{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dcbfe504-5300-46f6-9888-698f8ffde1a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package                   Version\n",
      "------------------------- --------------\n",
      "anyio                     4.9.0\n",
      "appnope                   0.1.4\n",
      "argon2-cffi               25.1.0\n",
      "argon2-cffi-bindings      21.2.0\n",
      "arrow                     1.3.0\n",
      "asttokens                 3.0.0\n",
      "async-lru                 2.0.5\n",
      "attrs                     25.3.0\n",
      "babel                     2.17.0\n",
      "beautifulsoup4            4.13.4\n",
      "bleach                    6.2.0\n",
      "certifi                   2025.7.9\n",
      "cffi                      1.17.1\n",
      "charset-normalizer        3.4.2\n",
      "comm                      0.2.2\n",
      "contourpy                 1.3.2\n",
      "cycler                    0.12.1\n",
      "debugpy                   1.8.14\n",
      "decorator                 5.2.1\n",
      "defusedxml                0.7.1\n",
      "executing                 2.2.0\n",
      "fastjsonschema            2.21.1\n",
      "filelock                  3.18.0\n",
      "fonttools                 4.58.5\n",
      "fqdn                      1.5.1\n",
      "fsspec                    2025.5.1\n",
      "h11                       0.16.0\n",
      "httpcore                  1.0.9\n",
      "httpx                     0.28.1\n",
      "idna                      3.10\n",
      "ipykernel                 6.29.5\n",
      "ipython                   9.4.0\n",
      "ipython_pygments_lexers   1.1.1\n",
      "ipywidgets                8.1.7\n",
      "isoduration               20.11.0\n",
      "jedi                      0.19.2\n",
      "Jinja2                    3.1.6\n",
      "json5                     0.12.0\n",
      "jsonpointer               3.0.0\n",
      "jsonschema                4.24.0\n",
      "jsonschema-specifications 2025.4.1\n",
      "jupyter                   1.1.1\n",
      "jupyter_client            8.6.3\n",
      "jupyter-console           6.6.3\n",
      "jupyter_core              5.8.1\n",
      "jupyter-events            0.12.0\n",
      "jupyter-lsp               2.2.5\n",
      "jupyter_server            2.16.0\n",
      "jupyter_server_terminals  0.5.3\n",
      "jupyterlab                4.4.4\n",
      "jupyterlab_pygments       0.3.0\n",
      "jupyterlab_server         2.27.3\n",
      "jupyterlab_widgets        3.0.15\n",
      "kiwisolver                1.4.8\n",
      "MarkupSafe                3.0.2\n",
      "matplotlib                3.10.3\n",
      "matplotlib-inline         0.1.7\n",
      "mistune                   3.1.3\n",
      "mpmath                    1.3.0\n",
      "nbclient                  0.10.2\n",
      "nbconvert                 7.16.6\n",
      "nbformat                  5.10.4\n",
      "nest-asyncio              1.6.0\n",
      "networkx                  3.5\n",
      "notebook                  7.4.4\n",
      "notebook_shim             0.2.4\n",
      "numpy                     2.3.1\n",
      "overrides                 7.7.0\n",
      "packaging                 25.0\n",
      "pandocfilters             1.5.1\n",
      "parso                     0.8.4\n",
      "pexpect                   4.9.0\n",
      "pillow                    11.3.0\n",
      "pip                       25.1.1\n",
      "platformdirs              4.3.8\n",
      "prometheus_client         0.22.1\n",
      "prompt_toolkit            3.0.51\n",
      "psutil                    7.0.0\n",
      "ptyprocess                0.7.0\n",
      "pure_eval                 0.2.3\n",
      "pycparser                 2.22\n",
      "Pygments                  2.19.2\n",
      "pyparsing                 3.2.3\n",
      "python-dateutil           2.9.0.post0\n",
      "python-json-logger        3.3.0\n",
      "PyYAML                    6.0.2\n",
      "pyzmq                     27.0.0\n",
      "referencing               0.36.2\n",
      "requests                  2.32.4\n",
      "rfc3339-validator         0.1.4\n",
      "rfc3986-validator         0.1.1\n",
      "rpds-py                   0.26.0\n",
      "Send2Trash                1.8.3\n",
      "setuptools                80.9.0\n",
      "six                       1.17.0\n",
      "sniffio                   1.3.1\n",
      "soupsieve                 2.7\n",
      "stack-data                0.6.3\n",
      "sympy                     1.14.0\n",
      "terminado                 0.18.1\n",
      "tinycss2                  1.4.0\n",
      "torch                     2.7.1\n",
      "torchaudio                2.7.1\n",
      "torchvision               0.22.1\n",
      "tornado                   6.5.1\n",
      "traitlets                 5.14.3\n",
      "types-python-dateutil     2.9.0.20250708\n",
      "typing_extensions         4.14.1\n",
      "uri-template              1.3.0\n",
      "urllib3                   2.5.0\n",
      "wcwidth                   0.2.13\n",
      "webcolors                 24.11.1\n",
      "webencodings              0.5.1\n",
      "websocket-client          1.8.0\n",
      "widgetsnbextension        4.0.14\n"
     ]
    }
   ],
   "source": [
    "!pip list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e598708-037b-4cdb-bdcc-fbc6589a02f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/karthik/Desktop/importants/venv/bin/python\n",
      "/Users/karthik/Desktop/importants/venv/bin/python3\n",
      "Python 3.13.5\n",
      "Python 3.13.5\n"
     ]
    }
   ],
   "source": [
    "# Check which Python you're using\n",
    "!which python\n",
    "!which python3\n",
    "!python --version\n",
    "!python3 --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5da9e198-31c2-4775-9f66-2653fbc1e14a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in /Users/karthik/Desktop/importants/venv/lib/python3.13/site-packages (3.10.3)\n",
      "Requirement already satisfied: numpy in /Users/karthik/Desktop/importants/venv/lib/python3.13/site-packages (2.3.1)\n",
      "Collecting pandas\n",
      "  Downloading pandas-2.3.1-cp313-cp313-macosx_11_0_arm64.whl.metadata (91 kB)\n",
      "Collecting scikit-learn\n",
      "  Downloading scikit_learn-1.7.0-cp313-cp313-macosx_12_0_arm64.whl.metadata (31 kB)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/karthik/Desktop/importants/venv/lib/python3.13/site-packages (from matplotlib) (1.3.2)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/karthik/Desktop/importants/venv/lib/python3.13/site-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/karthik/Desktop/importants/venv/lib/python3.13/site-packages (from matplotlib) (4.58.5)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /Users/karthik/Desktop/importants/venv/lib/python3.13/site-packages (from matplotlib) (1.4.8)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/karthik/Desktop/importants/venv/lib/python3.13/site-packages (from matplotlib) (25.0)\n",
      "Requirement already satisfied: pillow>=8 in /Users/karthik/Desktop/importants/venv/lib/python3.13/site-packages (from matplotlib) (11.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/karthik/Desktop/importants/venv/lib/python3.13/site-packages (from matplotlib) (3.2.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/karthik/Desktop/importants/venv/lib/python3.13/site-packages (from matplotlib) (2.9.0.post0)\n",
      "Collecting pytz>=2020.1 (from pandas)\n",
      "  Using cached pytz-2025.2-py2.py3-none-any.whl.metadata (22 kB)\n",
      "Collecting tzdata>=2022.7 (from pandas)\n",
      "  Using cached tzdata-2025.2-py2.py3-none-any.whl.metadata (1.4 kB)\n",
      "Collecting scipy>=1.8.0 (from scikit-learn)\n",
      "  Downloading scipy-1.16.0-cp313-cp313-macosx_14_0_arm64.whl.metadata (61 kB)\n",
      "Collecting joblib>=1.2.0 (from scikit-learn)\n",
      "  Using cached joblib-1.5.1-py3-none-any.whl.metadata (5.6 kB)\n",
      "Collecting threadpoolctl>=3.1.0 (from scikit-learn)\n",
      "  Using cached threadpoolctl-3.6.0-py3-none-any.whl.metadata (13 kB)\n",
      "Requirement already satisfied: six>=1.5 in /Users/karthik/Desktop/importants/venv/lib/python3.13/site-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n",
      "Downloading pandas-2.3.1-cp313-cp313-macosx_11_0_arm64.whl (10.7 MB)\n",
      "\u001b[2K   \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.7/10.7 MB\u001b[0m \u001b[31m33.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m1m30.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hDownloading scikit_learn-1.7.0-cp313-cp313-macosx_12_0_arm64.whl (10.6 MB)\n",
      "\u001b[2K   \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.6/10.6 MB\u001b[0m \u001b[31m42.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m1m43.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hUsing cached joblib-1.5.1-py3-none-any.whl (307 kB)\n",
      "Using cached pytz-2025.2-py2.py3-none-any.whl (509 kB)\n",
      "Downloading scipy-1.16.0-cp313-cp313-macosx_14_0_arm64.whl (20.7 MB)\n",
      "\u001b[2K   \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.7/20.7 MB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hUsing cached threadpoolctl-3.6.0-py3-none-any.whl (18 kB)\n",
      "Using cached tzdata-2025.2-py2.py3-none-any.whl (347 kB)\n",
      "Installing collected packages: pytz, tzdata, threadpoolctl, scipy, joblib, scikit-learn, pandas\n",
      "\u001b[2K   \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7/7\u001b[0m [pandas]━━━━\u001b[0m \u001b[32m6/7\u001b[0m [pandas]scikit-learn]\n",
      "\u001b[1A\u001b[2KSuccessfully installed joblib-1.5.1 pandas-2.3.1 pytz-2025.2 scikit-learn-1.7.0 scipy-1.16.0 threadpoolctl-3.6.0 tzdata-2025.2\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib numpy pandas scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3f9c13ab-1055-4f13-9d34-fb68dd684f9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyTorch version: 2.7.1\n",
      "MPS available: True\n"
     ]
    }
   ],
   "source": [
    "!python -c \"import torch; print('PyTorch version:', torch.__version__); print('MPS available:', torch.backends.mps.is_available())\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9cf6dd2c-f1ec-4ae0-9d62-22acd8d806b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:22:23,562 - INFO - PyTorch VAE Logger initialized - Device: mps\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🚀 Using MPS (Metal Performance Shaders) on M4 Mac\n",
      "🚀 PyTorch VAE Implementation for M4 Mac (Jupyter Optimized)\n",
      "============================================================\n",
      "PyTorch version: 2.7.1\n",
      "Device: mps\n",
      "MPS available: True\n",
      "\n",
      "📂 Loading MNIST dataset...\n",
      "📊 Loaded MNIST dataset:\n",
      "   Training samples: 60000\n",
      "   Test samples: 10000\n",
      "   Batch size: 128\n",
      "\n",
      "🏗️  Initializing VAE model (latent_dim=2, beta=1.0)...\n",
      "   Total parameters: 470,804\n",
      "   Model size: 1.80 MB\n",
      "\n",
      "🚀 Starting VAE training (20 epochs)...\n",
      "🚀 Starting training for 20 epochs\n",
      "📊 Model has 470804 parameters\n",
      "Epoch 1 [    0/60000 (  0%)] Loss: 71843.664062\n",
      "Epoch 1 [25600/60000 ( 43%)] Loss: 21454.763672\n",
      "Epoch 1 [51200/60000 ( 85%)] Loss: 21533.164062\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:22:27,619 - INFO - Epoch   1 | Total Loss: 188.3552 | Recon: 184.4511 | KL: 3.9041 | Val Loss: 164.1514 | Time: 3.77s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch   1 | Total Loss: 188.3552 | Recon: 184.4511 | KL: 3.9041 | Val Loss: 164.1514 | Time: 3.77s\n",
      "💾 New best model saved! Validation loss: 164.1514\n",
      "Epoch 2 [    0/60000 (  0%)] Loss: 22164.935547\n",
      "Epoch 2 [25600/60000 ( 43%)] Loss: 21070.710938\n",
      "Epoch 2 [51200/60000 ( 85%)] Loss: 20862.626953\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:22:31,661 - INFO - Epoch   2 | Total Loss: 166.2892 | Recon: 161.4309 | KL: 4.8583 | Val Loss: 158.2647 | Time: 3.75s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch   2 | Total Loss: 166.2892 | Recon: 161.4309 | KL: 4.8583 | Val Loss: 158.2647 | Time: 3.75s\n",
      "💾 New best model saved! Validation loss: 158.2647\n",
      "Epoch 3 [    0/60000 (  0%)] Loss: 20867.033203\n",
      "Epoch 3 [25600/60000 ( 43%)] Loss: 21339.685547\n",
      "Epoch 3 [51200/60000 ( 85%)] Loss: 21174.830078\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:22:35,755 - INFO - Epoch   3 | Total Loss: 163.2596 | Recon: 158.1717 | KL: 5.0879 | Val Loss: 156.5518 | Time: 3.80s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch   3 | Total Loss: 163.2596 | Recon: 158.1717 | KL: 5.0879 | Val Loss: 156.5518 | Time: 3.80s\n",
      "💾 New best model saved! Validation loss: 156.5518\n",
      "Epoch 4 [    0/60000 (  0%)] Loss: 20824.765625\n",
      "Epoch 4 [25600/60000 ( 43%)] Loss: 21083.958984\n",
      "Epoch 4 [51200/60000 ( 85%)] Loss: 20471.845703\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:22:39,954 - INFO - Epoch   4 | Total Loss: 162.0264 | Recon: 156.8154 | KL: 5.2110 | Val Loss: 154.8731 | Time: 3.90s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch   4 | Total Loss: 162.0264 | Recon: 156.8154 | KL: 5.2110 | Val Loss: 154.8731 | Time: 3.90s\n",
      "💾 New best model saved! Validation loss: 154.8731\n",
      "Epoch 5 [    0/60000 (  0%)] Loss: 21634.666016\n",
      "Epoch 5 [25600/60000 ( 43%)] Loss: 21157.478516\n",
      "Epoch 5 [51200/60000 ( 85%)] Loss: 20514.263672\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:22:44,328 - INFO - Epoch   5 | Total Loss: 160.9149 | Recon: 155.6240 | KL: 5.2909 | Val Loss: 153.7889 | Time: 4.04s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch   5 | Total Loss: 160.9149 | Recon: 155.6240 | KL: 5.2909 | Val Loss: 153.7889 | Time: 4.04s\n",
      "💾 New best model saved! Validation loss: 153.7889\n",
      "Epoch 6 [    0/60000 (  0%)] Loss: 19320.017578\n",
      "Epoch 6 [25600/60000 ( 43%)] Loss: 20710.652344\n",
      "Epoch 6 [51200/60000 ( 85%)] Loss: 20804.113281\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:22:49,933 - INFO - Epoch   6 | Total Loss: 160.2166 | Recon: 154.8810 | KL: 5.3356 | Val Loss: 153.2334 | Time: 5.18s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch   6 | Total Loss: 160.2166 | Recon: 154.8810 | KL: 5.3356 | Val Loss: 153.2334 | Time: 5.18s\n",
      "💾 New best model saved! Validation loss: 153.2334\n",
      "Epoch 7 [    0/60000 (  0%)] Loss: 20998.095703\n",
      "Epoch 7 [25600/60000 ( 43%)] Loss: 21136.589844\n",
      "Epoch 7 [51200/60000 ( 85%)] Loss: 20785.716797\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:22:55,510 - INFO - Epoch   7 | Total Loss: 159.5602 | Recon: 154.1608 | KL: 5.3994 | Val Loss: 152.9650 | Time: 5.11s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch   7 | Total Loss: 159.5602 | Recon: 154.1608 | KL: 5.3994 | Val Loss: 152.9650 | Time: 5.11s\n",
      "💾 New best model saved! Validation loss: 152.9650\n",
      "Epoch 8 [    0/60000 (  0%)] Loss: 20216.537109\n",
      "Epoch 8 [25600/60000 ( 43%)] Loss: 20081.449219\n",
      "Epoch 8 [51200/60000 ( 85%)] Loss: 21143.548828\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:23:01,493 - INFO - Epoch   8 | Total Loss: 159.0484 | Recon: 153.6075 | KL: 5.4409 | Val Loss: 152.1594 | Time: 5.51s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch   8 | Total Loss: 159.0484 | Recon: 153.6075 | KL: 5.4409 | Val Loss: 152.1594 | Time: 5.51s\n",
      "💾 New best model saved! Validation loss: 152.1594\n",
      "Epoch 9 [    0/60000 (  0%)] Loss: 20348.201172\n",
      "Epoch 9 [25600/60000 ( 43%)] Loss: 20497.261719\n",
      "Epoch 9 [51200/60000 ( 85%)] Loss: 19692.919922\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:23:07,288 - INFO - Epoch   9 | Total Loss: 158.7079 | Recon: 153.2501 | KL: 5.4578 | Val Loss: 151.6601 | Time: 5.35s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch   9 | Total Loss: 158.7079 | Recon: 153.2501 | KL: 5.4578 | Val Loss: 151.6601 | Time: 5.35s\n",
      "💾 New best model saved! Validation loss: 151.6601\n",
      "Epoch 10 [    0/60000 (  0%)] Loss: 19823.056641\n",
      "Epoch 10 [25600/60000 ( 43%)] Loss: 19976.890625\n",
      "Epoch 10 [51200/60000 ( 85%)] Loss: 19339.699219\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:23:13,055 - INFO - Epoch  10 | Total Loss: 158.2190 | Recon: 152.7258 | KL: 5.4931 | Val Loss: 151.2626 | Time: 5.37s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  10 | Total Loss: 158.2190 | Recon: 152.7258 | KL: 5.4931 | Val Loss: 151.2626 | Time: 5.37s\n",
      "💾 New best model saved! Validation loss: 151.2626\n",
      "Epoch 11 [    0/60000 (  0%)] Loss: 20556.484375\n",
      "Epoch 11 [25600/60000 ( 43%)] Loss: 19633.451172\n",
      "Epoch 11 [51200/60000 ( 85%)] Loss: 19624.287109\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:23:18,909 - INFO - Epoch  11 | Total Loss: 157.9164 | Recon: 152.4047 | KL: 5.5116 | Val Loss: 150.9700 | Time: 5.37s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  11 | Total Loss: 157.9164 | Recon: 152.4047 | KL: 5.5116 | Val Loss: 150.9700 | Time: 5.37s\n",
      "💾 New best model saved! Validation loss: 150.9700\n",
      "Epoch 12 [    0/60000 (  0%)] Loss: 20420.570312\n",
      "Epoch 12 [25600/60000 ( 43%)] Loss: 20442.427734\n",
      "Epoch 12 [51200/60000 ( 85%)] Loss: 19959.730469\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:23:24,246 - INFO - Epoch  12 | Total Loss: 157.4736 | Recon: 151.9246 | KL: 5.5490 | Val Loss: 150.4663 | Time: 5.01s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  12 | Total Loss: 157.4736 | Recon: 151.9246 | KL: 5.5490 | Val Loss: 150.4663 | Time: 5.01s\n",
      "💾 New best model saved! Validation loss: 150.4663\n",
      "Epoch 13 [    0/60000 (  0%)] Loss: 22069.693359\n",
      "Epoch 13 [25600/60000 ( 43%)] Loss: 20043.304688\n",
      "Epoch 13 [51200/60000 ( 85%)] Loss: 20020.326172\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:23:28,706 - INFO - Epoch  13 | Total Loss: 157.2043 | Recon: 151.6333 | KL: 5.5711 | Val Loss: 150.0154 | Time: 4.16s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  13 | Total Loss: 157.2043 | Recon: 151.6333 | KL: 5.5711 | Val Loss: 150.0154 | Time: 4.16s\n",
      "💾 New best model saved! Validation loss: 150.0154\n",
      "Epoch 14 [    0/60000 (  0%)] Loss: 19839.341797\n",
      "Epoch 14 [25600/60000 ( 43%)] Loss: 20097.576172\n",
      "Epoch 14 [51200/60000 ( 85%)] Loss: 20107.408203\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:23:32,893 - INFO - Epoch  14 | Total Loss: 156.9955 | Recon: 151.4061 | KL: 5.5894 | Val Loss: 149.9174 | Time: 3.84s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  14 | Total Loss: 156.9955 | Recon: 151.4061 | KL: 5.5894 | Val Loss: 149.9174 | Time: 3.84s\n",
      "💾 New best model saved! Validation loss: 149.9174\n",
      "Epoch 15 [    0/60000 (  0%)] Loss: 20016.656250\n",
      "Epoch 15 [25600/60000 ( 43%)] Loss: 19423.392578\n",
      "Epoch 15 [51200/60000 ( 85%)] Loss: 21607.128906\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:23:37,613 - INFO - Epoch  15 | Total Loss: 156.6990 | Recon: 151.0585 | KL: 5.6405 | Val Loss: 150.2038 | Time: 4.35s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  15 | Total Loss: 156.6990 | Recon: 151.0585 | KL: 5.6405 | Val Loss: 150.2038 | Time: 4.35s\n",
      "Epoch 16 [    0/60000 (  0%)] Loss: 19617.134766\n",
      "Epoch 16 [25600/60000 ( 43%)] Loss: 19511.328125\n",
      "Epoch 16 [51200/60000 ( 85%)] Loss: 19582.843750\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:23:42,356 - INFO - Epoch  16 | Total Loss: 156.4200 | Recon: 150.7703 | KL: 5.6497 | Val Loss: 149.6098 | Time: 4.43s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  16 | Total Loss: 156.4200 | Recon: 150.7703 | KL: 5.6497 | Val Loss: 149.6098 | Time: 4.43s\n",
      "💾 New best model saved! Validation loss: 149.6098\n",
      "Epoch 17 [    0/60000 (  0%)] Loss: 20652.857422\n",
      "Epoch 17 [25600/60000 ( 43%)] Loss: 19974.960938\n",
      "Epoch 17 [51200/60000 ( 85%)] Loss: 19229.871094\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:23:46,511 - INFO - Epoch  17 | Total Loss: 156.3926 | Recon: 150.7293 | KL: 5.6632 | Val Loss: 149.5000 | Time: 3.86s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  17 | Total Loss: 156.3926 | Recon: 150.7293 | KL: 5.6632 | Val Loss: 149.5000 | Time: 3.86s\n",
      "💾 New best model saved! Validation loss: 149.5000\n",
      "Epoch 18 [    0/60000 (  0%)] Loss: 20997.859375\n",
      "Epoch 18 [25600/60000 ( 43%)] Loss: 19943.279297\n",
