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This behaviour is the source of the following dependency conflicts.\n","cudf-cu12 24.6.1 requires pyarrow<16.2.0a0,>=16.1.0, but you have pyarrow 15.0.0 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed pyarrow-15.0.0\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["pyarrow"]},"id":"e9d9a3579bf9499482baa09a69f804b5"}},"metadata":{}}]},{"cell_type":"markdown","source":["#Import required Dependencies for a Computer Vision Project"],"metadata":{"id":"J5GR-MxNiv7E"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"PJwyjSxIh4Dj"},"outputs":[],"source":["import zipfile\n","import os\n","import glob\n","from sklearn.model_selection import train_test_split\n","import tensorflow as tf\n","from tensorflow.keras.preprocessing.image import ImageDataGenerator\n","from PIL import Image\n","from transformers import ViTFeatureExtractor, ViTForImageClassification\n","import requests\n","from transformers import AutoImageProcessor\n","import pandas as pd\n","from torch.utils.data import Dataset\n","import matplotlib.pyplot as plt\n","from datasets import Dataset\n","from datasets import load_metric\n","from transformers import AutoImageProcessor, TrainingArguments, Trainer,AutoModelForImageClassification\n","import numpy as np\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":104,"referenced_widgets":["9cf24ce2f5c84428922849ef9c9b5f6f","4ea609468f4f4100a06c50ab840b19d7","70716665cec6424e8cba535206a935e2","15ae836ecf4d4256889162ff211ce6e1","b667113d86f7445ea7286dcf448bf31c","a16e5821b53d4dd481d2078973cb2120","8cddf7417b6d4a37a62f94e145ca2aac","df74b3154788495797da0f2ce02e343a","f56510048cce46beb38e8a5d555b1061","d23f973bd9a54e58ab5be1a1c2cf70fb","1abc2d13fefe4146ad5c0dd5ecd21e0f"]},"id":"8UGse36eLeeb","outputId":"3db340ce-8e37-47ec-c0a4-e4b4df160261","executionInfo":{"status":"ok","timestamp":1728231727247,"user_tz":-120,"elapsed":1897,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-2-21f3ecd7ee32>:1: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n","  metric = load_metric(\"accuracy\")\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/1.65k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9cf24ce2f5c84428922849ef9c9b5f6f"}},"metadata":{}}],"source":["metric = load_metric(\"accuracy\")"]},{"cell_type":"markdown","source":["# Connecte Google Colab à Google Drive"],"metadata":{"id":"0uxbLoVqjhQV"}},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AnDkl0IljBqu","executionInfo":{"status":"ok","timestamp":1728231757436,"user_tz":-120,"elapsed":27920,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"304c098d-d729-4d0e-96d6-f52f8bcad17d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["def unzip_file(zip_path, extract_to):\n","    \"\"\"\n","    Unzip a zip file and maintain its directory structure.\n","\n","    Args:\n","    zip_path (str): The path to the zip file.\n","    extract_to (str): The directory to extract the files into.\n","    \"\"\"\n","    with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n","        # Extract all the files in the specified directory\n","        zip_ref.extractall(extract_to)\n","        print(f\"Files extracted to {extract_to}\")\n","\n","zip_file_path_train = '/content/drive/MyDrive/deeplearning/Financial_Data.zip'\n","zip_file_path_test = '/content/drive/MyDrive/deeplearning/Financial_data_test.zip'\n","\n","destination_path = '/content/'\n","unzip_file(zip_file_path_train, destination_path)\n","unzip_file(zip_file_path_test , destination_path)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TJkG-kIdjsjw","executionInfo":{"status":"ok","timestamp":1728231888063,"user_tz":-120,"elapsed":125597,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"34323840-aa85-4440-bac7-269c7bd32463"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Files extracted to /content/\n","Files extracted to /content/\n"]}]},{"cell_type":"code","source":["# Define the folder paths for training and test images\n","folder_path_train = '/content/Financial Data'\n","folder_path_test = '/content/Financial_data_test'\n","\n","# Count the total number of .jpeg images in the training folder\n","total_images_train = len(glob.glob(os.path.join(folder_path_train, '**/*.[jJ][pP][eE][gG]'), recursive=True))\n","\n","# Count the total number of .jpeg images in the test folder\n","total_images_test = len(glob.glob(os.path.join(folder_path_test, '**/*.[jJ][pP][eE][gG]'), recursive=True))\n","\n","# Print the total number of .jpeg images in the train and test datasets\n","print(f\"Total number of .jpeg images_train: {total_images_train}\")\n","print(f\"Total number of .jpeg images_test: {total_images_test}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cWX2U0yMlFMy","executionInfo":{"status":"ok","timestamp":1728232084501,"user_tz":-120,"elapsed":998,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"73726261-0b7d-465c-db39-30aef643172b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Total number of .jpeg images_train: 47996\n","Total number of .jpeg images_test: 39996\n"]}]},{"cell_type":"code","source":["def create_full_img_class_df(base_path):\n","    \"\"\"\n","    Create a DataFrame with image paths and their corresponding labels by traversing the directory structure.\n","\n","    Args:\n","    base_path (str): The path to the directory containing subdirectories of images.\n","\n","    Returns:\n","    DataFrame: A DataFrame with columns 'img' for image paths and 'label' for their corresponding labels.\n","    \"\"\"\n","    data = pd.DataFrame()\n","    img_rows = []\n","    label_rows = []\n","\n","    # List all subdirectories (each representing a label)\n","    for label in sorted(os.listdir(base_path)):\n","        label_path = os.path.join(base_path, label)\n","        if os.path.isdir(label_path):\n","            # List all image files in the subdirectory\n","            for img_file in sorted(os.listdir(label_path)):\n","                if img_file.endswith(('.jpg', '.jpeg', '.png')):\n","                    img_path = os.path.join(label_path, img_file)\n","                    img_rows.append(img_path)\n","                    label_rows.append(label)\n","\n","    data['img'] = img_rows\n","    data['label'] = label_rows\n","\n","\n","    return data\n","\n","\n","\n","# Create the DataFrame\n","df_train = create_full_img_class_df(folder_path_train)\n","df_test= create_full_img_class_df(folder_path_test)\n","df_train.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":226},"id":"rqN3LQYMlnJ2","executionInfo":{"status":"ok","timestamp":1728232102584,"user_tz":-120,"elapsed":11,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"34404ecd-2de6-4223-a532-a857a269bede"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                 img          label\n","0  /content/Financial Data/advertisement/00000004...  advertisement\n","1  /content/Financial Data/advertisement/00000016...  advertisement\n","2  /content/Financial Data/advertisement/00000016...  advertisement\n","3  /content/Financial Data/advertisement/00000016...  advertisement\n","4  /content/Financial Data/advertisement/00000018...  advertisement"],"text/html":["\n","  <div id=\"df-1d57c374-67bf-4bb3-839e-82e0a1718f94\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>img</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>/content/Financial Data/advertisement/00000004...</td>\n","      <td>advertisement</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>/content/Financial Data/advertisement/00000016...</td>\n","      <td>advertisement</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>/content/Financial 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\"handwritten\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":6}]},{"cell_type":"markdown","source":["\n","\n","# Split the data into training and validation sets\n","\n","\n"],"metadata":{"id":"j9Luf28umis4"}},{"cell_type":"code","source":["X_train,X_val, y_train, y_val = train_test_split(df_train['img'], df_train['label'], test_size = 0.2, stratify = df_train['label'])\n","# Display the DataFrame\n","print(X_train.shape[0])\n","print(X_val.shape[0])\n","print(df_test.shape[0])\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qkNlvjA-ls5_","executionInfo":{"status":"ok","timestamp":1728232111596,"user_tz":-120,"elapsed":1411,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"48a1ba96-ddd4-49ca-bbfd-8680269d11b8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["38396\n","9600\n","39996\n"]}]},{"cell_type":"markdown","source":["# Converting the labels to numeric format"],"metadata":{"id":"7YODQJSGoOdN"}},{"cell_type":"code","source":["# Find all unique labels in y_train\n","unique_labels_train = y_train.unique()\n","# Create a single mapping dictionary based on the training labels\n","label2id = {label: i for i, label in enumerate(unique_labels_train)}\n","# Create the inverse mapping dictionary from id to label\n","id2label = {i: label for label, i in label2id.items()}\n","# Convert the labels in y_train and y_val to indices\n","y_train_mapped = y_train.map(label2id)\n","y_val_mapped = y_val.map(label2id)\n","y_test = df_test['label'].str.replace('_', ' ') # Replace underscores with spaces in the test labels to match the format of training and validation labels\n","# Some categories in the test set contain underscores, which do not exist in the training/validation labels.\n","# This replacement ensures consistent labels across all sets and allows correct label mapping.\n","y_test_mapped = y_test.map(label2id)\n","# Print label2id,id2label\n","print('label2id :', label2id)\n","print('id2label :', id2label)\n","print('y_train_mapped :', y_train_mapped)\n","print('y_val_mapped :', y_val_mapped)\n","print('y_test_mapped :', y_test_mapped)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AH293qk0m4Ay","executionInfo":{"status":"ok","timestamp":1728232116291,"user_tz":-120,"elapsed":1048,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"7115f64f-7407-4c0d-8cee-9affbeb002e9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["label2id : {'memo': 0, 'resume': 1, 'scientific report': 2, 'file folder': 3, 'invoice': 4, 'form': 5, 'budget': 6, 'email': 7, 'specification': 8, 'presentation': 9, 'handwritten': 10, 'advertisement': 11, 'scientific publication': 12, 'questionnaire': 13, 'news article': 14, 'letter': 15}\n","id2label : {0: 'memo', 1: 'resume', 2: 'scientific report', 3: 'file folder', 4: 'invoice', 5: 'form', 6: 'budget', 7: 'email', 8: 'specification', 9: 'presentation', 10: 'handwritten', 11: 'advertisement', 12: 'scientific publication', 13: 'questionnaire', 14: 'news article', 15: 'letter'}\n","y_train_mapped : 24320     0\n","37518     1\n","44690     2\n","11316     3\n","20556     4\n","         ..\n","32168     9\n","6173      7\n","35935    13\n","30048     9\n","17162    10\n","Name: label, Length: 38396, dtype: int64\n","y_val_mapped : 36727     1\n","3131      6\n","5626      6\n","10057     3\n","23102    15\n","         ..\n","14953     5\n","10140     3\n","26127     0\n","8612      7\n","10364     3\n","Name: label, Length: 9600, dtype: int64\n","y_test_mapped : 0        11\n","1        11\n","2        11\n","3        11\n","4        11\n","         ..\n","39991     8\n","39992     8\n","39993     8\n","39994     8\n","39995     8\n","Name: label, Length: 39996, dtype: int64\n"]}]},{"cell_type":"code","source":["# Create a DataFrame for the training set\n","train_df = pd.DataFrame({\n","    'img': X_train,\n","    'label': y_train_mapped\n","})\n","# Create a DataFrame for the validation set\n","val_df = pd.DataFrame({\n","    'img': X_val,\n","    'label': y_val_mapped\n","})\n","test_df = pd.DataFrame({\n","    'img': df_test['img'],\n","    'label': y_test_mapped\n","})\n","# Reset the index of train_df and val_df before converting them to Hugging Face datasets\n","train_df = train_df.reset_index(drop=True)\n","val_df = val_df.reset_index(drop=True)"],"metadata":{"id":"7RdIsDA2u79a"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Create Hugging Face datasets\n","test_dataset = Dataset.from_pandas(test_df)\n","train_dataset = Dataset.from_pandas(train_df)\n","val_dataset = Dataset.from_pandas(val_df)\n","print(test_dataset)\n","print(train_dataset)\n","print(val_dataset)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0x8Go6fjr4Bo","executionInfo":{"status":"ok","timestamp":1728232125028,"user_tz":-120,"elapsed":1027,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"1ff1b88a-8215-4af8-cca4-9533e98d1ecc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Dataset({\n","    features: ['img', 'label'],\n","    num_rows: 39996\n","})\n","Dataset({\n","    features: ['img', 'label'],\n","    num_rows: 38396\n","})\n","Dataset({\n","    features: ['img', 'label'],\n","    num_rows: 9600\n","})\n"]}]},{"cell_type":"code","source":["# Function to display an image from the dataset\n","def show_image_from_dataset(dataset, index):\n","    # Retrieve the image path and label from the dataset\n","    img_path = dataset['img'][index]\n","    label = dataset['label'][index]\n","\n","    # Open the image using PIL\n","    image = Image.open(img_path)\n","\n","    # Display the image with its label\n","    plt.imshow(image)\n","    plt.title(f\"Label: {label}\")\n","    plt.axis('off')  # Hide axes for better visualization\n","    plt.show()\n","\n","# Example: Display the first image in the train_dataset\n","show_image_from_dataset(train_dataset, 