10_6_LoRA with Pytorch__M.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\">\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LoRA with PyTorch\n",
    "\n",
    "Estimated time needed: **60** minutes\n",
    "\n",
    "As an AI engineer, you are tasked with fine-tuning a model for sentiment analysis on the IMDB dataset, starting with a model that is pretrained on the AG News dataset. By leveraging Low-Rank Adaptation (LoRA), the model is initially trained on AG News, benefiting from its extensive labeled data and broad categorization capabilities. This robust foundation enhances the model’s language understanding.\n",
    "\n",
    "Subsequently, LoRA is used to fine-tune the model on the IMDB dataset, adapting its knowledge to the nuances of movie reviews for sentiment analysis. This two-phase process — starting with AG News and refining with IMDB data — ensures that the model is both well-rounded and specialized, achieving superior performance in sentiment analysis tasks.\n",
    "\n",
    "**Note: If you are already familiar with training a model on the IMDB dataset, you can run the cells and then jump to the Low-Rank Adaptation (LoRA) section**\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "![Documents Overload](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-GPXX0Y15EN/docs.png)\n",
    "\n",
    "```Efficiency in parameter updates:``` LoRA introduces only a small fraction of additional parameters compared to the total number of parameters in a large model. This makes the training process faster and less resource-intensive because fewer parameters need to be updated during backpropagation.\n",
    "\n",
    "```Preservation of pretrained knowledge:``` By keeping the majority of the model's weights fixed and only adjusting them through low-rank matrices, LoRA helps preserve the rich representations that the model learned during pretraining. This is particularly beneficial for tasks that do not require drastic deviations from the behavior learned during pretraining.\n",
    "\n",
    "```Customization to specific tasks:``` Despite the minimal updates, the changes introduced by LoRA are significant enough to adapt the model to specific tasks. This lets you fine-tune large models on specialized tasks without the need for extensive retraining.\n",
    "\n",
    "```Reduction in overfitting:``` Because only a limited number of parameters are adapted, the risk of overfitting is lower compared to full model fine-tuning, especially when adapting to smaller datasets.\n",
    "\n",
    "```Scalability:``` LoRA scales well with model size. As models become larger, the relative increase in the number of parameters introduced by LoRA becomes even smaller, making it a particularly attractive option for adapting very large models.\n",
    "\n",
    "```Compatibility and simplicity:``` The method can be easily applied to different types of neural networks, especially those based on the transformer architecture. It doesn't require major changes to the existing architecture, which simplifies integration into existing pipelines.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __Table of Contents__\n",
    "\n",
    "<ol>\n",
    "    <li><a href=\"#Objectives\">Objectives</a></li>\n",
    "    <li>\n",
    "        <a href=\"#Setup\">Setup</a>\n",
    "        <ol>\n",
    "            <li><a href=\"#Install-required-libraries\">Install required libraries</a></li>\n",
    "            <li><a href=\"#Import-required-libraries\">Import required libraries</a></li>\n",
    "            <li><a href=\"#Defining-helper-functions\">Defining helper functions</a></li>\n",
    "        </ol>\n",
    "    </li>\n",
    "    <li>\n",
    "        <a href=\"#Data-pipeline\">Data pipeline</a>\n",
    "        <ol>\n",
    "            <li><a href=\"#Tokenizer\">Tokenizer</a></li>\n",
    "        </ol>\n",
    "    </li>\n",
    "    <li>\n",
    "        <a href=\"#IMDB-dataset\">IMDB dataset</a></li>\n",
    "        <ol>\n",
    "            <li><a href=\"#Dataset-composition\">Dataset composition</a></li>\n",
    "            <li><a href=\"#Applications\">Applications</a></li>\n",
    "            <li><a href=\"#Challenges\">Challenges</a></li>\n",
    "            <li><a href=\"#Train-and-validate\">Train and validate</a></li>\n",
    "            <li><a href=\"#Data-loader\">Data loader</a></li>\n",
    "            <li><a href=\"#Neural-network\">Neural network</a></li>\n",
    "        </ol>\n",
    "     </li>\n",
    "    <li>\n",
    "        <a href=\"#Train-the-model-on-the-full-dataset\">Train the model on the full dataset</a></li>\n",
    "        <ol>\n",
    "            <li><a href=\"#Train-the-model\">Train the model</a></li>\n",
    "        </ol>\n",
    "     </li>\n",
    "    <li>\n",
    "        <a href=\"#Low-Rank-Adaptation-(LoRA)\">Low-Rank Adaptation (LoRA)</a></li>\n",
    "        <ol>\n",
    "            <li><a href=\"#LoRA\">LoRA</a></li>\n",
    "            <li><a href=\"#Rank\">Rank</a></li>\n",
    "            <li><a href=\"#Understanding-LoRA-in-PyTorch\">Understanding LoRA in PyTorch</a></li>\n",
    "            <li><a href=\"#Applying-LoRA\">Applying LoRA</a></li>\n",
    "            <li><a href=\"#Loading-the-model\">Loading the model</a></li>\n",
    "        </ol>\n",
    "     </li>\n",
    "    <li>\n",
    "        <a href=\"#Exercise:-Apply-LoRA-to-a-different-network\">Exercise: Apply LoRA to a different network</a>\n",
    "    </li>\n",
    "</ol>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n",
    "\n",
    "After completing this lab you are able to:\n",
    "\n",
    "- Construct and train a neural network from the ground up\n",
    "- Fine-tune a neural network in the conventional manner by unfreezing specific layers\n",
    "- Use LoRA to fine-tune a neural network\n",
    "- Comprehend the functions of LoRA and the reasons behind its effectiveness\n",
    "- Save and load models that employ LoRA efficiently\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install required libraries\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following required libraries are __not__ preinstalled in the Skills Network Labs environment. __You must run the following cell__ to install them. Note that it can take between __5 and 10 minutes__ to install the required libraries:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.2/6.2 MB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m\n",
      "\u001b[?25hDownloading mpmath-1.3.0-py3-none-any.whl (536 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m536.2/536.2 kB\u001b[0m \u001b[31m56.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading cmake-3.31.1-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (27.8 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27.8/27.8 MB\u001b[0m \u001b[31m21.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hDownloading lit-18.1.8-py3-none-any.whl (96 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m96.4/96.4 kB\u001b[0m \u001b[31m12.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: mpmath, lit, sympy, nvidia-nvtx-cu11, nvidia-nccl-cu11, nvidia-cusparse-cu11, nvidia-curand-cu11, nvidia-cufft-cu11, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cuda-cupti-cu11, nvidia-cublas-cu11, networkx, filelock, cmake, nvidia-cusolver-cu11, nvidia-cudnn-cu11, triton, torch\n",
      "Successfully installed cmake-3.31.1 filelock-3.16.1 lit-18.1.8 mpmath-1.3.0 networkx-3.4.2 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.2.10.91 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusparse-cu11-11.7.4.91 nvidia-nccl-cu11-2.14.3 nvidia-nvtx-cu11-11.7.91 sympy-1.13.3 torch-2.0.1 triton-2.0.0\n",
      "Collecting torchtext==0.15.2\n",
      "  Downloading torchtext-0.15.2-cp311-cp311-manylinux1_x86_64.whl.metadata (7.4 kB)\n",
      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.11/site-packages (from torchtext==0.15.2) (4.66.4)\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.11/site-packages (from torchtext==0.15.2) (2.31.0)\n",
      "Requirement already satisfied: torch==2.0.1 in /opt/conda/lib/python3.11/site-packages (from torchtext==0.15.2) (2.0.1)\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.11/site-packages (from torchtext==0.15.2) (1.24.1)\n",
      "Collecting torchdata==0.6.1 (from torchtext==0.15.2)\n",
      "  Downloading torchdata-0.6.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchtext==0.15.2) (3.16.1)\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchtext==0.15.2) (4.12.2)\n",
      "Requirement already satisfied: sympy in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchtext==0.15.2) (1.13.3)\n",
      "Requirement already satisfied: networkx in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchtext==0.15.2) (3.4.2)\n",
      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchtext==0.15.2) (3.1.3)\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchtext==0.15.2) (11.7.99)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchtext==0.15.2) (11.7.99)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu11==11.7.101 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchtext==0.15.2) (11.7.101)\n",
      "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchtext==0.15.2) (8.5.0.96)\n",
      "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchtext==0.15.2) (11.10.3.66)\n",
      "Requirement already satisfied: nvidia-cufft-cu11==10.9.0.58 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchtext==0.15.2) (10.9.0.58)\n",
      "Requirement already satisfied: nvidia-curand-cu11==10.2.10.91 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchtext==0.15.2) (10.2.10.91)\n",
      "Requirement already satisfied: nvidia-cusolver-cu11==11.4.0.1 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchtext==0.15.2) (11.4.0.1)\n",
      "Requirement already satisfied: nvidia-cusparse-cu11==11.7.4.91 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchtext==0.15.2) (11.7.4.91)\n",
      "Requirement already satisfied: nvidia-nccl-cu11==2.14.3 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchtext==0.15.2) (2.14.3)\n",
      "Requirement already satisfied: nvidia-nvtx-cu11==11.7.91 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchtext==0.15.2) (11.7.91)\n",
      "Requirement already satisfied: triton==2.0.0 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchtext==0.15.2) (2.0.0)\n",
      "Requirement already satisfied: urllib3>=1.25 in /opt/conda/lib/python3.11/site-packages (from torchdata==0.6.1->torchtext==0.15.2) (2.2.1)\n",
      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.11/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch==2.0.1->torchtext==0.15.2) (69.5.1)\n",
      "Requirement already satisfied: wheel in /opt/conda/lib/python3.11/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch==2.0.1->torchtext==0.15.2) (0.43.0)\n",
      "Requirement already satisfied: cmake in /opt/conda/lib/python3.11/site-packages (from triton==2.0.0->torch==2.0.1->torchtext==0.15.2) (3.31.1)\n",
      "Requirement already satisfied: lit in /opt/conda/lib/python3.11/site-packages (from triton==2.0.0->torch==2.0.1->torchtext==0.15.2) (18.1.8)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.11/site-packages (from requests->torchtext==0.15.2) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.11/site-packages (from requests->torchtext==0.15.2) (3.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.11/site-packages (from requests->torchtext==0.15.2) (2024.8.30)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.11/site-packages (from jinja2->torch==2.0.1->torchtext==0.15.2) (2.1.5)\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/lib/python3.11/site-packages (from sympy->torch==2.0.1->torchtext==0.15.2) (1.3.0)\n",
      "Downloading torchtext-0.15.2-cp311-cp311-manylinux1_x86_64.whl (2.0 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m76.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading torchdata-0.6.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.6 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.6/4.6 MB\u001b[0m \u001b[31m110.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: torchdata, torchtext\n",
      "Successfully installed torchdata-0.6.1 torchtext-0.15.2\n",
      "Requirement already satisfied: torchdata==0.6.1 in /opt/conda/lib/python3.11/site-packages (0.6.1)\n",
      "Requirement already satisfied: urllib3>=1.25 in /opt/conda/lib/python3.11/site-packages (from torchdata==0.6.1) (2.2.1)\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.11/site-packages (from torchdata==0.6.1) (2.31.0)\n",
      "Requirement already satisfied: torch==2.0.1 in /opt/conda/lib/python3.11/site-packages (from torchdata==0.6.1) (2.0.1)\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchdata==0.6.1) (3.16.1)\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchdata==0.6.1) (4.12.2)\n",
      "Requirement already satisfied: sympy in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchdata==0.6.1) (1.13.3)\n",
      "Requirement already satisfied: networkx in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchdata==0.6.1) (3.4.2)\n",
      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchdata==0.6.1) (3.1.3)\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchdata==0.6.1) (11.7.99)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchdata==0.6.1) (11.7.99)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu11==11.7.101 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchdata==0.6.1) (11.7.101)\n",
      "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchdata==0.6.1) (8.5.0.96)\n",
      "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchdata==0.6.1) (11.10.3.66)\n",
      "Requirement already satisfied: nvidia-cufft-cu11==10.9.0.58 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchdata==0.6.1) (10.9.0.58)\n",
      "Requirement already satisfied: nvidia-curand-cu11==10.2.10.91 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchdata==0.6.1) (10.2.10.91)\n",
      "Requirement already satisfied: nvidia-cusolver-cu11==11.4.0.1 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchdata==0.6.1) (11.4.0.1)\n",
      "Requirement already satisfied: nvidia-cusparse-cu11==11.7.4.91 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchdata==0.6.1) (11.7.4.91)\n",
      "Requirement already satisfied: nvidia-nccl-cu11==2.14.3 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchdata==0.6.1) (2.14.3)\n",
      "Requirement already satisfied: nvidia-nvtx-cu11==11.7.91 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchdata==0.6.1) (11.7.91)\n",
      "Requirement already satisfied: triton==2.0.0 in /opt/conda/lib/python3.11/site-packages (from torch==2.0.1->torchdata==0.6.1) (2.0.0)\n",
      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.11/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch==2.0.1->torchdata==0.6.1) (69.5.1)\n",
      "Requirement already satisfied: wheel in /opt/conda/lib/python3.11/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch==2.0.1->torchdata==0.6.1) (0.43.0)\n",
      "Requirement already satisfied: cmake in /opt/conda/lib/python3.11/site-packages (from triton==2.0.0->torch==2.0.1->torchdata==0.6.1) (3.31.1)\n",
      "Requirement already satisfied: lit in /opt/conda/lib/python3.11/site-packages (from triton==2.0.0->torch==2.0.1->torchdata==0.6.1) (18.1.8)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.11/site-packages (from requests->torchdata==0.6.1) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.11/site-packages (from requests->torchdata==0.6.1) (3.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.11/site-packages (from requests->torchdata==0.6.1) (2024.8.30)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.11/site-packages (from jinja2->torch==2.0.1->torchdata==0.6.1) (2.1.5)\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/lib/python3.11/site-packages (from sympy->torch==2.0.1->torchdata==0.6.1) (1.3.0)\n",
      "Collecting plotly==5.22.0\n",
      "  Downloading plotly-5.22.0-py3-none-any.whl.metadata (7.1 kB)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in /opt/conda/lib/python3.11/site-packages (from plotly==5.22.0) (9.0.0)\n",
      "Requirement already satisfied: packaging in /opt/conda/lib/python3.11/site-packages (from plotly==5.22.0) (24.0)\n",
      "Downloading plotly-5.22.0-py3-none-any.whl (16.4 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.4/16.4 MB\u001b[0m \u001b[31m38.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m0:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: plotly\n",
      "  Attempting uninstall: plotly\n",
      "    Found existing installation: plotly 5.24.1\n",
      "    Uninstalling plotly-5.24.1:\n",
      "\u001b[31mERROR: Could not install packages due to an OSError: [Errno 13] Permission denied: '/opt/conda/etc/jupyter/nbconfig/notebook.d/jupyterlab-plotly.json'\n",
      "Consider using the `--user` option or check the permissions.\n",
      "\u001b[0m\u001b[31m\n",
      "\u001b[0mCollecting pandas==2.2.2\n",
      "  Downloading pandas-2.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (19 kB)\n",
      "Requirement already satisfied: numpy>=1.23.2 in /opt/conda/lib/python3.11/site-packages (from pandas==2.2.2) (1.24.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.11/site-packages (from pandas==2.2.2) (2.9.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.11/site-packages (from pandas==2.2.2) (2024.1)\n",
      "Collecting tzdata>=2022.7 (from pandas==2.2.2)\n",
      "  Downloading tzdata-2024.2-py2.py3-none-any.whl.metadata (1.4 kB)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas==2.2.2) (1.16.0)\n",
      "Downloading pandas-2.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.0 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.0/13.0 MB\u001b[0m \u001b[31m109.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m0:01\u001b[0m\n",
      "\u001b[?25hDownloading tzdata-2024.2-py2.py3-none-any.whl (346 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m346.6/346.6 kB\u001b[0m \u001b[31m34.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: tzdata, pandas\n",
      "Successfully installed pandas-2.2.2 tzdata-2024.2\n",
      "Collecting matplotlib==3.9.0\n",
      "  Downloading matplotlib-3.9.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n",
      "Collecting contourpy>=1.0.1 (from matplotlib==3.9.0)\n",
      "  Downloading contourpy-1.3.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.4 kB)\n",
      "Collecting cycler>=0.10 (from matplotlib==3.9.0)\n",
      "  Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n",
      "Collecting fonttools>=4.22.0 (from matplotlib==3.9.0)\n",
      "  Downloading fonttools-4.55.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (164 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m164.7/164.7 kB\u001b[0m \u001b[31m11.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hCollecting kiwisolver>=1.3.1 (from matplotlib==3.9.0)\n",
      "  Downloading kiwisolver-1.4.7-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.3 kB)\n",
      "Requirement already satisfied: numpy>=1.23 in /opt/conda/lib/python3.11/site-packages (from matplotlib==3.9.0) (1.24.1)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.11/site-packages (from matplotlib==3.9.0) (24.0)\n",
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    }
   ],
   "source": [
    "!pip install numpy==1.24.1\n",
    "!pip install -U portalocker==2.8.2\n",
    "!pip install torch==2.0.1\n",
    "!pip install torchtext==0.15.2\n",
    "!pip install torchdata==0.6.1\n",
    "!pip install -U plotly==5.22.0\n",
    "!pip install pandas==2.2.2\n",
    "!pip install matplotlib==3.9.0\n",
    "!pip install scikit-learn==1.5.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import required libraries\n",
    "\n",
    "The following imports the required libraries:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from itertools import accumulate\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import os\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import torchtext #; torchtext.disable_torchtext_deprecation_warning()\n",
    "from torchtext.vocab import build_vocab_from_iterator, GloVe, Vectors\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "\n",
    "\n",
    "from torch.utils.data import DataLoader\n",
    "import numpy as np\n",
    "from torchtext.datasets import AG_NEWS\n",
    "from IPython.display import Markdown as md\n",
    "from tqdm import tqdm\n",
    "\n",
    "from torchtext.vocab import build_vocab_from_iterator\n",
    "\n",
    "from torch.utils.data.dataset import random_split,Dataset\n",
    "from torchtext.data.functional import to_map_style_dataset\n",
    "from sklearn.manifold import TSNE\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "import pickle\n",
    "\n",
    "from urllib.request import urlopen\n",
    "import io\n",
    "\n",
    "import tarfile\n",
    "import tempfile\n",
    "\n",
    "# You can also use this section to suppress warnings generated by your code:\n",
    "def warn(*args, **kwargs):\n",
    "    pass\n",
    "import warnings\n",
    "warnings.warn = warn\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining helper functions\n",
    "\n",
    "The following are some helper functions to help with plotting, saving, and loading files. These functions are not the main focus of this lab, you do not have to dwell on these too long. However, do run the cells in this section to define these helper functions:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(COST,ACC):\n",
    "    fig, ax1 = plt.subplots()\n",
    "    color = 'tab:red'\n",
    "    ax1.plot(COST, color=color)\n",
    "    ax1.set_xlabel('epoch', color=color)\n",
    "    ax1.set_ylabel('total loss', color=color)\n",
    "    ax1.tick_params(axis='y', color=color)\n",
    "\n",
    "    ax2 = ax1.twinx()\n",
    "    color = 'tab:blue'\n",
    "    ax2.set_ylabel('accuracy', color=color)  # You already handled the x-label with ax1\n",
    "    ax2.plot(ACC, color=color)\n",
    "    ax2.tick_params(axis='y', color=color)\n",
    "    fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "def save_list_to_file(lst, filename):\n",
    "    \"\"\"\n",
    "    Save a list to a file using pickle serialization.\n",
    "\n",
    "    Parameters:\n",
    "        lst (list): The list to be saved.\n",
    "        filename (str): The name of the file to save the list to.\n",
    "\n",
    "    Returns:\n",
    "        None\n",
    "    \"\"\"\n",
    "    with open(filename, 'wb') as file:\n",
    "        pickle.dump(lst, file)\n",
    "\n",
    "def load_list_from_file(filename):\n",
    "    \"\"\"\n",
    "    Load a list from a file using pickle deserialization.\n",
    "\n",
    "    Parameters:\n",
    "        filename (str): The name of the file to load the list from.\n",
    "\n",
    "    Returns:\n",
    "        list: The loaded list.\n",
    "    \"\"\"\n",
    "    with open(filename, 'rb') as file:\n",
    "        loaded_list = pickle.load(file)\n",
    "    return loaded_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data pipeline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tokenizer\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A tokenizer takes as input a document and breaks it up into individual tokens. Now, you might wonder, what's a token?\n",
    "This example might help you understand it better.\n",
    "\n",
    "Imagine a token as a puzzle piece of a jigsaw puzzle. Each word, number, or small part of a word is a token. When you tokenize a document, you break it into these puzzle pieces so that a computer can understand and work with the text more easily, just like how you solve a puzzle by arranging its pieces.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, import the **```get_tokenizer```** function from **```torchtext.data.utils```**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchtext.data.utils import get_tokenizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll create the tokenizer. We'll set it to the \"basic_english\" tokenizer that is provided by `torchtext`. The \"basic_english\" tokenizer is designed to handle basic English text and splits the text into individual tokens based on spaces and punctuation marks.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = get_tokenizer(\"basic_english\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our dataset is going to be an iterable. Therefore, We'll use a generator function **```yield_tokens```** to apply **```tokenizer```**. The purpose of the generator function **```yield_tokens```** is to yield tokenized texts one at a time. Instead of processing the entire dataset and returning all of the tokenized texts in one go, the generator function processes and yields each tokenized text individually as it is requested. The tokenization process is performed lazily, which means the next tokenized text is generated only when needed, saving memory and computational resources.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def yield_tokens(data_iter):\n",