      "Epoch 18 [51200/60000 ( 85%)] Loss: 20796.626953\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:23:50,521 - INFO - Epoch  18 | Total Loss: 156.1792 | Recon: 150.5035 | KL: 5.6757 | Val Loss: 148.9831 | Time: 3.71s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  18 | Total Loss: 156.1792 | Recon: 150.5035 | KL: 5.6757 | Val Loss: 148.9831 | Time: 3.71s\n",
      "💾 New best model saved! Validation loss: 148.9831\n",
      "Epoch 19 [    0/60000 (  0%)] Loss: 19659.544922\n",
      "Epoch 19 [25600/60000 ( 43%)] Loss: 20654.642578\n",
      "Epoch 19 [51200/60000 ( 85%)] Loss: 19714.167969\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:23:54,803 - INFO - Epoch  19 | Total Loss: 155.9202 | Recon: 150.2393 | KL: 5.6809 | Val Loss: 149.0982 | Time: 3.94s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  19 | Total Loss: 155.9202 | Recon: 150.2393 | KL: 5.6809 | Val Loss: 149.0982 | Time: 3.94s\n",
      "Epoch 20 [    0/60000 (  0%)] Loss: 19935.322266\n",
      "Epoch 20 [25600/60000 ( 43%)] Loss: 19195.658203\n",
      "Epoch 20 [51200/60000 ( 85%)] Loss: 19496.019531\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-14 09:23:59,161 - INFO - Epoch  20 | Total Loss: 155.7000 | Recon: 150.0054 | KL: 5.6946 | Val Loss: 148.6912 | Time: 4.05s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  20 | Total Loss: 155.7000 | Recon: 150.0054 | KL: 5.6946 | Val Loss: 148.6912 | Time: 4.05s\n",
      "💾 New best model saved! Validation loss: 148.6912\n",
      "📊 Training metrics saved to vae_logs_latent2_beta1.0/training_metrics.csv\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📈 Comprehensive training curves saved to vae_logs_latent2_beta1.0/comprehensive_training_curves.png\n",
      "✅ Training completed successfully!\n",
      "\n",
      "🎨 Creating visualizations...\n",
      "📊 Extracting latent representations for 3000 samples...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karthik/Desktop/importants/venv/lib/python3.13/site-packages/torch/utils/data/dataloader.py:683: UserWarning: 'pin_memory' argument is set as true but not supported on MPS now, then device pinned memory won't be used.\n",
      "  warnings.warn(warn_msg)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🌌 Latent space visualization saved to vae_logs_latent2_beta1.0/latent_space_visualization.png\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x400 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔄 Reconstruction comparison saved to vae_logs_latent2_beta1.0/reconstruction_comparison.png\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8EAAAPZCAYAAAAxzSj2AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAAnAlJREFUeJzt3QecHWd5L/4jq/fee7Pljm3ZjgsxDjam2ZcaLu0CgcDNxcaQ0BLIhZBguIFcIJDQkhC4hgsxEFNMiA2xMbjgKltukixbkq3ee9f5f97zv7vZXWuekWa12vJ+v5/PgrXPnpk5c+admefMOfPrVa/X6zUAAADIwAmdvQAAAABwvGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGxogoFuZfXq1bVPfepTtZe85CW1adOm1YYMGVLr27dvbcSIEbXTTjut9vu///u1v/3bv62tWLGisxeVil7wghfUevXq1fyzbNmyo55Gesz73//+2llnnVUbPnx4YxsZPXp0be7cubVLLrmkdvXVV9e++tWv1vbs2VPLwW233dZqnb71rW+tdXX1er32la98pXbhhRc2xvcJJ5zQvPyf//znaz3dmjVrGttt03NOz3/58uXhY17/+te3ep3TGGjrFa94Rau/ST+vec1rwun+8z//83MeU/STXiuArq5PZy8AwJHYt29f7SMf+UjtC1/4Qm3//v3PqW/durXx8+ijj9ZuuOGG2nvf+97G3/Xu3btTlrc7Sie6b3vb25r//bGPfaz28Y9/vNbd/OhHP6q94Q1vqO3atavV7zdt2tT4efLJJ2u3335743cve9nLalOmTOmkJSWStr+//Mu/rOVqwoQJtSuuuKJ20003Nb8pcP311zf2g4ezffv2xrbfUts3O9avX1/72c9+9pzH/uQnP2mMjVGjRh3T5wDQVWmCgS4vXa178YtfXPvVr37V6vf9+vVrXOkbP358bffu3bWlS5fWnnrqqeYTxvRDXlatWlV74xvf2KoBnj17duMKcJ8+fRpX19IbJWl7oWv72te+1urf5557buPTH0l6PXOQmtimJjj5P//n/xQ2wd///vdbbdfnnHNO49MxLaUm+nBvIqY3Gb/zne80PiFxJMaMGdP4RMXhDB48+IimAdCZNMFAl5dOzNo2wH/8x39c+/M///PnfPQufVz6u9/9bhYfl+S5vve979V27tzZ/O/PfOYzz/lIaDrh//Wvf91oCNLHTema1q5d2/zfEydOrN1zzz213Fx11VWNj/Fv3Lix8e9FixbV7r333sYbAm2lBrmlw33k/Zvf/Gbzf6dPyRw6dKj5zcL0SZAjbYJPPfXURtMN0F35TjDQpT3yyCO1b3zjG61+l5rfv/mbvznsd8/SyfL73ve+xkde05W/ttIJ309/+tPGd4dnzJhRGzhwYG3QoEG1k046qfZHf/RHtSeeeOKIv6f6H//xH42P06aPEA4YMKBxYvi5z32u8Ar0sZz3D37wg8bv0zpIv0vf+Ux+85vfNJ7/pZde2rgCOnLkyMZ6SN+LPf300xvzeeihhw77fb+WH4VO/uIv/qLVfNt+NDpddUrf2Uwf2Uwf3UxX5tN85s+f33hs04n74Tz22GON9TB27NjGekjr7n/9r/912KtUR2Px4sWt/v3CF77wOX+TljP9Pm1X6VMELaVlTh/BffWrX91YpvS8+vfv33id0lXI1JR8+9vfbjQPZd+bTOsrvaave93rGs8zXSE7//zzG69dk1tuuaWxLGm9pe+3/+7v/m7t3//9358z7fSat5x2eu3TJySuu+66xnKmdZiapbTcbV/fo5Eem7aRNM1hw4Y1nnv6uPhrX/vaxrIWSW88XXnllbWpU6c2xkJ63KRJkxpXI9/xjnc0tpODBw8e0TI0Pce2b24d7nviaRy1/H0aY1//+tcb6zktf9vvlKfX7Yc//GFjPaXXs2kMzpo1q/ER+l/84heHXab0WracT3qtf/vb3zbGfxpjaV5pzN16662t1skFF1zQeN3T65vuY3C0jXzaVtP3fKNmN3nmmWea9wFNj0vPp6UHH3yw1bZx2WWX1Z7//Oc3//v+++9v7G8BslAH6MI+8pGPpI6y+Wfs2LH13bt3V5rWtm3b6i95yUtaTa/tT9++fetf+cpXnvPYSy65pNXf/bf/9t8Kp3Httdd26Lzf/OY3P+ext956a+Nv3/3ud4fzSD+9e/eu/+M//mPz9L/xjW+UPib9fOxjH2t+zGOPPVY/8cQTw7+fMGFC/c4773zO8/nFL35RHzhw4GEf87u/+7v1c889t9Xvnn766SN+jd/znve0euzznve8+ve+97362rVrj+jx99577xGtiyuuuKK+b9++Vo9tux4vv/zy+qBBgw77+L/7u7+rf+5zn6v36tXrObUTTjihfuONN7aadloHbZ/X/PnzDzvt/v371//t3/6t1ePT9tHyb97ylrccdqwdbnla/rztbW+rHzhwoNXjjmSbSz/bt28/otfgSKbVtE1Mnz69dGw0/e2mTZvql156aem0X/e619X37t3bapnStt/yb6688srGODrc2Prxj39cf+9733vYaQ8YMKB+zz331I/Gfffd95x94P79+1v9zac+9alWf/PqV7/6OdO55pprWv3NP//zP9e//OUvt/rdn/zJnxx2Gdpu22mfBNCdaYKBLq3tSet//a//tfK0Xvaylz3nZPLFL35xYx79+vVr/n1qBH72s5+FjWj6GTJkSP33fu/36nPmzHlOE7NixYoOnXc62T7rrLPqL33pSxuNQMsmOM1/3rx59ec///n1q666qtF8n3zyyc85GV+1alXjMemx6aS5bVOVHpN+3/STmsmmZmLKlCmt/jatg/Qc205j9OjR9ZUrVzY/l/TY9Nxb/s2IESPql1122XOWsW0TcyRuuOGGwuZm8uTJ9Ve84hX1L3zhC83PvagJTg38+eef33iN0jq88MILn9O4pya2pcO9mZDe2Lj44ovrp59++nPWf3qdUpOctqEZM2a0qqc3GKImuOXfpWZ71KhRrX4/cuTIVo1/WRP813/9189Zvhe84AWN559ew5a1D33oQ82PS69ty8Z58ODBjeeTmsS0LYwbN+6om+Cm7a3lPNN6arktrlu37rBNcNObAOedd15j2cePH9+8/bzwhS98znNMb7r8zu/8Tr1Pnz6tau94xzvCJjj9pO0hjd+247/pjY/hw4c3Xpu0LbWsv+hFL6ofrbbbz09+8pNW9VNOOSWspzdsxowZ0+q5b926tb5+/fpWzz0ta9sG+3DbdppWy9fjcPsJgK5MEwx0aW1P7lqegDdJzc3hGoSWJ/rp6mPLWmpsWl7tWbRoUaOpbaqfdtppYSOaTr6XLVvWqKWTxrYn2N/85jc7bN6pafzNb37TXD906FDz9JYsWVLfsmXLYdfll770pVbTSVeBohPdlld+W/roRz/a6u8+/elPt6p/5zvfaVW/+uqrm2uf/exnW9VSA7F69erm+oc//OF2NcHpKuWRXO1Lbzx88IMffM5VzbTuFi9efNhpr1mzptHkNU0jNcnR+kvNYXrtk4MHDzb+vmU9Tevhhx9u1Hfu3FmfOHFiq/ry5cvDJvgDH/hAcz01M2m7aVn/i7/4iyNqgtNzbrn9zZo1q9UbFzt27KifffbZrdZd05sId9xxR6vp3n777c9Zb48//njjjYe2V1fLtB1vh9O2CU7/Tp9SaJJe3/Tz85///DlvEjz66KOt1k/LK7vptUvLXdQEt3zt0idTpk6d2qqe9knPPPNMo56279SYN9XSf7f9FEGZv/mbv2k1/d///d9vrt1///2taodrZH/wgx8UXilOb6RFDfTRfFok2m8AdCW+Ewxk4V//9V9b/XvDhg2N78ylfMz082d/9metbpKUvhsX5dN++MMfrk2fPr3x3+k7ty996Utb1VeuXNlh8/6TP/mT2kUXXdT87/QdxfQdwCR9tzF9pzR95zF9Jzh9H7EpX7XtTW+KvoNcpu3zueuuu5qfS/r5l3/5l+fErzRp+73SdNOq9L3bJv/zf/7P2tChQ2tVpZv9pAiY9Pqk72kWSTfH+uu//uvaRz/60Va/T9/dTLX3vOc9jTuPp+97NmW1puVsedOtsvWXviPa9J3k9Bqk74e2lL4rnL6nnaTvpbatt9yG2krrqOV3tNPdetNzbin6Dm/bv9uxY0erdZief9Pr+Za3vKVVPa2fpu8tN42BJn/1V39V+8d//MfGjezSnbqTefPmNabXtI12pDT/k08+udVzST8//vGPW/3dO9/5ztopp5zS/O/0HetXvepVz/n+fpGWr136DnT67nNL73rXu5qjt9J2k75j3WTv3r2NfcDReNOb3tTqHgfp+aRIuORb3/pWq79Nd0dvez+E9B3mllp+z7jtd47b/i1AT+Tu0ECXlm5clG6i1GTFihXP+Zt0c5p0Q6Ply5fX7rvvvsNO5+mnn2717zvvvLN03ukx6cY7h9P27qypeWopneh21LzTCfvhpBP31PzeeOONtSPRdBJ9tNo+n7bZpIe7aU+6KVJqRtJr1FJTI9Ek3agoNe8LFiyoVZWakk996lONhjrdLCjdLCyt89Sst3xdki9+8Yu1T3ziE81vQqQGPjURBw4caPf6a/vc2jb3beNr2tbbLmtLc+bMaTTO0fTarusjfT2XLFnS+DmSx0yePLn23//7f2/c+Cq5+eabGz8tm/Pf+73fa7wB0/ImTB2laGy0fVOp7WuTnHnmmY2M8aL1crxe28MZN25c48ZaTW8opZuipWVNd4D+v//3/4Z3hU532f63f/u35n+nN4fSPrPJK17xisa4a4pXOpLM4BSP1PJGXADdjSvBQJd24YUXtvp3uiNz2zsIf/WrX23Edbz73e8+pvNuedWvrXQn3pZSg3e85p3uuns46a7DbRvgdLKe7micmuN05+GWjleOcrojb2fk8qYT+9Q4fPKTn2zctXfz5s2Nuym3Xc9Nb6ykK5zpzsgtG+B0V+d09+u0/tJP28Yz0vbu5elqcEvpKnN31XL7/PKXv9zY9tKV1HR39pbSFc/0xkJqmo70zZn2KBobbbf1tnefPlqd8dq2bW7TXaLTGw7r1q0rzQZuuU2nBjq9iZKuVKefdKW+Zb0pMxigJ9MEA11a+thhyxPWdFUjxRAdrZkzZ7b6d4ov+X/3RSj8efnLX35MnsOxnnfbE+4mKfu2pRQ59PDDDzeu1KY3CdIVu8iRNgYtn096TPrYa9nzSfE/SYqlaaltJEs6QX/qqadqVa1Zs6awuU9N8Z/+6Z8+p0Fpugr86KOPNq6ANXne857XuIr985//vLH+0uvWVSxduvQ5byyk5W+p7UeVj3T7TNtJ2ev52c9+ttVjUgOcGuG0LaSPTqfX9Qtf+ELzm0PpMccju7tobLR9jgsXLnzO36SxEj2ms6UIqnRlveV4b/umTlk2cFOTmz5q3/Kn7RuLPhIN9HSaYKBLS1cy/9t/+2+tfpcamZTlejRXF9PV0LZZw4f7uGM6Ify7v/u72jXXXNOOpe6cebc9kW151TI1h+n7kpHUJLZdnrLnk5qbdAV+27Zth20q0nNt+qhsUzZpSynvOb2x0SQt4+GmdaT+4R/+ofFdz9SApWzZw33/NV0RbnlFL32k93DrL32HtalBTlez03a3a9euWleQ1lH6GHeT9HWAT3/6063+pu26LpK+t9xyW0lNU8uPNDfZvn174yO46ep6k7Q+0pX2lm9mpO+hp+/AvvnNb258NL3lNthZ2r6p9LWvfa3Vd7pTQ5nyg1u+udPyI8NdQdoWW2b/prF3xx13hNnAKfv3cA1/GZnBQE/nO8FAl5c+brl48eLGdzqbGpL0fc90pXP+/PmNRiY1Nvfee2/hNF70ohfVLr/88uabBaXvPM6dO7d29tlnNz7CmU7mn3zyyebvDqaPbx4rx2vev/M7v9NYV02uvfbaxkdR+/fvX7v77rvDj1gn6WORLX3jG99oLFfTR7/TFfipU6c2bsyVak1NTbpRVnpu6fmk12LLli2N73E33fznYx/7WPM03/72tzduSNVUS69rmm/6jnVqult+/7uq1Ny8973vbfykG4Wl7xinZix97Pmhhx5q9bfppk9NVyvTx0jTFeumm0Ddc889tRNPPLGxfGm50hsXqTk6Xh8jL5Oa3rTu0xXf1LSkRrhJeh3Krvw3SVfGP/KRjzR+kvTmUvoIeHreaf2l8ZauiC9atOg535VOVxXTzcXST7oB1EknndSYd7qin8Zjy22u5Q2rjrfUuKfvCzd9jzVd8U/ba9ru0psfaVlbPrd0RbUzl7dIWq6//du/LbxS3PZ7vGmctpTGbtur+C33Fy2nna4GF/1t+tRBumlakbQfSl8lAOiyOvv21ABHIsWQ/I//8T9axZhEPyn78q/+6q9aTSPlYl5xxRVH9PgUeRTFFLWN7SmLF+rIeTdJsSttY3haZpr+5V/+ZWFMTpOUr1q0XAsXLmz+u/Tfc+fOPaLnk+bb0s0339zIKT3c36YonjPPPPOInu/hpNf8SKNcUp5tiv9p6W//9m8L/z5FPbWN5DmabaBtzE76+5bS69Gy3pT9fLiIpHPPPbeR43u45UwRRj/96U9bTbssJzhJ8WMpu7hsvaUx2GTz5s1HtK5T1vAjjzxSPxpVIpIiGzdubOQCly1rig/as2fPMXvtjmYMH4kzzjjjsMvdNtooRVK1zY9OOdhF2sZdtYxaOpqIpPY+P4DjwcehgW4hXclLHxVOVybTVeB0tTRdeUpXOdPHANN35dJNYdJHp1NES7qq2HRlq+VdUdP3O2+66abGxwbTFcL0MdB0JTBdDUuROOlKZfruZ9tIlfY6HvNOH5f85S9/WfvgBz/YuLN0+ne6GpOu2KQrXRdffHHpNNKdYf/wD/+wccW3bcxKS+mqabqq+vWvf70RD5VuSJReizTPdEfvFOGUrjql5UkRUC2lq+JpedJypavM6XHpCmK6Ypw+ltr2pkNHIz33NM90ZTJdzUzrOV3dTd8VTR/3Tuvlla98ZWM9/+IXv2h8dLel9FH09P3fdFU9/X167Hnnnde4opbuJN1VpG0nfWQ5fRoiffQ4jY+0HaU7/aar/lU+ypuuLD/44IONOzmnOyWnbTZtn2kdpKvCr33taxtj8Nlnn2111+N0d+K03tI6S9/5Tus0bTvpqmRad2kcpo/WtowJ6gxpedIN0tKnI9J6SjeFStteWndpu0j3H2j6/nf6fVf1tre97Tm/S/vCF7/4xa1+l/YjLb/jnm6GlT45UyRFdKVx3yR90iOtD4CeqFfqhDt7IQCAYumj8i1v1CSiBgCqcyUYAACAbGiCAQAAyIYmGAAAgGz4TjAAAADZcCUYAACAbGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGxogjliM2bMqH3+85/v7MUAjiHjGnoe4xp6HuP62NIEF1izZk3t2muvrc2ZM6c2YMCA2vjx42sXXXRR7ctf/nJt165dte7ieA6YRx99tPbqV7+6Mc9evXoZqHQ5xvXR+/rXv157/vOfXxs5cmTj57LLLqvdc889x2XecCSM66P3wx/+sDZ//vzaiBEjaoMHD64973nPq/2f//N/jsu84UgY1+3z3e9+t3Eu/opXvOK4z7u76NPZC9AVPfXUU42Blg4O1113Xe3000+v9e/fv7Zw4cLa1772tdrkyZNrV111VactX71erx08eLDWp0/XevnSTmnWrFm11772tbX3ve99nb040IpxXc1tt91We/3rX1+78MILGyci/+t//a/ai170osabXmmdQWcyrqsZNWpU7SMf+Uht3rx5tX79+tV++tOf1t72trfVxo0bV7viiis6e/HInHHdPsuWLau9//3vb7yBTaDOc1xxxRX1KVOm1Hfs2HHY+qFDh5r/e/PmzfW3v/3t9TFjxtSHDh1av/TSS+sLFixorn/sYx+rn3nmmfVvfetb9enTp9eHDRtWf93rXlfftm1b898cPHiwft1119VnzJhRHzBgQP2MM86o33DDDc31W2+9tZ5eqp/97Gf1s88+u963b9/G75588sn6VVddVR83blx98ODB9fnz59dvueWW5sddcskljce1/Gny61//un7xxRc35pee6zXXXNPq+a5du7b+8pe/vFFPy3X99dc3lv9zn/vcEa3Do/lbOB6M6/aP6+TAgQONdfLNb37ziB8DHcW4PjbjOjnrrLPqH/3oR4/qMdARjOvq4zodoy+88ML6P/zDP9Tf8pa31P/Lf/kvR7Xuc6IJbmPDhg31Xr161T/1qU8d0d9fdtll9SuvvLJ+77331hcvXlz/kz/5k/ro0aPrGzdubB58Q4YMqb/qVa+qL1y4sH777bfXJ0yYUP+zP/uz5mn81V/9VX3evHn1n//85/WlS5fWv/GNb9T79+9fv+2221oNvjQob7755sagS9NPg/wrX/lKY7pp3unglQbL8uXLG49Lf5MG1ic+8Yn66tWrGz9JenwarGkgpcfdcccdjYPfW9/61uZleslLXtLYadx11131++67rzGgBg4cqAmmWzKuj824TtKJQ1qen/zkJ0f8GOgIxvWxGdepofjFL35RHzRoUGOZoTMZ1+0b1//zf/7P+ite8YrGf2uCY5rgNu6+++7Ghv7DH/6w1e/TgEobbPr54Ac/2PwuTnpHac+ePa3+dvbs2fWvfvWrzYMvHVhavuP0gQ98oH7++ec3/js9NtXvvPPOVtNI72q9/vWvbzX4brzxxtLlP/XUU+tf/OIXw2Y0Tfud73xnq9+l53LCCSfUd+/eXV+0aFFjfvfcc09z/fHHH2/8ThNMd2RcH5txnfzRH/1RfdasWY1pQmcyrts3rrds2dJYR3369Gmc8P/jP/5j6TJDRzOuq4/rNI3JkyfX169f3/i3JjjWNT/M3gWlG8EcOnSo9sY3vrG2d+/exu8eeuih2o4dO2qjR49u9be7d++uLV26tNWX4ocOHdr874kTJ9bWrVvX+O8nn3yy8V3ayy+/vNU09u3bVzvrrLNa/S7dxKKlNO+Pf/zjtZtuuqm2evXq2oEDBxrzXrFiRfhc0nI//PDDtW9/+9vNv0tviKTn9/TTT9cWL17c+J7DOeec01xP3xtK382AnsS4Prpx/elPf7pxs430PeH0/WDoiozrIxvX6XkuWLCgsWy//OUva3/8x3/cuK/HC17wgtLHwvFmXMfjevv27bU3v/nNjZtZjhkzJpw//z9NcBvpLnTpbmqLFi1q9ft0YEgGDhzYauNPAymdELbVckPt27dvq1qaftrQm6aRpAHU9iYz6SYALaU7OLaUvvR+yy231D772c82ljst22te85rGwI2keb7rXe+qvec973lObdq0aY3BBz2Jcd3+cZ2WJzXBv/jFL2pnnHFGu6YFx4Jx3b5xfcIJJzSWJUl3h3788cdrn/rUpzTBdCrjutq4Tk1/uiHWlVde2fy7pueYGuq0PmfPnn3U0+3JNMFtpHeT0rtBX/rSl2rXXHPNczb4ls4+++zGLdzTxpXeZarilFNOaQyy9K7RJZdcclSPveOOO2pvfetba6985SubB1UaAC2luz6mO9i1Xe7HHnus+eDXVnq3Kb2bdf/999fOPffcxu/S4NmyZctRPjvoGozr9o3rv/7rv6598pOfrP37v//7c94Jh85iXB/b43U6YW66wgadxbiuNq7TY9Lds1v66Ec/2rhC/IUvfKE2derUo3puOZATfBh///d/39j40sne9773vca7o2nju/7662tPPPFErXfv3o2/S3mZF1xwQSOD6+abb25s+HfeeWcjduC+++47onmlj2ekd5JSpNA3v/nNxjs5DzzwQO2LX/xi49+RuXPnNrL+0seZ0kcr3vCGNzS/69Mk7RRuv/322sqVK2sbNmxo/O5DH/pQYzmvvvrqxmOXLFlS+9GPftT4d3LSSSfVXvziFzfepfrtb3/bGITveMc7Wr37djjpna80vfST/jvNM/13+qgJdDbjutq4TpFIf/7nf177p3/6p8Z80wlH+ml69xw6k3FdbVynK77pClaKoknr7G/+5m8aOcFvetObjmhdQEcyro9+XKevKJ122mmtftLV8PT80n+nZpw2Sr4znK1Vq1bVr7766vrMmTMbt0JPd5Y777zz6p/5zGfqO3fubP679EX7dFvzSZMmNf5u6tSp9Te+8Y31FStWtLo1e0vpS+3pi/It78z4+c9/vn7SSSc1pjF27NjG7eF/9atftfpCfroNfEtPP/1041bw6W5xab5f+tKXGrdjv/baa5v/Jt1VLt3NLt30ouXLnb5sf/nllzeeV7rJQPqbT37yk831dAe7l73sZY3HTZs2rfnW8tEX8tPytL0VfPpJywRdgXF99OM61Q83rtM6gK7AuD76cf2Rj3ykPmfOnMadbEeOHFm/4IIL6t/97ncrvgJw7BnXRz+u23JjrFiv9D9tG2MAAADoiXwcGgAAgGxoggEAAMiGJhgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGz0OdI/7NWrV8cuCfQg3SV+27iGI2dcQ89jXEOe49qVYAAAALKhCQYAACAbmmAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbmmAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbfTp7AQAAjrVevXpVqp1wwgmVHteeeZZN9+DBg5Vqhw4dCqcLkCtXggEAAMiGJhgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGyISAKoqGrMStljO+JxSb1er1Rrz3Tb87j2LBPdRzRW+vQpPk0ZMGBAON2hQ4dWqg0ZMqSwNnLkyHCegwcPrjR2d+7cGU53w4YNhbX169cX1jZv3lxY27t3bzjPAwcOFNaMTWj/MbkjGJtHzpVgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbmmAAAACyoQkGAAAgGyKSjvJW51G9aq0sSqWqsun27t270mMPHjxYqVZWj27rfujQoXC6bglPR43rqvEt/fv3D+c5bNiwwtrYsWMLa1OmTAmnO378+EqRMtu2bSusbdmyJZzn2rVrK8W3lE13165dhbX9+/dXinbh+Cs7FvXt27fSOBk3blw43WnTphXWZsyYUVibPXt2YW3OnDnhPKNlivYXUZRRsnjx4sLaggULCmsLFy4srC1fvjyc59atWwtr+/btq3y8ho5S9Vge7YPK9kPRfmb06NHhdFevXl1Ye+aZZwprO3bsqDz+ono9s3NpV4IBAADIhiYYAACAbGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBs9NiIpI66TfrgwYMLa0OGDCmsDR06tLA2YsSIylFGHRVLEc1z+/bthbUNGzZUjlzYuXNnpTiGskiUqrFMdC9VI8raE4M0cODAytEIp5xySmFt/vz5hbUzzjgjnO706dMLa/369as0rp966qlwng899FBh7YknniisLV26NJxuFK8U7UuM+eMvGmPtiR+JIr+iKKPk5JNPLqydeuqplR43derUcJ7RuI+Oq7t37w6nG803WkeDBg2qHKUSjaNo/IlIorNEx/Lo+BeNk2TSpEmVjtfR48pikKJzj5UrV1Y6lpedTx/M7NjpSjAAAADZ0AQDAACQDU0wAAAA2dAEAwAAkA1NMAAAANnQBAMAAJANTTAAAADZ6LY5wWW5n1EeX//+/Stn9s6ZM6ewNm/evMLazJkzC2sTJ04M5xnlD+/fv79SrSxbN8osXLt2bWFt0aJF4Tyffvrpwtrq1asLazt27Ainu2fPnkqZaNE6oOdoT05wtL+IMkFPP/30cJ6XXnppYe3MM8+stC8p24dFuYPRGIpyXMtyYKPM0LJxvW3btrBO1xGNobKc4Cijc+zYsZUze6P6hAkTKuV/7927N5znxo0bK42/6JwlGT58eKXn+bznPa+wtnnz5nCeW7ZsKazt2rWr8nG1J+aN0jX2NVXP/aPxlUyfPr2wdsopp1Q+vx88eHCl41+0Dp599tlwnlHGdz0Ym1GGcHflSjAAAADZ0AQDAACQDU0wAAAA2dAEAwAAkA1NMAAAANnQBAMAAJCNPt011iS6PXhZJEN0K/RTTz01nO7zn//8wtppp51WWJs8eXKl2ISy25lv3769ckxBtH6jddSvX7/K8RFR9NLOnTsrxz1F9eh5RjUxDl1P1deybH8RbdMjR46sFD9y8cUXh/OM9hdRfEtZ3Ey0v4jiiqLtvSy+ZcyYMYW1UaNGFdYGDBgQTjd63YzdriV6rcqOcVF0SbSNlG2X0fa+YcOGSsepsuNqFMkX7WeiKMSyY3I0FqJ9SVnc2pNPPllYW79+feXzgJ4YtcLxizTsiH1UFNNWdhyrevxL1qxZU2m/GO1LoseV7Y/3BfuvstelOx53XQkGAAAgG5pgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbmmAAAACy0aUjkiJlt+qObh8+duzYwtqZZ54ZTjeKRJkxY0alyIUnnnginOfy5csrxaGURalE0S/RLd+j269HMQ7J0KFDK023LOImujV7d7xtO8dWWZRKtF3OmTOnsHbhhRcW1s4+++xwntF+KIoQifYHybp16yrFkEVjtyy+JYpVqBphU7YejOuupT2vR7R/j7atTZs2hdN96qmnCmtr166tVbFt27awvmvXrkr7oXHjxoXTnT17dmFt2rRplfZtEydODOc5bNiwyrFX0BmiY0pUKztHiI6B0bl22X4x2r9Fy1QWgxSpGpVYpjvGFroSDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABkQxMMAABANjTBAAAAZEMTDAAAQDZ6bE5wlNsV5fFFmaDJ+PHjC2s7duworD3yyCOFtd/+9rfhPJctW1YpkzDKB0zmzp1bWBs4cGBhbcSIEZWzywYMGFApRyzKCy2ryxDOQ7RPKMu0HD16dGHtrLPOKqydccYZhbVJkyZVzgd8+umnC2uLFi0Kp7tixYrC2v79+wtrEyZMqJRRWpbLvnfv3sLagQMHwulGy1u2T6DrKMuDjo5jGzZsKKzt2bMnnG6UBRwdi6LtbuPGjZXHdZQ1Gj3PsjEWndO053gd5ZRG+9so97kjc0rpGdqzDUSPjbbZqGdIBg8eXGmctCcnuOrjomNu2f6tntn4cyUYAACAbGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsaIIBAADIRreNSCoT3e48ijkaM2ZMON3o1v9RHEMUa7J48eJwnitXrqwUE1L2XKZMmVLplu9RVENZ5EJ0i/rouZTdRj6KWsntlu+5isZmFPmVzJs3r7B24YUXFtamT59eOcYtGtcPPfRQYe2xxx4Lp7t+/fpKyxTFzUQxK2X7mmhfHO1nysZ1WewOx1f0ekSRHGXxgtE2sHXr1nC6UQxStO3t3LmzUpxT2TyjdTR8+PDK+7fouBsdr8vGUFQ3/uiKqp67lkWKjhw5stLxsewYFx0fo/1mtO+L9l+J8+X/5EowAAAA2dAEAwAAkA1NMAAAANnQBAMAAJANTTAAAADZ0AQDAACQjS4dkdSeW3VHkShRnEfZbdKjGJ9NmzZVikPZsGFDOM8ouiS6vXoU1ZCMGzeusDZx4sRK67Y9zyWqReu9bFvJ7ZbvPVkU8dOnT59K23Ny8cUXF9ZOO+20wtqQIUMKaytWrAjnuXDhwsLakiVLCmvr1q0LpxtFuET7iyjyJIpnKVsP0f62LD4iWibjumuJXo+yiKTosXv37q005su2r+iYEtXKxsKgQYMqRbSURRpG0Y5RvFK0vGXRg1Hd+KO7nQdUPU6VPXbYsGGVz12jY9z27dsr9Ru7d++uPM/cuBIMAABANjTBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABko093vQ16e2IKBg8eXHmZosiAbdu2VYr/KXsuUSRRdGv2qVOnhtON6iNGjKh0y/eyyIXolu9RFMaBAwfC6YpryEO0T4giDM4555zKEUmjR4+uFFPwxBNPhPNctGhRYW3NmjWV9iVlMUjRvi9af9H+tCyipSyuIRLta4z5riV6PdoTcRft+8uOC9H+Ippn1ZiVsmPn2LFjC2uTJk0KpxtFuPTv37/SMbcs0nDr1q2FNWOTzhKN6+h8OTr+RfFlZefpUbRqNIbKrF27trC2efPmyvtF/pMrwQAAAGRDEwwAAEA2NMEAAABkQxMMAABANjTBAAAAZEMTDAAAQDY0wQAAAGSjS+cER6Icv2TAgAEdkhMc5W9FuXlRduCsWbMqL8/IkSMLayeeeGLlnOAo92zjxo2FtS1btoTzjLLNovzTQ4cOhdOl5+f/lWXgRjmbz3/+88Ppjh8/vrC2c+fOSlm/CxYsCOe5YsWKSvMsy1zt3bt3pTzRaB81YcKEcJ5Vs5R37doVTnf//v1hne6hLDc2qpftE6pme0ZjIcq9njZtWjjPcePGVRpHZTnBw4YNq3R83LFjR2Ft/fr14Tyj/VB7jsnRa1o121k2cT6icR2d30d592U5wdExLhrX0fgryxFetWpVYW3fvn3hdDkyrgQDAACQDU0wAAAA2dAEAwAAkA1NMAAAANnQBAMAAJANTTAAAADZ6NIRSdGt8qMYkLIIpShmpSx6KaqPHTu2sHbqqadWjoCIbusexZpMnz49nG60vNHz3LBhQ2Ft27Zt4Ty3b99eKX6qPbfMF6vQfUSvYzJw4MDC2sknn1xYmzt3bjjdKG4gijK68847C2sLFy4M5xmNoyh+pCyaJIqHGzJkSGFt1KhRHRLfEo35KDLtSOKg6B7KjnHRuI+25yjKrywuJYpFmz17dqUIpLJxFMWszJw5M5zumDFjalVEMUdl4y+KKItes7Lzs+ixVfd9juUk0b4m2pdE59JlMUhV48vKYkWjeCWxoceGK8EAAABkQxMMAABANjTBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2um1EUlnkQnS7/CgOpUy/fv0KaxMnTqwUxxDFOJTduj2abv/+/cPpRjEGu3btqjTdsnUb1d3yPQ9RPEZZRFkUMXL22WdXii0piwq5//77C2v33HNPpWilsjEW7b/aE6k2ZcqUwtrIkSMrx7NEr9uaNWsqR6qJROk+ou2ybFxHUUfRcXXatGnhdKdOnVqpFkWClUWpRM8leuzw4cPD6UbRjlEM2aZNmyq/LlGk2u7duytPNxrX0TnC3r17w+mShyg6L4oEi2I4o2297LgabbPR8pSNz2i6jn/HhivBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABkQxMMAABANjTBAAAAZKNL5wRHyjKyomyuKL8zytEsq0c5icOGDaucYRrlDkaZq2WZelHuWVSLlqcs63f//v2VMtzKXm95ol1LNBaifOqyzOwZM2YU1ubMmVNpnsm6desKa4sWLSqsrVy5srC2devWDsnMbk+2Z5S5euKJJ1bORl29enVh7bHHHquUb5rIDu8ZYz46ZiSzZs0qrM2bN6+wdsYZZ4TTnTt3bqVtOjr+RXm9Zfuw6DygbB8VHTuj/Ve/fv0Ka+PHj6/8ukTTLcv/3rNnT6Vztygftmxf4Tyg54heyyi/eseOHZWPq9E8o+lGOcDJ5s2bK50Tc2y4EgwAAEA2NMEAAABkQxMMAABANjTBAAAAZEMTDAAAQDY0wQAAAGSjS0ckRbckL7t1eBRPsnTp0sLagAEDwulu3LixUvxBVCu7hXoUnRDFJpRFOUTRE5MmTeqQiKTo9vVV45PK5isaoWvFpfTv379yXFgU2RFFA0XbXbJmzZrC2tq1awtru3btqrQ9l8V9RNFnZXEzJ598cmHtwgsvrBSRVDauH3744UoRSVEcSmLsdh/RcWrkyJHhY0877bTC2llnnVXpcWXzjSJRqka8ldWrzrMsUi3aX0yePLnycxk+fHileKVof1p2HhU9NtqnRuuHnqVsrFQZJ2Wi8+no/DTa1pP169cX1kQEdjxXggEAAMiGJhgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGx024iksviRbdu2FdaWLVtWOYpn5cqVleJdoliTsiijKIZl+/bthbUJEyaE040iZaKIpH79+lW+pXu0fqPImLLpilLpWqIogkGDBlWK80imTp1aKe4j2h+UxZTt3bu3UuRJFAVVNo6iSLUzzzwznO5LXvKSwtoLXvCCwtrgwYMLa0uWLAnnedNNN1Xa35btx43rnjHmozidsniuqFZ2jIvGYBSpE22XZdtkNK4jZeceUT2a59ixYyvtZ8rOW6LHlkXdPfXUU4W1PXv2VI6bIQ9RRFJ0Ph2Nk7JxW/X8Yt26deF0y2IC6ViuBAMAAJANTTAAAADZ0AQDAACQDU0wAAAA2dAEAwAAkA1NMAAAANnothFJUZxOsnv37sLahg0bKj0uWb9+fWFt+PDhlWJhykS3X4/ik+bOnRtO98ILL6wUxxBFYQwYMKDyre2j11tUStcTvZbRNhLFlowZMyac5+jRoytte1EcStn4HDduXKX4pCjqIxkxYkRh7aSTTiqsXXHFFeF0zzrrrEqxaCtWrCis/ehHPwrn+Zvf/KbS/qtsP27c9wxR/FYycuTIwtrEiRMr7Q/K4lKi8RlFJJXF9VWdZ1lUyo4dOyodr6PlKYsoq3qsLzvfiY4BUexcdMyh5yh7naN6tO1F+5no2Fh2LFq9enVhbe3ateF0o31C9DyrnkvTmivBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABkQxMMAABANjTBAAAAZKNL5wRHyrL6oty8KI8vyv0sy+XdunVrpQyyskyvqjliZdmoVddv2bqPRBmA5CHKnoxqZZmXVfMBy8ZRNM9NmzZVelwyY8aMShnfU6dODacbrcNly5YV1n7yk58U1m644YZwnqtWraq0H5JnmIeyDNyqubxR3mxZPnE0Pnfv3l05/zs6R9i4cWNhbd26deF0o2WKxlG/fv0qTbMsm3jDhg2FtZUrV4bT3bx5c6VlKjs/Iw/RuI+O9dH+oOzcNNou169fX6mWHDx4sLAmJ7jjuRIMAABANjTBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABko8fm1VSN8Sm7tXgUPxJFEUQxIe2JHKp6q/hk9OjRleIj2hN5UvWW73Qv0XYQbT9RzFiybdu2SuMo2taTCRMmFNZOPvnkSs+lLL4lik3r3bt35ViTJ554orD2/e9/v7D24x//uLD2zDPPhPOMYmOi10WUQ88Rvc5r164NH/v4448X1saPH19pnJQdA6NtdsuWLZWigcr2YVFcStl0qx53o8eVxT1F0VXRvrhsPx5FRUWxc1H0pX1Jz1EWlRhFHQ0cOLCwNmDAgErbVtl2Ge0vosi09qwH58vHhivBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABkQxMMAABANnpsRFIkupV+2W32Dx48WCmKIIociqKVyupjxowprJ144omVY2Gi6ImdO3cW1rZv3x7Osz1xUHQt0ViJXucoMmDVqlWVo1QmTZpUeYxNnjy50hgri3KIRPuLdevWFdbuuOOOcLrf+973Cmt33313pQiIKCqlvftUeoZozEeROMkDDzxQKbrk6aefDqc7fPjwSvuh6Bi3efPmcJ7Rc42iVKJ5HkmESxXtGZvR+iuLhYnqO3bsOK7rgM4RRfyUHa/79KnWukTbT3Q8LosEi/YJZWMhGoPR+UV0jl52nu2Y/J9cCQYAACAbmmAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbcoKPotaenOAo06ssE23YsGGV8k2j3NSyLLEopzTKZoyyRhM5f3mItq3du3dXzgm+6667KmX1Pfnkk+F0TzrppOOap50sWrSosHb77bdXylRNVq9eXVjbu3dvpddMriBlomNjWX78Y489VmmfMHjw4Mp5otGxKKrt2bMnnGe0f4umWzbGolzVquugbJrR/i1a3rJc8ehcKVq/ZfmndB9RBm5UK9v2opzpDRs2FNZWrFhRef/27LPPdkj+d3t6FY6MK8EAAABkQxMMAABANjTBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