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"Guiding Future STEM Leaders through Innovative Research Training" ~ thinkingbeyond.education
Project: stephanie's main branch
Path: ThinkingBeyond Activities / BeyondAI-2024-Mentee-Projects / nafiul / Diabetic_Retinopathy_Detection_Using_CNN.ipynb~1
Views: 1103Image: ubuntu2204
{
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"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"Vision Transformer Code: https://colab.research.google.com/drive/1UD_bybmnndzhi-tvuaqvHNHP5RWIq5zY?usp=sharing"
],
"metadata": {
"id": "GBAOoWEIDh-O"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8WGhXYYdqa--"
},
"outputs": [],
"source": [
"from tensorflow import lite\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"import numpy as np\n",
"import pandas as pd\n",
"import random, os\n",
"import shutil\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.image import imread\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"from tensorflow.keras.metrics import categorical_accuracy\n",
"from sklearn.model_selection import train_test_split\n",
"\n"
]
},
{
"cell_type": "code",
"source": [
"import kagglehub\n",
"\n",
"# Download latest version\n",
"path = kagglehub.dataset_download(\"sovitrath/diabetic-retinopathy-224x224-2019-data\")\n",
"\n",
"print(\"Path to dataset files:\", path)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0RcpxR65T4EV",
"outputId": "3fab5ed6-d269-41c3-a70e-5a97dc459ede"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading from https://www.kaggle.com/api/v1/datasets/download/sovitrath/diabetic-retinopathy-224x224-2019-data?dataset_version_number=4...\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 238M/238M [00:02<00:00, 95.5MB/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Extracting files...\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Path to dataset files: /root/.cache/kagglehub/datasets/sovitrath/diabetic-retinopathy-224x224-2019-data/versions/4\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Add an additional column, mapping to the type\n",
"df = pd.read_csv(r'../root/.cache/kagglehub/datasets/sovitrath/diabetic-retinopathy-224x224-2019-data/versions/4/train.csv')\n",
"\n",
"diagnosis_dict_binary = {\n",
" 0: 'No_DR',\n",
" 1: 'DR',\n",
" 2: 'DR',\n",
" 3: 'DR',\n",
" 4: 'DR'\n",
"}\n",
"\n",
"diagnosis_dict = {\n",
" 0: 'No_DR',\n",
" 1: 'Mild',\n",
" 2: 'Moderate',\n",
" 3: 'Severe',\n",
" 4: 'Proliferate_DR',\n",
"}\n",
"\n",
"\n",
"df['binary_type'] = df['diagnosis'].map(diagnosis_dict_binary.get)\n",
"df['type'] = df['diagnosis'].map(diagnosis_dict.get)\n",
"df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
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"id": "TVjQOD_DknKd",
"outputId": "3e77f7ea-3701-4fa5-dca7-66051f2dc433"
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"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" id_code diagnosis binary_type type\n",
"0 000c1434d8d7 2 DR Moderate\n",
"1 001639a390f0 4 DR Proliferate_DR\n",
"2 0024cdab0c1e 1 DR Mild\n",
"3 002c21358ce6 0 No_DR No_DR\n",
"4 005b95c28852 0 No_DR No_DR"
],
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"\n",
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"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df",
"summary": "{\n \"name\": \"df\",\n \"rows\": 3662,\n \"fields\": [\n {\n \"column\": \"id_code\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3662,\n \"samples\": [\n \"90960ddf4d14\",\n \"4e0656629d02\",\n \"3b018e8b7303\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"diagnosis\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 0,\n \"max\": 4,\n \"num_unique_values\": 5,\n \"samples\": [\n 4,\n 3,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"binary_type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No_DR\",\n \"DR\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"Proliferate_DR\",\n \"Severe\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"source": [
"train_intermediate, val = train_test_split(df, test_size = 0.15, stratify = df['type'])\n",
"train, test = train_test_split(train_intermediate, test_size = 0.15 / (1 - 0.15), stratify = train_intermediate['type'])\n",
"\n",
"#print number\n",
"print(train['type'].value_counts(), '\\n')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PwW1qeynVi3Z",
"outputId": "9dd1e339-35cd-4e25-c65c-966f29f12fae"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"type\n",
"No_DR 1263\n",
"Moderate 699\n",
"Mild 258\n",
"Proliferate_DR 207\n",
"Severe 135\n",
"Name: count, dtype: int64 \n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#create directories for train, val and test. update it everytime the code runs with shutil.rmtree\n",
"base_dir = '.\\dataset'\n",
"\n",
"\n",
"train_dir = os.path.join(base_dir, 'train')\n",
"val_dir = os.path.join(base_dir, 'val')\n",
"test_dir = os.path.join(base_dir, 'test')\n",
"\n",
"if os.path.exists(base_dir):\n",
" shutil.rmtree(base_dir)\n",
"\n",
"#make directories for train, val and test\n",
"os.makedirs(train_dir, exist_ok=True)\n",
"os.makedirs(val_dir, exist_ok=True)\n",
"os.makedirs(test_dir, exist_ok=True)\n",
"#ensure the directories have the same columns\n",
"valid_types = ['No_DR', 'Mild', 'Moderate', 'Severe', 'Proliferate_DR']\n",
"df = df[df['type'].isin(valid_types)]\n",
"assert all(train['type'].isin(valid_types))\n",
"assert all(val['type'].isin(valid_types))\n",
"assert all(test['type'].isin(valid_types))"
],
"metadata": {
"id": "2yChIiyNVkKY"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#copy the files from the sourcefile to train, validation and test directory respectively\n",
"src_dir = r'../root/.cache/kagglehub/datasets/sovitrath/diabetic-retinopathy-224x224-2019-data/versions/4/colored_images'\n",
"for index, row in train.iterrows():\n",
" diagnosis = row['type']\n",
" binary_diagnosis = row['binary_type']\n",
" id_code = row['id_code'] + \".png\"\n",
" srcfile = os.path.join(src_dir, diagnosis, id_code)\n",
" dstfile = os.path.join(train_dir, diagnosis)\n",
" os.makedirs(dstfile, exist_ok = True)\n",
" if os.path.exists(srcfile):\n",
" shutil.copy(srcfile, dstfile)\n",
"for index, row in val.iterrows():\n",
" diagnosis = row['type']\n",
" binary_diagnosis = row['binary_type']\n",
" id_code = row['id_code'] + \".png\"\n",
" srcfile = os.path.join(src_dir, diagnosis, id_code)\n",
" dstfile = os.path.join(val_dir, diagnosis)\n",
" os.makedirs(dstfile, exist_ok=True)\n",
" if os.path.exists(srcfile):\n",
" shutil.copy(srcfile, dstfile)\n",
"\n",
"for index, row in test.iterrows():\n",
" diagnosis = row['type']\n",
" binary_diagnosis = row['binary_type']\n",
" id_code = row['id_code'] + \".png\"\n",
" srcfile = os.path.join(src_dir, diagnosis, id_code)\n",
" dstfile = os.path.join(test_dir, diagnosis)\n",
" os.makedirs(dstfile, exist_ok=True)\n",
" if os.path.exists(srcfile):\n",
" shutil.copy(srcfile, dstfile)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"for subdir in [train_dir, val_dir, test_dir]:\n",
" print(f\"\\nContents of {subdir}:\")\n",
" for root, dirs, files in os.walk(subdir):\n",
" print(f\"{root}: {len(files)} files\")\n",
"\n",
"\n"
],
"metadata": {
"id": "RnSpw1uQbj5O",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b5d264a6-60c5-4d27-de41-98b301aa82d8"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Contents of .\\dataset/train:\n",
".\\dataset/train: 0 files\n",
".\\dataset/train/Moderate: 699 files\n",
".\\dataset/train/Mild: 258 files\n",
".\\dataset/train/Severe: 135 files\n",
".\\dataset/train/Proliferate_DR: 207 files\n",
".\\dataset/train/No_DR: 1263 files\n",
"\n",
"Contents of .\\dataset/val:\n",
".\\dataset/val: 0 files\n",
".\\dataset/val/Moderate: 150 files\n",
".\\dataset/val/Mild: 56 files\n",
".\\dataset/val/Severe: 29 files\n",
".\\dataset/val/Proliferate_DR: 44 files\n",
".\\dataset/val/No_DR: 271 files\n",
"\n",
"Contents of .\\dataset/test:\n",
".\\dataset/test: 0 files\n",
".\\dataset/test/Moderate: 150 files\n",
".\\dataset/test/Mild: 56 files\n",
".\\dataset/test/Severe: 29 files\n",
".\\dataset/test/Proliferate_DR: 44 files\n",
".\\dataset/test/No_DR: 271 files\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"train_path = train_dir\n",
"val_path = val_dir\n",
"test_path = test_dir\n",
"\n",
"train_batches = ImageDataGenerator(rescale = 1./255).flow_from_directory(train_path, target_size=(224,224), shuffle = True)\n",
"val_batches = ImageDataGenerator(rescale = 1./255).flow_from_directory(val_path, target_size=(224,224), shuffle = True)\n",
"test_batches = ImageDataGenerator(rescale = 1./255).flow_from_directory(test_path, target_size=(224,224), shuffle = False)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qOXxFZg-fLsL",
"outputId": "138309c1-cf63-499d-9c81-de0d7eafa7bc"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Found 2562 images belonging to 5 classes.\n",
"Found 550 images belonging to 5 classes.\n",
"Found 550 images belonging to 5 classes.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#cnn model\n",
"model = tf.keras.Sequential([\n",
" layers.Conv2D(8, (3,3), padding=\"valid\", input_shape=(224,224,3), activation = 'relu'),\n",
" layers.MaxPooling2D(pool_size=(2,2)),\n",
" layers.BatchNormalization(),\n",
"\n",
" layers.Conv2D(16, (3,3), padding=\"valid\", activation = 'relu'),\n",
" layers.MaxPooling2D(pool_size=(2,2)),\n",
" layers.BatchNormalization(),\n",
"\n",
" layers.Conv2D(32, (4,4), padding=\"valid\", activation = 'relu'),\n",
" layers.MaxPooling2D(pool_size=(2,2)),\n",
" layers.BatchNormalization(),\n",
" layers.Flatten(),\n",
" layers.Dense(32, activation = 'relu'),\n",
" layers.Dropout(0.15),\n",
" layers.Dense(5, activation = 'softmax')\n",
"])\n",
"\n",
"model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-5),\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
"\n",
"history = model.fit(train_batches,\n",
" epochs=50,\n",
" validation_data=val_batches)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YM5WCYAziRiy",
"outputId": "f97c448d-db1c-4a63-bfc4-d8661f49a40a"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/10\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
" self._warn_if_super_not_called()\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 1s/step - accuracy: 0.4029 - loss: 1.5315 - val_accuracy: 0.5673 - val_loss: 1.5060\n",
"Epoch 2/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 1s/step - accuracy: 0.6735 - loss: 0.9081 - val_accuracy: 0.6727 - val_loss: 1.3945\n",
"Epoch 3/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 1s/step - accuracy: 0.7026 - loss: 0.8166 - val_accuracy: 0.6945 - val_loss: 1.2591\n",
"Epoch 4/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 1s/step - accuracy: 0.7062 - loss: 0.8137 - val_accuracy: 0.6855 - val_loss: 1.1358\n",
"Epoch 5/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 1s/step - accuracy: 0.7130 - loss: 0.7965 - val_accuracy: 0.7127 - val_loss: 0.9882\n",
"Epoch 6/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 1s/step - accuracy: 0.7532 - loss: 0.7223 - val_accuracy: 0.7327 - val_loss: 0.8859\n",
"Epoch 7/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 1s/step - accuracy: 0.7584 - loss: 0.7100 - val_accuracy: 0.7382 - val_loss: 0.8226\n",
"Epoch 8/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m94s\u001b[0m 1s/step - accuracy: 0.7351 - loss: 0.7226 - val_accuracy: 0.7327 - val_loss: 0.7908\n",
"Epoch 9/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 1s/step - accuracy: 0.7378 - loss: 0.7089 - val_accuracy: 0.7545 - val_loss: 0.7670\n",
"Epoch 10/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m140s\u001b[0m 1s/step - accuracy: 0.7330 - loss: 0.7138 - val_accuracy: 0.7545 - val_loss: 0.7665\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score\n",
"\n",
"# Custom Callback to compute metrics after each epoch\n",
"class MetricsCallback(tf.keras.callbacks.Callback):\n",
" def __init__(self, validation_batches):\n",
" self.val_batches = validation_batches\n",
" self.sensitivity = []\n",
" self.specificity = []\n",
" self.f1_scores = []\n",
"\n",
" def on_epoch_end(self, epoch, logs=None):\n",
" # Get predictions and ground truths\n",
" y_pred_probs = self.model.predict(self.val_batches) # Probabilities\n",
" y_pred = np.argmax(y_pred_probs, axis=1) # Predicted classes\n",
" y_true = self.val_batches.labels # True labels\n",
"\n",
" # Compute confusion matrix\n",
" cm = confusion_matrix(y_true, y_pred)\n",
" tp = np.diag(cm) # True positives for each class\n",
" fp = np.sum(cm, axis=0) - tp # False positives for each class\n",
" fn = np.sum(cm, axis=1) - tp # False negatives for each class\n",
" tn = np.sum(cm) - (tp + fp + fn) # True negatives for each class\n",
"\n",
" # Avoid division by zero\n",
" recall = tp / (tp + fn + np.finfo(float).eps) # Sensitivity (Recall)\n",
" specificity = tn / (tn + fp + np.finfo(float).eps)\n",
" precision = tp / (tp + fp + np.finfo(float).eps)\n",
" f1 = 2 * (precision * recall) / (precision + recall + np.finfo(float).eps)\n",
"\n",
" # Store averages across classes\n",
" self.sensitivity.append(np.mean(recall))\n",
" self.specificity.append(np.mean(specificity))\n",
" self.f1_scores.append(np.mean(f1))\n",
"\n",
"# Initialize callback\n",
"metrics_callback = MetricsCallback(validation_batches=val_batches)\n",
"\n",
"# Train the model with the callback\n",
"history = model.fit(train_batches,\n",
" epochs=50,\n",
" validation_data=val_batches,\n",
" callbacks=[metrics_callback])\n",
"\n",
"# Plot Accuracy\n",
"plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
"plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Accuracy')\n",
"plt.title('Accuracy vs Epochs')\n",
"plt.legend()\n",
"plt.savefig('cnn acc vs epoch.png')\n",
"plt.show()\n",
"\n",
"# Plot Sensitivity, Specificity, and F1-score\n",
"epochs = range(1, len(metrics_callback.sensitivity) + 1)\n",
"plt.plot(epochs, metrics_callback.sensitivity, label='Sensitivity')\n",
"plt.plot(epochs, metrics_callback.specificity, label='Specificity')\n",
"plt.plot(epochs, metrics_callback.f1_scores, label='F1 Score')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Metrics')\n",
"plt.title('Sensitivity, Specificity, and F1-Score vs Epochs')\n",
"plt.legend()\n",
"plt.savefig('cnn spec f1 vs epoch.png')\n",
"plt.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "Qj9X5SIpKRFg",
"outputId": "c196f1c2-626f-4974-84c9-0b742bf74033"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 440ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 1s/step - accuracy: 0.8073 - loss: 0.5413 - val_accuracy: 0.7327 - val_loss: 0.7587\n",
"Epoch 2/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 418ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 1s/step - accuracy: 0.8078 - loss: 0.5459 - val_accuracy: 0.7400 - val_loss: 0.7507\n",
"Epoch 3/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 327ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 1s/step - accuracy: 0.8205 - loss: 0.5216 - val_accuracy: 0.7327 - val_loss: 0.7526\n",
"Epoch 4/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 320ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 1s/step - accuracy: 0.8220 - loss: 0.5207 - val_accuracy: 0.7327 - val_loss: 0.7552\n",
"Epoch 5/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 332ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 1s/step - accuracy: 0.8213 - loss: 0.5000 - val_accuracy: 0.7364 - val_loss: 0.7481\n",
"Epoch 6/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 316ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 1s/step - accuracy: 0.8227 - loss: 0.5007 - val_accuracy: 0.7400 - val_loss: 0.7574\n",
"Epoch 7/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 429ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 1s/step - accuracy: 0.8283 - loss: 0.4953 - val_accuracy: 0.7364 - val_loss: 0.7528\n",
"Epoch 8/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 324ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 1s/step - accuracy: 0.8353 - loss: 0.4914 - val_accuracy: 0.7418 - val_loss: 0.7406\n",
"Epoch 9/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 326ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 1s/step - accuracy: 0.8286 - loss: 0.4643 - val_accuracy: 0.7382 - val_loss: 0.7535\n",
"Epoch 10/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 322ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 1s/step - accuracy: 0.8268 - loss: 0.4771 - val_accuracy: 0.7436 - val_loss: 0.7535\n",
"Epoch 11/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 432ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 1s/step - accuracy: 0.8484 - loss: 0.4625 - val_accuracy: 0.7382 - val_loss: 0.7537\n",
"Epoch 12/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 331ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 1s/step - accuracy: 0.8515 - loss: 0.4411 - val_accuracy: 0.7345 - val_loss: 0.7652\n",
"Epoch 13/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 322ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 1s/step - accuracy: 0.8459 - loss: 0.4426 - val_accuracy: 0.7364 - val_loss: 0.7536\n",
"Epoch 14/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 326ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 1s/step - accuracy: 0.8416 - loss: 0.4430 - val_accuracy: 0.7345 - val_loss: 0.7559\n",
"Epoch 15/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 324ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 1s/step - accuracy: 0.8490 - loss: 0.4353 - val_accuracy: 0.7455 - val_loss: 0.7509\n",
"Epoch 16/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 436ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 1s/step - accuracy: 0.8572 - loss: 0.4196 - val_accuracy: 0.7400 - val_loss: 0.7536\n",
"Epoch 17/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 337ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 1s/step - accuracy: 0.8584 - loss: 0.4279 - val_accuracy: 0.7509 - val_loss: 0.7486\n",
"Epoch 18/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 380ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 1s/step - accuracy: 0.8700 - loss: 0.4032 - val_accuracy: 0.7491 - val_loss: 0.7484\n",
"Epoch 19/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 317ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 1s/step - accuracy: 0.8678 - loss: 0.4065 - val_accuracy: 0.7436 - val_loss: 0.7501\n",
"Epoch 20/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 329ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 1s/step - accuracy: 0.8708 - loss: 0.3996 - val_accuracy: 0.7473 - val_loss: 0.7510\n",
"Epoch 21/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 433ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 1s/step - accuracy: 0.8808 - loss: 0.3935 - val_accuracy: 0.7382 - val_loss: 0.7557\n",
"Epoch 22/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 434ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 1s/step - accuracy: 0.8671 - loss: 0.4118 - val_accuracy: 0.7400 - val_loss: 0.7632\n",
"Epoch 23/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 429ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 1s/step - accuracy: 0.8693 - loss: 0.3885 - val_accuracy: 0.7527 - val_loss: 0.7595\n",
"Epoch 24/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 314ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 1s/step - accuracy: 0.8671 - loss: 0.3895 - val_accuracy: 0.7418 - val_loss: 0.7526\n",
"Epoch 25/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 433ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 1s/step - accuracy: 0.8796 - loss: 0.3842 - val_accuracy: 0.7364 - val_loss: 0.7678\n",
"Epoch 26/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 429ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 1s/step - accuracy: 0.8792 - loss: 0.3600 - val_accuracy: 0.7491 - val_loss: 0.7655\n",
"Epoch 27/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 431ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 1s/step - accuracy: 0.8941 - loss: 0.3391 - val_accuracy: 0.7400 - val_loss: 0.7691\n",
"Epoch 28/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 321ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 1s/step - accuracy: 0.8877 - loss: 0.3532 - val_accuracy: 0.7418 - val_loss: 0.7658\n",
"Epoch 29/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 325ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 1s/step - accuracy: 0.8948 - loss: 0.3400 - val_accuracy: 0.7436 - val_loss: 0.7761\n",
"Epoch 30/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 324ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 1s/step - accuracy: 0.8936 - loss: 0.3344 - val_accuracy: 0.7527 - val_loss: 0.7652\n",
"Epoch 31/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 320ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 1s/step - accuracy: 0.8954 - loss: 0.3314 - val_accuracy: 0.7327 - val_loss: 0.7747\n",
"Epoch 32/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 370ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 1s/step - accuracy: 0.9006 - loss: 0.3306 - val_accuracy: 0.7436 - val_loss: 0.7662\n",
"Epoch 33/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 433ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 1s/step - accuracy: 0.8943 - loss: 0.3268 - val_accuracy: 0.7436 - val_loss: 0.7721\n",
"Epoch 34/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 314ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 1s/step - accuracy: 0.9086 - loss: 0.3117 - val_accuracy: 0.7436 - val_loss: 0.7863\n",
"Epoch 35/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 315ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 1s/step - accuracy: 0.9065 - loss: 0.2979 - val_accuracy: 0.7418 - val_loss: 0.7767\n",
"Epoch 36/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 334ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 1s/step - accuracy: 0.9149 - loss: 0.3005 - val_accuracy: 0.7455 - val_loss: 0.7790\n",
"Epoch 37/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 431ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 1s/step - accuracy: 0.9074 - loss: 0.2953 - val_accuracy: 0.7364 - val_loss: 0.7779\n",
"Epoch 38/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 320ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 1s/step - accuracy: 0.9103 - loss: 0.3055 - val_accuracy: 0.7509 - val_loss: 0.7785\n",
"Epoch 39/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 491ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 1s/step - accuracy: 0.9185 - loss: 0.2735 - val_accuracy: 0.7418 - val_loss: 0.7780\n",
"Epoch 40/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 422ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 1s/step - accuracy: 0.9141 - loss: 0.2989 - val_accuracy: 0.7364 - val_loss: 0.7859\n",
"Epoch 41/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 506ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 1s/step - accuracy: 0.9229 - loss: 0.2660 - val_accuracy: 0.7382 - val_loss: 0.7951\n",
"Epoch 42/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 429ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 1s/step - accuracy: 0.9228 - loss: 0.2747 - val_accuracy: 0.7491 - val_loss: 0.7982\n",
