diff --git a/ml_model/.gitignore b/ml_model/.gitignore index 4dd4743..4e8adb9 100644 --- a/ml_model/.gitignore +++ b/ml_model/.gitignore @@ -1,4 +1,7 @@ *.keras *.h5 *.pt -*.onnx \ No newline at end of file +*.onnx +*.pth +*.npy +*.json \ No newline at end of file diff --git a/ml_model/notebooks/LSTM.ipynb b/ml_model/notebooks/LSTM.ipynb deleted file mode 100644 index 025052b..0000000 --- a/ml_model/notebooks/LSTM.ipynb +++ /dev/null @@ -1,267 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\Shravya H Jain\\Desktop\\ML+AI\\Micro-Doppler-Based-Target-Classification\\.venv\\Lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", - " warnings.warn(\n", - "c:\\Users\\Shravya H Jain\\Desktop\\ML+AI\\Micro-Doppler-Based-Target-Classification\\.venv\\Lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model saved at epoch 1 with validation loss: 0.4525\n", - "Epoch [1/10], Loss: 0.4525\n", - "Model saved at epoch 2 with validation loss: 0.3909\n", - "Epoch [2/10], Loss: 0.3909\n", - "Model saved at epoch 3 with validation loss: 0.1003\n", - "Epoch [3/10], Loss: 0.1003\n", - "Epoch [4/10], Loss: 0.1022\n", - "Model saved at epoch 5 with validation loss: 0.0183\n", - "Epoch [5/10], Loss: 0.0183\n", - "Epoch [6/10], Loss: 0.7165\n", - "Epoch [7/10], Loss: 0.0261\n", - "Epoch [8/10], Loss: 0.3348\n", - "Epoch [9/10], Loss: 0.0270\n", - "Epoch [10/10], Loss: 0.0190\n" - ] - } - ], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "from torchvision import models, transforms\n", - "from torch.utils.data import DataLoader\n", - "from PIL import Image\n", - "import os\n", - "from sklearn.preprocessing import LabelEncoder\n", - "\n", - "\n", - "class CustomImageDataset(torch.utils.data.Dataset):\n", - " def __init__(self, base_dir, subfolders, transform=None):\n", - " self.base_dir = base_dir\n", - " self.subfolders = subfolders\n", - " self.transform = transform\n", - " self.image_paths = []\n", - " self.labels = []\n", - " \n", - " for subfolder in subfolders:\n", - " folder_path = os.path.join(base_dir, subfolder)\n", - " label = subfolder\n", - "\n", - " for img_name in os.listdir(folder_path):\n", - " if img_name.lower().endswith(('.png', '.jpg', '.jpeg')):\n", - " img_path = os.path.join(folder_path, img_name)\n", - " self.image_paths.append(img_path)\n", - " self.labels.append(label)\n", - " \n", - " self.label_encoder = LabelEncoder()\n", - " self.labels = self.label_encoder.fit_transform(self.labels)\n", - "\n", - " def __len__(self):\n", - " return len(self.image_paths)\n", - "\n", - " def __getitem__(self, idx):\n", - " img_path = self.image_paths[idx]\n", - " image = Image.open(img_path).convert('RGB')\n", - " \n", - " if self.transform:\n", - " image = self.transform(image)\n", - " \n", - " label = self.labels[idx]\n", - " return image, label\n", - "\n", - "# Define the LSTM model with a pretrained CNN for feature extraction\n", - "class PretrainedCNN_LSTM(nn.Module):\n", - " def __init__(self, num_classes):\n", - " super(PretrainedCNN_LSTM, self).__init__()\n", - " \n", - " # Pretrained CNN (ResNet18)\n", - " self.cnn = models.resnet18(pretrained=True)\n", - " self.cnn.fc = nn.Identity() # Remove the fully connected layer\n", - " \n", - " # LSTM\n", - " self.lstm = nn.LSTM(input_size=512, hidden_size=256, num_layers=2, batch_first=True)\n", - " \n", - " # Classification layer\n", - " self.fc = nn.Linear(256, num_classes)\n", - " \n", - " def forward(self, x):\n", - " # Feature extraction with CNN\n", - " batch_size, time_steps, C, H, W = x.size()\n", - " c_in = x.view(batch_size * time_steps, C, H, W)\n", - " \n", - " # Apply CNN to each image in the sequence\n", - " cnn_features = self.cnn(c_in)\n", - " cnn_features = cnn_features.view(batch_size, time_steps, -1)\n", - " \n", - " # Pass features through LSTM\n", - " lstm_out, (hn, cn) = self.lstm(cnn_features)\n", - " lstm_out = lstm_out[:, -1, :] # Take the output from the last time step\n", - " \n", - " # Classification\n", - " out = self.fc(lstm_out)\n", - " return out\n", - "\n", - "# Define transformations\n", - "transform = transforms.Compose([\n", - " transforms.Resize((64, 64)),\n", - " transforms.ToTensor(),\n", - " transforms.Normalize([0.5], [0.5])\n", - "])\n", - "\n", - "# Define dataset and dataloader\n", - "base_dir = r'DIAT-uSAT_dataset'\n", - "subfolders = [\n", - " r\"3_long_blade_rotor\", \n", - " r\"3_short_blade_rotor\", \n", - " r\"Bird\", \n", - " r\"Bird+mini-helicopter\", \n", - " r\"drone\", \n", - " r\"rc_plane\"\n", - "]\n", - "\n", - "# Initialize dataset and dataloader\n", - "dataset = CustomImageDataset(base_dir=base_dir, subfolders=subfolders, transform=transform)\n", - "dataloader = DataLoader(dataset, batch_size=32, shuffle=True)\n", - "\n", - "# Initialize the model, loss, and optimizer\n", - "model = PretrainedCNN_LSTM(num_classes=6) # 6 classes in the dataset\n", - "criterion = nn.CrossEntropyLoss()\n", - "optimizer = optim.Adam(model.parameters(), lr=0.0002)\n", - "\n", - "# Variables to track the best validation performance\n", - "best_loss = float('inf')\n", - "best_model_path = \"best_model_CustomVGGWithAttentionattention.pt\"\n", - "\n", - "# Training loop\n", - "num_epochs = 10\n", - "for epoch in range(num_epochs):\n", - " model.train()\n", - " for images, labels in dataloader:\n", - " # Reshape the images to [batch_size, time_steps, C, H, W]\n", - " images = images.unsqueeze(1) # Add time_steps dimension if needed\n", - "\n", - " # Forward pass\n", - " outputs = model(images)\n", - " loss = criterion(outputs, labels)\n", - "\n", - " # Backward pass and optimization\n", - " optimizer.zero_grad()\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " # For demonstration, we assume `loss.item()` is the validation loss\n", - " # Normally, you would compute this on a validation set\n", - "\n", - " # Check if current loss is the best so far\n", - " if loss.item() < best_loss:\n", - " best_loss = loss.item()\n", - " torch.save(model.state_dict(), best_model_path) # Save the model\n", - " print(f\"Model saved at epoch {epoch+1} with validation loss: {best_loss:.4f}\")\n", - "\n", - " print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_13264\\567059229.py:36: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " model.load_state_dict(torch.load(\"best_model_CustomVGGWithAttentionattention.pt\"))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test Accuracy: 98.02%\n" - ] - } - ], - "source": [ - "import torch\n", - "from sklearn.metrics import accuracy_score\n", - "from torch.utils.data import DataLoader\n", - "from torchvision import transforms\n", - "\n", - "# Function to evaluate the model on a test dataset\n", - "def evaluate_model(model, dataloader, device):\n", - " model.eval() # Set the model to evaluation mode\n", - " true_labels = []\n", - " pred_labels = []\n", - "\n", - " with torch.no_grad(): # Disable gradient calculation\n", - " for images, labels in dataloader:\n", - " images, labels = images.to(device), labels.to(device)\n", - "\n", - " # Add time_steps dimension if needed\n", - " images = images.unsqueeze(1) # Shape: [batch_size, time_steps, C, H, W]\n", - "\n", - " # Forward pass\n", - " outputs = model(images)\n", - " _, predicted = torch.max(outputs, 1)\n", - "\n", - " # Collect true and predicted labels\n", - " true_labels.extend(labels.cpu().numpy())\n", - " pred_labels.extend(predicted.cpu().numpy())\n", - "\n", - " # Calculate accuracy\n", - " accuracy = accuracy_score(true_labels, pred_labels)\n", - " return accuracy\n", - "\n", - "# Set up for testing\n", - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "\n", - "# Load the saved model\n", - "model = PretrainedCNN_LSTM(num_classes=6)\n", - "model.load_state_dict(torch.load(\"best_model_CustomVGGWithAttentionattention.pt\"))\n", - "model.to(device)\n", - "\n", - "# Define the test dataset and DataLoader (similar to the training set)\n", - "test_dataset = CustomImageDataset(base_dir=base_dir, subfolders=subfolders, transform=transform)\n", - "test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n", - "\n", - "\n", - "accuracy = evaluate_model(model, test_dataloader, device)\n", - "print(f'Test Accuracy: {accuracy * 100:.2f}%')\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/ml_model/notebooks/Vgg16Vgg19.ipynb b/ml_model/notebooks/Vgg16Vgg19.ipynb deleted file mode 100644 index 8064920..0000000 --- a/ml_model/notebooks/Vgg16Vgg19.ipynb +++ /dev/null @@ -1,2883 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# %pip install numpy torch torchvision scikit-learn matplotlib pillow \n", - "import os\n", - "import numpy as np\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "import torchvision.transforms as transforms\n", - "import torchvision.models as models\n", - "from torch.utils.data import DataLoader, random_split\n", - "from sklearn.preprocessing import LabelEncoder\n", - "import matplotlib.pyplot as plt\n", - "from PIL import Image" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.5.1+cu118\n", - "11.8\n" - ] - } - ], - "source": [ - "import torch\n", - "print(torch.__version__)\n", - "print(torch.version.cuda)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n" - ] - } - ], - "source": [ - "import torch\n", - "print(torch.cuda.is_available())" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-2.117904..2.169412].\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset Details:\n", - "Total Images: 4849\n", - "Training Images: 4121\n", - "Validation Images: 242\n", - "Test Images: 486\n" - ] - } - ], - "source": [ - "# %% [markdown]\n", - "# # Time-Variant Data Augmentation Techniques\n", - "\n", - "# %% Imports\n", - "import os\n", - "import numpy as np\n", - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "from torch.utils.data import Dataset, DataLoader, random_split, Subset\n", - "from torchvision import transforms\n", - "from sklearn.preprocessing import LabelEncoder\n", - "from PIL import Image\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# %% Time-Variant Data Augmentation Techniques\n", - "class TimeVariantDataAugmentation:\n", - " @staticmethod\n", - " def window_slicing(data, slice_percentage=0.9):\n", - " \"\"\"\n", - " Perform window slicing on time-variant data\n", - " \n", - " Args:\n", - " data (torch.Tensor): Input time-variant data\n", - " slice_percentage (float): Percentage of original width to keep (default: 0.9)\n", - " \n", - " Returns:\n", - " torch.Tensor: Augmented time-variant data\n", - " \"\"\"\n", - " # Ensure input is a torch tensor\n", - " if not isinstance(data, torch.Tensor):\n", - " data = torch.tensor(data, dtype=torch.float32)\n", - " \n", - " # Get original dimensions\n", - " orig_width = data.shape[1]\n", - " \n", - " # Calculate slice width\n", - " slice_width = int(orig_width * slice_percentage)\n", - " \n", - " # Generate random starting point\n", - " max_start = orig_width - slice_width\n", - " start_point = torch.randint(0, max_start + 1, (1,)).item()\n", - " \n", - " # Extract slice\n", - " sliced_data = data[:, start_point:start_point + slice_width]\n", - " \n", - " # Interpolate back to original size\n", - " augmented_data = torch.nn.functional.interpolate(\n", - " sliced_data.unsqueeze(0), \n", - " size=orig_width, \n", - " mode='linear', \n", - " align_corners=False\n", - " ).squeeze(0)\n", - " \n", - " return augmented_data\n", - "\n", - " @staticmethod\n", - " def window_warping(data, warping_factors=[0.5, 2.0]):\n", - " \"\"\"\n", - " Perform window warping on time-variant data\n", - " \n", - " Args:\n", - " data (torch.Tensor): Input time-variant data\n", - " warping_factors (list): Warping factors to apply (default: [0.5, 2.0])\n", - " \n", - " Returns:\n", - " torch.Tensor: Augmented time-variant data\n", - " \"\"\"\n", - " # Ensure input is a torch tensor\n", - " if not isinstance(data, torch.Tensor):\n", - " data = torch.tensor(data, dtype=torch.float32)\n", - " \n", - " # Get original dimensions\n", - " orig_width = data.shape[1]\n", - " \n", - " # Select window (10% of original width)\n", - " window_width = int(orig_width * 0.1)\n", - " start_point = torch.randint(0, orig_width - window_width + 1, (1,)).item()\n", - " \n", - " # Select random warping factor\n", - " warping_factor = np.random.choice(warping_factors)\n", - " \n", - " # Extract window\n", - " window = data[:, start_point:start_point + window_width]\n", - " \n", - " # Warp window\n", - " warped_window_width = int(window_width * warping_factor)\n", - " warped_window = torch.nn.functional.interpolate(\n", - " window.unsqueeze(0), \n", - " size=warped_window_width, \n", - " mode='linear', \n", - " align_corners=False\n", - " ).squeeze(0)\n", - " \n", - " # Reconstruct full data\n", - " augmented_data = data.clone()\n", - " end_point = start_point + warped_window_width\n", - " \n", - " # Replace window section with warped window\n", - " if warped_window_width < window_width:\n", - " augmented_data[:, start_point:end_point] = warped_window\n", - " else:\n", - " augmented_data[:, start_point:start_point + window_width] = warped_window[:window_width]\n", - " \n", - " return augmented_data\n", - "\n", - " @staticmethod\n", - " def jittering(data, mean=0, std_dev=0.03):\n", - " \"\"\"\n", - " Add Gaussian noise to time-variant data\n", - " \n", - " Args:\n", - " data (torch.Tensor): Input time-variant data\n", - " mean (float): Mean of Gaussian noise\n", - " std_dev (float): Standard deviation of Gaussian noise\n", - " \n", - " Returns:\n", - " torch.Tensor: Augmented time-variant data\n", - " \"\"\"\n", - " # Ensure input is a torch tensor\n", - " if not isinstance(data, torch.Tensor):\n", - " data = torch.tensor(data, dtype=torch.float32)\n", - " \n", - " # Generate Gaussian noise\n", - " noise = torch.normal(mean, std_dev, size=data.shape)\n", - " \n", - " # Add noise to original data\n", - " augmented_data = data + noise\n", - " \n", - " return augmented_data\n", - "\n", - "# %% Stochastic Augmentation Class\n", - "class StochasticAugmentation:\n", - " def __init__(self, augmentation_methods):\n", - " \"\"\"\n", - " Create a stochastic augmentation pipeline\n", - " \n", - " Args:\n", - " augmentation_methods (list): List of augmentation methods to choose from\n", - " \"\"\"\n", - " self.augmentation_methods = augmentation_methods\n", - " \n", - " def __call__(self, data):\n", - " \"\"\"\n", - " Randomly select and apply one augmentation method\n", - " \n", - " Args:\n", - " data (torch.Tensor): Input time-variant data\n", - " \n", - " Returns:\n", - " torch.Tensor: Augmented time-variant data\n", - " \"\"\"\n", - " # Randomly select an augmentation method\n", - " method = np.random.choice(self.augmentation_methods)\n", - " \n", - " # Apply the selected method\n", - " return method(data)\n", - "\n", - "# %% Custom Image Dataset Class\n", - "class CustomImageDataset(Dataset):\n", - " def __init__(self, base_dir, subfolders, transform=None, label_encoder=None):\n", - " self.base_dir = base_dir\n", - " self.subfolders = subfolders\n", - " self.transform = transform\n", - " self.image_paths = []\n", - " self.labels = []\n", - " \n", - " for subfolder in subfolders:\n", - " folder_path = os.path.join(base_dir, subfolder)\n", - " label = subfolder\n", - " \n", - " for img_name in os.listdir(folder_path):\n", - " if img_name.lower().endswith(('.png', '.jpg', '.jpeg')):\n", - " img_path = os.path.join(folder_path, img_name)\n", - " self.image_paths.append(img_path)\n", - " self.labels.append(label)\n", - " \n", - " if label_encoder is not None:\n", - " self.label_encoder = label_encoder\n", - " self.labels = self.label_encoder.transform(self.labels)\n", - " \n", - " def __len__(self):\n", - " return len(self.image_paths)\n", - " \n", - " def __getitem__(self, idx):\n", - " img_path = self.image_paths[idx]\n", - " image = Image.open(img_path).convert('RGB')\n", - " \n", - " if self.transform:\n", - " image = self.transform(image)\n", - " \n", - " label = self.labels[idx]\n", - " return image, label\n", - "\n", - "# %% Augmentation Visualization Function\n", - "def visualize_augmentation():\n", - " # Create more complex sample data with multiple frequencies\n", - " t = torch.linspace(0, 10*torch.pi, 200)\n", - " sample_data = torch.sin(t) + 0.5 * torch.sin(3*t) + 0.25 * torch.sin(5*t)\n", - " sample_data = sample_data.unsqueeze(0)\n", - " \n", - " # Create augmentation methods with specific transformations\n", - " augmentation_methods = [\n", - " (\"Window Slicing\", lambda x: TimeVariantDataAugmentation.window_slicing(x, slice_percentage=0.7)),\n", - " (\"Window Warping\", lambda x: TimeVariantDataAugmentation.window_warping(x, warping_factors=[0.5])),\n", - " (\"Jittering\", lambda x: TimeVariantDataAugmentation.jittering(x, mean=0, std_dev=0.2))\n", - " ]\n", - " \n", - " # Visualization\n", - " plt.figure(figsize=(15, 12))\n", - " \n", - " # Original Data\n", - " plt.subplot(4, 1, 1)\n", - " plt.title(\"Original Data\")\n", - " plt.plot(sample_data.numpy().flatten(), label='Original')\n", - " plt.legend()\n", - " plt.xlabel(\"Time Steps\")\n", - " plt.ylabel(\"Value\")\n", - " \n", - " # Augmentations with Difference Plots\n", - " for i, (name, aug_method) in enumerate(augmentation_methods, start=2):\n", - " # Apply augmentation\n", - " augmented_data = aug_method(sample_data)\n", - " \n", - " # Augmented Data Subplot\n", - " plt.subplot(4, 1, i)\n", - " plt.title(f\"{name} Augmentation\")\n", - " plt.plot(sample_data.numpy().flatten(), label='Original', alpha=0.5)\n", - " plt.plot(augmented_data.numpy().flatten(), label='Augmented', color='red')\n", - " plt.legend()\n", - " plt.xlabel(\"Time Steps\")\n", - " plt.ylabel(\"Value\")\n", - " \n", - " # Calculate and display difference metrics\n", - " mse = torch.nn.functional.mse_loss(sample_data, augmented_data).item()\n", - " max_diff = torch.max(torch.abs(augmented_data - sample_data)).item()\n", - " \n", - " plt.text(0.02, 0.95, f\"MSE: {mse:.4f}\\nMax Diff: {max_diff:.4f}\", \n", - " transform=plt.gca().transAxes, \n", - " verticalalignment='top')\n", - " \n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "# %% Define dataset parameters\n", - "base_dir = r'DIAT-uSAT_dataset'\n", - "subfolders = [\n", - " \"3_long_blade_rotor\",\n", - " \"3_short_blade_rotor\", \n", - " \"Bird\", \n", - " \"Bird+mini-helicopter\", \n", - " \"drone\", \n", - " \"rc_plane\"\n", - "]\n", - "\n", - "# %% Prepare Label Encoder\n", - "label_encoder = LabelEncoder()\n", - "label_encoder.fit(subfolders)\n", - "\n", - "# %% Define transformations\n", - "transform = transforms.Compose([\n", - " transforms.Resize((224, 224)),\n", - " transforms.RandomHorizontalFlip(),\n", - " transforms.ColorJitter(brightness=0.2, contrast=0.2),\n", - " transforms.ToTensor(),\n", - " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # Normalize with ImageNet stats\n", - "])\n", - "\n", - "# %% Load dataset\n", - "dataset = CustomImageDataset(base_dir, subfolders, transform=transform, label_encoder=label_encoder)\n", - "\n", - "# %% Seed and shuffle\n", - "torch.manual_seed(42)\n", - "indices = torch.randperm(len(dataset))\n", - "shuffled_dataset = Subset(dataset, indices)\n", - "\n", - "# %% Split dataset\n", - "train_size = int(0.85 * len(shuffled_dataset))\n", - "val_size = int(0.05 * len(shuffled_dataset))\n", - "test_size = len(shuffled_dataset) - train_size - val_size\n", - "train_dataset, val_dataset, test_dataset = random_split(shuffled_dataset, [train_size, val_size, test_size])\n", - "\n", - "# %% Create data loaders\n", - "train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)\n", - "val_loader = DataLoader(val_dataset, batch_size=8, shuffle=False)\n", - "test_loader = DataLoader(test_dataset, batch_size=8, shuffle=False)\n", - "\n", - "# %% Visualize Augmentation\n", - "visualize_augmentation()\n", - "\n", - "# %% Visualize one sample from dataset\n", - "sample_image, sample_label = next(iter(train_loader))\n", - "sample_image = sample_image[0]\n", - "sample_image = sample_image.permute(1, 2, 0)\n", - "\n", - "plt.figure(figsize=(10, 10))\n", - "plt.imshow(sample_image)\n", - "plt.title(f\"Sample Image (Label: {sample_label[0]})\")\n", - "plt.axis('off')\n", - "plt.show()\n", - "\n", - "# %% Print dataset details\n", - "print(\"Dataset Details:\")\n", - "print(f\"Total Images: {len(dataset)}\")\n", - "print(f\"Training Images: {len(train_dataset)}\")\n", - "print(f\"Validation Images: {len(val_dataset)}\")\n", - "print(f\"Test Images: {len(test_dataset)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Load dataset\n", - "dataset = CustomImageDataset(base_dir, subfolders, transform=transform, label_encoder=label_encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "torch.manual_seed(42) \n", - "indices = torch.randperm(len(dataset)) \n", - "shuffled_dataset = torch.utils.data.Subset(dataset, indices)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "train_size = int(0.85 * len(shuffled_dataset))\n", - "val_size = int(0.05 * len(shuffled_dataset))\n", - "test_size = len(shuffled_dataset) - train_size - val_size\n", - "train_dataset, val_dataset, test_dataset = torch.utils.data.random_split(shuffled_dataset, [train_size, val_size, test_size])" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)\n", - "val_loader = DataLoader(val_dataset, batch_size=8, shuffle=False)\n", - "test_loader = DataLoader(test_dataset, batch_size=8, shuffle=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.9980307..2.5005665].\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Visualize one sample\n", - "sample_image, sample_label = next(iter(train_loader))\n", - "sample_image = sample_image[0] \n", - "sample_image = sample_image.permute(1, 2, 0) \n", - "\n", - "plt.imshow(sample_image)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\Shravya H Jain\\Desktop\\micro-classify-main\\Micro-Classify\\.venv\\Lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", - " warnings.warn(\n", - "c:\\Users\\Shravya H Jain\\Desktop\\micro-classify-main\\Micro-Classify\\.venv\\Lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n", - "c:\\Users\\Shravya H Jain\\Desktop\\micro-classify-main\\Micro-Classify\\.venv\\Lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG19_Weights.IMAGENET1K_V1`. You can also use `weights=VGG19_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n" - ] - } - ], - "source": [ - "# Model definitions using VGG16 and VGG19\n", - "class CustomVGG(nn.Module):\n", - " def __init__(self, base_model, num_classes):\n", - " super(CustomVGG, self).__init__()\n", - " self.features = base_model.features\n", - " self.avgpool = base_model.avgpool\n", - " self.classifier = nn.Sequential(\n", - " nn.Linear(base_model.classifier[0].in_features, 256),\n", - " nn.ReLU(inplace=True),\n", - " nn.Dropout(p=0.5),\n", - " nn.Linear(256, num_classes)\n", - " )\n", - " \n", - " def forward(self, x):\n", - " x = self.features(x)\n", - " x = self.avgpool(x)\n", - " x = torch.flatten(x, 1)\n", - " x = self.classifier(x)\n", - " return x\n", - "\n", - "# Initialize models\n", - "vgg16_base = models.vgg16(pretrained=True)\n", - "for param in vgg16_base.parameters():\n", - " param.requires_grad = False\n", - "model_vgg16 = CustomVGG(vgg16_base, num_classes=6)\n", - "\n", - "vgg19_base = models.vgg19(pretrained=True)\n", - "for param in vgg19_base.parameters():\n", - " param.requires_grad = False\n", - "model_vgg19 = CustomVGG(vgg19_base, num_classes=6)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "CustomVGG(\n", - " (features): Sequential(\n", - " (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (1): ReLU(inplace=True)\n", - " (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (3): ReLU(inplace=True)\n", - " (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (6): ReLU(inplace=True)\n", - " (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (8): ReLU(inplace=True)\n", - " (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (11): ReLU(inplace=True)\n", - " (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (13): ReLU(inplace=True)\n", - " (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (15): ReLU(inplace=True)\n", - " (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (17): ReLU(inplace=True)\n", - " (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (20): ReLU(inplace=True)\n", - " (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (22): ReLU(inplace=True)\n", - " (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (24): ReLU(inplace=True)\n", - " (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (26): ReLU(inplace=True)\n", - " (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (29): ReLU(inplace=True)\n", - " (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (31): ReLU(inplace=True)\n", - " (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (33): ReLU(inplace=True)\n", - " (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (35): ReLU(inplace=True)\n", - " (36): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " )\n", - " (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n", - " (classifier): Sequential(\n", - " (0): Linear(in_features=25088, out_features=256, bias=True)\n", - " (1): ReLU(inplace=True)\n", - " (2): Dropout(p=0.5, inplace=False)\n", - " (3): Linear(in_features=256, out_features=6, bias=True)\n", - " )\n", - ")" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Device configuration\n", - "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - "model_vgg16.to(device)\n", - "model_vgg19.to(device)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Loss and optimizer\n", - "criterion = nn.CrossEntropyLoss()\n", - "optimizer_vgg16 = optim.Adam(model_vgg16.classifier.parameters(), lr=0.0001)\n", - "optimizer_vgg19 = optim.Adam(model_vgg19.classifier.parameters(), lr=0.0001)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\Shravya H Jain\\Desktop\\micro-classify-main\\Micro-Classify\\.venv\\Lib\\site-packages\\torch\\optim\\lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "# Learning rate scheduler\n", - "scheduler_vgg16 = optim.lr_scheduler.ReduceLROnPlateau(optimizer_vgg16, mode='min', factor=0.5, patience=2, min_lr=1e-7, verbose=True)\n", - "scheduler_vgg19 = optim.lr_scheduler.ReduceLROnPlateau(optimizer_vgg19, mode='min', factor=0.5, patience=2, min_lr=1e-7, verbose=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Training function\n", - "def train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, num_epochs=15):\n", - " model.train()\n", - " best_acc = 0.0\n", - " \n", - " for epoch in range(num_epochs):\n", - " running_loss = 0.0\n", - " running_corrects = 0\n", - " \n", - " for images, labels in train_loader:\n", - " images = images.to(device)\n", - " labels = labels.to(device, dtype=torch.long)\n", - " \n", - " optimizer.zero_grad()\n", - " \n", - " outputs = model(images)\n", - " loss = criterion(outputs, labels)\n", - " loss.backward()\n", - " optimizer.step()\n", - " \n", - " _, preds = torch.max(outputs, 1)\n", - " running_loss += loss.item() * images.size(0)\n", - " running_corrects += torch.sum(preds == labels.data)\n", - " \n", - " epoch_loss = running_loss / len(train_loader.dataset)\n", - " epoch_acc = running_corrects.double() / len(train_loader.dataset)\n", - " \n", - " print(f\"Epoch [{epoch+1}/{num_epochs}], Loss: {epoch_loss:.4f}, Accuracy: {epoch_acc:.4f}\")\n", - " \n", - " model.eval()\n", - " val_loss = 0.0\n", - " val_corrects = 0\n", - " \n", - " with torch.no_grad():\n", - " for images, labels in val_loader:\n", - " images = images.to(device)\n", - " labels = labels.to(device, dtype=torch.long)\n", - " \n", - " outputs = model(images)\n", - " loss = criterion(outputs, labels)\n", - " \n", - " _, preds = torch.max(outputs, 1)\n", - " val_loss += loss.item() * images.size(0)\n", - " val_corrects += torch.sum(preds == labels.data)\n", - " \n", - " val_loss /= len(val_loader.dataset)\n", - " val_acc = val_corrects.double() / len(val_loader.dataset)\n", - " print(f\"Validation Loss: {val_loss:.4f}, Accuracy: {val_acc:.4f}\")\n", - " \n", - " scheduler.step(val_loss)\n", - " \n", - " # Save the best model\n", - " if val_acc > best_acc:\n", - " best_acc = val_acc\n", - " torch.save(model.state_dict(), f\"best_model_{model.__class__.__name__}.pt\")\n", - " \n", - " model.train()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Train VGG16 model\n", - "train_model(model_vgg16, train_loader, val_loader, criterion, optimizer_vgg16, scheduler_vgg16)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch [1/15], Loss: 0.5034, Accuracy: 0.8275\n", - "Validation Loss: 0.1749, Accuracy: 0.9545\n", - "Epoch [2/15], Loss: 0.2482, Accuracy: 0.9170\n", - "Validation Loss: 0.1124, Accuracy: 0.9835\n", - "Epoch [3/15], Loss: 0.1816, Accuracy: 0.9386\n", - "Validation Loss: 0.1005, Accuracy: 0.9752\n", - "Epoch [4/15], Loss: 0.1498, Accuracy: 0.9517\n", - "Validation Loss: 0.0804, Accuracy: 0.9752\n", - "Epoch [5/15], Loss: 0.1330, Accuracy: 0.9510\n", - "Validation Loss: 0.0880, Accuracy: 0.9669\n", - "Epoch [6/15], Loss: 0.1140, Accuracy: 0.9612\n", - "Validation Loss: 0.0609, Accuracy: 0.9835\n", - "Epoch [7/15], Loss: 0.0960, Accuracy: 0.9704\n", - "Validation Loss: 0.0526, Accuracy: 0.9917\n" - ] - } - ], - "source": [ - "train_model(model_vgg19, train_loader, val_loader, criterion, optimizer_vgg19, scheduler_vgg19)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "from sklearn.metrics import classification_report\n", - "\n", - "def test_model(model, test_loader, criterion, class_names=None):\n", - " \"\"\"\n", - " Test the model on a test set and generate metrics.\n", - " \n", - " Args:\n", - " model (torch.nn.Module): The trained model.\n", - " test_loader (DataLoader): DataLoader for the test set.\n", - " criterion (torch.nn.Module): Loss function.\n", - " class_names (list): Optional list of class names for the report.\n", - " \n", - " Returns:\n", - " all_labels (list): True labels for the test set.\n", - " all_preds (list): Predicted labels for the test set.\n", - " \"\"\"\n", - " model.eval() # Set model to evaluation mode\n", - " test_loss = 0.0\n", - " test_acc = 0.0\n", - " all_preds = []\n", - " all_labels = []\n", - "\n", - " with torch.no_grad(): # Disable gradient computation for inference\n", - " for images, labels in test_loader:\n", - " images, labels = images.to(device), labels.to(device, dtype=torch.long)\n", - " outputs = model(images)\n", - " loss = criterion(outputs, labels)\n", - " test_loss += loss.item() * images.size(0)\n", - " _, preds = torch.max(outputs, 1)\n", - " test_acc += torch.sum(preds == labels).item()\n", - " \n", - " # Collect all predictions and labels for the classification report\n", - " all_preds.extend(preds.cpu().numpy())\n", - " all_labels.extend(labels.cpu().numpy())\n", - "\n", - " # Calculate average loss and accuracy\n", - " test_loss = test_loss / len(test_loader.dataset)\n", - " test_acc = test_acc / len(test_loader.dataset)\n", - "\n", - " print(f\"Test Loss: {test_loss:.4f}, Accuracy: {test_acc:.4f}\")\n", - "\n", - " return all_labels, all_preds\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test Loss: 0.0869, Accuracy: 0.9815\n", - "Test Loss: 1.8079, Accuracy: 0.1622\n", - "Classification Report for VGG16:\n", - " precision recall f1-score support\n", - "\n", - " 3_long_blade_rotor 0.99 0.97 0.98 75\n", - " 3_short_blade_rotor 0.98 0.97 0.97 90\n", - " Bird 0.99 1.00 0.99 82\n", - "Bird+mini-helicopter 1.00 0.95 0.97 81\n", - " drone 1.00 1.00 1.00 87\n", - " rc_plane 0.94 1.00 0.97 72\n", - "\n", - " accuracy 0.98 487\n", - " macro avg 0.98 0.98 0.98 487\n", - " weighted avg 0.98 0.98 0.98 487\n", - "\n", - "Classification Report for VGG19:\n", - " precision recall f1-score support\n", - "\n", - " 3_long_blade_rotor 0.13 0.03 0.04 75\n", - " 3_short_blade_rotor 0.00 0.00 0.00 90\n", - " Bird 0.00 0.00 0.00 82\n", - "Bird+mini-helicopter 0.16 0.95 0.28 81\n", - " drone 0.00 0.00 0.00 87\n", - " rc_plane 0.00 0.00 0.00 72\n", - "\n", - " accuracy 0.16 487\n", - " macro avg 0.05 0.16 0.05 487\n", - " weighted avg 0.05 0.16 0.05 487\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", - "c:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", - "c:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n" - ] - } - ], - "source": [ - "# Test model with model_vgg16\n", - "all_labels_vgg16, all_preds_vgg16 = test_model(model_vgg16, test_loader, criterion)\n", - "\n", - "# Test model with model_vgg19\n", - "all_labels_vgg19, all_preds_vgg19 = test_model(model_vgg19, test_loader, criterion)\n", - "\n", - "# Ensure class_names is defined using the label encoder or fallback to numeric class names\n", - "if 'label_encoder' in locals() and hasattr(label_encoder, 'classes_'):\n", - " class_names = label_encoder.classes_\n", - "else:\n", - " # Create class names based on unique labels found in the dataset\n", - " class_names = [str(i) for i in range(max(max(all_labels_vgg16), max(all_labels_vgg19)) + 1)]\n", - "\n", - "# Generate and print the classification report for model_vgg16\n", - "report_vgg16 = classification_report(all_labels_vgg16, all_preds_vgg16, target_names=class_names)\n", - "print(\"Classification Report for VGG16:\")\n", - "print(report_vgg16)\n", - "\n", - "# Generate and print the classification report for model_vgg19\n", - "report_vgg19 = classification_report(all_labels_vgg19, all_preds_vgg19, target_names=class_names)\n", - "print(\"Classification Report for VGG19:\")\n", - "print(report_vgg19)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.nn.functional as F\n", - "\n", - "class CustomCNN(nn.Module):\n", - " def __init__(self, num_classes=6):\n", - " super(CustomCNN, self).__init__()\n", - " self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)\n", - " self.bn1 = nn.BatchNorm2d(16)\n", - " self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n", - "\n", - " # 2nd Convolutional Block\n", - " self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)\n", - " self.bn2 = nn.BatchNorm2d(32)\n", - " self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n", - "\n", - " # 3rd Convolutional Block\n", - " self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n", - " self.bn3 = nn.BatchNorm2d(64)\n", - " self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)\n", - "\n", - " # 4th Convolutional Block\n", - " self.conv4 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n", - " self.bn4 = nn.BatchNorm2d(128)\n", - " self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)\n", - "\n", - " # Global Average Pooling\n", - " self.global_avg_pool = nn.AdaptiveAvgPool2d((1, 1))\n", - " \n", - " # Fully Connected Layers\n", - " self.fc1 = nn.Linear(128, 512)\n", - " self.fc2 = nn.Linear(512, 6) # Output for 6 classes\n", - "\n", - " # Dropout for Regularization\n", - " self.dropout = nn.Dropout(0.5)\n", - "\n", - " def forward(self, x):\n", - " # 1st Convolutional Block\n", - " x = self.pool1(F.relu(self.bn1(self.conv1(x))))\n", - " \n", - " # 2nd Convolutional Block\n", - " x = self.pool2(F.relu(self.bn2(self.conv2(x))))\n", - " \n", - " # 3rd Convolutional Block\n", - " x = self.pool3(F.relu(self.bn3(self.conv3(x))))\n", - "\n", - " # 4th Convolutional Block\n", - " x = self.pool4(F.relu(self.bn4(self.conv4(x))))\n", - "\n", - " # Global Average Pooling\n", - " x = self.global_avg_pool(x)\n", - " \n", - " # Flatten the output\n", - " x = torch.flatten(x, 1)\n", - " \n", - " # Fully Connected Layers\n", - " x = F.relu(self.fc1(x))\n", - " x = self.dropout(x)\n", - " x = self.fc2(x)\n", - " \n", - " return x\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "CustomCNN(\n", - " (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (bn3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (pool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (conv4): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (bn4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (pool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (global_avg_pool): AdaptiveAvgPool2d(output_size=(1, 1))\n", - " (fc1): Linear(in_features=128, out_features=512, bias=True)\n", - " (fc2): Linear(in_features=512, out_features=6, bias=True)\n", - " (dropout): Dropout(p=0.5, inplace=False)\n", - ")" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Initialize Deeper CNN model\n", - "model_deepercnn = CustomCNN(num_classes=6)\n", - "model_deepercnn.to(device)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Loss and optimizer for Deeper CNN\n", - "optimizer_deepercnn = optim.Adam(model_deepercnn.parameters(), lr=0.0001)\n", - "scheduler_deepercnn = optim.lr_scheduler.ReduceLROnPlateau(optimizer_deepercnn, mode='min', factor=0.5, patience=2, min_lr=1e-7, verbose=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch [1/20], Loss: 1.1535, Accuracy: 0.5977\n", - "Validation Loss: 0.5996, Accuracy: 0.7975\n", - "Epoch [2/20], Loss: 0.6531, Accuracy: 0.7704\n", - "Validation Loss: 0.4103, Accuracy: 0.8430\n", - "Epoch [3/20], Loss: 0.4873, Accuracy: 0.8260\n", - "Validation Loss: 0.3268, Accuracy: 0.8678\n", - "Epoch [4/20], Loss: 0.3914, Accuracy: 0.8719\n", - "Validation Loss: 0.3115, Accuracy: 0.8926\n", - "Epoch [5/20], Loss: 0.3274, Accuracy: 0.8940\n", - "Validation Loss: 0.2440, Accuracy: 0.9050\n", - "Epoch [6/20], Loss: 0.2959, Accuracy: 0.9015\n", - "Validation Loss: 0.1570, Accuracy: 0.9504\n", - "Epoch [7/20], Loss: 0.2666, Accuracy: 0.9131\n", - "Validation Loss: 0.1633, Accuracy: 0.9463\n", - "Epoch [8/20], Loss: 0.2434, Accuracy: 0.9197\n", - "Validation Loss: 0.1108, Accuracy: 0.9628\n", - "Epoch [9/20], Loss: 0.2063, Accuracy: 0.9287\n", - "Validation Loss: 0.1668, Accuracy: 0.9380\n", - "Epoch [10/20], Loss: 0.1969, Accuracy: 0.9352\n", - "Validation Loss: 0.0758, Accuracy: 0.9793\n", - "Epoch [11/20], Loss: 0.1865, Accuracy: 0.9362\n", - "Validation Loss: 0.1051, Accuracy: 0.9711\n", - "Epoch [12/20], Loss: 0.1618, Accuracy: 0.9498\n", - "Validation Loss: 0.1949, Accuracy: 0.9174\n", - "Epoch [13/20], Loss: 0.1548, Accuracy: 0.9505\n", - "Validation Loss: 0.0535, Accuracy: 0.9876\n", - "Epoch [14/20], Loss: 0.1482, Accuracy: 0.9539\n", - "Validation Loss: 0.0565, Accuracy: 0.9876\n", - "Epoch [15/20], Loss: 0.1322, Accuracy: 0.9541\n", - "Validation Loss: 0.0678, Accuracy: 0.9876\n", - "Epoch [16/20], Loss: 0.1217, Accuracy: 0.9607\n", - "Validation Loss: 0.0644, Accuracy: 0.9752\n", - "Epoch [17/20], Loss: 0.1021, Accuracy: 0.9670\n", - "Validation Loss: 0.0597, Accuracy: 0.9793\n", - "Epoch [18/20], Loss: 0.0847, Accuracy: 0.9755\n", - "Validation Loss: 0.0405, Accuracy: 0.9917\n", - "Epoch [19/20], Loss: 0.0827, Accuracy: 0.9731\n", - "Validation Loss: 0.0369, Accuracy: 0.9917\n", - "Epoch [20/20], Loss: 0.0947, Accuracy: 0.9687\n", - "Validation Loss: 0.0258, Accuracy: 0.9959\n" - ] - } - ], - "source": [ - "# Train Deeper CNN model\n", - "train_model(model_deepercnn, train_loader, val_loader, criterion, optimizer_deepercnn, scheduler_deepercnn, num_epochs=20)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test Loss: 0.0784, Accuracy: 0.9671\n", - "Classification Report for Custom CNN:\n", - " precision recall f1-score support\n", - "\n", - " 3_long_blade_rotor 1.00 0.89 0.94 72\n", - " 3_short_blade_rotor 0.93 0.96 0.95 85\n", - " Bird 1.00 1.00 1.00 76\n", - "Bird+mini-helicopter 1.00 0.96 0.98 78\n", - " drone 1.00 1.00 1.00 85\n", - " rc_plane 0.90 0.98 0.94 90\n", - "\n", - " accuracy 0.97 486\n", - " macro avg 0.97 0.97 0.97 486\n", - " weighted avg 0.97 0.97 0.97 486\n", - "\n" - ] - } - ], - "source": [ - "# Test the custom CNN model\n", - "all_labels_customcnn, all_preds_customcnn = test_model(model_deepercnn, test_loader, criterion)\n", - "\n", - "# Ensure class_names is defined using the label encoder or fallback to numeric class names\n", - "if 'label_encoder' in locals() and hasattr(label_encoder, 'classes_'):\n", - " class_names = label_encoder.classes_\n", - "else:\n", - " # Create class names based on unique labels found in the dataset\n", - " class_names = [str(i) for i in range(max(all_labels_customcnn) + 1)]\n", - "\n", - "# Generate and print the classification report for the custom CNN\n", - "report_customcnn = classification_report(all_labels_customcnn, all_preds_customcnn, target_names=class_names)\n", - "print(\"Classification Report for Custom CNN:\")\n", - "print(report_customcnn)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_35756\\3699078133.py:112: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " model_deepercnn.load_state_dict(torch.load(\"best_model_CustomCNN.pt\"))\n", - "Testing: 100%|██████████| 61/61 [00:10<00:00, 6.00it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test Loss: 0.0833 Test Acc: 0.9712\n", - "\n", - "Classification Report:\n", - " precision recall f1-score support\n", - "\n", - " 3_long_blade_rotor 0.99 0.93 0.96 72\n", - " 3_short_blade_rotor 0.99 0.91 0.94 85\n", - " Bird 1.00 1.00 1.00 76\n", - "Bird+mini-helicopter 0.97 0.99 0.98 78\n", - " drone 1.00 1.00 1.00 85\n", - " rc_plane 0.90 1.00 0.95 90\n", - "\n", - " accuracy 0.97 486\n", - " macro avg 0.97 0.97 0.97 486\n", - " weighted avg 0.97 0.97 0.97 486\n", - "\n", - "\n", - "Confusion Matrix:\n", - "[[67 1 0 2 0 2]\n", - " [ 1 77 0 0 0 7]\n", - " [ 0 0 76 0 0 0]\n", - " [ 0 0 0 77 0 1]\n", - " [ 0 0 0 0 85 0]\n", - " [ 0 0 0 0 0 90]]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Classification Report for Custom CNN:\n", - " precision recall f1-score support\n", - "\n", - " 3_long_blade_rotor 0.99 0.93 0.96 72\n", - " 3_short_blade_rotor 0.99 0.91 0.94 85\n", - " Bird 1.00 1.00 1.00 76\n", - "Bird+mini-helicopter 0.97 0.99 0.98 78\n", - " drone 1.00 1.00 1.00 85\n", - " rc_plane 0.90 1.00 0.95 90\n", - "\n", - " accuracy 0.97 486\n", - " macro avg 0.97 0.97 0.97 486\n", - " weighted avg 0.97 0.97 0.97 486\n", - "\n" - ] - }, - { - "ename": "ValueError", - "evalue": "multiclass format is not supported", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[44], line 125\u001b[0m\n\u001b[0;32m 122\u001b[0m \u001b[38;5;28mprint\u001b[39m(classification_report(all_labels_customcnn, all_preds_customcnn, target_names\u001b[38;5;241m=\u001b[39mclass_names))\n\u001b[0;32m 124\u001b[0m \u001b[38;5;66;03m# Generate the Precision-Recall Curve\u001b[39;00m\n\u001b[1;32m--> 125\u001b[0m \u001b[43mplot_precision_recall_curve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mall_labels_customcnn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_probs_customcnn\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[1;32mIn[44], line 99\u001b[0m, in \u001b[0;36mplot_precision_recall_curve\u001b[1;34m(all_labels, all_probs)\u001b[0m\n\u001b[0;32m 97\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[0;32m 98\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(class_names)):\n\u001b[1;32m---> 99\u001b[0m precision, recall, _ \u001b[38;5;241m=\u001b[39m \u001b[43mprecision_recall_curve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mall_labels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mall_probs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 100\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(recall, precision, lw\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mclass_names[i]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m Precision-Recall curve\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 101\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRecall\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[1;32mc:\\Users\\Shravya H Jain\\Desktop\\micro-classify-main\\Micro-Classify\\.venv\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 208\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m 209\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m 210\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 211\u001b[0m )\n\u001b[0;32m 212\u001b[0m ):\n\u001b[1;32m--> 213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m 216\u001b[0m \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m 217\u001b[0m \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m 218\u001b[0m \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m 219\u001b[0m msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[0;32m 220\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 221\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 222\u001b[0m \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[0;32m 223\u001b[0m )\n", - "File \u001b[1;32mc:\\Users\\Shravya H Jain\\Desktop\\micro-classify-main\\Micro-Classify\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:1002\u001b[0m, in \u001b[0;36mprecision_recall_curve\u001b[1;34m(y_true, y_score, pos_label, sample_weight, drop_intermediate, probas_pred)\u001b[0m\n\u001b[0;32m 993\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m 994\u001b[0m (\n\u001b[0;32m 995\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprobas_pred was deprecated in version 1.5 and will be removed in 1.7.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 998\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m 999\u001b[0m )\n\u001b[0;32m 1000\u001b[0m y_score \u001b[38;5;241m=\u001b[39m probas_pred\n\u001b[1;32m-> 1002\u001b[0m fps, tps, thresholds \u001b[38;5;241m=\u001b[39m \u001b[43m_binary_clf_curve\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1003\u001b[0m \u001b[43m \u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_score\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpos_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpos_label\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\n\u001b[0;32m 1004\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1006\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m drop_intermediate \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(fps) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m 1007\u001b[0m \u001b[38;5;66;03m# Drop thresholds corresponding to points where true positives (tps)\u001b[39;00m\n\u001b[0;32m 1008\u001b[0m \u001b[38;5;66;03m# do not change from the previous or subsequent point. This will keep\u001b[39;00m\n\u001b[0;32m 1009\u001b[0m \u001b[38;5;66;03m# only the first and last point for each tps value. All points\u001b[39;00m\n\u001b[0;32m 1010\u001b[0m \u001b[38;5;66;03m# with the same tps value have the same recall and thus x coordinate.\u001b[39;00m\n\u001b[0;32m 1011\u001b[0m \u001b[38;5;66;03m# They appear as a vertical line on the plot.\u001b[39;00m\n\u001b[0;32m 1012\u001b[0m optimal_idxs \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mwhere(\n\u001b[0;32m 1013\u001b[0m np\u001b[38;5;241m.\u001b[39mconcatenate(\n\u001b[0;32m 1014\u001b[0m [[\u001b[38;5;28;01mTrue\u001b[39;00m], np\u001b[38;5;241m.\u001b[39mlogical_or(np\u001b[38;5;241m.\u001b[39mdiff(tps[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]), np\u001b[38;5;241m.\u001b[39mdiff(tps[\u001b[38;5;241m1\u001b[39m:])), [\u001b[38;5;28;01mTrue\u001b[39;00m]]\n\u001b[0;32m 1015\u001b[0m )\n\u001b[0;32m 1016\u001b[0m )[\u001b[38;5;241m0\u001b[39m]\n", - "File \u001b[1;32mc:\\Users\\Shravya H Jain\\Desktop\\micro-classify-main\\Micro-Classify\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:817\u001b[0m, in \u001b[0;36m_binary_clf_curve\u001b[1;34m(y_true, y_score, pos_label, sample_weight)\u001b[0m\n\u001b[0;32m 815\u001b[0m y_type \u001b[38;5;241m=\u001b[39m type_of_target(y_true, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my_true\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 816\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (y_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m (y_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m pos_label \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)):\n\u001b[1;32m--> 817\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m format is not supported\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(y_type))\n\u001b[0;32m 819\u001b[0m check_consistent_length(y_true, y_score, sample_weight)\n\u001b[0;32m 820\u001b[0m y_true \u001b[38;5;241m=\u001b[39m column_or_1d(y_true)\n", - "\u001b[1;31mValueError\u001b[0m: multiclass format is not supported" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import torch\n", - "import numpy as np\n", - "import tqdm\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc, precision_recall_curve\n", - "import torch.nn.functional as F # Ensure this import is added\n", - "\n", - "# Class names for your classification task\n", - "class_names = [\n", - " \"3_long_blade_rotor\", \n", - " \"3_short_blade_rotor\", \n", - " \"Bird\", \n", - " \"Bird+mini-helicopter\", \n", - " \"drone\", \n", - " \"rc_plane\"\n", - "]\n", - "\n", - "# Test Function with Metric Collection\n", - "def test_model(model, test_loader, criterion, device=\"cuda\" if torch.cuda.is_available() else \"cpu\"):\n", - " model = model.to(device)\n", - " model.eval()\n", - "\n", - " running_loss = 0.0\n", - " running_corrects = 0\n", - " total_test = 0\n", - " all_preds = []\n", - " all_labels = []\n", - " all_probs = []\n", - "\n", - " with torch.no_grad():\n", - " for inputs, labels in tqdm.tqdm(test_loader, desc=\"Testing\"):\n", - " inputs = inputs.to(device)\n", - " labels = labels.to(device).long() # Ensure labels are of type torch.long\n", - "\n", - " # Forward Pass\n", - " outputs = model(inputs)\n", - " loss = criterion(outputs, labels)\n", - " _, preds = torch.max(outputs, 1)\n", - "\n", - " # Store metrics\n", - " running_loss += loss.item() * inputs.size(0)\n", - " running_corrects += torch.sum(preds == labels.data)\n", - " total_test += labels.size(0)\n", - "\n", - " all_preds.extend(preds.cpu().numpy())\n", - " all_labels.extend(labels.cpu().numpy())\n", - " all_probs.extend(F.softmax(outputs, dim=1).cpu().numpy()) # Softmax for probability scores\n", - "\n", - " test_loss = running_loss / total_test\n", - " test_acc = running_corrects.double() / total_test\n", - "\n", - " print(f\"Test Loss: {test_loss:.4f} Test Acc: {test_acc:.4f}\")\n", - "\n", - " # Classification Report\n", - " print(\"\\nClassification Report:\")\n", - " print(classification_report(all_labels, all_preds, target_names=class_names))\n", - "\n", - " # Confusion Matrix\n", - " cm = confusion_matrix(all_labels, all_preds)\n", - " print(\"\\nConfusion Matrix:\")\n", - " print(cm)\n", - "\n", - " # Plot Confusion Matrix\n", - " plt.figure(figsize=(8, 6))\n", - " plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n", - " plt.title(\"Confusion Matrix\")\n", - " plt.colorbar()\n", - " tick_marks = np.arange(len(class_names))\n", - " plt.xticks(tick_marks, class_names, rotation=45)\n", - " plt.yticks(tick_marks, class_names)\n", - " plt.ylabel('True label')\n", - " plt.xlabel('Predicted label')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " # ROC Curve & AUC (For multi-class classification)\n", - " if len(class_names) > 2: # Multi-class classification\n", - " all_probs = np.array(all_probs)\n", - " fpr, tpr, roc_auc = {}, {}, {}\n", - " for i in range(len(class_names)):\n", - " fpr[i], tpr[i], _ = roc_curve(all_labels, all_probs[:, i], pos_label=i)\n", - " roc_auc[i] = auc(fpr[i], tpr[i])\n", - " plt.plot(fpr[i], tpr[i], lw=2, label=f'{class_names[i]} (AUC = {roc_auc[i]:.2f})')\n", - "\n", - " plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n", - " plt.xlabel('False Positive Rate')\n", - " plt.ylabel('True Positive Rate')\n", - " plt.title('Receiver Operating Characteristic (ROC) Curve')\n", - " plt.legend(loc='lower right')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " return all_labels, all_preds, all_probs\n", - "\n", - "# Function to plot Precision-Recall Curve (for each class in multi-class classification)\n", - "def plot_precision_recall_curve(all_labels, all_probs):\n", - " plt.figure(figsize=(8, 6))\n", - " for i in range(len(class_names)):\n", - " precision, recall, _ = precision_recall_curve(all_labels, np.array(all_probs)[:, i])\n", - " plt.plot(recall, precision, lw=2, label=f'{class_names[i]} Precision-Recall curve')\n", - " plt.xlabel('Recall')\n", - " plt.ylabel('Precision')\n", - " plt.title('Precision-Recall Curve for Each Class')\n", - " plt.legend(loc='lower left')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "# Example Usage\n", - "if __name__ == \"__main__\":\n", - " # Assuming test_loader is defined\n", - " model_deepercnn = CustomCNN(num_classes=6)\n", - " model_deepercnn.load_state_dict(torch.load(\"best_model_CustomCNN.pt\"))\n", - " model_deepercnn.eval()\n", - "\n", - " criterion = nn.CrossEntropyLoss()\n", - "\n", - " # Test the model\n", - " all_labels_customcnn, all_preds_customcnn, all_probs_customcnn = test_model(model_deepercnn, test_loader, criterion)\n", - "\n", - " # Generate and print the classification report for the custom CNN\n", - " print(\"\\nClassification Report for Custom CNN:\")\n", - " print(classification_report(all_labels_customcnn, all_preds_customcnn, target_names=class_names))\n", - "\n", - " # Generate the Precision-Recall Curve\n", - " plot_precision_recall_curve(all_labels_customcnn, all_probs_customcnn)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_35756\\956647135.py:89: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " model_deepercnn.load_state_dict(torch.load(\"best_model_CustomCNN.pt\"))\n", - "Testing: 100%|██████████| 61/61 [00:10<00:00, 5.84it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test Loss: 0.0808 Test Acc: 0.9671\n", - "\n", - "Classification Report:\n", - " precision recall f1-score support\n", - "\n", - " class_0 1.00 0.90 0.95 72\n", - " class_1 0.99 0.91 0.94 85\n", - " class_2 1.00 1.00 1.00 76\n", - " class_3 0.95 0.99 0.97 78\n", - " class_4 1.00 1.00 1.00 85\n", - " class_5 0.89 1.00 0.94 90\n", - "\n", - " accuracy 0.97 486\n", - " macro avg 0.97 0.97 0.97 486\n", - "weighted avg 0.97 0.97 0.97 486\n", - "\n", - "\n", - "Confusion Matrix:\n", - "[[65 1 0 4 0 2]\n", - " [ 0 77 0 0 0 8]\n", - " [ 0 0 76 0 0 0]\n", - " [ 0 0 0 77 0 1]\n", - " [ 0 0 0 0 85 0]\n", - " [ 0 0 0 0 0 90]]\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Classification Report for Custom CNN:\n", - " precision recall f1-score support\n", - "\n", - " class_0 1.00 0.90 0.95 72\n", - " class_1 0.99 0.91 0.94 85\n", - " class_2 1.00 1.00 1.00 76\n", - " class_3 0.95 0.99 0.97 78\n", - " class_4 1.00 1.00 1.00 85\n", - " class_5 0.89 1.00 0.94 90\n", - "\n", - " accuracy 0.97 486\n", - " macro avg 0.97 0.97 0.97 486\n", - "weighted avg 0.97 0.97 0.97 486\n", - "\n" - ] - } - ], - "source": [ - "import torch\n", - "import numpy as np\n", - "import tqdm\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc\n", - "import torch.nn.functional as F # Ensure this import is added\n", - "\n", - "# Assuming class_names is defined somewhere, for example:\n", - "class_names = ['class_0', 'class_1', 'class_2', 'class_3', 'class_4', 'class_5']\n", - "\n", - "# Test Function with Metric Collection\n", - "def test_model(model, test_loader, criterion, device=\"cuda\" if torch.cuda.is_available() else \"cpu\"):\n", - " model = model.to(device)\n", - " model.eval()\n", - "\n", - " running_loss = 0.0\n", - " running_corrects = 0\n", - " total_test = 0\n", - " all_preds = []\n", - " all_labels = []\n", - " all_probs = []\n", - "\n", - " with torch.no_grad():\n", - " for inputs, labels in tqdm.tqdm(test_loader, desc=\"Testing\"):\n", - " inputs = inputs.to(device)\n", - " labels = labels.to(device).long() # Ensure labels are of type torch.long\n", - "\n", - " # Forward Pass\n", - " outputs = model(inputs)\n", - " loss = criterion(outputs, labels)\n", - " _, preds = torch.max(outputs, 1)\n", - "\n", - " # Store metrics\n", - " running_loss += loss.item() * inputs.size(0)\n", - " running_corrects += torch.sum(preds == labels.data)\n", - " total_test += labels.size(0)\n", - "\n", - " all_preds.extend(preds.cpu().numpy())\n", - " all_labels.extend(labels.cpu().numpy())\n", - " all_probs.extend(F.softmax(outputs, dim=1).cpu().numpy()) # Softmax for probability scores\n", - "\n", - " test_loss = running_loss / total_test\n", - " test_acc = running_corrects.double() / total_test\n", - "\n", - " print(f\"Test Loss: {test_loss:.4f} Test Acc: {test_acc:.4f}\")\n", - "\n", - " # Classification Report\n", - " print(\"\\nClassification Report:\")\n", - " print(classification_report(all_labels, all_preds, target_names=class_names))\n", - "\n", - " # Confusion Matrix\n", - " cm = confusion_matrix(all_labels, all_preds)\n", - " print(\"\\nConfusion Matrix:\")\n", - " print(cm)\n", - "\n", - " # Plot Confusion Matrix\n", - " plt.figure(figsize=(8, 6))\n", - " plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n", - " plt.title(\"Confusion Matrix\")\n", - " plt.colorbar()\n", - " tick_marks = np.arange(len(class_names))\n", - " plt.xticks(tick_marks, class_names, rotation=45)\n", - " plt.yticks(tick_marks, class_names)\n", - " plt.ylabel('True label')\n", - " plt.xlabel('Predicted label')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " # ROC Curve & AUC (For binary classification, adjust if necessary)\n", - " if len(class_names) == 2: # Binary classification\n", - " fpr, tpr, thresholds = roc_curve(all_labels, np.array(all_probs)[:, 1], pos_label=1)\n", - " roc_auc = auc(fpr, tpr)\n", - " plt.figure(figsize=(8, 6))\n", - " plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')\n", - " plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n", - " plt.xlabel('False Positive Rate')\n", - " plt.ylabel('True Positive Rate')\n", - " plt.title('Receiver Operating Characteristic (ROC) Curve')\n", - " plt.legend(loc='lower right')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " return all_labels, all_preds, all_probs\n", - "\n", - "# Example Usage\n", - "if __name__ == \"__main__\":\n", - " # Assuming test_loader is defined\n", - " model_deepercnn = CustomCNN(num_classes=6)\n", - " model_deepercnn.load_state_dict(torch.load(\"best_model_CustomCNN.pt\"))\n", - " model_deepercnn.eval()\n", - "\n", - " criterion = nn.CrossEntropyLoss()\n", - "\n", - " # Test the model\n", - " all_labels_customcnn, all_preds_customcnn, all_probs_customcnn = test_model(model_deepercnn, test_loader, criterion)\n", - "\n", - " # Generate and print the classification report for the custom CNN\n", - " print(\"\\nClassification Report for Custom CNN:\")\n", - " print(classification_report(all_labels_customcnn, all_preds_customcnn, target_names=class_names))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_35756\\714240698.py:117: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " model_deepercnn.load_state_dict(torch.load(\"best_model_CustomCNN.pt\"))\n", - "Testing: 100%|██████████| 61/61 [00:10<00:00, 5.85it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test Loss: 0.0853 Test Acc: 0.9691\n", - "\n", - "Classification Report:\n", - " precision recall f1-score support\n", - "\n", - " 3_long_blade_rotor 0.99 0.92 0.95 72\n", - " 3_short_blade_rotor 0.99 0.91 0.94 85\n", - " Bird 1.00 1.00 1.00 76\n", - "Bird+mini-helicopter 0.96 0.99 0.97 78\n", - " drone 1.00 1.00 1.00 85\n", - " rc_plane 0.90 1.00 0.95 90\n", - "\n", - " accuracy 0.97 486\n", - " macro avg 0.97 0.97 0.97 486\n", - " weighted avg 0.97 0.97 0.97 486\n", - "\n", - "\n", - "Confusion Matrix:\n", - "[[66 1 0 3 0 2]\n", - " [ 1 77 0 0 0 7]\n", - " [ 0 0 76 0 0 0]\n", - " [ 0 0 0 77 0 1]\n", - " [ 0 0 0 0 85 0]\n", - " [ 0 0 0 0 0 90]]\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAJOCAYAAAAqFJGJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAADErklEQVR4nOzdd1QU598F8Lu0pYNIU0TBRrGhqAQLFogoihRjTdSoMdFoTDQm0SQa0zSJiTFVE7uxF0DsHQs2LNh7Q6UIIr2zz/uHL/tzBXRXwKHczzkc3dmZ2e8OC8ydp4xMCCFARERERERUBlpSF0BERERERFUfgwUREREREZUZgwUREREREZUZgwUREREREZUZgwUREREREZUZgwUREREREZUZgwUREREREZUZgwUREREREZUZgwUREREREZUZgwURqc3BwQFvv/221GXUOF27dkXXrl2lLuOFZsyYAZlMhqSkJKlLqXRkMhlmzJhRLvu6c+cOZDIZli5dWi77A4ATJ05AT08Pd+/eLbd9lrdBgwZhwIABUpdBRM/BYEFUSSxduhQymUz5paOjAzs7O7z99tt48OCB1OVVapmZmfj222/RsmVLGBoawszMDJ07d8by5cshhJC6PLVcunQJM2bMwJ07d6QupZjCwkIsWbIEXbt2hYWFBeRyORwcHDBixAicPHlS6vLKxapVqzB37lypy1DxKmv64osvMHjwYDRo0EC5rGvXriq/kwwMDNCyZUvMnTsXCoWixP08evQIn3zyCZycnKCvrw8LCwv4+vpiy5Ytpb52Wloavv76a7Rq1QrGxsYwMDBA8+bN8dlnnyE2Nla53meffYaNGzfi7Nmzar+vmvDZJapMZKKq/NUlquaWLl2KESNG4JtvvoGjoyNycnJw7NgxLF26FA4ODrhw4QL09fUlrTE3NxdaWlrQ1dWVtI6nJSQkwNvbG5cvX8agQYPQpUsX5OTkYOPGjTh48CAGDhyIlStXQltbW+pSn2vDhg3o378/9u/fX6x1Ii8vDwCgp6f3yuvKzs5GcHAwduzYAS8vL/j7+8PCwgJ37tzBunXrcO3aNcTExKBevXqYMWMGvv76ayQmJsLS0vKV11oWffr0wYULFyos2OXk5EBHRwc6OjplrkkIgdzcXOjq6pbL5zo6OhqtW7fGkSNH4OnpqVzetWtX3Lx5E7NmzQIAJCUlYdWqVYiKisLnn3+O77//XmU/V69ehbe3NxITEzFixAi0bdsWKSkpWLlyJaKjozF58mTMnj1bZZtbt27Bx8cHMTEx6N+/Pzp16gQ9PT2cO3cOq1evhoWFBa5du6Zc38PDA05OTli+fPkL35cmn10iKieCiCqFJUuWCAAiKipKZflnn30mAIi1a9dKVJm0srOzRWFhYanP+/r6Ci0tLbFp06Ziz02ePFkAED/88ENFlliijIwMjdZfv369ACD2799fMQW9pHHjxgkA4tdffy32XEFBgZg9e7a4d++eEEKIr776SgAQiYmJFVaPQqEQWVlZ5b7f3r17iwYNGpTrPgsLC0V2dvZLb18RNZVkwoQJon79+kKhUKgs79Kli2jWrJnKsuzsbNGgQQNhYmIiCgoKlMvz8vJE8+bNhaGhoTh27JjKNgUFBWLgwIECgFizZo1yeX5+vmjVqpUwNDQUhw4dKlZXamqq+Pzzz1WW/fzzz8LIyEikp6e/8H1p8tkti7J+n4mqEwYLokqitGCxZcsWAUDMnDlTZfnly5dFv379RK1atYRcLhfu7u4lnlw/fvxYfPTRR6JBgwZCT09P2NnZiaFDh6qc/OXk5Ijp06eLRo0aCT09PVGvXj3xySefiJycHJV9NWjQQAwfPlwIIURUVJQAIJYuXVrsNXfs2CEAiM2bNyuX3b9/X4wYMUJYW1sLPT094erqKhYtWqSy3f79+wUAsXr1avHFF1+IunXrCplMJh4/flziMTt69KgAIEaOHFni8/n5+aJJkyaiVq1aypPR27dvCwBi9uzZYs6cOaJ+/fpCX19feHl5ifPnzxfbhzrHueh7FxERIcaOHSusrKyEubm5EEKIO3fuiLFjx4qmTZsKfX19YWFhId544w1x+/btYts/+1UUMrp06SK6dOlS7DitXbtWfPfdd8LOzk7I5XLRvXt3cf369WLv4c8//xSOjo5CX19ftGvXThw8eLDYPkty7949oaOjI15//fXnrlekKFhcv35dDB8+XJiZmQlTU1Px9ttvi8zMTJV1Fy9eLLp16yasrKyEnp6ecHFxEX///XexfTZo0ED07t1b7NixQ7i7uwu5XK48UVR3H0IIsW3bNuHl5SWMjY2FiYmJaNu2rVi5cqUQ4snxffbYP31Cr+7PBwAxbtw4sWLFCuHq6ip0dHREaGio8rmvvvpKuW5aWpr48MMPlT+XVlZWwsfHR5w6deqFNRV9hpcsWaLy+pcvXxb9+/cXlpaWQl9fXzRt2rTYiXlJ6tevL95+++1iy0sKFkII8cYbbwgAIjY2Vrls9erVAoD45ptvSnyNlJQUYW5uLpydnZXL1qxZIwCI77///oU1Fjl79qwAIEJCQp67nqaf3eHDh5cY4oo+008r6fu8bt06UatWrRKPY2pqqpDL5eLjjz9WLlP3M0VU1ajfJktEkijqBlGrVi3lsosXL6Jjx46ws7PDlClTYGRkhHXr1iEwMBAbN25EUFAQACAjIwOdO3fG5cuXMXLkSLRp0wZJSUkIDw/H/fv3YWlpCYVCgb59++Lw4cN499134eLigvPnz+PXX3/FtWvXEBYWVmJdbdu2RcOGDbFu3ToMHz5c5bm1a9eiVq1a8PX1BfCku9Jrr70GmUyG8ePHw8rKCtu3b8eoUaOQlpaGjz76SGX7b7/9Fnp6epg8eTJyc3NL7QK0efNmAMCwYcNKfF5HRwdDhgzB119/jcjISPj4+CifW758OdLT0zFu3Djk5OTgt99+Q/fu3XH+/HnY2NhodJyLvP/++7CyssL06dORmZkJAIiKisKRI0cwaNAg1KtXD3fu3MG8efPQtWtXXLp0CYaGhvDy8sKECRPw+++/4/PPP4eLiwsAKP8tzQ8//AAtLS1MnjwZqamp+Omnn/Dmm2/i+PHjynXmzZuH8ePHo3Pnzpg4cSLu3LmDwMBA1KpV64VdQLZv346CggIMHTr0ues9a8CAAXB0dMSsWbNw+vRpLFy4ENbW1vjxxx9V6mrWrBn69u0LHR0dbN68Ge+//z4UCgXGjRunsr+rV69i8ODBeO+99zB69Gg4OTlptI+lS5di5MiRaNasGaZOnQpzc3OcOXMGO3bswJAhQ/DFF18gNTUV9+/fx6+//goAMDY2BgCNfz727duHdevWYfz48bC0tISDg0OJx2jMmDHYsGEDxo8fD1dXVzx69AiHDx/G5cuX0aZNm+fWVJJz586hc+fO0NXVxbvvvgsHBwfcvHkTmzdvLtZl6WkPHjxATEwM2rRpU+o6zyoaPG5ubq5c9qKfRTMzMwQEBGDZsmW4ceMGGjdujPDwcADQ6PPl6uoKAwMDREZGFvv5e9rLfnbV9ez3uUmTJggKCkJISAj++ecfld9ZYWFhyM3NxaBBgwBo/pkiqlKkTjZE9ETRVes9e/aIxMREce/ePbFhwwZhZWUl5HK5SpO9t7e3aNGihcrVLYVCITp06CCaNGmiXDZ9+vRSr+4VdXv477//hJaWVrGuCPPnzxcARGRkpHLZ0y0WQggxdepUoaurK5KTk5XLcnNzhbm5uUorwqhRo0SdOnVEUlKSymsMGjRImJmZKVsTiq7EN2zYUK3uLoGBgQJAqS0aQggREhIiAIjff/9dCPG/q70GBgbi/v37yvWOHz8uAIiJEycql6l7nIu+d506dVLpHiKEKPF9FLW0LF++XLnseV2hSmuxcHFxEbm5ucrlv/32mwCgbHnJzc0VtWvXFu3atRP5+fnK9ZYuXSoAvLDFYuLEiQKAOHPmzHPXK1J0dffZFqSgoCBRu3ZtlWUlHRdfX1/RsGFDlWUNGjQQAMSOHTuKra/OPlJSUoSJiYnw8PAo1l3l6a4/pXU70uTnA4DQ0tISFy9eLLYfPNNiYWZmJsaNG1dsvaeVVlNJLRZeXl7CxMRE3L17t9T3WJI9e/YUa10s0qVLF+Hs7CwSExNFYmKiuHLlivjkk08EANG7d2+Vdd3c3ISZmdlzX2vOnDkCgAgPDxdCCNG6desXblOSpk2bil69ej13HU0/u5q2WJT0fd65c2eJx9LPz0/lM6nJZ4qoquGsUESVjI+PD6ysrGBvb4833ngDRkZGCA8PV15dTk5Oxr59+zBgwACkp6cjKSkJSUlJePToEXx9fXH9+nXlLFIbN25Eq1atSryyJ5PJAADr16+Hi4sLnJ2dlftKSkpC9+7dAQD79+8vtdaBAwciPz8fISEhymW7du1CSkoKBg4cCODJQNONGzfC398fQgiV1/D19UVqaipOnz6tst/hw4fDwMDghccqPT0dAGBiYlLqOkXPpaWlqSwPDAyEnZ2d8nH79u3h4eGBbdu2AdDsOBcZPXp0scG0T7+P/Px8PHr0CI0bN4a5uXmx962pESNGqFwZ7dy5M4AnA2IB4OTJk3j06BFGjx6tMmj4zTffVGkBK03RMXve8S3JmDFjVB537twZjx49UvkePH1cUlNTkZSUhC5duuDWrVtITU1V2d7R0VHZ+vU0dfaxe/dupKenY8qUKcUmPyj6GXgeTX8+unTpAldX1xfu19zcHMePH1eZ9ehlJSYm4uDBgxg5ciTq16+v8tyL3uOjR48AoNTPw5UrV2BlZQUrKys4Oztj9uzZ6Nu3b7GpbtPT01/4OXn2ZzEtLU3jz1ZRrS+a0vhlP7vqKun73L17d1haWmLt2rXKZY8fP8bu3buVvw+Bsv3OJars2BWKqJL566+/0LRpU6SmpmLx4sU4ePAg5HK58vkbN25ACIFp06Zh2rRpJe7j4cOHsLOzw82bN9GvX7/nvt7169dx+fJlWFlZlbqv0rRq1QrOzs5Yu3YtRo0aBeBJNyhLS0vlH8nExESkpKTg33//xb///qvWazg6Oj635iJFJw3p6ekq3TKeVlr4aNKkSbF1mzZtinXr1gHQ7Dg/r+7s7GzMmjULS5YswYMHD1Smv332BFpTz55EFp0cPn78GACU9yRo3Lixyno6OjqldtF5mqmpKYD/HcPyqKton5GRkfjqq69w9OhRZGVlqayfmpoKMzMz5ePSPg/q7OPmzZsAgObNm2v0Hopo+vOh7mf3p59+wvDhw2Fvbw93d3f4+flh2LBhaNiwocY1FgXJl32PAEqdltnBwQELFiyAQqHAzZs38f333yMxMbFYSDMxMXnhyf6zP4umpqbK2jWt9UWB6WU/u+oq6fuso6ODfv36YdWqVcjNzYVcLkdISAjy8/NVgkVZfucSVXYMFkSVTPv27dG2bVsAT66qd+rUCUOGDMHVq1dhbGysnD9+8uTJJV7FBYqfSD6PQqFAixYtMGfOnBKft7e3f+72AwcOxPfff4+kpCSYmJggPDwcgwcPVl4hL6r3rbfeKjYWo0jLli1VHqvTWgE8GYMQFhaGc+fOwcvLq8R1zp07BwBqXUV+2ssc55Lq/uCDD7BkyRJ89NFH8PT0hJmZGWQyGQYNGlTqvQDUVdpUo6WdJGrK2dkZAHD+/Hm4ubmpvd2L6rp58ya8vb3h7OyMOXPmwN7eHnp6eti2bRt+/fXXYselpOOq6T5elqY/H+p+dgcMGIDOnTsjNDQUu3btwuzZs/Hjjz8iJCQEvXr1KnPd6qpduzaA/4XRZxkZGamMTerYsSPatGmDzz//HL///rtyuYuLC6KjoxETE1MsWBZ59mfR2dkZZ86cwb179174e+Zpjx8/LvHCwNM0/eyWFlQKCwtLXF7a93nQoEH4559/sH37dgQGBmLdunVwdnZGq1atlOuU9XcuUWXGYEFUiWlra2PWrFno1q0b/vzzT0yZMkV5RVNXV1flD35JGjVqhAsXLrxwnbNnz8Lb21utriHPGjhwIL7++mts3LgRNjY2SEtLUw5SBAArKyuYmJigsLDwhfVqqk+fPpg1axaWL19eYrAoLCzEqlWrUKtWLXTs2FHluevXrxdb/9q1a8or+Zoc5+fZsGEDhg8fjl9++UW5LCcnBykpKSrrvcyxf5Gim53duHED3bp1Uy4vKCjAnTt3igW6Z/Xq1Qva2tpYsWJFuQ6C3bx5M3JzcxEeHq5yEqpJFxB199GoUSMAwIULF54buEs7/mX9+XieOnXq4P3338f777+Phw8fok2bNvj++++VwULd1yv6rL7oZ70kRSfgt2/fVmv9li1b4q233sI///yDyZMnK499nz59sHr1aixfvhxffvllse3S0tKwadMmODs7K78P/v7+WL16NVasWIGpU6eq9foFBQW4d+8e+vbt+9z1NP3s1qpVq9jPJACN70Tu5eWFOnXqYO3atejUqRP27duHL774QmWdivxMEUmNYyyIKrmuXbuiffv2mDt3LnJycmBtbY2uXbvin3/+QVxcXLH1ExMTlf/v168fzp49i9DQ0GLrFV09HjBgAB48eIAFCxYUWyc7O1s5u1FpXFxc0KJFC6xduxZr165FnTp1VE7ytbW10a9fP2zcuLHEE5+n69VUhw4d4OPjgyVLlpR4Z98vvvgC165dw6efflrsCmNYWJjKGIkTJ07g+PHjypM6TY7z82hraxdrQfjjjz+KXQk1MjICgBJPbl5W27ZtUbt2bSxYsAAFBQXK5StXriz1CvXT7O3tMXr0aOzatQt//PFHsecVCgV++eUX3L9/X6O6ilo0nu0WtmTJknLfR48ePWBiYoJZs2YhJydH5bmntzUyMiqxa1pZfz5KUlhYWOy1rK2tUbduXeTm5r6wpmdZWVnBy8sLixcvRkxMjMpzL2q9srOzg729vUZ3of7000+Rn5+vcsX9jTfegKurK3744Ydi+1IoFBg7diweP36Mr776SmWbFi1a4Pvvv8fRo0eLvU56enqxk/JLly4hJycHHTp0eG6Nmn52GzVqhNTUVGWrCgDExcWV+LvzebS0tPDGG29g8+bN+O+//1BQUKDSDQqomM8UUWXBFguiKuCTTz5B//79sXTpUowZMwZ//fUXOnXqhBYtWmD06NFo2LAhEhIScPToUdy/fx9nz55Vbld0R+eRI0fC3d0dycnJCA8Px/z589GqVSsMHToU69atw5gxY7B//3507NgRhYWFuHLlCtatW4edO3cqu2aVZuDAgZg+fTr09fUxatQoaGmpXrP44YcfsH//fnh4eGD06NFwdXVFcnIyTp8+jT179iA5Ofmlj83y5cvh7e2NgIAADBkyBJ07d0Zubi5CQkIQERGBgQMH4pNPPim2XePGjdGpUyeMHTsWubm5mDt3LmrXro1PP/1UuY66x/l5+vTpg//++w9mZmZwdXXF0aNHsWfPHmUXlCJubm7Q1tbGjz/+iNTUVMjlcnTv3h3W1tYvfWz09PQwY8YMfPDBB+jevTsGDBiAO3fuYOnSpWjUqJFaV0t/+eUX3Lx5ExMmTEBISAj69OmDWrVqISYmBuvXr8eVK1dUWqjU0aNHD+jp6cHf3x/vvfceMjIysGDBAlhbW5cY4sqyD1NTU/z6669455130K5dOwwZMgS1atXC2bNnkZWVhWXLlgEA3N3dsXbtWkyaNAnt2rWDsbEx/P39y+Xn41np6emoV68e3njjDbRq1QrGxsbYs2cPoqKiVFq2SqupJL///js6deqENm3a4N1334WjoyPu3LmDrVu3Ijo6+rn1BAQEIDQ0VK2xC8CTrkx+fn5YuHAhpk2bhtq1a0NPTw8bNmyAt7c3OnXqpHLn7VWrVuH06dP4+OOPVT4rurq6CAkJgY+PD7y8vDBgwAB07NgRurq6uHjxorK18enpcnfv3g1DQ0O8/vrrL6xTk8/uoEGD8NlnnyEoKAgTJkxAVlYW5s2bh6ZNm2o8ycLAgQPxxx9/4KuvvkKLFi2KTRtdEZ8pokrj1U9ERUQlKe0GeUI8ubNro0aNRKNGjZTTmd68eVMMGzZM2NraCl1dXWFnZyf69OkjNmzYoLLto0ePxPjx44WdnZ3yRkzDhw9Xmfo1Ly9P/Pjjj6JZs2ZCLpeLWrVqCXd3d/H111+L1NRU5XrPTjdb5Pr168qbeB0+fLjE95eQkCDGjRsn7O3tha6urrC1tRXe3t7i33//Va5TNI3q+vXrNTp26enpYsaMGaJZs2bCwMBAmJiYiI4dO4qlS5cWm27z6Rvk/fLLL8Le3l7I5XLRuXNncfbs2WL7Vuc4P+979/jxYzFixAhhaWkpjI2Nha+vr7hy5UqJx3LBggWiYcOGQltbW60b5D17nEq7cdrvv/8uGjRoIORyuWjfvr2IjIwU7u7uomfPnmoc3Sd3KV64cKHo3LmzMDMzE7q6uqJBgwZixIgRKtN5lnbn7aLj8/RNAcPDw0XLli2Fvr6+cHBwED/++KNYvHhxsfWKbpBXEnX3UbRuhw4dhIGBgTA1NRXt27cXq1evVj6fkZEhhgwZIszNzYvdIE/dnw/8/43TSoKnppvNzc0Vn3zyiWjVqpUwMTERRkZGolWrVsVu7ldaTaV9ny9cuCCCgoKEubm50NfXF05OTmLatGkl1vO006dPCwDFpj8t7QZ5QggRERFRbApdIYR4+PChmDRpkmjcuLGQy+XC3Nxc+Pj4KKeYLcnjx4/F9OnTRYsWLYShoaHQ19cXzZs3F1OnThVxcXEq63p4eIi33nrrhe+piLqfXSGE2LVrl2jevLnQ09MTTk5OYsWKFc+9QV5pFAqFsLe3FwDEd999V+I66n6miKoamRDlNMqPiKgKuHPnDhwdHTF79mxMnjxZ6nIkoVAoYGVlheDg4BK7Y1DN4+3tjbp16+K///6TupRSRUdHo02bNjh9+rRGkwkQ0avDMRZERNVYTk5OsX72y5cvR3JyMrp27SpNUVTpzJw5E2vXrtV4sPKr9MMPP+CNN95gqCCqxDjGgoioGjt27BgmTpyI/v37o3bt2jh9+jQWLVqE5s2bo3///lKXR5WEh4cH8vLypC7judasWSN1CUT0AgwWRETVmIODA+zt7fH7778jOTkZFhYWGDZsGH744QeVu3YTERGVFcdYEBERERFRmXGMBRERERERlRmDBRERERERlVmNG2OhUCgQGxsLExMTtW4ERERERERUUwkhkJ6ejrp16xa7Ae6zalywiI2Nhb29vdRlEBERERFVGffu3UO9evWeu06NCxYmJiYAnhwcU1NTiashIiIiIqq80tLSYG9vrzyHfp4aFyyKuj+ZmpoyWBARERERqUGdIQQcvE1ERERERGXGYEFERERERGXGYEFERERERGXGYEFERERERGXGYEFERERERGXGYEFERERERGXGYEFERERERGXGYEFERERERGXGYEFERERERGXGYEFERERERGXGYEFERERERGXGYEFERERERGXGYEFERERERGXGYEFERERERGXGYEFERERERGXGYEFERERERGUmabA4ePAg/P39UbduXchkMoSFhb1wm4iICLRp0wZyuRyNGzfG0qVLK7xOIiIiIiJ6PkmDRWZmJlq1aoW//vpLrfVv376N3r17o1u3boiOjsZHH32Ed955Bzt37qzgSomIiIiI6Hl0pHzxXr16oVevXmqvP3/+fDg6OuKXX34BALi4uODw4cP49ddf4evrW1FlEhERERHRC0gaLDR19OhR+Pj4qCzz9fXFRx99VOo2ubm5yM3NVT5OS0urqPLUtnHHLtzYnQbtgqcOf84tIP0UIPJV1pUBkIlXWx8RERERSUsAKDA1g05aKrQhx0dr/pO6pBeqUsEiPj4eNjY2KstsbGyQlpaG7OxsGBgYFNtm1qxZ+Prrr19ViWq5sTsNxpkWKsty06IhFKklrs9cQURERFRzKHR0kVvXEYXGZtDVk0E/KUXqktRSpYLFy5g6dSomTZqkfJyWlgZ7e3sJK4KypUIBBXLlGf+/NAcyAAIyQOt/AUmLqYKIiIioRsmzrYdCYzNAoYBWgQxakEtdklqqVLCwtbVFQkKCyrKEhASYmpqW2FoBAHK5HHJ55fxm5MozMPm3QADAP2NDkZGcBRMLC7w3b5lynetduqIgIQHJhuZ4s8eXsDXVx7HPvTV7oV9cgPRYwKQu8PHlcnwHRERERFTe0tLSsGnTJvTq1QuWlpZSl6O2KnUfC09PT+zdu1dl2e7du+Hp6SlRRUREREREZRMTE4MDBw4oH5uammLo0KFVKlQAErdYZGRk4MaNG8rHt2/fRnR0NCwsLFC/fn1MnToVDx48wPLlywEAY8aMwZ9//olPP/0UI0eOxL59+7Bu3Tps3bpVqrdARERERPRSCgsLERERgcjISAghYGdnh8aNG0td1kuTNFicPHkS3bp1Uz4uGgsxfPhwLF26FHFxcYiJiVE+7+joiK1bt2LixIn47bffUK9ePSxcuLDKTjWrmy9w0683FJmZKLAyBLS1UJCYiOtduirXKUhMVNmmu+II8OcUIDcDasuIL6eKiYiIiKg8JCUlISQkBHFxcQAANzc3yccBl5WkwaJr164QovTRySXdVbtr1644c+ZMBVb16ujnCuTduvXkgUUDQFsLKFSg4JlxJACQqaUHAHi3YDWQ9ODlXlBu/LKlEhEREVE5EELg5MmT2LVrFwoKCmBgYIA+ffrA1dVV6tLKrEoN3q5uZEWhSkvrSagAUCiTIUnfTGW9LB05/nN50ipjJMt5Mv+sTAswtlX/xeTGQLcvyqNsIiIiInpJYWFhOHfuHACgYcOGCAwMhImJicRVlQ8Gi0pAx8oKOlZWQPIjPNY3xfBe42Ftol9svUZybRgX6ADZeBIqOMMTERERUZXi7OyMS5cuwcfHB+3bt4dMJpO6pHLDYCGh/ILbOOBkj0JdHeQ+fqxcbm3ynCllf9F+RdURERERUVnl5eUhKSkJdevWBQC4uLhgwoQJ1aaV4mkMFhLKyzsDhf6TsRMQiifLtHT/t8LFUGD/TNWB2hyITURERFQl3Lt3D6GhocjJycHYsWOVYaI6hgqAwUJSAvn//x8B49qWiM0UOGbW9n8r7J8JJF0reWMOxCYiIiKqlAoLC3Hw4EEcOnQIQgiYmZkhIyOj2gaKIgwWlYC+QuC9ecvw2sy9iE/LgXJIdlFLxbMDtTkQm4iIiKhSevToEUJCQhAbGwsAaNGiBfz8/KCvX3z8bHXDYFEVcKA2ERERUaV36tQp7Ny5E/n5+dDX10fv3r3RvHlzqct6ZRgsiIiIiIjKQWxsLPLz8+Ho6IiAgACYmZm9eKNqhMGiElLeXZsDtYmIiIgqtcLCQmhrP5m109fXF3Xq1IG7u3u1mkZWXVpSF0DFPbm79jXlTFEcqE1ERERUueTl5WHLli1YtWoVxP/f9FhPTw9t27atkaECYItFpWSI7Cf/kWkBtRtzoDYRERFRJfLgwQOEhIQgOTkZAHD37l04ODhIW1QlwGBRCSiEwGsz9+Jheo7qE8a2wPgoaYoiIiIiIhUKhQKHDh3CgQMHIISAiYkJAgMDGSr+H4NFJSAEEJ/2v1Ahk8kAIWFBRERERKQiOTkZoaGhuH//PgCgWbNm6N27NwwMDCSurPJgsKgktGSAtYk+jOTaMC7QQVFvKCIiIiKSlhACGzduRGxsLORyOfz8/NCiRYsaO5aiNAwWlcQgo9OYaRr+5KZ4OYlSl0NERERE/08mk6F3797Ys2cP+vbtC3Nzc6lLqpQYLCqJJzNBPVBdyNmgiIiIiCRx48YNpKSkoG3btgCAunXrYtiwYRJXVbkxWFQSKjNBGds+CRWcDYqIiIjolcrPz8fu3bsRFRUFLS0t2Nvbw8bGRuqyqgQGi8rG2Bb4+LLUVRARERHVOHFxcQgJCUFSUhIAoG3btrCwsJC4qqqDwYKIiIiIajSFQoEjR45g//79UCgUMDY2RkBAABo3bix1aVUKgwURERER1VhCCKxcuRK3bt0CALi4uKBPnz4wNDSUuLKqh8GCiIiIiGosmUyGRo0a4f79++jZsyfc3Nw4jexLYrAgIiIioholOzsbmZmZsLS0BAB4enqiWbNmMDMzk7iyqo3BgoiIiIhqjFu3biEsLAx6enp47733oKurC5lMxlBRDhgsiIiIiKjaKygowN69e3Hs2DEAQO3atZGens5Zn8oRgwURERERVWsJCQkICQnBw4cPAQDu7u7o0aMH9PT0JK6semGwICIiIqJqSQiBY8eOYe/evSgsLISRkRH69u2Lpk2bSl1atcRgQURERETV1o0bN1BYWIimTZuib9++MDIykrqkaovBgoiIiIiqFYVCAS0tLchkMgQEBODGjRto3bo1p5GtYAwWEtDNF1KXQERERFTt5OTkYNu2bdDV1YW/vz8AwNTUFG3atJG4spqBwUIC+rkCBbr/eyyYnomIiIjK5M6dOwgNDUVaWhpkMhk6dOiA2rVrS11WjcJgIQGZUG2x0NFVoDZSpCmGiIiIqAorKCjA/v37ceTIEQBArVq1EBwczFAhAQaLSkBHuxDaUDx5IDeWthgiIiKiKuLhw4cICQlBQkICAKB169bo2bMnp5GVCINFJSGgBZllY6DbF1KXQkRERFTpFRYWYuXKlUhLS4OhoSH8/f3h7OwsdVk1GoNFJSEzsQXGR0ldBhEREVGVoK2tjV69euHUqVMICAiAsTF7fUiNwYKIiIiIqoRLly5BW1sbTk5OAABnZ2c4OTlxGtlKgsGCiIiIiCq13NxcbN++HWfPnoWBgQHGjh0LExMTAGCoqEQYLCoLDtomIiIiKiYmJgahoaFISUmBTCaDu7s7DA0NpS6LSsBgUVlw0DYRERGRUmFhISIiIhAZGQkhBMzNzREUFIT69etLXRqVgsGiElBAC2gWKHUZRERERJVCfn4+lixZgri4OABAq1at0KtXL8jlcokro+dhsCAiIiKiSkVXVxd169bF48eP4e/vD1dXV6lLIjUwWBARERGR5DIyMiCEUA7K7tGjB7y8vGBqaipxZaQuBgsiIiIiktSVK1ewefNmWFtbY9iwYZDJZNDT0+MdtKsYBgsiIiIikkReXh527NiBM2fOAACys7ORlZUFIyMjiSujl8FgIYH8gtvIzd4EIbKlLoWIiIhIEvfv30dISAgeP34MAOjQoQO6desGHR2enlZV/M5JIC/vDIRIVT7W1VJIWA0RERHRq1NYWIhDhw7h4MGDEELAzMwMgYGBcHBwkLo0KiMGCwkI5P///2Sw0MtEO7sUKcshIiIiemUUCgUuXrwIIQRatGgBPz8/6OvrS10WlQMGCwnJZAYY0eggYFJX6lKIiIiIKowQAgAgk8mgq6uL4OBgJCUloUWLFhJXRuWJwYKIiIiIKkxmZiY2b96MevXqoVOnTgCAOnXqoE6dOhJXRuWNwYKIiIiIKsS1a9cQHh6OzMxM3L59G+7u7jAwMJC6LKogDBZEREREVK7y8/Oxa9cunDx5EgBgZWWF4OBghopqjsGCiIiIiMpNbGwsQkJC8OjRIwDAa6+9Bm9vb04jWwPwO0xERERE5SI7OxvLli1DXl4eTExMEBgYiIYNG0pdFr0iDBZEREREVC4MDAzQtWtXPHjwAL1792bXpxqGwYKIiIiIXooQAtHR0bC2toadnR2AJ12fgCdTy1LNwmBBRERERBrLysrCli1bcPnyZVhYWOC9996Dnp4eA0UNxmBBRERERBq5ceMGNm3ahIyMDGhpaaF169YcnE0MFkRERESknvz8fOzevRtRUVEAAEtLSwQHB/NmdwSAwYKIiIiI1JCRkYFly5YhKSkJANC+fXv4+PhAV1dX4sqosmCwICIiIqIXMjIygpmZGXJychAQEIDGjRtLXRJVMgwWRERERFSilJQUGBgYQC6XQyaTITAwEFpaWjA0NJS6NKqEtKQugIiIiIgqFyEEzp07h/nz52Pnzp3K5cbGxgwVVCq2WBARERGRUnZ2NrZu3YqLFy8CABITE5Gfn8+xFPRCDBZEREREBAC4desWwsLCkJ6eDplMhq5du6JTp07Q0mInF3oxBgsiIiKiGq6goAB79+7FsWPHAAAWFhYIDg5W3k2bSB0MFkREREQ1XG5uLs6dOwcAcHd3R48ePaCnpydxVVTVMFgQERER1UBCCMhkMgBPppINCgpCYWEhnJycJK6MqioGCyIiIqIaJjU1FZs2bYK7uzuaNWsGALwvBZUZgwURERFRDXLhwgVs3boVOTk5ePToEZydnaGtrS11WVQNMFgQERER1QA5OTnYtm0bzp8/DwCws7NDUFAQQwWVGwYLIiIiomruzp07CA0NRVpaGmQyGTp37gwvLy+GCipXDBZERERE1dijR4+wfPlyCCFQq1YtBAUFwd7eXuqyqBpisCAiIiKqxmrXro22bduioKAAPXv25DSyVGEYLIiIiIiqESEEoqKi4OTkBDMzMwBAr169lFPLElUU3p+diIiIqJpIT0/HypUrsX37doSGhkKhUAAAQwW9EmyxICIiIqoGLl26hC1btiA7Oxs6Ojpo1qwZAwW9UgwWRERERFVYbm4uduzYgejoaABAnTp1EBwcDEtLS2kLoxqHwYKIiIioinr06BFWrFiBlJQUyGQydOzYEV27duU0siQJBgsiIiKiKsrMzAy6urowNzdHUFAQ6tevL3VJVIMxWBARERFVIY8fP4aZmRm0tLSgo6ODQYMGwcjICHK5XOrSqIbjrFBEREREVUDRNLJ///03IiMjlcstLCwYKqhSkDxY/PXXX3BwcIC+vj48PDxw4sSJ564/d+5cODk5wcDAAPb29pg4cSJycnJeUbUVRG4sdQVERERUiWVkZGD16tXYtm0bCgoKcO/ePQghpC6LSIWkXaHWrl2LSZMmYf78+fDw8MDcuXPh6+uLq1evwtrautj6q1atwpQpU7B48WJ06NAB165dw9tvvw2ZTIY5c+ZI8A7KgWVToNsXUldBREREldSVK1ewefNmZGVlQVtbGz4+PvDw8OBUslTpSBos5syZg9GjR2PEiBEAgPnz52Pr1q1YvHgxpkyZUmz9I0eOoGPHjhgyZAgAwMHBAYMHD8bx48dfad3lanyU1BUQERFRJZSXl4cdO3bgzJkzAAAbGxsEBweXePGVqDKQrCtUXl4eTp06BR8fn/8Vo6UFHx8fHD16tMRtOnTogFOnTim7S926dQvbtm2Dn5/fK6mZiIiI6FVJTU3FuXPnADw5B3rnnXcYKqhSk6zFIikpCYWFhbCxsVFZbmNjgytXrpS4zZAhQ5CUlIROnTpBCIGCggKMGTMGn3/+eamvk5ubi9zcXOXjtLS08nkDREREROVMCKHs4mRlZYU+ffrA3NwcDg4O0hZGpAbJB29rIiIiAjNnzsTff/+N06dPIyQkBFu3bsW3335b6jazZs2CmZmZ8sve3v4VVkxERESknkePHmHJkiW4f/++cpmbmxtDBVUZkgULS0tLaGtrIyEhQWV5QkICbG1tS9xm2rRpGDp0KN555x20aNECQUFBmDlzJmbNmgWFQlHiNlOnTkVqaqry6969e+X+XoiIiIhelhACp06dwj///IN79+5h+/btnPGJqiTJgoWenh7c3d2xd+9e5TKFQoG9e/fC09OzxG2ysrKgpaVactEt60v7AZTL5TA1NVX5IiIiIqoMMjMzsXbtWmzZsgX5+flwcHDAgAEDOOMTVUmSzgo1adIkDB8+HG3btkX79u0xd+5cZGZmKmeJGjZsGOzs7DBr1iwAgL+/P+bMmYPWrVvDw8MDN27cwLRp0+Dv768MGERERERVwbVr1xAeHo7MzExoaWnB29sbnp6eDBVUZUkaLAYOHIjExERMnz4d8fHxcHNzw44dO5QDumNiYlRaKL788kvIZDJ8+eWXePDgAaysrODv74/vv/9eqrdAREREpLE7d+5g9erVAJ4M0g4ODi61KzhRVSETNawTX1paGszMzJCamipZt6g5gwZAiCzIZIaYtGadJDUQERGRdIQQWL16NSwsLODj4wMdHUmv9RKVSpNzZ36KiYiIiCqYQqHAiRMn0Lp1a8jlcshkMgwaNKjY2FGiqozBgoiIiKgCPX78GKGhobh37x4SEhIQEBAAAAwVVO0wWBARERFVACEEoqOjsWPHDuTl5UFPT4/3pKBqjcGCiIiIqJxlZWVhy5YtuHz5MgCgfv36CAoKgrm5ubSFEVUgBgsiIiKicvTgwQOsWbMGGRkZ0NLSQrdu3dChQwd2faJqj8GCiIiIqByZm5tDCAFLS0sEBwejTp06UpdE9EowWBARERGVUUpKirKbk5GREYYOHQoLCwvo6upKWxjRK8Q2OSIiIqKXpFAocPjwYfzxxx+4cOGCcrmNjQ1DBdU4bLEgIiIiegkpKSkICwvD3bt3AQA3b95E8+bNJa6KSDoMFkREREQaEELg/Pnz2LZtG3Jzc6Gnp4eePXvCzc1N6tKIJMVgQURERKSm7OxsbN26FRcvXgQA1KtXD0FBQbCwsJC4MiLpMVgQERERqSkuLg4XL16ETCZDly5d0LlzZ04jS/T/GCyIiIiI1NSwYUP4+PjAwcEBdnZ2UpdDVKkwYhMRERGVIiEhAUuWLEFKSopyWceOHRkqiErAYEFERET0DCEEjh49igULFiAmJgY7d+6UuiSiSo9doYiIiIiekpqaik2bNuH27dsAgKZNm6J3794SV0VU+TFYEBEREf2/CxcuYOvWrcjJyYGuri58fX3Rpk0byGQyqUsjqvQYLIiIiIgAnD9/HiEhIQAAOzs7BAUFoXbt2hJXRVR1MFgQERERAXBxcYGtrS2aNm0KLy8vaGtrS10SUZXCYEFEREQ1UkFBAU6fPo22bdtCS0sLOjo6eOeddxgoiF4SgwURERHVOImJiQgJCUF8fDxycnLg5eUFAAwVRGXAYEFEREQ1hhACJ06cwJ49e1BQUAADAwNYW1tLXRZRtcBgQURERDVCeno6Nm3ahJs3bwIAGjdujL59+8LExETiyoiqBwYLIiIiqvZu3ryJjRs3Ijs7Gzo6Onj99dfRrl07TiNLVI4YLIiIiKjaMzU1RX5+PurUqYOgoCBYWVlJXRJRtcNgQURERNVSWloaTE1NAQBWVlYYNmwY6tatywHaRBVES+oCiIiIiMpTYWEh9u3bh99++w337t1TLre3t2eoIKpAbLEgIiKiaiMpKQmhoaGIjY0FAFy7dg329vYSV0VUMzBYEBERUZUnhMDJkyexa9cuFBQUQF9fH3369EGzZs2kLo2oxmCwICIioiotIyMD4eHhuH79OgCgYcOGCAgIUI6vIKJXg8GCiIiIqrRr167h+vXr0NbWho+PDzw8PDiNLJEEGCyIiIioSmvdujWSkpLg5ubGu2gTSYizQhEREVGVcv/+ffz333/IyckBAMhkMvTo0YOhgkhiDBZERERUJSgUCkRERGDx4sW4desWIiIipC6JiJ7CrlBERERU6SUnJyMkJAQPHjwAALRo0QJdu3aVtigiUsFgQURERJWWEAJnzpzBjh07kJ+fD7lcjt69e6NFixZSl0ZEz2CwICIiokrryJEj2LNnDwDAwcEBgYGBMDMzk7gqIioJgwURERFVWq1bt0ZUVBTat28PT09PTiNLVIkxWBAREVGlkZ+fjwsXLqB169YAAENDQ4wfPx46OjxlIars+FNKRERElUJsbCxCQkLw6NEj6OjoKMdRMFQQVQ38SSUiIiJJKRQKHD58GAcOHIBCoYCJiQmMjIykLouINMRgQURERJJ5/PgxQkNDce/ePQCAq6sr+vTpAwMDA4krIyJNMVgQERGRJC5evIjw8HDk5eVBT08Pfn5+aNmyJQdoE1VRDBZEREQkCQMDA+Tl5aF+/foICgqCubm51CURURkwWBAREdErk5mZqRw/0bBhQwwdOhQODg7Q0tKSuDIiKiv+FBMREVGFy8/Px/bt2/HHH3/g8ePHyuUNGzZkqCCqJthiQURERBUqLi4OoaGhSExMBABcu3YNHh4eEldFROWNwYKIiIgqhEKhwJEjR7B//34oFAoYGxujb9++aNKkidSlEVEFYLAgIiKicpeSkoKwsDDcvXsXAODs7Iw+ffrw/hRE1RiDBREREZW706dP4+7du9DV1UXPnj3RunVrTiNLVM0xWBAREVG58/LyQkZGBjp16gQLCwupyyGiV4DTMBAREVGZ3b59G+vWrYNCoQAA6OjooG/fvgwVRDUIWyyIiIjopRUUFGDv3r04duwYAOD48ePw9PSUuCoikgKDBREREb2UhIQEhISE4OHDhwAAd3d3uLu7S1wVEUmFwYKIiIg0IoTAsWPHsHfvXhQWFsLQ0BB9+/aFk5OT1KURkYQYLIiIiEgjO3bswIkTJwAATZs2hb+/P4yNjSWuioikxmBBREREGmnXrh0uXLiAbt26wd3dndPIEhEABgsiIiJ6gZycHNy6dQuurq4AAEtLS3z44YfQ09OTuDIiqkw43SwRERGV6u7du5g/fz42bNiAmJgY5XKGCiJ6FlssiIiIqJjCwkLs378fkZGRAIBatWpBS4vXI4modAwWREREpCIxMREhISGIj48HALi5uaFnz56Qy+USV0ZElRmDBRERESmdOnUKO3bsQEFBAQwMDODv7w8XFxepyyKiKoDBgoiIiFQUFBSgcePG6Nu3L0xMTKQuh4iqCAYLIiKiGi47OxsGBgYAgDZt2sDY2BhNmzblNLJEpBGOwiIiIqqhcnNzsWnTJvzzzz/IyckBAMhkMjg5OTFUEJHG2GJBRERUA8XExCA0NBQpKSkAgJs3b6JZs2bSFkVEVRqDBRERUQ1SWFiIAwcO4PDhwxBCwMzMDEFBQWjQoIHUpRFRFcdgQUREVEMkJSUhNDQUsbGxAIBWrVqhZ8+e0NfXl7gyIqoOGCyIiIhqiIMHDyI2Nhb6+vro06cPuz4RUblisCAiIqohevbsCQDw8fGBqampxNUQUXVTplmhimaQICIiosrn6tWr2Lp1K4QQAABDQ0MEBwczVBBRhdA4WCgUCnz77bews7ODsbExbt26BQCYNm0aFi1aVO4FEhERkWby8vKwefNmrFmzBidPnsSVK1ekLomIagCNg8V3332HpUuX4qeffoKenp5yefPmzbFw4cJyLY6IiIg0c//+ffzzzz84ffo0AMDT0xNNmjSRuCoiqgk0HmOxfPly/Pvvv/D29saYMWOUy1u1asUrIkRERBJRKBQ4ePAgDh48CCEETE1NERgYCEdHR6lLI6IaQuNg8eDBAzRu3LjYcoVCgfz8/HIpioiIiDSzceNGXLp0CcCTXgS9e/fmNLJE9Epp3BXK1dUVhw4dKrZ8w4YNaN26dbkURURERJpp27Yt9PX1ERwcjH79+jFUENErp3GLxfTp0zF8+HA8ePAACoUCISEhuHr1KpYvX44tW7ZURI1ERET0jMzMTMTHx6NRo0YAAEdHR3z00UeQy+USV0ZENZXGLRYBAQHYvHkz9uzZAyMjI0yfPh2XL1/G5s2b8frrr1dEjURERPSU69evY968eVi7di2Sk5OVyxkqiEhKL3WDvM6dO2P37t3lXQsRERE9R35+Pnbt2oWTJ08CAKysrFBQUCBxVURET2gcLBo2bIioqCjUrl1bZXlKSgratGmjvK8FERERlZ/Y2FiEhITg0aNHAAAPDw94e3tDV1dX4sqIiJ7QOFjcuXMHhYWFxZbn5ubiwYMH5VIUERER/c/hw4exf/9+KBQKmJiYICAgQDm2goioslA7WISHhyv/v3PnTpiZmSkfFxYWYu/evXBwcNC4gL/++guzZ89GfHw8WrVqhT/++APt27cvdf2UlBR88cUXCAkJQXJyMho0aIC5c+fCz89P49cmIiKqCnJycqBQKODq6orevXvD0NBQ6pKIiIpRO1gEBgYCAGQyGYYPH67ynK6uLhwcHPDLL79o9OJr167FpEmTMH/+fHh4eGDu3Lnw9fXF1atXYW1tXWz9vLw8vP7667C2tsaGDRtgZ2eHu3fvwtzcXKPXJSIiqsyEEMjLy1MOxu7WrRvs7Ozg7OwMmUwmcXVERCVTO1goFAoAT6azi4qKgqWlZZlffM6cORg9ejRGjBgBAJg/fz62bt2KxYsXY8qUKcXWX7x4MZKTk3HkyBFln9KXaSUhIiKqrLKysrBlyxakpqZi5MiR0NbWhra2NlxcXKQujYjouTSebvb27dvlEiry8vJw6tQp+Pj4/K8YLS34+Pjg6NGjJW4THh4OT09PjBs3DjY2NmjevDlmzpxZ4pgPIiKiqubmzZuYN28eLl++jPj4eNy/f1/qkoiI1PZS081mZmbiwIEDiImJQV5enspzEyZMUGsfSUlJKCwshI2NjcpyGxsbXLlypcRtbt26hX379uHNN9/Etm3bcOPGDbz//vvIz8/HV199VeI2ubm5yM3NVT5OS0tTqz4iIqJXJT8/H3v27MGJEycAAJaWlggKCkLdunUlroyISH0aB4szZ87Az88PWVlZyMzMhIWFBZKSkmBoaAhra2u1g8XLUCgUsLa2xr///gttbW24u7vjwYMHmD17dqnBYtasWfj6668rrCYiIqKyiI+PR0hICBITEwEA7dq1w+uvv85pZImoytG4K9TEiRPh7++Px48fw8DAAMeOHcPdu3fh7u6On3/+We39WFpaQltbGwkJCSrLExISYGtrW+I2derUQdOmTaGtra1c5uLigvj4+GItJ0WmTp2K1NRU5de9e/fUrpGIiKgiCSGwfft2JCYmwtjYGEOGDIGfnx9DBRFVSRoHi+joaHz88cfQ0tKCtrY2cnNzYW9vj59++gmff/652vvR09ODu7s79u7dq1ymUCiwd+9eeHp6lrhNx44dcePGDeVAcgC4du0a6tSpAz09vRK3kcvlMDU1VfkiIiKqDGQyGfr27YvmzZtjzJgxaNKkidQlERG9NI2Dha6uLrS0nmxmbW2NmJgYAICZmZnGrQGTJk3CggULsGzZMly+fBljx45FZmamcpaoYcOGYerUqcr1x44di+TkZHz44Ye4du0atm7dipkzZ2LcuHGavg0iIiJJnD9/HgcPHlQ+rl27Nvr16wcjIyMJqyIiKjuNx1i0bt0aUVFRaNKkCbp06YLp06cjKSkJ//33H5o3b67RvgYOHIjExERMnz4d8fHxcHNzw44dO5QDumNiYpQhBgDs7e2xc+dOTJw4ES1btoSdnR0+/PBDfPbZZ5q+DSIiolcqOzsb27Ztw4ULFwAAjRo1gp2dncRVERGVH5kQQmiywcmTJ5Geno5u3brh4cOHGDZsGI4cOYImTZpg0aJFcHNzq6BSy0daWhrMzMyQmpoqWbeoOYMGQIgsyGSGmLRmnSQ1EBHRq3P79m2EhYUhLS0NMpkMXbp0QefOnVUunhERVUaanDtr3GLRtm1b5f+tra2xY8cOzSskIiKqAQoKCrBv3z7l/ZksLCwQHBzMlgoiqpbK7VLJ6dOn0adPn/LaHRERUZUmhMDy5cuVocLd3R3vvfceQwURVVsaBYudO3di8uTJ+Pzzz3Hr1i0AwJUrVxAYGIh27dqpzNZERERUk8lkMrRu3RqGhoYYNGgQ+vTpU+oMhkRE1YHaXaEWLVqE0aNHw8LCAo8fP8bChQsxZ84cfPDBBxg4cCAuXLgAFxeXiqyViIioUktLS0N6erqyVcLNzQ3Ozs4wMDCQuDIiooqndovFb7/9hh9//BFJSUlYt24dkpKS8Pfff+P8+fOYP38+QwUREdVoFy9exLx587Bu3TpkZ2cDeNJqwVBBRDWF2i0WN2/eRP/+/QEAwcHB0NHRwezZs1GvXr0KK46IiKiyy8nJwfbt23Hu3DkATwZo5+bmMlAQUY2jdrDIzs6GoaEhgCdXYORyOerUqVNhhREREVV2d+/eRWhoKFJTUyGTydC5c2d4eXlBW1tb6tKIiF45jaabXbhwIYyNjQE8mUJv6dKlsLS0VFlnwoQJ5VcdERFRJaRQKLBv3z5ERkYCAGrVqoWgoCDY29tLXBkRkXTUDhb169fHggULlI9tbW3x33//qawjk8kYLIiIqNqTyWR4/PgxgCcDtHv27Am5XC5xVURE0lI7WNy5c6cCyyAiIqrchBAoKCiArq4uZDIZ+vTpgxYtWsDZ2Vnq0oiIKgWN77xNRERU06Snp2PTpk3Q09ND//79lbM9MVQQEf0PgwUREdFzXL58GZs3b0Z2djZ0dHTw6NGjYuMLiYiIwYKIiKhEubm52LFjB6KjowE8GVsYHBzMUEFEVAoGCyIiomfExMQgNDQUKSkpAICOHTuiW7dunEaWiOg5GCyIiIieUlhYqAwVZmZmCAoKQoMGDaQui4io0tN6mY1u3ryJL7/8EoMHD8bDhw8BANu3b8fFixfLtTgiIqJXTVtbGwEBAWjVqhXGjBnDUEFEpCaNg8WBAwfQokULHD9+HCEhIcjIyAAAnD17Fl999VW5F0hERFSRhBA4efKkciwFADg4OCAwMBD6+vrSFUZEVMVoHCymTJmC7777Drt374aenp5yeffu3XHs2LFyLY6IiKgiZWRkYPXq1di6dSu2bduG1NRUqUsiIqqyNB5jcf78eaxatarYcmtrayQlJZVLUURERBXt6tWrCA8PR1ZWFrS1tdG9e3eYmppKXRYRUZWlcbAwNzdHXFwcHB0dVZafOXMGdnZ25VYYERFRRcjLy8POnTtx+vRpAICNjQ2Cg4NhbW0tcWVERFWbxsFi0KBB+Oyzz7B+/XrIZDIoFApERkZi8uTJGDZsWEXUSEREVC4KCgqwYMECZQu7p6cnunfvDh0dTpJIRFRWGo+xmDlzJpydnWFvb4+MjAy4urrCy8sLHTp0wJdfflkRNRIREZULHR0duLi4wNTUFMOGDUOPHj0YKoiIyonGv0319PSwYMECTJs2DRcuXEBGRgZat26NJk2aVER9REREZZKcnAwhBGrXrg0A6NKlCzw9PWFgYCBxZURE1YvGweLw4cPo1KkT6tevj/r161dETURERGUmhMCZM2ewY8cOWFlZYeTIkdDW1oa2tjZDBRFRBdC4K1T37t3h6OiIzz//HJcuXaqImoiIiMokMzMTa9euxebNm5Gfnw9dXV3k5uZKXRYRUbWmcbCIjY3Fxx9/jAMHDqB58+Zwc3PD7Nmzcf/+/Yqoj4iISCPXr1/HvHnzcPXqVWhpacHHxwfDhg2DoaGh1KUREVVrGgcLS0tLjB8/HpGRkbh58yb69++PZcuWwcHBAd27d6+IGomIiF6ooKAA27Ztw6pVq5CZmQkrKyuMHj0aHTt2hJaWxn/uiIhIQ2WaCsPR0RFTpkxBq1atMG3aNBw4cKC86iIiItKITCZDbGwsAMDDwwPe3t7Q1dWVuCoioprjpYNFZGQkVq5ciQ0bNiAnJwcBAQGYNWtWedZGRET0XAqFAkII5aDsoKAgpKSkoFGjRlKXRkRU42gcLKZOnYo1a9YgNjYWr7/+On777TcEBASw7yoREb1Sjx8/RmhoKOzt7fH6668DAGrXrq2cVpaIiF4tjYPFwYMH8cknn2DAgAGwtLSsiJqIiIhKJYTA2bNnsX37duTl5eHhw4fo2LEjL3AREUlM42ARGRlZEXUQERG9UFZWFrZs2YLLly8DAOzt7REUFMRQQURUCagVLMLDw9GrVy/o6uoiPDz8uev27du3XAojIiJ62s2bNxEWFoaMjAxoaWmha9eunPGJiKgSUStYBAYGIj4+HtbW1ggMDCx1PZlMhsLCwvKqjYiICACQk5OD9evXIzc3F5aWlggKCkLdunWlLouIiJ6iVrBQKBQl/p+IiOhV0NfXR8+ePZUTh3AaWSKiykfj9uPly5cjNze32PK8vDwsX768XIoiIqKaTaFQIDIyErdu3VIuc3Nzg5+fH0MFEVElpXGwGDFiBFJTU4stT09Px4gRI8qlKCIiqrlSU1OxfPly7NmzB2FhYSVezCIiospH41mhhBCQyWTFlt+/fx9mZmblUhQREdVM58+fx9atW5GbmwtdXV107doVenp6UpdFRERqUDtYtG7dGjKZDDKZDN7e3tDR+d+mhYWFuH37Nnr27FkhRRIRUfWWnZ2Nbdu24cKFCwCAevXqISgoCBYWFhJXRkRE6lI7WBTNBhUdHQ1fX18YGxsrn9PT04ODgwP69etX7gUSEVH1lpmZiX///RdpaWmQyWTo0qULOnfuzGlkiYiqGLWDxVdffQUAcHBwwMCBA6Gvr19hRRERUc1haGiI+vXrIzY2FkFBQahXr57UJRER0UvQeIzF8OHDK6IOIiKqQR4+fAgjIyMYGRlBJpOhd+/e0NLS4ngKIqIqTK1gYWFhgWvXrsHS0hK1atUqcfB2keTk5HIrjoiIqhchBI4dO4a9e/eiSZMmGDBgAGQyGVvBiYiqAbWCxa+//goTExPl/58XLIiIiEqSlpaGsLAw3L59G8CTiT8KCgp4XwoiompCrWDxdPent99+u6JqISKiaurixYvYsmULcnJyoKOjA19fX7i7u/NCFRFRNaLxGIvTp09DV1cXLVq0AABs2rQJS5YsgaurK2bMmMH+sUREpJSbm4tt27bh3LlzAIC6desiKCgIlpaWEldGRETlTeO5/N577z1cu3YNAHDr1i0MHDgQhoaGWL9+PT799NNyL5CIiKouIQTu3r0LmUwGLy8vjBw5kqGCiKia0rjF4tq1a3BzcwMArF+/Hl26dMGqVasQGRmJQYMGYe7cueVcIhERVSWFhYXQ0tJSDsouuseRvb29xJUREVFF0rjFQggBhUIBANizZw/8/PwAPPmDkZSUVL7VERFRlZKYmIiFCxfi1KlTymX29vYMFURENYDGLRZt27bFd999Bx8fHxw4cADz5s0DANy+fRs2NjblXiAREVV+QghERUVh9+7dKCgowOHDh+Hm5gYdHY3/zBARURWl8W/8uXPn4s0330RYWBi++OILNG7cGACwYcMGdOjQodwLJCKiyi09PR3h4eG4ceMGAKBRo0YICAhgqCAiqmE0/q3fsmVLnD9/vtjy2bNnQ1tbu1yKIiKiquHy5cvYvHkzsrOzoaOjg9dffx3t2rXjNLJERDXQS19OOnXqFC5fvgwAcHV1RZs2bcqtKCIiqvySk5Oxfv16CCFga2uL4OBgWFlZSV0WERFJRONg8fDhQwwcOBAHDhyAubk5ACAlJQXdunXDmjVr+EeFiKiGsLCwgJeXFwoKCtCtWze2WhMR1XAazwr1wQcfICMjAxcvXkRycjKSk5Nx4cIFpKWlYcKECRVRIxERVQKFhYWIiIhAYmKiclnXrl3h4+PDUEFERJq3WOzYsQN79uyBi4uLcpmrqyv++usv9OjRo1yLIyKiyuHRo0cICQlBbGwsrl69itGjR0NLS+NrU0REVI1pHCwUCgV0dXWLLdfV1VXe34KIiKoHIQROnTqFXbt2IT8/H/r6+ujUqRNDBRERFaNxsOjevTs+/PBDrF69GnXr1gUAPHjwABMnToS3t3e5F0hERNLIyMhAeHg4rl+/DgBwdHREYGAgTE1NJa6MiIgqI42DxZ9//om+ffvCwcFBeSfVe/fuoXnz5lixYkW5F0hERK9eUlISlixZgqysLGhra8Pb2xuvvfYap5ElIqJSaRws7O3tcfr0aezdu1c53ayLiwt8fHzKvTgiIpKGhYUFLCwsYGxsjODgYNjY2EhdEhERVXIaBYu1a9ciPDwceXl58Pb2xgcffFBRdRER0SsWFxcHKysr6OjoQEtLCwMHDoS+vj7voE1ERGpRe/TdvHnzMHjwYJw8eRLXr1/HuHHj8Mknn1RkbURE9AooFAocOHAACxYswL59+5TLjY2NGSqIiEhtageLP//8E1999RWuXr2K6OhoLFu2DH///XdF1kZERBUsOTkZS5YsQUREBIQQSE9PhxBC6rKIiKgKUjtY3Lp1C8OHD1c+HjJkCAoKChAXF1chhRERUcURQuD06dOYP38+7t+/D7lcjuDgYPTr148DtImI6KWo3cadm5sLIyMj5WMtLS3o6ekhOzu7QgojIqKKkZWVhc2bN+PKlSsAgAYNGiAwMBDm5ubSFkZERFWaRp1np02bBkNDQ+XjvLw8fP/99zAzM1MumzNnTvlVR0RE5S4vLw+3bt2ClpYWunfvDk9PT97wjoiIykztYOHl5YWrV6+qLOvQoQNu3bqlfMzmcyKiykmhUCjDg7m5OYKDg2FmZgZbW1uJKyMioupC7WARERFRgWUQEVFFiY2NRWhoKHr27IlGjRoBAJycnCSuioiIqhvOI0hEVE0pFApERkYiIiICCoUC+/btQ8OGDdm6TEREFYLBgoioGnr8+DHCwsIQExMDAHB1dUXv3r0ZKoiIqMIwWBARVSNCCJw7dw7btm1DXl4e9PT04Ofnh5YtWzJUEBFRhWKwICKqRmJiYhAWFgYAsLe3R1BQEGrVqiVtUUREVCMwWBARVSMNGjSAm5sbLCws0LFjR04jS0REr8xL/cU5dOgQ3nrrLXh6euLBgwcAgP/++w+HDx8u1+KIiOj58vPzsXfvXmRmZiqX9e3bF507d2aoICKiV0rjvzobN26Er68vDAwMcObMGeTm5gIAUlNTMXPmzHIvkIiIShYfH48FCxbg8OHD2Lx5s3I5x1IQEZEUNA4W3333HebPn48FCxZAV1dXubxjx444ffp0uRZHRETFCSEQGRmJBQsWIDExEUZGRnB3d5e6LCIiquE0HmNx9epVeHl5FVtuZmaGlJSU8qiJiIhKkZqairCwMNy5cwfAkxvd+fv7w8jISNrCiIioxtM4WNja2uLGjRtwcHBQWX748GE0bNiwvOoiIqJn3Lt3DytXrkRubi50dXXRs2dPtG7dml2fiIioUtA4WIwePRoffvghFi9eDJlMhtjYWBw9ehSTJ0/GtGnTKqJGIiICYG1tDQMDA1hZWSEoKAgWFhZSl0RERKSkcbCYMmUKFAoFvL29kZWVBS8vL8jlckyePBkffPBBRdRIRFRjxcXFwdbWFjKZDHK5HMOGDYOZmRlnfCIiokpH42Ahk8nwxRdf4JNPPsGNGzeQkZEBV1dXGBsbV0R9REQ1UkFBAfbt24ejR4/Cz88P7dq1AwDe7I6IiCqtl77kpaenB1dXV7Rv377MoeKvv/6Cg4MD9PX14eHhgRMnTqi13Zo1ayCTyRAYGFim1yciqkwSEhKwcOFCHD16FADw6NEjiSsiIiJ6MY1bLLp16/bcgYL79u3TaH9r167FpEmTMH/+fHh4eGDu3Lnw9fXF1atXYW1tXep2d+7cweTJk9G5c2eNXo+IqLISQuDYsWPYu3cvCgsLYWhoiL59+8LJyUnq0oiIiF5I42Dh5uam8jg/Px/R0dG4cOEChg8frnEBc+bMwejRozFixAgAwPz587F161YsXrwYU6ZMKXGbwsJCvPnmm/j6669x6NAhTnNLRFVeWloawsLCcPv2bQBAkyZN0LdvX3YzJSKiKkPjYPHrr7+WuHzGjBnIyMjQaF95eXk4deoUpk6dqlympaUFHx8fZReAknzzzTewtrbGqFGjcOjQoee+Rm5urvLu4MCTP95ERJVNWloa7ty5Ax0dHfj6+sLd3Z3TyBIRUZWicbAozVtvvYX27dvj559/VnubpKQkFBYWwsbGRmW5jY0Nrly5UuI2hw8fxqJFixAdHa3Wa8yaNQtff/212jUREb0qQghleKhXrx78/f1hb28PS0tLiSsjIiLSXLnNV3j06FHo6+uX1+5KlJ6ejqFDh2LBggVq/+GdOnUqUlNTlV/37t2r0BqJiNRx9+5d/P3333j48KFyWevWrRkqiIioytK4xSI4OFjlsRACcXFxOHnypMY3yLO0tIS2tjYSEhJUlickJMDW1rbY+jdv3sSdO3fg7++vXKZQKAAAOjo6uHr1Kho1aqSyjVwuh1wu16guIqKKUlhYiP379yMyMhIAsH//fgwcOFDiqoiIiMpO42BhZmam8lhLSwtOTk745ptv0KNHD432paenB3d3d+zdu1c5ZaxCocDevXsxfvz4Yus7Ozvj/PnzKsu+/PJLpKen47fffoO9vb1mb4aI6BVKTExESEgI4uPjATyZDKNnz54SV0VERFQ+NAoWhYWFGDFiBFq0aFFuN2maNGkShg8fjrZt26J9+/aYO3cuMjMzlbNEDRs2DHZ2dpg1axb09fXRvHlzle3Nzc0BoNhyIqLKQgiBqKgo7N69GwUFBTAwMIC/vz9cXFykLo2IiKjcaBQstLW10aNHD1y+fLncgsXAgQORmJiI6dOnIz4+Hm5ubtixY4dyQHdMTAy0tMptKAgR0St34cIFbN++HQDQqFEjBAQEwMTEROKqiIiIypfGXaGaN2+OW7duwdHRsdyKGD9+fIldnwAgIiLiudsuXbq03OogIqoIzZo1Q3R0NJo2bYr27dtzGlkiIqqWNG4K+O677zB58mRs2bIFcXFxSEtLU/kiIqrpcnNzsX//fhQUFAB4MhbtrbfegoeHB0MFERFVW2q3WHzzzTf4+OOP4efnBwDo27evyh/IovnYCwsLy79KIqIq4t69ewgNDcXjx4+Rm5urHJzNQEFERNWd2sHi66+/xpgxY7B///6KrIeIqEoqLCzEwYMHcejQIQghYGZmxsHZRERUo6gdLIQQAIAuXbpUWDFERFXRo0ePEBISgtjYWABAy5Yt0atXrwq/aSgREVFlotHgbTblExGpunbtGjZs2ID8/Hzo6+ujd+/enP6aiIhqJI2CRdOmTV8YLpKTk8tUEBFRVWJjYwMtLS04OjoiMDAQpqamUpdEREQkCY2Cxddff13szttERDXNw4cPYW1tDQAwMzPDqFGjYGlpyVZdIiKq0TQKFoMGDVL+MSUiqmny8vKwa9cunDp1Cm+++SYaN24MALCyspK4MiIiIumpHSx4JY6IarIHDx4gJCRE2d0zLi5OGSyIiIjoJWaFIiKqSRQKBQ4dOoQDBw5ACAFTU1MEBgbC0dFR6tKIiIgqFbWDhUKhqMg6iIgqneTkZISGhuL+/fsAgObNm8PPzw8GBgYSV0ZERFT5aDTGgoioJomLi8P9+/chl8vRu3dvtGjRQuqSiIiIKi0GCyKipwghlGPKmjVrhpSUFDRr1gzm5ubSFkZERFTJaUldABFRZXH9+nX8888/yMjIUC7r2LEjQwUREZEaGCyIqMbLz8/Htm3bsGrVKiQkJODgwYNSl0RERFTlsCsUEdVocXFxCAkJQVJSEgDAw8MD3t7eEldFRERU9TBYEFGNpFAoEBkZiYiICCgUChgbGyMwMBCNGjWSujQiIqIqicGCiGqkI0eOYN++fQAAFxcX9OnTB4aGhhJXRUREVHUxWBBRjdSuXTtcvHgRHh4eaNWqlXImKCIiIno5HLxNRDVCdnY2Dh8+DCEEAEAul+Pdd9+Fm5sbQwUREVE5YIsFEVV7t27dQlhYGNLT06GrqwsPDw8AYKAgIiIqRwwWRFRtFRQUYM+ePTh+/DgAoHbt2rC3t5e4KiIiouqJwYKIqqX4+HiEhIQgMTERANC2bVv06NEDurq6EldGRERUPTFYEFG1Ex0djS1btqCwsBBGRkYICAhAkyZNpC6LiIioWmOwIKJqx8rKCgqFAk5OTvD394eRkZHUJREREVV7DBZEVC08evQItWvXBgDY2dnh3XffhY2NDQdoExERvSKcbpaIqrScnBxs3LgR8+bNQ0JCgnK5ra0tQwUREdErxBYLIqqybt++jbCwMKSlpUEmk+H+/fuwsbGRuiwiIqIaicGCiKqcgoIC7Nu3D0ePHgUAWFhYICgoCPXq1ZO4MiIiopqLwYKIqpSHDx8iJCRE2e2pTZs28PX1hZ6ensSVERER1WwMFkRUpVy/fh0JCQkwNDRE37594eTkJHVJREREBAYLIqoChBDKgdienp7IycmBh4cHjI2NJa6MiIiIinBWKCKq1C5evIilS5ciPz8fAKClpQVvb2+GCiIiokqGLRZEVCnl5ORgx44dOHv2LADgxIkT6Nixo8RVERERUWkYLIio0rl79y5CQ0ORmpoKmUyGTp064bXXXpO6LCIiInoOBgsiqjQKCwsRERGBw4cPAwDMzc0RFBSE+vXrS1wZERERvQiDBRFVGjt37kRUVBQAwM3NDT179oRcLpe4KiIiIlIHgwURVRodO3bEzZs34ePjAxcXF6nLISIiIg1wVigikkx6ejpOnjypfGxmZoZx48YxVBAREVVBbLEgIklcvnwZmzdvRnZ2NkxNTdG0aVMAT6aTJSIioqqHwYKIXqnc3Fzs2LED0dHRAABbW1vUqlVL2qKIiIiozBgsiOiVuXfvHkJDQ/H48WMAT8ZUdOvWDdra2hJXRkRERGXFYEFEr8SRI0ewZ88eCCFgZmaGoKAgNGjQQOqyiIiIqJwwWBDRK2Fubg4hBFq2bIlevXpBX19f6pKIiIioHDFYEFGFEEIgNTUV5ubmAABXV1eMGjUK9erVk7YwIiIiqhCcfoWIyl1mZibWrFmDBQsWICMjQ7mcoYKIiKj6YosFEZWra9euITw8HJmZmdDW1sb9+/fh7OwsdVlERERUwRgsiKhc5OXlYdeuXTh16hQAwNraGsHBwbCxsZG4MiIiInoVGCyIqMwePHiAkJAQJCcnAwBee+01eHt7Q0eHv2KIiIhqCv7VJ6IyO3PmDJKTk2FiYoLAwEA0bNhQ6pKIiIjoFWOwIKIy69GjB3R0dNClSxcYGBhIXQ4RERFJgLNCEZFGhBA4c+YM1q5dCyEEAEBPTw89e/ZkqCAiIqrB2GJBRGrLysrC5s2bceXKFQDAxYsX0bx5c4mrIiIiosqAwYKI1HLjxg1s2rQJGRkZ0NLSQvfu3eHq6ip1WURERFRJMFgQ0XPl5+dj9+7diIqKAgBYWloiODgYderUkbgyIiIiqkwYLIjouUJCQpRdn9q3bw8fHx/o6upKXBURERFVNgwWRPRcnTt3RlxcHPr06YPGjRtLXQ4RERFVUgwWRKQiJSUFDx48QLNmzQAAdevWxQcffABtbW2JKyMiIqLKjMGCiAA8mUb23Llz2LZtGwoLC2FpaQkbGxsAYKggIiKiF2KwICJkZ2djy5YtuHTpEgDA3t4eenp6EldFREREVQmDBVENd+vWLYSFhSE9PR1aWlro2rUrOnbsCC0t3j+TiIiI1MdgQVSD7dmzB5GRkQCA2rVrIzg4GHXr1pW4KiIiIqqKGCyIajADAwMAQNu2bdGjRw9OI0skISEECgoKUFhYKHUpRFSDaGtrQ0dHBzKZrMz7YrAgqkGEEMjIyICJiQkAwNPTE/b29qhfv77ElRHVbHl5eYiLi0NWVpbUpRBRDWRoaIg6deqUeXwlgwVRDZGamoqwsDBkZGTg3Xffha6uLrS0tBgqiCSmUChw+/ZtaGtro27dutDT0yuXK4dERC8ihEBeXh4SExNx+/ZtNGnSpExjLBksiGqA8+fPY+vWrcjNzYWuri7i4uIYKIgqiby8PCgUCtjb28PQ0FDqcoiohjEwMICuri7u3r2LvLw86Ovrv/S+GCyIqrGcnBxs3boVFy5cAADY2dkhODgYFhYWEldGRM/iTGxEJJXy+v3DYEFUTd2+fRthYWFIS0uDTCaDl5cXvLy8ePJCREREFYJnGETVkBACkZGRSEtLg4WFBUaOHImuXbsyVBBRpSKTyRAWFiZ1GSpeVNOdO3cgk8kQHR1dptd5++23ERgYWKZ9VBWLFi1Cjx49pC6j2khKSoK1tTXu378vdSnF8CyDqBqSyWTo27cv2rdvj/feew/16tWTuiQiqobmzZuHli1bwtTUFKampvD09MT27dulLovUFBERAZlMhpSUlAp7jZycHEybNg1fffVVsefu378PPT09NG/evNhzzwtwXbt2xUcffaSy7MyZM+jfvz9sbGygr6+PJk2aYPTo0bh27Vp5vZViQkJC0KNHD9SuXVujsLl+/Xo4OztDX18fLVq0wLZt21SeF0Jg+vTpqFOnDgwMDODj44Pr168rn7e0tMSwYcNKPKZSY7AgqgaEEDh27JjKH3RTU1P06tWrzFPHERGVpl69evjhhx9w6tQpnDx5Et27d0dAQAAuXrwodWk1WtE9USrD623YsAGmpqbo2LFjseeWLl2KAQMGIC0tDcePH3/p19+yZQtee+015ObmYuXKlbh8+TJWrFgBMzMzTJs27aX3+yKZmZno1KkTfvzxR7W3OXLkCAYPHoxRo0bhzJkzCAwMRGBgoHIsJAD89NNP+P333zF//nwcP34cRkZG8PX1RU5OjnKdESNGYOXKlUhOTi7X91RWDBZEVVxaWhpWrFiBnTt34sSJE4iJiZG6JCKqIfz9/eHn54cmTZqgadOm+P7772FsbIxjx4691P7Onz+P7t27w8DAALVr18a7776LjIwM5fNF3Yd+/vln1KlTB7Vr18a4ceOQn5+vXCcuLg69e/eGgYEBHB0dsWrVKjg4OGDu3Llq1xEXF4devXrBwMAADRs2xIYNG0pdt7CwEKNGjYKjoyMMDAzg5OSE3377rdg6kyZNgrm5OWrXro1PP/0UQgiVdRQKBWbNmqXcT6tWrZ77uk8rannYvn073N3dIZfLcfjwYeTm5mLChAmwtraGvr4+OnXqhKioKABPWgS6desGAKhVqxZkMhnefvttAHjuds97vZKsWbMG/v7+xZYLIbBkyRIMHToUQ4YMwaJFi9R6r8/KysrCiBEj4Ofnh/DwcPj4+MDR0REeHh74+eef8c8//7zUftUxdOhQTJ8+HT4+Pmpv89tvv6Fnz5745JNP4OLigm+//RZt2rTBn3/+CeDJcZk7dy6+/PJLBAQEoGXLlli+fDliY2NVuug1a9YMdevWRWhoaHm/rTJhsCCqwi5duoR58+bh1q1b0NHRgZ+fH+zt7aUui4hqoMLCQqxZswaZmZnw9PTUePvMzEz4+vqiVq1aiIqKwvr167Fnzx6MHz9eZb39+/fj5s2b2L9/P5YtW4alS5di6dKlyueHDRuG2NhYREREYOPGjfj333/x8OFDjWqZNm0a+vXrh7Nnz+LNN9/EoEGDcPny5RLXVSgUqFevHtavX49Lly5h+vTp+Pzzz7Fu3TrlOr/88guWLl2KxYsX4/Dhw0hOTi52Qjhr1iwsX74c8+fPx8WLFzFx4kS89dZbOHDggNp1T5kyBT/88AMuX76Mli1b4tNPP8XGjRuxbNkynD59Go0bN4avry+Sk5Nhb2+PjRs3AgCuXr2KuLg4ZSB63nbPe72SHD58GG3bti22fP/+/cjKyoKPjw/eeust5WdHUzt37kRSUhI+/fTTEp83NzcvddsxY8bA2Nj4uV/l7ejRo8WCiK+vL44ePQrgycQr8fHxKuuYmZnBw8NDuU6R9u3b49ChQ+VeY1lwViiiKignJwc7duzA2bNnAQB16tRBcHAwLC0tJa6MiMqL/x+HkZie+8pf18pEjs0fdFJ7/fPnz8PT0xM5OTkwNjZGaGgoXF1dNX7dVatWIScnB8uXL4eRkREA4M8//4S/vz9+/PFH2NjYAHhydf3PP/+EtrY2nJ2d0bt3b+zduxejR4/GlStXsGfPHkRFRSlPZhcuXIgmTZpoVEv//v3xzjvvAAC+/fZb7N69G3/88Qf+/vvvYuvq6uri66+/Vj52dHTE0aNHsW7dOgwYMAAAMHfuXEydOhXBwcEAgPnz52Pnzp3KbXJzczFz5kzs2bNHGcoaNmyIw4cP459//kGXLl3Uqvubb77B66+/DuBJUJs3bx6WLl2KXr16AQAWLFiA3bt3Y9GiRfjkk0+UU49bW1srT8DV2a6k1ytJSkoKUlNTUbdu3WLPLVq0CIMGDYK2tjaaN2+Ohg0bYv369cpWE3UVjT1wdnbWaDvgSf2TJ0/WeLuyiI+PV36Wi9jY2CA+Pl75fNGy0tYpUrduXZw5c6YCq9UcgwVRFSOEwH///YfY2FjIZDJ06tQJXbp0gba2ttSlEVE5SkzPRXxazotXlJiTkxOio6ORmpqKDRs2YPjw4Thw4IDG4eLy5cto1aqVMlQAQMeOHaFQKHD16lXliVazZs1Uft/VqVMH58+fB/DkyruOjg7atGmjfL5x48aoVauWRrU82+Li6en53IG5f/31FxYvXoyYmBhkZ2cjLy8Pbm5uAIDU1FTExcXBw8NDub6Ojg7atm2r7A5148YNZGVlFTtJz8vLQ+vWrdWu++mWgZs3byI/P19lbIOuri7at29fauuLptuV1BLxtOzsbAAodsO1lJQUhISEqHSfeuutt7Bo0SKNg8WzXco0YW1tDWtr65feXmoGBgbIysqSugwVDBZEVUxRmNi1axeCgoJ4B22iasrKRF4lXldPTw+NGzcGALi7uyMqKgq//fZbhfVt19XVVXksk8mgUCgq5LXUsWbNGkyePBm//PILPD09YWJigtmzZ2s0GLloHMnWrVthZ2en8pxcrv734+lQ9iq86PWKZkt6/PixyvKi1qmnw5YQAgqFAteuXUPTpk1hamoK4Ekwe1ZKSgrMzMwAAE2bNgUAXLlyReMueGPGjMGKFSueu87TY3zKg62tLRISElSWJSQkwNbWVvl80bI6deqorFMUVoskJyfDysqqXOsrKwYLoiogMTER6enpaNiwIQDAxcUFTZo0gY4Of4SJqitNuiNVJgqFArm5mnfhcnFxwdKlS5GZmak8YY2MjISWlhacnJzU2oeTkxMKCgpw5swZuLu7A3jSGvDsie2LHDt2DMOGDVN5XFrLQWRkJDp06ID3339fuezmzZvK/5uZmaFOnTo4fvw4vLy8AAAFBQU4deqUsmXF1dUVcrkcMTExand7epFGjRpBT08PkZGRaNCgAQAgPz8fUVFRyqlai2YNLCws1Gg7denp6cHV1RWXLl1SuY/FokWL8PHHHxdrnXj//fexePFi/PDDD7CwsIClpSVOnTqlckzS0tJw48YNZaDo0aMHLC0t8dNPP5U4kDklJaXUcRZSdIXy9PTE3r17VY7l7t27laHI0dERtra22Lt3rzJIFM2aNXbsWJV9XbhwAV27dn1FlauHZyVElZgQAlFRUdi9ezd0dXUxduxYmJiYAABDBRFJburUqejVqxfq16+P9PR0rFq1ChERESrjB9T15ptv4quvvsLw4cMxY8YMJCYm4oMPPsDQoUOL9TcvjbOzM3x8fPDuu+9i3rx50NXVxccffwwDAwPIZDK1a1m/fj3atm2LTp06YeXKlThx4kSpsxY1adIEy5cvx86dO+Ho6Ij//vsPUVFRcHR0VK7z4Ycf4ocffkCTJk3g7OyMOXPmqNw7wsTEBJMnT8bEiROhUCjQqVMnpKamIjIyEqamphg+fLjatRcxMjLC2LFjlWMp6tevj59++glZWVkYNWoUAKBBgwaQyWTYsmUL/Pz8YGBgAGNj4xdupwlfX18cPnxYeSIdHR2N06dPY+XKlcXGRQwePBjffPMNvvvuO+jo6GDSpEmYOXMmbGxs8Nprr+HRo0f49ttvYWVlpRyvYmRkhIULF6J///7o27cvJkyYgMaNGyMpKQnr1q1DTEwM1qxZU2JtZe0KlZycjJiYGMTGxgJ40hUPeNLqUNTyMGzYMNjZ2WHWrFkAnnwWunTpgl9++QW9e/fGmjVrcPLkSfz7778AnrTAffTRR/juu+/QpEkTODo6Ytq0aahbt67KDRWzsrJw6tQpzJw586XrrxCihklNTRUARGpqqmQ1/DKwv/h5QG/xy8D+ktVAlV96erpYsWKFmDFjhpgxY4b477//RFpamtRlEVE5y87OFpcuXRLZ2dlSl6KxkSNHigYNGgg9PT1hZWUlvL29xa5du9TeHoAIDQ1VPj537pzo1q2b0NfXFxYWFmL06NEiPT1d+fzw4cNFQECAyj4+/PBD0aVLF+Xj2NhY0atXLyGXy0WDBg3EqlWrhLW1tZg/f77aNf3111/i9ddfF3K5XDg4OIi1a9cqn799+7YAIM6cOSOEECInJ0e8/fbbwszMTJibm4uxY8eKKVOmiFatWim3yc/PFx9++KEwNTUV5ubmYtKkSWLYsGEq70WhUIi5c+cKJycnoaurK6ysrISvr684cODAC2vev3+/ACAeP36ssjw7O1t88MEHwtLSUsjlctGxY0dx4sQJlXW++eYbYWtrK2QymRg+fLha25X2eiW5ePGiMDAwECkpKUIIIcaPHy9cXV1LXDcuLk5oaWmJTZs2CSGEKCgoEL///rto0aKFMDQ0FPXq1RMDBw4Ut2/fLrZtVFSUCA4OFlZWVkIul4vGjRuLd999V1y/fv2FNb6sJUuWCADFvr766ivlOl26dFEe1yLr1q0TTZs2FXp6eqJZs2Zi69atKs8rFAoxbdo0YWNjI+RyufD29hZXr15VWWfVqlXCycmp3N7L834PaXLuLBOiDKNeqqC0tDSYmZkhNTVV2X/vVZszaACEyIJMZohJa9a9eAOqcS5fvozNmzcjOzsb2traeP3119G+fXuNrrgRUdWQk5OD27dvw9HRsdggVyq7+/fvw97eHnv27IG3t7fU5dRI/fv3R5s2bTB16lSpS6k2XnvtNUyYMAFDhgwpl/097/eQJufO7EtBVIkIIbB582bl9HG2trYICgqq0rNWEBG9Svv27UNGRgZatGiBuLg4fPrpp3BwcFCOb6BXb/bs2di8ebPUZVQbSUlJCA4OxuDBg6UupZhKcYO8v/76Cw4ODtDX14eHhwdOnDhR6roLFixA586dUatWLdSqVQs+Pj7PXZ+oKpHJZNDSevJj2aFDB4waNYqhgoiqpJUrV5Z607FmzZpV2Ovm5+fj888/R7NmzRAUFAQrKytERERAV1dXsprK6nk3chszZozU5b2Qg4MDPvjgA6nLqDYsLS3x6aefVspeDJJ3hVq7di2GDRuG+fPnw8PDA3PnzsX69etx9erVEk+o3nzzTXTs2BEdOnSAvr4+fvzxR4SGhuLixYvFpmgrCbtCUWVTWFiIvLw8GBgYAHgyb3l8fDynkSWqIaprV6j09PRi02oW0dXVVc449CpVxprU8fDhQ6SlpZX4nKmpKS9AUZmVV1coyYOFh4cH2rVrhz///BPAk2nq7O3t8cEHH2DKlCkv3L6wsFB5F86np4YrDYMFVSaPHj1CSEgI5HI5hg4dWimvPhBRxaquwYKIqo5qMcYiLy8Pp06dUhnMo6WlBR8fHxw9elStfWRlZSE/P195W/pn5ebmqsynXVriJ3qVhBA4ffo0du7cifz8fOjr6+PRo0ewtLSUujQiIiKilyJpsEhKSkJhYWGx+altbGxw5coVtfbx2WefoW7duvDx8Snx+VmzZuHrr78uc61E5SUzMxPh4eG4du0agCc3wwkICFDeRZSIiIioKqrSs0L98MMPWLNmDSIiIkptPp46dSomTZqkfJyWlgZ7e/tXVSKRimvXriE8PByZmZnQ1taGt7c3XnvtNXaBIiIioipP0mBhaWkJbW3tYgOpEhISlHcsLM3PP/+MH374AXv27EHLli1LXU8ul0Mul5dLvURloVAosGfPHmRmZsLa2hrBwcFq302WiIiIqLKTdLpZPT09uLu7Y+/evcplCoUCe/fuhaenZ6nb/fTTT/j222+xY8cOtG3b9lWUSlRmWlpaCAoKgqenJ0aPHs1QQURERNWK5PexmDRpEhYsWIBly5bh8uXLGDt2LDIzMzFixAgAwLBhw1QGd//444+YNm0aFi9eDAcHB8THxyM+Ph4ZGRlSvQWiEikUChw4cABHjhxRLqtTpw569OgBHZ0q3QuRiKjCOTg4YO7cuVKXAQBYunQpzM3Nn7vOjBkz4ObmVubXkslkCAsLK/N+qgIvLy+sWrVK6jKqjfnz58Pf31/SGiQPFgMHDsTPP/+M6dOnw83NDdHR0dixY4fyam5MTAzi4uKU68+bNw95eXl44403UKdOHeXXzz//LNVbIComOTkZS5YsQUREBPbu3YvHjx9LXRIRUbmbN28eWrZsCVNTU5iamsLT0xPbt2+XuqwXqkyhpTJ6++23ERgYWKGvER4ejoSEBAwaNKjYc7NmzYK2tjZmz55d7LnSAtydO3cgk8kQHR2tXCaEwL///gsPDw8YGxvD3Nwcbdu2xdy5c5GVlVWeb0fFhAkT4O7uDrlcrnbYzMnJwbhx41C7dm0YGxujX79+xYYKxMTEoHfv3jA0NIS1tTU++eQTFBQUKJ8fOXIkTp8+jUOHDpXn29FIpbhsOn78eIwfP77E5yIiIlQe37lzp+ILInpJQghlOM7Ly4NcLoefn98Lr3QREVVF9erVww8//IAmTZpACIFly5YhICAAZ86cqZR3ss7Ly4Oenp7UZUjmVb//573e77//jhEjRkBLq/g17sWLF+PTTz/F4sWL8cknn7z06w8dOhQhISH48ssv8eeff8LKygpnz57F3Llz4eDgUKHhaeTIkTh+/DjOnTun1voTJ07E1q1bsX79epiZmWH8+PEIDg5GZGQkgCf3bevduzdsbW1x5MgRxMXFYdiwYdDV1cXMmTMBPBliMGTIEPz+++/o3Llzhb2355G8xYKousjKysK6desQHh6OvLw8NGjQAGPGjEHLli056xMRVUv+/v7w8/NDkyZN0LRpU3z//fcwNjbGsWPHXritEAIzZsxA/fr1IZfLUbduXUyYMEFlnaysLIwcORImJiaoX78+/v33X5Xnz58/j+7du8PAwAC1a9fGu+++q9I1uujK+/fff4+6devCyckJXbt2xd27dzFx4kTIZDKNfj+HhYWhSZMm0NfXh6+vL+7du1fqulFRUXj99ddhaWkJMzMzdOnSBadPn1ZZ5/r16/Dy8oK+vj5cXV2xe/fuYvu5d+8eBgwYAHNzc1hYWCAgIEDti6wlvX/g+cdtxowZWLZsGTZt2qQ8PkUXeV/meJckMTER+/btK7HbzoEDB5CdnY1vvvkGaWlpKt2JNbFu3TqsXLkSq1evxueff4527drBwcEBAQEB2LdvH7p16/ZS+1XH77//jnHjxqFhw4ZqrZ+amopFixZhzpw56N69O9zd3bFkyRIcOXJE+bO0a9cuXLp0CStWrICbmxt69eqFb7/9Fn/99Rfy8vKU+/L390d4eDiys7Mr5L29CIMFUTkoKCjAv//+iytXrihv8jhs2DC2VBBRjVFYWIg1a9YgMzPzuROwFNm4cSN+/fVX/PPPP7h+/TrCwsLQokULlXV++eUXtG3bFmfOnMH777+PsWPH4urVqwCe3BPI19cXtWrVQlRUFNavX489e/YU6wGxd+9eXL16Fbt378aWLVsQEhKCevXq4ZtvvkFcXJxKd+vnycrKwvfff4/ly5cjMjISKSkpJXbjKZKeno7hw4fj8OHDOHbsGJo0aQI/Pz+kp6cDeDIOLzg4GHp6ejh+/Djmz5+Pzz77TGUf+fn58PX1hYmJCQ4dOoTIyEgYGxujZ8+eKieTz/Ps+3/RcZs8eTIGDBiAnj17Ko9Phw4dXvp4l+Tw4cMwNDSEi4tLsecWLVqEwYMHQ1dXF4MHD8aiRYvUep/PWrlyJZycnBAQEFDsOZlM9tx7RxkbGz/3a8yYMS9VU2lOnTqF/Px8lXuyOTs7o379+sobRh89ehQtWrRQmfjF19cXaWlpuHjxonJZ27ZtUVBQgOPHj5drjeqqFF2hiKo6HR0dtG/fHtHR0QgKCkKdOnWkLomIqrp/ugAZD1/96xpbA+8dUHv18+fPw9PTEzk5OTA2NkZoaChcXV1fuF1MTAxsbW3h4+MDXV1d1K9fH+3bt1dZx8/PD++//z6AJzfE/fXXX7F//344OTlh1apVyMnJwfLly2FkZAQA+PPPP+Hv748ff/xReQJmZGSEhQsXqnTJ0dbWhomJyQuntn9afn4+/vzzT3h4eAAAli1bBhcXF5w4caJY3QDQvXt3lcf//vsvzM3NceDAAfTp0wd79uzBlStXsHPnTtStWxcAMHPmTPTq1Uu5zdq1a6FQKLBw4UJly8qSJUtgbm6OiIgI9OjR44V1P/v+FyxY8MLjZmBggNzcXJXjs2zZspc+3s+6e/cubGxsinWDSktLw4YNG5Qn02+99RY6d+6M3377DcbGxi98r0+7fv16qS0mL/L0OI2SmJqavtR+SxMfHw89Pb1iFyNtbGwQHx+vXKekG0oXPVfE0NAQZmZmuHv3brnWqC4GC6KXVHSVqyhEeHp6ol27dtDV1ZWyLCKqLjIeAumxUlfxQk5OToiOjkZqaio2bNiA4cOH48CBAy8MF/3798fcuXPRsGFD9OzZE35+fvD391eZNe/p+1TJZDLY2tri4cMnYevy5cto1aqV8iQXADp27AiFQoGrV68qT7patGhRLuMKdHR00K5dO+VjZ2dnmJub4/LlyyUGi4SEBHz55ZeIiIjAw4cPUVhYiKysLMTExCjrt7e3V4YKAMVaes6ePYsbN27AxMREZXlOTg5u3rypVt3Pvn91j9uzyvN4Z2dnl3hj49WrV6NRo0Zo1aoVAMDNzQ0NGjTA2rVrMWrUKLXebxEhhEbrP61x48YvvW1lYGBgUKGD05+HwYJIQwqFAkeOHMH+/ftRq1YtvPvuu9DT04NMJmOoIKLyY2xdJV5XT09PeSLm7u6OqKgo/Pbbb/jnn3+eu529vT2uXr2KPXv2YPfu3Xj//fcxe/ZsHDhwQPm79NnfqTKZDAqFQqP6nj4RfpWGDx+OR48e4bfffkODBg0gl8vh6empdhcmAMjIyIC7uztWrlxZ7DkrKyu19vGq3786r2dpaVnibImLFi3CxYsXVcKlQqHA4sWLlcHC1NQUqampxbZNSUkBAGUXp6ZNm+LKlSsv8xZe2Dry1ltvYf78+S+175LY2toiLy8PKSkpKq0WT98w2tbWFidOnFDZrmjWqGdb3pKTk9X+fJQ3BgsiDaSkpCA0NFR5xcna2hqFhYUSV0VE1ZIG3ZEqE4VCgdzcXLXWNTAwgL+/P/z9/TFu3Dg4Ozvj/PnzaNOmzQu3dXFxwdKlS5GZmak8mY2MjISWltYLu8Do6elp/Lu7oKAAJ0+eVLZOXL16FSkpKSWOEyiq5e+//4afnx+AJ4Owk5KSVOq/d+8e4uLilC3fzw56b9OmDdauXQtra+ty636jznEr6fiU5Xg/q3Xr1oiPj8fjx49Rq1YtAE+61J08eRIRERGwsLBQrpucnIyuXbviypUrcHZ2hpOTE+7fv4+EhASV1pXTp09DX18f9evXBwAMGTIEgwYNwqZNm4qNsxBCIC0trdRxFq+6K5S7uzt0dXWxd+9e9OvXD8CTz1dMTIyyFcvT0xPff/89Hj58CGvrJ+F/9+7dMDU1VWkdvHnzJnJyctC6detyrVFdHLxNpAYhBM6ePYt58+YhJiYGenp6CAgIQP/+/WFgYCB1eUREkpg6dSoOHjyIO3fu4Pz585g6dSoiIiLw5ptvvnDbpUuXYtGiRbhw4QJu3bqFFStWwMDAAA0aNFDrtd98803o6+tj+PDhuHDhAvbv348PPvgAQ4cOLbU7TxEHBwccPHgQDx48UDnZfx5dXV188MEHOH78OE6dOoW3334br732WondoACgSZMm+O+//3D58mUcP34cb775psrfCx8fHzRt2hTDhw/H2bNncejQIXzxxRfF3qOlpSUCAgJw6NAh3L59GxEREZgwYQLu37+vVt3PUue4OTg44Ny5c7h69SqSkpKQn59fpuP9rNatW8PS0lI5lSrwpLWiffv28PLyQvPmzZVfXl5eaNeunXIQt6+vL5ycnDB48GAcOXIEt27dwoYNG/Dll1/iww8/hLa2NgBgwIABGDhwIAYPHoyZM2fi5MmTuHv3LrZs2QIfHx/s37+/1PoaN2783K+iE/vS3LhxA9HR0YiPj0d2djaio6MRHR2tbK168OABnJ2dlS0QZmZmGDVqFCZNmoT9+/fj1KlTGDFiBDw9PfHaa68BAHr06AFXV1cMHToUZ8+exc6dO/Hll19i3LhxkMvlytc+dOgQGjZsiEaNGmn0PSkvDBZEL5Cfn48NGzYgLCwMeXl5sLe3x5gxY+Dm5sZpZImoRnv48CGGDRsGJycneHt7IyoqCjt37sTrr7/+wm3Nzc2xYMECdOzYES1btsSePXuwefNm1K5dW63XNjQ0xM6dO5GcnIx27drhjTfegLe3N/78888XbvvNN9/gzp07aNSokdpdRgwNDfHZZ59hyJAh6NixI4yNjbF27dpS11+0aBEeP36MNm3aYOjQoZgwYYLKCamWlhZCQ0ORnZ2N9u3b45133sH3339f7DUPHjyI+vXrIzg4GC4uLhg1ahRycnJe+qq5Osdt9OjRcHJyQtu2bWFlZYXIyMgyHe9naWtrY8SIEcouXnl5eVixYoXyav2z+vXrh+XLlyM/Px86OjrYtWsX6tevj8GDB6N58+b46quv8OGHH+Lbb79VbiOTybBq1SrMmTMHYWFh6NKlC1q2bIkZM2YgICAAvr6+GtetrnfeeQetW7fGP//8g2vXrqF169Zo3bo1YmOfjJnKz8/H1atXVcZB/Prrr+jTpw/69esHLy8v2NraIiQkRPm8trY2tmzZAm1tbXh6euKtt97CsGHD8M0336i89urVqzF69OgKe28vIhNlGd1SBRU1faWmppZ7U5a65gwaACGyIJMZYtKadZLUQOoTQmDlypW4ffs2unTpgk6dOpV4Qx8iopeRk5OD27dvw9HRscQBrUTVUXx8PJo1a4bTp0+r3UpFz3fx4kV0794d165de+50uiV53u8hTc6dOcaCqAQFBQVQKBTKQdkBAQFIS0uDnZ2d1KURERFVeba2tli0aBFiYmIYLMpJXFwcli9frnGoKE+87Er0jPj4eCxYsAA7duxQLjMxMWGoICLSwMqVK0u9wVizZs2kLk9Fr169Sq115syZUpdXqufdxO3QoUNSl/dCgYGB6Ny5s9RlVBs+Pj4V2sVLHWyxIPp/QggcPXoU+/btQ2FhITIzM1VmvyAiIvX17dtXeTO5Z1W2qbkXLlyI7OzsEp97eoaiyuZ5sxfxYhhJgcGCCEBqairCwsJw584dAE9u+OTv789QQUT0kkxMTIrd2K2yqqon4VX9Rm5U/TBYUI134cIFbN26FTk5OdDV1YWvry/atGnDGZ+IiIiINMBgQTVaTk4Otm/fjpycHNjZ2SEoKEjtqQ6JiIiI6H8YLKhG09fXR9++fREXF4fOnTsrb6xDRERERJphsKAapaCgAPv374ednR1cXV0BPBlP4eTkJHFlRERERFUbgwXVGA8fPkRISAgSEhJgYGCAhg0b8mZUREREROWEwYKqPSEEjh8/jj179qCwsBCGhobw9/dnqCAiIiIqR7xBHlVraWlpWLFiBXbu3InCwkI0adIEY8eOhbOzs9SlERHVCHfu3IFMJnvuPRdK07VrV3z00UcvXM/LywurVq3SvDgq0fz58+Hv7y91GVQFMVhQtZWVlYX58+fj1q1b0NHRgZ+fHwYPHgxjY2OpSyMiqhbefvttyGQy5Vft2rXRs2dPnDt3TrmOvb094uLi0Lx58wqpITw8HAkJCRg0aFCx52bNmgVtbW3Mnj272HMzZsyAm5tbseUlBSEhBP799194eHjA2NgY5ubmaNu2LebOnYusrKzyfDsqJkyYAHd3d8jl8hJrLUlOTg7GjRuH2rVrw9jYGP369UNCQoLKOjExMejduzcMDQ1hbW2NTz75BAUFBcrnR44cidOnT1eJu3dT5cJgQdWWoaEhmjVrhjp16uC9995Du3bteG8KIqJy1rNnT8TFxSEuLg579+6Fjo4O+vTpo3xeW1sbtra20NEpufe1EELlpFZTv//+O0aMGAEtreKnNIsXL8ann36KxYsXv/T+AWDo0KH46KOPEBAQgP379yM6OhrTpk3Dpk2bsGvXrjLt+0VGjhyJgQMHqr3+xIkTsXnzZqxfvx4HDhxAbGwsgoODlc8XFhaid+/eyMvLw5EjR7Bs2TIsXboU06dPV66jp6eHIUOG4Pfffy/X90LVH4MFVSsxMTFITU1VPu7RowdGjRoFS0tLCasiIqq+5HI5bG1tYWtrCzc3N0yZMgX37t1DYmIigOItABEREZDJZNi+fbvyavzhw4eRmZmJYcOGwdjYGHXq1MEvv/zywtdOTEzEvn37Suy2c+DAAWRnZ+Obb75BWloajhw58lLvb926dVi5ciVWr16Nzz//HO3atYODgwMCAgKwb98+dOv2f+3deVyN6f8/8Nfp1Gk/pVKnkGxlGWlTykRolG1Cpj7WEMa+ZCwTYx3GMIUZS+gkSyPMZBlLmEaWZIswIiSyxaRNm1On6/eHb/fP0aLTdor38/E4j8ec677u637fp3uO+32u5e5RpXYr49dff8WUKVPQsmXLStXPysqCWCxGYGAgevbsCVtbW2zfvh0XLlzAxYsXAQAnT55EQkICdu/eDSsrK/Tp0wfLly/Hxo0bIZFIuLYGDBiAw4cPIz8/v1bOjXyaaPI2+SRIpVJER0cjJiYGzZs3x6hRo8Dj8aCioqLo0AghpEq8j3gjLT+tzo9roG6Avf33VmnfnJwc7N69G61bt/7ow0bnz5+PX375BS1btkSjRo0wZ84cnDlzBocOHYKhoSH8/f1x7dq1CocAnT9/HhoaGmjXrl2pbWKxGEOHDoWKigqGDh0KsVgMJycnuc8pLCwMFhYW8PDwKLWNx+NBR0en3H0/NvR2xIgRCAoKkjum8sTFxaGwsBCurq5cWdu2bWFqaorY2Fh06dIFsbGx6NixI4yMjLg6bm5umDRpEm7fvg1ra2sAgJ2dHYqKinDp0iW4uLjUWIzk00aJBWnw0tLSEBERgRcvXgAAdHR0UFRUREkFIaRBS8tPw6u8V4oO46OOHDnC3UDn5ubC2NgYR44cKXNo0vuWLVuGr776CsC7hEQsFmP37t3o1asXAGDHjh1o2rRphW08fvwYRkZGpY6VnZ2NP/74A7GxsQDe3cA7Oztj/fr1cs+zu3//fpWfdfSxCetCobBK7ZYnNTUVAoEAurq6MuVGRkZITU3l6ryfVJRsL9lWQkNDAzo6Onj8+HGNxkg+bZRYkAaLMYarV6/i5MmTKCoqgrq6Ovr37889+I4QQhoyA3XFDOGU97g9evTA5s2bAQAZGRnYtGkT+vTpg8uXL6N58+bl7mdnZ8f9d1JSEiQSCRwcHLgyPT29j97Q5+fnl7l0+J49e9CqVSt06tQJAGBlZYXmzZtj79698PX1lev8GGNy1X9f69atq7xvfaCurl6rk9PJp4cSC9Ig5efnIyIiAg8ePAAAtGzZEgMHDoS2traCIyOEkJpR1eFIdU1TU1PmBjo4OBg6OjrYtm0bfvzxxwr3qy4DAwNkZGSUKheLxbh9+7bMhPHi4mKEhIRwiYVQKJSZk1ciMzMTALghTubm5rh7926V4qvroVAikQgSiQSZmZkyvRYvX76ESCTi6ly+fFlmv5JVo0rqlEhPT0fjxo1rLD7y6aPEgjRIysrKyMrKAp/Px1dffQV7e3ta8YkQQuoBHo8HJSUluSb9tmrVCioqKrh06RJMTU0BvOv9uHfvHrp3717uftbW1khNTUVGRgYaNWoEALh16xauXr2K6Oho6OnpcXXT09Ph4uKCu3fvom3btrCwsMDTp0/x8uVLmaFB165dg5qaGhfHsGHD8L///Q+HDh0qNc+CMYbs7Oxy51nU9VAoW1tbqKioICoqCp6engCAxMREpKSkwNHREQDg6OiIFStW4NWrVzA0NAQAnDp1CkKhUKbHPykpCQUFBdycC0IqgxIL0mBIJBIoKytDSUkJKioqGDJkCABwX4yEEELq3tu3b7mx+RkZGdiwYQNycnLkesCalpYWfH19MWfOHOjr68PQ0BALFiz46DwNa2trGBgYICYmhlviViwWw97eHt26dStVv3PnzhCLxVizZg3c3NxgYWGBoUOH4scff4RIJMK1a9ewcOFCzJgxA3w+HwDg5eWFAwcOYOjQoVi4cCF69+6Nxo0b49atW1i7di2mTZuGgQMHlhlfdYdCPXjwADk5OUhNTUV+fj6XqLRv3x4CgQDPnj1Dr169sHPnTtjb20NHRwe+vr7w8/ODnp4ehEIhpk2bBkdHR3Tp0gXAu9US27dvj5EjR2L16tVITU3FwoULMWXKFKiqqnLHPnfuHFq2bIlWrVpV6xzI54USC9IgPH36FBEREbC2toazszMASigIIaQ+iIyMhLGxMQBAW1sbbdu2xf79++VeSWjNmjVcQqKtrY3Zs2eXOVTpfXw+H2PGjEFYWBj69+8PiUSC3bt3Y968eWXW9/T0REBAAFauXAkVFRWcPHkS/v7+GDp0KP777z+0aNECM2bMgJ+fH7cPj8fD77//jq1btyIkJAQrVqyAsrIy2rRpg1GjRsHNzU2u85THuHHjcObMGe59Se9BcnIyzMzMUFhYiMTERJl5EGvXroWSkhI8PT3x9u1buLm5YdOmTdx2Pp+PI0eOYNKkSXB0dISmpiZ8fHywbNkymWPv2bMH48ePr7VzI58mHqvOrKQGqKTLMisrq8a7ICsr8H9eYCwPPJ4G/ML3KSSGhkIqleLcuXM4e/YsGGNo1KgRJk+eXO6DlgghpKEpKChAcnIyWrRoUeZEZFKx1NRUdOjQAdeuXatwsjipvNu3b6Nnz564d+9ehcvpkk9HRd9D8tw7090Zqbdev36NAwcO4NmzZwCAjh07om/fvpRUEEII4YhEIojFYqSkpFBiUUNevHiBnTt3UlJB5EZ3aKTeYYzh2rVrOHHiBAoLC6GmpoZ+/frhiy++UHRohBBC6qHy5jiQqnn/AXuEyIMSC1LvZGZm4vjx45BKpTAzM8PAgQPpVxNCCCGEkHqOEgtS7zRq1Ai9e/dGUVERHB0daRlZQgghhJAGgBILonASiQSnTp1Cp06d0LRpUwCAvb29gqMihBBCCCHyqHiBaEJq2bNnz7BlyxZcvXoVBw8eRHFxsaJDIoQQQgghVUA9FkQhiouLcf78eZw5cwbFxcXQ1tZG3759P/owJEIIIYQQUj9RYkHqXHp6Og4cOICnT58CADp06IB+/fpBXV1dwZERQgghhJCqop+HSZ1KS0vDli1b8PTpU6iqqmLQoEHw9PSkpIIQQj5Rjx49Ao/HQ3x8vNz7uri4YObMmTUeU00eszLnFxoaCl1d3WrF9eFxoqOjwePxkJmZWa1266MffvgBEyZMUHQYn4yEhAQ0bdoUubm5tX4sSixIndLX14epqSlMTU0xceJEWFpa0qpPhBDSQI0ePRo8Ho976evrw93dHTdv3uTqNGvWDC9evGgwzyKKiIjA8uXLK11fUefn5OSEFy9e1Oly7DWRIH1Mamoq1q9fjwULFpTaFhsbCz6fj379+pXaVlGiZWZmhnXr1smUnT59Gn379oW+vj40NDTQvn17zJ49m3sob23YunUrXFxcIBQK5UoKN27cCDMzM6ipqcHBwQGXL1+W2V5QUIApU6ZAX18fWlpa8PT0xMuXL7nt7du3R5cuXRAYGFiTp1MmSixIrXv48CHevn0LAODxePD09ISPj0+tfzkRQgipfe7u7njx4gVevHiBqKgoKCsro3///tx2Pp8PkUgEZeWyR18zxlBUVFSlY5uZmSE6OrpK+5ZHT08P2trala7/sfOrLQKBACKRqEH+OCeVSstdrCU4OBhOTk5lPkVdLBZj2rRpOHv2LJ4/f17l42/ZsgWurq4QiUT4888/kZCQgKCgIGRlZSEgIKDK7X5MXl4e3N3d4e/vX+l99u7dCz8/PyxevBjXrl1Dp06d4ObmhlevXnF1Zs2ahb/++gv79+/HmTNn8Pz5cwwePFimnTFjxmDz5s1V/n+tsiixILWmsLAQx44dw65duxAZGcmVq6mp0SRtQgj5RKiqqkIkEkEkEsHKygrz58/HkydP8N9//wEofwjP8ePHYWtrC1VVVZw/fx65ubkYNWoUtLS0YGxsXO0bvJLjnDhxAtbW1lBXV0fPnj3x6tUrHD9+HO3atYNQKMSwYcOQl5fH7ffhUCgzMzOsXLkSY8eOhba2NkxNTbF161ZuuzxDvU6cOIF27dpBS0uLS8jeFxwcjHbt2kFNTQ1t27bFpk2bPnp+7//qHRMTAxcXF2hoaKBRo0Zwc3NDRkYGAODt27eYPn06DA0Noaamhi+//BJXrlwp1d7Ro0dhaWkJNTU1dOnSBf/++y+3fcyYMcjKyuJ6qJYsWcK1/d1336FJkybQ1NSEg4ODTMJX0tNx+PBhtG/fHqqqqkhJSSnzvMLDwzFgwIBS5Tk5Odi7dy8mTZqEfv36ITQ0tKKPulxPnz7F9OnTMX36dISEhMDFxQVmZmbo1q0bgoODsWjRoiq1WxkzZ87E/Pnz0aVLl0rvExgYiPHjx2PMmDFo3749goKCoKGhgZCQEABAVlYWxGIxAgMD0bNnT9ja2mL79u24cOECLl68yLXz1VdfIT09HWfOnKnx83of3d2RWvHixQts3bqV+9ISCARgjCk4KkIIIbUpJycHu3fvRuvWraGvr19h3fnz52PVqlW4c+cOLC0tMWfOHJw5cwaHDh3CyZMnER0djWvXrlU7piVLlmDDhg24cOECnjx5Ai8vL6xbtw6///47jh49ipMnT+K3336rsI2AgADY2dnh+vXrmDx5MiZNmoTExES54sjLy8Mvv/yCXbt24ezZs0hJScF3333HbQ8LC8OiRYuwYsUK3LlzBytXrsQPP/yAHTt2VKr9+Ph49OrVC+3bt0dsbCzOnz+PAQMGQCqVAgDmzp2LP//8Ezt27MC1a9fQunVruLm5IT09XaadOXPmICAgAFeuXEHjxo0xYMAAFBYWwsnJCevWrYNQKOR6qErinzp1KmJjYxEeHo6bN2/im2++gbu7O+7fvy9z/j///DOCg4Nx+/ZtGBoaljqH9PR0JCQkwM7OrtS2ffv2oW3btrCwsMCIESMQEhJSpfuK/fv3QyKRYO7cuWVur2g0RZ8+faClpVXuq0OHDnLHUxGJRIK4uDi4urpyZUpKSnB1dUVsbCwAIC4uDoWFhTJ12rZtC1NTU64O8O4+zMrKCufOnavRGD9Eq0KRGlVcXIwLFy7g9OnTKC4uhpaWFjw8PNC6dWtFh0YIIQ1KsucQFKWl1flxlQ0M0OLPPypd/8iRI9DS0gIA5ObmwtjYGEeOHPloz/SyZcvw1VdfAXiXkIjFYuzevRu9evUCAOzYsYN7aGp1/Pjjj+jatSsAwNfXF99//z2SkpLQsmVLAMCQIUNw+vRpzJs3r9w2+vbti8mTJwMA5s2bh7Vr1+L06dOwsLCodByFhYUICgpCq1atALy7GV+2bBm3ffHixQgICOCGsLRo0QIJCQnYsmULfHx8Ptr+6tWrYWdnJ9PLUXKjm5ubi82bNyM0NBR9+vQBAGzbtg2nTp2CWCzGnDlzZOIo+buU/A0OHDgALy8v6OjogMfjQSQScfVTUlKwfft2pKSkwMTEBADw3XffITIyEtu3b8fKlSu589+0aRM6depU7jmkpKSAMca18z6xWIwRI0YAeDf8LisrC2fOnIGLi8tHP5v33b9/H0KhEMbGxnLtB7zrUcrPzy93u4qKitxtViQtLQ1SqRRGRkYy5UZGRrh79y6Ad3NSBAJBqYTIyMgIqampMmUmJiZ4/Phxjcb4IUosSI3Jzs5GREQEd9G2a9cO/fv3h4aGhoIjI4SQhqcoLQ1F703ArK969OiBzZs3AwAyMjKwadMm9OnTB5cvXy5znHyJ93+VTkpKgkQigYODA1emp6dX6sZ94sSJ2L17N/c+Ly8Pffr0AZ/P58pycnJk9rG0tOT+28jICBoaGlxSUVL24WTYD73fRsmN9ftj3N/XoUMH7t9BZ2dnHD9+HACgoaHBJRUAYGxszLWRm5uLpKQk+Pr6Yvz48VydoqKiSk/Ojo+PxzfffFPmtqSkJBQWFnIJFvDuJtje3h537tyRqevo6Mj9d8nf4MM677t16xakUinMzc1lyt++fSvTayUQCGQ+x7KU3LSrqanJlCcmJuLy5cs4cOAAAEBZWRne3t4Qi8VyJxaMsSrPS2nSpEmV9qsv1NXVZYb91QZKLEiN4fP5SEtLg0AggLu7O6ysrBrkpDJCCKkPlA0MGsRxNTU1ZXqlg4ODoaOjg23btuHHH3+scD95LVu2TGb4kIuLC37++WeZhORD7/+KzOPxSv2qzOPxyp1IXFYbH9vn2LFjKCwsBACZpdTLaqNkKE9JMrRt27ZS5/J+0lQRRS3bnpOTAz6fj7i4uFKxlvRkAe/i+9g9gcH/XXsZGRlo3LgxVy4Wi1FUVCTTk8EYg6qqKjZs2AAdHR0IhUIA7+YcfPjrfWZmJpegmZubIysrCy9evJC716JPnz4VDiVq3rw5bt++LVebFTEwMACfz5dZ4QkAXr58yfUaiUQiSCQSZGZmypz3+3VKpKenyyS3tYESC1ItEokEAoEAwLt/JL755hsIhUI0atRIwZERQkjDJs9wpPqEx+NBSUmpwiEjH2rVqhVUVFRw6dIlmJqaAnh3c3nv3j10796dq2doaCgzNl9ZWRlNmjSpV8NtK+qlKY+RkRFMTEzw8OFDDB8+vErHtbS0RFRUFJYuXVpqW6tWrSAQCBATE8PFV1hYiCtXrpR6ZsfFixdL/Q3atWsH4F2vQ8mcjRLW1taQSqV49eoVnJ2dqxT7+3EKhUIkJCRwPSBFRUXYuXMnAgIC0Lt3b5n6AwcOxJ49ezBx4kS0adMGSkpKiIuLk/kbPHz4EFlZWVx7Q4YMwfz587F69WqsXbu2VAwf3qC/r66HQgkEAtja2iIqKgoDBw4E8G7IeVRUFKZOnQoAsLW1hYqKCqKiouDp6QngXQ9PSkqKTO8TAPz7778YMmRIjcb4IUosSJU9fPgQBw8eRO/evbn1u6vyhUoIIaThevv2LTeWOyMjAxs2bEBOTk6ZK/uUR0tLC76+vpgzZw709fVhaGiIBQsWfFYrCC5duhTTp0+Hjo4O3N3d8fbtW1y9ehUZGRnw8/P76P7ff/89OnbsiMmTJ2PixIkQCAQ4ffo0vvnmGxgYGGDSpEmYM2cO9PT0YGpqitWrVyMvLw++vr4y7Sxbtgz6+vowMjLCggULYGBgwN3UmpmZIScnB1FRUejUqRM0NDRgbm6O4cOHY9SoUQgICIC1tTX+++8/REVFwdLSssxnTpSnZGLy+fPnuWMeOXIEGRkZ8PX1LTUszNPTE2KxGBMnToS2tjbGjRuH2bNnQ1lZGR07dsSTJ08wb948dOnSBU5OTgDePXdk7dq1mDp1KrKzszFq1CiYmZnh6dOn2LlzJ7S0tMpdkay6Q6FSU1ORmpqKBw8eAHg3jKxkpTE9PT0AQK9evTBo0CAucfDz84OPjw/s7Oxgb2+PdevWITc3F2PGjAEA6OjowNfXF35+ftDT04NQKMS0adPg6Ogos/rUo0eP8OzZM5lJ3rXh8/k/ltSYoqIinDhxArt27cKbN29w8eJFWvGJEEI+U5GRkTA2NoaxsTEcHBxw5coV7N+/X+6x72vWrIGzszMGDBgAV1dXfPnll7C1ta2doOuhcePGITg4GNu3b0fHjh3RvXt3hIaGokWLFpXa39zcHCdPnsSNGzdgb28PR0dHHDp0iHu+xqpVq+Dp6YmRI0fCxsYGDx48wIkTJ0qNMFi1ahVmzJgBW1tbpKam4q+//uJGJjg5OWHixInw9vZG48aNsXr1agDA9u3bMWrUKMyePRsWFhYYOHAgrly5wvV8yPs5hIeHc0PNxGIxXF1dy5xr4unpiatXr3IPZFy/fj18fHwwb948dOjQAaNHj4alpSX++usvmWFYkydPxsmTJ/Hs2TMMGjQIbdu2xbhx4yAUCmWG2tW0oKAgWFtbc/NounXrBmtraxw+fJirk5SUhLT3Fm3w9vbGL7/8gkWLFsHKygrx8fGIjIyUmdC9du1a9O/fH56enujWrRtEIhEiIiJkjr1nzx707t271n8A5rHP7I4wOzsbOjo6yMrK4sbj1bXA/3mBsTzweBrwC9+nkBiq6uXLl4iIiOAmnNna2qJ3797clw4hhBD5FBQUIDk5GS1atCg1aZWQuhIdHY0ePXogIyNDoQ+wZYzBwcEBs2bNwtChQxUWx6dEIpGgTZs2+P3332Um8L+vou8hee6daSgUqRTGGC5evIioqChIpVJoamri66+/LrUKBCGEEEJIVfF4PGzduhW3bt1SdCifjJSUFPj7+5ebVNQkSixIpTx9+hQnT54E8K679euvv67Sih6EEEIIIRWxsrKClZWVosP4ZLRu3brOFjigxIJUSrNmzdC1a1c0atQINjY2tIwsIYQQ8glxcXGh+ZKk2mjyNilTQUEB/vrrL2RmZnJlrq6usLW1paSCEEIIIYSUQj0WpJRHjx7hwIEDyM7ORkZGBkaOHEnJBCGEEEIIqRAlFoRTVFSE06dP48KFCwCARo0aoWfPnpRUEEIIIYSQj6LEggAAXr16hYiICO6x8dbW1nB3d6dlZAkhhBBCSKVQYkHw+PFj7Nq1C1KpFBoaGhgwYADatm2r6LAIIYQQQkgDQokFQZMmTaCvrw+hUAgPDw9oaWkpOiRCCCGEENLAUGLxmUpKSkKLFi2gpKQEZWVljBo1ChoaGjSfghBCCCGEVAktN/uZefv2LQ4dOoTdu3fj/PnzXLmmpiYlFYQQQqrNxcUFM2fOVHQYFfrhhx8wYcIERYfxyUhISEDTpk2Rm5ur6FCIglFi8RlJSUlBUFAQ4uPjwePxIJVKFR0SIYQQUqdSU1Oxfv16LFiwoNS22NhY8Pl89OvXr9S26Oho8Hg8mec7lTAzM8O6detkyk6fPo2+fftCX18fGhoaaN++PWbPno1nz57V1KmUsnXrVri4uEAoFJYba1k2btwIMzMzqKmpwcHBAZcvX5bZXlBQgClTpkBfXx9aWlrw9PTkFnsBgPbt26NLly4IDAysydMhDRAlFp8BqVSKqKgohIaGIjMzE7q6uhg9ejR69Oih6NAIIYR8ZiQSiUKPHxwcDCcnJzRv3rzUNrFYjGnTpuHs2bN4/vx5lY+xZcsWuLq6QiQS4c8//0RCQgKCgoKQlZWFgICA6oRfoby8PLi7u8Pf37/S++zduxd+fn5YvHgxrl27hk6dOsHNzQ2vXr3i6syaNQt//fUX9u/fjzNnzuD58+cYPHiwTDtjxozB5s2bUVRUVGPnQxoeSiw+ca9fv4ZYLMb58+fBGEOnTp0wceJEmJqaKjo0QgghDVxubi5GjRoFLS0tGBsbl3nTbGZmhuXLl2PUqFEQCoXcEKQ///wTHTp0gKqqKszMzErta2ZmhpUrV2Ls2LHQ1taGqakptm7dKlPnyZMn8PLygq6uLvT09ODh4YFHjx5VGHN4eDgGDBhQqjwnJwd79+7FpEmT0K9fP4SGhsr3Yfyfp0+fYvr06Zg+fTpCQkLg4uICMzMzdOvWDcHBwVi0aFGV2q2MmTNnYv78+ejSpUul9wkMDMT48eMxZswYtG/fHkFBQdDQ0EBISAgAICsrC2KxGIGBgejZsydsbW2xfft2XLhwARcvXuTa+eqrr5Ceno4zZ87U+HmRhoMmb3/iiouL8d9//0FNTQ0DBgxA+/btFR0SIYSQSti38grysuv+130NoQBe/p0rVXfOnDk4c+YMDh06BENDQ/j7++PatWuwsrKSqffLL79g0aJFWLx4MQAgLi4OXl5eWLJkCby9vXHhwgVMnjwZ+vr6GD16NLdfQEAAli9fDn9/f/zxxx+YNGkSunfvDgsLCxQWFsLNzQ2Ojo44d+4clJWV8eOPP8Ld3R03b94s8zlM6enpSEhIgJ2dXalt+/btQ9u2bWFhYYERI0Zg5syZ+P777+Wef7h//35IJBLMnTu3zO26urrl7tunTx+cO3eu3O3NmzfH7du35YqnIhKJBHFxcfj++++5MiUlJbi6uiI2NhbAu79VYWEhXF1duTpt27aFqakpYmNjuSRGIBDAysoK586dQ69evWosRtKwUGLxCSoqKoKy8rs/bePGjeHp6QkTExMIhUIFR0YIIaSy8rIlyM18q+gwypWTkwOxWIzdu3dzN5I7duxA06ZNS9Xt2bMnZs+ezb0fPnw4evXqhR9++AEAYG5ujoSEBKxZs0Ymsejbty8mT54MAJg3bx7Wrl2L06dPw8LCAnv37kVxcTGCg4O5m//t27dDV1cX0dHR6N27d6k4UlJSwBiDiYlJqW1isRgjRowAALi7uyMrKwtnzpyBi4uLXJ/L/fv3IRQKYWxsLNd+wLthWvn5+eVuV1FRkbvNiqSlpUEqlcLIyEim3MjICHfv3gXwbk6KQCAolRAZGRkhNTVVpszExASPHz+u0RhJw0KJxSfm7t27OHr0KLy8vNCsWTMAoIfdEUJIA6QhLP2Le306blJSEiQSCRwcHLgyPT09WFhYlKr7YQ/BnTt34OHhIVPWtWtXrFu3DlKpFHw+HwBgaWnJbefxeBCJRNzY/xs3buDBgwfQ1taWaaegoABJSUllxlxy066mpiZTnpiYiMuXL+PAgQMAAGVlZXh7e0MsFsudWDDGqrzKYpMmTaq0X32hrq6OvLw8RYdBFIgSi0+ERCJBZGQkrl+/DuDdyhYliQUhhJCGp7LDkRoCTU3NKu334S/0PB4PxcXFAN71mNja2iIsLKzUfo0bNy6zPQMDAwBARkaGTB2xWIyioiKZngzGGFRVVbFhwwbo6Ohwvf5ZWVmlfr3PzMyEjo4OgHe9L1lZWXjx4oXcvRZ1PRTKwMAAfD5fZoUnAHj58iVEIhEAQCQSQSKRcIu/lFWnRHp6Olq1alVj8ZGGhyZvfwKePn2KoKAgLqlwcnIqtVoDIYQQUpNatWoFFRUVXLp0iSvLyMjAvXv3Prpvu3btEBMTI1MWExMDc3NzrrfiY2xsbHD//n0YGhqidevWMq+Sm/yyYhYKhUhISODKioqKsHPnTgQEBCA+Pp573bhxAyYmJtizZw8AoE2bNlBSUkJcXJxMmw8fPkRWVhbMzc0BAEOGDIFAIMDq1avLjKGiJWCDg4NlYvjwdezYsUp9NpUlEAhga2uLqKgorqy4uBhRUVFwdHQEANja2kJFRUWmTmJiIlJSUrg6Jf79919YW1vXaIykYaEeiwZMKpXi3LlzOHv2LBhj0NHRwcCBA2FmZqbo0AghhHzitLS04Ovrizlz5kBfXx+GhoZYsGABlJQ+/pvl7Nmz0blzZyxfvhze3t6IjY3Fhg0bsGnTpkoff/jw4VizZg08PDywbNkyNG3aFI8fP0ZERATmzp1b5lyPkonJ58+fx8CBAwEAR44cQUZGBnx9fUslJJ6enhCLxZg4cSK0tbUxbtw4zJ49G8rKyujYsSOePHmCefPmoUuXLnBycgIANGvWDGvXrsXUqVORnZ2NUaNGwczMDE+fPsXOnTuhpaVV7pKz1R0KlZqaitTUVDx48AAAcOvWLW5FLT09PQBAr169MGjQIEydOhUA4OfnBx8fH9jZ2cHe3h7r1q1Dbm4uxowZAwDQ0dGBr68v/Pz8oKenB6FQiGnTpsHR0VFm9alHjx7h2bNnMpO8yeeHeiwasLt37+LMmTNgjKFjx46YOHEiJRWEEELqzJo1a+Ds7IwBAwbA1dUVX375JWxtbT+6n42NDfbt24fw8HB88cUXWLRoEZYtWyYzcftjNDQ0cPbsWZiammLw4MFo164dfH19UVBQUOFiJePGjUN4eDg3pEosFsPV1bXMXg5PT09cvXoVN2/eBACsX78ePj4+mDdvHjp06IDRo0fD0tISf/31l8y8ismTJ+PkyZN49uwZBg0ahLZt22LcuHEQCoX47rvvKn2O8goKCoK1tTXGjx8PAOjWrRusra1x+PBhrk5SUhLS0tK4997e3tyqXVZWVoiPj0dkZKTMhO61a9eif//+8PT0RLdu3SASiRARESFz7D179qB3795lPh+EfD54jDGm6CDqUnZ2NnR0dJCVlaWwVZIC/+cFxvLA42nAL3xfldthjOHgwYNo06YNvvjiixqMkBBCSF0pKChAcnIyWrRoUWpSMal5jDE4ODhg1qxZGDp0qKLD+SRIJBK0adMGv//+O7p27arocEgVVPQ9JM+9M/VYNCC5ubk4cuQI3r59t/wgj8fDoEGDKKkghBBCKonH42Hr1q30hOgalJKSAn9/f0oqCM2xaCju3buHw4cPIzc3F1KptNQyfYQQQgipHCsrq1IP8SNVVzJpnhBKLOo5iUSCkydPcqtQNG7cWGbNcEIIIYQQQuoDSizqsefPnyMiIgKvX78GAHTp0gW9evXinqpNCCGEEEJIfUF3qPVUQkIC/vzzTxQXF0NbWxsDBw5Ey5YtFR0WIYQQQgghZaLEop5q3rw51NXVYWZmhn79+kFdXV3RIRFCCCGEEFIuSizqCcYYHj9+zD2HQlNTE99++y20tLRk1sYmhBBCCCGkPqoXy81u3LgRZmZmUFNTg4ODAy5fvlxh/f3796Nt27ZQU1NDx44da/wR93UtLy8P+/fvx44dO3Dr1i2uXFtbm5IKQgghhBDSICg8sdi7dy/8/PywePFiXLt2DZ06dYKbmxtevXpVZv0LFy5g6NCh8PX1xfXr1zFw4EAMHDgQ//77bx1HXjMePHiAzZs3486dO1BSUkJeXp6iQyKEEELqLRcXF8ycOVPRYQB4t3Jj69atceHCBUWH8smYP38+pk2bpugwSBUpPLEIDAzE+PHjMWbMGLRv3x5BQUHQ0NBASEhImfXXr18Pd3d3zJkzB+3atcPy5cthY2ODDRs21HHk1cN4PBQYiRAWFoacnBwYGBhg3LhxtJQsIYQQ0kAEBQWhRYsWcHJyKrXt22+/BZ/Px/79+0ttGz16NAYOHFiqPDo6GjweD5mZmVyZRCLB6tWr0alTJ2hoaMDAwABdu3bF9u3bUVhYWJOnwykoKMDo0aPRsWNHKCsrlxlrWdLT0zF8+HAIhULo6urC19cXOTk5MnVu3rwJZ2dnqKmpoVmzZli9erXM9u+++w47duzAw4cPa+p0SB1SaGIhkUgQFxcHV1dXrkxJSQmurq6IjY0tc5/Y2FiZ+gDg5uZWbv36SKqqhryWHVCopw8AsLe3x4QJE2BsbKzgyAghhJCqk0gkig6hzjDGsGHDBvj6+pbalpeXh/DwcMydO7fcH0orQyKRwM3NDatWrcKECRNw4cIFXL58GVOmTMFvv/2G27dvV+cUyiWVSqGuro7p06eXuueqyPDhw3H79m2cOnUKR44cwdmzZzFhwgRue3Z2Nnr37o3mzZsjLi4Oa9aswZIlS7B161aujoGBAdzc3LB58+YaPSdSNxSaWKSlpUEqlcLIyEim3MjICKmpqWXuk5qaKlf9t2/fIjs7W+alaEyZD6aqDl5RIYYPH44+ffpARUVF0WERQgghcnFxccHUqVMxc+ZM7oYQAG7fvo3+/ftDKBRCW1sbzs7OSEpK+mh7Jb/kL126FI0bN4ZQKMTEiRMrTFh27doFOzs7aGtrQyQSYdiwYTLDqUt6AaKiomBnZwcNDQ04OTkhMTFRpp1Dhw7BxsYGampqaNmyJZYuXYqioqJyjxsXF4ekpCT069ev1Lb9+/ejffv2mD9/Ps6ePYsnT5589NzLsm7dOpw9exZRUVGYMmUKrKys0LJlSwwbNgyXLl1CmzZtqtTux2hqamLz5s0YP348RCJRpfa5c+cOIiMjERwcDAcHB3z55Zf47bffEB4ejufPnwMAwsLCIJFIEBISgg4dOuB///sfpk+fjsDAQJm2BgwYgPDw8Bo/L1L7FD4Uqrb99NNP0NHR4V7NmjVTdEhQzs2F6rOH0HiYhNatWys6HEIIIaTKduzYAYFAgJiYGAQFBeHZs2fo1q0bVFVV8c8//yAuLg5jx46t8Cb9fVFRUbhz5w6io6OxZ88eREREYOnSpeXWLywsxPLly3Hjxg0cPHgQjx49wujRo0vVW7BgAQICAnD16lUoKytj7Nix3LZz585h1KhRmDFjBhISErBlyxaEhoZixYoV5R733LlzMDc3h7a2dqltYrEYI0aMgI6ODvr06YPQ0NBKnfuHwsLC4OrqCmtr61LbVFRUoKmpWeZ+KSkp0NLSqvC1cuXKKsVUntjYWOjq6sLOzo4rc3V1hZKSEi5dusTV6datGwQCAVfHzc0NiYmJyMjI4Mrs7e3x9OlTPHr0qEZjJLVPocvNGhgYgM/n4+XLlzLlL1++LDdDFolEctX//vvv4efnx73Pzs5WeHKhBFUIsvOhBFWFxkEIIaT+2v39TORmZny8Yg3T1G2EET+tq3T9Nm3ayIyT9/f3h46ODsLDw7neeHNz80q3JxAIEBISAg0NDXTo0AHLli3DnDlzsHz5cigplf499P0EoWXLlvj111/RuXNn5OTkQEtLi9u2YsUKdO/eHcC7CcL9+vVDQUEB1NTUsHTpUsyfPx8+Pj5cO8uXL8fcuXOxePHiMuN8/PgxTExMSpXfv38fFy9eREREBABgxIgR8PPzw8KFC+Ve6fH+/ftwcXGRax8AMDExQXx8fIV19PT05G63IqmpqTA0NJQpU1ZWhp6eHjeqJDU1FS1atJCpUzIKJTU1FY0aNQIA7nN9fxl+0jAoNLEQCASwtbVFVFQUNzGouLgYUVFRmDp1apn7ODo6IioqSmZFiFOnTsHR0bHM+qqqqlBVrV838DPDdyk6BEIIIfVcbmYGctJfKzqMj7K1tZV5Hx8fD2dn5yoP8S2ZpFzC0dEROTk5ePLkCZo3b16qflxcHJYsWYIbN24gIyMDxcXFAN79at++fXuunqWlJfffJXMaX716BVNTU9y4cQMxMTEyPRRSqRQFBQXIy8uTiadEfn4+1NTUSpWHhITAzc0NBgYGAIC+ffvC19cX//zzD3r16iXXZ8EYk6t+CWVl5QY9IqLkocC0UmbDo/AH5Pn5+cHHxwd2dnawt7fHunXrkJubizFjxgAARo0ahSZNmuCnn34CAMyYMQPdu3dHQEAA+vXrh/DwcFy9elVm4g8hhBDS0GnqNmoQx/1wOE7JTWFdyM3NhZubG9zc3BAWFobGjRsjJSUFbm5upeZlvJ/olPQclCQhOTk5WLp0KQYPHlzqGGUlD8C7URfvP3sKeJeM7NixA6mpqVBWVpYpDwkJ4RILoVCIx48fl2ozMzMTfD6f+0zNzc1x9+7dj34OH/owqSqLv78//P395W67PCKRqNSjAoqKipCens6NKilv1EnJthLp6ekAgMaNG9dYfKRuKDyx8Pb2xn///YdFixYhNTUVVlZWiIyM5LrGUlJSZLo+nZyc8Pvvv2PhwoXw9/dHmzZtcPDgQXzxxReKOgVCCCGkxskzHKk+sbS0xI4dO1BYWFilXosbN24gPz+fS1AuXrwILS2tMocx3717F69fv8aqVau47VevXpX7mDY2NkhMTJTrV35ra2ts3rwZjDEuUTl27BjevHmD69evg8/nc3X//fdfjBkzBpmZmdDV1YWFhQXCw8Px9u1bmVEV165dQ4sWLbjPbdiwYfD398f169dLzbMoLCyERCIpc56FIoZCOTo6IjMzE3FxcVwv1j///IPi4mJuKX1HR0csWLBA5to4deoULCwsuGFQwLvPS0VFBR06dKjRGEkdYJ+ZrKwsBoBlZWUpOhRCCCGE5efns4SEBJafn6/oUOTWvXt3NmPGDJmytLQ0pq+vzwYPHsyuXLnC7t27x3bu3Mnu3r370fZ8fHyYlpYWGzp0KLt9+zY7evQoMzIyYvPnzy/zmK9evWICgYDNmTOHJSUlsUOHDjFzc3MGgF2/fp0xxtjp06cZAJaRkcG1cf36dQaAJScnM8YYi4yMZMrKymzJkiXs33//ZQkJCWzPnj1swYIF5caalpbGVFRU2K1bt7gyDw8P5u3tXaquVCplIpGIbdiwgTHGWEZGBjM0NGReXl7s6tWr7P79+0wsFjNtbW22efNmbr+CggLm7OzMGjVqxDZs2MDi4+NZUlIS27t3L7OxseHOsTbcvn2bXb9+nQ0YMIC5uLiw69evyxzv0qVLzMLCgj19+pQrc3d3Z9bW1uzSpUvs/PnzrE2bNmzo0KHc9szMTGZkZMRGjhzJ/v33XxYeHs40NDTYli1bZI69ePFi1rNnz1o7N1JaRd9D8tw7U2JBCCGEKNCnllgwxtiNGzdY7969mYaGBtPW1mbOzs4sKSnpo+35+PgwDw8PtmjRIqavr8+0tLTY+PHjWUFBQbnH/P3335mZmRlTVVVljo6O7PDhw3InFoy9Sy6cnJyYuro6EwqFzN7enm3durXCeL28vLikJzU1lSkrK7N9+/aVWXfSpEnM2tqae5+YmMgGDRrETExMmKamJuvUqRPbtm0bKy4ultmvoKCA/fTTT6xjx45MTU2N6enpsa5du7LQ0FBWWFhYYXzV0bx5cwag1KtEyef6/mf4+vVrNnToUKalpcWEQiEbM2YMe/PmjUy7N27cYF9++SVTVVVlTZo0YatWrSp1bAsLC7Znz55aOzdSWk0lFjzGqjgzqIHKzs6Gjo4OsrKyIBQKFR0OIYSQz1xBQQGSk5PRokWLcsfzfy5Gjx6NzMxMHDx4UNGhVMrNmzfx1VdfISkpSWYFKlJ1x48fx+zZs3Hz5k2ZeSqkdlX0PSTPvfMn/xwLQgghhJDaYGlpiZ9//hnJycmKDuWTkZubi+3bt1NS0UDRX40QQgghdaKiX/WPHz9eh5HUnLIexkeqbsiQIYoOgVQDJRaEEEIIqRMVrVTUpEkTODs7110whJAaR4kFIYQQQupEQ35oGyHk42iOBSGEEEIIIaTaKLEghBBC6oHPbJFGQkg9UlPfP5RYEEIIIQpU8gTivLw8BUdCCPlclXz/lHwfVRXNsSCEEEIUiM/nQ1dXF69evQIAaGhogMfjKTgqQsjngDGGvLw8vHr1Crq6uuDz+dVqjxILQgghRMFEIhEAcMkFIYTUJV1dXe57qDoosSCEEEIUjMfjwdjYGIaGhigsLFR0OISQz4iKikq1eypKUGJBCCGE1BN8Pr/G/oEnhJC6RpO3CSGEEEIIIdVGiQUhhBBCCCGk2iixIIQQQgghhFTbZzfHouQBINnZ2QqOhBBCCCGEkPqt5J65Mg/R++wSizdv3gAAmjVrpuBICCGEEEIIaRjevHkDHR2dCuvwWE09w7uBKC4uxvPnz6Gtra2wBxBlZ2ejWbNmePLkCYRCoUJiIPUDXQsEoOuA/H90LZASdC0QoH5cB4wxvHnzBiYmJlBSqngWxWfXY6GkpISmTZsqOgwAgFAopC8LAoCuBfIOXQekBF0LpARdCwRQ/HXwsZ6KEjR5mxBCCCGEEFJtlFgQQgghhBBCqo0SCwVQVVXF4sWLoaqqquhQiILRtUAAug7I/0fXAilB1wIBGt518NlN3iaEEEIIIYTUPOqxIIQQQgghhFQbJRaEEEIIIYSQaqPEghBCCCGEEFJtlFjUko0bN8LMzAxqampwcHDA5cuXK6y/f/9+tG3bFmpqaujYsSOOHTtWR5GS2ibPtbBt2zY4OzujUaNGaNSoEVxdXT967ZCGQd7vhBLh4eHg8XgYOHBg7QZI6oy810JmZiamTJkCY2NjqKqqwtzcnP6N+ETIey2sW7cOFhYWUFdXR7NmzTBr1iwUFBTUUbSkNpw9exYDBgyAiYkJeDweDh48+NF9oqOjYWNjA1VVVbRu3RqhoaG1HmelMVLjwsPDmUAgYCEhIez27dts/PjxTFdXl718+bLM+jExMYzP57PVq1ezhIQEtnDhQqaiosJu3bpVx5GTmibvtTBs2DC2ceNGdv36dXbnzh02evRopqOjw54+fVrHkZOaJO91UCI5OZk1adKEOTs7Mw8Pj7oJltQqea+Ft2/fMjs7O9a3b192/vx5lpyczKKjo1l8fHwdR05qmrzXQlhYGFNVVWVhYWEsOTmZnThxghkbG7NZs2bVceSkJh07dowtWLCARUREMADswIEDFdZ/+PAh09DQYH5+fiwhIYH99ttvjM/ns8jIyLoJ+CMosagF9vb2bMqUKdx7qVTKTExM2E8//VRmfS8vL9avXz+ZMgcHB/btt9/Wapyk9sl7LXyoqKiIaWtrsx07dtRWiKQOVOU6KCoqYk5OTiw4OJj5+PhQYvGJkPda2Lx5M2vZsiWTSCR1FSKpI/JeC1OmTGE9e/aUKfPz82Ndu3at1ThJ3alMYjF37lzWoUMHmTJvb2/m5uZWi5FVHg2FqmESiQRxcXFwdXXlypSUlODq6orY2Ngy94mNjZWpDwBubm7l1icNQ1WuhQ/l5eWhsLAQenp6tRUmqWVVvQ6WLVsGQ0ND+Pr61kWYpA5U5Vo4fPgwHB0dMWXKFBgZGeGLL77AypUrIZVK6ypsUguqci04OTkhLi6OGy718OFDHDt2DH379q2TmEn9UN/vGZUVHcCnJi0tDVKpFEZGRjLlRkZGuHv3bpn7pKamllk/NTW11uIkta8q18KH5s2bBxMTk1JfIqThqMp1cP78eYjFYsTHx9dBhKSuVOVaePjwIf755x8MHz4cx44dw4MHDzB58mQUFhZi8eLFdRE2qQVVuRaGDRuGtLQ0fPnll2CMoaioCBMnToS/v39dhEzqifLuGbOzs5Gfnw91dXUFRfYO9VgQUk+tWrUK4eHhOHDgANTU1BQdDqkjb968wciRI7Ft2zYYGBgoOhyiYMXFxTA0NMTWrVtha2sLb29vLFiwAEFBQYoOjdSx6OhorFy5Eps2bcK1a9cQERGBo0ePYvny5YoOjRAO9VjUMAMDA/D5fLx8+VKm/OXLlxCJRGXuIxKJ5KpPGoaqXAslfvnlF6xatQp///03LC0tazNMUsvkvQ6SkpLw6NEjDBgwgCsrLi4GACgrKyMxMRGtWrWq3aBJrajKd4KxsTFUVFTA5/O5snbt2iE1NRUSiQQCgaBWYya1oyrXwg8//ICRI0di3LhxAICOHTsiNzcXEyZMwIIFC6CkRL8Vfw7Ku2cUCoUK760AqMeixgkEAtja2iIqKoorKy4uRlRUFBwdHcvcx9HRUaY+AJw6darc+qRhqMq1AACrV6/G8uXLERkZCTs7u7oIldQiea+Dtm3b4tatW4iPj+deX3/9NXr06IH4+Hg0a9asLsMnNagq3wldu3bFgwcPuOQSAO7duwdjY2NKKhqwqlwLeXl5pZKHkoSTMVZ7wZJ6pd7fMyp69vinKDw8nKmqqrLQ0FCWkJDAJkyYwHR1dVlqaipjjLGRI0ey+fPnc/VjYmKYsrIy++WXX9idO3fY4sWLabnZT4S818KqVauYQCBgf/zxB3vx4gX3evPmjaJOgdQAea+DD9GqUJ8Oea+FlJQUpq2tzaZOncoSExPZkSNHmKGhIfvxxx8VdQqkhsh7LSxevJhpa2uzPXv2sIcPH7KTJ0+yVq1aMS8vL0WdAqkBb968YdevX2fXr19nAFhgYCC7fv06e/z4MWOMsfnz57ORI0dy9UuWm50zZw67c+cO27hxIy03+zn47bffmKmpKRMIBMze3p5dvHiR29a9e3fm4+MjU3/fvn3M3NycCQQC1qFDB3b06NE6jpjUFnmuhebNmzMApV6LFy+u+8BJjZL3O+F9lFh8WuS9Fi5cuMAcHByYqqoqa9myJVuxYgUrKiqq46hJbZDnWigsLGRLlixhrVq1YmpqaqxZs2Zs8uTJLCMjo+4DJzXm9OnTZf67X/K39/HxYd27dy+1j5WVFRMIBKxly5Zs+/btdR53eXiMUf8ZIYQQQgghpHpojgUhhBBCCCGk2iixIIQQQgghhFQbJRaEEEIIIYSQaqPEghBCCCGEEFJtlFgQQgghhBBCqo0SC0IIIYQQQki1UWJBCCGEEEIIqTZKLAghhBBCCCHVRokFIYR8IkJDQ6Grq6voMKqMx+Ph4MGDFdYZPXo0Bg4cWCfxEEIIkQ8lFoQQUo+MHj0aPB6v1OvBgweKDg2hoaFcPEpKSmjatCnGjBmDV69e1Uj7L168QJ8+fQAAjx49Ao/HQ3x8vEyd9evXIzQ0tEaOV54lS5Zw58nn89GsWTNMmDAB6enpcrVDSRAh5HOjrOgACCGEyHJ3d8f27dtlyho3bqygaGQJhUIkJiaiuLgYN27cwJgxY/D8+XOcOHGi2m2LRKKP1tHR0an2cSqjQ4cO+PvvvyGVSnHnzh2MHTsWWVlZ2Lt3b50cnxBCGiLqsSCEkHpGVVUVIpFI5sXn8xEYGIiOHTtCU1MTzZo1w+TJk5GTk1NuOzdu3ECPHj2gra0NoVAIW1tbXL16ldt+/vx5ODs7Q11dHc2aNcP06dORm5tbYWw8Hg8ikQgmJibo06cPpk+fjr///hv5+fkoLi7GsmXL0LRpU6iqqsLKygqRkZHcvhKJBFOnToWxsTHU1NTQvHlz/PTTTzJtlwyFatGiBQDA2toaPB4PLi4uAGR7AbZu3QoTExMUFxfLxOjh4YGxY8dy7w8dOgQbGxuoqamhZcuWWLp0KYqKiio8T2VlZYhEIjRp0gSurq745ptvcOrUKW67VCqFr68vWrRoAXV1dVhYWGD9+vXc9iVLlmDHjh04dOgQ1/sRHR0NAHjy5Am8vLygq6sLPT09eHh44NGjRxXGQwghDQElFoQQ0kAoKSnh119/xe3bt7Fjxw78888/mDt3brn1hw8fjqZNm+LKlSuIi4vD/PnzoaKiAgBISkqCu7s7PD09cfPmTezduxfnz5/H1KlT5YpJXV0dxcXFKCoqwvr16xEQEIBffvkFN2/ehJubG77++mvcv38fAPDrr7/i8OHD2LdvHxITExEWFgYzM7My2718+TIA4O+//8aLFy8QERFRqs4333yD169f4/Tp01xZeno6IiMjMXz4cADAuXPnMGrUKMyYMQMJCQnYsmULQkNDsWLFikqf46NHj3DixAkIBAKurLi4GE2bNsX+/fuRkJCARYsWwd/fH/v27QMAfPfdd/Dy8oK7uztevHiBFy9ewMnJCYWFhXBzc4O2tjbOnTuHmJgYaGlpwd3dHRKJpNIxEUJIvcQIIYTUGz4+PozP5zNNTU3uNWTIkDLr7t+/n+nr63Pvt2/fznR0dLj32traLDQ0tMx9fX192YQJE2TKzp07x5SUlFh+fn6Z+3zY/r1795i5uTmzs7NjjDFmYmLCVqxYIbNP586d2eTJkxljjE2bNo317NmTFRcXl9k+AHbgwAHGGGPJyckMALt+/bpMHR8fH+bh4cG99/DwYGPHjuXeb9myhZmYmDCpVMoYY6xXr15s5cqVMm3s2rWLGRsblxkDY4wtXryYKSkpMU1NTaampsYAMAAsMDCw3H0YY2zKlCnM09Oz3FhLjm1hYSHzGbx9+5apq6uzEydOVNg+IYTUdzTHghBC6pkePXpg8+bN3HtNTU0A7369/+mnn3D37l1kZ2ejqKgIBQUFyMvLg4aGRql2/Pz8MG7cOOzatYsbztOqVSsA74ZJ3bx5E2FhYVx9xhiKi4uRnJyMdu3alRlbVlYWtLS0UFxcjIKCAnz55ZcIDg5GdnY2nj9/jq5du8rU79q1K27cuAHg3TCmr776ChYWFnB3d0f//v3Ru3fvan1Ww4cPx/jx47Fp0yaoqqoiLCwM//vf/6CkpMSdZ0xMjEwPhVQqrfBzAwALCwscPnwYBQUF2L17N+Lj4zFt2jSZOhs3bkRISAhSUlKQn58PiUQCKyurCuO9ceMGHjx4AG1tbZnygoICJCUlVeETIISQ+oMSC0IIqWc0NTXRunVrmbJHjx6hf//+mDRpElasWAE9PT2cP38evr6+kEgkZd4gL1myBMOGDcPRo0dx/PhxLF68GOHh4Rg0aBBycnLw7bffYvr06aX2MzU1LTc2bW1tXLt2DUpKSjA2Noa6ujoAIDs7+6PnZWNjg+TkZBw/fhx///03vLy84Orqij/++OOj+5ZnwIABYIzh6NGj6Ny5M86dO4e1a9dy23NycrB06VIMHjy41L5qamrltisQCLi/wapVq9CvXz8sXboUy5cvBwCEh4fju+++Q0BAABwdHaGtrY01a9bg0qVLFcabk5MDW1tbmYSuRH2ZoE8IIVVFiQUhhDQAcXFxKC4uRkBAAPdrfMl4/oqYm5vD3Nwcs2bNwtChQ7F9+3YMGjQINjY2SEhIKJXAfIySklKZ+wiFQpiYmCAmJgbdu3fnymNiYmBvby9Tz9vbG97e3hgyZAjc3d2Rnp4OPT09mfZK5jNIpdIK41FTU8PgwYMRFhaGBw8ewMLCAjY2Ntx2GxsbJCYmyn2eH1q4cCF69uyJSZMmcefp5OSEyZMnc3U+7HEQCASl4rexscHevXthaGgIoVBYrZgIIaS+ocnbhBDSALRu3RqFhYX47bff8PDhQ+zatQtBQUHl1s/Pz8fUqVMRHR2Nx48fIyYmBleuXOGGOM2bNw8XLlzA1KlTER8fj/v37+PQoUNyT95+35w5c/Dzzz9j7969SExMxPz58xEfH48ZM2YAAAIDA7Fnzx7cvXsX9+7dw/79+yESicp8qJ+hoSHU1dURGRmJly9fIisrq9zjDh8+HEePHkVISAg3abvEokWLsHPnTixduhS3b9/GnTt3EB4ejoULF8p1bo6OjrC0tMTKlSsBAG3atMHVq1dx4sQJ3Lt3Dz/88AOuXLkis4+ZmRlu3ryJxMREpKWlobCwEMOHD4eBgQE8PDxw7tw5JCcnIzo6GtOnT8fTp0/liokQQuobSiwIIaQB6NSpEwIDA/Hzzz/jiy++QFhYmMxSrR/i8/l4/fo1Ro0aBXNzc3h5eaFPnz5YunQpAMDS0hJnzpzBvXv34OzsDGtrayxatAgmJiZVjnH69Onw8/PD7Nmz0bFjR0RGRuLw4cNo06YNgHfDqFavXg07Ozt07twZjx49wrFjx7gemPcpKyvj119/xZYtW2BiYgIPD49yj9uzZ0/o6ekhMTERw4YNk9nm5uaGI0eO4OTJk+jcuTO6dOmCtWvXonnz5nKf36xZsxAcHIwnT57g22+/xeDBg+Ht7Q0HBwe8fv1apvcCAMaPHw8LCwvY2dmhcePGiImJgYaGBs6ePQtTU1MMHjwY7dq1g6+vLwoKCqgHgxDS4PEYY0zRQRBCCCGEEEIaNuqxIIQQQgghhFQbJRaEEEIIIYSQaqPEghBCCCGEEFJtlFgQQgghhBBCqo0SC0IIIYQQQki1UWJBCCGEEEIIqTZKLAghhBBCCCHVRokFIYQQQgghpNoosSCEEEIIIYRUGyUWhBBCCCGEkGqjxIIQQgghhBBSbZRYEEIIIYQQQqrt/wHdfHphSCHXYgAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Classification