2etWP8D7bVW/P392UPc/o1u1RFEEUgzRkyJBwnsOHD68UkXTyySeH0z3xxBMrLdNDDz1UWPv1r38dznPZsmWFtW3btlW6PX1XvF18V1uerjSuo3mWRSP07du3sNa/f//C2sCBA8PpDho0qNLYjSI7du3aFc4zinKIHlsWMyZGpOMY13Q3HbUttGe6XW0fZVx3recSnUuXHeujc9cobjSqlc0zikGKIguTLVu2VDrWizQsdyTrwZVgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbmmAAAACyoQkGAAAgGyKSjuHzjOJdevfuXfl28FFES3tiYaLopWg9RFFGGzduDOdZNfqlu93yvbssby7jGo4F4xp6HuO6e4nWQ3SuHT2uoyK/yiIN6TgikgAAAKAFTTAAAADZ0AQDAACQDU0wAAAA2dAEAwAAkA1NMAAAANkQkdSDtec1ix4bbTLdJWqgo3WX9WBcw5EzrqHnMa6h5xGRBAAAAC1oggEAAMiGJhgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGz06ewFoGtm33WX3DwAAICj4UowAAAA2dAEAwAAkA1NMAAAANnQBAMAAJANTTAAAADZ0AQDAACQjV51WTgAAABkwpVgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbmmAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbmmAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBuaYI7YjBkzap///Oc7ezGAY8i4hp7HuIaex7g+tjTBBdasWVO79tpra3PmzKkNGDCgNn78+NpFF11U+/KXv1zbtWtXrbs4ngPmn//5n2u9evVq9ZPWHXQVxnU1W7Zsqb373e+uTZw4sda/f//aiSeeWPvZz3523OYPEeP66L3gBS94zvE6/bzsZS87LvOHMsZ1NWleJ510Um3gwIG1qVOn1t73vvfV9uzZc9zm35306ewF6IqeeuqpxkAbMWJE7brrrqudfvrpjRO/hQsX1r72ta/VJk+eXLvqqqs6bfnq9Xrt4MGDtT59ut7LN2zYsNqiRYua/50OqtAVGNfV7Nu3r3b55ZfXxo0bV/v+97/fWE/Lly9vrEfobMZ1NT/84Q8bY7vJxo0ba2eeeWbtta99bacuFyTGdTXf+c53ah/+8Idr//RP/1S78MILa4sXL6699a1vbZyL/+///b87e/G6njrPccUVV9SnTJlS37Fjx2Hrhw4dav7vzZs319/+9rfXx4wZUx86dGj90ksvrS9YsKC5/rGPfax+5pln1r/1rW/Vp0+fXh82bFj9da97XX3btm3Nf3Pw4MH6ddddV58xY0Z9wIAB9TPOOKN+ww03NNdvvfXWenqpfvazn9XPPvvset++fRu/e/LJJ+tXXXVVfdy4cfXBgwfX58+fX7/llluaH3fJJZc0Htfyp8mvf/3r+sUXX9yYX3qu11xzTavnu3bt2vrLX/7yRj0t1/XXX99Y/s997nOF6+0b3/hGffjw4Ue9vuF4MK6rjesvf/nL9VmzZtX37dt31OscOppxXW1ct5X+Nq2TovUIx5NxXW1cv/vd767/3u/9Xqvf/fEf/3H9oosuOsI1nxdNcBsbNmyo9+rVq/6pT33qiP7+sssuq1955ZX1e++9t7548eL6n/zJn9RHjx5d37hxY/PgGzJkSP1Vr3pVfeHChfXbb7+9PmHChPqf/dmfNU/jr/7qr+rz5s2r//znP68vXbq00Uz279+/ftttt7UafGlQ3nzzzY1Bl6afBvlXvvKVxnTTvD/60Y82Bsvy5csbj0t/kwbWJz7xifrq1asbP0l6fBqsaSClx91xxx31s846q/7Wt761eZle8pKXNHYad911V/2+++6rX3jhhfWBAweWNsG9e/euT5s2rTHftGN45JFHKr4ScOwY19XHdXrMG9/4xvof/uEfNg70p556av2Tn/xk/cCBAxVfDTg2jOvq47qt0047rTHGobMZ19XH9be//e3Gxajf/va3jX+n55KeVzpm81ya4Dbuvvvuxob+wx/+sNXv04BKG2z6+eAHP9j8Lk56R2nPnj2t/nb27Nn1r371q82Db9CgQa3ecfrABz5QP//88xv/nR6b6nfeeWeraaR3tV7/+te3Gnw33nhj6fKnE9QvfvGLzf8+3LtGadrvfOc7W/0uPZcTTjihvnv37vqiRYsa87vnnnua648//njjd9HgS8/hm9/8Zv3BBx9s7DjSO1hp/TzzzDOlyw0dybiuPq5POumkxsnAH/zBHzQOxN/97nfro0aNqn/84x8vXW7oSMZ19XHdUjphTn/fdOIMncm4bt+4/sIXvtC4Ut2nT5/G3//3//7fS5c5V13rw+xd2D333FM7dOhQ7Y1vfGNt7969jd899NBDtR07dtRGjx7d6m93795dW7p0aasvxQ8dOrT53+nmMuvWrWv895NPPtn4gn/6zl1L6bs6Z511VqvfzZ8/v9W/07w//vGP12666aba6tWrawcOHGjMe8WKFeFzScv98MMP17797W83/y69IZKe39NPP934DkH6nsM555zTXJ83b17pdwAvuOCCxk+T9H2Ek08+ufbVr3619pd/+ZfhY6EzGNfl4zo9Pn0fOH0Pq3fv3o3Hr1y5svaZz3ym9rGPfSx8LHQG47p8XLf0j//4j43vXJ533nlH/Bg43ozr8nF92223Nb5D/fd///e1888/v/Hc0s3F0jn4n//5n4ePzZEmuI10F7r0BfKWN3dKZs2a1fj/dLe1lht/Gkhpo2ur5Ybat2/fVrU0/bShN00jSQMofdG/pXQTgJYGDx7c6t/vf//7a7fcckvts5/9bGO507K95jWvaXWzi8NJ83zXu95Ve8973vOc2rRp0xqD71hIzzvtQNIghM5kXFcf12ldpOeaGuAm6c2tdOfOtEz9+vWrNF1oL+O6/cfrnTt31r773e/WPvGJT7RrOnCsGNfVx3VqdN/85jfX3vGOdzT+nd7cSmP8ne98Z+0jH/lI7YQThAK1pAluI72blN4N+tKXvlS75pprnrPBt3T22Wc3TgTTuzXpXaYqTjnllMYgS+8aXXLJJUf12DvuuKNx17dXvvKVzYNq2bJlrf4mnaCmO9i1Xe7HHnusMWAPJ73blN7Nuv/++2vnnntu43dpZ5RiUo5Gmm+6k99LX/rSo3ocHGvGdfVxne7Qme44mU4Ymg6g6QCdTjw0wHQm47r9x+sbbrihcVXtTW9601E9H+goxnX1cZ2uaLdtdJvewE5XmmnNWwKHkT5GkDa+9LGH733ve7XHH3+8sfFdf/31tSeeeKJ5g7rssssaH/99xSteUbv55psbG/6dd97ZeLflvvvuO6J5pY9npHeSUo7XN7/5zcbHNx544IHaF7/4xca/I3Pnzm3EHCxYsKDx0Yo3vOENze9sNUk7hdtvv73x8cUNGzY0fvehD32osZxXX31147FLliyp/ehHP2r8O0n5Yi9+8Ysb71L99re/bQzC9K5Sy3ffDie9k5zWQ7q1fXoO6aCaolSa3pGCzmRcVxvXf/RHf1TbtGlT4yNVqflN75anj1ul3GDobMZ1tXHd8qPQaZ20/TgpdCbjutq4vvLKKxs5yunTHelj1ekqdbo6nH7f8tNc/D+d/aXkrmrVqlX1q6++uj5z5szGF8zTneXOO++8+mc+85n6zp07m/8ufdE+3dZ80qRJjb+bOnVq406qK1asaHVr9pbSl9rTF+Vb3ur985//fOMGNGkaY8eObdwe/le/+lWrL+Sn28C39PTTTzduBZ/uFpfm+6UvfalxO/Zrr722+W/SXeXS3ezSjW1avtzpy/aXX35543mlmwykv2l597h0B7uXvexljceluz033Vo++kL+e9/73sbf9uvXrz5+/Pj6S1/60voDDzxQ8RWAY8+4PvpxnaQbhqSbiKTHpbgkd4emKzGuq43rJ554ojGfdLdb6GqM66Mf1/v372/ctDLdGCzdpTot0//4H//jOcvN/69X+p+mhhgAAAB6Mh+HBgAAIBuaYAAAALKhCQYAACAbmmAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBt9jvQPe/Xq1bFLAj1Id4nfNq7hyBnX0PMY15DnuHYlGAAAgGxoggEAAMiGJhgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGxoggEAAMhGn85egO6mV69ex/xxJ5xQ/b2IaLr1ej18bFQ/dOhQ5ekCXW8f1B7GPDlpzxjrqPEZjUHjE+DouRIMAABANjTBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABko8dGJEWxQ3379i2s9e/fP5zukCFDCmvDhg0rrI0ePbqwNmnSpHCeY8aMKawNGDCgcmzC5s2bC2urVq0qrK1cubKwtmXLlnCeu3btKqzt27evsHbgwIFwugcPHiysiXuio0RxKGVRKVG9d+/ehbV+/fqF0432Naeddlphbdq0aZXGZvLQQw8V1h599NHC2p49e8LpGp+0J14wGkfReUB0nJ84cWI4z+nTpxfWhg8fXmlZk+3btxfW1q1bV+l4vXXr1g45XkfH48S4ho5Tdu5RNZa13o7Y1a465l0JBgAAIBuaYAAAALKhCQYAACAbmmAAAACyoQkGAAAgG5pgAAAAstGnp0YjRPEHgwYNKqyNGjUqnO6UKVMKazNnziysPe95zyusnXLKKeE8p06dWinSqSzWJIozWrZsWWFtwYIFlWrJ8uXLK0U27dy5M5xu9Fz3799fOcoBqu6HymIKokiUqBbFoiWzZs0qrL361a8urM2ePbvS/iDZtm1bYe2pp56qHJFEHqqOseg4X3asHzt2bGHtrLPOKqydd9554TyjY30UX1Z2ThMdx9avX19Ye/jhhwtrd999dzjPxx9/vLC2du3aysfrvXv3drsoFY6fsmNn1ce2J9KwqrLtuep8o/1FWdxa9NgTgloUN1p2Pt1Vz8NdCQYAACAbmmAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbPTYnuE+fPpVyNocPHx5Od8KECYW1008/vVLu4PTp08N5Dh06tLB24MCBSuugPTmlUVZYWe5nVI+yfqNcwbIMMrmDdFTuYHtE22U0z8GDB4fTPe200wprJ510UmFt5MiRlfK9yzK+o3FtbOahbAxFx/PoODZw4MBwuuPGjSusXXDBBYW1l7/85YW1c845p/I8o+cZHcPKjrszZ86sdH5Rdu5x++23V8oYLttfRM8l2l/QvcZ11Vzespzb/v37V8oOr1pL+vXrV6mnGDZsWDjdIUOGVF6mqufhu3btqpTxvW3btnC6W7ZsqdSrdCZXggEAAMiGJhgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGx06YikjorPiGIKoluvl0UoRbdJj+IPVq5cWfl25tu3b698S/LouVa9tf2IESMqxz1Fr0sUqZAcPHgwrJO3sn1JR8Qglc2zbJuuEmVUFsc2efLkSvuSRYsWhfN88sknC2u7d+8OHwvR+IuiSUaPHl15LLzsZS8rrJ177rmVjmFlESPRGCuLBorWURQVFUW0PO95zwvnGUW0ROc00TlL2T4hOm+pus+kY5QdN6N4s2hcl8UARmNw1KhRlc5Py/Yl0WOjWlnsajR2q54Tl0XIRuPzmWeeKawtXrw4nO4TTzxRObaps7gSDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABkQxMMAABANjTBAAAAZKNLRyR1VPxIdAv+sqidsluPF1m/fn1hbevWreFjly1bVljbtGlTpdvTl0UnVI2CKlt/vXv3rvS6lMU9RfPtqKgteo5oG4lq7YlWivYl0didPXt2ON1TTjmlUhxDFI1wzz33hPNcs2ZNYU18Ge2JUomOU/PmzQune8UVV1SKTxoyZEhhbdu2beE8ly5dWulYXhYlFq3DMWPGVIpFK4uFmTlzZmHtzDPPLKytWLEinO6GDRsqRamISDr+ou2u7Hw4GtdRDNL48ePD6U6bNq3SNjtnzpzK84yOne2J9YqOj1GMVLRfjKLNyvY1fYLXLOpjkkGDBh3z3qmjdc2lAgAAgA6gCQYAACAbmmAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBuaYAAAALIhJ/gopxtlXe3bt6+wtmrVqkrZgWX5nVHeV1lW2IgRIypNN8pL279/f4e8LmU5wVVzXqE92rNtRY+N8vbOPffccLpRLmi0j7rvvvsKa4888kg4z71794Z18laWERllz0+cOLGwdv7554fTPeeccypl5G7fvr2w9vDDD4fz/M1vflPpWL9r167K6yjKCZ41a1Zh7bTTTgvnOW7cuEqvy4wZM8LpLlmypFIOc9l5AMc3JzjKlE369+9fWBs6dGhhbcKECeF0o3zwM844o1KGcJnNmzdXys/duHFjON1omx41alSlLPMoQ7gsf7hv0DeUZR5H5/9dNePblWAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbPTYi6eDBg5UeW3bL9ygeKLq1+I4dOyota1lcSjTPsliK6LlGtzqPlrcswqDq61L2eotBorvp3bt3YW327NmFtd/7vd8LpxvFIzz99NOFtV/84heFtU2bNoXzNP6IolSibT0ZPnx4YW3u3LmFtQsuuKByXFh0LHrqqacKa7/85S/DeT766KOVolTK4gWj4/W6desqxT1FUSll9ShisSyKJop+WbNmTfhYus64LjvHjCKSouNUFM2VTJ8+vbA2adKkShGBy5cvD+f5+OOPV3rszp07K/cU0XlAFENWtr+Njtc7gl4lii8re2xXPUdwJRgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGxoggEAAMhGl45I6qhbake3D49u256MGTOmsDZ69OhKt4o/dOhQ5XlGym5fH63fvXv3Ftb27NlT+blE6z5a3q56e3WoKooJufTSSwtrp556ajjdKAbiiSeeKKw9/PDDlaYJZVEqZVE80THuvPPOK6ydeOKJlY83zz77bGHt9ttvL6zdd9994TyjuKLo2Nme9Rsdr6PjahQhVRbDMmHChEq1sn1fWUwl3Uf0Wg4ZMqSwNn78+HC6UT06V4yijH7zm99Ujj6LIgQHDBhQOe5p5MiRlcZY2f5248aNhbXVq1cX1jZs2BBOtz29QWdxJRgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGxoggEAAMhGj70XfRQnEMUVjR07Npzu1KlTC2sTJ04srG3btq3S8pTp27dv5YikAwcOFNZ27NhxzGvJrl27Kt1KPno9oSsqi/o4+eSTC2uvetWrKkcuRBEtv/jFLwpra9euLayJKKOjjrnJlClTCmtnnHFGYW3gwIHhdKPj7uOPP15Yu/feeytFK5XFhBw8eLDy8TqqR9Ndv359YW3NmjWVn8ugQYMqn0dFUZTROQ1dS9l5WTTuhw4dWlgbMWJEON1oG4nifxYsWFBYW7hwYeXjanR8jOLAkjlz5hTW5s+fXykiKRrzybJlyyrFSG0M1m1ZVFtXPYdwJRgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBs9Nic4ChTL8ouK8v0inIJo9zd/fv316qK8rWi6bYndzBaD+PGjatVNWTIkMLa4sWLK2cz7tu3r/IyQUcoy8q88sorC2unnnpq5X3JXXfdVVj7+c9/XlgzhmiP6HgS7ffLcoKjPMyy7Mko+zoaJ0uXLq2UdV+W2Xvo0KFaV8pyLcv9jPYJUQ76yJEjw+lGWefR8ka1rppD2pP17t27cp5vdC7dr1+/yvnVmzZtKqxt37690jaZjBkzptJzOe2008LpvuAFL6iUkR6dB5SdL0fn2itXrqyUu95Z+7f2ciUYAACAbGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsaIIBAADIRo+NSIpulx9FOezduzecbnT78KgW3bZ9y5YtlW8HH9WiKKiy27pPnDixsHb66adXelzZbejvv//+wtoTTzwRTnf37t2FNdEJdJQoAuLcc88NH/vSl7600nSffPLJcLo33XRTYW3dunXhY6Gq6Lg6ePDg8LFTp06tFK9UFlf02GOPVapFx+Syc4SqEUllx6mqx7HodYnOH8pikKLpRvuvsmidKAaJ4y96PcpiOKtuz2VxfVFUTzQ+o9jCsrinKAI1ihS96KKLwulG9REjRhTWlixZUqlWVt8YxKZF+7b2xJt1JleCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsaIIBAADIhiYYAACAbHTbiKSy221H9ehW51u3bg2n+9RTT1WKVVi9enWl+KRkx44dlW4l369fv3C6gwYNKqxNnz69sDZs2LDC2rRp08J5Tp48udJjo5iMstctiqWAMtG+JIpcuOqqq8LpTpkypVJ0ya9+9atwunfccUdhbf/+/eFjoaooTmfo0KHhY8eNG1dpumXHzuXLlxfWVq1aVVjbuXNn5YikqjFI7YlIiuJdosdF67bsWB+dX5TtZ6J15HjdtbQn2iY6196+fXthbc2aNeF0ozEYRX9W3Z7L4qDmzJlTWDvnnHPC6UbnENE6WrRoUWFtwYIF4TyfffbZSuceh3rg2HQlGAAAgGxoggEAAMiGJhgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGz06coZZFEtyuwqq0dZV1EuV5kodzDK5SrLJq6a2xVlB5blqUX5bqeffnph7eDBg+E8Bw8eXFgbM2ZMYW3AgAEdlmUHkb59+xbWnv/851eqleUSPvroo4W1m266KZzu2rVrK2eRQlXR8Sba75fld0bHuG3btoXTjXKEoyzgffv2VTo2dmSWZnSMi9b9oEGDCmtTp04N5zlq1KhKx/p169aF043qZeuX4ys6ZpRt69EYi86Xy6Yb5Y7379+/0jG3LCd4woQJlcZR9Liy8/uHHnqosPbLX/6ysLZw4cLK+8Vdu3ZVHpvRPqGrnnu4EgwAAEA2NMEAAABkQxMMAABANjTBAAAAZEMTDAAAQDY0wQAAAGSjS0ckRbf9L4v/iW53HkUGlE139+7dleKVolp0S/Jk//79tSrKbjPfp0+fSrdCrxprVRY9UbbuIyKSaI8oUm3WrFmFtVe/+tWFtUmTJoXz3LFjR2HttttuK6w9+OCD4XT37t0b1uF4j6HoWFN2rIqOf9Hx5Egi+6poT9RHe45T0TqMzmlmzJhRWDvnnHPCeY4cObLSudDSpUvD6a5evbqwZv/VtURjs2z8RWMleuyWLVvC6UaRmdE4iR43ceLEcJ7z5s2rdI5QFuf6yCOPFNZuuOGGwtpvfvObSvFTZT3H/mB/W7Y/7ah4uI7kSjAAAADZ0AQDAACQDU0wAAAA2dAEAwAAkA1NMAAAANnQBAMAAJCNTo9Iim4fHsUc9e/fP5zu0KFDC2vjxo0rrI0aNSqc7p49eyrdmr0z4n/K5hndLn7ChAmFtZkzZ1aKaiiLhdmwYUOl9d7e2Ap6vrIxNGzYsMLaZZddVlg788wzK0cjLFy4sLD2H//xH5XGSWIs0N1EsThRlEpZ9NLgwYMLayNGjCisbd26tXIMSNWYkPYcr6dPn15Ye8ELXlBYO/vssyvPc+XKlYW1hx9+OJzu+vXrK8Uz2rd1r4ikKFInemxZbGjVOLZozJ9++unhPKOxEvUNURxY8q//+q+VzgOeeeaZSvFlZa/LoXbEHHXH8elKMAAAANnQBAMAAJANTTAAAADZ0AQDAACQDU0wAAAA2dAEAwAAkI1uG5EURSAlY8aMKaxNnjy5UjRQWVTP9u3bC2vbtm0rrO3fvz+cZ3Qr+ShWIYp9SebMmVNYu/zyywtrZ5xxRqXXLNmyZUth7YknnqgUWVF2y3coi1KpOhZGjx5daVtPbrvttsLaY489Vnl/EcVBdccIA7qHaB+8c+fO8LHR8THaZsuOcXPnzq0UMRJZt25dWI/iSaLnUhYvOGvWrMLaC1/4wkoRSRMnTgznGb1uDz74YKVa2fE8ikiiaymL06l6vCmLNIz6huicODr3j8ZQcuqpp1aKeLvjjjvC6d56662VYsiiGKmyMeQ84D+5EgwAAEA2NMEAAABkQxMMAABANjTBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2Oj0nuGoW2IABA8LHjhgxorA2atSowtrUqVM7ZJkGDhxYKQusLH8xep7z5s0Lp3vRRRcV1p73vOdVWn8bNmwI53n33XcX1h5++OFK2YuJ3DOibMGyPNErrriiUj5nZMmSJWF94cKFlTLH27Meov1Xe8aQ8UeUX112XIgye6NM2UmTJoXTjTLto+UdP358YW3VqlXhPKNjVf/+/QtrM2fODKd71llnFdZOP/30Slnme/bsCecZ5f3+/Oc/r5zBHOWq2pf0HFVfy7LH9enTp1L2dXTOG42vsvP7Rx55pLD2s5/9LJzuU089VSkLOOoLjKEj50owAAAA2dAEAwAAkA1NMAAAANnQBAMAAJANTTAAAADZ0AQDAACQjS4dkdSeW4D37t27UkzB8OHDw+lGkQwnnnhipcihKAIi6devX6Uoh2h5knHjxlVaf+vWrSus3XbbbeE8b7jhhkpRUVGcReKW8HmI4n/69u1bOX7knHPOqbRP2LFjR2HtoYceCue5bNmySvu+aB20Jwbp0KFDlR4HyYEDBwprmzdvDh8bRYxEUX9RRGAybdq0wtrYsWMLa2effXZhbcuWLeE8o7EbxaxE0S5l9egcYf369YW1u+66K5znt771rcLaggULCmvbtm2rvK3Y1xCdf5aNhQsvvLCw9ru/+7uFtZEjR4bzjM5P/+M//qOwdv/994fTjcaKGKSO50owAAAA2dAEAwAAkA1NMAAAANnQBAMAAJANTTAAAADZ0AQDAACQjU6PSIpuAR7F4kTRJGWxQ1HET1RLJk+eXFibM2dOYe2ss84qrPXpE78MUT2KRoiiUpJdu3YV1hYvXlxYu+mmmwpr//Iv/xLOc+nSpYW13bt3V4pvgWTQoEGVYlaSUaNGVYrzWL58eaXYl2TDhg2FtX379lUeC6KO6GrH8rLInCVLlhTWbrnllkqxaMl5551XWJs6dWqlaKWyMRSth7Lol0h0fIzW3y9/+cvC2ve///1wntE+bOfOnZX2mYn9ENFYiOLLktNPP72w9ju/8zuFtSlTphTWtm/fHs7zgQceKKzdfvvtlXuKaKw47+14rgQDAACQDU0wAAAA2dAEAwAAkA1NMAAAANnQBAMAAJANTTAAAADZ0AQDAACQjU7PCY5ysKKszCgHuCyHbu/evZXzDKPMryi7bObMmYW1MWPGVM4JjrJ+V61aFU737rvvrpQt+MQTTxTWNm/eHM4zyn6WiUZ79OrVq3IuZbQ/efzxxwtrixYtKqw9+uij4Ty3bNlSaR8V5ZCWPVf5nHSUqsfc5Nlnn6302E2bNlXOpT/rrLMKazNmzCisjRgxIpxndBzbsWNHYW3t2rXhdB9++OHC2q233lpYW7BgQWFt/fr14TyjdS+PnDInnFB8nW3YsGGFtVmzZoXTjc61J02aVKmnWLFiRTjPO+64o7C2ePHiwtqePXsq5wQ7lnc8V4IBAADIhiYYAACAbGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBs9Kof4X22o/iRjhLNM7r1etK7d+/CWr9+/QprAwYMCKc7ZMiQSrXBgwfXqopihaKIpLIYqage3Uo+imhx2/butR46Y1x3lGifUDb+Jk+eXFibMGFCpfiDJUuWVI5IKotBonMY18d/eaNj+cCBA8PpRnFG48ePL6yNHTu20vlD2djduXNnpfjFsgilKHopOpaXbc/dZXtvr+7yPLvauC5bnmh8Tpw4sbD2O7/zO+F058+fX1ibM2dOpeN1WaThzTffXCkqcfv27ZXP77vLdtlVHcn6cyUYAACAbGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsaIIBAADIRpeOSOqKqq4HtzrPS3d5vY1rOHLGNfQ8xnU1ffr0CetDhw4trE2fPr2wdu6554bTnTt3bqVItWeeeaaw9tBDD4XzXLx4cWFt8+bNlWKZkkOHDoV1qhORBAAAAC1oggEAAMiGJhgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIaIJOgAIheg5zGuoecxrqs54YT4Olr//v0La6NHjy6sTZgwofJ8o0iibdu2Fda2bNkSznPnzp2FtQMHDnT7basnEpEEAAAALWiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsaIIBAADIhiYYAACAbMgJhg7QXbLhjGs4csY19DzG9fEXZf2WPc/o9ar6WnaXbYAjJycYAAAAWtAEAwAAkA1NMAAAANnQBAMAAJANTTAAAADZ0AQDAACQjSOOSAIAAIDuzpVgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbmmAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbmmAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBuaYI7YjBkzap///Oc7ezGAdjCOoecxrqHnMa47lia4wJo1a2rXXnttbc6cObUBAwbUxo8fX7voootqX/7yl2u7du2qdRfHcwDt37+/9olPfKI2e/bsxjo788wzaz//+c+Py7zhcIzjo/foo4/WXv3qVzfm2atXr8L5/t3f/V3jb9J6Pf/882v33HPPcVk+MK47ZlzffvvttSuvvLI2adKkxt/ceOONx2XZIDGuO2Zcf+pTn6qde+65taFDh9bGjRtXe8UrXlFbtGjRcVm+rq5PZy9AV/TUU081Bt6IESNq1113Xe3000+v9e/fv7Zw4cLa1772tdrkyZNrV111VactX71erx08eLDWp0/Xevk++tGP1q6//vra17/+9dq8efNq//7v/1575StfWbvzzjtrZ511VmcvHpkxjqtJJxuzZs2qvfa1r629733vO+zffO9736v98R//ce0rX/lKowFOB94rrriicWBNB1noKMZ1x43rnTt3Nt68/oM/+IPaq171quO+jOTLuO64cf2rX/2q9u53v7vRCB84cKD2Z3/2Z7UXvehFtccee6w2ePDgWtbqPMcVV1xRnzJlSn3Hjh2HrR86dKj5vzdv3lx/+9vfXh8zZkx96NCh9UsvvbS+YMGC5vrHPvax+plnnln/1re+VZ8+fXp92LBh9de97nX1bdu2Nf/NwYMH69ddd119xowZ9QEDBtTPOOOM+g033NBcv/XWW+vppfrZz35WP/vss+t9+/Zt/O7JJ5+sX3XVVfVx48bVBw8eXJ8/f379lltuaX7cJZdc0nhcy58mv/71r+sXX3xxY37puV5zzTWtnu/atWvrL3/5yxv1tFzXX399Y/k/97nPFa63iRMn1r/0pS+1+t2rXvWq+hvf+MYjXPNw7BjH1cZxS0V/e95559Xf/e53t3rukyZNqn/qU586oulCVcZ1x43rltLy/Ou//usRTQ/ay7g+PuM6WbduXWO5fvWrX9Vz5+PQbWzcuLF28803N941KXqHJH3koEl692XdunW1f/u3f6vdf//9tbPPPrv2whe+sLZp06bmv1m6dGnjY0U//elPGz/pXZlPf/rTrT6q8K1vfatxVSV9tCG9m/OmN72p8XctffjDH2487vHHH6+dccYZtR07dtRe+tKX1n75y1/WHnzwwdqLX/zixkeZVqxY0fj7H/7wh7UpU6Y0PqK8evXqxk/T8qS/TR+hePjhhxtXdX7zm9/Urr766uZ5vfWtb60988wztVtvvbX2/e9/v/b3f//3jecZ2bt3b+MjLC0NHDiwMW04nozj6uO4zL59+xrr6LLLLmv+3QknnND491133dWuaUPEuO64cQ2dxbg+vuN669atjf8fNWrUMZ92t9PZXXhXc/fddzfeIfnhD3/Y6vejR49uvOuTfj74wQ82v6uT3mHas2dPq7+dPXt2/atf/WrzO1KDBg1q9Q7UBz7wgfr555/f+O/02FS/8847W00jvcv1+te/vtU7UjfeeGPp8p966qn1L37xi+E7Q2na73znO1v9Lj2XE044ob579+76okWLGvO75557muuPP/5443fRu0xpeU855ZT64sWLG++y3XzzzfWBAwfW+/XrV7rccCwZx9XHcUuHm+/KlSsb02j7XNP6SFeIoaMY1x03rttyJZjjxbg+fuM6nZu/7GUvq1900UVHNM2ermt9uL0LSzd9OXToUO2Nb3xj44pn8tBDDzXeFRo9enSrv929e3fjXZ8m6Qvr6QvpTSZOnNj87s6TTz7Z+Ez/5Zdf/pyrLW2/Rzt//vxW/07z/vjHP1676aabGu82pc/6p3k3vSNVJC13eifq29/+dvPv0jEvPb+nn366tnjx4sb3Hs4555zmevqOb/quRuQLX/hC7Q//8A8bf5vetUs3yHrb295W+6d/+qfwcXC8GMfl4xi6G+PauKbnMa6P/bhOV9sfeeQRn9D8fzTBbaS70qUGru2d09IXz5s+3ttyMKSBddtttz1nOi033L59+7aqpemnDb9pGkkaUOmL/y2lmwK01PZjIu9///trt9xyS+2zn/1sY7nTsr3mNa9pDORImue73vWu2nve857n1KZNm9YYjFWMHTu28fGTPXv2ND7eku4wmT5K0rTu4HgxjquP4zJjxoyp9e7du7Z27dpWv0//njBhQofMExLjuuPGNXQW4/r4jOv00ev00fB0F/j0kW00wc+R3l1K7w596Utfql1zzTXhndPS9xDSLd3TuzfpXacqTjnllMagS+8iXXLJJUf12DvuuKPxHYJ0B+amQbZs2bJWf9OvX7/GHe3aLne6K1wawIeT3n1K726l71qku8klaee0ZcuWI1qu9L3gtGNJkUk/+MEPar//+79/VM8L2ss4bv84LpKWJb1bnb4TlaIWknRykf7d8vtNcKwZ1x03rqGzGNcdO67TFee0Xv/1X/+18ebBzJkz2z3NnsKNsQ4jfRk9bYzpYxDpy+vpC/FpY0zxP0888UTjKkiSbgRzwQUXNE4E05f600BIcUAf+chHavfdd98RzSt9XCO9s5S+lP/Nb36z8XGOBx54oPbFL36x8e/I3LlzG1/CX7BgQeOjFm94wxua3+lqknYS6V2flStX1jZs2ND43Yc+9KHGcqYT1vTYJUuW1H70ox81n8CedNJJjS/wp3etfvvb3zYG5Tve8Y5W78YdTvrbtDzpVve//vWvG9NIy/PBD37wiNYFHEvGcbVxnN7RTtNLP+m/0zzTf6ePkDVJ8UgpCi09t7Re/+iP/qgRr5K+/gAdybjuuHGdTuib/iZJH9NM/132UU9oL+O648Z1+gh0Wo/f+c53Gs89vYmQfnbv3n1E66tH6+wvJXdVq1atql999dX1mTNnNm6NPmTIkMZNXz7zmc/Ud+7c2fx36Yv36TbnKR4k/d3UqVMbkUArVqxodav2ltIX19MX2Fve+v3zn/98/aSTTmpMY+zYsY3bxTfdvrzpC/rptvAtPf30041bw6ebT6X5pniidHv2a6+9tvlv7rrrrsat3/v379/qVu3py/eXX35543mlmw6kv/nkJz/ZXF+9enXjy/PpcdOmTWu+1Xz0pfvbbrutfvLJJzcek25o8OY3v7lxEx3oLMbx0Y/jtDxtIx7ST1qmltKNQNI0043v0jpNNzeB48G47phx3fRc2v685S1vOerXCI6Wcd0x4/pw9fTzjW98o567Xul/OrsRBwAAgOPBx6EBAADIhiYYAACAbGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsaIIBAADIRp8j/cNevXp17JJAD9Jd4reNazhyxjX0PMY15DmuXQkGAAAgG5pgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbmmAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbmmAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbmmAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBt9OnsBALqyXr16VaqdcEL8HmP02Pao1+vHtdYeHTVd6IqiMW8sQM87D2jPcb7sHKKqqsf6Q4cOVZ5uV+VKMAAAANnQBAMAAJANTTAAAADZ0AQDAACQDU0wAAAA2dAEAwAAkI1uG5HUu3fvsN6vX7/C2oABAwprgwYNCqc7cODAwtrgwYMrLW+fPvHLcPDgwcLa3r17C2v79u0Lp7tr167C2u7duytN98CBA5WfS3T79fbcmr073rado9eeuKK+fftWqkX7gxEjRoTzHDt2bGFt3LhxlR5Xtn/bs2dPYW3t2rWFtfXr14fz3LBhQ2Ft27ZtlfZBZfua/fv3V9rPQNkxeejQoYW16dOnh9M999xzK51fLFy4MJzufffdV1jbsWNHYc3xj5xUPQ9oT08RnfsPGTIknG50fhGN3ejcv6yniM4R9gXH3C1btoTTjY71XfV47UowAAAA2dAEAwAAkA1NMAAAANnQBAMAAJANTTAAAADZ0AQDAACQjT5d+Xbm0S3J2xM/Mnny5MLatGnTwunOmDGj0jwnTpxYWBs+fHjlW6hHtyxfvXp1ON2nnnqqsLZ8+fJKtTVr1lS+hXrVWKayaKaoJj6ie+mo+INojEXxBxMmTCiszZ07N5zn/PnzC2vz5s2rNM+ySIYoVmHlypWFtUcffTSc54MPPlhYW7p0aeXope3btxfWdu7cWSlSzZinbH8xevTowtrFF18cTveFL3xhpeiSsu1yyZIllaLGxIXR01Q91kfjL+o3kmHDhlXaX5RFGkbn/9G5R9nyRqJz4lWrVhXWli1bFk432tdEMW6debx2JRgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsdHpOcJTbFWUBR7m7ycknn1xYO/300ys9Lpk1a1ZhbdKkSZUyxqKM0rLsrSgfcPPmzeF0TznllEqZhA899FBh7ZFHHgnn+eyzzxbWNm3aVDkrLMoZi7JlZYbmIcoVLNsPRbm7U6dOLaydccYZ4Tyj/VC0f4syCZOBAwdWGidRJmFZznKUPxzl+Ua54WU5wdHYNa4p2/dHou2nLJ8zygWNxtjkyZPD6UbjfuvWrYW13bt3V8oLhc4am2XjNjqeR8fyAQMGVDrOl42/aOxOmDAhnG7U5wwdOrSwNmrUqMrHv6g32LNnT2Ft/fr14XSj/VB0DhG9nh2dc+5KMAAAANnQBAMAAJANTTAAAADZ0AQDAACQDU0wAAAA2dAEAwAAkI0uHZEU3bK8LCJp9uzZhbV58+YV1mbOnBlON4o6iuI+ovifHTt2VI4fieKVonVbFpcS3Uo+ulX88OHDw3muW7eucoxNRCQK7YlciGJPogiDadOmFdbGjBlTeVxv3LixUoRB2biPYgqiKIKyiKQoyiFanrL4gyjCJdp/QXuOGVH0YNl2N2jQoErnD9F+Junfv3+l8dme/aLjKp2hPRFJ0ViIxlA0bsv6keiceMqUKeF0o31CdA4RPS46tyir9wmO12Xn6NF5VLR+o31q2f62vfsoV4IBAADIhiYYAACAbGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsdHpEUnQ786q1slus79u3r7C2Zs2acLrLli2rFP+zdu3aSnEoZTEiI0eOLKyNHTs2nG60DqOIiK1bt3bI7cqj51kWpRLdRl3MQx6i17ns1v4DBw4srI0fP76wNmHChMqRC9EYi/YJ+/fvrzwWoki1KKKlLKYg2qfu3r27ctxT9FxFJNFR+4toe45iu8rGWBTRUrY9R7Em7YlBgo7SGdtlNK6jeUbjtiwGMIoGjc7Rk1GjRhXWpk6dWun8Iuo3yvY1u4Pjddm5dBStGu03o1imsrin9nIlGAAAgGxoggEAAMiGJhgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGx0ek5wlB21c+fOwtq2bdvC6UaZvVEmaFm2Z5SRu2LFisLaM888U/m5RMsbZQGX5Q+X5aJVeV2ijLGyfLJoWyjLZpQTTHtE+XbRGIuydcuyiaN9SZTzFz2uLFM72r9FeYXt2S9u3769ck5wNO6Na8pE20h0zIjyqVetWlV5LAwbNqxSNvGRHAOLGCd0lo7a9qpONzom9+vXL3xsdHyMalGGcDJu3LhK043yc7ds2RLOc/Xq1YW1Z599trC2Y8eOyr3Brl27Ku/7OpIrwQAAAGRDEwwAAEA2NMEAAABkQxMMAABANjTBAAAAZEMTDAAAQDY6PSIpivOIbqm9YcOGyrcAj26FHkUYlMV9RNEI0fMcPHhwOM/oNukjR46sFK1UFrkQRZdEMUhRtERZPVpHZcRA5KHq69ynT7yri6KOZs6cWVgbP3585YikKBotigyI9otlYywa19E8o/VTtl+Mpls25kWf0Z2iG8uiS/r3719Y6927dzjd6LHRWKhag+4oGkdRLGhZlNG0adMKaxMmTCisjRkzpnJEUjQ+V65cWVh7+OGHw3nee++9lSKSdpdEoEbH+vacB3QkV4IBAADIhiYYAACAbGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsdOmIpOiW2lG8SLJp06bC2ujRoytHgUT1KJIoei5R9EEyduzYSstTFtESraOy9Vtk8+bNYb1qJIMoB8r06tWrclzY7NmzC2vz5s2rFI0QxQaVjc9BgwZVjhOIIpSi+JaqkWntGZ9RBFJ7pgsdpWybjMZ1tB+KolLKoh2jfULZGIOuNo6iY3nZGKsagTp16tRwnhMnTqzUU0QximXPJYqCfeihhwprd911VzjPJUuWVDp/OBCcI5Tta6rGHXb0OYArwQAAAGRDEwwAAEA2NMEAAABkQxMMAABANjTBAAAAZEMTDAAAQDY6PSIpuv11dDvu3bt3h9ONbvMdGTJkSFifMmVKpdukR7d8L4tIimKQouWN4lCSjRs3FtbWrl1b6TbzZfEtUYRSVNu5c2c43Wj9ilnpOaLXuU+fPpWijJLzzjuvsDZnzpxKy7N169Zwnr17964UkVQWfVY1qi1af1HsRNm4j6YrvoWuKDpmlJ17RMeqaMwPHTo0nO7gwYMLa/v37y+sGWP0NNExJTonjs7Ry84RovEZxZgOHz48nO6OHTsKa8uWLSus3X333YW1J598svI8DwR9V9n5fWdGHVXlSjAAAADZ0AQDAACQDU0wAAAA2dAEAwAAkA1NMAAAANnQBAMAAJANTTAAAADZ6NI5wVG+XZSLV5YTvGfPnkoZm8m4ceMKa+PHj6803bKc4Kge5aW1J1s3ykSLcovLMtGqLm/Zc4myzeg5om02GmOzZ88Opzt9+vRK+6EtW7YU1tatWxfOc/369ZX2b2XbepRFGo3PKEMxGvPJmjVrKu2/yrIDu2q2IPmKzh/KsrijjO8oB7hsP+T4R08SjZNk0KBBlc7DJ02aVOlxyYwZMwpr8+bNq3wMW7FiRWFtwYIFlbKAt2/fHs4z2l8cCvYzPfF47EowAAAA2dAEAwAAkA1NMAAAANnQBAMAAJANTTAAAADZ0AQDAACQjU6PSOqoSIDoFuFRNMmqVavC6UZRIdEyRbd0j2Jf2hMVFUU1JP369asU1zB27NjC2qhRo8J5RtONYpC2bt0aTnfv3r2Ftdxu+Z5rdEI0xqJos7JItaVLlxbWnnrqqcLakiVLwnlG8UrRuI5ixpJhw4ZVink4/fTTC2uTJ0/ukBg344/OEm17Bw8erByRFD02OuZG+6+yMRYd46C7iWL+ys4jo3P0KAZw4sSJ4TznzJlTWJswYUJhbfXq1eF0n3322UoxSNu2bau0D+rIc+J6NzyeuxIMAABANjTBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABko0tHJEXKIgGiGIM1a9YU1hYtWhROd/fu3YW14cOHF9b69u1bKZ4l2bx5c6XnGcUxJCNHjqwUiTJz5szC2oknnhjO84wzziisbdq0qXJ0VXS7+Ci6quxW8hxfZXFhUXRCNP6iSIVkw4YNlSIMFi5cWDkaIYpxi7bLsoikMWPGVIqPiCJYomiJsucC3U00/sriGaOYkCjibciQIeF0y47n0J1EY6EsIil6bFSLjmNR9GcycODASn1BdP5QFoMUxblG5/7dMaqos7gSDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABkQxMMAABANjTBAAAAZEMTDAAAQDa6bU5wmf3791fKo126dGnlPMwov3Pv3r2VcoCTLVu2VMosHDBgQDjdKJcwyhqdP39+YW3o0KHhPGfPnl1YO+usswprixcvrpy1tmPHjsKanODjn/cb1cryAaNtOsq9jnK6y/L4lixZUlh7+umnK+1nynL+ohz0srzQaD8U5QdGOctlGabRdKMMxbK8d+gM0XZZts1Gx5tI2T6qrA7HW3Qsb08WcHQMKxsLgwYNKqyNGDGi8vnyrl27CmurV68urD3yyCPhdFetWlXp3H/fvn2Vz2uj43W9Yq27ciUYAACAbGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsaIIBAADIRp/uGqUS3Xq97LHR7cOj26AnGzZsqFURxSZEt0Evi1eKblleto6i5xJFxkTznDFjRjjPyZMnF9amTJlSWDvllFPC6f7qV78qrK1ZsyZ8LMdeNP6ieIP+/fuH041ikMaNG1c5Vigagxs3biysbdu2rVI0UFmMW9X9YlkMRDQ+J0yYUDkWJoooi6KiRJTR3UTRZmXRJdExuT1xM1FNDBkdpSzSMDqeR+cBZVGbY8eOrXSOGZ0/lMUyRecB0Tnm8uXLw+lGj40iWTvj2NmrZB/VHSOUXAkGAAAgG5pgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbmmAAAACy0ekRSVVvx112a/aqMSwDBgwIpxvdRv3AgQO1jlA17qlseaLbmUfTXbt2baVa2XMZNGhQpfiWsliYsqgoqoley2icDBw4sNLrmEyfPr2wNnXq1MrjOoo1iWqR9kSeRPuvsrFw/vnnF9YuueSSwtqwYcMKa6tWrQrnec8991SKYiuLb+mOkQt0f9HYLdsmo206ikUrE523dMZ5CZTFCkXH88GDB1eKMkpmzZpV6RwhOscsG5tRfOq6desqR6Bu3ry58jJVVXZuktPxWHcAAABANjTBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABko0tHJEXKYm/69etXKQqkLKJlyJAhlaIIoniDstuOb9u2rVIcQxRzVHab9Gj9ReuoLL4lWn/R7fbLbhUfxdiUxbBwfCOSopiCSZMmhfOcOXNmYW3atGmVxl979gnR2CzbR0X1iRMnFtYuv/zycLpXXnllpfiI6LncfffdlSOSduzYUVgzNulpEUnRsSoaC2XH6yhaLjpe79mzJ5wudMRxPhk6dGhhbcaMGYW1yZMnh9M95ZRTKp0HRMf5snGyc+fOShFJUa0sQinaJ0SvS1kEUnTcrffAGKSIK8EAAABkQxMMAABANjTBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABko9NzgqNMqqq1pG/fvpWyasvyycaNG1cpMy3KAlu9enU4zw0bNhTWNm/eXFjbu3dvON0BAwZUyhO97LLLCmvnn39+OM/hw4cX1jZt2lRYW7JkSeV1JIv0+IvGQpRpOXjw4HC6Y8aMKayNHz++cv53VI8yhqdMmVIpV7AsU/vss8+uPMaifdTu3bsLa/fff39h7bvf/W44z6eeeqpSbmpumYR0f2V5vtEYi2pl0432qVGGsJxuOsugQYMqHTujHODk5JNPrjROouNNdI5eVl+5cmXl8/tofEbL254sc/6TK8EAAABkQxMMAABANjTBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2Oj0iKRLdvr/sFuDR7cOjOIFRo0aF0502bVqliJZoecpuzb5x48ZKsUK9e/cOpxvdov60004rrM2bN69SPEt7Ilp++9vfhtON1oMYiOMvGp9RFMiBAwfC6UbbdBS/NWPGjHC60T5h/vz5leINTjghfo8x2teMHDmyUrRZsm3btsLaHXfcUVj7+te/Xli79957w3mKYaEnKRu7Vcffnj17Ku/7omjHoUOHFtYcG+koZeeYUVxRFJ8URSGWxRZGkXzRufbatWsrx3CuWrWq0uOSffv2Hfeoo6rTrffA6CVXggEAAMiGJhgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGx06YikSFmcQBTDsn379spxRVHEwYgRIwprM2fOrBTtUhavVDX2pSxyIboFfbRuo1vFJ3fffXdh7cc//nFhbcGCBZUjWqLlpbrodvnRbf+jmKz169eH83z22WcLaytXrqwcuRDVR48eXSkCouq4LdvPPP300+Fjb7zxxsLa9773vcLa0qVLC2u7du3qsDg76AxVx2dZrFB0LIriDstimaJ61WN52XmUsUs0TsrGULR9Rce4rVu3htONYgL37t1b6fxi8+bNHXLuUfZcysZgR7wuUb2e2Zh3JRgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsdOmc4Civav/+/eFjo2yu9mRkRfON8smi2rx588J5Tpo0qbA2ePDgwlrfvn3D6e7cubOwtmzZssLaU089VVi7//77w3neeeedhbWHH364Ur5iWS4tXWt8Rjmaa9asqZzRuW3btsLaihUrwumedtpphbWpU6dWGn9leaKrV68urN11112FtV//+tfhdB9//PFK6yjKK8wtOxCqjutnnnmm0vExyiMvyzGNMleNa9qjd+/elfNoo/OyKLM3Oscse2w0TrZs2VKpVjbP6LwlOs8uW0fR2D148GDlcR3V65ntE1wJBgAAIBuaYAAAALKhCQYAACAbmmAAAACyoQkGAAAgG5pgAAAAstGrfoT3wy67FfrxVrY80W3do+ig/v37h9MdPnx4pYiDKOZo7Nix4TwnTJhQWBsyZEjl2KB169ZVqj377LOFtbVr14bzjKKOopiH6HbwXfGW793lNvOdMa5POOGESrWysduvX7/C2sCBA8PpDhgwoNJ0I7t37w7rUXRC9NiycV02VqjOuO45onXUp09xeuTQoUPD6Y4aNarS+UNZlEoUw7Jr165KcXXdZXvuaN1lPXTGuG7P+XJ0XI3OXaPHJYMGDaq0jqKo0ii6sWyMRbVonu2JOuou22xnOpJ15EowAAAA2dAEAwAAkA1NMAAAANnQBAMAAJANTTAAAADZ0AQDAACQjW4bkdRRy1v2uCjCJXpse9ZfR6376KU/dOjQMa/lpLvcvr67jWvoTMZ1HqrGJ5XFM0ZxKAcOHOgR21531F3WbVeLNCxbnmi9Ro8ti0qMROegHRU51F22n9zURSQBAADAf9IEAwAAkA1NMAAAANnQBAMAAJANTTAAAADZ0AQDAACQjR4bkQSdqbvcMt+4hiNnXNOedd9dtp/cdJfXxbiGIyciCQAAAFrQBAMAAJANTTAAAADZ0AQDAACQDU0wAAAA2dAEAwAAkA1NMAAAANno09kLAADQE3SXzFmA3LkSDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABkQxMMAABANjTBAAAAZKNX3f38AQAAyIQrwQAAAGRDEwwAAEA2NMEAAABkQxMMAABANjTBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABkQxMMAABANjTBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2NMEUmjFjRu3zn/98Zy8GcAwZ19DzGNfQ8xjXHUsT/P+sWbOmdu2119bmzJlTGzBgQG38+PG1iy66qPblL3+5tmvXrlp3cTwHzKOPPlp79atf3Zhnr169DjvftP7OOOOM2rBhwxo/F1xwQe3f/u3fjsvygXHdMeO6pU9/+tONv3vve997XJYPjOuOGdcf//jHG7WWP/PmzTsuywfGdccdr1euXFl705veVBs9enRt4MCBtdNPP71233331XLXp7MXoCt46qmnGgNtxIgRteuuu66xcfTv37+2cOHC2te+9rXa5MmTa1dddVWnLV+9Xq8dPHiw1qdP13q50k5p1qxZtde+9rW1973vfYf9mylTpjROkufOndt4Ht/85jdr/+W//Jfagw8+WDv11FOP+zKTD+O648Z1k3vvvbf21a9+tfFGFxwPxnXHjut0XP7FL37R/O+u9jzomYzrjhvXmzdvbqzbSy+9tHERauzYsbUlS5bURo4cedyXt8upU7/iiivqU6ZMqe/YseOw9UOHDjX/9+bNm+tvf/vb62PGjKkPHTq0fumll9YXLFjQXP/Yxz5WP/PMM+vf+ta36tOnT68PGzas/rrXva6+bdu25r85ePBg/brrrqvPmDGjPmDAgPoZZ5xRv+GGG5rrt956az29ND/72c/qZ599dr1v376N3z355JP1q666qj5u3Lj64MGD6/Pnz6/fcsstzY+75JJLGo9r+dPk17/+df3iiy9uzC8912uuuabV8127dm395S9/eaOeluv6669vLP/nPve5I1qHR/O3I0eOrP/DP/zDEf0tVGVcd+y43r59e33u3LmNZU3LeO211x7RNKE9jOuOG9dN6wOON+O648b1hz70ocZ8ea7sPw69cePG2s0331x797vfXRs8ePBh/yZ9xKBJerdl3bp1jXdT7r///trZZ59de+ELX1jbtGlT898sXbq0duONN9Z++tOfNn5+9atfNa6GNvnUpz5V+9a3vlX7yle+0vgoQ3r3Jn1MIf1dSx/+8Icbj3v88ccbV1p27NhRe+lLX1r75S9/2biS+uIXv7h25ZVX1lasWNH4+x/+8IeNK6+f+MQnaqtXr278NC1P+tv0kYmHH3649r3vfa/2m9/8pnb11Vc3z+utb31r7Zlnnqndeuutte9///u1v//7v288z2MpvYv23e9+t7Zz587Gx6KhoxjXHT+u07p92cteVrvsssuOyfSgjHHd8eM6XSGaNGlS4+rSG9/4xublhY5iXHfsuP7xj39cmz9/fmO9jRs3rnbWWWfVvv71r7d7uj1CPXN33313452aH/7wh61+P3r06Ma7POnngx/8YPO7OOkdpT179rT629mzZ9e/+tWvNr8DNWjQoFbvOH3gAx+on3/++Y3/To9N9TvvvLPVNNK7Wq9//etbvQN14403li7/qaeeWv/iF78YvhOUpv3Od76z1e/ScznhhBPqu3fvri9atKgxv3vuuae5/vjjjzd+dyyuGD388MON9di7d+/68OHD6zfddNMRTROqMq47dlz/3//7f+unnXZaYz6JK8EcD8Z1x47rdNXrX/7lX+oPPfRQ/ec//3n9ggsuqE+bNq3V+oFjzbju2HHdv3//xs+f/umf1h944IHGekpXm//5n/+5nruu9eH2LuSee+6pHTp0qPFO6N69exu/e+ihhxrvAqUvlre0e/fuxrs8TdIX1IcOHdr874kTJza/m/Pkk082PsN/+eWXt5rGvn37Gu/OtJTeuWkpzTvduOKmm25qvLt04MCBxrzL3qlNy53eefr2t7/d6vsN6fk9/fTTtcWLFze+53DOOec019PNMNJ3M46Fk046qbZgwYLa1q1bG+9uveUtb2m823bKKacck+nDkTKu2z+u0zvV6eYlt9xyS+PmJdDZjOtjc7x+yUte0vzf6arX+eefX5s+fXrtX/7lX2pvf/vb2z19OBrG9bEZ12ke6Xmk71on6Tk+8sgjjavg6Xw8Z9k3wekudOljFosWLWr1+/RRoCTdRa3lxp8G0m233fac6bTcUPv27duqlqafNsKmaSRpAKUv+reUbgLQUtuPhbz//e9vnHh+9rOfbSx3WrbXvOY1jYEbSfN817veVXvPe97znNq0adMag68j9evXr7G8SRrg6WY6X/jCFxo31IGOYFx33LhOHz9LJxPpI2gtv+pw++231770pS81TlZ69+7dIfMmb8Z1xx+v266nE088sdE0QEcxrjt2XKf11fai08knn1z7wQ9+UMtd9k1wejcpvRuUTt6uueaawu8jJOmkL93CPb1bk95lqiJtiGmQpXeNLrnkkqN67B133NH4zsArX/nK5kG1bNmy5zSc6YS07XI/9thjzY1oW+ndpvRuVjq5Pffccxu/SzujLVu21DpC2hE1vasHHcG47rhxnb57le7Y2dLb3va2xvw+9KEPaYDpMMb18T1ep2VOV9fe/OY3H/NpQxPjumPHdbozdNs3GFLTPX369Frusr8xVpK+fJ42vvRxgfRl9fQF+LTBXH/99bUnnnii+aQu3QAm3dDpFa94ReNL/GnDv/POO2sf+chHjjhvK308I72TlL6En+KC0gHmgQceqH3xi19s/DuSYobSl+7TR4vTRyve8IY3NL+z1STtFNIVmZQJtmHDhsbv0olpWs70Bfz02HTjix/96EfNX8hPH1dOX9hP71L99re/bQzCd7zjHa3efTuc9M5Xml76Sf+d5pn+u+W7xn/6p3/aWJ60rtKJc/p3egcvfbwFOpJx3THjOj3X0047rdVPOmlJJzLpv6EjGdcdd7xOzzV9ValpXaUT/bQ+X//61x/R+oKqjOuOG9fped59992Nj0On33/nO99pxE69+93vPqL11aN19peSu4pVq1bVr7766vrMmTMbt0IfMmRI/bzzzqt/5jOfqe/cubP579IX7dNtzSdNmtT4u6lTp9bf+MY31lesWFEYMZC+qJ6+sN7yVu+f//zn6yeddFJjGmPHjm3cHv5Xv/pVqy/kp9vAt/T00083bgU/cODAxny/9KUvPeeGNHfddVfjVu/pS/AtX970ZfvLL7+88bzSTQbS33zyk59srq9evbr+spe9rPG4dCOMplvLR1/IT8vT9lbw6SctU5M/+IM/aEynX79+jef5whe+sH7zzTcf9esDVRjXHTOu23JjLI4n47pjxnWKkZk4cWLjeD158uTGv1MkDBwPxnXHHa9/8pOfNG5mmaY9b968+te+9rWjem16ql7pfzq7EQcAAIDjwcehAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsaIIBAADIhiYYAACAbGiCAQAAyEafI/3DXr16deySQA/SXeK3jWs4csY19DzGNeQ5rl0JBgAAIBuaYAAAALKhCQYAACAbmmAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbmmAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBt9OnsBAACOtV69elWqnXDCCZVqHTXP5MCBA4W1gwcPFtYOHTpUWKvX6+E8AXoyV4IBAADIhiYYAACAbGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsiEjqBqpGLnTUdKNaWeRCVI9qUcwDdJb2RJ501Jivuk9oT1xK1ceWjeuq+wu6l6qRRP379w+nO2TIkMLa0KFDKz1u5MiR4TyHDx9eWOvXr19hbe/eveF0N27cWFhbt25dpcft3LkznOe+ffsKa47JUK495+gdxbHzP7kSDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABkQxMMAABANjTBAAAAZENEUheISymLUoliIKI4hrIohyiuYdeuXYW1HTt21Krq06dPpTiGbdu2hdPdvXt3pSgHt4rPQ3tihToqvmXw4MGFtdGjRxfWJk+eHE43qkf7iz179hTWtm/fHs5z7dq1hbXVq1dXim8pm2+0v9i/f384XY6vsmNcdFwYNGhQYW3MmDHhdKdOnVpYmzZtWmFtzpw5hbV58+aF85w0aVJhbcCAAZXjipYtW1ZYe/jhhwtrDz30UGFtyZIl4Tyj8Rkdcw8ePBhO13GX7hZpGI3daD8zc+bMcLrRWFm6dGlhbc2aNZXj1qJ51jMbm64EAwAAkA1NMAAAANnQBAMAAJANTTAAAADZ0AQDAACQDU0wAAAA2RCRdAxvsd67d+9Ktb59+4bzHDZsWKU4lCgC4kgilKrEJ0W1snp0y/coDqUsEiWq5XY7+FzHZllEUtUYpCg2IRq3yezZswtrZ599dmFt/vz54XRPPPHEwtqQIUMqRZ4888wz4TyjiJbHHnusckTLqlWrCmtbtmwprB04cKCwZsx3rWNje2KQpk+fXnksnH766ZVqM2bMCOc5fvz4Ssf6sliT6Fg/duzYSlFsZfvFRYsWFdbWrVtXKZYwMQbpTufoZcfOKFLt+c9/fjjdaJ9w//33F9buvvvuSsfNI4kwy4krwQAAAGRDEwwAAEA2NMEAAABkQxMMAABANjTBAAAAZEMTDAAAQDY0wQAAAGRDTvAxzBONcsb69etXKSOxLM83yg6MssuSUaNGVcr5i2qbN28O5xnll23cuLHSem9vRiw9X9k2ENWjsTtixIjC2ty5c8N5XnLJJYW18847r7B28sknVx7XVXNKhw8fHs4zmm6UCbpjx45wutH+JMoJ5viL9tF9+sSnGgMHDqy0PU+aNCmc7rRp0yo9NsoELcvY3LRpU6V1VLaPisZYlBMcZR5v27YtnGc0xqLH7tu3L5wudJRoHEXjLzrOJ0OHDq10rD/ttNPC6Ub7k+h8OeoLonPpZP/+/YW1AwcO1HLiSjAAAADZ0AQDAACQDU0wAAAA2dAEAwAAkA1NMAAAANnQBAMAAJCNLCOS2hOnU/WxUURE//79w3kOHjy4sDZ69OhK8Ull0UxR3FMUpVJ2e/WovmfPnkqxTO1Z95GyKAx6zriO4keieKAorujiiy8O5xnFIEXxZlFUQ9k42r59e4ds71FUVBTlEO3byl4X0WddS/R6RMeTstd5wIABlfft0TJVjdhatmxZWI+Oj5GyiJZhw4ZVOoeIxt/MmTPDeS5durSw9uyzzxbWdu7cGU7XsZX26Ih9f1kMZxSbNmHChMLauHHjKkeqReOkPTFkVc+V6kHcYXflSjAAAADZ0AQDAACQDU0wAAAA2dAEAwAAkA1NMAAAANnQBAMAAJCNLCOS2iO6RXjV24eX3Zo9ijKKIkbK4keiejTPKIJl9erV4Tx37NhRqbZ///5wumURHFXWvRiHnqNs+4jGwowZMwprF110UaVaMn369EqRMWvWrAmnG43BzZs3d8i+JBqf0X6xLPosilTriH0xnRNpEu2HoyiQbdu2hdNdvnx5YW3r1q2Vopei41RZPdqeowikZNq0aYW1E088sbA2ZsyYStEuZfFK7Ykvyy2GhWOr6jZSdryJRFFtUYxi9LiySLXdu3dX2i+WnS9H66Ge2fhzJRgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsZJkT3FE5WNF0o1y8KJOwLD9w6NChlWrJkCFDKmWbbd++vbC2fv36cJ5RxmmU29ie3LOqNbqeaBxFtbKc4FGjRhXWzjjjjMLaeeedV1ibPXt2OM9oeZ999tnC2uOPPx5Od/HixYW1nTt3FtbGjh1bWJs1a1Y4z4EDB1aaZ9m4jrIQo8xVjr/25EFH2fNbtmypPN3osWXZ10U2bNhQOfczOtZHWaNl+4upU6dWymDu169fOM9oeaPlieZZ9tjccko5tjpq+4n2F9H5ctn5fbSP2rx58zHPI0+c9/4nV4IBAADIhiYYAACAbGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsZBmR1B5VY1iiKIIoAqks/mDSpEmVIk/KYk22bdtWWHvmmWcKa4899lg4z2XLllWKUim77X1Ub090Bz1jbEbbenLiiScW1l7wghdUelxZ/Eg0jh588MHC2gMPPBBOd+XKlZXGQhR9VhbfMm7cuEoREWVRKlGEkrHbtUTbVllkx+7duytNN3pcWexe3759K0U27dq1K5xntL0PGjSosNa/f/8O2b9F0y0bQ9H4O3jwYPhY6E7KYhTHjBlTWBs/fnyl/UzZPmzjxo2V9kNl+1sxZP/JlWAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbIpKOUhRTEEUjDBgwoNLt1cviR6JaWURSFImybt26SvEtTzzxRDjPKHopilxwS3faM/4mTJgQTveSSy4prJ199tmVooNWrVoVznPBggWFtYULF1aKVkp27NhRaR1FYyxa78ngwYMLa6NHj64cSxFFuNgndC3R61EWpxNFEkUxPdHj2rO9RxEjZbFe0T4hWp5Ro0aF043iEKPHRmMsWrfJvn37CmvGJl1RdKyKalF8WdkYi45x0Zgvi3HbtGlTpYgk8WVHzpVgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbmmAAAACyoQkGAAAgGyKSjlIUNzBw4MDC2ogRIypHGQ0bNqxSvFK0PMn69esLa4sWLaoU7RLd0r0seiIicoGyiIMhQ4YU1ubPnx9O99JLL600xqJ4g7K4sCgGafny5YW1nTt3htONIhmiKKOhQ4dWWrdl+7f2RJ9FES72CT0nIil6bBTFE9XKIn6qjqGyKJVoHI0ZM6awNnHixHC6UcxbND6jdb958+ZwnlE9Gptlrwt0hijeLDrPLqtHEUllMWRRpOGGDRuO+b6N1lwJBgAAIBuaYAAAALKhCQYAACAbmmAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBtygo9Sv379CmujRo2qlCMW1ZLp06cX1k466aTC2q5du8LprlmzprD2wAMPVHpc1RzgRO4nUQ5w0rdv38LalClTKuUAJ5MnT640jhYvXlxYu//++8N5Pvnkk4W17du3Vx4nUZb5gAEDCmsjR46snGEa5Z9u27at0vMsy0K0v+g5qr6WZfuLKO+3f//+lXKvo7zesuN5dI4QHefL8sqj5xnliq9fvz6c55YtWzrkWB+9blGWa7Sd2B9Qtv1E5+9lOcHR+UV0fFy0aFE43VWrVhXWNm3aVFiTxX1suBIMAABANjTBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABkQ0TSUdxePRk4cGClW6yPHTu2sDZt2rRwnnPnzq003Si+JVmxYkWl2t69ezskpiCKTRB/kIco3icZPHhwYe30008vrJ188smVo3ieeeaZwtpvfvObSjFjybp16zpkez948GBhbfjw4ZX2JVE8RFmkzI4dOwprGzZsCKfbnhgWekb02aBBgyptz8mQIUMqbe9RXFEUJVa2TFEtijssi3GL4p42btxYKYIl2bNnT60jzqOi7SGKfon2bc4RKNv2onFSFn02Z86cSn3B5s2bK58HROclHBuuBAMAAJANTTAAAADZ0AQDAACQDU0wAAAA2dAEAwAAkA1NMAAAANkQkXSUUQ7RbfijmIeJEycW1qZOnVo5fmTXrl2FtZUrV4bTXbp0aWFty5YtlWIKoD0RBtEYSsaPH19YO++88wpro0aNCqcbxRjce++9hbW77767sLZ8+fJwnrt37660nynbRw0dOrSwNmDAgMLauHHjKtXKXtNoPxTtZ8riUkSi9IxokmTMmDGVIgTL4gUnTZpUKRIlWp4odqksxi3aD0XzLIuKimILyyJaIlH0S/Q823MeFcUyRfsDKNtGov1QWQxgFFEWjb8ooqwsJtC5dsdzJRgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGxoggEAAMiGJhgAAIBsZJkTHGXYleXbRVmI/fr1q5QPGOUAl3n22WcLa4sWLQofu2rVqsLazp07K2WXtSe7U+5nzxGNoz59inc7w4YNC6d74oknFtbmzp1badwma9asKaw98cQThbVnnnmmsLZt27Zwnvv27auUdViWpRzta6ZPn15YmzdvXuV9VLQeHn300cLa1q1bw+nKSew+evfuXVgbO3Zs+NhTTz21Ui3aZpOpU6dWyt2N8kSj/VdZfm40jsqylKPjYzSOon3xyJEjw3nOmDGjVkVZ/veuXbsqPc8DBw4U1uwrKNt+orE7ceLEcLpDhw6tNP7WrVsXTnfTpk2FNefEHc+VYAAAALKhCQYAACAbmmAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBs9NiIpikSJohzKYgqGDBlSKQZi9OjRhbWBAweG89y9e3dhbfXq1YW15cuXh9ONHrt3795K8S1l3PI9D1Esx4ABAypHqZx00kmVokmiSI6yuLAoPimabhTnUTaOon1UFIGUnHXWWYW1iy++uLA2Z86cyvEjDzzwQGFt4cKFlaLYEvuL7jOuo2PnpEmTwumeffbZlSKSZs6cGU637NhaRTQ2y2JYonizshi3qvuaMWPGFNZOOeWUcJ5R7OPw4cMLaytXrgynu2HDhkr72yhWbv/+/eE87UvyEI2jKOao7LgajetobEbn2cnmzZsLa7bZjudKMAAAANnQBAMAAJANTTAAAADZ0AQDAACQDU0wAAAA2dAEAwAAkI0+PTGqoezW/lGtLFIhijqaMGFCpfiIsviRLVu2VIoTiGIIkh07dlSKb4nWfdkt3ctet6rTpfvEFEQxY2WRJ1OmTKkUYRCNoWTTpk2V4sKifUkUBVW2jkaOHFlYu+CCC8LpvvSlL60UkRTtox5++OFwnjfeeGOlqDaxJj1HNP6mT58ePvbEE08srM2YMaNyrElkz549lbbLsm2y6jYb7WfKlrfqviSKbCp7TaP9RbSPL4uZis5LNm7cGE4Xom16xIgRHbIvic4fnnnmmXaNezqWK8EAAABkQxMMAABANjTBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2emxEUnSb9GHDhlW6hXpZXMPkyZMrRQaU3SI9qq9cubKwtn79+nC6O3furBTzUDXmqGy6dC/RdhBFdkTRQWPGjKkcURZNN4rdKBufUfRZFG9WFn02duzYwtrpp59eWHvRi14UTnfevHmV9otPPPFEYe273/1uOM+77rqrsLZ9+/bK68j+ovuM+ag2dOjQcLrDhw8vrI0fP77ydKPtZ/fu3YW1AwcO1KqKYtOi/VB0PC4bR9HyRmM+il0q056oyeixUSwTlO1rBg0aVOlYHu2DymLTVqxYUekcvewY2J44Uo6MK8EAAABkQxMMAABANjTBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABko8cGskVZc1FOcJQDXJbfOXLkyFoVUf5fsm/fvsLa6tWrC2tr164Np1s1IzDKLjt06FD4WNlmRBnC7RnXUT5gWZ5o7969K013y5YthbXBgweH85w1a1Zhbfbs2YW1cePGVd5fLFy4sFIW8I9+9KNwnlEmeZSvaH/Qc0Sv5datW8PH7t27t9IxJcoGT/r3718pqzbKEN61a1c4z+i5RuNk3bp1HXK8jtZB9DzLzk02bNhQORs1WkfR87Qvoew8IDoPj2pl20+U8f3ss88W1jZv3hxON5pv1Vx2Y+HIuRIMAABANjTBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABko9tGJEW3By+LYYliFcaOHRtON4paieYZxSqUxQpFMQVRrEIU35IcPHiwVkXV27Ynbt1OFIdSFicQbdPROCqLFRo/fnxhbd68eZXGUFl8SxS9FCmLm3nwwQcLa9dff31h7T/+4z8qx7dEsUzR62J/0HMcOHCgsPb000+Hj3300UcrRRpG8yyLKYsiTzZu3Fg5erBqRFI0z7J4oOi4G62jstil6LHRvrjs3GPTpk2V1l/Vcxa6l7LzyOjYGu0vhgwZUukYVhYJFm3v0flOe8+naT9XggEAAMiGJhgAAIBsaIIBAADIhiYYAACAbGiCAQAAyIYmGAAAgGz02IikKJYjijKKamVxRVGEQRQF0qdP/DJEsQpRXEMUy1S2jqretr3sdYnq4lK6l+j1iqIsomiSZ555pnKUShRzVDbGogil4cOHV95fRHbv3l1pPdx8883hdH/wgx8U1hYuXFhY27ZtW+VokmhbMK57jui1jKJAVq5cGU73jjvuqBQ/MnXq1MoxZNH2HsX0lMW4RfVoutH+oGwMdkaUSnR+Ee3jyx67c+fOwpqIpDz07t07rPft27fSMTmKBIu2u7Lz+yjyK3pc2T41GtfR8yyLXXVM/k+uBAMAAJANTTAAAADZ0AQDAACQDU0wAAAA2dAEAwAAkA1NMAAAANnQBAMAAJCNLp0TXDUjq6we5XZt3LixVtWBAwcq5ZqVZfxFuYNr1qyplIlWlrkX1aIMsrL8MflkeYi2kSiPb/ny5eF0o+1y3bp1hbUzzzwznO6cOXMKa2PGjKn0PKN807LM49tvv72w9thjj4XTjfZh+/btK6zJ+qVMtB1Ex78NGzaE033ggQcKa0uXLq2UA9yezNDouURjqD3TLRtj0XlCVIsyV8vOo6LHRvu+snUU1aO86bL8U3qG9uReRxnV0flytJ8p29dE5x7R9tye82nH62PDlWAAAACyoQkGAAAgG5pgAAAAsqEJBgAAIBuaYAAAALKhCQYAACAbvepHeC/t9tyyvKro9v3RrfuTPn2K058GDBhQWBs4cGA43SFDhhTW+vXrV2l5otiEssiFrVu3Fta2bdsWTjeKinL79fbpLuuoM8Z1e6LPoqix/v37Vx7X0T4hGrtVo6DK6lGsQtn+ortse91Rd1m3nTGue9Jz6S6vc2euv/ZMt6tFHXWX17snjev2PM/oXDs6R4/iDocOHRrOMzoPiCLgVq1aVfn8vquNk+7mSMa1K8EAAABkQxMMAABANjTBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2unREUlecZ9Vl6qhb8Isy6pq6y7rPJXIBjgXjGnoe4zoP7Vl/0WOdh3dNIpIAAACgBU0wAAAA2dAEAwAAkA1NMAAAANnQBAMAAJANTTAAAADZ6FPrwjrj1uJuZw4AAD1He87v9QY9kyvBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABkQxMMAABANjTBAAAAZEMTDAAAQDY0wQAAAGRDEwwAAEA2NMEAAABkQxMMAABANjTBAAAAZEMTDAAAQDZ61ev1emcvBAAAABwPrgQDAACQDU0wAAAA2dAEAwAAkA1NMAAAANnQBAMAAJANTTAAAADZ0AQDAACQDU0wAAAA2dAEAwAAUMvF/wd3al6mRMhrFQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x1000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✨ Generated samples saved to vae_logs_latent2_beta1.0/generated_samples.png\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x300 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🌊 Latent interpolation saved to vae_logs_latent2_beta1.0/latent_interpolation.png\n",
      "\n",
      "✅ VAE training and analysis complete!\n",
      "📁 All outputs saved to: vae_logs_latent2_beta1.0/\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "import logging\n",
    "from datetime import datetime\n",
    "import time\n",
    "\n",
    "# Set random seeds for reproducibility\n",
    "torch.manual_seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "# Check for MPS (Metal Performance Shaders) availability on M4 Mac\n",
    "if torch.backends.mps.is_available():\n",
    "    device = torch.device(\"mps\")\n",
    "    print(f\"🚀 Using MPS (Metal Performance Shaders) on M4 Mac\")\n",
    "elif torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda\")\n",
    "    print(f\"🚀 Using CUDA GPU\")\n",
    "else:\n",
    "    device = torch.device(\"cpu\")\n",
    "    print(f\"⚠️  Using CPU only\")\n",
    "\n",
    "class VAELogger:\n",
    "    \"\"\"Logger class to track VAE training progress and save comprehensive metrics\"\"\"\n",
    "    \n",
    "    def __init__(self, log_dir=\"pytorch_vae_logs\"):\n",
    "        self.log_dir = log_dir\n",
    "        os.makedirs(log_dir, exist_ok=True)\n",
    "        \n",
    "        # Setup logging for Jupyter notebook\n",
    "        if hasattr(self, 'logger'):\n",
    "            # Clear existing handlers to avoid duplicate logs in Jupyter\n",
    "            for handler in logging.root.handlers[:]:\n",
    "                logging.root.removeHandler(handler)\n",
    "        \n",
    "        logging.basicConfig(\n",
    "            level=logging.INFO,\n",
    "            format='%(asctime)s - %(levelname)s - %(message)s',\n",
    "            handlers=[\n",
    "                logging.FileHandler(f'{log_dir}/pytorch_vae_training.log'),\n",
    "                logging.StreamHandler()\n",
    "            ],\n",
    "            force=True  # Force reconfiguration for Jupyter\n",
    "        )\n",
    "        self.logger = logging.getLogger(__name__)\n",
    "        \n",
    "        self.metrics = {\n",
    "            'epoch': [],\n",
    "            'train_loss': [],\n",
    "            'train_recon_loss': [],\n",
    "            'train_kl_loss': [],\n",
    "            'val_loss': [],\n",
    "            'val_recon_loss': [],\n",
    "            'val_kl_loss': [],\n",
    "            'learning_rate': [],\n",
    "            'epoch_time': []\n",
    "        }\n",
    "        \n",
    "        self.logger.info(f\"PyTorch VAE Logger initialized - Device: {device}\")\n",
    "    \n",
    "    def log_epoch(self, epoch, metrics_dict):\n",
    "        \"\"\"Log comprehensive metrics for a single training epoch\"\"\"\n",
    "        for key, value in metrics_dict.items():\n",
    "            if key in self.metrics:\n",
    "                self.metrics[key].append(value)\n",
    "        \n",
    "        log_msg = (f\"Epoch {epoch:3d} | \"\n",
    "                  f\"Total Loss: {metrics_dict['train_loss']:.4f} | \"\n",
    "                  f\"Recon: {metrics_dict['train_recon_loss']:.4f} | \"\n",
    "                  f\"KL: {metrics_dict['train_kl_loss']:.4f} | \"\n",
    "                  f\"Val Loss: {metrics_dict['val_loss']:.4f} | \"\n",
    "                  f\"Time: {metrics_dict['epoch_time']:.2f}s\")\n",
    "        \n",
    "        print(log_msg)  # Use print for better Jupyter display\n",
    "        self.logger.info(log_msg)\n",
    "    \n",
    "    def save_metrics(self):\n",
    "        \"\"\"Save all training metrics to CSV file for analysis\"\"\"\n",
    "        df = pd.DataFrame(self.metrics)\n",
    "        csv_path = f'{self.log_dir}/training_metrics.csv'\n",
    "        df.to_csv(csv_path, index=False)\n",
    "        print(f\"📊 Training metrics saved to {csv_path}\")\n",
    "        return df  # Return dataframe for immediate analysis in Jupyter\n",
    "    \n",
    "    def plot_comprehensive_training_curves(self):\n",
    "        \"\"\"Create comprehensive training visualization with multiple subplots\"\"\"\n",
    "        plt.style.use('default')  # Reset style for Jupyter\n",
    "        fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
    "        \n",
    "        # Plot 1: Total Loss\n",
    "        axes[0, 0].plot(self.metrics['epoch'], self.metrics['train_loss'], 'b-', label='Training', linewidth=2)\n",
    "        axes[0, 0].plot(self.metrics['epoch'], self.metrics['val_loss'], 'r-', label='Validation', linewidth=2)\n",
    "        axes[0, 0].set_title('Total Loss Comparison', fontsize=14, fontweight='bold')\n",
    "        axes[0, 0].set_xlabel('Epoch')\n",
    "        axes[0, 0].set_ylabel('Loss')\n",
    "        axes[0, 0].legend()\n",
    "        axes[0, 0].grid(True, alpha=0.3)\n",
    "        \n",
    "        # Plot 2: Reconstruction Loss\n",
    "        axes[0, 1].plot(self.metrics['epoch'], self.metrics['train_recon_loss'], 'b-', label='Training', linewidth=2)\n",
    "        axes[0, 1].plot(self.metrics['epoch'], self.metrics['val_recon_loss'], 'r-', label='Validation', linewidth=2)\n",
    "        axes[0, 1].set_title('Reconstruction Loss', fontsize=14, fontweight='bold')\n",