0)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"HsNOHMlvxFsQ","executionInfo":{"status":"ok","timestamp":1728232130866,"user_tz":-120,"elapsed":1022,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"15be2f48-ee00-40a4-f91f-bca91c819070"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAS0AAAGbCAYAAACRcMaGAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADt4klEQVR4nOz9d5Bl13XYC//W3ifc3DlNDsAggwQIECRIiqBIiZJJS5RNyc922ZZdpXplyc+q+izHKlv6/pFLny3J5fTscpBl69nPMi3JSqYoiqQoMQAkQSIQeTCYPJ3DjSfsvb4/zu2eGWBAzQCYQXfP+QE93X373HP2OffsddZeUVRVKSkpKdkhmLd7ACUlJSXXQim0SkpKdhSl0CopKdlRlEKrpKRkR1EKrZKSkh1FKbRKSkp2FKXQKikp2VGUQqukpGRHUQqtkpKSHUUptEquildeeQUR4Z/+03/6lu3zC1/4AiLCF77whbdsnyW7n1Jo7WL+03/6T4gIX//619/uoVw3zp49y4/8yI8wOjpKq9XiB3/wB3n55Zff7mGVXEeCt3sAJSVvlE6nw4c+9CHW19f5B//gHxCGIb/4i7/IBz/4Qb71rW8xMTHxdg+x5DpQCq2SHcu//tf/mhdffJHHHnuMBx98EIDv//7v5+677+bnf/7n+dmf/dm3eYQl14NyeXiTk6Yp/+gf/SPe9a53MTIyQr1e5wMf+ACf//znX/c9v/iLv8jBgwepVqt88IMf5Omnn37NNs899xyf/OQnGR8fp1Kp8MADD/Cbv/mbf+J4er0ezz33HEtLS3/itp/61Kd48MEHtwQWwO23386HP/xhfvVXf/VPfH/JzqQUWjc5Gxsb/Pt//+955JFH+Lmf+zl+5md+hsXFRT760Y/yrW996zXb/+f//J/55//8n/MTP/ET/P2///d5+umn+e7v/m7m5+e3tvn2t7/Ne97zHp599ln+3t/7e/z8z/889XqdT3ziE/z6r//6dxzPY489xh133MG//Jf/8jtu573nySef5IEHHnjN39797ndz/Phx2u321V2Ekh1FuTy8yRkbG+OVV14hiqKt137sx36M22+/nX/xL/4F/+E//IfLtn/ppZd48cUX2bt3LwDf933fx0MPPcTP/dzP8Qu/8AsA/ORP/iQHDhzga1/7GnEcA/DjP/7jvP/97+fv/t2/yw/90A+96XGvrKyQJAlzc3Ov+dvma+fOneO2225708cq2V6UmtZNjrV2S2B571lZWSHPcx544AEef/zx12z/iU98YktgQaHVPPTQQ/zu7/4uUAiTz33uc/zIj/wI7XabpaUllpaWWF5e5qMf/SgvvvgiZ8+efd3xPPLII6gqP/MzP/Mdx93v9wG2hOKlVCqVy7Yp2V2UQquEX/7lX+bee++lUqkwMTHB1NQUv/M7v8P6+vprtr311ltf89qxY8d45ZVXgEITU1X+4T/8h0xNTV329dM//dMALCwsvOkxV6tVAJIkec3fBoPBZduU7C7K5eFNzq/8yq/woz/6o3ziE5/gb//tv8309DTWWv7xP/7HHD9+/Jr3570H4Kd+6qf46Ec/esVtbrnlljc1ZoDx8XHiOOb8+fOv+dvma3v27HnTxynZfpRC6ybnU5/6FEeOHOHXfu3XEJGt1ze1olfz4osvvua1F154gUOHDgFw5MgRAMIw5CMf+chbP+AhxhjuueeeKwbOPvrooxw5coRms3ndjl/y9lEuD29yrLUAXNrf5NFHH+UrX/nKFbf/jd/4jctsUo899hiPPvoo3//93w/A9PQ0jzzyCP/23/7bK2pBi4uL33E81xLy8MlPfpKvfe1rlwmu559/ns997nP88A//8J/4/pKdSalp3QT8x//4H/n0pz/9mtd/8id/ko9//OP82q/9Gj/0Qz/Exz72MU6cOMG/+Tf/hjvvvJNOp/Oa99xyyy28//3v56//9b9OkiT8s3/2z5iYmODv/J2/s7XNv/pX/4r3v//93HPPPfzYj/0YR44cYX5+nq985SucOXOGJ5544nXH+thjj/GhD32In/7pn/4TjfE//uM/zr/7d/+Oj33sY/zUT/0UYRjyC7/wC8zMzPC3/tbfuvoLVLKz0JJdyy/90i8p8Lpfp0+fVu+9/uzP/qwePHhQ4zjW++67T3/7t39b/8pf+St68ODBrX2dOHFCAf0n/+Sf6M///M/r/v37NY5j/cAHPqBPPPHEa459/Phx/ct/+S/r7OyshmGoe/fu1Y9//OP6qU99amubz3/+8wro5z//+de89tM//dNXdY6nT5/WT37yk9pqtbTRaOjHP/5xffHFF9/oJSvZAYhq2fewpKRk51DatEpKSnYUpdAqKSnZUZRCq6SkZEdRCq2SkpIdRSm0SkpKdhSl0CopKdlRXHVw6ffIJ6/nOK4JRUG2fkGQ77h9yc4ms5A0QuJOSuzBKShy3Z+4Ho/I8FYbBgYJxbHZdfecwQdd/vzfc/yFn1gg1jaCxxNg8DduFHOvTRN7zTY3YBzXDWPMZflyJbuTwFgO7TuAkc3btfjMr/fNe8lzcVNmUQY1vv3sWKFlMFhrt3LnSnYvzjlW5hexhZqDit4QPUcAGUopRfGiOFE8pfB6O9mxuYdePT715dLwJkBU6a2uU3GXaD1yUaBcdxSwBicejCCZYrW8794udqSmJZf8V7L7ERUCFczQlqRvg0lAjHDnvXcSVSuU8urtZcdqWq+meAIXN7XgkS3joVLI5ht7pymg4re0gYsG3Eu3eDVvxRiF4nwvWmLksqO/9rhXVljerpl56Whk61+jgqAIilEweqXtdeuML9+DXvLb5ef1eraqzTtp82/OK+dOzeNyxWAueWBefKdeZv260hnJZd9luP2rR3fpOegl7zFXHOXmHgRwXLxKmz/tPnaN0CowIAZRsFsfmJBj8DdcqVS8eCxgVYeilMIeIu513vMW3GJqh3tyIA5RQQiHf7yCF+h1Din6eoL+eq7JtLg2uil4Lx5PETyKRYmU4bUUCv1LAY+iqIAOH1JG9ZKH1+t7/TaXm695bVhLQEUQDxvL61jlEr/lpSJvKLKuoAXK8MklyvDzkIv3g1xefMOobu1Nt0Tj5j7NJWL0UgHpUYlwJqXilIwKmQmIfPo613lns2uElqBE9DGqeCIcITlSaDukGF5PUFwn1GJ9C4NgyFFyBDd0n0fXbWlrcMNz1WE4SDHBcgz5FSbU64og467w1+FEuV7GJBUCDbc0wy3dQxQ1Cc4bVAMUiyPAy/AqSlqMSQOUCOMDAhyQclHTDinukuw1h7VcqrldxF+8kpeqQHgcKsNH0KVCR0PQKz0cN8/JgXiEHFCsVhAt7g6VQvA6AbQQtGZLZyp+z6gUglfccF8OwReCThTVCItDSEEz3o4Vxo1g1wgtEDJiPAFOQpwoKgmYHlEeEeiNbXKgKLlZp7j1DCqKl2IKhC7j4qLt4hJOX1+EXDUZFi8higXZnGcGow6jVxDcr3NPW8+WDemiAAFwr/+mN03x6PEokCMCqEHVYF2N4mo5VFIwXYxkWBTvm+CbgMfSxpgeqKI6gqOBkmNMG8hRfW33HgX8FS69wWIwOFX8lgABiFENinfqptDyQ4HxWm02x+GGOqGoRQkKEWYTcuPIjJIbHQo0RUgQVSITUrExgsHiiTgDOCqVatFxSATwDGxCZgzLC3tI2o5AO1jN8USvGctuYBcJLcVIghGHMZbAGwInBC4kw5LdYOOtoEQ+RwmHN2lYLGPEkZIPJyZbcWa6tXh8M8eEQCHwxd69eBSHFze0Cb324/b6OtY1I3gtxleUXBsKL7FcvyWi4CVDTYpIBlbBCeKFwCcoAUpIIHaowUY4icjE4WSVUCyxGERrOOMYMCAnKcasIcbHl8R6XXINAD+8PTYXZcYYBnTxYYqJLRoIihsGWm4MNSMoNB6lVq8y0lQCc4UlmRbaUVzxjI2FNFsWlQGDIGVq/xxxs4ozvgjlUAEfIQyYmQ7YtzfC2PVCO8v2IialPpLSGBlAsIqhj8lqDPws//jvr/L8pz2VrI7xAZDsQj1rVwktwWgdJUeDHnnQJ9MA6+uFusyNXd8r0JfC7gKOyHpaFcVITkQdxOCcQ8QghrcsSFYlw5kBKkpUs2TaYWyygg9y/BXWQP6Kh1XGZ4RqE4wIcaWyaeS5rnEGioIRnHaZmKxjA6FaaXDm5BKSN4tjmz7Tk4ZD+2KsGbC81ufU6ZQkqTE5FnPskKWiq1RcTD+b4fmX1+kn68zNKntmQoIr3Ad6hesQRRE9l6OVkNpoTFgP8eTFcoyssBkOhbjiqdcjRloWI5drNwKEucVqhtgeYdQmDHuIGRBmEYZuYYYUhU1NWE3xs/iLx0HxJiqEqnFAXnyJJ1NHW2tYO4d6RdRjSW9gHPuN5RqElhmaQgtzKDBcm1/9Lq7oWxnG21zLlL247eWeptQqWa3PJ/6vA4zfPU8WpIADDa6oZVxvzNbSISUOehycq9CKHU1WsECSplhjCcLhUuMtsLsZbzHe4kWxMeTSo9LoYRCsf+01uJIRGoBQwXgQITB9Ll7r62iIH9q0IMGbDcCBt4iEOBJUimVuIH2MtgGHlxEcEY6EkIxA2xj6qAlJ5CzvNVVUlYCEwOdYfa1qWXgnL32hWLrLUJKpOJxx6KYNyYeIDy63Z20uHa9QCDi3UghF3VwiCngDoScffuYiimhhiM+HqwVVGc61oWPBa6EpeouwuczNiOhS0w3GKhGVyoDIFRqbv6INU1CBKArJBhE+qSFesMMFMOKxWpyn36Y6zVWPqlhX5+R4clsF9VTo4f2rXflDrUGGT04dWm9UL1lqXPS3mMuWH1eHSqGki7jiOCqAxUlGoIb3vWOde7/7m+CqiAbFsuxGBSJegrzKuyTDWOrXuq7f4uPqJa7wrYklW69fDXqpKeuyR/b1G7tcakO7dPKrFMuxwjU4VPaGBnbtDOOmLnroIAYvVEiIXTLc98V/X//4l1jvhocbDgCjAhpc3Iv4y3enw0/7CoLitSEaWmynHnvpy0OLlkUKG//WHvzWAIVXXxqPGEPslB//m30Wf9jitToUQDmv+bw0JDeWqO45/kzIb/2nNueeiyEJ8BKjNiHIMggcuYwQ0r70qmwLrlpoeekhCs6EpEEfsV2spDjvXiO0CgohFUURaZKiKBYh2Lr+ihgDVsld4Vu72ukg4rChxwSKkg1vVIOQEVU8ga1i8xCrGUa6w/EV3qObgk0X+9a/w/O+htOXS7eX1/zlOrJ5F8irvr36HF4di3Xp3+XKQ/9OvP7uL9vfq7ff+uU7HOjar9ifIFwv+bNgUGcw0mPfwZS9hxzeJAieIGu9dl+aoSQ4E3PnOwwPfXic3/1vls/8tzaLCyGp1IHCzIIkCAO4gvPi7eSqhVbh1cnRMOH9PzDOkbtz5ianSOPkEqPkRbz3GFPkBzoXFtqUEXLZfIoX72k2m3TbnS1hdjUjsawyNZExPlEfPg2LoDqjgvEhe/a9iLEbSDYGeRWxg9dfBpWU7GRUMBoV4RbeoBIiEoHkiKZXUAUUXIzxo6Ad5mbO8Jf/xgwf+p4ZfunfhaS2i7dgCBDJEfXXUbd+Y1yDQaqG0MfYDT78pyd58PvOU/WnCV3CtXi9rqyTXSu22JPMX6YC+yJ6D1GDeEPh2QmGnrkrBxaWlOxoRIs5oA5jZevhrCjOXOF+F48GKcb1CH1EmDSomCWO3dbh3d99G+dX1lGzDmq2PJnbbdpctdAyDDB4xIXEskrFHifSGs5GV9S0rrgPBXsFW7MXyK+hWIN1IcYP3zC0Z6EWozGYPvg6mAHepCBZYfvSYLtd+5KSN42qRbVBYQTLilARyQvnAsFr9SxfKaL2g0XEW3Ct4pFuV6jFHslCIKMQDWb4fXvpWldviJceqiGiEUEeEaVjmLyGkbQwSl4FCrgrBAyLQpRf7UiKp4iabHjc4QVVwfgUpAtSRMIj+dD7UqZWl+xSxOHtRvGjFm4eNCpMJVzBEE86nIgNwOLFkEsN1QrL8wOMa2E1pHAc5cO0pO01e67epqUxjgAxXcRm4KtgB0MP4VWelLx+mM81yfLNw12WMqGo5EB1mIIC4ocBnZQ1t0p2L8a/WhPY8s3zujNrGE2PpAQK6oXqeI2NpAdq8dZjPXjjC8/pNuINBmK8KmbnWr1S1/SH19v8Suv1y3d0pZ9KSnYTr7uGuLIb9HU3U4Vutz98xfCavMptNId2ZD2tkpKStxgVkkEOWGQr0fqK4eBvO6XQKikpATFkmaJ+U2BtH83q1ZRCq6SkZJhtMEwZGmYebFdKoVVSUjLMv9wsqFikB6FmW+pbpdAqKSlBUKw3CPkwZGgY27gNNa5SaJWUlFzCNpRSr6IUWiUlJTuKUmiVlJTsKEqhVVJSsqO4aqElBFhShJzUVjBe8Wpep5ZWybWir/669JeSkpItrlpoeSxGEkSENVpF2QpT5vS9tQxTKFQoCscbVK53ndOSkp3F1QstSXEaMXBNnn9pFY/Fm4xSFXhrkGFJ9qIse1GNPxfFbUefc0nJ28hVCy2VHC8hmdZYawdgwqIka8lbxyV56CrQ7fdJ8tc2Fy0puZm5+iKACqgdmlmCwubit18G+I7m0sLmqtSrwwazV+jwUlJys3LVQstqUDQYNYNheVdfNIS8wU1QdzObKV9FLVaDOr/V2aikpKTgmuppCQ6jHqMWMYOh57CcUW8FFwWWFqtE3by6yjV2WCsp2dVctdByKIZCYBkXgRTNM8u59NawKaBe9cLWjyUlJQVlcGlJScmOohRaJSUlO4pSaJWUlOwo3mBji+3Oq1qr70r0kp8uP8/dfNYlJbtK09psnHQxac8XPRK/w9aoAQ2KH1G8Bqhu98uihXdRHVvdtRG8WDxl2k/J7maXalpwVUGvkl+ilGXDlknbXWANEUEpyuOK5oXnUQGKnMVS3yrZrewqobUZ4+SHLZAEHYYRXFmAKUWyn6oBU3TUNZhhgf/tPOk3z2rzHDc7bZdlIUp2P7tKaBUT1hTLJBUMHqt5EVV+hS2VCKWKJwQtSu8YZ4qG2ds6UVkRVQyKk4DUxGQS0cuUmvXU6W9rkVtS8mbYIWuhq0G3lAwvlvNLbQZZ/h30DgENyfOYZ1/YIMli0GjLvrX9KXRKEPo5XNjIeO5sm4307R5XScn1ZddpWsVUFiamxglxoANQD5tdc7c0KMGTg3WMT9WQ0AMZiB/ai7Yahg+3Zut9bzeKgFg8ASpCLBmzdcueZo1QXCHLSkp2KVcttNRkGB8hOHLr8BIDKdthEl+kSCsKNCfAgLphscIc8UA+ig/nUZNis7HCjmX6zIxZhBxRj4qSG48SAQbDAJGsMHr7ENku5zvsSSekVIygqogOUDwqtrB3qRTj3a0RIHrpiRUnp1vnWNbU3a1cvdASj1BMbi8OT4ghYfvMhIvVEKw6wG297IlAK/ioR0/nQDPqpo9FMOowuOHEFlQMXgx5FoI3RNHQ5L3ZfXcbcLGCjQccngAkwIsWmqRe/qkIO2TF+wbYai46dLaICrqt7ZElb5ZrmIWXVnSQV/2+fRBeOzqhSO7uhcpv/YHy7eNNsmgdbP9V2xfvEG/orHsWF3LwEeIriI8vfYxvC5RhdYit5ay9+KU3QdjD5rlvCms8gkPUX558XrKruAab1uYkMIjYi991extQRAVxNYxCqCnHjnlmpjwmnQLSK3gJFWNSxscroCFGBggpQsC201fkkjBatYAiEqAejBHEa2GjGxrsd5sQ8wi5GowBUUXEYQDVovRrWeptd/IGNC2h2+mhXrfdHH5dxAMDqmnAvQcHTNcXiHw4XPJdafsEMf1CYEmCSIpIynY6YRXFi+KHQaanTnVwavBq2VjPUPdqIbV9xv5WoWJp57DUychtRC+D3FicsWVxyl3MNQotCGzI+uo6zumOMHWqKGmY4AKPOEOcBYTe4INlVJIrvkd0aMRWg6hFNAAN2W6ayqViqNmKC4+iCnEcYYy9LDtx53BJkOzWtysnY3kFZ2JstUFGwFo3JVOLN7vMKV5yGVdviKcw/IZqGHd1BqZLvEN868aHgAGbA4rRYZ17sVcUQ+ILz6HgQAUlYPuZs4vzAIuqMjFKoQ0qxBVQTS8Gbcj2cCD8iWixzPNBimiIuCqIw5vkilqxeEfN94njELwyM1pDvNsm7pKS68XVP5KkEFpZ6ugvbbBTbg1BsArgh5N3s4zxlZeGclnJUL/16vYSWJsjlMJ2AyDu4hBfU1Z+e2mIr4so3igpVUQMkUlAMrwB6177eQUGGgbwl3Qs2n4fVclbzBuQPIXOZYx53foJJSVvBC8wMBU2kkkSN4oLErxNUK2wYwRvyXXnjalLqqj6K+b0lZS8YdSQJBF/+MV5Tr6SgK+AVlBfpRRaJZu8MYvlUFiVMXwlbyUC1ALhox/ai8VjXAZiEdkZttOSG8PVC62hgLo8I6+k5K1D1BOyAcEKlhCTRYBi7Dr47ee9LXl7uKYO09nwuwVyc1nucUnJW4IIhAqoQ82mt3dnOH1KbgxXLbREi7jqzfJ4XrgYaF1S8lYgw2QqjYa/DytsaBl3VXKR8hFWUlKyoyiFVklJyY6iFFolJSU7ilJolZTsAC62LBk2ytsqgHjzNTMphVZJyY7BFOla4lAMRdWw/O0e1A2ndMuUlOwANsOLijqUUlSrBRCP1ZvLiV9qWiUlO4ZiKegxrHcdF1YyMol2RImot5JS0yop2WkIhKEFGyBGbrruS6XQKinZMWwG33rqkaBGUDe4WKboJuGaK5cCW51rLvml5CagqB/qULR4uKsCDtWiiJVuVRk1w5WMgioexYsfGmTc1p9QU9w9KsPXNn/f/DKbvrLL/hseCL2J2lcUDUyGXRpUsWRYnxCS30TiquDqhdZmagUKhKC28GSU3FSoyXBiSUx12Dijh2qIkhX9FkXJqSIo4j2oJQmEQZiDqyI+JTPD7kFuuJ1anHEoMToszijqkLyJCmQWcuvJjYB4hAxQnNxsC4XieoJFh75D1N40gnuTa/jUfVHJc7P7jjB8XN5scv5mRsBXCXKLtRkqgtMmgbOoRGSBI07q5JVlfC6oqQGGKI+KaqqmB/kMoVkt3GE2QdXiwzVEFCVHXBNv+ohdB6eIZIR5HfERKjIsU5OCOOxW383dfw++usP5xVJ226Z98A3jGsot5xQWv5svmK1kEwXJEdNHpEMaVsmkitUNVBsoPaCLqMdRIbeK2g6VLEQY4EJQqwRsoL6JJwSNcKoY5xEijI/JTQZ+HC+GOKsDGWiO4FERchNgNMf4HCW8GWRWySVcg6Y17LQMsNkYVG1ZCfAmw0lMz+9BbYd+NooNBiROMApBENGLVvHpQTwC2iPRJh2NMdkYNpgniAe4dIzILqE+Jzch+BZiBwTOYIMeqQiBb5JQQYMuKjkQE7t1RBQvjaInpd5kAUolwDU3ay0Mo95Z1IESDu0LJTcHQjdr8i8+NeC9H3k3f/ybT/KnP9zgGy9lRPWUw+MzvNjex4TLWel4xhRMRfjW2RniepdDtskjH23y3z8DD9w1zerCAks6RtX3icZjWpoRSpX1bo8jk2MsDAwnTmbc8/A9PP34U/zQuyYZD5cLy4QU92PJzcc1GOLNsNW6ZW2lS9ZTpCzOdpOh+NDjp4/xu99eYz0/jK2NcjJpsTZdYbUxy6dPT3I6DlmdGePpJcOe2+7h8VdmOJ5UaOsY59ZqPLcxxu89VWFB7uG5zjHazTHOhAdJZm/ny6/UOKt7Wa02WK3N8gr7+R+PjfLt5YP03CHwDcSDF48aV2paNyHX1ELMqiEXJekFRN0WWj83dDqXd87NgCIspE3muznd0Qg/McFn17uca1lOnelzOo6oa5OnTjpMtUW4v8q/f3yDkVtvIZgY8OW1Li88N8rEXSHPP7NIvzNGfXSDU+0Z0u4aZ07DbPUY62nCf/3qgIP3HORc9ByDhQkOj7yCtV2QPshmH8qSm5Fr9BkPm8qpIL7oIVhy8yCAzdb4rrtrtKOM6gGPkQXGj4xQ8Z7O+RPcNRvTbXcYnR4hHglJTEiavISxA+RgjpCAXeX2d9exPI3P12g0Imr1Olnfc+6p5xifalJ1k9juV3j4YIhMnGc27NIK+njAS4BRg3gpQr9K+XVT8YYCXUpRdXMiqhwL57nl0DpZEGJcj0CV3FTIbA6HHNaHWIRczhWewixGGGB8q4iODFZQreC0CeoQkq3GuVoV/Ef6OF1CfBchxcoS4i/gJCR0a6iRwgjv9WK0ZclNxRsSWuV9crMiqG3hTCHAxFjwgvUx1tthoGgIeCwxmrsi4DTo4FWBChhF8gqB9gG3FfsnKqAGl4ZYbPG7VdRahHVi74cNtWNUfWGMlYCbLvGupMw9LLl6VMAjWAaIi1ET4MVg1CHqYSiwgK1IdzWgPqTQzxPwcZEMZMGLoGIRNViviEJmi22t72JVsXkVJBlG2wcIhkBTFIsTg4gvH6I3GaXQKrk2xCEyQLSKR/BYxCQUGYZuWO/pomdZNAAfg2SAA7UIjiAPUDF4KUL9zFDbqjoK7UvaoFVUDE7CIhAVQ6AZQg4I3hhsaau46SiFVsk1YVRB66jkRSYOjqKxnC0Sal4jRHyRaKJDfUgL+1WRt+owl/TO1EtVJokpIvCLbcwlFToVO/Rm33xVO0tKoVVyDVzMhpDh/3LZX651Z9/5XRc3eE12ndxsxVhKLqWMDi0pKdlRlEKrpKRkR1EKrZKSkh3FGxJapcPmEpRhITbFSz6sqmlQNVt/Y/NnsqIemV7LFfRAXnjnNBoeMAeSYTVQAzjwIQ4ZbivF8fGo2qI2hzIMSyjKC3kN8ARFDVIfFp4/b4ZjLGqTFt5B2Sw3t3myb9WV2z4ol53axaKru/BcdwFvSGiVBtBLKVKavFG8KVz9ooI3WsQ12T65TXA2RYkpetdd2/43vWhFHJQiLka8xYmAetQMEG/JTeFXEQXjA4QMMwzaVHGoBoiCN31Ua6jGuKBLag1ZkJObgCTweLHDGCpBxRTfgV0psIbI0MGw2U/QiaBmpyxE5HW+didlGs+bxoHpg0aIbwAetRu4sAd5EyVCNQZviy/pD2OWrg7VALSCmAESXABXBbHgm4QuQRiQG0VsG6M18FWEPpAWweaygaFSCCMfETmLSg+1HQJNwDmcrCO5LYrTugriLdgMs1mldrMkEWYXT4fLz0pV6fUTGtX4bRrPtfLqT2X3ztIyjefNshlMiYJkxbJKG+TZBIF3GA1Q08WYtFjp+fAy/VZVEXn9K6omBVKMq4Ks4qVCFrTRoEfcn0DzKVy4htEI4yp0w4TITRKwhlGLmnWcxhjNsa6PyUJU6wyCGKOKySq4KCdyId54UIe3OSoOu5UUryAGj714rrsU9R6MICLEUfh2D+ea8d5v3U/f4bba0ZRxWm8WFaBarMmkA0bJ8gN8+6UGL50bEFQd+w9N0l0e0GqucuuhOi1dQ9R/R2H1qoMMvwJSv4evfG2MVXOO+w9G7B/zqFic7bG0GPHk2hgNCbjjsFIPEoy0yHyFP3o24x2zEZGO8jvzlokgZmJU6Lscpxnjvk2qE0QhLJ7zHDowz5GxLtanmC3BxW5+gANgRIpEJAFjd8rysMB7T7fXo1Gvv91Dua6UQuvNstngw8eIKEgXDRISW+V4d4LJkWm++NvL/MhfOsinf+MUz78c8IMPrdCo/slaFjBMezFAjiekJ4Yvfm0P/sgUrYlV5qbOIwjG5rTTBl87Psuk6zDSWObWPW1MNkYojhdOTTHZTJmrj/Jrf7zEXdOGh94xxh8/v8RqcpD797zM/JIwOZ6ydHKKYDJjzwTECqGmN+JKbhMu/Tx2loQWY6hUKoWKpUVLt92obV31o8Qow253Di8BqamgkpVrRXRYw9yAr4JGGLvOsdscYi9w7NgK05MbnDz1MmFcZc/eHBsUk0FE/mQPlThUEkDB19GgDfVnaE6f55XFFR57rEqeNXFugswL3Y5lrV/nxKmINI0xXghwyGDAE4sNvnE6YdL2yMMpRPuMDPoMFhNMOkc1VpK+ZXQ24+mnXkDVF/YzDEWT0N27NNTLXIg6PNedRDFuG9jhw5BdOzevWtMq0lw9gqMzsJxeh31zCfiiBPPNiwABwmD4VIsIHbR0hQ8/UONA5TS3/WnDS8uWuz8SYpcvDM3ZV+tSN6goanKEmFoW8sPfmxBPjNFPPZ1XYrDreJSZqZyPv2uMvCWwUidVQ1qZR6jxXQ9VaCeesWqHv/ThSXKzzu0TZ7j/u3ukgDHCajslkRHaaY9bGlPU3DrWGPQS39ru5eK57by5fjFW49JKwjvvPK6Oa+p7WDyNDEkCG+s5aGXrhr55uXIfukgyHpzuE+RLqDgmp8exdAnjDn541a9K01ILGoMmCD2iIOHo/ohcc0yshO98CZMDrkkUZ7z72NdRH8BYRBgM8PTJqXHbzCqha2NcjptokBJSyfoYBW9XQRIOTOSoWSLXEGtAVNBLDbvX4/JtB16d2vj2jOJNU3xMO3X0V89VCy0/7DmnWLwLyFKLaAyUmfZXRsnFIWEIxuAlw6vDWIfxlovC7mpusmHLNiwmd4QmIRJQL0g+hXhfFMfzCUEyhrgA8QJOkMACHm/aRXdoLGm4irOQMYIxMT5IMVIkKKsW/S3VF/FmcJW2t5KSG8RVC61LV/we0GFr8/JWvjJGldiDUAXnqIoMVfdhhPpVXjjBoeKLa60RogHiLGoysB2UGmo9KjneFMXxCIpyLlaLwnqB5pAXsV0g2MyhboBoiohHfVGfSojAB0U8mc1QKbvdlGw/rsl7qFIYLIubOS8M0LvZzHE1bJoTtuK1hoiipocbXmKrOV6KQsTmGnuMGlxRDA8D1gHD9B3XHAaqSlH9M6/iTYYaP2xZH2N8PIwhy3FhghewPsa4EG9TlAQI8MO+llYcVoaCUnS47+LcdLj8KOVYydvJNWhailGDigcsKvkw4eFmv4ULbaaowqlFsTrvELEURe4c4quIVzBgJKCojX51exf8ZgMkkCLsoesi8tATpSFNUbB9xAzwLqKTz5FZQ820qbAOQUrf5PR9kxpCQIoQIlkN0RwxFuOVXGIMOeI93hjEdIdt6+tYcoQcHXoRS3YfihZ9JLGo+iLMRnzRO2SbKSZXL7TEYzVCyVGtoHiMH0ZM3Oz3sfrC1ieCUUWpkFKnqwmhDZEsxNgcI12sMQTDpOWrw1PkN1ogILPCb/4RML2f5NwCf+EjVSLpY/OAV9YP8ptPKnky4E+9x3BrU7FpSD+s8n//l4i9+w5x7JY2d+1boCKOgTSxIliXkscxpJZcKjz2nOe+I4doVs8S4gqBK0NNS0ujwK5EIKgJaacIbRI1wwyP7Te9r2F5OBy+livCy5Chg08cgQdxdXKm+fIzKc8uHyCuzbDae55j+0aQZJ6H35kylqVXP/F10yBewds2ThpMH5rg0Rf28cDdFlwfF/YZmBG++YpjcnyKdxxYZ6x6AcnGEB3Q0ojm2GG+0Z9j+VzAlx9LmLtjjAvnI8ZGVtkIZoizNiYbEGLwtSWe/uw0P/xIwv7acVQCMhsTOEEk5eYOcdmdqEK9XqPfS5BhnMB2ZWflKWxDFMETAAajOTDA2RQfe5JkkUG+iNM+3nZoVtpUNL2mJ5cS4SUESbFeqGTC3QeX2R8/xr0z83zxD5ZZdwOMPcl9t3jWFxZ5+qTnD56MeaHXolOv0q5sMDJ+mtuqF5jyZ2iO9Ln9ljqZdFjpxJw+V2VhHtRkHNrfYLoa0M6g5zaQvIbXCmmQoSqvu1RQ1a2vkp2HiLC6ulp8ftvcU1ym8bxJikk8zM4TkGCAyHnuvm2CuVZIa2qV1eVxxkcSwrxDNR1HZNjv7ypQKcqkWPrYNCAgYSJI+dgjo4y4lIfvn8DHa5CPcKDR4f/4wCSJhSwJGAvPUBvEeFvjY/ePYf0itr5Majz1/BlG3j+HD5dxOk/FTxJbJWvPY6aFw/u6zI6sI70WIBjtFt7F13nObYZEbAquMkRiZ6HqqdVqbKzrNdZ7u/GUQutN4zGaolIhlyqBQiyecbPBxFwF0TbTswMMgmoNb9aLOKqr1LeEAYFa0BoEbXIzwrmNAzz6wjK3H7DcOnWBKKsT5Rln28KzS4rrdXjwnipVYkQdAxGeW1hh74yl2a7SauRgcvZVTiO2R4rBmy71PCFoWHIR5oIu4hNctIFQoZLWipS2K4xx67VLbva32xZypXHuWjE69O4W1pvN0JprPVthMBiAxtt6aQil0HprkM3oNYtqjDhHiAfpIT4g0ASPQSXGaQVw17BrKYoKipAEIZnCf/kj4ejBQ8wvnuauyZQwt6gJmV88wKc+fZi7jpzinoOnYNRBEjMIRvntzynvetce7t7X5amnVhht1BibCOn2V8jTSUQS0kBY2nBMTVnyDUNUH6e31uHOg31GbQQyeN1O9PNrCdVqTK1isfhhldStdjo3Fi0msMOiUpTTsZq/wcm8vdHh/SGSoZqRmxqhuotS+6pPV8kzQQwIBiUvjLVXuSK4kZRC680ighIVMeuaXZJKocNQBQfEw9eVwF+9wIJieYhkCBkiDjuYotXKeeehCzRNzoW2ZXy0jWGUsJpRHV2jNTlgvp9SqUVMhjnkTaJ4hYW1cVanY37/8Tbv+/45fusPz9Ifd1R6e9gbneX5JOD0+YPMjp2GyihTtVNMdA5z8MCLNGxGuNm+/jUTQWiOToIVnPZBc4JLnvk3HCmWqN7GnJkf0KobxmoUpaZl9zkRVAwZAWtmlG+vVfhA9RQuGiUkvepPQMQQBXWSNC2ukTggBK6+YOWNohRab5rXy8u7dHK/8dy93BThnGFusS5Dgnk+efcBTs7nhCN1zr3UoPVwn0hSatEG33dfzmjFcLrboBHXGG2tEZk1PvK+nOboeTbWIw4d2+BdMy/T3wMLzSZzURvqEStfqpBNRtxztEEl6zDd7DDmW0zUcozL8bKZOP1qlIr0Ua+Aw2zJ7LerAJcMQzOU0ZYlDocxR7sxWEMcXhICNfhexnjmWVNDK8yuOcAqzx1iXt/Zsl0ohda2x0JehzzABZBFPWqNFWZGJ9gnSxwZEcLUIrlwZHyByp4WdW+5hzWQFYxvU9Mmd+0ZpyHnCOo93n0wIvIL/MB9U6RxmzDvkGCZ++ABLqy+xHsPrdI0OWoTwmyNwHcRgqL0M/BqQSSqWB2wZfQa1kXR4d1/wwWFgjEG9Y5mNUDUDztjvx2Duc4IYHJybeCDFm0TMTLm0GusgaYKYRTS7/dLm1bJm8OQYkwXgiq5QJrP8r++6ug6z22tBu99VwfJYqyt0pcRfvmzVd5/qM+HJg1ZZYCxPbwXfuvTKfffKRw9Kpza8Lzy0iGO3pIy7TosYlnoRTTCU7x/apV+WsPWEyLfR8yw/JDardLSr0GGJVGUIvdRzTAYdbicvNGSYng4g0e83ywEtEs9moI4gwsq9NXz8kqXh0ZTMEU3pmshTZLigeO3nx3rUkqhtc0xvhAW3uZYcur5FDSnee/RGv/7d05x731jxPYM3ma8vDrH8ZU9+O4p7v6+nHrUxmTjGOsZmAO0wwrf6uc8errDxjMt5utjnFs4xbMvj3Pwvilur57ipW/tp+G6/LUf6hGH54tCj76GUUG0yEl8dRyPAl5sYQvRwmYk5BTNx8wlBvnLEjWHr126l9e8+IbYXJZebH4mw8r2b9Xy8Jqt3NcN8YLRCnHepUWPR46MoAKhz3mdtfyV9yPgdRjusM27EG3v0ZUgPkRcBR2WDvSmjQyWOPXCM9x9i+XrXwzo5wJ+QM2/zJ+9Y4nYOX79m56T86MMoj7dOKeZpxx/dp4Xv7XIZHeJQ3OGTnuB6qjn2MElHqo+y0snNjjvQYIcn1cLrWkY/V4Iq+/0BDaXCYtN0SEMhh7QiCLnMgMs4itAQNEj0hZiQDLeWhvY9dDyLql1ogIaoFp0XCp6Td54FItKQEqANwGWFG/sZc+Gq8Fs1RK8tKjg9qPUtLY5zkBmDRATuAhjNvjE/fvJIyGiixtUiaRJoDA31WZqbIn33WXo99eZ8FXCLCTXJf7UD+wjD7tUs5y+RPj8BEFmqVS7DIylshFw9+w4zq7QdAlj8QZFMrgBKWqmvb6FqjDeyiXLEQHUB6jWCk+U6RaVQTQqtC+TgQztYH7YD/ItElivdXu8hQZ4FdAQsRuor6OmR6HJRYgkoAE3fKqLQxTmIoBligfNtXmp0aI6ca5uM1vvda0Bbzel0NrmGHIizRAXYvKILIx4/twC8/02t423uGVqhbBXxYUVnr9Q45sXGtwykfLeA10Cl6Ja2MG++u1lpqdrVIzj4Kglr1ygIQ6fOZ7sj3FbI+CQO4lGPVKaGLdpuP6TJ+CVtyi0nFyqYHqIUYyGRTNbGeBNNpzkFhGLaoAS8taXvrkeAsQOhZNHTVaUw1aPGVbkuKEi60qhcG/EdjfMYrBiS+9hyZtDCfEaYhmgJiPTOs+eauCm7+fLv/ccP/HncvbFS2ggdPQgX//6B3jWf5m7/s9JmrZHnAgdc4Df/HLAsSPjvPed8N+ffIrQ30G2FjA5kfFbCwe4I36Rn3ioQxQEZFGPQA1W33wpmkAToI14Rd04iodggdxPIZJgNS36M4rHG4fx2/+W3BS2SIr6Go640CClh7AzOxcJQhCEZC7d9rmHpU1rmyMqiBfUOJzNyIOQNK/R7ih5FPDUC3X6MoqnThSsMxGepD6ywRPzhvNrNZxx+Np54tZZKs0Bzxwf0OnN8fXnxniaiNMLfaQ/xtq5kE53HJtVqKWVocB6k2MXj5E1jItR3yQ1CR0Tcn5tP88dV7wfQagMlyB+GNC4zR/zUAgs0x2mvFgWFoXnn1WKYMwdikCWpcP4tu3NNTzW3q5AwR3M1V6u18npKxZYCUaGZZQ1JNBVPnBvHR8fp3nnOv0LLUKnBGI5Nm74a598mk4OSEQsA3pVoaID/ur791CpnSVKHT3b4HvuOofmKdVmj3t7X2K0lxDNLuJzj/cTECwUXa2HI7uyb08u+7sMK8Zt+dbUgJvEhRv0w5Sl3iRff7KPiUZ4960BhOvg+6BBsa1G3ODF1RvEgIaoONTkYBIqzQaevNQCbgBXLbSEEIsrcuhMjhIgZKi8DYbHHcHQbK3B8OeiKmRR9XUzN2+zOmQRra1itt4qmx4qMTipo6IYm1PRNneOv4KXHIgwB/qItMGkNKRBrRkWyy5nCT3kaeESH52bh2AFyQX8BFmYEOXFGPeN5QStgNxXweeEmuF84T2UYSpI0SfDYNQgZgPRGHF1RLqgFmeFIK3igw6JFWJvhw03EtpW+cYrh1hZ9NyxP2BmT5e672F9OkyyHpZ19jtjyhdlxmsgGUY9s1MR3mfYXdDkZXMmb34q25FrKLdsKfKQhl4e3cxPuthZpuQiiuJtghAMa9b4YfPTora+8YIhR30TbwZ4rWHVv+ZSCr6odKo6LH3bAg2K9zLcXAz4FiExXjwYwbgiMTuQBMWhdjjRbEqRA1kB00fIiBRc2MFoiLcZgVOMFku1wihbaEJWLKohavqoRkAMplfcCz6mX10nThpELsMHISId1k2N3/3SLdQrPT7yjnVqtkswqGKMDoW0Gbrm/c64i4bVWzcxakAdduit2xHn8DrslLFfUzeebLgaMN5gMHgx297T8PYhiAaFh1xM4RkTAFcsLQjwXig+AouRFDPsnsNmzfnhhBYc4gOMG9pMhhuJWrzxqPVIOo7xDmMWIA9BBJUAJceQYn2Go0FqIyLTwTqLswbEYJ3B+CKyylCUYjXkIAlKDWUY5KqCSo66Cp4QlWx4jGhYZhpcMCAcNPHAht3Lp5+0BKHjg+/YYDR3qAMXJKia1/gJd8qkueix2zEj3lVcU2OL3IDxQuQNNjc4LLb86K6IqEFcjDf5UBRUEZ8g0sfpCJBhBWAAzhUVSgErnkLrKKLJPbbYlwIUnXacyRAVrKuCKcIHsN0iXscOCrlou6gWWrAXQXwEkhUtwyTDm5zchBiNiqKEGhS2Mw0LD59rQNgGtahZHz6ggqFmVcGSo5IgPkR9jGhGOAjIo4wk7pG7UT7zeID2he9//yJWHEk0IMpDvHQQYkR3X8WFkuvPVQstg5KhWIU4E9L1wTBlo1S1roQjoGcm8WEfr0KQ1whlg8Am9PMmYnuYeBmb1xEsko3iwozADYYdc4YGbTVDk08KxpGbkIGMYzWgavpFsrKLycN1em6SjDuRIKNqVgnzHkJELsJADxAEKxhdpcdRBmGG1UKDk8ABPawYvIJSg9RSISEmAMlJaJCaCcR2IRknDgaEcgpxHnE5Kg7POMZ3WY8cX32+Qi2o8N0PLRNKH1xIFgobtR7NQVQIrPJpV/IGuGqhZVFyKaJmJfUsn1kYahDXGHm769BX/VbMxE4S8q8/4wnHaqSDhKqL2T86wdzUDC/OZyyuGvYd3Mu+fQntxTa3HnGMaEzDKFZ90d6LQsMVyYEELwGZzvCFL3WYmox4123LhLmFdAxfW+Wl+VE+95UaNlziL35klglzFsSTBZ7PPpUwOxdz28w4v/pZx3LT8uC+FqdfWKPvZxibuIDrzhI0Vlk4X2dm7CQ/8IEA3zMYYlbaU3z2CWVi7yFOPtPljiMNHnzHJDU/wBiY9xOMhgl5Ps3nns0Y5AEfv7MD1XVIDIHPEATRCvhwGGVfPvDeWt6KnMjt/yS5aneNogReURSnFJ4p8zYVedtOKIg6ZFgtUtXgJcBELU6fniWK9zJmD/PEmQdZ7bT42pN9jt43RaaH+fbCLE882eLLzx/hdDfn+QseIyA6IDUhjhDrcxQ/9OQVXsa1wQQ9DrLaHWMtP0AWbiBZlUwavHTqVhaWZ+mZGoOghjcQaUTYHGOpMcNpqXOmPUdsb2XvdMSF9iRffuYuziw2MdSQZIL1pVHmszlyV8G4hMRUsfEML58WXjitrKwf5sUTM3Rpkds+zjv+n98N+cbaDL/xJTCJ5fvekdIKO4iCpQLiMOKIs6FTYvvPjR2D+mE1C9Xi6w3tRC5W6Bh+OCpFGtl2m+NXrWn5oZG4cMQXy0S9NEfppubiVSiy74SV5VWqusZ77pji5cc8Rw8EkJ/g/jsbvPzV40zbo3RqXeqdRfZP3knYDRgdCQlcB1EdprQU1RIUixJjNCGkS3djhS/+ccoLjUkqdfiB7xllRGrY7AKzk57RxgZPPZ5w90HYu2cdcSOYfsaLj4ecyx1+o0e1nZKeP8tdcxVa8SlG4nXiXOl2MsYak6Sp8umvH+Zj73wOr1V++8mYqYOjeJ8wdSzl+DOn+cJXK9x36wj7m9PceqDB1z4bc//9C7zzaIVq1sfaJUxeKyLHN8M5RAvb29vxMe1CNrvnaPHERIzgdViJQ7nqcjwqw5oYhTt6W38+2z9nYkdwUWE1wxIfkxOWv/FXq4yPLLD3Ay3ereeI4w5jotxxGCQ4wZoRxvw6bfs4o2ygWiHwKWgFI7boMWgcbBbg855A+nzie6ZYzhpEzQHqeoTV85i0xbG5iB/9Mx5jDRXvaGgLkwRosMx77n+ad/opgkFMn0WCMGIkibh7/xKJrOCI6KZdnAVnjuNEefGbFmMyrHo+8ODLNLVKngl57XHkoQB6I4zGFmte4cPvivjI/U0Cs06YgziBbB+E/SI5+pIkue08IXYiasBjSPMccRBag4gg19BVx79KqxItqj6I38GaVsl3wmx9F1WMUSKbMNfsEWUQ1trE2SJJdYVKB6o2Rm2bhnXEOTSDDcK0iprO8BkXYj1DgVXst6ilZzGkTNQWGDFdhDYSZoSuaOgaS8pk7RWMZBjvCNwGaAXxFVq5oeU8xq7j4vN4N0YYCsYrIVVyMVTibhGBT4qXCofuPIfxq2Qyy7Gkg5hFNPToYARsB4nbSDYBpk1kM5QNRDPQDj5ug1bAx6WQup5IYbpRsTgJMGJQo0Xw8jVceO8d/SQhjqPLStMMD7GtKIXWm0RFtozvmx+uqCdAhx2nLaHxBD5F0xqGYcdoB1UfADlxGhVrbe9RiYaxWcnwSVmUclHj8MZjnBBqQuDyIrrdKEiEtw6RhEgV8YL4Gtiloo6VGyPIaiA91A4I8xCy8SIANlhFpYNhreiik8fgYnzgcLV1nNuLmDYaybDMTALGolTxtgN2ATQqSuyZDt41UWNQGkCM3Sn5hDsUAaxCjpDnAUmSMtGyiA4LMF4lVgxxEBb33KbpZ/i13epqlULrLeCi6XP4k14S/GnBSAc0xjqHD1JQj2iAUcHZFJu3UNtBfEhuA7w4Qp8VYQE+KvL5TIb3YAhBLd5maBhj1OJFyI0QeovNq/hwrYhY903wNUR6qBU8MY4GgVqMXcWZohGs8TUCTREfoOJxUYbRAE8NIwNCl+GMolLYNUVSRHLEV/EmRYmAHNwIogZDCq6wZRVZEzsjPWdHops5qkIUhYSBRTR7na5Jr48oRGIKe9hQ1/JDm7zZZs+cUmi9SQpvjSuMoSogRTCniuLUAdWi/7QL0HBAEiihcwSZJTGNYWu5HuKjIr8QP5ziRddqMUVhOfERwgAdhj4ohtRYvBgil2B8EZSqmqGuAbhhRUswWkNVMaKkgcM4cEaxPgdtIlKUvMEoKjliFNEBUVoF9bggQJ3Bag6mg/gJIMObCkhWVITQoib51jzZqkK6nZ7RuxAR1FNIFh0QBBbxSpHUffUqkqCFB1x0qzKN0csylrYNpdB6C9hs6rCZ5qwISSaca09SrXhaDc95Y2nIHrobTVYGTYJwjaqm9PwexivzTMYJITlG/bAVlwy9Qpai1HKG8TGOKh0jnGuP0Qz6zMYL5OEAP5gjj5bJawPC1JBjCI1itcuAkNBbTL9JRdfJGGM5alI1y2TZCLHZoOrb2GERvsxNsuFqEJ1jI9tDnGf0Ngy10QQxk9Rsm5qLi2Rp1wQVhHzoGfSA4WJp5lJoXVe0uN9EwCUJagxhUHyO1/TIEBDR4Wd4cd/bMU2vFFpvJZsOMhEWNyr8i9+aYG7vgAfuj/hPv13nr33vBLpymv/xzS71WUvjrLAQzvHwPR2OzQbcN7ZGHAxjZHSz5IsWaTimDX6EJBzwe185QKfSZP0C3H7bnaCnOf/CHm57YJzFjZCZyhrPnWhy791V0qUO/VYbmwRUuzH3HVmiO6jyK7/e5f/40+P8wVd71EdzPv6OgDAN0aDLuZUKf/B0h6lDU3z59FHeM7HM808+Q+XIEZJ+n/fdU+VdE10qdDB5WIytsK68Pdf9JqYoLmvwzlOLK8MXdRjy4NmND43S2PBWsul0USUIm4zWxmmZMb7xjXFMfhuPPrXMXe/0zDVX+DMP72eyNk6ap8w0Rji5MA4SbAmri/E1RVJ1UeLGoiKcPQ97DiRIOMZvfnWaV9anWUgP8OUnQl56aR9/+HXLSnqULzwa8uVvwjefG+Ozjx7k2aWQHg1MAzqDg6SM0LcHWPd30XX78X6EzFZx8TjnVpusDSZ47kSTsBYxtafF1OE5lrujPPNsldwIuLEimbuMFH37GHoPrQjWg/WbNqih4NqFlJrWW4kMhY0Cbo2RYImGWILI8Bc/eIb50/M0u3t4R63PLb1TvGzhsE6xfrJDc7+9YiBg4ZtMCyM4jjCP+egH1vmdJ2JGJmb5wZFl+vEyt7/vec6vrrDSD9gTKRvJIo1qn8HKAMZhjpAHD3eox13Wk5T2huXc2iSnT77CQ+88yNceW+M9d1tqjRSfLKKrbfbVlIcnn+bO6QatTo3R0RPEB2NqiWBywSlI0C4mie7gqp07nC1j+eZDUzZzJ3Ynonp1prYPy5+h6LYS4k3KX/oHi/y5/2uFyOXD5lY3KcP268UtYrYii0HxkpOFhjCzRUyVqxdeOKMYVyPMuySx4Iylkm/26Xv1tczBxzhjMSSIN7jA4yRH8hCoEkgPr6awfXnB2a3GX0Mbm0cNBM7hA0HSCsv9aWqtU/TSSaqh0FmD0bFlQtNHkj2sOiWOUwLXJ0CRwGFUSIIQcUqQB0i4jteIMK8Whvfv2GKs5HqwaUO1KjC0haopijbKtQguDfi3//EwuSg/8aPnyYJOERpjBtzImnlm7sU/cZtS03oL2Lw1NlMpzFYVYiHMHYE6RMKiPTuCqqLkEAih90UjVNzrxNWYIm9vs/GoybFOCqO5ODy9okwNDsUjVoq6XOKH3sxiH+odIgHGGYzJmKqfQnxKLZgHDaiPhEUohgsQs8a43QxvGCY2e0E8VDSlqKiqqLdYKErU3JhLXfIqFLbq2pmhpu4ANYL1u7OYQSm03iyXLOkum7jDihjWD43Tarc6yxe9BIvUFoMd2iBez4hthqkvQzf2ZQeyxStDwbRlBZPCKH7palMK8TIMz9jMLosvEZPZMJIwAPEXX5eL41KzeejCOyibS8JSYr1tCIrB4SQkI8S7nCBS8Cm79YMpDfElJTsYUcV6hxfLykA4fr6N9xTZDdsxyOotoNS0Skp2BUq1HjAXjWIkH5bP3p2UQqukZEdTNNQNNKNODgFYr+zmmLlSaL1J9HWeZ69j6SopeUtREVQtVlOsJjC0VTrCIj/07R7gdaAUWm8B3lzupTGbvfzK2KWS647ijQ67GxXNbhWzldy+GymF1ptGMD7Y+nnzu25F0JSUXF82e1Ru+tVkFxvhoRRabxoZxmMVSdNF4TUvFo/B3PRNP0quN5tVRoq6bpv1QYbNfbG7MpWnFFpvEhVwKojxGM2GTU4tSFAUJCp1rZLrieiWgHKyOZ3zIntllypbpdB6kyiGgW0QkBNJD7xnq010Scl1R1AxOAnIJBoGL3sMuhuVLOCagktlmGaymVlXVLK82VEMr5wZsLwe4F0V1eLLa0yRleiKyg1qULVFas2wBlKRjK9bXzosdYsWXX28DPMGt+rfstkOaes9TrTYL8XPqEeHy4Oib1LxfuXivovId1eMxQfDEiaei4cfvldc0V1IFb/1d8WLx4sW799aimx2InL4zaWybqY2uYt/V4ZpTIovFjLDC+mLMeG2hlnsd1hJU4cG5mIkmxsU5zfc7uJ5FtfKq9l8d/F+LVpkXdxS2cwcVUDVFNdPGR4lH47r4rW7eH22yb2voFi8WAa5cn5hDScBXmVbFvB7K7h6oSWWgDp5kBCII7MBnnxz6t20GBy3zMFMo0NIF48jkwpdabGqs6zms/R1hNTVyE2Ap+hUk5mIno1JpEqiMalERR9JX0xTZ4VMPInGrA8O0EvncBrh1KMaDidjRi8A5+p4I/Qjj+JwJkDV41By41Egk5S2n6ZLi4QW3ibgLblr4sTjSfASkotlMOy5mGFY9jN0/BR9q8VEwOOM0LcOZ+xQYITk4kmlqHPvxYB3qA/JrIALSDB0gpBcwEkhFFKpkFqKoofeoPTx4nFaJdUYnMHh8OpxCE5rpAI5ReVWZICTlFQCMiN4XwhIvMHjyU2V3Njhg0NQNajG5FbIrcOp4FRJRUgCh/eVoRAM8TYk5+LyP5eA1IT0jcWbYFu5WIwqoc9pGsfe8RqBy4sU512qal3D8jADQF0TkR7LF2qIGwPWrsvAdg5KaBxWXTFhsKCeE2eX+dVH9+D6BznUPMED7/Mcm1mk058mrCTUNeR3vxGRyihxdpK73j1DVXvU3IANG1HXhFl6PHOyxn/9jFCtr/GxH6ww1qojxuO6U8xUOiwuR5w+1eDOe5R+7lhKpqnYhHMbyty40O01mIkXyIM1funXDMlIyP23jnP3nj4BMauBpyFzOM2o5RG+kvK5r63w7gN7CPw4/+0PlrAj43zsuzJqNWXK5fi8jg/X2JAEk+8nIWckuEBNQlaYIHMb1IOAUFJyaqy6EZo4TncT2qdDbjkygpeMKbPK2WWwjQnGoga5XaKaJaRaY5kO46HDmUlENkgSSBJ47BtjfPi942xULxBnXdSNcWG9AUTMTS1S8ymWPjlFV5kwFVyQk4dK4AzOenIZAZcTuQqE53jm2f00JmB2doUgM0iQ41wTb8YRu0CUO1RjFnuWz3/F87EP1RiTlbf7xiuQTe0XsjwnyzIa1QjvClfQblwjXoPQ2ixOVzx9stztWvXz2ikWDIKAKSo2HJiucN/RaRZ6DeLuPn7l8zVunb6Db5zI+NA7Vvjhe3OykSp//GzMsfq7+fKvA8ESI72QZzrKw7f1+WvvWaF+YI65dzYZdKp8+Rtw9tk93PqueZY7MZOjt3Dhwsucd3v50mqKyhQXVsY4NnWKZ3qGIyMJzz4j/NhHx7j1QMqgc4RvpxVsK+Lrf7TGxMExvrWc8o7JUc6dF8ayM9TGDvGtxXFu32d46vgaR2+bYZDVeezFAU+8MMk7Di/yctJif3o7i/Y81YVDLKVPojN383c/dJrfe6zB8rowEx3j6ep56tLmqeOjfPzYCM9knnxjigtfeJJjdzX5m9+1zG98foTaO/bTOX+CxmyVj99r+cZTo3zqq3D/0aN4naAxcoInHqvz7g80eGw+YPXRM1zwDzJqlmgPljhS28upXkY2U+HhuTYPTSzz+6/MsnR+io/cd5rlbIrf+P1p9oxWqc2+zHrvIL2VNaJBnZkjFebnH+TcmWX2n065cLzK4dvOcvaVjPtvX+PBOyf4wydrnFuYoTaxzuK6QczqdlodFo2cBLCCywoNO7DmWkrE7yiuwaYVAFUwG+ANU3M5YrvXbWA7CdHCMqPDEiEBnpEQppJz7I2eZK5xkulWhX5/ldrYGo3AEPo17OAE771rBF2/wHgAs3XLPYemmag6ZmqWmjjc6hLx+kv8mY+e41C9w8PvOM+R8fPoRp9OL6feSBkbHZC2FZ8KLjvLiBj2NacJBwljQUacK1ZWkeo8rdYyTbPMtKvxgVubyOIomgbM7E0wxpL2e+yZW6eRJ9x90HH6+AKnzj/PWGOVB25dZ2o0oyIpIn3yToJYSy4R4y0FcaRpH7GWlbUePrM0oipH9zepmQHaXaNuhJG6wacZxlfZOzfD+mqfLI1pb4D4nM6G45a7Io7dOc6pV84yOREx2mpy4eQ6lWgBYZX+OiSdgF47ANsjtD3WzhmM8XhjOXe+z4X5FeYXHMkgwAQhSyurrLUtg0GfhbUV+ukEq90FlrqLVIznwnxMlgqz+y3VWpV7b42oZZallYRBUCxHD491qW0XLetViAhhGGA2O07vUq6+CKD5s0TaoB+sELuIj/3tZf7a/2eZqh/sSml+tVx+c8hWawsvsEqTxEFd+3Syg1RkjQ0vjFRh1J5jIW2idpxKbul6wfpVRg1sGEPFZIz4Prmv0wkccdQhWR9DbEZYSVjuzBE1hG43oFbpIYMqSRxh8nPU3QguqIJbp582ma0vE9hV5ldvZaNuaNY7sGaJRpdZ6uyjHnbJTUqcxhibMMAxFnti9Wz0G3g3TT06ybI3TNoqG21Lq9GhjSfLZ/jiN0/w8Yc9DYlZ7Y8xQAk1RH2Oq6bk/QrjUZd5L1T8GN1Bh2Yk7Im7tLG0s5CKG8NVO4zRoyeOBddgtrJCP8upSIONbp04sKQ+pcaAnkwT5H0GSRM7ukjgqph2g/rIaYKozcpgmtRFjIeCD/q0fU6g4H3Aho8YdHpEfgJfPQ9ulPZ6zJ49CXm/Qq11ju5yi72jCYHNOOtHeenCBrfuj2i2HaP1taK927a484v7zRGgYoquS+qwslkf/irHuBuLAAoOpQeuBaScOK5oPgIy2B6f3dvIxWathddMBERD6gaqcQreMmUugMlpieK9IdUxRqwS6Tw+FOo2RxCcCxiRAG89a36MCm0qQK8zRaPmUaoYGTDWSFhJHGt9y2wlQUbP0JO91PuOXjzPiAlwQUad8+S+Tur2MDqxwrhdBzyMVDG5oVl7GWcVyZuYKng2mMwqkDRJq2epjK5R7Xfph8qebICYNo1JQQ2MZTFZvMb3v2+CQCGRPs3aIpEJibVLQo1zPcOt1TXyIGQqzIhSj/WOapywUQ1xwSrjaYxqF+wAckdno0GltsLGMoyOVwhJyeN1TGQYyyPyKKamL9DNx+nFK0zHOVYXkHqfxE+ynoVUogFeHe0kYzLq0MSivspCP2Gm2aY9XaW/dJrRYIZcUg4fPo/aNSSaIcAyOtbHBwOczZnLHWMTMXGeYes9VDenzaWNIzarzl6DoHhrbr5h4o6QAbkG1PBFFVO5tt6HO4WrFlqBhqTiMKQoysAKqq5oO7Qbr8xVI3hTrLIDnyLkZNQ5vVLl699qUj0acnLRcagl5Mk6a4tVxDqO3DrDMxdO8K49B3nqguGufV1un93gs8dH6XRrHJoM+YPHlU+83zBXc/yH3w24892jzJ8I+cRDXbK0yb/5PYczDe688wyH99zHs0+sc9uRgI4dkFwYRe0rfO89Ndb6DX7nCc87bh1j/kKdzOa01w2H9zXpZhusoEy6kEpuGdBkeT4hDISZOw6TzkfMjM/zxFrI3AA+ePsIXzrnWVmpM1sd46m1RY6NVDi3nrLvwF6OL51nLpogSftEM32eWpjkp+58hTNrFb651CdID9Jdg1dWL/Ced89ybvFFjlZu5cT50xyaFR4+mrKs4wzaKX/0Nc/teyqkgwEa72cjtNwXL/LHazXeuXeWk/M11nzGX73vJZ5ZGef8egNfE9Y3Qs4t58SThuQbNT7ywAK3H0k4dXac//mtPTxy12n+KLqdjW89w/0zU2TNGktPPcORPTOcP2/ZO9FmpTNHbSyhGq7huiHf+mafH/7YCIfqLxeNbE3R2xINhoW2U+BtmAsyDE9RQ1sylnyTY2RF2+ldatN6A8GlHiOW1sgIYpZ251W5BgQl8ClOilgZlZiOGeW5pYynT+4nWVtjYXU/L9s2e/dNcuZliEZzvjI/hp2wjFYm+cxXFzh124B9sy2+8KXDvHQm450PjPPtpZQHVjJua12gMjPDZ14OqazfwdLCWfZMZASNgHS1TtId49knZ3H1EOemOH3+PE9+s8Yn/5Sjkq+xtFShmxzl20sv8ezpY8ybOuNiODWfsb5uEbdBdWmZ3Iyzd3+HVSZxlZQnnmvTTGbZr/DShRHuvHuegany+T/s8cRTMe977yz90YN0TmTM11b44wuzLKx2ueVQk9WTx5i68xS1uE7mV+kbzx88vgeH4c6DTXqDMb59skWabrAqEasnGpw70eC+A54LJy1LMkF3aYHHFnKmpg7yctwgWRaWxlOe795Gz51iLHTU0wSTR3zpuTG++NKdfOjdpwhOGyqzbUZMi4VgnC997Tyzd9T5zcc3iEfup52fY/GVAYcmoDm5yHMLd/P0i3uZCatceOkEMzMh//uxEY7ds8DKqTuYOTRgIzuJ9EHrIak1WEkRNYhaxAcIMSopSH6DFS1BxWK9J8i6aB4joZKLxe5Ss9YbiohXVdY31tGhAfpmF1wXMQiekIxjB6vsm9qg65c48cqA6cmUIGrz8B2zOG0zNtLl5Jl19h86xWgwzt6xNqN+lT/7iKfbD2lML3D3WeEd0+sEYvjQkTbf1bK0Vz7H3L41QuC770+p+Yzxag+i53i5O8+RsZBWtcrtEy3untvAkLB/v+V+8wx76iEPTr6EasLGoMvISJ1zKxXqlZxRUdbWutQmVtGgTS/r0woD+u2TTE+M8a6557mltYTYmA8+MMM77hyl21+kX13hcAsWNlJueXiR9tkNqmPrBLcvkUcpUXCGUJc53Gzy19/fpR6vYQPPudmQoDnOgAXicA2dGxB0GkTBEnsnJmiYiP33BYRVj/UL3MqAwV5hXzPnrvZz3DXb4ckV4baxlLg/4E/fc5a7juXsHV1hTSzREYNsdBi8a4OoPyB0PR56qEan/VWOTi1TNQ1uP5iTDFaYnKox1upy7+Q6e1tw+BZPpXoBkR7tqUVGxtr4iZBq1MZ4gWCAeLBegB5FhGcFEbYCX29UjJQKeCxGPY1Qma5UIElxWOzF0N1dxVUb4r9PfphUHCBEYrnnr/b5R//fZepmgZtZam02U/VYAs0wroeakNxWcSQEbqSIbDd9FIvxhem+m1aph6DhIjY5QG57ZFohD3o0/TqDyBHkU1SSlCzqY7MK+AgjkMWrmHycfuAJNcF6jxPHhjRokiJYomwD46sMYnCmqFWfpLOsZYLJYhqVDcJog0g8oh0yUyGjSaxrCIZ0MEklaEOwQi6jGO0QZjEuyMncCCKWpV6NvHmeKaeYfAypnSdMpkE6eCN44zHqEOlj8xZIG82qLId1aqZD5BusW0MrT/FhSpwanIFuVEfokaUjtOjixWC9Q40ncIIzAzKEflzHaEo9aZJFa4ivEqrDqUFMjs0tg+oK9V6d3EKuFfrZGCPSxkudQM6gJiCRCt1KSKvvScKEMGsS+AFq+2RBRDxQBtWULhGtfgKRw2UtIhGMWUVNgmqjaDbic+DGCS0vikcIc0siHVZklhm3QRIpsb+GmbkbDfGXokC706bs0cnQnuHxw/b1iEU99HLllfYIk/EEsEQShAwGI4TOQ2uD//1Yg0fuF2pmmabLeWq5xtL8DJPRCT546wSrg5j5c7BnusX5cwkTYx02OlVGgxpL50aYrKYMGoIxoySdLvXGOF//RsJ736mcuBBybGKOmfozQJ1Qe5jc8uTSBv/zjwZ0dJY793j23THGbdOe9bMTzIwLY/U2Z9eOstLPeOyljAfeUWXC7GWlbZhqNellFdrtHvH5PvfesUStVmexP8KihpxbqHB47zjrmWdMLL2KY2l+Cmsjxuo99kVrzEuV5fwoT74UcOvUKnubnvn1VfbUD9CL+wRhQjbIefbFkLnxmPX1hLsO93GBwZsm/f46e20ddZN8Y3mVO8ZHWGeD6doFXHuc4ycqHDwkIDmTcQeP8PxgivF8lL7fYFQjfv+xJb73gWmSIMNGNfpdwQzmeOGlC3zP/ULH9en1DxIE5xAf0M1DRl1Gu9vi1FKdaD1i5vYeK2f7zI2PE8QBWlGW1mL2hB2mqze2soeoYsWhVEhkhMWuYbpqCbRPMb133yS9BqElbHakKlLcBLMptXbfdbl6VDGaY8XgJcCbGt5UODEf8GuP3U+YrrC+VOfIu1q8cKqNbsxgojO00zmSVc9Gcw/jg4ieTxl0pjk2swG3LvNHj05yYiOieXaJF8/dyWBxkWoQs3dugcyPc2hknOPdF6kG7+TkyqPMTdcI+yNEZy3/9n84/sIPBvzQHXup+DWUGgbhtj0DHr59Ats8woX+Bp991PDEHnjlGc/H7lrjB+9Y4v/+KhzfCAjG7uKZz68yGw/IXcQMwvnuMhNHm9ROCbfd8xzPnG7w+5/dx5135/zP38/48Dvew7Pzpzkc1lms91nqNNHWUR6e/jp//qF5fvuzR1nIZ9G1Lk9GM0xOpNA/Q5qP4qMqtCz7JhNeenKcW+4IOfviS7ywuId0pcrM/pyn1o/wF97xHPuDiG9+cZzjB6f44sp+/ubDZ5gatPjsOWF5vsu4sfyf9wtOYn7pD/YwVYUNhe+6zbE6sZ/ffz5go9rASov2Wsi+6iRnT7f57vuF556Z5D/8eo2JY4eJI8FUG4RJwlgNRufWePRzNe73R0jPLzPYyBk4qE/M8vhTG/ydH2kxXX0ZEeEqFzBvxQ2IqMeLkDpDt92BGphdHKd11UJrM026QIqyKz4EezNLLCjyKAKsKkYcOWDIGY1D+r01Dt++SFQTHr47R2qrRFmVPB/w/MIqh44kPHbKMXvrDIunXuH2/TlryTpO17nl0Aznl9ocG/WspYvYGcO0hkxP9Th9ukctThk1PSarF3hlcYTmWI+9Ixnn1uC2e8dIOj0eeybhfXdXCTQniXvUUoPvDDjX/QpHD8ygd8SQefbsXeSWo6AmozG2RtVPckvtHDKzgbU57Y0RIhMyU1vmnUcmOLFyhjw3SNTn8MEed03kfGU6Jxu7wJ31Llm7zvIZxYy2ODx3lttGz5NrhX2zlsXlBaZaKenaGrMT+1g+FzK+bwMbpsR1oWNWefBoFc8i6dgK7zhc46QscGwsZKnjkLrDuC77Z3N6tSXe2ZihwSpjYzA6mjNTnSDtLGLCkJwNvntvTLfrSeKY+w+d59w3UsZHpzm/fp53zlX4+ul5xseFtLmKGMeRI4d498MpU9WMXitHvaOq0ArPkQQNWuMe31PavXX2H6iw1jacn89xeZW4Mux/eUNTRYomvcb0GDfKu+cMBkXfQNXcS9PANwM3tqPou2qb1vfKnyMzKYIQacjBH1riF37RUQ3O3vSaVlElcrNEiKAizK86nl+b5d5bVwjyUepJhyzuYlydJLT0XJ0oPE8vP8iIruJwRGbActRiTzvD2AEdRqj7Lj1t0a+tEqdjVPN5BrGQyDhoA+/XeeK5gHvu7hCLAVejrRPE4VlqSU7FGCx9fJCCM3TcNBp2qGaGfuQxeQVVT2TXqQ0M87VJfGoZ0RWcr5JHlsTXsOJAerS0h6SQVSKMdEl0EsIV8qSGsxUqrLNuxjh9oofrjXD02IBxnSeUkIGJ2KBJICEbRnAba8RpyMiohSwkkBxn2kQSofk4vXidqXZAXl0kZ4KuNqlFryBpnUEEJnNI4KnSwXhHL59ATTHdaqaD5mNFQjY5KSFNXWRFj2DjhC4rjPkaSb+FjZao4qimFZLqKhsmJkqmyKI2TrqQHybWDppZkCpqumQ+IDA5Ksorp2PaacSxqQvsad3oxrV6yb8XueYeBRrwb4Y2rb/xo+fJhzYt3S02rZJXcennORRg0xMRtYkBsbMkmkLYJwkh0CYVv0rLdejRoCKvEKcxeWgJ8pCF5SpPnazxyF0xFbGshU0aeUaoHbwL2HAT1KVNnGckbGCN8MFjAZIXcUKZSamywcIFZXkwy75DS0g2QpBaVFJqWZWAlaLIai9muZLx+18f43tvDYmqOdU0IKaHo+gmnaUtKlZxwSpkVfAhGihkLZwZ5dzZC4w3J6mNhjT6A4xR6madxsEWIiu0koTUNOjrOJV8nRmfoCZngh5BNCAL66x74eRySESDmXpK1MyJ7TJGUwbBLEnFEvpFmoMc6yxPn6wQNpU7mo4MT6oTVHWJmnRI/R6eOC/cMdWgGp8n0hpeY2L6GI2pJYu8eHyEO4+OY+waDR+SoTipkotHXYuKGVDzK0jSAw1JTI+ltR6TIxkxMYZ+0dUZAEfrgKAh2NwP6+/c6Ke4DsvTGFCwbJZfvqZdbH3b7ilApdB6s8hWaSeQIuhBEdq9Cr/8RzWmZ0MuLE5xtLUHO3OC5eMprtLktn1zPLu6Tk3HWVkJuP0u4eHZc3zj+YDKTItffrzLZD3muf48e6ttfviuOl96vs5Xn7+Tv/Rnn2BiI+IXPt1mbPQYt+xbYnF1mgO1cR472+PIoR7LHeXMS/s5eF+NlQs1pqsLSDbNSHOZD7wv4cSZvTxzfJLaXmXFC2E15fmzTb5wconq7CRLvSZz/RqnFzx337VAtzPLRCXlheUWlYkxBmdewczdytkXO3zX4Tv5ytoF3ndA+eARx7dfHOXLCyHx1AEm8z69cIPVxVHixCIbHW57SFjt7KOaN5m/cI7Rub28cPI4oyN1ksattGptbpMF7j24wm88MaB5ywz9foXllz1z+1o8Nz+Gv5ByZrRDMD5g/bzj9ok6R/YE/N4LVZ6Yr3CyFzFZd3TXPGFzT+FNrfapRTlPD0a5cGKZxM3gegnB9Ci95ToDTUi6NWZnlwgGo9jKOVY33kWfU5x+rsmf/76cQ6MDKj7HygC0Aq5GaBN8RhGz9TaFGagYBrmQZRmtqsVqzrUIT0XxOiwcuL1lVim03iybWfZbLS2GaRWDNOZsfg9nN15maX2Ws4vr7DdHyHqeRtDj+Lyy0rM0p8d4fCVBT6zxvmnPXCOkwjLfTKbYWO8T1+Z45717CPQ0/Y5lvR+St8GlVcYaDfLmfk4urrB8rk7QCHHVo6yn0Ns4ScWO0ju/imTQmol4eWGKdx6BwPVYWGvRiCfJ2vPcUenQMJ4Xu55qOIb6A3R1kaX2CLXpMVZ659jozjE7N6B9poMxk5xLc6LzTWrxGPMbOcfPVpmxNb7r4IALvSkkGMP1Rnj8lTPs2dfk6KEK586kHNw7SdLv8ehzFe44OsmSW6BRcdTqI5g0ZsR6vv1syj33VXFmFK2OcOJMSN4bo+8bdOafIZQWKm00SNhbzch7AWP3xCRZhZPrAw5OWJbmhdkjc0xMJnz9+AL7RiIC9jIycoFOJ2DRT9Dpz7CSnid0GeOdUU5l4+ggQn0PG+5jb63G0+dCqiYkCCbpDfqsrZxnqjWHZaMoHigxmHUgQHz89phKFLwIagLUDHUl1WsKuxARoiiinyXoNi9ns4tsWkO7EooXCxhQV5SdVb1udYUuVrEsgh9EC03LY1nLLajSC2tE6RqdQZMRalSqG2Bi2nSIiXlyeZZbJxbYY5ZopyMQhZzLYhoIvXbOzNg6DQb0qLDiGkxEK9QGNQZeWa+ME8o6kvSpELNuKzifUAktdmNAtVZh4JpIsEjbRoyZderJGO0gAaMMjCAaMWI3yAeWdtaiaqYYmGUqmjMIDLFPSfIpqtE5ElejR53/9cVzfOKh22jUzpCK8PKK5WilxVTtPF1X49RGm6lmjcQLYWhoSEomllC6WA+L3ZBmrY7NIwg7pM5gfIXT/QovrHs+euQCVQ1J0yoDk5PmOT1tElbOU8taGALqQYIoOBcioQczoO1S4sE0JoYgX8PoLJ3oPFU/SS4poaYsJCn1ahUvEY8+WePI4S7TcY/FvIZkNVrhKnGkVHyfgR8hFEeuhtyuYLoTNEczIlkiCxw2HQG7Aa6BaAjibvB0KMpB5hKRS1hkaGiK0Qy5BluU+oB/91+OkuQ5P/GjZ/DhYGjTSthsCHsjuOlsWoJHxZISsrjSY2qsQizplqH8uhxTL/GyDCOiVQSPYXVtluUlOHbHSWpRi1YlJfRrODwh6/SzJlaE90+9RBJCmtWohRHt2iL7vKU+iBnM5TgX4XxARXP200PV4wOPBEpLV1GbUKk4MD3mXI+cGku5Q2oR1lp64RpNK8xqjvEV8koKApI5bNogqFrEV4nNBlJvYP05aixxLrmF8XABYzyN7DipqZHkylS0xp//HsuIPzUsgOwwjRrV+io5AXG4yNGwhSHHOGVZAjwxDc3Jg4BAlblWhvUJedhHbJdaaNAMjlYCjo5tEOfFAyC2KVYyKhUY1RUya6nmBuMtSznUQiGyXWweID5jHFi3MTkDJiLF5yuYrMq6CuNRG29jxu0UoTmHVc8H7h4hkIyq94wEGVltASUkyWJSU6EedemooSkZmbUkWBx9fN6kT0A/jRiLaohPsDdcYG3ed0qgOdYX1UVkWLJ6eEte3X4EvM1QJ4g4RA0qRfzhdguf2FVCa/PSWgOtRoy1Au7GpLFeFhAiwsKS8B9/rcNt903y+Gf2c8fdFer1HqsLjvFKj7sOwje+0qJWrdMazblgDtC2KTNZTsWMcPJcwnvvgUrW4ZsvzzE52eF0atk/nrH60hitA126aY2TF6bZW+sx4dscu3WN5UGDby+Ms3wBLiy1eOi75nnsqQoPHDhKJz3J3Ijl/Lyg5OQdy3wy4MC+PZhaRjNX2iuO/bNtpsZy/usfHubj7zK8dDbioTtfZqAz/K//t8MjH5/hdEd5aKZHNerx5IkR/vdX6rzvA1AhoS5TuMoMWbBGd8Oz5Dz7myNoZ5lqtUa65qiPCO+cXuRk3/PK4iQPTMdE8Qp/9EqLRtMi2TikbTROyTKLimffVIWTZzMONix7pzf41O9UGb+jwZ5RIelbJmohaytt1sbHObtW5wOTz3J4XPnv36yhBOw1RzkynXK27QmjY/SXhcZkl+lqwrHWGp3ONE90hdSsMr8as9w9wgNHL/DSQo1bpi2rq46F7ln2jIzyvrmYteWYX3lsjfffehurC+d45IEerXjxhq48hKIqpyLkXknSlFrFXntEvijVWkiWZVt7Lth+BRF2kdCSohGCeqw4aiEY57b6Et4QLnkgeWOIx0MO3mv5nU8Ja4/v4egdz/DcNw7zA4+8iIqQRFVeWGqwsDDLabeXZmeZ2cGAdnwbbuwU72ms0e/2eH5V6dQn+PWnJ3jXkSWq6xVWL3Q5/8WU0N7L07U/4oGHm9ziO/zhccuFzijtC2uY7hx/+NSzVDb28wdfGGXJC3vnlphoHSDx52mQcm65Tx7UeOLsPPe16nzt+IBHHpziB1odzmWWZ3qT/P7pUQ7e3WWcDqmr8Vt/3IM9I4xkXW49WOHrZz1n5XY+9blnmB0L2TdW4/kFmD40ycrZs0zeMsf5BYfVcU6fG2DdLIePbXDb/jbPnr2F//dzMXM/4JgMEn7nK0pzVIj8YZYWvs7MwX3IIGWkE/LHusRKPeM9BzP2z6Toeo2XB3fx7WdeYqDKhD+Gy1+kFY7yzWeVA7dPcHtzniDsc2z8bj79uCXRDqfcBk8+Ps7e6Srpc5Z7bnuGuXcrAx3wy/8b+ov7ePB7Ir49qOLP55wfjPHE105xx9F7OJl2eHphhoOTL9GIPJ1ByO9+y1Kdyhm74PnAoRtzq11OIbTUGMI4omgZItcobzxIjg4jMrcew9vQML+LhBaAFKYr77BSpDjcaJviZjT09Jjysfd7ls7P81d+UHjhuW8yVusyfX/Kodl1KnnMsdkV5lrQdRm9idOMDfrQyfj/famL9gyu26URJjx4+AVa0RQPjqxxzGTM7o94zlR4LBlnYvwMf/aRiBF/hlhzPnhwjFPnXqS+x3J6/gWCvft59Jued94/Txgss2d6wIWzj7N33widhRVun/LMjF/g1knPoclF9uyBA/vPUJOIh/Z8i1tNQOPIgD1Zm2a8zocf2sdG1CVp1Lml3qEVbHDvnib7x57jyN4FarU+9XyaZ89EzBzsszHZI5paIdvokzPCwdkR/vALL3LvbEwtqXKomvD9706J3TwtE/GDDxsINhisv4zfnxGOniHvp9SzFlEc0at1uG08JNIBD7+nzunKS9jmOhfWU45OnCMUTzT1LHfWxiBfxLqYD81sYGsv8V0PKXfUYHqlzh2PtJmafYG1C02S7homC2lFy/zF7xkjWBplYv8pbp1cYkq7nOy+TKvp6a9/htv3j0B9ldloQBQM+MB9o0zU1iHoc3j0bWjOq4AUMYKpV/LM0Ygtl4eKXg2O0bE666urhdhS2LSXFWwfbWsXGeIvhtld2iFIh7G9122Iunm84sbRzaceRQsnzXNMBD4PCDVBTRUnELqcrLKOyRrYpEFaWwcyUt/g9060iKzyoUMrWF/FeyESR44nRIGEjXCEz5wMmYpD7pt1NAc9+vGAKA8IadKXlFzqJFnG15Yr3L+nx5SeLVqoS4scweQxadglS6aIgw1CzcgDxXjB5iF908SkAbFZJreFQHamSaiL9G2TepbgTRdPk8CHqGyABKADkBE0U4wdkEQxQa7g6pxPHcsLlrsOJEQpZBUhtVmRnOsiCBwiG0jeBA3Jgw7OeDwhcZ6joSHqG5CUTtwk8glCipMGRnoYTYpzA3ITEaURhoRubAnoEw8qOFtB2MD4AGc80CDMPRp0yYMcm8WIpKRSIVClY0aouz5CH3EWH/YIfEyOZRA1qKWdokyNpMX53+AigACZCYqKXt5TNYL17pqG4cXxe1+6n5deWOEn/vI8ahSrGd44jJaG+OuHFuJJLns6CNdzhVi4h4c6tHoQg8OSmYjjyyOcPLPKsUP7UZORhgOitEpDlshcjTyfYbaWI3HK6bUxKlGLjrO8+wC0OwHLgyZZ0uCxpzf4/ocHxHmbF9YmqY72WFue5f37l/GryvqgxYluzPNP1mmOOO44GPDYs+e5bapBPRb2jCZ8+2nL/j0PEgYDTLjOYtbgnsZp1gdz/Lc/UN7/zkPMVjv06xk1lzETdsl7Eb/+jQ4P33+AxISsrQ0YH00YDSbpG2BtFm9rZLUu9cSyt9lhkB7keC/nYN2wPrDYOGUtjZkxOSs9pTZiiGdqvNxTjK6TuCZLp5Y4vG+KBbUcqZ+nGQqhWUN8DTRE6BMmAWIHpD4aVo9Qar6D8QFKiPEKQ8Eap1MY2sQiuLCDV0M1jwmyBj7sYzTEmh7k02AUbxQXZmA8NhsD43DiCDXAy4DQZKBddFghVGmRWIeGHRr9YX9HifAiyNby6sZwsWiBx3tPlmRUqzEexQy1sKtDWJw/hVCl8BZmePHINqyKsKuElmxpVPay17jOAX9OimA+IUex5BLz4tmE3/vKBPUxyx9+acDMMWUla9BZPsDhyT5PPtvg7sPr/OgP5Ly0OMo//9WE8VtvIax1aPV6LPUnMMk8lWAS2+zzXcbz8jnlt05Oc3jPPJ/5zBzvftc459ZeQfyt/3/2/ixIsivN78R+3zl39TU89shYck8kdtTWhdqLVdUbm93cjBxS5HBkpuFIGhvJRjLZvMlMepJeRI1kNqbhiOIMJXJYxuHS7GY3m71UV3V17SigsAOJ3JfI2H13v9s5Rw/XI5FAAV3IBhKVmfAfEBnh2/Xr1+/53+9851uQ+Hlqdp6ivcdzV1oMc/jJjXXavYTTT0V4vTf4py8q1hbm8caOK+YY/+DXbrC1NUd7aPm9Z+vkMkM1HFCPe/z64z6SC/1Kla9/MySzBYPaBk8uprR3CgbZgP1XExZPJFwwM1T9nP/Lr8Z849WIZ/oDTldrvLI7RGcBg6LJY1HIq2I4MxPzo0tbNOrzSN8QHV1Bbvb5wniJf/LMNv/Jry7z8WPQUrsERk1qsSvwUnAe/kSccLoclBgEQauE0q8ZgNe7ZXUrV3ZdxuWlqDkPIQfbBJ2VgSq2tIzFhIgkOCcIAbiyKlVcjIEyl08EPDdZ3Umrbybpkb/554eMFdAUVJWDWAEGlNxZNx6rWVus82pvXDZowWCVxS8CnNxJjZu7zx1047k/eMv07C0Txbv9vpS+BQdaHHEQkJsmw8xSnXU8+YkVRu0hWbvPx4+GfPZ4jc+creAHB7TTBvvpCZYXG6jNlFNLEc18xGMzMZHfJs56OOWjKysk3YKarzDtbbo3DEvNCq0YbCegWelRlTE1m8GeJR718Nr7tMaGqhKOHfepEBAX6zAe0JEqylOsRQXrdcdakFLN+hyttag2ZukaRZ44FuYC4sJyct4nzRK2txcQ1cAFHp/6wizzzZz1OUceBSzOKbIrY2rxOrXxIsuzM6wstagtWfKsT7VpmalZzhxrMTAN1hYcG+sRFzsFq4uKnb1ZvvdDQ27XcUQoxngMKB3Fb1Zh59DCPayDfmtQ3Xa7fOKbFzO5/cImk/8dCoumQGEpg1XKPpZqYjXJW7Yib7mvfH/5uZVjF+fQ1qGcoJwufywoe5gT+x63I4KnfG7NTOCeLT31YPm03jYPfNOTdfeuFI4yGlm5ssm7FUXhyrgkkweIG6KKOjpIyEkgX4a4h1EQGoeSNud6c/yHl2f40iNjjscWHQ/wug1UdZ/EtAjskMjLMCg6SlPPq+QuBX+Io4GTHJEBXrGKtQOc53CFRlyAI8XzLMrAKMoIx4LnLEPfoym7uKJGrmKEgIIc5Rye7qMd2GKWsdJ4ykMVCTYqII/JnUEpQacFvmdJ/AKMj+enhImjo3wCjoLbwtMdCqkQFJahLgidxVLFFDV+fGnIJ876+G7A2CwiboipJnhul6or8IsazsVYcbcE5K58g64UKyveZAJYFi889FPeMyf4OzHxqR7mHgqC2AIRW/pz3+P00DrhT39wnJffGPKf/d0+IiOMLgjyD9fS+sj5tG6tl0yuyG8vs3G3UO7WZAQceBisJOioi1hB+2Xoha8G4F3GiAZTQ9s+WMdDtTGrXx6gLVTSDEuGDi1YRaA6KJWAyhEX05IcT4+J8CfO/gRsCP4A8S5DMVdOhZRBSIGsvGRO2sOHwRjEMEOApY54PiE5hU4InQYKEI2yORJ2qFuNE4OSGGeHGJ2h/W45JZMWgqJKAbpAjMEGjlmTkqubKOmiyPGcAr+gKh6KDFWA9Yc89bE+ceKD5OjqOerDBnoQgbRA5TglWFUuwqu7aDKXqViC8wJyI29aWvewVr0dK4ocDQKeVmhj7jALRBiNxlh7j8U3vAP3hWi922F8xxASJWRZhud5qEmXHLmL34Pc8p6UU9PytkVbsLaGUxlWjxHro2yjfExSjOQUuopSKcpZ6qMK4EhCgzYtTFBgvTG+yUhVBUeIshpP2qTSwLkQdB/lMmAAdg6nMiTooWwATJosoHBSlPW+bAOLR66EKFGYMMdDk/opngmxUuDEQxmfVKdYsYQuJ9c+GrAqnjRQaCFOsFImMXkmwqoc66rk3oDCy/HyjMJP8KwgZkimA3w7wImPIkMkp56V+2VEE2Yxyo1BGxwalMKqHMGgJ7WhHI5bzZ+cTKYvkwZu7nDB5c3bt778W695+5TncIpZ2nG51XRHGbXYI1I+7tYKXLkBeXMv7h3bS0qPbaGExAhFltGIBK2lXBd6r5sRCAIPkbJz/L2cfviefVoOg0NNqpXmKEKs3B7HcRd403QqrYoy+Le0aPBuxb5Z0WQqpFCH8SklH65X621HQnKc7qBdgbIOZS3lNHUEWR0kpxBhFFqScIByA5Rs47sUoy2etRgXodMaCgiyClGR46UeYVIhNBlBEaCLOp4J8F2OV1RQJkC7Am0VnvHBxuV+OYXPAGUd2vVB2qAGuGCLeFzBKyAsMoIiB7F4eQVd1NF5jEgK3gG+HeJZh5IUobTYnBphdZdcpygZ4Bdg7QwOwctqFIToPAAnWAJ07nPoefRtOqmMIATGIaLI/YIkUKTaJ1OA81GU4lWWWJRyldaEWBSoDCQFW8XhlfXaxSLWBxsBQ8Q6HAVCihEh1w7UkEwpcqVwaJTz8GxetoFTZViJVWBVjlOGXMU4JzBZbLlX7JHDhH1FTqANWV6UaWST/pvvfUOOwPPAurdZmPfKJ32TO3DEW0ChnQAGZ6tlRvndNGOQyQmiJmIFDltaL2oEkuEs5K7KS29kJFmEco7I9/AOg0udu8tXxTfdtOq22wZFTowxNXKJySaNFVIvII0zkrCNbwqCwoILwSmca6HzJtHYx/k5ASniqkheB6+DkZRx0KAI22QeiDOAQaxG7ABxCUpGk9NMgQ3K3Egbg61hXYhVEV7aAPFQ2QzOzmOVovAKrDdAHJNp3QDPZFg/RechKo9R1sdIBVxOpj3EeEjRmAiLxkiCSIEmxeox2lGu/lkhMClWZ4jzMTql0I6cBoWeiDkaozTKVPBNRlAYfGsw4jAS4myVwksoVCl6RhcUusC6Cs5EFAhFkGLE4IxP6mcUysMwS64qOBvjbH3ioBZUVkMXAUKKyoOyOYQpaNZCNFBIRKYBU0FcjpgCcWNyCSgkuKtn1J0izuGblNAmzDbi287FO9iGHC5I8KYSlhu/51x67/mzvbnfDgt0+2PS7O7lJR0et7LsiymtB1eUvgbrITYC5+PEIZKwvh4ShPbWvr5lAelDR8CEHGzOcXm3xk4vZlj45KqKNjCQhKv9FoVKCFKDX3gkERReghiHE4POFMpUuTKY5freUbrGK8ugoNFphJ9XALA6K2PFzAJimxgCMDWwFuftoO0ILw/IlGMQGTrVG+zjyFUVJQUWh1EV2sajpxdJVROVV1AkaJfiXIBzTTLVItUeuRTc3D1F0l1CJEW5IZL76Cym3Z1hbFfQDAlyH+cqCA4bZmV7LfEZVMbkHjgbITZEWUFsjJMcg4d1BuX6WGXRhUfPakZFk964YDiaJVUe5H6ZTF0UOBOQew2umx4vbTfYHMbkzFNQp9AZBsPB0IKrYUSTBwMKKY+lU6VF61TZMQiV49kc5YQwd4QmRxeaXrpGWzRGRXg2xTfmHjRANIhPbsDcB36p98N79mndFkKJQzMaF+TGL2Na7uLKXBnQZ5BJj0UnalIc9HDi7Wgf9GjMt1A2/7M296EyzBv83/+4zvU84kjlIb78mX30Xs6XznQp+hv8k99s8Rc+51Mf7aFnBqRBTupXyIsZisFLfG29SVIx/DffLhj6C3z+kceJx+AHV4n1ArOVHg8vG252ZrmyWbB0ZI43rhqyyiIuTTmykLDdaXJqJaDTrmIyix5mDL06P/n+Lr/4tRrtzpClpQEnWgP+ybeqPP7oCnl7nxOri5xsbPK9G3WMKtCygC99xl5AO51ldK3PyMzyyCkY+BrX9TDeIs99+2W++heOMehlnFqC1fkO33rxIebjMYM8I6rNEAz36O0dI623Wduoc+3qJnOLAeNsCeO6fHYj5bWrx3kta7Ds++wmMWsz+3zr2YI0O83RU2P+2iMXKKKMeuKhnSXxhvzotTVeeOPjnHr0HKsVx0yjjufts7cZs7/lsTw/zyOPv0BElRcutKj7TXoCw3zEQlShMxIa3pjt/QHHji7Ta+9g9CqD7S7j2hzObfHLTx4QZz7KyD3lDRYUCOROsbM3ZHWxjnMfdtnnD4876sZziEPT6SeMUwXVd3SHfzDIxKk1WZ2zyqeQEDB4blxOG1E0GzFiinuqhEYUjTm71mT7wirdg21evaS5dF6zuhyyHIfsVbb5pz85QyxL+Crk6FLB1sAniWaojD7FJ2f3UH4BmeOrT+6jR8LvPicce+gIu+0GH2uFnFqO+OELAQMv4JrzOXdlnktbDZ546DwXL/j09DxIj2/9oWblsQG7r8+jKgF9HC/aDf70Jx6fO32D059+A1Vb4tVLC4ht8PLuZf7OVyr8y+8d58zCAZ3gCJF3HpfPcm1niS+esWzurvHSuYjtiqa3bbi0tcTxWpcfPjfPjbah/9Qu83MZ3+4HLCWG3fEC3fOaT88O2dqd4fp14fi4wvM/HPLpL89z7kLE/HyHj69dYsfO8Y9/s8axpYBPfgIWURTVFnu9Fv6WR/+UpaotmCpK9fGcQvt9ls5eY6AjvvvyMdLRsyh/g+Nnx+gZw/e2IhZPx3jZPM+8FLJ2quDVvYha9DH+3ZVvE5ovEkd9Tjxyk2d+dATPZmwlKyy3fCQRNmbqFOo8xtcoqfy8T69bHK6TWzSGAD8uXSql7497bmr3QXCHouUm/yks/oeUsGA5tLcKQga2Qnu7y/GFGk4ZkBw/SMDlaIKyQ9CH+E3dLpO3v6vvBnztkT0W12qseAMGfh/nbxDWPbzwBr/8cU2W32RgFcl+g6efPKBnrjOjb/LD83P4C/tUcsfZoxWQjBONhL//ZUtjyfLjG5YjsUNJjzMnjvPsS9s8dLJJ2L/MyY1lZuuXWVg/yQt7eyxXUn7xq1UO4pjt50d89osZ9ajHcHCJ4+uznFkdgdJ86dQ+AzXmyuvCE2c1s/mAX3x6QCMeM9M8IBwOudrNma15aB/ONN9Aooy1SoNOlPGxhYyFqiGuXGZls8+nTniELuDXnhxjugmvnEt45MQCS2HKseObbI5T5uf3WG1q1k+3sTYhioYEonhktc9/9Td28cKI0CkiNeaJZY+95i4rsymXNy1nVhx5JcO3AV4a8XC9RW04RKKM9NRPaNshs9V59tuWlRVH6O3g8phWJWEmqhFkPR5ZBFt0OHIWEm+Lhj+iazf5+BNNRgNBX9ol9gacPqHo7eQE+QpBNsZ59i77cu8QJzjRoPyyAKZw60L/IHIHwaV/nUwJnnM4PPJHe/z3X4djrQvIXfgC39yinawAKnIJSZyPTUc0ory0tNybMc7qnYMg7iplcKlCnEM5O/FflkF91gSM/JSqGWC0x0D7VNIqHim5Bn8UYwKh8FPCPKdQNRQjMr+Lsgt4dgxunrGMcf4AzynCwoKtoQuH8RIsNcSlKDE469H3a9TsHsr4jKKCWmLIJaYfwfZP5jl2ep/AH1GoEGtjwtSRRZZxlJf5e6ZB0Q+JvQESWYI8IwsSwrSKxcN4BTBGk6Gtg6JBGniEbkCiPYweECYBnosZKyFyCnEFiMHg45SPZ6GgQClT5qBIjrGzoEZ4bkiuYzxrsKJRhZD7Gl/aFCrHmUUwELt9rBdgdYbOGjjnk/ldrFTRZhan+mAStIyxyidIAS8hVZpc1lGyj5OUMF2kCLchWyK0I6zqIBKCtVzY3UBHKcutPtrlhDZFlMO6CorinU+ID9uymSw0WfFInM/WXsr6YoBnR3CHgbHf/f5RXnxtxN//e0OQMcbLCQrvQ42M/0CDS28FmzspByh3liZwp8hb/iply3MZNTIIAaffwdn+87m6OGQi3G9WeyjQ9LMIbSp00gXwPJwyfOdHAY9/cp9q3GVXzVAVIXQ9etQ5GIbUgjqeFZSqYIaaxA6JAkM6CuiNZpmvOa4N5ojCDjtXPc4cncWTXVJd4+YVTSezfPWkz14ekhctXBGTqD5WZ3A8Y6QqfOel46S1fZ4+PiL1hrTbqwxqhu6rIx5/wuPbP9jl4Y8doaISqjJic3eBhbpG8pxARfTHmkB7aAmwXkp/7CjUOpfeGPLJU0vU1AEHwL/60RqPHgtZrLcJ/RzyITrWZEOfWtinM2ox9g3jUcBsrU3DneB7z++z8PBRqnKZ2caQmhvTN3VC02JBMjZtk2tjn7WwhhRdVgKPHEU/00hxnEJfx2NMbjRoHw8f6+ah6IFUKIoKf/wdyxOfWmI27ON7Ac+8sMHppYJjjZAtbx5sQcAMW3pAljf54Y9Czh6LObFyjnpRoE0Z2/V2PXA/p6YWh97fQBnWFzTaZQ/s1BDudHroyuBJJxYwE2f43RYKue3fn77/509ZwuMwJsygseJxs+Pzj765TtgacfVyldnqDGJH7Fws+C8/2WGuv8j/+zstPvEQfPX4iN9+AX50JeDxE0cJRahEG/zr74x49OElelsdWtLh3LU1Hj55nosdj4UTq2x+b5fHP3GMg72I5rzi+k6DZX+bT5wd89/+y2PUWoY0seRBzkzzcXbHQxa18MrL83grAVd7M+j0DS5fq9Ocm6d9+QZrpxbYF8cfv9Qsy9Hkn+LGdh0/vIQyq+hoh8A4GgHcTAKohqT7m9joUXp7z3F0qUqjKfwPv1nwAznKn1zxWIhSQiMsh6u0zTaVcJGluevsnjvKJ7/cY6+7wP/4x1eo1NZJbgw5WzmCdXvsdY5ium22Ri3mil3+q1+33PAu8aedRxhcaTBfX+PvfOI85/YqfP13G6ycWsLmBhmv0OsNETVPNDMiriS89MITnD52E0XCi68FvGotKwsrIFd47plH+FufeYH5Wsj/43cjApY4M+uxa69y5cIsKxvCT36zxd/6jV2eXG6XMXjy1svlz+3sk9JYtYBxlsFwwEytgtJyK47xQePP4Yh35RVF7F2ZFt5vlKeuve22YCyYwjJfWNp7e8w3W4hcohZm1I5H1FXB6GCOYhCydW0ftTHHciQ0PZ9Fm3Hhaopow8GVgPbZiB+fg1/72BxnW3B21UMix4KknH6iglFXcVLn+GwL17/CqVmDl0eMOkPW53tE8yGLizOkZpdiM6NVhSdOblJZKNi86rOxWGdtLWN25oC0l3LjZptCGhw5MmL/5j61mQp2kBA1ChyG7rDPfAxHVwr612YIGnPsXVDU5jZZaQV09/YwiwnzzZCT422WVzXzlZThzgwyzlk6pTHFiFk9YuHYFqutlPauYqbiE83sM4cmKLbwZEQ/m0FlcKzqM1vvU6gKVdWicZDQioY4E9BIC/S4jTY+Oo2YCS2Sx+T+NkdW6kCPR447ZL/DsaVrZDSwcsBcI2A5WiLB8OSTByw1m8S6z/qRPXrXfZ4+6/Psc202HirY6VR4+LMR3W5OsRSj1OSiDWU3aXnrvODD5lZYlVKEcfymi+IBHZ7v2af1i/I3yZXDdwVOoHiszf/nn1uOtW4+kGr+XjlsbCkT35tFU0iAdREub5JLRjcV/KiNdlVUHjET7WDMLJ0gx9dDqmlAYaoMCk0YajKBix2ff/YsLKxtcHnP8cm5i/zlh6HmHTAyNRrpEM/WGFQ7FMUsNZMz0B51U2VcDPjnv+f4K3/VUHMjNCHaDWh7C1TcdSRvIlQR45V5jUaDLii8HJvuMUhiaq0mzmV4ro+1LZQ2GBtgPIdkC2i9xVC1yF3Cy7874AufW0fHHUSPUH6bIlvBuBhFvywVU6xiJIFwH1dUqRYGJw4T9OkNj/HsT+DJpzUtMwSXgNdnX68RmwEGn1B1iGyOkQomnUPrXVKOUFM3GHqWdj7LsF/Ht7sMRxHfe7bPX/0rRwm4SlzAOF0iCq9jbJNCVdDSIUhnsOEOiQ6o2SFRntOtFLjxOlWbkQNJUJDnIVHYJyoywgJEOZxm0ifwTXH4sGtp3Tr/xGHwKJwwGg5p1uJbCd93knD0wPm0bsdhmJ1tEoVDHujJ83tCMKJRk8oAMlnnNBiuJ300moV4jOclSOahK9uIVfj6gKYaYPNyeVoqV6lbH5U3qagujzQX+T9/PmNU6TMu5lmWSxRZnWxUw6+PcP4YJ2NubGnqjYRabYuwaCLSpungP/6NckWpnjiM3ycThe8OuHGg8dIKG0s9UENyIggM7f0azZrGBEdY0iMORiF7acpDNY8sGBG4LqJ82knA869bonpAvbbF8qLiU78qIBfQsouXnSDLfVTQBdtllBuubY+JgyHtvuWTaxEeuyTeHEb1cTYijg946vEKFWVw3gBdWALnqJiEatElVBkZVTy6RLnHgXQoogF+e4dX+nB8VViTLbxKG+f1KZozHPuLVcJ0i0iPSXVCHA0ZGQi9Dj5jIrWJ728jJqDqRuTaMrJHafa3ScMO1zPLUhjTsts4rSjyJgeDORI9YjYIqepO6cu8raT3z2PicctDIyCiqMUVlL1NPB/AoXkHwaVv1kwQ59Pt9klTC5UH8KjcIbcWJCZdW5WzdDuO//qfptTXzvLQvMfqqSHf+YOM3/i1KiuzBd/6tkcWfhLtbRKkM4SNId1+jAoVjzRGfOtcRLRcoEeGZLzNf/qVJi9fCDk/qiJ5k6okfOGzOd9+LeT6+Rke+4WjXNze46uPWR6Z7/Fbz1e4Nsg501iCqM8wO8G17R0eXqzS3h6iG02UsuzbGD3bp/v6TT659nG+d9DjzHpC0U5xexVO/5JGmwKd17BKE6uY16549NwZvvT0Nf7lDxJGtSVm3IjFqMovrSt+/GqFlwdL9I3m5FxOtxPhW48b5zye/HtX2N1v8G++W2Vl4wxbvV2CVp3BzfN87anH+fHmDp87OmChGvPPv1FjoaX45U8XjEeWb517DKnH6GiANGawr1X58RtdznwyZ4kqv3J8yLcvROzUPNxwka29S5xdeISfXGizfsQnbjUZXewyJuT0yYD25QU2Nva40K2wOqu4mlmO6wUeOrHDnzxXZbH1EDevzfD549BY6/Hf/16fo4/Pce2VdT72iX3OvTrml54ecqqRoyShTFD/cH1dh7415XIsQupVCUwbO+mB+CCOzjuOiD9s3JgXgnX6z3zNRwU18WkdThSdc9QrEUeWHDftiMudBS4+v8dmvsbNQZt6dY5nrowY6CWixpjulZDVZUvhHSVr3ODSKxVeuT5gKWsy2q+wXLeMJMebDbnSUTTMAvuDVzCJT9M6bkQ1Lp5zbHUi+p8+wJqAV55bx9Z36akVXjuvWFzv8CfPORY/bekVQ3Z2loljS55EpJsR81Jw+aZhJw4obB0ZdVnN61zcucH6WkYkOYVnUc7nzLEKz7zWoaI9zPAkl7bb5DebnDlR4ZdOPkdcOcGf/OYxVh4fUfW3EQzj8QDnPBKVMvYjXtnbYD+poKMRnbam0VjgmQuO5zp1HjrZo2r7XBxtcG3rJl/6rIdQ47uvxaRehS88LmzttFCdkJguV15fovL4COvvUejTvPKqcHajyQ/OLTO8eoQi3WP9M4rvXexhc8szPwnZ0zHZ/gq7/hXs7BLRjQ77xrG+kRMPW3A+4dpZzbOjmOPbPZorfbRpYdM5bnar7P/4RUa94zze3uNMXSOS4+SnykjcdcpCAg7PGQyWzW7K2WqO1RHavktYxn3Oe/Zp/bL8DTLFJE5LyB7r8Y//ueX47OUHUs3fL4UNeH23Tl+aVL0CPxzR77dYq/WZDYec2wnpsoHy9mBcJapsg6pTeB1apsbYaiSrMSgsUu/xVGuTNKvxRjukMGvUWm3OhEP29x1X+zFHjwg7e5rF1TZLZo/XOkuYSFiop3R7FQKTcaM7S03HFLqDV/GxYykLDnpV3LiPH/iMJKdR8bi8JazFLSrpHgvrlwnzGuAwIlzcnWXU1Ty8FvLisM+Ma9Ae+ejWiMdmLpIX81xqbxBFPQYjSxhEFD1wZsTp9TFW2rx8cIqGCynSLVS9glMpDS/geifgzMoelQK+v7dIY5zx6NEbeC7iXNYgNRXqWYatJvS3YypejjILZLUepxcukKQr3Niv0qqn3LgS4CHoOGf9aI9zuxt4uaY7SohndqnlS9RrN9gfj9moL3GhXXB0vsd8lLB5dZ69SkzfOh7zUqK5Puc2G3gRZFmDamXM3kGdMytXWNJjnGQ44g+1qgiUUYyCQ7mcMSHnux6PNfoUuoJ2d9Y89n7xaU1F6y5QriiVPy6v4FQGkqFUjsubiKSM3CxW6sTeDVQR4sSSezlBClLEJBWDn6WgQ67nwjIOL/Swqo8uYgo/RdsEqxNcsQRYnE5xolAOrAnwTYrCMg4D/CzGs5bUE7oDn1Y1QnMdRYx1IakY+olHVUd4XhfjpUQOdDpLUt2lN2wyY0OCcJucZlkRwo7Y8au0xgsMMZgQWmZAZFJGgUOT4xlBXIKzc+ReQlbUif0OCOgkRKwCz8NqwOWlHZ964I1JogBlBW8cgpcyDvsUbpkgUUT+NsbGiMrxzJBuuMTWvmGjUoFgQJAHKJcwjh1e4ZEmMcOwTi3vUJU2iV4gIIV8hPgFxi4w1iEVdrEieLmHUQFKj1FmTKJDEE2UeuB1sSoid018aePZshyMcxFC/qH6kW45bVzBSCpcGlZ4tLJDLiHeHU4P7xfRuofSPh8cRITERDx/fY6ZSpdM5lH+gGoFtocRxgxJ3CxbWx0eO7OILbp4do60OMAUHo+1fIYm59xmAssrfOu7m/zdpwWVhrx0dZ76rM8bF/f4pUebDLIOXQpU7rHg1/jxjSM8tD7g5e6Yz8xrllWfP7wwT36Q8vATOfuZ5Q//YJcvf+osy4uLdNsZSRjg15u8/INtJIvZ2Fjhxs1LPPKxWfauGRoPrfP9127wS8d8TrmYy6M1BiZhrbbFf7jYojoGF80x2xrx2MaYZQwXNk9xZd/ixzXm432ypErfC/nhizv8wsnTfPzMRVQ14YWXqqhYoSsexdBnrj4gxdAg4vUty3pzme1xj6NhQPeGx8hT9G4G/PqTFbT4YH3EaApn+PpvF/y1z8+R1oVTcwPqacArB4qWnuMPfq/P/EmIsfzKEz43JGRzC07NO67frLO6LLy6nbNSmWW/UKzUNXt7GV7k8UgrITQa43VJ4hDfZfiFxnM9nLgyhcZ5H7qV9SaTLlCiGSYGW7mHihTeBaaidZfoDur8o//pNGurL5PIKaK5A+abR3jhxjaz3QbzDw2x4To/+qNL9LJVjm+sUuxeZH1hgYdnX+Cla0f4xhtnkNfrHG9qGpUDXthu8G8uniV99TL9V76ATg74ydVjVJtdsqjFTG/ApUD48cE8P77ZY/1rBzS8Kv/0ew+xuH7Ai9+qcKO3x7hzGvPcInHtEqP9FfaCLhW/ztogQlohm70NXtgRvvvNKo8/1OX8f6gRLT2EsjcZSZWv/5Hm1Z2A//WvrnL55QqrDxdsyjLRFUeRwVePC//f/wAX5AnCRp1TzRfJ9zNW1o5zbT9nwS4xwx7xqRrfvrTE4sYRrp9/hab6JDZ5gaAR4XYrmFnhOwc1dvw2X1rzuPyGIZmDmVrAp5Mxs/U+nglwSgCNeHW+/2yVK5HPF48t8OXTN3nu8jJpT7GjAqJ8gZ4pCwJ+81nLgf0kw/Qn/NvvH+cznwj4D68OOBkv8Pr+Jh97eIHN5y6zshiw+mvCPGM8q7DOgZnHSo5yqiwHrQrUpEzzoc/3w6RMXhMQDy/wyjAMkQc2/3AqWneJyMt56uwVqosd4nHOjfY2wXzOI6tdtB6wXu/Svql4fLnCTjaiEm+yz5hPrF8h1gknj2zy6ot9FucfIu3sECaaJdvjqZlNbLRDN1kikddYWF2iQRu/vsfcEYh2xizVl8m2tmhW91FeyKdrr1J3XR57/DQXdnJcmrCz+X3OrgaY+YRxFmKyDotLHeormjTfAudRa44woyGPPZSx285ZDjs04w5f+OwMT+wNObbQ4rXqdZ6aW6F5cIFgVVB2C9+E/OKXoLF7nqrymQ23WDnaYOu1Z/i1T3dp+Zq5Ro9mMORsS1HN91lcGtO++Qpeo0tdDzh2dpEfb13msY8t8vqlPTbmG1y7nvCVh6ts3hjwxvmcjz+h0V4bSWN8qzh+5oD6aJbl+gG1KCYJMhbcAZYGxhszLzP4uoNYw2eO1vnOuR9Qr+SsuDbhwOOztSGnVnK+8NSYRv085w8K8IXvfWfI5x9tslgFryjTsUxkyZSgUGhTQcjLctc2+lDPszLMQpVVW61jsNshr1fLqrDyYIYjTX1adwnnfBJpkuicWlo2GUi8CG0yHFFZp93maEY4lZHbRUQnBBxQmAZKJYgJGMcFlbGl8Mfk0kDZmMLfxxTHsLpDLYnRKsG5MaIGGGbRxgIeog5QNmKs5vBpkwUFqS7wrcUvBGUVRscYG6HUCKX6iFWIaWJpYrzrJDoGKYhyn4AOQe7RCwyhKXv+9XUFrQRTxFRdH23KksvDsI5jTJhHiJcyUCFhXsH3r5LTxDlLZDWJ0oRFwTgcoswcjgJUSuBycrOEqD5W54wKOMjrHPe3ywqnNkR5HXQ6i2c8imDEKPARC56xaGKUS8ioUngOIwnaePgqQxcDrGiG3gZeMSCwIc46sjAnF0fd9HEmLAsJegE7nT7z9RFNGaDzCJSHUQOsq+GRoixYiXBqPOmr+GGOCEdZAjpgpHw6ScBsmFFxAxwed1KceOrT+ohjjM/zr8K+neHscWjO3iQZeSz6bbQNeOnFWdaeHGBGNTxP098Kma8Zisoc37iheGoj5Jht0+sv0S5qfPf8Lrao8MuPjPjTl2cYJgVnlxucqu2zVcwwF0bUdJ+BtbhEI3FMnhzDmoK+8mn6MaMkxA9zRoOYuWhAZ1hHvBqZGxHWMky+gctaWG8L3wl5uMoPn0+or81ztL6Nbx+iFfQYjS3d8RwrQZubnTE3ByHnC8VvrC+SFxvsmQ56f8xT8wW9QmOTBn9ysUmrcsBnN+boGw/racaZJdN1aiqlm1ZQRYWaN2JgPGwlpNKtUhQ1ZmeukeJhipR+9jiFus5sbBnly/STkLlan8Aq/LFmpGJSv89MYhgpYWvc4sWXuqwdOcvG2iXyBJQcx3djfvzGkE+ueHQCj2XpEbLNYHSSaupzfheilmWj2WVrU7MdhZxcU9R8Ic012fARqipnJr4MMsIR4wjfktL1oSEGIUOLI9AKpQ2uMDyow/vB/FT3AEnm8Vvf75KtNfmdV5uceHyOc6/6/M0vv87HFsf822/CUfcol1/WLK9vk+c19pzH2tihFxUnZlL6zQH/3e8rzOCAzKzwq19KkHGPf//HLSpzIc9+07H88WW+e7nF//Lpc3xm4xT/+JsJ1YU5bmzlxEGfMKtwc0d4/OgsV7s7zFVWuXRhidMP7fCN5yyf+5zHsO3QPILVN8hGMZ1uC+xRdOWAh49atq4pXuue4iebYz7+CLz+4gxLR17jf/tlYWZmmatdw6vnTzK+cpmt/ZjFjTEbiebMl2P+0Ws1wtGQa9tL/OVfVAy8bf7hvwnw5+dohh0uXVrhiUfbvHa9QZL2eeT0UXo3XsFbqnHhfELsFP+HvzpPtxC+dzFl52CO0bDDf/7LAS9etPzb71T4u192nDnm8a/+2NEbH6PSfJ3PrJ/gR9f3aFePYbJzfOvlmL/qHSPrH/Cnr8wyowfkR0+Tb1/k29cD/pMnM45lR/j6b2fUwmPsuR3+0lfrJHaTZ15ssHB2ht5mmyvPLTGqXOX65TmeOHWFv/GVWSp5CH4K9sMfTmVUfog4BdbR2e1SX26ACyjD5D/0XbrrPHAdpu8VwgBOL9WYNTWOtzxUr4lkDQplcEHO2qMhaZpTmVPEVSGcgYFq8PIVYbCrGLYVOQFho4K/HDJbc7SCHVTkWFmJqNseX3hilqEacuZ0wpGZAk91qM/3cfMF8apHHnpUa0d5/KkWx045Fo5FnH58xNyZHWZPJfhnaoyqXeK5WWwQkSkPv5FRW+5hWwodh4w6B5ycdcS+YmmjwcjOcX73KUx9iSJWxFLgJXvUbZuiGbLLmIVqld1um05oUJGjWW9wNEhYMwMqMuDs+gq16irbN5fom02CBsytVqiu5OznIa25iJOrVXrRPHkMhT8kDAWcYtsO6LoAcAyyHAkWSIYpJmgTrI+JllIWN2I2k4DNjmaQ9ajPZpx8bEBj1pAmCf2iRTifk9sDUi9jYSmhUt2l0BWyhRW2qnUef8hjqXIB6zmqtYIj8wGBNyQt9lk5cgyvEuIvxLx8bURq6ziX3SoJ/qHiFM55Zbcl0czNzKDE40G2R6Y+rbuERdPJK/SYoSr7iBewN66xHLapOUtHZaTSoBgL9ZqQJ45cQeEsxlkaXsasZLRtkwGKMFM0ohEq6DFMazBqQpgyjvu4XpNlXaC8AZ2gIBm3IKzhTEKQQy5DGmLYcTWWgxGdYkxTxbxSrFJJ+wx3HSdOW8gttihrpQ0kJFAZZqyYq/oYdcA4Dygs/Hf//hRL1TZ/7yt9KrLDyMbsuipON0nyEateRh9Fyz8gsQHOVHEOZtwI5e0zMouMcehkBRPfJLY+bafAOYwWZvUYXcBVc4TZ4DJzeBTi6KdV2qpKSM6GdLmpPfKsyZK/je9l9GydUdbAC/soFZCPM7Sex3gdKvRRUmc4MPzuc0v86me3KGzMrFV0yFnWfYSIbdOgUDnLakxohuR+TnvcIgoq2GKIkxjPz+mnHmnQwc9qLPo5PiMUkxZ2H3qclkE5i1WOTJoocnyXgJuUXX6PTH1aH3FGxucPL84iqcdsI2B5tsrSwogrV32OLqdUxaOV97CRx0AJLc8gSuGcQvQAKUIK8Zl1+8yI4roK6HSPMDOjqakDdGjJw4DYxvjRAL8o+1LW82Wa/i7OjcBV2PF6DIcNlquWdQoKFzPjK6K8z6rqMTYNZPEAFwxpZVAEgu8cLacwMqZoWlReQyvB9wVlLf/737iCR45WKWM3g/KarLCHNiGp32Nzb5nMV5g4Ys7LCPSQ3FURNQLTZHO4RCxtQjJmnSA2Im3sk+3UWW30cc7hVIWTwS5eHoOFSAQvGlERQZEyLmowFtbDXRSK3NWomjlq/i4qq2KDhFElpjLKUM6QK2EriVkP9vnrX9yi0I6lbAT5DFE4IlcRduzYvRqBt8X6cTA00GaPoCJc2c053lL4akRoc+pRH6PKEsfKZqVg/RxCDMoWeTk4jcEnt4pILGIL3B1WLr1fmIrWXcIZw7jTYPPyVVZOtfgn317kL35sxCvjKl8eb3Ph8hwfO32NhzY6/Pb3W6TFBgu1MTe3HcdP9Om1YWAD/vLHr3Jx6wi//aLmTHORTuBzasFx/rUzHFndYpQ0iWSXL31M8cJrXXbzZQqp0E33WImPc3Vnj5W0xXOtTVJ/jnEv4ezKHp85XeE7z1ueG0SE/QWOb6wRbe4wjC1/+/Ob3Nib549fDnn8RIXvPjvmyU/N8/qVEVEtILYNqvUOuzsNMnOTOK8SLHuwWUXN1njtuQ5Hn6zQaqxz4fxN4pXTuH3HZ57IOduy/OM/cCzMLZFtV3nyEyH9vQpps0lykPB3PhdxvZvzzMsV6ms19m60ObXi8bnjl9nutrjRjrh4YQ5/MaN74Hjk6DIXboxZXm1z6ZrhRKPBdr/KscU5Xj3YZCWucanfIFhY4Y1rl/k/fqlJ0tf8q+9WmA1GhF5Oa9Xn2uZRDno3KPqrZNUt9vurHNiEqLrMKM340Q99/su/2WE93gYVgKngWYDs8Bv/ueiDE8ERYiVk7DRvXBvy8NEaHjkPomDBVLTuGrUg46kTHW5sC587bXnmeo03ru5iK0u05SbP30yoLCnOSATZIv/u+46vPb3Ity9d4eL+An415vpWwa88uc8rgyEz4UN0R4oDajx75ThXb66zNdyi29ngY4/XGatr7GSWV3+0QC+aJThi+YM/qfClX3iCbrfLQTYkatZwPc3KsQT0DZbXlhleWSXGcm6/j91rsHT8NAl/xIV0nj9+1pDJKjdG22RvHOGl7oD1tItpz1Nt5fR3N2hWxzST43z7xi6zvWXWH71IUTRp95rsHmQcZIbr2yucTNtcbPs8upAxM6vYz4esNtf5rZ+08faaLBwZMz/bwtgr7PWqfPN781Q2zrC8/qfsvuTx5KkqVw7m+OHrY0a9xxiOf8Li3CzPXipQrQXGO5s068fYHva4FCyzs1OhUi3YjVY4f3WH7EBzeqWCC/bwsiWCMOZGdoJ8oMgPfkJ98ShSvUkkITr2+PH5I5jlAeevR6zNZvSqbQaqTE52eozY4EMObXhnHGUkvEVjRJEWjlw0gQpQrrgH9vCDZ+rTuks45+hkITd3Q1bmQ15t16DfYSer8fB6wTATZioDNmoH9MczbCZz1OMuP7lY0PDr9IZDiBS/empI11S5dN6xslzlerqHrsZU0gVCtUVGDV2MOLm0S7/w2b80z8WsgQ16xKEiVfscDefIVEJN5dhkldn1LeqZYZDXeW08oGnq4EZkQZXUBjw5s8fuoMYLlzQPLcP2IKE5u8iNvMNGYAnGQubG6KiFrzr4ic93dqokmzFf/PwO6f4KI+kStyJGXYuu+MS2TaM6oho59rrzjLIeDW+BTZWyu5ny6OKYQvocD2v0rOKlSyuEswWLs+dxe2usHelzoxewPwoJvTpe9SYm1cxGllHRoKr2qegUIzUu9DLmZmL8NGKQh3z/5R0+8fFlgmKbU7UBogKujVPe2D9CbxywNn9Ap6d4bLmJGQUUxVVGzLM/0Ki4zlA67O07fuVsTksSlMqB7K71+7wTJs31AEUmQjcJacQQmgGC5k528n7xaU1F627hBKwiDQAb0kuEHf84kb6CX/RZVY6kiMk9R40RxgMpanjjFoPmTcK8hfHGaDNA2yqZB4UUFHmTrt/jaLaP0TMYV0VLF0yEdjHbWiNKiHSPqFflRlWzlmbggS7GZDZi4GfMeA4/9ynCa6jsGE6liEvxii5W++ReDZ3XKfx9fKswuuz+7GxIYEs/SuZZcvHQTpHaJkE+IMLgfLCqi9g5dKGwKgUZMTbLHGiNN+gR1BSRs/iBgaKKOA8THoAtQGsyBSYJ8VNNVSzaDTCRR+oa+GqEoosxSxjJ0ZPO1EaFBIzB9RjQohgtkqoOLV1gfYhdglMJiTQIbUahFDfSJoGGJiG9ATR1ilfvYXUX34Z4SZU00GReTnOclw2BlYdT3JEg3D0cQgbW4RRk0kCT4dkEJwHT4NIp7xkLbI9r7I6X8XXKC6+2GSxrgu4CBy8l/C/+luabL1e5tLPI5z814nIPjtcKHqve4CddTb09j646XjkoeHi+wusHe8zNzNPuWZKBcORszPObVQYdj8WjlhsHa6zOZPzO1Yinm9s8OV/h918KeGZ4lEdPHrBYEQ72ChbEsXt1l7/8S11udlb47v5XWF9KuHA14+mNlLWW4WJ/iee2WxyrWnZ0izgVUi/leNzihX2f+cZNZoolnH+TblInTavYOGFRN2gnAWtLXbavPsxC/QYnZwueOVigItA/SLmc1th7o8Kjpy2SGj7/2T261wpe2dxgbn6Wq+2Y2fUBwwNDY+YI21vXWFsd8vEFx+X9GrtZlX57ncW1Hle3Kqy2RuhuyF6lj81nsJ7hlxcuc+3GPH/6YkFjvclj8ym77giN+CahL1wZzfN0a4uqDPgXX4859WlYmJ3ld36nS31myFe+9jiXt/o8tJZw/YZmpl5he8/wufUhR6q74PXRJrgnLtZlPS2NEjtJ3hYMCi13tnJ4PzEVrbtETsi/eXaWb74Q8rXHhYNRzKn1iL0iZCcxJG6XPe3T3l/jf/qDN3g+X+NXTlzh4U/WeeG5kPYLK4wXe5zfjvn0L1T51vURC1mN2RMeZ6odxl7AN17b4KUXejzx5BAGq/zL7Yv4c8t88uGccLFDnkcM9y1vVHx+98Yynet7PH1mlhkXkNoGr1/q8YNXF/l6esBTJ+ZIBj/iNz5/gm99W7je3ODbV7s8dnaL85ePMFu7zLPZBj8cZfzCUsxBr06r6DOyG+T9y8wtrnJldI3UNvlLnyl45nmP3/hiQk97/NH3YLRb4xNPz9AeVCj0iO1CkWzC027EhRs+P3wxp3Ys4Ju/H/GlXwnp9/aozWgu3TzC6aLL2cUtnn8Jrg4N2wd1/PMORw2d5LjGCU6sbPL9Z3rEi8f53C8OyGyTA6nS8m7y3LbiUn+Z8X5O0jzCQn6VtTXF2sM+4+U10K9zPjvF4gnN7qbl977V5PX+DI/eHHHhXI+1pVn64w6bF0b8F79SwVMOyH/ep9ibOD2pNOEmDviyN+mDKVlT0bpreJLzsdMZnaHwqYc7XL5e0Myv43kRNC2u0ByfGbLvrvALj8wyawY8PDdG6RGfPRnxo/0bhEcM69WEMwtthv2CzxwTfvx6h5O1kFAuMtcMObYxx3qrxtzRbRaOBbx+fkx9cUAWWMKozUNLTZbnchZyYWgLHlmwbCYdMjWksSp8Ubf5bH2fG1c2+dKZkJp+ns9//BjfuHSTY0fmqfQVTxwZsNLqMkpuwrVdfvmhiGs7l6iYOq9v9lg64mj6WzwylzLq7HFWcga1V3l8LiTQjr/2+Q6d3YKFJcf+RcWKJ9TSnIUTPlGumK8VfPVTBW6mwA2EEwse8YkRw17K8tKYceeAMI843nJUKhmnFlNSOwA3ohkP6avLHJvNWfk85MVNQn+fpTV4yBQsxX3SrEJ/vIvfSpGFPY6RMhs7QhNzvLrJESVkw4uEYZ+zj7ZQtZTo6hZffLLGMdnjyPwSl6+mzPuWAMCae8aIkbf8K+hbAa73yA7eBaY+rbuFBUvBMPCJM4WTAK1vYIuHEMaIHjCmiu/GFL6gswgPW3ZYdgvk4QGuCAidT+4PSYipppokzhBnqA/HpKHDygJBliEqwSrF2POI8yFeHmI9yH2LZ3o4Nwe2wAQpFAFKDdHZPMbLQAzaagocWg9IzRFM0EWZGQIjZP6YwN0kp8pQhcQAyQyBtwtKoQtHLj792BIUVapDIY0UkTvAisLio11OgUehQwKbIlbj/F3AR1wEzmLJca6G5xxSaPAHGNvESYFvh1iJMdrhnEarnML6ACjJyFWlbBhiBvg2AdMkDzQeXcRApiugDbmX4RUevstw1qeQGL/wcU6wqosWwVmN0wWonFRF+FisdXgS4uWm9PHIh995524z9Wl95BGycYPcRuTKktuIhhmSmAwRHxP4mP4MmVelklry8Zj5xoBhOkunH6JXhKJfJcZjLD6xAb8yJMsN2/0qSXSEYbdLXAtJRw2qlX26PZ8wgn5SQwUF/ZHGhSGXz1V4dH6JubUbaNcj92vstFsYYwgDRT+NkRlFkipktMi//vYcf/nzTebDIc7lqKSChE9hGPIvvzPP17405MI1WPJHNOdiKtqxEhuuvGqpVDUzusK4qKAkg8iyrMcEDNFmsWwtjwMLhgUK5XBO4bsCTzJSv4Mt5hClEDSie4itYFUVJykKUORIHpTpKjJAj3xcbYRyEKQaEwvGH2C9jMxU8W2ARmGtojYyWM9hqCMYItPHqBqIAa0onEMLeM4Dm08EMAIXYHVB7hm0s2Wu3wMnW/cHU9G6SySuzj/4bY9OReMtfILOsM3nVlf47mvXCItV1tbGvPj6ArX5bap6jnwc8b/5G8/y4ksBv/tcg6NrmlfPV1loHcGMXuEvPpryxU96/OiFFX77XJ+N4w1uXF+npq5zcWuVL3y84KWrM5yar/Dc8yPOnJ2lP7jMYDTPZq/Ff/4Zw8JygNIzuCLgv/3dk4x1n3F3g0Z+Ae9El+6VGY4+ZBkmc/y/fsunsrBM0rvM0Ypiq2hgqzHFzgbFt68zcyRm7B/wp//iBPNHtvjf/cUxz7yWEa22+N4fK2jUaTVOELda/K++coFAXcWpAV5WK+OcvD7imijVKdNNrIegiDIPjAUc2BhUAhQUyiGSI5Ok5LLsSkYhVRQBzvUoJCRQPoUovEII0gp5kKOkhxiH0yFOBWAFpTKcOKx2wAhBUCaAw96FruxdqazFBAPUpNSLlQjfaJCfQ57hFODPI1qTNlnygLbc/sBwYC0ErHHpWko/0ewxg+gBEav0r/2YucUKtdkWrjPHpfEWtlOl2iuoV+ssVz3yJzL8akLnmuPMowb8EZUKNGbAxJZKS7OkxiQNCKgxvw7N+oD5xxNWT1XpX4rJR4a1UwmpbvMHP8748icdoiyrJ3rsDsd4QZ/PP5WwPXLc7C9ydOECym5SK2DmaJ2b53L+0hcX+fr3R+wmIc3jNzHxEJM0IFrg009us9jShK7HmRNLvD4sWDizgr/QRhUj9FyFH7/c5quPzIDXx9N7ODcDOJRLUIWi7Naswc6AGJw3worgXIjglcJlQ3BBuVqmTBmiIRlGLMYzaAu5P8IGGqNzRAy6mCPzxmANflbFOZ/CS/HyGG37OFcn0zUCmwAeYssaoE6nGC/BEqAzTSEe2mk8q/AoyqntezS03t33Uhboe79j6Gf5dh7EMXoHPq2/SaZyAhvhZET+SM4//nrKxtzVB/LAvDPv9kl/+hBapzgYhFhVIUUxTnJmI03qMjxilOuj45DMWDwX0MtzlmuOa9ct7STk4VOawiVlRUpjaOgxvkopiOhmNfxASBOfStAnEY/A5OQqxHcpuVYMbcgrr3b52CNHCNnDc7C1n7K6rFBoXrk0pLUwRy0QfJ2AhmJYoRoOSWw5CUMFuDyl6htGJia3ISqwjFVCYBS9YZWLFzZ55MkGy+oAY6qkTlPYCuJl2CIH36NSjIm0BXIUGbjwbcetDI4sr6EON+kfWLZlUAgF8pZ2dW7S9d1iRVCWsomIWJQra7Y756OsR+FlQIFyk8+EQdnSSoMAK2VnaHuriovDYctrs1Noq249Jq7sIX4njVAdh2EJb96XZRmBH6A+gL6EDrCiJr03D4+nwk36b04bW1B+AVbASPn7o9dh+u2f9Z01X4llvj4GxuUd1dsf7U9+j299A7ORw4rPsfUaxzAo1cFzadmtxj/sOikEpCwEafmiSWXfOky2M6AcdEJNhyw8HqDcFqFLQQsnlnTp8LYFj2/4IL3SP4NDDBCNAIhv14eg/BV641IrrIdjiNWOudoS64/Pgj5AO/BlTCSAGpQv8iaH59b2BAj56VF/+AQzuVfd9uhE0N6xfXPZeejNDs/qzd/O4VSOsm5iz5hJgb7DqZ0HWNTkiKnbNi+o29vcvBmeKe9HZCaSaC2mMBB8cDXclZtMp29hkA+5/+KHyR01a4UyQdNI2QreqAc3gO1ncWigHvYPeL/B0YJDXIa2CSIONanPJCI4J7jbTkp5lzdzZQc8AAI7Qk26D1vR4MouxDiHkrJvjLjyc4jcSVk1hzhBW4O2AzyncPkQUT6gbjsu735AHIBzb3mOe9vtd33t2wa6yJvWzK3obwERM6mAUP4cHr53GsvlAH+n93bv6bj/LEorbbI9UVQrFay1H8jQKT+jmSROq8mlzYJjWuXBMenlZ8va4p5TaPfR9OM79+ap7D4g357g0JhJmRGLszkowd7xFVMmoYUObE45zVO3Hjkc3m8Kx3sXLCdgROMkLjvPqENRuK2Zw0TF30m8Dm3yW+/PW0XovQje7e/xlq2KwiAgGnEG7cwtr5FF3fr7nbcskwvPockGzr71i/1zC9bbjCmh/Jz6LUfhfVBeeSaJ0x44i8ZOup4/mDU+37Pq2MkBVoB2imyQUvRTZIYHUcx/JmmaIQhB6PNBTJEdTKrjTnwTCgoEJ6qcZVn7HgZOedUVwIqeXHllImMToZXbuvPJn8cRXC73l8ZJATicC0q/E+5dzc5b+/42i0pu25/3OozdobXo7K3tGedo93MsPnMND3Gl9VEgWFGgVemQf1vXZcfk3NYezr15jMUrny8TcXyvluCfzZtBoBxaf+977JTniBGfXHyyLCP2FMEDPCbfsxS70t5EUV4l3CAl6yXcK4p1y+F568pqAVOaB7f7KD+Qd4IwDAhCf3Ii//QxcEBaaFITMTZVUhOAVThnMWKxUvohHAZcAZMpt5XDJX1NQUQviRkOBZRfCpHckrXytVaw6MnnLacGDkiI6Jg5Bq5SXu2dTOwvNZnal4PZ3fLqlH7KoamUnh4nFHjk+KROYcVMvGUZihxtJtNOirKjNRo7GUCCA6MYJDCeuN8S4xiPNQhYZ0t7zyqMCIXy6Y4UiSn3LRddBnuKxWEoRJGLj5NylbEgYpgVWAxOlZKsRKhVI+K6T2oirI0wBBgXsDcSRi5kpy+Yw/3k8JhAoRUZjm7qk2vF/iAvXSDA2Aip9XHilYI8sbLv9HQS3Nt+PjgsGivlz0FviHHlKH1A2x7egaWlLMqGGBIKDPE4QOjj3pdz8oPFOYXF0XGLaHedepDhZRVEypicD+Adbv116zPfWrV561GwoviTixWu7S5xqev49FmPM+E+jZmUkeRAwpEoZpin7CUhWnysihj3Q87O7mJtxLntdX7nGeF4o8sjT1W4udNnY9WjGAyJKzEu6FDtz3NpVGH9yDUGPZ9QLAtLwvdfHbDXe5rUvMBnT83jG01ghKX5a1zOmrjUEEUx4jJcVjA0LXTU5be/PcN/9otbBFnAtw7mqIUNbuy9xCfXq2TFPOM8o2cHLPMof7K3w2dP7HM6KHhmq0bVy4i8GDUe0wwX+JPNhI1WhxNH4MXtgP3nm3zsiwOCLGdEnagwnM8NHYHe5UVOHDsgGXVpLM6x0k9QjR4r9QNe7y1xZcfyxZMp2XCWb7xq2TheYSMecDOZpZKMqTUKRLXYURV++OMhXzjTIlMH+HnIN17c4gsff5x//8OX+fXPH2EmukzVejT8nHOjCgM0YQ6/8+0lfvUXb3LxWpVPrXdJjMdL+xryKp8+Zln2LiJYjArQ76gI7766fLf043CZwbM5HgVH56u3nZMPJnfklConGuWVXN9jqxPiHMr1KUKfy5sxaVLl7IkF5twW6AKo8EFZhT89S3gnS0sR1GZ549V1stCn4yz/zQ9DjoUeoU0p/DZ/51cyrnRW+b/+ScjDM5qWP6CyUGV1YYuDrMLXv1OjW6wzLL7P9XOrPPu9HR46e5K5udeIGrP0zRLVkea75z0+/rhPt3OcyL3C358ZofGxyufFqyv84dYpGv3nWViu8198WfGtF5dxxZi+v0K4dJEF2+R3v7nK3/tcjf7WHkPls6NS/tl3PTaOzhHNPMr57xZkqsFcYrjoZ6h+i6vJJjNqntMntvlXzz6BJ+fQqUHNPUKWbVOvPULzyA/41s4iN7dCtjc13/jmMk8utDkwFRbii1wYfZxqY5tY1dl+aYxEZ2if32L9YBnUDf7nfztjZ2uBc1vwyFKFqKZItOFGu4XLfX7/smL/co9jjwu+2acbrLE7yFgZrPLcGzm1xkl64x7/v3MrBMlVfvMHa3zh1A2OzBmiOOWbN+rcuLbKmSXN5cFp/uC7GXt9j9QMsZUnuT7ss7mboPxdvnaqjlNjIlPw7oFab7vvA7lYvjty6/dEqNxtj9xbQ/QD44HxpDuBQoc4G3OkOUTHQqVIyIIQzwWI+3DrTCos8+YGXzy9iNMHVHzL5dBQbSzRHtd4fF2o8yrH5g7466dmEG+G5dqIzXZC1RgIhY+t7mLtAK/VoRZdZuXzTZrz1+juD9ho5uztDgk3FL/qV1hxGTerPpXZPSpScGwuJe1f4i+cSen3z7F0Wjjw96gon0cWtwm1ZctVOSjGzEnCL3+sxlx0lac+UyfHUVOa/+gTPjY4T5oaVh+usDl+g6yzgvWus3oGTreFRxttch3xidkDqhWDHUVIbYd6XVMkF3i4osnDm+zkDu+X1nhhO+PYkQushSEzXsTryet02gWVmW3G1To3+1dY3Vhmv7PHZz49RhPQ9Pc52fLIxz3mGxnLVcNsXREHKV85ukOvukJlococHtvZK1ROK6rha6SzAYW6xPpaQLzwOqo2YGFpCzlosN68hu9qfOFkl9dUhYc3BiiXMm87jOMax5YqXLp5gUcblhNhlXV/SJxbnPjoSWjG4Qn19gWEd1odnfLB8Z6DS7+i/yrKRggJDoeuGP5vv7nHw4/t3ROC7gDrApSxpFFGWIwQo8j0LIEtSqfxh7ijFijQGOWDtMlllmBk2NZNvvPGmM88HrMx7oDKSaQGXoHTQ6ypUst8xmEb30YkLiIuHGjIVYCQkbJA4HYRq3CeIxqH5GIoojG5jKnaGMMQI8sgXcLcx7N7jPUS1tbR/g2wQemsVqWvLfX71MYh46BBXIwo9AhtqhgNnjEIQ4wXs7W7iN+4SbPiGDNLxXZxjClUgLgKYRGgXEbuJ4hTBBnkuou2dYznIJ9B6YJC9ZFikXGQY/BopV3GegFHQd+NOOi3Odny8LIq6BG5qqJJ8HKNVYKTDKMFP7cUskwebBLkNZw/JkhirEowCnJVh6KKkn2UThi7AJEYq7uEwyM4v0sWDAicoW1P07I3ifMY4+UoN6LQDl00EElQDFGFh/VKR3q5iDkZPhPfprMOUeo20bp/pmoPbHDpvYo48EwOjLASkauY0CoCmyDklLlqH97RL52tZRCDlSbKxXi2x1J4k7/2UJMLm4pXd2ZZO6F45lKXv3AaMinz3VKEbl6jVtSx0sd6OYXEKJtwabvPxrLw/HnD8nJItX6DiBbjmsKkMT6asYnRDowkZKpBag2iZjnfCeiN52j5N1leihklPqvBLlvDgDfSJZ5uafJBRh54fOeNGZarM7SWE44EHTrJWUTayPyIUHlEacalLY/9+hEqu12OH1d0TI1F6VBTPQauxWDcQAcFw0Dj2RYvvr7DE/WYI4s3GdhZUufxxqsZXrjDp48bXriQ04wd4UJA1ljj2R+lPHI2oFZpM6JKamfwfY9KscPYzjAoLPN+yiizjA6q9MKMtVmPg4rBH7cYMmLZbDE0s/y7Hzm++uQ8v/V6jUeXY55/LWWhcIz9OWLf8MTpJn/6Rs4XPu5zOm7jlI8uDFLUUVbIfMg1BCrFoVGT9lxvX1UsE715/4F7U96VD0i03NtuHS7tfng4gcJ3aFvHtyleFmPRjOMRQVE6Tj/M/TGiePYqfOfyaXQcMcMOnWyDY/UBYgKujhsMTU74qkd4cIlPPFzjt77fo6c3OFJt84NLp1heHqH3VwiW++RpQaW6yKDfJ3llA9e/zNz2PK+YGf7ThwtWwl3+h392lNqpMZe74Kt1mB+ydWGBT0cXyOdOY2pbYGs0+4tcu1Dht67m/J9+fQ2de/zRj4/yytwFrmw+iYuH+HGFpZ5jeGGPJxbgB89HLM1t8IIa8T87k/EXVrtc3q3Rrxj2t+u8tC18e/Msnzp7gf/4qU2ef/VR/vWFeRZmr2C3ZzEzZ7nRXeLU8R06yz7/6IeLjIsEu3OSv/1rVymKNkkScz1o8uw3UsYc42T+A+ZOhOjaHP/iT33S4Ayt2ctUiwajwRNc6F3ii5+sce7cHgtxg+3acZKXbzLTcgRJwUx1ltWNDloCnh8/xlODfd4YC90f5nzp8TNc30o4cjxk86bQnLUoVWVcDFA4lClDJpS6iVW1cqAUGrEemrx0aXEoWApj4OBgwPxCE5ydRM//LAvLvfnrcKp5h2Pno5h7+L6jz9ytf+3k5+eHcoAYPKMQnSM6ISx4l5Weu4t2joZf44ULIW6gGQ/69CRhO/f5o590ccMclTnaeyOK0RDPWXa2NFsvGpJxwJrf4+ElS3vTQ/Kc4b5hac6jt91nsD0gbo4wntDIoaimaAxeNuCpJ4WKKhj6BrsvLBz0mF1vce61HjsXoW4OMLYgRXMkc/TaFi0Zw/1dxsOQoOcwqSU0l6jWM0b70M1C/GSXTzwuhDtCoC1F0Ke2mJJuCo8tdqnPCiE9Go0xxlNEXsTIjGh4gg5CdA617h5ZTYgKhRvk1I2wEe8R2y3yoIeMLLqvCegwvnqThROK4V6CcgWNmRrb5wM6PeG1nSYXDir0R5b+0FHRmtX1kBPBTepRm1NzYwpV8MhcgfIVAZa6XKAe9WnmHY4vDVFssnhcWM0vs9TsUQ16/MLaS5xtJAgKTYZgwFUmEe2CclJOCdUkyVtK35ZzPraoUuQz2CLCocvHpCif9y7SIrfS4Nz7Sul5ezDFBx9UcW/xgfi03v6lyDv89ZHDQlHUuepVqQQ9gn6LfhESzF5jt7tAS1lEKYZAzeyxVDvgG9/3WNtYZ3FRk6UZtcaI7u4SUa0LTvCiPsP2CkoC8LvYsEVv6HOk8Sr1PKfdeQi9cIMkrbJPSMU6aqOIoLnHwXgVIxk1b4RnxzgXk6QR880uuCGXsxVmgx384RI9CgK/T6QbpIOMWpxix1BrGG5kTWpBm1ZwwIiY1MX4eQcdGrrtM9R0QsO/QeGW2FQ5vudIcwV+BdIx8zqjogp28xm0NgQ2xI8vYpVjNFzGV46Bc6TjDeLmNeIc4so2ibS49PoczZVN/LCJMys402aGBClS4jDDOrA6AFF0syp1nRBLB+tCrqdNFqKMbecx73XwnSHVikrRY+gtENkyh09h8ZwpBetW/P6fFcogOOvhnI+gJmKVoeQwH/DdUq7AKg/HxHKwZW6kvCVv4GdzK8fh7dNRd+fSdb/4tD4A0drlsLg+uDKz3NlbjsmPLA5wCoshD3O8FLw8JAkV+0PLUsWgKcgkQotDuTGFmUeCA6SoocSAMziXIy5G9BDrcsQs4SSfOKKHWLeEzx5e7uF8MDZASUGmanguR9scp3IK5QEF2k1y7awr04QIcC7ECvhFF+UsuQrL6gnaA1eg8HEMywqjGCBCnEKZEVYVFJ6PkwLPeIjV5NrhlMO3OdgYpwocA0QUyjVwRQDSR5yHIsdJjJEKNtjHy1IUIc5VMAJOK4xK8V2CmAYwxKl8krgd4vBRblSeaaqs+mDw8EyKQ8h9H9+BMik5dQTBczniLFYZcAGW/DDTacLtl913OIcPK06IveWIPwzSLfcDsJPVRHdolb3dhQJDF1AUlkqoULY49IBONvDnE61bPrYHWLTe5/SwTA6xotnaH9BPTJkyMQUrilzCssywCIVXlgZGfOZrAZryPA6tRZkAZ+dQqkBRlCVLHIitloNJlV1WhFnwhogCpRyhEyI3xDNNkCqIRYkGfDxXRrAbf4yRCOUMvhW0CVEmRhGgHGgSUBlhoRG3gPFDbOBwulypFDysgJUAKxptPYQcqwpwVZRpABrnGuXJpA2Z51MojSqqaCt4hcYv6ngmxkpKEXZxHiAxaIs4ryyBbAfkfoQRH5EBns3RdoRvPLC1iT0fo2xtkp502Dz1cP8mEf7KgHgoGzAMoHAgJkLpLr7NEUlAGZTzsF6CJxrlzKSUy+1epXcZrWInIqRAFAgolSMqLy0te5jTKbc9951OEkeeF2We4weAc47hcFgmYz/AvC9H/K2oFBGqjQaeFhxFGSXvHtzgtveEGKweQB7g2QKnE1zoo8ShncHochqgbYpgcPjlc1CIyhA1RozFicLqEaXfQ4E3BJcgtgKugnIaqwwWH3Ex6CHWVVAMy4ocKivLs7igzEIRPcn+9xABxQinRiCKNMwwqsA3ugwvsGm5wiGHU6UCpzSoHCUGEwal4JChXBdcFXE+1WKMUCC3egPqSQqSA5WhJMHqCEeOFYdSBygzLHN/VY7RRSnaajipC5ViqeBU6R+6lasog1LQKHMgsYc+JrBSQ7AEJkU7jZUYIcUqhVMepWp6KJtj0SBvHwq3Z0S+/f5JnBZeWQanzIJG5LbXTPyryCSV7B3sg8gzBFrw5M0cx/djR4gIcRyXIRdvCTR9sHhfojUJqcM5RxQGZSmVSZ7bR1qwKCP0AyNgYqIsKf0XNiRwY3CKXIeT6k4pYh0aW1ZjsBHiPJCkLDtiq2g7AhRYBa4CaoQ1Mah+eay9UjCUteAyxMyiyND4OFdBigAwOD2e9Mab+E2cgKki+OAsojv4RZVgHGODnEK8SWB1OfWHsoaaZ0LEWQo/wUqBn1VBOzJVxamcwA0QG5PrCp4pS7BYcYBGFQ2UmcGpMm/VSRWnhxitEbOE70aIczjXxKk+xsWISyaLKQrUGFyEcx7oHCM+ToJJziOTwS+l1aRyammZRI4kOBNS6PRWxxqrMrQxGJ3hKBPf31LO5t2/3PL3W4RGTW4fCtTti1P6p7fhyuxSDThjJ0LzVjvvjrg9wPVBTTqc8D5DHgRB4TmDZ8a85er0gWSw37/IRBScl8Bkuodkt/wP2uUTKXjTHyG2LNIHGWIn5V4k5VYZOjWmPLYVREbl0FI5YqOyFIkweWw4eY0B5+FUSvmFaHC8peCdE4smw2mHnzUAhQsy3lL87s1nUxqIOU4cypaD0ekc8PEYl74yajgRtCsQseA8tC0DQp0yGEKcaGTSow9bVho0uqDQQpQLyoDxhogLgInQOuFW1VMpwIXlMHeHAvG2vRUAXZ6Vk5ru+rAqK6BcaXWW2RLmp17/rtxeRVUOX3eb0/72+96lfNNbouXlHUTtDrhdYPU75cc+YHwgcVq3JS9M7nhQD9edcGht3n7Ve1PJ33Ill8Nft1uo7/b6w8fcbc9Xb12xfYsP5fbb7+BWlsm+3LKo4GdW6JRDG1tuuy23fabD+l32lmCU4iITUU5ucxEfOrItyim00QgJSFa+zrlJRVI3+ay379mfkZr1U899l1e8a3med9vyndz/s8aBHO7Ce37Fz3yHj8DYe2Ai4qfce7hbolrWfBfrUy6rpROfp5qUwSkFTom59RynUwpiFLqcLsodWEJTHmimojXlruIQUEUZxyQ1rLOIKtDOYFEU4perby67tYKH+ORaM8hahJKixSB8xP0NU24xjU+YcteQiQWFFBgRUlvhynbBWEUYPArxyCWgkACLR+loz7ES08+F515KKJQPKnv3sIEpHzmmovU23KS+eVk1Qk2qoMpPNVSYchuHK8busKqTw0oZvOkAbJmsPsgNL14vJo54hRNNVni8cimj0CFWS7lA6hSXOx5hZbGskvxzTg+bcm8xFa23UQbyO6wTjIqwRCAeIm4qXO9CKVTq1o+Vss+fFcGIQhVVQNMd9fE3lojyAjXpDqRtxHdeqjEuhEIsGQHO5nznQpOllZxqPkLbiOnUcMohU9F6G4cF3BAYjcb0+sNSrNxHYmHmz4c4UClIVjrUrY8uqnh5HW0DRHXJJOTFazVOVg7QaowygpBjvCHMKCI7hGIGz+ZcyebRY1iqDgGNVcWDGic55c/BVLTezm1RBYEPvm9vxfhMDa13583QBjNJ/0lARsAYg8+4CLg6jjjqOTI/w3gjcD57mc9is0rojfFt2Zbklc4SZ5YcNTcCJ2WO4FS2pkyYitbbKMMDyzbjfgBxxcOV2a/TCcq7IE5Q1kNcmVxrPUOuxxh/hFM5uJBrB1WW5wp8XeH6aI62KIYS8/LFGp9aNqR+A6e7pLbBhUsDHjlh0ZKj5DBOa3r0p5RMRevtyGGE+mGO2eFV/h3Kf0wpEYfcqi0VYFyDzM2T2hWsq1J4jos3Yx5bLBh6Bd94NqKdbvCD7QqxGXN0tk2ifIzyud6bp2FyZmp75NqC5Cj74VadnXJvMxWtd2Hij+fBLqf2weFwFCoH69EzM/yz38v5yZU6Tmdc7y7SHvdYndljt7PMMFni+dccqQ751CM5Q+vxzBXDt15s8JvPw8ljDbTap1AeVgzKBJPW9VOmTEXrz0Q5QVk1DRH6WTjBFTFjH3AFz21mrJ54hGd2YWe8wbevCB873aJiEr5/TmF6PR5br/KlxX20G/JbL/gEwUny2kkuZ7PcLFL6yRp+IaASrLLcvc6BU+43pqL1Lsjb/pvyZ6OsJso8BoHPpd0anz/xCp/YKPi911YoMDxROeBgeILvbytcZFmZ2aKeZfzw6jJxtcrnGjeZcxf59bMdjrUc//6P1uimFRItOLHTb2DKLaaiNeV948RhvSFBntPJE8bJGBvWeKwx4LULW6wuz1H4Q75/LuIvfUp45CGP37sY0KHO+c2cp48N6Yjj2Z2Qp9b2eGz+Kh9/eMzvvBLTSxdQecjUpzXlkGnu4ZT3jQPGsaaSOFo4vvJwSpzU+XG3ztOP1rlybYfXVEpU7fD0ehfFAT+5usDuVszRtQPqgeXFrZgjdceCzbAmYfXkJrrexKQGU+2iXDC1eKcAU9Ga8gEgCNqA8arkuUKLZtsJ373Q4W9+OqHujxFT4+zMGIwQuILPrF7FuEWSXs4fbs4yvpDyq0+XlR80Y4K0xenakCw8wOT16ZRgyi2mojXlfSPOEWc5RhWM3AzPvhwgR7r8lU80WdEdgmGXIirQOAqlKTQYo1GMWakYXuuEfO4X2gSNDmOEajakOm6SBTF+Oj/pbJP/vD/mlHuEqWhN+QAQoIIiYS7s8IXHlwhbbQ7eGLFbSZg/kqJNCDaC3Oe1iyEXbs4wWxvxySc6/LWjV9HOUQxO8P0Xxjz66BL1eAu/EJyyWC9Fimms1pSSqdU95YPBQEENxLBav0rDDXhjp8HroyoHzJMR43Qb4yVsthv84WvzfOuKz0EwR+IXoA8QHD8857HLEj3lk0Vd0AWYsgX9lCkwtbSmfCA4kBwrgjjBy8c4F+DP+Hz3Oc3mNZ+TjYJPPJrgPLACRzdyzMhx/aKGyjzRrEcuIw4y4ff/uMuJ2SMs1To8cTbC87Yntdan19gpU9Ga8kGhDEqSMuXG+ogJOLHWZaYa0IyFIOmjdQZqwGNnFnnIXMd3gtg9Kjoua5bplL/ya1VMIiw3NXpcResBuGms3JQ3mYrWXeIwgvtW+sntTSpu3XfY1EFuPXY/Dk4n4JTFcxYxFayuY7XlWPMSJyoBTlvE1FGuihQpK7UdtJ209tJDbOEQGRHaGk8s5ICAVUhzAFbhrJqmfU65xVS07hqujOQuFhAZYXVGWSqvALxJb8gMR1D+SPqW1l73Gw4fMT5gsMF1cAFhVkVMgZt0u8YEKKnjuwxnPZT4OOujxSHOB5cSFg4nZWswW/igLGUj1Wn10iklU9G6izixiLcLpOCalH3wNE4EpwxOlb38nDtsKXa/mhMy6SJWFutTpjrxPlmcFiBESEGnOOcQEUTZskGsK5uk4tSb/U4xOMybxTamgjXlNqaezbuEOIW2PlYlWAnABYge4ohwUsY05VQxImiGaMN9W8ngsE3prV6lh/mawpulfm71Mr317Lf1Jjx88W3P5fbHp0wpmVpadw3BWZ9uFqFDCLwOogyE+wRZgWdiUGWHaCcglFOrKVOm/NlMResukovHH/1pxJlPxixV6/T2Nde3Ah57KMNLQyr1LXzJcK6JUe6tBsaUKVPeken08C5hpWBQBGylAa9eWef/+Q8UN9pVrkmVf/hsi3/4+xHdog44VBHjVMK0DvqUKT+bqWjdJZwSrvTnCOZi7OxFmg81sWvCG5uGKzdr3OgMMKqBJURkhHbF1Mqa8vPDHXaiKoteOgXalmWH7jWm08O7hLKWtcpFls+GqEpK8WUfLx6x8XSFPMiJbJ8Zfw9lNU401swjMkTuwZNkyoOPOJkIlEJwOHGlaOHuudjBqWjdLZxmPsxxQQYSQP0ajpCFZh+xFnEWKSyIo9BjjKT405X9KT8nnJRdwZ24svsUwD3qZZ2K1l3CKYfDw4igSHEEQB8hQNsqOB9nQVSBkjFITvl13HsnyZSPAo6w5qGDAsSVwcD3aOjgVLTuFk4hLkQBypWHWSiDSpN4t+zCbCKUVSgs2t6DZ8eUjwxOLEbnOG3LoGegDBqWe064pqJ113AUOivTDJ0tI96dBYQgawAWxOBUjsUiThCnb1s/PMxIlMkVz00Shw9TF8vL4D12Pk25XxFHtVHBCwelb+v2aOF7bFV7Klp3CeUsqgi4Mqqx5hmSEJw/pDKKUJ6HUyM8o8l1TlsUdQWxHZDTAmUQ05sEj1fJ7QxKHaDSCkoZcs/hVEZkpk6wKR8MDkXhgYgCJ2hrgACh4F6LIJyK1l3CiceN7Bj/9b/T/N2vZPz4WkJtaYOF4R6N5RRsTN3Nc/Nmn8u0WAtyji2NuXngULqgUfXwwhrtUUo6cETROrsXtjn7aJPr/ZC6n/HZlR1kmpc35QPAIRTq8O/SZWHRaCZVN+4hpqJ1l7AoXrmWEVSO8qPzbzDw5knHCwwGMed/2CQd79CSBmvLFXb8iNdeeIFW7VG225ep+S2WV1O2h/MUcRe3uY8Kz7A2O0dkHT+8pjkiCZ9d2WOaTDzlo8ZUtO4WTiHJJl/7BSHd9fAXxuRyiX5ScGymzbg+5NGFEf3ePitLNYJqhtM7eD6knWv48ZAjXsqILmurjoPsHKvNhDwbc3K2wVoUMxWsKR9FpqJ1l9AU/OKjFqPPo+er5FGOSkPsEUemMhIfZlJHIWBlQJT7FG6E0yO8ow7lYKRvkImhlQljf5vA5Dhb5VPH+1B4ZZzXlCkfMaaidZdwOKwWLILndTAuIpIU4wrCtEJYFARFilZC6uV4Ji3rPHgZzoR4Vqi7EU4PEBSx9REboPIGgexh1LQixJSPJlPRulsIaCNo8VDWEuEh5KhCgFKgUAoljsAFOGXLlWUTgng4EbAGIcKJwlmNiMV5HZxkyK1GD/eWk3TKlLvNVLTuEmW9u0mlePFQGJwIIlKW53SAlHFW2tpbYTGCIM68uREUAqhD/5UYxAWTh6eCNeWjx1S07jKlGMmt34d3ym3PuL2A508X7JR3uDmVqykfXaalaaZMmXJfMRWtKVOm3FdMRWvKlCn3FVPRmjJlyn3FVLSmTJlyXzEVrSlTptxXTEVrypQp9xVT0ZoyZcp9xfsQrWl445QpDw7vVKH03qpYesh7Fi3floVQrCrKfDoX4LC4e/SDTZky5b3jnI92BiTFM4JzASIZhfImlUvvHd6zaAkKbst/c5KD8plaXFOm3P84FA4pE/Wdpsx5LXsX3Gu8Z9GyKBAzaa5gsJLRH9/RJqZMmXJPIxgUTjTiFOIcytl7ziy5A8VR5UdyAc5ZrIK0aOKmojVlyn2PEotWKYjFCVi516TqTe7A0qIsi0IA+KQ5XL2ag5sWipgy5X5HuYxmnNNqaJwUGAVWCVbuPXf8e1cccZTS5YH4OGdJsnLuO2XKlPsboaAWZtTrMY4CIw49eUTcvSVb71lxnLPgNI4MI5aKMWSevqfNyClTprxHnKYhI1xkEecR2AIoUPegI/4O5nZlR2MmYQ7KOaxMAx6mTHkwEBQGK4crhu7Nrub3mG5N53ZTpky5r5iK1pQpU+4rpqI1ZcqU+4qpaE2ZMuW+YipaU6ZMua+YitaUKVPuK6aiNWXKlPuKqWhNmTLlvmIqWlOmTLmvmIrWlClT7iumojVlypT7iqloTZky5b5iKlpTpky5r5iK1pQpU+4rpqI1ZcqU+4qpaE2ZMoVSCgSFwyiFuEnF0nuw3PJUtKZMmYJYH0EIXMbIV4jRiCswcu9JxLQrxZQpU0AsShfMNQMUdtITQt1z7cNgamlNmTIFcJIjZBw/oghIQRziPMTZn/eu/RRTS2vKlCkgDu2EqvQQ40AEh0Y5e881kZ9aWlOmTAEEsQGeHaPtofNdAYZ7zRU/Fa0pU6bgEJzTlMuFh6aVIPeYYMF0ejhlypRDpMARghzOCAV3r/UPYypaU6ZMYSJSb/5z+733HH+u6aGDe/XzTJky5QHnzyVa4sDhEJGpdk2ZMuVD5c81PXTOoUSBc/egm27KlHuLOx0jh4aAu3XbTW7fXRNBeOt4lkOH/D1mmbx30bp9vitgrOLqlT2sZboGOWXKu+De9vtnIbc/WcCJAxziHO5WSs3tK3xMotffp/ngJuIoBicgToHzJu91b8Vq3YGlJYACcaUeu5h+ewxWTUVrypQ/A8dbNebPfnJpT1kRjJokMTuHxqJcUUqTE96iIu79z3cEhaCx4rBiUM6hJpt9z/v+IfE+Vg/fduCmTJnyU9waJXegK0ZZnJSTQnE+WK+0hFw2mSre/gMfTBCAABrlAsRakBynUkqvtce9NNanIQ9TpnwI3PJT/f/bO/Mgy66zsP++c8699+29znTP3jPaF1u7MZKlsi2MwayBKsDZCBD4BwpCqpJKqIQkRaqSUElRCZCEhGAgmBQhxI6dxSDHNsRgYVuSLVmypJFmNHvP9P5ev+3ee86XP+7rmZ7R2OqxR1a3uL+p6X793r3n3fW73/edb9mCVmQ0AjxWMoQMARRD6gyYQoyo+pFZCJB93dtnNOC8IMSAAxXUFF4u2WaO61JolZS8joycKQiCyCWX+ldGMMGNlgkEyVFrCQGsVgipYsWMNKANH3P2GmO+NsF4MucRAuLTUSy8HW359uK6CK1XH67N0vkbOwOxzR4KXztvmh35C44IaiBXxZgN99Nr3AySgQREYzJpktEk2AqiyxhnkWBQdSNH+XVyKGtCyGMiWSYyK1iGqBpkGzqsr0FoafG00CLGf2j6uDwhSIwXN1qiwBCwmiPqCERcnEwVRUNAxFwc7xLXItUuv6N1VGFxI1Mqx5AC3liCGFxQzHVwVr6eqBiGPkJDwqBvIcQYE6FecWIJISCFnbCJ7fYM3CqKSsBrRHcAqyurTE3USZKEVy4Ig2F+2dKVSoXhMN2SabXdEAS7pqycW0JDwCCvYW4Vx4aRLykPVeYXh/QyS2AWYyKq1QanT82DGlAL2K97O40GIhZ55NuEd76nBiHFoKi+6qJ7w9my0AoSsKFwyBljGLge+fwunv6zBtb1gEv2ureBfuLJ83FOnkhZWIRAggrUGpbueveK+200M7llCtV5g4mJCVZXV1EEE4R8dcDKKwtUU0fsDUOrDO32qwt0Jf00x6eW5QUPQbE24EOOiTKyPEfkiomi7Tats0UEJVZPTp31NKLfrTJeh8T1ONeG3EPYVMcpjgNZll+XWbJvPEIyqCLDXRgFE4pZOaOjK/gqp9BcsZuq9UujiYBmqE5e5600hMhxeC7gH13AqUMIo9iw7XXcr0nTgqKWoXhHhSrPf3qFf/R4din4bdNFJeJQ6ZLlljwHNR2CeNRYNOhVhNa1alqXvsu5JfI8RwI4caAWCbOYUNj+NhRxLhe/alMczKteb47s+2qvr/MYgmAkRiRCgwHNgBSVjHDJrri01zs4l0qATCFHCMZS0SpD9ShCU/xFLxCb/EFQeUO3+eshE/AErFgkKHbT5X/V586rZMSlNy5dxtfbbDOF5rbNBNTVuMY4rWKHRA1RnqAaEwZy0cy7FLcbMJKiaolIcLgiYI2AwV80566PprAxVoIdqclKcZEEUVQK/5rZOBl6+aqvev1an79OYyiQ2hzVACKFX8PkmKDEPuHVEnHnhpwoMMTgTU4wGWiO1QQfRmbOpam2wiekGxrGZR9u4TXXsOzrNZ5iNGBURtf/JRf6VkIhNseobz+X+BvDloXWRki/x1+S8l4xMuCiMBstGzAMxY3eSEE2yrcKxrvi4BdRcqM1riWiV674fWnVfKTOGhSLEmmhYeUo+TY/30Ig9tkmH4WDEKMEsqvODu1coSUITi0u5KgW+1aU9bWFRnzVB8HO3FeAgLCRgnOtV3wpqF7NloWWUVtM34qnqGYIqBTBb1cgGIxGI7MxIBcL5StB0quMHrg2tfTqPjAdycKwoVTppZNuuJ4+rS3p9Nc4ogGtbBrbsNHWCb3a8dm5Qgs29JAIowkQUDJUcnIsr86x27n7KnCZ433zWRyd3Vex/Q20N5ata1pYhEAoEpMuvns14SEojuHFJaCYdVQMnvgqo38tp+nVp9tpMSMZKESlRwgiuE0pCV/byFdzZBVmsY5+elIQM0q1MITR8dpy9gZKakYalVwSUqIymgC52jbtTBTITBhplZt8jSJYb7+Ct2ZnCi24ZOJdPGObLciSa2brPi0zKH5tfu+rXEdXc++8xipfN5t1qSIpYTQZLNd2fWxW3XXjrw2/ykhYIQZVUBUsDk+OwQEpisULWPXX9J02bGgU5lWfvdnMhGJfAxfPmhZT/Ff3IcFOvsM3OzQu84qwk/fqjWPLQquxa+313I5thd9kjYgUkcw+BFAdOYTNyIQRvFfabcFluzEhJxChKNdaW1s2/Xyzc/W9/Iu87yXXwpaF1i9/uPF6bse2wssmv68UmlUY+ZU25n5UBRHDyornX/9Sysv/15KElIAjiMduw35xJSVvBrYstGYOP/t6bse2wn/Fx+GG6bJhwglTh5p80/e+hRc/NcTkKYE6OvLelWKrpOT6s/XZw3Cl93Bz3FART3O5wf6VIi03f34lX23dze/zFZb7aly53Obtufy3kastt2mbN7VYUslJa+fITQ1hgNJAL/aLY2QqXr4XV+fyYyMX19RNn8plS28e9coZN9k0lm4a49V/X1r3ynTeS9955U951Z5dbQS5bM83b/GVn5eUbJ0th9UOTZNMKyNzKUXV4U0gjMpYDKxF1ZIbU9QCEgOSIt5hQlRMOIYIEwQVMKnFZEVEldcqSITxBpGA8RHilUCFjKQIEA0W1CF2CBIQ+phhjAJeihxHE2JMSDDqKIqaFVPqJhhMAAl1CHWMl1E8ULG+CYJRwfhKkWLhI0w2hkkjTO4J6sioQIggxMW4eQWTJljvSYJSDZ6+S8gi8FEgcxnB5mQuwxXRrfSSFHU9vMvoxxneejA5gh+lOQWCCwytAwO5BW9y1ArIkGCE4Ay4AUiRzBozwNIlEBNcRohyhpEipqiJ5LBF9UvJEfFYLGoVbzxWlBiK2U4REI+QEkRJnS/c4lYIdohBR2azYopRQAQvCUEUI0W2BBgyq0XaFxZHTCJdIiKMJIg4fNQFyb/a5VZyHZCr/H8zsGVNK9IOxnRBBPWTaKggJmB8hskTrF1FwjgujZFovRBc1uJdjmhUOLB9ILcRvQiq6RjEp0mtwYUKgRQrCT0HiViM6TMkojKsgOszlF3EaiHvkskEYnr06w3qwZK5FSLvSKOUOEvA1wg2xVsl0kBqPZEvIvS7kSPWNjZvEbAYyQlGsRoTJGAElAyjfbxLUQGnHfDjRT0ju4LzBmsS8ijQiav0jGMYtXn7+2vc+/YhvWEHhobBuSqf+J+QvhxjG8Lb3ttgbnaRpX5Glyl0KfD0x2q4wVms5Bgs642Eg3dOk5h5zj/tydYCwSQE1+fwI/O863v3c+FUlz/87QHZwjTi62QmYf/dA972aIoGy+N/1uXsE7PYfADaBarE0ifXCbqmCu4UUV4l0zq5rI+mDYo0DoMlEJN4i5qMWB1BITdDhIi+jXE6JMljggwQ8RjNGTqPyxtFJVs8wQTwgaHJiNjHcvMs6seZ7DWwwaP4N81NVPKNZctC6+TiNK5xE52sgTcrZKsVJsZrODskigMrvRr7kpSlsIu9VklCRntwiGGjQzasUg9dWjIgJ2Mtb5G6iOAPk2ublh/SCBW8abI6jIg1xvkGeeIxOXid4nytwp6Bo+aFQdKhlltqeUYIOX0midMIazvkNifYPi4bIwpDvCipGFSrJL5NRZQgE7jcUuT2WYINaKgSEEQDYrsMKwPI9mFyh7fnsB689LEaY7wjaIxkLZwd4tST+CaTTpltdPAt2LW/x/T+KZ58IuXEiQs0WjXe/o6D7B5fIKXF/vs79LI+zz0xyfp8DeMjcht46L3n+dl/dhof5fzLH5njC5+sUQlQuWWdH/4ne3jxxS7f9N37wKzy4X/TZ5hm1GY8P/YvPBWx5KbPvd9Z5xd+tEt2os7A5ERBSGmgklGp9th9j2Iqt5CuneP80waTFaasaIRiyExGNJtibqmTzA9ZOebJQs5YGhMFxRCjZPTiPlO3NDFunPWeY/2VNs1BROwt3gRSUXLnqdw15J2PThGGwrP/6wzmpQaqllBO+L+uvFmP7paF1r/6TAupzkFjyItPLlJbrfCXHq1w7sRJdOZe7ph8kReH6zy9nPBT7xzHxAv84Z+kNG6b5uTxJe6aS7nrcMaphYTHnp1hoBcY6GFW12/mgf0v8N13rLIuMX/w6QHTu9Y59dQUNz9iqXRWGWZ7eHJhjO+ZW+W+QxW+9Pw01WTI0ZXd0L3A8TDFO26dwqfz+EqHRV/j7tYCh2tdPn/2VkwmvNRZ49a9CffUU47nlpAl9Ls5p7v7ieyA5X4Tl3fpDHp83wM1+h7++BVD3vBEF8a5Y1+TY2fXaE5PcW4xRkTYm5zm3htG9bSzCR77TeGTv7NKHsX8jX+oHHrLMueeAxMSumem+Y9/dx6jFdI9K/zT38p45jlHWOxTzRIsGfbmlB/8iZv4zBPPcuRtDfphDcMMuTnLA987R2d5wG/8g4wH3rbC3/ypGh//zwt0LlQYbzRZPtbkN37xOMM44ed/bYp9d3qOnV4m1wpJsKCW3HbY+1DgjkcsH/3dz/FN772NQa9H5zlDUUjO4+2Q2u4hD/zw/Xz2iy9y/w/cwLOPHePCZ6MiYNekBI0JKM3pBt/5V27hdz7wDI25PUSDhHDSE/tidtVLwKhy9yOHOPb5x5mZhnf/5CE++nOnqXSbvHkMlu3HRos/hVHLv8vZyUd+yz6tsdYYy6fWqWvO/be0uGXPkDsOjdFdq3J2aQLNx1lejTnTNQy9wYvQTw2D9TGSqMLc3AxWLVONOnRzWjbi/DGYP1snbk5ixJGQEw+UI7vHCYOcqG65IF26UcLKQp+Bp4iaTvZxOkRc0IRPPDfLc13D731WOXpijtW1SV54/iCRqSJZhYXTjtPHpnmpM8cTpw8SNGY96/Ls/B6emZ/mM+emOZnu5Qtre2knc5z2B1DGWFuokZ6E3dkc80s386Vzu1mrHeGpsw0++UKT09HtPHd2ipDvwuZVYjlD4tuYDOrja9z9jhYf/702dnkM5xtEKlSHDskcNx4J7J+O+MzvNGDoCDan37rA9/+kcurcGn/wq4azqwdYObdALmsMYse+Gxf50uMd3EJE+6VAXJkkbhriMMXa8R6/8ve/wMq8Y/ZAn0prDYYdAjmR9lEGIKvkpsvuWyaZu3mGuQNjTO53uPqw8OuNzDWVjF0HE6rdiMEnUk488VkefPAQUTAMTI6KR8iBiL5vM7Cn2XVwkr03jFGdSMltv5g11cLNHhAe/09HOfVkYHqmzvSgSteMk5vtV1zuzYQgFwsGBCmE2LVk+G5ntqxp/ehbz2Df3kXzMSJjWTvcZ6Z1lm9/2FKZeY4kPss9UuHGfEgcL+K85b47ewzsIlOH24xplyhvIc5z+w2evWMN7jrQYz59mcl4CW9TTJrz8L1jTIwl3DSXMhMipthNVOtw17dm7K5YvFUkPYP1Ee8+vEqrW2PsQI2sc55GXME2huzTefqrk/T3BnZPrfPyqmOXs9y+Z5EQOWppjTMvWKb3WmYrF6h6w6Fmjz2ViNrYBYz0mZ4xVBaqLAx7TFiDP2NYa64zHgyP7KkRstOMH+zQixO8RIi2MJKTU+HgXQtkyRTPPD6ODTmZCah0ISjd2HPwkXG+dGbAwvNalBqRlINvaXHonha/+tPruGCIrMG53WANUW2Sw4fXePKxHJGIU+0ux9MK1UOezkvr2KxFPtjHN3/fOu//W9O88JTn5FOLiM5Q8RVSPBZPxjStZo6rXuCmu2MOH2jzfH3IijEQQGiA1pifv8Db9jbY831TPPTtFbIzq/STFcb6e5HQw4yedTlKZTwnsw6RNjfcOMEzz54gNxEEwQYhmAi0wzt/fI79tzf46C+8yMSwdSlBtOR1YaOoT1JNGA6G6Gj2f7vVe/9a2LLQer5Tp5nnJN1JfJxiKj3yNCFrTHLELTJRb/PUi03OtGuw+xYmsnnOqjLZanBssUdll1K1feYXW1SaGfMyzkIsaPB01po83dtFo9kjGzOc6w5ws+B9yttuXsfaeVDPmu7ilcEsbqzKkXqPI9Xz3PRwlZMvTtK8aZ0LEpimwzseXWCpfwPH21PsnrnAxNwaxo8ztb6KDJo0kohHHhpSTRw2LNOq96ngeGZhhpZrcXJ5HOyQ+/Z7mLjA/hsW0CxBXUIUUiScIMgYQTJy2vSTOpm0EBztZMBtbx/n/LmM/hmDmJzcBGzIUGIqlSF33zfGS5/vkraFYJsMoy7v+J7dVIenOHSLMDUz5EjjNPc8VOHMS4oJA1KpURvroeqZaEywq75EvrAORKSx4zt/2vDoDzf51O+u8di/q2KXD2J0Y2YwG80eZVijPPXnPT70gZjv8Bl7D0ScUYOqEPCoh/Rcwkc++Bj5rkleefE802Ga3Cb03RDrc6wqXncxVh8wVhMWl47Rdwm3jd+CaCAJlkDAS0oUhNsfnOWh7/L8+r99nMm5Q/TPDMmvmoNacr2Q0b/EJaQ6vCzFc6ezZaH11Ikm1co4/niHk3oP2cIXmT7QIh0K1Yd6NCbgmeU6L71UZSmPWH5ZSKdv4CavPH3c4u5pMzG7wAvzE/z3P5vAJi0aswmZX2Kv75LUB+w5lNPVBn69ymc+nfFd7xXeaobEVHH0aOeBZ84nvHR0ioMTKUfutaSS89++2OVBt48/PbnCu25ucZ/L+T+nJ3j+TAUbmhyeNiz1pviWG9a5rdnh9MIuPvzxPnZPk1o04F279nDTgSGf+qJlMXVUTIu6zTi7dIG/9r6cvVM5kTGIDoAIjMGwjEXxoYENDsMaURjDRSl33jnJy8+lmIEnaIQJhkiFoSjViSH7Dvb4o/+wigszEDIwFZZfXmfsgZgf+bEJoolztKrr3PXIDB/7vRXyfsKpY8rUgZhcGoy1VqgHQ1iewmvE5ME+7/qBCv/791f50K84KusZVvpYUUKIEI0JxhPlDT73IeV9P3EbP/53Inw25FMfWUXCnlHkVVFitxoSbr93D0tunhvn9vBHv3aURh8qPqA6jmcdIWftnOczHx3w6CPvpt3u88rnX8aOkqA3IrkcyvKpdT7xW4vMhLfikgq5O40S2I71x98sKEBQOmtt4HJhtdMF15aF1q2N87S7YzSn1lkbPs2R2TonlheZHK8iGVRTy537U8LaCSr13bQOJCxnp4jXI5rGsnpmCbO3R4Zw042WODuLxIZWvcq4rONEWTzlGJ/t01+vsMcK8dKAz3dy3n5kD2m9Te+84cKpAbfMKnXXwZgehgqdPGVpKSfzdTReIQ8JTc5