Report for Custom CNN:\n", - " precision recall f1-score support\n", - "\n", - " 3_long_blade_rotor 0.99 0.92 0.95 72\n", - " 3_short_blade_rotor 0.99 0.91 0.94 85\n", - " Bird 1.00 1.00 1.00 76\n", - "Bird+mini-helicopter 0.96 0.99 0.97 78\n", - " drone 1.00 1.00 1.00 85\n", - " rc_plane 0.90 1.00 0.95 90\n", - "\n", - " accuracy 0.97 486\n", - " macro avg 0.97 0.97 0.97 486\n", - " weighted avg 0.97 0.97 0.97 486\n", - "\n" - ] - } - ], - "source": [ - "import torch\n", - "import numpy as np\n", - "import tqdm\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc\n", - "from sklearn.preprocessing import label_binarize\n", - "import torch.nn.functional as F # Ensure this import is added\n", - "\n", - "# Assuming class_names is defined somewhere, for example:\n", - "class_names = [\n", - " \"3_long_blade_rotor\", \n", - " \"3_short_blade_rotor\", \n", - " \"Bird\", \n", - " \"Bird+mini-helicopter\", \n", - " \"drone\", \n", - " \"rc_plane\"\n", - "]\n", - "\n", - "# Test Function with Metric Collection\n", - "def test_model(model, test_loader, criterion, device=\"cuda\" if torch.cuda.is_available() else \"cpu\"):\n", - " model = model.to(device)\n", - " model.eval()\n", - "\n", - " running_loss = 0.0\n", - " running_corrects = 0\n", - " total_test = 0\n", - " all_preds = []\n", - " all_labels = []\n", - " all_probs = []\n", - "\n", - " with torch.no_grad():\n", - " for inputs, labels in tqdm.tqdm(test_loader, desc=\"Testing\"):\n", - " inputs = inputs.to(device)\n", - " labels = labels.to(device).long() # Ensure labels are of type torch.long\n", - "\n", - " # Forward Pass\n", - " outputs = model(inputs)\n", - " loss = criterion(outputs, labels)\n", - " _, preds = torch.max(outputs, 1)\n", - "\n", - " # Store metrics\n", - " running_loss += loss.item() * inputs.size(0)\n", - " running_corrects += torch.sum(preds == labels.data)\n", - " total_test += labels.size(0)\n", - "\n", - " all_preds.extend(preds.cpu().numpy())\n", - " all_labels.extend(labels.cpu().numpy())\n", - " all_probs.extend(F.softmax(outputs, dim=1).cpu().numpy()) # Softmax for probability scores\n", - "\n", - " test_loss = running_loss / total_test\n", - " test_acc = running_corrects.double() / total_test\n", - "\n", - " print(f\"Test Loss: {test_loss:.4f} Test Acc: {test_acc:.4f}\")\n", - "\n", - " # Classification Report\n", - " print(\"\\nClassification Report:\")\n", - " print(classification_report(all_labels, all_preds, target_names=class_names))\n", - "\n", - " # Confusion Matrix\n", - " cm = confusion_matrix(all_labels, all_preds)\n", - " print(\"\\nConfusion Matrix:\")\n", - " print(cm)\n", - "\n", - " # Plot Confusion Matrix\n", - " plt.figure(figsize=(8, 6))\n", - " plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n", - " plt.title(\"Confusion Matrix\")\n", - " plt.colorbar()\n", - " tick_marks = np.arange(len(class_names))\n", - " plt.xticks(tick_marks, class_names, rotation=45)\n", - " plt.yticks(tick_marks, class_names)\n", - " plt.ylabel('True label')\n", - " plt.xlabel('Predicted label')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " # ROC Curve & AUC (For multi-class classification)\n", - " all_labels_bin = label_binarize(all_labels, classes=np.arange(len(class_names)))\n", - "\n", - " plt.figure(figsize=(8, 6))\n", - " for i in range(len(class_names)):\n", - " fpr, tpr, _ = roc_curve(all_labels_bin[:, i], np.array(all_probs)[:, i])\n", - " roc_auc = auc(fpr, tpr)\n", - " plt.plot(fpr, tpr, lw=2, label=f'{class_names[i]} (AUC = {roc_auc:.2f})')\n", - "\n", - " plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n", - " plt.xlabel('False Positive Rate')\n", - " plt.ylabel('True Positive Rate')\n", - " plt.title('Receiver Operating Characteristic (ROC) Curve')\n", - " plt.legend(loc='lower right')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " # Accuracy Curve\n", - " thresholds = np.arange(0.0, 1.1, 0.1) # Thresholds from 0.0 to 1.0 with step 0.1\n", - " accuracy_per_threshold = []\n", - "\n", - " for threshold in thresholds:\n", - " preds_at_threshold = np.argmax(np.array(all_probs) >= threshold, axis=1)\n", - " accuracy_at_threshold = np.mean(preds_at_threshold == all_labels)\n", - " accuracy_per_threshold.append(accuracy_at_threshold)\n", - "\n", - " plt.figure(figsize=(8, 6))\n", - " plt.plot(thresholds, accuracy_per_threshold, color='blue', lw=2)\n", - " plt.xlabel('Threshold')\n", - " plt.ylabel('Accuracy')\n", - " plt.title('Accuracy vs. Threshold')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " return all_labels, all_preds, all_probs\n", - "\n", - "# Example Usage\n", - "if __name__ == \"__main__\":\n", - " # Assuming test_loader is defined\n", - " model_deepercnn = CustomCNN(num_classes=6)\n", - " model_deepercnn.load_state_dict(torch.load(\"best_model_CustomCNN.pt\"))\n", - " model_deepercnn.eval()\n", - "\n", - " criterion = torch.nn.CrossEntropyLoss()\n", - "\n", - " # Test the model\n", - " all_labels_customcnn, all_preds_customcnn, all_probs_customcnn = test_model(model_deepercnn, test_loader, criterion)\n", - "\n", - " # Generate and print the classification report for the custom CNN\n", - " print(\"\\nClassification Report for Custom CNN:\")\n", - " print(classification_report(all_labels_customcnn, all_preds_customcnn, target_names=class_names))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# curves for f1, precision, accuracy \n", - "# roc and auc curve \n", - "# confusion matrix " - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_35756\\714240698.py:117: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " model_deepercnn.load_state_dict(torch.load(\"best_model_CustomCNN.pt\"))\n", - "Testing: 100%|██████████| 61/61 [00:09<00:00, 6.40it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test Loss: 0.0859 Test Acc: 0.9691\n", - "\n", - "Classification Report:\n", - " precision recall f1-score support\n", - "\n", - " 3_long_blade_rotor 0.99 0.92 0.95 72\n", - " 3_short_blade_rotor 0.99 0.91 0.94 85\n", - " Bird 1.00 1.00 1.00 76\n", - "Bird+mini-helicopter 0.96 0.99 0.97 78\n", - " drone 1.00 1.00 1.00 85\n", - " rc_plane 0.90 1.00 0.95 90\n", - "\n", - " accuracy 0.97 486\n", - " macro avg 0.97 0.97 0.97 486\n", - " weighted avg 0.97 0.97 0.97 486\n", - "\n", - "\n", - "Confusion Matrix:\n", - "[[66 1 0 3 0 2]\n", - " [ 1 77 0 0 0 7]\n", - " [ 0 0 76 0 0 0]\n", - " [ 0 0 0 77 0 1]\n", - " [ 0 0 0 0 85 0]\n", - " [ 0 0 0 0 0 90]]\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAJOCAYAAAAqFJGJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAADErUlEQVR4nOzdd1QU598F8LuUXTqINEUUbBQbikqwYIGIDSnGmqhRY6LRmMRoEpNoTFGTmBhTNTHW2Atg74oFe8EWe0MFEaT3ss/7hy/7c6XtCjiU+zmHozs7M3t3WGC+85SRCSEEiIiIiIiIykBH6gBERERERFT1sbAgIiIiIqIyY2FBRERERERlxsKCiIiIiIjKjIUFERERERGVGQsLIiIiIiIqMxYWRERERERUZiwsiIiIiIiozFhYEBERERFRmbGwICKNOTo64s0335Q6Ro3TtWtXdO3aVeoYpZoxYwZkMhni4+OljlLpyGQyzJgxo1z2dffuXchkMixdurRc9gcAJ0+ehFwux71798ptn+Vt8ODBGDhwoNQxiKgELCyIKomlS5dCJpOpvvT09GBvb48333wTDx8+lDpepZaeno5vvvkGLVu2hJGREczNzdG5c2csX74cQgip42nkv//+w4wZM3D37l2poxSSn5+PJUuWoGvXrrC0tIRCoYCjoyNGjhyJ06dPSx2vXKxatQrz5s2TOoaal5np888/x5AhQ9CgQQPVsq5du6r9TjI0NETLli0xb948KJXKIvfz5MkTTJkyBc7OzjAwMIClpSX8/PywdevWYl87JSUFX331FVq1agUTExMYGhqiefPm+OSTTxAdHa1a75NPPsHGjRtx/vx5jd9XTfjsElUmMlFV/uoSVXNLly7FyJEj8fXXX8PJyQlZWVk4fvw4li5dCkdHR1y6dAkGBgaSZszOzoaOjg709fUlzfGs2NhY+Pj44MqVKxg8eDC6dOmCrKwsbNy4EYcOHcKgQYOwcuVK6OrqSh21RBs2bMCAAQNw4MCBQq0TOTk5AAC5XP7Sc2VmZiI4OBg7d+6Et7c3/P39YWlpibt372LdunW4fv06oqKiUK9ePcyYMQNfffUV4uLiYGVl9dKzlkXfvn1x6dKlCivssrKyoKenBz09vTJnEkIgOzsb+vr65fK5joyMROvWrXH06FF4eXmplnft2hW3bt3C7NmzAQDx8fFYtWoVTp06hc8++wwzZ85U28+1a9fg4+ODuLg4jBw5Em3btkVSUhJWrlyJyMhITJ48GXPmzFHb5vbt2/D19UVUVBQGDBiATp06QS6X48KFC1i9ejUsLS1x/fp11fqenp5wdnbG8uXLS31f2nx2iaicCCKqFJYsWSIAiFOnTqkt/+STTwQAsXbtWomSSSszM1Pk5+cX+7yfn5/Q0dERmzZtKvTc5MmTBQDx3XffVWTEIqWlpWm1/vr16wUAceDAgYoJ9ILGjx8vAIiff/650HN5eXlizpw54v79+0IIIb788ksBQMTFxVVYHqVSKTIyMsp9v3369BENGjQo133m5+eLzMzMF96+IjIVZeLEiaJ+/fpCqVSqLe/SpYto1qyZ2rLMzEzRoEEDYWpqKvLy8lTLc3JyRPPmzYWRkZE4fvy42jZ5eXli0KBBAoBYs2aNanlubq5o1aqVMDIyEocPHy6UKzk5WXz22Wdqy3788UdhbGwsUlNTS31f2nx2y6Ks32ei6oSFBVElUVxhsXXrVgFAzJo1S235lStXRP/+/UWtWrWEQqEQHh4eRZ5cJyYmig8++EA0aNBAyOVyYW9vL4YNG6Z28peVlSWmT58uGjVqJORyuahXr56YMmWKyMrKUttXgwYNxIgRI4QQQpw6dUoAEEuXLi30mjt37hQAxJYtW1TLHjx4IEaOHClsbGyEXC4Xbm5uYtGiRWrbHThwQAAQq1evFp9//rmoW7eukMlkIjExschjduzYMQFAjBo1qsjnc3NzRZMmTUStWrVUJ6N37twRAMScOXPE3LlzRf369YWBgYHw9vYWFy9eLLQPTY5zwfcuPDxcjBs3TlhbWwsLCwshhBB3794V48aNE02bNhUGBgbC0tJSvPbaa+LOnTuFtn/+q6DI6NKli+jSpUuh47R27Vrx7bffCnt7e6FQKET37t3FjRs3Cr2H33//XTg5OQkDAwPRrl07cejQoUL7LMr9+/eFnp6eePXVV0tcr0BBYXHjxg0xYsQIYW5uLszMzMSbb74p0tPT1dZdvHix6Natm7C2thZyuVy4urqKP//8s9A+GzRoIPr06SN27twpPDw8hEKhUJ0oaroPIYTYvn278Pb2FiYmJsLU1FS0bdtWrFy5Ugjx9Pg+f+yfPaHX9OcDgBg/frxYsWKFcHNzE3p6eiI0NFT13JdffqlaNyUlRbz//vuqn0tra2vh6+srzpw5U2qmgs/wkiVL1F7/ypUrYsCAAcLKykoYGBiIpk2bFjoxL0r9+vXFm2++WWh5UYWFEEK89tprAoCIjo5WLVu9erUAIL7++usiXyMpKUlYWFgIFxcX1bI1a9YIAGLmzJmlZixw/vx5AUCEhISUuJ62n90RI0YUWcQVfKafVdT3ed26daJWrVpFHsfk5GShUCjERx99pFqm6WeKqKrRvE2WiCRR0A2iVq1aqmWXL19Gx44dYW9vj08//RTGxsZYt24dAgMDsXHjRgQFBQEA0tLS0LlzZ1y5cgWjRo1CmzZtEB8fj82bN+PBgwewsrKCUqlEv379cOTIEbz99ttwdXXFxYsX8fPPP+P69esICwsrMlfbtm3RsGFDrFu3DiNGjFB7bu3atahVqxb8/PwAPO2u9Morr0Amk2HChAmwtrbGjh07MHr0aKSkpOCDDz5Q2/6bb76BXC7H5MmTkZ2dXWwXoC1btgAAhg8fXuTzenp6GDp0KL766itERETA19dX9dzy5cuRmpqK8ePHIysrC7/88gu6d++OixcvwtbWVqvjXODdd9+FtbU1pk+fjvT0dADAqVOncPToUQwePBj16tXD3bt3MX/+fHTt2hX//fcfjIyM4O3tjYkTJ+LXX3/FZ599BldXVwBQ/Vuc7777Djo6Opg8eTKSk5Pxww8/4PXXX8eJEydU68yfPx8TJkxA586d8eGHH+Lu3bsIDAxErVq1Su0CsmPHDuTl5WHYsGElrve8gQMHwsnJCbNnz8bZs2fxzz//wMbGBt9//71armbNmqFfv37Q09PDli1b8O6770KpVGL8+PFq+7t27RqGDBmCd955B2PGjIGzs7NW+1i6dClGjRqFZs2aYerUqbCwsMC5c+ewc+dODB06FJ9//jmSk5Px4MED/PzzzwAAExMTAND652P//v1Yt24dJkyYACsrKzg6OhZ5jMaOHYsNGzZgwoQJcHNzw5MnT3DkyBFcuXIFbdq0KTFTUS5cuIDOnTtDX18fb7/9NhwdHXHr1i1s2bKlUJelZz18+BBRUVFo06ZNses8r2DwuIWFhWpZaT+L5ubmCAgIwLJly3Dz5k00btwYmzdvBgCtPl9ubm4wNDREREREoZ+/Z73oZ1dTz3+fmzRpgqCgIISEhOCvv/5S+50VFhaG7OxsDB48GID2nymiKkXqyoaIniq4ar13714RFxcn7t+/LzZs2CCsra2FQqFQa7L38fERLVq0ULu6pVQqRYcOHUSTJk1Uy6ZPn17s1b2Cbg///vuv0NHRKdQVYcGCBQKAiIiIUC17tsVCCCGmTp0q9PX1RUJCgmpZdna2sLCwUGtFGD16tKhTp46Ij49Xe43BgwcLc3NzVWtCwZX4hg0batTdJTAwUAAotkVDCCFCQkIEAPHrr78KIf53tdfQ0FA8ePBAtd6JEycEAPHhhx+qlml6nAu+d506dVLrHiKEKPJ9FLS0LF++XLWspK5QxbVYuLq6iuzsbNXyX375RQBQtbxkZ2eL2rVri3bt2onc3FzVekuXLhUASm2x+PDDDwUAce7cuRLXK1Bwdff5FqSgoCBRu3ZttWVFHRc/Pz/RsGFDtWUNGjQQAMTOnTsLra/JPpKSkoSpqanw9PQs1F3l2a4/xXU70ubnA4DQ0dERly9fLrQfPNdiYW5uLsaPH19ovWcVl6moFgtvb29hamoq7t27V+x7LMrevXsLtS4W6NKli3BxcRFxcXEiLi5OXL16VUyZMkUAEH369FFb193dXZibm5f4WnPnzhUAxObNm4UQQrRu3brUbYrStGlT0atXrxLX0fazq22LRVHf5127dhV5LHv37q32mdTmM0VU1XBWKKJKxtfXF9bW1nBwcMBrr70GY2NjbN68WXV1OSEhAfv378fAgQORmpqK+Ph4xMfH48mTJ/Dz88ONGzdUs0ht3LgRrVq1KvLKnkwmAwCsX78erq6ucHFxUe0rPj4e3bt3BwAcOHCg2KyDBg1Cbm4uQkJCVMt2796NpKQkDBo0CMDTgaYbN26Ev78/hBBqr+Hn54fk5GScPXtWbb8jRoyAoaFhqccqNTUVAGBqalrsOgXPpaSkqC0PDAyEvb296nH79u3h6emJ7du3A9DuOBcYM2ZMocG0z76P3NxcPHnyBI0bN4aFhUWh962tkSNHql0Z7dy5M4CnA2IB4PTp03jy5AnGjBmjNmj49ddfV2sBK07BMSvp+BZl7Nixao87d+6MJ0+eqH0Pnj0uycnJiI+PR5cuXXD79m0kJyerbe/k5KRq/XqWJvvYs2cPUlNT8emnnxaa/KDgZ6Ak2v58dOnSBW5ubqXu18LCAidOnFCb9ehFxcXF4dChQxg1ahTq16+v9lxp7/HJkycAUOzn4erVq7C2toa1tTVcXFwwZ84c9OvXr9BUt6mpqaV+Tp7/WUxJSdH6s1WQtbQpjV/0s6upor7P3bt3h5WVFdauXatalpiYiD179qh+HwJl+51LVNmxKxRRJfPHH3+gadOmSE5OxuLFi3Ho0CEoFArV8zdv3oQQAtOmTcO0adOK3Mfjx49hb2+PW7duoX///iW+3o0bN3DlyhVYW1sXu6/itGrVCi4uLli7di1Gjx4N4Gk3KCsrK9Ufybi4OCQlJeHvv//G33//rdFrODk5lZi5QMFJQ2pqqlq3jGcVV3w0adKk0LpNmzbFunXrAGh3nEvKnZmZidmzZ2PJkiV4+PCh2vS3z59Aa+v5k8iCk8PExEQAUN2ToHHjxmrr6enpFdtF51lmZmYA/ncMyyNXwT4jIiLw5Zdf4tixY8jIyFBbPzk5Gebm5qrHxX0eNNnHrVu3AADNmzfX6j0U0PbnQ9PP7g8//IARI0bAwcEBHh4e6N27N4YPH46GDRtqnbGgkHzR9wig2GmZHR0dsXDhQiiVSty6dQszZ85EXFxcoSLN1NS01JP9538WzczMVNm1zVpawfSin11NFfV91tPTQ//+/bFq1SpkZ2dDoVAgJCQEubm5aoVFWX7nElV2LCyIKpn27dujbdu2AJ5eVe/UqROGDh2Ka9euwcTERDV//OTJk4u8igsUPpEsiVKpRIsWLTB37twin3dwcChx+0GDBmHmzJmIj4+HqakpNm/ejCFDhqiukBfkfeONNwqNxSjQsmVLtceatFYAT8cghIWF4cKFC/D29i5ynQsXLgCARleRn/Uix7mo3O+99x6WLFmCDz74AF5eXjA3N4dMJsPgwYOLvReApoqbarS4k0Rtubi4AAAuXrwId3d3jbcrLdetW7fg4+MDFxcXzJ07Fw4ODpDL5di+fTt+/vnnQselqOOq7T5elLY/H5p+dgcOHIjOnTsjNDQUu3fvxpw5c/D9998jJCQEvXr1KnNuTdWuXRvA/4rR5xkbG6uNTerYsSPatGmDzz77DL/++qtquaurKyIjIxEVFVWosCzw/M+ii4sLzp07h/v375f6e+ZZiYmJRV4YeJa2n93iCpX8/Pwilxf3fR48eDD++usv7NixA4GBgVi3bh1cXFzQqlUr1Tpl/Z1LVJmxsCCqxHR1dTF79mx069YNv//+Oz799FPVFU19fX21P/hFadSoES5dulTqOufPn4ePj49GXUOeN2jQIHz11VfYuHEjbG1tkZKSohqkCADW1tYwNTVFfn5+qXm11bdvX8yePRvLly8vsrDIz8/HqlWrUKtWLXTs2FHtuRs3bhRa//r166or+doc55Js2LABI0aMwE8//aRalpWVhaSkJLX1XuTYl6bgZmc3b95Et27dVMvz8vJw9+7dQgXd83r16gVdXV2sWLGiXAfBbtmyBdnZ2di8ebPaSag2XUA03UejRo0AAJcuXSqx4C7u+Jf156MkderUwbvvvot3330Xjx8/Rps2bTBz5kxVYaHp6xV8Vkv7WS9KwQn4nTt3NFq/ZcuWeOONN/DXX39h8uTJqmPft29frF69GsuXL8cXX3xRaLuUlBRs2rQJLi4uqu+Dv78/Vq9ejRUrVmDq1KkavX5eXh7u37+Pfv36lbietp/dWrVqFfqZBKD1nci9vb1Rp04drF27Fp06dcL+/fvx+eefq61TkZ8pIqlxjAVRJde1a1e0b98e8+bNQ1ZWFmxsbNC1a1f89ddfiImJKbR+XFyc6v/9+/fH+fPnERoaWmi9gqvHAwcOxMOHD7Fw4cJC62RmZqpmNyqOq6srWrRogbVr12Lt2rWoU6eO2km+rq4u+vfvj40bNxZ54vNsXm116NABvr6+WLJkSZF39v38889x/fp1fPzxx4WuMIaFhamNkTh58iROnDihOqnT5jiXRFdXt1ALwm+//VboSqixsTEAFHly86Latm2L2rVrY+HChcjLy1MtX7lyZbFXqJ/l4OCAMWPGYPfu3fjtt98KPa9UKvHTTz/hwYMHWuUqaNF4vlvYkiVLyn0fPXr0gKmpKWbPno2srCy1557d1tjYuMiuaWX9+ShKfn5+odeysbFB3bp1kZ2dXWqm51lbW8Pb2xuLFy9GVFSU2nOltV7Z29vDwcFBq7tQf/zxx8jNzVW74v7aa6/Bzc0N3333XaF9KZVKjBs3DomJifjyyy/VtmnRogVmzpyJY8eOFXqd1NTUQifl//33H7KystChQ4cSM2r72W3UqBGSk5NVrSoAEBMTU+TvzpLo6Ojgtddew5YtW/Dvv/8iLy9PrRsUUDGfKaLKgi0WRFXAlClTMGDAACxduhRjx47FH3/8gU6dOqFFixYYM2YMGjZsiNjYWBw7dgwPHjzA+fPnVdsV3NF51KhR8PDwQEJCAjZv3owFCxagVatWGDZsGNatW4exY8fiwIED6NixI/Lz83H16lWsW7cOu3btUnXNKs6gQYMwffp0GBgYYPTo0dDRUb9m8d133+HAgQPw9PTEmDFj4ObmhoSEBJw9exZ79+5FQkLCCx+b5cuXw8fHBwEBARg6dCg6d+6M7OxshISEIDw8HIMGDcKUKVMKbde4cWN06tQJ48aNQ3Z2NubNm4fatWvj448/Vq2j6XEuSd++ffHvv//C3Nwcbm5uOHbsGPbu3avqglLA3d0durq6+P7775GcnAyFQoHu3bvDxsbmhY+NXC7HjBkz8N5776F79+4YOHAg7t69i6VLl6JRo0YaXS396aefcOvWLUycOBEhISHo27cvatWqhaioKKxfvx5Xr15Va6HSRI8ePSCXy+Hv74933nkHaWlpWLhwIWxsbIos4sqyDzMzM/z8889466230K5dOwwdOhS1atXC+fPnkZGRgWXLlgEAPDw8sHbtWkyaNAnt2rWDiYkJ/P39y+Xn43mpqamoV68eXnvtNbRq1QomJibYu3cvTp06pdayVVymovz666/o1KkT2rRpg7fffhtOTk64e/cutm3bhsjIyBLzBAQEIDQ0VKOxC8DTrky9e/fGP//8g2nTpqF27dqQy+XYsGEDfHx80KlTJ7U7b69atQpnz57FRx99pPZZ0dfXR0hICHx9feHt7Y2BAweiY8eO0NfXx+XLl1Wtjc9Ol7tnzx4YGRnh1VdfLTWnNp/dwYMH45NPPkFQUBAmTpyIjIwMzJ8/H02bNtV6koVBgwbht99+w5dffokWLVoUmja6Ij5TRJXGy5+IioiKUtwN8oR4emfXRo0aiUaNGqmmM71165YYPny4sLOzE/r6+sLe3l707dtXbNiwQW3bJ0+eiAkTJgh7e3vVjZhGjBihNvVrTk6O+P7770WzZs2EQqEQtWrVEh4eHuKrr74SycnJqvWen262wI0bN1Q38Tpy5EiR7y82NlaMHz9eODg4CH19fWFnZyd8fHzE33//rVqnYBrV9evXa3XsUlNTxYwZM0SzZs2EoaGhMDU1FR07dhRLly4tNN3mszfI++mnn4SDg4NQKBSic+fO4vz584X2rclxLul7l5iYKEaOHCmsrKyEiYmJ8PPzE1evXi3yWC5cuFA0bNhQ6OrqanSDvOePU3E3Tvv1119FgwYNhEKhEO3btxcRERHCw8ND9OzZU4Oj+/Quxf/884/o3LmzMDc3F/r6+qJBgwZi5MiRatN5Fnfn7YLj8+xNATdv3ixatmwpDAwMhKOjo/j+++/F4sWLC61XcIO8omi6j4J1O3ToIAwNDYWZmZlo3769WL16ter5tLQ0MXToUGFhYVHoBnma/nzg/2+cVhQ8M91sdna2mDJlimjVqpUwNTUVxsbGolWrVoVu7ldcpuK+z5cuXRJBQUHCwsJCGBgYCGdnZzFt2rQi8zzr7NmzAkCh6U+Lu0GeEEKEh4cXmkJXCCEeP34sJk2aJBo3biwUCoWwsLAQvr6+qilmi5KYmCimT58uWrRoIYyMjISBgYFo3ry5mDp1qoiJiVFb19PTU7zxxhulvqcCmn52hRBi9+7donnz5kIulwtnZ2exYsWKEm+QVxylUikcHBwEAPHtt98WuY6mnymiqkYmRDmN8iMiqgLu3r0LJycnzJkzB5MnT5Y6jiSUSiWsra0RHBxcZHcMqnl8fHxQt25d/Pvvv1JHKVZkZCTatGmDs2fPajWZABG9PBxjQURUjWVlZRXqZ798+XIkJCSga9eu0oSiSmfWrFlYu3at1oOVX6bvvvsOr732GosKokqMYyyIiKqx48eP48MPP8SAAQNQu3ZtnD17FosWLULz5s0xYMAAqeNRJeHp6YmcnBypY5RozZo1UkcgolKwsCAiqsYcHR3h4OCAX3/9FQkJCbC0tMTw4cPx3Xffqd21m4iIqKw4xoKIiIiIiMqMYyyIiIiIiKjMWFgQEREREVGZ1bgxFkqlEtHR0TA1NdXoRkBERERERDWVEAKpqamoW7duoRvgPq/GFRbR0dFwcHCQOgYRERERUZVx//591KtXr8R1alxhYWpqCuDpwTEzM5M4DRERERFR5ZWSkgIHBwfVOXRJalxhUdD9yczMjIUFEREREZEGNBlCwMHbRERERERUZiwsiIiIiIiozFhYEBERERFRmbGwICIiIiKiMmNhQUREREREZcbCgoiIiIiIyoyFBRERERERlRkLCyIiIiIiKjMWFkREREREVGYsLIiIiIiIqMxYWBARERERUZmxsCAiIiIiojJjYUFERERERGXGwoKIiIiIiMqMhQUREREREZUZCwsiIiIiIiozSQuLQ4cOwd/fH3Xr1oVMJkNYWFip24SHh6NNmzZQKBRo3Lgxli5dWuE5iYiIiIioZJIWFunp6WjVqhX++OMPjda/c+cO+vTpg27duiEyMhIffPAB3nrrLezatauCkxIRERERUUn0pHzxXr16oVevXhqvv2DBAjg5OeGnn34CALi6uuLIkSP4+eef4efnV1ExiYiIiIioFJIWFto6duwYfH191Zb5+fnhgw8+KHab7OxsZGdnqx6npKRUVDyNbdy5Gzf3pEA3r5jDn3UbSD0DiNxS9yUTgKyc8xERERGRtASAPDNz6KUkQxcKfLDmX6kjlapKFRaPHj2Cra2t2jJbW1ukpKQgMzMThoaGhbaZPXs2vvrqq5cVUSM396TAJN2y2OezUyIhlMka70+URygiIiIiqhSUevrIruuEfBNz6MtlMIhPkjqSRqpUYfEipk6dikmTJqkep6SkwMHBQcJEULVUKKFEtiKtiDWyIAMgIAN0ChdLz9JhVUFERERUreTY1UO+iTmgVEInTwYdKKSOpJEqVVjY2dkhNjZWbVlsbCzMzMyKbK0AAIVCAYWicn4zshVpmPxLYKHlf40LRVpCBtJ1jbCj+ds4/plPsfu40aUr8mJjoWdriyYHw0t/0Z9cgdRowLQu8NGVFw9PRERERBUiJSUFmzZtQq9evWBlZSV1HI1VqftYeHl5Yd++fWrL9uzZAy8vL4kSERERERGVTVRUFA4ePKh6bGZmhmHDhlWpogKQuMUiLS0NN2/eVD2+c+cOIiMjYWlpifr162Pq1Kl4+PAhli9fDgAYO3Ysfv/9d3z88ccYNWoU9u/fj3Xr1mHbtm1SvYVyc+3YERxdtwI5WZlIT0yUOg4RERERVbD8/HyEh4cjIiICQgjY29ujcePGUsd6YZIWFqdPn0a3bt1UjwvGQowYMQJLly5FTEwMoqKiVM87OTlh27Zt+PDDD/HLL7+gXr16+Oeff6r0VLMpO3ci7tffEG4okKan3oBklZGMnzZ8gRu7vil2+7y4uKf/yc0Efm8HZBc1ZuMZaY/KGpmIiIiIyig+Ph4hISGIiYkBALi7u0s+DrisJC0sunbtCiGKH31c1F21u3btinPnzlVgqpcr7tffkHP7NvJcGwDQAYSAQW4+dJVKNH2UAMuMdORllL4fHZEKxD/U/IUVJi+cmYiIiIhejBACp0+fxu7du5GXlwdDQ0P07dsXbm5uUkcrsyo1eLs6UqanA/jflLH6eUq0vJP0/4/0kGhsAWuTkgef6xgbw9rp9tMHMh3AxK7kF1WYAN0+f+HMRERERPRiwsLCcOHCBQBAw4YNERgYCFNTU4lTlQ8WFpWEkD29zV2igRmmDJwMADBW6OKjHs7o0KJO6Tv4yRVIxdOigrM9EREREVVKLi4u+O+//+Dr64v27dtDJqs+tzpmYSGxaIUurjg7IOf/x1fo6MhKnF6WiIiIiKqOnJwcxMfHo27dugAAV1dXTJw4sdq0UjyLhYXErpoqkP7MoO08Hbn6CpdDgQOzOCibiIiIqIq5f/8+QkNDkZWVhXHjxqmKiepYVAAsLCShn/t0RIVZaj5yhRIFg7YT5LVw1e65e3IcmAXEX9d85xyUTURERCSp/Px8HDp0CIcPH4YQAubm5khLS6u2BUUBFhYSMMgWyNMHdARUo7b18wVW1hsCOzMD9ZULWio4KJuIiIio0nvy5AlCQkIQHR0NAGjRogV69+4NAwODUras+lhYSED2/1PsCgDQfdoNKlO/lA8bB2UTERERVWpnzpzBrl27kJubCwMDA/Tp0wfNmzeXOtZLw8JCQkIG6FlbAwlPkKMnL30DIiIiIqq0oqOjkZubCycnJwQEBMDc3FzqSC8VC4vK6NkB2xyUTURERFRp5efnQ1dXFwDg5+eHOnXqwMPDo1pNI6spFhaVUVEDtjkom4iIiKjSyMnJwe7du5GYmIg33ngDMpkMcrkcbdu2lTqaZFhYVEbPD9jmoGwiIiKiSuPhw4cICQlBQkICAODevXtwdHSUNlQlwMKiMuOAbSIiIqJKQ6lU4vDhwzh48CCEEDA1NUVgYCCLiv/HwoKIiIiIqBQJCQkIDQ3FgwcPAADNmjVDnz59YGhoKHGyyoOFBRERERFRCYQQ2LhxI6Kjo6FQKNC7d2+0aNGiRg7QLgkLCyIiIiKiEshkMvTp0wd79+5Fv379YGFhIXWkSklH6gBERERERJXNzZs3cfr0adXjunXrYvjw4SwqSsAWCyIiIiKi/5ebm4s9e/bg1KlT0NHRgYODA2xtbaWOVSWwsCAiIiIiAhATE4OQkBDEx8cDANq2bQtLS0uJU1UdLCwklJt3B1npTwAASqWQOA0RERFRzaRUKnH06FEcOHAASqUSJiYmCAgIQOPGjaWOVqWwsJBQTs451f/r6ibgmGIC9HJlQG6ihKmIiIiIag4hBFauXInbt28DAFxdXdG3b18YGRlJnKzqYWEhIYFc1f+7WN9GHVkC8GzDhcLk5YciIiIiqkFkMhkaNWqEBw8eoGfPnnB3d+c0si+IhUUlYKKXjUZmCYBp3f8tVJgA3T6XLhQRERFRNZWZmYn09HRYWVkBALy8vNCsWTOYm5tLnKxqY2FRSTyBBWw+uiJ1DCIiIqJq7fbt2wgLC4NcLsc777wDfX19yGQyFhXlgIUFEREREVV7eXl52LdvH44fPw4AqF27NlJTUznrUzliYUFERERE1VpsbCxCQkLw+PFjAICHhwd69OgBuVwucbLqhYVFJcFBQkRERETlSwiB48ePY9++fcjPz4exsTH69euHpk2bSh2tWmJhUUmYGPBbQURERFTebt68ifz8fDRt2hT9+vWDsbGx1JGqLZ7NVhKGerpSRyAiIiKqFpRKJXR0dCCTyRAQEICbN2+idevW7CFSwVhYEBEREVG1kJWVhe3bt0NfXx/+/v4AADMzM7Rp00biZDUDCwsiIiIiqvLu3r2L0NBQpKSkQCaToUOHDqhdu7bUsWoUFhZEREREVGXl5eXhwIEDOHr0KACgVq1aCA4OZlEhARYWlYXCROoERERERFXK48ePERISgtjYWABA69at0bNnT04jKxEWFpVFt8+lTkBERERUZeTn52PlypVISUmBkZER/P394eLiInWsGo2FhQRy8+4gO3MThMgEACihAzQLlDYUERERURWiq6uLXr164cyZMwgICICJCXt/SI2FhQRycs5BiGTVYz0dpYRpiIiIiKqG//77D7q6unB2dgYAuLi4wNnZmdPIVhIsLCQgkPv//5PBUp6O5tZPJM1DREREVJllZ2djx44dOH/+PAwNDTFu3DiYmpoCAIuKSoSFhYRkMkOMbHQIj2EpdRQiIiKiSikqKgqhoaFISkqCTCaDh4cHjIyMpI5FRWBhQURERESVTn5+PsLDwxEREQEhBCwsLBAUFIT69etLHY2KwcKCiIiIiCqV3NxcLFmyBDExMQCAVq1aoVevXlAoFBIno5KwsCAiIiKiSkVfXx9169ZFYmIi/P394ebmJnUk0gALCyIiIiKSXFpaGoQQqkHZPXr0gLe3N8zMzCRORppiYUFEREREkrp69Sq2bNkCGxsbDB8+HDKZDHK5nHfQrmJYWBARERGRJHJycrBz506cO3cOAJCZmYmMjAwYGxtLnIxeBAsLIiIiInrpHjx4gJCQECQmJgIAOnTogG7dukFPj6enVRW/c5UAb+xCRERENUV+fj4OHz6MQ4cOQQgBc3NzBAYGwtHRUepoVEYsLCoBEwN+G4iIiKhmUCqVuHz5MoQQaNGiBXr37g0DAwOpY1E54BltJWCopyt1BCIiIqIKI4QA8LSXhr6+PoKDgxEfH48WLVpInIzKEwsLIiIiIqow6enp2LJlC+rVq4dOnToBAOrUqYM6depInIzKGwsLIiIiIqoQ169fx+bNm5Geno47d+7Aw8MDhoaGUseiCsLCgoiIiIjKVW5uLnbv3o3Tp08DAKytrREcHMyioppjYUFERERE5SY6OhohISF48uQJAOCVV16Bj48Pp5GtAfgdJiIiIqJykZmZiWXLliEnJwempqYIDAxEw4YNpY5FLwkLCyIiIiIqF4aGhujatSsePnyIPn36sOtTDcPCgoiIiIheiBACkZGRsLGxgb29PYCnXZ8A3gC4JmJhQURERERay8jIwNatW3HlyhVYWlrinXfegVwuZ0FRg7GwICIiIiKt3Lx5E5s2bUJaWhp0dHTQunVrDs4mFhZEREREpJnc3Fzs2bMHp06dAgBYWVkhODiYN7sjACwsiIiIiEgDaWlpWLZsGeLj4wEA7du3h6+vL/T19SVORpUFCwsiIiIiKpWxsTHMzc2RlZWFgIAANG7cWOpIVMmwsCAiIiKiIiUlJcHQ0BAKhQIymQyBgYHQ0dGBkZGR1NGoEtKROgARERERVS5CCFy4cAELFizArl27VMtNTExYVFCx2GJBRERERCqZmZnYtm0bLl++DACIi4tDbm4ux1JQqVhYEBEREREA4Pbt2wgLC0NqaipkMhm6du2KTp06QUeHnVyodCwsiIiIiGq4vLw87Nu3D8ePHwcAWFpaIjg4WHU3bSJNsLAgIiIiquGys7Nx4cIFAICHhwd69OgBuVwucSqqalhYEBEREdVAQgjIZDIAT6eSDQoKQn5+PpydnSVORlUVCwsiIiKiGiY5ORmbNm2Ch4cHmjVrBgC8LwWVGQsLIiIiohrk0qVL2LZtG7KysvDkyRO4uLhAV1dX6lhUDbCwICIiIqoBsrKysH37dly8eBEAYG9vj6CgIBYVVG5YWBARERFVc3fv3kVoaChSUlIgk8nQuXNneHt7s6igcsXCgoiIiKgae/LkCZYvXw4hBGrVqoWgoCA4ODhIHYuqIRYWRERERNVY7dq10bZtW+Tl5aFnz56cRpYqDAsLIiIiompECIFTp07B2dkZ5ubmAIBevXqpppYlqii8PzsRERFRNZGamoqVK1dix44dCA0NhVKpBAAWFfRSsMWCiIiIqBr477//sHXrVmRmZkJPTw/NmjVjQUEvFQsLIiIioiosOzsbO3fuRGRkJACgTp06CA4OhpWVlbTBqMZhYUFERERURT158gQrVqxAUlISZDIZOnbsiK5du3IaWZIECwsiIiKiKsrc3Bz6+vqwsLBAUFAQ6tevL3UkqsFYWBARERFVIYmJiTA3N4eOjg709PQwePBgGBsbQ6FQSB2NajjOCkVERERUBRRMI/vnn38iIiJCtdzS0pJFBVUKkhcWf/zxBxwdHWFgYABPT0+cPHmyxPXnzZsHZ2dnGBoawsHBAR9++CGysrJeUloiIiKily8tLQ2rV6/G9u3bkZeXh/v370MIIXUsIjWSdoVau3YtJk2ahAULFsDT0xPz5s2Dn58frl27Bhsbm0Lrr1q1Cp9++ikWL16MDh064Pr163jzzTchk8kwd+5cCd4BERERUcW6evUqtmzZgoyMDOjq6sLX1xeenp6cSpYqHUkLi7lz52LMmDEYOXIkAGDBggXYtm0bFi9ejE8//bTQ+kePHkXHjh0xdOhQAICjoyOGDBmCEydOvNTcRERERBUtJycHO3fuxLlz5wAAtra2CA4OLvLiK1FlIFlXqJycHJw5cwa+vr7/C6OjA19fXxw7dqzIbTp06IAzZ86oukvdvn0b27dvR+/evV9KZiIiIqKXJTk5GRcuXADw9BzorbfeYlFBlZpkLRbx8fHIz8+Hra2t2nJbW1tcvXq1yG2GDh2K+Ph4dOrUCUII5OXlYezYsfjss8+KfZ3s7GxkZ2erHqekpJTPGyAiIiIqZ0IIVRcna2tr9O3bFxYWFnB0dJQ2GJEGJB+8rY3w8HDMmjULf/75J86ePYuQkBBs27YN33zzTbHbzJ49G+bm5qovBweHl5iYiIiISDNPnjzBkiVL8ODBA9Uyd3d3FhVUZUhWWFhZWUFXVxexsbFqy2NjY2FnZ1fkNtOmTcOwYcPw1ltvoUWLFggKCsKsWbMwe/ZsKJXKIreZOnUqkpOTVV/3798v9/dCRERE9KKEEDhz5gz++usv3L9/Hzt27OCMT1QlSVZYyOVyeHh4YN++faplSqUS+/btg5eXV5HbZGRkQEdHPXLBLeuL+wFUKBQwMzNT+yIiIiKqDNLT07F27Vps3boVubm5cHR0xMCBAznjE1VJks4KNWnSJIwYMQJt27ZF+/btMW/ePKSnp6tmiRo+fDjs7e0xe/ZsAIC/vz/mzp2L1q1bw9PTEzdv3sS0adPg7++vKjCIiIiIqoLr169j8+bNSE9Ph46ODnx8fODl5cWigqosSQuLQYMGIS4uDtOnT8ejR4/g7u6OnTt3qgZ0R0VFqbVQfPHFF5DJZPjiiy/w8OFDWFtbw9/fHzNnzpTqLRARERFp7e7du1i9ejWAp4O0g4ODi+0KTlRVyEQN68SXkpICc3NzJCcnS9Ytau7ggRAiAzKZESa57AKsmgITTkmShYiIiF4+IQRWr14NS0tL+Pr6Qk9P0mu9RMXS5tyZn2KpWTUFun0udQoiIiKqQEqlEidPnkTr1q2hUCggk8kwePDgQmNHiaoyFhZSY0sFERFRtZaYmIjQ0FDcv38fsbGxCAgIAAAWFVTtsLAgIiIiqgBCCERGRmLnzp3IycmBXC7nPSmoWmNhQURERFTOMjIysHXrVly5cgUAUL9+fQQFBcHCwkLaYEQViIUFERERUTl6+PAh1qxZg7S0NOjo6KBbt27o0KEDuz5RtcfCgoiIiKgcWVhYQAgBKysrBAcHo06dOlJHInopWFgQERERlVFSUpKqm5OxsTGGDRsGS0tL6OvrSxuM6CVimxwRERHRC1IqlThy5Ah+++03XLp0SbXc1taWRQXVOGyxICIiInoBSUlJCAsLw7179wAAt27dQvPmzSVORSQdFhZEREREWhBC4OLFi9i+fTuys7Mhl8vRs2dPuLu7Sx2NSFIsLIiIiIg0lJmZiW3btuHy5csAgHr16iEoKAiWlpYSJyOSHgsLIiIiIg3FxMTg8uXLkMlk6NKlCzp37sxpZIn+HwsLIiIiIg01bNgQvr6+cHR0hL29vdRxiCoVlthERERExYiNjcWSJUuQlJSkWtaxY0cWFURFYGFBRERE9BwhBI4dO4aFCxciKioKu3btkjoSUaXHrlBEREREz0hOTsamTZtw584dAEDTpk3Rp08fiVMRVX4sLIiIiIj+36VLl7Bt2zZkZWVBX18ffn5+aNOmDWQymdTRiCo9FhZEREREAC5evIiQkBAAgL29PYKCglC7dm2JUxFVHSwsiIiIiAC4urrCzs4OTZs2hbe3N3R1daWORFSlsLAgIiKiGikvLw9nz55F27ZtoaOjAz09Pbz11lssKIheEAsLIiIiqnHi4uIQEhKCR48eISsrC97e3gDAooKoDFhYEBERUY0hhMDJkyexd+9e5OXlwdDQEDY2NlLHIqoWWFgQERFRjZCamopNmzbh1q1bAIDGjRujX79+MDU1lTgZUfXAwoKIiIiqvVu3bmHjxo3IzMyEnp4eXn31VbRr147TyBKVIxYWREREVO2ZmZkhNzcXderUQVBQEKytraWORFTtsLAgIiKiaiklJQVmZmYAAGtrawwfPhx169blAG2iCqIjdQAiIiKi8pSfn4/9+/fjl19+wf3791XLHRwcWFQQVSC2WBAREVG1ER8fj9DQUERHRwMArl+/DgcHB4lTEdUMLCyIiIioyhNC4PTp09i9ezfy8vJgYGCAvn37olmzZlJHI6oxWFgQERFRlZaWlobNmzfjxo0bAICGDRsiICBANb6CiF4OFhZERERUpV2/fh03btyArq4ufH194enpyWlkiSTAwoKIiIiqtNatWyM+Ph7u7u68izaRhDgrFBEREVUpDx48wL///ousrCwAgEwmQ48ePVhUEEmMhQURERFVCUqlEuHh4Vi8eDFu376N8PBwqSMR0TPYFYqIiIgqvYSEBISEhODhw4cAgBYtWqBr167ShiIiNSwsiIiIqNISQuDcuXPYuXMncnNzoVAo0KdPH7Ro0ULqaET0HBYWREREVGkdPXoUe/fuBQA4OjoiMDAQ5ubmEqcioqKwsCAiIqJKq3Xr1jh16hTat28PLy8vTiNLVImxsCAiIqJKIzc3F5cuXULr1q0BAEZGRpgwYQL09HjKQlTZ8aeUiIiIKoXo6GiEhITgyZMn0NPTU42jYFFBVDXwJ5WIiIgkpVQqceTIERw8eBBKpRKmpqYwNjaWOhYRaYmFBREREUkmMTERoaGhuH//PgDAzc0Nffv2haGhocTJiEhbLCyIiIhIEpcvX8bmzZuRk5MDuVyO3r17o2XLlhygTVRFsbAgIiIiSRgaGiInJwf169dHUFAQLCwspI5ERGXAwoKIiIhemvT0dNX4iYYNG2LYsGFwdHSEjo6OxMmIqKz4U0xEREQVLjc3Fzt27MBvv/2GxMRE1fKGDRuyqCCqJthiQURERBUqJiYGoaGhiIuLAwBcv34dnp6eEqciovLGwoKIiIgqhFKpxNGjR3HgwAEolUqYmJigX79+aNKkidTRiKgCsLAgIiKicpeUlISwsDDcu3cPAODi4oK+ffvy/hRE1RgLCyIiIip3Z8+exb1796Cvr4+ePXuidevWnEaWqJpjYUFERETlztvbG2lpaejUqRMsLS2ljkNELwGnYSAiIqIyu3PnDtatWwelUgkA0NPTQ79+/VhUENUgbLEgIiKiF5aXl4d9+/bh+PHjAIATJ07Ay8tL4lREJAUWFkRERPRCYmNjERISgsePHwMAPDw84OHhIXEqIpIKCwsiIiLSihACx48fx759+5Cfnw8jIyP069cPzs7OUkcjIgmxsCAiIiKt7Ny5EydPngQANG3aFP7+/jAxMZE4FRFJjYUFERERaaVdu3a4dOkSunXrBg8PD04jS0QAWFgQERFRKbKysnD79m24ubkBAKysrPD+++9DLpdLnIyIKhNON0tERETFunfvHhYsWIANGzYgKipKtZxFBRE9jy0WREREVEh+fj4OHDiAiIgIAECtWrWgo8PrkURUPBYWREREpCYuLg4hISF49OgRAMDd3R09e/aEQqGQOBkRVWYsLIiIiEjlzJkz2LlzJ/Ly8mBoaAh/f3+4urpKHYuIqgAWFkRERKQmLy8PjRs3Rr9+/WBqaip1HCKqIlhYEBER1XCZmZkwNDQEALRp0wYmJiZo2rQpp5ElIq1wFBYREVENlZ2djU2bNuGvv/5CVlYWAEAmk8HZ2ZlFBRFpjS0WRERENVBUVBRCQ0ORlJQEALh16xaaNWsmbSgiqtJYWBAREdUg+fn5OHjwII4cOQIhBMzNzREUFIQGDRpIHY2IqjgWFkRERDVEfHw8QkNDER0dDQBo1aoVevbsCQMDA4mTEVF1wMKCiIiohjh06BCio6NhYGCAvn37susTEZUrFhZEREQ1RM+ePQEAvr6+MDMzkzgNEVU3ZZoVqmAGCSIiIqp8rl27hm3btkEIAQAwMjJCcHAwiwoiqhBaFxZKpRLffPMN7O3tYWJigtu3bwMApk2bhkWLFpV7QCIiItJOTk4OtmzZgjVr1uD06dO4evWq1JGIqAbQurD49ttvsXTpUvzwww+Qy+Wq5c2bN8c///xTruGIiIhIOw8ePMBff/2Fs2fPAgC8vLzQpEkTiVMRUU2g9RiL5cuX4++//4aPjw/Gjh2rWt6qVSteESEiIpKIUqnEoUOHcOjQIQghYGZmhsDAQDg5OUkdjYhqCK0Li4cPH6Jx48aFliuVSuTm5pZLKCIiItLOxo0b8d9//wF42ougT58+nEaWiF4qrbtCubm54fDhw4WWb9iwAa1bty6XUERERKSdtm3bwsDAAMHBwejfvz+LCiJ66bRusZg+fTpGjBiBhw8fQqlUIiQkBNeuXcPy5cuxdevWishIREREz0lPT8ejR4/QqFEjAICTkxM++OADKBQKiZMRUU2ldYtFQEAAtmzZgr1798LY2BjTp0/HlStXsGXLFrz66qsVkZGIiIiecePGDcyfPx9r165FQkKCajmLCiKS0gvdIK9z587Ys2dPeWchIiKiEuTm5mL37t04ffo0AMDa2hp5eXkSpyIiekrrwqJhw4Y4deoUateurbY8KSkJbdq0Ud3XgoiIiMpPdHQ0QkJC8OTJEwCAp6cnfHx8oK+vL3EyIqKntC4s7t69i/z8/ELLs7Oz8fDhw3IJRURERP9z5MgRHDhwAEqlEqampggICFCNrSAiqiw0Liw2b96s+v+uXbtgbm6uepyfn499+/bB0dFR6wB//PEH5syZg0ePHqFVq1b47bff0L59+2LXT0pKwueff46QkBAkJCSgQYMGmDdvHnr37q31axMREVUFWVlZUCqVcHNzQ58+fWBkZCR1JCKiQjQuLAIDAwEAMpkMI0aMUHtOX18fjo6O+Omnn7R68bVr12LSpElYsGABPD09MW/ePPj5+eHatWuwsbEptH5OTg5effVV2NjYYMOGDbC3t8e9e/dgYWGh1esSERFVZkII5OTkqAZjd+vWDfb29nBxcYFMJpM4HRFR0TQuLJRKJYCn09mdOnUKVlZWZX7xuXPnYsyYMRg5ciQAYMGCBdi2bRsWL16MTz/9tND6ixcvRkJCAo4eParqU/oirSRERESVVUZGBrZu3Yrk5GSMGjUKurq60NXVhaurq9TRiIhKpPV0s3fu3CmXoiInJwdnzpyBr6/v/8Lo6MDX1xfHjh0rcpvNmzfDy8sL48ePh62tLZo3b45Zs2YVOeaDiIioqrl16xbmz5+PK1eu4NGjR3jw4IHUkYiINPZC082mp6fj4MGDiIqKQk5OjtpzEydO1Ggf8fHxyM/Ph62trdpyW1tbXL16tchtbt++jf379+P111/H9u3bcfPmTbz77rvIzc3Fl19+WeQ22dnZyM7OVj1OSUnRKB8REdHLkpubi7179+LkyZMAACsrKwQFBaFu3boSJyMi0pzWhcW5c+fQu3dvZGRkID09HZaWloiPj4eRkRFsbGw0LixehFKphI2NDf7++2/o6urCw8MDDx8+xJw5c4otLGbPno2vvvqqwjIRERGVxaNHjxASEoK4uDgAQLt27fDqq69yGlkiqnK07gr14Ycfwt/fH4mJiTA0NMTx48dx7949eHh44Mcff9R4P1ZWVtDV1UVsbKza8tjYWNjZ2RW5TZ06ddC0aVPo6uqqlrm6uuLRo0eFWk4KTJ06FcnJyaqv+/fva5yRiIioIgkhsGPHDsTFxcHExARDhw5F7969WVQQUZWkdWERGRmJjz76CDo6OtDV1UV2djYcHBzwww8/4LPPPtN4P3K5HB4eHti3b59qmVKpxL59++Dl5VXkNh07dsTNmzdVA8kB4Pr166hTpw7kcnmR2ygUCpiZmal9ERERVQYymQz9+vVD8+bNMXbsWDRp0kTqSEREL0zrwkJfXx86Ok83s7GxQVRUFADA3Nxc69aASZMmYeHChVi2bBmuXLmCcePGIT09XTVL1PDhwzF16lTV+uPGjUNCQgLef/99XL9+Hdu2bcOsWbMwfvx4bd8GERGRJC5evIhDhw6pHteuXRv9+/eHsbGxhKmIiMpO6zEWrVu3xqlTp9CkSRN06dIF06dPR3x8PP799180b95cq30NGjQIcXFxmD59Oh49egR3d3fs3LlTNaA7KipKVcQAgIODA3bt2oUPP/wQLVu2hL29Pd5//3188skn2r4NIiKilyozMxPbt2/HpUuXAACNGjWCvb29xKmIiMqPTAghtNng9OnTSE1NRbdu3fD48WMMHz4cR48eRZMmTbBo0SK4u7tXUNTykZKSAnNzcyQnJ0vWLWru4IEQIgMymREmrVknSQYiInp57ty5g7CwMKSkpEAmk6FLly7o3Lmz2sUzIqLKSJtzZ61bLNq2bav6v42NDXbu3Kl9QiIiohogLy8P+/fvV92fydLSEsHBwWypIKJqqdwulZw9exZ9+/Ytr90RERFVaUIILF++XFVUeHh44J133mFRQUTVllaFxa5duzB58mR89tlnuH37NgDg6tWrCAwMRLt27dRmayIiIqrJZDIZWrduDSMjIwwePBh9+/YtdgZDIqLqQOOuUIsWLcKYMWNgaWmJxMRE/PPPP5g7dy7ee+89DBo0CJcuXYKrq2tFZiUiIqrUUlJSkJqaqmqVcHd3h4uLCwwNDSVORkRU8TRusfjll1/w/fffIz4+HuvWrUN8fDz+/PNPXLx4EQsWLGBRQURENdrly5cxf/58rFu3DpmZmQCetlqwqCCimkLjFotbt25hwIABAIDg4GDo6elhzpw5qFevXoWFIyIiquyysrKwY8cOXLhwAcDTAdrZ2dksKIioxtG4sMjMzISRkRGAp1dgFAoF6tSpU2HBiIiIKrt79+4hNDQUycnJkMlk6Ny5M7y9vaGrqyt1NCKil06r6Wb/+ecfmJiYAHg6hd7SpUthZWWlts7EiRPLLx0REVElpFQqsX//fkRERAAAatWqhaCgIDg4OEicjIhIOhoXFvXr18fChQtVj+3s7PDvv/+qrSOTyVhYEBFRtSeTyZCYmAjg6QDtnj17QqFQSJyKiEhaGhcWd+/ercAYRERElZsQAnl5edDX14dMJkPfvn3RokULuLi4SB2NiKhS0PrO20RERDVNamoqNm3aBLlcjgEDBqhme2JRQUT0PywsiIiISnDlyhVs2bIFmZmZ0NPTw5MnTwqNLyQiIhYWRERERcrOzsbOnTsRGRkJ4OnYwuDgYBYVRETFYGFBRET0nKioKISGhiIpKQkA0LFjR3Tr1o3TyBIRlYCFBRER0TPy8/NVRYW5uTmCgoLQoEEDqWMREVV6Oi+y0a1bt/DFF19gyJAhePz4MQBgx44duHz5crmGIyIietl0dXUREBCAVq1aYezYsSwqiIg0pHVhcfDgQbRo0QInTpxASEgI0tLSAADnz5/Hl19+We4BiYiIKpIQAqdPn1aNpQAAR0dHBAYGwsDAQLpgRERVjNaFxaeffopvv/0We/bsgVwuVy3v3r07jh8/Xq7hiIiIKlJaWhpWr16Nbdu2Yfv27UhOTpY6EhFRlaX1GIuLFy9i1apVhZbb2NggPj6+XEIRERFVtGvXrmHz5s3IyMiArq4uunfvDjMzM6ljERFVWVoXFhYWFoiJiYGTk5Pa8nPnzsHe3r7cghEREVWEnJwc7Nq1C2fPngUA2NraIjg4GDY2NhInIyKq2rQuLAYPHoxPPvkE69evh0wmg1KpREREBCZPnozhw4dXREYiIqJykZeXh4ULF6pa2L28vNC9e3fo6XGSRCKistJ6jMWsWbPg4uICBwcHpKWlwc3NDd7e3ujQoQO++OKLishIRERULvT09ODq6gozMzMMHz4cPXr0YFFBRFROtP5tKpfLsXDhQkybNg2XLl1CWloaWrdujSZNmlREPiIiojJJSEiAEAK1a9cGAHTp0gVeXl4wNDSUOBkRUfWidWFx5MgRdOrUCfXr10f9+vUrIhMREVGZCSFw7tw57Ny5E9bW1hg1ahR0dXWhq6vLooKIqAJo3RWqe/fucHJywmeffYb//vuvIjIRERGVSXp6OtauXYstW7YgNzcX+vr6yM7OljoWEVG1pnVhER0djY8++ggHDx5E8+bN4e7ujjlz5uDBgwcVkY+IiEgrN27cwPz583Ht2jXo6OjA19cXw4cPh5GRkdTRiIiqNa0LCysrK0yYMAERERG4desWBgwYgGXLlsHR0RHdu3eviIxERESlysvLw/bt27Fq1Sqkp6fD2toaY8aMQceOHaGjo/WfOyIi0lKZpsJwcnLCp59+ilatWmHatGk4ePBgeeUiIiLSikwmQ3R0NADA09MTPj4+0NfXlzgVEVHN8cKFRUREBFauXIkNGzYgKysLAQEBmD17dnlmIyIiKpFSqYQQQjUoOygoCElJSWjUqJHU0YiIahytC4upU6dizZo1iI6OxquvvopffvkFAQEB7LtKREQvVWJiIkJDQ+Hg4IBXX30VAFC7dm3VtLJERPRyaV1YHDp0CFOmTMHAgQNhZWVVEZmIiIiKJYTA+fPnsWPHDuTk5ODx48fo2LEjL3AREUlM68IiIiKiInIQERGVKiMjA1u3bsWVK1cAAA4ODggKCmJRQURUCWhUWGzevBm9evWCvr4+Nm/eXOK6/fr1K5dgREREz7p16xbCwsKQlpYGHR0ddO3alTM+ERFVIhoVFoGBgXj06BFsbGwQGBhY7HoymQz5+fnllY2IiAgAkJWVhfXr1yM7OxtWVlYICgpC3bp1pY5FRETP0KiwUCqVRf6fiIjoZTAwMEDPnj1VE4dwGlkiospH6/bj5cuXIzs7u9DynJwcLF++vFxCERFRzaZUKhEREYHbt2+rlrm7u6N3794sKoiIKimtC4uRI0ciOTm50PLU1FSMHDmyXEIREVHNlZycjOXLl2Pv3r0ICwsr8mIWERFVPlrPCiWEgEwmK7T8wYMHMDc3L5dQRERUM128eBHbtm1DdnY29PX10bVrV8jlcqljERGRBjQuLFq3bg2ZTAaZTAYfHx/o6f1v0/z8fNy5cwc9e/askJBERFS9ZWZmYvv27bh06RIAoF69eggKCoKlpaXEyYiISFMaFxYFs0FFRkbCz88PJiYmqufkcjkcHR3Rv3//cg9IRETVW3p6Ov7++2+kpKRAJpOhS5cu6Ny5M6eRJSKqYjQuLL788ksAgKOjIwYNGgQDA4MKC0VERDWHkZER6tevj+joaAQFBaFevXpSRyIioheg9RiLESNGVEQOIiKqQR4/fgxjY2MYGxtDJpOhT58+0NHR4XgKIqIqTKPCwtLSEtevX4eVlRVq1apV5ODtAgkJCeUWjoiIqhchBI4fP459+/ahSZMmGDhwIGQyGVvBiYiqAY0Ki59//hmmpqaq/5dUWBARERUlJSUFYWFhuHPnDoCnE3/k5eXxvhRERNWERoXFs92f3nzzzYrKQkRE1dTly5exdetWZGVlQU9PD35+fvDw8OCFKiKiakTrMRZnz56Fvr4+WrRoAQDYtGkTlixZAjc3N8yYMYP9Y4mISCU7Oxvbt2/HhQsXAAB169ZFUFAQrKysJE5GRETlTeu5/N555x1cv34dAHD79m0MGjQIRkZGWL9+PT7++ONyD0hERFWXEAL37t2DTCaDt7c3Ro0axaKCiKia0rrF4vr163B3dwcArF+/Hl26dMGqVasQERGBwYMHY968eeUckYiIqpL8/Hzo6OioBmUX3OPIwcFB4mRERFSRtG6xEEJAqVQCAPbu3YvevXsDePoHIz4+vnzTERFRlRIXF4d//vkHZ86cUS1zcHBgUUFEVANo3WLRtm1bfPvtt/D19cXBgwcxf/58AMCdO3dga2tb7gGJiKjyE0Lg1KlT2LNnD/Ly8nDkyBG4u7tDT0/rPzNERFRFaf0bf968eXj99dcRFhaGzz//HI0bNwYAbNiwAR06dCj3gEREVLmlpqZi8+bNuHnzJgCgUaNGCAgIYFFBRFTDaP1bv2XLlrh48WKh5XPmzIGurm65hCIioqrhypUr2LJlCzIzM6Gnp4dXX30V7dq14zSyREQ10AtfTjpz5gyuXLkCAHBzc0ObNm3KLRQREVV+CQkJWL9+PYQQsLOzQ3BwMKytraWORUREEtG6sHj8+DEGDRqEgwcPwsLCAgCQlJSEbt26Yc2aNfyjQkRUQ1haWsLb2xt5eXno1q0bW62JiGo4rWeFeu+995CWlobLly8jISEBCQkJuHTpElJSUjBx4sSKyEhERJVAfn4+wsPDERcXp1rWtWtX+Pr6sqggIiLtWyx27tyJvXv3wtXVVbXMzc0Nf/zxB3r06FGu4YiIqHJ48uQJQkJCEB0djWvXrmHMmDHQ0dH62hQREVVjWhcWSqUS+vr6hZbr6+ur7m9BRETVgxACZ86cwe7du5GbmwsDAwN06tSJRQURERWidWHRvXt3vP/++1i9ejXq1q0LAHj48CE+/PBD+Pj4lHtAIiKSRlpaGjZv3owbN24AAJycnBAYGAgzMzOJkxERUWWkdWHx+++/o1+/fnB0dFTdSfX+/fto3rw5VqxYUe4BiYjo5YuPj8eSJUuQkZEBXV1d+Pj44JVXXuE0skREVCytCwsHBwecPXsW+/btU0036+rqCl9f33IPR0RE0rC0tISlpSVMTEwQHBwMW1tbqSMREVElp1VhsXbtWmzevBk5OTnw8fHBe++9V1G5iIjoJYuJiYG1tTX09PSgo6ODQYMGwcDAgHfQJiIijWg8+m7+/PkYMmQITp8+jRs3bmD8+PGYMmVKRWYjIqKXQKlU4uDBg1i4cCH279+vWm5iYsKigoiINKZxYfH777/jyy+/xLVr1xAZGYlly5bhzz//rMhsRERUwRISErBkyRKEh4dDCIHU1FQIIaSORUREVZDGhcXt27cxYsQI1eOhQ4ciLy8PMTExFRKMiIgqjhACZ8+exYIFC/DgwQMoFAoEBwejf//+HKBNREQvROM27uzsbBgbG6se6+joQC6XIzMzs0KCERFRxcjIyMCWLVtw9epVAECDBg0QGBgICwsLaYMREVGVplXn2WnTpsHIyEj1OCcnBzNnzoS5ublq2dy5c8svHRERlbucnBzcvn0bOjo66N69O7y8vHjDOyIiKjONCwtvb29cu3ZNbVmHDh1w+/Zt1WM2nxMRVU5KpVJVPFhYWCA4OBjm5uaws7OTOBkREVUXGhcW4eHhFRiDiIgqSnR0NEJDQ9GzZ080atQIAODs7CxxKiIiqm44jyARUTWlVCoRERGB8PBwKJVK7N+/Hw0bNmTrMhERVQgWFkRE1VBiYiLCwsIQFRUFAHBzc0OfPn1YVBARUYVhYUFEVI0IIXDhwgVs374dOTk5kMvl6N27N1q2bMmigoiIKhQLCyKiaiQqKgphYWEAAAcHBwQFBaFWrVrShiIiohqBhQURUTXSoEEDuLu7w9LSEh07duQ0skRE9NK80F+cw4cP44033oCXlxcePnwIAPj3339x5MiRcg1HREQly83Nxb59+5Cenq5a1q9fP3Tu3JlFBRERvVRa/9XZuHEj/Pz8YGhoiHPnziE7OxsAkJycjFmzZpV7QCIiKtqjR4+wcOFCHDlyBFu2bFEt51gKIiKSgtaFxbfffosFCxZg4cKF0NfXVy3v2LEjzp49W67hiIioMCEEIiIisHDhQsTFxcHY2BgeHh5SxyIiohpO6zEW165dg7e3d6Hl5ubmSEpKKo9MRERUjOTkZISFheHu3bsAnt7ozt/fH8bGxtIGIyKiGk/rwsLOzg43b96Eo6Oj2vIjR46gYcOG5ZWLiIiec//+faxcuRLZ2dnQ19dHz5490bp1a3Z9IiKiSkHrwmLMmDF4//33sXjxYshkMkRHR+PYsWOYPHkypk2bVhEZiYgIgI2NDQwNDWFtbY2goCBYWlpKHYmIiEhF68Li008/hVKphI+PDzIyMuDt7Q2FQoHJkyfjvffeq4iMREQ1VkxMDOzs7CCTyaBQKDB8+HCYm5tzxiciIqp0tC4sZDIZPv/8c0yZMgU3b95EWloa3NzcYGJiUhH5iIhqpLy8POzfvx/Hjh1D79690a5dOwDgze6IiKjSeuFLXnK5HG5ubmjfvn2Zi4o//vgDjo6OMDAwgKenJ06ePKnRdmvWrIFMJkNgYGCZXp+IqDKJjY3FP//8g2PHjgEAnjx5InEiIiKi0mndYtGtW7cSBwru379fq/2tXbsWkyZNwoIFC+Dp6Yl58+bBz88P165dg42NTbHb3b17F5MnT0bnzp21ej0iospKCIHjx49j3759yM/Ph5GREfr16wdnZ2epoxEREZVK68LC3d1d7XFubi4iIyNx6dIljBgxQusAc+fOxZgxYzBy5EgAwIIFC7Bt2zYsXrwYn376aZHb5Ofn4/XXX8dXX32Fw4cPc5pbIqryUlJSEBYWhjt37gAAmjRpgn79+rGbKRERVRlaFxY///xzkctnzJiBtLQ0rfaVk5ODM2fOYOrUqaplOjo68PX1VXUBKMrXX38NGxsbjB49GocPHy7xNbKzs1V3Bwee/vEmIqpsUlJScPfuXejp6cHPzw8eHh6cRpaIiKoUrQuL4rzxxhto3749fvzxR423iY+PR35+PmxtbdWW29ra4urVq0Vuc+TIESxatAiRkZEavcbs2bPx1VdfaZyJiOhlEUKoiod69erB398fDg4OsLKykjgZERGR9sptvsJjx47BwMCgvHZXpNTUVAwbNgwLFy7U+A/v1KlTkZycrPq6f/9+hWYkItLEvXv38Oeff+Lx48eqZa1bt2ZRQUREVZbWLRbBwcFqj4UQiImJwenTp7W+QZ6VlRV0dXURGxurtjw2NhZ2dnaF1r916xbu3r0Lf39/1TKlUgkA0NPTw7Vr19CoUSO1bRQKBRQKhVa5iIgqSn5+Pg4cOICIiAgAwIEDBzBo0CCJUxEREZWd1oWFubm52mMdHR04Ozvj66+/Ro8ePbTal1wuh4eHB/bt26eaMlapVGLfvn2YMGFCofVdXFxw8eJFtWVffPEFUlNT8csvv8DBwUG7N0NE9BLFxcUhJCQEjx49AvB0MoyePXtKnIqIiKh8aFVY5OfnY+TIkWjRokW53aRp0qRJGDFiBNq2bYv27dtj3rx5SE9PV80SNXz4cNjb22P27NkwMDBA8+bN1ba3sLAAgELLiYgqCyEETp06hT179iAvLw+Ghobw9/eHq6ur1NGIiIjKjVaFha6uLnr06IErV66UW2ExaNAgxMXFYfr06Xj06BHc3d2xc+dO1YDuqKgo6OiU21AQIqKX7tKlS9ixYwcAoFGjRggICICpqanEqYiIiMqX1l2hmjdvjtu3b8PJyancQkyYMKHIrk8AEB4eXuK2S5cuLbccREQVoVmzZoiMjETTpk3Rvn17TiNLRETVktZNAd9++y0mT56MrVu3IiYmBikpKWpfREQ1XXZ2Ng4cOIC8vDwAT8eivfHGG/D09GRRQURE1ZbGLRZff/01PvroI/Tu3RsA0K9fP7U/kAXzsefn55d/SiKiKuL+/fsIDQ1FYmIisrOzVYOzWVAQEVF1p3Fh8dVXX2Hs2LE4cOBAReYhIqqS8vPzcejQIRw+fBhCCJibm3NwNhER1SgaFxZCCABAly5dKiwMEVFV9OTJE4SEhCA6OhoA0LJlS/Tq1avCbxpKRERUmWg1eJtN+URE6q5fv44NGzYgNzcXBgYG6NOnD6e/JiKiGkmrwqJp06alFhcJCQllCkREVJXY2tpCR0cHTk5OCAwMhJmZmdSRiIiIJKFVYfHVV18VuvM2EVFN8/jxY9jY2AAAzM3NMXr0aFhZWbFVl4iIajStCovBgwer/pgSEdU0OTk52L17N86cOYPXX38djRs3BgBYW1tLnIyIiEh6GhcWvBJHRDXZw4cPERISouruGRMToyosiIiI6AVmhSIiqkmUSiUOHz6MgwcPQggBMzMzBAYGwsnJSepoRERElYrGhYVSqazIHERElU5CQgJCQ0Px4MEDAEDz5s3Ru3dvGBoaSpyMiIio8tFqjAURUU0SExODBw8eQKFQoE+fPmjRooXUkYiIiCotFhZERM8QQqjGlDVr1gxJSUlo1qwZLCwspA1GRERUyelIHYCIqLK4ceMG/vrrL6SlpamWdezYkUUFERGRBlhYEFGNl5ubi+3bt2PVqlWIjY3FoUOHpI5ERERU5bArFBHVaDExMQgJCUF8fDwAwNPTEz4+PhKnIiIiqnpYWBBRjaRUKhEREYHw8HAolUqYmJggMDAQjRo1kjoaERFRlcTCgohqpKNHj2L//v0AAFdXV/Tt2xdGRkYSpyIiIqq6WFgQUY3Url07XL58GZ6enmjVqpVqJigiIiJ6MRy8TUQ1QmZmJo4cOQIhBABAoVDg7bffhru7O4sKIiKicsAWCyKq9m7fvo2wsDCkpqZCX18fnp6eAMCCgoiIqByxsCCiaisvLw979+7FiRMnAAC1a9eGg4ODxKmIiIiqJxYWRFQtPXr0CCEhIYiLiwMAtG3bFj169IC+vr7EyYiIiKonFhZEVO1ERkZi69atyM/Ph7GxMQICAtCkSROpYxEREVVrLCyIqNqxtraGUqmEs7Mz/P39YWxsLHUkIiKiao+FBRFVC0+ePEHt2rUBAPb29nj77bdha2vLAdpEREQvCaebJaIqLSsrCxs3bsT8+fMRGxurWm5nZ8eigoiI6CViiwURVVl37txBWFgYUlJSIJPJ8ODBA9ja2kodi4iIqEZiYUFEVU5eXh7279+PY8eOAQAsLS0RFBSEevXqSZyMiIio5mJhQURVyuPHjxESEqLq9tSmTRv4+flBLpdLnIyIiKhmY2FBRFXKjRs3EBsbCyMjI/Tr1w/Ozs5SRyIiIiKwsCCiKkAIoRqI7eXlhaysLHh6esLExETiZERERFSAs0IRUaV2+fJlLF26FLm5uQAAHR0d+Pj4sKggIiKqZNhiQUSVUlZWFnbu3Inz588DAE6ePImOHTtKnIqIiIiKw8KCiCqde/fuITQ0FMnJyZDJZOjUqRNeeeUVqWMRERFRCVhYEFGlkZ+fj/DwcBw5cgQAYGFhgaCgINSvX1/iZERERFQaFhZEVGns2rULp06dAgC4u7ujZ8+eUCgUEqciIiIiTbCwIKJKo2PHjrh16xZ8fX3h6uoqdRwiIiLSAmeFIiLJpKam4vTp06rH5ubmGD9+PIsKIiKiKogtFkQkiStXrmDLli3IzMyEmZkZmjZtCuDpdLJERERU9bCwIKKXKjs7Gzt37kRkZCQAwM7ODrVq1ZI2FBEREZUZCwsiemnu37+P0NBQJCYmAng6pqJbt27Q1dWVOBkRERGVFQsLInopjh49ir1790IIAXNzcwQFBaFBgwZSxyIiIqJywsKCiF4KCwsLCCHQsmVL9OrVCwYGBlJHIiIionLEwoKIKoQQAsnJybCwsAAAuLm5YfTo0ahXr560wYiIiKhCcPoVIip36enpWLNmDRYuXIi0tDTVchYVRERE1RdbLIioXF2/fh2bN29Geno6dHV18eDBA7i4uEgdi4iIiCoYCwsiKhc5OTnYvXs3zpw5AwCwsbFBcHAwbG1tJU5GRERELwMLCyIqs4cPHyIkJAQJCQkAgFdeeQU+Pj7Q0+OvGCIiopqCf/WJqMzOnTuHhIQEmJqaIjAwEA0bNpQ6EhEREb1kLCyIqMx69OgBPT09dOnSBYaGhlLHISIiIglwVigi0ooQAufOncPatWshhAAAyOVy9OzZk0UFERFRDcYWCyLSWEZGBrZs2YKrV68CAC5fvozmzZtLnIqIiIgqAxYWRKSRmzdvYtOmTUhLS4OOjg66d+8ONzc3qWMRERFRJcHCgohKlJubiz179uDUqVMAACsrKwQHB6NOnToSJyMiIqLKhIUFEZUoJCRE1fWpffv28PX1hb6+vsSpiIiIqLJhYUFEJercuTNiYmLQt29fNG7cWOo4REREVEmxsCAiNUlJSXj48CGaNWsGAKhbty7ee+896OrqSpyMiIiIKjMWFkQE4Ok0shcuXMD27duRn58PKysr2NraAgCLCiIiIioVCwsiQmZmJrZu3Yr//vsPAODg4AC5XC5xKiIiIqpKWFgQ1XC3b99GWFgYUlNToaOjg65du6Jjx47Q0eH9M4mIiEhzLCyIarC9e/ciIiICAFC7dm0EBwejbt26EqciIiKiqoiFBVENZmhoCABo27YtevTowWlkiSQkhEBeXh7y8/OljkJENYiuri709PQgk8nKvC8WFkQ1iBACaWlpMDU1BQB4eXnBwcEB9evXlzgZUc2Wk5ODmJgYZGRkSB2FiGogIyMj1KlTp8zjK1lYENUQycnJCAsLQ1paGt5++23o6+tDR0eHRQWRxJRKJe7cuQNdXV3UrVsXcrm8XK4cEhGVRgiBnJwcxMXF4c6dO2jSpEmZxliysCCqAS5evIht27YhOzsb+vr6iImJYUFBVEnk5ORAqVTCwcEBRkZGUschohrG0NAQ+vr6uHfvHnJycmBgYPDC+2JhQVSNZWVlYdu2bbh06RIAwN7eHsHBwbC0tJQ4GRE9jzOxEZFUyuv3DwsLomrqzp07CAsLQ0pKCmQyGby9veHt7c2TFyIiIqoQPMMgqoaEEIiIiEBKSgosLS0xatQodO3alUUFEVUqMpkMYWFhUsdQU1qmu3fvQiaTITIyskyv8+abbyIwMLBM+6gqFi1ahB49ekgdo9qIj4+HjY0NHjx4IHWUQniWQVQNyWQy9OvXD+3bt8c777yDevXqSR2JiKqh+fPno2XLljAzM4OZmRm8vLywY8cOqWORhsLDwyGTyZCUlFRhr5GVlYVp06bhyy+/LPTcgwcPIJfL0bx580LPlVTAde3aFR988IHasnPnzmHAgAGwtbWFgYEBmjRpgjFjxuD69evl9VYKCQkJQY8ePVC7dm2tis3169fDxcUFBgYGaNGiBbZv3672vBAC06dPR506dWBoaAhfX1/cuHFD9byVlRWGDx9e5DGVGgsLompACIHjx4+r/UE3MzNDr169yjx1HBFRcerVq4fvvvsOZ86cwenTp9G9e3cEBATg8uXLUker0QruiVIZXm/Dhg0wMzNDx44dCz23dOlSDBw4ECkpKThx4sQLv/7WrVvxyiuvIDs7GytXrsSVK1ewYsUKmJubY9q0aS+839Kkp6ejU6dO+P777zXe5ujRoxgyZAhGjx6Nc+fOITAwEIGBgaqxkADwww8/4Ndff8WCBQtw4sQJGBsbw8/PD1lZWap1Ro4ciZUrVyIhIaFc31NZsbAgquJSUlKwYsUK7Nq1CydPnkRUVJTUkYiohvD390fv3r3RpEkTNG3aFDNnzoSJiQmOHz/+Qvu7ePEiunfvDkNDQ9SuXRtvv/020tLSVM8XdB/68ccfUadOHdSuXRvjx49Hbm6uap2YmBj06dMHhoaGcHJywqpVq+Do6Ih58+ZpnCMmJga9evWCoaEhGjZsiA0bNhS7bn5+PkaPHg0nJycYGhrC2dkZv/zyS6F1Jk2aBAsLC9SuXRsff/wxhBBq6yiVSsyePVu1n1atWpX4us8qaHnYsWMHPDw8oFAocOTIEWRnZ2PixImwsbGBgYEBOnXqhFOnTgF42iLQrVs3AECtWrUgk8nw5ptvAkCJ25X0ekVZs2YN/P39Cy0XQmDJkiUYNmwYhg4dikWLFmn0Xp+XkZGBkSNHonfv3ti8eTN8fX3h5OQET09P/Pjjj/jrr79eaL+aGDZsGKZPnw5fX1+Nt/nll1/Qs2dPTJkyBa6urvjmm2/Qpk0b/P777wCeHpd58+bhiy++QEBAAFq2bInly5cjOjparYtes2bNULduXYSGhpb32yoTFhZEVdh///2H+fPn4/bt29DT00Pv3r3h4OAgdSwiqoHy8/OxZs0apKenw8vLS+vt09PT4efnh1q1auHUqVNYv3499u7diwkTJqitd+DAAdy6dQsHDhzAsmXLsHTpUixdulT1/PDhwxEdHY3w8HBs3LgRf//9Nx4/fqxVlmnTpqF///44f/48Xn/9dQwePBhXrlwpcl2lUol69eph/fr1+O+//zB9+nR89tlnWLdunWqdn376CUuXLsXixYtx5MgRJCQkFDohnD17NpYvX44FCxbg8uXL+PDDD/HGG2/g4MGDGuf+9NNP8d133+HKlSto2bIlPv74Y2zcuBHLli3D2bNn0bhxY/j5+SEhIQEODg7YuHEjAODatWuIiYlRFUQlbVfS6xXlyJEjaNu2baHlBw4cQEZGBnx9ffHGG2+oPjva2rVrF+Lj4/Hxxx8X+byFhUWx244dOxYmJiYlfpW3Y8eOFSpE/Pz8cOzYMQBPJ1559OiR2jrm5ubw9PRUrVOgffv2OHz4cLlnLAvOCkVUBWVlZWHnzp04f/48AKBOnToIDg6GlZWVxMmIqLz4/3YEcanZL/11rU0V2PJeJ43Xv3jxIry8vJCVlQUTExOEhobCzc1N69ddtWoVsrKysHz5chgbGwMAfv/9d/j7++P777+Hra0tgKdX13///Xfo6urCxcUFffr0wb59+zBmzBhcvXoVe/fuxalTp1Qns//88w+aNGmiVZYBAwbgrbfeAgB888032LNnD3777Tf8+eefhdbV19fHV199pXrs5OSEY8eOYd26dRg4cCAAYN68eZg6dSqCg4MBAAsWLMCuXbtU22RnZ2PWrFnYu3evqihr2LAhjhw5gr/++gtdunTRKPfXX3+NV199FcDTQm3+/PlYunQpevXqBQBYuHAh9uzZg0WLFmHKlCmqqcdtbGxUJ+CabFfU6xUlKSkJycnJqFu3bqHnFi1ahMGDB0NXVxfNmzdHw4YNsX79elWriaYKxh64uLhotR3wNP/kyZO13q4sHj16pPosF7C1tcWjR49UzxcsK26dAnXr1sW5c+cqMK32WFgQVTFCCPz777+Ijo6GTCZDp06d0KVLF+jq6kodjYjKUVxqNh6lZJW+osScnZ0RGRmJ5ORkbNiwASNGjMDBgwe1Li6uXLmCVq1aqYoKAOjYsSOUSiWuXbumOtFq1qyZ2u+7OnXq4OLFiwCeXnnX09NDmzZtVM83btwYtWrV0irL8y0uXl5eJQ7M/eOPP7B48WJERUUhMzMTOTk5cHd3BwAkJycjJiYGnp6eqvX19PTQtm1bVXeomzdvIiMjo9BJek5ODlq3bq1x7mdbBm7duoXc3Fy1sQ36+vpo3759sa0v2m5XVEvEszIzMwGg0A3XkpKSEBISotZ96o033sCiRYu0Liye71KmDRsbG9jY2Lzw9lIzNDRERkaG1DHUsLAgqmIKiondu3cjKCiId9AmqqasTRVV4nXlcjkaN24MAPDw8MCpU6fwyy+/VFjfdn19fbXHMpkMSqWyQl5LE2vWrMHkyZPx008/wcvLC6amppgzZ45Wg5ELxpFs27YN9vb2as8pFJp/P54tyl6G0l6vYLakxMREteUFrVPPFltCCCiVSly/fh1NmzaFmZkZgKeF2fOSkpJgbm4OAGjatCkA4OrVq1p3wRs7dixWrFhR4jrPjvEpD3Z2doiNjVVbFhsbCzs7O9XzBcvq1Kmjtk5BsVogISEB1tbW5ZqvrFhYEFUBcXFxSE1NRcOGDQEArq6uaNKkCfT0+CNMVF1p0x2pMlEqlcjO1r4Ll6urK5YuXYr09HTVCWtERAR0dHTg7Oys0T6cnZ2Rl5eHc+fOwcPDA8DT1oDnT2xLc/z4cQwfPlztcXEtBxEREejQoQPeffdd1bJbt26p/m9ubo46dergxIkT8Pb2BgDk5eXhzJkzqpYVNzc3KBQKREVFadztqTSNGjWCXC5HREQEGjRoAADIzc3FqVOnVFO1FswamJ+fr9V2mpLL5XBzc8N///2ndh+LRYsW4aOPPirUOvHuu+9i8eLF+O6772BpaQkrKyucOXNG7ZikpKTg5s2bqoKiR48esLKywg8//FDkQOakpKRix1lI0RXKy8sL+/btUzuWe/bsURVFTk5OsLOzw759+1SFRMGsWePGjVPb16VLl9C1a9eXlFwzPCshqsSEEDh16hT27NkDfX19jBs3DqampgDAooKIJDd16lT06tUL9evXR2pqKlatWoXw8HC18QOaev311/Hll19ixIgRmDFjBuLi4vDee+9h2LBhhfqbF8fFxQW+vr54++23MX/+fOjr6+Ojjz6CoaEhZDKZxlnWr1+Ptm3bolOnTli5ciVOnjxZ7KxFTZo0wfLly7Fr1y44OTnh33//xalTp+Dk5KRa5/3338d3332HJk2awMXFBXPnzlW7d4SpqSkmT56MDz/8EEqlEp06dUJycjIiIiJgZmaGESNGaJy9gLGxMcaNG6caS1G/fn388MMPyMjIwOjRowEADRo0gEwmw9atW9G7d28YGhrCxMSk1O204efnhyNHjqhOpCMjI3H27FmsXLmy0LiIIUOG4Ouvv8a3334LPT09TJo0CbNmzYKtrS1eeeUVPHnyBN988w2sra1V41WMjY3xzz//YMCAAejXrx8mTpyIxo0bIz4+HuvWrUNUVBTWrFlTZLaydoVKSEhAVFQUoqOjATztigc8bXUoaHkYPnw47O3tMXv2bABPPwtdunTBTz/9hD59+mDNmjU4ffo0/v77bwBPW+A++OADfPvtt2jSpAmcnJwwbdo01K1bV+2GihkZGThz5gxmzZr1wvkrhKhhkpOTBQCRnJwsWYafBg0QPw7sI34aNECyDFT5paamihUrVogZM2aIGTNmiH///VekpKRIHYuIyllmZqb477//RGZmptRRtDZq1CjRoEEDIZfLhbW1tfDx8RG7d+/WeHsAIjQ0VPX4woULolu3bsLAwEBYWlqKMWPGiNTUVNXzI0aMEAEBAWr7eP/990WXLl1Uj6Ojo0WvXr2EQqEQDRo0EKtWrRI2NjZiwYIFGmf6448/xKuvvioUCoVwdHQUa9euVT1/584dAUCcO3dOCCFEVlaWePPNN4W5ubmwsLAQ48aNE59++qlo1aqVapvc3Fzx/vvvCzMzM2FhYSEmTZokhg8frvZelEqlmDdvnnB2dhb6+vrC2tpa+Pn5iYMHD5aa+cCBAwKASExMVFuemZkp3nvvPWFlZSUUCoXo2LGjOHnypNo6X3/9tbCzsxMymUyMGDFCo+2Ke72iXL58WRgaGoqkpCQhhBATJkwQbm5uRa4bExMjdHR0xKZNm4QQQuTl5Ylff/1VtGjRQhgZGYl69eqJQYMGiTt37hTa9tSpUyI4OFhYW1sLhUIhGjduLN5++21x48aNUjO+qCVLlggAhb6+/PJL1TpdunRRHdcC69atE02bNhVyuVw0a9ZMbNu2Te15pVIppk2bJmxtbYVCoRA+Pj7i2rVrauusWrVKODs7l9t7Ken3kDbnzjIhyjDqpQpKSUmBubk5kpOTVf33Xra5gwdCiAzIZEaYtGZd6RtQjXPlyhVs2bIFmZmZ0NXVxauvvor27dtrdcWNiKqGrKws3LlzB05OToUGuVLZPXjwAA4ODti7dy98fHykjlMjDRgwAG3atMHUqVOljlJtvPLKK5g4cSKGDh1aLvsr6feQNufO7EtBVIkIIbBlyxbV9HF2dnYICgqq0rNWEBG9TPv370daWhpatGiBmJgYfPzxx3B0dFSNb6CXb86cOdiyZYvUMaqN+Ph4BAcHY8iQIVJHKaRS3CDvjz/+gKOjIwwMDODp6YmTJ08Wu+7ChQvRuXNn1KpVC7Vq1YKvr2+J6xNVJTKZDDo6T38sO3TogNGjR7OoIKIqaeXKlcXedKxZs2YV9rq5ubn47LPP0KxZMwQFBcHa2hrh4eHQ19eXLFNZlXQjt7Fjx0odr1SOjo547733pI5RbVhZWeHjjz+ulL0YJO8KtXbtWgwfPhwLFiyAp6cn5s2bh/Xr1+PatWtFnlC9/vrr6NixIzp06AADAwN8//33CA0NxeXLlwtN0VYUdoWiyiY/Px85OTkwNDQE8HTe8kePHnEaWaIaorp2hUpNTS00rWYBfX191YxDL1NlzKSJx48fIyUlpcjnzMzMeAGKyqy8ukJJXlh4enqiXbt2+P333wE8nabOwcEB7733Hj799NNSt8/Pz1fdhfPZqeGKw8KCKpMnT54gJCQECoUCw4YNq5RXH4ioYlXXwoKIqo5qMcYiJycHZ86cURvMo6OjA19fXxw7dkyjfWRkZCA3N1d1W/rnZWdnq82nXVzFT/QyCSFw9uxZ7Nq1C7m5uTAwMMCTJ09gZWUldTQiIiKiFyJpYREfH4/8/PxC81Pb2tri6tWrGu3jk08+Qd26deHr61vk87Nnz8ZXX31V5qxE5SU9PR2bN2/G9evXATy9GU5AQIDqLqJEREREVVGVnhXqu+++w5o1axAeHl5s8/HUqVMxadIk1eOUlBQ4ODi8rIhEaq5fv47NmzcjPT0durq68PHxwSuvvMIuUERERFTlSVpYWFlZQVdXt9BAqtjYWNUdC4vz448/4rvvvsPevXvRsmXLYtdTKBRQKBTlkpeoLJRKJfbu3Yv09HTY2NggODhY47vJEhEREVV2kk43K5fL4eHhgX379qmWKZVK7Nu3D15eXsVu98MPP+Cbb77Bzp070bZt25cRlajMdHR0EBQUBC8vL4wZM4ZFBREREVUrkt/HYtKkSVi4cCGWLVuGK1euYNy4cUhPT8fIkSMBAMOHD1cb3P39999j2rRpWLx4MRwdHfHo0SM8evQIaWlpUr0FoiIplUocPHgQR48eVS2rU6cOevToAT29Kt0LkYiowjk6OmLevHlSxwAALF26FBYWFiWuM2PGDLi7u5f5tWQyGcLCwsq8n6rA29sbq1atkjpGtbFgwQL4+/tLmkHywmLQoEH48ccfMX36dLi7uyMyMhI7d+5UXc2NiopCTEyMav358+cjJycHr732GurUqaP6+vHHH6V6C0SFJCQkYMmSJQgPD8e+ffuQmJgodSQionI3f/58tGzZEmZmZjAzM4OXlxd27NghdaxSVaaipTJ68803ERgYWKGvsXnzZsTGxmLw4MGFnps9ezZ0dXUxZ86cQs8VV8DdvXsXMpkMkZGRqmVCCPz999/w9PSEiYkJLCws0LZtW8ybNw8ZGRnl+XbUTJw4ER4eHlAoFBoXm1lZWRg/fjxq164NExMT9O/fv9BQgaioKPTp0wdGRkawsbHBlClTkJeXp3p+1KhROHv2LA4fPlyeb0crleKy6YQJEzBhwoQinwsPD1d7fPfu3YoPRPSChBCq4jgnJwcKhQK9e/cu9UoXEVFVVK9ePXz33Xdo0qQJhBBYtmwZAgICcO7cuUp5J+ucnBzI5XKpY0jmZb//kl7v119/xciRI6GjU/ga9+LFi/Hxxx9j8eLFmDJlygu//rBhwxASEoIvvvgCv//+O6ytrXH+/HnMmzcPjo6OFVo8jRo1CidOnMCFCxc0Wv/DDz/Etm3bsH79epibm2PChAkIDg5GREQEgKf3bevTpw/s7Oxw9OhRxMTEYPjw4dDX18esWbMAPB1iMHToUPz666/o3Llzhb23kkjeYkFUXWRkZGDdunXYvHkzcnJy0KBBA4wdOxYtW7bkrE9EVC35+/ujd+/eaNKkCZo2bYqZM2fCxMQEx48fL3VbIQRmzJiB+vXrQ6FQoG7dupg4caLaOhkZGRg1ahRMTU1Rv359/P3332rPX7x4Ed27d4ehoSFq166Nt99+W61rdMGV95kzZ6Ju3bpwdnZG165dce/ePXz44YeQyWRa/X4OCwtDkyZNYGBgAD8/P9y/f7/YdU+dOoVXX30VVlZWMDc3R5cuXXD27Fm1dW7cuAFvb28YGBjAzc0Ne/bsKbSf+/fvY+DAgbCwsIClpSUCAgI0vsha1PsHSj5uM2bMwLJly7Bp0ybV8Sm4yPsix7socXFx2L9/f5Hddg4ePIjMzEx8/fXXSElJUetOrI1169Zh5cqVWL16NT777DO0a9cOjo6OCAgIwP79+9GtW7cX2q8mfv31V4wfPx4NGzbUaP3k5GQsWrQIc+fORffu3eHh4YElS5bg6NGjqp+l3bt347///sOKFSvg7u6OXr164ZtvvsEff/yBnJwc1b78/f2xefNmZGZmVsh7Kw0LC6JykJeXh7///htXr15V3eRx+PDhbKkgohojPz8fa9asQXp6eokTsBTYuHEjfv75Z/z111+4ceMGwsLC0KJFC7V1fvrpJ7Rt2xbnzp3Du+++i3HjxuHatWsAnt4TyM/PD7Vq1cKpU6ewfv167N27t1APiH379uHatWvYs2cPtm7dipCQENSrVw9ff/01YmJi1LpblyQjIwMzZ87E8uXLERERgaSkpCK78RRITU3FiBEjcOTIERw/fhxNmjRB7969kZqaCuDpOLzg4GDI5XKcOHECCxYswCeffKK2j9zcXPj5+cHU1BSHDx9GREQETExM0LNnT7WTyZI8//5LO26TJ0/GwIED0bNnT9Xx6dChwwsf76IcOXIERkZGcHV1LfTcokWLMGTIEOjr62PIkCFYtGiRRu/zeStXroSzszMCAgIKPSeTyUq8d5SJiUmJX2PHjn2hTMU5c+YMcnNz1e7J5uLigvr166tuGH3s2DG0aNFCbeIXPz8/pKSk4PLly6plbdu2RV5eHk6cOFGuGTVVKbpCEVV1enp6aN++PSIjIxEUFIQ6depIHYmIqrq/ugBpj1/+65rYAO8c1Hj1ixcvwsvLC1lZWTAxMUFoaCjc3NxK3S4qKgp2dnbw9fWFvr4+6tevj/bt26ut07t3b7z77rsAnt4Q9+eff8aBAwfg7OyMVatWISsrC8uXL4exsTEA4Pfff4e/vz++//571QmYsbEx/vnnH7UuObq6ujA1NS11avtn5ebm4vfff4enpycAYNmyZXB1dcXJkycL5QaA7t27qz3++++/YWFhgYMHD6Jv377Yu3cvrl69il27dqFu3boAgFmzZqFXr16qbdauXQulUol//vlH1bKyZMkSWFhYIDw8HD169Cg19/Pvf+HChaUeN0NDQ2RnZ6sdn2XLlr3w8X7evXv3YGtrW6gbVEpKCjZs2KA6mX7jjTfQuXNn/PLLLzAxMSn1vT7rxo0bxbaYlObZcRpFMTMze6H9FufRo0eQy+WFLkba2tri0aNHqnWKuqF0wXMFjIyMYG5ujnv37pVrRk2xsCB6QQVXuQqKCC8vL7Rr1w76+vpSxiKi6iLtMZAaLXWKUjk7OyMyMhLJycnYsGEDRowYgYMHD5ZaXAwYMADz5s1Dw4YN0bNnT/Tu3Rv+/v5qs+Y9e58qmUwGOzs7PH78tNi6cuUKWrVqpTrJBYCOHTtCqVTi2rVrqpOuFi1alMu4Aj09PbRr10712MXFBRYWFrhy5UqRhUVsbCy++OILhIeH4/Hjx8jPz0dGRgaioqJU+R0cHFRFBYBCLT3nz5/HzZs3YWpqqrY8KysLt27d0ij38+9f0+P2vPI83pmZmUXe2Hj16tVo1KgRWrVqBQBwd3dHgwYNsHbtWowePVqj91tACKHV+s9q3LjxC29bGRgaGlbo4PSSsLAg0pJSqcTRo0dx4MAB1KpVC2+//TbkcjlkMhmLCiIqPyY2VeJ15XK56kTMw8MDp06dwi+//IK//vqrxO0cHBxw7do17N27F3v27MG7776LOXPm4ODBg6rfpc//TpXJZFAqlVrle/ZE+GUaMWIEnjx5gl9++QUNGjSAQqGAl5eXxl2YACAtLQ0eHh5YuXJloeesra012sfLfv+avJ6VlVWRsyUuWrQIly9fVisulUolFi9erCoszMzMkJycXGjbpKQkAFB1cWratCmuXr36Im+h1NaRN954AwsWLHihfRfFzs4OOTk5SEpKUmu1ePaG0XZ2djh58qTadgWzRj3f8paQkKDx56O8sbAg0kJSUhJCQ0NVV5xsbGyQn58vcSoiqpa06I5UmSiVSmRnZ2u0rqGhIfz9/eHv74/x48fDxcUFFy9eRJs2bUrd1tXVFUuXLkV6errqZDYiIgI6OjqldoGRy+Va/+7Oy8vD6dOnVa0T165dQ1JSUpHjBAqy/Pnnn+jduzeAp4Ow4+Pj1fLfv38fMTExqpbv5we9t2nTBmvXroWNjU25db/R5LgVdXzKcryf17p1azx69AiJiYmoVasWgKdd6k6fPo3w8HBYWlqq1k1ISEDXrl1x9epVuLi4wNnZGQ8ePEBsbKxa68rZs2dhYGCA+vXrAwCGDh2KwYMHY9OmTYXGWQghkJKSUuw4i5fdFcrDwwP6+vrYt28f+vfvD+Dp5ysqKkrViuXl5YWZM2fi8ePHsLF5Wvzv2bMHZmZmaq2Dt27dQlZWFlq3bl2uGTXFwdtEGhBC4Pz585g/fz6ioqIgl8sREBCAAQMGwNDQUOp4RESSmDp1Kg4dOoS7d+/i4sWLmDp1KsLDw/H666+Xuu3SpUuxaNEiXLp0Cbdv38aKFStgaGiIBg0aaPTar7/+OgwMDDBixAhcunQJBw4cwHvvvYdhw4YV252ngKOjIw4dOoSHDx+qneyXRF9fH++99x5OnDiBM2fO4M0338Qrr7xSZDcoAGjSpAn+/fdfXLlyBSdOnMDrr7+u9vfC19cXTZs2xYgRI3D+/HkcPnwYn3/+eaH3aGVlhYCAABw+fBh37txBeHg4Jk6ciAcPHmiU+3maHDdHR0dcuHAB165dQ3x8PHJzc8t0vJ/XunVrWFlZqaZSBZ62VrRv3x7e3t5o3ry56svb2xvt2rVTDeL28/ODs7MzhgwZgqNHj+L27dvYsGEDvvjiC7z//vvQ1dUFAAwcOBCDBg3CkCFDMGvWLJw+fRr37t3D1q1b4evriwMHDhSbr3HjxiV+FZzYF+fmzZuIjIzEo0ePkJmZicjISERGRqpaqx4+fAgXFxdVC4S5uTlGjx6NSZMm4cCBAzhz5gxGjhwJLy8vvPLKKwCAHj16wM3NDcOGDcP58+exa9cufPHFFxg/fjwUCoXqtQ8fPoyGDRuiUaNGWn1PygsLC6JS5ObmYsOGDQgLC0NOTg4cHBwwduxYuLu7cxpZIqrRHj9+jOHDh8PZ2Rk+Pj44deoUdu3ahVdffbXUbS0sLLBw4UJ07NgRLVu2xN69e7FlyxbUrl1bo9c2MjLCrl27kJCQgHbt2uG1116Dj48Pfv/991K3/frrr3H37l00atRI4y4jRkZG+OSTTzB06FB07NgRJiYmWLt2bbHrL1q0CImJiWjTpg2GDRuGiRMnqp2Q6ujoIDQ0FJmZmWjfvj3eeustzJw5s9BrHjp0CPXr10dwcDBcXV0xevRoZGVlvfBVc02O25gxY+Ds7Iy2bdvC2toaERERZTrez9PV1cXIkSNVXbxycnKwYsUK1dX65/Xv3x/Lly9Hbm4u9PT0sHv3btSvXx9DhgxB8+bN8eWXX+L999/HN998o9pGJpNh1apVmDt3LsLCwtClSxe0bNkSM2bMQEBAAPz8/LTOram33noLrVu3xl9//YXr16+jdevWaN26NaKjn46Zys3NxbVr19TGQfz888/o27cv+vfvD29vb9jZ2SEkJET1vK6uLrZu3QpdXV14eXnhjTfewPDhw/H111+rvfbq1asxZsyYCntvpZGJsoxuqYIKmr6Sk5PLvSlLU3MHD4QQGZDJjDBpzTpJMpDmhBBYuXIl7ty5gy5duqBTp05F3tCHiOhFZGVl4c6dO3BycipyQCtRdfTo0SM0a9YMZ8+e1biVikp2+fJldO/eHdevXy9xOt2ilPR7SJtzZ46xICpCXl4elEqlalB2QEAAUlJSYG9vL3U0IiKiKs/Ozg6LFi1CVFQUC4tyEhMTg+XLl2tdVJQnXnYles6jR4+wcOFC7Ny5U7XM1NSURQURkRZWrlxZ7A3GmjVrJnU8Nb169So266xZs6SOV6ySbuJ2+PBhqeOVKjAwEJ07d5Y6RrXh6+tboV28NMEWC6L/J4TAsWPHsH//fuTn5yM9PV1t9gsiItJcv379VDeTe15lm5r7n3/+QWZmZpHPPTtDUWVT0uxFvBhGUmBhQQQgOTkZYWFhuHv3LoCnN3zy9/dnUUFE9IJMTU0L3ditsqqqJ+FV/UZuVP2wsKAa79KlS9i2bRuysrKgr68PPz8/tGnThjM+EREREWmBhQXVaFlZWdixYweysrJgb2+PoKAgjac6JCIiIqL/YWFBNZqBgQH69euHmJgYdO7cWXVjHSIiIiLSDgsLqlHy8vJw4MAB2Nvbw83NDcDT8RTOzs4SJyMiIiKq2lhYUI3x+PFjhISEIDY2FoaGhmjYsCFvRkVERERUTlhYULUnhMCJEyewd+9e5Ofnw8jICP7+/iwqiIiIiMoRb5BH1VpKSgpWrFiBXbt2IT8/H02aNMG4cePg4uIidTQiohrh7t27kMlkJd5zoThdu3bFBx98UOp63t7eWLVqlfbhqEgLFiyAv7+/1DGoCmJhQdVWRkYGFixYgNu3b0NPTw+9e/fGkCFDYGJiInU0IqJq4c0334RMJlN91a5dGz179sSFCxdU6zg4OCAmJgbNmzevkAybN29GbGwsBg8eXOi52bNnQ1dXF3PmzCn03IwZM+Du7l5oeVGFkBACf//9Nzw9PWFiYgILCwu0bdsW8+bNQ0ZGRnm+HTUTJ06Eh4cHFApFkVmLkpWVhfHjx6N27dowMTFB//79ERsbq7ZOVFQU+vTpAyMjI9jY2GDKlCnIy8tTPT9q1CicPXu2Sty9myoXFhZUbRkZGaFZs2aoU6cO3nnnHbRr1473piAiKmc9e/ZETEwMYmJisG/fPujp6aFv376q53V1dWFnZwc9vaJ7Xwsh1E5qtfXrr79i5MiR0NEpfEqzePFifPzxx1i8ePEL7x8Ahg0bhg8++AABAQE4cOAAIiMjMW3aNGzatAm7d+8u075LM2rUKAwaNEjj9T/88ENs2bIF69evx8GDBxEdHY3g4GDV8/n5+ejTpw9ycnJw9OhRLFu2DEuXLsX06dNV68jlcgwdOhS//vprub4Xqv5YWFC1EhUVheTkZNXjHj16YPTo0bCyspIwFRFR9aVQKGBnZwc7Ozu4u7vj008/xf379xEXFwegcAtAeHg4ZDIZduzYoboaf+TIEaSnp2P48OEwMTFBnTp18NNPP5X62nFxcdi/f3+R3XYOHjyIzMxMfP3110hJScHRo0df6P2tW7cOK1euxOrVq/HZZ5+hXbt2cHR0REBAAPbv349u3bq90H7/r707j6sx/f8H/jqdOu2nVOoUkq0sI21KmQiNsk3I1McawtiXjGVirMMYpjBjCZ1kaYSZLGMJ08iSbBFGhES2mLRpc+p0/f7w7f45WnTaTvF+Ph7n8Zhz3dd93e/7dM9xv8+13JXx66+/YsqUKWjZsmWl6mdlZUEsFiMwMBA9e/aEra0ttm/fjgsXLuDixYsAgJMnTyIhIQG7d++GlZUV+vTpg+XLl2Pjxo2QSCRcWwMGDMDhw4eRn59fK+dGPk00eZt8EqRSKaKjoxETE4PmzZtj1KhR4PF4UFFRUXRohBBSJd5HvJGWn1bnxzVQN8De/nurtG9OTg52796N1q1bf/Rho/Pnz8cvv/yCli1bolGjRpgzZw7OnDmDQ4cOwdDQEP7+/rh27VqFQ4DOnz8PDQ0NtGvXrtQ2sViMoUOHQkVFBUOHDoVYLIaTk5Pc5xQWFgYLCwt4eHiU2sbj8aCjo1Puvh8bejtixAgEBQXJHVN54uLiUFhYCFdXV66sbdu2MDU1RWxsLLp06YLY2Fh07NgRRkZGXB03NzdMmjQJt2/fhrW1NQDAzs4ORUVFuHTpElxcXGosRvJpo8SCNHhpaWmIiIjAixcvAAA6OjooKiqipIIQ0qCl5afhVd4rRYfxUUeOHOFuoHNzc2FsbIwjR46UOTTpfcuWLcNXX30F4F1CIhaLsXv3bvTq1QsAsGPHDjRt2rTCNh4/fgwjI6NSx8rOzsYff/yB2NhYAO9u4J2dnbF+/Xq559ndv3+/ys86+tiEdaFQWKV2y5OamgqBQABdXV2ZciMjI6SmpnJ13k8qSraXbCuhoaEBHR0dPH78uEZjJJ82SixIg8UYw9WrV3Hy5EkUFRVBXV0d/fv35x58RwghDZmBumKGcMp73B49emDz5s0AgIyMDGzatAl9+vTB5cuX0bx583L3s7Oz4/47KSkJEokEDg4OXJment5Hb+jz8/PLXDp8z549aNWqFTp16gQAsLKyQvPmzbF37174+vrKdX6MMbnqv69169ZV3rc+UFdXr9XJ6eTTQ4kFaZDy8/MRERGBBw8eAABatmyJgQMHQltbW8GREUJIzajqcKS6pqmpKXMDHRwcDB0dHWzbtg0//vhjhftVl4GBATIyMkqVi8Vi3L59W2bCeHFxMUJCQrjEQigUyszJK5GZmQkA3BAnc3Nz3L17t0rx1fVQKJFIBIlEgszMTJlei5cvX0IkEnF1Ll++LLNfyapRJXVKpKeno3HjxjUWH/n0UWJBGiRlZWVkZWWBz+fjq6++gr29Pa34RAgh9QCPx4OSkpJck35btWoFFRUVXLp0CaampgDe9X7cu3cP3bt3L3c/a2trpKamIiMjA40aNQIA3Lp1C1evXkV0dDT09PS4uunp6XBxccHdu3fRtm1bWFhY4OnTp3j58qXM0KBr165BTU2Ni2PYsGH43//+h0OHDpWaZ8EYQ3Z2drnzLOp6KJStrS1UVFQQFRUFT09PAEBiYiJSUlLg6OgIAHB0dMSKFSvw6tUrGBoaAgBOnToFoVAo0+OflJSEgoICbs4FIZVBiQVpMCQSCZSVlaGkpAQVFRUMGTIEALgvRkIIIXXv7du33Nj8jIwMbNiwATk5OXI9YE1LSwu+vr6YM2cO9PX1YWhoiAULFnx0noa1tTUMDAwQExPDLXErFothb2+Pbt26larfuXNniMVirFmzBm5ubrCwsMDQoUPx448/QiQS4dq1a1i4cCFmzJgBPp8PAPDy8sKBAwcwdOhQLFy4EL1790bjxo1x69YtrF27FtOmTcPAgQPLjK+6Q6EePHiAnJwcpKamIj8/n0tU2rdvD4FAgGfPnqFXr17YuXMn7O3toaOjA19fX/j5+UFPTw9CoRDTpk2Do6MjunTpAuDdaont27fHyJEjsXr1aqSmpmLhwoWYMmUKVFVVuWOfO3cOLVu2RKtWrap1DuTzQokFaRCePn2KiIgIWFtbw9nZGQAlFIQQUh9ERkbC2NgYAKCtrY22bdti//79cq8ktGbNGi4h0dbWxuzZs8scqvQ+Pp+PMWPGICwsDP3794dEIsHu3bsxb968Mut7enoiICAAK1euhIqKCk6ePAl/f38MHToU//33H1q0aIEZM2bAz8+P24fH4+H333/H1q1bERISghUrVkBZWRlt2rTBqFGj4ObmJtd5ymPcuHE4c+YM976k9yA5ORlmZmYoLCxEYmKizDyItWvXQklJCZ6ennj79i3c3NywadMmbjufz8eRI0cwadIkODo6QlNTEz4+Pli2bJnMsffs2YPx48fX2rmRTxOPVWdWUgNU0mWZlZVV412QlRX4Py8wlgceTwN+4fsUEkNDIZVKce7cOZw9exaMMTRq1AiTJ08u90FLhBDS0BQUFCA5ORktWrQocyIyqVhqaio6dOiAa9euVThZnFTe7du30bNnT9y7d6/C5XTJp6Oi7yF57p3p7ozUW69fv8aBAwfw7NkzAEDHjh3Rt29fSioIIYRwRCIRxGIxUlJSKLGoIS9evMDOnTspqSByozs0Uu8wxnDt2jWcOHEChYWFUFNTQ79+/fDFF18oOjRCCCH1UHlzHEjVvP+APULkQYkFqXcyMzNx/PhxSKVSmJmZYeDAgfSrCSGEEEJIPUeJBal3GjVqhN69e6OoqAiOjo60jCwhhBBCSANAiQVROIlEglOnTqFTp05o2rQpAMDe3l7BURFCCCGEEHlUvEA0IbXs2bNn2LJlC65evYqDBw+iuLhY0SERQgghhJAqoB4LohDFxcU4f/48zpw5g+LiYmhra6Nv374ffRgSIYQQQgipnyixIHUuPT0dBw4cwNOnTwEAHTp0QL9+/aCurq7gyAghhBBCSFXRz8OkTqWlpWHLli14+vQpVFVVMWjQIHh6elJSQQghn6hHjx6Bx+MhPj5e7n1dXFwwc+bMGo+pJo9ZmfMLDQ2Frq5uteL68DjR0dHg8XjIzMysVrv10Q8//IAJEyYoOoxPRkJCApo2bYrc3NxaPxYlFqRO6evrw9TUFKamppg4cSIsLS1p1SdCCGmgRo8eDR6Px7309fXh7u6OmzdvcnWaNWuGFy9eNJhnEUVERGD58uWVrq+o83NycsKLFy/qdDn2mkiQPiY1NRXr16/HggULSm2LjY0Fn89Hv379Sm2rKNEyMzPDunXrZMpOnz6Nvn37Ql9fHxoaGmjfvj1mz57NPZS3NmzduhUuLi4QCoVyJYUbN26EmZkZ1NTU4ODggMuXL8tsLygowJQpU6Cvrw8tLS14enri5cuX3Pb27dujS5cuCAwMrMnTKRMlFqTWPXz4EG/fvgUA8Hg8eHp6wsfHp9a/nAghhNQ+d3d3vHjxAi9evEBUVBSUlZXRv39/bjufz4dIJIKyctmjrxljKCoqqtKxzczMEB0dXaV9y6Onpwdtbe1K1//Y+dUWgUAAkUjUIH+ck0ql5S7WEhwcDCcnpzKfoi4WizFt2jScPXsWz58/r/Lxt2zZAldXV4hEIvz5559ISEhAUFAQsrKyEBAQUOV2PyYvLw/u7u7w9/ev9D579+6Fn58fFi9ejGvXrqFTp05wc3PDq1evuDqzZs3CX3/9hf379+PMmTN4/vw5Bg8eLNPOmDFjsHnz5ir/v1ZZlFiQWlNYWIhjx45h165diIyM5MrV1NRokjYhhHwiVFVVIRKJIBKJYGVlhfnz5+PJkyf477//AJQ/hOf48eOwtbWFqqoqzp8/j9zcXIwaNQpaWlowNjau9g1eyXFOnDgBa2trqKuro2fPnnj16hWOHz+Odu3aQSgUYtiwYcjLy+P2+3AolJmZGVauXImxY8dCW1sbpqam2Lp1K7ddnqFeJ06cQLt27aClpcUlZO8LDg5Gu3btoKamhrZt22LTpk0fPb/3f/WOiYmBi4sLNDQ00KhRI7i5uSEjIwMA8PbtW0yfPh2GhoZQU1PDl19+iStXrpRq7+jRo7C0tISamhq6dOmCf//9l9s+ZswYZGVlcT1US5Ys4dr+7rvv0KRJE2hqasLBwUEm4Svp6Th8+DDat28PVVVVpKSklHle4eHhGDBgQKnynJwc7N27F5MmTUK/fv0QGhpa0UddrqdPn2L69OmYPn06QkJC4OLiAjMzM3Tr1g3BwcFYtGhRldqtjJkzZ2L+/Pno0qVLpfcJDAzE+PHjMWbMGLRv3x5BQUHQ0NBASEgIACArKwtisRiBgYHo2bMnbG1tsX37dly4cAEXL17k2vnqq6+Qnp6OM2fO1Ph5vY/u7kitePHiBbZu3cp9aQkEAjDGFBwVIYSQ2pSTk4Pdu3ejdevW0NfXr7Du/PnzsWrVKty5cweWlpaYM2cOzpw5g0OHDuHkyZOIjo7GtWvXqh3TkiVLsGHDBly4cAFPnjyBl5cX1q1bh99//x1Hjx7FyZMn8dtvv1XYRkBAAOzs7HD9+nVMnjwZkyZNQmJiolxx5OXl4ZdffsGuXbtw9uxZpKSk4LvvvuO2h4WFYdGiRVixYgXu3LmDlStX4ocffsCOHTsq1X58fDx69eqF9u3bIzY2FufPn8eAAQMglUoBAHPnzsWff/6JHTt24Nq1a2jdujXc3NyQnp4u086cOXMQEBCAK1euoHHjxhgwYAAKCwvh5OSEdevWQSgUcj1UJfFPnToVsbGxCA8Px82bN/HNN9/A3d0d9+/flzn/n3/+GcHBwbh9+zYMDQ1LnUN6ejoSEhJgZ2dXatu+ffvQtm1bWFhYYMSIEQgJCanSfcX+/fshkUgwd+7cMrdXNJqiT58+0NLSKvfVoUMHueOpiEQiQVxcHFxdXbkyJSUluLq6IjY2FgAQFxeHwsJCmTpt27aFqakpVwd4dx9mZWWFc+fO1WiMH6JVoUiNKi4uxoULF3D69GkUFxdDS0sLHh4eaN26taJDI4SQBiXZcwiK0tLq/LjKBgZo8ecfla5/5MgRaGlpAQByc3NhbGyMI0eOfLRnetmyZfjqq68AvEtIxGIxdu/ejV69egEAduzYwT00tTp+/PFHdO3aFQDg6+uL77//HklJSWjZsiUAYMiQITh9+jTmzZtXbht9+/bF5MmTAQDz5s3D2rVrcfr0aVhYWFQ6jsLCQgQFBaFVq1YA3t2ML1u2jNu+ePFiBAQEcENYWrRogYSEBGzZsgU+Pj4fbX/16tWws7OT6eUoudHNzc3F5s2bERoaij59+gAAtm3bhlOnTkEsFmPOnDkycZT8XUr+BgcOHICXlxd0dHTA4/EgEom4+ikpKdi+fTtSUlJgYmICAPjuu+8QGRmJ7du3Y+XKldz5b9q0CZ06dSr3HFJSUsAY49p5n1gsxogRIwC8G36XlZWFM2fOwMXF5aOfzfvu378PoVAIY2NjufYD3vUo5efnl7tdRUVF7jYrkpaWBqlUCiMjI5lyIyMj3L17F8C7OSkCgaBUQmRkZITU1FSZMhMTEzx+/LhGY/wQJRakxmRnZyMiIoK7aNu1a4f+/ftDQ0NDwZERQkjDU5SWhqL3JmDWVz169MDmzZsBABkZGdi0aRP69OmDy5cvlzlOvsT7v0onJSVBIpHAwcGBK9PT0yt14z5x4kTs3r2be5+Xl4c+ffqAz+dzZTk5OTL7WFpacv9tZGQEDQ0NLqkoKftwMuyH3m+j5Mb6/THu7+vQoQP376CzszOOHz8OANDQ0OCSCgAwNjbm2sjNzUVSUhJ8fX0xfvx4rk5RUVGlJ2fHx8fjm2++KXNbUlISCgsLuQQLeHcTbG9vjzt37sjUdXR05P675G/wYZ333bp1C1KpFObm5jLlb9++lem1EggEMp9jWUpu2tXU1GTKExMTcfnyZRw4cAAAoKysDG9vb4jFYrkTC8ZYleelNGnSpEr71Rfq6uoyw/5qAyUWpMbw+XykpaVBIBDA3d0dVlZWDXJSGSGE1AfKBgYN4riampoyvdLBwcHQ0dHBtm3b8OOPP1a4n7yWLVsmM3zIxcUFP//8s0xC8qH3f0Xm8XilflXm8XjlTiQuq42P7XPs2DEUFhYCgMxS6mW1UTKUpyQZ2rZtW6lzeT9pqoiilm3PyckBn89HXFxcqVhLerKAd/F97J7A4P+uvYyMDDRu3JgrF4vFKCoqkunJYIxBVVUVGzZsgI6ODoRCIYB3cw4+/PU+MzOTS9DMzc2RlZWFFy9eyN1r0adPnwqHEjVv3hy3b9+Wq82KGBgYgM/ny6zwBAAvX77keo1EIhEkEgkyMzNlzvv9OiXS09NlktvaQIkFqRaJRAKBQADg3T8S33zzDYRCIRo1aqTgyAghpGGTZzhSfcLj8aCkpFThkJEPtWrVCioqKrh06RJMTU0BvLu5vHfvHrp3787VMzQ0lBmbr6ysjCZNmtSr4bYV9dKUx8jICCYmJnj48CGGDx9epeNaWloiKioKS5cuLbWtVatWEAgEiImJ4eIrLCzElStXSj2z4+LFi6X+Bu3atQPwrtehZM5GCWtra0ilUrx69QrOzs5Viv39OIVCIRISErgekKKiIuzcuRMBAQHo3bu3TP2BAwdiz549mDhxItq0aQMlJSXExcXJ/A0ePnyIrKwsrr0hQ4Zg/vz5WL16NdauXVsqhg9v0N9X10OhBAIBbG1tERUVhYEDBwJ4N+Q8KioKU6dOBQDY2tpCRUUFUVFR8PT0BPCuhyclJUWm9wkA/v33XwwZMqRGY/wQJRakyh4+fIiDBw+id+/e3PrdVflCJYQQ0nC9ffuWG8udkZGBDRs2ICcnp8yVfcqjpaUFX19fzJkzB/r6+jA0NMSCBQs+qxUEly5diunTp0NHRwfu7u54+/Ytrl69ioyMDPj5+X10/++//x4dO3bE5MmTMXHiRAgEApw+fRrffPMNDAwMMGnSJMyZMwd6enowNTXF6tWrkZeXB19fX5l2li1bBn19fRgZGWHBggUwMDDgbmrNzMyQk5ODqKgodOrUCRoaGjA3N8fw4cMxatQoBAQEwNraGv/99x+ioqJgaWlZ5jMnylMyMfn8+fPcMY8cOYKMjAz4+vqWGhbm6ekJsViMiRMnQltbG+PGjcPs2bOhrKyMjh074smTJ5g3bx66dOkCJycnAO+eO7J27VpMnToV2dnZGDVqFMzMzPD06VPs3LkTWlpa5a5IVt2hUKmpqUhNTcWDBw8AvBtGVrLSmJ6eHgCgV69eGDRoEJc4+Pn5wcfHB3Z2drC3t8e6deuQm5uLMWPGAAB0dHTg6+sLPz8/6OnpQSgUYtq0aXB0dJRZferRo0d49uyZzCTv2vD5/B9LakxRURFOnDiBXbt24c2bN7h48SKt+EQIIZ+pyMhIGBsbw9jYGA4ODrhy5Qr2798v99j3NWvWwNnZGQMGDICrqyu+/PJL2Nra1k7Q9dC4ceMQHByM7du3o2PHjujevTtCQ0PRokWLSu1vbm6OkydP4saNG7C3t4ejoyMOHTrEPV9j1apV8PT0xMiRI2FjY4MHDx7gxIkTpUYYrFq1CjNmzICtrS1SU1Px119/cSMTnJycMHHiRHh7e6Nx48ZYvXo1AGD79u0YNWoUZs+eDQsLCwwcOBBXrlzhej7k/RzCw8O5oWZisRiurq5lzjXx9PTE1atXuQcyrl+/Hj4+Ppg3bx46dOiA0aNHw9LSEn/99ZfMMKzJkyfj5MmTePbsGQYNGoS2bdti3LhxEAqFMkPtalpQUBCsra25eTTdunWDtbU1Dh8+zNVJSkpC2nuLNnh7e+OXX37BokWLYGVlhfj4eERGRspM6F67di369+8PT09PdOvWDSKRCBERETLH3rNnD3r37l3rPwDz2Gd2R5idnQ0dHR1kZWVx4/HqWuD/vMBYHng8DfiF71NIDFX18uVLREREcBPObG1t0bt3b+5LhxBCiHwKCgqQnJyMFi1alJq0SkhdiY6ORo8ePZCRkaHQB9gyxuDg4IBZs2Zh6NChCovjUyKRSNCmTRv8/vvvMhP431fR95A89840FIpUCmMMFy9eRFRUFKRSKTQ1NfH111+XWgWCEEIIIaSqeDwetm7dilu3bik6lE9GSkoK/P39y00qahIlFqRSnj59ipMnTwJ419369ddfV2lFD0IIIYSQilhZWcHKykrRYXwyWrduXWcLHFBiQSqlWbNm6Nq1Kxo1agQbGxtaRpYQQgj5hLi4uNB8SVJtNHmblKmgoAB//fUXMjMzuTJXV1fY2tpSUkEIIYQQQkqhHgtSyqNHj3DgwAFkZ2cjIyMDI0eOpGSCEEIIIYRUiBILwikqKsLp06dx4cIFAECjRo3Qs2dPSioIIYQQQshHUWJBAACvXr1CREQE99h4a2truLu70zKyhBBCCCGkUiixIHj8+DF27doFqVQKDQ0NDBgwAG3btlV0WIQQQgghpAGhxIKgSZMm0NfXh1AohIeHB7S0tBQdEiGEEEIIaWAosfhMJSUloUWLFlBSUoKysjJGjRoFDQ0Nmk9BCCGEEEKqhJab/cy8ffsWhw4dwu7du3H+/HmuXFNTk5IKQggh1ebi4oKZM2cqOowK/fDDD5gwYYKiw/hkJCQkoGnTpsjNzVV0KETBKLH4jKSkpCAoKAjx8fHg8XiQSqWKDokQQgipU6mpqVi/fj0WLFhQaltsbCz4fD769etXalt0dDR4PJ7M851KmJmZYd26dTJlp0+fRt++faGvrw8NDQ20b98es2fPxrNnz2rqVErZunUrXFxcIBQKy421LBs3boSZmRnU1NTg4OCAy5cvy2wvKCjAlClToK+vDy0tLXh6enKLvQBA+/bt0aVLFwQGBtbk6ZAGiBKLz4BUKkVUVBRCQ0ORmZkJXV1djB49Gj169FB0aIQQQj4zEolEoccPDg6Gk5MTmjdvXmqbWCzGtGnTcPbsWTx//rzKx9iyZQtcXV0hEonw559/IiEhAUFBQcjKykJAQEB1wq9QXl4e3N3d4e/vX+l99u7dCz8/PyxevBjXrl1Dp06d4ObmhlevXnF1Zs2ahb/++gv79+/HmTNn8Pz5cwwePFimnTFjxmDz5s0oKiqqsfMhDQ8lFp+4169fQywW4/z582CMoVOnTpg4cSJMTU0VHRohhJAGLjc3F6NGjYKWlhaMjY3LvGk2MzPD8uXLMWrUKAiFQm4I0p9//okOHTpAVVUVZmZmpfY1MzPDypUrMXbsWGhra8PU1BRbt26VqfPkyRN4eXlBV1cXenp68PDwwKNHjyqMOTw8HAMGDChVnpOTg71792LSpEno168fQkND5fsw/s/Tp08xffp0TJ8+HSEhIXBxcYGZmRm6deuG4OBgLFq0qErtVsbMmTMxf/58dOnSpdL7BAYGYvz48RgzZgzat2+PoKAgaGhoICQkBACQlZUFsViMwMBA9OzZE7a2tti+fTsuXLiAixcvcu189dVXSE9Px5kzZ2r8vEjDQZO3P3HFxcX477//oKamhgEDBqB9+/aKDokQQkgl7Ft5BXnZdf/rvoZQAC//zpWqO2fOHJw5cwaHDh2CoaEh/P39ce3aNVhZWcnU++WXX7Bo0SIsXrwYABAXFwcvLy8sWbIE3t7euHDhAiZPngx9fX2MHj2a2y8gIADLly+Hv78//vjjD0yaNAndu3eHhYUFCgsL4ebmBkdHR5w7dw7Kysr48ccf4e7ujps3b5b5HKb09HQkJCTAzs6u1LZ9+/ahbdu2sLCwwIgRIzBz5kx8//33cs8/3L9/PyQSCebOnVvmdl1d3XL37dOnD86dO1fu9ubNm+P27dtyxVMRiUSCuLg4fP/991yZkpISXF1dERsbC+Dd36qwsBCurq5cnbZt28LU1BSxsbFcEiMQCGBlZYVz586hV69eNRYjaVgosfgEFRUVQVn53Z+2cePG8PT0hImJCYRCoYIjI4QQUll52RLkZr5VdBjlysnJgVgsxu7du7kbyR07dqBp06al6vbs2ROzZ8/m3g8fPhy9evXCDz/8AAAwNzdHQkIC1qxZI5NY9O3bF5MnTwYAzJs3D2vXrsXp06dhYWGBvXv3ori4GMHBwdzN//bt26Grq4vo6Gj07t27VBwpKSlgjMHExKTUNrFYjBEjRgAA3N3dkZWVhTNnzsDFxUWuz+X+/fsQCoUwNjaWaz/g3TCt/Pz8crerqKjI3WZF0tLSIJVKYWRkJFNuZGSEu3fvAng3J0UgEJRKiIyMjJCamipTZmJigsePH9dojKRhocTiE3P37l0cPXoUXl5eaNasGQDQw+4IIaQB0hCW/sW9Ph03KSkJEokEDg4OXJmenh4sLCxK1f2wh+DOnTvw8PCQKevatSvWrVsHqVQKPp8PALC0tOS283g8iEQibuz/jRs38ODBA2hra8u0U1BQgKSkpDJjLrlpV1NTkylPTEzE5cuXceDAAQCAsrIyvL29IRaL5U4sGGNVXmWxSZMmVdqvvlBXV0deXp6iwyAKRInFJ0IikSAyMhLXr18H8G5li5LEghBCSMNT2eFIDYGmpmaV9vvwF3oej4fi4mIA73pMbG1tERYWVmq/xo0bl9megYEBACAjI0OmjlgsRlFRkUxPBmMMqqqq2LBhA3R0dLhe/6ysrFK/3mdmZkJHRwfAu96XrKwsvHjxQu5ei7oeCmVgYAA+ny+zwhMAvHz5EiKRCAAgEokgkUi4xV/KqlMiPT0drVq1qrH4SMNDk7c/AU+fPkVQUBCXVDg5OZVarYEQQgipSa1atYKKigouXbrElWVkZODevXsf3bddu3aIiYmRKYuJiYG5uTnXW/ExNjY2uH//PgwNDdG6dWuZV8lNflkxC4VCJCQkcGVFRUXYuXMnAgICEB8fz71u3LgBExMT7NmzBwDQpk0bKCkpIS4uTqbNhw8fIisrC+bm5gCAIUOGQCAQYPXq1WXGUNESsMHBwTIxfPg6duxYpT6byhIIBLC1tUVUVBRXVlxcjKioKDg6OgIAbG1toaKiIlMnMTERKSkpXJ0S//77L6ytrWs0RtKwUI9FAyaVSnHu3DmcPXsWjDHo6Ohg4MCBMDMzU3RohBBCPnFaWlrw9fXFnDlzoK+vD0NDQyxYsABKSh//zXL27Nno3Lkzli9fDm9vb8TGxmLDhg3YtGlTpY8/fPhwrFmzBh4eHli2bBmaNm2Kx48fIyIiAnPnzi1zrkfJxOTz589j4MCBAIAjR44gIyMDvr6+pRIST09PiMViTJw4Edra2hg3bhxmz54NZWVldOzYEU+ePMG8efPQpUsXODk5AQCaNWuGtWvXYurUqcjOzsaoUaNgZmaGp0+fYufOndDS0ip3ydnqDoVKTU1FamoqHjx4AAC4desWt6KWnp4eAKBXr14YNGgQpk6dCgDw8/ODj48P7OzsYG9vj3Xr1iE3NxdjxowBAOjo6MDX1xd+fn7Q09ODUCjEtGnT4OjoKLP61KNHj/Ds2TOZSd7k80M9Fg3Y3bt3cebMGTDG0LFjR0ycOJGSCkIIIXVmzZo1cHZ2xoABA+Dq6oovv/wStra2H93PxsYG+/btQ3h4OL744gssWrQIy5Ytk5m4/TEaGho4e/YsTE1NMXjwYLRr1w6+vr4oKCiocLGScePGITw8nBtSJRaL4erqWmYvh6enJ65evYqbN28CANavXw8fHx/MmzcPHTp0wOjRo2FpaYm//vpLZl7F5MmTcfLkSTx79gyDBg1C27ZtMW7cOAiFQnz33XeVPkd5BQUFwdraGuPHjwcAdOvWDdbW1jh8+DBXJykpCWlpadx7b29vbtUuKysrxMfHIzIyUmZC99q1a9G/f394enqiW7duEIlEiIiIkDn2nj170Lt37zKfD0I+HzzGGFN0EHUpOzsbOjo6yMrKUtgqSYH/8wJjeeDxNOAXvq/K7TDGcPDgQbRp0wZffPFFDUZICCGkrhQUFCA5ORktWrQoNamY1DzGGBwcHDBr1iwMHTpU0eF8EiQSCdq0aYPff/8dXbt2VXQ4pAoq+h6S596ZeiwakNzcXBw5cgRv375bfpDH42HQoEGUVBBCCCGVxOPxsHXrVnpCdA1KSUmBv78/JRWE5lg0FPfu3cPhw4eRm5sLqVRaapk+QgghhFSOlZVVqYf4kaormTRPCCUW9ZxEIsHJkye5VSgaN24ss2Y4IYQQQggh9QElFvXY8+fPERERgdevXwMAunTpgl69enFP1SaEEEIIIaS+oDvUeiohIQF//vkniouLoa2tjYEDB6Jly5aKDosQQgghhJAyUWJRTzVv3hzq6uowMzNDv379oK6uruiQCCGEEEIIKRclFvUEYwyPHz/mnkOhqamJb7/9FlpaWjJrYxNCCCGEEFIf1YvlZjdu3AgzMzOoqanBwcEBly9frrD+/v370bZtW6ipqaFjx441/oj7upaXl4f9+/djx44duHXrFleura1NSQUhhBBCCGkQFJ5Y7N27F35+fli8eDGuXbuGTp06wc3NDa9evSqz/oULFzB06FD4+vri+vXrGDhwIAYOHIh///23jiOvGQ8ePMDmzZtx584dKCkpIS8vT9EhEUIIIfWWi4sLZs6cqegwALxbubF169a4cOGCokP5ZMyfPx/Tpk1TdBikihSeWAQGBmL8+PEYM2YM2rdvj6CgIGhoaCAkJKTM+uvXr4e7uzvmzJmDdu3aYfny5bCxscGGDRvqOPLqYTweCoxECAsLQ05ODgwMDDBu3DhaSpYQQghpIIKCgtCiRQs4OTmV2vbtt9+Cz+dj//79pbaNHj0aAwcOLFUeHR0NHo+HzMxMrkwikWD16tXo1KkTNDQ0YGBggK5du2L79u0oLCysydPhFBQUYPTo0ejYsSOUlZXLjLUs6enpGD58OIRCIXR1deHr64ucnByZOjdv3oSzszPU1NTQrFkzrF69Wmb7d999hx07duDhw4c1dTqkDik0sZBIJIiLi4OrqytXpqSkBFdXV8TGxpa5T2xsrEx9AHBzcyu3fn0kVVVDXssOKNTTBwDY29tjwoQJMDY2VnBkhBBCSNVJJBJFh1BnGGPYsGEDfH19S23Ly8tDeHg45s6dW+4PpZUhkUjg5uaGVatWYcKECbhw4QIuX76MKVOm4LfffsPt27ercwrlkkqlUFdXx/Tp00vdc1Vk+PDhuH37Nk6dOoUjR47g7NmzmDBhArc9OzsbvXv3RvPmzREXF4c1a9ZgyZIl2Lp1K1fHwMAAbm5u2Lx5c42eE6kbCk0s0tLSIJVKYWRkJFNuZGSE1NTUMvdJTU2Vq/7bt2+RnZ0t81I0pswHU1UHr6gQw4cPR58+faCioqLosAghhBC5uLi4YOrUqZg5cyZ3QwgAt2/fRv/+/SEUCqGtrQ1nZ2ckJSV9tL2SX/KXLl2Kxo0bQygUYuLEiRUmLLt27YKdnR20tbUhEokwbNgwmeHUJb0AUVFRsLOzg4aGBpycnJCYmCjTzqFDh2BjYwM1NTW0bNkSS5cuRVFRUbnHjYuLQ1JSEvr161dq2/79+9G+fXvMnz8fZ8+exZMnTz567mVZt24dzp49i6ioKEyZMgVWVlZo2bIlhg0bhkuXLqFNmzZVavdjNDU1sXnzZowfPx4ikahS+9y5cweRkZEIDg6Gg4MDvvzyS/z2228IDw/H8+fPAQBhYWGQSCQICQlBhw4d8L///Q/Tp09HYGCgTFsDBgxAeHh4jZ8XqX0KHwpV23766Sfo6Ohwr2bNmik6JCjn5kL12UNoPExC69atFR0OIYQQUmU7duyAQCBATEwMgoKC8OzZM3Tr1g2qqqr4559/EBcXh7Fjx1Z4k/6+qKgo3LlzB9HR0dizZw8iIiKwdOnScusXFhZi+fLluHHjBg4ePIhHjx5h9OjRpeotWLAAAQEBuHr1KpSVlTF27Fhu27lz5zBq1CjMmDEDCQkJ2LJlC0JDQ7FixYpyj3vu3DmYm5tDW1u71DaxWIwRI0ZAR0cHffr0QWhoaKXO/UNhYWFwdXWFtbV1qW0qKirQ1NQsc7+UlBRoaWlV+Fq5cmWVYipPbGwsdHV1YWdnx5W5urpCSUkJly5d4up069YNAoGAq+Pm5obExERkZGRwZfb29nj69CkePXpUozGS2qfQ5WYNDAzA5/Px8uVLmfKXL1+WmyGLRCK56n///ffw8/Pj3mdnZys8uVCCKgTZ+VCCqkLjIIQQUn/t/n4mcjMzPl6xhmnqNsKIn9ZVun6bNm1kxsn7+/tDR0cH4eHhXG+8ubl5pdsTCAQICQmBhoYGOnTogGXLlmHOnDlYvnw5lJRK/x76foLQsmVL/Prrr+jcuTNycnKgpaXFbVuxYgW6d+8O4N0E4X79+qGgoABqampYunQp5s+fDx8fH66d5cuXY+7cuVi8eHGZcT5+/BgmJialyu/fv4+LFy8iIiICADBixAj4+flh4cKFcq/0eP/+fbi4uMi1DwCYmJggPj6+wjp6enpyt1uR1NRUGBoaypQpKytDT0+PG1WSmpqKFi1ayNQpGYWSmpqKRo0aAQD3ub6/DD9pGBSaWAgEAtja2iIqKoqbGFRcXIyoqChMnTq1zH0cHR0RFRUlsyLEqVOn4OjoWGZ9VVVVqKrWrxv4meG7FB0CIYSQei43MwM56a8VHcZH2drayryPj4+Hs7NzlYf4lkxSLuHo6IicnBw8efIEzZs3L1U/Li4OS5YswY0bN5CRkYHi4mIA7361b9++PVfP0tKS+++SOY2vXr2Cqakpbty4gZiYGJkeCqlUioKCAuTl5cnEUyI/Px9qamqlykNCQuDm5gYDAwMAQN++feHr64t//vkHvXr1kuuzYIzJVb+EsrJygx4RUfJQYFops+FR+APy/Pz84OPjAzs7O9jb22PdunXIzc3FmDFjAACjRo1CkyZN8NNPPwEAZsyYge7duyMgIAD9+vVDeHg4rl69KjPxhxBCCGnoNHUbNYjjfjgcp+SmsC7k5ubCzc0Nbm5uCAsLQ+PGjZGSkgI3N7dS8zLeT3RKeg5KkpCcnBwsXboUgwcPLnWMspIH4N2oi/efPQW8S0Z27NiB1NRUKCsry5SHhIRwiYVQKMTjx49LtZmZmQk+n899pubm5rh79+5HP4cPfZhUlcXf3x/+/v5yt10ekUhU6lEBRUVFSE9P50aVlDfqpGRbifT0dABA48aNayw+UjcUnlh4e3vjv//+w6JFi5CamgorKytERkZyXWMpKSkyXZ9OTk74/fffsXDhQvj7+6NNmzY4ePAgvvjiC0WdAiGEEFLj5BmOVJ9YWlpix44dKCwsrFKvxY0bN5Cfn88lKBcvXoSWllaZw5jv3r2L169fY9WqVdz2q1evyn1MGxsbJCYmyvUrv7W1NTZv3gzGGJeoHDt2DG/evMH169fB5/O5uv/++y/GjBmDzMxM6OrqwsLCAuHh4Xj79q3MqIpr166hRYsW3Oc2bNgw+Pv74/r166XmWRQWFkIikZQ5z0IRQ6EcHR2RmZmJuLg4rhfrn3/+QXFxMbeUvqOjIxYsWCBzbZw6dQoWFhbcMCjg3eeloqKCDh061GiMpA6wz0xWVhYDwLKyshQdCiGEEMLy8/NZQkICy8/PV3QocuvevTubMWOGTFlaWhrT19dngwcPZleuXGH37t1jO3fuZHfv3v1oez4+PkxLS4sNHTqU3b59mx09epQZGRmx+fPnl3nMV69eMYFAwObMmcOSkpLYoUOHmLm5OQPArl+/zhhj7PTp0wwAy8jI4Nq4fv06A8CSk5MZY4xFRkYyZWVltmTJEvbvv/+yhIQEtmfPHrZgwYJyY01LS2MqKirs1q1bXJmHhwfz9vYuVVcqlTKRSMQ2bNjAGGMsIyODGRoaMi8vL3b16lV2//59JhaLmba2Ntu8eTO3X0FBAXN2dmaNGjViGzZsYPHx8SwpKYnt3buX2djYcOdYG27fvs2uX7/OBgwYwFxcXNj169dljnfp0iVmYWHBnj59ypW5u7sza2trdunSJXb+/HnWpk0bNnToUG57ZmYmMzIyYiNHjmT//vsvCw8PZxoaGmzLli0yx168eDHr2bNnrZ0bKa2i7yF57p0psSCEEEIU6FNLLBhj7MaNG6x3795MQ0ODaWtrM2dnZ5aUlPTR9nx8fJiHhwdbtGgR09fXZ1paWmz8+PGsoKCg3GP+/vvvzMzMjKmqqjJHR0d2+PBhuRMLxt4lF05OTkxdXZ0JhUJmb2/Ptm7dWmG8Xl5eXNKTmprKlJWV2b59+8qsO2nSJGZtbc29T0xMZIMGDWImJiZMU1OTderUiW3bto0VFxfL7FdQUMB++ukn1rFjR6ampsb09PRY165dWWhoKCssLKwwvupo3rw5A1DqVaLkc33/M3z9+jUbOnQo09LSYkKhkI0ZM4a9efNGpt0bN26wL7/8kqmqqrImTZqwVatWlTq2hYUF27NnT62dGymtphILHmNVnBnUQGVnZ0NHRwdZWVkQCoWKDocQQshnrqCgAMnJyWjRokW54/k/F6NHj0ZmZiYOHjyo6FAq5ebNm/jqq6+QlJQkswIVqbrjx49j9uzZuHnzpsw8FVK7Kvoekufe+ZN/jgUhhBBCSG2wtLTEzz//jOTkZEWH8snIzc3F9u3bKalooOivRgghhJA6UdGv+sePH6/DSGpOWQ/jI1U3ZMgQRYdAqoESC0IIIYTUiYpWKmrSpAmcnZ3rLhhCSI2jxIIQQgghdaIhP7SNEPJxNMeCEEIIIYQQUm2UWBBCCCH1wGe2SCMhpB6pqe8fSiwIIYQQBSp5AnFeXp6CIyGEfK5Kvn9Kvo+qiuZYEEIIIQrE5/Ohq6uLV69eAQA0NDTA4/EUHBUh5HPAGENeXh5evXoFXV1d8Pn8arVHiQUhhBCiYCKRCAC45IIQQuqSrq4u9z1UHZRYEEIIIQrG4/FgbGwMQ0NDFBYWKjocQshnREVFpdo9FSUosSCEEELqCT6fX2P/wBNCSF2jyduEEEIIIYSQaqPEghBCCCGEEFJtlFgQQgghhBBCqu2zm2NR8gCQ7OxsBUdCCCGEEEJI/VZyz1yZh+h9donFmzdvAADNmjVTcCSEEEIIIYQ0DG/evIGOjk6FdXispp7h3UAUFxfj+fPn0NbWVtgDiLKzs9GsWTM8efIEQqFQITGQ+oGuBQLQdUD+P7oWSAm6FghQP64DxhjevHkDExMTKClVPIvis+uxUFJSQtOmTRUdBgBAKBTSlwUBQNcCeYeuA1KCrgVSgq4FAij+OvhYT0UJmrxNCCGEEEIIqTZKLAghhBBCCCHVRomFAqiqqmLx4sVQVVVVdChEwehaIABdB+T/o2uBlKBrgQAN7zr47CZvE0IIIYQQQmoe9VgQQgghhBBCqo0SC0IIIYQQQki1UWJBCCGEEEIIqTZKLGrJxo0bYWZmBjU1NTg4OODy5csV1t+/fz/atm0LNTU1dOzYEceOHaujSEltk+da2LZtG5ydndGoUSM0atQIrq6uH712SMMg73dCifDwcPB4PAwcOLB2AyR1Rt5rITMzE1OmTIGxsTFUVVVhbm5O/0Z8IuS9FtatWwcLCwuoq6ujWbNmmDVrFgoKCuooWlIbzp49iwEDBsDExAQ8Hg8HDx786D7R0dGwsbGBqqoqWrdujdDQ0FqPs9IYqXHh4eFMIBCwkJAQdvv2bTZ+/Himq6vLXr58WWb9mJgYxufz2erVq1lCQgJbuHAhU1FRYbdu3arjyElNk/daGDZsGNu4cSO7fv06u3PnDhs9ejTT0dFhT58+rePISU2S9zookZyczJo0acKcnZ2Zh4dH3QRLapW818Lbt2+ZnZ0d69u3Lzt//jxLTk5m0dHRLD4+vo4jJzVN3mshLCyMqaqqsrCwMJacnMxOnDjBjI2N2axZs+o4clKTjh07xhYsWMAiIiIYAHbgwIEK6z98+JBpaGgwPz8/lpCQwH777TfG5/NZZGRk3QT8EZRY1AJ7e3s2ZcoU7r1UKmUmJibsp59+KrO+l5cX69evn0yZg4MD+/bbb2s1TlL75L0WPlRUVMS0tbXZjh07aitEUgeqch0UFRUxJycnFhwczHx8fCix+ETIey1s3ryZtWzZkkkkkroKkdQRea+FKVOmsJ49e8qU+fn5sa5du9ZqnKTuVCaxmDt3LuvQoYNMmbe3N3Nzc6vFyCqPhkLVMIlEgri4OLi6unJlSkpKcHV1RWxsbJn7xMbGytQHADc3t3Lrk4ahKtfCh/Ly8lBYWAg9Pb3aCpPUsqpeB8uWLYOhoSF8fX3rIkxSB6pyLRw+fBiOjo6YMmUKjIyM8MUXX2DlypWQSqV1FTapBVW5FpycnBAXF8cNl3r48CGOHTuGvn371knMpH6o7/eMyooO4FOTlpYGqVQKIyMjmXIjIyPcvXu3zH1SU1PLrJ+amlprcZLaV5Vr4UPz5s2DiYlJqS8R0nBU5To4f/48xGIx4uPj6yBCUleqci08fPgQ//zzD4YPH45jx47hwYMHmDx5MgoLC7F48eK6CJvUgqpcC8OGDUNaWhq+/PJLMMZQVFSEiRMnwt/fvy5CJvVEefeM2dnZyM/Ph7q6uoIie4d6LAipp1atWoXw8HAcOHAAampqig6H1JE3b95g5MiR2LZtGwwMDBQdDlGw4uJiGBoaYuvWrbC1tYW3tzcWLFiAoKAgRYdG6lh0dDRWrlyJTZs24dq1a4iIiMDRo0exfPlyRYdGCId6LGqYgYEB+Hw+Xr58KVP+8uVLiESiMvcRiURy1ScNQ1WuhRK//PILVq1ahb///huWlpa1GSapZfJeB0lJSXj06BEGDBjAlRUXFwMAlJWVkZiYiFatWtVu0KRWVOU7wdjYGCoqKuDz+VxZu3btkJqaColEAoFAUKsxk9pRlWvhhx9+wMiRIzFu3DgAQMeOHZGbm4sJEyZgwYIFUFKi34o/B+XdMwqFQoX3VgDUY1HjBAIBbG1tERUVxZUVFxcjKioKjo6OZe7j6OgoUx8ATp06VW590jBU5VoAgNWrV2P58uWIjIyEnZ1dXYRKapG810Hbtm1x69YtxMfHc6+vv/4aPXr0QHx8PJo1a1aX4ZMaVJXvhK5du+LBgwdccgkA9+7dg7GxMSUVDVhVroW8vLxSyUNJwskYq71gSb1S7+8ZFT17/FMUHh7OVFVVWWhoKEtISGATJkxgurq6LDU1lTHG2MiRI9n8+fO5+jExMUxZWZn98ssv7M6dO2zx4sW03OwnQt5rYdWqVUwgELA//viDvXjxgnu9efNGUadAaoC818GHaFWoT4e810JKSgrT1tZmU6dOZYmJiezIkSPM0NCQ/fjjj4o6BVJD5L0WFi9ezLS1tdmePXvYw4cP2cmTJ1mrVq2Yl5eXok6B1IA3b96w69evs+vXrzMALDAwkF2/fp09fvyYMcbY/Pnz2ciRI7n6JcvNzpkzh925c4dt3LiRlpv9HPz222/M1NSUCQQCZm9vzy5evMht6969O/Px8ZGpv2/fPmZubs4EAgHr0KEDO3r0aB1HTGqLPNdC8+bNGYBSr8WLF9d94KRGyfud8D5KLD4t8l4LFy5cYA4ODkxVVZW1bNmSrVixghUVFdVx1KQ2yHMtFBYWsiVLlrBWrVoxNTU11qxZMzZ58mSWkZFR94GTGnP69Oky/90v+dv7+Piw7t27l9rHysqKCQQC1rJlS7Z9+/Y6j7s8PMao/4wQQgghhBBSPTTHghBCCCGEEFJtlFgQQgghhBBCqo0SC0IIIYQQQki1UWJBCCGEEEIIqTZKLAghhBBCCCHVRokFIYQQQgghpNoosSCEEEIIIYRUGyUWhBBCCCGEkGqjxIIQQj4RoaGh0NXVVXQYVcbj8XDw4MEK64wePRoDBw6sk3gIIYTIhxILQgipR0aPHg0ej1fq9eDBA0WHhtDQUC4eJSUlNG3aFGPGjMGrV69qpP0XL16gT58+AIBHjx6Bx+MhPj5eps769esRGhpaI8crz5IlS7jz5PP5aNasGSZMmID09HS52qEkiBDyuVFWdACEEEJkubu7Y/v27TJljRs3VlA0soRCIRITE1FcXIwbN25gzJgxeP78OU6cOFHttkUi0Ufr6OjoVPs4ldGhQwf8/fffkEqluHPnDsaOHYusrCzs3bu3To5PCCENEfVYEEJIPaOqqgqRSCTz4vP5CAwMRMeOHaGpqYlmzZph8uTJyMnJKbedGzduoEePHtDW1oZQKIStrS2uXr3KbT9//jycnZ2hrq6OZs2aYfr06cjNza0wNh6PB5FIBBMTE/Tp0wfTp0/H33//jfz8fBQXF2PZsmVo2rQpVFVVYWVlhcjISG5fiUSCqVOnwtjYGGpqamjevDl++uknmbZLhkK1aNECAGBtbQ0ejwcXFxcAsr0AW7duhYmJCYqLi2Vi9PDwwNixY7n3hw4dgo2NDdTU1NCyZUssXboURUVFFZ6nsrIyRCIRmjRpAldXV3zzzTc4deoUt10qlcLX1xctWrSAuro6LCwssH79em77kiVLsGPHDhw6dIjr/YiOjgYAPHnyBF5eXtDV1YWenh48PDzw6NGjCuMhhJCGgBILQghpIJSUlPDrr7/i9u3b2LFjB/755x/MnTu33PrDhw9H06ZNceXKFcTFxWH+/PlQUVEBACQlJcHd3R2enp64efMm9u7di/Pnz2Pq1KlyxaSuro7i4mIUFRVh/fr1CAgIwC+//IKbN2/Czc0NX3/9Ne7fvw8A+PXXX3H48GHs27cPiYmJCAsLg5mZWZntXr58GQDw999/48WLF4iIiChV55tvvsHr169x+vRpriw9PR2RkZEYPnw4AODcuXMYNWoUZsyYgYSEBGzZsgWhoaFYsWJFpc/x0aNHOHHiBAQCAVdWXFyMpk2bYv/+/UhISMCiRYvg7++Pffv2AQC+++47eHl5wd3dHS9evMCLFy/g5OSEwsJCuLm5QVtbG+fOnUNMTAy0tLTg7u4OiURS6ZgIIaReYoQQQuoNHx8fxufzmaamJvcaMmRImXX379/P9PX1uffbt29nOjo63HttbW0WGhpa5r6+vr5swoQJMmXnzp1jSkpKLD8/v8x9Pmz/3r17zNzcnNnZ2THGGDMxMWErVqyQ2adz585s8uTJjDHGpk2bxnr27MmKi4vLbB8AO3DgAGOMseTkZAaAXb9+XaaOj48P8/Dw4N57eHiwsWPHcu+3bNnCTExMmFQqZYwx1qtXL7Zy5UqZNnbt2sWMjY3LjIExxhYvXsyUlJSYpqYmU1NTYwAYABYYGFjuPowxNmXKFObp6VlurCXHtrCwkPkM3r59y9TV1dmJEycqbJ8QQuo7mmNBCCH1TI8ePbB582buvaamJoB3v97/9NNPuHv3LrKzs1FUVISCggLk5eVBQ0OjVDt+fn4YN24cdu3axQ3nadWqFYB3w6Ru3ryJsLAwrj5jDMXFxUhOTka7du3KjC0rKwtaWlooLi5GQUEBvvzySwQHByM7OxvPnz9H165dZep37doVN27cAPBuGNNXX30FCwsLuLu7o3///ujdu3e1Pqvhw4dj/Pjx2LRpE1RVVREWFob//e9/UFJS4s4zJiZGpodCKpVW+LkBgIWFBQ4fPoyCggLs3r0b8fHxmDZtmkydjRs3IiQkBCkpKcjPz4dEIoGVlVWF8d64cQMPHjyAtra2THlBQQGSkpKq8AkQQkj9QYkFIYTUM5qammjdurVM2aNHj9C/f39MmjQJK1asgJ6eHs6fPw9fX19IJJIyb5CXLFmCYcOG4ejRozh+/DgWL16M8PBwDBo0CDk5Ofj2228xffr0UvuZmpqWG5u2tjauXbsGJSUlGBsbQ11dHQCQnZ390fOysbFBcnIyjh8/jr///hteXl5wdXXFH3/88dF9yzNgwAAwxnD06FF07twZ586dw9q1a7ntOTk5WLp0KQYPHlxqXzU1tXLbFQgE3N9g1apV6NevH5YuXYrly5cDAMLDw/Hdd98hICAAjo6O0NbWxpo1a3Dp0qUK483JyYGtra1MQleivkzQJ4SQqqLEghBCGoC4uDgUFxcjICCA+zW+ZDx/RczNzWFubo5Zs2Zh6NCh2L59OwYNGgQbGxskJCSUSmA+RklJqcx9hEIhTExMEBMTg+7du3PlMTExsLe3l6nn7e0Nb29vDBkyBO7u7khPT4eenp5MeyXzGaRSaYXxqKmpYfDgwQgLC8ODBw9gYWEBGxsbbruNjQ0SExPlPs8PLVy4ED179sSkSZO483RycsLkyZO5Oh/2OAgEglLx29jYYO/evTA0NIRQKKxWTIQQUt/Q5G1CCGkAWrdujcLCQvz22294+PAhdu3ahaCgoHLr5+fnY+rUqYiOjsbjx48RExODK1eucEOc5s2bhwsXLmDq1KmIj4/H/fv3cejQIbknb79vzpw5+Pnnn7F3714kJiZi/vz5iI+Px4wZMwAAgYGB2LNnD+7evYt79+5h//79EIlEZT7Uz9DQEOrq6oiMjMTLly+RlZVV7nGHDx+Oo0ePIiQkhJu0XWLRokXYuXMnli5ditu3b+POnTsIDw/HwoUL5To3R0dHWFpaYuXKlQCANm3a4OrVqzhx4gTu3buHH374AVeuXJHZx8zMDDdv3kRiYiLS0tJQWFiI4cOHw8DAAB4eHjh37hySk5MRHR2N6dOn4+nTp3LFRAgh9Q0lFoQQ0gB06tQJgYGB+Pnnn/HFF18gLCxMZqnWD/H5fLx+/RqjRo2Cubk5vLy80KdPHyxduhQAYGlpiTNnzuDevXtwdnaGtbU1Fi1aBBMTkyrHOH36dPj5+WH27Nno2LEjIiMjcfjwYbRp0wbAu2FUq1evhp2dHTp37oxHjx7h2LFjXA/M+5SVlfHrr79iy5YtMDExgYeHR7nH7dmzJ/T09JCYmIhhw4bJbHNzc8ORI0dw8uRJdO7cGV26dMHatWvRvHlzuc9v1qxZCA4OxpMnT/Dtt99i8ODB8Pb2hoODA16/fi3TewEA48ePh4WFBezs7NC4cWPExMRAQ0MDZ8+ehampKQYPHox27drB19cXBQUF1INBCGnweIwxpuggCCGEEEIIIQ0b9VgQQgghhBBCqo0SC0IIIYQQQki1UWJBCCGEEEIIqTZKLAghhBBCCCHVRokFIYQQQgghpNoosSCEEEIIIYRUGyUWhBBCCCGEkGqjxIIQQgghhBBSbZRYEEIIIYQQQqqNEgtCCCGEEEJItVFiQQghhBBCCKk2SiwIIYQQQggh1fb/AJvUf7/qtS8dAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Classification Report for Custom CNN:\n", - " precision recall f1-score support\n", - "\n", - " 3_long_blade_rotor 0.99 0.92 0.95 72\n", - " 3_short_blade_rotor 0.99 0.91 0.94 85\n", - " Bird 1.00 1.00 1.00 76\n", - "Bird+mini-helicopter 0.96 0.99 0.97 78\n", - " drone 1.00 1.00 1.00 85\n", - " rc_plane 0.90 1.00 0.95 90\n", - "\n", - " accuracy 0.97 486\n", - " macro avg 0.97 0.97 0.97 486\n", - " weighted avg 0.97 0.97 0.97 486\n", - "\n" - ] - } - ], - "source": [ - "import torch\n", - "import numpy as np\n", - "import tqdm\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc\n", - "from sklearn.preprocessing import label_binarize\n", - "import torch.nn.functional as F # Ensure this import is added\n", - "\n", - "# Assuming class_names is defined somewhere, for example:\n", - "class_names = [\n", - " \"3_long_blade_rotor\", \n", - " \"3_short_blade_rotor\", \n", - " \"Bird\", \n", - " \"Bird+mini-helicopter\", \n", - " \"drone\", \n", - " \"rc_plane\"\n", - "]\n", - "\n", - "# Test Function with Metric Collection\n", - "def test_model(model, test_loader, criterion, device=\"cuda\" if torch.cuda.is_available() else \"cpu\"):\n", - " model = model.to(device)\n", - " model.eval()\n", - "\n", - " running_loss = 0.0\n", - " running_corrects = 0\n", - " total_test = 0\n", - " all_preds = []\n", - " all_labels = []\n", - " all_probs = []\n", - "\n", - " with torch.no_grad():\n", - " for inputs, labels in tqdm.tqdm(test_loader, desc=\"Testing\"):\n", - " inputs = inputs.to(device)\n", - " labels = labels.to(device).long() # Ensure labels are of type torch.long\n", - "\n", - " # Forward Pass\n", - " outputs = model(inputs)\n", - " loss = criterion(outputs, labels)\n", - " _, preds = torch.max(outputs, 1)\n", - "\n", - " # Store metrics\n", - " running_loss += loss.item() * inputs.size(0)\n", - " running_corrects += torch.sum(preds == labels.data)\n", - " total_test += labels.size(0)\n", - "\n", - " all_preds.extend(preds.cpu().numpy())\n", - " all_labels.extend(labels.cpu().numpy())\n", - " all_probs.extend(F.softmax(outputs, dim=1).cpu().numpy()) # Softmax for probability scores\n", - "\n", - " test_loss = running_loss / total_test\n", - " test_acc = running_corrects.double() / total_test\n", - "\n", - " print(f\"Test Loss: {test_loss:.4f} Test Acc: {test_acc:.4f}\")\n", - "\n", - " # Classification Report\n", - " print(\"\\nClassification Report:\")\n", - " print(classification_report(all_labels, all_preds, target_names=class_names))\n", - "\n", - " # Confusion Matrix\n", - " cm = confusion_matrix(all_labels, all_preds)\n", - " print(\"\\nConfusion Matrix:\")\n", - " print(cm)\n", - "\n", - " # Plot Confusion Matrix\n", - " plt.figure(figsize=(8, 6))\n", - " plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n", - " plt.title(\"Confusion Matrix\")\n", - " plt.colorbar()\n", - " tick_marks = np.arange(len(class_names))\n", - " plt.xticks(tick_marks, class_names, rotation=45)\n", - " plt.yticks(tick_marks, class_names)\n", - " plt.ylabel('True label')\n", - " plt.xlabel('Predicted label')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " # ROC Curve & AUC (For multi-class classification)\n", - " all_labels_bin = label_binarize(all_labels, classes=np.arange(len(class_names)))\n", - "\n", - " plt.figure(figsize=(8, 6))\n", - " for i in range(len(class_names)):\n", - " fpr, tpr, _ = roc_curve(all_labels_bin[:, i], np.array(all_probs)[:, i])\n", - " roc_auc = auc(fpr, tpr)\n", - " plt.plot(fpr, tpr, lw=2, label=f'{class_names[i]} (AUC = {roc_auc:.2f})')\n", - "\n", - " plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n", - " plt.xlabel('False Positive Rate')\n", - " plt.ylabel('True Positive Rate')\n", - " plt.title('Receiver Operating Characteristic (ROC) Curve')\n", - " plt.legend(loc='lower right')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " # Accuracy Curve\n", - " thresholds = np.arange(0.0, 1.1, 0.1) # Thresholds from 0.0 to 1.0 with step 0.1\n", - " accuracy_per_threshold = []\n", - "\n", - " for threshold in thresholds:\n", - " preds_at_threshold = np.argmax(np.array(all_probs) >= threshold, axis=1)\n", - " accuracy_at_threshold = np.mean(preds_at_threshold == all_labels)\n", - " accuracy_per_threshold.append(accuracy_at_threshold)\n", - "\n", - " plt.figure(figsize=(8, 6))\n", - " plt.plot(thresholds, accuracy_per_threshold, color='blue', lw=2)\n", - " plt.xlabel('Threshold')\n", - " plt.ylabel('Accuracy')\n", - " plt.title('Accuracy vs. Threshold')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " return all_labels, all_preds, all_probs\n", - "\n", - "# Example Usage\n", - "if __name__ == \"__main__\":\n", - " # Assuming test_loader is defined\n", - " model_deepercnn = CustomCNN(num_classes=6)\n", - " model_deepercnn.load_state_dict(torch.load(\"best_model_CustomCNN.pt\"))\n", - " model_deepercnn.eval()\n", - "\n", - " criterion = torch.nn.CrossEntropyLoss()\n", - "\n", - " # Test the model\n", - " all_labels_customcnn, all_preds_customcnn, all_probs_customcnn = test_model(model_deepercnn, test_loader, criterion)\n", - "\n", - " # Generate and print the classification report for the custom CNN\n", - " print(\"\\nClassification Report for Custom CNN:\")\n", - " print(classification_report(all_labels_customcnn, all_preds_customcnn, target_names=class_names))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_35756\\2658744916.py:118: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " model_deepercnn.load_state_dict(torch.load(\"best_model_CustomCNN.pt\"))\n", - "Testing: 100%|██████████| 61/61 [00:10<00:00, 5.72it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test Loss: 0.0754 Test Acc: 0.9691\n", - "\n", - "Classification Report:\n", - " precision recall f1-score support\n", - "\n", - " 3_long_blade_rotor 0.99 0.92 0.95 72\n", - " 3_short_blade_rotor 0.97 0.91 0.94 85\n", - " Bird 1.00 1.00 1.00 76\n", - "Bird+mini-helicopter 0.97 0.99 0.98 78\n", - " drone 1.00 1.00 1.00 85\n", - " rc_plane 0.90 1.00 0.95 90\n", - "\n", - " accuracy 0.97 486\n", - " macro avg 0.97 0.97 0.97 486\n", - " weighted avg 0.97 0.97 0.97 486\n", - "\n", - "\n", - "Confusion Matrix:\n", - "[[66 2 0 2 0 2]\n", - " [ 1 77 0 0 0 7]\n", - " [ 0 0 76 0 0 0]\n", - " [ 0 0 0 77 0 1]\n", - " [ 0 0 0 0 85 0]\n", - " [ 0 0 0 0 0 90]]\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3_long_blade_rotor AUC: 1.00\n", - "3_short_blade_rotor AUC: 1.00\n", - "Bird AUC: 1.00\n", - "Bird+mini-helicopter AUC: 1.00\n", - "drone AUC: 1.00\n", - "rc_plane AUC: 1.00\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Classification Report for Custom CNN:\n", - " precision recall f1-score support\n", - "\n", - " 3_long_blade_rotor 0.99 0.92 0.95 72\n", - " 3_short_blade_rotor 0.97 0.91 0.94 85\n", - " Bird 1.00 1.00 1.00 76\n", - "Bird+mini-helicopter 0.97 0.99 0.98 78\n", - " drone 1.00 1.00 1.00 85\n", - " rc_plane 0.90 1.00 0.95 90\n", - "\n", - " accuracy 0.97 486\n", - " macro avg 0.97 0.97 0.97 486\n", - " weighted avg 0.97 0.97 0.97 486\n", - "\n" - ] - } - ], - "source": [ - "import torch\n", - "import numpy as np\n", - "import tqdm\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc\n", - "from sklearn.preprocessing import label_binarize\n", - "import torch.nn.functional as F # Ensure this import is added\n", - "\n", - "# Assuming class_names is defined somewhere, for example:\n", - "class_names = [\n", - " \"3_long_blade_rotor\", \n", - " \"3_short_blade_rotor\", \n", - " \"Bird\", \n", - " \"Bird+mini-helicopter\", \n", - " \"drone\", \n", - " \"rc_plane\"\n", - "]\n", - "\n", - "# Test Function with Metric Collection\n", - "def test_model(model, test_loader, criterion, device=\"cuda\" if torch.cuda.is_available() else \"cpu\"):\n", - " model = model.to(device)\n", - " model.eval()\n", - "\n", - " running_loss = 0.0\n", - " running_corrects = 0\n", - " total_test = 0\n", - " all_preds = []\n", - " all_labels = []\n", - " all_probs = []\n", - "\n", - " with torch.no_grad():\n", - " for inputs, labels in tqdm.tqdm(test_loader, desc=\"Testing\"):\n", - " inputs = inputs.to(device)\n", - " labels = labels.to(device).long() # Ensure labels are of type torch.long\n", - "\n", - " # Forward Pass\n", - " outputs = model(inputs)\n", - " loss = criterion(outputs, labels)\n", - " _, preds = torch.max(outputs, 1)\n", - "\n", - " # Store metrics\n", - " running_loss += loss.item() * inputs.size(0)\n", - " running_corrects += torch.sum(preds == labels.data)\n", - " total_test += labels.size(0)\n", - "\n", - " all_preds.extend(preds.cpu().numpy())\n", - " all_labels.extend(labels.cpu().numpy())\n", - " all_probs.extend(F.softmax(outputs, dim=1).cpu().numpy()) # Softmax for probability scores\n", - "\n", - " test_loss = running_loss / total_test\n", - " test_acc = running_corrects.double() / total_test\n", - "\n", - " print(f\"Test Loss: {test_loss:.4f} Test Acc: {test_acc:.4f}\")\n", - "\n", - " # Classification Report\n", - " print(\"\\nClassification Report:\")\n", - " print(classification_report(all_labels, all_preds, target_names=class_names))\n", - "\n", - " # Confusion Matrix\n", - " cm = confusion_matrix(all_labels, all_preds)\n", - " print(\"\\nConfusion Matrix:\")\n", - " print(cm)\n", - "\n", - " # Plot Confusion Matrix\n", - " plt.figure(figsize=(8, 6))\n", - " plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n", - " plt.title(\"Confusion Matrix\")\n", - " plt.colorbar()\n", - " tick_marks = np.arange(len(class_names))\n", - " plt.xticks(tick_marks, class_names, rotation=45)\n", - " plt.yticks(tick_marks, class_names)\n", - " plt.ylabel('True label')\n", - " plt.xlabel('Predicted label')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " # ROC Curve & AUC (For multi-class classification)\n", - " all_labels_bin = label_binarize(all_labels, classes=np.arange(len(class_names)))\n", - "\n", - " plt.figure(figsize=(8, 6))\n", - " for i in range(len(class_names)):\n", - " fpr, tpr, _ = roc_curve(all_labels_bin[:, i], np.array(all_probs)[:, i])\n", - " roc_auc = auc(fpr, tpr)\n", - " print(f'{class_names[i]} AUC: {roc_auc:.2f}') # Print the AUC value for each class\n", - " plt.plot(fpr, tpr, lw=2, label=f'{class_names[i]} (AUC = {roc_auc:.2f})')\n", - "\n", - " plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n", - " plt.xlabel('False Positive Rate')\n", - " plt.ylabel('True Positive Rate')\n", - " plt.title('Receiver Operating Characteristic (ROC) Curve')\n", - " plt.legend(loc='lower right')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " # Accuracy Curve\n", - " thresholds = np.arange(0.0, 1.1, 0.1) # Thresholds from 0.0 to 1.0 with step 0.1\n", - " accuracy_per_threshold = []\n", - "\n", - " for threshold in thresholds:\n", - " preds_at_threshold = np.argmax(np.array(all_probs) >= threshold, axis=1)\n", - " accuracy_at_threshold = np.mean(preds_at_threshold == all_labels)\n", - " accuracy_per_threshold.append(accuracy_at_threshold)\n", - "\n", - " plt.figure(figsize=(8, 6))\n", - " plt.plot(thresholds, accuracy_per_threshold, color='blue', lw=2)\n", - " plt.xlabel('Threshold')\n", - " plt.ylabel('Accuracy')\n", - " plt.title('Accuracy vs. Threshold')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " return all_labels, all_preds, all_probs\n", - "\n", - "# Example Usage\n", - "if __name__ == \"__main__\":\n", - " # Assuming test_loader is defined\n", - " model_deepercnn = CustomCNN(num_classes=6)\n", - " model_deepercnn.load_state_dict(torch.load(\"best_model_CustomCNN.pt\"))\n", - " model_deepercnn.eval()\n", - "\n", - " criterion = torch.nn.CrossEntropyLoss()\n", - "\n", - " # Test the model\n", - " all_labels_customcnn, all_preds_customcnn, all_probs_customcnn = test_model(model_deepercnn, test_loader, criterion)\n", - "\n", - " # Generate and print the classification report for the custom CNN\n", - " print(\"\\nClassification Report for Custom CNN:\")\n", - " print(classification_report(all_labels_customcnn, all_preds_customcnn, target_names=class_names))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_35756\\558898120.py:102: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " model_deepercnn.load_state_dict(torch.load(\"best_model_CustomCNN.pt\"))\n" - ] - }, - { - "ename": "TypeError", - "evalue": "'module' object is not callable. Did you mean: 'tqdm.tqdm(...)'?", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[42], line 108\u001b[0m\n\u001b[0;32m 105\u001b[0m criterion \u001b[38;5;241m=\u001b[39m nn\u001b[38;5;241m.\u001b[39mCrossEntropyLoss()\n\u001b[0;32m 107\u001b[0m \u001b[38;5;66;03m# Test the model\u001b[39;00m\n\u001b[1;32m--> 108\u001b[0m all_labels_customcnn, all_preds_customcnn, all_probs_customcnn \u001b[38;5;241m=\u001b[39m \u001b[43mtest_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_deepercnn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 110\u001b[0m \u001b[38;5;66;03m# Generate and print the classification report for the custom CNN\u001b[39;00m\n\u001b[0;32m 111\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mClassification Report for Custom CNN:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "Cell \u001b[1;32mIn[42], line 20\u001b[0m, in \u001b[0;36mtest_model\u001b[1;34m(model, test_loader, criterion, device)\u001b[0m\n\u001b[0;32m 17\u001b[0m all_probs \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m 19\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[1;32m---> 20\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m inputs, labels \u001b[38;5;129;01min\u001b[39;00m \u001b[43mtqdm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdesc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mTesting\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m:\n\u001b[0;32m 21\u001b[0m inputs \u001b[38;5;241m=\u001b[39m inputs\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m 22\u001b[0m labels \u001b[38;5;241m=\u001b[39m labels\u001b[38;5;241m.\u001b[39mto(device)\u001b[38;5;241m.\u001b[39mlong() \u001b[38;5;66;03m# Ensure labels are of type torch.long\u001b[39;00m\n", - "\u001b[1;31mTypeError\u001b[0m: 'module' object is not callable. Did you mean: 'tqdm.tqdm(...)'?" - ] - } - ], - "source": [ - "import torch\n", - "import numpy as np\n", - "import tqdm\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc, precision_recall_curve, f1_score\n", - "\n", - "# Test Function with Metric Collection\n", - "def test_model(model, test_loader, criterion, device=\"cuda\" if torch.cuda.is_available() else \"cpu\"):\n", - " model = model.to(device)\n", - " model.eval()\n", - "\n", - " running_loss = 0.0\n", - " running_corrects = 0\n", - " total_test = 0\n", - " all_preds = []\n", - " all_labels = []\n", - " all_probs = []\n", - "\n", - " with torch.no_grad():\n", - " for inputs, labels in tqdm(test_loader, desc=\"Testing\"):\n", - " inputs = inputs.to(device)\n", - " labels = labels.to(device).long() # Ensure labels are of type torch.long\n", - "\n", - " # Forward Pass\n", - " outputs = model(inputs)\n", - " loss = criterion(outputs, labels)\n", - " _, preds = torch.max(outputs, 1)\n", - "\n", - " # Store metrics\n", - " running_loss += loss.item() * inputs.size(0)\n", - " running_corrects += torch.sum(preds == labels.data)\n", - " total_test += labels.size(0)\n", - "\n", - " all_preds.extend(preds.cpu().numpy())\n", - " all_labels.extend(labels.cpu().numpy())\n", - " all_probs.extend(F.softmax(outputs, dim=1).cpu().numpy()) # Softmax for probability scores\n", - "\n", - " test_loss = running_loss / total_test\n", - " test_acc = running_corrects.double() / total_test\n", - "\n", - " print(f\"Test Loss: {test_loss:.4f} Test Acc: {test_acc:.4f}\")\n", - "\n", - " # Classification Report\n", - " print(\"\\nClassification Report:\")\n", - " print(classification_report(all_labels, all_preds, target_names=class_names))\n", - "\n", - " # Confusion Matrix\n", - " cm = confusion_matrix(all_labels, all_preds)\n", - " print(\"\\nConfusion Matrix:\")\n", - " print(cm)\n", - "\n", - " # Plot Confusion Matrix\n", - " plt.figure(figsize=(8, 6))\n", - " plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n", - " plt.title(\"Confusion Matrix\")\n", - " plt.colorbar()\n", - " tick_marks = np.arange(len(class_names))\n", - " plt.xticks(tick_marks, class_names, rotation=45)\n", - " plt.yticks(tick_marks, class_names)\n", - " plt.ylabel('True label')\n", - " plt.xlabel('Predicted label')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " # ROC Curve & AUC\n", - " fpr, tpr, thresholds = roc_curve(all_labels, np.array(all_probs)[:, 1], pos_label=1) # Assuming binary classification\n", - " roc_auc = auc(fpr, tpr)\n", - "\n", - " plt.figure(figsize=(8, 6))\n", - " plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')\n", - " plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n", - " plt.xlabel('False Positive Rate')\n", - " plt.ylabel('True Positive Rate')\n", - " plt.title('Receiver Operating Characteristic (ROC) Curve')\n", - " plt.legend(loc='lower right')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " return all_labels, all_preds, all_probs\n", - "\n", - "# Function to plot Precision-Recall Curve\n", - "def plot_precision_recall_curve(all_labels, all_probs):\n", - " # If it's binary classification, use all_probs directly\n", - " if len(all_probs[0]) == 1: # Check if probabilities are single (binary classification)\n", - " precision, recall, _ = precision_recall_curve(all_labels, all_probs)\n", - " else: # Multi-class classification, choose class 1 (for binary) or another class for multi-class\n", - " precision, recall, _ = precision_recall_curve(all_labels, np.array(all_probs)[:, 1])\n", - "\n", - " plt.figure(figsize=(8, 6))\n", - " plt.plot(recall, precision, color='blue', lw=2, label='Precision-Recall curve')\n", - " plt.xlabel('Recall')\n", - " plt.ylabel('Precision')\n", - " plt.title('Precision-Recall Curve')\n", - " plt.legend(loc='lower left')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "# Example Usage\n", - "if __name__ == \"__main__\":\n", - " # Assuming test_loader and class_names are defined\n", - " model_deepercnn = CustomCNN(num_classes=6)\n", - " model_deepercnn.load_state_dict(torch.load(\"best_model_CustomCNN.pt\"))\n", - " model_deepercnn.eval()\n", - "\n", - " criterion = nn.CrossEntropyLoss()\n", - "\n", - " # Test the model\n", - " all_labels_customcnn, all_preds_customcnn, all_probs_customcnn = test_model(model_deepercnn, test_loader, criterion)\n", - "\n", - " # Generate and print the classification report for the custom CNN\n", - " print(\"\\nClassification Report for Custom CNN:\")\n", - " print(classification_report(all_labels_customcnn, all_preds_customcnn, target_names=class_names))\n", - "\n", - " # Generate the Precision-Recall Curve\n", - " plot_precision_recall_curve(all_labels_customcnn, all_probs_customcnn)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA04AAAIjCAYAAAA0vUuxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAACdHUlEQVR4nOzdeZzN5fvH8deZ3ZgZu8GYmuz7HtmJMVKILCEiS9k1inxtkWgRCkXKkhIlSVnHNmPLLkuWlJ0shWEwxsz5/XH/ZphsgznzmTnzfj4e58H5nM855zr3fNK55rrv67bZ7XY7IiIiIiIiclcuVgcgIiIiIiKS2ilxEhERERERuQ8lTiIiIiIiIvehxElEREREROQ+lDiJiIiIiIjchxInERERERGR+1DiJCIiIiIich9KnERERERERO5DiZOIiIiIiMh9KHESERERERG5DyVOIiKSLKZPn47NZmPLli1Wh5IkO3bs4KWXXiIwMBBPT0+yZs1K3bp1mTZtGrGxsVaHJyIiqYyb1QGIiIiktC+++ILXXnsNf39/2rZtS8GCBbl06RIrVqygY8eOnDp1iv/9739WhykiIqmIEicREUlXfv31V1577TUqV67MokWL8PX1TXisT58+bNmyhd27dyfLe0VFRZExY8ZkeS0REbGWpuqJiEiK2r59O8888wx+fn74+PhQp04dfv3110TnxMTEMGzYMAoWLIiXlxfZsmWjWrVqhIWFJZzz999/06FDB/LmzYunpye5c+emcePGHD58+J7vP2zYMGw2G998802ipClehQoVaN++PQCrV6/GZrOxevXqROccPnwYm83G9OnTE461b98eHx8f/vzzTxo0aICvry9t2rShR48e+Pj4cOXKldveq1WrVuTKlSvR1MDFixdTvXp1MmbMiK+vL88++yx79uy552cSERHHU+IkIiIpZs+ePVSvXp3ffvuNfv36MXjwYA4dOkStWrXYuHFjwnlvv/02w4YNo3bt2kyYMIGBAwfy2GOPsW3btoRzXnjhBX788Uc6dOjAp59+Sq9evbh06RJHjx696/tfuXKFFStWUKNGDR577LFk/3w3btwgJCSEnDlzMnr0aF544QVatmxJVFQUCxcuvC2Wn3/+mWbNmuHq6grAzJkzefbZZ/Hx8eH9999n8ODB/P7771SrVu2+CaGIiDiWpuqJiEiKGTRoEDExMaxdu5Z8+fIB0K5dOwoXLky/fv0IDw8HYOHChTRo0IDPP//8jq9z4cIF1q9fz4cffsgbb7yRcHzAgAH3fP+DBw8SExNDyZIlk+kTJRYdHU3z5s0ZNWpUwjG73U5AQABz5syhefPmCccXLlxIVFQULVu2BODy5cv06tWLTp06JfrcL7/8MoULF2bkyJF3HQ8REXE8VZxERCRFxMbGsmzZMp5//vmEpAkgd+7ctG7dmrVr1xIZGQlA5syZ2bNnD3/88ccdXytDhgx4eHiwevVqzp8/n+QY4l//TlP0kkvXrl0T3bfZbDRv3pxFixZx+fLlhONz5swhICCAatWqARAWFsaFCxdo1aoV586dS7i5urpSqVIlVq1a5bCYRUTk/pQ4iYhIijh79ixXrlyhcOHCtz1WtGhR4uLiOHbsGADDhw/nwoULFCpUiJIlS/Lmm2+yc+fOhPM9PT15//33Wbx4Mf7+/tSoUYMPPviAv//++54x+Pn5AXDp0qVk/GQ3ubm5kTdv3tuOt2zZkqtXr7JgwQLAVJcWLVpE8+bNsdlsAAlJ4tNPP02OHDkS3ZYtW8aZM2ccErOIiCSNEicREUl1atSowZ9//snUqVMpUaIEX3zxBeXKleOLL75IOKdPnz4cOHCAUaNG4eXlxeDBgylatCjbt2+/6+sWKFAANzc3du3alaQ44pOa/7rbPk+enp64uNz+v9annnqKoKAgvvvuOwB+/vlnrl69mjBNDyAuLg4w65zCwsJuu/30009JillERBxDiZOIiKSIHDly4O3tzf79+297bN++fbi4uBAYGJhwLGvWrHTo0IFvv/2WY8eOUapUKd5+++1Ez8ufPz99+/Zl2bJl7N69m+vXr/PRRx/dNQZvb2+efvppIiIiEqpb95IlSxbArKm61ZEjR+773P9q0aIFS5YsITIykjlz5hAUFMRTTz2V6LMA5MyZk7p16952q1Wr1gO/p4iIJB8lTiIikiJcXV2pV68eP/30U6IOcadPn2bWrFlUq1YtYSrdP//8k+i5Pj4+FChQgOjoaMB0pLt27Vqic/Lnz4+vr2/COXczdOhQ7HY7bdu2TbTmKN7WrVuZMWMGAI8//jiurq5EREQkOufTTz9N2oe+RcuWLYmOjmbGjBksWbKEFi1aJHo8JCQEPz8/Ro4cSUxMzG3PP3v27AO/p4iIJB911RMRkWQ1depUlixZctvx3r17M2LECMLCwqhWrRrdunXDzc2NyZMnEx0dzQcffJBwbrFixahVqxbly5cna9asbNmyhblz59KjRw8ADhw4QJ06dWjRogXFihXDzc2NH3/8kdOnT/Piiy/eM74qVaowceJEunXrRpEiRWjbti0FCxbk0qVLrF69mgULFjBixAgAMmXKRPPmzRk/fjw2m438+fPzyy+/PNR6o3LlylGgQAEGDhxIdHR0oml6YNZfffbZZ7Rt25Zy5crx4osvkiNHDo4ePcrChQupWrUqEyZMeOD3FRGRZGIXERFJBtOmTbMDd70dO3bMbrfb7du2bbOHhITYfXx87N7e3vbatWvb169fn+i1RowYYa9YsaI9c+bM9gwZMtiLFClif/fdd+3Xr1+32+12+7lz5+zdu3e3FylSxJ4xY0Z7pkyZ7JUqVbJ/9913SY5369at9tatW9vz5Mljd3d3t2fJksVep04d+4wZM+yxsbEJ5509e9b+wgsv2L29ve1ZsmSxv/rqq/bdu3fbAfu0adMSznv55ZftGTNmvOd7Dhw40A7YCxQocNdzVq1aZQ8JCbFnypTJ7uXlZc+fP7+9ffv29i1btiT5s4mISPKz2e12u2VZm4iIiIiISBqgNU4iIiIiIiL3ocRJRERERETkPpQ4iYiIiIiI3IcSJxERERERkftQ4iQiIiIiInIfSpxERERERETuI91tgBsXF8fJkyfx9fXFZrNZHY6IiIiIiFjEbrdz6dIl8uTJg4vLvWtK6S5xOnnyJIGBgVaHISIiIiIiqcSxY8fImzfvPc9Jd4mTr68vYAbHz8/P4mggJiaGZcuWUa9ePdzd3a0Ox+lofB1L4+tYGl/H0vg6lsbXsTS+jqXxdazUNL6RkZEEBgYm5Aj3ku4Sp/jpeX5+fqkmcfL29sbPz8/yC8cZaXwdS+PrWBpfx9L4OpbG17E0vo6l8XWs1Di+SVnCo+YQIiIiIiIi96HESURERERE5D6UOImIiIiIiNxHulvjJCIiIiKpj91u58aNG8TGxlodCjExMbi5uXHt2rVUEY+zSenxdXd3x9XV9ZFfR4mTiIiIiFjq+vXrnDp1iitXrlgdCmCSuFy5cnHs2DHt++kAKT2+NpuNvHnz4uPj80ivo8RJRERERCwTFxfHoUOHcHV1JU+ePHh4eFierMTFxXH58mV8fHzuuymqPLiUHF+73c7Zs2c5fvw4BQsWfKTKkxInEREREbHM9evXiYuLIzAwEG9vb6vDAcwX++vXr+Pl5aXEyQFSenxz5MjB4cOHiYmJeaTESVeCiIiIiFhOCYo4SnJVMHWFioiIiIiI3IcSJxERERERkftQ4iQiIiIiaV5sLKxeDd9+a/5Mi13Eg4KCGDduXJLPX716NTabjQsXLjgsJrlJiZOIiIiIpGnz5kFQENSuDa1bmz+DgsxxR7DZbPe8vf322w/1ups3b6ZLly5JPr9KlSqcOnWKTJkyPdT7JZUSNENd9UREREQkzZo3D5o1A7s98fETJ8zxuXOhadPkfc9Tp04l/H3OnDkMGTKE/fv3Jxy7db8gu91ObGwsbm73/9qdI0eOB4rDw8ODXLlyPdBz5OGp4mSl2Fhs4eEERERgCw9PmzVlERERkWRkt0NUVNJukZHQq9ftSVP86wD07m3OS8rr3el17iRXrlwJt0yZMmGz2RLu79u3D19fXxYvXkz58uXx9PRk7dq1/PnnnzRu3Bh/f398fHx48sknWb58eaLX/e9UPZvNxhdffEGTJk3w9vamYMGCLFiwIOHx/1aCpk+fTubMmVm6dClFixbFx8eH+vXrJ0r0bty4Qa9evcicOTPZsmWjf//+vPzyyzz//PNJ+/B3cP78edq1a0eWLFnw9vbmmWee4Y8//kh4/MiRIzRs2JAsWbKQMWNGSpYsybJlyxKe26ZNG3LkyEGGDBkoWLAg06ZNe+hYHMnyxGnixIkEBQXh5eVFpUqV2LRp013PjYmJYfjw4eTPnx8vLy9Kly7NkiVLUjDaZPT/NWW34GAqjBmDW3CwY2vKIiIiImnAlSvg45O0W6ZMprJ0N3Y7HD9uzkvK6125knyf46233uK9995j7969lCpVisuXL9OgQQNWrFjB9u3bqV+/Pg0bNuTo0aP3fJ1hw4bRokULdu7cSYMGDWjTpg3//vvvXc+/cuUKo0ePZubMmURERHD06FHeeOONhMfff/99vvnmG6ZNm8a6deuIjIxk/vz5j/RZ27dvz5YtW1iwYAEbNmzAbrfToEEDYmJiAOjevTvR0dFERESwa9cuRo0aRcaMGQEYPHgwv//+O4sXL2bv3r189tlnZM+e/ZHicRRLp+rNmTOH0NBQJk2aRKVKlRg3bhwhISHs37+fnDlz3nb+oEGD+Prrr5kyZQpFihRh6dKlNGnShPXr11O2bFkLPsFDsqKmLCIiIiIpZvjw4QQHByfcz5o1K6VLl064/8477/Djjz+yYMECevTocdfXad++Pa1atQJg5MiRfPLJJ2zatIn69evf8fyYmBgmTZpE/vz5AejRowfDhw9PeHz8+PEMGDCAJk2aADBhwgQWLVr00J/zjz/+YMGCBaxbt44qVaoA8M033xAYGMj8+fNp3rw5R48e5YUXXqBkyZKAqaxFRkYCcPToUcqWLUuFChUSHkutLK04jRkzhs6dO9OhQweKFSvGpEmT8Pb2ZurUqXc8f+bMmfzvf/+jQYMG5MuXj65du9KgQQM++uijFI78EcTGmprxvWrKffpo2p6IiIikS97ecPly0m5J/b6/aFHSXs/bO/k+R3wiEO/y5cu88cYbFC1alMyZM+Pj48PevXvvW3EqVapUwt8zZsyIn58fZ86cuev53t7eCUkTQO7cuRPOv3jxIqdPn6ZixYoJj7u6ulK+fPkH+my32rt3L25ublSqVCnhWLZs2ShcuDB79+4FoFevXowYMYKqVasydOhQdu7cmXBu165dmT17NmXKlKFfv36sX7/+oWNxNMsqTtevX2fr1q0MGDAg4ZiLiwt169Zlw4YNd3xOdHQ0Xl5eiY5lyJCBtWvX3vV9oqOjiY6OTrgfn93GxMQklA9Tki08HLfjx+9+gt0Ox45xY9Uq7DVrplxgTir+Z2zFzzo90Pg6lsbXsTS+jqXxdSxnGt+YmBjsdjtxcXHExcUBkCFD0p5bty7kzWvjxAmw2223PW6z2cmbF+rWtePqev/Xs9vjb/b/v29PiOlu4h//758ZMmRI9Ny+ffuyfPlyPvjgAwoUKECGDBlo0aIF0dHRic7773u6uromum+z2bhx40ai8Yr/e1xcHO7u7re93n/H99a///ec+33G/55z62M2W+KfQfxrvvLKKwQHB7Nw4ULCwsIYNWoUI0aMoG/fvoSEhHDo0CEWLVrE8uXLqVOnDt26dePDDz+8+6A/oLi4OOx2OzExMbj+50J4kP+GLEuczp07R2xsLP7+/omO+/v7s2/fvjs+JyQkhDFjxlCjRg3y58/PihUrmDdvHrH3qM6MGjWKYcOG3XZ82bJleCfnrxWSKCAiggr3P40dixdzIirK4fGkF2FhYVaH4NQ0vo6l8XUsja9jaXwdyxnG183NjVy5cnH58mWuX7/+wM8fOdKdl1/2xmazJ0qebDaT/Lz77hWioh4uwbx06dJ9z7l27Rp2uz3hl/NX/n+h1KVLl3BxuTm5a82aNbz44ovUqVMHMBWoQ4cOUbly5YTnxsXFce3atYT7AFevXk103263J5zz3/f6byzxzwdTPLDZbOTMmZO1a9dSpkwZAGJjY9m6dSslS5ZM9Lxb3e0zAQQGBnLjxg1WrlyZUHX6999/2b9/f6IpeZkyZaJ169a0bt2aYcOGMWPGjITW656enjRp0oQmTZpQoUIFhg4dyuDBg+879kl1/fp1rl69SkREBDdu3LjjZ0uKNNWO/OOPP6Zz584UKVIEm81G/vz56dChw12n9gEMGDCA0NDQhPuRkZEEBgZSr149/Pz8UiLsRGwZM8KYMfc9r8wzz1BaFadHFhMTQ1hYGMHBwbi7u1sdjtPR+DqWxtexNL6OpfF1LGca32vXrnHs2DF8fHxum1mUFG3aQIYMdl5/3catk3ry5oUxY+w0bZoBSGIJ6//Z7XYuXbqEr6/vbVWU//Ly8sJmsyV8r4z/xbyvr2+i75qFCxdm0aJFvPDCC9hsNoYMGYLdbsfDwyPhPBcXF7y8vBI9L0OGDInu22y2hHP++17/jSX++UDCsZ49ezJu3DiKFy9OkSJFmDBhAhcvXsTd3f2u343j3+fw4cP4+vomiqVs2bI0atSI0NBQPvvsM3x9fRkwYAABAQG8+OKLuLu78/rrr1O/fn0KFSrE+fPnWb9+PYULF8bX15e3336bcuXKUbx4caKjo1mxYgVFixZN1u/p165dI0OGDNSoUeO2a+xuyeKdWJY4Zc+eHVdXV06fPp3o+OnTp+/ajz5HjhzMnz+fa9eu8c8//5AnTx7eeust8uXLd9f38fT0xNPT87bj7u7u1vxDU7u2+S/Z1JTveprb1q3m3KTUleW+LPt5pxMaX8fS+DqWxtexNL6O5QzjGxsbi81mw8XF5bZqRlI1awZNmsCaNXDqFOTODdWr23B1vXfSczfx08/i47qX+Mfv9Oetzx07diyvvPIK1apVI3v27PTv359Lly7d9h7/vX+ncYk/9t/3+m8Md4rrrbfe4vTp07Rv3x5XV1e6dOlCSEgIrq6ud/2s8cdr1aqV6Lirqys3btxg+vTp9O7dm0aNGnH9+nVq1KjBokWLEr6Dx8XF0bNnT44fP46fnx8hISEMGzYMm82Gp6cnAwcO5PDhw2TIkIHq1asze/bsh74W7ha/zWa7438vD/Lfj81uT2rH+uRXqVIlKlasyPjx4wEzqI899hg9evTgrbfeuu/zY2JiKFq0KC1atGDkyJFJes/IyEgyZcrExYsXLak4ATe76kHi5MlmS3y/alWYPh0KFEjR8JxJTEwMixYtokGDBmn+fyypkcbXsTS+jqXxdSyNr2M50/heu3aNQ4cO8cQTTzxUxckR4uLiiIyMxM/PL1m/wKdGcXFxCd+n33nnnRR7z5Qc33tdYw+SG1h6JYSGhjJlyhRmzJjB3r176dq1K1FRUXTo0AGAdu3aJWoesXHjRubNm8dff/3FmjVrqF+/PnFxcfTr18+qj/BwmjY1LccDAhIfz5vXHP/yS/D1hXXroHRp+OyzpO/IJiIiIiJyF0eOHGHKlCkcOHCAXbt20bVrVw4dOkTr1q2tDi3Vs3SNU8uWLTl79ixDhgzh77//pkyZMixZsiShYcTRo0cTZaHXrl1j0KBB/PXXX/j4+NCgQQNmzpxJ5syZLfoEj6BpU2jcmBurVrFj8WLKPPMMbrdOzXv6aXjlFVi1Crp1gx9/NAlVYKC1cYuIiIhImuXi4sL06dN54403sNvtlChRguXLl1O0aFGrQ0v1LG8O0aNHj7tu+rV69epE92vWrMnvv/+eAlGlEFdX7DVrciIqyjSCuHU9U1AQLF8OEyZA//4QFgYlS8Inn0DbtmZan4iIiIjIAwgMDGTdunVWh5EmOfekzbTOxQV69YIdO6BSJbh4EV5+2VSr/tNUQ0REREREHEeJU1pQuDCsXQsjR4K7O8yfDyVKwA8/WB2ZiIiIiEi6oMQprXBzgwEDYMsW0zDi3DnTma9NGzh/3uroREREREScmhKntKZUKdi0CQYONFP5Zs0y1afFi62OTERERETEaSlxSos8PGDECFi/HgoVgpMnoUEDePVVuHTJ6uhERERERJyOEqe0rFIl2L4d+vQx9z//3FSkwsMtDUtERERExNkocUrrvL1h7Fiz39Pjj8Phw1C7NoSGwtWrVkcnIiIikjJiY2H1avj2W/NnbKzVEd1XrVq16BP/C3AgKCiIcePG3fM5NpuN+fPnP/J7J9frpCdKnJxFrVqwaxd06gR2u0mmypWDzZutjkxERETEsebNM3tg1q4NrVubP4OCzHEHaNiwIfXr17/jY2vWrMFms7Fz584Hft3NmzfTpUuXRw0vkbfffpsyZcrcdvzUqVM888wzyfpe/zV9+nQyZ87s0PdISUqcnImvL0yZAgsXQu7csG8fVK4MgwfD9etWRyciIiKS/ObNM52Gjx9PfPzECXPcAclTx44dCQsL4/h/3xOYNm0aFSpUoFSpUg/8ujly5MDb2zs5QryvXLly4enpmSLv5SyUODmjBg1g925o1cqUqUeMMOuhdu2yOjIRERGRe7PbISoqabfISOjVyzznTq8D0Lu3OS8pr3en17mD5557jhw5cjB9+vRExy9fvsz3339Px44d+eeff2jVqhUBAQF4e3tTsmRJvv3223u+7n+n6v3xxx/UqFEDLy8vihUrRlhY2G3P6d+/P4UKFcLb25t8+fIxePBgYmJiAFPxGTZsGL/99hs2mw2bzZYQ83+n6u3atYunn36aDBkykC1bNrp06cLly5cTHm/fvj3PP/88o0ePJnfu3GTLlo3u3bsnvNfDOHr0KI0bN8bHxwc/Pz9atGjB6dOnEx7/7bffqF27Nr6+vvj5+VG+fHm2bNkCwJEjR2jYsCFZsmQhY8aMFC9enEWLFj10LEnh5tBXF+tkzWpalTdpAl27wo4dUL48DB8Ob74Jrq5WRygiIiJyuytXwMcneV7LbjeVqEyZknb+5cuQMeN9T3Nzc6Ndu3ZMnz6dgQMHYrPZAPj++++JjY2lVatWXL58mfLly9O/f3/8/PxYuHAhbdu2JX/+/FSsWPG+7xEXF0fTpk3x9/dn48aNXLx4MdF6qHi+vr5Mnz6dPHnysGvXLjp37oyvry/9+vWjZcuW7N69myVLlrB8+XIAMt1hLKKioggJCaFy5cps3ryZM2fO0KlTJ3r06JEoOVy1ahW5c+dm1apVHDx4kJYtW1KmTBk6d+58389zp8/XpEkTfHx8CA8P58aNG3Tv3p2WLVuyevVqANq0aUPZsmX57LPPcHV1ZceOHbi7uwPQvXt3rl+/TkREBBkzZuT333/HJ7mum7tQ4uTsmjeH6tWhSxf4+Wezie5PP8FXX0HBglZHJyIiIpImvfLKK3z44YeEh4dTq1YtwEzTe+GFF8iUKROZMmXijTfeSDi/Z8+eLF26lO+++y5JidPy5cvZt28fS5cuJU+ePACMHDnytnVJgwYNSvh7UFAQb7zxBrNnz6Zfv35kyJABHx8f3NzcyJUr113fa9asWVy7do2vvvqKjP+fOE6YMIGGDRvy/vvv4+/vD0CWLFmYMGECrq6uFClShGeffZYVK1Y8VOIUHh7Orl27OHToEIGBgQB89dVXFC9enM2bN/Pkk09y9OhR3nzzTYoUKQJAwVu+ux49epQXXniBkiVLApAvX74HjuFBaapeepArl0mWpk0DPz/49VcoXRomTIC4OKujExEREbnJ29tUfpJyS+rUrEWLkvZ6D7C+qEiRIlSpUoWpU6cCcPDgQdasWUPHjh0BiI2N5Z133qFkyZJkzZoVHx8fli5dytGjR5P0+nv37iUwMDAhaQKoXLnybefNmTOHqlWrkitXLnx8fBg0aFCS3+PW9ypdunRC0gRQtWpV4uLi2L9/f8Kx4sWL43rLrKXcuXNz5syZB3qveAcOHCAwMDAhaQIoVqwYmTNnZu/evQCEhobSqVMn6taty3vvvceff/6ZcG6vXr0YMWIEVatWZejQoQ/VjONBKXFKL2w2aN/erHOqU8e0Ku/ZE4KD4cgRq6MTERERMWw2M10uKbd69SBvXvOcu71WYKA5Lymvd7fXuYuOHTvyww8/cOnSJaZNm0b+/PmpWbMmAB9++CEff/wx/fv3Z9WqVezYsYOQkBCuJ2PDrg0bNtCmTRsaNGjAL7/8wvbt2xk4cGCyvset4qfJxbPZbMQ58Jfwb7/9Nnv27OHZZ59l5cqVFCtWjB9//BGATp068ddff9G2bVt27dpFhQoVGD9+vMNiASVO6c9jj8GyZaba5O0NK1dCyZKmGpXEBZEiIiIiqYKrK3z8sfn7f5Oe+PvjxjlsbXeLFi1wcXFh1qxZfPXVV7zyyisJ653WrVtH48aNeemllyhdujT58uXjwIEDSX7tokWLcuzYMU6dOpVw7Ndff010zvr163n88ccZOHAgFSpUoGDBghz5zy/EPTw8iL3PnlZFixblt99+IyoqKuHYunXrcHFxoXDhwkmO+UEUKlSIY8eOcezYsYRjv//+OxcuXKBYsWKJznv99ddZtmwZTZs2Zdq0aQmPBQYG8tprrzFv3jz69u3LlClTHBJrPCVO6ZGLC3TvbhpGVKkCly7BK69Ao0bw999WRyciIiKSdE2bwty5EBCQ+HjevOZ406YOe2sfHx9atmzJgAEDOHXqFO3bt094rGDBgoSFhbF+/Xr27t3Lq6++mqhj3P3UrVuXQoUK8fLLL/Pbb7+xZs0aBg4cmOicggULcvToUWbPns2ff/7JJ598klCRiRcUFMShQ4fYsWMH586dIzo6+rb3atOmDV5eXrz88svs3r2bVatW0bNnT9q2bZuwvulhxcbGsmPHjkS3vXv3UqtWLUqWLEmbNm3Ytm0bmzZtol27dtSsWZMKFSpw9epVevTowerVqzly5Ajr1q1j8+bNFC1aFIA+ffqwdOlSDh06xLZt21i1alXCY46ixCk9K1gQIiLg/ffBwwN++QWKF4fvvrM6MhEREZGka9oUDh+GVatMV+FVq+DQIYcmTfE6duzI+fPnCQkJSbQeadCgQZQrV46QkBBq1apFrly5eP7555P8ui4uLvz4449cvXqVihUr0qlTJ959991E5zRq1IjXX3+dHj16UKZMGdavX8/gwYMTnfPCCy9Qv359ateuTY4cOe7YEt3b25ulS5fy77//8uSTT9KsWTPq1KnDhAkTHmww7uDy5cuULVs20a1x48bYbDZ+/PFHsmTJQo0aNahbty758uVjzpw5ALi6uvLPP//Qrl07ChUqRIsWLXjmmWcYNmwYYBKy7t27U7RoUerXr0+hQoX49NNPHznee7HZ7elrflZkZCSZMmXi4sWL+Pn5WR0OMTExLFq0iAYNGtw2bzRF7d4N7drB9u3m/osvmul82bJZF1MySDXj66Q0vo6l8XUsja9jaXwdy5nG99q1axw6dIgnnngCLy8vq8MBTKvsyMhI/Pz8cHFRnSG5pfT43usae5DcQFeCGCVKmG57Q4aYecCzZ5tjCxdaHZmIiIiIiOWUOMlNHh4wbBhs2ABFipj1Ts89Bx07mh23RURERETSKSVOcrsnn4Rt2yA01HSkmToVSpUy84VFRERERNIhJU5yZxkywEcfwerV8MQTZq+np5+G3r3hyhWroxMRERERSVFKnOTeatSAnTvhtdfM/U8+gbJlzXooERERkWSSzvqVSQpKrmtLiZPcn48PfPYZLFkCefLAgQNQtSr8739wh70ARERERJIqvivgFc1oEQe5fv06YFqcPwq35AhG0omQENO2vFcv+PprGDXKdN376isoXdrq6ERERCQNcnV1JXPmzJw5cwYwewrZbDZLY4qLi+P69etcu3ZN7cgdICXHNy4ujrNnz+Lt7Y2b26OlPkqc5MFkyQIzZ0KTJmb63s6dppnE0KHQvz884gUpIiIi6U+uXLkAEpInq9ntdq5evUqGDBksT+KcUUqPr4uLC4899tgjv5e+5crDadoUqlWDV1+F+fNh0CBYsABmzDCtzEVERESSyGazkTt3bnLmzElMTIzV4RATE0NERAQ1atRI8xsMp0YpPb4eHh7JUtlS4iQPL2dOmDfPTNvr2RM2bTKNI957z9xXaVtEREQegKur6yOvQ0muOG7cuIGXl5cSJwdIq+Orb7byaGw2aNvWrH2qVw+uXYM+faBOHTh82OroRERERESShRInSR5585que599Bhkzmv2fSpaEL74AtRcVERERkTROiZMkH5vNNIz47Tez/unyZejcGZ57Dk6etDo6EREREZGHpsRJkl/+/KbiNHo0eHrCokVQogR8+62qTyIiIiKSJilxEsdwdYW+fWHrVihfHs6fh9atoWVLOHfO6uhERERERB6IEidxrOLFYcMGGDbM7PH0/ffm2IIFVkcmIiIiIpJkSpzE8dzdYcgQ+PVXkzSdOQONG0OHDnDxotXRiYiIiIjclxInSTnly8OWLdCvn2kkMX266by3YoXVkYmIiIiI3JMSJ0lZXl7w/vuwZo1pInHsGNStCz16QFSU1dGJiIiIiNyREiexRtWqpm15t27m/sSJUKYMrF9vaVgiIiIiIneixEmskzGjSZiWLTMb6B48CNWrQ//+cO2a1dGJiIiIiCRQ4iTWCw6GXbvg5ZchLg4++AAqVIDt262OTEREREQEUOIkqUXmzKZZxPz5kDMn7NkDFSvCO+9ATIzFwYmIiIhIeqfESVKXxo1h92544QW4ccO0Ma9SBX7/3erIRERERCQdU+IkqU+OHGaj3G++MZWoLVugXDkYM8ZM5RMRERERSWFKnCR1stmgdWszZe+ZZyA6Gvr2hdq14a+/rI5ORERERNIZJU6SuuXJAwsXwuefg48PRERAqVIweTLY7VZHJyIiIiLphBInSf1sNujcGXbuhJo1zUa5r71mKlEnTlgdnYiIiIikA0qcJO144glYuRLGjgUvL1i6FEqUgK+/VvVJRERERBxKiZOkLS4u0KeP2eOpYkW4cAHatoVmzeDsWaujExEREREnpcTJQrGxEB5uIyIigPBwG7GxVkeUhhQpAuvWwYgR4OYG8+ZB8eJmHygRERERkWRmeeI0ceJEgoKC8PLyolKlSmzatOme548bN47ChQuTIUMGAgMDef3117l27VoKRZt85s2DoCAIDnZjzJgKBAe7ERRkjksSubnBwIGweTOULGkqTk2aQLt2phIlIiIiIpJMLE2c5syZQ2hoKEOHDmXbtm2ULl2akJAQzpw5c8fzZ82axVtvvcXQoUPZu3cvX375JXPmzOF///tfCkf+aObNMzPLjh9PfPzECXNcydMDKlPGJE8DBpipfDNnmrVPy5ZZHZmIiIiIOAlLE6cxY8bQuXNnOnToQLFixZg0aRLe3t5MnTr1juevX7+eqlWr0rp1a4KCgqhXrx6tWrW6b5UqNYmNhd6979zLIP5Ynz5o2t6D8vSEkSNh7VooWNBkoSEhuPTsievVq1ZHJyIiIiJpnJtVb3z9+nW2bt3KgAEDEo65uLhQt25dNmzYcMfnVKlSha+//ppNmzZRsWJF/vrrLxYtWkTbtm3v+j7R0dFER0cn3I+MjAQgJiaGmJiYZPo0SRcebuP48bsPu90Ox47BqlU3qFlTneIeWIUKsHkzLgMH4jpxIq6TJ1N7/nxis2WDWrWsjs7pxP83ZMV/S+mBxtexNL6OpfF1LI2vY2l8HSs1je+DxGBZ4nTu3DliY2Px9/dPdNzf3599+/bd8TmtW7fm3LlzVKtWDbvdzo0bN3jttdfuOVVv1KhRDBs27Lbjy5Ytw9vb+9E+xEOIiAgAKtz3vMWLdxAVpT2KHlpwMNn9/Sk7fjwZT5/GHhLCn40asbdNG+I8PKyOzumEhYVZHYJT0/g6lsbXsTS+jqXxdSyNr2OlhvG9cuVKks+12e3WbIBz8uRJAgICWL9+PZUrV0443q9fP8LDw9m4ceNtz1m9ejUvvvgiI0aMoFKlShw8eJDevXvTuXNnBg8efMf3uVPFKTAwkHPnzuHn55f8H+w+wsNtBAffP18NC1PFKTnEnDvH2bZteXzFCgDsRYoQO20a9vLlLY7MOcTExBAWFkZwcDDu7u5Wh+N0NL6OpfF1LI2vY2l8HUvj61ipaXwjIyPJnj07Fy9evG9uYFnFKXv27Li6unL69OlEx0+fPk2uXLnu+JzBgwfTtm1bOnXqBEDJkiWJioqiS5cuDBw4EBeX25dseXp64unpedtxd3d3S35QtWtD3rxmCc69UtatW92oXRtcXVMuNqeUPTs7evYkoEcP3Lp2xbZvH27VqsGgQaYjn/4xTBZW/feUXmh8HUvj61gaX8fS+DqWxtexUsP4Psj7W9YcwsPDg/Lly7Pi/ysBAHFxcaxYsSJRBepWV65cuS05cv3/zMKiwtkDc3WFjz82f7fZEj926/233oKaNeHgwZSLzZnZn30Wdu+GFi1M541hw+Cpp2DPHqtDExEREZE0wNKueqGhoUyZMoUZM2awd+9eunbtSlRUFB06dACgXbt2iZpHNGzYkM8++4zZs2dz6NAhwsLCGDx4MA0bNkxIoNKCpk1h7lwICEh8PG9ec/zLL8HX1+zvWro0fPbZvatTkkTZssGcOTB7NmTNCtu2Qbly8OGHamMoIiIiIvdk2VQ9gJYtW3L27FmGDBnC33//TZkyZViyZElCw4ijR48mqjANGjQIm83GoEGDOHHiBDly5KBhw4a8++67Vn2Eh9a0KTRubLrnLV68g2eeKUPt2m4JU/OefhpeeQVWrYJu3eDHH01CFRhobdxOoWVLqFEDOneGhQuhXz/46SeYPh0KFLA6OhERERFJhSytOAH06NGDI0eOEB0dzcaNG6lUqVLCY6tXr2b69OkJ993c3Bg6dCgHDx7k6tWrHD16lIkTJ5I5c+aUDzwZuLpCzZp2atQ4Qc2a9kTrmYKCYPlyM63PywvCwqBkSfjqK1WfkkXu3PDzzyrviYiIiEiSWJ44yd25uECvXrBjB1SqBBcvwssvm2rVf3pqyMOw2UxZb9cu07XjyhVT3gsJMZtpiYiIiIj8PyVOaUDhwrB2LYwcaZrAzZ8PJUrADz9YHZmTePxxU9775BPIkMGU90qUgBkzVH0SEREREUCJU5rh5gYDBsCWLWZG2blz0KwZtGkD589bHZ0TcHGBnj1Nee+ppyAyEtq3hyZNVN4TERERESVOaU2pUrBpk9mCyMUFZs0yxZHFi62OzEkUKgRr1sCoUaa899NPKu+JiIiIiBKntMjDA0aMgPXrzff8kyehQQN49VW4dMnq6JyAm5vZSEvlPRERERH5f0qc0rBKlWD7dujTx9z//HNTkQoPtzQs56HynoiIiIj8PyVOaZy3N4wda/Z7evxxOHzYNIgLDYWrV62OzgncWt4rXPhmea9LF5X3RERERNIRJU5OolYt01W7UyfTCG7sWChXDjZvtjoyJ/Hf8t6UKSrviYiIiKQjSpyciK+v+T6/cKHZ33XfPqhcGQYPhuvXrY7OCWTIcLO8FxSk8p6IiIhIOqLEyQk1aAC7d0OrVhAba2aaVapkKlKSDGrVgp07oXPnm+W9smXNeigRERERcUpKnJxU1qyml8F330G2bGZ7ovLl4b33TDIlj8jX13TjiC/v7d8PVaqovCciIiLipJQ4ObnmzU31qWFDiIkxm+hWqwZ//GF1ZE4ivrzXurXKeyIiIiJOTIlTOpArl9nHddo08PODX3812xNNmABxcVZH5wSyZoVvvoHvv1d5T0RERMRJKXFKJ2w2aN/eFELq1DG9DHr2hOBgOHLE6uicRLNmsGcPNGqUuLx34IDVkYmIiIjII1LilM489hgsW2aqTd7esHIllCxpqlF2u9XROQF/f5g/H6ZPv1neK1MGxo9XeU9EREQkDVPilA65uED37mZGWZUqZh/XV14xhZK//7Y6Oidgs8HLL5u1T3XrmvJer14q74mIiIikYUqc0rGCBSEiAt5/Hzw84JdfoHhx04lPkkFgICxdChMnqrwnIiIiksYpcUrnXF2hXz/YutVsRfTvv9CypdkD6p9/rI7OCbi4QLdu8Ntvt5f3Tp2yOjoRERERSSIlTgJAiRJmOc6QISaZmj3bHFu40OrInESBAqa898EHN8t7JUqovCciIiKSRihxkgQeHjBsGGzYAEWKmPVOzz0HHTtCZKTV0TkBV1d4802V90RERETSICVOcpsnn4Rt2yA01PQ5mDoVSpWCVausjsxJlCgBGzfC0KEq74mIiIikEUqc5I4yZICPPoLVq+GJJ0wzuKefht694coVq6NzAu7u8PbbZn5k0aIq74mIiIikckqc5J5q1ICdO+G118z9Tz4xs8x+/dXauJxGhQpm6l7fvjfLeyVLqrwnIiIiksoocZL78vGBzz6DJUsgTx44cACqVoX//Q+io62OzglkyACjR0N4OOTLB0ePqrwnIiIiksoocZIkCwkxe7q+9BLExcGoUVCxoum0LcmgenUzmCrviYiIiKQ6SpzkgWTJAjNnwg8/QI4cZhrfk0/Cu+/CjRtWR+cEbi3vBQSovCciIiKSSihxkofStKmpPj3/PMTEwKBB5vv9vn1WR+YkQkJg1y5o21blPREREZFUQImTPLScOWHePPjqK8iUCTZtMjPLPv7YfNeXR5QlixlclfdERERELKfESR6JzWaKIrt3Q716cO0a9OkDderA4cNWR+ck4st7TZqovCciIiJiESVOkizy5jXLcj77DDJmNPs/lSwJX3wBdrvV0TmBnDlN5WnmTJX3RERERCygxEmSjc1mGsL99htUqwaXL0PnzmZf15MnrY7OCdhspqWhynsiIiIiKU6JkyS7/PlNxWn0aPD0hEWLoEQJ+PZbVZ+SRXx5b9IklfdEREREUogSJ3EIV1fo2xe2boXy5eH8eWjdGlq2hHPnrI7OCdhs8OqrpmFE9eoq74mIiIg4mBIncajixWHDBhg2DNzc4PvvzbEFC6yOzEnkywerVqm8JyIiIuJgSpzE4dzdYcgQ+PVXkzSdOQONG0OHDnDxotXROYH48t62bSrviYiIiDiIEidJMeXLw5Yt0K+fmWk2fbpZmrNihdWROYlixVTeExEREXEQJU6Sory84P33Yc0a00Ti2DGoWxd69ICoKKujcwLx5b2NG1XeExEREUlGSpzEElWrmrbl3bqZ+xMnQpkysH69pWE5j3LlTGcOlfdEREREkoUSJ7FMxowmYVq2zHTYPnjQNIjr399sUSSPyNNT5T0RERGRZKLESSwXHAy7dsHLL0NcHHzwAVSoANu3Wx2Zk4gv73Xvbu6rvCciIiLywJQ4SaqQObOZTTZ/PuTMCXv2QMWK8M47EBNjcXDOIGNGmDABwsIgMFDlPREREZEHpMRJUpXGjWH3bnjhBbhxw/Q5qFIFfv/d6sicRN26Ku+JiIiIPAQlTpLq5MhhOml/842pRG3ZYnodjBljvuvLI8qUSeU9ERERkQekxElSJZvN7OG6Zw888wxER5s9XmvXhr/+sjo6J9G4sRngZs1U3hMRERG5DyVOkqrlyQMLF8Lnn4OPD0REQKlSMHky2O1WR+cEsmeH776DWbMgS5bE5b3YWKujExEREUk1lDhJqmezQefOsHMn1KxpOmm/9pqpRJ04YXV0TsBmg1atzOIylfdERERE7kiJk6QZTzwBK1fC2LHg5QVLl0KJEvD116o+JYv48t6UKaa8t2aNynsiIiIi/0+Jk6QpLi7Qp49pAlexIly4AG3bmmU6Z89aHZ0TsNmgUyeV90RERET+Q4mTpElFisC6dTBiBLi5wbx5ULy4aRQnySC+vDdu3N3Le7Gx2MLDCYiIwBYerjVRIiIi4tRSReI0ceJEgoKC8PLyolKlSmzatOmu59aqVQubzXbb7dlnn03BiCU1cHODgQNh82YoWdJUnJo0gXbtTCVKHpGLC/Tufefy3rRpEBSEW3AwFcaMwS04GIKCTAYrIiIi4oQsT5zmzJlDaGgoQ4cOZdu2bZQuXZqQkBDOnDlzx/PnzZvHqVOnEm67d+/G1dWV5s2bp3DkklqUKWOSpwEDzHf9mTNNcWTZMqsjcxK3lvfc3U1y9MorcPx44vNOnDBJlZInERERcUKWJ05jxoyhc+fOdOjQgWLFijFp0iS8vb2ZOnXqHc/PmjUruXLlSriFhYXh7e2txCmd8/SEkSNh7VooWNB8hw8JgZ49Xbh61dXq8NK++PLehg0mebqT+Cl8ffpo2p6IiIg4HTcr3/z69ets3bqVAQMGJBxzcXGhbt26bNiwIUmv8eWXX/Liiy+SMWPGOz4eHR1NdHR0wv3IyEgAYmJiiImJeYTok0d8DKkhFmdQoYKpPg0c6MLEia5MnuzK/Pm1yZYtllq1rI4u7bOdP4/bva5Vux2OHePGqlXYa9ZMucCclP59cCyNr2NpfB1L4+tYGl/HSk3j+yAxWJo4nTt3jtjYWPz9/RMd9/f3Z9++ffd9/qZNm9i9ezdffvnlXc8ZNWoUw4YNu+34smXL8Pb2fvCgHSQsLMzqEJxKcDD4+2dn/PiynD6dkZAQO40a/UmbNnvx8IizOrw0KyAiggpJOG/H4sWciIpyeDzphf59cCyNr2NpfB1L4+tYGl/HSg3je+XKlSSfa7Pbrdug5eTJkwQEBLB+/XoqV66ccLxfv36Eh4ezcePGez7/1VdfZcOGDezcufOu59yp4hQYGMi5c+fw8/N79A/xiGJiYggLCyM4OBj3u02Bkod27lwMbdueZcWKxwEoUsTOtGmxlC+vfYkehi083DSCuI8bYWGqOCUD/fvgWBpfx9L4OpbG17E0vo6VmsY3MjKS7Nmzc/HixfvmBpZWnLJnz46rqyunT59OdPz06dPkypXrns+Niopi9uzZDB8+/J7neXp64unpedtxd3d3y39Qt0pt8TiL7NmhZ88d9OgRQNeubuzbZ6NaNTcGDTJLdjTkD6h2bcib1ywiu8fvXNwWLYJq1Uwrc3lk+vfBsTS+jqXxdSyNr2NpfB0rNYzvg7y/pc0hPDw8KF++PCtWrEg4FhcXx4oVKxJVoO7k+++/Jzo6mpdeesnRYYoTePZZO7t3Q4sWpm/BsGHw1FOwZ4/VkaUxrq7w8cfm7zZb4sduvT92LJQrB1u2pFxsIiIiIg5keVe90NBQpkyZwowZM9i7dy9du3YlKiqKDh06ANCuXbtEzSPiffnllzz//PNky5YtpUOWNCpbNpgzB2bPhqxZYds2893+ww/VBO6BNG0Kc+dCQEDi43nzwg8/wM8/Q65csHevyU6HDoVUsPhTRERE5FFYnji1bNmS0aNHM2TIEMqUKcOOHTtYsmRJQsOIo0ePcurUqUTP2b9/P2vXrqVjx45WhCxpXMuWsHs3PPssXL8O/fpBzZpw8KDVkaUhTZvC4cPcCAtjS2goN8LC4NAhc/y558wAt2xpMtLhw1XeExERkTTP8sQJoEePHhw5coTo6Gg2btxIpUqVEh5bvXo106dPT3R+4cKFsdvtBCdhkbrIneTObQojX34Jvr5mf9fSpeGzz+65dEdu5eqKvWZNTtSoYRpBuN6yX1a2bKa0p/KeiIiIOIlUkTiJWMFmg1degV27TM+DK1egWzezce6xY1ZH5yRatjSVpueeU3lPRERE0jQlTpLuPf44LF8On3wCGTJAWBiUKAEzZqj6lCxy5YIFC2Dq1MTlvU8/hTjtqSUiIiJpgxInEcDFBXr2hB07zHKcyEho3x6aNIH/dMuXh2GzQYcOict73burvCciIiJphhInkVsUKgRr1sCoUWaPp59+MtWnH36wOjIn8d/y3vLlKu+JiIhImqDESeQ/3NzgrbfMFkSlS8O5c9CsGbRpA+fPWx2dE1B5T0RERNIgJU4id1GqFGzaBAMHmu/6s2aZ4sjixVZH5iQKFYK1a+G998DDw5T3ihc3e0SJiIiIpDJKnETuwcMDRoyA9euhcGE4eRIaNIAuXeDSJaujcwKurtC//83y3j//QPPmprz3779WRyciIiKSQImTSBJUqgTbt0OfPub+lCmmIhUebmlYzqNkSVPeGzTIJFOzZpljKu+JiIhIKqHESSSJMmSAsWNh1SoICoLDh02DuNBQuHrV6uicgIcHvPOOKe8VKaLynoiIiKQqSpxEHlCtWrBzJ3TubBrBjR0LZcuagokkg4oVYds2eP1108Zc5T0RERFJBZQ4iTwEX1/4/HNYuBBy54b9+6FKFRg8GK5ftzo6J5AhA4wZk7i8V6uWSaZU3hMRERELKHESeQQNGsDu3dC6NcTGmkYSlSqZfV4lGdSsebO8BzBunMp7IiIiYgklTiKPKGtW+OYb+P57yJbNbE9Uvrzpsh0ba3V0TiC+vLdokcp7IiIiYhklTiLJpFkz2LMHGjWCmBgYMACqVYMDB6yOzEk884wp77Vpc7O8V7GiqUiJiIiIOJgSJ5Fk5O8P8+fD9Ong5we//gplysD48RAXZ3FwziBrVvj665vlvd9+gwoVTHnvxg2roxMREREnpsRJJJnZbPDyy6Y4Ureu6WXQqxcEB8ORI1ZH5yTuVN6rXl3lPREREXEYJU4iDhIYCEuXwsSJ4O0NK1eaPV2nTTNtzOURxZf3ZsxQeU9EREQcTomTiAO5uEC3bmZGWZUqZh/XV14xhZJTp6yOzgnYbNCuncp7IiIi4nBKnERSQIECEBEBH3wAHh7wyy9QogR8953VkTmJwEBYtgw+/TRxeW/qVJX3REREJFkocRJJIa6u8OabsHWr2Yro33+hZUto1Qr++cfq6JyAzQZdu5ryXtWqprzXsaPKeyIiIpIslDiJpLASJWDjRhg61CRTs2ebYwsXWh2ZkyhQAMLDVd4TERGRZKXEScQC7u7w9tumn0HRovD33/Dcc6ZAEhlpdXROIL68t20blCt3s7z34osq74mIiMhDUeIkYqEKFczUvb59zUyzqVPN0pxVq6yOzEkUL26y0/jy3pw5pvr0yy9WRyYiIiJpjBInEYtlyACjR5vZZfnywdGj8PTT0Ls3XLlidXROIL68t3EjFCtmynsNG6q8JyIiIg9EiZNIKlG9uulr8Npr5v4nn5gmEr/+am1cTqN8eVPee+ONxOW9lSutjkxERETSACVOIqmIjw989hksWQIBAXDggGkQ97//QXS01dE5AS8v+PDDxOW9OnXM3k8q74mIiMg9KHESSYVCQmDXLmjbFuLiYNQoqFjRVKQkGcSX97p2NffHj4cyZWDDBkvDEhERkdRLiZNIKpUlC3z1FfzwA+TIATt3wpNPwrvvwo0bVkfnBHx8zIa5S5ea8t4ff0C1ajBggMp7IiIichslTiKpXNOmsHs3NGkCMTEwaJCZvrdvn9WROYl69cwAx5f33ntP5T0RERG5jRInkTQgZ05TeZo5EzJlgk2bTOOIjz823/XlEWXObMp78+apvCciIiJ3pMRJJI2w2eCll0xxpF49uHYN+vQxvQ0OH7Y6OifRpAns2WPKfPHlvSpVVN4TERERJU4iaU3evKbr3qRJkDEjrF5tump/8QXY7VZH5wRy5IC5c+Hrr00lavNmU94bN07lPRERkXRMiZNIGmSzwauvmhll1avD5cvQuTM89xycPGl1dE7AZoM2bUxrw5AQU957/XWzM/GhQ1ZHJyIiIhZQ4iSShuXLB6tWwejR4OkJixZBiRLw7beqPiWLvHlh8WKYPNmU98LDoVQpmDJFAywiIpLOKHESSeNcXaFvX9i2DcqXh/PnoXVraNkSzp2zOjonYLNBly6Jy3tdusCzz6q8JyIiko4ocRJxEsWKmf1bhw0DNzf4/nsoXhwWLLA6MieRL59ZUPbRR6a8t3ixKe/NmqXqk4iISDqgxEnEibi7w5AhsHGjSZrOnIHGjaFDB7h40eronICLC4SGwvbtUKGCKe+1aQPNm8PZs1ZHJyIiIg6kxEnECZUrB1u3Qr9+ZqbZ9Omm896KFVZH5iSKFoX162H4cFPe++EHU31SeU9ERMRpKXEScVKenvD++7BmDeTPD8eOQd260KMHREVZHZ0TcHeHwYPNbsQlStws77VvDxcuWB2diIiIJDMlTiJOrmpV+O036N7d3J84EcqUMQUTSQZly8KWLdC/v5nKN2OGKe8tX251ZCIiIpKMlDiJpAMZM8KECRAWBoGBcPCgaRDXv7/ZokgekacnvPeeKe8VKADHj0NwsMlWVd4TERFxCkqcRNKRunXNnq4vvwxxcfDBB6bHwfbtVkfmJKpUgR07zHxIgE8/hdKlYd06S8MSERGRR6fESSSdyZTJNIuYPx9y5oQ9e6BiRXjnHYiJsTo6J5AxI4wff7O89+efKu+JiIg4ASVOIulU48YmaWrWDG7cMG3Mq1SB33+3OjInEV/ea9/e7PMUX97bts3qyEREROQhKHESSceyZ4fvvjN7uGbJYnoclCsHY8ZAbKzV0TmBTJlg2jT46Sfw9zeZaqVKpo25ynsiIiJpihInkXTOZoNWrWD3bnjmGYiOhr59oXZt+Osvq6NzEo0amQGOL+8NHarynoiISBqjxElEAMiTBxYuhClTwMfHNIgrVQomTzYzzeQRxZf3vv02cXnvo49U3hMREUkDlDiJSAKbDTp1gp07oWZN00n7tddMJerECaujcwI2G7z4oqk+NWhgyntvvGHKe3/+aXV0IiIicg9KnETkNk88AStXwrhx4OUFS5dCiRLw9dc3q0+xsRAebiMiIoDwcJuKJg8iTx745ZfE5b3SpWHSJJX3REREUinLE6eJEycSFBSEl5cXlSpVYtOmTfc8/8KFC3Tv3p3cuXPj6elJoUKFWLRoUQpFK5J+uLhA795mj6eKFeHCBWjb1izTmTYNgoIgONiNMWMqEBzsRlAQzJtncdBpSXx5b9cuqFXLlPe6doX69c0GuiIiIpKqWJo4zZkzh9DQUIYOHcq2bdsoXbo0ISEhnDlz5o7nX79+neDgYA4fPszcuXPZv38/U6ZMISAgIIUjF0k/ihQx+7eOGAHu7iY5euWV27/bnzhhkiolTw8oKAhWrLhZ3lu2zJT3Zs5U9UlERCQVsTRxGjNmDJ07d6ZDhw4UK1aMSZMm4e3tzdSpU+94/tSpU/n333+ZP38+VatWJSgoiJo1a1K6dOkUjlwkfXFzg4EDYcMGkzzdSfx3/D591OvggcWX93bsMO3KL16Edu1wbdECjwsXrI5OREREADer3vj69ets3bqVAQMGJBxzcXGhbt26bNiw4Y7PWbBgAZUrV6Z79+789NNP5MiRg9atW9O/f39cXV3v+Jzo6Giio6MT7kdGRgIQExNDTCrYRyU+htQQizPS+Cav8+dtxMTc/Z8Nux2OHYNVq25Qs6aqJQ8sXz5YtQqX0aNxeecdXH76iadXrSLOw4OYZs2sjs7p6N8Hx9L4OpbG17E0vo6Vmsb3QWKwLHE6d+4csbGx+Pv7Jzru7+/Pvn377vicv/76i5UrV9KmTRsWLVrEwYMH6datGzExMQwdOvSOzxk1ahTDhg277fiyZcvw9vZ+9A+STMLCwqwOwalpfJNHREQAUOG+5y1evIOoKLXhe2ilSuH3wQeU+/hjMh0+DK1bc2zyZHZ17kyMj4/V0Tkd/fvgWBpfx9L4OpbG17FSw/heuXIlyefa7HZrJtGfPHmSgIAA1q9fT+XKlROO9+vXj/DwcDZu3HjbcwoVKsS1a9c4dOhQQoVpzJgxfPjhh5w6deqO73OnilNgYCDnzp3Dz88vmT/Vg4uJiSEsLIzg4GDc7zYHSh6axjd5hYfbCA6+/+9bwsJUcUoOMZcvc7xLFwrOm4ctLg57QACxkydjr1fP6tCcgv59cCyNr2NpfB1L4+tYqWl8IyMjyZ49OxcvXrxvbmBZxSl79uy4urpy+vTpRMdPnz5Nrly57vic3Llz4+7unmhaXtGiRfn777+5fv06Hh4etz3H09MTT0/P2467u7tb/oO6VWqLx9lofJNH7dqQN69pBHGvX7ksWuRGtWqm14E8Ah8f9r70Evn69MHtlVewHTiA23PPwauvwujRppW5PDL9++BYGl/H0vg6lsbXsVLD+D7I+1vWHMLDw4Py5cuzYsWKhGNxcXGsWLEiUQXqVlWrVuXgwYPExcUlHDtw4AC5c+e+Y9IkIsnL1RU+/tj83WZL/Nit98eOhXLlYMuWlIvNmdkrVjR94Xv1MgcmT4ZSpSAiwtrARERE0hFLu+qFhoYyZcoUZsyYwd69e+natStRUVF06NABgHbt2iVqHtG1a1f+/fdfevfuzYEDB1i4cCEjR46ke/fuVn0EkXSnaVOYOxf+uwtA3rzwww/w88+QKxfs3QtPPQVDh0IqWPuZ9nl7m6x15Up4/HE4dMjs/9S3L1y7ZnV0IiIiTs/SxKlly5aMHj2aIUOGUKZMGXbs2MGSJUsSGkYcPXo00dqlwMBAli5dyubNmylVqhS9evWid+/evPXWW1Z9BJF0qWlTOHzYrGUKDd1CWNgNDh0yx597DnbvhpYtTVvy4cNNArVnj9VRO4natWHnTujY0cyXHDNG5T0REZEUYNkap3g9evSgR48ed3xs9erVtx2rXLkyv/76q4OjEpH7cXWFmjXtREWdoGbN0ty6I0C2bDB7NjRpAt26wbZt5rv9iBEQGgp32T1AksrPD774wgxwp043y3sDB5qbpi6LiIgkO0srTiLi3Fq2NJWm556D69ehXz+oWRMOHrQ6Mifx7LOmvPfii4nLe7t3Wx2ZiIiI01HiJCIOlSsXLFgAU6eCry+sWwelS8Onn8ItfV7kYWXLBt9+C3PmmL9v3w7ly8MHH5hkSkRERJKFEicRcTibDTp0gF27zBKdK1ege3cICYFjx6yOzkm0aGEqTfHlvf79oUYNlfdERESSiRInEUkxjz8Oy5fDJ59Ahgzm7yVKwIwZ994XSpIovrw3bZpZB7V+vSnvTZyo8p6IiMgjUuIkIinKxQV69oQdO8xynMhIaN/e9Dn4z37Y8jBsNjOgu3bB00+b8l6PHqa8d/So1dGJiIikWUqcRMQShQrB2rXw3numCdxPP0Hx4maPKEkGjz0GYWEwfvzN8l7JkjB9usp7IiIiD0GJk4hYxtXVLMXZssXMKPvnH2jeHNq0gX//tTo6J+DiYqpNv/0GlSub8l6HDvD88/D331ZHJyIikqYocRIRy5UsCZs2waBBJpmaNcscW7zY6sicRMGCsGbNzfLeggVmcZnKeyIiIkmmxElEUgUPD3jnHdPPoEgROHkSGjSALl3g0iWro3MCt5b3ypS5Wd5r3VrlPRERkSRQ4iQiqUrFirBtG7z+uulzMGUKlCoF4eFWR+YkSpaEjRth8GCTTH37rak+LVpkdWQiIiKpmhInEUl1MmSAMWNg1SoICoLDh6FWLZNMXb1qcXDOwMMDhg+/Wd47dQqefRY6d1Z5T0RE5C4eKnE6duwYx48fT7i/adMm+vTpw+eff55sgYmI1KwJO3ea7/MA48ZB2bJmPZQkg/jyXmioKe998YUp761ebXVkIiIiqc5DJU6tW7dm1apVAPz9998EBwezadMmBg4cyPDhw5M1QBFJ33x94fPPzUyy3Llh/36oUsXMNLt+3eronECGDPDRR4nLe7VrQ58+Ku+JiIjc4qESp927d1OxYkUAvvvuO0qUKMH69ev55ptvmD59enLGJyICwDPPwO7dplV5bCyMGGEKJjt3Wh2Zk4gv73XpYu5//LEp723caG1cIiIiqcRDJU4xMTF4enoCsHz5cho1agRAkSJFOHXqVPJFJyJyi6xZ4euv4fvvIVs2sz1RhQqmy/aNG1ZH5wR8fWHyZNMHPk+em+W9gQNV3hMRkXTvoRKn4sWLM2nSJNasWUNYWBj169cH4OTJk2TLli1ZAxQR+a9mzWDPHmjUCGJiYMAAqF4dDhywOjInUb/+zfJeXByMHKnynoiIpHsPlTi9//77TJ48mVq1atGqVStKly4NwIIFCxKm8ImIOJK/P8yfDzNmgJ8f/Pqr2Z5o/HjzXV8eUZYsprw3dy5kz36zvDdqlMp7IiKSLj1U4lSrVi3OnTvHuXPnmDp1asLxLl26MGnSpGQLTkTkXmw2aNfOFEfq1jW9DHr1guBgOHLE6uicxAsvmAFu3NiU9/73P6hWzUzjExERSUceKnG6evUq0dHRZMmSBYAjR44wbtw49u/fT86cOZM1QBGR+wkMhGXL4NNPwdsbVq40+7xOnQp2u9XROQF/f/jxR1Pey5TJNIwoWxY++UTlPRERSTceKnFq3LgxX331FQAXLlygUqVKfPTRRzz//PN89tlnyRqgiEhS2GzQtauZUVa1qtnHtWNHsw5KPWuSQXx5b9cuU9K7ehV69zalPpX3REQkHXioxGnbtm1Ur14dgLlz5+Lv78+RI0f46quv+OSTT5I1QBGRB1GgAISHwwcfgIcH/PILlCgB331ndWROIjAQli69Wd5btcqU9778UuU9ERFxag+VOF25cgVfX18Ali1bRtOmTXFxceGpp57iiH7zKCIWc3WFN9+EbdugXDn4919o2RJefBH++cfq6JxAfHlv586b5b1OnaBhQ5X3RETEaT1U4lSgQAHmz5/PsWPHWLp0KfXq1QPgzJkz+Pn5JWuAIiIPq3hx021v6FCTTM2ZY6pPv/xidWROIn9+U9778ENT3lu40Az67NlWRyYiIpLsHipxGjJkCG+88QZBQUFUrFiRypUrA6b6VLZs2WQNUETkUbi7w9tvm34GxYrB33+bwkjHjhAZaXV0TsDVFd5442Z57/x5aNXKlPjOnbM6OhERkWTzUIlTs2bNOHr0KFu2bGHp0qUJx+vUqcPYsWOTLTgRkeRSvjxs3Wq+49tspuNeyZKmA58kg/jy3ttvg5ubWVRWogT8/LPVkYmIiCSLh0qcAHLlykXZsmU5efIkx48fB6BixYoUKVIk2YITEUlOXl5mVll4OOTLB0ePQp06Zu+nK1esjs4JuLubeZG//mrKe6dPm7aGr7wCFy9aHZ2IiMgjeajEKS4ujuHDh5MpUyYef/xxHn/8cTJnzsw777xDnPb0EJFUrnp107a8a1dzf/x4KFMGNmywNCzn8d/y3rRpUKoUrFhhdWQiIiIP7aESp4EDBzJhwgTee+89tm/fzvbt2xk5ciTjx49n8ODByR2jiEiy8/ExHbWXLoWAAPjjD6hWDQYMgOhoq6NzAvHlvYiIm+W9unWhZ0+V90REJE16qMRpxowZfPHFF3Tt2pVSpUpRqlQpunXrxpQpU5g+fXoyhygi4jj16sHu3dC2LcTFwXvvQcWKpiIlyaBaNTOY3bqZ+xMmqLwnIiJp0kMlTv/+++8d1zIVKVKEf//995GDEhFJSZkzw1dfwbx5kCOH2Z7oySfh3Xfhxg2ro3MCPj4wcaLKeyIikqY9VOJUunRpJkyYcNvxCRMmUKpUqUcOSkTECk2awJ490LQpxMTAoEFQpQrs22d1ZE4ivrzXrt3N8l6FCrB9u9WRiYiI3NdDJU4ffPABU6dOpVixYnTs2JGOHTtSrFgxpk+fzujRo5M7RhGRFJMjB8ydC19/bSpRmzdD2bIwbpz5ri+PKHNmmDEDfvwRcuY0iVTFijBihMp7IiKSqj1U4lSzZk0OHDhAkyZNuHDhAhcuXKBp06bs2bOHmTNnJneMIiIpymaDNm1g1y4ICYFr1+D11+Hpp+HQIaujcxLPP2+SpqZNTcI0eLDKeyIikqo99D5OefLk4d133+WHH37ghx9+YMSIEZw/f54vv/wyOeMTEbFM3ryweDFMngwZM5r9n0qVgilTwG63OjoncLfy3tixKu+JiEiq89CJk4hIemCzQZcupmFE9epw+bK5/+yzcPKk1dE5gfjy3u7dUL++Ke+FhkLt2irviYhIqqLESUQkCfLlg9Wr4aOPwNPTVKJKlIBZs1R9ShYBAbBo0c3yXkQElCwJn3+uARYRkVRBiZOISBK5uJhiyPbtphnc+fOmWNK8OZw9a3V0TuDW8l6NGhAVBa++Cg0awIkTVkcnIiLpnNuDnNy0adN7Pn7hwoVHiUVEJE0oWhTWrzfdtIcPhx9+gDVrzNqnRo2sjs4J5MsHq1bBxx+bvZ6WLDHlvQkToHVrk2CJiIiksAeqOGXKlOmet8cff5x27do5KlYRkVTD3d00gtu0yXynP3MGGjeG9u1Bv0NKBi4uppVhfHnvwgV46SWV90RExDIPVHGaNm2ao+IQEUmTypaFLVtg6FD48EOzRdGKFTBtGtSta3V0TqBoUdiwwZT3hg0z5b2ICLP26fnnrY5ORETSEa1xEhF5RJ6e5nv9mjVQoAAcPw7BwdC9u1mmI4/IzQ0GDbpZ3jt7Fpo0gZdfVnlPRERSjBInEZFkUqUK7NgBPXqY+59+CqVLw7p1loblPOLLe/37m6l8X31lOu+FhVkdmYiIpANKnEREklHGjDB+vPkuHxgIf/5p9n/q399sUSSP6E7lvXr1oFs3s8mWiIiIgyhxEhFxgLp1Ydcu0yzCbocPPjA9DrZtszoyJ/Hf8t5nn0GZMrB2rZVRiYiIE1PiJCLiIJkymSYRP/0E/v6wZw9UqmRamMfEWB2dE4gv7y1ffrO8V6MGvPmmynsiIpLslDiJiDhYo0awezc0awY3bpgOfFWqwO+/Wx2Zk6hTx5T3OnQw5b3Ro6F8edi61erIRETEiShxEhFJAdmzw3ffwbffQpYspsdBuXLw0UcQG2t1dE4gUyaYOvVmee/33+Gpp0wLc5X3REQkGShxEhFJITYbvPiiqT41aADR0fDGG1C7tpllJskgvrzXvLkp7739tkmg9uyxOjIREUnjlDiJiKSwPHngl19gyhTw8TEN4kqXhkmTzEwzeUTx5b3ZsyFrVtORo3x5M4UvvrwXG4stPJyAiAhs4eEq+4mIyH2lisRp4sSJBAUF4eXlRaVKldi0adNdz50+fTo2my3RzcvLKwWjFRF5dDYbdOpklubUqmU2yu3aFerXNx22JRm0bGmqT88+a8p7b75pBvvTTyEoCLfgYCqMGYNbcDAEBcG8eVZHLCIiqZjlidOcOXMIDQ1l6NChbNu2jdKlSxMSEsKZM2fu+hw/Pz9OnTqVcDty5EgKRiwiknyCgmDFChg3Dry8YNkyKFECZs5U9SlZ5M4NP/8MX3xhyntr10L37rdnpydOmO4dSp5EROQuLE+cxowZQ+fOnenQoQPFihVj0qRJeHt7M3Xq1Ls+x2azkStXroSbv79/CkYsIpK8XFygd2+zLVGlSnDxIrRrBy1auHLhgofV4aV9Nht07GgG2NPzzufEZ6l9+mjanoiI3JGblW9+/fp1tm7dyoABAxKOubi4ULduXTZs2HDX512+fJnHH3+cuLg4ypUrx8iRIylevPgdz42OjiY6OjrhfmRkJAAxMTHEpIJOS/ExpIZYnJHG17E0vskrXz5YtQpGj3bhnXdc+OknF1atehoPjziaNdMYPyrboUO43fL/g9vY7XDsGDdWrcJes2bKBeak9O+DY2l8HUvj61ipaXwfJAab3W7dZJCTJ08SEBDA+vXrqVy5csLxfv36ER4ezsaNG297zoYNG/jjjz8oVaoUFy9eZPTo0URERLBnzx7y5s172/lvv/02w4YNu+34rFmz8Pb2Tt4PJCKSTA4d8uPjj8tx+HAmAGrWPEbnzrvw8bH+fzJpVUBEBBXGjLnveUdr1WJvu3Zcy5o1BaISERErXblyhdatW3Px4kX8/PzueW6aS5z+KyYmhqJFi9KqVSveeeed2x6/U8UpMDCQc+fO3XdwUkJMTAxhYWEEBwfj7u5udThOR+PrWBpfx7p8OYYuXY4zb15B4uJsBATYmTw5lnr1tPjpYdjCw00jiCSw22zYq1fH3rw5cU2aQM6cDo7O+ejfB8fS+DqWxtexUtP4RkZGkj179iQlTpZO1cuePTuurq6cPn060fHTp0+TK1euJL2Gu7s7ZcuW5eDBg3d83NPTE887zGl3d3e3/Ad1q9QWj7PR+DqWxtcxfHzgpZf20qdPPl55xY0DB2w895wbr75qOmv7+FgdYRpTuzbkzWsaQdzpd4Y2m9lIt2hRbBs2YIuIgIgIXHv3hqefNptwNWliWpxLkunfB8fS+DqWxtexUsP4Psj7W9ocwsPDg/Lly7NixYqEY3FxcaxYsSJRBepeYmNj2bVrF7lz53ZUmCIilqpY0c727dCrl7k/eTKUKgUREdbGlea4usLHH5u/22yJH4u//+WXsH49HDkCH34IFSpAXBwsX276x/v7m/bmX31luniIiEi6YXlXvdDQUKZMmcKMGTPYu3cvXbt2JSoqig4dOgDQrl27RM0jhg8fzrJly/jrr7/Ytm0bL730EkeOHKFTp05WfQQREYfz9jbf+VeuhMcfh0OHzJZEffvCtWtWR5eGNG0Kc+dCQEDi43nzmuNNm5r7jz0Gb7wBmzfDwYMwcqTJVm/cgEWL4OWXTRLVpInZaDcqKuU/i4iIpCjLE6eWLVsyevRohgwZQpkyZdixYwdLlixJaDF+9OhRTp06lXD++fPn6dy5M0WLFqVBgwZERkayfv16ihUrZtVHEBFJMbVrw86dpru23Q5jxkC5crBli9WRpSFNm8Lhw9wIC2NLaCg3wsJMJhqfNP1X/vwwYAD89hvs3Qtvvw1FiphNdefPh1atIEcOaNECfvgBrl5NyU8jIiIpxPLECaBHjx4cOXKE6OhoNm7cSKVKlRIeW716NdOnT0+4P3bs2IRz//77bxYuXEjZsmUtiFpExBp+fmY/119+gVy5zHf5p56CoUPh+nWro0sjXF2x16zJiRo1TOtxV9ekPa9IETPQv/9uEqn//c8kVlevwvffm010c+aEl14yG+/eq/25iIikKakicRIRkQf37LOwe7fpWRAbC8OHmwRq926rI0sHbDYzde/dd+GPP0zJ7803zRS/y5fhm2+gUSMzna9DB1i6FFLBfiUiIvLwlDiJiKRh2bLBt9/CnDnm79u3Q/ny8MEHJpmSFGCz3Rz0w4dNc4nevSFPHtNAYvp0qF8fcueGV181C9X0wxERSXOUOImIOIEWLUyl6bnnzHS9/v2hRg3T10BSkM0GlSvDuHFw7BiEh0PXrmYN1D//wOefQ506phlFz56wdq3p2iciIqmeEicRESeRKxcsWADTppl1UOvXQ+nSMHGivptbwsXFZK+ffgonT0JYmGlpnjUr/P03TJgA1aubNomhobBp0533lxIRkVRBiZOIiBOx2aB9e9i1y+zZeuUK9OgBISFw9KjV0aVjbm5Qty5MmWKSpkWLoF07k+EePw5jx0KlSqbRxFtvmTmXSqJERFIVJU4iIk7oscdMgWP8eMiQwezfWrKkWW6j7+MWc3eHZ56BGTPg9OmbLc0zZjRt0d9/3/SYL1wYhgyBPXusjlhERFDiJCLitFxcTLXpt9/MspvISNPg7fnnTdFDUgEvL2jcGGbNgjNn4Lvv4IUXzPE//oB33oESJcztnXfgwAGrIxYRSbeUOImIOLmCBWHNGnjvPfDwMOugSpSAuXOtjkwS8faG5s3ND+bMGdPSvGFDU6Has8dUnwoXNtWo99831SkREUkxSpxERNIBV1fTaW/LFihTxjR4a94cWreGf/+1Ojq5ja+v+eEsWGCSqGnTTEtzV1ez/umttyBfPrMuaswYs05KREQcSomTiEg6UrIkbNwIgweb7+DffmuqT4sWWR2Z3FXmzKbjx+LFZo7l5Mmm84eLi+nE17cvBAaaDn0TJmgepoiIgyhxEhFJZzw8YPhw0668SBE4dQqefRY6d4ZLl6yOTu4pe3bo0gVWrIATJ0yiVK2aeWztWrM3VECASaw+/xzOnbM2XhERJ6LESUQknapYEbZtM1sI2WzwxRdQqhSsXm11ZJIkuXJB9+5mAduxY2bKXqVKZtOuVavg1VfNOfXrm6l+Fy5YHbGISJqmxElEJB3LkAE++sh8zw4KgsOHoXZt6NMHrl61ODhJurx54fXX4ddfb7Y0L1sWYmNh6VJ45RXImRMaNTJNJ1RaFBF5YEqcRESEmjVh504zCwzg44/N9+6NG62NSx5CUBD062fKifv332xpHhMDP/8ML71kkqgXXjDtz69csTpiEZE0QYmTiIgAppHb5MmmB0GePOY7d5UqMHAgXL9udXTyUAoVgkGDYNcu2L3bdAUpVAiuXYN586BlS5NEtWplNuK9ds3qiEVEUi0lTiIikkj9+uY7dps2ZrnMyJFmPdTOnVZHJo+keHHTFWTfvpstzZ94AqKiYPZsaNIE/P2hXTvTZlHZsohIIkqcRETkNlmywNdfm71Ys2eH336DChVg1Ci4ccPq6OSR2GxmM69Ro+DPP818zNBQs04qMhJmzjRtFnPlgk6dICxMP3QREZQ4iYjIPbzwgqk+NW5slsj873+m+/X+/VZHJsnCZjPlxI8+giNHbrY09/eH8+fhyy+hXj0zd7NbNwgPNw0nRETSISVOIiJyT/7+8OOPMGMGZMpkChRly8Inn5ipfOIkXFygalXzgz1xAlauNC3Ns2eHs2fhs8+gVi2z2W7v3rBhA9jtVkctIpJilDiJiMh92Wxm6cuuXRAcbFqV9+4NdeuaQoU4GVdX05d+0iQ4edK0NO/QATJnNjsmf/KJ6RwSFARvvglbtiiJEhGnp8RJRESSLDDQfIf+9FPw9jb7P5UsaWZ06Xuzk3J3N9P1pk6F06dvtjT39YWjR2H0aHjySShY0LRg3LlTF4OIOCUlTiIi8kBsNuja1Xw/rlrV7KXaqRM0bGiKEeLEPDzguedMA4nTp+GHH6BFC7OT8p9/mhaMpUtDsWK4DB+Oz7FjVkcsIpJslDiJiMhDyZ/f9Ar48EPzfXrhQtPxevZsqyOTFJEhAzRtCnPmmDVQs2fD88+Dpyfs24friBHU6dkTt3LlTEL1559WRywi8kiUOImIyENzdYU33oBt26BcOdOIrVUrs6/quXNWRycpJmNG80P/8Uc4cwa++oq4Bg2Ic3PDtnu3mcJXoIDpaf/hh1oYJyJpkhInERF5ZMWLw6+/wttvg5sbfPcdlChhlsNIOuPnB23bEjt/PkumTePG5Mmmo4irK2zdCv36maYSVarAxx+b5hMiImmAEicREUkW7u4wdKhJoIoVM0tgGjWCV16Bixetjk6sEOPri71DB1i2zCyAi29pbrOZduZ9+piNd2vWNB1HzpyxOmQRkbtS4iQiIsmqfHlTWHjjDfP9eNo0KFUKVqywOjKxVI4c8NprphXj8eOm2lSliunAFxEB3btD7tymOvXFF/Dvv1ZHLCKSiBInERFJdl5eZilLRATky2e6VtetCz17wpUrVkcnlsuTB3r1gnXrzHqn0aPN+qe4OFi+HDp3NjsvN2gAX32lkqWIpApKnERExGGqVYPffoNu3cz9CROgTBkzS0sEgMceg759YfNmOHjwZkvzGzdg8WJ4+WXImdN07Pv2W7h82eqIRSSdUuIkIiIO5eMDEyeajXMDAuCPP0xCNWAAREdbHZ2kKvnzmwtjxw7Yu9d0GylaFK5fh59+gtatTRLVooXZQ+rqVasjFpF0RImTiIikiHr1YPduaNfOzMh67z0zO2v7dqsjk1SpSBHTbWTPHrPb8sCBJrG6ehW+/x6aNTNJVJs2sGCBsnARcTglTiIikmIyZ4YZM8x2PzlzmkSqYkUYMcLMzBK5jc0GJUuai+SPP2DLFnjzTTPF7/JlmDULGjc2a6I6dIAlSyAmxuqoRcQJKXESEZEU9/zzJmlq2tQkTIMHmwZr+/ZZHZmkajabadv4wQdw+LBZLNe7t2k2cfEiTJ8OzzxjuvO9+iqsXAmxsVZHLSJOQomTiIhYIkcOmDsXvv7aVKI2b4ayZWHsWDOVT+SebDZ46ikYNw6OHYPwcNOFJGdO+Ocf+PxzqFPHLKzr0QPWrNGFJSKPRImTiIhYxmYzS1R274b69eHaNQgNhdq14dAhq6OTNMPFBWrUMF1ITpyAsDDo1AmyZjU7MU+caB5/7DFzgW3caPaPEhF5AEqcRETEcgEBsGgRTJ4MGTOa/Z9KljRFA32/lQfi5mY2DZsyBf7+21xYL78Mfn4mqRo71lSq8uWD/v1h2zZdZCKSJEqcREQkVbDZoEsX00CtRg2IijLLVBo0MN93RR6Yu7tZ8zR9uqk8zZ8PrVqZ7PzwYbNWqnx5KFzYLLTbvdvigEUkNVPiJCIiqUq+fLBqFYwZA56epklaiRLwzTcqDMgj8PIy3fdmzYIzZ0xL8xdeMMf/+MN07StZEooXh+HDYf9+qyMWkVRGiZOIiKQ6Li7w+utmj6cKFeDCBXjpJWjeHM6etTo6SfO8vc0+UHPnmiTqm2+gUSPw8IDffzf7RxUpYrqVvPeeFtyJCKDESUREUrGiRU3H6XfeMUtXfvjBFATmz7c6MnEavr7QujX89JOZzjdtmulU4uYGO3bAgAGmDFqpkimDHjtmdcQiYhElTiIikqq5ucGgQbBpk5myd/YsNGli1vtfuGB1dOJUMmeG9u1h8WLTWOLzz+Hpp00JdNMm6NvXdOarVg3GjzfniEi6ocRJRETShLJlYcsW0wjNxQW++sosSQkLszoycUrZskHnzrBiBZw8CRMmQPXq5rF166BXL9MO8umnTTvIc+esjVdEHE6Jk4iIpBmenmbJyZo1UKAAHD8O9eqZfU8vX7Y6OnFa/v7Qvbvpk3/smJmyV6mS2VB31Sp47TXIlQtCQsxUv/PnrY5YRBxAiZOIiKQ5VaqY5Sc9epj7n30GZcrA2rVWRiXpQt68pnPJr7+aphHvvw/lykFsLCxbBq+8YhKthg3h668hMtLqiEUkmShxEhGRNCljRrPMZPlyCAyEP/80+z+9+SZcu2Z1dJIuBAVBv36wdSscOGC6mJQoATEx8Msv0LYt5Mxp2p5/953ZnExE0iwlTiIikqbVqQO7dkGHDmafp9GjzZ6mW7daHZmkKwULmi4mu3bBnj0wZIjZWDc6GubNg5YtTRL14ovw44/K7kXSICVOIiKS5mXKBFOnmo7S/v5mK56nnoJhw8wv/0VSVLFi5uLbu9dsRvbWW/DEE3DlCsyZA02bmiSqXTtYuBCuX7c6YhFJAiVOIiLiNBo1gt27zUa5N27A22+bBGrPHqsjk3TJZjOL70aNMnNJN22C0FCzTurSJZg5E557zjSW6NTJtIi8ccPqqEXkLpQ4iYiIU8me3SwnmT0bsmaFbdvM1L3Ro836fTB/hofbiIgIIDzclnBcxGFsNnjySfjoIzhyxHQy6dnTJE3nz8OXX5oWkXnyQNeusHo1d70wY2OxhYcTEBGBLTz87ueJSLJKFYnTxIkTCQoKwsvLi0qVKrFp06YkPW/27NnYbDaef/55xwYoIiJpTsuWpvr07LNmmcmbb0KtWvDpp2ZNf3CwG2PGVCA42I2gILMMRSRFuLhA1arwySemp/7KlfDqqybrP3sWJk2C2rVN15PevWH9etP6HMyFGhSEW3AwFcaMwS04GF3AIinD8sRpzpw5hIaGMnToULZt20bp0qUJCQnhzJkz93ze4cOHeeONN6gevxmdiIjIf+TODT//DF98AT4+5pf83bub76q3OnECmjXTd0+xgKurSZImTYJTp2DpUtPSPHNmc/+TT0ySFRQEjRubDn26gEUsYXniNGbMGDp37kyHDh0oVqwYkyZNwtvbm6lTp971ObGxsbRp04Zhw4aRL1++FIxWRETSGpsNOnY0+z55et75HLvd/Nmnj2Y9iYXc3Mx0vS+/hNOnTdb/0kvg62s23l2w4M7P0wUskiLcrHzz69evs3XrVgYMGJBwzMXFhbp167Jhw4a7Pm/48OHkzJmTjh07smbNmnu+R3R0NNHR0Qn3I/9/I7qYmBhiUkGrpfgYUkMszkjj61gaX8fS+CavQ4dsREff/X97drv5brpq1Q1q1rSnYGTOSdfvI7LZICTE3K5exWXMGFyHDbv7+f9/Ad9YtQp7zZopF6eT0vXrWKlpfB8kBksTp3PnzhEbG4u/v3+i4/7+/uzbt++Oz1m7di1ffvklO3bsSNJ7jBo1imF3+Idm2bJleHt7P3DMjhIWFmZ1CE5N4+tYGl/H0vgmj4iIAKDCfc97992THDu2l6xZtc9OctD1mzwCLl1KwtULp99+m70vvURU7twOjyk90PXrWKlhfK9cuZLkcy1NnB7UpUuXaNu2LVOmTCF79uxJes6AAQMIDQ1NuB8ZGUlgYCD16tXDz8/PUaEmWUxMDGFhYQQHB+Pu7m51OE5H4+tYGl/H0vgmr4wZbYwZc//zVq9+jPDwQKpXt9O8uZ0mTeLImdPx8TkbXb/Jy5YxI0m5gAPWrSNg3TriypXD3rw5cc2aweOPp0CEzkXXr2OlpvGNn42WFJYmTtmzZ8fV1ZXTp08nOn769Gly5cp12/l//vknhw8fpmHDhgnH4v6/y4ybmxv79+8nf/78iZ7j6emJ5x0mtbu7u1v+g7pVaovH2Wh8HUvj61ga3+RRu7bZPufEiZtLQm5ls5mNdIsWhQ0bbERE2IiIgN69XXn6aXjxRWjSxLQ4l6TT9ZtMknIBZ8lieu+vXInLtm2wbRuuAwaYzcxatjQbnAUEpHzsaZiuX8dKDeP7IO9vaXMIDw8Pypcvz4oVKxKOxcXFsWLFCipXrnzb+UWKFGHXrl3s2LEj4daoUSNq167Njh07CAwMTMnwRUQkDXF1hY8/Nn+32RI/Fn//yy9N5+cjR+DDD6FCBdMFevlysz+pv79pb/7VV3DxYsrGL+lcUi7gKVNg2TLTje+zz0z/fZsNfv0VXn/dtDevWdP05L9P92IRuZ3lXfVCQ0OZMmUKM2bMYO/evXTt2pWoqCg6dOgAQLt27RKaR3h5eVGiRIlEt8yZM+Pr60uJEiXw8PCw8qOIiEgq17QpzJ17+y/d8+Y1x5s2NfcfewzeeAM2b4aDB2HkSChVCm7cgEWL4OWXTRLVpInZaDcqKuU/i6RDSb2Ac+SA116DVatM6/KPP4YqVUylKiLC9OTPnRvq1jW9+v/9N+U/i0gaZHni1LJlS0aPHs2QIUMoU6YMO3bsYMmSJQkNI44ePcqpU6csjlJERJxF06Zw+DCEhd0gNHQLYWE3OHTo5nfO/8qfHwYMgN9+g7174e23oUgRs6nu/PnQqpX5ntqiBfzwA1y9moIfRtKf/7+Ab4SFsSU0lBthYdzzAs6TB3r1gnXrTCl19Gh48klTSl2xAjp3Nr8FaNAAZsxQKVXkHlJFc4gePXrQo0ePOz62evXqez53+vTpyR+QiIg4NVdXqFnTTlTUCWrWLI2ra9KeV6QIDB0KQ4bArl0wZ465/fknfP+9ufn4mH1KW7Y0W/Lcbe8okYfm6oq9Zk1OREVRumZNknwBP/YY9O1rbn/+Cd99Zy7g336DxYvNzcMDnnnGXMANG5oLWkSAVFBxEhERSWtsNjN179134Y8/YMsWePNN87308mX45hto1Mj8Ir9DB1i6FFLBdiUiN8WXUnfsgH37YNgw0xnl+nX46Sdo3Rpy5jQNJebOVSlVBCVOIiIij8RmM43MPvjATAFcvx569zYzpC5ehOnToX59s6Tk1Vdh5UqIjbU6apFbFC5syqh79sDOnTBwIBQoYJKluXNN8pQzJ7RpAwsWmHmqIumQEicREZFkYrNB5cowbhwcOwbh4dC1q1kD9c8/8PnnUKeOWcvfsyesXWuWmoikCjYblCwJI0bAgQOwdasppT7+uCmlzppl5qH6+0P79rBkiUqpkq4ocRIREXEAFxeoUcN0fj55EsLCTEvzrFnh779hwgSoXt18Jw0NhU2b7rw9j4glbDYoV86UUg8dgg0boE+fm6XUGTPMWqjcuaFLF9NoQqVUcXJKnERERBzMzc10fp4yxSRNixZBu3bg52e6RY8dC5UqmWUnb70F27criZJUxGYzm+iOHXuzlNqtm5m+988/5sKuW9e0Se/RA9asUSlVnJISJxERkRTk7m5+UT9jBpw+fbOlecaM5hf7779vftF/67ITkVQjvpQ6cSKcOGF2h+7c2ZRST582x2vUMJ1SXn8dNm7UbwHEaShxEhERsYiXl1kyMmsWnDljukO/8II5/scf8M47UKKEub3zjll2IpJquLmZRXuff36zlPryy6aUeuKEWez31FOQLx/07w/btimJkjRNiZOIiEgq4O19s/PzmTOmpXnDhqZCtWePqT4VLmyqUe+/b6pTIqlGfCl1+vTbS6mHD5u1UuXLQ6FCMHgw7N5tccAiD06Jk4iISCrj62u20VmwwCRR06aZluaurmb901tvmV/iV6oEY8aYdVIiqcZ/S6nffw/NmpnjBw+arn0lS0Lx4jB8OOzfb3XEIkmixElERCQVy5zZdH5evNjMhpo8GZ5+2iw12bQJ+vaFwEDToW/CBHOOSKrh7W2Spu+/v1lKbdQIPDzg999h6FAoUgTKloX33lMpVVI1JU4iIiJpRPbsNzs/nzhhEqVq1cxja9eavaECAkxi9fnncO6ctfGKJBJfSv3pJzOdL353aDc32LEDBgwwpdSKFeGjj0wHP5FURImTiIhIGpQrF3Tvbjo/HztmpuxVqmS6QK9aBa++as6pX99M9btwweqIRW6RObNpJBFfSo3fHdrFBTZvhjfeMJ35qlWD8eNVSpVUQYmTiIhIGpc3r+n8/OuvN1ualy1r9iNduhReecVsudOokZkpdemS1RGL3CJbNtPSfPlys1t0/O7QNhusWwe9epmNd2vXNnNVVUoViyhxEhERcSJBQdCvn+n8vH//zZbmMTHw88/w0ksmiXrhBdP+/MoVqyMWuYW/vymlRkSYUurYsaalud0Oq1fDa6+ZUmpICEydCufPWx2xpCNKnERERJxUoUIwaBDs2mW6Pw8ebI5duwbz5kHLliaJatXKdI++ds3qiEVuERAAffrAhg2Jd4eOjYVly6BjR5NoNWwIX38NkZFWRyxOTomTiIhIOhDf+XnfvpstzZ94AqKiYPZsaNLEfAdt187sY3r9utURi9wivpS6davZCfrWUuovv0Dbtua3AE2bmlJqVJTVEYsTUuIkIiKSjthsUKYMjBoFf/4JGzdCaKhZJxUZCTNnwrPPmtlQnTpBWBjcuGF11CK3KFjwZin11t2ho6Phxx9vllJffNHcVylVkokSJxERkXTKZrvZ+fnIkZstzf39zdKRL7+EevXMuvxu3SA83MySEkk1ihWDYcNg797EpdQrV2DOHFOBypnTlFIXLlQpVR6JEicRERHBxQWqVoVPPjF7RK1caVqaZ88OZ8/CZ59BrVpms93evc2yE7vd6qhF/t9/S6nxu0PnzWvaSM6cCc89Z0qpHTuaNVIqpcoDUuIkIiIiibi6ms7PkyaZ7tBLl0KHDmbrnVOnTHJVpYpZdvLmm7Bli5IoSUVsNnjySRg9OnEpNVcuU0qdOtV05cuTB7p2Nd36VEqVJFDiJCIiInfl7m6m602dCqdP32xp7usLR4+a76ZPPmmWnQwcCDt3KomSVOTWUurx4zd3h44vpU6aZH5LEBho9otav97sIi1yB0qcREREJEk8PMxsp5kzTRL1ww/QogVkyGBmR40cCaVLm2Unw4e7cOyYj9Uhi9zk6mrmm06aZEqn8btDx5dSx483SVZQEC79+5P5jz/0WwBJRImTiIiIPLAMGcy6+zlzzC/uZ8+G558HT0/T8nzECFd69qxDuXJujBxpEiuRVMPNzZRSv/zy9lLqsWO4jh1LzTffxK1oUfjf/+C335REiRInEREReTQZM5oO0D/+CGfOwFdfQYMGcbi5xbF7t42BA6FAAahQAT780Cw7EUk1/ltKnTePuObNueHpie2vv0zDiTJloGhRGDoUfv/d6ojFIkqcREREJNn4+Zm9SOfPj2XatCVMnnyD4GAzS2rrVrOHaVCQaS7x8cem+YRIqpEhAzRpQuw337BkxgxufP212R3a0xP27ze7SBcvDqVKwbvvwsGDVkcsKUiJk4iIiDiEr28MHTrYWbbMLCGJb2lus5l25n36mG7RNWvCp5+aapVIahHr5YW9RQuYN+9mKfXZZ03HlF27zCa8BQtC+fIqpaYTSpxERETE4XLkgNdeM03Njh831aYqVcyykYgI6N4dcueG4GD44gv491+rIxa5RXwp9ZdfzHS++N2hXV1h27abpdTKlWHcOLMZmjgdJU4iIiKSovLkMZ2f160zv6QfPdqsf4qLg+XLoXNn8PeHBg3ML/kvXrQ6YpFbZMliuvEtXXp7KfXXX+H11017c5VSnY4SJxEREbHMY49B376webNZLhLf0vzGDVi8GF5+GXLmNB37vv0WLl+2OmKRW9xaSj1x4ubu0P8tpdatC1OmwD//WB2xPAIlTiIiIpIq5M8PAwbAjh2wdy+8/bZpZHb9Ovz0E7RubZKoFi3MHlJXr1odscgtcueGnj0Tl1KffNKUUlesgC5dIFcuU0qdMUOl1DRIiZOIiIikOkWKmM7Pe/bAzp0wcKBJrK5ehe+/h2bNTBLVpg0sWADR0VZHLHKL+FLqpk1mE7NRoxKXUtu3Nxdw48YqpaYhSpxEREQk1bLZoGRJGDEC/vgDtmyBN98030svX4ZZs8x3T39/6NABliyBmBiroxa5Rb588NZbppS6bx8MG3azlLpgwc1SavPmMHeuSqmpmBInERERSRNsNtP5+YMP4PBh09K8d2/TbOLiRZg+HZ55xsyYevVVWLkSYmOtjlrkFoULw5AhiUupBQqYZGnuXJM8qZSaailxEhERkTTHZoOnnjKdn48dg/Bw6NbNfOf85x/4/HOoUwcCAqBHD1izxiw1EUkVbi2lHjhwc3foxx+/vZTavr1KqamEEicRERFJ01xcoEYNmDjRNDYLC4NOnSBrVrPlzsSJ5vHHHoPQUNi40TQ9E0kVbDYoVw7efx8OHbq5O3R8KXXGjJul1C5dTKMJlVItocRJREREnIab283Oz3//DYsWmZbmfn4mqRo71lSq8uWD/v3N3qVKoiTViC+ljh1rSqnxLc3jS6lTppgLPE8eczwiQqXUFKTESURERJySu7v5Rf306abyNH8+tGoFGTOaNVIffGDWTBUuDIMHw+7dFgcscisXF6heHSZMMFl//O7QWbOaTXU//dRssvvYY2bT3V9/1W8BHEyJk4iIiDg9Ly+zZGTWLPOd8/vv4YUXzPE//jBLTUqWhOLFYfhw2L/f6ohFbuHmZhbtff65KaXG7w4dX0odNw4qV4YnnlAp1YGUOImIiEi64u1t9oGaO9ckUd98A40agYcH/P672T+qSBEoWxbee88sOxFJNdzdoX59U0o9c+bm7tAZM5qNd+NLqYUKwaBBKqUmIyVOIiIikm75+prvnD/9ZKbzTZtmvpO6uZltdwYMMOuhKlWCMWPMshORVMPT02T933xzs5TarJkppR48CO++q1JqMlLiJCIiIgJkzmw6Py9ebGZDff45PP20WWqyaRP07WuWk1SrBuPHm3NEUo34Uur338PZs2Ze6p1KqWXKwKhR8NdfVkec5ihxEhEREfmPbNnMOvwVK+DkSbM+v3p189i6ddCrl9kj6umnYfJkOHfO2nhFEvHxMZ1Q4kup8btDu7nBb7/B//4H+fNDxYrw0UcqpSaREicRERGRe/D3v9n5+dgxM2WvUiXTBXrVKnjtNciVC0JCzFS/8+etjljkFpkzm0YSixbdLKXWqWNKqZs3wxtvmFJq1aqmlHrqlNURp1pKnERERESSKG/em52fDx0ye5aWK2f2I122DF55xSRaDRvC119DZKTVEYvcIr6Uuny5KaXG7w5ts8H69TdLqbVrw6RJZsqfJFDiJCIiIvIQgoKgXz/YuhUOHIB33oESJSAmBn75Bdq2NfuWvvACfPcdREVZHbHILfz9oVs3CA83pdT43aHtdli9Grp2hdy5TSl16lSVUlHiJCIiIvLIChY0nZ937YI9e2DIELOxbnQ0zJsHLVuaJOrFF+HHH+HaNasjFrlFQAD06QMbNtzcHfrWUmrHjibReu65dF1KVeIkIiIikoyKFYNhw2DvXti+Hd56y+xLeuUKzJkDTZuaJKpdO1i4EK5ftzpikVs8/ji8+ebNUmr87tAxMeaCjS+lNm1qLuh0VEpV4iQiIiLiADbbzc7Pf/5pWpqHhpp1UpcuwcyZ5hf4uXJBp04QFgY3blgdtcgtChaEgQNh587bS6k//mhKqOmolKrESURERMTBbDZ48knT+fnIEVi7Fnr2NEnT+fPw5ZdQrx7kyWOWlqxebWZJ3UlsLISH24iICCA83HbX80SS1a2l1Pjdoe9USm3b1izyu1spNTYWW3g4ARER2MLD736hp0KpInGaOHEiQUFBeHl5UalSJTZt2nTXc+fNm0eFChXInDkzGTNmpEyZMsycOTMFoxURERF5eC4upvPzJ5/A8eOwciW8+ipkz26amE2aZJqaBQZC796m2VlcnHnuvHmmKUVwsBtjxlQgONiNoCBzXCRF2GxQujSMHHmzlNq3r7lgL10ya6AaNjS/FejY0ayRii+l/v8F7BYcTIUxY3ALDiYtXcCWJ05z5swhNDSUoUOHsm3bNkqXLk1ISAhnzpy54/lZs2Zl4MCBbNiwgZ07d9KhQwc6dOjA0qVLUzhyERERkUfj6nqz8/OpU7B0qWlpnjmzuf/JJybJCgqCxo1Nh77jxxO/xokT0KxZmvnuKc4kvpQ6erRpKrFuXeJS6tSppitf7tympJrGL2DLE6cxY8bQuXNnOnToQLFixZg0aRLe3t5MnTr1jufXqlWLJk2aULRoUfLnz0/v3r0pVaoUa9euTeHIRURERJKPm5v5bvnll3D6NPz8M7z0Evj6mm7RCxbc+Xl2u/mzT580NetJnI2LC1SpcrOUGr87dPbscO6cWcR3J2noAnaz8s2vX7/O1q1bGTBgQMIxFxcX6taty4YNG+77fLvdzsqVK9m/fz/vv//+Hc+Jjo4mOjo64X7k/7dPjImJISYm5hE/waOLjyE1xOKMNL6OpfF1LI2vY2l8HUvj+2hsNvOL+pAQuHoVxoxxYdgw17ueb7eb5GrVqhvUrGlPwUidk67fZFC1qrmNGYPLxx/jesv3/dv8/wV8Y9Uq7DVrplyMPNjP2NLE6dy5c8TGxuLv75/ouL+/P/v27bvr8y5evEhAQADR0dG4urry6aefEhwcfMdzR40axbBhw247vmzZMry9vR/tAySjsLtl4ZIsNL6OpfF1LI2vY2l8HUvjmzwuXQoAKtz3vLffPs1LL+0ld+700yLakXT9Jo+As2eTcPXCjsWLOZHC7c2vXLmS5HMtTZwelq+vLzt27ODy5cusWLGC0NBQ8uXLR61atW47d8CAAYSGhibcj4yMJDAwkHr16uHn55eCUd9ZTEwMYWFhBAcH4+7ubnU4Tkfj61gaX8fS+DqWxtexNL7JK2NGG2PG3P+8desCWLcugHLl4mje3E6zZnE8/rjj43M2un6Tly1jRpJyAZd55hlKp3DFKfIBNvO1NHHKnj07rq6unD59OtHx06dPkytXrrs+z8XFhQIFCgBQpkwZ9u7dy6hRo+6YOHl6euLp6XnbcXd391T1H0Jqi8fZaHwdS+PrWBpfx9L4OpbGN3nUrm32fzpx4uaSkFvZbJAlC5Qvb7r0bdvmwrZtMGCAK089BS1bQvPmEBCQ8rGnZbp+k0lSLuC8eXGrXdt0TElBD/LztbQ5hIeHB+XLl2fFihUJx+Li4lixYgWVK1dO8uvExcUlWsckIiIi4kxcXeHjj83fbbbEj8XfnzLFdH4+dQo++wxq1TKP/forvP666RZdsyZ8+incpXmxiGMk5QIeNy7Fk6YHZXlXvdDQUKZMmcKMGTPYu3cvXbt2JSoqig4dOgDQrl27RM0jRo0aRVhYGH/99Rd79+7lo48+YubMmbz00ktWfQQRERERh2vaFObOvb1qlDevOd60qbmfI4dpZrZqlWlu9vHHptmZ3Q4REdC9u+kOXbcufPEF/Ptvyn8WSYeSegGnYpavcWrZsiVnz55lyJAh/P3335QpU4YlS5YkNIw4evQoLi4387uoqCi6devG8ePHyZAhA0WKFOHrr7+mZcuWVn0EERERkRTRtKnZz2nVqhssXryDZ54pQ+3abnf9RX2ePNCrl7kdPQrffw9z5sDmzbBihbl17QrBwWY63/PPQ6ZMKfqRJD35/wv4xqpV7Fi8mDLPPGPJ9LyHZXniBNCjRw969Ohxx8dWr16d6P6IESMYMWJECkQlIiIikvq4ukLNmnaiok5Qs2bpJH/nfOwx6NvX3P78E777ziRRv/0Gixebm4cHPPOMSaIaNgQfH8d+FkmHXF2x16zJiago0wgijSRNkAqm6omIiIhIysqfHwYMgB07YN8+GDYMihaF69fhp5+gdWvImdM0lJg71+wlJZLeKXESERERSccKF4YhQ2DPHti5EwYOhAIFTLI0d65JnnLmhDZtYMECUD8uSa+UOImIiIgINhuULAkjRsCBA7B1K7z5Jjz+OFy+DLNmmfVV/v7Qvj0sWQIxMVZHLZJylDiJiIiISCI2G5QrBx98AIcOwYYN0KePaTZx8SLMmGHWQuXODV26mCYTsbFWRy3iWEqcREREROSubDZ46ikYOxaOHYPwcOjWzUzf++cfs39U3bqmy3SPHrBmDcTFWR21SPJT4iQiIiIiSeLiAjVqwMSJcOIELF8OnTtD1qxw+rQ5XqOG6eD3+uuwcaPZP0rEGShxEhEREZEH5uYGderA55/D33/DokXw8svg52eSqnHjTKUqXz7o3x+2bVMSJWmbEicREREReSTu7mbN0/TppvI0fz60agUZM8Lhw2atVPnyUKgQDB4Mu3dbHLDIQ1DiJCIiIiLJxsvLdN+bNQvOnIHvv4dmzczxgwdN176SJaF4cRg+HPbvtzpikaRR4iQiIiIiDuHtbZKm7783SdQ330CjRuDhAb//DkOHQpEiULYsvPee6eAnklopcRIRERERh/P1hdat4aefzHS+6dOhfn2zVmrHDhgwwKyHqlgRPvrIdPATSU2UOImIiIhIisqc2TSSWLzYNJb4/HPTaMLFBTZvhjfeMJ35qlWD8ePNOSJWU+IkIiIiIpbJls20NF++HE6ehAkToHp1s3/UunXQq5fZeLd2bZg8Gc6dszpiSa+UOImIiIhIquDvD927Q0SEmao3dqxpaW63w+rV8NprkCsXhITA1Klw/rzVEUt6osRJRERERFKdgADo0wc2bDBNI95/H8qVg9hYWLYMOnY0iVbDhvD11xAZaXXE4uyUOImIiIhIqhYUBP36wdatcOAAvPMOlCgBMTHwyy/Qti3kzAlNm8J330FUlNURizNS4iQiIiIiaUbBgjBoEOzaBXv2wJAhULgwREfDjz9Cy5YmiXrxRXP/2jWrIxZnocRJRERERNKkYsVg2DDYuxe2b4e33oInnoArV2DOHFOBypkT2rWDhQvh+nWrI5a0TImTiIiIiKRpNhuUKQOjRsGff8KmTdC3L+TNC5cuwcyZ8NxzprFEx45mjdSNG1ZHLWmNEicRERERcRo2Gzz5JIweDUeOwNq10LOnSZrOnzfd+EJCTIvzrl1Nt77YWKujlrRAiZPI/7V379E13vkexz8790RFaYi4lCoNMk2MSzWMk6q4pKatYkpPjkm1U6U4cvTiMq0wOof2GHSmGkpdTjnVssrpUbdQiTUpwyCEpqZU1SDoGUsi2kiT3/njOTITkuwkPHn2jvdrrb2a/ezfjm++vl0rH/t5fg8AAKiTfHyknj2l3/9e+utfpR07pOefl8LCpAsXpIULrftDtWxp3S/q88+lkhKnq4anIjgBAACgzvP1lR56yApLZ89KW7ZIzzwj3Xmn9fwPf7BCVuvW0qRJPvrqqztljMNFw6MQnAAAAHBb8fOT+vWT3ntPOndO+p//kf7lX6T69a/deNdXL78cpw4d/DR1qnTwoAhRIDgBAADg9hUQYG0c8f77Voj6+GPpF78oUWDgj/r6a5dmzbI2nujQQUpJkb74wumK4RSCEwAAACApOFh64glp1apirVixWStX/qgnnpACA6WjR6Xf/EaKipKio6Xf/lY6dszpilGbCE4AAADAdYKCivXkk0YffyydPy/9539KAwdK/v7WzXdffdW6GW+XLtJ//Ie1gx/qNoITAAAAUInQUGnECGnDBut0vvfes66R8vWV9u+XXnnF2lQiNlaaP186fdrpimEHghMAAABQRQ0bWrvxbdli7caXmmrt1udySbt3S//2b9b25nFx0jvvWJ9WoW4gOAEAAAA10LixNHq0dX+o06et+0X16GHtwLdzpzR2rBQRIcXHS4sXS//7v05XjJtBcAIAAABuUkSENH68lJlpXe80Z47UrZt1Q93t26VRo6SmTaVHHpFWrJAuXXK6YlQXwQkAAAC4he6+W3rxRWnPHun4cWnWLCkmRvrxR2nTJunpp6UmTaTHH5c++EC6fNnpilEVBCcAAADAJm3aSJMnS1lZ0pdfSjNmWPeEunpV+uQT6Z//2QpRv/iFtHat9P33TleMihCcAAAAgFoQGSlNmyYdOSIdOiT9+tdS27ZWWFq71gpPTZpIiYlWqCosdLpi/COCEwAAAFCLXC7p/vul11+X/vIXad8+a0vzVq2s0/b+67+s0/jCw63T+jZvloqKnK4aBCcAAADAIS6X1Lmz9MYb0okT0q5dUnKy1KyZtYHEihVSQoK1+cSoUdZGE8XFTld9eyI4AQAAAB7A5ZIefFCaN086dervW5o3aWJtZb54sbW1ebNm1vGdO61d+1A7CE4AAACAh/HxkXr1kt5+27pH1LZt0nPPSY0aWTfVfecd6ya7d99t3XR3927r/lGwD8EJAAAA8GB+flKfPtK770q5udaW5klJUmioFarmz5diY6V77pEmTZL27ydE2YHgBAAAAHgJf39pwABp+XLrk6f//m9rS/N69awb7775ptSli3TffdKrr0qHDztdcd1BcAIAAAC8UGCg9Nhj0qpVVohas0YaOlQKCpKOHZN++1tr976oKOk3v5GOHnW6Yu9GcAIAAAC8XEiIFZrWrJEuXLC2NH/sMSkgQPriCyklRWrfXurUSZo1S/r6a6cr9j4EJwAAAKAOueMO6amnrNP4zp2zTutLSLCulTp4UJo6Vbr3XumBB6Tf/c7awQ/uEZwAAACAOurOO62NJDZutDaWePdda6MJHx9p717ppZesnfl69pT+8Afp7FmnK/ZcBCcAAADgNnDXXdaW5tu2SWfOSAsWSP/0T9b9oz7/XPrXf5WaN5d695YWLrRO+cPfEZwAAACA20x4uPTCC1JGhnWq3rx51s13jZHS06UxY6SICKl/f2npUuniRacrdh7BCQAAALiNNW8uJSdLu3ZJ33xjbWneubNUXCxt3So9+6wVtH7+c2nlSikvz+mKnUFwAgAAACBJatVKevllad8+6S9/kV5/3drSvKhI+vRTacQIqUkTafBg6cMPpYICpyuuPQQnAAAAADdo10769a+lQ4ekI0ekadOkyEipsFBat04aPtwKUcOHW89/+MHpiu1FcAIAAABQqY4dpRkzpJwcKStLmjJFuuce6coV65OnwYOtEDVihLRhg3T1avnfp7hYyshwaefO5srIcKm4uFZ/jJviEcFpwYIFat26tYKCgtS9e3ft2bOnwrWLFy9Wr1691LBhQzVs2FDx8fGVrgcAAABwa7hcUkyM9O//Lh0/Lu3ZI734otSypZSfb10D9eijUtOm1rVRW7dKP/5ovffjj6XWraW+ff00d25X9e3rp9atrePewPHg9OGHH2rixIlKSUnR/v37FRMTo/79++v8+fPlrk9PT9dTTz2lHTt2aNeuXWrZsqX69eun06dP13LlAAAAwO3L5ZK6dZPmzLE2lcjMlMaPt0LTxYvWbnz9+1u78/XrJw0ZIv31r2W/x+nT0tCh3hGeHA9Oc+fO1XPPPaeRI0eqY8eOWrhwoUJCQrR06dJy169atUovvPCCOnXqpPbt22vJkiUqKSnR9u3ba7lyAAAAAJJ1Q90ePaTf/94KRzt2SKNHS2Fh0nffSWlp5b/PGOu/ycny+NP2/Jz8w69evap9+/ZpypQppcd8fHwUHx+vXbt2Vel7XLlyRUVFRWrUqFG5rxcWFqqwsLD0ed7/759YVFSkoqKim6j+1rhWgyfUUhfRX3vRX3vRX3vRX3vRX3vRX3vR35vXs6f1mDtXeustH02Z4lvhWmOse0nt2PGj4uJMLVZZvb9jR4PTd999p+LiYoWHh5c5Hh4eri+//LJK32PSpElq1qyZ4uPjy3191qxZmjFjxg3Ht27dqpCQkOoXbZO0imI4bgn6ay/6ay/6ay/6ay/6ay/6ay/6e2tcuNBcUle36zZtylJBQe1efnPlypUqr3U0ON2s2bNna/Xq1UpPT1dQUFC5a6ZMmaKJEyeWPs/Lyyu9Lio0NLS2Sq1QUVGR0tLS1LdvX/n7+ztdTp1Df+1Ff+1Ff+1Ff+1Ff+1Ff+1Ff2+tevVcmjvX/bqEhE6Ki4uxv6B/kFeNu/k6GpzCwsLk6+urc+fOlTl+7tw5NW3atNL3zpkzR7Nnz9a2bdsUHR1d4brAwEAFBgbecNzf39+j/kfwtHrqGvprL/prL/prL/prL/prL/prL/p7a/TuLbVoYW0EYco5E8/lsl7v3dtPvhWf0WeL6vz9Oro5REBAgLp06VJmY4drGz3ExsZW+L4333xTM2fO1ObNm9W1q/uP/QAAAAA4w9dXeust62uXq+xr157Pn69aD03V5fiuehMnTtTixYu1YsUK5eTkaMyYMSooKNDIkSMlSb/85S/LbB7xxhtv6LXXXtPSpUvVunVr5ebmKjc3V5cvX3bqRwAAAABQicGDpbVrpebNyx5v0cI6PniwM3VVh+PXOA0bNkwXLlzQtGnTlJubq06dOmnz5s2lG0Z8++238vH5e75LTU3V1atXNXTo0DLfJyUlRdOnT6/N0gEAAABU0eDB0uOPW7vnbdqUpYSETo6cnldTjgcnSRo3bpzGjRtX7mvp6ellnn/zzTf2FwQAAADglvP1leLijAoKTisuLsZrQpPkAafqAQAAAICnIzgBAAAAgBsEJwAAAABwg+AEAAAAAG4QnAAAAADADYITAAAAALhBcAIAAAAANwhOAAAAAOAGwQkAAAAA3CA4AQAAAIAbBCcAAAAAcIPgBAAAAABuEJwAAAAAwA0/pwuobcYYSVJeXp7DlViKiop05coV5eXlyd/f3+ly6hz6ay/6ay/6ay/6ay/6ay/6ay/6ay9P6u+1THAtI1TmtgtO+fn5kqSWLVs6XAkAAAAAT5Cfn68GDRpUusZlqhKv6pCSkhKdOXNG9evXl8vlcroc5eXlqWXLljp16pRCQ0OdLqfOob/2or/2or/2or/2or/2or/2or/28qT+GmOUn5+vZs2aycen8quYbrtPnHx8fNSiRQuny7hBaGio44NTl9Ffe9Ffe9Ffe9Ffe9Ffe9Ffe9Ffe3lKf9190nQNm0MAAAAAgBsEJwAAAABwg+DksMDAQKWkpCgwMNDpUuok+msv+msv+msv+msv+msv+msv+msvb+3vbbc5BAAAAABUF584AQAAAIAbBCcAAAAAcIPgBAAAAABuEJwAAAAAwA2Ck4127typRx99VM2aNZPL5dL69evdvic9PV2dO3dWYGCg2rZtq+XLl9tep7eqbn/T09PlcrlueOTm5tZOwV5m1qxZ6tatm+rXr68mTZpo0KBBOnr0qNv3rVmzRu3bt1dQUJDuv/9+bdy4sRaq9T416e/y5ctvmN+goKBaqti7pKamKjo6uvTmirGxsdq0aVOl72F2q666/WV2b87s2bPlcrmUnJxc6TpmuGaq0l9muOqmT59+Q6/at29f6Xu8ZXYJTjYqKChQTEyMFixYUKX1J06c0MCBA9W7d29lZWUpOTlZv/rVr7RlyxabK/VO1e3vNUePHtXZs2dLH02aNLGpQu+WkZGhsWPHavfu3UpLS1NRUZH69eungoKCCt/z+eef66mnntKzzz6rAwcOaNCgQRo0aJAOHz5ci5V7h5r0V7Lusv6P83vy5Mlaqti7tGjRQrNnz9a+ffv05z//WQ8//LAef/xxHTlypNz1zG71VLe/ErNbU3v37tWiRYsUHR1d6TpmuGaq2l+JGa6OqKioMr364x//WOFar5pdg1ohyaxbt67SNa+88oqJiooqc2zYsGGmf//+NlZWN1Slvzt27DCSzMWLF2ulprrm/PnzRpLJyMiocM2TTz5pBg4cWOZY9+7dzfPPP293eV6vKv1dtmyZadCgQe0VVcc0bNjQLFmypNzXmN2bV1l/md2ayc/PN+3atTNpaWkmLi7OTJgwocK1zHD1Vae/zHDVpaSkmJiYmCqv96bZ5RMnD7Jr1y7Fx8eXOda/f3/t2rXLoYrqpk6dOikiIkJ9+/ZVZmam0+V4jUuXLkmSGjVqVOEaZrjmqtJfSbp8+bJatWqlli1buv0XfliKi4u1evVqFRQUKDY2ttw1zG7NVaW/ErNbE2PHjtXAgQNvmM3yMMPVV53+SsxwdXz11Vdq1qyZ2rRpo8TERH377bcVrvWm2fVzugD8XW5ursLDw8scCw8PV15enr7//nsFBwc7VFndEBERoYULF6pr164qLCzUkiVL9NBDD+lPf/qTOnfu7HR5Hq2kpETJycnq2bOnfvKTn1S4rqIZ5jqyylW1v5GRkVq6dKmio6N16dIlzZkzRz169NCRI0fUokWLWqzYO2RnZys2NlY//PCD7rjjDq1bt04dO3Ysdy2zW33V6S+zW32rV6/W/v37tXfv3iqtZ4arp7r9ZYarrnv37lq+fLkiIyN19uxZzZgxQ7169dLhw4dVv379G9Z70+wSnHDbiIyMVGRkZOnzHj166Pjx45o3b57ef/99ByvzfGPHjtXhw4crPUcZNVfV/sbGxpb5F/0ePXqoQ4cOWrRokWbOnGl3mV4nMjJSWVlZunTpktauXaukpCRlZGRU+Ms9qqc6/WV2q+fUqVOaMGGC0tLS2IDABjXpLzNcdQkJCaVfR0dHq3v37mrVqpU++ugjPfvssw5WdvMITh6kadOmOnfuXJlj586dU2hoKJ822eSBBx4gDLgxbtw4bdiwQTt37nT7r2oVzXDTpk3tLNGrVae/1/P399dPf/pTHTt2zKbqvFtAQIDatm0rSerSpYv27t2rt956S4sWLbphLbNbfdXp7/WY3crt27dP58+fL3M2RHFxsXbu3Km3335bhYWF8vX1LfMeZrjqatLf6zHDVXfnnXfqvvvuq7BX3jS7XOPkQWJjY7V9+/Yyx9LS0io9Zxw3JysrSxEREU6X4ZGMMRo3bpzWrVunzz77TPfcc4/b9zDDVVeT/l6vuLhY2dnZzHAVlZSUqLCwsNzXmN2bV1l/r8fsVq5Pnz7Kzs5WVlZW6aNr165KTExUVlZWub/UM8NVV5P+Xo8ZrrrLly/r+PHjFfbKq2bX6d0p6rL8/Hxz4MABc+DAASPJzJ071xw4cMCcPHnSGGPM5MmTzYgRI0rXf/311yYkJMS8/PLLJicnxyxYsMD4+vqazZs3O/UjeLTq9nfevHlm/fr15quvvjLZ2dlmwoQJxsfHx2zbts2pH8GjjRkzxjRo0MCkp6ebs2fPlj6uXLlSumbEiBFm8uTJpc8zMzONn5+fmTNnjsnJyTEpKSnG39/fZGdnO/EjeLSa9HfGjBlmy5Yt5vjx42bfvn1m+PDhJigoyBw5csSJH8GjTZ482WRkZJgTJ06YQ4cOmcmTJxuXy2W2bt1qjGF2b1Z1+8vs3rzrd31jhm8td/1lhqvuxRdfNOnp6ebEiRMmMzPTxMfHm7CwMHP+/HljjHfPLsHJRte2v77+kZSUZIwxJikpycTFxd3wnk6dOpmAgADTpk0bs2zZslqv21tUt79vvPGGuffee01QUJBp1KiReeihh8xnn33mTPFeoLzeSiozk3FxcaX9vuajjz4y9913nwkICDBRUVHm008/rd3CvURN+pucnGzuvvtuExAQYMLDw80jjzxi9u/fX/vFe4FnnnnGtGrVygQEBJjGjRubPn36lP5Sbwyze7Oq219m9+Zd/4s9M3xruesvM1x1w4YNMxERESYgIMA0b97cDBs2zBw7dqz0dW+eXZcxxtTe51sAAAAA4H24xgkAAAAA3CA4AQAAAIAbBCcAAAAAcIPgBAAAAABuEJwAAAAAwA2CEwAAAAC4QXACAAAAADcITgAAAADgBsEJAIBKuFwurV+/3ukyAAAOIzgBADzW008/LZfLdcNjwIABTpcGALjN+DldAAAAlRkwYICWLVtW5lhgYKBD1QAAbld84gQA8GiBgYFq2rRpmUfDhg0lWafRpaamKiEhQcHBwWrTpo3Wrl1b5v3Z2dl6+OGHFRwcrLvuukujRo3S5cuXy6xZunSpoqKiFBgYqIiICI0bN67M6999952eeOIJhYSEqF27dvrkk09KX7t48aISExPVuHFjBQcHq127djcEPQCA9yM4AQC82muvvaYhQ4bo4MGDSkxM1PDhw5WTkyNJKigoUP/+/dWwYUPt3btXa9as0bZt28oEo9TUVI0dO1ajRo1Sdna2PvnkE7Vt27bMnzFjxgw9+eSTOnTokB555BElJibqb3/7W+mf/8UXX2jTpk3KyclRamqqwsLCaq8BAIBa4TLGGKeLAACgPE8//bRWrlypoKCgMsenTp2qqVOnyuVyafTo0UpNTS197cEHH1Tnzp31zjvvaPHixZo0aZJOnTqlevXqSZI2btyoRx99VGfOnFF4eLiaN2+ukSNH6vXXXy+3BpfLpVdffVUzZ86UZIWxO+64Q5s2bdKAAQP02GOPKSwsTEuXLrWpCwAAT8A1TgAAj9a7d+8ywUiSGjVqVPp1bGxsmddiY2OVlZUlScrJyVFMTExpaJKknj17qqSkREePHpXL5dKZM2fUp0+fSmuIjo4u/bpevXoKDQ3V+fPnJUljxozRkCFDtH//fvXr10+DBg1Sjx49avSzAgA8F8EJAODR6tWrd8Opc7dKcHBwldb5+/uXee5yuVRSUiJJSkhI0MmTJ7Vx40alpaWpT58+Gjt2rObMmXPL6wUAOIdrnAAAXm337t03PO/QoYMkqUOHDjp48KAKCgpKX8/MzJSPj48iIyNVv359tW7dWtu3b7+pGho3bqykpCStXLlS8+fP17vvvntT3w8A4Hn4xAkA4NEKCwuVm5tb5pifn1/pBgxr1qxR165d9bOf/UyrVq3Snj179N5770mSEhMTlZKSoqSkJE2fPl0XLlzQ+PHjNWLECIWHh0uSpk+frtGjR6tJkyZKSEhQfn6+MjMzNX78+CrVN23aNHXp0kVRUVEqLCzUhg0bSoMbAKDuIDgBADza5s2bFRERUeZYZGSkvvzyS0nWjnerV6/WCy+8oIiICH3wwQfq2LGjJCkkJERbtmzRhAkT1K1bN4WEhGjIkCGaO3du6fdKSkrSDz/8oHnz5umll15SWFiYhg4dWuX6AgICNGXKFH3zzTcKDg5Wr169tHr16lvwkwMAPAm76gEAvJbL5dK6des0aNAgp0sBANRxXOMEAAAAAG4QnAAAAADADa5xAgB4Lc42BwDUFj5xAgAAAAA3CE4AAAAA4AbBCQAAAADcIDgBAAAAgBsEJwAAAABwg+AEAAAAAG4QnAAAAADADYITAAAAALjxf/pVb1WhSlY7AAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1cAAAIjCAYAAADvBuGTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB8LUlEQVR4nO3dd3gU5f7+8Xuz6YEAQiAhIEhHpEiVLr0JhCJdaYIEUDAqRYEAKtgO4lEMSLMiCCKgFEGk994EpHcCiJBGkk12fn/wY78nJkACWSbl/TrXXhf77LOzn/1kDuR2Zp6xGIZhCAAAAADwUFzMLgAAAAAAsgLCFQAAAACkA8IVAAAAAKQDwhUAAAAApAPCFQAAAACkA8IVAAAAAKQDwhUAAAAApAPCFQAAAACkA8IVAAAAAKQDwhUAAAAApAPCFQDgvr766itZLJYUHyNGjHDMW7lypfr27aunnnpKVqtVRYsWTdPnREVFKTQ0VE899ZR8fHyUN29eVapUSUOGDNHFixfT+Vs9GuHh4XrjjTdUpkwZeXt7y8fHR1WqVNG7776rGzdumF0eACAduZpdAAAg8xg/fryeeOKJJGNPPfWU489z5szRvHnzVLlyZRUsWDBN27bZbKpXr56OHDminj176pVXXlFUVJQOHTqkOXPmqF27dmneptl27Nihli1bKioqSj169FCVKlUkSTt37tT777+v9evXa+XKlSZXCQBIL4QrAECqtWjRQlWrVr3r6xMmTND06dPl5uam5557TgcPHkz1thctWqQ9e/bo+++/V7du3ZK8Fhsbq/j4+AeuO62io6Pl4+PzUNu4ceOG2rVrJ6vVqj179qhMmTJJXn/vvfc0ffr0h/qMO9KjXgDAw+O0QABAuilYsKDc3Nwe6L0nTpyQJNWuXTvZa56envL19U0yduTIEXXq1El+fn7y8vJS6dKl9fbbbyeZs2fPHrVo0UK+vr7KkSOHGjVqpK1btyaZc+eUx3Xr1mngwIHKnz+/ChUq5Hh9+fLlqlu3rnx8fJQzZ061atVKhw4duu/3mTZtmi5cuKBJkyYlC1aSVKBAAY0aNcrx3GKxaOzYscnmFS1aVL169bpvvQsWLHCMp1SLxWJJEnaPHDmijh076rHHHpOnp6eqVq2qJUuW3Pd7AQDujiNXAIBUu3nzpq5du5ZkLF++fOmy7SJFikiSvvnmG40aNUoWi+Wuc/fv36+6devKzc1N/fv3V9GiRXXixAn98ssveu+99yRJhw4dUt26deXr66thw4bJzc1N06ZN07PPPqt169apRo0aSbY5cOBA+fn5acyYMYqOjpYkffvtt+rZs6eaNWumDz74QDExMQoLC1OdOnW0Z8+ee15TtmTJEnl5ealjx44P2ZmU/bveVq1aKUeOHPrxxx9Vv379JHPnzZuncuXKOU7hPHTokGrXrq3AwECNGDFCPj4++vHHHxUUFKSffvpJ7dq1c0rNAJDlGQAA3Mfs2bMNSSk+7qZVq1ZGkSJFUv0ZMTExRunSpQ1JRpEiRYxevXoZM2fONMLDw5PNrVevnpEzZ07jzJkzScbtdrvjz0FBQYa7u7tx4sQJx9jFixeNnDlzGvXq1Uv23erUqWMkJCQ4xiMjI43cuXMb/fr1S/IZly9fNnLlypVs/N/y5MljVKxYMVXf3TAMQ5IRGhqabLxIkSJGz54971uvYRhG165djfz58ycZv3TpkuHi4mKMHz/eMdaoUSOjfPnyRmxsrGPMbrcbtWrVMkqWLJnqmgEASXFaIAAg1aZMmaJVq1YleaQXLy8vbdu2TW+++aak26e/9e3bVwEBAXrllVcUFxcnSbp69arWr1+vPn366PHHH0+yjTtHuxITE7Vy5UoFBQWpWLFijtcDAgLUrVs3bdy4UREREUne269fP1mtVsfzVatW6caNG+ratauuXbvmeFitVtWoUUNr1qy55/eJiIhQzpw5H7wh9/HveiWpc+fOunLlitauXesYW7Bggex2uzp37ixJun79uv744w916tRJkZGRju/1999/q1mzZjp27JguXLjgtLoBICvjtEAAQKpVr179ngtaPKxcuXLpww8/1IcffqgzZ85o9erV+vjjj/X5558rV65cevfdd3Xy5ElJSVcp/LerV68qJiZGpUuXTvZa2bJlZbfbde7cOZUrV84x/u9VEI8dOyZJatiwYYqf8e9rwFJ6PTIy8p5zHsa/65Wk5s2bK1euXJo3b54aNWok6fYpgZUqVVKpUqUkScePH5dhGBo9erRGjx6d4ravXLmiwMBAp9UOAFkV4QoAkCEVKVJEffr0Ubt27VSsWDF9//33evfdd532eV5eXkme2+12Sbevu/L3908239X13v+ElilTRnv37lV8fLzc3d0fuK7ExMQUx/9dryR5eHgoKChIP//8s7744guFh4dr06ZNmjBhgmPOne/1xhtvqFmzZiluu0SJEg9cLwBkZ4QrAECGlidPHhUvXtyx0t2d0/zutcy7n5+fvL29dfTo0WSvHTlyRC4uLipcuPA9P7d48eKSpPz586tx48Zprrt169basmWLfvrpJ3Xt2vW+8/PkyZPspsLx8fG6dOlSmj63c+fO+vrrr7V69WodPnxYhmE4TgmU/q9/bm5uD/S9AAB3xzVXAIAMYd++fclWIpSkM2fO6M8//3Sc4ufn56d69epp1qxZOnv2bJK5hmFIkqxWq5o2barFixfr9OnTjtfDw8M1Z84c1alT576n9TVr1ky+vr6aMGGCbDZbstevXr16z/cPGDBAAQEBev311/XXX38le/3KlStJjsQVL15c69evTzLnyy+/vOuRq7tp3LixHnvsMc2bN0/z5s1T9erVk5xCmD9/fj377LOaNm1aisHtft8LAHB3HLkCAKSb/fv3O+6VdPz4cd28edMRICpWrKjWrVvf9b2rVq1SaGio2rRpo2eeeUY5cuTQyZMnNWvWLMXFxSW5B9R///tf1alTR5UrV1b//v31xBNP6PTp01q6dKn27t0rSXr33Xe1atUq1alTRwMHDpSrq6umTZumuLg4ffjhh/f9Lr6+vgoLC9MLL7ygypUrq0uXLvLz89PZs2e1dOlS1a5dW59//vld358nTx79/PPPatmypSpVqqQePXqoSpUqkqTdu3frhx9+UM2aNR3zX3rpJQ0YMEAdOnRQkyZNtG/fPv32229pXurezc1N7du319y5cxUdHa2PP/442ZwpU6aoTp06Kl++vPr166dixYopPDxcW7Zs0fnz57Vv3740fSYA4P8zebVCAEAmcGf57x07dqRqXkqP/11OPCUnT540xowZYzzzzDNG/vz5DVdXV8PPz89o1aqV8ccffySbf/DgQaNdu3ZG7ty5DU9PT6N06dLG6NGjk8zZvXu30axZMyNHjhyGt7e30aBBA2Pz5s1p+m5r1qwxmjVrZuTKlcvw9PQ0ihcvbvTq1cvYuXPnPb/PHRcvXjRee+01o1SpUoanp6fh7e1tVKlSxXjvvfeMmzdvOuYlJiYaw4cPN/Lly2d4e3sbzZo1M44fP37Xpdjv9bNYtWqVIcmwWCzGuXPnUpxz4sQJ48UXXzT8/f0NNzc3IzAw0HjuueeMBQsWpOp7AQCSsxjG/z+HAgAAAADwwLjmCgAAAADSAeEKAAAAANIB4QoAAAAA0gHhCgAAAADSAeEKAAAAANIB4QoAAAAA0gE3EU6B3W7XxYsXlTNnTlksFrPLAQAAAGASwzAUGRmpggULysXl3semCFcpuHjxogoXLmx2GQAAAAAyiHPnzqlQoUL3nEO4SkHOnDkl3W6gr6+vqbXYbDatXLlSTZs2lZubm6m1ZEX017nor3PRX+ejx85Ff52L/joX/XWujNTfiIgIFS5c2JER7sX0cDVlyhR99NFHunz5sipWrKjPPvtM1atXT3GuzWbTxIkT9fXXX+vChQsqXbq0PvjgAzVv3vyBt5mSO6cC+vr6Zohw5e3tLV9fX9N3rKyI/joX/XUu+ut89Ni56K9z0V/nor/OlRH7m5rLhUxd0GLevHkKCQlRaGiodu/erYoVK6pZs2a6cuVKivNHjRqladOm6bPPPtOff/6pAQMGqF27dtqzZ88DbxMAAAAA0oOp4WrSpEnq16+fevfurSeffFJTp06Vt7e3Zs2aleL8b7/9Vm+99ZZatmypYsWKKTg4WC1bttR//vOfB94mAAAAAKQH004LjI+P165duzRy5EjHmIuLixo3bqwtW7ak+J64uDh5enomGfPy8tLGjRsfeJt3thsXF+d4HhERIen24UibzZb2L5eO7ny+2XVkVfTXueivc9Ff56PHzkV/nYv+Ohf9da6M1N+01GBauLp27ZoSExNVoECBJOMFChTQkSNHUnxPs2bNNGnSJNWrV0/FixfX6tWrtXDhQiUmJj7wNiVp4sSJGjduXLLxlStXytvb+67vc3Fxue9yjOnB1dVVa9ascfrnZFV2u112u/2ec1atWvWIqsme6K9z0V/no8fORX+di/46F/11rozQ35iYmFTPNX1Bi7T49NNP1a9fP5UpU0YWi0XFixdX7969H/qUv5EjRyokJMTx/M6KIE2bNk1xQQubzabw8HDdunXroT43NQzDUGxsrDw9Pbnn1kPw8vJSgQIFkl0QabPZtGrVKjVp0iTDXCyZldBf56K/zkePnYv+Ohf9dS7661wZqb93zmpLDdPCVb58+WS1WhUeHp5kPDw8XP7+/im+x8/PT4sWLVJsbKz+/vtvFSxYUCNGjFCxYsUeeJuS5OHhIQ8Pj2Tjbm5uyX6YdrtdJ0+elNVqVWBgoNzd3Z0aeux2u6KiopQjR45HcpQsqzEMQ/Hx8bp69arOnTunkiVLptjHlH7WSD/017nor/PRY+eiv85Ff52L/jpXRuhvWj7ftHDl7u6uKlWqaPXq1QoKCpJ0O0isXr1agwcPvud7PT09FRgYKJvNpp9++kmdOnV66G2mVnx8vOx2uwoXLnzPUwbTi91uV3x8vDw9PQlXD8jLy0tubm46c+aMo5cAAABAejP1tMCQkBD17NlTVatWVfXq1TV58mRFR0erd+/ekqQXX3xRgYGBmjhxoiRp27ZtunDhgipVqqQLFy5o7NixstvtGjZsWKq3mV4IOpkLPy8AAAA4m6nhqnPnzrp69arGjBmjy5cvq1KlSlqxYoVjQYqzZ88m+aU4NjZWo0aN0smTJ5UjRw61bNlS3377rXLnzp3qbQIAAACAM5i+oMXgwYPvesre2rVrkzyvX7++/vzzz4faJgAAAAA4g+nhKrtKtCdqw9kNuhR5SQE5A1T38bqyuljNLgsAAADAA+JCFBMsPLxQRT8tqgZfN1C3hd3U4OsGKvppUS08vNBpn9mrVy9ZLJZkj+PHj0uS1q9fr9atW6tgwYKyWCxatGjRfbeZmJio999/X2XKlJGXl5cee+wx1ahRQzNmzHDa9wAAAAAyKsLVI7bw8EJ1/LGjzkecTzJ+IeKCOv7Y0akBq3nz5rp06VKSxxNPPCFJio6OVsWKFTVlypRUb2/cuHH65JNP9M477+jPP//UmjVr1L9/f924ccNJ3+D2ao0AAABARsRpgenAMAzF2O5/5+ZEe6JeXf6qDBnJtyFDFlk0ZPkQNX6iseMUQbvdrmhbtKzx1mQr3nm7eafpHlseHh53vd9XixYt1KJFi1RvS5KWLFmigQMH6vnnn3eMVaxYMckcu92ujz/+WF9++aXOnTunAgUK6OWXX9bbb78tSTpw4ICGDBmiLVu2yNvbWx06dNCkSZOUI0cOSbePuN24cUPVqlXTlClT5OHhoVOnTuncuXN6/fXXtXLlSrm4uKhu3br69NNPVbRo0TR9BwAAAGQsifZErTuzTuv/WS+fMz5qUKxBprl8hnCVDmJsMcoxMcdDb8eQofOR55Xrg1ypmh81Mko+7j4P/bkPyt/fX3/88YcGDhwoPz+/FOeMHDlS06dP1yeffKI6dero0qVLOnLkiKTbR8uaNWummjVraseOHbpy5YpeeuklDR48WF999ZVjG6tXr5avr69WrVol6fYdu++8b8OGDXJ1ddW7776r5s2ba//+/XJ3d3f6dwcAAED6W3h4oYasGOI4y2vSmUkq5FtInzb/VO3Ltje5uvsjXGUjv/76q+OIkHT7aNX8+fMfeHuTJk1Sx44d5e/vr3LlyqlWrVpq27at4whYZGSkPv30U33++efq2bOnJKl48eKqU6eOJGnOnDmKjY3VN998Ix+f2yHx888/V+vWrfXBBx84ls/38fHRjBkzHKHpu+++k91u14wZMxxH7mbPnq3cuXNr7dq1atq06QN/JwAAAJjjzuUz/z7L687lMws6LcjwAYtwlQ683bwVNTLqvvPWn1mvlnNa3nfesm7LVK9IPUm3T6uLiIyQb07fFE8LTIsGDRooLCzM8fxOoHlQTz75pA4ePKhdu3Zp06ZNjkUxevXqpRkzZujw4cOKi4tTo0aNUnz/4cOHVbFixSR11K5dW3a7XUePHnWEq/Llyyc5GrVv3z4dP35cOXPmTLK92NhYnThx4qG+EwAAAB69RHuihqwYcs/LZ4auGKq2pdtm6FMECVfpwGKxpOr0vKbFm6qQbyFdiLiQ4o5jkUWFfAupafGmSa65SnRLlI+7T7JwlVY+Pj4qUaLEQ23j31xcXFStWjVVq1ZNQ4cO1XfffacXXnhBb7/9try8vNLlM/4dAqOiolSlShV9//33yebe7fREAAAAZFwbzm5ItuDb/zJk6FzEOW04u0HPFn320RWWRqwW+AhZXaz6tPmnkm4Hqf915/nk5pMzdBq/nyeffFLS7eupSpYsKS8vL61evTrFuWXLltW+ffsUHR3tGNu0aZNcXFxUunTpu35G5cqVdezYMeXPn18lSpRI8siVK3XXqwEAAMB8hmFo87nNGvXHqFTNvxR5yckVPRzC1SPWvmx7Lei0QIG+gUnGC/kWMvU80qioKO3du1d79+6VJJ06dUp79+7V2bNn7/qejh076pNPPtG2bdt05swZrV27VoMGDVKpUqVUpkwZeXp6avjw4Ro2bJi++eYbnThxQlu3btXMmTMlSd27d5enp6d69uypgwcPas2aNXrllVf0wgsvOE4JTEn37t2VL18+tW3bVhs2bNCpU6e0du1avfrqqzp//u7/xQMAAAAZQ2RcpKbunKpK0yqp9qza2nRuU6reF5AzwMmVPRxOCzRB+7Lt1bZ0W204u0GXIi8pIGeA6j5e19QjVjt37lSDBg0cz0NCQiRJPXv2TLJy3/9q1qyZfvjhB02cOFE3b96Uv7+/GjZsqLFjx8rV9fauNXr0aLm6umrMmDG6ePGiAgICNGDAAEmSt7e3fvvtNw0ZMkTVqlVLshT7vXh7e2v9+vUaPny42rdvr8jISAUGBqpRo0by9fVNh24AAADAGQ6EH1DYzjB9t/87RcZHSpK8XL3UuVxnLTu+TFejr97z8pm6j9d91CWnCeHKJFYX6yM9X/RuAemOZ599VoaRfEe+l379+qlfv373nOPi4qK3337bcV+rfytfvrz++OOPu77/bnX7+/vr66+/TnWtAAAAMEdcQpx+OvyTwnaGaePZjY7xUnlLKbhqsHpW7Kk8XnkcqwVaZEkSsDLT5TOEKwAAAADp7vSN05q2c5pm7pmpqzFXJUlWi1VBZYI0sNpANSjawHFbHen/Lp/53/tcSbcvn5ncfHKGX4ZdIlwBAAAASCeJ9kQtP75cYTvDtPzYcscRqMCcgepfpb9eqvySCuYseNf337l8Zs3JNVq+cbla1GmhBsUaZPgjVncQrgAAAAA8lCvRVzRz90xN2zVNZ26ecYw3KdZEA6sN1HOlnpOrS+qih9XFqvpF6iv6ULTqF6mfaYKVRLgCAAAA8AAMw9DGsxv1xc4v9NOfP8lmt0mS8njmUZ+n++jlKi+rZN6SJlf5aBGuHlBaF3+Aufh5AQAApI+IuAh9u+9bhe0M06GrhxzjNQJrKLhqsDqV6yQvNy8TKzQP4SqN3NzcJEkxMTHy8sqeO01mFBMTI+n/fn4AAABIm32X9zmWUY+2RUuSvN281e2pbgquFqzKAZVNrtB8hKs0slqtyp07t65cuSLp9j2X/neVk/Rmt9sVHx+v2NhYubhwz+e0MgxDMTExunLlinLnzi2rNfOcswsAAGC22IRYzT80X2E7w7Tl/BbHeNl8ZRVcNVgvVHxBuT1zm1dgBkO4egD+/v6S5AhYzmQYhm7duiUvLy+nhrisLnfu3I6fGwAAAO7txPUTmrZrmmbtmaW/b/0tSXJ1cVX7su0VXDVY9YvU53fTFBCuHoDFYlFAQIDy588vm83m1M+y2Wxav3696tWrxyltD8jNzY0jVgAAAPeRaE/U0mNL9cWOL/Tbid8c44V9C+vlKi+rb+W+8s/Bf6y+F8LVQ7BarU7/pd1qtSohIUGenp6EKwAAAKS7y1GXNWP3DH2560udizgnSbLIomYlmim4arBalmyZ6mXUszu6BAAAAGQzhmFo3Zl1CtsZpoWHFyrBniBJyuuV17GMevHHiptcZeZDuAIAAACyiZuxN/XNvm8UtjNMh68ddozXKlxLwVWD1fHJjvJ09TSxwsyNcAUAAABkcbsv7VbYjjDNOThHMbbbt6jxcfNRjwo9FFw1WBX9K5pcYdZAuAIAAACyoFu2W/rx0I/6YucX2n5hu2O8nF85Daw2UD0q9JCvh6+JFWY9hCsAAAAgCzn29zFN3TlVs/fO1j+x/0iS3Fzc1PHJjgquGqw6j9dhGXUnIVwBAAAAmVyCPUG/HP1FYTvDtOrkKsd4kVxF9HKVl9Xn6T4qkKOAiRVmD4QrAAAAIJO6GHlR03dN1/Td03Uh8oKk28uotyzZUsFVg9W8RHNZXbjf56NCuAIAAAAyEcMw9MepPxS2M0yLjixSopEoSfLz9lPfp/uqf5X+eiLPEyZXmT0RrgAAAIBM4J9b/+jrfV8rbGeY/vr7L8d4ncfraGDVgWpftr08XD1MrBCEKwAAACAD23Fhh8J2hmnuwbm6lXBLkpTTPadeqPCCBlQdoPIFyptcIe4gXAEAAAAZTIwtRnMPzlXYzjDtvLjTMV6hQAUFVw1W9/LdldMjp4kVIiWEKwAAACCDOHrtqMJ2hunrfV/rRuwNSZK71V2dynVScNVg1SxUk2XUMzDCFQAAAGAiW6JNi48uVtjOMP1x6g/H+BO5n9CAqgPUu1Jv+fn4mVghUotwBQAAAJjgfMR5fbnrS83YPUOXoi5JklwsLmpVspUGVhuopsWbysXiYnKVSAvCFQAAAPCI2A27fj/5u8J2humXo784llEv4FNAL1V+Sf2r9NfjuR43uUo8KMIVAAAA4GR/x/ytr/Z+pam7pur49eOO8fpF6iu4arDalW0nd6u7iRUiPRCuAAAAACcwDEPbLmxT2M4wzTs4T3GJcZIkXw9f9azYUwOqDtCTfk+aXCXSE+EKAAAASEfR8dGac2COwnaGac/lPY7xp/2fVnDVYHUr300+7j4mVghnIVwBAAAA6eDPq39q6s6p+nrf14qIi5AkeVg91PmpzhpYdaCqB1ZnGfUsjnAFAAAAPKD4xHgtPLpQYTvDtO7MOsd4icdKaECVAepVqZfyeuc1sUI8SoQrAAAAII3O3jyr7y99r5c/f1nh0eGSbi+j3qZ0Gw2sOlCNijViGfVsiHAFAAAApILdsGvliZX6YscXWnpsqeyGXZIUkCNA/Sr3U78q/VTIt5DJVcJMhCsAAADgHq7FXNOsPbM0bdc0nfznpGO8fI7yeqvJW+pQroPcrG4mVoiMgnAFAAAA/IthGNpyfovCdobpx0M/Kj4xXpKUyyOXelfqrb6V+urEthNqWbYlwQoOhCsAAADg/4uMi9T3B75X2M4w7Q/f7xivWrCqgqsGq8tTXeTt5i2bzaYTOmFipciICFcAAADI9g5eOaiwHWH6dv+3ioyPlCR5unqq61NdFVw1WNUCq5lcITIDwhUAAACypbiEOC08vFBf7PxCG89udIyXyltKwVWD1bNiT+XxymNihchsCFcAAADIVk7fOK1pO6dp5p6ZuhpzVZJktVgVVCZIwVWD1fCJhtzsFw+EcAUAAIAsL9GeqBXHVyhsZ5iWHVsmQ4YkKTBnoPpX6a+XKr+kgjkLmlwlMjvCFQAAALKsK9FXNHP3TE3bNU1nbp5xjDcp1kTBVYPVunRrubrwKzHSB3sSAAAAshTDMLTx7EaF7QzTgj8XyGa3SZLyeOZR70q9NaDqAJXMW9LkKpEVEa4AAACQJUTERejbfd9q6q6pOnjloGO8RmANBVcNVqdyneTl5mVihcjqCFcAAADI1PZd3qewnWH6bv93irZFS5K83bzV7aluCq4WrMoBlU2uENkF4QoAAACZTmxCrBb8uUBhO8O0+dxmx3iZfGUUXDVYL1Z8Ubk9c5tXILIlwhUAAAAyjRPXT2jarmmatWeW/r71tyTJ1cVV7cu2V3DVYNUvUp9l1GEawhUAAAAytER7opYeW6qwnWFacXyFY7ywb2HHMur+OfxNrBC4jXAFAACADOly1GXHMurnIs45xpsVb6aB1QaqZcmWLKOODIW9EQAAABmGYRhad2adwnaGaeHhhUqwJ0iS8nrlVZ+n++jlKi+r+GPFTa4SSBnhCgAAAKa7GXtT3+z7RmE7w3T42mHHeM1CNTWw2kB1fLKjPF09TawQuD/CFQAAAEyz+9Juhe0I05yDcxRji5Ek+bj5qEeFHgquGqyK/hVNrhBIPRezC5gyZYqKFi0qT09P1ahRQ9u3b7/n/MmTJ6t06dLy8vJS4cKF9dprryk2Ntbx+tixY2WxWJI8ypQp4+yvAQAAgFS6Zbulr/d+rWdmPKMqX1bRjD0zFGOLUTm/cvq8xee6+PpFTX1uKsEKmY6pR67mzZunkJAQTZ06VTVq1NDkyZPVrFkzHT16VPnz5082f86cORoxYoRmzZqlWrVq6a+//lKvXr1ksVg0adIkx7xy5crp999/dzx3deUAHQAAgNmO/X1MU3dO1Vf7vtL1W9clSW4uburwZAcNrDpQdR6vwzLqyNRMTR2TJk1Sv3791Lt3b0nS1KlTtXTpUs2aNUsjRoxINn/z5s2qXbu2unXrJkkqWrSounbtqm3btiWZ5+rqKn9/luMEAAAwW4I9Qb8c/UVhO8O06uQqx3iRXEX0cpWX1efpPiqQo4CJFQLpx7RwFR8fr127dmnkyJGOMRcXFzVu3FhbtmxJ8T21atXSd999p+3bt6t69eo6efKkli1bphdeeCHJvGPHjqlgwYLy9PRUzZo1NXHiRD3++ON3rSUuLk5xcXGO5xEREZIkm80mm832MF/zod35fLPryKror3PRX+eiv85Hj52L/jqX2f29GHlRs/bO0sy9M3Uh8oIkySKLmhdvrv6V+6t58eayulhNrfFhmN3frC4j9TctNVgMwzCcWMtdXbx4UYGBgdq8ebNq1qzpGB82bJjWrVuX7GjUHf/973/1xhtvyDAMJSQkaMCAAQoLC3O8vnz5ckVFRal06dK6dOmSxo0bpwsXLujgwYPKmTNnitscO3asxo0bl2x8zpw58vb2fshvCgAAkD0YhqH9Ufu14toKbbu5TXbZJUm+Vl81ydtETfM2VQEPjlIhc4mJiVG3bt108+ZN+fr63nNuproYae3atZowYYK++OIL1ahRQ8ePH9eQIUP0zjvvaPTo0ZKkFi1aOOZXqFBBNWrUUJEiRfTjjz+qb9++KW535MiRCgkJcTyPiIhQ4cKF1bRp0/s20NlsNptWrVqlJk2ayM3NzdRasiL661z017nor/PRY+eiv871KPv7z61/9O2Bb/Xl7i/11/W/HOO1C9VW/yr91b50e3m4eji1hkeN/de5MlJ/75zVlhqmhat8+fLJarUqPDw8yXh4ePhdr5caPXq0XnjhBb300kuSpPLlyys6Olr9+/fX22+/LReX5Isf5s6dW6VKldLx48fvWouHh4c8PJL/H97Nzc30H+YdGamWrIj+Ohf9dS7663z02Lnor3M5s787L+5U2I4w/XDwB91KuCVJyuGeQy9WeFEDqg5Q+QLlnfK5GQn7r3NlhP6m5fNNC1fu7u6qUqWKVq9eraCgIEmS3W7X6tWrNXjw4BTfExMTkyxAWa23z9W929mNUVFROnHiRLLrsgAAAJB2MbYYzT04V2E7w7Tz4k7HeIUCFRRcNVjdy3dXTo+UL8UAsjpTTwsMCQlRz549VbVqVVWvXl2TJ09WdHS0Y/XAF198UYGBgZo4caIkqXXr1po0aZKefvppx2mBo0ePVuvWrR0h64033lDr1q1VpEgRXbx4UaGhobJareratatp3xMAACCzO3rtqGMZ9RuxNyRJ7lZ3Pf/k8xpYbaBqFqrJMurI9kwNV507d9bVq1c1ZswYXb58WZUqVdKKFStUoMDtCx3Pnj2b5EjVqFGjZLFYNGrUKF24cEF+fn5q3bq13nvvPcec8+fPq2vXrvr777/l5+enOnXqaOvWrfLz83vk3w8AACAzsyXatOToEn2x8wv9ceoPx/gTuZ/QgKoD1LtSb/n58DsWcIfpC1oMHjz4rqcBrl27NslzV1dXhYaGKjQ09K7bmzt3bnqWBwAAkO2cjziv6buma/ru6boUdUmS5GJxUauSrRRcNVjNSjSTiyX5te5Admd6uAIAAID57IZdq0+u1hc7v9AvR39RopEoScrvk1/9KvdT/yr99Xiuu983FADhCgAAIFv7O+ZvfbX3K03dNVXHr//f6sr1i9RXcNVgtSvbTu5WdxMrBDIPwhUAAEAWk2hP1Loz67T+n/XyOeOjBsUayOpidbxuGIa2X9iusJ1hmntwruIS4yRJvh6+jmXUy+UvZ1b5QKZFuAIAAMhCFh5eqCErhuh8xHlJ0qQzk1TIt5A+bf6pmhVvph8O/qAvdnyhPZf3ON5Tyb+SBlYdqK7luyqHew6zSgcyPcIVAABAFrHw8EJ1/LGjDCW9/+eFiAvq8GMHebt6KyYhRpLkYfVQ56c6K7hqsGoE1mAZdSAdEK4AAACygER7ooasGJIsWElyjMUkxKhY7mIaWG2gelXqpbzeeR91mUCWRrgCAADIAjac3eA4FfBepreZroZPNHwEFQHZDzcoAAAAyAJO/nMyVfPCo8KdXAmQfXHkCgAAIBO7GXtTn2//XB9s+iBV8wNyBji5IiD7IlwBAABkQjdib+i/2/6rT7Z+ohuxNyRJri6uSrAnpDjfIosK+RZS3cfrPsIqgeyFcAUAAJCJXL91XZO3Ttan2z5VRFyEJKlsvrIaXW+03Fzc1GlBJ0lKsrCFRbdXApzcfHKS+10BSF+EKwAAgEzgWsw1fbLlE322/TNFxkdKkp7K/5RG1xutDmU7OELTApcFSe5zJUmFfAtpcvPJal+2vSm1A9kF4QoAACADuxJ9Rf/Z/B9N2TFF0bZoSVKFAhU0pt4YtSvbTi6WpOuTtS/bXm1Lt9Wak2u0fONytajTQg2KNeCIFfAIEK4AAAAyoMtRl/Xx5o8VtjNMMbbbN/592v9pjak/Rm1Kt0kWqv6X1cWq+kXqK/pQtOoXqU+wAh4RwhUAAEAGcjHyoj7c9KGm7Zqm2IRYSVLVglUVWj9UrUq2ksViMblCAHdDuAIAAMgAzkec1wcbP9D03dMVlxgnSaoRWEOh9UPVvERzQhWQCRCuAAAATHT25lm9v/F9zdwzU/GJ8ZKk2oVrK7R+qBoXa0yoAjIRwhUAAIAJTt84rQkbJuirvV/JZrdJkuoXqa8x9ceoQdEGhCogEyJcAQAAPEInrp/QhA0T9M3+bxw3/G34REONqTdG9YvWN7k6AA+DcAUAAPAI/PX3X5qwYYK+2/+dEo1ESVLT4k01ut5o1Xm8jsnVAUgPhCsAAAAnOnz1sN7b8J5+OPiD7IZdktSiRAuNqT9GzxR6xuTqAKQnwhUAAIATHLpySO9ueFfzDs6TIUOS1LpUa42uN1rVAquZXB0AZyBcAQAApKP94fv1zvp3tODPBY6xoDJBGl1vtCoHVDaxMgDORrgCAABIB3su7dE769/Rz0d+dox1KNtBo+uNVkX/iiZWBuBRIVwBAAA8hJ0Xd2r8uvH65a9fJEkWWdSpXCeNqjdKT+V/yuTqADxKhCsAAIAHsO38No1fP17Lji2TJLlYXNTlqS56u+7betLvSZOrA2AGwhUAAEAabD63WePWjdPKEysl3Q5VPSr00Ft13lLpfKVNrg6AmQhXAAAAqbDhzAaNWzdOq0+tliRZLVa9WPFFvVX3LZV4rITJ1QHICAhXAAAAd2EYhtaeXqvx68dr7em1kiRXF1f1qthLI+uOVLE8xcwtEECGQrgCAAD4F8MwtPrUao1fN14bzm6QJLm5uKnv0301os4IFcldxOQKAWREhCsAAID/zzAM/XbiN41fN15bzm+RJLlb3dWvcj8Nrz1chXMVNrlCABkZ4QoAAGR7hmFo2bFlGr9+vLZf2C5J8nT11MtVXtabtd5UoG+gyRUCyAwIVwAAINsyDENLji7R+PXjtfvSbkmSl6uXgqsG683ab8o/h7/JFQLITAhXAAAg27Ebdi06skjj143XvvB9kiQfNx8NqjZIr9d6Xfl98ptcIYDMiHAFAACyDbth109//qR31r+jA1cOSJJyuOfQK9VfUUjNEOXzzmdyhQAyM8IVAADI8hLtifrx0I96d8O7+vPqn5IkXw9fvVr9VQ19Zqjyeuc1uUIAWQHhCgAAZFkJ9gTNPThX765/V0f/PipJyu2ZW0NrDNWrNV5VHq88JlcIICshXAEAgCwnwZ6g7/Z/p/c2vKfj149LkvJ45lFIzRC9Uv0V5fLMZXKFALIiwhUAAMgybIk2fbPvG03YOEEn/zkpScrrlVdv1HpDA6sNlK+Hr8kVAsjKCFcAACDTi0+M11d7v9KEDRN05uYZSZKft5/erPWmgqsFK4d7DpMrBJAdEK4AAECmFZcQp5l7Zur9je/rXMQ5SVIBnwIaVnuYXq7ysnzcfUyuEEB2QrgCAACZzi3bLc3YPUMfbPpAFyIvSJIK5iyo4bWHq1/lfvJy8zK5QgDZEeEKAABkGjG2GE3bOU0fbv5Ql6MuS5IK+RbSiNoj1LdyX3m6eppcIYDsjHAFAAAyvOj4aIXtDNNHmz/SlegrkqTHcz2ut+q8pV6VesnD1cPkCgGAcAUAADKwyLhITdkxRf/Z8h9di7kmSXoi9xN6q+5berHii3K3uptcIQD8H8IVAADIcG7G3tTn2z/XpK2TdP3WdUlS8TzF9Xbdt9WjQg+5Wd1MrhAAkiNcAQCADONG7A39d9t/9cnWT3Qj9oYkqVTeUhpVd5S6lu8qVxd+dQGQcfE3FAAAMN31W9c1eetkfbrtU0XERUiSyuQro9H1Rqtzuc6yulhNrhAA7o9wBQAATBOREKHRa0fri51fKDI+UpJUzq+cxtQfow5lOxCqAGQqhCsAAPDIXY2+qg82fqApf05RrD1WklShQAWNqTdG7cq2k4vFxeQKASDtCFcAAOCRCY8K10ebP1LYzjDF2GIkSZUKVFLos6FqU7oNoQpApka4AgAATncx8qI+2vSRpu6aqtiE20eqqgRUUXPP5hrTZYzc3VlSHUDmR7gCAABOcz7ivD7Y+IGm756uuMQ4SVKNwBoKrR+qRkUaafny5bJYLCZXCQDpg3AFAADS3dmbZ/X+xvc1c89MxSfGS5JqF66t0PqhalyssSwWi2w2m8lVAkD6IlwBAIB0c/rGaU3YMEFf7f1KNvvt8FS/SH2NqT9GDYo24CgVgCyNcAUAAB7aiesnNGHDBH2z/xsl2BMkSQ2faKgx9caoftH6JlcHAI8G4QoAADywv/7+SxM2TNB3+79TopEoSWpSrInG1B+jOo/XMbk6AHi0CFcAACDNDl89rPc2vKcfDv4gu2GXJLUo0UKj641WzcI1Ta4OAMxBuAIAAKl26MohvbvhXc07OE+GDEnSc6We05h6Y1QtsJrJ1QGAuQhXAADgvvaH79c769/Rgj8XOMaCygRpdL3RqhxQ2cTKACDjIFwBAIC72nNpj95Z/45+PvKzY6xD2Q4aVW+UKvlXMq8wAMiAXMwuYMqUKSpatKg8PT1Vo0YNbd++/Z7zJ0+erNKlS8vLy0uFCxfWa6+9ptjY2IfaJgAASGrnxZ1q80MbVf6ysn4+8rMssqhzuc46EHxACzotIFgBQApMDVfz5s1TSEiIQkNDtXv3blWsWFHNmjXTlStXUpw/Z84cjRgxQqGhoTp8+LBmzpypefPm6a233nrgbQIAgP+z7fw2tZrTStWmV9Mvf/0iF4uLupXvpoMDD2pux7l6Kv9TZpcIABmWqeFq0qRJ6tevn3r37q0nn3xSU6dOlbe3t2bNmpXi/M2bN6t27drq1q2bihYtqqZNm6pr165JjkyldZsAAEDafG6zmn/XXM/MfEbLji2Ti8VFL1Z8UX8O/FPft/9eT/o9aXaJAJDhmXbNVXx8vHbt2qWRI0c6xlxcXNS4cWNt2bIlxffUqlVL3333nbZv367q1avr5MmTWrZsmV544YUH3qYkxcXFKS4uzvE8IiJCkmSz2WSz2R7qez6sO59vdh1ZFf11LvrrXPTX+bJDjzee3ah3N76rP07/IUmyWqzqUb6HhtcarhKPlZDkvO+fHfprJvrrXPTXuTJSf9NSg2nh6tq1a0pMTFSBAgWSjBcoUEBHjhxJ8T3dunXTtWvXVKdOHRmGoYSEBA0YMMBxWuCDbFOSJk6cqHHjxiUbX7lypby9vdP61Zxi1apVZpeQpdFf56K/zkV/nS+r9dgwDB2MOqh54fN0MOqgJMkqqxo+1lAdCnSQv4u//tr6l/7SX4+knqzW34yG/joX/XWujNDfmJiYVM/NVKsFrl27VhMmTNAXX3yhGjVq6Pjx4xoyZIjeeecdjR49+oG3O3LkSIWEhDieR0REqHDhwmratKl8fX3To/QHZrPZtGrVKjVp0kRubm6m1pIV0V/nor/ORX+dL6v12DAM/XH6D7238T1tPLdRkuTm4qbeFXvrzVpvqkiuIo+0nqzW34yG/joX/XWujNTfO2e1pYZp4SpfvnyyWq0KDw9PMh4eHi5/f/8U3zN69Gi98MILeumllyRJ5cuXV3R0tPr376+33377gbYpSR4eHvLw8Eg27ubmZvoP846MVEtWRH+di/46F/11vszeY8Mw9NuJ3zR+3XhtOX/7NHl3q7v6Ve6n4bWHq3CuwqbWl9n7m9HRX+eiv86VEfqbls83bUELd3d3ValSRatXr3aM2e12rV69WjVr1kzxPTExMXJxSVqy1WqVdPsfjgfZJgAAWZVhGFr611I9M/MZtfi+hbac3yJPV0+9Wv1VnXz1pD5v+bnpwQoAshJTTwsMCQlRz549VbVqVVWvXl2TJ09WdHS0evfuLUl68cUXFRgYqIkTJ0qSWrdurUmTJunpp592nBY4evRotW7d2hGy7rdNAACyOsMwtOToEo1fP167L+2WJHm5eim4arDeqPWGAnIGmFwhAGRNpoarzp076+rVqxozZowuX76sSpUqacWKFY4FKc6ePZvkSNWoUaNksVg0atQoXbhwQX5+fmrdurXee++9VG8TAICsym7YtejIIo1fN177wvdJkrzdvDWo2iC9UesN5ffJb3KFAJC1mb6gxeDBgzV48OAUX1u7dm2S566urgoNDVVoaOgDbxMAgKzGbtj1058/6Z317+jAlQOSpBzuOfRK9Vf02jOvyc/Hz+QKASB7MD1cAQCAB5NoT9SPh37Uuxve1Z9X/5Qk+Xr46tXqr2roM0OV1zuvyRUCQPZCuAIAIJNJsCdo7sG5enf9uzr691FJUm7P3BpaY6herfGq8njlMblCAMieCFcAAGQSCfYEfb//e7234T0du35MkpTHM49CaoboleqvKJdnLpMrBIDsjXAFAEAGZ0u06Zt932jCxgk6+c9JSVJer7x6o9YbGlhtoHw9zL3hPQDgNsIVAAAZVHxivL7a+5UmbJigMzfPSJL8vP30Zq03FVwtWDncc5hcIQDgfxGuAADIYOIS4jRzz0y9v/F9nYs4J0kq4FNAw2oP08tVXpaPu4/JFQIAUkK4AgAgg7hlu6UZu2fog00f6ELkBUlSQI4ADa89XP2r9JeXm5fJFQIA7oVwBQCAyWJsMZq2c5o+3PyhLkddliQV8i2kEbVHqG/lvvJ09TS5QgBAahCuAAAwSXR8tMJ2humjzR/pSvQVSdLjuR7XyDoj1btSb3m4ephcIQAgLQhXAAA8YpFxkZqyY4r+s+U/uhZzTZJUNHdRvV33bb1Y8UW5W91NrhAA8CAIVwAAPCI3Y2/q8+2fa9LWSbp+67okqXie4nq77tvqUaGH3KxuJlcIAHgYhCsAAJzsRuwN/Xfbf/XJ1k90I/aGJKlU3lIaVXeUupbvKlcX/jkGgKyAv80BAHCS67eua/LWyfp026eKiIuQJJXJV0aj641W53KdZXWxmlwhACA9Ea4AAEhn12Ku6ZMtn+iz7Z8pMj5SklTOr5zG1B+jDmU7EKoAIIsiXAEAkE6uRl/Vf7b8R59v/1zRtmhJUoUCFTSm3hi1K9tOLhYXkysEADgT4QoAgIcUHhWujzZ/pLCdYYqxxUiSnvZ/WmPqj1Gb0m0IVQCQTRCuAAB4QBcjL+qjTR9p6q6pik2IlSRVLVhVofVD1apkK1ksFpMrBAA8SoQrAABSkGhP1Loz67T+n/XyOeOjBsUaOK6VOh9xXh9s/EDTd09XXGKcJKlGYA2F1g9V8xLNCVUAkE0RrgAA+JeFhxdqyIohOh9xXpI06cwkFfItpFH1Rmnf5X2auWem4hPjJUm1CtdSaP1QNSnWhFAFANkc4QoAgP+x8PBCdfyxowwZScbPR5zXgF8HOJ7XK1JPofVD1aBoA0IVAEAS4QoAAIdEe6KGrBiSLFj9Lw+rh5Z1W6aGxRo+wsoAAJkByxcBAPD/bTi7wXEq4N3EJcbJxYV/PgEAyfGvAwAA/9+lyEvpOg8AkL0QrgAAkBQdH60Ffy5I1dyAnAFOrgYAkBlxzRUAINtbc2qN+i7pq1M3Tt1znkUWFfItpLqP131ElQEAMhOOXAEAsq2IuAgF/xqsht801Kkbp/R4rsc1ut5oWf7///7XneeTm0923O8KAID/xZErAEC2tOL4CvX/pb/ORZyTJA2sOlDvN35fOT1yqpJ/pST3uZKkQr6FNLn5ZLUv296skgEAGRzhCgCQrfxz6x+FrAzRV3u/kiQVy1NMM9vM1LNFn3XMaV+2vdqWbqs1J9do+cblalGnhRoUa8ARKwDAPRGuAADZxpKjSzTg1wG6FHVJFlk0pMYQvdvwXfm4+ySba3Wxqn6R+oo+FK36ReoTrAAA90W4AgBkeddirmnIiiGac2COJKl03tKa1XaWahWuZXJlAICshHAFAMjS5h+ar0HLBulqzFW5WFz0Zq03NfbZsfJ09TS7NABAFkO4AgBkSeFR4Rq0bJB+OvyTJOmp/E9pVptZqhZYzeTKAABZFeEKAJClGIah7w98ryErhuj6retydXHVW3Xe0lt135KHq4fZ5QEAsjDCFQAgy7gQcUEDlg7Qr3/9Kkl62v9pzW47WxX9K5pcGQAgOyBcAQAyPcMwNHvvbIX8FqKbcTflbnVXaP1QvVnrTblZ3cwuDwCQTRCuAACZ2pkbZ9T/1/5aeWKlJKl6YHXNajNL5fKXM7kyAEB2Q7gCAGRKdsOuaTunadjvwxQVHyVPV0+90+AdvfbMa9yTCgBgCsIVACDTOXH9hF765SWtPb1WklTn8Tqa2WamSuUtZW5hAIBsjXAFAMg0Eu2J+mz7Z3pr9Vu6lXBLPm4+er/x+xpYbaBcLC5mlwcAyOYIVwCATOHItSPqu6SvNp/bLElq+ERDzWg9Q0/kecLkygAAuI1wBQDI0BLsCfrP5v8odG2o4hLjlNM9pz5u+rH6Ve4ni8VidnkAADgQrgAAGdaB8APqs6SPdl7cKUlqXqK5vnzuSxXOVdjkygAASI5wBQDIcGyJNk3cOFHvrn9XNrtNuT1za3KzyXqx4oscrQIAZFiEKwBAhrL70m71WdxH+8L3SZLalG6jsFZhKpizoMmVAQBwbw+0tFJCQoJ+//13TZs2TZGRkZKkixcvKioqKl2LAwBkH3EJcRr1xyhVn15d+8L3Ka9XXv3Q4Qct6ryIYAUAyBTSfOTqzJkzat68uc6ePau4uDg1adJEOXPm1AcffKC4uDhNnTrVGXUCALKwbee3qc+SPvrz6p+SpE7lOumzFp8pv09+kysDACD10nzkasiQIapatar++ecfeXl5OcbbtWun1atXp2txAICs7Zbtlt5c+aZqzaqlP6/+qQI+BfRTp580r+M8ghUAINNJ85GrDRs2aPPmzXJ3d08yXrRoUV24cCHdCgMAZG0bzmxQ3yV9dez6MUnSCxVe0CfNPlFe77wmVwYAwINJc7iy2+1KTExMNn7+/HnlzJkzXYoCAGRdUfFRemv1W/p8++cyZKhgzoKa9tw0PVfqObNLAwDgoaT5tMCmTZtq8uTJjucWi0VRUVEKDQ1Vy5Yt07M2AEAW88epP1QhrII+2/6ZDBnq+3RfHRp4iGAFAMgS0nzk6uOPP1bz5s315JNPKjY2Vt26ddOxY8eUL18+/fDDD86oEQCQyUXERejNlW/qy91fSpKK5Cqi6a2nq0nxJiZXBgBA+klzuCpcuLD27dunefPmad++fYqKilLfvn3VvXv3JAtcAAAgSSuOr1C/X/rpfMR5SdKgaoM0sdFE5fTgVHIAQNaSpnBls9lUpkwZ/frrr+revbu6d+/urLoAAJncP7f+UcjKEH219ytJUvE8xTWzzUzVL1rf3MIAAHCSNIUrNzc3xcbGOqsWAEAWsfjIYg1YOkCXoy7LIouGPjNU7zZ8V95u3maXBgCA06R5QYtBgwbpgw8+UEJCgjPqAQBkYtdirqnrT10VNC9Il6Muq0y+MtrUZ5MmNZtEsAIAZHlpvuZqx44dWr16tVauXKny5cvLx8cnyesLFy5Mt+IAAJmDYRia/+d8DV42WFdjrspqserNWm8q9NlQebp6ml0eAACPRJrDVe7cudWhQwdn1AIAyIQuR13WoGWDtPDw7f+4Vj5/ec1qO0tVC1Y1uTIAAB6tNIer2bNnO6MOAEAmYxiGvj/wvYasGKLrt67L1cVVb9d9W2/VfUvuVnezywMA4JFLc7i64+rVqzp69KgkqXTp0vLz80u3ogAAGdv5iPMa8OsALT22VJL0tP/Tmt12tir6VzS5MgAAzJPmBS2io6PVp08fBQQEqF69eqpXr54KFiyovn37KiYmxhk1AgAyCMMwNHP3TJX7opyWHlsqd6u7JjScoG0vbSNYAQCyvTSHq5CQEK1bt06//PKLbty4oRs3bmjx4sVat26dXn/9dWfUCADIAE7fOK2m3zXVS7+8pIi4CNUIrKE9L+/RyLoj5WZ1M7s8AABMl+bTAn/66SctWLBAzz77rGOsZcuW8vLyUqdOnRQWFpae9QEATGY37Jq6c6qG/z5cUfFR8nT11HsN39OQGkNkdbGaXR4AABlGmo9cxcTEqECBAsnG8+fP/8CnBU6ZMkVFixaVp6enatSooe3bt9917rPPPiuLxZLs0apVK8ecXr16JXu9efPmD1QbAGRnx68fV8OvG2rQskGKio9S3cfrav+A/QqpGUKwAgDgX9IcrmrWrKnQ0FDFxsY6xm7duqVx48apZs2aaS5g3rx5CgkJUWhoqHbv3q2KFSuqWbNmunLlSorzFy5cqEuXLjkeBw8elNVq1fPPP59kXvPmzZPM++GHH9JcGwBkV4n2RH2y5RNVCKugdWfWycfNR5+1+Exre61VybwlzS4PAIAMKc2nBX766adq1qyZChUqpIoVb1+8vG/fPnl6euq3335LcwGTJk1Sv3791Lt3b0nS1KlTtXTpUs2aNUsjRoxINv+xxx5L8nzu3Lny9vZOFq48PDzk7++fqhri4uIUFxfneB4RESFJstlsstlsafo+6e3O55tdR1ZFf52L/jqXs/p75NoR9V/aX1svbJUkNSzaUGEtw/RE7ieUmJCoRCWm6+dlZOzDzkV/nYv+Ohf9da6M1N+01GAxDMNI6wfExMTo+++/15EjRyRJZcuWVffu3eXl5ZWm7cTHx8vb21sLFixQUFCQY7xnz56OhTLup3z58qpZs6a+/PJLx1ivXr20aNEiubu7K0+ePGrYsKHeffdd5c2bN8VtjB07VuPGjUs2PmfOHHl7e6fpOwFAZpVoJGrRlUWae3mubIZNXi5e6h3YW00eayKLxWJ2eQAAmCImJkbdunXTzZs35evre8+5DxSu0svFixcVGBiozZs3JzmlcNiwYVq3bp22bdt2z/dv375dNWrU0LZt21S9enXH+J2jWU888YROnDiht956Szly5NCWLVtktSa/RiClI1eFCxfWtWvX7ttAZ7PZbFq1apWaNGkiNzdW40pv9Ne56K9zpWd/D1w5oH6/9tPuy7slSc2LN9eUFlNU2LdwepSaabEPOxf9dS7661z017kyUn8jIiKUL1++VIWrNJ8WOHHiRBUoUEB9+vRJMj5r1ixdvXpVw4cPT+smH9jMmTNVvnz5JMFKkrp06eL4c/ny5VWhQgUVL15ca9euVaNGjZJtx8PDQx4eHsnG3dzcTP9h3pGRasmK6K9z0V/nepj+xifG6/2N7+vd9e/KZrcpt2dufdr8U71Q4QWOVv0P9mHnor/ORX+di/46V0bob1o+P80LWkybNk1lypRJNl6uXDlNnTo1TdvKly+frFarwsPDk4yHh4ff93qp6OhozZ07V3379r3v5xQrVkz58uXT8ePH01QfAGRluy7uUrXp1RS6NlQ2u01BZYL058A/9WLFFwlWAAA8gDSHq8uXLysgICDZuJ+fny5dupSmbbm7u6tKlSpavXq1Y8xut2v16tX3XXlw/vz5iouLU48ePe77OefPn9fff/+dYt0AkN3EJsTqrdVvqcaMGtofvl/5vPNpboe5WthpoQJy8vckAAAPKs2nBRYuXFibNm3SE088kWR806ZNKliwYJoLCAkJUc+ePVW1alVVr15dkydPVnR0tGP1wBdffFGBgYGaOHFikvfNnDlTQUFByRapiIqK0rhx49ShQwf5+/vrxIkTGjZsmEqUKKFmzZqluT4AyEq2nt+qPov76PC1w5KkzuU667MWn8nPx8/kygAAyPzSHK769eunoUOHymazqWHDhpKk1atXa9iwYXr99dfTXEDnzp119epVjRkzRpcvX1alSpW0YsUKx42Kz549KxeXpAfYjh49qo0bN2rlypXJtme1WrV//359/fXXunHjhgoWLKimTZvqnXfeSfG6KgDIDmJsMRqzZow+2fqJ7IZdBXwKKKxVmNqVbWd2aQAAZBlpDldvvvmm/v77bw0cOFDx8fGSJE9PTw0fPlwjR458oCIGDx6swYMHp/ja2rVrk42VLl1ad1vk0MvL64HutwUAWdWGMxvUZ0kfHb9++7rTFyq8oMnNJ+sxr8fu804AAJAWaQ5XFotFH3zwgUaPHq3Dhw/Ly8tLJUuW5KgQAGQwUfFRGvn7SH2+43NJUmDOQE17bppalWplcmUAAGRNaV7Q4o4cOXKoWrVqypkzp06cOCG73Z6edQEAHsLvJ39X+bDyjmDVr3I/HRp4iGAFAIATpTpczZo1S5MmTUoy1r9/fxUrVkzly5fXU089pXPnzqV7gQCA1LsZe1P9f+mvJt820ekbp1UkVxGtemGVvmz9pXJ55jK7PAAAsrRUh6svv/xSefLkcTxfsWKFZs+erW+++UY7duxQ7ty5NW7cOKcUCQC4v+XHluupsKc0ffd0SdKgaoN0cOBBNS7W2OTKAADIHlJ9zdWxY8dUtWpVx/PFixerbdu26t69uyRpwoQJjuXTAQCPzvVb1zVs6TB9s+8bSVLxPMU1q+0s1StSz+TKAADIXlJ95OrWrVvy9fV1PN+8ebPq1fu/f7iLFSumy5cvp291AIB72npjqyp9WUnf7PtGFlkU8kyI9gfvJ1gBAGCCVB+5KlKkiHbt2qUiRYro2rVrOnTokGrXru14/fLly8qVi/P5AeBRuBp9VYOWDtL80/MlSWXyldHstrP1TKFnTK4MAIDsK9XhqmfPnho0aJAOHTqkP/74Q2XKlFGVKlUcr2/evFlPPfWUU4oEANxmGIZ+PPSjBi8frGsx1+QiF71R6w2NazBOnq6eZpcHAEC2lupwNWzYMMXExGjhwoXy9/fX/Pnzk7y+adMmde3aNd0LBADcdjnqsgYuHaifj/wsSXrK7yn1ytNLrz77qtxc3UyuDgAApDpcubi4aPz48Ro/fnyKr/87bAEA0odhGPp2/7caumKo/on9R64urhpVd5TeeOYN/f7b72aXBwAA/r9UhysAwKN3PuK8Xv71ZS07tkySVDmgsma3na0KBSrIZrOZXB0AAPhfhCsAyIAMw9DMPTP1+srXFREXIXeru8Y9O05v1HpDri781Q0AQEbEv9AAkMGcvnFaLy15SatPrZYkPVPoGc1qM0tl/cqaXBkAALgXwhUAZBB2w66wHWEa/vtwRdui5enqqfcavqchNYbI6mI1uzwAAHAfhCsAyACOXz+uvkv6av2Z9ZKkekXqaUbrGSqZt6TJlQEAgNRySa8NnTt3Tn369EmvzQFAtpBoT9SkLZNUIayC1p9ZLx83H33e4nOt6bmGYAUAQCaTbuHq+vXr+vrrr9NrcwCQ5R2+elh1ZtfR6ytf162EW2pcrLEODjyoQdUHycWSbn89AwCARyTVpwUuWbLknq+fPHnyoYsBgOwgwZ6gjzZ9pLHrxio+MV6+Hr76T9P/qO/TfWWxWMwuDwAAPKBUh6ugoCBZLBYZhnHXOfxSAAD3tj98v3ov7q3dl3ZLklqWbKlpz01TId9CJlcGAAAeVqrPOwkICNDChQtlt9tTfOzevduZdQJAphafGK+xa8eqypdVtPvSbuXxzKNvgr7Rr11/JVgBAJBFpDpcValSRbt27brr6/c7qgUA2dWui7tU9cuqGrdunBLsCQoqE6RDAw/phYovcMQfAIAsJNWnBb755puKjo6+6+slSpTQmjVr0qUoAMgKYhNiNX7deH246UMlGonK551Pn7f4XJ3KdSJUAQCQBaU6XNWtW/eer/v4+Kh+/foPXRAAZAVbzm1RnyV9dOTaEUlS53Kd9VmLz+Tn42dyZQAAwFlSfVrgyZMnOe0PAO4jxhajkN9CVHtWbR25dkT+Ofy1sNNCze04l2AFAEAWl+pwVbJkSV29etXxvHPnzgoPD3dKUQCQGa07vU4Vwirok62fyJChnhV76tDAQ2pXtp3ZpQEAgEcg1eHq30etli1bds9rsAAgu4iKj9LgZYP17NfP6sQ/J1TIt5CWdluqr4K+0mNej5ldHgAAeERSfc0VACC530/+rpeWvKQzN89IkvpV7qePmnykXJ65TK4MAAA8aqkOVxaLJdnqVqx2BSC7uhl7U2+sfEMz9syQJBXNXVTTW09X42KNTa4MAACYJdXhyjAM9erVSx4eHpKk2NhYDRgwQD4+PknmLVy4MH0rBIAMZulfS/Xyry/rQuQFSdLgaoM1sfFE5XDPYXJlAADATKkOVz179kzyvEePHuleDABkZNdvXdfQFUP17f5vJUklHiuhmW1mql6ReiZXBgAAMoJUh6vZs2c7sw4AyNB+PvyzgpcGKzw6XC4WF732zGsa32C8vN28zS4NAABkECxoAQD3cDX6qgYvH6wfD/0oSSqbr6xmtZ2lZwo9Y3JlAAAgoyFcAUAKDMPQvEPz9MryV3Qt5pqsFquG1x6u0fVHy9PV0+zyAABABkS4AoB/uRR5ScFLg7X46GJJUoUCFTSrzSxVKVjF5MoAAEBGRrgCgP/PMAx9s+8bDf1tqG7E3pCbi5tG1RulEXVGyN3qbnZ5AAAggyNcAYCkczfP6eVfX9by48slSVUCqmh229kqX6C8yZUBAIDMgnAFIFszDEMzds/Q6ytfV2R8pDysHhr77Fi9UesNubrwVyQAAEg9fnMAkG2d+ueU+v3ST6tPrZYkPVPoGc1qM0tl/cqaXBkAAMiMCFcAsh27YdeU7VM0YvUIxdhi5OXqpfcavqdXa7wqq4vV7PIAAEAmRbgCkK0c+/uY+izpo41nN0qS6hWpp5ltZqrEYyVMrgwAAGR2hCsA2UKiPVGTt07WqDWjFJsQKx83H33Y5EMNqDpALhYXs8sDAABZAOEKQJb359U/1WdxH227sE2S1KRYE33Z+ksVzV3U3MIAAECWQrgCkGXZEm36aPNHGrdunOIT4+Xr4atJTSepz9N9ZLFYzC4PAABkMYQrAFnSvsv71GdJH+2+tFuS1LJkS017bpoK+RYyuTIAAJBVEa4AZCnxifF6b/17mrBxghLsCcrjmUefNv9UPSr04GgVAABwKsIVgCxj58Wd6r24tw5eOShJalemnb5o9YX8c/ibXBkAAMgOCFcAMr3YhFiNXTtWH23+SHbDLj9vP33e8nM9/+TzHK0CAACPDOEKQKa2+dxm9VncR0f/PipJ6vpUV33a/FP5+fiZXBkAAMhuCFcAMqUYW4xG/TFKk7dOliFD/jn8NbXVVLUt09bs0gAAQDZFuAKQ6aw9vVYvLXlJJ/45IUnqWbGnPmn2ifJ45TG5MgAAkJ0RrgBkGpFxkRrx+wh9sfMLSVIh30L68rkv1aJkC5MrAwAAIFwByCRWnlipfr/009mbZyVJ/Sv314dNPlQuz1wmVwYAAHAb4QpAhnYj9obeWPmGZu6ZKUkqmruoZrSeoUbFGplcGQAAQFKEKwAZ1q9//aoBvw7QhcgLkqRXqr+iCY0mKId7DpMrAwAASI5wBSDD+Tvmbw39bai+2/+dJKnkYyU1s81M1S1S1+TKAAAA7o5wBSBDWXh4oQYuHajw6HC5WFwU8kyIxjUYJ283b7NLAwAAuCfCFYAM4Ur0FQ1eNljz/5wvSSqbr6xmt52tGoVqmFwZAABA6hCuAJjKMAzNPThXryx/RX/f+ltWi1XDaw/XmPpj5OHqYXZ5AAAAqUa4AmCaS5GXFLw0WIuPLpYkVShQQbPbzlblgMomVwYAAJB2hCsAj5xhGPp639d67bfXdCP2htxc3DS63mgNrzNc7lZ3s8sDAAB4IIQrAE6RaE/UujPrtP6f9fI546MGxRrI6mLV2Ztn9fKvL2vF8RWSpKoFq2pWm1kqX6C8yRUDAAA8HBezC5CkKVOmqGjRovL09FSNGjW0ffv2u8599tlnZbFYkj1atWrlmGMYhsaMGaOAgAB5eXmpcePGOnbs2KP4KgB0e8W/op8WVZPvm2jSmUlq8n0TFf20qAb8MkBPffGUVhxfIQ+rh95v9L629N1CsAIAAFmC6eFq3rx5CgkJUWhoqHbv3q2KFSuqWbNmunLlSorzFy5cqEuXLjkeBw8elNVq1fPPP++Y8+GHH+q///2vpk6dqm3btsnHx0fNmjVTbGzso/paQLa18PBCdfyxo85HnE8yfj7ivKbtnqbI+EjVLFRTewfs1fA6w+XqwgF0AACQNZgeriZNmqR+/fqpd+/eevLJJzV16lR5e3tr1qxZKc5/7LHH5O/v73isWrVK3t7ejnBlGIYmT56sUaNGqW3btqpQoYK++eYbXbx4UYsWLXqE3wzIfhLtiRqyYogMGXedk9szt9b2XKsy+co8wsoAAACcz9T/ZBwfH69du3Zp5MiRjjEXFxc1btxYW7ZsSdU2Zs6cqS5dusjHx0eSdOrUKV2+fFmNGzd2zMmVK5dq1KihLVu2qEuXLsm2ERcXp7i4OMfziIgISZLNZpPNZnug75Ze7ny+2XVkVfQ3fa07sy7ZEat/uxF7Q+tPr1f9IvUfUVVZF/uv89Fj56K/zkV/nYv+OldG6m9aajA1XF27dk2JiYkqUKBAkvECBQroyJEj933/9u3bdfDgQc2cOdMxdvnyZcc2/r3NO6/928SJEzVu3Lhk4ytXrpS3t/d963gUVq1aZXYJWRr9TR/r/1mfqnnLNy5X9KFoJ1eTfbD/Oh89di7661z017nor3NlhP7GxMSkem6mvthh5syZKl++vKpXr/5Q2xk5cqRCQkIczyMiIlS4cGE1bdpUvr6+D1vmQ7HZbFq1apWaNGkiNzc3U2vJiuhv+vI546NJZybdd16LOi04cpUO2H+djx47F/11LvrrXPTXuTJSf++c1ZYapoarfPnyyWq1Kjw8PMl4eHi4/P397/ne6OhozZ07V+PHj08yfud94eHhCggISLLNSpUqpbgtDw8PeXh4JBt3c3Mz/Yd5R0aqJSuivw/PMAztv7r/nnMssqiQbyHHsuxIH+y/zkePnYv+Ohf9dS7661wZob9p+XxTF7Rwd3dXlSpVtHr1aseY3W7X6tWrVbNmzXu+d/78+YqLi1OPHj2SjD/xxBPy9/dPss2IiAht27btvtsE8GDiEuL00pKX9PrK1x1jFlmSzLnzfHLzyQQrAACQJZm+WmBISIimT5+ur7/+WocPH1ZwcLCio6PVu3dvSdKLL76YZMGLO2bOnKmgoCDlzZs3ybjFYtHQoUP17rvvasmSJTpw4IBefPFFFSxYUEFBQY/iKwHZSnhUuBp900iz9s6Si8VFk5pO0oLnFyjQNzDJvEK+hbSg0wK1L9vepEoBAACcy/Rrrjp37qyrV69qzJgxunz5sipVqqQVK1Y4FqQ4e/asXFySZsCjR49q48aNWrlyZYrbHDZsmKKjo9W/f3/duHFDderU0YoVK+Tp6en07wNkJ3su7VHbuW11LuKccnnk0ryO89SsRDNJUlCZIK05uUbLNy5XizotOBUQAABkeaaHK0kaPHiwBg8enOJra9euTTZWunRpGcbd76NjsVg0fvz4ZNdjAUg/C/5coJ6LeirGFqNSeUtpSZclKp2vtON1q4tV9YvUV/ShaNUvUp9gBQAAsjzTTwsEkLnYDbtC14Tq+fnPK8YWo2bFm2lr361JghUAAEB2lCGOXAHIHKLio9RzUU8tPLxQkhTyTIg+aPKBXF34qwQAAIDfiACkypkbZ9RmbhvtD98vd6u7praaqt5P9za7LAAAgAyDcAXgvjac2aAOP3bQ1ZirKuBTQAs7L1StwrXMLgsAACBDIVwBuKcZu2do4NKBstltetr/aS3usliFcxU2uywAAIAMh3AFIEUJ9gSF/Baiz7Z/Jkl6/snnNbvtbPm4+5hcGQAAQMZEuAKQzPVb19V5QWf9fvJ3SdI7Dd7R23XflsViMbkyAACAjItwBSCJw1cPq83cNjp+/bh83Hz0bbtv1a5sO7PLAgAAyPAIVwAclh1bpi4LuigyPlJFchXRkq5LVKFABbPLAgAAyBS4iTAAGYahjzZ9pOfmPKfI+EjVK1JPO/rtIFgBAACkAUeugGwuNiFW/X/pr2/3fytJ6l+5vz5r+Zncre4mVwYAAJC5EK6AbOxS5CUFzQvS9gvbZbVYNbn5ZA2qNoiFKwAAAB4A4QrIpnZc2KGgeUG6GHlReTzzaP7z89WoWCOzywIAAMi0CFdANjTnwBz1XdJXsQmxKpuvrH7p+ouKP1bc7LIAAAAyNRa0ALIRu2HXW6vfUveF3RWbEKtWJVtp60tbCVYAAADpgCNXQDYRERehHgt76Je/fpEkDa89XO81fE9WF6vJlQEAAGQNhCsgGzj5z0m1+aGNDl09JA+rh2a2manuFbqbXRYAAECWQrgCsrg1p9ao4/yOun7rugJyBGhRl0WqHljd7LIAAACyHMIVkIV9seMLvbr8VSUaiapWsJp+7vyzAn0DzS4LAAAgSyJcAVmQLdGmV5e/qqm7pkqSupXvphmtZ8jLzcvkygAAALIuwhWQxVyLuaaOP3bUujPrZJFFExtN1LDaw7gxMAAAgJMRroAs5ED4AbWZ20anb5xWTvecmtNhjp4r9ZzZZQEAAGQLhCsgi1h8ZLF6/NxDUfFRKpanmJZ0WaJy+cuZXRYAAEC2wU2EgUzOMAy9t/49Bc0LUlR8lBo+0VDbX9pOsAIAAHjEOHIFZGIxthj1WdxH8w7NkyQNrjZYk5pNkpvVzeTKAAAAsh/CFZBJnY84r6C5Qdp1aZdcXVw1peUU9a/S3+yyAAAAsi3CFZAJbTm3Re3mtVN4dLjyeefTT51+Ur0i9cwuCwAAIFsjXAGZzNd7v1b/X/srPjFe5fOX15KuS1Q0d1GzywIAAMj2WNACyCQS7Yl6Y+Ub6rW4l+IT4xVUJkib+24mWAEAAGQQHLkCMoEbsTfU9aeuWnF8hSRpdL3RGvvsWLlY+O8jAAAAGQXhCsjg/vr7L7X5oY2O/n1UXq5e+iroK3Uq18nssgAAAPAvhCsgA1t5YqU6L+isG7E3VMi3kBZ3WazKAZXNLgsAAAAp4JwiIAMyDEOfbv1ULb5voRuxN1SzUE3t6LeDYAUAAJCBceQKyGDiEuI0cOlAzdo7S5LUq1IvTW01VR6uHiZXBgAAgHshXAEZSHhUuDr82EGbzm2Si8VFHzf5WEOfGSqLxWJ2aQAAALgPwhWQQey9vFdtfmijcxHnlMsjl+Z2nKvmJZqbXRYAAABSiXAFZAAL/lygnot6KsYWo1J5S2lJlyUqna+02WUBAAAgDVjQAjCR3bBr7Nqxen7+84qxxahp8aba2ncrwQoAACAT4sgVYJKo+Cj1XNRTCw8vlCSFPBOiD5p8IFcX/m8JAACQGfFbHGCCMzfOqM3cNtofvl/uVndNbTVVvZ/ubXZZAAAAeAiEK+AR23h2o9rPa6+rMVeV3ye/fu78s2oVrmV2WQAAAHhIhCvgEZqxe4YGLh0om92mp/2f1qIui/R4rsfNLgsAAADpgHAFPAIJ9gSF/Baiz7Z/Jkl6/snnNbvtbPm4+5hcGQAAANIL4Qpwsuu3rqvzgs76/eTvkqTxz47XqHqjuDEwAABAFkO4Apzo8NXDajO3jY5fPy4fNx992+5btSvbzuyyAAAA4ASEK8BJlh1bpi4LuigyPlJFchXRkq5LVKFABbPLAgAAgJNwE2EgnRmGoY82faTn5jynyPhI1X28rnb020GwAgAAyOI4cgWko9iEWPX/pb++3f+tJKlf5X76vOXncre6m1wZAAAAnI1wBaSTS5GXFDQvSNsvbJfVYtXk5pM1qNogFq4AAADIJghXQDrYcWGHguYF6WLkReXxzKP5z89Xo2KNzC4LAAAAjxDhCnhIcw7MUd8lfRWbEKuy+cpqSdclKvFYCbPLAgAAwCPGghbAA7Ibdr21+i11X9hdsQmxalWylba+tJVgBQAAkE1x5Ap4ABFxEeqxsId++esXSdLw2sP1XsP3ZHWxmlwZAAAAzEK4AtLo5D8n1eaHNjp09ZA8rB6a2WamulfobnZZAAAAMBnhCkiDNafWqOP8jrp+67oCcgRoUZdFqh5Y3eyyAAAAkAEQroBU+mLHF3p1+atKNBJVrWA1/dz5ZwX6BppdFgAAADIIwhVwH7ZEm15d/qqm7poqSepWvptmtJ4hLzcvkysDAABARkK4Au7hWsw1dfyxo9adWSeLLJrYaKKG1R7GjYEBAACQDOEKuIsD4QfUZm4bnb5xWjndc2pOhzl6rtRzZpcFAACADIpwBaRg8ZHF6vFzD0XFR6lYnmJa0mWJyuUvZ3ZZAAAAyMC4iTDwPwzD0Hvr31PQvCBFxUepQdEG2v7SdoIVAAAA7sv0cDVlyhQVLVpUnp6eqlGjhrZv337P+Tdu3NCgQYMUEBAgDw8PlSpVSsuWLXO8PnbsWFksliSPMmXKOPtrIAuIscWo609dNWrNKEnS4GqD9VuP35TXO6/JlQEAACAzMPW0wHnz5ikkJERTp05VjRo1NHnyZDVr1kxHjx5V/vz5k82Pj49XkyZNlD9/fi1YsECBgYE6c+aMcufOnWReuXLl9Pvvvzueu7py9iPu7XzEeT3/0/PadWmXXF1cNaXlFPWv0t/ssgAAAJCJmJo6Jk2apH79+ql3796SpKlTp2rp0qWaNWuWRowYkWz+rFmzdP36dW3evFlubm6SpKJFiyab5+rqKn9/f6fWjqzjaPRRvTz7ZYVHhyuvV1791Okn1S9a3+yyAAAAkMmYFq7i4+O1a9cujRw50jHm4uKixo0ba8uWLSm+Z8mSJapZs6YGDRqkxYsXy8/PT926ddPw4cNltVod844dO6aCBQvK09NTNWvW1MSJE/X444/ftZa4uDjFxcU5nkdEREiSbDabbDbbw37Vh3Ln882uI6uavWe23j7+thKMBD3l95R+ev4nPZH7CfqdTth/nYv+Oh89di7661z017nor3NlpP6mpQaLYRiGE2u5q4sXLyowMFCbN29WzZo1HePDhg3TunXrtG3btmTvKVOmjE6fPq3u3btr4MCBOn78uAYOHKhXX31VoaGhkqTly5crKipKpUuX1qVLlzRu3DhduHBBBw8eVM6cOVOsZezYsRo3blyy8Tlz5sjb2zudvjEykkQjUd9c/EaLry6WJNXIVUNDHx8qLys3BgYAAMD/iYmJUbdu3XTz5k35+vrec26mClelSpVSbGysTp065ThSNWnSJH300Ue6dOlSip9z48YNFSlSRJMmTVLfvn1TnJPSkavChQvr2rVr922gs9lsNq1atUpNmjRxnAqJh3Mz9qZ6LOqh307+Jkl6vsDzmvXCLHm4e5hcWdbD/utc9Nf56LFz0V/nor/ORX+dKyP1NyIiQvny5UtVuDLttMB8+fLJarUqPDw8yXh4ePhdr5cKCAiQm5tbklMAy5Ytq8uXLys+Pl7u7u7J3pM7d26VKlVKx48fv2stHh4e8vBI/ou1m5ub6T/MOzJSLZnZX3//pTY/tNHRv4/Ky9VL05+brhync8jD3YP+OhH7r3PRX+ejx85Ff52L/joX/XWujNDftHy+aUuxu7u7q0qVKlq9erVjzG63a/Xq1UmOZP2v2rVr6/jx47Lb7Y6xv/76SwEBASkGK0mKiorSiRMnFBAQkL5fAJnOyhMrVWNGDR39+6gK+RbSxj4b1enJTmaXBQAAgCzC1PtchYSEaPr06fr66691+PBhBQcHKzo62rF64IsvvphkwYvg4GBdv35dQ4YM0V9//aWlS5dqwoQJGjRokGPOG2+8oXXr1un06dPavHmz2rVrJ6vVqq5duz7y74eMwTAMfbr1U7X4voVuxN5QzUI1taPfDlUOqGx2aQAAAMhCTF2KvXPnzrp69arGjBmjy5cvq1KlSlqxYoUKFCggSTp79qxcXP4v/xUuXFi//fabXnvtNVWoUEGBgYEaMmSIhg8f7phz/vx5de3aVX///bf8/PxUp04dbd26VX5+fo/8+8F8cQlxGrRskGbumSlJ6lWpl6a2mioPV66vAgAAQPoy/e66gwcP1uDBg1N8be3atcnGatasqa1bt951e3Pnzk2v0pDJhUeFq8OPHbTp3Ca5WFz0cZOPNfSZobJYLGaXBgAAgCzI9HAFOMPey3vV5oc2OhdxTrk8cmlux7lqXqK52WUBAAAgCyNcIctZ8OcC9VzUUzG2GJXKW0pLuixR6XylzS4LAAAAWZypC1oA6clu2DV27Vg9P/95xdhi1LR4U23tu5VgBQAAgEeCI1fIEqLjo9VzUU/9dPgnSdJrz7ymD5t8KFcXdnEAAAA8GvzmiUzvzI0zaju3rfaF75O71V1TW01V76d7m10WAAAAshnCFTK1jWc3qv289roac1X5ffLr584/q1bhWmaXBQAAgGyIcIVMa8buGRq4dKBsdpsq+VfS4i6L9Xiux80uCwAAANkU4QqZToI9QSG/heiz7Z9Jkp5/8nnNbjtbPu4+JlcGAACA7IxwhUzl+q3r6rygs34/+bskafyz4zWq3ihuDAwAAADTEa6QaRy+elht5rbR8evH5e3mrW/bfav2ZdubXRYAAAAgiXCFTGLZsWXqsqCLIuMjVSRXES3uslgV/SuaXRYAAADgwE2EkaEZhqGPNn2k5+Y8p8j4SNV9vK529NtBsAIAAECGw5ErZFixCbHq/0t/fbv/W0lSv8r99HnLz+VudTe5MgAAACA5whUypEuRlxQ0L0jbL2yX1WLV5OaTNajaIBauAAAAQIZFuEKGs/PiTrWd21YXIy8qj2cezX9+vhoVa2R2WQAAAMA9Ea6Qofxw4Af1WdJHsQmxKpuvrJZ0XaISj5UwuywAAADgvljQAhmC3bDrrdVvqdvCbopNiFWrkq209aWtBCsAAABkGhy5guki4yLVfWF3/fLXL5KkYbWGaUKjCbK6WE2uDAAAAEg9whVMdfKfk2rzQxsdunpIHlYPzWgzQz0q9DC7LAAAACDNCFcwzZpTa9Rxfkddv3VdATkCtKjLIlUPrG52WQAAAMADIVzBFGE7wvTqileVYE9Q1YJVtajzIgX6BppdFgAAAPDACFd4pGyJNr26/FVN3TVVktStfDfNaD1DXm5eJlcGAAAAPBzCFR6ZazHX1PHHjlp3Zp0ssmhio4kaVnsYNwYGAABAlkC4wiNx8MpBtfmhjU7dOKUc7jk0p/0ctS7d2uyyAAAAgHRDuILTLT6yWD1+7qGo+CgVy1NMS7osUbn85cwuCwAAAEhX3EQYTmMYht5b/56C5gUpKj5KDYo20PaXthOsAAAAkCVx5ApOEWOLUd8lfTX34FxJ0qBqg/RJs0/kZnUzuTIAAADAOQhXSHfnI84raG6Qdl3aJVcXV33e4nO9XPVls8sCAAAAnIpwhXS19fxWBc0NUnh0uPJ65dVPnX5S/aL1zS4LAAAAcDrCFdLNN/u+Ub9f+ik+MV7l85fX4i6L9USeJ8wuCwAAAHgkWNACDy3Rnqg3V76pnot6Kj4xXkFlgrSpzyaCFQAAALIVjlzhodyMvakuP3XRiuMrJEmj6o7SuAbj5GIhtwMAACB7IVzhgR37+5ha/9BaR/8+Ki9XL81uO1udn+psdlkAAACAKQhXeCCrTqxSpwWddCP2hgr5FtKizotUpWAVs8sCAAAATMO5W0gTwzD06dZP1fz75roRe0M1C9XUjn47CFYAAADI9jhyhVSLS4jToGWDNHPPTElSz4o9Ne25afJw9TC5MgAAAMB8hCukSnhUuDr82EGbzm2Si8VFHzX5SK8985osFovZpQEAAAAZAuEK97X38l61+aGNzkWcUy6PXJrbca6al2hudlkAAABAhkK4wj0t+HOBei7qqRhbjEo+VlK/dP1FpfOVNrssAAAAIMNhQQukyG7YNXbtWD0//3nF2GLUtHhTbXtpG8EKAAAAuAuOXCGZ6Pho9VzUUz8d/kmS9Nozr+nDJh/K1YXdBQAAALgbfltGEmdunFHbuW21L3yf3K3umtpqqno/3dvssgAAAIAMj3AFh41nN6r9vPa6GnNV+X3y6+fOP6tW4VpmlwUAAABkCoQrSJJm7J6hgUsHyma3qZJ/JS3usliP53rc7LIAAACATINwlc0l2BP0+m+v67/b/ytJev7J5zW77Wz5uPuYXBkAAACQuRCusrHrt66r84LO+v3k75Kk8c+O16h6o7gxMAAAAPAACFfZ1OGrh9Vmbhsdv35c3m7e+rbdt2pftr3ZZQEAAACZFuEqG1p2bJm6/tRVEXERKpKriBZ3WayK/hXNLgsAAADI1LiJcDZiGIY+2vSRnpvznCLiIlT38bra0W8HwQoAAABIBxy5yiZiE2LV/5f++nb/t5KkfpX76fOWn8vd6m5yZQAAAEDWQLjKBi5FXlK7ee207cI2WS1WTW4+WYOqDWLhCgAAACAdEa6yuJ0Xd6rt3La6GHlReTzzaP7z89WoWCOzywIAAACyHMJVFvbDgR/UZ0kfxSbEqmy+slrSdYlKPFbC7LIAAACALIkFLbIgu2HX26vfVreF3RSbEKtWJVtpS98tBCsAAADAiThylcVExkWq+8Lu+uWvXyRJw2oN04RGE2R1sZpcGQAAAJC1Ea6ykJP/nFSbH9ro0NVD8rB6aEabGepRoYfZZQEAAADZAuEqi1hzao06zu+o67euKyBHgBZ1WaTqgdXNLgsAAADINghXWUDYjjC9uuJVJdgTVLVgVS3qvEiBvoFmlwUAAABkK4SrTMyWaNOry1/V1F1TJUndynfTjNYz5OXmZXJlAAAAQPZDuMqkrsVc0/Pzn9fa02tlkUUTG03UsNrDuDEwAAAAYBLCVSZ08MpBtfmhjU7dOKUc7jk0p/0ctS7d2uyyAAAAgGyNcJWBJdoTte7MOq3/Z718zvioQbEG+vWvX9Xj5x6Kio9SsTzFtKTLEpXLX87sUgEAAIBsz/SbCE+ZMkVFixaVp6enatSooe3bt99z/o0bNzRo0CAFBATIw8NDpUqV0rJlyx5qmxnRwsMLVfTTomryfRNNOjNJTb5vosc+fExB84IUFR+lBkUbaPtL2wlWAAAAQAZhariaN2+eQkJCFBoaqt27d6tixYpq1qyZrly5kuL8+Ph4NWnSRKdPn9aCBQt09OhRTZ8+XYGBgQ+8zYxo4eGF6vhjR52POJ9kPCIuQpLUvHhz/dbjN+X1zmtGeQAAAABSYGq4mjRpkvr166fevXvrySef1NSpU+Xt7a1Zs2alOH/WrFm6fv26Fi1apNq1a6to0aKqX7++Klas+MDbzGgS7YkasmKIDBl3nXPo6iG5WEw/6AgAAADgf5h2zVV8fLx27dqlkSNHOsZcXFzUuHFjbdmyJcX3LFmyRDVr1tSgQYO0ePFi+fn5qVu3bho+fLisVusDbVOS4uLiFBcX53geEXH7CJHNZpPNZnvYr5om686sS3bE6t/ORZzTmpNrVL9I/UdUVdZ15+f7qH/O2QX9dS7663z02Lnor3PRX+eiv86VkfqblhpMC1fXrl1TYmKiChQokGS8QIECOnLkSIrvOXnypP744w91795dy5Yt0/HjxzVw4EDZbDaFhoY+0DYlaeLEiRo3blyy8ZUrV8rb2/sBvt2DW//P+lTNW75xuaIPRTu5muxj1apVZpeQpdFf56K/zkePnYv+Ohf9dS7661wZob8xMTGpnpupVgu02+3Knz+/vvzyS1mtVlWpUkUXLlzQRx99pNDQ0Afe7siRIxUSEuJ4HhERocKFC6tp06by9fVNj9JTzeeMjyadmXTfeS3qtODIVTqw2WxatWqVmjRpIjc3N7PLyXLor3PRX+ejx85Ff52L/joX/XWujNTfO2e1pYZp4SpfvnyyWq0KDw9PMh4eHi5/f/8U3xMQECA3NzdZrVbHWNmyZXX58mXFx8c/0DYlycPDQx4eHsnG3dzcHvkPs0GxBirkW0gXIi6keN2VRRYV8i2kBsUayOpiTWELeBBm/KyzE/rrXPTX+eixc9Ff56K/zkV/nSsj9Dctn2/aqgju7u6qUqWKVq9e7Riz2+1avXq1atasmeJ7ateurePHj8tutzvG/vrrLwUEBMjd3f2BtpnRWF2s+rT5p5JuB6n/def55OaTCVYAAABABmPqknMhISGaPn26vv76ax0+fFjBwcGKjo5W7969JUkvvvhiksUpgoODdf36dQ0ZMkR//fWXli5dqgkTJmjQoEGp3mZm0L5sey3otECBvoFJxgv5FtKCTgvUvmx7kyoDAAAAcDemXnPVuXNnXb16VWPGjNHly5dVqVIlrVixwrEgxdmzZ+Xi8n/5r3Dhwvrtt9/02muvqUKFCgoMDNSQIUM0fPjwVG8zs2hftr3alm6rNSfXaPnG5WpRpwWnAgIAAAAZmOkLWgwePFiDBw9O8bW1a9cmG6tZs6a2bt36wNvMTKwuVtUvUl/Rh6JVv0h9ghUAAACQgXEnWgAAAABIB4QrAAAAAEgHhCsAAAAASAeEKwAAAABIB4QrAAAAAEgHhCsAAAAASAeEKwAAAABIB4QrAAAAAEgHhCsAAAAASAeEKwAAAABIB4QrAAAAAEgHhCsAAAAASAeEKwAAAABIB65mF5ARGYYhSYqIiDC5EslmsykmJkYRERFyc3Mzu5wsh/46F/11LvrrfPTYueivc9Ff56K/zpWR+nsnE9zJCPdCuEpBZGSkJKlw4cImVwIAAAAgI4iMjFSuXLnuOcdipCaCZTN2u10XL15Uzpw5ZbFYTK0lIiJChQsX1rlz5+Tr62tqLVkR/XUu+utc9Nf56LFz0V/nor/ORX+dKyP11zAMRUZGqmDBgnJxufdVVRy5SoGLi4sKFSpkdhlJ+Pr6mr5jZWX017nor3PRX+ejx85Ff52L/joX/XWujNLf+x2xuoMFLQAAAAAgHRCuAAAAACAdEK4yOA8PD4WGhsrDw8PsUrIk+utc9Ne56K/z0WPnor/ORX+di/46V2btLwtaAAAAAEA64MgVAAAAAKQDwhUAAAAApAPCFQAAAACkA8IVAAAAAKQDwpXJ1q9fr9atW6tgwYKyWCxatGjRfd+zdu1aVa5cWR4eHipRooS++uorp9eZWaW1v2vXrpXFYkn2uHz58qMpOBOZOHGiqlWrppw5cyp//vwKCgrS0aNH7/u++fPnq0yZMvL09FT58uW1bNmyR1Bt5vQgPf7qq6+S7b+enp6PqOLMJSwsTBUqVHDcoLJmzZpavnz5Pd/D/pt6ae0v++6De//992WxWDR06NB7zmP/fTCp6S/7b9qMHTs2Wb/KlClzz/dklv2XcGWy6OhoVaxYUVOmTEnV/FOnTqlVq1Zq0KCB9u7dq6FDh+qll17Sb7/95uRKM6e09veOo0eP6tKlS45H/vz5nVRh5rVu3ToNGjRIW7du1apVq2Sz2dS0aVNFR0ff9T2bN29W165d1bdvX+3Zs0dBQUEKCgrSwYMHH2HlmceD9Fi6fTf7/91/z5w584gqzlwKFSqk999/X7t27dLOnTvVsGFDtW3bVocOHUpxPvtv2qS1vxL77oPYsWOHpk2bpgoVKtxzHvvvg0ltfyX237QqV65ckn5t3LjxrnMz1f5rIMOQZPz888/3nDNs2DCjXLlyScY6d+5sNGvWzImVZQ2p6e+aNWsMScY///zzSGrKSq5cuWJIMtatW3fXOZ06dTJatWqVZKxGjRrGyy+/7OzysoTU9Hj27NlGrly5Hl1RWUyePHmMGTNmpPga++/Du1d/2XfTLjIy0ihZsqSxatUqo379+saQIUPuOpf9N+3S0l/237QJDQ01KlasmOr5mWn/5chVJrNlyxY1btw4yVizZs20ZcsWkyrKmipVqqSAgAA1adJEmzZtMrucTOHmzZuSpMcee+yuc9h/H05qeixJUVFRKlKkiAoXLnzfIwW4LTExUXPnzlV0dLRq1qyZ4hz23weXmv5K7LtpNWjQILVq1SrZfpkS9t+0S0t/JfbftDp27JgKFiyoYsWKqXv37jp79uxd52am/dfV7AKQNpcvX1aBAgWSjBUoUEARERG6deuWvLy8TKosawgICNDUqVNVtWpVxcXFacaMGXr22We1bds2Va5c2ezyMiy73a6hQ4eqdu3aeuqpp+467277L9e03V9qe1y6dGnNmjVLFSpU0M2bN/Xxxx+rVq1aOnTokAoVKvQIK84cDhw4oJo1ayo2NlY5cuTQzz//rCeffDLFuey/aZeW/rLvps3cuXO1e/du7dixI1Xz2X/TJq39Zf9Nmxo1auirr75S6dKldenSJY0bN05169bVwYMHlTNnzmTzM9P+S7gC/kfp0qVVunRpx/NatWrpxIkT+uSTT/Ttt9+aWFnGNmjQIB08ePCe50vj4aS2xzVr1kxyZKBWrVoqW7aspk2bpnfeecfZZWY6pUuX1t69e3Xz5k0tWLBAPXv21Lp16+4aAJA2aekv+27qnTt3TkOGDNGqVatYNMEJHqS/7L9p06JFC8efK1SooBo1aqhIkSL68ccf1bdvXxMre3iEq0zG399f4eHhScbCw8Pl6+vLUSsnqV69OqHhHgYPHqxff/1V69evv+9/nbvb/uvv7+/MEjO9tPT439zc3PT000/r+PHjTqouc3N3d1eJEiUkSVWqVNGOHTv06aefatq0acnmsv+mXVr6+2/su3e3a9cuXblyJckZFYmJiVq/fr0+//xzxcXFyWq1JnkP+2/qPUh//439N21y586tUqVK3bVfmWn/5ZqrTKZmzZpavXp1krFVq1bd8xx2PJy9e/cqICDA7DIyHMMwNHjwYP3888/6448/9MQTT9z3Pey/afMgPf63xMREHThwgH04lex2u+Li4lJ8jf334d2rv//Gvnt3jRo10oEDB7R3717Ho2rVqurevbv27t2b4i/+7L+p9yD9/Tf237SJiorSiRMn7tqvTLX/mr2iRnYXGRlp7Nmzx9izZ48hyZg0aZKxZ88e48yZM4ZhGMaIESOMF154wTH/5MmThre3t/Hmm28ahw8fNqZMmWJYrVZjxYoVZn2FDC2t/f3kk0+MRYsWGceOHTMOHDhgDBkyxHBxcTF+//13s75ChhUcHGzkypXLWLt2rXHp0iXHIyYmxjHnhRdeMEaMGOF4vmnTJsPV1dX4+OOPjcOHDxuhoaGGm5ubceDAATO+Qob3ID0eN26c8dtvvxknTpwwdu3aZXTp0sXw9PQ0Dh06ZMZXyNBGjBhhrFu3zjh16pSxf/9+Y8SIEYbFYjFWrlxpGAb778NKa3/Zdx/Ov1ezY/9NX/frL/tv2rz++uvG2rVrjVOnThmbNm0yGjdubOTLl8+4cuWKYRiZe/8lXJnsztLf/3707NnTMAzD6Nmzp1G/fv1k76lUqZLh7u5uFCtWzJg9e/YjrzuzSGt/P/jgA6N48eKGp6en8dhjjxnPPvus8ccff5hTfAaXUl8lJdkf69ev7+j1HT/++KNRqlQpw93d3ShXrpyxdOnSR1t4JvIgPR46dKjx+OOPG+7u7kaBAgWMli1bGrt37370xWcCffr0MYoUKWK4u7sbfn5+RqNGjRy/+BsG++/DSmt/2Xcfzr9/+Wf/TV/36y/7b9p07tzZCAgIMNzd3Y3AwECjc+fOxvHjxx2vZ+b912IYhvHojpMBAAAAQNbENVcAAAAAkA4IVwAAAACQDghXAAAAAJAOCFcAAAAAkA4IVwAAAACQDghXAAAAAJAOCFcAAAAAkA4IVwAAAACQDghXAAA8JIvFokWLFpldBgDAZIQrAECm1qtXL1kslmSP5s2bm10aACCbcTW7AAAAHlbz5s01e/bsJGMeHh4mVQMAyK44cgUAyPQ8PDzk7++f5JEnTx5Jt0/ZCwsLU4sWLeTl5aVixYppwYIFSd5/4MABNWzYUF5eXsqbN6/69++vqKioJHNmzZqlcuXKycPDQwEBARo8eHCS169du6Z27drJ29tbJUuW1JIlSxyv/fPPP+revbv8/Pzk5eWlkiVLJguDAIDMj3AFAMjyRo8erQ4dOmjfvn3q3r27unTposOHD0uSoqOj1axZM+XJk0c7duzQ/Pnz9fvvvycJT2FhYRo0aJD69++vAwcOaMmSJSpRokSSzxg3bpw6deqk/fv3q2XLlurevbuuX7/u+Pw///xTy5cv1+HDhxUWFqZ8+fI9ugYAAB4Ji2EYhtlFAADwoHr16qXvvvtOnp6eScbfeustvfXWW7JYLBowYIDCwsIcrz3zzDOqXLmyvvjiC02fPl3Dhw/XuXPn5OPjI0latmyZWrdurYsXL6pAgQIKDAxU79699e6776ZYg8Vi0ahRo/TOO+9Iuh3YcuTIoeXLl6t58+Zq06aN8uXLp1mzZjmpCwCAjIBrrgAAmV6DBg2ShCdJeuyxxxx/rlmzZpLXatasqb1790qSDh8+rIoVKzqClSTVrl1bdrtdR48elcVi0cWLF9WoUaN71lChQgXHn318fOTr66srV65IkoKDg9WhQwft3r1bTZs2VVBQkGrVqvVA3xUAkHERrgAAmZ6Pj0+y0/TSi5eXV6rmubm5JXlusVhkt9slSS1atNCZM2e0bNkyrVq1So0aNdKgQYP08ccfp3u9AADzcM0VACDL27p1a7LnZcuWlSSVLVtW+/btU3R0tOP1TZs2ycXFRaVLl1bOnDlVtGhRrV69+qFq8PPzU8+ePfXdd99p8uTJ+vLLLx9qewCAjIcjVwCATC8uLk6XL19OMubq6upYNGL+/PmqWrWq6tSpo++//17bt2/XzJkzJUndu3dXaGioevbsqbFjx+rq1at65ZVX9MILL6hAgQKSpLFjx2rAgAHKnz+/WrRoocjISG3atEmvvPJKquobM2aMqlSponLlyikuLk6//vqrI9wBALIOwhUAINNbsWKFAgICkoyVLl1aR44ckXR7Jb+5c+dq4MCBCggI0A8//KAnn3xSkuTt7a3ffvtNQ4YMUbVq1eTt7a0OHTpo0qRJjm317NlTsbGx+uSTT/TGG28oX7586tixY6rrc3d318iRI3X69Gl5eXmpbt26mjt3bjp8cwBARsJqgQCALM1isejnn39WUFCQ2aUAALI4rrkCAAAAgHRAuAIAAACAdMA1VwCALI2z3wEAjwpHrgAAAAAgHRCuAAAAACAdEK4AAAAAIB0QrgAAAAAgHRCuAAAAACAdEK4AAAAAIB0QrgAAAAAgHRCuAAAAACAd/D+NPGBS1NwmIgAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# Sample data for plotting (Replace this with your actual values)\n", - "# Example epoch numbers (change as needed)\n", - "epochs = range(1, 6) # Adjust to match the length of your data\n", - "\n", - "# Replace with your actual training losses per epoch\n", - "train_losses = [0.8, 0.6, 0.4, 0.3, 0.2] \n", - "# Replace with your validation losses per epoch\n", - "val_losses = [0.9, 0.7, 0.5, 0.4, 0.3] \n", - "\n", - "# Replace with your actual training accuracies\n", - "train_accuracies = [0.65, 0.75, 0.82, 0.88, 0.92] \n", - "# Replace with validation accuracies\n", - "val_accuracies = [0.62, 0.72, 0.80, 0.85, 0.90] \n", - "\n", - "# Replace with F1 scores per epoch (optional if you don't need this)\n", - "f1_scores = [0.60, 0.70, 0.78, 0.84, 0.89] \n", - "\n", - "# Plot Loss Curve\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(epochs, train_losses, label='Training Loss', color='blue', marker='o')\n", - "plt.plot(epochs, val_losses, label='Validation Loss', color='red', marker='o')\n", - "plt.title('Loss Curve')\n", - "plt.xlabel('Epochs')\n", - "plt.ylabel('Loss')\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.show()\n", - "\n", - "# Plot Accuracy Curve\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(epochs, train_accuracies, label='Training Accuracy', color='blue', marker='o')\n", - "plt.plot(epochs, val_accuracies, label='Validation Accuracy', color='red', marker='o')\n", - "plt.title('Accuracy Curve')\n", - "plt.xlabel('Epochs')\n", - "plt.ylabel('Accuracy')\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.show()\n", - "\n", - "# Plot F1 Score Curve\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(epochs, f1_scores, label='F1 Score', color='green', marker='o')\n", - "plt.title('F1 Score Curve')\n", - "plt.xlabel('Epochs')\n", - "plt.ylabel('F1 Score')\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import matplotlib.patches as patches\n", - "\n", - "\n", - "fig, ax = plt.subplots(figsize=(15, 6))\n", - "\n", - "\n", - "colors = {\n", - " 'input': '#d3d3d3', # Light gray for Input Layer\n", - " 'conv': '#6495ed', # Blue for Convolution + ReLU\n", - " 'pool': '#a9a9a9', # Dark gray for Max Pooling\n", - " 'dense': '#ffd700', # Gold for Fully Connected + ReLU\n", - " 'output': '#32cd32' # Lime for Output (softmax)\n", - "}\n", - "\n", - "def draw_block(ax, x, y, width, height, label, color):\n", - " rect = patches.Rectangle((x, y), width, height, edgecolor='black', facecolor=color)\n", - " ax.add_patch(rect)\n", - "\n", - " ax.text(x + width / 2, y + height / 2, label, color='black',\n", - " ha='center', va='center', fontsize=9, weight=\"bold\", rotation=90)\n", - "\n", - "block_width = 0.4\n", - "\n", - "\n", - "draw_block(ax, 0, 1, block_width, 1, 'Input\\n(224x224x3)', colors['input'])\n", - "\n", - "# Conv + ReLU Layers\n", - "conv_layers = [\n", - " ('Conv 64\\n224x224', block_width), ('Conv 64\\n224x224', block_width), ('Pool\\n112x112', block_width),\n", - " ('Conv 128\\n112x112', block_width), ('Conv 128\\n112x112', block_width), ('Pool\\n56x56', block_width),\n", - " ('Conv 256\\n56x56', block_width), ('Conv 256\\n56x56', block_width), ('Conv 256\\n56x56', block_width), ('Pool\\n28x28', block_width),\n", - " ('Conv 512\\n28x28', block_width), ('Conv 512\\n28x28', block_width), ('Conv 512\\n28x28', block_width), ('Pool\\n14x14', block_width),\n", - " ('Conv 512\\n14x14', block_width), ('Conv 512\\n14x14', block_width), ('Conv 512\\n14x14', block_width), ('Pool\\n7x7', block_width),\n", - " ('Conv 512\\n7x7', block_width), ('Conv 512\\n7x7', block_width), ('Conv 512\\n7x7', block_width), ('Conv 512\\n7x7', block_width) # Additional layers\n", - "]\n", - "\n", - "x_offset = block_width\n", - "for layer, width in conv_layers:\n", - " if 'Conv' in layer:\n", - " draw_block(ax, x_offset, 1, width, 1, layer, colors['conv'])\n", - " else: \n", - " draw_block(ax, x_offset, 1, width, 0.5, layer, colors['pool'])\n", - " x_offset += width\n", - "\n", - "# Adaptive Average Pooling\n", - "draw_block(ax, x_offset, 1, block_width, 0.5, 'AvgPool\\n7x7', colors['pool'])\n", - "x_offset += block_width\n", - "\n", - "# Fully Connected + Dropout + ReLU Layers\n", - "draw_block(ax, x_offset, 1, block_width, 1, 'FC 256\\n+ Dropout', colors['dense'])\n", - "x_offset += block_width\n", - "\n", - "# Output Layer\n", - "draw_block(ax, x_offset, 1, block_width, 1, 'Output\\n6 Classes', colors['output'])\n", - "\n", - "\n", - "ax.set_xlim(0, x_offset + 3)\n", - "ax.set_ylim(0, 3)\n", - "ax.axis('off')\n", - "\n", - "\n", - "plt.title('Custom VGG19 Architecture (Fine-Tuned)')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", - " warnings.warn(\n", - "c:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG19_Weights.IMAGENET1K_V1`. You can also use `weights=VGG19_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n", - "C:\\Users\\incha\\AppData\\Local\\Temp\\ipykernel_25336\\996431065.py:32: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " model.load_state_dict(torch.load('best_model_CustomVGG.pt'))\n" - ] - } - ], - "source": [ - "import torch\n", - "import torchvision.models as models\n", - "\n", - "# Define your CustomVGG class as before\n", - "class CustomVGG(nn.Module):\n", - " def __init__(self, base_model, num_classes):\n", - " super(CustomVGG, self).__init__()\n", - " self.features = base_model.features\n", - " self.avgpool = base_model.avgpool\n", - " self.classifier = nn.Sequential(\n", - " nn.Linear(base_model.classifier[0].in_features, 256),\n", - " nn.ReLU(inplace=True),\n", - " nn.Dropout(p=0.5),\n", - " nn.Linear(256, num_classes)\n", - " )\n", - " \n", - " def forward(self, x):\n", - " x = self.features(x)\n", - " x = self.avgpool(x)\n", - " x = torch.flatten(x, 1)\n", - " x = self.classifier(x)\n", - " return x\n", - "\n", - "# Load the base VGG19 model\n", - "vgg19_base = models.vgg19(pretrained=True)\n", - "\n", - "# Initialize your CustomVGG model\n", - "num_classes = 6\n", - "model = CustomVGG(vgg19_base, num_classes)\n", - "\n", - "# Load the saved state_dict\n", - "model.load_state_dict(torch.load('best_model_CustomVGG.pt'))\n", - "model.eval() # Set the model to evaluation mode\n", - "\n", - "# Convert to TorchScript\n", - "scripted_model = torch.jit.script(model)\n", - "\n", - "# Save the scripted model\n", - "torch.jit.save(scripted_model, 'scripted_model_CustomVGG.pt')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n", - "C:\\Users\\incha\\AppData\\Local\\Temp\\ipykernel_25336\\4004522494.py:34: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " model.load_state_dict(torch.load('best_model_CustomVGG.pt'), strict=False)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error loading state_dict: Error(s) in loading state_dict for CustomVGG:\n", - "\tsize mismatch for features.19.weight: copying a param with shape torch.Size([512, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 512, 3, 3]).\n" - ] - } - ], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torchvision.models as models\n", - "\n", - "# Define your CustomVGG class\n", - "class CustomVGG(nn.Module):\n", - " def __init__(self, base_model, num_classes):\n", - " super(CustomVGG, self).__init__()\n", - " self.features = base_model.features\n", - " self.avgpool = base_model.avgpool\n", - " self.classifier = nn.Sequential(\n", - " nn.Linear(base_model.classifier[0].in_features, 256),\n", - " nn.ReLU(inplace=True),\n", - " nn.Dropout(p=0.5),\n", - " nn.Linear(256, num_classes)\n", - " )\n", - " \n", - " def forward(self, x):\n", - " x = self.features(x)\n", - " x = self.avgpool(x)\n", - " x = torch.flatten(x, 1)\n", - " x = self.classifier(x)\n", - " return x\n", - "\n", - "# Load the base VGG16 model\n", - "vgg16_base = models.vgg16(pretrained=True)\n", - "\n", - "# Initialize your CustomVGG model\n", - "num_classes = 6\n", - "model = CustomVGG(vgg16_base, num_classes)\n", - "\n", - "# Load the saved state_dict with strict=False\n", - "try:\n", - " model.load_state_dict(torch.load('best_model_CustomVGG.pt'), strict=False)\n", - "except RuntimeError as e:\n", - " print(f\"Error loading state_dict: {e}\")\n", - "\n", - "model.eval() # Set the model to evaluation mode\n", - "\n", - "# Dynamic quantization\n", - "quantized_model = torch.quantization.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8)\n", - "\n", - "# Save the quantized model\n", - "torch.save(quantized_model.state_dict(), 'quantized_model_CustomVGG.pt')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\incha\\AppData\\Local\\Temp\\ipykernel_1200\\2254952076.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " model_deepercnn.load_state_dict(torch.load(\"best_model_CustomCNN.pt\", map_location=device))\n" - ] - } - ], - "source": [ - "# Load the saved model state\n", - "model_deepercnn.load_state_dict(torch.load(\"best_model_CustomCNN.pt\", map_location=device))\n", - "model_deepercnn.eval() # Set to evaluation mode\n", - "\n", - "# Script the model\n", - "scripted_model = torch.jit.script(model_deepercnn) # Or use torch.jit.trace for static input shapes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from torch.utils.mobile_optimizer import optimize_for_mobile\n", - "\n", - "# Optimize the model\n", - "optimized_model = optimize_for_mobile(scripted_model)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Save the optimized model\n", - "optimized_model.save(\"deepercnn_mobile.pt\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generated data shape: (200, 128, 128), Labels shape: (200,)\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "from scipy.signal import spectrogram\n", - "import cv2\n", - "\n", - "# Parameters\n", - "sampling_rate = 1000 # Hz\n", - "duration = 1.0 # Signal duration in seconds\n", - "time = np.linspace(0, duration, int(sampling_rate * duration))\n", - "\n", - "def generate_synthetic_signal(frequencies, num_samples=100, img_size=(128, 128)):\n", - " dataset = []\n", - " labels = []\n", - " \n", - " for _ in range(num_samples):\n", - " # Choose a random frequency to simulate a band\n", - " freq = np.random.choice(frequencies)\n", - " \n", - " # Create a sine wave and add noise\n", - " signal = np.sin(2 * np.pi * freq * time) + np.random.normal(0, 0.2, time.shape)\n", - " \n", - " # Generate spectrogram\n", - " _, _, spectro = spectrogram(signal, fs=sampling_rate)\n", - " \n", - " # Resize spectrogram to match model input\n", - " resized_spectro = cv2.resize(spectro, img_size)\n", - " dataset.append(resized_spectro)\n", - " labels.append(frequencies.index(freq)) # Assign a class index\n", - " \n", - " return np.array(dataset), np.array(labels)\n", - "\n", - "# Example usage\n", - "frequencies = [50, 100, 150, 200] # Simulate four radar bands\n", - "synthetic_data, synthetic_labels = generate_synthetic_signal(frequencies, num_samples=200)\n", - "print(f\"Generated data shape: {synthetic_data.shape}, Labels shape: {synthetic_labels.shape}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Expand dimensions for compatibility (add a channel dimension if needed)\n", - "synthetic_data = np.expand_dims(synthetic_data, axis=-1) # For grayscale input" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_35756\\4282892681.py:8: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " model.load_state_dict(torch.load(model_path))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model has been converted to ONNX and saved at custom_cnn.onnx\n" - ] - } - ], - "source": [ - "import torch\n", - "\n", - "# Path to the saved .pt file\n", - "model_path = \"best_model_CustomCNN.pt\"\n", - "\n", - "num_classes = 6 \n", - "model = CustomCNN(num_classes=num_classes)\n", - "model.load_state_dict(torch.load(model_path))\n", - "model.eval() \n", - "\n", - "# Define a dummy input matching the input size of the model\n", - "dummy_input = torch.randn(1, 3, 224, 224) # Batch size = 1, 3 channels, 224x224 resolution\n", - "\n", - "\n", - "onnx_file_path = \"custom_cnn.onnx\" \n", - "torch.onnx.export(\n", - " model, \n", - " dummy_input, \n", - " onnx_file_path, \n", - " export_params=True, \n", - " opset_version=11, \n", - " do_constant_folding=True, \n", - " input_names=['input'], \n", - " output_names=['output'], \n", - " dynamic_axes={ \n", - " 'input': {0: 'batch_size'}, \n", - " 'output': {0: 'batch_size'}\n", - " }\n", - ")\n", - "\n", - "print(f\"Model has been converted to ONNX and saved at {onnx_file_path}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting onnx\n", - " Downloading onnx-1.17.0-cp312-cp312-win_amd64.whl.metadata (16 kB)\n", - "Requirement already satisfied: numpy>=1.20 in c:\\users\\shravya h jain\\desktop\\micro-classify-main\\micro-classify\\.venv\\lib\\site-packages (from onnx) (1.26.4)\n", - "Collecting protobuf>=3.20.2 (from onnx)\n", - " Downloading protobuf-5.29.1-cp310-abi3-win_amd64.whl.metadata (592 bytes)\n", - "Downloading onnx-1.17.0-cp312-cp312-win_amd64.whl (14.5 MB)\n", - " ---------------------------------------- 0.0/14.5 MB ? eta -:--:--\n", - " -------- ------------------------------- 3.1/14.5 MB 15.4 MB/s eta 0:00:01\n", - " ----------------------- ---------------- 8.4/14.5 MB 21.7 MB/s eta 0:00:01\n", - " ------------------------------------ --- 13.4/14.5 MB 22.7 MB/s eta 0:00:01\n", - " ---------------------------------------- 14.5/14.5 MB 21.8 MB/s eta 0:00:00\n", - "Downloading protobuf-5.29.1-cp310-abi3-win_amd64.whl (434 kB)\n", - "Installing collected packages: protobuf, onnx\n", - "Successfully installed onnx-1.17.0 protobuf-5.29.1\n" - ] - } - ], - "source": [ - "!pip install onnx" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "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": "3.12.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/ml_model/notebooks/confusion_matrix.png b/ml_model/notebooks/confusion_matrix.png index 3788444..6d14185 100644 Binary files a/ml_model/notebooks/confusion_matrix.png and b/ml_model/notebooks/confusion_matrix.png differ diff --git a/ml_model/notebooks/customCnnWithCNN.ipynb b/ml_model/notebooks/customCnnWithCNN.ipynb index 9f1d7a9..376e452 100644 --- a/ml_model/notebooks/customCnnWithCNN.ipynb +++ b/ml_model/notebooks/customCnnWithCNN.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 46, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -21,12 +21,12 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABdIAAASlCAYAAACspitqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3hUZdoG8PtMT++9EwKhdxAQEcHeRcXeC5ZVV9dv1bXtuqu79t4Lir2AXRRRpPcqnfTeezL9fH/MnEmAEGYmM5nknPt3XVzXbkgmLxDPnPO8z3s/giiKIoiIiIiIiIiIiIiIqFuqQC+AiIiIiIiIiIiIiKg/YyGdiIiIiIiIiIiIiKgHLKQTEREREREREREREfWAhXQiIiIiIiIiIiIioh6wkE5ERERERERERERE1AMW0omIiIiIiIiIiIiIesBCOhERERERERERERFRD1hIJyIiIiIiIiIiIiLqAQvpREREREREREREREQ9YCGdiIiIiMjPHn30UQiC4NXXLliwAIIgoLCw0LeL6qKwsBCCIGDBggV++x5ERERERAMZC+lEREREREexa9cuXHHFFUhJSYFer0dycjIuv/xy7Nq1K9BLC4jly5dDEATXL71ej4SEBJx44ol4/PHHUVNT4/Vr7969G48++qhfNwyIiIiIiLzFQjoRERERUTcWLVqE8ePHY9myZbj22mvx6quv4vrrr8fvv/+O8ePHY/HixW6/1oMPPoiOjg6v1nHllVeio6MDGRkZXn29P9xxxx1YuHAh3nzzTdx7772Ijo7GI488gmHDhuG3337z6jV3796Nf/7znyykExEREVG/pAn0AoiIiIiI+pu8vDxceeWVGDRoEFasWIG4uDjX7915552YMWMGrrzySuzYsQODBg066uu0tbUhJCQEGo0GGo13t95qtRpqtdqrr/WXGTNm4MILLzzkY9u3b8cpp5yCuXPnYvfu3UhKSgrQ6oiIiIiIfI8d6UREREREh3nqqafQ3t6ON99885AiOgDExsbijTfeQFtbG5588knXx6Uc9N27d+Oyyy5DVFQUjj/++EN+r6uOjg7ccccdiI2NRVhYGM455xyUlZVBEAQ8+uijrs/rLiM9MzMTZ511FlatWoXJkyfDYDBg0KBB+OCDDw75HvX19fjb3/6GUaNGITQ0FOHh4Tj99NOxfft2H/1NdRozZgyef/55NDY24uWXX3Z9vKioCLfeeiuGDh2KoKAgxMTE4KKLLjrkz7NgwQJcdNFFAIBZs2a5omOWL18OAPjmm29w5plnIjk5GXq9HtnZ2Xjsscdgs9l8/ucgIiIiIuoOC+lERERERIf57rvvkJmZiRkzZnT7+yeccAIyMzPxww8/HPF7F110Edrb2/H444/jxhtvPOr3uOaaa/DSSy/hjDPOwP/+9z8EBQXhzDPPdHuNBw8exIUXXoiTTz4ZzzzzDKKionDNNdcckt+en5+Pr7/+GmeddRaeffZZ3Hvvvdi5cydmzpyJ8vJyt7+Xuy688EIEBQXhl19+cX1s48aNWLNmDS655BK8+OKLmD9/PpYtW4YTTzwR7e3tABx/n3fccQcA4IEHHsDChQuxcOFCDBs2DICj0B4aGoq7774bL7zwAiZMmICHH34Y9913n8//DERERERE3WG0CxERERFRF01NTSgvL8e5557b4+eNHj0a3377LVpaWhAWFub6+JgxY/Dxxx/3+LVbtmzB559/jrvuugvPPfccAODWW2/Ftdde63a3+L59+7BixQpXsf/iiy9GWloa3nvvPTz99NMAgFGjRmH//v1QqTr7Z6688krk5ubinXfewUMPPeTW93KXVqvFkCFDkJeX5/rYmWeeeUQMzNlnn42pU6fiq6++ckXozJgxAy+++CJOPvlknHjiiYd8/scff4ygoCDX/58/fz7mz5+PV199Ff/+97+h1+t9+ucgIiIiIjocO9KJiIiIiLpoaWkBgEOK492Rfr+5ufmQj8+fP/+Y32PJkiUAHMXzrv7yl7+4vc7hw4cf0jEfFxeHoUOHIj8/3/UxvV7vKqLbbDbU1dUhNDQUQ4cOxZYtW9z+Xp4IDQ11/R0COKQAbrFYUFdXh8GDByMyMtLtNXR9jZaWFtTW1mLGjBlob2/H3r17fbd4IiIiIqKjYCGdiIiIiKgLqUDetRjcnaMV3LOyso75PYqKiqBSqY743MGDB7u9zvT09CM+FhUVhYaGBtf/t9vteO6555CTkwO9Xo/Y2FjExcVhx44daGpqcvt7eaK1tfWQv5OOjg48/PDDSEtLO2QNjY2Nbq9h165dOP/88xEREYHw8HDExcXhiiuuAAC//TmIiIiIiLpitAsRERERURcRERFISkrCjh07evy8HTt2ICUlBeHh4Yd8vGv3tD+p1epuPy6Kout/P/7443jooYdw3XXX4bHHHkN0dDRUKhXuuusu2O12n6/JYrFg//79GDlypOtjf/nLX/Dee+/hrrvuwtSpUxEREQFBEHDJJZe4tYbGxkbMnDkT4eHh+Ne//oXs7GwYDAZs2bIFf//73/3y5yAiIiIiOhwL6UREREREhznrrLPw1ltvYdWqVTj++OOP+P2VK1eisLAQN998s1evn5GRAbvdjoKCAuTk5Lg+fvDgQa/X3J0vv/wSs2bNwjvvvHPIxxsbGxEbG+vT7yV9v46ODpx66qmHfOzqq6/GM8884/qY0WhEY2PjIV8rCEK3r7l8+XLU1dVh0aJFOOGEE1wfLygo8O3iiYiIiIh6wGgXIiIiIqLD3HvvvQgKCsLNN9+Murq6Q36vvr4e8+fPR3BwMO69916vXl8qNL/66quHfPyll17ybsFHoVarD+lQB4AvvvgCZWVlPv0+ALB9+3bcddddiIqKwm233dbjGl566SXYbLZDPhYSEgIARxTYpc77rq9hNpuP+LsjIiIiIvIndqQTERERER0mJycH77//Pi6//HKMGjUK119/PbKyslBYWIh33nkHtbW1+OSTT5Cdne3V60+YMAFz587F888/j7q6Ohx33HH4448/sH//fgBH78721FlnnYV//etfuPbaazFt2jTs3LkTH330EQYNGtSr1125ciWMRqNrgOnq1avx7bffIiIiAosXL0ZiYuIha1i4cCEiIiIwfPhwrF27Fr/++itiYmIOec2xY8dCrVbjf//7H5qamqDX63HSSSdh2rRpiIqKwtVXX4077rgDgiBg4cKFRxTniYiIiIj8iYV0IiIiIqJuXHTRRcjNzcUTTzzhKp7HxMRg1qxZeOCBBw7JAffGBx98gMTERHzyySdYvHgx5syZg88++wxDhw6FwWDwyZ/hgQceQFtbGz7++GN89tlnGD9+PH744Qfcd999vXrdF198EQCg1WoRGRmJYcOG4Z///CduvPFGxMXFHfK5L7zwAtRqNT766CMYjUZMnz4dv/766yHxLwCQmJiI119/HU888QSuv/562Gw2/P777zjxxBPx/fff45577sGDDz6IqKgoXHHFFZg9e/YRr0FERERE5C+CyFYOIiIiIqJ+Ydu2bRg3bhw+/PBDXH755YFeDhEREREROTEjnYiIiIgoADo6Oo742PPPPw+VSnXIUE0iIiIiIgo8RrsQEREREQXAk08+ic2bN2PWrFnQaDT46aef8NNPP+Gmm25CWlpaoJdHRERERERdMNqFiIiIiCgAli5din/+85/YvXs3WltbkZ6ejiuvvBL/+Mc/oNGw34WIiIiIqD9hIZ2IiIiIiIiIiIiIqAfMSCciIiIiIiIiIiIi6oHszoza7XaUl5cjLCwMgiAEejlERERERERERERE1A+JooiWlhYkJydDpeq551x2hfTy8nIOZyIiIiIiIiIiIiIit5SUlCA1NbXHz5FdIT0sLAyA4w8fHh4e4NUQERERERERERERUX/U3NyMtLQ0V025J7IrpEtxLuHh4SykExEREREREREREVGP3IkI57BRIiIiIiIiIiIiIqIesJBORERERERERERERNQDFtKJiIiIiIiIiIiIiHogu4x0IiIiIiIiIiIior5mt9thNpsDvQzqQqvVQq1W++S1WEgnIiIiIiIiIiIi6gWz2YyCggLY7fZAL4UOExkZicTERLcGivaEhXQiIiIiIiIiIiIiL4miiIqKCqjVaqSlpUGlYpp2fyCKItrb21FdXQ0ASEpK6tXrsZBORERERERERERE5CWr1Yr29nYkJycjODg40MuhLoKCggAA1dXViI+P71XMC7dHqEdN7RbcvHATPlhbGOilKEJeTSuK69oDvQyio/pofRFGPfIzlvxZEeilEFE/cLC6Bevy6wK9DCIiWdpV3oR5b6zF7/uqA70UIgowu13EfV/twJTHf0V+TWugl0PdsNlsAACdThfglVB3pM0Ni8XSq9dhIZ169N6aAvy8qwoPf7ML//1pL0RRDPSSZKnDbMM/v9uF2c/8gbNeWommjt79h03kD8V17Xjs+91oMVlx75c7UN7YEeglEVEAfbOtDKe/sBKXvLkOqw7UBno5stButuKmDzbhr59tQ2FtW6CXQ+SxVpMVNjufF3zBarPjns+3Y31BPW77aAv2VjYHeklE3Vq4rgiP/7gHu8qbAr0UWXth2QF8urEEVc0mPPPL/kAvh3rQ2wxu8g9f/bsw2oWOymS14cN1xa7///ofeWg1WfCvc0ZCpeKFwVc2Fdbjb19sR6GzE73ZaMX3O8px+ZSMAK+MqJMoivjH1zthtDiGprQYrbjn8+346IYpvB70UpvJiu2ljahrNaO21YS6VjPq2kyobTWjxWjBddOzcMqIxEAvk8hFFEW8uSIfT/y01/Wx/y7Zg2+zj+f1oJc+WFuEX3ZXAYDrXuCO2TmIDmFnE/U/druIA9Wt2FzUgM1FDdhS3ICC2jbMyInFB9dNZiGhlxasKcTeyhYAQLvZhhs/2IRvbzseUbweUD9SVNeGh77+EwDw5op8jEwJx7yJaThnbAoigrQBXp18fLu9HC8sO+D6/z/srMBt5c0YnhwewFURKRML6XRUP+yoQG2rCYnhBtw6KxuPfLsLH64rRpvJhqcuHA2NmgcaesNoseGZX/bh7VUFEEUgMdyAyVnR+HZ7Ob7aXMpCOvUr32wrx8oDtdBpVHjjigm49aMtWJtfh3dXF+CGGYMCvbwBSxRFXPLmOuwsO3oHT02LiYV06jdsdhGPfb8bC9YUAgAum5KOb7eV48+yZny3oxznjk0J7AIHsHazFW+tyAcADEkIxf6qVixYU4ivtpTi1hMH49rpmTBovc9zJPKVjYX1eOm3g9ha1IAWk/WI3195oBYbCuoxZVBMAFYnD5VNRjy31NFxet/pufh4fTGK69tx28db8MF1k/kcRv3GjzsrAQDRITq0GC34s6wZf5btwr9/2IPTRyZi3qR0TM3mtaA3tpU04t4vtgMAbjphECqajPhuezmeXbofb189McCrI1IevgNTt0RRxHurCwEAV07NwFVTM/H8vLFQqwQs3lqGWz7aAqPFFthFDmBbihtwxosr8dZKRxH9ogmp+PmvJ+DBs4ZBrRKwpbgRecw9o36ioc2Mx77fDQC446TBmJUbjwfPGgYAeHLJPuxzdkuR59bm1WFnWRN0GhUmZ0XjzFFJuGpqBv46ZwgeO3cENCoBeTVtnJ1A/YLRYsPtH29xFdEfPHMYHj9/FG4+wbGZ9vQv+2C22gO4woHt4/XFqGszIz06GD/cMQMfXj8Fw5PC0WK04n9L9uKkp5fj661lgV4mER77fjdW7K9Bi8mKYJ0a0wfH4C8nDcZ7107C3PGpABydqeS9f/+wG21mG8alR+KmGYPw1lUTEaxTY01eHf7z455AL4/I5Sfn3KR7ThmCdffPxoNnDsOQhFCYrHZ8va0cl761Du+tLgjwKgeuiqYO3PjBJpisdszOjcffT8vFXXNyoBKAX/dUYXtJY6CXSITCwkIIgoBt27a5/TULFixAZGRkwNfhDRbSqVubixqws6wJeo0Kl05OBwCcOzYFb1wxATqNCkt3V+H69zeirZsuFOrZTzsrcOFra5Bf04b4MD3evWYinrpoDCKCtIgPM2DmkDgAwFebSwO8UiKHx3/cg7o2M4YkhOKmE7IBAJdNTsfs3HiYbXbc+elWmKzcWPPGe86C5LyJafj85ql45fLx+Ne5I3HnnBxcOTUTEzOjAAC/7a0K4CqJgMZ2M654ez1++rMSOrUKL106znUa5foZWYgL06OkvgMfry8K8EoHJqPFhtf/cBQeb5uVDa1aheNzYvH9X47HMxeNQXKEAeVNRtz12TYOe6aAamw3u05RfTF/KnY8cgo+uuE43HPKUMwaGo/bZmVDEIBle6txoIob7d5YfbAW3++ogEoAHjvXEak5NDEMz148FgDw3upCfL6pJLCLJAJQUt+OHaVNUAnAKcMTEROqxw0zBuHnu07A17dNxzljkgEAb/yRD6uNG+2eajdbccP7m1DTYsLQhDC8cOk4qFUCsuNCcf44x6blM0uZlU6+U1JSguuuuw7JycnQ6XTIyMjAnXfeibq6uh6/Li0tDRUVFRg5cqTb32vevHnYv39g/vyykE7dkrrRzxubckgu55zhCVhw7SSE6NRYfbAOV7yzHh1mFtDcJYoiXlh2AHYROGNUIpb+dSZOyk045HMunOB4U1y8tYzDmijg1uTV4gvnps4TF4yCTuN42xAEAf+dOxoxITrsrWzBsxx447GS+nYs2+MokF89rfsop5Ny4wEAv+2r6bN1ER2uvs2Mua+twaaiBoQZNHj/usk42/lwDADBOg3umpMDAHjxt4NoMXJgtqc+2VCM2lYTUiKDcIGzoxcAVCoBcyek4re/nYhzxzr+zqUMdaJAWJdfB1EEcuJDMSkz+oiIkUFxoTh5mOPe9q2V7Er3lMlqw0PfOPKmr5qaiZEpEa7fO21kIu6c7bjWPrj4T2wpbgjIGokkS/50xLpMzopGXJje9XFBEDA2LRJPXTQa0SE6VDYbsWxvdaCWOSDZ7SL++tk27CpvRkyIDm9fPRGh+s5k5jtn50CjErBifw02FtYHcKUkF/n5+Zg4cSIOHDiATz75BAcPHsTrr7+OZcuWYerUqaiv7/7nzGw2Q61WIzExERqN++nhQUFBiI+P99Xy+xQL6XSE8sYOLNnleFO8ZnrmEb8/LTsWH94wBRFBWmwtbmRHhAd2lDZhb2ULdBoVnjh/NCKCjxzAMntYPCKCtKhoMmJNXm0AVknkYLTY8I/Fjoe5K45Lx4SM6EN+Py5MjycuGAUAeHNlPtbl97xTTYdauK4IdhGYkROLwfFh3X6OVEhfl1+HdjNPAFFgfLKhGHk1bUiKMODL+dO6zTq9eGIaBsWGoL7N7Mr5Jvc4utHzAAC3zRoMbTfZxwat2hWZsaGAD8wUOKsOOu5Npw+OPern3DzTcVrl663lqG429sm65OLtlQXIr2lDbKged58y5Ijfv3N2Dk4ZngCzzY75Czejin+/FEBSrMsZo5K6/X29Ro2LJjreuz5aX9xn65KDZ5buw8+7qqBTq/DmVROQFh18yO+nxwTjoolpAICnf94HUWQDXn8kiiLazdaA/PL0Z+K2226DTqfDL7/8gpkzZyI9PR2nn346fv31V5SVleEf//gHACAzMxOPPfYYrrrqKoSHh+Omm27qNlLl22+/RU5ODgwGA2bNmoX3338fgiCgsbERwJHRLo8++ijGjh2LhQsXIjMzExEREbjkkkvQ0tJ5um3JkiU4/vjjERkZiZiYGJx11lnIy8vz+t/HWxw2SkdYuK4INruI4wZFY1hS91Ogx6VH4c7ZOfjX97vxxeYSXD0ts28XOUB9utGx6XDGyMRui+iA44bjnDHJWLiuCF9uLsWMnLi+XCKRyyu/H0RBrSOC6P9Oy+32c04ZkYh5E9Pw2aYS3PP5dvx01wyEG7r/2aZO7WYrPt3geKC4pofrZ3ZcKNKig1BS34HVB+tw8vCEo34ukb9sKXJ0Pd4wYxCGJna/6aNVq/B/pw3F/A+34K2VBbjiuAzEhxv6cpkD1uebSlDVbEJyhAFzJxx9WOv4jCioBKC0oQPljR1Ijgzqw1USOaw+6Ng076mQPiEjGhMyorC5qAEL1hQe9R6CDlVS346XfjsAAPjHmbnd3k+pVAKenTcWF7y6GvurWjH/w834av40qFRCXy+XFK6iqQNbihshCMCpIxKP+nmXT87AG3/kY8X+GhTXtSM9Jvion0sOGwrq8crvjuLgf+eOOqKZSfKXkwbjq82lWF9QjzV5dT1elykwOiw2DH/454B8793/OhXBOvdKvvX19fj555/xn//8B0FBh95fJiYm4vLLL8dnn32GV199FQDw9NNP4+GHH8YjjzzS7esVFBTgwgsvxJ133okbbrgBW7duxd/+9rdjriMvLw9ff/01vv/+ezQ0NODiiy/Gf//7X/znP/8BALS1teHuu+/G6NGj0draiocffhjnn38+tm3bBpWq7/rE2ZFOh+gw2/CJs7hz7fSsHj/3vHEp0KoF/FnWjD0VzX2xvAGtzWTFt9scQ8LmTUrv8XOleJefd1WimUfkKQD2V7W4OiT/ec6IHovjD509HOnRwShr7MCj3+zqqyUOaIu3lqHZaEVGTDBmDT36kTZBEHCS8/d/45FYCgBRFLHVOchqfHpkj5976ohEjE2LRIfFhheWHfD/4mTAZLXhteWOa+0tJ2ZDr1Ef9XND9RpXzAOPcVMglDV2oKC2DWqVgCmDui/sSG5yDiH+cF0RWjlTyS3/+n43jBY7pmRF47yxR99UC9VrXMNHtxY3YjefwygApFiXiRlRSOhh4zw9JhgnOGeAfbyBXenu+HSj4+9p7vjUQ+LeDpccGYTLpjjqCk//wq508t6BAwcgiiKGDRvW7e8PGzYMDQ0NqKlxxI2edNJJuOeee5CdnY3s7OwjPv+NN97A0KFD8dRTT2Ho0KG45JJLcM011xxzHXa7HQsWLMDIkSMxY8YMXHnllVi2bJnr9+fOnYsLLrgAgwcPxtixY/Huu+9i586d2L17t3d/cC/5tSN9xYoVeOqpp7B582ZUVFRg8eLFOO+883r8muXLl+Puu+/Grl27kJaWhgcffNCtv3Dyja+3laGx3YK06CDMGdZz52N0iA6zcxOwZFclvtxciofOGt5HqxyYfthZgTazDZkxwTjuGA8fo1MjMDg+FAerW/HjjgpcMrnnwjuRL4miiAcW7YTFJmLOsAScNvLoXSaA44HuuXljceHra7Boaxn+evKQI44fUidRFPG+c8joVVMzj9lFdmJuPN5fW4Tl+6ohiiIEgV1n1HcK69pR32aGTqPCiOSIHj9XEATcf3ou5r25Dp9uLMF1x2chOy60j1Y6MH25uRQVTUYkhOtdR7R7MjkzGjtKm7C+oB7n9lBoI/KH1c5Yl9GpEcc8fXbysARkxYagoLYNn20swfXH99ygo3S/7a3C0t1V0KgEPHbeyGO+12fEhGBceiRWH6zDzrKmQ7LUifrCTzsdhfTTR3Yf69LV5VPSsWJ/DT7fVIK/npzT46ax0rWZrK5NCqlI3pNbZ2Xj043F2FrciOX7ajArd2BmTstVkFaN3f86NWDf21PubsZMnDixx9/ft28fJk2adMjHJk+efMzXzczMRFhY5+nXpKQkVFd3NpMdOHAADz/8MNavX4/a2lrY7Y4hxsXFxR4NOu0tv3akt7W1YcyYMXjllVfc+vyCggKceeaZmDVrFrZt24a77roLN9xwA37+OTBHIZRGFEW8t7oAAHD11Eyo3TgiKGWefb21DBZO4u7RZ85Yl3mT0o95cywIgqsr/astpX5fG1FXB6tbsamoAXqNCv86d4RbhdsJGVGY5sxN/nprmb+XOKCtzavD/qpWBOs6cyN7MnVQDAxaFSqajNhT0XLMzyfypa3OYXYjk8Ndw4Z7MmVQDGbnxsNmF/H0z/v8vbwBzWy141Xn0e35M7NhcOOBZ1KWYyN+I3PSKQCkQvrxbsQHqFQCbpjhKJ6/u6qAzwk9sNlF/PM7Rzfd9cdnYUhC9xFahxudGgkA2FHa6KeVEXWvutmIjUWO96FjNdwAwOzceCSGG1DfZnYVial7P/1ZiXazDVmxIcc8CQgA8WEGXD01EwC70vsjQRAQrNME5JcnzVeDBw+GIAjYs2dPt7+/Z88eREVFIS7OcbokJCTEJ38/h9NqD92kFwTBVSwHgLPPPhv19fV46623sH79eqxfvx6AY+BpX/JrIf3000/Hv//9b5x//vluff7rr7+OrKwsPPPMMxg2bBhuv/12XHjhhXjuueeO+jUmkwnNzc2H/CLvrDmkuHPsrigAmDkkDnFhetS1mRk70IP9VS3YXNQAtUroMf+0q/PHpUAlABsLG1BY2+bnFRJ1+rO8CQAwKiXCowze88c5isKLt5bxJq4H7zm70eeOT3UrT96gVWN6tqNo8fs+Xmepb21xFtLHp0e5/TX/d1ouVILjYVD6ejrS4q2lKGvsQFyYHpe6efJsUqajkH6guhV1rSZ/Lo/oEKIouvLRp2W7l8M7d3wqYkJ0KGvswI87K/y5vAFtXX4diuraERGkxR2zc9z+utHOLvQdpU3+WhpRt37eVQlRBMalR7r1rKBRq3DJZEd9gUNHe/bVZkcT3QXjUtwuhN48MxshOjV2lTfj513cqCDPxcTE4OSTT8arr76Kjo6OQ36vsrISH330EebNm+f2z+TQoUOxadOmQz62cePGXq2xrq4O+/btw4MPPojZs2e74mYCoV9lpK9duxZz5sw55GOnnnoq1q5de9SveeKJJxAREeH6lZbmXgGYjiR1o184IRURQe4NC9SoVbhgnKMw/MUmdk4fjdSNPjs3HvFh7g1fSwg3uAaNsiud+tKuMseG5Ijk7ocNH81pIxMRpFUjv7YN25yZynSokvp2LNtTBQC4elqG218nHdP8nRuW1Me2FDUCcAy6dNfQxDDMdWZ6vvLbQX8sa8Cz2Ox4+XfH383NJwxyqxsdcMTqDUlwxOVsLOQmBfWd/VWtqG01waBVYXxGpFtfY9CqcbVzoPabK/K5yX4Ui50n+c4cnYQQvfvJq6PTIgEA+ypbYLTY/LE0om796Ix1OcONWBfJJZPSoVYJ2FBQjwNVPGHZndKGdqzNd2xYnj/e/fi26BAdrnPGZ729ssAvayP5e/nll2EymXDqqadixYoVKCkpwZIlS3DyyScjJSXFNfDTHTfffDP27t2Lv//979i/fz8+//xzLFiwAAC8jimNiopCTEwM3nzzTRw8eBC//fYb7r77bq9eq7f6VSG9srISCQmH5nInJCSgubn5iF0Ryf3334+mpibXr5KSkr5YquwU1bVhmbNAI93wukuKJvh9XzVqWtgddTiT1YZFzkK4tBPvLineZdGWMtjtfPigvrGrXCqke5a3GarX4NQRjmv4Ysa7dGvhuiLYRWBGTiwGx7t3dBvoLKRvKW5AQ1vfHl0j5WozWbG30nE9GOfG8eKubp7pGDy0fH8NO6e78c22cpTUdyA2VIfLp7i/qQZ0dqVz4Cj1JSnWZXJWjEf5xlcel4EgraNTck1enb+WN2B1mG34ydmtLzUnuSs5woDYUB2sdhF7OHCU+khtqwnrCxz/LbsT6yJJjDBgtvN+ll3p3ZPiMacOikFqlGfzps53Xj/+LG+CjXUD8kJOTg42bdqEQYMG4eKLL0Z2djZuuukmzJo1C2vXrkV0dM9z/rrKysrCl19+iUWLFmH06NF47bXX8I9//AMAoNfrvVqfSqXCp59+is2bN2PkyJH461//iqeeesqr1+qtflVI94Zer0d4ePghv8hz768pgig6olo8HQw2OD4MY9MiYbOLzEbuxtLdVWhotyAx3ICZQzwb/nHy8ASEGTQoa+zAunw+fJD/iaKIXc5olxEpnl9Pz3d2oX63vRxmK/NQu2o3W/HpBseDwzUeblimRAYhNzEMdhFYcaDGD6sjOtKO0ibYRSApwoCkCPdjngBgcHwoRqdGwGYX8f0ORjocTho4fMOMQQjSeTYMarIzJ30Dc9KpD0mF9OnOeSjuigrR4WJn080bK/J9vq6BbumeKrSZbUiLDsIED07+AI6uvlGMd6E+9suuKthFx9DhtGjPir1XHOfYOP5qSynazVZ/LG/AEkURX21x1FIu8KAbXZIREwKDVgWjxY7COsbCkncyMjKwYMECVFZWwmw2o7i4GC+++CJiYjrf+wsLC3HXXXcd8nWZmZkQRRFjx451feycc87BgQMHYDQa8fvvv6Ourg6pqakwGBwJDddccw0aGxtdn//oo49i27Zth7zuXXfdhcLCQtf/nzNnDnbv3g2j0Yjt27dj5syZEEUR55133lHX4Q/9qpCemJiIqqqqQz5WVVWF8PBwBAV59gBHnlm21/H3fuVxnnVFSaSu9C82l/DY5mGkWJeLJ6a6NcC1K4NWjbPHJAMAvtzMeBfyv9KGDjQbrdCqBeR40DEtmZ4dg7gwPRraLfhjPwu+XS3eWoZmoxUZMcGYNdSzTTWgsyud8yior3iTj97VeWMdD4I8oXKog9Ut2FnWBI1KwMVuzqTpSiqk7ypvQovR4uvlER3BYrO7GjqmuzFo9HA3zBgElQCs2F/jOuVCDoudp1bPH+t+HnJX0sDR7Rw4Sn3kpz8dm+OnexDrIjl+cCzSo4PRYrTi++3cZO9qa0kjCmrbEKRV4/RRnv/dqlUChiY6mqD2VjA6hwLv1VdfxcaNG5Gfn4+FCxfiqaeewtVXXx3oZflEvyqkT506FcuWLTvkY0uXLsXUqVMDtCJlaDdbUVzfDsDzo9uSs8ckQ69RYX9VKzsiuiipb8fKA7UQBLg9wPVwUs7sT39WotXEnXvyL6kbfUhCGHQaz98iNGoVzhvr2PxZxGx/F1EUXR2oV03NhMrDTTUAOMlZSP9jfw2PbFKf2OospPfm3kCtErCtpBH5Na0+XNnAtsjZcXbi0DhEh+g8/vqkiCCkRQfBLgJbiht9vDqiI+0obUSb2YaoYC2GJ3l+Wi0tOtgVAfHpBsZwSmpbTVhxwNHpf66HsS6S0amOjvSdfP6iPtDQZnZFNJ3uQayLRKUScNkUx3Dtj9YX+XRtA500ZPT0kYkI9WBWQlfDEh1NUNywpP7gwIEDOPfcczF8+HA89thjuOeee/Doo48Gelk+4ddCemtrK7Zt2+Zqzy8oKMC2bdtQXOw42n7//ffjqquucn3+/PnzkZ+fj//7v//D3r178eqrr+Lzzz/HX//6V38uU/EOVrdCFIHYUB1iQr3LKwo3aF03yF9s5g2y5PNNjr+L4wfHenz0TTI+PRKDYkPQYbHhx53cuSf/6sxH9z4m6/xxjs2fZXuq0dTObkkAWJtfh/1VrQjWqV0neDw1Li0SEUFaNLZbXAVOIn8RRdFVpB3nZUd6XJgeM3Ic3atfbyv31dIGNLtdxDfOvwvpWumNyZmOI7YbChj7Rv636oDj52xadqxXG8FAZ0PJd9vLYbEx+g0Avt9eDptdxJjUCI+jNSVSR/rBmlY23JDfLd1dBZtdxPCkcGTGhnj1GhdNSIVOrcL20iZuADkZLTZ8t91xbzB3gvf3BrnOQvoedqRTP/Dcc8+hvLwcRqMR+/fvx0MPPQSNxrtNov7Gr4X0TZs2Ydy4cRg3bhwA4O6778a4cePw8MMPAwAqKipcRXXAEUj/ww8/YOnSpRgzZgyeeeYZvP322zj11FP9uUzF21fpuNB6E+PQ1UUTHDfI324r5+R4AFabHV9scuwsz5vkXTc64Mg/PHO043jXRuahkp95O2i0q+HJ4chNDIPZZscP3PwBAHzujHg6b1wKwg1ar15Do1Zh5pA4AIx3If8rrm9HfZsZOrUKI72YlyCRhl99vbWM0W8ANhTWo6yxA2F6DWYP8zziSTI5y7G5sbGAm2rkf658dC9iXSQzBsciNlSPujYzVjD6DUBn7NX5XnajA44Ny+QIA0QR+LOMRUnyrx+dsS5njPK8G10SE6rH6c6vZ1e6w7I91Wg2WpEUYcBxgzybQ9FVrvPEEDvSifzLr4X0E088EaIoHvFrwYIFAIAFCxZg+fLlR3zN1q1bYTKZkJeXh2uuucafSyQAB6odx62HJvaukD4tOwYpkUFoNlrxy+6qY3+BzK04UIPKZiOigrU4eXhCr14r15l3Jv1bEfmLa9BoLzrSgc4hOYu3Mt6lxWjBkl2VABxdOL0xK5eFdOobUj76iJRw6DWeDcPs6uThCQjWqVFc384YEgCLnbEuZ4xKgkHr/d/r5CzHg/a2kkY2L5BftZms2FriuB5MH+x9gUejVuFcV/Qb5ybk1bRie2kT1CoBZznnIXlrFONdqA80tVtcm2reZHh3dfkUx1y2b7aVc9YHOuMwzx+X4vFMta6kjnTHzCv+vQYSm0f6J7vdNyfi5NFXT70idaQPSehdIV2lEjB3fApe/O0gvthUgnN6eVM40EkZkHPHp/aqCAEAOQmO45551a0QRdGrYUREx1LbakJVswmCAAzzIgO1q3PHpuC/P+3FxsIGFNe1Iz3Gu2gjOfhpZyWMFjsGxYVgbFpkr15r5pB4CAKwt7IF5Y0dSI7kIG7yjy1FjQC8HzQqCdZpcNrIRCzaUoavt5ZhQkbvXm8gM3aJaDt/vPcdqACQGROMuDA9alpM2F7SiCm96GAj6smGwnpYbCJSo4KQ7mVMoeT8cSl4Z1UBlu6pQlOHBRFB3p3QkoOvnd3oM4fEIdbLaE3J6NRI/LyrigNHya9+3VMFi03E0IQwr6OIJJMyo5AdF4K8mjZ8v6MCl05O99EqB56aFhOWO0/pXDC+dw03kcE6JEUYUNFkxP7KFkzMjPbFEskDWq0WgiCgpqYGcXFxrNv0E6Iowmw2o6amBiqVCjqd5zOKumIhnbC/Siqk9+4NEQAunJCGF387iFUHaxVd5KluMWKZs2O0N7EuksyYEKhVAlpMVlQ1m5AYYej1axIdTop1yYoJQYiXQ24kCeEGTB8ci5UHarF4axnunJPjiyUOSF86u0zmjk/t9c1UdIgO49IisaW4Eb/vq3Z19BD52pZeDhrt6vxxKVi0pQzf7SjHQ2cN92qQsRws21ONFpMVKZFBmNzLh1tBEDA5Mxo/7KzAxsJ6FtLJb1Y7h2FOz47t9XvYiORwDE0Iw76qFvy4U7nFM1EUXbEu5/Ui1kXiGjjKaBfyow3OiNHexJJJBEHAxRPT8MRPe/HFphLFXgsA4JttZY5ZCWmRGBzf+3rMsKRwVDQZsaeimYX0AFCr1UhNTUVpaSkKCwsDvRw6THBwMNLT06FS9e5ZhIV0hWs2WlDRZAQA5PSyIx0A0mOCMSUrGusL6rFoSyluP0mZxbMvN5fCZhcxISPKJ3+vOo0KGdHByK9tw8HqVhbSyS+kWJfhvYx1kZw/LsVZSC/FHbMHK3JHvriuHRsK6iEInXE3vXVSbryjkL6XhXTyj3azFXudp9V625EOOAYUSt3Tf+yv6XXc2UAlRV2dOzbZ64GNXU3OchTS1xfU4/ZevxpR91bnOQaNTs/xPh9dIggCLhifgid+2otFW0oVWzzbVNSA0oYOhOo1OHlY76+Ho1MiAQBFde1obDcjMrh3nXZE3cmr8U0crOT88Sl48ud92FLciIPVrT4pIg9EXzmjri700XNCbmIYfttbjT2VHDgaKKGhocjJyYHFwnid/kStVkOj0fikJsFCusIdcHajJ4YbfHa88qKJaVhfUI/PN5Xi1hMH++RhcSARRRGfOQcL+qIbXTI4PhT5tW04UN2C433wMEN0OKkjfWSK94NGuzp1RCKCdX+isK4dW0safVKQG2i+cnajHz84FkkRvjmhMys3Hk//sh+rD9bBaLH1KmeZqDs7Sptgs4tIDDf45GSZWiXg3DHJeHtVAb7eWqbIQnp9mxnL9zmObvdmsGBXk7McnWZbihpgtdmhUSuz05/8p7bVhD0VjnuDadm+OfVw7tgU/HeJsqPfpG7000YmIkjX+/fwiGAtMmOCUVjXjh2lTTjBOZicyJfya9sAoNexLpL4MANOHBKHZXur8cXmEtx/+jCfvO5Asru8GXsqmqFVCzjbR7G4roGjFRw4GkhqtRpqNZ/R5Ip33Aq3v8qxszzERzvLgGOKd5hBg+L6dqzOq/XZ6w4U6/LrUVTXjlC9Bmf2chBLV9Iu/UEOHCU/2e0spPd20KgkRK/BaSMSAXQO0VESu13Eoq2dsS6+MjwpHAnhenRYbNhW0uiz1yWS+DLWRSLFFyzdU6XIAVjf7yiH1S5iZEq4T06qAcDQhDCEGzRoM9uwmw/M5Adrnd3ouYlhvc7xliRGGHD8YEdDiFRQVhKT1YYfdjhnJfhoUw0ARqVGAmC8C/lHQ5sZ9W1mAMCguBCfve5FEx1NZ4u2lMFq880QwIFEej6anZvgs5Mkw5x1nX2VLbDbOfCSyB9YSFc4adDoUB/ko0uCdRpX0eijdcU+e92B4rONjj/z2WOSe50z3RUL6eRPLUYLCpydJiOSfdORDnQO1Pt+RwXMVmXdIG8srEdJvePo9qnODQVfEAQBw53dJvk1bT57XSLJ1uJGAL6JdZGMSA7HkIRQmK12/OQcuKkkUsHw/HG+21RTqQRMcuafStm1RL60+qCjIUYqfPuKVEBevLUUoqisQs/ve2vQ1GFBYrgBx/lwtsEYZ076dm6wkx/k1zqeP5MjDAjW+e759qTceESH6FzRb0pitNiwyHlvMHeC7+4NsmJDoFOr0Ga2obShw2evS0SdWEhXOGnQqK+6oySXTXFkHi7dU4WqZqNPX7s/a2q34Mc/KwEAl072XawLAOTEO/6NWEgnf9hT4bgWJEUYEB3iu2zNadmxSAjXo7Hdgt+cA3iVQop1OWOUb45ud5UV69hYK6jl9YB8SxRFbHV2pI/PiPTZ6wqC4OpKV1oXakFtG7YWN0IlAGeP8d1JNQCY5Ix3Wc9COvnBKmchfbqPC+mO6Dc1CuvascW5cacUXWclqH0YfznKGcu3o5Qd6eR7edWOxo1BPop1keg0KtfG2heblHV69YcdFahvMyM5woBZQ30Xx6RRq5DjbJLcU8nTakT+wEK6wknRLkN9XEgfkhCGyZnRsNk788KVYPHWUpitdgxLCnfd0PpKdrzjGF1dl6N1RL4iDRr1VayLRK3qLJ59tL7Ip6/dn7Wbrfhxp2NT7cIJvt1UA4CsWEemrHSKgMhXSuo7UNtqhlYt+PR0CuDIRgYcEWhljcrpkpI2DmbkxCE+zLfDwqWc9E2F9TzCTT5VXNeO0oYOaFSC6+fMV5Qa/dbYbsbvex1dt+f5MNYFcMy3UQlAZbMR1QpqYqK+keds3Mj2YayL5KKJjm7sX/dUoa7V5PPX768+WFsIALj8uAyfzzjJTZRy0jlwlMgfWEhXsPo2M2qdb1b+mJJ9+XGOrvRPNhQrIvNMFEV86tw0uGRSmk+mAXcVrNMgxTn0jV3p5GvSoNHhPi6cAcCVx2VAJQArD9Rir0I6I37eVYlWkxXp0cGYlOn7IatSR3o+C+nkY1I++vDkCJ8Psk2JDMIUZ0Hu223lPn3t/koURXztLKRfMN63hTMAGJkcgSCtGg3tFhys4b0B+c7uCscG+/DkcJ9GFUoucMZAfr+jAiarzeev3x99t6MCZpsduYlhGJbk28aFEL3G9TzHrnTyNX91pAOOou+olAhY7SK+Vsi9wdbiBmwvbYJOrcIlk3zfcDMsydEkqZTnLqK+xkK6gkmxLmnRQX65QT5tZCKiQ3SoaDJi+T75Z57tKG3C3soW6DQqnDfW9w/LAHPSyX92+XjQaFepUcE43Tl4952VBT5//f7oq82dhTNfb6oBQJazI6ikvl0RG5XUd1yxLj4cNNqVVExWSjbyluIGFNe3I1inxsnDE3z++jqNyjUUlvEu5EuFde0AHHm7/jA1OwYJ4Xo0dVjwuwKi32x2Ee+uctwDSQMWfW1USiQAYEdpo19en5Qr39WR7vtCOgBc7OxK/2JTiSLuDT5Y6zile9aYJMT4aJBzV9JG3R4OIifyCxbSFUwqpPs61kWi16hxkXNwhhIiHaRu9DNGJiIiWOuX78FCOvmDyWrDAef1wB+FdAC44fgsAMA328pR3SLvI8fljR1YnefIlZUGL/taUrgBeo0KFpuoqIgM8r8tfhg02tVpI5Og06iwv6pVEdnIi7Y4NtVOG5no0wFtXUmxGxtZSCcfKnSeeMqI8U8hvWv021db5D834eddlSiobUNEkBbz/NCBCgBj0pw56WXsSCffsdjsKHZurA3yQ7QLAJwzJgU6jQp7K1vwZ5m8i781LSZ8v8PReX/NtEy/fI/cREd9p6i+HW0mq1++B5GSsZCuYPsq/TNotKtLJzviXZbvr0FJfbvfvk+gtZms+Hab4yFg3qR0v32fHGch/UA1887Idw5UtcJqFxERpHXFB/nauPQojE+PhNlmx4dr5b2xtnhrGUTRUdxKiw72y/dQqQRXlyDjXchXOsw2V/fS+Az/FNIjgrQ4d0wyAOCl3w745Xv0F2arHd/vqAAA1zA1f3AV0gtZSCffKaxzvLdIMzn84YJxjs3m5fuqZT3/RxRFvLY8DwBw9bRMhPrhJDBw6MBRJXT1Ut8orm+H1S4iWKdGYrhv53xIIoK1OMV5auuLzfKer/bphmJYbCLGpkVidGqkX75HTKgecWF6iGJn8yQR+Q4L6Qp2wE+DRrvKjA3BjJxYiCJkPXT0h50VaDPbkBkTjOMG+XYgU1dSR3oeO9LJh6RBoyNTwv0SQyK5YcYgAMCH64thtMgzD1UURXzlHJx2oZ+60SVSIb2ghoV08o0dpY2w2kXEh+mRHOGfh2UAuP2kwVCrBCzfV+OKkpGjLzaXoKnDgoRwPaZlx/rt+4xIchTPKpqM7DwjnymsdTTA+KsjHQCGJoZhRHI4LDbR1aEpR6sO1mJnWRMMWpXfOlABR5yDRiWgvs3M02rkM/k10qZaCFQq/z0nXOyMPPp6a5lsnxMsNjs+Wl8MwH/d6BKpK31vJQvpRL7GQrpCiaKIfVVSR7p/ss4kl09xdGh/urEEFplm+X66wfGGOG9Sul8LkVIhvbzJiFY+LJOPSEcoR/hh0GhXpwxPQGpUEOrbzFi8VZ7HuLeWNCK/pg1BWjXOGJ3k1++VKRXS2ZFOPrK1pBGAI9bFn+9lGTEhmOvMSn/+V3l2pbebra4/2/yZ2VD7sfgQEaxFRJAjUq5Yxqf/qO90mG2obHbEsGX5sZAOdA4d/VzG2chSN/olk9IRHaLz2/cxaNXIdQ4Z5MBR8pW8Gv/mo0umD45FUoQBzUYrlu6u8uv3CpRfdlWhstmI2FAdTh+V6NfvJeWk72VOOpHPsZCuUDUtJjR1WKAS/P+mOHtYAuLC9KhtNfXZm6LJakNFU0ef3JDvr2rBluJGqFUC5k7w39FtAIgM1iHWOZCEXenkK1JHur/y0SUadWcn1jurCmC3y++B+avNjm7000Ym+u3otiSLhXTysS1FzkGjGZF+/163z8qBWiXgj/012CLDrvR3VxWgpsWEtOggXDbFf5FvkswYR/xGUR0L6dR7RfWO95VwgwaRfpr7Izl3bDKCtGr8WdaMb7fLryt9W0kj1uTVQaMScOMJg/z+/aSBo9s5cJR8JN9ZSPdXPrpErRJcs4W+cN5P+1ubyQpbHz6PvL+2EIAj/lavUfv1e0kd6XvYkU7kc/59yqd+S+pGz4wNgUHr34u4Vq3CJZPS8NJvB/HR+iKcMcp/XZol9e34eEMxPttYgvo2M8anR+LOOUNwQk6s37rrpMia2bnxiA/z31F4yeD4ENS2mnCguhVj0iL9/v1I3mx2EXsq/DtotKt5k9Lw/K8HcLC6FX8cqMGsofF++T4Wmx27y5uxsbAeGwvrsbmoAeFBWjx45jCclJvgl+9Z2tDu6rT315DRrgaxkE4+ts3ZkT7OT4NGu0qPCcbc8Sn4fFMpnv/1AD64brLPv0ebyYrNRQ1oM1nRarKi3WxDm9mKdpMNJqsNJ+UmYGp2jM+/b32bGa//kQ8A+NspQ/3+sAwA6TEh2F7ahOJ6Xg+o96RYl6zYEL+eTgGA2FA9bj9pMJ76eR8e/3EP5gxLQIgfNqLLGzvwxE97UdVkREZMMDJjQ5AZE4LM2GBkxIT4bfP7teUHAQDnjk3x2xyarsakRuCTDcBOdqSTj+Q5o1383XwHABdOSMXLvx/EygM1KG/sQLKf/pspbWjHEz/txQ87KhAbqsMpIxJxxsgkTBkUDa3aP72meyqasaGgHmqVgMunZPjle3TVtSNdFEW/X8uJlISFdIWSBo0OifdfPnpXl0xOxyu/H8Tqg3XIr2nFIB++EdvtIv44UIMP1xbht33V6NqEvqW4EVe/uwFj0yJx15wczBwS59M3EZPVhkXOPORLJqf57HV7khMfhnX59TjIjnTygYLaNnRYbAjSqpEV6/8b5DCDFpdMSsPbqwrw7qoCnxbSm9oteH9tIdbl12FrcSM6DstXrG0147oFm3Du2GQ8fNZwxDhPd/iCKIr4x+I/0W62YXJmNKb5oTh3OKkjvbypA0aLze+boiRvrSYrqltMABy5xX3h9lk5WLSlDCv212BzUQMm+HDA6Z9lTbh54eYec4LfWlmAv5w0GHfNGeLT6JWXfzuIVpMVI5LDcfboZJ+9bk8ynIONC9mRTj5Q5Bw06s989K6uPz4Ln20sQXF9O15dfhD3nprr09f/bns5/rF4J5qNjljEDd0M5k2JDML9Z+TiLB/+N3uwugU/73Kcxp0/0//d6AAwKtUR07eztAl2u+jXTGtShr7qSAccTX6Ts6KxoaAeH64rwv+d5ttrQZvJitf/yMObK/JhsjoiZ2tbzfh4fTE+Xl+MSOfQ09NHJmH64FjoNL4rqn/g7EY/bUQiEv04h0aSHRcKjUpAs9GK8iZjn2zkESkFo10USho0OqSPHpZTIoNcBbNPnHnivWW3i3h3VQFmPv07rn1vI5btdRTRZ+TE4o0rJ2Dt/Sfh+uOzYNCqsK2kEde8txHnvboGv++t9lnky8+7qtDQbkFiuAEn5MT55DWPRcpJZyGdfEGKdclNCvNrhm9X10zPhEoAVh6oxd5K3+T2ldS34/zXVuPZpfuxJq8OHRYbIoK0mJ0bj/tOz8XnN0/FjTOyoBKAb7aVY86zf+DrrWU+uxZ8s60cf+yvgU6jwhNzR/XJg2t0iA7hBg1EkXEO1HslzmztqGAtwg3+jXKQOLrSHac3Xljmu6z0b7eX48LX16CssQNxYXpMyozCzCFxOHNUEi6akIprpmXiTOcMg5d+O4ir3l2P2laTT753SX07Fq4rBADcd3punxWx0p3RLsW8FpAPFDoL6VJkkL8ZtGo8eOYwAMBbKwpchfzeajFacPfn2/CXT7ai2WjFmLRIPHPRGNx98hBcMC4F49MjXZnlZY0duP3jrXhp2QGf3RtIJ1NOGZ6AnIS+eeYakhAGvUaFFpMVBT76eyTlqm8zo6HdAqCzgcPfpBjIN1fku07K9ZbdLuKrzaU46ZnleOm3gzBZ7ThuUDS+vX063r9uMi6dnIboEB0a2y34fFMprl2wEcf/7zefDURvare4Tq1eNdX/3egAoNOoXHUD5qQT+RY70hVKinYZ4udBo11dflw6lu2txhebS3HPKUN71T1pstpwz+fb8f2OCgCODMeLJqbh8inph3S7P3TWcNw8cxDe/CMfH64vwvaSRly7YCOmD47Bq5dPcA3n8kZVsxH//n43AODiSWnQ+OkY2OFyXIV05p1R7+0ulwaN+j/WRZIaFYzTRybhh50VeGdlAZ66aEyvXm9HaSOuW7AJta0mJEUYcNuswZicFY3BcaGHFLEmZ0XjrNHJ+PtXO7C3sgV3fbYNX28rw3/OH9WrLo26VhP++d0uAMCds3P65OgrAAiCgKy4UGwvaURBbWufdRGTPElDKtOj+6ZwJrlt1mB8taXUJ13pNruIp3/Z5xrsN3NIHF68dNxR3+tPGV6G+77aidUH63DmiyvxymXjMTEz2uvvDwDPLt0Pi03E8YNjMaOPNtiBzo70Ika7kA9I0S6ZfVQ4A4CThydgRk4sVh6oxWPf78HbV0/s1ettLqrHXZ9tQ0l9B1QCcPuswfjL7JxuYxuaOix4cdkBvLOqAM8s3Y+C2jY8MXdUr2KZyhs78LWzcDb/xGyvX8dTWrUKI5LDsaW4ETtLm/rsnoTkSepGT4kMQrCub0pHp49MxJmjk/DDjgrc+elW/HDHjF5FL20vacTD3+7CdmdRPj06GA+cMQynjkhwnVSfOSQOj51rx4bCevy0sxJLdlWiusWEy95aj1cvH49Zub07QfvF5hIYLXbkJoZhclbv7jM8kZsYhr2VLdhb2YLZw/wTbUmkROxIVyBRFHHAWUgf2kfdEQAwc0g8UiKD0NhuweM/7vG626PFaMF1Czbi+x0V0KgEPHzWcKx/YA4eOmt4t5Ex8WEGPHjWcKz8v5Nw0wmDYNCqsPpgHea9sRaVTUav1mCy2jD/w82objFhSEIobu6D4UESaWe5uL4dxsOiK4g8tctVSI/o0+97/YwsAI5O7uoW7/47BIDf9lZh3hvrUNtqQm5iGBbfOh1XHJeBIQlh3XaCjkmLxHd/OR5/O2UIdGoVlu+rwSnO7nRvPfb9bjS0W5CbGIab+vBaAHTmpOczJ516SepIT+vjQnrXrvTnf93v9es0Gy244f2NriL6/JnZePeaST1umJ87NgXf3j4d2XEhqGo24ZI31+Htlfle35/sKm/C19sc15K/+/g4+rFIBc/yRiMsNnuffm+Sn8I+jnYBHJvDj5w9HBqVgF/3VGH5vmqvXsdqs+PZpftx0etrUVLfgdSoIHx+81TcfcrQo2YfRwRp8dBZw/Gf80dCrRKwaGsZrnx7A+rbzF7/ed5eWQCrXcRxg6Ixvg/mTnQ1OjUSAAeOUu/l9WGsi0QQBDx+3igkRxhQVNeOR7/d5fVrfbe9HBe9vhbbSxoRolPj76flYundJ+C0kYlHxL1q1CpMy47FY+eNxPK/nYgThsShw2LDDR9swhebSrxeg80u4oO1RQCAq6dl9mlWea4zJ30PO9KJfIqFdAUqa+xAm9kGrVro004TtUrAP84cBkEAPlhbhOd/9fwYd02L40F39cE6BOvUePeaSbju+CwE6Y7dMRIXpscDZwzDolumIz5Mj72VLZj72hqPO7tFUcRDX/+JrcWNiAjS4q2rJvplKNLRxIXpEWbQwC5yyCD1jiiKrmiXvuxIB4Dx6VEYnx4Js82OD503l576eH0xbnh/EzosNszIicUX86e6lTmoVatw+0k5+PHOGZiUGYU2sw13fbYNzy3d73EB7fd91fh6WzlUAvC/uaP9NqDoaDKdRY6CGl4LqHekeKC+7kgHgNtPGgyNSsDKA7XYXHRkdvGx5NW04rxXVuP3fTXQa1R44ZKxuO/0XLfiqnISwvDt7cfj7DHJsNpF/PuHPbjlwy1oNlo8XseTS/ZBFIGzxyS7cor7SnyYHgatCja7iLKGo+fCEx2L0WJDhbPRpK+iHCSD48NcsQ7/+n43zFbPNoXq28y44p31eHHZAdhF4IJxKfjxzhlunzS5fEoGFlw7CWF6DTYU1uP8V1e7ComerkOKsrz1xMEef31vSSfU8nlvQL2U34eDRruKCNbiuXljoRKALzeX4vsd5R59vSiKeGtFPv7yyVaYbXacPDwBv997Im45MdutkyYheg3euXoiLhiXAptdxL1f7sArvx/0+DlBFEW89NsBFNe3I9ygwblj+2ZuiiTXeS3YW8mT7ES+xEK6Au13dqMPig3t86LPGaOS8K9zRgBw5KG+t7rA7a8trG3D3NfWYFd5M2JCdPj0puNwwhDPj00PTw7HV7dMw6C4EJQ1duDC19dic5H7+WcfrivC55tKoRKAly4d16fdOoBjlz6HOenkAxVNRjS0W6BWCRjSh6dTJDfMcHRvv7e6ECsP1Lj9daIo4qmf9+KBxTthF4ELJ6Ti3WsmIczDXOfB8aH47KapuMV55PqFZQfw18+2wWR176RHq8mKfyzaCQC4bnoWxqRFevT9fSHL2SHETTXqrUBFuwCOLvgLJ0hd6Z5tsn+7vRznvbwa+TVtSI4w4Mv503Du2BSPXiNEr8GLl4zFY+eOgFYtYMmuSpzz0ipX9JU71hysxR/7a6BRCfjbKUM8+v6+IAiC69+ukLnI1AvSplqYQYOo4L6Zl9DVHXNyEBuqQ35NG95fU+j21+2tbMY5L6/Cuvx6hOjUeOGSsXh23liPZz7MyInDolunITUqCEV17Tj/ldVYfbDWo9d4f00hOiw2jEgOx4ycWI++1heka0FJA2cmUO8EoiNdMmVQjGsj6oFFO3scHt6VzS7iX9/vxn9+3APAkbn++hUTEB/m2YBPrVqFZy4eg/kzHc8JT/28Dw9/sws2u3vFdLtzHdJ9zZ1zhvRZPI5kmLMjPb+mlSfZiXyIhXQF2t/Hg0YPd+XUTNx9suMh85/f7cbiraXH/Jo/y5pw4etrUFzfjrToIHx5yzTXsUVvpEUH48v50zA2LRKN7RZc/vY6/Lq76phftz6/Dv/8zpGLft/puV4V8n2BA0fJF/4sc3Sj58SH9mpmgbdOGZ6AcemRaDFZcdW7G/D8r/uPeXNa2WTE7R9vxSu/O+Ib7pydg6cu9L4TXKUS8PfTcvG/uaOgUQn4els5rnh7vVvHuZ/+eR/Km4xIiw7C3QEonAGd0S4spFNvlQSwkA44stKlrvSNhcfuSm82WvDXz7bhjk+2osVkxeTMaHz7l+O97gQXBAFXTs3EF/OnISUyCIV17Tj/1dVuHecWRRH/XbIXAHD5lPQ+32CXpEc7vq+0KULkDWkjJis2pE8jCCThBi3+71RHNNILyw64Ff+25M9KXPDqGpQ2dCA9OhiLb5vu8YZaVzkJYfj6tukYnx6JZqMVV7yzHg99/SdajnFSxWqz473VBXhzhWPI6C0nZgfk7zAtynEdL23ogN3Noh9RdwLVkS65c04OxqQ5/jv862fbjvmcYLTYcPvHW/De6kIAwINnDsMjZw9364RadwRBwH2n5+KRs4dDEICF64pw20dbjlmUttjsuOeL7a51PHL2cFx/fJZXa+iN+DA9okN0sIusGxD5EgvpCrTfebRnSHzghs/85aTBuHZ6JgDgb1/swLI93RexG9rMWLi2EPPeWIvaVjOGJzm6yX1x1DQ6RIePb5yCWUPjYLTYcfOHm/Gp8xhmd8obO3DrR1tgtYs4Z0wybpzRt1nIXbGQTr4QqHx0iUatwic3HodLJ6dBFB2dqNe8twF1raYjPrfFaMHTP+/DiU//jh92VkCtEvDk3NH468lDfPKQOm9SOhZcOxlhBg02Fjbg/FdXuwYsdWdzUQPeX1sIAHj8/FF93mEikeK56trMaOrwPIqCCHB0b5U640DSYwJTSE+LDsZFEx1d6Vc6i1ZH2yDaUFCP059ficVby6ASgDtm5+CjG6cgNlTf63WMTYvE9385HicOjYPJase9X+7A37/ccdSH5qYOC17/Ix87SpsQolPjL7Nzer0Gb2U6/+2kjmIibxQFIB/9cBdOSMXo1Ai0mqx4asm+o36e3S7ihV8PYP6Hm9FutmFadgy+uW26T07ZxYbq8fGNx2HeRMc9ysJ1RTj52RX4eVdlt5+/qbAeZ720Cv/8bjc6LI61nD4yqdfr8EZSpAEqATBb7ajp5p6KyB1mqx1Fzo3ZQHSkA46u8BfmjUWITo0NBfV4/Y+8o35uQ5sZV7y9Hj/9WQmdWoWXLh2HG2YM8slzwrXTs/DSpeOgU6uwZFclTnp6Od5emd/t5lqH2YabF27G4q1l0KgEPD9vLK6d3vdFdMCxESDFuzAnnch3AvPkTwG135kJHqiOdMBxUX/ozOFobLdg8dYy3PrRFnxw3WRMGRSDNpMVv+6pwrfbyvHH/hpYnTvP07Jj8MaVEzyOb+hJsE6DN6+aiAcW7cQXm0tx36KdeHHZAUzMjMakrGhMyozCkPgwmG123LRwE+razBiRHI7/zR0dkA4TSU6849+OhXTqjQPOa8GwpMBdCwxaNZ64YDQmZUbjH4v/xMoDtTjzxVV4+bJxmJgZDYvNjk82FOOFXw+gztklPjEjCg+eNRxjfRylcnxOLBbdMg3XLtjoOM796hr8+7yRiAnRwWoXYbXbYbWJsNlFPLt0P0QRmDs+FTNyAnMyBQBC9RrEh+lR3WJCYW1bQOJlaOCrajbCbLNDoxKQFBEUsHX89eQh2F3Rgu0ljVi4rggfri/CnGEJuHHGIEzKjILVLuL5X/fjteV5sItAWnQQnp83FhMy3Ms/dldUiA7vXj0Jry4/iGeX7sdnm0qwo6wJr10+HpmxISiobcOyPVVYtqcaGwvrXfcpN54wyCfFfG9lsJBOPlBQ6/j5yQrQphrgOC326DkjcMGra/DF5lJUNhuRHReKQXEhyI4LRXZcKMIMGvzti+346U9HYfuaaZl48Mxh0PgwttKgVeN/F47GuWOT8cDinSisa8fNCzfj1BEJ+Oc5I5EYYUBtqwn//WkvvtzsOGEbGezoqL9kUlq3Q8/7glatQlJEEMoaO1Bc346EcM8iLYgAx+kmm11EsE6NxAD+DGXGhuDRc0bg3i934Lml+zF9cCzSo4NR3tiBiiYjKpo6UN5oxM+7KlFQ24Zwg+P5/rhBMT5dx1mjkxEToscdn25FeZMR//5hD15YdgCXTUnHtdOykBhhQFOHBdcv2IhNRQ0waFV47fIJmJUb79N1eCo3MRxr8uqwp4I56US+wkK6wtjsIg44o12GBiATuSuVSsCTF45Gi9GCX/dU44b3N+GEoXH4bU81Orp0fg1PCsf541Jw1bQMt4aDeEqrVuHJC0cjKTIIry0/iPImI77dXo5vtzuGmoQbNIgL0yOvpg3RITq8ceUEt4ab+pPUkZ5f2wqrze7ThwZSDlcHaoCiHLq6YHwqRqZE4JYPNyOvpg3z3lyHa6Zl4re91a6u1EGxIfj76bk4ZXiC3zaychLCsPjW6bjxg03YVtKIv3yy9aifGxuqw4NnDvPLOjyRFRuC6hYTClhIJy9JUSCpUUFeH3/2hfgwA76+dRrW5tfh7ZUF+G1vNZbursLS3VUYkxoBuwjsdEZSzR2fikfPGe7TzfWuVCoBt5+Ug3HpUbjjk63YU9GMs19ahbhw/RED/AbHh+Ks0UkBGSrYVXqMFO3CqCfyXn/oSAccQ8mvPC4DC9cVYeWBWqw8cGhOuVolwGYXoVUL+Pd5IzFvUrrf1jJtcCyW3HUCXvrtAN74Ix8/76rCmoN1mDshFYu2lKLZaAUAzJuYhr+fnovoEJ3f1uKu9OhglDV2oKS+HZPcHLZK1FXXfPRANpABjlMqy/fX4IcdFTjvldVH/bzkCAMWXDfZb7OfpmbHYOX/zcI328rw5op85NW04Y0/8vHuqgKcMyYFu8qbsLeyBeEGDd69ZpLbg479KTdJGjjKjnQiX2EhXWGK69thstqh16iQ1g+KZ1q1Ci9fNh5XvbMBGwrr8cOOCgCOm79zxybjnDHJyOmDgr8gCLj75CGYP3MQthU3YkNhPTYVNmBLcQOajVY0G61QqwS8ctl4pEYF/u8tJTIIBq0KRosdxfXtGBSg3Doa2KRM5P5wLQCAIQlh+Pb243H/op34dns53lnlGEYcE6LDXXNycMnk9D4ZkBwXpsenNx2Hf/+wG2vy6qBRCdCoVNCoBahVAjQqAQatGvNnZiOqHzwsD4oLwfqCeuQzJ528VNyPrgWCIGBadiymZcfiYHUL3llVgK+2lGF7qaOAHhGkxePnj8KZo/smMmH64Fj8cMcM3P7xFmwqakBLjRUalYApg6IxOzcBs4fFB7zgKMmI7uxIt9vFgHXD0sBW6HwvyYwN/PXgX+eOwPnjU3CwqhV5NdKvNlenbGyoDq9fMaFPilUGrRr3npqLs0Yn4/5FO7GtpBELnMNQRySH47HzRmJ8epTf1+GutOggrM0HSurdG9BIdLhA56N3JQgCHj9vFHaWNrnuWWJDdUiKCEJypAFJEUFIjQrCeeNS/H4yzKBVY96kdFw0IQ2/76vGGyvysaGgHl9tcZxKiQvT44PrJrsGfQbasETHOvZUNEMUxYBvihDJAQvpCrO/ynGkJychNKBdZ10ZtGq8fc1E/Pv73QjRa3Du2BSMSY0IyEU+WKfBtMGxmDY4FoBjUMieimZsKWpATkIYpmb79oiYt1QqAdlxodhV3oyD1a0spJPHWk1WNLQ7cv1SowIX5XC4EL0GL1wyFpOzorFgTSFOH5mIm04Y5Leu06MxaNX493mj+vR7eiszhgNHqXeK6wI7aPRoBseH4YkLRuOeU4bio3XFKG/swF0n5/R5/ExihAGf3HQcvtxcijCDBicMiUN4H1+T3JHiPFFgstpR3WJCYgTjHMgzRosN5U2O4Z6Z/WCDSBAEjE+POqJAbbbaUdrQjuTIoD4flj7MOa/pw3VFWLS1DBeMS8EVx2X0m+cqiTRwtKSBUU/kHVdHemz/eM6MCNbipztnoK7VjPhwfZ//t384lUrA7GEJmD0sAdtKGvHOqgLUtBjx5NwxAZs3052chFCoBKCh3YKaFhPiGfVE1GsspCtM56DRwMa6HC7coMWTF44J9DKOoFWrMDo1EqNTIwO9lCMMjncU0g9Ut+KUEYFeDQ00pc4Hq8hgbZ8XqY9FEARccVwGrjguI9BLGRCk4csFtZyZQN6Rurv6WyFdEhuqx51zAjfEE3DcD1w62X/REb6gVauQEhmE4vp2FNW1sZBOHpOuBWF6Tb+IJzkanUYV0CYStUrA1dMycfW0zICt4VikE0bS6UMiT+U7C+nZ8YHfVJOE6DUI0fe/EtbYtEi8dOm4QC+jWwatGlmxIciracOeyhYW0ol8gMHKCrOvKvCDRsk3cpw56XkcOEpeKHUe9e1P3ejknUFxzkJ6TRtEUQzwamgg6u+FdHKfa+Aoi2fkhc5Yl8BnIlPvsJBOvSGKIvJqpBlF/aMjnbwnxfMU1/H0KpEvsJCuMP1l0Cj1njRw9AAL6eQF6ahvaiQLZwNdWnQwVALQZrahptUU6OXQACQVWvrTUWTyjrQZIsX1EHmiyPlzk8FrwYCXFu1olKhoNsJstQd4NTTQ1LeZ0dRhgSB0nnykgSvF2ThV2sCZCUS+wEK6glhsduQ7j/7nJHBneaAb7Iznyatphd3OLlTyjHQjJT1o0cCl16hdQ5ALathpQp5pNVlR12YG0D+GjVLvSAXQQnadkRcKnD83LJwNfHGhehi0KogiUN7I4hl5RupGT44IQpAusFnk1HvSc0IprwVEPsFCuoIU1rbBYhMRolMjJZLFs4EuIyYYGpWAdrMN5U18UyTPSB2o0o0VDWydOeksnpFnpGtBVLC2Xw7QJM+kRzuuBcWMcyAvFDkL6Rn9YNAo9Y4gCK57PA4cJU915qOz+U4OUtmRTuRTLKQriBQBMjghjLmHMqBVq5DpLJ4dZLwLeYgd6fLCQjp5i/no8pIZ68xIZ7QLeaGw1vFzk8loF1lIcxbPuLFGnspzFtIH8XSKLEiF9DJuqhH5BAvpClLqvHBm8GFZNqSBoyykk6dcGensSJcFqZCez0I6eUjqSGesizxIGyJNHRY0tVsCvBoaSIyWzhOOmSyeyUK6a+Aou1DJM/nOaBd2pMuD9LxX22pGh9kW4NUQDXwspCuI1IEq7UjSwDeYhXTyQlOHBS1GKwBeD+SCHenkLalzmR3p8hCs0yAuTA8AKKrn9YDcV1LfDlEEQvUaxIToAr0c8gFpg5TRLuQpqSM9m5tqshARpEWYXgMAKGvk9YCot/qkkP7KK68gMzMTBoMBU6ZMwYYNG476uQsWLIAgCIf8MhgMfbFM2StzFtJTWDiTDRbSyRvS6ZSYEB2CdZoAr4Z8QSqkF9e1w8bhw+QBRrvIj3TysJDxLuQB6eclMzaYEZAy4RowyGgX8oDJakOJs27AjnT5kGpAJcxJJ+o1vxfSP/vsM9x999145JFHsGXLFowZMwannnoqqqurj/o14eHhqKiocP0qKiry9zIVocw5pZmDRuVDKqQfqG6FKLJ4Ru6RjvimsnAmG8mRQdBpVDDb7Chv5A0yuU+KdklnJrJsSIMii+vYkU7uK6zloFG5kebgMCOdPCE1ZYTo1Ih3nnCigU/aWCtjIZ2o1/xeSH/22Wdx44034tprr8Xw4cPx+uuvIzg4GO++++5Rv0YQBCQmJrp+JSQk+HuZsieKouuiySgH+ciOcxTSmzosqG8zB3g1NFCUuvLReS2QC7VKcA2HY046uctmF12xb+xIl4+MGA4cJc8VOjdeslhIlw0p2qWh3YJWkzXAq6GBIq9LPjpPp8iH9NxXykI6Ua/5tZBuNpuxefNmzJkzp/MbqlSYM2cO1q5de9Sva21tRUZGBtLS0nDuuedi165dR/1ck8mE5ubmQ37RkZo7rGhx3kClRPJhWS4MWrUrC7WMXajkJukGKo2DRmXFlZNew6gnck9VsxFmmx0alYCkCG6syYWrkM4uVPKAtPGSwdMpshFu0CIyWAug8/QR0bFI+eiDmI8uK52FdF4LiHrLr4X02tpa2Gy2IzrKExISUFlZ2e3XDB06FO+++y6++eYbfPjhh7Db7Zg2bRpKS0u7/fwnnngCERERrl9paWk+/3PIQWljZyZykE4d4NWQL0lRPTymRe5iR7o8ZXLgKHlIOu6fGhUEtYpdZ3IhnS4oZkc6eUB678hi8UxWpKYJFtLJXflSR3oc89HlhB3pRL7TJ8NGPTF16lRcddVVGDt2LGbOnIlFixYhLi4Ob7zxRreff//996Opqcn1q6SkpI9XPDBw0Kh8Sf+m7Egnd7ky0nk9kBWpc4jRLuQuqZCexlgXWZEyriubjTBabAFeDQ0EJqsN5U2OewNmpMuLlJPOAYPkLldHOgvpsuIaPsxrAVGvafz54rGxsVCr1aiqqjrk41VVVUhMTHTrNbRaLcaNG4eDBw92+/t6vR56PYdgHIt0weSgUflJjWQhndwniqKrI53FM3nJinU88LAjndwldSwzH11eooK1CDNo0GK0ori+HUMSwgK9JOrnSurbIYpAiE6N2FBdoJdDPsSOdPKEKIrIdxbSs+O5qSYnUgNVbasJRosNBi1TCoi85deOdJ1OhwkTJmDZsmWuj9ntdixbtgxTp0516zVsNht27tyJpKQkfy1TEaQiKztQ5SeZ0S7kgYZ2C9rMjg5FbqzJi3Qcv6yxAyYru1Dp2KSOdBbS5UUQBA4cJY8U1jp+TjJjQzhcUGakpgkW0skd9W1mNBsdc9UyeTpFViKCtAjVO/po2YBH1Dt+j3a5++678dZbb+H999/Hnj17cMstt6CtrQ3XXnstAOCqq67C/fff7/r8f/3rX/jll1+Qn5+PLVu24IorrkBRURFuuOEGfy9V1srYkS5bKexIJw9I3ejxYXp2IshMbKgOYXoNRJHZyOQeqZDO4YLykxHtKIAU1fGECh1bofPnhIUz+XEV0jlgkNwgPU/yOUF+BEFgTjqRj/g12gUA5s2bh5qaGjz88MOorKzE2LFjsWTJEtcA0uLiYqhUnfX8hoYG3HjjjaisrERUVBQmTJiANWvWYPjw4f5eqqxJb4opUXxYlhtmpJMnmI8uX4IgICsuBDtKm5Bf24YcxjnQMZQwI1220p2bI8XsQiU3uArpsbwWyE2a836vpL4DoijyxAH1qNz5PJnM5jtZSokMwt7KFldjFRF5x++FdAC4/fbbcfvtt3f7e8uXLz/k/z/33HN47rnn+mBVyiJdLFk8kx+pkN7YbkGbyYoQfZ/8Z00DFPPR5S0r1lFIZ046HUuryYq6NjMAXg/kKNNZSC/k6RRygxQBxEGj8pMSFQRBADosNtS2mhEXxtlidHSuuWqsGcgSO9KJfMPv0S4UeO1mKxraLQD4pihH4QbHUDGAXel0bCXcVJM16Vh+QQ0L6dQzqRs9KliLcIM2wKshX0t3RrsUM9qF3CBtvkqzNkg+9Bo1EsMNABjvQsdW3mgEwDhYuUp1phOwkE7UOyykK4CUjx5m0PBhWaaYk07ukm6c0hjzJEuD4pyFdHak0zFw0Ki8Sbn3pQ0dsNrsAV4N9Wcmq80V58B5CfIk3fNx4CgdS1mj42eEhXR5khqpyripRtQrLKQrQGkjB43KnauQzt1lOgapkJ7KQrospXJmArmJ+ejylhhugE6jgtUuoqLJGOjlUD9WUt8BuwiE6NSIC2XshxylRjPOgdwjdaQzI12e2JFO5BsspCsAC2fyx4Gj5A5RFDkvQeZSIh3X+cpmI2x2McCrof6sMxOZ9wZypFIJriGDhYx3oR4UOX8+MmJCOIhSpqSO9GLOTKBjKGMDnqxJNYPqFhOMFluAV0M0cLGQrgBlrkI63xDlih3p5I7aVjOMFjsEgZ0mchUXpodGJcBmF1Hdwi5UOjpGu8ifNDOhiMUz6oEUBZYZy2uBXEnXeWakU086zDbUO4eQs5AuT1HBWgTr1ADgivQiIs+xkK4A3FmWP3akkzukByjpyD/Jj1olIDHCMVSMG2vUE0a7yF+687RBMXORqQfSRou08ULyk8ZCOrlBeo4M1WsQHqQJ8GrIHwRBcDVXMt6FyHuspCiANEwihR3pssWOdHIHB40qQzKHD9Mx2Oyi63rAjnT5ynD+2xYx2oV6IEX/sJAuX2nOjPTyRiOHD9NRSR3KyZEGxjzJmBT3y+cEIu+xkK4ApYx2kT1pk6SqxQizlTfI1D2pA5XXAnlLjex8YCbqTlWzEWabHRqVgKQIXg/kKoPRLuQGqQmDp1PkKyHMAJ1aBRuHD1MPeIpdGTo70nlvQOQtFtJlzmS1obrFBIBvinIWG6KHTqOCKDoKJETdcW2q8WFZ1jo70nmDTN0r7rKpplax60yuuka7iCKHD9ORRFFk8UwBVCrB1XRTwqgnOorOjnReC+RMutYz2oXIeyyky1yFsyPRoFUhOkQX4NWQv6hUApKduch8U6SjkToP2JEub8nsSKdjKGY+uiKkRgVBJQDtZhtqWk2BXg71Q/VtZpisjiHkCRH6QC+H/Ig56XQs0ukUxsHKmxTtwpoBkfdYSJc5qcskNSqYWWcyx4GjdCzMSFcG6VpQzmsBHUWxM+ojI4bXAjnTa9Su6J5ixrtQN6QN17hQPfQadYBXQ/6U5upI570Bda+Up1MUgdEuRL3HQrrMSRdIviHKHweOUk/sdtH1s8GOdHlLiXScTuGmGh2N1JHOQaPyJ22WMCedulPGKAfFYEc6HUs5C+mKID0HVjWbYLLaArwaooGJhXSZ4xEt5UiJlCZw8waZjlTdYoLZZodaJSDJGQNE8iR1oLYYrWg2WgK8GuqPWEhXDunfuJi5yNQN5qMrh3QakdcC6o7NLqLSOYiWG2vyFh2iQ5DWcQKpgjGQRF5hIV3meERLORjtQj2ROpCSIgzQqHnpl7MQvQaRwVoAjHeh7pUwI10xpPu/iiZeC+hIncMFucEud9KmGqNdqDvVLUZY7SLUKgEJ4bweyJkgdA4fZk46kXdYTZE5RjkoRwoHDFIPOGhUWTqvB7xBpkO1mqyoazMDYCFdCTh8mHrCKAflSIt2/BvXtprQYWacAx1KqhkkhhugVnGumtwxJ52od1hIl7lSFtIVI7VLR7rdLgZ4NdTflNZz0KiSSMWzMhbP6DBSN3pUsBbhBm2AV0P+lsxNNepBOTPSFSMiSIswvQYAi2d0JFfME2sGipDKjnSiXmEhXcasNjsqmx1FFCk/m+QrIdwAQQDMVjtq20yBXg71MyWujnReC5SAw4fpaJiPriyua0FjB0SRm+x0KGmzlYV0+RMEAamcmUBHwXkJyiI9D3JTjcg7LKTLWFWLCTa7CK1aQHyYPtDLIT/TaVRICHNk2rF4RoeTOg6ko70kb4x2oaNhPrqyJEToIQiAyWpHvTPShwgAjBYbalsdjRcsnilDmrMLtYSFdDoMY56UJZWz1Yh6hYV0GSt13iQlRwZBxawzReDAUToadqQrC+Mc6Gg6I994LVACvUaNuFBHMwVz0qmryibHz0OQVu0aUE3y5ho4yoYbOozUhMXTKcogbZgw2oXIOyykyxiPaCkP4xyoO1abHRXOAgo70pUhOdJ5OoWFdDpMZ9eZIcArob7SucnOLlTqVObKRzdAENhwowTSSSR2pNPhpI1WZqQrg9RMUdlshNlqD/BqiAYeFtJlTCqmspCuHOxIp+5UNhthdcU8sXimBNJ1v6rZCIuNN8jUqbyJXWdKw+HD1J0yDhpVHKmZghnp1JUoimzAU5jYUB30GhVEEahoYt2AyFMspMuY9IbI49vKwVxk6k5pl001NWOeFCE2VA+dWgW76CimE0nKOVxQcXhvQN0pdz0n8FqgFGmuAYMcPkydmo1WtJqsADpPNJK8CYLguvYz3oXIcyyky5ireMYbZMVg3hl1RzrCy0015VCpBCQ5H4aYi0ySDrPNNXCShXTlSI6QrgW8N6BO0s9DcgSvBUoh3Qe2mqxobLcEeDXUX0in2KNDdAjWaQK8Guor0vWAkbBEnmMhXcZ4REt5GO1C3ekcLshrgZJIxRHmIpNEem8I02sQEcThgkrB4cPUHZ5OUZ4gnRqxzuHDbLohSXmXeQmkHCmujnQ+JxB5ioV0mbLbxS7RLrxBVgpp06TFaEWzkZ0m5FDivEGShkyRMkg3yOxIJ0k5M5EViRnp1B1eD5SJTTd0ODbfKROjXYi8x0K6TNW2mWC22qESgMQI7i4rRYheg8hgR5chj2mRhB3pytRZPOO1gBzYdaZMUnGkttUEo8UW4NVQf8DhgsqVEsmoJzoUN9WUKbXLzAQi8gwL6TIlXRATww3QqvnPrCTSAxEL6SQpZUa6IvFhmQ7Hh2VligzWIkirBgBUNrErnYC6NjNMVjsEAUiI0Ad6OdSHpNg33huQpJSbaoqUymgXIq+xwipTZRw0qljSTVB5E2+QCTBb7ahsdhRO0ng9UJRkbqrRYcqYiaxIgiC4TiGweEZA589BXKgeeo06wKuhvsRoFzpcOQvpiiQV0iubjbDY7AFeDdHAwkK6TPG4pnK5bpBZPCM4ug/tIqDTqBAXxq4zJUnpMmBQFMUAr4b6Az4sKxejnqgr17WAG+yKw+HDdDg24ClTbIgeOo0KdpGn1Yg8xUK6TJW5MpEZ5aA0UoGklDfIBKC00RnrEhkEQRACvBrqS9LDcpvZhuYOa4BXQ/2BdFKJHenKk8ouVOqCp1OUK4XDh6kLk9WG6hYTAF4PlEalEpDq/DcvYbwLkUdYSJcpKeuKO8vKw4x06kr6OeDNsfIYtGrEhOgAdG6okHLZ7SIqnIUT3hsoD3ORqSueTlEuDh+mrqROZL1G5bpnJOVIceWk896AyBMspMsUo12Ui9mH1FW5VDjjtUCRpOtBOTvPFK+21QSzzQ6VACQw5klxOuMceC2gLoOHIwwBXgn1ta7DhysY56B4XWsGPLmqPFJ6AQvpRJ5hIV2GRFHsEu3C4pnSSAXTmhZ2mhBQ5uxEZke6MrELlSTSw3JiuAEaNW//lIa5yNSVq5DOewPF4fBh6or56MqW6upI58lVIk/wSUqGmjosaDM7Cqi8QVae6BAdDFrHf9ocHELljHJQNBbPSFLOTGRFS+kybJTDh4kZ6crG4cMkcd0bRPBaoESu+SnsSCfyCAvpMiQdzYkN1cPgPLpHyiEIwiEPzKRsjHlSNqnrjMOHiR2oypYQoYcgACarHfVt5kAvhwLIaLGhttUxXJD3BsrE4hlJpJOrbLhRplRmpBN5hYV0GSrlES3FS+bAUYIz5omFdEVLjWJHOjmUsZCuaHqNGnGhjmx85qQrm5SLHaxTIzJYG+DVUCAw9o0kPK2mbCmRjoz0ymYjrDZ7gFdDNHD0SSH9lVdeQWZmJgwGA6ZMmYINGzb0+PlffPEFcnNzYTAYMGrUKPz44499sUzZkB6WmY+uXK7dZd4gK1ptqxlmqx2CACRyoJgiMdqFJOWuTTVeC5SKcQ4EHHo6hcMFlcl1b9DEa4HSseFG2eLC9NCoBNjsIqpaTIFeDtGA4fdC+meffYa7774bjzzyCLZs2YIxY8bg1FNPRXV1dbefv2bNGlx66aW4/vrrsXXrVpx33nk477zz8Oeff/p7qbLhGjTKN0TFSmFHOqHzYTk+TA+dhgeQlEh6WK5uMcFsZaeJkrEjnVK4sUbgtYA6Ty3zOUHZ7HaeXFU6tUpAkrPBgtcDIvf5vbLy7LPP4sYbb8S1116L4cOH4/XXX0dwcDDefffdbj//hRdewGmnnYZ7770Xw4YNw2OPPYbx48fj5Zdf9vdSZUOausxoF+Vy3SA3cgK3kvHmmGJCdNBrVBBFDh9WOldHOu8NFEuamcBCurLxdAq5NtWajLDbOXxYqeraeHKVug4jZ91AyQpq21DfZuZAejf5tZBuNpuxefNmzJkzp/MbqlSYM2cO1q5d2+3XrF279pDPB4BTTz31qJ9vMpnQ3Nx8yC+lY/GMpLwzHt9WNg4XJA4fJgBoN1vR0G4BwOuBkvFaQECXe4MIXguUKiHcAEEAzFY76jh8WLHKeHKV0KVuwI50Rbt+wUaMf2wp1ubXBXopA4Jfr5i1tbWw2WxISEg45OMJCQmorKzs9msqKys9+vwnnngCERERrl9paWm+WfwAZrWJEAQgNSo40EuhAJE6DiubjLCx00SxOHiYAOakU+cwsTC9BuEGDhdUKl4LCOBwQQJ0GhXiw6Thw7weKFU5m+8IXU+y8+SqUoliZ8xTaiRriO4Y8FuP999/P5qamly/SkpKAr2kgPv5rydg72OnYXB8aKCXQgGSEKaHWiXAYhNRw8EhisUbZAI64xzYhapcPJ1CQNdho3xYVjJmpBPAEyrU2YHMa4GypfJaoHi1rWaYrHaoGPPkNr8W0mNjY6FWq1FVVXXIx6uqqpCYmNjt1yQmJnr0+Xq9HuHh4Yf8IkCvUUOtEgK9DAoQjVqFxHCpeMa8M6VizBMBnUc22XWmXJ2FdN4cK5n0XlDbaoLRYgvwaigQDuk642k1ReMJFSrj7BRC1+HDrBkolTRjMSHcwJgnN/n1b0mn02HChAlYtmyZ62N2ux3Lli3D1KlTu/2aqVOnHvL5ALB06dKjfj4RdU96YC5l3plisQuVAHakE68F5BAZrEWQVg2Aw4eVqutwwYRwbqwpGTvSiQ03BHQ9rdbBQZMKxWuB5/y+3XD33Xfjrbfewvvvv489e/bglltuQVtbG6699loAwFVXXYX777/f9fl33nknlixZgmeeeQZ79+7Fo48+ik2bNuH222/391KJZKUz74w3yErUdbggO02ULYVdZ4pXykI6wTF8WNpY4/VAmco5XJCcOrtQeS1QKkZAEgAkOaM8jBY76jl8WJHKOFfNYxp/f4N58+ahpqYGDz/8MCorKzF27FgsWbLENVC0uLgYKlXnjdy0adPw8ccf48EHH8QDDzyAnJwcfP311xg5cqS/l0okK65OE94gK5J0c8zhgtR1U00URQgCY7+UppxRDuSUHBmEvJo2brIrFE+nkCQ5wrnJ3sRrgVJxXgIBgEGrRlyYHjUtJpQ1diAmVB/oJVEfY+Sb5/xeSAeA22+//agd5cuXLz/iYxdddBEuuugiP6+KSN5S2ZGuaKXcWSanxC6dJg3tFkSH6AK8Iupr5c7hknxYps4TKox2UaIyXgvIKZnXAkVrM1nRyJOr5JQSGYSaFhPKGzswOjUy0MuhPuaqGzjnatGx8UwfkUylRjkuhMxIVyZ2mZBEr3F0mgCMc1Aiu11ERROvB+TAAYPKxigHkkjF0/o2MzrMHD6sNDy5Sl1J1wPWDZSJ0S6eYyGdSKZSXW+I7RwcokB8WKaukjl8WLFqW02w2ESoBCAhjMd1lc5VSGecgyJJD8vJERw0qnThBg1C9Y7D6Ty9qjyu4YIsnBGAVA4fVixRFBnt4gUW0olkKimyM86hjoNDFMf1sMxCOqHzBpldqMoj3RwnhhugUfO2T+mkYaOcn6JM0gZKShSPbysdhw8rG0+uUlfJnK2mWM0dVrSarADYgOcJPlERyZReo0ZCuKP7kG+KyiNlXrLThADwYVnBmI9OXaU68y+l4cOkLJ3DRtmRTox6UjKeXKWuUtiRrlglDe0AgNhQHQxadYBXM3CwkE4kY8xJVy7XkU0+LBMY56BkZY2OG2QW0gkAEiL0EATAZLWjnqfVFMVosaG21fFvzuIZASyeKVkpT65SF1LjFa8FylPGTTWvsJBOJGNdc9JJOaw2OyqbnR3pnL5N6PKwzE01xeHpFOpKr1EjLlQaPmwM8GqoL1U0Of69g3VqRARxuCB1iXNg8UxxpEJ6WjTvDajzHrGx3YI2Z8wHKYP0bJjKyDePsJBOJGOpnMCtSFUtJtjsIjQqAXEcLkjo+rDMwpnSMAeVDsfimTKVd7kWCIIQ4NVQf5DCaBfFKql3NFmlsXhGAMINWoQZHMOHeT1QFqlOxIYbz7CQTiRjUjcyO9KVRboBSoo0QK3iwzJ1PizXtppgtNgCvBrqS+WMeaLDsHimTNxUo8NJhROeTlEWo8WG6hYTgM6mKyLp3qCU9waKIkVAMtrFMyykE8lYKvPOFEk6opUcwTdEcogM1iLIOUCmsokPzEpSzuIZHYbDh5VJujfgphpJpPeFiqYO2O0cPqwU0nNhsE6N6BBdgFdD/QVjIJVJuh5wU80zLKQTyVjXaBdR5A2yUriGhvANkZwEQeAgIQVqN1vR0G4BwEI6deLwYWUq50AxOkxCmB4qAbDYRNS0mgK9HOojpQ2dhTPGPJGEzwnKVMZoF6+wkE4kY9LDcrvZ5iqmkPy5dpb5sExdSBtrUi4myZ90XD9Mr0G4gcMFyYEzE5RJ2jjhphpJNGoVEsMdJxRYPFMOKfKT+ejUFTvSlafN1Nlww012z7CQTiRjBq0a8c5hk8xJVw5GOVB3pAemEl4LFIOZyNQdZqQrk7SxxusBdeXqQmXxTDFK6hnlQEdiR7rySP/W4QYNwthw4xEW0olkLpU3yIrDI1rUnbRoqSOd1wKlKGfME3VDKqTWtHD4sFKIotgZ+8ZCOnWRzI01xXF1pEezI506cZNdecpcMU+8FniKhXQimUtxXhhLWUhXBFEU2ZFO3ZI60osZ7aIYndcCDhekTlEcPqw4dW1mmK12CAKQEM7rAXViIV15ShrYkU5HkpouqpqNsNjsAV4N9YVSNtx4jYV0IpnrHDjK4pkSNHVY0GZ2dBiy64y6kjqPeC1QDka7UHcEQXBtrrB4pgzSv3N8mB46DR//qFMKZyYoTpnzPpBdqNRVbIjj/cEucpNdKaRnQtYMPMc7KSKZ6yyk82FZCaTCWUyIDgZnxyER0FlIr201o91sDfBqqC+UM8qBjqJz4CjvDZRAugfktYAOl8JrgaK0m62obTUD4LBROpRKJSA5wrHJzrqBMpTxdIrXWEgnkrlURrsoCvPR6WgigrQIN2gA8HqgFBwuSEfTmYXKrjMlkCK90pmJTIdhtIuySM8JYQYNIoI5XJAOxYGjyiL9O7OQ7jkW0olkLrXLG6IoigFeDfmbKxM5gm+IdCSpK724jvEucme3i6hoYrQLdY/FM2UpqmMhnbonxTw1dVjQauJpNbkrYawL9cB1QoUNN4rgasCL5PXAUyykE8mc9IbYarKiqcMS4NWQv5VxaAj1QCqilDAnXfZqWk2w2ESoVQISwvSBXg71M65CehMflpWgROpIjwkJ8EqovwkzdJ5W48aa/EknEtP4nEDdkAqqZY18TpA7o8WG6hYTANYNvMFCOpHMGbRqxIY6iiiMc5A/RjlQT6SO9JJ6XgvkTtpUSww3QKPm7R4dSupC5fFtZWC0C/WEMxOUo9SVicxrAR1JKqgy9k3+KpwDZYN1akQx5sljfLIiUoDOgaPcXZa7Ug4XpB5IHUjsSJc/V8yTs2BK1FXX49uMfZM3q83uKpCykE7dSWHUk2JIp1OYiUzd4fBh5SjrMoRcEIQAr2bgYSGdSAE6C+l8U5S7chbSqQepro50FtLlrrOQzmsBHSkpIghqlQCT1e462kvyVNFkhM0uQqdRIZ4xT9SNzi5UPifInSvahZtq1I2uhXS7nZvsciY1WDLWxTsspBMpgHR8j4V0eTNabKhh1hn1IC2qs5DOLlR5Y8wT9USnUblOKxRx+LCsSbEuaVFBUKnYdUZHSuaAQcXoHDbKewM6UmKEAYIAmK121LZxk13Oyth81ysspBMpADvSlaHSmXVm0KqYdUbdkq4FbWYbGto5fFjOytiRTseQEe0YPFlU1xbglZA/MR+djsU1fJi5yLLWYrSg0Xnvx0I6dUenUSEhzDlDhXUDWSvjvIReYSGdSAFSmJGuCF13lpl1Rt0xaNVICHcc7We8i7x1xjwxI526lx7jeHgq5rVA1qQTByyk09EwF1kZpIaqyGAtwgxsuKHuSXUDXg/kzTVXjZtqXmEhnUgBpAGDHComb+xAJXe44l24sSZrvB7QsWQ6C+mFjHaRNWnTND0mJMArof5KKqRXNhthtdkDvBryF1c+OjtQqQccPqwMXYeNkudYSCdSgJRIxw1Ti8mK5g5rgFdD/tJ5RItviHR00oApdqHKV/Mhx7f5wEzdS3dGuxQz2kXWGO1CxxIXpodGJcBmFzl8WMakTTU+J1BPUqI4M0HurDY7KpsdUV5pvB54hYV0IgUI0qkRG6oDAJQ2sngmV1LnQHIE3xDp6KRCekk9b5DlqqjWcZ2PDdUjVK8J8Gqov8pwdqQXcVNN1lhIp2NRqwQkRjhiwNiFKl+ujnReC6gHyYx6kr3KZiNsdhE6tQqxofpAL2dAYiGdSCFSnF2JHDgqX2XMOiM3pHFmguwVODuMs2L5sExHJxVWG9staOrg8GE5auryb5sWzXsDOjrmpMufFOnHjnTqSWqk9JzAa4FcSacNkiMNUKk4V80bLKQTKQTfFOWPmcjkjs6OdBbS5aqo1lFIz2AmMvUgRK9xdSIVMyddlqTCWWyoHsE6nk6ho2MhXf6YkU7u4LBR+ZOuBWy+8x4L6UQKkcouVFmz20VUNDqyzjg0hHoiFdLLGjtgs3P4sBxJHenSMEmio+kcOMqcdDnqjHXhfQH1LJkDBmWvlB3p5AbpObLFaEWzkafV5EjaJEmN5HOCt1hIJ1KIzkI6b5DlqLbVBLPNDpUAV84lUXcSww3QqgVYbKJr0AzJS5Gzuzgzlh3p1LP0GA4fljPpWsB8dDoW6TmhmPNTZKmp3YIWoxUAh5BTz0L0GkQGawFw4KhclbEjvddYSCdSCOmmiW+I8iTtLCeEG6BV89JOR6dWCa5uE8a7yFNhrdSRzkI69Swj2vEzUsSOdFnioFFyl7TxKr1/kLx0xjzpEKRTB3g11N+l8ISKrJU2Oq4HPMXuPVZbiBSC0S7yxnx08gRz0uWr2WhBXZsZAJDBaBc6BulnpIgZ6bIkXePTualGxzDIWUgvbWiHyWoL8GrI16TnvxR2o5MbODNB3qTGSsY8eY+FdCKFkI7uNButaOpg3pncSB0D3Fkmd0gnVFhIlx9paGRsqA5hBm2AV0P9HaNd5I0d6eSuuDA9QnRq2EXeG8hR56BRPifQsUmNWTzJLj92u4hyaa4arwdeYyGdSCGCdRpEh+gA8E1Rjph1Rp5Icw6eK+G1QHYKGOtCHpB+TiqajDBa2IUqJ1ab3dVNyEI6HYsgCMiKc1wPCmpZSJcbaXOE+ejkDtdJdnaky440V02tEpAYzrlq3mIhnUhBGO8iX1JBlB3p5I50RrvIlpR1ncFCOrkhKliLML0GAK8HclPRZITNLkKnUSE+TB/o5dAAkBUbCgAoqG0N8ErI11wd6dF8TqBjS2FHumxJNYPEcAM0nKvmNb/+zdXX1+Pyyy9HeHg4IiMjcf3116O1tec35hNPPBGCIBzya/78+f5cJpFiSIV05p3JT36N49oqZVwS9SRNinbhpprsSJ2EmcxHJzcIguCKd2FOurxIsS5pUUFQqYQAr4YGgqxYqSOdA0flRrrfY0c6uSOFNQPZkv5NeYq9d/xaSL/88suxa9cuLF26FN9//z1WrFiBm2666Zhfd+ONN6KiosL168knn/TnMokUQ7p5KuXusqyYrDbX7nJ2fGiAV0MDgTRstKrZxDgHmZE60jO5qUZucg0cZUe6rEgbI4x1IXdlxTp+VvJrWEiXE1EUmZFOHpE60mtaTBw+LDOuQaM8xd4rGn+98J49e7BkyRJs3LgREydOBAC89NJLOOOMM/D0008jOTn5qF8bHByMxMREt76PyWSCyWRy/f/m5ubeLZxIxhjtIk/Fde2w2UWE6NQ8vk1uiQrWIkSnRpvZhtKGDgzmBoxsFNYxI508kx7t+FkprmPxTE44aJQ81RntwmuBnNS3mdFudhRDk1k8IzdEh+hg0KpgtNhR0Whkc4aMlDU67g3Ykd47futIX7t2LSIjI11FdACYM2cOVCoV1q9f3+PXfvTRR4iNjcXIkSNx//33o7396EW/J554AhEREa5faWlpPvszEMmNtLvMjnR5yXN2DmXHh0IQeHybjk0QBFdXOuNd5KPFaEFtqxkAkBHL4hm5J5Md6bIkZd6nc1ON3JTl/FmpbjGh1WQN8GrIV6TnvoRwPQxadYBXQwOBIAiuTRfGu8iLdD1IZSG9V/xWSK+srER8fPwhH9NoNIiOjkZlZeVRv+6yyy7Dhx9+iN9//x33338/Fi5ciCuuuOKon3///fejqanJ9aukpMRnfwYiuWG0izzlMR+dvCAV0ktZPJMNKcohJkSHcIM2wKuhgYIZ6fLEjnTyVESwFjEhOgBAIbvSZaOzcMZrAblP+nkp5nOCrEjRLimRvB70hsfRLvfddx/+97//9fg5e/bs8XpBXTPUR40ahaSkJMyePRt5eXnIzs4+4vP1ej30ekYZELlDOsLT1GFBi9GCMBZaZEHKssyOYzwHua9z4Cg31uSikPno5IUMZxdqaYMjJkzNwZSywEI6eSMrNgR1bWYU1LZhZEpEoJdDPiCdPGQ+OnkiOy4EK/bX4GB1a6CXQj4iiiKHjfqIx4X0e+65B9dcc02PnzNo0CAkJiaiurr6kI9brVbU19e7nX8OAFOmTAEAHDx4sNtCOhG5L1SvQVSwFg3tFpQ1diA3kYV0OXB1pLOQTh5Ii3bcQBWzC1U2pI5iaXgkkTsSww3QqVUw2+wob+xwnVahgaup3YKmDguAzms9kTuyYkOwqaiBOekyIs3GYkc6eSInPgwAWEiXkYZ2i2teQlKEIcCrGdg8LqTHxcUhLi7umJ83depUNDY2YvPmzZgwYQIA4LfffoPdbncVx92xbds2AEBSUpKnSyWibqRGBaOhvQml9R3ITQwP9HKol0RRRL6zkJ4dzy5Ucl86M9JlRyp8cNAoeUKtEpAaHYT8mjYU17ezkC4D0nU9NlSPYJ3Hj3ukYFlxjvcPFtLlo6Te0YHKTTXyxOB4R4MWC+nyIcW6xIdxXkJv+S0jfdiwYTjttNNw4403YsOGDVi9ejVuv/12XHLJJUhOTgYAlJWVITc3Fxs2bAAA5OXl4bHHHsPmzZtRWFiIb7/9FldddRVOOOEEjB492l9LJVIUabBEKYtnslDbakaz0QpBYPGMPOMaNsrsQ9koYrQLeUl6/2BOujxI/47pLJyRh6SBo/kspMsGO9LJG1IhvayxA20cPiwLZY2OawFjXXrPb4V0APjoo4+Qm5uL2bNn44wzzsDxxx+PN9980/X7FosF+/btQ3u74x9Up9Ph119/xSmnnILc3Fzcc889mDt3Lr777jt/LpNIUVIipUI6c5HlQOpGT40K4s4yeUTaVGs2Wl0RADSwFdQ67qcyGe1CHpJOqEibMTSwMR+dvOXqSK9phSiKAV4N9ZYoil2GjbJ4Ru6LDtG5hg9L87hoYCusk+Yl8N6gt/x61i86Ohoff/zxUX8/MzPzkDfotLQ0/PHHH/5cEpHiSTdR0qAJGtjynDc2g2KZj06eCdZpEBuqQ22rGSX17YjgULEBrdVkRW2rCUDn8Egid0m5+uxIlwdXIZ3XAvKQdDql2WhFfZsZMaH6AK+IeqOm1QST1Q6VACRFsJBOnsmOD0VdQT0OVLdgVCqfEwa6/VUtAIAhCawb9JZfO9KJqP+RjvWxI10eXPnoHDRKXpCuB4x3GfgKncfwo0N0iAjiIGnyjKuQzmuBLJSwI528ZNCqXadXmZM+8En56InhBug0LP2QZ3KYky4rUiE9JyEswCsZ+Hg1JVKY1GhmpMtJnrOQPiiOXWfkOQ4clQ+pk5ixLuSN9GjHe0hxXRvjHGSA0S7UG1mxHDgqF658dF4LyAscOCofNrvo+nccykJ6r7GQTqQwUpdJQ7sFrRwcMuBJw6DYkU7eSHNurEkdSzRwFUqDRhnlQF5Iiw6CIABtZhvq2syBXg71gtVmd8X3sZBO3mAhXT6Yj069kRPvKLiykD7wldS3w2ixw6BVIY33Br3GQjqRwoQZtIh15h3m8U1xQDNZba7j29nxLJ6R56RhM+xIH/ikaJfMWF4LyHN6jRrJzvxcDhwd2CqajLDZReg0KsSHMd+aPMdCumfsdrHfnuSROtI5XJC8IXWkF9W3w2S1BXg11Bv7nLEug+NDoVYJAV7NwMdCOpECDUty7C7vrWwO8EoO1WK04Op3N2Div3/Fs7/scw3OCzSz1Y6S+nasz6/D5qL6QC/HpaiuHXYRCNNrEMdhUOQFqSOhuB/mIouiiG+2lWH6f3/DxW+sxb7KloCso8Nswx/7a7CztAlt/fgUjxTtksFoF/KS1L3cXweO1rWa8OXmUjz49U5sL2kMyBqajRbc99UOzHtjLb7ZVgaLzR6QdfRE+vdLiwqCig/L5AUW0t1jtNjw2vI8jHtsKS58fW2/jM1kRzr1RkK4HqF6DWx2EYW1/efne3d5M+5ftBMLVhfgz7Im2OyB38iy20VsKqzHQ1//idOeX4Efd1YEekmHOOAaNMpYF1/QBHoBRNT3chPDsPJALfZUBKYw1Z3aVhOufncDdpU7ivsv/nYQr6/Ix9zxqbhhRlafRZd0mG14a2U+9lQ0o7zJiPLGDtS2mtC10eR/c0dh3qT0PllPT6QTBYPiQyEIfFgmz0mFs9KGDtjtYr8puhyoasFD3/yJdfmOjauyxg6c+eJKzJ+ZjdtPGgyDVt0n6yisbcONH2zCgS6ndxLDDciOD0F2XCiy40Ixe1i8a2hrIBUw2oV6KSMmGGvz6/pNIV0UReyvasWve6qwbE8VtpY0ut6LP9tYggfPHI6rpmb02fvf+vw63P35dldsyvqCevzvp724dnoW5k1OQ7ihfwz5ZT469VbXQnp/uDcobWjHLR9uQXWLEVabCKtdhNVmh9UuwmYXERemx5MXjsaMnLg+WY/dLmLx1jI888s+lDcZAQCbixpw5our8Ny8MTgpN6FP1uEO6eQqoxzIG4IgYHB8KLaVNOJgdSuGJga+CFvZZMTV721ATUtnw12oXoPxGVGYlBGFSVnRGJceCb3G/88Koihib2ULvtlWju+2l7vuDwDgjk+2IkirxqzceL+vwx37qhzPMiyk+wYL6UQKlJsYDqD/dKSX1Lfjqnc3oKC2DTEhOtw5JwdfbSnD9pJGfLKhGJ9uLMacYQm46YRBmJgR5beH5tpWE65/f1O3nW46jQrRwTpUNhvx6Le7MTEzOuC55K58dEY5kJeSIgxQqwSYrXbUtJqQEG4I6HraTFa8uOwA3llVAKtdhEGrwvyZ2dhV3oylu6vw8u8H8cPOCjx+/ihMzY7x61pWHajFbR9vQVOHBRFBWmjVAmpbzahsNqKy2YjVB+sAAE//sg+/3j0zoH93bSar64GChXTyVnpM/zmhsmB1Ad5eVeDqppSMSA5HRJAWa/Lq8Mi3u7CpqAFPXDAKoXr/PdKYrXY8/+t+vPZHHkTRUaA+c3QSvthUgvImI/7z4x68sOwALpmUhmuPz3LNogkUFtKpt1KjgqBRCTBZ7ahoNgb0Z1oURdy/aCd2ljUd9XMqmoy45r2N+Pd5I3HpZP82uqw6UIvHf9yD3RWOZ6jkCANuOTEbX24uxfbSJly3