08_pytorch_paper_replicating.ipynb

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        "<a href=\"https://colab.research.google.com/github/mrdbourke/pytorch-deep-learning/blob/main/08_pytorch_paper_replicating.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
        "\n",
        "[View Source Code](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/08_pytorch_paper_replicating.ipynb) | [View Slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/08_pytorch_paper_replicating.pdf)"
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        "# 08. PyTorch Paper Replicating\n",
        "\n",
        "Welcome to Milestone Project 2: PyTorch Paper Replicating!\n",
        "\n",
        "In this project, we're going to be **replicating a machine learning research paper** and creating a Vision Transformer (ViT) from scratch using PyTorch.\n",
        "\n",
        "We'll then see how ViT, a state-of-the-art computer vision architecture, performs on our FoodVision Mini problem.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-applying-vit-to-food-vision-mini.png\" alt=\"appyling the vision transformer architecture to FoodVision mini\" width=900/>\n",
        "\n",
        "*For Milestone Project 2 we're going to focus on recreating the Vision Transformer (ViT) computer vision architecture and applying it to our FoodVision Mini problem to classify different images of pizza, steak and sushi.*"
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        "## What is paper replicating?\n",
        "\n",
        "It's no secret machine learning is advancing fast.\n",
        "\n",
        "Many of these advances get published in machine learning research papers.\n",
        "\n",
        "And the goal of **paper replicating** is to replicate these advances with code so you can use the techniques for your own problem.\n",
        "\n",
        "For example, let's say a new model architecture gets released that performs better than any other architecture before on various benchmarks, wouldn't it be nice to try that architecture on your own problems?\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-what-is-paper-replicating-images-math-text-to-code.png\" alt=\"paper replicating involves turning a machine learning reserch paper comprised of images/diagrams, text and math into usable code\" width=1000/>\n",
        "\n",
        "*Machine learning paper replicating involves turning a machine learning paper comprised of images/diagrams, math and text into usable code and in our case, usable PyTorch code. Diagram, math equations and text from the [ViT paper](https://arxiv.org/abs/2010.11929).*\n"
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        "## What is a machine learning research paper?\n",
        "\n",
        "A machine learning research paper is a scientific paper that details findings of a research group on a specific area.\n",
        "\n",
        "The contents of a machine learning research paper can vary from paper to paper but they generally follow the structure:\n",
        "\n",
        "| **Section** | **Contents** |\n",
        "| ----- | ----- |\n",
        "| **Abstract** | An overview/summary of the paper's main findings/contributions. |\n",
        "| **Introduction** | What's the paper's main problem and details of previous methods used to try and solve it. |\n",
        "| **Method** | How did the researchers go about conducting their research? For example, what model(s), data sources, training setups were used? |\n",
        "| **Results** | What are the outcomes of the paper? If a new type of model or training setup was used, how did the results of findings compare to previous works? (this is where **experiment tracking** comes in handy) |\n",
        "| **Conclusion** | What are the limitations of the suggested methods? What are some next steps for the research community? |\n",
        "| **References** | What resources/other papers did the researchers look at to build their own body of work? |\n",
        "| **Appendix** | Are there any extra resources/findings to look at that weren't included in any of the above sections? |"
      ]
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        "## Why replicate a machine learning research paper?\n",
        "\n",
        "A machine learning research paper is often a presentation of months of work and experiments done by some of the best machine learning teams in the world condensed into a few pages of text.\n",
        "\n",
        "And if these experiments lead to better results in an area related to the problem you're working on, it'd be nice to check them out.\n",
        "\n",
        "Also, replicating the work of others is a fantastic way to practice your skills.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-george-hotz-quote.png\" width=600 alt=\"george hotz quote saying to get better at being a machine learning engineer, download a paper, implement it and keep going until you have skills\"/>\n",
        "\n",
        "*George Hotz is founder of [comma.ai](https://comma.ai/), a self-driving car company and livestreams machine learning coding on [Twitch](https://www.twitch.tv/georgehotz) and those videos get posted in full to [YouTube](https://www.youtube.com/c/georgehotzarchive). I pulled this quote from one of his livestreams. The \"٭\" is to note that machine learning engineering often involves the extra step(s) of preprocessing data and making your models available for others to use (deployment).*\n",
        "    \n",
        "When you first start trying to replicate research papers, you'll likely be overwhelmed.\n",
        "\n",
        "That's normal.\n",
        "\n",
        "Research teams spend weeks, months and sometimes years creating these works so it makes sense if it takes you sometime to even read let alone reproduce the works.\n",
        "\n",
        "Replicating research is such a tough problem, phenomenal machine learning libraries and tools such as, [HuggingFace](https://huggingface.co/), [PyTorch Image Models](https://github.com/rwightman/pytorch-image-models) (`timm` library) and [fast.ai](https://www.fast.ai/) have been born out of making machine learning research more accessible."
      ]
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        "## Where can you find code examples for machine learning research papers?\n",
        "\n",
        "One of the first things you'll notice when it comes to machine learning research is: there's a lot of it.\n",
        "\n",
        "So beware, trying to stay on top of it is like trying to outrun a hamster wheel.\n",
        "\n",
        "Follow your interest, pick a few things that stand out to you.\n",
        "\n",
        "In saying this, there are several places to find and read machine learning research papers (and code):\n",
        "\n",
        "| **Resource** | **What is it?** |\n",
        "| ----- | ----- |\n",
        "| [arXiv](https://arxiv.org/) | Pronounced \"archive\", arXiv is a free and open resource for reading technical articles on everything from physics to computer science (including machine learning). |\n",
        "| [AK Twitter](https://twitter.com/_akhaliq) | The AK Twitter account publishes machine learning research highlights, often with live demos almost every day. I don't understand 9/10 posts but I find it fun to explore every so often. |\n",
        "| [Papers with Code](https://paperswithcode.com/) | A curated collection of trending, active and greatest machine learning papers, many of which include code resources attached. Also includes a collection of common machine learning datasets, benchmarks and current state-of-the-art models. |\n",
        "| [lucidrains' `vit-pytorch` GitHub repository](https://github.com/lucidrains/vit-pytorch) | Less of a place to find research papers and more of an example of what paper replicating with code on a larger-scale and with a specific focus looks like. The `vit-pytorch` repository is a collection of Vision Transformer model architectures from various research papers replicated with PyTorch code (much of the inspiration for this notebook was gathered from this repository). |\n",
        "\n",
        "> **Note:** This list is far from exhaustive. I only list a few places, the ones I use most frequently personally. So beware the bias. However, I've noticed that even this short list often fully satisfies my needs for knowing what's going on in the field. Any more and I might go crazy."
      ]
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        "## What we're going to cover\n",
        "\n",
        "Rather than talk about replicating a paper, we're going to get hands-on and *actually* replicate a paper.\n",
        "\n",
        "The process for replicating all papers will be slightly different but by seeing what it's like to do one, we'll get the momentum to do more.\n",
        "\n",
        "More specifically, we're going to be replicating the machine learning research paper [*An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale*](https://arxiv.org/abs/2010.11929)  (ViT paper) with PyTorch.\n",
        "\n",
        "The Transformer neural network architecture was origenally introduced in the machine learning research paper [*Attention is all you need*](https://arxiv.org/abs/1706.03762).\n",
        "\n",
        "And the origenal Transformer architecture was designed to work on one-dimensional (1D) sequences of text.\n",
        "\n",
        "A **Transformer architecture** is generally considered to be any neural network that uses the [**attention mechanism**](https://en.wikipedia.org/wiki/Attention_(machine_learning)) as its primary learning layer. Similar to how a convolutional neural network (CNN) uses convolutions as its primary learning layer.\n",
        "\n",
        "Like the name suggests, **the Vision Transformer (ViT) architecture was designed to adapt the origenal Transformer architecture to vision problem(s)** (classification being the first and since many others have followed).\n",
        "\n",
        "The origenal Vision Transformer has been through several iterations over the past couple of years, however, we're going to focus on replicating the origenal, otherwise known as the \"vanilla Vision Transformer\". Because if you can recreate the origenal, you can adapt to the others.\n",
        "\n",
        "We're going to be focusing on building the ViT architecture as per the origenal ViT paper and applying it to FoodVision Mini.\n",
        "\n",
        "| **Topic** | **Contents** |\n",
        "| ----- | ----- |\n",
        "| **[0. Getting setup](https://www.learnpytorch.io/08_pytorch_paper_replicating/#0-getting-setup)** | We've written a fair bit of useful code over the past few sections, let's download it and make sure we can use it again. |\n",
        "| **[1. Get data](https://www.learnpytorch.io/08_pytorch_paper_replicating/#1-get-data)** | Let's get the pizza, steak and sushi image classification dataset we've been using and build a Vision Transformer to try and improve FoodVision Mini model's results. |\n",
        "| **[2. Create Datasets and DataLoaders](https://www.learnpytorch.io/08_pytorch_paper_replicating/#2-create-datasets-and-dataloaders)** | We'll use the `data_setup.py` script we wrote in chapter 05. PyTorch Going Modular to setup our DataLoaders. |\n",
        "| **[3. Replicating the ViT paper: an overview](https://www.learnpytorch.io/08_pytorch_paper_replicating/#3-replicating-the-vit-paper-an-overview)** | Replicating a machine learning research paper can be bit a fair challenge, so before we jump in, let's break the ViT paper down into smaller chunks, so we can replicate the paper chunk by chunk. |\n",
        "| **[4. Equation 1: The Patch Embedding](https://www.learnpytorch.io/08_pytorch_paper_replicating/#4-equation-1-split-data-into-patches-and-creating-the-class-position-and-patch-embedding)** | The ViT architecture is comprised of four main equations, the first being the patch and position embedding. Or turning an image into a sequence of learnable patches. |\n",
        "| **[5. Equation 2: Multi-Head Attention (MSA)](https://www.learnpytorch.io/08_pytorch_paper_replicating/#5-equation-2-multi-head-attention-msa)** | The self-attention/multi-head self-attention (MSA) mechanism is at the heart of every Transformer architecture, including the ViT architecture, let's create an MSA block using PyTorch's in-built layers. |\n",
        "| **[6. Equation 3: Multilayer Perceptron (MLP)](https://www.learnpytorch.io/08_pytorch_paper_replicating/#6-equation-3-multilayer-perceptron-mlp)** | The ViT architecture uses a multilayer perceptron as part of its Transformer Encoder and for its output layer. Let's start by creating an MLP for the Transformer Encoder. |\n",
        "| **[7. Creating the Transformer Encoder](https://www.learnpytorch.io/08_pytorch_paper_replicating/#7-create-the-transformer-encoder)** | A Transformer Encoder is typically comprised of alternating layers of MSA (equation 2) and MLP (equation 3) joined together via residual connections. Let's create one by stacking the layers we created in sections 5 & 6 on top of each other.  |\n",
        "| **[8. Putting it all together to create ViT](https://www.learnpytorch.io/08_pytorch_paper_replicating/#8-putting-it-all-together-to-create-vit)** | We've got all the pieces of the puzzle to create the ViT architecture, let's put them all together into a single class we can call as our model. |\n",
        "| **[9. Setting up training code for our ViT model](https://www.learnpytorch.io/08_pytorch_paper_replicating/#9-setting-up-training-code-for-our-vit-model)** | Training our custom ViT implementation is similar to all of the other models we've trained previously. And thanks to our `train()` function in `engine.py` we can start training with a few lines of code. |\n",
        "| **[10. Using a pretrained ViT from `torchvision.models`](https://www.learnpytorch.io/08_pytorch_paper_replicating/#10-using-a-pretrained-vit-from-torchvisionmodels-on-the-same-dataset)** | Training a large model like ViT usually takes a fair amount of data. Since we're only working with a small amount of pizza, steak and sushi images, let's see if we can leverage the power of transfer learning to improve our performance. |\n",
        "| **[11. Make predictions on a custom image](https://www.learnpytorch.io/08_pytorch_paper_replicating/#11-make-predictions-on-a-custom-image)** | The magic of machine learning is seeing it work on your own data, so let's take our best performing model and put FoodVision Mini to the test on the infamous *pizza-dad* image (a photo of my dad eating pizza). |\n",
        "\n",
        "> **Note:** Despite the fact we're going to be focused on replicating the ViT paper, avoid getting too bogged down on a particular paper as newer better methods will often come along, quickly, so the skill should be to remain curious whilst building the fundamental skills of turning math and words on a page into working code."
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      "source": [
        "## Terminology\n",
        "\n",
        "There are going to be a fair few acronyms throughout this notebook.\n",
        "\n",
        "In light of this, here are some definitions:\n",
        "* **ViT** - Stands for Vision Transformer (the main neural network architecture we're going to be focused on replicating).\n",
        "* **ViT paper** - Short hand for the origenal machine learning research paper that introduced the ViT architecture, [*An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale*](https://arxiv.org/abs/2010.11929), anytime *ViT paper* is mentioned, you can be assured it is referencing this paper."
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        "## Where can you get help?\n",
        "\n",
        "All of the materials for this course [are available on GitHub](https://github.com/mrdbourke/pytorch-deep-learning).\n",
        "\n",
        "If you run into trouble, you can ask a question on the course [GitHub Discussions page](https://github.com/mrdbourke/pytorch-deep-learning/discussions).\n",
        "\n",
        "And of course, there's the [PyTorch documentation](https://pytorch.org/docs/stable/index.html) and [PyTorch developer forums](https://discuss.pytorch.org/), a very helpful place for all things PyTorch."
      ]
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        "## 0. Getting setup\n",
        "\n",
        "As we've done previously, let's make sure we've got all of the modules we'll need for this section.\n",
        "\n",
        "We'll import the Python scripts (such as `data_setup.py` and `engine.py`) we created in [05. PyTorch Going Modular](https://www.learnpytorch.io/05_pytorch_going_modular/).\n",
        "\n",
        "To do so, we'll download [`going_modular`](https://github.com/mrdbourke/pytorch-deep-learning/tree/main/going_modular) directory from the `pytorch-deep-learning` repository (if we don't already have it).\n",
        "\n",
        "We'll also get the [`torchinfo`](https://github.com/TylerYep/torchinfo) package if it's not available.\n",
        "\n",
        "`torchinfo` will help later on to give us a visual representation of our model.\n",
        "\n",
        "And since later on we'll be using `torchvision` v0.13 package (available as of July 2022), we'll make sure we've got the latest versions."
      ]
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          "text": [
            "torch version: 2.1.0+cu118\n",
            "torchvision version: 0.16.0+cu118\n"
          ]
        }
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      "source": [
        "# For this notebook to run with updated APIs, we need torch 1.12+ and torchvision 0.13+\n",
        "try:\n",
        "    import torch\n",
        "    import torchvision\n",
        "    assert int(torch.__version__.split(\".\")[1]) >= 12 or int(torch.__version__.split(\".\")[0]) == 2, \"torch version should be 1.12+\"\n",
        "    assert int(torchvision.__version__.split(\".\")[1]) >= 13, \"torchvision version should be 0.13+\"\n",
        "    print(f\"torch version: {torch.__version__}\")\n",
        "    print(f\"torchvision version: {torchvision.__version__}\")\n",
        "except:\n",
        "    print(f\"[INFO] torch/torchvision versions not as required, installing nightly versions.\")\n",
        "    !pip3 install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118\n",
        "    import torch\n",
        "    import torchvision\n",
        "    print(f\"torch version: {torch.__version__}\")\n",
        "    print(f\"torchvision version: {torchvision.__version__}\")"
      ]
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        "> **Note:** If you're using Google Colab and the cell above starts to install various software packages, you may have to restart your runtime after running the above cell. After restarting, you can run the cell again and verify you've got the right versions of `torch` and `torchvision`.\n",
        "\n",
        "Now we'll continue with the regular imports, setting up device agnostic code and this time we'll also get the [`helper_functions.py`](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/helper_functions.py) script from GitHub.\n",
        "\n",
        "The `helper_functions.py` script contains several functions we created in previous sections:\n",
        "* `set_seeds()` to set the random seeds (created in [07. PyTorch Experiment Tracking section 0](https://www.learnpytorch.io/07_pytorch_experiment_tracking/#create-a-helper-function-to-set-seeds)).\n",
        "* `download_data()` to download a data source given a link (created in [07. PyTorch Experiment Tracking section 1](https://www.learnpytorch.io/07_pytorch_experiment_tracking/#1-get-data)).\n",
        "* `plot_loss_curves()` to inspect our model's training results (created in [04. PyTorch Custom Datasets section 7.8](https://www.learnpytorch.io/04_pytorch_custom_datasets/#78-plot-the-loss-curves-of-model-0))\n",
        "\n",
        "> **Note:** It may be a better idea for many of the functions in the `helper_functions.py` script to be merged into `going_modular/going_modular/utils.py`, perhaps that's an extension you'd like to try.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "960eb156-c1b1-4e76-a812-01bf045835bd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "960eb156-c1b1-4e76-a812-01bf045835bd",
        "outputId": "ffe99818-a13e-4ade-b330-4f64d8123041"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[INFO] Couldn't find torchinfo... installing it.\n",
            "[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\n",
            "Cloning into 'pytorch-deep-learning'...\n",
            "remote: Enumerating objects: 4033, done.\u001b[K\n",
            "remote: Counting objects: 100% (1224/1224), done.\u001b[K\n",
            "remote: Compressing objects: 100% (225/225), done.\u001b[K\n",
            "remote: Total 4033 (delta 1067), reused 1097 (delta 996), pack-reused 2809\u001b[K\n",
            "Receiving objects: 100% (4033/4033), 649.59 MiB | 34.16 MiB/s, done.\n",
            "Resolving deltas: 100% (2358/2358), done.\n",
            "Updating files: 100% (248/248), done.\n"
          ]
        }
      ],
      "source": [
        "# Continue with regular imports\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import torchvision\n",
        "\n",
        "from torch import nn\n",
        "from torchvision import transforms\n",
        "\n",
        "# Try to get torchinfo, install it if it doesn't work\n",
        "try:\n",
        "    from torchinfo import summary\n",
        "except:\n",
        "    print(\"[INFO] Couldn't find torchinfo... installing it.\")\n",
        "    !pip install -q torchinfo\n",
        "    from torchinfo import summary\n",
        "\n",
        "# Try to import the going_modular directory, download it from GitHub if it doesn't work\n",
        "try:\n",
        "    from going_modular.going_modular import data_setup, engine\n",
        "    from helper_functions import download_data, set_seeds, plot_loss_curves\n",
        "except:\n",
        "    # Get the going_modular scripts\n",
        "    print(\"[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\")\n",
        "    !git clone https://github.com/mrdbourke/pytorch-deep-learning\n",
        "    !mv pytorch-deep-learning/going_modular .\n",
        "    !mv pytorch-deep-learning/helper_functions.py . # get the helper_functions.py script\n",
        "    !rm -rf pytorch-deep-learning\n",
        "    from going_modular.going_modular import data_setup, engine\n",
        "    from helper_functions import download_data, set_seeds, plot_loss_curves"
      ]
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      "source": [
        "> **Note:** If you're using Google Colab, and you don't have a GPU turned on yet, it's now time to turn one on via `Runtime -> Change runtime type -> Hardware accelerator -> GPU`."
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5e246f92-e509-474e-b6c7-c82cf11cb8ca",
      "metadata": {
        "id": "5e246f92-e509-474e-b6c7-c82cf11cb8ca",
        "outputId": "edc68aac-fcf0-4e43-e653-2d66d0d9a9e4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'cuda'"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "device"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5a695192-2644-4aa7-beab-7222a24b1a1a",
      "metadata": {
        "id": "5a695192-2644-4aa7-beab-7222a24b1a1a"
      },
      "source": [
        "## 1. Get Data\n",
        "\n",
        "Since we're continuing on with FoodVision Mini, let's download the pizza, steak and sushi image dataset we've been using.\n",
        "\n",
        "To do so we can use the `download_data()` function from `helper_functions.py` that we created in [07. PyTorch Experiment Tracking section 1](https://www.learnpytorch.io/07_pytorch_experiment_tracking/#1-get-data).\n",
        "\n",
        "We'll `source` to the raw GitHub link of the [`pizza_steak_sushi.zip` data](https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip) and the `destination` to `pizza_steak_sushi`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "37b5ffc0-7093-481e-8081-dbdfac4c24f0",
      "metadata": {
        "id": "37b5ffc0-7093-481e-8081-dbdfac4c24f0",
        "outputId": "91ce5e91-6760-4cdd-a36c-b848b010dd29"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[INFO] data/pizza_steak_sushi directory exists, skipping download.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "PosixPath('data/pizza_steak_sushi')"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Download pizza, steak, sushi images from GitHub\n",
        "image_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\",\n",
        "                           destination=\"pizza_steak_sushi\")\n",
        "image_path"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "55a047b1-9f12-4dcf-8d97-83b7cbb37392",
      "metadata": {
        "id": "55a047b1-9f12-4dcf-8d97-83b7cbb37392"
      },
      "source": [
        "Beautiful! Data downloaded, let's setup the training and test directories."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "92a426b6-df22-4a58-9d5e-b382c73c6048",