ypDFObxgRZ8L502tciKvcO9blSC2hmi9SqVQwUmG22iGptLlvNuPYepXz/ZT1dJo2MVnPYSYcwyTHhAyXVTBqENsGDBKmSLKYXDKMWOIY4lafo88qwcdYFcwo9rZnlemDdZI4sHqqCRqIyfFZwsd/U/mTD85gNGPibav81L/fzwd+/WVM5yaMrHL0synf/aMxT3/iBO/+QcfTL1RYWBFEquzZb0hczhOfOE+lcQBT6yO9OlnbjcrmpkhIiExg4WjOb//jBaoNR3c1xvWOEDMY+TwUI4YQlM99LOLGB+/gI792igtf3EdVL2BQUhxKjGUF+i2e/H0hjf8Uq2DzGBMcQ8lGHYoTPHDmhXVeOdpAtY+EAdbkODFvDgfLNmWnC6avxpaF1lvvGhKlQqXa415ZJskr1FxOoutUsoxBtovbZ9aYmnU46VGVZWrqGcg0R7rj7Kuus5oOePAtbd6XL7DkEnIiJFum1m8gSZeFvnCgntELhgdurTBTiyAdJ48HuKzD7NQBHh7PmTXH6UcRKzJFbWD469/ahAR2a8TtDUvFv8KjN+xlSI9h3sCGNrcfGGPWTyCcQqrHed/79jPbTHGDQLPapWsj7rgh5bYsJnNdCIbVtMEBa7D9LnnUYuAgdh0i7WF9BYgQ6VPXIalMEBlPq2ow2WFWTh8vblw2xaAbg01atM/N0ll7iSzqYbImQYB8lSxfIMqn6S4cJM3GibRJHFKMjvO5D6/yljub/PjfnmVtvc8Hf6mH60xgaCOkmKjFz/7ibiSzuOEhvvzpCh/4509Cby9ChBCQoMRZQuj0yXqrJPkkxl9RD02gUa0j55eY/8gJvEZU/ATeNunYHKPLWF8HBBcGuLCHau5xWpQCSu2QzHpMEFxwKEoUIMknCdom0MIbu6OL+pW8sYhuscXJz3/wO7j7rRGnnl0lqkzRfSXlwW9OqTegYQL/40/ggXs8/+X5nGZ2Mw/ue5ZvecuQo2sVfvlDN/Lw/ad59vQUj9y6zHvmLvBHnRbnjwtH2zeyr3+KZHcTzdb5oW9e5osn6vzx0Zjd+xzr3QkmJzvI2jxrg/3Upx3DLy9ycuwIJIabk0XC+glWJ2d4/guBn3nY89Y9p/jEmTleyRscyk+RmIgvL9RomVlWhkus1xvUIs+jh1/h5prnI0cPcGw15kBzkQlpstZoc1fT8v8WlF1dj0y0yPH45Xnuv7POVHWFam4xQfCux+eeO8jP/dUqbl4JUWDs5iqrL0PUi0e1VIvEnoET8lafA3MRZ74MkqVEfhwvAZF1XKhiVMhbysStFRZeaFPpFLNwQ6P4xJOMDxkMhsjaflp5hLDKoDZg4s4ZtBqNsgB6DBdTFl9eJUrrOK0g9IAIxaLSBx2nKNzTBYro9EvT5IrIEAkOoYrQQ2gi9MjsGsHPIOQYs4aESYoyQEIgkEqKNwFRgw1x4UszK5jQALNMCFNcKiFcCq7tgyG4Lu//e56//JMXSLQzOqPuOhfQfI2t2HP0NZfZsqZ14vg01dlVLhxr4vfeRjO9wJ+/+Dwz8TgHbl7j5LkKN87s5o7oOM3xc1RMTKxtut0mkzdFvJx54uYu7j1wEqvKfQ3l81HE8cxwbNVRb8KdhyeJ/DKxS3DT+/jYJ+e5++Aunn4ZfvzbBjz2uWkSHVLdn5AudmiHGd47F/OFV6a4daJGl3PMjUHmLE+u1Hnm5F6mxgIPHRGGWYqpzvLF9TUWexP80C2G9vAooZHTzzMazTUaGnGqXeMsloMTMe2h5/QJ5eTxGt96d5WVQcbTRz1vOTjO3tZKcWP7KjPj49jGIkZ249KI888n1PM+hkBqBESIA1TyQL4Ssdyuk4SiLnqQNi7UCYwjpKS2j6xXWXuih9MaqekQe0Mlr5GFlHQ4ScwKQTt0nCXJJ6E/5PwTQ5Q2lioqKU6VSqiAeoL0AI8dpTehhiArBAFRi+jIbStyqaOSCaiMI3QwokVLdsnAT4+m08FLAqZo1S74i+M4X1TkV8kQBR+aZCTAGEWnvsJRX1LytbBlofUT334ejdfo3D/A1zo0bhpQbXjSdo8k87znvkX27VtiPGvTmmoTrSs+9tyxN8PzJWoNpd97nInKOjmWVljnhj0RU7u/QC/zpMMBM3GCI7C/0eG+9Fne8/5d5O2niJoVzGCJd93s0cwT1/rM7e2TywXccIX799cYs4G5h/rYVsaQjHfPzfPOmXXmVztMugr1vYa0/zQPH1nHJkotW2fMCd5Zbps+S218jOVzfXrDs+yaqDAcnOUdh2OeXRGaay1YXOLh2wL95ZT5Y+vM3hWTmwFKzHA4QPoxGRZHSiUzCEoArDIqFlhEQ1k1SD4qQc1G37qAkKNkiCpGc4qOZwHVCqoxngwJARcyDBFGcjyCMgTNRu8rngGIHTXzBMEDOagbCYq0+F4Coht1vwqNR/VSL0rjGxRBDRFF0WwPRBeDHUQDEiI2KrcyEl0bgqsYsPixUfV2o+a9lgKr5Otgy+ZhNj+HV8VtbmclhcljgsXlMS44yAOIL1JwRrE6YPEiIIoNKRvpm0EMKgYvEMSTBEG8ghFSq4ikuNCnCH10qImKMsbBYyQUNeGFUZJzIRQuVhkQi2iMDzHB5qgR0IQQpEjWlQEVBkgIBHF4IrxEBHEoBmPamBCQbC+qNUK0TMw5TDaOhBiJLoBdR7XGQucGfuZ7EuZfGMf5jTLLKYzaipeUbH/ehOahJSmepRvly0UwIdDIirrmagJBBoQkjBKOPYIFcQRRgjjAgxkWJok4wI1abwesKBo8GAt4LIY8WAJNoOjkI3khloxGRWtwUYIpzBoJOuqrWNRYL9qU5YhTnKZIUDQMEQyqHiRDNUalqDRqNcdofyQEi8RfQ4qygBgIslaYPGbUlkyqRQXTUIV0AsIqVgJCFRgUDUDUliKrpOQ6s2Whtbi66+LrNE1xUYQRwauQSpWTSykreUzfVRBybhtXXJwwv9yml0OqQsUZ9k/VWF1dJw+GXB2DNBDUYDAYD0aEKIoY5CkeBxoVelnwhd6iysr8Cnmq7N43S7CFcbK6tExrqk5whVO56MbsR63kTbGrutH/JIzK24ySslQwFL6dwt+jqElRExCN0aCIyYrYIq2RZ0qWDajVK+jQ8cpjwtLpCoZhYSyJR7U6MpvKef2SkuvJ1n1a3z9z8bX3HutcoUWEgNWY7koPP8hxZIh4GhN1NHKstavkWUC8wTmhPh3T61QQb9DckGdFZ+GNGoMGIYotw9Si4ovyJ6P+hW7UF95nFg3g4nbhTBbQTBE7LDQ6HZllYoCcIJ5wlfpNlsHIgeyKWl0jwebNYGSyWgr/jcVqUcYGXSsc0QoiHlB8L0ayKSJpU/h2PErMtdaJLykpeW22LLQ6z116XZh2oyRMKZzIaBVRSEcaTHtN8QRyqRYuJi0SONvLOUpSrK8C4oomrpvyDFINWIlQBUPMxbrso3WKDtYQBsU2WBGMKqrF+2hE4U8qTETVUQk02dB8ZJTQO3YxchtCoSEBsVYIxHgRVDKsKlYjlCZISmG+OkAIpKgRjHgIVWAM5AKYHvi4FFslO4KNB+ylarLb17GxZaHlzdV2YqTm4EFG5XdHi2U6isYZtaUvJsWL6pfoqJeNAChFcPTlt7dno+jwqOTwxVLOl8oFb3wUCJhRGY7iyzI2Yo+Kv6+s27ip1rsKG446LWxJvAKaj+IfA94oXtPC/yYbpaXDxdMMSiAjGAFdR6WYEQz21W1HS0q2I8oAJcLmBqtZ8QA2HQw511AM5hvCloVW8hUnEOxXeP9qO/r13sKv9RT4Wg7u5mYVWxnzymWv3P+cTR0XS0p2BLl1DI0lmB7BrqEkxVWujlEJkG3DloVWd640dEpK3qy44GiqJao50BYqGWg+so9gO0mtLQut3/qvK6/ndpSUlLyBqAQQoTG2BHYZ8iZgi0Bg2aHm4eH9r7yOm1FSUvJGEsTgpciUMHkLCTEqXcTkoNur9tmWhVaQ7aMelpSUXF8kOGINYPuoRiApikFx28gwLNiy0EK3l4pYUlJyHZFQzMprzGX+K91+E0pb7zD9em5FSUnJG4yOZNUlkbDdNKwNSllUUlKyoyiFVklJyY6iFFolJSU7ilJolZSU7ChKoVVSUrKjKIVWSUnJjqIUWiUlJTuKLdeILykpKdkOlJpWSUnJjqIUWiUlJTuKUmiVlJTsKEqhVVJSsqMohVZJScmOohRaJSUlO4pSaJWUlOwoSqFVUlKyoyiFVklJyY7i/wP5EyMb6QNdcgAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# **VIT model training**"],"metadata":{"id":"eoAYPggubHkC"}},{"cell_type":"code","source":["model_checkpoint = \"google/vit-base-patch16-224\"  # Change to ViT model\n"," # pre-trained model to use as the base for fine-tuning(swin Transformer)\n","batch_size = 32\n","image_processor = AutoImageProcessor.from_pretrained(model_checkpoint)\n","\n","print(image_processor)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":622,"referenced_widgets":["82d54970354544ec91d4989be09769b5","97e242461fd0494fb6c27a9e47cb22c4","d24b4a4651c8467ebb7af993dbd40e07","49e012460aae4cb789c2aa9d3bafe42d","d6bde897932b4a53b8a2e8a5dc1b5445","9ac739e8ae7143d785a8da09c807c4b9","dc4239afe4cd4e6b9c04a0d1dc3d9995","b5606182c7fe4ea58ade05bde842ff66","a9c72a586e1442579c95f38333add9e6","f54ea8ee318b43a79d546f6542e7af54","5bd74ee93dff4483a49ffa4c97d12833","c0ebd87cb70a4121bfc48caf50b05052","3f99833f3bd1449ab2cc3b24373a0a4e","ac2b42894130481fae85eaf94f8f70b7","4dbcb054612246548bedeba6fce672ef","2aca119ad8c24e92b531ddb46f57bac7","b87d50e63cd545879a50ab198891a1d9","cdb8d5d253ab4eac8241f56e6cff4728","4644c02f194b47a3823a2e031e40402c","09ba4ce01bd94410b4a4af680b797787","40d41ffb06ff411cac478a31f8dc372e","ddb806b0c0c94d9394280392442bb624"]},"id":"OsBP3qJ3yIlm","executionInfo":{"status":"ok","timestamp":1728232191300,"user_tz":-120,"elapsed":3025,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"697bd46c-a54b-4f6d-f26f-535b64cc3fd3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["preprocessor_config.json:   0%|          | 0.00/160 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"82d54970354544ec91d4989be09769b5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/69.7k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c0ebd87cb70a4121bfc48caf50b05052"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Fast image processor class <class 'transformers.models.vit.image_processing_vit_fast.ViTImageProcessorFast'> is available for this model. Using slow image processor class. To use the fast image processor class set `use_fast=True`.\n"]},{"output_type":"stream","name":"stdout","text":["ViTImageProcessor {\n","  \"do_normalize\": true,\n","  \"do_rescale\": true,\n","  \"do_resize\": true,\n","  \"image_mean\": [\n","    0.5,\n","    0.5,\n","    0.5\n","  ],\n","  \"image_processor_type\": \"ViTImageProcessor\",\n","  \"image_std\": [\n","    0.5,\n","    0.5,\n","    0.5\n","  ],\n","  \"resample\": 2,\n","  \"rescale_factor\": 0.00392156862745098,\n","  \"size\": {\n","    \"height\": 224,\n","    \"width\": 224\n","  }\n","}\n","\n"]}]},{"cell_type":"code","source":["from torchvision.transforms import (\n","    Compose,\n","    Normalize,\n","    Resize,\n","    RandomHorizontalFlip,\n","    RandomApply,\n","    ColorJitter,\n","    ToTensor,\n",")\n","\n","# Extract mean and std for normalization\n","normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)\n","\n","# Determine the image size based on the image processor\n","if \"height\" in image_processor.size:\n","    size = (image_processor.size[\"height\"], image_processor.size[\"width\"])\n","    crop_size = size\n","    max_size = None\n","elif \"shortest_edge\" in image_processor.size:\n","    size = image_processor.size[\"shortest_edge\"]\n","    crop_size = (size, size)\n","    max_size = image_processor.size.get(\"longest_edge\")\n","\n","# Data preprocessing (Reading -> Resizing -> Normalization)\n","def preprocess(image):\n","    return Compose([\n","        Resize(size),  # Resize the image based on the size from the image processor\n","        ToTensor(),  # Convert to tensor\n","        normalize,  # Normalize the image\n","    ])(image)\n","\n","# Data augmentation (random brightness, contrast, flip)\n","def augment(image):\n","    return Compose([\n","        RandomApply([ColorJitter(brightness=0.2, contrast=(0.2, 0.4))], p=1.0),  # Random brightness & contrast\n","        RandomHorizontalFlip(p=0.5),  # Randomly flip horizontally\n","    ])(image)\n","\n","# Complete preprocessing + augmentation for training\n","def preprocess_train(image):\n","    img = preprocess(image)  # Preprocess the image (resize, normalize)\n","    img = augment(img)  # Apply data augmentation\n","    return img\n","\n","# Preprocessing for validation (no augmentation)\n","def preprocess_val(image):\n","    img = preprocess(image)  # Only preprocess (resize, normalize)\n","    return img\n","\n","# Example functions for batch processing\n","def preprocess_train_batch(example_batch):\n","    # Open the image from the file path and apply the transformations\n","    example_batch[\"pixel_values\"] = [\n","        preprocess_train(Image.open(image_path).convert(\"RGB\")) for image_path in example_batch[\"img\"]\n","    ]\n","    return example_batch\n","\n","def preprocess_val_batch(example_batch):\n","    # Open the image from the file path and apply the transformations (only preprocessing, no augmentation)\n","    example_batch[\"pixel_values\"] = [\n","        preprocess_val(Image.open(image_path).convert(\"RGB\")) for image_path in example_batch[\"img\"]\n","    ]\n","    return example_batch"],"metadata":{"id":"7WjSHJCF5D-r"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_dataset.set_transform(preprocess_train_batch)\n","val_dataset.set_transform(preprocess_val_batch)"],"metadata":{"id":"kjBBbAUf6QF5"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_dataset[0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_sz5-aa0FaBY","executionInfo":{"status":"ok","timestamp":1728232283355,"user_tz":-120,"elapsed":971,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"c346ab8d-cf7b-4e37-cc32-659a236c5c4d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'img': '/content/Financial Data/memo/0001210681.jpeg',\n"," 'label': 0,\n"," 'pixel_values': tensor([[[0.6283, 0.6283, 0.6283,  ..., 0.2725, 0.2725, 0.2725],\n","          [0.6283, 0.6283, 0.6283,  ..., 0.2725, 0.2725, 0.2725],\n","          [0.6283, 0.6283, 0.6283,  ..., 0.2725, 0.2725, 0.2725],\n","          ...,\n","          [0.6283, 0.6283, 0.6283,  ..., 0.6283, 0.6283, 0.6283],\n","          [0.6283, 0.6283, 0.6283,  ..., 0.6283, 0.6283, 0.6283],\n","          [0.6283, 0.6283, 0.6283,  ..., 0.6283, 0.6283, 0.6283]],\n"," \n","         [[0.5841, 0.5841, 0.5841,  ..., 0.1417, 0.1417, 0.1417],\n","          [0.5841, 0.5841, 0.5841,  ..., 0.1417, 0.1417, 0.1417],\n","          [0.5841, 0.5841, 0.5841,  ..., 0.1417, 0.1417, 0.1417],\n","          ...,\n","          [0.5841, 0.5841, 0.5841,  ..., 0.5841, 0.5841, 0.5841],\n","          [0.5841, 0.5841, 0.5841,  ..., 0.5841, 0.5841, 0.5841],\n","          [0.5841, 0.5841, 0.5841,  ..., 0.5841, 0.5841, 0.5841]],\n"," \n","         [[0.2110, 0.2110, 0.2110,  ..., 0.3014, 0.3014, 0.3014],\n","          [0.2110, 0.2110, 0.2110,  ..., 0.3014, 0.3014, 0.3014],\n","          [0.2110, 0.2110, 0.2110,  ..., 0.3014, 0.3014, 0.3014],\n","          ...,\n","          [0.2110, 0.2110, 0.2110,  ..., 0.2110, 0.2110, 0.2110],\n","          [0.2110, 0.2110, 0.2110,  ..., 0.2110, 0.2110, 0.2110],\n","          [0.2110, 0.2110, 0.2110,  ..., 0.2110, 0.2110, 0.2110]]])}"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":173,"referenced_widgets":["85a569cdb3e94e3f9af05428aad7b39c","74b7adfabded4a29a9cb0d8e8536243e","a84b5ac47a4944aabede30ebfe7567c4","3938b15134b24007a7b0e10a3b8a5347","9bed0bc9870d439baf11368ec649f3a3","3fee143deb3f47f5932961b4ca0b7942","32be097ee5414501b214738df3f42dc9","fc3e8f37d3fb402d834443c890d56679","bcb9e144eb1a47d8b12cd40fb30e44e0","a73cbf0d68e74fb6a626c04851de05aa","1514572b421b463ab6e8e1a6cca436d1"]},"id":"X9DDujL0q1ac","outputId":"d895468a-4ed3-4c62-f69a-3ad835e3b790","executionInfo":{"status":"ok","timestamp":1728232291882,"user_tz":-120,"elapsed":3698,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py:469: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/346M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"85a569cdb3e94e3f9af05428aad7b39c"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224 and are newly initialized because the shapes did not match:\n","- classifier.bias: found shape torch.Size([1000]) in the checkpoint and torch.Size([16]) in the model instantiated\n","- classifier.weight: found shape torch.Size([1000, 768]) in the checkpoint and torch.Size([16, 768]) in the model instantiated\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}],"source":["# Use your Hugging Face token here\n","hf_token = \"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\"\n","model = AutoModelForImageClassification.from_pretrained(\n","    model_checkpoint,\n","    label2id=label2id,\n","    id2label=id2label,\n","    ignore_mismatched_sizes = True,use_auth_token=hf_token\n",")\n"]},{"cell_type":"code","source":["# Display the number of parameters in the model\n","num_params = sum(p.numel() for p in model.parameters())\n","print(f\"Number of parameters in the model: {num_params}\")\n","# print(model)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WEXd2J7J8GZ1","executionInfo":{"status":"ok","timestamp":1728232334450,"user_tz":-120,"elapsed":1036,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"2392dbb2-2fbe-497f-b66e-468729adae3d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of parameters in the model: 85810960\n"]}]},{"cell_type":"code","source":["model_name = model_checkpoint.split(\"/\")[-1]\n","\n","\n","args = TrainingArguments(\n","    f\"{model_name}-finetuned-eurosat\",\n","    remove_unused_columns=False,\n","    evaluation_strategy=\"epoch\",\n","    save_strategy=\"epoch\",\n","    learning_rate=3e-3,\n","    per_device_train_batch_size=batch_size,\n","    gradient_accumulation_steps=4,\n","    per_device_eval_batch_size=batch_size,\n","    num_train_epochs=7,\n","    warmup_ratio=0.1,\n","    logging_steps=10,\n","    load_best_model_at_end=True,\n","    metric_for_best_model=\"accuracy\",\n","    push_to_hub=False,\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZtzC3yBL838l","executionInfo":{"status":"ok","timestamp":1728232363066,"user_tz":-120,"elapsed":445,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"11df600e-cf73-45f4-f6f6-395d1f82efa3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n","  warnings.warn(\n"]}]},{"cell_type":"code","source":["from transformers import AdamW, Trainer\n","\n","# Créez l'optimiseur AdamW avec les paramètres personnalisés, mais sans redéfinir le learning_rate\n","optimizer = AdamW(\n","    model.parameters(),\n","    betas=(0.9, 0.999),  # Les betas spécifiés\n","    weight_decay=0.3  # Décroissance des poids\n",")"],"metadata":{"id":"aj3AvVJm7Hew","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1728232371219,"user_tz":-120,"elapsed":820,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"edb50d78-907c-45b2-f4b4-42339335496a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:591: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  