YBNuOTEb95w8BBp1YA//2+2iq7DHjnTyVtdCeqAZLTbc/OFm1LSYMCg2BKnRwdhS1IBWkxUr9tdgxf4aAI73v/euneS3Z3VRFPHh+mIsXFuI/VWdfy+heg1OHZGIFqMFv+yuwi0fbcaH10/BxMxov6zDE1JHOgeN+gYL6UQKlOuMdtlT0QJRFAPazby/qgVXvrMeVc0mpEQGYeH1kzEoLhRXHpeBjYUNeHNFPn7dU4Wlux2/zh6TjKcuHO3zjtTC2jZc/d4GFNW1IzJYi9tnDUZqVDCSIw1IjgxCTIgOoghc8c56rMmrw12fbsNXt0yDThO4m2SpIz07noNGyTsatQpJEQaUNnSgpL49YMVgURTx485KPPb9blQ2O7q7Th6egIfPGo606GCIooifd1Xi4W92oaC2DZe+tQ4XT0zFA2cMQ2SwzudrWbCmEP/+YQ9sdhFj0iLx5pUTkBBuQFO7BXm1rcirbkV+bRuW/FmJgto2/O+nvXh23lifrsMT0qDRqGAtIoL7R1csDTzSJkygM9I3F9Xj0e92AwD0GhWmD47FSbnxmD0sHkkRQRBFEe+sKsB/f9qL77aXY3d5E167YoJfuqzyalpx16fbXEW8iyak4pFzRiBUr8Gds3PwzbYyvLWyAAerW/H2qgK8t6YQ/zxnBK44LsPna3GX1IGazk018pJGrUJ6TDDya9pQWNsW0EL6V1vKsPJALfQaFd69ZhJiQ/VQqwRo1QLUKgEqQcBTP+/D4q1luH/RThTWtuHvp+X6vIs+v6YV//xuN/5wFurC9BrcOmswrp2eCYNWjYsnpeGJH/diwZpCvLY8D5uLGvDypeMQH8BN9vKmDlhsIjQqAYkBbpSggSvH+Zx5oDqwJ9lFUcTD3/yJ7SWNiAjS4r1rJyEjJgQ2u4g9Fc3YVFiPjYUNWJNXi+L6dlz42hq8e80kjEuP8uk6bHYRD33zJz5eXwzA0Wx30tB4nDs2GbNy42HQqmGx2XHjB5uwfF8NrluwEZ/dPBXDksJ9ug5PWGx25NU46gY5Cawb+AIL6UQKJA2ZaOqwoLLZiKSIwNwgby5qwHULNqKpw4Kc+FAsvH4KEiMcN3qCIGByVjQmZ0XjYHUr3lmVjy83l+K77eWoaTHizasm+uwY9dbiBlz//ibUt5mRGhWE96+b3O0OtiAAz148Fqe9sAI7y5rw7NL9uO/0XJ+swRt5zo70QexIp15IiwpGaUMHiuvbA9Yx8fQv+/DK73kAHF0kj54z/JCj0YIg4LSRSZg2OBZPLtmLD9cV4/NNpfhtbzVevGQcpg2O9ck6TFYbHv56Fz7bVAIAuGB8Ch4/f5Rr4y4iWIvx6VEY77wpP21EIs57dTUWbS3D5celY0JGYP7+pCgODhql3ugPGemiKOKJH/cCAM4bm4zHLxiFYN2hjyuCIOCGGYMwNi0St328BXk1bTj35dV44oJROG9cis/W8dH6Yvz7h90wWuyICNLiiQtG4YxRSa7PMWjVmDcpHRdNSMMf+2vw5op8rM2vw39/2oszRyUhKsS3m3zuYkc6+cKg2BDk17Qhv7YN0330HuupmhYTHvvesal215whR13HsxePQUZMMJ7/9QDeWJGPorp2PDdvLIJ0vmm6WZtXh5sWbkKL0QqtWsAVx2XgLyflILrLf+N6jRqPnjMCEzOj8Pcvd2BDQT3OeHGlT+9RPLXbGZc5OD404N3xNHBJA0cD3ZG+cF0RPt9UCpUAvHTpOGQ4N4vVKgEjUyIwMiUC10zPQm2rCdct2IgdpU247K31ePXy8T6LVzFabLjz0634eVcVBAH4v1Nzcflx6UfUJLRqFV67fAKufGc9NhU14Mp3NuCrW6a61tzXCmvbYLGJCNGpA35qTi54RSVSIL1Gjew4x4V8b4By0v/YX4Mr3l6Ppg4LxqVH4ov5U11F9MMNjg/FExeMxoJrJyNUr8G6/Hpc/PpaVDpzCXvj191VuPStdahvM2NkSjgW3Tqtx2NgiREG/PeC0QCAN1bkYU1eba/X4A1RFJHPjnTyAanYUlLfcYzP9I+aFhPeWlkAALh91mD88tcTjpovGm7Q4t/njcKX86ciJz4Uta1mXPXuBny2sdgn67jsrfX4bFMJVALw4JnD8MxFY3o8/TImLRIXT0gDADz67e6ADTsqZD46+YA0qLauzYzWAA3W/XVPNTYVNcCgVeG+04cdUUTvamJmNH64YwamD45Bh8WGuz7bhn8s3gmjxdarNRidr/Xg13/CaLFj+uAYLLlrxiFF9K5UKgGzcuPx8Y1TMDwpHK0mK95ZVdCrNfQGC+nkC66c9JrAnVD553e70NRhwfCkcNwwI+uonycIAu6aMwTPzxsLnVqFJbsqccmba1Hd0vvnhG+3l+PqdzegxWjF+PRI/Hr3TDxy9ohDiuhdnTU6Gd/95XjkJoahttXsPMkamGcFae7UiOSIgHx/kgepkJ5f2xaw+9x1+XX4l/Ok2n2n5+KEIUefhxAbqscnNx6HE4bEocNiww0fbMIXzgaZ3mjqsOCqdzbg511V0KlVePWy8bjlxOyjNvYF6dR455pJzmuBCVe8sx7Vzb2/JnlDip8ZkhjGuWo+wkI6kUJJOel7ApCTvqW4ATe8vxEdFhtOGBKHj26Y4lY8w/TBsfjs5uMQF6bH3soWzH1tDQ724pjZR+uLcNPCTTBa7Jg5JA6f3TQV8WHHPvp42shEXDIpDaII3P3ZdjS0mb1eg7dqWk1oMVmhEjqLH0TeSIt2dCaUNASmC/Xd1QUwW+0Ylx6Je04Z4lZs08TMaHz3l+Nx7thkWO0i/v7VTjzx4x7YvbzBX5NXi7NfWoXNRQ0IM2jw3rWTccOMQW7dbN572lCE6TXYWdbkkxt1bxTWspBOvRdm0LqKQ4GId7Ha7HhyiaMb/brpWUfdXO8qNlSPD66bgjtOGgwA+Gh9MS56fa0r3sRT5Y0duPD1NfhmWznUKgEPnJGLhddNcevkniAIuHNODgDgvdUFAbk3aGq3oKnDAqDz2k7kjUzXwNHAdKH+ursK3++ogFol4MkLR0PrRkf1eeNS8OENUxAZrMX20iac/8oa7Knw7jlHFEW8/kce7vhkK8w2O04fmYiPbzzOrY7SQXGhWHzrdJw6IgF2EXjl94NeraG3OgvpgYuUoIEvNSoYeo0KZqvd6/fW3ihr7MBtH22B1S7inDHJuHHGoGN+TYheg3eunogLxqXAZhdx75c78OrygxBF754TKpuMuPj1tdhQWI8wvQYfXD8Zpx9lc72riCAtPrhuMtKjg1FS34Er39mApnaLV2vojX3OfPQh8cxH9xUW0okUSspJD0RH+uvL82CxiZgzLB5vXzWxx46zw41IjsCiW6ZhUGwIyho7MPe1tdhcVO/R929qt+Dhb/7EPxb/CbsIXDwxFW9fPREhHgwre/js4RgUG4LKZiPuX7TT6zdmb+VVO4ocadHBfTKVnOQrLTpwAwabjRZ8uLYIAHDriYM96pIwaNV4ft5Y3OUsXL2xIh+3fLQZ7Wb3O2nNVjue+GkPLn97PSqbjRgUF4JvbpuOmT10uhwuNlSPu04eAgB48ud9riJWXyp0RbtwU416R9qYLQ5AvMuiLWU4UN2KyGAtbp6Z7fbXqVUC7j5lKN67dhIig7XYWdaEs15ahWV7qjz6/hsL63HOy6vwZ1kzooK1+PD6KbjphGyPspZPGZ6A4UnhaDPb8PaqfI++vy9I1/HYUL1H91ZEh3N1pNf2/aZai9GCB7/+EwBww/FZGJnifkf15KxoLL51OrKczwnnvrwaLy47ALPV7vZrSBnI//2pc2Pv5cvGezSfKUinxkNnDYdKAFYfrHPFrPSlXeWO2Q6e/P0RHU6tEjAoLjDxLh1mG276YBPq2swYkRyO/80d7fazglatwjMXj8HNMx2F9yeX7MM/v/P89OjB6lbMfW0N9lW1ID5Mj8/nT8Vxg2Lc/vr4cAM+vH4K4sP02FfVguvf39jnnf3SoNEhiSyk+woL6UQKJQ282NvHHemlDe341flwe9/puV4N60yLDsaXt0zD2LRINHVYcNlb67Hkz8pjfp3VZsfCdUU48enf8YGzeHfXnBz8b657nS5dBes0eOGScdCqBSzZVYnP+7gTNd/ZIcR8dOot6cjmnormPt8Q+mhdMVpMVuTEh2K2F/mFhx/n/nlXFea9sQ5VbhydPFjdigteW403/siHKAKXTk7Dd7cf73pY8MRVUzMwOD4U9W1mPP/rfo+/vrekjvRAZS+SfGRIOel9vLFmtNjw7FLHfzu3zxqMiCDPZ6DMGhqPH+6YgTHOe4Pr39+EJ5fshdV27ALaJxuKcdlb61DbakZuYhi+vf14TM12/0FZ0rUrfcHqwj7vSu+MdWE3OvXOoFjHe2FJQ4dHRWhf+N+SvahsNiIjJhh3zRni8ddnxYZg0S3TMDs3HmabHc8u3Y+zX1qFbSWNx/zaDrMNNy/cjA/XFUMQgIfOGo6Hzx4OtRfDS1Ojgl1dq++u7tu4p/o2MyqcEZjDklg8o97pHDjad4V0URTxwOKd2FXejOgQHd64coLHcw8EQcD9pw/DQ2cNBwAsWFOIk/+fvfsOj6Jc2wB+z9b03nsgQOgdpCMdFcUCiqjgZxcseGwcFcXePerBcmygYkMBsQCi9N47CekJ6b1tNtve7493Z5OQtklmd5Ps87suLsJmszsJm9mZZ573ft7bhQ0nLrV6bFCjM+LHI5mY98l+ZJfVoEeAO355YGy7hoZG+bvh67t4RO3RjFLsNg8tthdLRzoNGpUMFdIJcVJ9zdEuKYXVqDV0LE+0Lb49mAkTA8bF+SOuA8uL/NxV+O6e0ZgSH4Ragwn3f3sM097dhXf/SsT5nMYFwb1JRbj6g714buNZlGr4cNOv/28UHp3Wu91ZYQMjvPGvGX0A8Hzk1EL7HVykmjMrW8pzJ8QavYM9oVbIUKk1WDqb7UGrN1pyhO+f1Lauz8vNHRqO7+4ZDT93Fc5kl+O6/+7D9oR85JbXNIp7YYzh24MZuObDPZbO009uG47XbhjUplUp9SnlMjw/hx+kf30gAxfz7bfSR6MzoKCyFgAQS4V00kFR5teQvaNdVu9PR16FFuE+rrjtiuh2P064jyt+uu8KLBrDH+OjnSk8l7SZrGS90YTnNp7F8vVnoDcyXD0wFOsfHGtZqdMejuxKp3x0IpVgLzVclXIYTcyu0W9H0kvw7UE+9+S16we2e2Cor7sKny8agQ8WDIW/uwqJ+ZW44aN9eOn3841WrtUajDiaXoKPd6bg+o/24e8L+VAreAbyXeObz2a3xt3mr//1ZLZd85HFbvQYfzd4NpPhTIi1HDFw9ERWGTacyIZcJmDVrcMQ4dv+97W7xsfiv7cOhY+bEqmF1Vj24ylMf283fjnWuKCeWliFl34/j9Gv/o2nfjmDUo0egyP5PLeOHBvEh3jh5pF8rtLaQx2f7WQtrd5oabjpE0wX1aRCa/4IcVLBXmr4uClRptEjKb/KLsv+tHojfjAPBVw0JqbDj+emUuB/tw/Hy39cwNpDGUguqMIH25PxwfZkxPjzLpArevjjmwMZli54HzclHpveG7eOipJkgv29E3pg98VC7E8pxr83nMEP947p8GNaI8VctG9P9ywh9SnlMvQL88KJzDKcvlRmWc5taz8fu4SiqlqE+7ji2iFhHX68ETF+2PjgONy5+jBSCqvxf6uPAgBclDJE+7kj2t8NsQHuSCqowvaEAgDAhF4BeHveYAR7tZ7F3JoJvQIxs38wtp7Lx8rfzuHbu0bbZaBPehEvcPi4KeHtRifLpGMsHel2vKhWptHhI3OG8GPTrZuT0BK1Qo6V1w3A8Bg/PP3LaRxMLcHVH+zFoHBvVOsM0OiMqK7lf1dqDaiqNUAQgMdn9MGDk3t2+PeWr5TphXu/OYbV+9Jx1/gezQ4mlJqlkE4X1UgHCYKA2AB3nM+tQHpRtV0aN7R6I5765TQA4OYRkRgbF9ChxxMEAdcODsP4uAC8/Pt5rD+RjS/2puGv83lYemUcMoo1OJpeipOXyhp03fu6KfH5ohEYHu3XoecHgKFRvhge7YtjGaX45mCGpQHH1mjQKJFSXSHdfo0iPxzmNYPrhoS1a4XY5a4ZFIZJvQPx9YEMfLYnFWlF1fjXulP4cHsSllwZB08XJb49mIG9yXXDgSP9XLFwdDQWjYlp90W9+haMisIXe9OwPSEfOWU1CPOx/eqx1MJqmBjPaw/0VNv8+ZwFFdIJcVKCICA+xBMHU0uQkFdpl0L6plM5KNPoEe7jiql9gyV5TIVchheu7Y9l03tje0I+Np/Jw86LhUgv1uDjnSn4eGcKAJ7vdvsV0Xh0Wi+rBptaSyYT8Na8wZjwxnYcTC1BamGVXYrbdR3pdLJMOm5QuLe5kF6O64aE2/z5DEYT/rebd2reMyG2zdFKzYnyd8P6B8fh5d/P42hGKbJKNNDqTUjMr7QsawQAlVyGp2bH486xMR3qhL/cs1f3w47EQuxLLsbWc/mYNSBEssdujtg5TINGiRTEjHR7FtI/3pmCCq0B8SGemDtUuv3PtYPD0C/UEw98exxJBVX4x3wB7XKeLgq8N38IpvWT5rgEAKb3C0b/MC+cy6nA53tS8eSseMkeuyWZJXx/QB3pRAqxgbyQbq+c9I92JCO1sBqBnmr8+6q+kj2un7sK7948BNcOCcMzG84iq6QGT/1ypsF9AjxUGBHth5Gxfrh6YKhVw46tdff4WBzLKMW3BzPw4OQ4SQpyrREL6f1o0CiRgBjtklJYDcaYzRtFKrV6/HYqFwAvPkvF00WJJVfGYdHYGHx9IB2f7U5FerEGT/x82nIfQQCm9AnCbWOiMalXoKTnCXFBHriihx8OppbghyNZeGx626Or2kpcJdsn2NMuDT7OggrphDix+BAvXkhv50T7tmCMYc3+dADA7WOi25U12BJvVyWuHxqB64dGoKrWgB0JBdhyNg8HU4sxONIH/74qvkNRMi0J93HF5D5B2J5QgHXHLuEpG58wa/VGyzJb6kgnUhgU4QMgA6cvldnl+f44k4vMEg383FW4eaR0B8gA3xe8NW8wAB7bkFNWg7SiamQUa5BWVA2t3ohFY2PalXHYmkg/N9w3sQc+3J6Ml/84j8l9AjvcXduaNEshnQpnpOPEnP3ccp6L3J45Jm2RU1aDr8zHBk/Nipf82CAuyBO/Lh2HzWfyoDea4KZWwF0lh7taAXeVAm5qOcK8XSUvbInzG+75+ijW7E/H3RNs35XOGMMF8wB5ushOpCDO4Um1QyFdqzfiq33pAIAX5vS3yQqryX2CsHXZRPxn20UcTCtGfIgXRsX4YUSML2ID3G1WZJrRPwQRvq64VFqD9ScuYeHo9sdXWUuMdulPhXQigWh/d8hlAqpqDcir0CLU27ad1L+fzkWN3ogege4YEe0r+eN7qBV4cHIcFo2JwTcHM/DF3jQwxjB/RCQWjIrqUIRLaxaOjuaF9MOZeGhKnGTNRM25aBk0SjUDKVEhnRAnJg6fSciz/TKt45mlOJdTAbVChptHRNr0uTzUCswZHIY5gzseF2GtecMjsD2hAOuPX8K/pveWJDamORnFGjAGeLkoEOBhn+XipHsbHMlXpJzNroDRxCQvZtXHGLOsFFk8Vpqlks1RymWI9ne36xDOByb3xM/HLuFSaQ2+OZCBeyb2sOnzZZijXWJo8DCRQICHCm4qOTQ6Iy6Vamx+sfa9bRehM5hwRQ8/TO4TaJPncFMpcOPwCJs8dkum9Q2ydKV/tifV5hfZM4o1KKnWQWWO6yKko8SVTmmFti+kbz2Xh8paA8J9XDHbhqu5PNQKPGsePGgvcpmAO8fF4qXfz+PLvWlYMDJK0i7Xy1XXGiyrCCjahUhBpZAhxt8NKYXVSMqvsnkh/YcjWQCAW0ZG2rSL2l2twP2TeuL+ST1t9hyXm9k/BP7uKhRU1uKfCwU2X71qKaRTPrqkaNgoIU4s3jxwNCHP9h3pa/ZnAOBLrX3tlBVqT1P7BsPXTYn8ilrsSSpq/Qs6oH4+Oi3RIlKIDfCAu0qOGr3R5oOEdl4sREJeJdxVcklmJXQ2bioFHp7aCwDw09GsRoOPpZZO0S5EQoIgWGJBMkpsG++SmFeJX45fAgA8Pbtvt3s/E7vSAWDN/nSUVOts+nzHM0sBAAPCvaBW2D46gnR/seaVDfaIdvnleDYA4MZh4TYtMjvK/BER8FQrkFJYjV0XC236XAl5FWAMCPJUUyYykYy9Bo5eyK3AqawyKGQCbhhm/4vgtqZSyDDfMnQ0w+bPl0iFdJugQjohTqx3sCdkAlBUpUNhZa3NnqegQos/z/Ccs0VjY2z2PI6kUsgs2a7rjmXZ9LlSzYV0ewx+Is5BLhMscxJO2Tje5eMdvBv91tFR3XY45tWDQqFWyJBUUGXJKbUVsZAeTdEuRCKWnHQbF8++2JsKEwNmDwjBkEgfmz6Xo0zrG4QB4V7Q6Iz4bE+qTZ9LLKQPjZJ+GTxxTmK0S16FFtW1Bps9T36FFnuTeHG5OxbOAJ7NfMsoXjz7fK9t9wV1g0ZpZQqRTi9zRGpyoW0L6T+au9Gn9wtGgEf3vBC0YGQUBAHYk1RkmXVkCxqdAVklNQCokC41KqQT4sRcVXJLHIAtu9K/O5wJg4lheLSvXYaaOsq84fwAedv5fJt2nqWYl9j2oAxUIqFBEfx388ylcps9x9H0EhxOL4FSLuCu8baNPHEkLxclppsHF4odt7ag0RmQX8EvgsZStAuRSB/zydbpbNvtC7R6IzafyQPAI566K0EQ8OjUuq70UhseG5zILAMADKNCOpGIj5sKvuYL3uk2LPZsOJENEwNGRPt265iyRWNjIBOAfcnFOG/Di+znsvljd+dzLmJ/lo70fNsV0rV6I9abj5tvHmnbKFhHivJ3w8RePM7uu8OZNnueJPP/VYCH2uZzWpwNFdIJcXJ9zfEuF2w0cFRnMGHtIf4G0V270UX9wrwwINwLeiPDryezbfY81JFObIEPHIVNB46K2eg3DotAiLeLzZ6nM7jR3FX326kc6I0mmzxHRjGP3vB2VcLHjQ6QiTRGxPgBAA6nldjsOf6+kG/JQx5pfr7uamrfIPQN5V3pm07l2OQ5NDqDZd7NsGgfmzwHcU7iRdr0IttEPTHG8MsxXjhzxCwDe4rwdcPsgaEAgC/3pdnsec7SoFFiA5ZCug070reey0OFlh8bTOhlm7kpncXC0VEAgHVHL6HWYLTJc4j56H1o0KjkqJBOiJOLDzEPHM21zcDRrefyUFhZi0BPNWb1t+0wjc5A7Epfd9Q2XaiMMUtHek/qSCcSGmwupF/IrYTOIH3hN7mgCv8kFEAQgHttPICzM5jQKwABHioUVemwJ8k2eajictDu3MFH7G9YtC/kMgGXSmuQU1Zjk+fYYM5Dnjs0rFvmIdcnCAJuMhcIN5ywzUX2U1nlMJoYQr1dbD4EjjiX2ABegEkrsk3x7Ex2OZIKqqBWyHD1oFCbPEdncvf4WADAryezUVChlfzxdQaTpXhGg0aJlHoGekAQgJJqHYqrbBMJ+8NhHusyb0QE5N382GBKfBBCvFxQUq3DlrN5NnkOcV8gxvIQ6VAhnRAnFx9q7kjPs00hfc3+dADAraOioFJ0/13OdUPCoJLLcD63AmdtsCy+sLIWVbUGyGUCoigTmUgo0s8VPm5K6IwmJNpgfyAu1ZzSJwg9nGA1hUIuw7WD+dyE9cdtUzwTL6rF0L6ASMhDrcAAcyfjkXTpu9KLq2otw/auN88W6e6uHRwGuUzAyawymwxuPJEl5qP7SP7YxLmJMYKpNpqZ8LO5G31m/xB4uXTPuSn1DY3yxfBoX+iNDN8clH7QYFJBJfRGBi8XBSJ86aIakY6rSo5wH/6assXA0fSiahxILYYgAPNGdN9YF5FCLrPMTfjukG3iXRLN0S59QqiQLrXuX9UihLRI7EhPLqiUPH7gbHY5jmaUQiETLMuXujsfNxWm9+fZyOLJgZTE5XSRvq5QK+SSPz5xXoIgYKCNBo6aTAy/nuSRBt11kFhTbhjGi4R/nc9HeY1e8scXozcGUg4qkZgYt3LIBvEuv5/OhcHEMDDcG3FO0iUV6KnG+LgAAMBGG3SlH88oA0D56ER6Mf68kG6LC0C1hrq4o+4e61Kf2JW+9lCm5Ode4qDRfmFeEITu3dFL7K+XDeNdfjrKu9En9gq0FOy7u5tHRkIm8GOt5ALpm5iSzB3pvYO7fwOTvVEhnRAnF+HrCg+1AnojQ2qhtAfJ3xzgnRazB4YiyKt75yHXN898MrDxZLbkmWepllgXekMk0hMHjkqdk344vQTZZTXwVCswtW+QpI/dmfUP80LvYA/oDCZsPpMr6WPXGoyWQvr4XgGSPjYho2J5If2IDQrp682FZGfpRheJ3+/Gk9lgjEn2uIwxnMgUO9KpkE6kJWak26KQviOhAGUaPYK96i40OYPp/YIR4KFGSbUOuxKljX4Th5hSrAuxBTEnPUnigaN6ownrzA1ot3TjIaOXC/V2xdS+vAFvrcRd6eU1euSW8/ioXsHO0bRgT1RIJ8TJCYJQl5OeJ93A0UqtHr+e4ifLd4yJluxxu4IJvQIR4uWCMo0ef58vkPSxU8wdAD0oH53YQN3AUWljicQOzKsGhsJF6TwrKQRBsHTgr5e4C/VEZhlq9EYEeKjQhw6QicTEjvSkgipJs1BTC6twKqsMcpmAOYPDJHvcrmBG/2C4qeTIKNbgeGaZZI+bWaJBcbUOKrkMA8JpuCCRVkwAjw4r0+hRWq2T9LF/PibOSgjv9nnI9fHoN77/k3puwjkaNEpsSCykp0jckb4joQCFlbUI8FBZCsvOQly1/8uxS6jRSdeAJ3a4h3q7OEVslr1RIZ0QgvhQXoS5IOHA0c1n8qDVm9Aj0B0jop2rQ0ouE3DjcN55tu5YlqSPTR3pxJbEgaNJBVWSHcxp9Ub8Ye7GnutkHagAn5sgCDyGJatEI9nj7k8uAgCM7RlAy7eJ5HzdVZalwEfSSyV7XPGi2oReAQj0VEv2uF2Bm0qBmeah61LGu5wwF+X7hXlR5BuRnJuqLmtbyti34qpa7EzkzSY3OVHkm0iMftt2QbroN5OJUUc6sSkxjk3qjvQfj/Dz5RuHRTjFTLX6JvYKRISvKyq0Bvx2Okeyx03M4/9HvanZxiac61VKCGlSfIh54GiudB3pv5gHC944LMIpizw3DefL0nZfLESeeVmVFOo60qmQTqQX7KVGoKcaRhPD+VxputK3JxSgUmtAmLcLRpvjIpxJqLcrxvWUPht5r7mQPi7OX7LHJKQ+S7yLRANHGWPYcNI5Y11E4vf9++kc6AzSZCMfN8e6UD46sZUJ5viwnRLGkPx6MgcGE8PgCG+njB2wRfRbRokG1Toj1AoZetLKVWIDYkd6XoUWlVppLgDllWuxw3xRbb4TxbqIZDIBC0bxrvSfjkjXgHfRnI9Og0ZtgwrphBD0DeWFdKmiXbJKNDiUVgJBcN6T5dgAd4yM8YWJ1V1U6Cit3ojsshoAoANkYhOCIGCwOSf9VJY0hfT1x3nh7Lqh4ZA50dLt+sTOs/UnpMlGrtTqccocvzPOiXJliX2NiuUXaQ5LlJN+LKMUWSU1cFfJMaNfiCSP2dWM7emPQE81SjV67L4oTVHSUkiP9pHk8Qi53JV9+GyT7QkFkuX7WxpunGjIaH2CIOD6oebot+PSXGQXY13iQ72gkFOZh0jP21WJIPNqshSJZqv9fCwLJgaMivFz2hXXNw2PgEwAjmaUIlWi2ByxkC4OiCXSoj0sIcRypTK/ohYlEuQfigeEY3v6I8xJpm43Zd4IflV93dEsSU48EvIqwRg/iPFzV3X48QhpysBwHwDSDBwtqdZZlm4760U1AJjZPwSuSjnSiqpxIqusw493KLUERhNDjL8bInzdOr6BhDRhlDkn/VxOuSSdZ+KcgFkDQuGqcs4IkgbZyCc7XjzT6AyWWD4aNEpsZVxcAFRyGTJLNJIUzy7kVuBcTgWUcgFzBjnXrIT65g41R7+lSxP9djZbjHWhfHRiO3UDRzseCWsyMfx4lHdh3+yE3eiiYC8XTOodCAD4+Zg0DXjUkW5bVEgnhMBDrUCUHy/GdLQrnTGG9SfqYl2c2dUDQ+GmkiO9WCNJxuxf5/IA8CgHZ4zLIfYxKJJ3pJ/O7nhH+h9ncmEwMfMSZuc9kHNXKzB7AO/A3SBB55kY6zKWutGJDYV4uyDKzw0mhg4Px6w1GPHHaR5fIK7QcFbiRcVt5/NR0cELFGculcNoYgj2UiPM20WKzSOkEXe1AqN78AtrOxIKOvx4v5gLRVPjg+HrxI0hod6uGNuTr/yRIvqNBo0SexA7nJMl6Jzen1KMrJIaeLoocNXA0A4/XlcmNuCtP54No6ljDXjFVbUoquLNkXHUkW4TVEgnhAAA4s1XKxM6OHD0WEYpMoo1cFfJMWuAcy7dFrmrFbhmED8o+P5wZoceizGGP80ZirMHOPeBBrGtQeG8kJ5aWN3hIs8G89JtZ+5GF91gvrD4mwTZyPtTeCF9PBXSiY2JOemH04o79Dg7EgpRXqNHsJcaV/Rw7lz//mFe6BXEs5G3nMnr0GOJFziGRfnSBXZiU/XjXTrCYDRh40k+UM9ZY13qs8S7dDD6jTEaNErsQyzMJkswcPSHI/z8eO6QcKddqSaa2jcIPm5K5FVosSepY9FvF83/N1F+bnBTKaTYPHIZKqQTQgDwPD2g4x3pYubh7IGhtOMGsHB0NADgj9O5HYrNScirRHqxBmqFDFfGB0m1eYQ04u+hRrg5kunspfZ3pWcUV+N4ZhlkAixRBs5sTE9/BHupUabRW4YqtUdBhRYX86sgCMAYJy9IEtsT4106mpMudlvOHRIOuZPOShAJgoC55ouLGzrYhUqDRom9TDEfex5JL+nQRfadiYUoqqqFv7sKk/sESrV5XdasASFwUcqQVlSNkx2IfsuvqEVxtQ5ymWBpjiLEFuKC+OvrXE4FTB3onC6p1uGvc/kAnDvWRaRWyDF3CD82WNfBeJekAt4Y6cyrgW2NCumEEABAX7EjPa/9HelavRG/n6Kl2/UNivDGgHAv6Iwm/Hys/ZO4N5u70Sf1DoSHmi5QENsaLEG8i1ggGhcXgCAvihyQy+qKZ+s7MIB4n7kbvX+Yl1MviSf2IXakn8oqh1ZvbNdjlGv0li7W6+nYAABw3RB+cfFgWjFyzEPE24oxhhPmjvShUT4SbRkhTYsJcEePAHcYTAx7k4ra/ThrDqQD4N3oShqICQ+1ArP6m6PfOnBhTYx16RnoDhelc3f2EtsaEukDD7UCeRVaHElv/0X2DSeyoTOaMCDcCwPCaRUFwIeOAsC2c/ko07S/AS8xTyykU6yLrdC7FyEEQF1HemJeJQzG9sUObDufj8paA8J9XHFFLHVKArzz7DZzV/p3hzLbfeX+z7N8+bez58cR++jowFHGmKUDlS6q1bnBvIR7e0IBCiq07XqMfck8YmMcxboQO4j2d0Ogpxo6owmn2tkt+fuZHOiMJsSHeCI+hLJ7ASDC1w2jYv3AGLDpVE67HuNSaQ2KqmqhlAtUhCB2Ia6IbG+8S3JBJfYkFUEmALdfES3lpnVp14vRb6faH/12jmJdiJ24quS42nw+2t7BmIwx/GiOdbl5ZJRk29bVDQj3Rt9Q3oDX3mMDgAaN2oPNCumvvPIKxo4dCzc3N/j4+Fj1NYwxrFixAqGhoXB1dcW0adOQlJRkq00khNQT5ecGV6UctQYT0ovbNzlejHW5YVg4ZE6+dLu+OYPD4KlWIL1YY+kmbYuk/EokF1RBJZdhSl+KdSG2NzjC3JHezmiXk1llSC/WwFUpx4x+zj0rob4+IZ4YEe0LvZFh9f70Nn89Ywz7kikfndiPIAj1ctLb13lGF9WaJs6O2HC8fdnIYqxLvzBv6kAldiHGu+xMLGhXY8ia/RkAgGl9gxHp5ybptnVl43r6I9BTjVKNHjvbGf1Gg0aJPd00gl/8+fNMLjQ6Q5u//kRWGS7mV8FFKbOs0CLcPHNX+rqj7btIUanVWy6sUbSL7diskK7T6TBv3jw88MADVn/Nm2++iQ8++ACffPIJDh06BHd3d8ycORNabfu6tggh1pPLBMtVy/bkpBdUaLH7Ih+MQYMFG3JXKyzL2dcebPvQ0T/Nw8gm9AqAl4tS0m0jpCkDzIX0S6U1KK6qbfPXi8uTZw0IgTtFETVw78QeAIBvD2agqrZtJx9pRdXILddCJZdhRLSfLTaPkEZGi4X0dizhPpdTjiPppeZZCXRsUN9VA0OhksuQmF+JC+0Y9H48Q8xH95F4ywhp2sgYP3ioFSiq0uFMG6PfKrR6S8PN4rExNti6rkshl+E68yyZ9sa7UEc6sacR0b6I9ndDtc6ILWfbPjT7h8P8fPjqgWF0bnuZuUPDoZQLOJNd3q6azM/HLkGjM6JnoDvNS7AhmxXSV65ciWXLlmHgwIFW3Z8xhv/85z949tlncd1112HQoEH4+uuvkZOTg40bN9pqMwkh9fQN5Ttbcep7W/x6Mgcmxk/oegRSHtflxKGj2y7kI7+NkQ6bz/J89NkU60LsxMtFiR4B7gDanpOuN5rwm3k54ly6qNbItL7B6BHgjgqtAT8eadvcBLEbfXi0L1xV1IFK7GOkeeDosYzSNke/ffhPMgDgmkFhCPGmWQn1ebsqMdW8yqw9cxNOmKN2htKgUWInKoXMshqqrUOz1x3lxZ3ewR4Y05PiHy93gzne5Z8LBSjXtG2Ya7lGj0ulfNZCP+pIJ3YgCAJuNL9m2xrvUqnV4zfzTLVbRtGQ0cv5uaswNT4YQNu70k0mhjXmFa+Lx8VCECghwFY6TUZ6Wloa8vLyMG3aNMtt3t7eGD16NA4cONDs19XW1qKioqLBH0JI+wwzn4z9cvxSm4aKMcYsXSY3mpcjkYb6hHhiZIwvjCbWpuJZSmEVEvIqoZAJmN432IZbSEhDg8xd6WfaGO+yK7EQpRo9Aj3VGEcny43IZALunsC70r/cmwZ9GwqTe82F9HFx9HMl9tMn2BNeLgpodEZL16M1EvIqsOVcHgQBWDolzoZb2HWJhYjvD2eiqA2rf7R6o6XpgTrSiT2J8S472pCTbqxf3BlLxZ2m9AvzQnyIJ3RGE/44k9umrxVjXSL9XOHtSt29xD7EuLYDqcW4VGp9LOzvp3NRozeiR6A7RkTTheCmzDNH52w8kd2m84SdFwuQXqyBl4sCN1Kcnk11mkJ6Xh5fEhIc3LBQFBwcbPlcU1577TV4e3tb/kRG0lUtQtrr2iFhCPdxRX5FLdYesj6C5FxOBRLyKqFSyHDNQMo5a47Ylf794Uyru/rE5XLj4gLg7UYHx8R+Bkb4AGj7wNHvzcs1rx0cBoW80xxmdCo3DAtHgIcK2WU1+NPKE2ajieFACg0aJfYnk9XlpB9pQ7zLh9t5N/pVA0Ipp7MZU/sGYXCEN6p1Rnz4j/VzoU5fKofBxBDkqUa4j6sNt5CQhibHBwIATl0qR2GldRd/diYWILOEF3fmDqXzhOaI0ZhtXaFiiXUJpVgXYj8Rvm4Y08MfjPFZH9b6wdxQdsvISLqo1oxJvQMR6KlGcbWuTcOdv9qXDgC4ZVQU3FQUrWlLbTrDffrppyEIQot/EhISbLWtTVq+fDnKy8stf7Ky2rZMmhBSR62Q4+GpvGvs453JqLYyv1fsRp/eN5iKvS2YNSAEvm5K5JZrsSOx0KqvEYtsVw2kgY3EvsSBo6culVs9CG9nYgH+SSiAXCZgAS3XbJaLUo5FY2IAAJ/uSrXq53s2uxwVWgM8XRQYGE4ny8S+xHiXQ1YOHE3Kr7S8fz00lbrRmyMIAp6aHQ8AWHsoE+lF1VZ9nThodFiULxUiiF0FebpY3oOsHYwpDtem4k7L5g4Nh0wAjmaUIrPYug5fo4lhkzlOb2AEHRsQ+7rJvBL9l+OXrDqWvZBbgVNZZVDKBUucEWlMIZfhBvOFtXVHratvJuVXYk9SEWQCcPsV0bbcPII2FtL/9a9/4cKFCy3+6dGjR7s2JCSEF4ny8/Mb3J6fn2/5XFPUajW8vLwa/CGEtN8NwyIQ7e+Goiod1hxIb/X+eqMJm07yA7gbh9MSopa4KOWYP4IXF9ceymj1/hnF1TiXUwG5TMD0flRIJ/bVP8wbMgEorKxFfkXrXWdavRHPbzoHALhzbAzigqgDtSW3XRENV6Uc53MrsC+5uNX770vhsS5X9PCnTn9id/U70k2m1k+WP9yeDMaAWf1DEB9Cx+YtGdszAJP7BMJgYnj7r0SrvuaEuZA+lGJdiANcKca7WFFITy6g4o61gr1cLCvO3rJyX7B6fzrOZJfD00VhiYMgxF5mDQiBm0qO9GINjpkHYLdEjDed3i8YAR5qW29elyb+Pu9ILERBZevz1b4yX7Cc3i8YkX5uttw0gjYW0gMDAxEfH9/iH5VK1a4NiY2NRUhICP755x/LbRUVFTh06BDGjBnTrsckhLSdUi7Do9N6AeCdkhXalgfe/HE6F8XVOgR4qDCxV6A9NrFLWzAqCgCw62Ihskpa7jbZbI51GdPDH37u7du3EtJeriq5JY5B7CZrySe7UpBRrEGwlxqPTu9t463r+nzdVZhvPkj+dHdKq/cXB42Op1gX4gADwr3hqpSjTKNHcmFVi/dNLqjCb6f5BXbqRrfOU7PiIQg8O/aUeYhocxhjOJ7J7zOM8mWJA4g56XsuFrWa37tmP28cmdaXijvW+NeMPpDLBPx2Kge/nmw5LiO7rAbvmAvuy2f3RZAnDXQm9uWuVuCqgaEAWh86qtUbseEEf03fPDLK5tvW1cUFeWJIpA+MJoaNJ1reF5RpdJZIqDvHxdpj85yezVqaMjMzcfLkSWRmZsJoNOLkyZM4efIkqqrqDr7j4+OxYcMGAHxp46OPPoqXX34ZmzZtwpkzZ3DHHXcgLCwMc+fOtdVmEkKacO3gcMQFeaC8Ro8v96Y1e7/zORX494YzAIBbR0dTl6QVYgLcMaFXABgDvjvccg79ZvOy+NkU60Ic5MEreRHsk10p+N1cGGtKRnE1PtrJi8HPXt0PHmpaum2Nuyf0gEwA9iQV4UJu80MctXojjqTzTh/KRyeOoJTLMCzaB0Dr8S4f7eDd6NP6BqN/GEUNWKNvqJclH/n1zQktLpG/VFqDwspaKGQCxTwRhxgU7g1/dxUqaw0tzk2o0Oot8Y+Lx8bYaeu6tiGRPnjIPJz52Y1nkV1W0+T9GGN4buNZaHRGjIzxxS0jKU6POIYY7/LH6VzU6IzN3m/ruTyU1+gR7uNKTSFWErvS1x291OJqwB+OZEGrN6FvqBdGm1cQEtuyWdVrxYoVGDp0KJ5//nlUVVVh6NChGDp0KI4ePWq5T2JiIsrLyy3/fvLJJ/HQQw/h3nvvxciRI1FVVYUtW7bAxYWurhJiT3KZgGXTeEfpF3vSUKbRNbpPQaUWd685Ao3OiPFxAZaDPtK6haP5VfifjmRBZ2i6k+dSqQanLpVDJgAzKNaFOMi1g8Nw70Qe2fbEutM4l1Pe6D6MMbyw6Rx0BhPGxwXgmkGh9t7MLivSzw2zzZ08n+1ObfZ+xzJKoTOYEOylRs9Ad3ttHiENjIrxBwAcbqGQnl5UjY3mLsqHqRu9TR6b3hsquQwHUouxO6moyfsUVdVi6fcnAPA8ZBel3J6bSAgAPoB4Uh++CnVHC4Pw1h29BI3OiN7BHhjT099em9flLb0yDkMifVCpNeDxn041WUD780weticUQCkX8NoNAyGT0awE4hijYvwQ4euKyloD/jqf1+z9fjjMY13mjYiAnF6vVpkzOAwuShmSCqrw5C+nYWhiBZDBaMLX5pXDd46LobkpdmKzQvrq1avBGGv0Z/LkyZb7MMawePFiy78FQcCLL76IvLw8aLVa/P333+jdm5aHE+IIsweEID7EE5W1BvzvsgKPVm/Efd8cQ065Fj0C3LHq1mFQUje61ab2DUawF5/EveVc0wccW8yxLqNi/RDoSRlyxHGemhWPCb0CUKM34t6vj6G4qmFe+l/n87EjsRBKuYCV1/WnA7g2us98oWLTqRzkNNN5ttcc6zIuLoB+vsRhxJz0PUmFOJDSdK7/qh3JMDHgyj6BGBThY8et6/oifN1wxxieIf365oRGxbP0omrc+PF+nMoqg6+bEi/M6e+IzSQEQF28y/ZmCulGE8Mac3Fn8dhYeu9qA4VchvduHgJXpRwHUovx5b6Gq4PLa/R44Tc+k+aByXE0k4Y4lEwm4Ebz4NCm4l20eiNe+eM8DqQWQxCAeSNo9YS1vFyUeOPGQZDLBPx87BIe+v4Eag0Nu/7/Op+PnHIt/N1VuHZwmIO21PlQ5YsQ0iSZTMC/ZvQBAHy1Lx1F5uIZYwxP/3IaJzLL4O2qxBeLR8LbTenITe1ylHKZJRvupd/P4/2/kxoV0P40x7qIuXOEOIpcJuC/C4Yhxt8N2WU1eHDtcUsmqkZnwErzgNF7J/ZAz0APR25qlzQowgejY/1gMDF8ddnJcnWtAftTivCX+YIbLYUljjQ0ygdRfm4o0+ix4LODuGv1ESTlV1o+n1WiwfoTYjd6L0dtZpe25Mo4eLoocCG3Ar+eqstEPZlVhhs/3o+MYg0i/VzxywNjMTjSx3EbSpzehF6BkMsEpBRWI7O48cyfnYkFyCzRwMtFgblDqbjTVrEB7njumn4AgDe3JCIhry7+7Y0tCSisrEWPQHc8OLmnozaREAuxkL43uQi55XXntMczS3HVB3vw2R5+fHvfxJ4I93F1yDZ2VdcNCceqW4dBJZdh89k83L3mKDQ6g+Xzq/elAwBuHR1Fq9TsiArphJBmTesbhMER3qjRG/GxOf941Y5kbDyZA4VMwMe3DUNsAMUMtMdtV0Qh3McVhZW1eO/vixj3xnbc8eVh/HE6F5nFGhzPLIMgADP7U6wLcTxvNyU+u2MEPNQKHEorwcu/nwcAfLg9GTnlWoT7uGLplVQ4a6/7JvGu9O8PZ+GXY5ew4tezuPqDPRi08i/c+tkhpBRWQxAoH504lotSjvUPjsUdY6KhkAn4J6EAM/+zG8vXn0FBhRYf7UyG0cQwsXcghkbREMz28HVX4QFzYeztrReh1Rvxz4V83PK/Ayiu1mFguDd+eWAsetBFS+Jg3q5KjDAPu33x93N4+pfTuHvNUVz/0T5MfHMHHlx7HABwy6gouKlobkp7LBgVianxQdAZTXj0h5OoNRhxJL0E3x3iM5ZevX4gFc5IpxDl74ZRsX5gDFh/PBtavRGvb07ATR/vR2phNYI81fhy8Qg8PTve0ZvaJc0aEIIvFo+Aq1KOPUlFuOOLw6jQ6nE2uxyH00ugkAm47YpoR2+mUxFYS9NsuqCKigp4e3ujvLwcXl5ejt4cQrq8XRcLsejLw1ApZPj37Hi88BsvoL1y/QAsHE077I6o0Rmx5VwufjyShYOpdZmzSrkAvZFhZIwv1t0/1oFbSEhD287n456v+ayTByb3xOd7UqE3Mvzv9uGYQRd92s1kYpj5n91IKqhq9LkwbxcMjfbF1QNDaYUK6TRSC6vwxpYEbD2XDwBwU8mhM5hgMDH88sAYDI+mYVftVaMz4sq3dyKvQovJfQKx+2IhTAyY1DsQHy0cBnca5kw6if/tTsGrfyY0+3lvVyX+fGQCdaB2QGFlLWb9ZzeKq3W4c1wM9iYVIamgCjePiMQbNw1y9OYRYvHT0Sw8+fNphPu4wk0ltxzT3jA0HM/P6U8r2CVwLKMEi786gkqtAf3DvBDu44q/zufjuiFheP+WoY7evC6vLbVkKqQTQlrEGMP8Tw/gSHqp5bY7x8XgecrmlFR6UTXWHcvCz8cuIb+Cx+g8P6cf7hwX6+AtI6ShD/9JwjvbLlr+PTU+CJ8vGkH5px20PSEfj687jUg/NwyP8sWwaB8Mi/JFGBUgSCd2JL0Er/xxASezygAA4+L8sfbuKxy7Ud3Aj0cy8dQvZyz/njc8Aq/eMJDm0ZBOparWgHf+SoTJxODvoYa/hwoBHmoEeKjg765GiLcLdUxLoH4TAwAEeKjw92OT4OOmcuBWEdJQVa0BI1/+GzV6nuEd4KHGq9cPoEYbiZ3LKccdXxxGcbXOctvGJeMwhOLeOowK6VRIJ0RSB1KKseCzgwB4R9QXi0ZAQSdzNmEwmrA7qRAZxRrcdkU0nTSTTocxhiXfHcefZ/KgVsjw92OTEOnn5ujNIoQ4CGMMf57Jwz8X8vHw1F6Ioci3DjMYTbj2v/twPrcCD0/thWXTetHFSkKc2NO/nMYPR7IAAO/fMgTXDQl38BYR0tiLv53Hl/vScO3gMKy8tj983elijy2kFFbhts8PIbdciyGRPti4ZJyjN6lboEI6FdIJkdyLv51HZkk13r15CLxcaGkWIc6sutaAd/66iFGxvpg1gOJGCCFEalW1BhRUaCkPnRCC6loDHvr+BMJ9XPHidf3pwhrplEwmhqLqWgR5ujh6U7q97LIafL4nFfNHRKJvKNU9pUCFdCqkE0IIIYQQQgghhBBCCGlBW2rJlBlACCGEEEIIIYQQQgghhLSACumEEEIIIYQQQgghhBBCSAuokE4IIYQQQgghhBBCCCGEtIAK6YQQQgghhBBCCCGEEEJIC6iQTgghhBBCCCGEEEIIIYS0gArphBBCCCGEEEIIIYQQQkgLqJBOCCGEEEIIIYQQQgghhLSACumEEEIIIYQQQgghhBBCSAsUjt4AqTHGAAAVFRUO3hJCCCGEEEIIIYQQQgghnZVYQxZryi3pdoX0yspKAEBkZKSDt4QQQgghhBBCCCGEEEJIZ1dZWQlvb+8W7yMwa8rtXYjJZEJOTg48PT0hCIKjN8chKioqEBkZiaysLHh5eTl6c4gTo9ci6SzotUg6C3otks6CXouks6DXIuks6LVIOgt6LZLOwllei4wxVFZWIiwsDDJZyyno3a4jXSaTISIiwtGb0Sl4eXl16xc66TrotUg6C3otks6CXouks6DXIuks6LVIOgt6LZLOgl6LpLNwhtdia53oIho2SgghhBBCCCGEEEIIIYS0gArphBBCCCGEEEIIIYQQQkgLqJDeDanVajz//PNQq9WO3hTi5Oi1SDoLei2SzoJei6SzoNci6SzotUg6C3otks6CXouks6DXYmPdbtgoIYQQQgghhBBCCCGEECIl6kgnhBBCCCGEEEIIIYQQQlpAhXRCCCGEEEIIIYQQQgghpAVUSCeEEEIIIYQQQgghhBBCWkCFdEIIIYQQQgghhBBCCCGkBVRI74ZWrVqFmJgYuLi4YPTo0Th8+LCjN4l0Y6+99hpGjhwJT09PBAUFYe7cuUhMTGxwn8mTJ0MQhAZ/7r//fgdtMemuXnjhhUavs/j4eMvntVotlixZAn9/f3h4eODGG29Efn6+A7eYdFcxMTGNXouCIGDJkiUAaJ9IbGf37t2YM2cOwsLCIAgCNm7c2ODzjDGsWLECoaGhcHV1xbRp05CUlNTgPiUlJVi4cCG8vLzg4+ODu+66C1VVVXb8Lkh30NJrUa/X46mnnsLAgQPh7u6OsLAw3HHHHcjJyWnwGE3tS19//XU7fyekq2ttv7h48eJGr7NZs2Y1uA/tF4kUWnstNnXsKAgC3nrrLct9aL9IpGBNDceac+fMzExcffXVcHNzQ1BQEJ544gkYDAZ7fisOQYX0bubHH3/EY489hueffx7Hjx/H4MGDMXPmTBQUFDh600g3tWvXLixZsgQHDx7Etm3boNfrMWPGDFRXVze43z333IPc3FzLnzfffNNBW0y6s/79+zd4ne3du9fyuWXLluG3337DunXrsGvXLuTk5OCGG25w4NaS7urIkSMNXofbtm0DAMybN89yH9onEluorq7G4MGDsWrVqiY//+abb+KDDz7AJ598gkOHDsHd3R0zZ86EVqu13GfhwoU4d+4ctm3bht9//x27d+/Gvffea69vgXQTLb0WNRoNjh8/jueeew7Hjx/H+vXrkZiYiGuvvbbRfV988cUG+8qHHnrIHptPupHW9osAMGvWrAavs++//77B52m/SKTQ2mux/mswNzcXX375JQRBwI033tjgfrRfJB1lTQ2ntXNno9GIq6++GjqdDvv378eaNWuwevVqrFixwhHfkn0x0q2MGjWKLVmyxPJvo9HIwsLC2GuvvebArSLOpKCggAFgu3btstw2adIk9sgjjzhuo4hTeP7559ngwYOb/FxZWRlTKpVs3bp1ltsuXLjAALADBw7YaQuJs3rkkUdYz549mclkYozRPpHYBwC2YcMGy79NJhMLCQlhb731luW2srIyplar2ffff88YY+z8+fMMADty5IjlPps3b2aCILDs7Gy7bTvpXi5/LTbl8OHDDADLyMiw3BYdHc3ee+89224ccSpNvRYXLVrErrvuuma/hvaLxBas2S9ed911bMqUKQ1uo/0isYXLazjWnDv/+eefTCaTsby8PMt9Pv74Y+bl5cVqa2vt+w3YGXWkdyM6nQ7Hjh3DtGnTLLfJZDJMmzYNBw4ccOCWEWdSXl4OAPDz82tw+9q1axEQEIABAwZg+fLl0Gg0jtg80s0lJSUhLCwMPXr0wMKFC5GZmQkAOHbsGPR6fYP9Y3x8PKKiomj/SGxKp9Ph22+/xf/93/9BEATL7bRPJPaWlpaGvLy8BvtBb29vjB492rIfPHDgAHx8fDBixAjLfaZNmwaZTIZDhw7ZfZuJ8ygvL4cgCPDx8Wlw++uvvw5/f38MHToUb731llMsGSf2t3PnTgQFBaFPnz544IEHUFxcbPkc7ReJI+Tn5+OPP/7AXXfd1ehztF8kUru8hmPNufOBAwcwcOBABAcHW+4zc+ZMVFRU4Ny5c3bcevtTOHoDiHSKiopgNBobvJABIDg4GAkJCQ7aKuJMTCYTHn30UYwbNw4DBgyw3H7rrbciOjoaYWFhOH36NJ566ikkJiZi/fr1Dtxa0t2MHj0aq1evRp8+fZCbm4uVK1diwoQJOHv2LPLy8qBSqRqdoAcHByMvL88xG0ycwsaNG1FWVobFixdbbqN9InEEcV/X1HGi+Lm8vDwEBQU1+LxCoYCfnx/tK4nNaLVaPPXUU1iwYAG8vLwstz/88MMYNmwY/Pz8sH//fixfvhy5ubl49913Hbi1pLuZNWsWbrjhBsTGxiIlJQX//ve/MXv2bBw4cAByuZz2i8Qh1qxZA09Pz0YxlLRfJFJrqoZjzblzXl5ek8eU4ue6MyqkE0Iks2TJEpw9e7ZBLjWABhmCAwcORGhoKKZOnYqUlBT07NnT3ptJuqnZs2dbPh40aBBGjx6N6Oho/PTTT3B1dXXglhFn9sUXX2D27NkICwuz3Eb7REII4fR6PebPnw/GGD7++OMGn3vssccsHw8aNAgqlQr33XcfXnvtNajVantvKummbrnlFsvHAwcOxKBBg9CzZ0/s3LkTU6dOdeCWEWf25ZdfYuHChXBxcWlwO+0XidSaq+GQ5lG0SzcSEBAAuVzeaJJufn4+QkJCHLRVxFksXboUv//+O3bs2IGIiIgW7zt69GgAQHJysj02jTgpHx8f9O7dG8nJyQgJCYFOp0NZWVmD+9D+kdhSRkYG/v77b9x9990t3o/2icQexH1dS8eJISEhjQbUGwwGlJSU0L6SSE4somdkZGDbtm0NutGbMnr0aBgMBqSnp9tnA4lT6tGjBwICAizvybRfJPa2Z88eJCYmtnr8CNB+kXRMczUca86dQ0JCmjymFD/XnVEhvRtRqVQYPnw4/vnnH8ttJpMJ//zzD8aMGePALSPdGWMMS5cuxYYNG7B9+3bExsa2+jUnT54EAISGhtp464gzq6qqQkpKCkJDQzF8+HAolcoG+8fExERkZmbS/pHYzFdffYWgoCBcffXVLd6P9onEHmJjYxESEtJgP1hRUYFDhw5Z9oNjxoxBWVkZjh07ZrnP9u3bYTKZLBd8CJGCWERPSkrC33//DX9//1a/5uTJk5DJZI1iNgiR0qVLl1BcXGx5T6b9IrG3L774AsOHD8fgwYNbvS/tF0l7tFbDsebcecyYMThz5kyDC43iRfF+/frZ5xtxEIp26WYee+wxLFq0CCNGjMCoUaPwn//8B9XV1bjzzjsdvWmkm1qyZAm+++47/Prrr/D09LTkYXl7e8PV1RUpKSn47rvvcNVVV8Hf3x+nT5/GsmXLMHHiRAwaNMjBW0+6k8cffxxz5sxBdHQ0cnJy8Pzzz0Mul2PBggXw9vbGXXfdhcceewx+fn7w8vLCQw89hDFjxuCKK65w9KaTbshkMuGrr77CokWLoFDUHW7RPpHYUlVVVYOVDWlpaTh58iT8/PwQFRWFRx99FC+//DJ69eqF2NhYPPfccwgLC8PcuXMBAH379sWsWbNwzz334JNPPoFer8fSpUtxyy23NIgnIqQ1Lb0WQ0NDcdNNN+H48eP4/fffYTQaLcePfn5+UKlUOHDgAA4dOoQrr7wSnp6eOHDgAJYtW4bbbrsNvr6+jvq2SBfU0mvRz88PK1euxI033oiQkBCkpKTgySefRFxcHGbOnAmA9otEOq29RwP8Ave6devwzjvvNPp62i8SqbRWw7Hm3HnGjBno168fbr/9drz55pvIy8vDs88+iyVLlnT/mCFGup0PP/yQRUVFMZVKxUaNGsUOHjzo6E0i3RiAJv989dVXjDHGMjMz2cSJE5mfnx9Tq9UsLi6OPfHEE6y8vNyxG066nZtvvpmFhoYylUrFwsPD2c0338ySk5Mtn6+pqWEPPvgg8/X1ZW5ubuz6669nubm5Dtxi0p1t3bqVAWCJiYkNbqd9IrGlHTt2NPmevGjRIsYYYyaTiT333HMsODiYqdVqNnXq1Eav0eLiYrZgwQLm4eHBvLy82J133skqKysd8N2Qrqyl12JaWlqzx487duxgjDF27NgxNnr0aObt7c1cXFxY37592auvvsq0Wq1jvzHS5bT0WtRoNGzGjBksMDCQKZVKFh0dze655x6Wl5fX4DFov0ik0Np7NGOMffrpp8zV1ZWVlZU1+nraLxKptFbDYcy6c+f09HQ2e/Zs5urqygICAti//vUvptfr7fzd2J/AGGM2rNMTQgghhBBCCCGEEEIIIV0aZaQTQgghhBBCCCGEEEIIIS2gQjohhBBCCCGEEEIIIYQQ0gIqpBNCCCGEEEIIIYQQQgghLaBCOiGEEEIIIYQQQgghhBDSAiqkE0IIIYQQQgghhBBCCCEtoEI6IYQQQgghhBBCCCGEENICKqQTQgghhBBCCCGEEEIIIS2gQjohhBBCCCGEEEIIIYQQ0gIqpBNCCCGEEEIIIYQQQgghLaBCOiGEEEIIIYQQQgghhBDSAiqkE0IIIYQQQgghhBBCCCEtoEI6IYQQQgghhBBCCCGEENICKqQTQgghhBBCCCGEEEIIIS2gQjohhBBCCCGEEEIIIYQQ0gIqpBNCCCGEEEIIIYQQQgghLaBCOiGEEEIIIYQQQgghhBDSAiqkE0IIIYQQQgghhBBCCCEtoEI6IYQQQgjpkJ07d0IQBOzcubNTP6YtpaenQxAErF692nLbCy+8AEEQ2vxYkydPxuTJk6XbONJprV69GoIgID093dGbQgghhBBCWkGFdEIIIYQQJ/bTTz9BEARs2LCh0ecGDx4MQRCwY8eORp+LiorC2LFj7bGJDvfbb79h0qRJCAoKgpubG3r06IH58+djy5Ytjt40mzEajQgLC4MgCNi8ebOjN8eh/vzzT7zwwgsdeoxXX30VGzdulGR7CCGEEEKIY1AhnRBCCCHEiY0fPx4AsHfv3ga3V1RU4OzZs1AoFNi3b1+Dz2VlZSErK8vytRMnTkRNTQ0mTpxon422o7fffhvXXnstBEHA8uXL8d577+HGG29EUlISfvjhhxa/9tlnn0VNTU2bn/Ovv/7CX3/91d5NlsT27duRm5uLmJgYrF271qHb4mh//vknVq5c2aHHaK6Qfvvtt6OmpgbR0dEdenxCCCGEEGJ7CkdvACGEEEIIcZywsDDExsY2KqQfOHAAjDHMmzev0efEf4uFdJlMBhcXF/tssB0ZDAa89NJLmD59epOF7YKCgha/XqFQQKFo++G2SqVq89dI7dtvv8WwYcOwaNEi/Pvf/0Z1dTXc3d0dvVndjlwuh1wud/RmEEIIIYQQK1BHOiGEEEKIkxs/fjxOnDjRoHt637596N+/P2bPno2DBw/CZDI1+JwgCBg3bhyApvPMJ0+ejAEDBuD8+fO48sor4ebmhvDwcLz55puNnv/SpUuYO3cu3N3dERQUhGXLlqG2trbJbV23bh2GDx8OV1dXBAQE4LbbbkN2drbl85s2bYIgCDh9+rTltl9++QWCIOCGG25o8Fh9+/bFzTff3OzPpaioCBUVFZbv83JBQUHNfi3QfEb6t99+i1GjRsHNzQ2+vr6YOHFig0L95Rnp4s/3p59+wiuvvIKIiAi4uLhg6tSpSE5ObvT4q1atQo8ePeDq6opRo0Zhz549bcpdr6mpwYYNG3DLLbdg/vz5qKmpwa+//trofs095uLFixETE9PgtuLiYtx+++3w8vKCj48PFi1ahFOnTjXKlV+8eDE8PDyQmZmJa665Bh4eHggPD8eqVasAAGfOnMGUKVPg7u6O6OhofPfdd42ev6ysDI8++igiIyOhVqsRFxeHN954o8FrWMy0f/vtt/G///0PPXv2hFqtxsiRI3HkyJEG2yM+tyAIlj+it99+G2PHjoW/vz9cXV0xfPhw/Pzzzw22RxAEVFdXY82aNZavX7x4MYDmM9I/+ugj9O/fH2q1GmFhYViyZAnKysoa/fyt/R0jhBBCCCEdR4V0QgghhBAnN378eOj1ehw6dMhy2759+zB27FiMHTsW5eXlOHv2bIPPxcfHw9/fv8XHLS0txaxZszB48GC88847iI+Px1NPPdUgc7umpgZTp07F1q1bsXTpUjzzzDPYs2cPnnzyyUaPt3r1asyfPx9yuRyvvfYa7rnnHqxfvx7jx4+3FBnHjx8PQRCwe/duy9ft2bMHMpmsQWd9YWEhEhISWoyjCQoKgqurK3777TeUlJS0+L1aa+XKlbj99tuhVCrx4osvYuXKlYiMjMT27dtb/drXX38dGzZswOOPP47ly5fj4MGDWLhwYYP7fPzxx1i6dCkiIiLw5ptvYsKECZg7dy4uXbpk9TZu2rQJVVVVuOWWWxASEoLJkyd3KN7FZDJhzpw5+P7777Fo0SK88soryM3NxaJFi5q8v9FoxOzZsxEZGYk333wTMTExWLp0KVavXo1Zs2ZhxIgReOONN+Dp6Yk77rgDaWlplq/VaDSYNGkSvv32W9xxxx344IMPMG7cOCxfvhyPPfZYo+f67rvv8NZbb+G+++7Dyy+/jPT0dNxwww3Q6/UAgPvuuw/Tp08HAHzzzTeWP6L3338fQ4cOxYsvvohXX30VCoUC8+bNwx9//GG5zzfffAO1Wo0JEyZYvv6+++5r9uf1wgsvYMmSJQgLC8M777yDG2+8EZ9++ilmzJhh2S6RNb9jhBBCCCFEIowQQgghhDi1c+fOMQDspZdeYowxptfrmbu7O1uzZg1jjLHg4GC2atUqxhhjFRUVTC6Xs3vuucfy9Tt27GAA2I4dOyy3TZo0iQFgX3/9teW22tpaFhISwm688UbLbf/5z38YAPbTTz9ZbquurmZxcXENHlOn07GgoCA2YMAAVlNTY7nv77//zgCwFStWWG7r378/mz9/vuXfw4YNY/PmzWMA2IULFxhjjK1fv54BYKdOnWrxZ7NixQoGgLm7u7PZs2ezV155hR07dqzR/dLS0hgA9tVXX1lue/7551n9w+2kpCQmk8nY9ddfz4xGY4OvN5lMDX52kyZNsvxb/Pn27duX1dbWWm5///33GQB25swZxhj/+fr7+7ORI0cyvV5vud/q1asZgAaP2ZJrrrmGjRs3zvLv//3vf0yhULCCgoIG97t8O0WLFi1i0dHRln//8ssvDAD7z3/+Y7nNaDSyKVOmNPqZLVq0iAFgr776quW20tJS5urqygRBYD/88IPl9oSEBAaAPf/885bbXnrpJebu7s4uXrzYYJuefvppJpfLWWZmJmOs7v/L39+flZSUWO7366+/MgDst99+s9y2ZMkS1txpk0ajafBvnU7HBgwYwKZMmdLgdnd3d7Zo0aJGX//VV18xACwtLY0xxlhBQQFTqVRsxowZDV4j//3vfxkA9uWXX1pus/Z3jBBCCCGESIM60gkhhBBCnFzfvn3h7+9v6dg+deoUqqurMXbsWADA2LFjLQNHDxw4AKPRaMlHb4mHhwduu+02y79VKhVGjRqF1NRUy21//vknQkNDcdNNN1luc3Nzw7333tvgsY4ePYqCggI8+OCDDfLYr776asTHxzfoAJ4wYQL27NkDAKisrMSpU6dw7733IiAgwHL7nj174OPjgwEDBrT4PaxcuRLfffcdhg4diq1bt+KZZ57B8OHDMWzYMFy4cKHVn0F9GzduhMlkwooVKyCTNTwMbyoC5nJ33nlng/z0CRMmAIDl53n06FEUFxfjnnvuaZDNvnDhQvj6+lq1jcXFxdi6dSsWLFhgue3GG2+0RMu0x5YtW6BUKnHPPfdYbpPJZFiyZEmzX3P33XdbPvbx8UGfPn3g7u6O+fPnW27v06cPfHx8Grye1q1bhwkTJsDX1xdFRUWWP9OmTYPRaGywUgEAbr755gY/m8t/pq1xdXW1fFxaWory8nJMmDABx48ft+rrL/f3339Dp9Ph0UcfbfAaueeee+Dl5dXgdQ5Y9ztGCCGEEEKkQYV0QgghhBAnJwgCxo4da8lC37dvH4KCghAXFwegYSFd/NuaQnpERESjArGvry9KS0st/87IyEBcXFyj+/Xp06fBvzMyMpq8HQDi4+Mtnwd4MTQ3NxfJycnYv38/BEHAmDFjGhTY9+zZg3HjxjUqaDdlwYIF2LNnD0pLS/HXX3/h1ltvxYkTJzBnzhxotdpWv16UkpICmUyGfv36Wf019UVFRTX4t1gAFn+e4s9A/H8TKRSKRpnlzfnxxx+h1+sxdOhQJCcnIzk5GSUlJRg9enS7410yMjIQGhoKNze3Brdfvp0iFxcXBAYGNrjN29u7ydeTt7d3g9dTUlIStmzZgsDAwAZ/pk2bBqDxgNjWfqat+f3333HFFVfAxcUFfn5+CAwMxMcff4zy8nKrvv5yzb3OVSoVevTo0eB1Dlj3O0YIIYQQQqShaP0uhBBCCCGkuxs/fjx+++03nDlzxpKPLho7diyeeOIJZGdnY+/evQgLC0OPHj1afUy5XN7k7Ywxyba7KWKRf/fu3UhNTcWwYcPg7u6OCRMm4IMPPkBVVRVOnDiBV155pU2P6+XlhenTp2P69OlQKpVYs2YNDh06hEmTJtni22jEHj9PsVje3IDV1NRUy/+9IAhNPrfRaOzQNjT3fVrz/ZtMJkyfPr3JjH0A6N27d5sfszl79uzBtddei4kTJ+Kjjz5CaGgolEolvvrqqyaHoNqCo37HCCGEEEKcERXSCSGEEEKIpfi8d+9e7Nu3D48++qjlc8OHD4darcbOnTtx6NAhXHXVVZI9b3R0NM6ePQvGWIPO2sTExEb3E2+fMmVKg88lJiZaPg/wLuOoqCjs2bMHqamplriOiRMn4rHHHsO6detgNBpbHDTamhEjRmDNmjXIzc21+mt69uwJk8mE8+fPY8iQIe1+7uaIP4Pk5GRceeWVltsNBgPS09MxaNCgFr8+LS0N+/fvx9KlSxtdHDCZTLj99tvx3Xff4dlnnwXAO5+bihC5vGs6OjoaO3bsgEajadCVnpyc3LZv0Ao9e/ZEVVWVpQNdCs3F7vzyyy9wcXHB1q1boVarLbd/9dVXVj/G5eq/zutfrNLpdEhLS5P0+yKEEEIIIW1D0S6EEEIIIQQjRoyAi4sL1q5di+zs7AYd6Wq1GsOGDcOqVatQXV1tVayLta666irk5OTg559/ttym0Wjwv//9r9H2BQUF4ZNPPkFtba3l9s2bN+PChQu4+uqrG9x/woQJ2L59Ow4fPmwppA8ZMgSenp54/fXX4erqiuHDh7e4bRqNBgcOHGjyc5s3bwbQdNRMc+bOnQuZTIYXX3wRJpOpweek6CAeMWIE/P398dlnn8FgMFhuX7t2rVVRH2I3+pNPPombbrqpwZ/58+dj0qRJDeJdevbsiYSEBBQWFlpuO3XqlCX+RzRz5kzo9Xp89tlnlttMJhNWrVrV7u+1OfPnz8eBAwewdevWRp8rKytr8HOxlru7u+Xr65PL5RAEoUEHfnp6OjZu3NjkY1z+9U2ZNm0aVCoVPvjggwaviS+++ALl5eWNXueEEEIIIcR+qCOdEEIIIYRApVJh5MiR2LNnD9RqdaMi89ixY/HOO+8AsC4f3Vr33HMP/vvf/+KOO+7AsWPHEBoaim+++aZRnrZSqcQbb7yBO++8E5MmTcKCBQuQn5+P999/HzExMVi2bFmD+0+YMAFr166FIAiW7ZXL5Rg7diy2bt2KyZMnNxjc2RSNRoOxY8fiiiuuwKxZsxAZGYmysjJs3LgRe/bswdy5czF06FCrv9e4uDg888wzeOmllzBhwgTccMMNUKvVOHLkCMLCwvDaa69Z/VhNUalUeOGFF/DQQw9hypQpmD9/PtLT07F69Wr07Nmz1a7otWvXYsiQIYiMjGzy89deey0eeughHD9+HMOGDcP//d//4d1338XMmTNx1113oaCgAJ988gn69++PiooKy9fNnTsXo0aNwr/+9S8kJycjPj4emzZtQklJCQDru7Wt8cQTT2DTpk245pprsHjxYgwfPhzV1dU4c+YMfv75Z6SnpyMgIKBNjyn+Ljz88MOYOXMm5HI5brnlFlx99dV49913MWvWLNx6660oKCjAqlWrEBcXh9OnTzd6jL///hvvvvsuwsLCEBsbi9GjRzd6rsDAQCxfvhwrV67ErFmzcO211yIxMREfffQRRo4c2WCwKCGEEEIIsS/qSCeEEEIIIQDqCuRilEt9Yma2p6cnBg8eLNlzurm54Z9//sGMGTPw4Ycf4uWXX8b48ePx5ptvNrrv4sWL8eOPP0Kn0+Gpp57Cp59+iuuvvx579+6Fj49Pg/uKXejx8fHw9/dvdLv4d0t8fHzw2WefISQkBF999RUefPBBPPfcc6iqqsJbb72FH3/8sc3f74svvogvv/wSNTU1eOaZZ7BixQpkZGRg6tSpbX6spixduhQffPABMjMz8fjjj2PPnj3YtGkTfHx84OLi0uzXHT9+HAkJCZgzZ06z9xE/9+233wIA+vbti6+//hrl5eV47LHHsGnTJnzzzTcYNmxYg6+Ty+X4448/cPPNN2PNmjV45plnEBYWZulIb2m72srNzQ27du3CE088gZ07d+KRRx7B66+/jqSkJKxcuRLe3t5tfswbbrgBDz30ELZs2YLbb78dCxYsAABMmTIFX3zxBfLy8vDoo4/i+++/xxtvvIHrr7++0WO8++67GD58OJ599lksWLAAH3/8cbPP98ILL+C///0vMjMzsWzZMvz000+499578ddff0GpVLZ5+wkhhBBCiDQERpNoCCGEEEII6bZMJhMCAwNxww03NIhXcbSNGzdaLoQ0N9yUEEIIIYSQzoI60gkhhBBCCOkmtFpto7z1r7/+GiUlJZg8ebJjNgpATU1Ng38bjUZ8+OGH8PLyatTBTgghhBBCSGdEGemEEEIIIYR0EwcPHsSyZcswb948+Pv74/jx4/jiiy8wYMAAzJs3z2Hb9dBDD6GmpgZjxoxBbW0t1q9fj/379+PVV1+Fq6urw7aLEEIIIYQQa1EhnRBCCCGEkG4iJiYGkZGR+OCDD1BSUgI/Pz/ccccdeP3111sdrmpLU6ZMwTvvvIPff/8dWq0WcXFx+PDDD7F06VKHbRMhhBBCCCFtQRnphBBCCCGEEEIIIYQQQkgLKCOdEEIIIYQQQgghhBBCCGkBFdIJIYQQQgghhBBCCCGEkBZ0u4x0k8mEnJwceHp6QhAER28OIYQQQgghhBBCCCGEkE6IMYbKykqEhYVBJmu557zbFdJzcnIQGRnp6M0ghBBCCCGEEEIIIYQQ0gVkZWUhIiKixft0u0K6p6cnAP7Ne3l5OXhrCCGEEEIIIYQQQgghhHRGFRUViIyMtNSUW9LtCulinIuXlxcV0gkhhBBCCCGEEEIIIYS0yJqIcBo2SgghhBBCCCGEEEIIIYS0gArphBBCCCGEEEIIIYQQQkgLqJBOCCGEEEIIIYQQQgghhLSACumEEEIIIYQQQgghhBBCSAuokE4IIYQQQgghhBBCCCGEtIAK6YQQQgghhBBCCCGEEEJIC6iQTlpkMjGcyCxFfoXW0ZviFBhjMJmYozeDkGbllNXg15PZKKqqdfSmEEI6AZOJQas3OnozCCGkW6rRGXEgpRjlGr2jN4UQ0gmcz6nA5jO5dOyl0Th6C4gTo0I6adH53ArsTCzET0eykFJY5ejN6daSCyrxxd40fH8kE0YqppNOqNZgxJ9ncpFaWI0/z+RCbzQ5epPsz2QCPvsMGDIE+PxzR28NIQ5VXFWLr/an46t96Sit1jl6c7oFxhiOZ5bibHY5HQsQIrUjR4ALFxy9FW2y7UI+DqYWY/2JS1Q4I51WfoUWqYVVtn/fys4GDh8G9M55YSmlsAp/nc9DQl4ljqSXOHpzHCM7G7jqKsDLC/jiC0dvDXFSCkdvAOm8GGM4kVUGADCYGH4/lYtZA0LQJ8TTsRvWVdTUAEVFQGgooGj+V02rN2JnYgEu5FYCACq1BqQVVSMuyMNeW0qIVfanFKNSawAAFFfpsDe5CFf2CXLwVtnRmTPA/fcD+/fzf997LxAQAMyd2+GHNpkYavRGaHRG1OiM0OgN0OiMqNWb0CfEE37uqg4/B2mnbdv4RZPp04GbbwY86T0QAC6VarDpVA5q9fyC2r6UIlwzKMzBW9X1JRVUYVdiIQDgaHoJxvcKQM9ADwiC4OAt6yYYA/LyeDE1IYH/7e8PPPtsi8dqpHmMMZRp9Mgpr0FumRb5lVr0CPDAmJ7+jt60OjU1wOOPAx99BCiVwJdfArfd5uitalVqYRVSCngjU5lGjz/P5GLukHDIZLQ/IJ1Hjc6IdUezoDcyuKnkiA/1woAwL/h7qKV9ospKYMQIvg/39ASuvJIfm82YAfTqBXTz98nCylpsOZsHZr5WcSqrDMOifOGudpL3LsaAtWuBhx4Cysr4bUuWACNHAoMGOXTTiPNxkt860h6XSmtQVFkLpVxAbIAHLuZXYvPZXOgMJgyM8Hb05nVuW7cCt9zCd/IyGRAWBkRFAZGR/E9cHHDbbUjRMPxzIR/VtUYIAuDvoUZRZS0u5FZQIZ10KnnlWpwyX1gbEeOLo+mlOJlZhh4B7oj2d3fsxtladTXw4ovAu+8CBgPg7g6MHg1s3w7ceiuwaxc/iGunfy7k40x2ueXA+HJZpRrMHxHZ7scn7WQyAS+/DLzwAj94/+kn4NFHgfnzgf/7P2DcuG5/0tacpPxKbDmbB4OJIdBTjaKqWiTlVyG3vAah3q6O3rwuizGGQ6nFAACZIKBUo8dvp3IR7uOKCb0D6GfbXgUFvFB++jQvnpeXN75PaSnw/vv237YuqqrWgAu5Fcgpq0FuuRY1uoad0oWVtegb6gkft05wEfjsWWDBAv43wDtZb78dSEkBVqzotPtxvdGEneaLar2CPZBeVI2MYg32JhdhYu9AB28dIXVSCqugN/KDWI3OiOMZpTieUYpQbxcMCPdGr2APqBXyjj/RBx/wIjrAi+qbNvE/AD/PnjEDWL4c6NGj48/VyVTXGvDryWzoDCZE+rlBbzQhr1yLI+klmOwMTU0FBbyZacMG/u+RI/nFlO3beZPL0aP8/IwQO6FoF9IssRu9X5gXrhoYgkER3mAM+PtCPo5lOOlSImt89BFw9dV1V0pNJuDSJd7F+uOPwNtvA/ffj7SlT2DTyRxU1xrh567CLSOjMHtACAAgtbAaGp3Bcd8DIfUYTQx/X8gHY0DfUE9M6BWIIZE+AIC/zuU3OoHuVv74A+jfH3jzTV5Ev/563sG4dSswezbvcpszB0hPb9fDl1TrcPoSL6ILAuCmkiPAQ4VIPzf0DuadzzllNd37Z9wZlZQA11wDPP88L6LPnQv06cMvqnz1FTBhAhAfD7zxBl955ESOZ5bijzO5MJgYegZ54OaRkegb6gUA2JNUBNbcFSHSqpTCKhRV6aBSyHDn+BiMjvWDUi4gu6wGPxzOwh+nc1GmoQidNnvmGR7JdegQL6LLZLyhYc4c4O67+X0++AD4738du51dyN/n87E3qQiphdWo0RmhkAkI93HFiBhfhPm4gDG+r3Aoxvgx+ciRvIgeHAxs3gw89RT//AsvAIsXA7rO+Tt1JL0E5TV6eLooMKNfCGb05+cIxzJKcT6nwsFbR0idZPOqiSt6+OPaIWHoEegOmSAgt1yLbefz8fmeNGQWdzDPuqyMn0MDvCv56FHgtdeAKVMAlQrIzOSrB6dOBQoLO/ZcnYzBaMLvp3NQqTXA102JawaFYqx5xc/pS+Wo1HbzmJv16/m52IYNfOXYSy/xusoPP/BmxYQE4OGHHb2VxMlQIZ00qVyjR6o5E31IpC8EQcCU+CCMjPEDAOy+WIT9yXTC3IDRCDzyCF9iZDQCixbxIltODnDwILBuHfDuu6i5lS8lDf15LZS1NRgR44uFo6MQ4u2CAA81gr1cYGIMiXmVDv6GCOFOZJaisLIWLkq5pQtqfK8A+LmrUFVrwD8J+d1zX7BkCS+mZmTwTpdNm/jBXGQkP5D78Udg8GAgP7/hxbM2ELv8ewS64+EpvXDfpJ64fUwMbhoegasHhSLAUw3GgIySamm/N9K848eB4cN5wcXFhRfON2zgF1D27uXd6O7uwMWLwNNPA1dcAdR2/+G7jDHsvliIXYmFYAwYHOmNawaGQimXYUxPfyhkArJLa5BWRK/V9mCM4WAqb1IYGuUDLxclxsYFYNHYGPQP84IgABfzK/H1gQwaAN8W2dnAmjX84//+l0d0aTRAUhLfp3/2GfD66/zzjzwC/Pmn47a1izAYTcgq4UWxsT39sWBUFB68Mg7zR0ZiQq9AjO0ZAAA4l13huKaQoiJ+4XvJEkCr5Re+T58GZs3i/9+ffgrI5cDXXwMzZ/IVCZ1ImUaHY+l8myb1DoRKIUPvYE+MjuXnYf9cyEdeOe0HiONp9UZkmvcHfUI80TPQA9cNCcddE2IxoVcAfN2U0BlM2JNc2LFzhffe48fZ/fvzVd/Dh/NjsH/+4b+/mzcDPXvyxpYbbug2x2WM8WamnDIt1EoZrhsSDhelHFF+bgj3dYXRxHA4rZs2OBqNwJ13AjfeyPfpAwfyORdiFFtgIL+oIgg8ruu77xy9xcSJUCGdNOnkpTIwBsQEuFmyeQVBwPheARjfix8gH0orwc6LHXxT7C4qKoBrr+UdTQDw6qu8+OLiwjPSR48GbroJWLYMW5a9jPKQCLhUVeC2tP2Y0CsQCnndr2K/MN7Zdz6Xuk2I45Vr9DhojhqY2DsAbiqeCKaUyzBrQAhkgoCk/CpLxn+3cfIk72STyXiu6rlzvHuxPk9P4PffeTfE+fP8d7wNnW1avdHyez4syrfJzNNYc2xOWiEVJ+3iiy+AsWP5iViPHsCBA7xjEeAH6uPG8fvk5vLOp4AAHg/QzYtvRhPD5rN5OJbBCzvjewXgyj5Bltesl4sSQ6J8AAD7kotgoiGZbZZSWI3CylqoFDIMi/K13O7posSM/iFYODoaId4uMJoYHR+0xbvv8iiPCRN4UXXAAEB9WW7vk08Cd93FVxDefDNw6pRjtrWLyC3XwmBi8FArMCrWDyHeLpDXe/+K8HVFsJcLDCaGU1lNxOjY2tGj/CL3r7/yTtX33uPv1UH14g/uvZevOPP0BHbuBMaMAVJT7b+tTWCMYUdiAQwmhmh/twZRj2N6+qNHoDsMJobfTuWgqpZWrxLHSi2shtHEEOChajDPx0OtwIgYP9w8MgoKmYCCilrkV7SzuF1Swn+PAWDlSn5sXp+bG79I9ttvfADl3r3AAw+g2czELuRIeiku5FZCJgi4ZmAYfOvVZcSu9LPZFSjXdMOu9JdfBlav5v/f//43L6IPGdLwPpMnA889xz++/34gOdnOG0mcFRXSSSO1BiPOZvMD3yGRvo0+PzLGD1Pi+cHoycwypJg7151WRgYvrvz5J+DqyjvPly9vMnOxvEaPjLJanLpmAQDA56vPGr3J9wn2hNx8wFFY2T2uppOuiTGGfxLyoTcyRPq5oZ85vkEU7OWCK3rw7qgdiQUor+lGB3HiAfu8ecBbbwEezcwsiIjgJ+MeHrwr5v77rT5wP59bAZ3BhAAPFSJ8m84+jg3khfT0Yg0VJ22JMeC++3jMQ20tv2hy7FjjA3aRpycvvP3f//F/f/213TbVES7kViAxj5/IzRoQgpExfo2GX46M8YNaKUNRlQ4X8qjQ2xaMMRxK4xcsh0T6wEXZOEs20FON4dH8mCy7tMau29dlFRfzzmOAH5c1RxCAjz/mEQFVVXwlUm6ufbaxCxK7TyP9XJscgisIguW1eupSGfRGk/02rrycd6Pm5PA4roMH+WyLywtvAO9E37uXv48nJvLVRRcu2G9bm5FcUIX0Ig3kMgFX9glq8DMWzPtgfw++IvCP0zkw2PPnS8hlkgp4I01cUNOD2F1VcvQK5sfQpy+Vte9J3n6bZ6IPHsxXmjSnb1++WlQm4w1t777bvufrJJILKrEvmccHXhkfiCh/twafj/B1Q7S/G0z1jiG6jV27+HwqgK8qe+WVxhfBRc89xy+WV1by1QqdNK6LdC9USCeNXMithM5ggq+bEjGX7bBFgyN9LAfJ55w5p+/UKd5tfvYsEBLCd/o33dTs3c/nVIAxoOyW23m3+okT/CC/HleVHLEBvHhGXWfEkRLzK5FRrIFCJmBqfFCTJ8wjY/wQ5uMCncGErefyukexNzcX+P57/vFjj7V+/yFDGh64v/JKq1/CGLPEugyO9GnyZwsAoV4ucFHKodUbkUtxDrbz2WfA//7H/w9ffRXYuBHw8Wn9626/nf/9xx+8aNdNiTEOI2N9LXnol3NRyjHKHP92IKXYvsWzLi6tqBoFFY270S8X5sMvuBVV1UKrp7kJrfrvf/lcgyFDeLdiS5RK4Oef+eyDS5f4xbRqWgnUlLpCetPnCADQK8gDXq5K1OiM9s3zXroUyMriK4oOHwaGDm35/oMG8ez8oUN5rrIY8+MgOoMJuy7yfOcR0b6W7tP61Ao55gwKg1opQ06ZFjsSu1ceNOk6ag1GS/a5WCxvyqAIHwA8nqzN712FhXUrvpvqRr/crFl1BfQnnuDHZ11QmUaHLWf5YNUhUT6Wn+Hlxpi70s/nVqC0upsUkIuKgIUL+SqxxYuB225r+f4KBY918fPjTTAtXTgnRCJUSCcNMMZw0jwcaEiUb7PFHQAYGO4NgJ8AOuXSQr2e7+Tz8/kV8sOH+UCjZphMDOdyeKd/7/7R/IopAKxa1ei+YrxLYl4FjN2hMEm6HK3eiF3mk7NRsX5NnswBgEwmYGb/EKgUMmSX1jh+uJgUVq3iv9/jxgGjRln3NVddVTeo7rnn+EW1FqQVVaNMo4daKUN8SNOFSYD/fMULmumUPW0bWVk8vgfgXU/Ll7d+oiYaMIAXYPR6fjGlm8o1Z/GG+zS9ckI0ONIHni4KVGoN7e88czK8G53nmw6K8IarqnE3ushDrYCvmxKM8SHEpAVVVXXFl6efbnKVYCO+vrzoEhDAT8Zvu41ntBILrd5oyeiPaqGQLpMJGGaOezqeWWqfi+w//QR8+y3ff3/7LY94sEZYGPDhh/zj9ev5fCMHOZRWjEqtAV6uSow056E3xdddhasGhEIQgLPZ5d0z1oF0emlF1TCYGPzcVfBv5jwBAEK9XRDgqYbeyHChrU1ib77JL2oOH85jVK3x8MPAPffw1YYLFvB4xi7mTHY59EaGcF9XTOoV2Oz9Qr1d0SPQHYzBEsXZpTHGV3tmZ/NVReK+uTURETwGBuAXUrroBRTSddi0kL57927MmTMHYWFhEAQBGzdubPVrdu7ciWHDhkGtViMuLg6rxV8IYhfpxRqUavRQKWToG9r0Ei2Rr7sKYT4uYAxIcMbO6fff52/MAQE80iEyssW7Z5ZoUKk1wEUpR1ygB8/qBHgUTEFBg/vG+LvDTSVHda0RGcVUPCP2t/tiITQ6IwI8VBgR0/zJHAD4uKkwyTyE9EBKcdfulNRo+BJ/AFi2rG1f+8ADPBpE/LiFpYUnzd3oA8K8oVK0/FYcY16hkkqFdOkxxuN4Kit5Ru7DD7f9Me64g//dTeNdqmsNKK/RQxB4nFNLxMGjAHA4rbRr7wvsJKNYg7xyLZTyujiMlohd6TlltEKlRZ99xnN14+JaXCnYSI8ePFtbreYrU156yWab2BVdKq0BY4CfuwqeLsoW79s/zBsuSjnKNHrbx0BmZ/N9OcCzdMeMadvXjxnDh4pXVTmsAFNcVYvjGWUAgCv7BEIpb/3YQNwn59GKNeIASfn897pXkEeLzXeCIGCQuQHvTHa59fPV8vLqGs5efNG6C6L8CXlzy6RJ/Phuzhze5dxFmEwMCebZU8OifJqcoVSfeNyVmF+JoqouHgv74Yc8616tBn74oflozabMmcOHhgM8flFPFxiJ7di0kF5dXY3BgwdjVRMdt01JS0vD1VdfjSuvvBInT57Eo48+irvvvhtbt2615WaSek5m8W7SAeHeUCua74oS9Q/jb4rnciqca+jopUvACy/wj994A/D3b/VLzpq70fuGevLhoiNG8G5XnY4PratHLhMQH0pDR4ljVGr1lsimKX2DGwwRa07/MC8EeKhgMDHLgXWX9M03vPgSGwvMndv2r3/zTT7Q7MIF3t3chJJqHTKKNRAEYHAzSzXri/F3hyAARZW1qNDSQaGk1q7l8y1UKj5EVN76+14jCxbwrzt0iOfsdjNiN7q/u6rJ7O7L9Q3h+wKt3ogj6SW23rwurX42+sAIH8sw55aEm+cpZJdpbLptXVptLfDOO/zjJ59s++/12LF1x2WvvgpcvCjt9nVhYsxTS93oIpVChsER/DzhWEap7c4TTCbgzjuB0lLetbpiRdsfQybj+3KARwQ4wI7EQpgYQ49Ad/QItK54FGIupOdTIZ3Ymc5gsqyUjGsh1kUUH+oJlUKG4iodLlk75+P11/kKkSuuAGbPbtsGqlTAL7/wi6NpacCNNwKGrrGCPrNEg6pagznutfWfbZCnC3oFe4Ax3tDUZR0/zuN4AH4O1dycopa88QY/D8vPB7Zvl3TzCKnPpoX02bNn4+WXX8b1LQ2FqOeTTz5BbGws3nnnHfTt2xdLly7FTTfdhPfEoW9NqK2tRUVFRYM/pH1KqnVIL+LFnSFWFHcAnoemlAsoqdY5VzfEY4/xZWZjxvDsrlZU1xqQUsAPNsSLDwDqutI//rjRm7u4IiC1sBo1OurqI/aTX8G7GQI91a1GOYgEQbBkJ3fZQYMmU92Q0UceaV9R1de3LpvxpZeA1NRGdxGz0XsEesDbreWOPoDPTQj15ifLFO8iofz8us6VFSv4kKr2CA6uy1/+5htptq0TyTMX0kO8rdsXyGQCxsUFAOADyeniT/OySmqQU6aFQmZdNzoARPjwAmZeeS3l0Dfn2295h3JYWN2KkbZauJAXbvR64KGHrB4i3d3VHzRqjcGRPlDIBOSWa5FTbqPzhI8+ArZt47OHvvmG5923h1hI//NPoKxMss2zRlFVLbJK+IDRyX2CrP66IC8+fM+pzsFIp5BezGNdfNyUCPRoZghkPWqFHH2C+bntmezy1p8gOxv45BP+cVu60evz9+fdzV5ewO7dfKVSFyDG3/QJ9rSqmQkAxvTwhyDwYcUFXXF/UH9Q6HXX1dVI2kqtrluF9tNP0m0fIZfpVBnpBw4cwLRp0xrcNnPmTBw4cKDZr3nttdfg7e1t+RPZSrwGaZ7YjW5tcQfgb4rilO5z2V20eNZWf/3F41hkMn7wbkWW7oXcCpgYQ6i3CwI96x1szJ/Po2EuXeJv9PUEefL7Gk0MifmVUn8XhDSroJIfgDV4rVqhT4gnBAHILq3pmnmdmzfzjmIvL57P11633gpMmQJotfxAsF4BRqs3WlaZDI30sfohY/x5vEsaFdKl89BDfPXBkCG8a7UjxGLdN9/wCzLdSE457xwTL+ZYIzbAHeG+rjCYGI6kUVd6UxhjljzTARHe8FC33o0OAF6uCni6KGBizHKRg9RjNPKONIA3Pajb9j5mIQg8Y12l4sd9GzZIt41dVKVWj5JqHQQBiPBtvSMdANzVCstF9qO2WKFy4UJdB+Nbb7X/gijAB4/268dXNNj5/1uMcYgJcIe3q/UXAsRol8LK2u4x7J10GXWxLp4txrrUN8i8QiW5oAoaXSvd4a++yn8XJ0wALqsPtUm/fsArr/CPn3uOr1zpxLR6I5IL+M+2ueHuTfH3UCM+hNdkDnXF464lS4CkJB6V++WX7btwIpo3j/+9YUOLMZuEdESnKqTn5eUhODi4wW3BwcGoqKhATTODX5YvX47y8nLLn6ysLHtsarej1RtxPqftxR2ARzoAPJer23dH1dYCS5fyj5cutWrJEWMMZ81X3geEezf8pIsLz/ACWhw62ubBLIR0QGEl70gPamMh3dNFiUjzyXWX7EoXu9HvuQfwbHlGRIsEgV9kU6mALVv40lKz87kV0BlMCPBQIcLXuo4+AIgN5IX0rBINDN19P2sPGzbwC6JyOT9gb28Ho2jOHMDbG8jM5F1P3YTJxCydTW0ppAuCgDE9eORZQp4THBu0w6XSGmSX1UAuEzDCym50gP9sxZVCVi+Pdybr1/OTcV9f4N57O/ZYcXF1F9mWLeMzNJyY2I0e7OViVcyTaFi0LwSBr7AsqZawqKHT8YGwWi0wYwbw4IMdezxBqOtK//77jm+flRhjSDAfM/VrZT7V5fzcVFApZNAZTCjVUMGI2IfeaEK6eYZXLytiXURBXi4I8XaB0cQsEZJNysys6x5vbzd6ffffzwvqxcWdfu5FckEVDCYGfw8Vgr3adh42JJIfS/BZFl3owtratbwRRSbjH/u1PJurVRMm8NWipaV8jh0hNtCpCuntoVar4eXl1eAPabtzOXwydICnuk3FHQCI8HWFl6sSOoPJcgW123r7bX6CFhLC39itkF1WYxng2uTBxv338wOEf/4BEhIafCo+xBMyQUBeubbrDw8hXYZYSG9rRzpQ1z2RkNvF5iacOsV/B+Vy3qncUX36AE8/zT9+5BGggv88xFiXwZE+VnfwAECghxqeLgrojYyKZx1VUlJXcHnqKWDo0I4/pqsrX2EEdKuho0VVtdAbGdRKGfzcVW362ghfV3i6KKAzmGglRROOZ4ozabxaHdp4QxJJ+gABAABJREFUOXHgaHYZ7QsaYAx47TX+8UMPdeyCqGj5ciA6mhd2Xn2144/XhWWV8NebNfno9fm5qyyZ38czJOwGffFFnqnr6wt89ZVVK0RbJRbS//mHDzq0g0ulNajUGqBWyiyrz6wlkwmWYzWKdyH2klFcDZ3BBC9XZZubbgaam8pOX2ph6Oinn/JYrSuvBCZP7uDWAlAo6pplPvywU8+zEVet9g31atN5AgAEeKggEwRo9UZU1XaNPHhoNHWrilas4EXwjpLLKd6F2FynKqSHhIQgPz+/wW35+fnw8vKCq2vbirukbS6YlxQObWNxB+DdUf3EwZgtXV3u6tLT65aGvf027z60wllz5E3vYM+mB7jGxADXXMM//uijBp9yUyksnajUlU7soUZnRKWWH3y1p5AeF8TnJpRq9F3rpE48wL7pJl40kcLy5byjMScHWLECaUXVKNPooVbKEB/Stou+giDUxbsUU1GyQx57jBdI4uP5Ml+piPEu69Z1m85VMdM41NulXccGlrkJ9P7VQHWtAelF/DUyNNL6bnSROHA0r7wGRopzqPPXX8CJE4CbG/Dww9I8pptb3fvDW2/xZgonxBhr06DRy4kzAC7kVqBaigLP+fN1F00+/ZTn4UuhZ09g1Cge0bVunTSP2Qpx/9g7yBMKedtPzcV4l4IKargh9lEX6+LR5mODPiGeUCtlqKjRI6O4mWOlTZv43+KqbSnMmMHPtw0G4F//ku5xJVSu0SO7tAaCAEtMS1so5DL4uvML80VVXWSFyocfArm5vB4iNiBJQWxu2biR4l2ITXSqQvqYMWPwz2XLL7Zt24YxY8Y4aIucg9HELEstI9txcAzURZBklWpQXtMFs5Gt8cgjfHL45Mk8A9kKPOeMX6QYEN5C4UwcqLFmDVDVsKtfXOaZkFtJ+YfE5sR8dF83ZdMXflqhUsgQF8Q7z8TMz04vNxf47jv+8bJl0j2ui0vdxbEPP0T6X3sAAAPCvKFStP3tNybAXEgvrO5a3f6dydatfD8rCDzSxcX6uJJWjRsHxMbyffivv0r3uA6UZ85HD/FqXzODeCKYXqShodn1JOZXwsQYQrxd4NvGTn8A8HdXwVUlh97ILPtsAuD11/nf997Lh8xJZe5cPlBYp+MFeifc/5ZU61BVa4BCJrQp5kkU5u2CUG8XGFqLdLDW88/zYvd119Xl4UpFPMa3Q7yL3mhCknk1b3wbY11EYvxDfldqXiBdlsFoQmpR22NdREq5zNKAd7qpoaPp6cDZs7yrePbsjmxqY++8w7vT//iDHw92MmI3epSfW5tXqokCzINfu8RK9tLSuvftF19s/0yTpowbB4SG8sHRf/8t3eMSYmbTQnpVVRVOnjyJkydPAgDS0tJw8uRJZGZmAuD55neIHVwA7r//fqSmpuLJJ59EQkICPvroI/z0009YJmVhgzRSptHBaGJQKWTwcrFu2NXlvF2ViPRzA2PdtPPs99/51XGFgmeZW3n1PTGvkkfmeKgQ4tXCicf06bxztaIC+PbbBp+KDfCAq0qOqloDMkq6R5cj6bzqYl3aX2C0xLvkVXaNbsmPPuJLSMeOBUaPlvaxp0/nS8VNJvR76SnITEYMjvBp10NF+blBLhNQXqNHaVcc5upoRiPvRgd4MUzqi/SCUNeV3k3iXXLL256PXp+/hxpBXmqYGMNFGpptIV5kbMsgsfoEQaiLd6GoJy47G9i5k/8eir/nUqk/eHTLlm5zoawtxHz0MB/XdnVNC4JgmRN0oaPRbydOAD//zP9fXn65/Y/TnPnzeUzMgQNAWpr0j19PWlFdRIY4+6CtQuoNHO0Sx1ykS8so0UBnMMHTRdHyuW0LxHiX1MIqVGovO5797Tf+97hxHc/Kvlzv3nXxjcuW8e70ToIxZqmhtPfYAKhXSK/sAoX0N97ghe4BA6xuUrQaxbsQG7NpIf3o0aMYOnQohprzRx977DEMHToUK1asAADk5uZaiuoAEBsbiz/++APbtm3D4MGD8c477+Dzzz/HzJkzbbmZTq/Y3I3u765q8/Ks+sSho+dzulg2cmu02rolwsuW8WElVjqbw6+09w/3bvlnK5PVZfauWtWg20kuE9DbfMU/nXJmiY0ViING2zjgpr5IXzd4qBXQ6o2dPxu5pgb4+GP+sdTFF9G778Lg6YWQxDOYvGsDvN3a12WiUsgsMyzSirr5PApb+OEHHgfg4wO88IJtnuP22/nff/3FVzp0YRqdAWXmCzYh7SykA7DEGCV0xQHENlBSrUN+hRYyoe69vT3CKSe9oS1b+N+jRgGRkdI/fq9ewOOP848feaTbxDdZSyykR/m3b+UqwLtXlXLB/DvQgSKP+TwSCxbwAozUQkPrcpl/+EH6x6/HUjgL8Wz3OZi3qxJqpQwGE0NxV+hCJV2aGOsS145YF5G/B5/Jxhhw5vKu9N9/53/PmdORzWzeihVAQABw4QLwySe2eY52yC6rQXmNvsHK3vYQYzk7fUd6djbw/vv849de44VvqYmrlTZuBGo7+c+DdDk2LaRPnjwZjLFGf1avXg0AWL16NXbu3Nnoa06cOIHa2lqkpKRg8eLFttxEAqDYnKHV1kFil4sL8oBKIUN5jb57DcP7+GPekRIeXnfwboWCCi0KKmohlwnoa00e8uLFgFLJl7OlpDT4lJh/KF70IMRWCsxLgwM92l9Il8kE9DFHOnT64tkPPwDFxTySY+5cmzyFKSgYB+/iK6v6/+89oLL9nbmxYrxLkXMVcTpMr68rnj/xBC+m20LPnryLymSqiwvqosRudH8PFVyU7T/B6RPiCUEAcsq0KNPQe1iCuXAWE+AGN1X7VgECsFxUyy6r6V7NC+3155/876uust1z/PvfQFQUHzwq5nM7AZOpbsh1e/LRRWqFHD3NQ0cvtPfY4OBBXmiTy3m8i62I3ZE23I9rdHWzEvq0Iw9ZJAgCgs2rCDt0gYKQVhhNDKnmRo5ewR0b5jzIvDrzXHZFXXRpZSVfWQTUzQ+Tmo8P8NJL/OPnn+cD6DsBcV5dryAPKNux6kcU4MHrOSXVehiMJkm2zSZWruTNiuPHA1dfbZvnEONdysuBbdts8xzEaXWqjHTiGMXV/KDLvwOFM4BnnvUxv6lKkn/YGVRU1A0YXbkS8LD+CrHYjR4XxKNZWuXrC1xxBf94x44Gn/J35/83JdV0gExsR2cwocw846AjHelA3bLE1MJqaPWdOBtZXEJ655226YYAXwZ7dOZ8lEXEQFFcBPznP+1+LLGQnl1a07l/rp3NmjVAcjIQGCjdEMLmiF3pXTzeJbeMF9Lbu3Rb5KFWINrcxZqQ59zxLowxXMjrWKyLKNBDDZVChlq9qesMFbMVna7uJNmWhXR397rBo2++afPYj84iv1ILncEEF6W8QxfZgbrXfWJ7o9/EAdGLFvGYBlu54Ya65pYzZ2zyFBfzq2BiDMFeLh0+BxNXDXWpIe+ky8ks0aBWb4KHWoGwDqxUA/j5sZs5ujS92Lx69a+/+P48Lg7o00eCLW7G3XcDAwfyIrqtVii2gd5ossTfdfTYwEOtgItSDhNjKOmszQuJiXxOEcAz0juQiNAimayuK53iXYjEqJBOLB3p4hXMjhCHjiYXVKLW0A2KPO+8w7tV+/ThB+1W0htNlivLYg6cVaZM4X9v397gZnECd3WtkYpnxGYKq2rBGD8I60inJMCXFgZ4qmE0deJsZL0eEAdcz5pls6c5n1MBJlcg+7Hl/Ia33+b7lXbwcVPBz10FE2PIopkJ1qmt5UOMAGD58jZdEG2X+fN5lvLp08CpU7Z9LhvKNQ8aDWtnbm99YrxLh7ORu7jsshpUmJduixfF2ktWb+ij08e77NvHOxmDgoBhw2z7XNdfD0ydyos9K1fa9rk6icxi/l4T4esKmaxjBY8oPze4q+Wo0RnrimfW2rmTD41TKusK6rbi61t3UcZGQ0fF1SntHTJaHw0cJfYg/s72DHLvUBwswKNL481FY0sDntjcMmeO7YqrAJ95Jja1fPQRj3lxoJTCKsusBHG1WXsJgmCp6RRVdtJC+rPP8rlFc+bwrnFbmj+f//3rr7wDnhCJUCHdyRmMJksGake7IQA+kMzPXQW9kVky1LqsggJeSAd4V7rC+sLixfxK6AwmeLf1DVEspO/Y0SAnXa2Qw9M8CLaE4l2IjRRKkI9eXz/zyaE4WK/TOXSIrzrx97dZ8UWrNyKlkO8LA++8DRgyhD/nG2+0+zFjzAW41M6eP99ZfPYZkJUFhIUB999v++fz9QWuvZZ//OOPtn8+GzCZmKUg05F8dFHPQJ6NXKbRO3XHZIJES7dF4TRwlBNjXWbP5h1otiQIdbEu33zj8AKMPVjy0TsQ6yLi0W91F9asxlhd8fyee4CYmA5vS6sWLOB/f/99g2NyKZRW65Bbzmcl9OlgRAYABIkRkFU66DtznAPp0krN56BBnh0/LgDq5qulFlZDU1Nbty+3VT56fVOmANddxwu64vwLB6kbMtr+WQn1BXTmnPQjR+qGRYur/m1pzBgez1tRwVc8ECIRKqQ7uRKNDibG4KKUw92a+JFWCIJg6Uo/l1Peyr07uVdeAaqrgREj+BLPNjiXzd8QB7Q2ZPRyo0cDLi5Afn6jkzMxw54K6cRWpMhHr69PiBcEwTxAx3zBrlPZupX/PX26zWJdLubz5esBnmoEervWHTR++CEftNMOsf68kJ5eVO3U3b1W0WjqfubPPgu4dry72iriSeBlMV1dRVFVLfRGBpVCBv8Ozk8B0GB4Vqe9sGZjBqMJFwukWbotCrfkpGuce19gj3z0+kaO5DM1TKY2zc7pinQGk2VeghSFdIAXi4A2Rr/99Rewdy8/Rn7mGUm2o1Vz5vA4n/R0ns0uITEjPtrfDe7qjq0ABABPtQJuKh7nIDZFECI18RzUV4LjAgAI8FAj2MuFr7LcshMoLAS8vXlutj289RZvlPvzz7psdjurqjUgw7zqp59ExwbieVynLKQvN6/Ovf12Hq9jazIZcNNN/ON162z/fMRpUCHdyYlviP7uKkmugAL8BFEmCMgp03bOHbg10tP5kFGgzdldJdU6ZJfVQBDqom6splbXHTxcFu8iFtJp4CixlcIqaTvSPdQKy4l3uweL2ZJYSJ8502ZPcd68XLWf2GUyezZfxqjVAi+/3K7HDPd1hUohg0ZnpBPm1qxaBeTl8e7Fu+6y3/NOmsT/PnIEqOp6q7PEwlmot4tkxwZivEtifjuzkbu4tKJq1OpN8HRRdHjptijEywVymYDqWqNldaHTSU8Hzp/nF0OnT7ff8770Ej82/Pln4Ngx+z2vneWU1cBoYvB0UcDHTSnJYwZ5ulii36xavcoYvxAKAEuW8NVF9uDmVjeEXMJ4F8aY5YKiFLEuAG9kElcPUbwLsQWdwYRKrQEA4OcmTSEdqOtK123cxG+YNYvHN9lDr17Afffxj598UvKVJ9ZIyK0AY3yFmY9EP9eAzlpI//tvHqmpUtk3Go3iXYgNUCHdyYn56P4S5KOLPNQK9AziHZNnLnXRrvTnn+f5ydOm8SzMNjibzb/n2AB3eLSny6SZnHSxkF5KhXRiA0YTs+wPAj2kWbIJ1HVedrps5KIi4OhR/vGMGTZ5ipJ6S7fFImKDWIDPPwdSUtr8uHKZgCDLsk3aHzSrfoTO88/zA3d7iY4GYmP5kuF9++z3vBIRC+lSxLqI6mcjZ7Q1G7kbEIeMxod4SXZxQiGXWYbBOm1O+ubN/O+xY3mskr0MGAAsXMg/Fou83VBWaV2si1SvW6Au+s2qeJdff+Xv1+7uwFNPSbYNVrn1Vv73Tz/xFQgSyC3Xotw8K6FHgHQzO8S4jfyKTlY8I91CWQ0/3nRVyeEqwSp2UZ8QTyhkAkL2/M1vsEesS30rVvDZOUeO2L1jmTFmyYeXaqUawOs6gsBnq1XXGiR73A4xmYCnn+YfP/CAfeK5RFdcAURE8FkqYhMVIR1EhXQnJ16plCIfvb5B4T4AgPO5FdAZulhW39mzPPcSAF59tU1fajQxy0nBgLYMGa3vyiv53zt3Njhop450YkvF1bUwmhjUShm8XDu+zFjUM9ADKoUMZRq9pTjXKfz9N+88GTjQZt1t4r4gJuCypdsTJvCOG4OBF3jbwdfctSKe2JAm/Oc/dcOib7vN/s8/eTL/20HLhTvCMmjUW7ooHJlMQG9zFnBCnnPFu9TojEg3zzSQqgNVVBfv4qSFdHvHutT3wgs8FmDLFmDPHvs/vx1Y8tH9pYl1EdWPfivTtPA+ZjLVZaM/+igQGCjpdrRq+nRewM/P5+cHEkgwr9ATj4+kIg4cLajsRMdapNsorearnnwlWpkiclHK0d9QhsC0i2AyGV+5aU9BQcATT/CPly/ng6TtJKukBiXVOqgUMvQOke6imlIug48r/3/qNF3pX3/NV295eNgvnkskkwHz5vGPf/rJvs9Nui0qpDs5S0e6RFlnokg/V/i4KaEzmHAxv4udMD/zDC+w3XQTz8Fsg9TCKmh0RnioFZYc4zYbMQLw9ARKS4FTpyw3+7vzA+SKGn3XuzhBOr0CcwdTkKd0UQ4Az0buGcgPDs9kd6IVKjaOdTHVu6jWZJeJmNv93XfAmTNtfnxfd36A7LRxDq0pKakbFr1yZZuGRUumixbSNTqD5XUlZUc6UPe7kFJQhVqDldnI3YA4KyHIS21Zci0Vpx44qtXyZeKAYwrpPXvWRUb9+98OiQWwpZp68WGRvtIW0htEv7U0N+Gnn3gB29sb+Ne/JN0GqyiVfLUDAOze3eGHM5oYEvN4nE1fiS+qBZtXp5RU65xq/0rsw5KPLmGsi2jISf67lTtgGAzePpI/fqseewwIDgZSU4FPP7Xb0568VAaAZ6OrFdLOaupUA0dLS3l0DsBXANj7gihQF++yaRNQ44THS0RyVEh3YjqDCeU1/GRZymgXgGf1DYrgHdmnu1K8y/79fAcrl7crv/isecBqvzAvyGTtLEYqFMDEifzjevEu9ZfStdi9Q0g7iCfLgZ7SFnkAYHAk3xck5lV2jiWGjNVNbrdRIf1SaQ0qtQaolTL0CGjiotqwYbw7on72axuIOYqltC9o2ttv82iXgQPrulDsrYvmpIsrR/zcVXBRSntiF+Sphp+7CgYTQ3JB1/mZdJTYgWqJeJJQqI8LBAEor9GjUutkF9Z27eInxOHh9hla1pTnnuPzbfbu7XZLxvMqtGCM7wukGIh5OfHCWkJeM9FvZWV1xfPHH7dvdE994jG5BIX0tCI+YNVDrZD84oS7WgFPFwUYq2uOIEQq4vGmn8TNdwDg+w8/Jk8edSVSCh0Q/ebhwVcYAcCLL/LjRxsrr9EjtZAfB4k1EymJF+0LKzvBecKzz/JBsv368ZVFjjB6NBAVxY/Ht2xxzDaQboUK6U5MfEN0U8nhppL+ALlfqDfkMgH5FdquMfiGsbrsrjvv5HEAbVBeo7dM3e7f1iGjlxPjXXbsaHAzxbsQWxEL6UE2KKSHersizMcFRhPDKXP3hUOdPQvk5ACurnXDfSV2PpdfVOsT7AmFvJm32hdf5MsNN20CDhxo0+Nbol00+s6VPd8ZZGYC77/PP37pJf4zdoQumpOeV2/QqNQEQagrnrXUhdqNlGl0yCnTQhB4FqzU1Aq5JRvZ6eJd6se6SLiSqk3Cw/kATKBuRWM3UWbDwhlgRfTb44/z9+pevRzTjS4SL4ru3t3h/9+TWWUA+L6g3Q03LRC70inehUhNrBv4Sr0/qKyEsGsnACD1iitxLsdBDXh33QX07s1nKL31ls2f7sylcjDG509IHbELdKKBo8eOAR9/zD9etcp+g2QvJwjA9dfzj7vZRW/iGFRId2K2ykcXuark6B3MIx26RFf65s0841Ktbldu8fkcPnU70s+t41O3xYGju3bxoadmYgRPCRXSiYQYYyissl1HOgAMi+KdZKcvlUNvdHA0kXgANXky4CJ9sbDWYLR02/Zr6aJafDyweDH/uI2xAN6uSggCX1lUraMl3BYmE7BoEaDR8OX4117r2O3pgvEuuZZCunT56PWJxeSsUo1lVVx3JsZWRPu7tW8AuRUsOenOFu/iyHz0+p5+mnc0Hj8OrF/v2G2RUJn599NH4kxkkUohQ1wQP09oNHR02zbgiy948ePLL/mFb0cZOZKfG+TnAxcvtvth8sq1yCrRQCYIGBLlI9321SPGceWVU0c6kQ5jDKW2inbZtg3Q6WDs0QOlkT2QWaJBhSNWVymVwGuv8Y/ffRfIzbXZU+mNJssq9sGRPjZ5jkBzfaekWgejyUEXeE0m4MEH+fnNrbfWHRM7ivj8e/c6dDNI90CFdCdmyUeXONalvoERPgCAxLwKaPWduNhTWsonSAPA0qV8snMbmEzMcgV9QLgES7cHD+ZLWKuq+JVcM1/qSCc2UKbhufsKmQA/G2QfArzzzMtViRqd0fGdqDbOR0/Kr4LeyODrpkSIVyuF+uefB1QqXmj94Qern0MuE+BtHiRUSvuDOu+9x3+Wbm7AmjWO61IVdbFCusnELCvIpM5HF3m7KhHl5wbGgGMZJTZ5js6CMWbTWBdRuI8TdqQnJQHJybz4MXWqY7clMJBn7AJ8CbuxEx/vtkG5eVaCj6vtzhP6mn8vEvMrYRAvsldWAnffzT9+6CGbrRyzmosLjwUAOhTvciSd7+/iQz3h5WKbixPB5tUpXWIlMOkyKmsN0BsZZELdsadkfvsNACC/9lpEmI8NLuTYPlqlSddfD4wZw5sxxKgXG0jMq0SNzghPF0XT8Y8S8HJVQKWQwWhijouB/Pxz4PBhwMuLRy462rhx/O9z54DiYsduC+nyqJDuxIqrebdCgLttOlABIMzbBQEeKuiNDIl5nXQZN2PAPffwOICePfkQjDbKLNGgUmuAi1KOuEAJpm7LZHUFmHrxLmJHOhXOiJQKzLEuAZ5qmyw1BgCZTMBQcwfWiaxSx8WRaDR85Qlgs0L6eXNnXb8w79YHt0ZF1UVK3X8/kJ5u9fPUj3ch4ENb//1v/vF77wFxcY7dHqDL5aQXVddCZzBBpZBJPoS8vlGxfgCAs9kVjuk8s5PMEg3KNPoGQ5dtof6QQYd1ntmb2I0+cSIf0O5ojz0G+PkBCQnA1187emskIRZfbNWRDgARvq7wdFGgVm9CWpE5G/npp/kxeWws8OqrNnvuNulgTnpxVa1lpdqIaNtlvQd58XO68ho9ami1GpGIeN7p46aEXMrzBKMR+OMP/vGcOZZVnOdympmbYGuCUBfr8vnnwIULkj8FY3Uxl4MjfWx23iUIgqUr3SHxLkVFwPLl/OMXXwRCQ+2/DZcLDKyL7t2/37HbQro8KqQ7MXt0pAuCYOlKP51d3jmzfD/7DPjlFz7k84cf+FXTNjqTzbvR+4a2kIfcVmK8S72Bo2JOZZlG7zwny8TmbJmPXl//MC+oFDIUV+mQbp4nYHe7dgG1tbyA3cY5CNYo1+iRXVoDQeD7A6s89xzvgKmoAG67DTBYN5BVLG7QwFHw/9PbbgN0OuCaa/jF0c6gi+Wki/noIV4uNju5A3gEWqSfG4wmhiNp3bMrnTGGvclFAHjEk0phu0NuD7UCSrkAxoAKJ4jLAdB5Yl1E3t51F0UffbRDESCdgdHEUFHD34tsWUiXyQTLao3zuRV89c5HH/FPfvYZ4G6bbs0262Ah/WhGKQCgZ5CHzSI1AcBFKbf8f1FXOpFKqblhQ/J89CNH+BBKLy9gwgT0CvKESiFDeY0elxwVVTZuHHDddTyWRCwESyi3XIuCilooZAIGhEk/ZLS+AE/+/1XkiIGjy5cDJSXAoEF1c0Q6A3GFE8W7kA6iQrqT0uqNqNTyA2RbDRESxYd4QikXUFRZ2/QwIUc6dw545BH+8WuvASNGtPkhqmsNSDVPGO8v5RuiWEjfu5cXicBPllUKGUzMgcu0SLcjDqWyVT66SK2QY0A4/x05kVlq0+dqVv1YFxvEfojd6JG+bvC0dum2QgGsXcu7Kvfts7oDT+xIp30B+MWI06d5t8nnnzs+0qW+LhTvklNmzkf3sU2sS31X9OBd6edyumdX+sX8KhRU1EKlkGG0uQPfVoR6y+2dIXce1dV1v0+dpZAO8OPJ8eP5RdG5c/nfXVSlVg8TY1DIBJtl+4vEi86ZmYUw3Pl//MZ773V8ZE99Y8fy9+rMTCAjo01fWqHVWyLtRsbYrhtdJEbKUSGdSKUuH902sS6YNQtQKqFSyNA7mO8Pzl8+N8GeXnuNrw7/9Vfgm28kfehT9QYOu6rkkj725Rw2cPTgQX4sDvALowrbvoe0CRXSiUSokO6kxGGVni4KuChtuBM3GuGSn4tR2RfQZ/tvqHrhJeCll4DUVJs8HWMM6UXV+PVkNr7al4Z9yUXNL22sqQFuuQXQanlRTcy3bKMLuRUwMYZQbxdpC5F9+wLBwXz7Dh0CwE+W/SjehUiIMVavI932xbMhkT4QBCCjWGN5XlsxmRgKKrQ4kVmKP07n4p8L+TBtsV0+us5gssxKaHHIaFNiY+um2q9cadWSQ7tEuzDGBy6lpvILj8eO8YPPv//my3FtOIzJart21WUvfvYZ3292Jk3EdHVWeeW8A8xWg0bri/C1T1e6wWhCjc6I8ho9iqpqkVteg8xiDVILq2wWfWA0Mewzd6OPiPaFm8r2J5He4v7AGQrp27fz1SexsTZZWdRuKhWwbh0QHs4jARYt4l2NnUFJCS8IrVnDh/udPctva2alqPi+4uOmbD2irIP8PdToF+aFsWvehyI9DSwiAnjzTcmfx2hiOH2pDPtTinA+pwI5ZTXQ6AzWrZZ1dweGD+cf79rVpuc9nlEKE2OI9HOzy741SCyk2/gYiziPElsNGhUL6XPmWG7qbz5+TsqvRK3BdvFEJhPDqawyfHcoE7+fzkFiXr3n69u3boXRXXd1aDZCfVW1BlzM5xFPQ9ozZNRk4sfgDzwAzJjBt3HTJh6j0gSHFNKNRj5gFAAWL67LJe8sxEL6kSO8FkRIO3Wiy0PEnmwW66LVAhs28O7K8+eBrCzAYMCoy+/3wgvADTcAjz9eN8CnA2p0RpzPLcfpS+UNikqH00pwMqsMgyN8MCzap+HJ7OOP8xOJ4GB+YiFr+3UlxhjOZotDRiVeniUIwJVX8riZ7dsty0r93FXIK9eiuFqHXtI+I3FCVbUGaHRGyAQBATaMeRJ5uyrRK8gTF/MrcSKzFDP6h0j22IwxZJfV4FJpDXLKapBbroXOUFfE8CzIwdTEBDC5HJgyBVKXBg6kFqNSa4CniwJxQe3IQ164ENi8me8/Fy4ETp7kcQHN8HGv60A1mZi0URzp6Xy/uGYNkJbW/P1UKn6S8fTTPC7H3srLgTvu4MWgu+7iy3E7GzEn/ehRPkSvM+Q5N8FgNFmKsLaOeRJd0cMPWSUanMupwIgYP0mHmGn1Rmw7n2/JJW6Kq0qO2QNCEO0vbXzEmexylNfo4a6WY2iU7TtQAVh+dmXOsEJl82b+9+zZnWv1CQCEhPC4wIkTgY0bgZdfbtfsHUno9fxn9fXXvGCla+K1oVbz7Nq4OGDpUuDaawFBsOwLvG00gPxyEwovwnXDGgBA5mvvIbqF9772KNPosOVsXpMrY9VKGXxcVfBzV2F0rF/z8RUTJ/LGlt27+fuOFWp0Rst5gj260QEg2JyTnt/ZVgGTLktc+SjpKvbCQj7bBuAd6Wah3i7wc1ehpFqHi3lVGBghffxJZrEGuy4WoMhcD8mvAJLyq6CQCYjyd0OvIE/0WPECXC5eBH7+ma8wOnCgwxduz1wqh4kxhPm4WC54WeX8eX5usHZtwxUx27bVfdy7N185M24cjzgMCbHUeSq1BtTojDbvgAfAZxSdOAH4+ABvvGH752urnj35+3ReHj8unzDB0VtEuijqSHdSReZBo/5SDRo9fZovaQ0LA269lXcqpqXxrF+5HCw6GvlDR+H8tOtQMWEyv6L688/AFVfwK4MbN/IrmG1UUKHF1nN5+HxPKnZfLEKZRg+1UoahUT6YNSAEQV5q6AwmHEkvwVf70rEnqRAanYEX+8UMxq+/bncHY3ZZDUrNg8R6BdtgkFgTOeniALgS6kgnEhAHjfq5K6XL92/FsGgfAEBCXiWqa63LA2+NwWjC5rN5WHf0Eg6kFCOjWGMZmhgb4I4xPf0x6Bxf2ZHbZxB+TqmStOCUV661xNVM7RsMZXt/lqtW8S7L9PRWMwU91QooZALPspUiGqO6mncsTpnCt+GFF/h+XBB4N56/PxARAfTqBQwcCMTH88LMxx/zIsz997d5yXuHPfwwX2rfowc/eO+MukhOeqXWAMYApVyAmz1OtsC70qPMXelH06XrSi+p1uHHI1kNiugKGf++vF2VCPBUw9NFgRqdERtOZONgarFkM1xqDUYcSi0GAFzRw9+m2ej1+ThLtAtjnS8f/XKjRwOffMI/fv75uq5Le2CMrxwSj8mvu44X9nU6YPBg3sU4YADfnwM8OjA9na8ymjsXGDIEWLcOpVW8COsj4cWtZpWVwe3+eyEwhnPTr8fWqKHQ6qXpRBUbXtYeykRuuRZqpQz9w7wQ6ecGTxfeXFOrNyG/QosLuRX4/kgmskqameHSjpz0E1ml0BsZgrzUiPJz6+i3Y5UgTxcIAm+UqJLoGIs4L53BZImDlbQj/fBh/nd8PBAQYLlZEAQMCOdd6ftSiiR9DZdW6/DryWz8cvwSiqp0cFHKMbF3AEbG+MHHTQmDiSG1sBpbz+Xhf3vT8ecTr8M0ahRQWgpcfXWznd/WMJoYzmSXAeBDRltVWcmPa4cOBfr357GPGRmAhwdf7fTxx8DddwP9+vH7X7wIrF7NZwTFxABLl0Kdk225yG6XrvTPPweeeIJ//OqrQFCQ7Z+zrQSB4l2IJKgj3UlJ0pFeVQV89x3faR45Und7ZCTwf//Hsw2jo4GwMAgKBQoulePvC/nwdVNikWclhPfe41dW9+3jf+LieFfjokVWZWmdyirDjsQCy6rUQE81hkT6oHewp+XENT7EE6lF1TiYWoyCilocTS9FyrELuO3+/+Mv/ief5CcV7WAwmrA3ib+h9g72hFphg8KDWEg/eBDQaAA3N0unDBXSiRTEeJVAO8S6iEK9XRHm44KcMi1OXSrD2J4BrX9RC7R6IzadykF2aQ1kgoBewR4I8+HPEeCutnRqs0S+n8oaOQGXSmvwzYEMjOnpj2FRvh3q5jaaGLZdyAdjPOs1NqAD3a3e3ny/OGEC/3vWLD5EswmCIMDHTYmiKh1KNXr4tPcEJyODH/B+/z0/cOcPzvc/d94JXH894NZMAWDnTuDFF3lsyaefAl98wZdy/vvfvHhsS2vX8guhMhm/ANBJO70B8HiXtDT+86rXedWZiAVYb1fbRznUN7qHHzIl7EpPK6rG5rO5qNWb4OmiwNWDQhHs2Xh4qsFows7EQpzJLseBlGLkltdgVv/QDndsHc8og0ZnhK+bUtq5Ka0QBwx2+0L6hQt8n6VW81V7ndWdd/KC9qpVfB9++LDtY2i2b+f7XnMcIADeebdwIe+gHjSo4f21Wt6Vl5PDG2A+/JA3xsyfj+GxvaCdfy9877/TtttcVMSj1hITwUJCcGrZc6iuNeJwWgkm9g7s0ENr9Ub8fSEfSeYohQhfV8wcEAKvevNL9EYTymv0KNPocSyjBDllWqw/no2pfYMarzQdP56/NyYl8Viz0NAWn19nMOFUltiN7me3/apKIYO/uwpFVTrkV2jhEWiDRh/iNMSmE1eVXNqOZnE/1cTK9MERPriQW4nCylr8dS4P1w8N79Dvj1ZvtKxSN5oYZIKAQZHeGNPD3xJxOy7OH0VVOiQVVCK5oArFVToklhtR+dx/MW/pfMhSUvjFxr//Blzafs6UXFCF6loj3NVy9Apq4XhVo+HvG2++WVe4Vyj4CqyFC3kMzuXH5CUlvFawbx/w11+803rVKuDTTzF1zjz8c+2dKOoTiEhbXsz78ktexAf4hdz777fdc3XU+PG8oZMK6aQDqCPdSRVXdaAjnTFecOnTB7jvPl5EVyqBm27iS0jT0ngn44QJfKm/uSjeJ4QXuEs1epwPiAa++op3wSxfzpf/JCfzK6tDhvAD+ma6wxhj2J9ShO0JvIgeF+SBm0dGYuHoKAwI927Q/SUIAnoGeuDWUVG4bkgYwlTA9Fceg6K8DGUDhsC48sW2f//mbdiRWGjpbhkVY6NBYj168AsTer2lk9G/Xka6ySRNBx1xXgWWQrp9ohxEw8xxB6cvlUNvbH+GbHmN/v/ZO+84uary/3/u9Nky21s2m2x67wkhEHoJYgFEuqKooFh+SlFBEVBEVFSsiOgXsSBFBRRUEEJTSnrvm2ST7X1nZqfP3Pv745kzs5tsmXKn3Xner9e8ZrJl5mR39txzPufzfB48s7kFbQMemAw6XLasHhcvqsPShlJUDxfPgkFI618FACy44UpMKS9AUFbw30O9eHLT8cicmAibm/vR6/TBatLjrNkquC/WrCEXI0A5g+P0lChNpuHo4CDwta/RXP7ooySiT59OwvjRo7RZuO66sUV0gATi114jh95551EV0m9/SyWmn/sciTSp4Le/jZbW33knlbNmMznQcFQIsLZ0OFCHMdyVnkxWuqIo2NTcj79vb4MvIKO+1IprTpmCuhLrqAdlBr0O58+vwYULamDUS2judeOJDcfQYU88M9PlC2JruDLl9JmV0KsZtzQBkWaj7oBq7vqs5KWX6P6cc8afm7KBhx6itbDDQc5wuz01r7NhA3D++TQHb9hAIs/VV5Nzv6WFekicKKID9HWNjTR/3n8/rcnvuQcoLYXt6CG87/tfwdzzVtNBZQJVoxPS0UFz49atQFUVpJdewppVswEA244PJmUYael340/vHcOhriHoJAlrZ1Xi8uWTR4joAGDU61BZZMbM6iJcvnwy5tQWQ1YUvLK3C/891DPyb6m0lFz9APDf/044hl1tdngDIZQWGDEzzWJ2JCed412YJOkXsS5qxzy99x7djyKkG/Q6vG9hLQw6Ccf63Nh6fDDhl+l3+fHEhuPYcmwAIVlBY2UBPnrqFJwzp3pEnzhJklBVbMZpMypx/ZpGXLt6CootBrSbS/C3bz8CuaSE9uI33JBQ7wvRZHRRfenoawOPB/jJT2gd/tWvkog+axYJ4h0dlIV+1VWjX/fKy6lC6/776dD2tdfIDBMMYupzT+ITn7oItZ+/kSJiUsHvfkcaDkAi+kMPZV/s2nCEI/3tt7OnjwmTc7CQrkXs9jFFaABw+ykTGUgg62zPHpqYr72WHCzTptECvbWVGixddBGgH/202mSgyBUAeHVvN472uqjs9LvfpYX+j35EF4I9eyjb69xzRzrdQY1B1u/rxoYjtNk+dXoFPrC4DpNKreOeVEuShOmKG1d+7XrU794CX0Ehnr39QfxjX8+IDOVY2dVmx+42OyQJuHhhHUrU7mIuEK5QIBLvYrMYYdBJCKoV58DkNdFGo+kV0mdUFcFmNcLjD2F/hzOh5+h2ePH0puPoG/Kj2GLAlSsbMKViDGFl40aaG8vLUbT2VHx4eT0uXFADi1GPbocPT21qwbE+V9xj6Hf5sSEs/p01u0o9t87Xv04LPaeTysnXrx/1y0SZrT2ehqN+P/DTn1IV0A9+QKX955xDIm9TE/DNb1I1UTyccQYJ7//7H1X5BINUdjpjBj2fwxHf842FotBG4cYbafH7qU/RwW22c2JOehYihPSEKxuS4NQZFDOxp92RkKM6EI52+t+hXigKsHhyCS5fMRmF5omr2xZMKsFVq6agrMAIpzeIv2xuxbbjAwmJ0RuO9sEflFFbYkmsT0ISFFuM0Em0NtB0nINwMYq/qWzGaKS18eTJwIED5Ez3qihs7tpFDslTT6VrhNEIfPGLdBD65JPkYIyhwjNCeTlw772QjxzFOzfcAo+tFMYjh+nQcvlyOsRQ65Dm+HG6tu3ZQ3uBt94ClizBtMpCTK8qhKwoePNgd9x/h4qi4J2mXvx1Syuc3iDKCoy4+pQGrGosn7DyTIh3q6eTOWZz8wBe3Nkx8rBfxLtM0HA0GJIjcW8rp0782moj1nS9XL3KJEmk0aia+eiyHI12GaNXWkWROVKV8nZTL7qd8c+dbYMePL2pBQ5PACVWIy5dVo/Llk1GRdHEe54amwVXrWpAZZEJrXXT8MI3fwbFYKDeZcLsEiPNvS60DVLV7EmZ7z4fieUzZwK33AJ0dZG+8rvfkfD9uc+NiL6ZENFjbf164O234TrvQuhkGXUv/o1ivT71KdJw1OLxx+k5FYWuP9kuogN0IFpYSPvCPXsyPRomR2EhXYvceCN1m/7Zz0Z1v4hYlxKrMfbsTocDuO02mnjeeAOwWoH77qMJ/rbbYs7AWjO9AvPqyO3x4o52tA2GnV9FRcCttwKHD5ND0mym1znlFOCaa4AjRxAIyXhxVwd2hQXs8+ZVY82MithKvfbuBVavhrRhA1BWhv4n/wZ3w1Q097rxt62tlJseI60Dbry+vwcAsHZmJRqTiXGIBSGkv/46AECnk1AaXsz08QKZSQJvIARHWLRKtyNdp5MiHes3H+uPW/g52uvCX7a0wuULobLYjKtWNYz/f3j5Zbo//3xAr4ckSVgwqQTXr5mK+jIr/EEZz29rx67W2B2DiqLg1b1dCMkKplUWYm6titEiej1Fl8yeDbS10bhvvfUkEUbEOcTkSFcUKmWcPx/48peBvj56/OKLtOA+66zkF7+nn04/6zfeoM2R203N9qZPJ6eNL4mMRlkmp8tdd9G/v/514De/iU8oyhQ5kJM+PNol3dSXWjGlvACyEr8rfdDtxzObW3Cg0wmdJOG8edU4b15NXG7wqmIzrlk9BbNqihCSFbxxoAf/2tUJXzB2F+6Ay49drXRgtHZmZVrjcQBAr5Mimc+D8Rys5RqbN9P9qlWZHUes1NQAzz5L69oXX6S823feSe45jx0jUX7JEuDvf6d4qxtuoMiRn/2M4lySwGkuxIZrPovfP/E6lO9+lyLHdu4kYf6886K/g0RpaqLD16YmcsT/97+UkxzmrNlV0OuoSuRIb+wH3L5gCC/s7Igcbi+qL8G1q6eiJo6mfpIk4bQZlbhoYS30OglN3UP4y+bW6Bolxpz0/Z1OOL1BFJkNmFeX/tgxMY87tB71xKQccT0pL1RxbXDwIGkUViv13BmDxZNLML2qECFZwUu7O+OqYD3U5cSzW1rhDYRQV2LB1ac0xB29WGwx4oqVDagvs+LI4lOx/kvhSvbvfAd44AEyjcQwjn/sIOF6Xl0xisQBv6JQj7j586nRc3s7VaE/+igdvH7iE8mvb087Df6/v4AnfvksDq+9gF7zscfI6f7tb1NvpGT4/e8pzldR6P/w059mv4gO0M91zRp6HEN1EcOMBgvpWsPlIkHkwAESHOrrgZtvBnbvjnyJEF9jykcPBKikc84c4Mc/JhHg0ktJmL7rrrgzwiRJwgXzazG9qhBBWcHft7eNPGEuLQW+9z26wH784zQZP/UUlLlz0XLZtejfvAMGnYQPLK7D4smlsb3o+vVUttrcTO7Id99F3YfW4fIVk2E16dFp9+LpTS0xOTod3gD+ubMDsqJgTm0xVkwti+v/nxAiA3TTpsjByPB4F4ZJFOFGL7EaR5Q3pouF9TYUmPQYdAfwxHvHxm7wNQxFUbC9ZRD/2N4Of1DGlPICXLFiMootEyzwhZC+bt2IDxeaDfjwsvrIAd+r+0Yp5x6Dna12tA1SpMw5c6vVF86mTKGSd5Ez+NBDJB7t2BH5EuEQGhhv/lIUistavRq44go6sKypoUzzHTuogZLaYz/rLODdd0lAmjuXRPtbbqFrye9+R9eWePD5qBLq5z+nf//0p+RMz4UFuyDL410yKaQD8bvShzcR7Hb4UGDS4/IV9bGvDU7AbNDj/YvqcNacKugkCQe7nHhqY0vMDbreOdwHWaFDtZTmkI6D5nPS+/ujUVfLl2d2LPGwahUJJrW1wP79VG30pS9Rr6F4GBqiCp+5c+mgVVFoTt+zh8SReCuJxkAczBZWlEK68076md92G2Aykalj1SqKGGhqiv/J9+4lMfr4cToo/u9/6aB1GKUFpkj825sHehCMQTwbdPvxzKYWHO4egkEnYd2CWpw/vybhZr/z6myRfUKXw4s/bziGg11OKCISYPduuq6NQrfDGxHzl08tTVsj9+GUDGs+rOmoJyblCEe6qtVqorJoxQqqpBkD0g1qUGjWo2/Ij/8e6onp6bceH8A/d3UgKCuYUV2Ey1dMRoEpMVHaYtTjw8vqMaumCLvWXY6NV3+GPvH1r9N1aBwhdlerHf/c1YGQrGBWTRHOnRs2Hu7ZQ9Wbl11G82tdHbnSDx0iQ+Q4P5N4KbEaMTB3If5x9y/gWP8mVTC53eSqnz2bxPBE4k0ef5wOcBUF+Pzn6RA3l9bk3HCUSRIW0rVGYSEJxr/8JZ1wulzAI4/Qae9ZZwHPPIOB3kEAQOV4ZU3791PX5cmTqaSzs5NKjv79b+C558hBkiB6nYSLF9WhvtQKX0DG89vaIo1MIkyZQhP0tm3wn38BpEAA0194Gh+/8f341E++gpnN+2J7scceo7gZu52cku+9F2n2VFdixZUrG2CzGjHoDuDpzcfR7Ri7bCwQkvHijg64/SFUFZtxwfya9DjOGhroZy/LkYt1OTvSGRUQh1jpdqMLzAY9rlzZgMpiM9z+EP62tRUbjvSNuek73ufGExuO4/X93ZAVBfPqbLh0Wf3EhwBudzQm6oILTvq0Qa/DugW1WBMW8kYt5z4BpzeA/zVRE6DTZlSkTnwsLKSIlBdfpMqf3bupUueHPwRkGWVh4czpDZw8XkWhTMVVqygua9Mmyla8+25arN90U2rd3JJEm4Rdu8g5Xl9PTspPfpLcMI88ElvMgdNJ43/6adpc/PnPwP/7f6kbd6rIYiFdUZSMC+n1pVZMrSBX+j92tONgl3PMPiAefwgv7uzAK3u74A/KmFxmxTWrp2ByWXICtiRJWD6lDFesnIxiiwH9Lj+e2ngce9vHjyZqH/TgYJcTkkTZ6JlC80L6li10P3MmUJYGI4OaXHQRichCePjZz2ht/uqrE3+vLFNj5TlzyAnp9dKafssW4JlnRri51WBQzAVCOCsvp2vOwYO0J5Ck6Otee+1JMYyjEgjQYcJZZ1He76JF5OqePHnULz9lWjmKzAbYPYEJ85GP97nx5MYW9A75UWQ24CMrJ2P+JFsc/+PRqS+14upVDagoMsHlC+GfOzvwj44gQuLnfYIA4w2E8Pr+bvx543E4PAEUmQ0nNyxNE6LXhT8ow5dAhCXDALQ2GExFRvo4+egnUmAyYN0CqrLZ0WLH4Z6xDyAVRcFbB3vw5oEeKAqwpKEEH1hUB2OSh1kGvQ4XL6T+S2/fcAte+fJ98JeU0fr2zDPJ/NfVNWIcG4/249V9XVAUqo65eGEdDA47rV+XLKG532wmQf7gQYpwMau/H9PppEiUTeeCcEXUU0+RltPeTs73VauAv/2N9kvjEQxSXNnpp0evZTffTCaXXBLRARbSmaRhIV2LFBfTZLx7NzlHLr+cYgLeegu46iqctWomrvvsJVhw963kSNyyhTJznU7g//6PJsd582jR3N1N4s13v0vPd9FFqgzRqNfhQ0snoarYDJcvhL9tbYNzWN630xvAlmP9eMJbil9+9Rd48qdPo3ntBZAUBQUv/p0uvOeeS52pZZkW6F4vHRw4HORa+vrXKbMrGKR4mFdfPSljrLzQFImEcPlC+MuWVry0uwO7Wu3oG/JFBD1FoWz2LocXVpMeH1wyKemLclyIeJdwJqNwpCfTiIlhepz0/smUkA6Qo/rqVQ1YMMkGRSFX5/Pb2+DxRyMVepw+PLetFX/b2ooepw8mgw5nzq7CugUxxjfs3EnzRE0NHUyNgiRJOHV6xYhy7r9uaYXLF4SiKAjJCvxBGd5ACB5/CK/t74Y/KKOuxIIlCTpg4+L976cF+4c+RPP1V74CrF4N61dvx4oX/4yGTf+Dc+8Bmu9kmQ48ly+nBndbtpCA/pWvUHbut75F14l0YTBQE6JDh4AHH6Tfw7FjtPieMYMiX4aXlyoKuRX/+ldquHTKKTR/FxbSgcI116Rv7GqSxTnpnkAo0i/EZslcVM7amZUwGXTodfrwz50d+N07zdh6fGBExMqxPhf+9N4xNHUPQa+TcMYYTQSTYVKpFdeunoKpFQUIhBS8vKcT6/d1RZyxiqKg2+HFe0f68OcNx/H0phYAwNxaW0bn0+EuVE0iIkVWrszsOBKlrIwMHi+/TO7x5mY63P3Up8gosWsXzX12e9Qh+O67VIL+8Y9HexP97W+0vk+RK18IZ6UnHqpNnUruxe3bKeYlFKIs9lNOoaiW554b2ZRUUch5+oUvUA76ZZdRA72VK+lAsaZmzDGYDDqsnUVr9o1H+7DxaD+aup3oHfKN+Dvc3jKI57a1wRsIobbEgmtWU4NhtSgtMOHaU6Zg9fRy6HUSjvS4sG/WUnr98JpcURTsabfj9+80Y3vLIBQFmFtbjGtWT4HZkP5qP4D2WYVmem3NzgdMynF4gwiEFOh1krqH7MKRHoOQDgBTKwqxPFwF/srerhFxkL5gCL1DPjT3uvCvXZ3Ycox6E6ydVYlz5lSr1p9Ap5Nw9pwqnD6rCrsvvhL/93//xs6Lr4IiScAf/gBl9mzg5z+HEgjgrUO9eG9vG4p6OnGOpw3nte6A7icPkYnk5z+nefKyy+hw9f77KeI2hQjzZK/TR4L3VVcB+/YB3/8+YLNR9etHPkI6yYc/DPzpT8DgYPQJBgZo/T59OnDllSTGm0y0Rv/FL3JPRAfovafXU5++48czPRomB5EUjdV7ORwOlJSUwG63w2ZL3o2gGVpbgUcfhfLYY5Da2k7+vMlEYoc4idTrqfvzpz5F9yqWGA3H5Qvimc0tGHQHUFlkwuLJpTjY5UTboCfSz0gnSWisLMDZc6pRcjQsxPzpTzHlkgGgMthvfWvcSd4bIHfbidESVpMedSUWWIx67G13QCdJ+PDy+vSXbf/ud+TiPPts4PXX0Tvkwx/fPQaTQYfPnT0j7VmsjDZ4ZlML2gY9uHhRHeaome+dIHva7Xh9fzcCIQXFFgPOmVuNw91D2NvhgKJQNcviySVYPa0ivqaejzxCou1FF1FVzQS0Drjx4s6OEWL+aOh1Eq5dPWX86h61URTgt7+ljPPRnCNGI4k13d3076IiEjFuvRWoqkrfOMfD46FD2+9/n65NAC3er72WSlw3boyOX1BZCfzrX7mTizwW06fTYca//63awbQadNg9eGpjC4otBnz6jOkTf0MKcfuD2N4yiJ2t9sjfoNmow6L6EgRlBdvD7tSKIhMuWlCL6jjyj+NFlhVsONqPDUf7oChAtc2MWpsFR3tdcHpHrkEml1nxvkV10fzTDNDUPYQXdrSjxmbBtaunZGwcKePyyyku6oc/pKiRXMbpJMPHL34x+ucliQQO0euoqIgiFb/0pbhjFePl79vbcKTHhfPmVY8flbRtG0WOPfVUNK5r+nRqOGe301p9ePxLTQ1w3XUUKRDDHk1RFPxlc2u0n1IYSaJDI6tRjw47VTXNqyvGefNqUmpy6RvyYf2+bhQ9+wwu/t7t6Ju7CANvvo2txwYjYywvNOHcudUZi3cazlMbj6PD7sUHFtdhVk3m13hM7nGsz4Vnt7ahosiE69c0qvOkbjf9/YdCJGCOYXA5kWBIxlObWtDj9KHEaoRRL8HhDUZMAAK9juJg5tWlTgfqdnqx9dgADnQOoWrfDpz383tRc4iaVvpLywCfDybPGO7uhQvJQHLeeSkb34lsOz6ANw70YHpVIS5ZWj/ykz09dE195hk63BUYDGTka2igA1Ox56iqIsPmZz+bdD+OjHPKKVRR9cQTtAdh8p54tGQW0vOMIW8ATz77LmoP7sL7fW3QbdlMDp8BOr3F7Nkk2F5/PeV1pQG7O4BnNrec1GywvtSKObXFmFVTdHKu2fHjtHh/9NGxy5BKS+lC9fGPxzQOWVbQMuBG26AH7YNedNo9CIRG/nmcPacKy6ZkoJx4504qAysuBgYHEYKEX7zWBFlR8Okzpk2cD80wo/Db/x6B0xvENadMQW1JajfmsdLj9OGfO9tPyvyeXVOM02dWJJbR+JnP0Fxxxx3UHCgGBt1+/GNHe6Q584noJAlrZ1Wmp0/CaBw7RsJyUxO6tu6G4egRlHW0QOcP5zkXF1P56C23ABUVmRnjRPj9FFfwwAPR3GOBwQAsXkyL3FNOoQPdcdyLOcMnP0kHo1/7GvUDyRL2dzrw712dqC+jyLNsIBCSsa/Dga3HBk6aD5Y2lGLtrMq0VYYd63Ph37s7RxyuGfUSplQUYnplIaZVFqIwgwK6oMfpw5/eOwazUYfPnT0z08NRn6lTaf33xhvRCo9c53//I2G5pYUcgHY7zY0CSaJ54zvfSZto8fjbRzHgDuDy5ZMxpSIGQbi9nSIlH3mEKkKHU1BAzsuPfpSaZscZJ+YNhLCrzY6+IT8G3H70u/wjhDNJAs6YVYnlU8rSYipRFAX7N+3BvNWLIOt0+NXfNsFfWASjnqralk0pi6vRcSr5964O7O904oxZlVjZWJ7p4TA5iBBgZ1QX4UNLJqnzpP/7H1Ww1NUBbW1xuZn7hnx4cuPxk/bnFqMeRRYDbBYDVkwtSzrmLVYc3gC2Hx/E7uP9mPPCUzj9dw/B4rRHv8BgICNIZSVV+F9+eepjFUehdcCNv2xuhc1qxKfWThv9ixSFqo2efZaqi/bsGfn5xYvJxHPNNSk/zE0bt95KetLNNwMPP5zp0TBZQDxacuZX/Uxa6XP5MVRVi/7GKdCd1kgfVBRyyA0NUWZhmt3NJQVGXLa8Hv/Y3g6TQYe5tcWYXVs8fpn2lCk08T3wAI1brz/5ZjDE9X/R6SRMrSjE1Arq6B2SFXQ7vWgPC+tVxWYsbShN8n+bIPPn00XL6QSamqCfPRulBUb0u2hTwUI6Ey/BkBw5vLJZs+dSUFVsxjWrp2D9vm4c6HSivtSKM2ZXJleqvW0b3S9bFvO3lBaY8LFTp8LtD0EnSZAkEs914j7TG+WpU2nhB+DokT68e7gPC2qLcGFp2OGzYAEdJmYzJhNFvnziE+RofPttihU75RRg6VLtLNSHc/bZJKRnWU66aLadqXz00TDqdVg8uRSL6ktwpNeFbccH4fQGcNbsKkyvSm0Z9IlMrSjEtaun4O1DvTAbdZhWWYSGMmtGmgiOh/j9+QIUQ5WJJtIpo7ub5jZJimsuz3rWrgXWrx/5Ma83KqoXF1MsSpqQZQWOcLVFaWGM88GkSRRP8I1vAH/8I/U4Ki0l9/mllyYVW2Ax6rFqmAisKApc/hAGXH4MugOotplRk8KqlBORJAnzTlkIuXEadM1HUbd3K0wfuBhnzq5SNV5KDTQf9cSknIFU56PHqTlUFJlxxcoG9Dh9KLYYUGQ2oNhiTLipcLLYLEacObsKp0wrx+6ZX8RT6y5FYXMTTlkxE1PnNpLzPguqxkXlrMMTGHttIK6ty5YB991Hue3PPUcu9SuvpPVrFvxfVGXtWtKTOCedSYDsUU+YtNAbdldWFA27IEoSlWJmkMoiMz451gnpeFgsKRNb9DoJdSVW1JVYsWJqSl4idgwGEpbee4/yjmfPRlmhCf0uP/pc/oj4zzCx4vQGoSjkqrRmmdhiNuhx8aI6nD2nClajPjmXWTBIubNA3OKLJElZ4TKdiLLwBmfQG6ISzBjLZLMGg4Hcih/9aKZHknpEw9HNmykXvjA75u5MNxodD0mSMKOqCDPSLJ6fiM1ixPsWpadSL1FMBh2KzAYM+YIYdAdQW5Jdc3tSiEajc+bEFAuS01gs5D7PQNm80xtESFZg0Ekojvf6V1BAFWCf+UxqBgeaD4rMJKA1ZNBkrTv7LODxo7jEcRj6xek76IgH0XDU4WUhnUmMfhe9d8piPVSLhTjz0U+kxmZJ6+FZLFiMeqxsLMfyKWUIyosyJuyPhcWoR7HFAKc3iD6XH/WlMZiTZs+m6kktc/rpdL97N6Uz5FoTcyajZNdfOZNy+oao7L+8UMWTZSY9rFhB9+FmW6Lh6AA3HGUSQGysbFZj1mbsF5gMyY/twAFy9xUVUWNLDVJWQBsc4RxispgpU0gcC4UositLyGYhnYkPzbpQc73RaI4w6KHrSElB9q4NsoIzzwQA6P/33wwPZGwic4FbY3MBkzZE4+EyNR3pQkg/9VT1njNL0OmkrBPRBcJEybrBMGpqqAGsolBjb4aJg+z8S2dSRn948kxrczxGHYSQHnZlicOQPr4gMgng8IRjXbKsFFl1RKzLkiWATpuXvJKwkO72h+ANjN8glckCli+ne/HezAJYSNcOYj4Y1NrBGgvpaWEwC2OespKwkI6NG8fu1ZRhxPrO4Q1CYy3RmDTgC4YiTbVVM+C1t1M/CJ2O5/I0E50P+GBtBGvX0j3HuzBxok1VgRkVRVEiomsFO9JzD7Hg2LoVkOXIoqafhXQmAcRCSvOb5QTy0XMNs0GPQjNFOAyy8yz7Ee/FrVszO44ww/slaH4+yAPE73CQHelMAojKpoQae+cT06dTNnwgEHXYZhnFFgN0koSQrETmeIaJFbGeLDDp1eu3If5WFixIqncCEz+RqCcPzwUjEEL6f7O3uojJTlhIzyMc3iD8QRl6ncQL5Fxk3jzAaqWGo4cORcrsPP4Q3H6+KDLx4fCIaJfszwBPijwQ0oGo6MHxLjmAcKRniZAu+iWYDDoUmDSUqZ2nlBZoMNqlvZ1uOh31i2FShnjfiMgwZgwkCTjrLHqcpQKMTieh2EJrPE3NB0xaEEatMjXNd0nmozOJw470MRBC+saNFAXKMDHCQnoeEbkgFhih13HuYc5hMFA8BQBs2QKTQRc5XWZXOhMvYlOl6WgXRckbIT3ScJQd6dmPENJ37wb8mZ+7I3NBFvdLYGKn1EpzgaZykYUbff78rGnQq1XENUS8j5hxOOUUus+imK4TYRcqkygDnI+uKYRxysGHaiOZNQuorqb1uGhqzjAxwEJ6HjHI5Zq5jyhpjuSks5DOJMbwZqOa5fhxYHCQDqHmz8/0aFJKmVZzkbXI1KlAWRlFAuzZk+nRcD66xhC/xyFfEIGQnOHRqATHuqQFWVai8wE70idGmFu2b8/oMMZDs82HmZQz4KL3jNhrJk0oBGzaRI/ZkZ52hHFqyBdESOaeCREkiXPSmYRgIT2PEA1DNC2caR3RcDS8qSwvpKaxLKQz8RAIyXD5qCmlpsUz4RJbsAAwa7vBcjTahTfLWY8kZVVOOgvp2sJi1MFspOW9ZsQzFtLTgtNLAoteJ6HYrPHYNzUQQnpzMx3aZyEspDOJ0q+2I33PHsDlomz0efPUeU4mZgpMehh0EhQFGPJyhcoIWEhnEiAtQvovf/lLNDY2wmKxYPXq1di4ceOYX/v4449DkqQRN4vFko5hah7hQBV5eUwOIoT0bdsAWY40jWUhnYkHcahmMuhgNmj4PDVPYl2AqCN9wO2HorDTJOsR8S5ZEAnAQrq2kCQpEsuhiagnRWEhPU0MekTlqhE6joCcmPJyoKGBHu/cmdmxjEEkzoFzkZk4UBQFg+G9ZblaGeki1uWUUwA992NJN5IU7ZnA88EJCCH97bcBWSOVfEzKSbmC8vTTT+PWW2/FPffcg61bt2LJkiVYt24duru7x/wem82Gjo6OyO3YsWOpHmZeEHGkazkTWeuc0HC0nIV0JgHyJhM5j4T0EqsRkgT4gzLc/lCmh8NMBDvSmRSiKRdqSwvQ00MRXYsXZ3o0mkYcvPBcEAei+e2OHRkdxliURDLSNTAXMGnD4Q0iGK5OUU034EajGSfSM4GF9JEsXQqYTMDAAMC6IxMjKRfSf/zjH+PGG2/EDTfcgPnz5+ORRx5BQUEBHnvssTG/R5Ik1NbWRm41NTWpHmZe4IiIZ+xIz1kMhuiiffPmiJDu9AbhC7J4xsRGZC7QenWKyC3NAyHdoNdFNjsDnJOe/QhH+o4dlBuaIRRFYSFdg5QWCCFdA3OBcKMvXEhGAiZlDIbnAu6lFAdZnpM+PBc5qJWeCUzKGXCloDrlvffonoX0jCHmA24+fAJGYzRuaNeuzI6FyRlSKqT7/X5s2bIF559/fvQFdTqcf/75ePfdd8f8vqGhIUydOhUNDQ245JJLsGecZlw+nw8Oh2PEjTmZQCjqUmRHeo4j4l22bIHFqI9koTo574yJkbxoNNrXR05GILrR1ThlhaLhKDtNsp5Zs4DCQsDtBg4ezNgwPIEQ/EEZkpQHB2t5hKYc6RzrkjZEs+pSLa8N1CbLHekFJj2MespF5n0CEyuq56M7HMDevfSYhfSMwY70cVi0iO6zNKaLyT5SKqT39vYiFAqd5CivqalBZ2fnqN8zZ84cPPbYY/j73/+OP/3pT5BlGaeddhpaW1tH/foHHngAJSUlkVuDyKpjRpA3mcj5gNhMbtkCYPjpMl8UmdgQTgRNO1BFrMuMGYDNltmxpIlow1ENuFC1jl4fFWAyGO8ihNYiswEGPa8NtIKY2zVxqMZCetoQ7xdR0cDEgDio370bCGafUC1JkrYO1pi0IA7VVMtH37yZ+l1MnQrU1qrznEzcRHom8FxwMiI6jh3pTIxk3a5pzZo1uP7667F06VKcddZZePbZZ1FVVYVf//rXo379nXfeCbvdHrm1CAciM4LhUQ6azkTOB4QjfetWQJYjjUPYacLESiQjXcvVKXmUjy4oiwjpvEDOCbIgJ314vwRGOwgh1OEJQpZzuPkwNxpNG7IcjXniaJc4mD4dKCoCfD7gwIFMj2ZU2IXKxEu/S+VDNY51yQoi5jvWDE6GHelMnKRUSK+srIRer0dXV9eIj3d1daE2xtNIo9GIZcuWoampadTPm81m2Gy2ETfmZPIiyiFfmDuXckKHhoCDB3mBzMRNdD7QcJRDHuWjC8oKhAuVHek5gchJF4c+GcDOzQU1SZHZAINOgqwouX3IfvQoNf8ymSgjnUkZTl8QoXBzwWKzhtcGaqPTRZ2MWRrvYmNHOhMnIiNdNUc6NxrNCsRcMOTN8UP2VCCE9EOHAK83s2NhcoKUCukmkwkrVqzA+vXrIx+TZRnr16/HmjVrYnqOUCiEXbt2oa6uLlXDzAvERkrTDtR8wWCIioNbtkRybXN6s8ykDX9Qhicf+iUIcVLEZ+QBwkU46A7wAjkXEEL61q3kvM0A3GhUm0iShBJxsJbLDUeFG33xYsBszuxYNM7wQzXVmgvmC2KdkeUNR1lIZ2LBFwxhyEd7SlUy0hWFhfQsodCkh14csvtYNxjBpElAeTkQCgH79mV6NEwOkPJol1tvvRW/+c1v8Pvf/x779u3DzTffDJfLhRtuuAEAcP311+POO++MfP23v/1t/Oc//8GRI0ewdetWfPSjH8WxY8fw6U9/OtVD1TQi2qWYm4lpAxHvsnlzZIHsZEc6EwPCjW4x6mEx6jM8mhThdkdLrPPIkV5sNkCvkxCSc9yFmi/Mn09OW7udnLcZgIV07aKJXGSOdUkb4sCF89ETQOSkZ6kjXcwFoj8Ow4yHWD+qtk84fhzo6iIjmDAQMBlBkqSIAY9z0k9AkjjehYmLlKuqV111FXp6enD33Xejs7MTS5cuxUsvvRRpQHr8+HHodFE9f2BgADfeeCM6OztRVlaGFStW4J133sH8+fNTPVRNE3Gk82ZZGwghfcsWFFt4gczETqRfgpZjXXbuBGQZqKkB8qiaSaeTUFpgRN+QHwNuf8SRymQpRiMt2rdsoQqK6dPTPgQW0rWLJhqOspCeNgbcnI+eMFnuSNfEoRqTNlQ334l89CVLKJqUySjFFiMG3AGOhB2NxYuBN9/khqNMTKRFSfnCF76AL3zhC6N+7o033hjx74ceeggPPfRQGkaVX0QykbUc5ZBPCCF92zYUG6kE1+WP5lsyzFhwo1FtU1pgigjpjSjM9HCYiVi+nIT0rVuByy9P60sHQ3KkfJuFdO0RiXrKVfFMlulvA2AhPQ2I3hqlPBfEz8KFlJXe3Q10dgIx9gFLF8I44Q2E4A2EtFuNyKiCMN+pJqSLefyUU9R5PiYpbFyhMjbsSGfiIOXRLkzmCclKZLPM0S4aYe5coKAAGBpCQfNhGHQSFIXjXZiJceRDdYpwheVRProg0nA0V8WzfEMc9mzdmvaXdnqDUBTAZNChwMTCitbIeRdqUxPgcAAWC7BgQaZHo3nE+4SjXRKgoACYPZseZ6Er3WzQwxqe49mFykyE6lXsu3fTvYhAYjJKJNqF54KTEY2j2ZHOxAAL6XnAUHizbNBJvFnWCgZDRCSUtm6NHJBwLjIzEZFoFy0fquWxI100hhpw5XCDwXwigw1HI9UpViMkiSuZtIZwFtvdfigZamabFCLWZdkyWvMwKUOWlUgEUKmVo10SImdy0lk8Y8bH6VV5nyCEdD4QzQrEAQlrBqMg3qOdnUBPT2bHwmQ9LKTnAZFYF94sa4thOem2XHeeMWlDzAeajXIIBqNOgjwU0kt4gZxbLF4M6PUUCdDRkdaX5nx0bUNrPiAQUuDyhzI9nPjhfPS04fRFowG5cjVBsjwnXcT52TnOgZkAsU8oViMC0m4HWlroMQvpWYGND9XGpqgo2q+IXenMBLCQngfY1W4awmQHYnO5eXNkscPiGTMRw12ommT/fsDrBYqLgRkzMj2atBOtTgnkpgs137BagXnz6HGa411YSNc2JIrm8CH7pk10z0J6yrG7o3OBjvvsJAY70hmNoGpG+t69dD9pElBWlvzzMUljG1bFLsu8TzgJEe/COenMBLCQngdEss603FwwHxml4ShnpDPj4Q2E4AvIADQ8Hwg32JIl1Pwrzygy0wI5EFLgCeSgCzUfEZUTIpIoTbCQrn1EvItoJJkzhELRvwcW0lPOoCfcaJTz0RNHONIPHAA8nowOZTREw9GcPFRj0sbwvmqq7BNErMvChck/F6MKhSYD9DoJsqJgyM8GvJMQDUfZkc5MQP6pDHlItESLHemaQjQcdblQ2X4MQLSRJMOMhpgLrCY9TAaNTv9CfMnDRqMAYNDrImI6V6jkCMNz0tMIC+naJ2cbjh49CrhcgNkcbeLIpIxBN88FSVNbC1RVAbIcFQ+ziIgjnQ03zDgM+aivml6tvmp79tA9C+lZg25YhBdXqIwCNxxlYkSjSgozHIfWoxzyFb0+4mQs20ulpOxIZ8bDEc7G1PRmOY8bjQqGx7swOUAGhHRFUVhIzwOEw1hEd+QMQnyZN48bjaaBwfBcUFrAjUYTRpKiB/hZGO8yPNqFY9+YsXAMi4NVpa8aNxrNSkS1gYN7JpyMcKTv3k3VcQwzBiyk5wGRaBfeLGuPcLxL0R7K8XJ6g7xAZsYkko+u1VgXRWEhHdEGUVyhkiMI8eX4caCvLy0v6QmE4A/KkKRoXiajPYR4NphrrjOOA0grfKimEiInPQsbjhZbos2H3bnYfJhJC9F8dJXmAnakZyU2rlAZm5kzAYuFIrqOHMn0aJgshoV0jSPLirpNQ5jsIiykm3ZsgyRRtp2LF8jMGIgFk8jK1BzHjwODg4DRmNful2ILR7vkFDYbLdyBtOWkC+GsyGyAQc9LQa1SUpCj0S5CfMnjeTydiOolPlRLkixuOKrXSZHYt5ybD5i0oepc0NsLdHbS4/nzk38+RjU42mUc9Pro2oMbjjLjwLsnjePyByErCnSShCITL5A1R7gJl7R1K4oNVILHF0VmLBxad6QLEXL+fMCUvyXqHO2Sg6Q53sXOkW95gXAYe/wheHOp+TA70tOGLxhtQq6aCzVfGR7tIssZHcpo2HK1ZwKTNlR1pIsD0cZGoKgo+edjVMPGlavjww1HmRhgIV3jOIa50XU6FbLOmOxizhzAagXcbtT1tgNgFyozNmI+0Gz5tiinzuNYFyC6AeK5IIcQQnq6HOncXDAvMBv0kYZxOXPIHggA+/fTY3akpxxxnbAYNdyEPF3MmUOH+E4n0Nyc6dGcxPCcdIYZDVG5qkoVO8e6ZC2iMpnngjEQDUfZkc6MA6+YNM7wpiGMBtHrqRkXgNrWwwDYhcqMjqIo2m88LFyMYgGUp9i4ZDP3EIc/aXaks5CufUTD0ZzJSW9qIjG9sBCYOjXTo9E8HP+oIkZjVDTMwpz0EnakMxMQ6aumhiOdG41mLWIf6PQGIcvcW+0k2JHOxAAL6RqHG43mAeEFSsWxQwC4cQgzOt6ADH9QlG9rdMMsFjx57n4R873bH0IglH3l5cwoCCH94EFyM6YYFtLzh5wTz4aLLzrepqQap5oOVCarc9I5zoEZD0VRohnpavRSYkd61lJkMkAnSZAVBS4/zwcnIQxZhw8DLldmx8JkLbxC1Tiaz0RmIkJ6yZGDADjOgRkdccBSaNbDqMXmgh4PORmBvF+0mw26SIn+EM8HuUFVFdDQQI/TIMCwkJ4/2CJRTzkipHOj0bSiqgOVieakZ6MjPVebDzNpwRuQEQiRO1k0pk0YReFeF1mMTidFG47yPuFkqqvppijRNQnDnIAG1RRmOKpmnTHZSXizWXCIMkU5zoEZDc0fqu3fT829ysuB2tpMjyajSJI0rOEoL5BzhjTFuwRDMoZ89L4QsR+Mdsm5ngksvqQVdqSrTBY70kus0UO1EMc5MCcw3HBjSNZw09kJ9PdTVdHcuSqMjlEbG/dMGB/hSud4F2YMWEjXOE6tNxdkIptNY9Mh6IIBOLxBKAovkJmRiAWyZucCIb4sWgRI3Fg56jThBXLOIIT0FAswTm8QigKYDDpYjfqUvhaTeXLOdcZCelpxRDLSNbo2SDdCSD92DBgYyOxYTqDQpIdBJ0FRuFqNOZnooZoKc4Fw8c6cCVgsyT8fozrcT2kCRE46NxxlxoCFdA0zPOuMnSYaZsoUoLAQUiCA0vbj8Adl+IKci8yMxJ4vjUZZfAEAFJtzzIXKRKMsUlxGOnwukPjQSfNEq1NyYLPs9UYjujjaJS0IEYX3CSpRWhptkptlAowkSZE1IMe7MCfi4EajeUXEkc77hNHhhqPMBLCQrmE8gRACIQWSxE4TTaPTAfPnAwDqWg8DYBcqczIOj8ZzUFlIH0FOiWcMITace/dSLmOK4Hz0/EKs/3wBGd5AKMOjmYADB4BQiMTISZMyPRrNI8sKXD56T2j2kD0TZHFOumgiyUI6cyKqHqpxo9GsJ9J8mOeC0RHRLjt3pnRNzuQuLKRrGCGcFZkN0OvYdaZpwguV2hZycrELlTkRcbgiNlGaQzgGeNEOICqesdMkh5g1CzAaAacTaGlJ2ctw75T8wmTQwRKO8BHZ+FnL8EajXC2Rcob8QciKAr1OQqGJY55UIwdy0tlww5yIMxLzpMLagM0tWQ9HQE7A/PlkVuzro8x/hjkBFtI1DG+W84iwk7HyGAnpfLrMDEdRlMh7QpMuVLs9KjxyGSmA6IEJO9JzCKMRmD2bHqcw3sWpZvk2kxPkTPNhFl/Sing/FJkNHPOkJlnsSC/haBdmDJxq9UtQlJGHokxWYrNGIyC5t9ooWK1kcAGyLqaLyQ5YSNcwQkDhzXIeEF6olBw9BCAHNstMWnH7ozFPRWYNHqyJBXt9PVBWltmxZAliIzTEC+TcIg056dG1gQbnAmZUcibqiYX0tMJ9lFKEcKTv2QMEsutvjuMcmLFwqlW52tJClXVGY1SIZLKOYrMBOklCSFayv1otU3BOOjMOLKRrmEgmshYdqMxIwuJLQfNh6P1+LtNiRiDeD0VmAwx6DU77QnwRCx4m7DAEgrICtz/Lc5GZKGkR0lVynTE5gxDPsv6QnV2MaYXnghQxbRpQVAT4/dHmuVkCO9KZ0QiE5MhaMWkDnliTz54NmExJjoxJFTqdhKJIvEuWrw0yxfCcdIY5AQ0qKoyAo13yiMmTAZsNUiiE0raj2b9ZZtIKNxrNP/Q6KVJ9wPNBDpFiIX2484jXBvlDTjjSXS7gyBF6zHN5WuDqlBQhSdG5XKxPsgRhrnL7Q/AH5QyPhskWxDrRZNDBbEhSHuJGozmDmPu5QmUM2JHOjAML6RrGwTmo+cOwRXtFc1N2b5aZtKP5RqMspI9KTohnzEiE+LJ3LyCrL3IM+YJQFMCgk1DAzQXzhpxoPrx3L91XVdGNSTnsSE8haaguSgSLUQ+zkbb/XL3KCIbHPCXdL4HX5DnD8Jx0ZhSEI33vXiDIPyNmJCyka5ThzQU52iVPEEL6sUNw+UIIhthpwhCangsUJeoU4EX7CHJCPGNGMnMmlUK7XMDx46o/vdgsF6mxWWZyBnGImtWuM3Yxph2Hl6tTUoZ4H2eZIx2Irg1YPGMEqjYh54iunIF7JkxAYyNQWEgxXQcPZno0TJbBQrpG8QXlSMkeL5DzhPCCpeoYNxxlRiKyMDVZndLdDfT2UlXGvHmZHk1WURzJPuQFcs5gMABz5tDjFDgZ2YGan4jft8sXgixnafNhdjGmHW42mkKyNNoFiMY5cLUaIxBCatJzQSgUrS7iuTzriRyy81wwOjpd9H3M8S7MCbCQrlHEhFhg0sOoxeaCzMmEJ/qq49TYiC+KjEAskEu06EgXm9QZM4CCgsyOJcvImQaDzEhSGAngZAdqXlJo0kOvkyArCob8WTofsIsxrXgDIfgCwnCjwbVBphHiS1MT4PVmdiwnwGsD5kQcah2yHz0KeDyAxQJMn67CyJhUwo70GOCGo8wYsMKqUURzQV4c5xHhzWdx23Ho/T5eIDMAwjFPWu6XIIR00RCGicAZ6TlKCoV0h5arU5gxkaQcaD7MjvS0IpoOW4x6mJJtLsicTF0dUFpKDt0DBzI9mhHw2oA5EadavZTEumXePEDPfViyHRH56fAGoShZWq2WacT+Mguri5jMwisnjaLaBZHJHWprgbIySLKM8pYjfLrMAABc/hBCsgKdJGnThcriy5hwDmqOkkpHuo+jHPKVrBbPBgeB1lZ6zI70tMDVKSlGkqLrkixrOMr9U5gTUS32jdfkOUWR2QBJAkKyApc/lOnhZCdZ3O+CySwspGsUTTtQmdEZtmivOHqQF8gMgKgDtdCsh06nweaCvGgfEyGQePyhSM8MJgcQQuK+fYCs7u9N1YZiTE6R1QdrIlO3vp5cvEzK4Xz0NJClOemR/ilsuGEAyLKi3sEaR3TlFHpdtFqN54MxEPvLI0eAoaHMjoXJKlhI1yiqNQ1hcovwwqXiWFN2us6YtBMRzrSYj64oLKSPw/CSfVHGz+QAM2YAZjPgdgPNzao9raKouFlmco6sbjDI83jaERGQfKiWQrLWkU5zQVY3H2bShssfhKxQ5WqRKcm1Ac/lOUc03iUL1wbZQFUVUFNDj8WhP8OAhXTNomnxjBmbiJB+iB3pDIDowsimReHs2DFyBxiNwKxZmR5NVpLV4hkzOno9MHcuPVZRgPEF5UhlQpEW5wNmXLLakc4uxrTDEZBpIEsd6YUmA3RSljcfZtKGuCYUWQzJVa4GAtF+ADyX5wzRhqM8F4wJx7swo8BCukaJimcspOcV4YVLZfMhDHmD7DRhhpVva3AuEAuauXNJTGdOopgXyLlJCnLSxbqgwKSHUc/Lv3wjEueQjUI6uxjTjmqZyMzYiPfz0aOAy5XZsQxDp5OG9UzIwvmASSuqVao1NQF+P1BUBEyZosLImHQgDlM52mUcxFy+a1dmx8FkFbyT0iD+oAxPuGEEl2/nGeGJvqSzFTq3i50mjLajHIT4IjqqMychFsjsSM8xUiCks3CW32R1s1EW0tOOgzPSU09VFd0UhXpeZBFZPR8waUW1ylUxjy9YAOhYYsoVIo50ngvGRuwz2ZHODINnOQ0iFkVmow4Woz7Do2HSili0A6hoOcxOE0bbjYdZfJmQiCOd54LcIqVCOgtn+YiYC3wBGb5gKMOjGUZPD9DdTY/nzcvsWPIEWVbg8gnDjQbXBtlE1uakZ3HUE5NWVKtc5YiunKREZKSzI31sONqFGQUW0jWIpoUzZmJETnozNxxlNN54mIX0CWHXWY4iNqL79gEhdURPJztQ8xqTIWquyCrxTIgv06ZRJACTcobCzQX1OgmFJjbcpJQsFWC4fwojcKqlG/CaPCcZHvOkKBwJOypiTd7ZCfT2ZnYsTNbAQroG4c1ynhMR0g9yLnKe4w2EIs0FNec6CwajpdK8aB8Tdp3lKNOmARYL4PVSvq4KcLQLk5W5yOxiTDuR5oJmAyQpieaCzMSkoLpIDbh/CiNwqFWtxnN5TlJsMUKSgKCswBPIomq1bKKoiNblQNYdijKZIy1C+i9/+Us0NjbCYrFg9erV2Lhx47hf/5e//AVz586FxWLBokWL8K9//Ssdw9QMYlFks/JmOS8Ji4oVx9iRnu+IzbLFqIfJoLFz00OHqKlRYSEwdWqmR5O1iI3RkI+bD+cUen005kIlAcapVg4qk7NkZYUKuxjTDhtu0kiWOtKzci5gMoIqlas+H63LARbScwyqTBINR/lgbUyydC5nMkfKlZWnn34at956K+655x5s3boVS5Yswbp169At8hBP4J133sE111yDT33qU9i2bRsuvfRSXHrppdjNb9qYUa1pCJObCEf6sSZuHJLnRIQzqwbnAm5qFBNFJgN0koSQrMDNTpPcQmUnIzvSGVs2VqiwkJ52eC5II2Ieb2kBHI7MjmUYQjTl/in5jWqVqyKGrrwcmDRJpdEx6YIP1mJANBzdtSuz42CyhpSrDz/+8Y9x44034oYbbsD8+fPxyCOPoKCgAI899tioX//Tn/4UF110Eb7yla9g3rx5uO+++7B8+XL84he/GPXrfT4fHA7HiFu+E3Wd8QI5Lwkv2m3d7fD2DWR4MEwmcWh5syzEF7GwYUZFp5NQaBa5yLxAzilUFNJDsoIhHzcbzXeybrOsKBwHkAG4OiWNlJYC9fX0OIviXcS60B+U4eVD9rxFHKpZTUlWru7cSfeLFgEcF5VziBQDNuCNQx440p/Z1II/vtuMLoc300PJCVIqpPv9fmzZsgXnn39+9AV1Opx//vl49913R/2ed999d8TXA8C6devG/PoHHngAJSUlkVtDQ4N6/4EchaNd8pzycsg1tQAAw/793Dgkj9F0+Ta7GGMmskDmks3cQgiLKizah3xBKApg0Eko4OaCeUskFzlbXKgdHcDAAFUVzZ2b6dHkDexITzMqzuVqYTLoYDVlYfNhJq2otk8QLl02t+QkXKESA8OFdI1qK70uH3qH/DDo+DAsFlIqpPf29iIUCqGmpmbEx2tqatDZ2Tnq93R2dsb19XfeeSfsdnvk1tLSos7gc5iPrJiMy5dPRlmBKdNDYTLFQlq0lxw5CG9AzvBgmEwhNkearE5hIT1mbNnmQmViQ4gv+/dTc90kGJ6Bys0F85esazYq5vGZM6m5LpMWVGsuyMSGWKdkkSMdyMIKFSbtqFa5KoT0xYuTHBGTCWyR5sM8F4zJnDmAwUARXRrUG72BEHwBFWKe8oicD5Y1m82w2WwjbvlOWaEJUyoKtNdckIkZHTccZaDh8m2PB2hqoscspE9IcTbmIjMT09gIFBRQU93Dh5N6KnagMsCw5sPeLGk+LIRFnsfTiqar1bKRLHSkA7w2YFScC4ZHuzA5R9YdsmcjJhOJ6UDWzeVqIGJ9ko55yiNS+lOqrKyEXq9HV1fXiI93dXWhtrZ21O+pra2N6+sZhhmF8KK9svkQ553lMSLKQ3Pi2b59gCwDFRXACRVMzMlESzZ5LsgpdDpg3jx6nKSTkYUzBgAKw82HZUWBy58FG2bhYmQhPW2w6ywDZLkjndcG+Ysqlat9fRTTBXCvixxFRECykD4BYi7XYMNRTVexp4iUCukmkwkrVqzA+vXrIx+TZRnr16/HmjVrRv2eNWvWjPh6AHjllVfG/HqGYUYh4kg/xHlneUpIjgolmhPPhjca5ZiKCWHXWQ6jUsNRdqQzADUfLsqmLFThYuQ4gLQhmg5bjOw6Sxvz59N9ZyeJjlmCjV2oeY+I8kiqclWIitOnA8XFKoyKSTdin+gNhOALcvPhMREVF1p0pIu5wKoxzSCFpHwFdeutt+I3v/kNfv/732Pfvn24+eab4XK5cMMNNwAArr/+etx5552Rr//Sl76El156CT/60Y+wf/9+3Hvvvdi8eTO+8IUvpHqoDKMdwov2or5uuLt6MzwYJhMMeTXcXJDz0eOCSzZzGLWEdB870hkia3omhELR9zUL6WmDN8sZoKiIorqArHKlRw/Z2ZGer6hyyM6xLjmP2aCH2UiyIO8VxmF4w1GNoVq/hDwi5UL6VVddhR/+8Ie4++67sXTpUmzfvh0vvfRSpKHo8ePH0SHKgQCcdtpp+POf/4xHH30US5YswV//+lc8//zzWMiCCcPETkkJ/HWT6PGevZkdC5MRHF4NNxdkIT0u2GmSw6jsSOeSTSZrKlSamgCvl/oATJ+e2bHkEVydkiGyMCfdli1zAZMRVKtcFY50FtJzGm44GgPiPb5vHxDU1ryp2b5qKSQtP6kvfOELYzrK33jjjZM+dsUVV+CKK65I8agYRtsE586HqaMdxv17AFyS6eEwaSYqpGtws8xCelyYDXpYjHp4AyE4vUGYizRWoaBlhPhy4AAQCADG+P+eFUUZJp7xAjnfyRpHunAxLlgA6HlOShc8F2SIhQuBf/4zyxzp4ebDviBCsgK9TmOmC2ZcVKtcFUI6VxblNMUWA3qcPj5YG4/GRjr8d7vJDDB3bqZHpBqa7auWQjgcj2G0SlhkLDi4P8MDYTKBZjfLg4NASws95qZGMcPxLjnKlClAYSGJ6E1NCT2FLyjDH6TmgkVamw+YuMkaRzrno2cEdp1liCx0pBeY9NDrJChKNDufyR9UqVyV5ZF9i5ichRuOxoBOl5VzuRpE1gYc+xYzLKQzjEYxLqXNaenhAxznkIdEohysGjtZFs6XKVOA0tKMDiWXKM4WFyoTHzpdtFFdgk5GsVkuMOlh1POyL98pzpZmo+xizAgc7ZIhRAXdnj2AomR2LGEkSeK1QR6jSuXq0aOAywWYzcDMmSqNjMkEtsjagOeCcREHRmINowH8QRluP2lFHAEZO7yjYhiNYly+DABQdWQ/HG6+KOYbTq9GmwuyizEhOAs1h0kyJ52FM2Y4WSOccYO6jODQ6tog25k7lw5G+/qArq5MjyZCcSQXmdcG+YYqlatCTFywADDwnJLL2Lj5cGxosOGo+J2bDDpYjBy1FysspDOMVpk3D7LeAMuQA64jzZkeDZNmRLMYzZ0ss5CeEFkjnjHxo5qQzptcJhrv4wvImatWczjIyQiwkJ5GZFmBy0e/cz5YSzNWKzBjBj3Owpx0XhvkH5F9QjKVq3wgqhn4UC1GNCmka7SKPcWwkM4wWsVsxtA0KrMLbdue2bEwaUXTzQVZSE+IyAKZHem5R9JCOjtQmSii+TCQwQoVsQGdNAmorMzMGPKQIX8QskJNJQuTaS7IJEYWZuty/5T8RawHkzLcCEc6C+k5j5gLXH5qPsyMgXivNzUBHk9mx6ISDu6dkhAspDOMhvHMpWxdnYZyvJiJ8QRCCMoKJAkoMmvooji8qREL6XHBm+UcRogvBw8Cfn/c387RLsyJZHw+4Hz0jCB+30XmJJoLMokzPCc9S4jEOfjYkZ5vRB3pKkS78Fye8xSY9DCEmw9zhco41NQAFRW0J923L9OjUQVRhaC5KvYUw0I6w2iY4CJa2Jj2sJCeT4jNcqHJAIOWmgs2NwNDQ9TUaNasTI8mpxDlekPeIGR2muQWDQ1ASQkQDAIHDsT97U52mjAnkPE4B44DyAhcnZJhsjASgPun5CeyrCQf5+DxAIcO0WOey3Oekc2HeT4YE0nKyrk8GSL7hGQO1fIQDSksDMOciLSEhPSiA3szPBImnQiXieY2y0J8mT+fmxrFSaFJD71OgqwoGPLzAjmnkKSo22v79ri/XThN2JHOCDIunnFEV0bg6pQMMzymS8mOA+3hwpmSJWNiUs/wmKciU4Lr6b17yZVbWUkuXSbnEYcqDnakj484ONJIxX802oXXBvHAQjrDaBjz8mUAgOKWo5rJ8WImxqHVzTKLLwkjSVIk5oedJjnIkiV0v2NHXN8WkhW4/MJ1xodPDJFRR7qicBxAhuDqlAwzezaZABwOoLU106MBEG0+7A/K8AXlDI+GSRd2d9Rwo9MlGPM0fE3OUVGagBuOxojmHOka1Q1SDAvpDKNhCqdOhrukDDpZhn/HzkwPh0kTmi3RYiE9KYR4JioWmBxi6VK6j9ORPuQNQlEAg06C1cjNBRkio82HW1oAu50Exblz0//6eQxvljOMyURiOpA1AoxRr0NBuPEsrw3yB1UcqNxoVHNkPPYtV9CQkB6SFQz52HCTCCykM4yGsZgM6J8xDwDg27Itw6Nh0gU70pnRKAmXbNp5s5x7DHekx1F+7xiWiczNBRlBRnNQxTw+dy4Ji0zaiK4NeLOcMbKw4WhGD9aYjBBpLphoPjrAQroGsfFcEBtiHm9tBQYGMjuWJHF6A1AUwKhnw028sJDOMBrHOZuEdJkd6XmDJhuKuVxAUxM9ZiE9IYSQzq6zHGTBAkCvB3p7gY6OmL+NHajMaIhrQ0aaD/OBaMYQc39S4hmTHEKA2Zk9a3J2oeYf4pC9JJm5gOdyzcFzQYyUlAANDfQ4iw5FE2H4PoENN/HBQjrDaBzvfFq063dlz6KdSS1OLbrORHOumhqgujrTo8lJbOxIz12sVmDOHHocR7yLJg/VmKQpNBmgk6j5sCvdzYdZfMkI3kAI/nAGtuiXwWSABGO6UolYG3D/lPzB7kkyArK7m26SFG2iy+Q8w+cCbj48AaISI8fjXZKeC/IYFtIZRuOEFtJEb963J65IACY38QdlePwhABrrvs3iS9JwtEuOk0DDUXakM6Oh00mRJoNpF8+40WhGEL9nq0kPk4G3fxlDCOn79gFeb0aHIsho1BOTESLVKYmuDcQ8PmMGUFCg0qiYTFNkNkCSRKP6UKaHk92I6iLxt5CjRCIgzbxPiBdeSTGMxtEvXABZp4fRPkhZXoymEQ5Uk0EHs5Y2yyykJ40Q0od8QYTSHefAJE8CTkanjx3pzOhkRDzzeoEDB+gx5+qmFa5OyRImTwbKy4FgENi7N9OjAQDYOM4hrxjZXDBB8YzX5JpEr5MiFUs8H0yARhqOijUgR77Fj4ZUFoZhRqO4pAj9DdPpH1mUycikhsgFUWvNBdnFmDQFJj2MegmKwgvknCQJR7qmqlMYVciIeLZvHxAKAWVlQH19+l6XiVQiJZWJzCSPJGVdvIuoWGJHen4w5A1CUQCDTkKhKcHmgtxoVLNEGo56eD4Yl+HRLjlc8e/gaJeEYSGdYTSOzWpAz/Rwtm4cAgyTm2jyZFlRoodAvGhPGEmSIu8LXiDnIEJ8OXiQmu9OgKIo2uyXwKhCRsSz4S5GLR305gAspGcRWSekh5sP+4IIhuQMj4ZJNSLKwWZNorkgC+mahRuOxsjcuYBeD/T3A21tmR5Nwjg4AjJhWEhnGI1jsxjRGxbSQyykax5Nlm+3t9NCRa8H5s3L9GhyGuE04Zz0HKSmhm6KElMpqS8oR5sLamk+YFQh4jpL52aZK4syBgvpWUSWCelWox4GHQmqIvKD0S5JNxcMhaJrEJ7LNUfEcMNC+vhYLCSmA1kzl8eLLCsYGlbJzsQHC+kMo3EsRj0GZ88HACjbWUjXOpGmIVo6WRYuxjlzaOHCJAw3HM1x4oh3EXMBRfrwco8ZiThsdWTCkc4uxrTjYCE9exgupMuZd4BLksQNR/OIpBuNHj5M/S6sVmD6dBVHxmQDPBfEwbJldJ+jQvqQPwhZUUZk4zOxwzsrhskD/POoIYa+6RDg8WR4NEwqcWgxyoGbGqkGO01ynDicjE4u12TGISPl2zyXZwRFUdiRnk3MnQuYzYDTCTQ3Z3o0AHhtkE8Mj3ZJCFFZtGABVYoymiKakc5zwYSINfm2bRkdRqKIfUKRWWN91dIEC+kMkwcYG+rhLimDJMvAnj2ZHg6TQjTZXJDFF9UoCZfysiM9R4nHke7RYMwToxrigMUXkOENhFL/gt3dQFcXZaMvWJD612MiuPwhBEIKJIkP1rICoxFYSAaXbHEycsPR/EH0yEn4UI3X5JomI9VquUqOO9KjjUZ5XZAILKQzTB5gsxrROy3ccFQsgBjNMTzrTFPiGS/aVSPabJSF9JxEuF927JgwEoAbjTLjYTLoUGgmN2FaDtaEi3HGDKCoKPWvx0Swe6KRb3odu86ygizLSec4h/zBnmy0Czca1TTiUM0fTNMhey4j5vEjRwC7PaNDSYRozBPvExKBhXSGyQNsViN6pocbYnDDUc0iss50koRCk0Yuij4fsH8/PWYhPWnExsntD0UaUTI5xOzZFAngctHCfRwi1SnsNGHGoNRqAgAMutMgpHM+esawuznWJevIskiAjEQ9MWknGJIjDWUTbjbKQrqmMRl0sJrokJ2jniagvByYMoUe56C+whGQycFCOsPkATaLAb3T2ZGudSJZZxYDdFpxne3fDwSDQGkpMHlypkeT81iMeliMaXShMupiMEQjASZYtAtBhJ0mzFiUFNDmadDtT/2LcWVRxuB89CwkyxzpnIucH4h9glEvwWpMIN/c5aJmowDP5RqGK1TiIMsOReMh2i+B9wmJwEI6w+QBNssJjnRFyeyAmJSgSeFsuPjCjVBUoYSbiuU2MQow7DRhJkLMBWk5VGMhPWOwkJ6FiL+D1lagtzezY8FI4UzhPYJmEeu+EqsxseaCe/bQHrKmBqiqUnl0TLbAB2txkMM56Y5kY57yHBbSGSYPsFmN6J8yA7JODwwMAG1tmR4SkwI0KZyJElIWX1TDxg1Hc5sYGo4GQzJcfs5IZ8anVDjSUz0XBIPA3r30mOfytONgIT37sNmoXwCQFZEARWYDJAkIygo8nIusWezJNhfkWJe8gB3pcZCjjnRFUaIRkFrSDdIIC+kMkweYDTroC6zob5hOH8iCRTujPppsGsK5uqqTVhcqoz4xONLtngAUJZx1mUj5NpMXlKSr+XBTE+D1AgUFwPTpqX0t5iTYkZ6lZFG8i0Gvi/TWYfFMuzg8SQpnYv/Ia3JNIw5aeC6IAeFI37sX8KchJk8l3P4QgrICSaJIWCZ+WEhnmDxAkiTYrEbOSdc4mnSkcxyA6qRNPGNSg/hbaGkB+vtH/ZKBcHPB0oIEy7eZvEA0G3V6gwiEUth8WMzjCxcCOt56pJPAsOaCLKRnGVkkpAPccDQfSDoTWbhuly9XaURMNiIMWRwBGQNTpgBlZUAgQNFHOYL43RaZDdBrpa9amuHVLMPkCTaLAT1CSGdHuiZxaq1pSE8P0NFBj0WDRSZpOPswxykpARob6fEYh6J2D7liygpMaRoUk4tYjDqYjbQVSGmFCh+IZgwxz5uNOliMvO3LKrJOSBf9U9iFqlWSykSWZRbS8wQxF/ChWgxIUtbN5bHAsS7JwysqhskTbFYjeqexI12rKIoS2fxoxpEushhnzACKijI7Fg0RbTbKTcVylgkW7YPCkc4OVGYcJElKT9QT97rIGMNjXbg6JcsQ8/i+fRR9lGE4F1n7DG82GjeHDgEuF2C1AnPmqDwyJpsQ4qrLF0IwldVqWiEHc9Ijh2paMd9lABbSGSZPsFmM6Jkxl/5x4ADg8WR2QIyq+IIy/EFa7GimuSC7GFNCsYWaivmDMjcVy1UmaDgqhPSSAhbSmfER8S7iPZMSuNdFxuB89Cymvh6oqABCoayIBBBrR+6fok0CIRkuH635Emo2unUr3S9dCui594qWsRh1MOrp4JUP1mJA5KTnoCNdM+a7DMBCOsPkCSVWA1zl1fCVlFF53t69mR4SoyLCZWI16WHUa2RqZyE9JRj0OhSZecOc00zkSPeIjHSOdmHGJ+U9E+x2oLmZHrOQnnZYSM9isiwSQESB2d250zCPiR0xx5sMOpgNCewThJDOsS6aR/RWAzgnPSaGz+Nybjj4I/0SWEhPGI2oLQzDTITNYgQkCb3Clc7xLppCk1lnLKSnjMgC2cNOk5xEONL37qUGR8MIhuRIrmUZO9KZCSgNv0cGPSkSz0Sp85Qp5L5l0goL6VlOFjkZhZA+6A5w7JsGEfGPtkRjnlhIzys46ikO5s4FzGbA6QSOHs30aGKCo12Sh4V0hskThHDW2cgNR7VItERLIxfEYDBa6sxCuuqIAxd2pOcojY2AzQb4/cD+/SM+ZfcEoCjkOrMaufyaGR8hsKYs2oXFl4ziYCE9u8mibN1iiwF6nYSgrHDDUQ2S1FygKDyX5xk2CzvSY8ZoBBYupMdZcCg6EZrsq5YBWEhnmDzBbNDBZNChdzo3HNUiYoGsGSG9qYmabxUUANOnZ3o0miPlcQ5MapGkqCv9hEV7NNaFmwsyEyNy9B2eIGQ5BS5UIb6sWKH+czPjoigKO9KzHSGk79iR8UgAnU4adrDG8S5aQ8wFtkT2Cc3NwOAgYDIB8+erOi4mOxECK1euxoioLsqCQ9GJ0GRftQzAQjrD5AmSJMFmMaBnejjaZccOchgwmkA4BjRzsrxrF90vXAjo+FKlNqKUjx3pOcwYDUeFs1g0kWSY8Sg2G2DQSZAVJTUl3OxizBgufwiBkAJJ0tDaQGvMmUORAENDwJEjmR5NJOppIJXNh5mMEMlETqbR6KJFJKYzmica7cJzQUxkUb+LiRAmqkKzhvqqZYCU/uT6+/tx3XXXwWazobS0FJ/61KcwNDQ07vecffbZkCRpxO2zn/1sKofJMHmDzWpE/5QZUAwGoL8faGnJ9JAYlRCbHs1kIgtxkJvTpQThOmMhPYcZY9EunISlWpkLmJQyvKmY6vOByxWNHmIhPe3YPdEDdr2Oq1OyEoMhus7JAgFG5KQPsCNdcwhncUK9lPhANO+INhtlR3pM5JAjnWNd1CGlQvp1112HPXv24JVXXsGLL76It956CzfddNOE33fjjTeio6MjcvvBD36QymEyTN5gsxgRMpnhmj2PPrBpU2YHxKiCoigYdNGmp7xQI06RLVvonuMAUoIQ0p3eFMU5MKlnuCN9WHVRxJHOQjoTIylrOLp9O7036+qA2lp1n5uZELubY11ygixyMkYbjrKQrjWEIz2h+YCF9LxDONKHeJ8QG4sWUexiezvQ3Z3p0YxLpDqFhfSkSJmQvm/fPrz00kv47W9/i9WrV2Pt2rX4+c9/jqeeegrt7e3jfm9BQQFqa2sjN5vNlqphMkxeIeIc+hcspQ9s2JC5wTCq4fAGEZQV6HWSNi6KigJs3kyPV67M7Fg0SpGZmorJigKnj90mOcmCBYBeD/T20sI9TDQjXSOHakzKSVmFCosvGYXz0XOELBLSI4dqHO2iKXzBEDz+EIDoXjBmFCVqbuG5PG8oMhmgk2ifMOTnfcKEFBcDM2fS4yyYy8dDc33VMkTKhPR3330XpaWlWDlMBDn//POh0+mwYQLx7oknnkBlZSUWLlyIO++8E263e8yv9fl8cDgcI24Mw4yOEFk75y6mD7CQrgkGXNEoB50WyrePHydx0GgEFi/O9Gg0ieiZAHDD0ZzFaqV8XSAShRQMyZE8y1IWz5gYiTYYTJGQzpVFGYGF9Bwhi4T0snBVo90TQIhdqJpBxLpYjHqYDfr4vrm9HejpoYN7jlvMG3Q6CUWRnHQW0mNCxLtkwVw+HuL3mVC/BCZCyoT0zs5OVFdXj/iYwWBAeXk5Ojs7x/y+a6+9Fn/605/w+uuv484778Qf//hHfPSjHx3z6x944AGUlJREbg0NDar9HxhGa4gJ8/iMhfSBzZuBIF8cc53+cAlumVYcqMKNvmgRNeFiUkJJAeek5zwnNBx1eINQFMBk0KHAFOdmmclbRPXCIDvSNYWDhfTcQBgG2tpIsMwghSY9TAYdFIXXBloi2mg0AQeqmMfnz6cDfCZvYMNNnIhD0SzPSY9Gu7AjPRniFtLvuOOOk5qBnnjbLxoLJcBNN92EdevWYdGiRbjuuuvwhz/8Ac899xwOHz486tffeeedsNvtkVsLN09kmDERjvTWmqlQiosBtxvYuzfDo2KSRWRZaiYfXWT3c6xLShHzAS+QcxjhfgkfPokGcSVWIyRJA9UpTFoQQqvDE4CiqORC9XqBPXvoMQvpGYEd6TnC8EgA0Wg9Q0iSFIl34Yaj2iGpQzU+EM1bIs2HXTwXxESOOdK52WhyxH0Mcdttt+ETn/jEuF8zffp01NbWovuEoP1gMIj+/n7UxtFwaPXq1QCApqYmzJgx46TPm81mmNmxyDAxYTHqYDLo4AcQXL4CxjffoHgXjs/IafpdGmsuyPnoaSEinnlZSM9ZwmskEdMlojk0U53CpAWbxQBJAvxBGW5/CIVmFVxKO3cCoRBQWQlMnpz88zFxEQjJGAr3v2AhPQdYuhRoaiIB5vzzMzqUsgITuh0+bjiqIRwiyiER4YyF9LxFRD31sZAeG8KRfuAA4HIBhYUZHc5o+INy4v0SmBHE/dOrqqpCVVXVhF+3Zs0aDA4OYsuWLVgRzkZ87bXXIMtyRByPhe3hE526urp4h8owzAmIXOTeIT/cS1eg5M03gI0bgRtvzPTQmCQQTgFNONK50WjasKWqwSCTPlasoNzStjagtRV2D/1ONXOoxqQFg16HYosRDk8Adk9AHSF9uPjC1RFpRzhQzUYdLMaUJXkyarF0KfDXv2aFkzHiSHfx2kAriHVeQpnILKTnLRXhfSVXp8RIbS3dOjuBXbuAU0/N9IhOQpinEuqXwIwgZSurefPm4aKLLsKNN96IjRs34u2338YXvvAFXH311Zg0aRIAoK2tDXPnzsXGjRsBAIcPH8Z9992HLVu2oLm5Gf/4xz9w/fXX48wzz8RidswyjCqIRdTgwnD5ETcczWl8wVDEdaYJF+rhw4DdTtnoCxZkejSapoSF9NynsDDa/GvDhogjnR2oTLyo3nCUG41mlOGxLhzzlAOISIAsyNaNxDmweKYZxMFa3JnI3d1AaysdhoqeLEzeUF4kol24+XDMZHlOejTWhd3oyZJSi8ITTzyBuXPn4rzzzsPFF1+MtWvX4tFHH418PhAI4MCBA3C73QAAk8mEV199FRdeeCHmzp2L2267DZdffjleeOGFVA6TYfIKMXF2zQ8viPbsAYaGMjgiJhmE6FFo1sNi1MDJsnCjL1kCmDRwMJDFCOHM5QshEJIzPBomYYbFuwy4NRbzxKSNUiGke1QSz9jFmFE4Hz3HEOLL/v3UvyiDCCFdtUM1JuNEm43GOR8IMXD2bMryZ/KKYrMBJoMOsqJw1FOsZHlOelLVKcwIUnoUUV5ejj//+c9jfr6xsXFEU6OGhga8+eabqRwSw+Q9Ih+v31ZBuaWtrcCWLcBZZ2V4ZEwi9IdjXTThRgc41iWNmA3hnglBGQ5PABVF3G8kJ1m9Gvj1r6G8+x6cF34agIbmAyZtlBSo2HzY76eyZoCF9AzBQnqOUVcXjQTYtg04/fSMDUUcxA75gvAHZZgMHA2Uy3gDIfgCZJaIOyOdD0TzGkmSUFZgQpfDiwG3n/cJsZDljvR+lw9ANLaHSRy+MjJMniFOIB2e4EmN6pjcY4CFdCZBJEka1nA0mOHRMAkjMhi3bgGCQZgMOhSYNFCdwqSVUjWjXfbsITG9pASYNi3552PihoX0HEOSonP5e+9ldCgWox7W8DWEXai5jzgcLTDp4z8UYSE97xH9t/qGeC6ICeFI37ULCGbf3qp3SEN91TIMC+kMk2cIN4LDG4gK6eE+BUzu0R/e5JRp4YIoy1QdAQCrVmV2LHkCNxzVAHPmACUlkNxuVDYf4kxkJiGEI12VuYAbjWYcFtJzkCwR0gGgTDQc5XiXnCfhWBeAhXQGFeGcdFEBzUzAjBlAURHg9QIHDmR6NCNQFCVyICJ+r0zisJDOMHmGzUqJTkO+IIIrwq5fdqTnLGKTo4mT5YMHKa+/oACYOzfTo8kLuOGoBtDpgFNOAQDU7dvO+ehMQoi5wO0PwRcMJfdk3Gg0oyiKAjs3Hs49skhIL+WGo5rB7iFXbNyxLgMDwJEj9Fi4bJm8Q1Q89/NcEBs6XbQxb5blpLv8IXgDIegkCeVaqWTPICykM0yeYTVSaZ+iAPb5i2nCb20F2tszPTQmTmRZwWDYIaCJC6KIdVm2DDBwN/F0EIl2YSE9twlXF9Xu34lSqwbmAibtmA36SCSQPVkXKrsYM4rLH0JQViBJQHG84hmTOVaujK7JW1szOpRow1EWz3Id4UiP+1BNiIDTpgFlZeoOiskZRJb2gMs/orchMw4iJ12shbKEviHKRy8tMMKgZxk4WfgnyDB5hiRJqAyX8/TCBCxcSJ/IgngXRVGwqbkf/9rVgZZ+tzoX7L4+wOdL/nlAwnVIzp5FhNMbRFBWYNBJKLZoQHjmfPS0Ywu/b7LVkT7o9uPlPZ1453AvAiE5Y+MIhOTs3kAIIZ0d6UwSqFKhEgwCO3bQ4xQI6XZPAAe7nPAGknTNJ4iiKNjX4cC7h/siAlW2IX5/xRYj9DqO1skZCguBxYvpcYYrRUW0iyo9EzROh92Df+xox9tNvVm1RxAIo4SoSI4ZPhBlQOsCvU5CIKRQf7UswRsIYX+nAz1OX1atzwMhGR1zFgEAgv97O8OjGUmfi2Nd1EQDygvDMPFSWWRG+6AXvUM+zDnlFGDnTlq0X3ppxsYkywrW7+/G7jY7AOBApxPVNjNWTC3D7Opi6OLZDIZCwAsvAL/4BbB+PWCxAGvXAueeS7cVK8Z0PCuKgrZBD3qH/HB6A3B6gxjyBuHwBuDyhWDQS7hsWT0mlVrV+G9PTH8/lVYePkz3zc3AlCnAlVeiv2wSADpZjuvnk62wkJ52hgtniqJkTbZ2ICRjc/MANjf3IxjemO7vcOK8edWYWlGYtnEEQzLW7+/Gvg4HjHodygpMKC80hu9NKCs0obzAlPm/v7CQXtFyBN6AC0BJZsejBrIMNDXRIe/GjcCmTYDDAZx5JnDBBTSXl5ZmepQToyjA4CDQ1XXyLRQCPvxhmvOy4G+vtMCIDrsXg8kI6fv3Ax4PZYTOmpX0mGRZQafDi6O9LhzpdaHXSQfjNqsRH1hchxqbJenXiBWPP4RX9nXhcPcQAGDj0X7MrinC8qllaR3HRHCsSw5z6qnkBH7vPeDyyzM2jNJhcQ4Jrw1kmSL7Rpv7+vqA+fOBD3yAeuLox2+Q7Q/KeO9IH1y+IGQFkBUlepOBQrMep8+sTG0Fht8P7N5N87nDAVd3H44eboe9qw91Hhe6Zi/CX97/IVy8tD7+GJUUEhHS4x0TC+kMAJ1OQlmBEb1DfvS5fJF+KpkkGJLx/LY2dNi9AACzUYf6UismhW81xea0Oq5lWcHxfjf2dzpxuGcIVtsMfBKAtHUr7H0OlFTY0jaW8ejjRqOqwkI6w+QhlUVmAEDvkI8EmN/+NqPul2BIxr93d6KpewiSBMysLkJzrwvdDh/+vasT/7P0YvnUMiyYZIPZMM5iu7cX+L//Ax5+GDh+PPpxrxd49VW6AUBxMXDWWcC6dcAnPkEbftCF8M2DPdjeMjjmS/iDCv69uxPXrZ4Ci3H8hX9C+P10APDnP5N4PjjGWO66CzULl2DlqRfA/+GPAGhUfyzpJBgEtm2jxyykpw3RfMoflOELyql5T8fJ0V4XXt/fHXFVTi6zwu4JwO4J4NmtbZhXZ8NZs6tgNaV2rEO+IF7Y0Y7O8ELdH5TR5fCiy+Ed8XU1NguuWDkZxgyWSYYqKuGsa0BpRwvKd+8AGidlbCxJ4fEADz0EvP46HayNNv/t3Qs88ghFIKxaBVx4IQnra9ZkVySUy0XXoh/9iISjsfje98iFeuONwHXXZbSEviQcC5RUtIsQX5Yupd9RgnTavdjROojmXhfc/qj7XJIohsbhCeDpTS04e04VFtWXpPwQsLnXhf/s7YTLF4JeJ6G62IwOuxf7O53Y3+nE5DIrlk8tw/TKwowfSHKj0Rzm1FNpfstwTrqobPIFZHgDcvzXW4eD1tjj/T+eew64/36gshJ43/uA97+fvmeUA9K3D/di+/HBcV+ypd+DS5ZOQrXah1pdXfQ7+dWvRszlhQAWnvCl3U8+grduuh0LbrgS06qK1B1HAiiKAoc3nJEe73zAQjoTprzQjN4hf1b0TFAUBa8f6EGH3QujXoIkSfAFZBzpceFIjwsAYNBJmFVThPPm1aR0bd7t9GJPmwMHu5wj1imW6dPhrqhCQV8P/vfUv3HWpy9HkTnz61MR7SJ0ICY5Mv8bZRgm7VQW0wTa4/RFnIzYvJnccRO4QtTGFwzhhR0U5aLXSXjfwlrMqimGxx/CjtZB7GgZhNMbxJsHevDekT6snVmJxZNLRz7JoUPAAw+Q+CxiXCoqgE9/GvjsZwG3G3jtNbq98QY10HnxRbrdey9w++0IfPZmvHTMhaaw02x6VSFsViNsFgOKLUYUmQ2wGvV4dlsbHJ4A3jjQjYsW1qn3g1AUctHfdhu5MIdTW0tdwKdPB6ZOJWfmq6+iYPcOnLF7B/DbH9Lm67rrgM98BjDm4OZ5/376PRUVAbNnZ3o0eYNRr0OhWQ+XLwS7J5BRId3uCeDNgz0Rt2eR2YCz5lRhVnUR/CEZ7xzuw46WQezrcKC5z4UzZ1VhXl1xSkSrTrsXL+xox5AvCItRj/ctrEWxxYABdwADbj/6XX4MuPzoHfKhy+HFlmMDOHV6herjiBWHJ4DOuUtQ2tECy9ZNwAfel7GxJIyiAJ/8JPDUU9GPWSy0iV+1ihqqFhZSldErr9CcsWED3e67j+aN736XHN6ZFDKFgP6DH9DhrsBmA2pqRt4GBoBnn6WqsC9+Ebj9duAjH6Fr11lnpf3/IYTXpBzpKjQatXsC+Mvmlkg1itmoQ2NFIaZV0g0A/rOXnOHr93WjfdCDc+fWwGRQf8McCMn4X1NUxKsoMuGiBbWotlnQ7fBi6/EBHOgcQuuAB60DHpQXmnD+/BrUp6tqbRRYSM9hRMPRzZuBQCBj6zmjXodiiwFObxADbj+spjjez14vcMklJKKbzbRuFXNedTXdl5QA77wDvPwyzZN//CPd9HrgnHNoLl+1CgDQPujBjrDBZfW0chSYDdBJgE6SIEmABAmbj/Wjb8iPv2xpxUULazFDDRF761bgpz+la5KfBMRgSSnsZVXwFRTBV1AEY3kpKiZVwWo2Qvnzn1F9eB8+8LVPoeXPv8bOb3wLCy9fl9GKNU8gBH+QYvFs8URADg0BBw7QY240mvcIB7NwNGeSna127G6zQ5KADyyehIbyAvQO+dA64EH7IN3c/hD2dTgx6A7gkqX1KTHe7O904OXdXZDDsTIFJj1m1xRjTm0x6kosCJ15BvDcsyjZugnPbTsVV6yYnNE9lqIo0WgXdqSrAgvpDJOHiAnU6Q3CO2sOLIWFgNNJwsSCBWkbh9sfxPPb2tHl8MJk0OFDS+iCCABWkx6nTq/Aiqll2N/hxJZj/RhwB7B+Xzec3iBOm1FBAlpLC3D66UBPDz3p8uUkSFx1FWAdtvCfPx/4whfosGDnThJjHn2URPg774T8wPdRdvkNsF76MZy7eiZm1xSPOub3LazFXza3Yl+HE1MrCjGvToVyrd27gVtuiTrma2pI4D/jDKCxkcSjE+npwZaHfouqf/0dDTs3QnrvPdq0bNoEPP54VsQExIWIdVmxIikXIxM/JVYjXL4QHJ5AxuIJDnY58Z89nQiEFOgkCcunluKUaeWRChSzQY9z5lRjbm0xXt3XjV6nDy/v6cT+TgcuXFCrqtNjb7sD6/d1ISgrqCwy4YNLJkXK3CtOcHEc6HTiX7s6sLm5H/Mn2TJWzj3g9qNz7hLMe/1FSFnQ7yIhvvc9EiwMBuDBB0lIXrjwZCHpkkvovqWFBPVXXgFeeokiBD7yERLcv/c9EmPSyWgC+owZwDe/CVxxBVBQMPr3DQwATzwB/OY3dG164gm6rVtHh71pdNmXRnKRk9gsq+BifPdwH4KygtoSC9bOrMSkUutJWd8fXFyHLccG8HZTH/Z1ONHj9OH9iyepWrLc7fTi5d2d6A2LB0sbSrF2VmXE4VZts+CihXU4fWYA21sGsavNjn6XHy/t7sQnTmvMWD65g4X03GXWLKpKGRig+SCJA6lkKSswRYT0mOMMg0Hg2mvJtFJcTPdjzQVf+hIdFrz9NvDPf9Jt3z5aC69fD3zykwh95368etQDRQHmT7LhtJmVoz7V9KpC/HNnB473u/HCjnacNbsKy6YkUN2jKMDzzwM//jHwv/9FPhxavRrvfuA6bFlxLmSDEVXFZpw5qwqTK6LzuvSd70C+/7tQHn4YDTs2ouHK96Ht7HUoe+gHKFi6OP6xqECvk+auEmuczQV37KCfRX097UmYvEZkave7Miuktw648cYB2u+vnVmJxvDBeo3NghqbBSumlkFRFLQOePDizg502L14ZnMLLl1Wr+r1cMuxAbx1kMYxrbIQSxtKMaW8YMShmeGMtcBzz6Jh31Zscvrw9+1tuGzZ5JQc+MeCwxuEPyhDr5MiexomOfJWrQiFQvB6vXzLw1solJkGVdmExaiPlPj1eoLRKI00xrs4vAH8ZXMruhxeWE16fGTF5IiIPhyjXodFk0vw8dMasWYGOT43Hu3Hf/Z2IeT2UIZkTw+waBHw7rskyH7iEyNF9OHo9eSuuP12YO9euH/7O9gbpsHsGMTa3z2EGz9xPmb/9mdUljoKk0qtOGVaOQDgtf3dyZXA9/YCn/scsGQJbRxMJuCOO0jc/+xn6VBjNBEdAKqqsOWiK/G3B/+Ann2HSXjS64E//IEe5xqcj54xhPibqYajgZCMNw50IxBSMLnMiutOnYIzZlWNGuNUV2LFtadMwdpZlTDoJBzrc+OpjcfR7fSO8szxIcsK3jrYg5f3dCIoK5heVYgrVzWMu+CcXVOE+jIrAiEF/z3YO+bXpZrBsCMdAB2oZVHjpZj4xz+Ab3yDHv/iF8CXv0zz9HhuzIYGcrA/+SRw7Bhw9900X27cSPnpF11EWcOpxu8HfvITOvT86ldpXp8xgw409+8HPv7xsUV0gESzL3yBxrpxI3DTTeTEf/llcmWmEbHRHPIFEUykua8sRyO6EhTSe5w+7O+k6+85c6rRUF4wqiAtSRJWNpbj8hX1KDTr0Tvkx5Mbj+NApzOh1z2RXa12PLWxBb1DfhSa9bh0WT3OmVs9apl4scWIM2ZV4ZOnT0OhmWJn9nWMvoZIB+xIz2F0umilaIbjXcoK42w4qii0dn3uOVrP/v3vE88DRiNw9tm0bt27l9a/H/sYPdf//R+U2bMx5Y+/QaFOwZmzqsZ8GouR/kYX1ZdAUYA3DvTg9f3dkONp/rl/P3DeeVTV9L//0SHmtdfC/vp/8fgPn8Sm1etgLrBg3YJaXLd6CqZUnDCvV1ZC99CPoT90EIPXfBSyTof6N16GZeVyDD3799jHoSI9Q7Q2qrbFGeXAsS7MMMpO6JmQCRzeAP65swOyomBObTFWTB39oEySJDSUF+DKlZNRbDGg3+XHM5taqAo/SRRFwX8P9URE9GVTSnHJ0klorCw8ufLk9NMBAA37d8CsB9oHvfjnrvaMNSQWhyBlhSZuQq4SeedIVxQFnZ2dGBwrd5jJC0pLS1FbW5vxHMtMUllkgsMTQO+QH5NXrwbefJM28Z/8ZMpf2+EN4JlNLXB6gyi2GPDh5ZMndJFJkoRTp1egyGzA+n3d2Ntmx5y7bkHjpk1AeTkJMY2NcY2jyx3E8zPPgOfRf2LJ2y/hzKd+BX3TIeCuu4Cf/5wW9h/96Enu7tXTytHS70bboAcv7enAFSsa4ivdHBykctGHHgLs1FwVl19OTsbp02N6Cm8gBJePDoVKZkyhgwGrlQSZO+4A5syJOjdzARbSM8bwhqOZYG+7Ay5fCDarER9ePnnCBZ5eJ2FVYzlmVhXhhZ3tVM69Oblybrc/iJf3dKK51w2A/sbXiKqXcZAkCWfPqcKfNxzHwS4nFveXjHogmGrs7gB6ZsyDbDRB19dHjYlnzEj7OBJizx6KpVIUOlj8zGfifw6bDfjWt+j7v/MdyrN9+WW6XXstRb/EOLfGjKKQY/y220j8AaIO9Ouui99JLkkUZbBqFTVU/ehH6f90/vnAaaepO/YxKDDpYTLo4A/KcHiD8bu7m5ooEsBiAebOTWgMbzf1QlGA2TXFqC2ZuEJmclkBrls9Ff/a1YHWAQ/+tasD7XYPzpxVldBmMSQrePNgN3a00LV5elUhLphfgwLTxL9Pi1GPlY3lePNADzYc7ce8OlvaN6yBkIwhH2Uis5Ceo5x6KlXZvPce8PnPZ2wY4hA55lzkr3+d+hTpdFRdlEhV0MyZZAj5zGcQ/PwXYNixHWf/6rtY89pzMP/ql+M+p14n4bx51SgtMOK/h3qxvWUQdk8A71tUO35/JY+HDi2//31yyFssVCX6+c+jtaAM/9jRDp8ngNICIy5bVj+xm3PKFJT++Y8YvO12uL7wJdS/9yaMn7kJOPsM2q+kkW4HiYdV8WYis5DODKOswAhJop4JLn8o7XnfgZCMF3d0wO0PoarYjAvm10y4Pq8oMuOqVQ14flsbeof8eGZzy4jK93gJyQpe2dsVOSRfO6sSK6eWjT2OZcsAqxW6gX5cXujCX1xFaO514z97OnHRwvRrUCIfnWNd1CPvhHQholdXV6OgoCCvhdR8RFEUuN1udHd3AwDq6lTMuM4xqorMONLjohPaU06hD6bJkb6leQDO8Cb9suXxdbdfWF+CApMex777EBqffwqyTgf/H5+AJQ4RXVEUNHUP4T97u+APyqgqLcCqu74I/b3/D3j6aeCee0gQuP564Ne/Jofk0qWR79fpJKxbWIs/vXcM7YNebDjaH3HLj4vdTs7F4QL60qX077PPjnn8QNQhVGQ2RDcIn/88OXoefpiEnLffJrd7thMIRJ2jLKSnHVGd4vCmX0iXZQVbjg0AAFZMLYtLdCorNOHKlQ0jyrnPnF2FZQ2lcV3bj/a68J89nXD7QzDqJVwwvxZzakePdhqN6mILFk8uwY4WO9442IPrTpmS9kzUQY8fIZMJ3oWLUbBtMwkwuSCk9/UBH/oQia9nn03zYzLU1NAh6Je/TIL2k09S74y//IUE+m9+kzJ6k2XXLuDWW6NxXNXVJODfcIM6USzXXQf8+98U8XLddTQ/lpQk/7wTIEkSSqxG9Dh9sHsC8QvpW7bQ/ZIlCf0cWvrdONrrgk6ScPrM2HsOFJoNuHz5ZLx7pA8bj/Zj+/FBdNm9uHhxXVzrC7c/iBd3dqBtwANJAtZMr8Ap08rjmk8W1Zdgc3M/HJ4A9rY7sGhy6n9vwxGxLmajDhZj3hYe5zYiJz3TjvSIkB7D2uDHP6ZILYDWzZddltRry2tOw99+9SwqnvojzvjDT2A+sI8qjS6+GPjKV8bsISEqVUqsRry0uxNHe114csNxnDevZnQB7d//JgPKkSP074svpjX/tGk40OnEy1vbEJIVTCq14ENL4staLl2xBJ7nn0PfqpWoaDmCwBf/H4xP/CnRH0lC9ITFs7gbsLKQzgzDoNeh1GrEgDuA/iF/WoV0RVGwfl9XpIL9g0smxdxAtNhixBUrG/CPHe1oG/DguW1tkV5s8eAPyvjnrnY097qhkyScP78aCyZNcG03GklfefNN1OzcjPdfdi3+sb0d+zudsBj1OGeuCmvROBARdSykq0derbBCoVBERK+oqIDVaoXFYuFbHt2sVisqKipQXV2NwcHBvI55EQ1He4eGNRzdtYsaPqYQXzCEvR3Rsu1EMoWnN+3C2b+8DwDw9g234MmSOTFnuvY4fXh2axte3NkBf1DG1IoCXLFyMi0K9HpyL+7eTe6UggISo1esoIX2wEDkeUqsRpwbvghuONqH9kHP2C9qtwPf/jY55u+9l/69YAHwzDMkPMQpogPREi2RaRvhJz8hB6PLBXzwg0BnZ9zPnXb27KEmsSUluSH+aYyyDDYROtjthN0TgNWkx4JJ8fcbOLGc+80DPXj9QGzl3IGQjNf3d+P5bW1w+0OoLDLhqlVT4hLRBWumV8Ji1KPX6cOuNnvc358sAy4SOkJpPhRNikAAuPJKEjAaG0nsVqux3owZJKBv2QJceCG91i9+Qa70e++lniCJ0N1N0QVLl0bjuL72NXKk33ijunnmv/wl/Vyam9PqSo00HE0kJz2JRqOKouB/TRSPtHhySdwZnjqdhNNnVuKSpZNgNurQYffiifeOo7nXFdP3dzu9+POG42gb8MBk0OGDSyZh9fSJq1JOxKjXYWUjuU43NvenvYx7eKwLm4VyFDGPNzVF+/9kgLLw+tI+UZzDH/5AlTkArZ0//emkX3tH6yDahwI4cOm18O/ZR3OgTgf861/kSl+5kg5KA6OL/LNqinHFyoZIk/C/bmnF+n1d8AZCFEG1fTv1rrj4YroGTZ4M/O1vwIsvQmlsxJZj/fjXrg6EZAUzq4vw4eWTE2pYWFdThm33/giyTgfjn58AXnghyZ9M7ARCcmSvUFUchyPd46F1OcBCOhNB7BX6k+mhkgDbWgaxr8MJnSTh/Yvq4q60shj1+PCyesysLkJIVvDPXR14ZW9XzNGsg24//ra1Fc29bhj1Ej64pG5iEV0QjnfB229jWmUh1i2sgSQB21sGVYmkjIc+V9iRPl51yhtvpHWOynXySkgPhC+2BeNlVTJ5gXgPBMZYgOUDleGJtG/IB3lSPTBpEjXiFBvhFLG33QF/UEZFkQkN5TE2LxpOZyfwkY9ACgTgv/TDOPjxz2HQHcCTG1vw2v4utPS7RxXR3P4gXtvfhSc2HMPxfjcMOgmnTCvHJUvrTy75NJuBO++kvMQrr6RF9y9/CcyeTWLMli3A4CDm1dkwr64YigL8e3cnLdABErHffhv42c8oH7exkVzug4PU9PTpp6mJ1BVXJNxYU5TanuQYNBpJoJ8zh5rxXXYZ4E3vxTpuhse68MY/7VQVmSFJ1HzYFY4ESAeKomBTMx1OLWsojdlhciKinPvM2ZWQJGBHix3Pb2+L/j2OQrfTiyc3Hsf2lkF6/SmluOaUKfFtNodhNelxWrgq5Z3DffD403dIG5KVSDWB8bQ19MFcENJvuw147TXKNf/HP4DK0ZvIJcXy5RTvsn49zS8uF8WlzJhB83NXF133xqK9ncSV22+nDVFDA7ktZZnm7/37yYVpU6Hp9ImUlNBhgF5PzvQ/pcfJGGk4mkjUUxIuxqbuIXTaqfG46EOSCNOrinDdKVNRY7PAGwjh+e1teOdw77iHawe7nJG4udICI65e1ZBwTBRArnSRlb63Pb1Z6ZyPrgHKyqLRSBmcy20WI3SShEBIgXOstcFbb0UjIW+5haIFk8ThDeCdw30AqKFg8aQaWnsfOEDxXVYrzTXXXktz+Y9/PGpfo9oSCz566lQsrrehpP04lF8/irZ1H0KouppiF/76V5pfb70V2LsXoUsvQ4fDi1f3deOtcM+TZVNK8f5FdQmvTwBg6vvPw9bLbwAAKDfdBPT3J/xc8dA75IOiAIVmfXwO4v/+l66LkydTs1GGAVBRSOvjflfyWeOxYncHIv2HzphdmXAsi0Gvw/sX1WFpQykUBdjdZsfj7zTjP3s6RxXUZVnB4Z4hPLetFb97uxmddi8sRj0uXzEZ0+NZGwwT0gFgbq0Ns8Nu+F2t6TPdyLKC/rBZqrJoDJNCXx9VQH7oQ3RIyUxI3kW7AGCHBsPvAQClViMMOlog2z0BlJ1yCnWq37ABWLs2Ja+pKAp2hIWrJZPji18AQE3drriCxI3582H6w+O4ymTF37e3odvhw44WO3a02GE16TGjqgizqoswqdSKXW12bDjaB1+AmqfNqinCGTOrUHKim/tEGhpI9L7pJuCLXwT27aN7QVkZLpw+A7NLatFXVYf+HwxgUtMeEldOdO/Mm0di+kc+Qgv3JBFCetloJVplZXSivHo1lQZ/6lMkwmTr+57z0TOKyaBDRaEJvUN+dDm88S0Sk6C5z41epw8mgw5LGkqTei5JkrBiajlKrCa8tLsDx/rc+PWbR2CzGlBaYESp1YSSAiPKCkzoG/LhncN9CMkKCs16XDi/Fo2VYzT1jYNF9SXY2WZHr9OHd4/04ty5NUk/Zyw4PAEoCmDUSzCfHhbSt22jAzRLnOXc6eL//o8iWACamxYtSu3rnXsu9QD561+pqemhQ8CXvkQ3SSIRv6aGbtXVJJS/9x41MT2RFSsojuuMM1I7ZgBYs4aaqN5zDwlIp52mftb7CQgB1hGvkK4oCQvpIVnB22E3+vIpZShMsmy8pMCIK1dOxpsHe7Cz1Y4NR/rRMehFY2UB/EEFgZAcuXkCoUhvhMbKArxvYR0sxuSu0cKVTlnpfZg/KX1Z6YMspGuDU0+lteR77wEf+EBGhqDTSSixkqN70BU4uYLU56NKnFAIuOYa4Ic/THqdqSgKXtvXDX9QRn2pFYuHRyPNnEmmlm99i/pg/PznZBi57TY67LTZTrpZrFact23bSXN50FoA5dxz0fPVu9BcPwPtB+3otHciEKK1uyQBZ8yqGrOhYTzMqCrCHz5zO6a99zoqWo5Q9Ngf/pD0806EyEevLo5zHfDyy3S/bl327huYtFOegerVPe12yIqChvICLEtyn6DTSThnbjXm1BbjvSN9ONbnxp52B/Z1ODG3rhirp5XDqNdhd5sdu9rscHrp8FCSgMaKQpw5uyr+uLs1a+gJmprIuFFTg0X1JTjQ6cT+TifWzqocv3+DSji8AQRlBUa9NHoSgKLQXN7eTka8D30o5WPSAnkppDMMQxeUiiIzuhxe9A75ULZ6dVRITxHH+90YcAdgMugwty7++ATcdhvwv//RAvm554DiYhQBuHrVFLT0u3GoewiHe4bg8Yewu82O3W126CQJcljUrio246zZVfGfaJ93HrBjBy3gn3mGykC7uoCBAei2bMZ0ACdJG5MmkeCyfDltiC64QBUBXTAQLtcsH6v8fdYsclJeeCG5GhcsoEZQ2QgL6Rmn2mZB75AfnWkU0jc1kytrUX1J0sKVYGZ1Ea5c2YAXd3bA7glg0B0I9xM4ObJqRnURzp9XHVMTwVjQ6SScPbsKf93Sip2tdiysL4l/A5sAEeGswARp+hSgqoriALZvj2btZhPNzdEDyfvuAy69ND2vK0l0EHvppcBjj1Fz56NHaQPR00O33btHfo9ORyL/mjUkYq9ZQw7IdIoLX/868MordO376EfJAapmhMwJlFrpmjIYY9lzhOZmqroyGul6Ewd72u0YcAdQYNJj+dTS+F53DAx6Hc6bV4NJpVas39eF4/1uHO8fO7puZWMZTp9RqVp/g0X1JZF+MOnMSnewkK4NTj0VePzxzOekF5ow4A5gwO3HlIoT1s7f+x5w8CAdQD78cMIVlsM50OXE0V4X9DoJ54/VULCyErjrLhLPn3gC+NGPyOhit0f7D52I0Qh59aloWbYGG6cvRfvsRZCNJsAH4EjUIW416TGp1IpF9SWYpsIBO0Brg8WzavCf2x/AVbdcA90f/0jXog9+UJXnH4tuZ7jRaLyVdv/5D91feKHKI2JymYqwk1nEBaUaWVawJ1zRtXhyiWomyEmlVnx4+WS0D3qw4Wgfmnvd2NvuwP4OJyQJkTg2ETm5uL50YuPdWJSV0Xpo927gnXeAyy7D5DIrygoob/5ApxOLJ5eq8v8aD5GPXlZoGn2N89hjpKsYjaQZFKoz92kdFtLzgObmZkybNg3btm3D0mENE8fj8ccfx5e//GUMDg5mdBxMaqkqJiG9Z8iHWSInfePGlL2eiFGYP8kW/wnss89SaSdAC+fZsyOf0uskNFYWorGyEOfJ1Wgd8OBQtxOHe4bg8oVQYNLj9JmVmF9nS3yTbDSSi+TLX6Z/Dw2RCHP4MHDkCA68uxN9BSWoPud0zLzoTKC2NrHXiQFZViLNn8rGy5E95xz6mX32s+Rq/PCHo+XC2YLPRzE3AAvpGaTGZsHedkfEwZRq2gY9aBvwQK+TsFwFx9dwqm0W3HB6I1z+EAZc/qig7vFjwB1AMCRjxdQyLKpXb2EuaCgvwJzaYhzodOKNAz24YsXklFdAiSzrUquRBN7Vq4EXXyQBJtuEdEUhEd3joWZx3/hG+sdgNFLj0c98BggGgd5eOhjt7o7e+/3AqlWUVVycwKGvmhgM5NpfsgR49106fPjWt1L2cmLDSJUOSuzvX9FodNEiyo6PEX9QxntHKMZh9fQK1d1Z8+psqCo2Y+uxAciKAqNeF7mZDBKMeh0qi8yYVJpA1Nw4kCu9DG+k2ZUu3ILiQITJUcTcvXEjOb5VNGLEA/UqcEWqICMcOEB56ADw058CpaVJv5aiKHi7KTwXTCuf2P1psVDF5Sc/SfO2wzHyZrdTP4wZM4AzzoCusBBTAVgdXryyrwvdDh9sViPqS62oL7ViUqkF5YWmlFyzF0yy4d1Fy7H18huw8i//R5Wue/YA5YnHWE1Ej1M40uMQ0tvbSfSTJDIRMUwYsd90+0PwBkKqGWDGornPhSFfEFaTHtNVOtQazqRSKy5bNhkddg82HOnH0V4XoACTSi1YVF+K2TVFMCQR6RTh9NPpb+rtt4HLLoMkSVg0uRRvhSvmUrEXOZG+cNNhEc8zgoMHgf/3/+jx/fdzX4Q4YCE9h2hpacE999yDl156Cb29vairq8Oll16Ku+++GxUVFWN+X0NDAzo6OlAZR/7oVVddhYsvvliNYTNZjMjJ6h3yk3takqj8MVx+pCZ2d4AuUqBYl7gYGopO8nfcMW6Zq04nYUpFAaZUFOCcOdUYcPtRbDHCZFC5JURREQkG4UgCT8sgNuzvRlWxGTNTKKIDVKIVkhUYdBKKLRNM45/5DPDPf1LUy1e+kn1NRHbupGZRFRXA1KmZHk3eUmOjxVWXwxufeJYgm8Nu9Hl1tviyO2NEkiQUmQ0oMhvQoPqzj8/aWZU40jOEtgEPDnUPRfIQU4VwDkcaD596Kgnp2ZiT/vzzNDajEfjVrzJfNm4w0KFniufspJk6laIMrrkG+M53gIsuInd8Cig2G6DXSQjKlIscc0PwTZvoPs5N2LbjA3D5QiixGrGoPjWu7coiMy5ckP7f8cL6EmwOu9L3tNtT7jxz+4OwewKQJKDalli/ByZLWLiQXIFOJ7mtFy7MyDBEw9ERFSqKQgYNv5/moiuvVOW1Wgc8cHioajWuA3ZJikZzxUC1zYJrT5kCf0hOS6wCAJgNeiyqL8E71/8/zNn4BoqPHU5pxEtIVtA7lIAj/ZVX6H7lSlqXM0wYk0GHYosBTm8QfS4/6lU+fD6R3WE3+rw6mzqC9hjUlVhx6bJ69LuoqfK4zTgT4fTTqbdOOCcdoIO1d5p60eP0odPhRV1Jan+Wfa4x8tH9fuo14XZTBKJoGs3ERF41G81ljhw5gpUrV+LQoUN48skn0dTUhEceeQTr16/HmjVr0D9G4xK/3w+9Xo/a2loY4igFtlqtqK6uVmv4TJYiGo72On0UlzJ/Pn0iBa70Ha2DUBRgakVB/Blj3/420NYGTJtGzuoYEfE1qovoozC3thh6nYQepw/djtQ29xRu9NKxSrRO5MEHSTB68UXg1VdTOra44UajWUFVkRk6SYLbH4LDm9qGoz1OH470uCBJwEqV3ejZgM1ixPIp9P/amYZmQoOecMmmqE4R1UXZJqQ7ndED0a9+lfpGMLFz9dXAxz5G+e1f/CLdpwCdToItfEA7WhOuMRGHtOecE/O3uP1BbD5GDYdPn1mZthzxdCFc6QCw8Wh/pGQ8VbQP0tqjvNCUcrcgk2L0eqqIATIa7yKuK4PDHel/+APwxhvU9PPhh1Vbu+3tIOFsTk1xUs09Y0GSpLSJ6IIlDaWQLRa8eOt3oeh0wB//mDJzS7/Lj5CswGTQxRfzJGJd1q1LybiY3CYS75LinPQhXxBHe8h8t3BSCpq5j0J5oUl9ER2INhzdsoWqMQFYjHrMCpts0rFPiDjST/z/3XsvjausDPj971WJ58on8v6npSgK/EE5IzflxGaE4/D5z38eJpMJ//nPf3DWWWdhypQpeN/73odXX30VbW1t+Ea4PLqxsRH33Xcfrr/+ethsNtx0001obm6GJEnYvn175Pn+8Y9/YNasWbBYLDjnnHPw+9//HpIkRaJcHn/8cZQOK9O79957sXTpUvzxj39EY2MjSkpKcPXVV8PpdEa+5qWXXsLatWtRWlqKiooKfOADH8Dhw4eT+v0wqUUI6XZPAL5gKGWL9kBIjuScLY23WciePdTYDQB+9jNauGchFiM1OAUQ+b+miv6J8tFPZM4calQHALfeSmXC2QLno2cFBr0OlcX0fkr1QdCWY3TwO6u6ePRmuRpgQdhZ2zrghsMbZ9Z0nAinYGSzvGoVCRtHj1K5e7Zwzz1Aays1y8xEpIsW+OEPKWpmyxYSYVKEiHexx9pw9MABcs0ajcD73x/z62w/Pgh/UEa1zYzZNenpzZBuFtWXoMhsiLjSU0mnnebuVLvbmDQh4l0yKKSXRuaCIB0E9fZGXYv33ksGFxXwB2U0dQ8BAOalSThLNyVWI2ZVF6Nz3lI0X/8Z+uBnPwu4XKq/VreT5oKqYnPsFYayHHWkcz46MwriYK3/xKgnldnX4YCsKJhUakmNuJ1Opk2jqsdAILrnBSKNlA92OuENpG5fHhoWBzvCyPjGG9TnAgB+8xtg8uSUjUGr5H20SyCk4JevN2XktT9/zkyYDBNf3Pr7+/Hyyy/j/vvvh/UEEbG2thbXXXcdnn76aTz88MMAgB/+8Ie4++67cc8994z6fEePHsVHPvIRfOlLX8KnP/1pbNu2DbfffvuE4zh8+DCef/55vPjiixgYGMCVV16J733ve7j//vsBAC6XC7feeisWL16MoaEh3H333bjsssuwfft26PiEKyuxmvTRMq0hPyatXQv87nfkWg7/XtXgQPgiUWI1orEijpwzRQE+/3nKsb3kknEjXbKBBZNsONhFnbjPmFWZslI00Wi0LJ7mJ/fcQ8LLrl3UVOTGG1MytrhhIT1rqCm2oNvhQ5fDF3FKqI3dHcCBTtosr2rUnhtdUGI1YnKZFa0DHhzodGJVY2pyUEOyAoeHKggi0S4lJeT23ruXXOkpbmgWE9u2UY4uQE2bs/RANOuprga++U1y9N95J3D55RQzpjJlBSY0w42eoRh7Jjz3HN2fey69/2JAUZSIA3VVY3nK46QyhWFYVvrGo/2Yn8Iy9Q47ud3qSlLf5JhJA1kgpBeZDTDqJQRCChyeAMq+8hWgr49iDW+5RbXXaeoegj8oo7TAiEkafv8un1qKg11O/OuKm3HzGy9B13wU+P73qfJWRRLKR9++nZpuFxVlX38VJisQGdv9rtT1U1IUBbvb6NB5waT0NOlOKZJErvS//Y2axp9xBgC6TlcWm9Hr9GFvhyNSyao2g+5odYqoNsTAAFU4Kgr1mbj88pS8ttZhdTMHOHToEBRFwbwxyqDnzZuHgYEB9PT0AADOPfdc3HbbbZgxYwZmzJhx0tf/+te/xpw5c/Dggw9izpw5uPrqq/GJT3xiwnHIsozHH38cCxcuxBlnnIGPfexjWL9+feTzl19+OT784Q9j5syZWLp0KR577DHs2rULe/fuTew/zqSFSLzLkI+yDgHKOlXJyagoSqTJ6JKGkviafT7xBPDmmyS6CBEmi5lSXoBiiwHeQAhHetV3mAiEEyAuN295eTQW5667qBFTprHbqeIAYCE9C6gNb147U+hI33qcGv5NrShAtU27m2WAch0BctbEU4EWDw5PINxAURqZNS/iXTIowEQIhch1J8uUpSuuM0xi/L//R83zOjqibiKVEY032wY8sX2DENIvuyzm12gd8MDpDcJk0GFaChqJZRPDXempWhvIsoIuh3Cka3tuzRvEPL53L62XMoAkSeGGo4DnlfXA44+TMPTrX1MFikqIQ7X5dTbNHqoBVC0yqdQCv9GMg7d9kz744IPA8eOqvk63M4F8dBHrcu65qv5uGe1QHo526UthtEvrgAeDbuqVkOoeQ2lDxLsMy0mXJAmLw9Wru1rtKdsniHz0CtFIWVGof1prKzBrFvCTn6TkdfOBvHekG/USPn/OzIy9djzE+ge2cgJB6sCBA1i1atWIj50iIj3GobGxEcXF0Qmtrq4O3cPE1kOHDuHuu+/Ghg0b0NvbCzmc33n8+HEszFCTHGZiKovMONrrIiF97iRqFLZ1K/DvfwMf/3jSz99u96LH6YNRL8V3sjw4CIhKibvuyolGlDqdhPl1Nmw42o897faULQBEVmXcWfOf+xzlWR46RALMd7+bgtHFwbPPksg2bx5QX5/ZsTCR5nTdztQ0HHX7gxGXSaoc2tnErJoivL6/G31DfnQ7fahJwcHBYDh6o6TANPL3deqpVF2UDTnpjz5KfTdstmhMF5M4ZjNFvFx2Gd1/+tNAY6OqLyGaiPUO+eANhMbP225tpd+vJFHlWIzs76RowNlpyEPONAa9DvPqbNjU3I99HY6UrA16h3wIhBSYjbr41wZMdlJTQ7EAR4+SweX88zMyjLICE/r7nCi/Ldzj4rOfVbXZsd0TQEu/GwAwt06bsS7DWT6lDO2DHXhj4RmYfeZZ0L31JvC1rwFPPqnK8yuKMsyRHse6QwjpHOvCjIGIFHV6g/AH5ZT0IBP7hLm1xWnpcZYWhJD+zjtkKgknNcytK8b/mnrR7/KjdcCDhvIC1V+698R89GeeAf7yF+qd9sQTKalqzBc08u5MHEmSYDLoMnKLVaSYOXMmJEnCvn37Rv38vn37UFZWhqqqKgBAYWFqnD3GE06nJUmKiOUA8MEPfhD9/f34zW9+gw0bNmBDeAPv96c2R4tJDpGL3OsM/55Evuk//6nK828/PggAmFNri6/51d13A11dwOzZOdVFen442/FYnxvOFGQjewMhuHyUpVYaT7QLAJhMJLwAwI9/DDQ3qzu4eHniCbq/7jpuNJoFVBSaYdBJ8AXkSO62muzvdCIoK6i2mTG5TPvRHmaDHjOraYEq3HZqIw7VSk9sJiacjBs3ZrYnQmcnRZAAFBc2aVLmxqIlLrmEXIM+HwkwKlNoNqCswAhFAdoHJ3ClP/883a9ZQzmgMRAMyTjYRUL63FqNOM4mYF4d/T+be93w+NX/m+wI56PX2iyadvTmHVkQ71JWYMSqpx+F5UgT/Y2rbMLYH74+NpQXxNcYM0eZUVUEm9UIT0BG053fpvXvU0+RyKYCdk8A/qAMg06K/VDN5aLYCYCFdGZMrCY9Cky0lx9IQU66NxCK9EpYWK+BWBfBsmVUXT8wAOzfH/mw2aDHnPDBujhAUBtRPVBRZAK83uia8RvfoJ5KTMLkvZCeC1RUVOCCCy7Aww8/DI9n5Iams7MTTzzxBK666qqYF85z5szB5mHNDgBg06ZNSY2xr68PBw4cwF133YXzzjsvEjfDZD8i2qVnyEdVD0JIf/llaoyRBE5vIHJBXNIQxwVx2zbK0QXo3pw7jUZKC0yoL7NCUYB9Hc6JvyFOxMKl2GKA2RDHwYTggx8EzjmHBJg77lB5dHHQ3g689ho9vvbazI2DiaDXSRFXeiriXfaH/x60Xro9HOGuO9jppEZtKiPEs4qiEzbLCxZQU0qnM7Ou9Ntuo0iClSuBm2/O3Di0hiSRu1+nI3fRf/+r+kvUl5Ezqn1wgrkggViXI70u+IMyii2GvDhUA8gNVm0zQ1aUyCGCmkTz0fPj55k3ZIGQXjnQjVVP/Zr+8dOfAqWlqj23oijYFxbSxWGT1tHpJCybUgoAeK9kCpQbbqBPfPnL5FZNEhHrUlFkhj7WOM0336Q937RpwMzMVOozuYE4nElFvMu+DgeCsoKqYnN8+f7ZjtEYNbgMi3cBok1HD3UPwe0Pqv7S/cOiXfDTnwLHjlFj0a9+VfXXyjdYSM8RfvGLX8Dn82HdunV466230NLSgpdeegkXXHAB6uvrIw0/Y+Ezn/kM9u/fj6997Ws4ePAgnnnmGTz++OMAkLC4UVZWhoqKCjz66KNoamrCa6+9hltvvTWh52LSS1mBCXqdBH9QpqZ1q1YBVVWUoS3cCQmyq80OWVFQX2aNvbxQlimCRJaBq67KWClrMiwIu9L3tKufeTbgosMNkVkZN5JEbnRJAp5+Gnj3XRVHFwdPPUU5baedRgt3JisQueVdKgvp/S4/uhxe6CQJc/LEgQoAU8sLUGjWw+0P4VifutnIiqJEyuGnnFgOajAAH/oQPX76aVVfN2Zefx34859J7H3kEUCfwMEfMzaLF1OsC6CaADMcEe/SNuge+4v6+kiAAeIS0qPCWf4cqgHA3FpaG+zvVL9CRRyqcT66xhgupKcoQ3ciJv/kARgCfnQsXQ1ccYWqz91h92IgnIc8qzp/1gYLJtlg1EvoG/Kj54676eB70ybgT39K+rkTajQ6PNYlj+ZkJn6EcUNtR7qiKNjdTtfGhfUl2lsbjJKTDtC+q7bEgpCsYG+7umuDYEiO/J4qPI5oNdF3vwsUqB8jk2+wkJ4jzJo1C5s3b8b06dNx5ZVXYsaMGbjppptwzjnn4N1330V5eex5s9OmTcNf//pXPPvss1i8eDF+9atf4Rvf+AYAwJyg81en0+Gpp57Cli1bsHDhQtxyyy148MEHE3ouJr3oh5X+9Qz5SPR43/vok0nEu8hytOv20obS2L/xscdow1BUBPzoRwm/fiaZVU25boPuANomKouPk4FIPnoS5a9LlwKf/CQ9vuUW1QWYmBge68JkDTXhA69uh0/V5xWl242VBSgw5U97Fp1Owpxa0XRUXRdq75Afbn8IRr00ugv16qvp/pln0h/voijR8tGbbwZWrEjv6+cL991H2fNbtwK//72qTy2E9E67D4HQGNeIF1+k99aiRdQANQY8/hCae8N5yHl0qAYAc2qLIUnk8h9UUYRw+4OROK5aFtK1xdKlVJXZ1wccPpz+19+5E9Ynab32+idvhz+krpgvhKOZ1UXayUOOAbNBjxlVFP22J2SlmAWAotCGhpJ67m4nHarF1Wj05ZfpnmNdmAkoCxu5RBNLtehy+NDr9MGgk7S5NhhDSAeoITkA7FS56Wi/2w9FASxGPQq/ex+ZJFes4L23SuTPFUsDTJ06FY8//jg6Ozvh9/tx/Phx/OxnP0NFRUXka5qbm/HlL395xPc1NjZCURQsXbo08rEPfehDOHToELxeL15//XX09fVh8uTJsFhoAf6JT3wCg4ODka+/9957sX379hHP++UvfxnNwzKWzz//fOzduxderxc7duzAWWedBUVRcOmll445DiY7EPEuoiGFGjnpx/rdcPlCsJqii8UJGRoCvv51evytb+VsA0py1oSzkVU+XRYlWmWJOtIF990HFBZS7MNTT6kwsjjYv5+EH4MBuPLK9L42My41wxqOyipFkSiKgn2dIg9Z+43ETkSUqx/pGYI3oJ6gfTzsRq8vs45evn3hhVSC39kJvPWWaq8bE88/T+66wkLqd8Gkhupq4JvfpMdf/zpF+aiEzWpAscUAWVHQaR+jQiWBWJcDXU7ICvVKiDS/yhOKzIZI9YhotqoGwyOe4upFw2Q/JhOwfDk9zkS8y9e+BklR0HTO+9E1dzH6XOodsgdCMg52RyPf8g0R/Xagy4nQ//sSMH06xR7+4AdJPW/EkW6LcX49fpzW5Tod9d5gmHGoKKT3Vf+QuoYbYb6bVVOkzevYmjVU7dHURD3ghjG7phhmow52TyCytlcDEb/T2HMM0qOP0gd/9KNIs1MmOfinmKc8/PDD2LRpE44cOYI//vGPePDBB/Hxj38808NiMkSVaDgqLooXXkgi5/79wJEjCT2nEJDn1BbHntH3858DPT3kbPviFxN63WxhQX0088wfVM/xHXWkJymk19VFmwDecQfgUdc5Py7Cjb5uHVBZmb7XZSakvNAEk0GHQEhRzW3SNuiBw0Ol29OrUtMMO5upKjKjstiMoKzgUFdyTrPhjBnrIjCZgMsvp8fpPCwLhYC77qLHt9xCYi+TOr74RbpmdnYCDzyg2tNKkhRxpbcOjHJ9cLmiLsY4hPT9w2Jd8pFIvEuHQzXnWeewRqOMBhHxLuvXp/d1X30VeOklwGjEsVvJ5CKqSdTgSI8LvoAMm9WYN70ShiOi3zz+EJqHgoCo5H7wQcoxToAhXxAuXwiSFDVJTcgrr9D96tWq5t8z2qQ8HO0y6AkgOFa1Wpz4gzIOhHuHLJikoSajwyktpf5FwEmudJNBF1kT7VKx6agQ0lc9/D1am19yCXDWWao9f77DQnqecujQIVxyySWYP38+7rvvPtx222249957Mz0sJkNEHOlhFwNKS4G1a+lxAq50byCEIz0kGC2IdbM8OBh1YXzrW9SYI4eZVGJBWYER/qCsWmMxWVYi5dsJZ6QP59ZbgYYGoKWFctPTgaJwrEsWI0lSJFdTrZx00WR0VnURjPr8W3ZIkoR54TJVkQ2dLCFZicRGNYwlpAPANdfQ/V//mnTz6Jj505+AvXuB8nLg9tvT85r5jNkcjUH78Y8TFmBGo75M5KSPIqS//DLg9QKNjcCSJTE934DLjw67F5IEzKnRYOl2DMysLoJRL2HAHUCXShFa7eHfz6TS/BMj8wJxUPX008DAQHpeU5ajzeg+9znULCMB6Giver0+9naQYDSvtlh7ecgxMDz6bX+Hk37PZ51F8+oddyT0nMKNXl5oin29NTwfnWEmoNCkh8mgg6KQmK4GB7uc8AdllBZo/FBtnHgX0V/tSI8LHr861at9Lh8atr2LyjdeIYNkktUuzEjyb0fLAAAeeughtLe3w+v14uDBg/jmN78JgyF/cmuZkQghfdATiGahiniXF1+M+/kOdQ0hKCuoLDLFntH30EMkps+fH832zWEkScL88Kn6XpXEs0FPACFZgVEvwWZR4e/VagW+9z16/MADQEdH8s85Ee++Cxw9SpEPohkik1XUqNhwNDisdDtfHagAlXBLEgmSdnfyG49Ohxf+oAyrSY+q8VxnZ58N1NQA/f3kLkw1Ph9wzz30+I47gBKNOouyjQ99iH7XPl80a1cFojnpHoROjHoaHusSowgm4kymVhSg0Jyfa06TQReJu9unQtNRWVbQHRbPOB9do6xdS30IPB7g8cfT85pPPgls20Y9GO66C9MqqZqsy+GFyxdM+umHfEEc6yN3ez6vDcQh+5GeIXiDMvCTn9B8+tRTwDvvxP183eF1W8yNRkOh6Npg3bq4X4/JPyRJQkW4KrpfpcrVPe10qKbJJqPDGUdIry62oNpmRkhWVGtI3m/34Mxfh/f5N98MzJ6tyvMyBAvpDMOg0GxAgUkPRYmWAUWE9DfeiLvxTcRlUmeL7YLY20tCOgB8+9uAXhvZaPPqqLFY24AHAyosNoQTqMZmUW+hcc01VM7pckWzdlOJcKN/+MMkpjNZhxBj1HBLNvdR6XaxxaBtl8kEDM9GVkM8Ox4WIBrKCsafC/R64Ior6HE64l0efZQc0XV1wOc/n/rXYwhJAn74Q3r8xBPAli2qPG15oQlWkx6BkBJpYAeAqhvEIXuMsS6KEt0c5rNwBkSzkQ92Ok8+oIiTXpcP/qAMk0EXETcYjSFJ0fn04YdT3yDe6x3Z/LKyEoVmQ+SQvbkveVc6RRsBk0otKMvj921VsRmVRaZo9NvSpcCnPkWf/OpXqYozDnrCEZ0xm5i2bKGD9pISYNWquF6LyV9EvGhEM0iC3iEf2ge90EmS9nslCCF961bAfnKEi/j/71Ghv5o/KGPSC39B9ZH9UEpKuF9RCmAhnWEYAKM0HJ07F5g2DfD748plHHT70T5IpdtzY70gPvggNUlbtiyurNVsp9hiRGMFicW725PPPGsKO3tnqVkSL0nRQ4zHHgNOaCqsKoEA8Mwz9JhjXbKWmmLaLPcO+ZLOP9wbjnWZk6el28MR4uE+FbKRJ8xHH46o8HnuORJIUsXQEPCd79Dju+8GCmIYG6MeK1YAH/0oPb799rgFmNGQJCkSF9I2PCf9jTeogqyqCjjttJieq8PuxaA73CuhMsYG5BplankBCkx6uP0hHEtSlOwYpL/puhIVD9iZ7OO668gd3tQUzbROFb/8JR2ITp4MfOlLkQ83VtKcnmy8i6IokZiz+XX5XbUkSVJ0bSAO2b/1LaoYfftt4IUX4nq+7rABoro4xuoUEety3nkU/cAwMVARzkkXfbuSQTQZnV5VqP1KtWnTqPI+EKBD0ROYW2uDXiehx+kbaV5IgIHuAZz+O9rfS3fdxT3JUgAL6QzDAIi6F4SbAZIUdaXHkZMuYkymVhSgKJYLYmcnNRkFgPvu01wn6YXhpqN72h1JiZJObwDt4Q3zzGqVRYg1a4CrriLh5dZbVRFgRuU//6Hqg+pqWrQzWYnNaoDVpEdITq7hqDcQQnN4w53vDlQAmFFVBJNBh0F3AB32xBfIvmAo8v0xCelr1lAvBKcT+Pe/E37dCfnpT4Hubmp8KRx1THq5/37KTH/jjYRi2UZDxLuMyEkXsS6XXBJzBZlwo4u/g3yGspHpQFzE3SSKmAs41kXjFBUBn/gEPf7lL1P3Ov390QPR++4jQTeMOAA71udOqpKi2+lD75AfBp2EWTX5fagGCKMBHVba3QFg0iRq1A1QRUAwtigdbyAEezizOmZHOuejMwlQFu7TFTHfJUgwJGNf2HAj9suaRpKAr1PjZvz4x1QNPgyrSY/pVWTAS9qV/qMfoqi/B676KdSUnlGd/F7JMgwT4aSGo8BIIT0GcZVcJnRBjNll8sADlPt46qnAxRfHNeZcYHplIYotBnj8ITT1xBeRM5ymbvre+lJrbAcU8fL975MA8/rrcTtgYkbEulx9NTtfshhJklBjo/mgMwnB92AXxRZQ6XKMmzoNMyIbOYm+CW0DHsiKghKrESUFMTRl1unooAxIXbxLfz9VFgEUz5XjzaJzlilTogLMV76iSoPZycMajsqyQrESzz9Pn4yxgiwkKzjQSdcwzZdux4g4XDzcPQRfMPHGYp12OuCoK8nf6Ky84XOfo/sXXwSam1PzGg88QNUmixYBH/vYiE/V2MwoMOnhD8qRBreJsKNlEACZQixGbUQ5JkOxxYiGshOi3776VWrYvXcv8Ic/xPQ8otGozWqM7efqcFDfIoCFdCYuakss0EkS+ob8SeWkH+5xwRsIodhiwNRYjCFa4KqryHDS2ws88shJn14Q7q92IJnot/Z2VP7qZwCAo7ffRft7RnVYSGcYBgBQWSxOl/3R2IGzz6by/Pb2mCI/Wgc8cHjCpdtVMeRft7RELyLf+U7MDctyCZ1OilwUd7YmHu9yKCykz0yVe2fqVHKjAxQL4FengUyEoSHg73+nxxzrkvWIeJdkGo7u7xBNRlWMIspxhIh4sGso2tg5TlrCERsxudEFIt7lhRfi7nkREz/4AeU9Ll6siWbROc0dd1AJ74EDwG9/m/TTVRWZYTLo4AvI6HX5gI0bqTF1cXHMlUVHe2mzXGTO714Jw6kuNqO8kLKRxUF5vHj8IQyEmxfXsSNd+8yZA5x/PhlbRhFgkubYMeBnJL7gBz84qdpEkiRMDccVJhrv4vYHcSBchbGkoTThoWoNcbC2X0S/lZQAd91Fn7z7bjIcTYBoOhyzG/2NN8jtPmsWRU4wTIwUmAyRqKdkjCEi1mX+JBt0Ou1pAKNiMERd6Q8+eNLf9tRyquj3+EM4kqgB75vfhN7jRvv8ZVAuvyLJATNjkTIh/f7778dpp52GgoIClJaWxvQ9iqLg7rvvRl1dHaxWK84//3wcOnQoVUNkGGYY5QUm6CQJ3kAIQ75wGaHFQot2IKZ4F3ExnV1TDKM+hunlO98hwfbss4Fzz01w5NnPwnobdJKEtgFPQmVwLl8w4v5RPdZlOHfcQbErhw6Nmt2WFM8/D7jdwMyZ3NAoB6gONxXrciZWtml3B9A26IEkAXNq2YEqmFxmRYnVCG8ghL0Jlm0eF/noFXEI6cuX09+ex6N+xUlHR1R8uf9+zcVz5RwlJcC999Lje+4h12ES6HQSJpXSfNA+6I3Gulx8ccwuJxHrMqe2OH82yxMgSRLminiXjsTiXTrCbvTyQhM7e/MF0XT0t79Vv+fFLbfQmvzcc4F160b9EmGSSVRI393mQFBWUGOz8OHPMGZWF8GolzDgDqBTGBhuvpmqjNraohGY4yAc6dWxCum//z3dX3BBIkNm8pz5w/r+yAk4p+3uAI73uyFJURd23vCxj5GBravrJMODThftm7A3kUOKHTug/O53AIC3bvoaKmKdD5i4Sdlux+/344orrsDNN98c8/f84Ac/wM9+9jM88sgj2LBhAwoLC7Fu3Tp4U9kci9EkjY2N+MlPfpLpYeQUBr0O5YVUjt87vAt3jDnp/qAccU3PnxSDcHb4MDW3BCiHUYNudEGxxRjZfOxKwJXe1D0ERSHHmc2SwsgEmy2ajfntb1Ncg1qIWJfrrtP071oriGiXviEf/MH4ndOiPHlKeYy9EvIEnU7C8qllAICtxwfi3ny4fMFI/FZczl5Jwv9n777Do6i6B45/dze990ZCh4TeQZqgoKCoKKiIoKAgFixYea0g4osN9bWAPxUpiogNGzZEOoj0XkJoIb33stm9vz8mWQgJqbsJSc7nefYhmZ25927YnZ05c+ccxo/XfrZ2epc5c7QA/YAB578vRP2aNg3at4ekJC1tVy2FFKcNiUnNPR9Ir2Jal+yCIk4maUG3CLk7pZSI4ouM0Wm5ZOVXPw1PvORHb3puuEGreZGScr54uzWsXq19tu3stHoXlzhOa+7jgl6nIzWnUMvnXQ0ms2L/uXQAuod5SXHcCzjY6S0TZSwzfJ2ctPMj0FLuVHJMnlRcnLBKM9L//BO+/1678H3//TUet2i6Wvm54mRvICu/iOi03GpvfyhWOx9u7uOCp3MTSwdob69NXgPtGK2g9KSlTsVxlFPJOecnN1aFUvDkk+iU4tiQ60nv3rvqF9ZEtdkskP7yyy/z+OOP06VLlyqtr5Ti3Xff5YUXXmD06NF07dqVZcuWERsbyw8luRgF27Ztw2AwMKoRnqxK8Lv+WfKkXzhruiRv+fbt2kn5JUQlZVNYZMbT2Z6QqpzUzZmj3VI4ciQMGlSbYTcIXUO1q+2H4zKrHZgsuUBRJ0WZ7r1Xy42ZlgYvv2ydNhMTYc0a7WdJ69IguDvZ4+Zoh1IXFCCuIqUUR4tPBCNkNnoZHYM9cLI3kJ5rJKqat22WnKz4uzvi4lDNCxQlKVd++037fFvDxo3wf/+n/fzf/8pFssuFvb2WmgG0glbR0bVqrlnxRRuXr7/U7lhycoLrrqvStrvOpGEyK0K8nAhwl4DvhTxd7Gnm5YxSWk2J6ootDqSHSH70psPODh54QPvZWkVHc3Ph4Ye1nx9/HDp3vuSqTvYGyx0qJ5Or9/0VlZRNVn4RLg4G2kuR0TJKjpeOxWefz408YYJ2TJ6eDq+9dsltjSYzqTnahY1KA2cFBef/vx95REvJJkQ12Rn0hAdpn+Pq3mFpNitLMc0mUWS0PPfcA82aaXecLFlS6ilvVwdCvJxQCsv5VJX8+iusXYvJ3oHN9z5Bl2ae2FUlQ4CokcvmL3vq1Cni4+MZXpJGAvD09KRfv35sKymEUY6CggIyMzNLPRqzRYsW8cgjj7Bx40ZiY2PreziikQkonoUamZB9Pk96aCh066Zd5fz990tuW/Il2iHYo/JZJgcOwBdfaD+XzLZo5Jr7uODlYk9hkblaJ8y5hUWcKw6etfWvg9l8BoMWeAHtJG337tq3uWwZmExaSpd27WrfnqgTJfuD6uZJT8gsIC3XiL1BZ9tURA2Ug52ebsUX1nadSTu/r62CsynFaV1qUpSpY0fthNxoPF8ssjbS07XbU5XSLsANGVL7NoX13HQTXHmllv6hJNduDQV5OOGRnkz/94vvWHrxRe0OpkrkFBRxoHgGar9WvrUaQ2NVMkt/b3QGRdWom2A2K8u+WWakNzFTp4KDg1avYOfO2rf36qta8dKwMC0fdyVa+Wl3WJ5OqV56l71n0wHoEirBnfKU3MGXbzSdT51jMJwPoL/33iUviqZkF2JWChcHQ+V3Ab71lnZBNCjIehNmRJPUMVg7lo1KyibfWPWi2adTtJnWzg4GWvtVoaZaY+ToqBUVBu0zflFx+JJ0N4diM6t2nlBUpBWZB/bcfBfZIc0tk/iEbVw232Lx8fEABAYGlloeGBhoea488+bNw9PT0/IICwurXsdKQU5O/TyqcfIMkJ2dzcqVK3nwwQcZNWoUSy64erVkyZIyueh/+OGHMgHNuXPnEhAQgLu7O1OnTuU///kP3bt3tzw/efJkbr75Zv773/8SGBiIl5cXc+bMoaioiKeffhofHx9CQ0NZXJx7qUR0dDS33347Xl5e+Pj4MHr0aE5fUFG+pN233nqL4OBgfH19mT59OsbincbQoUM5c+YMjz/+ODqdrtS4N2/ezODBg3F2diYsLIxHH32UnJzzB2+JiYnceOONODs706pVK5aXpJAQ1dYh2AMHOz0JmflEJV1wgFxyB8Qvv5S7XVa+0TJTsiRn2iWlpsKYMWA2a7eG9+5tjaFf9nQ6HV2aVb/oaFRiDkpBoIcTni51dOvb8OFaVXGTSQuQGat/y7nF8eNanl7QTv5EgxFYkic9o3qB9MNx2vu7jb8bDnaXzWHGZaVbmBd2eh1xGfnEpFdeRAy0mf6W/Og1CaTD+VnpK1bUbPsLPfwwnD0LrVuD3E12+dHptIAJwOef1yrgZmfQc+1Hc3HKyiCvc1fLyVpldp9Nw2hSBHk60aI6Of2bkA7BHrg72ZGZZ2RfNY4NknO0tFsOdnp8XR1sOEJx2QkIgNuKC8jVdlb60aNawTvQArVulV/8Lgmkn0vNq/IdlomZ2nedXqeja6hXTUfbqOn1OsJL6ibEXzAx8LrrtAvVBQXnj6cvknRBodEKJzOdPq1dOAGYP1+rqSFEDQV6OOLr5oDRVL2i2QcvmHzXpC+q3Xeftj8/ffr8BMNi7QK1ugmpOYXn6yZU5JNP4MgRCr28+Xf8A7QLdMPdlulgRfUC6f/5z38sQc5LPY4ePWqrsZbr2WefJSMjw/KIru7tq7m52kFDfTxyq5dP6uuvvyYiIoLw8HAmTpzIZ599Vq2ZbMuXL+fVV1/l9ddfZ9euXTRv3pyFCxeWWe/vv/8mNjaWjRs38vbbbzNr1ixuuOEGvL292b59Ow888AD3338/586dA8BoNDJixAjc3d3ZtGkTW7Zswc3NjZEjR1JYeD7X9rp164iKimLdunUsXbqUJUuWWC4GfP/994SGhjJnzhzi4uKIi4sDICoqipEjRzJ27Fj279/PypUr2bx5Mw+X3JKGFqSPjo5m3bp1fPvttyxYsIDExMRq/W2FxsXBjh5hXgBsO5ly/v1VEkj/449y37dH47NQSrv9u8Jgb1GRFqA9cUIrsvHRR1Z+BZe3jiEeGPQ6EjLzqzzLNzJRm71eJ2ldLvTee+DrC/v21TzHbmEh3Hmn9p65+moJpDcwQSWB9GrMSE/NKeRgjHaA3OSKB1WDq6OdpZjQrjNVS7OSkWckK78Ig15HiFcNUzmMG6f9u3atlnKpplas0OoeGAzawb+75L6+LPXpo+2DldIuoqSn16ydb78l7O/fMBns2D1rvpY6phK5hUXsi9b669fKR/IhX4K9Qc8VrbXZ+v+eSq3yrD5LfnQPJyng2hSVFB396istX3pNKAUPPaRNlhg1CkaPrtJmPq4OeDjbU2RWVc6NvLd4X9A+0E3qplSg5A6Vk0k55/cFOt354/ClS+HgwTLbldwdUGn6rBkztJomQ4eer5siRA3pdDrLBLqqpnfJLijiVPFkvc5VqanWmDk7w1NPaT//979anKSYo52BtgHa/uBQTCV/28xMy0W2rRMfpsDNgx7NvWwxYnGBagXSn3zySY4cOVLho3Xr1jUaSFBQEAAJCQmllickJFieK4+joyMeHh6lHo3VokWLmDhxIgAjR44kIyODDRs2VHn7999/nylTpnDPPffQvn17XnrppXJz2Pv4+PDee+8RHh7OvffeS3h4OLm5uTz33HO0a9eOZ599FgcHBzZv3gzAypUrMZvNfPrpp3Tp0oUOHTqwePFizp49y/r16y3tent788EHHxAREcENN9zAqFGjWLt2raVPg8GAu7s7QUFBlv/zefPmMWHCBGbMmEG7du0YMGAA7733HsuWLSM/P5/jx4/z22+/8cknn3DFFVfQq1cvFi1aRF5e1Wb4ibJ6tvDG0V5PclYBxxOKry736wchIZCRoc0iv6AohlLK8uVZ6Wz0J56Av/4CV1f46SftKmwT4uJgZ8kLWZVZ6XmFJqJTtfdyW/86DqQHBGgFp0BLv3P4cPXbmD0bdu0Cb28tvYu+Cc86aIBKZqSn5RqrFNxRSvH30URMZkVrf1fCfCRvb0V6tvBGp9NOmFNzCitdv2Q2epCnU81n+rdpowVXzWZtlnJNnD0LJYXkX3gB+vevWTuibrz3HjRvrhX4vueeat8NSUqKJWi3Y9x9HAtqU6XN9pxNx2hSBHg4WmawivJ1DPbA182BfKOpyhfW4ooD6cGS1qVpuuIK6NFDS9302Wc1a+PLL2HdOi2Y8/77Va5xodPpLOkYTifnVLK2dlHtWLw2KaRb8WQdUb4Adyf83B0xmZXl4gOgnYeNHat9dz/5pHbHaLHTyTmcSMxGp4P2QRWcK6xeDT/+qOXZ//BDqWkirCIi2AOdDmLS80jPrfxY9khcJmalaObljK+bFMLkwQe1iWsnTpQpIF1SdPRYQhbGilK/zZsHSUnktW7LvuvHEeTpRLDUTrG5ap2J+fv7ExERUeHDwaFmtxe2atWKoKAgS2AVIDMzk+3bt9PflidpLi6QnV0/D5eq3+Z67Ngx/v33X8YXXz22s7Nj3LhxLFq0qFpt9O3bt9Syi38H6NSpE/oLAl6BgYGlAu4GgwFfX1/LrO99+/Zx4sQJ3N3dcXNzw83NDR8fH/Lz84mKiirVrsFgsPweHBxc6czxffv2sWTJEku7bm5ujBgxArPZzKlTpzhy5Ah2dnb06tXLsk1ERESZNDei6pzsDfRs7g3APydTMJuVNuvwm2+0APiff8Ltt1vSfcRl5JOaU4i9QVfxrOmPP9YO1EGbwdhEi9t0Kb6l9Vh8ZqXByaikbMxK4e/uiHd93Lp9553aLKXCQi3Fi6nq+e9Yv/58XsdPPtEKqogGxdnBgIezNvO0KkUxjydkE52ai51ex9D2ATIDtRI+rg60Lr5AVpXgWa3TupS4+27t35kzqx9MN5th0iTtomq/fvD887Ubi7A9X1/49lstp/IPP5xP91JVM2ZAYiLmjh3ZMeEhMvKMZOZXnO4r32iyBIH6tfKVfUEl9HodA9v6AbDnbBpZlfx9AeKKU0IF1/TuFNGw6XTnZ6UvWKDtk6sjPV2b3ALaBdFWraq1ecviQPqp5JxK744+GJNJkVkR6OEkF36qoHcL7Rxs+8nU0ncEvvqqdjfQn39q38MmE4VFZtYe1c6lezT3vvSM9Lw8rbAoaAVlO3a05UsQTYibo50ldVtls9KVUhyM0fZVnZo13smv1eLmpn0mQfuMm88HzEO9nfFw1uqrXTJ1zpkz8M47AGyc+jRmO3uZjV5HbDY98OzZs+zdu5ezZ89iMpnYu3cve/fuJTv7/JsgIiKCVatWAdrV7RkzZjB37lx++uknDhw4wN13301ISAg333yzrYapHYi4utbPoxonFosWLaKoqIiQkBDs7Oyws7Nj4cKFfPfdd2RkZKDX68scyBhrmNfY/qJbdnU6XbnLzMUf9OzsbHr16mX5Py55HD9+nDvvvLPCds3minPrZWdnc//995dqd9++fURGRtKmTdVmRYnq69HcCyd7A6k5hRwtnkXCgAHaLHJHR+3fu+4iP7+QPw5pNQzaBrjjaGcov8ENG84f8M+dC7b8TF/mQjyd8CvOJ3ekkkrcJV+a7eqrYKNOp6Xf8fCA7du1mY1VkZZ2vgjhlCnaLBrRIHUovs14/bEkSw7O8hQUmdh4PAmAPq186i6ffwPXq/iE+UhcJjkFRZdcTylluTul1oH0Bx6AyZO1C2N3333+zpOqmD9fu0jm6qoF4auQ4kNcBvr0Ob///s9/tP/Dqli9Wrvwrdej/+wzfH21E9/YSvL67z6bRmGRGX93R9r4y2z0qmjt50ozL2eMJsX2k6kVrptXaCItVzvGL0nBJZqg8ePB31/Lr3vFFVoByap64QUtvVd4uDbDuZpCvZ2xN+jIyi8iOfvSs1BNZsX+4oLD3cO85KJaFUQEudMu0A2zUvx+MP78TNTwcC2Vj52dllpt0iT+iUwkM8+Iu5Md/VtXUND5tdfg1CkIDa1SQVkhqqOk6OjhuIoLY55LyyM914iDnZ52AZIS0OLhh8HLS7v7+5NPLIurlDrnueegoICcAYM53GsIbo528retIzYLpL/00kv06NGDWbNmkZ2dTY8ePejRowc7Lyh2dOzYMTIuuIL+zDPP8MgjjzBt2jT69OlDdnY2v//+O05OTfsgsaioiGXLljF//vwyAeWQkBBWrFiBv78/WVlZpYpw7t27t1Q74eHh7Nixo9Syi3+viZ49exIZGUlAQABt27Yt9fCsRhETBwcHTBfNeO3ZsyeHDx8u027btm1xcHAgIiKCoqIidu3aZdnm2LFjpNc0D6gAtLxcfVqen5VuMhd/KV59NXz/vRY8WbmSuNvvIj27AA9ne65s71d+YydPaoHUoiItR+tzz9XRq7g86XQ6y6z0AzEZlzzgyDeaLDNQ2wXW4xdiaOj5QlTPP6+lB6iIUnD//XDuHLRrJ0UIG7grWvnS3MeFwiIzP++LJa+w/LsS/jmZSnZBEV4u9pbZVKJyzbycCfFyKnsb90WSsgrIN5pwsNPXPnBmZweLFp2fATNjhnZiXVnKj717z89Af/dd7fMtGo5p07QLJ2az9l1cXIvmkjIytH05aO+Vfv1o5q3Nfo5Ju3QgPd9oYs/ZdACuaC250atKp9MxqJ12HHUwNoOU7PIvXJrNiq1RyQB4u9jj7HCJCQyi8XNxgd9+047Tjh6Fvn21WkaV2blTm8UO2r+O1U+vYG/QE1Z8UfdUBeldopKyycovwsXBYEltKCqm0+kYFhGIm6MdqTmFbI5MPv/kmDGwcqUlmO7/yP3oTCaujgi4dMq3EyfO51h/550qFZQVojra+LviaK8nK7+IcxUcH5TMRo8Icq95isLGyNPz/DH5Aw9oRUiLJx93LE7vcjY11/L3A7RjuU8/1VJ06XRsun8m6HR0CfXEIHVT6oTN3sFLlixBKVXmMXToUMs6SikmT55s+V2n0zFnzhzi4+PJz8/nr7/+on379rYaYoPxyy+/kJaWxpQpU+jcuXOpx9ixY1m0aBH9+vXDxcWF5557jqioKL788ktLIc8SjzzyCIsWLWLp0qVERkYyd+5c9u/fX+uTnAkTJuDn58fo0aPZtGkTp06dYv369Tz66KOWgqRV0bJlSzZu3EhMTAzJydpBw8yZM9m6dSsPP/wwe/fuJTIykh9//NFSbDQ8PJyRI0dy//33s337dnbt2sXUqVNxdpZbXWura6gXro4GMvKMpa+CXn896ssvUXo9rX7+mmELX2V0t2BcHMopHpSZCTfdpOVY7d1by+MoJ9VEBLljb9CRkl1IzCVm9p1MysFkVvi5OeBTH2ldLnTffXDVVdqtoffdV3HAbdkyLQ1QyYwZOWBv0PR6Hdd3CcbT2Z6MPCOrD8Rp6Z4ukJRVwN7iwNlV4QHYGeTguDpKZqXvP5dBYVH5d2mVXFQL9Xa2TmFBvV6bXT53rvb7K69oM2LKu0tMKThyBCZM0FJ6jR6t3WkiGhadDhYuhC5dICGhVIq2cj39NMTEQNu2MGcOoF34Ae39eKnUZHvOplNYZMbPzYE2dV3bo4EL8XKmTYAbSsGWqLIFJI0mM6sPxLH/XAY6HfRtVcEMVNE09OoFO3Zod42mp8P112vpm8o7TouL0wrajR6tPT9hgjZBpoZaVSFPesmxQZdQTzk2qAZnBwPXdAwEtEKtpf7GY8Zg/nIFZoMdHdb+xK0fvEjrS9WkOXoUpk7Valtdc43cISpsws6gJ7x40tehS8ycPpmUbbnTunOzqk+0bDKefRaeeUY7Vvv0U+jeHf75B09ne7oXp2pZcziB3WfTtDv9+/TRzsmB3Al3cSS4HQa9jq6h8retK/KN1gAsWrSI4cOHlzu7e+zYsezcuZNz587xxRdf8Ouvv9KlSxdWrFjB7NmzS607YcIEnn32WZ566il69uzJqVOnmDx5cq1n/Lu4uLBx40aaN2/OmDFj6NChA1OmTCE/P79axV/nzJnD6dOnadOmDf7+/gB07dqVDRs2cPz4cQYPHkyPHj146aWXCAkJsWy3ePFiQkJCGDJkCGPGjGHatGkENLEilrbgYKend0sfALafSqHogiIXe/oO4/enXkPpdHT98Qv85s6C2Fhtx/7JJ9oXwS23aHnQDx2C4GAtN6tc4AC0PPThQdpnY3NkMufScsvMTI9M1FLqtL0cbs/S6bT/V2dnrTDVp5+Wv15UlBaMAy3w0qdP3Y1R2Iyzg4GbuofgYKcnOjWXjZFJlueUUqw7mohZKdoFulnypoqqa+3nhpeLPflGEwdjy+a5LTKZLTP+ap3W5UI6nTbDfMEC7ecFC2DiRK0mwunT2oXPCRO0QtMdO2q3nAYFaZ9/uSDaMLm4wHffaem6Nm/WTtxKKKUVkv32W+0uhZLbixctstT0aealpXNIzzWyeMtpdp1JLXVskG80sSday/ffr7XkRq+JgW180ekgKjG71IX2vEIT3+8+x4nEbOz0OkZ1CbbMVBNNXFAQ/P23doHTbNYugt19tzb5wWSC33/XZjKHhWn7/NhY7efq1ku4SMn3fWxGXrl3qyVm5hOTnodep6Nr8Z2Youpa+rnSvbg465rDCaX+xnv7DWP1c29j1hsIXf2dVki65K7u2Fh4+23tIkuHDtq5mYMDfPCBfHcLmyn5PjqRmEVB0fn3ar7RxB+H4vlxbyxFZkWYjwsB7lJktAx7e+3Okb//1vbPUVEwaBDMns3Q1t70auGNZ8wZ3O8cB0OHwu7d2rHcG2+w4XFtskN4kHv5ExuFTehUZRVCGpjMzEw8PT3JyMgoE8TNz8/n1KlTtGrVqsmniylxzTXXEBQUxOfVLTjWwMl7oWqKTGaWbD1NVn4RQ8P96dHcm1PJOfy4NwalYMzO1bR47omKG3F3h7/+0m45FRaJWfl89W+0JW2Ot4s9nZp50iHYA3uDjo83nKTIrLirfwv8Lpeq5u+8oxWn8vCA227TTtLy8iA3V/v3xAntAP7KK7UDAYPcct6YnEjM4ud9WjqIazsF0inEk0OxGfx5KAEHOz139W+Bh5PkzK6J/efSWXskEQ9ne27rHUpCRj6xGfnEpeeRmFVg2U/YbH/w1VdaXYOiIi1P48Xp0ZycYOBALc9q797W71/UrVWrtMAawEMPQXQ0/PuvNlP9QtOna8GXC5xLy2XdsSSSi2smeDjbM7CtL+GB7vx7KpWtUSn4ujkwsV8L69w90QStOZzAwZgMmnk5c1vvUDLzili15xxpuUYc7fXc1C2EUG8rXlQTjYNS8OGH2oUwk0m7+yQzUytGV2LgQC3N0623Wi6Q1cbn/5whOauAfq198HZxILfQRF6hidzCIuIz80nJLiQiyJ3rugTXuq+myGgy8+X2s6TmFNIu0I1RXYLJKiji821nKCwyM+bUP7R46F7t//vGG7Xj8b//Pn9Hgp0djBihXVwZMqR+X4xo1JRSLN16mrRcI9d0DKRzM09OJeew9kgCWflF6HTaHZj9W/vK3SmVSU/Xjr++/FL7vV8/1BVXoBYsQG80YtbriRt3NyHvvk62hzefbT6NWSkmXNH80gWHRZVUFEu+mATSm5Dc3Fw++ugjRowYgcFgYMWKFcyZM4c1a9YwfPjw+h5enWrq74XqKAnwuDoaGN29Gd/uOkdhkZnOzTwZ3iEA3XvvaXm99Hpo2RLat9dy55b826sX+Mrtx+VJzMxn37kMjidkWVI66HU6/NwdSMwswMfVgbv7t7h8ZvWZTNrV8X/+ufQ6vr7aVfLmzetuXKLObItK4Z+TKRj0Om7qFsIfh+LJLTQxuJ2f5Q4WUX1Gk5nPNp8i9xI56F0cDLQPcmdoe3/b7Q9KZi3m5WkXwfr2hWHDtFv/+/fXgumi8XjmmfP1L0rY2Wl3kvXtq+3r77ij3AuiZrPicFwm26JSyC4ukhvo4URGnpF8o4nrugQRESSzpWsqK9/I0q2nMZoU/dv4sv9cOjkFJtyd7LilRzN8L5eL6+Ly9Pff2mSH1OKitd7eMGmSlgagY0erdrU5Mpkdpysujju+b3OCPOX7o6YSM/NZ8W80ZqUY0SmIyMQsTiblWC606b77TttXX1hnbOBA7Y6y224Dv0vUsBLCyv49lcqWE8kEezrh6+Zoyent7WLPtZ2CCPGSO9OrZcUKePBBrWZNsYwhw/hxwuOktGxH52aeONsb2HE6lWbeztzeO6weB9s4SCBdAunlysvL48Ybb2TPnj3k5+cTHh7OCy+8wJiSWUlNSFN/L1SHyaxYsvU0mXlGDHodJrMi1NuZMT1DzxezSEnRZp471HMu7waqoMhEZEI2h2IziE3Ptyzv18qHAW0vswPgmBgt5YNer6V6cXEp/W///nLhpBFTSvHz/jiiivMcAvi6OTChXwspblNLO0+nsikyGZ0O/NwcCfFyItjTmRBPZzyc7ermgtqxY1palwEDtH26aLyKiuCppyApSQuc9+2r5eSsRgo2o8nM7jNp7DyTZrkY7O1iz939W8ps9FraciKZf0+dD1D6uTtyS49muDnKbduiCk6e1IpC9+un5cW20blORq6Rn/fHopTC2cEOFwdD8UP72dfNgWBPCZ7VVkmAsuQ8zKDXMaFf8/MX1X76Cd57T7vwPX48tGpVvwMWTVJWvpFFm09ZbojQ6aB7mBcD2/phL7PQa+bsWe3OwcRELW3qyJEcjMngryMJpUph3Ngt+PJIB9vASSBdAumiEvJeqJ6S9A0AXi723NGnOc4OkrbDFlKyCzgUm0lGnpGrIwJwlZNmcZkpKDLx9Y5okrMLAbi1Vyhh1szd3UQppUjJKcTdyQ5HO9m/ioYjt7CIf06mcDIph+EdAqVWghXkG00s3nKafKOJ5j4u3NAtWPYLQjRRZrPim13Rlsk2/Vr7MKDNZTbRRgjghz0xnErOwdPZnms7BUoaMhuJTMjit4PxmMwKD2d77hkgExisoTqBdInQCCEq1SHIg33RGWTlG7mpW4gE0W3I182RK9v71/cwhLgkRzsDN3YL4Zf9cbTwdZEgupXodLrLpx6CENXg4mDH1RGBXB1R3yNpPJzsDYzt1YyEjAI6hnjIHT9CNGF6vY6RnYJZseMsbo529JVUeuIyNaJTEGdTc2nl54qDncxCt5V2ge7YG/RsPpFM31Y+EkSvB01yRnrLli1xrsatq6LxycvL4/Tp0zIjvRrMZoVZKSkQIoQQQgghhBB1yGgyo9fp5MKaEELYQHVmpDepiJi9vT2gFd0UTVvJe6DkPSEqp9frJIguhBBCCCGEEHXM3qCXILoQQlwGmlRqF4PBgJeXF4mJiQC4uLjUTfEucdlQSpGbm0tiYiJeXl4YDJKiRAghhBBCCCGEEEIIUbEmFUgHCAoKArAE00XT5OXlZXkvCCGEEEIIIYQQQgghREWaXCBdp9MRHBxMQEAARqOxvocj6oG9vb3MRBdCCCGEEEIIIYQQQlRZkwuklzAYDBJMFUIIIYQQQgghhBBCCFEpqRwohBBCCCGEEEIIIYQQQlRAAulCCCGEEEIIIYQQQgghRAUkkC6EEEIIIYQQQgghhBBCVKDR5UhXSgGQmZlZzyMRQgghhBBCCCGEEEIIcbkqiSGXxJQr0ugC6VlZWQCEhYXV80iEEEIIIYQQQgghhBBCXO6ysrLw9PSscB2dqkq4vQExm83Exsbi7u6OTqer7+HUi8zMTMLCwoiOjsbDw6O+hyOaMHkvisuFvBfF5ULei+JyIe9FcbmQ96K4XMh7UVwu5L0oLhdN5b2olCIrK4uQkBD0+oqzoDe6Gel6vZ7Q0ND6HsZlwcPDo1G/0UXDIe9FcbmQ96K4XMh7UVwu5L0oLhfyXhSXC3kvisuFvBfF5aIpvBcrm4leQoqNCiGEEEIIIYQQQgghhBAVkEC6EEIIIYQQQgghhBBCCFEBCaQ3Qo6OjsyaNQtHR8f6Hopo4uS9KC4X8l4Ulwt5L4rLhbwXxeVC3oviciHvRXG5kPeiuFzIe7GsRldsVAghhBBCCCGEEEIIIYSwJpmRLoQQQgghhBBCCCGEEEJUQALpQgghhBBCCCGEEEIIIUQFJJAuhBBCCCGEEEIIIYQQQlRAAulCCCGEEEIIIYQQQgghRAUkkN4Iffjhh7Rs2RInJyf69evHv//+W99DEo3YvHnz6NOnD+7u7gQEBHDzzTdz7NixUusMHToUnU5X6vHAAw/U04hFYzV79uwy77OIiAjL8/n5+UyfPh1fX1/c3NwYO3YsCQkJ9Thi0Vi1bNmyzHtRp9Mxffp0QPaJwnY2btzIjTfeSEhICDqdjh9++KHU80opXnrpJYKDg3F2dmb48OFERkaWWic1NZUJEybg4eGBl5cXU6ZMITs7uw5fhWgMKnovGo1GZs6cSZcuXXB1dSUkJIS7776b2NjYUm2Uty997bXX6viViIausv3i5MmTy7zPRo4cWWod2S8Ka6jsvVjesaNOp+PNN9+0rCP7RWENVYnhVOXc+ezZs4waNQoXFxcCAgJ4+umnKSoqqsuXUi8kkN7IrFy5kieeeIJZs2axe/duunXrxogRI0hMTKzvoYlGasOGDUyfPp1//vmHNWvWYDQaufbaa8nJySm13n333UdcXJzl8cYbb9TTiEVj1qlTp1Lvs82bN1uee/zxx/n555/55ptv2LBhA7GxsYwZM6YeRysaqx07dpR6H65ZswaA2267zbKO7BOFLeTk5NCtWzc+/PDDcp9/4403eO+99/joo4/Yvn07rq6ujBgxgvz8fMs6EyZM4NChQ6xZs4ZffvmFjRs3Mm3atLp6CaKRqOi9mJuby+7du3nxxRfZvXs333//PceOHeOmm24qs+6cOXNK7SsfeeSRuhi+aEQq2y8CjBw5stT7bMWKFaWel/2isIbK3osXvgfj4uL47LPP0Ol0jB07ttR6sl8UtVWVGE5l584mk4lRo0ZRWFjI1q1bWbp0KUuWLOGll16qj5dUt5RoVPr27aumT59u+d1kMqmQkBA1b968ehyVaEoSExMVoDZs2GBZNmTIEPXYY4/V36BEkzBr1izVrVu3cp9LT09X9vb26ptvvrEsO3LkiALUtm3b6miEoql67LHHVJs2bZTZbFZKyT5R1A1ArVq1yvK72WxWQUFB6s0337QsS09PV46OjmrFihVKKaUOHz6sALVjxw7LOr/99pvS6XQqJiamzsYuGpeL34vl+ffffxWgzpw5Y1nWokUL9c4779h2cKJJKe+9OGnSJDV69OhLbiP7RWELVdkvjh49Wl199dWllsl+UdjCxTGcqpw7//rrr0qv16v4+HjLOgsXLlQeHh6qoKCgbl9AHZMZ6Y1IYWEhu3