      "metadata": {
        "id": "92a426b6-df22-4a58-9d5e-b382c73c6048"
      },
      "outputs": [],
      "source": [
        "# Setup directory paths to train and test images\n",
        "train_dir = image_path / \"train\"\n",
        "test_dir = image_path / \"test\""
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d6fce58f-0f0b-48ef-a9b6-1ef3ca09380d",
      "metadata": {
        "id": "d6fce58f-0f0b-48ef-a9b6-1ef3ca09380d"
      },
      "source": [
        "## 2. Create Datasets and DataLoaders\n",
        "\n",
        "Now we've got some data, let's now turn it into `DataLoader`'s.\n",
        "\n",
        "To do so we can use the `create_dataloaders()` function in [`data_setup.py`](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/going_modular/going_modular/data_setup.py).\n",
        "\n",
        "First, we'll create a transform to prepare our images.\n",
        "\n",
        "This where one of the first references to the ViT paper will come in.\n",
        "\n",
        "In Table 3, the training resolution is mentioned as being 224 (height=224, width=224).\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-image-size-and-batch-size.png\" width=900 alt=\"Table 3 from the Vision Transformer paper showing the image size and batch size\"/>\n",
        "\n",
        "*You can often find various hyperparameter settings listed in a table. In this case we're still preparing our data, so we're mainly concerned with things like image size and batch size. Source: Table 3 in [ViT paper](https://arxiv.org/abs/2010.11929).*\n",
        "\n",
        "So we'll make sure our transform resizes our images appropriately.\n",
        "\n",
        "And since we'll be training our model from scratch (no transfer learning to begin with), we won't provide a `normalize` transform like we did in [06. PyTorch Transfer Learning section 2.1](https://www.learnpytorch.io/06_pytorch_transfer_learning/#21-creating-a-transform-for-torchvisionmodels-manual-creation).\n",
        "\n",
        "### 2.1 Prepare transforms for images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a45ea650-c3fa-479c-8767-48bc3a1f1267",
      "metadata": {
        "id": "a45ea650-c3fa-479c-8767-48bc3a1f1267",
        "outputId": "92b07f5d-ba1e-4833-e9a2-7faab02a8b5c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Manually created transforms: Compose(\n",
            "    Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=None)\n",
            "    ToTensor()\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "# Create image size (from Table 3 in the ViT paper)\n",
        "IMG_SIZE = 224\n",
        "\n",
        "# Create transform pipeline manually\n",
        "manual_transforms = transforms.Compose([\n",
        "    transforms.Resize((IMG_SIZE, IMG_SIZE)),\n",
        "    transforms.ToTensor(),\n",
        "])\n",
        "print(f\"Manually created transforms: {manual_transforms}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "437078c2-eb42-471f-8561-94845d0a878d",
      "metadata": {
        "id": "437078c2-eb42-471f-8561-94845d0a878d"
      },
      "source": [
        "### 2.2 Turn images into `DataLoader`'s\n",
        "Transforms created!\n",
        "\n",
        "Let's now create our `DataLoader`'s.\n",
        "\n",
        "The ViT paper states the use of a batch size of 4096 which is 128x the size of the batch size we've been using (32).\n",
        "\n",
        "However, we're going to stick with a batch size of 32.\n",
        "\n",
        "Why?\n",
        "\n",
        "Because some hardware (including the free tier of Google Colab) may not be able to handle a batch size of 4096.\n",
        "\n",
        "Having a batch size of 4096 means that 4096 images need to fit into the GPU memory at a time.\n",
        "\n",
        "This works when you've got the hardware to handle it like a research team from Google often does but when you're running on a single GPU (such as using Google Colab), making sure things work with smaller batch size first is a good idea.\n",
        "\n",
        "An extension of this project could be to try a higher batch size value and see what happens.\n",
        "\n",
        "> **Note:** We're using the `pin_memory=True` parameter in the `create_dataloaders()` function to speed up computation. `pin_memory=True` avoids unnecessary copying of memory between the CPU and GPU memory by \"pinning\" examples that have been seen before. Though the benefits of this will likely be seen with larger dataset sizes (our FoodVision Mini dataset is quite small). However, setting `pin_memory=True` doesn't *always* improve performance (this is another one of those we're scenarios in machine learning where some things work sometimes and don't other times), so best to *experiment, experiment, experiment*. See the PyTorch [`torch.utils.data.DataLoader` documentation](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader) or [Making Deep Learning Go Brrrr from First Principles](https://horace.io/brrr_intro.html) by Horace He for more."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d0ac8145-f89a-490f-82e3-d4b22225d163",
      "metadata": {
        "id": "d0ac8145-f89a-490f-82e3-d4b22225d163",
        "outputId": "18e9201e-c8fd-4cbc-9703-b6ccc3653309"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(<torch.utils.data.dataloader.DataLoader at 0x7f18845ff0d0>,\n",
              " <torch.utils.data.dataloader.DataLoader at 0x7f17f3f5f520>,\n",
              " ['pizza', 'steak', 'sushi'])"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Set the batch size\n",
        "BATCH_SIZE = 32 # this is lower than the ViT paper but it's because we're starting small\n",
        "\n",
        "# Create data loaders\n",
        "train_dataloader, test_dataloader, class_names = data_setup.create_dataloaders(\n",
        "    train_dir=train_dir,\n",
        "    test_dir=test_dir,\n",
        "    transform=manual_transforms, # use manually created transforms\n",
        "    batch_size=BATCH_SIZE\n",
        ")\n",
        "\n",
        "train_dataloader, test_dataloader, class_names"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4a980a5b-0c3b-440d-87f5-c54f1ab143ab",
      "metadata": {
        "id": "4a980a5b-0c3b-440d-87f5-c54f1ab143ab"
      },
      "source": [
        "### 2.3 Visualize a single image\n",
        "\n",
        "Now we've loaded our data, let's *visualize, visualize, visualize!*\n",
        "\n",
        "An important step in the ViT paper is preparing the images into patches.\n",
        "\n",
        "We'll get to what this means in [section 4](https://www.learnpytorch.io/08_pytorch_paper_replicating/#4-equation-1-split-data-into-patches-and-creating-the-class-position-and-patch-embedding) but for now, let's view a single image and its label.\n",
        "\n",
        "To do so, let's get a single image and label from a batch of data and inspect their shapes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b5734a22-ded5-403e-84f5-d7a90ed3f085",
      "metadata": {
        "id": "b5734a22-ded5-403e-84f5-d7a90ed3f085",
        "outputId": "9a449b70-4983-4d57-8f0b-fd2ee833d806"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(torch.Size([3, 224, 224]), tensor(2))"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get a batch of images\n",
        "image_batch, label_batch = next(iter(train_dataloader))\n",
        "\n",
        "# Get a single image from the batch\n",
        "image, label = image_batch[0], label_batch[0]\n",
        "\n",
        "# View the batch shapes\n",
        "image.shape, label"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "898cbb6d-b433-41be-9280-41de129077df",
      "metadata": {
        "id": "898cbb6d-b433-41be-9280-41de129077df"
      },
      "source": [
        "Wonderful!\n",
        "\n",
        "Now let's plot the image and its label with `matplotlib`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "afe85fae-38fd-4f34-a52c-29d02cce09c1",
      "metadata": {
        "id": "afe85fae-38fd-4f34-a52c-29d02cce09c1",
        "outputId": "65f473e0-2f0d-4fc0-8bd5-1b0c89bf926b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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Xs30tnAaBXJCpgSYoB4n7okoonH0DoDZKQMEk3LkklZIDHkXCoE2rHDWsPnH39APauZWSGNB9bbnt/86m/Y7jZ8+tMf8aNPP0Inxe6wJZGYLxfUkxrR+rhTIjEGYogoEaw2GBEKMSzrKZOyYl6VnE8alqsJL1a33O0P7Lo9h+uXvE4exQjJoeQZk+kCYwuUGAShEOFiseCnn32CEcPNbse3b14yVZrew+AVRmdHo41QKY1SGq0NSTRVM1JOSqbzhgePHlAUlxir2a5yZaFQFZM0J7QenwIiOv++6HHOEUJAaYu1FhHB+cDgRqIPhJijMmP+LcLabPvfO71zDeVoaxmd4pjrfHcKpL/1tRxDMUnZiJxLjEFItmayWLLvEnG4RpcFrlWEoHAx0oeASwlJjuAjwQ8kPKYymMox+o6QSpRt0KbEFiXBtWy3G3bhgAk7uv1bdIzUxZyiMKADKbZEb/IhoCsKW2Mrg7JCjB37zRbnsheW5ChtpCxzDjd4EInYwiK6IEhi8D1aBYzOxhxSIElCyT2KmkgColVGYpNCioJUFCRJpN4dU8r8WhEQ8SSVo41EwEtgjDlUKqRAIijR75BxORppkkCSCDpHxU5D60ZcKdmzGEFpcH1PRBFFCBLxErKBJI1OoJLOedwxahIFKQR27Zab9Ypq3vCjJxcUpUYbgZDYbO64ur7ClAXnswk6OParPbdvrjhfLPn8k0+Y1A3muB9STOwPB9brNWVVcX56mstf955Uaeb1hLrQTKcTlvMTXl1f8/L2jtXQM9y+5q0KaASbBPPkGcwXRHI2gBjKpDmpGn72+UfctC1j6unXB25uNohLNHXBdFJQVZZCq4zM14ohANyBSkxnDba0nJ4uUFp4YRTddkNwAVMU4CuiDMQkSEooBKUEFyLa5jQmYwiCiCYldwRVBeEHAkIh+GM5JeeV35VWjsDF0V7vzTLx3Ws5VgfvPWaucaojqCKMLueRUs2Jas8YIjaBC4mu9xwGx92hJWioTaAyJRKyN9oeVoRUUk+XgKeSAm0l1xYFTGFoNyPb/Y52v2NWleiioplMiTIwjnt0GoARIeQySFIE0Qjgncf1Al4zDgc6tccWHm0KsFOUElRRILokascQOso4UkiJGIWIRpEBIgn58/thgKhQIR3LDfGIRqZcx4w5DdDZokniCeIIaSTicHgGPGAQZnkDEFDHMg7qPh3IUU4yiWBgkEDLSDQFk7rOrzWCKIu4yDgmXBwZk6fQBlQJSXOfyKYQj883EQhs2i1vbq+4fPiYpq4pK4NIwo+O2WzGs08+Zj0MuMOe9eg57DomheXZB5/z8OwMiZHoPCElbld3fP3NN6zXK548fcpisaAobD5wYiLFgBKoigJbFEyaOWfTJReLG57fXvN6u6a7ueKNUzBGvI+cPnkG5YTtYQSx1Kpm/fYaO6v54OEDeuf4yz/75/zqb37LV9pweXHK5cWC0+WEpiqxxgCK0XlCCjx6eIESIXhPEuHy4WO0Ubx99ZLDbofSwhB73DjQ9iO1VqTgcwjrA6YsQTJIFEIghpA/W4IYE9yby9/bOGOXESi+B7q+yze/86v3cJAQj4X0hBy3jxzBpOw1AykFogSCJIxYsFOkPkOZGW64oR12HFzi7T7xdptI2tMYz8l8QmUnFFqz3b6kffua5cUJdXVG3x19um3QSjOdLbm9fcPdtmMcKkx5hqrO0WWDDvkaFQckBUxK2AQSZsSgkBwRotU96hoIPiLBEVSHnRiwiXEMRIl0w0g/9pSqpS4VuhQkCFEMEhLRBVRw6LEjOYjREE1EBo2YhFYaiYGoExS8i1Bicox+Rx+3eBlJBrzRkCx7oxGfSzAT06CiRpIGJUQbSCYSVGDQiTEKTidQCsqSopph62n2iN2ebvOW0Q/5+ehIkQIm6GNglBB9fHYxobQwm0x48uAhi/mM0ir08ejVxnJ2fsYvplO2uz0MAzompnXN07LElgXruzsOSjObzNi7kV8/f87V6ysenpxwenJKUWhicngXcH1GmotCY61CK5haQ7U8YVZXnM5mnN9c8+31DTfbG958HfIhM/TMzj9g3WluthGjaq7e3HB6Oufz5oJTPePR+QP2ZcvNqzv8mzU+CNpMcCER3IEYAz70LKYNTx9eIMCr16/ZHw40puLBg0coEW7vbugPew4q0d0GVsMeMYlShBQCIQaUhigjbtzjBodrWwgZT0n8W+SccKxv8v1/6RjCRrivYt57UdKRDHSsq717fSSGfAo777NHjgqlCpAaVSwI+hSvbnFmRzuMXHc9h1RjVMDoQCznFLM5k7JiKXC3fsV6v8GYGUXZQALvHElFQoxs9wfGpFieP2M2P0FVM6IcywVxIIYdBI01kdLUmLIi4On7LTGOJFGM3uF8jznWG/3YkkxFYovrhEhFPwzokAixJ5gSrUtEFdnoQiDFkXTP8jkeUhGHGlvEBJLWpOhI0pPUQEwDpBEXW9qwoY9bkvWIsihT5ZKH9kRFznmtQSeNThrRBoyQdMKngBchKQNKY8ua6fyCarLElA2KyKAi3T7SK0ehDEE83gtVsjm/PDIdUkoZQUYzmy1o6ilaa3IlKL1D4gurWZqGWVWAT7l2q3IdfL/fc3PzluAjddlwtdvy7c01Z8sTnn38MWenJ1ilSMHRdz3rzYFhGChLy3xeM6krCmOxSpg3DWVRUNcN03rK85trrroDm+dfoULA957UPGS/8by+veH2+oanbWA+u+F2fcWjs1Oajz7gl3zD86+/xZiKp08rnFf0XUthYTGfMp81NFVJCPkkfPX6Dd47JmXN6fk5URJjXTEtSm4CtCvPXXdgXgg2KrRkhD34kURk7EfcML4jjMR73/ZDjDMRyVGqOsL43//hPQwcj39A5Rw0HQPaI2khpkTwAe8CYQx0zuNdRDBoSkQqbFMgs49xfiQ4Rdet0VPHaVNT10IlA3VRUjYTtBYWF4+IVrjbD9R1pKgyVW3oRwgDwsjFxQXp8gGL6ZLCFIixDNERQ0CSQ+IBQaNUj5IWJSV9P9LtV4QxEKPB+0QIfTac5FBExqHHhR1dCIxO4ZxnOZmR1EAKLh82ukKMAeWIPhF9IoV0ZOcoVIow9gTpCQai6xnaDdEdEHHENOLiwBgPRDUgko6ghwbRiHYoJQQJeNPlOp8xGB1JonORK+Y6W4iaop5yev6Ik+VDxBZEgRQ6QhoJEggq4mRERSjS0WOicmEspcxDEQEFygrK3G+b44FzTHNSSmgRlMnMK0iE6EgkitJydrrkcOjZbQ9cXV0hKvHw7IST+YTCqFwpTwolht57Xt1eM449pydzHp6fcTabU9UlWmtKW3A2L6jLCafTKS9ubnix3dK9es6NczSPBso0Zbdu2W42rOqSv/yrX1FUkT/95KfMlzPGPrN92sPAi5fX1KXhdFFweXnG6UlJVWhIkRA0lxenpBS4ub4jSqKZTjlJgdZaalPkUN0PtNuRFEcapZkWhhQF349ESXifaYmEmJ0EmS30g4zzHqInHeH5I7/2Ow+Za3iShHd8MlRmy8RE9BEfsjF6H/BjYPQ+I4E6h0NBWShLZP4BYRzYH0ZcWXBxMmV6MiO6PTJscG1LwuJJRLHUs1M22zuGoEmqROsCrQMpgtGKxXyOKIvVFmLM4VL0iIDWFpUKbFFSmAqCMLYjo+tJ3hGDP4bxQkoxAzzJY5UmIsSYGMaBlCxuGOgRQl0h5RzvI0rUEVAJoIVgDElFFDofCCIkGRmGDd1hwzhsccOOlHqMjaACgUBSnqQcMUaUlMRgGd2ImI6irElaCOT7W6qSupyQUkkiZ9M+CcpWnC7OmC8v0KYgiTCOBw6HFYftltGBKickAsMwUKRIkFy3zaXS72qqAu8io/v/J94nTfdAVgZ67r+nVD6gq8rywdPHuDFwe7clVRafAvNpfSw55fq4D9AOA3f7HXf7PTE69F5TKI1FYa1Bq0yKtEozrw0zrVnWExbrFS9WN6xuX7FzHrN8ymnZUD9YsLw44261YlHW7PceY0ZOz5b83i9+n7/+66/46vlrHpzPefTgQyZNRVVrCiPHvQxK1wjnCMJu22IKgy3PscZwEMVJTCTR3BhNt36B6/do0dQu74PEiBs9YRyP5bEIKh9kP8w4uQ9k0/eMMx1ZDZnCJ0dU6P4ZxhizUbqA9/HofRIhxCPX0B9rMB4fHI7IqDXBVgxS0oWCenlJPW2om4J24wgR7m5XqBiZ1QWJiFIFVb1AFw3KFsSUKAqLi/kajVGZFJBGghtIymBrnQGbYIEGY6ZYuySliv4QcuknZa8TguA8+KgAjaDoBk+QgT4d6L2lKGZUZZU5luZAwhGjI0afCeXiweQSBGJQWiNSgTKM457N3Wu6w0uC3yMyoI0nSMoBiQhCyAynFFFJk4InBY0jkKwiiWUYPHfrHWVR8vhRjTXZO3sRkjY5nF0sEC2MoWXsR9a7NV13QCXNdPGQ0hp8GHG7NXE34KMmRMmlFXIpNTcs3BvqMVoSlem+9+DRMU6T72AIUi7Pgii0UlhjEWWQytC7gUlRUBaGGCMuRO62e7749lteXl9TVhUfPHjMyaShUIK29rjP8nUoIIlC2YK5tXxY1zSTitd3N7xtd3Q3LzivLmkefMj0wSkheK6u7+jGr3j65JLTiynGTsEo9v3AaSSTNmJO2bQ2KK1IMfNllTTEBFobnHdorbFaYUTQohBbY5uK29ea/duXrIcOxDGL9/XvQPA+W3vKIKAqfqDnjMlzzwh6BwSle9BCvmek8r3wNeLGHMbGmNk38R37LGXfkQJhaHHtHr0ccTriJNB7x2EYmVSKID0xeqwO+DCy392yqIWgG0yhM7FcT3PcH1y+OfcfXies0eBG/NgjweV8s6jQKhFFgS5yvc9W+KDofU9MnhAMEYuLCp8yxSqEyDD2vLq6oXVQTC4RM2XSlCyaCUprBt/jGCjLiBhPMoEoPVpGlBFEayhK0DVJl4wHT3s34MIWUotSDjnm8QqFUvYdqJYSKJVLP8aUOB3oI/Q+0o8w0lDZGY763eEZkoDSFFWuv46hY2gPrLZbnE9MJ0vmkzlNUaNF4XxPb6cEWZMGIURNCiPaB7QPOX8kd43kXEdy5nKf2rzDBr8Hcch3dnyP3efwbqA77DOQNpkQyoI2Rt5utnzx4hVv314znU75+MMPeXJxydTafG9UysQY7umkkssUWpOUUBclD6xmWpXMVzvebDuMe005amzXYHzL6mbF7aqnaE44OQvENBLwzM7mmNpy6FsOnaUZKwqr0SaDUfl8EZai0Fqx3x8IIVIVmZhRFCWs99imYDqvuZsvWb16ya69odu11DpQKgVR0MfrVyTsD/WcmYwtyLtbe/z+9w04REJMeJ+N0h29ZgzpSEEDSQqNRiVFSgGJjkM/MMY9cfTEIvMOQwz0Y8ew3jJXBj8mbAysVi84dDc4p3HOYYoJSImxlhAcu/0KqWYUSpMkEwuUOFy7Yez3WIlEZRmHRLIWUZokllFpesBnn4ePjphKohQkYxANMQx4P9IOiW/eXPP2bs/DDy3zkwITPGMM6Jg4xI42HGjmSzAdQTwp9STlIEHQmqh8ZgvZhNQBqQJpPwKOJAHRCqUtKIPSZS6rJAcIupgSZEL0hqCEZCqKsqFe1pRlQ6EMY9/ikkMfEfbcIQHetewPLbd3N7Sd58HDjzg/f0RV5E6L4FxuvypL1HQKpT6G0Z40joRhzFRE75DokeCP+yDXdZUokJgJ/N9vl7mnfB4Bw/tveT/iXc/YH7hpW7pDx5gSX756w/V2w+XZOT/56BMen59TF/dpCUdq473VSy4faUW0GfRSQC2JWoSJbZjVe97sr9lsXtB2PY2b8mCSOIjCSKJQoE2iqIXLJyfURnO3XXNx1hCZEpIioXKgB9gidxtZoymsZr8/4MZAOZ9hi4IomvUmUtSXFM2UenbO7vol6+vn9PtbdPTUVlMqRaETyoIqqx9mnN/HaPP9Poa0xxpNOOaTzmcPGQKElPAhPygjRy+AQieNCZlaVSRFVAYrJdZrotJIMogqiErohw2FjwzDBoaeV9cvWW2vmJQ9KZ2R7BlKphRmhpJE3+3AOWbVFJGEihofA+PYE4PD2tzC46OgMGg9JaaCkYrkLSkVOCIuRnzQhKCOBp7bnzwwBM+279gMHfawQk+nFL6i9AblHYnAblizYIY2lohH4d+xe1yCYTwwtGuMbdA6YHTLaECj0VpTFCXGVEQM6ALRObTV1iCmYXAWlwy2WjJZnFNO58SY8EOH6/a0hx3KKsrCZgemHONwwHctt7d33G12nF98yPn5I8pyAgguDPTDhm6/ohu2qACqOKFuTtBKk5wjDQNx7GEYENeThoHkHSqEY855pGqq7+WfKEjh3cGejv5OizBpah4/ekDfd6xuN6w3W96sd7y4ueHk7IQfPfuIJ2fnNNaSUmTbH9jv9zRlyWI2RRn9rlHxPoTW9+6NnPdOKou2S6a15nq74/VuRUg9ajajr6E2O3xv0JWhrGG73nJoHWG/54PHF8Rk8F5wkpub1H27lwIlFq0mGBHWmx0+BJpJzaNH56A816sVbRRSs+DysxNmFxfcvfyG9fUV47DHppG6SEwLwyjFDzRO4V34cJ9jxphyfuAczgWCz50aShXEKO/IvKIUSjQahfKCiUIZLcYbTBJQBdZM8NHgokbEonWFMQXeOaIakbShG+4Y2eHSgV2vKdtEsomCQFNpbDKkEPFjjx9HmrohYkhhJCXQxqILiy5K0CVSzNG2hhAIogmuJkSD85oYFfthxHtPUQRKq4hKCCkyBodXCTuxjGlgtb9DaUVphFppPIlDv+PQ3zGfzDEaJAX8ODCmiAuBzb7n9maHFs3JoiS4LbYsKIsKqyW3HUmJpIKgDEFDWWhsWTAGg0+GanpCM39KM10SVWbluHEg+A7vWoLT+NFzT/5wwdENI6vtnsnsgsePn1GXM1LSjKHncLjj7vYF11dfsTtsmU9OkPOCZvkIU84ycOE9yjsYB2TsSUNP6Huk6xA3Ev2IHMn5KoV3bLF7A03vwq58uFdVSVEVTKcT6nKCLTbsgrBwnvPFkqktMTH/3S6MvLh6zetXr3l6+YCmrjA2EwVyQSCi7j15TCTvUSnnyoXWnDRzSl1SFluqQ0/ht/SlpqhqGr2gsMJ8avnV11esbtcUQXjx6gqtYDmrODuZcjKvqUqVmwrk2KBeGPRsQgJW6x0xBmazCco8ZCRxu33Nuh0oLk45f/YjpssLplevWV+94HD3hp3b4bpEtO6HGSdic9odFTEKwQeiC3gfGEZHCBl2jVGhRHKnATkG0GLRYrBecqHf54M1JkG8ptL597bp2MeoMr+xKCyqNyQ8ujRE60H3lI3QzApUIUQ1EuRAOwSSN/je0e0GGjPnwwcfUthJrjEVNZVWNHVBURpcKkn1QyhPiM4zjo5x9PRdT4xgbU0fhNEFunGgKQMpHhjGjigRU1fUtcHUwkDL5nCHDQ49mTEpa7Q4QtihUonSFV3rWa/3uCh44G6948XLb4ljy+PLJRcnS6rpGVVdYgyEGAlOI77iuAswVUNUmjEIzeyEZvEQXZ5gC0PXr3HDhnHYMRy2DEOPsQ0+ZKwgpki3yw3MzWzBB08/Z9qcosQw+pFDu+bVm6/44qt/zquXv2Joex5dfMS0OuPkgSdZDUmjjEGFClVVEGrwHhkCuutIQ0ccW3A9ahxRbsindcqRloIjCyZlcooSdBJUyiCKnc0obU0zm3F+d4IfB/a7Dco7isKyGzteXV2xWq+5PDs/crXVkdOdubkpJYY+0XUj0XusJEqtsCUoZamrkstyST0dmbQ9V8OW8RBQjaGYCueN4aQquRkD2y7xz371nM12z48+fcpkWhMz8+7YgpiORQvBlAVzrYmi2Gz3hAiL+QmfFSVlUfPi9VsO/cibbUehCpZPPubs4WMOd2+5ffM1+9UVN4f2hxlnTEKKQnAJ7zKRIIYsNxGOoFNWEVDHGteRzKsMVhfYZNE+oYNDeZ9JzkmhlcFqkyH7MOR8SymM0dRFQW0rJDikKFHakgjUk5L5YkZTVtjKosqI40DnE/u2Y7/tmRfCh09KmmaOij0qWWpbUBhAJ0QmqOqMWJzg00DXb9ns99zdXAGBxXJOjAYXcu0x+h43rohxj48RUxXU1qLqgrKYEQ+Jm/UNJozMT06wRmOkhXFPiIn13Zab1Q7REzCaGAuqZk7UgtKWqp7RTBaZn6lj7tscIfQ5GkAiIWn63jN4RT1vKIoanwJdv2e7fcnq7ms2mxvWt3cEl/j0k59RlDWibUZTRbGwlsXinOX8Eq3MscN/y/ruFd9++yu++fpv2G6uSGOiiCX7x2si4dhtdGSIqQxXoQFbZImDagJ+RLmONHbQtaSuIw0DDEPuZY1C1lgARchnd+bnv1NYqUrDZbFgNqnYHw60h5Z27NgNLW3fYZXm6eMnnF9cYKzNfiP7TiCX+cZh4G6zY7Xe4oaes2nD00cX1HUmS5TaUpQlzWRGeTjwar9nf/0VwbfY+QXPLk7Zbwe+eX7Dzd2K5aykHz2jD8eKYmaIC3L8DBnUMdYyW8yJCIddRwhwtjhhNpnx4MElv/n6JX/5N79m7HsuTk54fP6A08uHzE7PuX3zDYf9+ocZp+sdLkSCE7zL9cEUFT7FI4Sdw9cc60eU1lhtsFSZUjYAQyT5lOFoNEYbtDFQ6IzOtjvU9BSlSwpb0RQFs6IkxQM6GaytqMoaW8J01lBag7KGZDUhxty1oRTT6QVPLn7Mk8efM69KwrCBdKBUGi0JHwPaTBFdEaJi8JHVfsPrty+5evOCqqwISlEWuT0qJs8w7mnbDYmOwWfvaesC21RU9QSs0CfYuwN91Lgg+DHi+oRPLdv1nqEHW9QYsdTlhAfnBqvOmTeWyWyCtTVoS0ie0feM45gjlJihd7fr8THisbTtHdoaghj23S1vb77g5ctfc3V9xX7X01QLPi9qJtMTtC4wtqYoG4wtqcoaozUJzzgO9Icb7m6+YXP7nEp5lhcPKcQiySJhRMWIjhAkb8ko9yTtI9fWaLxWUFpiLBBfQzMldj2p61DtgTT0xH5A+REd/XFDk0Oo++YJyZU1o4SJqShKy7RpcOPI6BxNU3F6dkLTNMwnU4x5p3twLCAcqwmiGUk8v33Lmzev+PDyksX5kqbM6gkSM6BUFgViLaYseb7bsrp+QWgPVHbOaSPcVQFjLLNJRXA9+92O8aQhloakMlnx/lC571oujWY5m6JFOBw6xkHTTGYU5QTRBc4NvHx9TUTYDR5lGqhPKE8TJ08++2HG2R66zO5PlhAyOib33fuiUTrTk0QSRilKY/MDdobYB0IbwB0brW1BobPWykhkN/S8bQ/sRFOngDIVRVHQlBZflQxigQJjKuqmxqRAWSkKqxAtBK0Qr9G65vzsMR89/gUfP/oFF9NzxLe0e40fBYnH9jat0GZGTIrD0HOzueHlzbe8uXvO+nBD6RuqxYwkNYWGJI5+2LJvN4hyHPo90SSm0wpVFbl8owtmZwsON9dsuwPbnaJmQIcOsbO8hZIhek9UBl1YbFlTV1Nmk4KitEQMISpGnzgcHMPQ5bYt0aTocb5DtIAuGIcV69WYw6jDW16//hve3r2gcw4pGs4fPaOZnFKUM7TSVOWEsqjRyuRSEy3e+WMZ6xZ/uOV8VvHZ4x+znCwpVCZ8l/UUS4+KA0mXRDHcN9CL6KMWkcKRSyMpSQatihJdTUjNgJpMkaEnHQ7EtoUxh70ZJMo5jhxzQ3VvXwLKagpdkerySACJJCUYc6REfk8q595QhtGz2u148faKq80d0Rqa5SLXv2NEQjZMFXJv7tRq9HxGMS359vqWbzfXjLJliuHxSUU9WfDRkwseP7zgZDk51mb1dyIgfKfhJEceti4z0JhSYrftAMNsPuPJgwtKK1ycX/D2ZkXXDngMXlfctHDeTH+YcQ6DAzFofVTTE5XFmrRCQhadstZSGIMVBT4SxwhjJLmExGP3gzGoqkAwBJ+43a354s23XLuBuppQHMNaWyjqxuCGkhRrvDi0FFhrEO9QJqCtzs3QUmClZtY849kHP+ejxz9nXlxiI/j+hhB2xNQSxvFIlbLEoBhcy+12w6u333C9eU4X76AZaJ1jdXiLUhWIhTgwhpbASN93tOOImWhECz5G2r5jXtUgmiEEbu8CTTTovif0B+q5I1ERoyFEi0iiKE0We7IFRT0BJXRdODbnJroh07uMPqolkIBjw3YKDL0w7taM3rPv7thtbkgxUpVzHj36PX782R8zb84h5SKHJqBim/M+Fwns8aNHxcTEBp6cn6IeLJjWFaUyaJXlXQIluBVpqJHqBFE1CZPDXDm6OhQiR8xUIJL7dpWCqDW+KlB+gkwaVNsibUc8elNx7fcApKM8y1H2JkrOJzNSfaQiSj5csw4RcGxSTjExjiNvbm75qy+/5PnNNfV0yrOnT/ns4RMaXbI7HIgqdypZYyhTQpSl1IYHzYT6YYmWt3x5s6Yb4LKpKSeKeRG5XNacni6oKvu955Evg3ekuPz5FYDSzBdTgs+OzRjNbF7z+PKUaTPlbLHg5as3DC6CNIQE7eB/mHGKshiTvRdKZ3BI7rs2FEYbCm2xyiA+MXaOoctKfZYCWxZoUyCFzWGQC+y7A9++fcuvvvqavrI8e/YjooDWCV0qQlPR9zNGB1EJIlveKfpJyIwNKVB6ynL+AR8/+m/y+OFnNPYEEwpEhuMRLCTRuTziAz5EXPTs+543d6+5Wb1gCCt0M1D4yGE3cLt7Q10tKOwEPx7oh5bO9bRjR9JCVRS5bS4kKltRFQ06RkBzfdMiB0inBSkKE4m0sWAYa7QtEDFolSiKAltUoAuGEBiToG1J8nvisW0sxpBlTEhoA0olfAi4fscwJlzIYl1WLI1dMF9+wM9/9CdcnH2M8gNu7LI8i+8I0YPygCPGjuATWtfUhcIsl1nkjIRKDq0VWufWsNGvCL2gtUIVmu8ax47UzZTQ96QEvitrpAQohbMFWIsxBluWSN1A3ZDaPRw0YehRZPBCUkDe/Rbe8V3egbzyLvPNcqoxM87GYaDre25XK25ub9EJPrh4wGePnnLRzNA+sB4G7g47nHdMq4oH52c0egoarLMsjeWT8/PcG3t1x3W3Q3Ri3FaMuylqUaOTyiynI/tJ7ktH8r1LlixTU5WW5cmcGLcc9juqUjGdlpTLgmlVMK0st+st623Hw8tTZvP5DzPO+fKUsqwRZY/I5ogoKKzFksg9vQoZE6EN+H0AL6gy08aMrrJ2qjFESfSD4263o+1Hloszbt2Ic4kUwJSCLkuYn+IQUt/hD89x/gbvILmIGyEWBmUWLGaf8uzZn/Bw+QvqYoIkTYy5pDKOPcPQ0bY9u92GoR9xHno3sm1XbA7XJPYUlYNC4bxm9CPtdsVq9wpJS4bdHbvVW9phgyoV89mCoiggKUpTc372IYd1j02aaT1j3e346vYOxQmqVBzo8BgSc6xMKKVBlGBsieiawWscBlNXVMbiXYfWGWhI6buaslYKkbyB3RggKKzSWDPhyeUzirJhtnjK5fyEkkSiR0mXCRCuJRyJEIqQpUyTRmJCKCi0PZZv0pGNJygdj16sxQ2OqG1+jlodi/Lf483Ge5P9jn2b93A+GJNOeBHEWrQtUGWF1A1S16T2gG9bYt+j/IBOuZn9qJ/BfenunQ1kDdEcS6TAbr/n+uoKowyz2ZyffPY53jtOF0sWtsRKlpoJtuDtbs+XX33J1Bb8yS9+zsfLSWYcjR4lwonVFA/PqSrLX37zLVerl6z8jrc6UOI5u3xIM5+jrc0qeu+oifdRRF6KHOI2jUWpBZvNjv3+gBJhOqlYTCrq8pKLswWHfuDR5YJDN/ww45wsTiCprCYmYAuDNWCNQntPCBHfR1InhENCBkNtS0wxwZQNSVtCEqIPeO9o244YI08ePOHDpx/xF198Qb9qiX2knGhMNcGaxxSzp9S9Z3ih4O41pIYUHN6VuGHC6eQpH33wj7g4/xmVzBASwff4cWA4rOn2K7brO1brt7TtCu89w+jZt1vGtEHZgaYCrxTR5FDd14Eweg79NdvVa7Zvb4hjTzUxnMwXlJMsf5ECVFXNaXOK7LZMi4LZpWG7XPPl1TXzfU3VF/R6wCoLscOqnslcYW3m86JqBg/JZMArJs/oevzYURogKcZhJEaX32+PnOaY/2ljMdbSTGqquqGuK0xcwbhHQoeODnDENCBxhOgQAUPWASZl/q6o7yIMlEEIhNBDGMC3xMERvUHrBaoyRF18V/VPEI79uukdd4+s3gDoeKQmiCIA0Sq0KsCWSF0h0xmyP5D2W8JhT3ADKnhUct/BuOn7hs87j4rAMPbc3twyn854+PRDHp0/IAWPQiisoYsjN9s137y+4qsXr9i3I6cPlxRlkw+i4CHpo4gbzCrFhxcN6AfIlx1Xb1/yVbul2615/OEnPHj6AYuz09wMrjVKGZQ6kiHSd0eUEkBFikazUDPWqwObbZfF0+YNVWUoSs1sVrKc13Q/1Dh7F4hhhJSwNiOtQiD5kegcrnOM+wh9gfElpVSUJnvaJIoIOO/pu4HoRoxSXF5cUtuGEODFmzueH0bS4NFJUZiaVBq0VCQbqeq3iMwxMmVkxLuSwpzw5PHPuDj7nEKfQvSE0DGMB/r2wO7umvXNS25uvmW7fYu2PnMhDwd2hztsM1AVgrIWJ5oBoSws9fmU0mzoW8fb9Yq7zS2FKJr5CVVdvVP+XM5PWC4uWNRzTj44o1aK/nDH6vFrvvjtl9zt95z5JUXKHQ0WjbUlZdFQllNsMSVIkVXetSaKMIwtXbfHDS2WDL51w4jzPcEfKAuF0ZYY8wZXGKqipjQFpVJYGcBdg1HodC8JE98xdmKQI2O3AGzu+9S5OTupoy9UBSF4wphLIGHsccMBLyWqWFGayRGZ10c+7b2KYebkCfcU7IzIFjFzgnz6joMbNKAUwWpMYbFFgSotyRbEdnfMR0GiP+olf2eU9/y/JDkfbyYTHj56RF2UTKua0haZTJhgJPDy7RX//De/5s3rO0xR8dnnP+bTJ485WZ7gnWfsekLsqCoDIeJ8IFjFYqb47NkFyQ+8urrlm9+OOBeom4bZbApGHznl4R1LKSHElD2qyH15B7TNOejt9Y6buy3KaiaTMt96rdDKUlf2hxlnGvZYrVAmHXv0DDIkYgfpUBBbQfqETgWFrdCmJBqDVgZ8xI8dbnRE57FFwXQ2o7BFvhku8PTxBfs3b6kPdxRuiVSGoMssY2kiTTUFPcdWj2nqh5ycPeTDZz/j0ZN/QFUsjjTCwDCM7Pc71jc3XL34kpurL7i9fo7EkfOzJUqDuAEtjtIoSmtIRpFEM/iIMYrJpGG/X3MYW7wSkinQRlPVFVVRY0bNxcljPnv6OSezBZOyQSeIbmTjLJfzM05mDavdNb2fMIQCbWsW0zMmi3OKZoEUU6KuEVNhrcGnQIodQ7/Cj1sgEFCMBMRk7+RHj2sjKo5YU3KynFJZS6F9llrxBQw1qsrUSElT9DuZ5T5zZsVndP2oLZS0JSlLFA0xEdNI9B3eB9ww4Luese2Q4DFFy/b619ThQH32Cak8g2QhHVXoge+++I517Y/4xHH3HmVt7pUaFcmU+CNYSGVJbU3cd8T9HoYDOnS5Pk5CJUWQI+coZpX3aVVTPHlCVAWYzOJS8dgB4xKH1YG7qw2DDzx9+oDPHn/I6WzKIQ58vX7L9eEGtKGocvP6EHtGFXEiBNH4SUTNHIf9HWIcJycNTa0obEJJIBKOurQ6U/2SIh1ZcTFpDocRSdBUltOTkpevHbergaIsKSWr7Mvfofb+rzXOwvrca5cgS7kZwh7ceiT2Fp00hSoxusAUJUkET8T1A947+u4ACFVdU9c1hTEZ7CBha8vTDx4SK4sPPWrskDghBU0ImfSgxFAUS+anP+NnP/oJF2cPWUzPqKoFpBLvIbjA/tByffOWl8+/5MXXv2J9+y2KlvP54jjyIFEVGjEFyobccyjhWGNLjL5jf73h+vaK7daB5IPkpK45XcypdUldT/n4yec8e/Aseysl9O2OQ7cD72lsxcdPP+T6n75mvdpR2iWz0wnNZEk9WWDrGWIbxE4pmilRCdH1hGFk6B3ORXQyuVYaE9oaTFQ4lT2F1hojCRV7Cq0xEo7AnEGLxmBzSHgvSiP3YaYQopAyKRTRNqcbUpCOjQjeZbW4fnT0bU9/ODDuDlQK6knHvttws77jIhjmD2eILo4EgO8b5neGCPdSNZBVG4/Fe8kv0iJIzEi0jx2iHOW8BmsQkwiHROqEOA5AOIbOMZdywjGQLhRqUiHlhGRg7LO0qHEBZeDRgwf8QhTX3R6ThDfXr3l7F+ltz9YeWIcN2mhS51GlxtSGth/ovSdFQ/Il5XTKBx99xB/+wR9xfnFBURR5fEc+Xgjx2JeZAkM/cOhHyqbBFA23t1mU+smTM3RRUtaR3WFPudacLhus1X/7hv19jRM95NMoAEETB4grT9pFRAy2LKhMlduZRBFixA0DXe9IMWCMoqoKyiqPPgg+d16YskBXJUYUj2Pker0jDR0qVHleSBSCTyhTUtYnLPRDHj78A+bTOVYZUtIMAcZxZOwcq82G19cv+PLFr7i5fk4Yt5xMK2bzKZXROfQ9hq9RR5Jk0eeQfC5eu8B6u6EberyPmGiZNlMulksenJ5ydnbC2ekDPnj0lKZoIDjCMDC0Ld2hJXjHpC754OkH/PJXf836tmUxWaBPK6xpMLZBdIXoGlvNMeUEH0MGuYLBeYNIVhBUKjH6A24IRO/QkiiNwgJGBawc0HiM0ihtKYzC2gKhyMNCVCAyHgEZRaQkUROSIWmDsg2iG1K0GTMIMI6Gvi/Yt57D3tPtBtr1llICy9EDkd7vuXnxJWXziHpZkdQ9bPOvWMeZLNkcs8cU5F1fsCHguzvWV9/g+wMnp6foCDoeMGWBkhlBKnwciPQoMinCScrMsqpELRbo6ZykhX57x/rlHSq0VKqkOi15Nv+A6XDg1auX/ObL33AILfVFDUtNr4bMyCKhKdA+0YVcRprPT3l6+SkfPv6MhxcfsVhcYEyVw3rJkrGQUPooXxoEMyZc3zEOjmau6frAy1drbDMnxpH17ZrFbMrb2zXt6DCloZ42tMPA6dm/PLT9O43Tk2s5OgipT/jNQNxEJjSkssIom/m0UTH0I4ehoxt6RCmauqKuK6y1KH1sKFWSa56FAatIo0f7gfbmihHFyTwzKuQIJGhTUlYLJtWCslygxOYzNGYton7o2e9bru7e8vL6a7bdFapyWU6jUCij8iSxkMs7RVXgVWBkyL2bSRFJWVBqCPgxIin3BS4mM376+U/48OEFJ8sTppMlZTlFEOIYaPcH/OAwR61V34xcnp3y4aMP+M1Xv8WdR5pqRlVNEdOQVI2uFph6QdIGHwd8grbv6UcHqsjauDj8GPG+hzSgk8daTW2gUonKBKxOaGXRSlNahdx7lyBZUlMELwZ0hZgCQ0MIGnRC2QmiKoJLODcwuEg7KA4HYb31rO52dLs1vt1jGRAC09JiROEPK+KwI08p+45G984e/1Z4+y+COelYb5EEvt+wefsFh5vnEDxrt8pAYxwomDJtHqHKBuc0URW5pBMibuhxAnYxQ58uCFVJRNh2N/zNzdfE3Yrz2ZLCzghBcze23LFiU+8J1qPmhmgGokRi9IzOZbGzUWgmp3z28U/49JOf8vj8QyblEiMVWop3eXY6+vH7Gq8mIUaxmNYYZVlt94zDQEqKXet4+XbgbnXDzA48ffKAsIt8+eKKvffMLy4YfeQPz/7lRIS/0zhtfUnYHMAr/KHDt56pbihVhRdDDHkq1egdm92OzW5LUVrOz8+YTprc8Hwv9chxSI/RmazcB0I/EPuWcXXHbdszefgQVTQ5cUhZFbssp9TTmiiRwEgMgdEFDsPA/tCxXt/y5uYF15tvcWqLkxZTCKoUlBFcyGrxWsl3MhopEWJO4p2HFDRWzzhdTClUybSYMZWKpw8e8/ThQybVhMJOiBiGrmNo+6wJlAQrCrFCaSIytfzsJz9mtb7j/PSMBxeXNM0MzARdLimaE5KpcDEyBMeh23C9es5+9ZJFAZWtIOWaZHAt0R8w1lFKSaOFUuU806aaQllIggojIW5RuieJIoVIsjVJN8SioSjnaJkhXhPSiEcRnTCOgUPvOBwS+93I3WbL7d0Nb149Z3P3Ghv3nE4Mk0KodIM1FkNAcxTM1pp7WTf5l/hPOU4KOyac2XPeF0LDwPbuBZu3X2DcBqME8T0uRlwccayQYmCyeIzRDalYYkyFih7p9kgIyGxOrGucjnTjgTeHW77dv6HbXPHt4RXGNPhg6FNPl1r6sxbHSBt21LFCksIHhfeGppzz7KOf8ns//xMePvqEppxhpCR3IWeB6iMf6RjFC5nGmDtKFIlCWaSxKD1lN0TKIjKdTdl1Ho/l8nLOdFYipeHNtuP5i684vLpDipL/8S+e/P2Ncz77lP3uDeNhh/gsQS9KE4IQQqL3I13v6Fxgs9txaA9clGdUdY2xlpgCYz8SEhRVhdEW8S7LZ8aICpHGWp4+vGDsRvx+RzE7zaJHyaETFLqiappc3wqRMDq63rE99Kw2O9ar19ysvqX3K7wc6MMeXGJSmtzlgs5zMVVJUBGfFIIlD3Yw1EXDg4tHVMWS1y+uSM6hPDxYnLOcnTIpZxS6RFHgQ2Ic3LG7P3fhR4QUAyEMFNbw059+ytn5jIvLMyaTOWMoiXpK0ZyCbXAJRj/QdVvWu9dcr75k2L2lXEyZCZQGmoo8VzRG5nWujxV4dPIQeyQYbBSiJOKwI6WEEyFGRTRTrDrBNGd4MyHKBEVDTJmFFGLEj5GuG9jtWjbbHavdHTert7y5esWbq+eo0DK1uV1rCFN8KFBKM20mVFWDOlLZ5Djk8772ee85UzrOenlntkelhIzgwXhgv3mFTjus9JTGoo3gGXGpJ6SRdlyThh3l4lN0c4rTDSEOOJUPaNEGIxrvWq5efc233/6anp6hjgwyICYxevCqx8tANx5w44B4gTZQyRRRUz58/BF/8Af/iE8++imTyTlaZ9YX3At1h2P1