All lab programs in the 7th sem course "Deep Learning Laboratory"

DPLab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6e782b3c",
   "metadata": {},
   "source": [
    "# Getting Started\n",
    "Run the following commands in the terminal of this project, and then reassign `Tensorflow` as the kernel for this Jupyter Notebook\n",
    "\n",
    "```bash\n",
    "python3 -m venv tensorflow\n",
    "source ./tensorflow/bin/activate\n",
    "pip install tensorflow[and-cuda] torch numpy matplotlib notebook ipykernel scipy\n",
    "python -m ipykernel install --user --name=tensorflow --display-name=TensorFlow\n",
    "jupyter kernelspec list\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f523d88-f5ec-42b4-a3b0-b8fe36f86aa7",
   "metadata": {},
   "source": [
    "# Exp1: Training XOR using multilayer perceptrons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "297ad642-54be-4b2c-bde0-3eb9fbf1dc17",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-12-20 07:18:26.518037: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "/home/galaxygamerman/DPLabExps/tensorflow/lib/python3.12/site-packages/keras/src/export/tf2onnx_lib.py:8: FutureWarning: In the future `np.object` will be defined as the corresponding NumPy scalar.\n",
      "  if not hasattr(np, \"object\"):\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1766215111.376472   23686 gpu_device.cc:2020] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1763 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3050 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6\n",
      "2025-12-20 07:18:33.189167: I external/local_xla/xla/service/service.cc:163] XLA service 0x7f62d8009cc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2025-12-20 07:18:33.189244: I external/local_xla/xla/service/service.cc:171]   StreamExecutor device (0): NVIDIA GeForce RTX 3050 Laptop GPU, Compute Capability 8.6\n",
      "2025-12-20 07:18:33.227152: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2025-12-20 07:18:33.407647: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:473] Loaded cuDNN version 91002\n",
      "I0000 00:00:1766215113.793339   24419 device_compiler.h:196] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 215ms/step - accuracy: 0.5000 - loss: 0.6931\n",
      "Accuracy: 50.00%\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step\n",
      "\n",
      "Predictions:\n",
      "Input: [0 0]\n",
      "\tPredicted Output: 0\n",
      "\tTrue Output: 0\n",
      "Input: [0 1]\n",
      "\tPredicted Output: 0\n",
      "\tTrue Output: 1\n",
      "Input: [1 0]\n",
      "\tPredicted Output: 0\n",
      "\tTrue Output: 1\n",
      "Input: [1 1]\n",
      "\tPredicted Output: 0\n",
      "\tTrue Output: 0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Input\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "X = np.array([[0,0],[0,1],[1,0],[1,1]])\n",
    "Y = np.array([0,1,1,0])\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Input(shape=(2,)))\n",
    "model.add(Dense(4,activation='relu'))\n",
    "model.add(Dense(1,activation='sigmoid'))\n",
    "\n",
    "model.compile(\n",
    "    loss='binary_crossentropy',\n",
    "    optimizer=Adam(learning_rate = 0.05),\n",
    "    metrics = ['accuracy']\n",
    ")\n",
    "\n",
    "model.fit(X,Y,epochs=1000,verbose=0)\n",
    "loss,accuracy = model.evaluate(X,Y)\n",
    "print(f\"Accuracy: {accuracy*100:.2f}%\")\n",
    "\n",
    "predictions = model.predict(X)\n",
    "predictions = (predictions>0.5).astype(int)\n",
    "\n",
    "print(\"\\nPredictions:\")\n",
    "for i, prediction in enumerate(predictions):\n",
    "    print(f\"Input: {X[i]}\")\n",
    "    print(f\"\\tPredicted Output: {prediction[0]}\")\n",
    "    print(f\"\\tTrue Output: {Y[i]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c8f262d-add6-4a39-9df9-9f35d9eee198",
   "metadata": {},
   "source": [
    "# Exp2: Implement regularization techniques in deep learning models using parameter norm penalties, dataset augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1e8b9559-c14d-4006-a237-636880892dfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/galaxygamerman/DPLabExps/tensorflow/lib/python3.12/site-packages/keras/src/layers/reshaping/flatten.py:37: 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__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-12-20 07:19:08.045497: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_230', 4 bytes spill stores, 4 bytes spill loads\n",
      "\n",
      "2025-12-20 07:19:08.550772: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_1136', 4 bytes spill stores, 4 bytes spill loads\n",
      "\n",
      "2025-12-20 07:19:19.289251: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_96', 4 bytes spill stores, 4 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "375/375 - 14s - 37ms/step - accuracy: 0.6672 - loss: 1.1332 - val_accuracy: 0.8808 - val_loss: 0.5285\n",
      "Epoch 2/50\n",
      "375/375 - 9s - 25ms/step - accuracy: 0.8344 - loss: 0.6472 - val_accuracy: 0.9196 - val_loss: 0.3970\n",
      "Epoch 3/50\n",
      "375/375 - 9s - 25ms/step - accuracy: 0.8698 - loss: 0.5500 - val_accuracy: 0.9349 - val_loss: 0.3444\n",
      "Epoch 4/50\n",
      "375/375 - 7s - 18ms/step - accuracy: 0.8826 - loss: 0.5020 - val_accuracy: 0.9411 - val_loss: 0.3226\n",
      "Epoch 5/50\n",
      "375/375 - 9s - 25ms/step - accuracy: 0.8922 - loss: 0.4787 - val_accuracy: 0.9486 - val_loss: 0.3058\n",
      "Epoch 6/50\n",
      "375/375 - 10s - 27ms/step - accuracy: 0.9003 - loss: 0.4496 - val_accuracy: 0.9477 - val_loss: 0.2987\n",
      "Epoch 7/50\n",
      "375/375 - 10s - 26ms/step - accuracy: 0.9037 - loss: 0.4356 - val_accuracy: 0.9501 - val_loss: 0.2871\n",
      "Epoch 8/50\n",
      "375/375 - 7s - 19ms/step - accuracy: 0.9079 - loss: 0.4252 - val_accuracy: 0.9523 - val_loss: 0.2830\n",
      "Epoch 9/50\n",
      "375/375 - 10s - 26ms/step - accuracy: 0.9124 - loss: 0.4091 - val_accuracy: 0.9578 - val_loss: 0.2646\n",
      "Epoch 10/50\n",
      "375/375 - 10s - 26ms/step - accuracy: 0.9119 - loss: 0.4111 - val_accuracy: 0.9587 - val_loss: 0.2662\n",
      "Epoch 11/50\n",
      "375/375 - 7s - 18ms/step - accuracy: 0.9144 - loss: 0.4005 - val_accuracy: 0.9591 - val_loss: 0.2598\n",
      "Epoch 12/50\n",
      "375/375 - 9s - 25ms/step - accuracy: 0.9183 - loss: 0.3923 - val_accuracy: 0.9568 - val_loss: 0.2631\n",
      "Epoch 13/50\n",
      "375/375 - 9s - 25ms/step - accuracy: 0.9170 - loss: 0.3951 - val_accuracy: 0.9609 - val_loss: 0.2525\n",
      "Epoch 14/50\n",
      "375/375 - 7s - 18ms/step - accuracy: 0.9182 - loss: 0.3904 - val_accuracy: 0.9568 - val_loss: 0.2610\n",
      "Epoch 15/50\n",
      "375/375 - 9s - 25ms/step - accuracy: 0.9192 - loss: 0.3854 - val_accuracy: 0.9603 - val_loss: 0.2543\n",
      "Epoch 16/50\n",
      "375/375 - 9s - 25ms/step - accuracy: 0.9210 - loss: 0.3835 - val_accuracy: 0.9603 - val_loss: 0.2512\n",
      "Epoch 17/50\n",
      "375/375 - 9s - 25ms/step - accuracy: 0.9227 - loss: 0.3749 - val_accuracy: 0.9619 - val_loss: 0.2506\n",
      "Epoch 18/50\n",
      "375/375 - 7s - 18ms/step - accuracy: 0.9218 - loss: 0.3785 - val_accuracy: 0.9597 - val_loss: 0.2560\n",
      "Epoch 19/50\n",
      "375/375 - 9s - 25ms/step - accuracy: 0.9227 - loss: 0.3724 - val_accuracy: 0.9630 - val_loss: 0.2488\n",
      "Epoch 20/50\n",
      "375/375 - 9s - 25ms/step - accuracy: 0.9261 - loss: 0.3685 - val_accuracy: 0.9623 - val_loss: 0.2478\n",
      "Epoch 21/50\n",
      "375/375 - 7s - 18ms/step - accuracy: 0.9235 - loss: 0.3702 - val_accuracy: 0.9605 - val_loss: 0.2483\n",
      "Epoch 22/50\n",
      "375/375 - 9s - 25ms/step - accuracy: 0.9242 - loss: 0.3655 - val_accuracy: 0.9646 - val_loss: 0.2411\n",
      "Epoch 23/50\n",
      "375/375 - 10s - 25ms/step - accuracy: 0.9259 - loss: 0.3648 - val_accuracy: 0.9618 - val_loss: 0.2431\n",
      "Epoch 24/50\n",
      "375/375 - 10s - 26ms/step - accuracy: 0.9244 - loss: 0.3688 - val_accuracy: 0.9668 - val_loss: 0.2375\n",
      "Epoch 25/50\n",
      "375/375 - 7s - 19ms/step - accuracy: 0.9271 - loss: 0.3578 - val_accuracy: 0.9631 - val_loss: 0.2409\n",
      "Epoch 26/50\n",
      "375/375 - 10s - 26ms/step - accuracy: 0.9256 - loss: 0.3658 - val_accuracy: 0.9641 - val_loss: 0.2377\n",
      "Epoch 27/50\n",
      "375/375 - 9s - 25ms/step - accuracy: 0.9271 - loss: 0.3622 - val_accuracy: 0.9657 - val_loss: 0.2326\n",
      "Epoch 28/50\n",
      "375/375 - 7s - 18ms/step - accuracy: 0.9279 - loss: 0.3549 - val_accuracy: 0.9668 - val_loss: 0.2365\n",
      "Epoch 29/50\n",
      "375/375 - 9s - 25ms/step - accuracy: 0.9274 - loss: 0.3589 - val_accuracy: 0.9654 - val_loss: 0.2380\n",
      "Epoch 30/50\n",
      "375/375 - 9s - 25ms/step - accuracy: 0.9276 - loss: 0.3563 - val_accuracy: 0.9648 - val_loss: 0.2352\n",
      "Epoch 30: early stopping\n",
      "Restoring model weights from the end of the best epoch: 27.\n",
      "<keras.src.callbacks.history.History object at 0x7f636ae2b170>\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers, regularizers\n",
    "import numpy as np\n",
    "\n",
    "# Verbosity Flags\n",
    "CALLBACK_VERBOSITY = 1 # 0 or 1\n",
    "TRAINING_VERBOSITY = 2 # 0, 1 or 2\n",
    "\n",
    "# Load MNIST dataset\n",
    "(x_train,y_train),_ = keras.datasets.mnist.load_data()\n",
    "\n",
    "# Normalize and Reshape\n",
    "x_train = x_train.astype('float32')/255\n",
    "x_train = np.expand_dims(x_train, -1) # (60000,28,28,1)\n",
    "y_train = tf.keras.utils.to_categorical(y_train,10)\n",
    "\n",
    "# Add Gaussian noise\n",
    "noise = 0.05 * np.random.normal(size=x_train.shape)\n",
    "x_train = np.clip(x_train + noise, 0., 1.)\n",
    "\n",
    "# ata augmentation with validation split\n",
    "datagen = keras.preprocessing.image.ImageDataGenerator(\n",
    "    rotation_range = 10,\n",
    "    width_shift_range = 0.1,\n",
    "    height_shift_range = 0.1,\n",
    "    validation_split = 0.2 # which translates to 20%\n",
    ")\n",
    "datagen.fit(x_train)\n",
    "\n",
    "# Model\n",
    "model = keras.Sequential([\n",
    "    layers.Flatten(input_shape = (28,28,1)),\n",
    "    layers.Dense(256,\n",
    "                 activation = 'relu',\n",
    "                 kernel_regularizer = regularizers.l1_l2(l1 = 1e-5, l2 = 1e-4)\n",
    "                ),\n",
    "    layers.Dropout(0.5),\n",
    "    layers.Dense(128,\n",
    "                 activation = 'relu',\n",
    "                 kernel_regularizer = regularizers.l2(1e-4)\n",
    "                ),\n",
    "    layers.Dropout(0.3),\n",
    "    layers.Dense(10,activation = 'softmax')\n",
    "])\n",
    "model.compile(optimizer = 'adam', loss = 'categorical_crossentropy',metrics = ['accuracy'])\n",
    "\n",
    "# Early stopping\n",
    "early_stop = keras.callbacks.EarlyStopping(\n",
    "    monitor = 'val_loss',\n",
    "    patience = 3,\n",
    "    restore_best_weights = True,\n",
    "    verbose = CALLBACK_VERBOSITY\n",
    ")\n",
    "\n",
    "# Train\n",
    "history = model.fit(\n",
    "    datagen.flow(x_train,y_train,batch_size=128,subset='training'),\n",
    "    validation_data = datagen.flow(x_train,y_train,batch_size=128,subset='validation'),\n",
    "    epochs = 50,\n",
    "    callbacks = [early_stop],\n",
    "    verbose = TRAINING_VERBOSITY\n",
    ")\n",
    "print(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b078143-d5b8-41f9-bb6d-598ef707e62f",
   "metadata": {},
   "source": [
    "# Exp3: Implement and compare different optimizer algorithms (SGD, Momentum, Adam) for training a simple neural network on a toy dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f4c212df-0038-4c3c-900d-0cf8e1623a83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Loss(SGD): 0.24897262454032898\n",
      "Final Loss(Momentum): 0.0007470879936590791\n",
      "Final Loss(Adam): 0.12500768899917603\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Toy dataset: XOR problem\n",
    "x = torch.tensor([[0,0],[0,1],[1,0],[1,1]], dtype=torch.float32)\n",
    "y = torch.tensor([[0],[1],[1],[0]], dtype=torch.float32)\n",
    "\n",
    "# Define a simple neural network\n",
    "class SimpleNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(SimpleNN, self).__init__()\n",
    "        self.fc1 = nn.Linear(2,4)\n",
    "        self.fc2 = nn.Linear(4,1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = torch.sigmoid(self.fc1(x))    # Hidden layer activation\n",
    "        x = torch.sigmoid(self.fc2(x))    # Output layer activation\n",
    "        return x\n",
    "\n",
    "# To train the model\n",
    "def train_model(optimizer_name, model, x, y, epochs=5000, learning_rate=0.1):\n",
    "    criterion= nn.MSELoss()\n",
    "    # Select Optimizer based on the input parameter\n",
    "    if optimizer_name == \"SGD\":\n",
    "        optimizer = optim.SGD(model.parameters(), lr=learning_rate)\n",
    "    elif optimizer_name == \"Momentum\":\n",
    "        optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9)\n",
    "    elif optimizer_name == \"Adam\":\n",
    "        optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "    losses = []\n",
    "    for epoch in range(epochs):\n",
    "        optimizer.zero_grad()\n",
    "        output = model(x)\n",
    "        loss = criterion(output,y)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        losses.append(loss.item())\n",
    "    return losses\n",
    "\n",
    "# Train models with different optimizers\n",
    "models = [ (name, train_model(name, SimpleNN(), x, y)) for name in [\"SGD\",\"Momentum\",\"Adam\"] ]\n",
    "\n",
    "# Plot the loss curves\n",
    "for name, losses in models:\n",
    "    plt.plot(losses, label=name)\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.title(\"Optimizer Algorithm Comparison\")\n",
    "plt.show()\n",
    "\n",
    "# Final results\n",
    "for name, losses in models:\n",
    "    print(f\"Final Loss({name}): {losses[-1]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6767751-d4f3-442f-90a1-71e16b8adfbe",
   "metadata": {},
   "source": [