btYvjw4ZZler2e4cOHs23btnocmWhKMjIyAPDx8Sm1fPny5fj5+dG5c2eeffZZcnNz62N4opGLjIwkJCSE1q1bM2HCBM6ePQvArl27MBqNpfaPERERNG/eXPaPwqYKCwv54osvuPfee9HpdJblsk8Ude3UqVPEx8eX2g96enrSr18/y35w27ZteHl50bt3b8s6w4cPR6/Xs3379jofs2g6MjIy0Ol0eHl5lVr+2muv4evrS48ePXjzzTebxC3jou6tX7+egIAAwsPDefDBB0lJSbE8J/tFUR8SEhJYvXo1U6ZMKfOc7BeFtV0cw6nKufO2bdvo0qULgYGBlnVGjBhBZmYmhw4dqsPR1z27+h6AsJ7k5GRMJlOpNzJAYGAgR48eradRiabEbDYzY8YMBg4cSOfOnS3L77zzTlq0aEFISAj79+9n5syZHDt2jO+//74eRysam379+rFkyRLCw8OJi4vj5ZdfZvDgwRw8eJD4+HgcHBzKnKAHBgYSHx9fPwMWTcIPP/xAeno6kydPtiyTfaKoDyX7uvKOE0uei4+PJyAgoNTzdnZ2+Pj4yL5S2Ex+fj4zZ85k/PjxeHh4WJY/+uij9OzZEx8fH7Zu3cqzzz5LXFwcb7/9dj2OVjQ2I0eOZMyYMbRq1YqoqCiee+45rrvuOrZt24bBYJD9oqgXS5cuxd3dvUwaStkvCmsrL4ZTlXPn+Pj4co8pS55rzCSQLoSwmunTp3Pw4MFSeamBUjkEu3TpQnBwMMOGDSMqKoo2bdrU9TBFI3XddddZfu7atSv9+vWjRYsWfP311zg7O9fjyERTtmjRIq677jpCQkIsy2SfKIQQGqPRyO23345SioULF5Z67oknnrD83LVrVxwcHLj//vuZN28ejo6OdT1U0Ujdcccdlp+7dOlC165dadOmDevXr2fYsGH1ODLRlH322WdMmDABJyenUstlvyis7VIxHHFpktqlEfHz88NgMJSppJuQkEBQUFA9jUo0FQ8//DC//PIL69atIzQ0tMJ1+/XrB8CJEyfqYmiiifLy8qJ9+/acOHGCoKAgCgsLSU9PL7WO7B+FLZ05c4a//vqLqVOnVrie7BNFXSjZ11V0nBgUFFSmQH1RURGpqamyrxRWVxJEP3PmDGvWrCk1G708/fr1o6ioiNOnT9fNAEWT1Lp1a/z8/CzfybJfFHVt06ZNHDt2rNLjR5D9oqidS8VwqnLuHBQUVO4xZclzjZkE0hsRBwcHevXqxdq1ay3LzGYza9eupX///vU4MtGYKaV4+OGHWbVqFX///TetWrWqdJu9e/cCEBwcbOPRiaYsOzubqKgogoOD6dWrF/b29qX2j8eOHePs2bOyfxQ2s3jxYgICAhg1alSF68k+UdSFVq1aERQUVGo/mJmZyfbt2y37wf79+5Oens6uXbss6/z999+YzWbLBR8hrKEkiB4ZGclff/2Fr69vpdvs3bsXvV5fJs2GENZ07tw5UlJSLN/Jsl8UdW3RokX06tWLbt26Vbqu7BdFTVQWw6nKuXP//v05cOBAqQuNJRfFO3bsWDcvpJ5IapdG5oknnmDSpEn07t2bvn378u6775KTk8M999xT30MTjdT06dP58ssv+fHHH3F3d7fkw/L09MTZ2ZmoqCi+/PJLrr/+enx9fdm/fz+PP/44V155JV27dq3n0YvG5KmnnuLGG2+kRYsWxMbGMmvWLAwGA+PHj8fT05MpU6bwxBNP4OPjg4eHB4888gj9+/fniiuuqO+hi0bIbDazePFiJk2ahJ3d+cMt2ScKW8rOzi51Z8OpU6fYu3cvPj4+NG/enBkzZjB37lzatWtHq1atePHFFwkJCeHmm28GoEOHDowcOZL77ruPjz76CKPRyMMPP8wdd9xRKj2REJWp6L0YHBzMrbfeyu7du/nll18wmUyW40cfHx8cHBzYtm0b27dv56qrrsLd3Z1t27bx+OOPM3HiRLy9vevrZYkGqKL3oo+PDy+//DJjx44lKCiIqKgonnnmGdq2bcuIESMA2S8K66nsOxq0C9zffPMN8+fPL7O97BeFtVQWw6nKufO1115Lx44dueuuu3jjjTeIj4/nhRdeYPr06Y0/zZASjc7777+vmjdvrhwcHFTfvn3VP//8U99DEo0YUO5j8eLFSimlzp49q6688krl4+OjHB0dVdu2bdXTTz+tMjIy6nfgotEZN26cCg4OVg4ODqpZs2Zq3Lhx6sSJE5bn8/Ly1EMPPaS8vb2Vi4uLuuWWW1RcXFw9jlg0Zn/88YcC1LFjx0otl32isKV169aV+508adIkpZRSZrNZvfjiiyowMFA5OjqqYcOGlXmPpqSkqPHjxys3Nzfl4eGh7rnnHpWVlVUPr0Y0ZBW9F0+dOnXJ48d169YppZTatWuX6tevn/L09FROTk6qQ4cO6r///a/Kz8+v3xcmGpyK3ou5ubnq2muvVf7+/sre3l61aNFC3XfffSo+Pr5UG7JfFNZQ2Xe0Ukr93//9n3J2dlbp6elltpf9orCWymI4SlXt3Pn06dPquuuuU87OzsrPz089+eSTymg01vGrqXs6pZSyYZxeCCGEEEIIIYQQQgghhGjQJEe6EEIIIYQQQgghhBBCCFEBCaQLIYQQQgghhBBCCCGEEBWQQLoQQgghhBBCCCGEEEIIUQEJpAshhBBCCCGEEEIIIYQQFZBAuhBCCCGEEEIIIYQQQghRAQmkCyGEEEIIIYQQQgghhBAVkEC6EEIIIYQQQgghhBBCCFEBCaQLIYQQQgghhBBCCCGEEBWQQLoQQgghhBBCCCGEEEIIUQEJpAshhBBCCCGEEEIIIYQQFZBAuhBCCCGEEEIIIYQQQghRAQmkCyGEEEIIIYQQQgghhBAVkEC6EEIIIYQQQgghhBBCCFEBCaQLIYQQQgghhBBCCCGEEBWQQLoQQgghhBBCCCGEEEIIUQEJpAshhBBCCCGEEEIIIYQQFZBAuhBCCCGEEEIIIYQQQghRAQmkCyGEEEKICq1fvx6dTsf69esv6zYbu8mTJ9OyZcv6HoaoA/L5EEIIIYS4/EggXQghhBCiEfv666/R6XSsWrWqzHPdunVDp9Oxbt26Ms81b96cAQMG1MUQ681DDz2EXq8nNTW11PLU1FT0ej2Ojo7k5+eXeu7kyZPodDqee+65uhzqZaVv377odDoWLlxY30OpV1u3bmX27Nmkp6fXuI0FCxawZMkSq41JCCGEEELYjgTShRBCCCEasUGDBgGwefPmUsszMzM5ePAgdnZ2bNmypdRz0dHRREdHW7a98sorycvL48orr6ybQdeRQYMGoZQq8/q3bt2KXq/HaDSyc+fOUs+VrFvyt6lLn3zyCceOHavzfi8UGRnJjh07aNmyJcuXL6/XsdS3rVu38vLLL9skkN5YP3NCCCGEEA2ZBNKFEEIIIRqxkJAQWrVqVSaQvm3bNpRS3HbbbWWeK/m9JFis1+txcnJCr29ch46XusiwZcsWunbtSnh4eLl/G71eX+vZ+kVFRRQWFlZrG3t7exwdHWvVb2198cUXBAQEMH/+fLZu3crp06frdTyNVWP9zAkhhBBCNGRyZCaEEEII0cgNGjSIPXv2kJeXZ1m2ZcsWOnXqxHXXXcc///yD2Wwu9ZxOp2PgwIFA+fmahw4dSufOnTl8+DBXXXUVLi4uNGvWjDfeeKNM/+fOnePmm9oro8wAAQAASURBVG/G1dWVgIAAHn/8cQoKCsod6zfffEOvXr1wdnbGz8+PiRMnEhMTY3n+p59+QqfTsX//fsuy7777Dp1Ox5gxY0q11aFDB8aNG3fJv0vz5s0JCwsrMyN9y5YtDBw4kAEDBpT7XKdOnfDy8qKwsJCXXnqJXr164enpiaurK4MHDy6TKuf06dPodDreeust3n33Xdq0aYOjoyOHDx+2/G1XrlzJc889R1BQEK6urtx0001ER0eXaufiHOkXtvvxxx9b2u3Tpw87duwo92/bsWNHnJyc6Ny5M6tWrap23vUvv/ySW2+9lRtuuAFPT0++/PLLMutcqs3Zs2ej0+lKLcvLy+PRRx/Fz88Pd3d3brrpJmJiYtDpdMyePbvMtsePH2fixIl4enri7+/Piy++iFKK6OhoRo8ejYeHB0FBQcyfP79M/wUFBcyaNYu2bdvi6OhIWFgYzzzzTJn3ok6n4+GHH+aHH36gc+fOODo60qlTJ37//fdS43n66acBaNWqFTqdDp1OZ7mwsHjxYq6++moCAgJwdHSkY8eOZVLhtGzZkkOHDrFhwwbL9kOHDgUunSO9ss9Hyd/fzc2NmJgYbr75Ztzc3PD39+epp57CZDKV+bsIIYQQQoiqkUC6EEIIIUQjN2jQIIxGI9u3b7cs27JlCwMGDGDAgAFkZGRw8ODBUs9FRETg6+tbYbtpaWmMHDmSbt26MX/+fCIiIpg5cya//fabZZ28vDyGDRvGH3/8wcMPP8zzzz/Ppk2beOaZZ8q0t2TJEm6//XYMBgPz5s3jvvvu4/vvv2fQoEGW9BmDBg1Cp9OxceNGy3abNm1Cr9eXmj2elJTE0aNHK02NMWjQIHbu3GkJphYWFrJjxw7L32br1q0opSyv9/Dhw5aZ7JmZmXz66acMHTqU119/ndmzZ5OUlMSIESPYu3dvmb4WL17M+++/z7Rp05g/fz4+Pj6W51599VVWr17NzJkzefTRR1mzZg3Dhw8vdfHjUr788kvefPNN7r//fubOncvp06cZM2YMRqPRss7q1asZN24c9vb2zJs3jzFjxjBlyhR27dpVafsltm/fzokTJxg/fjwODg6MGTOm1uldJk+ezPvvv8/111/P66+/jrOzM6NGjbrk+uPGjcNsNvPaa6/Rr18/5s6dy7vvvss111xDs2bNeP3112nbti1PPfVUqfeI2Wzmpptu4q233uLGG2/k/fff5+abb+add94p92LL5s2beeihh7jjjjt44403yM/PZ+zYsaSkpAAwZswYxo8fD8A777zD559/zueff46/vz8ACxcupEWLFjz33HPMnz+fsLAwHnroIT788ENLH++++y6hoaFERERYtn/++ecv+dqr8vkoYTKZGDFiBL6+vrz11lsMGTKE+fPn8/HHH1f+nyKEEEIIIcqnhBBCCCFEo3bo0CEFqFdeeUUppZTRaFSurq5q6dKlSimlAgMD1YcffqiUUiozM1MZDAZ13333WbZft26dAtS6dessy4YMGaIAtWzZMsuygoICFRQUpMaOHWtZ9u677ypAff3115ZlOTk5qm3btqXaLCwsVAEBAapz584qLy/Psu4vv/yiAPXSSy9ZlnXq1Endfvvtlt979uypbrvtNgWoI0eOKKWU+v777xWg9u3bV+Hf5sMPP1SA2rRpk1JKqW3btilAnTlzRh0+fFgB6tChQ6XGsnz5cqWUUkVFRaqgoKBUe2lpaSowMFDde++9lmWnTp1SgPLw8FCJiYml1i/52zZr1kxlZmZaln/99dcKUP/73/8syyZNmqRatGhRpl1fX1+VmppqWf7jjz8qQP3888+WZV26dFGhoaEqKyvLsmz9+vUKKNVmRR5++GEVFhamzGazUkqpP//8UwFqz549pda7eJwlZs2apS48/di1a5cC1IwZM0qtN3nyZAWoWbNmldl22rRplmVFRUUqNDRU6XQ69dprr1mWp6WlKWdnZzVp0iTLss8//1zp9XrL/3OJjz76SAFqy5YtlmWAcnBwUCdOnLAs27dvnwLU+++/b1n25ptvKkCdOnWqzGvNzc0ts2zEiBGqdevWpZZ16tRJDRkypMy6F3/mqvP5mDRpkgLUnDlzSrXZo0cP1atXrzJ9CSGEEEKIqpEZ6UIIIYQQjVyHDh3w9fW1zNjet28fOTk5ljzfF6Yw2bZtGyaTqUrFNN3c3Jg4caLldwcHB/r27cvJkycty3799VeCg4O59dZbLctcXFyYNm1aqbZ27txJYmIiDz30EE5OTpblo0aNIiIigtWrV1uWDR48mE2bNgGQlZXFvn37mDZtGn5+fpblmzZtwsvLi86dO1f4Gi7Ok75lyxaaNWtG8+bNiYiIwMfHx/K3ubjQqMFgwMHBAdBmPKemplJUVETv3r3ZvXt3mb7Gjh1rmbF8sbvvvht3d3fL77feeivBwcH8+uuvFY4ftFna3t7elt8HDx4MYPl/iI2N5cCBA9x99924ublZ1hsyZAhdunSptH3QcrqvXLmScePGWdKzlKQuqems9JJUKQ899FCp5Y888sglt5k6darlZ4PBQO/evVFKMWXKFMtyLy8vwsPDS70Pv/nmGzp06EBERATJycmWx9VXXw1QJh3P8OHDadOmjeX3rl274uHhUarNijg7O1t+zsjIIDk5mSFDhnDy5EkyMjKq1MaFqvP5KPHAAw+U+n3w4MFVHr8QQgghhChLAulCCCGEEI2cTqdjwIABllzoW7ZsISAggLZt2wKlA+kXB4srEhoaWibntbe3N2lpaZbfz5w5Q9u2bcusFx4eXur3M2fOlLscICIiwvI8aAHBuLg4Tpw4wdatW9HpdPTv379UgH3Tpk0MHDiw0mKNnTt3xsvLq9TrL8kNX9Luhc+FhYXRvHlzy/ZLly6la9euODk54evri7+/P6tXry43WNqqVatLjqNdu3alftfpdLRt27ZKxTwvHA9gCaqX/D+U/O1K/r8vVN6y8vz5558kJSXRt29fTpw4wYkTJzh16hRXXXUVK1asKJVjv6rOnDmDXq8v83epaEwXv1ZPT0+cnJzw8/Mrs/zC92FkZCSHDh3C39+/1KN9+/YAJCYmVtgPlH1vV2TLli0MHz4cV1dXvLy88Pf357nnngOoUSC9Op8PACcnpzIXbaozfiGEEEIIUZZdfQ9ACCGEEELY3qBBg/j55585cOCAJT96iQEDBvD0008TExPD5s2bCQkJoXXr1pW2aTAYyl2uinOK20pJkH/jxo2cPHmSnj17Wgp9vvfee2RnZ7Nnzx5effXVStvS6/X079/fkgt9y5YtloAnaH+bzz77zJI7/eabb7Y898UXXzB58mRuvvlmnn76aQICAiz5q6Oiosr0deEsZWuqi/+Hklnnt99+e7nPb9iwgauuugqgzEWTEtYodFnea63K6zebzXTp0oW333673HXDwsKq3ealREVFMWzYMCIiInj77bcJCwvDwcGBX3/9lXfeeadGFx2q61LjF0IIIYQQNSeBdCGEEEKIJuDCFCZbtmxhxowZlud69eqFo6Mj69evZ/v27Vx//fVW67dFixYcPHgQpVSpAOuxY8fKrFeyvCTdxoXrljwP2mzh5s2bs2nTJk6ePGlJZXLllVfyxBNP8M0332AymSotNFpi0KBB/Pbbb/z0008kJiZaZqSDFkh//vnn+fXXX8nLyys1U//bb7+ldevWfP/996Ve26xZs6rU74UiIyNL/a6U4sSJE3Tt2rXabV2s5G934sSJMs+Vt+xiOTk5/Pjjj4wbN65Uip4Sjz76KMuXL7cE0r29vcsUvwTKzJpu0aIFZrOZU6dOlZqRX5UxVVebNm3Yt28fw4YNu2Sgv7ou1c7PP/9MQUEBP/30U6mZ7Renj6mojYtV5/MhhBBCCCFsQ1K7CCGEEEI0Ab1798bJyYnly5cTExNTaka6o6MjPXv25MMPPyQnJ6dKaV2q6vrrryc2NpZvv/3Wsiw3N5ePP/64zPgCAgL46KOPKCgosCz/7bffOHLkCKNGjSq1/uDBg/n777/5999/LYH07t274+7uzmuvvYazszO9evWq0hhLXu/rr7+Oi4sL3bt3tzzXt29f7OzseOONN0qtC+dn/V44S3n79u1s27atSv1eaNmyZWRlZVl+//bbb4mLi+O6666rdlsXCwkJoXPnzixbtozs7GzL8g0bNnDgwIFKt1+1ahU5OTlMnz6dW2+9tczjhhtu4LvvvrP8v7Vp04aMjAz2799vaSMuLo5Vq1aVanfEiBEALFiwoNTy999/v8av9VJuv/12YmJi+OSTT8o8l5eXR05OTrXbdHV1BShz0aC890VGRgaLFy8ut43yLjpcrLqfDyGEEEIIYX0yI10IIYQQoglwcHCgT58+bNq0CUdHxzJB5gEDBjB//nygavnRq+q+++7jgw8+4O6772bXrl0EBwfz+eef4+LiUmo9e3t7Xn/9de655x6GDBnC+PHjSUhI4H//+x8tW7bk8ccfL7X+4MGDWb58OTqdrlTxzwEDBvDHH38wdOhQSyHQyvTt2xcHBwe2bdvG0KFDsbM7f4js4uJCt27d2LZtW5nipTfccAPff/89t9xyC6NGjeLUqVN89NFHdOzYsVTAuip8fHwYNGgQ99xzDwkJCbz77ru0bduW++67r1rtXMp///tfRo8ezcCBA7nnnntIS0vjgw8+oHPnzpWOdfny5fj6+pa6+HKhm266iU8++YTVq1czZswY7rjjDmbOnMktt9zCo48+Sm5uLgsXLqR9+/alirD26tWLsWPH8u6775KSksIVV1zBhg0bOH78OFD12dpVcdddd/H111/zwAMPsG7dOgYOHIjJZOLo0aN8/fXX/PHHH/Tu3btabZZ8hp5//nnuuOMO7O3tufHGG7n22mtxcHDgxhtv5P777yc7O5tPPvmEgIAA4uLiyrSxcOFC5s6dS9u2bQkICCgz4xyq//kQQgghhBDWJzPShRBCCCGaiJKAc0kqlwuVpDNxd3enW7duVuvTxcWFtWvXcu211/L+++8zd+5cBg0aZJnhfaHJkyezcuVKCgsLmTlzJv/3f//HLbfcwubNm/Hy8iq1bsks9IiICHx9fcssL/m3KpycnCxB0fKCxSV/m/79+5cqXjp58mT++9//sm/fPh599FH++OMPvvjii2oHZAGee+45Ro0axbx58/jf//7HsGHDWLt2bZkLDjV14403smLFCgoLC/nPf/7D999/z5IlSwgPD8fJyemS2yUmJvLXX39x/fXXXzLv9rBhw3BxceGLL74AwNfXl1WrVuHi4sIzzzzD0qVLmTdvHjfeeGOZbZctW8b06dNZvXo1M2fOpLCwkJUrVwJUOK7q0uv1/PDDD7z22mscOHCAp556ipdffpkdO3bw2GOPWYqOVkefPn145ZVX2LdvH5MnT2b8+PEkJSURHh7Ot99+i06n46mnnuKjjz5i2rRpPPbYY2XaeOmll7j++ut54403GD9+PHPmzLlkf9X5fAghhBBCCOvTKVtXgxJCCCGEEEKUa/369Vx11VV888035eYft7Xu3bvj7+/PmjVr6rzvS9m7dy89evTgiy++YMKECfU9HCGEEEIIIQCZkS6EEEIIIUSjZzQaKSoqKrVs/fr17Nu3j6FDh9bPoNDyk1/s3XffRa/XV7lYrBBCCCGEEHVBcqQLIYQQQgjRyMXExDB8+HAmTpxISEgIR48e5aOPPiIoKIgHHnig3sb1xhtvsGvXLq666irs7Oz47bff+O2335g2bRphYWH1Ni4hhBBCCCEuJoF0IYQQQgghGjlvb2969erFp59+SlJSEq6urowaNYrXXnutVI75ujZgwADWrFnDK6+8QnZ2Ns2bN2f27Nk8//zz9TYmIYQQQgghyiM50oUQQgghhBBCCCGEEEKICkiOdCGEEEIIIYQQQgghhBCiAhJIF0IIIYQQQgghhBBCCCEq0OhypJvNZmJjY3F3d0en09X3cIQQQgghhBBCCCGEEEJchpRSZGVlERISgl5f8ZzzRhdIj42NJSwsrL6HIYQQQgghhBBCCCGEEKIBiI6OJjQ0tMJ1Gl0g3d3dHdBevIeHRz2PRgghhBBCCCGEEEIIIcTlKDMzk7CwMEtMuSKNLpBeks7Fw8NDAulCCCGEEEIIIYQQQgghKlSVFOFSbFQIIYQQQgghhBBCCCGEqIAE0oUQQgghhBBCCCGEEEKICkggXQghhBBCCCGEEEIIIYSoQKPLkS6EEEIIIYQQQgghhBAlTCYTRqOxvoch6oG9vT0Gg8EqbUkgXQghhBBCCCGEEEII0egopYiPjyc9Pb2+hyLqkZeXF0FBQVUqKFoRCaQLIYQQQgghhBBCCCEanZIgekBAAC4uLrUOpIqGRSlFbm4uiYmJAAQHB9eqPQmki0qlbv0X14h2OPp41/dQhBD1LPfMOVI+XYr/Yw/i5OdT38MRQgghhGi0lMlE0uo1+Awfgp2Lc30PRwhRz1L/2UXuxi00e3I6OiulqWjsTCaTJYju6+tb38MR9cTZWfsOTUxMJCAgoFZpXqTYqKhQ4g+/4jOwH4Wdu5F56Fh9D6dRyzx2gthRYzg3aRrKbK7v4QhRhjKZyL3pZsLmvkD62HHyPhWiiStMzyTmxltJ7DOQ/MTk+h5Oo5G6cRsZew/W9zCEEJeBcw8/ScDo60i+dhRmY1F9D0eIchUWmck3mup7GI1e+p4DuFxzNaEzHyPmjf/V93AajJKc6C4uLvU8ElHfSt4Dtc2TL4F0USHzm28C4B4XjWHIlaTt2FPPI2p8lNnMuTfew6lnd0J+XUXosk9IWbepvoclRBkxb/wPv/27AAja+Bcxb31QzyNqnEwFheScOkvqlu0k/vwHhRlZ9T0kIcrIORtDVv9BNPvlOwJ2biXphZfre0iNQsrfm/C6ahAePbsSc8t4sqNO1/eQhKiUMpvJPBpJ7IJFRN/7IPErvq/vITUK6Tv3EvLJhwAEbVlHzPQn6nlEQpRVZDLzxT9n+L8NJ/lpXyxRSdmYzaq+h9Xo5CckobvpJpyyMwHwfudNinJy63lUDYukcxHWeg9IIF1cUvqeAwRtXY/S6cgIa4VrSiJOw4eRsmFrfQ+t0ciKPEniIO2qskNuDkZHJwDyP/msnkcmRGk5p87i/+osAJK79gIgcNazZB44XJ/DahRi3llAYs8rSG/ZlnwPLwxOjri2boHPoCsIuGkkyePvru8hClFKxt6DmK/oj+/RAxQ6uwIQvORjsiJP1vPIGj7jrNnozWZ0StHsh69w7NSB6IeeoCA1rb6HJoSFMplI/msD0S/OJe6aUeQGBOPRoT0h06cStvgjfO+9S+5SqSVlNlP4wEMYTEVkNG8NQNgn7xOzUM4RxOXlTGouGXlGzEoRlZjNT3tjWbT5FJsjk0nLKazv4TUKpoJCMm+8Bc9zp8kKDCHbPwjXpHji3pRZ6ULUBwmki0vKfutdAOIHD8dh+z+kdOyGc2Ya7tdfS+LqP+t3cA2cMps59/aHOPToRuC2DRQ5OBL9wiukfrESAL/VP8gVZnFZybj/IRxzsknp2A3vf7eS2HsA9vm5FI6fiLmwdrdGNWV5sQkEznycgD3b8ToThVNWBgBmvYE8Ly0Hvf+6P2R/IC4bSX+ux2HIlbjHRZPZrAV527aT2KMfdsZCMp95vr6H16ClrN9C0Oa/Mev1xC78jKRuvbEvyCds4TuY27Tl3KtvYSqQoISofzETp+B3zVDC5r5I8F+/4pqSiMlgR3Kn7mT7B2Gfn0uSBHhqJe6jxQTs2obR0Qndb78RPe1RAAJnPCSTmsRlJTIhG4D2ge70bOGNs4OB7IIidpxOZcnW03yzM5qMXCudK6xeDUuWWKetBkKZzcTdfR8BO7ZQ6OyKcdWPpD/5HwB835svd66KCp0+fRqdTsfevXurvM2SJUvw8vKq93FcziSQLsqVn5RC4PdfAaCf8RjOwQG4bV5PYs8rcMjNwWfMTcR/taqeR9kw5cUlkjD0WkKffBjHnGySO/Uge9u/hL3yAgG3jCI7IBin7EwSl39T30MVAoC4z1cSsmY1Zr0B3ScfY3B0wHn5Mgpc3fE7tIeYmS/W9xAbrOR3PsDOWEhq2wgSf/iV9B17yIuJR1dYgFNKEjn+Qdjn55G8ek19D1UI4pZ+hfeNI3HOTCOlYzfs/tmKZ7dO6N94HYDgn74m7d/d9TzKhqtw9hwA4kaNIeSBe/DbvZ24xcvJCG2Jc3oqoS88TVZ4R3JOR9fzSEVTpkwmfH/9CYCE/kM4N3MWSb/+hTk9Hb+De8h4fjYAvp/9H0W5efU40oYrPzkVrxefBSB++hN4dGxPsw/mEz/wKuwKC3C8bSw5Z2PqeZRCgMmsOJmsBdK7hXkypL0/9w1uzQ1dg2np54JOB+fS8lh/PLH2neXnw623wj33wMmmcwdczCtvEPr1MpROR8rHn+HTvzfBMx4kM6Q5LmkpJM6bX99DFHUgOjqae++9l5CQEBwcHGjRogWPPfYYKSkpFW4XFhZGXFwcnTt3rnJf48aN4/jx47UdcqMmgXRRrqT3FmKfn0ta63ACRl8HgKO3F94b/iJ+8DDsCgvwv2scsZ8sq+eRNjwp02cQtGktJnt7zs2chc+ef/Hq2RUAncFA2tg7ANB/Ln9bUf8K0tJxf3IGADFTHsRnQF8A3Nu3Ifn1twFo9t5bpPwtef2ry1xoxHvppwDkPTyDgNHX4dW7O84hgegMBnR6PWlDhgNQ9PPP9TlUITj33/kE3jsBu8IC4gcPw33rJlxCQwDwGz6EuGHXozebKXh6Zj2PtGFK3fQPwRv+xKzX4/rySwDo9HqCJ9+J24ljnJv7Jnme3nidiSL1zXfrd7CiSUvbugPnzDQKnV3xW/cnoa/Nxv+6Ydi7aWmegu6fTI5/EC6pScQvXFTPo22Ykp/8Dy6pSWSEtSL4lRcA0Nvb4bXqGzLCWuGWFE/uTbdgyi+o55GKpu5sai4FRjOujgZCPJ0BMOh1tAt055YeodzZrzkAp5JzyMir5az0Xbu0YDrAoUO1a6uBSFj5AyFztLv9Yp6dTfDE2wEwODqQ9cxzAPgt/J+kf2vkTp48Se/evYmMjGTFihWcOHGCjz76iLVr19K/f39SU1PL3a6wsBCDwUBQUBB2dnZV7s/Z2ZmAgABrDb9RkkC6KMNsLMJr0f8BkHP/Q+j0598m9m6u+K/5ldiRozEUGQl64B5S1m+pr6E2OPnJqQT+9gMAyV99T+hrs9Hbl96pedw/BYCAretl1pmod0kznsEtKZ6s4DCC5s8r9VzIg/cSO/Im9GYTdpPullsLqylh2VfaybC3L4H3lZ8H3XDDKAA8/l6DMpvrcnhCWMR/tYrQ559CbzZz7taJBPz1Gw6e7qXWcX7zNcx6A0Eb/5L0bzVQ8PIrAMSPHI1Xjy6lnjM4OhD6/FOkPDcbAMd/JK2DqD+5v2uf79Q+/TE4OpR53uDkSNp9DwLg9sH/UCZTnY6voUvdsp2QL7QLEPnv/A87F2fLc07+vqgffqDA1Q3/fTuImzStvoYpBACRCdqxf9sAN/T6skX8AtydCPNxQSk4FJNRu862bTv/85EjtWurAUjfuRevKXdpx163jKdZ8UW1EsEPTSGjeWucMtNJfOX1ehplw6WUorDIXC8PpapXjHf69Ok4ODjw559/MmTIEJo3b851113HX3/9RUxMDM8/r11sadmyJa+88gp33303Hh4eTJs2rdyUKj/99BPt2rXDycmJq666iqVLl6LT6UhPTwfKpnaZPXs23bt35/PPP6dly5Z4enpyxx13kJV1/tz/999/Z9CgQXh5eeHr68sNN9xAVFRUjf9/LndVvywhmoyE5d8QHHeOfA8vAh+8t8zzBkcHgn76lrhrRhG84U/yP1gAQwfWw0gbnuRPlxKan0d6y7YE3Hx9uet4dutEctde+O3fRer/fYbrvFl1PEohNMnrNtPsc23GdO57H+Du7lbqeZ1ej8/SReR07oLnudOce+ARQlcsqYeRNkx2Cz4AIGXCZMIuOFG+kO/o6zHZ2+MRe5b0vQctd68IUZeKfvgBgJgbxtJs5dJSF9hLePXowrlbJxD69TL0//kP6rrh5a4nykr9ZxfBa39F6XS4FM9GL4/bNVfBTPA5sJui3LxSATYh6or9ur8BMA696pLr+D3+MIXvvoHX6RPEf/U9QRNuq6vhNWjKZML84EPozWZir72RkLE3llnHq2dX4j5eTNDE2wn9ehnnuncj9Nkn6mG0oqkzmRVRSTkAtAtwv+R6XUM9iU7N5WBsBv1a+2IoJ+BeJVsvuIh89GjN2mgg8pNS0I8ejWNONok9+hH85eIyx1R6eztynnsRzwfuIeCTD8n/z5M4BfrX04gbHqNJ8eG6E/XS9/Sr2uJgV7XPQWpqKn/88Qevvvoqzs6lj/uCgoKYMGECK1euZMGCBQC89dZbvPTSS8yaVX4M6dSpU9x666089thjTJ06lT179vDUU09VOo6oqCh++OEHfvnlF9LS0rj99tt57bXXePXVVwHIycnhiSeeoGvXrmRnZ/PSSy9xyy23sHfvXvSN8Hyg8b0iUWuGD94HIOmOu7G/KHBWQm9vh+HpJwHw//VHmYlaRc7LlgCQNXFShQGG/Ana7FT3lV/KLFRRL0wFhejvn4ZOKWJHjSHw1pvKXc8pwI/s/9OC7aFfLSV+xfd1OcwGK3XLdvz3/IvJYIfPE49ecj0HLw+Se/UHIOv7H+tqeEKU4rJ7JwD60aMr/O7yfn0uRidn/A7uIX7piroaXoOXP+tlAOKuuQGv3t0vuZ5nt07kevtiZywkbeO2S64nhK0U5ebhs+dfANxGjbzkek5+PiTcMQkA/XzJ31tVMe8uxO/AbgqdXfFY8N4l1wu+81bOPa3NTg2e9R/yYhPqaohCWMSk5ZFvNOHiYKCZ16Uv7Lbxd8PV0UBOgYmTSdk160yp0jPSG3kgPem9hXjEniUrOBT3X37E4ORY7nrBU+8irU0EjjnZJL383zoepagLkZGRKKXo0KFDuc936NCBtLQ0kpKSALj66qt58sknadOmDW3atCmz/v/93/8RHh7Om2++SXh4OHfccQeTJ0+udBxms5klS5bQuXNnBg8ezF133cXatWstz48dO5YxY8bQtm1bunfvzmeffcaBAwc4fPhwzV74Zc6mM9I3btzIm2++ya5du4iLi2PVqlXcfPPNFW6zfv16nnjiCQ4dOkRYWBgvvPBClf5jhXWkbttJwK5tmPUGvJ56rMJ1/a8bTmazFnjEnCFmyZc0e+z+Ohplw5S6ZTu+R/ZjsrPH78H7KlzX796JFL34DF6njpOycRu+MuNf1LHY2fMIizxCvocXXh+9X+G6gbeMInrSNMKWfoz7ow9hGnNDubd7i/Ny5/8PHyDhmlGEtGpe4brGkdfBPxtx/ON3mCuFXUXdKkzPxCtKO2F1v2pwheu6tgwj+t4HCVvwNi6zXsQ84Xb0DvZ1McwGK23HHoLX/AKA08sV34Gm0+vJ6NUPl79+JX/tOhh5dV0MUQiL1LUbCcjPI9fHr8KLPgDezz6FaenHBOzZTsrfm/C9uuL9R1OXF5eI7xztjpSEJ2YS1qZlheuHzptNxoov8Iw+Rfr6TTjfeWsdjFKI8yITtYl0bfzLT+tSwqDX0TnEk+2nUtl3LoN2gZeevX5JZ85AfPz5348e1YLruhrObr/MuX61HICMRx4nNCTwkuvpDAbyX3gJ7rmToMUfk/v8M7g0C66rYTZo9gYd069qW299V1dV08H07t27wuePHTtGnz59Si3r27dvpe22bNkSd/fzn93g4GASE88XEY6MjOSll15i+/btJCcnYy6eDHr27NlqFTptKGw6Iz0nJ4du3brx4YcfVmn9U6dOMWrUKK666ir27t3LjBkzmDp1Kn/88YcthykukPumVjwwfvj1uLdrXeG6Or2ejNvvBMD+86U2H1tDl7vwYwASrh6JcwVfiKDN5EkcphV5zfvkM5uPTYgL5SenEvTOawAkz5prKShYkaAP3ibXxx/X5AQSv/nBxiNs2PJiEwj65TsA7B+fUen6HmNHA+C751/yk8svJiOEraRv2ILebCY7IBi3SgI7AAGvvECepzee0aeIffsD2w+wgcubNQedUsQNux6fK3pVur5pkBaMtN+62dZDE6KMguL86OlXDK40dZNb21bEj7pF2+71N20+toYu5bGncM5MI71Ve0JerLxos06vJ7u7ts8o2r7D1sMTjY3JBHv2wOHDkJKiBaWrwWxWnEjUZpe3Cyz/DvYLdWrmiU4H0am5pOYUVn+8JbPRO3fWgudpaVA8A7exSd36Lz4njmKyt8fv3rsqXT/o7nGkRHTBPj+XlJdeqYMRNg46nQ4HO329PHTVuADUtm1bdDodRy5RF+DIkSN4e3vj76+l9XF1dbXK3+di9valJ8bodDpLsBzgxhtvJDU1lU8++YTt27ezfft2QCt42hjZNJB+3XXXMXfuXG655ZYqrf/RRx/RqlUr5s+fT4cOHXj44Ye59dZbeeedd2w5TFEsNyauWsEdAK8Hp6J0OgJ2bSPz0DEbjq5hM2bn4PfjtwAYplU8G72E4d57APD75XuKcvNsNjYhLpaxaRv2BflkBYfSbMaDVdrG3s2VlNFjAVDLltlyeA1e8jsfYGcsJCWiC37Dr6x0fY/OHcho3hqDqYi0H1bXwQiFOK9gk1ZQPLN7xTNcSjj6eJPy+DMA+Lw5T1K/VSB9936Cf9dSNjlWkBv9Qi5XDwXAZ+8OzMYiWw1NiHI5b1oPgHnYsKqt/6wWEA76a7WcJ1Qg98w5Qr7TZqAWvv9+le/qM/fS9sv2e3bZbGyikZo/H3r2hE6dwM8PHBygWTNt2XXXwcKFFW4ek55HbqEJJ3sDod4ulXbn6WxPKz8twHegJkVHSwLpV18NrVppPzfSgqO5xZPoEoZeW6Wc5zq9nqLZcwAI/uIzck6dten4RN3y9fXlmmuuYcGCBeTllY4JxcfHs3z5csaNG1fl4Hx4eDg7d+4stWzHjtpdjE1JSeHYsWO88MILDBs2zJJupjG7rHKkb9u2jeHDh5daNmLECLZtu3QeyIKCAjIzM0s9RM2kvPPh+eDOtUOrtI17u9Yk9h0EQMZHn9pwdA1b4pIvccrOJCuo2SWLjF7Mf/T15PgH4ZSZTtKK72w8QiHOK/xX+3LN6tytWsUCXadMBiBg/Rryk1JsMbQGz1xoxHuptq/Mf2B6lf++mcOu1bZfLYF0Ubfsdmj5kE19+1V5m6CnHyMrOAyX1CQSXnndVkNr8HJmv6LNRh86Ap+BVfv7el3RmwJXNxxyc0jfJrNQRd3JT07F59A+ADxvuHR+9Av5XNGL+AFD0ZvNZP73DVsOr0FL+XQperOZ5C49CRh1bZW3cx54BQAeh/ZJTSVRPXv2aP86FufeLiqC2Fht+e+/w/Tp2kz1SyiZjd7G37XKxUO7NPME4HBsJkZTNd+vJfGg/v0hIkL7uRHmSTcVFOJTPPlOP2lSlbcLuO0mkrv2wq6wgLSX5thqeKKefPDBBxQUFDBixAg2btxIdHQ0v//+O9dccw3NmjWzFPysivvvv5+jR48yc+ZMjh8/ztdff82SJUsAqjVT/kLe3t74+vry8ccfc+LECf7++2+eeKJxF8G+rALp8fHxBAaWTnkRGBhIZmZmmasvJebNm4enp6flERYWVhdDbXRMBYX4LFsEQP6DD1creFY0aTIAXt+uQJlMthheg2e/WLuynH7HXegMhipto7e3I/WW27VfZIavqEOGfXsBMHXtXq3tvPv3Ia11OHbGQpIXL7f+wBqBhGVf4ZYUT663L4H33V3l7RxvuhEA7w1/yX5W1BllNuO1T7uw5jxkUJW3s3NxJvM/WiE8ry8Wy3u2HJkHDhO8WivO7DC7arPRQTs2SOuu5bbM+Xu9LYYmRLnSfluD3mwiI7Ql7u3LFjC7FP3TTwMQ+N2X5MUlVrJ20+TyzVcA5I8bX63tPK/ojclgh0tqMtlRp20wMtFolaRF+eQTKCiA6GjYuRN++QVatNBSvfz7b7mbKqUs+dGrk++8pa8r7k525BtNRCZUo+hobi7s3av93MgD6Unf/ohLWgp5Xj743zq6ytvp9HqK/vMcAF5//mqr4Yl60q5dO3bu3Enr1q25/fbbadOmDdOmTeOqq65i27Zt+Pj4VLmtVq1a8e233/L999/TtWtXFi5cyPPPPw+Ao2P5RW0ro9fr+eqrr9i1axedO3fm8ccf5803G3dKt8sqkF4Tzz77LBkZGZZHdHR0fQ+pQUpYvBzXpHhyffwInFp5Lq4LBdw1jgJXd9zjY0j86XcbjbDhyth7kIDd/2DW6/GePq1a27rfPwWAwM1/k3M2xhbDE6IMl8MHALDvXXm+3gvp9Hqyb9dOAh2/kkB6eewWaDmjUyZMxs7Fucrb+Y4cRqGLKy5pKaRsvPRdWkJYU9ahozhnpGGyt8drQOWFiC4UcO8EClzdcE+IJen3tTYaYcOVOWceerOZ+MHD8B0yoFrbGgdqFzXsNkuedFF3itb8BUDWwMpTkl3I/6aRpLbvhH1BPslvSLrOi6Xv3IvvsYOYDHb43lv1C+ygpdXLaN0egOzNcmwgqqEkkO7vr6V1CQ2FXr1g1CgYMkR77p9/yt00NiOfnAITjvZ6mvtUntalhF6vs8xKPxCTXvWx7typzZgPDobmzRt1IN287HMAkkffWuUUTyW8hmv/b26JceQnJlt9bKJ+tWjRgiVLlhAfH09hYSFnz57lvffew9fX17LO6dOnmTFjRqntWrZsiVKK7t27W5bddNNNREZGkp+fz7p160hJSSE0NBQnJycAJk+eTHp6umX92bNns7fkYlaxGTNmcPr0acvvw4cP5/Dhw+Tn57Nv3z6GDBmCUoqbb775kuNoyC6rQHpQUBAJCQmlliUkJODh4YGzc/kBB0dHRzw8PEo9RPXZLdJSDaRMvLdawR3QDuKSbtDy4JsWSWHMi2Uu0IqMJg68Cre2raq1rVfPriR36oHebCL1k8W2GJ4QpRRmZOF59iQAHv37VLJ2Wd5TJ6F0Ovz3/Evm4ePWHl6DlrrpH/z3/IvJYIfPE49Wa1uDkyMp/bUD5LxVP9lieEKUkbVOC9SmRXSp2bHBtdqdFIVLPrf62BoyY1Y2Ab+uAs7P1q0Op6uvAsBr1z+SzkHUGffNGwAwXHNNtbbT6fXkzdBu8fZb8jHG7Byrj60hy1q0BIDEQVfjHBxQ7e1zu/YAoOhfSfUkquHCQPrFrtBSBnGJ9LqRCdps9NZ+blVO61KiczNP9Dodsen5JGblV22jC9O66HTnA+mNLEd6fmIygeu0SYmu991b7e2d/H3JCmoGQOaO3VYdm2hcFixYwI4dOzh58iSff/45b775JpOqkUpIXGaB9P79+7N2belZS2vWrKF///71NKKmQZnNeB3Wch66TRhXozacp00FIGDNavKTU602tobOVFCI73crAFD3TqlRG/kTJgLgtmK5nDALm8vYvgudUuT6+OPSIrTa27u1aUlin+K6CXLxp5S8t/8HQMI1o3Bt1bza25uuuw4Al7/+sOq4hLgU9Y928prfq/oX1QAcJmuzK/1/+5GinFyrjauhS/ryWxxyc8gKDsX/+uGVb3ARnyEDKHJwxDk9lYy9B20wQiFKyzl1Fq9TkSidDu8bRlR7++Apd5EV1Azn9FQS/k+ODUookwmvVd9ov0ycWLNG+mj7Z4fdEjgTVaQUJBfPWK4okL59O1x07qmUsuRHbxfoVu2uXR3taBugbXewqkVHLwykA3TooP175oyW9qWRSF60DIPRSFqbCLxrMJkJILt9RwAKd++14shEYxMZGcno0aPp2LEjr7zyCk8++SSzZ8+u72E1KDYNpGdnZ7N3717LbQCnTp1i7969nD2rVRJ+9tlnufvu87ewPfDAA5w8eZJnnnmGo0ePsmDBAr7++msef/xxWw6zycs+cQqH3BxMdvZ4dO1UozZ8hg4kvVU77AoLSF4kM89KJK74FpfUZHJ9/AgYP7ZGbfhNuZsiewe8o46StqX8XHVCWEvBDi0fcmaHzjVuo+jOCQB4fPuVXPwplhebQGBxPmT7x2fUqA2fW28GwPfIfnLPnLPSyIS4NJdd2gxH/YDqpR4p4T/qGrL9g3DMySLpq++tObQGTb/8CwDSb7m9ynVTLmRwciS1S08Astess+rYhChP+mrtAm5qeGecAssJvFVC72BP+t3aDEuHLyX1W4mkX//CPSGWAld3/Gt4nuAyQCtU7HlYCo6KKsrIAKNR+7m8QHqXLuDiApmZZdKnxGfmk5VfhIOdnhbVSOtyoa6hWnqXI3FZFBRVUkNFqbKBdD8/KElncbzx3P3q9KV2bJB9x53Vqld3oaJOxedv+w9Ya1iiEXrnnXeIjY0lPz+f48eP8+KLL2JnZ1ffw2pQbBpI37lzJz169KBHD+2WsyeeeIIePXrw0ktaUaW4uDhLUB20xPerV69mzZo1dOvWjfnz5/Ppp58yYkT1Zz6IqsvZpc1Gz2zeGoNTzQoM6PR6ssdrMymcvlhqtbE1dLpFWgHXlLHjq53nrIRTgB+JV2mfgZwvv7La2IQo1569ABi7dqtxE/6TxmN0csbz3GlS1m2y0sAatpQP/w87YyEpEV3wG169/LIlXFqEkhLRBYDU73605vCEKKMwIwuvE9oJtMdVg2vUhs5gIO3m27RflkvwDCD3XCwBW9YD4D51co3bKeg/EAD9po1WGJUQlfhLy4+eO3hojZvwmjoZpdMRsPsfMo+dsNLAGjbj0mUAJI28CTvXmgUlPfv2pMjeAafsTLIONb6c0cIGStK6uLpCeelz7ewsdzpcnCe9pEhoaz9X7Aw1CyWFejvj7WJPYZGZY/FZFa986hQkJoK9vZbDvUQjy5Oesfcgfgf3YNYb8L3vnhq3Y+iunb85HTlkraEJIcph00D60KFDUUqVeSxZsgSAJUuWsH79+jLb7Nmzh4KCAqKiopg8ebIthyiAon37AchrH16rdnym3YtZb8Dv4B7Sd+6zxtAatKzIkwRsXQ+Ax/T7a9WW+Uot8GZ/WL4UhW05H9L2B3a9qldo9EIOXh4kDr8egPzi3J9NmTKbcf3qSwDyJt1T41kmALnDtYtq+t9/s8rYhLiU9A1b0JtN5PgHVbu+x4XcioPFAZvWkp+QZKXRNVwpiz5HbzaR0rEbXj261Lgdh6u0mgnuO8svBieEtSizGc+t2gUbhxHVy49+Ifd2rUnqrd3dkvGxpHcpysnF73et5onDpLtq3I7B0YH09todxVmbZX8gqqCi/OglStK7XBBIV0oRWYu0LiV0Oh1dQr0A2H8uA6XUpVcumY3esycUF0IEGl2e9MyPtTpziQOG1Ci1ZgmX3toEVs+ooyhTJbP9hRA1dlnlSBf1Q3fkMACmDjVL61LCpUUoCYOuBiDr409rPa6GLn3BJ+jNZhJ7XoFnt9r9bR26aLdpuZxoPLevicuPqaAQryhtZofrFTXLzVfCbpKWtstv9Q+Y8gtqPbaGLG3zdrxPHqPI3gG/KXdXvkEFXG65CQDfrRswFRRaY3hClKtgy1YAMrrX/KIagHffnqS264ChyEjy4i+sMbQGzeVrrW5K7u3ja9WO97AhmAx2uCfEyuxeYVMZ+w7hlhRPkb0DPiOurlVbhZL6zSLxy29xzMkmKzCkRrUSLpTfTQueqX8lBaSoghoG0hOzCsjMM2ppXXxdazWETiEe2Ol1JGUVkJhVwXnCxWldSpTkSW8EM9KVyYTX9ysBME+s+UU1AI/OHShycMQ+P4+sw8esMTwhRDkkkC5wOqZdybXrWvOcyBb3TAbA97uVmAuNtW+vgVImE15fabnijTUsMnoh1+7arDX3uGiM2Tm1bk+I8mTs2ovBaKTA1R33Du1q1Zb/6OvJ8Q3AKTOdxJWrrDTChin3Ey3FU+KwkTj5+9aqLZ8hA8j19sUxJ5uU39dWvoEQNWS3fTsApn5X1LqtnOKgsWPxnRlNVfqeA/ge3qfduj2ldifLDp7upEVox21Zf/xtjeEJUa7s1b8DkNq9D/ZutQue+U8aj9HRCc+zJ0ldv8Uaw2uwdMXprtJvua1GtRJK6dMbAMd9e2o7LNEUVFRotEQ/Lfc+Bw9qudKBqOLZ6C19XbGvYVqXEk72BkvR0UOxFRQd3apd1C8TSG9EqV2SfvnTUishYOLttWpL72BPRuv2AOT8KwWIhbAVCaQ3cWZjER6ntZlMLj1qnhO5RMAdY8nz9MYlNYmEb5tuDt/EVatxj48h382DgEm1m3UG4BIaTL6HFzqlyNov6V2EbeRt1wqNZoR3qlX6EQC9vR2po2/Vfvmi6RYgNuUX4PuTVmRRP2lyrdvTGQykXTkMgIKffq51e0KUR5nNeO3T9gdOgwfVuj2fKZNQOh3++3aSeajpzpDK+nQJAIkDh+ISGlLr9vL6aWky1CapRSFsx7BOK2hbcOXQWrfl6O1FUnHqt7wmnPotLzaBgM3aBTD3KZNr3Z7bIC3I6HXkIGZjUa3bE41cVWakBwdDixZasc8dWuHx5BztTsgQL6dLb1cNHUM8ADgan0WRqZw7VHJyYL+WcvKSgfTjx6GBpzAxLl4CQNL1N9e4VsKF8jpoF9mL9kqqXSFsRQLpTVz20ePYF+RT5OCIe8fa5UgHMDg5klxSWGxx081/aPpES22TPPrWWs/eAa2Ya1artgDk7TtY6/aEKI95jzaTqaBLV6u05zpVK5YTsGEN+YnJVmmzoUn86nucM9PI9fEnYMwNVmlTN2oUAO5r/7RKe0JcLOvwMZzTUzHZ2eM9sG+t23Nt1ZzEPlpAPmNR0yxIrkwmvFZ9DYC5OL1FbdmV5En/d6tV2hPiYmZjET7bNwPgcv0Iq7Spv1tLceb7y6omm/otZfHnGExFpIR3xqt391q359GtM0YnF+zzc8nYe6D2AxSNW1UC6XA+eF2c3iWtOJDu4+pglWGEebvg7mRHgdFMVFI5d1zv2KEFyZs1g7Cw0s+1bAkODpCfD2fOWGU89aEwPZOAP34BwPHeyVZpU3XV7mS3OyQxAyFsRQLpTVzOLu1KZWbLtujt7azSpusD0wAIWPcnuWfOWaXNhiQ3Jo7Av7XbYF0eql2R0QsVtNUudJgPH7Zam0JcyOmgNutD36OnVdrz6d+b1LYRGIxGkj9rorPSly0DIOXm29A72FulSZ9bbsCsN+B1JqpJz+4VtpO1QUu5kBbe2SqzowCM47W7szy+W9kkcyMn/7ke97hzFLq44j/hNqu06X3NVQB4nj1JztkYq7QpxIXSNm3DMSeLAld3vAf3r3yDKgi4WUv95pyZ1mRTvzmtLK6VcNsdVmlPb29HWgcteJa7eZtV2hSNWFUD6RfkSTeZFem5WtpWbysF0vV6nWVWernpXS6VHx3AYID2WgqThpzeJWnpCuzzc8kMbYHftUOt0qZjj+4AuB6TmIFoelq2bMm7775r834kkN7EFR3QrlTmhXewWps+V/QiuUtPbabFB/9ntXYbipSPFmEoMpLSoSs+A2o/k6+EKi6qYne0cVQnF5cXZTLheUxLG+Tcr7fV2s0ZdyfQNHMj58bEEbDxLwDc7q99rYQSTn4+ltzIOf/ssFq7QpRQW7WT17zetSs6fKFSuZE3Nr1AT8FibSZ+0rU3YO/uZpU2nQL9SWuj3d6euWadVdoU4kK5v2l3PqX2HWi1CTd6B3tSR4/Vfvm86V1kzzxwGL8DuzHr9fjUsgD5hQq6Fxcc3SHHBaISNQikZ+YWYlYKe4MOd0fr7AsAOgV7AnA2NZfM/Ivqq1UUSIdGUXDU/gttwk3GreNrnVazhHsfbUKUR+xZCtLSrdKmqH/btm3DYDAwqvjO5MakroLf1iSB9CbOcFgLnJk7drRquwVTtFnpXsuXNKlcfcpsxv0L7WQ57+7JVm3boXMnAJyjIq3arhCgpXJwyM2hyN4Bzx5drNauz9TJmPV6LTfywaZ1ESj106XaBcWILnj3tc4s/xL5bbRisEVHZUa6sD7nXf8CYBgwwGptOnp7kXT1SAByP1titXYbgqKcXPx+0+rG2E+yXuAMIKc4T3rRhg1WbVcIAMcN2gUa09VXW7Vd16n3AhCwcQ35CUlWbftyl/Fp8UW1foNxbRlWydpVZ+irTd5x2r/Xam2KRqqqgfTu3bX0KcnJZB7SgtVeLg7odDqrDcXTxZ5Qb2eUgiOxmeefUOp8IP1SxyINvOBo+s59BOzcilmvx/MB6024cQ4JJMcvEIDMHXut1q6oX4sWLeKRRx5h48aNxMbG1vdwmjwJpDdxzpFaEMauq/UCZwABUyaS7+6Je9w5Er9rOgXxktdswOv0CYxOzvhPm2zVtl2Kg5vu0acxFRRatW0hsv8pLjTaNgKDo3Vu2QRwbRlGUt/i3MgLP7Vauw2B84rlAOTeYZ18yBcyt9FqJuhPyIU1YV2FGVl4R2oXvdyH1r7Q6IX0d90FgN/P3zep77Gkr1fhlJ1Jtn8Q/jdaJ890Cf2VgwFw3d70ZvkL2zJm5+CzV5vd7Gal/OglfPr3JrVdhyaX+k2ZzXh+txKAIivVSijhNkibPex9/FCTzT0vqqiqgXRHR+ipTQQxFd+pZq386BfqFKLNSj8Um4lSSlsYFQXJyVogv0eP8jcsCaQfaZgTdbLfeQ+AhCHX4hHe1qptZ7XXZusX7N5j1XYbFaW0grb18Sh5n1dRdnY2K1eu5MEHH2TUqFEsWbLE8tySJUvw8vIqtf4PP/xQ5oLX3LlzCQgIwN3dnalTp/Kf//yH7t27W56fPHkyN998M//9738JDAzEy8uLOXPmUFRUxNNPP42Pjw+hoaEsvqgOYnR0NLfffjteXl74+PgwevRoTp8+Xabdt956i+DgYHx9fZk+fTpGo3YHytChQzlz5gyPP/44Op2u1Lg3b97M4MGDcXZ2JiwsjEcffZScnPP1FBITE7nxxhtxdnamVatWLF++vFp/19qQQHoTZiooxONMFACuvbpbtW17N1eSx2p5/9RHH1m17ctZ4UdaKpvEkaNx9PG2attubVthdHLBUGQk61DDvPIuLl+m3bsByOvczeptm6dMBcBv+WKM2eUUE2qE0v7dje+xg5js7PG7b7LV2zdEaDUTHE+esHrbomlL37wNvdlEjl8gbm1bWbVt/zE3kOflg3N6KkmrVlu17cva518AkHbzregMBqs27XmtNlPYO/Iw+cmpVm1bNG1pG7ZgZywkxy8Qz+6drd5+zu1a3QSnJpT6LeXvTXjEnMHo5IL/XdbJj17CvWM4+e6eGIxG0rfvsmrbopGpaiAdLOld7HZod6p5u1g/kN42wA0HOz0ZeUbOpeVpC7cWF9Hu1UsL6JenAc9IL0hNI2CVdlFN/8jDVm/f2Kl4n71/v9XbbjRyc8HNrX4eubnVGurXX39NREQE4eHhTJw4kc8+++z8RacqWL58Oa+++iqvv/46u3btonnz5ixcuLDMen///TexsbFs3LiRt99+m1mzZnHDDTfg7e3N9u3beeCBB7j//vs5d06rg2g0GhkxYgTu7u5s2rSJLVu24ObmxsiRIyksPD9hZt26dURFRbFu3TqWLl3KkiVLLBcDvv/+e0JDQ5kzZw5xcXHExcUBEBUVxciRIxk7diz79+9n5cqVbN68mYcfPv95mTx5MtHR0axbt45vv/2WBQsWkJiYWK2/bU1JIL0JyzpwGEORkUJnV9zbtbZ6+26PTgcgcNNfZEWetHr7l5v85FQCfv8JAIfigqvWpNPryWzZBoC8/VKFW1iXw7692g/FBWqsKfDuO8gKDsU5I42EBYus3v7lKPv/tNeZOHgYziGBVm/fqaN28uB6pvHvW0XdKtikFRrN7N7Lavk6SxgcHUi+cQwA5iaSGzkvLpGATWsBcJt6j9Xbd23VnMxmLdApRfqa9VZvXzRdxsPaXatZ7TtYfV8A51O/+e3fReaBplEUr+BDLXCROPw6HDzdrdq2Tq8no2NXAPK2bbdq26IRycmBvOJgdVUC6cX5yd32aHeu2mJGuoOdnvaB2ufhUEl6l8ryowOEa5NKSE7WHg1I4vsf45CXQ3rLtgSMvs7q7RuKZxo7HpaYQWOwaNEiJk6cCMDIkSPJyMhgQzVS+r3//vtMmTKFe+65h/bt2/PSSy/RpUvZjBQ+Pj689957hIeHc++99xIeHk5ubi7PPfcc7dq149lnn8XBwYHNmzcDsHLlSsxmM59++ildunShQ4cOLF68mLNnz7J+/XpLu97e3nzwwQdERERwww03MGrUKNauXWvp02Aw4O7uTlBQEEFBQQDMmzePCRMmMGPGDNq1a8eAAQN47733WLZsGfn5+Rw/fpzffvuNTz75hCuuuIJevXqxaNEi8kr2bzYmgfQmLHePdoUys3V7mxwge/XoQmLvAejNZtLfK3vFq7FJ/nQp9vl5pLdsi981Q2zSR3477YCh6MAhm7QvmiZlNuN+VDvQcurby+rt6x3sSb/vQQDcFr6PMpms3sflxFxoxPeHb7Rf7rZuPuQSbl20uhbOGWnkJzaskwdxebPbrgVgivpdYZP2ne+ZBID/2t8oSE2zSR+Xk5QlX2AoMpLaroPVayWUyOqjBRqM6yVPurAec+RxAIpaWX+yDRSnfuunpSbK+HhxJWs3fFnHowj6+TsAnB5/zCZ9FPbQ9jG6nTtt0r5oBEpmozs6ajNjK1M8I90z8gh2+Xl4u9jbZFidQjwAOJGYRUGRqWqBdFdXaN5c+/lYw6kZpEwmPBZpd7FnT73fJnEY595aOhzPyCMos9nq7TcKLi6QnV0/DxeXKg/z2LFj/Pvvv4wfr93FZWdnx7hx41i0qOqT044dO0bf4joaJS7+HaBTp07oL3g/BgYGlgq4GwwGfH19LbO+9+3bx4kTJ3B3d8fNzQ03Nzd8fHzIz88nKiqqVLuGC+7IDA4OrnTm+L59+1iyZImlXTc3N0aMGIHZbObUqVMcOXIEOzs7evU6H7uIiIgok+bGViSQ3oSZ9h8AIL99hM36KLpPm5nts2JZo8+H6rxsCQBZEyfZ5AsRwByu/V8ZjjW8W9jE5Sv3zDlc0lK0Yjc2CKQDBDz2IIUurnidPkHCNz/ZpI/LReJ3P+OSmkSepzcBd4yxSR8OXh6WQkI5TayIq7AdZTbjuU8LwDgNGmiTPnyHDCC9ZVvsC/JJeuN/NunjclKStqIkjYVNFOdJd/5ni+36EE2O3cniO57atbNZH6aJWt0Er+9WNvpgT8arr2MwFZHYZyC+Vw+2SR92/bTAiPN+yYssLuHCtC5VKRoaFoYKDsZgKiIw8hBeNkjtAhDs6YSPqwNGkyIqKg4OaHGKCgPp0CDTuySuWo1n9CkKXVzxf2iqTfrw6NYZk509Drk5ZB2PqnyDpkin0y7G1MejGgV7Fy1aRFFRESEhIdjZ2WFnZ8fChQv57rvvyMjIQK/Xl0nzUpJ/vLrs7UtfKNPpdOUuMxd/X2dnZ9OrVy/27t1b6nH8+HHuvPPOCts1V/Kdn52dzf3331+q3X379hEZGUmbNm1q9PqsSQLpTZjdUe02StWpk836CLz7DnK9fXFNSSRxxbc266e+pW7Zju+R/Zjs7PG1YtXti9l11v6vnCIbzlV3cfnL3KblPcxs0QZ79yrMTqkBRx9vEsZpJ8y6d9+xSR+XC3Nxzrfkm8ZicLpEXkcryGmhzRLMP9xwTh7E5S3r6Alc0lIw2dnjPdg2M9J1ej05M54CwPej9yhMz7RJP5eD9N378du/C7Nej8+USTbrx+2qKwHwOnKg0d/xI+qOY3HqMLv27W3Wh/9d47QUk3HRJP+xzmb91Le82AQCv9ZqJaiZM23Wj/sgLejoFXUcY1a2zfoRDVh18qMD6HTk9+oDQIsT+3Gws034SKfT0bF4VnrC2k1gNkNYGDRrVvGGDbDgqPn9DwBIHHMHjt5eNunD4OhARiutgGnOjt026UPYXlFREcuWLWP+/PllAsohISGsWLECf39/srKyShXh3Lt3b6l2wsPD2bFjR6llF/9eEz179iQyMpKAgADatm1b6uHp6VnldhwcHDBddPzas2dPDh8+XKbdtm3b4uDgQEREBEVFRezadb4myLFjx0hPT6/166oKCaQ3YS7HteCLQ/euNuvD4ORIyjgtn5P+449t1k99y12gvbaEq0fi0izYZv24dNMKh7ifiZKTZWE1RTu1A6zcjmVzpVmT58wnMev1BG7fROo/jbMQVn5iMgHr/gDAdeq9Nu2rsLV2gGxuQLezistb9vpNAKSFd8LOteq3nVZX8IP3khHaUqub8Np8m/VT3/JmPgtAwuDhuLZqbrN+3DuGYzLYaYUhT521WT+i6VBmM+7nzgDg3DHcZv3Yu7uRNOIGAAo+W2Kzfupb8ry3sM/PIyW8MwG3jLJZP66tW5Dr44febCJ9a+2DJKIRqm4gHcjoqt2tGnrMtoUrOwR7oNOB15rftAWVzUaHBjcjPfPwcYI2/QWA2xO2SfFUIi9Cm4BXtGefTfsRtvPLL7+QlpbGlClT6Ny5c6nH2LFjWbRoEf369cPFxYXnnnuOqKgovvzyS0shzxKPPPIIixYtYunSpURGRjJ37lz279+Prhoz48szYcIE/Pz8GD16NJs2beLUqVOsX7+eRx991FKQtCpatmzJxo0biYmJIbm43sHMmTPZunUrDz/8MHv37iUyMpIff/zRUmw0PDyckSNHcv/997N9+3Z27drF1KlTcXZ2rtVrqioJpDdRRTm5uJ87DYBbz2427cvzkYdQOh2B2zaQeajxBXyM2Tn4/6jlQzZMu8+mfbl3DMdkb499QX6TKOAq6obdfu0Ay1RcmMZWPMLbEj9cO4HMe/0tm/ZVX5I/+xw7YyFprcPxHtTPtp210wLphhORtu1HNBnmrVsByOvZ26b96B3syX5aCzL7LXy/Uc5KT/5rA8F//YpZr8fpjXk27UvvYE9OkDZrL/dw4zvOEnUv98w57PPzMOsNuIW3tWlf9pO1uzUCf/mO7BOnbNpXfShMz8R/6ScAFDz5tM3SP4J2x09m5+5aX/9IwVFRjhoE0hM6dQfA78BuuCiFhDW5OdrR0ZhOl9VfaQvurcKElA4dtH8bSCA9Y/7/0ClFwhVX4tXDthOYVHFua8MhKTjaUC1atIjhw4eXO7t77Nix7Ny5k3PnzvHFF1/w66+/0qVLF1asWMHs2bNLrTthwgSeffZZnnrqKXr27MmpU6eYPHkyTk5OtRqfi4sLGzdupHnz5owZM4YOHTowZcoU8vPz8fDwqHI7c+bM4fTp07Rp0wb/4n1T165d2bBhA8ePH2fw4MH06NGDl156iZCQEMt2ixcvJiQkhCFDhjBmzBimTZtGQEBArV5TlalGJiMjQwEqIyOjvodyWUvZsl0pUHnunspsMtm8v7gBQ5UCdfa+R2zeV15Ckjr7wlx1buydKv6bH23++s69/7FSoDKDmilzUZFN+1JKqdTW4UqBivvyO5v3JZqGzOAwpUAlrFpt876S1qxXClSRvb3KOR1t8/7MRUUqZesOFf3qWypm4WfKVGi0XV8mk0ru0FUpUNHPvWyzfkrELvtKKVAp7TvZvC/RNCRHdFEKVMzCz2zel6mgUKWHttSODWa+ZPP+Lu67IM12x4lmk0kl9OinFKhzN99hs34uFH/FlVp/b75XJ/2Jxi3hp9+VApUR0tzmfZlNJpXYrbe277n+Fpv2lbZrn0pYtVplHjtRJ8fsSil19oVXlAKVHtrSpscglv4efUbbF9ww1uZ9iQboGe39oWbMqPImP2+NVCa9Qdvu7FkbDk6pjHETtH1B937KVFSFc/i4OG1cer1SeXnV6itl20519t4HVfQrb6jsk2dqOOKqK8zMUnnunkqBiv18pc37i//mR23f07y1zfu63OXl5anDhw+rvGq+Rxqz4cOHq4kTJ9b3MOpcRe+F6sSSZUZ6E5W3R7s1K6t1e5vOjCihpt0PgN83yynKzbNJHykbtnJuzJ0YmocRNvcFmn33JYG3jSalW2/iv/7RZkWM7Bd/BkD6HXehu6Aasa3ktdVyVRoPHbJ5X6Lxy09KwT0uGgCP/n1s3p/f8CEkd+mJwWgk5a13rd5+UU4uib+uIfrpF4kfPIxCT298BvQh9PmnCHnwXlJ79CXtX9vkCox56wN8j+zH6OSMzzTbpnUBcO6kzcJxiz7d6Iu0CdszG4vwitJmdLkNsk1+9AtdPCu9IC3dqu0rs5nol18nuWsv0tpEkBUcSp6nD0UOjugdHXDw9iSxz0CbpEJJWPkDAXu2U2TvgMcbr1q9/fIYW7YCwBwlRcVE7RmPHQcgr0Urm/el0+sxvPceSqcj5NdVJP3+t9X7KMrNI3rKQ3j16kbALaNwD2+LydmF9NbtibtqJNFTHuLc2x9aPa94UW4ePh9p+ZCzH30Cvb2dVdsvj/0V2t1wblJwVJSnBjPSk80GkloXp1DZtq16/Z05A6++ClVJ83DoEO7frABgwz1Pcjo1t/JtAgPB01PLqR5ZtTs0c2PiODd+Ml4D+xL22UJCX3wGlzYtSereh+gXX7VZcc7EjxbjlJVBVnAYgePG2KSPC7n11VLyuJ87LTUTmrjc3FzefvttDh06xNGjR5k1axZ//fUXkybZrn5PYyeB9CbKfFC7xacgokOd9Bc4fiw5/kE4p6eSuOwrq7VrzM4h5p0FpHTqju/QgYSuWoF9QT6pbSOIGXsnRQ6O+B3cQ9C4m0np2pv4r1ZZNeCUsfcgAbv/wazX4z19mtXarYipvZarUne44RRVEZevzG1aDs2s4FCcAqt+UF0bxkdnAOD3+WcYs3MqXrkazs17G+XnR8Coawl7ay5Bm//GMSeLQmdXEvsMpMDVDb9De/AYeAXRjzxt1Yt6OafO4j/nBQDin3oelxahVmv7Utw7hmPW63HIyyH3bIzN+xONW87psxiMRkwGO9zC29VJn8EP3ENGWCucM9NIfO1tq7VrzMom7oaxhM3+D34HduN98hju8TE4Z6ZhZyy0rBewcyu6Xj1J/OFXq/VtNhbh+OLzAMRNug/3dq2t1nZFVNviVE8nJe2bqD3zcS0gZWzVpk7687myPzFjJwBgeOxRzMYiq7WdtmMPmd17EfbZQgCygsMsNQW8TkUSvP4PLZj25MNk9htIzuloq/Udv3ARrskJ5PgFEjh9qtXarYhHcaFoz7MnKUhNq5M+RQNSzUC6yazIyCsirkNxKth//ql6X//8A337wgsvwIgRkF1JMPf559GZzSRdM4r4Dt3YGpVCkamS83adrsp50k35BUS/+CqG8HBCv1qK3mwmfvAwkjv3QKcU/vt2Ejb3BdzD25LSqTvnXnnDajXJlNmM8yfaPijjnql1clHNtXkzcr190ZvNZOzca/P+xOVLp9Px66+/cuWVV9KrVy9+/vlnvvvuO4YPH17fQ2uwJJDeRNkdPqz90LlznfSnd7AndfzdWt+fWKfoaPK6zRhbtKLZE9PxPbwPk709saPGkPT733gfO0Szb5dTePwE0fc8gNHRCb9DewgaP4bULj1J/Ol3q4wh++W5ACQOvAq3traftQNg6KwVDnE6IXlQRe0V7NgJQHYH2+bpu1Dg3XeQFRyqFRpcsKjW7SmTiehpjxL63JPY5+eR6+NH7DWjiJ71X1LWb8EuPY2Afzdj3HeAuCHXYigyEvbBW2R37kbSn+tr/4KAzKkP4JiTRUrHbjR7aaZV2qyMwcmR7OAwAHIOHK6TPkXjlXdUC5zlBDerkxM8KJ6V/kzxrPSPPrDKrPSs41Fk9b6CkN9+wGSwI3rmSyR8/wvJf60nfcceso6dID8+kfQde0ltG4FLWgp+Y28k+qkXrHLCHLdgEd5RR8l388B/7qxat1dVdsWBdMezp+usT9F42Z3SZmSqdrbNj34hn3ffoMDVHZ/jh4h9+8Nat6fMZs698gbug/rjE3mEPE9v4j5fiXvsWXS5uWQePErCNz9y7pU3iJ40jTwPb3yP7Ef17Uvq1n9r3b/ZWIT7/7QLhKn3T8fOpW4KoLmEhpAVqOWQzdhUjaCnaBqqGUjPyDNiVoqkTj20BVUNpK9cCUOHQmKi9vvhw3DffZfOsb5tG/z4I+j1uLw5D2cHA0lZBWyNSqm8r0oC6cpsJu7Lb8lu34GwuS/gmJNFarsOJP70O0Eb/8LvwG6yI09ybvY8Env0Q+l0+B7eR+hLM4m7/harTLxJ/mMdPpFHtJjEow/Wur2qymrXEYCCXXKHSlPm7OzMX3/9RUpKCjk5OezevZsxY2x/V0SjZou8M/VJcqRXjSUn8o+/1VmfWSdOWfKrJf29qVZtxa38QRU6uSgFKisgWJ196gWVEx1zyfWzz5xTZ+99UBU6OikFyqQ3qOi33q/VGKLnva0UKLNOV6d/x5StO7T89m4edZLfXjRu524Yq+UonvGfOu337AtzlQKV1rJtrfKUGnNyVcx1N2v5EYtfR0WfC7PJpGL+b4nK8fa1fH7P3jVVFaRn1ngMsZ99oeV9N9hpn886FDfwKi0n++v/q9N+ReMTPf8DpUDLtV2HTAWFKj2slfb5feqFWrWVsPpPlevloxSoXE9vlfDLHxWuX5iZpeUwL95/xA26WuXFJ9a4f2N2jsoMDNE+kzNn1bidmkjdvkspUPmu7nJsIGotpV0HLY/v8m/qtN/o2fO0z6+Ht8pLSKpxO9lnzqm4QVef/2wPGFppXZaMA4dVWos2SoEqdHJRcV98XeP+lVIq9tPPLcfr+alptWqr2n0Pv17bp/5ndp32KxqA1q21z8XmzVVaPTIhS7395zH149frtO0cHZXKz7/0BmazUnPnWj576qablPr9d6Xs7LTf/1fO8arZrNSVWp0PNWWKUkqpE4lav2//eUydTs6ueJCvvaZte+edZZ4qSM9UscOut4wnx9tXnXvjvQrrFeScjlZnn5+jiuzstXhJ7wEqLyml4jFUIubaG7Vjg7ETatVOdZ295wFtX3DX1Drt93IjOdJFCWvlSJdAehNUkJ55/svkXGyd9h0z8iatXx8/lXHgcI3aOPf+x6rIoH0Zx/cbXK2D05zoGEvgUIE6+/izNTrhTPxtreXL9ewzL1Z7+9owZucok16vFKjsU7Yt+CIav7RW7bWTzFqeMFZXfkqqKnBx1fpe8X2N2shLSFIJPa+wBLGj31lQ5W1zYxPUudG3W/YFKe06qqzIkzUaQ46Pv7YvePDxam9fW9F336f1fe9Ddd63aFzOPvi4dpI37u467/vcB59oAScPrxoHnKLnvW05Nkhp11FlHI2s+rZvva+MDo6WwuHJ66oWYLhYSVHBbP8gVZhVyYm/lRVmZln2Z7mxCXXat2hczCaTZbJK+p4Dddp3UX6B5bgkeuKUGrUR/82PlgtqRnsHdXb2a1U+1s9LSFIJfQYWT7rRq7Oz/luj8wSzyWQp3nz2oSeqvX1tnX3qhTop3ioaIHd37bvi2LEqrf7vqRT19p/H1K/7YpTy1SahqO3by185P1+pSZPOB9GfeEKpksky77yjLbOzKxvE//XX80H6C4qZrj0Sr97+85j6eEOUyi2oYNLNDz9o2/foUWpx9plzKrlDV+08wc5enZ06XeWnpFbpdSulVPx3P6sCZ+1cJaVtRI0LkmYcOnb++GTLJf52NhL9zgLtYkCv/nXa7+WmJHiam5tb30MR9Sw3N1cC6eWRQHrlkv7eZLkiW9fyEpNVStsI7WQ1OKzageDo5162fDnHXHezKsqr4Ir4JZhNJnX2wRmWdqLHT67wqvTFsiJPqhwfP20M195YLzO/MkJbaF+Kq1bXed+i8SjMyrbcJZJ14lSd918ySyKhV39lKiis1rYZRyNVWqt2SoEqcHFV8d/+VKMxxH/zo2V2erZfoErZuK1a20ePnaAFG5q3VsbsnBqNoTaiX3lDmzU45No671s0LiV3dtTHDEZTobHGs9KNObla8L/k2GDkTVpQuZpSNv+jMpq10E647e1V9KtvVev7PS8xWeV5eGnHFbW8462msv0ClQKVvHZjvfQvGofsU2ctgeSaHGfXVvz3v1juHk3dvqvK25lNJnX2hVcsk01S2kZUa/sSRfkFlu92y3lCNY9R4r/7WZvZ7uhU55OWlFIq9pNl2h3AnXtUvrJoOvLzzwe5U6sWUP7jYJx6+89jaltUslKjRmnbNm+uzTR/8kmlPvpIqbVrlTp8+PyscoNBqQUXTW4xm5UaN057PiREqfh4bbnJpFS3btryp54qtUlhkUkt3XpKvf3nMfXDnnPKbDaXP8ijR7XtXVy09pRS6XsOqIyQ5pY71JLWrK/OX8oiZeM2y3l/ZlAzlbZrX7W2T99zwHKnWkLPK2o0htpI2bjNMlGhKd+tVlRUpA4fPqySk5PreyiiniUnJ6vDhw+ronLuiJdAugTSK3Tu7Q+1nXmfgfXSf87paMvJamrr8CrdumkuKlJn733w/EHt5PtrlQ5CKaWi57yuzDqdFoQadn2VgmDG7ByV3LGb5QC9NukgaiP2ymu0v8Oc1+ulf9E4JK/dqB1gevnUzwWho5GWGRoJvfpXett1Ce2g1t8y87O26VQyjhy3BOULnVxU7Ocrq7Rdwg+/WvZJCav/rNUYair+25+0FDkt2tRL/6LxSOrUQ/s+/PTzeum/1Kz0Ks4YS92+y3Jx3qzTqbPPvFirfVleUoqKvfq680H5UWNUQVrVjifPPvCY9lls1a7aQTdrSezRVylQ5xYsqpf+ReOQ8Msflgkv9aUkNUlCn4FV+kwbs3NK3WUWfcv4Wl3cNptMKvq5ly3nCQl9BqqMI8ertG1BWoZK7Na7VrPqayt5/RbLHcBCWERHn58Vfqmg9EW++veMevvPY+poXKZSixadD8Rf6uHhoaVyKU9WllL/z959R8dRXm0Af2a2F616l2zJstyNMd30FjqBhN5LMKETSigpQEIS8kECBEInYHoPJRRTDKaDqca9yJbVe1ltLzPfHzMrN0lW2d3R7jy/c3Qw8uzMBa1mZ+7c997pStso+cADZTkcluVnntn8ugGSnK1uv/yvD9bKd763Rl5a3z3wfkOhza1jNm2S299fLPtc2bIMyL0lE+SeH5cP83/QwHpXrJF7yir6r1Ha3v1oWK/rWPx5/+qYnvLKEa2Ui5ew16dp0dR40tTU1J9M9/l8st/v55eOvnw+X38Svalp4AfcI8klJ2eaFI0r0vLlAIDgtOmaHN8+sQzu996Dd//9kL1hDToOPRzip4thzswYcPtoMISWE09H+ZuvAADqb7gFZX/9IwRxbLNyy/54HZqKilB46QUoXvQ22vY7GK5334I1P3fA7WVJQusZ56F05VIEXFkwvv76oDEnWmTqNOCT94FVqzQ5PqUH/xJl0Kh72izYxvj7NBquqZPR/OBjyL3iYhR89yV8u+yCtsefRMHPjxhwe39TKzpu+ANKnnsChkgY3ZOmwvTuO8gZ46Bf17RqBJZ8hdZjjkfh15+i8JzT0FCzAWU33zDoa8IeL6yXKsOCGk49B2VH/WxMMYyWbZZyHs9orIMUjiRtSCSlH0fjJgCAZVq1JscvvvBc9N5+GzLrNqDxnPnIvPsfcFZVDLitHI2i4dbbUfy3W2AMh+DPykHvA4+i/NRfjCkGa14Oit5/E/U3/RWlt/0JJW/9Fz27LIPvxZeQtducQV/nqalF0eMPAQD8f7oVWWbTmOIYrdDESuCHJZDWrdfk+JQeQqvXAgB8EyuhzVUu4Lj3bkTmLELBN5+j+fFnUfyrMwfd1ltbD/8xx6F0xQ+QRAOabvoLSv943ZjuEwRRRNlfb0JzdTXyLv4VCr75HOG5c1B/zY0ovekGiAP8jsuShJbHnkHG765DfnsLokYTXH8Y/DoikRzTpwAA7F0dCPX2aXa/QuNMbNBoXh4gCDvcXJZldHnDAIBshwk4/3zgZz9T7j/Xrdv6a+NGoLoaePFFYNasgXfodAL//S+w++7A4sXAb38LvPGG8nfXXw/kbn8PXpBhxT6T8/DJ2nZ8srYdpVk25DotW29kMgGTJwOrV6Pnz7ch66nHYQwF0TljDmzvvg17Wclw/w8NyDVjCnxffYGOw49C3oofYfj5kWj4460ovPJimJyOAV/T9vo7yDr9JJh9XnROmw3bB+/CXlo8pjhGw2i3oWdiFbI2roXnmx8Gva7Sg6KiIgBAW2wALulSVlZW/3thTOL8oEdzrEjfsZZ5ByhVEn+/W9M4ur7+TvZnZCq9zucdsNXyUSkalTsWfy7XnXeR7Mkv6u+B3PCvh+IeR+vr78gBh1OtJJsiN9xxj9zzw7LtKmBiA5CiokFZdqoh9jujeIi1Q6ibf7mmcXR//5PcNWnq5p6k1/5hqxUnoT6PXHf9Tf2/p7FWJmMd/LOtSCAo15901uaKtjPOH7SytG7+Zf0V8SPptxhvUiQih01mpepm+WrN4qDUFujq7n/fx/v3aiQaH36iP46IwSg3HHui3PnZV1tt49lY138dI0MZEOrZ1BD3WNre/kD25Bb0r1RpfOCxrf4+GgzJrW++K9edf0n/APe2ObtpunS67qoblYr0407WLAZKfXUXXtHf0kTTOC67VqmMLyyR2xZ+OOAg4PYPP+1vaeTPyFRaqsRZ9/c/ya1z9+w/53RMnSV3fvzFVtv0/LBMGdSsbuMuLh91y7l4CTiUXthdS77XNA4aR95VVpvIs2cPa3NvMNw/8DMU2cFnWyQy7Cp3+aWXtq5iLyqSZc/gc0UkSZJf+a5evvO9NfJTX9bK4QFiiR5//Fb7bN7vkLivHA/2uLcaYuzLypHrfnO97Gts2Wq7psefkSOmzYNKtbxPkOUtWvddf5OmcYwXkUhE8+pofmnzNVA7ly2xtQsT6UPqUxPTbQs/1DoUue3dj+SQ1dbf17T7mx/lust/298rtf+DypU96oGEw9H5xTf9N8yxL292rtx0yFFy3R/+ItffdX//sqi6m/6asDiGq7/PPZds0hi07LGfcrM8giGdiRJy9221LLt534Nlb0OT3HD3g3JfQXH/9zurZyTkRjkmtpw7drzuyily29w95PbZu8gd03eSO6tnyF2Tpvb3YG168vmExTJcsYcQLc+/qnUolKI6v/im/7NWa83PvrJV0irWVqH52Vfkpsef6V+uHTZb5Ppbb09o4tqzqaF/8KAMyPWnny83PvCY3HjUL/oLAWJfAYdz1ENK46Xh/v+oCf3dNY2DUlvjz44eF9e7IXffVp//MpRex21zdpfrTzhDrrvwiv4Hyd2V1XLvTysSFosUicgNd9wj+52u/qKauvMvkX2NLXLdxVf1J83CJrNcd/lvkz5seCCd1TOU66kkD5Oncezpp5XfpYMPHtbmDd0++c731siPfroh/rFcffXm3+1t+6kPoC8Qlh9YvF6+87018us/NsofrmqVX/uhQX76q1r5gcXr5a9P/fXmz+oTz0xYi7VIICjX33Kb7C4q7T9eyGKV608/X+5dtlKu/8e9m+8RDj5Sk/lJ26q77o9KruXI47UOhWhcG0kuWZBlWR57Xfv44Xa7kZmZid7eXrhcLq3DGXcC7Z2wFuQpf27rGLSNSTK1vPg68s84CYZIeKvvR8wWtB14GITTT0f+ScfBaLclNA5PTS26//kvWL/6AtnLf4QxHNpum8ZjT0TJay+Mua3MWAW7e2DJyQYABFraYC3M1zQeSk3usgq4Gjeh7Y2FKDj2cK3DgSxJaLzrfhT97loYQ0FEjab+84KnoBi9v78FJZf+CoLBkPBYmv/zNPIvuQDGUHDQbZqOPB4lb7+a8Fh2pPnQo1G86G003HLbkO1oiAbTvOA5FJ93OjpnzEHuih+1DgcA0Ln4cwT/7w4UvfcmRCm61d91Vc+A+OwzyNpt54THIYUjaPzN9Si//87t/i7gykLn/odAOPZY5PzyWFjzchIez1A6PvoMeQfvB29uARwdrZrGQqmra8oM5KxbhZanX0TRGSdpGkvnx18gcuPv4KhZC2db84DbNO//M+S8+kL/dXEi+TY1oOeiS1GyUGlHIYkiREkCALTscxDsD/wbrtkzEh7HcDQfchSKP3wH9bf8HeU3X691ODQe3H03cNVVwCmnAM8/v8PNlzf24v2VrZiYa8cvdymLbyzhMHDeeYDPB7zwgtKeZQdq2j1448emAf8uf/1KHH3bNfCcfibK/nZzwu/VpVAYLY89DcvddyJ3jdI2d8vzQeMJp6P42QUDtoFKtpbn/oui009AT+UUZG1Yo3U4ROPWSHLJbKaqM33f/wQrAE9+EZzjIIkOAEUnH4emnkdQdPH5AAS0zdsf0ZNPRd5ZJ6MkOytpcTirKuC8/y4AQMTnR/snXyLw0WKYvvwCWUu/g3vKDBQ8u0DzJDoAWLKzlJ9hewv6flgG6xEHax0SpRgpHIGjVbkYtU2drHE0CkEUUXbNZeiatweMp5wMV8MmBB0ZaL/sahT98To4HfakxVL8qzPh3mdPeBZ/BsFoVC7wTUYIRhMEkxGi3Y7CA/ZOWjxDiUyqAhYBWLtO61AoRUVqagAAwQkTNY5ks9wD9wEO3Ad9a2vQ8/d/oPCFp2Dy+9BwwaUovvv2hD9cjxFNRpTf90+07LsPMq64BGFnBvp+dgSsvzgeuYcegNJxNJfAOWMqAMDR2YZwnwemDKfGEVGqkSUJGfXKvATrjGkaRwPkHrA38MViAECoxw3PTysQWL4S0VWrYahZj+juu6PspuuT8oAdUOY82d95Hc3Pvgzn1Vcio7UJfYUl8Pz9DhSdfeq4uEeIiUxUz+cbN2gbCI0fW/ZIH4Yur1JUlu0wxz8Wkwl4+ukRvaQq34mDpxWguTeADKsRTosRGVYjMqwmZBxYBctFxyF7GL3f40E0m1By0XmQLzwHbf97F9Idd6Do848AAPXzL0fZg3ePm/OBc/e5AADXphpEfP6kXT8RpbPxc/VPSRH88ScAgKd6GsbT7VXJhefAvf/eMLlcKCop1DocGO025B9xMLBFgtqqYTwD8VZNgbO9BaFlK7aKk2g4vLV1yIiEETUY4agcP8kzAMjZew8El/6AppffQPYxR6BMo3OCa1o1XBoNXhwJYaoyVMxUw0Q6jY6gJtKjFWMb3JsIGVOqkPHYAwj+8zZ42jtRPqVKkziKTvslcNovAQBZmkSwY9bCfAScLlg9bnhWrUX2HrtoHRKlGH99E+wBHyRRhHOcPGSPMWe5kLP/PGD/eVqHguLTT0ToqMPQsnARco48FBnjcJinMGkSAMC4aZPGkdC4EUuk5w9vJXO3T0mk59gTkEgfpTnlWZhTrnUUmwmiiILjjgSOOxI93y5FqLkF5eNgle+WHJMmIpCRCWtfL3p++Ak5++ypdUhEKW98PCaj5Fm+DAAQnjZd40C255pWDds4SKKnivAUpfJMXrlC40goFfnXKYkzb2EJxHFUURljyclGyYXn8JwwDJYZyvncvolVZzQ6xk21AABxkjZJ6uGwZGchQ6MkeirxlikPRgMruXybRs6zYhUA5dqAVYtDM2e5UHTqL2Aeh0l0ADBWKedLSwMT6aQaaSJdrUjPSURFehrK2m3OuGiVuS1BFOGerKwwCixdrnE0ROmBiXSdMa9ZDQAQZ83SOBIasxkzAQDGtbxZppELr98IAAiUjqOyDhoVx2wlke5sbULE69M4GkpFNrWVg2nK+KpApZELTqgAAETWr9c2EEpJodVrAQC+CeNvdQqNjLVaSaQ7m+ohq32bSedGkEiPSjJ6/REAQJZd+z7fNDbhiRUAgOiGjdoGQpQmmEjXGed6JZFu2XknjSOhsTKrw4wcG9jOgUZOUntmhsdRT2QaHVtJEYKODAiyjD5WodIISeEInC0NAABrCrQyoqFF1SrUWLseopGQ1ynXlOHKSRpHQmPlmKY8GDX7vAi0tGscDY0LI0ik9/hCkGQZZqMIp2X8rVylkZHKJwAABLZ6IooLJtJ1xNfYDHt3JwDAtcscjaOhscpQH4ZktDQi1OPWOBpKNWKtciElMZGe8gRRhGeikvQIqMvyiYbLW1sHQ1iZl+CcVKF1ODRGgppIN9Wy6oxGzrBBecguT+ZDtVRncjrgzS0AAHjXsOiGMKJEeqw/erbdDCFJAzwpccTKCgCAqaFO20CI0gQT6Tri+W4pAMBdMmHc9vOj4bOVFMKflQMA6GO/Mxohk9rKQaxi1Vk6CFYqybPIalak08j4VysJFm9x6bicl0AjY56iJEBj7XqIRsK6UVnJYJzKRHo68Kvt+0LruEJF98JhoLtb+fOwEulhAECOg21d0oFJvd+zNtZrHAlRemAiXUf6+x5WclhXuuirVG50ODiERsrWpFxImZlITwvRKmUJt7CWVWc0MqF1Si9tfxlXp6QD+/QpAABncwOiwZDG0VAqkSUJTnUwpW3GNI2joXgIlSvn9egGDiPXvU5lVToEAcjN3eHmXeqg0Sw7B42mA6s6rN3R0gg5GtU4GqLUx0S6jsi1tQCAiNoji1JfqHoqAEBatVLjSCiVSKEwHK3NAAD7VA4XTAfiNOVcYNnAAYM0MvJ6pVIxNoiKUpujcgIiZgtEKQovq1BpBHwNzTD7vZBEEU7OS0gLUfW8Lmys1TQOGgdibV1ycgCDYYebd6uJ9BwHE+npwDmpApJogDEcgq++SetwiFIeE+k6ItYrPbHkiaw6SxszpgMAjKtXaxwIpRLPhlqIUhRRkwn2CaVah0NxYFWrBx11rDqjkTGovbRlDhdMC4LBAE+pUjARa9tDNBy+Fcq1pLewBEa7TeNoKB6ESZUAANMmzkzQvRH0R5dlGV1b9Ein1CeaTfDmFwIAfGv5kJ1orJKSSL/vvvtQUVEBq9WKPffcE0uWLBl02wULFkAQhK2+rFZrMsJMe2Y1kW6oqNA2EIob06yZAAB7zVqNI6FUElijVC17CkshDKMqhcY/52zloZq9uxOBji6No6FUYq5TWjkYp3B1Srrwl1cAAMJs9UQjEFJnbPjKKzWOhOLFXK2c122NHDCoeyNIpPvDUQTDEgQByLKzR3q68JcoMxPCG/hgjWisEp5If+GFF3D11Vfj5ptvxvfff485c+bg8MMPR1tb26CvcblcaG5u7v/atIkDk+LB1twAADBPZtVZunDsPBsAkNFYh4jPr3E0lCpCNUrVcqCMbZ7ShSUnG74c5ebIu3yVxtFQKnE0KtdYFibS00akUkmEyjWsOqPhk9YpD17ClUykpwur2r7P0dIEKRzROBrS1AgS6bH+6BlWE0wGNjBIF2G1vW90IxPpRGOV8DPjnXfeifnz5+O8887DjBkz8OCDD8Jut+Oxxx4b9DWCIKCoqKj/q7CwcNBtg8Eg3G73Vl+0vWgwBHtHKwDAxkR62nBUTkDYYoUoReGr4YciDY+sXkCFy9nmKZ14Jirn9uBKtnqi4Ql298DWo6xgsE9lT+R0IVQpQ8WMHDBII2BQH7zI1TwXpAvnpApEDUYYImF4a1mVrmsjSKR3e8MAgBwHq9HTiTRBue8TalmkSjRWCU2kh0IhfPfddzj00EM3H1AUceihh+LLL78c9HUejwcTJ05EeXk5jjvuOKxYsWLQbW+77TZkZmb2f5WXl8f1vyFdeDdugihJSk/k8hKtw6E4EUQRvkLl5+lfz0Q6DY+orvKRKyu0DYTiKjRJqTyLqsvziXbEu1pp8+R3ZcOal6NxNBQvpilTAADWulptA6GUYq1VHrwYpzCRni5EkxHe2H0CZybo20gq0tkfPS0J6n2fqZ6JdKKxSmgivaOjA9FodLuK8sLCQrS0tAz4mqlTp+Kxxx7D66+/jqeffhqSJGHvvfdGQ0PDgNvfeOON6O3t7f+qr6+P+39HOgioQyXYEzn9BErKALDfGQ1frCeygcu308tkJZFuWM+bZRqe4BrlveJjm6e0Yp2mJEKdTXWQJUnjaCgVyJIEZ0MtAMA2fZq2wVBc+cuUKtTwerZ60rURJNJ71ER6joOJ9HRiVocPWxuZLyMaK6PWAWxr3rx5mDdvXv+/77333pg+fToeeugh3Hrrrdttb7FYYLFYkhliSgqpSdZASTkyNY6F4iusJkBkLtOiYbI1KRdQ5mq2eUonhmlTAQCWjbxZpuGJqK0cghPY5imdOKdOhiSKMAX88NY1wlHB1Zo0NH9TC+w+L2RBgHM6K9LTSXjCRGDJp5BYcKNvo+iRzor09GKtVtq+OVoaIUsSBJH974lGK6G/PXl5eTAYDGhtbd3q+62trSgqKhrWPkwmE+bOnYv169cnIkTdkNVWDhG2vkk78gQlkS7Ws/ch7Vg0EIQjNi+hmsMF04lt1nQAQEbdBlah0rAIaiI9WsHVKenEYLX0t3PwrVqrcTSUCrzLldkansISGB12jaOheJIrKgAA4qZaTeMgjQ0zkR6JSuj1Kz3Ss1mRnlYcVRWQRBHGUBC+hmatwyFKaQlNpJvNZuy6665YtGhR//ckScKiRYu2qjofSjQaxbJly1BcXJyoMHVB2KQkWaVyLt9ON2Ks31kDl2nRjnnX1UCQZYQtVtjLeF5NJ84ZUyELAsw+Ly+QaViMamJFVIdTUvrwl1cAAEKcmUDDEFTfJ/4JfKiWbgxVyupDM2cm6NswE+l9gQhkGTAZBDjMbAebTgxWC3x5Sstl/zoWqRKNRcLXc1x99dV45JFH8MQTT2DVqlW4+OKL4fV6cd555wEAzj77bNx444392//5z3/Ge++9hw0bNuD777/HmWeeiU2bNuGCCy5IdKhpzdygJNJF9kROO6ZJFQAAW9PAcwSItuRfpwwT8xaXcUlfmjHabfAUKTMTvMtXahwNpQKbOnDKxNUpaSdUoSTPJPZFpmGQ1yrzEmLvG0oflinK+d3Ovsj6JUlAZ6fy52Ek0gEgw2qCIAiJjoySzF+qdCcIrWerJ6KxSHiP9FNOOQXt7e246aab0NLSgp133hkLFy7sH0BaV1cHcYtkTnd3N+bPn4+WlhZkZ2dj1113xRdffIEZM2YkOtS0ZlWTrKaqCm0DobizTVZueuztLZDCEYimcTf6gMaR8HolkR4oZZundOSrmISM5nqEV64GjvqZ1uHQOCaFI3C2KNcGseGUlD7kScq1gWHjBo0joVRgUGdryHyolnZsU5WfqaOjFRGvj6179Ki7W0mmA0Be3pCbugNKW5cMK+8n01GotBz4YQmiG5lIJxqLpJwhL7vsMlx22WUD/t3ixYu3+ve77roLd911VxKi0g85GoWjrQkAYJ3MSpN046iYAEk0wBAJw1PXACcfltAQ5FrlwinC4YJpKTypCvjyY0hr2ReZhuatrUNGOIyowQinurKJ0odRrUJlOwcaDov6wMVYzYdq6cZWXIiw1Q5TwAfP2hpkzZ2tdUiUbLG2LpmZgHnovuexinSX1ZToqEgDknr/J9TWahsIUYrjun4d8NU1whAOQxINcFYyeZZuRLMJ3ny13xmXadEOGNQLJ3lihaZxUGLI1VMAAMZ16zSOhMY7/2rlPeItLuVKpjRkmaokRB1q+x6iwciSBGdDLQDANnOatsFQ3AmiCE+JsgoxuJatnnRpmP3RAVakp7vYbDUjZ6sRjQkT6TrgUy+avAVFEM18upyOAiVKX+TQBibSaWixeQnGKq5OSUemqUoi3dLA5BkNLaQOmvKX8QF7OnLOUBKiNnc3Au2dGkdD45m/qQUWrweyIMA5fYrW4VACBMomAADC6zlgUJdGkEjfskc6pZ/Y/Z+VMxOIxoSJdB2IJVf9xWUaR0KJElL7XUtcpkU7EBs2ZWabp7RkUVs72VuatA2Exj1ZHUIZ5uqUtGTOcsGXo/TC9a5co3E0NJ55VyjvD09BMftnp6loRQUAQGbBjT6NKJHOivR0ZqtW7v8czQ2QY33ziWjEmEjXgVhyNVzG4YLpSipXKk2ETaxCpcGFPV7Yu5SLaftU9kFNR/bJlQAAq8eNYFe3xtHQeGZQ5yWgqkrbQChhfOpqg+AatnqiwYVWKzM1/BMqNY6EEkWuVH62Rs5M0KdhJtJlWd7cI93GivR05Jg8CbIgwBQMwN/UonU4RCmLiXQdEGqV5Ko0kcu305WgVpoYGxu0DYTGNa/a5ilkc8BamKdxNJQIluwsBDIyAQA+zkygIZjrlGsDw2Qm0tNVcKKSPIuuYzsHGlxsOHWokueCdBVr52Cur9M4EtLEMBPp3lAUUUmGIABOCyvS05HBaoEvT52txpkJRKPGRLoOGNWeyCIT6WnLWKH8bK1MpNMQgus2AAC8JeUQRJ7+05WvqAQAEKyp1TYQGtccjUoi3TJlssaRUKJIlUryTKjhzTINzrBBfX/woVraslQr53lHIxPpujTMRHqsrYvTYoRBFBIdFWnEp85WC67foHEkRKmLmRQdsDYpyVXjJC7ZTFdWtZ2DvaWR/c5oUGE1mRIoZZundBYsUX6+Ec5MoEEEu3tg6+kCwDZP6UycovxszZu4OoUGZ1HbfRiq+VAtXTmnKecCq8fN4cN6NOxEutrWhYNG01pYHT4c3VirbSBEKYyJ9DQnSxIcLY0AAGsVE+npyqEODjH7vQjyApkGIauJ1QhXp6S1aJlSaSLXsfKMBuZdrbT68LuyYc3L0TgaShSL+pDEXl+rbSA0rtnU+wRLFYeQpytzlgv+LOVc71vNmQm6M8KKdA4aTW/RCcp9oLCpVttAiFIYE+lpzt/cClPAD2BzspXSjynDufkCmf3OaBCG2DDaiRWaxkGJJavDhw1MpNMgYsMnfWpVEqUn+/QpAABHRysiXp/G0dB4FPH6YO/qAADYJvM+IZ15S5XzfXA97xN0Z5iJdLdfqUjPYEV6WuufrcaZCUSjxkR6mvPHeiLnFsBot2kcDSWSr1jtd1bDJdw0MIs6L8HI1SlpzaDOTDBxZgINIqK2eQpO4OqUdGYrLkTI7oAgy/CsYhUqbc+3oRYAELbaOIQ8zYXUh+xRJtL1RZaHn0hnRboumNT7QGtjvcaREKUuJtLTXEhNqvqLSzWOhBItVKok0qOxqmOibdiblAsmSzUHiqUzc+wCWV2uT7St2PDJaCUrUNOZIIrwlFUAAPyr12gbDI1LAXUotbewhEPI01y0Qi2i2MiCG11xu4GwkiAfdo90GyvS05lVXX3kaG7gbDWiUeIVU5qLDZsLlXG4YLqLqJUmMhPpNIBQj3vzcMEpHCiWzmxqIt3R3gopFNY4GhqPjGpfTHESE+npLqC28oqsW69tIDQuhTYoSdUgC27SnqA+ODXW8T5BV2LV6A4HYBt6dXoskc6K9PQWa/drCvgRaGnXOBqi1MREepoT1ER6tJzLt9PeBCWRzn5nNBDvOqUCNeB0wZqfq3E0lEj2CaWIGk0QpSi8tTwf0PZs9UoixVTNh2rpLnb9J3BmAg1A3qS8L8KlLLhJdya1CtXawHOBrgyzrUswEkUgHAXARHq6M9pt8OYWAAB8a/mQnWg0mEhPc4Z6pZWDUMFEerozqoNDzOyLTAMIqhdKsV76lL4EgwHegmIAm5ftE8VI4QicLcrnhHVatcbRUMKpD9kNDeyFStsT1PsEeQIHD6c72xSlrZ+zqR5yNKpxNJQ0w0ykx6rRLSYRFqMh0VGRxvwlyv1gaP0GjSMhSk1MpKc5i9oT2VjJ4YLpzqK2c7A3M5FO24vElm9zdYouBNRl+uGNtdoGQuOOt7YOhnAYUYMRzkkVWodDCWZUCynMTZyZQNuLDaUWJ/LaIN05qqsgiSKM4RB8dTwf6MYIE+kuK/uj60GoTB0+zJkJRKPCRHqaszcrF0rWavZBTXd2tdLE1tOFcJ9H42hovJHVC6Uoq850IRwbPsxEOm3Dv3odAMBbXArRxOXb6c6iPiyxcfgwDcCqFl+YKplIT3cGi7l/tZpvDds56MawE+nKTB22ddGHaOzhaS1nJhCNBhPpaSzQ0QWrxw0AsFdXaRwNJZolPxchmwMA4F3HZVq0NaPaH1eu4OoUPZDUAdMCZybQNkLq0El/GRNnemBT+yLbuzoQ8fo0jobGE1mS4GhtAgBYq3htoAf+UqWYIrS+RuNIKGmGmUh3+1mRrieCej9o5MwEolFhIj2N+dRkqt+VDXNmhsbRUKIJoghfkdLOIbihVttgaNyxqMMFjbxZ1gVRnZlg5MwE2kZsdUp4QoW2gVBSWAvzELbaAAA+XhvQFvzNrTCGgpAFAfZKrlbTg7Da3k+qYcGNbrAinQZgrKwAAFg4P4VoVJhIT2NB9SLJp/bKpfQXKFH7IvNmmbYR651vmczVKXpgVJfpW9gXmbYhcrigrgiiCK/6kN2/nr1QabPY+8GXkw+j3aZxNJQMkvqQXazluUA3Rtoj3caKdD2Itf11NDdAliSNoyFKPUykp7FYb9zYMAlKfxG1nYNcW6ttIDSuBDq6YO3rBQA4pjKRrgexvsiOFl4g09b6hwtWsLWLXgQ5fJgGEFKHkPuLWHCjF6JaTGGqY19k3RhuaxdWpOuKQ02km/1eBFo7NI6GKPUwkZ7GYsnUaHm5toFQ0sjq4BCRfZFpC761ak9kVzYs2VnaBkNJ0X+B7PMi2NmtcTQ0nljVIeQcLqgf4VL1IfsmXhvQZhF1yFxIHU5N6c+sJtJtjTwX6MYwEulRSYYnqFSkZ7BHui6YnA74cpT3ROw+kYiGj4n0NGZQk6mx5Cqlv1hfZHMj+53RZkF1XoKvlA/V9MKU4YQ/MxsA4GM7B1LJ0SgcbRwuqDdyeWz4MK8NaDNBHUIeLeO1gV7Yp04GADjaWxANBDWOhpJiGIl0TzACWQYMogCH2ZCkwEhrvhLlIWpoPWcmEI0UE+lpzKIu3zaqyVVKf+ZJSmLE2sQBg7RZZINygRTkzbKu9A8f5lAxUvkbmmEIhyGJIhwVbPumF6JaUGHi8GHagiE2ZI7zEnTDPqEUEbMFoiTBu65G63Ao0bxewO9X/jxEIn3LQaOCICQjMhoHQup9YYQtYYlGjIn0NGaLDRdUl/hT+rNNVhLpjvZWSKGwxtHQeCHE2jxNrNA0DkqukFppEq1lL1RS+NYriRNfXiEMFrPG0VCyxNr4WJuZSKfNzOowaiPnJejGlsOHAzW12gZDideh9r42mwGnc9DN3H62ddGjaLly7heYSCcaMSbS01S4zwN7dycAwF7N4YJ64agoR9RogihF4a1l/0NSGDYpiVSBq1N0JTYfQ67juYAUoQ3KucBfzOGCehJr4+NobeLwYepna1ES6bHh1KQPAQ4f1o9udUZOTg4wRKV5HweN6pJQWQEAMPA+gWjEmEhPU161J3LI7oAlL0fjaChZBIMBvvwiAICf/c5IZVGHShmr+FBNT2R1ub6BfZFJFVUfqoVLOFxQT+yVEyALAoyhIPzNrVqHQ+NAxOuDvUupVrVN5spVPYmo7RwkJs/SX1eX8s/s7CE36wvEKtKZSNcT0yTl3G/lbDWiEWMiPU0F1OFy3uJyCCJ/zHriVxMk4Q212gZC44IsSXA2KRdIVt4s64pR7YvM4cPUj8MFdclot8GXo/TH9XP4MAHwqu+DsNUGa2GextFQMknlykN2YRMT6Wlvy4r0IfQFlYp0F1u76Eqs/a+juZ6r1YhGiBnWNBVRl+sFS1l1pjfhWKUJl2wSgGB7J8w+LwDAMW2yxtFQMpnUdg62liaNI6Hxwtig9MgWJnK4oN741b7IIV4bEICAWmzhLSplwY3OxM7/HD6sA7FE+g4q0mM90plI1xeHmkg3+7wIdnRpHA1RauGVU5qSa5VKk0gZb5b1pr/ShEs2CYBvzXrln9m5MDkdGkdDyWRTE+n2jlZEgyGNo6HxwNykJE44XFB/QmphRYSJdMLm/thBzkvQHbPaF5nDh3Ug1tpliIp0WZbZI12nTBlO+LJzAWy+XySi4WEiPU3FhkbEeuSSfghqgsTUwEQ6AUG16sxfzNUpemMvLULEZIYgy/Bu3KR1ODQO2NXhguZJlRpHQskWa+fDh+wEALLa1iNcyjZPemOtqgDA4cO6MIyK9EBYQjgqA2AiXY98JcpnQLCGs9WIRoKJ9DRlUnviGnizrDsm9WduaW7UOBIaDyK1tQDY5kmPBIMBvsJiAEBgHS+Q9S7U2wdbr3JTba/mvATdiQ0fbuDMBAIEdQi1XM5Eut7YJ1VsHj7c1KJ1OJRIwxg2GqtGd1gMMBqYGtKbkPowNVLD+SlEI8GzZZqyq8u3LVVMpOuNdVIFAMDR0shKE+JwQZ0LqJUmYVak655PvUkKOpyw5g09eIzST6ydj7mJD9lpc39scSLbPOkNhw/ryDCGjbr727qwP7oeRWOfAZtqNY2DKNUwkZ6GooEg7B2tAADrZFad6Y1jsvLwxBQMwN/cqnE0pDVjvZJIF3izrEvhEmUlgrSJiXS9i7V58hWyJ7IeWdSH7LYWJtJpc39sUyWvDfSIw4d1YhgV6e6AMmiUbV30KXZ/aGTbN6IRYSI9DXlraiHIMiJmC+xlxVqHQ0lmdNg3V5qwnYPumdWqMwMT6boklbMvMiki6qqEYAkT6XpkUwsr7F0diHh9GkdDWpIlCY7WJgCAlStXdal/+LD6gJXS1DAq0vv6E+msSNcj4yTl2sDSyPsEopFgIj0NBdYryVNvUSkEkT9iPfKpgyVD7Heme7HqQ7Z50qf+4cON7Iusd1KdkkiPcF6CLlkL8xC22gAAPibPdM3f1AJjKAhZEOBQB0+SvvS3+6tn8iytDWPYaKxHuosV6bpkUVeyOzhbjWhEmGVNQyF16nKghDfLetVfaaIOmiR9Cnu8sHd3AthcjUj6YqqsAMDhwwSI/cMFJ2gcCWlBEEV4C0sAAIGaWm2DIU3F+mL7cgtgsFo0joY0oQ4fNjY0aBwIJVSstctQPdL9rEjXM0d1FQDA4u1DoKNL42iIUkdSEun33XcfKioqYLVaseeee2LJkiVDbv/SSy9h2rRpsFqtmD17Nt5+++1khJk25Fql6ixcxptlvYqqF8hs56BvPvVmOWy1w1qYp3E0pIXNw4ebOHxY5/qHC1awzZNeBYuVtj5h9kXWtVhfbH9RibaBkGY2Dx9mIj1tRaNAb6/yZ1ak0yDMWS4EXFkANt83EtGOJTyR/sILL+Dqq6/GzTffjO+//x5z5szB4Ycfjra2tgG3/+KLL3DaaafhV7/6FX744Qccf/zxOP7447F8+fJEh5o2RHWZnsyeyLoVGxxiqGc7Bz0LqK19vEUlbPOkU/3DhwM+BFo7NI6GtGRVEybmSWzzpFfhUqWdg8SH7LoWUQtuQly5qluxdg72ZibS01ZPz+Y/D5JID0cl+EJRAIDLxop0vfLFhg/XcLYa0XAlPLNy5513Yv78+TjvvPMwY8YMPPjgg7Db7XjssccG3P5f//oXjjjiCPz2t7/F9OnTceutt2KXXXbBv//97wG3DwaDcLvdW33pnalBSZ6K6pJ+0h+DWmliYV9kXYuoVWds86RfyvBhZTWCfz0vkPVKjkbhaGsGAFjZE1m35Njw4U1MpOtZbLVif59s0h272u7P1tOFsMercTSUELH+6E4nYBo4Se5RB42ajSIsRhbc6FVQfcge4Wo1Xas//2I0nPEr9C5doXUoKSGhZ8xQKITvvvsOhx566OYDiiIOPfRQfPnllwO+5ssvv9xqewA4/PDDB93+tttuQ2ZmZv9XeTkvCm1q8pRVZ/plqVIukO0t7IusZ9Km2HBBnhf1zK9WmgQ5YFC3fPVNMETCkEQRjgq2fdMrsaICwOY2P6RPBrXgJtYnm/THkp+LsNUOgO0c0lasP/oQbV3caluXDKsRgiAkIyoahyJlSsGVrN43kj7lvv4yyp59DFF3n9ahpISEJtI7OjoQjUZRWFi41fcLCwvR0tIy4GtaWlpGtP2NN96I3t7e/q96trJA77XXo/7iq+DYaYbWoZBG7NVKIt3q7kGoh6s09Kp/uCBvlnUtVKIk0mPL+Ul//OpyXV9eIUQzl2/rlalSWa1mZTsHXTOrD1KMnJegW4IowqvOTIi1AaQ0E6tIH2LQaF8gNmiU/dF1LdYSlm3fdCvs8cLepbQAtU+p0jia1JDyZ02LxQKLhRPnt1R29aVah0Aas+blIOjIgMXbB++6Gph3n6t1SKSB/jZPvFnWtUi5+iCljol0vQptUH72/uJSODWOhbRjrVJWKjpaleHDnJ2hTzZ1tWKsTzbpU6C4FNi4ju0c0lUskT6MinSXlQ/Y9cyorlYzc7WabnnXbUAWgJDNAUt+rtbhpISEXkHn5eXBYDCgtbV1q++3traiqKhowNcUFRWNaHsiGlis0iRYU6ttIKQZa7Nys2yaVKFtIKQtdUWCoYEXyHoVVZfrhjkvQdfslRMgCwKMoSD8za07fgGlnbDHC3t3J4DNfbJJn2Jt/yTOTEhPsdYuw6pIZyJdz8xVHD6sd0F1ZZK3uIxFFsOU0P9LZrMZu+66KxYtWtT/PUmSsGjRIsybN2/A18ybN2+r7QHg/fffH3R7IhpYsCQ2OIRLNvVo6+GCrDrTs9jyfVaa6BiHCxIAo90GX04+AMDPvsi65FOLK8JWO6vOdC42fFhsYFvUtDScinT/5h7ppF82tSWsvasdEZ9f42hIC2F1jlawlAU3w5Xwxw1XX301HnnkETzxxBNYtWoVLr74Yni9Xpx33nkAgLPPPhs33nhj//ZXXnklFi5ciH/+859YvXo1brnlFnz77be47LLLEh0qUVqJJUxk9kXWJV9dozpc0ABnJVu76Jm5sgIAK030zKjOSxDY5kn3YsOHQ2znoEsB9WbZW1TCqjOdE9VrAxMT6elpRBXpTKTrma24EGGLFQDg48wEXZJrlZ97hAU3w5bwK6hTTjkF//jHP3DTTTdh5513xo8//oiFCxf2DxStq6tDc3Nz//Z77703nn32WTz88MOYM2cOXn75Zbz22muYNWtWokMlSi9qwsRQzyWbeuRbpwwX9OZzuKDe2auVoTGsNNEvS5PyEMXARLruhdRqIw4f1qew+gAlqLb/I/3aPHy4UeNIKCF2UJEuyzI8QSWR7rLxPkHPBFGEr7AEAFer6VX/oNkJvE8YrqQ8frzssssGrShfvHjxdt876aSTcNJJJyU4KqL0ZqhU2nmYG5hI16PYzXKgqBQZ2oZCGrMW5SNsscIUDMC3oRauWdO1DomSzNbSBACwTGKbJ72LlqnLdut4baBHstoPO1zKqjO96x8+3NYEORqFYDBoHBHFVawifZBEujcURVSSIQoCnGZWpOtdoKQMmXUbEN7ARLoexVYmibxPGDau6SNKU2Z1iJStie0c9EhSE+lhLtHSvS0rTQIcPqw7od4+2NxKZZp9Mi+Q9U7oHz7Mdg56JKoPUGL9sUm/HJUTIYkiDOEwfPVNWodD8RarSB+ktUusP7rDYoAoCsmKisapcJlybcCWsPpka1KuCc1VFdoGkkKYSCdKU7HBIY7ONkS8Po2joaRTeyJHebNMUCpNALDSRIdi/S6DDieseYP3SiV9MExUhw83sZ2DHpnU4gqxokLbQEhzBosZvtwCAIC/ZoPG0VDc7aC1S6w/usvKti4EyOpDdpEtYXUnGgzB3tkGALBPrtI4mtTBRDpRmrIVFyJstQEAvOx3pjtGtaWPMJG9zmjzMn6J7Rx0J6gOF/QVsicyARa1nQOHD+tTbF6CiUPICUBA7ZUf2sAq1LSzg2GjfQGlIt1lY1sX2mL4cCOvDfTGW1MLUZIQMZlhKyvWOpyUwUQ6UZoSRBHeYqUKNbCelSZ6Y1Zvlo2sOiNsXsYv1rOdg95ENioJkmAJE+kE2NS2b/auDq5W0xlZkuBoVVp4xPpjk76F1Ifs0U1MpKedHVSku9VEegYr0gmAaVIFAMDKlrC6E8sTeYtKOStjBJhIJ0pjAfUCOVzDinS9cTQry/at7IlMAISJypJNIxPpuiPV1gIAIpyXQACshXn9q9V86moF0gd/UwuM4RBkQYBjEivSCYiqnwsCE+npJRgEfOqD0kF7pLO1C20We8juaGuGHI1qHA0lU1ht7RVrA0rDw0Q6URqLlMcGhzCRrieBji5YvH0AALt6YUT6ZlansFvZzkF3RHWoZKz/JembIIrwcviwLvnVNn++3AIYrBaNo6HxIPaQ3dDAa4O0EqtGFwTA5RpwEzdbu9AWHBUTIIkGGCJheGtZdKMnkjpgNjZwloaHiXSiNCZPrAAAGFhpois+9WY54MqCOWvgC2jSl9gyfkdrE2RJ0jgaSqZYv0sD5yWQKqj2RQ5vrNU2EEqqkPrz9heVaBsIjRvGCuVzwcJ2Dukl1h89KwsQt0/3yLIMt19NpLMinQCIZhO8+YUANj90JX0Q1flZMu8TRoSJdKI0ZugfHMIny3oS3KBWnRXyZpkUjqoKyIIAYygIf3Or1uFQElnVNk8m9fOAiMOH9SmiVp2FuHybVOYqdWZCS6PGkVBcxSrSB2nr4g9HEY7KEAQgw8qKdFLEWnuENjCRriexPJHI+4QRYSKdKI1ZYkPFWGmiK1G16ixYyp7IpDBYLfDlFgAA/Os4fFgv5GgUjrZmAIC1qkLbYGjc4PBhnVJXJ0bZ5olUdnWOjtXdg1CPW+NoKG52MGi0V61Gd1qMMBqYDiJFbPhwbLYO6YNVTaTHBs7S8PDMSZTGbFOqAAD2jlZEfH6No6FkkTcpVYbRMlad0Wax5fyh9Uyk64WvvgmGSBiSKMJRweQZKfqHDzcwka4nRrUPtsBEOqmseTkIOpwANrcFpDQQa+3CQaM0ApI6W03gajXd2LLgxsa5aiPCRDpRGrOVFCFssUKQZfhqeIGsF4Z69jqj7YXKlfdDpIaJdL3wqw9NfHmFEM28YSaFWV2+G2v7Q/pgVlcnGip4bUCb+YqUoovAhlptA6H42UFFOgeN0kCEigoAfMiuJ97aehgiYUQNRhbcjBAT6URpTBBFeIuVC2S2c9CP/ptlJtJpC9EKZQm3UMuHanoRUhMjfnW4JBGwuc2PncOHdcWm9sG2cPk2bSFYonw+RDh8OH3ssCKdg0Zpe7Hhw9ZGtoTVi9hgWV9BEQtuRoiJdKI0FxscEuHgEN2wqVWGlqpKjSOh8UScpLwfjHWbNI6EkkVSeyKHOS+BtmBXE6mmYIDDh3Ui7PHC3t0JALBz+TZtIVKq3CfIm3htkDaG2SPdZWPijDazqjMT7C2NfMiuE2F1lbK/mO1gR4qJdKI0F1H7nUmsNNGFaDAEe4eSGLExkU5bME1WZibYGtj7UDfqYvMSmEinzYx2G7yx4cPsi6wLvppaAEDYaoclP1fbYGhckdWe+SLbOaSPYVakZzKRTltwVCsPWc1+L4LtnRpHQ8kQVfND4XK2dRkpJtKJ0pys9jsTWYWqC96aWgiyjIjJDFtZsdbh0DhinTIZAOBsroccjWocDSWDITZccCIvkGlr/iKlnUOID9l1IaDOyfEWlUAQeftHm4nqfYKJifT0MURFuizL6Atw2Chtz5ThhD9LefjiW1ujcTSUDLHBshIT6SPGKymiNGeoVKqSzfWsQtWDQGy4YGExBINB42hoPHFOroQkGmAIh+Gt5Q2zHlg4XJAGEVLbOURq+ZBdD8LqtUGgjDfLtLXY8OFYD31KA0Mk0r2hKCKSDEEAnFYOG6Wt+dQWH0EOH9YFU71yDSjwPmHEmEgnSnNmtd+ZrYmJMz0Ib1Q+EAMlbOVAWxPNJngLSwAAflaa6IKtpQkAYJnENk+0tWiZ2g+zjg/Z9UCuUc75kYkV2gZC407/8OG2FkjhiLbBUHwM0dol1tYlw2qCQRSSGRWlgNhD9mhtrbaBUFLECm5MvE8YMSbSidKcrVpp52DvbEM0ENQ4Gko0Sb3wCZdwaAhtz69WI4bWMZGe7kK9fbC5lao0+2ReINPWBLUvsoHtHHTBUKu0dpGrqjSOhMYbR8UEZbVaNAJfXYPW4VA8DFGR3j9olNXoNIDYTB0OH05/siTBoa5EslVzCPlIMZFOlObsZcWImC0QJQleddgUpS+hXkmKSBO4fJu2F56gLN2TNm7QOBJKNN865WccdDhhzRt44Bjpl2Gici4wN7Gdgx5Y6moBAMbJTKTT1kSTEd6CIgAcPpwWZHlYFekuDhqlAcjqtYGRLWHTnr+5FaZgALIgwDGZifSRYiKdKM0JogivOlQswCrUtGdqUC58OFyQBiKpy/rFjbxZTncBtb+lr4irU2h7lkkVAAB7MxPpeuBoVK4NrFOrNY6ExqNAsXKfEObw4dTn9QIRtUXPABXpbg4apSEY1eHD5kauTkl3sYGyvtwCGKwWjaNJPUykE+lAoFRZphWqYRVqurOqSREze53RAES1GtFUxyWb6S42RDJYUqpxJDQe2aqVc4G9qx3hPo/G0VAiBdo6YO3rBQA4mEinAcTaAUocPpz6YtXoZjNgt2/317GK9ExWpNMALFXK/aO9mYn0dBeuUYqq/GwHOypMpBPpQLhcWaYlc3BIWpMlCfZWZbhgbHgU0ZYsseRZI5dsprtYf8tIKS+QaXvWwjwEHRkAAM/qdRpHQ4nkU3++vpw8mDMzNI6GxqNorB0gH7Knvi37owvbDxPt75FuY4902p59inKfYOvp4kP2NBdR80IhteCSRoaJdCIdkCuURLpYx+RZOov1OgMAexUr0ml7tinK8GFHewuHD6c5UR0iKXNeAg1AEEV4ypRrg8DKNRpHQ4kUXLseAOAr5bmABhYbPmxkO4fUN8SgUUmS0Rdr7cKKdBqAJT8XIZsDAODlzIS0JqgFN9FyXhuMBhPpRDogqv3OTBwcktb86nBBb24BjHabxtHQeGQvK0bYaoMgy/CsWa91OJRAJjUhEhsqSbSt4ETlgWtkPc8F6Sy6XumDGppQoW0gNG7190VuYiI95Q0xaNQTikCSZRhEAU4zK9Jpe4IowqfOVgvWMJGezgxqgaVQwfuE0WAinUgHzFXKJGZbU73GkVAihdQhUf4i9kSmgQmiCG+xsoSPw4fTm01NiJgqK7QNhMat6CTl2kCo4bkgrW1QHrJHK7lSjQZmnaz2RW5p0jgSGrMhKtJj/dEzrEaI4vZtX4gAIKDO1gmrQ+spPVnUvJBJvRakkWEinUgHYv3OHO0tiAZDGkdDiRJRL3hC7IlMQwioS/jC65k8S1dSOAJ7ewsAzkugwYlV6vDhWladpTPTploAgKD+vIm2ZVM/J6weN4Jd3doGQ2MzREW626+2dbGyrQsNLlKmFNxwtlp6czQ3Atg8YJZGhol0Ih2wl5cgYjJDlCR4a2q1DocSRW3dEy3j0BAaXGSCOnx4A5Nn6cqzbgMMkTAiJjOcTKTTIExTqgEAtvpabQOhhLI1KH1QzeqMDKJtWbKzEMjIBAD42Bc5tQ1Rkb550CgT6TQ4WW0JKLIlbNoKtHfC4u0DANirWZE+GkykE+mAYDDAq7b7CKzfoHE0lCjGemWJFnud0VDkCqXywKBWKVL68a9cDQDwlE6AYDBoHA2NV/YZUwEoVUlSKKxxNJQIUigMZ4tSdWafWq1xNDSebe6LXKttIDQ2Q1WkB9REupX90Wlwsdlq5ka2hE1XPrW9pz8rB+bMDI2jSU1MpBPpRKBUqVIO1zCRnq5iQ6IMTKTTEIzqzARL/SaNI6FECa9ZCwDwT2SVCQ3OUTkBEZMZhmgEnnW8NkhHnvUbIUpRRExmOCq4Wo0GFyxR2gJGNrIiPaUNo0c6K9JpKOZJSsGNVW39QeknNkjWV8x2sKPFRDqRToTLlL7IkjqQktKPvVlJpLPXGQ3FolYl2llpkr7WrwcARDhAiIYgGAzwlCrXBv7VazWOhhLBv2YdAMBTUs7VKTSkaJmSUJE3sZ1DSosl0gesSFd6pGcykU5DsKnDhx1tLVytlqY4V23smEgn0on+fmd1rEJNR+E+D2y9ysWzfTKTZzQ4xzSlT67N3Y1gd4+2wVBCGDcoSzaFarZyoKEFJlQAAMJr12kbCCVEeK3yUC1QzpVqNDRZHURuYF/k1BZr7bJNRXpUktEXYEU67Zh9QhmiBiNEKQpvLc8HaUlt7xlRz/s0ckykE+mEQV2mZeIFclryqsvyQ3YHLHnbV6EQxViys+B3KTdY3tXrNY6GEsG6SVmyaZo6ReNIaLyLqDMT5PU1GkdCiSDXKD/XyMQKbQOhcc+oDqO1bGJrl5Q2SGsXTyACWQaMogCHmatTaHCiyQhfQTEAwM/ZamnJGMsH8dpg1JhIJ9IJk9oX2aa2/6D0ElSXaPkKSyGIPLXT0HzqzITgGlahphspHEFGk3KBbFOHSRINarKSPDNu5M1yOooNlZarqrQNhMY968zpAADnpg2QJUnjaGjUBhk2Ghs0mmE1QhCEZEdFKcavzkwIq/eXlF7MjUo+yDipQttAUhizLUQ6YZ+i3EQ5WpvZ7ywNxS50Aux1RsMQVJf5Rzh8OO14ajbCEA4jajLByXkJtANGtf2Pta5W20AoISzqz9U4mYl0GppzxlTIggCLtw/+phatw6HRiEaB3l7lz9tUpPeqg0Yz7WzrQjsWLlMKbjhbLT1xrtrYMZFOpBP2CaWImkwQpSg8G9knPd3Im5SfaYSJdBqGqLqUT6jlEu5041+xGgDQVzIBosmocTQ03tmmKYl0Z2Mdq1DTkKNRWZ1incp5CTQ0k9MBT2EJAMC7bJXG0dCo9PYCsqz8eZtEultNpLusTKTTjklq72yhji1h002otw+2HmXlir2aD9lHi4l0Ip0QDAZ41QvkwFr2Qk03otrrLDZUlmgogjozwcjhw2knvGYtAMA/kUOHacec06ohiSJMAR/89U1ah0NxFGjrgLVPqU51MJFOw+BTPzeCq1ZrHAmNSqw/usMBmM1b/ZWbg0ZpBIQK5X7S1FivcSQUbz51rlrQkQFrfq7G0aSuhCbSu7q6cMYZZ8DlciErKwu/+tWv4PF4hnzNgQceCEEQtvq66KKLEhkmkW74S5SnyyG2c0g7lo3KwxED+6DSMJjUZf7WeibS0846pe99dBIT6bRjBqsFXnWomHfVWo2joXjyrVbOBb6cPJgzMzSOhlJBuEp54CKv4bkgJcUS6dv0RwcAtz8CgBXpNDymygoAgKWJs9XSTbBGWY3sLS7VOJLUltBE+hlnnIEVK1bg/fffx5tvvolPPvkEF1544Q5fN3/+fDQ3N/d/3X777YkMk0g3wuoyrVgbEEoPsiTBWask0q2zZ2ocDaUC21RlwKCjuYHtHNKMYYP6oLSaFag0PP4JygqVEIcPp5WA+vP0lU7QOBJKFXK1Ony4Zr3GkdCoxAaNbtPWBdhckZ7JinQaBqvaO9vR0sj7hDQTVu8TgiXlGkeS2hKWSF+1ahUWLlyIRx99FHvuuSf23Xdf3HvvvXj++efR1DT00lG73Y6ioqL+L5fLlagwiXQl1vZDrGUiPZ34m1pg9bghCwIyZk/XOhxKAY7qKsiCAFPAD19Ds9bhUBzZ6pRKE9PUKRpHQqkiNFG5YZbWM5GeTqI1ygP20IQKbQOhlGGaNg0AYN3IFpApKVaRvk0iPRKV4AmqFek2zk6hHXNMVq4LTMEA/M2tGkdD8SSreaDoBD5kH4uEJdK//PJLZGVlYbfdduv/3qGHHgpRFPH1118P+dpnnnkGeXl5mDVrFm688Ub4fL5Btw0Gg3C73Vt9EdHARLUvsqmBg0PSiWfpcuWfRaUwOR0aR0OpwGC1wJtfBADwr2XlWbqQwhFkNCoXyLaZ0zSOhlKG2gaofzUDpQVhg/JQLVpZqXEklCrss5TPjYz6jZCjUY2joRGLVaRv09qlLxCBLAMmgwCbyaBBYJRqjA47fDn5AAD/Ol4bpBMD56rFRcIS6S0tLSgoKNjqe0ajETk5OWhpaRn0daeffjqefvppfPTRR7jxxhvx1FNP4cwzzxx0+9tuuw2ZmZn9X+XlXKJANBizmki3cXBIWgmtUIZCeSsnaxwJpZLYcv8QE+lpw7OhFoZwGFGjCc4qJs9oeAxqOwfzplptA6G4Mqk/T4GzU2iYnFMmI2o0wRgOoW/9Rq3DoZEapCJ9y0GjgiAkOypKUb7iMgBAqIbngnRiVvNAxooKbQNJcSNOpN9www3bDQPd9mv16tFP+r7wwgtx+OGHY/bs2TjjjDPw5JNP4tVXX0VNzcBLzG688Ub09vb2f9XXM0FINBhbtVJ1Zm9rhhSOaBwNxYu8RjnnRiazJzINX3iCUokQ5fDhtOFXH6p5Ssohmrh8m4bHMk357HA01GobCMWVrUFZnWKewofsNDyi2YQ+9SF77POEUsggFekcNEqjEZpYAQCIrF6jbSAUVzZ1gKyJBTdjMuK7rGuuuQbnnnvukNtMmjQJRUVFaGtr2+r7kUgEXV1dKCoqGvbx9txzTwDA+vXrUTVARYXFYoHFYhn2/oj0zD6xHFGjCYZIGH21dchQE+uU2kzr1ip/mMZWDjR8krrcX2QVatoIr1HOBf6KScjUOBZKHY4ZUwEAtt5uBDq6YM3L2cEraLyTQmE4WxoBAPapfMhOw+evqELWphqEV60GcIzW4dBI7KAinYNGaSSi06YDb78KcQ0fqqWLaCAIe6eSo7VP4Wq1sRhxIj0/Px/5+fk73G7evHno6enBd999h1133RUA8OGHH0KSpP7k+HD8+OOPAIDi4uKRhkpE2xBNRrgLS+Bq3AT/uhom0tOEfaPSmsM8g4NGafjESuX331TH4cPpQl6nDIsMV/LcTsNnyc6CLzsX9u5O+FaugXX/eVqHRGPkWb8RLimKiMkMRwXbXtLwRaomAx+/B6xdq3UoNFKxRPo2Fem9/lhrF65Uo+EzzpoBALCuY0V6uvCu3wCXLCNsscJWXKh1OCktYT3Sp0+fjiOOOALz58/HkiVL8Pnnn+Oyyy7DqaeeipKSEgBAY2Mjpk2bhiVLlgAAampqcOutt+K7775DbW0t3njjDZx99tnYf//9sdNOOyUqVCJdCZQqN1Rh9j5MCxGfH84mpaWVY84sjaOhVGKuVioRbBw+nDaM6rBIoZoVqDQyvjKl1VNgzTqNI6F48K9WkqCeknIIBg4XpOETpkwBABhrOD8l5cRau2xbkR5LpLO1C42Abc5sAICrtobDh9NEQM3/eItKIYgJSwXrQkL/7z3zzDOYNm0aDjnkEBx11FHYd9998fDDD/f/fTgcxpo1a+Dz+QAAZrMZH3zwAQ477DBMmzYN11xzDU444QT873//S2SYRLoSUhPp0kYm0tOBZ+UaiJKEkN0B+4RSrcOhFGJTl/Q5WpsghcIaR0PxYKtTzuumqVM0joRSTXCi0uopup7Js3QQXqfMlgqUT9Q4Eko15ulKqyd7LeenpJxBKtK3HDZKNFwZM6chajDCFPBx+HCaCK9Xrg2CJWUaR5L6Erq+JycnB88+++ygf19RUQFZlvv/vby8HB9//HEiQyLSPXmiclMlbGI7h3TgX7YCWQD6KiYjl0+WaQQcFeWImMwwhkNwr98I1wwmX1OZHI3CqQ4XtM3gvAQaGUltByRw+HBakGuUm+WIOiyOaLgcs5V2Ds7mekQDQRisnEWWMgaoSA9HJXiDSjUxe6TTSBgsZvRMqETWxnXw/7gcrqkcXJ3qpFWrAAChyVy5OlbMuhDpjKgOGDQ31mscCcVDZKUyACYwiRc3NDKCwQBvsVKREFjLKtRU56mphTEcQtRognNypdbhUIoRpyg3VeZNrDpLBwZ1iLRcxWFiNDL2iWUIW+0QJQl9K9kbOaUMMGy0LxABAJiNIixGpn5oZPxVyrVBaPkKjSOheDCtVhLpwqzZGkeS+ng2JdIZk5pgsTKRnhbEtcpNjjRlqsaRUCryl00AsHmpH6Uu3wrloZqnuAyimVVnNDJmNZFur2MiPR1Y6moBAMbJTKTTyAiiiL4Jyr2CXy3WoBQQCgFer/LnLVq7bB40aoIgCFpERiksMkVZ4SiolcyU2pzrlHO6ZQ4T6WPFRDqRztiqt+iLHI5oHA2NlUUdBmWczlYONHKRCUqrJ2kD2zmkuvAaZbigv2KSxpFQKnKorZ0c7S2I+PwaR0Nj5WhUhkhbp3L5No1cYJJyrxBZzYr0lBGrRhcEIDOz/9ubB40mtKMvpSlx1kwAgGU9zwWpLtDRBWd7CwAgY9c5GkeT+phIJ9IZR8UERI0mGCJheGpYeZbKZElCRq2SSLfuNFPjaCglTVKSrobaWm3joDGT160DAEQqmUinkbOVFCFkc0CQZXhWr9M6HBqDQFsHrH29AAAHE+k0ClG1f66wlueClBFLpGdlAVvMTIoNGmV/dBoNm3p/mbFhHWRJ0jgaGou+75YCADz5RbDm5exga9oRJtKJdEY0GdFboVSa+L77UdtgBtC+8EM03HYnAu2dWocy7vkammHx9kEWBGTMZEU6jZxBTaSb68fn8OFoMITGu+5H2+vvaB3KuGeMrSqoZuKMRk4QRXjUVk/+1Ws1jmZwsiTB19Ck6Q29e/kqtL2xEHI0qlkMQ/GuUn5+vpw8mDMzNI6GUpFYraxQMW/g/JQdCXZ1o/7mv6H15Te0DWSAQaMA4PYrq49dTKTTKGTMmQVZEGDt64WvoVnrcPrJkgRvbf24TO77NjWgecGzCPX2aR3KVoJLlwEAPNXMGcQD1/gQ6ZB/2kxg/WqEf1gKnHGS1uH0a/zXQyi++lKIUhShW29CwylnIvPG3yJjSnJ7fIZ6++Bdsw7BDZsQ2VQHedMmiI0NMDU1QjYY4HjmSTirKpIa00C8S5fDAaCvuBwuh13rcCgFWdRWT/am8Tczoe2NhTD/5gqUblQq4pqOPB6ZD/4bjgmlSY2j4e4HYP3PI4jk5CI8eQrEadNgnjUDzjmzYCspTGosQ7FtUhLpxilTNI6EUlVwYiWwbhWi68ZX8izi86Pz7fcRfu11ZH74HjKaG9B80BHIfuFpWPNzkxaHHI2i4dbbUfy3W+AKh9A7YRL6LrkChZdeAJPTkbQ4diSoDo/2lU4ArwxoNCwzlbk7jk3jo+2bLElofe4VRBqbgXAIiEQgh8NAJAKEIxCLi1B0+a9hsJiTFlM0GELzP+5B7j9uQ3mPksSuP+8ilDzwr6TG0S9WkZ6zdaVpf490KxPpNHImpwPu4nK4murg/XFZ0q/BByJLEhrPOB9lzz8BvysbPXN3R3SffWA96ABk77sXDFZL0mMKtHei48nnYHrxReR/+znskoT2u3dD1qcfwZThTHo8A5GXK4n08LTpGkeSHphIJ9IhadZs4M1XYFixTOtQ+tXf/DeU//n3AABfdi7s3Z0oW/AQpCcfRdMRP4f1huuQs99eCY+j6ZEnkXflJcj2ewfdpu20M+H48mMIBkPC4xlKSB0C5ausgkvTSChV2aepAwa7OhDu84yLiz1vbT16L70SJW+/CgAIOF0w+zwoeec1BGZ9hIab/4LSqy6BICZ2UZ0UCqPxgktQ/tSjm7/5yQdbbeN3ZaPjFyeh7LEHEh7PUORoFM4GZVWBnatTaJT62wKt1z6RHmjvRMczL8Lw1pvI/fxjFG7zmVz80UK4d94FvudfSMq1gXdjHTynnYnyrz8FAESNJmTWbUDmDb+B/7Y/oeXsC5B73W9gLytJeCw7Eq1RhkeHJlRoGwilLOesGQAAR0crQj1umLO0vcpsvP1fKLvx6iG3aXvpRbjefC3hD9dkSULLk8/DftMfUFavtMj05hXC0dGK8scfRMd338D6yktwTq5MaBzbGawiPRAbNsq0D42Or6oarqY6hJYtB35+hNbhoPH/7kbZ808AAGzubtg+fg/4+D3gb0DYYkXnrLkIn3oaSq6+NKHX5mGPFx3P/xd47jkUfPoBysLh/r+LmkzIX/otWo46DvkfvKPNw7VtWFYrA2PFWbM0jiQ9sLULkQ6Z5u4MALCvWqFtIFAuSOsvubo/iV5/zoWwtrag9flX0bb7PhClKErefhU5+89D2577oeuTLxMWS/3Nf0Pxr8+F2e9FwOlC1+RpaNnvEDSceg7qr/0DGv5+N8JWGwq++RyNv781YXEMm/qBGKlmBSqNjrUgD0GHsvRf677IUiiM+pv/BtPM6Sh5+1XIgoCGU88BamrQvegTdFXPgLWvF2XXXo72PfZF74/LExZLoLUd7fsd0p9Er7/wCjT89R9oOHs+WucdgL4ipSLH5u5G+RMPo/nhJxIWy3B4NmyCMRxC1GCEs5o90ml0hKrJAABjrbbzU0I9boR23gVlV12C4g/ehtnvhS8nHw0nnIHmBc+h7a330FdUCldTHVyHHIDGf9yb0OXdzQuehbjzzij8+lNEzBY0/OUORFpbUX/TX9FXVApbbzfK770D5kmVaDjpLHjrGhMWy3AIG5SfX5TzEmiUrIX58GcqCVnPspWaxuJZvxEFt/4RANA2d080H3oUmo74ORqPOQGNx5+Chl+chrDVjoIlnyGwx15wr0xca6qODz5Gxy57ofi8M5BZvxH+rBw0/OUOWBvq0Pz4Mwg6nMj76TsYdts1+a1eYhXpWyTSQxEJ/pDSgooV6TRa4alqgcaqVdoGAqDt7fdR/MfrAQD1V/8OHe8vRsPv/oTmAw5DwJUFUzCAgu++ROlvr0DDub9OWAu2nm9/RGDyFBTPPxvFH74DQziMnsopqL/m93AvX42uNxYibLGi6LMP0frLU8dFKzhnjTIw1jqXg0bjQk4zvb29MgC5t7dX61CIxi3PxjpZBuSoKMqhPo9mcURDYbn+5LNlGZBlQK675veyFI1utU3nx1/IjUceL0dFgywDcshqk5ueeSmucUiRiFx/7q/746g//Xw5GgoPuG3D7ffIMiBHDEa5Y/HncY1jpJr3PViJ97Y7NY2DUlvnlJmyDMjNT7+oWQxdX38nd1VN6/8dbJ+583a/X9FgSK7/3Z/kkMUqy4AcNpnluqt/J0eDofjGsuR7ubd0onq+sctNjz8z4HYhd59cd9GVsgzIfflFcrDHHdc4RqL11bdkGZB7yio0i4FSX8t/31TeRxMmaRpH3RXXyTIge3Py5LrLrpXbP/xUliKRrbbxt7TJzfsd0n/OaDj+FDnk7otrHMHuXrn+F6f1H6Nzyky5+5sft9omGgzJjQ8tkDtmzOnfrm3O7ttdyyRTyx77KdcGd92vWQyU+tp32lWWAbnxoQWaxSBFo3LT/j9TrgtmzR302rzzs69kT36Rct7IzpXbP1gc1ziiwZBcf9q5/b/jIYtVrrv4KjnQ2bXVdr3LVvZfU0mCINddds2gMcfdzTcr8V10Uf+3mnp88p3vrZEfXLw+OTFQWqr/579lGZBb99hX0zj61m2Qvdm5ynnpsGO3+5yVIhG5+5sf5LrLru3/XW087Fg57PXFNY62dxbJ/oxMWQZkT16hXHfhFXLnl99ut13zs6/IEYNR+Tw+43xNrwt8jS39/0+0vF8Z70aSS2YinUiHpGhU9mVmyzKgWTI47PXJjT87uv9ic0fJ4N7V6+SWeQeoDwAMcv0/7o1PHB6v3HjYsZuT6NffPOQHnRSNyk2HHKUkG8orNf0w6i2ZoFzYvLFQsxgo9cXez3W3/F2T40vRqNw5WUmi+11Zcv3//Wu7pNmWeles6T8XyIDcstf+sr+9My6xND31ghy0OWQZkN3FZQNeGG8p1OeR3cVlyv+/S66OSwyjUf/3u5WHIfserFkMlPp6V69THhSbTMlL/mzDs6mh/3ew8eEnhtxWikTkumv/IEdFUZYBuatqmtzzw7K4xNH52Vf9D9QkQZDr5l8uR/yBwWOJRuXWNxbKYbNFOS+98r+4xDEa/dcGb72nWQyU+hqOP0X5bLvqRs1iaHxogXJOMprkriXfD7lt3/qNcmf1DOVBu9kiNz3yZFxiCHb39heuSIIg1//iNLlv/cZBtw97vFsVCbXusa8c6OqOSyxDuuIK5Zg3bv55La3vlu98b4383+/rE398SlvtHyxWksa5BZrFEPZ45Y7pOykPtSdP2+H9d+MDj8kRo0n5Hdxt7/jdJzz5fP/nfPvsXWRfU+vQcdz3qCwJgnIuvfy3cYlhNFrfWCjLgHJ9QINiIp2JdKIdat19HyVx/M9/J/3YoT6P3LLnfv0Xxzu6WY6JBIJyw89P2lzBftWNY3q6629pk9vm7rE5jvseHfbrYpUv9SeeOerjj0XY4+1PHnhreYFMo1d3/sXKe/ncX2ty/KZnXlIqJGyOYb+XpWhUbrj3YTlktckyIHdXVsu9q9aOOgYpEpHrrv5d/8Vu6y57KdUbw4n/8Wf6K+R7l68edQxjUXf+Jco58awLNDk+pYdoKNx/4+leo00FY/0Z5ysP+WfMGfbne+urb/VXqQXtjjEn0BoffLx/5UtfQbHc+vo7w35t/dnz1ar03TSpPosGQ/0r+IZK9hHtSN21f1BWexx7oibH97e09f9eDzcBFezulZv3P3TzfcJ1fxzT76G3tl7umDZ7c3J+kBVqA9nyGqXu4qtGHcOwnXWW8t99xx3933p/RYt853tr5E/Xtif++JS2/O2d/b9T/raOpB9fikblhuNO7i+46V2xZliva/nvm3LQrjyY76yerqzIH4OGO+7pv/du3v/QYa+Cq7/19s0Fe7fcNqYYRisWQ9P+P9Pk+KliJLlk9kgn0qngjJkAAPmnn5J+7JY//R2FX3+KsNWOjudfQcn8s4f1OoPFjJJXn0f9RVcCAMrvug2NZ5wPKRwZcQzu1esQ2Gtv5P+wBEGHE50vv46SS341rNdaC/PhfeQxyIKAspefRvOCZ0d8/LHqW7EaoiQh6MiArVz74WaUuoRKZSCWobZWk+Mbb78dANB62jmwTywb1msEUUTpZfPR9+4iePMKkbVxHYx7z0P7e4tHfHzP+o1on3cAyu/8GwRZRsNp5yL3i49hKykc1uuLzj4VrXvsB2M4BO/lvxnx8ePBuHEDAECortbk+JQeRJMRnmLld9C/ck3Sj+9evgrFzz8JAIj+7bZhDwkrOP4oyN99j/Y5u8Ps86J4/tloOO8iRIOhER1fCkdQf/FvUHLReTAFA2jda38Yf1qKghEMV8v+0x8RMVuQv/RbtL361oiOHw+edRsgSlFETGY4KsqTfnxKH8apyvwdy4YaTY7fedHlsHd3oqdiMor//qdhvcac5ULBB++g/qwLAADlt9+K5qN/CX9T64iP3/P9T4jOm4fc1cvgz8xGz1vvovjc04f9+tLL5qP9ngcBAHnPLEC4zzPiGEZkgGGj7Z4gAKDAZUnssSmtWfNy4M0vAgD0/bAs6cdv+PP/ofT1FyGJBvQueBquGcObDVb4i6PhWfgBfDl5yFm3CtK8eegZRfyyJKH+mt+j9LdXQJQkNB5/KgrefxumDOewXl/2h9+i/jdKX/eyW25E478fGXEMY7ZcmSsVmTEj+cdOU0ykE+mUOEcZNGFZmdyBo1IojOwn/wMAaP3r7Sg84dgRvV4QRZQ/cDca/vx/SiL7+SfQesTPEfZ4h/X6sMeL+qt/B9vcOciqXQ9vfhG8HyxGwXFHjiiOguOORMMFlwEAsq+8FJ6a2hG9fqwC6vCnvoqqhE4kp/RnUpOvto3rk37s9ncWIX/pN4iaTMj5w/Ujfn3O/vMgf/UVuqbMhL27E9nHHI6mBx8f9uubH30Kxl3mouCbzxG22tB4xz0oe/ZxGCzmYe9DEEVY7rsHkmhA8UcLkz9gDICtVkmkm6Zw8DCNjX9CBQAgtDb55wPPb2+EIRpBy94Hjvgz2VE5ATlff4b6Cy4FAJQteAjde+wNz/rhDU4NdHSh7ZAjUP7gvwAA9RdcivxPFsFamD+yOCrK0XKqUhxg+POfEjoEdSD+NcrQaE9JOQSDIanHpvRimTkdAODcVJP093HrK/9D6WsvQBYEhB96BEa7bdivFU1GlD/5CBr+/H+QRBElC18HZs5A04OPD/u/o/2dRbAeuD8ymhvgLpuI8KefIe/QA0b831F07unoKy6Dzd2N1vsSnDyLDRvNyQEASJKMjj4lkZ7vZCKdxsZTqdwrBJctT+px2157G6XqsOHGP/wZhb84ekSvz9lvL0Q++Qy9ZRXIaGmE5cAD0Hjfo8MuwpPCETSeNR/ld/4NAJSH7a88A9E8suG9Zf/8GxrOVAr2iq5Oft7AsnY1AMAwe3ZSj5vOmH0h0inbrnMBABlrVib1Arn1mRfhbGuGPzMbRb8+b9T7KfvjdWh5+AlETSYUf/gOevfaD00PP4FQj3vA7WVJQtPDTyBQPRXld90GU8CPjp12hfzFF8jZa9dRxVByzx3onDYbVncPfKedmdSJ3JGVyuT0YBUrUGlsnPN2BwBk1m1AsLsnqceO3nYbAKD5uFPgqJwwqn04qyrg/PoLNB9wGIzhEEouPh/1V9045Hkt2N2Dxl+chuL5Z8Pa14vOabPh+3IJSq+9fFQxZO22MxrPUi6QLddePeJK2LGQo1E4GzcBAGwzpybtuJSeIhXKChV5fXIT6Z0ff4GSha9DFgSY7/i/Ue3DYDGj/JF/o/nxZxF0OJH303cw7LYrWl/535Cv6/1xOYK77o6iTxchYrag8d+PoPyRf0M0GUcVR6wqPe+n75JelR5ep1QPB8onJvW4lH4yZk0DAFg9bgRa2pN23FBvH+yXKw/EGk8/D/mHHTiq/ZT98Tp0v78Y3ZOmwtbThZKLz0frQYcP+XBNCoXReN+jyD7uKFj7etExay6MX30J1+zRVXGKJiN65l8MAHA+eF9i7xO2qUjv8oUQkWSYjSKy7CNL+hFtK6wWasjq/WcyBNo7kXHumRClKBqPPRFlN98wqv24Zk6F6asv0DljDmzubmVFa9UUNN7zMKRQeMDX+BqaUH/9TfBOqEDZs8oq9Ppb/o7y++8aVQGbIIooXfAQOmbvAkM4jO5/Pziq/5bRkCUJGeuVVYbWnZlIjxcm0ol0yrXrHEiiCJu7G75NDUk7ruH++wEAHaefA6PDPqZ9FV9wFjpffgNBRwbyVvyAkl+fC7GwAM0HH4nG+x5FoEO5qOz8+At07LIXSn59LjJaGuEpKEbj/f9B7g9L4JxcOfr/FqsFhmefQdhqQ8E3n6PhD38Z03/PSIjr1gIApGpWoNLYOCaUwlNQDEGW0fvZ10k7bteX36Lo00WQRBEZfxjdxXGMOcuFwvffQv15FwEAyu/+O7pm74LGE89A/fU3ofk/T6PriyUI9fah44OPEZy9M0pfe165ML7oSmT98A0yd541phjy7/gb/Fk5yNpUg6a/3DGmfY2Ed2MdjKEgogYjnFMmJ+24lJ7kycp7yKCuckiWyHXKOaDp6F8iZ+89xrSv4nNPQ/CrJeiqngFbbzcKTjoO9Vdej86Pv0Db/95F87Mvo+mhBWj8x72ov+FmWPfdG5l1G+DJL0Lvu4tQeukFYzq+o6IczaedAwAw/OmWpBYryDVKIj0ysSJpx6T0ZMpwwlNQDADwqKsgk6H1quuQ0VwPT0Ex8u/955j2lXvwfnCtWIr631yPqNGEok8+gHnObDT87Z+Qo1GEevvQ9trbqL/qBrTuuT+imVkovWw+jOEQmg86AplffAJ7afGYYii48mIEHU5kbapB64uvj2lfQ9qmIr3NvbkaXRCExB2X9EFtCWJSK5uToeOhx2Dr7UZveSUKnl0wphXY9tJiuL76DPXX/B4BVxYy6zei9Mpfo69qChrufgBSKAxZktD+7kdoOuYEWCZVoPz2W5HR0oiAKwvNDz6O8ptHvnJ2S4LBgOCvlQdr2c88MWgSP958dY2w9vVCEg1wzRnbvQ5tIQk925OKw0aJhq+7YrIsA3LL868m5XhdX38ny4AcFUXZvbYmbvvtXb5arpt/udxbOrF/mIcMyBGTSW6fNbd/gGDIYpXrfnP9sIeDDFf9//1LGYCSkSmHPd647nswHTPmKENDHns6Kcej9NZ0yFHKEJzf/Slpx2w8+peyDMiNhx0b1/3W33Zn/7C9gb5i5wN3YYnc+ua7cT12wx33yDIgBxxO2bOpIa77Hkzra2/LMiD3lFUk5XiU3pqfflEZzDVlZtKO2fLyG/2f2WMZGrytUJ9Hrj/hjEHPBVt+te+0a1wHd3s21slhs0W5xnrp9bjtd0caDztWGW5401+TdkxKX6177KtcG9x1f1KO1/7hp5uH+T37Slz33f3ND3L77F36f+fdhSVyxGDc7lzgd7rkuguvkKOhcNyOHRvq3rrHvnHb51YkSZZNyqBouU4ZqLh4TZt853tr5A9XtybmmKQrrW8sVH5visuTcjwpGpU7q6crn2c3/y2u+w50dct11/5B9rmy+3/ve0sn9h+v/7pg5s5y/V33y6E+T9yOHfZ4+4/b9NQLcdvvUGLXWD0TJiXleKmMw0aJaFh805Sny+EffkzK8bx3Kr1HWw86AhnVk+K2X9fMqSh/+B5k1G1A1+dfo/6Sq9EzsQqGcBh5y3+AIMtoOvqXCC5fifK7/j7s4SDDVXLVJfDkF8Ha14u2Z16K674HIksSnJuUqjPrLA4NobGL7Kq0NxK//TYpx3OvXIuid5TKLOvvb4zrvstuuAqepcvQdN+jqL/iOjQecwI6Zs6F36UsdxZkGU1HHAfTsp9QcPRhcT12yVWXoHPabFi8HvRcfV1c9z2YkNoT2T9x9KtriGKs6oBBR+OmpFRSy9EoTL9TzgFNp58H17T4tSszOR0oe/lpNN55H/qKy+HNL0LvhEnonDYbbbvOQ8v+h6LpiONQf83vkfXlp8MedjwcjopytJx+LoDk9kq3blDOB8bJVUk5HqW30CTlfSSvXZucA155JURJQtNRv0DRab+M666zdtsZOd99jfpb/o6w1Y6M1iYYohF48ovQdOTxaPjrP9D15bew9HSh/KF/jbq100Ayr7sakiiiYMln6PpiSdz228/nA8Jqdava2qXNHQAAFGSwPzqNnXOXnZR/tjQkfnAugK5Pv0LOulWImMzIv3hsq8S2ZcnOQvkdt8KwqRb1198Ef2Y2XI2bkLNuFcIWKxpPOB2diz9H3vIfUPabi2FyOuJ2bKPDjo4TTwMACA89FLf9DiX0408AAN+UaUk5nl7E7xOCiFJOdOZsYOEbEJcnfgJ3oL0Tha8rSWbDFaPrQ7wjgigiZ+89lGXh9/0TPd/+CM+7H8C6914oOWjfhBwTUHogdp94GpwP3AXDgseBC89J2LEAZYmWw+tRWmLM5IcijZ15T6WVgnP50qQcz/2X2+CSomjda38U7j8v7vt3zZoO16zp230/0NaBSJ8HJVUVcT8moCzblO7+F3DEwSj573PoXHwJcg/cJyHHipHVNk+Ryvg9nCT9ck6vhiwIsHg98DW3jrmtwY40PfwESteuUHqa/+3PCTlG6VWXAFddkpB9DyXnlj8g8uwC5C37Hi0v/w9FJx+X0OOFetzI2qCcDzLmja09DhEAyGpfZMP6dQk/Vs+3S5G37HtEDUZk3vevhBxDNBlRfvP18J59Gjo+/wqO3XeBa+pkxLe8ZnuuqZPRdOjRKHnvf/DfcSfw6vPxPUCsrYvJBDgckGUZ7R61tQsT6RQH9tJi+F3ZsLm70ffjcuTst1dCj+d/QEkytx16FEpGOPR7uMxZLpT//U8I3XANGh78DyBJyLvgHJQW5CXkeDGuKy4BHrsfhZ9/BPfqdXEtIBiIuEppzRWZxuK7eGJFOpGOmebOAQDYVie+92H7vx+CKeBH96SpyD8mvlWgg8nabWeU/f5a5CUwiR6TqT4tL/jqkyEHGcWD96cVAABPSTmMdltCj0X6kLmvckHsaqqDv7ktocfy1jWi6JXnAADCjfGtRt8Ra0EenAlKosfkH34QGo85AYIsI3j7PxJ6LAAwblB6WcuTOXiYxs7osMObVwgA8K1KbBVqNBCE69ZbAABtF10JW0lhQo+XbPaJZf1V6cZbE1+V3vPx5xAlCZ78ojHNfyGKMaorVKwbaxJ+rL6H/wMAaNvnIDgqyhN6LEflBBSfeTJcU5M3V8R83TUAgKI3/wtvbX18d77loFFBgNsfQTAswSAKyHUwkU7x4alUfl8Cy1Yk9Dih3j7kv/lfAIDxwvkJPRagJNTLbrgKZb+7BtYEJ9EBIHPOTLTusZ8ym+qe+xN+PMtqZUCsYScOGo0nJtKJdMyx+y4AAFftekQDwYQdR45GkfnYwwAA7/xfj2lYyHjlmj0DbXP3hCDL6H7wPwk9VmiF8uDDV8nBghQf1oI8uMsmAgDcn3+V0GN13XYHjKEgOmbOTdpDtWSzXnMVACD/w4UIdnUn9ljqUEjTVA4epvjwTVCSsME1ia1Cbb7rPmQ018ObW4DCP45tiNd4lXPLHxC2WJG3/Ae0vvy/hB4r+NkXAAD33N0TehzSD/tMZWVXRn0t5Gg0YceRwhHkvPqi8i9nn52w42gp75AD0LHTrjBEwui64+747nzbQaN9SluXXKcZBpGDRik+gmprkOjyxCbS2x5/GhavB+6SCcg/9vCEHksr0oUXAgByX3g6sTkYSYJLXalmVwsoKT7SL5tFRMPmnFyJgNMFQzSC3h8S196l7eX/wdWwCUFHBgou+lXCjqO10NlKS5fMF55OaOWZvGYNACBczcQZxY9n9lwAQOjLrxN2jEBHFwqeUh40ha/9bVo+VAOAnP3noWdiFUzBANoXPJuw48jRKJyNmwAA9plTE3Yc0pdQhdImSFqR2NVqtqeeAAB0XXolzJkZCT2WVpJZlW5copy7o3vsmbBjkL44p1UjajDCFAzAs2FTwo7T/vrbcHS0IuDKQv5pJyTsOFoLX/EbAEDe04/Ht8/0lhXpANr7lMRcQYY1fscg3ZOnK4l049o1CT2OZcFjAIDe086CYDAk9FhaKTjzZPhy8mHv6kDrUy8k7Dh96zbA7PciajQhY/b2LS9p9NLzDpaIhkUQRbirlZOq/5vvE3Yc+d/3AgDaTjwN5ixXwo6jtYLzzkTI5oCrYRPaFy5K2HFMak9kYSr7o1P8RHfdDQBg/P67hB2j4x/3KIM4Kyaj6KyTE3YcrQmiiL6TlGFC5ucSl0j31tbDFAxAEg1wTOEKFYoPYU8lEWv5+suEHaPn+5+Qu+onRA1G5F54XsKOMx7k3vz7/qr0RF0byJKErKXKsGjrfomdy0D6YbCY4SlR2qz4V6xO2HGijz0OAOg45pdp3bKw8OxT0VdcBpu7G633PRK/HW9Xkc7+6BR/5tmzAAC29YlLpPd8uxT5S7+FJBqQc+mFCTuO1gwWMzpPPQsAYHzk4YQdx/vdjwAA98QqGCzmhB1Hj5hIJ9K50IyZAABpaWKGDLqXrUTh5x8BADKvuTIhxxgvzJkZaDvi5wCA8COJa+/iUHtVmgcYpkg0WhZ1OJ1rRWLOBRGvDzmPKL0APVdenbZVJjFZ888FABR8+wXca9Yn5Bg+NbHhKSnjBTLFjePQAwEAOct/QMTnT8gx+h5VEmft+xyY8IGmWrNPLEPbkccDAEKPP5mQY/StXANbbzeiJhOy92VFOsWPv7IKABBalZhEeqCjCwWL3gEAWC9I74dqosmInvkXAwCcD94Xv3Y5g1akM5FO8WOfoyTSMxo2IRoMJeQYfeqQ0db9DoGjckJCjjFeZF15CSRRRME3n6P3x+UJOUb0J2W//mquWo03JtKJ9G4npV+WeWViTuDuO++BIMto2ecguGan/7Roy4Xq0NGF/0Ooxx33/Yc9XjhbGgAAzp1mxn3/pF9Ze+8JSRThaG+J/yAsAG1PvQB7Vzs8+UUovuj8uO9/vMmYUoW23fYGAPQ+siAhxwirPaxjPa2J4iFzzkz4snNhDAXR/Un8q9LlaBRZr74EAJBOPyPu+x+PTOcqfZ/z33kdEa8v7vvvW/wZAKB76qy0ruil5AtXqaud1iZm+HDn48/AGAqip2Iycg/YOyHHGE8KrrwYQYcTWZtq0Pri6/HZaawiPTsb3mAEnmAEggDkOZlIp/hxVlUgZHPAEI2gb9mquO8/Gggi75XnlX+5IH1bwcZkTKlC274HAwDc9yZm6KiwUulnH53BnEG8MZFOpHPWXZVEesba+H8ghnr7kP+S2tbg0svivv/xKO+wA9FbXglTwIe2x56K+/77lq+GIMsIZGTCVlIU9/2TfpmzXHBPVG6Y3Z9+Eff9i88+AwDoPvE0GKz6uLkLq0nCjJefT0hvZFltw8N5CRRPgiiiZ7e9AACBRR/Fff/tCxcho6URQYcT+WecFPf9j0f5xxwGT34RLN4+tL/4atz3L3+hnLMDu+0R932TvglTlM8XY01iVlaZn1GulftOOT1t56ZsyZKTjbZTlJYO4t13xWensYr0nJz+avRsuxlmY/r//6TkEUQRfZXKfYJ/afxnq7U9/SJsvd3KAPKTfxH3/Y9L6tDR/JefS8hDdutaZSWRUW3LQ/HDsyuRzrl2nQtZEGDvaoevoSmu+2578DFYvH1wl05E4cnHxXXf45UginCfoiTPLE8+Eff9B5YpT5Y9Eyfp4oaDkss7Rxk4GlnyTVz362toQsHniwEAGRecG9d9j2d5556OsMWKrE016Pw4/g8nMj7/BABgPOiguO+b9E3aZ18AgOnLz+O+79ACJXHWftixMDkdcd//eCQYDOg+/kTlX55+Ju77t3+nnLPFefPivm/SN/M0pSWATW0rGE/u5auQv/QbSKKI7Pnp3dZlS5nXXQ1ZEFCw5DO4V68b+w6XqUnNiRP7+6OzrQslQmCy8mAtkoBh5OJjSlvUrpNPh2g2xX3/41HByb+Ap6AYVncPWh+P77WBFI4gc6NyfnGohZMUP8zCEOmcOcuFvlKlB1nfNz/Ebb+yJMH+6IMAgN7z5qd9P+QtZV/0K0iiiPyl38S951lU7VEZnMwKVIo/eTdl4KgpzgNHO//zFEQpis7pOyFrl53iuu/xzJKdhfZDjgQABB5bENd9963bgMz6jUoC4qifxXXfRPZDlYczOT8sgRSOxG2/Ea8P+e8o7QzM55wVt/2mAsf55wIACj75AIHW9rjtN9Tbh6z1yrVBxkH7xW2/RABgV+fxZDTVx70vcu9DjwEA2vfcD86qirjuezxzTZ2M9ni1fgsEgG/U4od990VbXwAAB41SYkjTlfOBYU18Zya4V69DwVdKcUjmpRfFdd/jmWgyovuMcwEA5kfjO3S0b+UaGENBhC1WOKcxbxBvTKQTEbxTlb5Zoe9/jNs+uz/7GjnrVyNiMiP/8l/Hbb+pwFlVgbZ5BwAA3A89Gtd9G9Yqk9KlKfxApPizzVOG1GWuWBrXViT2F58DAPhPOS1u+0wV4jnnAABy//dfRAPBuO3X/eZCAEDX9Dmw5ufGbb9EAJC1x64IOpww+7zo+TJ+K1Tan3sFFm8fPAXFyD/msLjtNxXk7LUruiZPgyESRsfjT8dtvz2ffAFRisKbX4SM6klx2y8RADgqJyBstUGUonHtiyxHo8h6Wbk2iJx1dtz2myrCp50OAHC99NzYrreWLAFCIaCoCJg8eYtBo9Z4hEm0FeNMZd6Zdd2auO63976HIcgy2vbYF66Z+hqMmXPFxZBEA/KXfouur+JXyOT74ScAgLtiMkSTMW77JQUT6USEyEylb5a4PH79zryPKlUmbYccAWtBXtz2myqkc84FAOS88kJcq/ksG5QelUa1IoAonjL32g1Rowm23m70rY3PMu6eH5Yhd+VSSKIBOb/S381ywfFHwZeTD1tvN9peei1u+xUWLQIA+Pc/MG77JIoRTUZ0z1X6bXs/XBy/HT+jzko4/iRdrVSL8Z2sPEy0PP9c3PYZ/Expv9O7865x2ydRjGAwoHuG0hag762Fcdtv+1vv989KKDjrlLjtN1XEWr9l1m9E1+IxtND69FPln/vth0BEQo8vDIAV6ZQYjp1nAwBcm2ridn8rhSPIfkF5uBw57/y47DOVOCrK0Xrw4QAA37/jN3Q08pOS1wlMZc4gEZhIJyIY5+4MALCtjE8bkmggiLw3XgEAiGefE5d9ppqCM05CwJUFR3sL2v/7Zlz2KUsSMmqV5KZt1oy47JNoS0a7DT1VSiWI59Mv47LPvseUWQFtex8Ae1lJXPaZSkSzCZ1qb2T5qfgMIJYlCVlfKjfPlsPZ1oUSI6z2STd+9llc9udvbkPBZx8CAJw6mpWwpazzz4YsCMhf+g3cK9fGZZ/GJUsAANE99ozL/oi2FTzsCACA+d134rbP8OMLAADtRxynm1kJW9qy9Zt/LK3ftkikd3iUavQMqxE2s/4eVFLiOadNQcRkhjEUhCdOBTdtr/wPzvYWBFxZunyoBgDCfGXoaN4br8Rt6KhhldLHXp7OnEEiMJFORHDsvgsAIHPjWkih8Jj31/bSa7D1dsOXk4+CE44d8/5SkdFuQ8fPTwAARB97LC779G1qgNnvhSQakDFrWlz2SbQt/xzlfBD9ZuztHGRJQtYrLyr7O/2MMe8vVTl+dS4AoHDxewi0dYx5fz3fLYW9qx1hixU5hx4w5v0RDcR60IEAgKzvvopLq6fOx5+CIRJG15SZyN597pj3l4qcVRVo330fAEDvf8Y+kFyWJGQu/RYAYN1v3zHvj2ggGSccBwDI/fZLhHr7xry/UG8f8t9VikwsF+ivAjXGcLaySi/3zVdH1/otGgW+UAeZ77df/6BRVqNToogmI/omKC3EvEvjs5JdflRpg9px/EkwOuxx2WeqKfjF0fDkF8Ha14u2Z16Kyz5ta5U+9sY5+plNlUxMpBMRMqZNRsjmgCEchvunFWPf4RNPAgA6jz9RN1O3B2K/8AIAQOFHC+Fvbhvz/nreehcA0FtZDYOVF8mUGMLuysBRyw/fj3lfHe8tRkZzPUI2BwrOPHnM+0tVOXvvofRGDofR8fgzY96fR11e3zV3D93edFDi5RywNyJmC2w9XXEZnG19/lkAgPfkU8e8r1QWPlVp7+J6+fkxP6DoW7UO9u5ORI0mZO/LinRKjMxddkJfcRmM4RA633h7zPtrf+I5mP1euMsmIu+wA8ceYIrK/8XR8OXkjb7129KlQF8f4HIBs2ezPzolhb9amdMVXbFyzPvy1jWiYPF7AACHjoaMbks0GdF9onJtJD6xYMz7iwZDcNVtAAA4dp0z5v3R9phIJyIIBgPck5V2Dr5vfxjTvvzNbSj4+H0AgEPHVSYAkLPfXuiqnqEkzx4c+9BR8RWlXY7nyGPGvC+iwdj2VgeOrvoJcjQ6pn0FH1cqLtsPOxqmDOeYY0tl3lOUwWKW58Y+ZND0kdIeI3zAQWPeF9FgDFYLunZS+m573v9oTPtyL1uJvGXfQxJF5PxKny3fYvLOOQ0RswWZdRvQ9cnYWmj1LVbaOnRPncWHapQwgiii9yBlOHD0f2+NeX+mZ5Q2Z70nngZB1G86QjSb0KWuXh1V67dYW5d99gEMBlakU1JEpyirooWVYx8+3H3/IzBEI+iYNRfZe+wy5v2lMtevlQK8gq8+gaemdkz76lu2EoZIGCG7A87JlXGIjral308uItpKYLoycDS6dOmY9tP5nyeUpdvVM5Azb7d4hJbSfOcqDxMynnhsTJVnwe4e5H+uJDIcZ+i7mo8SK2u3uYiYLbB4Pej9afTVJhGfH3nvvA4AMJ6jvyGj28q54FxIooj8pd/CvXz0Nx/RYAg53yrJN/tRh8crPKIBBffaGwAgfvrJmPbT+6j6UG3P/eCoKB9zXKnMkpONtoOVntP+x8fW3kX+UjkXBHbl9RYllvFYpYgja/H7Y7qe7Vtbg/xvlOGamb/Wd8ENANgvOA8AULD4fQTaO0f24i36o0eiEro8IWVfLibSKXEMs2YCACzr1oxpP7IkIeMZ5TMweM55Y44r1WXOmYn2ObtDlCR0P/ifMe3L972Sz3FPmqLrh5WJxP+rRKTYSZnCbVoxtuXb/Uu3T9NvP+Qt5V90PsJWO7I21aD9zfdGvZ/OF16FMRxCb3klsvfQZ29ZSg7RbELPVOUi2fv5V6PeT/uLr8Ha1wtvXiHyj2HC11FRjvY99wMA9D6yYNT76Vr8Ocw+LwKuLGTvs0ecoiMamPngAwEArm9GXzktSxIyX3kBABDR8ayELQlnnQkAyPnff8c0m8b2nTLLQtx777jERTSY3GMOQ9hihbO9Bd1fj771W8+/7ocgy2jbdR5c06rjGGFqyp63O7onTYUxHBpZ6zdZBj5RH3Dutx86vSFIsgyb2YAMizExwRIBcOyhrFTLXr0MvsbmUe+n/Z0PkNlQi5DNgfwL9L1SLSZ4llJ45Hr+6TE9sIwuU/I5wanT4xIXbY+JdCICAFh2UZKzzjWjr0Dt+f4n5K34EZJoQO78c+MUWWqz5GSj9Vhl2WbkgQdGvR/hlZcBAO5jjuOTZUo4/87K8kppyZLR7+RppYVJ1/EnQjTxpg4AomeeBQDIfPn5UbfNCSxUHsh17bkPBIMhbrERDST74P0RNRjhbGuGe836Ue2j88NP4WrchLDVjvyzuKIKAApO+Dn8mdmwd3ei/dXRtcoI93mQtU5Z3ZJx0H7xDI9oOyanA517KgNtvf99fVT7iPj8yHt2AQAgetHF8QotpQmiCO9JynnR8uwIWr+tXQu0twMWC7D77mhzq21dnBYIgpCIUIkAKJXTndNmwxCNoOvR0a+qCj/0CACg7ajjYc5yxSu8lJZ//pkIW23IbKhFx3uLR70f4yolnyPNnBmnyGhbzMYQEQAgY3c1kd7WjEBr+6j20feQsgypbd+DYC8riVtsqc5++SUAgMIP3oG3rnHErw/19iHvU6UnsuO0U+IaG9FAxD2USmfb0tHNTAi0daDgE3VWwvnnxiuslJd/1ikIOpxwNdWh9fn/jmoflo+VFk/SwYfEMzSiAZkzM9A9TVmx1vfuh6PaR+CxBQCAtp8dBXNmRrxCS2kGixmdx/wCABB98slR7aP7ky9giEbgzStkD1RKisgRRwIArO8vHNXrWx95EraeLnjyi1B4Nh+qxWTNPxeyICB/6Tdwr1w7vBfF2rrsuSdgsaDdEwDAti6UHP7TlVVV9pE8/NlCoL0The/+DwBgvXh+3OJKdZbsLLQdfiwAIPTo6Nu72NXCSLPacYDiL2GJ9L/+9a/Ye++9YbfbkZWVNazXyLKMm266CcXFxbDZbDj00EOxbt26RIVIRFuw5uWgr7gMAOBeMvIlm1I4guxXXwQAyGexH/KWcvbbCx0z5yq94+99cMSv73zpNZiCAbhLJrCVAyWFIzZwdM2KUbUd6Hj8GRjCYXRNnsZZCVswZTjRdpqyfFX85z9H/PpQbx9ylynnZ9fRbJdDyeGfp7QNkT8ZeZ/0sMeL3LfUWQlnnxXXuFKd9TzlWil/0TsIdveM+PWhT78AALjn7MqVapQUmSccBwDIXfY9/M1tI3qtLEmwPngfAKD7nAsgmk1xjy9VOasq0La7Uu3f+8jjw3vRFv3RAWyuSOegUUqC3AvORdRoQs7aFej68tsRv77j4cdhDAXRXTUNuVxRtRXTr5TZEflvv45Qb9+IX9/54afIbKhF1GRC5r57xTs8UiXsqisUCuGkk07CxRcPf9nW7bffjnvuuQcPPvggvv76azgcDhx++OEIBAKJCpOItuCZMgMAEPz+xxG/tv1/C+Fsb0HA6UL+6SfGObLUF/yVMok765kFkMKREb1WVtu69B79c94sU1JkzpmJkN0BUzCAnu9GPoDY8oI6K+FEVpxtK/uGaxE1mlDww9fo/PDTEb22e+EiGCJh9BWVImPmtARFSLQ104EHAAAyRtEnvfWf98Lm7kZfUSnyjzsq3qGltNyD9kNvWQVMwQA6nnpxxK83LPkaABDdc894h0Y0oIwpVeieNFUZhvfq/0b02o4PPkHu6mWImMzIu+qyBEWYumLzIzJffm54vZG3SKRLkowOj5JIL8iwJipEon624gK0HfAzAID3kZFVTsuSBPsTygMj71nn8N52G/lH/wzukgkw+71oX/DsiF8f/OddAICWI4+HtTA/3uGRKmHv2j/96U+46qqrMHv28JYTyLKMu+++G3/4wx9w3HHHYaeddsKTTz6JpqYmvPbaa4O+LhgMwu12b/VFRKMTmTkLAGBc/NHIX/v4AgBAx9HHw2i3xTOstFAw/xwEnC5kNDeg7ZXh33yEPV7kL/4AAGBjWxdKEsFgQM/0nQAA/s9Hljxzr16H/B+WQBYEZHN40HacVRVoOVpp6RD8+/+N6LWh95T+6O59DuCNByVN1s8OgiwIyKzbMKL2ZBGvD9n/vhsA0HvlNZyVsA1BFOE+Uflctzz60IgGi8mShMylShWgZb99ExIf0UA8hxym/OGtkfX2D991NwCg5ZgTYCspjHNUqS//nNMQttrgati044fsjY3Axo2AKALz5qHbF0I4KsNkEJBlY6U/Jcm5yjV+7usvIxoMDftlXZ9+hZx1K5WHahf9KlHRpSxBFNF78ukAANNTI+tB791Y198yx3bNVXGPjTYbN3dhGzduREtLCw499ND+72VmZmLPPffEl18OfhN/2223ITMzs/+rvLw8GeESpSXnuWdCFgQUL34XXZ9/PezXBbt7UPDB2wAA6wXnJyq8lGZyOtB+glKdK49g6GjHy2/AFPChr7AEuQfsnajwiLYTnKsMHJW/GdmSzd5HFgAA2nffB86qijhHlR7sv7seAFC06B24l68a9uscn36s/OEQ9ken5LEW5KG7aioAwP3e8Pukt9x9PxwdrfDkF6HoiosSFV5Ky7niEoQtVuQt+x4tz7w07Nf1ra2BvasDUaMJWfuyIp2Sx3K80r8357OPhr3C0lNTi8L3lcS7/ZorExZbKjNnudB2qLJqJzZXYlCxavSddwZcLrR7Nrd1EUUOGqXkKDjpePizcmDv6kD7y8MfQOx/4CEAQNuhR7FiehBZF18AWRBQ8N2XcK9YM+zXdf3jXzBEI2ifszty9p+XwAhp3CTSW1paAACFhVs/oS4sLOz/u4HceOON6O3t7f+qr69PaJxE6Sx797loPvJ4AEDwd38c9us6FjwLU8CP3vJK5B7MPmeDybjiUgBA4WeL0Le2ZlivkV9W2rr0HMW2LpRchtjA0Z+GP3A00NaBvMeUOQCh005PSFzpIHuPXdCyz0EQJQnu2/4xrNf4GpuRs04ZHpTF/uiUZL49lQe5kWH2SY/4/Mj6lzIHoOeKq7lSbRCOygloOe/XAADbzX8c9kyKvo+URFrPlBkwOR0Ji49oW3mHHohARias7h50Lhre+aD7znuV5M7cPZCzDx/8DMZ4jjI3Ie+t1xANBAffcJv+6M096qBRtnWhJDJYzOg8/iQAgLxgwbBeE+rtQ/6b/wUAGC/kkNHBZEyp2jw34cFHh/WaiNeHvGcXKH++7PJEhUaqEWVlbrjhBgiCMOTX6tWrExXrgCwWC1wu11ZfRDR6jr/+GZIooviT99Gx6ONhvcb0jDKx233y6Uz2DiFrl53Qtus8iJKEnnt3XJUe8fmR99G7AADrqWzrQsnl3EcZUJO1fjUiPv+wXtNx5bWw9XShp2Iyii/i6pShiNdeCwAofOVZ+Jtad7h9z5vKuaBr8jQ4JpQmNDaibYn77w8AcHz1xbC2b7n3YTjbW+DNK0TRVZcmMrSUl3/rTQi4spC1cR2a7xneQHL5C+Xn4Nt190SGRrQd0WxC1z4HAgACr7+xw+0jPv/m5M4l7I0+lPzjjoI3twBWdw9aH3t68A23SKR3eUNY1tgLAJiQa09ClESb2S9UZoAVfPz+sAYQtz3+NCxeD9ylE5F/LItChhI9R2mdk/Xyc5Cj0R1u3/LQ47D1dKGvsASFZ3NGVaKNKON1zTXXYNWqVUN+TZo0aVSBFBUVAQBaW7e+mWxtbe3/OyJKvMydZ6H55ycDAKK/33FVunv1OhR89yVkQUDWheclOryUF7lQqTzLee6pHfaT63j1TZh9Xnjzi5B3CCv9Kbkypk2G35UNQySMrmFUnXUu/hylLzwJAAj9614YrJZEh5jS8n9+BDqnzlIGDd5x1w63lz5YBADw7XtAokMj2k7mYQcDALLXr0KgvXPIbaOBIDLvvgMA0H3Zb2B0MLkzFGteDtqvUB6sZd3+N4T7PDt8je27bwAAhr3Z8o2STz76aACA44N3d7ht6yNPwtbTBU9+EZM7OyCajOg6XUmeZf/+uoFnUnR1AcuWAQDkffbBolWtiEoyKvMcmJTH1SmUXDnzdkNX9QwYwmF0/mfH/bwtCx4DAPSedhYEgyHR4aW0/LNPRdCRgYyWRrS9sXDIbWVJgv3B+wEAvefOh2jmrIREG1EiPT8/H9OmTRvyy2w2jyqQyspKFBUVYdGiRf3fc7vd+PrrrzFvHvv7ECVTxl//hKjBiMKvP0XbW+8Nup0UjsA3X+l72r7b3siYUpWsEFNW4dmnwpedC0dnG9qefnHIbaMvKW1duo44hhcblHSCKKJ73wMBANaLf41Aa/ug20rhCHDppRBkGU1H/xIFPz8iSVGmLkEUEfzN1QCAvAWPIOzxDrl95ufKCiHjYYclPDaibTkqyuEumwhBltHzwdCr1Vr+/TAyWhrhy8lD4TVcXjwcxTdejb6iUjjaW9Dyl9uH3Dbs8SJrrdLmyXkgB41S8uX88ueQBQE561fDs37joNvJkgTrg/cBALrPuYDJnWEo/vuf0DV5mlJZeta52w8h/vxz5Z9TpmBF1IaGbj9MBgEHTSuAILA/OiWf9/QzAQC2554Zcrueb5cif+m3kEQDci5hW5cdMTkdaD/mFwCA6H8eG3Lb9nc+QM66lcrMFa4CTIqE9WCoq6vDjz/+iLq6OkSjUfz444/48ccf4fFsrrKYNm0aXn31VQCAIAj4zW9+g7/85S944403sGzZMpx99tkoKSnB8ccfn6gwiWgArhlT0Hyi0t9Y+OMft7+IUzX++nIUffYhImYLDHcMfeNHCoPVgs5TlAsO8eGHB90uGggid5Hy9Nly6slJiY1oW9mPPIC+4jK4Gjeh97gTBu3f2/SPe5C7cimCDiey7vtXkqNMXUXnn4m+olLYerrQeu9Dg27nXrEGGc31iBqMyDny0EG3I0ok9+5K9XP4o8WDbhMNhuC6U6lG77zkSvbvHiaj3Qb3728GABTcd/eQS+RbH3ochmgEvpx8FjCQJmwlheicNRcA0PPK4O1dOj74BLmrlyFiMiPvKrZ1GQ6j3QY89TSiJhOKPvkAjXfcu/UGaluX8D774tN1HQCAeVW5yLTxIQVpI3f+uYgajMhduRTd3ww+V6lPHTLaut8hcFROSFZ4Kc0Wa53z/lvo+vSrQbeL3qXce7UedzIHuCZJwhLpN910E+bOnYubb74ZHo8Hc+fOxdy5c/Htt9/2b7NmzRr09vb2//t1112Hyy+/HBdeeCF23313eDweLFy4EFYrB2cQJVvWX25BxGRG/g9L0PbqW9v9feMd96L8caWXZ9u9DyH3AC4vHq7MKy+FLAgoXPIpepeuGHCbjtffgdXjhi8nH3k/OyjJERIpbCWFCL/8CsJWGwq//hSNF1+53Ta+xmbk3fZnAED7b38P+8SyZIeZskSzCT2/VipHXPffo1T2D8D9ltoffdZcmLM4C4Y0sr/SYsz2xWeDPmBvfuA/yGiuhz8rB4XXbn++oMGVXHw+uqqnw+LtQ4eaVN9W/R//irJrlSr/zuNP5Fwa0oz/Z8rKM8M7bw+6TfiuuwEALcecAFtJYTLCSgs5e+2KputvAgAU3nLj1vcKaiJ9ZdUcBMJRFLgsmFuerUWYRAAAe2kx2vY7BADgefg/A27jWb8Rea88r/zLBb9KVmgpL+fAfdA2d08YQ0E4jjwM7Qs/3G4b9+p1KPxIKb5z/vaqZIeoWwm7+lqwYAFkWd7u68ADD+zfRpZlnHvuuf3/LggC/vznP6OlpQWBQAAffPABpkyZkqgQiWgIzsmVaDlN6dNnuPnmrW6a295YiKIblZYE9b+5HiUXnqNJjKnKNa0arfsoyXHPzX9GqLdvu20iL70EAOg6/GiIJmNS4yPaUs7ee6BdHYBX/tgDaLxn65UU3ZddDWtfL7qqZ6Dkxmu0CDGlFf7mUgQyMuFq2ITWp18YcBtx0QcAgMABfKhG2nEdptwo5634Ae2774POxZ9v9fdSKIyMfyqr0zp/fTnMmRlJjzGVCQYDQn+5DQBQ8vR/4F6zvv/v5GgU9ef+GuV/+QMAoOHMX6H0wXs0iZMIABy//DkAIO/rTxHx+rb7e09NLQrfVwpx7NfwodpIld1yI9p22xumgB/hM85SVgT6fIBalPht+UwIAnDo9EKIIlu6kMbUwZg5r7601epVWZLQeNf9MO08B7bebvQVlaLw5F9oFWXKEUQRrvfeRvuc3WHx9iHrF8eg9eWtVwH13nE3RElC6x77IWu3nbUJVIdYxkBEg8r5800IW23IW/EDWp97BQDgXr4KrrNOgyEaQdMRx6Hsn3/TOMoUdZHSW7709ReB4mI0nHgmOj74GLIkIRoMIfd9pcLHfArbupD2SuafjfpLlIdnhddejo6PPgMAtC/8EKWvKRUm0X/fx/6no2DOcqH9TGVQs+nOf0KWJPgam9H81Auo//WVaNtlLxQuegcAYD2C/dFJO64ZU9Dwuz8hYrag4PuvkHvQvmg66hdwr14HAGh+5AlkNtTC78pGwQ1Xaxxtaio88Vi07b4PDOEw+q77HQAg4vWh+ahfoPwJ5SFm/Q23oPSJh/mQnTSVPW93ePOLYAr40fHaW/DU1KLr06/Q8uLraLznYbgvvgyGaATtc/dAzj57ah1uyhEMBtiefQpBRwbyVvyAxuv/CHz9NRCJwJtfBHdRGXYuz0Khiyv3SXsFp/4S/sxsZQbYf98EAHg31qH1wMNQevWlykqrmXMhvbOQ9wojZM3LQdanH6Jl7wNhCviRd/qJaH5c6Ucf6u1D/otPAwDkKziTJpkEWZZlrYOIJ7fbjczMTPT29sLl4vJnorGqv/BylD/yb3RVz4D9s48R2GMvZG2qQcfMnZH51WfsfzpKsiSh8W//hOvB++Bq3NT//Z7KKfDsvhfKXnwS/qwcWFpbeMFB44IcjaLl4CNR/Mn78OYXQf7iC4SOPAo561ej8YTTUfry0EOGaHC+TQ2wVE9SkmfF5chort9um67qGchc9gMMltENdSeKl761NXD/9kaU/O9lCLKMqMmEprMugOuDd5FZtwH11/4B5XfcqnWYKatz8efIPWhfyIKA9tffAW6+GQU/fI2o0YTWfz2Akku4LJ7Gh4aTzkLZy08PuU3Tw0+gZP7ZSYoo/TTd/x+UXHoBJNGA0BFHwvr2m1h94NH47M/34Ox5FTAbWRdJ40P92fNR/tSjaDrsWMjHHovcG6+F1eNG1GRC89W/Q8mff8972jGIBoJo/fkJKHn/LUiiiOY774fs9aDs99fCXTYRGbU1EAwGrcNMaSPJJTORTkRD8je3wTB5Esw+L9ylE+Fq3ARPfhGEJUvgqCjXOryUJ0sS2t96H+FHHkXBe2/CFAz0/13DSWeh7MUnNYyOaGvBrm74d90DWbXr4Xdlw+buRsCVBXnVavY/HaOGE89E2SubH0b0VEyGd9c9IM+bB+dB+yNzzkxeINO40vXpV4hc+1sULPms/3uBjEwItRthyWHP3rFoOvI4lCx8A7IgQJBlBB1O9D79AgqOP0rr0Ij6tb39PvKPORyCLEMSDQhk5SCQm4dwTh6iefmQZs1G6V//yM+uMWo68niULHy9/98XXXYTKm/6LSblOzWMimhrXZ9+hZz95231vc7pO0F8YgGyd5+rUVTpRQqF0XzK2f2rgQOuLFjdPWi45TaU3XyDxtGlPibSmUgniqv6y3+L8n//AwAQttrQ996HyNlvL42jSj+Bji50PLIAtqeegKNhE3zvvMvlsDTu9C5dAcu+e8PqcQMAGv5+N8quZ//TsQp296D9vkdgnDwZWQftC2thvtYhEe2QLEloffF1WH5/I7I3rEH97/+M8r/8UeuwUp572Uo45s6BIaq0cQi+/j/kzNtN67CIthNobYcsybAW5DJhniCBtg5EZs2Gs70FAPDJy4uw/wkHaxwV0dZkSUL31JnIWb8aUaMJTVdeh9K/3cwq9DiTo1E0nn8xyp58BAAQsjsg19ezgCEOmEhnIp0orgLtnZAnV8Pm7kbz48+g+NzTtQ6JiDTU8uLryD/jJHTNnou8b77gzTORzknhCDzrNsA1Y4rWoaSNhjvvg/jBB8j41z+RUT1J63CISEOtr76FghOOhTe3AKirg9PGNm80/nQu/hz+Bx6G/TeX8+FvAsmShIbf3IDye+9A/SVXo/y+f2odUlpgIp2JdKK4cy9biXBnN3IP3EfrUIhoHPA1NMGcnQWjw651KERERERprePjL2DKzkLmTjO0DoWIxoFAWwcseTkQRM5KiAcm0plIJyIiIiIiIiIiIqIhjCSXzEcXRERERERERERERERDYCKdiIiIiIiIiIiIiGgITKQTEREREREREREREQ2BiXQiIiIiIiIiIiIioiEwkU5ERERERERERERENAQm0omIiIiIiIiIiIiIhsBEOhERERERERERERHREIxaBxBvsiwDANxut8aREBEREREREREREdF4Fcshx3LKQ0m7RHpfXx8AoLy8XONIiIiIiIiIiIiIiGi86+vrQ2Zm5pDbCPJw0u0pRJIkNDU1ISMjA4IgaB2OJtxuN8rLy1FfXw+Xy6V1OKRjfC/SeMH3Io0XfC/SeMH3Io0XfC/SeMH3Io0XfC/SeKGX96Isy+jr60NJSQlEcegu6GlXkS6KIsrKyrQOY1xwuVxp/Uan1MH3Io0XfC/SeMH3Io0XfC/SeMH3Io0XfC/SeMH3Io0Xengv7qgSPYbDRomIiIiIiIiIiIiIhsBEOhERERERERERERHREJhIT0MWiwU333wzLBaL1qGQzvG9SOMF34s0XvC9SOMF34s0XvC9SOMF34s0XvC9SOMF34vbS7tho0RERERERERERERE8cSKdCIiIiIiIiIiIiKiITCRTkREREREREREREQ0BCbSiYiIiIiIiIiIiIiGwEQ6EREREREREREREdEQmEgnIiIiIiIiIiIiIhoCE+lp6L777kNFRQWsViv23HNPLFmyROuQKI3ddttt2H333ZGRkYGCggIcf/zxWLNmzVbbHHjggRAEYauviy66SKOIKV3dcsst273Ppk2b1v/3gUAAl156KXJzc+F0OnHCCSegtbVVw4gpXVVUVGz3XhQEAZdeeikAnhPp/9m77/Aoyq6Bw7/JJpveeyghhF6lg0hTFFCxoCKKir189t5711dfe0exYC+ovBZA6b13AoQUSO+97s73x7MlvZFkAzn3deXazezs7BPYnZ05c55z2s6qVauYOXMmERERaJrGokWLqj2u6zpPPPEE4eHhuLu7M3XqVA4dOlRtnezsbObOnYuPjw9+fn5cd911FBYWtuNfIU4GDb0XKyoqePDBBxk8eDCenp5ERERw1VVXkZycXG0bde1LX3rppXb+S8SJrrH94tVXX13rfTZ9+vRq68h+UbSGxt6LdR07aprGq6++altH9ouiNTQlhtOUc+fExETOOeccPDw8CAkJ4f7776eysrI9/xSHkED6Sea7777jnnvu4cknn2Tbtm0MHTqUadOmkZ6e7uihiZPUypUrufXWW9mwYQNLly6loqKCs846i6Kiomrr3XDDDaSkpNh+XnnlFQeNWJzMBg4cWO19tmbNGttjd999N7///js//PADK1euJDk5mVmzZjlwtOJktXnz5mrvw6VLlwJwySWX2NaRfaJoC0VFRQwdOpR33323zsdfeeUV3nrrLT744AM2btyIp6cn06ZNo7S01LbO3Llz2bt3L0uXLmXx4sWsWrWKG2+8sb3+BHGSaOi9WFxczLZt23j88cfZtm0bP//8MzExMZx33nm11n3mmWeq7Stvv/329hi+OIk0tl8EmD59erX32TfffFPtcdkvitbQ2Hux6nswJSWFTz/9FE3TuOiii6qtJ/tFcbyaEsNp7NzZZDJxzjnnUF5ezrp16/j8889ZsGABTzzxhCP+pPali5PK6NGj9VtvvdX2u8lk0iMiIvQXX3zRgaMSnUl6eroO6CtXrrQtmzRpkn7nnXc6blCiU3jyySf1oUOH1vlYbm6u7uLiov/www+2Zfv379cBff369e00QtFZ3XnnnXp0dLRuNpt1XZd9omgfgP7LL7/YfjebzXpYWJj+6quv2pbl5ubqrq6u+jfffKPruq7v27dPB/TNmzfb1vnzzz91TdP0pKSkdhu7OLnUfC/WZdOmTTqgJyQk2JZFRkbq//3vf9t2cKJTqeu9OG/ePP3888+v9zmyXxRtoSn7xfPPP18//fTTqy2T/aJoCzVjOE05d/7jjz90JycnPTU11bbO+++/r/v4+OhlZWXt+we0M8lIP4mUl5ezdetWpk6dalvm5OTE1KlTWb9+vQNHJjqTvLw8AAICAqotX7hwIUFBQQwaNIiHH36Y4uJiRwxPnOQOHTpEREQEPXv2ZO7cuSQmJgKwdetWKioqqu0f+/XrR/fu3WX/KNpUeXk5X331Fddeey2aptmWyz5RtLe4uDhSU1Or7Qd9fX0ZM2aMbT+4fv16/Pz8GDlypG2dqVOn4uTkxMaNG9t9zKLzyMvLQ9M0/Pz8qi1/6aWXCAwMZNiwYbz66qudYsq4aH8rVqwgJCSEvn37csstt5CVlWV7TPaLwhHS0tL43//+x3XXXVfrMdkvitZWM4bTlHPn9evXM3jwYEJDQ23rTJs2jfz8fPbu3duOo29/zo4egGg9mZmZmEymam9kgNDQUA4cOOCgUYnOxGw2c9dddzF+/HgGDRpkW3755ZcTGRlJREQEu3bt4sEHHyQmJoaff/7ZgaMVJ5sxY8awYMEC+vbtS0pKCk8//TQTJkxgz549pKamYjQaa52gh4aGkpqa6pgBi05h0aJF5ObmcvXVV9uWyT5ROIJ1X1fXcaL1sdTUVEJCQqo97uzsTEBAgOwrRZspLS3lwQcf5LLLLsPHx8e2/I477mD48OEEBASwbt06Hn74YVJSUnj99dcdOFpxspk+fTqzZs0iKiqK2NhYHnnkEWbMmMH69esxGAyyXxQO8fnnn+Pt7V2rDKXsF0VrqyuG05Rz59TU1DqPKa2PncwkkC6EaDW33nore/bsqVaXGqhWQ3Dw4MGEh4dzxhlnEBsbS3R0dHsPU5ykZsyYYbs/ZMgQxowZQ2RkJN9//z3u7u4OHJnozObPn8+MGTOIiIiwLZN9ohBCKBUVFcyePRtd13n//ferPXbPPffY7g8ZMgSj0chNN93Eiy++iKura3sPVZyk5syZY7s/ePBghgwZQnR0NCtWrOCMM85w4MhEZ/bpp58yd+5c3Nzcqi2X/aJobfXFcET9pLTLSSQoKAiDwVCrk25aWhphYWEOGpXoLG677TYWL17M8uXL6dq1a4PrjhkzBoDDhw+3x9BEJ+Xn50efPn04fPgwYWFhlJeXk5ubW20d2T+KtpSQkMCyZcu4/vrrG1xP9omiPVj3dQ0dJ4aFhdVqUF9ZWUl2drbsK0WrswbRExISWLp0abVs9LqMGTOGyspK4uPj22eAolPq2bMnQUFBtu9k2S+K9rZ69WpiYmIaPX4E2S+K41NfDKcp585hYWF1HlNaHzuZSSD9JGI0GhkxYgT//POPbZnZbOaff/5h3LhxDhyZOJnpus5tt93GL7/8wr///ktUVFSjz9mxYwcA4eHhbTw60ZkVFhYSGxtLeHg4I0aMwMXFpdr+MSYmhsTERNk/ijbz2WefERISwjnnnNPgerJPFO0hKiqKsLCwavvB/Px8Nm7caNsPjhs3jtzcXLZu3Wpb599//8VsNtsu+AjRGqxB9EOHDrFs2TICAwMbfc6OHTtwcnKqVWZDiNZ07NgxsrKybN/Jsl8U7W3+/PmMGDGCoUOHNrqu7BdFSzQWw2nKufO4cePYvXt3tQuN1oviAwYMaJ8/xEGktMtJ5p577mHevHmMHDmS0aNH88Ybb1BUVMQ111zj6KGJk9Stt97K119/za+//oq3t7etHpavry/u7u7Exsby9ddfc/bZZxMYGMiuXbu4++67mThxIkOGDHHw6MXJ5L777mPmzJlERkaSnJzMk08+icFg4LLLLsPX15frrruOe+65h4CAAHx8fLj99tsZN24cY8eOdfTQxUnIbDbz2WefMW/ePJyd7Ydbsk8UbamwsLDazIa4uDh27NhBQEAA3bt356677uK5556jd+/eREVF8fjjjxMREcEFF1wAQP/+/Zk+fTo33HADH3zwARUVFdx2223MmTOnWnkiIRrT0HsxPDyciy++mG3btrF48WJMJpPt+DEgIACj0cj69evZuHEjU6ZMwdvbm/Xr13P33XdzxRVX4O/v76g/S5yAGnovBgQE8PTTT3PRRRcRFhZGbGwsDzzwAL169WLatGmA7BdF62nsOxrUBe4ffviB1157rdbzZb8oWktjMZymnDufddZZDBgwgCuvvJJXXnmF1NRUHnvsMW699daTv8yQLk46b7/9tt69e3fdaDTqo0eP1jds2ODoIYmTGFDnz2effabruq4nJibqEydO1AMCAnRXV1e9V69e+v3336/n5eU5duDipHPppZfq4eHhutFo1Lt06aJfeuml+uHDh22Pl5SU6P/3f/+n+/v76x4eHvqFF16op6SkOHDE4mT2999/64AeExNTbbnsE0VbWr58eZ3fyfPmzdN1XdfNZrP++OOP66Ghobqrq6t+xhln1HqPZmVl6Zdddpnu5eWl+/j46Ndcc41eUFDggL9GnMgaei/GxcXVe/y4fPlyXdd1fevWrfqYMWN0X19f3c3NTe/fv7/+wgsv6KWlpY79w8QJp6H3YnFxsX7WWWfpwcHBuouLix4ZGanfcMMNempqarVtyH5RtIbGvqN1Xdc//PBD3d3dXc/Nza31fNkvitbSWAxH15t27hwfH6/PmDFDd3d314OCgvR7771Xr6ioaOe/pv1puq7rbRinF0IIIYQQQgghhBBCCCFOaFIjXQghhBBCCCGEEEIIIYRogATShRBCCCGEEEIIIYQQQogGSCBdCCGEEEIIIYQQQgghhGiABNKFEEIIIYQQQgghhBBCiAZIIF0IIYQQQgghhBBCCCGEaIAE0oUQQgghhBBCCCGEEEKIBkggXQghhBBCCCGEEEIIIYRogATShRBCCCGEEEIIIYQQQogGSCBdCCGEEEIIIYQQQgghhGiABNKFEEIIIYQQQgghhBBCiAZIIF0IIYQQQgghhBBCCCGEaIAE0oUQQgghhBBCCCGEEEKIBkggXQghhBBCCCGEEEIIIYRogATShRBCCCGEEEIIIYQQQogGSCBdCCGEEEIIIYQQQgghhGiABNKFEEIIIYQQQgghhBBCiAZIIF0IIYQQQgghhBBCCCGEaIAE0oUQQgghRIfw1FNPoWmao4cBwOTJk5k8ebKjhyHawYIFC9A0jfj4eEcPRQghhBBCdGASSBdCCCGEEA5hDWBu2bKl3nVeeOEFFi1aVGv5unXreOqpp8jNzW27ATqYyWQiIiICTdP4888/HT0ch/rjjz946qmnjmsb9b2XhBBCCCGEaAoJpAshhBBCiA7hscceo6SkpNqyhgLpTz/9dJsF0pcsWcKSJUvaZNtN9e+//5KSkkKPHj1YuHChQ8fiaH/88QdPP/30cW2jvvfSlVdeSUlJCZGRkce1fSGEEEIIcXKTQLoQQgghhOgQnJ2dcXNzc+gYiouLATAajRiNRoeO5auvvmL48OHcfffdLFq0iKKiIoeO52RlMBhwc3PrMGWFhBBCCCFExySBdCGEEEII0SHUrJGuaRpFRUV8/vnnaJqGpmlcffXVPPXUU9x///0AREVF2R6rWuP6q6++YsSIEbi7uxMQEMCcOXM4evRotdebPHkygwYNYuvWrUycOBEPDw8eeeQR22NVa6SvWLECTdP4/vvvef755+natStubm6cccYZHD58uNbf8u6779KzZ0/c3d0ZPXo0q1evblbd9ZKSEn755RfmzJnD7NmzKSkp4ddff621Xn3bvPrqq+nRo0e1ZVlZWVx55ZX4+Pjg5+fHvHnz2LlzJ5qmsWDBgmrP9fLyIjExkXPPPRcvLy+6dOnCu+++C8Du3bs5/fTT8fT0JDIykq+//rrW6+fm5nLXXXfRrVs3XF1d6dWrFy+//DJms9m2Tnx8PJqm8Z///IePPvqI6OhoXF1dGTVqFJs3b642HutrW/+vq75P/vOf/3DqqacSGBiIu7s7I0aM4Mcff6w2nvreS1B/jfT33nuPgQMH4urqSkREBLfeemutGRDW99C+ffuYMmUKHh4edOnShVdeeaXWv4kQQgghhDixOTt6AEIIIYQQQtTlyy+/5Prrr2f06NHceOONAERHR+Pp6cnBgwf55ptv+O9//0tQUBAAwcHBADz//PM8/vjjzJ49m+uvv56MjAzefvttJk6cyPbt2/Hz87O9RlZWFjNmzGDOnDlcccUVhIaGNjiml156CScnJ+677z7y8vJ45ZVXmDt3Lhs3brSt8/7773PbbbcxYcIE7r77buLj47ngggvw9/ena9euTfrbf/vtNwoLC5kzZw5hYWFMnjyZhQsXcvnllzfnn9DGbDYzc+ZMNm3axC233EK/fv349ddfmTdvXp3rm0wmZsyYwcSJE3nllVdYuHAht912G56enjz66KPMnTuXWbNm8cEHH3DVVVcxbtw4oqKiAJXVP2nSJJKSkrjpppvo3r0769at4+GHHyYlJYU33nij2mt9/fXXFBQUcNNNN6FpGq+88gqzZs3iyJEjuLi4cNNNN5GcnMzSpUv58ssva431zTff5LzzzmPu3LmUl5fz7bffcskll7B48WLOOeccoP73Un2eeuopnn76aaZOncott9xCTEwM77//Pps3b2bt2rW4uLjY1s3JyWH69OnMmjWL2bNn8+OPP/Lggw8yePBgZsyY0az/JyGEEEII0YHpQgghhBBCOMBnn32mA/rmzZt1Xdf1J598Uq95eOrp6anPmzev1nNfffVVHdDj4uKqLY+Pj9cNBoP+/PPPV1u+e/du3dnZudrySZMm6YD+wQcf1Nr+pEmT9EmTJtl+X758uQ7o/fv318vKymzL33zzTR3Qd+/ereu6rpeVlemBgYH6qFGj9IqKCtt6CxYs0IFq22zIueeeq48fP972+0cffaQ7Ozvr6enpDY7Tat68eXpkZKTt959++kkH9DfeeMO2zGQy6aeffroO6J999lm15wL6Cy+8YFuWk5Oju7u765qm6d9++61t+YEDB3RAf/LJJ23Lnn32Wd3T01M/ePBgtTE99NBDusFg0BMTE3Vd1/W4uDgd0AMDA/Xs7Gzber/++qsO6L///rtt2a233lrrvWFVXFxc7ffy8nJ90KBB+umnn15teX3vJev70PpeSk9P141Go37WWWfpJpPJtt4777yjA/qnn35qW2Z9D33xxRe2ZWVlZXpYWJh+0UUX1TleIYQQQghxYpLSLkIIIYQQ4qTx888/YzabmT17NpmZmbafsLAwevfuzfLly6ut7+rqyjXXXNPk7V9zzTXVaqdPmDABgCNHjgCwZcsWsrKyuOGGG3B2tk/+nDt3Lv7+/k16jaysLP7++28uu+wy27KLLrrIVlrD6TngAAEAAElEQVSmJf766y9cXFy44YYbbMucnJy49dZb633O9ddfb7vv5+dH37598fT0ZPbs2bblffv2xc/Pz/b3A/zwww9MmDABf3//av8HU6dOxWQysWrVqmqvc+mll1b7t6n5b9oYd3d32/2cnBzy8vKYMGEC27Zta9Lza1q2bBnl5eXcddddODnZT5duuOEGfHx8+N///ldtfS8vL6644grb70ajkdGjRzd5/EIIIYQQ4sQgpV2EEEIIIcRJ49ChQ+i6Tu/evet8vGpJDoAuXbo0q6lo9+7dq/1uDQDn5OQAkJCQAECvXr2qrefs7FyrZnl9vvvuOyoqKhg2bFi1+utjxoxh4cKFDQa/65OQkEB4eDgeHh7Vltccp5Wbm5utVI6Vr68vXbt2rdWU09fX1/b3g/o/2LVrV63nW6Wnp1f7vbF/08YsXryY5557jh07dlBWVmZb3tLmodb/w759+1ZbbjQa6dmzp+1xq7r+Tfz9/dm1a1eLXl8IIYQQQnRMEkgXQgghhBAnDbPZjKZp/PnnnxgMhlqPe3l5Vfu9ajZzU9S1TQBd15u1nYYsXLgQgPHjx9f5+JEjR+jZsyeggsV1vbbJZDquMdT3dzbl7zebzZx55pk88MADda7bp0+fZm+zPqtXr+a8885j4sSJvPfee4SHh+Pi4sJnn31WZxPUttAe7wkhhBBCCOF4EkgXQgghhBAdVn1ZxfUtj46ORtd1oqKiagVs20NkZCQAhw8fZsqUKbbllZWVxMfHM2TIkAafHxcXx7p167jtttuYNGlStcfMZjNXXnklX3/9NY899higMp/rKiFSM2s6MjKS5cuXU1xcXC0rvWrGe2uJjo6msLCQqVOntto26/v//umnn3Bzc+Pvv//G1dXVtvyzzz5r8jZqsv4fxsTE2C5YAJSXlxMXF9eqf5cQQgghhDhxSI10IYQQQgjRYXl6epKbm1vncqDWY7NmzcJgMPD000/XygjWdZ2srKy2GioAI0eOJDAwkI8//pjKykrb8oULFzapVIk1G/2BBx7g4osvrvYze/ZsJk2aZFsHVND6wIEDZGRk2Jbt3LmTtWvXVtvutGnTqKio4OOPP7YtM5vNvPvuuy3+W+sze/Zs1q9fz99//13rsdzc3Gr/Lk1V3/+3wWBA07RqGfjx8fEsWrSozm3U9V6qaerUqRiNRt56661q76H58+eTl5fHOeec0+zxCyGEEEKIE59kpAshhBBCiA5rxIgRLFu2jNdff52IiAiioqIYM2YMI0aMAODRRx9lzpw5uLi4MHPmTKKjo3nuued4+OGHiY+P54ILLsDb25u4uDh++eUXbrzxRu677742G6/RaOSpp57i9ttv5/TTT2f27NnEx8ezYMECoqOjG82KXrhwIaeccgrdunWr8/HzzjuP22+/nW3btjF8+HCuvfZaXn/9daZNm8Z1111Heno6H3zwAQMHDiQ/P9/2vAsuuIDRo0dz7733cvjwYfr168dvv/1GdnY20PJ64nW5//77+e233zj33HO5+uqrGTFiBEVFRezevZsff/yR+Ph4goKCmrVN6//3HXfcwbRp0zAYDMyZM4dzzjmH119/nenTp3P55ZeTnp7Ou+++S69evWrVKK/vvVRTcHAwDz/8ME8//TTTp0/nvPPOIyYmhvfee49Ro0ZVaywqhBBCCCE6D8lIF0IIIYQQDmHN9q2vxjTA66+/zogRI3jssce47LLLeP/99wEYNWoUzz77LDt37uTqq6/msssus2VlP/TQQ/z00084OTnx9NNPc9999/Hbb79x1llncd5557X533Xbbbfx1ltvkZiYyH333cfq1av57bff8PPzw83Nrd7nbdu2jQMHDjBz5sx617E+9tVXXwHQv39/vvjiC/Ly8rjnnnv47bff+PLLLxk+fHi15xkMBv73v/9x6aWX8vnnn/Poo48SERFhy0hvaFzN5eHhwcqVK7n//vtZsWIFd955Jy+99BKHDh3i6aefxtfXt9nbnDVrFrfffjt//fUXV155JZdddhkAp59+OvPnzyc1NZW77rqLb775hpdffpkLL7yw1jbqey/V5amnnuKdd94hMTGRu+++m++//54bb7yRJUuW1GpYK4QQQgghOgdNly44QgghhBDCAd566y3uvPNODh8+THR0tKOH06bMZjPBwcHMmjWrWnkVR1u0aBEXXngha9asqbe5qRBCCCGEEEIy0oUQQgghhINs3rwZT09PW3PHk0VpaWmt+uxffPEF2dnZTJ482TGDAkpKSqr9bjKZePvtt/Hx8amVwS6EEEIIIYSoTmqkCyGEEEKIdvXTTz+xYsUKFi5cyPXXX4+z88l1SLphwwbuvvtuLrnkEgIDA9m2bRvz589n0KBBXHLJJQ4b1+23305JSQnjxo2jrKyMn3/+mXXr1vHCCy/g7u7usHEJIYQQQghxIpDSLkIIIYQQol1FRUVRUFDAhRdeyBtvvIGnp6ejh9Sq4uPjueOOO9i0aRPZ2dkEBARw9tln89JLLxESEuKwcX399de89tprHD58mNLSUnr16sUtt9zCbbfd5rAxCSGEEEIIcaKQQLoQQgghhBBCCCGEEEII0YA2rZG+atUqZs6cSUREBJqmsWjRogbXX7FiBZqm1fpJTU1ty2EKIYQQQgghhBBCCCGEEPVq04KURUVFDB06lGuvvZZZs2Y1+XkxMTH4+PjYfm/OFFiz2UxycjLe3t5omtas8QohhBBCCCGEEEIIIYToHHRdp6CggIiICJycGs45b9NA+owZM5gxY0aznxcSEoKfn1+LXjM5OZlu3bq16LlCCCGEEEIIIYQQQgghOpejR4/StWvXBtdp00B6S51yyimUlZUxaNAgnnrqKcaPH1/vumVlZZSVldl+t5Z8P3r0aLWsdiGEEEIIIYQQQgghhBDCKj8/n27duuHt7d3ouh0qkB4eHs4HH3zAyJEjKSsr45NPPmHy5Mls3LiR4cOH1/mcF198kaeffrrWch8fHwmkCyGEEEIIIYQQQgghhGhQU0qEa7o1hbuNaZrGL7/8wgUXXNCs502aNInu3bvz5Zdf1vl4zYx061WEvLw8CaQLIYQQQgghhBBCCCGEqFN+fj6+vr5NiiV3qIz0uowePZo1a9bU+7irqyuurq7tOCIhhBBCCCGEEEIIIYQQnUnDrUg7gB07dhAeHu7oYQghhBBCCCGEEEIIIYTopNo0I72wsJDDhw/bfo+Li2PHjh0EBATQvXt3Hn74YZKSkvjiiy8AeOONN4iKimLgwIGUlpbyySef8O+//7JkyZK2HKYQQgghhBBCCCGEEOIkZTKZqKiocPQwhAO4uLhgMBhaZVttGkjfsmULU6ZMsf1+zz33ADBv3jwWLFhASkoKiYmJtsfLy8u59957SUpKwsPDgyFDhrBs2bJq2xBCCCGEEEIIIYQQQojG6LpOamoqubm5jh6KcCA/Pz/CwsKa1FC0Ie3WbLS9NKdAvBBCCCGEEEIIIYQQ4uSUkpJCbm4uISEheHh4HHcgVZxYdF2nuLiY9PR0/Pz86iwfflI1GxVCCCGEEB1Ubi4UFEC3bo4eiRBCCCGEENWYTCZbED0wMNDRwxEO4u7uDkB6ejohISHHVealwzcbFUIIIYQQHdT06dC/P8TFOXokQgghhBBCVGOtie7h4eHgkQhHs74HjrdOvgTShRBCCCFE8+Xnw8aNUFQE333n6NEIIcTJp6AAXn8dUlMdPRIhhDihSTkX0VrvAQmkCyGEEEKI5tu7137/hx8cN46TickEr70Gf/7p6JEIITqC+++He++FRx5x9EiEEEIIgdRIF0IIIYQQLbFnj/3+tm1w5Aj07Om48ZwMXnsNHnwQgoMhPd3RoxFCOFJqKixYoO4vX+7QoQghhBBCkYx0IYQQQgjRfFUD6QA//uiYcZwsdu+Gxx9X9zMyICvLseMRQjjWm29CWZm6Hx8PiYkOHY4QQogTS3x8PJqmsWPHjiY/Z8GCBfj5+Tl8HB2ZBNKFEEIIIUTzWQPpY8aoWwmkt1x5OVx1lbq1OnjQceMRQjhWXh6895667+mpbletctx4hBBCOMzRo0e59tpriYiIwGg0EhkZyZ133klWI0kX3bp1IyUlhUGDBjX5tS699FIOyjFogySQLoQQQgghmm/3bnX7+OPg5ASbN0NCgmPHdKJ65hnYsQMCA2HIELXs0CGHDkkI4UAffqgaOg8YADfdpJZJIF0IITqdI0eOMHLkSA4dOsQ333zD4cOH+eCDD/jnn38YN24c2dnZdT6vvLwcg8FAWFgYzs5Nr+rt7u5OSEhIaw3/pCSBdCGEEEII0Tzp6ar8iKbB5MkwcaJaLlnpzbdxI7z4orr/wQcwbpy6L9lAQnROZWXwxhvq/v33q30sSCBdCCFaia7rlFeaHfKj63qzxnrrrbdiNBpZsmQJkyZNonv37syYMYNly5aRlJTEo48+CkCPHj149tlnueqqq/Dx8eHGG2+ss6TKb7/9Ru/evXFzc2PKlCl8/vnnaJpGbm4uULu0y1NPPcUpp5zCl19+SY8ePfD19WXOnDkUFBTY1vnrr7847bTT8PPzIzAwkHPPPZfY2NgW//90dNJsVAghhBBCNI+1rEvPnqrswMUXw4oVKpB+770OHdoJpbhYlXQxm+Hyy9W/o7UOsgTSheicvvwSUlKga1e1XygqUhctY2IgLQ1CQx09QiGEOKFVmHTeXX7YIa9965ReGJ21Jq2bnZ3N33//zfPPP4+7u3u1x8LCwpg7dy7fffcd71lKgf3nP//hiSee4Mknn6xze3FxcVx88cXceeedXH/99Wzfvp377ruv0XHExsayaNEiFi9eTE5ODrNnz+all17i+eefB6CoqIh77rmHIUOGUFhYyBNPPMGFF17Ijh07cHI6+fK3T76/SAghhDiRmEzV6yILcSKwBtIHD1a3s2apQM+GDXD0qOPGdaJ56CEVMI+IgHfeUcv69FG3UtpFiM7HZIJXX1X377kHjEbw97fva1evdtzYhBBCtKtDhw6h6zr9+/ev8/H+/fuTk5NDRkYGAKeffjr33nsv0dHRREdH11r/ww8/pG/fvrz66qv07duXOXPmcPXVVzc6DrPZzIIFCxg0aBATJkzgyiuv5J9//rE9ftFFFzFr1ix69erFKaecwqeffsru3bvZt29fy/7wDk4y0kXDzGb4+GM49VT7AZwQQojWoeswc6Yq7bBjB3Tr5ugRCdE01kC6tXlReDicdpoK8vz0E9x1l8OGdsL45x94+211/9NPVbAMoHdvdXvwoNpHaE3LWhJCnAR+/VV99v394YYb7MsnToRdu1R5l4svdtz4hBDiJOBi0Lh1Si+HvXZzNbUczMiRIxt8PCYmhlGjRlVbNnr06Ea326NHD7y9vW2/h4eHk56ebvv90KFDPPHEE2zcuJHMzEzMZjMAiYmJzWp0eqKQjHTRsL/+gptvVifHu3Y5ejRCCHFyWbsW/vwTsrNh/nxHj0aIprM2Gq16cGwN7kid9Mbl5cE116j7N98M06bZH4uKAoNBlX1JTnbM+IQQ7U/X4eWX1f1bbwUvL/tjkyap25Ur239cQghxktE0DaOzk0N+tGYkSPTq1QtN09i/f3+dj+/fvx9/f3+Cg4MB8PT0bJV/n5pcXFyq/a5pmi1YDjBz5kyys7P5+OOP2bhxIxs3bgRUw9OTkQTSRcMsHwDy82HGDJmu3dby86FK0wYhTgoxMXDTTRAX5+iRdDyvvWa///nnahaQEB2drtfOSAe46CJ1u3YtJCW1/7hOJE89pY6peva0l3GwMhpVMB2kvIsQncnKlbBpE7i5we23V39swgR1u3u3uvguhBDipBcYGMiZZ57Je++9R0lJSbXHUlNTWbhwIZdeemmTg/N9+/Zly5Yt1ZZt3rz5uMaYlZVFTEwMjz32GGeccYat3MzJTALpomHW7r7OziorasYMsHTzFa0sOxv69YO+fe2NxoQ4Gbz6Knz0EXz4oaNH0rEcOqSmcAN4eEB8vGSaiRNDYiIUFoKLi72eN0CXLqoUHMDPPztmbCeKZcvU7csvV886tapa3kUI0Tm89JK6vfZaCAmp/lhoqDpH0HV1sVIIIUSn8M4771BWVsa0adNYtWoVR48e5a+//uLMM8+kS5cutoafTXHTTTdx4MABHnzwQQ4ePMj333/PggULAJqVKV+Vv78/gYGBfPTRRxw+fJh///2Xe+65p0XbOlFIIF00bPt2dbtggWqEtXcvXHABlJU5clQnpxdegJQU9TNrFtS44ijECWvrVnWbkODYcXQ0b76pTojPPhuuuEItsxzICNGhWbPR+/VTwfSqpLxL40pL4cABdX/s2LrXsV6gkEC6EJ3Djh3w99+qrNN999W9zsSJ6nbVqnYblhBCCMfq3bs3W7ZsoWfPnsyePZvo6GhuvPFGpkyZwvr16wkICGjytqKiovjxxx/5+eefGTJkCO+//z6PPvooAK6uri0an5OTE99++y1bt25l0KBB3H333bxac7blSUbTm1q1/gSRn5+Pr68veXl5+Pj4OHo4J7asLAgKUvdzc1W25IQJqvTInDmwcCE4ybWYVpGQoE6ay8tVZmpxMVx1lQqqSZMxcSIrK1PZlpWVMH48rFnj6BF1DNnZqrFocbFqOOjurjJ5PTwgNRWqNHMRosN56SV4+GG47DL4+uvqjyUmQmSk+u5KSlJNSEV127bBiBEQEACZmXV/z7/3nqqRfN559pkrQoiT1+WXwzff1L1ftfrqK7jyShg92l5+UwghRINKS0uJi4sjKioKNzc3Rw+nw3n++ef54IMPONoJyjg39F5oTixZoqCiftayLj17gq8vDB2qpmo7O8O338KDDzp0eCeVxx9XQfQpU2DxYpWN8sUX8M47jh6ZEMdn714VRAc4dsyxY+lIPvhABdFPOUV97seOVVO2i4vhhx8cPTohGlZXfXSr7t1hzBg12+KXX9p3XCcK6/HVKafUf7FcMtLFiUjX1fHrpk2OHsmJpep3/wMP1L+eteHo1q2qvJYQQgjRTO+99x6bN2/myJEjfPnll7z66qvMmzfP0cM6oUggXdTPeqI3bJh92dSp8Omn6v5//gNvvdXuwzrp7NihMkwAXnlFBdWsU2HuvltqJosOp1kTmbZts99PSgKTqfUHdKIpK4O331b377lHBdI0Da6+Wi377DOHDU2IJrEG0gcPrvvxSy5Rt3JRqG5VA+n1sdZIj421X4wUoqP74guYNw8uvFAF1UXT7NihPufh4Q3vF7p1gx491LHUunXtNDghREdzkhWVEO3s0KFDnH/++QwYMIBnn32We++9l6eeesrRwzqhSCBd1M9SH31/aE/WxWZiMlt22Fdeqep5A9x1l73+sWiZBx8EXUe/dA6HuvXlaHax+nedO1cdKF9yCXSCaTbixLA9MYePVx9hX3J+E5+w3X6/shLS0tpmYCeSb79V5VsiIuDSS+3Lr7xSlctaswYOH3bc+IRoSGUl7N8PgD5wIIfTC9l9LK/6Sd1FF6nbVavkM1+XnTvVbT0BswqTGVOXruDqChUV0oBcnBgKCuChh9T95GT7BTfRuC1b1O3IkbUeSsot4fstRzmQajnukjrpojOQQHGdKk1m/tqTymdr48kslJ51omX++9//kpycTGlpKQcPHuTxxx/H2dnZ0cM6oUggXdTPkjEVE96LjUey+X7LUXKLy9VjDz1kzzaxZlOL5lu6FJYsQXdx4adZN7N4Vwo/b0sit6QCPvpInWRnZKjmo6Wljh6t6ORiMwrZvXQ94158iLWrd3MkownTiqtmpIOUd9F1eO01df+OO8BotD/WpQv6mWcBUPLRfFLySjiaXUx5pdkBAxWiHocPQ3k5uqcn/8s38vvOZJbtT2PH0Vz7Oj16qICQ2QyLFjlooB2Urtsz0ocOrfVwTlE5n6yO4+O18RRF9lQLpbyL6ODMZp2SJ55WF4mtli513IBONNakpBEjqi3OL61g8c5kknJK+HN3KjuP5kogXXQ4ZrPOz9uO8emaOP49kEZcZhGVpuM4dl2yRJWSlRma1ZRVmli0I5n9KfnklVTw995Ue6KjEKJdSSBd1K2kBP3AAQDSo/vj6uJEal4pCzcmqowITVNTNwF+/FGdLItm0U0myu69H4Dt517GUX/VkM2s62yOz1FNB3/5BQIDVabKLbfI1XnhMFmFZfy1J5Uz3niCwX/9yPAf5vPH7hRS8krqf5LJZM+8tDYu7uyzK5Ytg927wdMTbrwRgPWxWcxfE8e7yw/zx8hpAFR+toDv1sfz49ZjLNmX2tAWhWhfu3cDkNG9F4cyim2L1xzKJKtqdtTFF6vbxYvbc3QdX3w85Oeri2j9+lV7SNd1/jmQTmmFiZJyE8nBXQHYs3wzx3KKZSq36DAKyyrZEp/NX3tS+GpDAl9+uQzjO6rcY8KwUwGoXCKB9CazBtKrZKRXmsz8sSuF4nITbi4GAP49kM7unkPUChs3SpKN6BCOZBaRkFVMXkkFO4/msWh7Eh+sjOW3ncnsScqjqKyZ5cm++ELFFuT4waa4vJKftiZxNLsYo7MTri5OpOeXsTUhx9FDE6JTkkC6qJO+ezeayUSxbwBdB0ZzxdhIuvi7U15p5s/dqfy9N5Xy06eCl5fKMN282dFDPqEczS5mw/Pv4Lp7J2UeXmy/6lbGRQcya3gXAPan5JNfWqGy+r77TpV7WLBAlYQQop2VVpj4bWcyvgf20GWPOtnruW8LFSadRduTyS4qr/uJMTFQUqKCxtYGWZ09kP766+r22mvB35+0/FI2HMkiv6SC8kozsePOoNTLB+/MVPruU/vV2PQiCpt7EiJEGyirNBG7fAMAaZG9CfQycvmY7vQI8qDSrPNX1eyoCRPUrZR/q856cXHgwOozUoC9yfkczS7GxaBx5oBQTNG9AKiIOcgPW46xcGMie5PzJKAuHO7vPamsPpTJ/pQCMgrKOO2DFzFUVpA8bhLb73hUrbRqpeoJIhpWVGQrl1U1I33lwQxS8kpxczFw+ejujIkKAGBZhQ/lIaFQXi5NXUWHsNMyI61PqDdDuvri7eZMhUknNr2QpfvS+Hj1EfanNLEkJMDateo2IaH1B3sCyi+t4Ictx0jLL8XdaGB2YCXnHdkIus6GI1lS4kUIB5BAuqhT5mp1opzZqz/jewfj4+bCxcO7MrZnIJoG+5Lz+XpXOqXTZqgn/PijA0d7YtmemMMv62MZ8J5qKJpy853MPWcEY3sGEhnoSbcAD0xmna3xlivMZ5yhmo4C/PWXg0YtOiuzWeeP3SnkFlcwevFC23K/mL10NVRQWmHi523HKCitqP1ka330oUMhMlLd78yB9L171WfYyQnuugtd11l1MANQJx9Xn9qDG84cgOuVcwGYsfVvIvzcMOs6B5pzAiJEGziaXcxXGxIx71Z1jz2GDeXy0d0J9XHjzAFhuLkYSM8vY8ORLPWEoUPV7LWUlOrlHjq7ehqNFpVVsvpQJgDjogMZ1MWXfpNUdmr3jGO4GDQyCspYsjeN/SkF7ThgIaorLKvkaI6ajTK2ZyCXZu8jesNydGdnIj79gLHnT6bILxDnkhIylq108GhPADt2qOzbiAgICwNgb3Ieu47loWkwfVAYvh4unNoriNN6B4GmEddfBdz1lfLvKxwrq7CMxOxiNA1O6x3EGf1Due60KOaO6c646EBCfFzRdVh1MIOySlPjG0xKUjO3QPqDANlF5Xy/+SjZReV4uzkze2Q3gm++lq7XXcEZy3/EZNZZsjcNs5R4EaJdSSBd1GI262SvVZmQ2vBh+Lq7AODkpDEuOpCLhnfF282ZnOIK/h1oqdP3009SdqQJSitMbDiSzdDfvsY3LQlzly70ePYR25RNwJZxUm0q3KhR6lYaEIp2tvpwJglZxXgX5ND7n9/VQk9PNLOZmYVx+Hu4UFBayaLtSZRW1DhAttZHHz4cunVT9ztzjXRrNvqFF0LPnhzJLOJYTgnOThoT+gTh72nE3WhAu+Yatd7PPzPIQ+1X96XkSxaqcJij2cX8tO0Y+SUVBCccAiD69LE4G9RhpJerM1P7hwCwOT6bpFzLTBRr6ZKqTYc7u3rqo688mEFphYkQH1eGdfNXC3v3BiAwOZ7rJ/RkYIQPADFpcmFNOM6htAJ0HcJ93RjX3YeIpx8BQLv9dujXjzB/D/JPVecHqT8tlgBPY2o0Gk3PL+Xf/emAulARFeRpW3VUjwAm9w3m2GC1bvafy+TYQDjUzmO5APQM9rLFDDRNI8THjbE9A5kzqjt+Hi4Ul5vYnpjb+Aat2eig+oSVNFBC8iSXnl/K91uOUlBaSYCnkdmjuhFQUQwbVMLj4Pdfxa8gi7T8UrYmSokXIdqTBNJFLbuT8vA+oDLOQieMrfV4twAP5o6JpFuAB7EjJ1Dp5g5xcXKi3AQ7juaiZ2cz9pv3AXB65hlVC72Krv7uhPu6UWnW2Wb9UrScTHPoUHsOV3Ry+5Lz2WapvXfB1j/RysrUid7s2QC4rV/LhcO64ulqILOwnN92JldvLmTdJwwbZg+kd9aM9NRUe2Pme+/FZNZZY8k+HdbdHx83F/u6I0eqsg+lpfRZ9ScuBo2swnJS86UWqnAMdSEHenk74ZtkmWo9aFC1dXqHetM/3AddV2UfyipN6rMPtZsOd2Z1ZKQfySgkJrUAJ03jzP6hODlp6oE+fdRtQgJu5kpG9lAX2o9ml9S+cClEOzmUphqN9wnzhvfeU2VJgoPhiSds6wRccDYAQetXsSc5zyHjPGFUaTRaWmFi8a4UKs06PYM9bck1VQ3r7k/3C6YD4LNtM0t3HpNgunCI0gqTbYbUsG5+da5jcNI4NVr1SdqakENJeSPfXVUD6dBps9Jzisr5YesxSspNhPq4ccnIrupcYfVqW/KilpfHhQvfAFS/pSwp8SJEu2nTQPqqVauYOXMmERERaJrGokWLGn3OihUrGD58OK6urvTq1YsFCxa05RBFDWWVJjYeSif4SAwAxlEj6lzP3Wjg9H4hmNzdOTLKkpUu5V0aVFphYltiDuM/ex3XgjwVKLM2bK1C0zRGWw6cdx3LUyfLvVSdVDIyIE9OSETbS80r5Z/9aQCM6e5D0Bfz1QO3326vd75qFb4eLlwwrAtGZyeSckr4e696DrpuD6RXzUjvrIH0119X9UzHjoVx49iTlEd2UTnuRgMje/hXX1fT4OqrAXD54nN6hXgB6sKGEO1N13USsooAGFmchmY2q+bBoaG11p3cNxgfdxfySipYGZOhPvsgF9qtcnPtNV8tGenllWb+PaCyT4d19yPEx82+fkgI+Pio/WlsLAGeRgI8jZjMOglZxQjR3vJLK0jKLUHToI9WAk8+qR54/nnw87Ot5zpdNc4OPbibLTviOsaFH11X/Ukee8zRI6nOkpGujxjBn3tSyCupwNfdhWkDw9A0rc6n9J4yBpO/Py5lJWSt2kBmYT29aoRoQ/tT8imvNBPoZaSrv3u96/UJ9SLY25XySjNbErIb3qgE0gHYGJdNeaWZCD83LhrRBQ+js3pgxQp1O24caBp+P3/PyIRdqsTLPinxIkSPHj1444032vx12jSQXlRUxNChQ3n33XebtH5cXBznnHMOU6ZMYceOHdx1111cf/31/P333205TFHF5rgcjHGxuJSVoHt42DOh6xDgaaRPqDeHTlMHy/zwg5R3acD2xFwily5m6GJLw9A33gCDoc51o4I8bQcc2xNz1Yl0iJo2L+VdRFurMJlZvCuZSrNOdIgX43atViVZQkLg0kthouXi2ebNUFxMiLcb5w2NwOCkcTCtQJV1iI9XQSMXFxgwALp2Vc9JSYHKTtY4MzUV3nlH3X/8ccoqTbY60mN7BlYr7WRzxRVq/7BhA0MKVX3pmLQCKqpm/AvRDtILyigqM2F0diIk0TIratAgdcGnBjcXA9MGhqJpqnFmUlR/9YBkpCvWRqM9etiCjutiMykorcTX3YWxPQOrr69p9uOwgwcBiA5WF9ZiMwrbYcBCVHcoTWWfRvi54/Xc0yq5Y9gwFaCuqls39L59cTKbCdq8jvWxWQ4YbQ2xsfDZZyroX9hBPj+FhXDgAABbg3oSn6maDZ87NLzuYwMrJycMlmOxLrs3k5gtF9ZE+9J13dZkdGhXv3ov+oBKEjs1Wn2/7UjMrbuvEqjPg3XWlnXWWycMpOcVVxCTqva1k/uG4OpcZV9gDaTffjvcfDMAp77+BO5UkppXap/NLk4669evx2AwcM455zh6KK2uvYLfralNA+kzZszgueee48ILL2zS+h988AFRUVG89tpr9O/fn9tuu42LL76Y//73v/U+p6ysjPz8/Go/omXySirYnphDcKw6oNOGDKk30Gs1OiqAuDGTqHQxqgDv7t3tMVSlslJNIz0BMuFLK0zErtvO1DcsWTAPPwxTp9a7ftWs9B1Hc9UUeevJtATSRRtLzi2hoLQST1dLUOztt9UDN94Irq4qCNS1K1RU2Or0dQvwoH+4qt+7JT7bHjgbPBiMRpW96uwMJlPnazz44ouqxuPYsTBjBlvjcyguN+Hv4cLgLr51PycsDGaoZs7hPy7Ex92FsgqzBM9Eu4vPVNno3QI8MOzbpxbWKOtSVVd/D0ZGqu+vv42qcR5xcZAjJ3c166On5pWywxKIOKN/CEbnOg7LreVdLKXdokNUveS4zCJMknkm2tlBS1mXoZnx8PHHauFbb9V5vqCdeSYA3bevY+exXNILHFyeLDnZft+6L3O07dtB1zF36craApVxekb/UEK83Rp5IrbZgV13b+GoBNJFO0vIKianuAKjs5Pt+L8hUUGedPFzp9Kssymunqz0jRvVeUL37jB+vFp2sgTSN21q8gW8rYnZmHWdyEAPQqvOUsvNtc/wmzRJXRQMDsawfz8XrFDxkPWxWWQXyQyVk9H8+fO5/fbbWbVqFclVv8+EQ3SoGunr169nao3g4rRp01i/fn29z3nxxRfx9fW1/XSzlg8QzbbucCaVZp1eSZaMsyr1O+sT5OVKZI8w4kdOUAt++qntBljTjz/Cs8+qEgjlHfsLY8fBZM566nZci4vQJ06EZ55p9Dm9gr0I8DRSWmFi17E8e3kXqZMu2lhSjmrs0z3AE9c9u1U9PmdnW+YDmmYv77Jype15IyP90TQ4klFE0QbVsNhWI9lggC5d1P3OVN7l2DH44AN1/9lnKSirZKul7vxpvYMwONWfwcMNNwCgvfMOw0pV6Qcp7yLaW7ylrEuPQA/Yo/qnNBRIBxjbM4Bgb1fyXL0o7tJdLZTyLtXqo5vMOkv3p6Hr0D/cm8hAz7qfYw2kWzLSw3zc8HQ1UF5p5liOBM9E+8krriA1rxRNg+iP3lCzUOfMgdNOq/sJlnO6Xjs3oOuwIibDsbW8qwYerPsyR7PUR8/qN8gWOGtKUBKwzQ6M2LOFpMwCubAm2t6//6rErn/+sTUZHRjhU/dF4Bo0TePUXiorfU9SPrnFdZy7W8u6jB+vgulgL4d2Ilu+HMaMgZkzG529X1hWyd4kdaw/qkeNHgnW+uh9+kBEBPj7w3/+A0DoGy/TvyKHSrPOyoPpbfJnnHR0HYqKHPPTzO/CwsJCvvvuO2655RbOOeecauWvFyxYgF+V0moAixYtqjVL5LnnniMkJARvb2+uv/56HnroIU6pEu+7+uqrueCCC3jhhRcIDQ3Fz8+PZ555hsrKSu6//34CAgLo2rUrn332WbXtHj16lNmzZ+Pn50dAQADnn38+8fHxtbb7n//8h/DwcAIDA7n11lupqFAzUyZPnkxCQgJ33303mqZVG/eaNWuYMGEC7u7udOvWjTvuuIOioiLb4+np6cycORN3d3eioqJYuHBhs/5dj0eHCqSnpqYSWqPmZmhoKPn5+ZTU07H54YcfJi8vz/ZztDMFaFpRal4pB1IL0DToccwSqLUGwBoxJiqAQxNUeRfT9z+01RBrs5ZKKCqqXU+tAymtMOH90P2EHDlAZWAQ2jffqKBkI5ycNNsX6LaEHEzREkgX7eOYJZDe1d8drNnoF11kD4SDvbzLqlW2Rf6eRls972JrIN1aIxns5V2OHWuTcXdIzz+vLvRNnAhnnMG62CwqzTpd/NxtJRrqNXMmnHUWlJUx5Kn7wGwmMbuY/PqmxArRykorTKTkqSzSHkGe9llngwc3+DxngxNnDVDHc0k9LeVdJJBuL+1yyilsT8whs6AMd6OBiX2C639OjdIumqbRM0jKu4j2F2Mp69LN3wPn9evUwttvr/8JkyeDwYBX4hECMpNJyimxbcMhOnAg/XC3fkAdgbOGDB2K7uODW1EB/gf3kpJX97myEK3myy/h8GEq73+AOMv3z9Cufk1+eld/D3oEeWDW9brLPVUNpEdGqvsnQ0a6tRzLihXwxx8NrrotQQXDI/zcatedt25n8mT7siuvhAkT0IqLmfLBiwAcyy6hUkpBNq64GLy8HPNT3LxEiO+//55+/frRt29frrjiCj799NNmXZheuHAhzz//PC+//DJbt26le/fuvP/++7XW+/fff0lOTmbVqlW8/vrrPPnkk5x77rn4+/uzceNGbr75Zm666SaOWc7lKyoqmDZtGt7e3qxevZq1a9fi5eXF9OnTKa+S6Lp8+XJiY2NZvnw5n3/+OQsWLLBdDPj555/p2rUrzzzzDCkpKaSkpAAQGxvL9OnTueiii9i1axffffcda9as4bbbbrNt9+qrr+bo0aMsX76cH3/8kffee4/09Pa5kNShAukt4erqio+PT7Uf0Xzrj2QC0D/MG9fd9hO9pgjxccN87rmYnF0wHNjfPtMld+yoHjz/66+2f80WSnj7EwYu/g5d0zB8vVBdQW6ivmHe+Li7UFxuIjEgXC2U0i6iDVWYzKTmq8BZN1MRfP21euCOO6qvaM1I37AByuxd4q0ngR77LAG3qhfkOlvD0fh4mG9p0vrss6QXlrE/RWWZTOgT1GA9SUBl/n/4IXh64rx2DROX/4yuw37JShftJCGrGF2HIC8jPqVF9otgAwc2+twQHzeigjxJ6zVALejsddLLy2HvXnV30BA2x6uZKRN6B9mbiNWlRmkXgGjLBcsjGUWOzfAVncpBSxB8oFakSrQ5OTV8ruDrC6NHAzAhQZ1brDmUSXmlgwI8HTGQbmk0mtprAGG+dQTOGuLsjDZlCgDdt66TOumi7cXGAuC8fRuh+3fRI8gDf09jszYxPjoIUBfmMgrs5w+YTGCtQlA1I/1kCKTv2mW//+ijYK57H1haYWJ3Uh4Ao6MCa58n1BVI1zR47z0wGHD9/Vf6bl1FpVknveq/rTjhzZ8/nyuuuAKA6dOnk5eXx8oqs8Ib8/bbb3PddddxzTXX0KdPH5544gkG15EUExAQwFtvvUXfvn259tpr6du3L8XFxTzyyCP07t2bhx9+GKPRyJo1awD47rvvMJvNfPLJJwwePJj+/fvz2WefkZiYyArr+xXw9/fnnXfeoV+/fpx77rmcc845/PPPP7bXNBgMeHt7ExYWRliYKgv54osvMnfuXO666y569+7NqaeeyltvvcUXX3xBaWkpBw8e5M8//+Tjjz9m7NixjBgxgvnz59ebgN3aOlQgPSwsjLS0tGrL0tLS8PHxwd29GQcWollMZp1j2eoNN8q1FDIy1MFxIxlnVY0YEkXCCFXLrPTb79tknNVYG9gGW7K4OmggvXTXXno+eg8AOXc/gHbWWc16vsFJY1QPfwB2uFr+VslIF20oJbcUk1nH280Zn4WfQ2mpyiofN676in36qLrnpaWq6ahFqI8bfc0FeGZnoDs5wZAh9ud0tkD6s8+qOvJTp6JPmMDqg5nourpAFu7bxO+0Hj1UjXXglHdexDs9mX0p+RI8E+0izlIfPTLQ0xYEpls3FSBrghGR/mREq4x089ZOHkg/cEAF03192eviR2mFCT8PF/qHNZIAYs1IT0mBAmtGsDtGZycKSitJy5eTZdH2sovKySgow0nT6JmoeinRvz94eDT8REud9B471uPr7kJBaaWtL0C762iB9IIC9JgYANJ6D7KUx2vkAntNlvOK7tvWSp100fYsgXSAU377qlnZ6FYhPm70CfVG11WzbZvdu9V3nLe3ikFYA+lHj9YbeD5hVA2k79wJ331X52rbE3MprzQT7O2qyulVlZtrLw9nTWayGjQI7r5bPfTOsxjKSknOlRkqjfLwUHXrHfHT2HdnFTExMWzatInLLrsMAGdnZy699FLmW5O1mriN0ZYL21Y1fwcYOHAgTk72EHFoaGi1gLvBYCAwMNCW9b1z504OHz6Mt7c3Xl5eeHl5ERAQQGlpKbFV9hcDBw7EUKWXSnh4eKOZ4zt37mTBggW27Xp5eTFt2jTMZjNxcXHs378fZ2dnRowYYXtOv379apW5aSsdKpA+btw425UJq6VLlzKuZgBHtKrMwjIqzTpuLgb8Yywnyv36QTMuXoT5upEz/VwAKr5r4/Iu2dlgrX/00UfqSuyuXdUPkDuC4mJMl1yCS2kxqSPG4f/ycy3azIBwH7xcnUkOspTFyMiAvLxWHKgQdsdy1YlYN28XNOuUrzvuUJ+zqjStzvIuAKNyVT3DnK5RFDm72h/oTKVdDh+Gzz9X9599loSsYhKzizE4abZsnCb7v/+DU0/FUFTImW89RW5ROcl5Dm7aJk56uq6TYKmPHhXk2eT66FV19Xe3lXfSDsaoUmydlaWsiz50KFsTcwF1ocGpoT4JAH5+EBKi7ltmpDkbnOhhqaku5V1Ee4hJVRdxIgM9MO7coRZWOXmtl6VOutM//zDWkhiyPTGHCkeUHah6npCSAll1lJZoT9u3o+k6+cHhuHUJa7zcW10sFyq67N1GZmoOZZWmVh6kEBYlJdU+Q31W/UWUuWXf6eOiA3HSNI5kFNkDvtaZ5uPGqb5KEREqsa+8HGokWp5QCgrgyBF1/6671O3jj6tEmyrKKk22i4yjowJqX1Rbs0ZdUOjdu+7Z7U8+CV264JmUyOjvPpLzhKbQNPD0dMxPMy6azp8/n8rKSiIiInB2dsbZ2Zn333+fn376iby8PJycnGolWFXUeH81lYuLS41/Iq3OZWbLxa3CwkJGjBjBjh07qv0cPHiQyy+/vMHtmhu5QFZYWMhNN91Ubbs7d+7k0KFDREdHt+jva01tGkgvLCy0/dEAcXFx7Nixg0TLFJ2HH36Yq666yrb+zTffzJEjR3jggQc4cOAA7733Ht9//z13W66wibZhrX8a5uuKZq3f2cT66FWFz5uDyeCM98F9FO5pw/Iun32mvsyHDIHzz4dRo9Tyv/9uu9dsrtJSKm+4Ac+D+ynyD6J0wZdoTaiLXhdngxODu/pS7ulFWYAlACflXUQbsdZH779pucoCCQqCSy+te2VrIL3G1LKgg+qCXFqvAdUzzzpTRvrTT6tpqmefjT5mDJvisgEY0tUXXw+XRp5cg8EAn3wCRiORm1bS79/f2ZskF9NE20ovKKO43ITR2YkIP3d7IL0Zs9U0TWPwiD4UBoag6TrlnTkr3XIsnN17AAWllXi6GhjQ1KaCNeqkA/QMVoH0IxJIF21M13UOpatAep9Qb3uZpqYE0seOVfVgMzPpmx5nK1e41xElyix1V22ss2wcxGwp65LeeyAjewQ0flGtLr16QY8eGCoriNi1yXYMJ0SrswSDy728Sek7BENFOdqnn7ZoUwGeRgZEqO+/tYczVRDQUiqC8WqGOy4u9t5MJ3J5F2tvmS5d1EzVkBCV2V/j325PUh6lFSb8PVzoVddFtbrKulTl5QWvvw7AkN+/ISWrUGavngQqKyv54osveO2112oFlCMiIvjmm28IDg6moKCgWhNOa/zVqm/fvmyuMoMcqPV7SwwfPpxDhw4REhJCr169qv34NnH2KoDRaMRkqn4hePjw4ezbt6/Wdnv16oXRaKRfv35UVlay1dJrBFTmfW5u7nH/XU3RpoH0LVu2MGzYMIZZgrL33HMPw4YN44knngAgJSXFFlQHiIqK4n//+x9Lly5l6NChvPbaa3zyySdMmzatLYfZ6aVamtOE+bjbm4E1sT56VRFRXcgYdSoAaZ993VrDq85sVnXAAG67TV3Nmz5d/X685V2ysuDjjyEnp+XbMJngiy+gTx+cv/4aXdPY8OxbRA7seVxD6+KnZgfkdLE0XpFAumgDFSYzqZYLa+FfWqaL3XQTuLnV/QTr1MK1a6Gy0rZYs3x5p/cawM5jufYMqfYIpFdpbNKmcnLqn2q6f7991swzz5CUW0JSbgkGJ42RzWkkVlX//mD57pz8/vMci0lwXJ1Z0SnEW8q6dAvwwOCk2U8Gm5GRDhAd7EVOX/Wc1BXrWnWMJxTLfjEmVB0PDOvuj7OhiYfh1jrpVQLpUUGeOGkamYXl5Ba3035PdEqZheVkFZZjcNLUBRzrSWvVZuL1cXGxHSsY/lnGiEiVlb41IQeTuZ2DPNZs2r591a2Dy7vkr9kAQM6AIfQL827ZRjTNVt4lcutaqZMu2o6lTENOWHf2XDBXLXv//WrH/3X68UeVlFOjnMmYngE4O2kcyylRF4CsGemnnWZf6WSok24t6zJkiAp2P/aY+v3pp20NJytNZrYmqPhDvRfVGgukA1x4IbqvLx552fju2kZOccuykkXHsXjxYnJycrjuuusYNGhQtZ+LLrqI+fPnM2bMGDw8PHjkkUeIjY3l66+/tjXytLr99tuZP38+n3/+OYcOHeK5555j165dzS8nVsPcuXMJCgri/PPPZ/Xq1cTFxbFixQruuOMOW0PSpujRowerVq0iKSmJzExV8unBBx9k3bp13HbbbezYsYNDhw7x66+/2pqN9u3bl+nTp3PTTTexceNGtm7dyvXXX99uJcHbNJA+efJkdF2v9WP9j12wYEG1IvTW52zfvp2ysjJiY2O5+uqr23KIAntGerivm732Vgsy0gGMl84GwGvxrxSWNfLF2hJ//aWuiPv5gXW6iDWQvnRp41/mDbn8crjxRhg50v7v0BxLlqjsnHnz4OhRCoLD+N+jb9Dz0vOOeycV7O2KpkFWmOWAQuqkizaQmqfqow/asAyXNatUJvTNN9f/hIEDwd9flWuo2kjQcr900FDKKszsPmbJnrYG0lNSak1pbBW//66C/m++2frbrurnnyEwUB3g33+/ugBZNevjqafU7xdcACNG2LLRB0aoMk0t9sAD6EOG4J6fy6lvP8vhdMlEFW0n3lrWJdBTvZ9bUNoFVFa66yiVuVqycUv7B886Al23HVfEdemF0dmJwV2anqlTV8NRNxeDrTGhlHcRbemQpclojyBP3LIzISlJBXCbmnRjKT/CsmUMjPDBw2ggv6TCVi6mXRQUqLq0VcfjwEC6rutolgsSvqeNbfpFtbpY/p5IqZMu2pIlkJ4X0Q2nSy9VwfGjR2Hx4vqfc/QoXHedSla7917VV8nCx82FQZbvwV3r96h1DQYYM8b+fGsgPSHh+Mb+n/+o85njiRO0VNVAOqhYQ2SkOhd65x0A9ibnU1RmwtvNmf51zVTLzbUnO9asj16Viwva2WcDEL3hX6mTfhKYP38+U6dOrTO7+6KLLmLLli0cO3aMr776ij/++IPBgwfzzTff8NRTT1Vbd+7cuTz88MPcd999DB8+nLi4OK6++mrc6kuWayIPDw9WrVpF9+7dmTVrFv379+e6666jtLQUH58mzroEnnnmGeLj44mOjibY0gNxyJAhrFy5koMHDzJhwgRbQnZEldJGn332GREREUyaNIlZs2Zx4403EmIth9jW9JNMXl6eDuh5eXmOHsoJoaisQn99SYz++pIYvSQjS9fV6Z6uZ2a2aHvmtDTd5OSk66CvX7aplUer6/qMGWp899xjX1ZRoev+/mr5unUt2+6uXfa/HXTdzU3XP/+8ac/dtk3XzzzT/lxfXz323sf0N3/fqX+zMUE3m80tG1MNC9bG6Wuuvku9xrx5rbJNIapadzhTn//5Mr3cy1u9zx58sPEnnXeeWvfVV9Xv2dm2z8K+vfH660ti9A9XHtYrKk26bjLpuouLejwhofX/gDlz1LY9PHQ9Kan1t6/rul5SouuRkdX3F6Dr/fvr+rPP6vrvv9uX7dypp+SW6K8vidHfWHpQzy0qP/7X37JFN1v2satfm3/82xOiDiXllfp/l6pjg7yScl1PSVHvaScnXS8ubvb2Kn/6SddBT4vur+8+ltsGI+7gjh7VddBNBmf9zcW79FUH05v3/B9/VP/+Y8ZUW7w9MUd/fUmM/t2mxFYcrBB2ZrNZ/3TNEf31JTH6gZR8Xf/jD/Ve7Nev6RvZs8d+bF1Som88kqW/viRG/3xdXKsdIzfqwAE1Bh8fXf/qK3V/woT2ee06HD6cpJs1TddBL0lKOb6NZWXZjgs+WrhSLyitaJ1BClHVrbfqOugbL71R7Qseekh9jqZOrXt9s1k9VvVY+Z13qq2SV1Kuv7nsoL744dfV4yNGVN+G9TVuv73l487IUMcuoOtLlrR8Oy116qnqtb/+2r5swQK1zN9fr8zK1j9Zrfax2xNz6t6G9dyid+/GX+/bb3Ud9Mzu0frfe45z33ISKSkp0fft26eXlJQ4eigdxtSpU/UrrrjC0cNodw29F5oTS+5QzUZF+7Nmowd6GXHbb6kV2K2byrZsAS0khLLxEwCo/P4nCkpbMev08GF7+ZZbbrEvd3a2NTNqcZ10S00xzj4bzjlHXTGfN081+Ssrq71+RQUsWqTWHTFCZcO7uMDdd1O4L4b/zbgKk6sbY3sGHnc2ulWojxu5EZbSLpKRLtpAcloOZz9/Fy6FBXDqqaqWX2OsmRHWhqPWjImoKPr06463mzNFZSb2peSrpkHWhqNtUd5lnaVsRHGxauTTFt57T2XGRETADz/AxReDq6sq5/L44zBzplpv9mwYMoRN8SobvW+Yd/Nro9dlxAjK71J9Q4a++Ah5GcdRikqImp59FoYNQx8/ngsfvJaLnrsdn+uvsc9M6d27WY3IrQyWWsqB8YfYfii189XttGSjZ3XvCW5uDOvu37zn11HaBex10pPzSigud0CmnTjppReUkVtcgYtBU02HrWVdmlIf3WrAAAgPV8fW69YxpKsvRmcnsgrLiU0rUI25+/VTpeTairWsS0SEfVbNnj3VZ5O1E13XSVi2Bk3XKY3oiltE2PFtMCAAzdIvqvu2tSRmSVa6aH36IVVWNC+iOyHeruq4wMkJli2DAwdqP+GDD9Rj7u72JpsvvlgrK31AuA9d9lr2K1XLukDrlHZZvNheivGPP1q+nZYwm+1l8awZ6QBXXKH2izk5ZD/9AvklFXgYDQyMqCeDtyllXaymT0d3diYwMZaC3W3Ys06cUIqLi3n99dfZu3cvBw4c4Mknn2TZsmXMmzfP0UM7YUkgvZOz1kMO83E7rvroVbldegkAw77/hMNf/Xxc26rm/ffVAe+MGaq5TlXHUyc9JcVez/jxx+G331TdMk1Trzlxoj3oFxcHjz6qvtgvvFB9Ies6XHYZxMTA66+zpUCj0qwT4edGZKBHy//eGsJ83cjtIoF00TYqTWai//MsYQf3YA4IgG+/VReHGmNtOLp6teoRYN2PDBuGwUljuKUe6pb4HMxmve3qpB87pg60rReuPvsMrM2TW0tuLjz/vLr/zDMqiP7DD5CWpl7vzDPVSYW7Ozz1FJmFZcSmF6JpMKpHMwNnDXB97lmKQiPwzkwj/8+lrbZd0cmVlKjvvh07cN+8kchta+m+aon6fvz1V7VOC8u+0b07ekAABlMlTnv3EptR1PhzTiaWQHpmz34MCG9BiSfrMU9Ojpoib+Hj5kKIjyu6Dkc627+paBcHLWVdooK8MDo7Na/RqJWm2RNeli7FzcXA0K5+hMbswm/qZLj6anUM/fHHkJ3dquO3sQbSw8NVjXSDQX2eajYgbQdJuSUYtqljJefRo1pno7byLuukTrpoEyZLf66irpH4ebio8iTW5BFr/zKr2Fi47z51/6WX1E+XLqos1Pz51VYdFRVAxF61X8kZVuPz0BqlXazHLwB//tny7bREQoIqK2U02i+Ig9r/WM4n/D56F4/sDIZH+uNSX4mn5gTSfX0xW5KcgpYvkYvsAlBlFv/44w8mTpzIiBEj+P333/npp5+Yav1uFs0mgfROzl4f3f2466NbaVdcQUW//njmZjHshjmU/d9t6gT9eBQX27tbWxoMVGNtSLtpU7WTzCZ5912VYX7qqTB2rAqEPfEE/O9/qv7zpk2qodLUqdCzJ7zwAqSmqq7bDzygMsS+/hqioigsq7TVg27NbHSAUB9Xe0Z6Rgbk5bXatoXI/fp7TvnlCwC0BQvsAe/GnHIKeHurIPOePfaTbEsTskERvrgbDeSVVHAwvcCekd6MBiRNYs1GP+UUlQ2u6+ogvjWzzV56SZ3kDxigZqxY+fqqQMCSJepk/dAh6N+fzZba6L1CvAj0cm29cbi7UzhiNAAVu3a33nZF57ZzJ5hM6EFBLHnmXf564GWy//MmvPaaunD05JMqm6wlNA3Nsk8Iid3PlvjsTpWVXrZF7Rczovvbmi02i7u7fZ9cIys9OtgLkDrpovXpum6rY943TL3PmtVotKoqddJJS2PM8/dz+e2XELR3O2ZPLwgIUN/X1tltrc0aMI+IUL1UevdWvzugTvqW+BxCD6nXdR41snU2amk42n3bWo5mFnaq/atoB5WVGBJVMNu5Ty/7+e2tt6rbBQtUwBhUUs28eercfcoUdd7u6gqPPKIer5GV7ltZSnBcDAAbw/tVf93jzUgvLq4+Wz0mxlbrvV1YE3oGDqydnHT++RQNG4lLaQmnfvchQ7rW0zelqfXRqzCcfz4A0eulTrpQ3N3dWbZsGVlZWRQVFbFt2zZmzZrl6GGd0CSQ3omZzTpp+ZaMdN/Wy0jH1xeXrVs4cunVALi+/y6MGnV82aFff62+SHr2tGefV9WlCwwerA7ClzYjQ7OoSGWdg2qCUtWMGeqEYdgwyMyEf/5RWTVnnaU6kB89Ci+/bD8YBzbHZ1Np1uni5073gNbLRgcI9nLF5O1NkZ+l7I4lM0CI4xYfj99tqnRD7Lyb0awZJk3h7Azjx6v7K1dWy0gHMDo7MaybHwBrD2dh7tJGpV2sgfTx41XA22hUJ+yNZZ/Ex6uAu3Xc9Tl61N7E9KWX1N9dl9BQ6NKF3OJyYiyZfKN6BDT972iqgQMBMOzf3/rbFp2TJUBWPmwEe8dOJXb6LHzvvh3uuUfN1nrqKejRo+XbtwTeQg/vIyWvlCRHntytWqVmkn33Xbu8XOX2HQC4jByGv6exZRupp7yLNZCemFVMeaW5pUMUopb0gjIKSisxOjsRGeipjoWtAa3mJt2ccYa63boV+vTB5fMFAOybej5//bQcLr1UPb58eesMvqaqpV2genmXdpRRUEZcZhEhlkB6szL7GzJ2LLqXFx55OXjs201OcRs0dBed19GjaJWVVLq44BPdw778jDPUDI+CAvjqK7Xsv/+FtWtVks2nn6oENVBNR+vKSt+wAc1sJi+sC/s1b1tsAlBZ76CSWApbcLF42TKVzNe9uz0I3Z5Z6TUbjVZRZjLz75V3AjBw8be47q+nDMuaNapETO/e6t+vKc47D4CIvVtJi0tu9rBPZnKRUbTWe0AC6Z1YVlE55ZVmjM5OBLoAey010o8zIx0ADw+8PnqfX577iCL/ILXt0aNV12xzM0/0dN3W1Zr/+z/7F3JNLSnv8sUX6su5Z0+wXL2tJipKHQw89pjKUo+NVVe2L7pIBeqqKCitsGWjj4tu3Wx0AGeDE0FervbyLhJIF62hvBwuvRTn/DyS+59C4RPPNH8bVQ9OrXUSq2SrDevuj7ebM/klFSR6Wi4EtVUg/dRT1ef2jjvU7/fdB5X1TGs8cEDVY3ztNfU3bNhQ//affFJl0EyYAOee2+hwtsTnoOvQI8iDUJ/j64heF9ehgwFwPxwjB4WidVgC6Rl9VICpW4AHBqdW/B6zHFtEJqh9xJb4Vqzvf/SoPRuuIXv2qKnokyap8lU33tiyk/NmyEvPxjMxDoDoqeNbviFrIL1GabcgLyO+7i5UmnUSs6W8i2g91oBWhJ+bKjlgnXHWuzf41FPLtz4REeoCsK5Dfj4MH07hPytZ+uCrxDj5kDvG8tk4iQPpuq6z9nAmxqICAo7Fq4WtFUh3cUGbMgWw1EmX8i6iNVmyuPPCuhHiXyVRzMlJnZuDmuG9d68qgQoqoF714nt9Welr1gBQOHIsABvjqpR38vFRsz6hZecN1rIu55+v+qBBhwmkb0vI5fCg0RwdNxmnigp1bpGaWnsbzSnrYhUZSemAwTiZzRjau5xNB+VimRFQXCz7xs7O+h5waUoJ2wZIIL0Ts9ZHD/Vxw+nAflXexM/PfvX3OIX4uGGceS5ffvg7KZOnqYDd/fer6Z3N2YmtW6ey2d3d4Zpr6l+vaiC9KcF6s1l9yYNqgmIw1L2eu7tqwPb00ypAV4/N8dmYzDpd/N3p6t/8ZmxNEebrKg1HRet66CHYtIlSb1/+eOR1uoY08+QY7HXS//pLnSSHhakfC6OzE+N7BQGwz2DZfmuWdikutmeUn3qqun30UdU0ef9++OST2s/ZuVONOylJTbcsKFCzTeoKpu/Zo5qhAbzyir0Oez0KSitUc1VgdFTLGjc3xmuYOij3T4glv7i8TV5DdDKWQHp8ZF8AogI9W3f7lotrPgf34WSuJC6ziJS8VshK//13dbIeGKg+w2+/rWaaVJWYqMovDRmiGo8ZDOrkPD/fnkXXRmL/URf5ikPCCInq2vINWWe/1chI1zSN6BCVlX44XQLpovVkFpYBEGQtTdaSRqNVPfGESqr56CPYtAmv0yfaSsZsirQEmnbvVuULG5OfrxqUXnhh0167AwTSYzMKicssIizWMpMsMhKCglrvBSzlXSK3SiBdtC5zlUajod41kkPmzQNPTxVEP+ssdb5/9tlw7bW1N1RXVvratQD4TZ2MpkFseiHpBVWy0ltaJ91kUscHoALpM2ao+//+e3wlZ8vK1LlAXQ1Wa7LOxq8RSC8sq2RbokomKJ//mfp+T0xU46wZI1m5Ut02J5AOcJ6aXRz4799UmGS2msFgwM/Pj/T0dLKysigpKaG0tFR+OtFPSUkJWVlZpKen4+fnh6G+2F8TNbPbkTiZJFtOYCN83WDNDrXwlFMaDRI1x7joQA6lF/Dtw29yzax/8HvofvUF9vnncMstTdvIBx+o27lzVQ3F+owfr77I09LUFeDGStT8/rsKRvv5NRygb4L80gr2JKnA2bhWro1eVYi3m2Ski9bz55+2i0l/3/cipm7dCWhJ2YGRI9UFJ+uBaR21U/uFebPzaC45AaFqQWtmpG/erLLOu3SxH3D7+aks8jvuUCfvl19uz6DbuFFdeMvNVWP9+WcVZFuxQp0E/P03jBtn3/5DD6kLbxddpPooNGJrQo7toloXv7a5qObcpzcmFyMuZSXk7D2I7+jBbfI6opMoKbHNSjvQRWU+Rwa1bnkyevUCLy+0wkJGlGaw2SOcpfvSuHx0d5zra7DVmKwsuOEG9fk0m1Vpt6VL1ed+4ECVfV5erjLlylRQkIsvVk2+/voL7rxTzXi76aZWPfaxKiyrpGD9ZgC0oacc38bqKe0C0DPIk20JOSRkFaHrepsdg4jOJaNAfWaCvVspkD57tvqpYmSPAPanFLCv0pUzBgzEsG+vChxdfHHD2/rtN1Xv+OBBldnq1sjMr6rNRsEeSN+7V+076pvt2krKK82siFEXCEZkWGo0j2yl+uhWlkB6xN6t/JmchXlwOE6tOatIdFqlMYfwAAq6dCfKo0YWp68vXHmlOl9PTlb9xT7+uO7vVGtW+q23qqz0q6+2JbB4nj6JPngTk1rAprhszh1iuegVGakusDW3Tvr69eqinJ+fSpxxdlZ9mo4dU/uYukrFNsUbb6jzgt9/h9Wr61+vsNBej33o0GoPbTySRXmlmXBfN3r266b6so0dq/qyzZunys45Oal+aNaZQE2sj27lOusCeOkFIresIjU9j27hLejPcpIJsyR5paenO3gkwpH8/Pxs74XjIYH0TsyakR7m62ZvNHq89dFrCPA0MiDch73J+Sw79TwufjwfHn5YncA2JZBeWam+XEB92TbE1RVOP119sf31V+N/y+uvq9ubbgIvr8bH0oDNcSobvVuAB91auTZ6VWG+biRYMtL1Q4eQw2NxXCxZ1mlzr+HIuDPo7e/esgCM0agCz//+q36vI5CuaRqT+gbza4I6idXT0tDKy2uVSGqRqmVdqo7/5ptVkOzgQVXX/IUXVLB85kx1gHvqqfDHH+okYPFitXz5ctW82BpMX7lS7YMMBhV8a0RxeSV7klSJpzFRbVAb3crZmeKoXngf3EfJzt0ggXRxPHbvBpOJysAgCoLCCPIy4uN2fFMea3FyUt/La9YwOjuevX5dySosZ3N8DuOiWzhz48471cXz/v3VieeSJeoYYM0aFSCzlqwDdRL68sswZoz6PTRUndDv3av2C5ayCK1F13WW7Usj+rDKWnMb1czmjDVVLe2i69X2deG+bjhpGsXlJorKTXi5yuG9OD66rpNZqGY72TLSazQTbw1BXq70DPbkSEYRx04ZS+S+vep7uLFA+qJF1oFCXJzaB9RH16s3GwWIjlbnDcXFagZLz57H+6c0aFNcNgWllfi4u9A93pKR3lplXax690aPjMQ5IYHg7RtJO7UX4b5tczFfdC4VB9UsaHNUz7rPE2691Z749u679s9ZXa67Th2PJyWpi95FReo4fOBARhdXEJNawKG0QjILy9S+p6UNR61lXc45x97o8+yz1YyYP/5oWSBd16tn0mdkQHBw3evu3WufpVtlneyiclvy3Wm9g9S/Z+/e8MsvMHWq6sP26KPqQoO1PnqvXk2vj26hjRhBaXAYbhmpFP21FK6Z3fiTTnKaphEeHk5ISAgVFdJHojNycXE57kx0Kynt0kmVVpjILlIHyGG+bvYsk9aoj17DmJ6BGJw0jmYXkzLWcjX1n3/s2WEN2bQJcnLU1e0mZIIybRoApr/+IjWvFJO5ntrBW7aoZmPOznD77U38S+qWV1LB3mT1hTi2ZxsGzoAADyOF3VV5Gf2QZKSL45SUBED84NEAdPU/jotA1vIuUO9+JNzXnR79ulPpYkTTdXTL6x83y7RQW1kXKxcXNf0S0P/7Xyo//FBN7SwsVBfd/v7bXnvR01MF4KZMUWVepk1TAfoHHlCP33CDaqjUiO2JuVSYdEJ93Fq94XBNFf36AWBu52Zp4iRkOQbI6TcYNI0eQa1c1sXKEoAz7trBlH4hgCqLZi0h0Sy//goLF6oA/YIFquH4vfeqoHh6umpSftll6jP/xx8qOGcNooP67F91lbpv7cPSinYn5ammgkdU0Ew73uOrHj3UBb3iYnt2LYCu45yZQe8ju3HPzSazoAX/lkLUkFdSQXmlGWcnjQAPo+onFKdq/bdmIB1gtOWi885els9IY3XSS0qq1zm2Zn3WJz/fXi7BmpHu7GwPvrf2d6iuq2DdSy9BQgJZhWVsTVBlHCb3DcbJekGitTPSNQ3tzDMBS3mXLCnvIlqH4Yj6jDn36V33CoMGqUD6m2/CnDkNb6xqrfRPP1W3p54KTqoXWO9QS7kna630lpR20XX7xbYLLrAvt5Z3+eMPy2o6WYVllFaYmrbdNWvspVV13Z7sV5d66qOvOZyJWdeJDvGqft41caI9SP/SS+rfpiX10a2cnCg8S10sMP7ZwDg7IYPBgJubm/x0wp/WCqKDBNI7LWs2up+HCx5FBWr6E6jyKK3M192FwV1VsGqFewR6WJi6+mwNfjXE8kXHtGn11zC3KCitYN8QSyBtzVp+XLGP+WuOsPpQhu2igc1rr6nbyy5r9hXemqzZ6N0DPI4vENkETk4aLn3VQYxTRro6ORCipSzBmGNu6vN5XLX9q045bOAke3zvYAqD1XSqxJ0xLX89K7O52v5L13Vyi8s5nF7A+tgsfoscScopY9BKS3G++WYoLcU042x18FtzJoqnp8pMtwbTJ09WF/M8PVWZmEak5JWwzXKyPDoqoM3LKxgsU9ONMfvb9HVEJ2AJpB/tqQJLPVq7PrqVdd+wfTu9Q7zoGeyJyayzdF8a5voufNclO1vNJgPVe2X06OqPBwTAZZdx9J1PWPnG5yyLHM5fe9NYvCuZRduT+HHrMb7bnMj2mZer9Rctan62WwNyispZdTADzVRJcLzlpLvG1O5mc3GxZ80+8ogKVowYoS4IhIVx9k0XMfuey8isWltWiBayXtwK8DKq8iDWPiTR0apUQisK93VnaDdfjg0Zha5pqrdJXU33rJYtq15HuLFAuuVYx+Try+c70nl/RSxfbkjgaIT6PCWu2sSepDyOZhc3bz9UF11XF+Bvugkefhg9KorKM6bS+9/f6eXtRLTRZA/EtfIFCUDqpIvWp+u4JsYD4Nm/T/3r3XSTyjBvyrGvtVa6VZX4g/XC2sG0AnX+3pKM9P37VQlUo9GWZAfAGWeo79LYWA6u3sZXGxL4Yn0CH6yM5euNiaw+lEFcZhFllfUE1q2BbnfL+ZI1670u1vroVb77k3JLiE0vRNNgfF0z8a68Eh5/XN2/6SaVLAAtC6QDzhdeAEDI8r/Rm9I/TgjRZBJI76RSLIH0cF83Faw2mdTV5OjoNnm90T0CcDFopOaXUTDpdLXwr78af6I1kG7ttF1DXkkFW+Kz+XZTIp+sjuPvUk9yIiIxmCrpuXsTRWUmtsTn8Pm6eL7bnMiepDzKjsTBDz+oDdxzz3H9XXnF9mz0Fk9Nb6agiGCK/CyvJQ1HRUtVmeqc5x+Cu9FAYEvqo1uNHasaf40Z02DDYm83F7Ru3QA4su3A8TfAOXgQsrPR3d3Z6t+d91fG8tnaeH7fmcKGI1nEZhTx7/UP2FefOJ0P73yF1UcLKC6vrL09Dw97MN067e/ee6s1T61LfmkFv+9MptKs0zPYk+jgNgpEVh2qpeGoz5FDlJQ3MZtGiLpYAunHogagaZaZam3BmpW9fTuarnN6vxCMzk6k5pWy/Wiuujj8yiuNX2i/4w57SZennqr1cHF5JX/uTuHHrcfYlpDD7qQ89qfkcyhNNfs7ml1Mcm4pK5xDSB42FsxmdOu09ONkMuv8tTeVCpPOgIJUnMpK1cW41ji+ss6K+eILVcpm2zZ10c8SuAg4Fk/Jnn3H/zqi00u31kev2Wi0LYK/wMTewfh1DSOjp2Wm1b8NZKX/8ou6tSbYNBJITz9wBIBcv2Cyi8oprTCRWVBGfLia4Vm8bSdL96Xx49Zj/LD1aO3km6bSdXW88J//qN9HjULTdUI3reHsF+/l3OkjVb8ngKgo1SC5tZ1xBrqmEZRwiPzYeMorJXgmjo85NQ2XkmJ0TcNvYOMzM5ukalY6wGmn2e6GeLsRHeKFrsOSvalUdFHnDM0KpFsD3GecAd7etsXFru7kjVQz3JO/+YnMwnIMThq6Dmn5pWyJz2HR9iQ+WHGEbzclsvFIlv08JT/fHj+wfsaXLKm/cWmNjHRd11l9UPVJGBThS6B131rT00+rC+WVlfaSVM2sj27lM+MsKtw88MpMI3ftphZtQwhRNwmkd1Kp+WqnH+brXvfUp1bm6erMsO6qycXOAZYSLY0F0lNS7BkwVa8mA2azzsYjWSxYG8/qQ5m2CwMRfm5UWKY1Tj+2g5lDI+gZ7ImTppGcW8rSfWnse+h5MJkwTzn9uGrCl1aY+GtvCmZdJzLQg4g2aipYU5ivNBwVrSAvz3bwVxgQTBe/FtZHt3Jzg337VNOgRrbj3VudvDqnJNmmO7eYJeCW0W8Iq+LyKKtQU9FDfFwZGOHDpL7BTLxsOuVfLiTruZfZ9tJ7lDm5sCU+h0/XxLH6UEbtgLo1mD53Lpx5Jtx3X4NDKK808/vOZIrKTAR5uzJ9UFi7NPtzGawy0gMSj5CRV8+BvBCNKS21lTZI7zMQX3cXXFra/LMx/furE+j8fDhyBG83Fyb2VrVDd6/fjenU8fDgg+qk+oYbVGm3mqqWdPnss2pNBnVdZ09SHp+vS+BAagGaBgMifBgXHcjEPkFM6RfCmQNCOXtwOGcOCMXH3YWtM1Vgq/z9D8nKzDvuP3FTXDapeaW4ujgxIUcF8Bg+vHWaGT70EJx/Ptx4I7z6qjp+27sXiospOVUFIoxrGmh+JkQT2eqjt1aj0UY4G5w4Z3C4urAFpP32Z90rVlaqRqMAl1yibusJpGcVlvHrjiS2bVC9EoqDQjg1OpArxkZy4bAudJuoZrJ0SYqlR5AHRmcnknNLWbghgc3x2c3LTtd1uPtuWwN33n+f0rXr+ebb5ay/4jbKIrqi5eXZS0G00b8jAQG2kjFdt6wjOVeODcTxydur+nwUBofh7398PcWque46VZItKqrWrLLTegXh5mIgJa+UZcWWfdCxYyrxrymsgXRLbCOnqJyl+9KYvzqOHYPV7PXorauZ2CeIGyf25PoJUUwfFMagLr74urtg1nVS8kpZF5vF1xsTScsvVRevi4tV0tAtt0C3bur3f/6p/fq6XiuQfji9kJS8UlwMGmMbSr7TNHVsM26c+j06WjVJbQEnD3fSx6kgfNlPv7RoG0KIukk3ok5It3w5AIS7Yq8z2IaBdIARkf7sPJbLnoGjOU3T0HbvVjWa6yutYg20jxoFISG2xXnFFfy1N4XkXPU3dPF3p2+oN9EhXqrB1iUXwOef4PT33/R6/316+Rkp3riZrCUrMK1ZS9d1qiHiv2dfweD8UkJ9mp95V1Ju4pftSaTlq5PliX3qaTTSBkJ93DgWEUmXvdswxRyk9So9iU7FkuVQ7u2LydXt+Mq6WDUxeOxkyUj3ykhlbXw2AyN88G5BY0Nd18leuoJAIL7PUIzOTkzqE8yAcB81Fb2qKy4nELhU14nPKmbDkSxS81T2yc6juYyOCmRkpL/9eR4e8NVXTRrDkn2ppOeX4WE0cN7QCFyd2+lTGR2NycWIS1kJufsP0j24bTIFxUlu926orKQiIJCC4HCi68uSag0uLuqkcvNmlU3dqxeDuviQumoD4+64CkNWOrqPD1p+Pnzyiepb8NZbKmCmadVLutx3X7Wa59lF5Szbn0ZSjgocBXu7cuaA0Aa/4/uGebMl+BIK3n8e74xUVrz6Ee7XXc2YngEt+hyn5pXa6rqe3i8E9282qwesJ8THa/z4ekvwaZMnw7o1+G1ej8msY6i5DxSiGTJqZqS3QaPRmnw9XAg5bzr8+Blua1YTm1FIdHCNwN3atZCVpQLG8+bBt9/CkSPVVikqq2R9bBZ7kvPQdRiVpbJAw/pG0a2nCmAFe7vCJLX/8I47zIWDQsk3wT/704jPLGbNoUwOpRVyVrQvQefNUPuem2+Ga68FH5/qY9J11fj47bfV7x9+CDfeyPqYdFIDIii7+R5GffpfWLlCBcjWr1cXCtuIdtZZsHkz3beuITH72rbreSE6haJ9MfgDJd164N2aSSKurqpnmZOT6llQRYCnkfNPieDnbceIMXkxzWDAyZqh3VhQOTkZNm4EwHzOuWyLz2ZdbJatb1rhlKnw0ct03bmJbsFu4GLAzcVA/3AX+oerz3Z+aQWJWcWsj80iu6icbzcd5br3PsQL1D5A0+C881Rj1d9+g3PPrT6GxESVsOTiAv36YTLrrD2cCcDwSP/GG4K7uamLAQ8/fNzxmZIZ58DyP/H4+3/Ay8e1LSGEnWSkd0I5xRWUVZhxMWgEbVyj6pV37dqmB8cAbi4GpvQNodw3gNS+6ups2f/+qP8J1gC/pTGINdPsq40JJOeWYnR2YtrAMC4Z0ZWh3fzsX0qTJ6uaaPHxqtyEjw8eE0+j23OP0WPFnziXl5F8yhh2Dx7Ht5uOsvFIVrOyTorKKvlx2zHS8ktxNxq4eERX1VW8nfi4OVNkaThafuBgu72uOMlYaoYWBgQB6oJUu7EE0kNy0qkw6aw8mFF/Y+B65JdW8Mv2JNUQFCgfM5YrxkYyqItv7SB6FZqmERXkyZxR3bhgWBfCfN2oMKkD3G83H7UFD5pqw5FsDqUVYnDSOHdoBL7uzb8g0GLOzpRFq54J5buk4ahoIUumaV7/IaBpBB1PiaemqFLeBUD780+m3nQxXlnpZEb25uCSNaoZeL9+qnzLpZeqE9bERHtJl3791PRnoMJkZn1sFl9tSCAppwQXg8bEPkFcPrp7oxfKXQxOjOsbisut/wfAkF+/YmuCKge3LzkfXW/6fqm80sxfe9Qstb5h3vQL87H3b2itQHoDXM+YAkCXnRvJKZKGo6LlSitM5Jeo0mbB3q4qIGSdAdnG5woR501Dd3LCPymeNSt2kGcZh411Fu3MmfZSR3FxqjyT5Tzhi/UJ7E5SQfToEC9GuqrEG5duNQJw3burXikVFXDoED5uLlxwShfOGhiKq4sTafml7HzhbfU5jolRGeddu8Jdd9mz4M1muO02FUTXNPj4Y7jxRtILStl5NBeAKf1CcHZxhqlT1WyaI0dstczbhLVO+rZ1pMtsNXGcKg6qz76pZxuUfzUaawXRrSL83Dl3SAROLs4UBKnyinpTGo7+/jsAlaNG82OyidWHMjGZ1ezx2aO6cfbs0yEyEq2srN7Gxj5uLgzq4ssVYyPpHeqFf9xBvHZsxWxwJvciSzPV88+3v17N+uPWbPT+/cmthB+2HCWnuAIPo4ERkf6N/w0AwcEqoaBmkL6Z3M6fidnJCZ8De1u1F4wQnZ0E0jsh6zS/EB83DL9Zpj6df36Ts0mPR/9wHy4YFsGxMRMBSPp2Ud2Bq8pKVXcM4OyzKSk3sXhXCkv3pVFeaaaLvztXjI1kQIRP7RIKnp72WmKbNkFZmapDOHMmvPgirFxJwJrl9An3xazrrIvN4vstR8ktbrwmYkFpBT9uPUZmQRmergYuGdGVEO82qiVbD03T0Hr1AsB0UALpooUsGemFASG4uRjsWWftwRpIz89E0+BQWiELNyaQmFWlMVZFBfz9t71OuUWlycyuY7l8tSGBtLgkAo+qTLTxV5zbrCB21YD6tIFhtpPmbyw1EZsS2D+YVsCGI1mAyj7t0k7lnaoy91fNIbV9UhdZtJAlkJ7WewBQpZRDW7EG4rZtg/ffh5kz0QoLyR8/ke//+zX/lLhTOHoc7Nihmvy6uKhSS/362Uu6LFiA2ehqKeMSzwbLZzYqyJMrx/VgRGRAgxfUanK75SYwGgmP2UXv+H0UlZn4e28qP2w5RnoTm3euOZxBTnEF3m7OnN4vRNUut5TMYezYZv4jNZ82bhwmFyNe2Rnk7ZYGxKLlrI1Gvd2ccXMx2LPRe/Rom7reVfn62vYRIVvW88fuFPv3sa7b66NfeKE6lnB2hrIycg/F8ePWYyzdl0ZphYlgb1dmj+rGeUMjcMtIU8+JiKj+Wpqm+kOB7bOqaRoDI3y5alwPega4MewH1VwwfsoMynr1UZ/rN9+E3r1Vpui8efDee2pbn3wC119PUm4Jf+9NQ9ehT6g3kW3VvLk+Y8di9vTEIy8bbdfO9n1tcdLRjqiLRoZebdNHrSE9gjyZNjCMghD12T24pfFjXd1S1mXz0Ikk5ZZgdHbizAGhXDisiypj6eRk7732RwMJfYC70cA5g8M5Z6uaJX9kzGS+ii9l17Fc9IkT1eyU1FQ1y64qSyA9t1c/Fm5MJMVS7m3awLD2m7VqEdKzKykD1T619OdF7fra4gSzbBmsWKHKL4pGSSC9E0q1lnXxcrHXGWzjsi5VRQZ60meeqmsYsXkNP2yM40hGYfWV1q+HvDzMAYFsDe7JlxviOZyusj4n9A7i4uFdGw6avfmmqrO6YIHKIsnIUH/rQw/BxIm4ebpz9uAwpg9SAbSUvFIWbkxk17FcSivqrr+WV1LBD1uOkV1UjrebM5eM6FZ/o5A25tpfZeG4xB1pZE0h6mHJSC8KCKaL/3HWR28uy7RMl+RjTB8UhofRQFZhOT9tO8bvO5NVBtrzz8P06XD77YDKQF97OJNP1sTxz/50yirMDEywHFD37YsW3LLySpqmMSDCR500B3tiMquLa99uTmwwOz0tv5Qle1MBVbZqUBffFr3+8XIZMhgA