{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "93131f7e-7556-4182-9eb5-ecf9c13cc375",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c75997ab-ac5c-4345-ad8a-ec07106e35f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.7.1'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e11c0543-2b40-4e80-830b-0bdab1f7aecc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='mps')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device(\"mps\" if torch.backends.mps.is_available() else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6f2682f7-b6c2-4ba8-aee2-084e1581e679",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100.0%\n",
      "100.0%\n",
      "100.0%\n",
      "100.0%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data import DataLoader\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Hyperparameters\n",
    "latent_dim = 100\n",
    "hidden_dim = 256\n",
    "image_dim = 784  # 28x28 for MNIST\n",
    "num_epochs = 50\n",
    "batch_size = 64\n",
    "lr = 0.0002\n",
    "\n",
    "# Generator\n",
    "class Generator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Generator, self).__init__()\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(latent_dim, hidden_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_dim, hidden_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_dim, image_dim),\n",
    "            nn.Tanh()  # Output in [-1, 1]\n",
    "        )\n",
    "    \n",
    "    def forward(self, z):\n",
    "        return self.model(z)\n",
    "\n",
    "# Discriminator\n",
    "class Discriminator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Discriminator, self).__init__()\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(image_dim, hidden_dim),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            nn.Linear(hidden_dim, hidden_dim),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            nn.Linear(hidden_dim, 1),\n",
    "            nn.Sigmoid()  # Output probability\n",
    "        )\n",
    "    \n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "# Data loading\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5,), (0.5,))  # Normalize to [-1, 1]\n",
    "])\n",
    "mnist = datasets.MNIST(root='./data', train=True, transform=transform, download=True)\n",
    "dataloader = DataLoader(mnist, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "# Initialize models and optimizers\n",
    "generator = Generator()\n",
    "discriminator = Discriminator()\n",
    "criterion = nn.BCELoss()\n",
    "optimizer_g = optim.Adam(generator.parameters(), lr=lr)\n",
    "optimizer_d = optim.Adam(discriminator.parameters(), lr=lr)\n",
    "\n",
    "# Training loop\n",
    "for epoch in range(num_epochs):\n",
    "    for i, (real_images, _) in enumerate(dataloader):\n",
    "        batch_size = real_images.size(0)\n",
    "        real_images = real_images.view(batch_size, -1)\n",
    "        \n",
    "        # Labels\n",
    "        real_labels = torch.ones(batch_size, 1)\n",
    "        fake_labels = torch.zeros(batch_size, 1)\n",
    "        \n",
    "        # Train Discriminator\n",
    "        optimizer_d.zero_grad()\n",
    "        real_loss = criterion(discriminator(real_images), real_labels)\n",
    "        z = torch.randn(batch_size, latent_dim)\n",
    "        fake_images = generator(z)\n",
    "        fake_loss = criterion(discriminator(fake_images.detach()), fake_labels)\n",
    "        d_loss = real_loss + fake_loss\n",
    "        d_loss.backward()\n",
    "        optimizer_d.step()\n",
    "        \n",
    "        # Train Generator\n",
    "        optimizer_g.zero_grad()\n",
    "        fake_images = generator(z)\n",
    "        g_loss = criterion(discriminator(fake_images), real_labels)  # Trick Discriminator\n",
    "        g_loss.backward()\n",
    "        optimizer_g.step()\n",
    "        \n",
    "        if i % 100 == 0:\n",
    "            print(f'Epoch [{epoch}/{num_epochs}] Batch [{i}/{len(dataloader)}] '\n",
    "                  f'D Loss: {d_loss.item():.4f}, G Loss: {g_loss.item():.4f}')\n",
    "\n",
    "# Generate and visualize fake images\n",
    "z = torch.randn(16, latent_dim)\n",
    "fake_images = generator(z).view(-1, 28, 28).detach().numpy()\n",
    "plt.figure(figsize=(4, 4))\n",
    "for i in range(16):\n",
    "    plt.subplot(4, 4, i+1)\n",
    "    plt.imshow(fake_images[i], cmap='gray')\n",
    "    plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7e9f43d1-6918-4b96-a80f-c79bfc3b1e6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/100] D Loss: 0.8596, G Loss: 1.3245\n",
      "Epoch [2/100] D Loss: 0.3178, G Loss: 2.9104\n",
      "Epoch [3/100] D Loss: 0.3401, G Loss: 2.9102\n",
      "Epoch [4/100] D Loss: 0.2912, G Loss: 2.9682\n",
      "Epoch [5/100] D Loss: 0.3431, G Loss: 3.6941\n",
      "Epoch [6/100] D Loss: 0.1103, G Loss: 5.0811\n",
      "Epoch [7/100] D Loss: 0.0681, G Loss: 3.6966\n",
      "Epoch [8/100] D Loss: 0.2119, G Loss: 4.1157\n",
      "Epoch [9/100] D Loss: 0.3644, G Loss: 3.5768\n",
      "Epoch [10/100] D Loss: 0.2263, G Loss: 4.7301\n",
      "Epoch [11/100] D Loss: 0.1162, G Loss: 5.1073\n",
      "Epoch [12/100] D Loss: 0.1169, G Loss: 2.5280\n",
      "Epoch [13/100] D Loss: 0.2090, G Loss: 2.9446\n",
      "Epoch [14/100] D Loss: 0.2468, G Loss: 3.4869\n",
      "Epoch [15/100] D Loss: 0.1906, G Loss: 3.8375\n",
      "Epoch [16/100] D Loss: 0.1582, G Loss: 3.6400\n",
      "Epoch [17/100] D Loss: 0.2786, G Loss: 2.1655\n",
      "Epoch [18/100] D Loss: 0.7477, G Loss: 3.0151\n",
      "Epoch [19/100] D Loss: 0.3153, G Loss: 2.9306\n",
      "Epoch [20/100] D Loss: 0.4000, G Loss: 2.4397\n",
      "Epoch [21/100] D Loss: 0.5692, G Loss: 2.2974\n",
      "Epoch [22/100] D Loss: 0.3500, G Loss: 2.5623\n",
      "Epoch [23/100] D Loss: 0.6981, G Loss: 2.1662\n",
      "Epoch [24/100] D Loss: 0.4444, G Loss: 3.2438\n",
      "Epoch [25/100] D Loss: 0.3402, G Loss: 1.9033\n",
      "Epoch [26/100] D Loss: 0.5941, G Loss: 3.2159\n",
      "Epoch [27/100] D Loss: 0.6237, G Loss: 2.5690\n",
      "Epoch [28/100] D Loss: 0.7592, G Loss: 2.4508\n",
      "Epoch [29/100] D Loss: 0.5631, G Loss: 2.2924\n",
      "Epoch [30/100] D Loss: 0.7717, G Loss: 1.4503\n",
      "Epoch [31/100] D Loss: 0.6966, G Loss: 1.9626\n",
      "Epoch [32/100] D Loss: 0.7658, G Loss: 0.7556\n",
      "Epoch [33/100] D Loss: 0.6487, G Loss: 1.5975\n",
      "Epoch [34/100] D Loss: 0.8720, G Loss: 1.2473\n",
      "Epoch [35/100] D Loss: 0.6736, G Loss: 1.5726\n",
      "Epoch [36/100] D Loss: 0.5748, G Loss: 1.8743\n",
      "Epoch [37/100] D Loss: 0.7471, G Loss: 1.4878\n",
      "Epoch [38/100] D Loss: 0.6604, G Loss: 1.5125\n",
      "Epoch [39/100] D Loss: 0.7956, G Loss: 2.1544\n",
      "Epoch [40/100] D Loss: 0.8610, G Loss: 1.1597\n",
      "Epoch [41/100] D Loss: 1.0077, G Loss: 1.3896\n",
      "Epoch [42/100] D Loss: 1.1202, G Loss: 2.7569\n",
      "Epoch [43/100] D Loss: 0.7415, G Loss: 1.5275\n",
      "Epoch [44/100] D Loss: 0.7672, G Loss: 1.3828\n",
      "Epoch [45/100] D Loss: 1.0165, G Loss: 2.3110\n",
      "Epoch [46/100] D Loss: 0.7884, G Loss: 1.3334\n",
      "Epoch [47/100] D Loss: 1.1905, G Loss: 0.8218\n",
      "Epoch [48/100] D Loss: 0.7332, G Loss: 1.4409\n",
      "Epoch [49/100] D Loss: 0.7835, G Loss: 1.8210\n",
      "Epoch [50/100] D Loss: 0.8132, G Loss: 1.0869\n",
      "Epoch [51/100] D Loss: 0.7135, G Loss: 2.3405\n",
      "Epoch [52/100] D Loss: 0.6854, G Loss: 1.5260\n",
      "Epoch [53/100] D Loss: 0.7862, G Loss: 1.8492\n",
      "Epoch [54/100] D Loss: 0.8746, G Loss: 1.0866\n",
      "Epoch [55/100] D Loss: 1.0941, G Loss: 0.9446\n",
      "Epoch [56/100] D Loss: 0.7720, G Loss: 1.2812\n",
      "Epoch [57/100] D Loss: 1.0350, G Loss: 0.6587\n",
      "Epoch [58/100] D Loss: 0.7528, G Loss: 2.1129\n",
      "Epoch [59/100] D Loss: 0.9216, G Loss: 0.9458\n",
      "Epoch [60/100] D Loss: 0.7405, G Loss: 1.4153\n",
      "Epoch [61/100] D Loss: 0.9845, G Loss: 2.1555\n",
      "Epoch [62/100] D Loss: 0.6611, G Loss: 2.0854\n",
      "Epoch [63/100] D Loss: 0.7004, G Loss: 1.8191\n",
      "Epoch [64/100] D Loss: 0.8364, G Loss: 1.2061\n",
      "Epoch [65/100] D Loss: 0.7410, G Loss: 1.8626\n",
      "Epoch [66/100] D Loss: 0.8975, G Loss: 2.2005\n",
      "Epoch [67/100] D Loss: 0.7804, G Loss: 1.2154\n",
      "Epoch [68/100] D Loss: 0.8136, G Loss: 2.2689\n",
      "Epoch [69/100] D Loss: 0.7192, G Loss: 1.8950\n",
      "Epoch [70/100] D Loss: 0.7860, G Loss: 1.8399\n",
      "Epoch [71/100] D Loss: 0.7883, G Loss: 1.5123\n",
      "Epoch [72/100] D Loss: 0.7713, G Loss: 1.1822\n",
      "Epoch [73/100] D Loss: 0.8958, G Loss: 2.8854\n",
      "Epoch [74/100] D Loss: 0.7927, G Loss: 1.6941\n",
      "Epoch [75/100] D Loss: 0.6758, G Loss: 1.5761\n",
      "Epoch [76/100] D Loss: 0.8422, G Loss: 1.1505\n",
      "Epoch [77/100] D Loss: 0.6597, G Loss: 1.9421\n",
      "Epoch [78/100] D Loss: 0.7658, G Loss: 1.5101\n",
      "Epoch [79/100] D Loss: 0.6828, G Loss: 1.5025\n",
      "Epoch [80/100] D Loss: 0.8562, G Loss: 1.5586\n",
      "Epoch [81/100] D Loss: 0.7598, G Loss: 1.7176\n",
      "Epoch [82/100] D Loss: 0.7867, G Loss: 1.5005\n",
      "Epoch [83/100] D Loss: 0.8104, G Loss: 2.4359\n",
      "Epoch [84/100] D Loss: 0.7692, G Loss: 2.3517\n",
      "Epoch [85/100] D Loss: 0.7785, G Loss: 2.0902\n",
      "Epoch [86/100] D Loss: 0.6641, G Loss: 1.6968\n",
      "Epoch [87/100] D Loss: 0.6338, G Loss: 1.8915\n",
      "Epoch [88/100] D Loss: 0.7428, G Loss: 1.4103\n",
      "Epoch [89/100] D Loss: 0.6918, G Loss: 1.5932\n",
      "Epoch [90/100] D Loss: 0.9212, G Loss: 1.3316\n",
      "Epoch [91/100] D Loss: 0.6881, G Loss: 1.9827\n",
      "Epoch [92/100] D Loss: 0.7779, G Loss: 1.5996\n",
      "Epoch [93/100] D Loss: 0.7242, G Loss: 1.6816\n",
      "Epoch [94/100] D Loss: 0.6647, G Loss: 2.4757\n",
      "Epoch [95/100] D Loss: 0.8397, G Loss: 1.2086\n",
      "Epoch [96/100] D Loss: 0.7217, G Loss: 1.9149\n",
      "Epoch [97/100] D Loss: 0.6855, G Loss: 2.7034\n",
      "Epoch [98/100] D Loss: 0.8326, G Loss: 0.9218\n",
      "Epoch [99/100] D Loss: 0.5173, G Loss: 2.0841\n",
      "Epoch [100/100] D Loss: 0.5779, G Loss: 1.9022\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data import DataLoader\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Hyperparameters\n",
    "latent_dim = 100\n",
    "hidden_dim = 256\n",
    "image_dim = 784\n",
    "num_epochs = 100\n",
    "batch_size = 128\n",
    "lr = 0.0002\n",
    "\n",
    "# Data loading (MNIST)\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5,), (0.5,))\n",
    "])\n",
    "mnist = datasets.MNIST(root='./data', train=True, transform=transform, download=True)\n",
    "dataloader = DataLoader(mnist, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "# Same Generator and Discriminator as above\n",
    "class Generator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Generator, self).__init__()\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(latent_dim, hidden_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_dim, hidden_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_dim, image_dim),\n",
    "            nn.Tanh()\n",
    "        )\n",
    "    \n",
    "    def forward(self, z):\n",
    "        return self.model(z)\n",
    "\n",
    "class Discriminator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Discriminator, self).__init__()\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(image_dim, hidden_dim),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            nn.Linear(hidden_dim, hidden_dim),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            nn.Linear(hidden_dim, 1),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "    \n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "# Initialize models and optimizers\n",
    "generator = Generator()\n",
    "discriminator = Discriminator()\n",
    "criterion = nn.BCELoss()\n",
    "optimizer_g = optim.Adam(generator.parameters(), lr=lr, betas=(0.5, 0.999))\n",
    "optimizer_d = optim.Adam(discriminator.parameters(), lr=lr, betas=(0.5, 0.999))\n",
    "\n",
    "# Training loop with visualization\n",
    "losses_g, losses_d = [], []\n",
    "for epoch in range(num_epochs):\n",
    "    for real_images, _ in dataloader:\n",
    "        batch_size = real_images.size(0)\n",
    "        real_images = real_images.view(batch_size, -1)\n",
    "        \n",
    "        # Train Discriminator\n",
    "        optimizer_d.zero_grad()\n",
    "        real_loss = criterion(discriminator(real_images), torch.ones(batch_size, 1))\n",
    "        z = torch.randn(batch_size, latent_dim)\n",
    "        fake_images = generator(z)\n",