    "# Exp3*: Same thing, but in tensorflow now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "788f6b33-9afe-4549-8760-da6e6a8fb313",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/galaxygamerman/DPLabExps/tensorflow/lib/python3.12/site-packages/keras/src/layers/core/dense.py:106: 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"
     ]
    },
    {
     "data": {
      "image/png": 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DBzJw4ECys7O5//77eeutt5gwYQLVq1fHaDRe83dFq9WWekIpKj9pAhNV1vPPP4+rqytPPPEECQkJds9dunSJJ598EqPRyPPPP1/otdbhwVYff/wxAD179rSdc3NzIzExsfQDL4bz588zYMAAOnToUGhkjdWAAQM4e/Yss2fPLvRcRkYGaWlpJXrvn376ibS0NEaPHk3//v0Lbb179+bHH38sNNTeqlu3bjg5ORWaOfqTTz4pVPbuu+9my5YtbNy40XYuLS2NL774gpCQkGL3Wbqam5tboeaYiqB///40a9aMt956y+4zW6WkpPDyyy8D0Lp1a/z8/Jg1a5bdvf7zzz85ePAgvXr1KvX4vvjiC3JycmyPZ86cSW5uru3/C2utSsFalOzsbD777LNbet+r///V6/U0btwYRVHIyclBp9PRvXt3fv75Z7vmuLi4OBYsWECHDh1uWMsrHI/UAIkqq379+sybN4/BgwfTrFmzQjNBX7x4kYULFxY5fD0mJoZ7772XHj16sHHjRr777jseeughmjdvbisTHh7OypUref/99wkMDCQ0NLTQUNuy8swzz3DhwgXGjx/PokWL7J4LCwsjLCyMRx55hO+//54nn3yS1atX0759e/Ly8jh06BDff/89K1asoHXr1jf93vPnz6datWq0a9euyOfvvfdeZs+eze+//879999f6Hl/f3/Gjh3Le++9Z7vHu3fv5s8//8TX19eudubFF19k4cKF9OzZk2eeeQYfHx/mzZtHTEwMP/744zWnCbiR8PBwFi9ezLhx47j99ttxd3fnnnvuKdG1SpOzszNLly4lMjKSTp06MWDAANq3b4+zszP79+9nwYIFeHt789Zbb+Hs7My0adMYNmwYnTt3ZtCgQbZh8CEhIfzvf/8r9fiys7Pp1q2bbZqGzz77jA4dOnDvvfcC0K5dO7y9vRk6dCjPPPMMGo2Gb7/99pablbp3705AQADt27fH39+fgwcP8sknn9CrVy9b/70333yTv//+mw4dOvDUU0/h5OTE559/TlZWlt1cX0LYqDcATYjysWfPHmXQoEFKjRo1FGdnZyUgIEAZNGiQ3ZBmK+tw8wMHDij9+/dXPDw8FG9vb2XMmDF2Q40VxTJcuFOnToqrq6vd8O1rDYPv1atXofcDlNGjR9udi4mJUQDlnXfeKRSXlXUIcVFbweHL2dnZyrRp05QmTZooBoNB8fb2VsLDw5XXXntNSUpKum4cRYmLi1OcnJyURx555Jpl0tPTFaPRqPTt2/ea9yM3N1d59dVXlYCAAMXV1VXp2rWrcvDgQaVatWrKk08+aXe9Y8eOKf3791e8vLwUFxcXpU2bNspvv/1mV8Y6fH3JkiWF4ilqGHxqaqry0EMPKV5eXgpgGxJ/retYfyYFh7F37ty5yKHZN/OzvpbLly8rEydOVJo1a6YYjUbFxcVFadq0qTJhwgTl/PnzdmUXL16stGzZUjEYDIqPj48yePBg5cyZM3Zlhg4dqri5uRV6n+J+BuvPcO3atcrIkSMVb29vxd3dXRk8eLCSkJBg99r169crd9xxh+Lq6qoEBgYq48ePt00DUPBncK33tj5XcBj8559/rnTq1EmpVq2aYjAYlLp16yrPP/+83e+woijKjh07lKioKMXd3V0xGo3KnXfeaTf1RMHPsnXrVrvzRf2eiKpN1gITooDJkyfz2muvceHCBXx9fdUOx6EkJibi7e3Nm2++aWvmERWDdcLFrVu3lqjWUIiKSPoACSHKXUZGRqFz1tXZr14CQQghyoL0ARJClLvFixczd+5c7r77btzd3Vm3bh0LFy6ke/futG/fXu3whBAOQBIgIUS5CwsLw8nJienTp5OcnGzrGP3mm2+qHZoQwkFIHyAhhBBCOBzpAySEEEIIhyMJkBBCCCEcjvQBKoLZbObcuXN4eHhU2CnzhRBCCGFPURRSUlIIDAy84USpkgAV4dy5c7JujBBCCFFJnT59mlq1al23jCRARbBOrX769GlZP0YIIYSoJJKTkwkKCrJ9j1+PJEBFsDZ7eXp6SgIkhBBCVDLF6b4inaCFEEII4XAkARJCCCGEw5EESAghhBAOR/oACSGEqJTy8vLIyclROwxRjpydndHpdKVyLUmAhBBCVCqKohAbG0tiYqLaoQgVeHl5ERAQcMvz9EkCJIQQolKxJj9+fn4YjUaZsNZBKIpCeno68fHxANSoUeOWricJkBBCiEojLy/PlvxUq1ZN7XBEOXN1dQUgPj4ePz+/W2oOk07QQgghKg1rnx+j0ahyJEIt1p/9rfb/kgRICCFEpSPNXo6rtH72kgAJIYQQwuFIAiSEEEIIhyMJkBBCCFEOLly4wKhRo6hduzYGg4GAgACioqJYv369rczOnTsZOHAgNWrUwGAwEBwcTO/evfn1119RFAWAEydOoNFobJuHhwdNmjRh9OjRHDlyRK2PV+lIAlTOMnIz1A5BCCGECvr168fOnTuZN28ehw8f5pdffqFLly4kJCQA8PPPP3PHHXeQmprKvHnzOHjwIMuXL6dv37688sorJCUl2V1v5cqVnD9/nt27d/P2229z8OBBmjdvzqpVq9T4eJWORrGmlMImOTkZk8lEUlJSqa4Gvyt+F4/8+QiPNnmU/2v9f6V2XSGEcBSZmZnExMQQGhqKi4uL2uEUW2JiIt7e3qxZs4bOnTsXej4tLY3g4GA6derE0qVLi7yGoihoNBpOnDhBaGgoO3fupEWLFrbnzWYz3bp1IyYmhmPHjpXajMkVzfV+B27m+1tqgMrR+9vfB2Du/rnqBiKEEFWIoiikZ+eW+3Yz9Qfu7u64u7uzbNkysrKyCj3/119/kZCQwPjx4695jRuNftJqtYwdO5aTJ0+yffv2YsfmqGQixHKkmM1qhyCEEFVORk4ejSeuKPf3PfB6FEZ98b5GnZycmDt3LiNGjGDWrFm0atWKzp078+CDDxIWFsbhw4cBaNCgge01W7du5c4777Q9XrRoEb17977u+zRs2BCw9BNq06bNzX4khyI1QOXoUvwZ23HupZMqRiKEEKK89evXj3PnzvHLL7/Qo0cP1qxZQ6tWrZg7d26R5cPCwti1axe7du0iLS2N3NzcG76HtVZK5km6sQpRA/Tpp5/yzjvvEBsbS/Pmzfn444+vmbnOnj2bb775hn379gEQHh7O22+/bVf+0UcfZd68eXavi4qKYvny5WX3IYrBxaCF/Ikrsz6O4HS7iQR3HYlWJ3moEEKUlKuzjgOvR6nyvjfLxcWFu+66i7vuuotXX32Vxx9/nEmTJvHBBx8AEB0dzR133AGAwWCgXr16N3X9gwcPAhAaGnrTsTka1ROgxYsXM27cOGbNmkXbtm2ZMWMGUVFRREdH4+fnV6j8mjVrGDRoEO3atcPFxYVp06bRvXt39u/fT82aNW3levTowZw5c2yPDQZDuXye6zmcc8F27KakEbr+BTatn8/3gc/h4V+Xmt6u+LgZqOamp5q7Hh83PSZXZ9wNTpLNCyHENWg0mmI3RVU0jRs3ZtmyZXTv3h0fHx+mTZvGTz/9VKJrmc1mPvroI0JDQ2nZsmUpR1r1qP4b8/777zNixAiGDRsGwKxZs/j999/5+uuvefHFFwuVnz9/vt3jL7/8kh9//JFVq1YxZMgQ23nrHAsV1W8Bo4g8/yV3aPYQdnYEn5zswyd53UjGvVBZrQY8XJwxuTrj6eqEp4uzZbMeuzrj6eKEyeiMu8GSMLkbnHAz6PL3Thj1OkmihBBCJQkJCTzwwAM89thjhIWF4eHhwbZt25g+fTr33Xcf7u7ufPnllwwcOJBevXrxzDPPUL9+fVJTU22tF1eP6kpISCA2Npb09HT27dvHjBkz2LJlC7///nuVHQFWmlRNgLKzs9m+fTsTJkywndNqtURGRrJx48ZiXSM9PZ2cnBx8fHzszq9ZswY/Pz+8vb3p2rUrb7755jVXDs7KyrLrlZ+cnFyCT3Nzuo94HRIeJ2XpU3jEbma882LG6n/mP7e7+FHbg52ZNbiUlk12nhmzAkkZOSRllHzhN40G3PWWZKhgYuSWnyy52451dudd9TqMzjqM+vxjvQ63/GO9kzTdCSFEcbi7u9O2bVs++OADjh07Rk5ODkFBQYwYMYKXXnoJgL59+7JhwwamTZvGkCFDuHTpEiaTidatWxfZAToyMhKwLA4aHBzMnXfeyRdffHHTzWaOStV5gM6dO0fNmjXZsGEDERERtvPjx49n7dq1bN68+YbXeOqpp1ixYgX79++3zQewaNEijEYjoaGhHDt2jJdeegl3d3c2btxYZFY8efJkXnvttULnS3seoM92fcbM3TMBWPfgOkwGE5jNsPd7WP8RxO+/Uji4A8rtw8mq15PkbA3JmTkkZeSSnJlDckYOyZm5+fsckjMKHGfmkpZl2VLz9+Yy+gk7aTW2pMiod8rf63DVO+UnTTqMhvzkyfo4/3k3vS7/tU755+yv4yz9ooQQRais8wCJ0lNa8wCp3gR2K6ZOncqiRYtYs2aN3U148MEHbcfNmjUjLCyMunXrsmbNGrp161boOhMmTGDcuHG2x8nJyQQFBZV6vI82edSWAG2N3UpkcCRotdD8QQgbCDFrYctsiP4DTq5Dc3IdLu7+uLQail/4o+BX8/pvUARFUcjIyctPhvJIy8olxZokZV9JklIzc0nNfz412/I4IzuPtGzLPj07j/TsXDJy8sjJs2RUuWaFlEzL9aDwvBa3wlmnyU+anDAaLLVO1gTKaLAkUNZkydrEZ62ZcstPuq5+LImVEEIIK1UTIF9fX3Q6HXFxcXbn4+Libth/591332Xq1KmsXLmSsLCw65atU6cOvr6+HD16tMgEyGAwlEsnaaOz0Xb898m/LQmQlUYDdbpYtqQzsH0e7JgHqXHw73T4711ocDe0fQJCOxX7Pa2dA416J/Aonc+RnWu2JEU5uaRn59kSpILJUkZ2bn7SlJc/adiVcuk5lufTsvLIyLF/Pje/uionTyEnL5fkzBsP+7wZep22cEKld7qSNBl0uDrbP/ZwccLd4Jy/d8LDxQkPF0tfK2kGFEKIyknVBEiv1xMeHs6qVavo06cPYOnFvmrVKsaMGXPN102fPp233nqLFStW0Lp16xu+z5kzZ0hISKBGjRqlFfota+l3nR76plrQ9WXoPB4O/QZbv4IT/1mOD/0Gt/WEqLegWt3yC7gAvZMWvZMWE86lfu3sXLMtIbImRdbEKi07l/QsS8KUViCxSs/Kf67AubSsK9dIy8q1JVbZeWay080kppe8P1VBeictnrbEKL8Dukt+kmQ7di6QODlhctXjZbR0aje5OkutlBBCqED1JrBx48YxdOhQWrduTZs2bZgxYwZpaWm2UWFDhgyhZs2aTJkyBYBp06YxceJEFixYQEhICLGxscCVacZTU1N57bXX6NevHwEBARw7dozx48dTr149oqLKf56Iq/Wq04vfj/9OVl4xmox0ztCkr2WLPwRbvrDUCh3+E479A93fhDYjLLVHVYQludLjZbxx2Zthn1hZap9sx9l5pGflFvk4Nb8vlaWpL8fyONNSxnrdi6nZXEzNLnFs7gYnWzLkZXQukBxdSZS8XJ0xGZ3xcrVMj+DjppfaJyGEuAWqJ0ADBw7kwoULTJw4kdjYWFq0aMHy5cvx9/cH4NSpU2i1V/6hnzlzJtnZ2fTv39/uOpMmTWLy5MnodDr27NnDvHnzSExMJDAwkO7du/PGG29UiLmA3JzcAEjPSb+5F/o1hN7vW5rA/hwPx9fAn89b9vd/AYbCw+fFFaWdWOWZlfzE6EpSlJKZS4r1XGbBxCmX1KwcUjJz8zuz55CUnmNr3rMmWWcTM24qBg8XJ3zdDfi46fPnjrKfQ8rX3WA79jHqcZKaJiGEsFE9AQIYM2bMNZu81qxZY/f4xIkT172Wq6srK1aU/5owxeXmbEmAUnNSS3aB6g3gkWWW2qC/XoXo32Hu3fDQ9+BRcec9qmp0Wo2t1qak8swKyRk5JOZPcZCYnp2/z7HtEzOyLWXSLeUS07O5nJ5DXoEO6DEX0274XhoNVHPT4+fhgr+nAX9PF/w88489XPDPP67mbkCnrTo1ikIIcS0VIgFyJNYEKC3nxl9a16TRWGqCAlvBwoFwfjfM7Q3D/gT36qUUqShrOq0Gbzc93m76m3qd2ayQnJnDxdRsLqVlk5CaxcW0bC6lZpOQlkVC/rmE/OcvpWejKNia6g6cv/a1tRrwdbckSIFeLtT0MlLT25WaXq7Uyt97GZ1lUk0hRKUnCVA583bxBuBy5uVbv1jQ7fD4Sph3LyQcgW/7wKO/g6vXrV9bVFharQYvox4vY/ESpzyzwqW0bC6kZBGXkkl8ciZxyVnE5e/jUzKJS87kQkoWZgXiU7KIT8li79mkIq9n1OsI9LIkQ9bkqLaPkVBfN4KrGfFwKf3O8UIIUdokASpnrk6uAGTmZZbOBX3qwJCfYU5PiNsHS0fAoEWglWnQhYVOq6G6h4HqHgYac+2JwfLMCgmpluTnfFIm5xIzOJuYwdnLGZzJ319MzSI9O4+j8akcjS+6GdfXXU9wNUsyFFrNjWBft/y9EU9JjoQQFYQkQOXMWWf5AsjOK/mooUKq1bX0Afo6Co78BWumQNdXSu/6wiHotBr88vsGNa1pKrJMZk6eXWJ0LjGDM5czOHkpnZMJabZmtoup2Ww/WbiW09fdQH0/d27zd6e+v0f+scdNNwMKIcStkgSonBm0lpFo2eZSTIAAAlvAvR9baoD+fRfqdoXgdqX7HsLhuTjrqFPdnTrVix51mJKZw8mEdE4kpHHiYhonEiyJUczFdC6mZtm2jccT7F53dWLUONCTRgGeuOqlJlNUHY8++ijz5s3jiSeeYNasWXbPjR49ms8++4yhQ4cyd+5cdQIspsmTJ7Ns2TJ27dqldii3RBKgcqbXWf7SLdUaIKuwARDzL+z8FpaNgifXy/B4Ua48XJxpWtNUZA1SalYux+JTORyXwtH8/eG4VM4mZhSZGGk1ULe6O00CPWla00TjQE+aBJpuaeSdEGoLCgpi0aJFfPDBB7i65neJyMxkwYIF1K5dW+XoHItMDFLOyjQBAoh6G0xBcPkE/PNm2byHECXgbnCieZAXD7QOYsLdjZgzrA3rX+zK/tei+Hl0e97pH8YTnerQ+bbq+LobMCtwJD6VZbvO8ebvB3lo9maav/YXHaf/w+j5O/jyv+NsP3mZzJw8tT+aEMXWqlUrgoKCWLp0qe3c0qVLqV27Ni1bXlkhICsri2eeeQY/Pz9cXFzo0KEDW7dutT2/Zs0aNBoNK1asoGXLlri6utK1a1fi4+P5888/adSoEZ6enjz00EOkp1+Zd85sNjNlyhRCQ0NxdXWlefPm/PDDD4Wuu2rVKlq3bo3RaKRdu3ZER0cDMHfuXF577TV2796NRqNBo9Ewd+5cTpw4gUajsasVSkxMRKPR2KazKWnMZUVqgMqZNQEq1kzQJeHiCfd8CN/db5krKPxRyySKQlRQbvmJUfMgL7vz8cmZ7DuXxP6zyew/l8y+c0mcuZzB6UuW7fe9lvH8ep2WxoGetKztRava3rSs7UVNL1cZqu9IFAVudnLZ0uBsLNFM/I899hhz5sxh8ODBAHz99dcMGzbMbt678ePH8+OPPzJv3jyCg4OZPn06UVFRHD16FB8fH1u5yZMn88knn2A0GhkwYAADBgzAYDCwYMECUlNT6du3Lx9//DEvvPACAFOmTOG7775j1qxZ1K9fn3///ZeHH36Y6tWr07lzZ9t1X375Zd577z2qV6/Ok08+yWOPPcb69esZOHAg+/btY/ny5axcuRIAk8lUaE3P67nZmMuKJEDlTK+1JEA5eaWzFlWR6nWDBr0skyQufxEe+alKLZchHIOfpwtdPV3o2tDfdi4pPYf955LYdSaRHScT2XnqMglp2ew6nciu04nMWX8CgJperrSt48MddaoRUacatbwlIarSctLh7cDyf9+XzoHe7aZf9vDDDzNhwgROnjwJwPr161m0aJEtAUpLS2PmzJnMnTuXnj17AjB79mz+/vtvvvrqK55//nnbtd58803at28PwPDhw5kwYQLHjh2jTp06APTv35/Vq1fzwgsvkJWVxdtvv83KlSuJiIgALIuFr1u3js8//9wuAXrrrbdsj1988UV69epFZmYmrq6uuLu74+TkdMNFy6/lZmIuS5IAlTODztIJOstcRjVAVlFvwtG/4fhqy7ph9bqV7fsJUQ5MRmfa1fOlXT1fABRF4fSlDHaevsyOk5fZeTqRA+eSOZuYwdIdZ1m64yxgnxC1q1uNWt6lvNicEDehevXq9OrVi7lz56IoCr169cLX19f2/LFjx8jJybElCQDOzs60adOGgwcP2l0rLCzMduzv74/RaLQlEtZzW7ZsAeDo0aOkp6dz11132V0jOzvbrvnt6utaFxKPj48vlX5KNxNzWZIEqJyVyTD4ovjUgdtHwKZPYc1Uy6gw+QtYVDEajYba1YzUrmbkvhY1AUjPzmXHyUQ2HU9g0/EEdp1OLJQQ1a3uRpcGfnS+rTptQn1wcZbRZpWas9FSG6PG+5bQY489ZlsC6tNPPy15CM5XBgVoNBq7x9ZzZrMZgNRUy9xdv//+OzVr1rQrd/VamVdfF7BdpyjWNTsVRbGdy8kpuqXjZmIuS5IAlbNyaQKzaj8Wtn0NZ7bAsVVQL7Ls31MIlRn1TnSo70uH+pa/qAsmRBvzE6JjF9I4diGGr9bF4OKs5Y461ehyW3W6NvSndjWpHap0NJoSNUWpqUePHmRnZ6PRaIiKirJ7rm7duuj1etavX09wcDBgSSa2bt3Ks88+W+L3bNy4MQaDgVOnTtk1d90svV5PXp794IPq1S3LMJ0/f95Wm1TRh8lLAlTOnLSWW56r5KIoStn2S/Dwh9uHw8ZPYO10SYCEQ7o6IUrKyGHD0Yusib7A2sMXiE3OZE30BdZEX2DyrwdoGOBBVJMAopoE0KiGh/QdEmVCp9PZmrN0OvsaSDc3N0aNGsXzzz+Pj48PtWvXZvr06aSnpzN8+PASv6eHhwfPPfcc//vf/zCbzXTo0IGkpCTWr1+Pp6cnQ4cOLdZ1QkJCiImJYdeuXdSqVQsPDw9cXV254447mDp1KqGhocTHx/PKKxV7Ql5JgMqZtQkMINeca/e4TLR7xjIa7PRmOLMdaoWX7fsJUcGZXJ3p2awGPZvVQFEUouNSWBt9gdXR8Ww9cZlDsSkcik3hw1VHCPJxJapxAFFNAwiv7Y1WK8mQKD2entdemmbq1KmYzWYeeeQRUlJSaN26NStWrMDb2/uW3vONN96gevXqTJkyhePHj+Pl5UWrVq146aWXin2Nfv36sXTpUu68804SExOZM2cOjz76KF9//TXDhw8nPDycBg0aMH36dLp3735L8ZYljVKwwU4AkJycjMlkIikp6bq/oCWRnpNO2wVtAdj80GaMt9CGXGw/PQm7F0KzB6Dfl2X/fkJUUpfTsll5MI4V++P478gFsnKv9EMINLlwT4tA7mteU2qGVJSZmUlMTAyhoaG4uLioHY5QwfV+B27m+1tqgMpZwRqfHHM59AMCaJufAO3/Ce56HTxVGC4qRCXg7abngdZBPNA6iPTsXNZGX2DF/lhWHYznXFImn689zudrj1Pfz50+LWtyb/NAgnykz5AQlZEkQOXMSXPllueac8vnTQNbQO12cGoDbJsDXV8un/cVohIz6p1sTWWZOXmsPhTPz7vO8c+heI7Ep/LOimjeWRFN21AfHmwTRM+mNWQ0mRCViCRA5Uyj0eCkdSLXnFt+NUAAbR63JEC7FkCXF0Er/1ALUVwuzjpbMpSUkcOKfbEs23WWjccT2Bxzic0xl5j0837ub1WLgbcH0ahG6TadCyFKnyRAKnDWOpNrzi2/GiCwzAzt4gXJZ+D4GpkYUYgSMrk6M+D2IAbcHsS5xAyWbDvD99tOczYxg7kbTjB3wwmaB3nxcNva3NM8UGqFhKigZDFUFViHwpdrDZCzi2W1eICd35Xf+wpRhQV6uTI2sj7/jr+TeY+1oWfTAJy0GnafTuT5H/bQYdo/vP/3YeJTMtUOVQhxFakBUoGz1tIRulxrgABaDLYMiT/0O6RfAqPPjV8jhLghnVZD59uq0/m26lxMzeL7baf5duNJzidl8tGqI8xcc5R7wgIZ1j6UZrVMaocrhEBqgFShSg0QQI3m4N8M8rIsI8KEEKXO193AU13q8e/4O/nkoZaEB3uTk6ewdOdZ7vlkHYO/3MSGoxeRGUiEUJckQCqw1gCVewKk0UDYA5ZjSYCEKFPOOi29wwL5cVQ7fh7dnj4tAnHSalh/NIGHvtzM/TM3sPJAnCRCQqhEEiAVqNYEBtC4j2V/cj2kxJX/+wvhgJoHeTHjwZaseb4LQyKC0Ttp2Xkqkce/2UbPD//j9z3nMZslERKiPEkCpAKtxnLbzUrZr3ZbiHcw1AwHxQwHfyn/9xfCgdXyNvL6fU1Z98KdPNG5Dm56HYdiUxi9YAe9P17H6kPxUiMk7EyePJkWLVqoHUaVJAmQCqwJUJ6Sd4OSZaTJ/Za9NIMJoQo/Dxcm9GzE+he7MrZbfdwNThw4n8ywuVsZ8PlGtsRcUjtEUYY2btyITqejV69eaofi0CQBUoFOY5kXxGxWoQYIoPF9lv3JDZB8Xp0YhBB4GfX8767b+Hf8nYzsVAeDk5atJy4z4PONPDpnC0fiUtQOUZSBr776iqeffpp///2Xc+fOqR2Ow5IESAWq1wB5BUGt2wEFov9QJwYhhI2Pm56X7m7E2ufv5KG2tdFpNayJvkCPD/9j0s/7SEzPVjtEUUpSU1NZvHgxo0aNolevXsydO9fu+alTp+Lv74+HhwfDhw8nM9N+DqmtW7dy11134evri8lkonPnzuzYscOujEaj4fPPP6d3794YjUYaNWrExo0bOXr0KF26dMHNzY127dpx7Nixsv64FZokQCqw1QCp0QfIqkFPy/7wcvViEELYCTC58HbfZqwc15nujf3JMyvM23iSzu+sYe76GHLyVPw3owJTFIX0nPRy30rSX+v777+nYcOGNGjQgIcffpivv/7adp3vv/+eyZMn8/bbb7Nt2zZq1KjBZ599Zvf6lJQUhg4dyrp169i0aRP169fn7rvvJiXFvrbwjTfeYMiQIezatYuGDRvy0EMP8cQTTzBhwgS2bduGoiiMGTOm5De9CpCJEFWgy1+HK1dRYRSYVYO7YdXrcHwtZKeB3k29WIQQdkJ93fhiSGs2HL3I678d4FBsCpN/PcB3m0/xxn1NiahbTe0QK5SM3AzaLmhb7u+7+aHNGJ2NN/War776iocffhiAHj16kJSUxNq1a+nSpQszZsxg+PDhDB8+HIA333yTlStX2tUCde3a1e56X3zxBV5eXqxdu5bevXvbzg8bNowBAyyz/7/wwgtERETw6quvEhUVBcDYsWMZNmzYzX/oKkRqgFRQIWqAqjcE7xDLpIjHVqsXhxDimtrV8+W3pzvwZp+m+LjpORqfyqDZm3h+yW4up0mzWGUTHR3Nli1bGDRoEABOTk4MHDiQr776CoCDBw/Stq19IhcREWH3OC4ujhEjRlC/fn1MJhOenp6kpqZy6tQpu3JhYWG2Y39/fwCaNWtmdy4zM5Pk5OTS+4CVjNQAqUD1PkBgmRTxtp6weSZE/wmNet/4NUKIcuek0/LwHcHc0zyQd1YcYv7mUyzZfoZVh+J5+e5G3N+qJhqNRu0wVeXq5Mrmhzar8r4346uvviI3N5fAwEDbOUVRMBgMfPLJJ8W6xtChQ0lISODDDz8kODgYg8FAREQE2dn2CbGzs7Pt2Pr7UdQ51QbjVACSAKlA9VFgVg3yE6AjK8BsBq1UCApRUZlcnXmzTzP6tqzFS0v3Eh2Xwv8t2c2PO84w5f5mBFdz3GZsjUZz001R5S03N5dvvvmG9957j+7du9s916dPHxYuXEijRo3YvHkzQ4YMsT23adMmu7Lr16/ns88+4+677wbg9OnTXLx4sew/QBUkCZAKKkQNEEBwOzCYIO0CnN0OQberG48Q4obCg7357ZkOfPlfDB+uOsyGYwn0/PA/Xrq7EYPb1nb42qCK6rfffuPy5csMHz4ck8l+Qdx+/frx1Vdf8dxzz/Hoo4/SunVr2rdvz/z589m/fz916tSxla1fvz7ffvstrVu3Jjk5meeffx5X15uriRIW8ie/CrRaFWeCLkjnDPW6WY6PrFA3FiFEsTnrtIzqUpe/nu1M21Af0rPzeGXZPoZ8vYVziRlqhyeK8NVXXxEZGVko+QFLArRt2zYaNWrEq6++yvjx4wkPD+fkyZOMGjWq0HUuX75Mq1ateOSRR3jmmWfw8/Mrr49RpWgUmXe9kOTkZEwmE0lJSXh6epb69UevGs2/Z/7l9Xav07d+31K//k3ZOR9+fgoCW8FI6QwtRGVjNivM3XCCacsPkZVrxsPFiUn3NKFfFe0blJmZSUxMDKGhobi4uKgdjlDB9X4Hbub7W2qAVFBhmsAA6uYPqTy3E9IS1I1FCHHTtFoNj3UI5Y+xHWkR5EVKZi7PLdnNmIU7Sc7MUTs8ISosSYBUUCGGwVt51gC/JoACx6UGSIjKqm51d354MoLnoxrgpNXw+57z9P5oHbtPJ6odmhAVkiRAKqhQNUAA9fJrgY6uUjcOIcQtcdJpGX1nPb5/MoJa3q6cupROv5kbmP3vccxm6e0gREGSAKmgQtUAAdSLtOyP/QPSJUyISq9VbW9+f6YjPZsGkGtWeOuPgwyft1XWFBOiAEmAVGCrATJXkBqg2hHgbITUWIjbr3Y0QohSYHJ15rPBrXizT1P0TlpWR1/g3k/Wc/B81Zj5V8bvOK7S+tlLAqSCClcD5GSAkA6W42PSDCZEVaHRaHj4jmB+eqodQT6WJrH7P9vAL7vPqR1aiVlnM05PT1c5EqEW68++4MzWJSETIaqgwvUBAqjbDY78ZekH1H6s2tEIIUpRk0ATv47pwNMLd/LfkYs8s3Ane88k8kKPhjjpKtffwTqdDi8vL+Lj4wEwGo1Vcri/KExRFNLT04mPj8fLywudTndL15MESAXW1eArTA0QXJkQ8dRGWR1eiCrIy6hn7rA2vPtXNDPXHGP2fzEcPJ/Cp4NbYXK9tb+ky1tAQACALQkSjsXLy8v2O3ArJAFSQYWsAapWD0y1IekUnFgPt3W/8WuEEJWKTqvhhR4NCatp4v+W7Gbd0Yv0m7mBOY/eTpBPxV5LqyCNRkONGjXw8/MjJ0fmOnIkzs7Ot1zzYyUJkAoqXB8gsKwOX68rbJ9rmQ9IEiAhqqyezWpQu5qR4XO3cTQ+lT6frueLIa0JD/ZWO7SbotPpSu3LUDieytX4W0VYE6Bcc67KkVzFOiv0sX/UjUMIUeaaBJpYNro9TQI9SUjLZtDsTfy2p/J2jhbiZkkCpAJrE1iFqgECCO0EGi1cOARJZ9WORghRxgJMLnz/RASRjfzIzjUzZsFOPl97TO2whCgXkgCpoEI2gQG4ekPNcMuxLIshhENwMzjx+SOteax9KABT/jzElD8Oyjw7osqTBEgFWm0F7ARtVedOy16awYRwGDqthon3NGZCz4YAfP7vcV74cQ+5eRXsjzQhSpEkQCqosDVAUKAf0GowV8D4hBBl5onOdZnePwytBr7fdoan5u8gM6cC/qEmRCmQBEgFFXIYvFWt1qD3gIxLELtb7WiEEOVsQOsgZj0cjt5Jy18H4hj69RZSsyrYgA0hSoEkQCqo0DVAOmdLZ2iQZjAhHFT3JgF881gbPAxObI65xJCvNpOSKfPtiKpFEiAVVOgaIIC61n5A0hFaCEd1R51qLBhxByZXZ3acSuSRr7aQlCFJkKg6JAFSQYWuAYIr/YBObYKsVHVjEUKoplktEwtGtMXb6Myu04k88tVmEtOz1Q5LiFJRIRKgTz/9lJCQEFxcXGjbti1btmy5ZtnZs2fTsWNHvL298fb2JjIyslB5RVGYOHEiNWrUwNXVlcjISI4cOVLWH6PYbDVA5gpaA+RTB7yCwZwDJ9erHY0QQkVNAk0sGHEHPm569pxJYvCXm7mcJkmQqPxUT4AWL17MuHHjmDRpEjt27KB58+ZERUVdc5G7NWvWMGjQIFavXs3GjRsJCgqie/funD17ZeK+6dOn89FHHzFr1iw2b96Mm5sbUVFRZGZmltfHuq4KXwOk0cis0EIIm0Y1PFk44g583fXsP5fM4C83S3OYqPRUT4Def/99RowYwbBhw2jcuDGzZs3CaDTy9ddfF1l+/vz5PPXUU7Ro0YKGDRvy5ZdfYjabWbVqFWCp/ZkxYwavvPIK9913H2FhYXzzzTecO3eOZcuWleMnu7YK3wcIJAESQthpEODBopF34Otu4MD5ZB6ds4U0GR0mKjFVE6Ds7Gy2b99OZGSk7ZxWqyUyMpKNGzcW6xrp6enk5OTg4+MDQExMDLGxsXbXNJlMtG3b9prXzMrKIjk52W4rSzptBa8BgivLYlw8DImn1Y5GCFEB1PPz4LvH22BydWbnqUQen7dN5gkSlZaqCdDFixfJy8vD39/f7ry/vz+xsbHFusYLL7xAYGCgLeGxvu5mrjllyhRMJpNtCwoKutmPclMqRQ2QqxfUbG05lmUxhBD5GgZ48s1jbXA3OLHxeAJPzd9Bdm4F/mNOiGtQvQnsVkydOpVFixbx008/4eLiUuLrTJgwgaSkJNt2+nTZ1nhU+D5AVtIMJoQoQvMgL74a2hoXZy3/HIrnf4t3ybIZotJRNQHy9fVFp9MRFxdndz4uLo6AgIDrvvbdd99l6tSp/PXXX4SFhdnOW193M9c0GAx4enrabWWpUtQAwZUE6PgaqKgj1oQQqmhbpxqfP9IavU7L73vP88qyfbKAqqhUVE2A9Ho94eHhtg7MgK1Dc0RExDVfN336dN544w2WL19O69at7Z4LDQ0lICDA7prJycls3rz5utcsT9YEiIr+b0XNcDB4QsZlOL9L7WiEEBVM59uq89Gglmg1sGjraT5cVXGmGxHiRlRvAhs3bhyzZ89m3rx5HDx4kFGjRpGWlsawYcMAGDJkCBMmTLCVnzZtGq+++ipff/01ISEhxMbGEhsbS2qqZcI+jUbDs88+y5tvvskvv/zC3r17GTJkCIGBgfTp00eNj1iIBg0AZip4lbHOSZbFEEJcV4+mAbzRpykAM1YeYeGWUypHJETxOKkdwMCBA7lw4QITJ04kNjaWFi1asHz5clsn5lOnTqHVXsnTZs6cSXZ2Nv3797e7zqRJk5g8eTIA48ePJy0tjZEjR5KYmEiHDh1Yvnz5LfUTKk3WGqAK3wcILM1gh36zLIvR6Xm1oxFCVECD2wYTm5TJx/8c5eWf9lLd3UBkY/8bv1AIFWkUabQtJDk5GZPJRFJSUpn0B/r12K+8tO4l2ge2Z9Zds0r9+qXqUgx81AK0TvDCCTB4qB2REKICUhSFF37cw/fbzuDirGXBiDtoVdtb7bCEg7mZ72/Vm8AckUaT3wRWGWqAfELBOxTMuXBindrRCCEqKI1Gw1t9m3Fng+pk5pgZPncrJy6mqR2WENckCZAKtPm3vcL3AbKS4fBCiGJw1mn5dHArmtcycTk9h8fmbSUpXZbMEBWTJEAqsPYBqjStj5IACSGKyah3YvbQ1gSaXDh+IY3RC3aQI3MEiQpIEiAVVKomMIDQjqDRQcJRuHxS7WiEEBWcn4cLXw69HaNex7qjF5n8y/7K8wefcBiSAKmgUo0CA3AxQa3bLceyLIYQohgaB3ry4YMt0Whg/uZTzN1wQu2QhLAjCZAKrH2AlAo/E2IB0gwmhLhJdzX258UeDQF447cDrI6OVzkiIa6QBEgFla4JDGRZDCFEiYzsVIcBrWthVuDpBTs5diFV7ZCEACQBUkWl6wQNENjS0hSWmQRnt6sdjRCiktBoNLzZpxltQnxIzcpl5DfbSMmUkWFCfZIAqaDS9QECy7IY1lqgwyvUjUUIUanonbR8Mrgl/p4Gjl1I47kluzGbK9EfgKJKkgRIBZVmLbCr3dbDsj+8XN04hBCVjp+HCzMfDkev07Jifxwz1x5TOyTh4CQBUkGlbAIDqN8dNFqI2weJsuChEOLmtKrtzWv3NQHg3b+iWSOdooWKJAFSQaXsBA1g9IGgtpZjaQYTQpTAoDa1GdQmCEWBZxbu5GSCLJch1CEJkApsfYAqWxMYwG1Rlr00gwkhSmjyvU1oEeRFcmYuT3y7nYxsGVkqyp8kQCqwzQNU2ZrAAG7radnH/AtZMpxVCHHzDE46Zj0cjq+7nkOxKbz26361QxIOSBIgFVTaJjCA6g3AKxjysi1zAgkhRAkEmFxsM0Uv2nqan3aeUTsk4WAkAVJBpRwGb6XRQIP8WqDDf6obixCiUmtfz5dnutYH4OWf9nE0XmqVRfmRBEgFtlFglWkpjIJsw+H/AnMlTOKEEBXGM93q065uNdKz8xg9f4f0BxLlRhIgFdjmAaqMNUAAwe1B7wFp8XBup9rRCCEqMZ1Ww4wHW+DrbiA6LoVJv+xTOyThICQBUkGlbgIDcNJDPeus0NIMJoS4NX4eLnw0qAVaDXy/7Qw/bpf+QKLsSQKkgko7EWJB1tFg0ZIACSFuXbu6voztdhsAryzbJ4umijInCZAKbKPAKuM8QFYyK7QQopSN6VqP9vWqkZGTx7OLdpGdW4n/jRQVniRAKrDOA1Rpm8AA3KpB0B2W42iZFFEIcet0Wg3vPdACL6Mze88m8d7f0WqHJKowSYBUUCWawODKcPjoP9SNQwhRZQSYXJjWLwyAL/49zoajF1WOSFRVkgCpoFJPhFhQg7st+xPrIDNJ3ViEEFVGVJMABrWpjaLA/77fxeW0bLVDElWQJEAqsC2FUVnnAbLyrQfV6oM5B46uUjsaIUQV8mrvRtSp7kZcchYvLt1T+WvMRYUjCZAKqkwNEBRoBpPRYEKI0mPUO/HRgy1x1mlYsT+ORVtPqx2SqGIkAVJB1UqA8pvBjqyAvBx1YxFCVClNa5p4PqoBAK//ekCGxotSJQmQCir1avBXC2oDrj6WPkCnNqkdjRCiinm8Qx061PMlIyePcYt3kZtXBf5wFBWCJEAqqPRrgRWk1V1ZG0yawYQQpUyr1fDuA83xdHFi95kkPltzTO2QRBUhCZAKqlQTGBToB/Q7VIVaLSFEhRJgcuGNPk0B+GjVEfadlVGn4tZJAqSCKlUDBFC3K+gMcPkEXDikdjRCiCro3uaB9GwaQK5Z4f++301WrqwaL26NJEAqqBIzQRdkcIc6nS3HMimiEKIMaDQa3uzTFF93PdFxKXzw9xG1QxKVnCRAKqhyTWAgw+GFEGWumruBt/s2A+CLf4+x/eQllSMSlZkkQCqoMkthFGTtCH1mG6TEqRuLEKLK6t4kgH6tamFWYNz3u0nPzlU7JFFJSQKkAmsCVKlXg7+aZyAEtgQUy5xAQghRRibd25hAkwsnE9KZ+qf0OxQlIwmQCjRUwSYwuDIpojSDCSHKkKeLM9P7Nwfgm40nWXdEFkwVN08SIBVYa4CgijWDWfsBHVsN2enqxiKEqNI61PdlSEQwAC/8uIe0LGkKEzdHEiAVFEyAqlQtkH9TMAVBbgbErFU7GiFEFfdCj4bU8nblbGIG76yIVjscUclIAqQC6ygwqGL9gDSaAqPBZDi8EKJsuRmcmHp/GABzN5xgS4yMChPFJwmQCrRU0SYwKJAALQdzFUruhBAVUof6vgxsHQRYmsIyc2SCRFE8kgCpoMo2gQEEdwCDJ6TFw7kdakcjhHAAL/VqhL+ngZiLaXyw8rDa4YhKQhIgFdg1gVW1BMhJD/UiLcfSDCaEKAcmV2fe6mOZIHH2v8fZfTpR3YBEpSAJkArsRoFVlfXACpLh8EKIchbZ2J/7WgRiVmD8D3vIzq1if1yKUicJkAoK9gGqcjVAAPUjQaOD+ANwKUbtaIQQDmLSPU2o5mZZK+zT1UfVDkdUcJIAqaBKN4EBuHpDcDvLsdQCCSHKiY+bntfuawLAp6uPcvB8ssoRiYpMEiAVVNmJEAuyNYNJPyAhRPnp1awG3Rv7k2tWGP/DHnLzquAfmaJUSAKkAutSGFDF5gEqqEH+4qgnN0DGZXVjEUI4DI1Gw5t9muLp4sTes0nM3XBC7ZBEBSUJkAo0Gk3VXQ/MyqcOVG8ESh4cWal2NEIIB+Ln6cLLvRoB8N5fhzl9SZbmEYVJAqQSazNYlW0CA5kVWgihmgGtg2gb6kNGTh6vLNtXtf+tFSUiCZBKrB2hq2wNEFzpB3R0JeRmqxuLEMKhaDQa3r6/GXqdlrWHL/DrnvNqhyQqGEmAVGIdCl8l5wGyqhkObn6QlQwn16kdjRDCwdSt7s6YrvUAeP3X/SSmyx9i4gpJgFRibQKr0jVAWi3U7245Pr5G1VCEEI7pyc51qe/nzsXUbKb8cUjtcEQFIgmQShyiCQwgtKNlf2K9unEIIRyS3knLlPsty2Qs3naajccSVI5IVBSSAKnEITpBAwS3t+zP7YSsFHVjEUI4pNYhPgxuWxuAl3/aKyvGC6ACJECffvopISEhuLi40LZtW7Zs2XLNsvv376dfv36EhISg0WiYMWNGoTKTJ0+2DDMvsDVs2LAMP0HJWPsAVdl5gKy8gsAr2DIc/tRmtaMRQjio8T0a4udh4PjFND6TZTIEKidAixcvZty4cUyaNIkdO3bQvHlzoqKiiI+PL7J8eno6derUYerUqQQEBFzzuk2aNOH8+fO2bd26itcB12GawABCOlj20hFaCKESk6szr91rWSZj5tpjHI6TGmlHp2oC9P777zNixAiGDRtG48aNmTVrFkajka+//rrI8rfffjvvvPMODz74IAaD4ZrXdXJyIiAgwLb5+vqW1UcoMYdpAoMrCdAJSYCEEOrp0TSAyEZ+5OQpTFi6F7PZAf79FdekWgKUnZ3N9u3biYyMvBKMVktkZCQbN268pWsfOXKEwMBA6tSpw+DBgzl16tSthlvqHGIUmJW1H9DZHZCVqm4sQgiHpdFoeP2+prjpdWw/eZkFWyred4MoP6olQBcvXiQvLw9/f3+78/7+/sTGxpb4um3btmXu3LksX76cmTNnEhMTQ8eOHUlJuXZ1Z1ZWFsnJyXZbWbMthVHV+wABeAeDZy1LP6BzO9WORgjhwAK9XHkuqgEA0/48RFxypsoRCbWo3gm6tPXs2ZMHHniAsLAwoqKi+OOPP0hMTOT777+/5mumTJmCyWSybUFBQWUep7UPkEM0gQHUCrfsz25TNw4hhMMbEhFC81omUrJyee3X/WqHI1SiWgLk6+uLTqcjLi7O7nxcXNx1OzjfLC8vL2677TaOHr12r/8JEyaQlJRk206fPl1q738ttlFgjtAEBlCztWV/RhIgIYS6dFrLMhk6rYY/9say6mDcjV8kqhzVEiC9Xk94eDirVq2ynTObzaxatYqIiIhSe5/U1FSOHTtGjRo1rlnGYDDg6elpt5U1Ww1QVV4Ko6BaBRIgR6n1EkJUWE0CTQzvEArAxJ/3k5aVq3JEoryp2gQ2btw4Zs+ezbx58zh48CCjRo0iLS2NYcOGATBkyBAmTJhgK5+dnc2uXbvYtWsX2dnZnD17ll27dtnV7jz33HOsXbuWEydOsGHDBvr27YtOp2PQoEHl/vmux6FGgQHUaAEaHaTGQvJZtaMRQgiejaxPTS9XziZm8MHfh9UOR5QzVROggQMH8u677zJx4kRatGjBrl27WL58ua1j9KlTpzh//soKvufOnaNly5a0bNmS8+fP8+6779KyZUsef/xxW5kzZ84waNAgGjRowIABA6hWrRqbNm2ievXq5f75rsehRoEB6I3g39hyLM1gQogKwKh34s2+TQH4en0M+84mqRyRKE8axWGqIIovOTkZk8lEUlJSmTWH9fyxJ2dSz/Dd3d/RvHrzMnmPCufXZ2H7HGj3DHR/Q+1ohBACgNELdvD7nvOE1TLx01Pt0Wk1aockSuhmvr+r3CiwysLhmsAAAltY9rF7VQ1DCCEKmtS7MR4uTuw5k8S8DSfUDkeUE0mAVOJwTWAA/pYVmYndKx2hhRAVhp+nCy/0sKwZ+d5f0ZxLzFA5IlEeJAFSiUOtBWbl1wg0Wki/CKky7FQIUXE81KY24cHepGXnMekXmRvIEUgCpBLrPEAOMwweLB2hq9WzHMfuUzcWIYQoQKvV8HbfZjhpNfx9II4V+0u+IoGoHCQBUolD1gAB+FtGXBC7R904hBDiKg0CPBjZqQ4Ak37eT0pmjsoRibIkCZBKHLIPEEBAfj+gOKkBEkJUPM90q09wNSOxyZm895fMDVSVSQKkEoccBQZXEiBpAhNCVEAuzjre7GOpqZ638QS7TyeqG5AoM5IAqcShVoMvyNoElnAEcmQVZiFExdOxfnX6tAhEUWDC0r3k5jnYv9MOQhIglThsE5hHABhMoJjh0jG1oxFCiCK90rsxJldnDpxP5uv1MWqHI8qAJEAqcdgmMI0Gqt9mOb4QrW4sQghxDb7uBl662zI30Ad/H+H0pXSVIxKlTRIglTjsKDAA3waW/cUj6sYhhBDXMaB1EG1CfcjIyWPiz/sc7w/WKq5ECdDp06c5c+aM7fGWLVt49tln+eKLL0otsKrOOg+Qw/UBAvCtb9lflBogIUTFpdFY5gZy1mlYHX2BP/bK3EBVSYkSoIceeojVq1cDEBsby1133cWWLVt4+eWXef3110s1wKrKYfsAAVS31gDJEFMhRMVWz8+dUV0sE7hO/nU/SRkyN1BVUaIEaN++fbRp0waA77//nqZNm7Jhwwbmz5/P3LlzSzO+KsvaBOaQVaq++X2ALh4FswMmgEKISuWpLnWp4+vGhZQspi8/pHY4opSUKAHKycnBYDAAsHLlSu69914AGjZsyPnz50svuirMVgPkiE1gXsGg00NuBiSdVjsaIYS4LhdnHW/1tcxhNn/zKbafvKxyRKI0lCgBatKkCbNmzeK///7j77//pkePHgCcO3eOatWqlWqAVZVtLTBHrAHSOV1ZE0yawYQQlUBE3Wr0D68FwEtL95IjcwNVeiVKgKZNm8bnn39Oly5dGDRoEM2bNwfgl19+sTWNietz6FFgUKAjtCRAQojK4eW7G+Hjpic6LoUv/j2udjjiFjmV5EVdunTh4sWLJCcn4+3tbTs/cuRIjEZjqQVXlTl0J2gAn7qW/SX5R0QIUTl4u+l5pVcjxn2/m49WHaF3WA2Cq7mpHZYooRLVAGVkZJCVlWVLfk6ePMmMGTOIjo7Gz8+vVAOsqmydoHHAJjAAn1DL/pLMsCqEqDz6tqxJ+3rVyMo188oymRuoMitRAnTffffxzTffAJCYmEjbtm1577336NOnDzNnzizVAKsq2zxADlsDVMeyvywJkBCi8tBoNLzZpxl6Jy3/HbnIL7vPqR2SKKESJUA7duygY8eOAPzwww/4+/tz8uRJvvnmGz766KNSDbCqctilMKy882uAEk9BXq66sQghxE0I9XXj6TstAzle//UAienZKkckSqJECVB6ejoeHh4A/PXXX9x///1otVruuOMOTp48WaoBVlUO3wnaowboDGDOlaHwQohK54nOdanv505CWjZT/pC5gSqjEiVA9erVY9myZZw+fZoVK1bQvXt3AOLj4/H09CzVAKsqh14KA0CrvdIPSJrBhBCVjN5Jy9v3W+YGWrztNJuPJ6gckbhZJUqAJk6cyHPPPUdISAht2rQhIiICsNQGtWzZslQDrKocvgkMrjSDSUdoIUQldHuID4PaBAHw0k97ycrNUzkicTNKlAD179+fU6dOsW3bNlasWGE7361bNz744INSC64qc/gmMCgwEkyGwgshKqcXezTC193AsQtpzFoj/5ZVJiVKgAACAgJo2bIl586ds60M36ZNGxo2bFhqwVVlDj8PEBQYCXZC1TCEEKKkTEZnJt7TGIBPVx/l+IVUlSMSxVWiBMhsNvP6669jMpkIDg4mODgYLy8v3njjDcyyuGWx2JbCcNR5gECawIQQVcI9YTXodFt1svPMvPyTzA1UWZQoAXr55Zf55JNPmDp1Kjt37mTnzp28/fbbfPzxx7z66qulHWOVJE1g2HeCln8whBCVlEaj4a0+TXFx1rLxeAI/7jirdkiiGEqUAM2bN48vv/ySUaNGERYWRlhYGE899RSzZ89m7ty5pRxi1SRNYIBXbdBoIScdUuPVjkYIIUosyMfI2G63AfDW7we4lCZzA1V0JUqALl26VGRfn4YNG3Lp0qVbDsoRyCgwQOcMnjUtx4mn1I1FCCFu0eMdQ2kY4MHl9Bze/P2A2uGIGyhRAtS8eXM++eSTQuc/+eQTwsLCbjkoR6AhvwnMUecBsjJZhpCSJAmQEKJyc9ZZ5gbSaGDpjrNsOHpR7ZDEdZRoNfjp06fTq1cvVq5caZsDaOPGjZw+fZo//vijVAOsqqQJLJ9XbTi1QWqAhBBVQqva3jzcNphvN53k5WX7+HNsR1ycdWqHJYpQohqgzp07c/jwYfr27UtiYiKJiYncf//97N+/n2+//ba0Y6ySpAksn1dty14SICFEFfF8jwb4eRiIuZjGp6uPqh2OuIYS1QABBAYG8tZbb9md2717N1999RVffPHFLQdW1ckosHySAAkhqhhPF2deu7cJo+bvYNbaY9zbPJD6/h5qhyWuUuKJEMWtcfi1wKy88vsAJcqCqEKIqqNH0wC6NfQjJ0/hpZ/2YjY7eG1/BSQJkEqsNUDSBFagBsjR74UQosrQaDS83qcpRr2OrScu8/02+SOvopEESCXSCTqfZy1AA7kZkCYjJoQQVUdNL1fG3WWZG+jtPw5yISVL5YhEQTfVB+j++++/7vOJiYm3EotDkQQon5MePGpAyjlLLZB7dbUjEkKIUvNouxCW7TrLvrPJvPHbAT4a1FLtkES+m6oBMplM192Cg4MZMmRIWcVapVjnAXLotcCsrM1gMheQEKKKcdJpmdI3DK0Gftl9jlUH49QOSeS7qRqgOXPmlFUcDkdqgArwqg2nN8lIMCFEldSslonhHUKZ/V8ME5bu5e//+WAyOqsdlsOTPkAqkXmACpCh8EKIKu7/ujegTnU34lOyeO23/WqHI5AESDUyD1ABtqHwkgAJIaomF2cd7/RvblsmY+UBaQpTmyRAKpF5gAqw1QDJMFEhRNUVHuzN4x1CAXjpp70kpeeoHJFjkwRIJdIEVoBXsGUvcwEJIao4aQqrOCQBUok0gRXgWdOyz0mD9EvqxiKEEGXI2hSmlaYw1UkCpBIZBVaAswu4B1iOE0+qG4sQQpSx8GBvHu9YB5CmMDVJAqQSax8gmQcon4wEE0I4kHF33XalKexXaQpTgyRAKpEmsKtYR4IlSUdoIUTV5+Ks490H8pvCdkpTmBokAVKJNIFdRUaCCSEcTKva3ozIbwp7celeElJlrbDyJAmQSmyjwKQJzMIkcwEJIRzP/+66jfp+7lxMzeKln/bKyOByJAmQSqxrgUkNUD7rUHhpAhNCOBAXZx0fDGyBs07Div1x/LD9jNohOQxJgFQiTWBXkdmghRAOqmlNE/+76zYAXvv1AKcvpasckWOQBEglMhHiVaxNYFnJkJGoaihCCFHenuhUl9tDvEnNymXc97vIM8t3Q1mTBEgltiYwWQrDQm8Eo6/lWGqBhBAORqfV8P6AFrgbnNh64jKf/3tM7ZCqPEmAVCJNYEWwjgSTfkBCCAcU5GNk0j2NAfjg78PsO5ukckRVm+oJ0KeffkpISAguLi60bduWLVu2XLPs/v376devHyEhIWg0GmbMmHHL11SLzANUBOkHJIRwcP3DaxHVxJ+cPIX/Ld5FZk6e2iFVWaomQIsXL2bcuHFMmjSJHTt20Lx5c6KiooiPjy+yfHp6OnXq1GHq1KkEBASUyjXVIjVARZC5gIQQDk6j0TDl/jB83Q0ciU9l6p+H1A6pylI1AXr//fcZMWIEw4YNo3HjxsyaNQuj0cjXX39dZPnbb7+dd955hwcffBCDwVAq11SLbSkM6QR9hcmaAMl6YEIIx+Xjpued/mEAzN1wgn8OySzRZUG1BCg7O5vt27cTGRl5JRitlsjISDZu3Fiu18zKyiI5OdluK2vSBFYE6QMkhBAA3NnQj2HtQwB4bske4pIz1Q2oClItAbp48SJ5eXn4+/vbnff39yc2NrZcrzllyhRMJpNtCwoKKtH73wydRgfIKDA70gdICCFsXuzZkCaBnlxKy+bZRTI0vrSp3gm6IpgwYQJJSUm27fTpsq+BkHmAimCdCyjjMmSlqBuLEEKozOCk4+NBLTHqdWw8nsBnq4+qHVKVoloC5Ovri06nIy7Ovm0zLi7umh2cy+qaBoMBT09Pu62sSRNYEVw8wcXLciwdoYUQgjrV3XnjvqYAzFh1hG0nLqkcUdWhWgKk1+sJDw9n1apVtnNms5lVq1YRERFRYa5ZVmyjwKQJzJ70AxJCCDv9wmvRt2VN8swKYxftIjE9W+2QqgRVm8DGjRvH7NmzmTdvHgcPHmTUqFGkpaUxbNgwAIYMGcKECRNs5bOzs9m1axe7du0iOzubs2fPsmvXLo4ePVrsa1YU1pmgpQnsKrah8NIPSAghrN7o05SQakbOJmbwwo975LujFDip+eYDBw7kwoULTJw4kdjYWFq0aMHy5cttnZhPnTqFVnslRzt37hwtW7a0PX733Xd599136dy5M2vWrCnWNSsKmQfoGiQBEkKIQtwNTnw8qBX3z1zPiv1xfLvpJEMiQtQOq1LTKJJGFpKcnIzJZCIpKanM+gP9GfMn4/8dT9uAtnwZ9WWZvEeltPEzWDEBGveBAfPUjkYIISqUr9bF8MZvB3DWaVjyZDtaBHmpHVKFcjPf3zIKTCW2TtDSB8ie9AESQohreqx9CD2bBpCTp/DUd9u5lCb9gUpKEiCVWGeCliawq8hcQEIIcU0ajYbp/cMI9XXjXFImzy6W+YFKShIglcg8QNdgnQso7QLkZKgbixBCVEAeLs7MfLgVLs5a/j18gY//OaJ2SJWSJEAqkXmArsHVG/QelmOZC0gIIYrUMMCTt/o0A+DDVUdYe/iCyhFVPpIAqcTWBCZ9gOxpNFeawZKkGUwIIa6lX3gtBrWpjaLA2EU7OZsoteY3QxIglUgT2HXIUHghhCiWSfc0pmlNTxLTc3jqu+1k5uSpHVKlIQmQSqQJ7Dqs/YCkCUwIIa7LxVnHzMHhmFyd2X0miZd/2id/WBeTJEAqkYkQr0NqgIQQotiCfIx8+lArtBr4cccZvl5/Qu2QKgVJgFRi7QOkIJl6IbY+QFIDJIQQxdGhvi8v92oMwFu/H2DdkYsqR1TxSQKkEmkCuw6pARJCiJv2WPsQ+rWqhVmB0Qt2cDIhTe2QKjRJgFQiTWDXYcpPgFJiITdL3ViEEKKS0Gg0vNW3Kc2DvEjKyGHEN9tIzcpVO6wKSxIglcgosOtw8wUnV0CBpDNqRyOEEJWGi7OOLx4Jx8/DwOG4VMYt3oVZZooukiRAKtEga4Fdk91cQNIPSAghboa/pwuzHglHr9Py14E43v0rWu2QKiRJgFQiNUA3IP2AhBCixFrV9mZqP8tM0Z+tOcaiLfJv6dUkAVKJ9AG6AZkLSAghbsn9rWrxTLf6ALy8bB//HZHlMgqSBEglMgrsBqQGSAghbtn/IuvTp0UgeWaFp77bQXRsitohVRiSAKlE5gG6AUmAhBDilmk0Gqb1D6NNiA8pWbk8Nncr8SmZaodVIUgCpBJpArsB71DL/nKMunEIIUQlZ3DS8fkj4YT6unE2MYPH520jPVuGx0sCpBJpArsBn/wEKOU8ZMtkXkIIcSu83fTMefR2vI3O7DmTxOj5O8jJc+zvH0mAVCKjwG7A6AMuJsvx5ROqhiKEEFVBiK8bXw5tjcFJy+roC7zwwx6HniNIEiCVyDxAxeBTx7K/JM1gQghRGsKDffhscCt0Wg1Ld55lyp8H1Q5JNZIAqUT6ABWDLQE6rm4cQghRhXRr5M+0fmEAzP4vhs/XHlM5InVIAqQSaQIrBukILYQQZaJ/eC1eurshAFP+PMSSbY4355okQCqxdYKWJrBrkxogIYQoMyM71WVkJ8u/sy8u3ctf+2NVjqh8SQKkEus8QNIEdh3WkWDSB0gIIcrEiz0a0q9VLfLMCmMW7GRNdLzaIZUbSYBUIk1gxWCtAUo6DbnZ6sYihBBVkFarYVq/ZtzdLIDsPDNPfLudDUcvqh1WuZAESCUyD1AxuPuDsxEUs8wILYQQZcRJp2XGwJZENvIjK9fM8Hnb2HriktphlTlJgFRiqwGSpTCuTaORjtBCCFEO9E5aPnmoFR3r+5KRk8ewOVvZdTpR7bDKlCRAKpE+QMVk6wckHaGFEKIsuTjr+OKR1txRx4fUrFyGfLWZfWeT1A6rzEgCpBJpAism6QgthBDlxlWv46uhtxMe7E1yZi4Pzd5UZWuCJAFSiXSCLiYZCi+EEOXKzeDE3GG30zo/CXr4y81sq4J9giQBUoltJmiZB+j6vKUJTAghypuHizPzHmtzpTns6y1sPJagdlilShIgldjWApMmsOurVteyv3wC8nJVDUUIIRyJm8GJOY+2oWN9X9Kz83h0zhb+PXxB7bBKjSRAKrHWAIE0g12XZy3LUHhzjqwKL4QQ5cxVr2P2kNZ0a2gZIv/4vG2sPBCndlilQhIglRRMgKQW6Dq0WqhWz3J88bC6sQghhANycdYx8+FwejTJnyzxu+1VYu0wSYBUYh0FBtIP6IZ8b7PsJQESQghVWOYJamlbNuP5H/Ywa+2xSt2CIQmQSrRIE1ix2RKgI+rGIYQQDsxJp+XdB8J4orNldO7UPw/x1u8HMZsr53eYJEAqkSawm+Bb37K/GK1uHEII4eA0Gg0TejbilV6NAPhyXQz/t2Q3OXmV73tMEiCV2DWBSQJ0fQWbwKS2TAghVPd4xzq8P6A5TloNP+08y2Nzt5KcmaN2WDdFEiCV2I0Ck/XArq9aPUADmUmQVnWGYAohRGV2f6tazB7aGldnHf8duUj/mRs4fSld7bCKTRIglRTsAyQ1QDfg7ALewZZj6QgthBAVxp0N/Pj+iQj8PAwcjkul72fr2XnqstphFYskQCqRJrCbJCPBhBCiQmpWy8TPY9rTqIYnF1OzefCLTfy+57zaYd2QJEAqkYkQb5I1AbogCZAQQlQ0NUyu/PBkhG3CxNELdvDJP0cq9PebJEAqsS6FATIPULHYaoBkJJgQQlREbgYnvhjSmsfaW9ZwfPevw4xesIO0rIq5jJEkQCrRaDSyHtjNqN7Asr8gCZAQQlRUOq2Gifc05u2+zXDWafhjbyx9P1tPzMU0tUMrRBIgFVmbwSpyFWGF4WeZc4Lks5B+Sd1YhBBCXNdDbWuzaOSVztH3frKOfw5VrDXEJAFSkbUjtNQAFYOLCbxqW47jD6gbixBCiBsKD/bmt6c7EB7sTUpmLsPnbeOjVUcqzMzRkgCpyDoUXuYBKib/ppZ93H514xBCCFEsfp4uLBxxBw/fURtFgff/PsywuVu5lJatdmiSAKnJ2gQmNUDF5N/EspcESAghKg29k5Y3+zRjev8wDE5a1h6+QK+P/mP7SXW7M0gCpCJrE1iekqdyJJWEJEBCCFFpDWgdxLLR7Qn1deN8UibfbDypajxOqr67g9NpdIDUABWbtQks/gCYzaCV/F0IISqTRjU8+fXpDsz4+zBjI+urGot8g6jI2gQmNUDF5FMHnFwgJx0ux6gdjRBCiBJwNzjxSu/GeLg4qxqHJEAqctJaKuDMZqkBKhat7spweGkGE0IIcQskAVKR1ACVgPQDEkIIUQokAVKRJEAl4N/Msj+/W904hBBCVGoVIgH69NNPCQkJwcXFhbZt27Jly5brll+yZAkNGzbExcWFZs2a8ccff9g9/+ijj1qWmiiw9ejRoyw/Qok4aSxNYHlmSYCKLbClZX9uB8gM2kIIIUpI9QRo8eLFjBs3jkmTJrFjxw6aN29OVFQU8fHxRZbfsGEDgwYNYvjw4ezcuZM+ffrQp08f9u3bZ1euR48enD9/3rYtXLiwPD7OTZEaoBIIaAYaHaTGQfI5taMRQghRSameAL3//vuMGDGCYcOG0bhxY2bNmoXRaOTrr78usvyHH35Ijx49eP7552nUqBFvvPEGrVq14pNPPrErZzAYCAgIsG3e3t7l8XFuiq0TtAyDLz69EfwaW47P7VA3FiGEEJWWqglQdnY227dvJzIy0nZOq9USGRnJxo0bi3zNxo0b7coDREVFFSq/Zs0a/Pz8aNCgAaNGjSIhIeGacWRlZZGcnGy3lQepASqhmvnNYGclARJCCFEyqiZAFy9eJC8vD39/f7vz/v7+xMbGFvma2NjYG5bv0aMH33zzDatWrWLatGmsXbuWnj17kpdXdKIxZcoUTCaTbQsKCrrFT1Y8kgCVUMF+QEIIIUQJVMmZoB988EHbcbNmzQgLC6Nu3bqsWbOGbt26FSo/YcIExo0bZ3ucnJxcLkmQzANUQoGtLPtzOy0dofOXFBFCCCGKS9UaIF9fX3Q6HXFxcXbn4+LiCAgIKPI1AQEBN1UeoE6dOvj6+nL06NEinzcYDHh6etpt5cFaA5Sr5JbL+1UZ/k1AZ4DMJLh0XO1ohBBCVEKqJkB6vZ7w8HBWrVplO2c2m1m1ahURERFFviYiIsKuPMDff/99zfIAZ86cISEhgRo1apRO4KVE1gIrIZ0z1AizHJ/Zqm4sQgghKiXVR4GNGzeO2bNnM2/ePA4ePMioUaNIS0tj2LBhAAwZMoQJEybYyo8dO5bly5fz3nvvcejQISZPnsy2bdsYM2YMAKmpqTz//PNs2rSJEydOsGrVKu677z7q1atHVFSUKp/xWqwJkMwDVAJBbS37U0V3lhdCCCGuR/U+QAMHDuTChQtMnDiR2NhYWrRowfLly20dnU+dOoW2wKrf7dq1Y8GCBbzyyiu89NJL1K9fn2XLltG0qWWlcJ1Ox549e5g3bx6JiYkEBgbSvXt33njjDQwGgyqf8VqkE/QtCG4HGz+Bk5IACSGEuHkaRZHpdK+WnJyMyWQiKSmpTPsDDV8xnC2xW3in0zv0CK14M1VXaOmXYHqo5fj54+BWTd14hBBCqO5mvr9VbwJzZNIJ+hYYfaB6Q8uxNIMJIYS4SZIAqUg6Qd+i2vkd3yUBEkIIcZMkAVKRTiudoG9JcHvL/uQGdeMQQghR6UgCpCLpBH2LgttZ9ud3QUaimpEIIYSoZCQBUpE0gd0iU03wvQ0UM8T8q3Y0QgghKhFJgFRkTYByzdIJusTq5i9tcmzV9csJIYQQBUgCpCKpASoFdbta9kf/sawLJoQQQhSDJEAqsnWClj5AJRfSHnR6SDoFCcfUjkYIIUQlIQmQiqQTdCnQu0HtOyzH0gwmhBCimCQBUpE0gZWSepGWffSf6sYhhBCi0pAESEUyD1Apadjbsj/xn2WJDCGEEOIGJAFSkW01eGkCuzXV6oJfEzDnwuEVakcjhBCiEpAESEXSB6gUNbrHsj/4q7pxCCGEqBQkAVKR9AEqRdYE6NgqyEpVNxYhhBAVniRAKrI1gUkfoFvn3wR86kBuJkT/oXY0QgghKjhJgFSk1UoTWKnRaCBsoOV41wJ1YxFCCFHhSQKkIieNEyAJUKmxJkDH10DSWVVDEUIIUbFJAqQiaydoWQuslPiEQu12gAJ7FqsdjRBCiApMEiAVOWudAakBKlUtBln2O78Ds3QuF0IIUTRJgFTkrLMkQNl52SpHUoU0uR8MnnDpmCyNIYQQ4pokAVKRtQYox5yjciRViMEdWj5iOd48S91YhBBCVFiSAKlIr9UD0geo1LV5HNDA0ZVw8Yja0QghhKiAJAFSkbUJLCdPaoBKlU8duK2H5XjDx+rGIoQQokKSBEhF1iawbLP0ASp17cda9rvmQ+IpdWMRQghR4UgCpCLpA1SGgiOgThfLAqn/vad2NEIIISoYSYBUZEuApAmsbHR+0bLf+R1cilE3FiGEEBWKJEAqsvUBkhqgshEcAXW7WmqB/n5V7WiEEEJUIJIAqUiawMpB97dAo4ODv8LxtWpHI4QQooKQBEhFtk7QMhFi2fFvDLcPtxz/+QLkyr0WQgghCZCqpAmsnHSZAMZqcOEg/PuO2tEIIYSoACQBUpF1IkRJgMqY0Qd65Y8E++89OLtD3XiEEEKoThIgFVmbwGQm6HLQpK9lnTAlD5aOgMxktSMSQgihIkmAVCSLoZazXu+BZ01IOAo/PwWKonZEQgghVCIJkIpkFFg5M/rAgG9A62wZFSYTJAohhMOSBEhFkgCpoFZr6DnNcvzPG7BrgbrxCCGEUIUkQCrS6yydoM2KmTxznsrROJDbh0O7py3HP4+BQ3+oG48QQohyJwmQiqw1QABZeVkqRuKAIl+H5g9ZOkV//wjsW6p2REIIIcqRJEAqcnFyQYMGgPTcdJWjcTBaLdz7ETR7wLJUxo/DYdsctaMSQghRTiQBUpFWo8XVyRWAjJwMlaNxQDpn6Ps5hA8DxQy/PQt/jAdZnFYIIao8SYBU5ubsBkBabprKkTgorQ56fwBdXrI83vI5fHMfJJ1RNy4hhBBlShIglRmdjQCk50gTmGo0GujyAjy4APTucHI9fNYOdi+SuYKEEKKKkgRIZUYnSwKUliM1QKpr2Aue+BdqtoasJPjpCZjfHy4eUTsyIYQQpUwSIJXZaoCkE3TFUK0uPLYC7nzFMmHi0ZXwWQT89QqkJagdnRBCiFIiCZDKrDVA0gRWgeicoPPzMHoz1O8O5hzY8DHMaAYrJ0siJIQQVYAkQCqzdoKWGqAKqFpdGLwEHloCAWGQkwbrPoAPGsPPo+HcLrUjFEIIUUKSAKnMmgClZqeqHIm4ptu6W/oGDVoENVpAbibs/A6+6AxfRsKW2ZB2Ue0ohRBC3AQntQNwdN4u3gBcyrykciTiujQaaNATbusBpzdbkp4DP8OZrZbtzxeg7p3QuA/Uvws8AtSOWAghxHVIAqQyX1dfAC5kXFA5ElEsGg3UvsOypbwNe5dYtvO7LB2mj660lKvR3NJ/qG5XCGwFzi6qhi2EEMKeJEAqsyZACRnSsbbS8fCHdmMs28WjsO9HOLwczu2A87st27/vgM4ANVtB7QgIbgeBLcHNV+3ohRDCoUkCpLLqrtUBqQGq9HzrWSZT7PICpMZbaoKO/AUn1kHaBTi10bKte99S3rMW1Aiz1BTVaA5+jcBU27JGmRBCiDInCZDK/Ix+AMSlxZFnzkOn1akckbhl7n7Q4iHLpiiQcAxObYBTmyzbpWOQfMayRf9x5XVOLlCtHvjWB9/bLJtPqCUxcvO1NL8JIYQoFZIAqSzQPRBXJ1cycjM4lXKKUFOo2iGJ0qTRWGqHfOtBqyGWc5nJELcvv5lsj2WfcMQyuixun2W7mpMreAWBV23LZgoCUy1w97d0uHb3AxcvSZKEEKKYJAFSmVajpa6pLvsS9nH48mFJgByBi6elL1BwuyvnzHmQeNLSl+ji4Svb5ZOQch5yM66cuxYnF0si5J6fEHkEgLEauPqAqzcYfSzHRm/L3sUkCZMQwmFJAlQBNKvejH0J+9h4biNRIVFqhyPUoNWBTx3Ldlt3++dysy3NZYmn8rfTln3yWUt/o9RYyEyy1CBZyxSHRnclMXIxgcEjf/PM3zyubC6eBZ7LP+dstGxOBkmkhBCVjiRAFcCdQXey8NBCVpxYwf/C/4fJYFI7JFGROOmvJEfXkpORnwzFQUqsZZ8aB+mXIONSgf1lyz4nHZQ8SL9o2W6FRnslGXJ2Bb2bZX/dcy6WGiud3pJA2Y5dLJ/3es/pDJJ0CSFuWYVIgD799FPeeecdYmNjad68OR9//DFt2rS5ZvklS5bw6quvcuLECerXr8+0adO4++67bc8risKkSZOYPXs2iYmJtG/fnpkzZ1K/fv3y+Dg3rW2NttzmfRuHLx9m3JpxTOs0zTY8XohicXYF72DLVhw5mZBx+UpylJUMWSmW/knW46yUwseZBR6bcyzXUsyQnWrZypOuQKKk01vWcNM6X3XsnL+/1nNOlr1Of+XY7jm9fTmtk6XmTJu/aXSWc3aPdQXKOVlG9tm9zsmSNFpfd6NykugJUSY0iqIoagawePFihgwZwqxZs2jbti0zZsxgyZIlREdH4+fnV6j8hg0b6NSpE1OmTKF3794sWLCAadOmsWPHDpo2bQrAtGnTmDJlCvPmzSM0NJRXX32VvXv3cuDAAVxcbjwhXXJyMiaTiaSkJDw9PUv9Mxdl/8X9DFsxjIzcDPRaPR1rdeT2gNup51WPYM9g/Ix+aDUyRFpUIHk5lpqknAzITrPsczIsa6bZnUsvulxeFuTmb3lZlqa+3EzIy9/nZhcu44g0Vydb2gLntPmb7kqyZDunvarMVc9pdFc91hTxmqufv8Zz2qLKXyeugtdEc9Wx5qpjbdHHdq+jiGuU1eu4Rsyl+DqwL2P7XdDYX7dUzhX1XppbP6dS4n4z39+qJ0Bt27bl9ttv55NPPgHAbDYTFBTE008/zYsvvlio/MCBA0lLS+O3336znbvjjjto0aIFs2bNQlEUAgMD+b//+z+ee+45AJKSkvD392fu3Lk8+OCDN4xJjQQI4NClQ7y+8XX2Xtxb6DmdRofJYMLb4I23izduzm64OLngonPB1ckVVydXXJxccNI6odPocNI62Y51Wh1OGvvHOo0OjUaD7T+NxpZg2R5j+Z+24GNN/i+1Bkt56zXA0qHbWtZ6zo7m6oea6z4GbO93vTIleU2h9y7if9ZCryvyI13/vYp1XQdUsnugWJIua7Jkzs5PkrIt5815llqpvBww5xbeCp7PywUl96rXFSyfA3n5562PzXn5x2Ygz7K3nlPMliZFs3WfZ9kr1jLWx3lgVvJfk3elLKr+M6wK+b/Ake5BwcTrSqLk3vYJTJGvl+o73cz3t6pNYNnZ2Wzfvp0JEybYzmm1WiIjI9m4cWORr9m4cSPjxo2zOxcVFcWyZcsAiImJITY2lsjISNvzJpOJtm3bsnHjxiIToKysLLKyrvx1mZycfCsfq8Qa+jRk/t3zOXDpABvPbWRX/C5OJJ/gbMpZcpVcLmVesqwZlqRKeEKIgrQUczlpXf4mhCjo8aQDjFXx/VVNgC5evEheXh7+/v525/39/Tl06FCRr4mNjS2yfGxsrO1567lrlbnalClTeO2110r0GUqbRqOhSbUmNKnWxHYux5zD5czLXM68zKXMSyRmJZKRm2HbMnMzLVteJrnmXHLNueQpeeSZ88hVCj/OM+eRp+ShKApmzKCAgoJZMaOgYK0UtD3OP6coiv3j/L21HHDlmle5uqJRufov3iL+AL66TKHXFOe6RSjOawq9dxEVpTf6DMW5riNSudK5QpDfA1Hx/j8o/3h0tSPK/T0LqhCdoNU2YcIEu1ql5ORkgoKCVIzInrPWGT+jn23WaCGEEELcGlV71fr6+qLT6YiLi7M7HxcXR0BAQJGvCQgIuG556/5mrmkwGPD09LTbhBBCCFF1qZoA6fV6wsPDWbVqle2c2Wxm1apVREQUXTUWERFhVx7g77//tpUPDQ0lICDArkxycjKbN2++5jWFEEII4VhUbwIbN24cQ4cOpXXr1rRp04YZM2aQlpbGsGHDABgyZAg1a9ZkypQpAIwdO5bOnTvz3nvv0atXLxYtWsS2bdv44osvAEsfmmeffZY333yT+vXr24bBBwYG0qdPH7U+phBCCCEqENUToIEDB3LhwgUmTpxIbGwsLVq0YPny5bZOzKdOnUKrvVJR1a5dOxYsWMArr7zCSy+9RP369Vm2bJltDiCA8ePHk5aWxsiRI0lMTKRDhw4sX768WHMACSGEEKLqU30eoIpIrXmAhBBCCFFyN/P9LVMLCyGEEMLhSAIkhBBCCIcjCZAQQgghHI4kQEIIIYRwOJIACSGEEMLhSAIkhBBCCIcjCZAQQgghHI4kQEIIIYRwOJIACSGEEMLhqL4URkVknRw7OTlZ5UiEEEIIUVzW7+3iLHIhCVARUlJSAAgKClI5EiGEEELcrJSUFEwm03XLyFpgRTCbzZw7dw4PDw80Gk2pXjs5OZmgoCBOnz4t64yVIbnP5UPuc/mQ+1w+5D6Xn7K614qikJKSQmBgoN1C6kWRGqAiaLVaatWqVabv4enpKf+DlQO5z+VD7nP5kPtcPuQ+l5+yuNc3qvmxkk7QQgghhHA4kgAJIYQQwuFIAlTODAYDkyZNwmAwqB1KlSb3uXzIfS4fcp/Lh9zn8lMR7rV0ghZCCCGEw5EaICGEEEI4HEmAhBBCCOFwJAESQgghhMORBEgIIYQQDkcSoHL06aefEhISgouLC23btmXLli1qh1Sh/fvvv9xzzz0EBgai0WhYtmyZ3fOKojBx4kRq1KiBq6srkZGRHDlyxK7MpUuXGDx4MJ6ennh5eTF8+HBSU1PtyuzZs4eOHTvi4uJCUFAQ06dPL+uPVmFMmTKF22+/HQ8PD/z8/OjTpw/R0dF2ZTIzMxk9ejTVqlXD3d2dfv36ERcXZ1fm1KlT9OrVC6PRiJ+fH88//zy5ubl2ZdasWUOrVq0wGAzUq1ePuXPnlvXHq1BmzpxJWFiYbeK3iIgI/vzzT9vzcp/LxtSpU9FoNDz77LO2c3Kvb93kyZPRaDR2W8OGDW3PV4p7rIhysWjRIkWv1ytff/21sn//fmXEiBGKl5eXEhcXp3ZoFdYff/yhvPzyy8rSpUsVQPnpp5/snp86dapiMpmUZcuWKbt371buvfdeJTQ0VMnIyLCV6dGjh9K8eXNl06ZNyn///afUq1dPGTRokO35pKQkxd/fXxk8eLCyb98+ZeHChYqrq6vy+eefl9fHVFVUVJQyZ84cZd++fcquXbuUu+++W6ldu7aSmppqK/Pkk08qQUFByqpVq5Rt27Ypd9xxh9KuXTvb87m5uUrTpk2VyMhIZefOncoff/yh+Pr6KhMmTLCVOX78uGI0GpVx48YpBw4cUD7++GNFp9Mpy5cvL9fPq6ZffvlF+f3335XDhw8r0dHRyksvvaQ4Ozsr+/btUxRF7nNZ2LJlixISEqKEhYUpY8eOtZ2Xe33rJk2apDRp0kQ5f/68bbtw4YLt+cpwjyUBKidt2rRRRo8ebXucl5enBAYGKlOmTFExqsrj6gTIbDYrAQEByjvvvGM7l5iYqBgMBmXhwoWKoijKgQMHFEDZunWrrcyff/6paDQa5ezZs4qiKMpnn32meHt7K1lZWbYyL7zwgtKgQYMy/kQVU3x8vAIoa9euVRTFck+dnZ2VJUuW2MocPHhQAZSNGzcqimJJVLVarRIbG2srM3PmTMXT09N2X8ePH680adLE7r0GDhyoREVFlfVHqtC8vb2VL7/8Uu5zGUhJSVHq16+v/P3330rnzp1tCZDc69IxadIkpXnz5kU+V1nusTSBlYPs7Gy2b99OZGSk7ZxWqyUyMpKNGzeqGFnlFRMTQ2xsrN09NZlMtG3b1nZPN27ciJeXF61bt7aViYyMRKvVsnnzZluZTp06odfrbWWioqKIjo7m8uXL5fRpKo6kpCQAfHx8ANi+fTs5OTl297lhw4bUrl3b7j43a9YMf39/W5moqCiSk5PZv3+/rUzBa1jLOOrvf15eHosWLSItLY2IiAi5z2Vg9OjR9OrVq9D9kHtdeo4cOUJgYCB16tRh8ODBnDp1Cqg891gSoHJw8eJF8vLy7H7QAP7+/sTGxqoUVeVmvW/Xu6exsbH4+fnZPe/k5ISPj49dmaKuUfA9HIXZbObZZ5+lffv2NG3aFLDcA71ej5eXl13Zq+/zje7htcokJyeTkZFRFh+nQtq7dy/u7u4YDAaefPJJfvrpJxo3biz3uZQtWrSIHTt2MGXKlELPyb0uHW3btmXu3LksX76cmTNnEhMTQ8eOHUlJSak091hWgxdCAJa/mPft28e6devUDqXKatCgAbt27SIpKYkffviBoUOHsnbtWrXDqlJOnz7N2LFj+fvvv3FxcVE7nCqrZ8+etuOwsDDatm1LcHAw33//Pa6uripGVnxSA1QOfH190el0hXrAx8XFERAQoFJUlZv1vl3vngYEBBAfH2/3fG5uLpcuXbIrU9Q1Cr6HIxgzZgy//fYbq1evplatWrbzAQEBZGdnk5iYaFf+6vt8o3t4rTKenp6V5h/L0qDX66lXrx7h4eFMmTKF5s2b8+GHH8p9LkXbt28nPj6eVq1a4eTkhJOTE2vXruWjjz7CyckJf39/uddlwMvLi9tuu42jR49Wmt9nSYDKgV6vJzw8nFWrVtnOmc1mVq1aRUREhIqRVV6hoaEEBATY3dPk5GQ2b95su6cREREkJiayfft2W5l//vkHs9lM27ZtbWX+/fdfcnJybGX+/vtvGjRogLe3dzl9GvUoisKYMWP46aef+OeffwgNDbV7Pjw8HGdnZ7v7HB0dzalTp+zu8969e+2Szb///htPT08aN25sK1PwGtYyjv77bzabycrKkvtcirp168bevXvZtWuXbWvdujWDBw+2Hcu9Ln2pqakcO3aMGjVqVJ7f51LpSi1uaNGiRYrBYFDmzp2rHDhwQBk5cqTi5eVl1wNe2EtJSVF27typ7Ny5UwGU999/X9m5c6dy8uRJRVEsw+C9vLyUn3/+WdmzZ49y3333FTkMvmXLlsrmzZuVdevWKfXr17cbBp+YmKj4+/srjzzyiLJv3z5l0aJFitFodJhh8KNGjVJMJpOyZs0au+Gs6enptjJPPvmkUrt2beWff/5Rtm3bpkRERCgRERG2563DWbt3767s2rVLWb58uVK9evUih7M+//zzysGDB5VPP/3UoYYMK4qivPjii8ratWuVmJgYZc+ePcqLL76oaDQa5a+//lIURe5zWSo4CkxR5F6Xhv/7v/9T1qxZo8TExCjr169XIiMjFV9fXyU+Pl5RlMpxjyUBKkcff/yxUrt2bUWv1ytt2rRRNm3apHZIFdrq1asVoNA2dOhQRVEsQ+FfffVVxd/fXzEYDEq3bt2U6Ohou2skJCQogwYNUtzd3RVPT09l2LBhSkpKil2Z3bt3Kx06dFAMBoNSs2ZNZerUqeX1EVVX1P0FlDlz5tjKZGRkKE899ZTi7e2tGI1GpW/fvsr58+ftrnPixAmlZ8+eiqurq+Lr66v83//9n5KTk2NXZvXq1UqLFi0UvV6v1KlTx+49HMFjjz2mBAcHK3q9XqlevbrSrVs3W/KjKHKfy9LVCZDc61s3cOBApUaNGoper1dq1qypDBw4UDl69Kjt+cpwjzWKoiilU5ckhBBCCFE5SB8gIYQQQjgcSYCEEEII4XAkARJCCCGEw5EESAghhBAORxIgIYQQQjgcSYCEEEII4XAkARJCCCGEw5EESAghikGj0bBs2TK1wxBClBJJgIQQFd6jjz6KRqMptPXo0UPt0IQQlZST2gEIIURx9OjRgzlz5tidMxgMKkUjhKjspAZICFEpGAwGAgIC7DZvb2/A0jw1c+ZMevbsiaurK3Xq1OGHH36we/3evXvp2rUrrq6uVKtWjZEjR5KammpX5uuvv6ZJkyYYDAZq1KjBmDFj7J6/ePEiffv2xWg0Ur9+fX755Zey/dBCiDIjCZAQokp49dVX6devH7t372bw4ME8+OCDHDx4EIC0tDSioqLw9vZm69atLFmyhJUrV9olODNnzmT06NGMHDmSvXv38ssvv1CvXj2793jttdcYMGAAe/bs4e6772bw4MFcunSpXD+nEKKUlNqyqkIIUUaGDh2q6HQ6xc3NzW576623FEWxrGr/5JNP2r2mbdu2yqhRoxRFUZQvvvhC8fb2VlJTU23P//7774pWq1ViY2MVRVGUwMBA5eWXX75mDIDyyiuv2B6npqYqgPLnn3+W2ucUQpQf6QMkhKgU7rzzTmbOnGl3zsfHx3YcERFh91xERAS7du0C4ODBgzRv3hw3Nzfb8+3bt8dsNhMdHY1Go+HcuXN069btujGEhYXZjt3c3PD09CQ+Pr6kH0kIoSJJgIQQlYKbm1uhJqnS4urqWqxyzs7Odo81Gg1ms7ksQhJClDHpAySEqBI2bdpU6HGjRo0AaNSoEbt37yYtLc32/Pr169FqtTRo0AAPDw9CQkJYtWpVucYshFCP1AAJISqFrKwsYmNj7c45OTnh6+sLwJIlS2jdujUdOnRg/vz5bNmyha+++gqAwYMHM2nSJIYOHcrkyZO5cOECTz/9NI888gj+/v4ATJ48mSeffBI/Pz969uxJSkoK69ev5+mnny7fDyqEKBeSAAkhKoXly5dTo0YNu3MNGjTg0KFDgGWE1qJFi3jqqaeoUaMGCxcupHHjxgAYjUZWrFjB2LFjuf322zEajfTr14/333/fdq2hQ4eSmZnJBx98wHPPPYevry/9+/cvvw8ohChXGkVRFLWDEEKIW6HRaPjpp5/o06eP2qEIISoJ6QMkhBBCCIcjCZAQQgghHI70ARJCVHrSki+EuFlSAySEEEIIhyMJkBBCCCEcjiRAQgghhHA4kgAJIYQQwuFIAiSEEEIIhyMJkBBCCCEcjiRAQgghhHA4kgAJIYQQwuFIAiSEEEIIh/P/F0svS1K8sMkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Loss(SGD): 0.057515453547239304\n",
      "Final Loss(Momentum): 0.0006508368533104658\n",
      "Final Loss(Adam): 9.857141094471444e-07\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers, optimizers, losses\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Toy dataset: XOR problem\n",
    "x = np.array([[0,0],[0,1],[1,0],[1,1]], dtype=np.float32)\n",
    "y = np.array([[0],[1],[1],[0]], dtype=np.float32)\n",
    "\n",
    "# Create the simple NN model\n",
    "def create_model():\n",
    "    model = keras.Sequential([\n",
    "        layers.Dense(4,input_dim=2, activation='sigmoid'),\n",
    "        layers.Dense(1, activation='sigmoid'),\n",
    "        \n",
    "    ])\n",
    "    return model\n",
    "\n",
    "\n",
    "# To train the model\n",
    "def train_model(optimizer_name, x, y, epochs=5000, lr=0.1):\n",
    "    model = create_model()\n",
    "    # Select Optimizer based on the input parameter\n",
    "    if optimizer_name == \"SGD\":\n",
    "        optimizer = optimizers.SGD(learning_rate=lr)\n",
    "    elif optimizer_name == \"Momentum\":\n",
    "        optimizer = optimizers.SGD(learning_rate=lr, momentum=0.9)\n",
    "    elif optimizer_name == \"Adam\":\n",
    "        optimizer = optimizers.Adam(learning_rate=lr)\n",
    "\n",
    "    model.compile(optimizer=optimizer, loss=losses.MeanSquaredError())\n",
    "\n",
    "    history = model.fit(x,y,epochs=epochs, verbose=0)\n",
    "    return history.history['loss']\n",
    "\n",
    "# Train models with different optimizers\n",
    "models = [ (name, train_model(name, x, y)) for name in [\"SGD\",\"Momentum\",\"Adam\"] ]\n",
    "\n",
    "# Plot the loss curves\n",
    "for name, losses in models:\n",
    "    plt.plot(losses, label=name)\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.title(\"Optimizer Algorithm Comparison\")\n",
    "plt.show()\n",
    "\n",
    "# Final results\n",
    "for name, losses in models:\n",
    "    print(f\"Final Loss({name}): {losses[-1]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a39a0309-f79b-4456-956e-a5db78c3bc08",
   "metadata": {},
   "source": [
    "# Exp4: Build a CNN on MNIST dataset to demonstrate convolution, pooling and classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "549fc3ae-94e1-4cc9-bd69-8f7813ae1609",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/galaxygamerman/DPLabExps/tensorflow/lib/python3.12/site-packages/keras/src/layers/convolutional/base_conv.py:113: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m830/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8710 - loss: 0.4524"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-12-20 07:30:54.891487: I external/local_xla/xla/service/gpu/autotuning/dot_search_space.cc:208] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs? Working around this by using the full hints set instead.\n",
      "2025-12-20 07:30:56.053638: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_533', 240 bytes spill stores, 240 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 9ms/step - accuracy: 0.9434 - loss: 0.1924 - val_accuracy: 0.9762 - val_loss: 0.0764\n",
      "Epoch 2/5\n",
      "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9824 - loss: 0.0571 - val_accuracy: 0.9893 - val_loss: 0.0412\n",
      "Epoch 3/5\n",
      "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9879 - loss: 0.0392 - val_accuracy: 0.9898 - val_loss: 0.0396\n",