warnings.warn(\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EVWfiBuv2uCS"},"outputs":[],"source":["def compute_metrics(eval_pred):\n","    \"\"\"Computes accuracy on a batch of predictions\"\"\"\n","    predictions = np.argmax(eval_pred.predictions, axis=1)\n","    return metric.compute(predictions=predictions, references=eval_pred.label_ids)"]},{"cell_type":"code","source":["trainer = Trainer(\n","    model,\n","    args,\n","    train_dataset=train_dataset,\n","    eval_dataset=val_dataset,\n","    optimizers=(optimizer, None),\n","    compute_metrics=compute_metrics\n",")\n"],"metadata":{"id":"s8Z5zBca8_Wm"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# **Results**"],"metadata":{"id":"Zz0JWiS2bRsw"}},{"cell_type":"code","source":["train_results = trainer.train()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":404},"id":"rdSTMTcCE1rN","executionInfo":{"status":"ok","timestamp":1728239763364,"user_tz":-120,"elapsed":5533315,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"7516c4c5-1a6e-4a4f-b1da-09274c5007c3"},"execution_count":null,"outputs":[{"data":{"text/html":["\n","    <div>\n","      \n","      <progress value='559' max='2100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [ 559/2100 30:31 < 1:24:26, 0.30 it/s, Epoch 1.86/7]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Accuracy</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>1.092900</td>\n","      <td>1.231154</td>\n","      <td>0.626563</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='2100' max='2100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [2100/2100 2:02:45, Epoch 7/7]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Accuracy</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>1.092900</td>\n","      <td>1.231154</td>\n","      <td>0.626563</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>0.939600</td>\n","      <td>1.274263</td>\n","      <td>0.597604</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>0.739900</td>\n","      <td>1.164538</td>\n","      <td>0.636667</td>\n","    </tr>\n","    <tr>\n","      <td>4</td>\n","      <td>0.629600</td>\n","      <td>1.147251</td>\n","      <td>0.650729</td>\n","    </tr>\n","    <tr>\n","      <td>5</td>\n","      <td>0.469600</td>\n","      <td>1.151481</td>\n","      <td>0.658646</td>\n","    </tr>\n","    <tr>\n","      <td>6</td>\n","      <td>0.349800</td>\n","      <td>1.056857</td>\n","      <td>0.701562</td>\n","    </tr>\n","    <tr>\n","      <td>7</td>\n","      <td>0.141800</td>\n","      <td>1.066469</td>\n","      <td>0.716667</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}}]},{"cell_type":"code","source":["metrics = trainer.evaluate()\n","# Log metrics for evaluation\n","trainer.log_metrics(\"eval\", metrics)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":179},"id":"LFjciQ6rKLfU","executionInfo":{"status":"ok","timestamp":1728242473980,"user_tz":-120,"elapsed":139725,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"7419ccd6-f4f5-4e4a-a7b9-49a38e9b54d4"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='300' max='300' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [300/300 02:17]\n","    </div>\n","    "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["***** eval metrics *****\n","  epoch                   =        7.0\n","  eval_accuracy           =     0.7167\n","  eval_loss               =     1.0665\n","  eval_runtime            = 0:02:18.75\n","  eval_samples_per_second =     69.185\n","  eval_steps_per_second   =      2.162\n"]}]},{"cell_type":"code","source":["test_dataset.set_transform(preprocess_val_batch)\n","print(test_dataset[0])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BwSUi3-cN3AX","executionInfo":{"status":"ok","timestamp":1728242889706,"user_tz":-120,"elapsed":1027,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"6a8caed9-c58e-40d6-fb59-14a9b128f04c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'img': '/content/Financial_data_test/advertisement/0000001863.jpeg', 'label': 11, 'pixel_values': tensor([[[ 0.9843,  0.9843,  0.9843,  ...,  0.9843,  0.9843,  0.9843],\n","         [ 0.9843,  0.9843,  0.9843,  ...,  0.9843,  0.9843,  0.9843],\n","         [ 0.9843,  0.9843,  0.9843,  ...,  0.9843,  0.9843,  0.9843],\n","         ...,\n","         [ 0.9843,  0.9843,  0.9843,  ...,  0.9843,  0.9843,  0.9843],\n","         [ 0.9843,  0.9843,  0.9843,  ...,  0.9843,  0.9843,  0.9843],\n","         [ 0.9843,  0.9843,  0.9843,  ...,  0.9843,  0.9843,  0.9843]],\n","\n","        [[ 0.8039,  0.8039,  0.8039,  ...,  0.8039,  0.8039,  0.8039],\n","         [ 0.8039,  0.8039,  0.8039,  ...,  0.8039,  0.8039,  0.8039],\n","         [ 0.8039,  0.8039,  0.8039,  ...,  0.8039,  0.8039,  0.8039],\n","         ...,\n","         [ 0.8039,  0.8039,  0.8039,  ...,  0.8039,  0.8039,  0.8039],\n","         [ 0.8039,  0.8039,  0.8039,  ...,  0.8039,  0.8039,  0.8039],\n","         [ 0.8039,  0.8039,  0.8039,  ...,  0.8039,  0.8039,  0.8039]],\n","\n","        [[-0.7176, -0.7176, -0.7176,  ..., -0.7176, -0.7176, -0.7176],\n","         [-0.7176, -0.7176, -0.7176,  ..., -0.7176, -0.7176, -0.7176],\n","         [-0.7176, -0.7176, -0.7176,  ..., -0.7176, -0.7176, -0.7176],\n","         ...,\n","         [-0.7176, -0.7176, -0.7176,  ..., -0.7176, -0.7176, -0.7176],\n","         [-0.7176, -0.7176, -0.7176,  ..., -0.7176, -0.7176, -0.7176],\n","         [-0.7176, -0.7176, -0.7176,  ..., -0.7176, -0.7176, -0.7176]]])}\n"]}]},{"cell_type":"code","source":["# Evaluation on the test dataset (using eval_dataset)\n","test_metrics = trainer.evaluate(eval_dataset=test_dataset)\n","\n","# Show evaluation metrics\n","trainer.log_metrics(\"test\", test_metrics)\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":179},"id":"M_yzkuNGbKEg","executionInfo":{"status":"ok","timestamp":1728243476779,"user_tz":-120,"elapsed":579802,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"b8cc0ce9-933d-4e45-c80d-9678e33f0819"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='1550' max='300' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [300/300 19:00]\n","    </div>\n","    "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["***** test metrics *****\n","  epoch                   =        7.0\n","  eval_accuracy           =     0.7151\n","  eval_loss               =     1.0775\n","  eval_runtime            = 0:09:38.99\n","  eval_samples_per_second =     69.079\n","  eval_steps_per_second   =      2.159\n"]}]},{"cell_type":"code","source":["import numpy as np\n","predictions = trainer.predict(test_dataset=test_dataset)\n","\n","# Obtenez les prédictions finales (classe avec le score le plus élevé)\n","predicted_classes = np.argmax(predictions.predictions, axis=-1)\n","\n","# Les labels réels\n","true_labels = predictions.label_ids\n","\n","# Comparer les prédictions aux labels réels\n","correct_predictions = (predicted_classes == true_labels)\n","\n","# Afficher combien de prédictions sont correctes\n","print(f\"Nombre de prédictions correctes: {np.sum(correct_predictions)} sur {len(true_labels)}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":54},"id":"7GiRtNt5f8hv","executionInfo":{"status":"ok","timestamp":1728244102459,"user_tz":-120,"elapsed":575725,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"0aa2e18a-cda4-4bf6-8f8b-10698639835a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Nombre de prédictions correctes: 28603 sur 39996\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import classification_report\n","\n","# Labels réels et classes prédites du dataset de test\n","print(classification_report(predictions.label_ids, predicted_classes))\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4lLkxTAUjbwI","executionInfo":{"status":"ok","timestamp":1728244139292,"user_tz":-120,"elapsed":516,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"4eee5617-8479-4ada-d273-5cc6b4dcaff4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.81      0.58      0.68      2492\n","           1       0.88      0.77      0.82      2536\n","           2       0.57      0.41      0.48      2498\n","           3       0.80      0.93      0.86      2527\n","           4       0.51      0.80      0.62      2477\n","           5       0.48      0.48      0.48      2506\n","           6       0.69      0.70      0.69      2505\n","           7       0.97      0.92      0.94      2516\n","           8       0.64      0.79      0.71      2472\n","           9       0.69      0.54      0.61      2489\n","          10       0.91      0.84      0.87      2532\n","          11       0.82      0.84      0.83      2515\n","          12       0.90      0.73      0.81      2569\n","          13       0.48      0.70      0.57      2435\n","          14       0.80      0.78      0.79      2463\n","          15       0.77      0.63      0.69      2464\n","\n","    accuracy                           0.72     39996\n","   macro avg       0.73      0.71      0.72     39996\n","weighted avg       0.73      0.72      0.72     39996\n","\n"]}]},{"cell_type":"markdown","source":["# **Saving the model**"],"metadata":{"id":"6vSZYinsbZH5"}},{"cell_type":"code","source":["# Specify the directory in your Google Drive where you want to save the model\n","save_path = '/content/drive/MyDrive/deeplearning/trained_modelVIT/'\n","\n","# Save the trained model\n","trainer.save_model(save_path)\n","\n","# Save the training state (optional, for resuming training later)\n","trainer.state.save_to_json(save_path + 'trainer_state.json')"],"metadata":{"id":"gvJ8DRo2mSdi"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Specify the path where your model is saved in Google Drive\n","model_path = '/content/drive/MyDrive/deeplearning/trained_modelVIT/'\n","\n","# Load the model\n","model = AutoModelForImageClassification.from_pretrained(model_path)\n","print(model)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cdkyHCxmNWbe","executionInfo":{"status":"ok","timestamp":1728244300878,"user_tz":-120,"elapsed":514,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"ed0df659-e679-4eed-8a30-eb86f3839aae"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["ViTForImageClassification(\n","  (vit): ViTModel(\n","    (embeddings): ViTEmbeddings(\n","      (patch_embeddings): ViTPatchEmbeddings(\n","        (projection): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n","      )\n","      (dropout): Dropout(p=0.0, inplace=False)\n","    )\n","    (encoder): ViTEncoder(\n","      (layer): ModuleList(\n","        (0-11): 12 x ViTLayer(\n","          (attention): ViTSdpaAttention(\n","            (attention): ViTSdpaSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","            (output): ViTSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","          )\n","          (intermediate): ViTIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","            (intermediate_act_fn): GELUActivation()\n","          )\n","          (output): ViTOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","          )\n","          (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","          (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","        )\n","      )\n","    )\n","    (layernorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","  )\n","  (classifier): Linear(in_features=768, out_features=16, bias=True)\n",")\n"]}]},{"cell_type":"code","source":["import torch\n","from PIL import Image\n","\n","# Chemin de l'image individuelle\n","path = '/content/Financial_data_test/invoice/2028719701_2028719702.jpeg'\n","image = Image.open(path)\n","image\n","# Appliquer le même prétraitement que dans preprocess_val_batch\n","# Assurez-vous que preprocess_val fait les mêmes transformations que celles appliquées sur tout le dataset\n","pixel_values = preprocess_val(image.convert(\"RGB\"))  # Utilisez preprocess_val ici\n","\n","# Ajoutez une dimension de batch pour que le tenseur soit de forme [1, num_channels, height, width]\n","pixel_values = pixel_values.unsqueeze(0)\n","\n","# Forward pass pour obtenir les prédictions\n","with torch.no_grad():\n","    outputs = model(pixel_values=pixel_values)\n","    logits = outputs.logits\n","    predicted_class_idx = logits.argmax(-1).item()\n","\n","# Afficher la classe prédite\n","print(\"Classe prédite :\", model.config.id2label[predicted_class_idx])\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FT4HRhYgkycE","executionInfo":{"status":"ok","timestamp":1728244330371,"user_tz":-120,"elapsed":1726,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"f6f3c86b-a241-408b-8cac-e355783fa04a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Classe prédite : invoice\n"]}]},{"cell_type":"markdown","source":["# **clonning to github**"],"metadata":{"id":"e6ZWgZVQb1A_"}},{"cell_type":"code","source":["# Step 1: Mount Google Drive\n","from google.colab import drive\n","drive.mount('/content/drive')\n","\n","# Step 2: Change directory to where you want to clone the repository\n","%cd /content/drive/MyDrive/\n","\n","# Step 3: Clone the GitHub repository (replace with your personal access token for authentication)\n","!git clone https://your-username:your-token@github.com/username/repository-name.git\n","\n","# Step 4: Navigate to the cloned repository\n","%cd /content/drive/MyDrive/repository-name\n","\n","# Step 5: Copy the file into the repository (make sure the path to the file is correct)\n","!cp /content/drive/MyDrive/deeplearning/VIT_model_training.ipynb /content/drive/MyDrive/repository-name/\n","\n","# Step 6: Stage, commit, and push changes\n","!git add VIT_model_training.ipynb\n","!git commit -m \"Add VIT_model_training.ipynb file\"\n","!git push origin main  # Replace 'main' with the correct branch name if different\n"],"metadata":{"id":"PzbJ5WQVd_Cd","executionInfo":{"status":"ok","timestamp":1730040826786,"user_tz":-60,"elapsed":2905,"user":{"displayName":"maryam teymouri","userId":"13555925591657049929"}},"outputId":"8fab45b7-bc98-40b7-a736-5fede40ea925","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n","/content/drive/MyDrive\n","Cloning into 'repository-name'...\n","remote: Support for password authentication was removed on August 13, 2021.\n","remote: Please see https://docs.github.com/get-started/getting-started-with-git/about-remote-repositories#cloning-with-https-urls for information on currently recommended modes of authentication.\n","fatal: Authentication failed for 'https://github.com/username/repository-name.git/'\n","[Errno 2] No such file or directory: '/content/drive/MyDrive/repository-name'\n","/content/drive/MyDrive\n","cp: cannot stat '/content/drive/MyDrive/deeplearning/VIT_model_training.ipynb': No such file or directory\n","fatal: not a git repository (or any parent up to mount point /content)\n","Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).\n","fatal: not a git repository (or any parent up to mount point /content)\n","Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).\n","fatal: not a git repository (or any parent up to mount point /content)\n","Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).\n"]}]}]}