    "        fake_loss = criterion(discriminator(fake_images.detach()), torch.zeros(batch_size, 1))\n",
    "        d_loss = real_loss + fake_loss\n",
    "        d_loss.backward()\n",
    "        optimizer_d.step()\n",
    "        \n",
    "        # Train Generator\n",
    "        optimizer_g.zero_grad()\n",
    "        fake_images = generator(z)\n",
    "        g_loss = criterion(discriminator(fake_images), torch.ones(batch_size, 1))\n",
    "        g_loss.backward()\n",
    "        optimizer_g.step()\n",
    "        \n",
    "        losses_g.append(g_loss.item())\n",
    "        losses_d.append(d_loss.item())\n",
    "    \n",
    "    print(f'Epoch [{epoch+1}/{num_epochs}] D Loss: {d_loss.item():.4f}, G Loss: {g_loss.item():.4f}')\n",
    "\n",
    "# Plot losses\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(losses_d, label='Discriminator Loss')\n",
    "plt.plot(losses_g, label='Generator Loss')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Generate and visualize results\n",
    "z = torch.randn(16, latent_dim)\n",
    "fake_images = generator(z).view(-1, 28, 28).detach().numpy()\n",
    "plt.figure(figsize=(4, 4))\n",
    "for i in range(16):\n",
    "    plt.subplot(4, 4, i+1)\n",
    "    plt.imshow(fake_images[i], cmap='gray')\n",
    "    plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cf2ae3c6-55d6-4cc4-bf24-dae9870007db",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-13 11:22:12,887 - INFO - Using MPS (optimized for efficiency)\n",
      "2025-07-13 11:22:12,888 - INFO - LITE MODE - Latent: 64, Epochs: 50, Batch: 64, Subset: 10000\n",
      "2025-07-13 11:22:12,889 - INFO - Loading MNIST subset...\n",
      "2025-07-13 11:22:12,906 - INFO - Using 10000 samples, 157 batches per epoch\n",
      "2025-07-13 11:22:12,908 - INFO - Simple Generator: Linear layers only (much faster)\n",
      "2025-07-13 11:22:12,931 - INFO - Simple Discriminator: Linear layers only (much faster)\n",
      "2025-07-13 11:22:12,934 - INFO - Generator params: 576,656 (vs 3.5M before)\n",
      "2025-07-13 11:22:12,934 - INFO - Discriminator params: 533,505 (vs 2.7M before)\n",
      "2025-07-13 11:22:12,938 - INFO - ==================================================\n",
      "2025-07-13 11:22:12,938 - INFO - STARTING LITE TRAINING (HEAT OPTIMIZED)\n",
      "2025-07-13 11:22:12,938 - INFO - ==================================================\n",
      "2025-07-13 11:22:15,868 - INFO - Epoch [1/50] Batch [1/157] D_Loss: 1.414 G_Loss: 0.727\n",
      "2025-07-13 11:22:16,196 - INFO - Epoch [1/50] Batch [51/157] D_Loss: 0.487 G_Loss: 1.930\n",
      "2025-07-13 11:22:16,509 - INFO - Epoch [1/50] Batch [101/157] D_Loss: 0.087 G_Loss: 5.107\n",
      "2025-07-13 11:22:16,822 - INFO - Epoch [1/50] Batch [151/157] D_Loss: 0.188 G_Loss: 9.829\n",
      "2025-07-13 11:22:17,650 - INFO - Epoch 1 - D_Loss: 0.499, G_Loss: 3.914, Time: 4.7s\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-13 11:22:18,681 - INFO - Epoch [2/50] Batch [1/157] D_Loss: 0.198 G_Loss: 10.054\n",
      "2025-07-13 11:22:19,003 - INFO - Epoch [2/50] Batch [51/157] D_Loss: 0.197 G_Loss: 10.293\n",
      "2025-07-13 11:22:19,325 - INFO - Epoch [2/50] Batch [101/157] D_Loss: 0.107 G_Loss: 3.773\n",
      "2025-07-13 11:22:19,643 - INFO - Epoch [2/50] Batch [151/157] D_Loss: 0.120 G_Loss: 4.523\n",
      "2025-07-13 11:22:19,939 - INFO - Epoch 2 - D_Loss: 0.149, G_Loss: 8.121, Time: 2.2s\n",
      "2025-07-13 11:22:20,903 - INFO - Epoch [3/50] Batch [1/157] D_Loss: 0.090 G_Loss: 4.251\n",
      "2025-07-13 11:22:21,220 - INFO - Epoch [3/50] Batch [51/157] D_Loss: 0.055 G_Loss: 5.018\n",
      "2025-07-13 11:22:21,535 - INFO - Epoch [3/50] Batch [101/157] D_Loss: 0.037 G_Loss: 6.808\n",
      "2025-07-13 11:22:21,853 - INFO - Epoch [3/50] Batch [151/157] D_Loss: 0.025 G_Loss: 6.606\n",
      "2025-07-13 11:22:22,134 - INFO - Epoch 3 - D_Loss: 0.050, G_Loss: 5.622, Time: 2.2s\n",
      "2025-07-13 11:22:23,103 - INFO - Epoch [4/50] Batch [1/157] D_Loss: 0.020 G_Loss: 6.747\n",
      "2025-07-13 11:22:23,420 - INFO - Epoch [4/50] Batch [51/157] D_Loss: 0.016 G_Loss: 6.293\n",
      "2025-07-13 11:22:23,732 - INFO - Epoch [4/50] Batch [101/157] D_Loss: 0.016 G_Loss: 6.285\n",
      "2025-07-13 11:22:24,054 - INFO - Epoch [4/50] Batch [151/157] D_Loss: 0.011 G_Loss: 6.005\n",
      "2025-07-13 11:22:24,333 - INFO - Epoch 4 - D_Loss: 0.019, G_Loss: 6.681, Time: 2.2s\n",
      "2025-07-13 11:22:25,286 - INFO - Epoch [5/50] Batch [1/157] D_Loss: 0.013 G_Loss: 7.839\n",
      "2025-07-13 11:22:25,602 - INFO - Epoch [5/50] Batch [51/157] D_Loss: 0.055 G_Loss: 5.665\n",
      "2025-07-13 11:22:25,914 - INFO - Epoch [5/50] Batch [101/157] D_Loss: 0.131 G_Loss: 7.865\n",
      "2025-07-13 11:22:26,231 - INFO - Epoch [5/50] Batch [151/157] D_Loss: 0.208 G_Loss: 4.971\n",
      "2025-07-13 11:22:26,514 - INFO - Epoch 5 - D_Loss: 0.080, G_Loss: 6.832, Time: 2.2s\n",
      "2025-07-13 11:22:27,479 - INFO - Epoch [6/50] Batch [1/157] D_Loss: 0.059 G_Loss: 5.910\n",
      "2025-07-13 11:22:27,797 - INFO - Epoch [6/50] Batch [51/157] D_Loss: 0.327 G_Loss: 5.019\n",
      "2025-07-13 11:22:28,110 - INFO - Epoch [6/50] Batch [101/157] D_Loss: 0.069 G_Loss: 7.908\n",
      "2025-07-13 11:22:28,422 - INFO - Epoch [6/50] Batch [151/157] D_Loss: 0.041 G_Loss: 7.707\n",
      "2025-07-13 11:22:28,705 - INFO - Epoch 6 - D_Loss: 0.125, G_Loss: 7.011, Time: 2.2s\n",
      "2025-07-13 11:22:29,668 - INFO - Epoch [7/50] Batch [1/157] D_Loss: 0.094 G_Loss: 9.911\n",
      "2025-07-13 11:22:29,985 - INFO - Epoch [7/50] Batch [51/157] D_Loss: 0.099 G_Loss: 7.200\n",
      "2025-07-13 11:22:30,294 - INFO - Epoch [7/50] Batch [101/157] D_Loss: 0.106 G_Loss: 6.806\n",
      "2025-07-13 11:22:30,604 - INFO - Epoch [7/50] Batch [151/157] D_Loss: 0.072 G_Loss: 8.220\n",
      "2025-07-13 11:22:30,884 - INFO - Epoch 7 - D_Loss: 0.160, G_Loss: 7.298, Time: 2.2s\n",
      "2025-07-13 11:22:31,835 - INFO - Epoch [8/50] Batch [1/157] D_Loss: 0.127 G_Loss: 7.432\n",
      "2025-07-13 11:22:32,158 - INFO - Epoch [8/50] Batch [51/157] D_Loss: 0.182 G_Loss: 7.185\n",
      "2025-07-13 11:22:32,503 - INFO - Epoch [8/50] Batch [101/157] D_Loss: 0.324 G_Loss: 4.899\n",
      "2025-07-13 11:22:32,816 - INFO - Epoch [8/50] Batch [151/157] D_Loss: 0.448 G_Loss: 5.160\n",
      "2025-07-13 11:22:33,117 - INFO - Epoch 8 - D_Loss: 0.254, G_Loss: 6.831, Time: 2.2s\n",
      "2025-07-13 11:22:34,086 - INFO - Epoch [9/50] Batch [1/157] D_Loss: 0.496 G_Loss: 4.999\n",
      "2025-07-13 11:22:34,405 - INFO - Epoch [9/50] Batch [51/157] D_Loss: 0.343 G_Loss: 5.772\n",
      "2025-07-13 11:22:34,719 - INFO - Epoch [9/50] Batch [101/157] D_Loss: 0.256 G_Loss: 4.703\n",
      "2025-07-13 11:22:35,030 - INFO - Epoch [9/50] Batch [151/157] D_Loss: 0.150 G_Loss: 6.448\n",
      "2025-07-13 11:22:35,309 - INFO - Epoch 9 - D_Loss: 0.330, G_Loss: 5.221, Time: 2.2s\n",
      "2025-07-13 11:22:36,308 - INFO - Epoch [10/50] Batch [1/157] D_Loss: 0.299 G_Loss: 8.258\n",
      "2025-07-13 11:22:36,653 - INFO - Epoch [10/50] Batch [51/157] D_Loss: 0.249 G_Loss: 5.360\n",
      "2025-07-13 11:22:36,972 - INFO - Epoch [10/50] Batch [101/157] D_Loss: 0.340 G_Loss: 4.701\n",
      "2025-07-13 11:22:37,305 - INFO - Epoch [10/50] Batch [151/157] D_Loss: 0.095 G_Loss: 6.552\n",
      "2025-07-13 11:22:37,599 - INFO - Epoch 10 - D_Loss: 0.267, G_Loss: 5.786, Time: 2.3s\n",
      "2025-07-13 11:22:38,595 - INFO - Epoch [11/50] Batch [1/157] D_Loss: 0.123 G_Loss: 8.758\n",
      "2025-07-13 11:22:38,928 - INFO - Epoch [11/50] Batch [51/157] D_Loss: 0.231 G_Loss: 4.707\n",
      "2025-07-13 11:22:39,252 - INFO - Epoch [11/50] Batch [101/157] D_Loss: 0.213 G_Loss: 6.000\n",
      "2025-07-13 11:22:39,577 - INFO - Epoch [11/50] Batch [151/157] D_Loss: 0.315 G_Loss: 5.685\n",
      "2025-07-13 11:22:39,862 - INFO - Epoch 11 - D_Loss: 0.252, G_Loss: 5.776, Time: 2.3s\n"
     ]
    },
    {
     "data": {
      "image/png": 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P1SQht3QhfaztZPtD3fN2W+3TkGtf+61Zs2Zp+fPPP3dsLVq08La/Shiaccte96JFi/J2CxYiQ1TFwigAMVOse75eYHGaUgwh5AsZAAAgAhiQAQAAIoABGQAAoLZoyJXB+vlDmpzqAaoXWELhKX/9618d27/+9S+vhqja49KlS526Pd9u3bo5to8//tirGatmYrXTmtSXq0q/tNcU6jfVb0PbdujQwal/8sknaflvf/ubY7vuuuu859O2bVvHtnDhQu859OzZ07G99957Xs1Y29KGQVU0zA2gVKhMGtx6pv6HP/zBsd18883eOSg6H0T3q++XGOELGQAAIAIYkAEAACKAARkAACACalxDtn5+1QCs1qfL5KkOZ3VY1RdtOswJEyY4No1vtrqgaoYhXdtqmIUuzZevbhzSVEslVjDUxxpbrMsm2mUVN954Y8f2zTffpOVJkyYF01i+8sorafnXv/51MIba9uOHH37oPfdc6UJDqTNt++j9WNepaPpU/S2pJKse+wzkigm2z/KyZcu8qW7HjBnj2HRez+zZs9Ny06ZN8753qmq53MrCFzIAAEAEMCADAADUltWeQtvq7kPpEPUYNiSlefPmjk3TI4bc23Y/Dz/8sGMbMmSIUx81alRa/tOf/uTYbrjhBqdu3YsNGzb0rvaktm+//dap2+suxHVS7DAldTPlu6KTnrNer3U9q0vW9k27du0c25w5c7xuaXVPWRf20KFDHdvIkSO9oRMaVnHXXXd53d16XfYebNKkiWNbsWKFt70KCb9gtSc/FV3xLcY2iXW1p4qu5KfSlLqsretZ3zttzTvhxRdfdGybbbaZU7fPkoalWmkqoX///t5ztymTqyr8k9WeAAAASgQGZAAAgAhgQAYAAKgtYU+hkI9C0pepvvzFF19k1WQTtt12W68uq9qjtWlozdVXX+3VEm655RbH9tVXX3mn52voktVKNXwnpMuo9mLbsqqn5oc0jpAmpP2vIT52v6rD2rSmurzhzjvv7L13pk6d6tjs/aE69fHHH+/UbZjFnXfe6djmzZvn1Fu1auUNtbP6stW3s/WxbS9dZtLaWH6xejTlmtaMS4WKLq2rmrE+9zbsyb5zsz3bvveFpizWuTnDhg3zvpc0FDJfbTzXtpWFL2QAAIAIYEAGAAAoZZe1dQuqeyIU9qR1ux91+9r96DE++OADrztTQ1luv/12rztkwIABTn38+PFp+YUXXnBsnTp18p67upNt6JW6cEOZuWJakSS0ClUhoVp2P+q+D4UDTZw40anb44RW7Xr33Xcd27nnnuvUn3322bT81FNPBaWQUBvYPla3vbqw8+1jXNb5k8vt3KxZM++21emGrCtYWUvf16EMfaG+6dy5c/DZOeigg9Jyx44dHduJJ57oPYd8M+dV973BFzIAAEAEMCADAABEAAMyAABAKaXO1FCSUGiT3WWuVXBCOmwoFZudNq+anZ6r1QFnzZoVTMVmQ60uuugixzZ8+HCnblcK0lSeH330kfe6iqXVF1vbCIVchVZH0d+pzmPbP6QtaSiCDStL+Prrr733lb0fdLWnPn36OPVPP/00LZ9//vnBNJv2txr2Zo+j11XIykO2j7XtSJ3pxz5jCVtuuWWmVIk1dWYhhNLp6vNqn/tQSOVQSYN71llneUNlVRfWcWCLLbbw2t5///1MVUPqTAAAgBKBARkAACACGJABAABKKQ65ojpoSBcuRItWn7/GedptVV+26Q+feOIJr2asqTXtUozZlgKzadymT5+e97mrlmDbIKTVVzWheHLF2lT3DKWNDM0pUJsup2bbTZfjbNmyZVp+8sknHZvGnttt7VKMCdOmTXPqy5cv9+pMIR1d28DGXYY099qi71YHpawZ10ZC72DNP2DnlWjqzJ/Nc3X22Wc7Nn3ubRpcTb2seQPseyDW54wvZAAAgAhgQAYAACilsKfq+MRXl2VoGr26/RYvXpw1ZV5CixYtvG7QuXPnOvX+/ft7z0dTMlqXSMi1HHLT14ZQiUJ+p/2Wb0hUwoIFC7yuKytLaHjSjBkznPrAgQO9K8SMHTvW604OhTblShEbshWSarYYxOquqyz777+/N0Wqui91xa2aJpZnuRC0De2zojZt/y5dumQNQ9RwR5UmTz31VKduV2t76623HFvfvn0zFUlfW1WpMwl7AgAAKBEYkAEAACKAARkAAKCUl1/MF9VhC/HHW1++TmnfaqutnHrr1q29GrJNubhy5Upv6JKmX/v4448dm+7XXpvVN3OlvFRCGmIh+yk2ofOytkJCtUIaqQ05S9h+++2devv27dNyhw4dHNuiRYuyhkJkS7Fo9SNd4nGDDTbwnrumRy0k5aVtr0JSZ0L+PPPMM07dtnNsmnFtILSMrKbB1Tkfdl6HPvcrzTu6adOmju2AAw5w6i+//HJa3n333YNzUDS8ykdNLsXJFzIAAEAEMCADAADU1rAnu626IzRji3XD6jGsu0Kz8kyePNmpW5e2rhLUq1evtDx69GjHpq6s2bNnp+XNN9/csfXo0cOp28xNFXXFVyYMqtiulULkBXsN6lbSUKJQJirrhtb2ffPNN5267Vc9Zrdu3dLyQw895NgaNGjg1G2YxTbbbOPYdtxxR6f++uuv57WCTa6MdKH7PLQSVFVkaqstYU+57v9Sus5SDHtSGjZsmDUbXjb3tg1natKkiWP78ssvvb9T7PM7ZcoUxxZyhdeEW5qwJwAAgBKBARkAACACGJABAADqYupM3Y/V4fRUrC6sIT9atxriY4895g2J2XnnnR3bc88959S33nrrtLx69WrvMfQcQiuN6HVpSIA9TiGhTdWpOxWSTk63teEH+ju7Kozqpdr+vXv3TsvDhw93bHPmzPGGPzz11FNenVjDjFTHtuerfWzPT/dj9TQNucgVIpWvraKUkraqFHLfhbT52CgVDTkUCmmfZX1WdF6JfSfoPJ7PTXihzjnq1KmTd7/6XtUwVbtym557aJ4LqTMBAADqGAzIAAAAEcCADAAAUMqpM0MxmJZcaRWtZnrIIYc4tieeeCItz5w5M6gPtGnTJi1fd911ju24447zps60uoKyxRZbOPU+ffp4U3J+8sknXr1Ar1k1xKqINS0G+abt1BR1oevdb7/9vBq+Lm9pl83UuPC77rrLu/Se9rHeg5aePXt695Mwa9astDx9+nRvH6s+pPGTtk2qSqMqVSozPyFki103LkVs31jNWPVenf+x7777epfG1OezpYlhVn152223der2t9rf9v2shFLUkjoTAACgjsOADAAAUEphT5pWMURF00iGXCAHHXSQY3v66aedunVhXnrppd7VfpYuXepddUTTM+6www5BN7nuy3dd6sJVF411n6j717qKQyslFYNi9XEotE1TXlq3kkoWGq505513puWrrrrKsY0bN84rQ8ydO9epW5f2wQcf7Ng05Z9N4xe6TnWBqavNuvDUZn+r9wphTxlve+SSwwh7qlpCUlAu8pX0dpcQxnfeeccb9qShVvo+Uze673yqCsKeAAAASgQGZAAAgAhgQAYAAKgLqTNzhSLYw6vPv1WrVl69VnU4qws2b97csQ0aNMgbEqUaotUzRo0a5T1X1bg1bZvVQTRkKLQ0nx4jlKouFt1J+60QLXrjjTdOywsXLnRsqgktWbIk6+8SzjzzzLQ8dOhQx3b44Yd7Q+RuvfXWYJtajVuXc1u1apU3zEnbMqQTh9LHsvxi3SCWZ7mQ/YTe5Zo61i63qO9L+xzpcqn67lTd2s5JsSmSs52DPY7uJ6QvFws0ZAAAgBKBARkAACACGJABAABKOQ65WJqHjbsNpWfU+FDVHaym+MUXXzi2l156yasv33fffU595MiR3nOYOnWqU99ggw28+me+qUVzxUtW59J8xepjvQar94dS1m200UZOXZde23TTTdPy7NmzHds999yTljt27OjYdDnO559/3nvN48ePd+p2X3ZZONWhVENWKpo6kzhkP6H5FqVGrBpyRdGcDZrG0j47oblDn0hKYn22rU6sKXNDKXSrI95fQUMGAAAoERiQAQAASmm1p0JSJYbSRjZp0sSb+kxTZ9ptb7rpJsd2/PHHO/XJkyd7XZ9du3b1ur632WYbp3766aen5cGDB2dC2H1pikXrhtFVi2z4TkwrjYSOHQptUqlBr9e6jrT/7bbnnHOOY/u///s/r/Sg/da3b9+03L59e8e21VZbOfVjjz02LV977bWOTa/FhkjNmTPH2wZ6zAULFuQdvgb5U4i8U9Nu29qIbdPQe1/T12pKYPvc6/twqpEG9X2hq/7ZFeA0TFKPad3oGkYbyzPJFzIAAEAEMCADAABEAAMyAABAbQl7UlsozEfTmVkNWTWJzp07e3WGG2+80amffPLJWbVGXbZv+vTpju3999/3Xpdq0bpUo71ObUabGk5DfQpZJq4mw55CttC1q05v9RpNWdetWzdv6rvbb7/dqR911FHe5TevueYabxu+/fbbTt2erw1dy6ZRWU1Z+82m0tQwvFAIXyH9SOrMukEphj2FxgTVczXt7FdffeUNRV3bvCPscrjZwkttysvWrVs7Nh0zrKasz2t1QNgTAABAicCADAAAUFtWewq5WQsJTdBtrYtBMyGpm8O6iHXK/c0335yWTz311EwI6/ZYsWJFcFvrbtcp9tZNrddVUTdksfaT7/7zdVnn6mP7W92PzeKlbqQNN9zQqVs3mLqjhgwZkpbPO++8TAgbcvH99987NpUX7L2kbjjrLtPrKsRlne9KYHXRZV1XQplK0WVdyPu6X79+Tn3ixIlp+d1333Vs+++/f1ru2bOnY3vhhRe82fy0DXUcsPaaCHPCZQ0AAFAiMCADAABEAAMyAABAbdGQK4NNjaaa7dlnn52Whw8f7thUs7WXYVd+SrChLPo71TqsNloZjbZBgwbeVYsKCW2KRXeqzDwBu3qLar9HH310Wh49erRjU83WHkdXfbGrP2kfh0KStI8LCUmz8xYK6eNQO2vbsdpT3aAUNeTQMfV69thjD6c+bdo0bxrLVeZ53XHHHR2b6s1221zXHHqfF7I6X0VBQwYAACgRGJABAAAigAEZAACgtqTOLOR36p8PpWvM1+evep7Gbtp6rrhOq1tqTGooJjIUexvSJXMROkaxdScbE5zt+n1oOsxC+jgUG6j7tbq8bmvnAuTqN7vMp+rLoXMPnV8M90q+oCHHRylqyMWaC6Pvh3qB5XurOhdDVYKGDAAAUCIwIAMAAESAGx9SACEXXCGEXJZ2ZSgNK9Fte/funZbHjx/v2KybWl0e6i6xrs+QazHXuYeOWQjVmeIt5JKtTApHu191MTVt2jQtr1y50rHptt27d0/LkydP9vabtre64m26zFxu8lAYXCGhX9W5ahdAdVCs+1b383PADV2IFFSK8IUMAAAQAQzIAAAAEcCADAAAUBdSZ1Zmanyx9IHQfoo1db8mKPa5VjS0Lde9EdJaQyEOoeMUEjpUiPZfSFrLQratKIQ91Q1KMewJCoOwJwAAgBKBARkAAKCUwp5CGbdCbt9ihUQpGp4SCsupCTd5KRJq01CGnFyu3Iq2qa7aFMocVhOhEhX9bSnLJABQdfCFDAAAEAEMyAAAABHAgAwAAFBKYU8AAABQdfCFDAAAEAEMyAAAABHAgAwAABABDMgAAAARwIAMAAAQAQzIAAAAEcCADAAAEAEMyAAAABHAgAwAABABDMgAAAARwIAMAAAQAQzIAAAAEcCADAAAEAEMyAAAABHAgAwAABABDMgAAAARwIAMAAAQAQzIAAAAEcCADAAAEAEMyAAAABFQP98N119/fae+evXqtNy4cWPH9v3336fln376ybGVlZU59XXWWSct//DDD15bq1atHNsXX3zh1OvX/3+X8vPPPzu2tdZaK+t5qy2hXr16ablhw4aO7dtvv/Wen+7X2n788UfHtu666zp1e7567lq36H4ri712iAN9XooB/Vz7+5k+Ls0+5gsZAAAgAhiQAQAAIqBeWZ6+EnXtWhexYt3U1nWbDWv/7rvv8na7hE5bXcLWhb7eeus5tlWrVgWv0xJyhevvmjZtmpZXrlwZdDXba1l77bW9x9RjqJu8suDmig9c1nUDXNa1H1zWAAAAJQIDMgAAQCnNslZXauhT3G6rs6x1P9ZNra5mO+taXTAh93FoVrPOCA/tx7qdE5YtW+bUQ/tZvnx53q4Ke8xQe+msb8iO3itV4faFmod+rv2sJTJdKOqkNsAXMgAAQAQwIAMAAEQAAzIAAEAphT01aNDAqduf6S6shqs6T7Nmzbxaq2JDlFQXDmmtGtoUCq3q0qWLU//ss8+8mbk22mgjbziVnt9XX32V9dyy1W0babayUNiTbltZamuohLa33jv5os+A7f+q0jcJe8of+rn293F9CbetaLbCXFkYq0LHJuwJAACgRGBABgAAKOXFJewnvn6K29AidQWsWLEi74xW1uXUvHnzoEumUaNGablDhw7ehSjmzZvn2PbYYw+nPmbMGK9727qh1UWmbi3r9srlbrcuEL0u66Ip9mISsRNyFWk72ftT77liuTJDrkuFEJz8CfVPZfrZ3i+F9Af9XLN93Ny865cuXRp0Wdv9FNIXIRe1Up2hVnwhAwAARAADMgAAQAQwIAMAAJRS2JP6+W1dtT6rF6j/XfU7q7WqzmBPTUOZunbt6tTnzJmTlkePHu3YDj/88LR83XXXObZLLrnEG5Z19NFHO7bp06c79SeffDIt77PPPo5t5MiR3jbQcCWri6hubdtHta1ia8qlFCoRCn/o37+/Y3vssccqtB+lVatWTn3JkiWZqqauhz3panH22TniiCMc28MPP1yh/dTGfq4tfXzWWWc5tptvvtnbboU8yx07dnTqOrco37YtpN8IewIAACgRGJABAAAigAEZAACglDRkjUO2WnDIV6+71yUWrf5sY4kTVq9enZZ79+7t2MaPH+/UDzzwwLR82GGHOTYb39ypUyfH9uijjzp1G8O8aNGi4Lk/9dRTXm3cxiyrTfUrW1e92eoVqpGo3lzbdSd7fm3atHFsixcv9qY4nTt3rncZzSZNmmRipi5qyPb8VM+1z5XqgPPnz/feEy1atMjETClqyKG0oblSilp7SM/dfPPNHdunn37qfbfrGFXRc68q0JABAABKBAZkAACAUkqdqeSb3k7dqprWcsGCBV53rXU9H3TQQUG37zbbbJOW33jjDcd22mmnpeWPPvrIsf3ud79z6ieeeGLWsKaE008/3albt+mpp57q2AYOHJiW27dv79gWLlyY8WHd9Nom1ZnCrTIU4g6yMoCGvem9Y/ejoSj2npswYYJj23XXXZ26dVOfcsopju2uu+5y6hrSl+8zUBdSKhZyzTZsUX+3atUq736svKD9PHHiRK9spW7qE044wbHdf//93n7W66jL/RxKX6vXbldQ0lAmXdXP/tamNtbn88UXX8xYdtttN+8xL774Ysf2j3/8I+ND3zX2vVuTfcwXMgAAQAQwIAMAAEQAAzIAAEAphT2pJmC1hNDSWhqqoykwrRbcp08fx2b1XrVdcMEFTn3cuHFpeerUqY5t8uTJafmJJ55wbHfffbd36cj77rvPsWndptZUjfvzzz9Py2PHjnVsX3/9tVe/0DAcu22uNKS1LRzGpg1VvVmXT7P3meqOuqznr3/9a28YxTfffBO876ubuhD2pOEqtp+17+z7Q+di6LY2heo777wT7GcNaaxualvYk2Lfq/ps6/PawNh0rojOsdl2222zpk/OttRvKfQxX8gAAAARwIAMAABQSmFP+rndtm1bb0Yr68JWt4a6ijbeeOO0PHv2bK976r333nNsV199tdeVYUOXEmbNmpWWn376acd2yy23eMNe1M1iV3BKaNmyZVp+4YUXHNuGG27odTWrG9SG92h4gA3dqI5VZ2JCw2Hsalfdu3f3yhs33XRTUN6wcoK9/7L1eWhll4qu+gIuuoqZfR569erl2Ox7YOjQoY5t8ODB3ud+0003dWwqDYX60ob+lEroYXVQyD2v730rL6gcOcGELapM+Pvf/96bhU8z9NmV+wrpY7UR9gQAAFDHYEAGAACIAAZkAACAUg57slqbTkW3mlDTpk0dm64MZae4a/iDTYt23HHHBUMcbHiQhhVZPffYY491bGeddZZTv/766726sKZxs+ek6d+GDx/u1X41ZMdep7al1eM1XahuW9vCYQpZTSbUvs2bN/dqlKpFT58+3alvv/32afmDDz7w7qeqqAthTxXtZ51voSuA2ftgs802c2w6X2WrrbbKOxSuKijFsKeq6uN6pq5tr8+y7eO+ffs6Nk2hu/vuu3tXC9TxpCog7AkAAKBEYEAGAACIAAZkAACAUopD1jSNVs9UXdjGD6tvXv3orVu39qY669atmzcd5rx587z6gGoSixcv9sYhW61Xl2m78cYbvTFvqjVNmjTJm6bNxmxni7v86quvsv5Or0U15NpGZZY9s3MRzj77bMemOpSNOXzwwQcdm/72/fff9/YbFH9Jv1zYeSbnnHOOY9N3jT2O5hu48sorvc9ydWjGtZ1C+li3bdSoUVq+6qqrHJv2jZ1j8/DDDzu2M844w6lb3bg6NOOKwBcyAABABDAgAwAAlFLYk7pS7c/UHWHdCKEUZRoWpWkL+/Xr5w0r0tCm5557Li137drVsdn0iDbFYrbVnqzbVF2UAwcOdOpbbrllWn733Xe9qw+pe0TDlULuf9vu+rtip/GLLVRCsW2jq4hZl7X2m7abTZvYqVMnx/bxxx9nYqIuhD0ptr/0vRPqZ021alMnaupMfQ/UdOrTuhb2ZPtYQ2obmjBQDWHUPrfpizt06ODYZsyYUXJ9zBcyAABABDAgAwAARAADMgAAQCmHPYWw2qZqB7rsWUhftuFAqhlfdtllTn3nnXdOyyeddJJjs0t46dT4e+65x6mfcsopafnmm292bDfccINTX7BggTcVn70WTZ2pmonVU3TJsHzTCNYGct0rVjdWDdlqS3qvqoa8wQYbeFNl2vkP2leEPRUHfc71nre6sT4rtg90voVqyLafp02bFuxnW9f9QOWfZav1ah/rPIFvjW6c61m2YXAzZ84M9rGdf0DYEwAAAHhhQAYAACgll3UhWDeruio025S1q0vw4IMP9ma7GjFihFM/8sgjva6sAw88MC1vt9123mxgmplp2bJljk1dz6NGjfJOz7duOXXJhFzWuh97LbG6rCuTecn+tlWrVo7tyy+/dOr2+rUtevTo4T0f66rSVZt09RibZS5bf9RlKtPPVmKwrmSVfrRv1e3YpUsXr82Gy+izrP2s9wT9nL1NC5Eq7W832mgjx6aSnu1jDQfqa1Zt0udc+81mcNQxQiUvlT1jhC9kAACACGBABgAAiAAGZAAAgFJKnan6kWoN+aLpMe1qTz179vSGPVk9OVvKy3322SctH3/88d5tDz300GDY08knn+zVvwcNGuRdecSm50x4/fXXvdq4Nrmdgq8au9VQdMp/bU+3p9g0q2PHjs07hd4nn3zi1K2+pavH2PuxMhpqZVauKsbvYuvnkGao2L60KXG171QznDNnjlNv06aNt5+tTc+pNvRz7H1sQ93GjRvnHSM23HBDx6YpirfYYou80mpWRiuvzj7mCxkAACACGJABAAAigAEZAACglOKQQ6nm1P9u61b3y5Ymz8YgqoY8ceLEtHzvvfc6tt69ezv1s88+2xuHbNOrffrpp45t8ODBTv2RRx7xxg/feOONTv3KK6/06hfHHHOMNwWnpn3MV5uvqG4fEwMGDHDqI0eO9C6xqXqubTer0Sfsv//+Xq1dl1Rs0aKFV0vU+9XqiRqXHqKml3qrafr37+/UH3vssbQ8b948x6ZxyTZOXzVDqxtrP0+ePNmp//rXv/beS9rP9piabyBEXe7nE0880an/5z//ScsLFy50bKF4/7vuusuxHXvssd5+01S31q6xz6oh275aunRpJsY+5gsZAAAgAhiQAQAASinsSd2sdiq4phu07lsNIbDuY3U5bL755o7tzTffTMujR492bOeff7539aenn37asbVr1y4t77nnnsGQhvfee8/rJu/evbtT/+6777zuVpuKb/HixUF32UcffeRN92ZDorSr1DVfCqESoRCCjh07OjZ1bQ4ZMiQtX3LJJd721hCXDz/80KlbaaRRo0aOTe/lml75p1TDnkL9bJ/HbM/Oddddl5Yvvvhix2b7Vlfs+eyzz5y6DZmxMkW2FIw1vZJXKYY9hcLD9F1pU1zqynl/+ctfHNtK06+a7vKdd97xrvKnz7KG2Nb0Ck+EPQEAAJQIDMgAAAARwIAMAABQShqyhtyElkiz2qYuidapUydvekzVqS+88MK0/L///S+YDtEuqWeX70o4/PDD0/Itt9zi2H73u9859V122SXrNP6EAw44wKkPGzYsLffq1cuxzZgxI6tGnG1ZQavFaCiHDb1SWyFLo8Wabs9eu+r5ej7WrktYnn766Wn5zjvvDC6xafVCDYlSKppSsa5ryIptu1zXZO9r1fRteOOtt97q2HQOir1HJk2a5Nj0HGpbP9dEH4dCYfV8rF3DS0804VT//e9/HdtOO+3knTcwa9asYL/ZNq7MMpMVBQ0ZAACgRGBABgAAiAAGZAAAgFLSkDXGy8bghvzxqvVpDKJdtlD1PPtbTdP22muveePRVG+2OvZee+0VjAl+//330/L222/v2F599VWnfuqpp6blyy+/3LFZvVf17pA2qlg9Xn+nmnKpL7+o95HemjaGXeNGQ2ktb7vtNqd+9dVXe2OWdb+FLClXFdQWDTnf+SgaP6r9Y+ecaBy+zvn4+9//7o1Bte+v2tjPNd3HOh9Iry80N2bRokVZt9MY9YThw4en5RUrVjg2jWEOadzVARoyAABAicCADAAAUEouaw0/sJ/86h6x6R9tSsNsK3LY1VvUPbHjjjt6U27ed999Tn3s2LFp+bTTTnNsjz/+eFreZJNNHFuXLl2c+j//+U+vW+vPf/6zU2/fvn1anjBhgmPr2rWrN3Xm7NmznbptP20v687WcIxYU2eGwkb0GNaNZFf3Sth2223zDoexrmZ1iar0YPvtqaeecmz6OFhJY/ny5Zl8XXTF6puYXdaF9LNtnzFjxji23Xbbzdt2+tzb5yNXP9t3jabeVZnIrkKnrk6LSnA//PBDpja7rEP3tfa/3dZKf9lSadp20zSm35n3rh7fruCVsNlmm3lXjdI2tauKWbe4ouNQsaRBXNYAAAAlAgMyAABABDAgAwAAlHLYk/Wrq889NKXc+vwT5s+fn5Zbtmzp2AYNGuQNOerfv79Tf+KJJ7zLeR166KFZw6MSNt10U6+2oeFKuoyfbQMbjqHa49KlSx2bhnJYnaSQ9JixasihpfeUUPiDLtlmdSi7NGfC559/npbPO+88x7ZgwQLv+YW06FxUR6hMzBpyIf1s21n7ecqUKU69R48eafmZZ55xbFb7u/TSS4PLL/qOXxf6uTrmCYTGCJ0LM27cOO/8IH3Op5j7QZ9lO14ounRtaM5HrH3MFzIAAEAEMCADAACU8mpP1nWhLmu7rf5OwwSsS6RVq1Zed7K6j3XVJuuC0hCHAQMGeFeE+dvf/ubNHKau5pkzZwZdoT4XaihkTF3PoRWPtC0LcbvFmt0n5CoKZe7Sc33kkUeyShS5QlfU7V8TWZpK1WVdVf1st9Xn4f7770/LxxxzTPCY9r2k753a3s+x9bGGL1l02xEjRqTlo446KnjMUPa+XMepbnBZAwAAlAgMyAAAABHAgAwAAFBKGrJqACGNwu5Sd6+/s6kr58yZ49j+8Ic/pOVJkyYF0+TZdHe66ofd7+mnn+7YhgwZ4tSt3jt37txgCIBdQUZ1Ydteq1atCqbfs6niQvqa6p2hEISYdCfbbnrOZ555pndeQOh8dJWuULrDqiJ0XXVRQw61x/HHH+9Ne6vnY+vNmzd3bBpeGNpPsdquFPu5qvo4tGLSueeem5aHDRuW9/nYVLYJ8+bNy+R7HcVqN/u+LnY46RrQkAEAAEoEBmQAAIAIYEAGAAAoJQ1Zl0Gz+kHI5x5aoks1CdVnbByhpmJr166dU2/cuLE37tfGBCuaxq9FixbeFJeqTdvjhOKrtYlVe7E6idqs3qz7UW26FGMXC6E60tvFRilpyMWiOjTb2CgVDbk6tOja+k5AQwYAACgRGJABAAAiwJ/PTAi5WTX1XcOGDfMKFVKXlLpZ7H40VEhX/bApL9VFHXIfa7pOGz6jbmh1hVtXfcjtom2g7WX3o+dX2112hbiu+vbtm5bHjh3r2GJ3V9V1CunnPn36pOXx48c7Nvo5XgoJHbKr9T366KMVfueV1bL7gS9kAACACGBABgAAiAAGZAAAgFJOnZmvXpRr91ZPKiQ9p2J1h1AaS9VzrU6tGrIeX3Vsq5PofkNLBYZSYIbSCOYKn6rtoRJ1kboY9lQXqWthT3WRMsKeAAAASgMGZAAAgAjI2w9ts2ZpSJC6cm32Kw3x0f3YlY5CoVX6uR9yoavNnqu6cjSUKTT9PuRy0P3aa9H9hNzS2pah/QAAQO2BL2QAAIAIYEAGAACIAAZkAACAUgp7AgAAgKqDL2QAAIAIYEAGAACIAAZkAACACGBABgAAiAAGZAAAgAhgQAYAAIgABmQAAIAIYEAGAACIAAZkAACACGBABgAAiAAGZAAAgAhgQAYAAIgABmQAAIAIYEAGAACIAAZkAACACGBABgAAiAAGZAAAgAhgQAYAAIgABmQAAIAIqJ/vhvXq1avaM4GCKSsrK+r+GjRo4NRXr16dlhs2bOjYVq1alZZ//vnn4H7XXnvttPzTTz95bU2bNnVsS5cudeprrbWW95jW9uOPPwbvXVvXa/7++++d+jrrrJOWf/jhh7yvy/5Oz1fPPdSPut9iYM9bz0fbKnRuoXeC/s5ua/tKj59rP8W65mK1q7aBvTY9ht1Wr6vYzzLv6/jIp4/5QgYAAIgABmQAAIAIqFeWp68EF0h8FNvNpa5ErfvcjOqe1fOqX79+Vld3Zdye6oK07vV1113XsamruViu1saNG6flb7/91rGpu9LuN+SyVZueezGoqmc55MIvhJBr17ddMZ8V3a+9LpVDigUu69oPLmsAAIASgQEZAACglGZZQ+1H3cDWxaLulnxnTqubWt3b1gWobjadfWvtarNu6vXWWy/v/TRq1MixrVy5Mu9j2m1zuaPsfkLtpe72qqCQWc5221wzg+11hY6Raya3rYe2zTVD3qL3hEonIUL3aCGuZtvPuSIToG7CFzIAAEAEMCADAABEAAMyAABABBD2VMIUO1RC9cuKZpBSXfabb77x3kc2JEqPF9J+VRO0+pzqgxtttJFTnz9/vld3bNeunVO3+9Jr/vrrr72aqeroFt1PKOypKsJsQs9yIRppIVq07edQSJjuN3R8u08NQ0tYvny595jaP/aYLVq0cGxffvml9xz0Pgzp2CEIe6pe9N6pqnA2C2FPAAAAJQIDMgAAQAQQ9gR5ud/U3WLd0pql6rvvvnPqTZo0yeq+1v3a7bK5Fe0xO3bs6NjmzJmTlr/44gvHtuuuuzr1t956y+tiXLx4sdeVqVmzbHupyyvklg1lgqqOcJiQW7oQ12ko9MxmTtP20NC30MIPGtpk3dLaz61atXLq9hxy9Y9lyZIlmXwpJHwK8mOdwIIulQk7q24XdUXgCxkAACACGJABAAAigAEZAAAgAupE2JM992OOOcaxPfDAA07dNodqXaFwlZqgqkMlbD20EHsohaJqi6FwINWwN9hgA6e+YsWKtDxy5EjHdvTRR6flwYMHO7Zhw4Z5dcgTTjjBsU2ZMsWpP/XUU2l59913d2yPPvqo917QNrD3kmrstn1UM6vusKdi7beQFbVUJ7Ztt8MOOzi2t99+Oy1fffXVjk373faJ3kuLFi3ybtuzZ0/H9sEHH3j7R68l1Jeh55Wwp+xsv/32Tn3ixIlF2W/Dhg2dus6DqQoIewIAACgRGJABAAAigAEZAAAgAkomDjlX/FkodaLVJidPnhxM82jjUFXbCp1TsTWgmkBTZ1otLxRXq9euaelCKS+tzrbVVls5tgkTJjj1/v37p+WZM2c6Nqsnql64zz77OPX1118/qz6Ybd6APd9XX33Ve52qIWub2HhnvZdtG2g6yuqgWPex3Y/eA/YamzZt6p0boP1jy5radOzYscG+e+aZZ9Ly3nvv7dj0Oq1WP23aNK8tlzar8dc+QnMp6hqaf2CFuR8+/vjj4G9t3+g9F6I6NOOKwBcyAABABDAgAwAAREDJhD3lWlmmefPmXreRneL+ySefOLa2bdt6XUnLli1zbOois67QXCsVleJqTyFXXShNorqMly5d6j2GdSUOGDDAsV1//fVOvV+/fllXbEo4+eSTvX28xRZbOPWjjjoqa1hTwllnneVd0en00093bOedd15abt26tfea1WWtoUz2XlG3a1WkZtRnKd8VnfSeVknHnqveL3bFrfbt2zu2jz76yOveVndmmzZt0vJtt93m2EaNGuXUb7rpprR86aWXOjYNmbLuTr1/p0+f7l0JSt8Rtr0KCVmrDWFPIbmlkPfh2oF3cIcOHbzu7fPPP9+xXXvttZmYIOwJAACgRGBABgAAiAAGZAAAgAgoGQ05pGGqTqy6m01VqNqjLtnWvXv3tPzhhx86tr59+zr1SZMmeafc5xv+UBmKrTvpNVjdJ7REntpCWvR2223n2GxYw4477ujYLrjgAqf+zjvvpOVx48Y5NttXjz32mGO7+eabvXrmXXfd5dgeeeQRb6jVEUcc4djefffdrOVsGrJtA5u6U3WwXGlIi0HoWS6knxW7bSi0SXXyXXbZxamvXLnSOx+gWbNm3n7W+/ef//xnWn7yyScdmy7daPeroVZW09bnWvvL1vV8bPvkCpOrLDX9vs51PrZtVGtf29xn2t76brfzEex9k21p1ULCoqoCNGQAAIASgQEZAAAgAqLO1GVdF+q6U/eZdSuFMkVZd2U215F1S2oWr9/+9rdel7Wu7FKKqEvFhnho+IFtfw13UFeRdSstWLDAsVn3pWZRsy5H7Stdtcu6q0aPHu3YRowY4dTvu+++tNyyZcvg6l9W0nj22We9ITjqdlO3rHW9qWvNukvV1V0d2HPV+9g+O7nc51aq0ExI9h7RY4wfP96p2+NcccUVju3yyy/3urNPOukkpz5mzJisWbuyregUcqFaiUH7TlfuquqVumrLu8XeAxpKttQ8A3/6058c29ChQ536V1995X2WVTorBfhCBgAAiAAGZAAAgAhgQAYAAIiAkgl7yoXVqFRfttqjahDnnHOOdz+qmalW2qdPH6+etWTJkkyphz3ZlY60LawGZEPOsp2X1XJU17HHOOOMMxzbvHnzvNq0TWmZ0KlTp7R89NFHO7aBAwd60ya+/PLLju2JJ55w6meeeWZa/vzzzx3bnXfemVeqTL1O1RZD6RarYm6CPh+2v/Q5zzf0LdfqV/a3GlakK4CFQqRsXVfq6tWrl1O3K4JdfPHFju3ee+/1PsvLly93bBMnTsxbRw+1pW0D7efaHvZUUX6Q+1/vFdsfNn1ytnfE1ltvnZanTJkSZapjvpABAAAigAEZAAAgAhiQAQAAIqBkNGTVklRbCC0HGNKBVf/85ptv8tLTYqCqdSe7vF5IO8uVBtAuZ6faqtX9bFxvwpdffunU99xzz7T86aefOjbb5926dQvGj5966qneJdo0DtnqUpquc9asWd5rVo1w8eLFXi3W1jWutSo0ZO1nW1ebvS69xtB+QkvxaXy/pkO0x9HYUnsv2eUvEw477DCnbn97//33B5f2tPq3TdGq5xfS33OlGi3kGaosNf2+rgxrmXtnp512cmxvvPGGd9shQ4Y4tsGDB3ufpZp4l6MhAwAAlAgMyAAAABEQlctaw26s209dYOpysCkx58yZ43W9qsv0mmuuceoXXnhhWp46dapj69GjR7W5j2MIe7KEwlj0PLSvmjRp4g15salKFy5cGFyRx6ZC7Ny5s2Nr166d1519++23e6/Fpq1M+P3vf+/Ut9xyy6zhL3qdmipSXc0hd5ltd/1dVdxXFX2WC/ldaAWwkNyUMHfuXO9qbKE0rBp6aF2WNsViwmuvveZ91xQSklSIzbZfaJWouuCyts+Avi/qB9KYhlIo23dAtvdHTYPLGgAAoERgQAYAAIgABmQAAIAIiGr5xdByZRr2pHqn1Y01vZrVja2enLDvvvs69RtuuCFrqrVSCIOqLHo9oXAYu63aNKwllE7Q6saaKlNDU3bYYQfvUns2rOWhhx5ybP/5z3+c+gUXXJCWBw0a5NhuueUWr56pGmUoTV8o7EdD7Wpa+wtpm6Frqqjuqddv+zWhY8eOWcOcNHxM05Vq/9iwOdX/dRlWy8cff5z38pQhnVj7MjRXoLaj7297L2k7/WjGgVxau33X5NKM7TFjfXfzhQwAABABDMgAAAAREJXLWrEuHg2NCGWVUne2zeKkmZAUe5zQFPuY3R4VJZSlKYQNa8rWbi1atPBmZTrggAOyhrRky6503HHHec9hn332ybpyT7bMXZMmTfK6mmfPnu3UR40a5Q1tsu2jMom6d60bTjPJhcL7arqf7bbqatZnyfa7Phv2Hujdu7dje+mll7z3k67gs9VWW3mfc93vrbfemvV32VzWr7zySl59p22n29o20G2tmzr2sKTKoveKPjsh2kn4kkXbzbqp9T1USKa0WOALGQAAIAIYkAEAACKAARkAACACokqdGZoqr3qAapFWT9JLsis42bCJhE022cSr86g+pOE8IV2kOvTlYqfb0+u19Vwr/VgaN27srffs2dOxrVy5Mi0ffPDBju3OO+906gcddJB3ZR8b2rTffvs5tnvuucepDxw40Kt1XXbZZU7dplzUe+X111/3pmTV9rL2UHhfIWkbK0qo70L6cmh1p2zPqG9uht5n2nY2Xemjjz7q2Gw6WztvIGH06NFO3c4l0HbV+9A+rzpfxWr+2h8hrVQ1y4qm4KwIsWvU9n399NNPO7ZddtnF+y5ZtGiRU2/dunVaXr58uXfeQgyQOhMAAKBEYEAGAACIAAZkAACACIgqDlnjCq2eq6nmNLZ02rRpXt3BxohqCrdf/epXeWsvVu/UbWti+cViE4qxVE3c6mOqrWvdpjjUeN1XX33Vqxlvt912Tv3ss8/2xg/bPtdl+f75z396NWXVhf/97397l+e0qRgTDjnkkLQ8YsSI4L1s21a1VnvvVEccckVTCuZKnWl1Wk1J+/zzz3tTUzZt2tSpb7zxxmn5tttuc2x9+/b1aoZ639l27t69u2Pr16+fN/XulClTvPvRGHKt17bcBCFOPvlkp3733Xd7nxVdRtPeSzNmzHBsW5mYcV2uddy4cd60qx06dMj7GYw1dSlfyAAAABHAgAwAABABUYU9hcIqcp1mviEF1uWV8Pbbb+cd9hTbak/FdpOrK9Veb2gFrVAaU01/uPnmmzu2d955Jy0/8cQTju3cc8916ueff37W3+kx99xzT8emIS/W7aWhMl27dvW65q1bU++VZcuWBd2