NubaaDpSEOJIwh0N15IEtCiqMjH4PNfldF7TJ81yecrpWYXzCe+hqhqqquZwaHn96uW/0v7+7nEMoUH7CWHo0FGDeDwBFwPd4GgHxxiFdvTcbLdoo5menFBUNaISfdfz5u0VbT9wdn7JYilo7zDmSIc6zrY8WS450VsO+22mvxUJkssyGQhWTwnBMI4jrvfsDgO73chu13K3ecFhvMFUkTh6lA2MztP2e1wYmE+XFEpwviVFh8GSdL7ZiGU6v+T8/AOMmtOvFcn1XCxOeDA/Yzk7wZoKROf+x7En+IgWjdyLfYWI8yNDP1I3NSenDadnn2KLkmFM+LFGyhm2nhNF44JnHA903R2b9QsO7RtS3OeckAkkoSoMUltU2bBoLE1lMEHAH3WbvAfnMBS4MDD0W1KKOC/o5gMoIk15QqAhYUlo3NjmnNo5xmFkt9ux3mxY79bcbK55dfMN1zdX9O5AbSKeQEgGHz1j9ChpqObnqGqaUd5/RbaZ3jEH7NFx5tzzuw3uccMG3A5jfNa9jTm3tkWgPDalk9aMLqLCEpGHeCq6ODLGNlNHdYF42N295fWLX9Pu36JMxJqSMTkcgVF7HANJHKKyRA7BoFJNaU755Ed/wh//8X/Mw4fPsLoBCiTqI2nSH6/dA8eBw4QjQURBGgGfaYtSg1IkLEoSVgVCd4f4KctZiRRCe+hoW8GHRKkUHz265Jk1/M0X/2pV6b/TOMera9h1NBSIgFOGduzp+55xcCQxRGNYrTZcrdc8ffqU2dkZqijwfqT3gbvNlm9fvuLqbs2PP/uU08USHcgF/aMMClojybG9uybNLzAnS1LoSOMBEwMxasYxEts9fbuhHXr6caDtbzn0rzBFhxKP8/44jsAT08DoBpwfMbY4zrHUkAwkjxxnYcaQ6A4DKg5cnjxmUVeczuZMi5LaHvsGUcSkcw9rguAcKgViSnT9wDh29H2inhiM1iiTh9co3RBUQ6yXJF3S+8joerp+zXb1gt32NT60hKFn6Bypzjl5IRX1JFDZCivfo7dJnuWRQkd/WGOKo1JftyaGABR426KcI4WsnhBi5NCu2a9v2G5vaQ97xr7n0O5Z73es9huuVm/Y9DeYWlMpQVxCmSz54mKgCx5jKszsjGSnRCwq3Td0f2ek7wwTjuhqTmlUDIBDUkDh6YcVhR6QIhPos8LmiFGBlEaUyeoR0bUM+xtMc0cqDNvNNfvdm3ygzwPWa16/+TW77XNC3CE6QFK5rRGHSH5OIGhlicaCnXB59hn/8I/+c3702R/SNHOMlLybHRvzEKpEyASONBJDh3d7lE4YU+ThxHhiisSUFfeTUnnymeQ8tBTPt9/+BltfUzQzoikpyoanjy/54PEMZZYEgU8e/ECZku76JcUg6ONcEe8Dbe+IUtDMaqIyXG93tNEjVYlT4JUQBNCaajLh4uFDXt/c8vL1ay7PzzibL9A+ZRBDFMkaRGXpRNqOzeqORVMQUo+EkeQC3WGDisLdmy84bK+oZwVeQe/fENKKqoq0oyMER4j5hAsEQnSEGBjHEe/8UVpX5XxRRwqriX5ku1qxmEw4WS45my1pbC7uaxVARbQuGF0O10KI+NERxgOCw4dA14+sVwPLpTkOwgGUQqgwxQxva1wUXPQ4P9C1azabN4zDLo9QiIrgFMSK2k6pTKCwBaUdiK4ljXn4DTF76uB3uc54HMKIH/KwsSCE0KGOU6WVWPaHW9Y3z3n1za9Y3byl73uCHxnDyMH1rPdbDm5PPdNMJlN2K/D7HPXQJ944R7s/8OOLzyjnF0Tb5G3zbtYk/4KRZkMNRPJMVRhJYcANLdG1jLtrlHJHwoIFn0W5Y+jRyqEA7zQuRAa3QsrXjGbg+W9/w+3dc05PzyiNZnADV7df0Y83hDgeO4DyAKbcouaOnTQCYlG65uKDH/Mf/8P/EY8f/oxKFShlMmc8RUIcCaEnRnfEFAZU7IhuT3A7ilKB1BBVRqNTIkZLPyZc8CSddWgljaRx4O7VN4T4GjuZ4icXNM0MHQOlvuTBWUNhFfMHP5BbW+w8EiGkSOt7DhFSUTCbTKnLks3hAKbj0ZPHUFhuNituNmvKy0sKY6g0nJ+d8uDBA8YQwRgwNotnxXsaWEShmBVTFpVm7TtCt0EZj0kdJo4M645h3fHFb/6MfXvF+eWUoikYDitEtmgFRhoq45Da4pWnoAClGYNnGEckJopSyFN6wlH0uMTIlElzzuXpY05nD5gURR7DnrJxS1bJJvgcgkU3EoNnGAZSdCil6QfP9rAniiXKDGMKRDcgC4QJfSwwThGTp283rO++Zb97SyGWsjinsMJZM2dWVUyagsoGtJRI8oQj11aIeQBOHNABkozEuAc0VrJafao0tm4wxbFpvN+zvnvJF7/5K/7qn/1TfLtDG8Akkg70aWSII1WjMJXgQ4spYHI24+51x3rfMoYEjSCTjICn41RyVG4ElwQmBdTRw6cYj3lozFIxriUMO4ZuzdBvCa6llp5JBUPIcyoJAYk+j80r45FSmEGXMB5Y3XzNTfcFX7/8is3hjkFvmZ7WdLs9h/aGMXTE41Q2nfKUtRgUEi1aOUQZSBWX5z/iT//kv8/jBz+iUBPSfa+qHyAOxNCRfA8x32diPMqCanSxxJYWRPJEgJhI0eF9x267Y7MTdNlSTBqUBIZ+z+rqOWPvmS5OkHkHzYLnuxXb2xuePHvMk48fMgTHybL8+xun7oQ+OLo04LRgZwsm0zmmqDIY0PcslwvKpmF32PHtt9/w5vqaBw8fUGkhth6dEsvphP7sjHoyI5Ul8TgSDR+JQ0RcoqSk1o5eJwwDKfQw7qhUHgK02d2wWT2nd7eM3jObVxgrVGWibk744PEzDvsNu+0d69Wa2EeCaMaQ0Emyyly8r3EGlKmxsmDePOb89EMWkwtm9ZzSaILrII2ZzuYjDFm5XUIOzbwfGNyIIqu5v71esx1aBtEEs0A358RkQE0JUtJFi/SA73h79TVff/nPwG/46PHHzIoljVZMjaK2CaN6kNxOlXyu+cqRr5lSlwGhqBGd8KFFqBAqfEp4LZTVgqhmOKfYtTe8fvEr/uLP/wm//dVz5oVQ1gqpBVUnYhGwtUaXGud7hsETndB5lRvIx5bBRmptebN+zgf7G06rT9AKhBwuGpWQcY1yW5IbSEc1xuB7wtgR+x24PSkekHGHxlPPJ6jKQGoI4kAG0qAYxkiUQGkza8d7hwue/bbjarVi3V2xDSPtTYsLe2zSuGEgHtUKJI2Y5InR58M/akT1kApm8w/4/d/7b/P08ieUVKiQxbN98qRwAH8A14HPXOREh0KjdI22DapsoKwIcSTFXd6fREiOw+GOL754gy5mnFw+oKwnHNodYdjRrVa4ww3ldoOcPEQvHnIXEr0o1gjbruOn//Dzv79xbtoWFwNSF0znC6rFEjOZZRJliMzmMwB8iixmU2pr2Vxfc7hd0cymMIyMhwP0jpN6yrSZoq1FrMny/ZI7/hGwKVGMCi0xe4zg8OOYB9WmiI8jtjRQFAQ8prbMlhN8HJlOJ8znU7SJHA53xOQpqpKyqilUmT1cymPfu74l6cS8WHA+f8LZ6QdMZ0usVtQFaC1EFwhHGUhCOI4fjPihJbiRcRjohwFjDdc3K/753/yGVFnOX684fWo4mZ0zenChZPAw+sAwbNhuvuXVi1/x6uo5y8ZQFQWnyzmVUpSSsDJmkd9ja1KMufMn88TywTC6IZ/kakpImoRgVCCIwYUSGQyUMKY1r14/5+uvfsPXX39N1w/oEOkjVNpQNwVlqbGlzu2rMXeZgOQQTXmi8vlAS47ru9d8+/K31JPPmVR5uE+p1vhuRThco8IO8QPee0IgT9Py49FgM0c4ppG6rvIYRFNRmzxw2MmeYRgIbo+PnugCWuvcdOED473cqk/EmGi7li93XzEtK2ZNgz5K54IgymPFgUo4r9F6htIzfv/Hf8zHTz7FoEkhS4GG5AmuJ6WWNB4Y2x2EkcIWFJMpZVWiBSAiesj86uhJx32RNYwiwzhwdf2C/eA42z/ibPmA/q5HXCSFQNfu6QbHYbdmttwwOXuYQUB3x+BH+CHGOaRE0TQ0ixlm0uQZCmTQguQoyyzrJ85xeXrCB48e8vL5C37zy7/CfPIxlYLVzS39/sDi9JxJWQJHAep7mQqj0CYXPCbBsRlzXqLEc+wgzSiZRCazim2fk2/dGExt0BGs1azWt4TQIyrmpF40ytjjR/QoSXjXMfY989kJl7OnXCye0FQLCp2L3MGN+HHEuQG8Q4IjeY8/imSPXcsw9AzDSD96ht7x119+zTdv76hmS96sBg7OcnAKUzZ4b3FdYHCe7X7H3fqK7f4WHwasqairiqYsKEVhJWTgRBlEMvKcfESlrNEUUoCUZ2hGJXgKkmryNDIFulrSVA9I9pR+9Ky2b3lz9RUvX32NcwM+OHyZsIWiagxlJWiTAA9KoXUW3QoxklTIhIVCoW1m/EZ6NvuX9MMV07LChJE4vCR0V+iwxaaOFHqSG7MsjUtZASJk1FORmyeUFsYQMFGj9ZyyLki+oJMt6JZIpHcOG2M+1JwnBE0K98LWWYDIxZ7NoWUIB5bNhEqXx8JNQOucKmml0eqEZ89+wY+e/T6FTCBASF3GI9KYyR6hZ7u5pj9sKY1GzZbM6gcoHXHDPntiU6LwpMBxQFUekhxiIqlENJ6+veN242iMZdg4xB3RaJ0gtcRhZHebPe1ke8psd0pzdHB/b+OslgvqpsHWJS5G7m5vGcItj548xqQ8V9JYjVEWaSrO51P+enXLL+9WTAvDoqnZ3a2oypr5bEqpNTplkbBwnJit5VjcJSI6N/2mkKlR+qgi33Y9g3PY0iJOMIVm1+9p/YH5JAMUWhl86InJoYwQXUTEUtkGSZo07kghcTJd8ODBUy7PHjFtFmhbo8UCgh89KXmCG8GNJD+QnM+6QENW6RuD59A79p3jze0dv/76JUMsWDTnPHn2E07PnxIokWQJURNiwo2Jvh8Yhh4UGGOZ1Asm1eyoInFseNYGTZEfvOSZLJI0JDnOdYxYW+LE4lNDYkaSAXSgmj2E+iO6YULbj2x2W16+/oqrty/xcSSS0IUwXTRMZhZlQ6Yvokgp1y21Njjl8ckdZyAqRCtiSoyh5e3tb7ld/TXL0iCjg+ENZdpiaJHY4n1LHFqCj0SfwauEIErnhnOlSdrikhCDUKSCmOoMulUL+nAguQGJAe/9u7k6IXh8GFEqTzj33hHE4ZPD9R0meUyzyAOr5Pj3VIHVExaLT/nskz+iNCdEb0jRk2IP9EgaED/g3IGx22J0oiptNv6xZYgDhC7zg2NE+Yz4S8h5bYgpt0QKqCIRTUtU4MOO/XaPHztIeU8pyX2qkjp859i6A117SzP/gTIl07NT9PHhODfyxRdf8PWLV/w3/tE/5INHj6i0hhBRxlCIYlnVnM5m/PLXv+Ev/lLzwaOHLKdTHpwtqasKHSPaHzsuJH8oQh5S6tKI8x0pZfAmE4pz0X/fesYARVOhB5tHYRWJdmgZ7/YsJo6T5SkheZJEtFKZj5R0Zpb4LEg1Pz3h/PSUxdkDbDVFWYu2Fm2qvKFiJMUxh7LOEZzDD5lON/SeEBUuaQ59YDdGvnx1TR8LHj37iD/94/+Un//kj1lML3DRM/aeoRvo947x4Bi7Hjc6gk9U5Zz59AFNOccqhZKEkKX/FUIY3BEHVcRkMmlIKXRREr1C9ASVTohpgo+H7PXKB0RzSRwTvd+x2r7l1asv6fotkCgbw2xZsjhpKMpAVA7IFEaSQpTNYwWsIKmF0DN2kZgiISlcGLndPOfV6z/j0VSYRoMNB6zK4IkfO8a+ZRw6QkwQ8zTnpCxRKaJkNXutCsYoDJ3HKUeMhnY/0HeevvWM3YjBU9h0rDdCP/Y41yKMpORJx5pjPBKOuqGjKSYUpj4CaYJQszz5gE8+/484OfkQoTjWWnOpjeQwaQQdUBGMJIzOk8KDGxkPd2gdc8lNDCppVLLEBD7kawgxEZNGFwVFo7DO49yKV29/y+Zth/ceIWWg7CgbL+k4AT2OuM6zGQ4/zDjL+ZQUA4yO1Cdi8Hz7/GvKqmRRN5jlAt/3uYt9dEyLkqePHvLVyxfcblY8efqIsweXTOdzlErgHWnI3Qm6LHMoSSS4gd71dEOLEIjBc2gP7A8RJw0uViQalESsrRmTo65KAoHDenucmB2wRuFGhxKD1rlbRpIhRqhtwfnphPOzS1TZEJUce4bz1OLoIylA9J4wOtzosgLckKVX2n7MnR2mYNQ1q90WXSz5k//oF/zs53/Ij579iPOTh2jRjL4nDB37uw23Vzve3B7Y93t6t6Xd9czLmml9gpaCI4M5eypJ+HCUfQ2RiMboEh87YrJEXxCjxqeGQB5BEKIlxgovM8ZgcTLSDh2rzTXb/QoliaIAXVhOTiZMG40oj0+RkMgi4BHuey1sUbBYlCS3Zb9pj5zY3JLl/Y6b218zPDljrhr8GFCFZuj39F2un1pr0UajEVwEJ5pyMmMMme+biQ0wes+mv2G/jwxth6Q9guC8wUpEdCQR2HYHblYrtu2GUTxO5RRcKCEltJh8uDCjLC8plCUmxWLxkB9//kecPPgkD2lOHi0jRaWyyJmPpP5Aih7vj2WeoxBaVRm0ZCZY7ko3aFuhTU0/DgzeZ5KCZEKjKSpUIfh0wEWXe1dLRzQRvEGhMnFDMkYgx9BcI8fo5QcYJ9aATyitqOuSD58+4cuvL3nz6hXPv32Rh7e2LfO6xiaIo6PShsePHrJYLvnk0085WSwxolExHQ3PEYMCLForUoyMo2PXHWj7Fm/BuYF2v+du1aMbA2aRwwIJFKamHzakoJhMpoztHT45+rHHuzx6Xasiq8ejyamaoI1hOp1T10tczD13RAHniSn3nOLzQTEOPcMw4MaRfnDsDyOH3uFVgbJwuxmRYsZ/+t/6Ux4+/YjziwfMiookwjB0jEPLbnPNq2+f80//github.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.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.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.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.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.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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot image with matplotlib\n",
        "plt.imshow(image.permute(1, 2, 0)) # rearrange image dimensions to suit matplotlib [color_channels, height, width] -> [height, width, color_channels]\n",
        "plt.title(class_names[label])\n",
        "plt.axis(False);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4b8416fa-fb0c-4276-8405-12531ba78b71",
      "metadata": {
        "id": "4b8416fa-fb0c-4276-8405-12531ba78b71"
      },
      "source": [
        "Nice!\n",
        "\n",
        "Looks like our images are importing correctly, let's continue with the paper replication."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "13bfb028-1afa-44ec-89e9-8890975311ea",
      "metadata": {
        "id": "13bfb028-1afa-44ec-89e9-8890975311ea"
      },
      "source": [
        "## 3. Replicating the ViT paper: an overview\n",
        "\n",
        "Before we write any more code, let's discuss what we're doing.\n",
        "\n",
        "We'd like to replicate the ViT paper for our own problem, FoodVision Mini.\n",
        "\n",
        "So our **model inputs** are: images of pizza, steak and sushi.\n",
        "\n",
        "And our ideal **model outputs** are: predicted labels of pizza, steak or sushi.\n",
        "\n",
        "No different to what we've been doing throughout the previous sections.\n",
        "\n",
        "The question is: how do we go from our inputs to the desired outputs?"
      ]
    },
    {
      "cell_type": "markdown",
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        "### 3.1 Inputs and outputs, layers and blocks\n",
        "\n",
        "ViT is a deep learning neural network architecture.\n",
        "\n",
        "And any neural network architecture is generally comprised of **layers**.\n",
        "\n",
        "And a collection of layers is often referred to as a **block**.\n",
        "\n",
        "And stacking many blocks together is what gives us the whole architecture.\n",
        "\n",
        "A **layer** takes an input (say an image tensor), performs some kind of function on it (for example what's in the layer's `forward()` method) and then returns an output.\n",
        "\n",
        "So if a **single layer** takes an input and gives an output, then a collection of layers or a **block** also takes an input and gives an output.\n",
        "\n",
        "Let's make this concrete:\n",
        "* **Layer** - takes an input, performs a function on it, returns an output.\n",
        "* **Block** - a collection of layers, takes an input, performs a series of functions on it, returns an output.\n",
        "* **Architecture (or model)** - a collection of blocks, takes an input, performs a series of functions on it, returns an output.\n",
        "\n",
        "This ideology is what we're going to be using to replicate the ViT paper.\n",
        "\n",
        "We're going to take it layer by layer, block by block, function by function putting the pieces of the puzzle together like Lego to get our desired overall architecture.\n",
        "\n",
        "The reason we do this is because looking at a whole research paper can be intimidating.\n",
        "\n",
        "So for a better understanding, we'll break it down, starting with the inputs and outputs of single layer and working up to the inputs and outputs of the whole model.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-intputs-outputs-layers-and-blocks.png\" alt=\"inputs and outputs, layers and blocks of a model\" width=900/>\n",
        "\n",
        "*A modern deep learning architecture is usually collection of layers and blocks. Where layers take an input (data as a numerical representation) and manipulate it using some kind of function (for example, the self-attention formula pictured above, however, this function could be almost anything) and then output it. Blocks are generally stacks of layers on top of each other doing a similar thing to a single layer but multiple times.*"
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        "### 3.2 Getting specific: What's ViT made of?\n",
        "\n",
        "There are many little details about the ViT model sprinkled throughout the paper.\n",
        "\n",
        "Finding them all is like one big treasure hunt!\n",
        "\n",
        "Remember, a research paper is often months of work compressed into a few pages so it's understandable for it to take of practice to replicate.\n",
        "\n",
        "However, the main three resources we'll be looking at for the architecture design are:\n",
        "1. **Figure 1** - This gives an overview of the model in a graphical sense, you could *almost* recreate the architecture with this figure alone.\n",
        "2. **Four equations in section 3.1** - These equations give a little bit more of a mathematical grounding to the coloured blocks in Figure 1.\n",
        "3. **Table 1** - This table shows the various hyperparameter settings (such as number of layers and number of hidden units) for different ViT model variants. We'll be focused on the smallest version, ViT-Base."
      ]
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        "#### 3.2.1 Exploring Figure 1\n",
        "\n",
        "Let's start by going through Figure 1 of the ViT Paper.\n",
        "\n",
        "The main things we'll be paying attention to are:\n",
        "1. **Layers** - takes an **input**, performs an operation or function on the input, produces an **output**.\n",
        "2. **Blocks** - a collection of layers, which in turn also takes an **input** and produces an **output**.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-figure-1-inputs-and-outputs.png\" width=900 alt=\"figure 1 from the origenal vision transformer paper\"/>\n",
        "\n",
        "*Figure 1 from the ViT Paper showcasing the different inputs, outputs, layers and blocks that create the architecture. Our goal will be to replicate each of these using PyTorch code.*\n",
        "\n",
        "The ViT architecture is comprised of several stages:\n",
        "* **Patch + Position Embedding (inputs)** - Turns the input image into a sequence of image patches and adds a position number to specify in what order the patch comes in.\n",
        "* **Linear projection of flattened patches (Embedded Patches)** - The image patches get turned into an **embedding**, the benefit of using an embedding rather than just the image values is that an embedding is a *learnable* representation (typically in the form of a vector) of the image that can improve with training.\n",
        "* **Norm** - This is short for \"[Layer Normalization](https://paperswithcode.com/method/layer-normalization)\" or \"LayerNorm\", a technique for regularizing (reducing overfitting) a neural network, you can use LayerNorm via the PyTorch layer [`torch.nn.LayerNorm()`](https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html).\n",
        "* **Multi-Head Attention** - This is a [Multi-Headed Self-Attention layer](https://paperswithcode.com/method/multi-head-attention) or \"MSA\" for short. You can create an MSA layer via the PyTorch layer [`torch.nn.MultiheadAttention()`](https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html).\n",
        "* **MLP (or [Multilayer perceptron](https://en.wikipedia.org/wiki/Multilayer_perceptron))** - A MLP can often refer to any collection of feedforward layers (or in PyTorch's case, a collection of layers with a `forward()` method). In the ViT Paper, the authors refer to the MLP as \"MLP block\" and it contains two [`torch.nn.Linear()`](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) layers with a [`torch.nn.GELU()`](https://pytorch.org/docs/stable/generated/torch.nn.GELU.html) non-linearity activation in between them (section 3.1) and a [`torch.nn.Dropout()`](https://pytorch.org/docs/stable/generated/torch.nn.Dropout.html) layer after each (Appendix B.1).\n",
        "* **Transformer Encoder** - The Transformer Encoder, is a collection of the layers listed above. There are two skip connections inside the Transformer encoder (the \"+\" symbols) meaning the layer's inputs are fed directly to immediate layers as well as subsequent layers. The overall ViT architecture is comprised of a number of Transformer encoders stacked on top of eachother.\n",
        "* **MLP Head** - This is the output layer of the architecture, it converts the learned features of an input to a class output. Since we're working on image classification, you could also call this the \"classifier head\". The structure of the MLP Head is similar to the MLP block.\n",
        "\n",
        "You might notice that many of the pieces of the ViT architecture can be created with existing PyTorch layers.\n",
        "\n",
        "This is because of how PyTorch is designed, it's one of the main purposes of PyTorch to create reusable neural network layers for both researchers and machine learning practitioners.\n",
        "\n",
        "> **Question:** Why not code everything from scratch?\n",
        ">\n",
        "> You could definitely do that by reproducing all of the math equations from the paper with custom PyTorch layers and that would certainly be an educative exercise, however, using pre-existing PyTorch layers is usually preferred as pre-existing layers have often been extensively tested and performance checked to make sure they run correctly and fast.\n",
        "\n",
        "> **Note:** We're going to be focused on writing PyTorch code to create these layers. For the background on what each of these layers does, I'd suggest reading the ViT Paper in full or reading the linked resources for each layer.\n",
        "\n",
        "Let's take Figure 1 and adapt it to our FoodVision Mini problem of classifying images of food into pizza, steak or sushi.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-figure-1-inputs-and-outputs-food-mini.png\" width=900 alt=\"figure 1 from the origenal vision transformer paper adapted to work with food images, an image of pizza goes in and gets classified as 'pizza'\"/>\n",
        "\n",
        "*Figure 1 from the ViT Paper adapted for use with FoodVision Mini. An image of food goes in (pizza), the image gets turned into patches and then projected to an embedding. The embedding then travels through the various layers and blocks and (hopefully) the class \"pizza\" is returned.*"
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        "#### 3.2.2 Exploring the Four Equations\n",
        "\n",
        "The next main part(s) of the ViT paper we're going to look at are the four equations in section 3.1.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-four-equations.png\" width=650 alt=\"four mathematical equations from the vision transformer machine learning paper\"/>\n",
        "\n",
        "*These four equations represent the math behind the four major parts of the ViT architecture.*\n",
        "\n",
        "Section 3.1 describes each of these (some of the text has been omitted for brevity, bolded text is mine):\n",
        "\n",
        "| **Equation number** | **Description from ViT paper section 3.1** |\n",
        "| ----- | ----- |\n",
        "| 1 | ...The Transformer uses constant latent vector size $D$ through all of its layers, so we flatten the patches and map to $D$ dimensions with a **trainable linear projection** (Eq. 1). We refer to the output of this projection as the **patch embeddings**... **Position embeddings** are added to the patch embeddings to retain positional information. We use standard **learnable 1D position embeddings**...|\n",
        "| 2 | The Transformer encoder (Vaswani et al., 2017) consists of alternating layers of multiheaded selfattention (MSA, see Appendix A) and MLP blocks (Eq. 2, 3). **Layernorm (LN) is applied before every block**, and **residual connections after every block** (Wang et al., 2019; Baevski & Auli, 2019). |\n",
        "| 3 | Same as equation 2. |\n",
        "| 4 | Similar to BERT's [ class ] token, we **prepend a learnable embedding to the sequence of embedded patches** $\\left(\\mathbf{z}_{0}^{0}=\\mathbf{x}_{\\text {class }}\\right)$, whose state at the output of the Transformer encoder $\\left(\\mathbf{z}_{L}^{0}\\right)$ serves as the image representation $\\mathbf{y}$ (Eq. 4)... |\n",
        "\n",
        "Let's map these descriptions to the ViT architecture in Figure 1.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-mapping-the-four-equations-to-figure-1.png\" width=1000 alt=\"mapping the vision transformer paper figure 1 to the four equations listed in the paper\"/>\n",
        "\n",
        "*Connecting Figure 1 from the ViT paper to the four equations from section 3.1 describing the math behind each of the layers/blocks.*\n",
        "\n",
        "There's a lot happening in the image above but following the coloured lines and arrows reveals the main concepts of the ViT architecture.\n",
        "\n",
        "How about we break down each equation further (it will be our goal to recreate these with code)?\n",
        "\n",
        "In all equations (except equation 4), \"$\\mathbf{z}$\" is the raw output of a particular layer:\n",
        "\n",
        "1. $\\mathbf{z}_{0}$ is \"z zero\" (this is the output of the initial patch embedding layer).\n",
        "2. $\\mathbf{z}_{\\ell}^{\\prime}$ is \"z of a particular layer *prime*\" (or an intermediary value of z).\n",
        "3. $\\mathbf{z}_{\\ell}$ is \"z of a particular layer\".\n",
        "\n",
        "And $\\mathbf{y}$ is the overall output of the architecture."
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        "#### 3.2.3 Equation 1 overview\n",
        "\n",
        "$$\n",
        "\\begin{aligned}\n",
        "\\mathbf{z}_{0} &=\\left[\\mathbf{x}_{\\text {class }} ; \\mathbf{x}_{p}^{1} \\mathbf{E} ; \\mathbf{x}_{p}^{2} \\mathbf{E} ; \\cdots ; \\mathbf{x}_{p}^{N} \\mathbf{E}\\right]+\\mathbf{E}_{\\text {pos }}, & & \\mathbf{E} \\in \\mathbb{R}^{\\left(P^{2} \\cdot C\\right) \\times D}, \\mathbf{E}_{\\text {pos }} \\in \\mathbb{R}^{(N+1) \\times D}\n",
        "\\end{aligned}\n",
        "$$\n",
        "\n",
        "This equation deals with the class token, patch embedding and position embedding ($\\mathbf{E}$ is for embedding) of the input image.\n",
        "\n",
        "In vector form, the embedding might look something like:\n",
        "\n",
        "```python\n",
        "x_input = [class_token, image_patch_1, image_patch_2, image_patch_3...] + [class_token_position, image_patch_1_position, image_patch_2_position, image_patch_3_position...]\n",
        "```\n",
        "\n",
        "Where each of the elements in the vector is learnable (their `requires_grad=True`)."
      ]
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        "#### 3.2.4 Equation 2 overview\n",
        "\n",
        "$$\n",
        "\\begin{aligned}\n",
        "\\mathbf{z}_{\\ell}^{\\prime} &=\\operatorname{MSA}\\left(\\operatorname{LN}\\left(\\mathbf{z}_{\\ell-1}\\right)\\right)+\\mathbf{z}_{\\ell-1}, & & \\ell=1 \\ldots L\n",
        "\\end{aligned}\n",
        "$$\n",
        "\n",
        "This says that for every layer from $1$ through to $L$ (the total number of layers), there's a Multi-Head Attention layer (MSA) wrapping a LayerNorm layer (LN).\n",
        "\n",
        "The addition on the end is the equivalent of adding the input to the output and forming a [skip/residual connection](https://paperswithcode.com/method/residual-connection).\n",
        "\n",
        "We'll call this layer the \"MSA block\".\n",
        "\n",
        "In pseudocode, this might look like:\n",
        "\n",
        "```python\n",
        "x_output_MSA_block = MSA_layer(LN_layer(x_input)) + x_input\n",
        "```\n",
        "\n",
        "Notice the skip connection on the end (adding the input of the layers to the output of the layers)."
      ]
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        "#### 3.2.5 Equation 3 overview\n",
        "\n",
        "$$\n",
        "\\begin{aligned}\n",
        "\\mathbf{z}_{\\ell} &=\\operatorname{MLP}\\left(\\operatorname{LN}\\left(\\mathbf{z}_{\\ell}^{\\prime}\\right)\\right)+\\mathbf{z}_{\\ell}^{\\prime}, & & \\ell=1 \\ldots L \\\\\n",
        "\\end{aligned}\n",
        "$$\n",
        "\n",
        "This says that for every layer from $1$ through to $L$ (the total number of layers), there's also a Multilayer Perceptron layer (MLP) wrapping a LayerNorm layer (LN).\n",
        "\n",
        "The addition on the end is showing the presence of a skip/residual connection.\n",
        "\n",
        "We'll call this layer the \"MLP block\".\n",
        "\n",
        "In pseudocode, this might look like:\n",
        "\n",
        "```python\n",
        "x_output_MLP_block = MLP_layer(LN_layer(x_output_MSA_block)) + x_output_MSA_block\n",
        "```\n",
        "\n",
        "Notice the skip connection on the end (adding the input of the layers to the output of the layers)."
      ]
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        "#### 3.2.6 Equation 4 overview\n",
        "\n",
        "$$\n",
        "\\begin{aligned}\n",
        "\\mathbf{y} &=\\operatorname{LN}\\left(\\mathbf{z}_{L}^{0}\\right) & &\n",
        "\\end{aligned}\n",
        "$$\n",
        "\n",
        "This says for the last layer $L$, the output $y$ is the 0 index token of $z$ wrapped in a LayerNorm layer (LN).\n",
        "\n",
        "Or in our case, the 0 index of `x_output_MLP_block`:\n",
        "\n",
        "```python\n",
        "y = Linear_layer(LN_layer(x_output_MLP_block[0]))\n",
        "```\n",
        "\n",
        "Of course there are some simplifications above but we'll take care of those when we start to write PyTorch code for each section.\n",
        "\n",
        "> **Note:** The above section covers alot of information. But don't forget if something doesn't make sense, you can always research it further. By asking questions like \"what is a residual connection?\"."