    "    for  _,text in data_iter:\n",
    "        yield tokenizer(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following loads a pretrained word embedding model called GloVe into a variable called `glove_embedding`:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that GloVe embeddings are typically downloaded using:\n",
    "# glove_embedding = GloVe(name=\"6B\", dim=100)\n",
    "# However, the GloVe server is frequently down. The code below offers a workaround\n",
    "\n",
    "\n",
    "class GloVe_override(Vectors):\n",
    "    url = {\n",
    "        \"6B\": \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/tQdezXocAJMBMPfUJx_iUg/glove-6B.zip\",\n",
    "    }\n",
    "\n",
    "    def __init__(self, name=\"6B\", dim=100, **kwargs) -> None:\n",
    "        url = self.url[name]\n",
    "        name = \"glove.{}.{}d.txt\".format(name, str(dim))\n",
    "        #name = \"glove.{}/glove.{}.{}d.txt\".format(name, name, str(dim))\n",
    "        super(GloVe_override, self).__init__(name, url=url, **kwargs)\n",
    "\n",
    "class GloVe_override2(Vectors):\n",
    "    url = {\n",
    "        \"6B\": \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/tQdezXocAJMBMPfUJx_iUg/glove-6B.zip\",\n",
    "    }\n",
    "\n",
    "    def __init__(self, name=\"6B\", dim=100, **kwargs) -> None:\n",
    "        url = self.url[name]\n",
    "        #name = \"glove.{}.{}d.txt\".format(name, str(dim))\n",
    "        name = \"glove.{}/glove.{}.{}d.txt\".format(name, name, str(dim))\n",
    "        super(GloVe_override2, self).__init__(name, url=url, **kwargs)\n",
    "\n",
    "try:\n",
    "    glove_embedding = GloVe_override(name=\"6B\", dim=100)\n",
    "except:\n",
    "    try:\n",
    "        glove_embedding = GloVe_override2(name=\"6B\", dim=100)\n",
    "    except:\n",
    "        glove_embedding = GloVe(name=\"6B\", dim=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following builds a vocabulary object from a pretrained GloVe word embedding model and sets the default index to the <unk> token:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from torchtext.vocab import vocab\n",
    "vocab = vocab(glove_embedding .stoi, 0,specials=('<unk>', '<pad>'))\n",
    "vocab.set_default_index(vocab[\"<unk>\"])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following prepares the text processing pipeline with the tokenizer and vocabulary. The text pipeline will be used to process the raw data strings from the dataset iterators.\n",
    "\n",
    "The function **```text_pipeline```** first tokenizes the input text, following which **```vocab```** is applied to get the token indices.\n",
    "\n",
    "The function **```label_pipeline```** simply converts labels into their integer values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def text_pipeline(x):\n",
    "  return vocab(tokenizer(x))\n",
    "\n",
    "def label_pipeline(x):\n",
    "   return int(x) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IMDB dataset \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following loads the IMDB dataset into a temporary folder. This might take some time, so please be patient.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "urlopened = urlopen('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/35t-FeC-2uN1ozOwPs7wFg.gz')\n",
    "tar = tarfile.open(fileobj=io.BytesIO(urlopened.read()))\n",
    "tempdir = tempfile.TemporaryDirectory()\n",
    "tar.extractall(tempdir.name)\n",
    "tar.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **IMDB dataset** contains movie reviews from the Internet Movie Database (IMDB) and is commonly used for binary sentiment classification tasks. It's a popular dataset for training and testing models in natural language processing (NLP), particularly in the context of sentiment analysis.\n",
    "\n",
    "### Dataset composition\n",
    "\n",
    "- **Reviews**: The dataset consists of 50,000 movie reviews, divided evenly into 25,000 training and 25,000 testing samples.\n",
    "- **Sentiment labels**: Each review is labeled as either positive or negative, indicating the sentiment expressed in the review. The dataset is balanced, with an equal number of positive and negative reviews in both the training and testing sets.\n",
    "- **Text content**: Reviews are presented as plain text and have been preprocessed to some extent. For example, HTML tags are removed, but the text retains its original punctuation and capitalization.\n",
    "- **Usage**: The dataset is commonly used to train models for binary sentiment classification, where the goal is to predict whether a given review is positive or negative based on its text content.\n",
    "\n",
    "### Applications\n",
    "\n",
    "- **Sentiment analysis**: The primary application of the IMDB dataset is in sentiment analysis, where it serves as a benchmark for various text classification algorithms.\n",
    "- **Natural language processing (NLP)**: The dataset is widely used in NLP research and applications, providing a basis for testing the effectiveness of different models and approaches in understanding human language.\n",
    "\n",
    "### Challenges\n",
    "\n",
    "The dataset is small, so it's hard to train a model from scratch.\n",
    "\n",
    "The following class is defined to traverse the IMDB dataset. The need to define this class arises from the fact that the IMDB dataset is split across a large number of files:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class IMDBDataset(Dataset):\n",
    "    def __init__(self, root_dir, train=True):\n",
    "        \"\"\"\n",
    "        root_dir: The base directory of the IMDB dataset.\n",
    "        train: A boolean flag indicating whether to use training or test data.\n",
    "        \"\"\"\n",
    "        self.root_dir = os.path.join(root_dir, \"train\" if train else \"test\")\n",
    "        self.neg_files = [os.path.join(self.root_dir, \"neg\", f) for f in os.listdir(os.path.join(self.root_dir, \"neg\")) if f.endswith('.txt')]\n",
    "        self.pos_files = [os.path.join(self.root_dir, \"pos\", f) for f in os.listdir(os.path.join(self.root_dir, \"pos\")) if f.endswith('.txt')]\n",
    "        self.files = self.neg_files + self.pos_files\n",
    "        self.labels = [0] * len(self.neg_files) + [1] * len(self.pos_files)\n",
    "        self.pos_inx=len(self.pos_files)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.files)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        file_path = self.files[idx]\n",
    "        label = self.labels[idx]\n",
    "        with open(file_path, 'r', encoding='utf-8') as file:\n",
    "            content = file.read()\n",
    "            \n",
    "        return label, content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following uses the `IMDBDataset` class defined above to create iterators for the train and test datasets:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "root_dir = tempdir.name + '/' + 'imdb_dataset'\n",
    "train_iter = IMDBDataset(root_dir=root_dir, train=True)  # For training data\n",
    "test_iter = IMDBDataset(root_dir=root_dir, train=False)  # For test dataart=train_iter.pos_inx\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following prints 20 samples from the training set:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 'The pilot of Enterprise has one thing that has been lacking since the original Star Trek: A dose of realistic, flawed personalities. The Utopian characters of the Next Generation got tiring, they were so noble as to be unbelievable. I also like the sub-plot that humans are bitter toward the Vulcans. Its funny seeing them as pretentious snobs. It makes me look forward to seeing when the humans become the dominant race between the two, though I don\\'t think it would work in the time frame of the show. The only negatives that jumped out at me were the \"quick cut off the ending at 2 hours\" feel of the end, which is common among many of the Trek shows. The second was the shameless dig for ratings by a couple of senselessly sexy scenes. It was out of place, a good science fiction show should be able to stand on its own without trying to pad the pre-teen audience with some skin. But its not my job to make the show profitable, so oh well.<br /><br />Lets see how the next episode does.')\n",
      "(1, '\"The Godfather\" of television, but aside from it\\'s acclaim and mobster characters, the two are nothing alike. Tony Soprano is forced to go to a psychiatrist after a series of panic attacks. His psychiatrist learns that Tony is actually part of two families -- in one family he is a loving father yet not-so-perfect-husband, and in the other family he is a ruthless wiseguy. After analysis, Dr. Melfi concludes that Tony\\'s problems actually derive from his mother Livia, who\\'s suspected to have borderline-personality disorder. Gandolfini is rightfully praised as the main character; yet Bracco and Marchand aren\\'t nearly as recognized for their equally and talented performances as the psychiatrist and mother, respectively. Falco, Imperioli and DeMatteo are acclaimed for their brilliant supporting roles. Van Zandt (from the E-Street Band) plays his first and only role as Tony\\'s best friend, and is quite convincing and latching. Chianese, the only recurring actor to have actually appeared in a Godfather film, plays Tony\\'s uncle and on-and-off nemesis. Many fans also enjoyed characters played by Pastore, Ventimiglia, Curatola, Proval, Pantoliano, Lip, Sciorra and Buscemi. Tony\\'s children are \"okay\" but not notable (with the exception of Iler\\'s stunning performance in the third-to-last episode, \"The Second Coming\"); Sirico and Schirripa are unconvincing and over-the-top, but the show is too strong for them to hold it back. Even as the show continues for over six season, it ceases to have a dull or predictable moment.<br /><br />**** (out of four)')\n",
      "(1, 'Pickup on South Street (1953), directed by movie maverick Samuel Fuller, contains a stunning opening that establishes a double complication. Subway rider Candy (Susan Peters) collides with pickpocket Skip McCoy (Richard Widmark dipped in shades of Sinatra cool). She\\'s unaware that she carries valuable microfilm; McCoy is unaware of grifting it. Both are unaware of being observed by two federal agents. Thus the grift sets in motion a degree of knowledges. Candy is doubly watched (Skip and the police) and therefore doubly naive; Skip, the overconfident petty thief, is singularly unaware, trailed by federal agents; the feds, all knowing, are ultimately helpless. They can\\'t stop the \"passing\" of government secrets or the spread of communism.')\n",
      "(1, \"OH WOW. I saw this film at the Irish International Film Fleadh in Manhattan on 12 March 2000. Both stars were in attendance and were available for questions afterward. WHAT A GORGEOUS FILM! Although set in Ireland amid Catholic/Protestant antagonism, the story could have happened anywhere between any two groups of people who hate each other. The horror of how quickly people can get carried away when they are given a chance to vent their hate and anger was woven beautifully with a moving love story drizzled with humor and fun. If this one does not get picked up in the USA, it would truly be most unfortunate.<br /><br />As for the stars and supporting players...FIRST RATE. They call Orla Brady the Irish Meryl Streep, I heard. It is my opinion that she is BETTER than Meryl Streep. They should be calling Meryl the American Orla Brady! And, Liam Cunningham's steady and powerful portrayal of a simple and private man sucked into a political war was brilliant.<br /><br />SEE THIS MOVIE.\")\n",
      "(1, 'Just after the end of WWII Powell & Pressburger were asked to come up with something to try to heal the rift developing between the UK & the USA. At the time there was a lot of \"Overpaid, over sexed and over here\" type of comments. Somehow they came up with this masterpiece.<br /><br />My favourite movie of ALL time. It\\'s got everything. Romance, poetry, emotion, religion, drama and very quirky.<br /><br />I can never explain exactly why, but it hits all the right buttons and although I\\'ve seen it hundreds of times (yes, really) I\\'m still guaranteed to be in tears at many points throughout.<br /><br />Was it the magnificent acting, the wonderful sets, the inspired script ? Who knows. But *DO* watch it and you\\'ll see what I mean.')\n",
      "(1, \"One of the great things about many of the superb Chinese movies you can find, if you are lucky, in the video stores, is they are very accurate retellings of actual, true stories. Farewell, my Concubine, The Emperor and the Assassin and this movie are perfect examples. The film makers take a true story and work hard to accurately create a movie without compromising the facts for dramatic or commercial convenience -- the hallmark of much Hollywood, and especially Disney films.<br /><br />In this story we follow the later years of an famous local street performer dubbed the King of Masks for his mastery of Sichuan Change Art. Along an having lost his only son many years earlier, he searches to find a male heir to carry on his rare and dying art in a society that forbids females to have such work. Master Wang is sold a son by a slave trader. All is well as he joyfully prepares to pass down his art. But the son eventually is found out to be a girl. From there, the story get very interesting, with a good performance by Master Liang of the Sichuan Opera -- a regional operatic style related to Peking Opera. Fans of Farewell My Concubine should look carefully at Master Liang's portrayal of a male playing the female role in Chinese Opera. It may help them come to understand that the players of these female roles were probably not homosexuals or castrati, but people who have be so psychologically conditioned as to be totally unaware of their own sexuality.\")\n",
      "(1, \"If you want to watch something that is for 'him' and 'her' so to say then this is the film to pick. I am a sucker for rom coms but my husband is not always so keen (what a guy!!!). Anyway I managed to get him to watch it because I told him it was about sport, and you know what, he loved it!!!<br /><br />Drew Barrymore is very funny and her leading man (sorry but can't remember his name) is equally as good. When I watched the film it was called 'The Perfect Match' but I think the title was changed for the UK as it is based on the book Fever Pitch and there was already a film made about football with that title (the same film but the UK version - phew!),<br /><br />Anyway all of the reviews on here will tell you more details if you need them buy girls, take it from me, get your hubby/boyfriend in front of the television on a Saturday night and you will both laugh and cry together. A real gem.\")\n",
      "(1, \"...I saw this on cable back in the late 1980's as I was a big wrestling fan since 1986. I saw this on VHS in a 'for sale' bin and bought it.<br /><br />In 1998, I started training as a wrestler after the Air Force and would always go back to watching this to see how it was a very accurate portrayal of people that are involved with wrestling ( families and friends that wouldn't understand us, the travel, the heartbreak, etc. ). Henry Winkler is funny and sometimes sad to watch as nobody else can understand what a genius he is creatively. A great way to separate himself The Fonz character he played on Happy Days at the time. Plus, look at the cast...William Daniels ( Knight Rider ), Polly Holiday ( Alice ), and wrestlers Roddy Piper and Chavo Guerrero Sr. If you get a chance, watch it.\")\n",
      "(1, 'I have a deep liking for this film despite it appearing deliberately less \\'polished\\' than the other Fred and Ginger films, not to mention the slightly problematic casting of Harriet Hillard in the lead romantic role. <br /><br />Once again with these films, the plot is of a minor consequence - Astaire plays a rather unlikely sailor (who happens to be a brilliant former hoofer (of course!)) and Rogers an aspiring performer in a seedy dime-a-dance music hall. Although their relationship is bright and fun to watch, they are bogged down by an un-involving main story of Hilliard and Randolph Scott not succeeding in finding any chemistry between them. <br /><br />Although it was a last minute decision, Hilliard was rather miscast in this as she doesn\\'t have the screen presence to give this film what it needs despite being sweet and likable throughout. The film may have benefited in promoting Lucille Ball\\'s wise-cracking worldy brassy character to a larger role as she simply shined in every small scene, and would have made a great Helen Broderick-type side-kick to Rogers in this kind of bright film (See \\'Stage Door\\', made the following year, for an example of wonderful scenes between these two fantastic actresses). That Scott\\'s one-dimensional Neanderthal character eventually falls in love with Hilliard\\'s is even harder to believe than Astaire being in the Navy! <br /><br />Now onto the important part, the singing and dancing: Nothing more can be said about \"Let\\'s Face the Music and Dance\" other than it is brilliant and moving and perfectly executed and I often finish watching that scene with tears in my eyes. However other songs in the film deserve some recognition as well; \"I\\'m putting all my Eggs in One Basket\" is a lovely example of the comedic instincts of Astaire and Rogers, and almost pokes fun at their reputation of bursting into spontaneous, perfectly synchronized dancing. Other highlights are \"I\\'d Rather Lead the Band\" and \"Let Yourself Go\" which show how these two talents could perform as brilliantly alone as together. I nearly forgot to mention that this is one of the few (if not the only) time we see Astaire brilliantly play on the piano; It seems this man\\'s talents were endless!<br /><br />Overall, I actually prefer this to \\'Top Hat\\' and \\'Swing Time\\' (although only just), as it is more earthy and performed so enthusiastically by all involved, it is hard to dislike the fun factor.')\n",
      "(1, \"In 1943, a group of RAF Officers, including Eric Wiiliams, decide to escape from a POW camp using a Gymnastic Vaulting Horse in the courtyard. In 1950, it was decided to film his account, and it kick-started a peculiar British Film Genre- the Military Prison Camp story that reached its apogee in Danger Within (1959).<br /><br />The Wooden Horse is one of the quietest films I have ever watched. There are no great dramatic moments, but a steady storyline eventually builds to a climax that has more tension because the story doesn't give way for unlikely drama, jump cuts or jacked up (somethings about to happen!) music. It is utterly of its time and works beautifully.<br /><br />Leo Glenn, Anthony Steel and David Tomlinson lead a curiously low key cast of extras and (I suspect) non-actors. Without exception, all are constantly mono-tonal and quiet. They keep emotion out of their roles. As so many were, until recently, ex-service, I suspect they recreated their war time roles as 'Officers and Gentlemen'.<br /><br />This unemotional approach does not detract from any dramatic tension. On the contrary, unlike most Wartime Escape Films, the story doesn't end at the barbed wire: and that fact alone keeps me glued to the end.\")\n",
      "(0, \"The movie's storyline is pat and quaint. Two women travel through the middle east and discover themselves. Unfortunately, if you are looking for a movie about the middle east and central Asia this is absolutely terrible.<br /><br />The producers of the film either did no research or were unbelievably lazy when filming it. To begin with, and most glaringly incorrect, the Nuristanis, as they were known in the thirties, and indeed since the 1890s and their forceful conversion by Abdul-Rahman Shah of Aghnaistan, were not nomads. In fact they have not been nomads since the Aryan invasions of central Asia over three milenia ago.<br /><br />Second, the city that is filmed as Tehran is not Tehran, which is understandable, however the geography of the area around the city could not be more strikingly DIFFERENT than the city of Tehran, which is surrounded on all side by a large mountain range, which predominates all of the cities views.<br /><br />Third, Persian, despite the fact it is spoken in Iran and Afghanistan, is never heard in movie. When there are native speakers who do not speak in German they speak in Arabic. The 'Persian' guards at the border, in fact, say to each other 'Ma hadha rujal' (This is not a man) and not 'in mard nist' as it would be in Persian. Also, the love song between the Indian princess and one of the main characters is obviously in Spanish. While talking in the garden one of the main characters says that the Quran uses the words 'Ferdos' and 'jehaan' and makes some reference to drugs afterwords. These words certainly never appear in the Quran as they are Persian for Paradise (indeed, Ferdos and Paradise are very distant cognates between our languages) and 'World' respectively, though Jehaan is admittedly close to 'Jehennan' which is hell in Arabic. When they encounter the nomads in the desert the language spoken is also Arabic, this despite the fact that there are NO native speakers of Arabic in Iran and Afghanistan and its use is primarily religious, with some use in education at that time.<br /><br />When they are stopped in Iran before they reach the Afghan border the people they encounter are wholly unlike any Iranian group. Their tents are typically bedouin with carpets decorating the walls and a high profile. In Iran it is also extremely uncommon for people to wear Turbans unless they are a cleric. The language spoken is clearly Arabic from the initial greeting of 'Ahlan wa Sahlan.' When they do reach Kabul the desert they find themselves in is sandy, totally unlike the rock dirt that is found in the arid parts of the Hindu Kush mountain range. There is an absence of the light green scrub that covers the ground in the summer and spring. The area is also not wholly consumed by the extreme mountains of the mountain range that won its name, The Indian Killers, because of its difficult and limiting ground.<br /><br />In short, the story line is the only thing in this movie that holds any water and it is still weak and common place. It lacks any real draw to it, being merely the tale of two women trying to learning about themselves as they get to Nuristan, however, even that is still-born and no real development is felt, leaving the characters in the end just where they were in the beginning and nothing has changed except that world war two has broken loose. In short, this is a really bad movie that I would have rated at one star except for the good footage of Bedouin and the deserts of the Levant, even if they are misnamed.\")\n",
      "(0, 'Overlong drama that isn\\'t capable of making any real point. So she became an actress - so what? She learned to love - big deal. There is a certain eccentricity among the characters and in the dialog and situations, but the kind which is bad for the movie, causing it to often seem absurd.<br /><br />Summer Phoenix, playing the lead, talks and behaves like a semi-retarded person, so there is no choice but to watch the movie as about a retarded girl that makes it in the world of theater - which was clearly not the intended point. We are told early on (in that \"Barry Lyndon\"-like narration) that she learned to hide her emotions, which certainly explains her autistic stone-face, but the movie suffers for it. She basically walks around like a zombie, and her success as an actress isn\\'t quite credible given her lack of emotions. Occasionally, the movie had that dull, sleepy feel of a Dogma 95 movie. Is it one? I wouldn\\'t be at all surprised.<br /><br />Summer Phoenix is sister of Joaquim Phoenix and the late River Phoenix. Nepotism rarely works.<br /><br />If you\\'d like to see my Hollywood Nepotism List, with over 350 pictures/entries, contact me by e-mail.')\n",
      "(0, 'I\\'ll just put it out here, that was the Worst sequel to a classic Disney film I\\'ve EVER seen. In 1950, Disney released what I hail as one of his greatest films of all time. Now Take away the great songs, add a poor plot that resembles that of a lost TV show. \"Put it together and what do you get?\" the biggest load of Crud I\\'ve ever seen. After i saw this, I thought it was all over for Disney. Cinderella should\\'ve ended with, \"and they lived happily ever after.\" not this garbage. This film did not deserve a sequel like this. I thought it would be like \"A twist in time\" which was moderate but better than this. Also, What do we care of Anastasia? She switched sides like Iago did, but it didn\\'t help anyone.')\n",