    "        axes[0, 1].set_xlabel('Epoch')\n",
    "        axes[0, 1].set_ylabel('Reconstruction Loss')\n",
    "        axes[0, 1].legend()\n",
    "        axes[0, 1].grid(True, alpha=0.3)\n",
    "        \n",
    "        # Plot 3: KL Divergence Loss\n",
    "        axes[0, 2].plot(self.metrics['epoch'], self.metrics['train_kl_loss'], 'b-', label='Training', linewidth=2)\n",
    "        axes[0, 2].plot(self.metrics['epoch'], self.metrics['val_kl_loss'], 'r-', label='Validation', linewidth=2)\n",
    "        axes[0, 2].set_title('KL Divergence Loss', fontsize=14, fontweight='bold')\n",
    "        axes[0, 2].set_xlabel('Epoch')\n",
    "        axes[0, 2].set_ylabel('KL Loss')\n",
    "        axes[0, 2].legend()\n",
    "        axes[0, 2].grid(True, alpha=0.3)\n",
    "        \n",
    "        # Plot 4: Learning Rate Schedule\n",
    "        axes[1, 0].plot(self.metrics['epoch'], self.metrics['learning_rate'], 'g-', linewidth=2)\n",
    "        axes[1, 0].set_title('Learning Rate Schedule', fontsize=14, fontweight='bold')\n",
    "        axes[1, 0].set_xlabel('Epoch')\n",
    "        axes[1, 0].set_ylabel('Learning Rate')\n",
    "        axes[1, 0].set_yscale('log')\n",
    "        axes[1, 0].grid(True, alpha=0.3)\n",
    "        \n",
    "        # Plot 5: Training Time per Epoch\n",
    "        axes[1, 1].plot(self.metrics['epoch'], self.metrics['epoch_time'], 'purple', linewidth=2)\n",
    "        axes[1, 1].set_title('Training Time per Epoch', fontsize=14, fontweight='bold')\n",
    "        axes[1, 1].set_xlabel('Epoch')\n",
    "        axes[1, 1].set_ylabel('Time (seconds)')\n",
    "        axes[1, 1].grid(True, alpha=0.3)\n",
    "        \n",
    "        # Plot 6: Loss Ratio Analysis\n",
    "        train_ratio = np.array(self.metrics['train_recon_loss']) / (np.array(self.metrics['train_kl_loss']) + 1e-8)\n",
    "        val_ratio = np.array(self.metrics['val_recon_loss']) / (np.array(self.metrics['val_kl_loss']) + 1e-8)\n",
    "        axes[1, 2].plot(self.metrics['epoch'], train_ratio, 'b-', label='Training Ratio', linewidth=2)\n",
    "        axes[1, 2].plot(self.metrics['epoch'], val_ratio, 'r-', label='Validation Ratio', linewidth=2)\n",
    "        axes[1, 2].set_title('Reconstruction/KL Loss Ratio', fontsize=14, fontweight='bold')\n",
    "        axes[1, 2].set_xlabel('Epoch')\n",
    "        axes[1, 2].set_ylabel('Ratio')\n",
    "        axes[1, 2].legend()\n",
    "        axes[1, 2].grid(True, alpha=0.3)\n",
    "        \n",
    "        plt.tight_layout()\n",
    "        plot_path = f'{self.log_dir}/comprehensive_training_curves.png'\n",
    "        plt.savefig(plot_path, dpi=300, bbox_inches='tight')\n",
    "        plt.show()\n",
    "        \n",
    "        print(f\"📈 Comprehensive training curves saved to {plot_path}\")\n",
    "\n",
    "class VAE(nn.Module):\n",
    "    \"\"\"Variational Autoencoder implementation with PyTorch\"\"\"\n",
    "    \n",
    "    def __init__(self, input_dim=784, latent_dim=2, hidden_dim=256, beta=1.0):\n",
    "        super(VAE, self).__init__()\n",
    "        \n",
    "        self.input_dim = input_dim\n",
    "        self.latent_dim = latent_dim\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.beta = beta\n",
    "        \n",
    "        # Encoder\n",
    "        self.encoder = nn.Sequential(\n",
    "            nn.Linear(input_dim, hidden_dim),\n",
    "            nn.BatchNorm1d(hidden_dim),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(0.2),\n",
    "            nn.Linear(hidden_dim, hidden_dim // 2),\n",
    "            nn.BatchNorm1d(hidden_dim // 2),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(0.2)\n",
    "        )\n",
    "        \n",
    "        self.fc_mu = nn.Linear(hidden_dim // 2, latent_dim)\n",
    "        self.fc_logvar = nn.Linear(hidden_dim // 2, latent_dim)\n",
    "        \n",
    "        # Decoder\n",
    "        self.decoder = nn.Sequential(\n",
    "            nn.Linear(latent_dim, hidden_dim // 2),\n",
    "            nn.BatchNorm1d(hidden_dim // 2),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(0.2),\n",
    "            nn.Linear(hidden_dim // 2, hidden_dim),\n",
    "            nn.BatchNorm1d(hidden_dim),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(0.2),\n",
    "            nn.Linear(hidden_dim, input_dim),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "    \n",
    "    def encode(self, x):\n",
    "        h = self.encoder(x)\n",
    "        mu = self.fc_mu(h)\n",
    "        logvar = self.fc_logvar(h)\n",
    "        return mu, logvar\n",
    "    \n",
    "    def reparameterize(self, mu, logvar):\n",
    "        std = torch.exp(0.5 * logvar)\n",
    "        eps = torch.randn_like(std)\n",
    "        z = mu + eps * std\n",
    "        return z\n",
    "    \n",
    "    def decode(self, z):\n",
    "        return self.decoder(z)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        mu, logvar = self.encode(x)\n",
    "        z = self.reparameterize(mu, logvar)\n",
    "        recon_x = self.decode(z)\n",
    "        return recon_x, mu, logvar\n",
    "    \n",
    "    def loss_function(self, recon_x, x, mu, logvar):\n",
    "        recon_loss = F.binary_cross_entropy(recon_x, x, reduction='sum')\n",
    "        kl_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n",
    "        total_loss = recon_loss + self.beta * kl_loss\n",
    "        return total_loss, recon_loss, kl_loss\n",
    "\n",
    "class VAETrainer:\n",
    "    \"\"\"Comprehensive VAE training class with logging and visualization\"\"\"\n",
    "    \n",
    "    def __init__(self, model, device, logger):\n",
    "        self.model = model\n",
    "        self.device = device\n",
    "        self.logger = logger\n",
    "        self.best_val_loss = float('inf')\n",
    "        \n",
    "    def train_epoch(self, train_loader, optimizer, epoch):\n",
    "        self.model.train()\n",
    "        \n",
    "        train_loss = 0\n",
    "        train_recon_loss = 0\n",
    "        train_kl_loss = 0\n",
    "        \n",
    "        epoch_start_time = time.time()\n",
    "        \n",
    "        for batch_idx, (data, _) in enumerate(train_loader):\n",
    "            data = data.to(self.device)\n",
    "            data = data.view(-1, 784)\n",
    "            \n",
    "            optimizer.zero_grad()\n",
    "            \n",
    "            recon_batch, mu, logvar = self.model(data)\n",
    "            total_loss, recon_loss, kl_loss = self.model.loss_function(recon_batch, data, mu, logvar)\n",
    "            \n",
    "            total_loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            train_loss += total_loss.item()\n",
    "            train_recon_loss += recon_loss.item()\n",
    "            train_kl_loss += kl_loss.item()\n",
    "            \n",
    "            if batch_idx % 200 == 0:  # Less frequent printing for Jupyter\n",
    "                batch_total = len(train_loader.dataset)\n",
    "                batch_processed = batch_idx * len(data)\n",
    "                percent_complete = 100. * batch_idx / len(train_loader)\n",
    "                \n",
    "                print(f'Epoch {epoch} [{batch_processed:5d}/{batch_total} '\n",
    "                      f'({percent_complete:3.0f}%)] Loss: {total_loss.item():.6f}')\n",
    "        \n",
    "        epoch_time = time.time() - epoch_start_time\n",
    "        \n",
    "        avg_train_loss = train_loss / len(train_loader.dataset)\n",
    "        avg_recon_loss = train_recon_loss / len(train_loader.dataset)\n",
    "        avg_kl_loss = train_kl_loss / len(train_loader.dataset)\n",
    "        \n",
    "        return avg_train_loss, avg_recon_loss, avg_kl_loss, epoch_time\n",
    "    \n",
    "    def validate_epoch(self, val_loader):\n",
    "        self.model.eval()\n",
    "        \n",
    "        val_loss = 0\n",
    "        val_recon_loss = 0\n",
    "        val_kl_loss = 0\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            for data, _ in val_loader:\n",
    "                data = data.to(self.device)\n",
    "                data = data.view(-1, 784)\n",
    "                \n",
    "                recon_batch, mu, logvar = self.model(data)\n",
    "                total_loss, recon_loss, kl_loss = self.model.loss_function(recon_batch, data, mu, logvar)\n",
    "                \n",
    "                val_loss += total_loss.item()\n",
    "                val_recon_loss += recon_loss.item()\n",
    "                val_kl_loss += kl_loss.item()\n",
    "        \n",
    "        avg_val_loss = val_loss / len(val_loader.dataset)\n",
    "        avg_val_recon_loss = val_recon_loss / len(val_loader.dataset)\n",
    "        avg_val_kl_loss = val_kl_loss / len(val_loader.dataset)\n",
    "        \n",
    "        return avg_val_loss, avg_val_recon_loss, avg_val_kl_loss\n",
    "    \n",
    "    def train(self, train_loader, val_loader, epochs=50, learning_rate=0.001):\n",
    "        optimizer = optim.Adam(self.model.parameters(), lr=learning_rate)\n",
    "        \n",
    "        # Fixed: Remove verbose parameter for newer PyTorch versions\n",
    "        scheduler = optim.lr_scheduler.ReduceLROnPlateau(\n",
    "            optimizer, mode='min', factor=0.5, patience=5\n",
    "        )\n",
    "        \n",
    "        print(f\"🚀 Starting training for {epochs} epochs\")\n",
    "        print(f\"📊 Model has {sum(p.numel() for p in self.model.parameters())} parameters\")\n",
    "        \n",
    "        for epoch in range(1, epochs + 1):\n",
    "            train_loss, train_recon, train_kl, epoch_time = self.train_epoch(train_loader, optimizer, epoch)\n",
    "            val_loss, val_recon, val_kl = self.validate_epoch(val_loader)\n",
    "            \n",
    "            # Check if LR was reduced\n",
    "            old_lr = optimizer.param_groups[0]['lr']\n",
    "            scheduler.step(val_loss)\n",
    "            new_lr = optimizer.param_groups[0]['lr']\n",
    "            \n",
    "            if new_lr < old_lr:\n",
    "                print(f\"📉 Learning rate reduced to {new_lr:.2e}\")\n",
    "            \n",
    "            metrics = {\n",
    "                'epoch': epoch,\n",
    "                'train_loss': train_loss,\n",
    "                'train_recon_loss': train_recon,\n",
    "                'train_kl_loss': train_kl,\n",
    "                'val_loss': val_loss,\n",
    "                'val_recon_loss': val_recon,\n",
    "                'val_kl_loss': val_kl,\n",
    "                'learning_rate': new_lr,\n",
    "                'epoch_time': epoch_time\n",
    "            }\n",
    "            \n",
    "            self.logger.log_epoch(epoch, metrics)\n",
    "            \n",
    "            if val_loss < self.best_val_loss:\n",
    "                self.best_val_loss = val_loss\n",
    "                self.save_model(f'{self.logger.log_dir}/best_vae_model.pth')\n",
    "                print(f\"💾 New best model saved! Validation loss: {val_loss:.4f}\")\n",
    "        \n",
    "        # Save metrics and plot\n",
    "        df = self.logger.save_metrics()\n",
    "        self.logger.plot_comprehensive_training_curves()\n",
    "        \n",
    "        print(\"✅ Training completed successfully!\")\n",
    "        return df  # Return metrics dataframe for analysis\n",
    "    \n",
    "    def save_model(self, filepath):\n",
    "        torch.save({\n",
    "            'model_state_dict': self.model.state_dict(),\n",
    "            'model_config': {\n",
    "                'input_dim': self.model.input_dim,\n",
    "                'latent_dim': self.model.latent_dim,\n",
    "                'hidden_dim': self.model.hidden_dim,\n",
    "                'beta': self.model.beta\n",
    "            }\n",
    "        }, filepath)\n",
    "\n",
    "class VAEVisualizer:\n",
    "    \"\"\"Comprehensive visualization tools for VAE analysis\"\"\"\n",
    "    \n",
    "    def __init__(self, model, device, logger):\n",
    "        self.model = model\n",
    "        self.device = device\n",
    "        self.logger = logger\n",
    "    \n",
    "    def plot_latent_space(self, data_loader, labels=None, n_samples=5000):\n",
    "        self.model.eval()\n",
    "        \n",
    "        latent_vectors = []\n",
    "        sample_labels = []\n",
    "        \n",
    "        print(f\"📊 Extracting latent representations for {n_samples} samples...\")\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            sample_count = 0\n",
    "            for data, label in data_loader:\n",
    "                if sample_count >= n_samples:\n",
    "                    break\n",
    "                \n",
    "                data = data.to(self.device)\n",
    "                data = data.view(-1, 784)\n",
    "                \n",
    "                mu, _ = self.model.encode(data)\n",
    "                latent_vectors.append(mu.cpu().numpy())\n",
    "                sample_labels.append(label.numpy())\n",
    "                \n",
    "                sample_count += len(data)\n",
    "        \n",
    "        latent_vectors = np.vstack(latent_vectors)[:n_samples]\n",
    "        sample_labels = np.concatenate(sample_labels)[:n_samples]\n",
    "        \n",
    "        plt.figure(figsize=(12, 10))\n",
    "        \n",
    "        if labels is not None:\n",
    "            scatter = plt.scatter(latent_vectors[:, 0], latent_vectors[:, 1], \n",
    "                                c=sample_labels, cmap='tab10', alpha=0.7, s=20)\n",
    "            plt.colorbar(scatter, label='Digit Class')\n",
    "            plt.title('VAE Latent Space Visualization (Colored by Digit Class)', fontsize=16, fontweight='bold')\n",
    "        else:\n",
    "            plt.scatter(latent_vectors[:, 0], latent_vectors[:, 1], alpha=0.7, s=20)\n",
    "            plt.title('VAE Latent Space Visualization', fontsize=16, fontweight='bold')\n",
    "        \n",
    "        plt.xlabel('Latent Dimension 1', fontsize=14)\n",
    "        plt.ylabel('Latent Dimension 2', fontsize=14)\n",
    "        plt.grid(True, alpha=0.3)\n",
    "        \n",
    "        plot_path = f'{self.logger.log_dir}/latent_space_visualization.png'\n",
    "        plt.savefig(plot_path, dpi=300, bbox_inches='tight')\n",
    "        plt.show()\n",
    "        \n",
    "        print(f\"🌌 Latent space visualization saved to {plot_path}\")\n",
    "    \n",
    "    def plot_reconstructions(self, data_loader, n_samples=10):\n",
    "        self.model.eval()\n",
    "        \n",
    "        data, _ = next(iter(data_loader))\n",
    "        data = data[:n_samples].to(self.device)\n",
    "        original_data = data.view(-1, 784)\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            reconstructed, _, _ = self.model(original_data)\n",
    "        \n",
    "        original_images = data.cpu().numpy().reshape(-1, 28, 28)\n",
    "        reconstructed_images = reconstructed.cpu().numpy().reshape(-1, 28, 28)\n",
    "        \n",
    "        fig, axes = plt.subplots(2, n_samples, figsize=(n_samples*2, 4))\n",
    "        \n",
    "        for i in range(n_samples):\n",
    "            axes[0, i].imshow(original_images[i], cmap='gray')\n",
    "            axes[0, i].set_title(f'Original {i+1}', fontsize=12)\n",
    "            axes[0, i].axis('off')\n",
    "            \n",
    "            axes[1, i].imshow(reconstructed_images[i], cmap='gray')\n",
    "            axes[1, i].set_title(f'Reconstructed {i+1}', fontsize=12)\n",
    "            axes[1, i].axis('off')\n",
    "        \n",
    "        plt.suptitle('Original vs Reconstructed Images', fontsize=16, fontweight='bold')\n",
    "        plt.tight_layout()\n",
    "        \n",
    "        plot_path = f'{self.logger.log_dir}/reconstruction_comparison.png'\n",
    "        plt.savefig(plot_path, dpi=300, bbox_inches='tight')\n",
    "        plt.show()\n",
    "        \n",
    "        print(f\"🔄 Reconstruction comparison saved to {plot_path}\")\n",
    "    \n",
    "    def generate_new_samples(self, n_samples=16):\n",
    "        self.model.eval()\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            z = torch.randn(n_samples, self.model.latent_dim).to(self.device)\n",
    "            generated_images = self.model.decode(z).cpu().numpy()\n",
    "            generated_images = generated_images.reshape(-1, 28, 28)\n",
    "        \n",
    "        grid_size = int(np.sqrt(n_samples))\n",
    "        fig, axes = plt.subplots(grid_size, grid_size, figsize=(10, 10))\n",
    "        \n",
    "        for i in range(grid_size):\n",
    "            for j in range(grid_size):\n",
    "                idx = i * grid_size + j\n",
    "                if idx < n_samples:\n",
    "                    axes[i, j].imshow(generated_images[idx], cmap='gray')\n",
    "                    axes[i, j].set_title(f'Generated {idx+1}', fontsize=10)\n",
    "                    axes[i, j].axis('off')\n",
    "        \n",
    "        plt.suptitle('Generated Samples from VAE', fontsize=16, fontweight='bold')\n",
    "        plt.tight_layout()\n",
    "        \n",
    "        plot_path = f'{self.logger.log_dir}/generated_samples.png'\n",
    "        plt.savefig(plot_path, dpi=300, bbox_inches='tight')\n",
    "        plt.show()\n",
    "        \n",
    "        print(f\"✨ Generated samples saved to {plot_path}\")\n",
    "    \n",
    "    def interpolate_between_images(self, data_loader, n_steps=10):\n",
    "        self.model.eval()\n",
    "        \n",
    "        data, _ = next(iter(data_loader))\n",
    "        img1, img2 = data[:2].to(self.device)\n",
    "        img1_flat = img1.view(1, -1)\n",
    "        img2_flat = img2.view(1, -1)\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            mu1, _ = self.model.encode(img1_flat)\n",
    "            mu2, _ = self.model.encode(img2_flat)\n",
    "            \n",
    "            interpolated_images = []\n",
    "            \n",
    "            for i in range(n_steps):\n",
    "                alpha = i / (n_steps - 1)\n",
    "                z_interp = (1 - alpha) * mu1 + alpha * mu2\n",
    "                img_interp = self.model.decode(z_interp)\n",
    "                interpolated_images.append(img_interp.cpu().numpy().reshape(28, 28))\n",
    "        \n",
    "        fig, axes = plt.subplots(1, n_steps, figsize=(n_steps*2, 3))\n",
    "        \n",
    "        for i in range(n_steps):\n",
    "            axes[i].imshow(interpolated_images[i], cmap='gray')\n",
    "            axes[i].set_title(f'Step {i+1}', fontsize=12)\n",
    "            axes[i].axis('off')\n",
    "        \n",
    "        plt.suptitle('Latent Space Interpolation Between Two Digits', fontsize=16, fontweight='bold')\n",
    "        plt.tight_layout()\n",
    "        \n",
    "        plot_path = f'{self.logger.log_dir}/latent_interpolation.png'\n",
    "        plt.savefig(plot_path, dpi=300, bbox_inches='tight')\n",
    "        plt.show()\n",
    "        \n",
    "        print(f\"🌊 Latent interpolation saved to {plot_path}\")\n",
    "\n",
    "def load_mnist_data(batch_size=128):\n",
    "    \"\"\"Load and preprocess MNIST dataset with proper data loaders\"\"\"\n",
    "    transform = transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "    ])\n",
    "    \n",
    "    train_dataset = torchvision.datasets.MNIST(\n",
    "        root='./data',\n",
    "        train=True,\n",
    "        download=True,\n",
    "        transform=transform\n",
    "    )\n",
    "    \n",
    "    test_dataset = torchvision.datasets.MNIST(\n",
    "        root='./data',\n",
    "        train=False,\n",
    "        download=True,\n",
    "        transform=transform\n",
    "    )\n",
    "    \n",
    "    train_loader = DataLoader(\n",
    "        train_dataset,\n",
    "        batch_size=batch_size,\n",
    "        shuffle=True,\n",
    "        num_workers=0,  # Set to 0 for Jupyter compatibility\n",
    "        pin_memory=True if device.type != 'cpu' else False\n",
    "    )\n",
    "    \n",
    "    test_loader = DataLoader(\n",
    "        test_dataset,\n",
    "        batch_size=batch_size,\n",
    "        shuffle=False,\n",
    "        num_workers=0,  # Set to 0 for Jupyter compatibility\n",
    "        pin_memory=True if device.type != 'cpu' else False\n",
    "    )\n",
    "    \n",
    "    print(f\"📊 Loaded MNIST dataset:\")\n",
    "    print(f\"   Training samples: {len(train_dataset)}\")\n",
    "    print(f\"   Test samples: {len(test_dataset)}\")\n",
    "    print(f\"   Batch size: {batch_size}\")\n",
    "    \n",