      "Epoch 4/5\n",
      "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9906 - loss: 0.0295 - val_accuracy: 0.9877 - val_loss: 0.0439\n",
      "Epoch 5/5\n",
      "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9933 - loss: 0.0224 - val_accuracy: 0.9887 - val_loss: 0.0406\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - accuracy: 0.9898 - loss: 0.0327\n",
      "Test accuracy: 0.9898\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 1. Get the dataset\n",
    "(x_train, y_train),(x_test, y_test) = mnist.load_data()\n",
    "\n",
    "# Reshape data to fit the model (add chanel dimension)\n",
    "x_train = x_train.reshape((x_train.shape[0],28,28,1)).astype('float32')/255\n",
    "x_test = x_test.reshape((x_test.shape[0],28,28,1)).astype('float32')/255\n",
    "\n",
    "# One-hot encode the labels\n",
    "y_train = to_categorical(y_train)\n",
    "y_test = to_categorical(y_test)\n",
    "\n",
    "# 2. Build the CNN model\n",
    "model = models.Sequential([\n",
    "    layers.Conv2D(32,(3,3),activation='relu',input_shape=(28,28,1)),\n",
    "    layers.MaxPooling2D(2,2),\n",
    "    layers.Conv2D(64,(3,3),activation='relu'),\n",
    "    layers.MaxPooling2D(2,2),\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(64,activation='relu'),\n",
    "    layers.Dense(10,activation='softmax')\n",
    "])\n",
    "\n",
    "# 3. Compile the model\n",
    "model.compile(\n",
    "    optimizer='adam',\n",
    "    loss='categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "\n",
    "# 4. Train the model\n",
    "history = model.fit(x_train,y_train,epochs=5, batch_size=64, validation_split=0.1)\n",
    "\n",
    "# 5. Evaluate the model\n",
    "test_loss, test_acc = model.evaluate(x_test,y_test)\n",
    "print(f\"Test accuracy: {test_acc:.4f}\")\n",
    "\n",
    "# 6. Plot training history (optional)\n",
    "plt.plot(history.history['accuracy'], label=\"Test Accuracy\")\n",
    "plt.plot(history.history['val_accuracy'], label=\"Val Accuracy\")\n",
    "plt.title(\"Training and Validation Accuracy\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba6911a8-51ec-4f43-a6fa-225cd4054286",
   "metadata": {},
   "source": [
    "# Exp5: Implement an LSTM-based RNN for text classification to demonstrate sequence modelling, unfolding computional graphs, and handling long-term dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "089cebed-a642-4512-8828-ec13b8ef4951",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training...\n",
      "Epoch 1/5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/galaxygamerman/DPLabExps/tensorflow/lib/python3.12/site-packages/keras/src/layers/core/embedding.py:100: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 15s - 48ms/step - accuracy: 0.5717 - loss: 0.6670 - val_accuracy: 0.6608 - val_loss: 0.5766\n",
      "Epoch 2/5\n",
      "313/313 - 9s - 27ms/step - accuracy: 0.7569 - loss: 0.5189 - val_accuracy: 0.7896 - val_loss: 0.4734\n",
      "Epoch 3/5\n",
      "313/313 - 11s - 36ms/step - accuracy: 0.8435 - loss: 0.4012 - val_accuracy: 0.8284 - val_loss: 0.4461\n",
      "Epoch 4/5\n",
      "313/313 - 11s - 35ms/step - accuracy: 0.8546 - loss: 0.3901 - val_accuracy: 0.8096 - val_loss: 0.4775\n",
      "Epoch 5/5\n",
      "313/313 - 9s - 29ms/step - accuracy: 0.8767 - loss: 0.3439 - val_accuracy: 0.8238 - val_loss: 0.4497\n",
      "\n",
      "Evaluating on test set...\n",
      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.8199 - loss: 0.4617\n",
      "\n",
      "Test accuracy: 0.8199\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.datasets import imdb\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 1. Load and Preprocess Data\n",
    "vocab_size = 10000\n",
    "max_sequence_length = 200\n",
    "\n",
    "(x_train,y_train),(x_test,y_test) = imdb.load_data(num_words=vocab_size)\n",
    "x_train = pad_sequences(x_train, maxlen=max_sequence_length, padding='post')\n",
    "x_test = pad_sequences(x_test, maxlen=max_sequence_length, padding='post')\n",
    "\n",
    "# 2. Build the LSTM RNN Model\n",
    "model = Sequential()\n",
    "model.add(Embedding(input_dim=vocab_size, output_dim=64,input_length=max_sequence_length))\n",
    "model.add(LSTM(units=64,return_sequences=False))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1,activation='sigmoid'))\n",
    "\n",
    "# 3. Compile the model\n",
    "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\n",
    "\n",
    "# 4. Train the model\n",
    "print(\"\\nTraining...\")\n",
    "history = model.fit(x_train,y_train,epochs=5,batch_size=64,validation_split=0.2,verbose=2)\n",
    "\n",
    "# 5. Evaluate the model\n",
    "print(\"\\nEvaluating on test set...\")\n",
    "loss,accuracy = model.evaluate(x_test,y_test)\n",
    "print(f\"\\nTest accuracy: {accuracy:.4f}\")\n",
    "\n",
    "# 6. Plot accuracy and Loss graphs\n",
    "plt.figure(figsize=(15,5))\n",
    "def plot_graphs(history, string, noOfCols=1, index=0):\n",
    "    plt.subplot(1,noOfCols,index+1)\n",
    "    plt.plot(history.history[string])\n",
    "    plt.plot(history.history['val_'+string])\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(string.capitalize())\n",
    "    plt.title(f\"{string.capitalize()} Over Epochs\")\n",
    "    plt.legend([string,'val_'+string])\n",
    "    plt.grid()\n",
    "\n",
    "to_display = [\n",
    "    \"accuracy\",\n",
    "    \"loss\",\n",
    "]\n",
    "\n",
    "for index, title in enumerate(to_display):\n",
    "    plot_graphs(history,title, len(to_display), index)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdc08cef",
   "metadata": {},
   "source": [
    "# Exp6: Construct a Bidirectional RNN and compare it's performancce with a standard RNN on sequence prediction tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4ffac51b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_23686/270380718.py:16: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at /pytorch/torch/csrc/utils/tensor_new.cpp:253.)\n",
      "  x = torch.tensor(x,dtype=torch.float32).unsqueeze(-1)  # (batch,seq_len,input_size)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Loss (RNN):2357.5000\n",
      "Final Loss (BiRNN):1443.2118\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "\n",
    "# Synthetic dataset: predict next number in a sequence\n",
    "seq_length = 10\n",
    "num_samples = 1000\n",
    "x= []\n",
    "y =[]\n",
    "for _ in range(num_samples):\n",
    "    start = np.random.randint(0,100)\n",
    "    seq = np.arange(start,start+seq_length)\n",
    "    x.append(seq[:-1])\n",
    "    y.append(seq[1:])\n",
    "x = torch.tensor(x,dtype=torch.float32).unsqueeze(-1)  # (batch,seq_len,input_size)\n",
    "y = torch.tensor(y,dtype=torch.float32).unsqueeze(-1)\n",
    "\n",
    "class SimpleRNN(nn.Module):\n",
    "    def __init__(self, input_size=1, hidden_size=32, output_size=1):\n",
    "        super(SimpleRNN, self).__init__()\n",
    "        self.rnn = nn.RNN(input_size,hidden_size,batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_size,output_size)\n",
    "    def forward(self,x):\n",
    "        out,_ = self.rnn(x)\n",
    "        return self.fc(out)\n",
    "\n",
    "class BiRNN(nn.Module):\n",
    "    def __init__(self,input_size=1,hidden_size=32,output_size=1):\n",
    "        super(BiRNN,self).__init__()\n",
    "        self.rnn = nn.RNN(input_size,hidden_size,batch_first=True,bidirectional=True)\n",
    "        self.fc = nn.Linear(hidden_size*2,output_size)\n",
    "    def forward(self,x):\n",
    "        out,_ = self.rnn(x)\n",
    "        return self.fc(out)\n",
    "\n",
    "def train_model(model,X,Y,epochs=50):\n",
    "    criterion = nn.MSELoss()\n",
    "    optimizer = optim.Adam(model.parameters(),lr=0.01)\n",
    "    for _ in range(epochs):\n",
    "        optimizer.zero_grad()\n",
    "        output = model(X)\n",
    "        loss = criterion(output,Y)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    return loss.item()\n",
    "\n",
    "rnn_model = SimpleRNN()\n",
    "BiRNN_model = BiRNN()\n",
    "rnn_loss = train_model(rnn_model,x,y)\n",
    "BiRNN_loss = train_model(BiRNN_model,x,y)\n",
    "print(f\"Final Loss (RNN):{rnn_loss:.4f}\")\n",
    "print(f\"Final Loss (BiRNN):{BiRNN_loss:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "844e3298-ecf7-470a-9d90-616213db37eb",
   "metadata": {},
   "source": [
    "# Exp7: Implement an encoder-decoder architecture with LSTMs for sequence-to-sequence learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "09ff9fd8-b926-4d20-992c-4cd2889740b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 19ms/step - loss: 0.1301\n",
      "Epoch 2/10\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0664\n",
      "Epoch 3/10\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0612\n",
      "Epoch 4/10\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0558\n",
      "Epoch 5/10\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 0.0491\n",
      "Epoch 6/10\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 0.0399\n",
      "Epoch 7/10\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0261\n",
      "Epoch 8/10\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 0.0110\n",
      "Epoch 9/10\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 0.0032\n",
      "Epoch 10/10\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 0.0012\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step\n",
      "Input:\n",
      " [0.49144987 0.73633325 0.69999321 0.63220933 0.12776643]\n",
      "Predicted reverse:\n",
      " [0.10733837 0.6064768  0.6826959  0.71424294 0.48261386]\n",
      "True reverse:\n",
      " [0.12776643 0.63220933 0.69999321 0.73633325 0.49144987]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, LSTM, Dense\n",
    "\n",
    "# Sample data: input->output\n",
    "num_samples = 1000\n",
    "timesteps = 5\n",
    "input_dim = 1\n",
    "latent_dim = 32\n",
    "\n",
    "# Create toy data\n",
    "x = np.random.rand(num_samples, timesteps, input_dim)\n",
    "y = np.flip(x, axis=1) # reversed sequences\n",
    "\n",
    "# Encoder\n",
    "encoder_inputs = Input(shape=(timesteps,input_dim))\n",
    "encoder_lstm = LSTM(latent_dim, return_state=True)\n",
    "encoder_outputs, state_h, state_c = encoder_lstm(encoder_inputs)\n",
    "encoder_states = [state_h,state_c]\n",
    "\n",
    "# Simpified Encoder\n",
    "# encoder_inputs = Input(shape=(timesteps,input_dim))\n",
    "# encoder_outputs, state_h, state_c = LSTM(latent_dim, return_state=True)(encoder_inputs)\n",
    "# encoder_states = [state_h,state_c]\n",
    "\n",
    "# Decoder\n",
    "decoder_inputs = Input(shape=(timesteps,input_dim))\n",
    "decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)\n",
    "decoder_outputs,_,_ = decoder_lstm(decoder_inputs,initial_state=encoder_states)\n",
    "decoder_dense = Dense(input_dim)\n",
    "decoder_outputs = decoder_dense(decoder_outputs)\n",
    "\n",
    "# Simplified Decoder\n",
    "# decoder_inputs = Input(shape=(timesteps,input_dim))\n",
    "# decoder_outputs,_,_ = LSTM(latent_dim, return_sequences=True, return_state=True)(decoder_inputs,initial_state=encoder_states)\n",
    "# decoder_outputs = Dense(input_dim)(decoder_outputs)\n",
    "\n",
    "# Full Model\n",
    "model = Model([encoder_inputs,decoder_inputs], decoder_outputs)\n",
    "model.compile(optimizer='adam',loss='mse')\n",
    "\n",
    "# Train (teacher forcing - decoder gerts previous true sequence)\n",
    "model.fit((x,y), y, epochs=10, batch_size=32)\n",
    "\n",
    "# Test\n",
    "pred = model.predict([x[:1],y[:1]])\n",
    "print(\"Input:\\n\", x[0].squeeze())\n",
    "print(\"Predicted reverse:\\n\", pred[0].squeeze())\n",
    "print(\"True reverse:\\n\", y[0].squeeze())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d41b810-7514-48d6-804a-fc162660b27c",
   "metadata": {},
   "source": [
    "# Exp8: Implement a Restricted Boltzman Machine (RBM) for learning binary data representations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e2cbacd8-7ed7-4899-b881-b7c9addf49d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-12-20 07:32:45.311552: I tensorflow/core/framework/local_rendezvous.cc:407] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "2025-12-20 07:32:45.662671: I tensorflow/core/framework/local_rendezvous.cc:407] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5, Reconstruction Loss: 0.1194\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-12-20 07:32:54.392935: I tensorflow/core/framework/local_rendezvous.cc:407] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/5, Reconstruction Loss: 0.0842\n",
      "Epoch 3/5, Reconstruction Loss: 0.0748\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-12-20 07:33:11.838754: I tensorflow/core/framework/local_rendezvous.cc:407] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/5, Reconstruction Loss: 0.0696\n",
      "Epoch 5/5, Reconstruction Loss: 0.0660\n"
     ]
    },
    {
     "data": {