      "(0, \"This film deals with two ex-football players who are Fred Williamson, (Mack Derringer) and Gary Busey, (Lenny) who work as private eyes and meet all kinds of ladies and men with some bad backgrounds. Mack Derringer is approached by his ex-wife Vanity (Jennifer Derringer) who works at having sex talk over the telephone. Jennifer is being threatened by one caller who wants to do horrible things to her and she asks for his help along with several other ladies. Mack & Lenny have more time on their hands and often go to Miami, Fl. golf courses or hang out in a Sports Bar where all kinds of city things go on. There is lots of punches, killings and plenty of double meaning words that bring this film completely down to a big ZERO. Don't waste your time, this film cost me only 50 cents and that was too much.\")\n",
      "(0, 'Stilted, stagy, strange and opaque, if visually striking ... a wannabe-erotic fantasy. Really boring, way too much male nudity (including father-son incest), and just a sort of shameless pointlessness. I will confess, however, that certain passages of dialogue, taken on their own terms, do have a lulling, haunting quality.')\n",
      "(0, \"OK, the movie is good but I give it a 1 because the idea of a computer virus becoming an organic virus is pure fairy tale. This kind of crap just adds to those uncomputer savvy moron's paranoid delusions that a computer virus is exactly like an organic virus. First of all, strings of code and dozens of 1s and 0s add up to computer virus. An organic virus is much more complex, even though it's way tinier. Though, it's considered one of the simplest forms in the universe, organic virus's attach burrow into your cells and attach themselves to the RNA, then change your own RNA code. Explain to me how something like that could be processed from a monitor? Maybe the radiation has some effect on the user's cornea that turns your eyeballs into these viruses? I could see that, but obviously, the writer didn't think of that.\")\n",
      "(0, 'Whattt was with the sound? It sounded like it was all dubbed.<br /><br />Otherwise, bad. Plot = bad. Accents = bad (even Dougray, and we live in Scotland), Acting = bad, Harp = bad, Sex scenes - bad/cringeworthy.<br /><br />Still, we watched it until the end in disbelief. How could such a good roll call of actors perform so badly? Will they ever get a decent job again? <br /><br />Bad, Bad, Bad. By the way, we gave it 3 because we at least were enticed to watch it to the end due to its bizarre plot, etc.<br /><br />And to the older reviewer - I totally agree, it was like a romantic farce from the 1940s. How did it get made in 2004?<br /><br />OK, OK, there were some OK bits. They had a nice house in Bristol. Dougray had a nice boat. Jennifer looked nice in a little outfit. But how come the sister got all the men?')\n",
      "(0, \"I find it hard to understand why this piece of utter trash was repackaged. The only saving grace in the whole thing is the body of Ariauna in her sexy uniform. Her humour is also to be appreciated. She is a definite plus but alas it would take a magician to salvage this garbage. However she must be positively recognised for her heroic effort & true professionalism. Can't say the same for her co star Lilith with her whining voice that grates on your nervous system. Appeared disinterested & gave the impression that just her presence on the set was all that was needed. All said apart from Ariauna's performance it is indeed utter trash.\")\n",
      "(0, 'Once again, like Charlie\\'s Angels, Inspector Gadget and Thunderbirds, a TV series is turned into a full length film and gets ruined for all the hundreds of people that watched. Basically the Duke cousins, Luke (Johnny Knoxville) and Bo (Seann William Scott) in Hazzard County, spend a lot of time driving very fast in \"The General Lee\". But they need to get their act together and stop their family farm being destroyed along with almost the whole town by nasty Jefferson Davis \\'Boss\\' Hogg (Razzie nominated Burt Reynolds) to make way for a huge coal mine. They get help along the way from their sexy cousin Daisy (Razzie nominated Jessica Simpson) and Uncle Jesse (Willie Nelson), while being chased by equally mean Sheriff Rosco P. Coltrane (Lost\\'s M.C. Gainey) and the police. Also starring Joe Don Baker as Governor Jim Applewhite, Jack Polick as Deputy Cletus Hogg, David Koechner as Cooter Davenport, Michael Weston as Deputy Enos Strate and Lynda Carter as Pauline. I think the only reason I give the film an extra star is because of a couple of impressive car stunts, and of course the gorgeous body of Simpson (I\\'ll admit her legs aren\\'t quite Catherine Bach, but still!), but besides that it is pretty boring. It was nominated the Razzies for Worst Picture, Worst Director for Jay Chandrasekhar, Worst Remake or Sequel, Worst Screen Couple for Simpson & Her \"Daisy Dukes\" and Worst Screenplay. Pretty poor!')\n",
      "(0, 'Margret Laurence probably didn\\'t intend on having any of her novels adopted for film, let alone the Stone Angel. Hagar, as a character, was one who constantly challenged the social norm (Gainsay who dare, anyone?), and ended up nearly sacrificing her humanity in the process. The symbols in the book (the Stone Angel, Silver Thread, etc, etc.) are constant reminders of this struggle of the old and new, and the carnage (so to speak) along the way.<br /><br />While the film is reasonably faithful to the plot of the book (but it isn\\'t really a plot kind-of storytelling, is it?), I think it missed the point on capturing the spirit of the film. Hagar\\'s defiance (for the sake of defiance) was not there. Bram could have been a lot more crude than portrayed, and Hagar\\'s father could have been played more \"traditionally\", so to speak. If the filmmaker would insisted on stronger portrayals, the film would drive the point straight to home.<br /><br />Along the same vein, why should we see cell phones, organic produce, and other modernizations? Are we trying make some points for the sake of making some points (e.g., the Muslim girlfriend and the Native people). Hagar and co. are everything but politically correct in the book, so why should we see that in the film version. Modernization may be an excuse for a low-budget operation, but using that as an excuse to send subliminal politically-correct messages that are totally irrelevant to the novel (and the film) seems like throwing punches below the intellect.<br /><br />There is also the audience. It seems that we have been conditioned to see bitter old people as cute and lovable. Why should be laugh every time Hagar is at her tantrums? I doubt Magaret Laurence wanted her readers to laugh at, or with, Hagar. These people are frustrated and are full of angst, and all we do is to laugh at them. I don\\'t think it did Hagar and other folks in her situation any justice.')\n"
     ]
    }
   ],
   "source": [
    "\n",
    "start=train_iter.pos_inx\n",
    "start=0\n",
    "for i in range(-10,10):\n",
    "    print(train_iter[start+i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following defines the mapping of numeric labels to positive and negative reviews:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'positive review'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_label = {0: \" negative review\", 1: \"positive review\"}\n",
    "imdb_label[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following checks to make sure that there are exactly 2 classes in the train dataset:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_class = len(set([label for (label, text) in train_iter ]))\n",
    "num_class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following are some token indices:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[466, 13077]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab([\"age\",\"hello\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and validate\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following converts the dataset into map-style datasets and then performs a random split to create separate training and validation datasets. The training dataset will contain 95% of the samples in the original training set, while the validation dataset will contain the remaining 5%. These datasets can be used for training and evaluating a machine learning model for text classification on the IMDB dataset. The final performance of the model will be evaluated on the hold-out test set:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Convert the training and testing iterators to map-style datasets.\n",
    "train_dataset = to_map_style_dataset(train_iter)\n",
    "test_dataset = to_map_style_dataset(test_iter)\n",
    "\n",
    "# Determine the number of samples to be used for training and validation (5% for validation).\n",
    "num_train = int(len(train_dataset) * 0.95)\n",
    "\n",
    "# Randomly split the training dataset into training and validation datasets using `random_split`.\n",
    "# The training dataset will contain 95% of the samples, and the validation dataset will contain the remaining 5%.\n",
    "split_train_, split_valid_ = random_split(train_dataset, [num_train, len(train_dataset) - num_train])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code checks if a CUDA-compatible GPU is available in the system using PyTorch, a popular deep learning framework. If a GPU is available, it assigns the device variable to \"cuda\" (which stands for CUDA, the parallel computing platform and application programming interface model developed by NVIDIA). If a GPU is not available, it assigns the device variable to \"cpu\" (which means the code will run on the CPU instead).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data loader\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In PyTorch, the **`collate_fn`** function is used in conjunction with data loaders to customize the way batches are created from individual samples. The provided code defines a `collate_batch` function in PyTorch, which is used with data loaders to customize batch creation from individual samples. It processes a batch of data, including labels and text sequences. It applies the `text_pipeline` function to preprocess the text. The processed data is then converted into PyTorch tensors and returned as a tuple containing the label tensor, text tensor, and offsets tensor representing the starting positions of each text sequence in the combined tensor. The function also ensures that the returned tensors are moved to the specified device (e.g., GPU) for efficient computation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn.utils.rnn import pad_sequence\n",
    "\n",
    "def collate_batch(batch):\n",
    "    label_list, text_list = [], []\n",
    "    for _label, _text in batch:\n",
    "        label_list.append(label_pipeline(_label))\n",
    "        text_list.append(torch.tensor(text_pipeline(_text), dtype=torch.int64))\n",
    "\n",
    "\n",
    "    label_list = torch.tensor(label_list, dtype=torch.int64)\n",
    "    text_list = pad_sequence(text_list, batch_first=True)\n",
    "\n",
    "\n",
    "    return label_list.to(device), text_list.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You convert the dataset objects to a data loader by applying the collate function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 64\n",
    "\n",
    "train_dataloader = DataLoader(\n",
    "    split_train_, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch\n",
    ")\n",
    "valid_dataloader = DataLoader(\n",
    "    split_valid_, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch\n",
    ")\n",
    "test_dataloader = DataLoader(\n",
    "    test_dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the what these data loaders generate:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1,\n",
       "         0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0,\n",
       "         1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0]),\n",
       " tensor([[   39,    16,    50,  ...,     0,     0,     0],\n",
       "         [ 4876, 20287,    16,  ...,     0,     0,     0],\n",
       "         [  378,    39,   321,  ...,     0,     0,     0],\n",
       "         ...,\n",
       "         [41647,    59,  1536,  ...,     0,     0,     0],\n",
       "         [  194,  7402,  1777,  ...,     0,     0,     0],\n",
       "         [   51,  1222,   818,  ...,     0,     0,     0]]))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label,seqence=next(iter(valid_dataloader ))\n",
    "label,seqence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural network\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code defines a class called `TextClassifier` that represents a simple text classifier that uses an embedding layer, a hidden linear layer with a ReLU avtivation, and an output linear layer. The constructor takes the following arguments:\n",
    "\n",
    "- `num_class`: The number of classes to classify.\n",
    "- `freeze`: Whether to freeze the embedding layer.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn\n",
    "\n",
    "class TextClassifier(nn.Module):\n",
    "    def __init__(self, num_classes,freeze=False):\n",
    "        super(TextClassifier, self).__init__()\n",
    "        self.embedding = nn.Embedding.from_pretrained(glove_embedding.vectors.to(device),freeze=freeze)\n",
    "        # An example of adding additional layers: A linear layer and a ReLU activation\n",
    "        self.fc1 = nn.Linear(in_features=100, out_features=128)\n",
    "        self.relu = nn.ReLU()\n",
    "        # The output layer that gives the final probabilities for the classes\n",
    "        self.fc2 = nn.Linear(in_features=128, out_features=num_classes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # Pass the input through the embedding layer\n",
    "        x = self.embedding(x)\n",
    "        # Here you can use a simple mean pooling\n",
    "\n",
    "        x = torch.mean(x, dim=1)\n",
    "        # Pass the pooled embeddings through the additional layers\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu(x)\n",
    "        return self.fc2(x)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model on the full dataset\n",
    "\n",
    "The model can then be trained on labeled data from the IMDB dataset with two classes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's create the model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TextClassifier(\n",
       "  (embedding): Embedding(400000, 100)\n",
       "  (fc1): Linear(in_features=100, out_features=128, bias=True)\n",
       "  (relu): ReLU()\n",
       "  (fc2): Linear(in_features=128, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=TextClassifier(num_classes=2,freeze=True)\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code line `predicted_label=model(text, offsets)` is used to obtain predicted labels from a model for a given input text and its corresponding offsets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.eval()\n",
    "predicted_label=model(seqence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following returns the shape of `predicted_label`. Because your dataset iterators are batching 64 inputs, `predicted_label` should return 64 rows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 2])\n"
     ]
    }
   ],
   "source": [
    "print(predicted_label.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each input, the model outputs two logits corresponding to the two classes in the classification task. If the value of the first logit is greater than the second, the predicted class is class 0, which maps to a negative review. If the second logit is greater than the first, the predicted class is class 1, which maps to a positive review:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.1962, 0.2551],\n",
       "        [0.1670, 0.2381],\n",
       "        [0.1859, 0.2503],\n",
       "        [0.1622, 0.2373],\n",
       "        [0.1931, 0.2539],\n",
       "        [0.1473, 0.2350],\n",
       "        [0.1604, 0.2467],\n",
       "        [0.1829, 0.2501],\n",
       "        [0.1955, 0.2537],\n",
       "        [0.1912, 0.2485],\n",
       "        [0.1843, 0.2487],\n",
       "        [0.1545, 0.2456],\n",
       "        [0.1179, 0.2261],\n",
       "        [0.1159, 0.2238],\n",
       "        [0.1920, 0.2495],\n",
       "        [0.1729, 0.2463],\n",
       "        [0.1897, 0.2507],\n",
       "        [0.1876, 0.2503],\n",
       "        [0.1917, 0.2514],\n",
       "        [0.2028, 0.2571],\n",
       "        [0.1552, 0.2388],\n",
       "        [0.1808, 0.2494],\n",
       "        [0.1439, 0.2449],\n",
       "        [0.1781, 0.2530],\n",
       "        [0.1563, 0.2433],\n",
       "        [0.1159, 0.2191],\n",
       "        [0.1921, 0.2511],\n",
       "        [0.1892, 0.2512],\n",
       "        [0.2016, 0.2543],\n",
       "        [0.1881, 0.2509],\n",
       "        [0.1671, 0.2470],\n",
       "        [0.1918, 0.2502],\n",
       "        [0.1689, 0.2509],\n",
       "        [0.1884, 0.2522],\n",
       "        [0.1800, 0.2493],\n",
       "        [0.1982, 0.2558],\n",
       "        [0.1929, 0.2509],\n",
       "        [0.1527, 0.2400],\n",
       "        [0.1922, 0.2521],\n",
       "        [0.1681, 0.2411],\n",
       "        [0.1889, 0.2502],\n",
       "        [0.1912, 0.2545],\n",
       "        [0.1663, 0.2428],\n",
       "        [0.1910, 0.2533],\n",
       "        [0.1895, 0.2529],\n",
       "        [0.1937, 0.2530],\n",
       "        [0.1950, 0.2521],\n",
       "        [0.2006, 0.2563],\n",
       "        [0.1753, 0.2447],\n",
       "        [0.1850, 0.2490],\n",
       "        [0.1999, 0.2545],\n",
       "        [0.1724, 0.2466],\n",
       "        [0.1930, 0.2505],\n",
       "        [0.1807, 0.2440],\n",
       "        [0.1953, 0.2525],\n",
       "        [0.1934, 0.2519],\n",
       "        [0.1981, 0.2559],\n",
       "        [0.1558, 0.2419],\n",
       "        [0.1757, 0.2447],\n",
       "        [0.1886, 0.2486],\n",
       "        [0.1472, 0.2356],\n",
       "        [0.1539, 0.2390],\n",
       "        [0.1932, 0.2542],\n",
       "        [0.1915, 0.2538]], grad_fn=<AddmmBackward0>)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following **`predict`** function takes in a text, a text pipeline, and a model as inputs. It uses a pretrained model passed as a parameter to predict the label of the text for text classification on the IMDB dataset:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(text, model, text_pipeline):\n",
    "    with torch.no_grad():\n",
    "        text = torch.unsqueeze(torch.tensor(text_pipeline(text)),0).to(device)\n",
    "\n",
    "        output = model(text)\n",
    "        return imdb_label[output.argmax(1).item()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'positive review'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(\"the is a good movie\",model,text_pipeline )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can create a function to evaluate the model's accuracy on a dataset:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(dataloader, model, device):\n",
    "    model.eval()\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for label, text in dataloader:\n",
    "            label, text = label.to(device), text.to(device)\n",
    "            outputs = model(text)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total += label.size(0)\n",
    "            correct += (predicted == label).sum().item()\n",
    "    accuracy = 100 * correct / total\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following evaluates the performance of your model on the test set:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate(test_dataloader , model, device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the current performance of the model is no better than average. This outcome is expected, considering that the model has not undergone any training yet.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model\n",
    "\n",
    "The following defines the training function used to train the model:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, optimizer, criterion, train_dataloader, valid_dataloader, epochs=100, model_name=\"my_modeldrop\"):\n",
    "    cum_loss_list = []\n",
    "    acc_epoch = []\n",
    "    best_acc = 0\n",
    "    file_name = model_name\n",
    "    \n",
    "    for epoch in tqdm(range(1, epochs + 1)):\n",
    "        model.train()\n",
    "        cum_loss = 0\n",
    "        for _, (label, text) in enumerate(train_dataloader):            \n",
    "            optimizer.zero_grad()\n",
    "            predicted_label = model(text)\n",
    "            loss = criterion(predicted_label, label)\n",
    "            loss.backward()\n",
    "            torch.nn.utils.clip_grad_norm_(model.parameters(), 0.1)\n",
    "            optimizer.step()\n",
    "            cum_loss += loss.item()\n",
    "        #print(\"Loss:\", cum_loss)\n",
    "        cum_loss_list.append(cum_loss)\n",
    "        acc_val = evaluate(valid_dataloader, model, device)\n",
    "        acc_epoch.append(acc_val)\n",
    "        \n",
    "        if acc_val > best_acc:\n",
    "            best_acc = acc_val\n",
    "            print(f\"New best accuracy: {acc_val:.4f}\")\n",
    "            #torch.save(model.state_dict(), f\"{model_name}.pth\")\n",
    "    \n",
    "    # save_list_to_file(cum_loss_list, f\"{model_name}_loss.pkl\")\n",
    "    # save_list_to_file(acc_epoch, f\"{model_name}_acc.pkl\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following sets the learning rate (LR) to 1, which determines the step size at which the optimizer updates the model's parameters during training. The CrossEntropyLoss criterion is used to calculate the loss between the model's predicted outputs and the ground truth labels. This loss function is commonly employed for multi-class classification tasks.\n",
    "\n",
    "The chosen optimizer is Stochastic Gradient Descent (SGD), which optimizes the model's parameters based on the computed gradients with respect to the loss function. The SGD optimizer uses the specified learning rate to control the size of the weight updates.\n",
    "\n",
    "Additionally, a learning rate scheduler is defined using StepLR. This scheduler adjusts the learning rate during training, reducing it by a factor (gamma) of 0.1 after every epoch (step) to improve convergence and fine-tune the model's performance. These components together form the essential setup for training a neural network using the specified learning rate, loss criterion, optimizer, and learning rate scheduler.\n",
    "\n",
    "For the sake of time efficiency, the number of epochs has been set to 2. This is to give you a practical demonstration of what the training process looks like. However, if you were to train this model in a real-world scenario, you would likely increase the number of epochs to a larger figure, such as 100 or more. Given the reduced training set defined earlier, it takes approximately 2 minutes to complete 2 epochs of training:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR=1\n",
    "\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=LR)\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have pretrained the model for 300 epochs using a GPU and saved this model for your convenience. However, to demonstrate the training process, the following code has been included that trains the model for just two epochs. Please note that you have limited the number of epochs to two because training on a CPU can be time-consuming. Even with just two epochs, you can expect the following code to run for approximately one minute.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 1/2 [00:19<00:19, 19.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New best accuracy: 50.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:39<00:00, 19.76s/it]\n"
     ]
    }
   ],
   "source": [
    "model_name=\"model_imdb_freeze_true2\"\n",
    "train_model(model, optimizer, criterion, train_dataloader, valid_dataloader, epochs=2, model_name=model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of evaluating the model trained on 2 epochs, let's load the pretrained model that was trained for 300 epochs:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "%%capture \n",
    "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/ZvhVWJU0flC7BmU1jjYxjg/model-imdb-freeze-true2.pth\n",
    "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/2RdN-JG4Rm5Gx3UNtOP4NA/model-imdb-freeze-true2-acc.pkl\n",
    "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/8qoGvWk0BdXRGoFAOT-dAw/model-imdb-freeze-true2-loss.pkl\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the cost and accuracy for each epoch for the pretrained model that was trained for 300 epochs. From the plot, it becomes evident that with just a few epochs, the accuracy exhibits significant volatility.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cum_loss_list=load_list_from_file(model_name.replace('_','-') + \"-loss.pkl\")\n",