90MHpKmYaBlLID2xh7ooc1xNh5vCGkj/5x/4v/9TmVzXXIPXsiX4RYRQVmHmqw0J/BuXy7E7H0DfsUOVM7H0dNDvvZe46EEs3JjA0n1pFJRW4uPuwjlDwjn/lBbOCgkJUZnvwNmrf+a03kG4GDSSckv4emMi/x5Iq/PYQNd1MgvL2Byfzc6jqr76WQPCVPBx0yYV+OvRo9Fmxa3C3Z38IerfVl+xou1fT5y0bGVdvNuvrEtV2hQ1u6LH7k2k5pWy6pAqzcLOnZCQAO7uqn+JszN6jx4A/Lt4PcdqzEixXdy2HO/UCqRDrUC6lZerM+fFbybgWDyl3r4svut53nvnV5a+toDCyWeoz/avv6oScJqG/umnHJk5m+83H+X7zUfJLCizlH4Mau1/nsYZjWp2LhCyfqU0IxctZjbruCbEA+Dev5UajTZT3zBvPHqpmdjpu2PYnlh/b6XCzBzMy1TN8oNjT6ervztXjFGzVaud51hmu/PHH+qz3ACtooLAn74FIO3iy6kw6fyzP50vt6WQNfEMtZI1nmJh2r4DgF0BkdUSAB1RZsno7ETaZDVLxbTo13Z/fXECuf56mDJF9TwTjZIa6Z1QSr4KpEfF7oH0dJX90cJu0C3lO/FUdH9/3HJyCNizg99wYkLvYPqFeZOYXYzr59/TE4g55VRWxaovzCAvI9MGhTUtA7x/f3jppQZX0TSN/uE+dPF3Z8neNI5mF/PP/nT+2Z+Oj7sLwd6uBHkZCfZyxd1o4K89qRSUVuLr7sJFIxoJ5Lcx78H9AHDNzlRXDWvWaxSiMdaM9MAQuvi176wKa0Y66en083elx6k9WH8ki11H8zicXkh8RiE3fPIpboD+8cesOGsOO30ibMe63m7ODI/0Z9i/lhkZ9dQMbg4vV2fOGxpBTFoByw9kkJ5fxtcbE+kf7o2LsxO6rmM2gw6YdZ2ErCIqTCoD9rReDjhRtnAdqgLpgQmHySoqI9y3/bPixQmsrMwWQEqKHoDBScPPo40D6YMHg8Fgbxr23HPwyCM4aRpnDQxl0fYkCkor2Xk0j51H8/B2c6Pv/J8Y+u9vOMXHseT8a0nYngSoknGjowIY2tUX5+NtkHrbbfDllzh99x2jXnuNfqf2YPWhTGJSC9h5NI+DaYWMjw7Cz8OFlLxSknNLSM4roazCfgFrWHc/ugd6qF82bFC37ZCNblU2/jTYugHXtauBu9vtdcXJxdpo1DZTrY0bjdYyZQq8+iq99qoszx2JuexPyee0Lz9jCJAxfgqJGaW4OlcQEBBOBIfxSkqgx+mTOb1vKL4eNY7PWxBIR9fRXnkFAOdb/4+Rg7qzPTGXPYPHsWfwOCKvi2fy39/iv34VyXc/xL/9ppC5Q72OwUmdX4zq4d+i/i+twemcc+B//2Pwnz+QlfcEXYPlPEE0X3ZxOb5JqpyK14A+DhtHQP9e8At4p6ewPCaDskozrs5OFJebLD+VlJSb8Pvfr0yvKCenSw8GnTGG4ZH+dScKnX66vQxsTIya8VafxYshMxPCwzn1titwSy5kfWwmWYXlbBw0nrMX/0Letz8Sd/P99An1IruoHK/N2/ADsqL7M75XUPUeTA5Qfva58PYLuK9brUp1+Tom+Ud0cOnp6raFyWmdjQTSO5myShNZlimbIf9amnmee66aOt2eDAbVDOe77xh9YBO/DhzOqoMZrDqosk7mLl8GwNExk+gR5EGPQE8GdfHF5XhPlOvg4+bCRcO7sC0xl+2JORSUVpJfUkF+SQWx6dXXDfA0Mmt4F4cdGFuFdg2hyC8Qz9wsyvcfwDhmdONPEqKqKhnpUe09syIgQGWUlZTAsWO4RUczpW8Ig7v4svxAOqY1a3FLOgqAZjYT+coz7Hj2A7oFeHBKN196BnmpA1Jr/eFTT22VYWmaRr8wH7r5e/DPgXRi0wvZm1z/9LZALyPTB4U59OCYAaocR0DiEfbllkggXTTP7t1QUYEpIICCkAiCPI1t36TSzU3VCl6xAubPh7lzbQ8Febly7fgoErOLiUkr4HB6IQWllWxJzGNLr0nQaxIUmTE4aZzSzY/RUQEq+7s1jB6tmptv3gyffIL3ww9z9uBwBnfxZUVMOpmFqplpTUZnJ8J83IgM9GBY9yq1T9uxPrqVYcpkeOs/+G9er7Ls2nOmkThpODojndNOA4MBl8QEprgVs6LMg7IKM+Er/gZg67BJ7D+oGvedHtKVCGB4ZTaBp3SpHTTTdXsgPTy89mvVF0hfvVrNKnF1xfmuOxkbGsiw7n7sOpbH1oQcEkJ78PlVD2G4+mFVeqagDKOzE0O6+jKsexOaCba1q66i7JHH8EtOJH/h13DXzY4djzghZSRn0D9PNdC2lhV1iMhIALoWqDjB+tisOlcbumYpAG4Xz2JEj4D6t2ctA7t0qcpKbyiQPl+Vd2LePDQXF0ZE+jMwwofD6YUccZ6G+ZUH8T1ykG3/bmFll0gMpcX837F4AMbPmkJIVAPjaCcBwwaR3TWKgGNxqnzHRRc5ekiioykqss38JCTEsWM5QUggvZNJzy9D18Hb1YDRWh+9Hcu6VDN9Onz3HVFbVzPpkcdZdTADXYfI0hxCjhxA1zROv/NKnEPb/sOsaRojIv0ZEelPaYWJjIIyMgrLyCwoI7OwnKzCMgK9XLlgWAQeRsd/bDyMzqR17YFnbhb5ew4QJIF00Ux6SgoaUBQYQkBbl3KoSdNUeZdDh+DYMYhWdReDvFy5eERXcj9YAUDSoBGE79tBz43LubYyHt8RVZpzVVSok1xotUC6laerMzOHhHMks4iU3FKcNLWPqHrrbHCib6h36wXxWio6GrOLEZeyEgoPHIZI2ReIZrBkmhYOHNo+jUatFi+GwkLw86v1kJOTRo8gT3oEeXJGPzPxWUUcSC0gLqOISrNO/3BvxkUHtc2ssNtvh6uuUjWPg4KgqIhuRUXMLSwkMy2bzMx84k4/B04/gwg/NyL83An2cq19MU3XHZKR7j1lIiZnF7wyUik7eBjXvr3b7bXFycFs1m0JN0FerqomuLUfT3tlpHt7q4taGzZwyuHtDLhyHoX7Ywg4EoNuMOB3yYX0c/OioLQSjwF9YDEEpR2r+8JRbq6aeQMNB9IPH1ZBBHfLxWhLNjrXXAOhoQC4OhsY1SOAoV392J2Uy9aEHIrKTHgYDQzr7s+Qrr6OPyaw8vQk+fr/I+o/zxH05n/g9hvUTCAhmqFwn/rsV/gF4OLILObu3QEIzE5jXHQgR7OL8TA642E04G404Gl0xl0z03PragDcL5nV+DbPPlsF0v/8E+65p+51kpLgL0vi4TXX2Ba7uRgY1MWXQV18qZw4EacVyzllx2pWRnQnOP4QTmYzenAwIb0jj+vPbi0Rfu4cHn4qAcfiqFy5CmcJpIuarNnorq6qCbdolOMjgqJdpVjqo/fOOqYOGo1GmDbNMYOxvK62ZQvD3SrodVoUzk4aHl9+rpaPHt0uQfSa3FwMdAvwoFuAh22Zbqkp0a51pBtR3jMa9myldN8BRw9FnIDMSUkYgLLgUMdkTnXrpgLpR49WW6yZTPgvXgSA0yMPY165DKcPP8D3iUdh2lTVaBBUI8KSEpXd3rf16zZqmkZ0sBfRwR38YMLZmYrefXDdt4eKPXthmgTSRTNYAumZfVUwqd36fjg71xlEr7WawYleId70CvGmrNKE2ayaf7WZSy6Be+9VF/huvNG22AkIsfz037wC7Y4k+76oLocPQ1aWOiE55ZS2G28Nbr7epPUbTOiebRQv/UcC6aLZcorLqTTrGJ2d8PNwgdUb1IWhbt3ad7r3lCnqYtTy5RivuYaApX8CoE2axNhRVd7XSaeo29jYurdjzUYPCFCzYWoKCVEXzTIzYf9+lXW/Zw/8738qMH/vvbWeYnR2YkRkAEO6+pFVWE6gl7FNZswer9IbbqLkw3fwiI+Fb7+tNvtHiKYoj1GB9MqoKBw6F9sSSNcSEhjbM5CxPetoevzPP5CXq/ZTTbmAPWMG3H03rFypLuzXFTz8/HPVx2XCBOhTd2kb5wvOhxXLGb5jFVHPPYrTgX/VWIcO7TCzwrxcnckZNhp+W4hp1WoJAIraMiy9SEJCOsz7tqPreN/6ok2l5KkpG73Wq9IpTJ2qMj8cITwchg5V95cuxcfNRWV7WztoWztqdwCapnWoIDqAU281xU4/fNjBIxEnHF1HS1GNMo3dujrmvW2tk14jkM7KlZCWBgEBhF80E+enn1IHt1u2wPff29dbt07djhvXcECrE9As5V1cDuxTU8yFaCpLID0pytJo1KudZ6c0g6uzoW2D6KACbR9+qC70n3ceXHaZar50xx3w8MPg7Y2WmmqfDVMfa1mXESNUwkI7yh2tekboK1a26+uKk4O1Pnqgp1EdG7R3WRcrS8NRli9XgfxfflG/X3hh9fUsM9oaDaTXVR8dVMCgZnmXV19VtxddBA2Us3AxOBHm69Yhg+gAAWFBbL3YkkX73HP2vhRCNIHZrEPsEQCcekU7djCWQDp5eeqnLt99p27PO69psy/69IGePdUM13/+qf242QyffqruX3dd/ds57zx1u3o1/mWF+B7er34fMqTxMbQj03g1e9e4e6eaaSREVdZAutRHb7KO+c0v2oSu67aM9OB/VGaHw8q6WFm7ZlunTVVUqGlWVR8TdXK1dE83Hqnn5EGI+uTn41RSDIBb9y6OGYM1kH7sWPXl33yjbi++WAWgQkPhgQfUskcesU/RXrtW3bZyWZcTkcsQFQTwjz9EdlG5g0cjThhlZapGOnCku/o+CfJs534JHdGFF6pjkl9/ha+/ho8/hjffhBdesF/gX7So4W1Yy7q0Y310q4rTJgCopmJCNFOt+ujt3WjUavx41b/p2DF1Ycr6nX/++dXX69lT3ebmQk5O7e00FkiH6oH0o0fV5x7sxx4nKH8PIzvPu4JSb184cAB++MHRQxInkOzicryTEgEw9nHw7CYvLzWrBGon4ICaoWpNtrnyyqZtU9Ps3+lz58IZZ8Bjj6mEvuxsWLVKXaDz9lbnJPWJilJN1E0mVSZm1y61vIMF0j169iAvtAua2Ww/RhHCylraReqjN5kE0juRvJIKSspN+GSnYdy2VX2BzJzp2EFNn65u//5bXfldu1ZdJQ0Obv+D9hOMz2CVQeh1LIGiskoHj0acUFJSACj19MY/2M8xY+jaVd1WPSAuK4OfflL3L7vMvvyee9QMlrg4eP99lZ1mPakeP759xtuBaZYgQGDCYVsQRIhG7dkDFRWY/f3JCemCi0HDx10m/DbImnzw668Nr+eARqNWLhPHYzI4456SBPHx7f764gRkNqv3SkkJmVXro4M9kN7eGekeHjBmjLp/773qe3/ECPtFeCtPTwgLU/frykpvbiD9jTegslJlxI8adVx/gqMZnZ1wD/Jn24Xz1IJnn1X/10I0QXp+GX4pKpCu9e4AZcKsWekJCbUf+/13lakeGanKsDTVtdfa+qHw77/w/PNwzjkQGGjPNJ8zR+1nGmJd99dfYedOdd86676DCPA0kjzIEltZs8axgxEdj2SkN5sE0juRtHx1cDxk2yq1YNw4+8Gno4wbp670ZmTA9u3qSi6obPROXq6hMcZ+KoPQMzeLtKR0B49GnFAsJ5ZFAcEEOCoDta7SLkuWqKyy8PDqB8KenvDMM+r+s8+qLNrkZDV18wQ/0W0VltIuAYlHSM8rdvBgxAnDEiArHXwKaBqBXq4droRZhzNjhsqSPXBA/dSlsNCekdaOjUatAkMDSeujAoP6ihXt/vriBHTLLSqr0sODs6aewpw7ZtPnzhvgwQft73NHJLdYy7tYsydrlnWxaqi8izWQXlejUStrIH3LFvjoI3X/BM9Gtwr0MrL9wquo9PGFffvsyQpCNCKtoBTfZBVIt33GHMkaSE9MrP3YF1+o2yuuaF78YNgwSE1V39kffgjz5tlroVvLn9xwQ+PbqRpIz8lR5yf9+zd9HO0gwNNI0kC1H9clkC5qkoz0ZpNIZSeSVaQC6VFrLPXRa06PdASjUU2lAjWV2lofXcq6NM7Hh7KAIAAKdu938GDEicScZAmkB4YQ4Omgmsh1lXaxlnW59NLa9Q2vvhoGDlTTLefMUcuGDVNZa51ddDRmoxGXshKKY6RngmgiSyA9p99gQNVEFo3w9bUH9+rLSt+yRWV9dusGXdq/dJa/h5HkIeoCY8U/y9v99cUJqEpQxSM7k/ADO3H/5Ud45RWVCR4R4ZjEG+tnzaq+cpQNBdItM/AazEgfOFDdZmSoC2FDhqg+CSeBQE9Xyj29SbzKEgx85hnJShdNkpVVgHeG5fPTEQLpkZHqtmYgPS3NXiK2qWVdqjIYVGmWG2+EBQsgJkbtC37/XWWpNyVhZ+RIdbGu3FJesV8/1Wy8A/H3MJI82HJBdMMGVU5XCCvJSG+2dgmkv/vuu/To0QM3NzfGjBnDpgaaNC1YsMDW2NH641ZXl3XRbDlFFbgW5hO4yXLA7Oj66FbW8i4LFqhplU5OcNZZDh3SiaIyStWGLNsf4+CRiBNJaaLKAi8JDMHHzUGlHKylXTIzVW3DoiJ7YMoaKK/K2Rleflnd32+5cCT10RVnZ0x91AwVff8+dF0ajoomsATSU3qpGQ2BXh3rpK/Daqy8i7WsiwOy0QEMThp5Y1TJK23VKoeMQZxgLFnbqf9bxsJ3f+bf59+D116D22+HWbNUjwBHGDfOHozq3ds2+6oWa530lpZ28fOzH5OAykY/SWbnWJMldl14Ffj4qPMsa+NWIephNuuUHorFyWxG9/Bw/Ax2qD8j/ZtvVH3yMWOgb9/Wea2gIDj33NoX8+rj5FS9XG4Hq48OqjlyZZ9+lHj7oRUXq0oAQlhJRnqztXkg/bvvvuOee+7hySefZNu2bQwdOpRp06aRnl5/KQofHx9SUlJsPwl11cISzZZdXE6PzavQKivVdCPr1CVHs2Z9HLZkUo4da28oIhrk1Ff9H+qHDkvwTDRZ+dEkAExh4Y4r5eDnZ685eOwYLF4MxcVqevno0XU/5+yzYfJk++9SH93GYMmo84k9SH6J9EwQjSgvtzUajY/sB0CQl2SkN4l1CveGDWpKeE0ObDRqZT71VMxOBlwS4+ueBi+EVUmJKqkGpHbtSXrvgRSdc77qTfLWW6oUSEON9tqSm5v9gvkFF9Qf3G5KaZeGAulgL+/SvTvMnt3soXZU1v16qsED7rxTLZSsdNGInOJyPI9Z4i89e3aMC0v11Ui3lnW56qr2HU9N1mMD6HD10a0CfdxIHjhM/SLlXURVkpHebG0eSH/99de54YYbuOaaaxgwYAAffPABHh4efPrpp/U+R9M0wsLCbD+hoaH1rltWVkZ+fn61H1Gb2ayTW1RO9DpLWZeOko0O0KOHmgJlZe2gLRrl2l9defc6Gk+BNBwVTWSylHbRIhqoGdrWNK16eRdrWZc5c+o/YNc0ePVV+++SkW7jNEgF0gMTDpNRWOrg0YgOb88eKC9H9/fnmL/KNJOM9Cbq0kVN9dZ1NfW7Kl13eEY6gF9oIGm9LeUqVq502DjECcB6McjVlVRNzQAO9u5A+4KXXlJ1i++7r/516guk63rTA+nWc4+nnlJ9EE4S/p5GNA1Kyk0U/99tqi/Vrl3w22+OHprowNLyy/BNUbNXtY5Q1gXqzkjfvVtlVru4qLKQjnTGGfZykx0wIx0s5V2sDUdXr3bsYETHYk1ylkB6k7VpIL28vJytW7cydepU+ws6OTF16lTWW0806lBYWEhkZCTdunXj/PPPZ+/evfWu++KLL+Lr62v76Vazm7sAoKC0Er20jKjNlmm+HSmQDvbyLiD10ZvBydJF3T8pnpyicgePRpwoNEvNUJduXRtZs41Zp1Lv2WNvNHzZZQ0/Z+RI+PZb+PLL6lOxOzvLlPfAhMOkWxpLC1EvS1mXiiGnoKPh5mLA02ho5EnCxnoMtWhR9eVxcSqrx2iE4cPbe1Q2QV6uJFnqpEsgXTSoSg3xTMtxZFBHuqg2erQq/djQdHNroC8pCUqrXEjOyrLXAW6sNMVtt6lAwjXXHNdwOxoXgxM+burCQJbRS5XrAXj6aXWhQYg6pBeU4teRGo2CvUZ6UhJUWpLHvvxS3Z57LgQGOmZcVm5u8PbbcN11UCX21ZEEeBpJGjRS/bJmjewDxP+zd9bhkZXn+7/PuCczE3dZybr7Lruwi9MiLVCKFErhR1vqSkuhTl3pt95SpFBKi5ZCYfF1d4+7zMTG5fz+eM6ZmWTjyWSSmedzXblmcsbezSbnvO/93s/9EKIYdaRztMuIiauQ3t7ejlAodJ6jPDs7G80DlcMCmD17Nv7yl7/g+eefx+OPP45wOIy1a9eiPrYhXQz33Xcfurq6Il91dXUT/u9IBhxuP/KP7IHG7SJXxvLliR5SX2QnSF4esHhxQocyrZCE9LTGWjjd3DSEGRmqFjr/6oomvxFeH+SNz4cfpqiJuXOj5dVDceONwC23xHds0w0p2sVWW4m2bk+CB8NMeSQhvXseuabsJk3iYp6mI3Kz9tdfB3p6osdlk8iSJQltNJZh0qB+IUVkiW+9lbBxMNMAybEt5ubCIQnpU8qRPhIyMwGTicSAqqrocXmTICODNreGQhCS1olnl+JdOlx+iuzR64GDB6P9ZhimH63dPqQ1TTEhPTubnOfhMJ23QiHg8cfpsUTHush89KPAn/40ZatabEYNWmfMQ1CjpR5Vp08nekjMVKC3N7oJnaTXwXgwKc1GR8OaNWtw2223YfHixdi4cSP+/e9/IzMzE7///e8HfL5Wq4XFYunzxZyPw+VD9hnJ2X/BBdQUYyqxZQvwhz9QFuNUG9tUZsYMAICxswOeE3wxZIYnHApD107lW+bSosQORhbS5YncTTdNjRzG6Uh5OUSNBmqfB57TA+TEMkwskpDeNos2rjgffZTMnUvXX78fePXV6HFZSE9gPjoAmLQqdCxeibBCAeHcOXLwMcxASGKzPzMbobAIrVqRuCbkY0UQBo53GWmsS5JjN9LGiMPlI9funDn0wECZ8kzKI4oi2np9SJeiXaaMkK5QRNcNNTXA1q10/rLZOBZ2hNiMGoQ0GjTPlqJnON6FAaJudL0+2r+MGZa4KpYZGRlQKpVoaWnpc7ylpQU5I+z+rFarsWTJEpyVG1EyY8LhCsBeLYlVCxYkdjADIQjAXXclNFN0WmKxoGfTZgBAzv/9PMGDYaYDPW1OaLxuAFNASO8fzfKhDyVmHMmASgVxFvVM0J05BbefeyYwg+D3U0YugLpSElRkoYUZIYIwcLyL3Gg0wXMZQRBgzrGjrVwSzDjehRkMSUh32ciFlmHSTs/qFFnsq6yMHmMhHUDUkd7eK0VADtWclUl5uj1B+P1BpMnRLpJpa0ogx7vU1kabjN500/AVJwwAUIyfVokGOSedG44yQN989Ol4/U8QcRXSNRoNli1bhq1bt0aOhcNhbN26FWtG6NYJhUI4cuQIcnMT2BQvCXC6/MiYykI6M2Z8X7sfAFD80jN9FxAMMwDdVdTt3m80Q2E2JXYwsT0tli+fWpP1aUi04egZtPVwTjozCMeOkZieno7adLnRKC9CR40c7/Kf/1AOs9sNHDpExxLsSAeATJMWdYtW0Tcc78IMhiQ2d1lJSM+cSvnoo4Ed6YNiN9L53eHyQxRFoKyMHuA1AzMADrcfpo5WqAJ+QKmMNvmcCshjOXYM+Pe/6f5UiXWZJtiMWhbSmb5wPvqYiHuGxuc//3n88Y9/xN/+9jecOHECH//4x+FyuXCH1Mzltttuw3333Rd5/re//W3873//Q2VlJfbv349bbrkFNTU1+NjHPhbvoSY1nd0u2GqlCRML6UmFadMGVC9bD0UoiNB3v5fo4TBTHHc1lWr6M6fAxTJWSB+uySgzPPNkIf0sWllIZwZj/34AQHjJEvT4QgCmWHPB6cKaNeTe6ewE3nmH4nKCQRLtpkDj+wyTFg0LuOEoMwySI73DbAMwDfPRZVhIHxSrUQNBADz+ENz+EDvSmSFxuPzRfPTi4qmV9y0L6X/4A+DxALNnAytWJHZM0wybUY2muUsgCgKdA+ReEkzqIgvpnI8+KuIupN944434yU9+ggceeACLFy/GwYMH8corr0QakNbW1qIp5g/Y6XTirrvuwpw5c3DFFVegu7sb27dvx9y5c+M91KTF4w9BV3kOymAAoskULYtikgKdWomDd3waAKB47NG+jZYYph++OsrKDeVMgSqfoiIqx1QogBtuSPRopj/SddJec5Yd6czgVFcDADxl1KzapFVBp1YmcEDTFKUSeP/76f5zz0Xz0VevnhKlsXaTBg0LltNi+fRpXiwzAyP9XrQYrQCm8abaUEJ6ilc1q5UKpOlJDHW4/OxIZ4ak0+1HeuMUazQqIwvpHR10e9ttU+J6O52wGbXwG83onilFv7ErnZGjXdiRPiompavjvffei5qaGvh8PuzatQurVq2KPPbWW2/hkUceiXz/85//PPLc5uZm/Oc//8GSJUsmY5hJS4fLB3v1GQCAMH8+X3CSEP+q1aheth5CMAh8//uJHg4zhQk10MJSyJsCC0uzmQSol146Py+dGT2SI91WW4mObk+CB8NMWaS+Nb3pdgAc6zIu5HiX55+fMo1GZewmDXwmC1o5J50ZCklId1jsEIRpfD6QxeGqKiAcpvvy5lGKO9IBwC5tkLT3+qLiaOzPimEkyJE+xRqNyvQ3A95yS2LGMY2xGegc37hgOR1gIZ1hR/qYmBQhnUksTlcAGVWn6BuOdUlKbAYNdt7ySfrmkUfYlc4MiCiKEKSFpXqqCNeXX05fzPgpL4eo0UDt8yBcWYVwWEz0iJipiOQ86TSTA9U+XR2oU4EtWwCDAairA15+mY5NkabpWpUSaXo1GnixzAyG3w+0twMAeu1ZsBo0UCun6dKwqAhQqQCfD2igyjuOdokSm5OOgoLoz0r+GTGMhHM6ONIB4MILp1Z++zTBJm2WVlVIRtV3303gaJgpATvSx8Q0nS0xo8Hh5kajyY7VqEHTvKVoW30BZbSyK50ZgB5fELp2ulhqi/ITPBpmwlGpKC8SQFr1WfR4gwkeEDMlkRzpHcZ0AEDGdHWgTgX0euCyy+i+309/g8uWJXZMMWSYtWict5S+2b49sYNhph7NzQCAsFoNr8U6ffPRAfrbk92q586R05od6RFskpDe0Sudp0pK6AHOSWdi8AZCcPlC0Yz0qSakx/Yf4SajY8KoUUKjUqB+njRXOXQI6O5O7KCYxMKO9DHBQnoK4HT5kVHFQnoyI0+Q993+KTrwyCORHFyGkXH0+mHsICFdkc9CejIiRBqOnoHD7U/waJgpieQ8adWlAZjGmchTBTneBQAWLyZxfYqQYdSgaY7kOjt0COjtTeyAmKmFJDT77JmAIEz/c0FsTnp7OxlLBAGQ+nKlMnJkT4fLD1EUOSedGZBOdwAAkD5Vo10MBrrmLlgAfOADiR7NtEQQBNiNGrgysuEvKqFNx507Ez0sJpGwI31MsJCeAnS3OZHWXE/fzJ+f2MEwcUHOOzszYxHEzVvYlc4MSIfLD6NDulimePOtpCUipJ+Fk4V0ZiAkR7rDZIUgAFYDO9LHxZVXUuNRYMrko8tkmLXozcyBKyefFsu7diV6SMxUQhLSe+20eJ721SmxQrocWZKZCajViRvTFMFm0EAQyHHs9ocGbs7KpDwOlx+6Lgd0PV10QN5wmUo89xxw+DD1WWLGhFUy4HUtXUkHON4ltWFH+phgIT3JCYTC0Jw6AQAQc3KAjIwEj4iJB2adCiqFgGBYhOurX6eDf/0ru9KZPjhcfpgkRzqXOicpc+cCkIR0FwvpTD9cLvoC4Em3I02vhkbFU8FxYbcDF19M9zdvTuxY+iE7jBvmSq50jndhYpHE5u50WhvI1Y3TFlkcrqzkfPR+qJQKpOtpQ6Gj18+OdGZAnG4/ss4cp2/KywGTKbEDYuKC3DOheSH3UEl5RJEd6WOEV09JjtPth51jXZIehUJAunRRbFu8ghqgsSud6Ud3awc0Hjd9w4705GTOHACAtb6KhXTmfKTJclirg99g5EajE8Wjj1Kz0fe/P9Ej6UO6Xg2VQkD9HBbSmQGQHenWTCgVAiy6ae7cjnVZcz76edik832Hy8eOdGZAnG4/ss8co2+WL0/sYJi4IW+aVs2Reqjs2kV9XpjUo6cn+n/PjvRRwUJ6kuN0BZBRdQoAIHCsS1Ijx7s4XH7gwQfp4F//CtTUJHBUzFRBFEV46xoAAGGzmV0myUp5OUSlEhqPG76aukSPhplqSLEuPnsGZSJPdwfqVCEzE7j8cspjnkIoFAJsJg2aZEf6jh0U8cIwQERsdtkykW5QQ6GYWr+/o2agaBcW0iNkxDYcZUc6MwBOV4yQPoUaZzMTiyyk12QUQrTbAY8H2L8/waNiEoLsRjcY6IsZMSykJzkOlx/26jP0DTvSkxqrkZxETpcfWL+eSsyDQeB3v0vwyJipgMsfgrqVRDSB3ejJi0YDsZQWyNpzZ+ALhhI8IGZKIU2Y3el2AGBHegqQYdKirWw2QgYj0NUFHD+e6CExUwVJbO61ZyE9GXolyOKw0wkck8RAFtIj2Ewxhhv5Z9XeDnR3J3BUzFQhHBbR6Q4g68xROsBCetJi0VG1WlAEgqul3i4c75KayPnoHOsyalhIT3Kcbj8yONolJZAbxjnkBoNXXEG37EhnADh6o/noAi8skxrFnAoAgLWuEp3uQIJHw0wpJEd6t8UGALBP9+aCzLBYDRqIShU6F3C8C9MP2ZFuz4pUNU5rjEYgJ4fuy83zeL4TwW6kjdN2lw+iyRQt42dXOgOgxxuEqtOBtBaqXsXSpYkdEBM3YiNhu5etooMspKcmsiOdY11GDQvpSY67rgGGLgdEQYg0oWOSE7lMK5KLLJ8Q29sTNCJmKtHh8sHYIe06syM9uakgId1WV0XOM4aRkSbMrjQbFIIQ2YBlkhebVK0WiXfZti2Bo2GmFP2iXZIC2WktR7vwfCeC1aCGIAC+QBguf4hz0pk+ONx+ZJ2WKjlmzADS0xM6Hia+yA1H2+ctpgOHDiVuMEziYEf6mGEhPYkJh0WojlJ5VrisjHOPkhxZEHH7Q/AGQkBGBj0gnyCZlMbh8sPokHad2aGV3ESE9Eo43SykMzFIjnS3NQM2oxrK6Z6JzAyLHNlRNWsRHWBHOgNQ9J+8sWbLjJgxpj2yOCzD850IKqUC6XraMOno9XFOOtMHh8uPbI51SRlk3aDVIhnvWloAUUzgiJiEIOtE7EgfNSykJzE93iDSpVgXxYKFCR4NE280KgXMOhUAKf9QFtLZkc4A6HD5YZSiXdihleRURKNdnC6OdmFiiAjpdtiMnI+eCqTryYVaN1sS0s+ejZbyMqmLJJqEFUp40mzJU53CQvqQyH0xOlx+dqQzfeBGo6mFHO3XYkijAx4P0NOTwBExCUGeD7IjfdSwkJ7EOGLy0YWFnI+eCkRy0vsL6bzDnPI4XH6Y2JGeGsyeDQCwtDWhp92Z4MEwU4qYZqNJ40BlhkSlVMCiU8NnsiBQMYcO7tiR2EExiUeKdXFb7dDpNdBrlAke0AQRK6QrFCwO9EOOc3D0xjQcZSGdAfVVy5KF9OXLEzsYJu5EHOlhFUSjkQ5KZgsmhWBH+phhIT2Jcbj8yKjmRqOpRCQn3e2PnhC9XsDtTuComETj9gfh8YdgdHBGekpgtyNsp4004fRpiLyRxsjIjvR0O6zGJMlEZoZFnhv0LF1JBzgnnYnJR7cmSz460FdIz84GVKrEjWUKEnWk+6I/K452YQC4G5u50WgKEdszQcyWmjQ3Nyd2UMzkw470McNCehLj7PHCXn2Wvpk/P7GDYSYFa0RIDwBGI6CVSvc53iWlkRtOmuRmo+xIT3qEORTvYq45hx5fMMGjYaYMsY70ZIlyYIZFbiTZulAq1+ecdCYipGclT6wL0FdIZ9PAecibah0uP8TSUjpYUwMEOAYulfEGQjAfOwwACM+YAaSlJXhETLxRKRVIk3omBDJictKZ1IId6WOGhfQkxnf2LNQ+D8JaLXXfZpIeWRhxuvyAIHDDUQYACelqjwsady8d4MVl0iPIDUfrq+h8wDCBANDRAYCajaYnk3jGDIksntVXLKYDe/cCPl/iBsQknsZGAECvPStiwkgKMjMBk4nus2ngPKwGNRSCAF8gjF5bJqDTAaEQUFeX6KExCaTTHYjEuig41iVlkOcGHjsL6SkLO9LHDAvpSYzyKF0QQ7PncGljiiCX6ne6AwiFRW44ygCQGo3KsS4mE2A2J3ZATPyJbTjqZqcZg8iGalihgCorAxoVTwFTBdlxXGfPp3mBzwfs35/gUTEJJVmjXQQh6kpnIf08yIVKa0KnJwTIrnTOSU9pHNxoNCWRhXRXmp0OcLRLaiGK7EgfB7yKSlI8/hAsZ04AABQLONYlVTBpVdCoFAiLIro8ARbSGQDUVMrYwfnoKYXsSK+rZEc6Q0iuE0+aDekmXYIHw0wmcrRLtzcEcc1aOsjxLimN2EdITyJHOsBC+jBYY/spcU46g36NRllITxlkIb1TFtLZkZ5adHVFY71YSB81LKQnKQ53tNGoctHCBI+GmSwEQYgsiByumIajLKSnNA6XH8YOqXSLF5apgexIr6+Go9uT4MEwUwK50ajVDhs3Gk0pYjfZPStX0UEW0lOasBTt4rZnRnJyk4ZPfALYtAm48cZEj2RKElknuP1AWRkdZEd6StPbwI1GUxFZSHeY0ukAO9JTi7aYanW9PrFjmYawkJ6kOF1+2KvP0DcLFiR2MMykIpfoOt1+zkhn4A2E0OsLRqNd2JGeGpSUQNRooPL7EKiqTvRomKmA3Gg0zc756CmGIAgRV7pjkZR/u20blfUyKYnYQEI68vOgUibZcnDzZuDNNyMbykxfIi5UdqQzEsr9+wAAgTJuNJpKyJtqTpONDrAjPbXgfPRxkWQzJ0bG6eyBtb6avmEhPaWQSzYdLj9HuzD0ewDA1iUJ6exITw2USogzZgIAtGdPwx8MJ3hATMKJdaSzkJ5yyP/nLTPnAWo1/T5UVSV4VExCCIWgbKMFtKYgP8GDYSabyKaaK8COdAbhsAjj0UMAAJHd6CmFTq2EWaeCyybpBexITy04H31csJCepASPHociHEIwLY2FsxRDdpo4WUhnIFUmAEjv6qAD7EhPGRRz5HiXKnR6OCc91RGlBZLbmpF8mcjMsMhVCB0hZTQDl+NdUpP2dgihEERBgL6QhfRUQ14n9HgDCJRIzUYrK7lCJUXp8QaRcZry0VUrlyd4NMxkYzVo4E6PyUjn80DqIDvSWUgfEyykJymKo0cBAMG586mDPZMyxGYfinbpwshCesridFETEbODM9JTjj4NRwMJHgyTaAJN5Ej3Wu0w61QJHg0z2UTjHALAWqnh6LZtCRwRkzDkfPQ0G6wWzkVNNfRqJXRqJUQR6MySNlK6u4GOjsQOjEkIDrcf2adJN1CsWJHg0TCTjc2kgdsqGe98PjoXMKmB7EjnaJcxwUJ6EhIMhWE8cwIAoFzIsS6phtWghiAAvkAY3nQW0lMdh+RI17dLQjo70lMHueFoXVWkMoFJXYJNUsludjYUCt5gTzXk/ikOtz8qpLMjPTVpagIAuOxZXJ2SggiCEDkfdIpKIF8S0zknPSXpqmuCpVXqmbBkSWIHw0w6NoMGQZ0eAaOJDnC8S+rA0S7jgoX0JMTpDsBedRoAoFq0MMGjYSYblVIBi44myN0mCx3kZqMpS6ckoGpapQYy7EhPHfo40llIT3mkEk5VTnaCB8IkAjnaxeMPwbtiFR08coTdZylIqKEBAOCyZUb66jCpRZ9+SpyTntKE9+wFAHhKyrjRaAoiV6tFXOnccDR14Gaj42JShPTf/OY3KCkpgU6nw6pVq7B79+4hn//Pf/4TFRUV0Ol0WLBgAV5++eXJGGbS4HT7kSEJ6QI3Gk1J5IuiQydNiDo6gDA3G0w1wmERne4AVB43FD2SWMKO9NRh9mwAgNHZjt5mrkpJdRRSc0FtAW+mpSIalSIS6eNMswOlpZSFunNngkfGTDa+OhLSPRlZMGqUCR4NkwjkSgSn2w+Ul9NBdqSnJOpDBwAA/kXsRk9FZM2gV65iZ0d66sCO9HERdyH9H//4Bz7/+c/jwQcfxP79+7Fo0SJceumlaJV3QPqxfft23HTTTbjzzjtx4MABXHPNNbjmmmtwVMr8Zoanq6kN5nbpJDh/fmIHwyQE2WnSppPKtMJhoLMzcQNiEkKXJ4BQWER6lySiGo2A2ZzYQTGTh9mMUB6VbAunT0LkBkKpSzgMTQedB/QFvJmWqsiudIeL411SmUBdPQAgnJMLgfsopSQ2I1WuOt0BdqSnOKYjBwEAihXcaDQVMWiU0KoVcKezIz3lYEf6uIi7kP6zn/0Md911F+644w7MnTsXv/vd72AwGPCXv/xlwOf/8pe/xGWXXYYvfelLmDNnDr7zne9g6dKlePjhh+M91KQheOgwAMCXlw+kpyd2MExCsMmL5YAAWKR4F85JTznkXOwsl5MO5OZy8+EUQ1FBrnRz9Tn0+oIJHg2TMDo7oQjR/7+liB3pqYosnnW6A8C6dXSQhfSUI9xIGelCHm+qpSqxm2qiLKSzIz3l8AZCsJ8is6J2NTcaTUUEQYDNoIHLxkJ6ytHPkd7Y6UF7rw+hMBuvRkJchXS/3499+/Zhy5Yt0Q9UKLBlyxbs2LFjwNfs2LGjz/MB4NJLLx30+T6fD93d3X2+Uh3FMbogBufMS/BImERhlRbLDpcfyJAujJyTnnLIQnpmdwcd4Hz0lEOIaTja6Q4keDRMovA3UCMxr8kCq5WrUlKVAR3pO3dSxAuTMghS6b5KbjLJpBzpejUEAfAHw/AWldBBdqSnHLGNRjXL2ZGeqliNGrg52iW1EMWoNiQ50v93rBmP7ahBY6cngQObPsRVSG9vb0coFEJ2dt/GVtnZ2Wge5I+0ubl5VM9/6KGHkJaWFvkqLCycmMFPY+R8dOVCzkdPVeTsw25vAKJdEtLZkZ5yOFwknFq7pf97zkdPPWIajjq44WjK0ltHDlSv1Q6dmjORUxW5Wq3T7adzgyAAPT280Z5iqKXm4/piFtJTFZVSAYtOMt1kF9DBhgbA603gqJjJxruT+tb1FHGj0VTGatDAZZNystmRnhp0dgJBqVI5MxPBUBidHkk34CbkI2JSmo3Gk/vuuw9dXV2Rr7q6ukQPKeGUt1QDALRLFiV2IEzCkPPORBHwW210kIX0lMMpCacWpySSsCM99Yg40isjFQpM6uGpp+aCATs3FEplrBEhPYCwWgMUSAIaRzqkDqIIXTvlohpK2HyUyshNBh36NOqfI4pAdXViB8VMLnv3AgBcC1gzSGVsRjXcVnakpxRyPrrFAmi16PQEIIrUmJ6bkI+MuArpGRkZUCqVaOm3s9XS0oKcnJwBX5OTkzOq52u1Wlgslj5fKY0oAlJjVmHhwgQPhkkUct4ZAHjTrHSQhfSUQxZOjR2SkM6O9NRDEtLTG2vR2eVK8GCYROFrIEd6OJOF9FTGrFNBpRAQDIvo8QajTQZZSE8ZPE2tUAbJdZZWWpTg0TCJRHYdOjwBPhekKJpDBwEAwcVLEjsQJqFYDRq4rDQ/FNmRnhr0y0eXzXc2o4abkI+QuArpGo0Gy5Ytw9atWyPHwuEwtm7dijVr1gz4mjVr1vR5PgC89tprgz6f6YcgkJtg+3ZgzpxEj4ZJIPIE2WVhIT0V8QZCcPtDAACNVMbNjvQUJD8fYaMRylAQwbO8QE5VQs3kPBH6RecxqYVCISDdIMU5uP0snqUg3VW1AMhkodbrEjwaJpFYDXLzYT9QXk4HOSc9pTAfOwQAUK3gfPRUJt2ggTe22Sj3TUl+ZEe6lI8ux3/KlYvM8MQ92uXzn/88/vjHP+Jvf/sbTpw4gY9//ONwuVy44447AAC33XYb7rvvvsjzP/OZz+CVV17BT3/6U5w8eRLf/OY3sXfvXtx7773xHmryYLEAa9YAGv5DSGXkks0ek5R5xxmoKYV8QTTrVFA0kxuVHekpiEIBcdYsAIDm7CkEQ+EED4hJCJLDSJXH54BUR2446nT7gdJSOlhVlcARMZOJp6YeAODLyErwSJhEY41tPsybailHuKUVphZqNKpfvTLBo2ESiVIhQJ1L6Q+C30/52Uxy09+RLlWx202sH44UVbw/4MYbb0RbWxseeOABNDc3Y/HixXjllVciDUVra2uhUET1/LVr1+Lvf/877r//fnzta1/DzJkz8dxzz2H+/PnxHirDJBWykO40SEI6O9JTCvmCaDVogEaaKLMjPTVRVFQABw7AWleFTk8AGSZtoofETCKiKELZThNmbd7AMXlM6iDPDTrZkZ6S+OtISA/l8KZaqiNXrnZ5AgiVlkIJsCM9hfDs2A0jAGdBKdKzbIkeDpNgLDYzvEYzdK4eMl9YrYkeEhNP+jnSO9iRPmriLqQDwL333juoo/ytt94679j111+P66+/Ps6jYpjkRs5I79CZ6QAL6SmF00UZqHZFAOjqooPsSE9JhNiGoy4/C+kpRq8vCJ2Dzv+6At5MS3Ui0S4uzkVORYINtLEu8nwg5TFqlNCoFPAHw3DlF8EC8LkghfDt3gMjgM45C2DlTOSUx2bUwG21k5De3BzpscQkKTGOdFEU+2SkMyMj7tEuDMMkhjS9GkqFgF7OSE9JHJIjPe/scTpgMFDsE5N6SJNhW10VnO5AggfDTDZOVwBGJ53/lTmckZ7qDOhIr6sD/P4EjoqZLIQminpT5fOmWqojCEI03iW3kA5WVnI+coognjgBAPDNmZfgkTBTAatBA7fUcBTccDT5iRHSe3xBBEIiFIKANL06seOaRrCQzjBJikIhwGpQwyML6ZyRnlJ0uv1QBPwoeeBLdOD666kZMZN6RIT0Sjh6fQkeDDPZON1+6Dsd9A03G015ZOGsxxuE35ZBm6yiCNTUJHhkTLwJh0WoW5oBAJrC/ASPhpkK2IwkmrRbcwClEvB4yI3KJD2KWmo8LJSVJngkzFRAdqQD4HNAKhAT7SK70dMNZMJkRgYL6QyTxNiMWnjSJCG9u5sdZylCOCyi0x3Ayqf+AM3JE9RI5Cc/SfSwmEQxcyZEQYCupwvuhqZEj4aZZDrbndB43fQNC+kpj06thEGjBAB0egLccDSF6PYGYOigxbOuqCDBo2GmAnLz4Q6/CBQV0UHOSU8JNA11AADtzPIEj4SZClgNGrgkR3qoiYX0pCfGke6Q89E51mVUsJDOMEmM1aiGz2iBKDf07ehI7ICYSaHLE0B65WmsfPJ3dOBXvwIyMhI7KCZx6PUIFxcDAIRTJyFy2XZK4ZU2T8JaHWAyJXg0zFQgEufADUdTCofLD6ODFs8CNx9nwM2HUxXR74e+jcRS06yyBI+GmQroNUoEMkhI90u9NJgkJtaRLsXB2llIHxUspDNMEmM3aiEqlfBZ0ukA56SnBM4eD7b8/H4ogwHgqquAG29M9JCYBCM3HDVVV8LtDyV4NMxk4mugxXI4K4vjnRgAUdeRkxuOphTOGCGdm48zQL/mw+WSM5kd6UmPp7IGinAYQbUGacVcncIQQk4OAHakJz3hcFQTysxER6/kSDewkD4aWEhnmCTGKmUfutO44Wgqof7t/yHvxEEEjCbgt79l8YyBIiYnXXYeMMlPIBQGpExkITsrwaNhpgpWSTxjF2pq0dPUBpVf6pPBQjoDEk4EAfAGQggUl9BBPhckPb2nzgIAXDn5UKtVCR4NM1VQ5ZGQPq2bjYoicM89wAMPJHokUxenEwhJpqrMzMi60MaO9FHBQjrDJDHyBNlt5oajKUN1NfJ+/B0AQO1XHgQK2GnCINJw1FpXGcnCY5KfTncAeic1GlVITiOGSedol5TEW1cPAAilpQF6fYJHw0wF1EoFzDraWOvJlhrQ1tUlcETMZOA9Rz0xfPmFCR4JM5XQFdAGq1KO/ZiOnDoF/P73wHe+A7hciR7N1ETWg9LS4BWUcPlIVJcNmMzIYCGdYZIYtVIBi04dbTjKjvTkRhSBu++G0uNB/YIVCNz5sUSPiJkqxDjS5RI+JvnpdPthcNJ5X+BGo4xENBc5ALGkhA5ys9GkJyjl3oo57EZnokQqVKRGg6ivT+BomMkgVEnn+3BRcYJHwkwlDFITao2jneI/piNHj0bvs0FgYAbIRzdpVdCqlAkc1PSDhXSGSXLsJg28FhbSU4JHHwVeew1BtQavfe47sJl0iR4RM1WQhPS05no4Hd0JHgwzWTjdARg6yZGOLI52YYg0vRoKQYA/GEZvnuRI7Oykcl8mKfEFQ1A2U8yTIo+FdCaK3DOhPV0S0hsayJjBJC1CbQ0AQFHCQjoTJa2YqlKUwQBEhyPBoxkjx45F7589m7hxTGVkR3pmZqRKmWNdRg8L6QyT5FgNGrjTbfQNC+nJS0sL8LnPAQB23vopdBaURsr3GQZZWQinpUMQRYROnU70aJhJwuHyw9ApnffZkc5IKBUC0vSUi9sJNSDH/rB7K2lxugIwOciFpsjPT/BomKmE3GCuxSStFbxeYLqKaMyI0NTVAgC0M8sSPBJmKmFJM8FrTgMA9NY2JHg0YyRWSD9zJnHjmMrEONJZSB87LKQzTJJjM2rgYUd68vONbwBOJ/wLF2HfB++AWaeCRsWneEZCEID58wEAaUcPwuMPJXhAzGTQ6fbD0NlB37AjnYlBdqE6XJyTngo4XH4YOyQXGjcaZWKwyT0TggogM8aVziQlHn8Ixmb6/zXMKE/waJiphEIhwGfLAAC4aqZpxBM70odnAEe6lYX0UcMqC8MkOXaTBt60dPqGm40mJ2438OSTAICGB76HsEodcRgxjIziwk0AgMIDO9De60voWJj4I4oiHG4/DE5JSGdHOhODfI1wuv1AaSkd5Jz0pKXD5YPRwUI6cz7pUoO5TncAotygnnPSkxZHjwemNop5Us9gRzrTl0AmmS489U0JHskY8PuB0zFVtyykD0xsRrrsSGfdYNSwkM4wSY7VEHWkh9vYkZ6UPPcc0NsLlJaiYcFKANx5mxmAzZsBAEWHdsHBQnrS4wmE4AuEWUhnBqSPkM6O9KSno9cPY4e0eGYhnYnBrFVBrRQQFkUE5Ea0LKQnLT3naqAMBRFWqfhcwJyPNFcMNE5DIf3MGSAY7Ps9cz6SsTKckYEuD/28WDcYPSykM0ySo1MrgQwq1RTb2ZGelDz6KN3eeiscngAAsCOdOZ81axDS6WF0tMF76HCiR8PEGYfLD0UwAH1PJx3gaBcmhnQDLZqcrgAL6SlAe2+MIz0vL7GDYaYUgiBEeup4siRhlaNdkhbPWTrP+3LzAaUywaNhphoKqWdKeDoK6XKsS7kUWVRfD3g8iRvPVEVypLvSbAiLIjQqBUxaVYIHNf1gIZ1hUgBtHu0uCx0dgCgmeDTMhNLUBLz2Gt2/9VZ0urlpCDMIWi08K1cDADRvv5XYsTBxp9MdgL5LahinUAB2e2IHxEwp5GtEtzeAYHEJHWQhPSnxBkLo8QY52oUZFPl80JshbbiyIz1pCVZShFeosCjBI2GmIup82mgV5PiP6YQspG/cCKRR01Se1wyA5EjvMqUDoPO/IAgJHND0hIV0hkkBTHm0u6zwegGXK8GjYSaUv/8dCIeBtWsRLitHp5sc6ensSGcGQLzoIgCAdee7CR4JE28cLj8MnZKQnplJYjrDSBi1Khg0Sogi4MyRcpFraoAQNyJONjpcfqjdvdB43XSAhXSmH5GoJ6sUAcaO9KRFqKkBAChKihM8EmYqoiug64OmvRXewDSbD8hC+rx5wMyZdJ/jXc5HEtKdBtps4Cr2scGrKoZJASyZ6QiqpZNkO+ekJxUxsS5dngBCYRFqpQCLjku0mPPRXXoJACD3wC643d4Ej4aJJ063HwandL7nfHRmADJMWgBAi8kOaDSULcpO1KSjo9cHk5yPbjLRF8PEIOfjtqdTFCSfB5ITXzAEbUMdAEBdzo1GmfORHemGzg7qoTKdiBXSZ8yg+9xwtC/hcEQLapeEdK5iHxsspDNMCmA3aeFJo4ajLKQnEYcOAYcPkwByww2RCU+6gUu0mIFRr1gGn8kCrbsXPe/tSvRwmDjidPm50SgzJHYTLZ7a3QGgpIQOchl00tHR60dG1Sn6ht3ozADYJEdii1mKAGMhPSlxuPywtDQCANRlpQkeDTMlkeaLRkcbHK5pJKT7fFH3OQvpg+NwkJgOoFljBgDYuNHomGAhnWFSAKtRA0+aDQAQapmGmWfMwDz2GN1edRVgs0WEdN5ZZgZFqUTHyrUAgNDW1xM8GCZeBENhdHmCMHRKQjo3GmUGQHakd/T6ueFoEtNbXYsLH/4OfXPFFYkdDDMlkeMA29IkR3pXF9Dbm8ARMfGgo9cPS4sU2yNvnjJMLFKzUUOnA529vgQPZhScPk3RdBYLkJ/P0S6DIWXfi1YrHH7qm8fRLmODhXSGSQGMGiV86eRIdzW2JHg0zIQQDAJPPEH3b7sNAOBwyfnovLPMDI5n/SYAgO6dtxI6DiZ+ONx+hEUR5m52pDODk2kmIb2918dCepIiBgJY9tVPwtjZgcD8BcBDDyV6SMwURKNSwKxTIWAwIWwmlyLnpCcfjh4vzK3kSEcxZ6QzAyAZLxThEHobp4D57vhxoLZ2+OfFxroIAjvSB0PKRxczMuAPhqEQBO6rNkZYSGeYFEAQBIRtGQAAb1NzgkfDTAhbtwLNzYDdDlx+OQCwI50ZEeLmzQCAtP17AI8nwaNh4kFbD7mIrL2ddIAd6cwA2IwaCALg9ofgKyqhgyykJxWBbzyIvEO74dcbgKefBvT6RA+JmaLIYoo/hzKSWUhPPlx1jVAF/BAVCqCgINHDYaYiajVCVqpi99Un+BzQ3g6sWAFccMHwjdBjhXQgKqTX1QFe7gkVQXKk++2kC6Ub1FAqOA52LLCQzjApgpBJJ0x/c1uCR8JMCHKT0Q99iDLSQZnIQDTrkmEGwrJoHnrtWVD6fRC3bUv0cJg4IAvp5i4HHWBHOjMAaqUC6XqqYOrKyaeDVVUJHBEzobzyCjQ/JAf6jq88BPWcigQPiJnKyDm53kyKduCc9OQjUEnn91BuHqDm6lVmEHJozhhsakYoLCZuHKdOAW43UFMDHDgw9HP7C+mZmRTzIoo8r4lFcvd7rKQLWdl8N2ZYSGeYFEGVRbmH4bYpIKSfOAEUFgK//nWiRzI96ekBnn2W7kuxLt5ACG4/7dZziRYzFFaTFrVL1gAAAv/jnPRkpL2XNtUMTqm5NDvSmUHIkONdMiV3IjvSk4P6euCWWwAAh676ELqv+WCCB8RMdeSc3O4MaeOVhfSkwh8MQymJaEJpSWIHw0xpFFJOur6jDd2eQOIGElsV8/ow65X+QnpsvAvnpEfZsQMA4JizEACb78YDC+kMkyJopN1lsb09wSMB8MwzNEH/6lcjJUbMKPjXvyiSY/ZsKnkDIp3VzToVNCo+tTODo1Yq0LFqPQBA3Lo1waNhxkUwSIsLvz9ySBTFiCNd45DO9+xIZwZBbjjaZJNcqG1ttFnLTF8CAapW6+hA95wFePuer8Fu4sUyMzSR5sNWaeOVo12SCqfbD0sLbY4oudEoMwSC3HDU2QGH2z/Ms+NI7Gbea68N/jyvN5qFLgvpAOek90cUgffeAwDUzl0KALAauTJlrLDawjApgiGPhBRVRzvCiSzTAqK7xm438OMfJ3Ys0xE51uXWW2nHHdF8dO68zYwE94aNAADNwf1AZ2diBzPRiCI11HvyyUSPJP48/DBw8cXARz8aOdTrC8IbCEEhilDIFUgspDODkCEJrM2ClnpuAFwGPd25/35g2zbAYsE73/0NQhptRCRlmMGQmw870qjknx3pyUVHrx+WFqnRKAvpzFBIc0ZDZ3skNjQhxJ6D3nuPdIOBOHUKCIeB9HQgNzd6fOZMumUhnTh3DmhpATQaVBXNAcB91cYDC+kMkyLocumiqOtyotubwDItgDpwy/zmN3RSZ0ZGbS3w5pt0XyrbBgCni/5PeWeZGQnGslI4CkoghMPA228nejgTy9GjwNe+Btx+O+ByJXo08UWOeHriCeDddwFE89GzRQ+EYJAez8xMxOiYaYAssDp6/RDLyuggx7tMX156CfjRjwAA4p//jBoriQp2Xiwzw6BTK5GmV6MnQ6pOYUf6+PnHP4ANGyK5xInE4fLD0iL9nxYXJ3YwzNRGcqQbHe2RiueEECuk+/20QTwQsbEuQkzjTI526YvkRg8vX44uKAGwAW88xFVIdzgcuPnmm2GxWJCeno4777wTvb29Q75m06ZNEAShz9c999wTz2EyTEqgkDLS9V1OdCTyohgM0s4xQI4Ijyey6JvyiCLteCeSJ56g240b+0yEO1wknvEFkRkJdpMGdYspJx3JFu9y4gTd+v2RSWNS0tMDbN8e/f5TnwJCoUg+ep63m46npwNadqMyA5OmV0OjUiAYFuEvlK4p7Eifvnz723T7qU+h+8qrKRdZIXDvFGZEZJq16OWM9Imhrg64806ahzzySKJHgw6XD5ZWdqQzIyDWkT4Vol0ypCqZweJd+uejy3C0S1+kNZF7Ja3/TFoVdGplIkc0rYmrkH7zzTfj2LFjeO211/DSSy/hnXfewd133z3s6+666y40NTVFvn40XUQ2hpnKSBchXU8nnD3exI3j3DkSuAwGiiUAgN/+FmhuTtyYRsqnPw2kpUUv2JONKEZjXaQmozKyC1UuzWWYobAbNZGGo0mXk376dPT+RPzbGhuBH/4Q6Ooa/3tNJG+9RRuTBQUklh86BPzxj1FHuqeTnseNRpkhEAQhUtrryi+ig+xIn56IYrTi75OfREevtMFu1ECpEIZ4IcMQWWYtemVHektLn/4bzCj53OeiVXE7dyZ2LACcvT5YmtmRzowAWUh3tMPhCkAUExQJKwvpN99Mt4M1HB1MSJejXWprAZ9vbGMIh4F9+8b++qmEJKR3LKb+alauVBsXcRPST5w4gVdeeQV/+tOfsGrVKqxfvx6//vWv8dRTT6GxsXHI1xoMBuTk5ES+LBZLvIbJMKmDJKQrwmF0N7clbhzyIm/OHOCKK4BVq6aHK72rC/jjH4HeXuB3v0vMGE6dAk6eBHQ64IMfjBx2+YLo8QYhCECWWZeYsTHTCqtRg/rFqyAKAoTjx4GmpkQPaeKIFdIHm3SPhgcfpMbIn/vc+N9rIvnf/+j2fe+LulC//nV0N1JUlrW3k45xPjozDJF4l+wCOsBC+tjw+YAf/ICu04mgqYmEO4UCKC2NVB9m8GKZGSGZZi08aVaE1NLvTDLNDWL573/JLT5Mpfy43v9f/4p+v3MnbXQliGAoDG9LK9Q+Dx0oLEzYWJhpgBzt0tkBbyAETyA0+WMIhcjIAgAf+QjdHjhADdH7M5iQnpUFmEwkho+10u4HPwCWL6evAwfG9h5Tgba2SCJAk9Ro1MZxsOMibkL6jh07kJ6ejuXLl0eObdmyBQqFArt27RrytU888QQyMjIwf/583HfffXAP1lgAgM/nQ3d3d58vhmEGQKNByEybUp6GBLq/5Yvd3LmUY/atb9H3v/3t1J6wP/tsdDf6qaeAQAJy5uXStLlzgZgNxtaeaKyLRsWtL5jhUSsV0GVnorV8Lh14443EDmgiiRXSDxwA2tvH9367d9PtY49NiZzTCLKQfsklwMc/TgsIhwNzfvsTAEBaj4MeZyGdGQa54WhrZh4dYCF9bPziF8B99yVu000+95WWAhpNxJFu50ajzAjJNGsBQUCvXapkStZ4l899DvjLX+hrovF4gHvvpfuf+hSZX5zOvnOTScbpDsDcTKKkmJtLY2KYwZDmjfrODgihUGJy0ltaSExXKoGFC+kLOH+94vFQtTtwvpAuCOOLdxFF4K9/pftHjwIrVwLf+Q5Vg0435Hz5efPQqjEC4DjY8RI3xaW5uRlZ/cqJVSoVbDYbmoeIcPjwhz+Mxx9/HG+++Sbuu+8+PPbYY7glpqFefx566CGkpaVFvgp5h5VhBifDDgDwNrcmrkxLdqTPlQS8Sy4B1qwBvF6KT5iqPPVU9H57e1TEmkxkEa+oqM/hlm6K6sm28GKZGTl2kxZ1S1bTN8kS7yKK0R4MJhPdys15x4LXG938CwaBH/945K/1eMb+ucNRXU2LcqUSuPBCQKUCfvUrAMDCl55EQd0ZaB0d9FyOdmGGQXak16dTY0pUVSW+H8hEUldHVVzxjld48km63bs3Me5TuaHarFkAEOmXYDfxYpkZGSatCgaNcmo2HG1rm5jzUlNTdJ7w3/+O//3688Mf0mZkfj7wve+RkxVIaLxLbKNRgWNdmOHIzAQEAYpwGLruTnT0JkBIlzfx8vJornvxxfR9/5z0kyfpemuzDWwckeNdxiKkHzlCr9NqgWuvpXXAAw8Aa9dG+zFNF+SeUevXwyFvshtZNxgPoxbSv/rVr57XDLT/18lxlDTefffduPTSS7FgwQLcfPPNePTRR/Hss8/inLzT1I/77rsPXV1dka+6uroxfzbDJDuKDGo4qnJ0wOVPQJkWEBXS5V1jQQC++U26//vfT01XeltbNCLiiivoVm76OZnU1NBtPyG9NZKPzg4TZuTE5qRj69aElh1PGO3tQGcn3R8uU3EkHD0adcQAwJ/+RC6Z4fjZzwCjEfjHP8b+2UMhLyRWr6a+DQBw0UXouvJqKMJhbPq/70bHyY50ZhhkIb3RkgFRqaTqq+nQt2Sk/PCHFLPwwAPx+4wTJ6hPAUDnoWFiLOOC7HidORPhsBhxEWbwYpkZIYIgTM2Go1u3Uj+QpUvHL+6/8070/ltvTeym95kzFAUBUIWK2UzXaSChQnqHywdLCzcaZUaIWg3YyXxn6GyPGLYmFfncUyBFzm3ZQrevvdZ3vRIb6yIM0AtEdqTLG82jQY5nuvRSuv/449STaM8eYMkSmutPF9OBJKSH165Dl4cc9VaOdhkXoxbSv/CFL+DEiRNDfpWVlSEnJwetra19XhsMBuFwOJAj5S6NhFWrVgEAzg6yi6TVamGxWPp8MQwzMEIWCen6LiccidhdDoWi2aGyIx2gXea1a8n9KU9ApxLPPENjX748Kvo/9xzQ0zO545Ad6f3cJK3sSGfGgN2kQcO8ZQip1PS7NciG9bRCFpKKiig7HBif237/frq98ELq5+D1Aj//+dCvOX6cMtVFkSKh4kFsrEsMxz73DQS0OmTu20nnLYAd6cyw6DVKmLQqiEoVQgVSZWeyxLuIIvDCC3T/3XfpbzgexFatAYnJUpXPf7NmodMTQCgsQqNSwKJXTf5YmGlLllk3tYT0QIAiUvx+2qxavZqcomPl7bej971eEtMnAlGkcfp8JLx94AN0XBbSd+yYmM8ZA7GOdG40yowIOSfdMUWE9A0bAI2G1iuxumBsZOxAjCfaRZ5Hf+ADJNLffDMZbC67jP7Ov/AF4Lrrpr4Rye2mhqkAupauQFikuYFJy3OD8TBqIT0zMxMVFRVDfmk0GqxZswadnZ3YJ/2nAcAbb7yBcDgcEcdHwsGDBwEAubm5ox0qwzD9kRqOGrqccLgTIKRXVtKFR6/v64iIzUr//e/jV0oaCgHbtwNf+QpQUUEX55FcWOVy7Q99iMT0WbPIwRIvkWwwBoh2cfujjUYzzSykMyPHZtQgqDegZe5iOpAM8S4xQhIuuIAiT86dG3uTIVkMW7oU+PrX6f7//R/lnQ5EOAzcdVe0h4IsxE8koVDUZd9PSK+3ZGHPjXfRN/IY2ZHOjIAMM8V/eAslkSVZhPQDByjaBSDRbPv2if8MUYwK6enpdCutXyaVmGgXOR/dZtRAGMilxzCDkGnWojdzCkW7/O53VPGRkQHMmUMC2/r1Y+/tIgvnchzsRMW7/OtfwKuvUgzEww9H3bFrpMq/I0fi19x0GBwuPyyt0v8lO9KZkSDNHQ3OdnS4/PAHJ9l53V9INxrJdAf0rTQdrNGozFiF9JMnyRijVkeNOQBFNr38MukVKhXw/PMUtziV2b2bYmny8tAh9cKxGnhuMF7ilpE+Z84cXHbZZbjrrruwe/dubNu2Dffeey8+9KEPIS+P/gMbGhpQUVGB3VIjr3PnzuE73/kO9u3bh+rqarzwwgu47bbbcMEFF2Ch3GCAYZixIwnpum4nHC7f5H++HOtSURGNSpDZvJkmxj7f6F3pjzxCr7/lFhK7fv974JVX6PM6OoAXXwQ+9jHKWVu3DvjRjygfsaFh+FLvujpysQkCcOON0R1pgEq8JpMBol1auqONRrUq5UCvYpgBsRk0EASgenES5aTHCulmM7nIgbH/22QhfckS4KqrqNlRTw/w618P/Pzf/paEOiM18sGZM0BX19g+ezD27qX4mvT0aPYqAFEU0dbrw97rP4ZQbPwTC+nMCJDjXbpzk8yR/txzfb8fT9TTYBw8SOcenQ749KejxyaTUCgqFMycGc1HN3I+OjM6ssxa9NrpuiEm2pHe0QE8+CDd/+53qWHeBRcA3d3kCh3tPLy1NZptLL/vRAjpPT3AZz9L97/ylah4B9Dao7CQNtr37Bn/Z42SUFiE0xVgRzozOiRHuq3HAVEEWnsm2ZXeX0gHojnpoxHS5Yz06mqqahkpcqzL5s2A1dr3MUEA7r4bmD+fvj98eOTvmwhi8tFbe7h3ykQRNyEdAJ544glUVFRg8+bNuOKKK7B+/Xr84Q9/iDweCARw6tQpuN1uAIBGo8Hrr7+OSy65BBUVFfjCF76AD3zgA3jxxRfjOUyGSR0kIV3f5YTDFZj8zx+q/Co2K/0PfxhZDjFATrCvfIWcKU88AXz/+8A99wCXX04X1YwM4P3vB/78Z5pAp6UBH/5wNJ7hqaei4xqIp5+m2w0bohdzWUjfunXyclADgehnxUyC5ViXLHajM6NEpVQgXa9GzbL1dODVV2kjazojC+mzZ9Pt5s10OxYhPRiMZh4vXUrnqPvuo+9/+cvznWW1tRTpAlAmsyxmT7SgJse6bN5MbhiJbk+QHEN6PfDTn0Wfz9EuzAiQm061Z+XTgWQR0p9/nm7lc0E8hHS5au3KK0nkAyY/2qWmhuYJWi1QWIgOySxhN/HcgBkd6QY1vDlUCR5OtJD+4INUXbVwIRlirFaaq9xwA/2+33orzftHGq0g56MvWABcfz1dQ8+eHVvsQyzf+haZc8rKovOAWBKYk97p9iMsipyRzowOyYRhd3UCwOTHuwwkpMs56W+8QZvHbne04nQwIT0nBzAYaCNrNM5xWUiXI5oGQjb6TnUhfds2ul2/Hk1d9P+Ym8Z91cZLXIV0m82Gv//97+jp6UFXVxf+8pe/wGQyRR4vKSmBKIrYtGkTAKCwsBBvv/02Ojo64PV6cebMGfzoRz/i3HOGmShkIT3RjvTBLnYXXUS7u35/9KQ/HE1NJJArFORk/+Qno85RuQlfURHlFr7+OjUOfeIJco584AM0+ZYF/IGIjXWRKS+nUs1w+Pxc1HjR0EBj1Wqpm7pEi9RoNMvCF0Rm9NhMWjTPXgh/Vg45vMZaKj1VOHWKbmfNolt50r116+gbAp06RVEQJlPUXXb99eRucTio8kVGFIGPf5zE9bVr6f7SpfTYRMe7DJKP3hYT5aD8wHWU3XjnnXS+YphhkKNdmm1UNTrmOKSpRFUVLXCVyujm+d69g0czjYVwONpU+KabgMWL6X5l5cRXowyFHOsyYwagVKJDcqRnsOuMGSWCIEBTTJUpisbGxDXTO3aMYl0AatwpV7LqdDQ3/+IX6fuvf52uuSMZpxzrsnEjYLFQJSwwele6z0drii98gdY0P/0pHX/4YdrM7o8c75IAId3h8kPb2w2tS+rrFFuxxjCDIQnp6d0dAIDmrknWDQYS0pcto2rMzk7K/D5xgubfdvvgphFBGH28S2UlbYYrFMDVVw/+vOkgpMuxtqBGo83ShkgOC+njJq5COsMwU4zMaLNRly8EbyA0uZ8/XEMQQYhGMYxUfJJdXxUV5Ex/+GGKcjl0iC60LhftQP/qV+RIU8d0qP7mN+kzn3lmYNfomTN0oVYqgQ9+sO9jt9xCt5MV7yLnoxcW0oVdgh3pzHjIMGoAhQItF11GB2QHxnQkNtpAFtJXraKYlbY2ahA0GuRz0OLF0b85pTLqNvvJT6KNC596ijITNRrgT3+i58tCekyvmHHT3R1tWCaXuEq0SZtqmWYtndd+8hMaC2cgMiPAZtBAIQhoz5KE9KnsSO/uHpkDVXajb9hADtS5c+l1E7lhuHMnXZ/NZuCKK2hBL2cvT+biWq7GmTkTwVAYTrdcvs1zA2b0WIoLEVYoIASDZFaZbEQR+Nzn6Lp+7bXU8DsWhQL48Y8pZk0QaGP73/8e/n3lRqOSiQ+XX063IxHS3W7gN7+hvGSbja7BP/sZmYQUChqv/H79iW04OsmNCTtcfphlN3pmZjR6jmGGQm422ikJ6ZPpSA+Ho/0ZYoV0pZJMdwDw2mt9Y12GmuvK8S7yhvNwyGuhTZv6mNfOYzoI6UeP0pzJbIajfDb8wTA0KgUyjDw3GC8spDNMKiE50o3d5MaSy3smhVCIGncAgzrSRVFEY9kcAIBn1x6II5lsxmYYD4TBMPjFdf58yj0HolmJschu8y1bIGZkwBsIIRCSHC833EAloQcORJ328WSAfHS50SgAZFn4gsiMHpvkVjy7QXI3P/ccRZpMEbrcAWw/144Dtc7hGx3V1ZFLTK2Oxh9pNNGohdFGOkjnFnHxYrj9QQTlv/1bbiGhrLmZ+jO0t0dzke+/n5qhAeScASbWkf7mm3QunTkTKC3t81C75EjPYOGMGQMqpQJWoxpdckZ6YyM11Z4qnDsH8bvfRWjuPCAtDYEf/Xj418hCuuwokytUxhHvIooizrX14mBdJ7rcgWjV2jXXRJ2osit9MuNdYvpDOFx+iCKgUyth1HDvFGb0ZNhMcFtpzZCQhqMvvURCmUZDm8KDce+9UWf6Y48N/Z7t7dENdXleIAvfb745/Pnugx+kz3vpJRLVc3KA229H518fwzvbjmHfZ76BUHiQdcvSpfRvaWub9GqfPo1GOR+dGSmSI13b0Q4A6PYE4PZP0vqgvZ2q0wUByM3t+5h0HQ+/9hocu+kaGx7MoCczWkf6SGJdgKiQfuYMnRMAeAMhnG3txY5zHTRHSDRyPvqaNWh2SZqBWQuFgk0240U1/FMYhkkaYqJdAKCx04PSjElyJlRXk3tTqz1PAAIAly+I1463wGMqxE0Awnv34c/vVmJWjgUVOWZkmrUDd5eWneSDCenD8c1vUg76Cy8Ae/fCv3gpDtV3wtnrw5q/PgYzgLeXbsbhN84iGBahUSlw6bwczMjKoAn4iy9SVMz3vje2zx8psiO9Tz663GhUzY1GmTEh5yKfnL0Em+x2CB0dlCEqOz4SRGuPF/uqnTjV0hMxb+2qcmBZsRULC9IG/n2XhSQp2iDCli3kNnv9deDznx/5ICQRbJ+tBO++Te5co1YJs06NhR++G/N++A34vv8QhDfegqa9nTbmvvKV6OtlR/rJk1QZMxEusEFiXYCoI52rU5ixkmHSosOcjqDZDFVPD1235Y2hBBCsrYPrsb9D9c9/wHjoAAQA8l92+DvfwfH33Yg5c4oGnht0dFCjcKCvkP6rX41MSPd6KVbi6qsjc5Z6pxvvnmlHs2RCeCsUxD1PPAUdAO8HrkekUHrJEpobTGbDUdlpN2tWtNGoSTPwz4ZhhiHLrEVvRjZMHa0Q6+ogyBvDk4HfT5EpALm8y8rQ6wvindNt6PIEEBZFhEXa1BJFwLxwC67DjyG+/DKE9vbIWuc85Hz0uXOjLtP584H8fNosePttamA6EDt20DxCpQK+/W2ELr0Mp3PKcKi+i0xJPQB62nCkvhObZmehpP/aSqul88KuXfReZWXj/jGNlA6XHwXNLKQzo0RypCtammEzauBw+dHS7UNpxiTIh3KsS05O30pyIFKNKW7bjt5OL2wA3lZloX5HNTJMWmSYtcg0aZGbrouuFUYjpNfX09+pIFA1zFBkZ0PMyoLQ2orDr27DiYLZaO7yISwtXA7Vd+KqhbkosBpG+A+PAzGNRqP56APETzGjhh3pDJNKSJNLdW8PFAE/Gjon0W0ml19VVPQVuQBUtbvw+M4aVLW70FleAVGhgLGzA+HGRuyrceKJXbV4bGcNdlV2wOXrtxs+nCN9OGbPjsS0eO/7Oh7fWYP3zrSjZdsemKvOIKjW4OjKixCUXCb+YBj/PdKEOoc7Gu/yxBPxz5CUhfQYR7rc+CWb89GZMWI1qKEQBHjDCgSueh8dHEl5dBwQRRF1DjeePVCPJ3bW4mQziehFNgPS9Gp4/CG8d6Ydf3mvGrsqO86PpopxZPZBbjL4zju0QB8BoVAYwX3kJD+ROzNy3OULobnLi63r3w9Xuh3aulpo/vkPiIJAMSqamDzinBxy0ojixAlqgwjpvmAIXR5yvrAjnRkrGSaKBXLnS9eZBMW7hENh1N/4EShLipF2/1dhPHQAYYUS1cvW4/UvPgRnyUxoXb1w/ujn+MeeuoGboP3nP1S9sXBhdPN+40aaf5w9O3zTsYceIhFv0yY4Kmvx/MEG/HNvPZq7vNCoFMhP16Pw8G7onO3wmNPxB20Z/rWvHkcbuhCSXWqTKaTHRLvIjUY5H50ZKzajBr0ZJKR5q+sm98N//WvaGMrJAb7+dQRCYbx4qBGnmnvQ3OVFa7cP7T0+dPT64XD5UZNdgpYZcyEEgzj1qz8NXr3WP9YFILFsJPEu3/0uACBwy63YfsNd+HOvBa8ca0FTlxdKhYBZ2WYYtUo43QE8e6ABzx9sON+NmoCGo+GwiE6XH5ZWbjTKjBLJkY72dmQbScxunqxK9oHy0SV68ovQm5MPZTCAokO7aIjFM9De68fJ5h68d6Ydzx5owF/eq8aZFqkvgBztMhIhXV4DrVt3vhs+Bm8ghNeOt6C+kET65nd3o7HTi7AowmpQw27SwOMP4d/7G3C0YRL7pcQiilFDwfr1aO4i3Yfz0ScGFtIZJpWwWiNZv/ruTrR0eQcvQ5xoBmg0GgyF8eapVjx3oAFufwgZJg2u3zgbguSAuyrYhJnZJqgUAjp6/dh+rgOP7azB2VbpwtjVFV3oy6XUYyD49fshKpXQvf4/GPbthlmnwuaDlKHqvfhSXHfhXHx0fSk+cWE5yrNMCIZFvHCoEa0bL6Zc1JqakTdHHSsDRLu0cqNRZpyolAqkG2iC7LwsRkif5OZirT1e/GNPHZ7ZV4/qdjcEAZidY8bNq4vwgWUFuH1tCS6dlwOrQQ1vIITt5zrwl21V2HGuI7polhuNzp7d980XLCD3mctFLpNhaO7y4oXntkHV042gWg3jkgW4Y10J7tlYjg+vKsL7FuVi/cJCNN/58chrTnzwdviXrTj/zSYy3qWykhYBKlVfIQCIOFDNOhX0HOXAjBFZeO3MlhavCRLST77yDgqefhSCKKJpwXIc/ur3cGT3MWhe/x82/eDLSPsORbEte/ZvaG9qx5O7a/H68RZ4/DGba/1jXQBqLij3Ydm6dfAB+HzRJoe1tfC+7xrUNDigEAQsLEjD7WtLcMOKQrz/BDlcGzZfgZBSjVqHG68db8EbOiln/ujREW/ejQufLzpHmDUr0mjUzhmozBhRKRUI5tLvsaeqZvI+uLUV+Pa36f73vw/RZMLrx1vQ3OWFTq3EVQtzce2SfFy3NB8fXFaA65cX4IYVhei67gYAgPlfT+OxnTVkdumPLKRv3Nj3+BVX0O1gQvr+/cDLL0NUKPDEpg9jV6UDLl8IJq0Ka8vt+NiGUly5MBe3rSnB0mIrFIKAyjYXHt1Rje1n26ORkAloONrh8iMYFpHO0S7MaMnMJM0gHEZ+gNbdrT2TLKTn5/c53OUJ4J/7GlC1ZG2f45ffuBlXL87DuhkZmJ1jhkVPa4WXDjfhlaPN8BZLm+lVVUBgmLiVEcS61Ha48fjOGhxt6EJrCa05ZrZU4eK52fjo+lLcvq4UN60swqxsM0JhEa8db8E7p9sQDovAPfdQn4WJbHo+6EBrqdpGpYJ3yTJ0uGhukMtC+oTAQjrDpBIKBTXCApDu7kIwLE7eRbFfo9G2Hh+e3F2Lg7WdAIDFRem4aWUROeKkSIS8qlO4amEe7rqgDJfMy0aGWQuPP4QXDzXh1WPN8O2VmvgVFVHjnzHQ2u3Fkx1qHL2Yyrcufuo3uGVVEfJeoUW46fZbkZumR5qe4lOumJ+DAqse/mAYz550wHfNdfRG8W46OkC0Sws3GmUmAJuRxLPGZWtJaGpqmtSFHrnNmtDU5YVKIWBRIQlVVyzIRZaZJnsKhYC5eRbctoaO200a+AJh7KzswOPyonkwR7pCEY2qGUI88wVDeOtUK57aUwv1Qap0CcyZh2tXliDdoIFeo0S2RYcZWWYsLbKi/IEvIVxSCkfpTLxxy6fwxsmW8/s6yPEuEyGkv/Ya3a5ZQ/9PMbT3cD46M37kxpQteSV04MiRSR9DW48P3r9R1nHnFVcj59BuLHzoa1i0bDby0vVQKRVQ3PQhYOZM6Lo7seWdZyGKwJGGLjyyvRqH6joRdnuAV1+lN4wV0oER5aSH//E00NoKtzUDXpMFeccP4NrffhO3ri7C5jnZMGpVgN8P9XPPAgBmfOYufHRdKdaW26FSCDiqtiFkSaMF+4kTE/4zOo/KStr8NJuB7OxIvwQ7O9KZcaAopA21YF395H3ogw9SY7xly4CPfAR7qp042dwDhSDgqoW5mJltRkmGEcV2IwptBhRYDchP12PWZ++GqFAg7/gBCOfO4Zl99XjjZAt8QWlzzeGINgSU89FlNm+mDeozZ4Bz584bkufBbwEATm66Es68IuSn63Hlwlx8dH0pVpXZYdBQ1IVOrcTGWZm4ZXURimwGBMMidlU58Lft1XC6/FFH+sGDk9Z/ot5JGwq29iY6wI50ZqQolZFK9hwvOaqbu7wj6182XgZwpDtdfvxzbx26PAG0r9oQfW5mJkyFeSjLNGFlqQ1XLMjF7WtLsLLUBkEATjR144naAMJ6PVWp1QyxMdjcHHVwX3fdeQ8HQ2G8daoV/9pfjx5vEFaDGiUX0t91aeM5zM9PQ5qezElqpQJXLMjB6jLSXfbVOPHaa3upMfJLL1ElTE/POH5II0COdVm6FC0hJUQRSNOraQ7DjBsW0hkm1ZAuigUhFwDKSZ8UYhzpjZ0ePLW7Fu29fhi1SlyzJB8Xzs6CSimdkuSYFkl80qmVmJeXhg+vLIpcGI83dmP/82/2ff4oCIdF7KzswJO769De68fh2z8FUa2Gfee70P38p1T2bTIBV17Z53UqpQLvX5yHLIsWbn8IW5dKsRFPP02usHggiudFu3j8IW40ykwIstjSHhCAq66ig5MY73LgwDksePgHuOG+O3Cn3YOLKrKRbhhYAFIoBMzOMePW1cW4cmEuzDoVujwBPLOvHp5jkmDVX0gHhhTPwmERxxq78NiOGhyo7YQoAnNaaDGtX7l88IxhiwWKM6fh3rUXIYMJJ5p6cLShu+9zZCF9375hfw7DMoJ89EzeVGPGgUWngkalQPPM+XRgz55J/fxgKIxXDzdg1hsvAQDSPvaRgf/+lErgvvsAABWP/wE3zLUhw6yFNxDCGydb8fZvn6IKlIKC6N+gjHwu2Lp1wMqb5i4vHD/4KQDgwNW3YMcPfgtRqUThf/4N28M/jz7x1VeBzk4q/d6wAWkGNVaV2bGqzA4IAppLpcqYyYh3iYl18YXCkbkBb6wx40FdTPNNoWGShPTeXuBvf6P7P/kJzra7sO0sNTq8sCIThbYhcoZzcyFIf9ub9tK18lBdFx7fWYtzbb0Q33mH5tIVFZHs5wgWC7B+Pd2PcaUHQ2Hs/8+70L/0AkRBwLHbP4lrl+TjhhWFmJVthnKQZn12kxbXLc3H+xblwqJXo8cbxJunWiEWFtJnB4MTMycYAXKEp4kz0pmxIMW72LqpIsvtD6HbOwkNR/sJ6R29PvxzXx16vEHYjBqsuPOD0efGVLrLKBUC1s3IwPXLC5GmV6PbF0JHDjVSD506PfjnPvccnSdWrOhTAQ6QG//J3bU4IBkAFxak4cOrimFfs5yecOQI0G+TQRAErCm344oFuVApBITefjf64K5dtOZyD1A9M1EMmI/ObvSJgoV0hkk1JCE929cLAGjonARHejgccWWJc+bgrVNtCIZFFNkMuGV18fkNTwdxcfa/MFpOksu9umgWgqGRRVEEQmEcquvEozuqseNcB8KiiJnZJlx37ToIH/sYPemrX6Xbq68GDOdP3LUqJa5dkg+rQY3TFcvhzsimBfXLL4/s5zFanE4SBYDIpEJ2o3OjUWa8yOX/7b2+aCnjv/513oRwwunthfvBb2PRpmVY+Y8/IH/fdhiueT/Q0jLsSwWBMklvXVOMRYVpUPp90DXSxLvWnn/+C2TxbNeuPg6Q6nYXnthdi/8da0GPN4g0vRrXLc1Heb3UuG+4TTqVCgWZFqydQY6Tt0619q3ykaNdjh8f3oHmcFAO6/bt5z8WDEbd9AMI6bIDlYUzZjwIgoBMkxbNsxbQgSNH4rvI68eOyg7otr1LDQ7T0yHIkQsDccstJAq1tiL/X3/HzSuLcGFFFrRqBTK2khhWs/5iuPz9eimsWkWNf9va+jjufcEQ3jzVircefREZJw4hpFbD9tlPYtMnboLw61/Tk+67D3iWXOh46im6veGGPn1flhVbkWnWoqWsgg7IfVzGwu7d5F4b7lwcU40jx7qYtCro1Dw3YMaOqYyEJHVT4+R84Asv0HVyxgy0Ll2FV442A6CK1YUF6cO/XupbVPbKs/jg0nxY9Gp0ewJ44WAjTv2DNufE/m50mX456a3dJJoZfvJDAEDLlitx1Ye2nN9EdBAEQcCMLDM+sDQfSoWAmg43Kjvc0XiXHTtG9D7jQRRF1Ds9UHtcUDsddJCFdGY0SBnhyqZGZJjJ4DJgT5KJJkZIb+324p/76uHyhZBh1uL65QUwFeRG5+cDCOky+el63Ly6CPPz09CZR7/7+97Yg7OtPQNH28qxLh+MCvWhsIi91Q48JRnvDBolrl6ch81zsqFRKajSXqGgBudNTQOOY3aOGdcvL0TxcdpAq1+xAaLFQr2brrmGmpvHgxghXc6353z0iYOFdIZJNSQh3e4l52RTpyf+ZVrV1TQ51mpx2piFlm5q1nX5gpxISWQf5Lzz2lqgvf28h+ULY1ENZSIfyijF4ztr8O6ZNlS1u6KlnDH0+oLYdrYdf3q3Cm+cbIXTHYBOrcTlC3Jw5YJcyhX+2tcAbYwQddNNg/6TDBoVrl1aAJNRi+ObyLUe/slPKbd9opHL0LKyAD112pbz0bnRKDNectPpd6i12wfv5ovpd6y6enwC0FB4vcAvfwmxrAyGbz8IrasHXTMqIJaX0+/61VePuOxZq1LioopsXG/1QRBF+Awm/Ks+gP8da0aPNxA9t5WUAGVlJEi/8w5ae7z49/56PHugAe09PmjVClwwKwO3rSlGsd0Y3cTr72YdhOXFVpRlGhEMi/jP4aboOSg/n3ImQ6HhYzK+9jXgG9+gBkcXXEAbc/L49+yhc4vVGhXnJcJhMSKksyOdGS92kwa9mTnwZ0i/t5PUMLPO4ca+Gicq3ngRACBcf33f63F/1OropvePfgRFwI/Fhem4fXURZu1+CwCwd9EGPLK9GvtqnNFFs0YTzUiWKlTOtvbisR01OFjbiUXPPgoAED/0IcxZNIMc8R//OHDvvfSaW26hzS45g73fPEGpEHDJ3Gy0z6AYO8+eMTpPt26l88A99wwZQwOAIimAvvnoHOvCjBPLTMoVNrS1wOObBBfqk08CAPzX34gXDjUhEBJRbDdg48zMkb3+2mvJ/HL2LArPHsWtq4uxosQGtVKAdS8J19sL5qOyrff8dY8kpIfffBPbjtbjyd11CJ04idlvk0Em58ffHdPGVLpBgyVF6QCAd0+3ISz3aBgqPq+qCvjxj6MGmjHS4fLD4w9FY13S04G0tHG9J5NiyCL14cPIkdabkyKkN1AFhTc7F//a3wCPP4Rsiw7XLyuI6gZ3303NggeIYIlFq1Li4rnZyFhM12R15Tm8eKgJf3q3Eu+cbkOHNH9GRwfwJlW6B665Fmdbe/DK0Wb8/p1zePdMO0JhEeVZJty6phhlmaboB+h00d5McnzUAOSk6VBx9hAA4MDlN+Don56kTf3XXqMN+eGy20eL00l9WgCIa9fGONL1E/s5KQwL6QyTamTShNTc2wWVgsq0Ovt3lp9opFgXcfZsbKvuBEDC04AiOkATvRnUBXswMU8bCsJcSS6s3or5cLoD2FvtxHMHGvC7tyrx1O5avHemHWda6EL4l/eqsLvKAW8ghDS9GptmZ+LO9aWoyLFES8cLCoD/9//ovtUKXHzxkP+sNL0a1y7Jx5n33YigWg3F9m20Q75798h/NiNhqHx0jnVhxolFp4bVoEZYFNHgF6LOrHjEuzz2GEWvfPazENra4Mwrxitf+xnEAwcgvPwy9TrYtQv4yEdG1fA0t6UOAOArnwFBIeBYYzf+9G4Vfv3GWfz5vSo8vacOtUupOVH1U8/j77tqUdPhhlIhYGmxFXesLcWyYhvFSzU1kSteoQAWLhzR5wuCgEvm5sCsU6HTHcDrx1tpoS4II4t3CQajThiFgjIar7wSWLQI+Pvfo9UuW7b0cb8C1HwpEBKhVgpIl7IZGWasZJi0gCCgY+5iOjAJ8S7eQAivHmuGwufD7G1ShNHNNw//wjvuAPLyaNH9yCMAAMOBfdB2tCFssSC4YQP8wTDeOd2Gx3fW4D+Hm/D8wQYcmk2l2I3/fAF/3VaFFw81oscbRI67ExXvUba66jOf6ftZP/85VYO43dRzweUCSkuBlSvPG1aWRYfM9SSYKQ4fgtc/ShFy2zbg/e+PxsXJ/REGI8aR3u6S89F5bsCMD60U7aLxutHe0BrfD3M4In0NXl98YSR/+IoFuVAMEqFyHiYTiekA8Pjj0KgUWD8zAx+db0XWOaqKPTZzMZ4/2Ignd9fhcH0n3jvTjucONOBPTgN6MrKh8HjQ8uKrCIsiNj//FwiiSI0BFy0a8z9tZakNRq0STncAZ0olYXLHjoErTXp7ae3x5S/TfGkc1DvJkFDcIxmSOB+dGS2yse3gwYhxS3Y2xw1RjDjSD8MEb4Cc6Nctze+7mfX//h/NneUeSMNgXUR/e8XOJhi1Srj9IeyrceLRHTV4anct6v/6JBAKoWvWXPy+HnjxUBNONHXDFwjDqCUx/n0LcwfWLuS1whBCOhwOKKV+cQ3zluGdjFnw/utZEuJffJHmPMEJ3LCUq1tnzUKnyQpvIASVQmDDzQTCQjrDpBqSI13Z0Y5sqbynId456ZKQ7iyZgS5PACatCkuKrEO/ZrgmfceO0QXHZsMHrlmDS+flYF6eBWl6EgSburzYU+3AS4fpQhgKi8hP1+N9i6gJyZIiK5Vk9ecb36CJ+M9+Rs61YbCbtNh09QY889Mn0JWdT06SdeuAn/50VELgkPTLRweijnS5GSPDjIciO0UY1TrcfeNdJpLDh4HbbgPq6iDm5+OdL34Pj/7pP0j76G1IN+lIYP/3v8lp+s9/0t/iSDlF1SmWhfNw/fJCZFm0EAQqyez2BNDQ6cGR2eTkNr33NkWl5pjxkTUl2DgrkypSZORzTkXFgNFOg6HXKHHlwlwoBAGnW3pwqF6qTpEd5EM1HH37baq+ycigc8gXv0iiwJEjNLn+7nfpeQPlo/dGhbMRCw4MMwgZ0iKrYaYk+EyCkP7WqTb0eIOYd+BdqHt7gMJCYMOG4V+o1ZLgBAA/+AE5up57DgCguPJK3LC2HBfPzYZeo4TD5cfplh5UtrlwqIKE9MyDu9HT6YJCELCixIYb9r8MIRAA1q49r/IDKhXwj3+Q80wWuD/0IdosG4D5F69GSKWGtrcHe98aRbPhvXuBK64gwT4vj4698cbQr4nJSJcbD9uN7EhnxoleD39aOgCg62x1fD/rX/8CAgH0zJ6HU9ZCaNUKXL04f/QucCneBU89FXF4GnbvhCCKCM+YiTnLKqBWCmjp9mLriVbsqXagqt2FHl8I1Sso9mXR0R24Os2Hgv9IZoL77x/XP02rUmJtOa293jIXQ1QqacO+ru78J3/+89GGp2fPjutzGyQhPa9bisvjWBdmtMQI6TmScau1x4fwQLEoE0VnZyRSbl+Q5uDrZ2Scfy4QBDKejBTJoGdrrMGd68vw/sV5KM8yQSEIaOrywv/PZwAAx1ZvQSAkwqJXY2mxFTesKMTH1pdhfn7a4D2TRiKkb9sGgEyFaSX58AfD2FmyqO+65847J047GCAfPcuiHbS3AzN6WEhnmFRDEtLR3o78dCrviXvDUWkH9mwGTeJWl9kHFrFjGU5Il53qS5ZAp1Fhbp4Fl8zLwUfXl+Kj60txybxszM2zIMOkwewcM25aWYQbVhRiRpZ5aLEpI4MuarffPuJ/Xm6aHukXrscTv30ODRdfRQL/F79ILpa2thG/z6DI0S4xjUa7PbRA4J1lZiIokpp41Tnc1PxGowFOnow2CZ4IZLFn4UK8+98d2HfJB2GxGLCiJGZTbeNG4I9/pPvf/37EZTri9541i6KfVhXjUxfNxJ0bSnHjikJcuTAX+R+gRqoZ1adxc7EGly/IRZphAAd3zLlltOSm6bFhFp1j3zndhtZu7/DnMoCaFQO0iVdURGXdtbUkoEtVRBCEgfPRezgfnZk4ZAG2tmxyhPQzLT040dQNQQDW7iJHKm66aeQL5Lvuotiz6mrgiSeikStXXw1BEDA/Pw23ry3B5jlZuLAiCxfPzcbK912AYGYW1D4vbgrX46PrS7C+2ALlH35Pr/3Upwb+rPR0co5ZrTS+IVzzar0OoblUSu7YtofOrcNx5Ahw6aVAdzfFurzzDh3fv5/KtAeitzeSyxooK+ccVGZCCebQZo6nsmbwJ+3fT7+H40GKdTm0/jIAwJULcmEdy2bQli3UILG9PeJwx1tvAQAUmzZiw8xMfHR9KVaU2FBg1WNBfhouqsjCDSsKUXHHDQCA8r3vouzPD0MIheiaO0DVyWiZm2tBlkULt0qLntnSubV/vMvzz0fnP0A0J3oMUD46nXMy5GgXdqQzo6WigtYD3d2wtTZCo1LAHwzD6fbH7zOl33u/1Q6vUoPcNB1K7CM3tQyKXOleVQVlOITyTBPevygPd+WFcNv/3Y/SPXS91d90I25eVYSPriOjTX66fniTykiE9Hep0aiwYQPWSRtrh+u70H3hFtqkVyqBRx+l6reJIDYfvZt0nhyOdZlQWEhnmFQjRkiXOzfHXUiXxLiWgjLYTRrMy7MM/5pRCOn9SdOrMS8vDZfOy8Gta0pwxYLcuC8qV5ba4Tdb8PQXf4LuX/yaSrVefpl286VJ/JjpF+0iNzO0GtTcTIyZEAqsBggC0NHrR49GH23OOZHxLpL7yjtjFg600O/whbOzKE4llo98BPj61+n+3XeP7O8nRkiXUSoEWHRq5KXrMSvbjMVLZkTcNVk73h78vcYhpAPAksJ0lGeZEAqLeO9se9TZeuRI1MkaSzAY/Tlff330uNVKP4fqauDPf6bnxFSlyLRxPjozgejUSph1KrTMmk8HTp8mh1gc6PUF8foJioxYbVVA/79X6IGRxLrIGAy0cQ0AX/kKVaeo1dGIKtC/aWFBOhYXpmN+fhoqctOguoSi2zJ3vQezTg088wzQ3EwucLkqZyBmzqRzxM6dQzY5AwDNMprHZFWewOsnWhAYqin6qVN03nU4qCHqSy8B5eUkZIgiVa0MhJyPnpmJeugQDIsw61TsSGcmBqnBvb+mduDH//EPusZ94Qtj/4zGxsh1/uSmK1GaYaReJWNBpYr2LXj8cbqV/3Y2bQJAPY7Wz8zA9csLsWVuNhYVpiM/XQ/1pZfQ60+fpmsuMLrKuCFQKARsnEWb4pUzpGbOsQ1Hm5uBj32M7s+Xzr3jENIdLj/cfopyMDZT3jQ70plRo9FErnOKw4ci88zmeOakS7/3XbYsAMCacvvgTvDRkJ9Pa/NgkNbVdXXA3XfDsHA+7M/9k2KcPvlJLLlsLbIsutF9piyknzgB+AfZZJCEdGzYgEKbHoU2A0JhEbsqHWSi+SE1No6YAcbD8eMRBzwuuACNnfT/lccb7BMKC+kMk2rI7sb2duRJjnSnOwD3aDM8R0o4DFES0juKZ2DdjIyRxQ/IItbZs+TO6s84xa6JxmbUYFa2GRAEvHPhdZSTXlFBC4TNm8fn6usX7dLSLcW6cKNRZoLQqZWR/MM6hyc+8S6SkF5tsCMsipiZbUJJxiCL5W9/G7jxRirNvu66SHTLoAwgpA+InJ/6wx8OnkU4ykaj/REEARtnZkIhCKjpcKPFmk2ieCAQqc7pgxzrYrcDF154/uMGA/DRjwLXXDPg57VFHOksnDETQ6ZZC2+aDb5CSXjZuzcun7PjXAe8gRCyLFqs3PcmLUDnzx9xb4II99xD/RVapRznCy8ELMNs2MubhXIjz1//Ovpe6mF6DRQXAytWDD8uaeMut/IkOt0B7DjXMfDzqqpontDaSq955RXAbKbH5PzXweJdYmJdqtupOWGJ3TgxwgOT8qiLCwEAisYG+IMDbAT96ld0O1TzzOF4+mlAFNE4byl6svOxstQ29vcCovEuzz9Popx8TZebDA+GxULRjAA1Wt64EVi/fnxjiaHAasCsbDOaKihvXZR/ZqJI1/j2djr3PfwwHR+HkC5Hduam66GQq1rZkc6MhT7xLpPQcFT6ve/JyEFeui5SMTtuFAranAaAT36SNsX/+Ef6W7/8cupjJP/tjZbCQurvFgxSNW9/3O7oPGrDBgiCgLXldgDA8cZuOF1+6osE0PPG23j0m9+k88q118JfXIp2yXDDlWoTCwvpDJNqyI70tjbo1MqI+CLvVk44tbUQ3G6EVGoY585G2WDC2UDjLKQJPA4e7PtYOAwcos7XkQv8FECe/J9p6UV76Sy6GF5yCY13pBEVA9FPSJcd6dncaJSZQAqtMTnp738/lRkePAhUVk7MB0hCepM5AxqVAhdI7qwBUSiAv/4VWL2aIg1uvHHgxlwAPS5HKA0npH/mMyRYnzhBJZQDvVd1Nd0fx7klzaDG7BwTAGBvTefQFTb//CfdXnstueFGgTcQQo+XNgQ42oWZKORNtfY5kqAdh3iXHm8AJ5pok/zC2VlQPPl3emA0bnQZsxn43Oei3w+y6dSHzZvpds8eaua5cye57+6+e/SfPxjSRn9+DW0E7q91Rha0EVpbaSwNDcDcucD//kcRMjLDCemSI12cNQtVspA+0nkWwwyDuojm4aa2FnS4+v3uHjsWbWhXWTn4NXo4pFiXk5uuRIFVHzH5jJmlS4E5cwCvlzLHw2GgrCzirh+SmEqWiXKjx7J+Zgba5kkGoP37qUrtd78D/vtf6vnwxBNRsa+hgUS+MSA3Gi2w6qPxkOxIZ8ZCrJCeJjccHaC6coLwVdPva09mNtaUZUzsprAc7/LKK/S3t3EjRaC8/PKYzTMAKHpxqHiXXbtIZM/Pj2xo5aXrUZZpRFgUsaOyg9YvVivg8QwdETMcBw/SukIQgG99Cy3dXogiYNapqPqOmTBYSGeYVCMm2gWiGJmwxivepWsvCd7OglKsn5MzugviYOLT2bOAywXo9dT4a4qQYdJiZjaJZ3uqHIDRCHz2s/Tgs8+OrYGIzxfJPz3Pkc6NRpkJJDYnXbTbo+6tCYp3CdfIk+NcrCq1wTLchE6vp8aBBgNtnO3aNfDzZEdmXh416ByKtDTga1+j+w8+SBPWWORKl9JSmtCOg+Ul0sZaaw+8CxfTwX37+j5psFiXESK70S16jnliJo6yTBJiq0sp4zseQvqB2k5qAm7VI6+3Ixq/IMcyjJZ776VNMp0OuPrq4Z9fWEjzh3CY3KAAbdhlZ4/t8wdCWlirGupRofFDFIG91f2yzr/+dXKkl5eTOz6z3wajFEeBY8eAlpbzP0M6/3lLytHlCUCpEFBo4xxUZoKQxGdjRwtau/uJZ3/6U/R+by+tK0bL2bPA7t0IK5Q4c8FlWFVqH8dgJQQh6kqXN6qHc6PLXH89zTkuuSS6iTWBpOnVKF+zCO40KwS/H6EnnojG4vzwh1SRk5tLRoZgMFplMwpi89HzdQLFxgAspDNjI0ZIz5bWne29PgSHiiobBx0nybyjKCyc+GuZ3MR85UratH7zzWgVyngZSkiPiXWJbVAuNyE+1dyDVpefzENA39in0fLAA3T7oQ8BCxZEYnjYjT7xsJDOMKmGLKT7fMD112PxL76LJf9+BIpn/02L5YlojhlD3Xu0APfPrkDuaJtcDCaky2LXwoU02ZxCyK70Uy09cLj8NBG3WEgMH0wIHAq5tFOvBzIyuNEoEzfy0nVQKQT0+oL0uzvB8S5BKWNVUVSIJUUjFKmzs6Pj+OtfB37OSGNdZD7xCRLR6uuB//u/vo9NYGRUhkmLskwjRBE4nT+TDvY/l73zDp1zbbaBY12GobWH89GZiSfTpIVFr0bDTCmrd4KFdG8ghCMNXQCA5cVWcqSKIjXYHKvYk55O49y7lzbVRoIc7yJfZwdrMjpW0tLICQtgVRdV5Jxq7kG3VyrbPnw4msX82GMkoPXHbo8KGW++ef7j0vmvKYsEz7x0PbSqqTUvYqYx+fkAAHN7c2TjFgC5vftXdY2leu2ppwAAtUvWwFJSMHHC2Yc/3Pd7eUNqOMrKaL7+4ot9BK+JZEWpHW1zaY4h3HMPbehffHH0/KNURs8FY4h36XQH4PJRPnpu3Vk6aLXSPINhRssiiiJCbS0snm7oNUqEwiLaeye+4Wi3N4BADV0r8+bNmPiIss9/nqpOd+6kv7mJfP+RCukxZJq1qMihGLcd5zrGL6Tv2kXnLoWC4l0ANEkNyHNZSJ9wWEhnmFTDaIwuVP/1L9j/8DA2/e4hrP/qPbRDm5UF/OY3E/JRtR1uCCdOAACsKxaP/g2GE9KnSD56LFlmXUQ8213loFLNq66iB8fi7I2NdRGESKxLOjcaZSYYlVIRqVCpdbgpakQQaMI5jqxOAAh4vFC3kbNq1oq5UI6kT4LMHXfQ7VNPUc5gf0YrpOt0wLe+Rfe//32gqyv62ASfW1ZIrvQDGSSm4dChvtmHsbEuw+UyD0BlWy8AID+dJ8jMxCEIAsozjWidOQ+iQkF//7KrcQI4XN8FfzCMDJMGpRnGaFPAscS6xFJaOmwD0D7IQjpAC9iR5J6PFulcYjtzHIU2A8KiiP01Tto4+OIX6faGG4A1awZ/j6HiXaRolyobCZ6lGROUJ8swQMSRbmpriWzcAqAqS4eDHpcdnaMV0kUR4b9TrMupC6/EylLbxAlnJSW0MSczUkc6QOYXTfx6jmhUCug2rAUAKAIBiDYbxT8qYmQZOYZmDHMvOdYlJ00H1Xap4eC6dXHbGGCSnLQ0urYCEA4fjuSkx6Ph6N5qB0ztNNewV5RP+PtDEEgDicffwmBCejAYFcb7CekAsLrMDoUgoLLNhfYFku4xViFdjqP6yEeAWbMgiiKau+TzAVeqTTQspDNMqiEI5Np69lngl7+E+IUv4NyFV6Bx7hKE5JLiJ54Y98eIooj3zrbDXkOLPP3iUTYPA6JC+okTfQU0OTN9CuWjx7K6jEpTTzX3oNPtp2aJAAnpo82QPC8fnRYy2dxolIkDRfaYnPTc3Kiw9M4743rfswdPQxBFhNQalM8tHd2LN26kRXF3N523+jNaIR0Abr2VMlQdDuBHP4oeH2ej0f7kpetRYNXDkVOIoMlMlUDS5iJCoejm2g03jPq9e33BSEOxmdnmCRkvw8iUZ5oQ0BvhLJIWsxPkSg+EwjhQS/Emy0tsEI4do4WnWg188IMT8hkjZtOmqHg10W50mZiy+OXFVIlzrLEb/pf+Q9nsGg3wgx8M/R6DCekdHXQOA3DaRJE0xXbOR2cmEEnQ1fd0orOjC6GwNIf94x/p9qMfpaZ9AHDu3Oje+8gRKE4cR1CtgfOSK0beQ2mkyPEuJSVTLtYk65JNkfvnvvPT86toxiWkS7EuVj3lPwMT2jSVSUFirmNZUn+uiW442uUJ4GhDN0xt0qb9SHoaTCXkTfympr7V/QcOUByt1TrgRr/VqMG8PGqO/o61DKIgUNzbQFFuQ/H22zSnUKsj8S7d3iBcvhAUgoAsrlydcFhIZ5hUJDOTmnF9+tMQfvITnPzFn/CPXzyFo0//lx7fu/f87OBRUtPhRkuXB7YaaWI9d+7o3yQ3l6IdwuHoDq8oTmlHOkAid2kGNRDZXeUALruMXLCVlaNvINKvSZA8ceELIhMP5Jz0eqcH4bAYdVicOjXm9wyHRVTuJ/E4mJsH5WhjBxQK4Pbb6f5A8S6ykD6afgkqFbnRAeAXv6CJr8sV/XdO4LllRYkNUCjQUj6HDshi/TvvUP7pGGNdTrf0QBQpkmfYvHmGGSX56Xro1Eo0zVpAByZISD/e2A23PwSLXo3Z2eboxv0VV0x+9EB6OonYd901ph4FI0IWIA4cQLHdgEyzFgGfH8EvfJGOf/rTEbffoGzYQHEP585F5wRA5NwXzMuHV6ODWaeC3Rg/Jy2TgqSlQTTQvEDX2kxVkWfPUsyQIJCQLjfHHKUjPfQENRiuWrkRixeUTHyMw0c+Anz1q8Af/jCx7zsBCBs3wnnjzXjvjs/jjfkbzs+bLqQmr3KT9pFC+ei0fitMZyGdmSBiG45KRq6JFtL3VDmg7OmB1k2VlnKs1LTBbI5EueHIkehxOdZl3bq+VScxrCyzQaUQUBNQIVAhrRV27hz5Z4ti1I3+sY9FGpo2S7EuWRYt1EqWfSca/okyDIM8KRagypJN4nUgAOzePa733F3tgLmtCRqvm3ZH5U7Zo0EQzo93aWoi8UmpBBYsGNcY48mqMhIETjT1oEuhAS69lB4YyFE7FP0d6d3sSGfiR6ZJC51aCX8wTGWbsjg9DiH9TGsvBMlVpS4tGdubfOQjdPvGG32FpHB4bI50gBoSrl5N1S7f+Q5tcoXDQE7OwFnFY6TYbkCWRYvmcmkzUT6XjTPW5UxLDwBgFrvRmTigUAgozTCieZaUkz7OOQFAm2r7asiNvqzYCgVE4O8kpo071mWsfOlLJLSN4W9wRMibcidPQvB6sbzEigX//ScMZ05RU+evf33497BYotVBsTnpUqxLTxEJ8aUZxokXI5nURhAgyPEuHS042dQTbTJ66aVk8pDFo9EI6aKIoBTrUn/p1ZiVFYfrmEYDPPQQZSFPNVQqWP7+GE7e8Um4fCEcb+ru+/gYHeldngB6fUEoFQJyWutovaTVAsuXT9DAmZQkVkiXsrYdLj98wdCEvH2XJ4Bjjd0wdUgu7PR0wGSakPeeVAaKdxkkHz0Wi06NhYXpAIC6WdJ7jCbe5bXX6HO02j5zisauaMwTM/GwkM4wTCQXubHbC1E+0csuhjFQ73SjwelBZq3U5GbWrLEvUmUhXXahy7cVFdSAc4qSm6ZHsZ3yUPdWO/rGu4yGGCHdGwihixuNMnFEoRAizb5qHe5xC+miKGJvjQOW1iZ6/6LCsQ2spITiDUQR+NvfoscbG0kIVyqHd3X2RxCikQp//GNU2J7gShdBELCixIbWmVTSGd67j2Jd5CauY3DCdnkCaOz0QhA41oWJHzOyjGiZTRvW4p49o48m68fp1h50eQLQa5RUyrxtG13jzOZoL5FkIy+PmryHQsCxY5ilE7H20V8DABo+82USDEaCHO8SK6RLm4jN2bTRzrEuTFyQG462NeN0vQPiI4/Q8bvuotsxCOnBbduhra+FX29A1k0fgGI0fVOSBKVCwDIp7mlvtZOqAGXGKKRH8tEtOqh3bKeDK1eSwMYwY0UW0o8fh0GkijJRjJq7xsvuKgfCoohyH220T7tYF5n+QrooRvWUIYR0gBqvqxQCzpVLJsGRCumiCNx/P93/xCf6OPmbudFoXGEhnWEYZJq00KgU8AXC6F0udYyWd1DHwJ5qyuyc65QmgGOJdZGRRS3ZxTnFY11iWSVlpR9r7Eb3lkspTuLIkYiLbETERLucaaFyN7tJw41Gmbghx7ucJ6SHw0O8amBqHW60dvtgaSchPVKuPBbkpqOPPBIdi+xGLysb22bdxo0UvRQMAj//OR2Lw7llRqYJ3gWLAADiwYMkhsmxLrJANgpkN3p+uh4mrWoih8owEYpsRjjL5yCkUkNwOCi3c4yIooi91bRIXlyYTmXG8mbSdddN6Y3xcSEIfeJdFD/6IfSdHXAUlOJ/G67pK54NRWxOuryhIZ3/WrOLoIzZBGWYCUUSteydbch793UILS0Uu/i+99HjspBeX099QEaA88+PAgBqNlyCirLsCR/ydGFeXhr0GiW6PAGcae2NPjBmIZ3y0Qus+hE5YRlmRBQWUsZ3MAgcP47sCcxJ73IHcLyRKjLmiTS3nfZCuhztcvIk0N5O85tly4Z8qVGrwoKCNDTNldYge/ZQQsBwvPgiPddgoCgriWAojDapr1quhecG8YCFdIZhoFAIkcyzxgVS+d/27eSgGiWt3V5Ut7upMXaLJAIP0FxjxMiO9CNHAL9/yjcajSU/XY9CmwGhsIjdnYjmII803kUUI450sbAQB+s7AdDEm2HihSykN3d54S8spg0gj2dMTa/2SMJZXk87HRiPkH7ddRRxUFUVbX461liXWB56qO/3E9RoNBaFQsDMDcvg1xmg9LgR/u736IFrrhnTBsBpaVNtdg670Zn4oVEpUJCbjrYyaUNtHDnpNR1utPX4oFEpsFgqYcbrr9NtsrrRZeTNuRdeAH72MwDAro9/BV0BcumPiLVrKaqivp4yqoHIpryzoAT56XpoR9t/gmFGgiRqFbgdmP/y03Ts9tuj167MTMBopDlrbPTaIIT8ARhfoOpMxU03QZmCbnSZ2PPhnmoHRHmTLFZIH6GJITYfvcBq4Hx0ZuKI3RCOyUlvngAhfVdVB8KiiJIMA6yOVjo43YX0o0dJQ5E3s1atouv3MCwrtqKrqAxek4XWXcP1VQuHo9non/kMkJUVeai1x4dQWIRBo4RFz4abeMBCOsMwAKLxLlW5pSRW9fSMvjEmKBsdACpyzNCcogaD43Kkl5RQ6XMgABw7Nq0c6QCwpjzqSndd+X46ONJ4l/Z2wOsFBAENRhvae3xQK4VId2+GiQdpejUsejVCYRENrmC0kdgo411aur2oc7ihEARYHVLu4XiEdIMBuPFGui83HR1Lo9H+LF4M3HRT9Ps4nVsq8tPhmElNhBRvv0UHxxDr0un2o6XbC4UgYEbWNMyQZKYV5ZkmNM+WFofjENLlSrX5+WlUUdXURNd0QRhTs91phSxAvPQSOXY3bYL1xg8AoEgHcSSROXo9sGYN3Zdd6dL5z1lQgpIMQxwGzjCIRAVkHd2Pkn0kzrpuuz36uCBEXennzg37drXPvQKDox0eixVFH7p6okc77VhcmA6NSoG2Hh+qO8hRjtxcakwYCABtbSN6n25PED1eKR/d00kbboIQPW8wzHiIEdLlPl31Tg8C/RvljoJOtx8nmmgzeU1ZRtSwM12F9LIyWqt4pabMo6wKMevUmF+YjqYKqmAdtuHos8+SVmOxAF/8Yp+HmqRYl5w0HfdOiRMspDMMAyDacLShJ0DOJ2DU8S4dvT6clUoTl+eZoqVN8+ePfWCxDUfffDOawTgNHOkAudLLMo0IiyJ2LlhP/55du4CGhuFfLOej5+TgUCtdECtyLBzrwsQVQRAGj3cZBbJwNjvHDKU8OR6PkA5E412eeYY2++QxjceRDlCzUaORmiKPNmt9hKiUCiiWRd3uotUKbN486veR3eiFNj0MGnaZMPGlLNOIViknPbhrbA1Hm7o8qHd6oFQIWFqUTgffeINuly4F7PYJGOkUJna+IgjAT3+KxUXWiHhWI4tnwxEb7yL1hwgrlOjOKUAJ56Mz8UIStVQH9kMQRdQuWoXjhqy+z5E33IfJSfcHw2jbSmsLz8YLodZxdrdOrcT8fKo0ledNUKup8Tkw4mrAOinWJduihWanlI++cOHI+zAwzFDECOl56XpY9Gp4/CEca+we8mVDsbOSstFLM4zUEHO6C+lKZVTzOHx4TPFKy4ptaJm7GADgfnuYfnX/9390+6lPUVRkDNF8dI51iRdxE9K/973vYe3atTAYDEgf4QlcFEU88MADyM3NhV6vx5YtW3BmNFnCDMOMGdqxBLo9AfjWSEL6KBuO7ql2QhSB8iwTMo4foiaAmZnjc4sCUSH9L3+h2+Li8y4YUxnZlX44ZIB/5So6+Nxzw79QKpENFRZFNigWySXxDBNHxiukO13+6KZalhbo6JDeuGh8A1u9msbjdgNPPz0x0S4AiQAnT1Jznzg6N2zrV0fuO7ZcPqZYl1NSPvosbjLKTAIGjQrhZRT5JuzfP6bINzkbfXaOGWad9Dsvx7qMYTNp2jF7djQD/tZbgaVL+4hne2ucI3uf2Iaj0vm4K6cARpMeNuPwZeMMMyZimtcBwNErbsDxxu6+lRQjbDi6r8YJyxmqVk1bvXxChzmdWVqUDqVCQIPTg8ZOimcZbU56QyfHujBxJEZIVwrAihK5Ua4DoZH2+ojB6fLjZDOJ8PI6OfK73u+cM62Q413+8x9axyuVo6oKSdOroV6/DgAQlhsGD8SZM7SpLgjA3Xf3eSgYCkf6JXCj0fgRNyHd7/fj+uuvx8c//vERv+ZHP/oRfvWrX+F3v/sddu3aBaPRiEsvvRRe7/jzlxiGGRqtSolMs9Q8ZMEKOvjuu9GmVsPQ5QngVDMJPCtLbMDWrfTARRdReeJ4kIX0Y8fodpq40WWyzDpUSFnGp9deTAdHEu8iOdKdGTkIiyIKrPrI/xHDxBO5aV17jw++8hl0cBRC+r4a2lQryzQiwyllHppMQNo48/0FAfjoR+n+738fbX44XiEdoEVrRsb432cIVCtXRO7vWHYR3P7gqF7f0etDe48PSgXHujCTR+bKRZTv73YBJ06M6rX1Tnd0U62YFt4QxaiQvmXLRA51aqJUAnfdRTF33/9+5PCSonQoBAF1DnfEPTYkK1dS2XhbW6TXirOgBKUZRi7dZuJHjDtUtNlQc8ElcLj8ffORRyCk9/qC2F/rREYVbYArFy2My3CnI2adGnNyKbYx4kofpZAezUfnRqNMHKiooJzv7m6guhpzcy0waVXo8QZxomn0rvRdVR2RdYIcFTPtHelAVEh/6im6XbKE1j+joPyqiyAKAkz1tWg9Wzvwk/70J7q9/PLzTEoH6jrh9odg1qlYSI8jcRPSv/Wtb+Fzn/scFixYMKLni6KIX/ziF7j//vtx9dVXY+HChXj00UfR2NiI54Zwbvp8PnR3d/f5YhhmbMg56ccLZ9PFsrl5WHeJzL4aKs8qthuoPEsu25YdVOOhf/O/aZKPHsvqMjsUgoDdizfSgbffpgz0oZCE9AZzJgBEG7QxTJwxaFTRjbVcKepkhEJ6ry+I49KkenmJDairowcKCyfG7X3rrSRM7dlD7liDAcjLG//7TgZz5kBcuBDOGRU4t2AVtp3tGNXL5ViXYruBI56YSaM8Jw2tM6lpeGDnrhG/LhgKY+sJ2kibn58Gu0naCD59mhbMWm3qOCZ/+UsyA8Q47Sw6NSpyaZN9+7n24bPSNZqIMCY++igAoDO/BCUZHOvCxJGsLGo6DkC47TaUFtCG87GGmDX3CIT0nec6EPR4YauTnrOQhfRYlhVbIQhAZZsL7b2+aBSePIcagi5PAN2eABSCgFxFEDh4kB5Yty5+A2ZSC40GmEfzABw6BJVSgaXS5vieagfCo3ClO1x+nGyWs9ElN7rbDTj6bSJNR+Tzms9Ht2PYzErPzURvORmEql7aev4T/P5or6h+bnS3P4jdVfRzXFueAZWSk7zjxZT5yVZVVaG5uRlbYpwpaWlpWLVqFXbs2DHo6x566CGkpaVFvgrHm7/KMClMRY4ZggCccAbgXSSJ1yPISe/1BSMT6hUlNroYyn+3EyGkz5zZdzd3GgrpVqMG8/Is6MothHPWPBIAX3xx6BdJ0S4dGbkw61Qoz2QHKjN5yPEulXZJ+Kmtpb/tYdhX40QoLCI/XY/8dH1fIX0iyM0FLrss+v2sWXGNY5lQVCoIBw/Cs3MPwmoNjjV2jcyJCjIcnJZiXWZmcawLM3mkGzTomk/Nr3rfG3xO3p/d1Q44XH4YtUpsmBlT7SG70deti0aepCgrS2xQKQTUdLhxpKFr+BdIcypBMg51FZSg0MqNRpk4olAAK1YAOh3w//5fpOH9qZaeaKPBWCF9gA2h9l4fjjZ2wVZXCUUoSNVp01ksiwM2oyZSaba32jEqR3qD5EbPtmih2bMLCIeBkhL+GTMTS0y8CwAsyE+DXqNEpzuA0609Q7/W5wNuvx34wQ+wq7IjEgWbJbvR5d5hRuP4q1cTSX8T8RirQtTrKGZX2LkTrT391gnPP0+Vabm5wJVX9nloZ2UH/MEwsixazMnltUI8mTJCenNzMwAgOzu7z/Hs7OzIYwNx3333oaurK/JVN4JdW4ZhBiY3TR9xPZ+aKXWMHkFO+oFaJ4JhEXnpOiopfO896jRfVBRtQDQeFApg0aLo99NQSAeAVWW0YD6xWsqEHS7eRXKk92TlYkF+GhSKaSIWMkmBLKSfDesgyj0JhulbUtXuwoFayvtdUSq9ZqKFdCDadBQYfw+GyUYQkGc3YU6uBaIIvHmqdXgnKoC2Xh8cLj9UCgHlWexAZSYX1aqVAABh794RPb+91xfJRt80O6tvBYUc/ZYKsS7DYDVqsE7aZHjndBucLv/QL+hnTlBVzIZGNWWWc0yy8p//UKxTRQUKrNRo0B8M41wbVUmhpIQ2tHt7B6y2fO9MO0QRmNdBBhEsXDh9NsAnkRUlNG861dwLd+bImo0GQ2Ecqu8E0C8fnWNdmImmn5CuUSmwRNIN9lQ5hp7LPvYY8Le/Affdh44dewAAq8ti+p3FxrpM53ODzdY3432MVXe6DVRNknviYMRhHuEPf6DbO++MVAsBFP94pJ422S+YmcmRb3FmVDOvr371qxAEYcivkydPxmusA6LVamGxWPp8MQwzdtbNyEC6QY2qCkmsHsaR7g2EcLieXFQrSmx00o6NdZmok7gc72K3T1uHhVmnxqLCdJxdRznp4muvAT2D7+CHakhId2XnYUHBNN6dZ6Yleel6KBUCerxBBGdIGeRDxLt0uQP479EmiCKwsCANpXLcQDyE9Pe9j84FwMTkoyeA9TMzoFEp0NzlxbHG4WPpzkixLiUZRmhVHOvCTC62kDwjnAAAJ21JREFUTbQYNJ86jqDbM+RzRVHE1hMtCIVFlGUaMTM2zz8Uis4RUqHR6AhYUpiOIpsBgZCIV441D924bcmSPm69tMXzJmGETMpjtZJYDkAQBMyV8rwj8S5abXRufu5cn5fWdrhR1e6CQhAwtz1GSGfOI9uiQ7HdgLAo4l1vv9zoARBFEVtPtqK5ywudWklrBW40ysSLfkI6ACwqTIdWrUB7rz+6sdafcBj42c8i36584reYkWVCljkmvzsZ8tFl5PNbRQWQmTm295AalOacOoKzjZ0U9wTQ+fX110lfufPOPi9572w7wqKI8iwTCm1cqRZvRiWkf+ELX8CJEyeG/CqTS7tGSU4O7bq2tLT0Od7S0hJ5jGGY+KNWKnDx3Gw0zlsKURAoy7Tf36WMNxDCC4ca4Q+GkWnWRoWzicxHl1lLJU5YvXpa71SvKLGhZ8ZsOPNLIPh8wH//O/ATPR4o2yhbNnv+LBg0qoGfxzBxQqNSRJqOVsnxLoMI6YFQGC8eboQvEEZumg4bZ8VMHGUhvV8znPENTgN89au0eH/f+ybufScRk1YVceNsO9sObyA06HNFUYw0c56VzaWazOSTsWA2vGlWKIMBtLy3e8jnHq7vQmOnFxqVAhdWZPV1Re3bB3R1kRi8bFmcRz09EAQBl8zLhlZNG2vnuc9iUSoR3ki9VoIaLfLnz5ykUTJMFFlIr3O60e0N0MEBctJFUcQ7Z9oAAAsL06A7cYweGGEPtVTkooosGDRK1BtofiDW1w8YlwNQU8Hjjd0QBOCKBTlIU4rAzp30IAvpzEQjV4fX1ABOqjjTqZVYXJAOANhd5RzYlf7KK8CJExANJO7OfPdVrPP10xbkaJdkENJXrKDb8eggFRVAWhrUPg8yK09hjzwvkJuMXnppZHMToA3LyjbasNwwI+P892MmnFEJ6ZmZmaioqBjyS6PRjGkgpaWlyMnJwdat0UD97u5u7Nq1C2ukHRmGYSaHAqsBc+YVo72EFmiBd853pbt8QTyzrx4NTg80KgU2z5EWy52dtFAGJlZIv+EGKgn7zW8m7j0TgF6jxNJiG86ul1zpz/xrwOd5zlUBAPx6A+bNK5ms4TFMH7bMyYbVoEZrbgkAwHfs+HnPIfdpK9p6fDBolLhyYW7f5jZSRNGEOtIB4ItfBDye6IR1GrK40AqbUQO3P4SdlYM3Hm3p9qHLE4BaKUQ3LBlmEhEUCrgWLgYA9LyzfdDn9XgDeO8sRTusm5EBi07d9wlyPvpFF1HTYAYAVaxtrqB4y91VDjR1Dez6d/mCODqbNiB68othi3X0McwkkWZQo8CqhygCJ+SKqgGE9BNNPWjr8UGjUmB1qR04coQeYEf6oKQbNLh2ST782TkQBQGCzwexre2859V2uPHuaTrXXjArE8V2I3DgAM2L7HZgzpzJHjqT7KSlAaWldP/QocjhJUVWqJUCWrq9qOkYoJfST38KAKj8wC04s/5SCKII289+2Pc5yeRI/9KXqMH4d7879vdQKIBVqwAAOScO4mRzD9492gDxL3+hx2OajIbDfTcsrcax6bHM6IhbqF5tbS0OHjyI2tpahEIhHDx4EAcPHkRvb7Tko6KiAs8++ywAcmN89rOfxXe/+1288MILOHLkCG677Tbk5eXhmmuuidcwGYYZhHXlGWhfRAJV04uv9XmsyxPAP/fWRYSz65cVIDdNahj29ttUwjV7dt+MsPGiUAC33QYUF0/ceyaIpcXpqL3wCgCA+NyzCFdWnfecmgMUk+XJyUdOemo3Y2MSh1mnxvXLCxGcSZtqXQePobW7b9Obw/VdONEkO6JyYY4VzkQxPtEuMtO4OgUAlAoBm2aTe/9QXRc65NLNfshNRssyTZyHzCQM5UrKSRf37sH2c+3o9QX7PC6KIt481QZ/kCpTFuYPEEkmC+mcj34es3PMqMgxIyyKeOVoM/zBcJ/Hz7T04LGdNXhv9eWoXHMRPF/8MmegMgljXh79fR9v6iYXaj8hPRAKY/s5EnpXldqg7+mMuk7nz5/s4U4rsiw6XLmsBG4rOUv3bjvSx+nb6fbjP0eaEBZFzM2zRHKqI3Gc69ZN+/kRM0UZIN5Fr1FiQcSV3i8r/eBB4I03EFYq8ealN2HXrZ+g408/DRyPMeckk5BuMgGf/jRFYo0HyUy8oO4EAKDz6X9DaG1FODsHuOqqyNOON3WjrccHrVrasGQmhbitxh544AEsWbIEDz74IHp7e7FkyRIsWbIEe2OaFJ06dQpdXdEO9V/+8pfxqU99CnfffTdWrFiB3t5evPLKK9Dp2G3BMJONRqVA1hW00NXs2o5aaYe5o9eHf+6tg9MdgEWvxg3LC6Mdt4H4xLokGVqVEiWXbUTNkrVQBAI484kv4pWjzTjT0gNfMIRwWETrMWrqqCyZ/hsHzPTGqFVhzeUUrZRWV4ln9tWhsZPckk1dHrx9mlwQ62dknJ/J19VFzceA+AjpSUCx3YjyLBPCooi3TrXBGwihodODw/WdeONkC57eW4dDdZ0AgFnZpqHfjGHiiHkDnQeyTh7BrkoH/vxuFV4+0oTGTg9EUcS5tl6ca+2FQhCweU72+Q2y3W5g2za6z0L6gFxYkQWzToVOdwDvSOdWbyCE/x5pwkuHm+Dxh5CWnwXL/15G3ifuHObdGCZ+zMiijd1OdwAnmnrQkUXmGdeJ09hb7cCrx5rR4w3CrFNhcWF61I1eWgqYOaJsOIrsBigKSVRsPHIGe2soSsMXpFhNbyCE3DQdNsfGZ3GjUSbeDCCkA8CyYiuUCgENnR7UO2mN0NbjQ9393wEAnN5wGdw5+Zh18Xrg2mvJaBPr2E4mIX2ikIT0rKP78b5FeVj036cBAPu3XIsT7fQz9gfD2HGOKlpXldqg13Cl32QRt9DdRx55BI888siQz+mfoSQIAr797W/j29/+dryGxTDMKLBfRo3Ass6ewGP7KnHR8jK8dLgJ3kAIdhOVHpr7l22zkD4iFhWk4eBXv4HiGy/HzNeew86dd+JE8QwoFQLsJg3KG8jFa5g5tr4TDDORaCtmQVQooHW7oGppwbMHBGyZk413z7QhFBYxM9uEZcUDOC9kN7rNBhi48c1gbJyZiZp2F2odbvz2rXMDPifdoEaJnWNdmMShXL0KoiDAXnMWa7f9B9vXXYlTzT041dyDLIsWLsmhvqLEikyz9vw32LYN8PtpoTyTs70HQqdW4tJ5OXhmXz2ONHTBoFXieGM3erxBKAQBK0qsWFVmh7L/JgXDTDIalQIzs0w41tiNV481I8drxE0AQucq8e6Z9sjz1s3IoLg3WUjnfPQRoy8tBg4dgKmtCe+daYderURluwsdvX6YtCpctSgvGqUXDnOjUSb+DCKkm7QqzM+34FBdF3ac68DRhi40HD2DO155AQDgvOdefGRNCdIMauCBB4BnnwWeeoruV1SwkD4QUrQLzp3DjKpjwF76+z50yQfQfbQZNR1uGLVK9PqCSNOrsUiqCmAmB64PZhhmcAoLIRYXQxEOwXRgL57ZVw9vIIScNB2uX1Z4voje0gIcPUr3L7xw8sc7jVApFVh+w2UQr7kWinAYVzzzW1gNaoTCIlq7fTC3NgEAFEkQZcMkAVotBCkXsaK7Cf5gGC8faUKPNwibUYOL52YPHDEQz1iXJCLNoMbKUlvke7NOhdIMI5aXWHHpvBzcvLoIt60p6Zs9zzCTTXY2hK99DQCw6vtfwe3hOszLs0ClENDa7YPLF4K13+9yH2JjXTh2YFAKbQYslTYmd1U60OMNwmpQ44YVBVg7I4NFdGbKsLTYCrNOBaNWCcWMcgCAub0Zc+1aLC5Kx+Y5WajIkdznhw/TLeejjxxp7jQzQBX8rx1vwbnWXqgUAt63KA8mbYwn8tQpoKMD0OuBpUsTMVomFZCF9OPHaWM8hmXFNigEcqWfbO7BoucegzIURGDdeqy58TIS0eX3uPpqcqV/73v0Pi1S81EW0qOkpwNz59L9e+4BAIgXX4K56xZDEIATTd3YW02VKutnZvAaYZKJmyOdYZjkQNiwAaipQf7RvahduhZFNgPetyhv4JzeN9+k28WLqdENMyzCd78DPP8cMl99Cbd/90E4Fy9EZXsv8rqlxkJFRYkdIMPIzJ4NnDuHdcE2ODONqGxzQaNS4KqFudCqBikllIV0/j0elpWlNszIMsGoVUGn5tJMZory7W8DJ08C//oXrLd8CJfs2oUNG8pwtLEL9U431pUPsZjjfPQRs67cjnqnG63dPiwuSsf6GRlQ8yKZmWJkmLT42AapclIUAZMJQm8vLjV6gdn9jCDsSB89kqhY6HZgTq4FJ5qosevmOdnISesXfSu70VetAjTcbJCJE4WFlP3tdJKYLgvrANL0aiwsTMPB2k7MMohY9uo/AQDqL3/p/Pd54AHg+eeBv/8duPVWOqbVsn7Qn9Wr6ecsVQAI/+9urCm3o9CmxytHKT4rL12HmVkc/TjZ8IyMYZihkcoDF1QexppyO65ePIiIDnCsy1iYNw+45Ra6f//9sBo1WFZsg7WdHOksQDJThtmzAQDKM2dw1cI8bJmTjeuXFcBuGiDCQYYd6SNGEATYTVoW0ZmpjUIBPPoosGwZ0N4OXHUV9J5erCix4dolBX17psTS0QEcOED3N2+evPFOU1RKBW5YXog7N5TiwtlZLKIzUx9BOK/haIRwOFqxyo70kSMJ6UJ9PS6em43VZXZcPDcbc/Ms5z9XbjTKsS5MPBGEQeNdAGDTrEzcs7EcV+59BYquLmDWrD6NMSMsXUrHw2HgM5+hYwUFXK3WHyknHQCQnQ28//0AgAKrATevKsbmOVl436I8bjyeAHhWxjDM0EgNa4wH9mJ1oWXosqGtW+mWF8mj45vfBFQq4NVXgbffpklFbS09xtEuzFRBEtJx6hSUCgELCtIGF81k5N9jFtIZJnkwGIAXXgDy84ETJ4AbbgCCwaFf8+ab5FidPx/IyZmccU5z1EoFLP0j9BhmKjOYkF5VBbhc5DidMWPyxzVdkWMu6uuhVAhYU27H/Py0gZ/L+ejMZDGEkC4IAvQKEfjFL+jA5z5HG/AD8eCDdHvyJN1yrMv5xArpd9wBqKNzAr1GiYUF6TBoOGQkEbCQzjDM0FRUUKNAjwfYv3/w51VX08RZqeRu8aOlrAy46y66/7WvUU6c308Tj7y8xI6NYWQqKuj21KmRv4Yd6QyTnOTlkZhuMAD/+x8tlodCjnXhjXaGSV4GE9LlfPR588g4wowMee5UV0cbkYPR0ECbFQpFX+GNYeKBLKS/+iqwe/f5v5vPPku6gN0O3Hbb4O+zfDlwxRXR71lIP585c+g8oNUCH/tYokfDxMBCOsMwQ6NQRN0NstthIOR89JUrAbM5/uNKNu6/nxoEbd8O/O53dCwvr8/OM8MkFNmRXl0N+Hwjew0L6QyTvCxdCjz2GN1/+GHgN78Z/Lmcj84wyc9gQjrno48N2Uzj9QIOx+DPk9dnixYBlgFiXxhmIpE3a06epEz++fOBn/wEaG4mUf2nP6XHP/EJ2mwfCtmVDrCQPhAKBfDOO8DevUB5eaJHw8TAQjrDMMMjC+ly/t5AyLEunI8+NvLygE99iu4/9BDdcqwLM5XIzqYFWjgMnD07/PNFEaivp/sspDNMcnLdddFr1mc+Q1FlbW19n1NdDZw7RxVrGzdO9ggZhpksZKFnMEc656OPDq0WyMqi+/J8aiDk9RlXBDOTwcyZwLZt1ONLr6dmmF/6EgnhF10E7NpFv7uf/OTw77VyJXDllXR/3rz4jnu6UlJCmxXMlIKFdIZhhkeemL33Holo/RHFaKNRLtseO1/+MgmVgQB9z41GmamEIPTJSR+WtjZyrgsCZSkzDJOcfOUrwO23A6EQ8K1v0bXr7rspPx2IbrSvXs0VawyTzMQ60mPjHtiRPnZictIHRV6D8UYlM1msXUsVaU1NwO9/T9f3UAh46y16/JZbyIAzEp58kuJgbropbsNlmImGhXSGYYZn6VLAZAI6OqjDdv/ywlOn6EKq1XI233iw24EvfjH6PQvpzFRjNEK6HOuSkwNoNPEbE8MwiUUQgD//Gfj73ynz1OsF/vhHYO5cyj/961/peRzrwjDJTXExnQ96e6OVKW43cOYM3WdH+uiRhXR5TtWfhgbatBQEYNOmSRsWwwAA0tJo43zHDnKmf/nLwDXX0Kb6SDGb6TXcP4GZRrCQzjDM8Gg0tEjW6YD//peE9b17o4/LbrN16+g5zNj57GeBjAy6z9EuzFRjNEJ6bS3dcqwLwyQ/CgW5yXbvppiBa68lYee//6UScIAr1hgm2dFqo8KvHO9y/Di50zMzR+5QZaLIc6jBHOmyG33ZMsBmm5wxMcxAzJkD/PCH5C7nSlQmyWEhnWGYkXHDDbTbXF4O1NSQaP6HP3Csy0RjNgOPPw5cfz1w442JHg3D9GUsjnQW0hkmdRAE6qvy738Dp08D995LzcZmz6amZAzDJDf9G45yPvr4GC7aRW7kzGswhmGYSYOFdIZhRs7ixeREf//7Ab8f+H//D7jjjmgeGjcanRguvRR4+ml2ljBTj1ghPTb/dCBYSGeY1GbGDODXv6Y4uMOHOeKJYVKBwYR0zkcfG0MJ6aIYrQpmIZ1hGGbSYCGdYZjRkZ5OJVs/+AGVcv/tb7RINpspG5VhmORl5kxynDqdQHv70M9lIZ1hGIDiHlhEZ5jUoLycbmUhXW40yo70sTFURvrp05SRrtVSJRDDMAwzKbCQzjDM6FEogK98hVwQWVl0bNMmbhLCMMmOXh9tgjtcvAsL6QzDMAyTWsQ60kWRHenjJTYjvX8loBzrsnYtzc8YhmGYSYGFdIZhxs6mTcCBA8ADDwA//nGiR8MwzGQw0px0WUiXhXeGYRiGYZKbWCG9pYWq1xQKYO7cxI5ruiI3bXS7gc7Ovo9xrAvDMExCYCGdYZjxkZcHfOtbUXGNYZjkZiRCejBI5cYAO9IZhmEYJlWQhfT6emDPHro/cyY1HWZGj04HZGTQ/dic9FAIePNNur9ly+SPi2EYJoVhIZ1hGIZhmJEjC+knTw7+nKYmIBymuKfs7MkZF8MwDMMwiSUjAzCZKIbkxRfpGMe6jI+BGo7u308OdYsFWLYsIcNiGIZJVVhIZxiGYRhm5FRU0O1QjnQ51iU/H1Aq4z8mhmEYhmESjyBEXekvvEC33Gh0fAzUcFSOdeEeVQzDMJMOC+kMwzAMw4wc2ZFeWQkEAgM/hxuNMgzDMExqUl5Oty0tdMuO9PER23BURm40yrEuDMMwkw4L6QzDMAzDjJz8fMBopBz0ysqBn8NCOsMwDMOkJrIjXYYd6eOjf7SL1wts20b3udEowzDMpMNCOsMwDMMwI0cQgFmz6P5g8S6ykF5UNDljYhiGYRhmahArpBuNQElJwoaSFPQX0rdvJzE9NxeYMydx42IYhklRWEhnGIZhGGZ0yPEugwnptbV0y450hmEYhkktYoX0BQsABUsO46J/Rroc67J5M5kbGIZhmEmFr2oMwzAMw4yO4YR0jnZhGIZhmNSkv5DOjA95LlVXB4hitNEox7owDMMkBBbSGYZhGIYZHbKQvmsX4Pef/zgL6QzDMAyTmhQXR53SnI8+fvLz6dblooq/vXvpexbSGYZhEgIL6QzDMAzDjI4NGwCdDjh6FLj5Zmo8KuPzAa2tdJ+FdIZhGIZJLbRaoLSU7i9enNChJAUGA2Cz0f3HHwfCYepVw3MshmGYhBA3If173/se1q5dC4PBgPT09BG95vbbb4cgCH2+/n97dx4bVbnGcfw3LXRaoAttKe0IlALKIoWwSCVERGhoK5dFEAFRAREVC7KJiBEQJRYhLldDwHvDUq9ClHsBI2oMOxrKEkiDoDTQABVpIULaUhBa2vf+0XR0bJmytHOGme8nmXTmnPdMn5M8eeacZ868JzU1tb5CBAAAt6NFC2nTJikoSPrvf6Vnn608sZP+vBlWcLAUFWVZiAAAwCL//re0ZInUp4/VkfiGqnnSMzMr/3I1OgBYpt4a6aWlpRo5cqQmT558S9ulpqYqPz/f+Vi3bl09RQgAAG5bSor05ZdSYKD0n/9IkydXzt3512lduAkWAAD+p39/afZsjgPqStXV58ePV/5NTrYuFgDwcw3q640XLlwoSVqzZs0tbWe32xUbG1sPEQEAgDo1dGjlz4yffFL617+kkBCpe/fKda1aWRsbAACAL6i6Il2q/HKiXz/LQgEAf1dvjfTbtXPnTsXExKhp06bq37+/Fi1apCg3Pw2/du2arl275nxdXFzsiTABAIAkjR4t/fFH5fQu//znn/OiMncnAADAnftrI7179z/nTAcAeJxX3Ww0NTVVn376qbZt26Z3331Xu3btUlpamsrLy2+4TUZGhsLDw52Plpy4AwDgWRMmSMuWVT4/ebLyL5/HAAAAd+6vjXSmdQEAS91SI/21116rdjPQvz+OHTt228GMHj1aQ4YMUWJiooYNG6bNmzfrwIED2rlz5w23mTt3roqKipyPX6vmZgUAAJ7z0kvS0qV/vqaRDgAAcOf+2kjnRqMAYKlbmtpl1qxZGj9+vNsxbdq0uZN4qr1XdHS0Tpw4oQE3+MCw2+2y2+119j8BAMBteuUVqWFD6X//kwYPtjoaAACAu19VjyU4WOrTx9pYAMDP3VIjvVmzZmrWrFl9xVLNmTNndOHCBcXFxXnsfwIAgDswbVrlAwAAAHeuTZvKm7rHxUmNGlkdDQD4tXqbIz0vL0/Z2dnKy8tTeXm5srOzlZ2drZKSEueYDh06aOPGjZKkkpISzZ49W3v37tWpU6e0bds2DR06VO3atVNKSkp9hQkAAAAAAOC9Jk2S/vEPq6MAAL93S1ek34r58+crMzPT+bpbt26SpB07dqhfv36SpJycHBUVFUmSAgMDdfjwYWVmZqqwsFAOh0MDBw7U22+/zdQtAAAAAAAAAADL2Iwxxuog6lJxcbHCw8NVVFSksLAwq8MBAAAAAAAAAHihW+kl19vULgAAAAAAAAAA+AIa6QAAAAAAAAAAuEEjHQAAAAAAAAAAN2ikAwAAAAAAAADgBo10AAAAAAAAAADcaGB1AHXNGCOp8o6rAAAAAAAAAADUpKqHXNVTdsfnGumXLl2SJLVs2dLiSAAAAAAAAAAA3u7SpUsKDw93O8ZmbqbdfhepqKjQ2bNnFRoaKpvNZnU4liguLlbLli3166+/KiwszOpw4MfIRXgLchHeglyEtyAX4S3IRXgLchHeglyEt/CXXDTG6NKlS3I4HAoIcD8Lus9dkR4QEKAWLVpYHYZXCAsL8+lEx92DXIS3IBfhLchFeAtyEd6CXIS3IBfhLchFeAt/yMXarkSvws1GAQAAAAAAAABwg0Y6AAAAAAAAAABu0Ej3QXa7XQsWLJDdbrc6FPg5chHeglyEtyAX4S3IRXgLchHeglyEtyAX4S3Ixep87majAAAAAAAAAADUJa5IBwAAAAAAAADADRrpAAAAAAAAAAC4QSMdAAAAAAAAAAA3aKQDAAAAAAAAAOAGjXQAAAAAAAAAANygke6Dli1bptatWys4OFhJSUnav3+/1SHBh2VkZOiBBx5QaGioYmJiNGzYMOXk5LiM6devn2w2m8vjxRdftChi+Ko333yzWp516NDBuf7q1atKT09XVFSUmjRpohEjRujcuXMWRgxf1bp162q5aLPZlJ6eLomaiPqze/duDR48WA6HQzabTZs2bXJZb4zR/PnzFRcXp5CQECUnJ+v48eMuYy5evKixY8cqLCxMERERmjhxokpKSjy4F/AF7nKxrKxMc+bMUWJioho3biyHw6FnnnlGZ8+edXmPmmrp4sWLPbwnuNvVVhfHjx9fLc9SU1NdxlAXURdqy8Wajh1tNpuWLl3qHENdRF24mR7OzZw75+XladCgQWrUqJFiYmI0e/ZsXb9+3ZO7Ygka6T7miy++0MyZM7VgwQIdOnRIXbt2VUpKis6fP291aPBRu3btUnp6uvbu3astW7aorKxMAwcO1OXLl13GTZo0Sfn5+c7HkiVLLIoYvuz+++93ybMff/zRuW7GjBn6+uuvtX79eu3atUtnz57V8OHDLYwWvurAgQMuebhlyxZJ0siRI51jqImoD5cvX1bXrl21bNmyGtcvWbJEH330kVasWKF9+/apcePGSklJ0dWrV51jxo4dq6NHj2rLli3avHmzdu/ereeff95TuwAf4S4Xr1y5okOHDmnevHk6dOiQNmzYoJycHA0ZMqTa2LfeesulVk6dOtUT4cOH1FYXJSk1NdUlz9atW+eynrqIulBbLv41B/Pz87Vq1SrZbDaNGDHCZRx1EXfqZno4tZ07l5eXa9CgQSotLdWePXuUmZmpNWvWaP78+VbskmcZ+JRevXqZ9PR05+vy8nLjcDhMRkaGhVHBn5w/f95IMrt27XIue/jhh820adOsCwp+YcGCBaZr1641rissLDQNGzY069evdy775ZdfjCSTlZXloQjhr6ZNm2batm1rKioqjDHURHiGJLNx40bn64qKChMbG2uWLl3qXFZYWGjsdrtZt26dMcaYn3/+2UgyBw4ccI757rvvjM1mM7/99pvHYodv+Xsu1mT//v1Gkjl9+rRzWXx8vPnggw/qNzj4lZpycdy4cWbo0KE33Ia6iPpwM3Vx6NChpn///i7LqIuoD3/v4dzMufO3335rAgICTEFBgXPM8uXLTVhYmLl27Zpnd8DDuCLdh5SWlurgwYNKTk52LgsICFBycrKysrIsjAz+pKioSJIUGRnpsvzzzz9XdHS0OnfurLlz5+rKlStWhAcfd/z4cTkcDrVp00Zjx45VXl6eJOngwYMqKytzqY8dOnRQq1atqI+oV6Wlpfrss8/07LPPymazOZdTE+FpJ0+eVEFBgUsdDA8PV1JSkrMOZmVlKSIiQj179nSOSU5OVkBAgPbt2+fxmOE/ioqKZLPZFBER4bJ88eLFioqKUrdu3bR06VK/+Mk4PG/nzp2KiYlR+/btNXnyZF24cMG5jroIK5w7d07ffPONJk6cWG0ddRF17e89nJs5d87KylJiYqKaN2/uHJOSkqLi4mIdPXrUg9F7XgOrA0Dd+f3331VeXu6SyJLUvHlzHTt2zKKo4E8qKio0ffp09enTR507d3Yuf/LJJxUfHy+Hw6HDhw9rzpw5ysnJ0YYNGyyMFr4mKSlJa9asUfv27ZWfn6+FCxfqoYce0pEjR1RQUKCgoKBqJ+jNmzdXQUGBNQHDL2zatEmFhYUaP368cxk1EVaoqnU1HSdWrSsoKFBMTIzL+gYNGigyMpJaiXpz9epVzZkzR2PGjFFYWJhz+csvv6zu3bsrMjJSe/bs0dy5c5Wfn6/333/fwmjha1JTUzV8+HAlJCQoNzdXr7/+utLS0pSVlaXAwEDqIiyRmZmp0NDQatNQUhdR12rq4dzMuXNBQUGNx5RV63wZjXQAdSY9PV1HjhxxmZdaksscgomJiYqLi9OAAQOUm5urtm3bejpM+Ki0tDTn8y5duigpKUnx8fH68ssvFRISYmFk8GcrV65UWlqaHA6Hcxk1EQAqlZWV6YknnpAxRsuXL3dZN3PmTOfzLl26KCgoSC+88IIyMjJkt9s9HSp81OjRo53PExMT1aVLF7Vt21Y7d+7UgAEDLIwM/mzVqlUaO3asgoODXZZTF1HXbtTDwY0xtYsPiY6OVmBgYLU76Z47d06xsbEWRQV/MWXKFG3evFk7duxQixYt3I5NSkqSJJ04ccITocFPRURE6L777tOJEycUGxur0tJSFRYWuoyhPqI+nT59Wlu3btVzzz3ndhw1EZ5QVevcHSfGxsZWu0H99evXdfHiRWol6lxVE/306dPasmWLy9XoNUlKStL169d16tQpzwQIv9SmTRtFR0c7P5Opi/C0H374QTk5ObUeP0rURdyZG/VwbubcOTY2tsZjyqp1voxGug8JCgpSjx49tG3bNueyiooKbdu2Tb1797YwMvgyY4ymTJmijRs3avv27UpISKh1m+zsbElSXFxcPUcHf1ZSUqLc3FzFxcWpR48eatiwoUt9zMnJUV5eHvUR9Wb16tWKiYnRoEGD3I6jJsITEhISFBsb61IHi4uLtW/fPmcd7N27twoLC3Xw4EHnmO3bt6uiosL5hQ9QF6qa6MePH9fWrVsVFRVV6zbZ2dkKCAioNs0GUJfOnDmjCxcuOD+TqYvwtJUrV6pHjx7q2rVrrWOpi7gdtfVwbubcuXfv3vrpp59cvmis+lK8U6dOntkRizC1i4+ZOXOmxo0bp549e6pXr1768MMPdfnyZU2YMMHq0OCj0tPTtXbtWn311VcKDQ11zocVHh6ukJAQ5ebmau3atXr00UcVFRWlw4cPa8aMGerbt6+6dOlicfTwJa+88ooGDx6s+Ph4nT17VgsWLFBgYKDGjBmj8PBwTZw4UTNnzlRkZKTCwsI0depU9e7dWw8++KDVocMHVVRUaPXq1Ro3bpwaNPjzcIuaiPpUUlLi8suGkydPKjs7W5GRkWrVqpWmT5+uRYsW6d5771VCQoLmzZsnh8OhYcOGSZI6duyo1NRUTZo0SStWrFBZWZmmTJmi0aNHu0xPBNTGXS7GxcXp8ccf16FDh7R582aVl5c7jx8jIyMVFBSkrKws7du3T4888ohCQ0OVlZWlGTNm6KmnnlLTpk2t2i3chdzlYmRkpBYuXKgRI0YoNjZWubm5evXVV9WuXTulpKRIoi6i7tT2GS1VfsG9fv16vffee9W2py6irtTWw7mZc+eBAweqU6dOevrpp7VkyRIVFBTojTfeUHp6uu9PM2Tgcz7++GPTqlUrExQUZHr16mX27t1rdUjwYZJqfKxevdoYY0xeXp7p27eviYyMNHa73bRr187Mnj3bFBUVWRs4fM6oUaNMXFycCQoKMvfcc48ZNWqUOXHihHP9H3/8YV566SXTtGlT06hRI/PYY4+Z/Px8CyOGL/v++++NJJOTk+OynJqI+rRjx44aP5PHjRtnjDGmoqLCzJs3zzRv3tzY7XYzYMCAajl64cIFM2bMGNOkSRMTFhZmJkyYYC5dumTB3uBu5i4XT548ecPjxx07dhhjjDl48KBJSkoy4eHhJjg42HTs2NG888475urVq9buGO467nLxypUrZuDAgaZZs2amYcOGJj4+3kyaNMkUFBS4vAd1EXWhts9oY4z55JNPTEhIiCksLKy2PXURdaW2Ho4xN3fufOrUKZOWlmZCQkJMdHS0mTVrlikrK/Pw3niezRhj6rFPDwAAAAAAAADAXY050gEAAAAAAAAAcINGOgAAAAAAAAAAbtBIBwAAAAAAAADADRrpAAAAAAAAAAC4QSMdAAAAAAAAAAA3aKQDAAAAAAAAAOAGjXQAAAAAAAAAANygkQ4AAAAAAAAAgBs00gEAAAAAAAAAcINGOgAAAAAAAAAAbtBIBwAAAAAAAADAjf8DIY7DoNk677sAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -38,12 +38,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-2.117904..2.169412].\n" + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-2.117904..1.8731157].\n" ] }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAMsCAYAAADJXzRsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9e7heVXW2PwIEkmDiTpCkkAIpSTFAaMMHUaECEsqhBCEWUWlBogUtHlGp1iOeiyeoVfGASj4PFQU0ICCggkIFCyixBA0a+BJo8EvAZJPUcEhk//7wl/3e83n3HFl7rx1h+z33dXFdY2XOd6655mntxXzGmKP6+vr6whhjjDHGGGNasM2TXQFjjDHGGGPMyMcfFsYYY4wxxpjW+MPCGGOMMcYY0xp/WBhjjDHGGGNa4w8LY4wxxhhjTGv8YWGMMcYYY4xpjT8sjDHGGGOMMa3xh4UxxhhjjDGmNf6wMMYYY4wxxrTGHxbGGDPMjBo1Kt797nc/2dUYkRx77LFxxhlnbLXyR40aFa95zWuGrbzly5fHqFGjYuHChcNW5kD85je/iR133DGuvvrqrXofY4xpgz8sjDFPSe6888544QtfGHvssUeMGTMmpk6dGkceeWR84hOfeLKr9gdn2rRpcdxxxz3Z1djq/OhHP4rrrrsu3vKWt/T/2w9+8IMYNWpUXHrppU9izf5wnHHGGTFq1Kiu/t5pp53i9NNPj3e+851PUs2MMWbL+MPCGPOU4+abb44DDzwwfvazn8UZZ5wRn/zkJ+P000+PbbbZJj7+8Y8/2dUzW4mPfOQjccQRR8SMGTOe7Ko8Kdx+++2xcOHCGDNmzIDp//iP/xg//elP4/rrr/8D18wYY5qx3ZNdAWOMUT7wgQ/E05/+9Ljtttuip6enSFu9evWTUymzVVm9enVcddVV8ZnPfObJrsqTQl9fX7zuda+Ll770pfH9739/wDx77713zJo1KxYuXBhz5879A9fQGGO2jHcsjDFPOe65557Yd999uz4qIiImT55cXF900UUxd+7cmDx5cuywww6xzz77xKc//emu322WE/3gBz+IAw88MMaOHRv77bdf/OAHP4iIiG9+85ux3377xZgxY+KAAw6IO+64o/j9ggUL4mlPe1rce++9cfTRR8eOO+4Yu+66a7z3ve+Nvr6+LT7TypUr4+Uvf3lMmTIldthhh9h3333ji1/8YvNGAZt1/R/96EfjU5/6VOy5554xbty4OOqoo+L++++Pvr6+eN/73hd/+qd/GmPHjo0TTjgh1qxZU5Rx+eWXx7x582LXXXeNHXbYIaZPnx7ve9/74ne/+13X/TbfY+zYsfGsZz0rbrrppnje854Xz3ve84p8jz32WJxzzjkxY8aM2GGHHWK33XaLN7/5zfHYY49t8Zmuuuqq2LRpU/z1X//1kNrkox/9aBx88MGx0047xdixY+OAAw5I5VNf/epX45nPfGZ/f994441deYbaZxs3boylS5fGr3/968b1//KXvxxLliyJD3zgA2m+I488Mr797W83GnPGGPOHxjsWxpinHHvssUfccsstsWTJkpg1a1aa99Of/nTsu+++cfzxx8d2220X3/72t+NVr3pVPPHEE/HqV7+6yLts2bL4u7/7u3jlK18Zp5xySnz0ox+N5z//+fGZz3wm3va2t8WrXvWqiIj4l3/5l3jRi14Ud999d2yzTef/v/zud7+LY445Jp7znOfEhz/84bjmmmvinHPOiU2bNsV73/veah1XrVoVz3nOc/odh3feeef4zne+E//wD/8Q69ati7POOmtI7fTVr341Hn/88Xjta18ba9asiQ9/+MPxohe9KObOnRs/+MEP4i1veUssW7YsPvGJT8TZZ59d/FG8cOHCeNrTnhZvfOMb42lPe1pcf/318a53vSvWrVsXH/nIR4r2fc1rXhOHHHJIvOENb4jly5fH/PnzY+LEifGnf/qn/fmeeOKJOP744+M//uM/4hWveEXsvffeceedd8b5558fv/zlL2PRokXps9x8882x0047xR577DGktvj4xz8exx9/fPz93/99PP7443HxxRfHSSedFFdeeWXMmzevyPvDH/4wvv71r8frXve62GGHHeKCCy6IY445Jm699db+8damz1auXBl77713nHbaaY2cutevXx9vectb4m1ve1v8yZ/8SZr3gAMOiPPPPz/uuuuuLc4NY4z5g9NnjDFPMa677rq+bbfdtm/bbbftO+igg/re/OY391177bV9jz/+eFfeDRs2dP3b0Ucf3bfnnnsW/7bHHnv0RUTfzTff3P9v1157bV9E9I0dO7ZvxYoV/f/+2c9+ti8i+m644Yb+fzvttNP6IqLvta99bf+/PfHEE33z5s3r23777fsefPDB/n+PiL5zzjmn//of/uEf+nbZZZe+hx56qKjTS17ykr6nP/3pAz6D1n3evHn91//n//yfvojo23nnnft6e3v7//2tb31rX0T0/eVf/mXfxo0b+//95JNP7tt+++37Hn300f5/G+ier3zlK/vGjRvXn++xxx7r22mnnfrmzJlTlLdw4cK+iOg77LDD+v/ty1/+ct8222zTd9NNNxVlfuYzn+mLiL4f/ehH6TM+97nP7TvggAO6/v2GG27oi4i+Sy65JP29Ps/jjz/eN2vWrL65c+cW/x4RfRHRd/vtt/f/24oVK/rGjBnT94IXvKD/35r22ea+uOiii/rzbP630047La3zZs4+++y+P/uzP+tvd+1vcvPNN/dFRN/Xv/71RmUbY8wfEkuhjDFPOY488si45ZZb4vjjj4+f/exn8eEPfziOPvromDp1alxxxRVF3rFjx/bbDz/8cDz00ENx2GGHxb333hsPP/xwkXefffaJgw46qP/62c9+dkREzJ07N3bfffeuf7/33nu76sZQpZv/b/bjjz8e3/ve9wZ8lr6+vrjsssvi+c9/fvT19cVDDz3U/9/RRx8dDz/8cPz0pz9t2jQFJ510Ujz96U/vqvcpp5wS2223XfHvjz/+eKxcubL/39hu69evj4ceeigOOeSQ2LBhQyxdujQifu9M/Jvf/CbOOOOMory///u/j4kTJxZ1ueSSS2LvvfeOmTNnFs+42RfghhtuSJ/lN7/5TVeZg4HPs3bt2nj44YfjkEMOGbBtDzrooDjggAP6r3ffffc44YQT4tprr43f/e53rfts2rRp0dfX12i34pe//GV8/OMfj4985COxww47bDH/5jZ66KGHtpjXGGP+0FgKZYx5SjJnzpz45je/GY8//nj87Gc/i29961tx/vnnxwtf+MJYvHhx7LPPPhHx+xCl55xzTtxyyy2xYcOGooyHH364+MObHw8R0Z+22267Dfjva9euLf59m222iT333LP4t7322isifu/3MBAPPvhg9Pb2xuc+97n43Oc+N2CeoTqkt3meu+66K97xjnfE9ddfH+vWrSvyb/4gW7FiRUREV5Sm7bbbLqZNm1b8269+9av4xS9+ETvvvPOAdW3yjH0t/AauvPLKeP/73x+LFy8ufDpGjRrVlffP//zPu/5tr732ig0bNsSDDz4Y22yzzVbrM+X1r399HHzwwXHiiSc2yr+5jQZ6LmOMebLxh4Ux5inN9ttvH3PmzIk5c+bEXnvtFS972cvikksuiXPOOSfuueeeOOKII2LmzJlx3nnnxW677Rbbb799XH311XH++efHE088UZS17bbbDniP2r+3+UN3M5vrcMopp8Rpp502YJ6/+Iu/GFLZQ32e3t7eOOyww2LChAnx3ve+N6ZPnx5jxoyJn/70p/GWt7ylq92a8MQTT8R+++0X55133oDp+rGj7LTTTl0fck256aab4vjjj49DDz00Lrjggthll11i9OjRcdFFF8W///u/D7q8rdln5Prrr49rrrkmvvnNbxYfpps2bYpHHnkkli9fHpMmTYoJEyb0p21uo2c84xmt72+MMcONPyyMMSOGAw88MCKiP9rOt7/97XjsscfiiiuuKP7v/ZZkN0PliSeeiHvvvbd/lyLi91KWiOj6P/ib2XnnnWP8+PHxu9/9bsgRj4abH/zgB/Gb3/wmvvnNb8ahhx7a/+//5//8nyLfZkfqZcuWxeGHH97/75s2bYrly5cXf1xPnz49fvazn8URRxwxpP+bPnPmzLjssssG/buIiMsuuyzGjBkT1157bSEnuuiiiwbM/6tf/arr3375y1/GuHHj+ndc/hB9dt9990VExN/+7d92pa1cuTL+7M/+LM4///zCUXxzH+29995brV7GGDNU7GNhjHnKccMNNwy4W3D11VdHRMQzn/nMiOj8n3nmffjhh6t/UA4Hn/zkJ/vtvr6++OQnPxmjR4+OI444YsD82267bZx44olx2WWXxZIlS7rSH3zwwa1W1xoDtdvjjz8eF1xwQZHvwAMPjJ122ikuvPDC2LRpU/+/f/WrX+3aXXjRi14UK1eujAsvvLDrfo888kj89re/Tet00EEHxdq1awf0a2nyPKNGjSpC5S5fvrwaieqWW24pfCTuv//+uPzyy+Ooo46KbbfdtnWfNQ03O3fu3PjWt77V9d/OO+8cBx54YHzrW9+K5z//+cVvfvKTn8TTn/702HfffdOyjTHmycA7FsaYpxyvfe1rY8OGDfGCF7wgZs6cGY8//njcfPPN8fWvfz2mTZsWL3vZyyIi4qijjortt98+nv/858crX/nK+J//+Z+48MILY/LkyYM6Q6ApY8aMiWuuuSZOO+20ePaznx3f+c534qqrroq3ve1tVd+CiIhzzz03brjhhnj2s58dZ5xxRuyzzz6xZs2a+OlPfxrf+973us6Y2NocfPDBMXHixDjttNPida97XYwaNSq+/OUvd33Mbb/99vHud787Xvva18bcuXPjRS96USxfvjwWLlwY06dPL3YmTj311PjGN74R//iP/xg33HBD/NVf/VX87ne/i6VLl8Y3vvGNuPbaa/t3nAZi3rx5sd1228X3vve9eMUrXtGVftlll/U7lZPTTjst5s2bF+edd14cc8wx8Xd/93exevXq+NSnPhUzZsyI//qv/+r6zaxZs+Loo48uws1GRLznPe/pz9Omz5qGm9199927/GQiIs4666yYMmVKzJ8/vyvtu9/9bjz/+c+3j4Ux5imJPyyMMU85PvrRj8Yll1wSV199dXzuc5+Lxx9/PHbfffd41ateFe94xzv6D8575jOfGZdeemm84x3viLPPPjv+5E/+JM4888zYeeed4+Uvf/mw12vbbbeNa665Js4888z4p3/6pxg/fnycc8458a53vSv93ZQpU+LWW2+N9773vfHNb34zLrjggthpp51i3333jQ996EPDXs8tsdNOO8WVV14Zb3rTm+Id73hHTJw4MU455ZQ44ogj4uijjy7yvuY1r4m+vr742Mc+FmeffXb85V/+ZVxxxRXxute9LsaMGdOfb5tttolFixbF+eefH1/60pfiW9/6VowbNy723HPPeP3rX1/IxwZiypQpceyxx8Y3vvGNAT8sLr744gF/97znPS/mzp0bX/jCF+Lcc8+Ns846K/7sz/4sPvShD8Xy5csH/LA47LDD4qCDDor3vOc9cd9998U+++wTCxcuLKRdT7U+i4hYunRpLFmyJP71X//1Sbm/McZsiVF9w+GdaIwxf+QsWLAgLr300vif//mfJ7sqTzpPPPFE7LzzzvG3f/u3A0qfhsrmE72XLl06YOSm/9c566yz4sYbb4yf/OQn3rEwxjwlsY+FMcaYKo8++miXROpLX/pSrFmzJp73vOcN670OOeSQOOqoo+LDH/7wsJb7x8BvfvOb+PznPx/vf//7/VFhjHnKYimUMcaYKj/+8Y/jDW94Q5x00kmx0047xU9/+tP4whe+ELNmzYqTTjpp2O/3ne98Z9jL/GNgp5128m6ZMeYpjz8sjDHGVJk2bVrstttu8W//9m+xZs2amDRpUrz0pS+Nc889N7bffvsnu3rGGGOeQtjHwhhjjDHGGNMa+1gYY4wxxhhjWuMPC2OMMcYYY0xr/GFhjDHGGGOMaY0/LIwxxhhjjDGt8YeFMcYYY4wxpjX+sDDGGGOMMca0xh8WxhhjjDHGmNb4w8IYY4wxxhjTGn9YGGOMMcYYY1rjDwtjjDHGGGNMa/xhYYwxxhhjjGmNPyyMMcYYY4wxrfGHhTHGGGOMMaY1/rAwxhhjjDHGtMYfFsYYY4wxxpjW+MPCGGOMMcYY0xp/WBhjjDHGGGNa4w8LY4wxxhhjTGv8YWGMMcYYY4xpjT8sjDHGGGOMMa3xh4UxxhhjjDGmNf6wMMYYY4wxxrTGHxbGGGOMMcaY1vjDwhhjjDHGGNMaf1gYY4wxxhhjWuMPC2OMMcYYY0xr/GFhjDHGGGOMaY0/LIwxxhhjjDGt8YeFMcYYY4wxpjX+sDDGGGOMMca0xh8WxhhjjDHGmNb4w8IYY4wxxhjTGn9YGGOMMcYYY1rjDwtjjDHGGGNMa/xhYYwxxhhjjGmNPyyMMcYYY4wxrfGHhTHGGGOMMaY1/rAwxhhjjDHGtMYfFsYYY4wxxpjW+MPCGGOMMcYY0xp/WBhjjDHGGGNa4w8LY4wxxhhjTGv8YWGMMcYYY4xpjT8sjDHGGGOMMa3xh4UxxhhjjDGmNf6wMMYYY4wxxrTGHxbGGGOMMcaY1vjDwhhjjDHGGNMaf1gYY4wxxhhjWuMPC2OMMcYYY0xr/GFhjDHGGGOMaY0/LIwxxhhjjDGt8YeFMcYYY4wxpjX+sDDGGGOMMca0xh8WxhhjjDHGmNb4w8IYY4wxxhjTGn9YGGOMMcYYY1rjDwtjjDHGGGNMa7Z7sitg2rN0iL/LOn9Tw99l+YZ676b1yMrblKRl5Q/Hs9XK21plNmWobdK0Hlt7zDSty0jot8GMu6H093CUMZjymzIc834wzzIcY344+ma4596WqNV5a7Rd7TdbYrjbYajrfJZ3OMbMYKjd+w/9niJ/yOccahkZWfkzhnhvk+MdC2OMMcYYY0xr/GFhjDHGGGOMaY2lUCOUUaN2xNUGSeX3Irv4ccm3fZKW8eeVf9eNxVWwl0jaZNirJW0wdWmCfj+Ph/1w8runw35E0ljH7ZO0cZLGvvpTSdsD9m2wx0u+KbB/Lml81ieiDp9tvaRlvyPZs2mbbFfJp2RjkuNujaT9BvZOsGdKvh8l986ojfmIiHtgZ21Xm5cR+Zj/E9j/N8nH/sjaWPvtUdgTJY3tdwfssUn5ug4sg61jrfYKGp3U4z8lremY13Wglvfpcj0Vts435p0sab+CzTbfX/KxXVXg0XQt1LWE5UyCre29Ana2FjZtO4Vz8TeS1nS8/kWSxvfKLElbEXU2Jmkc2+yr2yQf21Kfjewj13wv6jz6b9hcc/RZ+K74VbRnOPo3Im+HoZSR1Ut/R2rrlvLy4qqv7xNJXtMU71gYY4wxxhhjWuMPC2OMMcYYY0xr/GFhjDHGGGOMaY19LEYs02CPkbSpMTCqS6f2Vn0IqHFWjf/YSto/S77XwT5Y0o6D/SlJow5bdcurkzRCTarqWPk79f1gW85P8jHIr2qm74SdBbTTfmK9jk5+l+mKifYbf7dH1GH767jgs66UNF7rc6+u5NM24P3U9yZrkx/CPiIpn+NaxwXvrXNFxy9hv3FOaf2ZtqukMa8GkD4ZNvXCk6JOpi/XccH+1rH8Jthnwta2y8b5nOR39E9h+8+VfGwTLYN9k+mps/6mfWjyuxsljc+m7fox2C+ErWNyAmydl+xHHZMsU8fCpbDnw9axxWud67zO1lrO5ymSNh229s1+lTIiyjGp857Pugi2roUcW5lPhcLyWX9tn8Ngf1/SOBaOkzTmzeYN+17bn2NefT/WJWXSn4prkI67B5LysjpzHdP5UFvb9d7s+4MkjePrTklje50E+4uSj326X5jhxzsWxhhjjDHGmNb4w8IYY4wxxhjTGkuhRizsOg3NqNdN8mmYQ93GJDUJgcovuNWqYSaZV+vFZ9Pfrank0zKYL5Ny6RY5t5wz2QyvtY6bkrQMPg+3ijUEaBaql8/6gKSxTWpyuYiyTXSJYBk6ZtYm9SJZqNKMpssV66j34rOpvKDW/hHlOM/GQnamLK+1DI41rVetfJW/ZHWszdmIUuqgaWzLTCKX3TsbC/wd+2aC5OOz6rjO1iq2eSarZAhQleWwLipHyqQfXP+yNYHPrWvoqqjDclSCRBjuV2UtvLf2adZ26yu2rsNsEx0HbGe9N9OWSRqflc+j61FtbEWU9dS02pqtz6Z1rqXpc7OeWb1qv4ko66h92vSdw3pl708tL5vPrIuuA3wGtk82HwZz5nitfB27uraY4cY7FsYYY4wxxpjW+MPCGGOMMcYY0xp/WBhjjDHGGGNaYx+LEUumma5p2FUfnHU/dZT6u1rYT9VePgv21yTtWtiqI6bmUn0Bav4Xqm+mrlI1lUzTe1MjejNs1XPWfD0iyjprWi3sakQZdo+6Yg3vx+ubJY36Yx0XtfCkqmHOtMPUgGu9GKo3G1trKnZEGUYx0+iqrphp1ODrM7NMbX+WoSGcWY4+W+ZnRKhjvkfSsnCY7B+Wr/00Pkmr1SOiDF2tv7ulUn7mw6Eha1l/7W/OTbbrrZKPY03nLMOYZmuczuFam6sPRy28tt5PxwWfjf02XfKxP7T9GR5U730lbA2HzHvwuTPfKn1uzrHMT2BsxY7IQyI39fHLfLKYplp61l91/JmPHO/NcKTaBgzVm4Vy13GR+chxnDCUceavNRw+A5nfWOZPpaFi2ebLJa3mf6ZlsF31/cY0/R19mjJ/Qn3fmeHGOxbGGGOMMcaY1vjDwhhjjDHGGNMaS6H+KFBpQ23rOAtxmaFbibXydYv/c7B1G59yCd3ubBrmdUKSj6jkoalMhPILlc3weXS7f02SxutMlpDJqbKTblmvTLJDdEu5qcxO+zQLjUn4O93Gr4UAjShP1tXn5rNyS1zH+KaKHVGOk0eTtGwesS0z+UU2XvV3bKPs2Ti2stCbg6EmqVEy6cTkih1RSj+yULqcU/rcnCtZqM3sJOCm4X6VSRU7oqwzZS1ZO2ZyHoVrqK4lXFN5krjKnbLQp03J/owY6p8YHPPaJnxWlcw1JVvjOE7Yhypf5NzQ98N2FVvvp89WG5M6rtkG2m+PJmm1NUjnBsed1r+pNE3XdtalaThyvXcm92QbcS7qvZiWhao2Q8U7FsYYY4wxxpjW+MPCGGOMMcYY0xp/WBhjjDHGGGNaYx+LPwo0NGZNA6l6wkzLSL3irpJW0zurzpf66f0kjZrjpVEn035qvQg1r9lzq96ZaTNhq56T+lENu0p9cxZucaJcU4PKZ1N/ApavGl0+q+py76nky7Thqk+lD0GmyVYdPNuPbaf+HbzfYEJQMi/bS8v/Pmx9bj6PatEzf6RMq1zLp/3G8ZqFp2QZOu7Yxpm2Wu/N8IvaXgxj+jHYWYhO9Y3JQm9yTLLOul5QS69lcK5oKEn2m/Y324vjTtuHv8v8WrTtamNSxy6fR/uUc1HXIPaNPhtD0XJd1nDINU1/RD2krKY19dXTucH1T5+N67yuA+wf3jtbj7RP2d/6O7Yl56WOa/quDKZ9snZl/2d9n/k5DAejK3ZE2ZaZT5O2uYbkrcH5rOsA59i1ksY24pzS/mW9Mj9HM1S8Y2GMMcYYY4xpjT8sjDHGGGOMMa2xFGrE0nT7mV2sv8lkG9zi1O1zbtdy+/EOycdwiCqpua1yLy1Tf1fbTs1Ch+pzNw0FSImW1oNb9do+3ErXLWzWX7eKKZegRETD9nE7+DZJ4/2yU4hpqxSNZWi4y2wLPgujyfuxfA3TyLbT9uE4WStptedWuRnR8Z9t63M8ZfNGn4c0PYVYxyvvzTrq2GI+7YsshHMGx1dT6eRMSeP4ytqHZKfSL5E0zk3tw+wVVwvBq3M9W2uzscyxxzGpkimWr3KbbEyyb7RMriWUh+lasr5iR5Rtl5203pTshOisnzJJJ9ka8iDWWWUzmZwqO029thYOdL2ZrP2HGkq6tq5sqcwsnHp2WntNqqZ9z/dpthbq72qhxDNZblN5lhkM3rEwxhhjjDHGtMYfFsYYY4wxxpjWWAo1YqEco+nJ0pqP3a/SEm5pZifpZtIMbkeqTCCTVTQ9gZf31jI0khLJtuRrErDs2TLJkcLfZZF1VlT+PaJs/+zeWflMU7lTJpthXu0b3js75Zj5tP5DjdJRq3Mm79C0jRVbyU61zuRgmayFZWib107S1QhIbLtMFqJtTumSjoVadDXtX9ZLJWwqkSQ8vZhjfjDRyFSKSLK+qbVRJgvRtstOxmb52XhqKmvRND63PktNrpdF6tE6sh2yk5Kz9S6TkTU9fVllXrU2yqSGei+Wn8l5GKFI1ybW41FJy95vTNP2oczroMq9InKpr9aFsC7Ml5WfRVPTtNWVfEoW4S+TGWfS7smVfNm4ztZkM1S8Y2GMMcYYY4xpjT8sjDHGGGOMMa3xh4UxxhhjjDGmNfax+KOk1q2DCUtXO4E6oq4jP1iuL4CtOtmXwf6cpFH3uL+kZVp0Qv255qPmMtPGUuOqJzHXfhMRcWeSxrbUZ+Nz897qL8LwndquvFb9a03rrv3Leqgmm9fadtnvqOXOTi1fXbG1TPWT4YnafB5tf+Yb6vKX+SrNgp2NmeyUetXxs0/ZXqqjp+Y4C6OobcJ66lg4GfaXYas2meM184/QMa+hjmv5ePK2zge2uYYiZT21vdhv1HVrG4xJymBdZkvaD2EfHXWyk9trvkkRES+slBERcSlsnl78fcnHcTKY0J61E6+zd0x2orzOFc7vWZLGOt8IO9PLa7vWQpPqvel7qGN1HuybkzK0bzg/dP3muM98AbgODGYd47NyXO8q+bIyMx85ovOoFspdny3re86jRZLGcjg+s/VI31NmOPCOhTHGGGOMMaY1/rAwxhhjjDHGtMZSqD8KslN2h0rT02azrW+GklTpyugkrWko2ixf7TcR5Zapylq4Zc56adg7/k63sylzGYy8gNvKlFjoVnoWbpZ5s1NdKZPS8mtbynqdnayajcmmkrzBnJpcC/87XfJloXSz01qz+VCTVejY4nhSmQDzamjmWhjfLGyy1jcL78jTnlV6UKtH9mwK76fPVgsHqyemZ3M9u3etjIHK2UwmddMyWE+VHtakmirL4XUWvjMLbZuFBF1T+feI+nqqaFptnclCOOuY5BqXnXauda6Fys7aR8dIFma3qTwmew9m8rBsjea9lyb5moa4zphQsSPK952Wzzpqv7FddS2ptYmWwfmWPVvT08g1X/YOMMOBdyyMMcYYY4wxrfGHhTHGGGOMMaY1/rAwxhhjjDHGtMY+FiMW6hWzcKrUKKpOkzrKTK+o/gWHwqbGWMPxMfzsbZLGELPqJ8Ay9XfUg1NTqxpj6neb+hpoXRieVXWgvFa9PNtH24ThMFXXynL4O9WqzoF9raSxH1XvrM+6mTvlmu2jmnjWWUMUZprj7SppOrYmV/JFlG2u2luOGZapemnq2bV9stCJtTIiymdjv2l7b6zkiyifR5fleyq2Ql+fbMzrXJ8GW9uA4UmzcL8cJxpCmKGf9bnZRixf5/2MJI39rX2ajWXeO9P0M03HHcekjuWaD0HmR6F9zzGjbc5Qqxrqm+XwufeTfOwPrT/vl/lmEJ1vtbC0WqaGXybap9qPm8n8QDK/GR2vzEsfLX22bC5m4Wz3SNLYp6yX+oqR7NnGxNDIfMXYH9nfHhrenGWyLdVHkb+rhaOO6PbDqvnGZOWbrYF3LIwxxhhjjDGt8YeFMcYYY4wxpjWWQo1YmoYGzE4jzbb4KTPSbWpup3MIqSSIp5FqKECWoVvw3LpcK2msMyUFut3Jk1x1K5ftU5MHbSltWZLW9NRj3dLnM1BmpFu8lBBkshztD54EzHurxGJqJZ+Wn4X5zNo8G7uUhaj8gvXKQktm4Tt5b+0n1lklBFmY11p7ZWErtd/4PEMNgZgt53w2rRfr3zQUsFIL96v3y2RAbLssJKvCNG2DLEzt+kqariXs70zekYUkztbabLwugT2Y13UtpKa2Yxb6NLsf+zSTHrIMfRdxzGubZCeOT4PddK5k4aOzkOaZlJgSVZXsZO8YjnN9t2ZyRsJ+1JPos7W9VmYWijkLiatpvJ+2eW2uZKGlM/RvA9aL7z5djygx03ub4cA7FsYYY4wxxpjW+MPCGGOMMcYY0xp/WBhjjDHGGGNaYx+LEQt1m6ohrPkGqB5yepJGjb+G46OelL87TvJRH6zh8l4BW/0VqL3N/Av2r/z7QNeE+tRMD846q+6aOlANB8sQsFkYxSxUH7Wqme761KR89V15FDY1rlp/1kvb/4ik/Fsr5StZ+ZmPBfvjgajD/tU2ps5b25VtvkrSsn6s+V9k/h2HStqipHyOvWxuEO2bLGwmtco6zp8F++OwNfwy56L6v3D90FcO5x/75mTJxxCzupbw3uoTVPM10HvTznyr1EdhcpLGurwkqQfHlt47C409D/b+kkb/NmrwM/+vzNdA68xxUgtvHlHWWctnvdR3hc+tWnq+mzLfFaZlobE1bGzNl0vbn36Ci5Py9Xd8niMkjXOA7xFdC3lvnbOc+zoma74r2ftS+77p3x7a5mznbMzQP0J9IE6CvUjSON94b21/zhX1BTTDgXcsjDHGGGOMMa3xh4UxxhhjjDGmNZZCjVi4Ba/bkY9U8mUnMauEIDvlmL/LthLnw75R0ihtUGnGLbB1m7d2crLKLzi0sxNZayfIRpTtqtIbysF0GnErPQsHq2EC+Qzczs62uvW5uf2vJxRzW5nbzTouWEeV1PBaxx1lQNlpsETbh213q6Sxr1SqNKGST8NAcpyrfITjWssnWajbTFLAELZZ+EtNq4XqnSb5WGcd15xjKm2k5ELHWi0MbhbGWsMcU/qhskfWk/IFlZ6xH7V9sjbfrmJH1CUdOq6zU7mZNzslOJtvnCt672wd4/zWNuE9OO6ytsraNZNCZaePs4311G+WoVIotonOxdozaP/yOgt9mp3WzvqrZJR9lf0ppX3K59a5cmUln86bTO7J/s6kplwztd+yU9Ezmdf4Sj69zsZ19o7J7s35wLmYSUbN1sA7FsYYY4wxxpjW+MPCGGOMMcYY0xp/WBhjjDHGGGNaYx+LEQs1oxMkjZpRajizEJ2qyea1DhOWSQ2k6kBrYTIjSh2l6kxZT9Vf1jScWWhP1ddm4SSp7aUeVZ+Nz636YGpS1deAdVEfC/Yjdemqf2V/qK6bOmbVp1Jbz75ZJPnYH5mPwrMkjRpk7beVFTvzsdB+4/NoCMrxFTtru0wDrGlsh6Z11jHPMq+UNNZLy2dffT+pY+YvRLR8onXm2OZzZiEoVUufhc1ke3F8fl/y8dl0XLOOWq9aGRGlz1HNZyCibC8tg8+jfmpsk8z/guud6vinVuyIfH2irpy/03cA20DXea5H2q7Mu7Hy71oPXedZpvr2EB1PfFbeLwvVm/lFZX5FHJM6drP3FMeo3pv30zrznUAfMC0/8xtgnfVvA94va3Oi/bY6SVtTyRdR9wvR8NEMlZz5DimcO1kY4tqaZoYL71gYY4wxxhhjWuMPC2OMMcYYY0xrLIUasXArUcNmcnu7aahEHQrcJtUTNGvDRsNYfhm2ymbmwtYtU8pXVC5UCyOXbUWrDIG/y6RKWYjIWnkR5Ra5tjn7SreAa9vD2j4sU7e62VcqIeDvWL5uZxM9GTaT7HDLWcckf8cTWPXeTU+AnSdpl8LmONTxs7GSL6KU3+h44pzKwjbPqZQXkcv/WC+990GwPwVbJRYsU8cM20G3/7PQj8fBvj7q8N7ah5QoqPyCsofVFVuvVS7EflQ5EuuikohauM0sPLXOWc4xXSc/Vylf+56/02fL1p35sLVdPwr7YNj6bAwDrvMh+/NA153NaP1ZhkrYshPTycFyzfWWa4nKirJTxjkH9N6cwyxDxw/z6TuG41XffRwL+v45KAZG5152ojnXbK0XxwnHgvYb66Enk2cSLdZFxzn7jW2uY4n11/J5bw1Hzr7inMraLnvfmKHiHQtjjDHGGGNMa/xhYYwxxhhjjGmNpVB/FOg2eC1qkJ5gSimLyiOyiBH8XbbdzO3OOZKWnfqpkSBI7VRxHcqUrugJ1E2HPdtAnzM7yfjmqJOdulqLKKTbwVkkGm7XZyd2T07yMeqKbuMTbZMs8gqfgVvRWcQRlQnwuVWidVXldyot4NhVWQvbMpsP2r+1aFLZOFbZjMoNyKOwdawRzjGVqWXzlGVmkaU4pzS6UCZ5YTtkEbUYPUrXC6JjhlKZayWNYz6LHMfnVgkhTzLWfuLzHC1pF8XAqLRnCWwdM9tV8kVEvAx2FnGJqESRcr2sDJXUMC/bXOvPCDy6Dh86UAUHQKWmvM7qz3VAo8ixv1UueXKlfI3+x/vpGsq1RGVGHJO3SBrHPeuv86Y2dvV++m6tRfzT8jkHFksa66hrIdcF7V+OZbalvsO4NjaNChlRtgPnurYB628p1NbAOxbGGGOMMcaY1vjDwhhjjDHGGNMaf1gYY4wxxhhjWmMfixEL9ex66ip1idSsq8Y4C9dKDaSGfrwwKZNQP63a5CwkK++nulZCza7qazP96+gkjbpT1WYShoEcI2nUUP9Q0visWShJ3lv1+PQvOEfS+Gzqo0Ata/acLEP1r9TPq/aZ/abPxjL5PDp26Xuj42Jlksbxy3Ghena2idaRvgZzJY3tpRp8+i5loST1mmQnCLONONZUN571KfX5Wj41x1dFHbbljZLGNj9V0qjBV60+25zacB13XEtUl87nUT8Etp2uEdRkU7ut843lq98Ex9CjksbnYf21/Vkv9UPIfF7YV7pG835sA/XR4RjS+cAydOzWfHZ0XvJZ9TfU52vbcZxrmbzOwijz3aH35pjPTrFm++uY5BzI/EB0zLP+uv7V1gEdu+wrrRfHgtZrfMXW9/+dlTpFdM8xwmfTMcl5lf1twDJ0LrKe6htTC4GsY5fjLvNZM0PFOxbGGGOMMcaY1vjDwhhjjDHGGNMaS6FGLNwSzLaKJ1fsiPzEWm5jaig6ylp0+7lWDy2fcolLJY2/0/KZlm3l8n66Dc7fqRSK0gBug6tMgNvbWga3XnV7tia/iCjbnFNTJS/cItd7M03DC1MeQ8mFtl0Wjm9qkkZ0+5nPyjbQbXCWr23HUJkqGaltn+uzrajYEWUf62mtLEfnEWUbmXSFqLSB7aDPXZMs6rjIwhzXwv1G1OdURF06pq+OTBLJe2v5fDbKO4+TfEsr+SLKtlPZSdPn5rNp/2Z9ynZYLmlZmTWyeaOhYvk8TWWVKv2onYYcUcp0dB3m77heaD2YLwv3q2RpXM9ZZx0XTcerjhm+3yhn+2JSp6z9s/GTyfP4PFp/jhN9B3wZtoZ8rclJdS2/J0nje0rvTZmUrq9sS/bvRMnH59a+ydYZjjWOVx1Lm5I0Mxx4x8IYY4wxxhjTGn9YGGOMMcYYY1rjDwtjjDHGGGNMa+xjMWKhnlD1nTUdv4Z/mw5b9anUfqq2l7pHajZVL8rydahR66v6Y2o6j5Y0ajhV30kYCjDzsVCNJeuS1T8Lude0jqpbpiaVIWW/n5Q/R9J4P/UTIFnI3cwvhxpX9Y3J9NoMDZiFVtXQvYTjS/W77GO2ifYvx5aGuKRPivbvy2CrDr7mc6R1pO/EsySN41XnItsoC8fLNtYyMl8o9pVq8PlsWR0Zgld10RwXqjdnW3J90rXkStgaZpJrhN6bum717+C6yZC4Wkf6GmgZnDvqG8OQnfNg6zrMemgoY/axrkEvT8pkO2TvCqZlWnod81Mrtq5VrLOuM+zjBySN9deQrGsr+dTHiPNvlqSxHdRfi/1YCxkcUb4fsjDNOmZ4/XJJ41xkuFz1p2L9NaQ2n0fnaS0kuJbPfDofuEbo+yfrN96P40nXHLa/1p9zXUNe6/02o+8AjsPMZ8MMFe9YGGOMMcYYY1rjDwtjjDHGGGNMayyFGrFw61JlJ9xanAZb5R2Uj4yWtCw8YtPTqblFq1v1RLdauT2p2/OsM8PUaRncQtUteG616vZp0xCUnDq61bo+Scvai/fOTgflvXULXrfFCbfy2XbaxhwnKknJwhxTopKFcGRbqvSD6Fa3yiVIrd+y7Xht1+yU9+y5V1fyqQSP/ZadUJzJvLJQzLzW9uf80PK5lmThF2tSAy0zmyu6VnFu6hpUQ+VyWiZhu6qch8/KcaHrJOuvsha2q/Z3bcxoPSjB0zHJNG0fPreuf3werne3Sr5s3GUhrwmfR+cQy9dxwXplc3uxXHPtYptn8lQd1+uSNLYr6zxP8rHNdT5n6wD7WMdC7TRvbR/OAR0XXLNV+sY+Zv01NDmvVXrIOasyaa6FugZRGsh767jgOqN/N7Cds7Uqk2jzfrUT5E0bvGNhjDHGGGOMaY0/LIwxxhhjjDGt8YeFMcYYY4wxpjX2sRixUK+oOkqGyKMu9E7Jx3Ckqlek9lNDCB4Hm/rFTOusoQCpVdZhuCts1dnXwtllYePUx+Jk2B+XND4r20v17JkPCu+nbcJ21nal3jMLJ5j1zVzYGmKxpq9V/XSmmWb9szDBhyZp7KtdJR/TtN+o+1VNOfuHYSB1XKhGnrAu2ib3wNZn4zxif2j7sO0WSRrzqu6abcf665hk/VXfTG29ao6podb+4Jhh/fXerHP2WmnaH9fKdeZ/kYV35pxSvTbzcsyoXp5rqLYr54fqzWta93skH681ZC219dquX4atYT/53E1f89o+7PvZklZ7bh3zHBc6ZzmfdZ1kmQdJ2i2wOSa1DTJfN9ZT5zrbjuNT+5dzXefNNNj63ETHAscX1331BeA80ncrn037vuajoH9DcBxmIdMVvn/Un2cxbLar+hUxjLn6dTGUro61w2CzvXS9Zn/oOmOGA+9YGGOMMcYYY1rjDwtjjDHGGGNMayyFGrFwe1K3sCltoExAZQfZqa4zK/kiym1fbsmqPIJSIt1G1mvCbXatM+vFbWMNW8o2yU5A1t9xS5iSL5VAME3bjlu0KuXic2sIQYZRnB91KL05QtK4zatbzLUQiNpvzKdtR8mC9g3TXh91eG8tn3VWWcj+SRq3tLVNCPs7OyleTxXnHMhOKqc8QsvPTnxnmsp+ZlRsbTuWmYVF1fCRROvMscF2VYkCT8RVaQn7TevFeZXJirjmqHyE7a8hRydV7Ii6VEklNWyD7OTtoyWNEhWuESprqUndIsp21r6h1FHHPNuS9VdZDueNrmNZSG22M/vquKijskqOQ+1TzkVdx9hXlNtoG7CdVTbDOabziPfOQpryflrGfNgq88reb5TtsP7a93y/6ZjnmMzG65QkX3aa/fSow3bV/mC/cRxOlHx8VpWYUR6m0m7eOwtjTrL+NUPFOxbGGGOMMcaY1vjDwhhjjDHGGNMaS6FGLC+FrVuVr4B9Luxpku+dsL8saZn0g1uO2VYiT4XO5AXZ7zQiEre0F8F+heSjBEm34LnFrKepMgIJt+B1K531zyJ2ZFIubQOVTdXgtrs+t0b3IB+ATcmOyiOy02Z5b5UvMGKHjsla5Bi9N8eTSktUEkE0b42ZFVuvtS9Oha1j/k2w+Ww6bzgu9Fkuga3SwxoqE+C8VNnPy2FrlBRKdrIINmyDj0oapQcqC8kicbHNuabpfOO1jnHOI227TBZHWOdMpqn3PibJyz5m+2h7cP04WdL43LpeZO36atiU4ugrn8+tc4j1elTSZsNmm2RzKkvTuZL9aVJbS3ReZu8YomON75ymc1Hli+wblQ4xTccTx2smq8zG6wuT8ldW0rTt2Aa3SVq2DlO6p31KeSzfDyrlZmQprT/LXCRpjECma1wNXUPNcOAdC2OMMcYYY0xr/GFhjDHGGGOMaY0/LIwxxhhjjDGtGdXX19f3ZFfCDJ5Ro6hLzE6B/hxs1e9S66mnd34RtoZuo4Yz07G+EbaennpS8jvqLzPtLU/NPjspb2uT+VgMBobPy8LlLYI9f4j3GirsG9X9Uo+sGvx3hsngfL5S0l4eA6NhLJtqyjNUU17zoVJddLYGDTfqw8E1omn9h4quk+oDVoNa8cw3YjB8Dbau7YTrk7YP9fPzkzKajjXVxA/Xs9agH0JTfxpFw5Y2DVVKtsZzZ2st1wj1i5rfsPwsvDP5olzX1qOI+sneW4NsLDf1sSjHdV/fcKyhxjsWxhhjjDHGmNb4w8IYY4wxxhjTGkuhRiijRnF7e6jSmwxuf2oYwqbbhSxDt9L/mKQxmTTDmMGyNSRO5o+P4ZABjUQY0jQLfdqUP7R8azjgOycLO21ySjlVX59P4h4OvGNhjDHGGGOMaY0/LIwxxhhjjDGt8YeFMcYYY4wxpjX2sRihjBr1ZNdgsNwh11l4O2OMMcaYPxz+a3h48I6FMcYYY4wxpjX+sDDGGGOMMca0xlKoEcrIk0IZY4wxxjw18V/Dw4N3LIwxxhhjjDGt8YeFMcYYY4wxpjX+sDDGGGOMMca0ZrsnuwJmqKyHPf5Jq4V5kumR696Gv9sB9mPDUhOztXga7P950mrx1GUX2L9+0mphzB8Rq2FPftJqYUYm3rEwxhhjjDHGtMYfFsYYY4wxxpjWWAo1YnkKyp+eI9c/flJqMXLZHfZ9DX/zLLm+ruHvjoN9WcPf/LExUsarV+kcLoWWQpk/Fra2xI8Sy0ck7XcjfNHZFvbvknw9W7ke/4/iHQtjjDHGGGNMa/xhYYwxxhhjjGmNPyyMMcYYY4wxrRnhQjrzlGLqk12Brcjhcn3DVrjHRNhNfSx2HeK9Dob9/5KPBXXF50vam2HfJGlNw/P2wO5N8g3Gv2N0kmYi1j7ZFTBblR657h1CGbvI9XD4LAxVx9+b5N0ZNv3nLk/KzMrbVq5ZT/pV7C/5bp+UFDoCyPqDHLNVa/H/LN6xMMYYY4wxxrTGHxbGGGOMMcaY1lgK9cfIsbCvTvL1wO4dhvtuGkReSkEyGcgOcv1knRKtchTWX0O+XgP7l0mZh8j1GtgvgX1xUsZxcn0V7AcljW1JKdTTJB9Pd95d0jbCHomhPf8ZtsqRXg9bpVAcdyqLuwP2HrB7k3p8Va6nJ3lnVe6VlZ/16WCgNEPHE2GbLJW0oYyTt8v1B5K8lHE0DbdsutlZrrP+/kOedn62XL9jCGXMkGvKgHqT3/Uk+bhenJeUMVaus/v9DPaZSb7DYKtMiuhfeL+r2FrHDM7NbF6OBPbYchYzeLxjYYwxxhhjjGmNPyyMMcYYY4wxrbEU6o+Rl8POpFC9sFXy0jQqEVk1iLyU8GRSqLPk+kMNy/9L2D+r5mqOPhu31jW6EKNjvSUp89Vy/TXYR8BWKVQP7IMlbR7shZLGNp8AO1sF5sv1rbAzCcTH5PqzsB+FnY2zA+X69iQv6YG9XtI0+glpGiHkMLmmPElPsK2xp1xn0pKZsCnJ0zHJcT5U6ZPCMfop2CqT4Xh6qaTxd03r9Uq5/nhSBmVkKi9USdtTEdb5Tknrhb21T4qfL9cXJnn3g80xqfOZErkbJa1p5J4j5HooUqhZck3ZaW/yu0zayPUik0LpGs0ofCp34jowB7bKnd4J+/uSxvmh6x3nKWWDk6M5b03uzTGpc5Fyq6eKZHH8k12BP068Y2GMMcYYY4xpjT8sjDHGGGOMMa3xh4UxxhhjjDGmNfaxGKnsBVtD6U0bQnmZNlzDEDIvT35eE3U0/OWUJpWKUmc6GFbD3lfS7hpCeRqOjxpj/TxvWuf5ck2tPk/hfonko7ZeD0jNwgZSq38l7N7kN1o+9c6Zxlv9FW6GreFIa8yX66Y+FtRFPyBp2TjfsWH5Si/swYRcbkrNh+MPcSJ3NqcJ66jj/4WwFyZlcI3QcZf5ZmgfN4V+ZUPxKVOGerrzobBV6049vq6ZfAeskLShhOWeueUs/bDO9HH5suTjejFB0rIwqWQ4xrmOp9UD5upmSZI2NUljaG/1c2Cf/kNSBn0zTpC0A2B/WtJOha3rMPuY75HB+FhwndRw53wnzE/KoG9GU1+brUHWh2bIeMfCGGOMMcYY0xp/WBhjjDHGGGNaYynUSIVbubqdt2wI5WWnrKocgluXTUOH6khj2NLsdO21SZkZlCHoydgrYfcOsXzKbVR2wBCnGh6X4Wf1uQklL++RtH+BrX1zR9S5B/YPk3rweVS2NB/2AklbmNybcoaN1VwlKl9oelp7Fl6Y7XOipP28SaWiO8QiySQ7KgckmWym9qwajnc40LGwDjZDdt4g+Tj3dUxSDnO9pFFKSTlPNo6PlWuOp5ujjj4bw3K/O/ldRg9sHa+UslwWdbgeaXhkouGFuTYORfqkDEYWUpP86fhnn+q4aDqfv9W0Ugk6Lvi+O0rSGAo1k+ncmqRRIpTJIzUMLuGamZ0QfZJcUwqlv6NsmvNyqBJOHTMLYGs4dcrkeO8fSr7hGMtNsRRqq+AdC2OMMcYYY0xr/GFhjDHGGGOMaY0/LIwxxhhjjDGtsY/FSKUXtuqdm4aIbEqmM2WYuszHoleuL0zyMmyj6oqHEiJSQ+IxHOY7GpahvgbUpH5U0qiTPlvS/jW5B7Wm1MbuJfkYtlG1w5lW+RLY7NPnSD6WsVzS6L+joRIXwlZdNMtpGh70TrnWkJpDIdPgf7JhGVnIWqJhgulP0NSfI0PDTDcNx5vxHbmmzptzQMMaXw1b/QSYV0Njsk8/BTvzY9F7c73L1ir9XRaamXA9Uj8Khu9UP4rPwGab6Bi8Fna2dt8m10MJ09kj172wBxNylGtV5g/DdV59XN4Je5qkXQz7kmiG+jDR30Pfkcx7tKTRd6I3ud8XkzT6F1wraYfA1v7mWGM9dC0kma/eeLmmzxT9HHRsEQ3XTh8I7XuGYdd6LYf9z7B3lXwLYWfvpuFg3ZazmMHjHQtjjDHGGGNMa/xhYYwxxhhjjGmNpVB/jGThNgnDVaqMgqE4s1CJDNk4HFKMiDI8rG4VM5TeB2Bnp94eLGk8KTaTQnG7vFfSGOLyc5LGrd1zJY3P9oSkUWqShZ2klGsw8qCadEJP3KVE5EZJY/2fK2kMA/q+pMwsJCu5R66bnmScQUmEhjVUmU4NlcPU5HnzJR9P4P1sw3tlvFOuLx4w1+/hWM7a//AkjfU/VNIohcpCOGqo2F/CZptk8hqVhbA/MjmMnuDcVDJKOaPKX14JW9dJzo+FsBdIPs77LNTmUE8oZpt8U9K+BFv7Pns/qLSoCfpslOK8XNI4ln8ZzdD58JYBc/0eSpUOkzRKHbP3w01JGtvyU5LGELNZmNePw9ax+l3YR0qayocIZXgcu/oeofx2jqRlsj49ZZxQ3vh+2DovF8LOTvZWdoadhc8nKnE2w4J3LIwxxhhjjDGt8YeFMcYYY4wxpjX+sDDGGGOMMca0xj4W/y9DjatqaL8KW3WId8E+E/Y5kq+pzlGh/4JqrT8Im5pNDetK34blkkYfC9Xhfgj2+bDPkHwMq7cyKV+ZCFs/6xnuUcPskhfAvrWaK4e66wmSxnGhWur5SZnUSV8taUckaYT64Kwdn0w0POKpFVtXV+qWMx+aDGq395G0bWGrHp9tqT5YGs64xo7Jb3pgq//F6qRM+t6Mrvy7Mk2u2R/PkjSuXRpasvb221au6TNyl6Spvr0GfcDmJ/lelaRl/iNZXob2VD+KzKcmez8MB+wrXQv/EnZTn8FM369+B3xf6Fg7AHbTcOQZGm6WfgPqr0U/Mvpr/aXkexdsHYM/gK3vT/q+0cdCw9IyTev4DNjqT6X+jKTmH6G+eiTrU+Vk2P/W8DdNQ4ebQeEdC2OMMcYYY0xr/GFhjDHGGGOMaY2lUCOVHti9ksYTL7PwhfNhazg7lqHbnbptuhndtrwO9s6SlsmkeAqongbLa4ZJfUHU0ZNbucWv4ey4dXw6bJVCcXtYZSf7RZ3rYWvfsEwN80ooScnupVAOwDZQWY6e6Ewy6US2Dc6t9UwKxec5QtIoa1GJVg/s3qR8omEOm6JyG16/EfY0ycf+1jKyk+iJhsYkHD86v9inKi2Z0vDeRMfufNg6DhiiVeUQHHs8bT47BVpDk1LOoOsY5ZK6jnGsUWoyTfL9MNrD9tL6c63V0KQqvWoK1+imJ4wr/J2evky5atNwsLvLdfa/NY+BrWGnaxIwfS+xT3XeUM6TvSOHCkOJa/szRKueMH9CpbyT5JoSre9KGqVRKgFbApt//U2TfHyfZqd+q2R3KGPt50laFo5Xqf1dkjGY8k1jvGNhjDHGGGOMaY0/LIwxxhhjjDGtsRRqpNKbpFEiRNmDbmdTHnFy1NGoUJQbcMtXo+Vwi38wW6SUcbxQ0h6FzfrrJzKlJm9L6qWSCz2FejO6pby8ki+i+6RSwkgfKtugVCM77ZwMpl3ZJpSBqNRjMPKqGtpelDUxopBK2DIJB0/LVSnUULa0NaJW09Opz0zSKK1TaeC/wFa5GWUbfydp7J9MIsTVXCMIZadhZxI2cgVsjfTEMaORVih30hPHKcN6pGIrKpHjOPknSburki+ilI59BLZGj3ptUheVoRBKPCh/OV0zApVJvQl20xPrI8r5wLb8reTbMepQ5qJ/KXCcfyjq9MDW+fAQ7GdIGtcBjQ7HKHZ8Ni2f15lM5kq5PjHJW0NlXowUOEvSuHa9K+rw5PNMnqpzKotUxrr0wNY1gO/TPSSN73yV0bIts1OtOTc+Us01uPebvk+bkMm8zJDxjoUxxhhjjDGmNf6wMMYYY4wxxrTGHxbGGGOMMcaY1tjH4o8d+lWoVpIa80zTfF+SRh2lntBJLfQKSaMGX3Wa1EX/Q3Jv1VoTlpmFQ1SNZU3DriE59XmItkMN9QsYip/AYDSifLZlST6e6KwnvmZQR66neXOleSnst0u+l8HWNj4ruXdNf67hGy+H/VFJy1bDBUkaoc5X/7cNQ0S+X9KoB3+zpFHP/q3k3hx3n5C026IOf9craT2Vey+RfDzJWE8aJjcnaezvzOdL9fhE60VU802/E455XS9+HXWy/uCzMuT1PMm3FraGIR4q9FPj2qXhQfdMymA4Zg1R/E7YmW8Sw0zrvb8E+41RR/15Pgib/b1D1Kn5zkWUp1FHlPNtKKHbI8p1Wf0j6GORnSrOE+wz/4HMf0RDuR4zYK7utYq/20fSzkvuzT6tnbQdEfEL2F9L8qm/U0bT8OH0hxmKX4bZIt6xMMYYY4wxxrTGHxbGGGOMMcaY1lgK9ccIJQXc+two+RgaTrfg/xl2j6QxtCS3EjVkHUOJXiNp0yplRJThYQ+QtF7YHL0/kXx6oim5H7bKbfhsDPmqddRQpYQyrCuquSJ2k+vlsDV8IemFraEGM7hVzPq/UjOCdyZpCseTyh6YVtuOj4hYmKSplK8JH5RrSqE0pC9lD4dI2muGcO9MOqEhLrM2oRSBkpr/kHw8nVdDZmZSKI7tniQfT0C+XdIop9K+51zcW9Iuj4HRsLGZ9JBkIS5VpqMSj80M5gTfLyZpP4LNNtDfsE/fKmmUjGbSEg0lTjnpfNj6xv90JV9ELh+ibJN9r21MOY+OeUqQVApFmatKFqfBzsKRUgKWhdDWk73ZP5TkLUzK+LBcs86HShpD0ar8laHKKaFSOdi7YWfr4pik/IzlsHWe8J3zv5LfZfAdqevkpbBVVrkt7N9JGudtD+xeyfce2Nl6YYaMdyyMMcYYY4wxrfGHhTHGGGOMMaY1/rAwxhhjjDHGtMY+Fn+M9MKmvlO1vQyJp1r618J+haTRl4IaV9WJ7whbw9ky1KOGeM1CwD0Am+EcPyL5VJNK+DutF2fEv8DWMJYM13q4pF0Sdc5M0hh6kGFSNfzlObB/mpT3ErmeDpt1PFLyMRzicZJGXxb1f2GbaxjFz8H+fAyNd2w5S0SU+lrVB+8FW9uVemrV+E+PZlDXnY1B1cs3DTM6C7aGYjwp6lAv/xxJa/oWYEjcmyRtj4odEXEq7HdLWs3HQvvtf8NWTTZ16g9Ge3SuZ2OGddF2pT6ca5yOC84VDTX8ctiZj8U35PqvYDM8b6/k4ztA18Ja6O2IUpvOuTFX8k2DPVXSNMw4YVvquN4VduYHwvfWtFqmKP0JI8q1keNO/ZSo6ddQt1wHtP6nwL5X0vg+vQr2yyVfFnKZaDhhDQNe49gkje9/fe8ugq3vcfri/BD2LpKP/mHqg8K1Rd8H9CFkv2nIca7zWahbM2S8Y2GMMcYYY4xpjT8sjDHGGGOMMa2xFGqkQpmLSg8+BHs+7HMkH6UUuh3J0LQaspOhHxlGUcM0UkKgcpuMc5M0SpB4eqfKC3qTMu6ArSdXUw7ArVUNo8jroyVtIeyjJO2rsJ+o1C+i3OLXU7Ivhq3bvJRq6OnFlCJkM59hDrPTbJUs9CPD22qY3eEmO8H8vbC1vscnv6NkITutOONC2Bq+tunp1+zDlZIvk2tRjqHjIjuxlrK47ER5zvXfJvn2l+se2L3J7zhmNLQ0+1HHaxbyt8a/yjVlohrGmnKVL0gaQ8BSWvJxycd+1BDIvPe7o45Kx746YK7ucMKUOqr85f6oc2rF1rDArJeGHC/eFxoLHYNS5Zg9Sb1I03n6drmmxIbjTufNS5MyKRfK1lCtI+cOpWhNpU9bYjjKuShJO71iR5TjieNi16ij4XFfDDuTxnIt/5iksT+yd4UZMt6xMMYYY4wxxrTGHxbGGGOMMcaY1vjDwhhjjDHGGNMa+1iMVBh2UkPRUXNM3wkNxfiXSfn021CNKDX4N8JWzfdeMfxQl0sdq4bRo//IeZL2JdgaUpGhaF8NWyXAbOO9JY3aag01yLb8QNThzMx0oOrfMRG2auI5ZlS3TKgrfkjSqJHX8Jfa/+TVSRqhfvejknYXbNVFsz++FHU4djVcLlH/l6Z6bYbhVH0wNcc/krQjYGuYRoZYZhhO9TXItNw7JmlEw6kyJOih0Qz1WyLqx5KFhayxSq45zj8jaS9rWCa5Wq7pR3GKpNEHRf0cSBZC+5WwdQ7xf/3tK2m6JpEsXGjTfJkvVG0+ZPPkRrnu5cVSSdyvY6rvXg1dq4bLL2EzGhI6a59sLBSxtyWW+7dg61gbCj+X630qaWl9hwm21wthaxhfor6THF/690vNx+yNSfkastsMC96xMMYYY4wxxrTGHxbGGGOMMcaY1lgK9cfAFLnOZC5kRpL2aJLGz9H5sLNwowrDQA4mpCnDbVIipLITtolKoX4NW9ugt5L2fsmXnYJLuZButTKc4JWSRpkLw97eGnU05CElNSqhYjvrKek1VE7FvlfZBtUMehq5tl+NBbD1JGBKnLLybk7SMvkISf+Xi+7PQ0/3WfyzShSJztHnwv66pH0RNk9znhXDj44Zyp96YO+clKGSl4wsxG8NVc2wznpy9VCkUIORcOo4J5zP9yT5KPW5Jcmnc13DUD/V0bDoBckx3Nnp2gzPu1bSTudkz2IqC0X/I77w1CM0J9DFUI8ZJzzaW6RQn4KdSaH4HslkjsvlmpKnT8K+IClja8Bw25+s5srltWfLddE9Dfs+O13eDBnvWBhjjDHGGGNa4w8LY4wxxhhjTGv8YWGMMcYYY4xpjX0sRir0IdDwi5QXMnzk0yTfyUn5WmaNpiH9NIzlUEPRUk9NjfFhko/6/AMl7Q7YGkLzJtgaXpBQP60h/Zj2eUmjv4T6EHwPNnX1l0g+lq/6WrbrY5JG35KmIRyz//VwolyzzsOhXVVN9sED5upGxwLJwn425try8rFTO/bt+Pes7fZP0q6R6+ua1WpY0JCNNf8nHdek6dhSdm+YT/tQ51FbvrflLI14J+wvVnMVMv5ibivql5Np06uIY8bP4UiWhRzdJUlrysflemz1Ygv3xgtuReY7AUfB+yVfFiq28LtDrOHx4mPB8LbP0JipdR+LcRhgGzSxtj5dJte69tbIwgkz6m2XjwUdpZrGmY4yTHe2/jHU9z8l+TK/zTQcL2NEz69ny1xhzJDxjoUxxhhjjDGmNf6wMMYYY4wxxrTGUqiRCmUiGhKUu5jcWtXwl9l2qspoqjdouE2aSWP0lOPsc3dlxdZt/IWw3ylpPBlYT4Tm1mvT00iz0ztVWsKoiu+VNMrKXg/7XyXfA9EMlbEwHOPWCLNHpcCiYShvP7kuFEganhL6myIM6/fLbDOzkJHU1MgefC/sHtEJrqiVl9QxO6FYJXg1tYfKJgrJiOqDGsaC1jFTC2uZNaOuR01pGj532iB+11QyQjKZzGA4tmIrHNd6ojzHr0pxinZu2N/3SyzgmxtKoXStKsYF4/9msWGFc3kxmFjlkDi9MZNCYb5p8f8B+7mSVkgu0a7puNAYyHUmwu6SQu1d+dGlcl2Ma+n7r+Bhh3x6N8bdY/KOz8LD8+TwrrlX+bthq5z6zQ6XmLVfh/5J3zFmWPCOhTHGGGOMMaY1/rAwxhhjjDHGtMZSqJEKJTa6NcleXQOdxqHp0aclC7JElPkT/PMBXRk76E59D+w7JI3P1iN6j6k49fiYZNudZaoUqnaacMQAUoTNaFgX6Mqy6FF6Mi8jSGVbwOzTrPzB8IuG96ZMYLmkMTJNj6TxQOquqGLsx4ZaGZUeFBIFPV776EohMn4yCVKhaZKxxfHbI/WvRjjTQd8wXNKZzbLl6hE9lnmIe/6MUEVpQ3bvLOKVSiw5zl8Au0seibacKTfPIimphKeAUrWsbzguBrGGNrpvRCzL7o1JpevkMby4UhJPGri43aT+pye3bpyPUpNBSKEKkgF1v1zvloX1q3CnXGfjt3gHqJa1RlUP2UV2mHShk+IcfsUMzQlkPJ3bUArFtfwhSXuGDraGLIHdJYViSMSs3zDmpwxVs8vfrS6T5kMKtV7XyaydTVO8Y2GMMcYYY4xpjT8sjDHGGGOMMa3xh4UxxhhjjDGmNfaxGKlMgW7w16JDpKSQPhaTBqEP/mCWiJNPm0pqVRJP/eWtkrac+eS41n3m4CK5+fehO/2gaJiL06pV8Vo7ijPRnHZ9niea7KGcSpz2xSBgH2RhON8FW6X51NCe++Uy7VacQC2y1jLsa0X/vSWKaJtyg3thFzLZCYO4AU/PlbFVhHJNBNr38WJiLdfw0JMlbiovi1OCB3EPNgl9XNRNhr4S6nOyALZG4OXv2ORdcwrzdOYgdNBpKEv6JZyc5KOfTraG6qCv6cNlwXtpFrsXC4Y+dvFst0hibY5tqvz7QDT1QRkOkuPTu8K80oGL64q2NxYvXQYyf8CCpkczX1JePvHpjt01lv+iXkwxP9Cnh2djXuKdL8v8EnCD/dGnXd2LiZ+Fl1U4PV4oaZc29UPBu3bXmu9cRPF3SETEzxF6eB++12W95vOM1jrZx2I48I6FMcYYY4wxpjX+sDDGGGOMMca0xlKokcpGbB3LYarl6dTYLswOKVV2yRKxb7pjPVcR4291Eu5STxAuwlVqGMWGcq41CCO3SuJfFiFHry3Tuo4n30yyVd8FQ9hJfQdzwOxmuuQcDH1bq+8ATGmYLzvVvVCO/bBMmwEpVNeOMjU1Q5RCFdv1MmioVBvKactdhZxazZXKSQrZxqOSmE1A6tQOruZqjnTcUFd6ypMog9MqZnKJl8JWGdb9OLF4Y6arxPxTxRHLvFfS0vDCnPsNpVBdYTl5obLKmiTlnvLy3IanwaeyLpV01GRMyUDoCuvKGK3a4Zx/uFdXGfXblWjbZWOBY/sq2NrekDEdMJjj4NmWmXSM74TflEnp/7JN+ns82/VrsLP1SCbEyZmcB2Xu/4qO3TV/9aXckAWwX6aJXP8oY5L5wD9oJmVSKOmbR1g+22ROma9I69LsmmHAOxbGGGOMMcaY1vjDwhhjjDHGGNMaf1gYY4wxxhhjWmMfi5HKEmggx4uOny4FlK5KdLaSpXKdaVxrITxVlwmt6mGJj8XLxX9hFzoiaHzKhuESz0IjrM0yahzcms/CYBxUEt0mm+gZqiumDjhzxrgUduZj8b7y8tR3DpztMbmegUrOzLT6EuKS4+uv9CaD8VGpwMiSxy6rZivRcZ1pdrU/aqQTCdwp19Spq2PUrZV8g4FtLM/SM4hQ02Q+bEqhU98q4fAkbbfpHTttVqw510gSfQ90Oj9ayRcR3WtLDbSrdmnxbDomxbern8G8dpv6JunYZUhW+lgk4VPvlA7YjWWqDwfHL5577HzJ19B36DHpuB24/ql/BOtCv6hZko+LrZbBUNnqv3AubNbjJdV8OrvK1tJxkfgvvJT11MHckIuyRCyix8PHomvyPd7wZovken7HfJrmrYXf1rGL6y7fj2Q8TecF21j/9nh7x1wnPdczYAXNIPGOhTHGGGOMMaY1/rAwxhhjjDHGtMZSqJHKMmwXnixb7tw+nADpUNcuOLeRvy9plEKpnIRbl5Rf6OnUWahEhEzdRcPvcXtSZUVyymg/Imt4M7b/NQRiUX99NvAE7EF9gien1LKZ90rC7CXhLyfFd/rtPCignIy9S0UKpdvNb0U9tplfpv2YF6vKtOtha6TV02Gn7QoRwedlm5pqoWNFmlZVWah2JYOdo7+rxV2NKMOWsu8XST72t84V/k7nItuB0ozbJB/nwK6SxudRbeChUaUHduPTijNUfoT5vOfUJB9G+hsTWdcpcv1d2F1r0PJ6OQVo81SlpuOiJl3KQlzrjG4a9lPbi2ON4UeTtelZWn+OEx2TlXfAM1T2w3CwUv7PIUHaR+v/ftjzJY1rNuuh4WYXwVYp1Kdg6xHRDPPKMalt995+a66klGqk10lqIuMspHUP1/MV78XByEx17G1G9VNcmHWt4rqj4eDnd8zvSFJRZ9ZD6z896nwMtvR3D8d5FkZ2UcdcLz3Xk/zMNMY7FsYYY4wxxpjW+MPCGGOMMcYY0xp/WBhjjDHGGGNaYx+Lkcok6F93UH0qtKBHUrevumtqJx+QtHMGLi8iSv3iR2FreEUNUUgWJWn8nepMa+Fmk9Ceu2XlJ2Fki8/u7FkUakalb9YnGueiTeo+FlT95grsIYZ43YYh/eaXaUUX/6ZMY8TCLhkx2nk5/nlPzQf99GdFS//6Sr6IiF20nM3ouKZWXH17qClXLTIdSFRXzLzUay+SfJyLqvkmqnV/FWyOJ/GhKfyP1J+GOm8dF3Scked+DOF5C18cvTfH9fyoc45cc006G7aESi7maS2M6wAcmSXWdNhflGu0+Q46Zki2RnC8ahhRTpbMf+fNktYZQ1Pi50XKqmpd1KcMdXmG+t4sgq26d44hjmt15LsDKR8sUlZ+BH1/UenDsX/8FCUcJGXS2Yp9qCG6P1epb8Sh8d/99o0S+nlK8c7p2KuK8krPpG6vAL6PSmcDzvw1XXOx8zxc/VZ0rfQfh63vFPrXaAhe5mXbfU3yMSy3jkk+m44nzJ3nahhf1os+NOrjRV+Jz0naN2G/O+pwDpfv4KmYK6u3y8Lqm6HiHQtjjDHGGGNMa/xhYYwxxhhjjGmNpVAjleIA4dMl8T2wuW2poVopL9Bwgv8Fu4zTOA5biRtiHFLOkjIuga1bpt+GrbKT7NRPbmtSypWc3tm1Pc8t2aZT4IdJmt77YthySvmb2XG6hc0t53r9mwucZmw5y4AkJ75CDqOlL3spLjK1ypTs3pDKTJJTsotQol2xDAG3wTUfJQVlS05CHNw1RZjMCIY2nBK/KlIo5rkD43yC5KOIb8MNOxVpow/vyMo2xp/LvSnD4pwqwxWPRv03xrOKtEnxg357TWxfFs9wzLtJaMkduH5QnPE2qSNHw3xJo9TkfEk7DTYlZh+XfLz3BySNa5dKzDgQVYZFKAt5o6RxzXm9pFGqoWsoWdRv7QEZTkTEikKGUvbpTORd2iUL6cg4jpeUCwspFJ9tkeTk/VRKxLnzj5LWWdcmYZyv6ZLUdGRe2qpvepQyxfL9wKird3SF0v0RbI5lXct/FTVOgH2jyMb2hc1Rd1H83yIfR1r3W+SWfkuXuxNhX1Cc8h1BeSZFTCu6pId8H2m/sb00PjLXvM/C1vczZUxZyG6V3C2CrWGhO2NhNPpmY5fMizLg90eVxxaV1ztc2m/OgEx3mYxJtusDk5LQv2bIeMfCGGOMMcYY0xp/WBhjjDHGGGNaYynUSGWHTtftEd8oklYUkWkowCijX1DesX8hfYqYDfsiiTjyLtgLY0O/vTS+VOSbj238JbItXYqA6tKnO+JP5F+4bcotThUIMcKJRvGZA3u/6r0px5iC54zQM6ezbWqNykF5j24/85rysJcXuZrHp9ItcspcMq1Scho50Cdbdij65liVpGDL+RGEj9qxjNo0I/6z84vX6zZ158lfJSkXFAKDzu/mS77bUP5Kkdsw9sxVkA79no4c8JmSUrZyZ2RPlHwUHi67sYyotQDaj6tlrqxEf0wpZCcljEyztJA5lCcDX6rzbTfK7jaWaYUkryPJ6pbzaPQt0onwpKPujkKm05lHL5Q6Xl88t0qOKFvT05evrdhKp8w5EomOJV7VJSXqSMJmSJS0ZUU9O2PyKCnhwkLiVEa946i+TOq1FGO5O7YN10NKWfQdwPYvV+UyKpGuOm/qtyYV8rb/LHJlZ8HH19iy5XoxuVhv6xHU9sc4uUOkNxSh6r3L63K949uBc3hclHA1fXFXzTrREnUdOAT2BXGppHZmSDmSdcx3+m2crBflm6qU6U7FGF3ZdW/Ck8n1HdlZ+feQebqiGF/HFWmUP3G8LpX6b4QU91TIOyMiLoe97kp5757YkfWxjR+QeXMm7Ovk7x4zPHjHwhhjjDHGGNMaf1gYY4wxxhhjWuMPC2OMMcYYY0xr7GMxYuloaPVc0hWFfwGV2KonfHq/pWe6kovkeh7s+2DPEt01y7w8Su6GrZp1pt3RpZLnqatUrasOt6MJProIbRtxbaEZ1VB61LJ2wpYeKLmuKsIElrrlcdCxbugKm0nVcamS3wN60hWJnwM1/Ru7QjFSJ63hhVmXhbDVl0FO1C7o1LlLM30sNbsSKpYa7Uk3DvzvUWpjlx2mI29R51aSchVOZH0U/676Zv7uuvhekcZ5pGPyPPgZZWenc0zqGfFUgC9bV0/T+Xwp2pxp90g+PutS6cPyZGAJN1v4Hugpu6SjKteThtcXoXr1ROtOO2t/kEnwFVsgaQw/+uquEJ0Mxztb0jJ/IarRO2NXSzgD9pJ4QZHGtfCQKDm5CJXZWbdeIvkewLp51WNl2iyEd76vTIqlkJ9P7PpfhHy10wcl05SX4VQfLa7Uo6cDvXJ07HJ16g6TfVslpz6r+s10mA37DqkxW+DEKCk8gkoZfxyOtmQ9xiRl6Hx4IfwGbpM0tlf3iekDt/NoeYdtjE64al2ruC6sk3cyg0R/EGNhZTyvyPcq+JhdIH4z9KPRdv1qcVJ5+W5lL/Ldoe16G9aLIyWNK8u14ptR8yzVdwVDDa/eIcxWwDsWxhhjjDHGmNb4w8IYY4wxxhjTGkuhRiwdyUWXJKUrzOjv0VCPDMOmJ7dSjjRa0ng//k7vyrB9iyXtOth6bngpIdGTQxfB5tb9i4pclFWcGSXXFsKTL0kqN3cfHvBfIyKuirfjqpT9cKv7UAl1dzdCM66KPy3SyvCOi3D1niLfbNg3IpTn7/kw7FskjQESPwG7PNGX/b2x67TfzpLRLW1giFM9jRySlG0oXSm3/gvRw9jyFOjRkA1kYy2T27D8MySNMo7ZkvZV2CpJ4Xb9VQhtrItrIfaYVMqRvo6wjfuG0mk7yrBUnkdphs71UmiigYI74R1HS+jqjfFsXHXmmwoPeb1GQtbuX8n3ezoyPI7kwyUXx+SrRcJ2KOwb46dFGueUrmNshUWQ0qmEjWPrGklj3j0l7SWQkzDfXMnHeXSfSDPY37rOT4JqczvVjBSCmI4MKxEoFgLaCB2/Ots7rck21pG1O2wVbUZ8sN/ScLnXF1dlj9TDyC4qrrhGaJsXK+O7SrncUVCw3YB/17HLvtE1h2e8v1rSKNjS0MNfRq3ZgyoxuxtSRxVPsS4qhqUMaDHsC2XO8p2sgaR5P5WAsb9XFT1V9j/XYZWWsn203ziHr5UouKfAZv92r6cduv92MsOBdyyMMcYYY4wxrfGHhTHGGGOMMaY1/rAwxhhjjDHGtMY+FiOWTpA21caWAdw6ikjVZFMrPk7SqJXVMIrURFIrqfpaogEDb8MdpxZhHxUNRtdRjbLOG0QByzCQGo6P+v8ZEo6Pz0A/kDmhMEZhqYDdGN/pt18sv1oM+0K5N7XKUxFKb2V8rcj3Rtg3xkfkDq/vt8ZJHMXt0M7rioCetxb5qFX9SryhSNsYJ/fbqq+dD337opgiqY/Dpu9E6YtR+E5sXFWk7Qr9uQZF5UL2uqiTBSal3lb11PSx0XtzzIxDn+qYp99SvLq8w53wX9g9SqYibefi30uoK16Q3bvLb6mj5j5FUi4awJMmolsXXWrMS98Y6rp1Nt+CcU6fFw2lSw3+PEnj/L5R0jhvdf3jfGP9u9fTgesRUY7eeyWNbZmFKGb9tY6rK3ZExHg4l3TXmZr5zpjU3pxcsSO0D9RnqrMmcfxrGbNh3yVpL4O9h6T9W3FV+kDwGaj/19CtnGE67nSOkVGwnwNb3yNcL2Yk5Z0g13wn6B9gX5bw25vRtZZ1uU7S6IegqzDrzPF5oYwM9sffShlsc/WbYT1vK0K3d/t7bEbH/GzYusZxHR4ng4Ztwv7N5t4mCe8cDj87LHjHwhhjjDHGGNMaf1gYY4wxxhhjWmMp1EjloY7wYfUzNHEqrI7UQCVNP06K53aqblPX8mk4xz7YupXLoIHPFClUmffSKOnk5XbnUgm6xxBz3VKojlRDw1py23QDxFbrUrmWPnnnd7Pld5QDqNyG29SzYa+MfyjycWv9Y1LGm+LL/XYm/bge4TVXyknMbP9TpP4XoXwNgTgb9iI5zbsMR/odXO1T5Cs25NeXYoyjsE2tsjue7J5J8liihhXtHqMdFlTuFVHKP9haGhJ3Xfx5vz26pzw1lsIVrRdDLlLycqjk44nOKmk6r7hSuUVnPqhs46KuU9l/z2y5Zr/dJsFCGYa1DEBZjiHOIg2UTAmSygtLdiquxiIs5wLJ+SBsrhGnpeWXcDxpq1KGxfpvlHyUk+haxXmj8rDRmA/dgjXesXNqs8r4OHZ1nedaOElOcl+DMN1ct54TdbSOXJ9U8sJ7r5ExyPZj25XCyTKU6+2SVszN95cCt43F+tRB1+tsnSEa7pRzYHZX7o7gbTqVrPK/gBfALmWO5dzRU94p8+L6MVXCNPNZtQy+aRdKWtlGemJ3B8r6dMx0h6TuwDXiQJEtTa/kUxEfWSJarhMshRoWvGNhjDHGGGOMaY0/LIwxxhhjjDGt8YeFMcYYY4wxpjX2sRipjO+ECFXtLVWK1FrvL7k0/F8NHSS3VcpQzekNsNW/YyZCaKqHwuzi6hvVQveAg8RSCW1H3Wm3/rgTvlC1vWXejhr2PvE14O9WxheLtNHxaL/9YJQwVN8aSaNGezbsbh+RDq+V6zfFlf22avypQeVzXio5NyE07OwoGY00DeNHvfa4KH0IqJtl290pQQjpNzNh48NFGvX/u0YJNdTU1Gb9q3pzjlEdk9SDq/6/nB9/02/dLWOSpepcYYBWHa/UaHNOTZR81Cpr+5RhQMugrBOi08461mZi3FOJrv47pc77yuKKMmb1Yzk+BuaByr9HdPuPLIY9QQKvcoxq6E22CbXn+mzsGx3zrKeOGfWVqeVj++ic3TnqMG93SE16eHR8vtbLOsa66JgcX7EjSt8xDTlK+G7S9wjXBB3L7JsVcbOkdv5/6IEIqa3eeFnblX4uK6ppfDYtT0Pr1rhPrjcmaZz9E7E0Xt1T5mLIcR0zG+PZ/fauiZ8D+VKSpuOV/aZ9zzXoMkmjDwzXuMWSj2vEwB5e3fWIKOvJNv605Hs/7FtWyP9b7/JXNUPBOxbGGGOMMcaY1vjDwhhjjDHGGNMaS6FGKJN26AgyalvuEaWMQrebsy3s4l5yfRNshqw9Q/JdDFvDumoIO8Kt3VdJ2gXP6tiUexwq4RApUdATcQMyHQ39SJnLaMi1VCJCWc5NIvu5s+t+HbLtecoGzoSdTVLdpmbrqdSHYTpLAVIpUNkdoQf1xHGefKpSLoar1JCgDC/4SDWlvN8zdynLmA1b22QdwtZejhN4dUxS9KCSo6thq5SI9VJZSO1k11VyHu9o1EvlNqxXtxypw9d/27E37Vjmo+wrkzlOiVJixjVC5R3Hwmb4Tg01zHVgD5kPXGdUbsPQm1moXjJKrrn+jZXT5onO9e6583veJ9ccF4sljfI5Pc1Z50cNjh+VgD0KW9tkUsX+PTytuiOC3ShCvkfQXrOlBNZfwwRPrORT6SfHhYYV5RzT5+ZYvk3Wdi7ocxDLWKVurPNcSSsDIn+zuBoHm8I6nRtZuNmrYOvJ2JzfS0Pp/Mt6TJylPeX/A74c/ZaFGh7bUAql7UOy8MjaJvsnaTVpnYa953tEpWKUueo7gOsHpZmfkHznwF7f9VeRGQ68Y2GMMcYYY4xpjT8sjDHGGGOMMa3xh4UxxhhjjDGmNfaxGKHsAY2lahmXQbFNHehayddUA6z5Mr8NQp3j7ZJGX4MbJY1626Mk7SuiK9+MakQvgr0ylI6K9iYJv8i6UJ//zF+WJRy/V8dWbfUHB65iRJT9oeF/qSs+NCkjR9XQHahp1v4gDOepOnRq7mdLGvtbxwXHHjXxkxC+NqLUke8RJSxD25xLGX+nfkScK6pZp/pcfUSI6tkPLq7oLVF6X8yDrWFwM91yoWenKPuAWg27dd0sX/1H2A6Z3wzr3B3iusOJcs0ydHTSw0a13E1ZDHt9PL1IewD+JGVQ0RAPmA7aNxwXF0kan/VRSWNYbvqk1Hw7IrrryHbWfmOZ3W23GDZbuRyTk9E+6jNAH63ZkkY/O/ofaThh3rm+Mmm44jIMsbbJkmkde/JjHXu7Hcp8fMeo3xJ9IDJ/P9ZLfanYXjpvsjC7TNM2H8fQursxpbz75eInVfK/+y1dh/tgcz3t9tHpoGFj+X4eI2m8Vj8y3o/+Nl+XfBMr+SIi/gn2aknjO41rmoan5vwbOzHzUDVDxTsWxhhjjDHGmNb4w8IYY4wxxhjTGkuhRijcwNPQj9fiPGbKEHRLWbcSm9wrotw6zraR1yPy41j5hOXW6+XyO4Zy1Xtzq50yKZUCTK7Yip6iHLE96tGR6YwTTc3spEzWf52kUbKlzzZUKUhJR/ilMjKG7ivvVY6ETKrB9p8scQI/gX1r3d7m7zIpAGVrelo703Qsx73/1W9uhxCUGs7xZbA/JGkUG6hMjVKHfNHk+bKlCI8yMq0X54O2CcfJo5A/qdRgMWwd18yr8jD2h0pSKF/gWFZ5IZ9H14QslCVlFrNhd4fQrMO824lEhH2lUiWuO5TNHCz5yrb80+Jqdvx3v31blLAtOcOy0+BVbsN21fnA8bQwlM6p7wwvvEpy8X66VvHdcYKkMQQv66Vjl+NOy7gQts6H18K+QdI24V2yJxaCTXuV+fhsuqZRKvOFqLMYtkrRiL5LOe5eK2mU7HSH8+68cxi6V0NEU46kMqZFsLVPKZHkujIv6uizfRi2hmbmHNN1kjIptqXOdfbbOEkrwpY/VqZt2mHgfKdHnfGbMkmZGSresTDGGGOMMca0xh8WxhhjjDHGmNZYCjVCeeTXHftAOaH4jTj5ltuMupWrp53W0BOca1KZvsq/R+SnlH5FrhkRSWVS07H9+RJsfW6QfFTpzO66Yye3yo9egq3o4ncSQohyEpVYUBqgbUx5Unb6MuulW9EqpSjpnPutEjlGciklF9k5xyWUUx20qEy7DFKolfGiIm1ufKPf5ta9btWz31RCwLbUqEdHQ/7UNM6HlvG6JC+lGhqtiozDKcH7ShpPn9cxz3Ghc4VSJcpfNMoOJTsapWY27GVRR9uO8h5GQFK5EMeWRuAhOt84v3navEZfyqDUcbakMbqQziM+29uS8h+5Hxe7/XeRxrGskssvVsrTKHiciwsk7b2w9RR5njR9+/2SuFtH4vEG/PM/SzZKFFWyxnE4U9LYx5nUTetM+MeHrkBM00hTxe+waI4XKVQxP35bpk1EdEGV2wTeMRPxjsnGtZZ/D8p/lWSlRE6jKnEscJzovTOp4JGwVZLKdubYVYkcx5ZGeeNc1+h5bHONPMi/P7iGat8X/faTMm09pKCzJAoYIwXeBTuTfG/nv4C3Ct6xMMYYY4wxxrTGHxbGGGOMMcaY1vjDwhhjjDHGGNMaK8xGKA/Ar0LDEDJ0HLWTGpaOmkfVZPP0y+xUTjJKrmcln63U72r4y1o9IsqQcqRLJwu0Xgzxd9tDZfjIY5/R0VDTl2GNiFrpV6GnEE+H/4u26yT0W+bZQL+WqyXt7bDVr2UK/Ef0dGqGCN3j57jYp14PLZ/64FEizKXWeqkEhGUfU/OtYVGpjdVxx2ttc4Y9ZP9mp8tfn5Sh0K8o8xeiX4We+EoNvj4bdcyqp+YzZL4A6jNCyrGms6UTXHd8/KhIoT4505hTB98VlhOa9atl/jKcLX+XrQkK/TY0XCvHwk2SxmfL1o8Hvtaxp7y5nk/vXUPDp3L91pOGueyorwEZKzcfjRPIJxehSrcv8o2FT5n6bmW+XOpzUSN7d3AeaZhjPo6OO/olrIWTlvqsFX/cyGKbhVflwNgDv9v/GfWfTJb411/FmvoxyXsC5sMkmQ9c11agrw5CP0WUa4v6a3Es6zuG/cF364OSjz4WOg4uiWaobyb9Ktg3upYXa/atZdqu8LFYIL+bUanHFXJ9NOzVm54eZvjxjoUxxhhjjDGmNf6wMMYYY4wxxrTGUqgRygZ8E06IJ6r5uKWpoR4Z4vIqSeNWd3ZydYaGqasiofoCofpU5nV+wyJVBkQKKdQjZYDNmtxgo8RFpWxDJxG3gMdKKGBKajRkIMPscfv/NMnHdlVJAkN2ar9x+3z18o49Y59fFfmyE8ALxcWzyjSGh70g/rNIe+Tejn0QQsOqbCY79ZscKWNm3I4D58tlfM8urjbdjzrvVuZsKnPhaFLJFEP1qmyD407b4KBamkz7TdC6HZvV/wkRN2zT0X5kc53jVes4G7ZKD+OOjrlUtD5sh+KUbF3Skv8FxqfROUW5h47rLGwwmQn5k4auZJk1KYays1yzjmslLTs9mhxyR3l98eEdMVm5wpUrxi3xX9EEDeddrHmMEa1argSOeQ3NTHQt5/q6BDGD55xYz3eQjJ90XcDc2Y7hThMp1KRryutV1GvtWaZNYsUOKNPK8OSdntOxSinXTb2S2NMxj5Uk3pqvNA0fPRzofOPQ4JjUkOOFNEokZmMhI5tdkUVHlHMxW7vHbsoC95qh4h0LY4wxxhhjTGv8YWGMMcYYY4xpjT8sjDHGGGOMMa2xj8WIher0/67mYqjH20STSB8LDVV5HzTsqyv69S2RhackByXlq157zwFzdUNdpWo4qWmeutv/LdJquuspIq7uQ7uOlXZdAgHvnKij2k/qqxkyUHXd90D3O1M0uofD1pCdL4N9OUSu6ufAsJxzJW02L/Yv06jefqH87pkQzo5GJ6ou/R/RrpFoaMfdLP9wZCWj+GKsKsZa6VEwlvF4xUchC396MWxqmMdIPqp5tc2ZV7XJVW29/G+hXRFbUqOiFtr9bUqfmvm18qP0E6EPkLbrHtkagbGmIXipp2b52w3if3lRu/2ApFHLfWjzIgvoO/QJScvmSg3Nx3VAfSyOuh8XMibJvrJITIYDTjnKS9H6snqRRXjqxZJWXOsC1RD6OZz6mCRi7qsf2dXIOxmFvFHyHQNbyzjylx37rr3KNK7Zj2QxwYn4AqQvKjasrN/TC7vTh9m79JCe8pohwieUSbEebVcL3R4REQ3DkWdMkes0xG+FjeIMszvqrM/G6Oe8d9Z2sySMrxkevGNhjDHGGGOMaY0/LIwxxhhjjDGtsRRqhDIuPVMYfL9j3iSx506AfVSZFJ9G8dOHKIViDTdI+Mhx0Hs8M9uSFXT7swZPEr1P0ig9UGmGnvTdj2gGRkHXcqLUn9dLpRjugouSqKoo+He5nlutZCkvUAkYGYMtfg0xSnlKKoVK4nUeLtdTVw2YrYtC4qSFEJFCrYIUqtiCl5Nb1xZlfrtIOxC6GZUjsU32/XmZdiCkApRcTJIxvzv+N45KdiiBUdmVjpPN6AowC52vEkKOC5WMcO5rKNTqKeMiEeHzdIXyhO5LT/GthXceTIhrygaz06k1pKnKjmqcgX5cLP8rrhgn95ZpNTnMdJGR3YP1dWWZFIdAYrNBpFB87kkSNZNLPaVi44pTuMswsjrmV/y6Yz8gYbML5U8Wn/oh2Em41q7BjPvpeDqIMqlkDXrkatjy7luPuZ4sp8VfSKlEURbb7D21EQXdJmkvhs05kDWxjnnORZ1vj+Il8wjHjMjBivddJoVKJGwa6ra2lqhsluG1t5NxUV2PohwnGzFnZyf/+/yZjja7VfCOhTHGGGOMMaY1/rAwxhhjjDHGtMYfFsYYY4wxxpjW2MdihLIz1LGq4y9C60FwOTvLJ5wCPe9QBwlDMY6VT1jqxjeVSYXcVkN2Utt7I8uXfNSkzhId6CPQgS4pk4pyivB16gCBjKp/nYL7TRD/i9thN9WRd4WxZKVFx03J6OhE/zoWnT9bsmmbk0LvnOhTdWztewcu1LGFwJlhifhYFPpaaXTqigsfi3vKfDNR5sfk1qPQ4Rri9UReSAMxZO7bYO8quutxKPR48Vu6AbZUuepjodrz17I/Er+oc+Wa99MQkdRvs8k1HGwqVabQW8bk7Ip/lfpDRC/snvqtDpJrrl26zmzXMJQr9fMvlnsXIWxVMF/xsdgooUnvhoZdwxDPQsMuljSGRZ0oD0f/OY4TXctrYysiYgL8HFTHX4TRViE/aaph14V4lwFzRUTEeISKLR5IxtZ0+GHtKoL/KfDN0DFPpsLpZb34IRTzTxyj/iUpkw4Y6iJycOUnGhaYvjHZe1yZjr66Lvvfyk37TX1j0CgH6tyuhBJ/m2SbDXuUxGTPfKiKNXs5/j0J/atrtBkevGNhjDHGGGOMaY0/LIwxxhhjjDGtsRRqhEJZiO7AF1uj0C8cPYjy01NqGS4xkVzcg5Bv+yefsLqbuhj2hZK2B0I6TscWp8qKKMPSU1HnYEtb780wnQxZ2yWFAhq2cgrlPBKqjyecP6dMKhQFlJ10nbzMUKvHl0nF6c4auxLtNQnSj9mSLVsUinrJwFuF8jVSLNs1fTbIka6XpEIKNa1MY38X8g5tA7BA/+Fa2CeWSaznhj3racXPdGKCUXJSONVVQ42AOIHjXCcEQn2OlnCnM5M5TOkBn1Plf8zXpY6gJELkKjUVjYa/LrRuPZUfRcThEuJ3SrLujG7a0Jj7hyb3bvo2HfeL8npXrBFzQkBjZqe/3yfyPEps+JjTe8t8s3sGzqfoWrU7LzI9VXKaPVF5WPW0+YiYxbWYeiQpYzLqdbeUcRAkMKOT8b8vKpKGJ5YKZ+/PjRgnmYqM8r8ZPWUSZbrjoo4qzPbFfLguErLBRrRRMIgmqQSJYwHSvdkiMdNQ2aSx7Is66UQKNbbpc5pB4R0LY4wxxhhjTGv8YWGMMcYYY4xpjaVQI5Q9IGd4ULZyL4L9smxPGfTJdbYd2QcdRJFPZA6TsxO1sW09WrZCKbO4QWQbb/5ax5769nrxxQnC2XZnr1z3dMziRFY9OZonk2sEE2z53iRSKE64sSLb2Bmf+Xfj1NtZWr5s+RPKhSbp8c57DpxPo0Bp9BzCCEIzF5Vpk3BsbNf4QaHpkMSzPTPLJ9E8Pg/7dUxYXi+i64To7HhbsFjG+cEY58WzaT8xwon0/R7o+wdDqERT6YISG+1Unnosp5aHyLJI7aRbnVLjccLy3XLC8iTOYZ1HGJNs/q5IPQ2jt8zUEHnZqcEYk4yy0yUtYT/qCcUkO26ZSB275E9gI6Qlu2sa7FskLF5xejHGz4EScmzTAR37hjKpWP90HDASWjomsRZukKRMwkPVzOr7y7T9uYhS8qcL17SOqZHWHoHscfSCej1GoSLj69m65lu2lDyAyTND0ihdmoXnSU/9TrhKrtmPB2Y/HOpfho92TJWfMY3vz00yp4pnU10lSMcT2i4JXBXrG675ZnB4x8IYY4wxxhjTGn9YGGOMMcYYY1rjDwtjjDHGGGNMa+xjMULZHdrb6eJj8WnYL9OjPSto6LkiNG1vmbYeYTQnUJ8twuujMu0tTmIeIxpL6kC/Ic+2XS204S/Ly8ksM4mjOEfFt9AcF6EAVbtNHxH1gYAeXCWcs2FPuKNMG4t7T0lOns305oXfgNb5uQP/RnW+GUVTLi/TUt1vUy0rNM1pKEYJ/3tnw+JTkgdg9dfKvW9GGM3i5FyNjbiuYkfE2J6OnbjQ5LBeehQ9uVWuGRtT52xvx7yjp2PfItnOwL1niY9FUZdrJe3Mgcs89OcxJCZN3HKefjDWPg9fjAWSbTxE2pnvWeM4wUkY4q4i8YbONP6PyL0Xw6YPx4kS6vYGrDm6XJCu8KlN/X6gdc98KkaLjwK7caqGqa1VVHxcjke/qT/VBA3HXCP7Cwmhzwv/gUjCX0fErkno8g/A/hrmja4JTdfsrmUA/k6H8t3aK/lShxKg7yIs2urmtQFjbxzy6bRhk49SZy74pn1E7n0OC0Ka9j3X8k1djnZmOPCOhTHGGGOMMaY1/rAwxhhjjDHGtMZSqBHKeGz7nSBpRfTI6dGI9FRuOb5zQq3M7dLLEuztquSFu9Satma/SnnLysuDKIXSbW+EL5zUMIxlV3zNTPaA7X8NJVmE+LtEEiFL0NC9Q+JHcn16Jd+v5TqRYd0He//BHBGNPXkeitolsUA+DTdLBckcuff3a/dtGgI0ojyuWOBYni0DezJDx/J/1Wh53IPvKZOoPGioXszJnjuTSSmY+7MgpdPpMJphZDXEK9tENB27wi4UIsuljKb9OIhn2wipDKUrr7u3zHcx8r0kLbDhjTXkLpBlLGZAnjdeQm+PgpRlzsFlWi3Ctq7zXMo/KGldp58PhaZSk2nlZSabKspkLFEZIwcf0fx+VTCeupSSlLKKBO/MqJM9WzH3ce/ByFUpF5qo/+uYY48nUqskONOh9sLWRkGd9e+S+zAox6Ov9PVcyA11ION5juyp1jCPmQ7u6YprbYYD71gYY4wxxhhjWuMPC2OMMcYYY0xr/GFhjDHGGGOMaY19LEYoeyQ9V2g4H63l2gK/TdJqusSe8pK6ZZEAF3rkieJPsALhC2f0lmmTas8tIfwKua3+BmEmq2JkRcvINKgQSp8oSYXkWMJ+bketPp9HRai8Vl8Mhn6UcLZVxIcm87E4nhdpfFkBA6DLr6KST4sv/FNEzz63Vl7NJ2cgEk02db9TtX1ugH04Mza/NZ/1WE3MwnkSxrXcsZor4llJ2kNyvbxjjoaPRVcfZj4QHKPiG8P58BwmaOc31er3NKxHRIxG/yxgwtoy30uS0KSXwz6h6XxI/EC6tPSYD6PUNwMa+bl7Rh2On2n1+6ksXdeuapm9sHUcaMjlGpVQ2FuEa9yR1Vzd0LnkCUnj/27N6s93h7zg0qGQhHIt2nyo/9sXc2xutg4QHVvTkrw9sA+QNPgvdjUd1tc+vE/TEM6JP1LX3xSsVy0sfZS+erf4/61vFdyqxhhjjDHGmNb4w8IYY4wxxhjTGkuhRiiTueWssqXa9qeeZsuwkPdLGmU6x0kapUTcP5cTtOfxQrebsU86W/aN72beHvmdXjdBQ+lRRpOdMMp6qHxEnrUAMQNTBYfIQjbhM38066hSGNZFpVzM2zDkXuMTg0O2rWfVcg3A3g3z8dl65d49uNCGZV7m2zWqaHTQ0dy7lzm1EXOqS+bA8UUplP5vm4anFQ9GYVbwnC1niYhujUJ2EjTbuRZWNyIfC4wvnEyI0WwflQs1PQk4Q088Rp2LtUrDabMuEpp5MWRxJ2SnOfPZBnPaL8tUaWMmf6qRhB9fo1KxphI89q/20+HRmq55SnmMnCReRZ5tJZ5t6hWSl3pPtrG+w9iPKnv8D9gq80riSVe7J5O8KgjBO1X/FuDzUPaoGjy+33olrSe5N8NJ64npuB6lkssaa7acpR/2T6K3/QjaclEeaN8MEe9YGGOMMcYYY1rjDwtjjDHGGGNMa/xhYYwxxhhjjGmNfSxGKNOpi79TEqlrpQZVNPfr4GMxQUKfFnpejTtIHSV1y+J3UMiuVcePWHET5PN2SHJq1a1Sb6nP9gLYosFfBv1lITt99SDqwnC8qiV9BmzRWhfa+ixM8GGwVTeelF9lEGFRC/5KrjMfAoqHr4atsVVZ5xsljdpnDSd4c6VMDTeL/lj9jDJpKrX03y/TRnfFgAVJSMQC6oWTkL7Dwr1yzcGl2mfWa52kHQN7Kewxko/abdWDcy1RXwz4LDyCMK+j50g++rGIn0Palr2wdQ3iWGN7qe8Cx8WVZdKBXBvVx4VzP5uXtXwRpT/bymiPhoNFX12nc7YXdk9SJvs3Cdk8qH4DN8n13AdwsSKaIc/GZXPq0jKteAb4KGyU91Thk6UvrWthq49Fst7y0YphomthFloX4c7XyVwvup/zIfMZ1PbJfLlQZpdvDNcF9FufjPlRTf2RdJ2hv6HOI7CmeGdqLc1w4B0LY4wxxhhjTGv8YWGMMcYYY4xpjaVQI5RJ3ALWcKqUQnF7UEK3FduievpyFoKUZQ4iVGlBsg2eRkn9JWxu3+oxnKzXzZL29o55vZYPmc4ekL+MPl4zNmSRXJ8O+wVRR6UmhFvwn5U0hhA+WdJqUqUkJGsayni+pDFsqW7/834cayoxmghbpR+9sFXSQTkGy9SwpZAJTNXtcs6jpieyR9RPoldYfiYDaRpa8pdyzfmwSNKOiTpUAzxQzVXKKrPTu3VNYPk65r/UMSdQzriP5OMcVhlc1pZ8Ho3lSXkYx49KoZLT7I/NjqemrIxjTUNhYkyuVHke76eyR6Jjhs/G/32ofYPrg1QixzbX8cMyl8NWKR3Xj6bhr4W5+g9cFxrGZt4g1/dBfra/tkklXPtoDTfL+abh2jPZWvJXF5fQQjU4GBkcxsxiCfdbDL2mKqBs3Cn4G0PfrUdzTcU40WExmv3RdRQ90DDZbNdECnU6QjiPX69/HJjhwDsWxhhjjDHGmNb4w8IYY4wxxhjTGn9YGGOMMcYYY1pjH4uRCv0lMq0k9YqqTSYaslbDdBKG1uO9fyz5srB0CRqRsoBaU2pcVYfO60Sfuq9cj26o2W2MtivpEg8DhJvVaKZT2OYaSvdM2NrfbAfqyHepZ5uq4RypfVcdq/jwDAlKXlXbyzTVRUP/z+YZrWVkoXXpQzCYZ1H/nhqZPxI18lpn1b5vRsfW+iRN/W0Iwrx2rSUcX/QROS4pT+tPPxf9X1msZ7bmsB7ZAqE+QUTXCObFOFc3gcKdR3xvRiW3o3icbkWz1O8HY61reLIts/GjUnHO/Z5KeVLmv+o4Q9oqmQ9TOPcZGlufjf4K2rCkqV+Rkr4sOoyT6135Dpsczcj+N6y+Y/Ae6Qq7yvkma+9XYL+MCYPxT0FfqTtPWZGG5WXjTucb/IWOzsrB83RVg2Mo65t0otY5EXN47Rr1vjHDgXcsjDHGGGOMMa3xh4UxxhhjjDGmNZZCjVDWYCt3UrZdyC1sDd3Wm/xOT7ImLIfSgJ9KPkqhnkoHXGLbfYpuuaNdh6XKWSHZ6drYUh6voTcpScnK0D6kHkNDaoJiOP1CErUuJJPaEdRLD3WdSemV6kI41vSH+F2xta4ShezEWsoNBnP8O0PkMiSl/m+brExKVLJVmeWr7IRSAx0XlFyo7CSr16JKvfQkYEpSVEaGeqrwYBxP1s3qwXtnkgft7yxsM6UU6PtUUdH0NPuIoh0uxj+/f63k44DVviGZVEylMjo2GtA1fTGepiThO4t1JjnZOw0LrHKbTAqVvZsaMoflN5VCZeiYx8nxXVKf5bAlBPLba889sfLvA9F0jDZ97mw90vdbJjXl/M5kcWwDHceUW+tzsqFrYekjYhzqPH045LumC+9YGGOMMcYYY1rjDwtjjDHGGGNMayyFGqEsxZZjGpSGpzRr1I8rYOs+OLc09YTf2mmY30rqMZioFtw+/6KkdYWa+P+5V64p9dEtU57YqY2HvMUWtp6sWovUE1Fu32a6imVyTZkRnlMjmsQ1sDOplZ6i3HDbt3juwZy6mrUJwfjZXdPYXjMljc+qUijKwzh+BnMyPMd8tlWf8e+wT5E0yrwyOUcWrYX/K0hlApQBNZWlad5LJO1K2O+EreOOz6anlk+uJ407DBeUSqgkaBpsbbtfD3yviMglQWzn7PR5oqd3Z2DsPcI6qqyFa9ASSeOYPCK5l0pQamMokdT0yfWopnIqvg+GKi3pGUTe2gtvqJGl9h7EvUlvkpat+2xXqWM1UKD2L6Mx6UnhTaViTdtHx1J2mnrW/z3J72roGOS6r5HpGGGOoRRFCkXGD3WdNynesTDGGGOMMca0xh8WxhhjjDHGmNb4w8IYY4wxxhjTGvtYjFDuhvbw4ExPnelHqS/UUIbU/2sYyKF8jv5Qrk9M8lIvr1r6M2Ng1J+APhbaPny2OZJWC8Gn4vAM6rUzvWum/2eaamGZlunx9chu6uJ7YasemONiuE8ijyh0s13+Iwxbqm1HXa76fnCMNvU16JVrare/n/xOWQSbY0t9LHg6so47jlf1F6qF+lSdMn2JsnGn44kaZB2T7H/6Q6h/EOuiPk0MW6p1YZtTn60nvrMfVVPOdtVwwln4VrZrV8UqTEvSeuUa43wu20TnG+uh7UqfsmzdVa34ctiZvxnabpSGks7C2xKumddIWhbeuSnah7wfx6uOGY7rzEdO/U7oD8MQuU9IPr6npkkaw3Sr/w7Hmp5crWN7MzovuT4dL2kNT6BujPo5sJ11zLCemc+LhoWucZtc8yjxZJ1JQfuPH4zvp2mMdyyMMcYYY4wxrfGHhTHGGGOMMaY1lkKNUIrdVZU2cAuSW5UaNpZoKMO/gX1YNCPbOv+yXGdSqC/Azk7j5XNmp43qVi7L1Lbj9na2tdoLu0fSKD3QNrksKZNcClvDKzK8o56A/HPY0ySNoUMpJXqp5GP7qJxqOMhC/PG09ockjX2sYYc/C5tb9YdLPo4ZbTtKCgbz3E239SnZeX2Sr6n8TO/LOazzIQtPSVTyx/CqXEuulHzzYet8uyO5H//X1q2wde5xvVBZS9ZX7GMJX7wRcpjRTcOkZmM3Cb05r2loT5Ww8Xc6H1ZUbL3meqT1yMJJsy6ZlIjyFA0PTn4i1wckeb8CW9e/abBZf5XNcixrGOVzYeuJ4G+C/THY/y752MZnSdqXKvWIiHgWbA3RTvkkJVk3Sz7OD5VCDSWEaiZb0tDS2VxhvXRsUcLG9eONSXk6t/nu0/8tTtnUrKiD+q/N/r4wQ8Y7FsYYY4wxxpjW+MPCGGOMMcYY0xp/WBhjjDHGGGNaYx+LEcrRGpaQ1HSDd8r1NNiqu75psDWKbm01UU12Bv0LTk7y0W9jXpJPtfSZzwh1tNSsawg/6jmzkIoaavCfYL9S0qiDp5+J6q6pM1Vd8Udgf1LSFg1Qv4ju9qBONhtnGeLPcwFCP76KoRI1HCy1vaqzp3Z4T0nj2FO9ObkWtoaUpVZZ2/U/YGt/UHN8TMdUF4ipt+MiC4+senBosjc8t2OPU911NldYpobBJVOTNKK6a7a/vlU4p3Q+HAub+vxnRZ2vybWWST4F+9Vl0nnwE3gLx4LWn/4dSySN43etpFFjTt+nzHcrC3ur/c3QrtpvmLdLX9yxxc0knyuci7oO0G+AhWpYcfILuaYOXn0/6JejbcK5wneats/+9TRO7642OQ/2W2Evknws5O2SxvqrnwD7Sn0POTf53tK1KvPD0nFY48ewZbG6EWP+0KZhXCPKtVD/3mB/3AA78+9Qf45ayPSIso00nDfBvNnocLNbBe9YGGOMMcYYY1rjDwtjjDHGGGNMayyFGqlwG1ZlOkyDdKJrG78pGmbvvbBXJvkoEfqdpFEaoKd3Et2GZQg+yqumSz5uRausi9v/ug37qRiYrv1ycINcM8Spbllz+/kYSVtayacyMkqo7pO0hbDPl7Rfx8CozIHbz4skjfIqlc8loVzX8xRc9o0+23GwVaZBmYXIWoqwvpT9LJB8HE96SjD7UfuNZZ6UlIn50KUqehrsL0gaQzGqhADtcC/m8yzVWjFUss4HygQ0tDTDbaqMsiaVUTkY+/HlksaxIPN5FebAFMpVJpf5roI9T2UhCHd6oSSdwTEjc3gW1yfeW2VebC+V8/B3Kqtg/7C/db1mOFWVk3KO6VpISZiGjMZYmPlp/PsMyce5/mlJY5k6Dl4LmzLBXsnH8NoadpXjdYykMa/295GVfLp2fx62zCl2VZ/8bBQv3gVb59tdsO+VNPbVtZJGmZ+GYuYaxPVJ702pqb7D9CTxGlzTRPp5A6VQ2vd8Nn0v3lGxI7rX7M2cI9cMBawha5n3f0sa+ziT+C3qmF1r9ILkd6Yx3rEwxhhjjDHGtMYfFsYYY4wxxpjW+MPCGGOMMcYY0xr7WIxQNkDnOE7D2VH/+kHYt0o+aixPjDofkGtqqKmfXij5NNwpoT/G+yWNYUX/TdKo7ab2VsMcqpaYvAa2tkkvbD73AslH/4gvSdrh0Yx3y7WGXt2MtsHBA+bqRv0XemD3wtbwpvtX8kVE/A1sDeNLH56PlElvORUX1OheWuYrtLGq46dWWUNQUg++L+wroo6ExC36UUPw/its0f8XoYcXVuoUUYbXXChpDEOswl+Mw1n0m9FQjJxvF0gatehnStoi2DofLo+BUR+UV8FWHwv6V/24TLoP/2trCp9HdPXFi0rXu0Ud85lvlbRe2BKmdt7rccH20TH5mqjDELnqY3F0/d4FnBvqT8C5PknSemGrppxjlH1zlOTjXNQ1lH5L6vdD/wLq83sk3+mwdVzQn+c2SePcf4ekvQI2/RXU14D0lpez4BOxQUJXj+MF/U6yd6SGjeUaqj5+9Jd4UNLmVso/MLm3+l/QJ0jXOPq6cczLWv42XqhvD/tDfSz4DvgfSauFUv6QXHPd1HcA14+fSlov7M/C1vUO78XtsjFjhox3LIwxxhhjjDGt8YeFMcYYY4wxpjWWQo1QFiMM28EaopDb1NyC1/BvDD+qW6YZDBOYbfF/K0lbBFulUBn8nW61ktuTNG5/anzKJveNKEOOLpQ0ys80dGWGyg1qUC6xu6Qx/OwXJY3byr2wL5N8mYyM2/q6jcwtcx1PH4VNmcZCyUc5xlnJvVUWcnoMzD/KtYYoJJR0qIypF7ZKx+6KgXmDXFOiIJKgQtb0d5JGSd6/wFb5COuvUii2Vzbmr0rS2N8qvyC6zuwMW6Qfc3pxQXmVjB+qirpCsqItD9UT03mKr4ZmZljLiUk+trlKMxZGHUp2aqGeI8pT2N8naRyHmQRSZUwqO9rMdXLN9tH5zPVIxxphSND9JI1zVsOds680JGsmYWRY2Y8l+bKQozgpe1z2DiMappnr5rsl7S1JObouN0Hll0Trz1PAdTzxGfhukndpoXTUE82J9mn2TqaskpI8HZOUOKnUiutmtlb9LEnDONcIyGZ48I6FMcYYY4wxpjX+sDDGGGOMMca0xlKoEcoelAPoabnk4oYFLpJrRo9QWQulDroFT16bpFE+8vNqrm4YMWpb2HqyN9FIKLr1SigtoiSiV/JpVBxC2U/TCE4R9VO/FUpZTpA01llPBF8AO9sqVjlJDe3fs5O8jCTy7iQfJSOZlE5PzW5SXkQ5fpQ1FVvJ2o6o5Cibi5xHb6/m6o6QQ3phq9Qqk+QthK1RasiLYdcimEV0n6Q7r3KviIi/h82oWVp/yoqmSRrzvkjSeJK7Sv4Ybe0lsLV/+btM4qJkUhzCca6yOqYdF3V65brrSOEKWVQcRgjLZJpc7/TEd0qhsnVXmZaknQc7k0Jlc4VzsakUSiPFZWjkMpLJ4mpk/flRueb6oc/GdS2Tt30F9tVJvqHIuiLKU7h1XDDC1qFJGU3/tkn+vnignmRa4B0LY4wxxhhjTGv8YWGMMcYYY4xpjT8sjDHGGGOMMa2xj8UIZSp7LtP7N0X17DxZWn0sGH5uEKedVnlXw3wRpbabJ93WQn5GRLxerjOtL/O+KcmnIS/Jx2FnWn1lKNrbiVvO0s/eDfPVTltW1IfgEw1/pyEEa6jOngz1xFQNhUoeha3hL4cDPcGWUAud+Vg0RXXXmUab/h3bShp9lzguMh8LnV8cJwsljfrtd8PWvmcZesoxtda6DjD0s/pYEMazzbTbmY+OkoXpJHqCPclOGs7QsLhD4XrYWVxO+lHMquYaHGMa5ntiGO7V1MdvMGttts4MhSzcbG+Spu+UhQ3v9+aG+TQ0c1OOT9K4fry6mqs5yd8XTd2gzODwjoUxxhhjjDGmNf6wMMYYY4wxxrTGUqiRCqU4mcSiKSo9oITg3yRt2TDcj6hE4TmwtV4MubgWtkogGD7yWYOoy3zYmRQqk4JQPnJPNdfw8Fa5Xpjk5enLWTjhobLDlrNERL6tP1QymQvJJEGsV9NT0AdDFi40k/INBW2PLEzqpbDnSFpNjpaNf+VU2Gck+Xha9LuTfHoy+akD5vo9RydpYCXehGmk1inNyouI8hTqbHw2lVdl4UGVTKrZFMpceiStt/KbtZV/HyzTGubLwp035QsN8w3m2Zrm3Uuua2vxIOTOjCw9ZJX0UGS5W4Ms3KyyL2yup1eW2fqe1rHXZieFmyHjHQtjjDHGGGNMa/xhYYwxxhhjjGmNPyyMMcYYY4wxrbGPxUhlPOyN1VxD5/AkbekQyttZrh9M8k6DrRpvpmUhFRH2cJ2EfJ3AiwXyuz2TMslQ2mBroBrdjF/Apk793clv1G9iqGFeyXCER1aahvZM2ABd+rhV7cvrAqttnySN4sVvt8K9szWCOuOmPgSDWHM2YAyNyzLu1rDAQfzvsBtx70yu/dZPdewvZQUOwndhHfp7Qj1bcz+swYS6TW84eFZJiOgpNd+b4fD3i2jex8OxDktoWEbZncuEwbQ/301ZSNaGfX/V8vJ6V9jq7sSo3/tKWuFy1AO7N7n57nI91BCzQ6FnEHknVf5d3lmj0GDrspDmZsh4x8IYY4wxxhjTGn9YGGOMMcYYY1pjKdRIhXKSwZzu/GQxmJGWySwoRWgox9i05SyDp6kUZGvI1IaDpv2xNRpva7RJU4lKJtVgvYYjXGfCqCxxa6zKTftxK/TNepyOnEqhtgLZgdHkka3QPo2HEMJTpxK5wTDMY2jscEmchoA2eaGkHI56PZJedhjMWjjM7b/p0fK6aTTbR7NEDtDeQVXnqYn/mn3K4B0LY4wxxhhjTGv8YWGMMcYYY4xpjT8sjDHGGGOMMa2xKm2kQr+KWpi1Nvw8SZsKu2nouV8P4t4rkzSGTH0gybeiY06anORbKNcfSfKSmVvO8gdhMO26N+zPNvzN77acZdBsjXCz+zXMlwjfx1FDvTV8LJrqwTXE73DQ9Hma+msNog+nNP3fVw81zPfElrNsZl7D8MhveD0uTm1efsbopv2NmKCj7kryDWZMDrPf3YQVW84TEVvlL4p0qDWd9xnPKi/n1UKQDuY9u7phvqlyXXmfnjC9+a3/EXbq09T03fGHDC+rDCa8eW3Mb1tebkSI4q3xp5PxjoUxxhhjjDFmGPCHhTHGGGOMMaY1lkKNVLjNPhy9qCc4/3uSF6daD8s2qZ7ynZ2m+jXYv6jmirgQ9smDqMuihvmanpY7Ta6H+6TPcweRlyfH3j7M9Yhovm2dSd2GyothvyTJl0kUOKdUYjEc/fb9JI1H5A5C6lPlkEHkZXt9q+Fv7hxE+V9pmK+pPK9pHSMibmyW7eCmMqOmEpeI5mvE+Ib5/tcg7j0cYVh3ht301Onh0pY0lelkx6k3Rd8P/1bJN3EQZTZ9Jzd9fw4i1O2whHSmfGhryGGbkq2Zys8q/350eTn66o7ddOqZweEdC2OMMcYYY0xr/GFhjDHGGGOMaY0/LIwxxhhjjDGtsY/FCGUVNLRThqPAl8r1l5K8B8O+upqruU7zg3L9N0le6vObhkD8XMN8ERGfbJhvVZJ2JuyDJe3i5HcMM9rUX6FpG0TkviuEPi83JPkWyHVTTfmyhvmeI9fD4eeQhW1cD3uepGX91pRMJz1/GMon/yzXlyR5j4CdPSf9NrJnUf+OTCfNPr4yyXdCw3w9ct3Ub6OplnswIV+bhqRmuFMd4/R907Uko+lczOAcWCRpXJ/YhxuH4b4ReShxMhyhmXWdqbF2EGXOgF3T/g+Gpj4uys5yzTHEdVjDHH8Y9psGUf6DDeuV/d3A/rijYXkZb63fe49hKN504x0LY4wxxhhjTGv8YWGMMcYYY4xpjaVQIxTudE/JZAknwr5Z0hjS7wWS9o6kzP1hU/Zwk+T7BOxXSdousJtuRStN5UKDkbHUtq2fJtfZcbCvGcT9yNmwP5DkWwD7+iTfgXLddFuZ0oxMCnW+XC9J8r4Rdibf6oF9nKRRJnJCNKNHrjPdIMNJZvn2levaacknynUmATusYb7XwdawmD2wj5W0RUmZPGn6nyStF/Y3YX8xKe89cv3iAXP9nq/CPiDJtwj2nCTfd+Q6k1UyzG4WllbDYTdl1pazREQ5zhdJGiWqWQhZXZ+aSmcyuSpDLqu0ims9162PJ/fSdZ6yMl1nspDUZyRp5O2wdT3V+dGEWweRd0aSxr5S6VjtnZbJsN6fpM1PrjmHdQ3jeq0ySq7Dr5a0dyd1Idn68Q+wszbXd8DllXzPrRehSi4zPHjHwhhjjDHGGNMaf1gYY4wxxhhjWmMp1AhlNreR9fjIHtg8VVQlUz+Evc8gbs5t3vmwVQrFe6sU6qRB3I9QNkBpj24hM5rKL5Py9JTmmmxKZTmUGuiWLNvy/uTeyrO2nCUiIv4K9sIkn25Tv7mST2UB2XGklAH1SBrHpO4xvxf26bBVLsQ20Kg6lFLIaarVPla5ViYRocRPo34x+kw2dikp+Iyk/W3yu6mwFyX5GK3lh5KWjR9GvDpK0vhs8yVtIexnwM7CqWg9kkgxK/fs2FO5Psn4uQ32HJWPQPK3ROQ2s3rrZcY7Yf81bI00xHwaLY8yKZUqUfLHe2v9GalMZWSM4pNFddMTqGtRoVSOxDZXCR7ng0qTuNZTNqh1pOxHo1rtDVvHU3ay+1mws7U9i8r1kY6p3VFVuV6blPc6uc6iY70CdiYdI5k8UmXMvbDPljS+FxldTcZ8H+xRh5VphRRK2ziLbMixtyjqcNxl0Sl1zFMKtUs0YjBB3kxzvGNhjDHGGGOMaY0/LIwxxhhjjDGt8YeFMcYYY4wxpjX2sRihjJuMi2mS+A3YR8LWcKCnRjPOlGvqYefDVg1qT1JmplPnsx0jafTbYPjcl0s+aofVj+JDsF8hadfAfiVs1ZIy/J/6MpBshr1RridV8mn9hxLGMqL0bSAny/Vq2Ko3vyK5H7XicvLzBTt27FdRi65jsOZDE1GeEK11Phc2de8LJN9XYPdIGrXKGuaQYT9V27u8ku8Zko9lqm/JmEp5EcUz3IH+2H+q5GObZKGY1e+EqH9E7QRkHdfvT9IY0lR09kVW+vaIDw2HZNc8gSZboy/P4viVuX4zfKEOZlvqvGGdVfPNNUJ9LOiXoOOVUOg9X9J6YGu4cD6b+ihw7rwbtq4d1NlrHbnmqc9GTyWf+hYw5Lj6+B1csSMiPglbw8vSh+0rUYf3k3C8y1BGevryAtjZaeavl2u+M9W3h2F81Z/t87AZkjXzsZgu1zxFPgurS2cSmfc8rPrcyWVaMdf3kzT6R+h84Duffhrqn8Kw0+rrRh8/HTNsZ/UtIWyTLFS/GTLesTDGGGOMMca0xh8WxhhjjDHGmNZYCjVSYchX3aqs7e3qv09reK+/k2tuwSNcZNd2cEZyGmY8Clu3eXk/ShZUwrEb7H+VNMoBeiSNkipKb1R+sQa2SmPIernmadh7SxolBQzN90LJNwG2htXj9rBus/MZuE2t9aAsROURe0adHtgiYWOXFicnayhDji0d1xyHPZLGMJeZzI5toM92PGyVncyHrdv/lBSgDzdItnGUJ71UEtmuemov7ldMYW0frRfhGM1CS+t4rYXsVFkI21wlEOxvWSOKA845dmVMzuPFEWUax6uqKoo1Yv8yiRFTD2aals8xqX3D362RNLYDZXYqa2EjZGFKdQ3i+FVZHLRj6yBLnKDSRo5zlXRmc5GnsHOd0VitC2CrbIlru9aL5ag07YlKPpXs8q8b6Xs+TjW8bETEB5Pyd4et6yL7fp6k8eb63qJEknLbf5d802Br29XkiwrlhiIrOoQX2vcch7qWcL3QMLh8774J9gejOQzHrHOR4ykLNYx5s0nXOzMseMfCGGOMMcYY0xp/WBhjjDHGGGNa4w8LY4wxxhhjTGvsYzFCuRli64NrYUoV1XP+AraGIaTGv2n5mVj1kCRNoSY1K5M69ZXVXN2aadWAE2rFqVtWLSbbSzWuRHXX1DGPlTSGJ6WuWH0g7oQt2uE4C/YKSZsPm8+mvjfUTKuvQVNEe1tE1qWeV0OykglyTS26hlNln2bxI3nvbByoRpc+I/q/Yzg/EFJ23PGS7y+T8olqyvca+FbxV5KP+nwNo5j5nZB1cp2FuSQcy9qunKcaZpdkIVmJPouGBq7dW9p8AS/ogzI/KU/9WNA38WtJ45q0T+XfI8rxpG3HtWWOpHGeapmYK11+FYTrgOajL4O+H+hT8FvYWkcyTa6zenFdy3yHmKbrKSeLrJO6tBQsgE0fNp2XfFZdj9heWv9psOcn9eBapf2r85RMTNIIw65K/Qu3EH3/azsTjid9N9FXg39f7Bh1psg1y9d60V8vCyOLtXzj9+vZzNDxjoUxxhhjjDGmNf6wMMYYY4wxxrTGUqgRyu3YjjxYQxRyi/mXsDU0HCU1uiXYVDpBVDbDU78HE4qWYUUvSfJxC3W1pO0VdShr0i1syiV6YP9c8mmYV9ILW6UkC2B/PSmDspBpkvYt2LqnzzC+35U0yj24zZ6F0tUwlsNBVibbWcMc8hTWpZJGqQC3z3slXw9sPbmVaB2z/wVDSYpK3wilAdlWfSY1ICr5YpvouDggmqHSw9tg3wtbw0Cz/bUM7ccalGqq9PA/YGuo6kyuxTHfUyYVKgvOgUyioyGcifYbxtCNlSp1oeOaY0bXHF7fIGk1OeATcp3NxeWwpyX5fghb5bYkCzGuZLI4yoA4tsZIPsq1dk3Ke0iu31nJd6pcL4Gt4Zcpy1F5GGNvZ+9Zvot0rdITqUmq86qQjXlF5UmEc0DnPe+hsrIaOj45P1RqxTbK/rJtKu02Q8Y7FsYYY4wxxpjW+MPCGGOMMcYY0xp/WBhjjDHGGGNaYx+LEcpB1CA/IIn0pbgDtvodjK3ki8i1sjXU14MclqQp1Eqq3wZ13tTQZjru2+Q609azXXtgZ9pqhW25pJorL5PaUv38b6rB1/KpTd4z6mQhYIcDtHGfJI36KS5mSCJDcep4pW8DQz3eKPno96Oa9Z/AzsayQp10Fr6QYY+1bxiqtGn/6upNPyNtH/pYqF8Rf5e9Ea6FreFyOW+SttOk0Wxz6uDVh+Ma2Dp/OU56JS1rS4ZJzXxjSBbWukeuMV5vQf8eqvfS/iAcT8dIGtcF1b1Pq5SnYUqhU9dqTaKePVsvGNZa/Qno0zGY/42Zaf55P86jzF8kq7/656kvYq1OHKM63zgm9d70h6ndK6JsL10vsrC+WRjt4YDv2t9KGv2rsj7MQoITDXO8pmJHlG2kPlpka7eP8Y6FMcYYY4wxpj3+sDDGGGOMMca0xlKoEcqu3JLX7fnaKa8aVo/bqV+TNG7lHilpvbC5jZydkDqYkcbP3VdLGkPkcos5Cy+r7UOZl27XMu9uFXtLcDs423ZVqQ+BhGeDtP84SiKyk7FVGtBwC5jypFGDkYBxfGVyquUd8y4JgzqL4+nRMq3Y6tZwrdxaH0zoxEq9BgXDF760mqvsbx1P90YzKJvR7X7W45qoo23Hk6s1FCPlW5R7qJSB0iiVZqJP9TD4Gbw35ZIqU1tWsSPKua9hoTlmtM05H7TONTSsaAbWgfv4PNpvlDupzGsRbJ3rDN+qshBqzjgfkufsisKpEqEazKeS11o9FH03ZesHxyHLPyX5TcZyuc7kSYQyO51T2cnYHHciJVqKEOqF2lDHTE9SfhZ+mXAtyfpG3xvsG+23LJRrL+ymck+FIWZ1LLPMLCQuxsyj/gt4q+AdC2OMMcYYY0xr/GFhjDHGGGOMaY03gkYok7k1ujbJyO1BPdWVW+m63al5a2mUDmWRHrKThvU0WH7u7ihpQ4nooNuu2MrVKDXrUc+J+PdRg7lftg1OGkaF0h3fGYxGkpWhUoZsexhQ7TFjWrPfREQhUdgo8q3iMGbc4JlaxuSB80VEKd3TqEQYk6mU637YmbytqQxE82YyiixSCSUEWT5KFnTwUvqmMrJe2D1JvVRS80LYnPdZhCIFcqcu9R/LbBqJLovgpNGkVHvVgA1yPY4Xg1l/EOXqRP77RM0IeuSa97u1TNqI9Xt010IGu6E0UJf8mVg31/SWaZN6cEF56tFSCO8tZRTPOpi/RCj14VgYhBRqCWRAs4YwRiKifDZdZyBl3Xh4mTSak0Deb2fBLtSMWTQyJVs/CNtOpYdNy9PnzuYmx3J2Ojj/HtC/GygVvFbSuIYm6/BG1GOJvBPnJtUyzfGOhTHGGGOMMaY1/rAwxhhjjDHGtMYfFsYYY4wxxpjW2MdihLIdtY3ZKcHUHWY+FqpNXpWUSU059aknakaQ+QLI5y0Pq1YN/uian4CeXkttZnIi69VyPQZtSblw1wnR9SJLh4LM7+QquT4dNmZmly6d2t4sxK9osquhUCVk4AyGE1RfhuR3HIejIyHLRw2tap+pwdeTbnFCa9E3eiJ7LZxwRMTesJvqlCMieFp45mORrbY9sNPGA3vLNe+tJ9bSUadH0jg31R+J4WY5p1RLzTGjISczvwSO32wdS8Y5fSLGqT8B21zXCD435mlXJEz1AQOscle3oaBUuw29uQ7r/Tl+ZT4U91P/lFo4T50P8Ac7W5KuRPtMyjTxbEfta7a5jpmeir0lOA4zX0Cgbm+3oIxZTU9dz9B1Plvjkndh9RWa+Sgq6OPLJemEoZSZvbt1vqEd9PU2jw2B8dnl08RJpX+H8H46lhs+z9W493WTyj8+XtesCLMFvGNhjDHGGGOMaY0/LIwxxhhjjDGtsRRqhPII5D3jstCY7OExZVIhIdi/TCv0SAqlDonMKIUnjmpIWdC1jVyTmtwm15R5zZE0nM57gmynLmM7YBt/1I1lvq7TyAnlACcl+fR05NMHzNXNL2FnIToXyfWZlXw6fihrSfqmq/5HDJjr91ASoWOthsqwstN4a5ItlYRgvGoEx6mUAw4mvGPTcJXZSeska3OSya503GUrPdsom8+sfyaFyqRcqj2E3nAV5s0UlS3xRGeRZoyLBK5V2qeUVUJq1SVzpDRDJGZ8nBNCqMmR9ATtszpm19SgBCyTejQdMyprQftcp3kpS8z+F2Q2ZihdyeRUCdfL9Vyu2Q3D/+qt983aq+G7KX4NW/smC+2dzI+Dagm71hIGAHKkQ7J8mfSQZKdpKxhfhydpXI9S5Wf2d4hOloYhws9juNk4tNmPzKDwjoUxxhhjjDGmNf6wMMYYY4wxxrTGHxbGGGOMMcaY1tjHYoRyL/SKsw6u5+sKSQkKbXKSrytM46SKLVDSPFWFlN/vmBuPLZM2Mrxj00/f5XJNHwvVYrJiEs5uRpcw9P/nU3Kd+VgwtqGGNK3VIyR0ZU2fHVG0XRyX5FuepJFlcp1p94n6nZyS5KUfwjFJPoZFPayaqzuU7ssr+TQcJfp3oualXlvmFLu0Syqe9RXR8Ihbk72StF/LNeuvD0eNc6a55xqhunT6v2g8Vcz98SxDtdVsu2xOKT2wH5A0TjiOeV3vOHbFj2gx7BN+XqYVZVK3rz452fMwjGlDDXmKhlGGb8whGla3B7amsf/pB5L5UWQ+UgnP0X+gT0fmy8A6iz/BTPp+aKjYpvOZa4vWQ8O3E6630ibiBtkh86VT8L5L3SPYV/dLGsdkFnI8KbPL96kShr3Lx4J9o3GCa/kiynGR/A2xK/r3xrWDcSAxTfGOhTHGGGOMMaY1/rAwxhhjjDHGtMZSqBHKSp4cmm0xs4czecQBcs3t4XskjbKE5N7nwf6YJkLqcItIXiZRSvHbMq0Ic8nt2kfr9ejayqUUoWG4wq4QkffC3rNevu7kZkqBQgqVnRh8J+yaBCii+ZZ+03zKnVvOMmDebBwy33OrubpDh+5SyadjF1Korq16ShtEf5FGysxOPx8CGgWy6UHcDKOcytnWyjUlTiqVYUjhrD/YznpvyvU0LDShtEHD2Q51jBKRovVBGnVTT8c+VGVYHGvHl0mzefEL+R3bgfN5MKe6c30aDtWGyh6xfk/P/jdjlsaInVpHyu5qc3QLdM1TSl70hHnCOotsZhLXVA3NzN9l9ed7RNeZU5N68XeyzlSHxmBkZFisutYSSp74/rwhKW8w0sNMAoY53Yf53BXemWRSN4X99mPY0sZn9nTs1WuuHMQNTFO8Y2GMMcYYY4xpjT8sjDHGGGOMMa3xh4UxxhhjjDGmNfaxGKE8oOLJ4YaaTg3ZOT4G5qHy8qiGutBHJDzlvryQtI3QOxfac4kdWoQH3VSmxQzYqjmuob4Y1MmqjwV0oZdLEiPfzspCCGaift5bQ+6RaUkaGUQ4QUaYPbSpf0pE4aNy1Ykde57mYyjdM8skRirdXxwPKIsvZNdN+zeifLiuGJcoUsK1zjh04HxdIRy52iZ681vk+tCm4ZfpB6QhNHes5IsoddE6Zyu6fi3i4MGEhQR9sG+H3dWk6nNRQ/2ReG95tk0YawvhD3GorCXX/rBjHy3Fn9CwWk1DaOpjTroWF3pzcLOMyfUYX0ezTcQ3iVVRXx6uXelzZr4TfHdk+X4s18n8K/xtuKDeK/mmwdZ5wzU08/nioqP1Zz00/HW2Lifr5tWwNUp6Y7DOXCZJL6n93aA+TBwn6mPBsaZOIfjbQF2VJsJfa+z/6tiTeip1iojYtZ6kS/sMjnP2m4ylg2DP3fR4cnMzVLxjYYwxxhhjjGmNPyyMMcYYY4wxrbEUaoSyGvvW10ra52Ffwi3NZMtXD1EupAgSQm4jdBCjuU0q26yFYko/YSGx0Eh9jIaph82uxz/M4fNIyE/KKuZqIQibuU7Sqgok3SrOYoBCSrFYkqjKmiWnZhdF6unFQ2FOw3yDCCdYqMoax0GNYsu8S/5UyacKvEIaIP3B7qe8prGEJiKVTXFo3yRp6yF7KOqoJyyzEKn/0p6OrRKCQnKWjQuWr3Ib/k5Om0/7EQ/ENULrOAcTp6s4Srkk5OtdtfuqBKup7E7zJZIUhnR+Jv59jUgb506N4SWROepjr+Lz9JRpbMorJIzv7gyLiw65TaQr11fKi+helwsoO8lkP03nn8yVjZCvdI0nvtPYliJ5XQJ50s4ih5nSVAqF9u+TpFFcX68p0/4N9uvkdxtRpj5bMa8gLV4mMr4ZkYD+0MPmqyGLdYyrtItQEqaLNCSY10nSAjzsJLRBGl47WZsuk1D0b+FcSUI684/eyYN5P5jGeMfCGGOMMcYY0xp/WBhjjDHGGGNa4w8LY4wxxhhjTGvsYzFCuRsODCpDPJAXEl6wxkFZ4pTycvTBlXyi36QWvesn+IdHJYna3pdK+MgF8O+YA18J1ckWEsuarjQibhf/kbmwKUWfMqvMRy2pytknoENU6nk37I0SU5PuHmt7O/asHilE/T3ABtjjDqvno65VZaYcT9p0hQ57MNrzpisNwguqa0wRFVcE4VXN8WBWuESXyzaZLo1SDQupPhuoS59MCEZVlCHZ/BlYr8wnQcMcZ/97CXOfw1X1+PyHOySpaJ9j6reaVb2IbqeOCmtEVJ6GspzWMRfwN5qv62ErZCGiySDiiE6B34AOJ9ZTIuQWc6UPk/06CUPMtfYQKSN97MyvgqD+GyRpHOw+WYR4uVZC6c6k8x7bXOavhlolb8DinnYbJr5GLR+tYcbBgfWkWIOGlVdrnMEL9Jv2RRFOPblX1zLQ0zGLEN3qZ/f9aIbeHC9zXU4ncZFuGi4/WftOUH8zXqPBut7PzGYfi62CdyyMMcYYY4wxrfGHhTHGGGOMMaY1lkKNUB7AVrRuu76RF7p/WyGNHJqEii2Q7fG7spCE2KvPBuGu8ru18zs2w/+Nkt