"Epoch 43/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 422ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 1s/step - accuracy: 0.9129 - loss: 0.2866 - val_accuracy: 0.7345 - val_loss: 0.7970\n",
"Epoch 44/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 321ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 1s/step - accuracy: 0.9183 - loss: 0.2825 - val_accuracy: 0.7382 - val_loss: 0.7967\n",
"Epoch 45/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 321ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 1s/step - accuracy: 0.9215 - loss: 0.2690 - val_accuracy: 0.7436 - val_loss: 0.7990\n",
"Epoch 46/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 317ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 1s/step - accuracy: 0.9264 - loss: 0.2446 - val_accuracy: 0.7364 - val_loss: 0.7988\n",
"Epoch 47/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 395ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 1s/step - accuracy: 0.9116 - loss: 0.2727 - val_accuracy: 0.7436 - val_loss: 0.7939\n",
"Epoch 48/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 317ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m140s\u001b[0m 1s/step - accuracy: 0.9241 - loss: 0.2640 - val_accuracy: 0.7509 - val_loss: 0.7995\n",
"Epoch 49/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 537ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 1s/step - accuracy: 0.9366 - loss: 0.2348 - val_accuracy: 0.7545 - val_loss: 0.7973\n",
"Epoch 50/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 396ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m140s\u001b[0m 1s/step - accuracy: 0.9305 - loss: 0.2520 - val_accuracy: 0.7455 - val_loss: 0.7954\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"files.download('cnn acc vs epoch.png')\n",
"files.download('cnn spec f1 vs epoch.png')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"id": "fmRa8IcOTLkI",
"outputId": "af8ef24b-3d4e-4361-ed42-65f41e2f6cbc"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"\n",
" async function download(id, filename, size) {\n",
" if (!google.colab.kernel.accessAllowed) {\n",
" return;\n",
" }\n",
" const div = document.createElement('div');\n",
" const label = document.createElement('label');\n",
" label.textContent = `Downloading \"${filename}\": `;\n",
" div.appendChild(label);\n",
" const progress = document.createElement('progress');\n",
" progress.max = size;\n",
" div.appendChild(progress);\n",
" document.body.appendChild(div);\n",
"\n",
" const buffers = [];\n",
" let downloaded = 0;\n",
"\n",
" const channel = await google.colab.kernel.comms.open(id);\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
"\n",
" for await (const message of channel.messages) {\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
" if (message.buffers) {\n",
" for (const buffer of message.buffers) {\n",
" buffers.push(buffer);\n",
" downloaded += buffer.byteLength;\n",
" progress.value = downloaded;\n",
" }\n",
" }\n",
" }\n",
" const blob = new Blob(buffers, {type: 'application/binary'});\n",
" const a = document.createElement('a');\n",
" a.href = window.URL.createObjectURL(blob);\n",
" a.download = filename;\n",
" div.appendChild(a);\n",
" a.click();\n",
" div.remove();\n",
" }\n",
" "
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"download(\"download_09c4daec-a244-4b57-9fe7-25703f2fb2bb\", \"cnn acc vs epoch.png\", 39993)"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"\n",
" async function download(id, filename, size) {\n",
" if (!google.colab.kernel.accessAllowed) {\n",
" return;\n",
" }\n",
" const div = document.createElement('div');\n",
" const label = document.createElement('label');\n",
" label.textContent = `Downloading \"${filename}\": `;\n",
" div.appendChild(label);\n",
" const progress = document.createElement('progress');\n",
" progress.max = size;\n",
" div.appendChild(progress);\n",
" document.body.appendChild(div);\n",
"\n",
" const buffers = [];\n",
" let downloaded = 0;\n",
"\n",
" const channel = await google.colab.kernel.comms.open(id);\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
"\n",
" for await (const message of channel.messages) {\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
" if (message.buffers) {\n",
" for (const buffer of message.buffers) {\n",
" buffers.push(buffer);\n",
" downloaded += buffer.byteLength;\n",
" progress.value = downloaded;\n",
" }\n",
" }\n",
" }\n",
" const blob = new Blob(buffers, {type: 'application/binary'});\n",
" const a = document.createElement('a');\n",
" a.href = window.URL.createObjectURL(blob);\n",
" a.download = filename;\n",
" div.appendChild(a);\n",
" a.click();\n",
" div.remove();\n",
" }\n",
" "
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"download(\"download_d4f6269d-e3e7-4295-8b54-975e06234979\", \"cnn spec f1 vs epoch.png\", 38896)"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"model.save('64x3-CNN.keras')"
],
"metadata": {
"id": "Dio44z4w62QL"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"test_loss, test_accuracy = model.evaluate(test_batches)\n",
"print(f\"Test Accuracy: {test_accuracy * 100:.2f}%\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "44sSxmGdSrAH",
"outputId": "5b045e9a-46a5-47b5-a54b-b94f021a158f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 329ms/step - accuracy: 0.7009 - loss: 0.8493\n",
"Test Accuracy: 73.82%\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import roc_curve, auc\n",
"from sklearn.preprocessing import label_binarize\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"y_test = test_batches.labels\n",
"# Binarize the output (e.g., for 5 classes)\n",
"y_test_bin = label_binarize(y_test, classes=[0, 1, 2, 3, 4])\n",
"n_classes = y_test_bin.shape[1] # Number of classes\n",
"\n",
"# Ensure predictions are also in the right shape\n",
"y_pred_probs = model.predict(test_batches) # Probabilities for all classes\n",
"\n",
"# Compute ROC curve and AUC for each class\n",
"fpr = {}\n",
"tpr = {}\n",
"roc_auc = {}\n",
"\n",
"for i in range(n_classes):\n",
" fpr[i], tpr[i], _ = roc_curve(y_test_bin[:, i], y_pred_probs[:, i])\n",
" roc_auc[i] = auc(fpr[i], tpr[i])\n",
"\n",
"# Plot all ROC curves\n",
"plt.figure()\n",
"colors = ['blue', 'green', 'red', 'cyan', 'magenta']\n",
"for i in range(n_classes):\n",
" plt.plot(fpr[i], tpr[i], color=colors[i], lw=2,\n",
" label=f'Class {i} (AUC = {roc_auc[i]:.2f})')\n",
"\n",
"# Add random guess line\n",
"plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('Receiver Operating Characteristic for Multi-Class')\n",
"plt.legend(loc='lower right')\n",
"plt.savefig('cnn roc curve.png')\n",
"plt.show()\n",
"\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 544
},
"id": "8vxZfaCOJgiY",
"outputId": "73ce4818-40e5-4d5c-cadd-66a1a44ac9a0"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
" self._warn_if_super_not_called()\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 435ms/step\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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UJDnylJUANWzYUGZbDw8P7jagMte5vLhzc3Mxf/58rmzU0tISVlZWSE9Pl/kQV9a7H2QlCUvJH7yHDx8CABo0aCC1nba2tkJN1yYmJgDKv76ViQsAd5vQ0NAQZmZmsLKywmeffQYAcpMbeRQ5RlxcHABU+D4oi7LvD0V/d7/77jvcuHEDTk5O8Pf3R0REBJeUKqtkv8q+RnkePnzI3f4rreRWacnvVglFX/e7Bg8ezN1O2blzJwYPHlyp46hKQUEBkpOTpX7EYnGl3wulKfq34Msvv0R6ejo8PDzQrFkzzJw5E9euXePW6+rqYvHixThw4ABsbGwQGBiI7777DsnJyZV/4TUI9bmpofz9/blqqT59+qBt27YYPHgw7t69CyMjI0gkEgDAjBkz5H6bBWQ/zMojkUjQrFkz/PDDD3LXOzk5lbt/eHg4Fi5ciNTUVBgbG2P37t0YNGgQ11JQEu/QoUNl+uaUaN68udRzRb8BNm7cGDt37sS1a9cQGBgod5uSPwqKfJsurTLXWV7ckydPRmRkJD755BMEBATA1NQUAoEAAwcO5M5RWe+2EpVgKhrbx9PTEwBw/fp1+Pj4KLxfRXHFxcWhc+fO8PT0xA8//AAnJyeIRCLs378fy5Ytk7ku8q6rsseoLGXfH4r+7g4YMADt2rXDjh07cOjQISxZsgSLFy/G9u3b0a1btyrHrWmVrV4LCQmBrq4uRowYgfz8/HIrQstqhRKLxWX+zinr9OnT6Nixo9SyhIQENGjQANra2rh+/Xqlj63o34LAwEDExcVh165dOHToEFavXo1ly5Zh5cqV3NhAn3zyCXr16oWdO3fi4MGDmDdvHhYtWoRjx46hRYsWlY6xJqDkphYQCoVYtGgROnbsiF9++QWzZ8+Gm5sbAEBHR0eqg6w87u7uFZYmuru74+rVq+jcuXOlmrDDw8PxxRdfYNu2bbCxsUFmZiYGDhzIrbeysoKxsTHEYnGF8SqrZ8+eWLRoETZs2CA3uRGLxdi0aRPMzc3Rpk0bqXXymnzv3bvHtWgoc53LEx0djREjRmDp0qXcsry8PKSnp0ttV9nbB+Upad5/8OCB1B/soqIiJCYmyiSV7+rWrRuEQiH+/PNPpTsVl2fPnj3Iz8/H7t27pVp5yrsFWtljuLu7AwBu3LhRbtJf1vWv6vujPHZ2dvj444/x8ccf4/nz52jZsiUWLlzIJTeKnq/kd1WVZcjOzs64e/euzPI7d+5w61VBX18fffr0wZ9//olu3brB0tKyzG3Nzc1l3jdAcStSyTUoi6LX0tvbG4cPH5ZaZmtrCz09PXTq1AnHjh3D48ePK/zSJ4+ifwsAwMLCAqNGjcKoUaOQnZ2NwMBARERESA186O7ujv/973/43//+h/v378PHxwdLly7Fn3/+qXRsNQndlqolOnToAH9/f/z444/Iy8uDtbU1OnTogN9//x3Pnj2T2f7Fixfc49DQUFy9ehU7duyQ2a7kW/SAAQOQlJSEVatWyWyTm5vLVf2UpXHjxmjWrBm2bt2KrVu3ws7OTirREAqF3JgV8v74lo5XWa1bt+ZGBt27d6/M+rlz5+LevXuYNWuWzDfLnTt3SvWZOX/+PM6dO8d9sChzncsjFAplWlJ+/vlnmZFMS8bEkfeHrrL8/PxQr149rFq1CkVFRdzyv/76S6F79U5OThg7diwOHTqEn3/+WWa9RCLB0qVL8eTJE6XiKvmWXfq6ZGRkIDIyUuXH+OCDD2BsbIxFixbJVOCU3tfQ0FDubcKqvj/kEYvFMueytraGvb098vPzK4zpXVZWVggMDMTatWvx6NEjqXWVbcXr3r07zp8/jzNnznDLXr9+jT/++AMuLi5Kt4SWZ8aMGViwYAHmzZtX7nbu7u44e/YsCgoKuGV79+7F48ePKzyHou8vc3NzBAUFSf2U9NVasGABGGMYNmwYsrOzZfa9dOkS1q9fX+axFf1b8PLlS6nnRkZGaNCgAfe7kZOTI/O77O7uDmNjY6nfn9qKWm5qkZkzZ6J///5Yt24dxo8fjxUrVqBt27Zo1qwZxo4dCzc3N6SkpODMmTN48uQJrl69yu0XHR2N/v3748MPP4Svry/S0tKwe/durFy5Et7e3hg2bBj+/vtvjB8/HsePH0ebNm0gFotx584d/P333zh48CB3m6ws4eHhmD9/PvT09DB69GiZwbu+/fZbHD9+HK1atcLYsWPh5eWFtLQ0xMbG4siRI0hLS6v0tdmwYQM6d+6M3r17Y/DgwWjXrh3y8/Oxfft2nDhxAuHh4Zg5c6bMfg0aNEDbtm0xYcIE5Ofn48cff0S9evUwa9YsbhtFr3N5evbsiY0bN8LU1BReXl44c+YMjhw5gnr16klt5+PjA6FQiMWLFyMjIwO6urro1KkTrK2tK31tRCIRIiIiMHnyZHTq1AkDBgxAYmIi1q1bB3d3d4W+zS5duhRxcXGYMmUKtm/fjp49e8Lc3ByPHj1CVFQU7ty5I9VSp4gPPvgAIpEIvXr1wkcffYTs7GysWrUK1tbWchPJqhzDxMQEy5Ytw5gxY/Dee+9h8ODBMDc3x9WrV5GTk8N9GPn6+mLr1q2YPn063nvvPRgZGaFXr14qeX+8KysrC46OjggLC4O3tzeMjIxw5MgRXLhwQepbfVkxyfPTTz+hbdu2aNmyJcaNGwdXV1ckJiZi3759uHLlilLxAcDs2bOxefNmdOvWDVOmTIGFhQXWr1+PhIQEbNu2rdwB+pTl7e0Nb2/vCrcbM2YMoqOj0bVrVwwYMABxcXH4888/uda58vj6+gIoHtwzODgYQqFQ6d/b1q1bY8WKFfj444/h6emJYcOGoWHDhsjKysKJEyewe/dufP3112Xur+jfAi8vL3To0AG+vr6wsLDAxYsXuWEDgOIW5s6dO2PAgAHw8vKCtrY2duzYgZSUFKVfU43ER4kWqbyyBvFjjDGxWMzc3d2Zu7s7V2ocFxfHhg8fzmxtbZmOjg5zcHBgPXv2ZNHR0VL7vnz5kk2aNIkb7MnR0ZGNGDFCqiy7oKCALV68mDVp0oTp6uoyc3Nz5uvry7744guWkZHBbVdWKeb9+/e5gbFOnTol9/WlpKSwiRMnMicnJ6ajo8NsbW1Z586d2R9//MFtU1JWGhUVpdS1y8rKYhEREaxJkyZMX1+fGRsbszZt2rB169bJlMKWHsRv6dKlzMnJienq6rJ27dqxq1evyhxbketc3r/dq1ev2KhRo5ilpSUzMjJiwcHB7M6dO3Kv5apVq5ibmxsTCoUKDeL37nUqa3C3n376iTk7OzNdXV3m7+/P/vvvP+br68u6du2qwNVlrKioiK1evZq1a9eOmZqaMh0dHebs7MxGjRolVSZe1vQLJden9ABju3fvZs2bN2d6enrMxcWFLV68mK1du1Zmu5JB/ORR9Bgl27Zu3Zrp6+szExMT5u/vzzZv3sytz87OZoMHD2ZmZmYyg/gp+v6AnJLq0utKyoLz8/PZzJkzmbe3NzM2NmaGhobM29tbZgDCsmIq69/5xo0brG/fvszMzIzp6emxRo0asXnz5smN593Y5MVdMohfyfH8/f3LHMRPmfdsedepRFm/S0uXLuUGrWzTpg27ePGiQqXgRUVFbPLkyczKyooJBIJKDeJX4tKlS2zw4MHM3t6e6ejoMHNzc9a5c2e2fv16rkRe3nEV/Vvw9ddfM39/f2ZmZsb09fWZp6cnW7hwITf1TWpqKps4cSLz9PRkhoaGzNTUlLVq1Yr9/fffCr+GmkzAWA2ZMZAQDUpMTISrqyuWLFmCGTNm8B0OLyQSCaysrNCvXz+5t1sIIaS6oj43hBDk5eXJ3OffsGED0tLS0KFDB36CIoSQSqI+N4QQnD17FtOmTUP//v1Rr149xMbGYs2aNWjatCn69+/Pd3iEEKIUSm4IIXBxcYGTkxN++uknpKWlwcLCAsOHD8e3337L62zjhBBSGdTnhhBCCCG1CvW5IYQQQkitQskNIYQQQmqVOtfnRiKR4OnTpzA2NlbLUPaEEEIIUT3GGLKysmBvb1/hAJF1Lrl5+vRppeb7IIQQQgj/Hj9+DEdHx3K3qXPJjbGxMYDii1MyPT0hhBBCqrfMzEw4OTlxn+PlqXPJTcmtKBMTE0puCCGEkBpGkS4l1KGYEEIIIbUKJTeEEEIIqVUouSGEEEJIrULJDSGEEEJqFUpuCCGEEFKrUHJDCCGEkFqFkhtCCCGE1CqU3BBCCCGkVqHkhhBCCCG1CiU3hBBCCKlVeE1u/v33X/Tq1Qv29vYQCATYuXNnhfucOHECLVu2hK6uLho0aIB169apPU5CCCGE1By8JjevX7+Gt7c3VqxYodD2CQkJ6NGjBzp27IgrV67gk08+wZgxY3Dw4EE1R0oIIYSQmoLXiTO7deuGbt26Kbz9ypUr4erqiqVLlwIAGjdujFOnTmHZsmUIDg5WV5iEkLooH8A2AI/5DoRompiJcev5LbzMfcl3KDVai4gWMLUx5eXcNWpW8DNnziAoKEhqWXBwMD755JMy98nPz0d+fj73PDMzU13hEUKUFRUFzJ8PZGUpvEtOLpCZCTCJGuMCYMg+hgn7TL0nIdWSEEI0QzO+w6jxgnrew5EelNxUKDk5GTY2NlLLbGxskJmZidzcXOjr68vss2jRInzxxReaCpGQmqcSCYbKJCUpvYvBmx/1c9LIWQipDXIMcsAEDIavDbllGfX4SWyAGpbcVMacOXMwffp07nlmZiacnOiPFqnmNJlwVCLBUAsHhzJXMQDPX6egUFyksXDMcwHDN6d7pTsB01d8hVf1LKElEcP01SuNxUF4xgQABHxHUa3p5ifBKu0gCnXMkVKvNyAo7s7LxGkAbMrfWU1qVHJja2uLlJQUqWUpKSkwMTGR22oDALq6utDV1dVEeISozvz5wJ07mj9vOQmG2hgbA199BYSFlbnJw/REuC531WBQwIbtwLBrxY/fG3cYcf3nAiYAMp8By+gLUl2graWNJ9OewMaInw/o6o4xhpMnT+LEiRNgjMHWWB9zB+rC2Nj4zRYWvMVWo5KbgIAA7N+/X2rZ4cOHERAQwFNEpMbg89ZLZTx7Vvx/LS3Azk7951MgwdCUXXd2YdzecUjNSeWWMca4x9rpjVB0qweMjIBx49QXR+N/G3OPP2zxIRaJjJANwEhkhHHvTy97R1IrCAQCdG/YnRKbMmRnZ2PHjh2Ij48HAHh7e6N79+4QiUQ8R1aM1+QmOzsbDx484J4nJCTgypUrsLCwQP369TFnzhwkJSVhw4YNAIDx48fjl19+waxZs/Dhhx/i2LFj+Pvvv7Fv3z6+XgJRFXUnH9Xl1ouyPDyA27f5jkKjfj7/M56/fl7met2kLig6tBSmDsDSbWoM5M+3Dz9r9xl+1QOyAZjqmWFp8FI1npiQ6i0hIQHbt29HdnY2dHR00L17d/j4+PAdlhRek5uLFy+iY8eO3POSvjEjRozAunXr8OzZMzx69Ihb7+rqin379mHatGlYvnw5HB0dsXr1aioD1xR1JiCaTD74uPVSGSWtKbUYYwyDf1uE3TcOo0hcvKzQMhZ48+VP54UvSvd3EGY7IefYJxqPkxBSTCKRYP/+/cjOzoaVlRX69+8PKysrvsOSIWCl23vrgMzMTJiamiIjIwMmJiZ8h1O9vZvMaCoBUVfyUY1uvZBil59dRss/WspfKdYGvs4HmPyxRj091dyoNQxvW2/uA44NgCQADgCeqPG0hFR3ycnJuHjxIoKDg6Gjo6Ox8yrz+V2j+twQDSuvU6s6EhBKPqqlh+kPsfnGZuQU5qj82HGv4uSvkAhhdGM6TO3lJzbKNmpFAZgPQJk2x+UAQt88bgvgmRL7ElKbxMXFIT09Hb6+vgCKi3t69uzJc1Tlo+SGvPVuS428Tq2UgNQ5/aP648LTC2o/j9Hlz/Aieh4AQEugBZFQdR0T5wNQtvasdCqXDKBkzEBjOdsSUhtJJBIcP34cp06dgpaWFuzt7WGniQIHFaDkpi4qq+9MWbed6mCnVlKsQFygkcQGTAC9J8HQ09ZTy+FLftO1ACj6p7n0QIG2APJQnNjU7l5QhBTLzMzEtm3buH6vLVq0qJZ9a8pCyU1dpMgYKiW3nepAp1YiH2MMAWveDrNQ37Q+VvdaXeXj1o+sD6c/nSAoLO4onJoKSAp1INTSBpyrfHi5zgIQAxACcFR0p7eV6Dil8ogIqb7u37+PHTt2IDc3FyKRCCEhIWjSpAnfYSmFkpu6onRrTXljqNBtJ/JGWm4aYp/Fcs/fs38PXdy7lLtPRQV1IgbcegqUbp/hhsMTA3gku48qKJzQlMWw4k0IqQ2OHj2KU6eK03k7OzuEhYXBwoK/wfgqi5KbukJeaw3dbiJK+LXHrxVuU1GjoCHeJjb5AErPuaytDVirqdW7pM+MFopvMSlMiOKqqZrRzYCQKisZ7d/f3x9dunSBtnbNTBNqZtSkYhV1DqbbTUQJPRr2gLWhdYXblfy6lTWwsoEEXNnROV1gsGXxY3U3GPqByrgJKUtBQQE3snBAQAAcHR1Rv359nqOqGkpuaqOoKGDAAPnrqLWGlCEpMwkzDs9A/Kvi4dQLxYWVPpadHfBEXhaRDa7cKLAd8ORwpU9RrndLv6mMmxBZYrEYhw8fRlxcHMaOHQuRSASBQFDjExuAkpvaaf586efUOZgoYOXFldhyY4vcdeqqYlKXskq/qYybkGKvXr1CdHQ0nj59CgC4e/cumjVrxnNUqkPJTU0nrwfns2fS66lzMClHUmYS1l5eiz339kgtF7yZ9sDO2A7j/cbzEVqlySv9pjJuQordunULu3fvRn5+PvT09NCnTx80atSI77BUipKbmq68HpyenpTYkApN2DdBJrGJHReLFnYtFNpfXiFedWEH6mNDSImioiIcOnQIFy4Uj13l5OSE0NBQmJqa8hyZ6lFyU9OV1YOTbkGRMiRlJuHi04vc89Ll3gDgbOqMJtaKj2khL782pvs/hFQ7pRObNm3aoGPHjhAKhTxHpR6U3NQWZfbgJOSth+kP0eDnBiiSFMmsM9QxxOFhh9HSrqVSUx+8m19TXk1I9RQYGIiHDx+iS5cuaNCgAd/hqBUlN4TUUNkF2UpXNB2OPyw3sQEAH1sfBDgFyF2nCMqvCaleCgsLcefOHa6jsJGREcaPHw+BQMBzZOpHyQ0hNdC0mGlYfm45GFilj9GjYQ+0cWoDADAUGWJAkzKGD6iMQgAnAeS+s/zd54QQtUhNTUVUVBSeP38OLS0tbvqEupDYAJTcEFLjMMbwy4VfqpTYAMBw7+GqTWhKCwEQo55Dl/bueDYlqlm/ZkI06urVq9i3bx8KCwthaGjIjTpcl1ByQ0g1tP/+fnz171fIypedpImBcbeWTHVN0dqptdLH97H1Qe9GvQFUPB9UReRWSB1XYEcVzMNX1ng2JahfM6lLCgoKcODAAVy5cgUA4Orqir59+8K4Dvbwp+SGkGpo5uGZuPXiVoXb+dr7Yv+Q/VU6lyKTxCtC7t9PWwAT5Sy3BjCo6ueUN54NFw9oXBtSdzx//hzR0dF48eIFBAIB2rdvj3bt2kFLS4vv0HhByQ0hPJMwCSIvR+LSs0vcssT0RO6xkchI7n6WBpb4X8D/qnz+iuaDUkSZFVK2AD6vbGSKo/FsSF336tUrvHjxAkZGRggNDYWLiwvfIfGKkpuaquReQnUbNY0o7Uj8EYzZM0buuvqm9fHwk4caiYOqnQipWRhjXAfhRo0aoVevXmjUqBEMDQ15jox/dbO9qjYouZcgkRQ/r4P3VGs6xhhOPjyJrTe2lrlNWGMaYZoQIis5ORmRkZHIyMjglrVs2ZISmzeo5aamKn0vwcODRk2rgX469xM+OfiJ1LKI9hEIaRQCADDWNUYDi9o90BYhRDmMMVy6dAkxMTEQi8U4dOgQ+vfvz3dY1Q4lNzVRVBSQlFT82M4OuH2b33hIpZx5ckZmWW/P3vCx9anysZWpgKrudzbLKvcGqOSb1C35+fnYs2cPbt68CQBo2LAhevTowXNU1RMlNzVJySdW6dIWuh1VKyxovwBdG3RVSWIDVK4Cqrr+KlVU7g1QyTep/Z49e4bo6GikpaVBS0sLnTt3RkBAQJ0ZlE9ZlNxUd6W/gpe01pRGt6NqhVE+o+Bs5qyy4ylbAVWd54Mqr9wboJJvUvslJCTgr7/+glgshqmpKcLCwuDo6Mh3WNUaJTfVlbxWmtI8PYs/jcKowykpW22qgKJyb1JXOTo6ol69ejA3N0fv3r3r5IjDyqLkprqSl9g4OLz9ik1JDamKVQB2AVWcwaFsBWo6LiF1xPPnz2FpaQktLS3o6OhgxIgR0NfXp9tQCqLkprqSVw1FCU2tcD3lOlZeXIlzSef4CeARgHEaOpeOhs5DSC3BGMPZs2dx5MgRtG/fHoGBgQAAAwMDniOrWSi5qe6oGkptqjqnUmU97/MhCq0uSi17v5UQwteqO0e5FVDPVXeecukBGKuhcxFSC+Tm5mLnzp24d+8egOLWm9ID9RHFUXJD6ixVzalUIbtYwPby2+cmd6XXJ7ZH8j0HtZy6wgqoMQAWq+XUxckNfdkkRCGPHz9GdHQ0MjMzIRQKERwcDD8/P0psKomSG1JnqWJOpYoUWJ3Diz7vy12nle0Aq30nIMx0h8BB9X/AFKqA0gNgofJTE0IUxBjD6dOncfToUTDGYGFhgbCwMNip649SHUHJDanz1FVRlJabhh/P7sNX/8pfH+rfGn8voRGICanL0tLScPz4cTDG0LRpU/Ts2RO6urp8h1XjUXJTHZUegZjUSFE3ozBk+xAUSgq5ZR/6fIgApwAAgImuCXp69OQrPEJINVGvXj10794djDG0bNmSbkOpCCU31dH8+W8fV9dhY0m5om5FSSU2ADDSZyTaObfjKSJCSHXAGMOpU6fg5uYGB4fivnYtW7bkOarah5Kb6qh0+U51HTa2hlNX49i6K+vw1b9f4Unm2/tcoY1D0dm1M9rWb6v6ExJCaozs7Gzs2LED8fHxiI2NxYQJEyASifgOq1ai5KY6c3CgsW3URF2NY/OPz8fjzMfcc6FAiNUhq2GmZ6a6kxBCapyEhARs374d2dnZ0NbWRvv27SmxUSNKbkidpOrGsc3XN2Pn3Z14ll08wIy2ljZczFwwusVoSmwIqcMkEgn+/fdf/PPPPwAAKysr9O/fH1ZWVjxHVrtRckPqNFU0jiVnJ2PojqGQMAm3zKOeB25+fBOQAHhYteOrXHkD/BFCVCY/Px9btmxBYmIiAMDHxwfdu3eHjg4N3a1ulNwQUkUp2SlSiY1QIMQon1FADgAfAPf5iqxmiAIwH29n/34X5WKkphKJRNDR0YGOjg569uyJ5s2b8x1SnUHJTXVSMh9AuWPnk+psSLMh+LnbzzDXNwcOofonNo58B1Cc2CgyUDTVDZKaQCKRQCwWQ0dHBwKBAH369EFOTg4sLS35Dq1OoeSmOnl3PgAqA1cLVVdK5RXlcY+NREbFiQ0AiEtt1ARAU9WdUyXcAXzEdxBvW2y0AJQ1JqsxAKobJNVdZmYmtm3bBnNzc/Tp0wdA8YSXNOml5lFyU53ImwmcqJwqK6Ve5b7C+2vkT68gpT+ABVU7V21nB0ANA0UTohH379/Hjh07kJubi+TkZHTo0AFmZmZ8h1VnUXJTHdFM4Gqlykqpk49OSj13NXOt2gEJITWKWCzGsWPHcPr0aQCAnZ0dwsLCKLHhGSU3pM5SRaVU6Y7EFvoW+Pi9j6sYFSGkpsjIyEB0dDSevJmczt/fH126dIG2Nn208k2L7wAIijuBNG5MHYlruFmtZ8FYl/pJEVIXMMbw559/4smTJ9DV1cWAAQPQrVs3SmyqCUpuqoOSjsSSN60A1JGY1HJRABqjuFiLUnpSEwkEAnTt2hWOjo746KOP0LhxY75DIqVQism3qKi3FVLUkVitqNK++pBX/k0pPanuXr16hbS0NLi7uwMA3N3d4ebmRjN5V0OU3PCtdOmOhwd1JFYjVVfaM8YQ8yCmagepo94t/6ZSb1Ld3bp1C7t37wYAjBs3DhYWFgBAiU01RckN32gGcI1RdaX90YSj+P3S71UPrA6j8m9S3RUVFeHQoUO4cOECAMDR0RFCoZDnqEhFKLnhQ8n9kayst/dIaAZw9RAD2ArgKvBZRnGLgbEB8HEIgAtvfirJ7KkZvk34lns+NHkosKfUBvGVPzYhhH8vX75EdHQ0kpOTAQCtW7dGp06dKLmpASi54cO790cA6kSsLjEAhhQ/5Iq0swF8V/VD+735j/NfORtTyzUhNcqNGzewZ88eFBQUQF9fH3379kXDhg35DosoiJIbPpS+P2JnV5zY0C0p9bjHdwAofpd9wHcQhBBlPHnyBAUFBahfvz5CQ0NhYmLCd0hECZTc8MnODnhCPQ7U6cqV4om5AeALAEcBWFkC27ZV/pixz2Kx8N+FeJHzglsW0T4Cndw6yd+hIcqeNIkQUm0wxrgOwl26dIGFhQX8/PygpUWjptQ0lNyQWi0m5m1ycxPASQCelgACK3/MxdGLsd16u9QySaAEcKv8MWuCKBSXcGdVtKECqBqfVDfXrl3D9evXMWjQIGhpaUEoFMLf35/vsEglUXJDarW8/LePLcwBT5uq3QG8+PQi/r75N/fc28YbHV06oqNLxypEWTPIG5umqqinGeFbQUEBDhw4gCtXrgAALl++DF9fX36DIlVGyQ2pM1auBDCgasf49MinUs+PjzgOc33zqh20hnh3bJqqorFtCN+eP3+O6OhovHhRfIu5ffv2aNGiBc9REVXg/UbiihUr4OLiAj09PbRq1Qrnz58vd/sff/wRjRo1gr6+PpycnDBt2jTk5eVpKFpSV916cQtf/fMVrqdc55aNbTm2ziQ2pZWMTVPVn9sAaPADwgfGGC5fvoxVq1bhxYsXMDIywvDhw9GhQwfqX1NL8Npys3XrVkyfPh0rV65Eq1at8OOPPyI4OBh3796FtbW1zPabNm3C7NmzsXbtWrRu3Rr37t3DyJEjIRAI8MMPP/DwCkhdUCQpQucNnZGcncwt09bSxh+9/uAxKkJIZf3zzz/4559/AABubm7o168fDA0NeY6KqBKvyc0PP/yAsWPHYtSoUQCAlStXYt++fVi7di1mz54ts/3p06fRpk0bDB48GADg4uKCQYMG4dy5cxqNm2hW6TEPlTUgs+rnz8jLkEpsAKCnR8+qH5gQwosmTZrg7NmzaNOmDdq2bUtTKNRCvCU3BQUFuHTpEubMmcMt09LSQlBQEM6cOSN3n9atW+PPP//E+fPn4e/vj/j4eOzfvx/Dhg0r8zz5+fnIz3/bqzQzUwWfdkSj5I15qCim2lDg7+CPpR8sRYBjgIqPTAhRF8YYUlJSYGtrCwCwsrLC1KlToa+vz3NkRF14S25SU1MhFothY2MjtdzGxgZ3yvgkGzx4MFJTU9G2bVswxlBUVITx48fjs88+K/M8ixYtwhdffKHS2CuNpqWulHfHPFSGaTaADNXFYmVghbb126rugIQQtcrPz8fevXtx8+ZNjBgxAs7OzgBAiU0tV6OqpU6cOIFvvvkGv/76K1q1aoUHDx5g6tSp+OqrrzBv3jy5+8yZMwfTp0/nnmdmZsLJyUlTIUtT9bTUdYxCYx4eQPE8C6lvnheoN6a6IgpAEt9BEKKkZ8+eITo6GmlpaRAIBEhNTeWSG1K78ZbcWFpaQigUIiUlRWp56abDd82bNw/Dhg3DmDFjAADNmjXD69evMW7cOMydO1duL3ddXV3o6uqq/gVUhqqnpSayfgCQWMY6M82FUdvML/WYUnJS3THGcOHCBRw6dAhisRimpqYIDQ3l74st0TjekhuRSARfX18cPXoUffr0AQBIJBIcPXoUkyZNkrtPTk6OTAJTMjsrY6ruXaFGdnbA7dt8R1E75ZR67IW3E1a2BVDG7AhlEUvEmHV4Fk48PKGS0Gqy0n25KSUn1VleXh52796N22/+xjZq1Ai9e/em21B1DK+3paZPn44RI0bAz88P/v7++PHHH/H69Wuuemr48OFwcHDAokWLAAC9evXCDz/8gBYtWnC3pebNm4devXrRFPS1QFlVUZXuonQNQBV+Lf55+A9+OCs9xICetl7lD1gLOIDGpiHV2507d3D79m1oaWmhS5cuaNWqFVVD1UG8Jjfh4eF48eIF5s+fj+TkZPj4+CAmJobrZPzo0SOplprPP/8cAoEAn3/+OZKSkmBlZYVevXph4cKFfL0EokIVVUVpuotSWm6a1HN7Y3uM9xuv2SAIIUrx9vZGSkoKmjZtCgcHB77DITwRsBp1P6fqMjMzYWpqioyMDM1PYe/oCCQlAQ4ONBu4HCWXR15VlLFxcRelsIqaDdoAOP3mcRGq1HITfSsa/aP6AwC+7/I9/tf6f5U/WA3niOIOxQ4oHl2YkOoiNzcXx44dQ+fOnaGnV7dbVms7ZT6/a1S1FKkbFKqK0oC4tDi+QyCElOPx48fYtm0bMjIykJ+fj379+vEdEqkmKLkhRI5zT85h9lHZUbJruygUV0a9Oxg0jcxEqhPGGE6fPo1jx45BIpHA3NwcAQE0sCZ5i5IbQt7ILshGobgQAHAw7qDUuqbWTfkISePmAyhvMGgqAyd8y8nJwc6dO3H//n0AxVMp9OrVq/oM+UGqBUpuiMZUNEcUnwM3zzkyB9+d/g4SJpFZN7rFaHzg/gEPUWleyT+NFopn/y7NGFQGTviVnJyMTZs2ISsrC0KhEN26dUPLli2pGorIoOSGaIyic0TxMXDzigsr5CY2APBhiw/r3B9PO1DHYVL9lHQirVevHvr37y8zfQ8hJSi5IRqjyBxRJVVRmlYoKb4dZSQyQrv67bjlgc6BNEkmITzKz8/nbjkZGBhg6NChMDMzg0gk4jkyUp1RckM0rrpUQ8njbu6O/UP28x0GIQRAQkICtm/fjs6dO8PHxwcAYG1tzW9QpEaQnYyJkDrmv0f/Ia8oj+8wCCFvSCQSnDhxAhs3bkR2djYuXLhQs6bYIbyjlhtSp+UV5aHn5p7ccy0B5fuE8CkrKws7duxAQkICAMDHxwfdunWrc/3eSNVQckPqtPS8dKTnpXPPQxuH8heMGpU1fs27aDwbwqe4uDjs2LEDr1+/ho6ODnr06AFvb2++wyI1ECU3hLzhY+uDuYFz+Q5DLSoav+ZdNJ4N0bRXr17hr7/+AmMM1tbW6N+/PywtLfkOi9RQlNwQ8oaLmQvfIahNeePXvIvGsyF8MDc3R5s2bZCbm4vg4GDo6OjwHRKpwSi5IaQOofFrSHVy//59WFpawtzcHADQqVMn6ltDVIJ6TxJCCNEosViMw4cPY9OmTYiOjoZYLAYASmyIylDLDSGEEI3JyMhAdHQ0nrwZ7MrBwYHKvInKUXKjCSWTKvE5eZIGlTWHVB15+YSQMty9exc7d+5EXl4edHV1ERISAi8vL77DIrUQJTea8O6kSnxMnqRBFc0hVctfPiHkHWKxGEeOHMHZs2cBAPb29ggLC+P62hCiapTcaELpSZU8PPiZPEmDyptDSq1zRyUCOK2mY9cA5Y1lQ41mhE+MMTx8+BAA0KpVK3Tp0gVCoZDnqEhtRsmNJtnZAbdv8x2Fxmh8Dqnl7zyvY30TFRnLhhrNiCYxxiAQCKCtrY3+/fsjJSUFnp6efIdF6gBKbkjt8bLU44Goc7WAFY1lQ+PXEE0pKirCoUOHoKenh06dOgEoHseGbkMRTaHkRt2iooCkJL6jqHu+lL84NScVablp3PMXr19oKCDNobFsCJ/S0tIQHR2NZ8+eQSAQwMfHBxYWFnyHReoYSm7Ubf78t49rcE/asiqg5KmuVVGRlyMxds9YiJmY71AIqZVu3ryJ3bt3o6CgAPr6+ujTpw8lNoQXlNyoW+lsoAZ3JK6oAkqe6pbLbb25tdzEpr5JfQ1GQ0jtUVhYiIMHD+LSpUsAgPr16yM0NBQmJiY8R0bqKkpuNMXBAQgL4zuKSiuvAkoetVZFKYAxhjG7xmD/g/3cAGGlb0cNbDoQOlpv566xN7bHtPenaTxOQmo6xhg2btyIx48fAwDatm2Ljh07QkurjnV6I9UKJTdEKRqvgKqk2y9uY+2VtXLX6Qp1sa73Ouhq62o4KtUoq+S7mt4NJLWcQCBAy5Yt8fLlS/Tr1w/u7u58h0QIJTekdlp4ciH32ETXBBb6xff9RUIRJr43scYmNkDFJd/V7G4gqYUKCwuRnp4OKysrAICPjw8aNWoEfX19niMjpBglN6RWOv/0PFCv+PHE9ybim87f8BuQCpVX8k3l3kTdXrx4gaioKOTn5+Ojjz6CgYEBAFBiQ6oVSm5Ijfb89XPsvLMT+UX56JLaBZ6QHiDMXM8coY1DeYpOvajkm2jalStXsG/fPhQVFcHIyAjp6elcckNIdULJDamxGGPosrELrqVcAwDsjtstldxs6LMB/Zv0h562Hl8hElIrFBQUYP/+/bh69SoAwM3NDX379oWRkRHPkREiHyU3pEYrSWx8k3zR614vbrlIKMIH7h9QYkNIFaWkpCA6OhqpqakQCATo0KED2rVrB4Ggjs1vQmoUSm5IrTD0yVCp5zHTY2BjZMNTNITUHv/99x9SU1NhbGyM0NBQODs78x0SIRWi5IbUCkbapZrHPwWcnJz4C4aQWqR79+7Q1tZG586dYWhoyHc4hCiEkhtSYzxMf4j+Uf1xJ7W4EJqByd+wowaDUrGyxrApjcazIer07NkzXL9+HV26dIFAIICenh5CQkL4DosQpVQpucnLy4OeHvVpIJqx6fomXHh6Qe662tK3pqIxbEqj8WyIKjHGcPHiRRw8eBBisRhWVlZo0aIF32ERUilKJzcSiQQLFy7EypUrkZKSgnv37sHNzQ3z5s2Di4sLRo8erY44SR104P4B/BH7BwrEBQCAu6l3uXX1TevDSFR8K8rSwBLt09vzEqOqlTeGTWk0ng1Rpby8POzZswe3bt0CAHh4eMDT07OCvQipvpRObr7++musX78e3333HcaOHcstb9q0KX788UdKbohKSJgEw3cOR2pOqtz1G/psQHuXUglNhGbi0hQaw4ZoSlJSEqKjo5Geng4tLS0EBQXh/fffp2ooUqMpndxs2LABf/zxBzp37ozx48dzy729vXFH2WmjCSmFMYajCUdx6ekliJm4zMTGz94PAU4BGo6OkNrn8uXL2Lt3LyQSCczMzBAWFgYHBwe+wyKkypRObpKSktCgQQOZ5RKJBIWFhSoJitRNpx+fRpeNXWSWv+/4PvYN3sc9N9czp2+VhKiAhYUFGGNo3LgxQkJCqA8lqTWUTm68vLxw8uRJmbEOoqOjqfMZqZS7qXfxMvcldt3dJXd9kGsQN/ElIaRqSheCODs7Y8yYMbCzs6MvDKRWUTq5mT9/PkaMGIGkpCRIJBJs374dd+/exYYNG7B37151xEjULCoKmD8fyCqn/viZmuqPv/73a8w7Pk9m+WT/yejk2glWBlZo7dRaPSdXEUXKtxVFZd5EXRhjOHPmDE6ePInRo0fD0tISAGBvb89zZISontLJTe/evbFnzx58+eWXMDQ0xPz589GyZUvs2bMHXbrI3lIg1d/8+YCi3aWMVVx/vPee/IQ4vEk42tRvo9hBdgD4QnUxKUuZ8m1FUZk3UaWcnBzs3LkT9+/fBwBcvXoVnTt35jkqQtSnUuPctGvXDocPH1Z1LIQnJS02WlqAXTn1x8bGwFcqrD9ecX4FziWd455Pf386AMDfwV/x1ppHAN6d9FtLNfEpStHybUVRmTdRpUePHmHbtm3IzMyEUChE165d4evry3dYhKiV0smNm5sbLly4gHr16kktT09PR8uWLREfH6+y4Ihm2dkBTzRYf/zd6e+4x5YGllgavFT5gzwGpAYqdgTA010sKt8m1QljDKdOncLx48fBGEO9evUQFhYGW1tbvkMjRO2UTm4SExMhFotllufn5yMpKUklQZHa7feLv+PvW3/jadZTbtlvPX6r+oGDAewBoFP1QxFS0125cgXHjh0DADRv3hw9evSASCTiOSpCNEPh5Gb37t3c44MHD8LU1JR7LhaLcfToUbi4uKg0OFL7JGcnY8K+CVLzQjWq1whhXmFVP3hTUGJDyBve3t64ceMGmjZtCh8fH6qGInWKwslNnz59AAACgQAjRoyQWqejowMXFxcsXVqJ2wq1VUkJkrrKjFQkKgrQRIMbYww77+zEobhDUomNvrY+prSaov4ACKnlJBIJLl++DB8fHwiFQmhpaWHo0KGU1JA6SeHkRiKRAABcXV1x4cIFroyQlOHdEiRVlxmpyPz5bx+rM8RjCcfQ7+9+UstGeI/Aql6roCOk5hZCqiI7Oxvbt29HQkICUlNTERwcDACU2JA6S+k+NwkJCeqIo/YpXYLk4aHaMiMVKj22japDZIwh9lksXuW9ws47O2XWd23QtcYmNqXHtqnebXOktouPj8f27dvx+vVr6OjoUIdhQlDJUvDXr1/jn3/+waNHj1BQUCC1bsoUusUgxc4OuH2b7ygq5OAAhKmg20tpnx39DN/+963M8lmtZ2F0y9HwqOeh2hNqkLyxbapn2xyprSQSCU6cOIGTJ08CAKytrdG/f39qVScElUhuLl++jO7duyMnJwevX7+GhYUFUlNTYWBgAGtra0puCACgUFyIg3EH5a4b0GRAjU5sANmxbWhsGqJJmZmZ2L59Ox4+fAgAaNmyJbp27QodnZrZEkqIqimd3EybNg29evXCypUrYWpqirNnz0JHRwdDhw7F1KlT1REjqWGmHJiCFRdWQMKK+2kJIMDcdnMBAAFOAfC1rz0DiNHYNoQPRUVFePbsGUQiEXr27IlmzZrxHRIh1YrSyc2VK1fw+++/Q0tLC0KhEPn5+XBzc8N3332HESNGoF+/fhUfhPBOXcVcOYU5+OX8L1IVUXbGdviqE7VrEFIVjDGug7CFhQX69+8Pc3NzmQFVCSGVSG50dHSgpVU8vr21tTUePXqExo0bw9TUFI8fP1Z5gEQ9VFnM9brgNaYdnIarKVdRJCniEhszPTO87/g+JvtPrmK0hNRtGRkZ2L59O9q3bw83NzcAQIMGDXiOipDqS+nkpkWLFrhw4QIaNmyI9u3bY/78+UhNTcXGjRvRtGlTdcRI1ECVxVzbbm/DqthVMssDnQOxa+Cuyh+YEIK7d+9i165dyM3Nxf79+/Hxxx9zXzAJIfIpndx88803yHrzybhw4UIMHz4cEyZMQMOGDbFmzRqVB0jUSxXFXGm5aVLPBRDAxsgGH/t9XLUDE1KHicViHDlyBGfPngUA2NvbIywsjBIbQhSgdHLj5+fHPba2tkZMTIxKAyI125bQLQhvGs53GGpTMr4NjW1D1Ck9PR3R0dHcfH2tWrVCUFAQtLUrNXoHIXWOyr4CxMbGomfPnkrvt2LFCri4uEBPTw+tWrXC+fPny90+PT0dEydOhJ2dHXR1deHh4YH9+/dXNmxClFIyvo3kzXMa24aoWkZGBn7//XckJSVBT08P4eHh6Nq1KyU2hChBqXfLwYMHcfjwYYhEIowZMwZubm64c+cOZs+ejT179nBDfitq69atmD59OlauXIlWrVrhxx9/RHBwMO7evQtra2uZ7QsKCtClSxdYW1sjOjoaDg4OePjwIczMzJQ6b21WUgVVeuRhear5lFfVVunxbTxAY9sQ1TMxMYGHhwfS0tIQGhpKf98IqQSFk5s1a9Zg7NixsLCwwKtXr7B69Wr88MMPmDx5MsLDw3Hjxg00btxYqZP/8MMPGDt2LEaNGgUAWLlyJfbt24e1a9di9uzZMtuvXbsWaWlpOH36NDdYFc1ELu3dKqiKqGI+qUJxYdUPUsPYAaj+406TmiItLQ16enowMDCAQCBAz549ueE2CCHKU/i21PLly7F48WKkpqbi77//RmpqKn799Vdcv34dK1euVDqxKSgowKVLlxAUFPQ2GC0tBAUF4cyZM3L32b17NwICAjBx4kTY2NigadOm+OabbyAWi8s8T35+PjIzM6V+arPSVVAODuX/eHpWfT6pi08vYtaRWVUPnJA66ubNm/j999+xa9cuMFY8jIKOjg4lNoRUgcItN3Fxcejfvz8AoF+/ftDW1saSJUvg6OhYqROnpqZCLBbDxsZGarmNjQ3ulNH0EB8fj2PHjmHIkCHYv38/Hjx4gI8//hiFhYVYsGCB3H0WLVqEL774olIx1mR2dsATDQydu+XGFqnntkY0aR8hiigqKkJMTAwuXboEAMjNzUV+fj709PR4joyQmk/h5CY3NxcGBgYAAIFAAF1dXdjZ2aktMHkkEgmsra3xxx9/QCgUwtfXF0lJSViyZEmZyc2cOXMwffp07nlmZiacnJw0FXKtdSLxBOYdn4dbL25xy0IahaCdczseoyKkZnj58iWioqKQkpICAGjbti06duxIZd6EqIhSHYpXr14NIyMjAMXfOtatWyczA62iE2daWlpCKBRyb+4SKSkpsLWV/+3fzs5Oprm2cePGSE5ORkFBAUQikcw+urq60NXVVSgmorj5x+fj1KNTUssWdloILUHt++NcUv6dBSoBJ1V37do17N27F4WFhTAwMEDfvn1ptGFCVEzh5KZ+/fpYtertKLS2trbYuHGj1DYCgUDh5EYkEsHX1xdHjx5Fnz59ABS3zBw9ehSTJk2Su0+bNm2wadMmSCQS7hvOvXv3YGdnJzexqStKV0hpogrq5vObOPnoJPfcWGSMPp590MSqifpPzoOS8u/SqAScVEZhYSGOHz+OwsJCuLi4oF+/fjBWRa9+QogUhZObxMRElZ98+vTpGDFiBPz8/ODv748ff/wRr1+/5qqnhg8fDgcHByxatAgAMGHCBPzyyy+YOnUqJk+ejPv37+Obb75ROKHSCHXNSFkOeRVS6vx7OXj7YO6xkcgImXNqeSftN//XQnGVlDGoBJxUjo6ODsLCwnD//n0EBgbSbShC1ITXUaHCw8Px4sULzJ8/H8nJyfDx8UFMTAzXyfjRo0dSb34nJyccPHgQ06ZNQ/PmzeHg4ICpU6fi008/5eslyFLljJQKKl0hZWdXfMqqVkGVJ/5VPPc4zCtMfSeqZuwAaKCPNqllrly5AsYYWrRoAQBwcHCAg4MDz1ERUrvxPuTlpEmTyrwNdeLECZllAQEB3Fwr1ZIqZ6RUkqYqpEpoCbSwNmSt5k5ISA1SUFCA/fv34+rVqxAKhahfvz7q1avHd1iE1Am8Jze1lipmpKymsguyAQCelp4QCAQ8R0NI9ZOSkoLo6GikpqZCIBAgMDAQ5ubmfIdFSJ1ByQ1RyoCoAXyHQEi1xRjD5cuXceDAARQVFcHY2Bj9+vWjkdQJ0TBKbmq4qCjgzcTBapeel46oW1Hccwdj6jdASAnGGHbu3Ilr164BABo0aIA+ffrA0NCQ58gIqXsqldzExcUhMjIScXFxWL58OaytrXHgwAHUr18fTZrUznLg6mr+/LePVd13Of5VPMbtGcd1IBYz6Wkufuz6o2pPWM2UjG9DY9sQRQgEAlhYWEAgEKBTp05o06YN3bYlhCdK1yH+888/aNasGc6dO4ft27cjO7u4/8XVq1fLHCWYqE/p2b9V3Xd51aVVOJpwFAnpCUhIT8CjjEfcuvAm4fCy8lLtCauZkvFtJG+e02gk5F2MMeTm5nLP27Vrh3HjxqFt27aU2BDCI6WTm9mzZ+Prr7/G4cOHpQbO69SpU/WuYqrlHByAMBVVZd9/eR9TD0zFjjs7uGXmeuawNrSGtaE1vG28MbXVVNWcrBorPb6NJ2hsGyItLy8P0dHRWL9+PQoLCwEUT/5b1gjrhBDNUfq21PXr17Fp0yaZ5dbW1khNTVVJUIRfkw5MwqG4Q1LLjo84Dm9bb54i4pcdgNpZ90Yq6+nTp4iOjsarV6+gpaWFx48fw83Nje+wCCFvKJ3cmJmZ4dmzZ3B1dZVafvnyZRqYqoZ7/vo5Dtw/gGsp16SWN7dpjqbWTXmKipDqgzGG8+fP49ChQ5BIJDA1NUVYWBgcHR35Do0QUorSyc3AgQPx6aefIioqCgKBABKJBP/99x9mzJiB4cOHqyNG8kbpOaRKqGqWB7FEjPdWvSfVr8ZIZITYcbFwt3CvvhNiLuM7AFJX5ObmYvfu3bjzZgRyT09PhISEQF9fn+fICCHvUjq5+eabbzBx4kQ4OTlBLBbDy8sLYrEYgwcPxueff66OGMkb8uaQKlHVSqmU1ylSiQ0AtHJohYb1GlbtwOqUCWBbqedUcUvUaP/+/bhz5w6EQiG6dOkCf39/6jRMSDWldHIjEomwatUqzJs3Dzdu3EB2djZatGiBhg2r8YdgLfHuHFIlqjqXVKG4EM1/a84997H1wczWM9HLo1flD6oJOe88p4ZDokZBQUFIS0tDjx49YG9vz3c4hJByKJ3cnDp1Cm3btkX9+vVRv359dcREKqDqOaSuJF/By9yX3PPA+oEY3GxwOXtUQ30AuKvmUDS+DQGAnJwc3Lt3Dz4+PgAAU1NTjBkzhlprCKkBlE5uOnXqBAcHBwwaNAhDhw6Fl1ftHuukNkvJTsHg7YNx+dllqeWftfuMp4iqh5LxbUrQ+DZ1z6NHj7Bt2zZkZmZCX18fjRo1AgBKbAipIZTuJfr06VP873//wz///IOmTZvCx8cHS5YswRNNTkdNVGLrza04lnAMr/JeccvmB86HjZENj1Hxj8a3qbsYYzh16hTWrVuHzMxMWFhYwNTUlO+wCCFKUrrlxtLSEpMmTcKkSZOQkJCATZs2Yf369ZgzZw4CAwNx7NgxdcRZ56liDqmTD0/ij9g/kFeUBwC4/eLt6C1WBlbwtvXGhy0+rNpJahEa36Zuef36NXbs2IG4uDgAQLNmzdCjRw/o6uryHBkhRFlVmjjT1dUVs2fPhre3N+bNm4d//vlHVXGRd6hiDqmhO4bKVESVWNdnHbo37F65AxNSwyUmJmLbtm3Izs6GtrY2unXrhhYtWtBtKEJqqEonN//99x/++usvREdHIy8vD71798aiRYtUGRsppapzSD3KeFRmYtPQoiHaO7evZGSE1HzZ2dnIzs6GpaUl+vfvD2tra75DIoRUgdLJzZw5c7BlyxY8ffoUXbp0wfLly9G7d28YGBioIz7yjsrMIVUoLkSr1a24581tmmPf4H3cc3tj++o7SB8hasIY41pmmjZtCrFYjMaNG0vNmUcIqZmUTm7+/fdfzJw5EwMGDIClpaU6YiIqkp6XjsT0RKRkpyA5O5lb3sG5AxxNaLj40krKv7NAJeB1QXx8PA4fPowhQ4bAyMgIAODtXTfnTiOkNlI6ufnvv//UEQdRsUtPL6FdZDvkFuVKLTcWGWNp8FLFDyQBcAFAtkrDU4001R3q3fJvgErAayOJRIJ//vkH//77LwDgxIkT6NmzJ89REUJUTaHkZvfu3ejWrRt0dHSwe/fucrcNCQlRSWCkag48OCCT2ADAeL/x0NZSIqcdBWCD6uKqrkqXf9uhOLGhEvDaJSsrC9u2bcPDhw8BAC1atEBwcDDPURFC1EGhT7k+ffogOTkZ1tbW6NOnT5nbCQQCiMViVcVGqkDCJNzj7g27o75JfTiYOODj9z5W7kAHVRyYuniq5jB2AGjEptrnwYMH2LFjB3JyciASidCzZ080a9aM77AIIWqiUHIjkUjkPiY1w6T3JqFbw25VO4gJgEkqCUf17EHzSpEy3bx5E9HR0QAAGxsb9O/fH/Xq1eM5KkKIOind52bDhg0IDw+XGdiqoKAAW7ZswfDh9ClTK1kAWMh3EIQor0GDBqhXrx5cXV0RHBwMbe0qDe9FCKkBlK7/HTVqFDIyMmSWZ2VlYdSoUSoJihBCquLJkydgjAEAdHV1MXbsWPTo0YMSG0LqCKXf6aXHhijtyZMnNAeLhoglYuy9txd3Ut+t73nr5KOTGoyIkOpBLBbj6NGjOHPmDD744AMEBAQAAE2hQEgdo3ByUzIUuUAgQOfOnaW+AYnFYiQkJKBr165qCZJIi74VjYHbBvIdBiHVSnp6OqKjo5H0ZhK2rNLDehNC6hSFk5uSKqkrV64gODiYG/gKAEQiEVxcXBAaGqryAImsG89vKLytvrY+3nN4T43REMK/O3fuYNeuXcjLy4Oenh569+4NT08VldARQmochZObBQsWAABcXFwQHh4OPT09tQVVY6li6m4lRbSPgLdt2SOrBjgGwNKARpImtVNRUREOHz6M8+fPAwAcHBwQFhYGMzMzfgMjhPBK6T43I0aMUEcctYMqpu5WUpv6bRDkFqSRcxFS3bx48QIXL14EAAQEBKBz584QCoU8R0UI4ZtCyY2FhQXu3bsHS0tLmJuby+1QXCItTYVj4tc0VZ26mxCiFDs7O3Tr1g0mJibw8PDgOxxCSDWhUHKzbNkyGL9piVi2bFm5yQ1B5abu5tt/AFYCeHfGhlc8xEJIGUpuQ7Vs2RI2NjYAAD8/P56jIoRUNwolN6VvRY0cOVJdsRA+DQcQX856Gh6E8Ozly5eIiopCSkoK4uPjMWHCBGhpKT1UFyGkDlD6L0NsbCyuX7/OPd+1axf69OmDzz77DAUFBSoNjmjQ03LWaQH4UFOBECLr+vXr+OOPP5CSkgIDAwMEBwdTYkMIKZPSfx0++ugj3Lt3DwAQHx+P8PBwGBgYICoqCrNmzVJ5gETDGqN45sjSP6kA5vAZlHpFAdBsjRtRVGFhIXbv3o3t27ejoKAAzs7OGD9+PBo0aMB3aISQakzpmw337t2Dj48PACAqKgrt27fHpk2b8N9//2HgwIH48ccfVRwi0SgRAAe+g9CsUjVu0EyNG1FEdnY2Nm7ciOfPnwMAAgMD0b59e2qxIYRUqFLTL5TMDH7kyBH07NkTAODk5ITU1FTVRkeIBpQex5Zq3KoPAwMDGBoawtDQEP369YObmxvfIRFCagilkxs/Pz98/fXXCAoKwj///IPffvsNAJCQkMBVLxBSEzkAqGE1brVOQUEBtLS0oK2tDS0tLfTr1w8ApEZEJ4SQiijdvvvjjz8iNjYWkyZNwty5c7l739HR0WjdurXKAySE1A3Pnz/HqlWrEBMTwy0zMjKixIYQojSlW26aN28uVS1VYsmSJTQyKCFEaYwxXL58GQcOHEBRURHy8/ORk5MDAwMDvkMjhNRQlR695NKlS7h9+zYAwMvLCy1btlRZUISQuiE/Px/79u3jvjC5u7ujb9++lNgQQqpE6eTm+fPnCA8Pxz///MNNTpeeno6OHTtiy5YtsLKyUnWMhKhcFIqrpLIAPOM5lroqOTkZ0dHRePnyJQQCATp16oQ2bdrQCOiEkCpTus/N5MmTkZ2djZs3byItLQ1paWm4ceMGMjMzMWXKFHXESDQhj+8ANGs+gDsoHt9G8mYZlYFrTlFRETZt2oSXL1/CxMQEI0eORNu2bSmxIYSohNItNzExMThy5AgaN27MLfPy8sKKFSvwwQcfqDQ4oiG/8B2A5pWUf2sBsENxYkNl4Jqjra2NHj16IDY2Fr1796bbUIQQlVI6uZFIJNDR0ZFZrqOjw41/Q1QjKgqYP794svFn6rx3sqrUYzs1nqcaskPxIMxE/Z4+fYq8vDxuvJpGjRrBw8ODWmsIISqn9G2pTp06YerUqXj69O1kRElJSZg2bRo6d+6s0uDquvnzgTt3gKQkoCRvNDYGLidfVu2Jiko9/l61hyaEMYZz585h7dq1iI6ORkZGBreOEhtCiDoo3XLzyy+/ICQkBC4uLnBycgIAPH78GE2bNsWff/6p8gDrsqw39060tAA7u+LEpsP01Vh5f596TmgEoIl6Dk3qptzcXOzevRt37twBADg7O0MkEvEcFSGktlM6uXFyckJsbCyOHj3KlYI3btwYQUFBKg+OFLOzA548AXIKczBq12GpGbw9LT35C4yQcjx58gTbtm1Deno6hEIhunTpAn9/f2qtIYSonVLJzdatW7F7924UFBSgc+fOmDx5srriIu/47r/vMPfYXBRJ3t5D+qvfX3A0ceQxKkJkMcZw9uxZHDlyBBKJBObm5ggLC4O9vT3foRFC6giFk5vffvsNEydORMOGDaGvr4/t27cjLi4OS5YsUWd85I1fL/wqldjoaOmga4OuPEZUvZUex0YeGttGfQQCAVJTUyGRSODl5YVevXpBT0+P77AIIXWIwsnNL7/8ggULFmDBggUAgD///BMfffQRJTdqEhVV3JG4RKGkEACgK9RFF/cuGNhkICz0LXiKrvorGcemIjS2jeowxrhbTl27doWzszOaNWtGt6EIIRqncLVUfHw8RowYwT0fPHgwioqK8EytNcp11/z5bx8bl/oEtjK0wp5BezCk+RDNB1WDlB7HxqGMH0/Q2DaqwBjDqVOnsGnTJjDGABQPDdG8eXNKbAghvFC45SY/Px+Ghobccy0tLYhEIuTm5qolsLouq9T9lK++AqY+4i+WmozGsVGv169fY+fOnXjw4AEA4M6dO1IDfBJCCB+U6lA8b948qZFECwoKsHDhQpiamnLLfvjhB9VFR+DgAISFAVPpspJq5uHDh9i2bRuysrKgra2Nbt26wdOTqvcIIfxTOLkJDAzE3bt3pZa1bt0a8fHx3HNqglaPrPwsPM16WvGGlTo4gFvqOTSpnSQSCU6dOoUTJ06AMQZLS0v0798f1tbWfIdGCCEAlEhuTpw4ocYwSFkYGDqu76i+E3yhvkOT2mnfvn2IjY0FAPj4+KBbt240MB8hpFpRevoFdVixYgVcXFygp6eHVq1a4fz58wrtt2XLFggEAvTp00e9AfJJUIRLzy5xT1vYtlDt8S+WetxOtYfWhCgAjQE4vvND3dzV57333oO+vj769OmD3r17U2JDCKl2eE9utm7diunTp2PBggWIjY2Ft7c3goOD8fz583L3S0xMxIwZM9CuXQ38RFYYQ2oP6ZGfN/bdqNpTvCz1eI9qD60JJSXfSe/8lEzhSqXeVSeRSPD48WPuua2tLT755BN4e3vzGBUhhJSN9+Tmhx9+wNixYzFq1Ch4eXlh5cqVMDAwwNq1a8vcRywWY8iQIfjiiy+4GYZrJfMEFNj9yz0d2HQgTPVMy9mhEkqSGwcAQtUeWhPKK/mmUu+qy8rKwoYNG7Bu3ToklRp4iVprCCHVGa/JTUFBAS5duiQ1L5WWlhaCgoJw5syZMvf78ssvYW1tjdGjR2siTP5oFUk9/brj16o9PsPb5Kaeag+taSUl36V/bgMI4zOoGu7BgwdYuXIlHj58CG1tbWRllTXeMyGEVC9KT5ypSqmpqRCLxbCxsZFabmNjw80i/K5Tp05hzZo1uHLlikLnyM/PR35+Pvc8MzOz0vHyabj3cLhbuKv2oK8BFLx5XMOTG6I6EokEx44dw3///Qeg+P3Yv39/1KtHvySEkJqhUi03J0+exNChQxEQEMA1VW/cuBGnTp1SaXDvysrKwrBhw7Bq1SpYWloqtM+iRYtgamrK/Tg5Oak1RlUQS8TIbvIzEPSpek9Uur+NYpeT1HIZGRlYt24dl9j4+flhzJgxlNgQQmoUpZObbdu2ITg4GPr6+rh8+TLXKpKRkYFvvvlGqWNZWlpCKBQiJSVFanlKSgpsbW1lto+Li0NiYiJ69eoFbW1taGtrY8OGDdi9eze0tbURFxcns8+cOXOQkZHB/ZTuGFldzVt3CBmtpwCNd3LLhAI1dIgpndzQZxcBcPv2bTx+/Bi6uroICwtDjx49oK3NawMvIYQoTenk5uuvv8bKlSuxatUq6OjocMvbtGnDjX2hKJFIBF9fXxw9epRbJpFIcPToUQQEBMhs7+npievXr+PKlSvcT0hICDp27IgrV67IbZXR1dWFiYmJ1E91t3a79FwLukJdhDYOVf2JKLkh72jVqhVat26NcePGoUmTJnyHQwghlaL0V7K7d+8iMDBQZrmpqSnS09OVDmD69OkYMWIE/Pz84O/vjx9//BGvX7/GqFGjAADDhw+Hg4MDFi1aBD09PTRt2lRqfzMzMwCQWV6T5ee9fTzY+hv8MnI8zPXNVX+i1FKPKbmpk9LT03H8+HH06NEDIpEIAoEAXbp04TssQgipEqWTG1tbWzx48AAuLi5Sy0+dOlWpsuzw8HC8ePEC8+fPR3JyMnx8fBATE8N1Mn706BG0tHivWNcoic7bqpQuAXbqSWwAarmp4+7cuYNdu3YhLy8PIpEIPXr04DskQghRCaWTm7Fjx2Lq1KlYu3YtBAIBnj59ijNnzmDGjBmYN29epYKYNGkSJk2aJHddRdM+rFu3rlLnrK4uJF1A5vszVXOwPADhAMqqqn9d6jElN3WGWCzG4cOHce7cOQCAg4MD2rRpw3NUhBCiOkonN7Nnz4ZEIkHnzp2Rk5ODwMBA6OrqYsaMGZg8ebI6YqxTdt3dJfW8vmn9yh/sIIDdCm7rWPnTkJrj1atXiI6OxtOnxROxBgQEoHPnzhAKa+AIjoQQUgalkxuBQIC5c+di5syZePDgAbKzs+Hl5QUjIyN1xFfniCVi7rFefBg6uHSo/MHebZmRN7ixAEAIgOaVPw2pGRITE7Flyxbk5+dzc0N5eHjwHRYhhKhcpWs8RSIRvLy8VBkLeYfRrYnQEqiov9ECANSwVqfVq1cP2trasLa2RmhoKExNVTyVByGEVBNKJzcdO3aEQCAoc/2xY8eqFFBdlpydjG//+5bvMEgtkpOTAwMDAwCAsbExRo4cCXNzc7oNRQip1ZRObnx8fKSeFxYW4sqVK7hx4wZGjBihqrjqpCX/LZFeIKHB00jlXb9+HXv37kXv3r25VlZFR/YmhJCaTOlPz2XLlsldHhERgezs7CoHVNc8y3qG3Xd3o0BcgOOJx9+uyDOF6IU/f4FVA1EA5uPtzN/yPNNQLDVJYWEhYmJiuEE1r169SreQCSF1isqaBoYOHQp/f398//33qjpkrccYQ4f1HXDv5T3ZlSuvQGAo0nhM1cl8APKnT5VlrM5AapDU1FRERUXh+fPnAIDAwEC0b9+e56gIIUSzVJbcnDlzBnp6eqo6XJ2QW5QrP7HJcAIyHQFDzcdUnZS02GgBsCtnO2MAX6k/nGrv6tWr2LdvHwoLC2FoaIh+/fpVamBNQgip6ZRObvr16yf1nDGGZ8+e4eLFi5UexI8AogwvFByZC0iEQHwXQKINY2qOAFCc2DzhO4hq7tmzZ9i5cycAwNXVFf369aPhGQghdZbSyc275aNaWlpo1KgRvvzyS3zwwQcqC6yuEWTbAtcHQ0sLsLMDjI2Br6g5gijIzs4OAQEB0NXVRbt27erclCWEEFKaUsmNWCzGqFGj0KxZM5ibq2m+ozrOzg54omgzRQ6AowDyy1h/TjUxkeqHMYarV6/Czc2Nm+mevlwQQkgxpZIboVCIDz74ALdv36bkpjroAuA030EQTcvPz8e+fftw/fp11K9fHyNGjKCWGkIIKUXpv4hNmzZFfHy8OmIhymBQLrHxVlcgqhUFoDGKp7qiMm9ZycnJWLVqFa5fvw6BQICGDRuWO6gmIYTURUr3ufn6668xY8YMfPXVV/D19YWhoXRJT0kTOVFOflm3lhRRH8CUcta3ANCuCsfXIHnl39Svuvg21KVLlxATEwOxWAwTExOEhoaifv0qTKxKCCG1lMLJzZdffon//e9/6N69OwAgJCRE6hsjYwwCgQBisbisQxAFVKpCygHA/1QdCT/eLf+mMu/i21B79uzBzZs3AQAeHh7o3bs3N60CIYQQaQonN1988QXGjx+P48ePV7wxqTSqkCpG5d9vaWlp4cWLF9DS0kLnzp0REBBAt6IIIaQcCic3jDEAoNFO1cjBAQgL4zsKUh2UvN8EAgF0dHQQFhaG/Px8ODo68hwZIYRUf0r1uaFvi4SoX15eHnbv3g07Ozu0a1fcWcrKyornqAghpOZQKrnx8PCoMMFJS0urUkCE1GVJSUmIjo5Geno67t+/jxYtWtBIw4QQoiSlkpsvvvhCZoRiQkjVMcZw9uxZHDlyBBKJBObm5ggLC6PEhhBCKkGp5GbgwIGwtrZWVyx1Xl2ZSyoKxSXfWXLW1cWxbXJzc7Fz507cu1c8iaqXlxd69epFE9ESQkglKZzcUH8b9asrlVLyxrJ5Vx3J8yAWi7F69WqkpaVBKBQiODgYfn5+9H4jhJAqULpaiqiHrq4SlVK3AUSoMRg1e3csm3fVpbFthEIh3n//fZw9exb9+/eHra0t3yERQkiNp3ByI5FI1BkHUcaXAP4u9VzEVyBVU1fHssnJycHr16+5Cig/Pz/4+PhAR0eH58gIIaR2UHr6BVINvCj1WAhgAl+BEGU9fPgQ27Ztg7a2NsaNGwc9PT1uLBtCCCGqQVMJ8yQqCmjRQgUHegkgXAXHIWrFGMO///6L9evXIysrC0KhEDk5OXyHRQghtRK13PBk/nzgXtzb58LK/kvQv2C1l52djR07diA+Ph4A4O3tje7du0MkqqH3EwkhpJqjj0aeZL1TB+3mxk8cRL0SEhKwfft2ZGdnQ0dHB927d4ePjw/fYRFCSK1GyU01URuHDyprPJu6NJbN2bNnkZ2dDSsrK/Tv35+mUSDlkkgkKCgo4DsMQngjEomgpVX1HjOU3BC1qWg8m7owlk3v3r1x6tQpdOzYkToNk3IVFBQgISGBKlNJnaalpQVXV9cq37an5IaoTXnj2dTWsWzi4uIQFxeHDz74AABgYGDAPSakLIwxPHv2DEKhEE5OTir55kpITSORSPD06VM8e/YM9evXr9JgppTcELWrC+PZSCQSHD9+HKdOnQIAODk5oXHjxjxHRWqKoqIi5OTkwN7eHgYGBnyHQwhvrKys8PTpUxQVFVWptZuSG0KqKDMzE9u2bcOjR48AAL6+vmjQoAHPUZGaRCwWAwBV0JE6r+Q9IBaLKbmpcSSAayFgxATAi0YAACddp4onXCrxWn2hEeXcv38fO3bsQG5uLkQiEUJCQtCkSRO+wyI1FM0pRuo6Vb0HKLnRNDEAP+DkcwDQB1aUymjm8BQTqZSTJ0/i2LFjAAA7OzuEhYXBwsKC56gIIYRQrzVNuw3gioqOZQ1AV0XHIkqzsyvuJu3v748PP/yQEhtCyiAQCLBz506+w1DKy5cvYW1tjcTERL5DqTVmz56NyZMna+RclNxoWqkqz7uQINInEpE+kYhpGwOMhOI/4wBsB7W9adjr12/vCTZo0AAff/wxunXrBm1t+ocgdVNycjImT54MNzc36OrqwsnJCb169cLRo0f5Dg1AcSXa/PnzYWdnB319fQQFBeH+/fsV7rdw4UL07t0bLi4uMuuCg4MhFApx4cIFmXUdOnTAJ598IrN83bp1MDMzk1qWmZmJuXPnwtPTE3p6erC1tUVQUBC2b98OxpiiL1FpJ06cQMuWLaGrq4sGDRpg3bp1Fe5z8OBBvP/++zA2NoaVlRVCQ0NlEr8VK1agcePG0NfXR6NGjbBhwwap9TNmzMD69eu50drVitUxGRkZDADLyMhQ/cEdHBgDiv9flquMMRT/rNQqYIgAQwRYp/WdVB8PzxxY8Ust52rUGEVFRezAgQPs22+/ZWlpaXyHQ2qZ3NxcduvWLZabm8t3KEpJSEhg9vb2zMvLi0VHR7O7d++yGzdusKVLl7JGjRpx2wFgO3bs4CXGb7/9lpmamrKdO3eyq1evspCQEObq6lrutX79+jUzMTFhZ86ckVn38OFDZmRkxKZMmcLGjx8vs759+/Zs6tSpMssjIyOZqakp9/zVq1esSZMmzNHRka1bt47dvHmT3b17l/3xxx/M3d2dvXr1qjIvt0Lx8fHMwMCATZ8+nd26dYv9/PPPTCgUspiYmHL30dXVZXPmzGEPHjxgly5dYoGBgaxFixbcNr/++iszNjZmW7ZsYXFxcWzz5s3MyMiI7d69W+pYYWFhbMaMGWWeq7z3gjKf35TcqBIlN1JqS3KTlpbG/vjjDxYREcEiIiLYuXPn+A6J1DI1Nbnp1q0bc3BwYNnZ2TLrSn84v5vczJo1izVs2JDp6+szV1dX9vnnn7OCggJu/ZUrV1iHDh2YkZERMzY2Zi1btmQXLlxgjDGWmJjIevbsyczMzJiBgQHz8vJi+/btkxufRCJhtra2bMmSJdyy9PR0pquryzZv3lzm64qKimJWVlZy10VERLCBAwey27dvM1NTU5aTkyO1XtHkZsKECczQ0JAlJSXJbJuVlcUKCwvLjK8qZs2axZo0aSK1LDw8nAUHB5e5T1RUFNPW1mZisZhbtnv3biYQCLh/t4CAAJmkZfr06axNmzZSy9avX88cHR3LPJeqkhu6LUVIOW7duoXff/8dT58+hb6+PgYOHAh/f3++wyKEd2lpaYiJicHEiRNhaGgos/7dWzClGRsbY926dbh16xaWL1+OVatWYdmyZdz6IUOGwNHRERcuXMClS5cwe/Zsrix44sSJyM/Px7///ovr169j8eLFMDIyknuehIQEJCcnIygoiFtmamqKVq1a4cyZM2XGd/LkSfj6+sosZ4whMjISQ4cOhaenJxo0aIDo6Ogyj1MWiUSCLVu2YMiQIbC3t5dZb2RkVOat7pMnT8LIyKjcn7/++qvMc585c0bqegDFt9nKux6+vr7Q0tJCZGQkxGIxMjIysHHjRgQFBXH/Lvn5+dDT05PaT19fH+fPn0dhYSG3zN/fH0+ePFF7XybqKMAjGqur+ioqKsKhQ4e4e+pOTk4IDQ2Fqakpz5GRusLPD0hO1vx5bW2Bixcr3u7BgwdgjMHT01Ppc3z++efcYxcXF8yYMQNbtmzBrFmzAACPHj3CzJkzuWM3bNiQ2/7Ro0cIDQ1Fs2bNAABu5cw6nPzmAtrY2Egtt7Gx4dbJ8/DhQ7lJx5EjR5CTk4Pg4GAAwNChQ7FmzRoMGzas3Nf7rtTUVLx69apS187Pzw9Xrlwpd5t3X29pycnJcq9HZmYmcnNzoa+vL7OPq6srDh06hAEDBuCjjz6CWCxGQEAA9u/fz20THByM1atXo0+fPmjZsiUuXbqE1atXo7CwEKmpqVwBRsl1ffjwodz+TKpCyQ0hcpw7d45LbNq0aYOOHTtCKBTyHBWpS5KTgaQkvqMoG6tCh9etW7fip59+QlxcHLKzs1FUVAQTExNu/fTp0zFmzBiudaB///5wd3cHAEyZMgUTJkzAoUOHEBQUhNDQUDRv3rzKr6e03NxcmVYIAFi7di3Cw8O5VpVBgwZh5syZiIuL4+JTRFWunb6+vsYHCU1OTsbYsWMxYsQIDBo0CFlZWZg/fz7CwsJw+PBhCAQCzJs3D8nJyXj//ffBGIONjQ1GjBiB7777Tmo6kZLkKScnR60x020pPgnzuYcC0OBd1UmrVq3QoEEDDB48GEFBQZTYEI2ztQUcHDT/Y2urWHwNGzaEQCDAnTuKjj5a7MyZMxgyZAi6d++OvXv34vLly5g7d67UbOgRERG4efMmevTogWPHjsHLyws7duwAAIwZMwbx8fEYNmwYrl+/Dj8/P/z8889lXMPiF5OSkiK1PCUlhVsnj6WlJV69eiW1LC0tDTt27MCvv/4KbW1taGtrw8HBAUVFRVi7di23nYmJCTIyMmSOmZ6ezrX8WllZwczMTOlrB1T9tpStra3c62FiYiK31QYoroIyNTXFd999hxYtWiAwMBB//vknjh49inPnzgEoTlrWrl2LnJwcJCYm4tGjR3BxceGqq0qkpaVx10CdqOVGg6KigD9nAbvePC+0uMat87P34ycoFYhC8QzgWe8sf8ZDLJVVWFiICxcu4P3334eWlha0tbUxZMgQvsMidZgit4b4ZGFhgeDgYKxYsQJTpkyR6XeTnp4ut9/N6dOn4ezsjLlz53LLHj58KLOdh4cHPDw8MG3aNAwaNAiRkZHo27cvgOLbxOPHj8f48eMxZ84crFq1Su74Ka6urrC1tcXRo0fh4+MDoLj8+ty5c5gwYUKZr61Fixb4888/pZb99ddfcHR0lBmv59ChQ1i6dCm+/PJLCIVCNGrUCIcOHZI5ZmxsLDw8PAAUz3w9cOBAbNy4EQsWLJC5BZadnQ09PT25/W6qelvq3dtJAHD48GEEBASUuU9OTo7MZK4lX/jencVeR0cHjo6OAIAtW7agZ8+eUvveuHEDOjo66h/JvcIux7UMn9VSnp6MNXtTKcXA2O8tVjFEgOl/rc+Ss5JVH4+GeDLuJcn98eQvNIW8ePGC/frrrywiIoIdPXqU73BIHVRTq6Xi4uKYra0tVwp+7949duvWLbZ8+XLm6fn2nY9S1VK7du1i2trabPPmzezBgwds+fLlzMLCgqskysnJYRMnTmTHjx9niYmJ7NSpU8zd3Z3NmjWLMcbY1KlTWUxMDIuPj2eXLl1irVq1YgMGDCgzxm+//ZaZmZmxXbt2sWvXrrHevXtXWAp+7do1pq2tLTXsg7e3N/v0009ltk1PT2cikYjt3buXuyZ6enps8uTJ7OrVq+zOnTts6dKlTFtbmx04cIDb7+XLl8zT05M5Ojqy9evXs5s3b7J79+6xNWvWsAYNGqi9FHzmzJns9u3bbMWKFTKl4D///DPr1OltBe/Ro0eZQCBgX3zxBbt37x67dOkSCw4OZs7Ozly12N27d9nGjRvZvXv32Llz51h4eDizsLBgCQkJUudfsGCB1LHfRaXglaTR5EbCGNvIGJtc/LPGkLG/Sn3qr/RdyRABNih6kOpj0aCSkm+tN49L/3gyxqL4C61CV65cYQsXLmQRERFsyZIlLC4uju+QSB1UU5Mbxhh7+vQpmzhxInN2dmYikYg5ODiwkJAQdvz4cW4bvFMKPnPmTFavXj1mZGTEwsPD2bJly7jkJj8/nw0cOJA5OTkxkUjE7O3t2aRJk7hrM2nSJObu7s50dXWZlZUVGzZsGEtNTS0zPolEwubNm8dsbGyYrq4u69y5M7t7926Fr8vf35+tXLmSMcbYxYsXGQB2/vx5udt269aN9e3bl3t+/vx51qVLF2ZlZcVMTU1Zq1at5I7zk56ezmbPns0aNmzIRCIRs7GxYUFBQWzHjh1MIpFUGGNlHT9+nPn4+DCRSMTc3NxYZGSk1PoFCxYwZ2dnqWWbN29mLVq0YIaGhszKyoqFhISw27dvc+tv3brFfHx8mL6+PjMxMWG9e/dmd+7ckTl3o0aNyi3DV1VyI2BMjcMgVkOZmZkwNTVFRkaGVAc2lXB0LO4B6OAAPHkCnALQruzNV/quxIReE/Chz4dY03uNamPRIEcASQAcADzhORZFFRQU4MCBA1zzrqurK/r161dmSSkh6pSXl4eEhAS4urrK7chKNG/fvn2YOXMmbty4IXNLhlTOgQMH8L///Q/Xrl0rs9S9vPeCMp/f1OdGneLKXsWEDHsa7dFcLITz4sULREVF4cWLFxAIBGjfvj3atWtHf8AIIZwePXrg/v37SEpKgpOTE9/h1AqvX79GZGSkRqaroeRGUz4FeqwFnr96Dd3gL5DjdwSXBZf5jqpOYozh1atXMDIyQmhoqFrHWiCE1Fzy5ogilRcWFqaxc1FyoyER64CYl4Ck2TbgvSVS64xEdCtE3SQSCdcyY21tjfDwcNjZ2ckdWZUQQkjNRu3wGpKcAki0s4GgT7llxiJj+Dv4Y5zvOB4jq/2Sk5OxcuVKPHr0iFvWoEEDSmwIIaSWouRGgwQt1wLGb4f8/r3n7zg35hyaWKu53l9NogA0RvUdz4YxhosXL2L16tV48eIFDh8+XKWRQQkhhNQMdFtKU2wuQ9R0H/JLLQpyCypz85pgPoDS42sa8xWIHPn5+dizZw9u3rwJoHg01T59+kAgoJGgCSGktqPkRlP8fke+49tRK48NPwYrQ/UOP61uJSMSawHwAPAVj7GU9uzZM0RHRyMtLQ1aWlro3LkzAgICKLEhhJA6gpIbHuhr68PH1ofvMFTGDsBtvoN44/nz51izZg3EYjFMTU0RGhpKZZyEEFLHUHKjSRItrO79Bzq5doK5vjnf0dRKVlZW8PDwgEQiQe/evcucCI4QQkjtVS06FK9YsQIuLi7Q09NDq1atcP78+TK3XbVqFdq1awdzc3OYm5sjKCio3O2rFSbE6Jaj4WruyncktcrTp0+Rl5cHABAIBOjbty/Cw8MpsSGkmhAIBDITTlZ3BQUFaNCgAU6fPs13KLXGypUr0atXL42ci/fkZuvWrZg+fToWLFiA2NhYeHt7Izg4GM+fP5e7/YkTJzBo0CAcP34cZ86cgZOTEz744AMkJSVpOPIKTAYwUnqRkNrJVIoxhjNnzmDNmjXYu3cvVwmlo6ND/WsI0ZDk5GRMnjwZbm5u0NXVhZOTE3r16oWjR4/yHRoAYPv27fjggw9Qr149CASCCmfULrFy5Uq4urqidevWMus++ugjCIVCREVFyawbOXIk+vTpI7P8xIkTEAgESE9P55YVFBTgu+++g7e3NwwMDGBpaYk2bdogMjIShYWFir5EpV27dg3t2rWDnp4enJyc8N1331W4z4ULF9C5c2eYmZnB3NwcwcHBuHr1qtQ2jDF8//338PDwgK6uLhwcHLBw4UJu/YcffojY2FicPHlS5a/pXbwnNz/88APGjh2LUaNGwcvLCytXroSBgQHWrl0rd/u//voLH3/8MXx8fODp6YnVq1dDIpFUmzcSAEBsDvwivShNPw1meiqey0qNSsq8Hcv54bMEPDc3F1u3bsWhQ4cgkUjAGINYLOYxIkLqnsTERPj6+uLYsWNYsmQJrl+/jpiYGHTs2BETJ07kOzwAxUP+t23bFosXL1Z4H8YYfvnlF4wePVpmXU5ODrZs2YJZs2aV+TmliIKCAgQHB+Pbb7/FuHHjcPr0aZw/fx4TJ07Ezz//zFV6qlpmZiY++OADODs749KlS1iyZAkiIiLwxx9/lLlPdnY2unbtivr16+PcuXM4deoUjI2NERwcLJWETZ06FatXr8b333+PO3fuYPfu3fD39+fWi0QiDB48GD/99JNaXpuUCqfWVKP8/HwmFAplZksdPnw4CwkJUegYmZmZTE9Pj+3Zs0eh7TUyK7itLzfzNwNjv/n+xnTn6rK1sWtVf0418WRSL6HcH08Nx/bo0SP2ww8/sIiICPbVV1+x8+fPq3UGXULUrabOCt6tWzfm4ODAsrOzZda9evWKe4x3ZgWfNWsWa9iwIdPX12eurq7s888/ZwUFBdz6K1eusA4dOjAjIyNmbGzMWrZsyS5cuMAYYywxMZH17NmTmZmZMQMDA+bl5cX27dtXYawJCQkMALt8+XKF2164cIFpaWmxzMxMmXXr1q1j77//PktPT2cGBgbs0aNHUutHjBjBevfuLbPf8ePHGQDuuixevJhpaWmx2NhYmW0LCgrkXlNV+PXXX5m5uTnLz8/nln366aesUaNGZe5z4cIFBkDqtV67do0BYPfv32eMFc8Krq2tLXcm8NL++ecfJhKJWE5Ojtz1qpoVnNeWm9TUVIjFYtjY2Egtt7GxQXJychl7Sfv0009hb2+PoCD5Y8bk5+cjMzNT6keTtnnswYReEyB+7YxRLUZp9NxVUbrM26GcH09orgScMYb//vsPkZGRyMzMhIWFBUaPHo333nuPbkMRomFpaWmIiYnBxIkT5Y72bWZmVua+xsbGWLduHW7duoXly5dj1apVWLZsGbd+yJAhcHR0xIULF3Dp0iXMnj0bOjo6AICJEyciPz8f//77L65fv47FixfDyEi1U9icPHkSHh4eMDaWHb1rzZo1GDp0KExNTdGtWzesW7euUuf466+/EBQUhBYtWsis09HRKXME9UePHsHIyKjcn2+++abM8545cwaBgYEQiUTcsuDgYNy9exevXr2Su0+jRo1Qr149rFmzBgUFBcjNzcWaNWvQuHFjbm6+PXv2wM3NDXv37oWrqytcXFwwZswYpKWlSR3Lz88PRUVFOHfuXEWXqEpqdC+Qb7/9Flu2bMGJEydkpkYvsWjRInzxxRcajqz2sAPwhO8g3sjLy8O5c+fAGEPTpk3Rs2dP6Orq8h0WIWrh94cfkrMV+5KnSrZGtrg47mKF2z148ACMMXh6eip9js8//5x77OLighkzZnC3eoDiD/CZM2dyx27YsCG3/aNHjxAaGopmzZoBANzc3JQ+f0UePnwIe3t7meX379/H2bNnsX37dgDA0KFDMX36dHz++edKf8G6f/8+OnTooHRs9vb2FfYbsrCwKHNdcnIyXF2li1pKGhiSk5Nhbi5byWtsbIwTJ06gT58++Oqr4q+zDRs2xMGDB7kZvuPj4/Hw4UNERUVhw4YNEIvFmDZtGsLCwnDs2DHuWAYGBjA1NcXDhw8Ver2VxWtyY2lpCaFQiJSUFKnlKSkpsLW1LXff77//Ht9++y2OHDmC5s2bl7ndnDlzMH36dO55ZmYmjXtSQ+nr6yM0NBSpqalo2bIltdaQWi05OxlJWdWsUKIUVoWpTLZu3YqffvoJcXFxyM7ORlFREUxM3vZJnD59OsaMGYONGzciKCgI/fv3h7u7OwBgypQpmDBhAg4dOoSgoCCEhoaW+xlQGbm5uXK/MK9duxbBwcGwtLQEAHTv3h2jR4/GsWPH0LlzZ6XOUdnrp62tjQYNGlRq38rKzc3F6NGj0aZNG2zevBlisRjff/89evTogQsXLkBfXx8SiQT5+fnYsGEDPDw8ABS3cvn6+uLu3bto1KgRdzx9fX3k5OSoNWZeb0uJRCL4+vpKdQYu6RwcEBBQ5n7fffcdvvrqK8TExMDPz6/cc+jq6sLExETqR6OEBZo9Xy3CGMO///6La9euccucnZ3h6+tLiQ2p9WyNbOFg7KDxH1uj8r9YlmjYsCEEAgHu3LlT8calnDlzBkOGDEH37t2xd+9eXL58GXPnzkVBwdu/lREREbh58yZ69OiBY8eOwcvLCzt27AAAjBkzBvHx8Rg2bBiuX78OPz8//Pzzz0rFUBFLS0uZWzRisRjr16/Hvn37oK2tDW1tbRgYGCAtLU2qY7GJiQkyMjJkjpmeng6hUMjdbvLw8FD62gFVvy1la2srt0GhZJ08mzZtQmJiIiIjI/Hee+/h/fffx6ZNm5CQkIBdu3YBAOzs7KCtrc0lNgDQuHFjLubS0tLSYGWl3hH6eb8tNX36dIwYMQJ+fn7w9/fHjz/+iNevX2PUqOL+KcOHD4eDgwMWLVoEAFi8eDHmz5+PTZs2wcXFheubU/KPSmqH7Oxs7NixA/Hx8dDR0YGLi4vmE1NCeKTIrSE+WVhYIDg4GCtWrMCUKVNk+oikp6fL7Xdz+vRpODs7Y+7cudwyebcoPDw84OHhgWnTpmHQoEGIjIxE3759AQBOTk4YP348xo8fjzlz5mDVqlWYPHmyyl5bixYt8Ntvv4Exxn2R2r9/P7KysnD58mUIhUJu2xs3bmDUqFHc623UqBG2bNmC/Px8qdvmsbGxcHV15foODR48GJ999hkuX74s0++msLAQBQUFcvvdVPW2VEBAAObOnYvCwkIulsOHD6NRo0Zyb0kBxRViWlpaUl8qS55LJBIAQJs2bVBUVIS4uDiule3evXsAir+UloiLi0NeXp7cvkYqVWGXYw34+eefWf369ZlIJGL+/v7s7Nmz3Lr27duzESNGcM+dnZ0ZAJmfBQsWKHQuTVdLRTeOZogA0x0drPrzqZEDK34JDjycOz4+nn3//fcsIiKCff311yw2NpaqoUitVlOrpeLi4pitrS3z8vJi0dHR7N69e+zWrVts+fLlzNPzbR0lSlVL7dq1i2lra7PNmzezBw8esOXLlzMLCwtmamrKGGMsJyeHTZw4kR0/fpwlJiayU6dOMXd3dzZr1izGGGNTp05lMTExLD4+nl26dIm1atWKDRgwoMwYX758yS5fvsz27dvHALAtW7awy5cvs2fPnpW5T2pqKtPR0WHXr1/nlvXu3ZuFh4fLbCsWi5mtrS375ZdfGGPFVWLW1tZswIAB7OLFi+z+/ftszZo1zNjYmP3222/cfnl5eaxdu3bM3Nyc/fLLL+zKlSssLi6Obd26lbVs2VKhqq7KSE9PZzY2NmzYsGHsxo0bbMuWLczAwID9/vvv3Dbbt2+Xqp66ffs209XVZRMmTGC3bt1iN27cYEOHDmWmpqbs6dOn3HVo2bIlCwwMZLGxsezixYusVatWrEuXLlLnj4yMZG5ubmXGp6pqqWqR3GgSX8mNjWe86s+nJn+zt2XemkxuxGIxO378OPviiy9YREQEW7FiBXv+/LkGIyCEHzU1uWGMsadPn7KJEycyZ2dnJhKJmIODAwsJCWHHjx/ntsE7peAzZ85k9erVY0ZGRiw8PJwtW7aMS27y8/PZwIEDmZOTExOJRMze3p5NmjSJuzaTJk1i7u7uTFdXl1lZWbFhw4ax1NTUMuOLjIys1BfiAQMGsNmzZzPGGEtOTmba2trs77//lrvthAkTWIsWLbjnd+/eZX379mX29vbM0NCQeXt7s1WrVsl8ScvLy2OLFi1izZo1Y3p6eszCwoK1adOGrVu3jhUWFpYbX1VcvXqVtW3blunq6jIHBwf27bffSq0vuWalHTp0iLVp04aZmpoyc3Nz1qlTJ3bmzBmpbZKSkli/fv2YkZERs7GxYSNHjmQvX76U2uaDDz5gixYtKjM2VSU3Asaq0CusBsrMzISpqSkyMjJUf5vD0RFISgJsfYHk4iblbY23Icz3DzgcP4gn1aXsqAKNAZTcCfaEZibFlEgk+PPPP5GQkACguFm4W7duXLMpIbVZXl4eEhIS4OrqWmblJ9Gsa9euoUuXLoiLi6MuDypy8+ZNdOrUCffu3YOpqancbcp7Lyjz+c37CMW1CtMG0AvI6cd3JFWSVeqxpsaw0dLSgr29PXR0dNC3b1+EhIRQYkMI4U3z5s2xePFi7gsXqbpnz55hw4YNZSY2qsR7h+JaJWsygP8Bmh0nUG0cAISp8fgSiQS5ublcp7mOHTuiZcuW5XaGI4QQTRk5ciTfIdQqZQ22qw7UcqNKhbJjLZx3qCEzlmtYZmYm1q9fj02bNnFzQgmFQkpsCCGEVBm13KhJ3tI8hFwOwVG3o0C85rLVmuD+/fvYsWMHcnNzIRKJ8Pz5c9jZ2fEdFiGEkFqCkhs1KQwvxOGsw3yHUa2IxWIcO3YMp0+fBlA86FNYWBi11hBCCFEpSm4IACAKwHwUdyZ+pobjp6enY9u2bXjypmTM398fXbp04eYlIYQQQlSFPlkIgOLE5t2BwGXnw628PXv