wH374ode1alNQap9WRbq/Qtzi+abV1P22bNnSsS1ZsiQtn3DCCcEUqffee29avvzyyx3bW2+95ZUtVKqwoZEaCqcpOW04ZOg9pPeSSmDWha3hU/Z+0XYt9rukpsOe1NWsaVYvuugir6T0k7nnNSROV+2yIVL6/lKXdSjcsDog7AkAAKBEYEAGAACIAAZkAACACIhaQ7anprqkaguh0A2rJagtlB5TNSrVUa1eFFqGrVRTZ9r+UFso1WCXLl2cug2B0P1ccsklafnxxx8PLoX47rvvpuVdd93VsfXv3z8t33TTTY7td7/7nVPfbbfdsqbczBYOM2TIEG8Ylk3jOHPmTMemKVrtdauWZdtPU8JWxTyFij7LodSqucK37HKHdsm8bDqsTVe6wQYbeOcVaIjaUUcd5dTbtGmTlm+44YZMCDsHQJfxs+8aDXNSbH+F3jW1XUPORehe2deEzOnSnBraZO8VDUuNDTRkAACAEoEBGQAAIAIYkAEAACIgKg05hMZu2iUVc+mdVhNS/UqX6LLxc6pJqH4UapPqSKVZ7GNo7KDVM0P6vsb/We1OdZ9Zs2Y5Nvvbww8/3LF98MEHTt2mPxwzZoxjs3MMbGxiQseOHZ365MmTvduqZnXKKaek5auuusqrLX722WeZEKE5Bdam7VwVKf5ypcDMFz1XqwWHYj47derk1QE1FlzbddiwYWm5R48ejk3nINi4do0LHzt2rPeYmtrT3qO5YlmtFhx6Zur68ou2TfXZ+CFwz//jH//w1jXWuabzRChoyAAAACUCAzIAAEAERLXaU8jtoi5qdZNal/aKFSu84TMaPmVXItJVSTQcQ91ANlwlVzhEKaDuOHu9eu12dSUNQVPXt2XTTTd16jZ8SV1M8+fPd+oPPvhgWl60aJFjGzVqlDf9ZefOnZ36lVde6Q0zevTRR5367bff7pU77KpE6hbXc7f3ayi0qTpWe6poOkx1LYbCg9TWunXrtDxo0CDHds455zj1119/3euWPuigg9Jy27Ztg+F29jg2vC7bvRa6120btGvXzrHpPVHbVoArBHvtn3/+uTfsTd81es/XN2Gq2hc2vDGhffv2afnTTz8Nnl++7+tCpIZiwxcyAABABDAgAwAARAADMgAAQASUTNiT6gyartHqV5ri0p671bISFi5c6NU2VEPW6fhWj9Yp99VBsbUNbTfbFtreoVAdq62q3qvtb5dhe/XVVx3bAQcc4NRfeOGFtPzHP/7RsR1xxBFpedttt3Vsffr0cerffvttWp4+fbpjmzRpklO3/apzE2zInF1eMFdqV9Xq8w2JKhY6/yKUxtBqrXq/6RKLdj6G7mfLLbf0phUdOnSoUz/ppJPS8rPPPuvY7FJ92o7ad/bcVePX0Cbbznqd9jlX7bmQuSO1PewppJ9r+Nomm2ySdc6ALo258847Zyyh9JihezcGCHsCAAAoERiQAQAAIiBql7Wd/q7uqT322MOpv/nmm1lXF9LQiQMPPNAbSqNuaXWBaOhVTYc1FPv46sq0Lh/bF9o2+jt1s9p7p1mzZl739pw5cxzbbbfd5nUDqyvTure32WYbx/aXv/zFqdvQFbvKT8KMGTOcur2XQmFZIZe+/raQkJuqyNQVepZDbr9c4SD2t7ptSN6xoSu6rbq3r7vuurQ8cOBAx6bvCLtSlJUpsp27dbdrX1q3tF6X9qXdb6id1VZsaaKmJUYldO+obcSIEWn5mGOOCe43tMpfbOCyBgAAKBEYkAEAACKAARkAACACotaQQ/Tq1cup21WEdGq81WdUr9IUjFURZlKqGnK+q1npeejvbNo8DTM78sgj0/K0adO8aTVV51u+fLljs/rzeeed59guvvhir0ap56Pnbu8lvTesrq7hL6q5a7rMfNNTVoUuVoi2Wcg9ZkPaVPs999xz0/Idd9zh2DSdrdWCNVzJhs9oWKKGmtm2rEy7hvTvQrRR33bZ6pWlJt7XAwYMSMsjR47M+3eaave7GgghrY6Up2jIAAAAJQIDMgAAQAQwIAMAAERAyWrIxdIHanKprdhTZ1rdLaS5aRuGYpZVy7M6oOqsbdq0cepNmzbNus9syx2G4lPt0oCqO2qsuT2/0PKUSigWW9syFL9bFct6av+E0mNatM1Dz1IoXld/p+djNVvd1t4jueZ7WG0ytOSlnq8e086t0GOG2qAQW23QkCEMGjIAAECJwIAMAAAQAa6vqJZSnauslDLqjlMXZShUxaJu4JC7u2HDht4wFg1Js/vRsKeQW9yuHqNpFDU1pbpP7b7UZW1dmeoi1BAy+9vQPVcd6f8KcaVWdL96HVZu0FWztL+6du2alqdOneptR23zUBhUyA2t56vbhlY2q2rXcylRyH203XbbeVfpqsvwhQwAABABDMgAAAARwIAMAAAQAXUi7Km2Umy9SjXjfJfpy3UeoWUcQ+FAhYQV2fPR69BwLhvalCtkK1/9MNdyevmGE2kbVIWmnCt8qaLvgFB7hPo5dJzQEo+h8DH9ba5wR9/vCt22ohD2VPsh7AkAAKBEYEAGAACIgDoR9gT5oWEjNsREXbnWpq5C3U8ou1K+GZLUrscMreyj5xNyn2o9dEy7bei6FL2u6iZ0HYVk2ApRyLahe0sJubtrcpWebJRyFkCoGfhCBgAAiAAGZAAAgAhgQAYAACilsCcAAACoOvhCBgAAiAAGZAAAgAhgQAYAAIgABmQAAIAIYEAGAACIAAZkAACACGBABgAAiAAGZAAAgAhgQAYAAIgABmQAAIAIYEAGAACIAAZkAACACGBABgAAiAAGZAAAgAhgQAYAAIgABmQAAIAIYEAGAACIAAZkAACACGBABgAAiAAGZAAAgAion++G9erVC9Z9lJWVBe1rrbWWd1t7jFz7CR3THuPnn3/22tSu11jI+Vlbrv3kewxFr6WyhI4VshVyzsraa6+dln/66ae8+0ax2+p+Qu1vj5/rHPQ6bV3PNYReR3X2ca7jQc1QyPstH9Zdd12n/uOPP6bl+vXre+/5XM+yreuzYp8lPf6qVavyfidX9FlZZ511nPoPP/zg/W3oOvWY+o6w9UKOkc+zzBcyAABABDAgAwAAREC9sjx9JSHXRejzXz/3reukENdFrvMJ2QpxQ4fOp6IuXSXkNg+5RUPXVQwq2sfqrlVXlnWRaf/ne4xchG7j0HUV0sd6naH9FHJfhVzf2pbFAJd17XdZV1RizLUf+yyru7aQ/VhC77z64l4v5J1XyDvButjVvR7abyFjQj7PMl/IAAAAEcCADAAAUMqzrPP9bFcXpWJdEKEZr4XMVNZtdb++4+tvddaeHtNeW2XcgPnOJtfzKTbqLs13FnEuN5Jtp9BMyFyzqkNu8kLcxyFJRQm5mUJSSEiWCF2XuugAKoLeR6F3lX0Gcj3L9nnVYxTyLFtCz3KDBg0c27fffuvdT+PGjZ36ihUrvPtVrJu6kOgaxbZJRSRFvpABAAAigAEZAAAgAhiQAQAAakvYU2Wm8Fc0E1LoGIXonappWrtuq3rG999/X6GQpNC1hNqgukMlrLYUyjqTS3MJZePybZdtPxXtY93WHkfnODRs2NCrWRXSN6FsSEpF+7+iEPYUH8Xu55AuG3pX6Xno+zE0J8iGDul2oaxeofdhfXmONthgA6e+aNEib7iSPsv2ulVvtvtRQu/9Yr+v+UIGAACIAAZkAACAUg57Kta2+br5dJ/rrbee95gh92qukJj111/f667RafS+Y6gbJpc7O+T+LSQkobJUNBNYIf2t/WZdW6G+KOQ41qWUzSXWpEkT7zGXL1/uPWbIFa+E+jjkTgQoBqEQ0kLCeNT13KhRo7T8zTffeLdVN6++S+35NG3a1LE1a9YsLU+fPt2xdejQwSsp6fsi9B746quvvDZ9dlevXl2hkNuKyBB8IQMAAEQAAzIAAEAEMCADAACUkoZcSKrKik7hD2kbqkGEdI8uXbo49Xnz5qXlu+++27GdffbZTt1qC3vuuadj+/DDD536/PnzvZqN1VdytUe+KyeFQn2KQVWtmJRvCJguaq77sWEMPXr0cGyTJ09OyxMnTnRshxxyiFP/7rvv0vLuu+/u2F5//XWvfqRaUigkSgnNKbB9nCuVJ/w/tttuO6c+adKkGjuX2MiVsjjfNMh6X9t7PpTmVUOQrPasz+DBBx/s2O427+jbbrvNsV144YXed0vXrl0d2+eff+7VlPv06ePYxo0b513FStvS6uP6TrDtV5GV2vhCBgAAiAAGZAAAgAhgQAYAAIiACq/1ZvUD9ZWHYrFUo7DammoSVk9r166dY1u8eLFT33XXXdPyqFGjHNvee++dlseOHevYttxyS6c+cuRIry2kaar2Z/WKXKkcQ3HSVa0bV7SPQ4TidfV6bAq7Nm3aeLX/hJ49e6blW265xbHdcMMNaXn8+PGObcMNN3Tqb7zxRlreYYcdHJvGMtrr1rR9Cxcu9Gpmit1PqI+rIlVmKRNqq48++six6TwTq3dW9dKlsVHR5VJzva/ts6xzPuw7T+OF7bOisccrV650bL169UrLn332WTCPwb333puWTz75ZMem7yyr906dOrVCy1Pqs67tZdu9IvccX8gAAAARwIAMAABQyqkzQ9PqQyFJOv3duivUHWFdBZ07d3ZsS5cu9bpXzzjjDMd22mmnZXWHJMyaNcupWxem2u666y6nPmzYsLS82Wabed2i6s7U9rEhAMUKISuGmyvkxgn1cYsWLYJ95Qsh2HzzzfP+3eOPP+7UDz30UK+rqnXr1l4X9oQJExzb6NGjnfojjzySltu3b+/Ynn322bxkCL3PQ31MGk33uVcZy7oBNc2pvj/svaX3hD7bGjJT6oRC6xTbxvpe12f566+/9oYH9e7dOy13797dsY0ZM8ap29++9dZb3ndwd9mPvtsPO+ywtHznnXc6tgsuuMB776gcacMdVR5Vd7u9P0NpNQl7AgAAKFEYkAEAACKAARkAAKC2pM4Mbavami7ZZTVlnUZv9YzZs2c7tkGDBjn1ZcuWeX3+Vnf4+OOPHdsdd9zh1I899lhvWMUrr7ziDavQY1qaN2/u1FX7sm0USp1Z1RSyvGOoj1X7tdeverrVVjWUbejQoU79tddeS8szZsxwbJdeeqk3haL28dFHH+3VrzTdnt3XRhtt5Nj0fvXdG6pfheZjEPbkanSqaVrtUZfXUz1P5w5Y9tprL6+tJudxVMezrOE4to1DoWO6Xw1tsulrp02b5thOPPFEp/7ll196Q1E/NCmK//jHPzo2TZn7wQcfpOU//OEPXpvOE9A5BG3btvW+hxTbXjb0Ve9BNGQAAIAShQEZAACglMOeQuEZoZWA1P1jXZg6jd66RNQdpW5J60aw7hDNCmMzeunvEo444oi0fNBBBwVXGpk5c2bWsrorbFhTtrrvd9XtLquqPg5lTLIhBnalrWyuI3sPzJ071yt96Cpd2se2H88//3zHdsUVVzj1bbbZJi1PmTLFsdn7VY+h7tRQaFMhUkFtpJBwO99zne2+22OPPbzhU/oesBmhStFFnQv7fKhsaNtf3z96X9v96PNqpQZ1df/3v//1Pjt///vfHdtll12Wlr/44gvHtt9++3n3ayWtbO9vey+pbGhlNRsul21be3+oBFfZrHt8IQMAAEQAAzIAAEAEMCADAABEQL2yPB3dqvOEVnSyddVuVC+z08bVZlcC0uPrCj6WJk2aOPUVK1ak5YsuusixnXrqqU69S5cu3hAYXQnopJNO8mrc//73v726qWoSIT02pKEVW3sM9XHo2NrHej9YnVj1806dOnnbRdPmWX1Lj7lkyZK0fOSRRzq2a6+91qm3bNnSq5HpyjOnnHKKN4zGatH2Xs2mfVlCfVxIysOKUkrpOUMhSJoq0faV/jbXa86u5GXTQ+azklcxKLZure+cUJhiKOxJnw8b6hdaFaljx45em7ax3uPNjZ5r09Mm7Ljjjt7QJr0fbr/9dqf+m9/8xvtut6kz9R1VSN/Y9gnNI/HBFzIAAEAEMCADAABEAAMyAABAKWnIoRhV3UUoFkv1C6st6LabbLKJV0+0yxsmbL311t4YVatXaLpDq2EmTJ8+PS336dPHsb377rtefUV1QXtMXXJSNapQjGqoLUNLYNZkHLK2sd1WNSqbzu7AAw90bMOHD3fqdsk0qxmrhq+an8YlP/PMM2l5//33d2y6HKM9d21vG7uqaTU//fTTKPs42/Grm1ypKQvRfi25lsCMmWJryCF9N5SeN9QXhWjIOt9C33kWXbp2fTNX5/jjj3dshxxyiPf9rRqyvj9atWqVll966aW8lwFWjdu+X3QuS2hMyGc+CF/IAAAAEcCADAAAUMqpM0MuFvupnitNoHVzWJeCHkOnqetKI9tuu21aHjdunDekQVci6tevn1MfNmxYWj7ttNMc2znnnOMNr5o3b57XtaMhUerStdPjQyFkVZ3Sr6Kr3IRWL9Lr1fAg6yr65JNPgn3cv3//tDx48GDH1q1bN++KXuqCsm7zgQMHOraTTz7ZqVu5QVePse6qOXPmODZdBcamKwylGq0taRtD91Kua7RtrmFo9n2hLkBd7efGG29My++//75X4qqNqFvatrmmKA6FJ4UkFHXX2nBCu/petmfQPqPq3t7QhLTalfoStthiC6duV4N67rnnHNvbb7/t1O35avpQe79quFLI1azyWEjKywe+kAEAACKAARkAACACGJABAABKOewpFKoRCvFQ3cH65zXlpdWUVRPRFGrPP/98Wt5uu+28adE0lMmmU1ON+aGHHnJsO+20k1O3ad1C16lLkammbCkkdWax0yqGwhhCumeuPrbbah/bVKXNmjVzbL/97W+9SyNq+MMDDzyQdcnEbMuw2WUddVk4DbO44447vPeg1aJ1yc9QuFIhqTNrY9hTrhSt9r4OadG6n/nz5zv1zp07VyqNYXVS7PPRtskX1ZBD+9EQRvu+1nfecccd59RHjBjhDRmcbkJPZ82a5dgmTpzoTY9p3/PZxgj73n311Ve9oVY6byHUN6EwXtXqCXsCAAAoERiQAQAASinsSQl9xls3U8jNp2EwusrGpptu6nVDapiRdYmMGTPGsbVp0yYtv/DCC0H36h577OF1c4VCWzQTjXVL5nJVhFx0oRCyqqaifaxhXbaPtU1tHx966KGO7bHHHnPqJ5xwgjcMzq4u8/TTTzs2zZRmj6O2mTNnOvUGDRp4QyVsn+t9FAqPiKmPq4vQ/RLKDqX3km3HXM+V/a2+Wyoa4leq5JspTmUjbTfrhlU5xYaX9u3b17Hdf//9Tn3jjTf2Pjv77befV96xNg210uyNmoXx7rvv9t6D9jrVpudn32GhbFwVeZb5QgYAAIgABmQAAIAIYEAGAACoLas9FaKJab1FixbeKfc9evTwno+mR2zXrp1XBznrrLOyapbZwmdmz56dlnfZZRfHptPzbSpF1bNs6rgVK1YEw55C4UWh9GtVvUJMqI9DmpTWmzZtmrW/NeXpwoULHZvqxFaj0tA22zcHHHCAYzv22GOdutWaNMxJNWWbzlNDtmbMmJGWly9fHtTerJ5ZSDhEVWibNa1T51qVydpDz4OmLdR0paH3kL5rih1CWCixPsvaTvY+Vpttfzv3Ilsqza222ipr6JKuxHTiiSdmLNddd51TP+ywwzI+dt11V69Nn+WvvvrKey/YkCh9tgsJYc2nj/lCBgAAiAAGZAAAgAhgQAYAACilOORQCrWQzplLX7SxnKrZ2lRoqifvu+++Tv2RRx7xLuNol2Vr3ry5YxswYIA3ZlmXCrQxb6otqN5pyaXL5BuHWtF0ePkSWnot1Me5lmyz8buq7z766KNp+Ve/+lVwKcTrr7/eq8tb3efWW291bH/5y1+c+pZbbuld4lFTq1qtSVMz2ntFl/XUNrExm4Wk4itVQjql3ks2fapq89oetq7HsKkys9lj0oyrGr12m/8hpHuq9qt9ZXV7TUlr4/8nTZrk2LQf7bN+9dVXe+eVfPHFF17tWd8tdnnWhHPPPdep23SZutSrnduix9R463zzRlSE2vH0AwAAlDgMyAAAAKUU9hRKdxcKYwhNm1fatm3r1O2KIRouY1fs0WPqSiM2RErdmU888YRTnzx5clru3r27Y2vfvr13VRJ1VdhzUPeYuoWs276QkJTKukcK6WNNeWrdOPo7vT57D2jYmXX1hlZ9SVhvvfWy/i7hnXfeScv/93//59g0JOnxxx9Py1tvvbVj22GHHbwhGPb4CUuWLPH2hbaX7eNcqSOrso9jCHuqDLatrGsz4b333ivZlKTFDnsKpXINra6lhFIf21BTTWc8aNAgx3bNNdc49XvuuSctX3vttY7twQcfzBqOlM2dbM/n8MMPd2z6rrFpcUOyobr0NezJPssqa9qQqFBaTR98IQMAAEQAAzIAAEAEMCADAADUFg05lIZOf6f+ePtb1ejsfmzaxIRZs2Z5Q1k+/PBDx2aXbly0aJFjUw3Ahk5oek6bAlJTPar+bfVPbWLVKOx1FqIlFVtfDOkqheieqveH2tDqzZryVDXB3r17p+Vx48Y5tq5du3rTVuoxbUq98ePHOza9Tqs/a6jdqFGj0vLKlSuDoRI2Xab2m9X0tF1ro4ZcmaUPQ20Vup9Vv4tt+cXqTJ0ZOrbOfQi9k5WddtopLU+ZMiWoadswUbsUY8JJJ52U8S3beOqpp2Z8PPzww8FnZ+7cud7xxD6/upSuXnNoaV17nWpDQwYAACgRGJABAAAigAEZAACglFJnhjSXQtLQqbZm9xNalm6vvfbyxpSpBhDSKW2MW8Lf/vY3b2ypplVUbXqLLbbwatohzVDbwOoO2gbVqf2F9l9I6szvv//euyybxo9bPV2XUlMNec6cOd70qDaNpS63+Nprrzn1jz76KC1vuOGG3nhmTaVp03xq2r6QZqzUtF5Z0xSi3+q2VtPUfAOqf9p6RZbCK2VC83pC+rLex4qdc2Pv/4SxY8d64/k/++wzp963b1/v+VgeeOAB77yNhE8//dS73KJu269fP++zbO8VfXZD6TF1zkmu9ssFX8gAAAARwIAMAABQSi7rQlZ0Ck39VldR69atvWk17ee/TWGY0KhRI2/Yi3WdJNx2221pefvtt3dsDz30kFO//PLLsx4/W4hUKN1aKG1fKLQl3/CEqiAkPYRWjwnJENputr81Hekbb7zh2DQMyoZVvPLKK47tiiuuSMuHHnqoY7vllluc+pNPPulNzadheXZVKQ2nCq3gFAqHCN0PNR2SVB2Ewr7U3ar31rJly7zuQpVKrFSVK2wvFCJV29pY2yIkmVkpSN3UmjrTSny6ip59PhOee+65tLzbbrs5toEDB6blzTbbzLHpqn92v1dddZX3XNVtrv1v7xUNidKV/Gz7hca3ijzLfCEDAABEAAMyAABABDAgAwAAlHLqzJDN6he6e02hZv3sqtHauoYgqX/ehiQdc8wxju3ll1/2LqFoQ3I0XaYu76batNVRdVkwq0mpvqxtkm86QLUVW1PWfrTnpSElobAuTbdnz1PTatolF/V3PXv2dOojRoxIyxdeeKFjGzlyZFrWe0XTpdrj7Lnnno7tvvvuy/iwIXHax6pfhnTIQvq/KuYNxKZTh94fobkLqvfrMpshPU/b1Z5DVaQrzUWx+1nn49j7MRQSpeehKYG//PJL737se3fixImO7dxzz3XqgwcP9i6BO8gs3ajjhYa72tA31Zf1fW3fw9rH9l7Se0XT4lp7KJyukGUu18AXMgAAQAQwIAMAAJSSyzrk5grZcoUbWLu6WWwWpdmzZ3tDmRIee+yxtHzOOec4tgkTJqTlPfbYw7G98MILTn3SpElpecGCBcFsMzYkY9WqVV43h7p2QmFChWQxquoVYiq62pe6caxd92Oznak7SsMYrLvMhrkp6rLW7Fs245e6Od9//33vil4aEhIKwYm1j7Mdv6bp3r27VzZSuadJkybezEwvvvii9xjVsYpWZajOZ1ndwKHMU4U8y1ZitOGCmplLZaOpU6c6tr/+9a9peejQoY5t6dKlTn2TTTbxSkqKXe1J5THr+lZ5LiQ5huQnfS/qfrLBFzIAAEAEMCADAABEAAMyAABAKaXOLESvCIUQaD2Uts3qey1btvTqd7qtTrG3mmHHjh0d2/Dhw5261QR0NRmdtm51Em2D0Kooqr2Ewg7yDYmqbqw+kksTtXMDVEex/aapMlWXtytFvf32245typQpafmyyy5zbHfccYf33FV30vvz66+/9vabDZnTtJqheQOxhdzUNKo3htoxFNZS11Z0KtbKbaEwVa3bsNH58+c7tuOOOy5rGGI27de+vzfeeGPHdumll3rTWOo7YcaMGd73bGjVJn2329SZup9QmFgoHDi04psPvpABAAAigAEZAAAgAhiQAQAASikOOVesqfcABaSs09gw+1uN6wxt27x5c8dm9QLVgUNp0nLFLoaWmQzpwsWKCy223qjxd9rmxehjPYa9r1Rz0VSqVosOLdWpeq7GXdo0l3quId2pkD4upE9DMd359kEpxyFD9abODL03Qpq92kPvRz2GLpdrNVvdz0rzDs41B8mm4tUUlzpfJfS+tjY9hj6DoXk99l2Xa0nWbPCFDAAAEAEMyAAAAKW82lNoxQvrltTdhz7/Q8dUt6O6I0Lbhqaf6/GtK1yn2CuhFVRCoQShNJSFuD6L7bLW8wqFpNl20nPWvrHXoNdj203dbBqaYM8nFDoWSven567HCKXA1Psq5IIqZAW06uzjXMeD2uGyVmnIEpLtQqu65cKGLar7WI+5yy67eFeG+iFwTF2dLyQ/hd7JoXFIn49CwulCLut8nmW+kAEAACKAARkAACACGJABAADqgoaca6p36PBWA9D9FKLDhVITFqIL6m8rqsWFrjkUQpRL26gsNdHHhaSNrGi4QSH6fqiPC1k2Ucl326ru42zHgNqnIYfu+Youpav7Cb0vcqXgDD33awdCqwpJO6xtUBVzdQq5rnz6mC9kAACACGBABgAAKOXVnkIukNC09VDIT+gYuVwXIbdCyKWq099DLuIQoW1zuUfydSFV90pAobYIrV5USFuE3EiFhIcVsmJNRanodeVaFSb0O4CKoCGENhSxMtng8l19Tt9VmlnRjhG5QmMr8hwV+ixV9N0acotXBL6QAQAAIoABGQAAIAIYkAEAAEop7AkAAACqDr6QAQAAIoABGQAAIAIYkAEAACKAARkAACACGJABAAAigAEZAAAgAhiQAQAAIoABGQAAIAIYkAEAADI1z/8HSCjmRbFY8DsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x600 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-13 11:22:40,887 - INFO - Epoch [12/50] Batch [1/157] D_Loss: 0.267 G_Loss: 4.521\n",
      "2025-07-13 11:22:41,220 - INFO - Epoch [12/50] Batch [51/157] D_Loss: 0.164 G_Loss: 6.560\n",
      "2025-07-13 11:22:41,538 - INFO - Epoch [12/50] Batch [101/157] D_Loss: 0.360 G_Loss: 4.705\n",
      "2025-07-13 11:22:41,864 - INFO - Epoch [12/50] Batch [151/157] D_Loss: 0.204 G_Loss: 5.475\n",
      "2025-07-13 11:22:42,163 - INFO - Epoch 12 - D_Loss: 0.240, G_Loss: 5.424, Time: 2.2s\n",
      "2025-07-13 11:22:43,122 - INFO - Epoch [13/50] Batch [1/157] D_Loss: 0.163 G_Loss: 6.708\n",
      "2025-07-13 11:22:43,441 - INFO - Epoch [13/50] Batch [51/157] D_Loss: 0.314 G_Loss: 4.626\n",
      "2025-07-13 11:22:43,773 - INFO - Epoch [13/50] Batch [101/157] D_Loss: 0.132 G_Loss: 6.173\n",
      "2025-07-13 11:22:44,085 - INFO - Epoch [13/50] Batch [151/157] D_Loss: 0.378 G_Loss: 4.652\n",
      "2025-07-13 11:22:44,373 - INFO - Epoch 13 - D_Loss: 0.230, G_Loss: 5.482, Time: 2.2s\n",
      "2025-07-13 11:22:45,336 - INFO - Epoch [14/50] Batch [1/157] D_Loss: 0.259 G_Loss: 5.078\n",
      "2025-07-13 11:22:45,655 - INFO - Epoch [14/50] Batch [51/157] D_Loss: 0.147 G_Loss: 5.611\n",
      "2025-07-13 11:22:45,968 - INFO - Epoch [14/50] Batch [101/157] D_Loss: 0.212 G_Loss: 4.202\n",
      "2025-07-13 11:22:46,279 - INFO - Epoch [14/50] Batch [151/157] D_Loss: 0.119 G_Loss: 7.232\n",
      "2025-07-13 11:22:46,561 - INFO - Epoch 14 - D_Loss: 0.238, G_Loss: 5.009, Time: 2.2s\n",
      "2025-07-13 11:22:47,526 - INFO - Epoch [15/50] Batch [1/157] D_Loss: 0.522 G_Loss: 7.273\n",
      "2025-07-13 11:22:47,841 - INFO - Epoch [15/50] Batch [51/157] D_Loss: 0.183 G_Loss: 4.704\n",
      "2025-07-13 11:22:48,154 - INFO - Epoch [15/50] Batch [101/157] D_Loss: 0.415 G_Loss: 3.363\n",
      "2025-07-13 11:22:48,467 - INFO - Epoch [15/50] Batch [151/157] D_Loss: 0.218 G_Loss: 4.362\n",
      "2025-07-13 11:22:48,746 - INFO - Epoch 15 - D_Loss: 0.244, G_Loss: 4.437, Time: 2.2s\n",
      "2025-07-13 11:22:49,726 - INFO - Epoch [16/50] Batch [1/157] D_Loss: 0.514 G_Loss: 3.931\n",
      "2025-07-13 11:22:50,044 - INFO - Epoch [16/50] Batch [51/157] D_Loss: 0.189 G_Loss: 5.084\n",
      "2025-07-13 11:22:50,360 - INFO - Epoch [16/50] Batch [101/157] D_Loss: 0.212 G_Loss: 4.539\n",
      "2025-07-13 11:22:50,677 - INFO - Epoch [16/50] Batch [151/157] D_Loss: 0.151 G_Loss: 4.327\n",
      "2025-07-13 11:22:50,966 - INFO - Epoch 16 - D_Loss: 0.242, G_Loss: 4.478, Time: 2.2s\n",
      "2025-07-13 11:22:51,921 - INFO - Epoch [17/50] Batch [1/157] D_Loss: 0.183 G_Loss: 5.415\n",
      "2025-07-13 11:22:52,240 - INFO - Epoch [17/50] Batch [51/157] D_Loss: 0.397 G_Loss: 7.084\n",
      "2025-07-13 11:22:52,554 - INFO - Epoch [17/50] Batch [101/157] D_Loss: 0.333 G_Loss: 3.645\n",
      "2025-07-13 11:22:52,868 - INFO - Epoch [17/50] Batch [151/157] D_Loss: 0.181 G_Loss: 5.427\n",
      "2025-07-13 11:22:53,153 - INFO - Epoch 17 - D_Loss: 0.220, G_Loss: 4.776, Time: 2.2s\n",
      "2025-07-13 11:22:54,127 - INFO - Epoch [18/50] Batch [1/157] D_Loss: 0.107 G_Loss: 5.884\n",
      "2025-07-13 11:22:54,444 - INFO - Epoch [18/50] Batch [51/157] D_Loss: 0.216 G_Loss: 4.467\n",
      "2025-07-13 11:22:54,755 - INFO - Epoch [18/50] Batch [101/157] D_Loss: 0.212 G_Loss: 5.575\n",
      "2025-07-13 11:22:55,075 - INFO - Epoch [18/50] Batch [151/157] D_Loss: 0.205 G_Loss: 3.849\n",
      "2025-07-13 11:22:55,378 - INFO - Epoch 18 - D_Loss: 0.229, G_Loss: 4.560, Time: 2.2s\n",
      "2025-07-13 11:22:56,348 - INFO - Epoch [19/50] Batch [1/157] D_Loss: 0.166 G_Loss: 4.697\n",
      "2025-07-13 11:22:56,670 - INFO - Epoch [19/50] Batch [51/157] D_Loss: 0.530 G_Loss: 3.344\n",
      "2025-07-13 11:22:56,992 - INFO - Epoch [19/50] Batch [101/157] D_Loss: 0.307 G_Loss: 5.135\n",
      "2025-07-13 11:22:57,320 - INFO - Epoch [19/50] Batch [151/157] D_Loss: 0.153 G_Loss: 3.771\n",
      "2025-07-13 11:22:57,602 - INFO - Epoch 19 - D_Loss: 0.249, G_Loss: 4.461, Time: 2.2s\n",
      "2025-07-13 11:22:58,569 - INFO - Epoch [20/50] Batch [1/157] D_Loss: 0.382 G_Loss: 4.253\n",
      "2025-07-13 11:22:58,886 - INFO - Epoch [20/50] Batch [51/157] D_Loss: 0.380 G_Loss: 3.176\n",
      "2025-07-13 11:22:59,201 - INFO - Epoch [20/50] Batch [101/157] D_Loss: 0.239 G_Loss: 4.258\n",
      "2025-07-13 11:22:59,529 - INFO - Epoch [20/50] Batch [151/157] D_Loss: 0.216 G_Loss: 5.879\n",
      "2025-07-13 11:22:59,808 - INFO - Epoch 20 - D_Loss: 0.272, G_Loss: 4.471, Time: 2.2s\n",
      "2025-07-13 11:23:00,782 - INFO - Epoch [21/50] Batch [1/157] D_Loss: 0.237 G_Loss: 6.417\n",
      "2025-07-13 11:23:01,100 - INFO - Epoch [21/50] Batch [51/157] D_Loss: 0.330 G_Loss: 4.075\n",
      "2025-07-13 11:23:01,422 - INFO - Epoch [21/50] Batch [101/157] D_Loss: 0.220 G_Loss: 5.957\n",
      "2025-07-13 11:23:01,735 - INFO - Epoch [21/50] Batch [151/157] D_Loss: 0.061 G_Loss: 5.366\n",
      "2025-07-13 11:23:02,014 - INFO - Epoch 21 - D_Loss: 0.209, G_Loss: 4.974, Time: 2.2s\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-13 11:23:03,016 - INFO - Epoch [22/50] Batch [1/157] D_Loss: 0.125 G_Loss: 5.063\n",
      "2025-07-13 11:23:03,330 - INFO - Epoch [22/50] Batch [51/157] D_Loss: 0.137 G_Loss: 5.944\n",
      "2025-07-13 11:23:03,642 - INFO - Epoch [22/50] Batch [101/157] D_Loss: 0.156 G_Loss: 5.161\n",
      "2025-07-13 11:23:03,953 - INFO - Epoch [22/50] Batch [151/157] D_Loss: 0.131 G_Loss: 6.399\n",
      "2025-07-13 11:23:04,263 - INFO - Epoch 22 - D_Loss: 0.170, G_Loss: 5.673, Time: 2.2s\n",
      "2025-07-13 11:23:05,263 - INFO - Epoch [23/50] Batch [1/157] D_Loss: 0.085 G_Loss: 6.612\n",
      "2025-07-13 11:23:05,618 - INFO - Epoch [23/50] Batch [51/157] D_Loss: 0.198 G_Loss: 3.426\n",
      "2025-07-13 11:23:05,956 - INFO - Epoch [23/50] Batch [101/157] D_Loss: 0.142 G_Loss: 5.337\n",
      "2025-07-13 11:23:06,307 - INFO - Epoch [23/50] Batch [151/157] D_Loss: 0.283 G_Loss: 4.375\n",
      "2025-07-13 11:23:06,595 - INFO - Epoch 23 - D_Loss: 0.243, G_Loss: 5.066, Time: 2.3s\n",
      "2025-07-13 11:23:07,564 - INFO - Epoch [24/50] Batch [1/157] D_Loss: 0.246 G_Loss: 4.877\n",
      "2025-07-13 11:23:07,879 - INFO - Epoch [24/50] Batch [51/157] D_Loss: 0.196 G_Loss: 3.591\n",
      "2025-07-13 11:23:08,193 - INFO - Epoch [24/50] Batch [101/157] D_Loss: 0.164 G_Loss: 5.076\n",
      "2025-07-13 11:23:08,506 - INFO - Epoch [24/50] Batch [151/157] D_Loss: 0.220 G_Loss: 5.017\n",
      "2025-07-13 11:23:08,786 - INFO - Epoch 24 - D_Loss: 0.212, G_Loss: 5.454, Time: 2.2s\n",
      "2025-07-13 11:23:09,735 - INFO - Epoch [25/50] Batch [1/157] D_Loss: 0.376 G_Loss: 4.019\n",
      "2025-07-13 11:23:10,050 - INFO - Epoch [25/50] Batch [51/157] D_Loss: 0.268 G_Loss: 5.462\n",
      "2025-07-13 11:23:10,361 - INFO - Epoch [25/50] Batch [101/157] D_Loss: 0.176 G_Loss: 7.060\n",
      "2025-07-13 11:23:10,676 - INFO - Epoch [25/50] Batch [151/157] D_Loss: 0.318 G_Loss: 3.175\n",
      "2025-07-13 11:23:10,955 - INFO - Epoch 25 - D_Loss: 0.269, G_Loss: 4.973, Time: 2.2s\n",
      "2025-07-13 11:23:11,911 - INFO - Epoch [26/50] Batch [1/157] D_Loss: 0.259 G_Loss: 5.458\n",
      "2025-07-13 11:23:12,224 - INFO - Epoch [26/50] Batch [51/157] D_Loss: 0.416 G_Loss: 4.406\n",
      "2025-07-13 11:23:12,550 - INFO - Epoch [26/50] Batch [101/157] D_Loss: 0.185 G_Loss: 4.671\n",
      "2025-07-13 11:23:12,919 - INFO - Epoch [26/50] Batch [151/157] D_Loss: 0.239 G_Loss: 5.871\n",
      "2025-07-13 11:23:13,231 - INFO - Epoch 26 - D_Loss: 0.290, G_Loss: 4.670, Time: 2.3s\n",
      "2025-07-13 11:23:14,247 - INFO - Epoch [27/50] Batch [1/157] D_Loss: 0.355 G_Loss: 5.252\n",
      "2025-07-13 11:23:14,611 - INFO - Epoch [27/50] Batch [51/157] D_Loss: 0.354 G_Loss: 4.411\n",
      "2025-07-13 11:23:15,011 - INFO - Epoch [27/50] Batch [101/157] D_Loss: 0.322 G_Loss: 5.771\n",
      "2025-07-13 11:23:15,413 - INFO - Epoch [27/50] Batch [151/157] D_Loss: 0.497 G_Loss: 3.684\n",
      "2025-07-13 11:23:15,724 - INFO - Epoch 27 - D_Loss: 0.342, G_Loss: 3.951, Time: 2.5s\n",
      "2025-07-13 11:23:16,720 - INFO - Epoch [28/50] Batch [1/157] D_Loss: 0.169 G_Loss: 5.322\n",
      "2025-07-13 11:23:17,095 - INFO - Epoch [28/50] Batch [51/157] D_Loss: 0.243 G_Loss: 5.145\n",
      "2025-07-13 11:23:17,418 - INFO - Epoch [28/50] Batch [101/157] D_Loss: 0.291 G_Loss: 4.104\n",
      "2025-07-13 11:23:17,731 - INFO - Epoch [28/50] Batch [151/157] D_Loss: 0.321 G_Loss: 4.954\n",
      "2025-07-13 11:23:18,013 - INFO - Epoch 28 - D_Loss: 0.355, G_Loss: 3.937, Time: 2.3s\n",
      "2025-07-13 11:23:18,976 - INFO - Epoch [29/50] Batch [1/157] D_Loss: 0.367 G_Loss: 3.584\n",
      "2025-07-13 11:23:19,302 - INFO - Epoch [29/50] Batch [51/157] D_Loss: 0.571 G_Loss: 2.301\n",
      "2025-07-13 11:23:19,625 - INFO - Epoch [29/50] Batch [101/157] D_Loss: 0.377 G_Loss: 3.155\n",
      "2025-07-13 11:23:19,957 - INFO - Epoch [29/50] Batch [151/157] D_Loss: 0.408 G_Loss: 4.367\n",
      "2025-07-13 11:23:20,239 - INFO - Epoch 29 - D_Loss: 0.388, G_Loss: 3.713, Time: 2.2s\n",
      "2025-07-13 11:23:21,206 - INFO - Epoch [30/50] Batch [1/157] D_Loss: 0.328 G_Loss: 5.198\n",
      "2025-07-13 11:23:21,527 - INFO - Epoch [30/50] Batch [51/157] D_Loss: 0.388 G_Loss: 4.180\n",
      "2025-07-13 11:23:21,840 - INFO - Epoch [30/50] Batch [101/157] D_Loss: 0.284 G_Loss: 3.585\n",
      "2025-07-13 11:23:22,155 - INFO - Epoch [30/50] Batch [151/157] D_Loss: 0.301 G_Loss: 4.791\n",
      "2025-07-13 11:23:22,437 - INFO - Epoch 30 - D_Loss: 0.333, G_Loss: 4.205, Time: 2.2s\n",
      "2025-07-13 11:23:23,391 - INFO - Epoch [31/50] Batch [1/157] D_Loss: 0.571 G_Loss: 5.715\n",
      "2025-07-13 11:23:23,711 - INFO - Epoch [31/50] Batch [51/157] D_Loss: 0.334 G_Loss: 2.926\n",
      "2025-07-13 11:23:24,036 - INFO - Epoch [31/50] Batch [101/157] D_Loss: 0.267 G_Loss: 3.822\n",
      "2025-07-13 11:23:24,349 - INFO - Epoch [31/50] Batch [151/157] D_Loss: 0.307 G_Loss: 4.891\n",
      "2025-07-13 11:23:24,633 - INFO - Epoch 31 - D_Loss: 0.351, G_Loss: 4.128, Time: 2.2s\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x600 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-13 11:23:25,636 - INFO - Epoch [32/50] Batch [1/157] D_Loss: 0.423 G_Loss: 3.483\n",
      "2025-07-13 11:23:25,953 - INFO - Epoch [32/50] Batch [51/157] D_Loss: 0.502 G_Loss: 4.788\n",
      "2025-07-13 11:23:26,272 - INFO - Epoch [32/50] Batch [101/157] D_Loss: 0.248 G_Loss: 3.862\n",
      "2025-07-13 11:23:26,587 - INFO - Epoch [32/50] Batch [151/157] D_Loss: 0.297 G_Loss: 3.970\n",
      "2025-07-13 11:23:26,868 - INFO - Epoch 32 - D_Loss: 0.328, G_Loss: 4.403, Time: 2.2s\n",
      "2025-07-13 11:23:27,829 - INFO - Epoch [33/50] Batch [1/157] D_Loss: 0.348 G_Loss: 5.138\n",
      "2025-07-13 11:23:28,144 - INFO - Epoch [33/50] Batch [51/157] D_Loss: 0.211 G_Loss: 4.601\n",
      "2025-07-13 11:23:28,457 - INFO - Epoch [33/50] Batch [101/157] D_Loss: 0.236 G_Loss: 5.468\n",
      "2025-07-13 11:23:28,771 - INFO - Epoch [33/50] Batch [151/157] D_Loss: 0.325 G_Loss: 4.500\n",
      "2025-07-13 11:23:29,050 - INFO - Epoch 33 - D_Loss: 0.311, G_Loss: 4.536, Time: 2.2s\n",
      "2025-07-13 11:23:30,015 - INFO - Epoch [34/50] Batch [1/157] D_Loss: 0.383 G_Loss: 4.953\n",
      "2025-07-13 11:23:30,329 - INFO - Epoch [34/50] Batch [51/157] D_Loss: 0.253 G_Loss: 4.417\n",
      "2025-07-13 11:23:30,641 - INFO - Epoch [34/50] Batch [101/157] D_Loss: 0.426 G_Loss: 3.786\n",
      "2025-07-13 11:23:30,954 - INFO - Epoch [34/50] Batch [151/157] D_Loss: 0.419 G_Loss: 3.220\n",
      "2025-07-13 11:23:31,232 - INFO - Epoch 34 - D_Loss: 0.341, G_Loss: 4.485, Time: 2.2s\n",
      "2025-07-13 11:23:32,203 - INFO - Epoch [35/50] Batch [1/157] D_Loss: 0.517 G_Loss: 2.477\n",
      "2025-07-13 11:23:32,519 - INFO - Epoch [35/50] Batch [51/157] D_Loss: 0.407 G_Loss: 2.256\n",
      "2025-07-13 11:23:32,833 - INFO - Epoch [35/50] Batch [101/157] D_Loss: 0.437 G_Loss: 3.213\n",
      "2025-07-13 11:23:33,145 - INFO - Epoch [35/50] Batch [151/157] D_Loss: 0.509 G_Loss: 2.451\n",
      "2025-07-13 11:23:33,422 - INFO - Epoch 35 - D_Loss: 0.442, G_Loss: 3.479, Time: 2.2s\n",
      "2025-07-13 11:23:34,375 - INFO - Epoch [36/50] Batch [1/157] D_Loss: 0.686 G_Loss: 2.102\n",
      "2025-07-13 11:23:34,692 - INFO - Epoch [36/50] Batch [51/157] D_Loss: 0.358 G_Loss: 4.190\n",
      "2025-07-13 11:23:35,005 - INFO - Epoch [36/50] Batch [101/157] D_Loss: 0.343 G_Loss: 3.795\n",
      "2025-07-13 11:23:35,317 - INFO - Epoch [36/50] Batch [151/157] D_Loss: 0.439 G_Loss: 2.760\n",
      "2025-07-13 11:23:35,595 - INFO - Epoch 36 - D_Loss: 0.439, G_Loss: 3.209, Time: 2.2s\n",
      "2025-07-13 11:23:36,555 - INFO - Epoch [37/50] Batch [1/157] D_Loss: 0.443 G_Loss: 2.539\n",
      "2025-07-13 11:23:36,871 - INFO - Epoch [37/50] Batch [51/157] D_Loss: 0.595 G_Loss: 2.278\n",
      "2025-07-13 11:23:37,183 - INFO - Epoch [37/50] Batch [101/157] D_Loss: 0.492 G_Loss: 3.511\n",
      "2025-07-13 11:23:37,495 - INFO - Epoch [37/50] Batch [151/157] D_Loss: 0.374 G_Loss: 2.574\n",
      "2025-07-13 11:23:37,775 - INFO - Epoch 37 - D_Loss: 0.443, G_Loss: 3.252, Time: 2.2s\n",
      "2025-07-13 11:23:38,745 - INFO - Epoch [38/50] Batch [1/157] D_Loss: 0.327 G_Loss: 2.378\n",
      "2025-07-13 11:23:39,061 - INFO - Epoch [38/50] Batch [51/157] D_Loss: 0.474 G_Loss: 2.944\n",
      "2025-07-13 11:23:39,374 - INFO - Epoch [38/50] Batch [101/157] D_Loss: 0.268 G_Loss: 3.171\n",
      "2025-07-13 11:23:39,685 - INFO - Epoch [38/50] Batch [151/157] D_Loss: 0.497 G_Loss: 2.537\n",
      "2025-07-13 11:23:39,964 - INFO - Epoch 38 - D_Loss: 0.447, G_Loss: 3.178, Time: 2.2s\n",
      "2025-07-13 11:23:40,915 - INFO - Epoch [39/50] Batch [1/157] D_Loss: 0.484 G_Loss: 2.305\n",
      "2025-07-13 11:23:41,243 - INFO - Epoch [39/50] Batch [51/157] D_Loss: 0.554 G_Loss: 2.446\n",
      "2025-07-13 11:23:41,556 - INFO - Epoch [39/50] Batch [101/157] D_Loss: 0.564 G_Loss: 3.132\n",
      "2025-07-13 11:23:41,868 - INFO - Epoch [39/50] Batch [151/157] D_Loss: 0.445 G_Loss: 3.345\n",
      "2025-07-13 11:23:42,147 - INFO - Epoch 39 - D_Loss: 0.446, G_Loss: 3.142, Time: 2.2s\n",
      "2025-07-13 11:23:43,110 - INFO - Epoch [40/50] Batch [1/157] D_Loss: 0.472 G_Loss: 2.334\n",
      "2025-07-13 11:23:43,426 - INFO - Epoch [40/50] Batch [51/157] D_Loss: 0.500 G_Loss: 2.421\n",
      "2025-07-13 11:23:43,739 - INFO - Epoch [40/50] Batch [101/157] D_Loss: 0.437 G_Loss: 3.359\n",
      "2025-07-13 11:23:44,051 - INFO - Epoch [40/50] Batch [151/157] D_Loss: 0.531 G_Loss: 3.274\n",
      "2025-07-13 11:23:44,331 - INFO - Epoch 40 - D_Loss: 0.428, G_Loss: 3.358, Time: 2.2s\n",
      "2025-07-13 11:23:45,288 - INFO - Epoch [41/50] Batch [1/157] D_Loss: 0.536 G_Loss: 3.828\n",
      "2025-07-13 11:23:45,604 - INFO - Epoch [41/50] Batch [51/157] D_Loss: 0.369 G_Loss: 3.995\n",
      "2025-07-13 11:23:45,917 - INFO - Epoch [41/50] Batch [101/157] D_Loss: 0.412 G_Loss: 2.651\n",
      "2025-07-13 11:23:46,230 - INFO - Epoch [41/50] Batch [151/157] D_Loss: 0.381 G_Loss: 4.241\n",
      "2025-07-13 11:23:46,509 - INFO - Epoch 41 - D_Loss: 0.474, G_Loss: 3.121, Time: 2.2s\n"
     ]
    },
    {
     "data": {
      "image/png": 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pqMRx0aJFUcjK+bNcE6mqWjXwmoSsAQBICQZkAAACwIAMAEAAgs4hF7pQed26da22zP1NmDDB6hs8eLDVzicXKI+Jzk37yqeSUs55p3z4VhzTi6NvueWWVvuhhx6Kt4888kir79lnn03dOS6l83zggQda7SeffDJKq1L4LMvvQH1vTlXu+SiG6lqFUCKHDABASjAgAwAQAAZkAAACsEFINV3FyjO89tprGetKjWXLliUy/Z/OQcic4ltvvWX1DRw4MOfnLXXVMd2inh5V1oXrHPI999xjte+44454++WXX3bue+/eva2+zz//vIp7jWx0zrh+/fo538dR6HdN7dq1na9RbvT8CmvWrHEeJ/3ZLkYOuY7an08++cR5vej5BpYvXx5vd+3a1er7+uuvo+rCL2QAAALAgAwAQJrLnjbYYIPESz4OOOAAqz127Nic/7Zjx47x9uLFi60+GT7Rz7nrrrs635demaomplvzlRKksVTikEMOsdqPPvpo4q+x4YYbWm193ObPn+8MQ3/11VdW++abb3ZO4xjKdHs1dZ59n4cQQrs77rhjvD1u3LgoZGn8LOt9lq958cUXO79XjQceeCDenjt3bs7TGWtNmjSJt+fMmWP1NWjQwPldc+ihh0bVjbInAABSggEZAIAAMCADAFCOU2cmVdq0zz77WO133nnHebt748aNnXlhnWfo3r17vL1kyRLvPjRs2DDeXrVqlfN46ZIofQxatWrlzKeUWt4pqf3T+yqnwtP5yyeeeMJq77bbbvH2rFmzrL7mzZtb7Z49e2bMPevjn+3+AnmOFyxYEJVCDrmm6fMsP4/GRx995CxlCU1aPsvyGG+yySZW30svvRRvv/HGG1bfmDFjnLlf/fmcp+7V8OWiZb5ZT4epybEm22NznZpZlnplQw4ZAICUYEAGACDNM3XJn/z5rJRRaIhar6b04osvOmfp2WWXXay+8847L94+/PDDrb7zzz/fasuZm3TIukePHlZ7+vTpGUOSOrwpS7KMmTNnWm0Zpq6J0qq0kNeADleuXLnSOWOPLoeQ1+7GG29s9d11113O8JlONchzo1cY06Ub+YSpETk/D760xYwZM7zhzSTcdNNNVvvcc8+NSlm/fv2s9sSJE+PtTz/91Fm2qlfNO/roo632LbfcUlCZ03bbbWe18wk977777jm9L50Off755612PmHqfPELGQCAADAgAwAQAAZkAAACUHCSReaNW7ZsafUtXLgwSnoKxJ9++snq0/k8Wc4k84k6R9umTRurT5ey+PKU+lb+o446ypkHkdN36rykzlGkIWdcE7nt9u3bO/O5Bx54oLOsQpdN+KZ21VNl6nsM5P0HvvecbfpYeb0WY6WbUiKvNZ17HD16tDOXp78j+vfvn/i+jR8/PionMreqz43O0cvvx6ZNm1p9U6dOtdq6/NSluyhDNQ466CDnY/X1oO/r6NKlizP/7SqXq278QgYAIAAMyAAABIABGQCAcpw6U5N1ZDq3llSe8tlnn3XWEf7jH/+w2vfcc0+8/eabb3rzR3LaTb3vxXhf+hwknYsMfUpFuVTm66+/7szRPvXUU876Q51b0rWrI0eOtNpXXnllVJNCmjpzq622stoffvhhVAyvvvpqvH3CCSdYfXLZvMsvv9zq0/d8yOvgyCOPjEKWlqkzpT59+jjzxPr9FPr+1ld1xj/88INzWmR9P5DOBe+3335V3p9sfPeKMHUmAAApwYAMAEAAajxkLf3zn/+02meddVZBz6P39cILL3ROY6lXgZG36++www5W37HHHmu1jzvuuIzhVL0PxQqPVGeYK6myJ10q4SsXatSokdX+8ccf4+2tt97a6lu+fLkzlKpX9JJuvvlmq/3CCy9Y7eeeey6qSSGFrKuLDPt9/PHHVt9ee+3lnNLwpJNOKjikvsUWW8TbkyZNiqpbWkLW3bp1i7fHjRvn7NOlp4Xq3bu31X744Yed/brMSYe7d9ppp3j7lVdeiaobIWsAAFKCARkAgAAwIAMAkOYccjFypDpnKPOCVXkN+bx6WkW9rOOyZcucSyrKJR71Y7VSyyFXB53r1fnm1q1bO6e8PPvss52lbT6TJ0+22oMGDbLa33zzTVSTyjGHLO/H0MtWyvN1wQUXWH36vMtlOPVUjXqJTj3tZnUL9bPsu3fklFNOsfruvvvuxEs077zzTqs9YMAAq925c2fn+KG/2/NZ5rEYyCEDAJASDMgAAJRjyFreGq9DgnIFKf0a2cpufPsgw5tyxY9MzytXGNKrhyxatMhqy9vsdTiknEPWzZo1c658lc/r65B1ixYt4u0hQ4ZYfZdeemnG2XuyGThwoNXWpRw1LeSQdbbVdYpBzsalw6D6+yMfs2bNirc7dOgQVbdQP8v//e9/rfbs2bPj7YsuuigqhrriOtptt92sPl2u5Cub1NdHdXwn+xCyBgAgJRiQAQAIAAMyAAABCGrqTB+d15E5H02v7vPyyy87H6vfvswF61ykL1/hk9S0k3JKwarmzEI8xzJHrKfK1MctnzIVfbzl++zbt6/VN2XKFO/fVreQc8jVQe/rtttuG29vvvnmVt9dd93lLG3K53rRn7OkV1VLUw65UHrayny+qzqK6Y311Jk6h9ykSRPnvSo1/dnVyCEDAJASDMgAAASAARkAgADYhZ55qI6aLlmHunbtWqtPL6M4YsSIePuLL76w+uT+ZcutzJ8/35lD/uGHH3Led9fr56vQPFhayLpSnQMaP3681e7Vq1dOz6mPk54m8f7774+3p0+fbvUV61qu6RrIYkjq3oh88rlz5sxxXh+6DlrXSeeqOnLGpS5bzlheOzrf/KO4d+TFF1+0+oYOHWq1R40aFVU3mbdeunRpos/NL2QAAALAgAwAQDmWPennuf322+PtU089Nee/22STTaz2l19+6Qw56XC3z9y5c+Pttm3bWn3FCjXKUJvedxkeWbhwYcmVSsiQpH7v11xzjTNkve+++3pX4vKVT8kSuqRDToWErH3HIE1lTy1btnReq4XSK/jIFdY6depk9c2cOTO1KYW0fJZ912oSz2m8/fbb0Trbb7995FvByZfGCy31QNkTAAApwYAMAEAAGJABAAhAaqbO1NMq6hKkDTfcMN5euXKl1ffYY4/F24MHD/a+jvxb+ZzVlVvKZ8q5tOSdctWmTRurveOOO1ptudxb//79cy5t00tuylxjaHkmLU055GJYsGCB1W7evLkz95im91Xqn+V8SuR833l1VMmivh8oqeMm90G/5qpVqxJ5DXLIAACkBAMyAAABYEAGACDNU2cWe6pMo0GDBs5p8OSUi7rWVD/PnnvumfO0ioUusZiUpJdUDI1eRlPWq2633XZW35gxY6y2nkbPlaN68MEHvecYxbfPPvtY7eeff77Ktc2aziEj3KlT9f048vtaf+fVEs+bbbpg+V2fz3e3HiPk3yaVMy4EVzQAAAFgQAYAoBzLnnr37m21P//880SeV4avdDhCTr+np/TTZS/t27fPuPJTMcPJua7opEN0Se9PTZRKyNRDs2bNvFMh6tV8XFMq6lWA9DSboZc6lVvZk/zMGZMnT844dayxevXqnKZLTZtSL3saNGiQ1R47dmzix2I99f0Y2uecsicAAFKCARkAgAAwIAMAUI5lTzpnLKcsq8rSc/JvdR62VatWzud84403rPaiRYuqvQQp2639oeZEkiDLDfT7u/vuu612v379nMtvnnXWWc6cZCket1Iye/Zsq73VVltlXFbVuOuuu6ptv5BcDvvpp58u+mv+qj7nvtImfT+KLqutKfxCBgAgAAzIAACkqexJ31Je6K3pha76kdSKMXrVqGxhjiRU5dj5HlsKpRKy5EuvyjR06FBn6uHqq6+2+iZNmhRv16tXr9pX6SrWOShGuD20khgdPuzRo0e8femllzrLZxo2bBiVilL4LLtKTTOVr8nZsGSaUJfG6hSnb/zQfT179rTa06ZNy/h31YWyJwAAUoIBGQCAADAgAwBQjlNn5vMasp1PLk0/j8xN6xIjvTLQsGHD4u3vv//e6lu6dGlUbPncqi+nEUxCdZxjnVuS50Ou7mUsWbLEmYfS9xfI1WNCp/NpvuuqHKbO9NHTqS5evDgqRWnMIfvyuZ06dbL6vv32W+d3sl7Ra968eQXdn6OnUl27dq2zndTxzme6TnLIAACkBAMyAAABYEAGAKAcp870xdx1jF22Zb1qpryDzve66trGjRvnXLYv23KQMr9s/PWvf3VO16inA8w1F6OPT4jTu1WFL9er60p1Pl3nlKvbY489ZrUPPvjggp6nOu5FKBU1kTPW30NpyrlXJ19OVN+r47s/SM4TYVx33XXROhdeeGHk+06Q+7BixQqrT89HIN17771W+8QTT4wKkfQ8AfxCBgAgAAzIAACkqexJl9zIkISe8nLOnDnxdps2bXLeGR1iSLqsJ9ut+lV5bLH2QYZosoWBkt4vX5+cgnThwoUFv4Z8f7pMoRzpa15/7pJACLbm6bI9X6oq1HNcrO/r9cSx0GVveppN+dixY8daffvtt19Brz99+nTnVK5VCWfnck74hQwAQAAYkAEACAADMgAAacoh6zxxrrd+J5WH1VMuJjVVom+qymzkewt1Oa/qyDvVrl3bapMLTk65T51ZLkL5LIegVg1/rxYLU2cCAJASDMgAAKRppq5CZyRJKuRQrDCoDlHnEy7JtWSqlMIumRCiBpCUihL/vvThFzIAAAFgQAYAIAAMyAAApKnsCQAAFA+/kAEACAADMgAAAWBABgAgAAzIAAAEgAEZAIAAMCADABAABmQAAALAgAwAQAAYkAEACAADMgAAAWBABgAgAAzIAAAEgAEZAIAAMCADABAABmQAAALAgAwAQAAYkAEACAADMgAAAWBABgAgAAzIAAAEYIOcH7iB/dBffvkl3q5du7bV9/PPP8fb661nj/kVFRVW+9dff3U+tk6dOvH2Tz/95H0euX/y9bORr6FfR7+GT61atbz7l+vf6v259NJL4+3LLrus4NfIxfrrr+88N8V67/k8j7w+mjdvbvUtXLiw6PtXLL5rtxj7qo8Hal7S55lzXDPq168fb69atSrvc8wvZAAAAsCADABAAGpV5Bgr0WFp+Wc6tOl7ynr16lnt1atXR0mHZPTry1Cs3lf9vs4///x4+29/+1vBrynDq/o1Cw2hbrTRRlb7+++/j5KkUwY1HeYtVij8zjvvjLdPOeWUKGSErAtXaBqrnEPWoad3tGbNmsXbixcvjkJGyBoAgJRgQAYAIE0h63xCIL7H6jt55WN1yHTNmjU5v2aTJk3i7WXLlll98i36Xj/bvjZs2NBqyxCJ725t/b58dy/nEyIKJcwVGn3e9PFfu3ZtlBaErMsDn+XSR8gaAICUYEAGACAADMgAAJRqDll67bXXrPauu+5qtVu0aBFvL1q0KOfX95UZ6ce2bNky3l65cqVzxjHjiCOOiLenT59u9U2ZMsVqt2rVKt6eNm2ac999s4rpfr0/vvKpUPJOjRs3tto6h+97D8VQt25d770I8n1mO4YyH63Pjes5c3neXF+DHHLp8F13oXyWUTzkkAEASAkGZAAA0hSy9i0SoWe7kmUl2UJ5MryoQ4tNmzZ1Li6h22eccUbG2baMTTbZxLkoQf/+/a32gAED4u2bbrrJ+5oyvLhkyRJniZR+X/p5fDMKyT597JKefcgX5sqndCuf15DvKanXyBZS33vvvePtzp07W32XXHKJ1T7ttNPi7QcffNDqk6Vu+ZTo5YOQdXE0aNDAautUVnUr55B127ZtrfacOXOc70O35XdGVb4Phw8f7lzEJymErAEASAkGZAAAAsCADABAmsueZG5T5/pkO1teUObhdA5ALvas865nn322M098zTXXWH0nnXRSvD1kyBCrT+d+77rrLuf+jBo1Kufcr1zFSh87fQxk2YtvgXpdzlPoSlmlkHfy+fTTT632vffea7WvvPJK53SoM2fOtNpXXHFFvP3hhx9afV9++WVB58KXR892rSShVM7zYYcdZrUffvhhZ0ml/uzMmzfPavtK2qpDOeeQ8zkuv3pW66uOksqqIIcMAEBKMCADABAABmQAAEqlDtlXG6aXwvPV2eocwO23355xO1PuV+b+ttpqK6vvoIMOirf32GMPq69Xr15We9asWfH2tddea/V16tTJmZvU00XKOsfNNtvM6nv77bedxySfPEgoeaeqTBtZKH09yvzhLrvsYvU1a9Ys5ykM9Xt566234u1DDjnE6pP3NVx00UXeeuZCUYfs3vdGjRo5l0PVeeHnnnvOOceBsdtuu2X8O80350JVhPJZDsGv4jsw2/v44x//GG/fd999ibz+jTfeaLXPO++8RJ6XHDIAACnBgAwAQADsZYc8fKE938pLulxJlyo88sgjGaetNE4//fSMz5lpxSQZStJlLzJ83L59e2844tZbb423J0+ebPW9+eabVnvFihU5rdKkn8dXJqbJ91UdoeBCVmnKFvb1pTd878n3WD314QcffBBv77XXXlbf/PnzrXbr1q0znkNj7ty5VnvYsGHO1chkyVRVptvLZ/WpciePzz777GP17bTTTlb75ZdfjrdHjhzpLW/LtewpqRB1KShWquqWW26Jt8855xzvVMc6bZGEpELUheAXMgAAAWBABgAgAAzIAACUStmTzufKMh7dp3ORcko7nW/eeOONnXlA3/ST8u+Mq6++Ot7++uuvrb6vvvrKudyezndvscUWzvf5ySefWH0//vijc2m3nj17Opcfe+WVV6w+3+mpzlKJYpV75EPuw4EHHmj1DR061Hku5DnVZO45U/nSpEmTnHnGrl27ZlwyTp9/vZSnzov5UPbk3vcxY8ZYfYceeqjz7+QUvcWcKrPQ+wHKuexJf5fXymPffeNHaCh7AgAgJRiQAQBIU9mTJks+dOmIDFNnCw3J0KcOXcjZuPQMXzqELsPUeqYVWSKz4YYbWn0TJkyw2t99913G5zTef/99q71q1SrnajKSLrX64osvrPaUKVOc4RoZiq/ukph27dplPC41Rc6udOyxx1p9b7zxRrz9hz/8wepbvny51f7hhx+cJVJr1qxxvr4+xwsXLsy4b5lmbssnTI3ImfKSn4EDDjjA+50gvzOqazUnytZyI7/XdBqxq0gF6e/Db775pqTL0PiFDABAABiQAQAIAAMyAABpziHLHICO88t8Tb169Zx512z5Zpmb1q/RokULq7377rvH2zvuuKOztElPvfb6669bbTntpsztZlpFSuYvZsyY4V2NStIrYPlWvKrJHInMG+vzuHr16qK/vj7nMi971llnWX2y5OHKK690/p1x8MEHO3PG+tzIvLE+N0uXLo23R48ebfXp3LQ8x6WW9yomfe+IvA719KlakyZNEt8fWQaXqRQyDWpidTZNftf37dvX6ps+fbrz/pupU6cWfd/lPSZG8+bNo+rCL2QAAALAgAwAQAAYkAEASNPUmTrvIHM5esoymWvbe++9rb4XX3wxkanPWrVq5cw7fPnll1Zfr1694u0XXnjB6tNTVcrlvHR+WdadGrNmzXLuez71wzK/qHOask/XWfpqZgvhm7KuKnmnpJYXlDlDnVt84oknnNecnh5z0KBB8faCBQucr6FfR59jeQ3q5/FN3ZjPdc7UmZHz3oxsOWL5HZX0ZyVp5Tx1pv5e+yWPmnH5t/ksA1sTmDoTAICUYEAGACBNZU96CjtZ9qKno5RTFeoQsV41SIbvfH369a+44gqrPX78+Hh7zz33tPrmzZvnXIVHh7flyksyDJ5pNRk5BeZbb73lDJfpqUV16EKGaPJZOStpvhBPVcI/hYaK9PuVJXOyzC1TSZrUp08fq61X3/KFrGX6RZdD6GvJR4fYSyHUWB30+ZAppWxCD1OnXVIh4RtuuMH5mVtPhbN9q/zpz5jen+HDh8fbl112WRQifiEDABAABmQAAALAgAwAQAByTkrq+LzMH+jl7XxlLnrawFzLnjbaaCNv2dOuu+7qzB19//33zikgGzdubLVlbkFPf6mnQ3zwwQedz+Ojp2eUeRFfDjnXPGShfGUDOncjczu6Lyn6/cpjo/PA8rzq3Fb9+vWttlxWU99DoPPCvjykr0/vQ67HqKZLM0Kjp2iVpYadOnWqgT1C0tfqeeedZ7WHDh3q/OzqnPLWW2+d8T6iTELNG0v8QgYAIAAMyAAApGmmLh0q8IUzZUhW9+kw07fffhtv612RIUo9K89RRx1ltU8//fR4+7rrrrP6ZNhDz9rUsGFDq3322Wc7w6J69afu3bvH23fffbfVJ8PdunwqnxCvb5Wgmpzdx5eW8IXdq0Keq1GjRll9gwcPdr6+Dkv37Nkz52NYHaF5n3KfqUvOcJYtTaC/o9IU/i/nmbq0igJnAcznsTVxbTBTFwAAKcGADABAABiQAQBI82pPcppLPXXm4sWLc34e+fI6ByR16dLFanft2tVqL1q0KN4+4ogjrL7bbrst3l62bJnVp/OCTz/9tDNv/fXXX1vtRx99NN7u0KGD1XfLLbc4X0PnwWTO3Vcuo49PPqsGpSHvpN+fnjZx8803j7fffvvtnJ+3X79+zpyybxpNw/fxyOd45ZqzKrRcKk3nOR/6/eezIlmalHMO+f7777faxxxzjPOxF198sdX+6KOP4u2XXnopChk5ZAAAUoIBGQCAADAgAwCQ5jpk1/SX2WoFfY/V06TJPp0T6du3r7NeUed+5dSZcslE44ADDrDaM2fOjLdvvPFGq09PzSaPyZgxY6y+qVOn5pxDlvW1eupGX+1cKeSd5Gvq+uFx48Y5l1jU049Kutb84IMPttrfffddzvuX1DGWywb6lm3UnzO5NGep5hf1XADyszx//nyrT14jvilo06YUPsuF0udxlVhmVV//J598stX+73//m5q6c3LIAACkBAMyAABpWu1JhxXkdIh6RRYZdtM/03WpTp8+fZxTU8rwtg7l6TIjOT3lihUrrL4rrrgi3r766qu9q0gtWLAg3p42bZrVp0umjjvuuHj7gQcecO67/jtZMqb311depqfOTAvfe7jmmmvi7auuusrq69Wrl9V+//33nX3Nmzd3Xqv6+sy1DE9fdzrdIkNr2XTr1i3enjhxonPff/jhh6gU+VIvuvRMfh506Zv8/khziBqRMyxdW3xf6O9KXSKlv3fTjl/IAAAEgAEZAIAAMCADAJDmqTNlqYLO2fpyhk2bNrXastRJ5+Rkjkjnetu2bWu1ZV5uhx12sPpkW0/B+e6771rtuXPnOvPWeulIWU715ptvWn2yXEPnzXWO01f65ctTpqVUwreEobyODj/8cKvvoosucj62VatWzuUu9fUo71PQOWV9jvX+yddZuHBhIsf/wAMPtNpPPvmk87HlvvyiLBfT967IZTSNSZMmRWmVls9ydbjnnnvi7RNPPNH7WN/nMzSUPQEAkBIMyAAABIABGQCANOeQfX2yrXOiupZT1qDp6Scff/zxeHvWrFnePJysR3vkkUec++PLS+opObNNzXfddddlzD3rvHC2Qyz79WPlvut8p8ynpSXvJJesNG644YZ4++6777b6dI5Q/u0hhxzizOf37t3bW4csz6N+z3LaxmzTwFaHcsgh62Mu77no3Lmz81zqz1yakUP+P/kcC3lv0bx586KQkUMGACAlGJABAEhTyNoXvvWFR3SYVU+Ftu+++zrD2TIs/cQTTzhvjdf7o19z0aJF8fYLL7xg9elyqptuusn5Gm+99ZbVPu2005zhY1nupfdHH/L+/ftnnB5SH3d9fHR5TyhhLr1qk0xL6PfevXv3ePuyyy6z+oYMGeJ8v7qUbOjQofH2M888k3PpmC/dks/0jNmm4JTn0beCky7ny2dlqrSGM/XKbfLzqsPSoa/oUyhC1v9n5513jrdff/31nKe2DR0hawAAUoIBGQCAADAgAwCQphyyjtX7llOTdJ+e8lCWMegc6YMPPhhvL1++3Jv7laUTuuxF5uGmTp1q9d16663O5Rd1Pu/jjz92To+pyRyyzr/7coi+XH11lz3J85FP+Y8vL7vhhhtaffI9jR492urr16+f1W7SpInzNWQJ2sMPP2z1TZ48OUoLfY5910o55Bfbt29vtWfPnh2VonLOIbdp08Zqz/WUs+mlbOV3QujIIQMAkBIMyAAAlMpMXTpssHTpUmcIVj9PvXr14u127do5Z2rq1q2b1Tdw4ECr3bhxY+cKMXK1Hx2i1iHCYcOGxdsrV660+nQIW84Mow+jbOswvQyLa77b+nUJTtJhrmwzkxVa8iPfg37sMcccE2+feuqp3nIYOauaLkk78sgjnedJl4cVo3Qmn1W68lEOM3X16tXLah977LHx9l/+8hdnmkanuIr9+Simcg5ZF1pqmLb3ScgaAICUYEAGACAADMgAAATAnuOwQLokSeb6dP5O59pkeZDOrcoVfN59912r71//+pfV3nvvvePtxYsXW30TJ07MuG+ZXlPSuV+9mojMueqpHGVuQ04FmC234MtFFjtfkk/uxpcb0dOjynPcoUMHZ/lagwYNrD49Beaee+7pLMOS9zFkm6o0H7mW9+WTM9Yle7LMI005sULp8/PFF184S9j0dLVyWtb77rvP6jvhhBMS3lNUB/n9YMyYMSNap0uXLpFvrMnnXpY04BcyAAABYEAGACAADMgAAJRKHXI+dO1g8+bNM+aMda4p2zJbvv2Tr6lrizXfspK+JfWGDx9u9V1yySUF7auPr9Y5CcXKX8q8n57uU57Xjh07Wn2HH3641b755pudeWrZ1vcQJEVOz5rpvoFCj20+U88mIU15al0bf8ghh8TbDz30UFFeU9/nMGvWrKjY0vJZLtSIESOstqwv10ssbrPNNjlPpVro8qS++1yKhTpkAABSggEZAIA0haxl2FGHdnVYSYYh8wnr6ZCgXOkmW7hQPta32lBVSn102Nz3t/I1q7KCjwy3H3fccVbfnXfeGSXJVy5UaMhV09eK77iUQhlDvqvdXHjhhVbf+eefn/jrhRbOLEe6HFSX/FXnOZalZXpKYhSukO8rfiEDABAABmQAAALAgAwAQJrLnpIq3fE9j8xp6nIpPXWizOf63pIvh6nzmNmmYGzRokW8vXDhwihX+nllfj6fnHvSOVV9n4DvWMjjrf9OlzblSp+bfHLtxSLvXcjn3CSl3MueykWplz0houwJAIC0YEAGAKBUyp6SWk2nKuVJua7Kk8/70q+ZTxmODL9mC73m89hcXz+NYa58ysrKBSHr8sBnufQRsgYAICUYkAEACAADMgAAacohAwCA4uEXMgAAAWBABgAgAAzIAAAEgAEZAIAAMCADABAABmQAAALAgAwAQAAYkAEACAADMgAAAWBABgAgAAzIAAAEgAEZAIAAMCADABAABmQAAALAgAwAQAAYkAEACAADMgAAAWBABgAgAAzIAAAEYINcH1irVq2CXkD/XUVFRUHPU67q1q0bb69Zs6aox7JevXpWW77eBhtskPM5Xrt2rbN/vfXc/wZs3Lix1V68eLH3dZJQlWMo90c/j95X3zFo3759vD1z5kyr75dffomSVr9+fau9evVq52Plfuv39Ouvvzr/bv3113c+Vr9//R59x0o+NoTvFt8+6L7atWs7PyO+Y5nEfqHm5XJ98gsZAIAAMCADABCAWhU5xnl06KjQ8JAOl61atSqqSfp9tWzZMt6eP39+FBJ97FauXJno89epU