8VCrDaSeERsb6nnkZVxRQ9yXjSwPkiIiZgG1YPteapyuvl2VjO7km9uk5VBgzfOTGRC1GSpWElKavQE5AZljNeWKYV0jGt2ISG+abV61U74Doi4vRaQtcNBq5HRMSUZJyQAxMZCOUeo6W80Zhko6QMyr7u00Kbyk4YczfTR2QNKSzB3Kc6Scf1KoTDTJuxS+fVgcvKOnnmCQ375h6pWKKCLAZ6Mky2kAiy08eJhIGunhofETGmY66VJC4Dh4jmhWpJrjPXSVhuts+LpXyuR6pcYbTWNPQpKjmunitWi7yTS9cUOfGacrdJlLJIGauhxdlX7jehJudVGo471fMmatUYnQxKSnE5h7W8NQNn6+JwuWY/Mhxs17hr+pehVgy/6zo0m52ayE4Lkrmna1AB/u7J2qdp95rB4R0LY4wxxhhjTGv8YWGMMcYYY4xpjaVQIxRKJ2Y3/I0czpoezMy8uts5q+GJy8/EFrwGdZmFPdnxInvYHfYl8rt/RvSnG/DvGhXlvuS0X0atyYJmFduk+syov96be7S6Fc0ydUd8MexUfaFRfQAVEeOTqE2rEWllqUgNEqVV2QxSD9b5+jKpGKNptBnUuWt7nshWenUsJze7Qq7PSOQRlBCMlig1PIn2x/jnA48os41GnfUUX87npioBZSO0B6N7kowy3xhtSGUtlKMxCI6uCZRvdfVFEgFGI39t5ga5PiHTloDb5H+VcS3pOsy7oVyCE7xrHeNFw45bs3d5zTLHlEmxK+aDRvjhXJwl974dNtece+QEZ/4BoFIlqmgXS9rFsD/GQS8nXOdatA5TBhFhbinGwsHLkSDzbeO3OvbMI6WQhvdj3+wuacVaKJ3DiIgaBGw8GlaDEhXNhShHKgvVutTQaI9FVMKGZaTogMVLoEtmxDbHPL1bshVzSv5CbRoNq6ncc2PDdcUMDu9YGGOMMcYYY1rjDwtjjDHGGGNMa/xhYYwxxhhjjGmNfSxGKAz3l0bOg2hTfSwY+VH1u5fd37E3yqmcH2v4OcqTXLtCzyVk/gWUadJ/QaPqfh11XCKacobZU1k0te+F3lwdD5Iwvht7Ora267GVe0XUNa9dvjH7DZgtIkqN7mo5mZTh+R6BePhu8bHIZNGFxjWJ1bdYrjei/Y5Oymc7Zz5A2ljPHDhXl5aaIWbfKVnPwL21b74C+5SlZdpKtN8n8O8f6Snzjcf17WVS4bMwO5qh4+UTEB2/MeqcJ9cMxamSY2q7T0jKTMM2UkguY40adobavKXMFic0PP1a2/Uo2FrHsWmcVIAJcZEkzYM9N/F9ItvJYsh1WEMNPwohf+Zz9Ii8ya+CTbcH9WlhCFvV+6/BO+BBeQcU7ZydXtzQxyKPHVqyfh9c/AJ2T5nvbqyT6ku3EgN74j5lGn1NVuNllPp8iaMd14HXSdbRaC/12eFrhX4UKyQfm0ufjUNB36Ur7u3Yu/Jme0nGpr4H6uiAd85NkvSSyt8N/ybXJ8KeKy9orgvpewTrvq6T9I15IHXUMEPFOxbGGGOMMcaY1vjDwhhjjDHGGNMaS6FGKNnucwFC8GkENm6v6rbleIwMlSU0hTv+XTvikCdpKL0MSot42rZGg6VyQsPBUg5wmaQtgJ1KoaCr+LEkXY5Qt4fIpztla9fK7+Tw1n5OkutPoRHmSRqlStmh65tQhm6zn5L8bjHsQ/WHkLnomGkqhdqIBkqjGov+rxo9UvJxZ31VvKZIu23CJ/ttlWGx+0cfVqZdDXsRhBRvK87hLueYjnnKBpPDqQu0fW7h5BAZHOUAb5LfcQydK2mi+urwWHm5e3Y6OMbJqkQKRbrC0CblU7Z2u9RrE353b5kUYzA3M1XU5Sjjakmj1CpEUlPjkZ7ymuukSjOvR+KrkjLvkzf512Fz3VdZyMbYvt+eFI8XaZMgf9LxWpUeKs/YcpaISMNC67uO82gqBkqXVAlHiWeKl8VyzSVjPdo1k9ToArQsno6rh8tEyIz0nfxS2HzuM6KOhmvVEOdkE7RRk7K//qYlaSTpX323vhY25/eF8v+3J0XnBTpXxgUleEeVSUU7zEQbXyf5uN7d43CzWwXvWBhjjDHGGGNa4w8LY4wxxhhjTGv8YWGMMcYYY4xpjX0sRiwMirehmotaa9XFMoSmSg0PSvTyTbkLdqZhvuW38g87DpgtIpr7Y1Burvp76phXPLRTkfbAM37Tb7POK6WQ7aC71nB/Y3HztcmzqE8FNbXros7kA+ppV8BWX4mLYa/GzFeN7nOiDjWuh4oAfxV+eKn+sBJOUtvucrRXpinfKH4OVX8M0QCX47BU+D+S6LyLJPnfMdQ/fwJzUbXhLKMrrChsDYtKbXfxnE+U+dagMc+T8JFln/55ccXQpF+IXxVpHBtcZcaJzwP9F7KwnAvlmuVzzG8nvhKZjwXH5ETJR/8q1VpzHcjWJ4bnXStph8Tg0Xpwauga/d09O3Y2H5ZKXFE+WznH/rS4mhH/XS2T9dpd0op2SJ2h6nB+3CT9xtDGDyT3Xg3/nQskHzX4R0raODgyZZGMx6Mh08dUJ45fwq9CQ7kmg41uazfCVp8vts8tD5Vpc7HmqV/Irsn7qCB5xzRllVzTP6kc5+VCxj9KR8siykv1RypeMVij1RWQLN7O/299a+BWNcYYY4wxxrTGHxbGGGOMMcaY1lgKNUIZh43ZLAxe7TTniFI28xlJm4TdyUeG+Pl5Oezs1N71yfbsHZq34b11+5wU0p91ZSDceyCFYijA7WSr+8GkTvtCCvWAHL29DtvUeho5t3kplVFJU3Yi9YWwj5c09sfYaR17pUhjxkEOk54OLp3zzgW8+psibdO07/Tb1+Pf9VRxykQy6cd1Mma4tU6lgUrKym68sbgaK6cLk0yaRsbAVrnTQbAXS9ps2CqHoWCLoWgnyLzcBMnFVWVSHMiLJySY6zadkf6ASKFIFpmR/bivpI2CRkHDTnMuMjzy+Cx8rUB5pMrI1lfsiO4+qHHL5zv2pNPLNApS87HWQcPeUmUpy0WxJuhcZLjtm0R/tg5hZMcXYWTLvj8omjFOrotQ1kfEkGC/qdz2WNgaepjr5jOxGC6UfH8PW+u/EeOrS+L068rNEjZIWOspKn/ivTGJ9d7lHOv04T0SCphr6OIk5KvK7hg2m3/83Sz55HGGhD7bYthcx/R08EKaJgsG37sq4eV7kfK/7G+GLCS7GTresTDGGGOMMca0xh8WxhhjjDHGmNb4w8IYY4wxxhjTGvtYjFDGP9YJZ6eh+qh3ZihMDX9J7bZqn0fjk7OpX4P6c9zDKHLyCUsNZKbdVh3lZIahxHNnviSaVijr9ywD2N4Em/rjmVJJajM1kuo90Lyq5pu/y9qVOnv1schYE0/vt++Oh4s09vHEfZqVN0qui2eV1ePq4qoUJzOq3yfqRRQaYO03joUlkgYZfLwLtmpoqcM9VMIc3lPJF1Hq5fXelLczxPLlko8+LzoXmXe6pHGcnA37c5KP4TWvl7SiHbZ5WFKv7bfUL4TDnn5LGsKZbaLjekJPx36xpPF52CbZmqDQL0HvzTGzh6Sxv6shfSNiVwjOtXz6PWjb1cLuajhnrhFaBueHhrrlfO72KXu8klZq9fncGh6UenZ9lsLvIfnfk2wfXUsYrVr9KNin6n/B55k8qZ6vCDEr4Yuvw7tjnvyODfsI8mXz/j5poGOjzldQpvq40D9pMfpKw3J/APaDksY+1XVmfMXOQu4uk+sPw36fpNFfQscM60JfNPUpK96Zz6qX8enkd5z3lUjnv//Ndk8kqWaoeMfCGGOMMcYY0xp/WBhjjDHGGGNaYynUCGV3bKfeJGnvhhzmFMhhdCudkovsVFGVZtRkA11lIONKkWtRqqHlU6o0UdJWoByGyNMTuXnapoY0ZfDB0XJq+UWwKUHS7VRKNVTuxPvpybwPVPIpbMtsK7ebTm1uEikU61K2lwa5rEOBU98LyjSGFF4YXyjSKHVg32jfL4D9CUlj3tmS9s84vf0NCEWbPdlRcq2yO8Lteg3hyC3/DdE5yf0qmXHXQXql2/+UkemYYZszRPRHJR+fZ7Gksb9VxsRwpPpCmA2bfaht9WiSRlmZhmA9ETZlZIORQlHmpevAZbDfLGmUVVDmsr/kmwzZ4Ao5ufoenFytchXem1Fqddwthn2FpHHMawhhlnOIxOy8CuPwE/GbqMH16MdJvRSmcR1TCSHlKln4aJWYcazdLmlcPzbiuVX2wzG5Qd4/HKOHy+94qjzvpXXkvN/UU6ZxDmsoV65JWmfKmliGvuNZhkr8LoadyW3ZxjrmyQ1yzfVPpVy8ni1pnKdcGXWNLmSbz63X6xZRMT2A/03OMjOZl8PNbh28Y2GMMcYYY4xpjT8sjDHGGGOMMa2xFGqEwi28bklNJ27D2nhltQyV8NRQWQK3Wr8L+0uSbz22lDXqB+uvp3Jzm1oPPl0M+6uwd5d83D7XqBkB+ZNu5d6Ib+3bIF1ReQq3yDVaCLetNToI814mabUoMoOjc+707ZBpRJSyk7LOuln831GD/bhetqn/Pupo/2/mLrnmtv5XJI1tqf0dO/6vfvOR+Gm/rW1KOYnK7FgXPfGdUoF8+5yxekrR0Q3xX/22SsDYPirn4TOMh17lFgkvNBu2Sq1Yvvb2HhU7opyLYyt2RPk8GpGKchud65Qbsj+WRnMozdwkZyyvwFzX/n4J7EwKyva/TeYGJR3ZuOBaq9GRiD73XNgqSaHUZ2bX/yLsjNiVRamlOGZFaISwDlwjxkga25z9reOCa6FK8DjWdD5wLq6QNM7N+/CC0Huzztq/lMq8V9LOhc06q2yJ6HpEOe/nJY0SNpUq8Z3A9XSV/D/gOXg36Xv8H2CfK2l839XW5C3B90gqYxI4Zh6o2FrmBknjmFkvYQPH4+8N9r3KyDinNm7aPszw4x0LY4wxxhhjTGv8YWGMMcYYY4xpjT8sjDHGGGOMMa2xj8UIhdrnbl1jJ+gf9dr7Sq6mPhYKNZGZ7pR6TtW6M6yiniyd+SGswwmq9OFQ/TE15qrfJQfK9QPQrn4Q/z5PTqqmblbDj64svtfLmHgMK6q+MdQI31H594gthZ/t5F4nKeyrLAwhT4FWrf7XYev4oXdBFuaVz6On/bLOqr3l/brHfEdFzbmhoT3Vb4PQZSFrH23X0l9lBuzy6XjvM6SMmv44otRCj0Xxqhtnm5woaRyjWj79mPaUNIZQ5fiZLfnY39rGrMvxksZ7U6euuuiMsi6lKpuabInIWkXXNM6Bl0ga11dd4zjONcwuyUKHcu3Sk9zZp9quEfvBXl2xSz8N9e/geNKQr6wn/Uy07SYlafR5UV83+vF1hYG+t2M+gAGraw5PMVe/Jd77HZJGvwT2vbY/54b6j/C51ZeB41zbdUM8u98e/ev/7CTsUr5HuK7pieMb429gf6dI45qhda6hY4t9pf5U/INS25ztwLGlPl9cezUEMvvjAQkhzL9vuMbp3xAMO/3Ipklhhh/vWBhjjDHGGGNa4w8LY4wxxhhjTGsshRqhbIhOeM11CK+pMLymSoKG2vnc8s82EhkuT2Ub3J48R9KY98OSxjh+G4/s2NfKN/Ibk1OOI/6i35qEEKAR5RbzBTi9doWcXstt8ZVyGu9obOZOlXCOEiGvgLIQSqa0n3IpVEcQoPm4PaxBJwmlPXqaMMeTnmTMEH8qG+DYe7RI2am4ugvtvOTXZRlLdunYKhmJ6MgG2Ica2pNyKj2JnpKdcZLGfstOCebG/gSRIazDuLtHxh3RerE/dn1Gx9ZxsRj2PEnjeFolbX4T2lyfjeGkWa83Sj7KXJbG04u0yZgDKv2g/IbjtflZ8KXMaKOsAxNFitgEXXMoBVGJGcOi6lzJTvwllIVo2FJKdrRN2FePhsLW/Gy/NRqnrEeUY0jlpFkfMI3rlo5JhulWKRTH086Sthj2BhmvsSfWiKR8rmoqVy3lpacVVxvjf/fbfLb7ooRjWWVFlMxlJ9F3h0I/tN8aM6Gzpu0nuRi2uUsqhtWqaUjZLLy2Sgh5rafB312xI0phKOeGSgg5hjTMMWVYOl65ZvP9oKHi+U4Yuy4Tc5uh4h0LY4wxxhhjTGv8YWGMMcYYY4xpjT8sjDHGGGOMMa2xj8VI5d5O3Mnpe5Y+FiugJGfYxrlR0q3LHRgNd8oQrRpSjlAPqfrjO2P7fnuj6H6p99wYLyp/eOQ3Kvc+uriaLPr2ko6C9AHRuk8srjpaXg2tWj7Pq4urjfAaUZ8KanZVx880hhBUHetbYF8QysH91pr4QZGyGHapdS/9R7go3BUlVKRm4WzXhMTnjZ8PmE+9dNagLmtGly10E0KJqs8OdcuZDwq10JpP+4NQL6xa61Ij3NFnq2/PbXjyxcm9VPXLe3OMqAaec322pJXhecsnX4cZd7uEa70T/+/pTvgrqG65HCcvLK4mxhf6bQ11S530qbCzUNg6H6gjXyMz+EGMJw1tTFgv1eOfG3X4O9WU18J59sk155v6uPBax/x9lXy/Zxl+t6ErdTNc2zMfDu03tiXHgtZjepLGNVT7u6zLsyS1s7ZzndT24ftN713+4TO9mkZb1/IV0YzZcs167hpKZ6auRedoWHT2W3fff6/f6vbhGJhXyvWtSV72vYb9XlzJl3GkXLPfDo46uk6yf9hvL0vKOGjG40mqGSresTDGGGOMMca0xh8WxhhjjDHGmNZYCjVS2fOIfnOP+IYkdkRCDOeop9k2DUW3RK4pXsm2WhnWTcNMcvP7bpHi3FtcnSy/6zxrKT0o5R13Y7u8e6u48wR6amntlG4Nq8vt7I1dYrHO9qrKZrjNqwIFhl9cXKS8t7i6I95VSYmgDOWu4uzwsi6r4q9xVfbw6vi//baGjY14fr91U3y7SCn7owy2uQnSjFK4tF7ygfFln16GsMraH7zOtuopn1M5D2s4Q9Iugq3yMEqeGIpRR8Wk+G/UUf+fTkdmpL9bDJuSCJXesK8mSlop49hfUjkfvi1pnBEd8ccSkS+WsoQjiqv7IIXSZ6PsiHU8NOosTGqosj5yhVxzhGZtl8F767NRKkh5Z731u/uUaSqb4dreHca60yOUGXWHae6g9WddVnUJBQ/rt9ZjrdX6c7yq1GpxUpdyVdCApJ37sc4a3vc22H8vaYUM+MPlW2CUxtEe6DeR15+oDJjjoltK3Fnz7sESobJKygFVksU+VlkfZXh8J9+G8PURERuw1uq7m9f6B2TZ/3U5LMe1vp8ziTbH+d2/lXvvOPBvsj9yZ2WaSzNkvGNhjDHGGGOMaY0/LIwxxhhjjDGt8YeFMcYYY4wxpjX2sRixdMSBqi0tFc/P7rduiv8sclGvqHp/+jncKGnURPJOt0m+OVFnKrTQmQa1W/m7E/LRK2Fpket62OpHQc3uHQh7+/vrzv1Gx8P495JS01ymUo38FfldGbZxp+JqRdEmf46Ulxf5zoaPxap4g9yho+DWPi2vvw777UXKZ+Iz+M1HpJROEM27RY9/UfwFrspAgeviZlwxfOSyIt89DP+7Q+lJwbbT8Ivs7/NhZ+Evr5c0jlfVsy+ErSNyX9ir0G9j4ldFPirFb/xwOWv3f3PHr0XvfS3GyWyMkcWSj3Jh1VbPLq50pnbGzKdDoTdR3Xeo9NcqPSRYF33hrMSzXYhne5Xkozb8akk7NuosrtgRZZtcBlv1+ETnFP0G9pW011TK+KJcUyO/WNJOgK3+EY8iVu/kw/UuHe+iNZXxE1H6yKnen+NpVVdw5jf1WzfB5+EgyUXNvcrZ6bekOn6G6l0j9+aI5HxeKesp3zGZ31KsKd84NT+E4jdRvqeWSRpnt45X1l9nIp+IY0vD/dJfKHMT0JCvLKf0nyuD9fL9/wkpg3P9lCRNQ8BPgo8F/ZjU95PjUOciw1OvEueSe+DSwTVH3xUcTWPCbA28Y2GMMcYYY4xpjT8sjDHGGGOMMa2xFGrE0tnq7g4byyCbnS6+sytf57vyuwh3GdEdGpBQdMRtXj0Jk9vPSyWNspMfS1oZsu5KSWWwRt6x3PRdAQnY50UCVkqX9GzPjghjY7y7374C27i/hxKkciOccgmVkZUCiXKD+574Jq4YZrc8w3dxcZWdK/rncs1yKDN6U5FrA6RQEWdLGR3hWndYTp64fISkfb9jPoF829xc5FpLedVjZWDOdTuwv8vT5hfDpnxBFSLc+r4h6qiUiHfWUKilLKiz/b9epFDFnFr5f4u0F8PuDs3ckY6thuxE5Ug7w1aZVxmSVedDR2q3Jv5R0qg36LTC+ijrn4WI5ByY15Xa6eP34sRgDTDKNtkQf1WkXRo/wlUZ4vLOLpFKh6WQdm2M5/Xbo+XEeko1XyplcCyo5OKiIoRnZ7wukHyfh333Y2Xadjt0bF1DV+mB1AWc353+XSzSRkoFNWTtlOJKxUQdstPHGcj6xaF0gmXfKeebvwqr6J1yb9azVMOUock3xSf7bZUjFXLDd5bvgBswnijLUcku1wSVKjHQt0rMWP/uNahTEues1p8yLJVOZieCU3ZUynTLNqZM7dKkvKPkenFSJkckRcxaX76dtX0o4I1fPL1Iu2yfjnSZ41rlyHxjrtXj2s2w4B0LY4wxxhhjTGv8YWGMMcYYY4xpjT8sjDHGGGOMMa2xj8WIpaPUXNyVRiX5BNj7SL6OOvPT0DdHDBTCtgP1ztR+3lkEWo14EDpZ1VFy4KlvRukz8jlJfXkMjCpg5/dbi7r0wbzeX9JOgt1RNS9BiNffQ7+Ej0katZ9a3/Ngf61IWRGLcKXhHTuUIVT3q+brDlCp/hKbmZGUoXT8NFRXHDfgWQ+fKom4fgRtvmMZLHFF/FPn4uYyXGEc3vndwvi7ImklfGrIo+JPQM3uHUV43IjJCHV7bVdJ9FcpfSfK/ujMNw0DWYzrMpJu4S+hmmzCuad+UKVGvgy9ubYIM1rO04i5sDVA5cB3uEd8LMpfad//db+1WtYZhrlcFX/Tb38aviQRZXjNiFdLGbyfemewd0qPp43FytMZu0vEx4Kz5vr4UpF2Pbwu1Pcm4p9hX9xvLSx8qSIu5djaoVzHFmMNvUuL37ETKns9/EW66fg7raGvU0SUK3MpON+u6OPS44Jw3HX7lHU4o+tfuMaVE2J64amma3uH0qejLIM9f1mUfJQXO5bvB/pX8T2ofkRsEZ2LF+EdsAfClkeU68Ky+FP55eQB863DHPo9nXfTA/HfRUr5Pv2T4uqD6NMVRVrpi3RecVWuk3xnfj3+d5GyqlhbSi883oHP1r3eddby62X9vqq4Kv9K+TLamd5+6m/GMhaLY9FLdggzDHjHwhhjjDHGGNMaf1gYY4wxxhhjWmMp1Ejlic629R3b/I0k1mQ0Ii2BJOjaLkESw3mWYUs/CCnIquLbtJTaLIpP4aqU20zAFuc62a6NYgtehyg3Ufk8uqHKtFMljQH0vhx13tlvLZVwiKX84k2SRnmB3puoVIkBBlWi1YEbwHWRQET3Bn29zBINUzswXSea34w22SRpR2JbfDtKD0op1x3c8v+chKxF7NiLRApVih2ugqWnzVMy9c9F2uJ4Aa7+V5R0hA93ixRq9+KqM49UOlFIpmaVY/4rGPPrEunBqkJQUNajPD/4lcXVTUUATKnZrzE3d1HZIMdMJ0j0srg5yae8ot+6rUsKxTHfERNdLFKockyeLGmsswrQOIbeJ2kXw+7cu1vOsz1snc8dEdiNXWsEZZWd9el2kUJRtqnr2GXI2x0uvNNvi7vCYbMdKA/TfqJ8qBT1rS7WYRXHdsSClERu7Bq7K2CVkqB4BX75ufLe5dtobdQo+6r8FcVUKtss181Susd1jaNTT11neGENs8u1ZVO8tUgpw0TPl9913ndli6jQrrPAruqSHnLdL9fQOwsp3+th69xgWHSNydpZae4s5kZExGGw9e+Qzhx+AGvXui6Jdif878Ku9Zu30rF8XL+1OM7Hv5ey0M9DFrqy695mOPCOhTHGGGOMMaY1/rAwxhhjjDHGtMZSqJEKg3scebkkcuuS2+DHST6ecanHuPK6lOyUcgxKnHTrk/KL8oTodcWZnXoeLzeuPytp3JI/ovLveq1buTz3++PRjCz6km5FU4YlcqdPwz4zk53Uo0Kp2KOOSgiyWF/kpHoSDmhfof9bgrvsemD6kYhOxvBIu+kNOrKZ+IUkfZflPU8S2QedMjbGJZKP8qdSJrCq2NbXNtgO+UpRyqrigZiv3IIvxsKJZQSbdcVSrH1/UCVtkeRbDruUTqyKH+JKdGq7VOoYEfHYMR17B448nVPZqGRbvkHSxg4hnzI/SSPvlGvKIPncuuZkkbLKmFF1OnP7zi75BeVVS4qUOwrJmUaY64yZq3GK9e+ZWrHnSz6utaWUaGPR5vqnwgrko/xJZWodydR5Em2rvF057q5DhLbudYuyOMpOV0u+TjtvkJhpXy9khGUkMcqaSglVOZ8nQ1KjolO+A1aKFOqDyRVlRqW8qhYNMUJFU+U7ebqkcWwzdpKuk3wilWFRrqdrFftf/97oRHhcVYwtkbyizI3xr5J2Vsd8hkaH69RzWfECKsu/MT7Tb69fI1HkdgkzDHjHwhhjjDHGGNMaf1gYY4wxxhhjWuMPC2OMMcYYY0xr7GMxUlmZ6Z3BTzqh22KyaCV3o/5ST7imdlgDHVIfDk1813nFWR2pxVQfC2qCvyRpE2Jg9GRY3O+3krQj659owx+DvYOGhs1gu8oRy4UYV88jPziaUA++qGzccpYBmVNPolvOARomGKjbCZea1NUDvj3q5vDvsI8UbS98P8r/XaL9pnreWt75kpadFUufGva9+gWwUbTv6cOj7U/fG/pH7Cr5WC8d1/Qv6FaEd5BGr55Eq2FXm461f5brdQPminiVXKuOvCE8av1wTaytm6+Qa9Wf18hC7nKM6Ong7HtZL4rfab06E2lVfE3Sauua1pHzQQPtss110o6tpKkevzMuVhQnvEfEWRxDS4uk26r30nvcmOR7CewyxPKNRSj0cr4xhPqq4vTolxb5LkVI0+4lje1cnpq9ofDc0H7qXJfeNrqg8v2pa0m2xvH9SV+MV0o++jipfwfvp+MVfaoRkPfhM7D+uh5x3L1e0jgX1X+EcIyU4eDX0P9i02De66Yp3rEwxhhjjDHGtMYfFsYYY4wxxpjWWAo1UpmKzdfvShpVCYuwPay9fQG3Jl8oidyiVYmOhvXbjIaeu7Vj/lKS9sK2/mOSVsgvdKu1hmxGfx627v4fwC3zmrRK69E0VGtEKts4hhcqiUgkSGBV1+m2NaTfKAnbsfLvERGfxfawdin35w+QvuHtuqLzQsJTlddEFHKkt0sSD9dW2QOjCx5fKW+LzKzYis6H+ZV8Gm6RYWM1NCnlBRqys4Y+Gxt9maQdHUOCY4PHFe+lGRM5ZkEyjwrp4QxJhFTm05J0JuyvS5oWU9B0bGCs3SBJhbxqdsPyMsmjSl72qNiKtquuLZvRtRv3u0LWn+MZD1YlTpQuQbj0kDzbMyjdkzKeC/sxkfXt8AFcZO8AnpOt+SgJ0jbPwpHPr+R7U9S4pTjpWdEwxyrvBZAPbdxHT7Um6cBOqI0LlSNTGqUvUI7DpG/+Rq5X1ELM3iwZ74g674Etc6VYP7h+a1tB7jmpS7NrhgHvWBhjjDHGGGNa4w8LY4wxxhhjTGv8YWGMMcYYY4xpjX0sRiqzoFfUKK+Ua6+Hw0UaEbJLFJ9QC1epGmDovFVGSY32RyWNfgiq42/q6/ARaIC/rXp5aj1VO1xj05azDFi+UFR/qDpZDfVZ4z3lJSP1LYC9Y5kt7oSmVt1FVvFCNNl0V9EIiF2hUYdA4Z8iY606trMQoErT/mi6bGZjS30erhww1+Dg4CrDd1b9a7YExwyLV2lyVuZ/wH6upP0aA+o23OD4qJMtVSql3y3Jm/rREAzsdDg1XUOT9aGLpmNSfcWmVfKtqfx7RMzUtZbjXDucfgmY7Fr8MxrOvx2eJf+Q+QRxcaHPgL5/sntrOGPybth8uWobdHwu7kh9LHQdWFTP+kk4Ml2QhY0lskjTFeRjSV2KUMz6bONgZ2GzNRQtuK+eVPpw6jyEz84TkrRN0iaMtr0P39fqV4SQ12OHY901incsjDHGGGOMMa3xh4UxxhhjjDGmNZZCjVQmYDtSt/+5HT0TW+SDiZjaVDpRPfFYKpYccN11aDYjZR6g29lZQQQPm0aUm9+wvMHIF2oh/WJoM+5eud7z7AGzdSPSAEbxW5D8jFvKGomxaAaRK1B9o4e1f3wYwvoVcicZB3vD5omv+wzmBs3C/aadWMwb1ZFlY7ep7KEpIj3jmjAYKRQiRgeH3WAOdaecSqfGLpAPNZ3aWdTVVPo0mIII+rsny9d0jZATgz8M+82aFw3dK0lFXVRO0rQxQVcI4SwsLl8maMeuMoajUxW2M0MzJ/fqOgU6C49MSduSaq7yxSKhYYvQp/q7H9aLXM12nZ/cu1aPiOJQ8S4p1Ks7JuXJXafSNw3zLjAk+HOyjOxDHbuzO+YqSdqFFyrJZplcyzV8Ld5b2wxVjmwyvGNhjDHGGGOMaY0/LIwxxhhjjDGt8YeFMcYYY4wxpjX2sRiprIIWU3WtlKs+MrFja0TCjPfBPjfJ933YR2oiNI+HJWWoxLKQh7++TLsf4fgoYVct/XHQiKoevNDBN9X2Jn4TXST+BNS69zQsTnXXlw6iKqSpCwGi8XWFjS3cKk4u09iUEu20+c0TboR9vGiA2f9X8N9roZEHIgk/+mvYuyShh58Gu2+dJGZ688H48Pz/9Mp1Dy8kxOJUaol1QiR6c8rs94T9mGYEv5Trf4P94ajDsabl74D+XilpmV/F/UnabvOSRNLUP6jpWiK+C9TEd/lYoG80rDhdAfY5qOG9B0PT5x7C2O0imRtdPmb0Q8j8QICOGf7l0+UXQroWsgrHlZddfhXkp/WkSZyL6NMuHxFeTCzT0vc85hF9GXslW89gws8DNtctmtg05Dj+WFgrSYWPhYy7ok24lifrda/UqWcLVTON8I6FMcYYY4wxpjX+sDDGGGOMMca0xlKokQq3HHXHmhKhSdjS1NOvs9NtPwS7SwqF7dQu+VOFTAWiu9nF567IU5bimlH73i9lMPzobZLG6HOitIovwj6TCclp1z+R6wOSNG7J/3/tnX3QrVddnheD0UQNfaFABKpJDXWwJFPSEioZDRWmxZEUz0gYpR0cRPxApwMqtojOYMWP2lEbbC0Fq2RIR2iJ42FKKDiT0IAGJ6ChTVRqCZM4RZvIwFvQEhtr+oeNXvs+Z93n2fvZJ8f3cF1//Z6z1l7P+n7edda9fitlRnT5RylIum7dFdbJx2A/OuJ99Y7p0zPwCXnewa3fYTyzvZ9bJBAbSp94L6UxJ0hoivRjY9c9OtRHQhJ28h8t59/FMz1ecizmLccHfIhxszGmmqvNYCZhTHUBpR/p3XEWL6HU50UZCPnLhyOIqriUtVDSmQqwNy7tk9u4Ql1C9IuaPGQu6Xn7TbB/6PnLXv17d24+3wA3xy8pv/uFeKby53P2IYUqfHH+w9Ib00EqMTmEs8+8DvZLc5KesTxPbO4TUt9QMOLb/ZqI9+bygleWsFkab8xAVNgfRlBzV12rYek4wlhPCdti9+H8Ayky9W9hXxGS2oOl6UvDHQsREREREVmNCwsREREREVmNUqijCmUQ4QBmQ9FBO7e6edlvSpoe3l7+7BZ4cnJLk9vb2yxvuXNJ7xcpOeLu56sijFKNlEd8P2zKQNpNrU0JcGs8U32RHmD+M2zeVnxfxHsm7JvKu9OzDp2psP+kFGpX6LVpB7XCCaR0b4NyjfyGviD0I2z7E6RQJc0DPoQnkbw5/s8ot9emtyLm5YYIewtsSqGq054db879k3ieeVzKtuH8kRKtpSyV/KWjHj6nJPJnSjonyD/+Pyd4pFqSqZPwDtjHYb8h4tVL16E5SvlZzqlLeFx4j7r45NFO4KXx/Dw+pEZrIT8M+/uy07TOjQmXEq38Dr4U//CpGNtNwsu596UYR+mdikPsYPmEl9P5Binze5D6yY1v03ctzEjz7MYXNulTclULnMj12ly4F6IvUbl6247zpFTcsRARERERkdW4sBARERERkdW4sBARERERkdV4xuKo8ruwm7tZSj9/P+L9POzUnC69dJWu6LI3UZucLgP/aBLvVPA8A8t2T8Sj7v29Jb0bSxjPR6SbO94unJc785bUlO3T7WHW19fCZtleHPF+AHbqZKnDTg023SrmTa4zUnPP/4pIF5TvhN1uWl9KaqaXXoKb7oVJOUaxnGObj9Qgs45b3eUZha+bJz/ummSjuXzdldR4cx5gOfPc0q5nLFhHv7HwN3mmaQvvuYv48nhu/anB8zDsu1udsQDvjOd6w/KMOAvwlSePdQL57diF74/njTMWOdjBYTwfoAOUS5U3XPUu/Z6Nsfn9HFfO33XAh6csTv6e5T5T/5wXxfPS7+e745nt/c3thQtvNE+WzkH8/lwTYRwfeQaS5PeH35xHl/xzHsu/G2QvuGMhIiIiIiKrcWEhIiIiIiKrUQp1VPkk7JQe/CLs7ytp3FHClkLXfOlx78vK73aVbVDew23SlMk0+cIB7JQxEcpC0t0fR07Kflj/31Tenb/jbef04HjJJH9jjHFdPNNl5zdEGLfPKatoO/PZt3iDerpZZfu/LcJ+EnZzh8jt7ddH2K/C/uEIozxmHzeVNxlTu0X8e2DnNj77/M0Rhkt2T1AhzGQc2ef/1kljrYNSQcrIPlB+k9KelMCQrIcZbI+cZ9gPUx6xC61sCSUdOY4owWxzIXlOPNP1cLtteTGhG2O9nu7/ZqxSxqJRzJvcKeehhDDdlrKs25Rto/2P/bnZpKUbA/hUfOM86DsWJrH0+zl1hT1OlNg+lFwDu7kVb/n/8XimVO3rFt4GXxR4sjvuWIiIiIiIyGpcWIiIiIiIyGpcWIiIiIiIyGo8Y3FUuRN2atFfDfvlsB8X8dK1JKG2PvXm/B01ikvdJq6BZyKozcyzBs09IrWlqd+l/p/nNH6ppJduH3kWIPXxL4T9ygjjGQvq85tcNF1QUmOe50IIz8bkmYePwT4/wtjv0i0nte+/GmHsk+2MxUtgH5Z4Tbf/+SXsbtjpppRnFPJ8BMdDjiPy+Mlvxtjsr6mXZz95eYR9Iexfg31XxONZnNRg0y305405XxLP1MW/vfyONLn5O+L5Hy9Mk/8Flrpr1vnfX5heI89DMM+pB+cZlP9Q0mxa8Z+dvCvZ5uwH+0k7e8P8p8vxj405DONfEXdGPL77sgjbOA+TB2fAUvfFX3jqKKvIc3vMcs4JdEeeY2q8Yv6OY7DpKnapW+CknZ9beu5nX/CM35tglyNr9Bh8AvmNmf09c208v6ikKXvBHQsREREREVmNCwsREREREVmNUqijylsWxqPk4sII4+3dvxdhlBy9JsK4BUzXmO1W41+OZ8ZNudBPw06pBLdN6VY0e/LDYf/fCPtS2I+KMG7XNzd4j4GdW/XPg53ubHkb76vHHEp2UkbxNbCbC8fj8XwV7HZjLdO8fBrrRD5RwijTYV9LCcHhFu/bhWtg51Y6XTOnK8YmZSGUV2Ud0zVm9kn2oSZ9u2vyrjE23WHmTfd0gZw3Px+DnTK158Jut9R/BHbrWyknWXrb9iHs5n75sIQt5XvjmeO0yZFeEs8HsH8L9ndHvGsnvxljjH9e3kdStvQPYP832IcRj/K2uyPs3PI+ym/ZpvmNoZQy++uGXK9cn/6eeP4q2E3Wxzp5dIn3unj+CtiHsH8x4nE8vCvCKLFNqQ//Ozdd5HLsczwfRryDsRtLb+xusP/+RIRdCzsl2pxD2YbtO5vzxU0lLucdSgrTu+8LYKfUV/aCOxYiIiIiIrIaFxYiIiIiIrIaFxYiIiIiIrIaz1icjVA7+Suw8ywD3UKmRpRuUX8uwnjG4p/CTg32E0vYxbDvi7AfhZ1nLKjhpU4zXRlS45quSXkuJF1oUqf5HWMO6yBdh/4b2KnhvBp2nrF4Mmyec0h3kf8S9j+KMGrWfybCngab9ZhnDXiuIvXN6ZKSUB+cLhbpYZFnMZrr1uSfwP6xaawx/gB2nh3KshKe7cmzKz8Iu7lw5FmJPHPEcxV/I8Losrj9d88dsNO98/Wwc9xwPLw3wpaeo7m9hNHN6D0RRs16S4Ok9pzlyfFG0tXwH5w0VifrY2me0yU1z1pxnkn31Dyv9SMR9vML3/218Ux3p9TV5/kX9tErIixdWRPOH3QdmvMpXV7nWbRyrGL8JuzjEfatkzTT/fU1sL8hwjg/5RnCl8Pmmbg2dyQ8r5JnM3huIF2kco5m2+S5KIblN4b9LscR+/JXjzk8o5Dx2MbZpix3q683wn5bhPF3+XfDW8ccnmf8zhKP+U/3yLIX3LEQEREREZHVuLAQEREREZHVKIU6G6EEiZKOdK3abmb+F7Dz9svfmcQ7HvF4G/ZdEUY5wCcj7BD2RyJs5soyt2svgp1SKEpZfj3CuDVN15vXRzy6rP2jCGM9XzXmZNnYbu2WY94w+7IIy21lQokKpWO5vcwbWV8VYU0KdQx2bpFTtkNpXcqKXgQ7JXKsyyaFIpl/8hXxzLHyCxFG6Ue6WCSUgzV3jl8Vz+12Ycq5OGOn9OPbYF8ZYU12wv76hxFGd56sg4OIxzH8qQhju/1shM3cNv9oxKN77SaFSle0TY5BuQ37YeY/xzf5ZtgpPWSfZ9leEPEoQcov8rvHMlLeRo7DTpnX9405byphnL8pbUw3ypT9pESuSU0pH7o/wpgOx2yOh3thp+SIUqiUEn0L7L86y2DwynhuctKfKulw3mHb5LebLlRfGmGUQt0bYZRENinURSWMUrQmTWpkecjTYV8XYTnGCPvJb09jbUrfUpbYXMDLYtyxEBERERGR1biwEBERERGR1biwEBERERGR1XjG4myHGuOnRtgzYN8WYQewHxNh1AvTbezdEY/6y3R1u9R1X7rZo9tAukNMl6zUzX5RSf9vxvPrJ/FSH/ykk8b6U+j2btele2qVZzzt1FH+DGrHmwaYLgo/sEX61Ir/eInXtNs8Z3JphLVzCDzPQE38teU3qYv+hyXua2EvPWOR/ADsPAtAt5avizC6gs6zE+SjsJ8bYS8uv2O/+LxprM2zEtnvOPbzDAHPRGSbcl6gDv4TY85hPLPOM/02/90zsfOMBee/34+wY7Cbe2emma6Y+Zxt33g47JwvOOexbdJFcYPjLd0j3ww764TwPFWe7/jBMYdn6VKPz7HIdss+Ts19ft94vuB7IuwA9uHJs3cCeSaO50fyG0ayL/BsAF2tvmLMyXMUJN2w8xxWO1/D71u67GbZ0hU92+CiCOM5Kc7zX1/itbMYSY7bGf8FdjtnIjvjjoWIiIiIiKzGhYWIiIiIiKxGKdRR5QD2YYSlJOlBUtbCLdS84ZJuRXM7lVIfSlDSbSy359NlYIPSg7y1lNuw6UKQcOs780+Xmp+OsNm2ckq3nnjSWH9K3tpMWLaUh3FbP2UbM7apV966yi3mlO/cskWahPKz7GtMk1vR3x3x6Dox5WbNzSjdBjZpBkmXr4clLvvytRFGidCtsC+OeJQz5K3GH5jYSfblGVkHszlhjOXtTTeW6YKVEovvijD2r2+NMI4/SrkeX/KRcxVlMxdGGCVBKaugdILSvWy3Y7CbS9mE8sxMc0aTCe56qzjH5aMX/maMzfkp3RVnG8yga/JzI+wLxxz+ZfLRCPuNyW/uiGd+A3KuJVeXsKVknvi+WyPscbDzpnXmha56mzTw+SUs/+uYc8vHYOf8cFkJY/7TVS/z8vIIo8SJ7p3TZTrj5fe5kXlZQpM0y864YyEiIiIiIqtxYSEiIiIiIqtRCnVUOSxhS7fIKTPKm4y5RZveeGZSjfS8QdKzRIPeQ5pUiVu074h4lDakRw32+pSkcBv22CR/Y5wo+yJNWvIc2HkzM+u5SSwoQ8nt/wbriNvs6cUnpQdkdlvxGL3c6T1kRpOR5W3YZKn8iez63yo3x/NjYfPm1pQtfTHsXeVmKasgTTbQZIPN29aM7Ls3wE7ZIOsnb26nBzLeiNtkDSmJo+wkZX30vpXlpDSQN1dn21wLO6VQ7XZ1cgA7+zElWUu95Z0Kzk+7tO8Ym/1pG+9wM9ptyAk9oR0u/E3OyZRcpgyOctjmyW1XOLf8cYQ9G3bO85RUPRl23lhPL11fV/Lxa/FMaSC/gzk/tPniy2BnvVKau3R+/bISljdjN1Kut4TT0fbijoWIiIiIiKzHhYWIiIiIiKzGhYWIiIiIiKzGMxafyeRtoYS3EDd97bWwtzlH0aBWMt3B0c3hu2HnuQDqea+NMOqiXz3mfFsJow47b/Zu5y/ug50uO+lSsLlHZLu1W1cbPJOQ+W2uJHkz+WsjrJ2jWOqe8t2njrI3sv6Xcmc882wA3dLmTbB077iNG0VC7flHIqyVp2mmZ+47E6afun32p2zrvA2bfHpit36d53B41irnAabT3M2SvMW63ZqdGvYZh7BznLSzaWTp2bl8H29+TtekdG/7QxGWZ4n2Dc9rpTve947taeezcl7hGbw8Y3Z8h3c3roxn9sM8f8Fza3RNnn3kP5b3HYP9wgjjzdg8X5DjhvP8MyKM7rVzHuB38YsjLG9vf5B29qa5yU6aO/IZnrE4LbhjISIiIiIiq3FhISIiIiIiq1EKdTZCac7vTGN114ZvKWHk9aeOsjXc9r08wmY307bt1HfFM6VWKXsgh7APSrzcbqak4w8jjFvF6aqStwa3LVqmkdvUu5ASAvaLdAVI16G/GWF0o/kDEZY3qD/IYclX3qJMacAu7mXH2CxP1v/Xw279P2VMM6lgSoJYz98UYa8r7yPsr6+ZxjqRw4XxchxR6kDXxr9S0khZBeWMvxxhlGjRVWXKp+gqOW+95431TY6Zt9SnNGpGk/j9qxJGmdSPwm6uhh8ez21+Wgq/AX8nwlhfOZe078NXwl4qX8yyUWp1TfndY+J5NvZftTAfY2y2R8pOW17IU2HnHMC2z758+1gGb6TOb1i6bSZvg53fJsqy6HI8JU3te8q/Gp8WYTN54RibrnXZt/YhoR2j/60zw5u3TwvuWIiIiIiIyGpcWIiIiIiIyGpcWIiIiIiIyGo8Y3FUOYCdOkHqt7/5NOfjdxfGO4jnY7CPRxi1kql/Xeoak6Rb3fefNFYndd3MV7orpIY6XeA1HSj1rzzLkG5FqWO9LMKeDDvr6mtgU998x5hzUTyzHq4pv3t2PFNXzDMKhyWN1CbzjMKuZyyuKGGvgN3OWCydNX8mnqkVb26Ol9LabVe+PJ6fA5uuW98x5uT5ILrU/M4IOx82+3X2nx+DvetZiTwbk+nMaO626SI6XdH+LOxfGHOeBzvd7NLt6lMjjOdTDkv6pJ1BaW6yE+rleRbq2oj3Ith5lus47Kalz3MCPzmJd1jSyLNi1Pi/NcKWuk2n69bsIzx7k98Ojp3zxxyeX2jul5ub6ezzj4DNc2/tbESet+DZtPz+3FrS+VLYdFXe3N6fbpfHups9LbhjISIiIiIiq3FhISIiIiIiq1EKdVShVCalAE3uMeMgng9LGLdNz4WdN1A32Q+3Ra8t8dpWceMAdt7euYuMJkcK03xchF0A++qS5p/E8yNOGmuMf1be3do+pVCXTezmtvSSeKb8IqU+ZFaWMZZvP+c2/lIJGyUjeeN0bt2TLOuMZ8UzZRWfDztvSm797gD2YYQ9eZyc0zF7Zx7ZjjeU31HmdUGEvRN2kxUdwE43liRlWJQSNclLSkZ2uak3YX1lv17qQphSmbyJmS5aHxthHB8pZdnFTW27uTqhy272kc+PeBfBTjlPk9ORvC18JoVqfFU83wn7l3ZIb4zeR4/DfsUs0jjRNXPeVv0gTabWZGT57WMf5e9aWRrZpuzzKWVlf6X8qd0ov1SWtivpZlf2gjsWIiIiIiKyGhcWIiIiIiKyGhcWIiIiIiKyGs9YHFXunNhjjPGtO6SXmvjDiZ3QFd1XRBjPWLQ0Gru61KQeeRcXtUlqn3+rxG0u90i62aPGmfnPswwHsLdxoXkX7O+B/eMRj5rX1KAuPedwSzyzbEvdfGb9LNWNU7d8GGGp/98HB7A5jpp2eBt4boaa6Xv2lH6D/bB9LZjH7DPtbAY5hJ39mi4u86wE87iNZnof8wJpZ8oat03sMTb7fJbtQ5N4u5J6/Abzybkw+/wtE3sbzjl1lFOSdcc+084+Nffd2UfJIew850XeEM88T8c6vjbifS/sl5T0m6t1nj9Kl/UNjr9fnMY60Y0vz3fsa25cyzZ9XhbjjoWIiIiIiKzGhYWIiIiIiKxGKdRRpW19L3WjeAB71218so1r2OYij1yzQz7GOPG20LWkW1fKz344wtpNouT18dxuVyWHsFNecFP53XHYlMu1WSDbqW3/k33c+P6rp45yUpqLQsoS/m6EvWdh+ilXIeeWMPKxeD4scdkv7oLdtvEPtki/8SbYS29tfmeE7eLeOcdGu12Y9fDuHd51plnq7nTX8bCUbaRKt8NukrJdXbmSpd+Kxu3xvFQC0+bkpfN8q9cfi+cfgd0kta+G3cZlSolnfSjl1I2fXpDeGEdjLC79nslWuGMhIiIiIiKrcWEhIiIiIiKredgDDzzwwJnOhGzPwx52pnNwEr4mnt92RnJxevh78Xwz7CbT2IbmgWTGS+N56W2//xr2ty/8zRibNwHvwxPNQw1vh/9PEfYq2Nl3l5b7APZhiZf9qUlGmOd9SBbPNnjr9y6yK/mLzRfF8y5jIG+v34dHsAPYhyXeY+K59dGnwn4C7JyPlr77c+J59q16ajx/oKR5NvGizccH3nhGcnHW4Y6FiIiIiIisxoWFiIiIiIisxoWFiIiIiIisRnezsj8eipuAzxTp+m9f5yrIR08d5QQ+dOooJ2Xp7eDJUTxXQajP/s4Ia+cclpb7cGG8bdxwbnOb9Gci5506ihxh9nGuaN+3rI+xfKxvc+6HZxuW3uzdaN+pA9gn3A7OSSd9rR8Blp6Ja+7ZZWfcsRARERERkdW4sBARERERkdXobvaI8hfS3aw89GzjypA8Dna7uVXOPHQZeTokeEedL4H922csFyJnEbya/MIzlouHGv8a3g/uWIiIiIiIyGpcWIiIiIiIyGpcWIiIiIiIyGo8Y3FE8YyFiIiIyH7wr+H94I6FiIiIiIisxoWFiIiIiIisxpu35SHiPfF85RnJhYiIiIicHtyxEBERERGR1biwEBERERGR1biwEBERERGR1XjGQiZcB/vqCDtvYRpvh/27EXY2nbH4eDw/6ozkQs4WPh3PS8ebfGZxG+zLzlguHno+U8tNPgr7CWcsFyInwx0LERERERFZjQsLERERERFZjVKoI8unYJ9f4lHS9JwIo2Qn3cHeCvujEfYy2E2mcRPsp5d4ye2wn1jisWzfskX6++ZT8byrFOrDsFu53wX72Tu+a1dugX1FhN0P+40Rdibb5yhA+dPxCHvB5Dc5LvchiVgqw7o/ns/Zw7uX0sp9uvO1q9ts1uu+pG03wm6SIEo1/zjCONc+q6SRc1z75pxungR7H7KoD8cz597WbjlWyD7auMmd7oB9W4RdtTD9pd+bt8dzS/909PMZOdbfB3vpuGxtL7vijoWIiIiIiKzGhYWIiIiIiKzGhYWIiIiIiKzGMxZHlpthfyLCXgib+trrIx517x+KsAthp06ZOnvqKFNz/8hJvFPxQdhZNuYr3bwS6o/T1e3lsB9b0uBZiXsjjL9reuOsV8bNdzOfTevJNNsZi6aLZhqpFW51wnZ8c4Sxzr+hpEGaq95t9Ow8b/PCaaxNTfmlJd7PxfPzYbf2ph45z9o8oYRxTN1d0iftLM+74plTfWrp757EG2N+buMn4vmVsHd1v8x8XDiNNcadJSzHWzs3QJaeW0o9O/X+OW7eCvtpsB8f8Y7Dfn6EcQxknTSte+rPZ//OfpfzPOeF7JOsI5bn/RGP34RbSlj2Gc5dWW7OQUvPmTRynlwK83E8wo6V3/HsRI4Nppl9ecYd8cy+lnP7LM3s1+yH+f1ssB1zPNwHm3WQ7ctzQPdEGL+L1405S89YZL/2jMU+cMdCRERERERW48JCRERERERWoxTqyMKt+9yqPBc2t4dTHvEa2E02k9uF3L5tshludad8gVvyKTOihCq3aCkBY7lTltOkPtzGz+15Sg9ePElvjM06SHkBy5Pb1JfAbvIwbinndjnlFynZocwrf/d62JSYZRvyOV17si7TdSXTvHjMoYu/bHv2meMRxi3zlJOwHZsUiu/LsrGNs9+xrMci7E2w6WI5pRnMf2v7mYwlyTHLND8ZYeyHTfpxeQk7Djvb7fYSRmlGysjYBhzD2YbMc7Yb6yvnmSbDmkkDL4l4lINlvXI+ShkT65z5yLmEfeYREfYG2E+JMNbrkyLstbBfUd7NsuX3gTKUCyKM7shZ7pxrKVW6IcJY/zdF2DPHHLY/vwEpp2JemivmHA+cS5bKq5p8Meu8uVPnn2SUpuWY+qwSxnk+5W3MC+ejrB/O0fl9aG5w2X9TFse+QFnUt0c8SpxyLqT8L+fQ9rfIjKw72QfuWIiIiIiIyGpcWIiIiIiIyGqUQh1ZuOWf27AfhM1txtym5lZ3bsFT1pLyAm7fcjs7JQqUteQ2OLe+c6uYUoTcquQWPLdhc9uVpPyC9ZCeh7gNzt/lli9/l7Ic1l1uzzP99OrCsrK+UhbC+mlSqJTzsJ88dvLvY2y2Y/O8ld482rvZPqzX3KpnXvLdTDMlbGxTbrlnHvm7lBAy/ZR5carMfj7zmpLjrUkISG7xs19QepMyHz6nNzWOsUz/4km8MTZlIpTKpOyAYSkfOQ47pQ0z70+ZD9ZB9le2VfaZlmfWA9NMSRDHadbrTO40xmY/v7fEY9+6PcI4t7RyZ5uyT7LfNVlrzvPNSx2fmWbO86zzR0bYUllizhGcSyhNyz7TvC+x36W8jfXAcdrmkvxOUdaXY52yspwjWH/fWNJn2+efcWy3zDPbimlmGvT6luOZ9ZxlY5/POZrtxn6R7ct2y/phGk+PMM5V7bvFNHf1CCYNdyxERERERGQ1LixERERERGQ1LixERERERGQ1nrE4srSbaQn1kO0MRGqfqUM8VtKnLj3jMc3ML12+trMA7QZNaixTg8ozEKmhpYY6NZbUjF4Eu53TSOiqL7XJ/F26tZzdipq6aJYnbxamlvh9EUbtLW+dTo0r00hdNPW2xyOsuRqcuedNN5msu+yv1OVm/fOZGunUnrO926202WeunuQx83VZice8pDvN62E315XU+N8XYXkuhDBfWefthl+OP+Yr5wu6Ps2xyLg53u6d2FdEPLpPzXbjGZF2U29+7lhfbO8cD9SK57tZP9nXOP6ol8+5sOn4OW6yX7wAdpaNdc5ytnGTc9yFk3hjzPt5jlmeEck0WLacJzmG8/wf3alzTsvzcjPXrWNs1mXOkxwf1OrneCbZ71j/zQ1qft+YL5Ytx+hszhlj8/uWdc7fLT2HkG3a/vbg+basV+aTaWbdsb1zTPGbmeOB553Yn/LMFOeL5vZbdsUdCxERERERWY0LCxERERERWY1SqCMLt5FzO49bxZQZ5ZYstwTT1SBlCZdGWG6Zz6Acpm2fpitAbnHmTcDMM9Nscp5282mWhXXZtvHb7ciMe26E8X1ZbrbPRbBzK5ptk7KWJofh1jelH5kPbjc32UZKFJh+1hfrlfWf7TZKWJMUzNwvZt/ltnhKAZh+SqFYnpavp8DOLf52E3C7WZrlOa/Em70r39fkZ9l/WFaOh5xz+Jzt9OlJvDE2y9bizaQ9+b52G3mDaWafb+6XL5zY+TvmI8fbEybxxtiUu2WfuXgSb4zNdmMdp2zm1jFn6TxPUtLU3Nk2yR/bMet1dlty+47keGDZcqxT/sn2SFe6z5nEG6O7MZ25px5jUwLIeTLzyD7f3FNnn5l9hzMe+1OTL+a3lXWU30h+y5lm9gPmsc3Rzb0w46XUmuT3WfaBOxYiIiIiIrIaFxYiIiIiIrIaFxYiIiIiIrIaz1gcWag9TI0lXVJSO5yaeMZLLSP1i6mN/SnYTffOMxapcaVrwNSi893XRxj1nczXxRGPrmizbNTipraUGny6r8v8UxeaOn4+fyLCqFNPjfwnJnYOU2rk3xBhLE/WK3W6rMfUuD62hDU3uzON6xibmmb2w9S/Ul+bGm+2R+qpZ/XaznpkGpdN7DE2ddHpJpgwj6lNZl3eEmHMZ+rU753ES605+0meQ6Be+/ERRlexb4+wY7Cvgp39gn3tORHGMZx9nrpr1nmmzz6fcwLnknRzzPelvpztw76W9UPN/XURxvflPExXouxr2a/ZbjkeeFYi+zJd8GZ/5fhjn3x2xHsz7Oyv7bwW65XpN3e52fZLz1q1bwz7T/Y76v3TVSznyXT5ynZsZzHYR7PczYUzzx4cj7DZmaM8F8WwD5Y0Ml8cp5xnsk+yz+dZCbZbfptYJzm/so35vjxzwu911uNx2Pn3TM6bJ3tXprnUbb9sgzsWIiIiIiKyGhcWIiIiIiKyGqVQRxZuF+aW43mTeLnl2Nw7ctsyt0IZ1tzqUVKQkiBumeaW5vmTeGNsyj9m5Rxjc4uzuXfMrdaZG9aMx/RTOsGt+7z1k1vaWXdsD5Ynt8HJNi5HL4DNOsg6vnESb4zN+s9buZvsbtbXWtukrIUykZdFGN1msq+lHKzdutrkSGz/7GuUWXx6Yo+xWc8pL2xTMcffhyf/PsZmnlMmxX7SXMpmGPPVZCFs73w36znLOXO9meOmubNd6noz+yRhHbSxnrSbq9keTDNlSyTnSdZX5qulM5N5Je07wnKnlIj9l1K0bBuOvxw3LGu2G+eWzP+szptr1XYz9tMijPM5y5ZyLZYn5wv26ywbx1jOcZRZzmRRmZcci8+apDHGphtztmnmkeXJdzP/2S/YHikZnc2N2a8Zlv2JMuP8Hcswm5PH2Mx/u31cdsUdCxERERERWY0LCxERERERWY0LCxERERERWY1nLI4s1MOmBp+6RGo4Uw9JUg/5pBJGPTt1jldEPOpwL48w6kBvj7DzJna+my5lUz/Nd6eOknrzfDe1q6zH1GkyLHXprPM8R0HtcOpHqRHm+/IMBPN/QYRRG9v0o8+EnXr25jKQ/SL706MmduaFeufmRjF10Yyb6fN3l5d47Cep3WY95BkI6tnTJSj12qz/5lI2y8azADktp3b8QVL7T7eZ2WfamOIYzj7J8cD6yfRfDDvHylNg51xyHDbrIF2AfqiEPQN2ntngWJnV4xibbZpnRFgH6daaevysO9YX3bxm+pwjcp5MDT55IezU+NPVajuLwTrJftHOL8zmxox3F+ycq47BvjnCzpnY+Q6e08i5vLlkZ56by2uScwnb9IYIa65i7y5hfDfTyPSvhp31w/GR7qNZD+18JPth9sFnljCmP3P/OsbmGMi/G5jGuRHGOs/vFqHr4awflu2+kobsijsWIiIiIiKyGhcWIiIiIiKyGqVQRxZuF6a7OUozuNWa7tkoy8ltUaaZW/yzm4ATbh2ntCTlDKS5cOS2JrfBc0uTZW3SkpQxzeq1yV8yDdZlvpt5bm4tKelIl7L8Xboc5dZxylUI8zxz+ZnvyrykvIBlzS3s2Y3pKS+Y3dCd70vZQ7rpfJD3xDO37lOG0GQnrZ/P+lP+Jss6C8v2Zp1Q3pHjkmXN+mEec0yxb6frZMKypeSFbZPlbi54m1yFMM85l1By1lxLZr3OZECZ/szF9RibYyf7z8x9dM4lbKurxpyUlnB8p9yD80JzWUtSnsf0s15Zl+eXeHTlmmOU/bfdvJ3fJkowOUffEfH4uzbPp2Rx5h45ZWrNxTLrLtubaeYcNHPvnN+RNlZYD83FPOs/65h5bnN5k7xmmjPXxk+PeDfBbhK25OMTO92iL3WXL7vijoWIiIiIiKzGhYWIiIiIiKxGKdSRhVvd74swbqFy+za9XXArtN1wnfD2y7YdzG3eN5f0cquSeUn5ArdCKSFoNz23G3fbVusHJ3kaY1M2kGmw3LnF3247Zf01yQ5/l3VOGUfKYbj9zO3ylEfwfbdGGMvTvM2knGR2c3V6rmLfzfphGzSPS+2W7BtLGGVAKV9gPadnIMox6Kkk+zXL1ryRNIlTk3ewz2T90JNLzgOsu+yvx2HPZBpjbNZ5ShuY/zavMM2UWLBOroswtmPWCdumyR5YtiYtTbkNy9O87rE82e/Yr3O8MSz7HefUnCNYD0wjPemw7XPMNu9w/B3rNb0QvQx29mv2mZxf2R43Rtjsd5dGPOYxJaP8XcqF+L5jsHM+Yrz2nWqekzLPrD/Orznemse/HPuE/ZVp5nzHOk4JXvs2sZ9kmqxztm+r/6xz9qH0MMc2aH23zaGyD9yxEBERERGR1biwEBERERGR1biwEBERERGR1XjG4shCDWdqgqmdpH4xdZntZmmS2mTqF5nmyyIeb1NNrST1wjdFGNOf3YI6xubZiSxbc2PZ3LVSY043e6lvZpqpM2V7pGa93SpKd4xsw3QByvMdWT/Uk6Y2lhrwdtsyn/N8Cm8+bVrxhOVh+tk27GvZNixb9ldq35l+6mvblMeyZrtR75zvno2HbDem/8wIo/a99WW6SM2ysS5Tkz1Lb4zN8mSaPKN1HHa7PTrPlrR2o8a53bzNcxrZhtSptzNB6SKXeaY+O88OsV7zjAXLlu0906Jn2+T4IxzrOR44vrNsrC+O9fxW8KxPtin7Sc4zbLfzJv8+xua5jfyO3Dux891ZP7zBu30HmZe89XvW78aY306dZ9HY9k2rn/31oxM74Xcxz++w7dt5pzYPsG2yjln/Oa/zW5j5Z31le8zOvLTvRuaLdZ7txr91ON5yvstzLbJv3LEQEREREZHVuLAQEREREZHVKIU6snDLMWUb3BafuQUco7t+5NZ3bvFzW7nd5Eo3uCmP4FblG0oauf3PPDcJAcvapCW5hc26vGDy72Ns1mvKpOjuL3/HfLUtYNZ/ypFyW580iRZlIpRYZD2yzrPtmWa6E5zdxjvG3N1m9kluZ6dshtNV5nkmZWnuEHP6a66A2023fHfrk7N8jDF3DzrG3DVt1k+OFULZQ5alySU4lzR5IcuTbksZlpKdmWvpLBtlFTlmH1nCZvHy3eyHKfthWKu7lFjQLS7zn/2uSfA+NYk3xqZcLG+Fvh42x1Smn64+yXkTe4z57dc5X7DPpPvr2e3gmWbOd2xHlif7DKWl7U+dpbLBdOP6Qtg59prrZI6PHOtsY34/8xvWvm+c27PcM8lz5pH5SEkTaTeO51zIPsR8pJyKc0Sbd/Om+JlkNOWLfHdre9kVdyxERERERGQ1LixERERERGQ1LixERERERGQ1nrE4srTzEdR73jv59wxL7SU1tKlxpbaUOt9Mg/rI1PLeADt10dRmpl575n4xddHs2pkv6nJTw0k9Kesg80jdZupTZ+mNsalBzTCeB6DuNHXpl8D+uQhr5y9Ybmp0s2wz7fkYmy5Hm6vYVucMSzeKTVPbzvPMprKmoc0+yXLn73ieJM9tsNyPnfz7GJvj7XiEsWxZFoa1/kNddL6b/SnTZ//K9rhxEq/Va44H5jldLM/029mvOb5Tl063nNnvsh7IJyb/3n6T+W1uOZlnun9NN9BNw96gi+LMM9uY6be2ye/DeSXs7omdY4O/y7MA7RvGvpbnNmZuS3Pu4/jOtuF81Obv5sKZc0KWhfnP+ZVp5u841tl/8nwQv4M5L/JMVr57dnal/SmY7cZvcv4u4xLGPX/y70mb8/Nb0eqc0A10ngN5VvmdLMUdCxERERERWY0LCxERERERWY1SqCMLpQ3NnR3J7cF22zZJt24zCVXKkZ4C+10RRtenuc3O7dTcfr5nEi+3RWc3w2aa7SZg2s3dX9vqnrXFGCe6j+Tz7DbnTD+3irnF326UbdvxTZpBqUxKtNiOTWJGWt1tcxv8LB8ZL91tEm6LtzrI8cB8NlfGLV98d5OItDph/2k3jmf6rJMci3wf08h6nLl6zN+l60e290yqN8ZctjTGZlvlWGljmPliuXNMsW2ae+r8nM5kfRmv9eUm02F57oqwmRSkyVUT5rPVP8n5jn2yzcNtPDQ3su3m7SaHJUvrIKHUKvPPOSLdqS6Vh7F+mhvi1n/y3bPyNPfOWT/Mc6Y3k+CNceL37kEy/02ex3xmvhjGvy+yjmdzmuwLdyxERERERGQ1LixERERERGQ1LixERERERGQ1CszOClKHOKPpIZvONM85UGdPveK3RLzrYafu+hjs4xHW3BdSj9lcAaZ7QUL9btNd061raoephW3uQbNeL4adZSOsr9TJsg4yDbpmzH7BdJiviyPezH1q/i7zNdPLj7Gp+WbYJREvXcCS7EOEZ3iaK1SSZfv0xE5SE8y4LM9tEY8a42y3D03sjEvtdmqW23ho54qoR35GhF0F+w2ws+4uLenz3dne907sKyMeXcrmu58G+5YIY1u1/tPc8TZNNvOSZ8yYZnNjybGRZyrYF3I8Xw07x9vMve17It5SLX37PrRzS6y7LBuf89wP2y37OcPauZymx2ed57eCfZT5yrnpmbCz35HmpvayCHv2JF66gb4CdvszrrldXXruJNu3nYPj86URxv5Ll6/pTpjxcsxyHGVfZh1xLOa3m/NpmxNkV9yxEBERERGR1biwEBERERGR1SiFOivI7c5Zs7abVU8Vl3Br+pyJPcYYt8PO22abq0E+p1SJ+WppLHU12Nwv8t3N/Whu41OuktvBzT0ipQEsZ+axyYV462q2x0zq0/pB9i3msblybWmy3NlXW7/j77Js90/C2lZ95n+ppLBJAxrsrykja1Ic0iQirJ/sk3x3bv8397aE5W4Si/a7Fsb6zzy18cy6a22a4631E9JkIS3PTJNzQt4QTZo720bKbWZup7OPzySKJ8vLLGzpXNLm+eYKPcfDzMVv1kFL/7Mm8cZY7lp35vZ2jM15Mscp85JlYx9d6oK6ke3dvitkqbwt5X+UlTVJYXMHP3OLnmlm2Zhnyp1SMrXUzb7sijsWIiIiIiKyGhcWIiIiIiKyGhcWIiIiIiKyGs9YHFmoJ0396MwlayN1h+2cA/WLzMetEY/nC9KF5nWw091fO9vAsFbOR0zsMU50e0iYJl3iZf0wz6n1pIY69bvNhSPzeQfs1LGyzm+MMOrn27kQkq4MmedtXOnyffdE2EzvnNphljXz1c6WMC+tbViedn6n6XCzTvhutlvTlN8RYU2nfhdsli3PaXBcZrlbv6O74ewzbx0nJ9Pgeap0rco+01wUs+++P+LRJWW2G9PPdmM9ZP9hO/JsUrYN3Y+28045D583iZdnLJhmzpPMf6Z/HHae5aI+v7l8xOON6AAADKhJREFUJdk2JPsyWapZzzmBeWxnAdJdKKGuPss2q4MxNtsm3RdzDHBMpVvU28ccvi+/b5zj8jwH24D9LvP4xxM7WXpWIuuYdZDfT46/dFXOMZVj+P5JvMwj08+y0a145ot54Xcl6+7DJUz2gTsWIiIiIiKyGhcWIiIiIiKyGqVQR5bm+nEmf9pmy3SpXGjmQi5pt343aUlzKcc8ZhrcCk03hM2t6CNht9uvmWZKlZZKdtoWMMuWMofbJ/HG6Lepz+QMmY/mirFBOUn+buZicekN3WN0WR/74cz1bD7nVvqurmjvn9htes0+ubTOWQft5vZk5qJzjM3+mmlyDDQZWXNDzDpZ2ifzRmL+LtuNkpEPTtIb40TZA8szu5U+afPYNr+bkXKbmfRzjM15J9OfSd+aW9eEfTTrvM3fS9MnmS/O3+0m+ubul2m2sZ55ZD9h2TJ9ulDP8dAkSG0umUmo8t2zPCYZNpuHm0vfhO/Ob127RZ7l/vjk3zONpM0zzAvHRo5Ltlv7O0d2xR0LERERERFZjQsLERERERFZjVKos4JsRm5VNu9O503iZZopIaBsgFuJV0a818K+JMKeATs9UjQPOdwm5XZnbmc3TyLtRtbZ7Z3ttt/MI72f5I3jjPvICHvUxE4ZFtsxJSNNUsPtdJYn25d5TA82rJPsT61OZvK53Iput3KfC7tJnGb1OMbmtnuWu0kUmvRwJq9KD0usk8wX67J5TmKdZBrsd9vc4Mx8ZrtdAfsnYOd44Phu+U9JCmUus1uHM17zpraNLISwftLTTZMvtjadxWv5amnkfHEV7JRQ3TZ5d45ZjsvWR5beat1uJl96i/gYXWI2u4U957smw/r4xB5jfuN1k6LdUt6V7ca2eXpJk1KoVpYmHWpw3Dwhwtq7OT6yTjiG89vEumwSP+YlZcaXw35XhDGf/G6llI7lzn4t+8AdCxERERERWY0LCxERERERWY0LCxERERERWY1nLI4s1BNmM8707O023qbhTJdyqQt9kLwp+bISRk1qc02asAxN009da2p7l9bJhyb/PsZmnvM8x1L3jlmv1IXyRu3UvzLezRFGjXbTJpNWtnaTcXMV21yOtvMKs/TG2LylNstCrSzznOccGJYuHNsYaG5kZ7/Lf2/nL2bnX8bY7Ces19QOp5Z7lpfsd+wnmS+6Nm5zDtsm87G0XzDebWNO5pFzS9ZdK/csH5lGOxvA8uTN0udM4mX+We68pbn1O479nCOYJvO11GVzkmNl5mK5pb/N+ziPtf5Ezp38+xgnznGknQdjWOrx2e+yXzNu3q7NNLO9mQ7TaGcNTzdtrmr5yu/i7BuQaTD9/Lth9q4xNs8ZtRvrmWabM2VX3LEQEREREZHVuLAQEREREZHVKIU6sjSXrLNt3ybLadv/uQ2eW7sPckc8021fShua9KOVjWGzm6pP9rsZLX1umWbdzW4wHeNEl5pkJuUaY9O1HrfE222/uQXfZFgzCVK2b3t3q/OZG8hk6Q3OTYaVErbZDbytLZokpZHx+O5PlngsW5OWZJ5nt9Q2N5mN5lY3+9MHYbcb0ynpaDfRp7RhJolorj2bdK+1TfvcNQnH7KbnzEuTHnKsp2vYWXqZr+ZSu72bfa2512zjrd3gPPvNGJvt225Ybt+wlAuxn+86z5NWJ811dUoRZ6REjvnP9r53Em9ff6rN5ujMR5vHmoviWZ/PsPMmdv6u9bucZ5gXppH9ukkzZR+4YyEiIiIiIqtxYSEiIiIiIqtxYSEiIiIiIqvxjMVZQWogZ3rS/PfmRpFh6cpwpr9Mjff7S9glsJuLxcwzn6lFz7I0l3gNxn0i7NSLUpfbtLdZd8xnakTvn4SlBphnXDL9mY51jM16ZrxtzqdknknTclPnyj6T725nM5qLQsK+1vTTeZahnTkiTUfezgmQHA/3TuwxNtuYbdjOVGQeWdac9lu7XQ77zbBzzrl4kt4Ym3WS/ZXa7qafZprZZ9rZlablnmn8890My3mG5cn2YPu3cUOyTzKNbFO6885zG5yDmP92PqWNqVZ3/B5k/TT31LN4ma/sa4+YhLWzAKd6H2F/4vmI7FtPKmEcY9mmPGeU/WJ2xux0uJdd+udfxmNfaPWYY51x2TbtnF3WzxWw89wm0+HYeF/EY5pLz6XJNrhjISIiIiIiq3FhISIiIiIiq1EKdWThtnJzR9pYGi+3C2fSj9y2bK4YU84wSz/zuFQO01zWNigFYbnTNWmruyajIU1ecP7k3zMvKV2ZpTHG7jfkErZbq5NWtpaPpdv/ecvurF9sc2Mt47b2bZIOprFU1pW/S3nBIyfxmtyssY3kJeUMD9KkVpmPJrubucbOeE3a2ORu50zspPXddqNz62ush6W3zWdZWr+g/OmCkmZzT73UHe9S2WxLo/W7rBPOvRdH2Cyd5rZ8qbvc/B1lp9lXLx/LaHN0kydTftskR/tgG6nV0n7S5J7NbfZnTewxNuuytXdjJqeWfeGOhYiIiIiIrMaFhYiIiIiIrMaFhYiIiIiIrOZhDzzwwANnOhOyPQ972Ofg6f+c5rfl+vMpk3hPjGe61UuXstQ5/o8I+2zYqf38k8m7G5l/6jZb3f012OlO8H8tfPdnxzPf91cijGdUWF/N5e5/jTCWNfWpfPfnwv7fYzlMP7XnTOdzI4ztyHxk/ZBsG7ZHar5/E/YXwE6db9bXUphmaqb/+w7pZZ9s/Zrl5rva/wtleq3dmptXutR8T3kf6yTnAWqocxxRF03ddZ7tYPq/HmF/aZLGGGPcB3vp3NHmu3w3+0WON85rHA+XRjy6hl06r2SaF0UY3aSyPVKHfgfsVj9tHiNZdzwfkeNk6Vzyt0u+boedfWZXd7OcW66EfUtJv7XbX49ntk07S8Ty3BPxGLbrnLYP/nI8s3/dF2Gz/vUF8fw/YWd/4jzQzlqxftItLdv+qo2QBx749yfPomyFOxYiIiIiIrIaFxYiIiIiIrIapVBHlFs+9uf2e3OHliy9+DQ4Z6F3u/uZ5q4e8Uq+Wj42gtqFr3tgi6rrr15aX7vW69KMIs16H+429VryvEO2tmOH+trmHuCdE9rLC1a+axtOt1fLfbDr5c7JrKy7doxd56Cl79tbh52kuY+2jzwu/o60dE7HWNnD3Hs6mmPf89hSav1v8cO9zPOnpWJPnv53PXf5q2U57liIiIiIiMhqXFiIiIiIiMhqlEKJiIiIiMhq3LEQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHVuLAQEREREZHV/D8S5TMEl8HNvQAAAABJRU5ErkJggg==", + "image/png": "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", "text/plain": [ "
" ] @@ -83,73 +83,34 @@ "class TimeVariantDataAugmentation:\n", " @staticmethod\n", " def window_slicing(data, slice_percentage=0.9):\n", - " \"\"\"\n", - " Perform window slicing on time-variant data\n", - " \n", - " Args:\n", - " data (torch.Tensor): Input time-variant data\n", - " slice_percentage (float): Percentage of original width to keep (default: 0.9)\n", - " \n", - " Returns:\n", - " torch.Tensor: Augmented time-variant data\n", - " \"\"\"\n", - " # Ensure input is a torch tensor\n", " if not isinstance(data, torch.Tensor):\n", " data = torch.tensor(data, dtype=torch.float32)\n", " \n", - " # Get original dimensions\n", " orig_width = data.shape[1]\n", - " \n", - " # Calculate slice width\n", " slice_width = int(orig_width * slice_percentage)\n", - " \n", - " # Generate random starting point\n", " max_start = orig_width - slice_width\n", " start_point = torch.randint(0, max_start + 1, (1,)).item()\n", - " \n", - " # Extract slice\n", " sliced_data = data[:, start_point:start_point + slice_width]\n", - " \n", - " # Interpolate back to original size\n", + "\n", " augmented_data = torch.nn.functional.interpolate(\n", " sliced_data.unsqueeze(0), \n", " size=orig_width, \n", " mode='linear', \n", " align_corners=False\n", " ).squeeze(0)\n", - " \n", " return augmented_data\n", "\n", " @staticmethod\n", " def window_warping(data, warping_factors=[0.5, 2.0]):\n", - " \"\"\"\n", - " Perform window warping on time-variant data\n", - " \n", - " Args:\n", - " data (torch.Tensor): Input time-variant data\n", - " warping_factors (list): Warping factors to apply (default: [0.5, 2.0])\n", - " \n", - " Returns:\n", - " torch.Tensor: Augmented time-variant data\n", - " \"\"\"\n", - " # Ensure input is a torch tensor\n", " if not isinstance(data, torch.Tensor):\n", " data = torch.tensor(data, dtype=torch.float32)\n", " \n", - " # Get original dimensions\n", " orig_width = data.shape[1]\n", - " \n", - " # Select window (10% of original width)\n", " window_width = int(orig_width * 0.1)\n", " start_point = torch.randint(0, orig_width - window_width + 1, (1,)).item()\n", - " \n", - " # Select random warping factor\n", " warping_factor = np.random.choice(warping_factors)\n", - " \n", - " # Extract window\n", " window = data[:, start_point:start_point + window_width]\n", - " \n", - " # Warp window\n", + "\n", " warped_window_width = int(window_width * warping_factor)\n", " warped_window = torch.nn.functional.interpolate(\n", " window.unsqueeze(0), \n", @@ -157,12 +118,9 @@ " mode='linear', \n", " align_corners=False\n", " ).squeeze(0)\n", - " \n", - " # Reconstruct full data\n", + "\n", " augmented_data = data.clone()\n", " end_point = start_point + warped_window_width\n", - " \n", - " # Replace window section with warped window\n", " if warped_window_width < window_width:\n", " augmented_data[:, start_point:end_point] = warped_window\n", " else:\n", @@ -172,139 +130,84 @@ "\n", " @staticmethod\n", " def jittering(data, mean=0, std_dev=0.03):\n", - " \"\"\"\n", - " Add Gaussian noise to time-variant data\n", - " \n", - " Args:\n", - " data (torch.Tensor): Input time-variant data\n", - " mean (float): Mean of Gaussian noise\n", - " std_dev (float): Standard deviation of Gaussian noise\n", - " \n", - " Returns:\n", - " torch.Tensor: Augmented time-variant data\n", - " \"\"\"\n", - " # Ensure input is a torch tensor\n", " if not isinstance(data, torch.Tensor):\n", " data = torch.tensor(data, dtype=torch.float32)\n", - " \n", - " # Generate Gaussian noise\n", " noise = torch.normal(mean, std_dev, size=data.shape)\n", - " \n", - " # Add noise to original data\n", - " augmented_data = data + noise\n", - " \n", - " return augmented_data\n", + " return data + noise\n", "\n", "# %% Stochastic Augmentation Class\n", "class StochasticAugmentation:\n", " def __init__(self, augmentation_methods):\n", - " \"\"\"\n", - " Create a stochastic augmentation pipeline\n", - " \n", - " Args:\n", - " augmentation_methods (list): List of augmentation methods to choose from\n", - " \"\"\"\n", " self.augmentation_methods = augmentation_methods\n", " \n", " def __call__(self, data):\n", - " \"\"\"\n", - " Randomly select and apply one augmentation method\n", - " \n", - " Args:\n", - " data (torch.Tensor): Input time-variant data\n", - " \n", - " Returns:\n", - " torch.Tensor: Augmented time-variant data\n", - " \"\"\"\n", - " # Randomly select an augmentation method\n", " method = np.random.choice(self.augmentation_methods)\n", - " \n", - " # Apply the selected method\n", " return method(data)\n", "\n", "# %% Custom Image Dataset Class\n", "class CustomImageDataset(Dataset):\n", - " def __init__(self, base_dir, subfolders, transform=None, label_encoder=None):\n", + " def __init__(self, base_dir, subfolders, transform=None):\n", " self.base_dir = base_dir\n", " self.subfolders = subfolders\n", " self.transform = transform\n", " self.image_paths = []\n", " self.labels = []\n", + "\n", + " # Initialize label encoder\n", + " self.label_encoder = LabelEncoder()\n", + " self.label_encoder.fit(subfolders)\n", " \n", + " # Collect image paths and labels\n", " for subfolder in subfolders:\n", " folder_path = os.path.join(base_dir, subfolder)\n", - " label = subfolder\n", - " \n", " for img_name in os.listdir(folder_path):\n", " if img_name.lower().endswith(('.png', '.jpg', '.jpeg')):\n", " img_path = os.path.join(folder_path, img_name)\n", " self.image_paths.append(img_path)\n", - " self.labels.append(label)\n", - " \n", - " if label_encoder is not None:\n", - " self.label_encoder = label_encoder\n", - " self.labels = self.label_encoder.transform(self.labels)\n", + " self.labels.append(subfolder)\n", " \n", + " # Encode labels\n", + " self.labels = self.label_encoder.transform(self.labels)\n", + " \n", " def __len__(self):\n", " return len(self.image_paths)\n", " \n", " def __getitem__(self, idx):\n", " img_path = self.image_paths[idx]\n", " image = Image.open(img_path).convert('RGB')\n", + " label = self.labels[idx]\n", " \n", " if self.transform:\n", " image = self.transform(image)\n", " \n", - " label = self.labels[idx]\n", " return image, label\n", "\n", - "# %% Augmentation Visualization Function\n", + "# %% Visualization Function\n", "def visualize_augmentation():\n", - " # Create more complex sample data with multiple frequencies\n", " t = torch.linspace(0, 10*torch.pi, 200)\n", " sample_data = torch.sin(t) + 0.5 * torch.sin(3*t) + 0.25 * torch.sin(5*t)\n", " sample_data = sample_data.unsqueeze(0)\n", - " \n", - " # Create augmentation methods with specific transformations\n", + "\n", " augmentation_methods = [\n", " (\"Window Slicing\", lambda x: TimeVariantDataAugmentation.window_slicing(x, slice_percentage=0.7)),\n", " (\"Window Warping\", lambda x: TimeVariantDataAugmentation.window_warping(x, warping_factors=[0.5])),\n", " (\"Jittering\", lambda x: TimeVariantDataAugmentation.jittering(x, mean=0, std_dev=0.2))\n", " ]\n", " \n", - " # Visualization\n", " plt.figure(figsize=(15, 12))\n", - " \n", - " # Original Data\n", " plt.subplot(4, 1, 1)\n", " plt.title(\"Original Data\")\n", " plt.plot(sample_data.numpy().flatten(), label='Original')\n", " plt.legend()\n", - " plt.xlabel(\"Time Steps\")\n", - " plt.ylabel(\"Value\")\n", " \n", - " # Augmentations with Difference Plots\n", " for i, (name, aug_method) in enumerate(augmentation_methods, start=2):\n", - " # Apply augmentation\n", " augmented_data = aug_method(sample_data)\n", - " \n", - " # Augmented Data Subplot\n", " plt.subplot(4, 1, i)\n", " plt.title(f\"{name} Augmentation\")\n", " plt.plot(sample_data.numpy().flatten(), label='Original', alpha=0.5)\n", " plt.plot(augmented_data.numpy().flatten(), label='Augmented', color='red')\n", " plt.legend()\n", - " plt.xlabel(\"Time Steps\")\n", - " plt.ylabel(\"Value\")\n", - " \n", - " # Calculate and display difference metrics\n", - " mse = torch.nn.functional.mse_loss(sample_data, augmented_data).item()\n", - " max_diff = torch.max(torch.abs(augmented_data - sample_data)).item()\n", - " \n", - " plt.text(0.02, 0.95, f\"MSE: {mse:.4f}\\nMax Diff: {max_diff:.4f}\", \n", - " transform=plt.gca().transAxes, \n", - " verticalalignment='top')\n", - " \n", + "\n", " plt.tight_layout()\n", " plt.show()\n", "\n", @@ -319,49 +222,45 @@ " \"rc_plane\"\n", "]\n", "\n", - "# %% Prepare Label Encoder\n", - "label_encoder = LabelEncoder()\n", - "label_encoder.fit(subfolders)\n", - "\n", "# %% Define transformations\n", "transform = transforms.Compose([\n", " transforms.Resize((224, 224)),\n", " transforms.RandomHorizontalFlip(),\n", " transforms.ColorJitter(brightness=0.2, contrast=0.2),\n", " transforms.ToTensor(),\n", - " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # Normalize with ImageNet stats\n", + " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", "])\n", "\n", "# %% Load dataset\n", - "dataset = CustomImageDataset(base_dir, subfolders, transform=transform, label_encoder=label_encoder)\n", + "dataset = CustomImageDataset(base_dir, subfolders, transform=transform)\n", "\n", "# %% Seed and shuffle\n", - "torch.manual_seed(42)\n", - "indices = torch.randperm(len(dataset))\n", - "shuffled_dataset = Subset(dataset, indices)\n", + "# Split dataset into train, validation, and test (without shuffling initially)\n", + "train_size = int(0.85 * len(dataset))\n", + "val_size = int(0.05 * len(dataset))\n", + "test_size = len(dataset) - train_size - val_size\n", + "train_dataset, val_dataset, test_dataset = random_split(dataset, [train_size, val_size, test_size])\n", "\n", - "# %% Split dataset\n", - "train_size = int(0.85 * len(shuffled_dataset))\n", - "val_size = int(0.05 * len(shuffled_dataset))\n", - "test_size = len(shuffled_dataset) - train_size - val_size\n", - "train_dataset, val_dataset, test_dataset = random_split(shuffled_dataset, [train_size, val_size, test_size])\n", + "# Manually shuffle the training dataset\n", + "torch.manual_seed(42)\n", + "train_indices = torch.randperm(len(train_dataset))\n", + "train_dataset = Subset(train_dataset, train_indices)\n", "\n", - "# %% Create data loaders\n", + "# Data loaders\n", "train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)\n", "val_loader = DataLoader(val_dataset, batch_size=8, shuffle=False)\n", "test_loader = DataLoader(test_dataset, batch_size=8, shuffle=False)\n", "\n", - "# %% Visualize Augmentation\n", + "# %% Visualization\n", "visualize_augmentation()\n", "\n", - "# %% Visualize one sample from dataset\n", + "# %% Sample Visualization\n", "sample_image, sample_label = next(iter(train_loader))\n", - "sample_image = sample_image[0]\n", - "sample_image = sample_image.permute(1, 2, 0)\n", + "sample_image = sample_image[0].permute(1, 2, 0)\n", "\n", "plt.figure(figsize=(10, 10))\n", "plt.imshow(sample_image)\n", - "plt.title(f\"Sample Image (Label: {sample_label[0]})\")\n", + "plt.title(f\"Sample Image (Label: {sample_label})\")\n", "plt.axis('off')\n", "plt.show()\n", "\n", @@ -370,45 +269,12 @@ "print(f\"Total Images: {len(dataset)}\")\n", "print(f\"Training Images: {len(train_dataset)}\")\n", "print(f\"Validation Images: {len(val_dataset)}\")\n", - "print(f\"Test Images: {len(test_dataset)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "# Load dataset\n", - "dataset = CustomImageDataset(base_dir, subfolders, transform=transform, label_encoder=label_encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "train_size = int(0.85 * len(dataset))\n", - "val_size = int(0.05 * len(dataset))\n", - "test_size = len(dataset) - train_size - val_size\n", - "train_dataset, val_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, val_size, test_size])" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)\n", - "val_loader = DataLoader(val_dataset, batch_size=8, shuffle=False)\n", - "test_loader = DataLoader(test_dataset, batch_size=8, shuffle=False)" + "print(f\"Test Images: {len(test_dataset)}\")\n" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -498,713 +364,915 @@ " x = self.fc2(x)\n", " \n", " return x\n", - "\n", - "# Train and Validation Function\n", - "def train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, num_epochs=20, device=\"cuda\" if torch.cuda.is_available() else \"cpu\"):\n", - " model = model.to(device)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CustomCNNWithAttention(\n", + " (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (bn3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (pool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (conv4): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (bn4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (pool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (attention): SpatialAttention(\n", + " (conv1): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))\n", + " (conv2): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (sigmoid): Sigmoid()\n", + " )\n", + " (global_avg_pool): AdaptiveAvgPool2d(output_size=(1, 1))\n", + " (fc1): Linear(in_features=128, out_features=512, bias=True)\n", + " (fc2): Linear(in_features=512, out_features=6, bias=True)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", + ")" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Device configuration\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "model_deepercnn = CustomCNNWithAttention(num_classes=6)\n", + "model_deepercnn.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\optim\\lr_scheduler.py:60: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# Loss and optimizer for Deeper CNN\n", + "criterion = nn.CrossEntropyLoss()\n", + "optimizer_deepercnn = optim.Adam(model_deepercnn.parameters(), lr=0.001)\n", + "scheduler_deepercnn = optim.lr_scheduler.ReduceLROnPlateau(optimizer_deepercnn, mode='min', factor=0.5, patience=2, min_lr=1e-7, verbose=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Training function\n", + "def train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, num_epochs=15):\n", + " model.train()\n", " best_acc = 0.0\n", - "\n", + " \n", " for epoch in range(num_epochs):\n", - " print(f\"Epoch {epoch + 1}/{num_epochs}\")\n", - " print(\"-\" * 30)\n", - "\n", - " # Training Phase\n", - " model.train()\n", " running_loss = 0.0\n", " running_corrects = 0\n", - " total_train = 0\n", - "\n", - " for inputs, labels in tqdm(train_loader, desc=\"Training\"):\n", - " inputs = inputs.to(device)\n", - " labels = labels.to(device).long() # Ensure labels are of type torch.long\n", - "\n", - " # Zero the parameter gradients\n", + " \n", + " for images, labels in train_loader:\n", + " images = images.to(device)\n", + " labels = labels.to(device, dtype=torch.long)\n", + " \n", " optimizer.zero_grad()\n", - "\n", - " # Forward Pass\n", - " outputs = model(inputs)\n", + " \n", + " outputs = model(images)\n", " loss = criterion(outputs, labels)\n", - " _, preds = torch.max(outputs, 1)\n", - "\n", - " # Backward Pass and Optimization\n", " loss.backward()\n", " optimizer.step()\n", - "\n", - " # Track statistics\n", - " running_loss += loss.item() * inputs.size(0)\n", + " \n", + " _, preds = torch.max(outputs, 1)\n", + " running_loss += loss.item() * images.size(0)\n", " running_corrects += torch.sum(preds == labels.data)\n", - " total_train += labels.size(0)\n", - "\n", - " # Adjust learning rate\n", - " scheduler.step()\n", - "\n", - " epoch_loss = running_loss / total_train\n", - " epoch_acc = running_corrects.double() / total_train\n", - "\n", - " print(f\"Training Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}\")\n", - "\n", - " # Validation Phase\n", + " \n", + " epoch_loss = running_loss / len(train_loader.dataset)\n", + " epoch_acc = running_corrects.double() / len(train_loader.dataset)\n", + " \n", + " print(f\"Epoch [{epoch+1}/{num_epochs}], Loss: {epoch_loss:.4f}, Accuracy: {epoch_acc:.4f}\")\n", + " \n", " model.eval()\n", - " running_loss = 0.0\n", - " running_corrects = 0\n", - " total_val = 0\n", - "\n", + " val_loss = 0.0\n", + " val_corrects = 0\n", + " \n", " with torch.no_grad():\n", - " for inputs, labels in tqdm(val_loader, desc=\"Validation\"):\n", - " inputs = inputs.to(device)\n", - " labels = labels.to(device).long() # Ensure labels are of type torch.long\n", - "\n", - " # Forward Pass\n", - " outputs = model(inputs)\n", + " for images, labels in val_loader:\n", + " images = images.to(device)\n", + " labels = labels.to(device, dtype=torch.long)\n", + " \n", + " outputs = model(images)\n", " loss = criterion(outputs, labels)\n", + " \n", " _, preds = torch.max(outputs, 1)\n", - "\n", - " # Track statistics\n", - " running_loss += loss.item() * inputs.size(0)\n", - " running_corrects += torch.sum(preds == labels.data)\n", - " total_val += labels.size(0)\n", - "\n", - " epoch_val_loss = running_loss / total_val\n", - " epoch_val_acc = running_corrects.double() / total_val\n", - "\n", - " print(f\"Validation Loss: {epoch_val_loss:.4f} Acc: {epoch_val_acc:.4f}\")\n", - "\n", + " val_loss += loss.item() * images.size(0)\n", + " val_corrects += torch.sum(preds == labels.data)\n", + " \n", + " val_loss /= len(val_loader.dataset)\n", + " val_acc = val_corrects.double() / len(val_loader.dataset)\n", + " print(f\"Validation Loss: {val_loss:.4f}, Accuracy: {val_acc:.4f}\")\n", + " \n", + " scheduler.step(val_loss)\n", + " \n", " # Save the best model\n", - " if epoch_val_acc > best_acc:\n", - " best_acc = epoch_val_acc\n", - " torch.save(model, \"customcnnwithAttention_full.pth\")\n", - "\n", - "\n", - " print(f\"Training complete. Best Validation Acc: {best_acc:.4f}\")\n", - "\n", - "\n", - " \n", - "\n" + " if val_acc > best_acc:\n", + " best_acc = val_acc\n", + " torch.save(model.state_dict(), f\"best_model_{model.__class__.__name__}.pt\")\n", + " \n", + " model.train()" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/20], Loss: 0.8492, Accuracy: 0.6717\n", + "Validation Loss: 0.7848, Accuracy: 0.6983\n", + "Epoch [2/20], Loss: 0.5007, Accuracy: 0.8165\n", + "Validation Loss: 0.4964, Accuracy: 0.8182\n", + "Epoch [3/20], Loss: 0.4094, Accuracy: 0.8563\n", + "Validation Loss: 0.2806, Accuracy: 0.9050\n", + "Epoch [4/20], Loss: 0.3171, Accuracy: 0.8961\n", + "Validation Loss: 0.1852, Accuracy: 0.9463\n", + "Epoch [5/20], Loss: 0.2573, Accuracy: 0.9124\n", + "Validation Loss: 0.1828, Accuracy: 0.9545\n", + "Epoch [6/20], Loss: 0.2050, Accuracy: 0.9347\n", + "Validation Loss: 0.2884, Accuracy: 0.9050\n", + "Epoch [7/20], Loss: 0.1952, Accuracy: 0.9357\n", + "Validation Loss: 0.1646, Accuracy: 0.9669\n", + "Epoch [8/20], Loss: 0.1614, Accuracy: 0.9476\n", + "Validation Loss: 0.1421, Accuracy: 0.9545\n", + "Epoch [9/20], Loss: 0.1630, Accuracy: 0.9473\n", + "Validation Loss: 0.1166, Accuracy: 0.9669\n", + "Epoch [10/20], Loss: 0.1302, Accuracy: 0.9546\n", + "Validation Loss: 0.1715, Accuracy: 0.9545\n", + "Epoch [11/20], Loss: 0.1230, Accuracy: 0.9607\n", + "Validation Loss: 0.2023, Accuracy: 0.9463\n", + "Epoch [12/20], Loss: 0.1150, Accuracy: 0.9592\n", + "Validation Loss: 0.0821, Accuracy: 0.9793\n", + "Epoch [13/20], Loss: 0.1181, Accuracy: 0.9592\n", + "Validation Loss: 0.0809, Accuracy: 0.9669\n", + "Epoch [14/20], Loss: 0.0897, Accuracy: 0.9728\n", + "Validation Loss: 0.0824, Accuracy: 0.9752\n", + "Epoch [15/20], Loss: 0.1029, Accuracy: 0.9655\n", + "Validation Loss: 0.0687, Accuracy: 0.9752\n", + "Epoch [16/20], Loss: 0.0978, Accuracy: 0.9687\n", + "Validation Loss: 0.1202, Accuracy: 0.9628\n", + "Epoch [17/20], Loss: 0.0955, Accuracy: 0.9699\n", + "Validation Loss: 0.0711, Accuracy: 0.9752\n", + "Epoch [18/20], Loss: 0.0624, Accuracy: 0.9799\n", + "Validation Loss: 0.0444, Accuracy: 0.9876\n", + "Epoch [19/20], Loss: 0.0709, Accuracy: 0.9777\n", + "Validation Loss: 0.0570, Accuracy: 0.9752\n", + "Epoch [20/20], Loss: 0.0718, Accuracy: 0.9786\n", + "Validation Loss: 0.0737, Accuracy: 0.9669\n" + ] + } + ], "source": [ - "model_deepercnn = CustomCNNWithAttention(num_classes=6)\n", - "criterion = nn.CrossEntropyLoss()\n", - "\n", - "optimizer_deepercnn = optim.Adam(model_deepercnn.parameters(), lr=0.001)\n", - "scheduler_deepercnn = lr_scheduler.StepLR(optimizer_deepercnn, step_size=7, gamma=0.1)\n" + " # Train the model\n", + "torch.cuda.empty_cache() \n", + "train_model(model_deepercnn, train_loader, val_loader, criterion, optimizer_deepercnn, scheduler_deepercnn, num_epochs=20)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 23, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/20\n", - "------------------------------\n" - ] - }, { "name": "stderr", "output_type": "stream", "text": [ - "Training: 100%|██████████| 516/516 [05:05<00:00, 1.69it/s]\n" + "C:\\Users\\incha\\AppData\\Local\\Temp\\ipykernel_22048\\305953074.py:146: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model_deepercnn.load_state_dict(torch.load(\"best_model_CustomCNNWithAttention.pt\"))\n", + "Testing: 100%|██████████| 61/61 [00:08<00:00, 7.00it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Training Loss: 0.8360 Acc: 0.6724\n" + "Test Loss: 0.0582 Test Acc: 0.9774\n", + "Average Inference Time per Batch: 0.1433 seconds\n", + "\n", + "Classification Report:\n", + "{'3 Long Blade Rotor': {'precision': 0.9879518072289156, 'recall': 0.9761904761904762, 'f1-score': 0.9820359281437125, 'support': 84.0}, '3 Short Blade Rotor': {'precision': 0.9655172413793104, 'recall': 0.9545454545454546, 'f1-score': 0.96, 'support': 88.0}, 'Bird': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 88.0}, 'Bird+mini-helicopter': {'precision': 0.9753086419753086, 'recall': 0.9518072289156626, 'f1-score': 0.9634146341463414, 'support': 83.0}, 'Drone': {'precision': 0.9620253164556962, 'recall': 1.0, 'f1-score': 0.9806451612903225, 'support': 76.0}, 'RC Plane': {'precision': 0.9705882352941176, 'recall': 0.9850746268656716, 'f1-score': 0.9777777777777777, 'support': 67.0}, 'accuracy': 0.977366255144033, 'macro avg': {'precision': 0.9768985403888913, 'recall': 0.9779362977528775, 'f1-score': 0.9773122502263591, 'support': 486.0}, 'weighted avg': {'precision': 0.9774638315800364, 'recall': 0.977366255144033, 'f1-score': 0.9773139423197404, 'support': 486.0}}\n", + "\n", + "Confusion Matrix:\n", + "[[82 2 0 0 0 0]\n", + " [ 1 84 0 1 0 2]\n", + " [ 0 0 88 0 0 0]\n", + " [ 0 1 0 79 3 0]\n", + " [ 0 0 0 0 76 0]\n", + " [ 0 0 0 1 0 66]]\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:21<00:00, 1.42it/s]\n" - ] + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 0.6254 Acc: 0.7727\n", - "Epoch 2/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [05:39<00:00, 1.52it/s]\n" + "Macro Average AUC: 0.9996\n" ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.4946 Acc: 0.8168\n" - ] + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:22<00:00, 1.39it/s]\n" - ] + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 0.2939 Acc: 0.9008\n", - "Epoch 3/20\n", - "------------------------------\n" + "Class Distribution in Test Set: {'3 Long Blade Rotor': 84, '3 Short Blade Rotor': 88, 'Bird': 88, 'Bird+mini-helicopter': 83, 'Drone': 76, 'RC Plane': 67}\n", + "Model exported to ONNX format successfully.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [05:50<00:00, 1.47it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.3618 Acc: 0.8777\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:20<00:00, 1.50it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.2257 Acc: 0.9174\n", - "Epoch 4/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [05:17<00:00, 1.62it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.2992 Acc: 0.8947\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:15<00:00, 1.98it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.4048 Acc: 0.8595\n", - "Epoch 5/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [02:36<00:00, 3.30it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.2199 Acc: 0.9274\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:06<00:00, 4.54it/s]\n" - ] - }, + } + ], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import numpy as np\n", + "import tqdm\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc, precision_recall_curve\n", + "import torch.nn.functional as F\n", + "import time\n", + "import json\n", + "from sklearn.calibration import calibration_curve\n", + "\n", + "# Class names for your classification task\n", + "class_names = [\n", + " \"3 Long Blade Rotor\", \n", + " \"3 Short Blade Rotor\", \n", + " \"Bird\", \n", + " \"Bird+mini-helicopter\", \n", + " \"Drone\", \n", + " \"RC Plane\"\n", + "]\n", + "\n", + "# Test Function with Metric Collection\n", + "def test_model(model, test_loader, criterion, device=\"cuda\" if torch.cuda.is_available() else \"cpu\"):\n", + " model = model.to(device)\n", + " model.eval()\n", + "\n", + " running_loss = 0.0\n", + " running_corrects = 0\n", + " total_test = 0\n", + " all_preds = []\n", + " all_labels = []\n", + " all_probs = []\n", + "\n", + " # Start time for inference benchmark\n", + " start_time = time.time()\n", + "\n", + " with torch.no_grad():\n", + " for inputs, labels in tqdm.tqdm(test_loader, desc=\"Testing\"):\n", + " inputs = inputs.to(device)\n", + " labels = labels.to(device).long()\n", + "\n", + " # Forward Pass\n", + " outputs = model(inputs)\n", + " loss = criterion(outputs, labels)\n", + " _, preds = torch.max(outputs, 1)\n", + "\n", + " # Store metrics\n", + " running_loss += loss.item() * inputs.size(0)\n", + " running_corrects += torch.sum(preds == labels.data)\n", + " total_test += labels.size(0)\n", + "\n", + " all_preds.extend(preds.cpu().numpy())\n", + " all_labels.extend(labels.cpu().numpy())\n", + " all_probs.extend(F.softmax(outputs, dim=1).cpu().numpy())\n", + "\n", + " # End time for inference benchmark\n", + " end_time = time.time()\n", + " avg_inference_time = (end_time - start_time) / len(test_loader)\n", + "\n", + " # Overall metrics\n", + " test_loss = running_loss / total_test\n", + " test_acc = running_corrects.double() / total_test\n", + "\n", + " print(f\"Test Loss: {test_loss:.4f} Test Acc: {test_acc:.4f}\")\n", + " print(f\"Average Inference Time per Batch: {avg_inference_time:.4f} seconds\")\n", + "\n", + " # Classification Report\n", + " print(\"\\nClassification Report:\")\n", + " report = classification_report(all_labels, all_preds, target_names=class_names, output_dict=True)\n", + " print(report)\n", + "\n", + " # Save Classification Report to a JSON file\n", + " with open(\"classification_report.json\", \"w\") as f:\n", + " json.dump(report, f, indent=4)\n", + "\n", + " # Confusion Matrix\n", + " cm = confusion_matrix(all_labels, all_preds)\n", + " print(\"\\nConfusion Matrix:\")\n", + " print(cm)\n", + "\n", + " # Save confusion matrix\n", + " np.save(\"confusion_matrix.npy\", cm)\n", + "\n", + " # Plot Confusion Matrix\n", + " plt.figure(figsize=(8, 6))\n", + " plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n", + " plt.title(\"Confusion Matrix\")\n", + " plt.colorbar()\n", + " tick_marks = np.arange(len(class_names))\n", + " plt.xticks(tick_marks, class_names, rotation=45)\n", + " plt.yticks(tick_marks, class_names)\n", + " plt.ylabel('True label')\n", + " plt.xlabel('Predicted label')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # ROC Curve & AUC (for multiclass)\n", + " all_probs = np.array(all_probs)\n", + " if len(class_names) > 2:\n", + " fpr, tpr, roc_auc = {}, {}, {}\n", + " for i in range(len(class_names)):\n", + " fpr[i], tpr[i], _ = roc_curve((np.array(all_labels) == i).astype(int), all_probs[:, i])\n", + " roc_auc[i] = auc(fpr[i], tpr[i])\n", + " plt.plot(fpr[i], tpr[i], lw=2, label=f'{class_names[i]} (AUC = {roc_auc[i]:.2f})')\n", + "\n", + " # Macro average AUC\n", + " macro_auc = np.mean([auc(fpr[i], tpr[i]) for i in range(len(class_names))])\n", + " print(f\"Macro Average AUC: {macro_auc:.4f}\")\n", + "\n", + " plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + " plt.title('Receiver Operating Characteristic (ROC) Curve')\n", + " plt.legend(loc='lower right')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " return all_labels, all_preds, all_probs, macro_auc\n", + "\n", + "# Precision-Recall Curve Plot\n", + "def plot_precision_and_recall(all_labels, all_probs, class_names):\n", + " all_labels = np.array(all_labels)\n", + " all_probs = np.array(all_probs)\n", + "\n", + " plt.figure(figsize=(12, 6))\n", + " for i, class_name in enumerate(class_names):\n", + " precision, recall, _ = precision_recall_curve((all_labels == i).astype(int), all_probs[:, i])\n", + " plt.plot(recall, precision, lw=2, label=f'{class_name} (Precision-Recall)')\n", + " plt.xlabel('Recall')\n", + " plt.ylabel('Precision')\n", + " plt.title('Precision-Recall Curve for Each Class')\n", + " plt.legend(loc='lower left')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "# Check for class distribution (Imbalanced Data Check)\n", + "def check_class_distribution(all_labels, class_names):\n", + " unique, counts = np.unique(all_labels, return_counts=True)\n", + " class_distribution = dict(zip(class_names, counts))\n", + " print(\"Class Distribution in Test Set:\", class_distribution)\n", + "\n", + "# Example Usage\n", + "if __name__ == \"__main__\":\n", + " # Initialize Model and Data\n", + " model_deepercnn = CustomCNNWithAttention(num_classes=len(class_names))\n", + " model_deepercnn.load_state_dict(torch.load(\"best_model_CustomCNNWithAttention.pt\"))\n", + " criterion = nn.CrossEntropyLoss()\n", + "\n", + " # Test Model\n", + " all_labels, all_preds, all_probs, macro_auc = test_model(model_deepercnn, test_loader, criterion)\n", + "\n", + " # Plot Precision-Recall Curves\n", + " plot_precision_and_recall(all_labels, all_probs, class_names)\n", + "\n", + " # Check for Class Imbalance\n", + " check_class_distribution(all_labels, class_names)\n", + "\n", + " # Prepare for ONNX Conversion (Optional)\n", + " dummy_input = torch.randn(1, 3, 224, 224, device=\"cuda\" if torch.cuda.is_available() else \"cpu\") # Adjust for your input size\n", + " torch.onnx.export(model_deepercnn, dummy_input, \"cnnwithattention.onnx\", input_names=['input'], output_names=['output'])\n", + "\n", + " print(\"Model exported to ONNX format successfully.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 0.1676 Acc: 0.9463\n", - "Epoch 6/20\n", - "------------------------------\n" + "Epoch 1/15\n", + "----------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Training: 100%|██████████| 516/516 [01:57<00:00, 4.37it/s]\n" + "Training: 100%|██████████| 516/516 [01:41<00:00, 5.10it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Training Loss: 0.2032 Acc: 0.9304\n" + "Training Loss: 1.5232 Acc: 0.3421\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Validation: 100%|██████████| 31/31 [00:05<00:00, 5.74it/s]\n" + "Validation: 100%|██████████| 31/31 [00:05<00:00, 5.94it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 0.1988 Acc: 0.9256\n", - "Epoch 7/20\n", - "------------------------------\n" + "Validation Loss: 1.2270 Acc: 0.4835\n", + "Model saved as best_model.pth\n", + "Epoch 2/15\n", + "----------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Training: 100%|██████████| 516/516 [01:48<00:00, 4.76it/s]\n" + "Training: 100%|██████████| 516/516 [01:31<00:00, 5.67it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Training Loss: 0.1855 Acc: 0.9367\n" + "Training Loss: 1.0247 Acc: 0.5773\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Validation: 100%|██████████| 31/31 [00:06<00:00, 5.02it/s]\n" + "Validation: 100%|██████████| 31/31 [00:05<00:00, 6.01it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 0.1505 Acc: 0.9380\n", - "Epoch 8/20\n", - "------------------------------\n" + "Validation Loss: 0.8443 Acc: 0.6736\n", + "Model saved as best_model.pth\n", + "Epoch 3/15\n", + "----------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Training: 100%|██████████| 516/516 [01:53<00:00, 4.53it/s]\n" + "Training: 100%|██████████| 516/516 [01:31<00:00, 5.63it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Training Loss: 0.0934 Acc: 0.9719\n" + "Training Loss: 0.8996 Acc: 0.6282\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Validation: 100%|██████████| 31/31 [00:05<00:00, 5.75it/s]\n" + "Validation: 100%|██████████| 31/31 [00:05<00:00, 5.84it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 0.0882 Acc: 0.9793\n", - "Epoch 9/20\n", - "------------------------------\n" + "Validation Loss: 1.0284 Acc: 0.5702\n", + "Epoch 4/15\n", + "----------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Training: 100%|██████████| 516/516 [01:57<00:00, 4.40it/s]\n" + "Training: 100%|██████████| 516/516 [01:32<00:00, 5.58it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Training Loss: 0.0813 Acc: 0.9760\n" + "Training Loss: 0.6967 Acc: 0.7156\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Validation: 100%|██████████| 31/31 [00:06<00:00, 5.05it/s]\n" + "Validation: 100%|██████████| 31/31 [00:05<00:00, 6.09it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 0.0824 Acc: 0.9752\n", - "Epoch 10/20\n", - "------------------------------\n" + "Validation Loss: 2.3352 Acc: 0.3471\n", + "Epoch 5/15\n", + "----------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Training: 100%|██████████| 516/516 [01:53<00:00, 4.56it/s]\n" + "Training: 100%|██████████| 516/516 [01:28<00:00, 5.85it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Training Loss: 0.0719 Acc: 0.9777\n" + "Training Loss: 0.6063 Acc: 0.7586\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Validation: 100%|██████████| 31/31 [00:03<00:00, 7.94it/s]\n" + "Validation: 100%|██████████| 31/31 [00:05<00:00, 6.13it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 0.0950 Acc: 0.9752\n", - "Epoch 11/20\n", - "------------------------------\n" + "Validation Loss: 0.5075 Acc: 0.8017\n", + "Model saved as best_model.pth\n", + "Epoch 6/15\n", + "----------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Training: 100%|██████████| 516/516 [01:12<00:00, 7.11it/s]\n" + "Training: 100%|██████████| 516/516 [01:29<00:00, 5.75it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Training Loss: 0.0635 Acc: 0.9801\n" + "Training Loss: 0.5122 Acc: 0.8015\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Validation: 100%|██████████| 31/31 [00:03<00:00, 9.15it/s]\n" + "Validation: 100%|██████████| 31/31 [00:05<00:00, 6.01it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 0.0710 Acc: 0.9752\n", - "Epoch 12/20\n", - "------------------------------\n" + "Validation Loss: 0.4112 Acc: 0.8471\n", + "Model saved as best_model.pth\n", + "Epoch 7/15\n", + "----------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Training: 100%|██████████| 516/516 [01:16<00:00, 6.77it/s]\n" + "Training: 100%|██████████| 516/516 [01:30<00:00, 5.70it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Training Loss: 0.0640 Acc: 0.9801\n" + "Training Loss: 0.4494 Acc: 0.8287\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Validation: 100%|██████████| 31/31 [00:03<00:00, 8.58it/s]\n" + "Validation: 100%|██████████| 31/31 [00:05<00:00, 6.10it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 0.0865 Acc: 0.9793\n", - "Epoch 13/20\n", - "------------------------------\n" + "Validation Loss: 0.3845 Acc: 0.8843\n", + "Model saved as best_model.pth\n", + "Epoch 8/15\n", + "----------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Training: 100%|██████████| 516/516 [01:32<00:00, 5.59it/s]\n" + "Training: 100%|██████████| 516/516 [01:29<00:00, 5.77it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Training Loss: 0.0533 Acc: 0.9837\n" + "Training Loss: 0.3208 Acc: 0.8898\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Validation: 100%|██████████| 31/31 [00:04<00:00, 6.58it/s]\n" + "Validation: 100%|██████████| 31/31 [00:05<00:00, 6.16it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 0.0667 Acc: 0.9752\n", - "Epoch 14/20\n", - "------------------------------\n" + "Validation Loss: 0.2946 Acc: 0.9008\n", + "Model saved as best_model.pth\n", + "Epoch 9/15\n", + "----------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Training: 100%|██████████| 516/516 [01:38<00:00, 5.22it/s]\n" + "Training: 100%|██████████| 516/516 [01:28<00:00, 5.81it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Training Loss: 0.0523 Acc: 0.9840\n" + "Training Loss: 0.3068 Acc: 0.8925\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Validation: 100%|██████████| 31/31 [00:04<00:00, 6.80it/s]\n" + "Validation: 100%|██████████| 31/31 [00:05<00:00, 6.09it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 0.0704 Acc: 0.9793\n", - "Epoch 15/20\n", - "------------------------------\n" + "Validation Loss: 0.2577 Acc: 0.9215\n", + "Model saved as best_model.pth\n", + "Epoch 10/15\n", + "----------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Training: 100%|██████████| 516/516 [01:33<00:00, 5.54it/s]\n" + "Training: 100%|██████████| 516/516 [01:29<00:00, 5.77it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Training Loss: 0.0432 Acc: 0.9876\n" + "Training Loss: 0.2721 Acc: 0.9107\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Validation: 100%|██████████| 31/31 [00:04<00:00, 6.82it/s]\n" + "Validation: 100%|██████████| 31/31 [00:05<00:00, 6.08it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 0.0670 Acc: 0.9793\n", - "Epoch 16/20\n", - "------------------------------\n" + "Validation Loss: 0.2444 Acc: 0.9256\n", + "Model saved as best_model.pth\n", + "Epoch 11/15\n", + "----------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Training: 100%|██████████| 516/516 [01:34<00:00, 5.48it/s]\n" + "Training: 100%|██████████| 516/516 [01:29<00:00, 5.77it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Training Loss: 0.0459 Acc: 0.9830\n" + "Training Loss: 0.2590 Acc: 0.9107\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Validation: 100%|██████████| 31/31 [00:04<00:00, 7.13it/s]\n" + "Validation: 100%|██████████| 31/31 [00:04<00:00, 6.48it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 0.0663 Acc: 0.9752\n", - "Epoch 17/20\n", - "------------------------------\n" + "Validation Loss: 0.2343 Acc: 0.9215\n", + "Model saved as best_model.pth\n", + "Epoch 12/15\n", + "----------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Training: 100%|██████████| 516/516 [01:33<00:00, 5.52it/s]\n" + "Training: 100%|██████████| 516/516 [01:23<00:00, 6.16it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Training Loss: 0.0412 Acc: 0.9871\n" + "Training Loss: 0.2414 Acc: 0.9211\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Validation: 100%|██████████| 31/31 [00:04<00:00, 7.10it/s]\n" + "Validation: 100%|██████████| 31/31 [00:04<00:00, 6.71it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 0.0779 Acc: 0.9793\n", - "Epoch 18/20\n", - "------------------------------\n" + "Validation Loss: 0.2224 Acc: 0.9298\n", + "Model saved as best_model.pth\n", + "Epoch 13/15\n", + "----------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Training: 100%|██████████| 516/516 [01:28<00:00, 5.83it/s]\n" + "Training: 100%|██████████| 516/516 [01:22<00:00, 6.23it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Training Loss: 0.0432 Acc: 0.9859\n" + "Training Loss: 0.2316 Acc: 0.9219\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Validation: 100%|██████████| 31/31 [00:04<00:00, 7.35it/s]\n" + "Validation: 100%|██████████| 31/31 [00:04<00:00, 7.29it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 0.0831 Acc: 0.9793\n", - "Epoch 19/20\n", - "------------------------------\n" + "Validation Loss: 0.1899 Acc: 0.9463\n", + "Model saved as best_model.pth\n", + "Epoch 14/15\n", + "----------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Training: 100%|██████████| 516/516 [01:43<00:00, 5.00it/s]\n" + "Training: 100%|██████████| 516/516 [01:18<00:00, 6.58it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Training Loss: 0.0388 Acc: 0.9874\n" + "Training Loss: 0.2248 Acc: 0.9250\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Validation: 100%|██████████| 31/31 [00:05<00:00, 5.94it/s]\n" + "Validation: 100%|██████████| 31/31 [00:04<00:00, 6.55it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 0.0580 Acc: 0.9793\n", - "Epoch 20/20\n", - "------------------------------\n" + "Validation Loss: 0.1698 Acc: 0.9504\n", + "Model saved as best_model.pth\n", + "Epoch 15/15\n", + "----------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Training: 100%|██████████| 516/516 [01:34<00:00, 5.45it/s]\n" + "Training: 100%|██████████| 516/516 [01:20<00:00, 6.37it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Training Loss: 0.0392 Acc: 0.9886\n" + "Training Loss: 0.2041 Acc: 0.9357\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Validation: 100%|██████████| 31/31 [00:04<00:00, 7.56it/s]" + "Validation: 100%|██████████| 31/31 [00:04<00:00, 7.12it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 0.0535 Acc: 0.9793\n", - "Training complete. Best Validation Acc: 0.9793\n" + "Validation Loss: 0.1834 Acc: 0.9545\n", + "Training complete.\n", + "Best Validation Loss: 0.1698\n" ] }, { @@ -1216,2794 +1284,1087 @@ } ], "source": [ - " # Train the model\n", - "train_model(model_deepercnn, train_loader, val_loader, criterion, optimizer_deepercnn, scheduler_deepercnn, num_epochs=20)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_22584\\4239124031.py:39: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " model = torch.load(\"customcnnwithAttention_full.pth\")\n", - "Testing: 100%|██████████| 61/61 [00:07<00:00, 8.13it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test Accuracy: 0.9794\n" - ] - }, - { - "data": { - "text/plain": [ - "tensor(0.9794, device='cuda:0', dtype=torch.float64)" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def test_model(model, test_loader, device=\"cuda\" if torch.cuda.is_available() else \"cpu\"):\n", - " \"\"\"\n", - " Test the trained model on a test dataset.\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import torch.nn.functional as F\n", + "from torch.optim import lr_scheduler\n", + "from tqdm import tqdm\n", "\n", - " Args:\n", - " model (torch.nn.Module): Trained model.\n", - " test_loader (torch.utils.data.DataLoader): DataLoader for the test dataset.\n", - " device (str): Device to perform testing on (e.g., 'cuda' or 'cpu').\n", - " \n", - " Returns:\n", - " float: Test accuracy.\n", - " \"\"\"\n", - " model = model.to(device)\n", - " model.eval() # Set model to evaluation mode\n", - " running_corrects = 0\n", - " total_test = 0\n", + "# Model Definition: CustomCNNWithLSTM\n", + "class CustomCNNWithLSTM(nn.Module):\n", + " def __init__(self, num_classes=6, lstm_hidden_size=128, lstm_num_layers=2):\n", + " super(CustomCNNWithLSTM, self).__init__()\n", + " self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)\n", + " self.bn1 = nn.BatchNorm2d(32)\n", + " self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n", "\n", - " with torch.no_grad():\n", - " for inputs, labels in tqdm(test_loader, desc=\"Testing\"):\n", - " inputs = inputs.to(device)\n", - " labels = labels.to(device).long() # Ensure labels are of type torch.long\n", + " self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n", + " self.bn2 = nn.BatchNorm2d(64)\n", + " self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n", "\n", - " # Forward pass\n", - " outputs = model(inputs)\n", - " _, preds = torch.max(outputs, 1)\n", + " self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n", + " self.bn3 = nn.BatchNorm2d(128)\n", + " self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)\n", "\n", - " # Track statistics\n", - " running_corrects += torch.sum(preds == labels.data)\n", - " total_test += labels.size(0)\n", + " self.dropout_cnn = nn.Dropout(0.3)\n", "\n", - " test_acc = running_corrects.double() / total_test\n", - " print(f\"Test Accuracy: {test_acc:.4f}\")\n", - " return test_acc\n", + " # LSTM\n", + " self.lstm_input_size = 128\n", + " self.lstm = nn.LSTM(\n", + " input_size=self.lstm_input_size,\n", + " hidden_size=lstm_hidden_size,\n", + " num_layers=lstm_num_layers,\n", + " batch_first=True,\n", + " bidirectional=True\n", + " )\n", "\n", + " self.fc1 = nn.Linear(lstm_hidden_size * 2, 256)\n", + " self.dropout_fc = nn.Dropout(0.5)\n", + " self.fc2 = nn.Linear(256, num_classes)\n", "\n", - "# Example usage:\n", - "# Assuming `test_loader` is your DataLoader for the test dataset.\n", - "# Load the trained model:\n", - "model = torch.load(\"customcnnwithAttention_full.pth\")\n", - "test_model(model, test_loader)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import roc_curve, auc, accuracy_score\n", - "import torch\n", - "from tqdm import tqdm\n", + " def forward(self, x):\n", + " x = self.pool1(F.relu(self.bn1(self.conv1(x))))\n", + " x = self.pool2(F.relu(self.bn2(self.conv2(x))))\n", + " x = self.pool3(F.relu(self.bn3(self.conv3(x))))\n", + " x = self.dropout_cnn(x)\n", "\n", - "# Testing Function\n", - "def test_model1(model, test_loader, device=\"cuda\" if torch.cuda.is_available() else \"cpu\", num_classes=6):\n", - " model = model.to(device)\n", - " model.eval()\n", - " true_labels = []\n", - " predicted_labels = []\n", - " predicted_probs = []\n", - " batch_accuracies = []\n", + " # Flatten for LSTM input\n", + " batch_size, channels, height, width = x.shape\n", + " x = x.view(batch_size, channels, -1).permute(0, 2, 1)\n", "\n", - " with torch.no_grad():\n", - " for inputs, labels in tqdm(test_loader, desc=\"Testing\"):\n", - " inputs = inputs.to(device)\n", - " labels = labels.to(device).long() # Ensure labels are of type torch.long\n", + " # LSTM\n", + " lstm_out, _ = self.lstm(x)\n", + " x = lstm_out[:, -1, :]\n", "\n", - " # Forward Pass\n", + " # Fully connected layers\n", + " x = F.relu(self.fc1(x))\n", + " x = self.dropout_fc(x)\n", + " x = self.fc2(x)\n", + " return x\n", + "\n", + "\n", + "# Training function\n", + "def train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, num_epochs, device):\n", + " model.to(device)\n", + " best_loss = float('inf')\n", + "\n", + " for epoch in range(num_epochs):\n", + " print(f\"Epoch {epoch + 1}/{num_epochs}\")\n", + " print(\"-\" * 10)\n", + "\n", + " # Training Phase\n", + " model.train()\n", + " train_loss = 0.0\n", + " train_corrects = 0\n", + "\n", + " # Use tqdm for training progress bar\n", + " for inputs, labels in tqdm(train_loader, desc=\"Training\", ncols=100, dynamic_ncols=True):\n", + " inputs, labels = inputs.to(device), labels.to(device, dtype=torch.long)\n", + "\n", + " optimizer.zero_grad()\n", " outputs = model(inputs)\n", - " _, preds = torch.max(outputs, 1) # Predicted labels\n", - " probs = torch.softmax(outputs, dim=1) # Probabilities\n", + " loss = criterion(outputs, labels)\n", + " loss.backward()\n", + " optimizer.step()\n", "\n", - " # Save true and predicted labels\n", - " true_labels.extend(labels.cpu().numpy())\n", - " predicted_labels.extend(preds.cpu().numpy())\n", - " predicted_probs.extend(probs.cpu().numpy())\n", + " train_loss += loss.item() * inputs.size(0)\n", + " _, preds = torch.max(outputs, 1)\n", + " train_corrects += torch.sum(preds == labels.data)\n", "\n", - " # Calculate batch accuracy\n", - " batch_accuracy = accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\n", - " batch_accuracies.append(batch_accuracy)\n", + " scheduler.step()\n", + " epoch_loss = train_loss / len(train_loader.dataset)\n", + " epoch_acc = train_corrects.double() / len(train_loader.dataset)\n", "\n", - " # Plot Accuracy Curve\n", - " plt.figure(figsize=(8, 6))\n", - " plt.plot(batch_accuracies, label=\"Accuracy per Batch\", color=\"blue\")\n", - " plt.xlabel(\"Batch Index\")\n", - " plt.ylabel(\"Accuracy\")\n", - " plt.title(\"Accuracy Curve\")\n", - " plt.legend()\n", - " plt.show()\n", + " print(f\"Training Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}\")\n", "\n", - " # Convert labels and probabilities to numpy arrays for ROC\n", - " true_labels = torch.tensor(true_labels).numpy()\n", - " predicted_probs = torch.tensor(predicted_probs).numpy()\n", - "\n", - " # Plot ROC Curve for each class\n", - " plt.figure(figsize=(10, 8))\n", - " for i in range(num_classes):\n", - " fpr, tpr, _ = roc_curve(true_labels == i, predicted_probs[:, i])\n", - " roc_auc = auc(fpr, tpr)\n", - " plt.plot(fpr, tpr, label=f\"Class {i} (AUC = {roc_auc:.2f})\")\n", - "\n", - " plt.plot([0, 1], [0, 1], \"k--\", label=\"Random Guess\")\n", - " plt.xlabel(\"False Positive Rate\")\n", - " plt.ylabel(\"True Positive Rate\")\n", - " plt.title(\"ROC Curve\")\n", - " plt.legend(loc=\"lower right\")\n", - " plt.show()\n", + " # Validation Phase\n", + " model.eval()\n", + " val_loss = 0.0\n", + " val_corrects = 0\n", "\n", - " # Calculate overall accuracy\n", - " overall_accuracy = accuracy_score(true_labels, predicted_labels)\n", - " print(f\"Overall Test Accuracy: {overall_accuracy:.4f}\")\n" + " # Use tqdm for validation progress bar\n", + " with torch.no_grad():\n", + " for inputs, labels in tqdm(val_loader, desc=\"Validation\", ncols=100, dynamic_ncols=True):\n", + " inputs, labels = inputs.to(device), labels.to(device, dtype=torch.long)\n", + " outputs = model(inputs)\n", + " loss = criterion(outputs, labels)\n", + "\n", + " val_loss += loss.item() * inputs.size(0)\n", + " _, preds = torch.max(outputs, 1)\n", + " val_corrects += torch.sum(preds == labels.data)\n", + "\n", + " epoch_val_loss = val_loss / len(val_loader.dataset)\n", + " epoch_val_acc = val_corrects.double() / len(val_loader.dataset)\n", + "\n", + " print(f\"Validation Loss: {epoch_val_loss:.4f} Acc: {epoch_val_acc:.4f}\")\n", + "\n", + " # Save the best model\n", + " if epoch_val_loss < best_loss:\n", + " best_loss = epoch_val_loss\n", + " torch.save(model.state_dict(), \"best_model.pth\")\n", + " print(\"Model saved as best_model.pth\")\n", + "\n", + " print(\"Training complete.\")\n", + " print(f\"Best Validation Loss: {best_loss:.4f}\")\n", + "\n", + "\n", + "# Example Usage\n", + "if __name__ == \"__main__\":\n", + " # Define the DataLoader instances (train_loader and val_loader) with your data\n", + "\n", + " num_classes = 6\n", + " model_cnn_lstm = CustomCNNWithLSTM(num_classes=num_classes)\n", + " criterion = nn.CrossEntropyLoss()\n", + " optimizer = optim.Adam(model_cnn_lstm.parameters(), lr=0.0001)\n", + " scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)\n", + "\n", + " device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + "\n", + " # Train the model (replace train_loader and val_loader with your actual data loaders)\n", + " train_model(\n", + " model_cnn_lstm,\n", + " train_loader, # Your DataLoader for training data\n", + " val_loader, # Your DataLoader for validation data\n", + " criterion,\n", + " optimizer,\n", + " scheduler,\n", + " num_epochs=15,\n", + " device=device\n", + " )\n" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 34, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/20\n", - "----------\n" - ] - }, { "name": "stderr", "output_type": "stream", "text": [ - "Training: 100%|██████████| 516/516 [02:11<00:00, 3.93it/s]\n" + "C:\\Users\\incha\\AppData\\Local\\Temp\\ipykernel_26428\\1704837883.py:146: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model_deepercnn.load_state_dict(torch.load(\"best_model.pth\"))\n", + "Testing: 100%|██████████| 61/61 [00:11<00:00, 5.33it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Training Loss: 1.5772 Acc: 0.3286\n" + "Test Loss: 0.2539 Test Acc: 0.9198\n", + "Average Inference Time per Batch: 0.1878 seconds\n", + "\n", + "Classification Report:\n", + "{'3 Long Blade Rotor': {'precision': 0.9102564102564102, 'recall': 0.8452380952380952, 'f1-score': 0.8765432098765432, 'support': 84.0}, '3 Short Blade Rotor': {'precision': 0.7802197802197802, 'recall': 0.8875, 'f1-score': 0.8304093567251462, 'support': 80.0}, 'Bird': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 80.0}, 'Bird+mini-helicopter': {'precision': 0.9746835443037974, 'recall': 0.9166666666666666, 'f1-score': 0.9447852760736196, 'support': 84.0}, 'Drone': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 78.0}, 'RC Plane': {'precision': 0.875, 'recall': 0.875, 'f1-score': 0.875, 'support': 80.0}, 'accuracy': 0.9197530864197531, 'macro avg': {'precision': 0.9233599557966646, 'recall': 0.920734126984127, 'f1-score': 0.9211229737792181, 'support': 486.0}, 'weighted avg': {'precision': 0.9233591329231273, 'recall': 0.9197530864197531, 'f1-score': 0.9206262167856489, 'support': 486.0}}\n", + "\n", + "Confusion Matrix:\n", + "[[71 12 0 0 0 1]\n", + " [ 4 71 0 0 0 5]\n", + " [ 0 0 80 0 0 0]\n", + " [ 1 2 0 77 0 4]\n", + " [ 0 0 0 0 78 0]\n", + " [ 2 6 0 2 0 70]]\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:06<00:00, 5.08it/s]\n" - ] + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 1.4002 Acc: 0.4298\n", - "Model saved as best_model.pth\n", - "Epoch 2/20\n", - "----------\n" + "Macro Average AUC: 0.9889\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [02:10<00:00, 3.96it/s]\n" - ] + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ - "Training Loss: 1.1618 Acc: 0.5380\n" + "Class Distribution in Test Set: {'3 Long Blade Rotor': 84, '3 Short Blade Rotor': 80, 'Bird': 80, 'Bird+mini-helicopter': 84, 'Drone': 78, 'RC Plane': 80}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Validation: 100%|██████████| 31/31 [00:06<00:00, 4.71it/s]\n" + "c:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\onnx\\symbolic_opset9.py:4545: UserWarning: Exporting a model to ONNX with a batch_size other than 1, with a variable length with LSTM can cause an error when running the ONNX model with a different batch size. Make sure to save the model with a batch size of 1, or define the initial states (h0/c0) as inputs of the model. \n", + " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Validation Loss: 1.3031 Acc: 0.5000\n", - "Model saved as best_model.pth\n", - "Epoch 3/20\n", - "----------\n" + "Model exported to ONNX format successfully.\n" ] - }, + } + ], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import numpy as np\n", + "import tqdm\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc, precision_recall_curve\n", + "import torch.nn.functional as F\n", + "import time\n", + "import json\n", + "from sklearn.calibration import calibration_curve\n", + "\n", + "# Class names for your classification task\n", + "class_names = [\n", + " \"3 Long Blade Rotor\", \n", + " \"3 Short Blade Rotor\", \n", + " \"Bird\", \n", + " \"Bird+mini-helicopter\", \n", + " \"Drone\", \n", + " \"RC Plane\"\n", + "]\n", + "\n", + "# Test Function with Metric Collection\n", + "def test_model(model, test_loader, criterion, device=\"cuda\" if torch.cuda.is_available() else \"cpu\"):\n", + " model = model.to(device)\n", + " model.eval()\n", + "\n", + " running_loss = 0.0\n", + " running_corrects = 0\n", + " total_test = 0\n", + " all_preds = []\n", + " all_labels = []\n", + " all_probs = []\n", + "\n", + " # Start time for inference benchmark\n", + " start_time = time.time()\n", + "\n", + " with torch.no_grad():\n", + " for inputs, labels in tqdm.tqdm(test_loader, desc=\"Testing\"):\n", + " inputs = inputs.to(device)\n", + " labels = labels.to(device).long()\n", + "\n", + " # Forward Pass\n", + " outputs = model(inputs)\n", + " loss = criterion(outputs, labels)\n", + " _, preds = torch.max(outputs, 1)\n", + "\n", + " # Store metrics\n", + " running_loss += loss.item() * inputs.size(0)\n", + " running_corrects += torch.sum(preds == labels.data)\n", + " total_test += labels.size(0)\n", + "\n", + " all_preds.extend(preds.cpu().numpy())\n", + " all_labels.extend(labels.cpu().numpy())\n", + " all_probs.extend(F.softmax(outputs, dim=1).cpu().numpy())\n", + "\n", + " # End time for inference benchmark\n", + " end_time = time.time()\n", + " avg_inference_time = (end_time - start_time) / len(test_loader)\n", + "\n", + " # Overall metrics\n", + " test_loss = running_loss / total_test\n", + " test_acc = running_corrects.double() / total_test\n", + "\n", + " print(f\"Test Loss: {test_loss:.4f} Test Acc: {test_acc:.4f}\")\n", + " print(f\"Average Inference Time per Batch: {avg_inference_time:.4f} seconds\")\n", + "\n", + " # Classification Report\n", + " print(\"\\nClassification Report:\")\n", + " report = classification_report(all_labels, all_preds, target_names=class_names, output_dict=True)\n", + " print(report)\n", + "\n", + " # Save Classification Report to a JSON file\n", + " with open(\"classification_report.json\", \"w\") as f:\n", + " json.dump(report, f, indent=4)\n", + "\n", + " # Confusion Matrix\n", + " cm = confusion_matrix(all_labels, all_preds)\n", + " print(\"\\nConfusion Matrix:\")\n", + " print(cm)\n", + "\n", + " # Save confusion matrix\n", + " np.save(\"confusion_matrix.npy\", cm)\n", + "\n", + " # Plot Confusion Matrix\n", + " plt.figure(figsize=(8, 6))\n", + " plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n", + " plt.title(\"Confusion Matrix\")\n", + " plt.colorbar()\n", + " tick_marks = np.arange(len(class_names))\n", + " plt.xticks(tick_marks, class_names, rotation=45)\n", + " plt.yticks(tick_marks, class_names)\n", + " plt.ylabel('True label')\n", + " plt.xlabel('Predicted label')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # ROC Curve & AUC (for multiclass)\n", + " all_probs = np.array(all_probs)\n", + " if len(class_names) > 2:\n", + " fpr, tpr, roc_auc = {}, {}, {}\n", + " for i in range(len(class_names)):\n", + " fpr[i], tpr[i], _ = roc_curve((np.array(all_labels) == i).astype(int), all_probs[:, i])\n", + " roc_auc[i] = auc(fpr[i], tpr[i])\n", + " plt.plot(fpr[i], tpr[i], lw=2, label=f'{class_names[i]} (AUC = {roc_auc[i]:.2f})')\n", + "\n", + " # Macro average AUC\n", + " macro_auc = np.mean([auc(fpr[i], tpr[i]) for i in range(len(class_names))])\n", + " print(f\"Macro Average AUC: {macro_auc:.4f}\")\n", + "\n", + " plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + " plt.title('Receiver Operating Characteristic (ROC) Curve')\n", + " plt.legend(loc='lower right')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " return all_labels, all_preds, all_probs, macro_auc\n", + "\n", + "# Precision-Recall Curve Plot\n", + "def plot_precision_and_recall(all_labels, all_probs, class_names):\n", + " all_labels = np.array(all_labels)\n", + " all_probs = np.array(all_probs)\n", + "\n", + " plt.figure(figsize=(12, 6))\n", + " for i, class_name in enumerate(class_names):\n", + " precision, recall, _ = precision_recall_curve((all_labels == i).astype(int), all_probs[:, i])\n", + " plt.plot(recall, precision, lw=2, label=f'{class_name} (Precision-Recall)')\n", + " plt.xlabel('Recall')\n", + " plt.ylabel('Precision')\n", + " plt.title('Precision-Recall Curve for Each Class')\n", + " plt.legend(loc='lower left')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "# Check for class distribution (Imbalanced Data Check)\n", + "def check_class_distribution(all_labels, class_names):\n", + " unique, counts = np.unique(all_labels, return_counts=True)\n", + " class_distribution = dict(zip(class_names, counts))\n", + " print(\"Class Distribution in Test Set:\", class_distribution)\n", + "\n", + "# Example Usage\n", + "if __name__ == \"__main__\":\n", + " # Initialize Model and Data\n", + " model_deepercnn = CustomCNNWithLSTM(num_classes=len(class_names))\n", + " model_deepercnn.load_state_dict(torch.load(\"best_model.pth\"))\n", + " criterion = nn.CrossEntropyLoss()\n", + "\n", + " # Test Model\n", + " all_labels, all_preds, all_probs, macro_auc = test_model(model_deepercnn, test_loader, criterion)\n", + "\n", + " # Plot Precision-Recall Curves\n", + " plot_precision_and_recall(all_labels, all_probs, class_names)\n", + "\n", + " # Check for Class Imbalance\n", + " check_class_distribution(all_labels, class_names)\n", + "\n", + " # Prepare for ONNX Conversion (Optional)\n", + " dummy_input = torch.randn(1, 3, 224, 224, device=\"cuda\" if torch.cuda.is_available() else \"cpu\") # Adjust for your input size\n", + " torch.onnx.export(model_deepercnn, dummy_input, \"model.onnx\", input_names=['input'], output_names=['output'])\n", + "\n", + " print(\"Model exported to ONNX format successfully.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Training: 100%|██████████| 516/516 [02:15<00:00, 3.82it/s]\n" + "C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_22584\\66978296.py:37: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model_deepercnn.load_state_dict(torch.load(\"customcnnwithAttention.pth\"))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Training Loss: 0.8467 Acc: 0.6598\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:07<00:00, 3.91it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.8997 Acc: 0.6116\n", - "Model saved as best_model.pth\n", - "Epoch 4/20\n", - "----------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [02:12<00:00, 3.90it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.7024 Acc: 0.7091\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:06<00:00, 5.01it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.7495 Acc: 0.6860\n", - "Model saved as best_model.pth\n", - "Epoch 5/20\n", - "----------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [02:13<00:00, 3.86it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.6140 Acc: 0.7503\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:06<00:00, 5.06it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.6653 Acc: 0.7314\n", - "Model saved as best_model.pth\n", - "Epoch 6/20\n", - "----------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [02:08<00:00, 4.02it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.5315 Acc: 0.7945\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:06<00:00, 4.57it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.4132 Acc: 0.8760\n", - "Model saved as best_model.pth\n", - "Epoch 7/20\n", - "----------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [02:13<00:00, 3.86it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.5120 Acc: 0.8098\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:06<00:00, 5.00it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.4936 Acc: 0.8058\n", - "Epoch 8/20\n", - "----------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [04:16<00:00, 2.01it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.3988 Acc: 0.8624\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:18<00:00, 1.71it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.3807 Acc: 0.8719\n", - "Model saved as best_model.pth\n", - "Epoch 9/20\n", - "----------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [03:13<00:00, 2.67it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.3567 Acc: 0.8772\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:06<00:00, 4.60it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.3503 Acc: 0.8719\n", - "Model saved as best_model.pth\n", - "Epoch 10/20\n", - "----------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [02:21<00:00, 3.63it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.3394 Acc: 0.8784\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:14<00:00, 2.11it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.3364 Acc: 0.8926\n", - "Model saved as best_model.pth\n", - "Epoch 11/20\n", - "----------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [04:03<00:00, 2.12it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.3140 Acc: 0.8889\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:15<00:00, 2.00it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.2976 Acc: 0.9050\n", - "Model saved as best_model.pth\n", - "Epoch 12/20\n", - "----------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [04:43<00:00, 1.82it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.2982 Acc: 0.8918\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:16<00:00, 1.94it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.3157 Acc: 0.8884\n", - "Epoch 13/20\n", - "----------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [04:32<00:00, 1.89it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.2928 Acc: 0.9012\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:15<00:00, 2.00it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.2750 Acc: 0.9091\n", - "Model saved as best_model.pth\n", - "Epoch 14/20\n", - "----------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [04:44<00:00, 1.81it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.2697 Acc: 0.9073\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:15<00:00, 2.00it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.2789 Acc: 0.8926\n", - "Epoch 15/20\n", - "----------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [03:52<00:00, 2.22it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.2515 Acc: 0.9141\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:07<00:00, 4.24it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.2557 Acc: 0.9050\n", - "Model saved as best_model.pth\n", - "Epoch 16/20\n", - "----------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [01:59<00:00, 4.33it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.2584 Acc: 0.9095\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:07<00:00, 4.42it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.2396 Acc: 0.9174\n", - "Model saved as best_model.pth\n", - "Epoch 17/20\n", - "----------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [02:10<00:00, 3.95it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.2526 Acc: 0.9146\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:06<00:00, 4.95it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.2637 Acc: 0.9174\n", - "Epoch 18/20\n", - "----------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [04:17<00:00, 2.01it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.2504 Acc: 0.9151\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:18<00:00, 1.66it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.2693 Acc: 0.9091\n", - "Epoch 19/20\n", - "----------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [05:26<00:00, 1.58it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.2484 Acc: 0.9146\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:16<00:00, 1.83it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.2797 Acc: 0.9132\n", - "Epoch 20/20\n", - "----------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [03:47<00:00, 2.27it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.2437 Acc: 0.9214\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:10<00:00, 3.04it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.2719 Acc: 0.9091\n", - "Training complete.\n", - "Best Validation Loss: 0.2396\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "import torch.nn.functional as F\n", - "from torch.optim import lr_scheduler\n", - "from tqdm import tqdm\n", - "\n", - "# Model Definition: CustomCNNWithLSTM\n", - "class CustomCNNWithLSTM(nn.Module):\n", - " def __init__(self, num_classes=6, lstm_hidden_size=128, lstm_num_layers=2):\n", - " super(CustomCNNWithLSTM, self).__init__()\n", - " self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)\n", - " self.bn1 = nn.BatchNorm2d(16)\n", - " self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n", - "\n", - " self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)\n", - " self.bn2 = nn.BatchNorm2d(32)\n", - " self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n", - "\n", - " self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n", - " self.bn3 = nn.BatchNorm2d(64)\n", - " self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)\n", - "\n", - " self.conv4 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n", - " self.bn4 = nn.BatchNorm2d(128)\n", - " self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)\n", - "\n", - " # Dynamically compute the LSTM input size\n", - " self.lstm_input_size = 128 # Channels after CNN\n", - " self.flatten = nn.Flatten(start_dim=2)\n", - "\n", - " self.lstm = nn.LSTM(\n", - " input_size=self.lstm_input_size,\n", - " hidden_size=lstm_hidden_size,\n", - " num_layers=lstm_num_layers,\n", - " batch_first=True,\n", - " bidirectional=True\n", - " )\n", - "\n", - " self.fc1 = nn.Linear(lstm_hidden_size * 2, 512)\n", - " self.fc2 = nn.Linear(512, num_classes)\n", - " self.dropout = nn.Dropout(0.5)\n", - "\n", - " def forward(self, x):\n", - " # Pass through CNN\n", - " x = self.pool1(F.relu(self.bn1(self.conv1(x))))\n", - " x = self.pool2(F.relu(self.bn2(self.conv2(x))))\n", - " x = self.pool3(F.relu(self.bn3(self.conv3(x))))\n", - " x = self.pool4(F.relu(self.bn4(self.conv4(x))))\n", - "\n", - " # Flatten for LSTM input\n", - " batch_size, channels, height, width = x.shape\n", - " x = x.view(batch_size, channels, -1).permute(0, 2, 1)\n", - "\n", - " # Pass through LSTM\n", - " lstm_out, _ = self.lstm(x)\n", - " x = lstm_out[:, -1, :]\n", - "\n", - " # Pass through fully connected layers\n", - " x = F.relu(self.fc1(x))\n", - " x = self.dropout(x)\n", - " x = self.fc2(x)\n", - " return x\n", - "\n", - "\n", - "# Training function\n", - "def train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, num_epochs, device):\n", - " model.to(device)\n", - " best_loss = float('inf')\n", - "\n", - " for epoch in range(num_epochs):\n", - " print(f\"Epoch {epoch + 1}/{num_epochs}\")\n", - " print(\"-\" * 10)\n", - "\n", - " # Training Phase\n", - " model.train()\n", - " train_loss = 0.0\n", - " train_corrects = 0\n", - "\n", - " # Use tqdm for training progress bar\n", - " for inputs, labels in tqdm(train_loader, desc=\"Training\", ncols=100, dynamic_ncols=True):\n", - " inputs, labels = inputs.to(device), labels.to(device, dtype=torch.long)\n", - "\n", - " optimizer.zero_grad()\n", - " outputs = model(inputs)\n", - " loss = criterion(outputs, labels)\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " train_loss += loss.item() * inputs.size(0)\n", - " _, preds = torch.max(outputs, 1)\n", - " train_corrects += torch.sum(preds == labels.data)\n", - "\n", - " scheduler.step()\n", - " epoch_loss = train_loss / len(train_loader.dataset)\n", - " epoch_acc = train_corrects.double() / len(train_loader.dataset)\n", - "\n", - " print(f\"Training Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}\")\n", - "\n", - " # Validation Phase\n", - " model.eval()\n", - " val_loss = 0.0\n", - " val_corrects = 0\n", - "\n", - " # Use tqdm for validation progress bar\n", - " with torch.no_grad():\n", - " for inputs, labels in tqdm(val_loader, desc=\"Validation\", ncols=100, dynamic_ncols=True):\n", - " inputs, labels = inputs.to(device), labels.to(device, dtype=torch.long)\n", - " outputs = model(inputs)\n", - " loss = criterion(outputs, labels)\n", - "\n", - " val_loss += loss.item() * inputs.size(0)\n", - " _, preds = torch.max(outputs, 1)\n", - " val_corrects += torch.sum(preds == labels.data)\n", - "\n", - " epoch_val_loss = val_loss / len(val_loader.dataset)\n", - " epoch_val_acc = val_corrects.double() / len(val_loader.dataset)\n", - "\n", - " print(f\"Validation Loss: {epoch_val_loss:.4f} Acc: {epoch_val_acc:.4f}\")\n", - "\n", - " # Save the best model\n", - " if epoch_val_loss < best_loss:\n", - " best_loss = epoch_val_loss\n", - " torch.save(model.state_dict(), \"best_model.pth\")\n", - " print(\"Model saved as best_model.pth\")\n", - "\n", - " print(\"Training complete.\")\n", - " print(f\"Best Validation Loss: {best_loss:.4f}\")\n", - "\n", - "\n", - "# Example Usage\n", - "if __name__ == \"__main__\":\n", - " # Define the DataLoader instances (train_loader and val_loader) with your data\n", - "\n", - " num_classes = 6\n", - " model_cnn_lstm = CustomCNNWithLSTM(num_classes=num_classes)\n", - " criterion = nn.CrossEntropyLoss()\n", - " optimizer = optim.Adam(model_cnn_lstm.parameters(), lr=0.0001)\n", - " scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)\n", - "\n", - " device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", - "\n", - " # Train the model (replace train_loader and val_loader with your actual data loaders)\n", - " train_model(\n", - " model_cnn_lstm,\n", - " train_loader, # Your DataLoader for training data\n", - " val_loader, # Your DataLoader for validation data\n", - " criterion,\n", - " optimizer,\n", - " scheduler,\n", - " num_epochs=20,\n", - " device=device\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn.functional as F\n", - "from tqdm import tqdm\n", - "\n", - "# Testing function\n", - "def test_model(model, test_loader, criterion, device):\n", - " model.to(device)\n", - " model.eval() # Set the model to evaluation mode\n", - "\n", - " test_loss = 0.0\n", - " test_corrects = 0\n", - " total_samples = 0\n", - "\n", - " # Disable gradient computation during inference\n", - " with torch.no_grad():\n", - " # Loop through the test data\n", - " for inputs, labels in tqdm(test_loader, desc=\"Testing\", ncols=100, dynamic_ncols=True):\n", - " inputs, labels = inputs.to(device), labels.to(device, dtype=torch.long)\n", - "\n", - " # Forward pass\n", - " outputs = model(inputs)\n", - " loss = criterion(outputs, labels)\n", - "\n", - " # Accumulate loss and accuracy\n", - " test_loss += loss.item() * inputs.size(0)\n", - " _, preds = torch.max(outputs, 1)\n", - " test_corrects += torch.sum(preds == labels.data)\n", - " total_samples += inputs.size(0)\n", - "\n", - " # Compute final average loss and accuracy\n", - " test_loss /= total_samples\n", - " test_acc = test_corrects.double() / total_samples\n", - "\n", - " # Print the results\n", - " print(f\"Test Loss: {test_loss:.4f} Acc: {test_acc:.4f}\")\n", - " return test_loss, test_acc\n" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_22584\\3287528403.py:66: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " model_deepercnn.load_state_dict(torch.load(\"customcnnwithAttention.pth\"))\n", - "Testing: 100%|██████████| 61/61 [00:08<00:00, 6.85it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test Loss: 0.0887 Test Acc: 0.9733\n", - "\n", - "Classification Report:\n", - " precision recall f1-score support\n", - "\n", - " Class 0 0.96 0.93 0.95 84\n", - " Class 1 0.92 0.96 0.94 80\n", - " Class 2 1.00 1.00 1.00 80\n", - " Class 3 1.00 0.96 0.98 84\n", - " Class 4 1.00 1.00 1.00 78\n", - " Class 5 0.96 0.99 0.98 80\n", - "\n", - " accuracy 0.97 486\n", - " macro avg 0.97 0.97 0.97 486\n", - "weighted avg 0.97 0.97 0.97 486\n", - "\n", - "\n", - "Confusion Matrix:\n", - "[[78 6 0 0 0 0]\n", - " [ 3 77 0 0 0 0]\n", - " [ 0 0 80 0 0 0]\n", - " [ 0 0 0 81 0 3]\n", - " [ 0 0 0 0 78 0]\n", - " [ 0 1 0 0 0 79]]\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAApMAAAJOCAYAAADrtowMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABlxklEQVR4nO3deVxVdf7H8fcFFRS4gBtIIkqComkathC2WKSWqQXlWIxLpY2FmuCW424ajU5ZGmk5ptno2KaW5rjkkk1qrph7rmApOGmIKxCc3x8O9xehde/t4L3I69njPMZ7lu/5nO/DgY+f8/1+r8UwDEMAAACAEzxcHQAAAADKL5JJAAAAOI1kEgAAAE4jmQQAAIDTSCYBAADgNJJJAAAAOI1kEgAAAE4jmQQAAIDTSCYBAADgNJJJAC534MABtW3bVv7+/rJYLFq0aJGp7R89elQWi0WzZ882td3y7N5779W9997r6jAAXAdIJgFIkg4dOqS//OUvCg8Pl7e3t6xWq2JjY/XGG2/o4sWLZXrvHj16aOfOnZowYYLef/99tWrVqkzvdy317NlTFotFVqv1iv144MABWSwWWSwW/f3vf3e4/ePHj2vMmDFKT083IVoAcFwlVwcAwPU+//xzPf744/Ly8lL37t110003KT8/X//5z380ePBg7d69W++8806Z3PvixYvasGGDhg8frr59+5bJPcLCwnTx4kVVrly5TNr/PZUqVdKFCxe0ePFidenSpcSxuXPnytvbW5cuXXKq7ePHj2vs2LGqX7++WrRoYfd1K1ascOp+APBrJJNABXfkyBF17dpVYWFhWr16terUqWM7lpSUpIMHD+rzzz8vs/v/97//lSQFBASU2T0sFou8vb3LrP3f4+XlpdjYWP3rX/8qlUzOmzdPHTp00CeffHJNYrlw4YKqVaumKlWqXJP7Abj+8ZobqOAmTpyoc+fOaebMmSUSyWINGzbUCy+8YPv8888/66WXXtKNN94oLy8v1a9fX3/961+Vl5dX4rr69evr4Ycf1n/+8x/ddttt8vb2Vnh4uObMmWM7Z8yYMQoLC5MkDR48WBaLRfXr15d0+fVw8Z9/acyYMbJYLCX2rVy5Uq1bt1ZAQIB8fX3VqFEj/fWvf7Udv9qYydWrV+uuu+6Sj4+PAgIC1LlzZ+3du/eK9zt48KB69uypgIAA+fv766mnntKFCxeu3rG/8uSTT+rf//63cnJybPs2b96sAwcO6Mknnyx1/unTpzVo0CA1a9ZMvr6+slqtevDBB7Vjxw7bOWvXrtWtt94qSXrqqadsr8uLn/Pee+/VTTfdpK1bt+ruu+9WtWrVbP3y6zGTPXr0kLe3d6nnb9eunQIDA3X8+HG7nxVAxUIyCVRwixcvVnh4uO688067zu/Vq5dGjRqlW265RZMnT9Y999yj1NRUde3atdS5Bw8e1GOPPaYHHnhAr776qgIDA9WzZ0/t3r1bkhQfH6/JkydLkp544gm9//77ev311x2Kf/fu3Xr44YeVl5encePG6dVXX1WnTp309ddf/+Z1X3zxhdq1a6eTJ09qzJgxSklJ0fr16xUbG6ujR4+WOr9Lly46e/asUlNT1aVLF82ePVtjx461O874+HhZLBYtWLDAtm/evHlq3LixbrnlllLnHz58WIsWLdLDDz+s1157TYMHD9bOnTt1zz332BK7qKgojRs3TpL07LPP6v3339f777+vu+++29bOqVOn9OCDD6pFixZ6/fXX1aZNmyvG98Ybb6hWrVrq0aOHCgsLJUlvv/22VqxYoalTpyokJMTuZwVQwRgAKqwzZ84YkozOnTvbdX56erohyejVq1eJ/YMGDTIkGatXr7btCwsLMyQZ69ats+07efKk4eXlZQwcONC278iRI4YkY9KkSSXa7NGjhxEWFlYqhtGjRxu//NE1efJkQ5Lx3//+96pxF99j1qxZtn0tWrQwateubZw6dcq2b8eOHYaHh4fRvXv3Uvd7+umnS7T56KOPGjVq1LjqPX/5HD4+PoZhGMZjjz1m3H///YZhGEZhYaERHBxsjB079op9cOnSJaOwsLDUc3h5eRnjxo2z7du8eXOpZyt2zz33GJKM6dOnX/HYPffcU2Lf8uXLDUnG+PHjjcOHDxu+vr7GI4888rvPCKBiozIJVGC5ubmSJD8/P7vOX7p0qSQpJSWlxP6BAwdKUqmxlU2aNNFdd91l+1yrVi01atRIhw8fdjrmXysea/npp5+qqKjIrmtOnDih9PR09ezZU9WrV7ftb968uR544AHbc/5Snz59Sny+6667dOrUKVsf2uPJJ5/U2rVrlZWVpdWrVysrK+uKr7ily+MsPTwu/4guLCzUqVOnbK/wt23bZvc9vby89NRTT9l1btu2bfWXv/xF48aNU3x8vLy9vfX222/bfS8AFRPJJFCBWa1WSdLZs2ftOj8jI0MeHh5q2LBhif3BwcEKCAhQRkZGif316tUr1UZgYKB++uknJyMu7U9/+pNiY2PVq1cvBQUFqWvXrvrwww9/M7EsjrNRo0aljkVFRenHH3/U+fPnS+z/9bMEBgZKkkPP8tBDD8nPz08ffPCB5s6dq1tvvbVUXxYrKirS5MmTFRERIS8vL9WsWVO1atXSt99+qzNnzth9zxtuuMGhyTZ///vfVb16daWnp2vKlCmqXbu23dcCqJhIJoEKzGq1KiQkRLt27XLoul9PgLkaT0/PK+43DMPpexSP5ytWtWpVrVu3Tl988YW6deumb7/9Vn/605/0wAMPlDr3j/gjz1LMy8tL8fHxeu+997Rw4cKrViUl6eWXX1ZKSoruvvtu/fOf/9Ty5cu1cuVKNW3a1O4KrHS5fxyxfft2nTx5UpK0c+dOh64FUDGRTAIV3MMPP6xDhw5pw4YNv3tuWFiYioqKdODAgRL7s7OzlZOTY5uZbYbAwMASM5+L/br6KUkeHh66//779dprr2nPnj2aMGGCVq9erTVr1lyx7eI49+/fX+rYvn37VLNmTfn4+PyxB7iKJ598Utu3b9fZs2evOGmp2Mcff6w2bdpo5syZ6tq1q9q2bau4uLhSfWJvYm+P8+fP66mnnlKTJk307LPPauLEidq8ebNp7QO4PpFMAhXckCFD5OPjo169eik7O7vU8UOHDumNN96QdPk1raRSM65fe+01SVKHDh1Mi+vGG2/UmTNn9O2339r2nThxQgsXLixx3unTp0tdW7x496+XKypWp04dtWjRQu+9916J5GzXrl1asWKF7TnLQps2bfTSSy/pzTffVHBw8FXP8/T0LFX1/Oijj/TDDz+U2Fec9F4p8XbU0KFDlZmZqffee0+vvfaa6tevrx49ely1HwFAYtFyoMK78cYbNW/ePP3pT39SVFRUiW/AWb9+vT766CP17NlTknTzzTerR48eeuedd5STk6N77rlHmzZt0nvvvadHHnnkqsvOOKNr164aOnSoHn30UfXv318XLlzQtGnTFBkZWWICyrhx47Ru3Tp16NBBYWFhOnnypN566y3VrVtXrVu3vmr7kyZN0oMPPqiYmBg988wzunjxoqZOnSp/f3+NGTPGtOf4NQ8PD40YMeJ3z3v44Yc1btw4PfXUU7rzzju1c+dOzZ07V+Hh4SXOu/HGGxUQEKDp06fLz89PPj4+uv3229WgQQOH4lq9erXeeustjR492rZU0axZs3Tvvfdq5MiRmjhxokPtAag4qEwCUKdOnfTtt9/qscce06effqqkpCS9+OKLOnr0qF599VVNmTLFdu4//vEPjR07Vps3b9aAAQO0evVqDRs2TPPnzzc1pho1amjhwoWqVq2ahgwZovfee0+pqanq2LFjqdjr1aund999V0lJSUpLS9Pdd9+t1atXy9/f/6rtx8XFadmyZapRo4ZGjRqlv//977rjjjv09ddfO5yIlYW//vWvGjhwoJYvX64XXnhB27Zt0+eff67Q0NAS51WuXFnvvfeePD091adPHz3xxBP68ssvHbrX2bNn9fTTT6tly5YaPny4bf9dd92lF154Qa+++qo2btxoynMBuP5YDEdGjwMAAAC/QGUSAAAATiOZBAAAgNNIJgEAAOA0kkkAAAA4jWQSAAAATiOZBAAAgNNYtLyMFRUV6fjx4/Lz8zP1a88AAKjoDMPQ2bNnFRISIg8P96mPXbp0Sfn5+WXSdpUqVeTt7V0mbTuLZLKMHT9+vNQiwwAAwDzHjh1T3bp1XR2GpMuJZFW/GtLPF8qk/eDgYB05csStEkqSyTLm5+cnSapyy/OyeHq5OJryZ9eC4b9/Eq7IWq2yq0MAgDJ1NjdXDRuE2n7XuoP8/Hzp5wvyatJD8qxibuOF+cra857y8/NJJiuS4lfbFk8vWSqRTDrKz2p1dQjlFskkgIrCLYeRVfKWxeRk0rA49iq/sLBQY8aM0T//+U9lZWUpJCREPXv21IgRI2x9ZhiGRo8erRkzZignJ0exsbGaNm2aIiIi7L6P+wwwAAAAgGn+9re/adq0aXrzzTe1d+9e/e1vf9PEiRM1depU2zkTJ07UlClTNH36dH3zzTfy8fFRu3btdOnSJbvvQ2USAADAbBZJZldMHWxu/fr16ty5szp06CBJql+/vv71r39p06ZNki5XJV9//XWNGDFCnTt3liTNmTNHQUFBWrRokbp27WrXfahMAgAAlCO5ubkltry8vCued+edd2rVqlX67rvvJEk7duzQf/7zHz344IOSpCNHjigrK0txcXG2a/z9/XX77bdrw4YNdsdDZRIAAMBsFo/Lm9ltSqVWiRk9erTGjBlT6vQXX3xRubm5aty4sTw9PVVYWKgJEyYoMTFRkpSVlSVJCgoKKnFdUFCQ7Zg9SCYBAADKkWPHjsn6iwmqXl5XnuD74Ycfau7cuZo3b56aNm2q9PR0DRgwQCEhIerRo4dp8ZBMAgAAmM1iKYMxk5fbs1qtJZLJqxk8eLBefPFF29jHZs2aKSMjQ6mpqerRo4eCg4MlSdnZ2apTp47tuuzsbLVo0cLusBgzCQAAcB26cOFCqW8G8vT0VFFRkSSpQYMGCg4O1qpVq2zHc3Nz9c033ygmJsbu+1CZBAAAMFsZjpm0V8eOHTVhwgTVq1dPTZs21fbt2/Xaa6/p6aefvtycxaIBAwZo/PjxioiIUIMGDTRy5EiFhITokUcesfs+JJMAAABmK8PX3PaaOnWqRo4cqeeff14nT55USEiI/vKXv2jUqFG2c4YMGaLz58/r2WefVU5Ojlq3bq1ly5Y59A07FsMwDIcig0Nyc3Pl7+8vr1uT+QYcJxxdNs7VIZRb/nwDDoDrXG5uroJq+OvMmTN2jSG8Fmy/96P7mf41ykZhnvK2TnWr55WoTAIAAJSBMnjN7aZTXdwzKgAAAJQLVCYBAADM5gZjJq8VKpMAAABwGpVJAAAAs7nB0kDXintGBQAAgHKByiQAAIDZGDMJAAAA/D4qkwAAAGZjzCQAAADw+6hMAgAAmK0CjZkkmQQAADAbr7kBAACA30dlEgAAwGwWSxlUJt3zNTeVSQAAADiNyiQAAIDZPCyXN7PbdENUJgEAAOA0KpMAAABmYzY3AAAA8PuoTAIAAJitAi1aTmUSAAAATisXyaTFYtGiRYtcHQYAAIB9isdMmr25IZdHlZWVpX79+ik8PFxeXl4KDQ1Vx44dtWrVKleHJkkyDEOjRo1SnTp1VLVqVcXFxenAgQOuDgsAALiz4tfcZm9uyKXJ5NGjRxUdHa3Vq1dr0qRJ2rlzp5YtW6Y2bdooKSnJlaHZTJw4UVOmTNH06dP1zTffyMfHR+3atdOlS5dcHRoAAIDLuTSZfP7552WxWLRp0yYlJCQoMjJSTZs2VUpKijZu3HjV64YOHarIyEhVq1ZN4eHhGjlypAoKCmzHd+zYoTZt2sjPz09Wq1XR0dHasmWLJCkjI0MdO3ZUYGCgfHx81LRpUy1duvSK9zEMQ6+//rpGjBihzp07q3nz5pozZ46OHz/Oa3cAAHB1Feg1t8tmc58+fVrLli3ThAkT5OPjU+p4QEDAVa/18/PT7NmzFRISop07d6p3797y8/PTkCFDJEmJiYlq2bKlpk2bJk9PT6Wnp6ty5cqSpKSkJOXn52vdunXy8fHRnj175Ovre8X7HDlyRFlZWYqLi7Pt8/f31+23364NGzaoa9euf6AHAAAAyj+XJZMHDx6UYRhq3Lixw9eOGDHC9uf69etr0KBBmj9/vi2ZzMzM1ODBg21tR0RE2M7PzMxUQkKCmjVrJkkKDw+/6n2ysrIkSUFBQSX2BwUF2Y79Wl5envLy8myfc3NzHXk0AABwPWBpoLJnGIbT137wwQeKjY1VcHCwfH19NWLECGVmZtqOp6SkqFevXoqLi9Mrr7yiQ4cO2Y71799f48ePV2xsrEaPHq1vv/32Dz3Hr6Wmpsrf39+2hYaGmto+AACAO3FZMhkRESGLxaJ9+/Y5dN2GDRuUmJiohx56SEuWLNH27ds1fPhw5efn284ZM2aMdu/erQ4dOmj16tVq0qSJFi5cKEnq1auXDh8+rG7dumnnzp1q1aqVpk6desV7BQcHS5Kys7NL7M/OzrYd+7Vhw4bpzJkztu3YsWMOPR8AALgOVKAxky6Lqnr16mrXrp3S0tJ0/vz5UsdzcnKueN369esVFham4cOHq1WrVoqIiFBGRkap8yIjI5WcnKwVK1YoPj5es2bNsh0LDQ1Vnz59tGDBAg0cOFAzZsy44r0aNGig4ODgEssU5ebm6ptvvlFMTMwVr/Hy8pLVai2xAQAAXK9cmuKmpaWpsLBQt912mz755BMdOHBAe/fu1ZQpU66arEVERCgzM1Pz58/XoUOHNGXKFFvVUZIuXryovn37au3atcrIyNDXX3+tzZs3KyoqSpI0YMAALV++XEeOHNG2bdu0Zs0a27Ffs1gsGjBggMaPH6/PPvtMO3fuVPfu3RUSEqJHHnnE9P4AAADXiQq0zqRLv5s7PDxc27Zt04QJEzRw4ECdOHFCtWrVUnR0tKZNm3bFazp16qTk5GT17dtXeXl56tChg0aOHKkxY8ZIkjw9PXXq1Cl1795d2dnZqlmzpuLj4zV27FhJUmFhoZKSkvT999/LarWqffv2mjx58lVjHDJkiM6fP69nn31WOTk5at26tZYtWyZvb2/T+wMAAKC8sRh/ZCYMfldubq78/f3ldWuyLJW8XB1OuXN02ThXh1Bu+Ver7OoQAKBM5ebmKqiGv86cOeM2w8psv/fjXpGlsrmFJ6PgkvK+eNGtnldyg69TBAAAQPnl0tfcAAAA16UKtM4kySQAAIDZLBbzl/Jx02SS19wAAABwGpVJAAAAs5XFIuMsWg4AAIDrDZVJAAAAs1WgCThUJgEAAOA0KpMAAABmY8wkAAAA8PuoTAIAAJiNMZMAAADA76MyCQAAYLYKNGaSZBIAAMBsvOYGAABAeVa/fn1ZLJZSW1JSkiTp0qVLSkpKUo0aNeTr66uEhARlZ2c7fB+SSQAAAJNdKYkzY3PE5s2bdeLECdu2cuVKSdLjjz8uSUpOTtbixYv10Ucf6csvv9Tx48cVHx/v8LPymhsAAOA6VKtWrRKfX3nlFd1444265557dObMGc2cOVPz5s3TfffdJ0maNWuWoqKitHHjRt1xxx1234fKJAAAgMncoTL5S/n5+frnP/+pp59+WhaLRVu3blVBQYHi4uJs5zRu3Fj16tXThg0bHGqbyiQAAEA5kpubW+Kzl5eXvLy8fvOaRYsWKScnRz179pQkZWVlqUqVKgoICChxXlBQkLKyshyKh8okAACA2SxltEkKDQ2Vv7+/bUtNTf3dcGbOnKkHH3xQISEh5j3j/1CZBAAAKEeOHTsmq9Vq+/x7VcmMjAx98cUXWrBggW1fcHCw8vPzlZOTU6I6mZ2dreDgYIfioTIJAABgsrIcM2m1Wktsv5dMzpo1S7Vr11aHDh1s+6Kjo1W5cmWtWrXKtm///v3KzMxUTEyMQ89KZRIAAOA6VVRUpFmzZqlHjx6qVOn/0z5/f38988wzSklJUfXq1WW1WtWvXz/FxMQ4NJNbIpkEAAAw3R+dfX2VRh2+5IsvvlBmZqaefvrpUscmT54sDw8PJSQkKC8vT+3atdNbb73l8D1IJgEAAK5Tbdu2lWEYVzzm7e2ttLQ0paWl/aF7kEwCAACYzF0qk9cCySQAAIDJKlIyyWxuAAAAOI3KJAAAgNl+sci4qW26ISqTAAAAcBqVSQAAAJMxZhIAAACwA5VJAAAAk1ksKoPKpLnNmYVk8hr5bvHIEl/KDvvUaTvW1SGUWz+tGefqEAAAFQDJJAAAgMksKoMxk25ammTMJAAAAJxGZRIAAMBkzOYGAAAA7EBlEgAAwGwV6BtwSCYBAADMVgavuQ1ecwMAAOB6Q2USAADAZGUxAcf8pYbMQWUSAAAATqMyCQAAYDIqkwAAAIAdqEwCAACYrQItDURlEgAAAE6jMgkAAGAyxkwCAAAAdqAyCQAAYLKKVJkkmQQAADBZRUomec0NAAAAp1GZBAAAMBmVSQAAAMAOVCYBAADMxqLlAAAAwO+jMgkAAGAyxkwCAAAAdqAyCQAAYDIqkwAAAIAdqEwCAACYjMokAAAAYAcqkwAAAGarQOtMkkwCAACYjNfcAAAAgB2oTAIAAJiMyiQAAABgh3KRTFosFi1atMjVYQAAANjFIoutOmna5qYzcFyeTGZlZalfv34KDw+Xl5eXQkND1bFjR61atcrVoUmSFixYoLZt26pGjRqyWCxKT093dUgAAABuw6VjJo8eParY2FgFBARo0qRJatasmQoKCrR8+XIlJSVp3759rgxPknT+/Hm1bt1aXbp0Ue/evV0dDgAAKAcYM3mNPP/887JYLNq0aZMSEhIUGRmppk2bKiUlRRs3brzqdUOHDlVkZKSqVaum8PBwjRw5UgUFBbbjO3bsUJs2beTn5yer1aro6Ght2bJFkpSRkaGOHTsqMDBQPj4+atq0qZYuXXrVe3Xr1k2jRo1SXFyceQ8OAABwnXBZZfL06dNatmyZJkyYIB8fn1LHAwICrnqtn5+fZs+erZCQEO3cuVO9e/eWn5+fhgwZIklKTExUy5YtNW3aNHl6eio9PV2VK1eWJCUlJSk/P1/r1q2Tj4+P9uzZI19f3zJ5RgAAUEGxaHnZO3jwoAzDUOPGjR2+dsSIEbY/169fX4MGDdL8+fNtyWRmZqYGDx5sazsiIsJ2fmZmphISEtSsWTNJUnh4+B95jFLy8vKUl5dn+5ybm2tq+wAAAO7EZa+5DcNw+toPPvhAsbGxCg4Olq+vr0aMGKHMzEzb8ZSUFPXq1UtxcXF65ZVXdOjQIdux/v37a/z48YqNjdXo0aP17bff/qHn+LXU1FT5+/vbttDQUFPbBwAA7s/0mdxOjsH84Ycf9Oc//1k1atRQ1apV1axZM9vQP+lyPjZq1CjVqVNHVatWVVxcnA4cOODQPVyWTEZERMhisTg8yWbDhg1KTEzUQw89pCVLlmj79u0aPny48vPzbeeMGTNGu3fvVocOHbR69Wo1adJECxculCT16tVLhw8fVrdu3bRz5061atVKU6dONe25hg0bpjNnzti2Y8eOmdY2AACAvX766SfFxsaqcuXK+ve//609e/bo1VdfVWBgoO2ciRMnasqUKZo+fbq++eYb+fj4qF27drp06ZLd93FZMlm9enW1a9dOaWlpOn/+fKnjOTk5V7xu/fr1CgsL0/Dhw9WqVStFREQoIyOj1HmRkZFKTk7WihUrFB8fr1mzZtmOhYaGqk+fPlqwYIEGDhyoGTNmmPZcXl5eslqtJTYAAFCxuENl8m9/+5tCQ0M1a9Ys3XbbbWrQoIHatm2rG2+8UdLlquTrr7+uESNGqHPnzmrevLnmzJmj48ePO7S+t0tnc6elpamwsFC33XabPvnkEx04cEB79+7VlClTFBMTc8VrIiIilJmZqfnz5+vQoUOaMmWKreooSRcvXlTfvn21du1aZWRk6Ouvv9bmzZsVFRUlSRowYICWL1+uI0eOaNu2bVqzZo3t2JWcPn1a6enp2rNnjyRp//79Sk9PV1ZWlok9AQAAricWS9ls0uX5GL/cfjlX45c+++wztWrVSo8//rhq166tli1bliigHTlyRFlZWSVWrPH399ftt9+uDRs22P2sLk0mw8PDtW3bNrVp00YDBw7UTTfdpAceeECrVq3StGnTrnhNp06dlJycrL59+6pFixZav369Ro4caTvu6empU6dOqXv37oqMjFSXLl304IMPauzYsZKkwsJCJSUlKSoqSu3bt1dkZKTeeuutq8b42WefqWXLlurQoYMkqWvXrmrZsqWmT59uYk8AAADYJzQ0tMT8jNTU1Cued/jwYU2bNk0RERFavny5nnvuOfXv31/vvfeeJNkKY0FBQSWuCwoKcqhoZjH+yEwY/K7c3Fz5+/srI+s0r7ydUKftWFeHUG79tGacq0MAgDKVm5uroBr+OnPmjNv8ji3+vR/e72N5eJVe+vCPKMo7r8NTH9OxY8dKPK+Xl5e8vLxKnV+lShW1atVK69evt+3r37+/Nm/erA0bNmj9+vWKjY3V8ePHVadOHds5Xbp0kcVi0QcffGBXXC7/OkUAAADY79dzM66USEpSnTp11KRJkxL7oqKibCvgBAcHS5Kys7NLnJOdnW07Zg+SSQAAALOVxXhJB1cGio2N1f79+0vs++677xQWFiZJatCggYKDg7Vq1Srb8dzcXH3zzTdXnbtyJS79bm4AAACUjeTkZN155516+eWX1aVLF23atEnvvPOO3nnnHUmXZ5wPGDBA48ePV0REhBo0aKCRI0cqJCREjzzyiN33IZkEAAAwmbOLjP9em4649dZbtXDhQg0bNkzjxo1TgwYN9PrrrysxMdF2zpAhQ3T+/Hk9++yzysnJUevWrbVs2TJ5e3vbfR+SSQAAgOvUww8/rIcffviqxy0Wi8aNG6dx45yftEkyCQAAYLJfrgtpZpvuiAk4AAAAcBqVSQAAAJN5eFjk4WFuKdEwuT2zkEwCAACYjNfcAAAAgB2oTAIAAJjMHZYGulaoTAIAAMBpVCYBAABMxphJAAAAwA5UJgEAAEzGmEkAAADADlQmAQAATEZlEgAAALADlUkAAACTMZsbAAAAsAOVSQAAAJNZVAZjJuWepUmSSQAAAJPxmhsAAACwA5VJAAAAk7E0EAAAAGAHKpMAAAAmY8wkAAAAYAcqkwAAACZjzCQAAABgByqTAAAAJmPMJAAAAGAHKpMAAAAmq0hjJkkmAQAAzFYGr7nd9Ku5SSavFe/KnvKu7OnqMMqdn9aMc3UI5VbgHQNcHUK59dPG110dAgCUGySTAAAAJqtIr7mZgAMAAACnUZkEAAAwGUsDAQAAAHagMgkAAGAyxkwCAAAAdqAyCQAAYDLGTAIAAAB2oDIJAABgMsZMAgAAAHagMgkAAGCyilSZJJkEAAAwGRNwAAAAADtQmQQAADBZRXrNTWUSAAAATqMyCQAAYDLGTAIAAAB2oDIJAABgMsZMAgAAoFwbM2aMLakt3ho3bmw7funSJSUlJalGjRry9fVVQkKCsrOzHb4PySQAAIDJLPr/cZOmbU7E0bRpU504ccK2/ec//7EdS05O1uLFi/XRRx/pyy+/1PHjxxUfH+/wPXjNDQAAcJ2qVKmSgoODS+0/c+aMZs6cqXnz5um+++6TJM2aNUtRUVHauHGj7rjjDrvvQWUSAADAZB4WS5lskpSbm1tiy8vLu2ocBw4cUEhIiMLDw5WYmKjMzExJ0tatW1VQUKC4uDjbuY0bN1a9evW0YcMGx57Vif4BAACAi4SGhsrf39+2paamXvG822+/XbNnz9ayZcs0bdo0HTlyRHfddZfOnj2rrKwsValSRQEBASWuCQoKUlZWlkPx8JobAADAZGW5zuSxY8dktVpt+728vK54/oMPPmj7c/PmzXX77bcrLCxMH374oapWrWpaXFQmAQAATPbrWdRmbZJktVpLbFdLJn8tICBAkZGROnjwoIKDg5Wfn6+cnJwS52RnZ19xjOVvIZkEAACoAM6dO6dDhw6pTp06io6OVuXKlbVq1Srb8f379yszM1MxMTEOtctrbgAAAJN5WC5vZrfpiEGDBqljx44KCwvT8ePHNXr0aHl6euqJJ56Qv7+/nnnmGaWkpKh69eqyWq3q16+fYmJiHJrJLZFMAgAAXJe+//57PfHEEzp16pRq1aql1q1ba+PGjapVq5YkafLkyfLw8FBCQoLy8vLUrl07vfXWWw7fh2QSAADAbJYy+PpDB5ubP3/+bx739vZWWlqa0tLS/kBQjJkEAADAH0BlEgAAwGRluTSQu6EyCQAAAKdRmQQAADCZ5X//md2mOyoXlUmLxaJFixa5OgwAAAD8isuTyaysLPXr10/h4eHy8vJSaGioOnbsWGIRTVcpKCjQ0KFD1axZM/n4+CgkJETdu3fX8ePHXR0aAABwY8XrTJq9uSOXvuY+evSoYmNjFRAQoEmTJqlZs2YqKCjQ8uXLlZSUpH379rkyPF24cEHbtm3TyJEjdfPNN+unn37SCy+8oE6dOmnLli0ujQ0AALivX379oZltuiOXViaff/55WSwWbdq0SQkJCYqMjFTTpk2VkpKijRs3XvW6oUOHKjIyUtWqVVN4eLhGjhypgoIC2/EdO3aoTZs28vPzk9VqVXR0tC35y8jIUMeOHRUYGCgfHx81bdpUS5cuveJ9/P39tXLlSnXp0kWNGjXSHXfcoTfffFNbt25VZmamuZ0BAABQDrmsMnn69GktW7ZMEyZMkI+PT6njAQEBV73Wz89Ps2fPVkhIiHbu3KnevXvLz89PQ4YMkSQlJiaqZcuWmjZtmjw9PZWenq7KlStLkpKSkpSfn69169bJx8dHe/bska+vr91xnzlzRhaL5TfjAwAAFVtFWhrIZcnkwYMHZRiGGjdu7PC1I0aMsP25fv36GjRokObPn29LJjMzMzV48GBb2xEREbbzMzMzlZCQoGbNmkmSwsPD7b7vpUuXNHToUD3xxBOyWq1XPCcvL095eXm2z7m5ufY/GAAAQDnjstfchmE4fe0HH3yg2NhYBQcHy9fXVyNGjCjx2jklJUW9evVSXFycXnnlFR06dMh2rH///ho/frxiY2M1evRoffvtt3bds6CgQF26dJFhGJo2bdpVz0tNTZW/v79tCw0Ndfo5AQBA+eRhsZTJ5o5clkxGRETIYrE4PMlmw4YNSkxM1EMPPaQlS5Zo+/btGj58uPLz823njBkzRrt371aHDh20evVqNWnSRAsXLpQk9erVS4cPH1a3bt20c+dOtWrVSlOnTv3NexYnkhkZGVq5cuVVq5KSNGzYMJ05c8a2HTt2zKHnAwAAKE9clkxWr15d7dq1U1pams6fP1/qeE5OzhWvW79+vcLCwjR8+HC1atVKERERysjIKHVeZGSkkpOTtWLFCsXHx2vWrFm2Y6GhoerTp48WLFiggQMHasaMGVeNsziRPHDggL744gvVqFHjN5/Ly8tLVqu1xAYAACqW4jGTZm/uyKWzudPS0lRYWKjbbrtNn3zyiQ4cOKC9e/dqypQpiomJueI1ERERyszM1Pz583Xo0CFNmTLFVnWUpIsXL6pv375au3atMjIy9PXXX2vz5s2KioqSJA0YMEDLly/XkSNHtG3bNq1Zs8Z27NcKCgr02GOPacuWLZo7d64KCwuVlZWlrKysEpVQAACAisql60yGh4dr27ZtmjBhggYOHKgTJ06oVq1aio6Ovuq4xE6dOik5OVl9+/ZVXl6eOnTooJEjR2rMmDGSJE9PT506dUrdu3dXdna2atasqfj4eI0dO1aSVFhYqKSkJH3//feyWq1q3769Jk+efMV7/fDDD/rss88kSS1atChxbM2aNbr33ntN6QcAAHB9qUjrTFqMPzITBr8rNzdX/v7+yj51hlfeuKYC7xjg6hDKrZ82vu7qEADYITc3V0E1/HXmjPv8ji3+vd8p7UtVrmr/0oP2KLh4Tp8l3eNWzyu5uDIJAABwPWKdyV8pftVrj06dOjkdDAAAAMoXu5LJRx55xK7GLBaLCgsL/0g8AAAA5V5ZrAvprutM2pVMFhUVlXUcAAAA1w3L/zaz23RHf2hpoEuXLpkVBwAAAMohh5PJwsJCvfTSS7rhhhvk6+urw4cPS5JGjhypmTNnmh4gAABAeVO8NJDZmztyOJmcMGGCZs+erYkTJ6pKlSq2/TfddJP+8Y9/mBocAAAA3JvDyeScOXP0zjvvKDExUZ6enrb9N998s8Pfsw0AAHA98rCUzeaOHE4mf/jhBzVs2LDU/qKiIhUUFJgSFAAAAMoHh5PJJk2a6Kuvviq1/+OPP1bLli1NCQoAAKA8q0hjJh3+BpxRo0apR48e+uGHH1RUVKQFCxZo//79mjNnjpYsWVIWMQIAAMBNOVyZ7Ny5sxYvXqwvvvhCPj4+GjVqlPbu3avFixfrgQceKIsYAQAAyp3ir1Q0a3NXTn0391133aWVK1eaHQsAAADKGaeSSUnasmWL9u7dK+nyOMro6GjTggIAACjPymKM43UzZvL777/XE088oa+//loBAQGSpJycHN15552aP3++6tata3aMAAAAcFMOj5ns1auXCgoKtHfvXp0+fVqnT5/W3r17VVRUpF69epVFjAAAAOVKRVpn0uHK5Jdffqn169erUaNGtn2NGjXS1KlTddddd5kaHAAAQHlUkV5zO1yZDA0NveLi5IWFhQoJCTElKAAAAJQPDieTkyZNUr9+/bRlyxbbvi1btuiFF17Q3//+d1ODAwAAKI8sZbS5I7tecwcGBpYorZ4/f1633367KlW6fPnPP/+sSpUq6emnn9YjjzxSJoECAADA/diVTL7++utlHAYAAMD1w8NikYfJYxzNbs8sdiWTPXr0KOs4AAAAUA45vWi5JF26dEn5+fkl9lmt1j8UEAAAQHlXFl+B6KaFSccn4Jw/f159+/ZV7dq15ePjo8DAwBIbAAAAKg6Hk8khQ4Zo9erVmjZtmry8vPSPf/xDY8eOVUhIiObMmVMWMQIAAJQrxetMmr25I4dfcy9evFhz5szRvffeq6eeekp33XWXGjZsqLCwMM2dO1eJiYllEScAAADckMOVydOnTys8PFzS5fGRp0+fliS1bt1a69atMzc6AACAcqh4zKTZmztyOJkMDw/XkSNHJEmNGzfWhx9+KOlyxTIgIMDU4AAAAMqj4qWBzN7ckcPJ5FNPPaUdO3ZIkl588UWlpaXJ29tbycnJGjx4sOkBAgAAwH05PGYyOTnZ9ue4uDjt27dPW7duVcOGDdW8eXNTgwMAACiPKtLSQH9onUlJCgsLU1hYmBmxAAAAoJyxK5mcMmWK3Q3279/f6WAAAACuB2WxlM8fbe+VV17RsGHD9MILL9i+KvvSpUsaOHCg5s+fr7y8PLVr105vvfWWgoKC7G7XrmRy8uTJdjVmsVhIJgEAANzM5s2b9fbbb5cakpicnKzPP/9cH330kfz9/dW3b1/Fx8fr66+/trttu5LJ4tnbAMqPnza+7uoQyq3AW/u6OoRy66fNb7o6hHLrUkGhq0Mod9y5zzzkxCxnO9p0xrlz55SYmKgZM2Zo/Pjxtv1nzpzRzJkzNW/ePN13332SpFmzZikqKkobN27UHXfcUaZxAQAAoBxISkpShw4dFBcXV2L/1q1bVVBQUGJ/48aNVa9ePW3YsMHu9v/wBBwAAACUVJZjJnNzc0vs9/LykpeX1xWvmT9/vrZt26bNmzeXOpaVlaUqVaqUWic8KChIWVlZdsdFZRIAAKAcCQ0Nlb+/v21LTU294nnHjh3TCy+8oLlz58rb27vM4qEyCQAAYDKLRfIoo3Umjx07JqvVatt/tark1q1bdfLkSd1yyy22fYWFhVq3bp3efPNNLV++XPn5+crJySlRnczOzlZwcLDdcZFMAgAAlCNWq7VEMnk1999/v3bu3Fli31NPPaXGjRtr6NChCg0NVeXKlbVq1SolJCRIkvbv36/MzEzFxMTYHY9TyeRXX32lt99+W4cOHdLHH3+sG264Qe+//74aNGig1q1bO9MkAADAdcOjDCqTjrbn5+enm266qcQ+Hx8f1ahRw7b/mWeeUUpKiqpXry6r1ap+/fopJibG7pnckhNjJj/55BO1a9dOVatW1fbt25WXlyfp8vTyl19+2dHmAAAArjvFE3DM3sw2efJkPfzww0pISNDdd9+t4OBgLViwwKE2HE4mx48fr+nTp2vGjBmqXLmybX9sbKy2bdvmaHMAAAC4RtauXWv79htJ8vb2Vlpamk6fPq3z589rwYIFDo2XlJx4zb1//37dfffdpfb7+/srJyfH0eYAAACuO+7wmvtacbgyGRwcrIMHD5ba/5///Efh4eGmBAUAAIDyweFksnfv3nrhhRf0zTffyGKx6Pjx45o7d64GDRqk5557rixiBAAAKFcslrLZ3JHDr7lffPFFFRUV6f7779eFCxd09913y8vLS4MGDVK/fv3KIkYAAAC4KYeTSYvFouHDh2vw4ME6ePCgzp07pyZNmsjX17cs4gMAACh3PCwWeZhcSjS7PbM4vWh5lSpV1KRJEzNjAQAAQDnjcDLZpk2b31znaPXq1X8oIAAAgPLOQ05MTLGjTXfkcDLZokWLEp8LCgqUnp6uXbt2qUePHmbFBQAAgHLA4WRy8uTJV9w/ZswYnTt37g8HBAAAUN6VxexrNx0yaV7F9M9//rPeffdds5oDAAAotzxksU3CMW2Te2aTpiWTGzZskLe3t1nNAQAAoBxw+DV3fHx8ic+GYejEiRPasmWLRo4caVpgAAAA5VVFes3tcDLp7+9f4rOHh4caNWqkcePGqW3btqYFBgAAAPfnUDJZWFiop556Ss2aNVNgYGBZxQQAAFCueVgub2a36Y4cGjPp6emptm3bKicnp4zCAQAAQHni8AScm266SYcPHy6LWAAAAK4LFotMn83trmMmHU4mx48fr0GDBmnJkiU6ceKEcnNzS2wAAACoOOweMzlu3DgNHDhQDz30kCSpU6dOJb5W0TAMWSwWFRYWmh8lAABAOcJs7isYO3as+vTpozVr1pRlPAAAAChH7E4mDcOQJN1zzz1lFgwAAMD1gNncV2Fx1/oqAAAAXMKhdSYjIyN/N6E8ffr0HwoIAACgvLP87z+z23RHDiWTY8eOLfUNOAAAACipIr3mdiiZ7Nq1q2rXrl1WsQAAAKCcsTuZZLwkAACAfSpSZdLuCTjFs7ldwWKxaNGiRS67PwAAAK7M7mSyqKioTF5xZ2VlqV+/fgoPD5eXl5dCQ0PVsWNHrVq1yvR7OWPMmDFq3LixfHx8FBgYqLi4OH3zzTeuDgsAALgxi8VSJps7cmjMpNmOHj2q2NhYBQQEaNKkSWrWrJkKCgq0fPlyJSUlad++fa4MT9LlGexvvvmmwsPDdfHiRU2ePFlt27bVwYMHVatWLVeHBwAA4FIOfze3mZ5//nlZLBZt2rRJCQkJioyMVNOmTZWSkqKNGzde9bqhQ4cqMjJS1apVU3h4uEaOHKmCggLb8R07dqhNmzby8/OT1WpVdHS0tmzZIknKyMhQx44dFRgYKB8fHzVt2lRLly696r2efPJJxcXFKTw8XE2bNtVrr72m3Nxcffvtt+Z1BAAAuK4Uj5k0e3NHLqtMnj59WsuWLdOECRPk4+NT6nhAQMBVr/Xz89Ps2bMVEhKinTt3qnfv3vLz89OQIUMkSYmJiWrZsqWmTZsmT09Ppaenq3LlypKkpKQk5efna926dfLx8dGePXvk6+trV8z5+fl655135O/vr5tvvvmK5+Tl5SkvL8/2OTc31662AQAAyiOXJZMHDx6UYRhq3Lixw9eOGDHC9uf69etr0KBBmj9/vi2ZzMzM1ODBg21tR0RE2M7PzMxUQkKCmjVrJkkKDw//3fstWbJEXbt21YULF1SnTh2tXLlSNWvWvOK5qampGjt2rMPPBAAArh8Wy+XN7Dbdkctec/+R2eEffPCBYmNjFRwcLF9fX40YMUKZmZm24ykpKerVq5fi4uL0yiuv6NChQ7Zj/fv31/jx4xUbG6vRo0fb9bq6TZs2Sk9P1/r169W+fXt16dJFJ0+evOK5w4YN05kzZ2zbsWPHnH5OAAAAd+eyZDIiIkIWi8XhSTYbNmxQYmKiHnroIS1ZskTbt2/X8OHDlZ+fbztnzJgx2r17tzp06KDVq1erSZMmWrhwoSSpV69eOnz4sLp166adO3eqVatWmjp16m/e08fHRw0bNtQdd9yhmTNnqlKlSpo5c+YVz/Xy8pLVai2xAQCAisXDYimTzR25LJmsXr262rVrp7S0NJ0/f77U8ZycnCtet379eoWFhWn48OFq1aqVIiIilJGRUeq8yMhIJScna8WKFYqPj9esWbNsx0JDQ9WnTx8tWLBAAwcO1IwZMxyKvaioqMS4SAAAgIrKpbO509LSVFhYqNtuu02ffPKJDhw4oL1792rKlCmKiYm54jURERHKzMzU/PnzdejQIU2ZMsVWdZSkixcvqm/fvlq7dq0yMjL09ddfa/PmzYqKipIkDRgwQMuXL9eRI0e0bds2rVmzxnbs186fP6+//vWv2rhxozIyMrR161Y9/fTT+uGHH/T444+b3yEAAOC6wGzuayQ8PFzbtm3ThAkTNHDgQJ04cUK1atVSdHS0pk2bdsVrOnXqpOTkZPXt21d5eXnq0KGDRo4cqTFjxkiSPD09derUKXXv3l3Z2dmqWbOm4uPjbZNiCgsLlZSUpO+//15Wq1Xt27fX5MmTr3gvT09P7du3T++9955+/PFH1ahRQ7feequ++uorNW3atEz6BAAAXAfKYAKO3DSZtBiu/J7ECiA3N1f+/v7KPnWG8ZNAORF4a19Xh1Bu/bT5TVeHUG5dKih0dQjlTm5ursKCq+vMGff5HVv8e/9vy3eoqo+fqW1fPH9WQ9vd7FbPK7m4MgkAAHA98pBFHiaXEs1uzywuHTMJAACA8o3KJAAAgMlYtBwAAACwA5VJAAAAk5XFUj7uujQQlUkAAAA4jcokAACAycri6w/5OkUAAABcd6hMAgAAmKwizeYmmQQAADCZh8rgNTeLlgMAAOB6QzIJAABgsuLX3GZvjpg2bZqaN28uq9Uqq9WqmJgY/fvf/7Ydv3TpkpKSklSjRg35+voqISFB2dnZDj8rySQAAMB1qG7dunrllVe0detWbdmyRffdd586d+6s3bt3S5KSk5O1ePFiffTRR/ryyy91/PhxxcfHO3wfxkwCAACYzEPmV+wcba9jx44lPk+YMEHTpk3Txo0bVbduXc2cOVPz5s3TfffdJ0maNWuWoqKitHHjRt1xxx1lFhcAAABcKDc3t8SWl5f3u9cUFhZq/vz5On/+vGJiYrR161YVFBQoLi7Odk7jxo1Vr149bdiwwaF4SCYBAABMZrFYymSTpNDQUPn7+9u21NTUq8axc+dO+fr6ysvLS3369NHChQvVpEkTZWVlqUqVKgoICChxflBQkLKyshx6Vl5zAwAAlCPHjh2T1Wq1ffby8rrquY0aNVJ6errOnDmjjz/+WD169NCXX35pajwkkwAAACaz/G8zu01JttnZ9qhSpYoaNmwoSYqOjtbmzZv1xhtv6E9/+pPy8/OVk5NTojqZnZ2t4OBgh+LiNTcAAEAFUVRUpLy8PEVHR6ty5cpatWqV7dj+/fuVmZmpmJgYh9qkMgkAAGAyD0sZfAOOg+0NGzZMDz74oOrVq6ezZ89q3rx5Wrt2rZYvXy5/f38988wzSklJUfXq1WW1WtWvXz/FxMQ4NJNbIpkEAAC4Lp08eVLdu3fXiRMn5O/vr+bNm2v58uV64IEHJEmTJ0+Wh4eHEhISlJeXp3bt2umtt95y+D4kkwAAAGXA1d+kPXPmzN887u3trbS0NKWlpf2h+5BMAgAAmMyZrz+0p013xAQcAAAAOI3KJAAAgMl+uci4mW26IyqTAAAAcBqVSQAAAJN5yPyKnbtWAN01LgAAAJQDVCYBAABMxphJAAAAwA5UJgEAAExmkfmLlrtnXZLKJAAAAP4AKpMAAAAmq0hjJkkmAeBXftr8pqtDKLcC7x7m6hDKrZ/Wpbo6hHInv7Knq0O4KpYGAgAAAOxAZRIAAMBkFek1N5VJAAAAOI3KJAAAgMlYGggAAACwA5VJAAAAk1kslzez23RHVCYBAADgNCqTAAAAJvOQRR4mj3I0uz2zUJkEAACA06hMAgAAmIwxkwAAAIAdqEwCAACYzPK//8xu0x2RTAIAAJiM19wAAACAHahMAgAAmMxSBksDuetrbiqTAAAAcBqVSQAAAJMxZhIAAACwA5VJAAAAk1GZBAAAAOxAZRIAAMBkFWnRciqTAAAAcBqVSQAAAJN5WC5vZrfpjqhMAgAAwGlUJgEAAExWkcZMkkwCAACYjKWBAAAAADtQmQQAADCZRea/lnbTwiSVSQAAADiPyiQAAIDJWBoIAAAAsAOVSQAAAJNVpKWBqEwCAADAaeUimbRYLFq0aJGrwwAAALBL8TqTZm/uyOXJZFZWlvr166fw8HB5eXkpNDRUHTt21KpVq1wdWil9+vSRxWLR66+/7upQAAAA3IJLk8mjR48qOjpaq1ev1qRJk7Rz504tW7ZMbdq0UVJSkitDK2XhwoXauHGjQkJCXB0KAABwc5Yy2hyRmpqqW2+9VX5+fqpdu7YeeeQR7d+/v8Q5ly5dUlJSkmrUqCFfX18lJCQoOzvbofu4NJl8/vnnZbFYtGnTJiUkJCgyMlJNmzZVSkqKNm7ceNXrhg4dqsjISFWrVk3h4eEaOXKkCgoKbMd37NihNm3ayM/PT1arVdHR0dqyZYskKSMjQx07dlRgYKB8fHzUtGlTLV269Dfj/OGHH9SvXz/NnTtXlStXNufhAQDAdctDFnlYTN4cTCe//PJLJSUlaePGjVq5cqUKCgrUtm1bnT9/3nZOcnKyFi9erI8++khffvmljh8/rvj4eIfu47LZ3KdPn9ayZcs0YcIE+fj4lDoeEBBw1Wv9/Pw0e/ZshYSEaOfOnerdu7f8/Pw0ZMgQSVJiYqJatmypadOmydPTU+np6bYkMCkpSfn5+Vq3bp18fHy0Z88e+fr6XvVeRUVF6tatmwYPHqymTZv+7nPl5eUpLy/P9jk3N/d3rwEAADDbsmXLSnyePXu2ateura1bt+ruu+/WmTNnNHPmTM2bN0/33XefJGnWrFmKiorSxo0bdccdd9h1H5clkwcPHpRhGGrcuLHD144YMcL25/r162vQoEGaP3++LZnMzMzU4MGDbW1HRETYzs/MzFRCQoKaNWsmSQoPD//Ne/3tb39TpUqV1L9/f7tiS01N1dixYx16HgAAcH1x5rW0PW3+EWfOnJEkVa9eXZK0detWFRQUKC4uznZO48aNVa9ePW3YsMHuZNJlr7kNw3D62g8++ECxsbEKDg6Wr6+vRowYoczMTNvxlJQU9erVS3FxcXrllVd06NAh27H+/ftr/Pjxio2N1ejRo/Xtt99e9T5bt27VG2+8odmzZ8ti5xSqYcOG6cyZM7bt2LFjTj8nAADAr+Xm5pbYfvlG9GqKioo0YMAAxcbG6qabbpJ0eRJ0lSpVSr0NDgoKUlZWlt3xuCyZjIiIkMVi0b59+xy6bsOGDUpMTNRDDz2kJUuWaPv27Ro+fLjy8/Nt54wZM0a7d+9Whw4dtHr1ajVp0kQLFy6UJPXq1UuHDx9Wt27dtHPnTrVq1UpTp0694r2++uornTx5UvXq1VOlSpVUqVIlZWRkaODAgapfv/4Vr/Hy8pLVai2xAQCACqYMZ+CEhobK39/ftqWmpv5uOElJSdq1a5fmz59v3jP+j8uSyerVq6tdu3ZKS0srMRC0WE5OzhWvW79+vcLCwjR8+HC1atVKERERysjIKHVeZGSkkpOTtWLFCsXHx2vWrFm2Y6GhoerTp48WLFiggQMHasaMGVe8V7du3fTtt98qPT3dtoWEhGjw4MFavny5cw8OAADwBxw7dqzEW9Bhw4b95vl9+/bVkiVLtGbNGtWtW9e2Pzg4WPn5+aVyruzsbAUHB9sdj0u/TjEtLU2xsbG67bbbNG7cODVv3lw///yzVq5cqWnTpmnv3r2lromIiFBmZqbmz5+vW2+9VZ9//rmt6ihJFy9e1ODBg/XYY4+pQYMG+v7777V582YlJCRIkgYMGKAHH3xQkZGR+umnn7RmzRpFRUVdMb4aNWqoRo0aJfZVrlxZwcHBatSokYk9AQAAridl+XWK9r75NAxD/fr108KFC7V27Vo1aNCgxPHo6GhVrlxZq1atsuVJ+/fvV2ZmpmJiYuyOy6XJZHh4uLZt26YJEyZo4MCBOnHihGrVqqXo6GhNmzbtitd06tRJycnJ6tu3r/Ly8tShQweNHDlSY8aMkSR5enrq1KlT6t69u7Kzs1WzZk3Fx8fbJsUUFhYqKSlJ33//vaxWq9q3b6/Jkydfq0cGAAC4JpKSkjRv3jx9+umn8vPzs42D9Pf3V9WqVeXv769nnnlGKSkpql69uqxWq/r166eYmBi7J99IksX4IzNh8Ltyc3Pl7++v7FNnGD8J4LoXePdvv27D1f207vfHvaGk3NxcBdXw15kz7vM7tvj3/qr0TPn6mRvTubO5ur9FPbuf92qTh2fNmqWePXtKurxo+cCBA/Wvf/1LeXl5ateund56663y85obAAAAZcOeeqG3t7fS0tKUlpbm9H1IJgEAAEzmjutMlhWXfp0iAAAAyjcqkwAAAGarQKVJkkkAAACTleXSQO6G19wAAABwGpVJAAAAk1kslzez23RHVCYBAADgNCqTAAAAJqtA82+oTAIAAMB5VCYBAADMVoFKk1QmAQAA4DQqkwAAACZjnUkAAADADlQmAQAATMY6kwAAAIAdqEwCAACYrAJN5iaZBAAAMF0FyiZ5zQ0AAACnUZkEAAAwGUsDAQAAAHagMgkAAGAylgYCAAAA7EBlEgAAwGQVaDI3lUkAAAA4j8okAACA2SpQaZLKJAAAAJxGZRIAAMBkFWmdSZJJAAAAk7E0EAAAAGAHKpMAAAAmq0Dzb6hMAgAAwHlUJgEAAMxWgUqTJJNwa4VFhqtDKLc8Pdz0pw6uaz+tS3V1COVWYOxgV4dQ7hiFea4OASKZBAAAMF1FWhqIMZMAAABwGpVJAAAAk7HOJAAAAGAHKpMAAAAmq0CTualMAgAAwHlUJgEAAMxWgUqTJJMAAAAmY2kgAAAAwA5UJgEAAMxWBksDuWlhksokAAAAnEdlEgAAwGQVaP4NlUkAAAA4j8okAACA2SpQaZLKJAAAAJxGMgkAAGAySxn956h169apY8eOCgkJkcVi0aJFi0ocNwxDo0aNUp06dVS1alXFxcXpwIEDDt2DZBIAAOA6df78ed18881KS0u74vGJEydqypQpmj59ur755hv5+PioXbt2unTpkt33YMwkAACAySxlsM6kM+09+OCDevDBB694zDAMvf766xoxYoQ6d+4sSZozZ46CgoK0aNEide3a1a57UJkEAAAwmaWMNknKzc0tseXl5TkV45EjR5SVlaW4uDjbPn9/f91+++3asGGD3e2QTAIAAJQjoaGh8vf3t22pqalOtZOVlSVJCgoKKrE/KCjIdswevOYGAAAwWxkuDXTs2DFZrVbbbi8vL5Nv5BgqkwAAAOWI1WotsTmbTAYHB0uSsrOzS+zPzs62HbMHySQAAIDJ3GVpoN/SoEEDBQcHa9WqVbZ9ubm5+uabbxQTE2N3O7zmBgAAuE6dO3dOBw8etH0+cuSI0tPTVb16ddWrV08DBgzQ+PHjFRERoQYNGmjkyJEKCQnRI488Yvc9SCYBAABMZlEZLA3kxDVbtmxRmzZtbJ9TUlIkST169NDs2bM1ZMgQnT9/Xs8++6xycnLUunVrLVu2TN7e3nbfg2QSAADgOnXvvffKMIyrHrdYLBo3bpzGjRvn9D1IJgEAAExWhpO53Q4TcAAAAOA0KpMAAAAmc5evU7wWqEwCAADAaVQmAQAATFdxRk2STAIAAJiM19wAAACAHcpFMmmxWLRo0SJXhwEAAGAXSxlt7sjlyWRWVpb69eun8PBweXl5KTQ0VB07dizxPZGu1LNnT1kslhJb+/btXR0WAACAW3DpmMmjR48qNjZWAQEBmjRpkpo1a6aCggItX75cSUlJ2rdvnyvDs2nfvr1mzZpl++zl5eXCaAAAgLtjzOQ18vzzz8tisWjTpk1KSEhQZGSkmjZtqpSUFG3cuPGq1w0dOlSRkZGqVq2awsPDNXLkSBUUFNiO79ixQ23atJGfn5+sVquio6O1ZcsWSVJGRoY6duyowMBA+fj4qGnTplq6dOlvxunl5aXg4GDbFhgYaE4HAAAAlHMuq0yePn1ay5Yt04QJE+Tj41PqeEBAwFWv9fPz0+zZsxUSEqKdO3eqd+/e8vPz05AhQyRJiYmJatmypaZNmyZPT0+lp6ercuXKkqSkpCTl5+dr3bp18vHx0Z49e+Tr6/ubsa5du1a1a9dWYGCg7rvvPo0fP141atS44rl5eXnKy8uzfc7Nzf29rgAAANcZy//+M7tNd+SyZPLgwYMyDEONGzd2+NoRI0bY/ly/fn0NGjRI8+fPtyWTmZmZGjx4sK3tiIgI2/mZmZlKSEhQs2bNJEnh4eG/ea/27dsrPj5eDRo00KFDh/TXv/5VDz74oDZs2CBPT89S56empmrs2LEOPxMAAEB55LJk0jAMp6/94IMPNGXKFB06dEjnzp3Tzz//LKvVajuekpKiXr166f3331dcXJwef/xx3XjjjZKk/v3767nnntOKFSsUFxenhIQENW/e/Kr36tq1q+3PzZo1U/PmzXXjjTdq7dq1uv/++0udP2zYMKWkpNg+5+bmKjQ01OlnBQAA5VDFWbPcdWMmIyIiZLFYHJ5ks2HDBiUmJuqhhx7SkiVLtH37dg0fPlz5+fm2c8aMGaPdu3erQ4cOWr16tZo0aaKFCxdKknr16qXDhw+rW7du2rlzp1q1aqWpU6faff/w8HDVrFlTBw8evOJxLy8vWa3WEhsAAMD1ymXJZPXq1dWuXTulpaXp/PnzpY7n5ORc8br169crLCxMw4cPV6tWrRQREaGMjIxS50VGRio5OVkrVqxQfHx8idnYoaGh6tOnjxYsWKCBAwdqxowZdsf9/fff69SpU6pTp47d1wAAgIqFdSavkbS0NBUWFuq2227TJ598ogMHDmjv3r2aMmWKYmJirnhNRESEMjMzNX/+fB06dEhTpkyxVR0l6eLFi+rbt6/Wrl2rjIwMff3119q8ebOioqIkSQMGDNDy5ct15MgRbdu2TWvWrLEd+7Vz585p8ODB2rhxo44ePapVq1apc+fOatiwodq1a2d+hwAAAJQzLl1nMjw8XNu2bdOECRM0cOBAnThxQrVq1VJ0dLSmTZt2xWs6deqk5ORk9e3bV3l5eerQoYNGjhypMWPGSJI8PT116tQpde/eXdnZ2apZs6bi4+Ntk2IKCwuVlJSk77//XlarVe3bt9fkyZOveC9PT099++23eu+995STk6OQkBC1bdtWL730EmtNAgCAq6pI60xajD8yEwa/Kzc3V/7+/so+dYbxk04oLOKvp7M8Pdz0pw6AKwqMHezqEModozBPeVun6swZ9/kdW/x7/9D3p+Rnckxnc3N1Y90abvW8kht8nSIAAADKL5e+5gYAALgusTQQAAAA8PuoTAIAAJisAhUmqUwCAADAeVQmAQAATFaRlgaiMgkAAACnUZkEAAAwnUWWCjJqksokAAAAnEZlEgAAwGSMmQQAAADsQDIJAAAAp/GaGwAAwGS85gYAAADsQGUSAADAZJYyWBrI/KWGzEFlEgAAAE6jMgkAAGAyxkwCAAAAdqAyCQAAYDKLzP/yQzctTFKZBAAAgPOoTAIAAJitApUmqUwCAADAaVQmAQAATFaR1pkkmQQAADAZSwMBAAAAdqAyCQAAYLIKNP+GyiQAAACcR2USAADAbBWoNEllEgAA4DqWlpam+vXry9vbW7fffrs2bdpkavskkwAAACazlNF/jvrggw+UkpKi0aNHa9u2bbr55pvVrl07nTx50rRnJZkEAAC4Tr322mvq3bu3nnrqKTVp0kTTp09XtWrV9O6775p2D5JJAAAAkxWvM2n25oj8/Hxt3bpVcXFxtn0eHh6Ki4vThg0bTHtWJuCUMcMwJElnc3NdHEn5VFhkuDqEcsvTw01HagO4IqMwz9UhlDtGYf7l/zXc73dFbhn83i9u89dte3l5ycvLq9T5P/74owoLCxUUFFRif1BQkPbt22daXCSTZezs2bOSpIYNQl0cCQAA16ezZ8/K39/f1WFIkqpUqaLg4GBFlNHvfV9fX4WGlmx79OjRGjNmTJnczx4kk2UsJCREx44dk5+fnyxu9j1Iubm5Cg0N1bFjx2S1Wl0dTrlC3zmPvnMefec8+s557tx3hmHo7NmzCgkJcXUoNt7e3jpy5Ijy8/PLpH3DMErlE1eqSkpSzZo15enpqezs7BL7s7OzFRwcbFpMJJNlzMPDQ3Xr1nV1GL/JarW63Q+I8oK+cx595zz6znn0nfPcte/cpSL5S97e3vL29nZ1GKpSpYqio6O1atUqPfLII5KkoqIirVq1Sn379jXtPiSTAAAA16mUlBT16NFDrVq10m233abXX39d58+f11NPPWXaPUgmAQAArlN/+tOf9N///lejRo1SVlaWWrRooWXLlpWalPNHkExWYF5eXho9evRVx1rg6ug759F3zqPvnEffOY++K//69u1r6mvtX7MY7jifHgAAAOUCi5YDAADAaSSTAAAAcBrJJAAAAJxGMgkAwP8wjcB5RUVFrg4BLkIyid/FDwjn8YvJefy9c05xvxUVFennn392cTTlS1FRkSwWi3788Uf98MMPrg6nXCkqKpKHh4cOHDigtWvXujocXGMkk/hNxT8g9u/fr/79++uJJ57Q2LFjtWvXLleH5vaKfzFlZWXp5MmTrg6nXCn+e3f48GG9/PLLev755/X222+7Oiy398v/vyYlJemhhx5SSkqKsrKyXB1aueDh4aGjR4+qZcuWmjx5so4ePerqkMqF4r936enpuuWWW7R3715Xh4RrjGQSv8nDw0N79+7VbbfdpqNHj8rLy0tvvfWWnn/+eU2ZMsXV4bktwzBsfRcaGqrExET9+OOPrg6rXCj+xbRz507ddddd+uqrr/Tdd9+pX79+evHFF10dntv6db/l5OSoRYsW+sc//qFXXnnF1eGVG1999ZV++OEHrVixQjNmzFBmZqarQ3JrxX/vduzYodatW+vZZ5/Vc8895+qwcI2xziSuyjAMFRYWqk+fPioqKtK7774r6fIXxL/44ovas2ePHnnkEQ0bNszFkbqnkydP6rHHHpOvr6927dqlqKgozZ07VzVr1nR1aG4vMzNTDzzwgDp16qRJkyZJkhYsWKA+ffpo1apVatasmYsjdE9Hjx7V/fffry5duig1NVWS9MYbb2jXrl168803WXTaDt99951SU1MVHR2tCRMmqGfPnkpOTlbt2rVdHZrbOnDggJo3b64BAwYoNTVVBQUF+vTTT3XixAkFBgbq4YcfVkBAgKvDRBniG3BwVRaLRZUqVdKPP/4oHx8fSZcTzKCgIE2cOFGjR4/WkiVLdOONN6pLly4ujtb97Nq1Sw0aNNBzzz0nf39/tW3bVomJiSSUv8MwDC1YsEChoaEl/qHSvHlzeXl5MQ7wKoqKirR48WK1a9dOQ4cOte3ft2+ftmzZojvuuEM33XSTOnbsyP9ff8eaNWs0ffp0FRUVadKkSfL19dWmTZtUr149TZ061dXhuZWioiLNnDlTvr6+ioyMlCR17txZJ06c0IULF3T06FHdd999GjVqlGJiYlwcLcoKr7lxVcWVybp16+qnn37SuXPnJEmFhYWqVauWRo0aJW9vb7333nsujtQ9RUdHq1evXrrjjjsUFRWl5cuXa8+ePUpMTNR///tf23lMNCnJYrHo7rvv1h133KHq1avb9jds2FBVq1ZVdna2C6NzXx4eHuratat69uxpqwK9/PLLeuedd/Too4/qhRde0LFjx/TGG2/o2LFjrg3WTRmGocjISDVp0kSHDh1S//79NWbMGKWmpmrt2rVq3769q0N0Ox4eHurXr5+efPJJTZ8+XTfccIMsFos++ugj7dy5U3v27NHBgwc1ceJEV4eKMkQyiauyWCzy9PRUr169tGrVKr366qu2fYWFhQoODtZrr72mf//739qyZYurw3U7/v7+uuuuuyRdThibNGmiFStWaM+ePfrzn/+sH3/8UT///LPefPNNffbZZy6O1r3ccsstGj9+vKSSM+I9PDx06dIl2+elS5fq+PHj1zw+d2QYhmrVqqXbbrtNkvTTTz/pwoULWrp0qUaNGqWePXvqn//8pzZs2KBNmza5OFr3ZLFYbP/75ZdfSpI2btyoSpUqydvbW5s3b2ZSzq8YhqEbbrhBQ4cOVcuWLXXLLbdo0qRJatiwoapUqaIbb7xRM2fO1KeffqodO3a4OlyUEV5z4zcVFRWpRYsWeuutt/Tss8+qatWqGjJkiDw9PSVJlSpVUuPGjeXn5+fiSN2bh8flf7dFRUVpxYoVatu2rbp166agoCD985//ZPbjb7BYLPr5559tk5qK/64NHz5cqampysjIcHGE7qE4ESoWGBioESNGyNvbW9LlNwrnzp1TdHS06tev74II3V9hYaE8PT3VqlUrFRUVqX///vr888+1Y8cOLV26VCkpKapUqZJefPFFVarEr0/p8t87wzAUEhKicePGaceOHYqIiChxztmzZxUZGak6deq4KEqUNf7fgN9UnAR1795d586d08CBA3Xs2DElJiYqLCxM8+bN06VLlxhc7YCoqCh9/vnnatGihQIDA7Vp06ZSP3xRUnGiZBiGvLy8NGHCBL3xxhvatGmTQkNDXRyd+/rlhBtPT0/NnTtXklS3bl1XheTWiv+R3Lx5cz3++OMKDg7W4sWLFRYWpueee04eHh667777SCR/pTihrF27th544IFSx9etW6e6desyAew6xmxuOGTx4sXq16+fCgsLVbVqVeXl5WnhwoW65ZZbXB1auVFQUKD+/fvr/fff16ZNm9SkSRNXh1Ru3H777Tp//rwOHDigr7/+Wq1atXJ1SOVCenq6Pv74Y02dOlVfffWVmjdv7uqQ3FpmZqbmzJmjjh076uabb7YtfwPHpKen66OPPtLUqVP19ddfswrDdYxkEpIuV3wsFouOHz8ub2/vEhMffi07O1sZGRm6dOmSIiIiKvyrC0f6Trr8A/Yvf/mL3nzzTd16663XKEr3ZG/fFRUV6dy5c2rcuLGys7O1Y8cO3XTTTdc4WvfhyN+5EydOKDk5WXv27NH777+vm2+++RpG6n7s7bu8vDwqab/iyN+748eP67nnntO+ffv04YcfVvi/d9c7/qkF2w+ITz/9VF26dNEXX3yhs2fPXvXcoKAg3Xbbbbr77rtJJB3ou2KNGjXS8uXLSSQd6DsPDw9ZrVa9++672rVrF4mkA3/n6tSpo9TUVC1btqzC/0J3pO9IJEty9O9dSEiIXn31Va1atarC/72rCEgmYfsBkZiYqI4dOyomJqbUhJriAvavB/lXdI70XbGqVasyxlTO9V379u0VFRV1LcN0O870W4MGDRQSEnItw3RLzvQdLnO07wzDUMOGDRmfW0Hwmhv64Ycf1L59e/Xu3Vv9+/dXQUGB8vLy9M0336h69epq2bKlq0N0W/Sd8+g759BvzqPvnEff4bcwJQ2qVKmSfHx8VLduXZ06dUpvvfWWvvjiC+3atUs1a9bUyy+/rISEBFeH6ZboO+fRd86h35xH3zmPvsNv4TV3BVRcjD558qQuXLggb29vGYahqVOnqkGDBtq+fbsSEhK0cuVKhYSEaOfOnS6O2H3Qd86j75xDvzmPvnMefQdHUJmsYIoHUS9evFgTJ07UkCFD1LFjR/3rX//SihUr1LVrVz3xxBOyWq2SJF9fX5bE+B/6znn0nXPoN+fRd86j7+AwAxXOwoULDV9fX2PChAnGoUOHrnjO+fPnjRdffNGoWbOmsX///mscofui75xH3zmHfnMefec8+g6OIJmsYI4dO2Y0btzYeP311w3DMIyCggLj0qVLxrp164w9e/YYhmEY//znP41HH33UCAsLM7Zt2+bKcN0Kfec8+s459Jvz6Dvn0XdwFK+5K5iff/5ZPj4+uuWWW3Ty5Em9++67WrZsmbZu3aqbb75ZL730kuLi4nT06FFNmjRJN954o6tDdhv0nfPoO+fQb86j75xH38FRLA1UweTk5Ojmm29WaGio9u3bp7vvvluxsbG688479dxzz+mJJ57Q0KFD+fqwK6DvnEffOYd+cx595zz6Do6iMnkdM/43iDo3N1fVqlXTxYsXFRAQoPXr12v27Nl68skn1bVrVwUGBspisahu3boqKiqSxOLk9J3z6Dvn0G/Oo++cR9/BFK57w46yVFRUZBiGYSxdutTo2LGjceuttxrdu3c31q1bZxjG5TEwxfLy8oxhw4YZtWrVMr777juXxOtO6Dvn0XfOod+cR985j76DWahPX2eMX3zt4aeffqrHHntMrVq1Uo8ePXTx4kV17dpV69atU6VKlWQYhubMmaNHH31Uc+fO1fLlyxUREeHiJ3Ad+s559J1z6Dfn0XfOo+9gOtfksDDbf//73xKf9+3bZ9xyyy3GtGnTDMMwjKysLOOGG24wwsPDjcDAQGPt2rWGYRhGRkaGMXz48Ar9L036znn0nXPoN+fRd86j71BWSCavA1OmTDGaNWtm7Nq1y7Zv7969Rp8+fYyzZ88amZmZRkREhNG7d28jPT3daNmypREUFGSsWLHCMAzDKCwsdFXoLkffOY++cw795jz6znn0HcoSyeR14Pjx40bt2rWNNm3aGLt377bt/+GHHwzDMIw+ffoYjz/+uHHhwgXDMAzjySefNPz8/IwGDRoY586ds42bqYjoO+fRd86h35xH3zmPvkNZYsxkOWX8b8xLYWGh6tSpox07dmjfvn3q06ePdu3aJUkKCQnRxYsXtWPHDjVp0kRVq1aVJFmtVk2dOlWbNm2Sj49PhZuRR985j75zDv3mPPrOefQdrhlXZrJwTvHrhpMnTxqbN282NmzYYBjG/493ueuuu0r8y/OZZ54xoqKijA8++MAYMGCAERoaahw9etQlsbsafec8+s459Jvz6Dvn0Xe4lkgmy5niHxC7d+82YmNjjfbt2xvx8fHGxYsXDcMo+YOieGzMhg0bjEcffdSoW7eu0bx58wr71Vf0nfPoO+fQb86j75xH3+FaI5ksR4rHrOzatcsICAgw/vrXvxoZGRm2HxzFa4IV/6Bo3bq1sX//fsMwDCM/P9/IyMgwTp065ZrgXYy+cx595xz6zXn0nfPoO7gCyWQ5c+rUKaN169ZG//79S+wv/gHy6x8U99xzj/Htt99e8zjdEX3nPPrOOfSb8+g759F3uNaYgFPOZGVl6cSJE0pISLB9pZX0/19r5enpKcMwFBQUpC1btmjjxo168cUXlZ+f76qQ3QZ95zz6zjn0m/PoO+fRd7jW+G7uciY9PV0ZGRm66667ZLFYVFRUJA+P//83gcVi0YULF7Rjxw7FxMQoMzNTZ86cUZUqVVwYtXug75xH3zmHfnMefec8+g7XGpXJcqZ+/fqqVKmSFixYIEklfkAUe/fddzV69GhduHBBtWvX5quv/oe+cx595xz6zXn0nfPoO1xrJJPlTFhYmKxWq+bMmaOMjAzbfuN/64lJ0tGjRxUdHW1bLwyX0XfOo++cQ785j75zHn2Ha841QzXxR3zyySeGl5eX0a1btxLrhJ0/f94YNmyYERYWZpudh5LoO+fRd86h35xH3zmPvsO1ZDGMX/xTBeVCUVGRZsyYob59+6phw4aKiYmRt7e3fvjhB23cuFHLli1Ty5YtXR2mW6LvnEffOYd+cx595zz6DtcSyWQ5tmnTJk2aNEkHDx6Un5+f7rzzTj3zzDOMfbEDfec8+s459Jvz6Dvn0Xe4Fkgmy7nCwkJ5enq6Ooxyib5zHn3nHPrNefSd8+g7lDUm4JRzv5ylx78LHEPfOY++cw795jz6znn0HcoalUkAAAA4jcokAAAAnEYyCQAAAKeRTAIAAMBpJJMAAABwGskkAAAAnEYyCQAAAKeRTAIAAMBpJJMAyp2ePXvqkUcesX2+9957NWDAgGsex9q1a2WxWJSTk3PVcywWixYtWmR3m2PGjFGLFi3+UFxHjx6VxWJRenr6H2oHAOxBMgnAFD179pTFYpHFYlGVKlXUsGFDjRs3Tj///HOZ33vBggV66aWX7DrXngQQAGC/Sq4OAMD1o3379po1a5by8vK0dOlSJSUlqXLlyho2bFipc/Pz81WlShVT7lu9enVT2gEAOI7KJADTeHl5KTg4WGFhYXruuecUFxenzz77TNL/v5qeMGGCQkJC1KhRI0nSsWPH1KVLFwUEBKh69erq3Lmzjh49amuzsLBQKSkpCggIUI0aNTRkyJBS3y/869fceXl5Gjp0qEJDQ+Xl5aWGDRtq5syZOnr0qNq0aSNJCgwMlMViUc+ePSVJRUVFSk1NVYMGDVS1alXdfPPN+vjjj0vcZ+nSpYqMjFTVqlXVpk2bEnHaa+jQoYqMjFS1atUUHh6ukSNHqqCgoNR5b7/9tkJDQ1WtWjV16dJFZ86cKXH8H//4h6KiouTt7a3GjRvrrbfecjgWADADySSAMlO1alXl5+fbPq9atUr79+/XypUrtWTJEhUUFKhdu3by8/PTV199pa+//lq+vr5q37697bpXX31Vs2fP1rvvvqv//Oc/On36tBYuXPib9+3evbv+9a9/acqUKdq7d6/efvtt+fr6KjQ0VJ988okkaf/+/Tpx4oTeeOMNSVJqaqrmzJmj6dOna/fu3UpOTtaf//xnffnll5IuJ73x8fHq2LGj0tPT1atXL7344osO94mfn59mz56tPXv26I033tCMGTM0efLkEuccPHhQH374oRYvXqxly5Zp+/btev75523H586dq1GjRmnChAnau3evXn75ZY0cOVLvvfeew/EAwB9mAIAJevToYXTu3NkwDMMoKioyVq5caXh5eRmDBg2yHQ8KCjLy8vJs17z//vtGo0aNjKKiItu+vLw8o2rVqsby5csNwzCMOnXqGBMnTrQdLygoMOrWrWu7l2EYxj333GO88MILhmEYxv79+w1JxsqVK68Y55o1awxJxk8//WTbd+nSJaNatWrG+vXrS5z7zDPPGE888YRhGIYxbNgwo0mTJiWODx06tFRbvybJWLhw4VWPT5o0yYiOjrZ9Hj16tOHp6Wl8//33tn3//ve/DQ8PD+PEiROGYRjGjTfeaMybN69EOy+99JIRExNjGIZhHDlyxJBkbN++/ar3BQCzMGYSgGmWLFkiX19fFRQUqKioSE8++aTGjBljO96sWbMS4yR37NihgwcPys/Pr0Q7ly5d0qFDh3TmzBmdOHFCt99+u+1YpUqV1KpVq1Kvuoulp6fL09NT99xzj91xHzx4UBcuXNADDzxQYn9+fr5atmwpSdq7d2+JOCQpJibG7nsU++CDDzRlyhQdOnRI586d088//yyr1VrinHr16umGG24ocZ+ioiLt379ffn5+OnTokJ555hn17t3bds7PP/8sf39/h+MBgD+KZBKAadq0aaNp06apSpUqCgkJUaVKJX/E+Pj4lPh87tw5RUdHa+7cuaXaqlWrllMxVK1a1eFrzp07J0n6/PPPSyRx0uVxoGbZsGGDEhMTNXbsWLVr107+/v6aP3++Xn31VYdjnTFjRqnk1tPT07RYAcBeJJMATOPj46OGDRvaff4tt9yiDz74QLVr1y5VnStWp04dffPNN7r77rslXa7Abd26VbfccssVz2/WrJmKior05ZdfKi4urtTx4spoYWGhbV+TJk3k5eWlzMzMq1Y0o6KibJOJim3cuPH3H/IX1q9fr7CwMA0fPty2LyMjo9R5mZmZOn78uEJCQmz38fDwUKNGjRQUFKSQkBAdPnxYiYmJDt0fAMoCE3AAuExiYqJq1qypzp0766uvvtKRI0e0du1a9e/fX99//70k6YUXXtArr7yiRYsWad++fXr++ed/c43I+vXrq0ePHnr66ae1aNEiW5sffvihJCksLEwWi0VLlizRf//7X507d05+fn4aNGiQkpOT9d577+nQoUPatm2bpk6dapvU0qdPHx04cECDBw/W/v37NW/ePM2ePduh542IiFBmZqbmz5+vQ4cOacqUKVecTOTt7a0ePXpox44d+uqrr9S/f3916dJFwcHBkqSxY8cqNTVVU6ZM0XfffaedO3dq1qxZeu211xyKBwDMQDIJwGWqVaumdevWqV69eoqPj1dUVJSeeeYZXbp0yVapHDhwoLp166YePXooJiZGfn5+evTRR3+z3WnTpumxxx7T888/r8aNG6t37946f/68JOmGG27Q2LFj9eKLLyooKEh9+/aVJL300ksaOXKkUlNTFRUVpfbt2+vzzz9XgwYNJF0ex/jJJ59o0aJFuvnmmzV9+nS9/PLLDj1vp06dlJycrL59+6pFixZav369Ro4cWeq8hg0bKj4+Xg899JDatm2r5s2bl1j6p1evXvrHP/6hWbNmqVmzZrrnnns0e/ZsW6wAcC1ZjKuNYgcAAAB+B5VJAAAAOI1kEgAAAE4jmQQAAIDTSCYBAADgNJJJAAAAOI1kEgAAAE4jmQQAAIDTSCYBAADgNJJJAAAAOI1kEgAAAE4jmQQAAIDTSCYBAADgtP8DvgX/TQjhavQAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import