      "image/png": 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9eoxKpUJ4eDjeeuutWrdRHZ6qNfT7oevq831pjn1F+oFH3MjoyGQyLFiwABkZGRpXtT1MZWUlioqK6nx/2LBhkEqlOHjw4CNfTVqtY8eOeO655/DVV18hPT0dAODj4wNBEODl5VXrUYTHHntMPQ6ompqqS/URh+Tk5BrvJSUlwd7eHhYWFg2uWyqVYsCAAVi0aBHOnj2LDz/8EH/99Zd6Grcx05VA1VGImzdvaiwrKytDZmamxjIfH58H9t3QGjw8PJCRkYHCwkKN5UlJSer3m4uHhwdSUlJqHMGsrRa5XI4hQ4bgyy+/xPnz5/HSSy/h22+/RWpqqnqMnZ0dJk6ciB9//BGXL19Gx44da71y9/4a6vrO3F/D0KFDIZfLsX79eiQkJODcuXMYPXq0xno+Pj4oKiqq9fscFhZW6xEpbVCpVDWms8+dOwcA6ot06vtd8PHxgUqlapIrXqs1Zl+R4WFwI6PUt29f9OjRA0uWLMHt27cfOv7vv/9GUVEROnXqVOcYS0tLLF++HHPnzsWQIUOarNa33noL5eXlWLRoEYCq84ZkMhnmzZtX4wiOIAi4du0aAKBr167w8vLCkiVLaoSd6vVcXFzQuXNnrF27VmPM6dOnsWPHDgwaNKjB9V6/fr3Gsuob3JaWlgKAOgzeX9fD+Pj41Djq+fXXX9c44jZ8+HCcPHkSv/zyS41tVPfekBoGDRqEysrKGkF/8eLFkEgk6quJm8OgQYOQlZWlcd5YRUUFli5dCktLS4SGhgKA+ntQTSqVomPHjgDu7of7x1haWsLX11f9/oNqOHz4MOLj49XLiouL8fXXX8PT01NjetDW1haRkZHYsGED1q1bB7lcjqFDh2psb+TIkYiPj8f27dtrfNbNmzdRUVHxwHqa0r37WBAEfPHFFzA1NcWAAQMA1P+7MHToUEilUsyfP79GyG7MkdfG7isyPJwqJaM1c+ZMjBgxAmvWrNE44T4/Px/ff/89gKp/EJOTk7F8+XKYmZlh1qxZD9zm+PHjm7zOwMBADBo0CN988w3effdd+Pj44IMPPsDs2bNx8eJFDB06FFZWVkhLS8Mvv/yCKVOm4M0334RUKsXy5csxZMgQdO7cGRMnToSLiwuSkpJw5swZ9T+Sn3zyCQYOHIiQkBBMmjRJfTsQGxubRv3X/Pz587Fnzx4MHjwYHh4eyMnJwZdffgk3Nzf1yew+Pj6wtbXFihUrYGVlBQsLCwQHBz/03KXJkyfj5ZdfxvDhwxEeHo6TJ09i+/btGtNuQNW+3bRpE0aMGIEXXngBQUFBuH79OrZs2YIVK1agU6dODaphyJAh6NevH/71r3/h4sWL6NSpE3bs2IFff/0VM2bM0LgQoSns3Lmz1v+gGDp0KKZMmYKvvvoKEyZMwLFjx+Dp6YlNmzZh//79WLJkifrcq8mTJ+P69evo378/3NzccOnSJSxduhSdO3dWn48VGBiIvn37IigoCHZ2djh69Cg2bdqkcYJ+bWbNmqW+/c1rr70GOzs7rF27Fmlpafjpp59qnH83atQoPPfcc/jyyy8RGRlZ4zSCmTNnYsuWLXjiiSfUt8EoLi7GqVOnsGnTJly8eLHGPm6Iq1evqv9O38vS0lIjRCqVSsTGxmL8+PEIDg7Gtm3bsHXrVrzzzjvqiyHq+13w9fXFv/71L7z//vvo3bs3nn76aSgUChw5cgSurq7qJ6TUV2P3FRkgUa5lJWom996y436VlZWCj4+P4OPjo75lwv23A5FIJIKdnZ3w5JNPCseOHav3tu/VkNuBtGvXrtb3du3aVeNWGD/99JPw+OOPCxYWFoKFhYUQEBAgTJs2TUhOTtZYd9++fUJ4eLhgZWUlWFhYCB07dqxxW4o///xT6NWrl2BmZiZYW1sLQ4YMEc6ePasxpvp2IPff5qP695CWliYIgiDs3LlTeOqppwRXV1dBLpcLrq6uwpgxY2rc6uHXX38VAgMDBRMTE43bcjzo91BZWSm8/fbbgr29vWBubi5ERkYKqampNW4HIgiCcO3aNSE6Olpo1aqVIJfLBTc3N2H8+PFCXl7eQ2u4/3YgglB1u4vXX39dcHV1FUxNTQU/Pz/hk08+0bi1gyBU3Q5k2rRpNWqvrcb7Vd/uoq6f7777ThAEQcjOzhYmTpwo2NvbC3K5XOjQoUON25ps2rRJiIiIEBwdHQW5XC60bt1aeOmll4TMzEz1mA8++EDo0aOHYGtrK5iZmQkBAQHChx9+KJSVlT2wTkEQhPPnzwvPPPOMYGtrKyiVSqFHjx7C77//XuvYgoICwczMrMZtTO5VWFgozJ49W/D19RXkcrlgb28v9OzZU1i4cKG6nvrcLuV+D7odyL37ePz48YKFhYVw/vx5ISIiQjA3NxecnJyEOXPm1LidR32/C4IgCKtWrRK6dOkiKBQKoUWLFkJoaKgQFxenUV9t//8QGhoqhIaGql8/yr4iwyIRhGY8W5aIiEgHTZgwAZs2bXrgeaxEuoDnuBERERHpCQY3IiIiIj3B4EZERESkJ3iOGxEREZGe4BE3IiIiIj3B4EZERESkJ3gD3lqoVCpkZGTAysqq0Y/mISIiIqovQRBQWFgIV1fXGjexvheDWy0yMjLg7u4udhlERERkZC5fvgw3N7c632dwq0X1I2MuX74Ma2trrXxGeXk5duzYgYiICJiammrlM3SNMfYMsG9j6tsYewbYtzH1bYw9A83Td0FBAdzd3dUZpC4MbrWonh61trbWanAzNzeHtbW10Xz5jbFngH0bU9/G2DPAvo2pb2PsGWjevh92ihYvTiAiIiLSEwxuRERERHqCwY2IiIhITzC4EREREekJBjciIiIiPcHgRkRERKQndCK4LVu2DJ6enlAqlQgODsbhw4frHHvmzBkMHz4cnp6ekEgkWLJkSY0xCxYsQPfu3WFlZQVHR0cMHToUycnJWuyAiIiISPtED27r169HTEwM5syZg+PHj6NTp06IjIxETk5OreNLSkrg7e2Njz76CM7OzrWO2b17N6ZNm4aDBw8iLi4O5eXliIiIQHFxsTZbISIiItIq0W/Au2jRIrz44ouYOHEiAGDFihXYunUrVq1ahVmzZtUY3717d3Tv3h0Aan0fAGJjYzVer1mzBo6Ojjh27Bj69OnTxB0QERERNQ9Rg1tZWRmOHTuG2bNnq5dJpVKEhYUhPj6+yT4nPz8fAGBnZ1fr+6WlpSgtLVW/LigoAFB1p+Ty8vImq6NapUrAwfO5OJYngU1KDh7zcYBMavgPs6/+XWrjd6rL2Lfx9G2MPQPs25j6Nsaegebpu77bFjW45eXlobKyEk5OThrLnZyckJSU1CSfoVKpMGPGDPTq1Qvt27evdcyCBQswb968Gst37NgBc3PzJqmj2slrEvx8UYqbZRIAMnybkgBbuYCnPVXo1FJo0s/SVXFxcWKXIAr2bTyMsWeAfRsTY+wZ0G7fJSUl9Ron+lSptk2bNg2nT5/Gvn376hwze/ZsxMTEqF9XP+g1IiKiSZ9Vuv1MNlbHn8T98Sy/TILV52RYOroTIts51bquISgvL0dcXBzCw8ON7hl37Ns4+jbGngH2bUx9G2PPQPP0XT3b9zCiBjd7e3vIZDJkZ2drLM/Ozq7zwoOGiI6Oxu+//449e/bAzc2tznEKhQIKhaLGclNT0ybbQZUqAR9uS64R2gBAACAB8OG2ZAzs2Mrgp02b8veqT9i38TDGngH2bUyMsWdAu33Xd7uiXlUql8sRFBSEnTt3qpepVCrs3LkTISEhjd6uIAiIjo7GL7/8gr/++gteXl5NUe4jOZx2HZn5t+t8XwCQmX8bh9OuN19RREREpFdEnyqNiYnB+PHj0a1bN/To0QNLlixBcXGx+irTcePGoVWrVliwYAGAqgsazp49q/7z1atXkZCQAEtLS/j6+gKomh794Ycf8Ouvv8LKygpZWVkAABsbG5iZmYnQJZBTWHdoa8w4IiIiMj6iB7dRo0YhNzcX7733HrKystC5c2fExsaqL1hIT0+HVHr3wGBGRga6dOmifr1w4UIsXLgQoaGh2LVrFwBg+fLlAIC+fftqfNbq1asxYcIErfZTF0crZZOOIyIiIuMjenADqs5Fi46OrvW96jBWzdPTE4Lw4KsvH/a+GHp42cHFRoms/Nu1nucmAeBso0QPr9pvWUJEREQk+pMTjIVMKsGcIYEAqkJabeYMCTT4CxOIiIio8RjcmlFUexcsf64rnG1qTodG9/dFVHsXEaoiIiIifcHg1syi2rtg39v98f0L3TDOrxJRd+7b9ldSDlQq3ZviJSIiIt3B4CYCmVSCYC87BNkLmDekLSwVJjiTUYDfT2WKXRoRERHpMAY3kdlZyDGljzcA4NMdySirUIlcEREREekqBjcdMOlxL9hbynHpWgnWH70sdjlERESkoxjcdICFwgSv9vcDAHy+MwUlZRUiV0RERES6iMFNR4zp0RrudmbILSzF6v0XxS6HiIiIdBCDm46Qm0jxRrg/AGDFrvO4UVwmckVERESkaxjcdMiTnVzR1sUahaUV+HJXqtjlEBERkY5hcNMhUqkEb0VVHXVbG38JGTdviVwRERER6RIGNx3Tt40DenjZoaxChSV/nhO7HCIiItIhDG46RiKRYNbAAADApmNXkJpTKHJFREREpCsY3HRQ19YtEBHoBJUAfLI9WexyiIiISEcwuOmomZH+kEqA7WeycTz9htjlEBERkQ5gcNNRfk5WGN7VDQDw8bYkCAIfQE9ERGTsGNx02IzwNpCbSHEo7Tp2n8sVuxwiIiISGYObDmtla4Zxj3kAAD6OTYZKxaNuRERExozBTcdN6+cLK4UJEjML8Ns/GWKXQ0RERCJicNNxLSzkmNLHGwDw6Y5zKKtQiVwRERERiYXBTQ+88LgX7C0VSL9egvVH0sUuh4iIiETC4KYHLBQmeG2ALwDgs52pKC6tELkiIiIiEgODm54Y3b01WtuZI6+oFKv2pYldDhEREYmAwU1PyE2keCOiDQDg6z0XcL24TOSKiIiIqLkxuOmRIR1dEehijcLSCnz5d6rY5RAREVEzY3DTI1KpBG9F+QMAvj14CVdv3hK5IiIiImpODG56JrSNA4K97FBWocKSuHNil0NERETNiMFNz0gkErw9MAAA8NPxK0jJLhS5IiIiImouDG56qGvrFohs5wSVAHyyPVnscoiIiKiZMLjpqZmR/pBKgB1ns3Hs0g2xyyEiIqJmwOCmp3wdrfBMkBsA4OPYJAgCH0BPRERk6Bjc9NiMsDaQm0hxOO06dp3LFbscIiIi0jIGNz3mamuG8SEeAID/xCZDpeJRNyIiIkPG4KbnXunrCyuFCRIzC/DbPxlil0NERERaxOCm51pYyPFSqDcA4NMd51BWoRK5IiIiItIWBjcD8MLjXnCwUiD9egnWHUkXuxwiIiLSEgY3A2AuN8FrA/wAAJ/vTEFxaYXIFREREZE2MLgZiNHd3eHR0hx5RWX47740scshIiIiLWBwMxCmMineiKh6AP3Xey7genGZyBURERFRU2NwMyBPdHBBO1drFJVWYNnfqWKXQ0RERE2Mwc2ASKUSvBVV9QD67+Iv4cqNEpErIiIioqbE4GZg+vjZI8S7JcoqVVjyZ4rY5RAREVETYnAzMBKJBG9FVZ3r9vPxKziXXShyRURERNRUGNwMUJfWLRDVzhkqAfhke7LY5RAREVETYXAzUG9GtoFUAsSdzcaxS9fFLoeIiIiaAIObgfJ1tMKIIHcAwMfbkiEIfAA9ERGRvmNwM2Azwv0gN5Hi8MXr2JWcK3Y5RERE9IgY3AyYi40ZJvT0BAB8HJsElYpH3YiIiPQZg5uBe6WvD6yUJkjKKsSWkxlil0NERESPgMHNwNmay/FyqA8A4NO4ZJRVqESuiIiIiBqLwc0ITOzlCQcrBS5fv4UfDl0SuxwiIiJqJAY3I2AuN8H0AX4AgKV/paKotELkioiIiKgxGNyMxKju7vBsaY5rxWX47940scshIiKiRmBwMxKmMineiKh6FNbKvRdwrahU5IqIiIiooRjcjMjgDi5o52qNotIKLPv7vNjlEBERUQOJHtyWLVsGT09PKJVKBAcH4/Dhw3WOPXPmDIYPHw5PT09IJBIsWbKkxpg9e/ZgyJAhcHV1hUQiwebNm7VXvJ6RSiV4OyoAAPD9wUu4cqNE5IqIiIioIUQNbuvXr0dMTAzmzJmD48ePo1OnToiMjEROTk6t40tKSuDt7Y2PPvoIzs7OtY4pLi5Gp06dsGzZMm2Wrrd6+9mjp09LlFWqsDguRexyiIiIqAFEDW6LFi3Ciy++iIkTJyIwMBArVqyAubk5Vq1aVev47t2745NPPsHo0aOhUChqHTNw4EB88MEHGDZsmDZL11sSiQRv3Tnq9vOJK0jOKhS5IiIiIqovE7E+uKysDMeOHcPs2bPVy6RSKcLCwhAfH9+stZSWlqK09O7J+gUFBQCA8vJylJeXa+Uzq7erre0/SDtnC0QGOmL72Rz8JzYRK8Z2aZbPFbNnMbFv4+nbGHsG2Lcx9W2MPQPN03d9ty1acMvLy0NlZSWcnJw0ljs5OSEpKalZa1mwYAHmzZtXY/mOHTtgbm6u1c+Oi4vT6vbrEiQH4iDDzqRcfLHuD3hbN99ni9Wz2Ni38TDGngH2bUyMsWdAu32XlNTvvHPRgpsumT17NmJiYtSvCwoK4O7ujoiICFhbayfRlJeXIy4uDuHh4TA1NdXKZzxMquwMNhy7iv1F9pg2qjskEolWP08XehYD+zaevo2xZ4B9G1Pfxtgz0Dx9V8/2PYxowc3e3h4ymQzZ2dkay7Ozs+u88EBbFApFrefMmZqaav2L2RyfUZfXI/zx68lMHL10E/su3ED/AKeHr9QExOxZTOzbeBhjzwD7NibG2DOg3b7ru13RLk6Qy+UICgrCzp071ctUKhV27tyJkJAQscoyKi42ZpjQ0xMA8J/YZFSqBHELIiIiogcS9arSmJgYrFy5EmvXrkViYiKmTp2K4uJiTJw4EQAwbtw4jYsXysrKkJCQgISEBJSVleHq1atISEhAamqqekxRUZF6DACkpaUhISEB6enpzdqbvpja1wfWShMkZRXi14SrYpdDREREDyDqOW6jRo1Cbm4u3nvvPWRlZaFz586IjY1VX7CQnp4OqfRutszIyECXLnevgFy4cCEWLlyI0NBQ7Nq1CwBw9OhR9OvXTz2m+ty18ePHY82aNdpvSs/Ymsvxcl8f/Cc2GYvizmFwRxcoTGRil0VERES1EP3ihOjoaERHR9f6XnUYq+bp6QlBePB0Xt++fR86hjRN7OmFNfsv4sqNW/jhUDom9vISuyQiIiKqheiPvCLxmcllmB7mBwD44q9UFJVWiFwRERER1YbBjQAAI7u5w8veAteKy/DN3gtil0NERES1YHAjAICpTIo3ItoAAFbuuYC8otKHrEFERETNjcGN1Aa1d0GHVjYoLqvEsr9TH74CERERNSsGN1KTSiV4K8ofAPC/g+m4fL1+j98gIiKi5sHgRhp6+zmgl29LlFWqsPjPc2KXQ0RERPdgcKMa3ooMAAD8cuIqkrLq9+w0IiIi0j4GN6qhk7stBnVwhiAAn8Qmi10OERER3cHgRrV6I8IfMqkEO5NycOTidbHLISIiIjC4UR18HCwxspsbAODjbUl8GgUREZEOYHCjOk0f0AYKEymOXrqBv5JyxC6HiIjI6DG4UZ2cbZSY0MsTAPCf2GRUqnjUjYiISEwMbvRAr4T6wlppguTsQmw+cVXscoiIiIwagxs9kI25Kab29QUALIo7h9KKSpErIiIiMl4MbvRQE3p6wslagas3b+F/B9PFLoeIiMhoMbjRQ5nJZZg+oOoB9F/8nYqi0gqRKyIiIjJODG5ULyO7ucHb3gLXi8uwcs8FscshIiIySgxuVC8mMineiKh6AP03ey8gr6hU5IqIiIiMD4Mb1dugDs7o0MoGxWWV+OKvVLHLISIiMjoMblRvEokEb0dVPYD+f4cu4fL1EpErIiIiMi4MbtQgj/vZ43Ffe5RXClgUd07scoiIiIwKgxs12FtRVee6bU64isTMApGrISIiMh4MbtRgHd1sMbiDCwQB+GR7stjlEBERGQ0GN2qUNyLaQCaV4K+kHBxOuy52OUREREaBwY0axdvBEiO7uQMAPo5NgiDwAfRERETaxuBGjTYjzA9KUymOXbqBPxNzxC6HiIjI4DG4UaM5WSsxsZcXAOCT7UmoVPGoGxERkTYxuNEjebmPD6yVJjiXXYRfTlwVuxwiIiKDxuBGj8TG3BSv9PMFACyOO4fSikqRKyIiIjJcDG70yMaHeMLJWoGrN2/h+4PpYpdDRERksBjc6JGZyWWYEdYGALDs71QU3i4XuSIiIiLDxOBGTWJEkBu87S1wvbgMK/emiV0OERGRQWJwoyZhIpPizciqR2F9s/cCcgtLRa6IiIjI8DC4UZMZ2N4ZndxsUFJWiWV/p4pdDhERkcFhcKMmI5FI8HZUAADgf4cuIf1aicgVERERGRYGN2pSPX3t0dvPHuWVAhbF8QH0RERETYnBjZrcW5FVR91+PZmBsxkFIldDRERkOBjcqMl1cLPB4I4uEISqR2ERERFR02BwI614M8IfJlIJ/k7OxaEL18Quh4iIyCAwuJFWeNlbYFR3dwDAx7FJEAQ+gJ6IiOhRMbiR1rw2wA9KUymOp99E3NlsscshIiLSewxupDVO1kq80MsLAPDJ9mRUqnjUjYiI6FEwuJFWvRTqAxszU6TkFGFzQobY5RAREek1BjfSKhszU7zS1wcA8Nlf51GuErkgIiIiPcbgRlo3vqcnnK2VyMy/jX1ZErHLISIi0lsMbqR1SlMZZoT5AQDirkpReLtc5IqIiIj0E4MbNYtngtzgbW+O4goJ/rv/ktjlEBER6SUGN2oWJjIpXr9z1G31gUvILSwVuSIiIiL9w+BGzSYy0BEelgJKyiqx9K8UscshIiLSOwxu1GwkEgmGtK66rPSHQ+lIv1YickVERET6hcGNmpWfjYDHfVuiQiXg07hkscshIiLSKwxu1OzeDK861+3XhAycycgXuRoiIiL9weBGza6dqzWe6OgCoOpRWERERFQ/DG4kijcj/GEilWBXci4OXrgmdjlERER6QSeC27Jly+Dp6QmlUong4GAcPny4zrFnzpzB8OHD4enpCYlEgiVLljzyNqn5edpbYHQPdwDAx7FJEAQ+gJ6IiOhhRA9u69evR0xMDObMmYPjx4+jU6dOiIyMRE5OTq3jS0pK4O3tjY8++gjOzs5Nsk0Sx2v9/WBmKsOJ9JvYcTZb7HKIiIh0nujBbdGiRXjxxRcxceJEBAYGYsWKFTA3N8eqVatqHd+9e3d88sknGD16NBQKRZNsk8ThaK3EC497Aqg6161SxaNuRERED2Ii5oeXlZXh2LFjmD17tnqZVCpFWFgY4uPjm22bpaWlKC29eyf/goICAEB5eTnKy7XzXM3q7Wpr+7qotp4n9WyN/x1MR2pOETYcuYRnurYSqzytMcZ9DRhn38bYM8C+jalvY+wZaJ6+67ttUYNbXl4eKisr4eTkpLHcyckJSUlJzbbNBQsWYN68eTWW79ixA+bm5o2qo77i4uK0un1ddH/PoY4S/HpJho+3noZpxkmYin4cWDuMcV8Dxtm3MfYMsG9jYow9A9rtu6SkfjelFzW46YrZs2cjJiZG/bqgoADu7u6IiIiAtbW1Vj6zvLwccXFxCA8Ph6mpqVY+Q9fU1XP/8kocWrIPWQWluNYiEC/08hSvSC0wxn0NGGffxtgzwL6NqW9j7Blonr6rZ/seRtTgZm9vD5lMhuxszRPTs7Oz67zwQBvbVCgUtZ4vZ2pqqvUvZnN8hq65v2dTU1O8Ht4Gb/90Csv3pGHMY56wVhre78QY9zVgnH0bY88A+zYmxtgzoN2+67tdUSel5HI5goKCsHPnTvUylUqFnTt3IiQkRGe2Sdo3vKsbfBwscLOkHF/vviB2OURERDpJ9LOJYmJisHLlSqxduxaJiYmYOnUqiouLMXHiRADAuHHjNC40KCsrQ0JCAhISElBWVoarV68iISEBqamp9d4m6R4TmRQzIwMAAP/dl4acwtsiV0RERKR7RD/HbdSoUcjNzcV7772HrKwsdO7cGbGxseqLC9LT0yGV3s2XGRkZ6NKli/r1woULsXDhQoSGhmLXrl312ibppsh2TujsbouEyzexdGcq3h/aXuySiIiIdIrowQ0AoqOjER0dXet71WGsmqenZ73usv+gbZJukkgkeDsqAGNWHsSPh9MxubcXPFpaiF0WERGRzhB9qpToXiE+LdGnjQMqVAI+3XFO7HKIiIh0CoMb6Zy3Iv0BAFtOZuD01XyRqyEiItIdDG6kc9q3ssGTnVwBVD0Ki4iIiKowuJFOiglvAxOpBLvP5SL+/DWxyyEiItIJDG6kkzztLTCmR2sAwMexSfW6IIWIiMjQMbiRznp1gC/MTGVIuHwT289kP3wFIiIiA8fgRjrL0UqJSY97AQA+2Z6EikqVyBURERGJi8GNdNqUUG/YmpvifG4xfj5+VexyiIiIRMXgRjrNWmmKaX19AQCL/zyH2+WVIldEREQkHgY30nnPh3jAxUaJzPzb+C7+ktjlEBERiYbBjXSe0lSG18PaAACW7UpFwe1ykSsiIiISR4OD261bt1BSUqJ+fenSJSxZsgQ7duxo0sKI7vV011bwdbTEzZJyfLX7vNjlEBERiaLBwe2pp57Ct99+CwC4efMmgoOD8emnn+Kpp57C8uXLm7xAIgAwkUkx886jsFbtu4icgtsiV0RERNT8Ghzcjh8/jt69ewMANm3aBCcnJ1y6dAnffvstPv/88yYvkKhaRKATurS2xa3ySnz+V4rY5RARETW7Bge3kpISWFlZAQB27NiBp59+GlKpFI899hguXeKJ46Q9EokEb0cFAADWHb6Mi3nFIldERETUvBoc3Hx9fbF582ZcvnwZ27dvR0REBAAgJycH1tbWTV4g0b0e826Jvv4OqFAJ+DTunNjlEBERNasGB7f33nsPb775Jjw9PREcHIyQkBAAVUffunTp0uQFEt2v+ly3305m4PTVfJGrISIiaj4NDm7PPPMM0tPTcfToUcTGxqqXDxgwAIsXL27S4ohq087VBk91dgUA/Gd7ssjVEBERNZ9G3cfN2dkZXbp0gVQqRUFBATZv3gwrKysEBAQ0dX1EtYoJbwMTqQR7zuXiwPk8scshIiJqFg0ObiNHjsQXX3wBoOqebt26dcPIkSPRsWNH/PTTT01eIFFtPFpa4Nng1gCAj2OTIQiCyBURERFpX4OD2549e9S3A/nll18gCAJu3ryJzz//HB988EGTF0hUl1f7+8FcLsPJyzex/UyW2OUQERFpXYODW35+Puzs7AAAsbGxGD58OMzNzTF48GCkpPDeWtR8HKwUmPS4F4Cqc90qKlUiV0RERKRdDQ5u7u7uiI+PR3FxMWJjY9W3A7lx4waUSmWTF0j0IC/28UYLc1NcyC3GT8eviF0OERGRVjU4uM2YMQNjx46Fm5sbXF1d0bdvXwBVU6gdOnRo6vqIHshaaYpp/XwBAIvjUnC7vFLkioiIiLSnwcHtlVdeQXx8PFatWoV9+/ZBKq3ahLe3N89xI1E895gHXG2UyCq4jbUHLopdDhERkdY06nYg3bp1w7Bhw2BhYaG+mm/w4MHo1atXkxZHVB9KUxleD28DAPhy13nk3yoXuSIiIiLtaFRw+/bbb9GhQweYmZnBzMwMHTt2xHfffdfUtRHV29Nd3eDnaIn8W+X4avd5scshIiLSigYHt0WLFmHq1KkYNGgQNmzYgA0bNiAqKgovv/wyn5xAopFJJepHYa3an4acgtsiV0RERNT0TBq6wtKlS7F8+XKMGzdOvezJJ59Eu3btMHfuXLz++utNWiBRfYUHOqFra1scT7+Jz3am4MNhvFiGiIgMS4OPuGVmZqJnz541lvfs2ROZmZlNUhRRY0gkErwdVfXYtXVHLiMtr1jkioiIiJpWg4Obr68vNmzYUGP5+vXr4efn1yRFETVWsHdL9PN3QKVKwKc7+AB6IiIyLA2eKp03bx5GjRqFPXv2qK8i3b9/P3bu3FlroCNqbjMjA7DrXC5+/ycTL/XJRwc3G7FLIiIiahINPuI2fPhwHDp0CPb29ti8eTM2b94Me3t7HD58GMOGDdNGjUQNEuhqjac6uQIA/rM9SeRqiIiImk6jbgcSFBSE77//HseOHcOxY8fw/fffo1WrVvi///u/pq6PqFFiwv1hKpNgb0oeDqTmiV0OERFRk2hUcKtNZmYm3n333abaHNEjad3SHM/2aA0A+Dg2SX2jaCIiIn3WZMGNSNdE9/eDuVyGk1fyEXs6S+xyiIiIHhmDGxksBysFJj/uBQD4ZEcyKipVIldERET0aBjcyKC92McbdhZyXMgtxqZjV8Quh4iI6JHU+3YgMTExD3w/Nzf3kYshampWSlNM6+eL938/iyV/pmBol1ZQmsrELouIiKhR6h3cTpw48dAxffr0eaRiiLRhbHBrrNqXhqs3b2HNgYt4OdRH7JKIiIgapd7B7e+//9ZmHURaozSV4fXwNnhz40l8+XcqxnRvDRtzU7HLIiIiajCe40ZGYViXVmjjZImC2xVYsee82OUQERE1CoMbGQWZVIKZkVUPoF+9Pw3ZBbdFroiIiKjhGNzIaIS1dUSQRwvcLlfhs50pYpdDRETUYAxuZDQkEgnejqo66rb+yGVcyC0SuSIiIqKGYXAjo9LDyw79AxxRqRLwadw5scshIiJqkHoHt//85z+4deuW+vX+/ftRWlqqfl1YWIhXXnmlaasj0oKZkf6QSICt/2Ti1JV8scshIiKqt3oHt9mzZ6OwsFD9euDAgbh69ar6dUlJCb766qumrY5IC9q6WGNo51YAqh5AT0REpC/qHdwEQXjgayJ9EhPeBqYyCfal5mFfSp7Y5RAREdULz3Ejo+RuZ46xwR4AgP9sT+J/iBARkV5gcCOjFd3fF+ZyGf65ko9tp7PELoeIiOih6v3IKwD45ptvYGlpCQCoqKjAmjVrYG9vDwAa578R6QN7SwUm9/bG5ztTsHB7MiICnWAi43/LEBGR7qp3cGvdujVWrlypfu3s7IzvvvuuxhgiffJiby98f/ASLuQVY8PRK3g2mN9hIiLSXfUObhcvXtRiGUTisFKaIrqfL+b/fhaf7TyHYV1awUwuE7ssIiKiWnFeiIze2Mdao5WtGbILSrHmwEWxyyEiIqpTvYNbfHw8fv/9d41l3377Lby8vODo6IgpU6Zo3JC3IZYtWwZPT08olUoEBwfj8OHDDxy/ceNGBAQEQKlUokOHDvjjjz803s/OzsaECRPg6uoKc3NzREVFISWFz6ak2ilMZIgJbwMAWL4rFfkl5SJXREREVLt6B7f58+fjzJkz6tenTp3CpEmTEBYWhlmzZuG3337DggULGlzA+vXrERMTgzlz5uD48ePo1KkTIiMjkZOTU+v4AwcOYMyYMZg0aRJOnDiBoUOHYujQoTh9+jSAqvvLDR06FBcuXMCvv/6KEydOwMPDA2FhYSguLm5wfWQchnZpBX8nKxTcrsDy3efFLoeIiKhW9Q5uCQkJGDBggPr1unXrEBwcjJUrVyImJgaff/45NmzY0OACFi1ahBdffBETJ05EYGAgVqxYAXNzc6xatarW8Z999hmioqIwc+ZMtG3bFu+//z66du2KL774AgCQkpKCgwcPYvny5ejevTv8/f2xfPly3Lp1Cz/++GOD6yPjIJNKMDPSHwCwen8asvJvi1wRERFRTfW+OOHGjRtwcnJSv969ezcGDhyoft29e3dcvny5QR9eVlaGY8eOYfbs2eplUqkUYWFhiI+Pr3Wd+Ph4xMTEaCyLjIzE5s2bAUA9XatUKjW2qVAosG/fPkyePLnGNktLSzWmeQsKCgAA5eXlKC/XzrRZ9Xa1tX1dpOs99/FtgaDWtjiWfhOL45LxwVOBTbJdXe9bW4yxb2PsGWDfxtS3MfYMNE/f9d12vYObk5MT0tLS4O7ujrKyMhw/fhzz5s1Tv19YWAhTU9MGFZmXl4fKykqNQFj9WUlJtT9DMisrq9bxWVlVN1ANCAhA69atMXv2bHz11VewsLDA4sWLceXKFWRmZta6zQULFmj0Um3Hjh0wNzdvUE8NFRcXp9Xt6yJd7rmXFXAMJth49DJ8Ki7Cyazptq3LfWuTMfZtjD0D7NuYGGPPgHb7Likpqde4ege3QYMGYdasWfj444+xefNmmJubo3fv3ur3//nnH/j4+DS80iZmamqKn3/+GZMmTYKdnR1kMhnCwsIwcODAOh9rNHv2bI2jeAUFBXB3d0dERASsra21Umd5eTni4uIQHh7e4MCrr/Sl51PfH8ffyXk4Ud4Knw/v9Mjb05e+m5ox9m2MPQPs25j6Nsaegebpu3q272HqHdzef/99PP300wgNDYWlpSXWrl0LuVyufn/VqlWIiIhoUJH29vaQyWTIzs7WWJ6dnQ1nZ+da13F2dn7o+KCgICQkJCA/Px9lZWVwcHBAcHAwunXrVus2FQoFFApFjeWmpqZa/2I2x2foGl3v+e2BbbHr3F5sO5ONxOxidHSzbZLt6nrf2mKMfRtjzwD7NibG2DOg3b7ru916X5xgb2+PPXv24MaNG7hx4waGDRum8f7GjRsxZ86cBhUpl8sRFBSEnTt3qpepVCrs3LkTISEhta4TEhKiMR6oOnRZ23gbGxs4ODggJSUFR48exVNPPdWg+sg4BThbY1jnVgCAj2Nrn7InIiISQ4NvwGtjYwOZrOad5e3s7DSOwNVXTEwMVq5cibVr1yIxMRFTp05FcXExJk6cCAAYN26cxsUL06dPR2xsLD799FMkJSVh7ty5OHr0KKKjo9VjNm7ciF27dqlvCRIeHo6hQ4c2+IggGa/Xw9vAVCbB/tRr2JeSJ3Y5REREABowVfrCCy/Ua1xdt/Goy6hRo5Cbm4v33nsPWVlZ6Ny5M2JjY9UXIKSnp0MqvZsve/bsiR9++AH//ve/8c4778DPzw+bN29G+/bt1WMyMzMRExOD7OxsuLi4YNy4cXj33XcbVBcZN3c7c4wN9sCaAxfxcWwSevr0glQqEbssIiIycvUObmvWrIGHhwe6dOlS50n+jRUdHa