    "acc_epoch=load_list_from_file(model_name.replace('_','-') + \"-acc.pkl\")\n",
    "plot(cum_loss_list,acc_epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, you load the model that has been trained for you. Please comment out these lines if you want to train the model yourself.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TextClassifier(\n",
       "  (embedding): Embedding(400000, 100)\n",
       "  (fc1): Linear(in_features=100, out_features=128, bias=True)\n",
       "  (relu): ReLU()\n",
       "  (fc2): Linear(in_features=128, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_state_dict(torch.load(model_name.replace('_','-') + \".pth\", map_location=device))\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following evaluates the model on the test data. The pretrained model achieves an accuracy of 66%.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66.292"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate(test_dataloader , model, device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Low-Rank Adaptation (LoRA)\n",
    "\n",
    "PyTorch and the Hugging Face library provide robust tools for model manipulation with LoRA, but they are not intuitive. In this section, you delve into building a LoRA (Low-Rank Adaptation) implementation from scratch using PyTorch. LoRA is a general method, but it's commonly applied to the Attention layer. For the sake of simplicity, in this lab, you apply it to a Vanilla neural network. This decision is made because accessing the Attention Parameters in the PyTorch Encoder module can be challenging.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LoRA\n",
    "1) For any arbitrary layer of a network, you have the model with pretrained parameters $ W_0 $, which are the parameters of the model. If you only consider the attention parameters for each layer, at a minimum $ 4 \\times m \\times n$ for each layer. For many models, this can reach in the trillions of learnable parameters. Each time you fine-tune a new dataset, you have to store trillions of parameters.\n",
    "\n",
    "2) $ \\Delta W $ represents two matrices $ B $ and $ A $, where $ B $ and $ A $ are constrained such that $ B \\in \\mathbb{R}^{m \\times r} $, $ A \\in \\mathbb{R}^{r \\times n} $, and $ r \\leq \\min(m, n) $. The total number of parameters is $ A $ and $ B $ is much smaller than $ W_1$  and much easier to store.\n",
    "\n",
    "$ W_1\\approx W_0 + \\Delta  W = W_0 + BA $\n",
    "\n",
    "\n",
    "\n",
    "3) To train and predict, the forward pass holds $W_0$ constant.\n",
    "\n",
    "$h = W_0 + \\Delta W  = W_0x + BAx $\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "To scale $\\Delta W \\times \\dfrac{\\alpha'}{r}$, where $\\alpha$ is a constant in $ r $.  Adjusting $\\alpha'$ is similar to tuning the learning rate if the initialization is properly scaled. Therefore, you set $\\alpha'$ to the first $ r $ you try and do not tune it further; just use $\\alpha$. This scaling reduces the need to retune hyperparameters. The final form is:\n",
    "\n",
    "$h =  W_0x +  \\dfrac{\\alpha'}{r} BAx=  W_0x +  \\alpha BAx $\n",
    "\n",
    "The following example illustrates the process.\n",
    "\n",
    "\n",
    "$\n",
    "W_0 + BA = \n",
    "\\begin{bmatrix}\n",
    "w_{11} & w_{12} & w_{13} & w_{14} \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n",
    "w_{21} & w_{22} & w_{23} & w_{24} \\\\\\\\\\\\\n",
    "w_{31} & w_{32} & w_{33} & w_{34} \\\\\\\\\\\\\n",
    "w_{41} & w_{42} & w_{43} & w_{44} \\\\\\\\\\\\\n",
    "\\end{bmatrix} +\n",
    "\\begin{bmatrix}\n",
    "a_1 \\\\\\\\\\\\\n",
    "a_2 \\\\\\\\\\\\\n",
    "a_3 \\\\\\\\\\\\\n",
    "a_4 \\\\\\\\\\\\\n",
    "\\end{bmatrix}\n",
    "\\begin{bmatrix}\n",
    "b_1 & b_2 & b_3 & b_4 \\\\\\\\\n",
    "\\end{bmatrix}\n",
    "$\n",
    "\n",
    "This illustrates the product of matrices $ A $ and $ B $, denoted as $ AB $, which can be added to $ W_0 $. However, the resulting matrix $ W_0 + AB $ is limited depending on the dimensions of $ A $ and $ B $. This limitation is due to the concept of rank.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rank\n",
    "The rank of a matrix is the number of dimensions the rows of the matrix \"live in.\"  A square matrix is said to be **full rank** if its <a href='https://en.wikipedia.org/wiki/Rank_(linear_algebra)?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMML0187ENSkillsNetwork31430127-2022-01-01'>rank</a> is equal to the number of its rows or columns. Let's make this idea more intuitive with an example.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sympy import Matrix, init_printing,Symbol\n",
    "from numpy.linalg import qr,eig,inv,matrix_rank,inv, norm\n",
    "from scipy.linalg import null_space\n",
    "from sympy import Matrix, init_printing,Symbol\n",
    "init_printing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_matrix_and_subspace(F):\n",
    "    assert F.shape[0] == 3, \"Matrix F must have rows equal to 3 for 3D visualization.\"\n",
    "    \n",
    "    ax = plt.figure().add_subplot(projection='3d')\n",
    "    \n",
    "    # Plot each column vector of F as a point and line from the origin\n",
    "    for i in range(F.shape[1]):\n",
    "        ax.quiver(0, 0, 0, F[0, i], F[1, i], F[2, i], color='blue', arrow_length_ratio=0.1, label=f'Column {i+1}')\n",
    "\n",
    "    if F.shape[1] == 2:\n",
    "        # Calculate the normal to the plane spanned by the columns of F if they are exactly two\n",
    "        normal_vector = np.cross(F[:, 0], F[:, 1])\n",
    "        # Plot the plane\n",
    "        xx, yy = np.meshgrid(np.linspace(-3, 3, 10), np.linspace(-3, 3, 10))\n",
    "        zz = (-normal_vector[0] * xx - normal_vector[1] * yy) / normal_vector[2] if normal_vector[2] != 0 else 0\n",
    "        ax.plot_surface(xx, yy, zz, alpha=0.5, color='green', label='Spanned Plane')\n",
    "\n",
    "    # Set plot limits and labels\n",
    "    ax.set_xlim([-3, 3])\n",
    "    ax.set_ylim([-3, 3])\n",
    "    ax.set_zlim([-3, 3])\n",
    "    ax.set_xlabel('$x_{1}$')\n",
    "    ax.set_ylabel('$x_{2}$')\n",
    "    ax.set_zlabel('$x_{3}$')\n",
    "    # ax.legend()\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_matrix_and_subspace(F):\n",
    "    assert F.shape[0] == 3, \"Matrix F must have 3 rows to represent 3D space.\"\n",
    "\n",
    "    ax = plt.figure().add_subplot(projection='3d')\n",
    "    \n",
    "    # Plot each column vector of F\n",
    "    for i in range(F.shape[1]):\n",
    "        ax.quiver(0, 0, 0, F[0, i], F[1, i], F[2, i], color='blue', arrow_length_ratio=0.1, label=f'Column {i+1}')\n",
    "\n",
    "    # Calculate the null space of the transpose of F\n",
    "    normal_vector = null_space(F.T)\n",
    "    \n",
    "    # Check that the null space is 1-dimensional\n",
    "    if normal_vector.shape[1] == 1:\n",
    "        normal_vector = normal_vector[:, 0]  # Simplify the array to 1D\n",
    "        # Create a meshgrid for the plane\n",
    "        xx, yy = np.meshgrid(np.linspace(-3, 3, 10), np.linspace(-3, 3, 10))\n",
    "        # Calculate corresponding z coordinates based on the plane equation ax + by + cz = 0\n",
    "        zz = (-normal_vector[0] * xx - normal_vector[1] * yy) / normal_vector[2] if normal_vector[2] != 0 else 0\n",
    "        ax.plot_surface(xx, yy, zz, alpha=0.5, color='green', label='Spanned Plane')\n",
    "    else:\n",
    "        print(\"The null space is not 1-dimensional, so a unique plane cannot be determined.\")\n",
    "\n",
    "    # Set plot limits and labels\n",
    "    ax.set_xlim([-3, 3])\n",
    "    ax.set_ylim([-3, 3])\n",
    "    ax.set_zlim([-3, 3])\n",
    "    ax.set_xlabel('X axis')\n",
    "    ax.set_ylabel('Y axis')\n",
    "    ax.set_zlabel('Z axis')\n",
    "    # ax.legend()\n",
    "\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the context of Low-Rank Adaptation (LoRA), where $B \\in \\mathbb{R}^{d \\times r}$, the matrix $B$:\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 0\\\\0 & 1\\\\0 & 0\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "⎡1  0⎤\n",
       "⎢    ⎥\n",
       "⎢0  1⎥\n",
       "⎢    ⎥\n",
       "⎣0  0⎦"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B=torch.tensor([[1,0],[0,1],[0,0]]).numpy()\n",
    "\n",
    "Matrix(B)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This $3 \\times 2$ matrix has columns that span a 2-dimensional subspace in $\\mathbb{R}^3$. Specifically, the columns of $B$ are:\n",
    "\n",
    "- $\\mathbf{b}_1 = \\begin{bmatrix} 1 \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ 0 \\\\ 0 \\end{bmatrix}$\n",
    "- $\\mathbf{b}_2 = \\begin{bmatrix} 0 \\\\\\\\ 1 \\\\ 0 \\end{bmatrix}$\n",
    "\n",
    "These columns are standard basis vectors for the $xy$-plane in $\\mathbb{R}^3$, and, thus, they span the $xy$-plane shown in green in the following image. Muliplying each column vector in blue by a scaler always falls in the plane.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " plot_matrix_and_subspace(B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this scenario, the vectors, despite each having three components, can reach any point on the two-dimensional green plane depicted in the image. These vectors span the green plane, which resides within a two-dimensional subspace. This subspace's dimension, also known as its 'rank', is two—corresponding to the dimensionality of the plane. If the rank were three, any point in the 3D space could be reached by some combination of the columns of $𝐵$. The rank of a matrix can be determined by using the matrix_rank function provided by NumPy.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_rank(B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, you plot a different matrix where the matrix spans a different plane, but the rank remains two.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rank of B 2\n"
     ]
    }
   ],
   "source": [
    "B_=torch.tensor([[1,0],[-2,1],[0,1]]).numpy()\n",
    "plot_matrix_and_subspace(B_)\n",
    "print(\"rank of B\",matrix_rank(B_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_rank(B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, you present the matrix ```A```. The rank of this matrix is also two.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 1 & -1 & 1 & 0\\\\-2 & 2 & 2 & 0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "⎡1   1  -1  1  0⎤\n",
       "⎢               ⎥\n",
       "⎣-2  2  2   0  1⎦"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A=torch.tensor([[1,1,-1,1,0],[-2,2,2,0,1]]).numpy()\n",
    "Matrix(A)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_rank(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the matrices $ C = BA $, if $B $ and $ A $ both have a rank of $ r $:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 1 & -1 & 1 & 0\\\\-2 & 2 & 2 & 0 & 1\\\\0 & 0 & 0 & 0 & 0\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "⎡1   1  -1  1  0⎤\n",
       "⎢               ⎥\n",
       "⎢-2  2  2   0  1⎥\n",
       "⎢               ⎥\n",
       "⎣0   0  0   0  0⎦"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C=B@A\n",
    "Matrix(C)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The columns of $ C $ will have the same rank as $ B $. Furthermore, the span of the columns of $ C $ will be the same as the span of the columns of $ B $.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rank of C 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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GW61dZNKOOs9hwyxJcuzYMaiqahSTt7e3MT8/D5fLZbQnK4rSES3WzUQG7UA7mgOs7JILhYJBMI6bpb1wCKVDIIoiJicnEYvFcPHiRfT19TX9QNuR8hIEAZOTkwCA8fFxQwDwnYKDWCyI3AiZGC8Xucxms+A4DpqmIRwOH5rIZSdFKO0+D7ObJVFWMLtZzszMoFgsIhAIlEQwjptlfXAIpQNAZku8Xi/Gx8db9v4gHVTNLhSRSAQzMzMYGRnB2bNnQdN0W+oy7ySCqgflIpezs7OQJAmapmFpaQn5fL5hkUs70EmEctDty5XcLNfX17GxsYHt7W2Ioui4WdYJh1AOEaqq4vbt27hz5w5OnjyJ48eP2/JCmf1GGnnoFUXB/Pw8tra29nVxtaPN991GKOVgGAYcx+HUqVMASkUuCdmQFuVwONw2OZJ3M6GUg0j3dHV1IZfL4fLly8jn84bZmNnNkhBMOBxu6/DrgwSHUA4JgiDgxo0bSKfT8Pv9OHnypG3HJi9lIy9oLpfD1NQUKIqy7OKym1A6dQ7lIFF+/eUil2QhSyQS2NjYgKqqJS3Kdk2LdwqhdMqAJaC/OwzDgKZpQxvOys1ybW3NcLM0E8yD6M9jB96dV32I0DQNkUgEk5OTCIfDOHPmDDY3N239jEbnPLa3t3Hr1q2SFJfVMd9pKa/D/nygci3HnIopF7kkU/zmGg3pIGuGGDqFUDqlOQDQo3Wr96CSm6W5dZwQPyGYd5Ob5bvjKjsEiqLg1q1bWFtbw7lz5zAyMoLd3d22CDkCtbWyFEXB3Nwctre38Z73vMfwF6l0TEmSbD3PTljQDxONXL+VyGUmk0E8Hjc8R4igIkmR1Sty2SmEQqRwOgH1RvdmN0vyvZjVrcvtkgnBdMp12g2HUA4ImUwG169fN+Td/X4/AD2P3g6ZFKA6oeRyOUxOToKm6boGFZ2UV3vQSidfKBRCKBTC8ePHDcXeRCJhSMLXK3LZKYTSCTUUApLyahTV3Cyj0SiWl5cNN0szwXTKdbcKh1DaDE3TsLa2hpmZGQwNDeHs2bMl4W+7hByrLdjb29uYmZnB6OhoxRSX1THfSUX5TlhA7bz+cs8RK5FLv99vRC+hUAgsy7bUDWg3HoSUV6Oo5ma5u7tr2CUTN8u+vj50dXU9sATjEEobIUkSpqamsLu7i4cffhgDAwP7Xph2EEql45IUVyQSwSOPPFI1xVUOOwlFFEUoimIUm+uRKHmnol3XXC5ySeRI4vH4PpHLTsE7IUKpBSs3y3Q6jUQiYelmGQwG0dvb2/YU2ec//3l8/vOfx+rqKgDgwoUL+IM/+AN85CMfaeg4DqG0CfF4HNevXze0r8ySG2a0k1DMBGBOcV27dq1hLS67ivLJZNIYmOQ4zpAo4TjuwE2uDjvldpCfz/O8oXcFlIpcAsCbb76JUChk3P/DMLTqpC4vRVEOpJBeyS6ZpC7/8A//EF/96lcxMDCAZ599Fr/+67+OU6dO2f7djI6O4j/9p/+E06dPQ9M0/N3f/R1+9md/FhMTE7hw4ULdx3EIxWaoqoqlpSXMz8/jxIkTOHHiRNWXpF2EYiaAra0t3Lp1C2NjYzhz5kxTL22r+mAk9bewsIBTp05hZWUFHo8HjzzyiPESxeNxw+TK5/MZi9thTZC/k0GmxQcGBrC7u4tLly4hk8kYU/wASlqUD8JvpNMilMM4F3PqUtM0nD17Fh/96Efxmc98Bl/84hfxp3/6p+jr68Mv/MIv4M/+7M9s+9yPfexjJf/+oz/6I3z+85/HG2+84RDKYSGfz2NiYgKZTAaPPvoowuFwzZewnREKUSze2dnBxYsX0d/f3/TxWkl5kfNIJBK4evUquru7cefOHePnVvl/snteXFxEoVBAMBg0fued5Dd+2Gk+8p36/X50dXVhbGwMmqYZ5BKLxbC8vFxV5NIudFINpV0pr0ZAxEf/0T/6R/jsZz+L3//938dzzz2H119/Hbu7u237XEVR8L//9/9GLpfDtWvXGvpbh1BsgKZp2N3dxcTEBEKhEMbHx8HzfF1/SwilHYXRmZkZ8DxfNeVWL5ollGw2i4mJCSP1R1JZ1Y7HcZzhdwHAcsDP3B77oNZfDjvlZj4H8/2jKGpfp5KVyKX5O6j3ea8GJ0KxBhlyDQQC8Hg8+Kmf+qm2fM709DSuXbuGQqEAv9+PF154AQ899FBDx3AIpUUQ06nV1VWcOXMGY2NjDT2IzcqkVMPW1hYKhQIGBgZw8eJFW16MZgiFpNqOHj2K06dP77u+eo/n8XgwMjJiaC0RiXjz7pmkx+qdv+gUAjrs87AilHJYiVwSgllbWzNSlGab5GakSDqthtIp5wLAiNLbibNnz2JychKpVApf/OIX8Wu/9mv44Q9/2BCpOITSArLZLK5fvw5JkvDEE0809YU3I5NSCYTcdnZ2jEXYrpeikdScqqqYnZ1FJBKpmGprNuIxD/gdPXq0pIi5sbGB2dlZY3E7TAXfetAJEQr5ThshNpZlDbtd4H6KMpFIYHl5GYIgGGq9jYhcOimvysjn820nFJ7nDV25Rx99FG+99Rb+y3/5L/irv/qruo/RmW9ah0PTNGxsbODmzZsYGBjA+fPnm160yILfaldJNpvF5OQkWJbF+Pg4JiYmDmUQ0Sx7X6ubzI7zM9dfTp48WbK4lddfuru7EQwGS6LCw8ZhL6D1RCi1UJ6iLBaLRopydnYWoigiFArVFLnspDRTJ0Uosiwb9/AgoaoqisViQ3/jEEqDkGUZN2/exPb2Ni5cuIDBwcGWXsZ6ZVKqgaSWjhw5gtOnT7dFbr4eQtnd3cX09DSGhoZw7ty5qi9kuyblreovpMBvFlgkKcbDHOrrBEJrx/W7XK59IpeE5KuJXHZSyquTyC2dTgNAWwnls5/9LD7ykY/gyJEjyGQy+Pu//3v84Ac/wLe+9a2GjuMQSgNIJBK4ceOGEQXY4atOptqbWfzNKa5Lly4ZQ2yA/d1j1QhA0zQsLi7i7t27uHDhAoaHh+s63kGg3AOe6Cxtbm4in8/jRz/6UcP1FzvRCRFKO8/Bym/ESuSyq6sLkiRBFMWOmNzvpJRXJpMB0F5C2d3dxa/+6q9ie3sboVAIjzzyCL71rW/hgx/8YEPHcQilDqiqipWVFdy+fRtHjhxp2pq3EppZ/MtTXOVdXHZHAJXOsVgsYmpqCsVisUSjrB4c9A7dXH9RFAWCIGB4eBjxeLxE/4oQTLtVYt+pEUo1VBK5TCQSiEajuHXrVonIZXd3d8uGc82gk1Je6XQaHMfZ0klXCX/zN39jy3EcQqmBQqGAyclJJJNJAPpEqd07l0YJZXNz0yA3kuJq9Zi1YEVQ8XgcU1NT6O7uxpUrVxpafDtBHJJoKJHuJaJ/FY/Hsby8bBRCSfRirr/YeQ6HicOOBswil3fv3sWVK1eMOtjm5ibm5ubgdrtLhlzbubASdFLKK5PJNG1NcNBwCKUCNE0zrHn9fj/Gx8fx6quvtmUIsV7FYUVRcPv2bWOy2ZziKkc7CUXTNKyurmJpaQlnzpzBkSNHHoiH3Qyr8y3XvyoUCkZxeXp62sj9k8XtIKbH243DJhQzVFUFy7JGhxig1yzNIpe5XM4QuWyXioKmaR2V8spms4cSpTUDh1AsoKoq5ufnsbS0hNOnT+Po0aOgabotUvNAfYs/SXFxHIennnqq5gPWLrl5SZIwMzODVCqFxx57zFBRbfZ4h4lan+92uzE8PGzUX3K5HOLxuBHBkOlxQjCNvvSHff3kHDqBUCqpHrMsi97eXvT29gK4L3JZ3sVHCCYYDLZMBORddCKUxuEQShlyuRxu3LiBQqGAxx9/vGTBpGkaiqLY/pm1CIWkuI4ePYpTp07Vbfxjd4SiKApef/11eL3ehtQAKh2vHeTcLhAZDLNTn1nEb25uDh6Pp+H6y2EvEp3SWVXvIl5J5DKRSGBrawuyLJe0KPv9/oavj5xLJ0Uo7ZC6aQccQrkHTdOwtbWFqakp9PX14fLly/umfQ86QpFlGbOzs3WluOo9ZrOIRqMQRRFHjx7FiRMnWl4ID3shbRXm6fETJ05AluWS4T4ilWEe7itf2JwIpfQ8gMajgvIuPkEQjO9hbW0NQOMil50YodjRUXoQcAgF98ULNzc3cf78eQwPD1s+dAcZoWQyGUxNTdWd4iqHXSklUrfZ2dkBwzA4efJky8ck6IQF1S6wLLuv/kLmX8jO2ax95fP5ABw+sXYKoTQzsV8OiqLg8/ng8/kwOjpqKXJJ5OIJ0Vvt/BVFMdr5OwGCIDgRyoOCdDqNt99+GxRF4dq1a8aLboWDMMPSNM1oYW0kxVXtmM2CeKgwDINLly4ZE/B2oFNe1nbB7XaXDPflcrkSn3HynfI8j3A4fGhF104jFDujAiuRS2JmZSVySXx4OqkgD+gpLydC6XCoqoq7d+/i1q1bGB0drWu2hGGYtkUoiqJAlmXcvn0b0WgUly9fNgqRzaDVGsXOzg6mp6eNeyMIwjvKU/6gZy9I/WVsbMxY2G7fvo1UKoXXX38dHo+nRH+sGXHFZtBJhNLuqMBsx3v8+HEoimJ0kJl9eMimUpKkA/seqiGXy1Xd6HYS3pWEIooiJicnEYvFcPHiRfT19dX1ILezhpLP5/H6668bcvOt7libTc+pqoqFhQVsbGzg4YcfxuDgoHE8u4v871aQhY1EMX19fSXzL4IglMy/WNVf7EKnCDIeRnMAwzD7RC6TySS2t7ehKApeeeUVBAKBkhblw4hcHELpYJDZEtKp1MjC3Y4aiqZpKBQK2Nvbw4kTJ3Dy5ElbXqxmCIAMcSqKsi/916425MPEYX8+QXlrrFlc8datW5BluWT+hWhf2YFOi1AOE2QOiWzwLl26ZBT45+bmSkQuy4VG2wlBEIyutk7Hu4ZQyM57cXERJ0+exPHjxxt+GOyOUEiKK5vNYmhoCKdPn7bt2I0u2LFYzOhwe+ihh/btxMjLbucC1CkL+mGh0r0sF1cUBMGYfyH1l1qF5VbP4aDRKe3LwH3ZFZfLhcHBQQwODlYUuQyFQm0hejMEQXAilE5CPp/H9evXIQgCHnvsMUNqo1HYGaFkMhlMTk7C5XJhYGDA9qJsvRGKpmlYWVnBysoKzp8/j9HRUcvfs5tQOmERexBg7lwy11/MhWWzNEl3d3dDef9OIZROkjqxOhcrkUvSaEGm+M1SPt3d3bY5iQqC0JBG3mHiHU0omqZhZ2cHExMTCIfDGB8fb6nIZkdRnnipzM3N4dixYzh16hRmZ2dtr83UU5QXRRHT09PIZrM1DcLIi2HXi3/Yi9hhfz5Bo+dRXlgm0iQkepmZmWnI3MohlP2op8vLqtGCtCjv7e1haWmpxEm0FZFLYlj2IOAdSyiKouDWrVtYW1vDuXPnbHEvpGkaoig2/feyLOPWrVuIxWIlXVztaEemabpqSimVSmFiYgLBYLAuom2HKdWDNCnfDthxL63qL6Q9+fbt28bkODEhK0/LdBKhdMJ5AM0pDZtFLo8dOwZFUZBOpw0layJyaY5g6lWayOfzToRymMhkMrh+/TpUVW1YUr0aWqmhZDIZTExMwO1272sGYBgGsizbco4ElUhK0zSsr69jfn4ep06dwrFjx+p6kc0pr1YhiiK2oltYTaxCfl3GieETlovduwHtMLcy5/1J/SWRSGB1dbVkwj8cDncMoXRSDcWOaIkMUJL0ulnkkowr1CtySVQXHgS8owhF0zTcuXMHt2/fxvDwMM6dO2e7b0mjKS+rFFf5C9yuCKX8mCRCisfjePTRRw1F13pgF6GkUil8/ZWv45X1V5CX8liVV8HcYRCkghgODOOhsYcw3D98IGZXh90U0O7Pt6q/ZDIZxONxRCIRLCwsgGVZMAyD3d3dhusvdqKTUl6KotjeHlyPyCVpUSZWCQzDGM0A7fKT/4//8T/i+eefN7ToxsfH8bnPfQ5nz55t6njvGEKRJAlTU1PY2dkBRVF46KGHbN95NRqhmFNcV65cMfrdy9EOQinv8spms5iYmIDL5cL4+HjDi7UdhLKxsYEvvvJFbKgboDgKlEwZL4qsyVgsLmL69jS8t70I0SGc6D2Bs6Nn0dfbd2gzAO3GQUYH5rQMqb8sLi4imUyW1F/M8y8Hdc87KeV1EORWLnJJpHrMreLf+973IMtyW8Uhf/jDH+I3f/M38dhjj0GWZfze7/0ePvShDxlDno3iHUEo8XgcN27cAM/zuHjxIiYnJ9vycDYSoaTTaUxOTsLtduOpp56quoC3O0IhnvOtSLm0YlWsqiompyfxlcmvQPJKCHlCKBQKJeREURTcbreRCsypOfw48WO8tvMaAlQAvXwvzg6ffUelxw47QmJZFl6vF6qq4sKFCyX1l9nZWUiSVNIWGwgE2nbPOylCOQzplXKpHkEQsLi4iBdffBGCIODnfu7n8Mwzz+D9738/PvrRj+LMmTO2fO43v/nNkn9/4QtfQH9/P65fv46f/MmfbPh4DzShqKqK5eVlzM3N4cSJEzhx4gQKhQJUVW1LbrieCMVcozh+/DhOnjxZ8zzaFaEQYcft7W1cvHgR/f39LR+z0UUwn8/jB2/8AN9f/T483R542Pp2WjRN6zuke5ukHWkHq2uroO/QCFGhltNjnUJGh30e5vfEqv5CCGZ1ddVoiyUFfjt3zZ1UQ1EUpa3Wz7VAUpWf/vSn8au/+qvo6+vD//yf/xOLi4v49re/jWw2i3//7/99Wz47lUoBQEPpcDMeWELJ5/OYmJhAJpMx6gEURRl5x3YQSq0IhagWJxKJqikuq+PaTSiSJCGfzyOZTOLatWu2iMs1Myz54ssvYiY9g1BvqOT7aPS74TjOyO+b02OeWx50MV043nsc50bPvaPTY+1ApfekXLnXXH/Z2dnBwsICXC5XSVtsK/4477aUV73IZDIAgPHxcfzsz/4sfud3fqdtn6WqKv71v/7XeOqpp/Dwww83dYwHjlA0TcPu7i4mJiYQCoX2GT2RhaSZ1r9aqBahkBQXKWw1smO2W9Jlb28Pt2/fBkVReOKJJ2xbXOslFE3TsLyyjC+9/iVE6Si6erps+XzzeZSnx95KvIW3dt+Cn/Kjm+vGmaEzODF8Aj09PR2bHuuEDqt6z6G8/kKEFePxeEnXEoleGq2/dNIi3klqw+l0GgAQCoXa/lm/+Zu/iZmZGbz66qtNH+OBIhRFUTA7O4vV1VWcOXMGY2Nj+x5CM6HY3bFitfCbU1wk7dbMsJod+XRN07C0tITV1VUcO3YMm5ubtne51YqkZFnGj2/8GF+7/TVQAQpBl3V3ip0LKU3T6O/uh6iIkBRJT4+tr4JepRGkghgJjOD86HkjPUaI6LBrGJ2AZiODcmFFURSN9mRz/YWkyGrVXzop5dVJ5JZOp8HzfNtTcJ/5zGfw1a9+FS+//HJFtYx68MAQSjabxfXr1yFJUtWpboqiQNO07XMdwP4IxZziarQN1ww7Ul7FYhE3b95EoVDAk08+CVVVsb6+3tIxy1ErQslms/j2j76NH239CIFw4MB2eUFPENlCFqqm30NzekzRFCyJS5ienYbntp4eO9ZzDKNdo6BAtaVFtF50AqHZFSXxPL9P94oQDHFOLNcfM39uJy3i7chuNIt0Ot1WP3lN0/Cv/tW/wgsvvIAf/OAHOH78eEvH63hCIXMcN2/exMDAAM6fP1+TrdspM08ilFZSXNWO2wwSiQQmJyfR3d2Ny5cvg2VZZDKZtrcimxGJRPD8y89jubiMrr6ulo7VyPkE3AGk8+mqv+NyuYzvJ6fm8Hbybby88TJCTAhv333bSI/Vs5O2Gw9KyqsRmHWvzM6J5fUXM8F0Wg2lU1Je2Wy2reZrv/mbv4m///u/x1e+8hUEAgFEIhEAeoqtmaaLjiYUWZZx8+ZNbG9v48KFCxgcHKzbt6QdRliEqO7evYuFhYWmU1zlaDZC0TQNd+/exeLiIs6cOYMjR44Y59IOeXirY6qqitn5WXz1+leRdWXR1d1l62dWAs/w4BiuKplYgXSPKYqCRD4BiqOwur4KZpVBgApUTI+1A50SobR7N252TiSyJOVT4xzHwe12IxaLHXpTRSdFKGQGpV1k+/nPfx4A8PTTT5f897/927/Fpz71qYaP17GEkkgkcOPGDbAsi/Hx8Ya6lNpFKGQBWFlZaSnFVY5mCEWSJMzMzCCVSuGxxx5DV1dXy8eshXJCEUURr7z5Cr6z/B3wIR48x8PNueHiXFBUBUJRMNJQVsdqFj7eB1ERkRNzTR+DoGJ6bNaDLlpPj5m7x+zOZR/2rvwwGgOs6i+3bt2CJEmYn59HsVgs0R876Kixk9Jv7bb/tXtT03GEoqoq7ty5g9nZWRw5cgSnTp1qeLfSDkJJpVKGp/pjjz1mq1hbo4s/SbcRkzCrdk1S6LdzwTCfZzKZxNdf/TquR68jGL5vNFSQCihIBf33KRp+tx8MxUCSJQiS0PI5hDwhZAqZikTVCqzSY9eT1zGdnAYt0ejhe3Bm8AxOjpy0ZaHrlAjlsEmN53m4XC50dXXh2LFjhu9IPB4vqb+QFFk7d+xAZ6W8MplM26bk24GOIhTiGJhMJnH58mX09PQ03YFiF6Fomoa1tTUsLCzg+PHjhiy1nWiEUDY2NjA7O1tzaLIdhlgkQllbW8PzP3oeG8oGunq7Kv6+qqnIFrLGvzmGg4f3ABqQZhpLVZF6SSqfavb0GwbHcAj2BI1r2JV2sba5hu/d/R6CdBDD/mGcGzmHkYGRptNjh72YdwKhAPejAivfEVJ/2dvbw+LiInieN2ov4XC4pfkXK3RSyiuXy7U1QrEbHUEomqYZ1rx+v7/lIrddhELSSslk0khxLS8v2x791DOMSVqmd3Z2SqTvK4G8EHaH74sri/j2/Ld1CZVAY73xkiJByksA9PQSz/AIeUKQFRmCJFTcsXMMB57lG66XtAIX6wJFUaWEWDZcuSQuYWZuBu45t5EeOzt6Fv29/XWlx5wIpfQ8rJ5Tq/pLKpVCPB7H+vo6bt++baj2hsNhW+ovnZTycgilQaiqivn5eSwtLeH06dM4evSoLdLRrS76JMXl8/nw1FNPGbugduluAZVDbUEQMDExAYZhMD4+XlcIbD6mHcjn89hKbOH1yOsI9AbqllCpBlERjYiDoRl4XB7QFI2iVERRLgIAvLwXsiojV2y9XlIv/C4/ClIBslq59dwyPZa6jjd230CACtSdHjvsxbxT5j/q7fJiGMaorQClqr3m+ot5/qWR69M0raNSXrlc7oGx/wUOmVByuRxu3LiBQqGAxx9/fF9huVm0Qiikc2phYQGnTp3C8ePHSx70dtRnqhHKzs4OpqenMTIygrNnz9b9ctjpXxKNRvHiKy/ize03EewK2pLyK188FFUpiQZ4lkfAFYCkSAa5HASC7iAyhQw0NHbfaJrWd5L3NpN78h7WNtfw0t2XEKJDGPQN4qHRh1pKj7UDnRKhNBsVlKv2mudf1tfXoWlaSXtyLVtesgHrBJIFHiw/eeCQCEXTNGxtbWFqagp9fX24fPmyrVPtzS765hRXJe/5dkcoBKqqYmFhARsbG3j44YcxODjY0DHNlr3NQtM0LC0v4fk3nkeMiYFiKFsXn0pkR1EU3JwbsVxM/zco+HifbkSmyMiL+YYXfKvPKP93rZmWRsCyrGGKJGsyVsQV3J67XZIeC9JBpFIp+Hy+QxMj7BRCsStS8ng8GBkZMeov2WzWqL8sLS2B47gSgilPrZP3pZMilOHh4cM+jbpxKE+xKIpYXFzEuXPnMDw83BZV4EYJJZlMYmpqCn6/vyTFZcexa6F88S8UCpiamoIkSbh27VpTO5RW5OYBfQbozetv4uuzXwcVoBBwBZDJZGzL+1f6zq3qJRq0khZhlmbh4fX0WF7KQ5Sbt2U2PpNpX43GKj02lZlCKpZCOBJGj0tPj50aOXXgbbKdQijtqFtQFIVAIIBAIICjR48a9RcSvRDPD0IuXV1dHRmhPCj2v8AhEYokSchkMhgZGWnL8RmGQbFYX5qkVorL6tjtmO8gkU8sFsPU1BR6e3tx9erVlnZKzWqEZbNZfOvVb+H1yOvwd/vbsluzusf11ktkVUamkDH+7ebc4FkeqqpCECvPvljB+EwbZlrqhc/tg8IriOxGIHtlRJko1jfX9e4xKogh/xDOj57H6MCoIVPSLnQSobT7PMz1l5MnT0KSJKM9mbgmksU7nU43XH9pBwRBeGDsf4FDIhSWZaGqatu6KeqNIiRJwvT0NNLpdMUUVznsVgYmoCgKa2tr2NjYwLlz5zA6OnooE/glEiplLcHtmL4nCLqDyBazTc2XlM+++HgfWIaFqIjIi/mKf+dlvSjIhbbI9FRCwB2AIApQVP0ZoiiqJD2maMq+9NjR8FGcGztXd/dYI+gUyZPDaA7gOA79/f2GT1A+n8f29jay2Sxu3rwJVVVL5l9q1V/agXw+79RQan7ovRdClmXbe8iB+gglmUxicnISgUCg4nBgpWPbvQCJoghVVRGJRPD444/bJlXdSISiqipuz93GC2+/gJwrh6GBIfAMD1mVIYiCsZO1O+VFgULAY1/tQtXUkmiDYzh4OH2Hn5fykBS9bdnv8mMnt4OAenC7v/KCv9W9tEqP3UjfwJs33oSf8t/vHhs+iZ6eHluGKzuBUDqhVdfj8aC3txdbW1t46qmnjPpLLBbD8vIyWJYtmX9pZbShXjgRSh0wS8y36/iVjm1OcZ0+fRrHjh1r6IWyO0Ih7ckUReHhhx+21feg3hpKsVjEqz9+Fd9Z+g74Lh4+zoe8mEce+u6eoRl4XV5k+Sxoxp6XnqIovVWY97R1vkRSJINEKFDw837wLI+9wt6BLaQ0RcPv8iNdaPw6y7vHorJ1emykf6QpF0WHUEpBhhqr1V/IcLHP5yuZf2lHY0U+n3cIpRZImN8OiXmgMqGIooiZmZmGUlxWx7YjQjH7qJw8eRLr6+ttcZisda7JZBJfe+VruBG/gWBP0PKlVlQFmUIGOSkHnuLRzXXDzbqNaKCZ++HlvaBBQxBbl2OpFzzLQ9ZkZIUsBEnQCcbt3zf7YieMJoMmyMQK5vSYChUrRT095pp16dL84WM4N3YOfT196O7urmu4slMIpVPOw6pmWKn+kkgkjPpLMBg0CCYYtH6XGoGmacZxHxQc2hxKuwQcKx272RRXOeyIUGRZxq1btxCPx40J/K2trbYU+ysd05BQee15bKqbDbkqFqUiipK++NIUDZ9Lr1lIUn16XSF3COvpdUiqZGgVtbtt1ufyoSgVS4YVy6VheFYXt9RUDYJ0v87RLNzcPdKt0mTQ6iJqTo8JqoCb2ZuYnJoEp3Lo4XtweuC00T0WDAb3fV6nEEqnDFjWK7tiVX8hBf7NzU2oqoquri4jRebz+Zq6z06EUu8Hs+yBEIqmaVhdXcXi4mJTKS6rY7ey8GezWUxOToLjOFy7ds0YcGvXfItVnl5RFEzcnMCLUy9C9skIeepLs1nK15ctmDzLw8N5oGoq8mK+ZAGnKApBdxBb0S3s7e0B0O9HLBYDy7Lwer3weDxwu922LnJBTxCZfO1hRVEWjRZkMvvCMiwkRWo4kvK7/ciL+ZZJqRGQ7jFyDVE5ivWtdXxv7XsIUfpwJekeI+mxTiGUTopQmiE2j8cDj8eD4eFhY/4lkUiU1F9I9NJI/aVQKByI/a9dOFRCaXfKSxRFTE9PI5PJ2DaJ30qEsr29jZmZGRw5cgSnT58ueXDbRSjlxxQEAd9/4/v44d0fwtPtaUhCpZ6ifPmi7HV5wdGcQSx3Nu8gmUyiu7sb8XgcQ0NDUFUVhUIBgiAgGo1CURS43W54PB54vd6mh14pSp+faaZGYzX74uX1Lp9asy/1Ttvb2TFnRWDl6bE7xTu4PX+ve4zRu8fCXBjpdLptNYB60Sk1FDtkV8z1lyNHjkBVVUN/bHNzE7Ozs/B6vUb0Uik1WSgUIMuyk/KqB+1OecmyjNdee63lFJfVsSVJauhvVFXF3Nwctra2cPHiRSNMNuMg/Ev29vbw4isv4nb2NkK9obbvCDVoEIoCPLwHkixha2sLlELhoVMPIVfIIR6PA7hfeCYieJIkIZ/PG2kEhmGMHaDH46kvJWFz7UJW5ZJjWc2+GARm02fWi3ojsPL02ER6AvFoHAObA+i70YfTg6eN7jGr9Fi70EnDhO1QGqZp2iAOQH++k8kk4vE4lpeXkc/nLesv6bT+HLUzQnn55Zfxx3/8x7h+/Tq2t7fxwgsv4Nlnn236eO+4CIXIuiiKYohN2vliNBqh5PN5TE5OQtO0qkZh7WhHJiRFJFS+9MaXEGfi6Ap3NXW8ZtqGg54g4uk4tra3wLIsBgYHUFALkFQJHMMh4AroxGMaSCSqvsFg0ChM5vN5JJNJ7O3tged5Iz1mlTrwcB4omtJWQcny2ZegOwiWZm3xfGkEQXewqQiMkHiMikHxKoiyenrs+2vfR5AKWqbH2gXyTHUCoRxEpMRxHPr6+tDX1wdAj0SI/tj09DRUVcX/+l//y5g/aWcNJZfL4eLFi/j1X/91fPzjH2/5eO+oGgpJcRFmN1vi2oVGFv69vT3cvHkTg4ODOHfuXNVQul0RiiRJ+NGPf4Sv3/466CCNgKv5h7MRKReKohD0BLG1t4WdnR2EQiHDoc84N0VCKp8CTdO6NS/vA0uzKMpFY7GmKMqITAC9oYFEL6mUrlRMfu71etHl62q686xZ8CyPolxEWtafOzL7okFDQSoYbctWaPb5NFqubYiGyocrzekxz7wHQTqIo91HcX70PPr7+uvqHmsE5LvqhBqKoigHruPldrsxPDxs1F9yuRxee+01fO1rXwOgr2Mf+MAH8IEPfAAf/ehHLTMczeIjH/kIPvKRj9h2vHdMhJJIJDA1NYVgMIgnn3wSP/zhD9sWvtYiQk3TsLS0hNXVVTz00EN1Scy0g1BUVcWbM2/ix3s/RiAcOLAXhaVZuDk3VjdXkUgk0N/fv0+PqFwNWVX3F/dJa3JezEPR9HtOFj6y+BWLReTzeWQyGRRSBUSoiEEw1Yr7di1efrcfBbFU6r589sXLecEyujpETsoZ19xsDYVneDAMU9Kh1iwqnYM5PZZX85jKTOHW7Vu6cyXXU5Iea1WipJNSXoddy6EoCn6/H5/97GcxPj6OX/mVX8E//MM/4KWXXsJf//Vfo6enBx/72McO7fxq4YGPUEgXl9lPhbwkiqLYqmIM1I5QRFHE1NQU8vk8nnzyybrDVbsJZXt7G2/deQtrhTWMjIxUNbCqF/WkvDy8B7IsY+nuEorFIkZGRpqaKN7XceXSoxdRFpGX7supuFwueD1ejA2OISWkjOiFFPfNtZfyZ6HV+1FP7UKD3oKMe0GK2feFoxt/Nkk6r5qkTKOoRa4cyyEYvu9cSbrHzOmxcyPnMDY41vRwZT3ncRDoJC8U4if/kz/5k3jf+96H/+f/+X8O+5Rq4lAJRRBayzeTFFc2my2RLCFKu+0o+leLUBKJBCYnJ9Hd3Y3Lly83lBawi1