    "    return train_loader, test_loader\n",
    "\n",
    "def run_vae_training(epochs=20, latent_dim=2, beta=1.0, learning_rate=0.001):\n",
    "    \"\"\"Main function optimized for Jupyter notebook\"\"\"\n",
    "    print(\"🚀 PyTorch VAE Implementation for M4 Mac (Jupyter Optimized)\")\n",
    "    print(\"=\" * 60)\n",
    "    \n",
    "    print(f\"PyTorch version: {torch.__version__}\")\n",
    "    print(f\"Device: {device}\")\n",
    "    print(f\"MPS available: {torch.backends.mps.is_available()}\")\n",
    "    \n",
    "    # Load MNIST dataset\n",
    "    print(\"\\n📂 Loading MNIST dataset...\")\n",
    "    train_loader, test_loader = load_mnist_data(batch_size=128)\n",
    "    \n",
    "    # Initialize VAE model\n",
    "    print(f\"\\n🏗️  Initializing VAE model (latent_dim={latent_dim}, beta={beta})...\")\n",
    "    vae_model = VAE(\n",
    "        input_dim=784,\n",
    "        latent_dim=latent_dim,\n",
    "        hidden_dim=256,\n",
    "        beta=beta\n",
    "    ).to(device)\n",
    "    \n",
    "    total_params = sum(p.numel() for p in vae_model.parameters())\n",
    "    print(f\"   Total parameters: {total_params:,}\")\n",
    "    print(f\"   Model size: {total_params * 4 / 1024 / 1024:.2f} MB\")\n",
    "    \n",
    "    # Setup logging\n",
    "    logger = VAELogger(log_dir=f\"vae_logs_latent{latent_dim}_beta{beta}\")\n",
    "    \n",
    "    # Train model\n",
    "    trainer = VAETrainer(vae_model, device, logger)\n",
    "    \n",
    "    print(f\"\\n🚀 Starting VAE training ({epochs} epochs)...\")\n",
    "    metrics_df = trainer.train(\n",
    "        train_loader=train_loader,\n",
    "        val_loader=test_loader,\n",
    "        epochs=epochs,\n",
    "        learning_rate=learning_rate\n",
    "    )\n",
    "    \n",
    "    # Create visualizations\n",
    "    print(\"\\n🎨 Creating visualizations...\")\n",
    "    visualizer = VAEVisualizer(vae_model, device, logger)\n",
    "    \n",
    "    if latent_dim == 2:  # Only plot latent space for 2D\n",
    "        visualizer.plot_latent_space(test_loader, labels=True, n_samples=3000)\n",
    "    \n",
    "    visualizer.plot_reconstructions(test_loader, n_samples=8)\n",
    "    visualizer.generate_new_samples(n_samples=16)\n",
    "    \n",
    "    if latent_dim == 2:  # Only interpolate for 2D\n",
    "        visualizer.interpolate_between_images(test_loader, n_steps=8)\n",
    "    \n",
    "    print(\"\\n✅ VAE training and analysis complete!\")\n",
    "    print(f\"📁 All outputs saved to: {logger.log_dir}/\")\n",
    "    \n",
    "    return vae_model, metrics_df, visualizer\n",
    "\n",
    "# Run the training (optimized for Jupyter)\n",
    "if __name__ == \"__main__\":\n",
    "    # Run with fewer epochs for faster testing in Jupyter\n",
    "    model, metrics, viz = run_vae_training(epochs=20, latent_dim=2, beta=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f0bc5d2-66bd-42e8-a860-ccd27f9d5a83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import essential PyTorch libraries for deep learning\n",
    "import torch  # Core PyTorch library for tensor operations and neural networks\n",
    "import torch.nn as nn  # Neural network modules (layers, activations, etc.)\n",
    "import torch.nn.functional as F  # Functional interface for operations like loss functions\n",
    "import torch.optim as optim  # Optimization algorithms (Adam, SGD, etc.)\n",
    "from torch.utils.data import DataLoader, TensorDataset  # Data loading utilities for batch processing\n",
    "\n",
    "# Import torchvision for computer vision datasets and transformations\n",
    "import torchvision  # Computer vision library with datasets and models\n",
    "import torchvision.transforms as transforms  # Image preprocessing transformations\n",
    "\n",
    "# Import standard Python libraries for data analysis and visualization\n",
    "import numpy as np  # Numerical computing library for array operations\n",
    "import matplotlib.pyplot as plt  # Plotting library for creating visualizations\n",
    "import pandas as pd  # Data manipulation library for handling structured data\n",
    "import os  # Operating system interface for file/directory operations\n",
    "import json  # JSON encoder/decoder for saving configuration data\n",
    "import logging  # Logging facility for tracking program execution\n",
    "from datetime import datetime  # Date and time handling utilities\n",
    "import time  # Time-related functions for performance measurement\n",
    "\n",
    "# Set random seeds to ensure reproducible results across different runs\n",
    "torch.manual_seed(42)  # Set PyTorch's random number generator seed\n",
    "np.random.seed(42)  # Set NumPy's random number generator seed\n",
    "\n",
    "# Device detection: Check for available acceleration hardware (MPS/CUDA/CPU)\n",
    "if torch.backends.mps.is_available():  # Check if Metal Performance Shaders available (Apple Silicon)\n",
    "    device = torch.device(\"mps\")  # Use MPS backend for M1/M2/M4 Mac GPU acceleration\n",
    "    print(f\"🚀 Using MPS (Metal Performance Shaders) on M4 Mac\")  # Confirm MPS usage\n",
    "elif torch.cuda.is_available():  # Fallback: Check if NVIDIA CUDA is available\n",
    "    device = torch.device(\"cuda\")  # Use CUDA for NVIDIA GPU acceleration\n",
    "    print(f\"🚀 Using CUDA GPU\")  # Confirm CUDA usage\n",
    "else:  # Last resort: Use CPU if no GPU acceleration available\n",
    "    device = torch.device(\"cpu\")  # Use CPU for computation\n",
    "    print(f\"⚠️  Using CPU only\")  # Warn about CPU-only execution\n",
    "\n",
    "class VAELogger:\n",
    "    \"\"\"\n",
    "    Comprehensive logging class for tracking VAE training progress\n",
    "    Handles metrics collection, file logging, and visualization creation\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, log_dir=\"pytorch_vae_logs\"):  # Initialize logger with default directory name\n",
    "        self.log_dir = log_dir  # Store the logging directory path\n",
    "        os.makedirs(log_dir, exist_ok=True)  # Create logging directory if it doesn't exist\n",
    "        \n",
    "        # Configure logging system for both file and console output\n",
    "        if hasattr(self, 'logger'):  # Check if logger already exists (Jupyter notebook compatibility)\n",
    "            for handler in logging.root.handlers[:]:  # Remove existing handlers to prevent duplicates\n",
    "                logging.root.removeHandler(handler)  # Clear each existing handler\n",
    "        \n",
    "        logging.basicConfig(  # Configure the logging system\n",
    "            level=logging.INFO,  # Set logging level to INFO (shows info, warning, error messages)\n",
    "            format='%(asctime)s - %(levelname)s - %(message)s',  # Define log message format with timestamp\n",
    "            handlers=[  # List of output handlers for log messages\n",
    "                logging.FileHandler(f'{log_dir}/pytorch_vae_training.log'),  # Write logs to file\n",
    "                logging.StreamHandler()  # Also display logs in console/Jupyter output\n",
    "            ],\n",
    "            force=True  # Force reconfiguration (important for Jupyter notebook compatibility)\n",
    "        )\n",
    "        self.logger = logging.getLogger(__name__)  # Create logger instance with current module name\n",
    "        \n",
    "        # Initialize dictionary to store training metrics for analysis\n",
    "        self.metrics = {\n",
    "            'epoch': [],  # List to store epoch numbers\n",
    "            'train_loss': [],  # List to store training total loss values\n",
    "            'train_recon_loss': [],  # List to store training reconstruction loss values\n",
    "            'train_kl_loss': [],  # List to store training KL divergence loss values\n",
    "            'val_loss': [],  # List to store validation total loss values\n",
    "            'val_recon_loss': [],  # List to store validation reconstruction loss values\n",
    "            'val_kl_loss': [],  # List to store validation KL divergence loss values\n",
    "            'learning_rate': [],  # List to store learning rate values at each epoch\n",
    "            'epoch_time': []  # List to store time taken for each epoch\n",
    "        }\n",
    "        \n",
    "        # Log successful initialization with device information\n",
    "        self.logger.info(f\"PyTorch VAE Logger initialized - Device: {device}\")\n",
    "    \n",
    "    def log_epoch(self, epoch, metrics_dict):\n",
    "        \"\"\"\n",
    "        Log comprehensive metrics for a single training epoch\n",
    "        Args:\n",
    "            epoch: Current epoch number\n",
    "            metrics_dict: Dictionary containing all metrics for this epoch\n",
    "        \"\"\"\n",
    "        # Store all provided metrics in the internal metrics dictionary\n",
    "        for key, value in metrics_dict.items():  # Iterate through all provided metrics\n",
    "            if key in self.metrics:  # Check if this metric is tracked by the logger\n",
    "                self.metrics[key].append(value)  # Add the metric value to the corresponding list\n",
    "        \n",
    "        # Create a formatted log message with key training statistics\n",
    "        log_msg = (f\"Epoch {epoch:3d} | \"  # Epoch number with fixed width formatting\n",
    "                  f\"Total Loss: {metrics_dict['train_loss']:.4f} | \"  # Training loss with 4 decimal places\n",
    "                  f\"Recon: {metrics_dict['train_recon_loss']:.4f} | \"  # Reconstruction loss\n",
    "                  f\"KL: {metrics_dict['train_kl_loss']:.4f} | \"  # KL divergence loss\n",
    "                  f\"Val Loss: {metrics_dict['val_loss']:.4f} | \"  # Validation loss\n",
    "                  f\"Time: {metrics_dict['epoch_time']:.2f}s\")  # Epoch duration in seconds\n",
    "        \n",
    "        print(log_msg)  # Display log message in console/Jupyter (better for notebook display)\n",
    "        self.logger.info(log_msg)  # Also write to log file\n",
    "    \n",
    "    def save_metrics(self):\n",
    "        \"\"\"\n",
    "        Save all collected training metrics to CSV file for later analysis\n",
    "        Returns:\n",
    "            pandas.DataFrame: DataFrame containing all metrics for immediate analysis\n",
    "        \"\"\"\n",
    "        df = pd.DataFrame(self.metrics)  # Convert metrics dictionary to pandas DataFrame\n",
    "        csv_path = f'{self.log_dir}/training_metrics.csv'  # Define path for CSV file\n",
    "        df.to_csv(csv_path, index=False)  # Save DataFrame to CSV file without row indices\n",
    "        print(f\"📊 Training metrics saved to {csv_path}\")  # Confirm save operation\n",
    "        return df  # Return DataFrame for immediate analysis in Jupyter\n",
    "    \n",
    "    def plot_comprehensive_training_curves(self):\n",
    "        \"\"\"\n",
    "        Create comprehensive visualization of training progress with multiple subplots\n",
    "        Generates a 2x3 grid of plots showing different aspects of training\n",
    "        \"\"\"\n",
    "        plt.style.use('default')  # Reset matplotlib style to default (important for Jupyter)\n",
    "        fig, axes = plt.subplots(2, 3, figsize=(18, 12))  # Create 2x3 subplot grid with large figure size\n",
    "        \n",
    "        # Plot 1: Total Loss Comparison (Training vs Validation)\n",
    "        axes[0, 0].plot(self.metrics['epoch'], self.metrics['train_loss'], 'b-', label='Training', linewidth=2)  # Blue line for training loss\n",
    "        axes[0, 0].plot(self.metrics['epoch'], self.metrics['val_loss'], 'r-', label='Validation', linewidth=2)  # Red line for validation loss\n",
    "        axes[0, 0].set_title('Total Loss Comparison', fontsize=14, fontweight='bold')  # Set subplot title\n",
    "        axes[0, 0].set_xlabel('Epoch')  # Label x-axis\n",
    "        axes[0, 0].set_ylabel('Loss')  # Label y-axis\n",
    "        axes[0, 0].legend()  # Add legend to distinguish lines\n",
    "        axes[0, 0].grid(True, alpha=0.3)  # Add semi-transparent grid for readability\n",
    "        \n",
    "        # Plot 2: Reconstruction Loss (Training vs Validation)\n",
    "        axes[0, 1].plot(self.metrics['epoch'], self.metrics['train_recon_loss'], 'b-', label='Training', linewidth=2)  # Training reconstruction loss\n",
    "        axes[0, 1].plot(self.metrics['epoch'], self.metrics['val_recon_loss'], 'r-', label='Validation', linewidth=2)  # Validation reconstruction loss\n",
    "        axes[0, 1].set_title('Reconstruction Loss', fontsize=14, fontweight='bold')  # Set subplot title\n",
    "        axes[0, 1].set_xlabel('Epoch')  # Label x-axis\n",
    "        axes[0, 1].set_ylabel('Reconstruction Loss')  # Label y-axis\n",
    "        axes[0, 1].legend()  # Add legend\n",
    "        axes[0, 1].grid(True, alpha=0.3)  # Add grid\n",
    "        \n",
    "        # Plot 3: KL Divergence Loss (Training vs Validation)\n",
    "        axes[0, 2].plot(self.metrics['epoch'], self.metrics['train_kl_loss'], 'b-', label='Training', linewidth=2)  # Training KL loss\n",
    "        axes[0, 2].plot(self.metrics['epoch'], self.metrics['val_kl_loss'], 'r-', label='Validation', linewidth=2)  # Validation KL loss\n",
    "        axes[0, 2].set_title('KL Divergence Loss', fontsize=14, fontweight='bold')  # Set subplot title\n",
    "        axes[0, 2].set_xlabel('Epoch')  # Label x-axis\n",
    "        axes[0, 2].set_ylabel('KL Loss')  # Label y-axis\n",
    "        axes[0, 2].legend()  # Add legend\n",
    "        axes[0, 2].grid(True, alpha=0.3)  # Add grid\n",
    "        \n",
    "        # Plot 4: Learning Rate Schedule Over Time\n",
    "        axes[1, 0].plot(self.metrics['epoch'], self.metrics['learning_rate'], 'g-', linewidth=2)  # Green line for learning rate\n",
    "        axes[1, 0].set_title('Learning Rate Schedule', fontsize=14, fontweight='bold')  # Set subplot title\n",
    "        axes[1, 0].set_xlabel('Epoch')  # Label x-axis\n",
    "        axes[1, 0].set_ylabel('Learning Rate')  # Label y-axis\n",
    "        axes[1, 0].set_yscale('log')  # Use logarithmic scale for better visualization of learning rate changes\n",
    "        axes[1, 0].grid(True, alpha=0.3)  # Add grid\n",
    "        \n",
    "        # Plot 5: Training Time per Epoch (Performance Monitoring)\n",
    "        axes[1, 1].plot(self.metrics['epoch'], self.metrics['epoch_time'], 'purple', linewidth=2)  # Purple line for epoch time\n",
    "        axes[1, 1].set_title('Training Time per Epoch', fontsize=14, fontweight='bold')  # Set subplot title\n",
    "        axes[1, 1].set_xlabel('Epoch')  # Label x-axis\n",
    "        axes[1, 1].set_ylabel('Time (seconds)')  # Label y-axis\n",
    "        axes[1, 1].grid(True, alpha=0.3)  # Add grid\n",
    "        \n",
    "        # Plot 6: Loss Ratio Analysis (Balance between Reconstruction and KL)\n",
    "        # Calculate ratio of reconstruction loss to KL loss (indicates balance)\n",
    "        train_ratio = np.array(self.metrics['train_recon_loss']) / (np.array(self.metrics['train_kl_loss']) + 1e-8)  # Add small epsilon to prevent division by zero\n",
    "        val_ratio = np.array(self.metrics['val_recon_loss']) / (np.array(self.metrics['val_kl_loss']) + 1e-8)  # Same for validation\n",
    "        axes[1, 2].plot(self.metrics['epoch'], train_ratio, 'b-', label='Training Ratio', linewidth=2)  # Blue line for training ratio\n",
    "        axes[1, 2].plot(self.metrics['epoch'], val_ratio, 'r-', label='Validation Ratio', linewidth=2)  # Red line for validation ratio\n",
    "        axes[1, 2].set_title('Reconstruction/KL Loss Ratio', fontsize=14, fontweight='bold')  # Set subplot title\n",
    "        axes[1, 2].set_xlabel('Epoch')  # Label x-axis\n",
    "        axes[1, 2].set_ylabel('Ratio')  # Label y-axis\n",
    "        axes[1, 2].legend()  # Add legend\n",
    "        axes[1, 2].grid(True, alpha=0.3)  # Add grid\n",
    "        \n",
    "        plt.tight_layout()  # Automatically adjust subplot spacing to prevent overlap\n",
    "        plot_path = f'{self.log_dir}/comprehensive_training_curves.png'  # Define path for saving plot\n",
    "        plt.savefig(plot_path, dpi=300, bbox_inches='tight')  # Save high-resolution plot to file\n",
    "        plt.show()  # Display plot in notebook/console\n",
    "        \n",
    "        print(f\"📈 Comprehensive training curves saved to {plot_path}\")  # Confirm save operation\n",
    "\n",
    "class VAE(nn.Module):\n",
    "    \"\"\"\n",
    "    Variational Autoencoder (VAE) implementation using PyTorch\n",
    "    \n",
    "    A VAE consists of:\n",
    "    1. Encoder: Maps input data to latent distribution parameters (mean, log_variance)\n",
    "    2. Reparameterization: Samples from latent distribution using the reparameterization trick\n",
    "    3. Decoder: Maps latent samples back to reconstructed data\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, input_dim=784, latent_dim=2, hidden_dim=256, beta=1.0):\n",
    "        \"\"\"\n",
    "        Initialize VAE architecture with specified parameters\n",
    "        \n",
    "        Args:\n",
    "            input_dim: Dimension of input data (784 for flattened MNIST 28x28 images)\n",
    "            latent_dim: Dimension of latent space (2 for easy visualization, higher for more capacity)\n",
    "            hidden_dim: Number of units in hidden layers\n",
    "            beta: Weighting factor for KL divergence loss (beta=1 is standard VAE)\n",
    "        \"\"\"\n",
    "        super(VAE, self).__init__()  # Initialize parent nn.Module class\n",
    "        \n",
    "        # Store architecture parameters as instance variables\n",
    "        self.input_dim = input_dim  # Store input dimension\n",
    "        self.latent_dim = latent_dim  # Store latent space dimension\n",
    "        self.hidden_dim = hidden_dim  # Store hidden layer dimension\n",
    "        self.beta = beta  # Store beta parameter for loss weighting\n",
    "        \n",
    "        # Build Encoder Network: Input → Hidden Layers → Latent Parameters\n",
    "        self.encoder = nn.Sequential(  # Sequential container for encoder layers\n",
    "            nn.Linear(input_dim, hidden_dim),  # First linear layer: input_dim → hidden_dim\n",
    "            nn.BatchNorm1d(hidden_dim),  # Batch normalization for training stability\n",
    "            nn.ReLU(True),  # ReLU activation function (inplace=True for memory efficiency)\n",
    "            nn.Dropout(0.2),  # Dropout layer with 20% probability for regularization\n",
    "            nn.Linear(hidden_dim, hidden_dim // 2),  # Second linear layer: hidden_dim → hidden_dim/2\n",
    "            nn.BatchNorm1d(hidden_dim // 2),  # Batch normalization for second layer\n",
    "            nn.ReLU(True),  # ReLU activation\n",
    "            nn.Dropout(0.2)  # Dropout for regularization\n",
    "        )\n",
    "        \n",
    "        # Separate linear layers for latent distribution parameters\n",
    "        self.fc_mu = nn.Linear(hidden_dim // 2, latent_dim)  # Linear layer to output mean (μ) of latent distribution\n",
    "        self.fc_logvar = nn.Linear(hidden_dim // 2, latent_dim)  # Linear layer to output log variance (log σ²) of latent distribution\n",
    "        \n",
    "        # Build Decoder Network: Latent → Hidden Layers → Output\n",
    "        self.decoder = nn.Sequential(  # Sequential container for decoder layers\n",
    "            nn.Linear(latent_dim, hidden_dim // 2),  # First decoder layer: latent_dim → hidden_dim/2\n",
    "            nn.BatchNorm1d(hidden_dim // 2),  # Batch normalization\n",
    "            nn.ReLU(True),  # ReLU activation\n",
    "            nn.Dropout(0.2),  # Dropout for regularization\n",
    "            nn.Linear(hidden_dim // 2, hidden_dim),  # Second decoder layer: hidden_dim/2 → hidden_dim\n",
    "            nn.BatchNorm1d(hidden_dim),  # Batch normalization\n",
    "            nn.ReLU(True),  # ReLU activation\n",
    "            nn.Dropout(0.2),  # Dropout for regularization\n",
    "            nn.Linear(hidden_dim, input_dim),  # Final layer: hidden_dim → input_dim (reconstruction)\n",
    "            nn.Sigmoid()  # Sigmoid activation to constrain output to [0,1] range (pixel values)\n",
    "        )\n",
    "    \n",
    "    def encode(self, x):\n",
    "        \"\"\"\n",
    "        Encode input data to latent distribution parameters\n",
    "        \n",
    "        Args:\n",
    "            x: Input tensor of shape (batch_size, input_dim)\n",
    "            \n",
    "        Returns:\n",