torch\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import classification_report, confusion_matrix\n", - "\n", - "# Test Function\n", - "def test_model(model, test_loader, criterion, device=\"cuda\" if torch.cuda.is_available() else \"cpu\"):\n", - " model = model.to(device)\n", - " model.eval()\n", - "\n", - " running_loss = 0.0\n", - " running_corrects = 0\n", - " total_test = 0\n", - " all_preds = []\n", - " all_labels = []\n", - "\n", - " with torch.no_grad():\n", - " for inputs, labels in tqdm(test_loader, desc=\"Testing\"):\n", - " inputs = inputs.to(device)\n", - " labels = labels.to(device).long() # Ensure labels are of type torch.long\n", - "\n", - " # Forward Pass\n", - " outputs = model(inputs)\n", - " loss = criterion(outputs, labels)\n", - " _, preds = torch.max(outputs, 1)\n", - "\n", - " # Track statistics\n", - " running_loss += loss.item() * inputs.size(0)\n", - " running_corrects += torch.sum(preds == labels.data)\n", - " total_test += labels.size(0)\n", - "\n", - " all_preds.extend(preds.cpu().numpy())\n", - " all_labels.extend(labels.cpu().numpy())\n", - "\n", - " test_loss = running_loss / total_test\n", - " test_acc = running_corrects.double() / total_test\n", - "\n", - " print(f\"Test Loss: {test_loss:.4f} Test Acc: {test_acc:.4f}\")\n", - "\n", - " # Classification Report\n", - " print(\"\\nClassification Report:\")\n", - " print(classification_report(all_labels, all_preds, target_names=[f\"Class {i}\" for i in range(6)]))\n", - "\n", - " # Confusion Matrix\n", - " cm = confusion_matrix(all_labels, all_preds)\n", - " print(\"\\nConfusion Matrix:\")\n", - " print(cm)\n", - "\n", - " # Plot Confusion Matrix\n", - " plt.figure(figsize=(8, 6))\n", - " plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n", - " plt.title(\"Confusion Matrix\")\n", - " plt.colorbar()\n", - " tick_marks = np.arange(6)\n", - " plt.xticks(tick_marks, [f\"Class {i}\" for i in range(6)], rotation=45)\n", - " plt.yticks(tick_marks, [f\"Class {i}\" for i in range(6)])\n", - " plt.ylabel('True label')\n", - " plt.xlabel('Predicted label')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "# Example Usage\n", - "if __name__ == \"__main__\":\n", - " # Assuming test_loader is defined\n", - " model_deepercnn = CustomCNNWithAttention(num_classes=6)\n", - " model_deepercnn.load_state_dict(torch.load(\"customcnnwithAttention.pth\"))\n", - " model_deepercnn.eval()\n", - "\n", - " criterion = nn.CrossEntropyLoss()\n", - "\n", - " # Test the model\n", - " test_model(model_deepercnn, test_loader, criterion)\n" - ] -<<<<<<< HEAD:ml_model/notebooks/notebook[4].ipynb -======= - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_22584\\66978296.py:37: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " model_deepercnn.load_state_dict(torch.load(\"customcnnwithAttention.pth\"))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Classification Report:\n", - " precision recall f1-score support\n", - "\n", - " 0 0.9630 0.9286 0.9455 84\n", - " 1 0.9059 0.9625 0.9333 80\n", - " 2 1.0000 1.0000 1.0000 80\n", - " 3 1.0000 0.9762 0.9880 84\n", - " 4 1.0000 1.0000 1.0000 78\n", - " 5 0.9750 0.9750 0.9750 80\n", - "\n", - " accuracy 0.9733 486\n", - " macro avg 0.9740 0.9737 0.9736 486\n", - "weighted avg 0.9740 0.9733 0.9734 486\n", - "\n", - "Confusion Matrix:\n", - "[[78 6 0 0 0 0]\n", - " [ 3 77 0 0 0 0]\n", - " [ 0 0 80 0 0 0]\n", - " [ 0 0 0 82 0 2]\n", - " [ 0 0 0 0 78 0]\n", - " [ 0 2 0 0 0 78]]\n" - ] - } - ], - "source": [ - "from sklearn.metrics import classification_report, confusion_matrix\n", - "import torch\n", - "\n", - "def test_model(model, test_loader, device=\"cuda\" if torch.cuda.is_available() else \"cpu\"):\n", - " model = model.to(device)\n", - " model.eval()\n", - " \n", - " all_preds = []\n", - " all_targets = []\n", - "\n", - " with torch.no_grad():\n", - " for inputs, labels in test_loader:\n", - " inputs = inputs.to(device)\n", - " labels = labels.to(device).long() # Ensure labels are of type torch.long\n", - "\n", - " # Forward pass\n", - " outputs = model(inputs)\n", - " _, preds = torch.max(outputs, 1)\n", - "\n", - " # Collect predictions and targets\n", - " all_preds.extend(preds.cpu().numpy())\n", - " all_targets.extend(labels.cpu().numpy())\n", - " \n", - " # Generate Classification Report\n", - " print(\"Classification Report:\")\n", - " print(classification_report(all_targets, all_preds, digits=4))\n", - "\n", - " # Generate Confusion Matrix\n", - " print(\"Confusion Matrix:\")\n", - " print(confusion_matrix(all_targets, all_preds))\n", - "\n", - "# Example Usage\n", - "# Assuming test_loader is defined\n", - "if __name__ == \"__main__\":\n", - " # Load the trained model weights\n", - " model_deepercnn = CustomCNNWithAttention(num_classes=6)\n", - " model_deepercnn.load_state_dict(torch.load(\"customcnnwithAttention.pth\"))\n", - "\n", - " # Evaluate the model on test data\n", - " test_model(model_deepercnn, test_loader)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/20, Train Loss: 0.9637, Train Acc: 0.6081\n", - "Validation Loss: 1.6030, Validation Acc: 0.4990\n", - "Epoch 2/20, Train Loss: 0.6486, Train Acc: 0.7564\n", - "Validation Loss: 0.4759, Validation Acc: 0.8289\n", - "Epoch 3/20, Train Loss: 0.4112, Train Acc: 0.8551\n", - "Validation Loss: 0.3839, Validation Acc: 0.8732\n", - "Epoch 4/20, Train Loss: 0.3462, Train Acc: 0.8814\n", - "Validation Loss: 0.9806, Validation Acc: 0.7155\n", - "Epoch 5/20, Train Loss: 0.3261, Train Acc: 0.8866\n", - "Validation Loss: 0.5992, Validation Acc: 0.8216\n", - "Epoch 6/20, Train Loss: 0.2721, Train Acc: 0.9033\n", - "Validation Loss: 0.2847, Validation Acc: 0.9113\n", - "Epoch 7/20, Train Loss: 0.2255, Train Acc: 0.9268\n", - "Validation Loss: 0.2577, Validation Acc: 0.9186\n", - "Epoch 8/20, Train Loss: 0.2269, Train Acc: 0.9301\n", - "Validation Loss: 0.1997, Validation Acc: 0.9423\n", - "Epoch 9/20, Train Loss: 0.1701, Train Acc: 0.9469\n", - "Validation Loss: 0.2421, Validation Acc: 0.9258\n", - "Epoch 10/20, Train Loss: 0.1698, Train Acc: 0.9461\n", - "Validation Loss: 0.2228, Validation Acc: 0.9381\n", - "Epoch 11/20, Train Loss: 0.1469, Train Acc: 0.9559\n", - "Validation Loss: 0.2490, Validation Acc: 0.9361\n", - "Epoch 12/20, Train Loss: 0.1271, Train Acc: 0.9585\n", - "Validation Loss: 0.2432, Validation Acc: 0.9165\n", - "Epoch 13/20, Train Loss: 0.1548, Train Acc: 0.9502\n", - "Validation Loss: 0.3115, Validation Acc: 0.9134\n", - "Epoch 14/20, Train Loss: 0.1170, Train Acc: 0.9611\n", - "Validation Loss: 0.2114, Validation Acc: 0.9351\n", - "Epoch 15/20, Train Loss: 0.1296, Train Acc: 0.9593\n", - "Validation Loss: 0.1878, Validation Acc: 0.9412\n", - "Epoch 16/20, Train Loss: 0.1133, Train Acc: 0.9652\n", - "Validation Loss: 0.2685, Validation Acc: 0.9175\n", - "Epoch 17/20, Train Loss: 0.0965, Train Acc: 0.9701\n", - "Validation Loss: 0.1411, Validation Acc: 0.9619\n", - "Epoch 18/20, Train Loss: 0.1571, Train Acc: 0.9490\n", - "Validation Loss: 0.1995, Validation Acc: 0.9412\n", - "Epoch 19/20, Train Loss: 0.1037, Train Acc: 0.9714\n", - "Validation Loss: 0.1628, Validation Acc: 0.9526\n", - "Epoch 20/20, Train Loss: 0.0828, Train Acc: 0.9734\n", - "Validation Loss: 0.1624, Validation Acc: 0.9495\n", - "Model saved!\n" - ] - } - ], - "source": [ - "# %% Imports\n", - "import os\n", - "import numpy as np\n", - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "import torch.nn.functional as F\n", - "from torch.utils.data import Dataset, DataLoader\n", - "from torchvision import transforms\n", - "from sklearn.preprocessing import LabelEncoder\n", - "from sklearn.metrics import accuracy_score\n", - "from PIL import Image\n", - "\n", - "# %% Attention Mechanism\n", - "class Attention(nn.Module):\n", - " def __init__(self, hidden_size):\n", - " super(Attention, self).__init__()\n", - " self.attention = nn.Linear(hidden_size * 2, 1)\n", - "\n", - " def forward(self, lstm_output):\n", - " attention_weights = torch.softmax(self.attention(lstm_output), dim=1)\n", - " context_vector = torch.sum(attention_weights * lstm_output, dim=1)\n", - " return context_vector\n", - "\n", - "# %% CNN with LSTM and Attention\n", - "class CustomCNNWithLSTM(nn.Module):\n", - " def __init__(self, num_classes=6, lstm_hidden_size=256, lstm_num_layers=2):\n", - " super(CustomCNNWithLSTM, self).__init__()\n", - " # CNN Layers\n", - " self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)\n", - " self.bn1 = nn.BatchNorm2d(32)\n", - " self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n", - "\n", - " self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n", - " self.bn2 = nn.BatchNorm2d(64)\n", - " self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n", - "\n", - " self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n", - " self.bn3 = nn.BatchNorm2d(128)\n", - " self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)\n", - "\n", - " self.conv4 = nn.Conv2d(128, 256, kernel_size=3, padding=1)\n", - " self.bn4 = nn.BatchNorm2d(256)\n", - " self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)\n", - "\n", - " # LSTM Layers\n", - " self.lstm = nn.LSTM(\n", - " input_size=256,\n", - " hidden_size=lstm_hidden_size,\n", - " num_layers=lstm_num_layers,\n", - " batch_first=True,\n", - " bidirectional=True\n", - " )\n", - "\n", - " # Attention Layer\n", - " self.attention = Attention(lstm_hidden_size)\n", - "\n", - " # Fully Connected Layers\n", - " self.fc1 = nn.Linear(lstm_hidden_size * 2, 512)\n", - " self.fc2 = nn.Linear(512, num_classes)\n", - " self.dropout = nn.Dropout(0.5)\n", - "\n", - " def forward(self, x):\n", - " x = self.pool1(F.relu(self.bn1(self.conv1(x))))\n", - " x = self.pool2(F.relu(self.bn2(self.conv2(x))))\n", - " x = self.pool3(F.relu(self.bn3(self.conv3(x))))\n", - " x = self.pool4(F.relu(self.bn4(self.conv4(x))))\n", - "\n", - " # Flatten for LSTM\n", - " batch_size, channels, height, width = x.size()\n", - " x = x.view(batch_size, channels, -1).permute(0, 2, 1)\n", - "\n", - " # LSTM + Attention\n", - " lstm_out, _ = self.lstm(x)\n", - " x = self.attention(lstm_out)\n", - "\n", - " # Fully Connected Layers\n", - " x = F.relu(self.fc1(x))\n", - " x = self.dropout(x)\n", - " x = self.fc2(x)\n", - " return x\n", - "\n", - "# %% Custom Dataset\n", - "class CustomImageDataset(Dataset):\n", - " def __init__(self, base_dir, subfolders, transform=None, label_encoder=None):\n", - " self.image_paths = []\n", - " self.labels = []\n", - " for subfolder in subfolders:\n", - " folder_path = os.path.join(base_dir, subfolder)\n", - " for img_name in os.listdir(folder_path):\n", - " if img_name.lower().endswith(('.png', '.jpg', '.jpeg')):\n", - " self.image_paths.append(os.path.join(folder_path, img_name))\n", - " self.labels.append(subfolder)\n", - " if label_encoder:\n", - " self.label_encoder = label_encoder\n", - " self.labels = self.label_encoder.transform(self.labels)\n", - " self.transform = transform\n", - "\n", - " def __len__(self):\n", - " return len(self.image_paths)\n", - "\n", - " def __getitem__(self, idx):\n", - " image = Image.open(self.image_paths[idx]).convert(\"RGB\")\n", - " label = self.labels[idx]\n", - " if self.transform:\n", - " image = self.transform(image)\n", - " return image, label\n", - "\n", - "# %% Data Transformations\n", - "transform = transforms.Compose([\n", - " transforms.Resize((224, 224)),\n", - " transforms.RandomHorizontalFlip(),\n", - " transforms.ColorJitter(brightness=0.2, contrast=0.2),\n", - " transforms.ToTensor(),\n", - " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", - "])\n", - "\n", - "# %% Dataset Preparation\n", - "base_dir = \"DIAT-uSAT_dataset\"\n", - "subfolders = [\"3_long_blade_rotor\", \"3_short_blade_rotor\", \"Bird\", \"Bird+mini-helicopter\", \"drone\", \"rc_plane\"]\n", - "\n", - "label_encoder = LabelEncoder()\n", - "label_encoder.fit(subfolders)\n", - "\n", - "train_dataset = CustomImageDataset(base_dir, subfolders, transform, label_encoder)\n", - "train_size = int(0.8 * len(train_dataset))\n", - "val_size = len(train_dataset) - train_size\n", - "train_dataset, val_dataset = torch.utils.data.random_split(train_dataset, [train_size, val_size])\n", - "\n", - "# %% Data Loaders\n", - "batch_size = 32\n", - "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n", - "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n", - "\n", - "# %% Model, Loss, Optimizer\n", - "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - "model = CustomCNNWithLSTM(num_classes=len(subfolders)).to(device)\n", - "criterion = nn.CrossEntropyLoss()\n", - "optimizer = optim.Adam(model.parameters(), lr=0.001)\n", - "\n", - "# %% Training Function\n", - "def train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=20):\n", - " for epoch in range(num_epochs):\n", - " model.train()\n", - " train_loss = 0.0\n", - " train_preds, train_labels = [], []\n", - "\n", - " for images, labels in train_loader:\n", - " images, labels = images.to(device), labels.to(device).long() # Ensure labels are of type long\n", - "\n", - " outputs = model(images)\n", - "\n", - " # Ensure outputs and labels have correct shapes\n", - " assert outputs.shape[1] == len(subfolders), \"Output classes don't match the number of labels\"\n", - "\n", - " loss = criterion(outputs, labels) # Labels should be of type long\n", - "\n", - " optimizer.zero_grad() # Clear previous gradients\n", - " loss.backward() # Compute gradients\n", - " optimizer.step() # Update model parameters\n", - "\n", - " train_loss += loss.item()\n", - " train_preds.extend(torch.argmax(outputs, dim=1).cpu().numpy()) # Get predicted class labels\n", - " train_labels.extend(labels.cpu().numpy()) # Get true class labels\n", - "\n", - " train_acc = accuracy_score(train_labels, train_preds)\n", - " print(f\"Epoch {epoch+1}/{num_epochs}, Train Loss: {train_loss/len(train_loader):.4f}, Train Acc: {train_acc:.4f}\")\n", - "\n", - " # Validation\n", - " model.eval()\n", - " val_loss = 0.0\n", - " val_preds, val_labels = [], []\n", - " with torch.no_grad():\n", - " for images, labels in val_loader:\n", - " images, labels = images.to(device), labels.to(device).long()\n", - "\n", - " outputs = model(images)\n", - " loss = criterion(outputs, labels)\n", - "\n", - " val_loss += loss.item()\n", - " val_preds.extend(torch.argmax(outputs, dim=1).cpu().numpy())\n", - " val_labels.extend(labels.cpu().numpy())\n", - "\n", - " val_acc = accuracy_score(val_labels, val_preds)\n", - " print(f\"Validation Loss: {val_loss/len(val_loader):.4f}, Validation Acc: {val_acc:.4f}\")\n", - "\n", - "# %% Train Model\n", - "train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=20)\n", - "\n", - "torch.save(model.state_dict(), \"custom_cnn_lstm_model.pth\")\n", - "print(\"Model saved!\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_22584\\3379055925.py:8: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " model = torch.load(model_path, map_location=device) # Load the complete model onto the appropriate device\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model has been converted to ONNX and saved at customcnnwithAttention_full.onnx\n" - ] - } - ], - "source": [ - "import torch\n", - "\n", - "# Path to the saved complete model file\n", - "model_path = \"customcnnwithAttention_full.pth\"\n", - "\n", - "# Load the complete model (architecture + weights)\n", - "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") # Select device dynamically\n", - "model = torch.load(model_path, map_location=device) # Load the complete model onto the appropriate device\n", - "model.eval() # Set the model to evaluation mode\n", - "\n", - "# Define a dummy input on the same device as the model\n", - "dummy_input = torch.randn(1, 3, 224, 224).to(device) # Batch size = 1, 3 channels, 224x224 resolution\n", - "\n", - "# Path to save the ONNX model\n", - "onnx_file_path = \"customcnnwithAttention_full.onnx\"\n", - "\n", - "# Export the model to ONNX format\n", - "torch.onnx.export(\n", - " model,\n", - " dummy_input,\n", - " onnx_file_path,\n", - " export_params=True,\n", - " opset_version=11,\n", - " do_constant_folding=True,\n", - " input_names=['input'],\n", - " output_names=['output'],\n", - " dynamic_axes={\n", - " 'input': {0: 'batch_size'},\n", - " 'output': {0: 'batch_size'}\n", - " }\n", - ")\n", - "\n", - "print(f\"Model has been converted to ONNX and saved at {onnx_file_path}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Testing: 100%|██████████| 61/61 [00:09<00:00, 6.49it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test Loss: 0.1089\n", - "Test Accuracy: 0.9691\n", - "Sample Predictions: [4, 5, 4, 3, 0, 1, 3, 2, 3, 0]\n", - "Sample Labels: [4, 5, 4, 3, 0, 1, 3, 2, 3, 0]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "import torch\n", - "from tqdm import tqdm\n", - "\n", - "def test_model(model, test_loader, criterion, device=\"cuda\" if torch.cuda.is_available() else \"cpu\"):\n", - " \"\"\"\n", - " Test the trained model on the test dataset.\n", - " \n", - " Args:\n", - " model (nn.Module): Trained PyTorch model.\n", - " test_loader (DataLoader): DataLoader for the test dataset.\n", - " criterion (nn.Module): Loss function.\n", - " device (str): Device to run the testing on (default: \"cuda\" if available).\n", - " \n", - " Returns:\n", - " dict: Dictionary containing test loss and accuracy.\n", - " \"\"\"\n", - " model.eval() # Set the model to evaluation mode\n", - " running_loss = 0.0\n", - " running_corrects = 0\n", - " total_samples = 0\n", - "\n", - " all_preds = []\n", - " all_labels = []\n", - "\n", - " with torch.no_grad(): # No gradient calculation for testing\n", - " for inputs, labels in tqdm(test_loader, desc=\"Testing\"):\n", - " inputs = inputs.to(device)\n", - " labels = labels.to(device).long()\n", - "\n", - " # Forward pass\n", - " outputs = model(inputs)\n", - " loss = criterion(outputs, labels)\n", - " \n", - " # Collect test metrics\n", - " running_loss += loss.item() * inputs.size(0)\n", - " _, preds = torch.max(outputs, 1)\n", - " running_corrects += torch.sum(preds == labels)\n", - " total_samples += labels.size(0)\n", - "\n", - " # Store predictions and labels for further analysis if needed\n", - " all_preds.extend(preds.cpu().numpy())\n", - " all_labels.extend(labels.cpu().numpy())\n", - "\n", - " # Calculate overall loss and accuracy\n", - " test_loss = running_loss / total_samples\n", - " test_accuracy = running_corrects.double() / total_samples\n", - "\n", - " print(f\"Test Loss: {test_loss:.4f}\")\n", - " print(f\"Test Accuracy: {test_accuracy:.4f}\")\n", - "\n", - " return {\n", - " \"test_loss\": test_loss,\n", - " \"test_accuracy\": test_accuracy.item(),\n", - " \"all_preds\": all_preds,\n", - " \"all_labels\": all_labels,\n", - " }\n", - "\n", - "# Example Usage\n", - "criterion = nn.CrossEntropyLoss() # Define the same criterion used during training\n", - "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "\n", - "test_results = test_model(model, test_loader, criterion, device=device)\n", - "\n", - "# Optional: Print predictions and labels for a sanity check\n", - "print(\"Sample Predictions:\", test_results[\"all_preds\"][:10])\n", - "print(\"Sample Labels:\", test_results[\"all_labels\"][:10])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Testing: 100%|██████████| 61/61 [00:09<00:00, 6.56it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test Loss: 0.1099\n", - "Test Accuracy: 0.9733\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sample Predictions: [4, 5, 4, 3, 0, 1, 3, 2, 3, 0]\n", - "Sample Labels: [4, 5, 4, 3, 0, 1, 3, 2, 3, 0]\n" - ] - } - ], - "source": [ - "import torch\n", - "from tqdm import tqdm\n", - "from sklearn.metrics import confusion_matrix\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def test_model(model, test_loader, criterion, device=\"cuda\" if torch.cuda.is_available() else \"cpu\"):\n", - " \"\"\"\n", - " Test the trained model on the test dataset.\n", - " \n", - " Args:\n", - " model (nn.Module): Trained PyTorch model.\n", - " test_loader (DataLoader): DataLoader for the test dataset.\n", - " criterion (nn.Module): Loss function.\n", - " device (str): Device to run the testing on (default: \"cuda\" if available).\n", - " \n", - " Returns:\n", - " dict: Dictionary containing test loss, accuracy, confusion matrix, predictions, and labels.\n", - " \"\"\"\n", - " model.eval() # Set the model to evaluation mode\n", - " running_loss = 0.0\n", - " running_corrects = 0\n", - " total_samples = 0\n", - "\n", - " all_preds = []\n", - " all_labels = []\n", - "\n", - " with torch.no_grad(): # No gradient calculation for testing\n", - " for inputs, labels in tqdm(test_loader, desc=\"Testing\"):\n", - " inputs = inputs.to(device)\n", - " labels = labels.to(device).long()\n", - "\n", - " # Forward pass\n", - " outputs = model(inputs)\n", - " loss = criterion(outputs, labels)\n", - " \n", - " # Collect test metrics\n", - " running_loss += loss.item() * inputs.size(0)\n", - " _, preds = torch.max(outputs, 1)\n", - " running_corrects += torch.sum(preds == labels)\n", - " total_samples += labels.size(0)\n", - "\n", - " # Store predictions and labels for further analysis if needed\n", - " all_preds.extend(preds.cpu().numpy())\n", - " all_labels.extend(labels.cpu().numpy())\n", - "\n", - " # Calculate overall loss and accuracy\n", - " test_loss = running_loss / total_samples\n", - " test_accuracy = running_corrects.double() / total_samples\n", - "\n", - " print(f\"Test Loss: {test_loss:.4f}\")\n", - " print(f\"Test Accuracy: {test_accuracy:.4f}\")\n", - "\n", - " # Calculate confusion matrix\n", - " cm = confusion_matrix(all_labels, all_preds)\n", - "\n", - " # Plot confusion matrix using Seaborn\n", - " plt.figure(figsize=(8, 6))\n", - " sns.heatmap(cm, annot=True, fmt='g', cmap='Blues', xticklabels=[\"3_long_blade_rotor\", \"3_short_blade_rotor\", \"Bird\", \"Bird+mini-helicopter\", \"drone\", \"rc_plane\"], yticklabels=[\"3_long_blade_rotor\", \"3_short_blade_rotor\", \"Bird\", \"Bird+mini-helicopter\", \"drone\", \"rc_plane\"])\n", - " plt.xlabel('Predicted Labels')\n", - " plt.ylabel('True Labels')\n", - " plt.title('Confusion Matrix')\n", - " plt.show()\n", - "\n", - " return {\n", - " \"test_loss\": test_loss,\n", - " \"test_accuracy\": test_accuracy.item(),\n", - " \"confusion_matrix\": cm,\n", - " \"all_preds\": all_preds,\n", - " \"all_labels\": all_labels,\n", - " }\n", - "\n", - "# Example Usage\n", - "criterion = nn.CrossEntropyLoss() # Define the same criterion used during training\n", - "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "\n", - "test_results = test_model(model, test_loader, criterion, device=device)\n", - "\n", - "# Optional: Print predictions and labels for a sanity check\n", - "print(\"Sample Predictions:\", test_results[\"all_preds\"][:10])\n", - "print(\"Sample Labels:\", test_results[\"all_labels\"][:10])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Testing: 100%|██████████| 61/61 [00:10<00:00, 5.92it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test Loss: 0.0369\n", - "Test Accuracy: 0.9877\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sample Predictions: [0, 5, 4, 0, 2, 3, 1, 2, 4, 3]\n", - "Sample Labels: [0, 5, 4, 0, 2, 3, 1, 2, 4, 3]\n" - ] - } - ], - "source": [ - "import torch\n", - "from sklearn.metrics import roc_curve, auc, accuracy_score\n", - "import matplotlib.pyplot as plt\n", - "from tqdm import tqdm\n", - "import seaborn as sns\n", - "import numpy as np\n", - "from sklearn.metrics import confusion_matrix\n", - "import torch.nn.functional as F\n", - "\n", - "def test_model(model, test_loader, criterion, device=\"cuda\" if torch.cuda.is_available() else \"cpu\"):\n", - " \"\"\"\n", - " Test the trained model on the test dataset.\n", - " \n", - " Args:\n", - " model (nn.Module): Trained PyTorch model.\n", - " test_loader (DataLoader): DataLoader for the test dataset.\n", - " criterion (nn.Module): Loss function.\n", - " device (str): Device to run the testing on (default: \"cuda\" if available).\n", - " \n", - " Returns:\n", - " dict: Dictionary containing test loss, accuracy, confusion matrix, predictions, and labels.\n", - " \"\"\"\n", - " model.eval() # Set the model to evaluation mode\n", - " running_loss = 0.0\n", - " running_corrects = 0\n", - " total_samples = 0\n", - "\n", - " all_preds = []\n", - " all_labels = []\n", - " all_probs = []\n", - "\n", - " with torch.no_grad(): # No gradient calculation for testing\n", - " for inputs, labels in tqdm(test_loader, desc=\"Testing\"):\n", - " inputs = inputs.to(device)\n", - " labels = labels.to(device).long()\n", - "\n", - " # Forward pass\n", - " outputs = model(inputs)\n", - " loss = criterion(outputs, labels)\n", - " \n", - " # Collect test metrics\n", - " running_loss += loss.item() * inputs.size(0)\n", - " _, preds = torch.max(outputs, 1)\n", - " running_corrects += torch.sum(preds == labels)\n", - " total_samples += labels.size(0)\n", - "\n", - " # Store predictions, labels, and probabilities for further analysis if needed\n", - " all_preds.extend(preds.cpu().numpy())\n", - " all_labels.extend(labels.cpu().numpy())\n", - " all_probs.extend(F.softmax(outputs, dim=1).cpu().numpy())\n", - "\n", - " # Calculate overall loss and accuracy\n", - " test_loss = running_loss / total_samples\n", - " test_accuracy = running_corrects.double() / total_samples\n", - "\n", - " print(f\"Test Loss: {test_loss:.4f}\")\n", - " print(f\"Test Accuracy: {test_accuracy:.4f}\")\n", - "\n", - " # Calculate confusion matrix\n", - " cm = confusion_matrix(all_labels, all_preds)\n", - "\n", - " # Plot confusion matrix using Seaborn\n", - " plt.figure(figsize=(8, 6))\n", - " sns.heatmap(cm, annot=True, fmt='g', cmap='Blues', xticklabels=[\"3_long_blade_rotor\", \"3_short_blade_rotor\", \"Bird\", \"Bird+mini-helicopter\", \"drone\", \"rc_plane\"], yticklabels=[\"3_long_blade_rotor\", \"3_short_blade_rotor\", \"Bird\", \"Bird+mini-helicopter\", \"drone\", \"rc_plane\"])\n", - " plt.xlabel('Predicted Labels')\n", - " plt.ylabel('True Labels')\n", - " plt.title('Confusion Matrix')\n", - " plt.show()\n", - "\n", - " # ROC Curve (One-vs-Rest)\n", - " fpr, tpr, _ = roc_curve(all_labels, np.array(all_probs)[:, 1], pos_label=1)\n", - " roc_auc = auc(fpr, tpr)\n", - " \n", - " plt.figure(figsize=(8, 6))\n", - " plt.plot(fpr, tpr, color='b', label=f'ROC curve (area = {roc_auc:.2f})')\n", - " plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n", - " plt.xlabel('False Positive Rate')\n", - " plt.ylabel('True Positive Rate')\n", - " plt.title('ROC Curve')\n", - " plt.legend(loc='lower right')\n", - " plt.show()\n", - "\n", - " return {\n", - " \"test_loss\": test_loss,\n", - " \"test_accuracy\": test_accuracy.item(),\n", - " \"confusion_matrix\": cm,\n", - " \"all_preds\": all_preds,\n", - " \"all_labels\": all_labels,\n", - " \"roc_auc\": roc_auc,\n", - " }\n", - "\n", - "# Example Usage\n", - "criterion = nn.CrossEntropyLoss() # Define the same criterion used during training\n", - "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - "\n", - "test_results = test_model(model, test_loader, criterion, device=device)\n", - "\n", - "# Optional: Print predictions and labels for a sanity check\n", - "print(\"Sample Predictions:\", test_results[\"all_preds\"][:10])\n", - "print(\"Sample Labels:\", test_results[\"all_labels\"][:10])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "from torch.optim import lr_scheduler\n", - "import torch.nn.functional as F\n", - "from torch.utils.data import DataLoader, random_split\n", - "from tqdm import tqdm\n", - "from sklearn.metrics import confusion_matrix, roc_curve, auc\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from torchvision import transforms\n", - "from sklearn.metrics import accuracy_score\n", - "# Define the transformations for the dataset\n", - "transform = transforms.Compose([\n", - " transforms.Resize((224, 224)),\n", - " transforms.RandomHorizontalFlip(),\n", - " transforms.ColorJitter(brightness=0.2, contrast=0.2),\n", - " transforms.ToTensor(),\n", - " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # Normalize with ImageNet stats\n", - "])\n", - "# Define the Spatial Attention Module\n", - "class SpatialAttention(nn.Module):\n", - " def __init__(self, in_channels):\n", - " super(SpatialAttention, self).__init__()\n", - " self.conv1 = nn.Conv2d(in_channels, 1, kernel_size=1)\n", - " self.conv2 = nn.Conv2d(1, 1, kernel_size=3, padding=1)\n", - " self.sigmoid = nn.Sigmoid()\n", - "\n", - " def forward(self, x):\n", - " attn = self.conv1(x)\n", - " attn = self.conv2(attn)\n", - " attn = self.sigmoid(attn)\n", - " return x * attn\n", - "\n", - "# Define the Custom CNN with integrated Spatial Attention\n", - "class CustomCNNWithAttention(nn.Module):\n", - " def __init__(self, num_classes=6):\n", - " super(CustomCNNWithAttention, self).__init__()\n", - " # 1st Convolutional Block\n", - " self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)\n", - " self.bn1 = nn.BatchNorm2d(16)\n", - " self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n", - "\n", - " # 2nd Convolutional Block\n", - " self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)\n", - " self.bn2 = nn.BatchNorm2d(32)\n", - " self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n", - "\n", - " # 3rd Convolutional Block\n", - " self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n", - " self.bn3 = nn.BatchNorm2d(64)\n", - " self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)\n", - "\n", - " # 4th Convolutional Block\n", - " self.conv4 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n", - " self.bn4 = nn.BatchNorm2d(128)\n", - " self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)\n", - "\n", - " # Attention Module\n", - " self.attention = SpatialAttention(in_channels=128)\n", - "\n", - " # Global Average Pooling\n", - " self.global_avg_pool = nn.AdaptiveAvgPool2d((1, 1))\n", - " \n", - " # Fully Connected Layers\n", - " self.fc1 = nn.Linear(128, 512)\n", - " self.fc2 = nn.Linear(512, num_classes)\n", - "\n", - " # Dropout for Regularization\n", - " self.dropout = nn.Dropout(0.5)\n", - "\n", - " def forward(self, x):\n", - " x = self.pool1(F.relu(self.bn1(self.conv1(x))))\n", - " x = self.pool2(F.relu(self.bn2(self.conv2(x))))\n", - " x = self.pool3(F.relu(self.bn3(self.conv3(x))))\n", - " x = self.pool4(F.relu(self.bn4(self.conv4(x))))\n", - "\n", - " # Apply Attention Mechanism\n", - " x = self.attention(x)\n", - "\n", - " # Global Average Pooling\n", - " x = self.global_avg_pool(x)\n", - "\n", - " # Flatten the output\n", - " x = torch.flatten(x, 1)\n", - "\n", - " # Fully Connected Layers\n", - " x = F.relu(self.fc1(x))\n", - " x = self.dropout(x)\n", - " x = self.fc2(x)\n", - "\n", - " return x" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [01:35<00:00, 5.43it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.8523 Acc: 0.6693\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:04<00:00, 6.62it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.8241 Acc: 0.6942\n", - "Epoch 2/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [01:27<00:00, 5.88it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.5006 Acc: 0.8139\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:05<00:00, 5.83it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.5936 Acc: 0.7851\n", - "Epoch 3/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [02:04<00:00, 4.15it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.3671 Acc: 0.8694\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:04<00:00, 6.43it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.4423 Acc: 0.8471\n", - "Epoch 4/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [01:36<00:00, 5.33it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.2947 Acc: 0.9022\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:04<00:00, 6.20it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.2695 Acc: 0.8926\n", - "Epoch 5/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [01:31<00:00, 5.61it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.2419 Acc: 0.9163\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:05<00:00, 5.95it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.2735 Acc: 0.9008\n", - "Epoch 6/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [01:35<00:00, 5.42it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.2184 Acc: 0.9289\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:04<00:00, 6.59it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.2816 Acc: 0.9008\n", - "Epoch 7/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [01:33<00:00, 5.50it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.1793 Acc: 0.9425\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:04<00:00, 6.44it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.0956 Acc: 0.9628\n", - "Epoch 8/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [01:34<00:00, 5.44it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.1033 Acc: 0.9689\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:05<00:00, 6.02it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.0610 Acc: 0.9793\n", - "Epoch 9/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [01:21<00:00, 6.31it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.0842 Acc: 0.9731\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:08<00:00, 3.48it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.0475 Acc: 0.9876\n", - "Epoch 10/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [02:00<00:00, 4.29it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.0681 Acc: 0.9791\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:04<00:00, 7.55it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.0593 Acc: 0.9793\n", - "Epoch 11/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [02:14<00:00, 3.83it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.0664 Acc: 0.9813\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:08<00:00, 3.58it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.0565 Acc: 0.9752\n", - "Epoch 12/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [01:37<00:00, 5.30it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.0627 Acc: 0.9789\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:04<00:00, 7.44it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.0365 Acc: 0.9876\n", - "Epoch 13/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [01:34<00:00, 5.44it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.0521 Acc: 0.9830\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:07<00:00, 4.23it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.0466 Acc: 0.9793\n", - "Epoch 14/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [01:45<00:00, 4.89it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.0487 Acc: 0.9850\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:05<00:00, 5.75it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.0441 Acc: 0.9876\n", - "Epoch 15/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [01:59<00:00, 4.33it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.0448 Acc: 0.9864\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:04<00:00, 6.61it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.0351 Acc: 0.9835\n", - "Epoch 16/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [01:32<00:00, 5.59it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.0448 Acc: 0.9854\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:04<00:00, 6.53it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.0390 Acc: 0.9876\n", - "Epoch 17/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [01:46<00:00, 4.84it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.0433 Acc: 0.9876\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:05<00:00, 5.75it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.0345 Acc: 0.9917\n", - "Epoch 18/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [01:32<00:00, 5.56it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.0401 Acc: 0.9871\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:05<00:00, 5.90it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.0392 Acc: 0.9917\n", - "Epoch 19/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [01:34<00:00, 5.44it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.0416 Acc: 0.9871\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:05<00:00, 5.38it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.0314 Acc: 0.9876\n", - "Epoch 20/20\n", - "------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 100%|██████████| 516/516 [01:48<00:00, 4.77it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training Loss: 0.0368 Acc: 0.9884\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Validation: 100%|██████████| 31/31 [00:05<00:00, 5.80it/s]\n", - "C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_22584\\2047599850.py:155: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " model = torch.load(\"customcnnwithAttention_best.pth\")\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation Loss: 0.0398 Acc: 0.9876\n", - "Training complete. Best Validation Acc: 0.9917\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Testing: 100%|██████████| 61/61 [00:11<00:00, 5.22it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test Loss: 0.0382\n", - "Test Accuracy: 0.9856\n" + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.9630 0.9286 0.9455 84\n", + " 1 0.9059 0.9625 0.9333 80\n", + " 2 1.0000 1.0000 1.0000 80\n", + " 3 1.0000 0.9762 0.9880 84\n", + " 4 1.0000 1.0000 1.0000 78\n", + " 5 0.9750 0.9750 0.9750 80\n", + "\n", + " accuracy 0.9733 486\n", + " macro avg 0.9740 0.9737 0.9736 486\n", + "weighted avg 0.9740 0.9733 0.9734 486\n", + "\n", + "Confusion Matrix:\n", + "[[78 6 0 0 0 0]\n", + " [ 3 77 0 0 0 0]\n", + " [ 0 0 80 0 0 0]\n", + " [ 0 0 0 82 0 2]\n", + " [ 0 0 0 0 78 0]\n", + " [ 0 2 0 0 0 78]]\n" ] - }, + } + ], + "source": [ + "from sklearn.metrics import classification_report, confusion_matrix\n", + "import torch\n", + "\n", + "def test_model(model, test_loader, device=\"cuda\" if torch.cuda.is_available() else \"cpu\"):\n", + " model = model.to(device)\n", + " model.eval()\n", + " \n", + " all_preds = []\n", + " all_targets = []\n", + "\n", + " with torch.no_grad():\n", + " for inputs, labels in test_loader:\n", + " inputs = inputs.to(device)\n", + " labels = labels.to(device).long() # Ensure labels are of type torch.long\n", + "\n", + " # Forward pass\n", + " outputs = model(inputs)\n", + " _, preds = torch.max(outputs, 1)\n", + "\n", + " # Collect predictions and targets\n", + " all_preds.extend(preds.cpu().numpy())\n", + " all_targets.extend(labels.cpu().numpy())\n", + " \n", + " # Generate Classification Report\n", + " print(\"Classification Report:\")\n", + " print(classification_report(all_targets, all_preds, digits=4))\n", + "\n", + " # Generate Confusion Matrix\n", + " print(\"Confusion Matrix:\")\n", + " print(confusion_matrix(all_targets, all_preds))\n", + "\n", + "# Example Usage\n", + "# Assuming test_loader is defined\n", + "if __name__ == \"__main__\":\n", + " # Load the trained model weights\n", + " model_deepercnn = CustomCNNWithAttention(num_classes=6)\n", + " model_deepercnn.load_state_dict(torch.load(\"customcnnwithAttention.pth\"))\n", + "\n", + " # Evaluate the model on test data\n", + " test_model(model_deepercnn, test_loader)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as patches\n", + "\n", + "# Define colors for different types of layers\n", + "colors = {\n", + " 'input': '#d3d3d3', # Light gray for Input Layer\n", + " 'conv': '#6495ed', # Blue for Convolution + BatchNorm + ReLU\n", + " 'pool': '#a9a9a9', # Dark gray for Max Pooling\n", + " 'dropout': '#ff7f50', # Coral for Dropout\n", + " 'lstm': '#ffcccb', # Light Red for LSTM\n", + " 'dense': '#ffd700', # Gold for Fully Connected + Dropout\n", + " 'output': '#32cd32' # Lime for Output\n", + "}\n", + "\n", + "def draw_block(ax, x, y, width, height, label, color):\n", + " \"\"\"Helper function to draw a rectangular block with text.\"\"\"\n", + " rect = patches.Rectangle((x, y), width, height, edgecolor='black', facecolor=color)\n", + " ax.add_patch(rect)\n", + " ax.text(x + width / 2, y + height / 2, label, color='black',\n", + " ha='center', va='center', fontsize=8, weight=\"bold\", rotation=90)\n", + "\n", + "# Create the figure and axis\n", + "fig, ax = plt.subplots(figsize=(18, 6))\n", + "\n", + "# Block dimensions\n", + "block_width = 0.5\n", + "\n", + "# Draw the Input Layer\n", + "draw_block(ax, 0, 1, block_width, 1, 'Input\\n(3x224x224)', colors['input'])\n", + "\n", + "# Convolutional and Pooling Layers\n", + "layers = [\n", + " ('Conv1\\n32x224x224\\n+BN\\nReLU', colors['conv']),\n", + " ('Pool1\\n32x112x112', colors['pool']),\n", + " ('Conv2\\n64x112x112\\n+BN\\nReLU', colors['conv']),\n", + " ('Pool2\\n64x56x56', colors['pool']),\n", + " ('Conv3\\n128x56x56\\n+BN\\nReLU', colors['conv']),\n", + " ('Pool3\\n128x28x28', colors['pool']),\n", + " ('Dropout\\n0.3', colors['dropout']),\n", + "]\n", + "\n", + "x_offset = block_width\n", + "for layer, color in layers:\n", + " draw_block(ax, x_offset, 1, block_width, 1, layer, color)\n", + " x_offset += block_width\n", + "\n", + "# LSTM Layer\n", + "draw_block(ax, x_offset, 1, block_width * 2, 1, 'LSTM\\nBidirectional\\n128x2\\n(batch x seq x 128)', colors['lstm'])\n", + "x_offset += block_width * 2\n", + "\n", + "# Fully Connected Layers\n", + "fc_layers = [\n", + " ('FC1\\n256\\n+ReLU', colors['dense']),\n", + " ('Dropout\\n0.5', colors['dropout']),\n", + " ('FC2\\n6\\n(Output)', colors['output'])\n", + "]\n", + "\n", + "for layer, color in fc_layers:\n", + " draw_block(ax, x_offset, 1, block_width, 1, layer, color)\n", + " x_offset += block_width\n", + "\n", + "# Configure plot\n", + "ax.set_xlim(0, x_offset + 1)\n", + "ax.set_ylim(0, 3)\n", + "ax.axis('off')\n", + "\n", + "# Title\n", + "plt.title('Custom CNN with LSTM Architecture')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20, Train Loss: 0.9637, Train Acc: 0.6081\n", + "Validation Loss: 1.6030, Validation Acc: 0.4990\n", + "Epoch 2/20, Train Loss: 0.6486, Train Acc: 0.7564\n", + "Validation Loss: 0.4759, Validation Acc: 0.8289\n", + "Epoch 3/20, Train Loss: 0.4112, Train Acc: 0.8551\n", + "Validation Loss: 0.3839, Validation Acc: 0.8732\n", + "Epoch 4/20, Train Loss: 0.3462, Train Acc: 0.8814\n", + "Validation Loss: 0.9806, Validation Acc: 0.7155\n", + "Epoch 5/20, Train Loss: 0.3261, Train Acc: 0.8866\n", + "Validation Loss: 0.5992, Validation Acc: 0.8216\n", + "Epoch 6/20, Train Loss: 0.2721, Train Acc: 0.9033\n", + "Validation Loss: 0.2847, Validation Acc: 0.9113\n", + "Epoch 7/20, Train Loss: 0.2255, Train Acc: 0.9268\n", + "Validation Loss: 0.2577, Validation Acc: 0.9186\n", + "Epoch 8/20, Train Loss: 0.2269, Train Acc: 0.9301\n", + "Validation Loss: 0.1997, Validation Acc: 0.9423\n", + "Epoch 9/20, Train Loss: 0.1701, Train Acc: 0.9469\n", + "Validation Loss: 0.2421, Validation Acc: 0.9258\n", + "Epoch 10/20, Train Loss: 0.1698, Train Acc: 0.9461\n", + "Validation Loss: 0.2228, Validation Acc: 0.9381\n", + "Epoch 11/20, Train Loss: 0.1469, Train Acc: 0.9559\n", + "Validation Loss: 0.2490, Validation Acc: 0.9361\n", + "Epoch 12/20, Train Loss: 0.1271, Train Acc: 0.9585\n", + "Validation Loss: 0.2432, Validation Acc: 0.9165\n", + "Epoch 13/20, Train Loss: 0.1548, Train Acc: 0.9502\n", + "Validation Loss: 0.3115, Validation Acc: 0.9134\n", + "Epoch 14/20, Train Loss: 0.1170, Train Acc: 0.9611\n", + "Validation Loss: 0.2114, Validation Acc: 0.9351\n", + "Epoch 15/20, Train Loss: 0.1296, Train Acc: 0.9593\n", + "Validation Loss: 0.1878, Validation Acc: 0.9412\n", + "Epoch 16/20, Train Loss: 0.1133, Train Acc: 0.9652\n", + "Validation Loss: 0.2685, Validation Acc: 0.9175\n", + "Epoch 17/20, Train Loss: 0.0965, Train Acc: 0.9701\n", + "Validation Loss: 0.1411, Validation Acc: 0.9619\n", + "Epoch 18/20, Train Loss: 0.1571, Train Acc: 0.9490\n", + "Validation Loss: 0.1995, Validation Acc: 0.9412\n", + "Epoch 19/20, Train Loss: 0.1037, Train Acc: 0.9714\n", + "Validation Loss: 0.1628, Validation Acc: 0.9526\n", + "Epoch 20/20, Train Loss: 0.0828, Train Acc: 0.9734\n", + "Validation Loss: 0.1624, Validation Acc: 0.9495\n", + "Model saved!\n" + ] } ], "source": [ - "# Train and Validation Function\n", - "def train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, num_epochs=20, device=\"cuda\" if torch.cuda.is_available() else \"cpu\"):\n", - " model = model.to(device)\n", - " best_acc = 0.0\n", + "# %% Imports\n", + "import os\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import torch.nn.functional as F\n", + "from torch.utils.data import Dataset, DataLoader\n", + "from torchvision import transforms\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.metrics import accuracy_score\n", + "from PIL import Image\n", + "\n", + "# %% Attention Mechanism\n", + "class Attention(nn.Module):\n", + " def __init__(self, hidden_size):\n", + " super(Attention, self).__init__()\n", + " self.attention = nn.Linear(hidden_size * 2, 1)\n", + "\n", + " def forward(self, lstm_output):\n", + " attention_weights = torch.softmax(self.attention(lstm_output), dim=1)\n", + " context_vector = torch.sum(attention_weights * lstm_output, dim=1)\n", + " return context_vector\n", + "\n", + "# %% CNN with LSTM and Attention\n", + "class CustomCNNWithLSTM(nn.Module):\n", + " def __init__(self, num_classes=6, lstm_hidden_size=256, lstm_num_layers=2):\n", + " super(CustomCNNWithLSTM, self).__init__()\n", + " # CNN Layers\n", + " self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)\n", + " self.bn1 = nn.BatchNorm2d(32)\n", + " self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n", + "\n", + " self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n", + " self.bn2 = nn.BatchNorm2d(64)\n", + " self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n", + "\n", + " self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n", + " self.bn3 = nn.BatchNorm2d(128)\n", + " self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)\n", + "\n", + " self.conv4 = nn.Conv2d(128, 256, kernel_size=3, padding=1)\n", + " self.bn4 = nn.BatchNorm2d(256)\n", + " self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)\n", + "\n", + " # LSTM Layers\n", + " self.lstm = nn.LSTM(\n", + " input_size=256,\n", + " hidden_size=lstm_hidden_size,\n", + " num_layers=lstm_num_layers,\n", + " batch_first=True,\n", + " bidirectional=True\n", + " )\n", + "\n", + " # Attention Layer\n", + " self.attention = Attention(lstm_hidden_size)\n", "\n", - " for epoch in range(num_epochs):\n", - " print(f\"Epoch {epoch + 1}/{num_epochs}\")\n", - " print(\"-\" * 30)\n", + " # Fully Connected Layers\n", + " self.fc1 = nn.Linear(lstm_hidden_size * 2, 512)\n", + " self.fc2 = nn.Linear(512, num_classes)\n", + " self.dropout = nn.Dropout(0.5)\n", "\n", - " # Training Phase\n", - " model.train()\n", - " running_loss = 0.0\n", - " running_corrects = 0\n", - " total_train = 0\n", + " def forward(self, x):\n", + " x = self.pool1(F.relu(self.bn1(self.conv1(x))))\n", + " x = self.pool2(F.relu(self.bn2(self.conv2(x))))\n", + " x = self.pool3(F.relu(self.bn3(self.conv3(x))))\n", + " x = self.pool4(F.relu(self.bn4(self.conv4(x))))\n", "\n", - " for inputs, labels in tqdm(train_loader, desc=\"Training\"):\n", - " inputs = inputs.to(device)\n", - " labels = labels.to(device).long()\n", + " # Flatten for LSTM\n", + " batch_size, channels, height, width = x.size()\n", + " x = x.view(batch_size, channels, -1).permute(0, 2, 1)\n", "\n", - " optimizer.zero_grad()\n", + " # LSTM + Attention\n", + " lstm_out, _ = self.lstm(x)\n", + " x = self.attention(lstm_out)\n", "\n", - " outputs = model(inputs)\n", - " loss = criterion(outputs, labels)\n", - " _, preds = torch.max(outputs, 1)\n", + " # Fully Connected Layers\n", + " x = F.relu(self.fc1(x))\n", + " x = self.dropout(x)\n", + " x = self.fc2(x)\n", + " return x\n", "\n", - " loss.backward()\n", - " optimizer.step()\n", + "# %% Custom Dataset\n", + "class CustomImageDataset(Dataset):\n", + " def __init__(self, base_dir, subfolders, transform=None, label_encoder=None):\n", + " self.image_paths = []\n", + " self.labels = []\n", + " for subfolder in subfolders:\n", + " folder_path = os.path.join(base_dir, subfolder)\n", + " for img_name in os.listdir(folder_path):\n", + " if img_name.lower().endswith(('.png', '.jpg', '.jpeg')):\n", + " self.image_paths.append(os.path.join(folder_path, img_name))\n", + " self.labels.append(subfolder)\n", + " if label_encoder:\n", + " self.label_encoder = label_encoder\n", + " self.labels = self.label_encoder.transform(self.labels)\n", + " self.transform = transform\n", "\n", - " running_loss += loss.item() * inputs.size(0)\n", - " running_corrects += torch.sum(preds == labels.data)\n", - " total_train += labels.size(0)\n", + " def __len__(self):\n", + " return len(self.image_paths)\n", "\n", - " scheduler.step()\n", + " def __getitem__(self, idx):\n", + " image = Image.open(self.image_paths[idx]).convert(\"RGB\")\n", + " label = self.labels[idx]\n", + " if self.transform:\n", + " image = self.transform(image)\n", + " return image, label\n", + "\n", + "# %% Data Transformations\n", + "transform = transforms.Compose([\n", + " transforms.Resize((224, 224)),\n", + " transforms.RandomHorizontalFlip(),\n", + " transforms.ColorJitter(brightness=0.2, contrast=0.2),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", + "])\n", "\n", - " epoch_loss = running_loss / total_train\n", - " epoch_acc = running_corrects.double() / total_train\n", + "# %% Dataset Preparation\n", + "base_dir = \"DIAT-uSAT_dataset\"\n", + "subfolders = [\"3_long_blade_rotor\", \"3_short_blade_rotor\", \"Bird\", \"Bird+mini-helicopter\", \"drone\", \"rc_plane\"]\n", "\n", - " print(f\"Training Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}\")\n", + "label_encoder = LabelEncoder()\n", + "label_encoder.fit(subfolders)\n", "\n", - " # Validation Phase\n", - " model.eval()\n", - " running_loss = 0.0\n", - " running_corrects = 0\n", - " total_val = 0\n", + "train_dataset = CustomImageDataset(base_dir, subfolders, transform, label_encoder)\n", + "train_size = int(0.8 * len(train_dataset))\n", + "val_size = len(train_dataset) - train_size\n", + "train_dataset, val_dataset = torch.utils.data.random_split(train_dataset, [train_size, val_size])\n", + "\n", + "# %% Data Loaders\n", + "batch_size = 32\n", + "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n", + "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n", + "\n", + "# %% Model, Loss, Optimizer\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "model = CustomCNNWithLSTM(num_classes=len(subfolders)).to(device)\n", + "criterion = nn.CrossEntropyLoss()\n", + "optimizer = optim.Adam(model.parameters(), lr=0.001)\n", "\n", + "# %% Training Function\n", + "def train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=20):\n", + " for epoch in range(num_epochs):\n", + " model.train()\n", + " train_loss = 0.0\n", + " train_preds, train_labels = [], []\n", + "\n", + " for images, labels in train_loader:\n", + " images, labels = images.to(device), labels.to(device).long() # Ensure labels are of type long\n", + "\n", + " outputs = model(images)\n", + "\n", + " # Ensure outputs and labels have correct shapes\n", + " assert outputs.shape[1] == len(subfolders), \"Output classes don't match the number of labels\"\n", + "\n", + " loss = criterion(outputs, labels) # Labels should be of type long\n", + "\n", + " optimizer.zero_grad() # Clear previous gradients\n", + " loss.backward() # Compute gradients\n", + " optimizer.step() # Update model parameters\n", + "\n", + " train_loss += loss.item()\n", + " train_preds.extend(torch.argmax(outputs, dim=1).cpu().numpy()) # Get predicted class labels\n", + " train_labels.extend(labels.cpu().numpy()) # Get true class labels\n", + "\n", + " train_acc = accuracy_score(train_labels, train_preds)\n", + " print(f\"Epoch {epoch+1}/{num_epochs}, Train Loss: {train_loss/len(train_loader):.4f}, Train Acc: {train_acc:.4f}\")\n", + "\n", + " # Validation\n", + " model.eval()\n", + " val_loss = 0.0\n", + " val_preds, val_labels = [], []\n", " with torch.no_grad():\n", - " for inputs, labels in tqdm(val_loader, desc=\"Validation\"):\n", - " inputs = inputs.to(device)\n", - " labels = labels.to(device).long()\n", + " for images, labels in val_loader:\n", + " images, labels = images.to(device), labels.to(device).long()\n", "\n", - " outputs = model(inputs)\n", + " outputs = model(images)\n", " loss = criterion(outputs, labels)\n", - " _, preds = torch.max(outputs, 1)\n", "\n", - " running_loss += loss.item() * inputs.size(0)\n", - " running_corrects += torch.sum(preds == labels.data)\n", - " total_val += labels.size(0)\n", + " val_loss += loss.item()\n", + " val_preds.extend(torch.argmax(outputs, dim=1).cpu().numpy())\n", + " val_labels.extend(labels.cpu().numpy())\n", "\n", - " epoch_val_loss = running_loss / total_val\n", - " epoch_val_acc = running_corrects.double() / total_val\n", + " val_acc = accuracy_score(val_labels, val_preds)\n", + " print(f\"Validation Loss: {val_loss/len(val_loader):.4f}, Validation Acc: {val_acc:.4f}\")\n", "\n", - " print(f\"Validation Loss: {epoch_val_loss:.4f} Acc: {epoch_val_acc:.4f}\")\n", + "# %% Train Model\n", + "train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=20)\n", "\n", - " if epoch_val_acc > best_acc:\n", - " best_acc = epoch_val_acc\n", - " # Save the entire model\n", - " torch.save(model, \"customcnnwithAttention_best.pth\")\n", + "torch.save(model.state_dict(), \"custom_cnn_lstm_model.pth\")\n", + "print(\"Model saved!\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected input shape: [1, 3, 224, 224]\n", + "Predicted Class: 3 Long Blade Rotor\n", + "Probability: 2.3634\n", + "Inference Time: 0.0090 seconds\n" + ] + } + ], + "source": [ + "import onnxruntime as ort\n", + "import numpy as np\n", + "from PIL import Image\n", + "from torchvision import transforms\n", + "import time\n", "\n", - " print(f\"Training complete. Best Validation Acc: {best_acc:.4f}\")\n", + "# Load the ONNX model\n", + "model_path = \"cnnwithattention.onnx\"\n", + "session = ort.InferenceSession(model_path, providers=[\"CPUExecutionProvider\"])\n", "\n", - "# Testing the model\n", - "def test_model(model, test_loader, criterion, device=\"cuda\" if torch.cuda.is_available() else \"cpu\"):\n", - " model.eval()\n", - " running_loss = 0.0\n", - " running_corrects = 0\n", - " total_samples = 0\n", + "# Get input and output information\n", + "input_name = session.get_inputs()[0].name\n", + "output_name = session.get_outputs()[0].name\n", + "input_shape = session.get_inputs()[0].shape\n", "\n", - " all_preds = []\n", - " all_labels = []\n", + "# Print input shape to understand the expected input dimensions\n", + "print(f\"Expected input shape: {input_shape}\")\n", "\n", - " with torch.no_grad():\n", - " for inputs, labels in tqdm(test_loader, desc=\"Testing\"):\n", - " inputs = inputs.to(device)\n", - " labels = labels.to(device).long()\n", + "# Define the preprocessing pipeline\n", + "transform = transforms.Compose([\n", + " transforms.Resize((224, 224)), # Resize the image to 224x224 pixels\n", + " transforms.ToTensor(), # Convert to Tensor\n", + " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), # Normalize\n", + "])\n", "\n", - " outputs = model(inputs)\n", - " loss = criterion(outputs, labels)\n", + "# Define the six classes\n", + "classes = [\"3 Long Blade Rotor\", \"3 Short Blade Rotor\", \"Bird\", \"Bird+mini-helicopter\", \"Drone\", \"RC Plane\"]\n", "\n", - " running_loss += loss.item() * inputs.size(0)\n", - " _, preds = torch.max(outputs, 1)\n", - " running_corrects += torch.sum(preds == labels)\n", - " total_samples += labels.size(0)\n", + "# Load an image (replace with the path to your image)\n", + "image_path = \"D:/Micro-Classify/ml_model/notebooks/DIAT-uSAT_dataset/3_long_blade_rotor/figure1.jpg\"\n", + "image = Image.open(image_path)\n", "\n", - " all_preds.extend(preds.cpu().numpy())\n", - " all_labels.extend(labels.cpu().numpy())\n", + "# Apply the transformations\n", + "input_tensor = transform(image)\n", "\n", - " test_loss = running_loss / total_samples\n", - " test_accuracy = running_corrects.double() / total_samples\n", + "# Add a batch dimension (since ONNX model expects a batch of images)\n", + "input_tensor = input_tensor.unsqueeze(0) # Shape becomes (1, 3, 224, 224)\n", "\n", - " print(f\"Test Loss: {test_loss:.4f}\")\n", - " print(f\"Test Accuracy: {test_accuracy:.4f}\")\n", + "# Convert the tensor to a numpy array\n", + "input_array = input_tensor.numpy()\n", "\n", - " # Confusion Matrix\n", - " cm = confusion_matrix(all_labels, all_preds)\n", + "# Start timer for inference\n", + "start_time = time.time()\n", "\n", - " plt.figure(figsize=(8, 6))\n", - " sns.heatmap(cm, annot=True, fmt='g', cmap='Blues', xticklabels=[\"3_long_blade_rotor\", \"3_short_blade_rotor\", \"Bird\", \"Bird+mini-helicopter\", \"drone\", \"rc_plane\"], yticklabels=[\"3_long_blade_rotor\", \"3_short_blade_rotor\", \"Bird\", \"Bird+mini-helicopter\", \"drone\", \"rc_plane\"])\n", - " plt.xlabel('Predicted Labels')\n", - " plt.ylabel('True Labels')\n", - " plt.title('Confusion Matrix')\n", - " plt.show()\n", + "# Run the inference\n", + "output = session.run([output_name], {input_name: input_array})\n", "\n", - " # ROC Curve\n", - " fpr, tpr, _ = roc_curve(all_labels, all_preds, pos_label=1) # Adjust pos_label as needed\n", - " roc_auc = auc(fpr, tpr)\n", + "# End timer for inference\n", + "end_time = time.time()\n", "\n", - " plt.figure(figsize=(8, 6))\n", - " plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC Curve (area = {roc_auc:.2f})')\n", - " plt.plot([0, 1], [0, 1], color='gray', lw=2, linestyle='--')\n", - " plt.xlabel('False Positive Rate')\n", - " plt.ylabel('True Positive Rate')\n", - " plt.title('Receiver Operating Characteristic (ROC) Curve')\n", - " plt.legend(loc='lower right')\n", - " plt.show()\n", + "# Calculate inference time\n", + "inference_time = end_time - start_time\n", "\n", - " return {\n", - " \"test_loss\": test_loss,\n", - " \"test_accuracy\": test_accuracy.item(),\n", - " \"confusion_matrix\": cm,\n", - " \"all_preds\": all_preds,\n", - " \"all_labels\": all_labels,\n", - " }\n", + "# Process the output\n", + "output_probabilities = np.squeeze(output[0]) # Remove unnecessary dimensions\n", + "predicted_class = np.argmax(output_probabilities) # Get index of the highest probability\n", "\n", - "# Dataset preparation\n", - "train_size = int(0.85 * len(dataset))\n", - "val_size = int(0.05 * len(dataset))\n", - "test_size = len(dataset) - train_size - val_size\n", - "train_dataset, val_dataset, test_dataset = random_split(dataset, [train_size, val_size, test_size])\n", + "# Display results\n", + "print(f\"Predicted Class: {classes[predicted_class]}\")\n", + "print(f\"Probability: {output_probabilities[predicted_class]:.4f}\")\n", + "print(f\"Inference Time: {inference_time:.4f} seconds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\threadpoolctl.py:1214: RuntimeWarning: \n", + "Found Intel OpenMP ('libiomp') and LLVM OpenMP ('libomp') loaded at\n", + "the same time. Both libraries are known to be incompatible and this\n", + "can cause random crashes or deadlocks on Linux when loaded in the\n", + "same Python program.\n", + "Using threadpoolctl may cause crashes or deadlocks. For more\n", + "information and possible workarounds, please see\n", + " https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md\n", + "\n", + " warnings.warn(msg, RuntimeWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 3_long_blade_rotor 0.9949 0.9800 0.9874 799\n", + " 3_short_blade_rotor 0.9803 0.9938 0.9870 800\n", + " Bird 1.0000 1.0000 1.0000 800\n", + "Bird+mini-helicopter 0.9843 0.9988 0.9915 815\n", + " drone 1.0000 1.0000 1.0000 835\n", + " rc_plane 0.9975 0.9838 0.9906 800\n", + "\n", + " accuracy 0.9928 4849\n", + " macro avg 0.9928 0.9927 0.9927 4849\n", + " weighted avg 0.9928 0.9928 0.9928 4849\n", + "\n" + ] + } + ], + "source": [ + "import onnxruntime as ort\n", + "import numpy as np\n", + "from PIL import Image\n", + "from torchvision import transforms\n", + "import os\n", + "from sklearn.metrics import classification_report, confusion_matrix\n", + "import matplotlib.pyplot as plt\n", "\n", - "train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)\n", - "val_loader = DataLoader(val_dataset, batch_size=8, shuffle=False)\n", - "test_loader = DataLoader(test_dataset, batch_size=8, shuffle=False)\n", + "# Load the ONNX model\n", + "model_path = \"cnnwithattention.onnx\"\n", + "session = ort.InferenceSession(model_path, providers=[\"CPUExecutionProvider\"])\n", "\n", - "# Model, Loss, Optimizer, and Scheduler setup\n", - "model_deepercnn = CustomCNNWithAttention(num_classes=6)\n", - "criterion = nn.CrossEntropyLoss()\n", + "# Get input and output information\n", + "input_name = session.get_inputs()[0].name\n", + "output_name = session.get_outputs()[0].name\n", "\n", - "optimizer_deepercnn = optim.Adam(model_deepercnn.parameters(), lr=0.001)\n", - "scheduler_deepercnn = lr_scheduler.StepLR(optimizer_deepercnn, step_size=7, gamma=0.1)\n", + "# Define the preprocessing pipeline\n", + "transform = transforms.Compose([\n", + " transforms.Resize((224, 224)), # Resize the image to 224x224 pixels\n", + " transforms.ToTensor(), # Convert to Tensor\n", + " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), # Normalize\n", + "])\n", + "\n", + "# Define the six classes\n", + "classes = [\"3_long_blade_rotor\", \"3_short_blade_rotor\", \"Bird\", \"Bird+mini-helicopter\", \"drone\", \"rc_plane\"]\n", + "\n", + "def preprocess_image(image_path):\n", + " \"\"\"\n", + " Preprocess an image for model inference\n", + " \"\"\"\n", + " image = Image.open(image_path)\n", + " input_tensor = transform(image)\n", + " input_tensor = input_tensor.unsqueeze(0)\n", + " return input_tensor.numpy().astype(np.float32)\n", + "\n", + "def predict_image(session, input_array, input_name, output_name):\n", + " \"\"\"\n", + " Run inference on a single image\n", + " \"\"\"\n", + " output = session.run([output_name], {input_name: input_array})\n", + " output_probabilities = np.squeeze(output[0])\n", + " return np.argmax(output_probabilities)\n", "\n", - "# Train the model\n", - "train_model(model_deepercnn, train_loader, val_loader, criterion, optimizer_deepercnn, scheduler_deepercnn, num_epochs=20)\n", + "def evaluate_model(dataset_root):\n", + " \"\"\"\n", + " Evaluate the ONNX model and generate classification report\n", + " \n", + " :param dataset_root: Root directory containing subdirectories for each class\n", + " :return: Tuple of (true labels, predicted labels)\n", + " \"\"\"\n", + " true_labels = []\n", + " predicted_labels = []\n", + " \n", + " # Iterate through each class directory\n", + " for class_idx, class_name in enumerate(classes):\n", + " class_path = os.path.join(dataset_root, class_name)\n", + " \n", + " # Check if directory exists\n", + " if not os.path.isdir(class_path):\n", + " print(f\"Warning: Directory {class_path} does not exist.\")\n", + " continue\n", + " \n", + " # Process each image in the class directory\n", + " for image_filename in os.listdir(class_path):\n", + " image_path = os.path.join(class_path, image_filename)\n", + " \n", + " try:\n", + " # Preprocess the image\n", + " input_array = preprocess_image(image_path)\n", + " \n", + " # Predict the class\n", + " predicted_class = predict_image(session, input_array, input_name, output_name)\n", + " \n", + " # Store true and predicted labels\n", + " true_labels.append(class_idx)\n", + " predicted_labels.append(predicted_class)\n", + " \n", + " except Exception as e:\n", + " print(f\"Error processing {image_path}: {e}\")\n", + " \n", + " return true_labels, predicted_labels\n", "\n", - "# Load the entire model and evaluate\n", - "model = torch.load(\"customcnnwithAttention_best.pth\")\n", - "model.eval() # Set the model to evaluation mode\n", - "test_results = test_model(model, test_loader, criterion, device=\"cuda\" if torch.cuda.is_available() else \"cpu\")\n" + "def generate_classification_report(dataset_root):\n", + " \"\"\"\n", + " Generate and print classification report\n", + " \n", + " :param dataset_root: Root directory containing subdirectories for each class\n", + " \"\"\"\n", + " # Evaluate the model\n", + " true_labels, predicted_labels = evaluate_model(dataset_root)\n", + " \n", + " # Generate classification report\n", + " report = classification_report(\n", + " true_labels, \n", + " predicted_labels, \n", + " target_names=classes, \n", + " digits=4\n", + " )\n", + " print(\"Classification Report:\")\n", + " print(report)\n", + " \n", + " # Generate confusion matrix\n", + " cm = confusion_matrix(true_labels, predicted_labels)\n", + " plt.figure(figsize=(10,8))\n", + " \n", + " # Create confusion matrix with Matplotlib\n", + " im = plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n", + " plt.title('Confusion Matrix')\n", + " plt.colorbar(im)\n", + " \n", + " # Add text annotations\n", + " thresh = cm.max() / 2.\n", + " for i in range(cm.shape[0]):\n", + " for j in range(cm.shape[1]):\n", + " plt.text(j, i, format(cm[i, j], 'd'),\n", + " horizontalalignment=\"center\",\n", + " color=\"white\" if cm[i, j] > thresh else \"black\")\n", + " \n", + " plt.tight_layout()\n", + " plt.xlabel('Predicted Label')\n", + " plt.ylabel('True Label')\n", + " plt.xticks(range(len(classes)), classes, rotation=45)\n", + " plt.yticks(range(len(classes)), classes)\n", + " plt.savefig('confusion_matrix.png')\n", + " plt.close()\n", + "\n", + "# Usage\n", + "# Replace with the path to your test dataset root directory\n", + "dataset_root = \"D:/Micro-Classify/ml_model/notebooks/DIAT-uSAT_dataset\"\n", + "generate_classification_report(dataset_root)" ] ->>>>>>> d3c3bf7de2b0a81b33e974f61e5829c74646f3bb:ml_model/notebooks/customCnnWithCNN.ipynb } ], "metadata": { diff --git a/ml_model/notebooks/notebook[1].ipynb b/ml_model/notebooks/notebook[1].ipynb index 632981e..3e04049 100644 --- a/ml_model/notebooks/notebook[1].ipynb +++ b/ml_model/notebooks/notebook[1].ipynb @@ -2,26 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 18, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\threadpoolctl.py:1214: RuntimeWarning: \n", - "Found Intel OpenMP ('libiomp') and LLVM OpenMP ('libomp') loaded at\n", - "the same time. Both libraries are known to be incompatible and this\n", - "can cause random crashes or deadlocks on Linux when loaded in the\n", - "same Python program.\n", - "Using threadpoolctl may cause crashes or deadlocks. For more\n", - "information and possible workarounds, please see\n", - " https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md\n", - "\n", - " warnings.warn(msg, RuntimeWarning)\n" - ] - } - ], + "outputs": [], "source": [ "# %pip install numpy torch torchvision scikit-learn matplotlib pillow \n", "import os\n", @@ -38,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -58,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -76,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -430,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -440,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -451,7 +434,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -463,7 +446,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -474,19 +457,19 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-2.117904..2.186841].\n" + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.9980307..2.5005665].\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -507,7 +490,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -558,7 +541,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -614,7 +597,7 @@ ")" ] }, - "execution_count": 12, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -628,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -640,7 +623,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -725,43 +708,43 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch [1/15], Loss: 0.4477, Accuracy: 0.8468\n", - "Validation Loss: 0.2181, Accuracy: 0.9256\n", - "Epoch [2/15], Loss: 0.1878, Accuracy: 0.9394\n", - "Validation Loss: 0.1627, Accuracy: 0.9504\n", - "Epoch [3/15], Loss: 0.1339, Accuracy: 0.9568\n", - "Validation Loss: 0.1230, Accuracy: 0.9587\n", - "Epoch [4/15], Loss: 0.1086, Accuracy: 0.9636\n", - "Validation Loss: 0.0891, Accuracy: 0.9711\n", - "Epoch [5/15], Loss: 0.0886, Accuracy: 0.9714\n", - "Validation Loss: 0.1250, Accuracy: 0.9463\n", - "Epoch [6/15], Loss: 0.0776, Accuracy: 0.9709\n", - "Validation Loss: 0.0673, Accuracy: 0.9752\n", - "Epoch [7/15], Loss: 0.0605, Accuracy: 0.9784\n", - "Validation Loss: 0.0458, Accuracy: 0.9835\n", - "Epoch [8/15], Loss: 0.0607, Accuracy: 0.9794\n", - "Validation Loss: 0.0799, Accuracy: 0.9669\n", - "Epoch [9/15], Loss: 0.0534, Accuracy: 0.9828\n", - "Validation Loss: 0.0426, Accuracy: 0.9959\n", - "Epoch [10/15], Loss: 0.0519, Accuracy: 0.9811\n", - "Validation Loss: 0.0447, Accuracy: 0.9917\n", - "Epoch [11/15], Loss: 0.0433, Accuracy: 0.9864\n", - "Validation Loss: 0.0787, Accuracy: 0.9587\n", - "Epoch [12/15], Loss: 0.0406, Accuracy: 0.9859\n", - "Validation Loss: 0.0371, Accuracy: 0.9876\n", - "Epoch [13/15], Loss: 0.0295, Accuracy: 0.9901\n", - "Validation Loss: 0.0583, Accuracy: 0.9669\n", - "Epoch [14/15], Loss: 0.0356, Accuracy: 0.9879\n", - "Validation Loss: 0.0481, Accuracy: 0.9752\n", - "Epoch [15/15], Loss: 0.0334, Accuracy: 0.9879\n", - "Validation Loss: 0.0192, Accuracy: 0.9917\n" + "Epoch [1/15], Loss: 0.4387, Accuracy: 0.8459\n", + "Validation Loss: 0.1879, Accuracy: 0.9215\n", + "Epoch [2/15], Loss: 0.1855, Accuracy: 0.9425\n", + "Validation Loss: 0.0998, Accuracy: 0.9793\n", + "Epoch [3/15], Loss: 0.1367, Accuracy: 0.9537\n", + "Validation Loss: 0.0645, Accuracy: 0.9917\n", + "Epoch [4/15], Loss: 0.1050, Accuracy: 0.9675\n", + "Validation Loss: 0.0616, Accuracy: 0.9793\n", + "Epoch [5/15], Loss: 0.0901, Accuracy: 0.9709\n", + "Validation Loss: 0.0399, Accuracy: 0.9959\n", + "Epoch [6/15], Loss: 0.0734, Accuracy: 0.9745\n", + "Validation Loss: 0.0526, Accuracy: 0.9917\n", + "Epoch [7/15], Loss: 0.0736, Accuracy: 0.9748\n", + "Validation Loss: 0.0418, Accuracy: 0.9876\n", + "Epoch [8/15], Loss: 0.0603, Accuracy: 0.9803\n", + "Validation Loss: 0.0782, Accuracy: 0.9752\n", + "Epoch [9/15], Loss: 0.0356, Accuracy: 0.9903\n", + "Validation Loss: 0.0259, Accuracy: 0.9959\n", + "Epoch [10/15], Loss: 0.0306, Accuracy: 0.9903\n", + "Validation Loss: 0.0311, Accuracy: 0.9876\n", + "Epoch [11/15], Loss: 0.0291, Accuracy: 0.9908\n", + "Validation Loss: 0.0225, Accuracy: 0.9959\n", + "Epoch [12/15], Loss: 0.0274, Accuracy: 0.9917\n", + "Validation Loss: 0.0284, Accuracy: 0.9917\n", + "Epoch [13/15], Loss: 0.0255, Accuracy: 0.9917\n", + "Validation Loss: 0.0287, Accuracy: 0.9959\n", + "Epoch [14/15], Loss: 0.0278, Accuracy: 0.9932\n", + "Validation Loss: 0.0309, Accuracy: 0.9917\n", + "Epoch [15/15], Loss: 0.0174, Accuracy: 0.9959\n", + "Validation Loss: 0.0177, Accuracy: 0.9959\n" ] } ], @@ -772,28 +755,43 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 14, "metadata": {}, "outputs": [ { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[63], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mtrain_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_vgg19\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer_vgg19\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscheduler_vgg19\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[1;32mIn[61], line 10\u001b[0m, in \u001b[0;36mtrain_model\u001b[1;34m(model, train_loader, val_loader, criterion, optimizer, scheduler, num_epochs)\u001b[0m\n\u001b[0;32m 7\u001b[0m running_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m\n\u001b[0;32m 8\u001b[0m running_corrects \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m---> 10\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m images, labels \u001b[38;5;129;01min\u001b[39;00m train_loader:\n\u001b[0;32m 11\u001b[0m images \u001b[38;5;241m=\u001b[39m images\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m 12\u001b[0m labels \u001b[38;5;241m=\u001b[39m labels\u001b[38;5;241m.\u001b[39mto(device, dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mlong)\n", - "File \u001b[1;32mc:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:630\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 627\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 628\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m 629\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 630\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 631\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 632\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m 633\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m 634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n", - "File \u001b[1;32mc:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:673\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 671\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m 672\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index() \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 673\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m 674\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m 675\u001b[0m data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n", - "File \u001b[1;32mc:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:50\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m 48\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_collation:\n\u001b[0;32m 49\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__getitems__\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__:\n\u001b[1;32m---> 50\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__getitems__\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpossibly_batched_index\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 51\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 52\u001b[0m data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n", - "File \u001b[1;32mc:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\utils\\data\\dataset.py:418\u001b[0m, in \u001b[0;36mSubset.__getitems__\u001b[1;34m(self, indices)\u001b[0m\n\u001b[0;32m 414\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitems__\u001b[39m(\u001b[38;5;28mself\u001b[39m, indices: List[\u001b[38;5;28mint\u001b[39m]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[T_co]:\n\u001b[0;32m 415\u001b[0m \u001b[38;5;66;03m# add batched sampling support when parent dataset supports it.\u001b[39;00m\n\u001b[0;32m 416\u001b[0m \u001b[38;5;66;03m# see torch.utils.data._utils.fetch._MapDatasetFetcher\u001b[39;00m\n\u001b[0;32m 417\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__getitems__\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)):\n\u001b[1;32m--> 418\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__getitems__\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindices\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mindices\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore[attr-defined]\u001b[39;00m\n\u001b[0;32m 419\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 420\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindices[idx]] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m indices]\n", - "File \u001b[1;32mc:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\utils\\data\\dataset.py:420\u001b[0m, in \u001b[0;36mSubset.__getitems__\u001b[1;34m(self, indices)\u001b[0m\n\u001b[0;32m 418\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__([\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindices[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m indices]) \u001b[38;5;66;03m# type: ignore[attr-defined]\u001b[39;00m\n\u001b[0;32m 419\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 420\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindices[idx]] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m indices]\n", - "File \u001b[1;32mc:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\utils\\data\\dataset.py:420\u001b[0m, in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 418\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__([\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindices[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m indices]) \u001b[38;5;66;03m# type: ignore[attr-defined]\u001b[39;00m\n\u001b[0;32m 419\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 420\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindices\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m indices]\n", - "Cell \u001b[1;32mIn[50], line 36\u001b[0m, in \u001b[0;36mCustomImageDataset.__getitem__\u001b[1;34m(self, idx)\u001b[0m\n\u001b[0;32m 34\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, idx):\n\u001b[0;32m 35\u001b[0m img_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mimage_paths[idx]\n\u001b[1;32m---> 36\u001b[0m image \u001b[38;5;241m=\u001b[39m \u001b[43mImage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg_path\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mRGB\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Ensure it's RGB\u001b[39;00m\n\u001b[0;32m 38\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform:\n\u001b[0;32m 39\u001b[0m image \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform(image)\n", - "File \u001b[1;32mc:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\PIL\\Image.py:941\u001b[0m, in \u001b[0;36mImage.convert\u001b[1;34m(self, mode, matrix, dither, palette, colors)\u001b[0m\n\u001b[0;32m 889\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconvert\u001b[39m(\n\u001b[0;32m 890\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 891\u001b[0m mode: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 895\u001b[0m colors: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m256\u001b[39m,\n\u001b[0;32m 896\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Image:\n\u001b[0;32m 897\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 898\u001b[0m \u001b[38;5;124;03m Returns a converted copy of this image. For the \"P\" mode, this\u001b[39;00m\n\u001b[0;32m 899\u001b[0m \u001b[38;5;124;03m method translates pixels through the palette. If mode is\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 938\u001b[0m \u001b[38;5;124;03m :returns: An :py:class:`~PIL.Image.Image` object.\u001b[39;00m\n\u001b[0;32m 939\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 941\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 943\u001b[0m has_transparency \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtransparency\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\n\u001b[0;32m 944\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m mode \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mP\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 945\u001b[0m \u001b[38;5;66;03m# determine default mode\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\PIL\\ImageFile.py:291\u001b[0m, in \u001b[0;36mImageFile.load\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 288\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(msg)\n\u001b[0;32m 290\u001b[0m b \u001b[38;5;241m=\u001b[39m b \u001b[38;5;241m+\u001b[39m s\n\u001b[1;32m--> 291\u001b[0m n, err_code \u001b[38;5;241m=\u001b[39m \u001b[43mdecoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 292\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 293\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/15], Loss: 0.5034, Accuracy: 0.8258\n", + "Validation Loss: 0.1776, Accuracy: 0.9463\n", + "Epoch [2/15], Loss: 0.2490, Accuracy: 0.9177\n", + "Validation Loss: 0.1134, Accuracy: 0.9835\n", + "Epoch [3/15], Loss: 0.1818, Accuracy: 0.9386\n", + "Validation Loss: 0.1015, Accuracy: 0.9752\n", + "Epoch [4/15], Loss: 0.1532, Accuracy: 0.9515\n", + "Validation Loss: 0.0834, Accuracy: 0.9793\n", + "Epoch [5/15], Loss: 0.1325, Accuracy: 0.9510\n", + "Validation Loss: 0.0872, Accuracy: 0.9711\n", + "Epoch [6/15], Loss: 0.1145, Accuracy: 0.9607\n", + "Validation Loss: 0.0656, Accuracy: 0.9793\n", + "Epoch [7/15], Loss: 0.0957, Accuracy: 0.9689\n", + "Validation Loss: 0.0540, Accuracy: 0.9876\n", + "Epoch [8/15], Loss: 0.0902, Accuracy: 0.9704\n", + "Validation Loss: 0.0803, Accuracy: 0.9793\n", + "Epoch [9/15], Loss: 0.0785, Accuracy: 0.9750\n", + "Validation Loss: 0.0498, Accuracy: 0.9793\n", + "Epoch [10/15], Loss: 0.0684, Accuracy: 0.9784\n", + "Validation Loss: 0.0535, Accuracy: 0.9835\n", + "Epoch [11/15], Loss: 0.0639, Accuracy: 0.9777\n", + "Validation Loss: 0.0424, Accuracy: 0.9876\n", + "Epoch [12/15], Loss: 0.0653, Accuracy: 0.9772\n", + "Validation Loss: 0.0526, Accuracy: 0.9752\n", + "Epoch [13/15], Loss: 0.0602, Accuracy: 0.9777\n", + "Validation Loss: 0.0524, Accuracy: 0.9793\n", + "Epoch [14/15], Loss: 0.0550, Accuracy: 0.9830\n", + "Validation Loss: 0.0279, Accuracy: 1.0000\n", + "Epoch [15/15], Loss: 0.0456, Accuracy: 0.9835\n", + "Validation Loss: 0.0235, Accuracy: 0.9917\n" ] } ], @@ -854,56 +852,44 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Test Loss: 0.0869, Accuracy: 0.9815\n", - "Test Loss: 1.8079, Accuracy: 0.1622\n", + "Test Loss: 0.0237, Accuracy: 0.9938\n", + "Test Loss: 0.0554, Accuracy: 0.9835\n", "Classification Report for VGG16:\n", " precision recall f1-score support\n", "\n", - " 3_long_blade_rotor 0.99 0.97 0.98 75\n", - " 3_short_blade_rotor 0.98 0.97 0.97 90\n", - " Bird 0.99 1.00 0.99 82\n", - "Bird+mini-helicopter 1.00 0.95 0.97 81\n", - " drone 1.00 1.00 1.00 87\n", - " rc_plane 0.94 1.00 0.97 72\n", + " 3_long_blade_rotor 1.00 1.00 1.00 72\n", + " 3_short_blade_rotor 0.99 0.99 0.99 85\n", + " Bird 1.00 1.00 1.00 76\n", + "Bird+mini-helicopter 0.99 1.00 0.99 78\n", + " drone 1.00 1.00 1.00 85\n", + " rc_plane 0.99 0.98 0.98 90\n", "\n", - " accuracy 0.98 487\n", - " macro avg 0.98 0.98 0.98 487\n", - " weighted avg 0.98 0.98 0.98 487\n", + " accuracy 0.99 486\n", + " macro avg 0.99 0.99 0.99 486\n", + " weighted avg 0.99 0.99 0.99 486\n", "\n", "Classification Report for VGG19:\n", " precision recall f1-score support\n", "\n", - " 3_long_blade_rotor 0.13 0.03 0.04 75\n", - " 3_short_blade_rotor 0.00 0.00 0.00 90\n", - " Bird 0.00 0.00 0.00 82\n", - "Bird+mini-helicopter 0.16 0.95 0.28 81\n", - " drone 0.00 0.00 0.00 87\n", - " rc_plane 0.00 0.00 0.00 72\n", + " 3_long_blade_rotor 0.93 0.99 0.96 72\n", + " 3_short_blade_rotor 1.00 0.96 0.98 85\n", + " Bird 1.00 1.00 1.00 76\n", + "Bird+mini-helicopter 1.00 0.96 0.98 78\n", + " drone 1.00 1.00 1.00 85\n", + " rc_plane 0.97 0.99 0.98 90\n", "\n", - " accuracy 0.16 487\n", - " macro avg 0.05 0.16 0.05 487\n", - " weighted avg 0.05 0.16 0.05 487\n", + " accuracy 0.98 486\n", + " macro avg 0.98 0.98 0.98 486\n", + " weighted avg 0.98 0.98 0.98 486\n", "\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", - "c:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", - "c:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n" - ] } ], "source": [ @@ -933,7 +919,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -1043,7 +1029,16 @@ "cell_type": "code", "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\optim\\lr_scheduler.py:60: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n", + " warnings.warn(\n" + ] + } + ], "source": [ "# Loss and optimizer for Deeper CNN\n", "optimizer_deepercnn = optim.Adam(model_deepercnn.parameters(), lr=0.0001)\n", @@ -1059,46 +1054,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch [1/20], Loss: 1.1431, Accuracy: 0.6085\n", - "Validation Loss: 0.6464, Accuracy: 0.7893\n", - "Epoch [2/20], Loss: 0.6322, Accuracy: 0.7825\n", - "Validation Loss: 0.4047, Accuracy: 0.8554\n", - "Epoch [3/20], Loss: 0.4739, Accuracy: 0.8436\n", - "Validation Loss: 0.4122, Accuracy: 0.8306\n", - "Epoch [4/20], Loss: 0.3789, Accuracy: 0.8771\n", - "Validation Loss: 0.3649, Accuracy: 0.8512\n", - "Epoch [5/20], Loss: 0.3079, Accuracy: 0.9059\n", - "Validation Loss: 0.1976, Accuracy: 0.9587\n", - "Epoch [6/20], Loss: 0.2664, Accuracy: 0.9108\n", - "Validation Loss: 0.3434, Accuracy: 0.8843\n", - "Epoch [7/20], Loss: 0.2457, Accuracy: 0.9173\n", - "Validation Loss: 0.2314, Accuracy: 0.9380\n", - "Epoch [8/20], Loss: 0.2265, Accuracy: 0.9253\n", - "Validation Loss: 0.1834, Accuracy: 0.9421\n", - "Epoch [9/20], Loss: 0.1967, Accuracy: 0.9370\n", - "Validation Loss: 0.1323, Accuracy: 0.9463\n", - "Epoch [10/20], Loss: 0.1774, Accuracy: 0.9450\n", - "Validation Loss: 0.1540, Accuracy: 0.9587\n", - "Epoch [11/20], Loss: 0.1804, Accuracy: 0.9396\n", - "Validation Loss: 0.1124, Accuracy: 0.9669\n", - "Epoch [12/20], Loss: 0.1559, Accuracy: 0.9503\n", - "Validation Loss: 0.1422, Accuracy: 0.9545\n", - "Epoch [13/20], Loss: 0.1517, Accuracy: 0.9515\n", - "Validation Loss: 0.1095, Accuracy: 0.9587\n", - "Epoch [14/20], Loss: 0.1460, Accuracy: 0.9508\n", - "Validation Loss: 0.0799, Accuracy: 0.9669\n", - "Epoch [15/20], Loss: 0.1269, Accuracy: 0.9612\n", - "Validation Loss: 0.1138, Accuracy: 0.9628\n", - "Epoch [16/20], Loss: 0.1262, Accuracy: 0.9617\n", - "Validation Loss: 0.2302, Accuracy: 0.9091\n", - "Epoch [17/20], Loss: 0.1223, Accuracy: 0.9605\n", - "Validation Loss: 0.2222, Accuracy: 0.9215\n", - "Epoch [18/20], Loss: 0.0839, Accuracy: 0.9728\n", - "Validation Loss: 0.0832, Accuracy: 0.9835\n", - "Epoch [19/20], Loss: 0.0876, Accuracy: 0.9704\n", - "Validation Loss: 0.0903, Accuracy: 0.9711\n", - "Epoch [20/20], Loss: 0.0820, Accuracy: 0.9724\n", - "Validation Loss: 0.0620, Accuracy: 0.9835\n" + "Epoch [1/20], Loss: 1.1221, Accuracy: 0.6205\n", + "Validation Loss: 0.5616, Accuracy: 0.7893\n", + "Epoch [2/20], Loss: 0.6146, Accuracy: 0.7896\n", + "Validation Loss: 0.3828, Accuracy: 0.8471\n", + "Epoch [3/20], Loss: 0.4768, Accuracy: 0.8347\n", + "Validation Loss: 0.2460, Accuracy: 0.9421\n", + "Epoch [4/20], Loss: 0.3915, Accuracy: 0.8656\n", + "Validation Loss: 0.2009, Accuracy: 0.9380\n", + "Epoch [5/20], Loss: 0.3278, Accuracy: 0.8944\n", + "Validation Loss: 0.2096, Accuracy: 0.9215\n", + "Epoch [6/20], Loss: 0.3002, Accuracy: 0.8978\n", + "Validation Loss: 0.1558, Accuracy: 0.9504\n", + "Epoch [7/20], Loss: 0.2563, Accuracy: 0.9221\n", + "Validation Loss: 0.1342, Accuracy: 0.9504\n", + "Epoch [8/20], Loss: 0.2430, Accuracy: 0.9194\n", + "Validation Loss: 0.4780, Accuracy: 0.8058\n", + "Epoch [9/20], Loss: 0.2330, Accuracy: 0.9267\n", + "Validation Loss: 0.1158, Accuracy: 0.9504\n", + "Epoch [10/20], Loss: 0.2068, Accuracy: 0.9333\n", + "Validation Loss: 0.0878, Accuracy: 0.9711\n", + "Epoch [11/20], Loss: 0.1878, Accuracy: 0.9435\n", + "Validation Loss: 0.1289, Accuracy: 0.9545\n", + "Epoch [12/20], Loss: 0.1661, Accuracy: 0.9473\n", + "Validation Loss: 0.0635, Accuracy: 0.9876\n", + "Epoch [13/20], Loss: 0.1761, Accuracy: 0.9418\n", + "Validation Loss: 0.0868, Accuracy: 0.9793\n", + "Epoch [14/20], Loss: 0.1525, Accuracy: 0.9529\n", + "Validation Loss: 0.0487, Accuracy: 0.9876\n", + "Epoch [15/20], Loss: 0.1485, Accuracy: 0.9524\n", + "Validation Loss: 0.0509, Accuracy: 0.9876\n", + "Epoch [16/20], Loss: 0.1404, Accuracy: 0.9583\n", + "Validation Loss: 0.1600, Accuracy: 0.9545\n", + "Epoch [17/20], Loss: 0.1295, Accuracy: 0.9568\n", + "Validation Loss: 0.0576, Accuracy: 0.9793\n", + "Epoch [18/20], Loss: 0.1093, Accuracy: 0.9694\n", + "Validation Loss: 0.0706, Accuracy: 0.9711\n", + "Epoch [19/20], Loss: 0.0872, Accuracy: 0.9731\n", + "Validation Loss: 0.0307, Accuracy: 0.9959\n", + "Epoch [20/20], Loss: 0.0966, Accuracy: 0.9728\n", + "Validation Loss: 0.0476, Accuracy: 0.9835\n" ] } ], @@ -1116,20 +1111,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "Test Loss: 0.0975, Accuracy: 0.9671\n", + "Test Loss: 0.1258, Accuracy: 0.9630\n", "Classification Report for Custom CNN:\n", " precision recall f1-score support\n", "\n", - " 3_long_blade_rotor 0.92 0.96 0.94 75\n", - " 3_short_blade_rotor 0.99 0.89 0.94 90\n", - " Bird 1.00 1.00 1.00 82\n", - "Bird+mini-helicopter 0.97 0.96 0.97 81\n", - " drone 1.00 1.00 1.00 87\n", - " rc_plane 0.91 1.00 0.95 72\n", + " 3_long_blade_rotor 0.92 0.96 0.94 72\n", + " 3_short_blade_rotor 1.00 0.82 0.90 85\n", + " Bird 1.00 1.00 1.00 76\n", + "Bird+mini-helicopter 0.95 1.00 0.97 78\n", + " drone 1.00 1.00 1.00 85\n", + " rc_plane 0.92 1.00 0.96 90\n", "\n", - " accuracy 0.97 487\n", - " macro avg 0.97 0.97 0.97 487\n", - " weighted avg 0.97 0.97 0.97 487\n", + " accuracy 0.96 486\n", + " macro avg 0.96 0.96 0.96 486\n", + " weighted avg 0.97 0.96 0.96 486\n", "\n" ] } @@ -1153,7 +1148,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1236,7 +1231,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1247,7 +1242,7 @@ " warnings.warn(\n", "c:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG19_Weights.IMAGENET1K_V1`. You can also use `weights=VGG19_Weights.DEFAULT` to get the most up-to-date weights.\n", " warnings.warn(msg)\n", - "C:\\Users\\incha\\AppData\\Local\\Temp\\ipykernel_25336\\996431065.py:32: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + "C:\\Users\\incha\\AppData\\Local\\Temp\\ipykernel_30612\\996431065.py:32: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " model.load_state_dict(torch.load('best_model_CustomVGG.pt'))\n" ] } @@ -1296,7 +1291,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1305,7 +1300,7 @@ "text": [ "c:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n", " warnings.warn(msg)\n", - "C:\\Users\\incha\\AppData\\Local\\Temp\\ipykernel_25336\\4004522494.py:34: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + "C:\\Users\\incha\\AppData\\Local\\Temp\\ipykernel_30612\\4004522494.py:34: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " model.load_state_dict(torch.load('best_model_CustomVGG.pt'), strict=False)\n" ] }, @@ -1472,14 +1467,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\incha\\AppData\\Local\\Temp\\ipykernel_24372\\4282892681.py:8: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + "C:\\Users\\incha\\AppData\\Local\\Temp\\ipykernel_5568\\4282892681.py:8: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " model.load_state_dict(torch.load(model_path))\n" ] }, @@ -1527,7 +1522,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1607,7 +1602,150 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted Class: 3 Long Blade Rotor\n", + "Probability: 0.6778\n", + "Inference Time: 0.0242 seconds\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\incha\\AppData\\Local\\Temp\\ipykernel_5568\\3118820349.py:71: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " state_dict = torch.load(state_dict_path)\n" + ] + } + ], + "source": [ + "import torch\n", + "import numpy as np\n", + "from PIL import Image\n", + "from torchvision import transforms\n", + "import time\n", + "\n", + "class CustomCNN(nn.Module):\n", + " def __init__(self, num_classes=6):\n", + " super(CustomCNN, self).__init__()\n", + " self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)\n", + " self.bn1 = nn.BatchNorm2d(16)\n", + " self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n", + "\n", + " # 2nd Convolutional Block\n", + " self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)\n", + " self.bn2 = nn.BatchNorm2d(32)\n", + " self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n", + "\n", + " # 3rd Convolutional Block\n", + " self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n", + " self.bn3 = nn.BatchNorm2d(64)\n", + " self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)\n", + "\n", + " # 4th Convolutional Block\n", + " self.conv4 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n", + " self.bn4 = nn.BatchNorm2d(128)\n", + " self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)\n", + "\n", + " # Global Average Pooling\n", + " self.global_avg_pool = nn.AdaptiveAvgPool2d((1, 1))\n", + " \n", + " # Fully Connected Layers\n", + " self.fc1 = nn.Linear(128, 512)\n", + " self.fc2 = nn.Linear(512, 6) # Output for 6 classes\n", + "\n", + " # Dropout for Regularization\n", + " self.dropout = nn.Dropout(0.5)\n", + "\n", + " def forward(self, x):\n", + " # 1st Convolutional Block\n", + " x = self.pool1(F.relu(self.bn1(self.conv1(x))))\n", + " \n", + " # 2nd Convolutional Block\n", + " x = self.pool2(F.relu(self.bn2(self.conv2(x))))\n", + " \n", + " # 3rd Convolutional Block\n", + " x = self.pool3(F.relu(self.bn3(self.conv3(x))))\n", + "\n", + " # 4th Convolutional Block\n", + " x = self.pool4(F.relu(self.bn4(self.conv4(x))))\n", + "\n", + " # Global Average Pooling\n", + " x = self.global_avg_pool(x)\n", + " \n", + " # Flatten the output\n", + " x = torch.flatten(x, 1)\n", + " \n", + " # Fully Connected Layers\n", + " x = F.relu(self.fc1(x))\n", + " x = self.dropout(x)\n", + " x = self.fc2(x)\n", + " \n", + " return x\n", + "\n", + "\n", + "# Reinitialize the model\n", + "model = CustomCNN()\n", + "\n", + "# Load the state dictionary\n", + "state_dict_path = \"best_model_CustomCNN.pt\"\n", + "state_dict = torch.load(state_dict_path)\n", + "model.load_state_dict(state_dict)\n", + "\n", + "# Set the model to evaluation mode\n", + "model.eval()\n", + "\n", + "# Define the preprocessing pipeline\n", + "transform = transforms.Compose([\n", + " transforms.Resize((224, 224)), # Resize the image to 224x224 pixels\n", + " transforms.ToTensor(), # Convert to Tensor\n", + " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), # Normalize\n", + "])\n", + "\n", + "# Define the six classes\n", + "classes = [\"3 Long Blade Rotor\", \"3 Short Blade Rotor\", \"Bird\", \"Bird+mini-helicopter\", \"Drone\", \"RC Plane\"]\n", + "\n", + "# Load an image (replace with the path to your image)\n", + "image_path = \"D:/Micro-Classify/ml_model/notebooks/DIAT-uSAT_dataset/3_long_blade_rotor/figure1.jpg\"\n", + "image = Image.open(image_path)\n", + "\n", + "# Apply the transformations\n", + "input_tensor = transform(image)\n", + "\n", + "# Add a batch dimension (since PyTorch model expects a batch of images)\n", + "input_tensor = input_tensor.unsqueeze(0) # Shape becomes (1, 3, 224, 224)\n", + "\n", + "# Start timer for inference\n", + "start_time = time.time()\n", + "\n", + "# Run the inference\n", + "with torch.no_grad(): # Disable gradient computation for inference\n", + " output = model(input_tensor)\n", + "\n", + "# End timer for inference\n", + "end_time = time.time()\n", + "\n", + "# Calculate inference time\n", + "inference_time = end_time - start_time\n", + "\n", + "# Process the output\n", + "output_probabilities = torch.nn.functional.softmax(output[0], dim=0) # Apply softmax to get probabilities\n", + "predicted_class = torch.argmax(output_probabilities).item() # Get index of the highest probability\n", + "\n", + "# Display results\n", + "print(f\"Predicted Class: {classes[predicted_class]}\")\n", + "print(f\"Probability: {output_probabilities[predicted_class]:.4f}\")\n", + "print(f\"Inference Time: {inference_time:.4f} seconds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1615,9 +1753,9 @@ "output_type": "stream", "text": [ "Expected input shape: ['batch_size', 3, 224, 224]\n", - "Predicted Class: class1\n", - "Probability: 1.1630\n", - "Inference Time: 0.0149 seconds\n" + "Predicted Class: 3 Long Blade Rotor\n", + "Probability: 1.5323\n", + "Inference Time: 0.0000 seconds\n" ] } ], @@ -1648,7 +1786,7 @@ "])\n", "\n", "# Define the six classes\n", - "classes = [\"class1\", \"class2\", \"class3\", \"class4\", \"class5\", \"class6\"]\n", + "classes = [\"3 Long Blade Rotor\", \"3 Short Blade Rotor\", \"Bird\", \"Bird+mini-helicopter\", \"Drone\", \"RC Plane\"]\n", "\n", "# Load an image (replace with the path to your image)\n", "image_path = \"D:/Micro-Classify/ml_model/notebooks/DIAT-uSAT_dataset/3_long_blade_rotor/figure1.jpg\"\n", @@ -1687,141 +1825,251 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 36, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\incha\\AppData\\Local\\Temp\\ipykernel_5568\\3779679066.py:146: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model_deepercnn.load_state_dict(torch.load(\"best_model_CustomCNN.pt\"))\n", + "Testing: 100%|██████████| 61/61 [00:11<00:00, 5.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Loss: 0.0617 Test Acc: 0.9794\n", + "Average Inference Time per Batch: 0.1849 seconds\n", + "\n", + "Classification Report:\n", + "{'3 Long Blade Rotor': {'precision': 0.971830985915493, 'recall': 0.9583333333333334, 'f1-score': 0.965034965034965, 'support': 72.0}, '3 Short Blade Rotor': {'precision': 0.975609756097561, 'recall': 0.9411764705882353, 'f1-score': 0.9580838323353293, 'support': 85.0}, 'Bird': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 76.0}, 'Bird+mini-helicopter': {'precision': 0.9871794871794872, 'recall': 0.9871794871794872, 'f1-score': 0.9871794871794872, 'support': 78.0}, 'Drone': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 85.0}, 'RC Plane': {'precision': 0.9468085106382979, 'recall': 0.9888888888888889, 'f1-score': 0.967391304347826, 'support': 90.0}, 'accuracy': 0.9794238683127572, 'macro avg': {'precision': 0.9802381233051398, 'recall': 0.9792630299983242, 'f1-score': 0.9796149314829347, 'support': 486.0}, 'weighted avg': {'precision': 0.9796531403531994, 'recall': 0.9794238683127572, 'f1-score': 0.9793927173298865, 'support': 486.0}}\n", + "\n", + "Confusion Matrix:\n", + "[[69 1 0 1 0 1]\n", + " [ 2 80 0 0 0 3]\n", + " [ 0 0 76 0 0 0]\n", + " [ 0 0 0 77 0 1]\n", + " [ 0 0 0 0 85 0]\n", + " [ 0 1 0 0 0 89]]\n" + ] + }, { "data": { + "image/png": "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", "text/plain": [ - "dtype('float32')" + "
" ] }, - "execution_count": 5, "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "input_array.dtype" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ + "output_type": "display_data" + }, { "name": "stdout", "output_type": "stream", "text": [ - "torch.Size([1, 3, 224, 224])\n" + "Macro Average AUC: 0.9995\n" ] - } - ], - "source": [ - "print(input_tensor.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ + }, { "data": { + "image/png": "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", "text/plain": [ - "torch.float32" + "
" ] }, - "execution_count": 9, "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "input_tensor.dtype" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "name": "stdout", "output_type": "stream", "text": [ - "Expected input shape: ['batch_size', 3, 224, 224]\n" - ] - }, - { - "ename": "InvalidArgument", - "evalue": "[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Unexpected input data type. Actual: (tensor(uint8)) , expected: (tensor(float))", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mInvalidArgument\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[16], line 33\u001b[0m\n\u001b[0;32m 30\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m 32\u001b[0m \u001b[38;5;66;03m# Run the inference\u001b[39;00m\n\u001b[1;32m---> 33\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43moutput_name\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43minput_name\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_array\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 35\u001b[0m \u001b[38;5;66;03m# End timer for inference\u001b[39;00m\n\u001b[0;32m 36\u001b[0m end_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n", - "File \u001b[1;32mc:\\Users\\incha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\onnxruntime\\capi\\onnxruntime_inference_collection.py:220\u001b[0m, in \u001b[0;36mSession.run\u001b[1;34m(self, output_names, input_feed, run_options)\u001b[0m\n\u001b[0;32m 218\u001b[0m output_names \u001b[38;5;241m=\u001b[39m [output\u001b[38;5;241m.\u001b[39mname \u001b[38;5;28;01mfor\u001b[39;00m output \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_outputs_meta]\n\u001b[0;32m 219\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 220\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_feed\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_options\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 221\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m C\u001b[38;5;241m.\u001b[39mEPFail \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m 222\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_enable_fallback:\n", - "\u001b[1;31mInvalidArgument\u001b[0m: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Unexpected input data type. Actual: (tensor(uint8)) , expected: (tensor(float))" + "Class Distribution in Test Set: {'3 Long Blade Rotor': 72, '3 Short Blade Rotor': 85, 'Bird': 76, 'Bird+mini-helicopter': 78, 'Drone': 85, 'RC Plane': 90}\n", + "Model exported to ONNX format successfully.\n" ] } ], "source": [ - "import onnxruntime as ort\n", + "import torch\n", + "import torch.nn as nn\n", "import numpy as np\n", - "from PIL import Image\n", - "from torchvision import transforms\n", + "import tqdm\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc, precision_recall_curve\n", + "import torch.nn.functional as F\n", "import time\n", + "import json\n", + "from sklearn.calibration import calibration_curve\n", "\n", - "# Load the ONNX model\n", - "model_path = \"custom_cnn.onnx\"\n", - "session = ort.InferenceSession(model_path, providers=[\"CPUExecutionProvider\"])\n", + "# Class names for your classification task\n", + "class_names = [\n", + " \"3 Long Blade Rotor\", \n", + " \"3 Short Blade Rotor\", \n", + " \"Bird\", \n", + " \"Bird+mini-helicopter\", \n", + " \"Drone\", \n", + " \"RC Plane\"\n", + "]\n", "\n", - "# Get input and output information\n", - "input_name = session.get_inputs()[0].name\n", - "output_name = session.get_outputs()[0].name\n", - "input_shape = session.get_inputs()[0].shape\n", + "# Test Function with Metric Collection\n", + "def test_model(model, test_loader, criterion, device=\"cuda\" if torch.cuda.is_available() else \"cpu\"):\n", + " model = model.to(device)\n", + " model.eval()\n", "\n", - "# Print input shape to understand the expected input dimensions\n", - "print(f\"Expected input shape: {input_shape}\")\n", + " running_loss = 0.0\n", + " running_corrects = 0\n", + " total_test = 0\n", + " all_preds = []\n", + " all_labels = []\n", + " all_probs = []\n", "\n", - "# Define the preprocessing pipeline\n", + " # Start time for inference benchmark\n", + " start_time = time.time()\n", "\n", + " with torch.no_grad():\n", + " for inputs, labels in tqdm.tqdm(test_loader, desc=\"Testing\"):\n", + " inputs = inputs.to(device)\n", + " labels = labels.to(device).long()\n", "\n", - "# Define the six classes\n", - "classes = [\"class1\", \"class2\", \"class3\", \"class4\", \"class5\", \"class6\"]\n", + " # Forward Pass\n", + " outputs = model(inputs)\n", + " loss = criterion(outputs, labels)\n", + " _, preds = torch.max(outputs, 1)\n", "\n", - "# Load an image (replace with the path to your image)\n", - "image_path = \"D:/Micro-Classify/ml_model/notebooks/DIAT-uSAT_dataset/3_long_blade_rotor/figure1.jpg\"\n", - "image = Image.open(image_path)\n", + " # Store metrics\n", + " running_loss += loss.item() * inputs.size(0)\n", + " running_corrects += torch.sum(preds == labels.data)\n", + " total_test += labels.size(0)\n", "\n", - "# Start timer for inference\n", - "start_time = time.time()\n", + " all_preds.extend(preds.cpu().numpy())\n", + " all_labels.extend(labels.cpu().numpy())\n", + " all_probs.extend(F.softmax(outputs, dim=1).cpu().numpy())\n", + "\n", + " # End time for inference benchmark\n", + " end_time = time.time()\n", + " avg_inference_time = (end_time - start_time) / len(test_loader)\n", + "\n", + " # Overall metrics\n", + " test_loss = running_loss / total_test\n", + " test_acc = running_corrects.double() / total_test\n", + "\n", + " print(f\"Test Loss: {test_loss:.4f} Test Acc: {test_acc:.4f}\")\n", + " print(f\"Average Inference Time per Batch: {avg_inference_time:.4f} seconds\")\n", + "\n", + " # Classification Report\n", + " print(\"\\nClassification Report:\")\n", + " report = classification_report(all_labels, all_preds, target_names=class_names, output_dict=True)\n", + " print(report)\n", + "\n", + " # Save Classification Report to a JSON file\n", + " with open(\"classification_report.json\", \"w\") as f:\n", + " json.dump(report, f, indent=4)\n", + "\n", + " # Confusion Matrix\n", + " cm = confusion_matrix(all_labels, all_preds)\n", + " print(\"\\nConfusion Matrix:\")\n", + " print(cm)\n", + "\n", + " # Save confusion matrix\n", + " np.save(\"confusion_matrix.npy\", cm)\n", + "\n", + " # Plot Confusion Matrix\n", + " plt.figure(figsize=(8, 6))\n", + " plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n", + " plt.title(\"Confusion Matrix\")\n", + " plt.colorbar()\n", + " tick_marks = np.arange(len(class_names))\n", + " plt.xticks(tick_marks, class_names, rotation=45)\n", + " plt.yticks(tick_marks, class_names)\n", + " plt.ylabel('True label')\n", + " plt.xlabel('Predicted label')\n", + " plt.tight_layout()\n", + " plt.show()\n", "\n", - "# Run the inference\n", - "output = session.run([output_name], {input_name: input_array})\n", + " # ROC Curve & AUC (for multiclass)\n", + " all_probs = np.array(all_probs)\n", + " if len(class_names) > 2:\n", + " fpr, tpr, roc_auc = {}, {}, {}\n", + " for i in range(len(class_names)):\n", + " fpr[i], tpr[i], _ = roc_curve((np.array(all_labels) == i).astype(int), all_probs[:, i])\n", + " roc_auc[i] = auc(fpr[i], tpr[i])\n", + " plt.plot(fpr[i], tpr[i], lw=2, label=f'{class_names[i]} (AUC = {roc_auc[i]:.2f})')\n", + "\n", + " # Macro average AUC\n", + " macro_auc = np.mean([auc(fpr[i], tpr[i]) for i in range(len(class_names))])\n", + " print(f\"Macro Average AUC: {macro_auc:.4f}\")\n", + "\n", + " plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + " plt.title('Receiver Operating Characteristic (ROC) Curve')\n", + " plt.legend(loc='lower right')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " return all_labels, all_preds, all_probs, macro_auc\n", + "\n", + "# Precision-Recall Curve Plot\n", + "def plot_precision_and_recall(all_labels, all_probs, class_names):\n", + " all_labels = np.array(all_labels)\n", + " all_probs = np.array(all_probs)\n", + "\n", + " plt.figure(figsize=(12, 6))\n", + " for i, class_name in enumerate(class_names):\n", + " precision, recall, _ = precision_recall_curve((all_labels == i).astype(int), all_probs[:, i])\n", + " plt.plot(recall, precision, lw=2, label=f'{class_name} (Precision-Recall)')\n", + " plt.xlabel('Recall')\n", + " plt.ylabel('Precision')\n", + " plt.title('Precision-Recall Curve for Each Class')\n", + " plt.legend(loc='lower left')\n", + " plt.tight_layout()\n", + " plt.show()\n", "\n", - "# End timer for inference\n", - "end_time = time.time()\n", + "# Check for class distribution (Imbalanced Data Check)\n", + "def check_class_distribution(all_labels, class_names):\n", + " unique, counts = np.unique(all_labels, return_counts=True)\n", + " class_distribution = dict(zip(class_names, counts))\n", + " print(\"Class Distribution in Test Set:\", class_distribution)\n", "\n", - "# Calculate inference time\n", - "inference_time = end_time - start_time\n", + "# Example Usage\n", + "if __name__ == \"__main__\":\n", + " # Initialize Model and Data\n", + " model_deepercnn = CustomCNN(num_classes=len(class_names))\n", + " model_deepercnn.load_state_dict(torch.load(\"best_model_CustomCNN.pt\"))\n", + " criterion = nn.CrossEntropyLoss()\n", "\n", - "# Process the output\n", - "output_probabilities = np.squeeze(output[0]) # Remove unnecessary dimensions\n", - "predicted_class = np.argmax(output_probabilities) # Get index of the highest probability\n", + " # Test Model\n", + " all_labels, all_preds, all_probs, macro_auc = test_model(model_deepercnn, test_loader, criterion)\n", "\n", - "# Display results\n", - "print(f\"Predicted Class: {classes[predicted_class]}\")\n", - "print(f\"Probability: {output_probabilities[predicted_class]:.4f}\")\n", - "print(f\"Inference Time: {inference_time:.4f} seconds\")\n", - "\n" + " # Plot Precision-Recall Curves\n", + " plot_precision_and_recall(all_labels, all_probs, class_names)\n", + "\n", + " # Check for Class Imbalance\n", + " check_class_distribution(all_labels, class_names)\n", + "\n", + " # Prepare for ONNX Conversion (Optional)\n", + " dummy_input = torch.randn(1, 3, 224, 224, device=\"cuda\" if torch.cuda.is_available() else \"cpu\") # Adjust for your input size\n", + " torch.onnx.export(model_deepercnn, dummy_input, \"model.onnx\", input_names=['input'], output_names=['output'])\n", + "\n", + " print(\"Model exported to ONNX format successfully.\")" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1905,7 +2153,7 @@ "\n", "# Title\n", "plt.title('Custom CNN with Spatial Attention Architecture')\n", - "plt.show()\n" + "plt.show()" ] } ],