BUVcXM7Rl8+fqXsZnfhN/vR07MGdPudin0WiHoCSKRSWBzaxMsy2J0dLTiy9mIX4sGrSR6MVsJk0ggU8joaTOfz8g9i6KIfD4PQRAQj8eN4r7X623pXrciF2P2fZFUCTzLw+/x1zX70o5W5Fr3n2M4cAxXEg1ZpscWbsM970aICTWcHjPb/x42FEU51G43M8iMVifcl3rxwEYoJMUVCoUwPj6+b/fZrjkXqwiFyLmQWZdmGgHsIJRisYhX3nwF313+LvguHpzM3Y/W7k27E7g5N1ysC4qq1K3QWylCIfMl27Ft7OzsIBAIoKenp+o9aMUAjFgJ+1w+SIp030pYlSEUBSNi4HkePM8jFAoZxX1CLpIkgaZpJJPJisX9cuztAa+87MLJY25cfNQeb3tJkQxiokDBy3vBMdy+2Zdmjb/qQaXviQxm1prBKU+PTWYm8ePJH+vOlVwPTg2cwqmRUxXTY50ygwIcfsrLjGw229ZmiHbggWsb1jQNd+7cwfLyctXFu12qwOXHlWUZ09PTSKVSuHr1alNyLlbHbRSJRAJfe+VrmEhMGC3B1VJU5i4lhmYQcAV0CRJTQbwcVscz6iVber2kr6+vrjRfq46S5nSTrMolKSUyL1IQCxAV0fg8c3E/lUohk8lAFEWkUqmSn3s8HsvI6uaEDwuzLBYW87j4aFOnbcDqukm3GwG5ty7WhUyxPWRS6f5bKQvUg/IW8Kgcxfr2Or6/rqfHhnxD+9JjnTIlD3RmyutBwgNVlBdFETdv3kQul6upytuuORfzcTOZDCYmJuDxeFqedaFpuuH5FuC+hMqXXvsSttXtEgmVett8y6MXF+eCi3Xpu9Ni5ejFy3khKRKW7i6hUCg0VC9pdkday1lx37WwpmsxRWIMw4BhGPT390PTNKO4n06nLVuT/W4/tiMqwBZAHdDap0EDKBjOla34vlRD+XfRipVAOczpMQ2aZXrsRP8JaJoGWZYPPd3UjkaeZpHL5doeoWSzWSwtLRn/vnPnDiYnJxEOh3HkyJGGj/fApLxIfaKrq8syxdXq8esFSU1tbm7i9u3bOHbsGE6dOmWLd0mj56soCm5M3cD/mf4/UHwKgu7Sidpm5ebLtbr8bj8YikEhW0Be04vBQXcQiVwCm5ubYBgGY2NjTe3sGjk/KzfHWijK90UfieovTdGQCzIy0ImHoii43W643W50d3dDURSjuL+7uwsv48UetweG6wfggrv9quWWnVzlsy9+lx8MzexrVGgE5fc/5AkhnU+3JRoCrNNjb+++DV7lMb07jdMDevdYb2/voRhcdVrKq91eKG+//TaeeeYZ49+/9Vu/BQD4tV/7NXzhC19o+HgdH6GYU1xnzpype7aknUZYkiRhbm4Oly5dMoaTWkWjNRRBEPDSay/h5Y2X4e3yws26Lc+11bqMWUCxqBTB0Rx6/D1IpBJYX1uHP+CvWS+xQqMpLw/vgaK2NqyoaiqyRf1aCnIBLM0i6NaHJ80FcYZh4Pf7EfAHEHAHEE1Hkc/nkb330S63iFgsZrQm270AeTgPZFWu2sllvhagsu9LNZi7q0ijQSpvT22oHtA0jd6uXqSYFHb2dsB2s9jY3sD317+PEB3CgGcA50fPY2xwDN3d3QeS/umklFcul2s7oTz99NO2SgB1dITSSIqrHO1IeQmCgJs3bxpT73aGo40QSomESk/7JVQIWIaFSqlY3lhGPB5Hf18/BnsH9V1yDXfEcjRyzuUuh3bBLKdSLgYpSiLcvBvpQtoo7kuintIMBnUCicfjkGVZb12+lx5rNO1Zfh8CrkBTSsf1zr5YgaEZ+Ny+tvrSWIF0rUmKZDlcuVpcxezCLFzzLnTRXTjSfQQPjT2Evl5dmt/ukQCgs1JegiA4NZS6P7hGhBKPxzE1NVV3iqscdhPK7u4ubt68iYGBAeRyOdvd2uohFE3TsLi0iC+98SUk2ERNCRU7HRY9vAcFqoBkLglKoIx6iXmXTJSGNU0XVqxnUax1fu1OwRjnYRKD5FkeLs5lSKoUJL24z7IaAAqPvAfG1L9ZdyyZTNZV3Aesr9uua7WcfeF1DTSz74umaWBpFi7WZcuQZCMw12kqKR7vG67MTuGtybd050quB6f69e4xO9NjnZTyyuVy6O3tPezTaAiH3uVV/jBpmoaVlRWsrKw0lOKqdPxWoaoqFhcXsba2hocffhg9PT2G34GdD14tQpEkCW9cfwPfnPumLqHC19dJZQehBD1BJLNJRPYi0DQNR44csVwoS4YRqfstsI1GL8D9bq2DTMEA97qb5GKJ0jAAuFk3MvEgICkYHk4Y/71cd4wU91OpFPb29uByuQxycblcls9yu9NNiqqUEL+LdcHFuaDIChgwyEv5A11Ey4mzHgn98u6xmBzDRmQDP9j4AYJUEAPeAZwfOW90jzW7s++klJcgCA+UnzxwyBGKpmklX2CxWMTNmzeRz+db9la3g1AKhQKmpqYgSRKuXbsGv99vHNNumZRqE/iZTAbffOWbeHP3TQTC9e/E7CCUoCeInfgOIpEIXC5X3S+cppW2wJr1rcw+KVbnRwrS5m6tg0C17qZEpgAJGsABXSFKL+6X7fhrFfc1TSshFwBgqINPNxXlIlhGF7IUNRE+lw88yzdF/I0i6A5aEmejm8by7rG7xbuYW5yDa+F+esw8XFlvhqOTUl4PmrkWcMiEAtwXYyMpru7ubly6dKnl/GirhBKLxTA1NYXe3l48+uijxvmSh83u+kylCGVrawvPv/I87kp3MTo6Coqi6rarbYVQWIaFm3VjbXsN8Xgcvb29oGkaiUSi9h9bYF+On/fCx/ngYkpTh37ej4JSgCjaP8lvhlWrbDWhxVRK/32vVwPNWOz4WVfF4j7RMSPRCzEV42gOmVQGQkGw3VSsGghxyopsED/pEivxfRHzxhxPqyBdaVb32A6TL3N6rKAV9PTY1FvoZroRZII42X8Sp0dOG9L8lUijk1JegiDs08DrdBwaoRCpBUmSsL6+jpWVFZw9exZjY2O2vFgMwzS1KJm7ys6dO2cs4gQURbWlg6y8I8uQULnxZeTdeQS7ghUn3XNi5aJrM4Ti4fSOquU13atheHgYbrcbuZw9cvBkgC8n5eCVvOjydhlF5GxBX+gOApqm6ak1zltTtTed1p+BYGj//SxvTfa5fGBoBpIilez4yaLX1dUFlmJxZ/UOCnIB+Uy+xFSsmeJ+vTCnm6w0tCr6vtSYSaoGhmbg5twV77GdXUbAvXSr14u+rj4U5SJiop4e++HGDy3TY0TepDxjcthwIpQGQFEUGIbBzZs3IUkSnnjiCVudyZqJUCRJws2bN/dpg1kdu50pr0KhgFd+rEuouLpc8HL788Hlk+5elxcUKBTkQkkdo1EE3UEkc0lsbm2CoqiS+RI7i/xmKKoCUEA8Fy+RH5EVGYIotK0gzzIseJZHplg7tUYilJAFoZhRbolsleojg5myKhst13aYilVDpTpNrWfEavaFpmidLOuYfSHpy2rt3u2wIfa7/BAkAaqq7kuPrYlrmFucg3vRjSAVNNJjfT36CEAnRCiapiGfzzsRSiMgpPLYY4/ZPiHbKKGkUilMTk7C7/fX7CprR4RCUl6JRAJffeWrmExM1u2qWD4d7uE94BgOalFFJlN/HSLkDSESiyASicDv96O3t7ftaRgaNLy816ghWMmPeHmdUEm3lR3wcB7ktFzdNQMSoYQa3POUp/rC3jAUTYGbdYPC/Xtbj6kYEbZstMOQpnSvmfI6TaObA6vZFzenX4fV7AvRAqt1j+0mlFq6Z6QNHACKWhHTuWm8PfU2/PCjm+tG8fUizoyeqZkeazecCKVB8DyP48ePt0VuoV5C0TQNGxsbmJubw8mTJ3H8+PGaD3c7IhSKoiCKIv72G3+Lba1UQqVR5MU88shDkARQ0IUbQd1bkC1UhhmagYfzYG17DbFYDL29vZbRot0RCk/zoGm66u7VKgXjYl1QFKXmfEUlBN1BRIRIQzpVqXunEAw2d/0kQogLuu2xoiigKRoBdwAMzZTUK8p1xxRFgSAIyOfziEQiAEpNxaqlaIhacKUorJWF3Gr2hWM5vXsTGopyfVpgdhJKo63X5nutKApurt3ElraFlzdfRpAKot/bj/Mj53Fk8EhJeuwgUCgUHig/eeCQCYXjuLb6ytc6tizLuH37NqLRKK5cuVLiKFgNdkcoiqLg1twt/GD9B6C76H0SKs2CoigoqlKyIJPohagMu1k3FFXByvoKBEEw6iWVjmcXofjdfsiaDFEW4Yb151lhX6qP94Km65fkD3p0y9xGryNdZ8rLCgx1z12xLEJQtNLifiWtLoZhLE3FjOI+x1mairnYe/7xFdSC7dwcmGdfAq4AinIRHs4Diq7dRGIXoYQ8oZZar4nqsTk9ti6uY2FpAfwCjxAdMtJjA30DbRuuJOeSz+fb6iffDhwqobRLwLGeY2ezWUxOToLjOIyPj1dcRCsd264IhUio/GDtB4hmozg1eMqW4wLWBECiF0BfXEVRRGQrAlVRMTo6eiDifCFPCEkhCVVTUSgU4PV6m/pcRVVKdt5kQVZU3YDKXEAmxfJm23NTpCjfYITCszwYmikhDsB6MS+pV1SwRAZKi/tk4Sk3FQsHwlBdKmimerrG7t22OUIwp8DI7Ium6ilN4sBp13kEPdbtyI3AitgqpscoP8JsGKcGTtXVPdYoCoUCVFV1CKWhD7fZBtiMaoSyvb2NmZkZHDlyBKdPn274IbArQtnd3cVXXvkK5nJz8HX7EE1Hbc8nV9qFhjwh7CR2sL29DZ/Ph4GhAXhd3pLopfxvW41QaJqGn/cjno1je3sbgL7b3tjYMHbZpEbQzD0oj14MIUhFBjQ0PdciSUBeaDxC8fLefZ1e9aJeS+RyUzFJkkArNKKpKPK7+arFfbsbLKpFCOZOOLMDp6RIiKkxUHQL6tOu5szOylHr3TOnxziGQ17L4+Wdl/HDjR8iQAWM7jE70mPptH49DqE08uFtUgQGrAlFVVXMz89jc3MTjzzyCAYGBpo+disRiqZpmF+Yxws/fsGQUCHnaiehWB3HqJdE9HpJT0+P8dCaoxeWZg2rWjvmEVysCzRNYy+1h+3tbXg8HhQKBQQCAQSDQcNZkQwAut1uQx+r2eglW8gaizpN66nEegyjypHJ6PeR4zXUG8gGXAHkpJxtkWy9lsi9wV6k82kMuAdKivuJRGLf5L5dz1qjk/7lDpwkegx5Qg09a+RZrtXyXS/qvR88o9f+ClKhpAvLzvRYOp02/OUfJBw6oRxUhJLP5zE5OWkIO7YiutZKhCJJEl5/+3V8c/6bYIKMIaHSquGUFcojCg/ngaqqWFlfQS6Xw9DQUEWBS1mVjR09BQoe3gMP40GMjjUcqfhcPoiyiFg8ht3dXXR3d6O7uxvZrJ4GsrLuFQShZo2gFgLuAHLivUVdQYkkv4/3QeZkuDl3RUMxAqNluM50V72F4WYXc0tLZM4DlmFLupvMO+pwOAxZlo30WCqlL/6aphnOgLXmLwoFvdstnaawu0thYYHGLz6nYiDsbSlCkFUZgiQYhFQtdUnA0nrLd3kqsRXUY/TlYl3QoFk+M5XSYz7KZ6THzoycQTgcRigUqvpZmUymLUrW7ca7glCi0SimpqYwMDCA8+fPtzy41GyEkk6n8a1Xv4U3dt9AMFyab203oQTcAaSFNDa3NqFpGsbGxure+ZMZCkmSkJfzYKj7cy+CJFSVSg95QkgX0ojFYkgmkxgYGCjRJ7KUXrn3YpbXCPb29uqOXsyOjuVQNRU5MYeclENBKpTm9y2UfgmhBGtkHxrZqdv5PSuq3lVF5ng8nAc8y++zRCbzGIFAAJqmIZ1OI5lMlpiKWaUdv/1tFjMzNESxjAAVFq+/yuD9H24tQiiPDGrNvvAsD5qiG440a6GWFbGb0xtY6rEGKO/USypJvLLzCn64/kME6WDN9BghlE6xRq4Xh04ohUL13WGzYBhGV+ddXMTq6ioeeughjIyM2HLsZiKUra0tfOmVL2FVWsXA4IDxcJJunnYSCqmXRCIReDwe9Pf3t/Sglsu+kwWMvPBkgQi4A0jkEtjd3UWxWMTo6GjDE+BW0Yu5w4llWYNc3G63UTuptWM2X7/lpDulS/IXpALubearRiiVOrnaDaMt+F40qUFDXsobaTCiBEDR+y2ReV4fOhweHoaiKCgUCvvSjoriQ2Sny4JMeAA0imrr6gnVUk3lsy/kuwFQ9+Jux3l4eS9EWWzYEpmAyPAQkPQYt8Chi+7CWNcYzo2ew1D/EDiOQyaTORA/+b/4i7/AH//xHyMSieDixYv48z//czz++ONNH+/QCaVdNRRy3O3tbTz55JO2Dgg1EqGoqorpW9P4ysRXkHfn0dXVVbKAmf3ceYa3lVAYmoGbcWN9Zx3RaBThcBhdXV1NH88q1VW+gLE0C79Hd3iMpqPY2NwAwzAYHR21jAwbvV4SvYRCoX0dTpRKIeALIMknm+4cK59051ke+VQAEGmEgtaRB89Yd3K1G7XagoEKnXCMLqVSyBeMBZRhGIO4k0ng9m1gbp7G7o5F3l9149JlEe97pgCPp/Xntd7ahZf36rMt96R5ymdfmp1LqnUeft6PvJy31Y+nPD02I8zg+s3rcMku/MV/+gv0cD0oCAW88sorePLJJ9sixfMP//AP+K3f+i385V/+JZ544gn82Z/9GT784Q9jfn4e/f39TR3zHdk2nEwmMTExAQC4cuWK7YWteiOUQqGAl998GS+tvFRRQsU85a5AgYt1IeAJVOy0qhduzg1REyFIAoqxYtV6iZ3gGA55KY9kOolIJIK+rj4MDw1D1vT0i50wRy9uzo1CsYBUJoVcLod4PA6WZY0UTrPpA1EWEcukAZ5GMKTsK4YbnVxNWPC2EiX6eB+KSrFh3TNzOkmDBi/rRdAdxM6ehKlbIubmaUS2zXl7DRQNaKp+riePKnj4kTl0hYFi0QOG8dqy2NW6F36XH3mpdFG38n3x8t66Zl+sYEUofre/aQ2zelGeHvv0H3wa11+6jrlvzuG5555DLpfDM888g7/4i79oyue9Ev70T/8Un/70p/FP/+k/BQD85V/+Jb72ta/hv/23/4bf/d3fbeqYhx6h2FlD0TQNd+/exeLiIk6fPo25ubm25CAZhql53vF4HF979WsNSagQhVeNvpfzptm6axVmBNwBZPIZrG+sAwBGR0dtGcAyp+Wsroc4K8YTcWPi3h+8rzDLMRw8vAfQ7u+q7YjIyOeC1tssSfRCUjjm+Qzzi1vvZ5MZlFCoNHoJefSiCkvrVgzNpkMaRcAdQK6Ya3mRi8eBmRkeX/5GHjsRGlC8gOIBKAVHTuRw7ryEs2dVTEzQ2Nig8TMf8iDYk4Uo9RldealUqm5TsUqo9T3Ue73l0Vit2Rer8zA/19XsDNqJ8fPj6NvsA+4Ar7/+Oqanp/Htb3+77sHreiCKIq5fv47Pfvazxn+jaRof+MAH8Prrrzd93EMnFLsiFFmWMTMzg0QigatXr6K7uxtLS0u2S6QA1SMUTdOwurqK519/HhFEGpZQMb9c+2oVvAc8o9cqKqU5Qp4QdhO72I5sw+12o1gs2jasWI1QQt4QUrkU9qJ7yGazlhGRpEiQ8velOtysG37eDy/vbbrAWkm3qdyQidReBEFAPB43GiLy+XzV6EVVgYwhu2K63rJOLiJqyTKsIWpZDc0SacitNzk0K5gZj1OYm6PvpbNMmmCUhqMnCzh3VsCZMyr8PhjX0/O0CJ7V6zSqpu0r7pPJfVLcr8dUzIxqKa9qzRW1UG32xer7MUvXH5RTaDkujl7ERx/+KP7fF/9feL26CsTFixdx8eJFWz+HbLLKRycGBgYwNzfX9HEPnVDsiFAymQwmJyfhdrvx1FNPGSF4PZFEM6hUQ5FlGdenruOr01+F6lcbllChabqyDP29TisyJ2JWsRVEAdAAr8tbUi8JBALIZrNtUXM1zvleF048E0ckEoGqqnV1kGnQUFSKyBQzcImuEqFBQRRq7vaNgbY6ZxDKay+pVArpdNp4scydY+ZoLpuloGkUKFqD369V7OTaJ2rJsPBy3nvfWwE/flvB5csqWvkamp0Gj8Xukcgcjb09c2ehhsGhAi4+wuHMGRVe7/1nTwMM18qQJ4ScmEPArdchzSKd9ZiK1erKq/R8EpkcO1Deam0lOkrOo1UJl2Zx5cgVfPihD4OiKGSz2QfOrRF4B9RQNjc3cfv2bRw7dgynTp0qeTDbIeIIWEcouVwOL732El7ZeAW+sA8803heuZH5DrMwn5tzg6M57O3uIZPIYHBwEF6v1ziWXYX+8k40nuXB0iyi6Si2t7fhcrkwNDTUVO+8lQEXx3CWu0mO4cCzfNMDbTRNw+VygWVZjIyMQJIkCIJgRC+k9uLxeJBM6otOMAiwzL1BujoWOVmRkVb03/vLv+SR3Ath7iaDf/4v8sg3GI01IxsTjVKYnaUxP08jGjWRCK3h2DEN586qGB5OQ5bTGBoasjwGBb1Tjyyu5HqA+yKdsiqX1PrKTcXKZ4rM99bsQ2JpeNbGjjkr0VE/rzeTHLRTKAA8fuxxfOD8B4x/C4LQ0qxcLfT29oJhGOzs7JT8952dHQwODjZ93Ac2QlEUBXNzc4hEIrh06RL6+vr2/U67iv7lRLW7u4svv/xlzOfn666XWKEZaRNSL1neWoaqqhgcHoTP4zNMsuwUdDQTChlW3EvsYWdnB11dXQiHww0f0+rcynf75uhFVmTImlxVobhRcBy3r/aSz+cRi8WwslIAMAS/mwYUBVm1sU6u73+fQTJJAZyA7ZiGTF6Ei2Phd/nh4/WNR7XJcDLAV88it7t7P50Vj90nEZrWcOy4hvPnFJw6pYJkIjMZFbmc9bNaawq9XObGw3v2WSID+2eKzPeWRIaKohjrQKORp10oSLp2Vk7JoQc9FZUI2oHxk+N4+szTJf8tl8u1dUqe53k8+uijeOmll/Dss88C0FN+L730Ej7zmc80fdxDJxRFURpOyQiCgMnJSVAUhfHx8YrdS+1KeZEIhUioPP/j55HiUujq7mrpuI0u/iFPCLvJXWxv6/USEh3sk+ngfXC73PtEBptFwHWv+B6PI5FIoL+/v60PP4le/G4/REWEi3PBw3na8rKbay89PT24exeA5IXXl8Dy3VUwDGOkb6ppNSkK8PVvsLg1oxeoOU7D//V/SWAYfXdcLBZ1T3dFrCjJX09b8M4OIREGifj9c6EZDSeOazh7VsHp02pFyRir8yezLfW2QStqfZbI5feWmIoVCgXEYjGkkimEg2Fk2awtpmL1gkRie3t74DiuokkagLrSsY3gfWfeh6dOPrXvvwuC0LQ0VL34rd/6Lfzar/0arl69iscffxx/9md/hlwuZ3R9NYNDJxRAjzbqLRzv7u5ienoaQ0NDOHfuXNWHrl0pLxL5vPLGK/jG3DfAhlj4+dYX1HoJhaEY+Fw+bOxuYG9vr+p8iQYN2WIWRVXfNbo5N1zcvcWrinVwpfPzcT6k8inE43EUCgWMjIw0bPhkPl69n28uvsvF+y80iV4A/WW3c1YAAIq5boApoG+AwdGjR436QDwehyzLhm2v1+s1ai+FAvD88xzW1mhQlIYPfUjB8eMq/H7ra7Vy32QZFkWpaEkmkch9EkkmzCleDSdOqDh3TsWpUypqfS2WKgUsb9SxmkW9lsjEVCyZTGJ4cBgczSGWjiGf1esvpLjv9eqtye2oA1IUZXjdV5JeqZSOlRVdMqbZDMD7z70fTxx/wvJnB+En/4lPfAJ7e3v4gz/4AyPT881vfrMlIntgCEVVVSwuLmJtbQ0PP/xwxbyvGe2KUAqFAlb2VnAzdROBnoBtO6l6Flg364YGDSsbK8hk7tdL6j2m1eJFU7X9RFiGhZtzQ5AFSBEJDMOU2AO3C7VSIJVedlERm1L5NSPkCSGazAI0jVDovlc5ud9khy0IgmHbK8sBfPvbvUgkaPC8hmeflXHiRP2bGkVVAA1I5VNQVdXQtbq7pmJquojZBQqppIlEWA0nT6o4d1YnkUZHQsyLtIfzQFZl21wxgfoskRkw4DkeKqUaaVNS3BcEATs7O9A0raT2YkfnIk3pbqEknVhLegXYn45tZvaFoih88PwHcfXo1Yq/cxCEAgCf+cxnWkpxleNQCYWmadA0DVmWq+5yi8UipqamIIoirl27VveNbkeEsrm5iS+/9mXcjN7EhbMXwNCMbamkWoTid/uRy+ewsbUBRVHqmi+pdsxK1sHlQ5UezgMVKqJJXV6f53kMDAzYsmOsdr0szcLFuerOp1vVXjy8rqrbSPRiLkan0/r9tZKtL7ftXV2V8JUXvSjkGXg8Ej74wT309LAQRU/dw3/mdtXNTQrz8zLm5lXd4EvlANkLlqVw5pyA0+fzOHmycRIhMN97q8HBdqB8AxByh7DH7IGhdakk0qZbXtyv11SsXjAUAzfvLknVNdMNWXH2RdMgFK19Xz5y4SO4NHap6nEfRD954JAJBahdOI/H45iamkI4HMaVK1ca2pnYWZQnEipfvvFlZNjMPo2hSl0vjaDa4h/yhrCX2MPW9lZJvaSVY5ajXL7e6/bq9rTFPPbie7q8CUWhu7u77aJ1bs4NTdNaKr6Xz72YC60CrFM65cXodJ3GWouLDF580QVZptA/oOLnnhXBMJwhHU98ScjUPrC/fhF0BXF7KY3ZWQbzCzQyabPnvIaTp0ScO1vAyZMqOA5wcS64GJchyd/oAB5ZQA9rgM/NuZETc8iKWeSlPNy8Wy/uW1gm1DIVs0o9VgJLs+AZft+zZUd7vdXsC0MxkFUZBamAn3nkZ/Dw8MM1jyMIwgPnJw90AKFU6vTSNA137tzB8vIyzp49i7GxscZ3ITalvAqFAn74xg/xvTvfg6vLBY/m2bdIl6eSfC5fwxPuVos/MaXa2NHrJUT6vd2QVRkUKCSyCURjUYiCiLPHz+Lu+l3YNetV6fv0u/0oiAVbi5/lcwg8y8PH+YwGA0VTDJ8LslHI5wFJqk0ob73F4KWXGAAUTpxQ8eyzEnieBRA0ohcytU9qLxzHQVVVFIsi9nY9WJ4PYno+h2zmfrjB8RpOnVJx/pyK48d1EjGjKBVLJfld1g6P1eDn/ZaDoe2Gz+VDQSrc1+WiqBLLBKCyJbKVqVh56rGSqRj5jgVp/4bCdnM70zPH0AyevfQszg2eq/13956XB81PHugQQimPIiRJwvT0NNLpNB5//PGmXcsYhoEktaZGGo/H8dVXvoqp5JTREixJUtVUmtnH3TzhXiuvX04obs4NaMDKxgrS6XTNekk9x6wHNEXD7/YjkUsgEolAlmUMDQ1BoiSIqgiWYRH0BBtOJVmh/NxamYxuBJIiISflkClmQFM0ur06SZu7xohsvdenwSow1jTgu99lcf26Xke6fFnBBz8oozxwLNdqEkUZCwsFzM3T2LjrRyHnB7gcAAocr+H06fskUm9AXl6rqCXJD+hkkhSS8KD9Gm9mBFz3fGo01fj+rRbycktkv8sPGrShAk1QnnokrcnJZLJkcj8UCAEsKpJtuwaAWYbFz136OZzuP1333+TzeSdCaQblUUQ6ncbExAT8fj/Gx8dbEp5jGKZpeXwiofKl176EHWqnREKF7HjqeQDLJ9x5ltdrEvc8OczEZF78/S4/coUcNrc3IcsyxsbGmtLjapRQjGHFlD6syPM8RkdHS3Z5kiIZQ2cURTW1M953nvcm0A9a/h3Qd8upfMrYAfMsDzfnxrpAAaqIUHB/pCRJwFe+wmFpSb8vzzwj44knKhOrqgLr6/q0+vwCDyHnA2QXAAq8L4ujR/MYGUmhry8Dr5eH1+uFqnoANPf8V5PkL8pFBFwB3E3cberYraCSpEmt90hVVWQL91PM5DtS1VJL5HLyJqZiqqhia2sLsiobqbFy3bF6DLYaBcdw+PkrP48TvSca+rt8Pu9EKE2dgGkWZWNjA3Nzczhx4gROnDjR8m6h2RqKLMt4e/JtfHXmq9D82j4JlVoiidVgnhEhkQBDMShIupS4qqoIeUKGVa7Vgt4IGiEUopwbTUYRiUQQCoX2CdKVH6+8zuFiXfqLfm/HXCsvr2na/brFAZMJ8YopX+DIdxSJMQDFIRz0IeTRXfqKchHZLIUvfpFFJEKDYTV87GdknDu3/zpVFVhbIyRCG770AOCiPRg9HceVqyyOHVPvLWxhyHIQgiAYO2zSWWaVvqkX5uiFoXS7BEBvtshr7R3aM8NK0qRahFIN9VoisyyLgZ4BiIqIoBw0JvfLTcU8Hk9dXV6NgGd5PHflORztOdrQ35EBUIdQmjkBloUoipienkY0GsWVK1dsU9VshlByuRy++9p38crGK/CH/ZZtseShM4vJNQNVK911eTgPGI1BIpXAxsZG09Pn5edaD6GEPCFkChnEE3HE43H09fVZhty1jle+M/a7/GBoxliMy4/V6BCdnSAzNZWQTlMApcEXyiKV16OUVNyF/9//N4hkUoM7kMVzvyhiZOT+/VBVYHVVlzyZX6BRyN9foNxuDWfOqrh8wYtQzw7iiT2Mjo6WfCbLsggGgzXTN2Q2oxHwjG6oRdKxpCPOjgizFirpkNmh4mBpiXxP1VpRFRTkgqEaQYr73d3d+9xAVVVFLBaDz+fbp+nWKNycG7/46C9itHu09i+XIZfT58OaTfUfJg6dUABgdXUVPp8P4+PjRgeMHWiUUHZ2dvDlV76MhfxCVQkViqJslTQB9J29AAE7qR3IMRnDQ8Po7+4HKEAotjadW+08KYpC0BNEMpfE7u4u8vl8S8OKZlTqhFNUfajSzeopi3YtYpVAZgdiUgzdqNzgkDJUhvX7t7pK4/kXNIjFNLoHNXziF2WMDvgAlcHsgoibsxIWFmgUC6bZDo9OIufOqjh6VEWXT68RCfnaHVWV0jdm2fh6oxcSNZpreCTCbudUOGnBrhR9tqNuQbr7SCu0m3WDc3H7LJHNxX2S4na73UbzhFkVoRF/dw/nwS899ksYCtWelbNCOq3fK4dQmgDP8/D7/bh69art+ct6CUXTNMzNz+H5Hz+PNJ+uS0LFTkLxu/zIFXOIJqOQZdmwyjUX9r287kwnSZJlh0q186wEMqwYS8cQiUQA6N4p1VqzW7luc5E1wAegKAoojUJsLwaPr7qUiV0gfuSJXKLm76ZTxAdFw82bNL7xTRaaSmF0TMWzPyshEqHx6o8KWFy8RyKKF5Dd8AVknL2QxZmzMo4cUY0ifavquVay8YIg1IxevLwXoiLWZcZVPifi431gmcqS79VAUzR8vK/qHFG7CuFmD5V9JlwWlsgEwWDQsA8vV0WoJzr0uXz45cd+Gf2B5hwPAV09ncjUPGjoGEJph25PPYQiiiJee+s1fGvxW2CD9Uuo2EUoIW8Ie0m9XkKkwMsfVmNg796zX1LYr1GnqHSeRK4klophe3u7Ia/5Vq6boijwFI+puSlD2jwv5FHMF5Gls2BYBoyLQSAYsH0Kn9SICnJ9EREx1lpaojE1pZ/L6JiKQEDDX/9/eIjF+/fK69Nw9oyA8+ezGBtTwdD6FDYZfOUZfh+ZtLKQmmXjgfvRSz6fLzG96uvqg6AJgMVH1SpCa9AMCXugNJWUl/JV2+EZmoGbc5cM/Vl+RhsIpZqXSSVLZEmRQFO0cT/KVRGsosNyU7GAO4BPPvZJ9PhbS9mn0+mmjMo6AYdOKCzLQhDstYYlqEUoqVQK33j1G3hr7y0Ew8GGSI0U0JsFKchv7m5id3cXXV1dYFkW2WztWkJ5YT/g0uVfKtUpygmAOBwmU8maWmDlaOXlZygGclHGrbVbhnQ8y+puh5IkIZ1OQxAEqAUVxVwRDM2A4ij4Ar66jJqqoVGXQ0mCUUQnZEIzGjbW7z8jPr8uA3/unIrR0VKvE5LuI9P+RbmIoCdozFTYDavohVZobO1tQRRF8DxvpG/M6cxG7mn5oCgx4FKUe8oK9xZwjuHAMmxdQ6l2E0rI3ZiXCYmayTpBmmTMtUDA+v6aTcW6/d346Ud/GrREt1xbzWaztqb+DxIdQSjtkJgHqhPKxsYGnn/1ecSZOEZHRhueNK5mhlULLlZfHO9s3EEqlcLAwAB8Ph8ymUzDx1Q1dd+OyzyxX04oJO0Si8WQSqUanm1pNjLjWR7xWByRvci+SX+KosDzPHp7e/VrUlXkcjlkMhkUhSKKhSI4mgPN0mDd+ovdyAvbjPteOr1/kVMVCv7AfRIZGalumMWzfEmNwtys4OW8KHJFuFhXw97ntUBRFPq7+pEupDHsG9YX/HudY+bdtSzLTe+C9xmK3TOsoigKklx/esxOQmnFGEtVVcvhV6KdZ563KjcVC7qC+NCJD0EWZNy6dQuKoqC7uxvhcBg9PT0V1dArIZPJNPw3nYJDJ5R2eZZUOraqqrg5cxNfnvgyip4ivB4vssUsGJrRO5IoZp/sgxWaXVhJvWRrW985knpJK8c0w1ynYGndd8PFuYxiazKXxM7Ozr7Pbic8nAd3N+4inUkbrcjVFhGapkt2g6IoIpVKQcgLQAEopAtgWAY0R8MX8FVUoqWoUoOoRpDJAIGAhkxGPy7NaBgd0XDhgoKTJ7WKysEEJL1mJbhJpFJyUg5FuWjIvatQIRQbl1ApR3mthmEYy921IAhGF5lV9NIIZFWGpEiQVRmyIhvDvIaqdRUyt4NQmnWzJLAiNisbiPJ6Uo+/B5987JOGm6WmaYbe2O7uLhYXF+F2u9HT02NkAmrJR2Wz2QOpJ7YDh04o7Y5QVFU1HpZCoYAfvPEDfO/O9+Dp9sDL3t+ZK6pS0sJbS7W2mcU/6Akilopha3sLHMdhbGysZKdtd+eYrMr6y0xrCDEh5It5JHeT4GgOA2MDTYXljZ6jj/dhdnEWoiSir6+v4d560urZ368XOVVVRTqdRjabhZSTIOZFPTXGUnB5XfD5fKBp2sjhN1sETyZpZDIUPF5NrxnkKaytUVhb0+/ZwKCKkydUnDypYnhYK4lUAq4AclKu7pRoSas1mQin9BRmNQXoctTj7GjeXUuSBI7jwLLsvujFXBuoBz7eh6JcNLrCzMO8pKvOStW61efdroHYWpFSeT2JZViMdo3iH1/8x/C77tddKYoyyPvYsWOQZRmJRALxeBwLCwsoFovGOEA4HIbf79/3uYRQHkR0BKG0Q2IegPEyKIqCVCqF//PK/8F0arouV0VzyG5VBG+khkLqJdvRbezs7FgODAKt12Ws4OV0K+BYSu/kCgQC6OnpMUyq6h1ANJ9jvYuAh/Fgem4amqZhZGTElrwwTdPo6upCV1eXsdsm0UuhUICQEvTuNa8bBW8BLMs2tdNL3dvsnjur4kMfkrG9TWF5mcbyMo1IhMbOvf+99po+X3L8hE4wFx8KIFusL71mdV7lE+Eu7l70olYXgCS1mkbtaytFL6lUal/nWKXoxe/26+dW4dktV7U2p2VzuVzTO3HD2sCGgdhGU2+9vl48e+lZw5e+EliWRV9fn+EoS1qS4/E4Vld1wzZCLuFwGDzPI5vNHniH1x/90R/ha1/7GiYnJ8HzPJLJZFPH6QhCaWeEAgDLK8t48a0XsUftlUio1It90+0uP4KuIBi69u7NxbpAUzRWN1eRTCaruhvaHaEEPUFE1SjEgohCvIDe3l4jQjBfE0Pr09P1+KLUA5qmoYoqZpZnQNM0xsbGbPGvKEd5p5OiKJDyEhKpBKKJKPg0D5ZhQbM03D53yaxGrcUjnbnfMkxRwPCwhuFhBT/xEwpyOQorKxSWV2jcWaFRKFCYvcVgdiKMr341iaFh1oheBgdLoxeCer/nEgFIc/RianklrdCNKjOXn0N5bcDsSRKJRCyjF7PxWb0wp2UpSi/uBz3BhiIymqLhdXltswpuRHZlpGsEn7j6CaNTshGQzrHR0VGoqopUKoVYLIa1tTW8+uqr+NznPge/3w9VVSHLclveGyuIoojnnnsO165dw9/8zd80fZxDJ5R2mWAB+gJTlIv47z/670AARp6zFZDunZycg1t2o4fvAcdw+9zoAL1eIogCNrc266pZ2EUoRu1ASEEQBEiSVDVCsPJF4RneUoq/1jlyLIdUIoWtnS24XC4MDw8fmJVrl68LOTYHt99tzBGk02nkCjkUCgXk6BxolgbjYsDzfNXrIDMoVirDPp+G97xHw3veo0JVge0tFmsrAcwuJbC7S2N7i8L2Fo1XXwW8Xg0nTurRy/Hjla1460F59OLm3EaU2czCWmtXbvYkIbUss2xJ2B9Gkk3C422+9qIo+iQ7iTKIJL+iKciLecuIzPAyKdinrlCv7MqR8BE89+hzhj1zK6BpukQ9/OTJk9jb28PnP/95bG5uore3Fx/4wAfw4Q9/GJ/61Kdamtyvhf/wH/4DAOALX/hCS8c5dEJpV8orm83iOz/6Dr6z9h109XXB47Y3J0kWVnOumGM4eHmv8aJGU1FsR7bBsixGR0dr5qPtIBQyuJXI6krBiqLA7/c3lG6y8kUBqnubA3rdaW1jDcl0EoFAAH19fQdWWAy5Q0gV7hdlreYIUqkUcrkc1IIKF+sCDx6xvRi8fu8+kyYyg2JlrGUGz7I4c9KFkdEErv0kkMlQWFmhsbxM4c4qDUGgMDPNYGaaAUVpGBnR7tVdmH1y9I2CoRlkChnd9dDkHGg1sNcqymVLfLwPO/EdfS4jot/3SqKL1VBOauaIjKKofdI9LM2CZ/d7mbSKelJex3qO4blHnwPHtGdh7+npwW/8xm9gZmYGxWIR/+bf/Bt885vfxNe+9jX883/+z9vymXajIwjFXDi3Azs7O3jh5RewWFhEQSm0ZVGzqndIioRMIQO/y4/t2DaS0SQGugcQ6A7UFcq3SigkBI+mdaVgt9uNYDDYUkpRVuUSZWEv74WX9+rCgiYRPj/nx+zSLIpiEb29vQcmG0EWHTOZWIFlWfT09KCnpwepVArRaBQ8z6MgFFDM32tL5mgwPAOfP4CMIbtS+ZhWqaZAQMPFiwouXgQUBdjY0OsuyysUYlEaGxsUNjZoACF4vD6cOU3h5EkVx4415rxYPldjNbDnYl2QVD1ytnqumnnnzEXwckdFc/RCai+kc6za51T6maZpJdI9Xt4LntU7xxiasdVdsta9ONV/Ch+/9HGwTPuXTEEQEAqFcPXqVVy9WtkmuBPREYQC1OcrXwuapuH23G18+e0vI83pEiqpdMrWugSB1RwKqZfc3b6LRCKB/v5+uPwuiLJomAWV2+ua0Qqh+F1+FKQCkmldk4uE0vF4vKnjWUHTNBTkAuSCDNbL6srCrBuyImNmYQaKqmB4ePjAOlQYmoGbddddiNY0DYlEAqlUCkNDQ/B6vSVDlblcDlpeQ3xHgVYcAMUJYNk8NG3/gljP1D3DAEeP6hpeP/VTur+KTi40Vlcp5AUWU1P64CRNaxgb06OXEydU9PZqyOcBq1tZj2dMueGb1+UFBQoFubHOMTOqdZGZoxdSe8nn89jd3S3xg/d6vfsk4+sBeXeSQtI4FxK92CFqWY1Qzg6cxbOXnq2rZmoHcrkchoeHWz7O7/7u7+Jzn/tc1d+ZnZ3FuXO1Tb/qRccQSqsFKFEU8aO3foRvL3wbbOi+hApN07Z3TgH7IxSfy4e8mMfW9haKxeI+gUXzC05mQqwGpoDGd45koCsejyOZTBqDkuSY7SBU4N7kdEHC4t1FMAyD86fOw+VytSXlUg6Swza3claDpmnY29tDPp/H8PCw8d2Yhyp7e3uhqioWFgoAn4HXJyG5lzAK+5yHg9/vR9Ab1D04Gtwhh0IarlxRcOWKglQqh8VFEYlEH5aXaSSTFO7epXD3Lo3vfU+v3RSLesTz9NMKTp3Sn7Vm9MD21cg4D3iWR4JJ1P2ckbpFPeRdyQ8+k8kYkSFJjdVTu3BzbiiqUvJMlQuPGkOIxPRNa+y7qfTOXRi+gI+952MHVgcE7POT/+3f/m186lOfqvo7J0405tNSC4dOKBRFgabpluooqVQK33jlG3gr+haCPcG2znZYHTfkCSGW1udL6qmXmAX4yE6Lpe9LVdRLKGZnxd3dXRSLxX2Ff7uvnxyPYzhk0hlsbG8YxfeiWkQxr+e/3ZwbLkZPuQhFay2pZuHlvRBlsW4lXEVRsLOzA1VVDbmXStCfRZ2Me3oZ9A71lgxVSmlJX4h5Cv6AHxzHNZVSZRgNIyMFXL0q44MfBOLx+9HL+hplTOoXixS++EUawaCG3/3t5udqzMhLef1/ch4e2oOAO6DPaVXYCBCLgWbrFmY/+PLoRVV1L5hMJrMvegHqF7a08kdhKMZI+dWClVzKxdGL+OjDHz3wAUNBEGwhFHO78kHh0AkFaK11mEiorClr6Ort2vfzdkYomqYh6AliO7aNSCSCYDBYcwq8HOadlqZp4GgOQXcQGqVVTI0BurcFy7CGUjBN05ZE1g5CdbEuRCIRJFIJ+P1+S1HJkol9hjVEBSvZ0daLxjW5JEQiEbAsW3fHGbH+DQVxf6hS0xe3rb0tfagyK0EURH2IkmXAe3ljqLIZhMMawmEFjz2mQJKAO3doPP88C4ACVBpdPjfS+doKyY1A0zRduqesw49jOKiK7ijKMRwomrJNf6w8etnb24MkSchkMojFYuA4zkiNhYNhFOViw8+LpT/KvYxAXsxbbkTKN3FXjlzBhx/68KFMqwuCcOD2v2tra4jH41hbW4OiKJicnAQAnDp1qiFy6xhCaTRCUVUVk9OTeHHyRYheEf3d/YbXhnkhbleE4uJc0CQNd7f0ekklQ6pGQFEUJFVCUkiCZVmwNAuPS5dgEETB2KV5eS9kVTaGFf1+P3p7eyvKj9h5/T7eh93ELmRZRk9PT12ikrIqG4uWsXukGYiSWLfyL9C4VlOhUDDuTyNEf8+OwujwokHD5/EhU8iUDFUWCgW99pLPIV/II5/Kg2EZULw+LV1rqLLSzzgOOH1axf/9f0so5lmsrXIYO56s+7pbgbnDz+fy6Qq8FA1N1dqSxqRpGm63G+Fw2DC8EgQBmXgGuzu7cHvcRv2l2ZR4JUl+s7ujmVAeP/Y4PnD+A/ZcYBOwK+XVCP7gD/4Af/d3f2f8+/LlywCA73//+3j66afrPk7HEEojEUo+n8cP3/ghvreqS6h4WE9JuyGR2dY0DXv0nu0Ris/lQy6TQyqnS1XYZUgFlBJAyUJM6Quxm3UjL+YRjUcRjUbR09NzYB1VPs6HlegKVE2Fz+czZjka2cXtE+BjdBUCRVN0HSvs/66MTq4GyCSXyxnNCaFQbWUEM0iEEgzq9sQudv8EunnID7ivxpDL5SCnZYg5XRKG5vYPVQK1i9EUBfT36um0gaH2uFlW++68vBdFqViym/fwHnA0Z3xXjQwzVjsHcl+I4dVgzyCyxayhM0a0sczRS7Pq05Uk+fNsHhIkjJ8cx9Nnnm75upoFmZ86aPvfL3zhCy3PoAAdQiiNCETGYjG8+PKLmEnPVJRQMctsgwY8rAchT8hS3r1RkHrJXmwPmqbhyJEjtvoWVIooNE0DS7OIZnUiKQpFnD1+Fi63S9frqrBA2RGh0BQNWqFxc+4mKIpCb08vRFE0bFPJS+71ehveRYqKaOx8aYqGn9e9cfKi7rfRTCdXKpUyuuxIc0IjIDMovWEeXJ3pHrOEhqZpRgutIQlDC6BYCoyLqWux8HAeyKrcsmpBLVi9P36337LpYN980j3ZkVreKI2cg3ny3lx7MUcvpHPM7XYbxf2Wope8hKyYxVMnnjpUMiHI5/MHnvKyCx1BKPWkvDRNw/LKMp5/4/mGJFQoikK2mIU7r89okCnwSqKPlUBTNALuALZjuh6Xy+XS++FtNsGxIgAytBbPxo1hxaHhIUiUBKko3W8Lpah9dsGtEgrHcMhlcljbWgPP8xgeHjau2Tw9be7g8fl8hqtds/UkQK+X0BStL1Yaahb2NU1DLBZDNpvF0NBQU9phmnZPx0v2IhgUUJAbXygpiiqxllUUBclk0vB6EbMiaIrWp8Hz+X1DlT6XT/fosHHOwgpWz0XQHUS2mK1Zo5JVudRRlPMamQazN0oj51DNZsBs1wvAeO7Koxdi19to9PLY8GN44ugTDf1Nu1AoFA48QrELHUMo1SIUWZbx4xs/xtdufw1UgELQVf/NLm/vtZpsryWQyLM8GJrB3e27iMfj6OvrA8MwiEajdZ9HI+drfsncrBuggFhad1Z0uVwlXiJAaVsoyREzDANJlpCjKkcvteDhPNiObCOaiMLn82FgYKDkRS2fnia+G7lcDslk0rAxJbvIRgrWXt5bUkBlGVb3Oqdg+V2pqoqdnR3IsoyRkZGmZSryeUDOBQFWgNff/K6bgKIosCxb4vWSTCYN8b3EvbZk4lQ53DdcUXKkHSiJDuqYb7GCBq3EZpdELxo0FKRC1eiFpN0arY/xPA+e50uil3w+j729vYaiF4qi8MHzHwSbYA+0NbgSFEVBsVh8IP3kgQ4ilEoRCpFQ+dH2j+Dv9jccEVRrSZYUyXiISwQSTX4oPpcPBbGA9c115PN5o16Sz1tPH7cKM6GQYcVUJoWdnR1D9roaynPELtYFH+szdMXqXah8vA+LdxYh5AVjSLLWrq9cuZakKGKxGGRZLkmNVVvwrXbJsiobk+AGadIMREVENp81Ot3MEVQzkPNBgMvC51fRDl0+ouTb09ODYDAISZL0tmRBgFt0YyWzohfzOb0tudEorxGUGK+5W/O7N2Nf9GJydiyfG9I0DUF3a14mtaIXlmUNcjFHLxRF4SMXPoJLY5dwI3ajIwglm83q98SJUJpHpRpKJBLBCy+/gOXiMkK9zTF2vSmf8l2+1+WFh/UgLaSxurZqqOaSxard8y1dni59WDERN+oBzXR+SKqEjJgxTMQCvO52KIhCxZ2jj/Ph1oLuPDc4ONhUHaJcS4u85LlczkhRkJ+bX/J6dqpm0iwWi4jtxdDt60b/QL++u7co7NeEpn/2fDQNUCyCNTS8mkEqpQ+fmms7PM+jr7cPIW8IiVwCmUwG2WwWYk6EmBfB0jq58G4efr/f1kVP0zRQoJoalqz7M8qdHRlWt1SAhoJYgJfzIitmEXDZVzOoFL1Eo1EoigKPxwOf14ePX/04Lo1dAgBjHuawkb7XYlivJXenoSMIheM4FAr320dVVcXs/Cy+/NaXkXFlEOpuPvxrxmOEoihwNIeN6AYikQi6Q90YGxozFjLSmdKO+RaaouHjfUgI+rCiOSpqFWa9J0KaLM1CkiXkpbx+3RpnFN/tdHQ0v+RmWfSdnR0Auqx3f3c/4kq87heb/H1XVxe8XV7kxJxe6+L1Qb28mIek1k5bURQFv1vXA0ul9AXbzowDkXxJp9P7ajvGZ+dToGkaoVAIoVBov9dLvoB8Om84VbYyVGmGHeZUjUBWZKSVtOFlIisyugPd8PJe22ZdzLCKXoqFIq72XkXmbgZv7L6Bnp4eFIv22jA3i0wmA5ZlHYOtVmCOUERRxCtvvoLvLH8HXJCDj298d2xGo97v5fUS4iFCFmKGZuBz+VCki6Bhb4jMMRzcnBvJXBLptP7SmaOiZlCxawyaPsF+D17ei3w2jztbd8CxHIZHWksdVUO5LLosypBFGeuRdUiSBJfLZRT2Ky2a6XQa0Wh03/yPqqklIokeTh/Uk1XZcmKfpmh4OI8RnRJjrZCFbH0zKJd8MRM0TdHw8B7LDjYrrxeiNyZl9KFKhtHJxeVxwev1NhS9MDQDN+NGtphtqyy6FcxeJnkpD17kAe5e9MJ79QFYUahbCaEReD1e/JMn/wlOD5w23BRjsRhEUcStW7ewvb1t2PUetMkVoBNKM00FnYKOIBRSlE8mk/j6q1/H9eh1BMNBW8L7RiIUn8uHolQ06iXDw8P7OoUUVUG6kIYsy1BUxcgPk11+s/DwHiiqgryUR1rQlVztkH+vJzXn5tzYjmxjL74Hr9eLsaExw8ip3l1+s3BzboADiq4ixgJjkGXdxY8425H8N8mBA0A8Hjd2+7V2ckRmBDBN7EMv7LM0q6sFm/L6aUO2vvVrK28UMBeHWYYFR9cvZ8IwjFHLIkOVqVRKH6oU8hCSguH1EggEwDBMxWeHSMDn5eaf12Zh5WVCzlNWTMrW92ovxGvIjuiFYzj8/JWfx4leXb/K7KYYjUZx6tQpox3e7AVPBngPIiVGCOVBRccQSiaTwRe+8QVsKBuWEirNot4IJegJIpFJYGt7y0j31OoO0aAhW8gaxOfi7qvvClJl2RSrz84Vc0imkigWi4aciR2oRSg+3oel1SXkhFyJ2Y+5fbfWLr9ZWGlysSxrpH3Mswdk5oV8n4ODgw2nBcyDoh7Oow8eUjQ0aMZ8EolQrIy1GoGiKNje3rZsFHCxLl0GpMkNSPlQpSzLRvQip2UUs0UjevH49N8jizbP8EYNzU7LiHpg5WVS6dksr72Q6J0C1VT0wrM8nrvyHI72HLX8uarqw7qDg4M4cuRISfQyNzcHSZLQ1dVlEEy7opdMJlPyfT1o6AhCYRgGS9ElZL1ZhBh72+VqRSgURSHoDiISj9SUMTGDkIj5hTBP6xvGVJquiFtppoAUoaPRqLE7sTN/Wo1QfJwPtxduQ5IlDAwMVCz6m3f5HMsZLoGNdI2VI+jW04jVyM6c/5Zl2ZjBYVkW29vbhmotmdqv9yUkytDmc3exund7OkYDqlDTWKsaJEkyWrzLdc683D2xQxvTOSzLWg9VCgKK+SJyTA40Q8Pj9YAJMm1XgrYCz+ip5PJIo15SK5dPaSR6cbEufOLqJzDaPVrxd8rFIc3RC7mnsVgM0WgUS0tLRvQSDofR3d1tW/RyGH7ydqIjCIXjOJzpP4N/+tQ/xUZiA/M781jcWUQ02/qcR7UIhQgsrkXWEIvFSjzXa8EsNW8FszGV2buBTOszlD6MmMjpzoqyLGN0dBSxWKxtcvPmc+fAYXp+GgAwOjpad9HfSimZoRjdi77OhSroDjZkWSuKIiKRiDFYSdO0MfMiCAK2traMYcJaMy+VZi2KchHZfBGCxAFgMNznh89DI19sLOVH9MMCgQDC4XDJYtlo63YzKB+qJE6VSlFBIplAJpXR02Hcwe2AeZYHBcoyImsmSrKKXjycvgkrj148nAe/9NgvYSg0VPl4mla1y8t8T0n0kkwmEYvFsLCwAFEUjeiF1F6ajTCy2ayT8moVpIZCURTGwmMYC4/hA+c/gFg2hvmdeSzsLGAjsdHUQlspQiHpltWNVQiCYFkvqefY9ZxT+QR40B0ES7PI5DPYWN8Ax3MYHR0FTdNtk5snYBkWoiBicX0RLMvWlHOvhvLrIj4bkmItWU+Dhs/ta4hM8vk8dnZ29i3Q5TMvhULBaEmuNPNSqyWZpLt4jwyFLiCdN10Xw9/fDVdYK0jXWXd39762T7OkyEGBoihwHIejw0eRl/KQFb0+lclkUMwV4WJciEfiYDgGrItFIBiwfRbDzbmhampFySM70m6VxB9pisbPX/l59Aeqp4/J+lDvtZNB1d7eXiN6icfjiMViWF5eBs/zRmqs0ejFiVBsAMMwlsOHPf4ejPvHMX5yHLliDou7i1jYWcDK3krd2kFWEUrIE0Iiq9dLANSsl1RCM4s/2aWms2lEIhGEu8MYGxoDoKfG2kEogP7iujk3otEodqI78Hg8GBwctHUBsSyAa/evqxFNLkB/uXZ3d2sKYJbXFKxmXvq7+xHJR6p20NwXhax8XRzLGVbL5on9TCaDvb09y3mhcr/7g4RZ7p+maQQCAaiqimKxqFtT37tXKACFbEGX43cx8PkbSyNawcN5IKlSVS8Tu+s4pLU/4A7gE1c/gV5/b82/IYTSTNrKHL2MjY1BURSj9rKwsIBisbiv9lLtenO5nEMoraIetWGfy4dLY5dwaewSZEXGnegdIzVm3iWXg3x5JKQNuoPYSexge3sbPp+vpU6qRmdRiFZRIpkwJFwCgYCxyJIUEk3T4FneFmFAcm1e1os763eQzWURCoUa9m1pFOYCuJvV7Y8BGDpq1aBpmiFPMjg42PALZp55oTQKqqRiL7kHQdAL0SRyKTd0Kpett0LJbpjSc/npRBrphEXX2b1hycMik/LUYrkFsjkiV1XVGKoUsgKKQlHvguNo8J7GvV4a0SOz+zkMeoL45GOfRNhXXVWCoNEIpRoYhimJXvL5PGKxGGKxGFZWVozohdReyjeyuVyuqUHiTkHHEIqqqpauaZa/z7A4PXAapwdOQ9M0bCY3DXLZy+yV/C45HkuzcHNuo15ih+x7vdEEKfwnhST29vYqpthUTdU1kQC4ZbdReDT7NjRzjl7Oi7kVvVOlr6/vQGUdSGrRPLjq5txwsS7L1Fglq95mQDqaClQB/f39xrCgIAhIpVLY29srmXlJpfTvo94ZFFVVcXfzLnK5HIaGhhD0BfUuP1Xv8iPDkoeB8qjILJw5MjKyb2C10lBlLp/TO+2Sgi4Jw1MIBKt7vfjd92pFdWy27I5Qurxd+ORjn0SXt6vuvyHpdruJzawWQaIXUntZWlpCoVAwopfu7m74fL4Dj1BWV1fxh3/4h/je976HSCSC4eFh/Mqv/Ar+3b/7d00NNXcMoQD6F9voLoGiKIx2j2K0exTvP/d+xHNxzEfmsbCr111UVYWbcUNRFKxurBovvx2dVPUQCsdw4Fke8Wwc29vbAKqn2Mw1H3PhkWfLfEPqKOxSFAWe4iFIAiiKalpGpVlU6uTa5+Z4T/QxI2SwHdmGoigt1XaA+/Lv5HOA0mHBcDgMWZaN1Fg8HsfW9jCAALxeEapKV30WVVXF7u4uJEkyxCiLchFFuairHXA+4x4IklDTwtY2WERFmqYZFtH1CGdWGqrMZrOQM7I+VFnB66VRR00707s9/h788mO/jKC7sQ3TQcmuMAxjpL4AGJ1j8XgcL7zwAv78z/8cLpcLZ8+eRSaTORAJ+7m5Oaiqir/6q7/CqVOnMDMzg09/+tPI5XL4kz/5k4aPR2ntbimqA5qm4cUXX8T73vc+W1tmBVHA0u4SvvidLyIqRiGrMoaGhlpaqMxYX19Hd3d3xXZbMqyYyWWwvb0Nj8djaZdrRjwehyTpbbyVQOTsaaqyJhdDM1CKCpbXlo3irCiKTbfaNopG6waSJGEnsgO/24/RkVFIqtS0dw0R9FS0+uXfVVXFf/8fDLY2WTz1VARHjqQr+rwoioJIJAIAGBwcLFmMOIYzuvnMqNWwYAco6DIu5jqVebjSjmefpHHS6TQKhQIYjdGvmWUQ8AfAuBkwbH2Ls6ZpWF1dtcVTqC/Qh19+7JfhdzWud5dKpTA9PY33vve9LZ1DK8jlcvjWt76F3//930c2m0U2m8V73/tefPzjH8dnPvOZAz2XP/7jP8bnP/95rKysNPy3HRGhUBTVkMlWvfDyXoz5xjA+NI6ucBe6x7qxtLeExd3FhorDlVCtJZkMK6bSKezu7iIcDtcl+FZP1LNPyLIsNeZiXYjFY4js6kVoIndvlpdPpVKGvLzP54Pb7bYlh0w6uRohk2KxaNS0Qr0hZEW9JuZiXXBzbkiKhLyYr6tDqtluKpqmkc3qi9rJkz3o6/Pv83khToGxWAw8z6O/v7/knrk5NxRV2UcmgPUsj6bpBWQ72ohpioaX95Y812bia1WFmaBc9JO0JVMShUg0YrTiUxy1b6iyHGab7lYwGBzELz32S4bhV6OoN9XeTvh8Pnz84x/H3/zN3+AnfuIn8M/+2T/DN7/5zbZYZNRCKpWqqWpeCR1BKEBzvvK1sL6+jrm5OXAch1MnTqG3txdnh85C0zRspbawsLOAhcgCdjO7TR2/0uJP1FtjsRhSqVRDheVWe/KD7iC2t7aRSqf2Fd8rycuXOy/6fL6mFp9G3RWB6la9JIUE6DUwIpsiiIJlsbeVbipVBTJGUR6WPi/ZbBaJRMJICeXzeSPdYzX1XwnlhX2fyweGYkqutxEQPTJzc4osy9je3gbLshgYGGjbgsmyLE6MnkAqn0KX2mUMVRaFIor5IrJ01vB6CQQDJc+VHYQy0jWCT1z9hNF51ww6RWkY0NNggUAAJ06cwL/8l//ywD9/aWkJf/7nf95UugvoMEKxK0JRVRWzs7OIRCK4cuUKZmdnSwqEFEVhpGsEI10jeObsM0gICSzsLGA+Mo/1xHrdnVvlhEKGFZO5JHZ2diCKYsOKvc2oIxP4eT9m5mcgSiJ6e3sR7g7Dy3tBgdo3rW/eafb09OxzXnS5XMbP60mNuVi9eF7ud1ENRM69r6+vpjS/uWuMQukiLCqi7jnfQgE8kwE0jQJNayg/FYZhwLIsCoUCwuGw4YdD0pM9gR6kXXqKrFGhRU3TSqRISFQmq7Ihj1INHM2BYZiS+04m9d1uty16cNVgnu2habpE9FOSJGNiXy2oKOZ0SRiKpeAL3N+0NHt+R8JH8NyjzxnPXrNopnbbLhBCaRW/+7u/i8997nNVf2d2dhbnzp0z/r25uYmf/umfxnPPPYdPf/rTTX1uxxBKpVmURlEsFjExMQFVVTE+Pg6Px1Mzndbt7cYTx5/AE8efQF7MY2l3CQu7C1jeXa66YzQv/i7WBZqmEc/EjZ3h2NhYU00GjZa1KIqCm3Lj5txNaJqG4eFheDweaydHev9OuNx5kRSrBUFAIpEAwzAlU+jlC0Aju3OgdateDfcXYZqiEXAFjOtrREPNDPMMSvn6RuZhzMrGhIg9jAeReATZbBbRaLSiz0u9MH83ZINCUdb6VUQTzJxiI+lDv9/f3tZwTY/EKw2KUhQFnudLnCqz2SwymQzEnC4hz1EcXIwL6VQa/kBjXi/Heo7huUefA8e0rpTcCSkvgkKhYAuh/PZv/zY+9alPVf2dEydOGP9/a2sLzzzzDMbHx/HXf/3XTX9uxxCKHRFKMpnExMQEwuEwHn74YWMH1Eh9xsN78J7R9+A9o++BoipYja7qqbHdhX2+EaSGQlSKk5mkIbvR7MvcKKEwFANVUjFzdwY0TWN0dNRyl2zl5Ojm3BAVEQWxUFJzYFkWwWAQwWDQUqDRPMfR7euuqcllBumOEkWxJate4H4B3DxrwVAMvG7rqKwayAxKubFWMplEIpGwTFuS3blZzJIQ8c7OTtWZl3qgaEqJFL+5VgYNkDW5pCGjUChge3sbXV1d+gxOm8iEeLg0onhA07TxTJF0K7lHqWRK93rh6vN6OdV/Ch+/9HGwjD3LV6ekvMh9acZIrxxEh6webG5u4plnnsGjjz6Kv/3bv22JXDuKUFqJUDY2NjA7O4tTp07h2LFjJQ9jswV/hmZwsv8kTvafxEfwEWwl9brL4s4iIumInv/mfBBEwegvb0QPzAqNEArP8kgmktja2YLL5TJ0ruqBVX1C07R9WlNmgUZN0yCKol7UT6YgJAREmEhN7xICIvBIURRGRkZaeomt2oKBe4uwqYbj5XR7AVEWUZD3F8sJSIRCRpOqRVEUKATcgX278/KUj9XMC6lRNWOORWplXt4LRVPg4TxwsS4IooBMNmM0f7TTj5zG/uJ/o1AUBbFYTDdVuzcfRNqSpazu9ULTtOH1Yh6qPDtwFs9eehYMbR8BdFLKK5/PH6if/ObmJp5++mkcPXoUf/Inf4K9vftzfIODgw0fr6MIpZlFX1VVzM3NYXt7G5cvXzZCbDMYhrHFXXG4axjDXcN4+uzTSApJfPfH38VKfAWbe5vIZDJN6YGVo15C8fE+3N24i2Q6iUAg0FKuvKQ+QVGGF0pBKpRMtZPUmMflgW/Yh0Q2UTLHQbxLSNeY+XxEUTTy+rVap2teewNtwYIkAPc28TzLw83qnVjlqbEUKcgHtapzG6SbqtbuvNLMi1UasZEOO7PAJNkQ5LI5pONpnBg9Ac7D2aKwYAWr4n+jsKrvUBRlRFVEly2dThtOlUJKAMMyOD14Gh86/SFQNvdcd0qEAuiEcpCDx9/5znewtLSEpaUljI6WqjE3kzruKEJpNEIpFouYnJyELMu4du1axU4qu+ozZnR5u3Bh4ALCahiP9T6GwGgAa8k1LO8tW7aN1ot6/Ft8vA9zS3MoikXD/McuaJpWsmAYU+2yLozIMAxcrAuZQsYyNZbL5fale2iaxu7uLkKhELq7u1sik3pk7ytBlEVjsaUpGn633/DXSKX04wUCKra3t6Gq6r4oiqH1a29mQS2/V4VCwUgjKopSkhqrNCti1RJNGhsGBgagcRpEWWyq3boWyLU30nRRDrKp8Hq9FS0iynXZFEXRpWL4IZx1ncVbb70FhmEQCoUwPDyMcDjc8mxNp0QosixDFMUDGWgk+NSnPlWz1tIIOoZQGk1LpVIpTExMoKurC48++mjVh6odMy6ZTAabm5tgWRbvHX8vWJbFVVzV6y6xVSzu6EKW1dRtrVArQvGwHszMz0BVVQwNDbVdpsE81e5z+8BSLBRNAU3RVVNjJN0Tj8chyzI4jgNN08b/bwa11IIbgaqpJWmbbCIIFF2AvAKKovalD3mGN4rjrYLM/5DCviRJhgqweeaFzL0Q6Z56dblKCvv3hmAbrSmZwTIsWJpt6dpFUcTW1palrH81MAyDD176ID584cPQNA3xeBzr6+tIJpOIx+MGAfX19WFkZKSpDEGnFOXT9wp5dm4QDxodQygcx+mqp3Vgc3MTt2/fxsmTJ3H8+PGaD6fdhLKzs4ObN2+iu7sbNE2XkBlDMzjZdxIn+07ipx/+aWyntvWi/s4CIqlIzWNXIhSapgEJmFnWi+9jY2MH6gVeLvZnCFla2AST1Jgg6HpO/f39RsE6Ho833AlVqWZhFzQNSGQEwJVHoFvCkZEj8PD3TMSKAlycC7IqtyWVRLqheJ4vmXkRBAHb29ugKAoDXQPIslkj2quly2WGudMPuFfYpzmISn36cIRIW4m6SedZMBhsOEJ9/Pjj+OD5DwLQ75VZeLFYLGJtbQ17e3u4e/cu7t69C47jEAqFMDo6iq6urrqIolNSXpmM/j0dZA3FbnQModSz6Kuqivn5eWxtbeHSpUt1dzEwDINisTkZDzM0TcPy8jLu3LmDRx55BMVisaSIZYWh0BCGQkN435n3IZVPGeRyN3bXcrdoRSg8yyOdTGMjsmGYTB3kC2CVZir3QvHyXrA0a3iGEIFH84Jn1QkFoCTdU74A1FuzaAVzcxJkWSfno0e7IakSpIJOkIQ0OYYDNDRkttUMzMOn0AAOHCLxCISMgN3dXbjdbqiqauidNbqpsDKmquS+6WJdUDW1JSIlnWdWHjG1MH5yHM+cfcbyZ6RGdebMGZw5cwayLCMajWJzcxOJRALRaNSQlu/v78fw8HBF4iUuoIeNdDoNjuNaEkQ9bBz+XbyHWkV5URQxOTkJURSr1kusYEdRXpZlzMzMIJVK4cknn0QgEMDGxkZDkU/IE8Jjxx7DY8ceQ1EqYnlvGfM781jaXTJ2gOWE4uW9WN9aRyKZMLzmD8xvugH5dbJQKYqC2F4MHM1h7MQYRFUsuR6rTqhcLodEImEsmKRY7XXrJNVKEbgWcrkcllckAF7QtAbO5GQYcAeQLWb3fR+G9WybNLmA+xI2mWLGEBQkzpWKokDTNEQikZZmXqzcN0kzBkMzkBSpJatismlopvPsJ0//JH7i9E/U/fssy2JwcBCDg4NGPW9tbQ3RaBQrKyuGdHx3dzdGR0cRCNw3E+uUlBdxazyw97sN6ChCqVQ4T6fTuHHjBkKhEK5cudLwbqLVlFc+n8eNGzfAsiyuXbtm7HRaOa6Lc+Gh4Yfw0PBDugx6/C4WdhZwa+MW7mp3Aei74/nleeQLecM/4aBAg4bP05gmF+ng4Xkeod4QikqxZDjPalqfdEKZawm5XA6ZRAYsz4J38yW1BDtBCtqyfBQAYN7AVrIpLtnhs9V3+M2CdFOZZ1AURcHe3h4YhsHIyAgoiqrYBNHMzIs54vTyXmjQ56uaLewTSZ3e3t6Gi8zvP/d+PHniyYb+xgxSzzt//jyA+zI029vb2Nvbw87OjrGxGRgYgCRJHZPyslMc9zDQUYRitThvbW3h1q1bOHHiBE6cONHUotJKl1cikcDExAQGBgZw/vz5kp2MXe3INE3jeO9xHO89jqdPPY0X8i+g/1g/vvnGN1EsFg9cdp6lWfAs39CsAfFSL5/QNg/nGdP6DIOitF+3iuM4dHV1YaR/BEJRQCaXQS6XM2oJpCW5mmd8PSAF7XRaN8X63vdNaSMNCHnrK/6X7/CJHExeytftKFoOlmbBMVxJN1UlXS6rJojymZd6pXMIfC4f8mIeqqYij7xxTh7eU3dhnygLWLlXVgMFCh966EO4euxq3X9TD4hqxdjYmDGxv7a2hng8jsXFRQD6pjWXy2F0dNQy9XoQIITiRCg2oDxCUVUVCwsL2NjYaKheYoVmF34yLHn27FkcOXJk38+Jgq+doGkaXe4uyFsyPnTsQzj/yHls5bYwvzOPtdhaSymIekA8wBvp6Mlms9jb26uZ2rCa1nexesGb7IKNeg2llaTGSJttuWe8z+drKGItN/DieR6ZDHmBKQQ8zRX/VU0t0eQicvWiLCIv5utKjZECuLlYXo8uV7WZl2QyWdJVVo2MA64AsmJ2Xw3PPKcE3KuXMSwkWdpX2CedagMDAw1tgihQ+Mh7PoLLY5fr/ptmQCb2H374YaiqiomJCWSzWfA8j0gkgq2tLaOONTQ0hL6+vgOrr+RyOSdCsQvmCEUURUxNTaFQKODatWst784bTU2Zi/9XrlwxDHGsjmtHhGLG5uYmAH2ROH/+PPq6+jDYM4grR66gKBWxEl3Bws4ClvaWWuq8sYKP96Eg12fbCuiLcyqVQiKRQH9/f8PfU/m0fsgTgqiIoECVpFjMswlmIctsNmtIydfj8WL2BjEbeBWLAFQaIa8bGZscFhuVq3ezbiiaUlIAb1aXq9LMSzQahaIo8Hg8Rp2K3INGZP9LjN8YHm5ebxTY3ttGNBbF4OBgQwsjTdH4mUd+Bu8ZeU/df9MqVFXF1NQUJEnC+Pg4OI6DqqpIJpNYX19HIpHA7OwsZmdn4fF40Nvbi9HRUbhcrrZFL07Ky0aQtFQmk8GNGzcQCARw7do1W3YHjRAKIbNisViz+N+op3w1aJqG27dvY319HaFQCIFAAEtLS7h16xZ6enoMbZ7zQ+dxfug8VFXFemIdC7t611hSSLb0+UHPvQWlzoFBTdMQjUYNB8xWFAKIp0ssF9P/bZJ0L8iFfV1GZs94c5vt1tZWxd24oijY3t4GTdP7uuSeukbjzjKHh97Tnk6ySqkxokRgJa5ply6X1cxLuc9Lf1c/9gp74F2NG66JiggxLyKZTCKVTOHUsVPwe/zIS/kSlYVKYGgGP3vxZ3F+6HxT19cMFEXBzZs3IYoiHn30UaNTjqZphMNhwwtEFEWsra1hd3cXGxsbWF9fB8uyxlBlT0+PreRy0Pa/7UDHEApJeb3xxhs4fvw4Tp48aVsusV5CyWazuHHjBvx+P5588smaZGbXfIuiKHjrrbeQTCZLrl3TNCOdRNJvoVDIIJejPUdxtOcoPnj+g9jN7BrDlNup7fqLqA3UDAjKd/qtzMNYtQWXS7q7OTdcjAuSur+zqpLHC0mNkYJ+Op22lH1xsS781DMaxJ9oXyeZGeWpsW5vtxGxyEUZoO4XtO3W5TLPvBAyZlVWb0sWhBJLg3rrCOX1KIVSjEYOorJgJUAK6FHpz13+OZwZOGPbNdaCoiiYmpqCLMu4cuVK1WeX53mcOnUKp06dgqqqiEaj2NjYQCqVQiwWM+5Xb28vxsbGmvJgNyObzTqEYhfI4v2e97ynKVGyaqhn4d/d3cXNmzdx5MgRnD59ui4ysyNCKRaLeO211yCKIh555JES61+KoozF8sSJEygUCohGo9jd3cXS0hK8Xq9BLn2hPvQH+vHUqaeQKWQMCf7V6GrFuovRydUAmRCBR6udfqPgaA4sU7st2DytX62zyrwgEj8OkpIj/04mk0ah2sf7ICr1y+7bjYA7gGQ+aUSFLMNCLsjIxDMY6B+A19fexSXsDyOVT+myLaY6lbmF21zYLweZXCc6duW/Y/7eyg3SaIrGL1z5BZzoO7HvuO2CmUwuX77c0EaIpmn09/cbQ7pkqDIajZYMVXZ1dWFkZKTuoUozBEFwCMUuEEJp1nqyGgihaJq2jyg0TcOdO3ewvLyMhx9+GENDQ3Ufl0wtN9vHnkwm8eMf/xgUReGJJ56o2V7pdrsxOjqK0dFRyLKMWCyGvb09TExMgKZp9Pb2or+/H+FwGJePXMblI5chyiJWoitY3FnE4t6iXiBGc51cxWIRkUjEkLpoJYJ0s3rxv55pbTMs00c0o0/rK6XT+rIsI5vNIhwOIxAIGEKWyWQSPpcPCVcCbo/7UDprrNqSY4mYocvl8/ng5byW/jUtw8LLpLxORVJjRN2gXPgTAKLRKARBqCtKNRf2eYbHL179RRztOWrfNdWAoiiYnJyEoihNjR6YQdM0PB4Pzp49i7Nnz0KWZezu7mJrawvxeBx7e3u6yOq9ubHh4eG6yCuXyx1oN2c70DGEQhZkWZZbDh3LQXbR5RILiqJgZmYGiUQCjz/+eMPpBfNxGyWUzc1NTE9Pw+1248knn2w4bURaSAcGBoxi4t7eHubn51EsFkvqLucGz+Hc4DmoqoqN5AYWdhawGlvFTnqn7s8jQ2p2eG2Uy7g0i/L0kXlafye2g2g0WtK6SqI9P+/HXnIP2Vy2xP6YFKrbPZNQblVcnjZyu91GAZ/Axbp0CRhFbknwkQIFv6e2lwmRMCHqBiSVSGZeyGZqaGiooWeXZ3n88mO/jNHu0dq/bBMImaiq2jKZWIFlWQwPD2N4eBiqqiKXy2F9fR2xWAzLy8tYXl4Gz/MIh8MYHR2F329tJpbL5dDf32/ruR00OoZQKIqy1QbYDLJAKIpi/P9CoYCJiQlQFIVr1641JXdgPm69D6mmaZibm8Pdu3fR09ODixcvtryAmYuJZ86cMXLwVnWXI+EjOBLWW6D3MntY3F3E4u4iNhObFRepdDqNaDRa4ljYLFpRC64FYpmbTCaRS+dw5vgZuNwu5It5qNBTY0FPEOl8Gh6vBx6vx/B4sdO3pCI0IOgtjQyILlcul6sqD2LlX0OuuV5ipkHD62rcy8Qs/NnT04NIJAJRFMGyLDY3N+ueeXFzbnzy8U9iKFR/FqBVKIqCiYkJaJqGy5cvt70FmKZpBAIBPPTQQwD0DfLW1hYikQh2d3eNdHEgEMDg4CAGBgaMcxIEwRZzrXrxj//xP8bk5CR2d3fR3d2ND3zgA/jc5z6H4eHhpo/ZMYQCtG6yVQnmTh/gvrNjb28vLly40HSnhjG8VycJKoqC69evIx6P4+jRo3XXaho9JzK/Uavu0hvqRV+gD+Mnx5EtZnVy2VnEamwVkiKV5MiHhoZabmmsNH1uF0jnmSAIGBwchEzJkIuyLivC+8GxHDL50sW0kv0xkYMhqZ5K9sf1wnA5zJc2HxDflXrTIkCZf829DjmWZqumxuzwMiHNGKqqYmxszOjMrGfmxct78cnHP4mB4ECNT7EPsiwbm8YrV64cyjQ8y7I4cuQIjhw5AlVVkclkjKHK+fl5zM/PY2ZmBrdv30Y0Gj3QlNczzzyD3/u938PQ0BA2NzfxO7/zO/iFX/gFvPbaa00fk9LasVVsEt/97ndx/vx5S5OsVvGd73wH165dQzqdxq1bt3D69GkcPXq05QWdHLfWzqJYLOL1119HoVDAhQsXWtoFNAtz3YWI55nrLuSFkxQJSztLeOntl7C4u4iu3q6W0pBELbidZEKshSVJwuDgYKkpFvRaC5nYJwuwqIhVZ3nMqZ5cLte0vAnpZDMv5mRxVhQFg4ODtu2cXawLbtYNSdWHDjVNM7xMWpGfV1UVkUgEmqZhcHDQ8trNXXaCIBgDqH1dffhn7/tnGO09uDQXIROapnHp0qWOkFYphyiK2NzcxH/+z/8Z/+N//A/D2uGDH/wg/sW/+Bf42Mc+dqAT+y+++CKeffZZFIvFpjs3O4pQfvCDH+D48eO2d3kBwEsvvYS+vj7s7u7i0qVLtpHWSy+9hKtXr1atv6TTabz55psAgKtXrx6oI1slmOsue3t7JXWXUCiE27dvAwAuXryI3dwuFnZ162MyK1IvrBZTu6EoCiIR3RqgfLEjO/NKxlBkAZY1GUJRqJj2M8ubCIIAURThcrmMukslwrVazKudr51gaAZ+lx8MzSCVTzVdsyLnS1EUBgcH624nliQJtELjJwZ/Ampehc/n0yPj3l6EQqG2NULIsowbN26AYZiOJRMzEokEfvqnfxr5fB4PPfQQ3nzzTezu7uLEiRNYXl4+kHOIx+P4jd/4DWxubuLVV19t+jgdl/JqRw1FkiQoioJ4PG7L5L0Ztablt7e3MTU1Bbfbjccff7xjpKmt6i57e3tYW1tDNpsFx3E4cuQIZFnGWHgMY+ExvP/c+xHNRrG4q8+7VKu7APW3BbcCSZIQiUTAcRz6+/tLFjuO4cDQTFWXwXIzKkOzqpjb15JcLm+Sy+VKuqDMlr4URVmachFdLqvztRtkeJJcn5fTJVNEWURBrk9lgQyEMgxToiNWCxRFob+rH//k8X+CLm8XJEkyouPJyUkAQG9vL/r6+hAOh23z9pEkCRMTE2BZ1pb6ZLuRTqfxMz/zM+B5Hj/60Y+MzaZZZ6yd+Lf/9t/iv/7X/wpBEPDkk0/iq1/9akvH66gI5Y033kA4HMbRo/a1E+ZyOdy4cQOFQqEtMy7///bOOyyqM33/9zD0DlJFFEVEkV5FY4u9Y2+/1STurjHqJiabxJiNJlfMZiPZmKiJMWuiJgY1Goi9F7ArvQmCSJE2wwDDFKaf3x98z8lQZWDKGTmf68p17eLAvAwz5z7v+zzPfaekpCAgIKDdjocgCBQVFaGkpASOjo4GO8PVhIaGBmRlZcHd3R22traoq6tDfX091SZM7l7IO0uRVEQV9Z/WPW3Vtmtp1mLH0Z1p6Z5CWpPY2Ni0i5Tt7fOzwIKVuRVM2aYdGlmqox5/LBa3NAY42jnC2sYapuamfx4ldsOXS1uQWSadmVSam5pTdi9kM0Nb1MXP3d1do/U62zhjZfRK2Fu1342rVCo0NTVRR68ikQiOjo7U7qWnN3zGJiZCoRCzZs2CTCbDjRs3tOImvnnzZnzxxRddPubRo0cYPnw4AFCf8bKyMnzyySdwcHDAmTNnel4rpJOgpKamwsbGBkOGaGfYqa6uDllZWfDy8kJ9fT2GDBmidUG5ffs2/Pz8WrX7kZ0ldXV18Pb2hr+/P+0dRKurq5Gfnw9/f38MGPDnWXdndRdXV1f069evVd3lad1TFHGK8KzhGRrEDb1uC+6K5uZm1NTUdNjG3JGVSW+xNLWEuak5FCpFlzkoBEHARGWCxqZGNAmbIJfLYWlpCQsLCwgEAo19uXqClZmVRlkm5DwPiBYPMoVK0Svxc7V1xYqYFbC16F7HUnNzM+rq6sDlctHQ0ABLS0sqmZFMRX0ecrkc6enpMDc3R3BwMO3FRCwWY86cORAIBEhOTu7UL1BTuFwueLyuj6WHDBnS4RHts2fP4O3tjTt37iA2NrZHz/9CHnkRBIGysjIUFRUhICAAXl5eePDggU6O09o6Dsvlcty9exdisRgjRoxodXGmI+RgZ2lpKUJCQtrttDqbd3n8+HG7eZdh7sMwzH0YCIJAVWOLQ3IRpwh1wjqtrlkgEIDL5XbYxmxrYavVbBISiUJCHROZslvadgmCaPdcthYtPlYOTg5wcHKgpvX5fD5VtK6vr4eNjY1OMl6sza0hVUg1EnMVoWrVSmwGM9Rx6+Bo5wg7RzuN1uhh74Hl0cthbd79iW8rKyvKXl6pVILH46Gurg55eXlQKBTUe8zFxaXDC6FcLkdaWhosLCwQEhJCi7CsrpBIJFiwYAEaGhqQkpKiNTEBQH0WewJ5dN+bdFta7VBycnKgVCqpYJyeoFKpkJ+fDy6Xi7CwMCp2NC0trWUOowMb+t7w4MEDeHl5wcvLC0KhEHfv3gVBEIiIiNA48lTfqFQqPHr0CDweD2FhYRrNmBAEQdVdOBwOBAJBq3kX9WOLelF9S/Qxp6Xu0puLfWNjIxoaGuDu7t7OpsLeyh6C5u455moLsumAzWKDYBHtDDbJmaB+/frB1taWKuqLxS11FU29s7pCPcukp8hkMlRVVcHOzg7Ozs6wMLWAlZlVy9GYVEzN83REf4f+WB69HJZmPTcKVYcgCMrEksvlQiAQwN7entoh29raQqFQIC0tDZaWlggODqa9mEilUixcuBAVFRVISUnRyJlDm9y/fx8PHz7ESy+9BCcnJzx58gQfffQRamtrkZeX1+NaL60E5dGjRxCLxQgMDOzR90ulUmRkZFATseoOuJmZmXBwcMDgwYO1tVwALcd0bm5usLCwQGZmJszMzDBq1CjaFN87Qy6XIzs7G3K5HKGhob1yCwZAzbtwuVzU19dTRoxt6y5imbjFZ6z2MZ7WPe12jYMcABQKhfDw8Gi33rbT5/qGFDNLM0uYs80hV7VM63O53A6DptS9s8RiMeRyOZXxYm1trXGRurMsE00ga1L29vZwcnJqtzMhxdPExATN0mbIVX/WZwY4DcCyyGWwMNPd+14qlVLHrzweD2w2m2rl1tSbyxDI5XIsXrwYxcXFSE5Ohre3t8HWkpOTgzfffBNZWVmUY/j06dPxr3/9C15eXj3+ubQSlKKiIjQ0NCAkJETj7yVjgp2cnBAYGNjuDDUnJwdWVlYYOnSotpYLAEhPT6e26fb29oiMjKT9+W1zczMyMjJgZWWFoKAgrU8Pd7fuolAqUMorxePaxyjiFHXaDaY+ANjW6kMfMy7Pw8GqvVszn88Hv4GPwQMHw8rK6rl39+S0vlgshkQigZmZGdU19ryjMU2yTDqDtMt3cnLq9s6anOdxtXPFgrAFMDfVrmVSV0gkEjx8+JB6XaRSKZydnanaC91yRRQKBZYvX47c3FykpKRotfGITtCuhtKTSfmamhrk5OTA19cXgwcP7vDDpy2reXXIgCmpVAo3NzcEBwfTvvje1NSEjIwMuLm5wd/fXydHBJrUXYa6DcVQt6EgCALV/OqWukttEbhCLoCWBgdyOtvLy6vdjElb63u90oH1v7ovl4eHB5QmSgilQmpa38TEBGKZGApl6/d5ZxkvZPwxKS5tExe14T5ANjhoapcvlonh6+qLReGLYMrW36VEJpMhIyMDDg4OCAwMbGnNFoupvPjCwkKq86/tDtkQKBQKrFq1CllZWUhOTn5hxQSg2Q6loqICT58+RUxMTLceTxAEiouLUVZWhuDg4C6N1QoLC6FUKimPnd4il8tx7949yiGUtJ4mj3ns7e1pJy4cDge5ubkYMmSIVlwCNEWTukuDuAG5Fbm4mnoV9dJ6uLq5trqQamP6uzewWCzYWti2KmarW794enp26S5gZWYFc7Y5ZEpZl47L5NEY2ZJMTp/b2NjAw9kDYkXvfn+yxuPi4qKxT9sw92FYELYAbBP97cilUinS0tJga2uLwMDADm+IyJmXuro61NW1NISQO5d+/frp9WhMqVRizZo1uHXrFq5fvw5/f3+9PbchoJWgVFdXo6CgAKNHj37uYxUKBbKzsyEUChEeHv5c65Pi4mI0NzcjKKj3MaMikQh3796lrLDJQTcejwcOh4O6ujqw2WxKXLrb+qhLysvLUVxcjJEjR7bKXDEkUqmUmtRvW3dhs9nIzMyEs7MzfHx9UMJriT4uqSsBiJYLulYt3TWgo+n/ro7lnoc52xyWZl3PhJDPQdrKs+Qs8ATdjz/uCKFQCA6H02GN53kEeAZgXsg8vb6vSTGxs7PrtgcfeYrQduaF3L1YW1vr7MZKqVTi9ddfx5UrV3Dt2jWMHDlSJ89DJ2glKBwOB1lZWRg3blyXjxOLxUhPT6faBLvjM/X06VPw+XyEhob2ao1cLhfp6ekwNTVFTExMh2e1KpWKCinicDhQqVR/GjK6uOi1xkIQBB4/fozq6mqEhobStvNMve7C4XCgVCpha2sLX1/fdnWXsvqylnRKzmONnXN7S0c7I3VfLk9Pz179fdksNqwtrAECEMs7cBL+P8fipuYm6mhMJBKhubmZMmYkM0u6uuCS3VNubm4aDxIGewVjdvBsve5wSTGxt7fHyJEje/zc5MwLOdBnYWFBfS61eeOnVCrx5ptv4tSpU7hy5UqvrzvGAq0Epb6+Hg8ePMDEiRM7fQyPx0NmZiY8PT0xfPjwbr8BysvLweVyERER0eP1lZSU4PHjx7Czs0NUVFS3LhwEQaCpqYkSF4lEAmdnZ+pOXNvZL+oolUrk5ORAJBIhLCzMKNLgOBwOcnJy4O3tDYIg2vmMqb9mBEGgpqmmpSW59jE4Ao5O12ZmYgY2m93KULInPlfdhQUWrC1aWpKlCilkChlsLW07FFF1Y0aRSERlvJBdY+qNF01NTeDxeB22Xj+P8IHhmD5yul7FRCKRIC0tDQ4ODr0Sk7aQdkzk7oWceSGPx3raqUkQBN555x389ttvuHTpEiIjI7WyXmOAVoLS1NSEmzdvYvLkyR3+e3l5OQoLCzF8+HCNW+4qKytRWVmJ6OhojddFEASys7NRXV0NT0/PXr2pyTNrLpeLpqYmODg4UNGi2uxMkUqlyMzMBJvNRkhICO1bKoGWGlpRURECAwOpeph63YV8zezt7SlBVr+7bhQ3oohThMLaQlTUV2h1uNHC1KIlO0WtzVmfvlwmMIG9lT0IEJAr5V2GbKlnvIjFYkilUiqzhLQ9IYO8NCHKJwpTA6Zq49fpNhKJBKmpqXByckJAQIDOhIwgCAiFQkpcmpqaYGdnRx2N2dl1b8CTIAh88MEH+Pnnn3HhwgWMGjVKJ+ulK7QSFLFYjMuXL2PatGmt/njkAF5tbS1CQ0N7FBNcU1ODp0+famwpoFQqce/ePQgEAvj5+cHHx0fj5+4MiUTSqoZgY2NDiYutrW2PPzxCoRAZGRnUh9DQ9ZvnQRAEnjx5gmfPnj33WK6juourqyvc3NxadfNI5BIUc4tRVFuEYm4xZIqee4p1ZGWiT1+ujhyT1UO22hpZtoXMLGlsbIRcLm9lZNndjJfRvqMx0b/zkwNdQIqJs7MzRowYodddkUwmo+aqeDweTE1NWxX2O7Pv37ZtG3744QecPXsWY8eO1dt66QKtBEUmk+H8+fOYPHkytUWXyWTIzMyEXC5HeHh4j+/iyXjcl156qdvf09zcjDt37kChUGjV8r4j5HI59Qauq6uj7npdXV3h6OjYbVHg8XjIzs7GwIEDMWTIENp1mrWFdDZoaGhAeHi4Ruf5beddgD+tJ9Q/9EqVEmW8MsqCX5M2W2tza0jlUiiJP2sZ5ACgPny5utPNxmKxqPx5iVzSblhUPSjNw8OjVVtyd+KPx/mNw1g//V4cm5ubkZaWZhAxaQtZEyU/n1KpFE5OTpRTso2NDQiCwGeffYZdu3bh1KlTePnllw22XkNCK0FRqVQ4ffo0Jk6cSJnppaenw97evtcDePX19cjJycH48eO7/fiHDx+CzWYjJiZGr/UH9bNdDqelLtDRhbItVVVVePToEUaMGGGQAC9NITv1pFIpwsLCejWtr1KpwOfzqePEzuouAFDNr6bSKWuaajr9mR35gnVlSqltTNmmMDUx7TIErCMsTC1gYWoBhUoBkVRERQy3bWUmj8bIluS2GS9mZmaYNGISYof0zCiwpzQ3NyM1NRUuLi4YPnw47W6KRCIRVdg/dOgQrl69Cjc3N2RlZSEpKQkzZsww9BINBq0EBWhJDRs7diwEAgGys7MxePBg+Pr69vpNxefzkZaW1q07h9LSUhQUFMDGxgZRUVEGrT+QGemkuEilUupc19XVFWZmZtSRUUVFBUJCQnp0JKhvSJscMzMzhISEaHVaX5O6C7+ZT3WMldeXU11VHU2fq/ty6TokjcxS6U1rNEEQaKhvgEqqgtcAL8gJebeOxkQiESQSCUYNGIWxw8dqvEvuDaSYuLq6GoVLd11dHTZt2oSTJ0/CxsYGbDYbM2bMwMqVKzFz5kxDL0/v0E5Qzp49i/79+6OyslKr+SWkceOUKVM6fQxBEMjNzUVlZSUtJ9/VC4ccDgdCoRCOjo5QKBSQyWTdmsehA2TbN9m1o+sLVXfrLlK5FMXcYpTWlaKgtqDVzoB0OO7JzIamdNQAoCnkXIxMJoOnpydMTU3/9OJimaBZ1tqLSx0WWJgaMBUDrQdSx4kqlYqqIbi4uOjkJkssFlMmrsYgJgRB4LvvvsO2bdvw66+/Yu7cuXjw4AHOnDkDV1dXvPXWW3pZx969e7F3716UlpYCAEaOHImtW7caZKdEO0E5d+4cWCyW1qNyxWIxUlJS2hX8SZRKJR48eAA+n48hQ4YYRf1BIBAgKysLMpkMKpUKdnZ21IXSxsaGluvn8/nIyMhA//794efnp/c1alJ3Ka8vx+Pax3hQ+ABl1WU9arPVFE2zTDqiu3Mx1ubWMGObQab4c1rfhGWC2cGzEeT15wAw2fpOirJ6IBY5HNhbxGIxUlNT4e7ujmHDhtHyvasOQRDYv38/Nm/ejIMHD2Lp0qUGW8vp06fBZrPh5+cHgiBw6NAhxMfHIyMjQ+/DlLQTlCtXrsDX11frNQCZTIZr165hypQp7T5gEokEd+/ehUwmQ0hISI/zBPSJWCxGRkYGZUGhVCpRV1cHDocDHo8HCwsLqmPM0F5GJFwul/Jco4OfUUd1F3JGiMzeILvPBvgNQLW4pfZSw6/RiUV+T7JM2qJSqVBTUwOCIDTKqzdnm8PGwgYThk1AQP+u7YnUA7Hq6+thbW1NDQf2pK4kEomQlpYGDw8Pg9xk9IRDhw7h7bffxv79+7Fy5UpDL6cdzs7OiI+Px5o1a/T6vLQyhwQAMzMznRyBUHeeSmWrD1ljYyMePHgAFouFUaNGGcWRUWNjIzXcSd7Nsdls9O/fH/3796fcjzkcDjIzM8Fisaidi7Ozs0HaiCsrK1FQUICRI0dqPTWzp5iYmMDJyQlOTk4YNmwYVXeprKzEo0ePYGZmBqVSiaCgoBYjSwzFWL+xaGpuQhGnpe5SxivTSjKljbkNmuW9yzJRH7L09PTU6O+sIlSYPGIyhrkPe+5j1QOx1Hd8WVlZANDKWfp5tTGRSITU1FT0798fQ4cONQoxSUhIwKZNm7B3717aiYlSqcTx48chEol6nLrYG2i3Q7l9+zY8PDy0nnRIEAQuXryI8ePHU63HFRUVyMvLg5WVFWJiYoxi+I8MwPHz8+vWcCfp9kvehcvlcri4uMDNzU0vRnlkImRZWZnRNAyoVCpkZWWhqakJNjY24PP5VN2FLFCr112e1D2h5l007cgCtJMyqVQqUV1dDTabDXd3d43ExIxthoXhC+Hr6tvj5wda+2ZxuVyIxWKqvdbV1bVdy79QKERaWppRicnx48exdu1afPPNN1i7dq2hl0ORk5OD2NhYSCQS2NraIiEhwSBNAbQTlPv378PJyUknRyKXL19GbGwsbGxs8OjRI5SXl8PFxcUoMqjJWOOSkhLqjrknP0MgEFDiIhKJWtnAaDsUjCAIFBQUUOmZmrrZGgKlUknVpcLDw2Fubt5qx9dV3UWlUqGioYKKPm4UNz73+bSRZaI+se/u7q7RhdmcbY7FEYvh4+LT4+fvDNJSvq6uDg0NDbCxsWnlZ5eeng4vLy+tdHHqg5MnT+K1117DF198gY0bN9JqzTKZDOXl5eDz+Thx4gT279+P5ORkrbmrdxfaCUpqaiqsra3h69u7u6WOuHr1KsLDw/H48WM0NDRorSVZ16hUqlYXZm01K5AfeA6HAz6f32lrbU8gfcTEYjHCwsJoF3jUEXK5HBkZGTAxMUFoaGiHxzVk3UW9jVu97qIuyhwBpyU8rLYI1fzqdqKhjZRJhUKBqqqqHk3sm5uaY3nUcgxw0u5pQEeQlvLk7kWpVMLOzg6DBw/Wu2FqTzh37hxWrVqFTz/9FG+//TbtrxmTJ0+Gr68v9u3bp9fnpZ2gkP5Tw4Y9/yxXU65fvw6VSgW5XK7VlmRdoj78FxoaqrMLs0wmoy6S9fX1sLKy6nG2C+luwGKxEBoaahRHiRKJpFWKZXeNPzuad1HvtCMRSAQtdZfalrqLNoLB5HI5qqqqYG1tDRcXF43+RpZmllgetRz9HfU7ACsQCKg5E3Nzc3C5XEgkklZHY72No9Y2V65cwbJly/Cvf/0LH3zwAe3FBABefvllDBw4EAcPHtTr89JOUPLy8iCTybS+VePz+bh79y4AwN/fv136Hx0hL3Lm5uYIDg7W24W5o2wX8iL5PIvv5uZmpKenU91ndH+NgT/nH0ibj542LXQ279K27iJTyFBS15LvUswtRrOs84CtzpDJZKiuroaNjY3G9i/W5tZYHr0cHvb6vaESCARIS0ujbIFI1EWZz+fD1taWet26a8qoK27cuIElS5bgn//8J7Zt20ZLMfnggw8wY8YMDBw4EAKBAAkJCfjiiy9w8eLFLufudAHtBKWgoABCoVArQVgkVVVVyM7Ohrm5Odzc3MDj8SCTyajitIuLi9Zz1XuLQCBARkYGZT9hKINH9WwX8qhCvaiv/rqRVjnu7u5GMZgG/LlmT09PrbasalJ3edb4jIo+bhA3PPdnk15i9vb2cHJy0mjNtha2WBG9Aq52+m2Nb2pqQnp6OgYNGoTBgwd3+jiZTEYdjfF4PLDZbKprzNnZWa83KLdu3cLChQuxceNGfPbZZ7R9P69ZswZXr15FdXU1HBwcEBwcjPfff1/vYgLQUFCKi4vB4/G0EkhDhks9ffoUzs7OCA0NBZvNpibOyYwSkUiEfv366SWjpDvU1dVRtjM+Pj60eSOrZ7twuVw0NzdT9QM2m41Hjx7Bx8eHVmvuioaGBmRmZsLHx6fLi1xv6cg+p7O6C1fApY7Gqhqr2tVdJBIJqqur4ejoCCcnJ43WYW9pjxUxK9DPpp9Wfq/u0tTUhLS0NI1fZ/Jmhizsk/5spMBou4lEnfv37yMuLg5/+9vfEB8fbxTvZzpAO0EpLS1FZWVlr0NplEol0tPTwePxMHDgwC6nb0mPJjLn3NHRkRoK1Pd5bkVFBR4/fkyreY3OIF+3yspKNDc3w9raGgMGDNDa9LQuIYcshw0bpvUW9a7oqu5CNkOQ71OhVEiZWJbyStEkbEJNTQ2cnZ3h4OCg0fM6WjtiZfRKOFo76uC36hw+n4/09HTq5qindPS6kc4Qrq6uvYp7aEtaWhrmzp2Lv/zlL/jmm28YMdEA2gnKs2fPUFJSgpiYmB7/DLlcjjt37qC5uRkBAQHw8vLq9vdKJBJKXBobG2FnZ0eJS287n7qCIAgUFRWhqqoKISEhGt99GorS0lKUlJRgxIgRUCgUWs920QWkK3NgYCDc3d0Nupbu1l2qa6tx6d4lwB6oV9Z3aWffFmcbZ6yMXgl7K90aWraFFJMhQ4ZofQygbV6JmZkZ1ZLcm+Hd7OxszJo1C4sWLcL3339Pq/etMUA7QamursajR48wZsyYHn2/QCDAvXv3QBAEIiMjNb6TU0e984nH4+nsIqlUKpGbmwuBQICwsDCdCpe2II8Ta2pq2rUyayvbRReUlZXhyZMnCAkJQb9++j36eR5k3YUUGADUEWxZWRkCAwPh4eEBgiDwrOEZHnNaoo/rRfWd/kxXW1esiFkBWwv9OkA0NjYiIyMDvr6+GDhwoE6fS6lUtjoak8vlVHQBaaHTHfLy8jBz5kzMnj0b+/fvN4qGErpBO0HhcrnIzMzEuHHjNP7e2tpaZGZmwtzcHDExMVo9Y1UoFJRXVl1dHVXg761XlnqLbUhIiMHrN91BpVIhNzcXTU1NCA8P7/J4Sz3bhcvlgiCIbmW7aBv1VMiwsLBe3WjoA7Lu8vTpU/B4PLBYrFb5Lurv7TphHVV3qWyopOouHvYeWB69HNbm+j1+1KeYtEXdkZvL5UIgEHR6pKhOQUEBZsyYgcmTJ+Pnn39mxKSH0E5QGhoacO/ePY0Sz8jjopKSEjg4OCAiIkKnbwj1Dh4ulwsTExNKXJ7XVquOSCRCRkYG7O3tMXLkSKN4E8vlcmRlZUGpVCIsLEwjASStOcgjxY6yXXSB+sS+sVj8Ay3Hv48fP0ZwcDAsLS27VXcRSUUo4rQMUk4YNoGKCdYXpJgMHTq0W9ZAuqbtkaKFhQW1c3FwcICpqSlKSkowdepUjBkzBkePHjWKzyFdoZ2gCAQCJCcnd7vljbTK4HA4GDBggN4T3tTbajkcDlQqFTWz0dUdeH19PbKysjBgwACj8TEi52IsLS17bVdDFlnJ100oFHbp+9RTyN2UQCDoVYS0viFtdkJDQ9vV07pbd9E3DQ0NyMjI0HujQ3dR3y1XVFTgtddeQ2BgIPLy8jBq1CicOnWKduMDxgbtBKW5uRmXLl3qNLdEHblcjrt370IsFmP48OEGvyNqewfe2axLdXU18vPz4e/vT8sPXkcIhUJkZGT0evivM5qbm6mLZENDA2xtbVs1Q/TkItmRL5cxUFJSgvLy8m4dzXVWd9H3kWJ9fT0yMzNpKyZtUSqVOHnyJNavXw+CICAWi/HSSy9hzpw5WLNmDRwdHQ29RKOEdoIil8tx7tw5TJ48ucu7BaFQiHv37kGlUiE8PJx2XVGdzbqwWCzU19fTsijcGeS8BjnhrOs7YLlcTl0g6+rqepTt0h1fLrpBEASKi4tRVVWF8PBwjc001eddSEsTZ2fnDusu2oQUE9KBwhiorq7G1KlT4evri7Nnz6KmpgZnzpzB6dOn8euvv+rtevL5558jMTERBQUFsLKywujRo/HFF1/A399fL8+vbWgnKCqVCqdPn8aECRM6nQHhcrlIT0+HmZkZoqOjjeIYQygUIjc3F0KhEARBwMnJyWCzLppA2uUb6s6z7R04me1C3oF3tFOSSCRIT0+HtbV1t325DA1BECgsLASHw0FERESvO/00mXfpDTweD1lZWRg+fLjWQ/F0BZfLxZQpU+Dl5YXz588b9PM3ffp0LFu2DFFRUVAoFNiyZQtyc3ORn59vFN2ebaGdoAAtkZZjxoxp94ISBIGSkhIUFRXBzs4OUVFRRnGxkMvlyM7OhlwupxwADDHroikVFRUoKipCYGAg3NzcDL0cKtuFbOUms13IIquZmRlEIhHS09N1djSnCwiCQH5+PhoaGhAREaGTGyRd1F2MUUx4PB6mTp2Kfv364fLly7S7GeVyuXBzc0NycnKPOl0NDS0F5fz584iIiGh1fkwQBLKyslBTUwNPT0+MHDnSKArZzc3NrVxs2x696GvWRRPIo5fKykqEhobS8jyZzHYhXzuRSAR7e3sIhUJ4eHhgxIgRRvH+IJsGhEIhwsPD9XK3rI26C2kPNGLECHh6eup6yVqhoaEB06ZNg62tLa5evUqrmzeS4uJi+Pn5IScnB4GBgYZejsbQUlAuXbqEoKAgKt1PLpfj/v37EAqFGDZsGC3yyLsDn89HZmYm3N3dMWzYsOfeLetq1kUTVCoVdbccHh5Oyw9dR9TU1CAvLw/m5uaQSqWd2sjTCTIzRiKRGKxpoCd1F2MUk6amJkyfPh2mpqa4du2a1jKFtIlKpcLcuXPR2NiIW7duGXo5PYKWgnLt2jX4+fnBzc0NYrEYd+/ehUKhQFhYmNEUsjkcDnJzc6nhLk3FQFuzLppAZq/IZDKEhYXp1HxPm7T15eoo24UUF02zXXSFUqlEZmYmNc9Dl8wYspW7s7oLKSbG4DVHIhQKMXPmTCgUCty4cYOWO24AWLduHc6fP49bt24ZRadcR9BSUFJSUjBw4ECYmZkhLS0NpqamiImJod15Z2eUl5ejuLhYa7WHns66aIJUKkVGRgbMzMwQEhJiFF1RwPN9uXqT7aIrFAoFMjIyqAAyur7WUqmU2jHX19fD1NQUMpkMvr6+GDx4MC2E+XmIxWLMnj0bQqEQycnJtL0h3bBhA06ePImUlBSdOl/rGloKyu3bt5GTkwOhUIiwsDBERkbS5g6uK8hOndraWoSGhurE3qO7sy6aQE7sOzg4YOTIkUZRyAb+9OUKDQ2ljke7QpNsF10hl8upDsWQkBCjaCoBWtps8/Ly4OjoCKFQCACtXjs6/h7Nzc2Ii4sDl8tFSkoKLRpL2kIQBDZu3IikpCTcuHEDfn5+hl5Sr6CloMyZMwcXLlzAm2++iY8//tgoLnBKpRLZ2dlobm7WW4a6NnJd+Hw+MjIy0L9/f60GTOkSbfhydZXtoqtMHKlU2qqd2Rje10BL63hubi6CgoLg5uZmsHkXTZBKpViwYAGePXuGlJQU2tZ63njjDSQkJODkyZOtZk8cHByM5kRGHVoJilKpxJYtW7B7927069cP1dXVCAsLw5w5c7B06VIMGDCAlhc88rjI1NQUISEhBttNaZrrQtYehg4dqncTv55CEAQePXqEuro6rcxrkLStHTg4OFDioo1sF4lEgrS0NMq3zdjEJDg4GK6uHac8Pq/uou/PrFwux+LFi1FcXIybN2/Setiys9fmwIEDeOWVV/S7GC1AG0EhCALLli1DRkYGzpw5Az8/P+Tn5+Pw4cM4ffo0CgoKEBQUhNmzZ2Pp0qW0OcMVCATIzMyEk5MTAgICaHOheF6uS2VlJQoKCoyquKovXy6pVEpdINWzXXqacU5m1vfr189o2pmBPzvnuhKTtrStu5AuB66urnBwcND550OhUGD58uXIzc1FSkqK0XSEvijQRlAA4OLFi4iOjm5ne0C6CR8+fBinTp1Cbm4uRowYgZkzZ2L58uUGO6rh8XjIzs7WmyVJT2k762JmZga5XI7hw4fDy8uLtutWh+xAk8vlGrsc9wa5XN6qqE8GObm5uXUr20UkEiEtLY1qHTeG1xpoEZP8/HwEBQV1W0za0tG8iy7rLgqFAqtWrUJqaiqSk5Ph6+ur1Z/P8HxoJSjdgSAIPH36FL/++itOnTpFWWXPnDkTy5Yt09vAI3mHP2LECKOZEiZnTLhcLuzt7cHn8w0y66IpZGYMm802aAeaSqVCfX09tXshCKLLC6RAIEBaWhoGDBgAX19fWr62HUGG3AUHB8PFxUUrP1O9mUQXdRelUonXXnsNt2/fxvXr143WC8vYMTpBUYcgCFRWVuLw4cM4efIkUlNTMWjQIMyYMQNLly5FWFiY1j/EZEG4oqICISEh3eouogPkEJ1YLKYmsg0x66IppC+XjY0NAgMDadNN1FG2C9kQ4eLigubmZqSnp8PHx8eo2kCrqqpQUFCgc/NS0meMw+H0uu6iVCqxdu1aXL16FdeuXcPIkSN1tm6GrjFqQVGHIAhwOBwkJCQgKSkJ9+7dg5eXF6ZNm4Zly5ZpJXRLqVQiLy8PTU1NCA0NNZqgJvVUyNDQ0A6bBvQx66IpxuLL1VG2C9BiZzJs2DCj6dbRl5i0hay7kPnwZAgWuWvu6u+uVCrxj3/8A6dPn8bVq1cREhKit3UztOeFEZS28Hg8HDlyBImJibhz5w5cXV0xbdo0LF68GKNHj9b4AimTyZCVlQWCIBAaGmo02RrknbKtrW237/B1MeuiKU1NTUhPT4eXl5fRBJABLbYkWVlZcHNzg0wm01q2i66prKxEYWFht2d6dEVndRfSZ0z9vUcQBN5++20cP34cly9fRkREhKGWzfB/vLCCok5jYyOOHTuGxMRE3Lx5Ew4ODpg6dSoWLVqECRMmPPciKxaLkZGRodFFmQ4IBAKkp6fD3d0d/v7+PbqQaWPWRVPINMvBgwfDx8dH6z9fV5B2OwEBAVTnnDayXXQNGTVsaDFpS0d1F3t7e9y6dQvz58/H3r178csvv+DChQsYNWqUoZfLgD4iKOoIBAKcOHECJ06cQHJyMqytrTF58mQsWrSow1CvxsZGZGZmGtXgH/BnB5qPjw98fHy0tm5NZ100hbwoG1NYE/BnIbsru52usl2cnZ0NcqNCiklYWBjtQuraIhKJkJeXh40bNyIvLw8mJib461//io0bNyIgIMBoPpsvMn1OUNQRi8VITEzEiRMncP36dbDZbEyaNAkLFy7EtGnTcPjwYeTl5WHTpk0GjxfWBDJiWNcdaM+bddEU8gyfLvkr3YU8LtKk9tCdbBddU1FRgeLiYoSFhdHWMLEtBEFg+/bt2LVrF9544w08fvwYly9fRv/+/XH79u0O/dx0RUpKCuLj45GWlobq6mokJSUhLi5Ob89PR/q0oKgjlUpx6tQp/Pbbb7h8+TIkEgkUCgXWrl2LTz/9lNapiiQEQaCsrAwlJSV6L6z2NtdFfd10OnZ5HuXl5ZSfWE/v8Ds6VlRvqdXFe49ct7GJSXx8PL788kskJSVhypQpAFpuDG/cuIEZM2bodZdy/vx53L59GxEREViwYAEjKGAEpR1yuRzr1q1DYmIioqOjkZqaCqlUivHjxyMuLg5xcXFaseLQNgRB4PHjx6ipqUFYWJhB8x40yXXRhi+XoXj69ClKS0sRHh6u1XWLxWJKnPl8vtazXYxVTL755hts374dx48fx6xZswy9pFawWCxGUMAISjvWrl2Lu3fv4uzZs/D29oZCocDly5dx5MgRXLx4EUKhEGPHjsW8efOwYMEC2NnZGXrJVDuzQCBAWFgYrQSvq1kXR0dHFBYWgsfjGVWYFymClZWVCA8P1+l7QNvZLuROUNsiqEsIgsC3336Ljz/+GAkJCbS8aDOC0gIjKG0oKSmBi4tLh3f4SqUSN27cQEJCAi5cuID6+nqMHj0a8+bNw8KFCw1S1JTL5cjKyqKCmujcztx21kUul4PNZmPYsGHw8PAwiu45cidYW1urVXPK7kBmu5BFfU2zXUpLS/H06VOjE5P9+/dj8+bNOHToEJYsWWLoJXUIIygtMILSQ5RKJe7cuYNff/0VFy5cQHV1NWJiYjBv3jwsXrwY/fr10/l5rkQiQUZGBiwtLREcHGwUF2Sg5cKYmZkJqVQKZ2dn8Hg8g8y6aArpdFxfX4+IiAiDDiyS4kzuXp6X7UIez0VERNAy/rYzDh48iLfffhs//vgjVq5caejldAojKC0wgqIFCILAgwcP8Ouvv+LcuXMoKytDVFQU5s6di6VLl8LNzU3r4iIUCpGRkUH7KfK2yGSyVlb/pqamBpl10RSVSkUdK5LWNXSBzHYhxYXMdiGL+lVVVSgrK0N4eLhRiUlCQgI2bNiAvXv34tVXXzX0crqEEZQWGEHRMgRBIDMzE4cPH8bZs2dRXFyM8PBwzJ49G8uWLdOKu29DQwMyMzNp73LcFnVfrq4CpnQ966IpKpWKCk+LiIighcB1hbpPFp/PB4vFgre3N7y9vWlVX+uK48ePY+3atdi1axf+/ve/G3o5z4URlBYYQdEhBEEgLy8Phw8fxpkzZ1BQUIDg4GDMmjULy5Yt69HAYW1tLfLy8jBs2DAMGDBARyvXPqQvl6aZINqeddEUpVKJrKwsyOVyhIeHG0UUNcmTJ09QXl4Ob29vNDU1UdkuZN2lJ9ku+uCPP/7AmjVrsGPHDmzYsIGWawRaTgmKi4sBAGFhYfjqq68wceJEODs7G01gnbZhBEVPkMVcMjCMtOggM12641dVXl6O4uLiXmVUGAJt+XL1dtZFUxQKBTIyMgC0XDDoWNfpCIIgUFJSgoqKCkRGRlImpm3buTXNdtEH586dw6pVq7B9+3Zs2rSJtmICADdu3MDEiRPbfX316tU4ePCg/hdEAxhBMQBkpgspLhkZGfDz86MyXdraSBAEgeLiYlRWViI0NNRoZgcA3flyaTLr0hPkcjnS09NhZmaGkJAQo2l4UG9pjoiI6NQRW9NsF31w+fJlLF++HB999BE2b95MazFh6BhGUAwMQRB49uwZlUaZmpoKHx8fzJgxA8uWLcPw4cPx97//HZMmTcKiRYuMZlYD0J8vl7ZzXWQyGdLS0mBlZYXg4GBa3Ll3B/LGo6qqqksx6ej7usp20UfN6MaNG1iyZAneffddbN26lRETI4URFBpBEARqa2tbZbqYmprCysoK3333HWbNmmU0d8qG8uXqba4L2ThgZ2eHkSNHGqWYREZG9vjGg8x2IY8W1ZsiXF1dddIqfevWLSxcuBD/+Mc/sH37dkZMjBhGUGhKbW0tpk6dColEAjc3Nzx48ABubm6YNm0alixZgtjYWNqKC118uTTNdWlubkZaWhqcnJyMyr2WIAgUFRWhpqZG68OWEomEEhcy24UUaG3Ure7fv4+4uDj8/e9/x44dO4zmNWfoGIMJyty5c5GZmQkOhwMnJydMnjwZX3zxhdHks+sSqVSK4OBgREVF4aeffoK5uTkaGhqoTJdbt27B0dERU6ZMweLFizF+/HhaiIt6rYduMw/Pm3