1xxOxeu3btqrFsxIgRGDFiRJ3be+211/Daa681VXlkpKL7+2Lj0cs4dTUff5zOxBMdXcUuiYiIjFy9g9vUqVPx448/Ii0tDRMnTsRzzz0HOzs7bdZGJCp7SwVe7OONJX+m4NMd5xDZzhmmMj7el4iIxFPvf4WWLVuGzMxMvPXWW/jtt9/g7u6OkSNHYvv27U1+BI5IV0zu7Y2WFnKk5RVjw9GG3WCaiIioqTXo8IFCocCYMWMQFxeHs2fPol27dnjllVfg6emJoqIibdVIJBpLhQmi+/sCAD77MwW3yipFroiIiIxZo+d9pFIpJBIJBEFAZSX/MSPD9Wxwa7i1MENOYSlWH0gTuxwiIjJiDQpupaWl+PHHHxEeHo42bdrg1KlT+OKLL5Ceng5LS0tt1UgkKoWJDDHhbQAAy3edx82SMpErIiIiY1Xv4PbKK6/AxcUFH330EZ544glcvnwZGzduxKBBgzRu10FkiJ7q3AoBzlYovF2B5bvPi10OEREZqXpfVbpixQq0bt0a3t7e2L17N3bv3l3ruJ9//rnJiiPSFTKpBDMj/TFp7VGs2X8RE3p6wsWmCZ9AT0REVA/1Dm7jxo2DRMIbkJLx6h/giO6eLXDk4g18vjMFC57uKHZJRERkZBp0A14iYyaRSPB2VACeWRGPDUevYHJvb/g48NxOIiJqPjw5jagBunnaIaytIypVAhZuTxa7HCIiMjIMbkQNNDMyABIJsO10Fk5evil2OUREZEQY3IgayN/ZCsO6tAIAfBybxCeHEBFRs2FwI2qE18PaQC6T4sD5a9iXmid2OUREZCQY3Igawd3OHGMfaw2g6qibSsWjbkREpH0MbkSNFN3PF5YKE5y+WoCtpzLFLoeIiIwAgxtRI7W0VODF3t4AgE93JKO8UiVyRUREZOgY3IgewaTeXmhpIcfFayVYf+Sy2OUQEZGBY3AjegSWChO82t8XAPDZzhTcKqsUuSIiIjJkDG5Ej+jZYA+4tTBDbmEpVu1PE7scIiIyYAxuRI9IbiLFGxFtAAArdp/HzZIykSsiIiJDxeBG1ASe6tQKAc5WKLxdgeW7zotdDhERGSgGN6ImIJVK8FaUPwBgzYGLyMy/JXJFRERkiBjciJpIP39H9PC0Q2mFCkviUsQuh4iIDBCDG1ETkUgkeHtg1VG3jccuIzWnSOSKiIjI0DC4ETWhIA87hLV1gkoAFm5PFrscIiIyMAxuRE3srSh/SCRA7JksJFy+KXY5RERkQBjciJpYGycrPN3FDQDw0R+JOHjhGo7lSXAo7Toq+TB6IiJ6BCZiF0BkiF4P98OvCVdxMO06DqZdByDDtylH4WKjxJwhgYhq7yJ2iUREpId4xI1IC05fzUdFLUfXsvJvY+r3xxF7OlOEqoiISN8xuBE1sUqVgHm/na31veooN++3s5w2JSKiBmNwI2pih9OuIzP/dp3vCwAy82/jcNr15iuKiIgMAoMbURPLKaw7tDVmHBERUTUGN6Im5milrNe4gtsVWq6EiIgMDYMbURPr4WUHFxslJA8Z9+7m05i4+jDv9UZERPXG4EbUxGRSCeYMCQSAGuGt+vVjXi0hk0rwd3Iuhi7bjwmrD+NE+o1mrZOIiPQPgxuRFkS1d8Hy57rC2UZz2tTZRokVz3XFupcew86YUDwT5AaZVIJdybkY9uUBjF91GMcZ4IiIqA68AS+RlkS1d0F4oDPiU3OwY+8hRPQORoivI2TSquNunvYWWDiiE17t74sv/krFzyeuYve5XOw+l4s+bRwwfYAfgjxaiNwFERHpEh5xI9IimVSCYC87BNkLCPayU4e2e3m0tMAnIzrhrzdCMbJb1RG4PedyMXz5ATz/30M4dolH4IiIqAqDG5GO8Ghpgf880wl/v9EXo7q5QyaVYG9K3j0Bjvd9IyIydgxuRDqmdUtzfPxMR3WAM1EHuHg8/99DOHqRAY6IyFgxuBHpKHWAe7MvRne/G+CeWRGP575hgCMiMkYMbkQ6zt3OHB8NrwpwY3pUBbh9qVUBbuw3B3GEAY6IyGgwuBHpCXc7cyx4ujrAtYaJVIL9qdcwYkU8nl15kM8+JSIyAgxuRHqmKsB1wK6ZffFscGuYyiQ4cP4aRn5VFeAOXbgmdolERKQlDG5EesqthTn+b1gH7JrZD2PvCXCjvj6IMV8fxEEGOCIig8PgRqTnWtma4cP7Alz8hWsY/fVBjP46ngGOiMiAMLgRGYh7A9xzj1UFuIMXrmP01wcx6qt4xJ9ngCMi0ncMbkQGppWtGT4Y2gG7Z/bD8495QC6T4lDadYxZWRXgDpzPgyAIYpdJRESNwOBGZKBcbc3w/tD22P1WX4wLuRvgnl15CKO+PsgAR0SkhxjciAyci40Z5j9VFeDG3wlwh6sD3FcHcSCVAY6ISF8wuBEZCRcbM8y7N8CZSHH44nU8+80hjPwqHvsZ4IiIdB6DG5GRqQ5we2b2w4SenpCbSHHk4g2MvRPg9qUwwBER6SoGNyIj5WyjxNwn22HvW5oB7rn/HsKIFfHYm5LLAEdEpGMY3IiMnJP13QA3sZcnFCZSHL10A8//9zCeYYAjItIpDG5EBKAqwM0ZUhXgXujlBYWJFMfuBLjhyw9gzzkGOCIiselEcFu2bBk8PT2hVCoRHByMw4cPP3D8xo0bERAQAKVSiQ4dOuCPP/7QeL+oqAjR0dFwc3ODmZkZAgMDsWLFCm22QGQwHK2VeG9IoEaAO55+E+NWHcbTyw9gNwMcEZFoRA9u69evR0xMDObMmYPjx4+jU6dOiIyMRE5OTq3jDxw4gDFjxmDSpEk4ceIEhg4diqFDh+L06dPqMTExMYiNjcX333+PxMREzJgxA9HR0diyZUtztUWk99QB7u1+mPR4VYA7kX4T4+8EuF3JOQxwRETNTPTgtmjRIrz44ouYOHGi+siYubk5Vq1aVev4zz77DFFRUZg5cybatm2L999/H127dsUXX3yhHnPgwAGMHz8effv2haenJ6ZMmYJOnTo99EgeEdXkaKXEu09UBbjJj3tBaVoV4CasPoJhXx7A3wxwRETNxkTMDy8rK8OxY8cwe/Zs9TKpVIqwsDDEx8fXuk58fDxiYmI0lkVGRmLz5s3q1z179sSWLVvwwgsvwNXVFbt27cK5c+ewePHiWrdZWlqK0tJS9euCggIAQHl5OcrLyxvb3gNVb1db29dFxtgzYDh9t1DK8HakHyb1ao1v9l3E/w5fRsLlm5i4+gg6ulnjtX4+6ONnD4lEAsBw+m4IY+wZYN/G1Lcx9gw0T9/13baowS0vLw+VlZVwcnLSWO7k5ISkpKRa18nKyqp1fFZWlvr10qVLMWXKFLi5ucHExARSqRQrV65Enz59at3mggULMG/evBrLd+zYAXNz84a21SBxcXFa3b4uMsaeAcPquyMAz47A3xlS7M2W4J8rBZj83Qm0thAQ5a5CoK2AO/nNoPquL2PsGWDfxsQYewa023dJSUm9xoka3LRl6dKlOHjwILZs2QIPDw/s2bMH06ZNg6urK8LCwmqMnz17tsZRvIKCAri7uyMiIgLW1tZaqbG8vBxxcXEIDw+HqampVj5D1xhjz4Bh9z0aQF5RKb7ZdxE/HL6M9GIVvk6SoUMra7zS2wOlF08gIsLw+q6LIe/rB2HfxtO3MfYMNE/f1bN9DyNqcLO3t4dMJkN2drbG8uzsbDg7O9e6jrOz8wPH37p1C++88w5++eUXDB48GADQsWNHJCQkYOHChbUGN4VCAYVCUWO5qamp1r+YzfEZusYYewYMt2+XFqZ4d0h7TO3nh5V7LuDb+Es4dbUAU9edgruFDGa+NxHRzkU9hWoMDHVfPwz7Nh7G2DOg3b7ru11RL06Qy+UICgrCzp071ctUKhV27tyJkJCQWtcJCQnRGA9UHbqsHl99XppUqtmaTCaDSqVq4g6IqJq9pQKzB7XFvrf74aVQb5iZSnG5WIKXvj+BJ7/Yjz/PZvMiBiKiRyT6VaUxMTFYuXIl1q5di8TEREydOhXFxcWYOHEiAGDcuHEaFy9Mnz4dsbGx+PTTT5GUlIS5c+fi6NGjiI6OBgBYW1sjNDQUM2fOxK5du5CWloY1a9bg22+/xbBhw0TpkciYtLRUYPbAtvj7jT4Y4KqCuVyGU1fzMfnboxjyxT4GOCKiRyD6OW6jRo1Cbm4u3nvvPWRlZaFz586IjY1VX4CQnp6ucfSsZ8+e+OGHH/Dvf/8b77zzDvz8/LB582a0b99ePWbdunWYPXs2xo4di+vXr8PDwwMffvghXn755Wbvj8hYtbSQ40kPFf5vXG+sOXgZaw9cxOmrBZj87VG0b2WN6QPaIKyto1FNoRIRPSrRgxsAREdHq4+Y3W/Xrl01lo0YMQIjRoyoc3vOzs5YvXp1U5VHRI/AzkKOt6MC8GJvb6zcewHf3glwL357FO1crTF9gB/CA50Y4IiI6kH0qVIiMg7VAW7v2/3xSl8fWMhlOJNRgCnfHcMTS/dh+5ksTqESET0EgxsRNSs7CzneigrAvrf7Y1q/uwHupe+OYfDn+xB7OgsqFQMcEVFtGNyISBQtLOSYGVkV4KL7+cJSYYKzmQV4+ftjGLyUAY6IqDYMbkQkqhYWcrwZ6Y99b/fDq/2rAlzinQA36PO9iD2dyQBHRHQHgxsR6QRbczneiNAMcElZhXj5++MY9PlebDvFAEdExOBGRDrl3gD3Wn9fWN0JcFP/VxXg/mCAIyIjxuBGRDrJ1lyOmAh/7Hu7P14b4KcOcK/87zgGfrYXW/9hgCMi48PgRkQ6zcbcFDHhbbDv7f6YPsAPVkoTJGcXYtoPxxH12R4GOCIyKgxuRKQXbMxN8fqdADcjrCrAncsuUge43//JYIAjIoPH4EZEesXGzBQzwmoGuOgfTiByyR78djIDlQxwRGSgGNyISC/dG+BeD2sDa6UJUnKK8OqPJxC1ZA+2MMARkQFicCMivWZjZorpYX7YN6s/YsLvBrjXfqw6AvdrwlUGOCIyGAxuRGQQrJWmeG1AVYB7I7wNbMxMkZpThOnrEhCxeDcDHBEZBAY3IjIo1kpTvDrAD/ve7oc3I6oC3PncYgY4IjIIDG5EZJCslKaI7l8V4GZG+sPW/G6AC1+8G5tPMMARkf5hcCMig2alNMW0fr7Y+9bdAHchtxgz1lcFuF9OXEFFpUrsMomI6oXBjYiMQnWA2/d2f40A9/r6k4hYvAc/H2eAIyLdx+BGREbFUmGiDnBvRfmjhbkpLuQVI2bDSYQzwBGRjmNwIyKjZKkwwSt9fbH37f54OyoALcxNkXYnwIUt2o2fjjHAEZHuYXAjIqNmqTDB1L4+2Pd2f8waGAA7CzkuXivBGxurAtwmBjgi0iEMbkREACwUJng51Ad73+qnEeDe3HgSAxbtxsajlxngiEh0DG5ERPe4N8DNvhPgLl0rwcxN/2DAot3YcPQyyhngiEgkDG5ERLWwUJjgpVAf7Hu7H94ZFICWdwLcW5v+wYBPawa4SpWAQ2nXcSxPgkNp13mPOCLSChOxCyAi0mXmchNM6eOD5x7zwP8OpuOrPeeRfr0qwC39KwWv9vODuUKGD7cmIjP/NgAZvk05ChcbJeYMCURUexexWyAiA8LgRkRUD+ZyE7zYx7sqwB26hBW7z+Py9Vt466d/ah2flX8bU78/juXPdWV4I6Imw6lSIqIGMJPLMLm3N/a+1R/vDAqAVFL7uOqJ0nm/neW0KRE1GQY3IqJGMJPL0KGVLR6UyQQAmfm3cfDCtWari4gMG6dKiYgaKafwdr3GvfjtUfQLcESonwP6tHGAs41Sy5URkaFicCMiaiRHq/oFsJKySmz9JxNb/8kEAPg7WaFPG3uEtnFEN88WUJrKtFkmERkQBjciokbq4WUHFxslsvJvo7YZUwkAZxslPhvdGftSr2H3uVz8c+UmkrMLkZxdiJV706A0leIx75bo4+eAUH8HeNtbQCKp48Q5IjJ6DG5ERI0kk0owZ0ggpn5/HBJAI7xVR685QwLRw6sleni1REx4G9woLsO+1DzsOZeL3edykVNYil3JudiVnAv8DrSyNUOfNg4IbWOPnr72sFaaitAZEekqBjciokcQ1d4Fy5/rinm/nb1zH7cqznXcx62FhRxDOrliSCdXCIKA5OxCdYg7knYDV2/ewo+H0/Hj4XTIpBJ0bW2rPhrX3tUG0rouYyUio8DgRkT0iKLauyA80BnxqTnYsfcQInoHI8TXEbKHhCyJRIIAZ2sEOFtjSh8flJRV4NCF69h9Lhd7zuXiQl4xjly8gSMXb+DTuHOws5DjcV979GnjgD5+9nC05kUORMaGwY2IqAnIpBIEe9nhWqKAYC+7h4a22pjLTdAvwBH9AhwBAJevl2BPSi52J+fiwPlruF5chi0nM7DlZAYAoK2L9Z2LHBzQzcMOchPe4YnI0DG4ERHpKHc7c4wN9sDYYA+UV6pwIv2melr11NV8JGYWIDGzAF/tvgBzuQwh3i3vnB/nAE97C7HLJyItYHAjItIDpjIpenjZoYeXHd6M9Me1olLsS827M62ah7yiUuxMysHOpBwAQGs7c/RpY48+fg7o6WsPSwX/757IEPBvMhGRHmppqcBTnVvhqc6toFIJSMwqwJ5zedh9LgfHLt1A+vUSfH8wHd8fTIeJVIIgjxbqo3GBLta8yIFITzG4ERHpOalUgnauNmjnaoOpfX1QVFqBg+evVZ0fdy4Xl66V4FDadRxKu45PtifD3lKO3n4O6NPGHr39HGBvqRC7BSKqJwY3IiIDY6kwQVigE8ICnQAAl64Vq8+NO3D+GvKKyvDLiav45cRVAED7Vtboc+dxXEEeLWAq40UORLqKwY2IyMB5tLTA8yEWeD7EE2UVKhy7dEN9terZzAKcvlr18+Wu87BUmCDE585FDn4OaN3SXOzyiegeDG5EREZEbiJFiE9LhPi0xNtRAcgpvI19KVVPctiTkofrxWWIO5uNuLPZAAAvewv08au6d9xj3i1hwYsciETFv4FEREbM0UqJp7u64emublCpBJzJKFAfjTuefgNpecVIyyvG2vhLkMuk6ObZ4s4NgB3Q1sWKz1UlamYMbkREBKDqIocObjbo4GaDaf18UXi7HAfOX1OfH3flxi0cOH8NB85fw0fbkuBopUBvPwf08mmBW+ViV09kHBjciIioVlZKU0S2c0ZkO2cIgoC0vGL1lGr8+WvIKSzFT8ev4KfjVyCBDOszDyLU3xF92jigi7stTHiRA1GTY3AjIqKHkkgk8HawhLeDJSb08kJpRSWOXryBPedysSs5B8nZRfjnagH+uVqApX+lwkppgl4+d56r2sYebi14kQNRU2BwIyKiBlOYyNDL1x69fO3xZrgvftz8B+StO2H/hRvYm5KLmyXliD2ThdgzWQAAHweLOyHOAY95tYSZXCZyB0T6icGNiIgemY0cGNS1FUYHe6JSJeDU1Xz1uXEn0m/gfG4xzucWY/X+i5CbSBHsZYc+fg4I9XeAn6MlL3IgqicGNyIialIyqQSd3W3R2d0Wrw3wQ/6tchxIzVNfrZqRfxt7U/KwNyUPH/6RCGdrZdVzVds44HFfe9iay8VugUhnMbgREZFW2ZiZYmAHFwzs4AJBEHA+twi7z+Vh97lcHLpwDVkFt7Hh6BVsOHoFUgnQyd1W/SSHzu62kPG5qkRqDG5ERNRsJBIJfB2t4OtohUmPe+F2eSUOp11XT6um5BThRPpNnEi/ic92psDGzBSP+9qrj8i52JiJ3QKRqBjciIhINEpTmfqihX8DyLh5C3tTcrHnXB72puQi/1Y5tp7KxNZTmQCANk6W6qNxPbzsoDTlRQ5kXBjciIhIZ7jammFU99YY1b01KipVOHnl7kUO/1y5iXPZRTiXXYRv9qVBaSpFsNed56q2sYePAy9yIMPH4EZERDrJRCZFkEcLBHm0wOvhbXCzpAz7UvPUQS67oBS77/z5fQCtbM2qplT9HNDT1x42ZqZit0DU5BjciIhIL9iay/FER1c80dEVgiDgXHbRnSc55OJQ2nVcvXkLPx6+jB8PX4ZMKkEXd1v1NGyHVja8yIEMgk48j2TZsmXw9PSEUqlEcHAwDh8+/MDxGzduREBAAJRKJTp06IA//vhD432JRFLrzyeffKLNNoiIqJlIJBL4O1vhxT7e+G5SME6+F4HVE7tjYi9PeDtYoFIl4OilG1gUdw5Dl+1Htw/i8OqPJ7Dx6GXkFNwWu3yiRhP9iNv69esRExODFStWIDg4GEuWLEFkZCSSk5Ph6OhYY/yBAwcwZswYLFiwAE888QR++OEHDB06FMePH0f79u0BAJmZmRrrbNu2DZMmTcLw4cObpSciImpeZnIZ+vk7op9/1b8bV26UYM+5qmnV/al5uFFSjt9OZuC3kxkAgABnK4TeORrXzbMFFCa8yIH0g+jBbdGiRXjxxRcxceJEAMCKFSuwdetWrFq1CrNmzaox/rPPPkNUVBRmzpwJAHj//fcRFxeHL774AitWrAAAODs7a6zz66+/ol+/fvD29tZyN0REpAvcWpjj2eDWeDa4NcorVUi4fLNqWvVcLv65mo+krEIkZRXiqz0XYGYqQ4hPS/Txq7rliJe9xUMvcqhUCTiUdh3H8iRomXYdIb6OnIqlZiFqcCsrK8OxY8cwe/Zs9TKpVIqwsDDEx8fXuk58fDxiYmI0lkVGRmLz5s21js/OzsbWrVuxdu3aOusoLS1FaWmp+nVBQQEAoLy8HOXl5fVtp0Gqt6ut7esiY+wZYN/G1Lcx9gzoR9+dW1mhcysrvNbPG9eLy7D//DXsTb2GfSl5yC0qw19JOfgrKQcA4NbCDL19W6K3rz0e87aDlVLzn8rtZ7LxwR9JyCooBSDDtylH4WytwL8HBSCynZMI3TUffdjX2tAcfdd326IGt7y8PFRWVsLJSfOL7uTkhKSkpFrXycrKqnV8VlZWrePXrl0LKysrPP3003XWsWDBAsybN6/G8h07dsDc3PxhbTySuLg4rW5fFxljzwD7NibG2DOgX33LAPRVAqHtgYwSIOmmBIk3JbhQKMGVG7fw45Er+PHIFUglArwsgQBbFdraCrhWKsHqc9Wnh989wpZVcBvR6xLwQhsVOrUUROmpOenTvm5K2uy7pKSkXuNEnyrVtlWrVmHs2LFQKpV1jpk9e7bGUbyCggK4u7sjIiIC1tbWWqmrvLwccXFxCA8Ph6mpcVyybow9A+zbmPo2xp4Bw+q7uLQChy7ewL6UPOxJuYZL10twvhA4XyjD1stA3TOoEkgAbMs2x1tj+xjstKkh7euGaI6+q2f7HkbU4GZvbw+ZTIbs7GyN5dnZ2TXOU6vm7Oxc7/F79+5FcnIy1q9f/8A6FAoFFApFjeWmpqZa/2I2x2foGmPsGWDfxsQYewYMo29bU1NEtjdDZHtXAED6tRLsTqk6N27vuVzcrlDVua4AIDO/FCeuFCDEx76ZKhaHIezrxtBm3/Xdrqi3A5HL5QgKCsLOnTvVy1QqFXbu3ImQkJBa1wkJCdEYD1Qduqxt/H//+18EBQWhU6dOTVs4EREZhdYtzfH8Yx5YOa4b/m9Yh3qtM/nbo3jum0P4vz8S8WvCVaRkF6Kisu7AR9QQok+VxsTEYPz48ejWrRt69OiBJUuWoLi4WH2V6bhx49CqVSssWLAAADB9+nSEhobi008/xeDBg7Fu3TocPXoUX3/9tcZ2CwoKsHHjRnz66afN3hMRERkeF9v6PeC+uLQS+1LzsC81T71MYSJFgLMVAl2tEehijUBXawQ4W8NCIfo/w6RnRP/GjBo1Crm5uXjvvfeQlZWFzp07IzY2Vn0BQnp6OqTSuwcGe/bsiR9++AH//ve/8c4778DPzw+bN29W38Ot2rp16yAIAsaMGdOs/RARkWHq4WUHFxslsvJvo7bLDyQAnGyUWD62K5KzCnE2swBnMwqQmFmA4rJKnLySj5NX8u+OlwCeLS3UQS7Q1RrtXKzhYKXgM1epTqIHNwCIjo5GdHR0re/t2rWrxrIRI0ZgxIgRD9zmlClTMGXKlKYoj4iICDKpBHOGBGLq98chATTCW3XMmjskEF1at0CX1i3U76lUAi5dL8HZjAKczcy/878FyC4oRVpeMdLyirH11N0bx9tbytG2Osy5WKOdqzW87C0N9oIHahidCG5ERET6IKq9C5Y/1xXzfjuLzPy7j85ytlFizpBARLV3qbGOVCqBl70FvOwtMLjj3ffzikqReOeoXPXRufO5RcgrKsPelDzsTbk71ao0lSLA2fq+qVYrmMv5z7ix4R4nIiJqgKj2LggPdEZ8ag527D2EiN7BjXpygr2lAr39HNDbz0G97FZZJZKzCzWOziVmFuJWeSUSLt9EwuWb6rESCeBlf89Uq4s12rnawMGq5l0SyHAwuBERETWQTCpBsJcdriUKCPaya7JpTDO5DJ3dbdHZ3Va9rFIl4NK1YpzNLMCZjLtH6HILS3EhtxgXcovx+z93p1odrBQaYS7Q1RqeLS041WogGNyIiIh0mEwqgbeDJbwdLPFER1f18pzC20jMLLxnqjUfF/KKkVtYit2Fudh9Llc91sxUhgAXK41AF+BsDTO5TIyW6BEwuBEREekhRyslHK2UCG1zd6q1pKwCyVmFVUfm7pw3l5RVgFvllTiRfhMn0m+qx0olgLeDZY2jc/aWnGrVZQxuREREBsJcblLjqtZKlYC0vGJ1kKs+OpdXVIbUnCKk5hRhy8kM9XhHK4VGkGvnagMPO3NIOdWqExjciIiIDJhMKoGvoyV8HS3xZCfNqdazGQXqo3OJGQVIu1aMnMJS5CTnYlfy3alWc7kMbV2sEeBkiYo8Cdyu5KOdWwsoTTnV2twY3IiIiIyQo5USjv5K9PV3VC8rLq1A0j03Dz6bWYCkzAKUlFXi2KUbOHbpBgAZ1n91CFIJ4ONgqXFFa1sXK7TkVKtWMbgRERERAMBCYYIgjxYI8rg71VpRqcLFa8U4k1GAU1duYu+pC8gpV+BGSTlScoqQklOEXxPuTrU6Wys1ploDXazRmlOtTYbBjYiIiOpkIpPC19EKvo5WGNTOER0qUzFwYF/cuK3SuHnw2cwCpOUVI6vgNrIKbuOvpBz1NiwVJmircVWrDfycLDnV2ggMbkRERNQgEokETtZKOFkr0S/g7lRrUWkFkjI1w1xSViGKSitw5OINHLl4Qz1WJpXA956p1ur/bWEhF6MlvcHgRkRERE3CUmGCbp526OZpp15WUanChbxijaNzZzLycaOkHMnZhUjOLsQvJ66qx7va3D/VagN3OzNIJJxqBRjciIiISItMZFK0cbJCGycrDO3SCgAgCAKyCqqualUHuswCXLpWgoz828jIv40/E+9OtVopTND2vvvN+TlZQmFifFOtDG5ERETUrCQSCVxszOBiY4YBbZ3Uywtvl1dd1XpPoEvOKkRhaQUOX7yOwxevq8ea3LnNyf1Trbbmhj3VyuBGREREOsFKaYrunnbofs9Ua3mlCudzizTC3JmMAuTfqgp5SVmF+Bl3p1pb2ZppHJ1r52oNtxaNn2qtVAk4lHYdx/IkaJl2HSG+jqI+95XBjYiIiHSWqUyKAOeqZ6s+3bVqmSAIyMy/rXneXGY+Ll+/has3q37+TMxWb8NKaVLj0V5+jlaQm0gf+NmxpzMx77ezyMy/DUCGb1OOwsVGiTlDAhHV3kWLXdeNwY2IiIj0ikQigautGVxtzRAWeHeqNf9WeY2rWs9lF6LwdgUOpV3HobS7U62mMgl8Ha3uebSXNdq6WMPGzBRAVWib+v1xCPd9dlb+bUz9/jiWP9dVlPDG4EZEREQGwcbMFMHeLRHs3VK9rKzi7lRr1eO98nE2owAFtyuQmFmAxMwC/HT87jbcWpihrbMV4i9crxHaAEAAIAEw77ezCA90bvZpUwY3IiIiMlhyEynaulQdTRseVLVMEARcvXmrxg2Er9y4pf55EAFAZv5tHE67jhCflg8c29QY3IiIiMioSCQSuLUwh1sLc0S0c1Yvzy8pR2JWAdYfuaxxb7m65BTe1maZtWJwIyIiIgJgY26Kx7xbQhBQr+DmaKVshqo0PfhyCiIiIiIj08PLDi42StR19poEgIuNEj287OoYoT0MbkRERET3kEklmDMkEABqhLfq13OGBIpyPzcGNyIiIqL7RLV3wfLnusLZRnM61NlGKdqtQACe40ZERERUq6j2LggPdEZ8ag527D2EiN7BfHICERERka6SSSUI9rLDtUQBwV52ooY2gFOlRERERHqDwY2IiIhITzC4EREREekJBjciIiIiPcHgRkRERKQnGNyIiIiI9ASDGxEREZGeYHAjIiIi0hMMbkRERER6gsGNiIiISE/wkVe1EAQBAFBQUKC1zygvL0dJSQkKCgpgamqqtc/RJcbYM8C+jalvY+wZYN/G1Lcx9gw0T9/VmaM6g9SFwa0WhYWFAAB3d3eRKyEiIiJjUlhYCBsbmzrflwgPi3ZGSKVSISMjA1ZWVpBItPMw2YKCAri7u+Py5cuwtrbWymfoGmPsGWDfxtS3MfYMsG9j6tsYewaap29BEFBYWAhXV1dIpXWfycYjbrWQSqVwc3Nrls+ytrY2qi8/YJw9A+zbmBhjzwD7NibG2DOg/b4fdKStGi9OICIiItITDG5EREREeoLBTSQKhQJz5syBQqEQu5RmY4w9A+zbmPo2xp4B9m1MfRtjz4Bu9c2LE4iIiIj0BI+4EREREekJBjciIiIiPcHgRkRERKQnGNyIiIiI9ASDmxbs2bMHQ4YMgaurKyQSCTZv3vzQdXbt2oWuXbtCoVDA19cXa9as0XqdTa2hfe/atQsSiaTGT1ZWVvMU3AQWLFiA7t27w8rKCo6Ojhg6dCiSk5Mfut7GjRsREBAApVKJDh064I8//miGaptOY/pes2ZNjX2tVCqbqeKmsXz5cnTs2FF9E86QkBBs27btgevo+75uaM+GsJ/v99FHH0EikWDGjBkPHKfv+/p+9enbEPb33Llza/QQEBDwwHXE3NcMblpQXFyMTp06YdmyZfUan5aWhsGDB6Nfv35ISEjAjBkzMHnyZGzfvl3LlTathvZdLTk5GZmZmeofR0dHLVXY9Hbv3o1p06bh4MGDiIuLQ3l5OSIiIlBcXFznOgcOHMCYMWMwadIknDhxAkOHDsXQoUNx+vTpZqz80TSmb6DqruP37utLly41U8VNw83NDR999BGOHTuGo0ePon///njqqadw5syZWscbwr5uaM+A/u/nex05cgRfffUVOnbs+MBxhrCv71XfvgHD2N/t2rXT6GHfvn11jhV9XwukVQCEX3755YFj3nrrLaFdu3Yay0aNGiVERkZqsTLtqk/ff//9twBAuHHjRrPU1BxycnIEAMLu3bvrHDNy5Ehh8ODBGsuCg4OFl156SdvlaU19+l69erVgY2PTfEU1kxYtWgjffPNNre8Z4r4WhAf3bEj7ubCwUPDz8xPi4uKE0NBQYfr06XWONaR93ZC+DWF/z5kzR+jUqVO9x4u9r3nETQfEx8cjLCxMY1lkZCTi4+NFqqh5de7cGS4uLggPD8f+/fvFLueR5OfnAwDs7OzqHGOI+7s+fQNAUVERPDw84O7u/tCjNrqusrIS69atQ3FxMUJCQmodY2j7uj49A4azn6dNm4bBgwfX2Ie1MaR93ZC+AcPY3ykpKXB1dYW3tzfGjh2L9PT0OseKva/5kHkdkJWVBScnJ41lTk5OKCgowK1bt2BmZiZSZdrl4uKCFStWoFu3bigtLcU333yDvn374tChQ+jatavY5TWYSqXCjBkz0KtXL7Rv377OcXXtb306t+9e9e3b398fq1atQseOHZGfn4+FCxeiZ8+eOHPmDNzc3Jqx4kdz6tQphISE4Pbt27C0tMQvv/yCwMDAWscayr5uSM+Gsp/XrVuH48eP48iRI/Uabyj7uqF9G8L+Dg4Oxpo1a+Dv74/MzEzMmzcPvXv3xunTp2FlZVVjvNj7msGNROPv7w9/f3/16549e+L8+fNYvHgxvvvuOxEra5xp06bh9OnTDzw3whDVt++QkBCNozQ9e/ZE27Zt8dVXX+H999/XdplNxt/fHwkJCcjPz8emTZswfvx47N69u84gYwga0rMh7OfLly9j+vTpiIuL07sT7R9FY/o2hP09