24MmTJ9DV1UXv3r25YbkJIYQQVaPkhgB4W/6theKZwI2h2jLwHj16YN++fejZs2eZQ3wTQgghqkDJDZFiB0AVYw2+evUKCQkJaNmyJYDiuU6GDRumgiMTQggh5aNScKJyt27dwu+//449e/YgPj6e73AIITwTCATYuXMn32Eo5eXLl7C2tkZiYiLfodQas2fPVukEp+Wh5IaoTFFREfbv34+oqCjk5+fD0dGRKqEIqeWSk5MxefJkuLm5QVdXF05OTujVqxeOHj3Kd2goLCzEp59+imbNmsHQ0BD29vYYPnw4nj59WuG+CxcuRO/eveHi4iKzLjg4GEKhEBcuXJBZ16FDB3zyyScyy9etWyczS3pmZibmzp0LT09P6OnpwdbWFkFBQdi+fTvUOTPSiRMn0LJlS+jq6qJBgwZYt25dhfv8/fff8PHxgYGBAZydnbFkyRKZbf766y94e3vDwMAAdnZ2+PDDD/Hy5Utu/YwZM7B+/XqNfOml5IaoRFpaGtasWcO92Vu3bo2RI0fKvJkJIbVHYmIifH19cezYMSxZsgTXr19HTEwMOnbsiIkTJ/IdHnJychAbG4t58+YhNjYW27dvx927dxESElLhfmvWrMHo0aNl1j169AinT5/GpEmTsHbt2krHlp6ejtatW2PDhg2YM2cOYmNj8e+//yI8PByzZs1CRkZGpY9dnoSEBPTo0QMdO3bElStX8Mknn2DMmDE4ePBgmfscOHAAQ4YMwfjx43Hjxg38+uuvWLZsGX755Rdum//++w/Dhw/H6NGjcfPmTURFReH8+fMYO3Yst42lpSWCg4Px22+/qeW1Salwas1aRq2zgusd4qbTznySyRCB4p+hHzAHTU6vXQkOrPKzgN+4cYN98803LCIigi1evJjdu3dPxdERUrvV1FnBu3XrxhwcHFh2drbMulevXnGP8c6s4LNmzWINGzZk+vr6zNXVlX3++eesoKCAW3/lyhXWoUMHZmRkxIyNjVnLli3ZhQsXGGOMJSYmsp49ezIzMzNmYGDAvLy82L59+xSO+fz58wwAe/jwYZnbREVFMSsrK7nrIiIi2MCBA9nt27eZqakpy8nJkVrfvn17NnXqVJn9IiMjuZnPGSueSdzQ0JAlJSXJbJuVlaW2WcFnzZrFmjRpIrUsPDycBQcHl7nPoEGDWFhYmNSyn376iTk6OnIznS9ZsoS5ubnJbOPwzoff+vXrmaOjY5nnUtWs4NShuIYrPT5NVVRlbJuCggIUFBSgfv36CA0NVf1s64TURX5+QHKy5s9rawtcvFjhZmlpaYiJicHChQu5+eFKK6/V1tjYGOvWrYO9vT2uX7+OsWPHwtjYGLNmzQIADBkyBC1atMBvv/0GoVCIK1eucBPpTpw4EQUFBfj3339haGiIW7duKTVrd0ZGBgQCQbnxnTx5Er6+vjLLGWOIjIzEihUr4OnpiQYNGiA6OlrpYgmJRIItW7ZgyJAhsLe3l1lf3us5efIkunXrVu7xf//9dwwZMkTuujNnzsjM8RQcHCz3VlqJ/Px8GBgYSC3T19fHkydP8PDhQ7i4uCAgIACfffYZ9u/fj27duuH58+eIjo5G9+7dpfbz9/fHkydPkJiYKPeWn6pQclPDyRufpioUHdtGIpFAS6v4rqaPjw9EIhEaN27MLSOEVFFyMpCUxHcUZXrw4AEYY/D09FR6388//5x77OLighkzZmDLli1ccvPo0SPMnDmTO3bDhg257R89eoTQ0FA0a9YMAODm5qbwefPy8vDpp59i0KBB5X4Je/jwodyk48iRI8jJyUFwcDAAYOjQoVizZo3SyU1qaipevXpVqWvn5+eHK1eulLuNjY1NmeuSk5Nl1tvY2CAzMxO5ubnQ19eX2Sc4OBjTpk3DyJEj0bFjRzx48ABLly4FUDzTt4uLC9q0aYO//voL4eHhyMvLQ1FREXr16oUVK1ZIHavkupYkRepCyU0N9+74NFWh6Ng2V69exalTpzBq1CgYGBhAIBCgSZMmVTw7IUSKrW21Pi+rQofXrVu34qeffkJcXByys7NRVFQklWxMnz4dY8aMwcaNGxEUFIT+/fvD3d0dADBlyhRMmDABhw4dQlBQEEJDQ9G8ueykxe8qLCzEgAEDwBirsM9Hbm4u9PT0ZJavXbsW4eHh3MjqgwYNwsyZMxEXF8fFp4iqXDt9fX00aNCg0vtXxtixYxEXF4eePXuisLAQJiYmmDp1KiIiIrgvtLdu3cLUqVMxf/58BAcH49mzZ5g5cybGjx+PNWvWSMUPFPdrUidKbmoJVY1PU56CggIcOHCA+9Zw7tw5dOzYUc1nJaSOUuDWEJ8aNmwIgUCAO3eUazs+c+YMhgwZgi+++ALBwcEwNTXFli1buJYAAIiIiMDgwYOxb98+HDhwAAsWLMCWLVvQt29fjBkzBsHBwdi3bx8OHTqERYsWYenSpeWWGJckNg8fPsSxY8cqvHVuaWmJV69eSS1LS0vDjh07UFhYKJUcicVirF27FgsXLgQAmJiYyO0MnJ6eDlNTUwCAlZUVzMzMlL52QNVvS9na2iIlJUVqWUpKCkxMTOS22gDFpfyLFy/GN998g+TkZFhZWXHVcCUtZ4sWLUKbNm0wc+ZMAEDz5s1haGiIdu3a4euvv+YmR05LSwNQfA3UiZIbopCS+6cvXrwAALRv3x6BgYE8R0UI4YuFhQWCg4OxYsUKTJkyRabfTXp6utx+LadPn4azszPmzp3LLXv48KHMdh4eHvDw8MC0adMwaNAgREZGom/fvgAAJycnjB8/HuPHj8ecOXOwatWqMpObksTm/v37OH78OOrVq1fha2vRogX+/PNPqWV//fUXHB0dZcbrOXToEJYuXYovv/wSQqEQjRo1wqFDh2SOGRsbCw8PDwCAlpYWBg4ciI0bN2LBggUyt8Cys7Ohp6cnd+69qt6WCggIwP79+6WWHT58GAEBAeUeEwCEQiEcHBwAAJs3b0ZAQACXpOTk5MjEKxQKAUi3VN24cQM6Ojrqb+2vsMtxLaOpaqlFOxZppFqqKlVOipBIJCw2NpZ9/fXXLCIign3//fcsPj5eTWcjpG6qqdVScXFxzNbWlnl5ebHo6Gh27949duvWLbZ8+XLm6enJbYdS1VK7du1i2trabPPmzezBgwds+fLlzMLCgqskysnJYRMnTmTHjx9niYmJ7NSpU8zd3Z3NmjWLMcbY1KlTWUxMDIuPj2eXLl1irVq1YgMGDJAbX0FBAQsJCWGOjo7sypUr7NmzZ9xPfn5+ma/r2rVrTFtbm6WlpXHLvL292aeffiqzbXp6OhOJRGzv3r3cNdHT02OTJ09mV69eZXfu3GFLly5l2tra7MCBA9x+L1++ZJ6enszR0ZGtX7+e3bx5k927d4+tWbOGNWjQQKraTJXi4+OZgYEBmzlzJrt9+zZbsWIFEwqFLCYmhtvm559/Zp06deKev3jxgv3222/s9u3b7PLly2zKlClMT0+PnTt3jtsmMjKSaWtrs19//ZXFxcWxU6dOMT8/P+bv7y91/gULFkgd+12qqpai5EaVSiU39WbWe5vcDAypscnNuXPnWEREBIuIiGAbNmxgWVlZajoTIXVXTU1uGGPs6dOnbOLEiczZ2ZmJRCLm4ODAQkJC2PHjx7lt8E4p+MyZM1m9evWYkZERCw8PZ8uWLeOSm/z8fDZw4EDm5OTERCIRs7e3Z5MmTeKuzaRJk5i7uzvT1dVlVlZWbNiwYSw1NVVubAkJCQyA3J/S8cnj7+/PVq5cyRhj7OLFiwwAO3/+vNxtu3Xrxvr27cs9P3/+POvSpQuzsrJipqamrFWrVlKvv0R6ejqbPXs2a9iwIROJRMzGxoYFBQWxHTt2cCXW6nD8+HHm4+PDRCIRc3NzY5GRkVLrFyxYwJydnbnnL168YO+//z4zNDRkBgYGrHPnzuzs2bMyx/3pp5+Yl5cX09fXZ3Z2dmzIkCHsyZMnUts0atSIbd68uczYVJXcCBhT4zCI1VBmZiZMTU2RkZGh+pJl/cNAXhcAgOVMS7w0fDMy48YYOOQF44kaOsU4AkgC4AD19LnJy8vDqlWr4OPjg7Zt20IgEKjhLITUbXl5eUhISICrq6vcjqxE8/bt24eZM2fixo0bVAWqIgcOHMD//vc/XLt2Te4tN6D894Iyn9/U50bNDG5/hJy44OLsoxIqGsemKuPTyMMYQ3x8PNzc3CAQCKCnp4cJEyaU+YtICCG1UY8ePXD//n0kJSXBycmJ73BqhdevXyMyMlIjnyf0iVXNKTqOjaLj05QnPz8fe/fuxY0bN9CzZ09uECtKbAghdVF5A9sR5YWFhWnsXPSppUpi1Y9Locg4NoqOT1OeZ8+eIfr/7d17XE35/j/w197V3qUbRqldiUoaxi2mhuHhMpmYXMeoaJoYd4WjrxKaKea4zJnB4IRByqWR0Qg/l1xzRGYkIiS6ySBH0k2ly37//vBoH9tud9NF2/v5eKzHoz7r81nrvT7JfrfW57M+ERHIycmBUChEWVnZWx6RMcYYax6c3DSUEwDKujfa4RvrPTZEhLi4OJw8eRIVFRXQ19fH+PHj+TYsY4yxFouTm4ZyXP7bfHF+88RRByUlJTh8+DCSkpIAAF26dMGYMWOUvsiJMcYYawk4uWkor805K9fzQZn6u/9Y58mTJ7hz5w6EQiGGDRsGe3t7ng3FGGOsxePkphGQRmJzh1Ar5ubmGDFiBCQSieytk4wxxlhLx5P33yH7AXyIV++uqdwacqp3cXEx/vjjD2RnZ8vKPv74Y05sGGOMqRS+c9MIcgtfyr4uelH7dtVN+37bqd4PHjzAH3/8gby8POTk5GDatGn8CIoxxphK4js3jUCq9lpGQ6+6WLcW2cnr075NXttsUP+p3kSEixcvIjQ0FHl5eWjTpg1GjhzJiQ1jrMkIBAKFBSffdc+ePYOhoSEyMjKaOxSV8cknn+CPP/5oknNxctPIPsgeDRsb4Ic6ZCeV074rtyQA9Xn1UVFREfbu3YvTp09DKpWiW7dumDlzpmzpecYYe1tZWVmYO3cuLCwsIBaLYWZmhlGjRuHMmTPNHRoAIDAwEDY2NtDW1kabNm3g4OCAv/76q8Z2K1aswJgxY9CxY0eFfY6OjlBTU0NcXJzCvsGDB1f58r/Q0FCFVdLz8/OxdOlS2NjYQFNTE0ZGRnBwcMCBAwfQmCsjnTt3Dra2thCLxbCyskJoaGi19QMDAyEQCBS2N1eCrxQeHg6BQICxY8fKlfv7+8PPzw9SqbSBrkQ5Tm4akfcn3sj+cziSkoAmfDEjACAnJwdbtmzBvXv3oK6ujpEjR2L8+PEQi8VNGwhjTGVlZGSgT58+OHv2LH766SckJiYiKioKQ4YMgaenZ3OHBwCwtrbGv//9byQmJuLChQvo2LEjPv/8czx9+lRpm6KiIgQHB2Pq1KkK+zIzMxEbGwsvLy/s2LGj3nHl5uaif//+2LVrFxYvXoyrV6/i/PnzcHFxga+vL/Ly8up97Oqkp6fDyckJQ4YMQUJCAv7xj39g2rRpOHHihNI2CxcuxOPHj+W2rl27YsKECQp1MzIysHDhQgwcOFBh34gRI1BQUIDjx48r7GtwNS6tqWIabVXweSRbETyr1ceEQNDqmNV1OkRDrvBdXl5OwcHBtHHjRsrKymqAIzLGGktLXRV8xIgRZGJiQoWFhQr7nj9/Lvsab6wK7uvrS507dyYtLS3q1KkT+fv7U2lpqWx/QkICDR48mHR0dEhXV5dsbW0pLi6OiIgyMjJo5MiR1Lp1a2rVqhV17dqVjh49WuuYKz8DTp8+rbTO/v37ycDAoMp9gYGB5OrqSklJSaSvr09FRUVy+wcNGkTz589XaBcSEiJb+ZyIaPbs2aStrU0PHz5UqFtQUEBlZWW1u6A68vX1pW7dusmVubi4kKOjY62PkZCQQADo/PnzcuXl5eXUv39/2r59O3l4eNCYMWMU2k6ZMoW+/vprpcduqFXBeUCxCnnx4gU0NTWhpqYGNTU1TJgwAWKxGCKRqLlDY4zVUV8AWc1wXiMAV2pRLycnB1FRUVixYkWVjyfefATzOl1dXYSGhkIikSAxMRHTp0+Hrq4ufH19AQBubm7o3bs3Nm/eDDU1NSQkJEBDQwMA4OnpidLSUpw/fx7a2tq4ffs2dHR0anVtpaWl2Lp1K/T19dGzZ0+l9WJiYmRr672OiBASEoKgoCDY2NjAysoKERERcHd3r9X5K0mlUoSHh8PNzQ0SiURhf3XXExMTgxEjRlR7/F9//RVubm5V7rt06RIcHBzkyhwdHeu0jtb27dthbW2tcHdm+fLlMDQ0xNSpUxETE1NlWzs7O6xevbrW56ovTm5URHp6Og4cOIDu3bvj888/B/DqPxDGWMuUBeBhcwdRjZSUFBARbGxs6tzW399f9nXHjh2xcOFChIeHy5KbzMxM+Pj4yI7duXNnWf3MzEyMHz8e3bu/Wu7GwsKixvMdOXIErq6uKCoqgrGxMU6dOoV27doprX///v0qk47Tp0+jqKgIjo6OAICvv/4awcHBdU5usrOz8fz583r1Xd++fZGQkFBtnfbt2yvdl5WVpbC/ffv2yM/PR3FxcY1vqC8pKUFYWBj8/Pzkyi9cuIDg4OAaY5NIJHjw4AGkUimEwsYbGcPJTTPbj1dTwAtQv3faSKVSnD9/HufPnwcRISUlBUOGDJH9lcMYa5kafhnehj0vvcWA13379mHDhg1ITU1FYWEhysvLoaenJ9vv7e2NadOmYffu3XBwcMCECRNgaWkJAJg3bx5mz56NkydPwsHBAePHj0ePHj2qPV/l+JLs7Gxs27YNzs7O+Ouvv2BoaFhl/eLiYmhqaiqU79ixAy4uLlBXf/XROXHiRPj4+CA1NVUWX228Td9paWnBysqq3u3fVmRkJAoKCuDh4SErKygogLu7O7Zt21Zt0gi8il8qleLly5eNutQPDyhuZpXvtnkIoHL8eG3vtxQUFGDPnj34z3/+AyJCr169MH36dE5sGFMBVyA/a7Kptto8kgJe3U0RCAS4c0fZ27mqdunSJbi5ueGLL77AkSNHcO3aNSxduhSlpaWyOoGBgbh16xacnJxw9uxZdO3aFZGRkQCAadOmIS0tDe7u7khMTETfvn2xcePGas+pra0NKysrfPLJJwgODoa6ujqCg4OV1m/Xrh2eP38uV5aTk4PIyEhs2rQJ6urqUFdXh4mJCcrLy+UGFuvp6VU5GDg3Nxf6+voAAAMDA7Ru3brOfQe8eiylo6NT7RYWFqa0vZGREZ48eSJX9uTJE+jp6dUq2di+fTtGjhwpd/cnNTUVGRkZGDVqlKxvdu3ahcOHD0NdXR2pqamyujk5OdDW1m70NQz5zk0ze/3dNsZ4ldjUZtZ4amoqIiMj8eLFC2hoaMDJyanaZ8iMMdaQ2rZtC0dHRwQFBWHevHkK425yc3OrHHcTGxsLc3NzLF26VFZ2//59hXrW1tawtrbGggULMHHiRISEhGDcuHEAADMzM8yaNQuzZs3C4sWLsW3bNsydO7fWsVfeOVCmd+/e2LNnj1xZWFgYTE1NFd7Xc/LkSaxZswbLly+HmpoaunTpgpMnTyoc8+rVq7C2tgYACIVCuLq6Yvfu3QgICFB4BFZYWAhNTU3ZHaLXve1jqX79+uHYsWNyZadOnUK/fv2qPSbwavhDdHQ0Dh8+LFduY2ODxET5ZYf8/f1RUFCA9evXw8zMTFZ+8+ZN9O7du8ZzvbUahxyrmHdttlR9ZkgVFxfTqlWrKDAwkDZt2kRPnz6tX8yMsXdCS50tlZqaSkZGRtS1a1eKiIigu3fv0u3bt2n9+vVkY2Mjq4fXZksdOnSI1NXVae/evZSSkkLr16+ntm3bymYSFRUVkaenJ0VHR1NGRgZduHCBLC0tydfXl4iI5s+fT1FRUZSWlkbx8fFkb29Pzs7OVcZXWFhIixcvpkuXLlFGRgZduXKFpkyZQmKxmG7evKn0um7cuEHq6uqUk5MjK+vZsyctWrRIoW5ubi6JRCI6cuSIrE80NTVp7ty5dP36dbpz5w6tWbOG1NXV6fjx47J2z549IxsbGzI1NaWdO3fSrVu36O7duxQcHExWVlZys80aUlpaGrVq1Yp8fHwoKSmJgoKCSE1NjaKiomR1Nm7cSEOHDlVo6+/vTxKJhMrLy2s8j7LZUoMGDaLly5crbddQs6U4uWkoTZjcEBElJibS4cOH5aZPMsZappaa3BARPXr0iDw9Pcnc3JxEIhGZmJjQ6NGjKTo6WlYHb0wF9/HxoQ8++IB0dHTIxcWF1q1bJ0tuXr58Sa6urmRmZkYikYgkEgl5eXnJ+sbLy4ssLS1JLBaTgYEBubu7U3Z2dpWxFRcX07hx40gikZBIJCJjY2MaPXo0Xb58ucbrsrOzoy1bthAR0ZUrVwiA0nYjRoygcePGyb6/fPkyDRs2jAwMDEhfX5/s7e3lrr9Sbm4u+fn5UefOnUkkElH79u3JwcGBIiMjSSqV1hhjfUVHR1OvXr1IJBKRhYUFhYSEyO0PCAggc3NzubKKigoyNTWlJUuW1OocVSU3f//9N2loaNCDBw+Utmuo5EZA1IivQXwH5efnQ19fH3l5eXID2N7afAAbXn35pJUdjHzjsPqz1Vg0YFG1zUzxaryNCV4971am8mV8nTp1aph4GWPvjJKSEqSnp6NTp05VDmRlTe/o0aPw8fHBzZs3G3VWz/tk0aJFeP78ObZu3aq0TnW/C3X5/OYxN++4iooKnD17FrGxsdDW1sasWbNq/U4Hxhhj9ePk5IR79+7h4cOHcmNGWP0ZGhrC29u7Sc7Fyc07LC8vDxEREfj771f3dLp27cp/1THGWBOpy4vtWM3+7//+r8nOxclNM6l8v42yd9skJyfj4MGDKCkpgVgsxujRo9G1a9cmjJAxxhhrmTi5aSaV77epVPluG6lUilOnTuHPP/8E8Optjl999RXatGnT1CEyxhhjLRInN83k9ffbWON/77YRCAR48eIFAMDe3h7Dhg2DmppaM0TIGGOMtUyc3DQzYwBJeHXHBkIhBAIBnJyc0L17d7n1VBhjjDFWOzy/rZkJy8tx7Ngx/P7777L1RsRiMSc2jDHGWD3xnZtm1DYnB6MjIhD3+NWw4szMTJibmzdzVIwxxljLxslNM7G4eRMD/t//g7i0FFpaWhg7diwnNowxxlgD4MdSjWBO6D5gwQOs/HgOTAG5zbysDK5HjuCzP/6AuLQUWR06YNasWbIF1RhjTNUIBAKFBSffdc+ePYOhoSEyMjKaOxSV4erqijVr1jTJuTi5aSAP8h/872vzToCeKfLFungIyG39//gDH8bHgwCcHzAA//HwaNhlIBhjrAllZWVh7ty5sLCwgFgshpmZGUaNGoUzZ840d2gKZs2aBYFAgF9++aXGuitWrMCYMWPQsWNHhX2Ojo5QU1NDXFycwr7BgwdX+fK/0NBQhVXS8/PzsXTpUtjY2EBTUxNGRkZwcHDAgQMH0JgrI507dw62trYQi8WwsrJCaGhojW1OnDiBTz75BLq6ujAwMMD48eOVJn4XL16Euro6evXqJVfu7++PFStWIC8v7+0vogac3DSQ9Ofp8gXSCrQtK4IJILfdGTAAhbq6OP7113j02WdYzmuWMMZaqIyMDPTp0wdnz57FTz/9hMTERERFRWHIkCHw9PRs7vDkREZG4s8//4REIqmxblFREYKDgzF16lSFfZmZmYiNjYWXlxd27NhR73hyc3PRv39/7Nq1C4sXL8bVq1dx/vx5uLi4wNfXt9ESgPT0dDg5OWHIkCFISEjAP/7xD0ybNg0nTpyots2YMWMwdOhQJCQk4MSJE8jOzsaXX35Z5XV98803+OyzzxT2ffTRR7C0tMSePXsa9JqqVKvlPVVIY60Kfm70Odmq4B//RST4bwGVlpdSaWkppaeny9UtKytr0HMzxlq2lroq+IgRI8jExIQKCwsV9j1//lz2Nd5YFdzX15c6d+5MWlpa1KlTJ/L396fS0lLZ/oSEBBo8eDDp6OiQrq4u2draUlxcHBERZWRk0MiRI6l169bUqlUr6tq1Kx09erTaOP/++28yMTGhmzdvkrm5Oa1bt67a+vv37ycDA4Mq9wUGBpKrqyslJSWRvr4+FRUVye0fNGgQzZ8/X6FdSEiIbOVzIqLZs2eTtrY2PXz4UKFuQUFBo31O+Pr6Urdu3eTKXFxcyNHRUWmb/fv3k7q6OlVUVMjKDh8+TAKBQO7nVnksf39/CggIoJ49eyoca9myZTRgwACl52qoVcH5tkEjEZTrIDcnF9u2bUNYWBiePHki26euzuO4GWM16AsoDNpriq1v7cLLyclBVFQUPD09oa2trbD/zUcwr9PV1UVoaChu376N9evXY9u2bVi3bp1sv5ubG0xNTREXF4f4+Hj4+flBQ0MDAODp6YmXL1/i/PnzSExMxI8//ljtYsJSqRTu7u7w8fFBt27danVtMTEx6NOnj0I5ESEkJARff/01bGxsYGVlhYiIiFod882YwsPD4ebmVuWdJB0dHaWfEzExMdDR0al2CwsLU3ruS5cuwcHBQa7M0dERly5dUtqmT58+EAqFCAkJQUVFBfLy8rB79244ODjIfi4AEBISgrS0NAQEBCg9lp2dHS5fvoyXL18qrdMQ3olP2aCgIPz000/IyspCz549sXHjRtjZ2Smtv3//fnz33XfIyMhA586d8eOPP+KLL75owohrQISeyQnYevEYysvLoaOj0+g/SMaYisnCq4F676iUlBQQEWxsbOrc1t/fX/Z1x44dsXDhQoSHh8PX1xfAq0c/Pj4+smO//t6vzMxMjB8/Ht27dwcAWFhYVHuuH3/8Eerq6pg3b16t47t//36VScfp06dRVFQER0dHAMDXX3+N4OBguLu71/rYAJCdnY3nz5/Xq+/69u2LhISEauu0b99e6b6srCyF/e3bt0d+fj6Ki4uhpaWl0KZTp044efIknJ2dMXPmTFRUVKBfv344duyYrM69e/fg5+eHmJiYav+Al0gkKC0tRVZWVqPOEG725Gbfvn3w9vbGli1bYG9vj19++QWOjo5ITk6GoaGhQv3Y2FhMnDgRq1atwsiRI/Hbb79h7NixuHr1Kj766KNmuAJ5paJS9LxxFKaPbqAcr37xxo0bV+1fFowxpsDo3T4vvcWA13379mHDhg1ITU1FYWEhysvL5SZWeHt7Y9q0abK7AxMmTIClpSUAYN68eZg9ezZOnjwJBwcHjB8/Hj169KjyPPHx8Vi/fj2uXr0KgUBQ6/iKi4uhqampUL5jxw64uLjIPrwnTpwIHx8fpKamyuKrjbfpOy0tLVhZWdW7fX1kZWVh+vTp8PDwwMSJE1FQUIDvv/8eX331FU6dOgWpVIpJkyZh2bJlNc78rUyeioqKGjfoGh9cNTI7Ozvy9PSUfV9RUUESiYRWrVpVZX1nZ2dycnKSK7O3t6eZM2fW6nyNOeYmq30W/dvz3xQYGEjfL1tG//nPf0gqlTboeRhjqqcljrl59uwZCQQCWrlyZY118dqYm9jYWFJTU6N//vOfFBcXR3fv3qXly5fLjUchIkpOTqa1a9fSsGHDSCQS0YEDB2T7MjMzafPmzTRu3DjS0NCgDRs2VHnedevWkUAgIDU1NdkGgIRCIZmbmyuNd9KkSTRx4kSF6xWLxSQUChWOt2TJElm9UaNG0eTJk6uMpUOHDkT06nOudevWNGPGjOq6rUrnz58nbW3tarc9e/YobT9w4ECFMUE7duwgPT09pW38/f2pb9++cmUPHjwgAHTp0iV6/vw5AZDrF4FAICs7c+aMrN2ff/5JAOjp06dVnquhxtw0652b0tJSxMfHY/HixbIyoVAIBwcHpc//Ll26BG9vb7kyR0dHpe9QePnypdwjofz8/LcPXIk7Xe4g2yAbJWJdhH8+Hsts+aV8jDHV1LZtWzg6OiIoKAjz5s1TGHeTm5tb5bib2NhYmJubY+nSpbKy+/fvK9SztraGtbU1FixYgIkTJyIkJATjxo0DAJiZmWHWrFmYNWsWFi9ejG3btmHu3LkKx3B3d69yfIm7uzumTJmi9Np69+6tMKMnLCwMpqamCp81J0+exJo1a7B8+XKoqamhS5cuOHnypMIxr169KrurIRQK4erqit27dyMgIEDhEVhhYSE0NTWrfLzzto+l3nycBACnTp1Cv379lLYpKiqC8I2ZvZULOkulUujp6SExMVFu/6ZNm3D27FlERESgU6dOsvKbN2/C1NQU7dq1q/Ya3lqN6U8jevjwIQGg2NhYuXIfHx+ys7Orso2Ghgb99ttvcmVBQUFkaGhYZf2AgAACoLA1xp2bCkEFnRlyhj49X0iilAY9PGNMhbXEOzdERKmpqWRkZERdu3aliIgIunv3Lt2+fZvWr19PNjY2snp47c7NoUOHSF1dnfbu3UspKSm0fv16atu2rezOTVFREXl6elJ0dDRlZGTQhQsXyNLSknx9fYmIaP78+RQVFUVpaWkUHx9P9vb25OzsXOuYazNb6saNG6Surk45OTmysp49e9KiRYsU6ubm5pJIJKIjR47I+kRTU5Pmzp1L169fpzt37tCaNWtIXV2djh8/Lmv37NkzsrGxIVNTU9q5cyfdunWL7t69S8HBwWRlZSU326whpaWlUatWrcjHx4eSkpIoKCiI1NTUKCoqSlZn48aNNHToUNn3Z86cIYFAQMuWLaO7d+9SfHw8OTo6krm5ucJssUrKZkt5eHjQt99+qzS+hrpzo/LJTUlJCeXl5cm2yltpDZ3cJJ9NJq81GTR52zPq+Wc6ecfW3IYxxohabnJDRPTo0SPy9PQkc3NzEolEZGJiQqNHj6bo6GhZHbwxFdzHx4c++OAD0tHRIRcXF1q3bp0suXn58iW5urqSmZkZiUQikkgk5OXlJesbLy8vsrS0JLFYTAYGBuTu7k7Z2dm1jrc2yQ3RqyETW7ZsISKiK1euEAC6fPlylXVHjBhB48aNk31/+fJlGjZsGBkYGJC+vj7Z29vLXX+l3Nxc8vPzo86dO5NIJKL27duTg4MDRUZGNuqQhujoaOrVqxeJRCKysLCgkJAQuf0BAQEKj+327t1LvXv3Jm1tbTIwMKDRo0dTUlKS0nNUldwUFxeTvr4+Xbp0SWm7hkpuBESN+BrEGpSWlqJVq1aIiIjA2LFjZeUeHh7Izc3FoUOHFNp06NAB3t7ecm+ADAgIwMGDB3H9+vUaz5mfnw99fX3k5eXxm4EZY++EkpISpKeno1OnTlUOZGVN7+jRo/Dx8cHNmzcVHsmw+tm8eTMiIyOrfGxXqbrfhbp8fjfrT0wkEqFPnz5yr+mWSqU4c+aM0ud//fr1U3itd03PCxljjLG6cHJywowZM/Dw4Ts8H7+F0dDQwMaNG5vkXM0+Fdzb2xseHh7o27cv7Ozs8Msvv+DFixeywV7ffPMNTExMsGrVKgDA/PnzMWjQIKxZswZOTk4IDw/HlStXsHXr1ua8DMYYYyqmqjWiWP1Nmzatyc7V7MmNi4sLnj59iu+//x5ZWVno1asXoqKiZKO9MzMz5W4J9u/fH7/99hv8/f2xZMkSdO7cGQcPHnwn3nHDGGOMsebXrGNumgOPuWGMvWt4zA1jr6jEmBvGGGP/8579rcmYgob6HeDkhjHGmlnlC9FKS0ubORLGmlfl70Dl70R9NfuYG8YYe9+pq6ujVatWePr0KTQ0NHjqMXsvSaVSPH36FK1atap28c3a4OSGMcaamUAggLGxMdLT06tcioCx94VQKESHDh3qtNBpVTi5YYyxd4BIJELnzp350RR7r4lEoga5c8nJDWOMvSOEQiHPlmKsAfCDXcYYY4ypFE5uGGOMMaZSOLlhjDHGmEp578bcVL4gKD8/v5kjYYwxxlhtVX5u1+ZFf+9dclNQUAAAMDMza+ZIGGOMMVZXBQUF0NfXr7bOe7e2lFQqxaNHj6Crq/vW8+jflJ+fDzMzMzx48IDXrWpE3M9Ng/u5aXA/Nx3u66bRWP1MRCgoKIBEIqlxuvh7d+dGKBTC1NS0Uc+hp6fHvzhNgPu5aXA/Nw3u56bDfd00GqOfa7pjU4kHFDPGGGNMpXBywxhjjDGVwslNAxKLxQgICIBYLG7uUFQa93PT4H5uGtzPTYf7umm8C/383g0oZowxxphq4zs3jDHGGFMpnNwwxhhjTKVwcsMYY4wxlcLJDWOMMcZUCic3dRQUFISOHTtCU1MT9vb2uHz5crX19+/fDxsbG2hqaqJ79+44duxYE0XastWln7dt24aBAweiTZs2aNOmDRwcHGr8ubBX6vrvuVJ4eDgEAgHGjh3buAGqiLr2c25uLjw9PWFsbAyxWAxra2v+v6MW6trPv/zyC7p06QItLS2YmZlhwYIFKCkpaaJoW6bz589j1KhRkEgkEAgEOHjwYI1tzp07B1tbW4jFYlhZWSE0NLTR4wSxWgsPDyeRSEQ7duygW7du0fTp06l169b05MmTKutfvHiR1NTU6F//+hfdvn2b/P39SUNDgxITE5s48palrv08adIkCgoKomvXrlFSUhJNnjyZ9PX16e+//27iyFuWuvZzpfT0dDIxMaGBAwfSmDFjmibYFqyu/fzy5Uvq27cvffHFF3ThwgVKT0+nc+fOUUJCQhNH3rLUtZ/DwsJILBZTWFgYpaen04kTJ8jY2JgWLFjQxJG3LMeOHaOlS5fSgQMHCABFRkZWWz8tLY1atWpF3t7edPv2bdq4cSOpqalRVFRUo8bJyU0d2NnZkaenp+z7iooKkkgktGrVqirrOzs7k5OTk1yZvb09zZw5s1HjbOnq2s9vKi8vJ11dXdq5c2djhagS6tPP5eXl1L9/f9q+fTt5eHhwclMLde3nzZs3k4WFBZWWljZViCqhrv3s6elJQ4cOlSvz9vamTz/9tFHjVCW1SW58fX2pW7ducmUuLi7k6OjYiJER8WOpWiotLUV8fDwcHBxkZUKhEA4ODrh06VKVbS5duiRXHwAcHR2V1mf16+c3FRUVoaysDG3btm2sMFu8+vbz8uXLYWhoiKlTpzZFmC1effr58OHD6NevHzw9PdG+fXt89NFHWLlyJSoqKpoq7BanPv3cv39/xMfHyx5dpaWl4dixY/jiiy+aJOb3RXN9Dr53C2fWV3Z2NioqKtC+fXu58vbt2+POnTtVtsnKyqqyflZWVqPF2dLVp5/ftGjRIkgkEoVfKPY/9ennCxcuIDg4GAkJCU0QoWqoTz+npaXh7NmzcHNzw7Fjx5CSkoI5c+agrKwMAQEBTRF2i1Offp40aRKys7MxYMAAEBHKy8sxa9YsLFmypClCfm8o+xzMz89HcXExtLS0GuW8fOeGqZTVq1cjPDwckZGR0NTUbO5wVEZBQQHc3d2xbds2tGvXrrnDUWlSqRSGhobYunUr+vTpAxcXFyxduhRbtmxp7tBUyrlz57By5Ups2rQJV69exYEDB3D06FH88MMPzR0aawB856aW2rVrBzU1NTx58kSu/MmTJzAyMqqyjZGRUZ3qs/r1c6Wff/4Zq1evxunTp9GjR4/GDLPFq2s/p6amIiMjA6NGjZKVSaVSAIC6ujqSk5NhaWnZuEG3QPX592xsbAwNDQ2oqanJyj788ENkZWWhtLQUIpGoUWNuierTz9999x3c3d0xbdo0AED37t3x4sULzJgxA0uXLoVQyH/7NwRln4N6enqNdtcG4Ds3tSYSidCnTx+cOXNGViaVSnHmzBn069evyjb9+vWTqw8Ap06dUlqf1a+fAeBf//oXfvjhB0RFRaFv375NEWqLVtd+trGxQWJiIhISEmTb6NGjMWTIECQkJMDMzKwpw28x6vPv+dNPP0VKSooseQSAu3fvwtjYmBMbJerTz0VFRQoJTGVCSbzkYoNpts/BRh2urGLCw8NJLBZTaGgo3b59m2bMmEGtW7emrKwsIiJyd3cnPz8/Wf2LFy+Suro6/fzzz5SUlEQBAQE8FbwW6trPq1evJpFIRBEREfT48WPZVlBQ0FyX0CLUtZ/fxLOlaqeu/ZyZmUm6urrk5eVFycnJdOTIETI0NKR//vOfzXUJLUJd+zkgIIB0dXVp7969lJaWRidPniRLS0tydnZurktoEQoKCujatWt07do1AkBr166la9eu0f3794mIyM/Pj9zd3WX1K6eC+/j4UFJSEgUFBfFU8HfRxo0bqUOHDiQSicjOzo7+/PNP2b5BgwaRh4eHXP3ff/+drK2tSSQSUbdu3ejo0aNNHHHLVJd+Njc3JwAKW0BAQNMH3sLU9d/z6zi5qb269nNsbCzZ29uTWCwmCwsLWrFiBZWXlzdx1C1PXfq5rKyMAgMDydLSkjQ1NcnMzIzmzJlDz58/b/rAW5Do6Ogq/7+t7FsPDw8aNGiQQptevXqRSCQiCwsLCgkJafQ4BUR8/40xxhhjqoPH3DDGGGNMpXBywxhjjDGVwskNY4wxxlQKJzeMMcYYUymc3DDGGGNMpXBywxhjjDGVwskNY4wxxlQKJzeMMTmhoaFo3bp1c4dRbwKBAAcPHqy2zuTJkzF27NgmiYcx1vQ4uWFMBU2ePBkCgUBhS0lJae7QEBoaKotHKBTC1NQUU6ZMwX//+98GOf7jx48xYsQIAEBGRgYEAgESEhLk6qxfvx6hoaENcj5lAgMDZdeppqYGMzMzzJgxAzk5OXU6DidijNUdrwrOmIoaPnw4QkJC5MoMDAyaKRp5enp6SE5OhlQqxfXr1zFlyhQ8evQIJ06ceOtj17R6PADo6+u/9Xlqo1u3bjh9+jQqKiqQlJSEb7/9Fnl5edi3b1+TnJ+x9xXfuWFMRYnFYhgZGcltampqWLt2Lbp37w5tbW2YmZlhzpw5KCwsVHqc69evY8iQIdDV1YWenh769OmDK1euyPZfuHABAwcOhJaWFszMzDBv3jy8ePGi2tgEAgGMjIwgkUgwYsQIzJs3D6dPn0ZxcTGkUimWL18OU1NTiMVi9OrVC1FRUbK2paWl8PLygrGxMTQ1NWFubo5Vq1bJHbvysVSnTp0AAL1794ZAIMDgwYMByN8N2bp1KyQSidwq3AAwZswYfPvtt7LvDx06BFtbW2hqasLCwgLLli1DeXl5tdeprq4OIyMjmJiYwMHBARMmTMCpU6dk+ysqKjB16lR06tQJWlpa6NKlC9avXy/bHxgYiJ07d+LQoUOyu0Dnzp0DADx48ADOzs5o3bo12rZtizFjxiAjI6PaeBh7X3Byw9h7RigUYsOGDbh16xZ27tyJs2fPwtfXV2l9Nzc3mJqaIi4uDvHx8fDz84OGhgYAIDU1FcOHD8f48eNx48YN7Nu3DxcuXICXl1edYtLS0oJUKkV5eTnWr1+PNWvW4Oeff8aNGzfg6OiI0aNH4969ewCADRs24PDhw/j999+RnJyMsLAwdOzYscrjXr58GQBw+vRpPH78GAcOHFCoM2HCBDx79gzR0dGyspycHERFRcHNzQ0AEBMTg2+++Qbz58/H7du38euvvyI0NBQrVqyo9TVmZGTgxIkTEIlEsjKpVApTU1Ps378ft2/fxvfff48lS5bg999/BwAsXLgQzs7OGD58OB4/fozHjx+jf//+KCsrg6OjI3R1dRETE4OLFy9CR0cHw4cPR2lpaa1jYkxlNfrSnIyxJufh4UFqamqkra0t27766qsq6+7fv58++OAD2fchISGkr68v+15XV5dCQ0OrbDt16lSaMWOGXFlMTAwJhUIqLi6uss2bx7979y5ZW1tT3759iYhIIpHQihUr5Np8/PHHNGfOHCIimjt3Lg0dOpSkUmmVxwdAkZGRRESUnp5OAOjatWtydd5c0XzMmDH07bffyr7/9ddfSSKRUEVFBRERffbZZ7Ry5Uq5Y+zevZuMjY2rjIGIKCAggIRCIWlra5OmpqZs9eS1a9cqbUNE5OnpSePHj1caa+W5u3TpItcHL1++JC0tLTpx4kS1x2fsfcBjbhhTUUOGDMHmzZtl32trawN4dRdj1apVuHPnDvLz81FeXo6SkhIUFRWhVatWCsfx9vbGtGnTsHv3btmjFUtLSwCvHlnduHEDYWFhsvpEBKlUivT0dHz44YdVxpaXlwcdHR1IpVKUlJRgwIAB2L59O/Lz8/Ho0SN8+umncvU//fRTXL9+HcCrR0rDhg1Dly5dMHz4cIwcORKff/75W/WVm5sbpk+fjk2bNkEsFiMsLAyurq4QCoWy67x48aLcnZqKiopq+w0AunTpgsOHD6OkpAR79uxBQkIC5s6dK1cnKCgIO3bsQGZmJoqLi1FaWopevXpVG+/169eRkpICXV1dufKSkhKkpqbWowcYUy2c3DCmorS1tWFlZSVXlpGRgZEjR2L27NlYsWIF2rZtiwsXLmDq1KkoLS2t8kM6MDAQkyZNwtGjR3H8+HEEBAQgPDwc48aNQ2FhIWbOnIl58+YptOvQoYPS2HR1dXH16lUIhUIYGxtDS0sLAJCfn1/jddna2iI9PR3Hjx/H6dOn4ezsDAcHB0RERNTYVplRo0aBiHD06FF8/PHHiImJwbp162T7CwsLsWzZMnz55ZcKbTU1NZUeVyQSyX4Gq1evhpOTE5YtW4YffvgBABAeHo6FCxdizZo16NevH3R1dfHTTz/hr7/+qjbewsJC9OnTRy6prPSuDBpnrDlxcsPYeyQ+Ph5SqRRr1qyR3ZWoHN9RHWtra1hbW2PBggWYOHEiQkJCMG7cONja2uL27dsKSVRNhEJhlW309PQgkUhw8eJFDBo0SFZ+8eJF2NnZydVzcXGBi4sLvvrqKwwfPhw5OTlo27at3PEqx7dUVFRUG4+mpia+/PJLhIWFISUlBV26dIGtra1sv62tLZKTk+t8nW/y9/fH0KFDMXv2bNl19u/fH3PmzJHVefPOi0gkUojf1tYW+/btg6GhIfT09N4qJsZUEQ8oZuw9YmVlhbKyMmzcuBFpaWnYvXs3tmzZorR+cXExvLy8cO7cOdy/fx8XL15EXFyc7HHTokWLEBsbCy8vLyQkJODevXs4dOhQnQcUv87Hxwc//vgj9u3bh+TkZPj5+SEhIQHz588HAKxduxZ79+7FnTt3cPfuXezfvx9GRkZVvnjQ0NAQWlpaiIqKwpMnT5CXl6f0vG5ubjh69Ch27NghG0hc6fvvv8euXbuwbNky3Lp1C0lJSQgPD4e/v3+drq1fv37o0aMHVq5cCQDo3Lkzrly5ghMnTuDu3bv47rvvEBcXJ9emY8eOuHHjBpKTk5GdnY2ysjK4ubmhXbt2GDNmDGJiYpCeno5z585h3rx5+Pvvv+sUE2MqqbkH/TDGGl5Vg1ArrV27loyNjUlLS4scHR1p165dBICeP39ORPIDfl++fEmurq5kZmZGIpGIJBIJeXl5yQ0Wvnz5Mg0bNox0dHRIW1ubevTooTAg+HVvDih+U0VFBQUGBpKJiQlpaGhQz5496fjx47L9W7dupV69epG2tjbp6enRZ599RlevXpXtx2sDiomItm3bRmZmZiQUCmnQoEFK+6eiooKMjY0JAKWmpirEFRUVRf379yctLS3S09MjOzs72rp1q9LrCAgIoJ49eyqU7927l8RiMWVmZlJJSQlNnjyZ9PX1qXXr1jR79mzy8/OTa/ff//5X1r8AKDo6moiIHj9+TN988w21a9eOxGIxWVhY0PTp0ykvL09pTIy9LwRERM2bXjHGGGOMNRx+LMUYY4wxlcLJDWOMMcZUCic3jDHGGFMpnNwwxhhjTKVwcsMYY4wxlcLJDWOMMcZUCic3jDHGGFMpnNwwxhhjTKVwcsMYY4wxlcLJDWOMMcZUCic3jDHGGFMpnNwwxhhjTKX8f8TUyV9g4njBAAAAAElFTkSuQmCC\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from google.colab import files\n",
"files.download('cnn roc curve.png')\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"id": "R0fHuQEqRp3J",
"outputId": "a5e08691-f8e4-4ad9-ed04-acfb81450d3f"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"\n",
" async function download(id, filename, size) {\n",
" if (!google.colab.kernel.accessAllowed) {\n",
" return;\n",
" }\n",
" const div = document.createElement('div');\n",
" const label = document.createElement('label');\n",
" label.textContent = `Downloading \"${filename}\": `;\n",
" div.appendChild(label);\n",
" const progress = document.createElement('progress');\n",
" progress.max = size;\n",
" div.appendChild(progress);\n",
" document.body.appendChild(div);\n",
"\n",
" const buffers = [];\n",
" let downloaded = 0;\n",
"\n",
" const channel = await google.colab.kernel.comms.open(id);\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
"\n",
" for await (const message of channel.messages) {\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
" if (message.buffers) {\n",
" for (const buffer of message.buffers) {\n",
" buffers.push(buffer);\n",
" downloaded += buffer.byteLength;\n",
" progress.value = downloaded;\n",
" }\n",
" }\n",
" }\n",
" const blob = new Blob(buffers, {type: 'application/binary'});\n",
" const a = document.createElement('a');\n",
" a.href = window.URL.createObjectURL(blob);\n",
" a.download = filename;\n",
" div.appendChild(a);\n",
" a.click();\n",
" div.remove();\n",
" }\n",
" "
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"download(\"download_49361e34-304f-4086-939c-a50de81b72cf\", \"cnn roc curve.png\", 48906)"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"#generate confusion matrix\n",
"from sklearn.metrics import classification_report\n",
"y_pred = np.argmax(y_pred_probs, axis=1) # Predicted class labels\n",
"print(classification_report(y_test, y_pred, target_names=test_batches.class_indices.keys()))\n",
"from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
"cm = confusion_matrix(y_test, y_pred)\n",
"disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=test_batches.class_indices.keys())\n",
"disp.plot(cmap=\"Blues\")\n",
"plt.savefig('cnn confusion matrix.png')\n",
"plt.show()\n",
"\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 658
},
"id": "bvpypyMW1zA1",
"outputId": "144aab14-2ed0-4b0d-f508-f3455e5334b6"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" Mild 0.47 0.50 0.49 56\n",
" Moderate 0.60 0.73 0.66 150\n",
" No_DR 0.89 0.97 0.93 271\n",
"Proliferate_DR 0.33 0.02 0.04 44\n",
" Severe 0.60 0.21 0.31 29\n",
"\n",
" accuracy 0.74 550\n",
" macro avg 0.58 0.49 0.49 550\n",
" weighted avg 0.71 0.74 0.71 550\n",
"\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from google.colab import files\n",
"files.download('cnn confusion matrix.png')\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"id": "VlBDK0727jAr",
"outputId": "81f9a05b-cd68-4080-e3bc-79a6aa236a27"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"\n",
" async function download(id, filename, size) {\n",
" if (!google.colab.kernel.accessAllowed) {\n",
" return;\n",
" }\n",
" const div = document.createElement('div');\n",
" const label = document.createElement('label');\n",