    "            mu: Mean of latent distribution (batch_size, latent_dim)\n",
    "            logvar: Log variance of latent distribution (batch_size, latent_dim)\n",
    "        \"\"\"\n",
    "        h = self.encoder(x)  # Pass input through encoder layers to get hidden representation\n",
    "        mu = self.fc_mu(h)  # Calculate mean of latent distribution from hidden representation\n",
    "        logvar = self.fc_logvar(h)  # Calculate log variance of latent distribution from hidden representation\n",
    "        return mu, logvar  # Return both parameters of the latent distribution\n",
    "    \n",
    "    def reparameterize(self, mu, logvar):\n",
    "        \"\"\"\n",
    "        Reparameterization trick: Sample from latent distribution in a differentiable way\n",
    "        \n",
    "        Instead of sampling z ~ N(μ, σ²) directly (which breaks backpropagation),\n",
    "        we sample ε ~ N(0, 1) and compute z = μ + σ * ε\n",
    "        \n",
    "        Args:\n",
    "            mu: Mean of latent distribution\n",
    "            logvar: Log variance of latent distribution\n",
    "            \n",
    "        Returns:\n",
    "            z: Sampled latent variable\n",
    "        \"\"\"\n",
    "        std = torch.exp(0.5 * logvar)  # Convert log variance to standard deviation: σ = exp(0.5 * log(σ²))\n",
    "        eps = torch.randn_like(std)  # Sample random noise ε from standard normal distribution with same shape as std\n",
    "        z = mu + eps * std  # Apply reparameterization trick: z = μ + σ * ε\n",
    "        return z  # Return sampled latent variable\n",
    "    \n",
    "    def decode(self, z):\n",
    "        \"\"\"\n",
    "        Decode latent variable to reconstructed output\n",
    "        \n",
    "        Args:\n",
    "            z: Latent variable tensor of shape (batch_size, latent_dim)\n",
    "            \n",
    "        Returns:\n",
    "            Reconstructed output tensor of shape (batch_size, input_dim)\n",
    "        \"\"\"\n",
    "        return self.decoder(z)  # Pass latent variable through decoder to get reconstruction\n",
    "    \n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Complete forward pass through VAE\n",
    "        \n",
    "        Args:\n",
    "            x: Input tensor\n",
    "            \n",
    "        Returns:\n",
    "            recon_x: Reconstructed input\n",
    "            mu: Mean of latent distribution\n",
    "            logvar: Log variance of latent distribution\n",
    "        \"\"\"\n",
    "        mu, logvar = self.encode(x)  # Encode input to get latent distribution parameters\n",
    "        z = self.reparameterize(mu, logvar)  # Sample latent variable using reparameterization trick\n",
    "        recon_x = self.decode(z)  # Decode latent variable to get reconstruction\n",
    "        return recon_x, mu, logvar  # Return reconstruction and distribution parameters\n",
    "    \n",
    "    def loss_function(self, recon_x, x, mu, logvar):\n",
    "        \"\"\"\n",
    "        Calculate VAE loss: reconstruction loss + KL divergence loss\n",
    "        \n",
    "        Args:\n",
    "            recon_x: Reconstructed input\n",
    "            x: Original input\n",
    "            mu: Mean of latent distribution\n",
    "            logvar: Log variance of latent distribution\n",
    "            \n",
    "        Returns:\n",
    "            total_loss: Combined VAE loss\n",
    "            recon_loss: Reconstruction loss component\n",
    "            kl_loss: KL divergence loss component\n",
    "        \"\"\"\n",
    "        # Reconstruction Loss: How well can we reconstruct the input?\n",
    "        recon_loss = F.binary_cross_entropy(recon_x, x, reduction='sum')  # Binary cross-entropy between reconstruction and original\n",
    "        \n",
    "        # KL Divergence Loss: How close is our latent distribution to standard normal?\n",
    "        # KL(q(z|x) || p(z)) where q(z|x) = N(μ, σ²) and p(z) = N(0, I)\n",
    "        # Formula: KL = -0.5 * sum(1 + log(σ²) - μ² - σ²)\n",
    "        kl_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())  # KL divergence formula\n",
    "        \n",
    "        # Total VAE Loss: Reconstruction + β * KL Divergence\n",
    "        total_loss = recon_loss + self.beta * kl_loss  # Combine losses with beta weighting\n",
    "        \n",
    "        return total_loss, recon_loss, kl_loss  # Return all loss components for monitoring\n",
    "\n",
    "class VAETrainer:\n",
    "    \"\"\"\n",
    "    Comprehensive training class for VAE with logging and model management\n",
    "    Handles training loop, validation, model saving, and performance tracking\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, model, device, logger):\n",
    "        \"\"\"\n",
    "        Initialize trainer with model, device, and logger\n",
    "        \n",
    "        Args:\n",
    "            model: VAE model to train\n",
    "            device: Computation device (MPS/CUDA/CPU)\n",
    "            logger: Logger instance for tracking progress\n",
    "        \"\"\"\n",
    "        self.model = model  # Store reference to VAE model\n",
    "        self.device = device  # Store computation device\n",
    "        self.logger = logger  # Store logger for tracking\n",
    "        self.best_val_loss = float('inf')  # Initialize best validation loss to infinity for model saving\n",
    "        \n",
    "    def train_epoch(self, train_loader, optimizer, epoch):\n",
    "        \"\"\"\n",
    "        Train model for one complete epoch\n",
    "        \n",
    "        Args:\n",
    "            train_loader: DataLoader for training data\n",
    "            optimizer: Optimizer for updating model parameters\n",
    "            epoch: Current epoch number\n",
    "            \n",
    "        Returns:\n",
    "            avg_train_loss: Average training loss for the epoch\n",
    "            avg_recon_loss: Average reconstruction loss for the epoch\n",
    "            avg_kl_loss: Average KL loss for the epoch\n",
    "            epoch_time: Time taken for the epoch\n",
    "        \"\"\"\n",
    "        self.model.train()  # Set model to training mode (enables dropout, batch norm training mode)\n",
    "        \n",
    "        # Initialize epoch-level metrics\n",
    "        train_loss = 0  # Accumulator for total training loss\n",
    "        train_recon_loss = 0  # Accumulator for reconstruction loss\n",
    "        train_kl_loss = 0  # Accumulator for KL divergence loss\n",
    "        \n",
    "        epoch_start_time = time.time()  # Record epoch start time for performance measurement\n",
    "        \n",
    "        # Training loop: iterate through all batches in the training dataset\n",
    "        for batch_idx, (data, _) in enumerate(train_loader):  # data: images, _: labels (not used in VAE)\n",
    "            data = data.to(self.device)  # Move batch data to computation device (MPS/CUDA/CPU)\n",
    "            data = data.view(-1, 784)  # Flatten 28x28 MNIST images to 784-dimensional vectors\n",
    "            \n",
    "            optimizer.zero_grad()  # Clear gradients from previous iteration\n",
    "            \n",
    "            # Forward pass through VAE\n",
    "            recon_batch, mu, logvar = self.model(data)  # Get reconstruction and latent parameters\n",
    "            \n",
    "            # Calculate loss components\n",
    "            total_loss, recon_loss, kl_loss = self.model.loss_function(recon_batch, data, mu, logvar)\n",
    "            \n",
    "            # Backward pass and parameter update\n",
    "            total_loss.backward()  # Compute gradients via backpropagation\n",
    "            optimizer.step()  # Update model parameters using computed gradients\n",
    "            \n",
    "            # Accumulate losses for epoch-level statistics\n",
    "            train_loss += total_loss.item()  # Add batch loss to epoch total\n",
    "            train_recon_loss += recon_loss.item()  # Add batch reconstruction loss to epoch total\n",
    "            train_kl_loss += kl_loss.item()  # Add batch KL loss to epoch total\n",
    "            \n",
    "            # Print progress every 200 batches (less frequent for Jupyter notebook)\n",
    "            if batch_idx % 200 == 0:\n",
    "                batch_total = len(train_loader.dataset)  # Total number of training samples\n",
    "                batch_processed = batch_idx * len(data)  # Number of samples processed so far\n",
    "                percent_complete = 100. * batch_idx / len(train_loader)  # Completion percentage\n",
    "                \n",
    "                # Print formatted progress message\n",
    "                print(f'Epoch {epoch} [{batch_processed:5d}/{batch_total} '\n",
    "                      f'({percent_complete:3.0f}%)] Loss: {total_loss.item():.6f}')\n",
    "        \n",
    "        epoch_time = time.time() - epoch_start_time  # Calculate total epoch duration\n",
    "        \n",
    "        # Calculate average losses by dividing by number of samples\n",
    "        avg_train_loss = train_loss / len(train_loader.dataset)  # Average loss per sample\n",
    "        avg_recon_loss = train_recon_loss / len(train_loader.dataset)  # Average reconstruction loss per sample\n",
    "        avg_kl_loss = train_kl_loss / len(train_loader.dataset)  # Average KL loss per sample\n",
    "        \n",
    "        return avg_train_loss, avg_recon_loss, avg_kl_loss, epoch_time  # Return epoch metrics\n",
    "    \n",
    "    def validate_epoch(self, val_loader):\n",
    "        \"\"\"\n",
    "        Validate model on validation dataset\n",
    "        \n",
    "        Args:\n",
    "            val_loader: DataLoader for validation data\n",
    "            \n",
    "        Returns:\n",
    "            avg_val_loss: Average validation loss\n",
    "            avg_val_recon_loss: Average validation reconstruction loss\n",
    "            avg_val_kl_loss: Average validation KL loss\n",
    "        \"\"\"\n",
    "        self.model.eval()  # Set model to evaluation mode (disables dropout, sets batch norm to eval mode)\n",
    "        \n",
    "        # Initialize validation metrics\n",
    "        val_loss = 0  # Accumulator for validation loss\n",
    "        val_recon_loss = 0  # Accumulator for validation reconstruction loss\n",
    "        val_kl_loss = 0  # Accumulator for validation KL loss\n",
    "        \n",
    "        # Validation loop: no gradient computation needed\n",
    "        with torch.no_grad():  # Disable gradient computation for efficiency and memory saving\n",
    "            for data, _ in val_loader:  # Iterate through validation batches\n",
    "                data = data.to(self.device)  # Move data to computation device\n",
    "                data = data.view(-1, 784)  # Flatten images to vectors\n",
    "                \n",
    "                # Forward pass (no backward pass needed for validation)\n",
    "                recon_batch, mu, logvar = self.model(data)  # Get model predictions\n",
    "                \n",
    "                # Calculate loss components\n",
    "                total_loss, recon_loss, kl_loss = self.model.loss_function(recon_batch, data, mu, logvar)\n",
    "                \n",
    "                # Accumulate validation losses\n",
    "                val_loss += total_loss.item()  # Add to total validation loss\n",
    "                val_recon_loss += recon_loss.item()  # Add to reconstruction loss\n",
    "                val_kl_loss += kl_loss.item()  # Add to KL loss\n",
    "        \n",
    "        # Calculate average validation losses\n",
    "        avg_val_loss = val_loss / len(val_loader.dataset)  # Average validation loss per sample\n",
    "        avg_val_recon_loss = val_recon_loss / len(val_loader.dataset)  # Average validation reconstruction loss\n",
    "        avg_val_kl_loss = val_kl_loss / len(val_loader.dataset)  # Average validation KL loss\n",
    "        \n",
    "        return avg_val_loss, avg_val_recon_loss, avg_val_kl_loss  # Return validation metrics\n",
    "    \n",
    "    def train(self, train_loader, val_loader, epochs=50, learning_rate=0.001):\n",
    "        \"\"\"\n",
    "        Complete training loop with validation and logging\n",
    "        \n",
    "        Args:\n",
    "            train_loader: DataLoader for training data\n",
    "            val_loader: DataLoader for validation data\n",
    "            epochs: Number of training epochs\n",
    "            learning_rate: Initial learning rate for optimizer\n",
    "            \n",
    "        Returns:\n",
    "            metrics_df: DataFrame containing all training metrics\n",
    "        \"\"\"\n",
    "        # Initialize optimizer with Adam algorithm\n",
    "        optimizer = optim.Adam(self.model.parameters(), lr=learning_rate)  # Adam optimizer with specified learning rate\n",
    "        \n",
    "        # Initialize learning rate scheduler (reduces LR when validation loss plateaus)\n",
    "        scheduler = optim.lr_scheduler.ReduceLROnPlateau(\n",
    "            optimizer,  # Optimizer to modify\n",
    "            mode='min',  # Reduce LR when monitored metric stops decreasing\n",
    "            factor=0.5,  # Multiply LR by this factor when reducing\n",
    "            patience=5  # Number of epochs with no improvement before reducing LR\n",
    "        )\n",
    "        \n",
    "        # Print training initialization information\n",
    "        print(f\"🚀 Starting training for {epochs} epochs\")\n",
    "        print(f\"📊 Model has {sum(p.numel() for p in self.model.parameters())} parameters\")\n",
    "        \n",
    "        # Main training loop: iterate through specified number of epochs\n",
    "        for epoch in range(1, epochs + 1):  # Epoch numbering from 1 to epochs\n",
    "            # Train for one epoch\n",
    "            train_loss, train_recon, train_kl, epoch_time = self.train_epoch(train_loader, optimizer, epoch)\n",
    "            \n",
    "            # Validate after each epoch\n",
    "            val_loss, val_recon, val_kl = self.validate_epoch(val_loader)\n",
    "            \n",
    "            # Update learning rate based on validation loss\n",
    "            old_lr = optimizer.param_groups[0]['lr']  # Store current learning rate\n",
    "            scheduler.step(val_loss)  # Update learning rate based on validation loss\n",
    "            new_lr = optimizer.param_groups[0]['lr']  # Get updated learning rate\n",
    "            \n",
    "            # Check if learning rate was reduced and notify user\n",
    "            if new_lr < old_lr:\n",
    "                print(f\"📉 Learning rate reduced to {new_lr:.2e}\")\n",
    "            \n",
    "            # Prepare metrics dictionary for logging\n",
    "            metrics = {\n",
    "                'epoch': epoch,  # Current epoch number\n",
    "                'train_loss': train_loss,  # Training loss for this epoch\n",
    "                'train_recon_loss': train_recon,  # Training reconstruction loss\n",
    "                'train_kl_loss': train_kl,  # Training KL loss\n",
    "                'val_loss': val_loss,  # Validation loss\n",
    "                'val_recon_loss': val_recon,  # Validation reconstruction loss\n",
    "                'val_kl_loss': val_kl,  # Validation KL loss\n",
    "                'learning_rate': new_lr,  # Current learning rate\n",
    "                'epoch_time': epoch_time  # Time taken for this epoch\n",
    "            }\n",
    "            \n",
    "            # Log epoch metrics using logger\n",
    "            self.logger.log_epoch(epoch, metrics)\n",
    "            \n",
    "            # Save model if validation loss improved\n",
    "            if val_loss < self.best_val_loss:  # Check if this is the best validation loss so far\n",
    "                self.best_val_loss = val_loss  # Update best validation loss\n",
    "                self.save_model(f'{self.logger.log_dir}/best_vae_model.pth')  # Save model state\n",
    "                print(f\"💾 New best model saved! Validation loss: {val_loss:.4f}\")\n",
    "        \n",
    "        # Save final metrics and create visualizations\n",
    "        df = self.logger.save_metrics()  # Save all metrics to CSV and return DataFrame\n",
    "        self.logger.plot_comprehensive_training_curves()  # Create comprehensive training plots\n",
    "        \n",
    "        print(\"✅ Training completed successfully!\")  # Confirm successful training completion\n",
    "        return df  # Return metrics DataFrame for further analysis\n",
    "    \n",
    "    def save_model(self, filepath):\n",
    "        \"\"\"\n",
    "        Save model state and configuration to file\n",
    "        \n",
    "        Args:\n",
    "            filepath: Path where to save the model\n",
    "        \"\"\"\n",
    "        torch.save({  # Save dictionary containing model state and config\n",
    "            'model_state_dict': self.model.state_dict(),  # Model parameters and buffers\n",
    "            'model_config': {  # Model configuration for reconstruction\n",
    "                'input_dim': self.model.input_dim,  # Input dimension\n",
    "                'latent_dim': self.model.latent_dim,  # Latent space dimension\n",
    "                'hidden_dim': self.model.hidden_dim,  # Hidden layer dimension\n",
    "                'beta': self.model.beta  # Beta parameter\n",
    "            }\n",
    "        }, filepath)\n",
    "\n",
    "class VAEVisualizer:\n",
    "   \"\"\"\n",
    "   Comprehensive visualization tools for analyzing trained VAE models\n",
    "   Creates various plots to understand latent space, reconstruction quality, and generation capability\n",
    "   \"\"\"\n",
    "   \n",
    "   def __init__(self, model, device, logger):\n",
    "       \"\"\"\n",
    "       Initialize visualizer with trained model, device, and logger\n",
    "       \n",
    "       Args:\n",
    "           model: Trained VAE model for analysis\n",
    "           device: Computation device (MPS/CUDA/CPU)\n",
    "           logger: Logger instance for saving visualizations\n",
    "       \"\"\"\n",
    "       self.model = model  # Store reference to trained VAE model\n",
    "       self.device = device  # Store computation device\n",
    "       self.logger = logger  # Store logger for saving plots\n",
    "   \n",
    "   def plot_latent_space(self, data_loader, labels=None, n_samples=5000):\n",
    "       \"\"\"\n",
    "       Visualize the 2D latent space representation of data points\n",
    "       Shows how different classes cluster in the learned latent space\n",
    "       \n",
    "       Args:\n",
    "           data_loader: DataLoader containing data to encode\n",
    "           labels: Whether to color points by class labels\n",
    "           n_samples: Number of samples to visualize\n",
    "       \"\"\"\n",
    "       self.model.eval()  # Set model to evaluation mode\n",
    "       \n",
    "       # Initialize lists to store latent representations and labels\n",
    "       latent_vectors = []  # List to store encoded latent vectors\n",
    "       sample_labels = []  # List to store corresponding class labels\n",
    "       \n",
    "       print(f\"📊 Extracting latent representations for {n_samples} samples...\")\n",
    "       \n",
    "       # Extract latent representations from data\n",
    "       with torch.no_grad():  # Disable gradient computation for efficiency\n",
    "           sample_count = 0  # Counter for number of samples processed\n",
    "           for data, label in data_loader:  # Iterate through data batches\n",
    "               if sample_count >= n_samples:  # Stop when we have enough samples\n",
    "                   break\n",
    "               \n",
    "               data = data.to(self.device)  # Move data to computation device\n",
    "               data = data.view(-1, 784)  # Flatten images to vectors\n",
    "               \n",
    "               # Encode data to latent space (use mean, ignore variance for visualization)\n",
    "               mu, _ = self.model.encode(data)  # Get latent mean (deterministic representation)\n",
    "               latent_vectors.append(mu.cpu().numpy())  # Convert to numpy and store\n",
    "               sample_labels.append(label.numpy())  # Store corresponding labels\n",
    "               \n",
    "               sample_count += len(data)  # Update sample counter\n",
    "       \n",
    "       # Combine all latent vectors and labels into single arrays\n",