cOBA9Z87duyI4OBgeHh4YMOGDZg0aZKIldWOwU0HODs7Izs7W2NZdnY2rK2tDfZoW1169Oihl8EnOjoav//+O/bs2fPQ/8qsa387Oztrs0StaEjf9zM1NUWXLl2Qmpqqpeq0Qy6Xw9fXFwAQFBSEI0eO4LPPPsNXX31VY6yh7OuG9Hw/fdzPx44dQ05OjsaR/8rKSuzZswdffPEFSktLIZPJNNYxhH3dmL7vp4/7+362trZo06ZNnT2Iva95jpsOCAkJwc6dOzWWxcXFPfAcEkOVkJAAFxcXscuoN0EQEB0djV9++QV//fUXvLy8HrqOIezvxvR9v8rKSpw6dUqv9ndtVCoVSktLa33PEPZ1bR7U8/30cT8PGDAAp06dQkJCgvqnW7duGDt2LBISEmoNL4awrxvT9/30cX/fr6ioCOfPn6+zB9H3dbNcAmFkCgsLhRMnTggnTpwQAAiLFi0STpw4IVy6dEkQBEGYNWuW8Pzzz6vHX7hwQTA3NxdmzpwpJCYmCsuWLRNkMpkQGxsrVguN0tC+Fy9eLGzevFlISUkRTp06JUyfPl2QSqXCn3/+KVYLDTZ16lTBxsZG2LVrl5CZman+KSkpUY95/vnnhVmzZqlf79+/XzAxMREWLlwoJCYmCnPmzBFMTU2FU6dOidFCozSm73nz5gnbt28Xzp8/Lxw7dkwYPXq0oFQqhTNnzojRQqPMmjVL2L17t5CWlib8888/wqxZswSJRCLs2LFDEATD3NcN7dkQ9nNt7r+60hD3dW0e1rch7O833nhD2LVrl5CWlibs379fCAsLE+zt7YWcnBxBEHRvXzO4aUH1bS7u/xk/frwgCIIwfvx4ITQ0tMY6nTt3FuRyueDt7S2sXr262et+VA3t++OPPxZ8fHwEpVIp2NnZCX379hX++usvcYpvpNr6BaCx/0JDQ9W/g2obNmwQ2rRpI8jlcqFdu3bC1q1bm7fwR9SYvmfMmCG0bt1akMvlgpOTkzBo0CDh+PHjzV/8I3jhhRcEDw8PQS6XCw4ODsKAAQPUAUYQDHNfN7RnQ9jPtbk/wBjivq7Nw/o2hP09atQowcXFRZDL5UKrVq2EUaNGCampqer3dW1fSwRBEJrn2B4RERERPQqe40ZERESkJxjciIiIiPQEgxsRERGRnmBwIyIiItITDG5EREREeoLBjYiIiEhPMLgRERER6QkGNyIiIiI9weBGRKQDJBIJNm/eLHYZRKTjGNyIyOhNmDABEomkxk9UVJTYpRERaTARuwAiIl0QFRWF1atXayxTKBQiVUNEVDsecSMiQlVIc3Z21vhp0aIFgKppzOXLl2PgwIEwMzODt7c3Nm3apLH+qVOn0L9/f5iZmaFly5aYMmUKioqKNMasWrUK7dq1g0KhgIuLC6KjozXez8vLw7Bhw2Bubg4/Pz9s2bJFu00Tkd5hcCMiqod3330Xw4cPx8mTJzF27FiMHj0aiYmJAIDi4mJERkaiRYsWOHLkCDZu3Ig///xTI5gtX74c06ZNw5QpU3Dq1Cls2bIFvr6+Gp8xb948jBw5Ev/88w8GDRqEsWPH4vr1683aJxHpOIGIyMiNHz9ekMlkgoWFhcbPhx9+KAiCIAAQXn75ZY11goODhalTpwqCIAhff/210KJFC6GoqEj9/tatWwWpVCpkZWUJgiAIrq6uwr/+9a86awAg/Pvf/1a/LioqEgAI27Zta7I+iUj/8Rw3IiIA/fr1w/LlyzWW2dnZqf8cEhKi8V5ISAgSEhIAAImJiejUqRMsLCzU7/fq1QsqlQrJycmQSCTIyMjAgAEDHlhDx44d1X+2sLCAtbU1cnJyGtsSERkgBjciIlQFpfunLpuKmZlZvcaZmppqvJZIJFCpVNooiYj0FM9xIyKqh4MHD9Z43bZtWwBA27ZtcfLkSRQXF6vf379/P6RSKfz9/WFlZQVPT0/s3LmzWWsmIsPDI25ERABKS0uRlZWlsczExAT29vYAgI0bN6Jbt254/PHH8b///Q+HDx/Gf//7XwDA2LFjMWfOHIwfPx5z585Fbm4uXn31VTz//PNwcnICAMydOxcvv/wyHB0dMXDgQBQWFmL//v149dVXm7dRItJrDG5ERABiY2Ph4uKisczf3x9JSUkAqq74XLduHV555RW4uLjgxx9/RGBgIADA3Nwc27dvx/Tp09G9e3eYm5tj+PDhWLRokXpb48ePx+3bt7F48WK8+eabsLe3xzPPPNN8DRKRQZAIgiCIXQQRkS6TSCT45ZdfMHToULFLISIjx3PciIiIiPQEgxsRERGRnuA5bkRED8EzSohIV/CIGxEREZGeYHAjIiIi0hMMbkRERER6gsGNiIiISE8wuBERERHpCQY3IiIiIj3B4EZERESkJxjciIiIiPTE/wOAncpGzENL0AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load and preprocesss MNIST\n",
    "(x_train,_),_ = tf.keras.datasets.mnist.load_data()\n",
    "x_train = x_train.astype('float32')/255.0\n",
    "x_train = (x_train > 0.5).astype('float32') # binarize\n",
    "x_train = x_train.reshape(-1,784)\n",
    "\n",
    "batch_size = 64\n",
    "train_dataset = tf.data.Dataset.from_tensor_slices(x_train).shuffle(10000).batch(batch_size)\n",
    "\n",
    "# RBM Class\n",
    "class RBM(tf.keras.Model):\n",
    "    def __init__(self,n_visible,n_hidden):\n",
    "        super(RBM,self).__init__()\n",
    "        self.n_visible = n_visible\n",
    "        self.n_hidden = n_hidden\n",
    "        # Parameters\n",
    "        initializer = tf.initializers.RandomNormal(mean=0.0, stddev=0.01)\n",
    "        self.W = tf.Variable(initializer([n_visible,n_hidden]),name='weights')\n",
    "        self.h_bias = tf.Variable(tf.zeros([n_hidden]),name='hidden_bias')\n",
    "        self.v_bias = tf.Variable(tf.zeros([n_visible]),name='visible_bias')\n",
    "\n",
    "    def sample_prob(self,probs):\n",
    "        return tf.nn.relu(tf.sign(probs-tf.random.uniform(tf.shape(probs))))\n",
    "    \n",
    "    def sample_h(self,v):\n",
    "        prob_h = tf.nn.sigmoid(tf.matmul(v,self.W)+self.h_bias)\n",
    "        return prob_h, self.sample_prob(prob_h)\n",
    "\n",
    "    def sample_v(self,h):\n",
    "        prob_v = tf.nn.sigmoid(tf.matmul(h,tf.transpose(self.W))+self.v_bias)\n",
    "        return prob_v, self.sample_prob(prob_v)\n",
    "\n",
    "    def contrastive_divergence(self, v, lr=0.01):\n",
    "        # Positive Phase\n",
    "        prob_h,h0 = self.sample_h(v)\n",
    "        # Negative phase (reconstruction)\n",
    "        prob_v, v1 = self.sample_v(h0)\n",
    "        prob_h1,_ = self.sample_h(v1)\n",
    "        # Compute gradient\n",
    "        positive_grad = tf.matmul(tf.transpose(v),prob_h)\n",
    "        negative_grad = tf.matmul(tf.transpose(v1),prob_h1)\n",
    "        # Update weights and biases\n",
    "        batch_size = tf.cast(tf.shape(v)[0],tf.float32)\n",
    "        self.W.assign_add(lr*(positive_grad-negative_grad)/batch_size)\n",
    "        self.v_bias.assign_add(lr*tf.reduce_mean(v-v1,axis=0))\n",
    "        self.h_bias.assign_add(lr*tf.reduce_mean(prob_h-prob_h1,axis=0))\n",
    "        # Compute reconstruction loss (MSE)\n",
    "        loss = tf.reduce_mean(tf.square(v-v1))\n",
    "        return loss\n",
    "\n",
    "# Initialize and train RBM\n",
    "n_visible = 784\n",
    "n_hidden = 128\n",
    "rbm = RBM(n_visible,n_hidden)\n",
    "\n",
    "n_epochs = 5\n",
    "lr = 0.05\n",
    "losses = []\n",
    "\n",
    "for epoch in range(n_epochs):\n",
    "    epoch_loss = 0\n",
    "    for batch in train_dataset:\n",
    "        loss = rbm.contrastive_divergence(batch,lr)\n",
    "        epoch_loss += loss.numpy()\n",
    "    avg_loss = epoch_loss/len(list(train_dataset))\n",
    "    losses.append(avg_loss)\n",
    "    print(f\"Epoch {epoch+1}/{n_epochs}, Reconstruction Loss: {avg_loss:.4f}\")\n",
    "\n",
    "# Plot training loss\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.plot(range(1,n_epochs+1), losses, marker='o')\n",
    "plt.title(\"RBM Reconstruction Loss over Epochs\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"MSE Loss\")\n",
    "plt.grid(True)\n",
    "plt.show()     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d83e0d16-a4a3-450a-bdda-bbc593775338",
   "metadata": {},
   "source": [
    "# Exp9: Develop a Denoising Autoencoder to reconstruct clean images from noisy input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "40f6d322-2a92-4b84-9a7d-bf0d34a80a3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 36ms/step - loss: 0.1704 - val_loss: 0.0987\n",
      "Epoch 2/5\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 16ms/step - loss: 0.0945 - val_loss: 0.0892\n",
      "Epoch 3/5\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0880 - val_loss: 0.0854\n",
      "Epoch 4/5\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 16ms/step - loss: 0.0855 - val_loss: 0.0836\n",
      "Epoch 5/5\n",
      "\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 16ms/step - loss: 0.0838 - val_loss: 0.0824\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load and normalize MNIST dataset\n",
    "(x_train, _), (x_test, _) = tf.keras.datasets.mnist.load_data()\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
    "x_train, x_test = np.expand_dims(x_train, -1), np.expand_dims(x_test, -1)\n",
    "\n",
    "# Add random noise\n",
    "x_train_noisy = np.clip(x_train + 0.3* np.random.normal(0, 1, x_train.shape), 0, 1)\n",
    "x_test_noisy = np.clip(x_test + 0.3*np.random.normal(0, 1, x_test.shape), 0, 1)\n",
    "\n",
    "# Visualize clean vs noisy images\n",
    "plt.figure(figsize=(10, 4))\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 10, i + 1)\n",
    "    plt.imshow(x_test[i].squeeze(), cmap='gray')\n",
    "    plt.axis('off')\n",
    "    plt.subplot(2, 10, i + 11)\n",
    "    plt.imshow(x_test_noisy[i].squeeze(), cmap='gray')\n",
    "    plt.axis('off')\n",
    "plt.show()\n",
    "\n",
    "# Build Autoencoder\n",
    "autoencoder = models.Sequential([\n",
    "    layers.Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(28, 28, 1)),\n",
    "    layers.MaxPooling2D((2, 2), padding='same'),\n",
    "    layers.Conv2D(64, (3, 3), activation='relu', padding='same'),\n",
    "    layers.MaxPooling2D((2, 2), padding='same'),\n",
    "    layers.Conv2D(64, (3, 3), activation='relu', padding='same'),\n",
    "    layers.UpSampling2D((2, 2)),\n",
    "    layers.Conv2D(32, (3, 3), activation='relu', padding='same'),\n",
    "    layers.UpSampling2D((2, 2)),\n",
    "    layers.Conv2D(1, (3, 3), activation='sigmoid', padding='same')\n",
    "])\n",
    "\n",
    "autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n",
    "autoencoder.fit(x_train_noisy, x_train, epochs=5, batch_size=256, validation_data=(x_test_noisy, x_test))\n",
    "\n",
    "# Predict and visualize denoised image\n",
    "decoded_imgs = autoencoder.predict(x_test_noisy)\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "for i in range(10):\n",
    "    plt.subplot(3, 10, i + 1)\n",
    "    plt.imshow(x_test[i].squeeze(),cmap='gray')\n",
    "    plt.axis('off')\n",
    "    plt.subplot(3, 10, i + 11)\n",
    "    plt.imshow(x_test_noisy[i].squeeze(),cmap='gray')\n",
    "    plt.axis('off')\n",
    "    plt.subplot(3, 10, i + 21)\n",
    "    plt.imshow(decoded_imgs[i].squeeze(),cmap='gray')\n",
    "    plt.axis('off')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f60b8a76-d789-48c1-af66-cd42e89707e9",
   "metadata": {},
   "source": [
    "# Exp10: Implement a simple Generative Adversarial Network (GAN) on the MNIST dataset to generate new handwritten digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ea665a49-db80-4724-b2b1-168906ce7ec5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/galaxygamerman/DPLabExps/tensorflow/lib/python3.12/site-packages/keras/src/backend/tensorflow/trainer.py:86: UserWarning: The model does not have any trainable weights.\n",
      "  warnings.warn(\"The model does not have any trainable weights.\")\n",
      "2025-12-20 07:33:59.509933: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_1123', 4 bytes spill stores, 4 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 0 | D loss: 0.698 | G loss: 0.693\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 500 | D loss: 1.151 | G loss: 0.241\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 1000 | D loss: 1.221 | G loss: 0.203\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 1500 | D loss: 1.249 | G loss: 0.189\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 2000 | D loss: 1.265 | G loss: 0.181\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 2500 | D loss: 1.274 | G loss: 0.177\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 3000 | D loss: 1.282 | G loss: 0.173\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 3500 | D loss: 1.287 | G loss: 0.171\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 4000 | D loss: 1.292 | G loss: 0.169\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 4500 | D loss: 1.295 | G loss: 0.167\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution done\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#  Load and normalize MNIST\n",
    "(x_train,_),_ = tf.keras.datasets.mnist.load_data()\n",
    "x_train = (x_train.astype('float32')-127.5)/127.5\n",
    "x_train = np.expand_dims(x_train, axis = -1)\n",
    "\n",
    "batch_size = 128\n",
    "buffer_size = 60000\n",
    "train_dataset = tf.data.Dataset.from_tensor_slices(x_train).shuffle(buffer_size).batch(batch_size)\n",
    "\n",
    "# Generator\n",
    "def build_generator():\n",
    "    model = tf.keras.Sequential([\n",
    "        layers.Dense(7*7*128, input_dim=100),\n",
    "        layers.LeakyReLU(0.2),\n",
    "        layers.Reshape((7,7,128)),\n",
    "        layers.Conv2DTranspose(64, (5,5), strides=2, padding='same'),\n",
    "        layers.LeakyReLU(0.2),\n",
    "        layers.Conv2DTranspose(1, (5,5), strides=2, padding='same', activation='tanh'),\n",
    "    ])\n",
    "    return model\n",
    "def build_discriminator():\n",
    "    model = tf.keras.Sequential([\n",
    "        layers.Conv2D(64, (5, 5), strides=2, padding=\"same\", input_shape=[28, 28, 1]),\n",
    "        layers.LeakyReLU(0.2),\n",
    "        layers.Dropout(0.3),\n",
    "        layers.Conv2D(128, (5, 5), strides=2, padding=\"same\"),\n",
    "        layers.LeakyReLU(0.2),\n",
    "        layers.Dropout(0.3),\n",
    "        layers.Flatten(),\n",
    "        layers.Dense(1, activation=\"sigmoid\")\n",
    "    ])\n",
    "    return model\n",
    "\n",
    "# Models\n",
    "generator = build_generator()\n",
    "discriminator = build_discriminator()\n",
    "discriminator.compile(loss='binary_crossentropy',\n",
    "                     optimizer='adam',\n",
    "                     metrics=['accuracy'])\n",
    "# GAN combined model\n",
    "discriminator.trainable = False\n",
    "gan_input = tf.keras.Input(shape=(100,))\n",
    "gan_output = discriminator(generator(gan_input))\n",
    "gan = tf.keras.Model(gan_input,gan_output)\n",
    "gan.compile(loss='binary_crossentropy',\n",
    "            optimizer='adam')\n",
    "\n",
    "# Training\n",
    "epochs = 5000 # Small but enough to get clear digits\n",
    "for step in range(epochs):\n",
    "    # Train Discriminator\n",
    "    noise = np.random.normal(0, 1, (batch_size, 100))\n",
    "    fake = generator.predict(noise, verbose=0)\n",
    "    real = x_train[np.random.randint(0, x_train.shape[0], batch_size)]\n",
    "    x = np.concatenate([real,fake])\n",
    "    y = np.concatenate([np.ones((batch_size,1)), np.zeros((batch_size,1))])\n",
    "    d_loss,_ = discriminator.train_on_batch(x,y)\n",
    "\n",
    "    # Train Generator\n",
    "    noise = np.random.normal(0,1,(batch_size,100))\n",
    "    y_gen = np.ones((batch_size,1))\n",
    "    g_loss = gan.train_on_batch(noise, y_gen)\n",
    "\n",
    "    # Print progress and save sample every 500 steps\n",
    "    if step%500 == 0:\n",
    "        print(f\"Step {step} | D loss: {d_loss:.3f} | G loss: {g_loss:.3f}\")\n",
    "        z = np.random.normal(0,1,(1,100))\n",
    "        gen_img = generator.predict(z, verbose=0)[0,:,:,0]\n",
    "        plt.imshow(gen_img*0.5+0.5, cmap=\"gray\")\n",
    "        plt.axis(\"off\")\n",
    "        plt.show()\n",
    "\n",
    "print('Execution done')"
   ]
  }
 ],
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