WRy+VIS0uDm5tbj7NjDIH65H5kZKRO24LlcnmrupV6sqKjo6PGr1laWhrmzJmDVatW4ZtvvjGa15yhcwwmKDt37kRsbCw8PT1RWVmJf/7znwCAO3fuGGI5tOPBgweIjIzs8MhFIBDg+PHjOHHiBFJSUmBjY4NJkyZh8eLFmDRpkkG6kcgpcmPx5VKfdWlqagKLxaJ2JsYS2UsQBAoLC8HlchEREaHXGROlUtmqqA+AEpfuZLtkZWVh1qxZWLJkCfbu3cuIyQsCbY68Tp06hbi4OEilUqPq9Tc0IpEIiYmJ+P3333Ht2jWYmZlRBfxp06bp5bVUqVTIycmBSCSi3RT58+Dz+dQxl1KpNMisS08wpJi0RaVStTpalMvlrYr6bd+DeXl5mDFjBubMmYP9+/fTYnfNoB1oISj19fVYt24dKisrcevWLUMvx2iRSCRUpsvVq1dBEARefvllzJ8/H7NmzdLJhV6hUCArKwsKhYL25pRtIR0HfH19qUE0fc+69ASCIFBQUIC6ujpERkbSakfV0dGik5MTKisrERgYCJlMhhkzZmDKlCk4dOgQIyYvGAYVlPfffx979uyBWCzGqFGjcObMGb2GQr3IyOVynD17FseOHcPly5chlUoxYcIEzJ8/H3PnztXKHW1HvlzGAo/HQ1ZWVpeOA7qedekJdDKo7A7Nzc3gcDjYtGkTLl++DDabDX9/fxw9ehQjR4409PIYtIxWBWXz5s344osvunzMo0ePMHz4cABAXV0d6uvrUVZWhk8++QQODg44c+YMLe4CXyQUCgUuXryIo0eP4tKlS1SmS1xcHObPn9+jPI/u+nLREXI+ZsSIEfD09OzW92h71qUnqItJZGSkUR0tlpaWYtKkSfDw8IC3tzcuX76MQYMG4ZNPPsHSpUsNsqZvv/0W8fHxqKmpQUhICHbv3o3o6GiDrOVFQauCwuVywePxunzMkCFDOjwWefbsGby9vXHnzh3ExsZqa0kMbVAqlbh+/TqV6dLQ0IAxY8Zg3rx5WLRoUbdyMnrqy0UHampqkJ+f36v5mN7OuvQEgiCQn5+PhoYGoxOTZ8+eYcqUKQgMDMTJkydhamoKkUiEixcvwsvLCzExMXpf07Fjx7Bq1Sp8//33iImJwddff43jx4+jsLBQr3NTLxq0qKEALT5VgwYNwvXr1zFhwgRDL6dPoFQqcfv2bfz66684f/48amtrMWrUKCrTxdnZuZ1YaMuXyxBUVlaisLAQwcHBcHFx0crP1HTWpafPkZeXBz6fj4iICKMSk+rqakyZMgV+fn44c+YMbRpuYmJiEBUVhT179gBouUnw9vbGxo0bsXnzZp09r0qlarWbJQjCqD5Dz8MggnL//n08fPgQL730EpycnPDkyRN89NFHlJOuhYWFvpfU5yEIAvfv36cyXcrLyxEdHU1luri6uuLcuXN48uQJ5s6dq1VfLn1A5qjrcthSF7ku6mISGRlpVJ8NDoeDKVOmYMCAAbhw4QJt1i6TyWBtbY0TJ060sptfvXo1GhsbcfLkSZ08r0KhgKmpKYRCIXJychAVFWVUdcfuYJCDb2trayQmJmLSpEnw9/fHmjVrEBwcjOTkZNq86foaLBYLo0aNwu7du1FcXIz79+8jOjoaP/30E/z8/BAaGooVK1ZAKBRi0KBBhl6uRjx9+hRPnjxBeHi4Tif3WSwW7Ozs4Ovri9jYWMTGxsLR0RHPnj1DSkoKUlNTUV5eDolE0q2fp1KpkJubi6amJqMTEx6PhxkzZsDDwwPnzp2j1drr6uqgVCrh7u7e6uvu7u6oqanRyXMqlUqYmpqivr4eY8eOxdGjR5GTkwOg5abhRYE2R14M9IQgCGzfvh2ffvop3N3dUV1djZCQEMyaNQtLly7tUaaLviCdd589e4aIiIgeNR9oC01zXUgxEQqFiIiIoNUF+Xk0NDRg2rRpsLOzw5UrV2g3y1NVVQUvL6929dr33nsPycnJuH//vk6el5zTIrNTPDw8Wh1/tT0OM0aMe/W9oLS0FGvWrMHgwYNhZWUFX19fbNu2DTKZzNBLoxW7du3Cl19+iUuXLqG8vJwaSjt58iSCg4MRGxuLTz/9FEVFRbS60yItSaqqqhAVFWVQMQEAS0tLDBw4EJGRkRg3bhwGDBiAxsZG3L17F3fv3sWTJ08gEAhAEAQlJiKRyOh2Jk1NTZg1axasrKxw6dIl2okJALi4uIDNZqO2trbV12tra+Hh4aGV5ygtLW3XoLRz5064ubnh6NGj6N+/P2pqanD48GH85z//QXV1NUxMTKBSqbTy/IaizwpKQUEBVCoV9u3bh7y8POzcuRPff/89tmzZYuil0Qp/f3+qUYLFYsHf3x/bt29HZmYmioqKsHDhQly+fBkRERGIiorC1q1bkZ+fb1BxIdtrORxOr5x3dYW5uTm8vLwQFhaGCRMmYPDgwRCJRHj48CFu376Nu3fvoqmpCeHh4UY1KCoQCDB79mywWCxcvnzZ4CLeGebm5oiIiMDVq1epr6lUKly9elUrHaYymQwvv/wyEhMTW33d1tYWCoUCJSUl2LlzJ9566y289dZbOH78OEaPHo36+nqj36EwR15qxMfHY+/evSgpKTH0UowKgiBQXl6OX3/9FadOnUJaWhoGDx5MZboEBwfr7VhMpVIhLy8PTU1NRtcRJZfLkZGRAZFIBAAGmXXpKWKxGLNnz4ZIJEJycrJBXaa7w7Fjx7B69Wrs27cP0dHR+Prrr/Hbb7+hoKCgXW2lJ9TU1MDDwwMEQaChoQHOzs5ITEzE559/jqqqKjg4OOD111/HzJkzUVFRgTfffBOnTp0yumaXtrxYLQa9hM/n0/6DQEdYLBYGDRqELVu24IMPPkBNTQ2V6fLdd9/B29sb06dPx9KlSxEeHq4zuw3SU0wsFhvdUREphCqVCmPGjIGpqSk165Kbm6uXWZee0tzcjLi4OPD5fKMQEwBYunQpuFwutm7dipqaGoSGhuLChQtaERMA1M9ZtmwZsrOzkZycjAULFmDQoEGorq7GuHHjYGNjAzabjZycHKhUKiiVSq08tyFhdij/R3FxMSIiIvDll1/ib3/7m6GX80JAEATq6upw5MgRJCUl4c6dO3B3d6cyXUaNGqW1C6NSqURWVhbkcjnCw8NpM+/QHVQqFbKysiCVShEREdFu7fqYdekpUqkUCxYsoDrZuus88KKiVCpbvacLCwsxb948ODo6IjExsVU8R21tLe7du4fVq1dj27Zt2LRpkyGWrFVeOEHR1P4FaBl4Gz9+PCZMmID9+/freol9loaGBhw9epTKdHFycsLUqVOxZMkSjB07tsfiolAokJmZCYIgEBYWZlS9/UqlEtnZ2ZDJZN0SQl3MuvQUuVyORYsW4cmTJ7h586ZOY56Njffeew/r1q3D4MGDUV5ejmnTpsHW1hYnTpygdin//ve/kZKSghUrVuD999839JK1wgsnKJrav1RVVWHChAkYNWoUDh48SOtz6heJpqamVpkutra2mDx5MhYvXoyXX36526JA1h3YbDZCQ0NpdRT0PMhdFenU3JNdlXqui3pcr5ubm07rRwqFAsuXL0deXh6Sk5ONbjZJl9y+fRuTJk1CRUUFXF1dAbTctE6fPh1mZmZITEyEj48P7t27B6lUivHjxxt4xdrjhRMUTaisrMTEiRMRERGBw4cPG9XF6EVCKBRSmS7Xr1+Hubk5lekyderUTi+0MpkM6enpsLS0RFBQkFH9/bQhJm3RdNalpygUCqxatQppaWm4ceMGfH19tfazjZG2x1zNzc0IDQ3FDz/8gPHjx1P/XlNTgzlz5kAgEODUqVMYNmyYAVetG/qsoFRWVmLChAkYNGhQu1wGbfWiM2iORCLBH3/8gePHj+Pq1atgsViYOHEiFixYgJkzZ1J33eXl5SgtLYWjoyMCAwONamepVCqRmZkJpVKJ8PBwnRzR6SrXRalU4tVXX8WdO3dw48aNF/Ki2FPKysowaNAgqFQqBAQE4LXXXsN7773X6jFcLhfR0dF45513sGHDBgOtVHf0WUE5ePAgXn311Q7/rY++JLRDLpfjzJkzVKaLXC7HhAkTEBsbi127duHVV1/Fhx9+SNtJ/Y4gxUSlUumt3qOtXBelUom1a9fi6tWruH79OgICAnS8cuPhww8/xFdffUVFSBMEARcXF/z1r3+lXBrIuRypVGpUHYia0GcFhcG4IDNd9u3bh7Nnz8LExASTJk3C/PnzERcXR9shOnWUSiUyMjIM2jzQ01wXpVKJjRs34uzZs7hy5QpCQkL0vHJ6c/fuXZiZmeHp06d4+PAhHj58iOTkZPj6+kIqlUIqlcLT0xO7du3CuHHjDL1cncEICg347LPPcPbsWWRmZsLc3ByNjY2GXhItycnJwZQpU7Bq1SpMnjwZR44cwcWLF9HY2IjRo0cjLi4OCxcu7Fami75RKBTIyMgAi8VCWFgYLeo93c11IQgCmzZtwokTJ3DlyhWEh4cbeOWGpTueWwcOHMC///1v3L59G8+ePUNubi7kcjnWrFmjp1UaBkZQaMC2bdsoV9off/yREZROWL58OUaOHNnqmEupVOLmzZtISEjA+fPnweFwEBsbSwWGdZTpom9IMTExMaFtJ1rbWRepVIpDhw5h7NixyMvLw2+//YaLFy8aJAyLTqgX4C9cuACJRAIWi4V58+YBaDmmNTMzw+PHjzFt2jQkJydj4MCBhlyyXmEEhUYcPHgQb731FiMonUDmSXSGUqnEgwcPqEyXZ8+eUZkuS5Ysgaurq97FRaFQID093ajamgmCQH19PT777DP89ttvaGhowOjRo7FmzRrMnTtXa+Fk2kCfu3v1MKwNGzbg0qVLYLFYMDExQXBwMI4dO0Y9lsfjwc/PD9999x2WLVumszXRDeNpjWHo8zyv5sBmsxEbG4s9e/bgyZMnuHv3LqKiorB//374+flh0qRJ+Oqrr1BVVaWXxgu5XI709HSYmpoajZgALVY6zs7OcHJyglwux8GDBzF37lzs27cPnp6e+OOPPwy9RAqZTIbFixdj3bp1On8uUkw+/vhjnDx5EomJiSgsLMT8+fNx/PhxTJ06FXK5HADQr18/DBgwAEKhUOfrohPMDoVGMDsU3UAQBHJycnD48GGcOXMGjx8/RmhoKJXpMmjQIK3vXMiBS1NTU4SEhBiNmAAtr9eOHTvw3//+F0lJSZgyZQr1bxUVFbC3t6ddnUpfn53i4mK89957eP311zF16lQkJCTgjTfewLvvvou9e/ciKCgISUlJsLS0xNmzZzFr1iydroduMDsUHbF582awWKwu/ysoKDD0MvsELBYLwcHB2LFjB/Ly8pCbm4tp06YhKSkJQUFBGD16NLZv347i4mKt7FzInYmZmZlRisnXX3+N+Ph4HDt2rJWYAIC3tzftxESfDBkyBPPmzUNkZCSSk5Px/vvv49tvv8WHH36IFStW4OLFiwgODoZMJutzYgIwbsM645133sErr7zS5WOGDBmin8UwULBYLAwfPhyfffYZtm/fjpKSEvzyyy84ffo0duzYAX9/f8ycORPLli3D8OHDNd65yOVypKWlwcLCAiEhIUY1cEkQBL799lt89tlnSEhI6JMXRODPWgmPx0O/fv1afd3ExASrV68GANy4cQNjx47FggULALSI7dq1a2FnZ2dUOTbaxHje7UaGq6srhg8f3uV/ffVNRxdYLBZ8fX3x8ccfIzU1FU+ePMGKFSuQkpKCmJgYhIeHY8uWLcjOzu7WzoUUE0tLS6MUk//973/Ytm0bDh48iLi4OIOtxdC7exaLhZycHISFhbXKmG97c1FcXIz8/HxYWVmBz+fjzJkzGD58OHbs2KGztdEdpoZCA8rLy1FfX49Tp04hPj4eN2/eBAAMHToUtra2Bl5d34MgCFRXV1OZLg8ePMDAgQMxffp0LFmyBJGRke0uLqSvmJWVFYKCgoxKTICWuYl33nkHP/30E1asWGHQtWhq8Apov4Zy584dLF26FNnZ2XBycurwMffu3cPcuXNhY2MDS0tL2NjYIDU1VSvPb6wwgkIDXnnlFRw6dKjd18noXQbDQWa6kOJy9+5deHh4UJkuMTExqK2txeeff47XXnvN6HYmAJCQkIANGzbg+++/f+4xLV3RtqDIZDL4+fnhv//9LxYtWtTpY/Ly8pCUlAQnJ6cXIs+ktzCCwsCgAfX19Th27Bh+//133L59Gw4ODmhuboafnx8uXrwIKysrQy9RI44fP47XX38du3btMspgOW3s7ttOvqtUKsjlcowfPx4LFy7Eu+++q5O1v4gwgsLA0EOKi4sxfvx4qFQq8Pl82NnZYcqUKVi8eDEmTpxI+6CvP/74A2vWrEF8fDzWr19vcEeBnqCt3X1xcTEyMjIQGxsLOzs7ODg4YMeOHcjIyMCRI0faWdSTqA87MjCC0qf59ttvER8fj5qaGoSEhGD37t2Ijo429LKMgtraWkycOBFhYWE4dOgQJBIJEhMTceLECVy/fh0WFhaYPHkyFi5c2GWmi6E4e/YsVq9eje3bt2PTpk199qJIEATEYjHmzp2L9PR0uLq6gs/nIzY2FpmZmbCyskJaWhqsra07FRWGP2EEpY9y7NgxrFq1Ct9//z1iYmLw9ddf4/jx4ygsLISbm5uhl0d7GhsbsXv3bmzZsqXdRUYikSApKQnHjx/HtWvXwGKx8PLLL2PhwoWYMWOGwa3LL126hBUrVmDr1q14//33+6yYqMPj8eDg4ID8/Hykpqaivr4eV69eRVlZGYKCgrB//37Y2dkxovIcGEHpo8TExCAqKgp79uwB0HJu7O3tjY0bN2Lz5s0GXt2Lg1QqxdmzZ3Hs2DFcuXIFCoUCEyZMwPz58zFnzhy911yuX7+OpUuX4r333sNHH33EiMn/0ZGDsFQqxe+//46vv/4aXl5eOHToEOzt7bvlNtxXYQSlDyKTyWBtbY0TJ060mjdYvXo1GhsbcfLkScMt7gVGoVDgwoULOHr0KC5dugSxWIxx48YhLi4OcXFxOm8Rv3nzJhYuXIg333wT27dvZ8SkA8iaCCkaMpkMx44dww8//ACCIHD27Nk+7RTwPBiZ7YPU1dVBqVTC3d291dfd3d1bDXIxaBdTU1PMnj0bhw8fRnV1NRITE+Hm5oaPP/4YPj4+mDdvHg4cOAA+n6/157537x6WLFmCN954gxGTLiBfFxMTExAEAXNzc6xYsQKvvPIK3N3doVKpDLxCesMICgODAWCz2Zg6dSoOHjyIiooKnD17Fj4+PvjPf/4DHx8fzJo1C/v27UN9fX2vn+vhw4dYsGABXnnlFXzxxReMmHQTFosFgiDAZrPx6quv4vDhw50OOTK0wAhKH8TFxQVsNhu1tbWtvl5bWwsPDw8DrarvwmazMXHiRPzvf/9DaWkprl27hoCAAOzatQtDhgzBtGnTsGfPHnC5XI3NK7OysjB//nwsX74cX3/9NSMmGkKKiomJidHNGBkCRlD6IObm5oiIiMDVq1epr6lUKly9ehWxsbEGXBkDm83GmDFj8O2336KkpAR37txBREQE/ve//2Ho0KGYNGkSdu7c2a1Ml9zcXMyZMwdxcXHYs2cPIyY9hHndug9TlO+jHDt2DKtXr8a+ffsQHR2Nr7/+Gr/99hsKCgra1VYYDA9BEMjOzqYyXYqKihAaGorZs2dj6dKlGDhwYKsLX0FBAWbMmIEpU6bg0KFDTKsrg15gBKUPs2fPHmqwMTQ0FLt27erzmeHGAEEQKCgowC+//IIzZ84gPz8fgYGBCAwMxCuvvAKCIDBnzhxMnz4dv//+OyMmDHqDERQGBiOGIAg8efIEv/zyC3bv3o2GhgYAgKOjIx48eAA/Pz8Dr7A1paWl+PTTT3Ht2jXU1NSgf//++H//7//hww8/ZOIcXgCYGgoDgxHDYrEwdOhQfPLJJ8jPz8egQYPg6uqKyMhIBAQEICoqCkeOHDH0MikKCgqgUqmwb98+5OXlYefOnfj++++xZcsWQy+NQQswOxSaQf45+lIhMCUlBfHx8UhLS0N1dTWSkpIMGvBkjIhEIowaNQpBQUH45ZdfwGazKRdeZ2dnzJ0719BL7JT4+Hjs3bsXJSUlhl4KQy+htx1qH0MkEsHGxsbQy9A7IpEIISEheO2116g4VQbNsLa2xtatWzF//nyqZuLs7GwU+SZ8Ph/Ozs6GXgaDFmB2KDRi2bJlsLW1xbfffksZCJIWEH3FJpvFYjE7lD5EcXExIiIi8OWXXxplHgtDa5gaCo1Yv349fvvtN0gkEgCgBqpqamr6hJgwGC89yYGvrKzE9OnTsXjxYkZMXhAYQaERXl5e8Pb2xrlz5wAAQqEQ33zzDYYMGYK9e/caeHUMDJ3zzjvv4NGjR13+N2TIEOrxVVVVmDhxIkaPHo0ffvjBgCtn0CZMDYUmqFQqDBkyBI6OjsjPzwefz8df/vIXFBUV4csvv8S6desAMAlxDPTE1dUVrq6u3XpsZWUlJk6ciIiICBw4cICxgn+BYP6SNIH8UK1fvx6///47AgMDwePxcOTIEbzxxhsAWkSnIzEhCEJjjycGBkNQWVmJCRMmYODAgfjyyy/B5XJRU1PDuFy/IDA7FBpAFt6rq6uRl5eHgoICrFy5Ert27WrlbtrZnRwpMiqVinJHZWCgI5cvX0ZxcTGKi4sxYMCAVv/G3BQZP8wOxcAolUqYmJigtLSUyrUGgKioKDg5OUEul3f6vQqFAufOncOhQ4dQWVkJExMToxQToVCIzMxMZGZmAgCePn2KzMxMlJeXG3ZhDFqHtIbp6D8G44cRFAPDZrORkpKCcePGwdHREQcOHMBrr72GK1euQKlUwszMrNPvffLkCW7fvo0ff/wRI0aMwKRJk/Dw4cN2j+vqw0qHD3JqairCwsIQFhYGAHj77bcRFhaGrVu3GnhlDAwMmsDMoRgQhUKB9evX4/z585g5cyZ2794NMzMznD9/HsuXL0d5eTns7e07/X6CICCTyWBhYQEul4tXXnkFHh4e2Lt3L8zNzcHn8yEWi+Hp6dkqB5usxZBHZYcOHcKKFSu6FC8GBgaG58HsUAwIi8VCUFAQdu7cie+++w5mZmYgCAL+/v7w8PCg2oc7QqVSoaioCD///DOuX78OV1dXxMfH4/z585SFBYfDQUxMDH755ZdW9RcTExNKTGpra7F9+3bcvXtXt7+skfD5558jKioKdnZ2cHNzQ1xcHAoLCw29LAYGo4DZodAMsi149OjRCAkJwd69eztsFf7qq6+wY8cO+Pn5oba2Fg0NDRg+fDgePHiA5uZmSkAWL14MGxsbHDx4EADw008/gcPhYN26dbCzs2u1a2HaN4Hp06dj2bJliIqKgkKhwJYtW5Cbm4v8/Pw+aYvDwKARBAMtuXz5MrF+/foO/625uZnw9vYmtm3bRtTW1hJNTU3ErVu3iMGDBxNz5swhFAoFoVQqCYIgiDNnzhCurq5EYWEh8a9//YuwtLQkNm7cSPD5fIIgCGL79u1EVVUVQRAE9T0EQRByubzV/++rcDgcAgCRnJxs6KUwMNAepm2YpkyePBmTJ0/u8N8kEgkaGxvRv39/uLm5AWgZLBOJRBg/fjzYbDZUKhUAYNiwYRgxYgTmz58PMzMzJCQkYP78+QCAvLw8fPTRR4iMjISnpydMTEzA4XDg5uYGU1PmrQG0GBcCYMwLGRi6AXPVMEJsbGzw5ptv4sMPP8TTp0/h6emJr776ChKJBDNmzADw58zK77//jps3b2LEiBE4cuQIRowYAaVSCTabjcOHDyMgIAAxMTEQCoVISEjAzz//jPLycowbNw7/+Mc/EB0dbchf1aCoVCq89dZbGDNmDAIDAw29HAYG2sMcmhshZmZm+OSTT/DNN98gLy8PbDYbbm5u8PHxweDBgwG0eCWtW7cOX331FdatWwcWi4URI0YA+HMQ8ujRo5g6dSocHR1x+vRpxMfHY+rUqfjxxx+hUqnw7rvv4saNG4b6NQ3O+vXrkZubi6NHjxp6KQwMRgGzQzFSTExMsGLFCqxYsQIAEBQUBC6XCysrKxw6dAifffYZPDw8kJSUBFdXV5w6dYqadzExMUFJSQnKysqoHY2TkxMIgsDKlSvh6+uLKVOm4Pz585RA9TU2bNiAM2fOICUlpd1ENwMDQ8cwgvKCMG7cOOp/+/v749VXX8Xf/vY3uLi4AAD69euHmzdvUo87ePAg/Pz8qF1LUFAQvL29sXDhQvzjH//A6tWrKbHpSxAEgY0bNyIpKQk3btzos4LKwNATmCOvF5BRo0bhgw8+oMQEABYtWoQDBw6Ay+UCAI4dO4ZZs2bBw8MDQIt1/rlz57By5Up8//33+O9//wsAVHG/r7B+/XocPnwYCQkJsLOzo4wLm5ubDb00gzJ37lwMHDgQlpaW8PT0xF/+8hdUVVUZelkMNIOZQ+lDpKenIzw8HBUVFRg0aBD++OMPzJ07FwcOHEBMTAwCAgJAEAR+/PFHfPDBB7hw4QIiIiIMvWy90lk0wIEDB4wiTldX7Ny5E7GxsfD09ERlZSX++c9/AgDu3Llj4JUx0AlGUPoYZIdXdnY2Bg8eDBaLhQ8//BAlJSV49dVXMWbMGPz+++/YsGEDKioq4OXlZeglM9CQU6dOIS4uDlKplLHsYaBgBIUBpaWl2L17NxISEmBiYgI/Pz9ER0djx44dzAQ9Qzvq6+uxbt06VFZW4tatW4ZeDgONYASFoRWFhYWwsLCAj4+PoZfCAGDv3r3Yu3cvSktLAQAjR47E1q1bDdIw8f7772PPnj0Qi8UYNWoUzpw5g379+ul9HQz0hbn1ZGiFv78/IyY0YsCAAfjPf/6DtLQ0pKam4uWXX8a8efOQl5fX65+9efNmynW6s/8KCgqox7/77rvIyMjApUuXwGazsWrVKlrEHzDQB2aHwsBgZDg7OyM+Ph5r1qzp1c/hcrng8XhdPmbIkCEwNzdv9/Vnz57B29sbd+7cQWxsbK/WwfDiwMyhMDAYCUqlEsePH4dIJNLKRdzV1RWurq49+l6ynVwqlfZ6HQwvDoygMDDQnJycHMTGxkIikcDW1hZJSUkICAjQ2/Pfv38fDx8+xEsvvQQnJyc8efIEH330EXx9fZndCUMrmBoKAwPN8ff3R2ZmJu7fv49169Zh9erVyM/P19vzW1tbIzExEZMmTYK/vz/WrFmD4OBgJCcnw8LCQm/rYKA/TA2FgcHImDx5Mnx9fbFv3z5DL4WBoRXMDoWBwchQqVRM7YKBljA1FAYGGvPBBx9gxowZGDhwIAQCARISEnDjxg1cvHjR0EtjYGgHIygMDDSGw+Fg1apVqK6uhoODA4KDg3Hx4kVMmTLF0EtjYGgHU0NhYGBgYNAKTA2FgYGBgUErMILCwMDAwKAVGEFhYGBgYNAKjKAwMDAwMGgFRlAYGBgYGLQCIygMDAwMDFqBERQGBgYGBq3ACAoDAwMDg1ZgBIWBgYGBQSswgsLAwMDAoBUYQWFgYGBg0AqMoDAwMDAwaIX/DxHw/MvImT4kAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"rank of C\",matrix_rank(C))\n",
    "plot_matrix_and_subspace(C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding LoRA in PyTorch\n",
    "\n",
    "LoRA (Low-Rank Adaptation) is relatively simple to initialize in PyTorch. You initialize LoRA with the dimensions of the input (`in_dim`), $ m $, output (`out_dim`), $n $, a rank (`rank`), $ r $, and a scaling factor `alpha`. The parameters are initialized as follows:\n",
    "\n",
    "```\n",
    "self.A = torch.nn.Parameter(torch.randn(in_dim, rank) * std_dev)\n",
    "self.B = torch.nn.Parameter(torch.zeros(rank, out_dim))\n",
    "```\n",
    "\n",
    "The use of  ```nn.Parameter``` makes these values learnable parameters.\n",
    "\n",
    "In the forward function, LoRA uses the notation $BAx$ PyTorch, the input vector is a row, so the output becomes $x^TA^TB^T$ will drop the trapose from now on. The forward pass is implemented as:\n",
    "```\n",
    "x = self.alpha * (x @ self.A @ self.B)\n",
    "```\n",
    "The use of  ```nn.Parameter``` makes these values learnable parameters.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LoRALayer(torch.nn.Module):\n",
    "    def __init__(self, in_dim, out_dim, rank, alpha):\n",
    "        super().__init__()\n",
    "        std_dev = 1 / torch.sqrt(torch.tensor(rank).float())\n",
    "        self.A = torch.nn.Parameter(torch.randn(in_dim, rank) * std_dev)\n",
    "        \n",
    "        self.B = torch.nn.Parameter(torch.zeros(rank, out_dim))\n",
    "        self.alpha = alpha\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.alpha * (x @ self.A @ self.B)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This class ```LinearWithLoRA```  copies the original linear model and creates a ```LoRALayer``` object. \n",
    "```\n",
    "self.linear = linear.to(device)\n",
    " self.lora = LoRALayer(\n",
    "            linear.in_features, linear.out_features, rank, alpha\n",
    "        ).to(device)\n",
    "```\n",
    "\n",
    "Then, in the forward method apply both the original linear model and the output Lora model to the input x and add them together ```self.linear(x) + self.lora(x)```. This corresponds to:\n",
    "\n",
    " $xW_0 + xAB $\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearWithLoRA(torch.nn.Module):\n",
    "    def __init__(self, linear, rank, alpha):\n",
    "        super().__init__()\n",
    "        self.linear = linear.to(device)\n",
    "        self.lora = LoRALayer(\n",
    "            linear.in_features, linear.out_features, rank, alpha\n",
    "        ).to(device)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \n",
    "        return self.linear(x) + self.lora(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying LoRA\n",
    "To fine-tune with LoRA, first, load a pretrained TextClassifier model with LoRA (while freezing its layers), load its pretrained state from a file, and then disable gradient updates for all of its parameters to prevent further training. Here, you will load a model that was pretrained on the AG NEWS dataset, which is a dataset that has 4 classes. Note that when you initialize this model, you set `num_classes` to 4. Moreover, the pretrained AG_News model was trained with the embedding layer unfrozen. Hence you will initialize the model with `freeze=False`. Although you are initializing the model with layers unfrozen and the wrong number of classes for your task, you will make modifications to the model later on that correct this:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TextClassifier(\n",
       "  (embedding): Embedding(400000, 100)\n",
       "  (fc1): Linear(in_features=100, out_features=128, bias=True)\n",
       "  (relu): ReLU()\n",
       "  (fc2): Linear(in_features=128, out_features=4, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from urllib.request import urlopen\n",
    "import io\n",
    "\n",
    "model_lora=TextClassifier(num_classes=4,freeze=False)\n",
    "model_lora.to(device)\n",
    "\n",
    "urlopened = urlopen('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/uGC04Pom651hQs1XrZ0NsQ/my-model-freeze-false.pth')\n",
    "\n",
    "stream = io.BytesIO(urlopened.read())\n",
    "state_dict = torch.load(stream, map_location=device)\n",
    "model_lora.load_state_dict(state_dict)\n",
    "\n",
    "# Here, you freeze all layers:\n",
    "for parm in model_lora.parameters():\n",
    "    parm.requires_grad=False\n",
    "model_lora"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the `for` loop in the above code froze all of the layers in the neural network, including the embedding layer.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, note that the original model was on a classification problem that had four classes, while the IMDB dataset has just 2 classes. To account for this, you will replace the final layer with a new linear layer where the number of outputs equals 2:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TextClassifier(\n",
       "  (embedding): Embedding(400000, 100)\n",
       "  (fc1): Linear(in_features=100, out_features=128, bias=True)\n",
       "  (relu): ReLU()\n",
       "  (fc2): Linear(in_features=128, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lora.fc2=nn.Linear(in_features=128, out_features=2, bias=True).to(device)\n",
    "model_lora"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's view all of the modules in the object.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TextClassifier(\n",
       "  (embedding): Embedding(400000, 100)\n",
       "  (fc1): Linear(in_features=100, out_features=128, bias=True)\n",
       "  (relu): ReLU()\n",
       "  (fc2): Linear(in_features=128, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lora"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your task now is to replace the hidden layer with a LoRA layer. You can access the hidden layer as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear(in_features=100, out_features=128, bias=True)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lora.fc1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following replaces this layer with a LoRA layer:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lora.fc1=LinearWithLoRA(model_lora.fc1,rank=2, alpha=0.1).to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the hidden layer again to ensure that it is indeed converted to a LoRA layer.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearWithLoRA(\n",
       "  (linear): Linear(in_features=100, out_features=128, bias=True)\n",
       "  (lora): LoRALayer()\n",
       ")"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lora.fc1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, training the model is similar, with the only difference being that, except for the output layer, only the learnable parameters \n",
    "```A``` and  ```B``` will be updated. The code to select the values for  `r` and `alpha`, which is not run, is nonetheless provided herein for your convenience.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TextClassifier(\n",
       "  (embedding): Embedding(400000, 100)\n",
       "  (fc1): LinearWithLoRA(\n",
       "    (linear): Linear(in_features=100, out_features=128, bias=True)\n",
       "    (lora): LoRALayer()\n",
       "  )\n",
       "  (relu): ReLU()\n",
       "  (fc2): Linear(in_features=128, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lora.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary><b>Click here to see code to select r and alpha</b></summary>\n",
    "    \n",
    "```python \n",
    "ranks = [1, 2, 5, 10]\n",
    "alphas = [0.1, 0.5, 1.0, 2.0, 5.0]\n",
    "\n",
    "results=[]\n",
    "accuracy_old=0\n",
    "# Loop over each combination of 'r' and 'alpha'\n",
    "for r in ranks:\n",
    "    for alpha in alphas:\n",
    "        print(f\"Testing with rank = {r} and alpha = {alpha}\")\n",
    "        model_name=f\"model_lora_rank{r}_alpha{alpha}_AGtoIBDM_final_adam_\"\n",
    "        \n",
    "        model_lora=TextClassifier(num_classes=4,freeze=False)\n",
    "        model_lora.to(device)\n",
    "        \n",
    "        urlopened = urlopen('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/uGC04Pom651hQs1XrZ0NsQ/my-model-freeze-false.pth')\n",
    "        \n",
    "        stream = io.BytesIO(urlopened.read())\n",
    "        state_dict = torch.load(stream, map_location=device)\n",
    "        model_lora.load_state_dict(state_dict)\n",
    "        \n",
    "        for parm in model_lora.parameters():\n",
    "            parm.requires_grad=False\n",
    "        \n",
    "        model_lora.fc2=nn.Linear(in_features=128, out_features=2, bias=True)\n",
    "        model_lora.fc1=LinearWithLoRA(model_lora.fc1,rank=r, alpha=alpha )\n",
    "        optimizer = torch.optim.Adam(model_lora.parameters(), lr=LR)\n",
    "\n",
    "        scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.1)\n",
    "        \n",
    "        model_lora.to(device)\n",
    "        \n",
    "        \n",
    "        \n",
    "\n",
    "        \n",
    "        train_model(model_lora, optimizer, criterion, train_dataloader, valid_dataloader, epochs=300, model_name=model_name)\n",
    "        \n",
    "        accuracy=evaluate(valid_dataloader ,  model_lora, device)\n",
    "        result = {\n",
    "            'rank': r,\n",
    "            'alpha': alpha,\n",
    "            'accuracy':accuracy\n",
    "        }\n",
    "\n",
    "        # Append the dictionary to the results list\n",
    "        results.append(result)\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "\n",
    "        if accuracy>accuracy_old:\n",
    "            print(f\"Testing with rank = {r} and alpha = {alpha}\")\n",
    "            print(f\"accuracy: {accuracy} accuracy_old: {accuracy_old}\" )\n",
    "            accuracy_old=accuracy\n",
    "            torch.save(model.state_dict(), f\"{model_name}.pth\")\n",
    "            save_list_to_file(cum_loss_list, f\"{model_name}_loss.pkl\")\n",
    "            save_list_to_file(acc_epoch, f\"{model_name}_acc.pkl\")\n",
    "            \n",
    "        \n",
    "```       \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Let's set up the training components for the `model_lora` model, defining a learning rate of 1, using cross-entropy loss as the criterion, optimizing with stochastic gradient descent (SGD), and scheduling the learning rate to decay by a factor of 0.1 at each epoch:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR=1\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(model_lora.parameters(), lr=LR)\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have pretrained a model using an identical procedure for 300 epochs and saved it for your convenience. However, to give you a taste of how training works in practice, run the following code to train the model for just 2 epochs.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:38<00:00, 19.21s/it]\n"
     ]
    }
   ],
   "source": [
    "model_name=\"model_lora_final2\"\n",
    "train_model(model_lora,optimizer, criterion, train_dataloader, valid_dataloader, epochs=2, model_name=model_name)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of evaluating the model you just trained for 2 epochs, lets have a look at the LoRA model pretrained on 300 epochs:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "%%capture \n",
    "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/JWPRb1RMhKLRMUWOKw9pxA/model-lora-final2.pth\n",
    "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/_dm02rLyTrwsXEQh2r32sQ/model-lora-final2-acc.pkl\n",
    "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/OZbVqKjoqOSIwnET8AB1KA/model-lora-final2-loss.pkl\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following shows the progression of the training of this model for 300 epochs:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cum_loss_list=load_list_from_file(model_name.replace('_','-') + \"-loss.pkl\")\n",
    "acc_epoch=load_list_from_file(model_name.replace('_','-') + \"-acc.pkl\")\n",
    "plot(cum_loss_list,acc_epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load actually load the model into model_lora:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TextClassifier(\n",
       "  (embedding): Embedding(400000, 100)\n",
       "  (fc1): LinearWithLoRA(\n",
       "    (linear): Linear(in_features=100, out_features=128, bias=True)\n",
       "    (lora): LoRALayer()\n",
       "  )\n",
       "  (relu): ReLU()\n",
       "  (fc2): Linear(in_features=128, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lora.load_state_dict(torch.load(model_name.replace('_','-') + \".pth\", map_location=device))\n",
    "model_lora.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And, let's evaluate its performance on the test data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 69.16$"
      ],
      "text/plain": [
       "69.16"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate(test_dataloader , model_lora, device)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You get a 3% improvement over a model trained from scratch by using LoRA. Note that this occurs despite the fact that the model fine-tuned with LoRA updated less parameters than the model trained from scratch!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ```model_lora.fc1``` attribute represents ```LinearWithLoRA``` which contains both the standard ```Linear``` layer ``(linear)`` and an additional ```LoRA``` layer ```(lora)``` which represents the ```LoRALayer```.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearWithLoRA(\n",
       "  (linear): Linear(in_features=100, out_features=128, bias=True)\n",
       "  (lora): LoRALayer()\n",
       ")"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lora.fc1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From ```model_lora.fc1.lora```, you can obtain the learnable parameters A and B.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B Parameter containing:\n",
      "tensor([[-4.3192e-01, -1.1071e+00,  2.4456e-01, -3.1031e-02, -2.0012e-01,\n",
      "         -6.7811e-01, -1.3552e-01, -2.7458e-01,  3.2278e-02,  6.7592e-02,\n",
      "          8.3020e-01,  1.1610e-05, -1.0894e-01,  7.7830e-05, -1.6789e-01,\n",
      "         -1.3309e-01, -5.1875e-01,  2.1928e-02, -6.5869e-02, -3.5834e-01,\n",
      "         -2.4473e-02, -1.1260e+00, -8.8752e-02, -7.0861e-03, -1.3263e-02,\n",
      "          0.0000e+00, -6.9039e-01, -8.6471e-02, -3.9146e-01, -2.2644e-01,\n",
      "         -8.7611e-01, -7.9929e-01,  0.0000e+00,  3.9646e-01,  5.2164e-01,\n",
      "         -4.2730e-01,  2.3550e-01,  4.0447e-02,  2.3289e-01, -4.5217e-01,\n",
      "          1.7721e-03, -4.7263e-01, -2.4343e-01,  6.3737e-01,  0.0000e+00,\n",
      "          2.6904e-03, -7.8828e-01,  2.2559e-02, -4.3776e-02,  3.0909e-01,\n",
      "         -1.6914e-01, -2.0294e-01, -4.2175e-01,  7.8840e-01, -3.1771e-01,\n",
      "         -2.0639e-01,  1.1487e-02, -5.7238e-01,  1.4071e-01, -2.8561e-01,\n",
      "          1.1753e-01, -1.6501e-04, -4.5406e-01,  0.0000e+00,  6.4464e-05,\n",
      "         -4.4552e-01, -1.4372e-01,  5.2899e-02, -9.7813e-01, -4.5834e-01,\n",
      "         -6.0968e-01,  5.5418e-02, -7.8054e-01, -1.2806e-01, -1.2291e-01,\n",
      "         -1.4927e-01,  1.1984e-01, -4.9344e-02, -4.0802e-01,  0.0000e+00,\n",
      "          1.0085e-01,  0.0000e+00, -1.1553e+00, -1.1726e-01,  0.0000e+00,\n",
      "         -1.5980e-01,  1.0715e-01, -6.6047e-01, -4.0122e-01, -3.3328e-01,\n",
      "          0.0000e+00, -8.4713e-05, -1.0361e+00, -2.0651e-01,  4.8110e-01,\n",
      "         -1.1648e+00, -1.3535e+00,  3.9261e-01, -5.9634e-04,  0.0000e+00,\n",
      "         -2.3484e-02, -2.9119e-02,  4.8014e-03, -9.4629e-02, -2.8612e-01,\n",
      "         -2.1896e-02, -3.7316e-01,  3.3309e-02, -1.2363e-01, -1.7977e-01,\n",
      "          4.3949e-05, -2.0580e-01,  4.9675e-01, -3.3358e-03,  2.3369e-01,\n",
      "         -2.5254e-01,  8.1739e-03,  1.1209e-01, -1.7311e-01, -8.0624e-02,\n",
      "         -1.3280e+00,  7.7398e-01, -1.2380e+00, -4.4729e-01, -6.5231e-01,\n",
      "         -3.0026e-01,  8.8543e-02, -1.9287e-04],\n",
      "        [ 2.8719e-01,  4.5585e-01, -2.3756e-01, -4.2161e-02,  1.0720e-01,\n",
      "         -8.3839e-01,  2.5098e-01, -4.7875e-01, -6.7113e-02, -1.8940e-01,\n",
      "         -9.7494e-01,  1.5431e-05, -1.9587e-01,  2.8327e-04,  1.3258e-01,\n",
      "         -1.4904e-01,  7.6676e-01, -1.9980e-01, -5.4798e-02, -5.3408e-02,\n",
      "         -1.8818e-01,  7.9588e-01,  9.6087e-02,  2.5530e-02, -7.2462e-01,\n",
      "          0.0000e+00, -1.4258e+00, -5.9680e-02,  1.7253e-03,  4.7575e-02,\n",
      "          4.2872e-01,  1.7875e-02,  0.0000e+00, -1.6925e-01,  1.6071e+00,\n",
      "         -7.2869e-01, -2.5870e-01,  2.7839e-02, -3.0237e-01,  6.9624e-01,\n",
      "         -2.8908e-04,  2.9164e-01,  4.7389e-02,  2.8530e-01,  0.0000e+00,\n",
      "         -5.9235e-02, -6.8176e-02, -4.6494e-02, -8.1741e-02, -6.3252e-01,\n",
      "          2.4469e-02,  7.2494e-02,  8.2158e-02,  6.3870e-01,  2.1799e-01,\n",
      "          7.5177e-01,  3.1872e-02,  1.4100e-01, -2.3390e-01,  3.3391e-01,\n",
      "         -3.8685e-01, -4.5463e-03,  1.4543e-01,  0.0000e+00,  3.8082e-05,\n",
      "          3.0410e-01,  4.7614e-02, -1.5299e-01, -3.4549e-01, -2.3156e-01,\n",
      "          2.2711e-01, -1.0894e-01,  1.7479e+00,  5.1043e-03,  1.4021e-02,\n",
      "          1.9368e-02, -1.0875e-01,  9.7929e-03, -7.4530e-02,  0.0000e+00,\n",
      "          1.9233e-02,  0.0000e+00,  5.1489e-01, -2.0680e-01,  0.0000e+00,\n",
      "          1.8348e-01,  5.8013e-01,  2.9426e-01, -8.2005e-01,  2.2163e-01,\n",
      "          0.0000e+00,  1.8530e-04,  5.1081e-01,  1.1597e-01, -4.4384e-01,\n",
      "          6.3387e-01,  9.7338e-01, -6.0469e-01, -6.5377e-02,  0.0000e+00,\n",
      "         -7.6416e-02, -4.1226e-02,  1.3478e-01, -2.0853e-01,  2.4558e-01,\n",
      "         -1.0312e-01,  4.7792e-01,  4.1285e-02, -4.4301e-02,  3.2861e-02,\n",
      "          6.9632e-04,  4.0895e-02,  1.1403e-01,  4.4740e-03, -2.8703e-01,\n",
      "          3.8271e-01, -2.0000e-03, -5.1493e-02,  2.4292e-01, -2.3110e-01,\n",
      "          1.1481e+00, -1.6331e+00,  5.0157e-01, -1.3528e-01,  3.8709e-01,\n",
      "          1.4637e-01, -1.2716e-01, -6.3597e-05]], requires_grad=True)\n",
      "\n",
      " Number of elements in the tensor B 256\n"
     ]
    }
   ],
   "source": [
    "B=model_lora.fc1.lora.B\n",
    "print(\"B\",B)\n",
    "print(\"\\n Number of elements in the tensor B\",B.numel())\n",
    "torch.save(B, 'B.pth')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A Parameter containing:\n",
      "tensor([[ 3.2952e-01,  9.3019e-01],\n",
      "        [ 2.3904e+00, -5.1022e+00],\n",
      "        [-2.4573e-01,  2.4733e+00],\n",
      "        [ 5.8014e-01,  3.9014e-01],\n",
      "        [ 1.6970e+00, -2.4614e+00],\n",
      "        [-1.2420e+00,  8.3014e-01],\n",
      "        [-2.0468e+00,  1.1629e+00],\n",
      "        [-5.9361e-01,  1.8099e-01],\n",
      "        [ 9.2466e-02,  1.1583e-01],\n",
      "        [-2.0841e-02,  1.5550e+00],\n",
      "        [ 1.3028e+00, -9.8381e-01],\n",
      "        [ 1.4320e+00, -3.3497e+00],\n",
      "        [-1.1637e+00,  1.9436e+00],\n",
      "        [ 2.4898e-01, -1.1353e+00],\n",
      "        [ 2.4423e+00,  1.0154e+00],\n",
      "        [ 2.4881e+00, -4.6765e+00],\n",
      "        [-1.8985e-01,  1.3426e+00],\n",
      "        [-1.1730e-01, -2.1925e+00],\n",
      "        [ 2.0193e+00, -8.5886e-01],\n",
      "        [-3.1268e+00,  3.5134e+00],\n",
      "        [ 1.0935e+00, -2.9263e+00],\n",
      "        [-1.0435e+00,  2.5428e-01],\n",
      "        [-8.6704e-01,  2.3570e+00],\n",
      "        [-5.2725e-02, -2.3731e-01],\n",
      "        [-4.3896e+00,  5.0369e+00],\n",
      "        [-1.1174e+00,  9.9728e-01],\n",
      "        [ 4.7710e-01, -1.2162e+00],\n",
      "        [ 3.3296e+00, -5.9627e+00],\n",
      "        [ 1.7393e+00, -2.0707e+00],\n",
      "        [-4.0115e-01,  2.2691e+00],\n",
      "        [ 4.8368e-04,  1.1773e+00],\n",
      "        [ 1.9858e+00, -1.6100e+00],\n",
      "        [ 6.9951e-01, -1.2869e+00],\n",
      "        [-2.6380e-01,  2.5743e+00],\n",
      "        [-3.9002e+00,  2.6775e+00],\n",
      "        [-2.1946e+00,  3.7126e+00],\n",
      "        [-3.2268e-01,  1.7346e-01],\n",
      "        [ 4.7590e+00, -2.9553e+00],\n",
      "        [-6.6704e-01,  8.1369e-01],\n",
      "        [ 1.0073e+00,  3.6606e-01],\n",
      "        [ 1.3186e-01, -3.4151e+00],\n",
      "        [-6.6832e-01,  3.7761e-01],\n",
      "        [-3.0825e-01,  1.1928e+00],\n",
      "        [ 1.8897e+00, -1.2788e+00],\n",
      "        [-1.4344e+00, -2.9294e-01],\n",
      "        [ 2.1678e+00, -2.5469e+00],\n",
      "        [-6.6993e-01,  2.2932e+00],\n",
      "        [-3.1326e+00,  4.3424e+00],\n",
      "        [ 4.2197e+00, -7.1153e+00],\n",
      "        [ 7.1775e-01, -4.0832e-01],\n",
      "        [-1.6389e+00,  1.4520e+00],\n",
      "        [-1.6150e+00,  2.4683e+00],\n",
      "        [ 1.5745e+00, -4.6085e+00],\n",
      "        [ 6.0900e-01, -1.0851e+00],\n",
      "        [-1.2270e+00,  4.0171e-01],\n",
      "        [-2.6879e-01,  1.5706e+00],\n",
      "        [ 2.0390e+00, -1.8152e+00],\n",
      "        [-1.2317e+00,  6.0501e-01],\n",
      "        [ 4.1927e-02, -2.4078e+00],\n",
      "        [ 1.4265e+00, -9.2525e-01],\n",
      "        [ 5.9394e-01, -7.4587e-01],\n",
      "        [ 6.1463e-01, -2.6220e-01],\n",
      "        [ 6.7605e-01, -2.1740e-01],\n",
      "        [-5.9436e-01,  9.3632e-01],\n",
      "        [ 2.8933e-01, -1.3208e-02],\n",
      "        [-2.0206e+00,  3.9333e+00],\n",
      "        [ 2.4903e+00, -1.1771e+00],\n",
      "        [ 9.5112e-01, -1.7163e+00],\n",
      "        [ 1.7097e+00, -8.8835e-01],\n",
      "        [-3.4182e+00,  2.9024e+00],\n",
      "        [ 1.3908e+00, -1.7813e+00],\n",
      "        [-1.9825e+00,  2.1889e+00],\n",
      "        [ 3.2187e-02, -1.7187e-01],\n",
      "        [ 2.3456e+00, -3.3756e+00],\n",
      "        [-1.3550e+00,  3.8261e+00],\n",
      "        [-2.2111e+00,  1.7149e+00],\n",
      "        [-1.6745e+00,  2.1577e+00],\n",
      "        [-2.0270e+00,  3.8298e+00],\n",
      "        [ 1.6872e+00, -1.8343e+00],\n",
      "        [-1.3611e+00,  8.4912e-01],\n",
      "        [-1.5436e+00,  2.8195e+00],\n",
      "        [-8.4964e-01,  1.7983e+00],\n",
      "        [ 1.7100e+00, -6.1945e-01],\n",
      "        [ 1.7025e+00, -3.0842e+00],\n",
      "        [ 1.5122e+00,  7.2826e-01],\n",
      "        [ 9.1957e-01, -2.4107e+00],\n",
      "        [-2.5545e+00,  4.1697e+00],\n",
      "        [ 1.7832e-01, -2.9886e+00],\n",
      "        [-4.0698e+00,  5.4074e+00],\n",
      "        [ 1.3872e+00, -2.1134e+00],\n",
      "        [-3.7505e+00,  4.7124e+00],\n",
      "        [-1.6772e+00,  3.0610e+00],\n",
      "        [-7.6127e-01,  7.7868e-01],\n",
      "        [-5.1529e-01,  7.3124e-02],\n",
      "        [ 2.5028e+00, -5.3530e-01],\n",
      "        [ 2.1893e+00, -2.8006e+00],\n",
      "        [-1.7899e+00,  1.7446e+00],\n",
      "        [-1.1793e+00,  4.6499e+00],\n",
      "        [ 1.1547e+00,  1.7930e+00],\n",
      "        [-1.7421e-01,  2.7170e+00]], requires_grad=True)\n",
      "\n",
      " Number of elements in the tensor A 200\n"
     ]
    }
   ],
   "source": [
    "A=model_lora.fc1.lora.A\n",
    "print(\"A\",A)\n",
    "print(\"\\n Number of elements in the tensor A\",A.numel())\n",
    "torch.save(A, 'A.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A and B have approximately 450 parameters. If you were to store the entire linear layer, you would have 12,800 parameters, which is around 28 times more. Remember, this is possibly the simplest model that you can have.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Number of elements in the tensor A 12800\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"\\n Number of elements in the tensor A\",model_lora.fc1.linear.weight.numel())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " alfa and the ouput layer  are also saved.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "alfa_=model_lora.fc1.lora.alpha\n",
    "torch.save(alfa_, 'alfa_.pth')\n",
    "torch.save(model_lora.fc2.state_dict(), 'out_layer.pth')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the model\n",
    "\n",
    "The main advantage of LoRA is that for fine-tuning, you only need to save the learnable parameters A and B, alpha, and the output layer in your classification example.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The saved files are converted to tensors and the linear layer, respectively.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A: torch.Size([100, 2])\n"
     ]
    }
   ],
   "source": [
    "A = torch.load('A.pth')\n",
    "print(\"A:\",A.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B: torch.Size([2, 128])\n"
     ]
    }
   ],
   "source": [
    "B = torch.load('B.pth')\n",
    "print(\"B:\",B.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 0.1$"
      ],
      "text/plain": [
       "0.1"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alfa_ = torch.load('alfa_.pth')\n",
    "alfa_ \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output layer:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_layer=nn.Linear(in_features=128, out_features=2, bias=True)\n",
    "output_layer.load_state_dict(torch.load('out_layer.pth'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model object is created and the pretrained parameters are loaded:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TextClassifier(\n",
       "  (embedding): Embedding(400000, 100)\n",
       "  (fc1): Linear(in_features=100, out_features=128, bias=True)\n",
       "  (relu): ReLU()\n",
       "  (fc2): Linear(in_features=128, out_features=4, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model_load_lora = TextClassifier(num_classes=4,freeze=False)\n",
    "model_load_lora.to(device)\n",
    "\n",
    "urlopened = urlopen('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/uGC04Pom651hQs1XrZ0NsQ/my-model-freeze-false.pth')\n",
    "\n",
    "stream = io.BytesIO(urlopened.read())\n",
    "state_dict = torch.load(stream, map_location=device)\n",
    "model_load_lora.load_state_dict(state_dict)\n",
    "\n",
    "model_load_lora"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The LoRA layer object is added to the original hidden layer.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_load_lora.fc1=LinearWithLoRA(model_load_lora.fc1,rank=2, alpha=0.1)\n",
    "model_load_lora.fc2=nn.Linear(in_features=128, out_features=2, bias=True).to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameters from fine-tuning are added.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_load_lora.fc1.lora.A=A\n",
    "model_load_lora.fc1.lora.B=B\n",
    "model_load_lora.fc1.lora.alpha=alfa_ \n",
    "model_load_lora.fc2=output_layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TextClassifier(\n",
       "  (embedding): Embedding(400000, 100)\n",
       "  (fc1): LinearWithLoRA(\n",
       "    (linear): Linear(in_features=100, out_features=128, bias=True)\n",
       "    (lora): LoRALayer()\n",
       "  )\n",
       "  (relu): ReLU()\n",
       "  (fc2): Linear(in_features=128, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_load_lora.to(device)\n",
    "model_load_lora.eval()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 69.132$"
      ],
      "text/plain": [
       "69.132"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate(test_dataloader , model_load_lora, device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This confirms that the model loaded correctly. You still get a 3% improvement in accuracy!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the following shows how you can make a prediction on the following article using the function **`predict`**:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "article=\"\"\"This was a lacklustre movie with very little going for it. I was not impressed.\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This markdown content generates a styled box with light gray background and padding. It contains an `<h3>` header displaying the content of the `article` variable, and an `<h4>` header indicating the predicted category of the news article which is provided by the `result` variable. The placeholders `{article}` and `{result}` will be dynamically replaced with actual values when this markdown is rendered.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "<div style=\"background-color: lightgray; padding: 10px;\">\n",
       "    <h3>This was a lacklustre movie with very little going for it. I was not impressed.</h3>\n",
       "    <h4>The category of the news article:  negative review</h4>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = predict(article, model_load_lora, text_pipeline)\n",
    "\n",
    "markdown_content = f'''\n",
    "<div style=\"background-color: lightgray; padding: 10px;\">\n",
    "    <h3>{article}</h3>\n",
    "    <h4>The category of the news article: {result}</h4>\n",
    "</div>\n",
    "'''\n",
    "\n",
    "md(markdown_content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Exercise: Apply LoRA to a different network\n",
    "\n",
    "The following code defines a neural network called `NNet`. \n",
    "\n",
    "`NNet` is a neural network that was originally written to identify hand-written digits from 32x32 images. Your task is to fine-tune this network to perform letter recognition using LoRA by replacing the section labeled `### REPLACE THIS ###` in the code block below. To enhance your understanding, apply LoRA to just the second linear layer, and replace the last layer with a layer that has 26 outputs, one for each letter in the English alphabet.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is what the model looked like before applying LoRA:\n",
      "NNet(\n",
      "  (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (fc1): Linear(in_features=400, out_features=120, bias=True)\n",
      "  (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
      "  (fc3): Linear(in_features=84, out_features=10, bias=True)\n",
      ")\n",
      "\n",
      "###############\n",
      "\n",
      "This is what the model looked like after applying LoRA:\n",
      "NNet(\n",
      "  (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (fc1): Linear(in_features=400, out_features=120, bias=True)\n",
      "  (fc2): LinearWithLoRA(\n",
      "    (linear): Linear(in_features=120, out_features=84, bias=True)\n",
      "    (lora): LoRALayer()\n",
      "  )\n",
      "  (fc3): Linear(in_features=84, out_features=26, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "#TODO\n",
    "\n",
    "class NNet(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(NNet, self).__init__()\n",
    "        # 1 input image channel, 6 output channels, 5x5 square convolution\n",
    "        # kernel\n",
    "        self.conv1 = nn.Conv2d(1, 6, 5)\n",
    "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
    "        # an affine operation: y = Wx + b\n",
    "        self.fc1 = nn.Linear(16 * 5 * 5, 120)  # 5*5 from image dimension\n",
    "        self.fc2 = nn.Linear(120, 84)\n",
    "        self.fc3 = nn.Linear(84, 10)\n",
    "\n",
    "    def forward(self, input):\n",
    "        # Convolution layer C1: 1 input image channel, 6 output channels,\n",
    "        # 5x5 square convolution, it uses RELU activation function, and\n",
    "        # outputs a Tensor with size (N, 6, 28, 28), where N is the size of the batch\n",
    "        c1 = F.relu(self.conv1(input))\n",
    "        # Subsampling layer S2: 2x2 grid, purely functional,\n",
    "        # this layer does not have any parameter, and outputs a (N, 6, 14, 14) Tensor\n",
    "        s2 = F.max_pool2d(c1, (2, 2))\n",
    "        # Convolution layer C3: 6 input channels, 16 output channels,\n",
    "        # 5x5 square convolution, it uses RELU activation function, and\n",
    "        # outputs a (N, 16, 10, 10) Tensor\n",
    "        c3 = F.relu(self.conv2(s2))\n",
    "        # Subsampling layer S4: 2x2 grid, purely functional,\n",
    "        # this layer does not have any parameter, and outputs a (N, 16, 5, 5) Tensor\n",
    "        s4 = F.max_pool2d(c3, 2)\n",
    "        # Flatten operation: purely functional, outputs a (N, 400) Tensor\n",
    "        s4 = torch.flatten(s4, 1)\n",
    "        # Fully connected layer F5: (N, 400) Tensor input,\n",
    "        # and outputs a (N, 120) Tensor, it uses RELU activation function\n",
    "        f5 = F.relu(self.fc1(s4))\n",
    "        # Fully connected layer F6: (N, 120) Tensor input,\n",
    "        # and outputs a (N, 84) Tensor, it uses RELU activation function\n",
    "        f6 = F.relu(self.fc2(f5))\n",
    "        # Gaussian layer OUTPUT: (N, 84) Tensor input, and\n",
    "        # outputs a (N, 10) Tensor\n",
    "        output = self.fc3(f6)\n",
    "        return output\n",
    "\n",
    "model_exercise = NNet()\n",
    "model_exercise.to(device)\n",
    "\n",
    "print('This is what the model looked like before applying LoRA:')\n",
    "print(model_exercise)\n",
    "print(\"\\n###############\\n\")\n",
    "\n",
    "# Freeze all parameters:\n",
    "for parm in model_exercise.parameters():\n",
    "    parm.requires_grad=False\n",
    "\n",
    "# Change final layer for one with 26 outputs:\n",
    "model_exercise.fc3=nn.Linear(in_features=84, out_features=26, bias=True).to(device)\n",
    "\n",
    "# Apply LoRA to the second linear layer\n",
    "model_exercise.fc2=LinearWithLoRA(model_exercise.fc2,rank=2, alpha=0.1).to(device)\n",
    "\n",
    "print('This is what the model looked like after applying LoRA:')\n",
    "print(model_exercise)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "class NNet(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(NNet, self).__init__()\n",
    "        # 1 input image channel, 6 output channels, 5x5 square convolution\n",
    "        # kernel\n",
    "        self.conv1 = nn.Conv2d(1, 6, 5)\n",
    "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
    "        # an affine operation: y = Wx + b\n",
    "        self.fc1 = nn.Linear(16 * 5 * 5, 120)  # 5*5 from image dimension\n",
    "        self.fc2 = nn.Linear(120, 84)\n",
    "        self.fc3 = nn.Linear(84, 10)\n",
    "\n",
    "    def forward(self, input):\n",
    "        # Convolution layer C1: 1 input image channel, 6 output channels,\n",
    "        # 5x5 square convolution, it uses RELU activation function, and\n",
    "        # outputs a Tensor with size (N, 6, 28, 28), where N is the size of the batch\n",
    "        c1 = F.relu(self.conv1(input))\n",
    "        # Subsampling layer S2: 2x2 grid, purely functional,\n",
    "        # this layer does not have any parameter, and outputs a (N, 6, 14, 14) Tensor\n",
    "        s2 = F.max_pool2d(c1, (2, 2))\n",
    "        # Convolution layer C3: 6 input channels, 16 output channels,\n",
    "        # 5x5 square convolution, it uses RELU activation function, and\n",
    "        # outputs a (N, 16, 10, 10) Tensor\n",
    "        c3 = F.relu(self.conv2(s2))\n",
    "        # Subsampling layer S4: 2x2 grid, purely functional,\n",
    "        # this layer does not have any parameter, and outputs a (N, 16, 5, 5) Tensor\n",
    "        s4 = F.max_pool2d(c3, 2)\n",
    "        # Flatten operation: purely functional, outputs a (N, 400) Tensor\n",
    "        s4 = torch.flatten(s4, 1)\n",
    "        # Fully connected layer F5: (N, 400) Tensor input,\n",
    "        # and outputs a (N, 120) Tensor, it uses RELU activation function\n",
    "        f5 = F.relu(self.fc1(s4))\n",
    "        # Fully connected layer F6: (N, 120) Tensor input,\n",
    "        # and outputs a (N, 84) Tensor, it uses RELU activation function\n",
    "        f6 = F.relu(self.fc2(f5))\n",
    "        # Gaussian layer OUTPUT: (N, 84) Tensor input, and\n",
    "        # outputs a (N, 10) Tensor\n",
    "        output = self.fc3(f6)\n",
    "        return output\n",
    "\n",
    "model_exercise = NNet()\n",
    "model_exercise.to(device)\n",
    "\n",
    "print('This is what the model looked like before applying LoRA:')\n",
    "print(model_exercise)\n",
    "print(\"\\n###############\\n\")\n",
    "\n",
    "# Freeze all parameters:\n",
    "for parm in model_exercise.parameters():\n",
    "    parm.requires_grad=False\n",
    "\n",
    "# Change final layer for one with 26 outputs:\n",
    "model_exercise.fc3=nn.Linear(in_features=84, out_features=26, bias=True).to(device)\n",
    "\n",
    "# Apply LoRA to the second linear layer\n",
    "model_exercise.fc2=LinearWithLoRA(model_exercise.fc2,rank=2, alpha=0.1).to(device)\n",
    "\n",
    "print('This is what the model looked like after applying LoRA:')\n",
    "print(model_exercise)\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Congratulations! You have completed the lab\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Joseph Santarcangelo](https://author.skills.network/instructors/joseph_santarcangelo) has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his Ph.D.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Wojciech \"Victor\" Fulmyk](https://www.linkedin.com/in/wfulmyk) is a Data Scientist at IBM, and a PhD Candidate in economics at the University of Calgary.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Ashutosh Sagar](https://www.linkedin.com/in/ashutoshsagar/) is completing his MS in CS from Dalhousie University. He has previous experience working with Natural Language Processing and as a Data Scientist.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "[Finetuning with LoRA -- A Hands-On Example](https://lightning.ai/lightning-ai/studios/code-lora-from-scratch)\n",
    "\n",
    "[TEXT CLASSIFICATION WITH THE TORCHTEXT LIBRARY](https://pytorch.org/tutorials/beginner/text_sentiment_ngrams_tutorial.html)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{## Change Log}\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{|Date (YYYY-MM-DD)|Version|Changed By|Change Description||-|-|-|-||2023-07-27|0.1|Joseph|Created Lab||2024-06-04|1.0|wfulmyk|First release|}\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "© Copyright IBM Corporation. All rights reserved.\n"
   ]
  }
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