" label.textContent = `Downloading \"${filename}\": `;\n",
" div.appendChild(label);\n",
" const progress = document.createElement('progress');\n",
" progress.max = size;\n",
" div.appendChild(progress);\n",
" document.body.appendChild(div);\n",
"\n",
" const buffers = [];\n",
" let downloaded = 0;\n",
"\n",
" const channel = await google.colab.kernel.comms.open(id);\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
"\n",
" for await (const message of channel.messages) {\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
" if (message.buffers) {\n",
" for (const buffer of message.buffers) {\n",
" buffers.push(buffer);\n",
" downloaded += buffer.byteLength;\n",
" progress.value = downloaded;\n",
" }\n",
" }\n",
" }\n",
" const blob = new Blob(buffers, {type: 'application/binary'});\n",
" const a = document.createElement('a');\n",
" a.href = window.URL.createObjectURL(blob);\n",
" a.download = filename;\n",
" div.appendChild(a);\n",
" a.click();\n",
" div.remove();\n",
" }\n",
" "
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"download(\"download_b059b73c-48e9-4e03-a601-5e77d7d71fdf\", \"cnn confusion matrix.png\", 27332)"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"#evaluate training and validation accuracy on a iteratively halving dataset\n",
"from math import ceil, pow\n",
"src_dir = r'../root/.cache/kagglehub/datasets/sovitrath/diabetic-retinopathy-224x224-2019-data/versions/4/colored_images'\n",
"for i in range(5):\n",
" total_files = len(train)\n",
" denom = pow(2, i)\n",
" print(denom)\n",
" files_to_copy = ceil(total_files / denom) # Half the files, rounded up if odd\n",
" print(files_to_copy)\n",
"\n",
" if os.path.exists(train_dir):\n",
" shutil.rmtree(train_dir)\n",
" os.makedirs(train_dir, exist_ok=True)\n",
"\n",
"\n",
" # Counter to track the number of files copied\n",
" copied_files = 0\n",
"\n",
" for index, row in train.iterrows():\n",
" if copied_files >= files_to_copy:\n",
" break # Stop when half of the files are copied\n",
"\n",
" diagnosis = row['type']\n",
" binary_diagnosis = row['binary_type']\n",
" id_code = row['id_code'] + \".png\"\n",
" srcfile = os.path.join(src_dir, diagnosis, id_code)\n",
" dstfile = os.path.join(train_dir, binary_diagnosis)\n",
" os.makedirs(dstfile, exist_ok=True)\n",
"\n",
" if os.path.exists(srcfile):\n",
" shutil.copy(srcfile, dstfile)\n",
" copied_files += 1 # Increment the counter\n",
"\n",
" for index, row in val.iterrows():\n",
" diagnosis = row['type']\n",
" binary_diagnosis = row['binary_type']\n",
" id_code = row['id_code'] + \".png\"\n",
" srcfile = os.path.join(src_dir, diagnosis, id_code)\n",
" dstfile = os.path.join(val_dir, binary_diagnosis)\n",
" os.makedirs(dstfile, exist_ok=True)\n",
" if os.path.exists(srcfile):\n",
" shutil.copy(srcfile, dstfile)\n",
"\n",
" for index, row in test.iterrows():\n",
" diagnosis = row['type']\n",
" binary_diagnosis = row['binary_type']\n",
" id_code = row['id_code'] + \".png\"\n",
" srcfile = os.path.join(src_dir, diagnosis, id_code)\n",
" dstfile = os.path.join(test_dir, binary_diagnosis)\n",
" os.makedirs(dstfile, exist_ok=True)\n",
" if os.path.exists(srcfile):\n",
" shutil.copy(srcfile, dstfile)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" for subdir in [train_dir, val_dir, test_dir]:\n",
" print(f\"\\nContents of {subdir}:\")\n",
" for root, dirs, files in os.walk(subdir):\n",
" print(f\"{root}: {len(files)} files\")\n",
"\n",
" train_path = train_dir\n",
" val_path = val_dir\n",
" test_path = test_dir\n",
"\n",
" train_batches = ImageDataGenerator(rescale = 1./255).flow_from_directory(train_path, target_size=(224,224), shuffle = True)\n",
" val_batches = ImageDataGenerator(rescale = 1./255).flow_from_directory(val_path, target_size=(224,224), shuffle = True)\n",
" test_batches = ImageDataGenerator(rescale = 1./255).flow_from_directory(test_path, target_size=(224,224), shuffle = False)\n",
"\n",
" #cnn model\n",
" model = tf.keras.Sequential([\n",
" layers.Conv2D(8, (3,3), padding=\"valid\", input_shape=(224,224,3), activation = 'relu'),\n",
" layers.MaxPooling2D(pool_size=(2,2)),\n",
" layers.BatchNormalization(),\n",
"\n",
" layers.Conv2D(16, (3,3), padding=\"valid\", activation = 'relu'),\n",
" layers.MaxPooling2D(pool_size=(2,2)),\n",
" layers.BatchNormalization(),\n",
"\n",
" layers.Conv2D(32, (4,4), padding=\"valid\", activation = 'relu'),\n",
" layers.MaxPooling2D(pool_size=(2,2)),\n",
" layers.BatchNormalization(),\n",
" layers.Flatten(),\n",
" layers.Dense(32, activation = 'relu'),\n",
" layers.Dropout(0.15),\n",
" layers.Dense(2, activation = 'softmax')\n",
" ])\n",
"\n",
" model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-5),\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
"\n",
" history = model.fit(train_batches,\n",
" epochs=10,\n",
" validation_data=val_batches)\n",
"\n",
"\n",
"\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "U39_KR12Ot-t",
"outputId": "002f0a49-d8a9-4b67-9804-58a475f6271b"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1.0\n",
"2562\n",
"\n",
"Contents of .\\dataset/train:\n",
".\\dataset/train: 0 files\n",
".\\dataset/train/No_DR: 1263 files\n",
".\\dataset/train/DR: 1299 files\n",
"\n",
"Contents of .\\dataset/val:\n",
".\\dataset/val: 0 files\n",
".\\dataset/val/No_DR: 271 files\n",
".\\dataset/val/DR: 279 files\n",
"\n",
"Contents of .\\dataset/test:\n",
".\\dataset/test: 0 files\n",
".\\dataset/test/No_DR: 271 files\n",
".\\dataset/test/DR: 279 files\n",
"Found 2562 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/10\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
" self._warn_if_super_not_called()\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m95s\u001b[0m 1s/step - accuracy: 0.7844 - loss: 0.4764 - val_accuracy: 0.5073 - val_loss: 0.8788\n",
"Epoch 2/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 1s/step - accuracy: 0.9100 - loss: 0.2328 - val_accuracy: 0.5073 - val_loss: 1.0356\n",
"Epoch 3/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 1s/step - accuracy: 0.9173 - loss: 0.2120 - val_accuracy: 0.5073 - val_loss: 1.1552\n",
"Epoch 4/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 1s/step - accuracy: 0.9285 - loss: 0.1944 - val_accuracy: 0.5109 - val_loss: 0.9722\n",
"Epoch 5/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 1s/step - accuracy: 0.9311 - loss: 0.1797 - val_accuracy: 0.5491 - val_loss: 0.6548\n",
"Epoch 6/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 1s/step - accuracy: 0.9346 - loss: 0.1593 - val_accuracy: 0.7582 - val_loss: 0.4198\n",
"Epoch 7/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 1s/step - accuracy: 0.9482 - loss: 0.1526 - val_accuracy: 0.9164 - val_loss: 0.2454\n",
"Epoch 8/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m89s\u001b[0m 1s/step - accuracy: 0.9395 - loss: 0.1521 - val_accuracy: 0.9236 - val_loss: 0.1954\n",
"Epoch 9/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 1s/step - accuracy: 0.9404 - loss: 0.1574 - val_accuracy: 0.9309 - val_loss: 0.1698\n",
"Epoch 10/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 1s/step - accuracy: 0.9452 - loss: 0.1475 - val_accuracy: 0.9327 - val_loss: 0.1669\n",
"2.0\n",
"1281\n",
"\n",
"Contents of .\\dataset/train:\n",
".\\dataset/train: 0 files\n",
".\\dataset/train/No_DR: 664 files\n",
".\\dataset/train/DR: 617 files\n",
"\n",
"Contents of .\\dataset/val:\n",
".\\dataset/val: 0 files\n",
".\\dataset/val/No_DR: 271 files\n",
".\\dataset/val/DR: 279 files\n",
"\n",
"Contents of .\\dataset/test:\n",
".\\dataset/test: 0 files\n",
".\\dataset/test/No_DR: 271 files\n",
".\\dataset/test/DR: 279 files\n",
"Found 1281 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n",
"Epoch 1/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 1s/step - accuracy: 0.7730 - loss: 0.4892 - val_accuracy: 0.5073 - val_loss: 0.6800\n",
"Epoch 2/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 1s/step - accuracy: 0.8863 - loss: 0.2912 - val_accuracy: 0.5073 - val_loss: 0.6994\n",
"Epoch 3/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 1s/step - accuracy: 0.8942 - loss: 0.2338 - val_accuracy: 0.5073 - val_loss: 0.7084\n",
"Epoch 4/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 1s/step - accuracy: 0.9099 - loss: 0.2347 - val_accuracy: 0.5073 - val_loss: 0.7110\n",
"Epoch 5/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 1s/step - accuracy: 0.9302 - loss: 0.2254 - val_accuracy: 0.5127 - val_loss: 0.6887\n",
"Epoch 6/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 1s/step - accuracy: 0.9352 - loss: 0.1886 - val_accuracy: 0.5218 - val_loss: 0.6459\n",
"Epoch 7/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 1s/step - accuracy: 0.9431 - loss: 0.1787 - val_accuracy: 0.5327 - val_loss: 0.6414\n",
"Epoch 8/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 1s/step - accuracy: 0.9385 - loss: 0.1534 - val_accuracy: 0.5982 - val_loss: 0.5673\n",
"Epoch 9/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 1s/step - accuracy: 0.9442 - loss: 0.1668 - val_accuracy: 0.7182 - val_loss: 0.4592\n",
"Epoch 10/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 1s/step - accuracy: 0.9510 - loss: 0.1555 - val_accuracy: 0.7745 - val_loss: 0.4030\n",
"4.0\n",
"641\n",
"\n",
"Contents of .\\dataset/train:\n",
".\\dataset/train: 0 files\n",
".\\dataset/train/No_DR: 335 files\n",
".\\dataset/train/DR: 306 files\n",
"\n",
"Contents of .\\dataset/val:\n",
".\\dataset/val: 0 files\n",
".\\dataset/val/No_DR: 271 files\n",
".\\dataset/val/DR: 279 files\n",
"\n",
"Contents of .\\dataset/test:\n",
".\\dataset/test: 0 files\n",
".\\dataset/test/No_DR: 271 files\n",
".\\dataset/test/DR: 279 files\n",
"Found 641 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n",
"Epoch 1/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 1s/step - accuracy: 0.6456 - loss: 0.6968 - val_accuracy: 0.6873 - val_loss: 0.6828\n",
"Epoch 2/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 1s/step - accuracy: 0.8668 - loss: 0.3267 - val_accuracy: 0.7309 - val_loss: 0.6757\n",
"Epoch 3/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 1s/step - accuracy: 0.8835 - loss: 0.2830 - val_accuracy: 0.5982 - val_loss: 0.6689\n",
"Epoch 4/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 1s/step - accuracy: 0.8973 - loss: 0.2757 - val_accuracy: 0.5145 - val_loss: 0.6677\n",
"Epoch 5/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 1s/step - accuracy: 0.9076 - loss: 0.2304 - val_accuracy: 0.5145 - val_loss: 0.6660\n",
"Epoch 6/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 1s/step - accuracy: 0.9293 - loss: 0.1915 - val_accuracy: 0.5145 - val_loss: 0.6673\n",
"Epoch 7/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 1s/step - accuracy: 0.9245 - loss: 0.1830 - val_accuracy: 0.5127 - val_loss: 0.6822\n",
"Epoch 8/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 1s/step - accuracy: 0.9402 - loss: 0.1654 - val_accuracy: 0.5436 - val_loss: 0.6704\n",
"Epoch 9/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 1s/step - accuracy: 0.9411 - loss: 0.1661 - val_accuracy: 0.5455 - val_loss: 0.6825\n",
"Epoch 10/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 1s/step - accuracy: 0.9319 - loss: 0.1554 - val_accuracy: 0.5618 - val_loss: 0.6682\n",
"8.0\n",
"321\n",
"\n",
"Contents of .\\dataset/train:\n",
".\\dataset/train: 0 files\n",
".\\dataset/train/No_DR: 168 files\n",
".\\dataset/train/DR: 153 files\n",
"\n",
"Contents of .\\dataset/val:\n",
".\\dataset/val: 0 files\n",
".\\dataset/val/No_DR: 271 files\n",
".\\dataset/val/DR: 279 files\n",
"\n",
"Contents of .\\dataset/test:\n",
".\\dataset/test: 0 files\n",
".\\dataset/test/No_DR: 271 files\n",
".\\dataset/test/DR: 279 files\n",
"Found 321 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n",
"Epoch 1/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 1s/step - accuracy: 0.6672 - loss: 0.6967 - val_accuracy: 0.5073 - val_loss: 0.6918\n",
"Epoch 2/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 2s/step - accuracy: 0.8493 - loss: 0.3656 - val_accuracy: 0.5073 - val_loss: 0.6956\n",
"Epoch 3/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.8947 - loss: 0.3088 - val_accuracy: 0.5073 - val_loss: 0.7002\n",
"Epoch 4/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.8497 - loss: 0.3160 - val_accuracy: 0.5073 - val_loss: 0.7053\n",
"Epoch 5/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 1s/step - accuracy: 0.8942 - loss: 0.2618 - val_accuracy: 0.5073 - val_loss: 0.7103\n",
"Epoch 6/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9385 - loss: 0.2016 - val_accuracy: 0.5073 - val_loss: 0.7169\n",
"Epoch 7/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 2s/step - accuracy: 0.9371 - loss: 0.1914 - val_accuracy: 0.5073 - val_loss: 0.7235\n",
"Epoch 8/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 1s/step - accuracy: 0.9385 - loss: 0.1765 - val_accuracy: 0.5073 - val_loss: 0.7315\n",
"Epoch 9/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9635 - loss: 0.1654 - val_accuracy: 0.5073 - val_loss: 0.7362\n",
"Epoch 10/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.9161 - loss: 0.1862 - val_accuracy: 0.5073 - val_loss: 0.7401\n",
"16.0\n",
"161\n",
"\n",
"Contents of .\\dataset/train:\n",
".\\dataset/train: 0 files\n",
".\\dataset/train/No_DR: 89 files\n",
".\\dataset/train/DR: 72 files\n",
"\n",
"Contents of .\\dataset/val:\n",
".\\dataset/val: 0 files\n",
".\\dataset/val/No_DR: 271 files\n",
".\\dataset/val/DR: 279 files\n",
"\n",
"Contents of .\\dataset/test:\n",
".\\dataset/test: 0 files\n",
".\\dataset/test/No_DR: 271 files\n",
".\\dataset/test/DR: 279 files\n",
"Found 161 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n",
"Epoch 1/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2s/step - accuracy: 0.6488 - loss: 0.7023 - val_accuracy: 0.5691 - val_loss: 0.6915\n",
"Epoch 2/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 2s/step - accuracy: 0.7992 - loss: 0.4822 - val_accuracy: 0.5364 - val_loss: 0.6905\n",
"Epoch 3/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 2s/step - accuracy: 0.8545 - loss: 0.3240 - val_accuracy: 0.5073 - val_loss: 0.6910\n",
"Epoch 4/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 2s/step - accuracy: 0.8747 - loss: 0.3692 - val_accuracy: 0.5073 - val_loss: 0.6940\n",
"Epoch 5/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 2s/step - accuracy: 0.9120 - loss: 0.2714 - val_accuracy: 0.5073 - val_loss: 0.6983\n",
"Epoch 6/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 2s/step - accuracy: 0.9072 - loss: 0.2942 - val_accuracy: 0.5073 - val_loss: 0.7036\n",
"Epoch 7/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 2s/step - accuracy: 0.8756 - loss: 0.2688 - val_accuracy: 0.5073 - val_loss: 0.7078\n",
"Epoch 8/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 2s/step - accuracy: 0.9226 - loss: 0.2576 - val_accuracy: 0.5073 - val_loss: 0.7089\n",
"Epoch 9/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 2s/step - accuracy: 0.9151 - loss: 0.2522 - val_accuracy: 0.5073 - val_loss: 0.7091\n",
"Epoch 10/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 2s/step - accuracy: 0.9447 - loss: 0.2030 - val_accuracy: 0.5073 - val_loss: 0.7103\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# Data\n",
"database_size = [2562, 1281, 641, 321, 161]\n",
"cnn_train_acc = [94.52, 95.10, 93.19, 91.61, 94.47]\n",
"cnn_val_acc = [93.27, 77.45, 56.18, 50.73, 50.73]\n",
"vit_train_acc = [91.67, 88.21, 70.20, 54.34, 62.67]\n",
"vit_val_acc = [92.73, 94, 69.27, 63.09, 63.09]\n",
"\n",
"# Plot\n",
"figure, axis = plt.subplots(1, 2, figsize=(10, 5))\n",
"# Define the y-ticks with 5 increments\n",
"y_ticks = np.arange(50, 101, 5)\n",
"\n",
"# CNN plot\n",
"axis[0].plot(database_size, cnn_train_acc, label='CNN Train Accuracy')\n",
"axis[0].plot(database_size, cnn_val_acc, label='CNN Validation Accuracy')\n",
"axis[0].set_xlabel('Database Size')\n",
"axis[0].set_ylabel('Accuracy')\n",
"axis[0].set_title('CNN Accuracy vs Database Size')\n",
"#axis[0].set_xticks(database_size) # Set the x-ticks explicitly\n",
"axis[0].set_yticks(y_ticks) # Set consistent y-ticks\n",
"axis[0].invert_xaxis() # Reverse the x-axis\n",
"axis[0].legend()\n",
"\n",
"# ViT plot\n",
"axis[1].plot(database_size, vit_train_acc, label='ViT Train Accuracy')\n",
"axis[1].plot(database_size, vit_val_acc, label='ViT Validation Accuracy')\n",
"axis[1].set_xlabel('Database Size')\n",
"axis[1].set_ylabel('Accuracy')\n",
"axis[1].set_title('ViT Accuracy vs Database Size')\n",
"#axis[1].set_xticks(database_size) # Set the x-ticks explicitly\n",
"axis[1].set_yticks(y_ticks) # Set consistent y-ticks\n",
"axis[1].invert_xaxis() # Reverse the x-axis\n",
"axis[1].legend()\n",
"plt.savefig('cnn vs vit.png')\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 507
},
"id": "geABaxbazdIg",
"outputId": "74682125-a198-477d-9aa0-1b11e69e5ffa"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x500 with 2 Axes>"
],
"image/png": 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Ovu79TL92x44dGfbXqFHDaN26te197dq1bd+/cuNa3cuvLLtYsWK291ndm3Xr1mX4vmAY1+5entP6vUWLFgZgjBs3zrYvKSnJqFOnjhEaGmokJycbhpHz+v7ZZ581/P39jdTU1Gx/35ut//r06WMARrFixYwuXboY77//foa/+3Tp9z6772cJCQlGvXr1jLCwMOPUqVOGYRjG4cOHDRcXF+Odd97JcO6OHTsMV1fXTPslf6ilW25KTEwMAH5+fjd0/WuvvZar1u4KFSrw8MMP89lnn3Hq1Klc/7wZM2bQqVMnW7yVK1emXr16GZ4unzt3jtWrVzNgwADKlSuX4fr0ruIWi4Xvv/+ee+65h/r162f6OTc6MVtERIStNfFKV7YwREdHc/78eVq0aMHBgwdtXbaXLl1KbGys7Yl+dvH06dOHDRs2ZOiKN2PGDMqWLUuLFi2uGV/Pnj3ZsmVLhmvnzJmDh4eH7Sl8QEAAAEuWLLnhFv/spE/Gc+Usu1fem9jYWM6fP0+zZs2Ij4/n33//vW6ZLi4utglKLBYLkZGRpKamUr9+/Syf/nfu3JnSpUvb3jds2JBGjRrxyy+/ZBlTYmIi58+ft3XxvrLMwMBANmzYkG3Pja1bt7Jv3z4efPBBLly4wPnz5zl//jxxcXG0adOG1atXX7MbW4kSJdi2bRtPPPEEFy9eZMqUKTz44IOEhoYycuTIDC0x13L69Gm6detGnTp1+OSTT2z7582bR7NmzShWrJgttvPnz9O2bVvS0tJYvXp1jsoXEcdycXGhV69erFu3LkP335kzZ1KiRAnatGlj158/e/ZszGYzXbt2te174IEHWLx4sW3YEsB3331HcHBwlqufpNdz3333HSaTiTfffDPbc27EE088kWlfTj7rc/p9oUePHnh6emb4PrJkyRLOnz/PQw89dM3Y7r//flxdXTP0Ytu5cye7du2iZ8+etn2BgYH8888/7Nu3Lye/cq74+vpmWzenpKRw4cIFKlWqRGBgYI5b1nNTv7u6uvL444/b3ru7u/P4449z9uxZtmzZAuS8vg8MDCQuLu6a3e5vtv6bOnUqH330ERERESxcuJAXXniB6tWr06ZNmyxX6snOoEGD2LFjB9999x0lS5YEYMGCBVgsFnr06JEhtpIlS1K5cuVrDp8T+1HSLTclfRbpay01ci03kkTnNlFPt3v3bv7++2+aNm3K/v37ba+WLVvy888/2x4gHDx4EOCay5CdO3eOmJiYPF8XMiIiIsv9a9asoW3btvj4+BAYGEhISAivvPIKgC3pTk+ErxdTz5498fDwsFXs0dHR/Pzzz/Tu3fu6X0i6d++O2Wy2VeyGYTBv3jw6duxo+7cQERHB0KFD+eKLLwgODqZ9+/Z8/PHHNzWeO136EldXPuT5559/6NKlCwEBAfj7+xMSEmL7gpLTnzlt2jRuvfVW2xi3kJAQFi1alOX1lStXzrSvSpUqGb6oRkZG8uyzz1KiRAm8vLwICQmx/d1eWebYsWPZuXMnZcuWpWHDhowYMcL27w+wfTHq27cvISEhGV5ffPEFSUlJ1/0dS5UqxeTJkzl16hR79uxh4sSJhISE8MYbb/Dll19e996kpqbSo0cP0tLSWLBgAR4eHhni+/XXXzPFlj7jcFbjQ0XEOfXu3RvANgfI8ePH+eOPP+jVqxcuLi52/dnTp0+nYcOGXLhwwVY3161bl+TkZObNm2c778CBA1StWhVX1+wX3zlw4ABhYWEEBQXlaYxZ1c85+azP6feFwMBA7rnnHtv9B+sD8dKlS193VvDg4GDatGmToYv5nDlzcHV15f7777ft+9///kdUVBRVqlShVq1aDBs2jO3bt1//l8+BS5cuZaibExISeOONN2zjnYODgwkJCSEqKirHdXNu6vewsDB8fHwy7KtSpQpAhvo5J/X9oEGDqFKlCh07dqRMmTIMGDDANkwv3c3Wf2azmcGDB7NlyxbOnz/PDz/8QMeOHVmxYgW9evXK0f359NNPmTp1KpMmTcowd8u+ffswDIPKlStnim/37t2qmx1ES4bJTfH39ycsLCzTRFK5kT52dMyYMXTu3Pm651eoUIGHHnqIzz77LMtxPdmZPn06AM899xzPPfdcpuPfffcd/fv3z3F5OZFdEpvdJCBZjZk7cOAAbdq0oVq1aowfP56yZcvi7u7OL7/8wgcffJDrCauKFSvG3XffzYwZM3jjjTeYP38+SUlJ132SDtZKrVmzZsydO5dXXnmF9evXc/ToUcaMGZPhvHHjxtGvXz9++OEHfvvtN5555hlGjRrF+vXrKVOmTK7ivVL6v7NKlSoB1klMWrRogb+/P//73/+oWLEinp6e/PXXX7z00ks5ujfTp0+nX79+dO7cmWHDhhEaGoqLiwujRo3K0KKfGz169GDt2rUMGzaMOnXq4Ovri8VioUOHDhli6tGjB82aNWPhwoX89ttvvPfee4wZM4YFCxbQsWNH27nvvfeebXz/1XK6FI/JZKJKlSpUqVKFTp06UblyZWbMmMHAgQOved2wYcNYt24dy5Yty/R3Z7FYuPPOO3nxxRezvDb9C4+IOL969epRrVo1Zs2axSuvvMKsWbMwDMOWjNvLvn372LRpE5D1Q80ZM2ZkWH4sL+S2boas6+ecftbnVJ8+fZg3bx5r166lVq1a/PjjjwwaNAiz+fptZL169aJ///5s3bqVOnXqMHfuXNq0aUNwcLDtnObNm3PgwAFb3fzFF1/wwQcfMGXKlOvWBdeSkpLC3r17MzxYePrpp5k6dSpDhgyhcePGBAQEYDKZ6NWrV47uTV7U71fLaX0fGhrK1q1bWbJkCYsXL2bx4sVMnTqVPn36MG3aNCBv67/ixYtz7733cu+999KyZUtWrVrFkSNHbGO/s7Jx40aeffZZBg4cmOn/h8ViwWQysXjx4iwfmOX0e4PkLSXdctPuvvtuPvvsM9atW0fjxo1zfX3FihV56KGH+PTTT2nUqFGOrnnttdeYPn16pmQvO4ZhMHPmTFq1asWgQYMyHR85ciQzZsygf//+VKhQAeCaDxJCQkLw9/e/7sOGYsWKAdbKIzAw0LY/qxnAs/PTTz+RlJTEjz/+mKG7+9XdgypWrGiLOz0pzU6fPn2477772LRpEzNmzKBu3brUrFkzR/H07NmTQYMGsWfPHubMmYO3tzf33HNPpvNq1apFrVq1eO2111i7di1NmzZlypQptgk/bsS3336LyWTizjvvBKyzel64cIEFCxbQvHlz23mHDh3KdG12X7Lmz59PhQoVWLBgQYZzsuqaCGTZLW/v3r22mb8vXrzI8uXLeeutt3jjjTeueR1YW6IHDRrEoEGDOHv2LLfddhvvvPMOHTt2tP2d+vv75+l6tRUqVKBYsWLX7V0ye/ZsJkyYwIQJE7IcelCxYkUuXbqktXRFConevXvz+uuvs337dmbOnEnlypVp0KCBXX/mjBkzcHNz49tvv82UIPz5559MnDiRo0ePUq5cOSpWrMiGDRtISUnBzc0ty/IqVqzIkiVLiIyMzLa1+8q6+Uq5qZtz+lmf0+8LAB06dCAkJIQZM2bQqFEj4uPjefjhh3MUT+fOnXn88cdtPdH27t3L8OHDM50XFBRE//796d+/P5cuXaJ58+aMGDHippLu+fPnk5CQkGF43Pz58+nbty/jxo2z7UtMTMx0z7Orm3NTv4N1srq4uLgMrd3pK52k18+5qe/d3d255557uOeee7BYLAwaNIhPP/2U119/nUqVKtmt/qtfvz6rVq3i1KlT2Sbd586dsw35unpCXbD+HzAMg4iICD38diLqXi437cUXX8THx4eBAwdmmME53YEDBzIts3C11157jZSUFMaOHZujn3llon769Onrnr9mzRoOHz5M//796datW6ZXz549WblyJSdPniQkJITmzZvz1VdfcfTo0QzlpI+BNZvNttnM05cgyeq89KTpyrE9cXFxtielOZH+JeTK8bfR0dFMnTo1w3nt2rXDz8+PUaNGZVpm5eqxux07diQ4OJgxY8awatWqHLVyp+vatSsuLi7MmjWLefPmcffdd2eo5GJiYkhNTc1wTa1atTCbzSQlJeX451xt9OjR/Pbbb/Ts2dPWGpLVvUlOTs4w7jidj49Pll3asipjw4YNrFu3Lss4vv/++wzjrTZu3MiGDRvo2LFjtuUBttnS06WlpWWKJzQ0lLCwMNt9qlevHhUrVuT999+3da2/UlZLv11pw4YNxMXFZdq/ceNGLly4QNWqVbO9dufOnQwcOJCHHnoo05J/6Xr06MG6detYsmRJpmNRUVGZ/h2IiHNLb9V+44032Lp1q91bucGadDdr1oyePXtmqpuHDRsGYFsuq2vXrpw/fz7D0lTp0j9zu3btimEYWS6hlX6Ov78/wcHBmcbdZlV3ZCenn/U5/b4A1nHJDzzwAHPnzuXrr7+mVq1a3HrrrTmKJzAwkPbt2zN37lxmz56Nu7t7pt6DVy9l5uvrS6VKlW6qbt62bRtDhgyhWLFiDB482LbfxcUl072ZNGlSpt4E6d8frk7Gc1O/g3UoVPoyq+nnfvrpp4SEhFCvXr1sy8yqvr/6PpnNZtvfQ/q9upn67/Tp0+zatSvT/uTkZJYvX47ZbM628SQtLY1evXqRnJzMd999ZxujfqX7778fFxcX3nrrrUx/B4ZhZLmkndifWrrlplWsWJGZM2fSs2dPqlevTp8+fbjllltITk5m7dq1zJs3j379+l23jIceeihXyWh6t/Q9e/Zct5V2xowZuLi40KlTpyyP33vvvbz66qvMnj2boUOHMnHiRO644w5uu+02HnvsMSIiIjh8+DCLFi2yLSvx7rvv8ttvv9GiRQsee+wxqlevzqlTp5g3bx5//vkngYGBtGvXjnLlyvHII48wbNgwXFxc+OqrrwgJCcmU0GenXbt2tieujz/+OJcuXeLzzz8nNDQ0Q0ulv78/H3zwAQMHDqRBgwa29US3bdtGfHx8hnvr5uZGr169+Oijj3BxcbEtoZYToaGhtGrVivHjxxMbG5thkhawLnH11FNP0b17d6pUqUJqaqqtBePKSXKyk5qaahsKkJiYyJEjR/jxxx/Zvn07rVq14rPPPrOd26RJE4oVK0bfvn155plnMJlMfPvtt1lOEFavXj3mzJnD0KFDadCgAb6+vtxzzz3cfffdLFiwgC5dutCpUycOHTrElClTqFGjRpaJbqVKlbjjjjt48sknSUpKYsKECRQvXtzWxczf35/mzZszduxYUlJSKF26NL/99lump/OxsbGUKVOGbt26Ubt2bXx9fVm2bBmbNm2ytQyYzWa++OILOnbsSM2aNenfvz+lS5fmxIkTrFy5En9//0xL6lzp22+/ZcaMGXTp0oV69erh7u7O7t27+eqrr/D09LTNC5CV9KEWzZs3t/19XHnfK1SowLBhw/jxxx+5++676devH/Xq1SMuLo4dO3Ywf/58Dh8+nKFro4g4t4iICJo0acIPP/wAYPeke8OGDezfv5+nnnoqy+OlS5fmtttuY8aMGbz00kv06dOHb775hqFDh7Jx40aaNWtGXFwcy5YtY9CgQdx33320atWKhx9+mIkTJ7Jv3z5bV+8//viDVq1a2X7WwIEDGT16NAMHDqR+/fqsXr3a1jKaEzn9rIecfV9Il7781MqVK3Pcmy9dz549eeihh/jkk09o3759hnIBatSoQcuWLalXrx5BQUFs3ryZ+fPnZ3v/r/bHH3+QmJhIWloaFy5cYM2aNfz4448EBASwcOFC20ReYO0F+e233xIQEECNGjVsw5SKFy+eocw6derg4uLCmDFjiI6OxsPDg9atW+eqfgfr8LcxY8Zw+PBhqlSpwpw5c9i6dSufffaZrVdETuv7gQMHEhkZSevWrSlTpgxHjhxh0qRJ1KlTx7ac2c3Uf8ePH6dhw4a0bt2aNm3aULJkSc6ePcusWbNsDzGyu3bKlCmsWLGCJ554IlOPxxIlSnDnnXdSsWJF3n77bYYPH87hw4fp3Lkzfn5+HDp0iIULF/LYY4/xwgsvXOdvW/Jcvs2TLoXe3r17jUcffdQoX7684e7ubvj5+RlNmzY1Jk2alGFphyuXDLvSvn37DBcXl2suGXa1vn37GsA1lwxLTk42ihcvbjRr1uya8UdERBh169a1vd+5c6fRpUsXIzAw0PD09DSqVq1qvP766xmuOXLkiNGnTx8jJCTE8PDwMCpUqGAMHjw4w5IUW7ZsMRo1amS4u7sb5cqVM8aPH5/tkmHZLeXx448/Grfeeqvh6elplC9f3hgzZoxt6ZSrl2358ccfjSZNmhheXl6Gv7+/0bBhQ2PWrFmZykxf6qtdu3bXvC9Z+fzzzw3A8PPzy7Q82cGDB40BAwYYFStWNDw9PY2goCCjVatWxrJly65bbvrfZ/rL29vbKF++vNG1a1dj/vz5RlpaWqZr1qxZY9x+++2Gl5eXERYWZrz44ovGkiVLMi2xcenSJePBBx80AgMDDcC2PJbFYjHeffddIzw83PDw8DDq1q1r/Pzzz5mW0EpfYua9994zxo0bZ5QtW9bw8PAwmjVrZmzbti1DTMePH7f92wkICDC6d+9unDx5MsNSNElJScawYcOM2rVrG35+foaPj49Ru3Zt45NPPsn0O/7999/G/fffbxQvXtzw8PAwwsPDjR49ehjLly+/5v3cvn27MWzYMOO2224zgoKCDFdXV6NUqVJG9+7djb/++ivTvb/y901fwi6r15XL7MTGxhrDhw83KlWqZLi7uxvBwcFGkyZNjPfff9+2TIuIFBwff/yxARgNGzbM8nheLhn29NNPG4Bx4MCBbM8ZMWKEAdg+Z+Pj441XX33ViIiIMNzc3IySJUsa3bp1y1BGamqq8d577xnVqlUz3N3djZCQEKNjx47Gli1bbOfEx8cbjzzyiBEQEGD4+fkZPXr0MM6ePZvtkmHnzp3LFFtOPuvT5eT7QrqaNWsaZrM5w/KUORETE2N4eXkZgDF9+vRMx99++22jYcOGRmBgoOHl5WVUq1bNeOedd677WZ2+bFX6y83NzQgJCTGaN29uvPPOO8bZs2czXXPx4kWjf//+RnBwsOHr62u0b9/e+Pfff43w8PBM/z4+//xzo0KFCrbvgOl1d07r9/SlYzdv3mw0btzY8PT0NMLDw42PPvoow8/JaX0/f/58o127dkZoaKjtu9vjjz9uW5Ir3Y3WfzExMcaHH35otG/f3ihTpozh5uZm+Pn5GY0bNzY+//zzDEuEXr1kWPq/x6xeLVq0yPBzvvvuO+OOO+4wfHx8DB8fH6NatWrG4MGDjT179mQbm9iPyTByuGaMiBQq27Zto06dOnzzzTc5HjMmIiIi9lW3bl2CgoJYvny5o0MRkTyiMd0iRdTnn3+Or69vhuVERERExHE2b97M1q1b6dOnj6NDEZE8pDHdIkXMTz/9xK5du/jss8946qmnMq1rKSIiIvlr586dbNmyhXHjxlGqVKlM86WISMGmpFukiHn66ac5c+YMd911V5azu4qIiEj+mj9/Pv/73/+oWrUqs2bNwtPT09EhiUgecmj38tWrV3PPPfcQFhaGyWTi+++/z3DcMAzeeOMNSpUqhZeXF23bts20/mFkZCS9e/fG39+fwMBAHnnkkSxnHBYRq8OHD5OQkMD333+Pn5+fo8MRkQJCdbaI/YwYMQKLxcLu3btp0aKFo8MRkTzm0KQ7Li6O2rVrZ7mwO8DYsWOZOHEiU6ZMYcOGDfj4+NC+ffsMaxD37t2bf/75h6VLl/Lzzz+zevVqHnvssfz6FURERIoE1dkiIiI3xmlmLzeZTCxcuJDOnTsD1ifmYWFhPP/887a15KKjoylRogRff/01vXr1Yvfu3dSoUYNNmzZRv359AH799Vfuuusujh8/TlhYmKN+HRERkUJLdbaIiEjOOe2Y7kOHDnH69Gnatm1r2xcQEECjRo1Yt24dvXr1Yt26dQQGBtoqb4C2bdtiNpvZsGEDXbp0ybLspKQkkpKSbO8tFguRkZEUL14ck8lkv19KREQkBwzDIDY2lrCwMMxm519oxF51tuprERFxZjmtr5026T59+jQAJUqUyLC/RIkStmOnT58mNDQ0w3FXV1eCgoJs52Rl1KhRmkBKRESc3rFjxyhTpoyjw7gue9XZqq9FRKQguF597bRJtz0NHz6coUOH2t5HR0dTrlw5jh07hr+/vwMjExERgZiYGMqWLVvkJztUfS0iIs4sp/W10ybdJUuWBODMmTOUKlXKtv/MmTPUqVPHds7Zs2czXJeamkpkZKTt+qx4eHjg4eGRab+/v78qcRERcRoFpQu1veps1dciIlIQXK++dtqBYhEREZQsWZLly5fb9sXExLBhwwYaN24MQOPGjYmKimLLli22c1asWIHFYqFRo0b5HrOIiEhRpDpbREQkew5t6b506RL79++3vT906BBbt24lKCiIcuXKMWTIEN5++20qV65MREQEr7/+OmFhYbbZUqtXr06HDh149NFHmTJlCikpKTz11FP06tVLs6CKiIjkIdXZIiIiN8ahSffmzZtp1aqV7X36uK2+ffvy9ddf8+KLLxIXF8djjz1GVFQUd9xxB7/++iuenp62a2bMmMFTTz1FmzZtMJvNdO3alYkTJ+b77yIiIlKYqc4WERG5MU6zTrcjxcTEEBAQQHR0tMaIiYiIw6leyprui4iIOJOc1ktOO6ZbREREREREpKBT0i0iIiIiIiJiJ0q6RUREREREROxESbeIiIiIiIiInSjpFhEREREREbETJd0iIiIiIiIidqKkW0RERERERMROlHSLiIiIiIiI2ImSbhERERERERE7UdItIiIiIiIiYidKukVERERERETsREm3iIiIiIiIiJ0o6RYRERERERGxEyXdIiIiIiIiInaipFtERERERETETpR0i4iIiIiIiNiJkm4RERERERERO1HSLSIiIiIiImInSrpFRERERERE7ERJt4iIiIiIiIidKOkWERERERERsRMl3SIiIiIiIiJ2oqRbRERERERExE6UdIuIiIiIiIjYiZJuERERERERETtR0i0iIiIiIiJiJ0q6RUREREREROxESbeIiIiIiIiInTh90h0bG8uQIUMIDw/Hy8uLJk2asGnTJtvxfv36YTKZMrw6dOjgwIhFRERERERErJw+6R44cCBLly7l22+/ZceOHbRr1462bdty4sQJ2zkdOnTg1KlTttesWbMcGLGIiEjRpAflIiIimbk6OoBrSUhI4LvvvuOHH36gefPmAIwYMYKffvqJyZMn8/bbbwPg4eFByZIlHRmqiFzFYjFIsVhISTNITbOQnPbfdkqaheRUg1SLBYAyxbwp5u2GyWRycNQicjMGDhzIzp07+fbbbwkLC2P69Om0bduWXbt2Ubp0acD6oHzq1Km2azw8PBwVroiISL5w6qQ7NTWVtLQ0PD09M+z38vLizz//tL3//fffCQ0NpVixYrRu3Zq3336b4sWL53e4InZjGAYpaQYp6QlrmoXUK96nZLOdntimpFlISb2cBKdePsdyeV+aJeP2NcpLTTMuJ8+Z31+9nWoxcvU7+nu6EhHiS0RxbyKCfYkI8SGiuA/lg73x83Sz050VkbyiB+UiIiJZc+qk28/Pj8aNGzNy5EiqV69OiRIlmDVrFuvWraNSpUqA9Yn5/fffT0REBAcOHOCVV16hY8eOrFu3DhcXlyzLTUpKIikpyfY+JiYmX34fcQ5XJrDZJY3XSjYznJdqIdVy+f3lBPfK7fTENyXNYj12xXZKqvW61CsS3iuT6Su3c5vAOiOTCdxdzLi5mHFzMV3+00yaxeBMbCIxialsOxbFtmNRma4N9vWgQrA1AY8I9iXi8p/hxb3xdMv6/7mI5C89KBcREcmayTAMp/42f+DAAQYMGMDq1atxcXHhtttuo0qVKmzZsoXdu3dnOv/gwYNUrFiRZcuW0aZNmyzLHDFiBG+99Vam/dHR0fj7++f571CYpSewWSWRmbavSGSzSy4ztdTmsEXX1oqbxbY12f3v/MKSwLq5mHF3MeN6OYF1v5zMul5OZt1t2/8luG5XnOuaxf5M265m3Mym7Lddr3P9Fdsu5uy7jiempHHkQjyHzsdx6Hwchy//efB8HOcvJWV7nckEYQFeRGSRkJcp5oWbi9NPWyGSpZiYGAICAgpcvdSkSRPc3d2ZOXOm7UF53759qVSpEnv27GH27Nl4e3tneFDu6+ub7YPyrB6Sly1btsDdFxERKZxyWl87fdKdLi4ujpiYGEqVKkXPnj25dOkSixYtyvLckJAQ3n77bR5//PEsjztrJW4Y1i65OUooM3QRvkYyamvRtZCcgy7D1+s+bG25tWSIszBIT1htSeQVCaWr2YR7FtvpCa57Ntvpia+bi/nysf+23VxMtlZf1/RtV2v5bi5m3K/aznDedRLYwiY2MYXD5+M5dCGOQ+fiOHT+EocuxHPo3CViElOzvc7VbKJskLc1IS/uY+uuHhHiQyl/T8xF6B5KwVNQk+68flCuh+QiIuLMclpfO3X38iv5+Pjg4+PDxYsXWbJkCWPHjs3yvOPHj3PhwgVKlSqVbVkeHh52mbjlwLlLjP9tb4ZW3atbeK/ZWlvIEtjsWlyvTihds2lJdb+c5GaVCOekZfWa265m3MzWbRezSRN4OTE/TzdqlQmgVpmADPsNw+BifIo1CT8ff/nPOA6dj+fw+TgSUtJsLedX83A1WxPxYB/KB/tc7rpufR/s665/D5Jr0fEpzP/rOAOali/S/34qVqzIqlWrMj0or1ChQpbnV6hQgeDgYPbv359l0j18+HCGDh1qe5/+kFxERKQgcfqke8mSJRiGQdWqVdm/fz/Dhg2jWrVq9O/fn0uXLvHWW2/RtWtXSpYsyYEDB3jxxRepVKkS7du3z/dYYxJSWLTjVJ6WmdMkMrsW16xaWTO1uGbTont14pupZfaqrs2uLiZclcBKPjGZTAT5uBPkE0S98KAMxwzD4ExMEgfPX7K2ktsS8jiORsaTlGphz5lY9pyJzVSur4crEcFZJ+QBXprQTTI7fjGeflM3sf/sJVLSLDzRoqKjQ3K4vHpQbq+H5CIiIvnJ6ZPu6Ohohg8fzvHjxwkKCqJr16688847uLm5kZqayvbt25k2bRpRUVGEhYXRrl07Ro4c6ZBKumyQN2/dW/O6Lau2bdfL51y5fWWyrARW5IaYTCZKBnhSMsCTJlflP6lpFk5GJXLwciJ++PLY8cMX4jh+MYFLSansOBHNjhPRmcoN8nG3JeS2xPzyDOve7k7/cSp2sPNENP2/3sS52CRK+nvSokqIo0NyqIL0oFxERCS/FJgx3fZUUMfOiUjeSkpN41hkPAfPXZ7Q7UIcB89Z/zwTk/2EbgAl/T2tiXj62PHLLeTlgrxxd9WEboXR73vOMmjGX8Qnp1GtpB9T+zegVIBXnpRdUOuluXPnZvmgPCAggISEBDp37szff/+d6UF5iRIlclR+Qb0vIiJSOBW6idTsSZW4iFxPXFIqhy9Yk/FD5+KsE7tdbim/GJ+S7XVmE5Qp5p2phTwi2IewQK8iNSleYTJn01FeWbiTNItB00rFmfxQPfzzcD151UtZ030RERFnUugmUhMRcSQfD1dqhgVQMywg07Go+GTbmPErX4fPxxGXnMbRyHiORsazau+5DNe5u5gpV9w7U3f1CiE+hPp5aHiJEzIMgw+W7WPi8n0A3F+3NKO73qreDCJSOKWlQvKly6848C8NHr6OjkqkwFHSLSJykwK93albzp265Ypl2G8YBudik6xjxq9KyI9ExpOcamH/2UvsP3spU5ne7i4Zlzq7out6MR/3/PrV5ArJqRaGL9jBd38dB+Dp1pUYemcVPRwREedhSbMmyEmXrvgz9vKfcVdsZ3Xs6n2XIDUxY/nFImDQenDzdMzvJ1JAKekWEbETk8lEqL8nof6e3F6heIZjaRaDk1EJti7r6WPHD523TugWn5zGrlMx7DoVk6ncQG83a4v4FTOrp48h9/XQx7o9xCam8OT0v/hz/3lczCbe7nwLDzQs5+iwRKSgs1j+a0nOMgmOvSqJjs0+QU66BKkJ9onTxd2a0F88BFumwu1P2ufniBRS+nYmIuIALmYTZYO8KRvkTbPKGWe8Tk61cOxivK11/MqW8lPRiUTFp7A1Poqtx6IylRvq55FpqbOIyxO6ebq55NNvV7icjk6k39SN/Hs6Fm93Fz5+8DZaVQt1dFgi4ggWC6TEXZUEX06Ss0qCk2OvOJZFEp0Sb584za7g7gsefpf/9L3iTz9w97li31XnZDj/8jFXd9j8Ffz8HPwxHm7rC+7e9oldpBBS0p3XEi6CV7Hrnycikg13VzMVQ3ypGJJ53FxCctp/E7pdMXb80Pk4LsQlczY2ibOxSWw8FJnhOpMJSgd6ZRg7nt5dvUwxL1xdNCY5K3tOx9Jv6kZORScS7OvB1H4NqFUm87h+EXFShpFNy/ANtiSnxNknTpPLfwmxR1aJr0/GpPnqc64+5mqHpXPrPAR/fgBRR2HTF9D0mbz/GSKFlJLuvJRwET5qAJXbQdu3wLdor9cqInnPy92F6qX8qV4q8wyZ0QkpmcaOH75gnW09NimV4xcTOH4xgT/2nc9wnavZRLkg7wzd1CsE+1CzdAABXnk3I3dBs3b/eR6fvoXYxFQqhvjwdf+GlA1Sy46IXRmGtfU3y5bk6yXIVyfRl49hh4V6TOZrJMi+V7QkX31ONq3Mrh7Wp6POzNUdWrwEPwyGNROgfn9r/CJyXUq689L+5RB3DrbOgH9/hjZvQL3+YFaXThGxvwAvN2qXDaR22cAM+w3D4EJccsblzq4YQ56UauHg5W7sVzKZoHpJfxpGBNEoIogGEUEE+9qh9cQJLfz7OC/O305KmkHD8kF81qcegd6awE4kE8OAlIS86WqdniQblryP02TOouv0lUmxD1l3x86mldnV0/mTZHu4tZe1e3nkAdjwKTR/wdERiRQIWqebPF7389gmWDQUTm+3vg+rC53GQ+nbbj5QEZE8ZrEYnIpJ5PBVY8f3n73E0cjMYw0rhvjQqEJxGkUE0TAiiFIBXg6I2n4Mw+CT3w/w3pI9AHS6tRTjutfO9/HwWo86a7ovecAwrDNS50VX6/TZsO2RJGPKIkG+iZZkN6+imSTbw/a5sOBR8AyEIdvBU0NupOjKab2kpBs7VOKWNNj0JawYCUkxgAnqD4A2r2u8t4gUGGdiEtl4KNL22nMmNtM5ZYO8aFi+OI0qWFvDywV5F9gltFLTLLzx4z/M3HAUgEebRTC8Y3XM5vz/fZRcZq1I3hfDgNSkPGpJvrxtpNkn1uwm4cpxS/IVx9y8lSQ7K0safNIYzu+BFi9Dq+GOjkjEYZR054LdKvHYM7D0ddg+x/reOxjajYTaD6giEZEC52JcMpsOWxPwDYci+edkNJarapAS/h40jChu65JeOdS3QCTh8cmpPD3zb5b/exaTCd68uwb9mkY4LJ4imVzmQIG5L6lJOVsf+XpdrdP3WVLtE6ebTzZdrdNbknPY1dr9cpJs1oSMRcbOBTC/P3j4w7PbwDvI0RGJOISS7lyweyV+6A/45QU496/1fbnG0GkclKiZ9z9LRCSfxCamsOXIRVtL+LbjUaSkZaxSgnzcaVC+GA0jrF3Sq5fyx8UBLcfXci42iUembWL78Wg8XM182KsuHW4p6dCYCkxymc/sdl9Sk7PoTn2DXa2TLoElJe9iu5Kb93VaknPY1drD15pwK0mWG2WxwKfN4MxOaPa8dR4jkSJISXcu5MuXm9RkWP8JrBpjnZXT5AK3PwktX9bMjyJSKCQkp/H3sf+S8L+OXiQxJeNYTz8PV+qVL0ajy63htUoH4O7quC/+B85dot/UjRyLTKCYtxtf9G1AvXDHDwNS0p21PL0v8wfAgZXWpDktOW8CvJqrV961JLv7aGJWcS67f4Y5va0PcIZsB59gR0ckku+UdOdCvn65iT4Ovw6H3T9a3/uVgvbvQs0u6nIuIoVKcqqFHSeiL3dHv8Dmwxe5lJSxm6ynm5nbyhW73B29OHXLBebbpGWbD0cy8JvNRMWnEF7cm6/7NyQi2Cdffvb1KOnOWp7el5k9Ye+vGfe5el5jLeTrtSRnsXyUkmQpzAwDPmsJp7ZCk6eh3duOjkgk3ynpzgWHfLnZt8za5fziIev7Cq3grvchuFL+/HwRkXyWZjHYfSqGDYci2XjoAhsPRXIxPmM3XDcXE7XLBNLw8uzo9cKL4eeZ92uF/7LjFEPmbCU51UKdsoF80be+Uy2HpqQ7a3l6XyIPWnuh2VqSfcFFK6mK5Mre32Bmd2uvjme3gV8JR0ckkq+UdOeCw77cpCTCmgnW9Q7TksDFHZo+C3cMBXfv/ItDRMQBLBaDA+cusf5yd/QNBy9wNjYpwzlmE9xSOoCG5a1JeIPyQRTzubn1sr/44yDv/LIbw4A7a5RgYq+6eLk7V4ukku6s6b6IOBnDgC/awonN0OgJ6DjG0RGJ5Csl3bng8Eo88iD88iLsX2p9H1gOOo6Fqh3zPxYREQcxDIOjkfGXW8Ktr6zWCq9aws/aHb1CEA3LBxHq75mj8tMsBm8v2sXUNYcB6NM4nDfvqel0E7uBE9RLTkr3RcQJHVgB33YBFw945m8IKO3oiETyjZLuXHCKStww4N+fYfHLEHPcuq/qXdBhNBQLd0xMIiIOdjIqgU2HI22J+P6zlzKdExHsY2sJbxgRRNmgzD2FElPSGDJ7K7/+cxqA4R2r8VjzCk67nJlT1EtOSPdFxAkZBky9C46uhfqPwN3jHR2RSL5R0p0LTlWJJ8fBqrGw7iPrupyuXtD8BesEFa7OM95QRMQRzl9KYvPhSNYftCbhu0/HcHUtVjrQy5aAN4wIopi3O49+s5ktRy7i7mLm/R61ubd2mGN+gRxyqnrJiei+iDipw3/C153A7AbP/GXttSlSBCjpzgWnrMTP/mudaO3wH9b3xStZJ1qr2MqxcYmIOJHohBS2HPmvJXzH8WhSLRmrNVeziVSLgb+nK5/3qU+jCsUdFG3OOWW95AR0X0Sc2LR74dAqqPsw3PeRo6MRyRdKunPBaStxw4Ad82DJqxB31rqv5v3WJcb8Szk2NhERJxSfnMpfR6LYeOgCGw5F8vexKJJTLZQO9OLr/g2oXMLP0SHmiNPWSw6Wl/dlzf7zRCekUC7Im/Di3naZJV+kSDm6Ab5qByYXeGoTFK/o6IhE7C6n9ZLWxnBmJhPc2gOqtIeV78LGz+CfBbBvKbQaDg0f1/ImIiJX8HZ35Y7KwdxRORiApNQ0dp+KJSLYhwAvJVXyn6/+PMTyf8/a3gf5uNsS8PAgb8oV97Fth/h5OO34fxGnUa4RVGoL+5dZh0re/6mjIxJxGmrppgC1KJzaBoueh+ObrO9L3AKdxkG52x0bl4iI5KkCUy/ls7y8L2N+/Zf1By9w9EI8F+KSr3mul5sL5YK8KXc5CQ8v7k345aQ8LNALNxfzTcUiUmic2AKftwaTGQZtgJAqjo5IxK7UvTwXCtSXG4sF/v4Wlr0JCRet++r0hjv/Bz7Bjo1NRETyRIGql/KRve5LbGIKRyPjOXohniOR8Ry5EM/RyDiOXIjnZFQClmt8U3Ixmygd6EV4cW9bS3m5IB/bex8P9UiTImbWA7DnF7ilK3T7ytHRiNiVku5cKJBfbuIuwPIR8Nc31veegdD2TbitL5hdHBmZiIjcpAJZL+UDR9yX5FQLJ6ISOHIhjqOXE/L0pPxoZDyJKZZrXh/s63FFl/WMSXlxH3d1W5fC5/QOmHIHYIIn10KJGo6OSMRulHTnQoH+cnNsIywaav2AAwi7zbo+Ylhdx8YlIiI3rEDXS3bkbPfFYjE4dynpciJ+RVIeGc/RC3FcjE+55vU+7i7WsePpLeTFvQm/nJCXCvDEVd3WpaCa2wd2/QDV74Ge0x0djYjdKOnOBWerxHMtLRU2fwkr3oakGMAEDR6B1q+BVzFHRyciIrlU4OslOylo9yU6IYVjkfEcvmDtqm7tvh7H0QvxnIpJzLTG/JVczSbKFPPKmJQHWceSlwvyxstdvdrEiZ3dDZ80Bgx4fDWUqu3oiETsQkl3LhS0Sjxbsafht9dhx1zre+9gaPc21O5lnQldREQKhEJTL+WxwnRfklLTOBaZYBs7bu2ybm0xP3YxgeTUa3dbD/XzyNBV/cqkvJi3m7qti+N9N9C69G2VDvDgHEdHI2IXhSbpjo2N5fXXX2fhwoWcPXuWunXr8uGHH9KgQQMADMPgzTff5PPPPycqKoqmTZsyefJkKleunOOfUZgqcQAOrYZFL8D5Pdb35ZpYZznXmBoRkQKh0NVLeaSo3BeLxeB0TGKGCd2OpE/0diGOmMTUa17v5+Gaafx4+pjyUgFeuJiVkEs+OL8PPm4IhgUGLocy9R0dkUieKzRJd8+ePdm5cyeTJ08mLCyM6dOn88EHH7Br1y5Kly7NmDFjGDVqFNOmTSMiIoLXX3+dHTt2sGvXLjw9PXP0MwplJZ6aDOs/tq6TmBIPJhe4/Ulo+TJ4+Dk6OhERuYZCWS/lAd0Xq6j45Axjx69Myk/HJF7zWncX8+Vu61esR365+3rZIG883dRtXfLQwidh20yo2AYeXuDoaETyXKFIuhMSEvDz8+OHH36gU6dOtv316tWjY8eOjBw5krCwMJ5//nleeOEFAKKjoylRogRff/01vXr1ytHPKdSVeNQxWDIcdv9kfe8XBh3ehRqd1eVcRMRJFep66SbovlxfYkoax66a0C09IT92MZ6UtGt/7Svp75lhPfIrk/JAb/d8+i2k0Ig8BB/VB0sqDFgC5W53dEQieSqn9ZJTLx6ZmppKWlpaphZrLy8v/vzzTw4dOsTp06dp27at7VhAQACNGjVi3bp1OU66C7XAstZZI/cthV9egIuHYV4/qNAK7nofgis5OkIRESkk8mNImFybp5sLlUv4UblE5l5taRaDU9EJWa5HfvRCPLFJqZyOSeR0TCIbD0Vmut7f09U6kduVSfnl7usl/T0xq9u6XC0oAur0hr+mWSf87fezoyMScQinTrr9/Pxo3LgxI0eOpHr16pQoUYJZs2axbt06KlWqxOnTpwEoUaJEhutKlChhO5aVpKQkkpKSbO9jYmLs8ws4k8p3wqD18OcE+PMDOLgSJjeGps9Cs+fBzcvREYqISAE3cOBAdu7cybfffmsbEta2bVvbkLCxY8cyceLEDEPC2rdvn6shYXLjXMwmyhTzpkwxb5pcdcwwDC7Gp2S5HvmRC/GcjU0iJjGVHSei2XEiOlPZ7q5myhbzss2uHn7FmPKyQV54uKrbepHVfBhsmwWH/7DOOxTR3NERieQ7p+5eDnDgwAEGDBjA6tWrcXFx4bbbbqNKlSps2bKFL7/8kqZNm3Ly5ElKlSplu6ZHjx6YTCbmzMl6psQRI0bw1ltvZdpfZLqrXTgAi1+E/cus7wPDoeNYqNrBsXGJiAhQMLtR58eQsIJ4XwqL+ORUjkUmZLke+fGLCaRasv86aTJBKVu3dR/bJG/lL7ea+3u65eNvIg6x6AXY9DmUvR0G/KohjlJoFIru5QAVK1Zk1apVxMXFERMTQ6lSpejZsycVKlSgZMmSAJw5cyZD0n3mzBnq1KmTbZnDhw9n6NChtvcxMTGULVvWbr+D0yleEXrPt47z/vVliDoCs3pC1U7QcTQElnN0hCIiUsDYY0hYkeyZ5qS83V2pWtKPqiUzd1tPTbNwMiqRI+ld1S8vfZa+HZ+cxsnoRE5GJ7L+YOZu68W83bJcjzy8uDehfh5a/qwwaDYU/voGjq2HA8uhUtvrXyNSiDh90p3Ox8cHHx8fLl68yJIlSxg7diwRERGULFmS5cuX25LsmJgYNmzYwJNPPpltWR4eHnh4eORT5E7KZIIa90LF1rB6LKz7GPYsggMroMUwaPw0uGrCFBERyRl7DAkbNWpUlj3TxLm4upgpV9y6JFmzq4bnG4bB+UvJWa5HfjQynvOXkrkYn8LF+Ci2HYvKVLanm5lyQVmvR1460At3V3P+/JJyc/zDoMEjsP4TWPmudTZzPUyRIsTpu5cvWbIEwzCoWrUq+/fvZ9iwYXh6evLHH3/g5ubGmDFjGD16dIbxYdu3b9eSYbl1dre168+RP63vi1eGTu9DhZYODUtEpCgqqPVSXg8Jy6qlu2zZsgXuvkj2LiWlcjSr9cgj4zgZlUjaNbqtm00QFuiV5Xrk4cV98PUoMG1LRcOls/BhbetStg/M0bBGKRQKTffy6Ohohg8fzvHjxwkKCqJr16688847uLlZx/+8+OKLxMXF8dhjjxEVFcUdd9zBr7/+qglZciu0unVGyR3zYMmrcGEffHMf3NIV2r0D/qWuX4aIiBRpeT0kTD3TCj9fD1dqhPlTIyzzl9WUNAsnLiZkuR75kcg4ElMsHL+YwPGLCazhQqbri/u4Z7keebni3oT4qtt6vvMNhYaPwpoPYeU7UKW9WrulyHD6lu78UFBbFOwmIcr6YbjpCzAs4O4HrV6Bho+Bi9M/pxERKfAKS7108eJFIiIiGDt2LI8++ihhYWG88MILPP/884D19wwNDdVEapJrhmFwLjbpv6XPLq9Hnt59PTIu+ZrXe7u7XO62nnk98tKBXri6qNu6XcRdgA9vheRL0ONb61BHkQIsp/WSkm5UiWfr5FZY9Dyc2Gx9X+IW6DQOyt3u0LBERAq7glov2XtIWEG9L5L/YhNTrhg/Hp9hTPmp6ASu0WsdF7OJ0rZu6xnXIw8v7o23uxogbsqKt2H1exBaA55YA2Y94JCCq9B0LxcHCqsDjyyFv7+BZSPgzE74qj3UeQjufAt8gh0doYiIOBENCRNn4efpxi2lA7ildECmY8mpFo5fvKKr+hVJ+dHIeJJSLRyNtG5nJdjX46rx4/8l5cV93NVt/XoaD4YNn8HZXfDPAqjVzdERididWrrRk/McibsAy96Ev7+1vvcMhLZvwm399IRSRCSPqV7Kmu6L2JvFYnA2Nsm65Jlt/Li1+/rhC/FEJ6Rc83pfD1fKBnlnGD8efjkhLxXgqW7r6X4fA7+/a524d/AGMLs4OiKRG6Lu5bmgSjwXjm2En4fCmR3W96XrWbuch9V1bFwiIoWI6qWs6b6Io0XHp2S7HvnpmESu9a3a1WyiTDGvLNckLxfkjZd7EUo8E2OsY7sTLkKXT6H29ed0EHFGSrpzQZV4LqWlWidZW/E2JMcCJmgwEFq/Bl6Bjo5ORKTAU72UNd0XcWaJKWnWbutXrUd+JDKe45EJJKdZrnl9qJ8H5Yv7XDHjujUhDw/yJtDbrfB1W/9jPCx/C4pFwFObwMXN0RGJ5JqS7lxQJX6DYk/Db69ZlxkD8AmBO0dan1YWtopBRCQfqV7Kmu6LFFRpFoPTMYkcuRB3RZf1eFureWxi6jWv9/N0vTyOPHNSXtLfExdzAfzelXTJum53/Hm4dxLc1sfREYnkmpLuXFAlfpMOroJfXoDze63vw5vCXe9DiRqOjUtEpIBSvZQ13RcpjAzDICo+5fKSZ5mT8jMxSde83t3FTJkgr8td1n1sM66HF/emTDFvPN2cuNv62o/gt1choBw8vQVc3R0dkUiuKOnOBVXieSA1GdZ/DKvGQko8mF3h9iehxcvg4evo6EREChTVS1nTfZGiKCE5jWO2butxVyyDFs/xi/GkpGX/Vd5kgpL+nlck4lck5UE+BHg7uEt3SoK1tfvSGescQQ0GOjYekVxS0p0LqsTzUNRR+HU4/Puz9b1fGHQYBTXuU5dzEZEcUr2UNd0XkYzSLAYnoxJsifiRyLgrlkGL51LStbutB3i5ZViP3NZ9vbg3Jfw8MedHt/UNn8LiF63fGZ/5G9y0hKAUHEq6c0GVuB3s/Q0WD4OLh63vK7a2djkvXtGhYYmIFASql7Km+yKSc4ZhEBmXnGE9cltSHhnPudhrd1v3cDXblj9LH0fepnoJygZ5522gKYkw6TaIOQEdxsDtT+Rt+SJ2pKQ7F1SJ20lKAvz5gfWVlgwu7tB0CDQbCm5ejo5ORMRpqV7Kmu6LSN6JT079r6v6FZO6WbutJ5BmyZwi+Hm48suzzfI+8d78Ffz8HPiEwrPbwD2PyxexEyXduaBK3M4uHIBfhsGB5db3geFw13tQpb1j4xIRcVKql7Km+yKSP1LTLJyMSsyQiK/49yz7z16ifngx5jzeOG9nTE9Nho/qWYcp3jkSmj6Td2WL2FFO6yVzPsYkRVXxivDQd9DjG+t4nagjMLMHzO5t/XAVEREREafh6mKmXHFvmlUO4aHbw3nlrupM7dcAXw9XNh+5yJRVB/L4B7pDi5es22smQFJs3pYv4mBKuiV/mEzWydSe2gRNnrHObv7vz/BRQ/hjvPUJp4iIiIg4pbJB3rx1b00APli6lx3Ho/P2B9zaC4IqQPwF6+RqIoWIkm7JXx6+0G4kPPGndT3v1ARY/hZMaWpd71tEREREnNL9t5WmU61SpFoMnp3zNwnJaXlXuIurdalZgLWTIDGPk3oRB1LSLY4RWh36LYIun4JPCJzfC9/cC/MfgdjTjo5ORERERK5iMpl4p8stlPD34OC5ON79ZXfe/oBa3SC4KiRGwfrJeVu2iAMp6RbHMZmgdi94ajM0fAxMZtg5HybVt37Qpl17bUkRERERyV+B3u683702AN+uP8LKf8/mXeFmF2h5ubV73ccQH5l3ZYs4kJJucTyvQOts5o+uhNL1IDkWfn0ZPmsJRzc4OjoRERERuUKzyiH0b1oegGHzt3Ph0rXX/M6VGp2hxC2QFAPrPsq7ckUcSEm3OI+wOvDIMrh7AngGwpkd8FU7+GEwxF1wcHAiIiIiku6lDtWoUsKX85eSeHnBDvJsFWKzGVoOt26vnwJx5/OmXBEHUtItzsVshvr94ektUPch676/p1vXbtw8FSwWx8YnIiIiIni6uTChZ13cXcws3XWGuZuP5V3h1TpBqdqQEmddQkykgFPSLc7JJxju+xgG/GbtYpRwEX4eAl/eCSe3Ojo6ERERkSKvRpg/z7erAsBbP+3i8Pm4vCnYZIJWr1q3N34BsWfyplwRB1HSLc6tXCN4bBV0GA3ufnBiM3zeCha9AAlRjo5OREREpEgb2KwCt1cIIj45jSFztpKalke9Eiu3g9L1rcvL/vlB3pQp4iBKusX5ubjC7U/CU5vglm5gWGDT5/BRfdg2B/JqDJGIiIiI5IqL2cS4HnXw83Rl67EoPl55IG8KNpmg9eXW7s1fQfSJvClXxAGUdEvB4V8Kun0JfX6E4pUh7hwsfAy+vhvO5vE6kSIiIiKSI6UDvXi78y0ATFyxj7+PXsybgiu0gnJNIC0J/hiXN2WKOICSbil4KrSAJ9dCmzfA1QuO/AlT7oDfXoekS46OTkRERKTIua9Oae6tHUaaxeC5OVuJS0q9+UKvbO3+6xuIOnrzZYo4gJJuKZhc3aHZ8/DURqh2N1hSYe1E+Lgh7PpBXc5FRERE8tnI+24hLMCTwxfieXtRHvVCLH8HRLQASwqsGps3ZYrkMyXdUrAFloNeM+DBuRAYDjEnYG4fmN4VLuTRmCIRERERua4Abzfe71EbkwlmbTzK0l15NOt4+kzmW2dC5MG8KVMkHynplsKhSnsYvAGavwgu7nBgOXzSGFa+CykJjo5OREREpEhoUjGYR5tVAODl77ZzLjbp5gst1wgqtQUjTa3dUiAp6ZbCw83LOu5n0Hqo2No66caqMfDJ7bD3N0dHJyIiIlIkPN+uCtVK+nEhLpmXvtuOkRfD/lq9Yv1z+xw4t/fmyxPJR06ddKelpfH6668TERGBl5cXFStWZOTIkRn+4/br1w+TyZTh1aFDBwdGLQ5XvCI8tAC6TwO/MLh4GGZ2h9m9IeqYo6MTERERKdQ8XF34sFdd3F3NrPj3LDM25MEEaKXrQdW7rEvHrhp98+WJ5COnTrrHjBnD5MmT+eijj9i9ezdjxoxh7NixTJo0KcN5HTp04NSpU7bXrFmzHBSxOA2TCWp2tk601uRpMLnAvz9bJ1r7YzykJjs6QhEREZFCq2pJP17qUA2Atxft4sC5PFhhJr21e+cCOLPr5ssTySdOnXSvXbuW++67j06dOlG+fHm6detGu3bt2LhxY4bzPDw8KFmypO1VrFgxB0UsTsfDD9q9DU/8aV3nMSUelr8FU5rCodWOjk5ERESk0OrfpDx3VAomMcXCc3O2kpJmubkCS9aCGvcBBvz+bp7EKJIfnDrpbtKkCcuXL2fvXuu4jW3btvHnn3/SsWPHDOf9/vvvhIaGUrVqVZ588kkuXLhwzXKTkpKIiYnJ8JJCrkQN6P8LdPkUfELg/F6Ydg98NxBiTzs6OhGRAk9DwkTkamazife71ybAy43tx6OZuHzfzRfacjhggt0/waltN1+eSD5w6qT75ZdfplevXlSrVg03Nzfq1q3LkCFD6N27t+2cDh068M0337B8+XLGjBnDqlWr6NixI2lpadmWO2rUKAICAmyvsmXL5sevI45mMkHtXvDUZmjwKGCCHfPgowawfgqkpTo6QhGRAktDwkQkKyUDPHm3Sy0APl65ny1HIm+uwNDqcEtX6/bKUTcZnUj+MBl5Mp2gfcyePZthw4bx3nvvUbNmTbZu3cqQIUMYP348ffv2zfKagwcPUrFiRZYtW0abNm2yPCcpKYmkpP+WL4iJiaFs2bJER0fj7+9vl99FnNDJv+HnoXDyL+v7krWg03go29CxcYlIkRcTE0NAQECBqpfuvvtuSpQowZdffmnb17VrV7y8vJg+fTpgbemOiori+++/v6GfURDvi4hYDZ27lQV/naBskBeLn22Or4frjRd2fp91nh7DAgNXQJl6eReoSC7ktF5y6pbuYcOG2Vq7a9WqxcMPP8xzzz3HqFHZP9WqUKECwcHB7N+/P9tzPDw88Pf3z/CSIiisLgxcBnd/AJ6BcHoHfHkn/PAUxF17iIKIiGRkryFhIlI4jLi3JqUDvTgWmcBbP/5zc4UFV4Zbe1m3V75z88GJ2JlTJ93x8fGYzRlDdHFxwWLJfhKG48ePc+HCBUqVKmXv8KQwMLtA/QHw9Bao85B139/fwkf1YMvXcI1/ayIi8h97DAnTHCwihYe/pxsf9KyDyQTzthzn152nbq7AFi+C2RUOLIej6/MmSBE7ceqk+5577uGdd95h0aJFHD58mIULFzJ+/Hi6dOkCwKVLlxg2bBjr16/n8OHDLF++nPvuu49KlSrRvn17B0cvBYpPMHT+GAYsgRK3QMJF+OlZa8v3ya2Ojk5ExOnNnTuXGTNmMHPmTP766y+mTZvG+++/z7Rp02zn9OrVi3vvvZdatWrRuXNnfv75ZzZt2sTvv/+eZZmag0WkcGkYEcSTLSoC8PKCHZyJSbzxwoIioM7lh3or3s6D6ETsx6nHdMfGxvL666+zcOFCzp49S1hYGA888ABvvPEG7u7uJCQk0LlzZ/7++2+ioqIICwujXbt2jBw5khIlSuT452iMmGSQlgobP4OV70JyLJjM0GAgtHoVvAIdHZ2IFAEFsV4qW7YsL7/8MoMHD7bte/vtt5k+fTr//vtvtteFhITw9ttv8/jjj2c6pjlYRAqf5FQL909ew84TMTSrHMy0/g0xm003VljUMZhYFywp0PcniGiet8GKXEdO6+ubmMHA/vz8/JgwYQITJkzI8riXlxdLlizJ36Ck8HNxhcaDoGYX+O1V2PmdNQn/53vrmt+39rDOhC4iIjb2GBLm4eGBh4dHnsYpIo7l7mpmQs86dJr4J3/sO8836w7Tr2nEjRUWWBbq9YVNX1gbS8o303c0cUpO3b1cxKH8S0G3r6DPD1C8MsSdhYWPwdd3w9nsW21ERIoiDQkTkZyqFOrHq52qAzBq8b/sOxN744U1ex5cPODoOjiwIo8iFMlbSrpFrqdCS3hyDbR5A1y94MifMKUpLH0Dki45OjoREacwadIkunXrxqBBg6hevTovvPACjz/+OCNHjgSsrd7bt2/n3nvvpUqVKjzyyCPUq1ePP/74Q63ZIkXQw7eH06JKCEmpFp6dvZXk1BucvNY/DBo8Yt1e+Q4478hZKcKcekx3fimIY+fEQS4egV+Hw55F1vf+paHDaKh+j7oziUieUb2UNd0XkcLlbEwi7Ses5mJ8Ck+0qMjLHavdWEGXzsKHtSElHh6YA1U75G2gItkoFOt0izidYuHwwEzrB3pgOYg5AXMfhhnd4MIBR0cnIiIiUmCE+nsy6v5bAfh09QE2HLxwYwX5hkLDR63bau0WJ6SkW+RGVO0AgzZA82Hg4g77l8EnjWHlKEi5ieUvRERERIqQDreUpEf9MhgGDJ27jZjElBsrqMmz4O4Lp7fD7p/yNkiRm6SkW+RGuXtD69fgyXVQoRWkJcGq0fDJ7bBvqaOjExERESkQ3rinJuWCvDkRlcCbP/xzY4X4FIdGT1i3fx8F11g5QSS/KekWuVnBleDhhdD9a/ArBRcPWbubz+5tXT9SRERERLLl6+HKBz3rYDbBwr9P8NO2kzdWUJOnwCMAzu6CXQvzNkiRm6CkWyQvmEzWdb2f2gSNnwKTC/z7M3zcEP78AFKTHR2hiIiIiNOqF16Mp1pVAuDVhTs4FZ2Q+0K8ikHjwdbt30eDJS0PIxS5cUq6RfKShx+0fwee+APKNbbOorlsBEy5Aw794ejoRERERJzW020qU7tMADGJqTw/dxsWyw1MiHb7k9bk+/xe2DEv74MUuQFKukXsoURN6L8YOk8B72A4vwem3Q3fPQqxZxwdnYiIiIjTcXMx80HPOni5ubD2wAW+WnMo94V4+kOTZ6zbv4+GtBucmE0kDynpFrEXkwnqPABPb4YGAwET7JgLH9WHDZ9CWqqjIxQRERFxKhVCfHnt7uoAjP11D/+ejsl9IQ0fszZ6XDwE22blcYQiuaekW8TevIpBp3Hw6AoIuw2SYmDxi/B5Szi2ydHRiYiIiDiVBxuWo021UJLTLAyZvZXElFyOzfbwhTues26vek9z64jDKekWyS+lb4OBy6DTePAMgNM74Mu28OPTEB/p6OhEREREnILJZGJMt1sJ9nXn39OxjPttT+4LqT8AfEtA9FH4+9u8D1IkF5R0i+Qnsws0eASe2gJ1elv3/fUNTLoNtkzTmpIiIiIiQLCvB2O63grA538cYs3+87krwN0bmj1v3V79PqQk5nGEIjmnpFvEEXxDoPMn0P9XCK0JCRfhp2fgq3ZwapujoxMRERFxuDbVS/Bgo3IAPD93G9HxuZwU7ba+4F8aYk/Clq/zPkCRHFLSLeJI4Y3h8VXQ/l1w94Xjm+CzlvDLi5AY7ejoRERERBzqtU7ViQj24XRMIq9+vwPDyMUyYm6e0PwF6/Yf4yA53j5BilyHkm4RR3Nxg8aD4alNUPN+MCyw8VOYVB+2z4XcVC4iIiIihYi3uysf9KyDi9nEz9tP8cPWk7kroM5DEFgO4s7Cpi/sE6TIdSjpFnEW/mHQfSo8/D0Ur2StHBY8CtPugbP/Ojo6EREREYeoUzaQZ9tUBuD173dy/GIuWqxd3aHFS9btNRMg6VLeByhyHUq6RZxNxVbw5Fpo/Tq4esHhP2BKU1j6JiTHOTo6ERERkXw3qGVF6pYLJDYplefnbiPNkouegLf2gqAKEH/B2ptQJJ8p6RZxRq4e1jFIgzdA1bvAkmp9OvtRQ9j1o7qci4iISJHi6mJmQs86eLu7sOFQJJ//cTDnF7u4QouXrdtrJmreHMl3SrpFnFmxcHhgFjww2zoeKeY4zH0YZnSHyFxUNiIiIiIFXHhxH0bcUxOAcb/tYeeJXCTPtbpBcFVIjIL1k+0ToEg2lHSLFARVO8KgDdDsBTC7wf6l8PHt8PtorTspIiIiRUb3+mVoV6MEKWkGz83ZSmJKWs4uNLtAy8ut3es+hvhI+wUpchUl3SIFhbs3tHkdBq2DCi0hLQl+HwWf3A77ljo6OhERERG7M5lMjO56KyF+Huw7e4nRi3Mx2WyNzlDiFkiKgXUf2S1Gkasp6RYpaIIrW2c47zYV/ErBxUMwoxvMeQiijzs6OhERERG7CvJx571utwLw9drDrN57LmcXms3Qcrh1e/0UiLtgpwhFMlLSLVIQmUxwy/3Wtb0bPwUmF9j9k3WitT8nQGqyoyMUERERsZuWVUPp0zgcgBfmbeNiXA6/+1TrBKVqQ0qcdZJakXygpFukIPPwg/bvwBN/QLnG1gpk2ZvwaTM4/KejoxMRERGxm+Edq1MxxIezsUkMX7ADIyeru5hM0OpV6/bGzyH2jH2DFEFJt0jhUKIm9F8MnSeDdzCc+xe+7gQLHlNlIiIiIoWSl7sLH/aqi6vZxK//nGb+lhwOs6vcDkrXh9QE+PMD+wYpgpJukcLDZII6D8LTm6H+I4AJts+Bj+rDhs/AksPZPUVEREQKiFtKB/DcnVUAGPHjPxy9EH/9i0wmaH25tXvzVxB9wo4Rijh50p2Wlsbrr79OREQEXl5eVKxYkZEjR2boOmIYBm+88QalSpXCy8uLtm3bsm/fPgdGLeJgXsXg7vHw6HIIq2udoXPxMPisJRzb5OjoRERERPLUEy0q0qB8MeKS0xg6dyupaZbrX1ShFZRrYl0N5o9x9g9SijSnTrrHjBnD5MmT+eijj9i9ezdjxoxh7NixTJo0yXbO2LFjmThxIlOmTGHDhg34+PjQvn17EhO1drEUcaXrwcDl0GkceAbA6e3wZVv48RmtTSkiIiKFhovZxPgedfD1cGXzkYtMWXXg+hdd2dr91zcQddS+QUqR5tRJ99q1a7nvvvvo1KkT5cuXp1u3brRr146NGzcC1lbuCRMm8Nprr3Hfffdx66238s0333Dy5Em+//57xwYv4gzMLtBgIDy1BWo/aN331zSYVM9awVhy8CRYRERExMmVDfLmrXtrAjBh2T62H4+6/kXl74CI5mBJgdXv2TdAKdKcOulu0qQJy5cvZ+/evQBs27aNP//8k44dOwJw6NAhTp8+Tdu2bW3XBAQE0KhRI9atW5dtuUlJScTExGR4iRRqviHQZbJ1srXQGpAQCT8+DV+1h1PbHR2diIiIyE27/7bSdKpVilSLwZDZW4lPTr3+Ra1es/759wyIPGjfAKXIcuqk++WXX6ZXr15Uq1YNNzc36taty5AhQ+jduzcAp0+fBqBEiRIZritRooTtWFZGjRpFQECA7VW2bFn7/RIiziS8CTy+Gtq9A+6+cHwjfNYCFr8EidGOjk5ERETkhplMJt7pcgsl/D04eD6Od3/Zff2LyjWCSm3BSINVY+0fpBRJTp10z507lxkzZjBz5kz++usvpk2bxvvvv8+0adNuqtzhw4cTHR1tex07diyPIhYpAFzcoMlT8NQmqNkFDAtsmAIfNYDt8yAna1yKiFxFk5+KiDMI9HZnXPc6AExff5SV/569/kWtXrH+uX0OnNtrv+CkyHLqpHvYsGG21u5atWrx8MMP89xzzzFq1CgASpYsCcCZMxnXIT5z5oztWFY8PDzw9/fP8BIpcvzDoPvX8PBCCKoIl87AgoEw7R44t8fR0YlIAaPJT0XEWdxROZgBTSMAGDZ/G+cvJV37gtL1oOpd1oaIVaPzIUIpapw66Y6Pj8dszhiii4sLlsuTP0VERFCyZEmWL19uOx4TE8OGDRto3LhxvsYqUmBVbA2D1kHr18DVEw7/AZObwtI3ITnO0dGJSAGhyU9FxJm82KEqVUr4cv5SMi9/tyNDr5sspbd271wAZ3bZP0ApUpw66b7nnnt45513WLRoEYcPH2bhwoWMHz+eLl26ANZxG0OGDOHtt9/mxx9/ZMeOHfTp04ewsDA6d+7s2OBFChJXD2g+DAZvgCodrbN4rpkAHzeC3T+ry7mIXJc9Jj/VxKcicqM83VyY0LMu7i5mlu0+w5xN1xlOWrIW1LgPMOD3UfkSoxQdTp10T5o0iW7dujFo0CCqV6/OCy+8wOOPP87IkSNt57z44os8/fTTPPbYYzRo0IBLly7x66+/4unp6cDIRQqoYuXhwdnQaxYElIPoYzCnN8zsAZGHHB2diDgxe0x+qolPReRm1Ajz54X2VQB466ddHDp/nR58LYcDJtj9o1Z3kTxlMq7b16Lwi4mJISAggOjoaI3vFkmXHA9/vA9rJlpbvl08oNnz0PRZcNNDLRF7Koj10uzZsxk2bBjvvfceNWvWZOvWrQwZMoTx48fTt29f1q5dS9OmTTl58iSlSpWyXdejRw9MJhNz5szJVGZSUhJJSf+NxYyJiaFs2bIF6r6IiGNZLAa9v9jAuoMXqFM2kPlPNMbV5RrtjvMfgZ3zrT3/Hpydf4FKgZTT+tqpW7pFxIHcvaHNG9bx3hEtIC0Jfn8XPrkd9i1zdHQi4mTsMfmpJj4VkZtlNpsY16M2fp6ubD0WxUcr91/7gpYvg8kMexfD8S35E6QUekq6ReTagitDnx+g21fgWxIuHoIZXWHOwxB93NHRiYiT0OSnIuKswgK9eLvzLQBMWrGfv45ezP7k4Mpway/r9sp38iE6KQqUdIvI9ZlMcEtX69retw8Gk4t1vNNHDWHNh5CW4ugIRcTBNPmpiDiz++qU5r46YaRZDJ6bs5W4pNTsT27xIphd4cByOLo+/4KUQktJt4jknKc/dHgXHl8NZW+HlDhY+gZMaQaH/3R0dCLiQJr8VESc3f/uu4WwAE+OXIjn7UXXWBYsKALqWCeBVGu35AVNpEbBnLBGxOEsFtg2C5a+DvEXrPtu7QXtRoJvqGNjEyngVC9lTfdFRG7W2gPn6f3FBgwDPnu4Hu1qZj2nBFFHYeJt1slk+/4MEc3yN1ApEDSRmojYl9kMdXvDU5uh/gDABNtnw6T6sOEzsKQ5OkIRERGRDJpUDOaxZhUAeHnBDs7GJmZ9YmA5qNfXur3yHVA7pdwEJd0icnO8g+DuD+DR5VCqDiRFw+Jh8HkrOL7Z0dGJiIiIZDC0XRWql/InMi6Zl+ZvJ9uOv82ety6ZenQdHFiRv0FKoaKkW0TyRul68OgK6DQOPALg1Db4oi389CzERzo6OhEREREAPFxdmNCzDu6uZlbuOcf0DUezPtE/DBo8Yt1Wa7fcBCXdIpJ3zC7QYCA8vRlqPwAYsOVrmFQP/vrGOg5cRERExMGqlvTj5Q7VAHhn0S4OnLuU9Yl3PAdu3nBiC+xdko8RSmGS66S7fPny/O9//+Po0WyeCImI+IZClynQ7xcIqQ4JkfDj0/BVezi9w9HRiRQZqrNFRLLXr0l57qgUTGKKhSGzt5KSlkXjgG8oNHzUuq3WbrlBuU66hwwZwoIFC6hQoQJ33nkns2fPJikpyR6xiUhBV74pPPEHtHsb3H3h+Eb4tDksfhkSYxwdnUihpzpbRCR7ZrOJ97vXJsDLjR0novlw2b6sT2zyrPV7zOnt8O/P+RukFAo3lHRv3bqVjRs3Ur16dZ5++mlKlSrFU089xV9//WWPGEWkIHNxgyZPw+CNUKMzGBbYMBk+qg875uuJsYgdqc4WEbm2kgGevNulFgCf/L6fzYezmIfGpzg0esK6vfJdDZeTXLvhMd233XYbEydO5OTJk7z55pt88cUXNGjQgDp16vDVV19lPwugiBRNAaWhxzR4aAEEVYRLZ+C7R+Cbe+HSWUdHJ1Koqc4WEclep1tLcf9tpbEY8NzcrcQmpmQ+qclT1oliz+6CXQvzP0gp0G446U5JSWHu3Lnce++9PP/889SvX58vvviCrl278sorr9C7d++8jFNECotKbWDQOmj1Grh6wqHV1hnORcRuVGeLiFzbW/fWpEwxL45FJvDWT7syn+BVDBoPtm7/PhosafkboBRoJiOXj7f/+usvpk6dyqxZszCbzfTp04eBAwdSrVo12zk7d+6kQYMGJCQk5HnA9hATE0NAQADR0dH4+/s7OhyRouP0DvisJVhSodcsqHaXoyMScQp5VS8Vtjpb9bWI2NPGQ5H0/GwdhgGTe99Gx1qlMp6QGAMf3goJF6HLp1C7l2MCFaeR03op1y3dDRo0YN++fUyePJkTJ07w/vvvZ6i8ASIiIujVS/8IReQ6StaCxk9Ztxe/BMlxjo1HpJBRnS0iknMNI4J4skVFAIYv3MGZmMSMJ3j6Q5NnrNu/j4a01HyOUAqqXLd0HzlyhPDwcHvF4xB6ci7iQMlx8HEjiD5mXQuz7QhHRyTicHlVLxW2Olv1tYjYW3Kqhfsnr2HniRiaVQ5mWv+GmM2m/05IugQf1ob483DvR3Dbw44LVhzObi3dZ8+eZcOGDZn2b9iwgc2bN+e2OBEp6tx9oOMY6/baSXD2X8fGI1KIqM4WEckdd1czE3rWwcPVzB/7zjNt3eGMJ3j4wh1DrNurxkJqcn6HKAVQrpPuwYMHc+zYsUz7T5w4weDBg/MkKBEpYqp1giodrWO7Fz2vZcRE8ojqbBGR3KsU6sernaoDMHrxv+w9E5vxhPqPgG8JiD4Kf3/rgAiloMl10r1r1y5uu+22TPvr1q3Lrl1ZzPQnIpITHceAqxcc+RO2z3F0NCKFgupsEZEb8/Dt4bSoEkJSqoUhs7eSlHrFbOXu3tDseev26vchJTHrQkQuy3XS7eHhwZkzZzLtP3XqFK6urnkSlIgUQcXCocUw6/aSV60zg4rITVGdLSJyY0wmE+91u5Vi3m7sOhXD+KV7M55wW1/wLw2xJ2HL1w6JUQqOXCfd7dq1Y/jw4URHR9v2RUVF8corr3DnnXfmaXAiUsQ0fhqCq1onJ1k+0tHRiBR4qrNFRG5cqL8no+6/FYDPVh9k/cEL/x1084TmL1i3/xwPyfEOiFAKilwn3e+//z7Hjh0jPDycVq1a0apVKyIiIjh9+jTjxo2zR4wiUlS4ukOny58jm7+C41scG49IAac6W0Tk5nS4pSQ965fFMOD5uduITkj572CdhyCwHFw6A5u/dFyQ4vRynXSXLl2a7du3M3bsWGrUqEG9evX48MMP2bFjB2XLlrVHjCJSlEQ0g1t7AgYseg4sade9RESypjpbROTmvX5PDcoFeXMiKoE3f9j53wFXd2j+onX7zw+sy4mJZCHX63QXRlr3U8TJXDoLk+pDUjR0fA8aPeboiETyleqlrOm+iIijbDlyke5T1mIxYOIDdbm3dpj1QFoqfNwAIg9Cmzf+m2BNioSc1ks3PIvKrl27OHr0KMnJGdemu/fee2+0SBERK99QaPM6/PICrBgJNe4Fv5KOjkqkwFKdLSJyc+qFF+Op1pWZuHwfry3cQf3wYoQFeoGLK7R4GRY+BmsmQoOB4Bng6HDFyeQ66T548CBdunRhx44dmEwm0hvKTSYTAGlp6goqInmg/gDYOgNO/m2dzbybxkqJ5JbqbBGRvPN060qs2nOWbcejeX7uNmYMbITZbIJa3eCPcXB+D6yfDC1fdnSo4mRyPab72WefJSIigrNnz+Lt7c0///zD6tWrqV+/Pr///rsdQhSRIsnsAnd/AJhg53w4+LujIxIpcFRni4jkHTcXMx/0rIOXmwvrDl7gqzWHrAfMLv8l2us+1rKnkkmuk+5169bxv//9j+DgYMxmM2azmTvuuINRo0bxzDPP5HmA5cuXx2QyZXoNHjwYgJYtW2Y69sQTT+R5HCLiAGF1rd20ABY9D6lJjo1HpIDJ7zpbRKSwqxDiy+t31wBg7K972H0qxnqgRmcIrQlJMbD2I8cFKE4p10l3Wloafn5+AAQHB3Py5EkAwsPD2bNnT95GB2zatIlTp07ZXkuXLgWge/futnMeffTRDOeMHTs2z+MQEQdp/Rr4hMKF/bB2oqOjESlQ8rvOFhEpCh5oWJY21UJJTrMwZPZWElPSwGyGVsOtJ2yYAnEXrl2IFCm5TrpvueUWtm3bBkCjRo0YO3Ysa9as4X//+x8VKlTI8wBDQkIoWbKk7fXzzz9TsWJFWrRoYTvH29s7wzma0VSkEPEKhPbvWrdXvw+RhxwajkhBkt91tohIUWAymRjT7VaCfd3ZcyaW95dcfohZ7W4oVRuSL8GaCQ6NUZxLrpPu1157DYvFAsD//vc/Dh06RLNmzfjll1+YONG+rVDJyclMnz6dAQMG2CaBAZgxYwbBwcHccsstDB8+nPj4eLvGISL5rFY3iGgOqYmw+EXQSociOeLIOltEpDAL9vVgTNdbAfjiz0Os2X8eTCZo9ar1hI2fQ+wZB0YoziRP1umOjIykWLFiGRJhe5g7dy4PPvggR48eJSzMujbeZ599Rnh4OGFhYWzfvp2XXnqJhg0bsmDBgmzLSUpKIinpv7GhMTExlC1bVut+ijizc3thchOwpEDP6VD9HkdHJGI39lyPOr/qbHvQOt0i4mxeWbiDmRuOUtLfk1+HNCPQyw2+aAsnNkOjJ6HjaEeHKHaU03opVy3dKSkpuLq6snPnzgz7g4KC8qXy/vLLL+nYsaMt4QZ47LHHaN++PbVq1aJ379588803LFy4kAMHDmRbzqhRowgICLC9ypYta/fYReQmhVSBps9atxe/BEmXHBuPiJNzdJ0tIlIUvNapOhHBPpyOSeTV73diALS+3Nq9+SuIOenI8MRJ5CrpdnNzo1y5cg5Z1/PIkSMsW7aMgQMHXvO8Ro0aAbB///5szxk+fDjR0dG217Fjx/I0VhGxk2bPQ2A5iDkBq/TkWORa8rvO1mojIlIUebu7MqFnHVzMJhZtP8X3W09AhVZQrgmkJVnX75YiL9djul999VVeeeUVIiMj7RFPtqZOnUpoaCidOnW65nlbt24FoFSpUtme4+Hhgb+/f4aXiBQA7t5w1/vW7XWfwJldjo1HxMnlZ52t1UZEpKiqXTaQZ9tUBuCN7//heFQCtHrFenDLNIg66sDoxBm45vaCjz76iP379xMWFkZ4eDg+Pj4Zjv/11195Flw6i8XC1KlT6du3L66u/4V84MABZs6cyV133UXx4sXZvn07zz33HM2bN+fWW2/N8zhExAlUaW+dHfTfn2HRUOj3i3WZDhHJJD/r7JCQkAzvR48ene1qIyIihc2glhX5fc9Z/joaxdC525j16B24RDSHQ6th9Xtw7yRHhygOlOuku3PnznYI49qWLVvG0aNHGTBgQIb97u7uLFu2jAkTJhAXF0fZsmXp2rUrr732Wr7HKCL5qMNoOLACjq6DbTOh7kOOjkjEKTmizob/VhsZOnRoptVGpk+fTsmSJbnnnnt4/fXX8fb2dkiMIiJ5ydXFzAc963DXh3+w8VAkn60+yJOtXoND7eDvGXDHcxCkpRqLqjyZvbyg02yoIgXQmg9h6RvgFQRPbwHvIEdHJJJnCnq9pNVGRKSomrvpGC9+tx03FxMLBzXllpUDYP8yqP0AdJni6PAkj+W0vlbSTcH/ciNSJKWlwJRmcG433NYX7tWaw1J4FPR6qX379ri7u/PTTz9le86KFSto06YN+/fvp2LFilmeM2LECN56661M+wvqfRGRws8wDJ6YvoUl/5yhUqgvi7p64zG1LZjMMHgjBFd2dIiSh+yyZBiA2WzGxcUl25eISL5wcYO7x1u3/5oGxzY6Nh4RJ+SIOlurjYhIUWYymRh1/62E+Hmw/+wlRm3zhqp3gWGB37XySlGV6zHdCxcuzPA+JSWFv//+m2nTpmX5NFpExG7Cm0Cd3rB1Bvw8FB77HVxy/bEmUmg5os7O69VGPDw88jI8ERG7C/Jx571ut9Jv6ia+XnuYu7s8Qf09v8DO76zLn5ao4egQJZ/lWffymTNnMmfOHH744Ye8KC5fFfRufCJFWtx5mFQPEqOg/ShoPMjREYncNHvXS/aqsy0WCxERETzwwAOMHv1fi052q42UKVOGVatW5bh81dciUpC8+cNOpq07QoifB2sipuK+92eofi/0/NbRoUkesVv38uzcfvvtLF++PK+KExHJGZ9gaDvCur3yHYg56dBwRAoCe9XZ11ttpF27dlSrVo3nn3+erl27XnPMt4hIQfdyx+pUCvXlXGwSoxK6YGCC3T/Cqe2ODk3yWZ4k3QkJCUycOJHSpUvnRXEiIrlzW18o0wCSL8GSVxwdjYhTs2ed3a5dOwzDoEqVKhn2ly1bllWrVnHhwgUSExPZt28fY8eOVWu1iBRqXu4uTOhZB1ezian7vDgW1tF6YOW7jg1M8l2uBz8WK1Ysw5qbhmEQGxuLt7c306dPz9PgRERyxGyGTuPhsxbwz0Ko+zBUauPoqEQcTnW2iIhj3VI6gKHtqjD21z08efxOfnb5FdPexXBiC5Su5+jwJJ/kOun+4IMPMlTgZrOZkJAQGjVqRLFixfI0OBGRHCt1KzR8HDZMhl9egCfXgZuno6MScSjV2SIijvd484r8/u85Nh6G3wNb0ypxmbW1+6HvHB2a5BOt040mZhEpNBJj4OOGEHsKWg6Hli87OiKRG6J6KWu6LyJSUB2LjKfjh39QLPkEqzxewEwaDPgNyjVydGhyE+w2kdrUqVOZN29epv3z5s1j2rRpuS1ORCTvePpD+8vjpP4YDxcOODYeEQdTnS0i4hzKBnnzv/tqcswowby05tadK992bFCSb3KddI8aNYrg4OBM+0NDQ3n3XU0KICIOVrMLVGgFaUnwyzBQZx4pwlRni4g4jy51S9OpVikmpnQmBVc4tBoO/eHosCQf5DrpPnr0KBEREZn2h4eHc/To0TwJSkTkhplM0GkcuLjDgeWw63tHRyTiMKqzRUSch8lk4p0ut5DqX4ZZqa2sO1e+owaCIiDXSXdoaCjbt2deW27btm0UL148T4ISEbkpxSvCHc9Zt38dbh3rLVIEqc4WEXEugd7ujOteh49T7yPJcIOj6+DACkeHJXaW66T7gQce4JlnnmHlypWkpaWRlpbGihUrePbZZ+nVq5c9YhQRyb07hkKxCOukar+PdnQ0Ig6hOltExPncUTmYTk3rMT2tLQApy9XaXdjlOukeOXIkjRo1ok2bNnh5eeHl5UW7du1o3bq1xoeJiPNw84S73rdub5gCp3c4Nh4RB1CdLSLinF7sUJUlxXqRYLjjdmoLxt4ljg5J7OiGlwzbt28fW7duxcvLi1q1ahEeHp7XseUbLUEiUojN7QO7foAyDWHAEjDn+lmjSL7L63qpsNTZqq9FpDDZdTKGNZMH8ajLT0T6VyfouXXWuWmkwMhpveR6oz+gcuXKVK5c+UYvFxHJHx1Gw/7lcHwj/P0t1Ovr6IhE8p3qbBER51MjzJ9NLYZw6Y+lBMXs5szG7yjRqJujwxI7yHWTT9euXRkzZkym/WPHjqV79+55EpSISJ7xD4NWr1i3l70JcRccG49IPlKdLSLi3B5uXY+lfl0ASPjtf6Skpjo4IrGHXCfdq1ev5q677sq0v2PHjqxevTpPghIRyVMNH4cSt0DCRVj6hqOjEck3qrNFRJyb2Wzi9ofeJAZvyqcd4be5UxwdkthBrpPuS5cu4e7unmm/m5sbMTFalkdEnJCLK3Qab93eOh2OrHNsPCL5RHW2iIjzK1WyFCeqDQCg6p6P+evweQdHJHkt10l3rVq1mDNnTqb9s2fPpkaNGnkSlIhInivXCG7rY91eNBTSUhwbj0g+UJ0tIlIwVO/8EnFmfyqZTvLLrEnEJambeWGS64nUXn/9de6//34OHDhA69atAVi+fDkzZ85k/vz5eR6giEieafsW7P4Zzu6C9ZOh6TOOjkjErlRni4gUEJ7+uDR7FlaNpHfCLN756T7e7Xabo6OSPJLrlu577rmH77//nv379zNo0CCef/55Tpw4wYoVK6hUqZI9YhQRyRveQXDn/6zbv4+G6OOOjUfEzlRni4gUHJ5NniDFozgR5jOk/D2LJf+cdnRIkkdueJ3udDExMcyaNYsvv/ySLVu2kJaWllex5Rut+ylShFgsMLUjHFsP1e+BntMdHZFIJvaqlwp6na36WkQKvbWT4LfXOG4Ec7/LJH5+rjWhfp6OjkqykdN6Kdct3elWr15N3759CQsLY9y4cbRu3Zr169ffaHEiIvnDbIZO48DkArt/gr2/OToiEbtTnS0iUkDUfwTDtwRlTOe5M2kpL87fzk22kYoTyFXSffr0aUaPHk3lypXp3r07/v7+JCUl8f333zN69GgaNGhgrzhFRPJOyVvg9iet27+8ACkJjo1HxA5UZ4uIFEDu3piaPQ/AU67fs27PCaavP+LgoORm5Tjpvueee6hatSrbt29nwoQJnDx5kkmTJtkzNhER+2k5HPzCIOoI/DHO0dGI5CnV2SIiBdhtfcG/NKVMkfRyWck7v+xm/9lLjo5KbkKOk+7FixfzyCOP8NZbb9GpUydcXFzsGZeIiH15+ELH0dbtPyfA+X0ODUckL6nOFhEpwNw8ofkLADzn8ROkJDBkzt8kp1ocHJjcqBwn3X/++SexsbHUq1ePRo0a8dFHH3H+vP0Xbi9fvjwmkynTa/DgwQAkJiYyePBgihcvjq+vL127duXMmTN2j0tECoHq90KlO8GSAoueB42ZkkLCUXW2iIjkkToPQWA5Ai2RPOq5kp0nYvhw+V5HRyU3KMdJ9+23387nn3/OqVOnePzxx5k9ezZhYWFYLBaWLl1KbGysXQLctGkTp06dsr2WLl0KQPfu3QF47rnn+Omnn5g3bx6rVq3i5MmT3H///XaJRUQKGZMJ7noPXD3h0CrY+Z2jIxLJE46qs0VEJI+4ukPzFwF4yv0nvElk8u8H2HQ40sGByY24qSXD9uzZw5dffsm3335LVFQUd955Jz/++GNexpfJkCFD+Pnnn9m3bx8xMTGEhIQwc+ZMunXrBsC///5L9erVWbduHbfffnuOytQSJCJF3KqxsPId8C0BT20CzwBHRyRFnD3qJUfU2XlN9bWIFClpqfBxA4g8yKLQxxh8tCVlinmx+Nlm+Hm6OTo6IR+WDAOoWrUqY8eO5fjx48yaNetmisqR5ORkpk+fzoABAzCZTGzZsoWUlBTatm1rO6datWqUK1eOdevW2T0eESkkmj4LxSvBpTOw4h1HRyNiF/ldZ4uIyE1ycYUWLwNwV8xcqgQaHL+YwIgfdzk4MMmtm0q607m4uNC5c2e7PzH//vvviYqKol+/foB1ORR3d3cCAwMznFeiRAlOnz6dbTlJSUnExMRkeIlIEebqAXe9b93e9Dmc3OrQcETsKb/qbBERyQO1ukFwVUyJUXxVbTNmE3z313F+2XHK0ZFJLuRJ0p1fvvzySzp27EhYWNhNlTNq1CgCAgJsr7Jly+ZRhCJSYFVsBbd0BcMCPz8HljRHRyQiIiJFndkFWlpbu8vs/oohd4QA8MrCHZyOTnRkZJILBSbpPnLkCMuWLWPgwIG2fSVLliQ5OZmoqKgM5545c4aSJUtmW9bw4cOJjo62vY4dO2avsEWkIGn/Lrj7wcm/YMvXjo5GREREBGp0htCakBTNYPdfuaW0P1HxKQybvw2LRSuvFAQFJumeOnUqoaGhdOrUybavXr16uLm5sXz5ctu+PXv2cPToURo3bpxtWR4eHvj7+2d4iYjgVxJav2bdXv4WXDrr2HhEChAt8SkiYidmM7QaDoDLpk+ZeG84nm5m/th3nmnrDjs2NsmRApF0WywWpk6dSt++fXF1dbXtDwgI4JFHHmHo0KGsXLmSLVu20L9/fxo3bpzjmctFRDJoMBBK3gqJ0bD0DUdHI1JgaIlPERE7qnY3lKoNyZeosOdzXr2rOgCjFv/L3jNaBtLZFYike9myZRw9epQBAwZkOvbBBx9w991307VrV5o3b07JkiVZsGCBA6IUkULBxRXu/gAwwbZZcPhPR0ckUiCEhIRQsmRJ2+vnn3+mYsWKtGjRgujoaL788kvGjx9P69atqVevHlOnTmXt2rWsX7/e0aGLiDg/kwlavWrd3vg5D93iRcuqISSnWnh29lZS0iyOjU+uqUAk3e3atcMwDKpUqZLpmKenJx9//DGRkZHExcWxYMGCa47nFhG5rjL1oV4/6/bPQyE12aHhiBQ0WuJTRMQOKreD0vUhNQHTmgmM7XYrxbzd2H0qhiX/ZL9ykzhegUi6RUTyXds3wTsYzu+B9R87OhqRAkVLfIqI2IHJBK0vt3Zv+pJQI5KHbw8HYNbGow4MTK5HSbeISFa8ikG7t63bq8ZClCozkZzSEp8iInZSoRWUawxpSfDHOHo0KIvJBGv2X+Dw+ThHRyfZUNItIpKd2r0gvCmkxMPilxwdjUiBoCU+RUTs6Mqx3VumUcZ0nhZVrGt3z96kz0hnpaRbRCQ7JhN0GgdmV9jzC/z7i6MjEnF6WuJTRMTOIppBRHOwpMDq93igYTkA5m85RnKqJlRzRkq6RUSuJbQ6NH7Kur34JUhW1y2R7GiJTxGRfNLqNeuff8+gdWgcoX4enL+UzLLdZxwbl2RJSbeIyPW0eBECykL0UVj9nqOjEXFaWuJTRCSflGsEldqCkYbbn+/To751zgtNqOacTIZhGI4OwtFiYmIICAggOjpaXddEJGv/LoLZD1q7mj+xBkKrOToiKcRUL2VN90VE5AontsDnrcFk5tzdX9NwviuGAauHtaJccW9HR1ck5LReUku3iEhOVOsEVTqCJRV+eQH0vFJEREQcqXQ9qP0gGBZCFj3CkNJ7AJi9Sa3dzkZJt4hITnUcA65ecPgP2D7H0dGIiIhIUXfvRKh5P1hSePrC29xlXs/czcdJSdOEas5ESbeISE4VC4cWw6zbv70GCRcdG4+IiIgUbS5ucP/ncGtPzEYak9w/okn8CpZrQjWnoqRbRCQ3Gj8NwVUg7hwsH+noaERERKSoc3GFzpOhzkO4YOEDt084uuJLR0clV1DSLSKSG67u1rW7ATZ/Bce3ODYeEREREbML3DuJ2JoP4WIyGHjhfSJXf+7oqOQyJd0iIrkV0Rxu7QkYsOg5sKQ5OiIREREp6sxm/Lp9xG++92E2GQSteAE2feHoqAQl3SIiN6bd2+ARAKe2wSZ14RIREREnYDKR2m40X6R2tL5f9Dysn+LYmERJt4jIDfENhTavW7dXjIRYTVgiIiIijte2RkmmeAxgSuo91h2/vgRrJzk2qCJOSbeIyI2qPwDC6kJSDPz2qqOjEREREcHd1Uy3+uUYndqLHwN6W3f+9hr8Mc6xgRVhSrpFRG6U2QXu/gAwwY55cPB3R0ckIiIiQq8GZQETz57tRPTtl5c7Xf4/+H00GIZDYyuKlHSLiNyMsLrQYKB1e9ELkJrk2HhERESkyCsf7EPTSsUxDPjS3B3avGk98PsoWPG2Eu98pqRbRORmtX4NfELhwj5YO9HR0YiIiIjwQMNyAMzZfIzUJkOg3TvWA3+8D0vfUOKdj5R0i4jcLK9AaP+udXv1+xB5yKHhiIiIiLSrUZLiPu6ciUli5Z5z0OQp6DjWenDtRFjyihLvfKKkW0QkL9TqZl2/OzURFr+oSkxEREQcyt3VTLd6ZQCYtfGodWejx6HTeOv2+k/glxfAYnFQhEWHkm4RkbxgMsFd48DsBvt+g39/dnREIiIiUsT1bFAWgN/3nOVEVIJ1Z4NH4N6PABNs+gJ+HqLE286UdIuI5JWQKtD0Gev24pcg6ZJj4xEREZEirUKIL40rFMdiwNxNx/47cNvD0HkymMzw1zT48SmwpDku0EJOSbeISF5q9gIEloOYE7BqjKOjERERkSLugUbWCdXmbj5GatoVLdp1HoAun4HJBbbOgIVPQFqqg6Is3JR0i4jkJXdv6PiedXv9J3Bml2PjERERkSKtfc0SFPN241R0Iqv2nst48Nbu0O1LMLvCjrmw4FFIS3FMoIWYkm4RkbxWtQNUuxssqbBoqMZJiYiIiMN4uLpknlDtSjW7QPdp1nlp/lkA8/tDanI+R1m4KekWEbGHDqPBzRuOroNtsxwdjYiIiBRhvS6v2b3i37Ocik7IfEL1u6HXDHBxh90/wdw+kJqUz1EWXkq6RUTsIbAstHzZur30dYiPdGw8IiIiUmRVDPGlUUTQ5QnVjmd9UpX28MAscPWEvYthdm9ISczfQAspJd0iIvZy+yAIqQ7xF2DZCEdHIyIiIkXYg5cnVJuz6ShpFiPrkyq1hQfngKsX7F8Ks3pBcnw+Rlk4OX3SfeLECR566CGKFy+Ol5cXtWrVYvPmzbbj/fr1w2QyZXh16NDBgRGLiFzm4gZ3j7du/zUNjm1ybDwiIiJSZLWvWZJAbzdORiey+uoJ1a5UoSU8NB/cfODgSpjZQ8ug3iSnTrovXrxI06ZNcXNzY/HixezatYtx48ZRrFixDOd16NCBU6f+396dx0VV9Q8c/wzbALKJsiqiIKAimCuhueWCmqZFLrgnaZppLqT5lFtaLmWZpfZkCmqiaS5ZuWP6uO+4r6gPLqCGC6CyCPf3xzzMz5FdlmH5vl+v+3Jm7pl7v3MEvnPuOfecGO22cqXcPymEKCFcm8IrfTSP/xwtS3EIIYQQQi9MjQ0JbKCZUC08qwnVnlf9Nei3Dkws4foeWPEOJCcUQ5RlU4ludM+aNQsXFxdCQ0Np0qQJNWrUoH379ri7u+uUU6vVODo6arcXG+VCCKFX7T4HUxu4cxoO/6TvaIQQQghRTgU1cQE0E6rFPsrlfu1qr0L/DaC21kwMu/wtSHpU9EGWQSW60b1x40YaNWpE9+7dsbe3p379+ixatChTuV27dmFvb4+XlxfDhg0jLi4ux+MmJycTHx+vswkhRJGpUBnaTtE8/vsLiL+t13CEEEIIUT7VtLekSXVb0tIV1hy9kfsbqjbSNLxNbeDmEVjWFZ4+KOowy5wS3ei+evUqCxcuxMPDg61btzJs2DBGjhzJ0qVLtWU6dOjAsmXLiIiIYNasWezevZuOHTuSlpaW7XFnzJiBtbW1dnNxcSmOjyOEKM8aDIAqjSAlEbb+S9/RCCGEEKKcCvLTtH1WHbmR/YRqz6vSAAb8AWa2cPsELH1TVmXJJ5WiKHmoaf0wMTGhUaNG7N+/X/vayJEjOXLkCAcOHMjyPVevXsXd3Z0dO3bQpk2bLMskJyeTnPz/687Fx8fj4uLCo0ePsLKyKtwPIYQQGWJOwk+tQEmHvuugZtZ/o4SIj4/H2tpa8tILpF6EEKLgklLT8PsygkdPUwl7tzGtvOzz9sY752DZm/D4Hth7Q//fwcKuaIMt4fKal0p0T7eTkxN16tTRea127dpER2d/47+bmxuVK1fmypUr2ZZRq9VYWVnpbEIIUeSc6kGT9zWPN4XI2pdCCCGEKHamxoa83aAKACtzm1DteQ51YOBfYOEAd8/C0s6QcKeIoixbSnSju1mzZly8eFHntUuXLuHq6prte27evElcXBxOTk5FHZ4QQuRf63+BhSPcvwr75uo7GiGEEEKUQ0FNNGt27zh/l7vx+egEsPOCgZvA0hnuXYCwTjJXTR6U6Eb36NGjOXjwIF9++SVXrlwhPDycn376ieHDhwOQmJjIxx9/zMGDB7l+/ToRERF07dqVmjVrEhAQoOfohRAiC6ZW0OFLzeM930BclH7jEaIQ3bp1i759+1KpUiXMzMzw8fHh6NGj2v0DBw5EpVLpbB06dNBjxEIIUT55OljSyLWiZkK1Yzfz9+bKNeHdv8DaBeKuQGgneJTPY5QzJbrR3bhxY9avX8/KlSupW7cu06ZNY+7cufTpo1nz1tDQkFOnTvHmm2/i6elJcHAwDRs2ZM+ePajVaj1HL4QQ2fB+G9xaQ1oybPoYSu7UGkLk2YMHD2jWrBnGxsZs3ryZc+fOMWfOnEzLeHbo0IGYmBjttnLlSj1FLIQQ5VtGb/fKw9Gk52VCtefZummGmttUgwfXNA3vB/8tgijLhhI9kVpxkYlZhBDFLi4KFrwKaSnQPQy839J3RKIEKY156ZNPPmHfvn3s2bMn2zIDBw7k4cOHbNiw4aXOURrrRQghSqqk1DSafLGD+KRnLB3UhJaeLzEp2qObENZZ0/C2doEBGzUN8nKiTEykJoQQZVYld3httObxlgmQnKDfeIQooI0bN9KoUSO6d++Ovb099evXZ9GiRZnK7dq1C3t7e7y8vBg2bBhxcXHZHjM5OZn4+HidTQghROHQTKhWFYCVh/IxodrzrKvCu5ugkgc8ugGhb8A/2U9oXV5Jo1sIIfTltTFQsQYkxMDfM/QdjRAFcvXqVRYuXIiHhwdbt25l2LBhjBw5kqVLl2rLdOjQgWXLlhEREcGsWbPYvXs3HTt2JC0tLctjzpgxA2tra+3m4uJSXB9HCCHKhV5NNH9Xd5y/k78J1Z5n5awZam5XCxJuayZXu3cx9/eVIzK8HBmuJoTQo8s7YEUgqAzh/d3g6KPviEQJUBrzkomJCY0aNWL//v3a10aOHMmRI0c4cOBAlu+5evUq7u7u7NixgzZtMq9bn5ycTHJysvZ5fHw8Li4upapehBCipHt7wT6ORz/k4wAvhreu+fIHSrwHy7pqlhMzrwwD/tAsM1aGyfByIYQoDTzaQp2uoKTBn2MgPV3fEQnxUpycnKhTR/fLVe3atYmOzn7IopubG5UrV+bKlayHIqrVaqysrHQ2IYQQhStjQrVVR15iQrXnWdjBwD/B0Ree/ANhb0DMqUKKsnSTRrcQQuhbh5lgYgE3D8OJ5fqORoiX0qxZMy5e1B1OeOnSJVxdXbN9z82bN4mLi8PJyamowxNCCJGNzr7OWJoaceP+U/ZF/VOwg5nbaiZTc24AT+/D0i5w+0ThBFqKSaNbCCH0zcoZWk3QPN4xGR5nP7GUECXV6NGjOXjwIF9++SVXrlwhPDycn376ieHDhwOQmJjIxx9/zMGDB7l+/ToRERF07dqVmjVrEhAQoOfohRCi/DIzMeSt+lUAzfJhBT9gRei/Aao2hqSHsLQr3Dxa8OOWYtLoFkKIksBvKDjUhacPYMckfUcjRL41btyY9evXs3LlSurWrcu0adOYO3cuffr0AcDQ0JBTp07x5ptv4unpSXBwMA0bNmTPnj2o1Wo9Ry+EEOVbr8aaIebbzt7hXkJyLqXzwNQa+q6Dav6Q/AiWdYPogwU/biklE6lROiesEUKUQdGHYEl7zeNBW6Haq/qNR+iN5KWsSb0IIUTR6TZ/H5E3HjK+Qy2GtXIvnIMmJ8LKXnB9DxhXgD5roHqzwjl2CSATqQkhRGlTzQ/q99M8/nM0pKXqNx4hhBBClBu9C2tCteepLaD3anBrBamP4ZdAuLqrcI5dikijWwghSpJ2n4OZLdw9B4d+1Hc0QgghhCgnOtdzwkJtxH/jnnDgaiHOL2NiDkG/Qs128OwphPeEKzsK7/ilgDS6hRCiJDG31TS8Af6eAY9u6jceIYQQQpQL5iZGdKvvDEB4YUyo9jxjU+i1Ajw7wrMkWBkEF7cU7jlKMGl0CyFESfNKH3Dx0wzD2vKJvqMRQgghRDmRsWb3trOx/JNYCBOqPc9IDT2WQa3OkJYCv/aF838W7jlKKGl0CyFESWNgAG98AypDOP8HXNqm74iEEEIIUQ54O1tTr6o1qWkKa48VwWg7IxPoHgbeb0F6KqwZAGc3FP55ShhpdAshREnkWBdeHaZ5vPljSH2q33iEEEIIUS5k9HavPBxNkSx0ZWgMb/8MPj0g/Rn8NghO/1b45ylBpNEthBAlVatPwNIZHlyHPXP0HY0QQgghyoEu9ZypYGLI9cKeUO15hkbw1o+aW+qUNFg3GCJXFs25SgAjfQcghBAiG2pL6DgTVveHfd+Bb0+o7KHvqIQQQghRhlVQG9G1fhXCD0Wz8vANmrpXLpoTGRjCmz9o/j2+DDYMg+R4qOZfNOd7nqEJ2Ncq+vP8jzS6hRCiJKv9pmaJjSvb4a+x0P93UKn0HZUQQgghyrDeTaoRfiiarWdiiUtMppKFumhOZGAAnb/TNIKP/AybxxXNeV5k4wqjThXPuZBGtxBClGwqFXSaDQv84dpuOLMWfN7Rd1RCCCGEKMPqVrHGp4o1p289Yt3xWwxu4VZ0JzMwgE5fg3llOPGLZrh5UbOwL/pzPEca3UIIUdLZukHzsfD3F7D1X+DRDkyt9R2VEEIIIcqwoCbVOL3+NCsPR/Ne8xqoinKknUoFrSdotiL2LC0dI8PindpMJlITQojSoNlHYOsOiXdg5xf6jkYIIYQQZdybrzhjbmLI1X8ec+jafX2HUygSklJ5dcZOxv12kicpz4rtvNLoFkKI0sBIDW/8bwbzI4vgdqRewxFCCCFE2WahNqLrK86AZvmwsuCvUzH8k5jM0f8+wMzYsNjOK41uIYQoLdxbQ91AUNLhrzGQXgz3PAkhhBCi3MpYs3vz6VgePE7RczQFt+bYTQC6N3Qp2uHyL5BGtxBClCYBX4KJJdw6BsfC9B2NEEIIIcownyrWeDtbkZKWztrjN/UdToFE3Uvk2H8fYKCCtxtUKdZzS6NbCCFKE0tHeP0zzeOIqZB4T7/xCCGEEKLMUqlU2t7ulYejURRFzxG9vN/+18vd0tMOByvTYj23NLqFEKK0afweOPpC0iPYPlHf0QghhBCiDOv6ijNmxoZE3XvMkesP9B3OS0lLV1j3v576Ho1civ380ugWQojSxtAIOn8LqODkSri+V98RCSGEEKKMsjQ15s16pXtCtf9cvsed+GQqmhvTprZDsZ9fGt1CCFEaVW0EDQdqHv81Fp6V/slNhBBCCFEyBflphpj/dTqGh09K33eO345qerm7vlIFE6PibwJLo1sIIUqrtpPBvDLcuwAH5+s7GiGEEEKUUfWqWlPbyYqUZ+msO35L3+Hky8MnKWw/dweA7o2q6iWGEt/ovnXrFn379qVSpUqYmZnh4+PD0aNHtfsVRWHSpEk4OTlhZmZG27ZtuXz5sh4jFkKIYmJWEdpP1zzePRsels4hX0IIIYQo2VQqFb2baO6FLm0Tqv0eeZuUtHTqOFnh7WytlxhKdKP7wYMHNGvWDGNjYzZv3sy5c+eYM2cOFStW1JaZPXs28+bN48cff+TQoUNUqFCBgIAAkpKS9Bi5EEIUk3q9wLUZpD6BzZ/oOxohhBBClFFd61fB1NiAy3c1S2+VFquP3gD018sNJbzRPWvWLFxcXAgNDaVJkybUqFGD9u3b4+7uDmh6uefOnctnn31G165d8fX1ZdmyZdy+fZsNGzboN3ghhCgOKhW8MQcMjODiX3Bxs74jEkIIIUQZZGVqTBdfzYRq4aVkQrVzt+M5ezseY0MVXV8p3rW5n1eiG90bN26kUaNGdO/eHXt7e+rXr8+iRYu0+69du0ZsbCxt27bVvmZtbY2fnx8HDhzI9rjJycnEx8frbEIIUWrZ1wb/4ZrHm8ZBymP9xiOEEEKIMkk7odqpGB49SdVzNLlbc0zTy922tgO2FUz0FkeJbnRfvXqVhQsX4uHhwdatWxk2bBgjR45k6dKlAMTGxgLg4KA77buDg4N2X1ZmzJiBtbW1dnNxKf612oQQolC1HA/WLvAoGv7ztb6jEUIIIUQZVN/FhlqOliQ/S2f9iZv6DidHKc/S+T3yNqDfoeVQwhvd6enpNGjQgC+//JL69eszZMgQBg8ezI8//lig406YMIFHjx5ptxs3bhRSxEIIoScmFaDjLM3j/d/DvYv6jUcIIYQQZY5KpSKoiaa3e+XhGyV6QrWdF+5w/3EK9pZqWnjY6TWWEt3odnJyok6dOjqv1a5dm+hozT0Ejo6OANy5c0enzJ07d7T7sqJWq7GystLZhBCi1PPqBJ4dID1Vs3Z3CU6EQgghhCidutWvgtrIgIt3Ejge/VDf4WRrzf/W5n67QVWMDPXb7C3Rje5mzZpx8aJub82lS5dwdXUFoEaNGjg6OhIREaHdHx8fz6FDh/D39y/WWIUQQu9UKug4G4zM4PoeOLVa3xEJIYQQooyxNjOm8/8mVFtZQidUu5uQxK5L9wD9Dy2HEt7oHj16NAcPHuTLL7/kypUrhIeH89NPPzF8uGbCIJVKxahRo5g+fTobN27k9OnT9O/fH2dnZ7p166bf4IUQQh8qukLLjzWPt30KT0vPkh5CCCGEKB16+2nmxPrz1G0ePS15E6qtP36LtHSFBtVscLez0Hc4JbvR3bhxY9avX8/KlSupW7cu06ZNY+7cufTp00dbZty4cYwYMYIhQ4bQuHFjEhMT2bJlC6ampnqMXAgh9Mh/BFT2hMf3YOd0fUcjypFbt27Rt29fKlWqhJmZGT4+Phw9elS7X1EUJk2ahJOTE2ZmZrRt25bLly/rMWIhhBAvo0G1ing6WJCUms7vkbf0HY4ORVFYc0wztLx7o5IxYXaJbnQDdO7cmdOnT5OUlMT58+cZPHiwzn6VSsXnn39ObGwsSUlJ7NixA09PTz1FK4QQJYCRiWbtboAji+HWMf3GI8qFBw8e0KxZM4yNjdm8eTPnzp1jzpw5VKxYUVtm9uzZzJs3jx9//JFDhw5RoUIFAgICSEpK0mPkQggh8uv5CdXCD0WXqAnVIm885MrdREyNDejs66TvcIBS0OgWQgjxEmq0AN+egAJ/joH0NH1HJMq4WbNm4eLiQmhoKE2aNKFGjRq0b98ed3d3QNPzMHfuXD777DO6du2Kr68vy5Yt4/bt22zYsEG/wQshhMi3t/43odqF2AQibzzUdzhaq/83gVrHuk5YmhrrORoNaXQLIURZ1X46qK0hJhKOLtF3NKKM27hxI40aNaJ79+7Y29tTv359Fi1apN1/7do1YmNjadu2rfY1a2tr/Pz8OHDggD5CFkIIUQA25ia84aPpSS4pE6o9TUnjz5P/W5u7of4nUMsgjW4hhCirLOyhzUTN44jPIeFOzuWFKICrV6+ycOFCPDw82Lp1K8OGDWPkyJEsXboUgNjYWAAcHBx03ufg4KDd96Lk5GTi4+N1NiGEECVHkJ9miPkfJ2OIT9L/hGpbz8aSkPyMqhXNeNWtkr7D0ZJGtxBClGWNBoFzfUiO18xmLkQRSU9Pp0GDBnz55ZfUr1+fIUOGMHjwYH788ceXPuaMGTOwtrbWbi4uJWNCHCGEEBqNXCtS096Cp6lp/B55W9/hsObYDQACG1TFwECl52j+nzS6hRCiLDMwhDe+AVRweg1c3a3viEQZ5eTkRJ06dXReq127NtHRmiGHjo6OANy5ozvi4s6dO9p9L5owYQKPHj3Sbjdu3CiCyIUQQryskjSh2s0HT9gfFQfAOyVoaDlIo1sIIcq+Kg2g8Xuax3+NhWfJ+o1HlEnNmjXj4sWLOq9dunQJV1dXAGrUqIGjoyMRERHa/fHx8Rw6dAh/f/8sj6lWq7GystLZhBBClCxv16+CiZEB52PiOXXzkd7iWHvsFooCTd0r4WJrrrc4siKNbiGEKA9e/wwq2EPcZdg/T9/RiDJo9OjRHDx4kC+//JIrV64QHh7OTz/9xPDhwwFNb8ioUaOYPn06Gzdu5PTp0/Tv3x9nZ2e6deum3+CFEEK8tIoVTOhUVzNiSV8TqqWnK/x2XDMaqnujktXLDdLoFkKI8sHMBgK+0Dz+z9fw4Lo+oxFlUOPGjVm/fj0rV66kbt26TJs2jblz59KnTx9tmXHjxjFixAiGDBlC48aNSUxMZMuWLZiamuoxciGEEAWVMcR848nbJOhhQrVD1+5z4/5TLNVGdPAuGWtzP08a3UIIUV74dNes3/0sCTaNAz3edyXKps6dO3P69GmSkpI4f/48gwcP1tmvUqn4/PPPiY2NJSkpiR07duDp6amnaIUQQhSWJjVscbOrwJOUNDaeLP4J1TImUOtczwkzE8NiP39upNEthBDlhUoFneaAgTFc3goX/tR3REIIIYQoA1QqFb3/19td3EPME5JS2Xxas/TkOw1L5ioX0ugWQojyxM4Tmo3UPN78CSQn6jceIYQQQpQJgQ2qYmJowJlb8ZwuxgnV/joVw9PUNNzsKtCgmk2xnTc/pNEthBDlTfMQsKkG8Tdh9yx9RyOEEEKIMqBiBRM6+mgmVAsvxt7uNcduAtC9oQsqVclZm/t50ugWQojyxsQcOn6leXxwAdw5p994hBBCCFEmaCdUi7xFYvKzIj9f1L1Ejv33AQYqeLtBlSI/38uSRrcQQpRHXh2gVmdIfwZ/jZFJ1YQQQghRYH41bHGrXIHHKWn8UQwTqv32v17uVl72OFiV3JUwpNEthBDlVYeZYGwO0QcgMlzf0QghhBCilFOpVNre7qKeUC0tXWHd8Yyh5SVvbe7nSaNbCCHKKxsXaPWJ5vH2ifDkvn7jEUIIIUSpF9hQM6HaqZuPOHOr6CZU+8/le9yJT6aiuTFtajsU2XkKgzS6hRCiPHv1A7CrDU/iIGKqvqMRQgghRClnW8GEgLqaCdWKsrf7t6OaXu6ur1TBxKhkN2tLdnRCCCGKlqExdP5G8/hYGNw4otdwhBBCCFH6BTXRrJf9e+RtHhfBhGoPn6Sw/dwdALo3KtlDy0Ea3UIIIVybQr3emsd/jYa0op9tVAghhBBll79bJapXMicx+Rl/nir8CdV+j7xNSlo6dZys8Ha2LvTjFzZpdAshhID208DUBmJPw5FF+o5GCCGEEKXY8xOqhR++UejHX3NMc8zS0MsN0ugWQggBUKEytJ2iebzzC4iP0Ws4QgghhCjdAhtWxdhQxckbDzl7u/AmVDt3O54zt+IxNlTR9ZWSuzb386TRLYQQQqPBAKjSCFISYOu/9B2NEEIIIUqxyhZq2ntrJlRbVYi93Rm93G1rO2BbwaTQjluUpNEthBBCw8BAM6maygDOroPL2/UdkRBCCCFKsd7/G2K+4cQtnqQUfM6YlGfp/B6puUe8tAwtB2l0CyGEeJ5TPWjyvubxr/3g7Aa9hiOEEEKI0svfrRKulcxJSH7Gn6cKfuvazgt3uP84BXtLNS087AohwuIhjW4hhBC62kyEmu3g2VNYMwB2fwWKou+ohBBCCFHKGBio6NVY09tdGGt2r/nf2txvN6iKkWHpacqWnkiFEEIUD5MKELQKXv1A8/zv6bBuMKQm6TcuIYQQQpQ67zSsipGBihPRDzkfE//Sx7mbkMSuS/eA0jW0HKTRLYQQIiuGRtBhBnT+FgyM4PQaCHsDEu7oOzIhhBBClCJ2lmraezsAsKoAvd3rj98iLV2hQTUb3O0sCiu8YiGNbiGEENlrNAj6rtOs4X3rKCx6XbOWtxBCCCFEHmWs2b3uxC2epqTl+/2KorDmmGZoefdGLoUaW3Eo0Y3uKVOmoFKpdLZatWpp97dq1SrT/qFDh+oxYiGEKIPcWsLgnVCpJsTfhMUBcGGTvqMSQgghRCnRzL0yLrZmJCQ946/T+Z9QLfLGQ67cTcTU2IDOvk5FEGHRKtGNbgBvb29iYmK02969e3X2Dx48WGf/7Nmz9RSpEEKUYZXc4b0dUKMlpD6GVb1h71yZYE0IIYQQuSrohGoZvdwd6zphaWpcqLEVByN9B5AbIyMjHB0ds91vbm6e4/7Ckp6eTkpKSpGfR4jyzNjYGENDQ32HIbJjVhH6roXN4+HoYtgxGf65pLnv20it7+iE0EpLSyM1NVXfYQhRakk+FkWhe6OqfLv9Esf++4CLsQl4OVrm6X1PU9L4I2Nt7oalawK1DCW+0X358mWcnZ0xNTXF39+fGTNmUK1aNe3+FStW8Msvv+Do6EiXLl2YOHEi5ubmhRpDSkoK165dIz09vVCPK4TIzMbGBkdHR1Qqlb5DEVkxNIbO34BdLdgyHiJXwP2r0PMXqFBZ39GJck5RFGJjY3n48KG+QxGi1JN8LAqbvaUpbWs7sOVsLCsPRzPlTe88vW/r2VgSkp9RtaIZr7pVKuIoi0aJbnT7+fkRFhaGl5cXMTExTJ06lebNm3PmzBksLS3p3bs3rq6uODs7c+rUKcaPH8/FixdZt25djsdNTk4mOTlZ+zw+Pvup6xVFISYmBkNDQ1xcXDAwKPEj8oUolRRF4cmTJ9y9excAJ6fSd79OueI3BCq5wZp3IfqAZoK13r+CfW19RybKsYwGt729Pebm5tJYEOIlSD4WRSnIrxpbzsay7vhNPulYC1Pj3EdUrDl2A9AsPWZgUDr/rpfoRnfHjh21j319ffHz88PV1ZXVq1cTHBzMkCFDtPt9fHxwcnKiTZs2REVF4e7unu1xZ8yYwdSpU/MUw7Nnz3jy5AnOzs6F3oMuhNBlZmYGwN27d7G3t5ehbSVdzbaa+7zDe8CD6/BzO+geCh7t9B2ZKIfS0tK0De5KlUpnT4gQJYXkY1FUmtesTBUbM249fMqm0zG83SDn4eI3Hzxhf1QcAIG5lC3JSlW3rY2NDZ6enly5ciXL/X5+fgDZ7s8wYcIEHj16pN1u3LiRbdm0NM2U9iYmJi8ZtRAiPzIubsn9mKWEnRcM/htcX4OUBE0D/MACmWBNFLuMvxlygVyIwiH5WBQFAwMVQU00S37lZUK1tcduoSjQ1L0SLral9+97qWp0JyYmEhUVle0wl8jISCD3YTBqtRorKyudLTcyRE2I4iG/a6WQuS30Ww/1+4GSDlsnwJ+jIE2+qIniJ39DhCgc8rskikr3Ri4YGqg4cv0Bl+8kZFsuPV3ht+M3/vee0tvLDSW80R0SEsLu3bu5fv06+/fv56233sLQ0JCgoCCioqKYNm0ax44d4/r162zcuJH+/fvTokULfH199R26KGIqlYoNGzboOwwhRAYjE3jze2j/BaCCY2Gw/C14cl/fkQlRJkyZMoVXXnlF32FkUr16debOnavvMIQQpYiDlSltatkDsPJw9iOOD127z437T7FUG9HBu3TPLVCiG903b94kKCgILy8vevToQaVKlTh48CB2dnaYmJiwY8cO2rdvT61atRg7diyBgYH88ccf+g67xIiNjWXEiBG4ubmhVqtxcXGhS5cuREREaMtUr14dlUrFwYMHdd47atQoWrVqpX0+ZcoUVCoVQ4cO1SkXGRmJSqXi+vXrmc5//fp1VCpVjltYWNhLfbaYmBide/4LYsaMGRgaGvLVV18VyvGEKLdUKmj6IQStAhMLuL4Hfm4D/1zWd2RClFhdunShQ4cOWe7bs2cPKpWKU6dOERISos3fGbk7u23gwIGZjpVbPp4yZcpLxX/kyBGdOXYKYuXKlRgaGjJ8+PBCOZ4QouQK8tOsRrX2+E2SUtOyLJMxgVrnek6YmZTueQVK9ERqq1atynafi4sLu3fvLsZoSpfr16/TrFkzbGxs+Oqrr/Dx8SE1NZWtW7cyfPhwLly4oC1ramrK+PHjc61PU1NTFi9ezNixY/Hw8Mg1BhcXF2JiYrTPv/76a7Zs2cKOHTu0r1lbW2sfp6WloVKp8jRDfGGuzb5kyRLGjRvHkiVL+PjjjwvtuC8jJSVF5g8QpZ9XBwjeBuG9NMuJ/dwGui8F99b6jkyIEic4OJjAwEBu3rxJ1aq6wydDQ0Np1KiRdgSfhYUFoGnoZsw5s3//fgIDA7l48aL2drmMSbCe93w+/vXXX5k0aRIXL17UvpZxbNDMXp2WloaRUe5fE+3s7PL6UXO1ePFixo0bx7///W/mzJmDqalpoR07vyQfC1G0WnjYaSdU23Imlm71q+jsT0x+xubTsQC809BFHyEWqhLd0y1e3gcffIBKpeLw4cMEBgbi6emJt7c3Y8aMydSrPWTIEA4ePMimTZtyPKaXlxetW7fm008/zVMMhoaGODo6ajcLCwuMjIy0z7ds2YKTkxMbN26kTp06qNVqoqOjOXLkCO3ataNy5cpYW1vTsmVLjh8/rnPs54eXZ/Sor1u3jtatW2Nubk69evU4cOBArjHu3r2bp0+f8vnnnxMfH8/+/ft19qenpzN79mxq1qyJWq2mWrVqfPHFF9r9GaMxbG1tqVChAo0aNeLQoUMADBw4kG7duukc78URBK1ateLDDz9k1KhRVK5cmYCAAAC++eYbfHx8qFChAi4uLnzwwQckJibqHGvfvn20atUKc3NzKlasSEBAAA8ePGDZsmVUqlRJZ1k8gG7dutGvX79c60SIQuHgDYN3gosfJD2CXwLhyM/6jkoUoYwRUc9vtWrV0u5v1apVpv0vjp4qjzp37oydnV2mkV+JiYmsWbOG4OBgQHd4uZ2dnTaX2traAmBvb6997fkL2hmez8fW1taoVCrt8wsXLmBpacnmzZtp2LAharWavXv3EhUVRdeuXXFwcMDCwoLGjRvrXDiHzMPLVSoVP//8M2+99Rbm5uZ4eHiwcePGXOvh2rVr7N+/n08++QRPT88sl39dsmQJ3t7eqNVqnJyc+PDDD7X7Hj58yPvvv4+DgwOmpqbUrVuXP//8M1PdZZg7dy7Vq1fXPs/I2V988QXOzs54eXkBsHz5cho1aoSlpSWOjo707t1bu5RWhrNnz9K5c2esrKywtLSkefPmREVF8Z///AdjY2NiY2N1yo8aNYrmzZvnWidClGWGBip6NtY0psOzmFDtr1O3eZqahptdBRpUsynm6AqfNLrzSVEUnqQ808um5HE24Pv377NlyxaGDx9OhQoVMu23sbHReV6jRg2GDh3KhAkTSE9Pz/HYM2fOZO3atRw9ejTPdZaTJ0+eMGvWLH7++WfOnj2Lvb09CQkJDBgwgL1793Lw4EE8PDzo1KkTCQnZT7QA8OmnnxISEkJkZCSenp4EBQXx7NmzHN+zePFigoKCMDY2JigoiMWLF+vsnzBhAjNnzmTixImcO3eO8PBwHBwcAM0XopYtW3Lr1i02btzIyZMnGTduXK51+KKlS5diYmLCvn37+PHHHwEwMDBg3rx5nD17lqVLl7Jz507GjRunfU9kZCRt2rShTp06HDhwgL1799KlSxfS0tLo3r07aWlpOl9y7t69y19//cWgQYPyFZsQBWJhB/03gm8vUNLgr7Gw6WNIy/n3UpRe3t7exMTEaLe9e/fq7B88eLDO/tmzZxdpPPrK2XnN1wBGRkb079+fsLAwnfetWbOGtLQ0goKCiqJqsvTJJ58wc+ZMzp8/j6+vL4mJiXTq1ImIiAhOnDhBhw4d6NKlC9HROc84PHXqVHr06MGpU6fo1KkTffr04f79nOd3CA0N5Y033sDa2pq+fftmyscLFy5k+PDhDBkyhNOnT7Nx40Zq1qwJaC6Qd+zYkX379vHLL79w7tw5Zs6cme9lriIiIrh48SLbt2/XNthTU1OZNm0aJ0+eZMOGDVy/fl1n+P6tW7do0aIFarWanTt3cuzYMQYNGsSzZ89o0aIFbm5uLF++XFs+NTWVFStWSD4WAujRyAUDFRy+dp8rd3U7l1YfvQlA94YuZWJSvxI9vLwkepqaRp1JW/Vy7nOfB2Bukvt/2ZUrV1AURaeHITefffYZoaGhrFixIsfe0AYNGtCjRw/Gjx+vc2/4y0pNTWXBggXUq1dP+9rrr7+uU+ann37CxsaG3bt307lz52yPFRISwhtvvAFoEr63tzdXrlzJth7i4+P57bfftD3iffv2pXnz5nz33XdYWFiQkJDAd999xw8//MCAAQMAcHd357XXXgMgPDyce/fuceTIEW1PQ8YXgPzw8PDI9MVz1KhR2sfVq1dn+vTpDB06lAULFgAwe/ZsGjVqpH0Omi+7GXr37k1oaCjdu3cH4JdffqFatWo6vexCFAtjU3jrR7DzhIjP4fBPEHcF3gkFMxt9RycKWcZopuyYm5sX6u1BudFXzs5rvs4waNAgvvrqK3bv3q39Ox0aGkpgYGCWvdZF5fPPP6ddu3ba57a2tjr5edq0aaxfv56NGzfq9DK/aODAgdqLBV9++SXz5s3j8OHD2d67np6eTlhYGN9//z0AvXr1YuzYsVy7do0aNWoAMH36dMaOHctHH32kfV/jxo0B2LFjB4cPH+b8+fN4enoC4Obmlu/PX6FCBX7++WedYeXPN47d3NyYN28ejRs3JjExEQsLC+bPn4+1tTWrVq3C2NgYQBsDaG4fCA0N1d6+9scff5CUlESPHj3yHZ8QZY2jtSmv13Jgx/k7rDoczWed6wAQdS+RY/99gKGBisAGVXI5SukgPd1lUH6usGews7MjJCSESZMmkZKSkmPZ6dOns2fPHrZt2/ayIWqZmJhkmm3+zp07DB48GA8PD6ytrbGysiIxMTHXK+vPHydj2bgXh4A9b+XKlbi7u2u/ULzyyiu4urry66+/AnD+/HmSk5Np06ZNlu+PjIykfv362gb3y2rYsGGm13bs2EGbNm2oUqUKlpaW9OvXj7i4OJ48eaI9d3ZxgaY3adu2bdy6dQuAsLAwBg4cWCauFIpSSKWC5mOhx3IwNoeonbC4HcRF6TsyUcguX76Ms7Mzbm5u9OnTJ9Pf7RUrVlC5cmXq1q3LhAkTtH/TspOcnEx8fLzOVhbVqlWLpk2bsmTJEkBz8XzPnj3aoeXFpVGjRjrPExMTCQkJoXbt2tjY2GBhYcH58+fzlY8rVKiAlZVVjvl4+/btPH78mE6dOgFQuXJl2rVrp62Pu3fvcvv27RzzcdWqVXUauy/Dx8cn033cx44do0uXLlSrVg1LS0tatmwJoK2DyMhImjdvrm1wv2jgwIFcuXJFe2tfWFgYPXr0yHIkohDlUW8/zRDz5ydU++2Yppe7pacd9lb6m9uhMElPdz6ZGRty7vMAvZ07Lzw8PFCpVDqTpeXFmDFjWLBggU7vaVbc3d0ZPHgwn3zySabhX/llZmaWqSE4YMAA4uLi+O6773B1dUWtVuPv75/rxYDnE17GMXMa6r148WLOnj2rM1FMeno6S5YsITg4OMuJaF6MPScGBgaZLoCkpmZet/jFxHv9+nU6d+7MsGHD+OKLL7C1tWXv3r0EBweTkpKCubl5rueuX78+9erVY9myZbRv356zZ8/y119/5fgeIYpcnTehoiusDIJ/LmkmWOv5C1R/Td+RiULg5+dHWFgYXl5exMTEMHXqVJo3b86ZM2ewtLSkd+/euLq64uzszKlTpxg/fjwXL17M8t7dDDNmzGDq1KkvHZO+cnZe8/XzgoODGTFiBPPnzyc0NBR3d3dtA6+4vJiPQkJC2L59O19//TU1a9bEzMyMd955J1/5GDQ5Obd8fP/+fZ3clp6ezqlTp5g6dare8vHjx48JCAggICCAFStWYGdnR3R0NAEBAdo6yO3c9vb2dOnShdDQUGrUqMHmzZvZtWtXju8Rojxp6WmPk7UpMY+S2Ho2ls6+zqw7njG0vHSvzf08aXTnk0qlyteQMX2wtbUlICCA+fPnM3LkyExJ5OHDh5nu6wbNzKUTJ05kypQpvPnmmzmeY9KkSbi7u+c4w/zL2rdvHwsWLNBe8b5x4wb//PNPoZ7j9OnTHD16lF27dun0VN+/f59WrVpx4cIFPDw8MDMzIyIigvfeey/TMXx9ffn555+5f/9+lr3ddnZ2nDlzRue1yMjIbK+GZzh27Bjp6enMmTNHO5P76tWrM507IiIixy+j7733HnPnzuXWrVu0bdsWF5fSP/OjKAOc6mkmWFsZBLePw7Ju0PkbaNBf35GJAnp+GUdfX1/8/PxwdXVl9erVBAcH6ywr5ePjg5OTE23atCEqKgp3d/csjzlhwgTGjBmjfR4fH5+vv2WlIWdn6NGjBx999BHh4eEsW7aMYcOG6X100r59+xg4cCBvvfUWoOn5zmqJ0IKIi4vj999/Z9WqVTq3SaWlpfHaa6+xbds2OnToQPXq1YmIiKB168yrIPj6+nLz5k0uXbqUZW+3nZ0dsbGxKIqirdPIyMhcY7tw4QJxcXHMnDlT+3P34pw2vr6+LF26lNTU1Gzz+3vvvUdQUBBVq1bF3d2dZs2a5XpuIcqLjAnV5u64zMrD0ViZGXMnPpmK5sa0qe2g7/AKjQwvL6Pmz59PWloaTZo0Ye3atVy+fJnz588zb948/P39s33fkCFDsLa2Jjw8PMfjOzg4MGbMGObNm1fYoePh4cHy5cs5f/48hw4dok+fPrleSc6vxYsX06RJE1q0aEHdunW1W4sWLWjcuDGLFy/WLqU2btw4li1bRlRUFAcPHtT27gcFBeHo6Ei3bt3Yt28fV69eZe3atdp7xF9//XWOHj3KsmXLuHz5MpMnT87UCM9KzZo1SU1N5fvvv+fq1assX75cO8FahgkTJnDkyBE++OADTp06xYULF1i4cKHOxYnevXtz8+ZNFi1aJBO2iJLF0hHe3QTeb0N6KmwcAVs/hfSs1+kUpZONjQ2enp5cuXIly/1+fn4A2e4HUKvVWFlZ6WxllYWFBT179mTChAnExMRkudZ2cfPw8GDdunVERkZy8uRJevfune/JQnOzfPlyKlWqRI8ePXTycb169ejUqZM2506ZMoU5c+Ywb948Ll++zPHjx7X3gLds2ZIWLVoQGBjI9u3buXbtGps3b2bLli2AZub8e/fuMXv2bKKiopg/fz6bN2/ONbZq1aphYmKizccbN25k2rRpOmU+/PBD4uPj6dWrF0ePHuXy5cssX75cZzm2gIAArKysmD59Ou+++25hVZ0QZUbGhGoHr95n7vZLAHR9pQomRmWnqVp2PonQ4ebmxvHjx2ndujVjx46lbt26tGvXjoiICBYuXJjt+4yNjZk2bRpJSUm5niMkJERnXc/CsnjxYh48eECDBg3o168fI0eOxN7evtCOn5KSwi+//EJgYGCW+wMDA1m2bBmpqalMnDiRsWPHMmnSJGrXrk3Pnj2196WZmJiwbds27O3t6dSpEz4+PjqzpQYEBDBx4kTGjRtH48aNSUhIoH//3Hvz6tWrxzfffMOsWbOoW7cuK1asYMaMGTplPD092bZtGydPnqRJkyb4+/vz+++/6wyVt7a2JjAwEAsLi0xLlwmhd8Zm8M4SaDVB8/zAD7CqNyTnvEqBKD0SExOJiorSzrHxooyexuz2l0fBwcE8ePCAgIAAnJ2d9R0O33zzDRUrVqRp06Z06dKFgIAAGjRoUKjnWLJkCW+99VaWvfqBgYFs3LiRf/75hwEDBjB37lwWLFiAt7c3nTt35vLly9qya9eupXHjxgQFBVGnTh3GjRunXcu8du3aLFiwgPnz51OvXj0OHz5MSEhIrrFlLOW2Zs0a6tSpw8yZM/n66691ylSqVImdO3dqVzRp2LAhixYt0un1NjAwYODAgaSlpeXpe4AQ5Y2zjRmtvTTf9U/efARA90ZlZ2g5gEp5mVm3ypj4+Hisra159OhRpqvoSUlJ2tkzTU3Lxo38ovxo06YN3t7eRTIioajI71w5dGYtbPgAniWBfR0IWqW597scyykvlVQhISF06dIFV1dXbt++zeTJk4mMjOTcuXPEx8cTHh5Op06dqFSpEqdOnWL06NFUrVqV3bt35/kckq9FaRUcHMy9e/fytGZ5SSG/U6I47Th3h/eWaW7fqONkxaaPSsda9nnN16XjRichRL48ePCAXbt2sWvXrlwnxhNC7+oGgk11WBUEd8/Boteh1wqo9qq+IxP5cPPmTYKCgoiLi8POzo7XXnuNgwcPYmdnR1JSEjt27GDu3Lk8fvwYFxcXAgMD+eyzz/QdthBF6tGjR5w+fZrw8PBS1eAWori18rLD0cqU2PikMtfLDdLoFqJMql+/Pg8ePGDWrFl4eXnpOxwhcle1IQz+G1b2gthTsLQLvPk91Oul78hEHuU0saaLi0u+erSFKCu6du3K4cOHGTp0qM4a6EIIXUaGBszt9Qq7L90jqEk1fYdT6KTRLUQZVNizywpRLKyrwKAtsP59OP+H5t97F+H1iWAgU5AIIUofWR5MiLx71a0Sr7pV0ncYRUK+xQghhCg5TCpA92XQfKzm+d5vYHU/SHms37iEEEIIIV6SNLqFEEKULAYG0GYSvPVvMDSBC3/CkgB4dFPfkQkhhBBC5Js0uoUQQpRM9XrBgD/BvDLEntZMsHbzmL6jEkIIIYTIF2l0CyGEKLmq+cGQvzVLiSXegbBOmiXGhBBCCCFKCWl0CyGEKNlsqkHwNvDsoFnL+7dB8PcMUBR9RyaEEEIIkStpdAshhCj51JbQKxyajtA83z0TfnsXUp/qNy4hhBBCiFxIo1uUCtWrV2fu3Lna5yqVig0bNmRb/vr166hUKiIjIwt03sI6jhCiEBgYQvvpmvW7DYzg7HoI7QQJsfqOTIgiN2XKFF555RV9h0GrVq0YNWqU9vmL+TkrueXsvCqs4wghRHGTRncZFhsby4gRI3Bzc0OtVuPi4kKXLl2IiIjQlqlevToqlYqDBw/qvHfUqFG0atVK+3zKlCmoVCqGDh2qUy4yMhKVSpXtutA+Pj6Z3pNh+fLlqNVq/vnnn3x/tpiYGDp27Jjv9+Vk4MCBdOvWTec1FxcXYmJiqFu3bqGeKzs3b97ExMSk2M4nRKnUoD/0/x3MKsLt4/BTa4g5qe+ohHgpXbp0oUOHDlnu27NnDyqVilOnThESEqLN3xm5O7tt4MCBL32e/Dpy5AhDhgzJ9/tykt0FhqLI/dl5+vQptra2VK5cmeTk5GI5pxCi7JJGdxl1/fp1GjZsyM6dO/nqq684ffo0W7ZsoXXr1gwfPlynrKmpKePHj8/1mKampixevJjLly/nOY7g4GBWrVrF06eZh4CGhoby5ptvUrly5TwfL4OjoyNqtTrf78svQ0NDHB0dMTIyKvJzAYSFhdGjRw/i4+M5dOhQsZwzO2lpaaSnp+s1BiGyVf01GLwTKntCwm1Y0gHObdR3VELkW3BwMNu3b+fmzcxL4oWGhtKoUSN8fX2xsLCgUqVKgKahGxMTQ0xMDGvXaiYWvHjxova177777qXPk192dnaYm5vn+30vo7hyP8DatWvx9vamVq1aeu9dVxSFZ8+e6TUGIUTBSKO7jPrggw9QqVQcPnyYwMBAPD098fb2ZsyYMZl6tYcMGcLBgwfZtGlTjsf08vKidevWfPrpp3mOo2/fvjx9+lT7pSDDtWvX2LVrF8HBwURFRdG1a1ccHBywsLCgcePG7NixI8fjvjjE7PDhw9SvXx9TU1MaNWrEiRMndMqnpaURHBxMjRo1MDMzw8vLS+dLyZQpU1i6dCm///67tqdg165dWQ4v3717N02aNEGtVuPk5MQnn3yikwxbtWrFyJEjGTduHLa2tjg6OjJlypRc60pRFEJDQ+nXrx+9e/dm8eLFmcrs27ePVq1aYW5uTsWKFQkICODBgwcApKenM3v2bGrWrIlaraZatWp88cUXAOzatQuVSsXDhw+1x3pxlEJYWBg2NjZs3LiROnXqoFariY6O5siRI7Rr147KlStjbW1Ny5YtOX78uE5cDx8+5P3338fBwQFTU1Pq1q3Ln3/+yePHj7GysuK3337TKb9hwwYqVKhAQkJCrvUiRLZs3eC9HeDeBlKfwOp+sGeOTLAmSpXOnTtjZ2dHWFiYzuuJiYmsWbOG4OBgQLf3187ODkdHRxwdHbG1tQXA3t5e+5q1tfVLnScuLo6goCCqVKmCubk5Pj4+rFy5Msf4XxxefvnyZVq0aIGpqSl16tRh+/btmd4zfvx4PD09MTc3x83NjYkTJ5KamgpoctHUqVM5efKkNh9nxPxi7j99+jSvv/46ZmZmVKpUiSFDhpCYmKjdnzGC7euvv8bJyYlKlSoxfPhw7blysnjxYvr27Uvfvn2zzMdnz56lc+fOWFlZYWlpSfPmzYmKitLuX7JkCd7e3trvCh9++CGQ9W1rDx8+1H7vgP/P2Zs3b6Zhw4ao1Wr27t2bp+9LycnJjB8/HhcXF9RqNTVr1mTx4sUoikLNmjX5+uuvdcpnfBe4cuVKrnUihHh50ujOL0WBlMf62fL4RfL+/fts2bKF4cOHU6FChUz7bWxsdJ7XqFGDoUOHMmHChFx7NmfOnMnatWs5evRonmKpXLkyXbt2ZcmSJTqvh4WFUbVqVdq3b09iYiKdOnUiIiKCEydO0KFDB7p06UJ0dHSezpGYmEjnzp2pU6cOx44dY8qUKYSEhOiUSU9Pp2rVqqxZs4Zz584xadIk/vWvf7F69WoAQkJC6NGjBx06dND2FDRt2jTTuW7dukWnTp1o3LgxJ0+eZOHChSxevJjp06frlFu6dCkVKlTg0KFDzJ49m88//zzLLx7P+/vvv3ny5Alt27alb9++rFq1isePH2v3R0ZG0qZNG+rUqcOBAwfYu3cvXbp0IS0tDYAJEyYwc+ZMJk6cyLlz5wgPD8fBwSFPdZjhyZMnzJo1i59//pmzZ89ib29PQkICAwYMYO/evRw8eBAPDw86deqkbTCnp6fTsWNH9u3bxy+//MK5c+eYOXMmhoaGVKhQgV69ehEaGqpzntDQUN555x0sLS3zFZ8QmZhaQ+/V0OR9zfOIz2H9+5CapN+4RMmgr5ydjws/RkZG9O/fn7CwMJTn3rdmzRrS0tIICgoqlKrIy3mSkpJo2LAhf/31F2fOnGHIkCH069ePw4cP5+kc6enpvP3225iYmHDo0CF+/PHHLEfSWVpaEhYWxrlz5/juu+9YtGgR3377LQA9e/Zk7NixeHt7a/Nxz549Mx3j8ePHBAQEULFiRY4cOcKaNWvYsWOHtnGb4e+//yYqKoq///6bpUuXEhYWlunCw4uioqI4cOAAPXr0oEePHuzZs4f//ve/2v23bt2iRYsWqNVqdu7cybFjxxg0aJD2AvzChQsZPnw4Q4YM4fTp02zcuJGaNWvmqQ6f98knnzBz5kzOnz+Pr69vnr4v9e/fn5UrVzJv3jzOnz/Pv//9bywsLFCpVAwaNCjLfNyiRYuXik8IkXfFM2a2LEl9Al866+fc/7oNJpkb0S+6cuUKiqJQq1atPB/6s88+IzQ0lBUrVtCvX79syzVo0IAePXowfvx4nXvDcxIcHEzHjh25du0aNWrUQFEUli5dyoABAzAwMKBevXrUq1dPW37atGmsX7+ejRs3ZkqeWQkPDyc9PZ3FixdjamqKt7c3N2/eZNiwYdoyxsbGTJ06Vfu8Ro0aHDhwgNWrV9OjRw8sLCwwMzMjOTkZR0fHbM+1YMECXFxc+OGHH1CpVNSqVYvbt28zfvx4Jk2ahIGB5jqWr68vkydPBsDDw4MffviBiIgI2rVrl+2xFy9eTK9evTA0NKRu3bq4ubmxZs0a7b15s2fPplGjRixYsED7Hm9vbwASEhL47rvv+OGHHxgwYAAA7u7uvPbaa7nW3/NSU1NZsGCBzv/H66+/rlPmp59+wsbGht27d9O5c2d27NjB4cOHOX/+PJ6engC4ublpy7/33ns0bdqUmJgYnJycuHv3Lps2bcp1NIMQeWZoBJ1mg50nbBoHp36FB9eh5wqwsNN3dEKf9JWz85ivMwwaNIivvvqK3bt3a+dTCQ0NJTAwMMte65eV23msra11LlqPGDGCrVu3snr1apo0aZLr8Xfs2MGFCxfYunUrzs6aev/yyy8z3Yf92WefaR9Xr16dkJAQVq1axbhx4zAzM8PCwgIjI6Mc83F4eDhJSUksW7ZM28Hwww8/0KVLF2bNmqW96FyxYkV++OEHDA0NqVWrFm+88QYREREMHjw422MvWbKEjh07UrFiRQACAgIIDQ3VjlqbP38+1tbWrFq1CmNjYwBt/gOYPn06Y8eO5aOPPtK+1rhx41zr70Wff/65zvcGW1vbHL8vXbp0idWrV7N9+3batm0L6ObjgQMHMmnSJA4fPkyTJk1ITU0lPDw8U++3EKLwSU93GaS8xNBKOzs7QkJCmDRpEikpKTmWnT59Onv27GHbtm15Ona7du2oWrWq9upqREQE0dHRvPvuu4CmpzokJITatWtjY2ODhYUF58+fz3NPd8YVYFNTU+1r/v7+mcrNnz+fhg0bYmdnh4WFBT/99FOez/H8ufz9/VGpVNrXmjVrRmJios59ci/eF5fR2MzOw4cPWbduHX379tW+9uKQtoye7uziSk5OznZ/XpmYmGSK/c6dOwwePBgPDw+sra2xsrIiMTFRW3eRkZFUrVpV5wvH85o0aYK3tzdLly4F4JdffsHV1ZUWLVoUKFYhMmn8HvRdq+n9vnEIFr0Od87qOyohclWrVi2aNm2qHRV25coV9uzZox1aXlznSUtLY9q0afj4+GBra4uFhQVbt27NVz52cXHRNrgh63z866+/0qxZMxwdHbGwsOCzzz57qXxcr149nRF9zZo1Iz09nYsXL2pf8/b2xtDQUPs8t3yclpbG0qVLM+XjsLAw7WjAyMhImjdvrm1wP+/u3bvcvn27wPkYoFGjRjrPc/u+FBkZiaGhIS1btszyeM7Ozrzxxhva//8//viD5ORkunfvXuBYhRA5k57u/DI211zB1te588DDwwOVSsWFCxfydfgxY8awYMECnZ7UrLi7uzN48GA++eSTLO9zepGBgQEDBw5k6dKlTJkyhdDQUFq3bq29+hoSEsL27dv5+uuvqVmzJmZmZrzzzju5Nv7zY9WqVYSEhDBnzhz8/f2xtLTkq6++KrLJyl5MxCqVKseh+xlX7P38/LSvKYpCeno6ly5dwtPTEzMzs2zfn9M+QNsD//wFmazuaTMzM9O5oAAwYMAA4uLi+O6773B1dUWtVuPv76/9/8nt3KDp7Z4/fz6ffPIJoaGhvPvuu5nOI0ShcG8N70VAeE+4HwWL20PgYvDKetZmUcbpK2fnMV8/Lzg4mBEjRjB//nxCQ0Nxd3fPtvFUEDmd56uvvuK7775j7ty5+Pj4UKFCBUaNGlWo+fjAgQP06dOHqVOnEhAQoO0xnjNnTqGd43n5zcdbt27l1q1bmYa0p6WlaUesFUc+BjLdIpjb96W85uN+/frx7bffEhoaSs+ePYttIjwhyjNpdOeXSpWvIWP6YGtrS0BAAPPnz2fkyJGZ/mg/fPgw033dABYWFkycOJEpU6bw5ptv5niOSZMm4e7uzqpVq/IU07vvvsv06dNZt24d69ev5+eff9bu27dvHwMHDuStt94CNFdys1uCLCu1a9dm+fLlJCUlaXu7X5wsbt++fTRt2pQPPvhA+9rzE56Appc34/7onM61du1aFEXRNhr37duHpaUlVatWzXPML1q8eDFjx47NtMzLBx98wJIlS5g5cya+vr5EREToDJPP4OHhgZmZGREREbz33nuZ9tvZaYbYxsTEaIfL5XXt8X379rFgwQI6deoEwI0bN3SWefP19eXmzZvaiwNZ6du3L+PGjWPevHmcO3dOOwReiCJR2UMzwdrq/nB9D6zsBa5NQVUMg7teGw01C97DJQpJKcjZGXr06MFHH31EeHg4y5YtY9iwYUVycTKn8+zbt4+uXbtqe3kzLvzWqVMnT8euXbs2N27c0N5OBJnz8f79+3F1ddWZlPX5+6Uh7/k4LCyMx48fa7/n7Nu3DwMDA7y8vPIUb1YybvV6cdLYL774gsWLF9OuXTt8fX1ZunQpqampmRr1lpaWVK9enYiICFq3bp3p+M/n4/r16wP5y8c5fV/y8fEhPT2d3bt3a4eXv6hTp05UqFCBhQsXsmXLFv7zn//k6dxCiIKR4eVl1Pz580lLS6NJkyasXbuWy5cvc/78eebNm5flUK8MQ4YMwdramvDw8ByP7+DgwJgxY5g3b16e4qlRowavv/46Q4YMQa1W8/bbb2v3eXh4sG7dOiIjIzl58iS9e/fO11JVvXv3RqVSMXjwYM6dO8emTZsy3Z/k4eHB0aNH2bp1K5cuXWLixIkcOXJEp0z16tU5deoUFy9e5J9//snyyvMHH3zAjRs3GDFiBBcuXOD3339n8uTJjBkzRnv1Or8iIyM5fvw47733HnXr1tXZgoKCWLp0Kc+ePWPChAkcOXKEDz74gFOnTnHhwgUWLlzIP//8o132bdy4cSxbtoyoqCgOHjyoHYlQs2ZNXFxcmDJlCpcvX+avv/7Kc6+Ch4cHy5cv5/z58xw6dIg+ffroXE1v2bIlLVq0IDAwkO3bt3Pt2jU2b97Mli1btGUqVqzI22+/zccff0z79u0LdIFCiDwxt4V+66HhQECB/+7TNMCLekvMftiqEDmxsLCgZ8+eTJgwgZiYmCzX2i7q83h4eLB9+3b279/P+fPnef/997lz506ej922bVs8PT0ZMGAAJ0+eZM+ePZkarx4eHkRHR7Nq1SqioqKYN28e69ev1ylTvXp1rl27RmRkJP/880+W62T36dMHU1NTBgwYwJkzZ/j7778ZMWIE/fr1y/ckohnu3bvHH3/8wYABAzLl4/79+7Nhwwbu37/Phx9+SHx8PL169eLo0aNcvnyZ5cuXa4e1T5kyhTlz5jBv3jwuX77M8ePH+f777wFNb/Srr76qnSBt9+7dOve45yS370vVq1dnwIABDBo0iA0bNmhXismYNBY0S6EOHDiQCRMm4OHhkeN3QiFE4ZFGdxnl5ubG8ePHad26NWPHjqVu3bq0a9eOiIgIFi5cmO37jI2NmTZtGklJuc/8GxISgoWFRZ5jCg4O5sGDB/Tu3Vvn/utvvvmGihUr0rRpU7p06UJAQAANGjTI83EtLCz4448/OH36NPXr1+fTTz9l1qxZOmXef/993n77bXr27Imfnx9xcXE6vd4AgwcPxsvLi0aNGmFnZ8e+ffsynatKlSps2rSJw4cPU69ePYYOHUpwcHCeE2ZWFi9eTJ06dbKc+O6tt97STjzm6enJtm3bOHnyJE2aNMHf35/ff/9du4b4xIkTGTt2LJMmTaJ27dr07NlTe9+asbExK1eu5MKFC/j6+jJr1qxMM67nFN+DBw9o0KAB/fr1Y+TIkdjb2+uUWbt2LY0bNyYoKIg6deowbty4TL0UwcHBpKSkMGjQoJepJiHyz9AYOs+FQVvhnSXFs7lmXvVAiLzKyJMBAQE690UX13k+++wzGjRoQEBAAK1atcLR0ZFu3brl+bgGBgasX7+ep0+f0qRJE9577z3t0pUZ3nzzTUaPHs2HH37IK6+8wv79+5k4caJOmcDAQDp06EDr1q2xs7PLctkyc3Nztm7dyv3792ncuDHvvPMObdq04YcffshfZTwnY1K2rO7HbtOmDWZmZvzyyy9UqlSJnTt3kpiYSMuWLWnYsCGLFi3S9noPGDCAuXPnsmDBAry9vencuTOXL1/WHmvJkiU8e/aMhg0bMmrUqDzn47x8X1q4cCHvvPMOH3zwAbVq1WLw4ME6K6HA/+fjjLl1hBBFT6W8zKxbxWTKlCmZhtJ6eXlp71VOSkpi7NixrFq1iuTkZAICAliwYEG+r3DGx8djbW3No0ePsLKy0tmXlJSknXX7+YaiECJ/li9fzujRo7l9+zYmJibZlpPfOSFyzkvlmeRrIQpuz549tGnThhs3buT4nVl+p4TIXV7zdYnv6X5+ncaYmBj27t2r3Td69Gj++OMP1qxZw+7du7l9+7bOsGUhhP49efKEqKgoZs6cyfvvv59jg1sIIYQQRSM5OZmbN28yZcoUunfv/tLD8IUQ+VfiG90Z6zRmbJUrVwbg0aNHLF68mG+++YbXX3+dhg0bEhoayv79+zNN2iGE0J/Zs2dTq1YtHB0dmTBhgr7DEUIIIcqllStX4urqysOHD5k9e7a+wxGiXCnxje7Lly/j7OyMm5sbffr00a5FeOzYMVJTU3VmZ6xVqxbVqlXjwIEDOR4zOTmZ+Ph4nU0IUTSmTJlCamoqERER+ZoDQAghhBCFZ+DAgaSlpXHs2DGqVKmi73CEKFdKdKPbz8+PsLAwtmzZwsKFC7l27RrNmzcnISGB2NhYTExMMi195eDgQGxsbI7HnTFjBtbW1trNxcWlCD+FEEIIIYQQQojyqkSv092xY0ftY19fX/z8/HB1dWX16tU6Sxbl14QJExgzZoz2eXx8vDS8hRBCCCGEEEIUuhLd0/0iGxsbPD09uXLlCo6OjqSkpPDw4UOdMnfu3MHR0THH46jVaqysrHS23JTgSd6FKFPkd00IURDyN0SIwiG/S0IUnlLV6E5MTCQqKgonJycaNmyIsbExERER2v0XL14kOjoaf3//QjunoaEhACkpKYV2TCFE9p48eQKgXe9UCCHyIuNvRsbfECFEwUg+FqLwlOjh5SEhIXTp0gVXV1du377N5MmTMTQ0JCgoCGtra4KDgxkzZgy2trZYWVkxYsQI/P39efXVVwstBiMjI8zNzbl37x7GxsYYGJSq6xRClBqKovDkyRPu3r2LjY2N9oKXEELkhaGhITY2Nty9excAc3NzVCqVnqMSovSRfCxE4SvRje6bN28SFBREXFwcdnZ2vPbaaxw8eBA7OzsAvv32WwwMDAgMDCQ5OZmAgAAWLFhQqDGoVCqcnJy4du0a//3vfwv12EKIzGxsbHK9RUQIIbKS8bcjo+EthHh5ko+FKDwqRW7YID4+Hmtrax49epTt/d3p6ekyxFyIImZsbCxX1IUgb3mpPMprvaSlpZGamlqMkQlRtkg+FiJv8pqXSnRPd0liYGCAqampvsMQQgghSqQpU6YwdepUnde8vLy4cOECAElJSYwdO5ZVq1bpjE5zcHAo9FgMDQ2lwSCEEKLEkBuUhRBCCFEovL29iYmJ0W579+7V7hs9ejR//PEHa9asYffu3dy+fZu3335bj9EKIYQQxUN6uoUQQghRKIyMjLK8B/TRo0csXryY8PBwXn/9dQBCQ0OpXbs2Bw8eLNQJUIUQQoiSRnq6hRBCCFEoLl++jLOzM25ubvTp04fo6GgAjh07RmpqKm3bttWWrVWrFtWqVePAgQP6ClcIIYQoFtLTjWZpBNDcCC+EEELoW0Y+Kk1znfr5+REWFoaXlxcxMTFMnTqV5s2bc+bMGWJjYzExMcHGxkbnPQ4ODsTGxmZ7zOTkZJKTk7XPHz16BEi+FkIIUTLkNV9LoxtISEgAwMXFRc+RCCGEEP8vISEBa2trfYeRJx07dtQ+9vX1xc/PD1dXV1avXo2ZmdlLHXPGjBmZJmcDyddCCCFKltzytTS6AWdnZ27cuIGlpSUqlapYzhkfH4+Liws3btyQ5WBegtTfy5O6Kxipv4KR+ssbRVFISEjA2dlZ36G8NBsbGzw9Pbly5Qrt2rUjJSWFhw8f6vR237lzJ8d1gCdMmMCYMWO0z9PT07l//z6VKlWSfF1KSP0VjNRfwUj9FYzUX+7ymq+l0Y1mObCqVavq5dxWVlbyQ1wAUn8vT+quYKT+CkbqL3elpYc7O4mJiURFRdGvXz8aNmyIsbExERERBAYGAnDx4kWio6Px9/fP9hhqtRq1Wq3z2otD1IuL/MwWjNRfwUj9FYzUX8FI/eUsL/laGt1CCCGEKLCQkBC6dOmCq6srt2/fZvLkyRgaGhIUFIS1tTXBwcGMGTMGW1tbrKysGDFiBP7+/jJzuRBCiDJPGt1CCCGEKLCbN28SFBREXFwcdnZ2vPbaaxw8eBA7OzsAvv32WwwMDAgMDCQ5OZmAgAAWLFig56iFEEKIoieNbj1Rq9VMnjw507A5kTdSfy9P6q5gpP4KRuqv7Fq1alWO+01NTZk/fz7z588vpogKh/zMFozUX8FI/RWM1F/BSP0VHpVSmtYjEUIIIYQQQgghShEDfQcghBBCCCGEEEKUVdLoFkIIIYQQQgghiog0uoUQQgghhBBCiCIije6XNGPGDBo3boylpSX29vZ069aNixcv6pRp1aoVKpVKZxs6dKhOmejoaN544w3Mzc2xt7fn448/5tmzZzpldu3aRYMGDVCr1dSsWZOwsLCi/nhFLi/1l5SUxPDhw6lUqRIWFhYEBgZy584dnTLltf7+85//0KVLF5ydnVGpVGzYsEFn/8CBAzP97HXo0EGnzP379+nTpw9WVlbY2NgQHBxMYmKiTplTp07RvHlzTE1NcXFxYfbs2UX90YpFbvWnKAqTJk3CyckJMzMz2rZty+XLl3XKlOf6e9GUKVMy/bzVqlVLu7+wfpeFeFmSswtGcnbBSM4uGMnZhUtytp4o4qUEBAQooaGhypkzZ5TIyEilU6dOSrVq1ZTExERtmZYtWyqDBw9WYmJitNujR4+0+589e6bUrVtXadu2rXLixAll06ZNSuXKlZUJEyZoy1y9elUxNzdXxowZo5w7d075/vvvFUNDQ2XLli3F+nkLW17qb+jQoYqLi4sSERGhHD16VHn11VeVpk2baveX5/rbtGmT8umnnyrr1q1TAGX9+vU6+wcMGKB06NBB52fv/v37OmU6dOig1KtXTzl48KCyZ88epWbNmkpQUJB2/6NHjxQHBwelT58+ypkzZ5SVK1cqZmZmyr///e/i+IhFKrf6mzlzpmJtba1s2LBBOXnypPLmm28qNWrUUJ4+faotU57r70WTJ09WvL29dX7e7t27p91fGL/LQhSE5OyCkZxdMJKzC0ZyduGSnK0f0uguJHfv3lUAZffu3drXWrZsqXz00UfZvmfTpk2KgYGBEhsbq31t4cKFipWVlZKcnKwoiqKMGzdO8fb21nlfz549lYCAgML9AHr2Yv09fPhQMTY2VtasWaMtc/78eQVQDhw4oCiK1F+G7BJ4165ds33PuXPnFEA5cuSI9rXNmzcrKpVKuXXrlqIoirJgwQKlYsWK2rpUFEUZP3684uXlVajx69uL9Zeenq44OjoqX331lfa1hw8fKmq1Wlm5cqWiKFJ/L5o8ebJSr169LPcV1u+yEIVJcnbBSM5+eZKzC0ZydsFJztYPGV5eSB49egSAra2tzusrVqygcuXK1K1blwkTJvDkyRPtvgMHDuDj44ODg4P2tYCAAOLj4zl79qy2TNu2bXWOGRAQwIEDB4rqo+jFi/V37NgxUlNTdT57rVq1qFatmvazS/3lbNeuXdjb2+Pl5cWwYcOIi4vT7jtw4AA2NjY0atRI+1rbtm0xMDDg0KFD2jItWrTAxMREWyYgIICLFy/y4MGD4vsgxezatWvExsbq/NxYW1vj5+en87Mn9afr8uXLODs74+bmRp8+fYiOjgYK73dZiMIkObtgJGcXPsnZL0dy9suRnF38jPQdQFmQnp7OqFGjaNasGXXr1tW+3rt3b1xdXXF2dubUqVOMHz+eixcvsm7dOgBiY2N1fmAB7fPY2Ngcy8THx/P06VPMzMyK8qMVi6zqLzY2FhMTE2xsbHTKOjg45Fo3GftyKlOW6i8rHTp04O2336ZGjRpERUXxr3/9i44dO3LgwAEMDQ2JjY3F3t5e5z1GRkbY2trq1F2NGjV0yjxfvxUrViyeD1PMMj5/Vj83z9eN1N//8/PzIywsDC8vL2JiYpg6dSrNmzfnzJkzhfa7LERhkZxdMJKzC5/k7JcnOTv/JGfrhzS6C8Hw4cM5c+YMe/fu1Xl9yJAh2sc+Pj44OTnRpk0boqKicHd3L+4wS6zs6k+8vF69emkf+/j44Ovri7u7O7t27aJNmzZ6jEyURR07dtQ+9vX1xc/PD1dXV1avXl1mvySL0ktydsFIzi58krNFcZKcrR8yvLyAPvzwQ/7880/+/vtvqlatmmNZPz8/AK5cuQKAo6NjptkAM547OjrmWMbKyqpM/GJkV3+Ojo6kpKTw8OFDnfJ37tzJtW4y9uVUpqzUX165ublRuXJlnZ+9u3fv6pR59uwZ9+/fz1f9lkUZny2rz/583Uj9Zc/GxgZPT0+uXLlSaL/LQhQGydkFIzm7eEjOzjvJ2QUnObt4SKP7JSmKwocffsj69evZuXNnpiEpWYmMjATAyckJAH9/f06fPq3zh2D79u1YWVlRp04dbZmIiAid42zfvh1/f/9C+iT6kVv9NWzYEGNjY53PfvHiRaKjo7WfvTzXX37dvHmTuLg4nZ+9hw8fcuzYMW2ZnTt3kp6erv2i6e/vz3/+8x9SU1O1ZbZv346Xl1eZGmb1oho1auDo6KjzcxMfH8+hQ4d0fvak/rKXmJhIVFQUTk5Ohfa7LERBSM4uGMnZxUtydt5Jzi44ydnFRL/zuJVew4YNU6ytrZVdu3bpTLn/5MkTRVEU5cqVK8rnn3+uHD16VLl27Zry+++/K25ubkqLFi20x8iYcr99+/ZKZGSksmXLFsXOzi7L5TM+/vhj5fz588r8+fPLxPIZudWfomiWLKhWrZqyc+dO5ejRo4q/v7/i7++v3V+e6y8hIUE5ceKEcuLECQVQvvnmG+XEiRPKf//7XyUhIUEJCQlRDhw4oFy7dk3ZsWOH0qBBA8XDw0NJSkrSHqNDhw5K/fr1lUOHDil79+5VPDw8dJbPePjwoeLg4KD069dPOXPmjLJq1SrF3Ny8TCyfkVP9KYpm+REbGxvl999/V06dOqV07do1y+VHymv9vWjs2LHKrl27lGvXrin79u1T2rZtq1SuXFm5e/euoiiF87ssREFIzi4YydkFIzm7YCRnFy7J2fohje6XBGS5hYaGKoqiKNHR0UqLFi0UW1tbRa1WKzVr1lQ+/vhjnTU/FUVRrl+/rnTs2FExMzNTKleurIwdO1ZJTU3VKfP3338rr7zyimJiYqK4ublpz1Ga5VZ/iqIoT58+VT744AOlYsWKirm5ufLWW28pMTExOscpr/X3999/Z1l/AwYMUJ48eaK0b99esbOzU4yNjRVXV1dl8ODBOks7KIqixMXFKUFBQYqFhYViZWWlvPvuu0pCQoJOmZMnTyqvvfaaolarlSpVqigzZ84szo9ZZHKqP0XRLEEyceJExcHBQVGr1UqbNm2Uixcv6hyjPNffi3r27Kk4OTkpJiYmSpUqVZSePXsqV65c0e4vrN9lIV6W5OyCkZxdMJKzC0ZyduGSnK0fKkVRlMLvPxdCCCGEEEIIIYTc0y2EEEIIIYQQQhQRaXQLIYQQQgghhBBFRBrdQgghhBBCCCFEEZFGtxBCCCGEEEIIUUSk0S2EEEIIIYQQQhQRaXQLIYQQQgghhBBFRBrdQgghhBBCCCFEEZFGtxBCCCGEEEIIUUSk0S2E0LFr1y5UKhUPHz7Udyj5plKp2LBhg77DEEIIIYqc5GshSg9pdAtRggwcOBCVSoVKpcLY2BgHBwfatWvHkiVLSE9Pz9exwsLCsLGxKZpA9eDevXsMGzaMatWqoVarcXR0JCAggH379mnLxMTE0LFjRz1GKYQQojyQfJ09yddCZGak7wCEELo6dOhAaGgoaWlp3Llzhy1btvDRRx/x22+/sXHjRoyMyuevbWBgICkpKSxduhQ3Nzfu3LlDREQEcXFx2jKOjo56jFAIIUR5Ivk6a5KvhciCIoQoMQYMGKB07do10+sREREKoCxatEj72pw5c5S6desq5ubmStWqVZVhw4YpCQkJiqIoyt9//60AOtvkyZMVRVGUZcuWKQ0bNlQsLCwUBwcHJSgoSLlz5472uBnv/fPPPxUfHx9FrVYrfn5+yunTp7Vl/vnnH6VXr16Ks7OzYmZmptStW1cJDw/XiXnNmjVK3bp1FVNTU8XW1lZp06aNkpiYqN2/aNEipVatWoparVa8vLyU+fPnZ1svDx48UABl165dOdYfoKxfv15RFEWZPHlypjoAlNDQUEVRFCUtLU358ssvlerVqyumpqaKr6+vsmbNmhyPL4QQQiiK5OvsSL4WImvS6BaiBMkuiSuKotSrV0/p2LGj9vm3336r7Ny5U7l27ZoSERGheHl5KcOGDVMURVGSk5OVuXPnKlZWVkpMTIwSExOjTfCLFy9WNm3apERFRSkHDhxQ/P39dY6bkcRr166tbNu2TTl16pTSuXNnpXr16kpKSoqiKIpy8+ZN5auvvlJOnDihREVFKfPmzVMMDQ2VQ4cOKYqiKLdv31aMjIyUb775Rrl27Zpy6tQpZf78+doYfvnlF8XJyUlZu3atcvXqVWXt2rWKra2tEhYWluVnT01NVSwsLJRRo0YpSUlJ2dbf80k8ISFB+9ljYmKUr7/+WjE3N9d+GZk+fbpSq1YtZcuWLUpUVJQSGhqqqNXqXL8oCCGEEJKvJV8LkR/S6BaiBMkpiffs2VOpXbt2tu9ds2aNUqlSJe3z0NBQxdraOtdzHjlyRAEyXXVftWqVtkxcXJxiZmam/Prrr9ke54033lDGjh2rKIqiHDt2TAGU69evZ1nW3d0905X2adOmKf7+/tke/7ffflMqVqyomJqaKk2bNlUmTJignDx5UqfM80n8eQcOHFBMTU218SclJSnm5ubK/v37dcoFBwcrQUFB2cYghBBCKIrka8nXQuSPTKQmRCmhKAoqlUr7fMeOHbRp04YqVapgaWlJv379iIuL48mTJzke59ixY3Tp0oVq1aphaWlJy5YtAYiOjtYp5+/vr31sa2uLl5cX58+fByAtLY1p06bh4+ODra0tFhYWbN26VXuMevXq0aZNG3x8fOjevTuLFi3iwYMHADx+/JioqCiCg4OxsLDQbtOnTycqKirbuAMDA7l9+zYbN26kQ4cO7Nq1iwYNGhAWFpbj542OjqZbt26EhITQo0cPAK5cucKTJ09o166dTgzLli3LMQYhhBAiN5KvJV8L8aLyOcODEKXQ+fPnqVGjBgDXr1+nc+fODBs2jC+++AJbW1v27t1LcHAwKSkpmJubZ3mMx48fExAQQEBAACtWrMDOzo7o6GgCAgJISUnJcyxfffUV3333HXPnzsXHx4cKFSowatQo7TEMDQ3Zvn07+/fvZ9u2bXz//fd8+umnHDp0SBvbokWL8PPz0zmuoaFhjuc1NTWlXbt2tGvXjokTJ/Lee+8xefJkBg4cmO3nffPNN/H39+fzzz/Xvp6YmAjAX3/9RZUqVXTeo1ar81wPQgghxIskX0u+FuJF0ugWohTYuXMnp0+fZvTo0YDm6nd6ejpz5szBwEAzYGX16tU67zExMSEtLU3ntQsXLhAXF8fMmTNxcXEB4OjRo1me8+DBg1SrVg2ABw8ecOnSJWrXrg3Avn376Nq1K3379gUgPT2dS5cuUadOHe37VSoVzZo1o1mzZkyaNAlXV1fWr1/PmDFjcHZ25urVq/Tp06dA9VKnTp1s1/lUFIW+ffuSnp7O8uXLdXod6tSpg1qtJjo6WttzIIQQQhSU5OusSb4W5Z00uoUoYZKTk4mNjdVZgmTGjBl07tyZ/v37A1CzZk1SU1P5/vvv6dKlC/v27ePHH3/UOU716tVJTEwkIiKCevXqYW5uTrVq1TAxMeH7779n6NChnDlzhmnTpmUZx+eff06lSpVwcHDg008/pXLlynTr1g0ADw8PfvvtN/bv30/FihX55ptvuHPnjjaJHzp0iIiICNq3b4+9vT2HDh3i3r172i8BU6dOZeTIkVhbW9OhQweSk5M5evQoDx48YMyYMZliiYuLo3v37gwaNAhfX18sLS05evQos2fPpmvXrlnGP2XKFHbs2MG2bdtITEzUXi23trbG0tKSkJAQRo8eTXp6Oq+99hqPHj1i3759WFlZMWDAgPz/xwkhhChXJF9LvhYiz/R7S7kQ4nkDBgzQLpVhZGSk2NnZKW3btlWWLFmipKWl6ZT95ptvFCcnJ8XMzEwJCAhQli1bpgDKgwcPtGWGDh2qVKpUSWcJkvDwcKV69eqKWq1W/P39lY0bNyqAcuLECUVR/n9ilj/++EPx9vZWTExMlCZNmuhMghIXF6d07dpVsbCwUOzt7ZXPPvtM6d+/v3ZSmXPnzikBAQGKnZ2dolarFU9PT+X777/XiX/FihXKK6+8opiYmCgVK1ZUWrRooaxbty7LeklKSlI++eQTpUGDBoq1tbVibm6ueHl5KZ999pny5MkTbTmem5ilZcuWOS5Bkp6ersydO1fx8vJSjI2NFTs7OyUgIEDZvXt3Pv/XhBBClDeSryVfC5EfKkVRlOJr4gshhBBCCCGEEOWHzF4uhBBCCCGEEEIUEWl0CyGEEEIIIYQQRUQa3UIIIYQQQgghRBGRRrcQQgghhBBCCFFEpNEthBBCCCGEEEIUEWl0CyGEEEIIIYQQRUQa3UIIIYQQQgghRBGRRrcQQgghhBBCCFFEpNEthBBCCCGEEEIUEWl0CyGEEEIIIYQQRUQa3UIIIYQQQgghRBGRRrcQQgghhBBCCFFE/g9Uqvg/GZHXcwAAAABJRU5ErkJggg==\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from google.colab import files\n",
"files.download('cnn vs vit.png')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"id": "-TU7NzwR-x5a",
"outputId": "e134c1f0-344a-43dd-8dd0-30bd052967ee"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"\n",
" async function download(id, filename, size) {\n",
" if (!google.colab.kernel.accessAllowed) {\n",
" return;\n",
" }\n",
" const div = document.createElement('div');\n",
" const label = document.createElement('label');\n",
" label.textContent = `Downloading \"${filename}\": `;\n",
" div.appendChild(label);\n",
" const progress = document.createElement('progress');\n",
" progress.max = size;\n",
" div.appendChild(progress);\n",
" document.body.appendChild(div);\n",
"\n",
" const buffers = [];\n",
" let downloaded = 0;\n",
"\n",
" const channel = await google.colab.kernel.comms.open(id);\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
"\n",
" for await (const message of channel.messages) {\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
" if (message.buffers) {\n",
" for (const buffer of message.buffers) {\n",
" buffers.push(buffer);\n",
" downloaded += buffer.byteLength;\n",
" progress.value = downloaded;\n",
" }\n",
" }\n",
" }\n",
" const blob = new Blob(buffers, {type: 'application/binary'});\n",
" const a = document.createElement('a');\n",
" a.href = window.URL.createObjectURL(blob);\n",
" a.download = filename;\n",
" div.appendChild(a);\n",
" a.click();\n",
" div.remove();\n",
" }\n",
" "
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"download(\"download_3547fb09-3ca6-46a8-9622-6b968af0982d\", \"cnn vs vit.png\", 56896)"
]
},
"metadata": {}
}
]
}
]
}