8fZp0NqvtChDlf+/PPPOV1H2a4p/byu/dHhdf28G264Yby9dOlSqy+f/fOFrPW+5vo8et91miIJ+j363ofvnMgQrA7D5hNO9oX39XGUr5HtPDdq1Mh5npMKb+tj6QvNyz79WUv6PJdLyLqeSLP5Ui8hIGQNAEBKMCADAJCmkHWxQiC+580nrCRDW747U/MJpemQU8OGDa32jz/+mNO+Fetu0KTvKvWFDvMJ1+pQpqTPTT53lzZr1swZgvSFCuWd6jrsqV9fhrP1OdYhUhmKT0o+IeNCJfVZ9j1PPndka/L60e9fHnNdFaDPjwxh6usun7SK3PeqhOJ9jy32Zxk1j5A1AAApwYAMAEAAGJABACiHHPJ7771ntbfddltnzm758uVREnQOSM4ApfPAc+fOtdrHH398vN2hQwer74YbbnDmllasWBFVt6TzTr5ZtHyv1bVrV6v9zTffOM/xsmXLCsph67bOSXbq1Cne/v77762+Bg0aWO0DDjgg3n7yySe9ZXgyL/nTTz85990325Q+tr7HVkcO2TeLlk+TJk2891DIUp6qlKDIY66PVatWrZzXkr53oWPHjvH27NmzrT79t7KkUF8DvutOk+dZP9Z3nws55NJHDhkAgJRgQAYAIABBlT35ZjvKNsl+69at4+3PPvvMGebSYVld5tKjR494+4svvrD6dFjaF4aU4T0dusq1XEqHAYs9IX0+ocx8rgdZnqLDgbKvc+fOVt+XX35ptf/zn//E2zvttJPVN3DgwIzXgnH44Ydb7cceeyzenjFjhtW3aNEiqy2f67vvvnPO6rZgwYKCS8hkWZYOixdjcYmkZt3Tcg37Nm3a1OrTJWynnHJKvP23v/3N6tt6662d5+qkk06y2qNHj3a+R32c5ef122+/tfqOOeaYePu+++6LkgjF65I5Qta5ufHGG632ueee65ztTJc71vQxIWQNAEBKMCADABAABmQAAALgXnU+gLi6b0Uhuai70aVLl3i7bdu2zikX77jjDqvv8ccft9pvvPGGc2o+vbqSb/90XqxQMtcly7eKQeeM9VSEksyB6dyzznv6Vujp06dPvD1r1iyr76WXXnLmbHV+Web7dfmLzmfutddezvKcAw880HnfgC77kTnMbNMkyn3S+Uu5D75jnhS9bzKn7FtdyTelpH4f+nm2224757nT5/mpp56Kt0eMGGH13Xvvvc4SymeeecZqy+tpjz32sPr088p7NeR1Ztx///1RrnxlT8WYarXUfabuB7rqqquc17LOGcvrKN97kmoKv5ABAAgAAzIAAAFgQAYAIAA1Xofsy7nssMMO8fa7775r9bVr186ZL2revLnVJ3MLMmdpXHzxxVZb1j2OHz/e6tN5MVlP7Kuh3mKLLay+iRMnWm35vvOpR0y6dlG/P/n8vqkg9d/5nlfm+o3+/fs7c73Dhg2z2rI+dPfdd/fuu4+sV3z++ee9Uyqef/75zjp0ed709JxLlizJ+Vj6+pKuNc/0Grn26fy2zLvqvNz2229v9S1evNh5rGR9uTFy5EhnzlCeO11Dru/b+Oqrr+LtRx55xHndGWPHjo23v/76a+e9ArLWOtP14qs3dz2uGOe5pmtua/qeo0z3pOjvnupGHTIAACnBgAwAQKmGrH1h6DPOOMNq33bbbfF27969rb7PP/88KoTeVxki06VDemUgGdLWU2fq9+JbBUaWyOgyHD3NYqGKPd2e7zzK96Sn9NRTI8pysf3228/qe+utt5ylKfqx/fr1c4Yn5XnVYcWrr77aav/hD3+Ity+55BJv2EuGyfWUp/KYZCt78oUk5bHUpTHVEbL2lYP4+nRZoLwO/v3vf1t955xzjjNcrFNM8jx369bN6nv44Yedx+bZZ5+12rvssku8/cknn3inzJX7rlcrSyqlIEP8uvSNqTNzo9Mm8rz97ne/86YGaxohawAAUoIBGQCAADAgAwAQgBove/KR+Sudq9G7LfdP57Y233xzZ/525syZVrtr167x9rRp06w+PXXixhtv7FyaT+6vLpdp06aN1Za5ST09p0/SeSed6851ekw9paK+VuRx0qVNcmnMjh07Os+b0ahRI+dryrzx7bffbvXNmTPHag8ePDjefu6557yvKad5XLhwofP46+tT59HlMZgyZUpUU+c42/KLvvPsW1JSn4OGDRs6ry15zo3u3bs7n/eHH35wTqWo+/SxkvnnuXPnWn26/FHmjfVjfWWJ+njJ866Xh/Qhh5wb33EihwwAABLBgAwAQABqPGTtm9kmn9U55O3wkyZNsvruuusu5/Po1Z+22mqreHvChAlWnw5LytCfXmlEllXoEJ0OWeY6o49+XNJhLh3KlLOhzZ49O+d0gi+0qdMJsiRNv74OocvVfXTZkwwn67SEDjXvtttuGWdzMubPn+8sq9DlXTKkr/ddX2e+0hl57ejVp4oRstavL8+Jfn3f9ecrk9OfBz07l6TTGDLcrcuD5GOzHXM5059OW+jrR84k5is9y7ayWa6z2/n+LgnlGLJez5OKCQEhawAAUoIBGQCAADAgAwBQDjlkPdWZzs8UujpKixYtnKs0HX/88VbfO++84yyx6Ny5szMPofNp+jU32mgjZ05I5iblFH7Gq6++arWz5cdDmDpT5wTlscmWV/M9j6+UqVmzZlZblrm0bdvWmefTKw3pPLEsbXvqqae8pRMy//zpp58685l65SOd+8y1hK/Y9wlky7XpaUflec62L/I96s+9LBmU+VqjdevWVnvevHnO4ypfY5999vGuACdzyJdddpnVp78jRowY4ZwiVR6DbHlr33Sz1Xk/SKnkkCvyOC6hv2dyyAAApAQDMgAAAWBABgAgAHaipwi22247qz1u3Lica/N8ebfNNtvMav/xj3+Mt5cvX+7M/bZv396bd5A5bt23bNkyqy1zYb78QFVyxrnm2ItB5hP1lJ6+aSN99ar6HgJ5DHWtqj5Op556qnM60oEDB8bbI0eO9F6Dcrk/fY71+3zzzTed9xTIazfbcnryWtbHQOZbdY6yGPS+yby+rgn2yTaVpiRrw3V9ucwZ6ykv9bHaf//9nUun9urVy3nMDz30UKtvxowZVnvJkiXOc+n7jipUdZznclARWK1xVXFVAAAQAAZkAADSFLLWIRYdpnTRIWpdDiHpEgc5VaEurTnnnHOcq8D07dvX6nv99dczTmmZKQztCyXpUpurrroq3j7hhBOcq/vMmjUrSkNIxhe+1cdfhhL1taAf6yurkSFSvUKQXnnp888/j7dvueUWq0+urDNs2DDvVKVyhS+92pNeBUiWZeUTztX0tJuSDssWmw4Zr1mzJqeSKH0t+h6rS5Dkakr6etGpChnC/vvf/+5MP+jrQ6/+9Morr8Tbd999t9Wny+/k95I+V/mU0/g+r75jif+jz42PTl2mHb+QAQAIAAMyAAABYEAGACBNOeRcc8bZ6HyZXPpN5rJ0rqt3795W3zbbbOPM2erShMsvv9xZLqPzQ7J86uWXX7b6dDnN73//+3h70KBBznxWPjlkvT/VmWvy5QjzmfLUl1PWJWnyeXzL8BlnnnlmvD116lTn+df3KejlL3v27BlvP/vss1Zfly5drPZrr73mzJnK19HXrm95Pd9Uo9Ux/Z8vR5rP59w3NaTM0+v3r+8V0edr7733jreff/55q69///7OY66Pq5w6s02bNt4lOXVJW67TYfqmwCz0vpty95MqO/N59NFHnWNEGvELGQCAADAgAwBQDjN1aTvvvLNzJSZfmZFeaUmHM2UoVIa18g05vfTSS/H2n/70J6uvY8eOzlIbWVplvPDCCxlne8r0mrn2FZsOHfpmJfIdU1/Zk35OX7nJ3LlzrfYbb7wRb7ds2dLq+/LLL52lbXp1obfeeitjWVOmULgMb8+ZM8f5vnT41DeLVU2f/3zC4r7H6vIpeW59s5HpPn2+5HmXM7Dpc9epUyerT6cfbr31VmfaSJ8veR3otJYsy9IrQWmFhv/xf+SsaZr+jiiFMLXEL2QAAALAgAwAQAAYkAEACECtihyTVjqXJHOIvlv/sz2PfKyeMk2u+nLGGWdYfTqnPGLECGfuUeaPsr3dfffdN97+73//a/XpshyZ+9L5V53j9D1PrnzTVyZBl2nIc6Vf2zcVpI8vt9q6deucj6EsadHH4oMPPvBOqShX/NL3NOj2XXfd5SzlkVN56ty479z47mPQ5yCp1YVyzfEn9fq+87zpppvmnDOUpYXGhRde6Fzt6eyzz3aWMuk8tb53QE7xq9+zrwwn2z0puR6fpM9zdZTPJSWf+yY2UN+z8viHPh1pLvvHL2QAAALAgAwAQAAYkAEASHMdcq41dr5ckjZq1CirffTRR8fbV1xxhTNnrHPKX3/9tXN6Tp2/1Uu/yZphXWep2zJPqGsgmzdvHm8vXbo053y8L5dUjHyiaz+y1Q/L96DfTz71l/I15s+fb/XpvF+PHj3i7WnTpjn3XdYk6/NvTJ8+3bmM44MPPmi1Zd2pXI7SaNy4sTNPnW2pQldfTeTB5PnzXWP5TOvq+9zrcyeXVDQuuOAC570K8jUPO+wwq69Zs2ZW+7vvvou3L7roIqvvf//7n/Oa9S3Rqo+PzsfLtp6Os5xrlHfZZRerPWnSpIKe55cifwfWNH4hAwAQAAZkAADKcepMbfz48fH29ttv7ww16lDFhx9+aLUnTJjgXFHo2GOPjbfvueceq08/r5yS8/3337f6+vXr5wyRvffee1bfvHnznK+h5RomLHY4M9uqTbn+nQ5X+lIGMlyrVwHaZJNNnGVQespVGb6cPHmy1dehQwfnvuupEOVUmXpVseuvvz7nFYJ851inPuTxKrScLB/5lCnm8zh5nvWxkedZh+9POukk52dZT0n79NNPO0PUOv3UtWtX5+pORxxxhNWWJW2vvPKK89zp9JMub5OP1fuTROljyHzT6erziMz4hQwAQAAYkAEACAADMgAAaZ46s379+s58iC/v2apVK2fOVi9vJ0uH9JRpeinEjz/+2JmTlVMy6tIV/bxy//RrDB8+3Dml3v777+9c8s9X8lKVEoikc8r5LBkoyzv08dY5Ukk/j3xsmzZtnFOn6ilR9X0Cch90CdJGG21ktS+99FLnMp6+vOhDDz1k9clctb529XSQcv90GZa+JqVi3DeQ1LSK+rqW14RvSU69POr5559vtWW+d8qUKVbfo48+6szf6uO66667Oqdllfeu6GtPv6bML+spN/X0nb5yQN90qsX+LIdGfu59U5Om7X35MHUmAAApwYAMAEAAGJABAEhzHbKvjk7GynXMXy+xuGjRoozL2RlHHnmks09OW6hz2o8//rizfrVv377enPaWW24Zb5933nnOGkjjhRdeyCmPnpZp8vKZGlHmXmWu35g9e7Yz17j33ns788I6x3bQQQdZ7dtuu825/Ka8jrQtttjCOY2frmXVdabyNXW+WT5W58F8tb46Zyzrr6ujDjkfct90znjNmjVWW17nnTt3tvrkdJQDBgyw+nROWS57qo+VfB45Baqx++67W+3ddtst3m7SpInV17RpU+dr6vMs89++6yzbcoC+Ot1Sp/P7J5xwQsnnjAvBL2QAAALAgAwAQDlMnanDNro8ZN9994237733XqtPrtBy2mmnOfuMtm3bOqdgHDt2bMbQdqYSHVk+1b59e6uvT58+zjIoX0lDNnvttVe8/eKLL+ZcRpE03/PrPll2pEuQ9Ao4m2++ebw9btw4q++4446Ltz/77DPntJU6LPzpp59afTNmzHCWpsgyJ716j7xuMp1HGVLXq4jJ6yzb+ZelV7pURoapdUqnplMTvhC6r7xNrrSkV3TSU1MOGzbMau+8884Zz6ueklaHQXWZnFy57cQTT/Tunwxpy9fIdzUuef709KHyb/VnpNQtWLDAausVtaSKGljxLBT8QgYAIAAMyAAABIABGQCAclx+UU9V+dxzz2UsI9J5Yp3rGzlypNWWOcWXX37Z6pM5K50/02UuMgd0zjnnWH16iUWZN9QlIfmUNei8sSufUt3LL8pjoUtcfPQxlnn5Hj16WH2y7GjTTTe1+nTOVpZT7bTTTlafzGNfeOGF3v2T15Ke4lLmjHV5nX5fX331lTOfqc+/zBvrPLGcVrMmll/0TYPq+zv9HmW/vo9Dvi+ZIzbuu+8+qz1ixIiMZYj6M6fLnPRSmrK8St+7oK9Dub+6TE6WO+rPuT4mMm+sv+vksc22JGup0fd1yOtBH6cnn3wyKlf8QgYAIAAMyAAABKDoIWsd5tMhORny0WG/66+/Pt5u0KCB1afLTOTMO+eee67V16lTJ2cpgpzNR4e2Ro8ebfVtvfXWVluWZJTCzDs6/JZr+FSXcOhwnLwG9IpOMmStX0/PpnTEEUc4Z0y65557Mr5eplI3mXp49dVXnSVaxhtvvOEslZGhtmyhTNnv+wyEXPKRrVxKfgZ0CVK/fv2cqQg9G5ecoW/MmDFW32uvvRZvjxo1yurTn/srr7zSmZrSM/atWLEi3p44caLzetafc98x0Z8D3wyGpe6JJ55wfnb0cTpIzdBXTviFDABAABiQAQAIAAMyAAABqFWRY9Iqn5yHb1UTX85F98m2b/UcPRWbXnlJ5iiylSfJ12zdurXVp6fU8+1fdUxrmHS+MZ+pM33nWJc4yCkv9fGXuaSOHTt6p1kdMmRIxhW8dF7yzDPPtPqeffZZZymTXtnnb3/7W+Sic8jyfeljoPPq8p4HXeYh+/RxLsa9Cfl8ln3XuM7Ny9y4fv/ysQMHDrT6nn/+eef50feKyFxv9+7drT59XIcOHepckUzml43Fixc7S/xkSV227zPZr4+P/F4q9nkOPUctv6+nTp1q9XXo0CEqRbl8X/MLGQCAADAgAwAQAAZkAABKNYecz/P4lisLnXwvunZRT79XDMXOIfvyh74+nT+UbV2Dq3PKPnKJPD39pKxv/uabb6w+nWv86KOPnLXvvqkQdQ21nmYzic9LtnslkpDUZ9lXe+3ry/Ye5d/qfZVTXOprSdY6G5MmTXLmaHWecubMmc7rZdq0aVEh8plOtzrvB0EU9ezZM5FznA9yyAAApAQDMgAA5RiyLjTEk085UKlq166dc/WjJPjCxzoMLcuF9LSE+bxGrmVv2VbIkY/1rbKTLXRYu3btgqYPzef61MdAtnXpl1xRKin6XPrOgdw3XYLkk20q0Vz78nkNzff94Ttfvv3JNk2sj3xNPUWrTnlVFSHrmqdXH5OlXi78QgYAIAAMyAAABIABGQCANOeQizFtZNrIkpmaKNmqzrInzTd1Zj77JXPROh+npzDM9Xl9OdpsU1Xq15D5aJ1DzadkK9ccts5/55rDzkc+n2X5HqtSkiXLlfRxTGraSF9+N9t5ru7vM33t5JOLzgU55PBQ9gQAQEowIAMAEICgyp7KlQy15RO6Sjq05gvB+kJ82ci/1SFZX2lKrmVOmfZP0qvuyOeVKzbl+7yyRCqp0HJ1rPaUT0mSTz4rt1XHe9SzrsmUR7brN9dSp6RCy+W+2lM5qiBkDQBAOjAgAwAQAAZkAADSlEMGAADFwy9kAAACwIAMAEAAGJABAAgAAzIAAAFgQAYAIAAMyAAABIABGQCAADAgAwAQAAZkAAACwIAMAEAAGJABAAgAAzIAAAFgQAYAIAAMyAAABIABGQCAADAgAwAQAAZkAAACwIAMAEAAGJABAAgAAzIAAAHYINcH1qpVq7h7grxVVFQk+nyc49I/x9V1nvVrFON9pE3t2rXj7bVr1xb1+Ky33nrO55f7Yfz666/x9i+//JLI69epU8dq//TTT4lcK7UCv6622267eHv8+PF57yu/kAEACAADMgAAAahVkeNv/nIJZw4YMCDefuedd6KQEbIuzPrrr594iK5Y0hqyDsE111wTb19yySVROZ1neY3rc+675nWoW4azkwwf161b17k/P//8s/N96Me2bNky3l60aJHVp/9Wvhff+0rqfeq/y+U1+YUMAEAAGJABAAgAIesUI2Rd+ghZl4dQP8vFuqtZhsb1c1Z4XqNBgwZWe9WqVc6/8+17tlB4MXCXNQAAKcGADABAABiQAQAIADnkFAsl75QtdxOyfv36We2JEydGIQkph5xPSQzS+Vnu0KGD1Z49e7azXGn16tVRMdQVr6Fn+NLXYMOGDePtH3/8seDvpeqY8YscMgAAKcGADABAAAhZ5+Hcc8+12jfddFNUk0IJc9WEH374wWo3b9682vdh1qxZ8XaXLl2csw2VSsgaxVOdi0sk9bw6ZSGvqw02sNct0otp9O3bN95+6qmnrL4tt9wy4+sZrVq1strTp0/PuXxK7p/uO/TQQ+PtRx55xPl3mf42V4SsAQBICQZkAAACwIAMAEAAyCF71KtXz3sM5C35NbFqUDnnkMsFOeQotYvVh/RZznWqymzH1JeHbdeuXbw9d+5cq2+nnXZylivpe3M222yzeLtOnTpW3/z58632KaecEm+3adPG6nvsscesdqNGjeLtFi1aWH1fffWV830ltVocOWQAAFKCARkAgAAwIAMAEICyzyHrPLHMO2y99dbeGrjtt98+3h4/fnxU3cghh2PZsmVWu3Hjxok8Lzlk977rpfiefPJJq33xxRfH2xMmTEjk9XXtra6TTcNnOZ9lCXUOd82aNRnzwJnystKQIUOs9qabbhpvH3DAAVZfA3Fe9dSZ+jjNmTMn3v7nP//pnQZU1ju/9957zvM6YMAAq++NN96IkkAOGQCAlGBABgAgAPb8ZjVMhyd0uKQY9Iol++67b84hh5oIU6fdd9995yyVSLOkQtSlQocvFy1aVNDzHHbYYVb7nXfeca5EJKdRNLbddtt4e8qUKd6pV3XqyiWpEHVN8oVOdRmPnvJSuuaaa6z2OeecE29vtNFGVl/t2rWt9j777JMx7KzDx/r74ZBDDrHa1157bbw9duxYq69ly5ZWe/LkyRlD75oOUedTXienDC1k+tz0X10AAJQABmQAAALAgAwAQACCyiFXR85Y++STTwoueUD+ksoZ65zf/vvv78wPfvjhh1Zb5nZWrlyZyP4gKjhn3KlTJ6v9+eef553bNXr06GG1b775Zmeesk+fPjlPHynLcNJyvfjynvnkRHVOWZZ+6ikv69ev77w3Ry9PKvO0O+ywg9U3V0y7OXjwYKtP3wtw4403Os+Nvq6mTp0aJTEuyXLYt99+O9FlV/mFDABAABiQAQAIQFAh65rQt2/fnB+rZ35BzZXSLFy40Oq755574u3333/f6nv99dedIVE5Y1BNrdqVVnpWp0KP3bRp05ylI/kYOXKkM7z5+OOPW33PPfec1d5xxx3j7Xnz5ll9chUhXRKz8847R2kgv7tmzZqV89/p8LZcbUmXgMlw7SabbGL13XDDDVZbzqp1xx13WH333Xefs1xKp0LkPvTu3du5gpOxYsWKgsL2ukRKh6mTxC9kAAACwIAMAEAAGJABAAhAald70vmLXEuS9PScOkchjRo1ymr/8Y9/DCrfWGqrPenyAl3GIHM5eqWf3//+9/H2I4884s1Jdu/ePd7+8ssvrb5vv/3WWzpR3UpltSeZX8z2+rLUyTd1o75XYJtttin4uMp8c8eOHa2+Ro0aldRqT3rVugULFiRyD0G3bt3i7X79+nnPjcwb63K1D0WZ4uabb+4siTKuuuoqZ37/iCOOsNr77bdfvL148eKCy8LkOdd9vvPIak8AAKQEAzIAAAFgQAYAIACpzSFXR64m9PccSg75oYcestqHH354Iq8vax71cmp6Cj2Z29tss82svs6dOzun8Zs0aZL3mOpcU3UrlRzyUUcdFW+PGTOmKO9Rvy+5BOD3338f9DKgxf4sy7xnUlMA69eQ7a5du3qX4xw6dGi8/corr1h9e+21l3OJR33tyHPcsGFDq0+fc1kbXZVjIHPn+j4ieZz1/QW++yHivyl4rwAAQGIYkAEACEDZT52JqssnRK1LmWT5kg7byRC1XtnlvPPOs/qaNWvmfM3Zs2db7VNOOSXefvPNN62+unXrWu3GjRvH28uWLXO+Bmz6XMpwpg7l6dJDXZpY6Gv6Vt554IEHnH+rQ7GybK6qq/nUFBmi1de4nhoyVwcddJDVfvLJJzN+bowjjzzSaj/99NPx9nXXXecM7b6pPp+yBE2XSv7rX//yTsmaVNjeV+4qn7eQ1+AXMgAAAWBABgAgAAzIAAAEgLKnFL/nUMqeknrNfHIuv/vd76z2xIkT4+3hw4c7c1vGBx98EG/ffPPNVp9epk9Px1fd0lr2pMvS5GveeeedVt9NN91U0GvoaRU/+eQT5z7oZTb1dJF6OsnqziGXQtmTfI3Bgwd7l7k95phj4u1PP/3U6tt3332dU+T69v3SSy+12g8//LDV1tPkJnF88lnGkakzAQBICQZkAADKsewpn5/4SdGrfkinn366cxUSFKZ3795W+/PPP3c+VpYgZSOvFR3mku1bbrnFWxIlQ1LnnHOO1bfzzjtbbblqjZzhyzjttNPi7YsuuigqNzfccIMzDNmmTZuivKacAUqv9rTddttZ7S222ML5PDpEPWvWrHi7Q4cOVl9aS50kXxi2fv368faqVaty/r72fXfLFZsyzXgnP2d6hq1a4jV1iZF+zZkzZ8bbrVu3tvqWLl1qtTfccMOcSr3yCVknPX7xCxkAgAAwIAMAEAAGZAAAAlCSZU96RRBfDlmWNOj3GXruqBTKnmS5gc4XLVy40GoPGjTIWTojp1vs1auX1fef//zHap955pnOc/yHP/zBWToxZMgQq2/UqFFRsaW17KlYnnnmmYyrAhmXX3651ZblbzpPWeh0kaXwWdZTlyZVBuVahSlT+8cff3SuFtezZ0/ntLf6voHddtst3r7iiiusvhUrVjg/y/r4yO+BpO5z0s+Ty3HmFzIAAAFgQAYAIAAMyAAABCC1OWSdB5FvQ+ci5b7rJf308+gp9UJWCjnk+++/P94eMGCA1afrfuWybB9//LHVJ8/rTjvtZPV99913ztrFjh07Wn1//etfndM61sTyi+SQbfXq1XPmgXWOTtahNm3aNApZKXyWfa/ZrVs3q3300UfH2507d7b6lonPma51P+SQQ5zLL8prI9O0uPJ5fXn0qpwL3xScTJ0JAEBKMCADAFCOU2cmxXcLuSyBMerWreucqvHaa68twt5BWn/99Z3pBF1KVGh5hnxePW2fNnbs2Hh79OjRVt8777zjDJvXRMg6TeSKW3LK0STNmTMn5+lKdXoKxeULyS5atMhq//vf/3aWN44Wn0m58lOmEtYJEybE240aNbL62rdvb7Xnz58fby9ZssS571UJWcsQ+urVq/P+e34hAwAQAAZkAAACwIAMAEAAUptD9pE5Y51T1ku0IcppWtGqTCOq88a5ymdKv3bt2sXb33//vfd5ZLnMuHHjvM/71Vdf5bwP5a5YeWOpefPmOef65L0LoU+DW2p0CZTOp8qpM/VjjzzySOcSmvr+oAMOOMC5lO6MGTO80yS79ldeN9m+v+TSlZmWr8wXv5ABAAgAAzIAAAFgQAYAIACpnTpzww03dOYFdZ5B5hB1fiDNSmG6vULJ6S+NDh06xNsNGjSw+lauXOls6+uoGFNVVgVTZ0bOz/KsWbOc9xEYt99+e7x91llnRSGryc+y/Azo+y30Z8c3T4AvR6uXX5TnrnHjxlZfjx49nN/lcqpdo2vXrvH2J598YvX961//cs4/oHPaMk+sc8b5nBumzgQAoAQwIAMAEIDUlj3pFXxkmFKHBnQZFNJv8eLFVnvSpEnx9vLly62+efPmWe22bds6V5T6+uuvo2KQYUG9f4gKCgnqz/lrr71mtWWYWodwQ0tNJC2fkHU+16Pv+Mvv2RUrVjj/Tn8Gddposfhs/+Uvf7H6+vTpY7Xfe++9jGkrY5999nGGrPW+53M9yFWldOg7n1LNTPiFDABAABiQAQAIAAMyAAABSG0OuWnTps7cws0332z1MW1e6dl1112t9siRI+NtuUSf0b17d2eep1g5Y428cTI+++wzZ9/gwYOdfaWeM872fn1LoOZDfnZ0KZPMrWYrifrhhx/i7VatWll9+4olF4855hjv1JTdunVzfsb0+5TTXOpSq9mzZ+d8v4HMG+v3Kf+2kOPML2QAAALAgAwAQABSG7L+4osvrLYMn7Rs2dIZckjzLEXIHPLSKQwZ8sq0+pNcMQhh05/XTTfd1PlYuYJQpr8tZ0mF7GWIVoesf/e738Xb06ZNs/p0eeGee+4Zb7/55ptW31VXXZXx9YzatWtbbRmmvuyyy7ypyqVLlzpD3/J19ApOuoQryTInjV/IAAAEgAEZAIAAMCADABCA1K72pMm88Ysvvmj1bbnlllEpKufVnnz+97//We2jjz7a+diHHnrIah9++OFRSMp9tSf9/mUucsaMGd78YprKHWvys+xboUiXK8lSHj0lsXysnBoz01THp556arx9wQUXOFdy69+/v9WnV3+S0+Lq87/JJps4rwd9begpMIuB1Z4AAEgJBmQAAALAgAwAQABKJodc055++mmrPWjQoKK/Jjnk3OSz9F6dOnWcOSv9d9VxvEoxhyyXSs1U55nrvAFjxoyx2scee6zVXrt2bUH7N3fuXKut623L+bMsn1dOx6nzz61bt/Y+z/Dhw53P06tXL+fz6OkoZV26ziHreuIlS5Y4c9x6ut1iIIcMAEBKMCADABAAQtYpUuwVa/I5x9OnT4+3e/ToUaQ9Kj/VsSpRPudZhgSnTJkS1bSNN97YGepetmyZ8+86duzoLK0pxfOsp5z0pQFGjBgRb5933nmJvL5+Dd3OdcrJOiqFpNMQoa3iJd+nnhJ0xx13zPr3/EIGACAADMgAAASAARkAgACQQ06xtJRKoHClWPakp2NM0xSXpf5ZzqdE0HdeGzdu7F0utVC1qrB/Sb1mrq9fSN6cX8gAAASAARkAgAAQsk6xUMJcKJ5SDFkjrM+yfGw++6FLqyT9PEm9vw1UuiOp1EehxyAfzNQFAEBKMCADABAABmQAANKUQwYAAMXDL2QAAALAgAwAQAAYkAEACAADMgAAAWBABgAgAAzIAAAEgAEZAIAAMCADABAABmQAAKKa9/8BaVjrzPrRpUAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x600 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-13 11:23:47,491 - INFO - Epoch [42/50] Batch [1/157] D_Loss: 0.312 G_Loss: 3.891\n",