    "       latent_vectors = np.vstack(latent_vectors)[:n_samples]  # Stack vectors and limit to n_samples\n",
    "       sample_labels = np.concatenate(sample_labels)[:n_samples]  # Concatenate labels and limit\n",
    "       \n",
    "       # Create latent space visualization\n",
    "       plt.figure(figsize=(12, 10))  # Create large figure for better visibility\n",
    "       \n",
    "       if labels is not None:  # If class labels are provided, create colored scatter plot\n",
    "           # Create scatter plot with different colors for each digit class\n",
    "           scatter = plt.scatter(latent_vectors[:, 0], latent_vectors[:, 1],  # X and Y coordinates from latent space\n",
    "                               c=sample_labels, cmap='tab10', alpha=0.7, s=20)  # Color by class, semi-transparent, small points\n",
    "           plt.colorbar(scatter, label='Digit Class')  # Add colorbar to show class-color mapping\n",
    "           plt.title('VAE Latent Space Visualization (Colored by Digit Class)', fontsize=16, fontweight='bold')\n",
    "       else:  # If no labels, create simple scatter plot\n",
    "           plt.scatter(latent_vectors[:, 0], latent_vectors[:, 1], alpha=0.7, s=20)  # Simple scatter plot\n",
    "           plt.title('VAE Latent Space Visualization', fontsize=16, fontweight='bold')\n",
    "       \n",
    "       plt.xlabel('Latent Dimension 1', fontsize=14)  # Label first latent dimension\n",
    "       plt.ylabel('Latent Dimension 2', fontsize=14)  # Label second latent dimension\n",
    "       plt.grid(True, alpha=0.3)  # Add semi-transparent grid for reference\n",
    "       \n",
    "       # Save and display the plot\n",
    "       plot_path = f'{self.logger.log_dir}/latent_space_visualization.png'  # Define save path\n",
    "       plt.savefig(plot_path, dpi=300, bbox_inches='tight')  # Save high-resolution image\n",
    "       plt.show()  # Display plot in notebook\n",
    "       \n",
    "       print(f\"🌌 Latent space visualization saved to {plot_path}\")\n",
    "   \n",
    "   def plot_reconstructions(self, data_loader, n_samples=10):\n",
    "       \"\"\"\n",
    "       Compare original images with their VAE reconstructions\n",
    "       Shows how well the model can reconstruct input data\n",
    "       \n",
    "       Args:\n",
    "           data_loader: DataLoader containing data to reconstruct\n",
    "           n_samples: Number of samples to show in comparison\n",
    "       \"\"\"\n",
    "       self.model.eval()  # Set model to evaluation mode\n",
    "       \n",
    "       # Get sample data for reconstruction comparison\n",
    "       data, _ = next(iter(data_loader))  # Get first batch from data loader\n",
    "       data = data[:n_samples].to(self.device)  # Take first n_samples and move to device\n",
    "       original_data = data.view(-1, 784)  # Flatten images for VAE input\n",
    "       \n",
    "       # Generate reconstructions\n",
    "       with torch.no_grad():  # Disable gradients for inference\n",
    "           reconstructed, _, _ = self.model(original_data)  # Get reconstructions from VAE\n",
    "       \n",
    "       # Convert tensors to numpy arrays for plotting\n",
    "       original_images = data.cpu().numpy().reshape(-1, 28, 28)  # Convert original images back to 28x28\n",
    "       reconstructed_images = reconstructed.cpu().numpy().reshape(-1, 28, 28)  # Convert reconstructions to 28x28\n",
    "       \n",
    "       # Create comparison plot with original and reconstructed images\n",
    "       fig, axes = plt.subplots(2, n_samples, figsize=(n_samples*2, 4))  # 2 rows, n_samples columns\n",
    "       \n",
    "       for i in range(n_samples):  # Iterate through each sample\n",
    "           # Plot original image in top row\n",
    "           axes[0, i].imshow(original_images[i], cmap='gray')  # Display as grayscale image\n",
    "           axes[0, i].set_title(f'Original {i+1}', fontsize=12)  # Set title for original\n",
    "           axes[0, i].axis('off')  # Remove axis ticks and labels\n",
    "           \n",
    "           # Plot reconstructed image in bottom row\n",
    "           axes[1, i].imshow(reconstructed_images[i], cmap='gray')  # Display as grayscale image\n",
    "           axes[1, i].set_title(f'Reconstructed {i+1}', fontsize=12)  # Set title for reconstruction\n",
    "           axes[1, i].axis('off')  # Remove axis ticks and labels\n",
    "       \n",
    "       plt.suptitle('Original vs Reconstructed Images', fontsize=16, fontweight='bold')  # Overall title\n",
    "       plt.tight_layout()  # Adjust spacing between subplots\n",
    "       \n",
    "       # Save and display the plot\n",
    "       plot_path = f'{self.logger.log_dir}/reconstruction_comparison.png'  # Define save path\n",
    "       plt.savefig(plot_path, dpi=300, bbox_inches='tight')  # Save high-resolution image\n",
    "       plt.show()  # Display plot in notebook\n",
    "       \n",
    "       print(f\"🔄 Reconstruction comparison saved to {plot_path}\")\n",
    "   \n",
    "   def generate_new_samples(self, n_samples=16):\n",
    "       \"\"\"\n",
    "       Generate completely new samples by sampling from the learned latent space\n",
    "       Demonstrates the generative capability of the VAE\n",
    "       \n",
    "       Args:\n",
    "           n_samples: Number of new samples to generate\n",
    "       \"\"\"\n",
    "       self.model.eval()  # Set model to evaluation mode\n",
    "       \n",
    "       # Generate new samples from latent space\n",
    "       with torch.no_grad():  # Disable gradients for inference\n",
    "           # Sample random points from standard normal distribution in latent space\n",
    "           z = torch.randn(n_samples, self.model.latent_dim).to(self.device)  # Random latent vectors\n",
    "           \n",
    "           # Decode latent samples to generate images\n",
    "           generated_images = self.model.decode(z).cpu().numpy()  # Generate images and convert to numpy\n",
    "           generated_images = generated_images.reshape(-1, 28, 28)  # Reshape to 28x28 image format\n",
    "       \n",
    "       # Create grid visualization of generated samples\n",
    "       grid_size = int(np.sqrt(n_samples))  # Calculate grid dimensions (assumes n_samples is perfect square)\n",
    "       fig, axes = plt.subplots(grid_size, grid_size, figsize=(10, 10))  # Create square grid\n",
    "       \n",
    "       # Display each generated sample in the grid\n",
    "       for i in range(grid_size):  # Iterate through grid rows\n",
    "           for j in range(grid_size):  # Iterate through grid columns\n",
    "               idx = i * grid_size + j  # Calculate flat index from grid coordinates\n",
    "               if idx < n_samples:  # Check if we have a sample for this position\n",
    "                   axes[i, j].imshow(generated_images[idx], cmap='gray')  # Display generated image\n",
    "                   axes[i, j].set_title(f'Generated {idx+1}', fontsize=10)  # Set title with sample number\n",
    "                   axes[i, j].axis('off')  # Remove axis ticks and labels\n",
    "       \n",
    "       plt.suptitle('Generated Samples from VAE', fontsize=16, fontweight='bold')  # Overall title\n",
    "       plt.tight_layout()  # Adjust spacing between subplots\n",
    "       \n",
    "       # Save and display the plot\n",
    "       plot_path = f'{self.logger.log_dir}/generated_samples.png'  # Define save path\n",
    "       plt.savefig(plot_path, dpi=300, bbox_inches='tight')  # Save high-resolution image\n",
    "       plt.show()  # Display plot in notebook\n",
    "       \n",
    "       print(f\"✨ Generated samples saved to {plot_path}\")\n",
    "   \n",
    "   def interpolate_between_images(self, data_loader, n_steps=10):\n",
    "       \"\"\"\n",
    "       Create smooth interpolation between two images in latent space\n",
    "       Demonstrates the continuity and structure of the learned latent space\n",
    "       \n",
    "       Args:\n",
    "           data_loader: DataLoader containing images to interpolate between\n",
    "           n_steps: Number of interpolation steps to create\n",
    "       \"\"\"\n",
    "       self.model.eval()  # Set model to evaluation mode\n",
    "       \n",
    "       # Get two sample images for interpolation\n",
    "       data, _ = next(iter(data_loader))  # Get first batch from data loader\n",
    "       img1, img2 = data[:2].to(self.device)  # Take first two images and move to device\n",
    "       img1_flat = img1.view(1, -1)  # Flatten first image for VAE input\n",
    "       img2_flat = img2.view(1, -1)  # Flatten second image for VAE input\n",
    "       \n",
    "       # Generate interpolation sequence\n",
    "       with torch.no_grad():  # Disable gradients for inference\n",
    "           # Encode both images to get their latent representations\n",
    "           mu1, _ = self.model.encode(img1_flat)  # Get latent representation of first image\n",
    "           mu2, _ = self.model.encode(img2_flat)  # Get latent representation of second image\n",
    "           \n",
    "           interpolated_images = []  # List to store interpolated images\n",
    "           \n",
    "           # Create interpolation steps between the two latent representations\n",
    "           for i in range(n_steps):  # Generate n_steps interpolation points\n",
    "               alpha = i / (n_steps - 1)  # Interpolation coefficient from 0 to 1\n",
    "               # Linear interpolation: z = (1-α)*z1 + α*z2\n",
    "               z_interp = (1 - alpha) * mu1 + alpha * mu2  # Interpolate between latent representations\n",
    "               \n",
    "               # Decode interpolated latent vector to generate image\n",
    "               img_interp = self.model.decode(z_interp)  # Generate image from interpolated latent vector\n",
    "               interpolated_images.append(img_interp.cpu().numpy().reshape(28, 28))  # Store as 28x28 image\n",
    "       \n",
    "       # Create visualization of interpolation sequence\n",
    "       fig, axes = plt.subplots(1, n_steps, figsize=(n_steps*2, 3))  # Single row with n_steps columns\n",
    "       \n",
    "       # Display each interpolation step\n",
    "       for i in range(n_steps):  # Iterate through interpolation steps\n",
    "           axes[i].imshow(interpolated_images[i], cmap='gray')  # Display interpolated image\n",
    "           axes[i].set_title(f'Step {i+1}', fontsize=12)  # Set title with step number\n",
    "           axes[i].axis('off')  # Remove axis ticks and labels\n",
    "       \n",
    "       plt.suptitle('Latent Space Interpolation Between Two Digits', fontsize=16, fontweight='bold')  # Overall title\n",
    "       plt.tight_layout()  # Adjust spacing between subplots\n",
    "       \n",
    "       # Save and display the plot\n",
    "       plot_path = f'{self.logger.log_dir}/latent_interpolation.png'  # Define save path\n",
    "       plt.savefig(plot_path, dpi=300, bbox_inches='tight')  # Save high-resolution image\n",
    "       plt.show()  # Display plot in notebook\n",
    "       \n",
    "       print(f\"🌊 Latent interpolation saved to {plot_path}\")\n",
    "\n",
    "def load_mnist_data(batch_size=128):\n",
    "   \"\"\"\n",
    "   Load and preprocess MNIST dataset with proper data loaders for VAE training\n",
    "   \n",
    "   Args:\n",
    "       batch_size: Number of samples per batch for training\n",
    "       \n",
    "   Returns:\n",
    "       train_loader: DataLoader for training data\n",
    "       test_loader: DataLoader for test data\n",
    "   \"\"\"\n",
    "   # Define data transformation pipeline\n",
    "   transform = transforms.Compose([  # Compose multiple transformations\n",
    "       transforms.ToTensor(),  # Convert PIL Image to PyTorch tensor and scale to [0,1]\n",
    "   ])\n",
    "   \n",
    "   # Load MNIST training dataset\n",
    "   train_dataset = torchvision.datasets.MNIST(\n",
    "       root='./data',  # Directory to store downloaded data\n",
    "       train=True,  # Load training split\n",
    "       download=True,  # Download data if not already present\n",
    "       transform=transform  # Apply defined transformations\n",
    "   )\n",
    "   \n",
    "   # Load MNIST test dataset\n",
    "   test_dataset = torchvision.datasets.MNIST(\n",
    "       root='./data',  # Directory to store downloaded data\n",
    "       train=False,  # Load test split\n",
    "       download=True,  # Download data if not already present\n",
    "       transform=transform  # Apply defined transformations\n",
    "   )\n",
    "   \n",
    "   # Create data loader for training dataset\n",
    "   train_loader = DataLoader(\n",
    "       train_dataset,  # Dataset to load from\n",
    "       batch_size=batch_size,  # Number of samples per batch\n",
    "       shuffle=True,  # Shuffle data for better training\n",
    "       num_workers=0,  # Number of subprocesses for data loading (0 for Jupyter compatibility)\n",
    "       pin_memory=True if device.type != 'cpu' else False  # Pin memory for faster GPU transfer\n",
    "   )\n",
    "   \n",
    "   # Create data loader for test dataset\n",
    "   test_loader = DataLoader(\n",
    "       test_dataset,  # Dataset to load from\n",
    "       batch_size=batch_size,  # Number of samples per batch\n",
    "       shuffle=False,  # Don't shuffle test data (order doesn't matter for evaluation)\n",
    "       num_workers=0,  # Number of subprocesses for data loading (0 for Jupyter compatibility)\n",
    "       pin_memory=True if device.type != 'cpu' else False  # Pin memory for faster GPU transfer\n",
    "   )\n",
    "   \n",
    "   # Print dataset information\n",
    "   print(f\"📊 Loaded MNIST dataset:\")\n",
    "   print(f\"   Training samples: {len(train_dataset)}\")  # Number of training samples\n",
    "   print(f\"   Test samples: {len(test_dataset)}\")  # Number of test samples\n",
    "   print(f\"   Batch size: {batch_size}\")  # Batch size for training\n",
    "   \n",
    "   return train_loader, test_loader  # Return both data loaders\n",
    "\n",
    "def run_vae_training(epochs=20, latent_dim=2, beta=1.0, learning_rate=0.001):\n",
    "   \"\"\"\n",
    "   Main function to run complete VAE training pipeline (optimized for Jupyter notebook)\n",
    "   \n",
    "   Args:\n",
    "       epochs: Number of training epochs\n",
    "       latent_dim: Dimension of latent space\n",
    "       beta: Beta parameter for VAE loss weighting\n",
    "       learning_rate: Learning rate for optimizer\n",
    "       \n",
    "   Returns:\n",
    "       model: Trained VAE model\n",
    "       metrics_df: DataFrame containing training metrics\n",
    "       visualizer: VAEVisualizer instance for further analysis\n",
    "   \"\"\"\n",
    "   print(\"🚀 PyTorch VAE Implementation for M4 Mac (Jupyter Optimized)\")\n",
    "   print(\"=\" * 60)\n",
    "   \n",
    "   # Display system and PyTorch information\n",
    "   print(f\"PyTorch version: {torch.__version__}\")  # Show PyTorch version\n",
    "   print(f\"Device: {device}\")  # Show computation device being used\n",
    "   print(f\"MPS available: {torch.backends.mps.is_available()}\")  # Show MPS availability status\n",
    "   \n",
    "   # Load MNIST dataset with specified batch size\n",
    "   print(\"\\n📂 Loading MNIST dataset...\")\n",
    "   train_loader, test_loader = load_mnist_data(batch_size=128)  # Load data with 128 samples per batch\n",
    "   \n",
    "   # Initialize VAE model with specified parameters\n",
    "   print(f\"\\n🏗️  Initializing VAE model (latent_dim={latent_dim}, beta={beta})...\")\n",
    "   vae_model = VAE(\n",
    "       input_dim=784,  # 28x28 MNIST images flattened to 784 dimensions\n",
    "       latent_dim=latent_dim,  # Specified latent space dimension\n",
    "       hidden_dim=256,  # 256 units in hidden layers\n",
    "       beta=beta  # Specified beta parameter for loss weighting\n",
    "   ).to(device)  # Move model to computation device\n",
    "   \n",
    "   # Display model information\n",
    "   total_params = sum(p.numel() for p in vae_model.parameters())  # Count total parameters\n",
    "   print(f\"   Total parameters: {total_params:,}\")  # Display parameter count with comma formatting\n",
    "   print(f\"   Model size: {total_params * 4 / 1024 / 1024:.2f} MB\")  # Estimate model size (4 bytes per float32 parameter)\n",
    "   \n",
    "   # Setup logging system with unique directory name\n",
    "   logger = VAELogger(log_dir=f\"vae_logs_latent{latent_dim}_beta{beta}\")  # Create logger with descriptive directory name\n",
    "   \n",
    "   # Initialize trainer with model, device, and logger\n",
    "   trainer = VAETrainer(vae_model, device, logger)  # Create trainer instance\n",
    "   \n",
    "   # Start training process\n",
    "   print(f\"\\n🚀 Starting VAE training ({epochs} epochs)...\")\n",
    "   metrics_df = trainer.train(  # Train model and get metrics DataFrame\n",
    "       train_loader=train_loader,  # Training data loader\n",
    "       val_loader=test_loader,  # Validation data loader (using test set)\n",
    "       epochs=epochs,  # Number of training epochs\n",
    "       learning_rate=learning_rate  # Learning rate for optimizer\n",
    "   )\n",
    "   \n",
    "   # Create comprehensive visualizations\n",
    "   print(\"\\n🎨 Creating visualizations...\")\n",
    "   visualizer = VAEVisualizer(vae_model, device, logger)  # Create visualizer instance\n",
    "   \n",
    "   # Generate latent space visualization (only for 2D latent space)\n",
    "   if latent_dim == 2:  # Only create latent space plot for 2D latent space\n",
    "       visualizer.plot_latent_space(test_loader, labels=True, n_samples=3000)  # Visualize latent space with class colors\n",
    "   \n",
    "   # Generate reconstruction comparison\n",
    "   visualizer.plot_reconstructions(test_loader, n_samples=8)  # Compare 8 original vs reconstructed images\n",
    "   \n",
    "   # Generate new samples from learned distribution\n",
    "   visualizer.generate_new_samples(n_samples=16)  # Generate 16 new samples from latent space\n",
    "   \n",
    "   # Generate interpolation visualization (only for 2D latent space)\n",
    "   if latent_dim == 2:  # Only create interpolation for 2D latent space\n",
    "       visualizer.interpolate_between_images(test_loader, n_steps=8)  # Create 8-step interpolation\n",
    "   \n",
    "   # Display completion information\n",
    "   print(\"\\n✅ VAE training and analysis complete!\")\n",
    "   print(f\"📁 All outputs saved to: {logger.log_dir}/\")  # Show where outputs are saved\n",
    "   \n",
    "   return vae_model, metrics_df, visualizer  # Return trained model, metrics, and visualizer for further use\n",
    "\n",
    "# Main execution block for running the VAE training pipeline\n",
    "if __name__ == \"__main__\":\n",
    "   \"\"\"\n",
    "   Main execution block - runs when script is executed directly\n",
    "   \n",
    "   This block demonstrates how to use the VAE implementation:\n",
    "   1. Trains a VAE model with default parameters\n",
    "   2. Creates comprehensive visualizations\n",
    "   3. Returns trained model and metrics for further analysis\n",
    "   \n",
    "   The returned objects can be used for:\n",
    "   - Further experimentation with different parameters\n",
    "   - Additional analysis of the learned representations\n",
    "   - Generating more samples or visualizations\n",
    "   \"\"\"\n",
    "   \n",
    "   # Run VAE training with optimized parameters for Jupyter notebook\n",
    "   # Parameters chosen for balance between training time and result quality\n",
    "   model, metrics, viz = run_vae_training(\n",
    "       epochs=20,  # 20 epochs for reasonable training time in notebook\n",
    "       latent_dim=2,  # 2D latent space for easy visualization\n",
    "       beta=1.0  # Standard VAE (beta=1) for balanced reconstruction/regularization\n",
    "   )\n",
    "   \n",
    "   # The returned objects can be used for further experimentation:\n",
    "   # - model: Trained VAE for generating samples or encoding data\n",
    "   # - metrics: DataFrame with training history for analysis\n",
    "   # - viz: Visualizer for creating additional plots\n",
    "   \n",
    "   # Example of further analysis (commented out):\n",
    "   # print(f\"Final training loss: {metrics['train_loss'].iloc[-1]:.4f}\")\n",
    "   # print(f\"Final validation loss: {metrics['val_loss'].iloc[-1]:.4f}\")\n",
    "   # viz.generate_new_samples(n_samples=25)  # Generate more samples"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}