      "2025-07-13 11:23:47,806 - INFO - Epoch [42/50] Batch [51/157] D_Loss: 0.189 G_Loss: 4.416\n",
      "2025-07-13 11:23:48,119 - INFO - Epoch [42/50] Batch [101/157] D_Loss: 0.594 G_Loss: 2.705\n",
      "2025-07-13 11:23:48,432 - INFO - Epoch [42/50] Batch [151/157] D_Loss: 0.336 G_Loss: 3.526\n",
      "2025-07-13 11:23:48,710 - INFO - Epoch 42 - D_Loss: 0.449, G_Loss: 3.213, Time: 2.1s\n",
      "2025-07-13 11:23:49,673 - INFO - Epoch [43/50] Batch [1/157] D_Loss: 0.465 G_Loss: 4.197\n",
      "2025-07-13 11:23:49,990 - INFO - Epoch [43/50] Batch [51/157] D_Loss: 0.288 G_Loss: 3.148\n",
      "2025-07-13 11:23:50,301 - INFO - Epoch [43/50] Batch [101/157] D_Loss: 0.571 G_Loss: 1.964\n",
      "2025-07-13 11:23:50,612 - INFO - Epoch [43/50] Batch [151/157] D_Loss: 0.386 G_Loss: 4.316\n",
      "2025-07-13 11:23:50,892 - INFO - Epoch 43 - D_Loss: 0.452, G_Loss: 3.185, Time: 2.2s\n",
      "2025-07-13 11:23:51,855 - INFO - Epoch [44/50] Batch [1/157] D_Loss: 0.575 G_Loss: 4.070\n",
      "2025-07-13 11:23:52,171 - INFO - Epoch [44/50] Batch [51/157] D_Loss: 0.508 G_Loss: 3.639\n",
      "2025-07-13 11:23:52,484 - INFO - Epoch [44/50] Batch [101/157] D_Loss: 0.321 G_Loss: 4.893\n",
      "2025-07-13 11:23:52,795 - INFO - Epoch [44/50] Batch [151/157] D_Loss: 0.369 G_Loss: 2.931\n",
      "2025-07-13 11:23:53,075 - INFO - Epoch 44 - D_Loss: 0.446, G_Loss: 3.338, Time: 2.2s\n",
      "2025-07-13 11:23:54,018 - INFO - Epoch [45/50] Batch [1/157] D_Loss: 0.476 G_Loss: 3.702\n",
      "2025-07-13 11:23:54,334 - INFO - Epoch [45/50] Batch [51/157] D_Loss: 0.416 G_Loss: 3.484\n",
      "2025-07-13 11:23:54,646 - INFO - Epoch [45/50] Batch [101/157] D_Loss: 0.500 G_Loss: 3.784\n",
      "2025-07-13 11:23:54,957 - INFO - Epoch [45/50] Batch [151/157] D_Loss: 0.517 G_Loss: 3.381\n",
      "2025-07-13 11:23:55,235 - INFO - Epoch 45 - D_Loss: 0.458, G_Loss: 3.254, Time: 2.2s\n",
      "2025-07-13 11:23:56,193 - INFO - Epoch [46/50] Batch [1/157] D_Loss: 0.452 G_Loss: 2.595\n",
      "2025-07-13 11:23:56,523 - INFO - Epoch [46/50] Batch [51/157] D_Loss: 0.549 G_Loss: 2.774\n",
      "2025-07-13 11:23:56,838 - INFO - Epoch [46/50] Batch [101/157] D_Loss: 0.455 G_Loss: 2.451\n",
      "2025-07-13 11:23:57,152 - INFO - Epoch [46/50] Batch [151/157] D_Loss: 0.312 G_Loss: 3.342\n",
      "2025-07-13 11:23:57,430 - INFO - Epoch 46 - D_Loss: 0.480, G_Loss: 3.068, Time: 2.2s\n",
      "2025-07-13 11:23:58,387 - INFO - Epoch [47/50] Batch [1/157] D_Loss: 0.429 G_Loss: 3.185\n",
      "2025-07-13 11:23:58,722 - INFO - Epoch [47/50] Batch [51/157] D_Loss: 0.573 G_Loss: 3.059\n",
      "2025-07-13 11:23:59,038 - INFO - Epoch [47/50] Batch [101/157] D_Loss: 0.666 G_Loss: 2.839\n",
      "2025-07-13 11:23:59,348 - INFO - Epoch [47/50] Batch [151/157] D_Loss: 0.418 G_Loss: 3.074\n",
      "2025-07-13 11:23:59,643 - INFO - Epoch 47 - D_Loss: 0.516, G_Loss: 3.013, Time: 2.2s\n",
      "2025-07-13 11:24:00,604 - INFO - Epoch [48/50] Batch [1/157] D_Loss: 0.447 G_Loss: 2.961\n",
      "2025-07-13 11:24:00,924 - INFO - Epoch [48/50] Batch [51/157] D_Loss: 0.595 G_Loss: 3.247\n",
      "2025-07-13 11:24:01,238 - INFO - Epoch [48/50] Batch [101/157] D_Loss: 0.449 G_Loss: 2.994\n",
      "2025-07-13 11:24:01,545 - INFO - Epoch [48/50] Batch [151/157] D_Loss: 0.427 G_Loss: 3.454\n",
      "2025-07-13 11:24:01,825 - INFO - Epoch 48 - D_Loss: 0.471, G_Loss: 3.184, Time: 2.2s\n",
      "2025-07-13 11:24:02,833 - INFO - Epoch [49/50] Batch [1/157] D_Loss: 0.600 G_Loss: 3.144\n",
      "2025-07-13 11:24:03,148 - INFO - Epoch [49/50] Batch [51/157] D_Loss: 0.230 G_Loss: 3.468\n",
      "2025-07-13 11:24:03,460 - INFO - Epoch [49/50] Batch [101/157] D_Loss: 0.541 G_Loss: 3.467\n",
      "2025-07-13 11:24:03,771 - INFO - Epoch [49/50] Batch [151/157] D_Loss: 0.451 G_Loss: 3.256\n",
      "2025-07-13 11:24:04,048 - INFO - Epoch 49 - D_Loss: 0.445, G_Loss: 3.259, Time: 2.2s\n",
      "2025-07-13 11:24:05,005 - INFO - Epoch [50/50] Batch [1/157] D_Loss: 0.424 G_Loss: 2.920\n",
      "2025-07-13 11:24:05,322 - INFO - Epoch [50/50] Batch [51/157] D_Loss: 0.326 G_Loss: 4.424\n",
      "2025-07-13 11:24:05,639 - INFO - Epoch [50/50] Batch [101/157] D_Loss: 0.404 G_Loss: 2.636\n",
      "2025-07-13 11:24:05,956 - INFO - Epoch [50/50] Batch [151/157] D_Loss: 0.300 G_Loss: 4.418\n",
      "2025-07-13 11:24:06,238 - INFO - Epoch 50 - D_Loss: 0.394, G_Loss: 3.880, Time: 2.2s\n",
      "2025-07-13 11:24:06,238 - INFO - Total time: 113.3s (1.9 min)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-13 11:24:06,306 - INFO - Lite model saved!\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data import DataLoader, Subset\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import logging\n",
    "import time\n",
    "\n",
    "logging.basicConfig(\n",
    "    level=logging.INFO,\n",
    "    format='%(asctime)s - %(levelname)s - %(message)s',\n",
    "    handlers=[\n",
    "        logging.FileHandler('gan_training_lite.log'),\n",
    "        logging.StreamHandler()\n",
    "    ]\n",
    ")\n",
    "logger = logging.getLogger(__name__)\n",
    "\n",
    "if torch.backends.mps.is_available():\n",
    "    device = torch.device(\"mps\")\n",
    "    logger.info(\"Using MPS (optimized for efficiency)\")\n",
    "else:\n",
    "    device = torch.device(\"cpu\")\n",
    "\n",
    "latent_dim = 64\n",
    "num_epochs = 50\n",
    "batch_size = 64\n",
    "lr = 0.0002\n",
    "image_size = 28\n",
    "channels = 1\n",
    "subset_size = 10000\n",
    "\n",
    "logger.info(f\"LITE MODE - Latent: {latent_dim}, Epochs: {num_epochs}, Batch: {batch_size}, Subset: {subset_size}\")\n",
    "\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5,), (0.5,))\n",
    "])\n",
    "\n",
    "logger.info(\"Loading MNIST subset...\")\n",
    "full_dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)\n",
    "indices = torch.randperm(len(full_dataset))[:subset_size]\n",
    "mnist_subset = Subset(full_dataset, indices)\n",
    "dataloader = DataLoader(mnist_subset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
    "logger.info(f\"Using {subset_size} samples, {len(dataloader)} batches per epoch\")\n",
    "\n",
    "class SimpleGenerator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(SimpleGenerator, self).__init__()\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(latent_dim, 128),\n",
    "            nn.ReLU(True),\n",
    "            nn.Linear(128, 256),\n",
    "            nn.BatchNorm1d(256),\n",
    "            nn.ReLU(True),\n",
    "            nn.Linear(256, 512),\n",
    "            nn.BatchNorm1d(512),\n",
    "            nn.ReLU(True),\n",
    "            nn.Linear(512, 28*28),\n",
    "            nn.Tanh()\n",
    "        )\n",
    "        logger.info(\"Simple Generator: Linear layers only (much faster)\")\n",
    "    \n",
    "    def forward(self, z):\n",
    "        return self.model(z).view(-1, 1, 28, 28)\n",
    "\n",
    "class SimpleDiscriminator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(SimpleDiscriminator, self).__init__()\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(28*28, 512),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            nn.Dropout(0.3),\n",
    "            nn.Linear(512, 256),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            nn.Dropout(0.3),\n",
    "            nn.Linear(256, 1),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "        logger.info(\"Simple Discriminator: Linear layers only (much faster)\")\n",
    "    \n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "generator = SimpleGenerator().to(device)\n",
    "discriminator = SimpleDiscriminator().to(device)\n",
    "\n",
    "total_params_g = sum(p.numel() for p in generator.parameters())\n",
    "total_params_d = sum(p.numel() for p in discriminator.parameters())\n",
    "logger.info(f\"Generator params: {total_params_g:,} (vs 3.5M before)\")\n",
    "logger.info(f\"Discriminator params: {total_params_d:,} (vs 2.7M before)\")\n",
    "\n",
    "criterion = nn.BCELoss()\n",
    "optimizer_g = optim.Adam(generator.parameters(), lr=lr)\n",
    "optimizer_d = optim.Adam(discriminator.parameters(), lr=lr)\n",
    "\n",
    "fixed_noise = torch.randn(16, latent_dim, device=device)\n",
    "\n",
    "g_losses = []\n",
    "d_losses = []\n",
    "\n",
    "logger.info(\"=\"*50)\n",
    "logger.info(\"STARTING LITE TRAINING (HEAT OPTIMIZED)\")\n",
    "logger.info(\"=\"*50)\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    epoch_start_time = time.time()\n",
    "    epoch_d_loss = 0\n",
    "    epoch_g_loss = 0\n",
    "    \n",
    "    for i, (real_images, _) in enumerate(dataloader):\n",
    "        real_images = real_images.to(device)\n",
    "        batch_size = real_images.size(0)\n",
    "        \n",
    "        real_labels = torch.ones(batch_size, 1, device=device)\n",
    "        fake_labels = torch.zeros(batch_size, 1, device=device)\n",
    "        \n",
    "        optimizer_d.zero_grad()\n",
    "        real_output = discriminator(real_images)\n",
    "        real_loss = criterion(real_output, real_labels)\n",
    "        \n",
    "        noise = torch.randn(batch_size, latent_dim, device=device)\n",
    "        fake_images = generator(noise)\n",
    "        fake_output = discriminator(fake_images.detach())\n",
    "        fake_loss = criterion(fake_output, fake_labels)\n",
    "        \n",
    "        d_loss = real_loss + fake_loss\n",
    "        d_loss.backward()\n",
    "        optimizer_d.step()\n",
    "        \n",
    "        optimizer_g.zero_grad()\n",
    "        fake_output = discriminator(fake_images)\n",
    "        g_loss = criterion(fake_output, real_labels)\n",
    "        g_loss.backward()\n",
    "        optimizer_g.step()\n",
    "        \n",
    "        epoch_d_loss += d_loss.item()\n",
    "        epoch_g_loss += g_loss.item()\n",
    "        \n",
    "        if i % 50 == 0:\n",
    "            logger.info(f'Epoch [{epoch+1}/{num_epochs}] Batch [{i+1}/{len(dataloader)}] '\n",
    "                       f'D_Loss: {d_loss.item():.3f} G_Loss: {g_loss.item():.3f}')\n",
    "    \n",
    "    avg_d_loss = epoch_d_loss / len(dataloader)\n",
    "    avg_g_loss = epoch_g_loss / len(dataloader)\n",
    "    \n",
    "    g_losses.append(avg_g_loss)\n",
    "    d_losses.append(avg_d_loss)\n",
    "    \n",
    "    epoch_time = time.time() - epoch_start_time\n",
    "    logger.info(f\"Epoch {epoch+1} - D_Loss: {avg_d_loss:.3f}, G_Loss: {avg_g_loss:.3f}, Time: {epoch_time:.1f}s\")\n",
    "    \n",
    "    if epoch % 10 == 0:\n",
    "        with torch.no_grad():\n",
    "            fake_images = generator(fixed_noise).cpu()\n",
    "            plt.figure(figsize=(6, 6))\n",
    "            for j in range(16):\n",
    "                plt.subplot(4, 4, j+1)\n",
    "                plt.imshow(fake_images[j, 0], cmap='gray')\n",
    "                plt.axis('off')\n",
    "            plt.suptitle(f'Epoch {epoch} - Lite Mode')\n",
    "            plt.show()\n",
    "        \n",
    "        if device.type == \"mps\" and hasattr(torch.mps, 'empty_cache'):\n",
    "            torch.mps.empty_cache()\n",
    "\n",
    "total_time = time.time() - start_time\n",
    "logger.info(f\"Total time: {total_time:.1f}s ({total_time/60:.1f} min)\")\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(g_losses, label='Generator')\n",
    "plt.plot(d_losses, label='Discriminator')\n",
    "plt.legend()\n",
    "plt.title('Training Losses')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "with torch.no_grad():\n",
    "    final_images = generator(fixed_noise).cpu()\n",
    "    grid = torch.cat([final_images[i*4:(i+1)*4] for i in range(4)], dim=3)\n",
    "    grid = torch.cat([grid[i] for i in range(4)], dim=2)\n",
    "    plt.imshow(grid[0], cmap='gray')\n",
    "    plt.axis('off')\n",
    "    plt.title('Final Results')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "torch.save(generator.state_dict(), 'generator_lite.pth')\n",
    "logger.info(\"Lite model saved!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df3792f6-6a89-40e6-a9ef-6471519a1180",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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