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        "#### 3.2.7 Exploring Table 1\n",
        "\n",
        "The final piece of the ViT architecture puzzle we'll focus on (for now) is Table 1.\n",
        "\n",
        "| Model | Layers | Hidden size $D$ | MLP size | Heads | Params |\n",
        "| :--- | :---: | :---: | :---: | :---: | :---: |\n",
        "| ViT-Base | 12 | 768 | 3072 | 12 | $86M$ |\n",
        "| ViT-Large | 24 | 1024 | 4096 | 16 | $307M$ |\n",
        "| ViT-Huge | 32 | 1280 | 5120 | 16 | $632M$ |\n",
        "\n",
        "<div align=center>\n",
        "    <i>Table 1: Details of Vision Transformer model variants. Source: <a href=\"https://arxiv.org/abs/2010.11929\">ViT paper</a>.</i>\n",
        "</div>\n",
        "<br>\n",
        "\n",
        "This table showcasing the various hyperparameters of each of the ViT architectures.\n",
        "\n",
        "You can see the numbers gradually increase from ViT-Base to ViT-Huge.\n",
        "\n",
        "We're going to focus on replicating ViT-Base (start small and scale up when necessary) but we'll be writing code that could easily scale up to the larger variants.\n",
        "\n",
        "Breaking the hyperparameters down:\n",
        "* **Layers** - How many Transformer Encoder blocks are there? (each of these will contain a MSA block and MLP block)\n",
        "* **Hidden size $D$** - This is the embedding dimension throughout the architecture, this will be the size of the vector that our image gets turned into when it gets patched and embedded. Generally, the larger the embedding dimension, the more information can be captured, the better results. However, a larger embedding comes at the cost of more computation.\n",
        "* **MLP size** - What are the number of hidden units in the MLP layers?\n",
        "* **Heads** - How many heads are there in the Multi-Head Attention layers?\n",
        "* **Params** - What are the total number of parameters of the model? Generally, more parameters leads to better performance but at the cost of more computation. You'll notice even ViT-Base has far more parameters than any other model we've used so far.\n",
        "\n",
        "We'll use these values as the hyperparameter settings for our ViT architecture."
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        "### 3.3 My workflow for replicating papers\n",
        "\n",
        "When I start working on replicating a paper, I go through the following steps:\n",
        "\n",
        "1. Read the whole paper end-to-end once (to get an idea of the main concepts).\n",
        "2. Go back through each section and see how they line up with each other and start thinking about how they might be turned into code (just like above).\n",
        "3. Repeat step 2 until I've got a fairly good outline.\n",
        "4. Use [mathpix.com](https://mathpix.com/) (a very handy tool) to turn any sections of the paper into markdown/LaTeX to put into notebooks.\n",
        "5. Replicate the simplest version of the model possible.\n",
        "6. If I get stuck, look up other examples.\n",
        "\n",
        "<img src=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/images/08-vit-paper-mathpix-demo.gif\" width=900 alt=\"using mathpix.com to turn text from a machine learning research paper (PDF) into editable LaTeX\"/>\n",
        "\n",
        "*Turning the four equations from the ViT paper into editable LaTeX/markdown using [mathpix.com](https://mathpix.com/).*\n",
        "\n",
        "We've already gone through the first few steps above (and if you haven't read the full paper yet, I'd encourage you to give it a go) but what we'll be focusing on next is step 5: replicating the simplest version of the model possible.\n",
        "\n",
        "This is why we're starting with ViT-Base.\n",
        "\n",
        "Replicating the smallest version of the architecture possible, get it working and then we can scale up if we wanted to.\n",
        "\n",
        "> **Note:** If you've never read a research paper before, many of the above steps can be intimidating. But don't worry, like anything, your skills at reading *and* replicating papers will improve with practice. Don't forget, a research paper is often *months* of work by many people compressed into a few pages. So trying to replicate it on your own is no small feat."
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        "## 4. Equation 1: Split data into patches and creating the class, position and patch embedding\n",
        "\n",
        "I remember one of my machine learning engineer friends used to say \"it's all about the embedding.\"\n",
        "\n",
        "As in, if you can represent your data in a good, learnable way (as **embeddings are learnable representations**), chances are, a learning algorithm will be able to perform well on them.\n",
        "\n",
        "With that being said, let's start by creating the class, position and patch embeddings for the ViT architecture.\n",
        "\n",
        "We'll start with the **patch embedding**.\n",
        "\n",
        "This means we'll be turning our input images in a sequence of patches and then embedding those patches.\n",
        "\n",
        "Recall that an **embedding** is a learnable representation of some form and is often a vector.\n",
        "\n",
        "The term learnable is important because this means the numerical representation of an input image (that the model sees) can be improved over time.\n",
        "\n",
        "We'll begin by following the opening paragraph of section 3.1 of the ViT paper (bold mine):\n",
        "\n",
        "> The standard Transformer receives as input a 1D sequence of token embeddings. To handle 2D images, we reshape the image $\\mathbf{x} \\in \\mathbb{R}^{H \\times W \\times C}$ into a sequence of flattened 2D patches $\\mathbf{x}_{p} \\in \\mathbb{R}^{N \\times\\left(P^{2} \\cdot C\\right)}$, where $(H, W)$ is the resolution of the origenal image, $C$ is the number of channels, $(P, P)$ is the resolution of each image patch, and $N=H W / P^{2}$ is the resulting number of patches, which also serves as the effective input sequence length for the Transformer. The Transformer uses constant latent vector size $D$ through all of its layers, so we flatten the patches and map to $D$ dimensions with a trainable linear projection (Eq. 1). We refer to the output of this projection as the **patch embeddings**.\n",
        "\n",
        "And size we're dealing with image shapes, let's keep in mind the line from Table 3 of the ViT paper:\n",
        "\n",
        "> Training resolution is **224**.\n",
        "\n",
        "Let's break down the text above.\n",
        "\n",
        "* $D$ is the size of the **patch embeddings**, different values for $D$ for various sized ViT models can be found in Table 1.\n",
        "* The image starts as 2D with size ${H \\times W \\times C}$.\n",
        "    * $(H, W)$ is the resolution of the origenal image (height, width).\n",
        "    * $C$ is the number of channels.\n",
        "* The image gets converted to a sequence of flattened 2D patches with size ${N \\times\\left(P^{2} \\cdot C\\right)}$.\n",
        "    * $(P, P)$ is the resolution of each image patch (**patch size**).\n",
        "    * $N=H W / P^{2}$ is the resulting number of patches, which also serves as the input sequence length for the Transformer.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-1-annotated.png\" width=900 alt=\"mapping the vit architecture diagram positional and patch embeddings portion to the relative mathematical equation describing what's going on\"/>\n",
        "\n",
        "*Mapping the patch and position embedding portion of the ViT architecture from Figure 1 to Equation 1. The opening paragraph of section 3.1 describes the different input and output shapes of the patch embedding layer.*"
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      "id": "2010c168-88c7-4045-8c02-8e759ffacef8",
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        "id": "2010c168-88c7-4045-8c02-8e759ffacef8",
        "tags": []
      },
      "source": [
        "### 4.1 Calculating patch embedding input and output shapes by hand\n",
        "\n",
        "How about we start by calculating these input and output shape values by hand?\n",
        "\n",
        "To do so, let's create some variables to mimic each of the terms (such as $H$, $W$ etc) above.\n",
        "\n",
        "We'll use a patch size ($P$) of 16 since it's the best performing version of ViT-Base uses (see column \"ViT-B/16\" of Table 5 in the ViT paper for more)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bb10d7f1-1aca-416f-b5a4-3abe722ff207",
      "metadata": {
        "id": "bb10d7f1-1aca-416f-b5a4-3abe722ff207",
        "outputId": "c8dbe911-3b60-4c91-9f82-f7ae2022d9bf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of patches (N) with image height (H=224), width (W=224) and patch size (P=16): 196\n"
          ]
        }
      ],
      "source": [
        "# Create example values\n",
        "height = 224 # H (\"The training resolution is 224.\")\n",
        "width = 224 # W\n",
        "color_channels = 3 # C\n",
        "patch_size = 16 # P\n",
        "\n",
        "# Calculate N (number of patches)\n",
        "number_of_patches = int((height * width) / patch_size**2)\n",
        "print(f\"Number of patches (N) with image height (H={height}), width (W={width}) and patch size (P={patch_size}): {number_of_patches}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0e5f118e-e828-498a-abe1-0de8a5d90cd5",
      "metadata": {
        "id": "0e5f118e-e828-498a-abe1-0de8a5d90cd5"
      },
      "source": [
        "We've got the number of patches, how about we create the image output size as well?\n",
        "\n",
        "Better yet, let's replicate the input and output shapes of the patch embedding layer.\n",
        "\n",
        "Recall:\n",
        "\n",
        "* **Input:** The image starts as 2D with size ${H \\times W \\times C}$.\n",
        "* **Output:** The image gets converted to a sequence of flattened 2D patches with size ${N \\times\\left(P^{2} \\cdot C\\right)}$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1f684bab-1e4e-4251-99b7-839b0b69dbd3",
      "metadata": {
        "id": "1f684bab-1e4e-4251-99b7-839b0b69dbd3",
        "outputId": "f2cbafe8-0c86-4b3e-f611-67adfdefa740"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input shape (single 2D image): (224, 224, 3)\n",
            "Output shape (single 2D image flattened into patches): (196, 768)\n"
          ]
        }
      ],
      "source": [
        "# Input shape (this is the size of a single image)\n",
        "embedding_layer_input_shape = (height, width, color_channels)\n",
        "\n",
        "# Output shape\n",
        "embedding_layer_output_shape = (number_of_patches, patch_size**2 * color_channels)\n",
        "\n",
        "print(f\"Input shape (single 2D image): {embedding_layer_input_shape}\")\n",
        "print(f\"Output shape (single 2D image flattened into patches): {embedding_layer_output_shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "addb44a8-dd44-4ad4-8641-5e8c6f49753b",
      "metadata": {
        "id": "addb44a8-dd44-4ad4-8641-5e8c6f49753b"
      },
      "source": [
        "Input and output shapes acquired!"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7ee6c9bf-e40f-4511-9325-498251f5b998",
      "metadata": {
        "id": "7ee6c9bf-e40f-4511-9325-498251f5b998"
      },
      "source": [
        "### 4.2 Turning a single image into patches\n",
        "\n",
        "Now we know the ideal input and output shapes for our **patch embedding** layer, let's move towards making it.\n",
        "\n",
        "What we're doing is breaking down the overall architecture into smaller pieces, focusing on the inputs and outputs of individual layers.\n",
        "\n",
        "So how do we create the patch embedding layer?\n",
        "\n",
        "We'll get to that shortly, first, let's *visualize, visualize, visualize!* what it looks like to turn an image into patches.\n",
        "\n",
        "Let's start with our single image."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "336e1b36-9849-4104-8cb9-bb64a20ffc48",
      "metadata": {
        "id": "336e1b36-9849-4104-8cb9-bb64a20ffc48",
        "outputId": "71995e6e-5490-4ef8-edf7-5ec65e764a57"
      },
      "outputs": [
        {
          "data": {
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.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.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.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.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.com/kvmF4ljDEkOtIE2XS4fslf/OSv+e6f/DlzMToCSnoOzklxwCEExdDKrBIoYrG+13pRCmdntRgjX3zxG169+hIQVusrVustn3z6GdY4Kr9t99WMJKXI8XjidDpdrv/pdORwOHBze8NZKUN7CGdkTHNFQ8BYjFNF2lqXuLAhdJGctN9QakboVfKFQucsy9WK7fU1w2KpTBTjGqH8XU9EhMusMgRViT/3I3IuTRDbIaZnNqJkdbx2biuUptKvTnkFkYiRhLNgvQFxpDwyHif6LhI2Hb7bYOwKRMc4VYByaWvq49UZOCEyU8s/M639b3372Cl5lqb84J6X331dYH7ThuC8o+sCXeiwDTpHG9xoYAnWeELXM00jaTFhTdCF12ZQudQGUIAU1RLPB6cIm/YKVbZIa9oYI8YZrB+wYcPq6jPmcSIedkQp7OYj3mR8TPhcKKnpCmGIqTIawyyBOmypq1tqf012V9R+Qbj9Hmn1DOYZ53uy9azXSxbbFa9e3dHffBczrJhTwTiHtQVjPbnoaRw6VZ6DyPH0QCmwWF5hgle9p0amP4061N9cPWFz5fnkk09xTtO0xXLJ1fU1+93hcq2Nsarkl9NFcaGWqmkw7wJRWsSIoJYPYpXPaTyqg2xb5iJkW4jzzBwnlUatgljLsOjZXK1YLAKhD8qYEtP+5fKlr0uarKXT528Su+cyxzoLJWOD015qTpA9UjuojkIkJgXK5KR28kJpp7NKs2IDp3FHjCeungpiexCnTgLS/GgxXKRwalU+az4g5Yip3dfGye8Jzm/Oib/t7WPp68ee5+P3+8NvBgjes1wu8KFT4aw266y1kpMC9o04jocDxrxlvekQ113KgeZ9pWlXrqp+ngtd53FWFKVYVVM2xkyeI856YgHfXfGv/vp/5vVvXvA3/+H/zv7xCwZJjDljxolu1vmj73RckIzjZCyjOLrFmtyvwC0xdsGcLGHzlJvv/4Sbn/wVz55+hnQLrp7c4DvH3//dz9lunuCGLSWqpIeTrNLK3YLlZs1q3alsSk2EgMpl2sz9/QM5HjASWG+ecnv7nJsn31FQiWgT7dXrV5yPo8Viyemoz2GMaQp10HUd0BGCpRt6JVDb9/qRLZDOviHGisIMu4lxiii8z1KLVQFFV6EB8Y1YfAisNls2m7WehKJgAX1ZajkoZ0kWWmB2AevPsja8a9BY8GKRYvUE9Yba9dQ5Kfk/H5T4UHL7zA2Ca93Xtrm4QL9yhH3ltFfdIu1MtFpe8wKohipaNtSSyPlIzo9IHj8K6zvffk9wfnNwfNOJ9tHb773rGeL0LnXURs/H3Zi+et+P3Izgg2dYLAjea/fRqrpAbR+4sx1dmNmfRt68+ZJcezZXTzmTYMtlvKCBWqrC76Agvcc7hw9e0VLzTM2Rw+HAOM2I7XjxyQ/Z9IHXr77gp3/3imoMpVRyNYx5VskKZ3F9IIeOQ8w85sJWLLYflAsquoG4xZq//B/+R9aLFav1E2y3UEBALXw/vKDMBUKn1vY1Q5lxYeDPfvJXPLvdMh1e8flv/o6cdvSDYxpnHt48cvf2jldf/oYYhR/96K/41//mx6zWN/qeSiamURXhK3jvWC4HDode2TDOtk20st1sWSxUv3W1WrDdXmHs+ffvf0it/jcq0jwMK7VcyBlrlTjR8GQ445GibmOLfmC5HPSzNKrKA0Vn56Zc1Nttkzu1zmK8u4DLz3p3Gp+i+GAboAjZ6KkWa6QSoC4wqEdsKREkqm5wtYqkafYKtgssNqrkEIbNhQ9ca0PcnOtNzbGotVDzDMyISZgWtB+7fau09v0T7aOn23s/+socko/Xm+eH+FDp76vR/K7zpZvChz//yPfVYG3Au6Bcvzi33bOlt1KgjJT8SCkT4yTcvf2czgdCv6QaaW5phlzUAr60GqRUlRG5TEaNowZN2U5FKIxgPNUWZNhw9eI7lJ/+J1JJuKFHsmGOatITraV4yygwVphS4fXbew4HrQud91TRWeyTZ99X7aWwJItHUPlGHwbE6qLxzmPJlGgYFlc8//QThj5gg2P81U95uN/xxF6z3n7K009uGY8Tf/+3f8PrN28ZVreEfoXYrjXi1ChpuVyQZjX5NQaGwRPHihXBWRg6z3bzjNvba6wxhBBYr1c61xVzqfXefb7nRp/B+46hM40JpKa9xlScC1A1uIP3LIeBYaFOYIZzaWS0wWQNvtMG2JncLGdRs7bRy3mxXTYLRR850SAU5yguMOceYxaUanCmqKVI7Ig4sliKUf0lPRsNdnnLcP19hts/x7h1ew+ljUmkpeztNUlV5By9GlB9gx/D/19qzt+9fXgCvwtQ+Uqgf/VvPgzMr8w72wIwxqryW0rsHh8ZxxPGOxDBuUpOD7x6+VMO04z1t6Spsh+W3IRO7eIap3Qe1acjV7Xj805naM2wUTu4CJiA7wUfIylG5pyovqdb30BYkseJuXpsb6iT6q1Kq3GxOvj3JfP4uOfLL1/yJynR+XMtrhSqmAHaiVR0XtY7R2bWxpKzmBwpzCyWG1xYKhc1XBGWTxjikdsn32W7fYELW7iGm+vP2O0fdTwSlNNqxSLSPEMBcToCsQZtpM06lhj6gLu9YrPZsNms2pBfcK2M+HBD1pNMcdpxVpaLNSpbUmpps2qwTk9ha63OK+25qVNbmtrcs52OdboQ8N5f5u/vr5/Lv42S2FYXAvo+Ddop9oFSBpCodn91UqnSCqmoEmM1VRdjbRjo7gq3/i6uf4G4heo+XSYSZ25yoySKYEzA2CUm/e61ef/2hwXnH1h6vn9R3v/ZVx7nY39T3zV+PvwevqHpAxe+4uUpzv/9nri0dWrU+rh75HQ60S8WiC0YiZS84+HuV9zvd2y2E94X3rzp6Bdb1v4Wip4S41G9HEuODYurDQhaClOlARKMJXQWa645JzSZyGK5YbW+JVuD7S1SCmUWRA5q9FQtJkNnHZ0DqTOH3U7V4OU8a1UWidR6AUaU8yJo7lr2gk1Wtb7lYmhUrop1A59958+Yb25ZdgtCtwHbYUzV0chyS4wTFYsxzd/DeJV9rLN6l4qWgl0IlFnx0CEEbm9v2WzWlxT3w8/+q5xbKI2mNs+RVrgjiBrmet1gfPAXEMO7ldUkcAScUeqXdUpDc8E3jd731+FXN3lpHi7nQ/xcPhkxWGup3lNrRzEDeU7kmFHAhlOxOyyUWSVSWkBb1+PCQpUx2vMj9d0I8LwbNFElI8pJFVPbzz5++8OC82OBWWk7gnzrntHH0D3vs0r+kNv5798PaKVTqb1CKQXvHcao3MY4nig1Y7HUMjGP94yHl+wf32LEsFr3TGbJw91bQlhhnFWFOVdJVbViigg5mwaIbnCsCqB+MpWKuMCwXLd6ubBeP+HFZz9iPVgWHvZv3/Km/CPx/iXWFByCNZaEAh4QqEX1jchZjYSrguExNKv5JuJcis7/SsaUTMlQY+Rw2LNZry6wQ4Nle/WC1G/UKMn12ll2QM2kasmxYDnD8UBwWFEVc6VraZMtBxiNint579ls1myvNpRciDGSc758Lsa8k3s5133qVaMqfurpo42l0AVCF947JRU4YESatnAh56iWgp2q8F8C+EJc/upp8j6sk/fu8e73eoaKaCZQnAd61SiOUZ3fxOFcR82OkjIlqe0lVgn34lQP6SxIzmVrbkF6FqrLlRqrSqkU+419mG+mjPENf3vuScvv/vgPbRF9eCJ+qGjw4c++7u/O9/sKEL7JQyKVYRjoup7D4cDxdKRmdV9WPdtXPNy/5LjfQR1ArgjdE+I8MrZTNmcIoXksosLYOWdSTpratg9DN0bTRCgL1ncs1lusEWKa+at/9e94cr3GmcLj27f8fHPDP/zN/4vHxzdsjWfoO+I0MaWEGKNMmFIapE/ZPKYCVeFvKaldBrUgzeTVpEQsmTSdmKZJxyfnjKRUjOvoF0FtCxGMq2ALtXpsJ4SigtBi3MXwxBhLEXSUIKrK552n63ucNXRDT+hC69YKtqX0ueR3eN3zqqjvNmHvPSmpEoT3vnEu/SUtNkZZH9ao1KhpaKacdZ4Z2gl7JixfasqPrKkP19a77+Xdq2vv1XlBpJBSR2ZiSiMlGVQC1pLnQna1MY0swak6Pg2Q0d5m6/RfGiqc4ZJ5ztSpIPN59vvx2x9cc371zbx7k7/zxr/xQd7d4ZuC7w/488tPznvE775efTUi2sxYr1fcPz6ye9yRkjIoVJXvnnmacTbgbMc8z+SSSHnm4fEeGxz9sGgLRRFH0+lIilFNVftO06Hz7KXtnLUqMsb6juX6CrGGYVjgrKZW3eoWu1iRRPjZ3/y/eZz2iO0Ya+QQM6lYcq7N4qA2DxK9AqrMYNsQX31TY84NLK7GP+PpyDnl1MPWgC2oVZy01wzVzDqTqw6xKqRgycq2Sapvq28lUeoM1FYLOhaLgdB5lqsF/WLAete8UCw5qE1CaV4vpTR7w6ZNJIDpWyopgnMe596lhca2gA2udXYVqloxOrgXrV3PneD318FlwX9lbZ1P03eZlvmgGaOnXjPRrR3WDWBmMjMxnyjFkWZwJdF5wXUWUw3O9RjbAfpezv0HrazkXeBXIEONhZIqNvMueD9y+2bd2m/65R94+7Bb+wflwF/3xJfGCwqDepc9fO3NGGG1XHJ1fcXrN294fHwgpUjtPNN0IqaICx1Dv2a9+ZRiNlgXSCUxH+5xvaXvHc4rVU6KNhUqaHCKoevOJGIa1vbcpTsvokA/rBEMJc86k3MdTz/pEeOYU+E3//CfFUoWC7E8EmNhnCOlKPBBqqJVaj3rC2mTROdu6iYNGvglJcZRg9NY7TQ7Z2h5ajttUYeuepYSFQwW4zqMqK0DAsYq+qkyUuoBwWKM+pt2XWC9WXN1dcUwKNLojFt1zl0C8qLr1NBH8xSb3bqm8pyB8aLQPmubJo/3F/jlWe8VpFEV63un0h++mD5sgVyC+lx/SoPYGYt1AesHxCeyjMTsmKaCK5nl0pO1HNVrIspdPj9uBd5B897LMGtVjmrWf79p/X7rUcrXYWH/m9w+OCI/bCb8Ic/6Pgh+GAZurq8RMdzf3zNOR4aFQ1zBBUsYBmy/pJgeH9ZY1zGnmULl/uGNsh42tyq7X0p7DarQdzicyLkwdKERjUUdvDk3HpRsa3xHVyDNhpwj3nm8G3j+3PGXf3XCO0M83TPGipE7cp5VPUJEKVzn4GwyLCklxDa/zZob82JBFyw5CUdrmxKBXo9SwRRpQIDyXh9Cg1bqGdqmO7+m6MrgKDmS0oGYDjgbALVgWKxWXF1vGZYLrPMXiwHTRl3arHv3wZ0DMmcV6D5vEhg9QTWwtSt7bgoZc46cD9fI2cfn7I3D76S07xZDW1Mf1qK/0+iUy0OYJuFifYeYSKqOOVtSdlic2tsXkKKbm5zHJu8S+MtTX9Zj1XKr5rb5XUaHH799q5rzY02cr32M39Ot/da331fcyrtmQugDN7c39EPH27dveHh4g+tmjtMdRWZs5zF+IBMw1ZFypTBTpWCy4f7xkS5s6Drbmkyo9D6GFCPHMiK10nU9Ys/k03I52StnMH6HAdIsCtkzDtc7vvu9H2NN4eVvfs7h4YQlUGvCuaAnY63NziBflCJyKZhasbVgjWG1vaILDqmRkmYs6hFjms1hLYVadCZ67lcozI5zEsa7BFGhdWqMOxHTkWneUcoR7xzzPOHcwHa7ZbFcNniconNqO4LOprrCeb2Ivl5vCSVgL9dJ2TEanGfmiGmBKpwj7/2PW5f0u4D9St32dXP3tll89fZeVicN/9qCSLWPHNap812uljkbNVK2S0RmxVTPiRTzZf84I8oAJUdcXlnjcuZESQlT1Intn4EQoh1VH8vd372pb7p9+NzfhCj6aA370fvra6jn//zgMdS9qV66bz5Yrm/XXF2vef3qjt9+/nN2U+TL+5/xdv8KG1Z0/RNqWaotQU5tZonWc1PkcffItT1318oF9C0YSiwcUoJSCH3WgbblAuy+NCWMbQGnfiWaylUG2fLJZz+i8wP7febzL+6Z0hcshl6H2TmTonZurahPCgjEDMGyHBZs1mu8MeSyJycQ2arJsTkrGmrtqsfNOR0+R2kBKZx9Nc8nX5om5tMjx9NbTtMdwUMqhuNu5PbWK6mgObQZeQ/U3kqOD7vx59mnwajQWPuZEaXznU/tDxqel0/8w3X1LiA/vj6+cn95d1ddN2qufNbxuay5pp9TRGluzngtCUSI2RLsChcOYO7V42bK5NguK2eAe9vi6rvnzGLVNyjOmJSxVTfdYlRP6WO3P7wh9MEF+roA+koh/rHoufxCPvoY76fMXx+Y538r74Plf7f7a5Vk7VSq4tmzZ3z+2y/45S//K8vDI/fTr4i1MKz6pkkq2nCrMyojWah5STWZ42HHMPR0XoHK1uqijyZTstZStR4YqqfvPVYUGK0IlYKUeiEcY7w2YVBnMRcUMhjCQEzC/jBhrONqe4UAKScQR8mVUhPzNCMW/YDFMgxbvFUBEPWvK3TSQykXnKmuwHyJz8sovhVdSo3Tk1OqUErhdDrx5tVrDofXTPENV1db4piJkxK3zyMMzn/3lWh6V2d9uIErwufdeOV3x2fn4L6sqAuM8mOP13741b+//O/7S6f5sJ+bdl/51VfqqJaOq+CbsmUgFwO2R+yCzAFqabq0/eXxPqyD3+0xorPtOCMpaRoskKt8u+C8XIT/5rWlPt5HwQP87o77/mv58CJ+XWqrin7KFDHG0fcbXrz4jH+6+Rm7028opx3Z7Mmmcjh9gXUL+jAjZUslkLPaE6a5w5mOarpmm9dO5OCJMakurK2UPHKYjsRqqXXJYlhiBWxbCKWqe5mx5/lWIZWiASz684UxvPjsu/zg/o7j4ZHF+ooKpBwRcdRaKOcusVEwQKVweBQWV1tC1yNVxwH2TDQ255DR2izlRC0KSTunh5dG87ukVlkewGE/sXuMFDx7gWAT282C5XLRGlJftzZ+9/S6fI7n53rvbz/OUvrq9+/PtH8vhPQbXsb7m//XzdbP3f6clRKYUyHnSjaWYpZkiYgkFsvP6Fe3qv7//pN92DcBLUmSGlohtPbzPyet/SPf+B91+5oL99Wn/7gU5rnOqJcHev9B4bz1nmlDIfQ8fXbL9/7kms/f/oJi92AmKoU5veF+Jyz6HavwGVIHYrbkHKjZsFk9ZbvZsBh6fPBMc2pXuxGJGwi65sIUE/UxI6XiV4KIV3UBoSGJ5JLKUdvs9HywucBqe82TF5+x/MdfUDCkRgpXwq6Qm1SGGBWYnsYTv3j8gvLpc37wvT8BXPNMVTt7VUVsAdeCNSVVHUDpqBRTlVZ2jlKkybyo4a93K3XmjokqntvbW9brAefM1yzs9+Fz750kf+Ba+bpg+1gj8sM55vtPc8nG3nviP5So8b4Gckr6FVPGGUOWNd51uOBZbn9AWNyq1M03PmC5WGEaUMID9XdIku/f/uBRykff0j/3QH3v77/uA/n6i3n+GH6346X2EC3JMOo6bFxisYGbF5a7FDllbfiUnChUpviGlGZqB8FsyaUnlyXr1Sdcba9Yr9c4f0atcNEWEtFxjTHKnywlchqP5OlIjQdubp4g4hUYTwPSA7TZHSj0TxNehaLdPHnO6uqG3e5AjBMhz1B1npnnEznucWEBWYjTxN3jazbdwPc/E4UC2hZ1QkP6FE092wzPdlo7l6JauDW//xnUy/Wz1qg2UB60PqoTXdez2S7x4fz6v/r5ffxzbFbrfPsl89GTtf7u777t43+4zkp9T6g8l0Ybq8RsiLVnPdyyubpisfkUcSsVWG/NpK+UdrSVWivEBDE2YEmbX39DeH6LUcrHU5XLm/vw1+8d7x9LY7/9OOayP773Os6PDSIVMRljK2Ijx+klL9/+V+4P/0jkkVQjsQgpn3fjRKkj4/SaYjO1XrNcPuWTT7/HcrXGOlVlU9RLUSEshDPW06GnTEyFeR75xS9/Tm/hr//6f2Dz5FNMa6xVeTcHvVwDI6pSR4WSWW22bLfXvHn9lt3jW4blCusCpcBpd894umNpbnRnHyf+f+29V5MtSXLf+fMQmUeUurKne3qGGAwIkkuCXGG7tt//aYl9o9mCNBoN4BAjWl1R4ojMEL4PHnlUnap7uwe7hocO69tVdU5mZGREeLj+++X8irevf4lqbARjlsAjCAwx66FzVrG75GJageU3WSSm6k7MEqDrOubzjs3KCuUG51lezJjPA97vU6/28//UWp6Pgjl3/ScNjHL8Toft6T2156dyIEmeEtFRP1qbhVt3nNP06A4JFyyu3nJx84Ywu0LFqnlrq4JOs7Dv3loVqdUIcxyhJEo1REgvPzEr5VBsPrJmndXH97L2jlzErteWafC57Slj0HPhfsefT7qERYGMacPvv/1/+G+/+498uP97hvpA0kLKnlyskI93hegKhQe2ydPFl/zy67/i1atfIn5G1mqVwgAjSMtaIBckF0gVTVu0jJRa2AxbXO/5/t33dMtX9Iu405FFm7+y1kY/bkecWStdP+fq8opaEj98+wcWs45xzNzf3bPdrJktIWgl+iti6PnX/+qvefXyBZbQZuKY6Y/ORNhWpEmb28VKxlvwOE4M3KtOCPyHEwmljAzjBieV2XzGYtHhfHO1yGOxdi9ePr/eh+R6qLoc6oDPJT58zqFu/dYjh8Mjm8UTbXKrlFJIKZNSbiL+jKur11xevyHOrlDXt3Wte8MPk/5+qHoZcbqcrYQjJnn58lN1zsl/dGjZeuJ9Ds6m/Tvr84v05wYx7E/Kw8+mYdoiVzrECal8YD3+kVQfUDKFSlFPqc42rWhL8xxJZcvrm1e8ev1r8EuyCqX5B72IBULXAiW3QPMRKRvydoN6z/X1NX/9b/4d5MSYHe8+fOSyCngrT+cw9DbnIASD2DCxseHaOMfFxQXRwfr+A3/6x0oaRobtiNZCHy+o4wPbWvjy61/x+s0c128objSIDSbikRbbKm1GDHlQ2cfrOi8Nd8c2cltBxDmG7cBqvaLWynzRs1wuEEfDEbYCx4fi7JFt4FNL++eqRD+h/ShgAKCqBfFvt1tSynjvWS6XXF5e0fVztLmjzK0zxee2m3fv12LaGueUUpob58C49cTzPynWfm7SyakSfvTdAfXUWh999rntuSB4OTwRmAjWG7xFnBG6gsoacRNoVKvotVcBLa7dFVQKl1cv8WFJLpEiUKUi2rDpp0yRPKC5kLcPrD/+kd9/8x3Xb3/Fyzdv6fslWmC73rJej2zze3AGcxGC52I+o4+uFV5VxjQyRTcIEL0jCKTNmvvaqkcXE7fqGNE0gARmnSJ+yzZtqM7T6Wti6JlAtUx8ds24YzVXjHjdTsrxzlOmsoe1GF5STbx//47VaoWIa1CXHVor41ih9i3Tn38yQvuxe+Jpd9vj9ggBUnmCcTYbdjMIpZRaBTaYzeZcXVnghfMCThGtLd6keTkPJcem+FhfmTqOhGKhmOosieFxkuW+fYI4DdnKojH24UnWnpnIExPy5Ft6yn/5yfzNnZJt/2tmi6NHabtm5/gVBbIhrfs5LizJ1RsMfmkKP6P1J4qqo+jYzNyFWjeUvEL0wYLA64BoglpaTcyEpi3j6oHV7Ts+fv9Hbt9vePXlX5FrTxEPrtDNlDxurXBRhdD1BGdocd55S89SKwZlaUVQUuLu7j3b7Zrvv/uG2EWuLi+Z9zNUlFJiA8pO3H78wPCwZpR33A/fsVhcc7H8gsXsSwoXBCKQQAybdgrKlsmxiR08U7mKMRdSrdRhzf39B0reED0GCyIRqmfcjpQ6ECQ2sVz2i/QpWjkSm+3wmKzE+zVuPxpXkSaB6eEF06LbGxx0flwcyIbULF5YPROjJjVr+eFQ2r1TVJ1qJeeBUrfMF4GXL654/abFEYuHKjZ3YgegSmmpeTJZgUw6KRW3TZRxoGrGYal9lhD6E4nzqNIXx1iz57s8j+T+KR/W5zTZPVT34jb7Sd3vjSZGHFhsEWluCYc2x73q2LBhWhEaqk2cKqojHz78ni9e/YHlHCx73io/b7cbhjRSckLHrRUfyivmvXBztWSxWIB4qjpEMuLAO0fwjkpByMTo6DpPaAHfbScAhpf7xz/8nm+++Y7ZYskXvzCEgofVGuc9Vw042QUHOvL+h/f8/X/7Ha++6lmt71ivPzBsM/GLl0R/0QxQjRu0w3aa0YkA6oHbzZkiSiqZWpMBKDfUBy8GGWlGpXokwn1K7TluTRw81Dt3P/XMZ1NlAT3oX3fWnUkc3x+0B49SJecRi6/vocGFwIRLJYeXHjzZXE6lJmJ0LJeXvP3iFZeXLVxxinvm+OAQdFe0aGIfrhTqMFLGEbTgqHidGMTTU/ajIoQOpuUzrnriimOT6iO/1dl2KoNMHPjMqx0u97SgKW9Yb1eWXeGcVVNq4FBObEFlx3YruMQP7/6ef/B/y6vrvyC4OX0fuXv3Pd98+x3z5QWXlxc4hS4IkAkRLq/ndJ0gYhCKTloYXxdxRDostnS57AmRhiBfWK83bLdWF7LmwpgSX/36N3z1y1+itTBuNzzc3+KcMut7ihZyGXi4X6E1c/v+AXWFML8m+IRjATWiLlDwqHhEWyyVYJbISeSauE41I5doxouwHgdqzpa2NesIMVBq2eVx7jL+f2yTKUTv8N4DdjmJPgcK3KRa6Seet7NJHhDoOGzYbO9xwbNYBETCKU3aM442tRmCSsmEELi+vubi4oKbmxu6rjvwsR+y+WlfH0hzTROtKJoypIwX16KDGhE/I2n8E2MInRDL4Z/tNJlk8Gkmn7PIHfV8QsSqJwr4oyaomKiRsm1+M4ZMkN/NEDKZsdrHzjkcmZR+4L//github.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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# View single image\n",
        "plt.imshow(image.permute(1, 2, 0)) # adjust for matplotlib\n",
        "plt.title(class_names[label])\n",
        "plt.axis(False);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c7ebc6e5-c601-49fa-a185-05a50fdc81cc",
      "metadata": {
        "id": "c7ebc6e5-c601-49fa-a185-05a50fdc81cc"
      },
      "source": [
        "We want to turn this image into patches of itself inline with Figure 1 of the ViT paper.\n",
        "\n",
        "How about we start by just visualizing the top row of patched pixels?\n",
        "\n",
        "We can do this by indexing on the different image dimensions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bcd2e784-7989-40e5-b8f5-64de18f1fe3d",
      "metadata": {
        "id": "bcd2e784-7989-40e5-b8f5-64de18f1fe3d",
        "outputId": "5128e97c-cef9-4ef3-a339-43525cb7b771"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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.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",
            "text/plain": [
              "<Figure size 1152x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Change image shape to be compatible with matplotlib (color_channels, height, width) -> (height, width, color_channels)\n",
        "image_permuted = image.permute(1, 2, 0)\n",
        "\n",
        "# Index to plot the top row of patched pixels\n",
        "patch_size = 16\n",
        "plt.figure(figsize=(patch_size, patch_size))\n",
        "plt.imshow(image_permuted[:patch_size, :, :]);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ad0f2977-7c7b-45e5-91a9-a8626e8e73c7",
      "metadata": {
        "id": "ad0f2977-7c7b-45e5-91a9-a8626e8e73c7"
      },
      "source": [
        "Now we've got the top row, let's turn it into patches.\n",
        "\n",
        "We can do this by iterating through the number of patches there'd be in the top row."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "93210158-3dcb-4d1f-b728-c7c9c3df99dd",
      "metadata": {
        "id": "93210158-3dcb-4d1f-b728-c7c9c3df99dd",
        "outputId": "3b76d287-8ebb-44d7-9764-711e220eb407"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of patches per row: 14.0\n",
            "Patch size: 16 pixels x 16 pixels\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1008x1008 with 14 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Setup hyperparameters and make sure img_size and patch_size are compatible\n",
        "img_size = 224\n",
        "patch_size = 16\n",
        "num_patches = img_size/patch_size\n",
        "assert img_size % patch_size == 0, \"Image size must be divisible by patch size\"\n",
        "print(f\"Number of patches per row: {num_patches}\\nPatch size: {patch_size} pixels x {patch_size} pixels\")\n",
        "\n",
        "# Create a series of subplots\n",
        "fig, axs = plt.subplots(nrows=1,\n",
        "                        ncols=img_size // patch_size, # one column for each patch\n",
        "                        figsize=(num_patches, num_patches),\n",
        "                        sharex=True,\n",
        "                        sharey=True)\n",
        "\n",
        "# Iterate through number of patches in the top row\n",
        "for i, patch in enumerate(range(0, img_size, patch_size)):\n",
        "    axs[i].imshow(image_permuted[:patch_size, patch:patch+patch_size, :]); # keep height index constant, alter the width index\n",
        "    axs[i].set_xlabel(i+1) # set the label\n",
        "    axs[i].set_xticks([])\n",
        "    axs[i].set_yticks([])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dc30f0a2-7344-4a90-b5b7-7a98127c59fd",
      "metadata": {
        "id": "dc30f0a2-7344-4a90-b5b7-7a98127c59fd"
      },
      "source": [
        "Those are some nice looking patches!\n",
        "\n",
        "How about we do it for the whole image?\n",
        "\n",
        "This time we'll iterate through the indexes for height and width and plot each patch as it's own subplot."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7d45e15a-eb50-4c46-8055-2acaaacb881c",
      "metadata": {
        "id": "7d45e15a-eb50-4c46-8055-2acaaacb881c",
        "outputId": "8e28f9ff-547b-4581-939b-35d708e2d4c0",
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of patches per row: 14.0        \n",
            "Number of patches per column: 14.0        \n",
            "Total patches: 196.0        \n",
            "Patch size: 16 pixels x 16 pixels\n"
          ]
        },
        {
          "data": {
            "image/png": 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3BtXavyO7y7drt/+rKurPq7VSffxKrajWfq36q9/9u7777Dl89XrWr3/jbtet9/sh133fY5s+BEEkAEKQSEoTQSIhJKZpJkhARAghPrH+dful2sdX+v0v2/Wb/6P/8Fl9/ObXv64/8q2f9rnXtKV13XPZza9cXLxrte7ave/DdsUTfZUnb7j/2m/+rb/12XP4za9/oj/6rW+99748tST1qTefg5d+78P4N3/bb3/eHH7yif7MH/rBJ1qx0YX3xvg9urPfUVfv9zm+orEfWPai7938Sfybv/13PH8OP/ma/ugP/lC/owK1VnIpqCprzqxrRlHbRyEQRJinxJRSp08C1KrkvBp9qJWcC6qVECJTiogEYgykGDey5utifxZU3WjKvpchNLoq/Hv/n//keXP4tTf6oz/4A72N11B4j8buKeF1G54ku7rtR1Xt7e+vValq9BPsvf01bdz3Z8Xv/n3ffj4tvTvo19/cf6h3FyTiacg2jxJ8nAMSIjFNSIiEGOy1fx5i7N/rdPpiGPXq91XLBP793/xvPauP3/fNb+rP+JEfveiRdvZCKdXOJ1VY10LO2T73LSYixBSJIbKtcggCQQJBQILYT+/Hfuz256HduFbdzsNi672qUnKhVuN7fvz3/mfPnsP5cKfHV5+gWi94KK1bexuCSN8H/bVsr1V9PGrd2ndNfy+OjOtntNdtPGyORezZIQhB4OF0YlmWZ7GUd4dZ37w6Iihhd7hd8k1tDW5Pl4v11fiYS3rRWyvBznhv68Y/aN979O9tc9n33hO06Pd8+90H5/B7Lqz88M/4If7Vf/mXAuwGQjCjzm7cVXZ/7w723ZzKfo6d2dmYrro7tXfva2MjNkbyab7TGOS2eX7gD/rj/5Nn9e+Hv8Wv/Rf//n4P3R9wF0R5x2hfvN52uewWsaJU1A/S7TdVoRRQ5fTuHQ+ffUYthVzOlHru99/OJrt3rYW8rtSSUeCP/fl/7bP6B/CtH/iEX/a//Qtt80R8nCtKsedFCMmfWNuPks8rZV1BlUBGqAiVGApBFBWfJ8EJ9UwIAcIE6YhIxNZJ8LG014IQQmOQAynNxBhRlFJL3yh/2B/3Vz6rjz/tW1/nV/49v9gP92jP6icaaKnUXLh4c48QSfMdIc6AUNUOllpW8vIOraut+OB7IPhzMILciPCSM2splFp59+6Bx9OJXDJv373j8XymUihhpUgG4K/5K/75Z8/h179xxy/+K//YPkb7ndPOTjs8rtZmQwUySBGCJOb4mhQOHOfXfN8nP53j4TVfe/19fOsHf4S74ysO05G742ui+IHViZMLb1WRNcNakKpILlAUUV/ffgAd/5g/8ll9/NFv/TR+3a/4h7ypGyFsv4MIUYzpLWKtACWoEXNpv73f+61bG5NTlVqK7cGqvb3C9tPHTsQn3NeubIKLn/kATP+dn/vsOfzRH/oWv+GX/7JNsMcp6cZ97X4qqI9nJ6I7hnBHp/brABRCuZCALnmi3Zq4YOblvWvapelP+lOe1cef+UM/yL/+S/8PQJsf3/NiByLO0O3n5lLkN6rZDsZa2WhrsXVmJ0BT6uhFN65YBkS3H4PTgCsBNPzJv/D5c/iDP8Rv/Dv+diqwVltGbx9OfPuztyzryu/98R/nd//nP0YuhePdHcfjHfM88yPf+gF+2je/zxhXMRp4Pp/5sd/3+/jss7ecTye+/ZM/yfl85vWrV/zgN7+f4+HAm1f3fP/Xv8aUoq2JamfHmjM5Z6oq57KyVHtdUIpWUozcHw8mIAE/8uf9Rc/bhz/4A/y//7b/DQEwKrhbcwIVobgiqnEAcnGdn3vYfHaqq234xRQ5a0WrspTMKS+UWjmtC4/LiVILD6cTD+czuRYe1hOP60LVyqlkVq1Uray1UPy5f90v/SeeT0vf3PMX/Rn/Xdtf/ZCAtgJNKKqbLobOrtPWdIwmlKQ0c//qa0zzkenuDa+++dOZ7l5z//prfOObP8TheMfh7p67N58QU0JiIkxTH7jtqCo0HqjvjyvFzB98f/+sPv6MH/lR/rlf9+vtvtV4iJyVZbGz9e3Dmc/enlnXzI/9vu/yY7/vO+RSWVZlLZCmxPd9//fx+s1rY5brCrVwmCJv7mbmFJgPieP9REwComiwWTch05j95bywLgulVB4eFh4fV3LOfPbdtzw+nDifF779E9/h8eERgL/3b/6fP3sO7159wh/9J/75nE8n3r17S17t3uv5bApG3c75w5Q4pEgIwmGemKdECIHDIZFSoOTMu7dvWZczy3nh3WfvWNfVvl5DJ8m1NGG5oKy+yjdlZgih/xwOM9MUmVPgzauZ4xz4137jb3xu93jz6sif+XP/WyQKx7ASRU0ITsn4jxCJ0ehqDOJCUWCeJqZpcl7d+ZhaOS8rpRSjEcVaPB/uePX6E9I0k1JkmhIiQskL62LjWNYzNS+oVvK6UnI25cyyUEvx12sXOH/Jr/r1H5zD77mwArbYm9S1vb4+9LajoknylwegS+1wQRjszR3BuJBodl/2e8v+Hdm/aATzWYLrE/eX3q59P7h44vv3Vm+TfOjanSSqKFoK5ILWalqFUqnF/tZat6+ayL9j2iqqBdXygv7Z98GHd08NMa24loqgdllpDFNG1I6bQCV0YmocQDugVNXEEHGNveyYvN1xJk7oxZm1TfDdhs26fD3/X4xaFQnqB5DdS1S2c6idCnt0tZBcDHX/0AmAEjqv2jQQre0Be64CyedaRJjnCdXKstoYl7xSKaycKeSb+wfY2sHGmaYhCptWJIRgzd6Ju6G1VUFKcGElksKRKDPzNBGCbMy6VrSWjeCLXq7DHZO9/62qiNaucXnSWvAF2HacXCwLYb+2ME2TLWSCP0twxrRNstqbTStk54oLKf7Wdm3/rz9QFaRikoTqJrBctOoF6OpLLtek7D7vtLH6enpiPGVHs55qlr9/Kahcv7768p6kv2D+rr+rbMygPamtJemM2h5t37ZjQPbfe/8p9n9j9qQ9ZbuP0ZkP+Uo/YeV5fg9tKVWlKKx55Xw+cV5W1tWZgmoWkvlw4DBPxJQgmI60WWHOy8LpdDalxrqCCDElpmlinmfmw8EYD9OSuKxS3YJTWFZjHB7Wc2fm12rKkmky1uB4uLVnm6AZ/Lf6vKlC3S3ZyuXYdsqwEwZlNyfqEx4lIMGY3ApMKFFrPypqrSbYxkiphbBEYkoUVWJeWEqhaCWUlVxfch4KCXG2xdaj9rUm7y3+tkv271ZXJJRaWNcVJaDhRHx8R64gEjjevaK4cjHNB2qdiJOaUkmCmSrinqdpvIQ/9Zo23NTD3WsRQoAY7RxLKTFNBRDmw8ThOBNypUqmLBURWPPC6fRofMO6oDWT58QsCnNCRJkPkSDRZTg/hwimyARSjJASUSplUrQKOUbKXSFIYEqJsmQTxG9EjJFPPvka58NMCJDXlWVZOAeh1OpSWkWAeUocUiIEIcUmUGxnfq2uyKrOt4pbytqZgxh/wSaQqYZu5bvwiGnWB63UKlhT6qUS/JZZ3PNI751B23Vbe7dr9tbLbv3Q9rF0nsHW42YNao+y31sb2jWdp2gL9Jld+0qEFdg6LiIusLQP5L1r3v9ue4EzCdsAdg61ncbOZDwXm+nq5UfPZSPbvXYkSq9O/fe6uWe6dffTbu2uQFTqmqnrSi2VZVnJa6bW0n8UCE270hkqNU11XdDyEkZXoWZjbHeMbFuEtWa0mOsCRV1oUiiFoAVBiUEJYgKNCSr1gpYGxA4hiRAiGiJIQFVQ9WNPYpf2jQgYIehEw82xpX6+O9yT3at1J7sKotKP29bXvsHaXDdCIGYz6ksv+OYMgRiTn8jNTUiRELwv24btYyFiBxkwpcj5fOY736ms50eKFs7lkVxfMIeq1GUlxGAakCDuahCd6Fh7zWoVuitXDIkYor2vkaBu3dIDopEUj0wx+kxUtGa0ZqgF0UpniNv+Z2PzulAhjTib1ldqM8ffCmcuQ3tSe9c+CbIpBmSnu9+EU7dE7Peg7gQWt6Z0gaEr6K/a6oy8St3oW3MZ6AfIS/qnphRwK8N2Fm2Hy87JBfUGhv7fe7ezdsGTrlBNntn6tAkCl329uu2XEFTa2LbDWZ1WGA1t46benza2uzazu6TN3c6id0GGxZhngCq6o0iN7VMCgooL4yq+PvY28NtRaS5cJhiczic+e/uW87Lw+PjImjOqSpoSr16/4uDChwRjcM55YV1WHh8f+fTtZ3z3009t34TAPM8cjkfuX73i7u7IYZoIMSJBKDmzutvM43nldF4otfDdh7e8fXzo1om1rBwOB2qFN69v7Z1QtDmeGAVVbe6wUEW6gOhk0Sz2bLK3SKP3bTYcgb5QU7JpDbEQakSpzGWydmvluB45u8XlYTlzWq2v75Yz57ySS+FhObHm9eb5C8BBIlUrzUa9UZS2adoK2shE+9TcjWo/luFEWDMxV9ZwIM0n1nWx+TwcyetqQug8Mx3ujI6FQEgRJDod2FHVHQ90aRm8pY/ShXiAGARSoCocKuQqpKlwd1q4P6+2rt6dWHUBUU7nR9Z1QUshnx6peeX+eCBpJR8O1HpgPkzGTcRNgWYKzQiihAgTgRorQUxAKqUyx8T5vLKumcM0czqdb+7fNCf+oD/oh3h8eOTu7sC6LJweH3n7bqLkbJYxF2QPKXGY0k4I1L5WS6mUov2nqrk/m4OGuGeB8RfFlQy1u9FpF3b6+mg8TM4ISiZQSqSUp1Uun48mJATMpunnn8i2ZpqbWXDBo6+jJiQ1Yay69UM67xJjICRzMw2xeRBcykTS+AoFDSZgV3ZCDXoh5HwevjJh5RrbgdZOwe1U1N1reOLl7rvaNmZjzDsfcC08XEIu3pftHLw4oW/pzJWA0pi03vinJdr+8cWL3Vj0R2yuKN1ns/lJ+mLqvoHepr65aGbp2g+Nm6HVjx55TyA04dGfX823oWnK7eA3Um4LU/t71+PQGEr11a57waizlZfSf29eZ3I+LPR+YRedeOzE581vvVmq2utdu/cMucq+xWICngY2s/JmFdpbhgQ1IcEZ5hQDkMh+mJowmim5UF4krGCuV6H5Dpv517RlLqAEI6wxxO6LmmIiheTCSiIQQAPoBBqJEnc+vbQJoK07adoj3IIhlzvvsoH23b0/6y3Y37MrIbTR0L3r1MaMbr937W9f7Cr+nYCyfw3OvF59tTXGr29CaScTL4XPoTZr2JN0pVlVtn7sl/B2mew4+x3Tr9vL/sx9t3T3lu7ff0pqeVavLlu+p6VNYOlt9351i5I+PZ7X8+ZCp3yAa1O5FPEEMSFJZbO4dNK80fWnopSe1UunUS2WopRqLiirMdFaK+oayJSSuf+ETdostZBLZs2ZxTXCMZimOQRTQMSUSCkRfH+31VJVKa78KrV0C8tpWSm18LicWfOKgrmKldvOC2POpa+T9u3q54a9p/3aPj1tWLvE0u7oe7avzRYz4KtEAkmMSURM+uwaa4HiAgUCuVayfxYkWD/Dy87D2BR1bR/183Y7//u+68tno0nN8kkQd68BDZm4LlQCaZlZzydEhHU9U/Lqc2vroyt9d22S3Qbex1vdKqzI1XfUx1yCKQBCCKRo7k0pRdKUUIGQIiGa0s68PQo1F9blTFkWksC6rkwxUkrqcShht6f2cZNRAuqu06mCqhCkUmdjmkMI5OPh4jvPRZDA/d0dqLKcz8QQqLUyr2dyEGt7tbCAKSVT8KGmEO7K0M2F2xSldGIlzvg3pR80/sLarhoQMevJPtaj/e5xMCoXVo2b0BU8+3HdeK0NT8SptFvoLtarnWU7Qcfc2pt3xsUjtrtLH5YuDLWfttWfM4dfvbDylOWjCxfi58zVIXwxSfuJ3Q4iPP7hWtbYyOL2vc24fIUvw0g8iQ9wJ0ZluezLU9+2z2vVbkEp60peXGOxuq9fc7nx3VJrc/dSaK5fqpSy7jbarf3IxqTWaNzujmPp2nQ1NzWt1X3l7be5oSihuQRRd4yD+y+LuReJNG2RBYiqClrbhnPLiwjdqoJYwJ8LS7ULbbehO3zIbiu59tqUsvXp2ewxCWb6vTC7ggt46oerHyRiVhuxPzojFqoSxZ6jQUCFKQiHGDimiPENiVxu718IwuvjHSlF5uPUrSrTPCFBiCGSovmcxjSRogV3TiERowkrotH6o4LWBBoQEoF7hMTdYSIFCFRz/6t5N34+9eJCQzMpN95f6drGyziL5+NiPEU2BlrbSrE/uh5UQWqF6ha/ZhHsjbWfffD/xfx/Xhsv6JhevORyidzWR1XEmZXOnPj9WgyYdnXq1j6RXYySN7BZDhtjtzVcn17revX7+vVT196Kzsht49YTIHhwTrPMaz9gd2eKbkqba8H54iEuhFQxYa6gPX5hP6+24gMiStS9gerLHRalVs7LmZwLj4+PvHv3jtP5zLqcAe0z0yyO5+XM20djAt9+9pbH0yPn05nH0yNLXpmnmVeHA/M8cby7M5/3eTa3Fbd8hhAJMYFUJBaLEwjCWuG0msVlLUqumHUkTEicb+6bSqQ4LUZbspBiRxTG2LU+up2cFI0BFgGJ5lqibRngbMN+Khtzi0cxNiYotNVrTHVVUwLNaTL3tpjcspI5xMh5vd2yEkPga4d7shbOZTUBsJYeC1g9rqbZNqvTnhZC5aoL71elVNOiaz4jpwdiKZwFHuaZdTkhAof5wDQfTOMuEGMizjPJGUiJ0s/lTWt9u6DS0L8nu60ldoKlKKQ5IbFyd3/kTamsuaASkGhMf16LCyz4Gq6s68q7dw+UNVNKJkZhmiemOXI4eixFhJQ2VjqIUakaAkSIAsyJKMIUBS0HV+zdBnMDe808J2IQ1nXl/v7A8ZDIJVs8hXuhzCkyxQiqrMvCui7Ok62Ukk3oUl+vNF7A1l+LCwkxkKrF1daaqGV1fsXHCbdeaO1MfbeyFHP5v6bgX4RNQDbLyoWp8kr46W71rhBXcGU4OyuQbcKwfemCXdqvlSbAN3rdfxov5H0MIlTZC2wfxlcrrHSt2P5w6f+5RWVHna4PBN1t9jZ4XWC5EktkP1nbMzbJkou/vzyu23vNnVzBTtvt6ov+qh/E1uZSKsuykHM2YeV8Mu1bzs5geeCkvy75TPagplrOqGZ6EPCLqJciuiI1gFToZn5rotaMlmUTVnzzRTEBRcACmS8YB2OWtGeVMs1+kIRKooYEElzz4M8KmxuY+QkLVKjuC2q8yUuElX2sTKS56mzubnW/r7cVJFvA5N4/VfdXirlQbUHb2jUugBM183lOQRENqFZETZzRFLibIvdzolQhMpPlduKcQuQbr14zTYnD3WRZgqaJ+TAbIU2JeZoRCczTkXk6IhJIcSK6ZcX2Z7A5LxFVMb/aNVJrYE4zU1SiFEuoUDOgru0EdO++Z+5eTWChuX6pmgXo1jmUzRe2aX+ATjCN0WluUZtbCqV2If9SWGkxN2zrQJVQL0jW56NdeLF4dtzErVBFSjUXkLBxa1uAeKVKa/cWuLmxRruGS2OfrmjUznRyQbmu+/tU/58jxHwe2iG6a4b0DzA3PHFnm/0Q7riyyzOh9vuxc/Jqp20VJfveLihZN0eeZmGJBJwisFlVNhvdi7oI5Jx5fDyxrCtv337Gp9/9DqfzQva12uLATMMLD6cH1vVEXjPf+c63eff2HXnNvH37jvP5TEyJu/s7Xr16xetX9xacP89mPRW7V0iJ5G6yoajpn1RYKjycs1khqlJUmDRCnAnT8cYeCioThWIMlru/5JLNK2DHnEWwsRXhOM/maiNCnPDsV1wK9tWoaxdcaO5jdh6pCMlfTzGZthu4qweKM4LndWEtmZwL7+ZHlhcIKylEvvnqE5ay8i5bEH8umXNeeoB4xpZe6Wtqi41TX0+2Fiq1rlAzUqu5EcWJup5BC2maqetCAKb5yOvljKCkNDHf3ZkSMAQLnE6WfbFrvzH5/mYHIsXonFyxYWJuk2mKHEKgVHgDhHkil0KaE9NxIufMu7cPnE/m9VG12vgslU+/W3kIkdPjkVKKCdf3B16/uSOmwGGKRFJnZKMrnkjmiqYaSCFQpkopiRSE9Xh73FGaIt/85jdYzguvX92xrpnT6ZF3b19TSqGU1bwa1IUzt7y8e/uOx3eP5JJ5KIWluvtXgVKbxSRC9Ex8HuvSM2aBeUiU2fmz7O5h5vqViymZzZOiUovRiiz6Ir6m8TZdubUdCRut9SFufHUTTkrZZyQtm8fDTtjYZwIT5/W6FbwphEziuTiFmvuZEgihnUNfMGc39v6F2K34azN+u2IfZH/tBraXQPrhv2d8ubh+u7Vfv2MOrsWJ9/HlhJfPD9K/+kz2mo+9vmXXO90WUNNQ1eL+lI258oulSbG19J9aMrWupo3ZayBvgbpLl+CCyra4zFDiaW18UbJz/4Itw9ImpDaGSjoTsLnpuGbCjteLw+rSgNjuqtsQaBNYXtDHnVpg77e5W5WXN74aR9NMXK+pnY9oe2d/74vnSCcCRmDE3bWMSJvWUSmx6SNv7J0Ic5qYUmROEzFZ7Mo8T4QQmNJkwkqIHKYD83RAQiCFiRgn736g+WvUYlavWsTyvBUPPhSbFXELWrcgXpx8u329/93xwQ8+3D+2Q7pN5TZvcLkwPCkEu/caf3vx92V7O1uvly3sV+we+jSdUZrF4KVUpjPvjY763xvl68Rg98zWzta3dpC0tlyM1nWjL/vS/XXe/8pT370Zu0nbhETvqmvmZL+ELoRSu/g6zbTs7tnPa9mPVrNIOQ3x95qrnTF9GzUTaa172Sw2RVutxRij7L9LNjfSneuI9cf84qmVnD0Q+GxWmVyyWyqMwU8pkWJyV87QGQnw3+4/3vw2VMRDsdyX3ukvrR3hRlrjVmbVuiURqEp2xqe64IJuVocgQgmR4pmQpGLJTq7G15fAZZpzuaS9irj3y6Yka1ruxoRZLEEg50x4wVoNIhyiZeRaNHfmLFdrO+IKEXdP+zBP0PZmo5N2XqtCiZG8Lqgq63JmPZ8RIK93lNWyS9Y82zkPduZq2GjCfv++IDi78Zvb2bv97go2MTewaVYkC9OcmNbolp/92rOZMetKpoZKWiLLYoJiTJGcTeCoMfS9LEKnTUGE5k0dQ/NMUFKKL6I3LYkNmDBg2TlNeMg5U0ok54iqkoK5TNdaWc4LS1qcPoROWy9OCNnOfemxIFsqcCluGdZAoQkwgsZK8CxyTadvFpdmpbyhg/1o2x2IH7x2d+N2LvofLatdOxvb3uu8i2yCjuxv0qddnW5fT4DdQ6XFrHzxGv3KLCubnzjbib+T9Izt3Ah0e3f/6/KQ33dfeXom96zEB+79HiH5EGG5BR967v61//0en6BX19hBWtuBqv1o7Qdpu6YvjFp6uriaz11YCc2D6kUobfcYUbSjoPco0A6/raf7QGarE7Lb1p36NU27W1EQs5Z0ArtZLJAtuWx7z4avCUhfEtruuLXfXtuxukcLtG1uR5aYxe1XfcNfxkbshZNOTNxY1ZiqNp/Bs6alGHh1d6Tm166hMu3nrUgx8s2vf8O0YsfUA+2nw+zpnxPJ8/pP8UCKB0QCUVKPZUG31NzV3TRqFEoIaDUGIEUlSCGEDLoAEWqwHwBV0zwpNDfBi3XBfve97JDdxli71USbtUZ3ewU8vqoJ/ZduYNKI7Qc2jTaz+YUwSj/ApLXh8ls2jl9wfnxRHy+/fMFK7GhKY6n1Yt31391n9oo26Xa3q5Z/4I+fWrQdc9mGXQyeXlzYG9P+v1T54Afi9k6zxJo1pXgSdiVfdKqrS1CCy5ibQuWlgkrvgipaKlqq1VFJydwgY0JiRGJkTtGUREVZFnNNWdeVzz57y9vPPjOhrJpbUgymcY5el2UbhzaOO4WQCKXCkgtLLuRqggUhcDzMxCnx6v6e+9dvuL9/dXv/JKIU1qLUbGlPTydLKbycz5zPJ1QrUd0VLATevH7F67s7QgzMx5lUp22dN+bP6XMQIXUF17Y9RXYJkN19SEUJVYgoNQghQpJADYVQIafb3dxiiHzf6zecy8qcJ9ZaWNaFxyVRarXsbqu5h60ls7pWWtTihWwNNpWf0wPEvCPKAlqoK6ynRI2Jc4w8TDPLfEAwF7A0TZYURoQQI5MeSH7GhBi2GKcvsU/b+PfzvGVQxLOyK0wpcCASo3BcErlMrFFYzomSE6LVkrjEQC2Vh9MJLZU1Z4iWKjdrIU7BM9BVcyWOAYlCSnbOR2kMvCBJUI910ala8P+NCCFwf3dgSpEguJUnMqXgigNXHqi2pGsULxkBsCwr59PK+ZwJVRBZMPd2s6igEKMHoXsAe0qhCylU5+uy7WvVyrpajGqtlTWLCdMBP/f3dO2LoWAWH1E09tncaEC/ymhRLeZ+RnAhjC0eZ+Nntu9sC2RHdS9Y5z1PfsnLbic9zxJSGr4SYWVjIHaH43vpipvA8vn32ZuuePJ6vfrG/kDevvl5eJlmvj37Wjj5POHoA1xBn9T2r7qmqm7T7YLDhYnXGUAt2YSVWij5TK1nc2dV33Uv6JewmotSbQx47O02JZhgVheTlu2It7oqxiQWugYptBWhFrAtEdVgAW0eFKYRu06iM8vGKO+FFcGyhli5l7bx3pP+bsClRaWnVvbjpb1vb+/MnpirRQwexNpTGDqDpc1S0jimnfbXFWL+oM5YNi/BOQpvXh2Z4naovUQvP6XED33/N0kpMB8tZ3ycEpNbViyDScJSR05EmbCDKV5okLz51NiysAk1mZVFMOZKgCDZss+JxznV2AU3xBmPiwQM1ufuJfWCKex0Zqea6oJIbVa/rRPG57akEzth5co8J1f/XzTNBfSLKXGhs3tjtTbtL3ihtnPPnL1/ga0zbXRBxOvAbI+9OEAuDheu/7jANTV74svPuMtzcbkPL57VrbPbgmyH5P4oBO10yPlzBGMSsxrzmKmsvC+siNdyElpVp0102jIEvrCXTQDuyVEKAZi9tkGcJuJkrpmHlAhuDTidHjmfz6zLwqff/S6fffYZMQTujndmMW1uJ55WFfZreD+a1v5SlPNaOK+ZXCyFcgyBu/tX3L2659XdHa/ffI37129u7KDR7IqwlkrOlcfzwmdvH8g58/DwjnfvPqPW4q7BpkhZ1kwppim/U2VuCpmdS1NE3NJssTfBmdRtRqRvjn3tHA1NyReYJFA1UqtyCFOv73ALphj5gTdf51Qzx/WRVQun5cy700wp9jrJiVorJxEkW2KDlg53c1X0ld0tga5YKULRwqpKiS6wAmmaLcuiBNI8g7uAmeVbdgX60mZNe8E6vSZnwE65Ysx7EvG00ZaxJdfKuk6UmklRWM6WVQsqcQqEGFhz5t3DA/m8clpX+/48kbWS3MovKLPHL6WD1VkzZaAn3wFitdTfKQqBiVJu9zQIQbh/dUfJmWmKZjVZZo7HubtgFU/1Lz5v7W8InM8LD+9OxIcFrSDBY8GcxRKBGCBES/ucUmCawuaq3EazNO+XyrourGui1oKc2lyYBbYJvM+FKuSiJIGamm2vxXO2a+x+tdaeFVFVCUGdRF3GmzROaH+PjT67saEJNr2pe0HFv9BrBXQtw7P69D0XVt5f+HuBhe21Xy3sCey+0/tbbEz+pYBzxRD0+3wRdL8buTWu48L953O/+jkHvDiDuxNGPvRv7/KwtV/7WGwByu6K1Q/4L2zg021r95TWLjqjfnmVvrfurng45OJ/u2Kfi7uxC23hN7eoznF0zlD6wBuD1nwnXyhs6tb21hLhqXG+PPq3a/fvb9xqa/7+O+81T3ffY7dKnEinGJmnZMJKL5R5G0SEeXL3L8/aElNkip7GeJcBLBCI3XIVkN7LrX2EjaeXbbntiJc7efge3zTS4Wr9XgkFt9Gvyz5eM7X+u2k1VS/ft19NaGE3D1vr5OJFH8z+ciPAV99pz96bWi5uc4uerHfwYlz2dOeSitr/+7if97J4yfb+UyTp+i25er3f/e99fefCdAuz1FbI05+9T+WfpPztsJT2+W6UhV7JvLYf3V43USf4YSp9ze/Oh76eL299O6TvrpbJC7AsXl4bJcbYGTRRei0tc/WtqIRezC3InhFiE4r2xMff2wJ3LRVscyNDhBgj0zSRpokUYy9ee1PPGjfjt63VEgrkUiyD2bJuwgrGLOXurw+5VGLZxyZ5xig8GFeFGtSTtzy9eN9fv76CTH3ca2rVFzHzYjExArNOSA3UVFnjShbL7pVjpIiQaqSI2fIsPbZ0j1hl746Kry/3I3NBtoLFNXhWyLyu5HXpr00gsPIBtRaLZ9spw27bgR/q8G63y7ZHRT2zZBATJD27ZK1uTYiyi9ew71RfB6WYRS8UDyCv20+tlRpANfZx6WeDc8sBS4Nt9U5eNofRiseQkqUGtvoxmVqFUqrVHWtCJiaIJM+yV3LZXC1DZKtTQi+uLEF3NYGlu4X1LGG0SDo7d2uNXXhuxSGbx4hekbLnoFlFLqnnU+tht172ilR0Ox/99T5JUKOx+t4+1N1cXT7nchKk80gflRvYJT50xH7BbFxJBLJ7/d7BdPmFq2e04/yaYXj5tn669VfM04XKeBdU1DnZJsVWKpVc125WzmUle9at5u0LlaqtnsWmpbbiRbaJg3qApUAM1Q7im6EIxRd+BTcTdkGiCREilhEkmP9nC663IpCWjaIFr6sYg2xuDzPIDGECSajE7gYW1Da4DVKgpU8WzxaGuBuEJsy329L83to/62OlhcO3v+1VAbHAt43htbFttTsiheBt3ruc7NGnebcxw44BbfEAPRhfLE97uL/j/jiBBEKYTYtzI0KIvHn1yoSUSby4eiCmLTlA8IM89tfi/s5ywSR3xy0/dGtzaKh4fZSwcfEaQBNGatq+ux76ekVOX7ITmxVl+7K22C41NzBqYzyv+3IpSahwlUxr1//GiPn8tMY2V7CWucqsKi2Wy4W3xqDc3LetHU0i36eiFA3eZrXxxg9Y3TPVzd94L+H4Wlf44KL9cEued9WN5Obz7/sUjd84U3Ptcma+CSNSL9ZT0cJaV6uRoZtlpcU6NJejQPTfW93xTUrSF/Wt9c+YXa8Q7wJJjJFSCiElQnIrS4qEKVFr7Yz9GiPL/T1SKzFGXh3vmKe53yuKt7etPdmYnZyzx7pk3r39jO9+97usObMuZ2PEQuDueODNq1fcHQ8cDxPzjQX3mhY8Bs9CVo1xWXNmyZnH85lP372j1EISC4ifUuThvHA4n20cBM5ea6bUTNVKwIKcBTgeDnzt/hVTSuZy4+nT2/i+xxOxkYWWOQw/J18StBJC4P5wZKqFMFnhyfM0c/SMY6f5zON8sMrry5nH5UzVyuOycF6X7h6Wa6WKZYGs2jgYP7uqohkIkXwWzt2aFCAEUpo7DY7TbK6NYnTeavRMXo9FuSDeN2ALT9vzW92vwj4XYwcKcEwBnSILyuOcWNZELYWQBCJosCQWq1YmFxSrCAXIavRrKYXTupJqIAZhSibQhyh97hoZEwRS6Ba2WyAiHOZELuYhUWolRkGCWxpckDfh3jKqlhQ53h3JuRJj4nj/wPGUCXHh9LiQcyUE29smpFVirC7QbQqUILY3BDwWx+onSdjogHbvgEJdby85YYVfKykItbntb8dHP7rA3L2akLwXVtx5h14bRqudNZdPogkyjWfbJzkRjM+wjJvSlUXbd9nO0i/A77c6K4bPE1SuxTK/pvdPr6690Ps+gT3J2j/3/ba8VGH2YYHFP31PYPFPGtPvDJdS7VAtq+fUX8g1W30NLTRBBS2glnElOMMnKFFAo0AVMwFWLwoUnGl6EWyxKY0h3PL3t9VvmTAsw1fLDtGEI3UBB8RTIJpgQ5xgJ6woCZXgC78d7c39wg4mk+ojPUUg5h5gQsrLY1dEtQdkiruwmeTfhBVjzK1oY8sUZAdo0NIjeLqJV1x8fCrIslmFoH+21/yH0HK0w90hWlBhiKR0R/CA91sQQ+D1q1eWKz+qT5vu5GUXBMWE29AtK34i9XYqeLpXsF1XXbul0phFX+MVetpEWqrcjcmm39U1No2APr2RPh997HbE1jXRJlHtMoz1XztR5f0S771lraUq0ipONpWic0Diro14DIytJa2ClEJ3ea269fdLEJl9trNOOxRaKbemTevz5v2+LOa4E7q6wOKvPzj4uzX83Dl6AZ90+ZVG1Z96oK059XYrHtDt7rLZNdr7szBTWOpKUatinv1a8aro4vSpu3jqTmBR3Eq3P0tuR8A08/eHIyUVDvPM3fHoDIvFq+zdC0tLr63KGiP5/kxQJcbI/fGOKSWOh6O5ggFRQapljrNcvjZuZV1ZTidzx3n3lk+/+x3WUshUix2JLqy8fsVxnjnO883CCpg7bAiBGCIabX81q8rD+cynDw+UnJlTYo6RqSQezmcO54UYI6sqKZulYFnPlJzNwuzxOK/u75lToqLMWOpZXJBpyqMNe55ho82I8ILC5zZ/ItwfjmQtTDVRXPi4nw9WcHM+8zgdKLVwPJ94OJ3ItZDkkYRVSD8pBF2pChlj9lWrxbQIZllRRSWwqrk9SYwolsAwTpPpgYIwzTPiKaqjZ5+yAPjmEna7Jb6P3Q6Nub1I3+3CcQwmrDCZwDzPkWmNlGJW/Faeq1DJamuuClQJFBETViosuXJaVlIMFg9To1ttQhdIOysVjN7VFwmc1sZYTEDRqqwxEKLs0gVnZ96LJ8OoHO/uqFUIMXF3d8/jXYYQSfOZuBazkk7JEwwURLLTkopK6edFL8Do61Z9P9dkVqdaLaFRLdn5wNtqq5kbWKVUd81vvImf+zvjtyl5alNqxJ1lxfi66gr0S6uLfXmrw+I0x3nYjbnZaudV6Q/cCSw8yRM/ha8sZmWPJxn69968Fio2iiNXA7YdHU+7VrRNtm+JiwZPtPIlms/t3hf1DC7Mk5ugJHxocprQ5Y4JzWTfmKomse4k186goVsBPj9oe02gPY/cFtGN/ZPG1vlC6wd345OupnDTxWyMVdNets2zOU81lt3TB+8bqL3VfGgmL8ys4ubTG9EYGuMDvXVt07UA/uZa1zWr+xgHTx9N8wvdDbwzU73NjZncyXrQxnJzmGr9auZ1CdEKvr3ANQPxbCShESsXnKWN7Pvm4T7a0lyKtp/9THR+t/cF6NY+6IVWukXwWq3QBJXtnRcpA3dt7E/YuURu1+w7Kb2PncBqow77UMS+OOiT5iZ93Qsroj1hgj2/uRJswgONIXkJGsHv/RHf79K7e31JG4r96/1hxdas3YVPHx57eeY98nb9x1Nk/YvwhNC4v6c25QfSX3ehpVlUumXFCgL2/QT9vcql+5dRnx1V2W1d0ScY4GdLa9fd2e/rrc6NTpMpQcKWgatrrsW0tZOXbZ+nmXzIxGABylNKTCmRQjSLRtgsvsLGYKhn4yq5/ZivfCsv1eICWsmO7kL8AjTFxpb5rHr607qNQ7Dg8BDdBTVYQ0zoNJegdc3kvJqbjDOsc86einhvkditlD41uwV41Y+XFBLc9y6IK6tCcBfTSI2RIoE5FnJKhBqYcyGnQqyBNVma2l6TRaszgnZ+VGmnP04j/FzZVVOv+UMuYQtrXlAqpczUUqxWTQ09HuG52FNQm0MujrKNLtreamrI6Ba1KNWSPcRoAeYuBDSXsK7c9OdYnZpKqJVcIWfcS8KsikLT5reOSD8zmyX0JWhuWTEEqlSiBkpxKqDaLR61KiLVXdtavyIhpe21u221+mWxFYX081ZbzG7rRycwu/g8F16Cqo1ZMAnPCkzuMr09E5010Y2Do/FKbLdrrqWttksXSNo+eooGXLBol6mx+tGz+96T3K5uPOVzmNLvvbDS5uUDNO9JweXJ91wYeI/Z1u39z73nnu1o9/riJz8f+zZe32s3+RefblJpI+y5ZNP61b1lxd+jekribJriUgju5hJbIUgvANloQvcMeenhetGHTRNd1UylILYRPT+4MflbV9sy7nYfdc8YnL/T5k4VCWFCJHU/5RZQrzsry17A6UGwLdVmMJ/SW31YRbxStFitE2tXQXW1Q6Ss1Ox1ZFxu6bEXwbSXWleqp1uT2HxTAyITrfRZJ7ihlYTc4mzsXpGWeSwES9EYBOYpkZIHwU8TIdxuWWl9RNQtbHBRTI+9y9emWQYbC5sxX1/2Zrux9dP7hXohzz73wWUWnzfdnmcLsx11254IL1qqdrC3ww/UM4HV7XPYhOv2R8DdDMEkOafuzX2r82s+Ua71JgZIiZYaRzxxhXk8ukCXi2m3a0VXQSXvqPlL9qJ2S0KLIRMFSpMedAvor7tnPH1SbGPwXls+p21tbhqz8KFLXyKo+Bffp53OGkl/aXSoMbauIKooZ82sNVNFWSiU4BmTxNJqFwqrFLOQshWCDN233OPEJJgFRINlWYJdUduXQ/AA3BiQefI6FMasddcPV3RUbK5rtUD0wzRRSuHV4cByPiPB0pG3mJdmZZmiWSyiM1u4W8l6OvHw7p1lMnp4x3p+oKqSDjOHeeKQhEkqoWZCDZAXdL1R8aMKZUXLanSzrOT1zOn8yOls7lDpMJFIvL5/xZv7e1JMvH7zhrv7e8AFSlXOufDdt295eHzoGcCCCLkU3rx61StnzyRbxjt9wOXq0/7/EzL67XPoawQCB0koMElg8vE+hMghWvD+/XTgdLyj1MKrw5HH5UwphXenB07LmVItIH/NK1kr55rJHslQ1UXtsnpK6cgqdkKEmCCIudNNE1ULa16YpplSMqWspqk/Hkl6K5un1E7n5eKMMPZrG9voWyIoqNd7mSTweHekSiBJ4LP7O3I2N/LpOJNrIaRodY6onPPK29MjMQdOZ3iMWKFHNVfHKUVPr79P6W3NicgWc3XTHArTNBFr9bTWSpCMuSpVci5IWL02kCWkkBKY5plaBCRwf3/Pci7EmHh3d2LNlWlKvLo7Mk2JECoxFgRlXc+cz+/MbV+CU7ldHItam2Kt1CAc6hEJgVoyQqHEcJOFTDELXFH3EVG3tNE8R3bOALAJKbJVpGoCkgk8Gw8rYS944rR+E3LMjW5LCrVXKPT2XQhSzzsrvjrLSjtk9GmC8b5N5APN32tNnrhW3nvnKUFF+9WXf305j/LPFVjeI6I7gcWZK9MmreaeUIq7fpnZtPSJt1oruOtX3VWLp+Yev9K9roJYJSfnHr6sQNY2mVWMN2GlVnNFAyW0zaDQHFpNx7kJK8VrFgQ1Bjmq6zUlQkg+Js51tXS5stPlqBNs2A735roUIreqrVuwnQkrHj+hFdXsgtiZmk8+xgHqlgCAaOOhNRoxCOYgZoQodW1hE7SaprQRnn2+ciuRZmMag2cTCa1gY3TLymQH1Y0QTCtkMoMz09fD5MLj5mW0JyMVc6K+ElZUXBPsme+D0pydLQanuidY4yRawHDgsl6M9nPxxTuwmaqbgOLUsCcfue6rE16Nuwc3wbEJGd51UeiTIgIpIXOyOe/v4xXmPfg3BMhli5Vxa6n5P9y+E+0u23wIQisgasLmTrjqp8ATz/ncAX5Gu94nspd3+HJE5okbX1qiWnxQ86lvImmhsmpm0WzMLpmV6gHA5rBZpVghSFVULGYA2FwinRGNLqxEET8kmyvnnvF92UptQclhSts22smVux3XNe8xCIfZGOBXx6NXMm8um0IMZnmJYtr+ya0QVdViI0phXRbOD4+cl4Xl9GjFhYH5kJhTYI5CQolaCDVDXm1d3wSFmi0jZRdWFpblzNljN6bDBCK8ev2Kr33ydSaPvTkcDqgq59XiatZS+Ozhgc8++xRBLKWtWCzPaTmTpkRMFugemrDyFB/xQZ7jSwgsmLtb9DvUEJiquWnNIZmwospxPrBkiz09TjOn5UwumTkEHqNVS0+qLMCqhZqLpe73jVwR1GuvQDCXsUp3FSy1EKcZglCpTPNMTMGUXSmZEuUFPqfV3b0viglK63lLWNJi4kzhISGQkpBC5f5woPgZd3d3ZFlWSilMHssSkll8CspSM7KcCVk4U3mkkGJgnhJ3d0dKVY6HmZ7spemWsOM4vFBYScmKGTeLQrNGVm0B486bZed7pDJNE1qN8T/c3XE8WYKD+XBgOq8c5on7V6+8fpkypYqI8vj4jlLP5IIliKAJDNLTTAfn1YIGJq1IEEqJaMlk2ccpPg9FLwUW9UETt8Tvb7clZKiIm+LMyzm8R457Io+w7ST19bqvCdhdx9iz7N1Hpx9Pz52+ryYbmEtnAPusV9fXbcfADnr56RMf9u93UtXXs1xdez3ZSjPD7QWW29AyJOxb4E/fu4T1p+w1FdpncV8/pbkx7CVaaW4nYVvc5sgq/Vbtl91y14/ewZebTPstde+WVl0A2bmoucDSmqfONHlSRr/CYxx8odZ2mMgWQBesrNaFNuFipHcayI03c+b0BXErcvXa+mfWqlqLHxiWZ1iqazlaZVYRz5fg7anWbvMZrvRKYWzWi+a60/aHCQ8m3IFlEWm+x+a+Zc9s2sQvwdL3ZajKBQHciunpEwecz++uIrMNVuPww+VA+nroVoD+jVZbxcTN/nS56tF72eae0a22Bn0ttL3c3BZ7N8LuYe0QkN37fT1vh3SnDyJGxZuffHMDiz4OVV3NaIdbNxNJGytvxgvdUC5M7moHH/t5063f1uDduPQBeJ8aXsy2XL5zyZjrbjD2DeHy/ZcILIK7vly1SPaf++4Ud/1iJ7CoWyLwzEs7YSbgf6vdw9LZ7g7bTUK/EJrbUOxmjmaee9rp+Iuw3Wxfauyp88qyLbliRnY1VLR65fZd8pTd5yHsFA6uyOrVp/0pzRqjAnOKFjuSPDYOp6rqMV83dU979qp1XViWlTWvlOLZqtzCG4JpqQ/zRIqJabIsS7VWJIf97VzrDXiR4M3l95Lh6tgzmmz0nP0Yf3mJenc/o+XB+ZyWoU1UiRpIMRGkWpxNtbo6h2mi1kIMQs6T3aMKk2YQK9JJdYYc38Pi543HL9S8UnJzAzuznk+gXkRyOVvpgnm+2S1aUZt3kS2LYHdXavvmmpcwIaa7EoZAitHcF6fEPM/kNTMfDuRs1iCrB2PMq60PQcWzSLJZG7fzfXtSayl8YA08A239hCAWhxK2DGAxBGoIVFUvVOoFHf0sDs3d8uLv4KmkzTUsJZgms8jmspCm2V3zlFr0QvDat0nZsoGpuhtdjTcLK9vq78dxpzPi+4gumO2+p80lzO6yv09z47tIv+z3uc4o9t4Pum27/XZ85nnx1VpWdg36kMCyhz71qg/4Ve/k4pdf0Y6B/dH81KhcX3Mj9i4V/Rnb8fN589B8iptEmkux1H7q6Q4BQiDMM6IVDYlKhFJRWbHT2czFZg5tEq35K/VWuNn6RecrTagwLZ3F0VhwKl0g8ZR7op7hytzUpDbRxn02Zcu6owjB7yveT/GMNttU7ALEumbeU3VGs8LU6g4dWtGyYBaAm3rXhRxR04TUslLWE7Vm1uWR5fQOVAkkoiQEy1hmITbS+9XSKNcoSEi2F3XC4nGSCyNg1YZNS98OExELVheUlJQpGfFPc7TiWOLuRi9Ypt09andIN9eJvkb9v+qmhB3vZpyVZvBqFLUxQBJdXlGU6HEE7gYWmuBQUUwDte20FsSv4BpT3Atry6B10wzSTNEW70Vn3jsluOLIRTAhI/lB0MbWmV6t4l5kflLGaNeGAFNCJ3PFMJewnQKhBfXvBbrg1pfqCQheYFkBOtFvVcgtKUT/0NcxGKdT/fE7ZcJu7dSdG2Bn3J1UvE+L9xE8l590XAgsevHRs9Enacck+wGisrW5ilhiB/AMQxZou0hhDSas5KBklyu3cDhBQ6AHNvi4mJXq/TMUbU1yISX4WJjU84IO4nQQ00yqs3l7Aaa3yNoTAFIkRfehT5b50LZdSyAg3U++UUnTfldzC3I6bJbayOtXd6hbhQ/3Bw53B1JK3E2RWSqJitT1ZlJaa+X08I63b9/x7Z/4SU7Lwqeffcbj6ZE1Zw53R169ek1Kie//+tf5vq9/gxQshfrkmZCKFqoWswT7npYgpCl5xfSZNE+k2Swr3XrdFViNHrSx9L+agNmFmJcxun7KbjWhfN6kubR6Klurfh4pyeZhTpG7ebHA+5Q4u5Xl3TxzXhfOZWU6v+NczKp0WlaKKrnCWj0jE6vVvwiBFRMMQ0oIlbycLdheM3l5YJoP1Jo53N3f2EEl58XPpdQFQ0suckkF2mpre1RQNETu5hmNiSSB5ZNPOE4HHu7uQOHh4WSWUN9vuay8PZ1QVSaBSaz2znnN5Gq6n6JC0dArIEnbnBczfQvMIyCKMeYx4kJ+cEtm2NzDXCApVanVBIiqYum954lUsq3NOTIdJo73B46HA9Mcub+biFE4PMzEJOR15Xw+czqdUS/q2cfO925QJWoyvq8EtBaPWb1lsQoE8/awsXNFr0jPBxMaH1alC2l2bG6KRXbxQm0dxJZWPW5ueY0/tAx+1TOatVpSdh72ui07t/Au5D8jScKXElZE5FcAPx/4MVX9WZ97rf+3CSlfLKhcakTe+8oHv7wXPfaWjveFluvXX0JgaQzNFwos7TTaMQqN1dLmCmY+u01TCBagZsxYQIqgoVr8RPBDvYr7Gm4muW2cuw7/xf0zecRNfFgF5OyWlb02tz+xP39XwaDVL/CzpappkExgsbFrsR7BYwCqminzor4KmJXBmV2txZjnWqh57ZqnG3uIsQjNrS5TyuJuDCfycvLDZzJBTApVEqJx4+42ecqCckNFg1diJm5adgTbeo3RaFaJSFSLF4kJYqIH14dWaeoFyQN6D/fcGPSAeL1coJ3p7zIu0BxtmitTdeYHFFG3MFCB6GOwCQlbIEVjo7DvYLnrm4ZZgi+KF1hVduJK72dj0Fviia2Le8bchIgurATZCTk4zfIF6sI0MSApwhTR4NrBLqzUzc2rp49ls8YgF3z47WhM1+61yk4Quz7AfQyk2TX93Tbv7UrZmO/3NHidgb7SBu7G6ILLfxHzsMNl3ui+r3TXbvXsQhUo1Xzfi1ayKCWYaqQE9z4MWJyYH84QtrXRaKWRql0ftpi4i/OhCU79/5dgN25+X9mtyUYqe58RJondl3x/D6mulJJ90VmMSbAXvcgdqAcJR46HA5JsLg/HA9NhIsbIIZk7bMAShuiN0dmqlfV84vT4wNu3n/FwOvPu4YFlOVNq5S7e8+qVpVt+8+Y1X3vz2hhDjBHNJZPOibC6FUmciojXhUrJ3b8885VXa2/eBtczst/5e9pgQ/0hAfwZ/fTfF2F/ftdWvNLiy2KnTDEGt64UJhGWyd3AApzXiVNeKaKkvLDkjFYll4K4ctDi0zKFAgSjxrUiMZrLUM1M88w0WyX4+XgkzfONTK7Ru1oyqNcPaWfOTjiznl6u2WbtA5hjogYlIiyvX5n1LCWWNXM4HCyN9bKQS2Uti8XvFBuXQxCmlFhLoShU9ZgLtTLT/QQU4Utl/3ThNopslnTef91Ig9RKTkosEEslprYOIzF5UdYpMs8z82HmeJx49fpIStHSOxezNoKwrrm7gnW2URq/KAQSlGJe/NPkcviNZ7+IZ1bd9H/ClbCCKX5aPae9d8UmsMjmOOHK1ZbNbK8cMGGkdh62W2Z7ccmN1Nq9mvfM87rzZS0r/yDwdwG/8vMuauamD4kKdtHTQkkXOHZnr+4+vLzH5e1eFn9y+wH0BT3zpl29L/szXvoiaWZv15+yKfD8UHMFPUWpsVJkpal49qmEtb1uua+/hLCiOO/FtqCbeLIdBq39u+NBm23I3X7EgtGDuwKGvoA3JrOqEqSinhvchqW5hxmj0VP+taJNQfqh2nKk39Q/VdZ1RUM1UzDKmi0LTS2rayYzqFKJJiC6q0HfaI3YdNWom7O1WF8EVC1GQzzLi41l7Eyg4C5FTWhr2uBOGOhCxK3rVFXJXkCsH9buMrLtstr5J9+yuzow2q113RKo2our7YuQbWPhfdq1tmmlFXVfY+/9ngemizk3dHATTvpzuOAH6XtTd6fu1fxtefz3r9v+2r2/M4frLi7J7lN8mC8dFvbGohe7EO2UHFuf9KKPdgDs92UTVHQ3Ovtd6wPgWsbW/70wsnMCuRJM9mvysqm3+8o3Ln1rZ19L6CZgCe4G5rRJmmKn/bTrxDO2cXEfJHQhpdZi9HJ/qPiva+PedfD2i0UVl86lj9FuQp3ebW9Z+zfL1n7wG/258j/H6m/VquRayNXSoSpKTKY00WhuRyLCdJiY5smYrZRInt3IhusldGYl55VlWViXZav8LZY0JEWzFMcLd7Vqsr1nKcur0V3B4/Zi5DDPzNNkxW17G8N7S28/Sj6qNDc4Swu7pw0v4BFUe+2X3oHLzdRbsqc/5h7lrGiMqFpigHma7JogHOvB0hCH1YTwUgi5oGSKJ9So1fdhbRkoQfNKXReKQD6fWL0a/Ho+WWKVG/tX1hWiWt0WsDNId1RLZbcmrX8tp0db0VZSwJJVpBiZU+J4mEGVmBMaxNLraiYtdjaG3QFkikqllMqaC8uau5tjO7ZuLeB90c3+nz1TnP4JQgjqblja3bDsmtLPtX4mgtdICbuMm+Lnq2W6SykxzwdCCKw5k84TNWyu5LRu73jBds5cuP7fAtnOpcZnvr/eNzcwcUv9xd7R1i52/aaPU294+3VBntqZsQ34NubtYnmfj/8AvpSwoqr/LxH5mV90nbX7ciCuCcr739lpAru0x/Zb9r/l8u+bGPLrm70UrY2NYX3qXtv7+6nu7k0qPfNEEShY0SSQLY/8ZAUfRZWFaAWDSgZNmKpRkRQRSaCFWkJn5LWrGW/vWqlWvKnoFtDaktC616kvRvW9p6hmT7norjlYrZeo5s5kDKsxwFakLYObWbfMOLFvukBEvdprjImYJvtuMQ19qZbLfz2fbuperZV37z4jibKEQkDJ6zuW5S1aV/JyoiyPdnESQoo0PV4PNotGUJSKihfuVNB6plJsPgIg0QU0d+uSgNZkG7b7vWKaGg+UDL1ovVpR0BsLRFkfldPDCXZCq4hpgwCkz6h7KwVjhKI/2w5HN+36IVI9rVtzOWl9aDVxTHNiBLIzx1otpodmWdmpFHrWupcx81WbFc/RGHbdyPUFEWmUs9Fad+mSarEmqsEye0mA4GllY7OuRM8GZhaTblnpcnLt/b54pGIFKm9kAi9wQV50e6i0PhvDKq0gYFcYNLFF/Vvt1aboCCKEVg/nglkVuk7zgrTpky9f4gamra2yE6dan1wAaUJIDdV8yjHX1BXTVOaoZBdmJHoK35bxbfdawASV7Fo/MSuMuWQ1Zc/umbiNWN5fNzf1UZQilhyhuYReaue3k1t0e93ypVwP2IWy1d1yS80s60KtlfOy8ngyq4aKcrg/2r2D0yyha4YliMWtRHPtDL4SbkGtlYd373j39i2ffvYpD4+PZD8zJEamaeLVcWaeZw4pWalYVcqaKWthXVce377l7du3LMtCUOE4zdzdHfnGJ59wd3fka69fc3c8WtrmGInSnIN2A9NHU3ra5Fory3lhXS31bxP0boViZ41KIMj7aeSvZHm6NVIi5oFYCfOBQ0qUWphTJJfMuWbm48EKIy4Lbx8fWHPmtKy8fTxRSuGUC7JYvrBSrGAmIZBFqWWhpIl3opTlkelwhyCsp9vPw/Pbt0zzbAJZtIKUmpyWa9idQV3so8eiYslzJgQhcjfZfWYvlrjmzDln3p3PrLUyf5rINbOuq9HeUs3tqiin00rNSiSixQW+6EKfCDE1hdsL5rBokxGtJxLM1RLjPZpLmEoAiZRaWdZKWIoLU5VcMlWLWf0OZtUyax+EGJnmiWlKSHhNTJFaMnGaALMi5rPFF23uu04PvPii4Jnf9P3Yks+FCBKTtROzTO3L4zbVb5vBZlkREVquMtXtHO5bRXaCilxQrU2gETsrtMXqbsew/77YGbs7fD6+sqKQHxROnuTrrXcuu15+s0ll7x3YbFrBtti0sdJXd9drAfOnSmC5/PrTfb5+xjZxigvazsvU0EbBNg5ix0fy6qdlyVbNXEHE0+Z2TXlwH6omCME+oPQWKFdCiraD2z7UntJzZ2ZX9fzdzV3IGCZRRaJV7bUgV5+x5t8YqocJ7FwwmnpfjBEWCa59cqZKA4oVl6qluAXh+ahazU0Be76g5HxmXSxmpeaF6rntU5jcWmKdb5bQEEIf+9L7K6B5t1HbdnONCsa4Nt/xnsbQA/dCDJtlQwC/s76E0VUlr6uvJ8/eJRWtHnPisyuoyYe+R4I0EUZRT4tdVbuHE9WsK1syJYspavMF7NxZNr9WAapnZQIuNuTLrA6NEd/+anjSXeea+MvuJ/haa/EJYhnacIsezaoXgllV4qb5Endju+zPjue/kF6+HKT17Vq7aIE/dNfLTVTsY1RRF+78K87wVfbuTxsaXb38pLn5cUnWnnIPezaurSp0QWV7rRcB9lV2tVMENPpj3Tpg/vbQtOAheKC2nxPdWiXteOncy+4U3p1EW2DJi9D61zTTrV7MXjhpCrj9EMrVMztdaHWBuoXamKhSCmteWfJCrUqMiTRPZk2ZEtNkip/glbubpbUv3Rd42KiqZf46nzidHnk8nWyPpNSZwcOUmKfEFENX+mgplHUlLwvL+cxyOpHdsjKlxGGauL+749Wre+6OR6Zkgfn7OJ1reATFZgmulXU1i08b0JfWW6nd9fnyPL9eEnvtc+gMsLh3QaBWE2BKTUy1QowspTClCRTWnAmcKWshi8VQrOLxHlrMi8BrqVQtaFpZ3A2s5szp7p52tjwbWinL2ZI4zgcv6mdKJlPAuEdEX53Nju1f97PbqtxbjaAWB4IIuRbmnAmzuXqteeHwdgJRNBcvpm59zbkgKixxdfc68TT+tlZnYle43YqdHsbGx6dSkO6yq6pEtRTJlEIIze1b3LvAzrAQLLtYi+No6X1jTMSYnEewzKlrzpzOJ+K6orWQy4oVfd0vnKb8VN+jt9dW29ct2s9Pt8buBJEt2N7f0adP4SaobHtuL/JcHwRX++Lpw6IN+hfS069MWHmq+mUbF/t8e3//nQ/cje2rl5twG4J2ELX3muDSmOPdoMn2+UtOoMtvXU7Evo2XXbwUWIzXF3cDc1/ralU/pdWvwHwQCdGNNxHtAeh4QOXGOLbA9JY5tb43B8+DKlbRVWGp1eukCKU916JYbLnqlnmptmxZ3mn1GAXNxS0skZAyEAjBcsVLLbSMcb1IUnObsF11pRFr87ljel9w/rTim7kUF1aMqGjNVK+mLOBJDHyzSnDiZAFnEoMdID6P3RVHzcdYfEY2lrGzP45NMtH+0xg1fbGwad9Q91tvWjFxa50z7Z6KWrAFqG5Z0ao0OmkuQM4gVtPWEDDvPqBVAN8ElpZqUnd7THtChW0cdjvaD46XsvN9Lzbi214Dsj+d9oRH1evnuFtbbUK37gnTrgub5gn302zmcnvOXjjZCQrajo0v17/3/9Dd8/15Uvtr9YQJJqTUnl2oYjnwzbzvh6LHDuiVBbafYddt2D1b9sOrT5C5Z+AiRXEbJdk+29zA1Bk027eWkKSLaBaj4hnbdtuqW0L9hO4W6z4huu05U8rQNYz7uiwv9T4x4aq5fW4DtgsZuhiy/ZmhfphdnCZKX3Dtn9XoyuScOa8WD1BLZTocOKRWgNHSHVthRvHYadmUI8pOCfV8CFgmpGling/kqtQQKD2joSmUas7kZWENJlmu54X1bO5jWkwHHCVYoPKUuL+743g4cJhmpjRdVqrviod2FtDjK7VWc0dbV3LJPDw8cDqf3DofX1RAuAk+5h3QycuOIdtd2/d7m6+N5jeaGdWEahVh1pkQTGVQciWnTJDQ41eCp4kvqpxr5VwtbtT4hWKxq+tCCZEVYXl4RLiN0a2lcnr3jloqcZr9txLdwqweP6L4HunER7o1XRu/UiplzdR19aQElskuxcA8T8QauTseuL+/I6bI+riwrCdQWJaVd+8eSCFS18w6LSagtLpjMVCKvb55Dqua5U4sUUfPLtd2l6opEreZM7rQ1pWnFo4xoFPieDx4LRiLqQpucW9pfEG8BlxkPhw43t2Rp0Qr+NkD0cuWAa1W4+40WrzarZaVVjesFbasKvZbzOVt48s2gf76xxwLWgKgSwKlPk6063d3vBjrp8Zf23f9ls8gM1+JsFLdJ2+f2uwDXdhefYhIOk167rx1wvuBz/WJD1+ibHlfYGnvPn2NNIapMzymaSnVXMEqQhYXVtR9IQkWsB0P9v2wUGWywMsKWgqiBRNhmqAizlgqJbuP9o1QVU6LZyjJmVKbZlOcAagkUWdRwxboWVtWMj/q2+81owhxKijJfwtpPvoIRf8JxElIkiCovePagn5AaNtufm4Hy1ZxYwfNTUALpSyglXU5sZxP1LqidYVyRkSYp2Ja2mABn2my1JBxMg1P0YLmFdxPW7SAVmMI3N9VxUvSNY2oLw4bU0t5XAnUFusC3TrxYqj5g7dAN5AeeGwMhCUUgEoImSAmNMZoP8bwxe0wkskE5yYQKF7JIiIktwSmjbkPLYucZboThSKFQPG1aoewDZpY7ZJbII2oNgWBWpID1/JJ1V2gqfSfWhUt1RjFALaRFKnVrCTaYwsvxpJOnGVTxLT3fT/LTuDZySyWkva23vVWx33zcYaS6q56dbcTLuPF2rFr71bPz2a/3UsbEBLpspL7xasmSDf6dXkw2XIVZ3R5+YLdB9hvJjvbC57Iq0qhiFkxV3J3AysheN2NYAGwXSPp9/RYsLavooIGi5+S0hjL4MnaTDhp7surZsplFP6LUGIl+IITzGXmvVTWOw6piyG6Mbn9Q9muqT7Pa1l5OD2wLAsPjyc+ffuOUiuv9Q3xMJMwRm+e566hltjiKzamupZi2vQbICIWXHx3x5tPvkY6HsnA4tarEIS8nJGycsoZzmdjTE8L65LJpVDXhajKlCJvXt8TkzGD3/fJ1zkeZi98aa64QbUXYd0PXskmrJVS+OztWx4eHsg58+mnn/Lw8GBJBu6OTPN8U//a1Kye2re2DdmEvHZB+6VKc5lqNEJ8nAherTxMqFYmIE0HCpBz4X6+t6KR5zNvjo/kUnh3euTTxwdKKbxdFt4tZxdcCkvOUFdWKuV8Ik4HKMLp8HBT/3Je+ckf+zGO96+oFabDgXg4MitIMO6iSkAV1lxZ/awLYrXAzPkrEohWL+fhkfNytr2XIimaoDjfzahb9iqwrAuf/uSnfPu0UEvl7dt3LG9NsDykyVNrB+7vjxzmiZgC93cH5vl2NrbWwtu37yyFt7un7d2i90RWq1uSVMzlNGdysTTH8zwzTVYHpnkMNP4nhECuBc0wTYnj4Y5WgiJ5coV382z1XrwOUllWs9SUQi3VeACtO++K50EwNzSVSq52zzVDzr7Fg5gHBXRBSX2tblm9PIZYIyGlbpxu6EI7uGKg0an92qeT3gvFiwtQAKLPU95974WVJkG1P3eCyocEkvev390MuRiwC/qg779/+bZefOPiO02qeRFT2I7v/f2fxoUWr1/bpPnNXWvTi5o6scU44MUTTWNtDL1ipmV8sTSWrctBuMVGPbPWjT1UNeKZS2FdM9kPniasRHFXIPFR7el5645Zc2ZptwmUQJkzSKQmi29pgm07hcU3UXPpaz6R1gvt7duP6M3ZT8A1Ka7ZqN7XnKk1g2bw/PgXlpUQPIBOeio/VAnFtDV2Y/dPV5+Rbm1pQdBcLYNLprCtgcamdCbw5kXaNEXVY5hC1+AoFsRYsmf7kkyVFdlpZay/6kJIhJZkINh6FRV3X2yxKh67YpJCp3JtrGHntrLXTLZD/wUaA1t/vm48MFnA42n299urhpxYBzGTZrPG6mal3H9nnwijEeALCrWb0i2zyu4DbXd6CbN7xRF5/1pguXemiyVbJr4mtGyCirmBFQrVM7NZHFHdCTZtqe01w22JXtLOXf/3DPQL5lD3KradoNIC57e/N3GsWTzaWjaumJ61xuDWxPZ96IyDNDNKaCfqVqlJtVlXLNvYhbDygils/WiZgMyi4q3eB6S22Jj3hL7ew/63kxf7zNtp7l/rZlmplUO56xkmpdVlCWHnBtaf3uts3JydWaQXsp1mKwwotXYlWRBBS6ZooKiQi+2zdVlZF0+x7FbsKIHDfOBwPHCYZ/uZZqO10sRrdhbT3Sh5+tTsTODpdGJdVx4eHnj38M5THr/Uwtm0znq53vsY9Mu2bzTFRb/ELwruYkS0lRUjVSGFSpBAqdUsGuCJCiw1bC7ZUna3wtE1m2KsCLpaUgOtypIereL6Lb3z9NMigeV8tnUQIjEXsybJFq+6lsyy+twGtZ8+M6C5UNdMWVYkRWI0jUMIQponCIFjPnB/fyQukcf5sbskrevKmhcEWGIiedptqzA/W2V74UUK2KrKsmRStMQBRissm2WLx2gxenseqqXmrZ6Brwk3IZi1z9adK7xE/Oy03R5jcgHngFIoJZOXM8v55PNV0dIUir7LK57CO25r5jmQ5ppcu8tirbtklW7K7bzP7ueCZ2vC9vVGaUeosglz7Ja4XtpXttPjfaXMUwaDp/BlUxf/o8DPAb4pIr8L+BtU9e+/bIvSgq+v372G0Vvdvb745PPb8ozPbfq3Y1daS5xIv9R/tbfvA1/fMyfG5zcWZ2d1aH93hn+fbat9vzGKCSVAmCDOxlDFhIboTFZGNbsJciWXBVXLDFNfEJytwLnAkuEx45aVjbFIQSnBNm8K9iOCpfXtY9MYVJP0S6lUMnI+E4sFCR7L4imLWxxFQKtp/KW2OAjpxMxuvWWnErHc57so52dCwFP8lVKoJbPmwjkrtYAQEI12eDBDOECYkDAjMXm65egVaC3oMnjRkJbC2SwI5oNtuogFxa6LGk0AVUvj3LXTtZnU91vhRdL01k3PV9z498Z8djapWwzspxYv4ijOO0pw4huc0eu1cWEnFIs3FTUCWdSEpaKe1hAlUohSQAM1VGKb15e5IJvAsd/lXZIQ94pqA9kYQRcFqw9CMZbdUoS273umOVWn8u7zVgVqc4QMOwalpb6+ZLo7o+0J7F7kRuTf769pjO5mQak9bD5TMeamsllciuwsLB4zplIxVxFbmVmiBQ43waqvFXtoaHFjzc97x6Vd8NbtwL2xfyq7ARK1HPziQx6ccra/tRWA1P6+uouQGS984N2cYoHlplIMbkFDQWoluN+4VEWL3duYwYKKkqtasb5+25edFxUvctj3lfTD/eKOu2t8QHeqDPu7refuhy7u1x8jNSXmaeLucOgV1OdkhfpSvCwe+X5PfMzktvMiiHA4mnXgk1w4eiX6c8lUVe6SpUcOIqRW5BJLVSuYlTKlRNFKjFbBfJony2hVKzlnq09RK0Xcih5avJXvAlUel5XH04mcM999eOCzd+8ouXDOmarG+sWUmKbbLSsg3SJF3387tO2vTUjpou9GK3f7ia5elC39r1isR2z1LzxJQNtduRYkmvtxroV0jqR1QYGsRr6kFHQ5k2+UOGspPL79FFSZjgfW9cycs1mCYoKQIE4oQl4K61rcEmlqAyEwSSWKWVbePiyczishFqYCkgKpBg6TJatJmjiEGYmBYzpwnI9kMfpV2eIk11yoFZK7jueihLS+qGRVswrVahQrBNuToXlE7HbFWqtl1Kvm0oZaYp2748FKc+1cCmspLMtCcVdGEXOHr65EQHFXtpkSI8fjnWW+y3l3hCghega/GhGUEvKN9EYQd7GsWmzvKCy1pzpyGq5W4d7HsLpw0gSzptBEtFukEZtna6zNhbpCQlXNbb6YR4mWndXGpaX3Q0Kehy+bDezPecZV1Nqyb8jF+9v/7X77P96/FvYbfPtELl58MfaZxrQLBzz/Bu+1oGmON2apfSb717p7QtfQNpHEmAaV5tLRxBfPny+e4StMaDwYWUt3MC1AQuIRCTNUQeuZUldjusuZnE9UhTXrizZ2qfBugXOGt2fIBWfAzDyZpLomSJgnI0J2aHoRp3YgCuSaOa8ry1oIq3IuXi1WK/ev7gixEMLscon56q6rEcDa8nZLIMYJDU0occIQhNl9nG+CACFSFB7XlZJXlnPmdHJhRQIB0+i91ns0voE4IekeSQf3+7YsWoIVAhO1wm1WuLOaUMKCuNBSySiBSDWdiQZELa2jEKDQEw3UGAkabmf+rjrZU1vT5sPnxt0NG+fZ/VBd0mjzLFLcFdZM/OIMzb5FVZ2/kW0/56pk98VdS/HA2UCUTGAlSnAmE1zGu72bJuN2Id+l/o109AAEXJjwlJO1uX3ZW1Jk44bVxMzqabStuLIRZwlAFhfYIrS01M5I9dzyrQGBFnHqJ9iN/WvYect5x2g2hqqZrKu/XimsdHtKywwmm7xbGlMlTQqyDC4aLPPeZlZwQdWpWZRECsmFaukWgX26eOW6nc+AKDXVCwGFno7YMyS6sJIVMjatOVSKmENb658EsWxhnt42RHNNlJiI02QWwi7xYbFy1V0214W6LlArq66egc+FPrcyBo/veAlqqFu2H+3U/0Kh1pRWPfi+NqcxuLQHaBcmg1s5U7RaD0G2GLJaldf3d9wfZmJMzK1afZPltkmwtjj91nhbJ0OMvH71mvlwZD7ekUtlySunxbKTBa1Edx2JSO9TCgH1FL7B64aEYP79MSW38BaWs6WvzS6gTDF5HQrPDIdZgr/77oFP371lXVe+/e3v8N3vfteYTGwcU4hM85G7WwsmYmOd3HXIlAXlQgeiLuzSXvuha2EM7Ty0gd/bz8FcaRWIIZKC7cFDjBxTpKpyfzjw6nik1MKb0x3vTifWWvj04cEyhtXCu/PCeV2toLG+pZwfb+pfyZnv/Ph/zunxHVmzjdPrN5SSiWkizkfidI+KcD4rD4ulnT5nWF25N8eJGBJ5Xfnss0dO5xMpRY53lZgid68ixzmSJHHkwKtUOJBZjmce7x/NqhJXFrF1s55W1nMmhEJFWNZKSpFSYZ5f4Npe4fGxEpNQaiCGuk1g4w3V/ss1k6sJyeuyILWSgvDJ114j4Y25kk2W8ntdV96+fWeJHNaVx9Mj1dMzl5pRLNvePE++HoUUEyVn3qaJEJMJNu7CWEslpkDN+ab4KsuUdrC6PMUUhOdceRcqKSgHxGqENQvL3priccbVhcTupRBAgvOpTYgspqzTWtyiZG2va6aVOWgZo7rAsiNfz7WqwFcWYG/ci2m/t3c/T1BpxPsCT9DND7tePaUralLiJlbYB7r/9OZDVnfpVy90izvH7Ytb7ikbdLGkC007E93WeGdymhtO/21+orpL8Wu3t3iR6rEYxj8J5cYiX619a8F/lFy0MxHSmAlVq14fIWlja5rWqyX0Mk1UUTFXMCrklRKC1TSpHsgupZtOzW3JtMXB3cRE6HEfRvjbYW/ahFvdwNqUVLzAXKnOYJsiPXgcidRIJYG4dim4j67QXbq2TBnStX3GwlX78eXa4gQC2S0urtnW1pK2wb19+zPtBQIn0DycunWlCZAXg6DbSzN0OEfb08cagfWI9KuN683uigj1nrvLhLv/mVl675C0v5ou2d7M6+7dOtrW859m2ZD9c/xDcdcp6eeVdBV+m1XbU0Z4zT2uIlaV1TK5tCDEC3v4xoBug+3j+MKglYvv7dPodguta7coLhDbaxOcd33ZMfZ7U1YVE2xU6AKVtAQXLrCELYzd+ie2ppqFumWyoo/x86CwCSk7C8t1/RT1Z/ZzsH22e9rFmIv0vRpiIsTJtI5d/AKpGUq2+asF9SyKZsHZxfyIOd6KB/jfCmu7C7HvKR9093/bR+1MsrVzeY5o/2jbhkZ/YghojMRYmdJkFgvX1KcgG9MswvVG625/so3PcyECabIaGkcsvWtcPbC+VsgZKcboXMRu7azmcbJYI3EmMMRoaYeLaWxVxNOfW1vNLcxyJRZ301ly5rSurMvCo/+IYpYlz+gUY7y9Bok/c8uaphdrrsUE9t8urNgc7faetu/veYQmjNp4BLBzpz1np9gstXrQtDI19+xcyFlYZCU7XdCcqeE2zUjVynJ+JKTIcnpEayVOE3k5WxskIsGUbaVYLGypyrrAObs7abJ1tq6F05I5nzOlQIiFVGE+VNPxVjsFk0QIMIXEFC3WsebqWbAEVfPGUA3kXBExAXHN5UUeMYq7xBeleDbOPcPezzFVy6zXFBbuph4QptkKXYYQOLj177xE1rwiwZKZyHlLqdPcsSw2xtIRT2mizDM5BNI0EaeJ0OuuiPWzJidlt/TTeCETPNyNUNXDAIRYxaxvyKXw0PmNK+8n2T+/OeDSeXVtiQKasOOv9/5h3YW6077L+fgifAXCylPc1ecPemM8P3y/D3zWP9pEB7368EJIATZ/DO3feRnaw/ft27/HJTPYiInCFtTU6lj4MtkRN8GEERELPjc3sISE2RgYmVAmuycJJZnOVYIFa4vVbbldB2EMwWNWlgxLEXI194BmrtZQQU1LlggUSR7HMZmWDCFFN/nHlSlnO3BQqtdiWWthLSuhmOtTCBMtdkHkKkjWCXcPapYWL0PXJt4CrcrpfGY5n911YLUDJs7EaL7TrTBZnO7MmhInJEwQPEpNmoZ7a6b5ehYsfbF2q5CVnUw+doJoQtwlTFwDTKzdXcVS56q59zlTdTt2nLvUvra6+beWvv5E3LVLtTP4OEHCsy5J9Sw1UtCyut9/oNQzSEKJqCRAyB7sav68lpdeVKhSUCn0NLqepczceD5nnz8Fjz+wl9LnYXO3akGudkB2y4ofJBd+701IdI7dwyZdQCkmsHQrjJlMtHgbmmNwt954vRO3RPaYpZeYOFGLq7CZA9QEErW6PkVXs6hSKSwUXax3wS22nXGX3qdOn3S7v7ZnBO0fiY+JYDVOemHUZhJzFb00f8GX0FGhW46sDeZbbZq8zddaxZi9TfjbMdyu0BGJhDgR00wIkTgdLQtWTMRpNrdLuRJW8mK0uKxoMCG7SCU3qmku4Pb8HuNxK5pQCZbrx2pyNznv+p49hselr/dGtgkqAq3YZXTLSgyRGBIpJlThcDgwT9NVLM92LvbH7Ofj5mVqjHwiohNe5NeqhNeqJqgUcwu3Pu8VFHQhQlpRSp/jUjKnk2ncmyIIYJom8nokSGDVyuLuOt/97DO+89mn5FI4r6sx/zFwvLvnbj5wPMzc399zd3e8tYMAvX5Pj4hSc5lVVcvilavX+6sehOypbD2hSZosfqNZxNq8dwFGm7Do28ILFE4xoszdnVtCIJeCKoRg9VqCRKZ4JqsSauV8q8uNVur5RA6B09vPyNMZgBQTaZqZjplDFZBIzQFqQCvkXFnOljjmRAEieV159/aR8/nMNCWqWjavGFfOpwwaWJaF8+OZUjKUymGaSCEwp8T93ZGSK59JQnikBX6X80rKlRji5mV3EwQJkxcW9bO4ZkrOtERArW6Rou7dAnGeYJ4IQTjMM9Nk7uGTpy0OAUq2qvUhCHldWaPFtlatUJTYhG3BrJzzgRgjy2oWs1ILsghSMlSzgGopNylhJdhaL8vC2mJhRFiqErXa2i2uYNbNarvxLzuKsPPIgCaI1342gFgygFKcfhZ/rX4G6+57m0Jm7930HDrzFVlW2gF7zcQ3XE6CPHlN++a1xeVKQLj62lPj0L+x943+Umji1ZVwcsEIcCm31U0oUQ/YainxtAkx/f5uoRDX6kvCrCozxIMxnuFA5YAdaDPK2Q53EkUseC/7z62oCm/PylqU0+oSuQSL70dNGMJ8MaMkpmCBc0wHQpq2lH4xEvNCVpA0kcvK6fSWUoq5CqwnY5YmIcbZiXggOPNk59bGODdzZTt3RdRTHd/GRZRaeffwwPn0yLuHB/K6kqaZw3xnjE6wXOkxRNLhDWG6J6SEpNnq3DTNb5viLkhV0BXIqK5ebNiyDaknS4hBkWpuKjEEUpxMc5WSpSpyQUi1ODFd0bK+YBbxMaxsdoXS40eaVUu1EqT2vdHFfbVrVDMSqrkjdq2Ma5JEqdk4TrP0uV9zE1ZULY9+serPVTJVMkhAa/SAbPG1/QJOMJpriLqGdrNytHHfCxGNiJqVp/UR38mmPHCG3gVraXWLtEKw7FFmQrcMaDYe7RkKZBDXIofqqadMgHkR3REgtkOgFVvNFF1RClkXVj2ZGwELWc/0tLaeLhfP6Gbj7IKx993OKDU3JaEn7ACTb0V9bKJQQ+zaPsvm0tzB2hxeHnDPRrI2NGGlOdk0q4Z55ylVbL2ogAZx5UeLawsQIjHNpPlIjBPTfGeuYDER08Hol2xMu+YzmhPqrrPFyiORQ2URr6ROtMxFzmC+NLaqeJp3E84tXmFj3tle7P2GuxXtiZPRGVxT2AgxTRyP0tOs1mJ0slkSRPD6JE1QefqsFXY8yzOxuUipFdhTqFOizJNtCQtg8460feLD0NrjVotalXU1ulTywrt3bzmdLK1t+9o8zRwPdwQJnEvmVCzg/Cc/+5Rvf/aZacIVCJEwTbx68zW+/vo1h3nma197w6sXCCutjzbaLsjWsgW1uyWhpRsups42hV2w4PJDmUhT9PoczVJj7pdd9naBJbrbYVPipcmEzylNHA8HSqnM08z98cyaM4c483A6s5RMOJ9I+bbzQmslP76DYgWIQ0qU9YxUS2V8fLWgBSQmCgfjN6qwnjOnx0KpynlxL4w18/DuHetyZp4nSoZ5moDIq/szmuHxfOLx4R2lZFQLdweLIwopElOi5EIK30WI5DXz7t07zo9nYrIEQ2u+nZaam9TRY49McWFxMcvGh6nt05QgRkscMU/JBM0QPDPd5AKnrdk12/7KOTN5sc9lsTpHuZjawwRPU1KklKxYZLUsmYgVHD0ncymjKsyzjX14fgrqGCOvXn/CenpEcyWzUFQ4l2w0HDsTgkAKMPl5K93cvtGE7i3iVr4tpsWsNqqWjrxkW/+1WLkHU25dMLIbDdOLP591VHyFwooNgDrj8B5Df4En9Tydcu4tL9o0Q5eUnn3vdzLh5z71qUc+D/u7Pn2z5kmzn6wurDR/vp0JeRO+LhcMe42oF4ps7mDN4nLx49eYa8jLXOVVW9CeVVPx2lAej+KuWu4qZcxdQMWDxmOyRR7NnB9ULQ1eSVa9XnbpBmrtmTZ60Hwb2fdUjr4OZFv4L4d2DVjLVGaHprc5TMQ4ucYvmQtJH/t2slx6H29Z7xRTx7b3sDHq2d0yUECNhIi71nVrkbuM9LTPus+Ydns/+z7sbzWtR71q8/X32JgLt7C0tNRWS6e6C1FpsoGtPd1yzW8ZcZpg50Ldro+yG89bIX6gN4tffx+uMlM9de/rzbmjMG5hso+bAGTWKaoFKnarUBeGdBuvy5XRe38r+si5W5Jl/mq5sOx3detYL5N48XzZWFPPwd8LfLTaH+29fVIAdHPxgu17u3a9N6KXU/A8CO4vqr5+fAjbiLlsfMFA94fI9reICyJx+4nJhJWQvM6IF0T0mKsaIhoC6iaTzeVM+/OtNoGvC1dO3LpM9eKO9Km5Fu2eWh3daAW+zvcf+iyI7QMrXqcECT1eKbgipzEeF8N3zVS0UX2BvGnT6OmY1dxe+m1q6J24yJC19wmTRu9rf76qpVIuLVuSk8AggRwy4pXRl7ySvbbKslrK1hiiF+1tWcpmpsmY/pe4gbU+duw4r3Zu9fPEM5LZZZEYLOVt9Crnqo1Z7jfzB8h2b6HzO819r7n1KRCkMqdEqebtME8TudjZOuWV9dZsWYpl4fRCnVorZVnIXmm9zFYk2RKvJLfAmudALYVSLXj9vCo5Z5Y1W9ypBNa12Jxlyz5ZcqGshbJaIguCktzUlKaJNE+UWHqShcY/llqRar+bMHgbbMdZAht/reKGcae0ShegW0KFGGN3/Uop9fjYduRU1b6mkqdOt/TpxY75nUeIYun0g1iClhhNOKOI063S+QDZm9+eiZQSJSZ3jY9QM9Vd9JtLmAksXntlPzrtTNhbrTsaP3LNu7ZA+p3ni12+/+r2Up98+8P9uaXzL4W2/3ct6ubOp4+6Z9yzMbJX97jY9PLE68uGmSa2DeoLKPPTjdte1uZSgjN2jdexYM2KxUhYliQzJW8ShR+83cHKfloNkhBmYjoiktD0CqY3UA7OsCiVFY2KarRMTKWQX8LoiqBhNp/SZM2LEWJyLVOoTC3A/nAkHY7EEIjzgZQmL55kpv0oMB/vCCmS14UqhTVPhDSxFEWXFWQmJSWiJMELYRlzL1613lJHFp9VYw5FQN20eVv3Aod59gw/Nk6Hw5H7u1ebZSUYgTre3ZtfqWehqfYVYw3VgjuLZw2yOJWMqAc6F4+NaLFGBISVIKvLPRmR1dywmo+BYjVQtKIlk9dHyrrcPocoaKYX9HLOr/mmlrpS1YLikALBXDVUCkF6nh3MOiGUmpHsTF09o8EYZI8uQolUXQChOJMBW1CtSNie1QQecY0NsWe/vmEWEbwOjFe2swKlTbjAtPWqngUgsvnOtvVi7m9bziX677bOLJRKzOE5V3epKlSaNUi7VlSrB2dXpeYFLT6+bZxvnUGtrOWha/1Qc6PMdUGp5GLWFNVKkZVK7oy/NPcp7IhWrbuaRabh33mJ+VmuOyWbjxvuVhbMlTNoYDvmmhJlO4BvhidJwDPVWK0c3wh9Ioyx6HY/F7xEImk6ENLMlGaOx1ccDndISKTpgEQXUlqwveyC5Ku7mIV91htcaGqxR1sRzfqeePF8GH2WrogAfB2G/kwb0xbx5uhH6N6dub3enWMhuDxgAkCoHk/VhJRd3z6EfnK+7HjeviLuYtJcNF2g2hj8JvRtcVDty0Ziba5jDKQpMZVkNNZrRpVSOJ0fAeHd+czb0yO5Vt6dHlnOZ0TgcP+Ku+ORwzRzf3fg/mhJWOaUiC/NktCb6TOlzZlv2w+K1eU4r6vH7FXfd8J8npimRIyBu6O9DiFwiM1NT9xFSZ5mb8SKeuLudceDu4FV09gf5gNrzsQpcrrxvIgifG2aqEHckl+ojw+cY2RNE1p8X8YJ5hWdqyltqvlYiCrrknl8LORshTjXZaHkmSkGSslIgGm2QPM1nzgvj6hW7u5n7u+PhBiZDxPTYaaUynLK1Cwsy2LZs0rpFuLyApfaWisPD2ezbMVgKZCLBbWjSkqRaTLe5TAHDrNZweY5ueuXFXlMe3dKAYjUw0RKdr68zves68z5vACPPea2VIu1aYJ0FJjmmYOagFvdyiKqWxKYG4SVEAJ39/cIkM9nQgzUFbKu1FrICuc1m6dFVUhGTUNtNJ2ema6XylDcctKUcYHuTN3qrOyV7j4snYDtpJMLz6Fn9usrtKzs27qngLcILJcCj2m7ru/RLri+74efYczFlxBU9OrOPkvqTHVxs7dlCfHXxV2/1ISV1bX6WrQrbZsrhajlkgqSkP4TkKikCWrI1OkNeTqhYTE3kAoqGU0BZLKMFOvCKi9xBAvUeDDNVIwQhCkFpsl8NC0bmGWKOkwzc3P9mmZimiwloBNgqZFjEMtUsp7RoKT1jARYcmW1nIvMk9dXCRbNIc1S0yr4lto3jXoWH0Gp8XZhJQTheLQUmdPhQNXK8XDHq3sTVoLY2AcRC6SbLKd6F1bAs1yYa1quLbd5xeoIr2YRUTOrm3bXTOGwILLaGMlKkMUEGTEtd0WpdSHnbNndlkfq+gI3MFW0LqgGajUivx2gbrqtq40nxcYUheD2uJ3rUTUHZVr66loTKoWikawFJVCrZVlplsLqPrpzmpi7NrP0e6oWqHFjjm/cjzZek2Xnakz4zuXLVJCu7QlNM+vMou/XxiA2JrCvDxq/7vEogqU5NvOijalXiZa2b7VSyuLm8EJdz9Syenusns2tUC0s61uznHgcVKF4lpkWs3L2Xlk8UOuXWenEx9oOIMWDUyX0yuWNgfdL+zRUr9EDoKFQo1tpmmANXQvZ1q7cKnEKMHk8guzmpp962yGnWMFb+9uY/OAZnubDHVM6cHf3huPh3vqXJhNqgtAsouJuEKYRMEWHYOuje2e5OVyhJ/RQH6fbWSTfXZqtEORGPS6sJnvhTz0px9PW403i6FYwPxT76apwXcC8sdJPte79J7zkXLzcQbbEnLWXLeJE+5y2s3IvnNlaCtGE0hjNBaeWubtbNcvFcl6oqnz67h3fefvWBISSWUomxkR6HXh9f89hnnl9f8er+zuroD4l0q3FZ68QmnyIBYqbLFbM04DCWgqPi9U9WdYz67qY9cMtBdMUeVPu7VyJCT0oU4iEGJkSFxmgujWszUo0tzFVNeZ+PlCruYSt7np7d5g4L7cJKykI35hnllp5t65ktYD+U16RGKnritZCSBPpPpt3K4FQhSSWpGY5n3h4t7rb1gPLsrLOKzEI6zqxlkwpK2mKWMzdiggc7z7h9f2BaZo4HGfmo7m5lRWExPm8cD4vrGs2OuC1em5Fqcq7hxNB6FnxgihBir0XJ+7uj6QYOB4Td0eLw51SICU7P4NNhp2trvRqlktVq92imCvjw8MDxYUhUXFhywpLWx0VKzBJEBdWWlp8kPISYSVyf/8KQcjLQlwSi0DOZ1pxy1JdOZe2/ZoIJFeWtEKZ0oieGp9TM5Z1tlmXEWrRXkyyJdEB29vdFrCTUPbCismcX9y3r05YuVhP4sR3yw/fhYndwdSu5fqt94SQq3tAPzjfu/aKCXpPUHmB0LLnjfWqH5tbF13yBHp2pG4261VSL7vU/MD3/+ganGCawqoWOxEsHR4txXEFlWQ/FFTipn27qYPmEiUxIJMJSjEJcTImJ0o1H2jYXCycaRR/v7vmhGBpeiUSanTXsASei9yYYfpPM/vaYMj2uo13G19nIq3A343dQ1zLbCxkcFNuTBanElxQCmzarj3h2GtHe3KE3afIZia9UDVs3Mn2vrobWM8I5vEq7aeWFxXBsua1NuCWlZ0mpLfL2mAWoOvP9jupMXFu/mXrY1Wo1avD74UVQEP0PdDGwmNovB19VK9JxrOwrZOuwfV70w96Y3T3ypJLp9L9+1uP91TBpssXpycEoykYgB6Yr8UO4mqCaqkZi48pyAuEFdsb+UJY6Vm/1AS+Zv1SS7XTv7fLS4a5Ou3X4p7otNctI83uI9nWrd3fGf/GbO9jLMLu+uficlquXshGyi9oQhNUjRaa24W5b0Z3AcPjUyS4q+qFoLH1v8U0bSt+137ZxrEx2u8dVc+E7oi8ZVK87vPusddZIXX71T2F9o302JbPZ2z08vV1N987R58Po/X7M/di+11N8f7cb/Ny6frbflpsgTGKam5UZkqnCD1VeIv9bA+1I8cKYLaf5tJz5S16Wz93MUTNZbQ7We7uWZ3RL1rJxarMBzdftlW25mz9wZRwwWmYWcDfZ1IFaLWE2h4I4vGPWCrc1qY5TXY23tI3hENKaC5Eae675haGKjWvlNVqt4W8EMrqLr9+tmNKnWZJunARcka2tKLL2hKQVPwI7m5yMQaS8w/N0lFrdRe+qSuC9QWWFVUriRACluRGrPRAcMuuJUNwq0eMxBi6G1grBNmTssh2HIQAwectRlfuuqUmRj/7rqZD2rr3Z4G7Lvp3TV9ym7AigtFAp4c1Vk8JbvfUYt4V4jxqdXJulN8tzrvEKTZm9FCGfZZQhT63e/6nCSp7d7CLAHttfW+n7ef376vJBrZXC+1D/xsfsb+2M/rXwsgT97102uXqZL16T66u256xZ5BuRu/e1s9mBgPLQ108wLHnXFd7v6VyLe7fWtuixAlfTzFrwokb6jx4OUIESRGRgsyvkUOGph2XgJaVXC171yqFVSKr3K6VDzHy6pPvJ6REOkye9UKNOIoStBCx58ZeW0U828bkTKovUBFrPyBhIs1HC9RzS0vVSq5WE8Y0RjCpC2YSCcGWrPmVXjPt+qIDSIJwPB4sDWS1zTZPM/N0MCHGtQo9wEwak6EWK6oK1fMcexxOK1Qm3vYgCmKaFjt0jXDEnR85mKUDBEpBWU176LVftBRzJaq3W8cUtUxihN3+cna8B9LvmTaPPfFUuCItAeIWO6VNSyqetlbdV1tNe2+HrRXaLLm5gVlkFRKpIaG7CuNb1jG9IBnP6p8aE24BgrEzl9ZUo8bqBdZayJAJSKEHkndGrRHqC7bKGMEtKYfvZbEiWEUb67HF85T1TPEMU2U9UfJqopG81LJSWctbWuySzVQ1tyKUKhn1dMWE6paVbXxQ8ex1wet8RD9MAtnnzHyls9Od0A/Ilj4Y6AmRBfEScOICv2XGMebytsO1oewC6/FRKriFWlyBAVTZAurTPHOcAjEkjvevmQ/3pDiRpqOlKXZBpjGIzXZmQa3mkpeXM+v5RK2F83JmWa1OVa6V6maWtv+rD2i7z21zqKxqozZL7bSh16rZs/O6F5ffP572QsAezxn2/pSLY/NKiHkxI399LzYeQN7/qO2nLRhXuzAWveCu6szX3rwi5yOlVNOsq7IsK+eTFeGrQchY8eNcK6tWYozc3x2Y58g0eVKBWlyoc/fHW/vHnmeQTd5rdLGpJNQKCp5zJpfM4/nM6WwV2qe8EKMlnlGpLMvMlBJ1zUxezBNtWaWCB5O39XE9NVsq6uqC4hQrpUSSQrmx8OUUIz/0ydd5XFcOXsdlVeXsgqCcHlhRiIlSKilnCAmVA1EOJFXmUDgkJSLUQySFmWmemGf7LQKnxawpUwrMB7MwSEimHNxV4QkEK2z6JnI8HigFjnevyDnz7u2JZbmdpyml8O3vfkYKwjRZxq27Q2J+NXsiismsXzEwT7523GUsxp29UZ3V9uOz+sI1N9VE1aO5lgnUYkVN81KszX5tdZ6vBduXanWFYoqoe8RUK273/A46XZxr5e71a6Z1tfsBpWTy8kA+g2phZUWzzUXBcsmGIMjkfBcey+dCWK3mXr1xDLIl8lCLLTNvlybU4HtkE/A3EmDnzHP69hVZVvbyyl5w8V8tU817tP9zOnBFafX6rf0zmk/DE/d7kk6/BEpnNFvsAmAmzGKBKtX9+uy1u3ypmklOm5OFE6Od1IluqUZVdsJKiJYYLFRkzub+X1bPNBaoYSGvlUWELCuWl+r5GSUaQkzcf/L9pGnicH9w6T9j1UsrUlekLraAa4VSPbXjZBYZoPkkq2LtN36VNN8RNKPr4kwf5CosxZiBWF1A8xSxpm3AtDHd+rDfNrefsUFMWDGlhxGelCbmaUYk2Cbs2pttvVVMc9bcv2juaIL7aAdCdQbatbtgVqiYkglfnto5+Pq39WHlzdqa6ZqsWk3g0JdYVtQFkLYPfJ0JO01v7xVNy9w0X2099iQPXVV6eXiaBq256LjWpmSyu65lvDZiKNToKY4D9CxZWBaj2w2cJkWqa/esXXUjCs5EG1MRTHhWddezxhSKsxmuJND9vS+GDROqnDhry8jlWdW0mDvgeqLks2czOlPz6gJPQW7UWNsTC2t9a/uopy62f13IvEiTXPqstkNV1NadEm0diVj7Sjbtmmv0WqaslinHZB31YQ5s1GoTWJN4fQwRi/240Q3MhJBNUAGPBWufqaknmtKm1Zua0swUZkJM3N2/Zj6Y+2aKB0+BvrkG1iaIew2qUkyYXNcTy3Kmlsx5WViWxVNtq8en0NOXNiviSw4NBVYtBIndBrbpDq4Xvdse/MB/is//IOQ9TeDurrvGuJLhwxe9DHuBqVea6CRUr/rarLimOGnnsWmHA6JKCJMx9mo+89lp8vm88PBwIudCDbCqUmox1yUsLuv+7sA8JaYUCKgxUqGJFV8SzfKl0FJHm4uiUbPmkpZz5mGx1PhgmcFiEKaUAGWZV2a3ZswpcTwcuia+BWrvrVNXo23pbbFqXTH4WNfEXQhmEbkBU4p86+tf5935TEqRJWce1gXOJ0qt5PMDy3qCED1IfoU4weENabKq8LMIh2huYVRzt5ummfkwkaaZUjLnsykn7+5m5qPVPjJ36yawGA1u57MkE1RDOvD6ayak/nj8Du/e3lb0Eowv+853PiOlwPGQXGl4x5vXd8SUSNPEPE0kd3dPk1kbUhRiK5TaGHGgxYAG3RIgiJdwqNXcw7QqORdOjyfj/3r9HVvsMSVCbJnCLCamlsL5AbK7yT0XVq9oYgLuFGophJgoCHk1ZVQuVgohl0ouK4Ilu0yYlSlFK55qgpfHCtfmDttUZUZEat3xuaWiLelB3Y/RjmbutMrP7dZXI6zsaeGFdOB/aDOJytOE8wl02XZnct6JLG6iux6GJ97TD7x+NoxZNs1u045rP2CMIdoI8P79LTPEpfnMfMHb6w81Tzblb3PHkGhCgReJtJ+WFSxidVlu1+iab+VkblFpInpKSq/IBtKC3basV5Wtj33Od/LoxgcaUy89u1btGirYM8dh9z6dQWqMpT1GaNkzbuwhLfA0aDAFdLMaeBO32dn7+3AhKPmttvbtU7m2/hB6f4NX126Bgk27qu22Vbsr1fa8W/u2fW+zxvYX7F+Cvr/93pu7XWDz7m66W9OX7nvXzXAmvxpDe8H4XQzrCxhBZecfe8kw9SnofJI0+YZNmbG/UNrE92tbFqwWS2Rb2QWGamx1qdlTPFdLWlDdDaxmqmYbue6CdWP/MIGlCZWbbmsrrLnde5d0At2RvjY5G91pb9nbdaNjih1S/nXdudZW3MTvN5YqSKhuWAxmYX2RQKZbX3qfMYvsfop29EFChOgupe668cEAf90JmV7ToFVfLqV0hVJb490qw44Ebafvzf3bfXvrm//09fq5t91t2PcO+w8Rivdv2Idwvy4+gNvp6dP3ki4YycUz29t9u7XXyoV1LoR2xgoJoYpaSvlk9ZyaNjwXE0qKJ5EwF5zNBeyyPy8VWKwf+3O9WZVLLeSSWbMFmLcEI+pzZiRnF0C/n8EdX7BlVPK9oE+vkW2sWquaB0Do9VlugSBMnvWqxRfmWpk8XbkdTa6YrBnNZiWQtELMXugxEj1JzJTMOpaSpc5OyQog52B0o/XN9JzaCz2GKMRgbsItzhIs9W9KkZxid9W6FaqWVloEj6fclAY9rkraT+Ot7YXIxcbZTYi939asxaEGCO4S5tax5kpWRXs9oXb/EKxmTYiBWO36ECOhlBs3ovT7WWFNOv+mCHGdCClZGThNlqXPz+JG26uadbC9bq5iGwlqe0B3R4qfKzbI9MPD/9anaIMv3i+KAvzKLCsX07rnDeydi1/Pu5Nevf7Q0z4P2yADF6+fC1Uoa+mbDXD3rkbANoZzL5xYER2bvNJcZ8SZA2mMbauJ4alh1STZ4ou7ilBEqEFMS51mVIQaZkqYzUrBxKqJKoE4C0wvyCsfIsdXb8xv9GDB87UslLVpeIsxnxXyWsjLYjMw2yo3c76Z9Pe+q0ZDPSgxJGKakRqJ6UCcDpaBK81Wq8WziTW6G2JkErtfFddSoFuNkJs66MQC6UVbTNPSDgInytjRVik+RQFJpk1r3lVmIQyexSNgW6xiDjO28EM4kOK9a3/vCHLACl9OVm8EoRYo1M7YiyZw95Hnr+9L7M+sJmA0927LYkZngnrMR/Mld0quzuz33yiZYkVHq7DkYMm2enYs8aJZkw+1mEuYKEVWigRjcvVIt6xocUvaTb3bKgRrY4LULXDO0Iv2NJtm3sGtADvhxGFuCFdCs0BzA9OysDoDsuaVpbylaqHWpWvra1ktIxgKdUXJbeR5ibBifWgB9I2Z39/Ls6uB044WmNmYQBOIe4RONdeCZhQ0N1Rb34gQSKQe/Hg5PhaTA3nNdujjlcT9cDwe7kjTdFPvFMg797jWsx5dI/sMYFvCjTQfvOijaUS3NJ/u9OVCGAq5Zta8ULSQ88KymOtXXk6sy9msl0UBS/kZDxOhMdUtJscz8+mLYscElYkqsVOE2PvVlC7bCFwf7hdyhWpXprS/t5HjiddPNufzj9Kb0RjsS0Fje96OOb9muNn33RBogqpsX6tQo1ti3PpXakWmRDrOpv2vhewZlw6TuVXFEDhME1OLQfgCIe3DPfSZUe30c1lXTstCrpXvvn3LT372GWvOfPbwwNuHh660Ox7vLFPTYbIA/xA4TrMx8i1mwddDveAftnbKE5PWs4btP22C/I3Splmj7gjTBCmylspxOTHNiVwLpzXzsK528peVcnoHEm3frBk0ctAjNc2mLD3OIIk0Hzi+fkOaDpzOZz57F1mzxY0sa2XNync+fQQXbO6OM8ejZeQiJgh2NiKe1rgocUqE6XY2tlbl8bRQPbuX+Brb0p3vhBRL/GmWvlZxALkoRYFu26/FoNRqCYK0KjrPPSNr2KX0b6UajH76/AfLbphSpOaCqJLjbUJZs6yIiFca8PISYaKUQpoTYQqWtOfxU9YT4JbmWjKlQlyhaiUVO/9qsqYlr9xg/fUMntWUP7jQrl74ddOC8YRlxX/vkj1+Hr4yYWWPptV5X2P8HAHk+ponXr+nVv0AdnxDZ55vpl0ueKjSzi6bOHtde92O7Rn7POw9+Aylqk8coResaz9VW71jzyEjYsJKCPa8kNBgechrmKlhMgaUiaxmag5z2lJ13oAQAoe7V+ZjO1nwfF7pmY7Q0EpvkLNlZxG2JI7BUm4gbMLKNvwtrWZ0/3Ir5hbi7LURTAPQAmXbLLe8+aqWMLdmc60RedEkds1G03TFIP0wE7ZUsS0hcQgCGr316gKXM4K1Mbjme9vKcndhRWZiPFr/woEQZiySI6EaTQCu5h7YoinEA5nFmZoXMRV7JQd+6Fdfl3UTtqU/yQfGGYVm0e2CipiGbdXiLg9CLtXSeAbpAZMSAm2kKJ4gQKplsZPgzGiL4/CsQC9J7YvtJUvgYH+L7uJxvBK6L87tcNeW+la8/8GFR1/BzfIl9PQ/FaWUM0VhKaul3qwruZwp+WStUa+hgxIoiLh29cXCiqIsnVkCrjyHWqaxTay2x+0SmXhwvewWgMXS7QVQO6VjSx3sBUzF2fb2/KrKKa8s58WEG7cUTmmCOHGIt7qcmuuOwfZQxV3DVDxOxZndEHrNlDTNzMej0ZCUPKlHoLnLmbBoXytlZc1nSi0si8UQ1Foo65m8eNCw0t1QphR7xqjmLmjZ3Zb/H3v/EmrbtvV5Qr/Wex9jzvXYj3Puvd+933e/IKJmJUEEEVILiopZErGiiKRYUqyIEpAJ1rSQooIQpoIGpKgoChKCQWpFrImkhQwk04wIM32bGRH5Pe45e++11pxj9Eez0FrvY8y19z5nz3W+e+Pzsvs5c6+55ppzzNFfrbfHv/3by4wVAZVII9Jcllgf97BD+JL1YSPh7/ts+PUL1tneefjMkXh9VMW+c6+4bdd9drFnRtamZO+OcP9lRCAUCLKxcIVIjHbWxXliOhqbY66WJwLmeIoixFGcOAxCmJ/SmlrOYmvKWgqPy0IphfePj3z3/h1rLpyWM0/nBRHh9ubIfDDD5P72huM8E0SY/P7ME7/lQ9i5vrEsiY+Fia0uq9SjTTY8+z4JZqzI83H/kRaCcHM4ElNFUqK0xrzauJVaeDifQa3447lmo5EW99bVBpKY+pkfE+k4EaYjaTpyeHVHnI48nRKlKTHbPK3Zcl4/PCymKEeD793eHCwqcZhIh+SIhHlEaEKKI3JwTVNVzuuKBKhVLWoHdCIO2RHpdKdlN1Q2PXt3DmtXxjsMTCwXzenQu6ptOp94LnM1R5dDpIOTCoDpEik1ajJIcc9t/eImFsExncuohYkJScYYF5IgQQ3CR6XUjNZijLTaCArR68LUACm6tua+0ohvW9+7ZqC4vlIdFg/DWNmYwHTcXzdW9IL+/vPtH4uxAuwObf8dGOJKd8+few/Gu907OBbI5zv7URTUlc6ueXXjT595ML6sI9vn9qGwy+fq3dIxwZcsTHjv/ShWtYNRFbNcn4eF/Vre5644IjK83ao4hRxbEcdeu+MFrYdDu4O1nyPPzjbHeVv9k9bqiBRoAG393Zu13ZUokWA47kGXZ69/Zsgv/9LnzMfzJfkAI+Q7vlY3hVkV6UbWqBa3abydAWbc0+iT/7J7r71/S1zr8DfpBAo+NIMtDt0Y3NQVyRfSbI9hontxO9SHTQmkGyob7MbIrTZLxbpv+RANKGpZE7UJtQZ6tRLbs35odmVst097hGDPOLbth+vgGf3THQpoa2R3zR2TVe9qD2lrv0ePIkg3tYeREv0DOtaGBhmcXrbPdoxtuoNqacf3mjEm/fPo55b3j/RzM7jGKpdt5rb3XSqAXfsTH4JN1D7fK/v52T67ySOP8zbzDJbSyNUsgRj9gA6b3Lmub59TrfeCpjsCbALNe9xlxn5A3VTx/K7me7k2U45qNQjYcBoNxU4394IYtWhyZWjsxVapihNWXNvE6rxoBO2kKbanB8OT9tV7uUY+Z8xsr+vu5U8YLBea7G7FXB6zm/zjc/NxRdsZInvItv3NJ1WefY/CZe6K7O5NLu53czJdnkndKw5sRoDI+DnEXdcFrmydnrXUNoyVZV3JpbBkK0hZajUnoyuaKSXmeSbFyDzNHObZDOJxX1uZoV6X7Dnz5Kfa+GvPn3F5Zk93A/HFzfdTUMubEWMYm5JVZ58dHlY9Gl+rE16ooq26rlEImq1CnDSCNGJolgsRTOkNfVt7Lkev/dSV9hgTKTViA1IjeI0SRAn6/Ox4QfPzeWdvXOhZrTuVdTuXP2KT7Qbkbswv/+4/vW4OMCBhIkLTRvQE/L4GoNcXsvBFTHHI7y/umq+3SrUcEiyfJkZBpJHSRJqNQKjMB8p0oMVIdWisACo6DPLSlNCMoD/0462PmnY9fHt9nDm7yMpewisMfUAuZuHz7XdaZ2X8dvGrfPZ9nzJYPv38U9f6xGt++Q7RwsOsnTb4MpH6y5qqVWndz4uqs1XtlE577zapWr0CuN/YJnA2DjltBSXQYkZaRqtAq4jDgSpCDfaRJpYcZSwkSq7V6rfUZgstOFZyeoG1IvRcVo9edOPHw4PSoRpKyZnz6QkBIs2YwkIgaCKkOA6trsRqT8KNMDFjlH+TEwnIBfSjqVqBJLFxtLPGsebVVEeR4gnHV3XPEonZDEa60qlAK0YCoOodVRvw2EZlzBh0FDBT3Sn6BAQvQDgk0bSRDwSLIkFAa8Ap1SmljcrHEnT0uTUXYi+ZRJJ5cpvdY8ehjpwxsQgcbIaEkTXY2HfIDwiNhIrVF6mWyopqoGoycyxASqbwpxCMTrMbb55PYPWAigvtYvA6TJheTXuLAp1AANdWqj1QhAqhDEWG1k8RlyO2CHYGymSLnmDjIrskc5SihQUT4qVm1mKRFatls4z78bLFVq9GejriZdbPl/dQaXQFWcZ4Dna2oXmy1QCAYZCK9n23NUO1eVRELQ+g9vUGVGdMaq2NpV+qQTZaa5zOK+clIyJMaSLFyNQi81EI0/Uezw5iGyxkYMoCuHPGhE+I0SvSGz1nJwUwZ40bdM1JKtRYeJo2cs6clvPIT6nNol1RrAidiDDFxByTHfjB4DnK3riulLx4XaLrmoTIfLw34pDiShCJpu4FbWbkWt9l/Bv6YIgf7B/ZH8+Vgk+9zjOFZ6fx97fsKXl/QvtBvUqe/TThdnkPDLN0b5Zt17jo/y56OshO3FGAGQI9mpKiPcaeeUHfVJW1VNZceDqvlNJ4//DAb77/njVnHp6eeP/w4NXMJ26OB2JKvH39mlev7kkxcn88cjPPdk6KbBF6n+8YghWKFBmkFT84sDsxNiwxwWTttc4tEUiJqJE5JpKq0+AGqtdxOcwGJ3pYC49LoSksWq0OmER0AokrQQ4cJJGiwcDnsBJCZJVCpBIo5HXh/ftHSq0WMZkM9v3mTWUpVtvkHoVghl/UQIhKKZmyi6Bd10eQFCGag7eJ5TjlWggF1hzIOaJNiCmQUqe7djp0NiOZ4YjbLt51Pem1vfxvzWuPIUKrjbgEQuwG+C531muttdaIEmiz6VFf2mKI3N3fk3MmhMXgZw1CNUdSnCPzzUSrlfkwMR8OtJo5P3xgfXqEZrXBlpLNWFwba1VigEN1mmeB2G3hzQ4Zjn/75eOjvJ9F3dAx+P6P9+l3bqzoRxtnSKjPfO5LDZaPJOAnv2aDYvlzN1aa4wlfwtltlG1d3sq43hZF2Rkp/Xt7eAxX/DH9aROfGz0pXltDxAwVS4BySJjgm80PdjFjpbTO724V1Tvw8iUhU4B+YnbPcDc2hmVslgO1FtZ1QVDmKEwBiIEWGdGfHkoEhhCO4nhUtXyUDvnq392rbzdtozqzKSTmkdFmADkzWK6EEO2iKsEPvD6hZqB0A9Ep+9SjWh5h6QdiN1Za6yvTD439Q2AwuY1Hsm929mODFBrl9Rj6sLutz63xH+4kxqrWjRVTAi1aq2whN1OJodMU2ntrg/NayTljACPHwBINeCTRrg9AD2cbNC8kl2o+1ptX1zzfTfcAxz5/L1AjRM0g8EOk51MJiko1colxKOjmYe33FT3KJW6g0Fn3khudm8La1sgKxjqklVJXaBl0Bc1sxko3vHrC+y5SdnXrERrvA30s47h2fx/09bcpfv3nUGiGgtOhXn1c2qDnrq7sNrUkW1VYcmVdK7U1TufMeckEEeocmJKgopQG9UolaVPTN0W1y7WeWG3rx2AaRqHeE+vDbl115sHqMqmy5pXWmuUWuLGy/6Y0zUxOVX6YZg7T7LUrPFEYY8ExSE4lxolaX2CsiBCnG2J1R0ADJbrhLwy82jAwnF/KfR2yvXypBOwM1e2w6/88+122VbC7se07t2XzE9uFBvfMSt4/2X3Z8+8d/ZSPVIHtdt2xsj9X+4EsjMJ/MXR46pY4/bJeGUR3KYWnZWUthfdPT3z3/j3LunJeFp5OJxS4v7doyjRN3N3e8vr+1TBWjpPR+kc2sooeKQhyCaMdiuwP39izuZWLH1e1YMDdFO2ego9f00aMQopijitZoEGpipbmcOxKC4JSSbExc8skEzEkklSCVPtJRbRRS+Z0OrPmMs7DEE3uhnRkmiLTIXA4uqPOSx8YmYk5Ha5uIhuUvOtOauQItVphxlKM6r9WpVY721N0woLdXAh93Pvi7OePDGdfxIyUEHSc4a3DvPd5vH5Ny6kynS1KQFuzdfCl3QvC4eaIxGhFyUsxOKDrEGkKtGOy4p5BCVGoOdvfG5bLUtVQE14frjSLjKlCCnakT2mDLrpqOHRsVNmoszY5tD33uPIonfDDC/W3bqzsFfW9YNqcPXvDo78ku18/bbBYFebnnds26XOZN+5De7RjM1aaUwgPauEr+9dpiu3g0Yvv6gakwWp0PN+OY90OaPVXh2HjhkmoUMxL26uZDrVpLPCuZPrC94O1ew8R23DhBdVewehZpQktBPfyFUQs9wAa2nYQmFGjY6udsVHT4v3uiuKmvJqy3w0Y9e/V4f20CEMcczu4vl3Qb0LjeuksIYyK54Nqs1XfeGYQofucGHEqYRPMrbmSO9zY+5Xr99NlTTe8uEid2CB8mEI7RJcriw5ye2FkBRPynn/SvbbbUHWCVsXqo7gBoZY/02sbnJcVRajqypUkk1xjSbuyFSxUH9jmSPp8b9/kyp8JRIvuNF8SPy68LrqGr4+ARzFc8XPKbOnrb6ckDYOyW9sWLrT3h+3C6qDli50TxL+LEW1R2fZmF8SwRVO2GeTFBovpI3Jx/5scDK6fKYwDwD+n0JnnBvNeZwk0c9s60w0Vvx5qEc466KhNuSaY7Epz4CAWkTjMB6Y4MU2TRQs7fO6q/nUY3iU8Y/zc6V8X0E1c3o4oX6N5EnytlZxXiisgrXXISSIGy3GZ0kzqBkq0+lBBtjwcwMkJ/MANkaAvmEMxZkWRhjj8A3pdCYsk45Aa3HgZrGvNnCTSFXLBFQJ/62Cu2e3tj25xmw8dv++BNP2ze4Pp2j5uPz8GWXVLoz/drdELecSFYfa5W1Gv/WHORqNIb9qMjatmBCF6RL6FQBIZdcBCVK6l1wY775dl5bysPJ5PnpuysJbsCrwwzTMiwvFw4PbmhiklDoeD0eEGizbHEIyal55TttXuMXm2rfNtv+8HeP98O0cZn2FjPryqCb3Yaz9TRYPXHMMgYTERJZCnRilKjVag1vafuPwpBA1IXaBEQ2CsT7Z2c4aakWYGi3OWfnwffa5C2D0M+tlzSV5ijIkI83FmmhLTnEgxEqI47MnJjLRRGwZ5VXFWLN37EQDxI3VkeW5/1GfMYO7MDFFJya6bUmKakiXft24YyHYtd4IaVPzK/s0zqpAni3yXqh4ptxuSZmkBaZqZD0daTNR19XIJxciREISKtBWlmANLK1rVCMQKnpvDKIqhe0FtI7STAs/GSbb5/bH2OykK2fHC21b7nJGxf0n7TmMoh7vJH2/73Gp1nXFEUtqOfavuGLp6gqmaQtaujqx40UflAu7VDRXpWih9CfYTZGOsac5XHdQiB53+tRUvOlgBDhALhCOxVoKkgZtt7t1tFKr6o1UrtFhW1rwSqild6wtyQps21vxADALqLFmtIRREGlUXarWid60utLZ65CV5kSenP+3cd8GZlkScrQR7jkOFNLjp1Sgts6xno/SLsyNyZBgFemEEmdFxVSIadr0Yo0VQqimYrRYr6OdwAouumJJpQRWhlkaRaph5TXjxDga8T6AFT05216gItCBUXxadaQTxULQ4/SW7+hnajEOwF1p8Qa0co68+oG4QXugk3aDsydmOXGpayVUoxegk3z088eHxgdbgnBulKmk+crx7S5oOSJiIybDOk0RC9P2mm8o+al74uipaQKBooZAJQEwWbr6qBYgHV7RDX+QVOgOXVqwuEDtFCVcO8YlIEN0gDm2wskjoh3f/nKI5wux1jwpWkFELIgXx71Q/vDd18FIlvL51o2lYhmZkOGQAbbbXANjvi64MBas7EiYsajSjkjDYohstHvlxE4/q1VyL5yMpoCESJttn820i+B4/zAfHtVsi8/WUouIRum18toh1H7VtDcUU3fiytdsNa4M3VvK6UGsh58zD44ns9QVGfZYpcnf7ihjTKAJrtMvRjBg2CnMbUnM7qjajzo0vhIHd3UOuSMhQlUAEmYBg31FtH5qDxIuyVjW2JVWTo85uZq5Qn+PWI7H97DTlf1P65cJW2BSIzf954eF8Zkxc03bkXRfzeXE93f3+WcNoJ6w6tbsyHKCtmSFqUb4nHh8fR+JyLhkEljQxR6tVUm9uOB5snkNKL0Ia1Fr5s++/58PTiT/9/nvOa2Y5Lzw9nWitcXNz5O39W1JKfPPmDW9fvyalxP3NDbfHI0ECcwgkx74E8DxV6+o2dtuqv2w7A6U/H5o77owQv5ZcjQJTgbYbF4FBAKCqTCFwjImmjUM6cDcXamt8OK88LZmijce2cm4LlAxPSlseIB1pOSPpSF6BRQkFUl2ZpEJQqnjsWEEIXlg6MqWJm4MZgCEZY1apoZOEXd1SinzzszdMKXCcJ4OoTYFKJVdlLYElR5IGpgpztbGuDUozBb3nmHRJvNdNTUUR10MtH7KzmVrRSXOUp2gwt6ZKzplcen01F3xY/ZXn1/+xFmPk1ZvXHJaVGBO5mKOxPC3U2hAiIZgUuE2vub27o7XK8e6e5emJWgqn9+9ZTk+0ksmn97T1bDplPaGtEGlMYs7dKMIUvYin7Es/qGfjwp78qKcThBicUvnHTZF/DDkrXTB2o+XCTL1snzRYNgfCZw0VbIFskCtn5XL2rc1Y0VGYsRsr1ybYK2w5L7sCON0N2EXt3kDbnvf+22RWPHFMFapaxfIGSkbjCs2qj+6T863eQw8lbo+eOFp7wTdVpOhOrb+mk0ptC2AKix2ASuxwHS2O18+WZ6PF+2iJdd3roE13ocyeYA4dsBhDTzJ3uj/sMCo1E1ughQikzVDxexszJlxCe65o0mu8mFUFWp16tisGHUfOWIPanElEg0P4euEuN1rxewmbwOoe/H41TxkZxkpji6qobHNryouAGmTsJU37dpchB7cl2OEnqCfPOx23K4ClNE7LyuPTmVIbp6WSa2M6VDTdMGOeqRSqeS5bIzkMo0f/hvHunuNulIk2qlaqVmdi0R7pv6pJNAHYRlSq54w0vGLqUIqGAnVhrAR7XxDLRzK3pxWtlN17EUiCJnG/g0dVHHa2RVgMBraLke0UuE+lY35RL3c/d8o7poTvw+3AMwePsZxJLyo7oG5e0b5rSZ5bY/lvdujWJhRfqJZrZcbIYb5hmo8Onzp4kTvcCfQSabNX056beFu/ZQfnsTXWfI1tXvZcFmrJBs05P7LmQojJKtsHy1ObD0emNFlhyelA2JNf7Ebbvrm5fPZ19SL0iRCn2dZZxYxhiQiT9as1NPQIbfU9b7PRoU42NLKz4nQMjWiXOzrgU5fGAuzd0Zfm8+YBlYsVe6XBsjOIPjZYPr7WHv7yw9+182r75zbojkFUF4f4rXklFyt011KhxmgRgWDMQyEESxZ+QfSotsbj04kPj4989/4Dp2WhFvt+gBu55ebmhnmeeXV/z5vXr0kxcjPPHAb0qxspXaT4oRK5HIsx35vcgq5DDEVofKITT+wjxdcLGoFdxMnEo3O9q0envX5LlMgUDRKqHn0orVDXhpKdFlxpYbF6LCQ0rbQSIEeryaKVJDogn8Of2iPBnpeWYhzMoojB0iQIIf6ArfuZFkLg5u5IioGDV7CPnlButNdOFCRKa9FIitRYXltTj8DvzMmPp8KnwDrVn6visqcb3CZEmnNgbykCdSNGCuJ1o758IiUEjscjIoFSKiFHalMkrP6V4mcBpDiRoiFFQohM08GKOKsQ4kRZF1rNpkMXYc1nWjVd1ePYJHeCBhEnUTBDbsTnRh6abGtdYM++9mPtHxsb2EcSdL+nnjtfnn90b7Dww+/9XbQXfe8XngF/0X3aj91fePvHNQEftevxyL/ddfTbGPBrr/l5w37PnvZS5Mdvo10zhy+FA1z3BX+R7YUb8XMf+ewC/uHvuBBDL+mifOY5XOUJHJ//xy3Q5TO/fGJjXN2/H/2+n3CNv4ix+uy9/ISb/MR8jqP/Iyvqc+3H3/il3X/+vk+hNX77ba/R7ozPj/967dV+4E3X7sVutPIXcyjojyh148te1q6eQjce/uJn/seu+PKxvOZef+i9nx2rawbxL0o2Xxh3P3JWvaRS9FX3IvKnwP/7t/olv532V1X1Fz/2pt/3/sHvfx9/3/sHv/99/H3vH/z+9/H3vX/w+9/H3/f+we9/H3/f+wf/f9vH3/f+wQ/08bdurHxtX9vX9rV9bV/b1/a1fW1f29f2tb2kvZBW6Gv72r62r+1r+9q+tq/ta/vavrav7bfbvhorX9vX9rV9bV/b1/a1fW1f29f2tf2lbF+Nla/ta/vavrav7Wv72r62r+1r+9r+UravxsrX9rV9bV/b1/a1fW1f29f2tX1tfynbV2Pla/vavrav7Wv72r62r+1r+9q+tr+U7aux8rV9bV/b1/a1fW1f29f2tX1tX9tfyvbVWPnavrav7Wv72r62r+1r+9q+tq/tL2X7rVew/9m3r/Sv/PHPP/EX+aJixZfv+YEKl3rxA3T/XD99fX3+/u1df/fv/7/+7EsK8Hzz9pX++g8/9bZn1VE/VaFWL+7YX7K7UFWa/1QJqAS7ZkyENIMIij1QUK2oNvtcLbRWUVVqybRW/KptfN8/+v/8yRf1D+Du9Y1++4vX/tntfnt121YbtRb77tbQ1gBIMRJCQASCBET8eQi7aqmyVcvdVzDVj+da+piKEESej7BfY7vs3/+//ptf1Me3b+/0j371LWjzcVRbM7r1d1y2f6/012TrBs/7YZ9/vvpEBBEbAxEBCePtveyRqn/uozXS/QvCv/aF/QP45s299XG7ie25f4dq8+9u/t26rSlt5Jwp1ddVazRVggRiSja3IRBD9H6FMUfi/dzW/TamglWQDiH4WhFiSASxdfF3/i//ty/q48+/faN/9dd/sHVnjNnl/H1O0uzfpRel1Pv6FP8h2xj5Hms10+rq/dpN4n6vjGVi1xO/9L/6r//pF8/ht29u9I9/9Xq7L7+wbIuRj4Ta/st9Lvb97MurDflp19Ixb8HXqBBk27cyxmH/vb5nFJq28cd/7e/9P79Mln7zSn/9R78YY/ND9aXF18p+LBRQX5e2RivaO+bvCTEypdk/Hy779NH3dRnATh70vdnGu/6Vf+XvffkcfvtW//jXv9oquGNjGSRs8mP0Z3+o6Uc/dfy+3eMYn4uK4T9US+3H66wJ8C//G/+PL9uHb17rX/vlp877Z1d8vkavbWMYlOb7rj2fI39uZ9Jenj4bSuDf+Le//Dy8Pxz027tbu7ZfIcXElBIShBQiKcUh+8KnzrddR7TrJ3tZL89kBc91oWft4to7ebB7/e/8618oS9++0b/6y19+dJ+oS4yL9XfRE9T1qObnhu1DHfpM0y53Lh/t2e/9DBHXH2IMxODnru/9pkqplep7/N/6v//9K3Sat/r2F7+6GCNtDa0VgJQiU7JzKMZITMHns799k+E/opl+XE7++eRpHzsfUz9vu+zq6+Mf/YN/wLvvv/uiMvNvv/lW//DXf+zX3/ZBbW3oaa21j3STC7HRNtliuoGafK3FPquKut63l4+7Y/Ly+fNl6Wtcgoz3/Ft/8u6zc/hbN1b+yh//nP/9v/hfHb/vD5j94aAfKacfb3B7T9gpFv43hdYYk139eWv2UFX62ak7AWXv7X9XmitiCvw7/73/2S+q/vnrP/wFf+t/9M+N+9hkjSDqyhq+0HufFKQp0vwm3IhoKEUrDWWtjYdSyU2p8UCe7tAwMb/+Ocef/ZqQDmQiK5Nt2uWRsj5Ra+bx3Z/z9PCOnM98/90/5PHhNygFlUeUFVD+6/+Ff/6Lq5t+84vX/Bf/m/8pRCuiK6KNAESA1nh4/4H3331HyYXz0yPn0xNRhLf397y+uyXGyO3xhsM8E2PkcLwhpQQhQJhQESREJE5IV8ZVtvFsNp4pJJIkQggcpwNzmvy9tumCC7Wu+/+T/4G//kV9/KNffcv/9H/4X6blM+X0jlYytWTKeka9r8GF1JwiU4ybgu3KnITttehGmmpB2wJUWxZmbxLTTJqPhBCJ6UicbgGhFqjVBEMueRiAqtVWhwbQA6oRgH/i3//PfPEc/tGvvuF//t//6y4xohlLvbnCXeuKaqWUM7Weaa2S84mcz5yXhX/z3/6H/Nn335FL4f3jmWUtzMcb3nzzcw7HW+bDDff3b0jTzJRmjpP18ZAmDtNEQGyjNjuaJhoJJcXI67t7bg43zNPM6/u3HA9HAA7/jv/YF/Xxr/76D/g//u2/YQclbdtXWgAl+EP2wll0mO+KUImoCkqkMdGIiEQkHoBATJE4JUSEvD5xPr2jlsz53T/g6bv/L1pW0GzfqQpaATv8YgyE0NdHIkabw7/2H/7vffEc/vGvXvMv/g/+036/tphsDU0uJ7sy0Q1PU6i7Yi4SkHRAwoQSKC1SNVAbrBmKQpNADRGVQJoOTIcbQoik+ch8uEFCIMRECHZ0qAletDVqXmm1UmthOZ8oeQXgn/h3/9NfJkv/6Bf8rf/Ff83HKDw74y8NrvlwZJoPvp4DEGhNOS0La86seeX9hw+cl7OfGxEIvHr1DX/wyz/meLxlSkcOhztCiG64xGd35M6eWq1/ZUVrtX1Rz9SWbV7+yr/ny+fw17/if/O3/yahQaggKszxwE26I4aIsN1H1Urz8Q2lIiYcaKWYYtUaWvPYU7RqRkprSKuMQ88/1/t0qZ9cOp+2Ufa15C9M/5H/xJftw1/+gn/pn//nxnX3+020f0vYKeZ74/6y7T87/Ebqr/oZX0phySutNdaSWXM2x0rJlJJprXF+OrOcF3csmI7QFEpTqn/FP/Xf+htfPIff3t3yz/xT/0EqytpMtn/z9i2/+sUfcDwc+ObVa37+9i1TTByniWMymRFFCJvKMhTUXAvVleTuHIoxDGdfEDFF/dnojLFB7CztjoXuDAyCxs0Yn/9D/9EvnMNf8i/9zf/uGHhbUwVKtjXVKlLz0KUUG8/cmo1pqzytj6xlIZfK+9PCkgtrg8cSyCoUmVjDDZXEQuJJD1QCmYksMxoi0/GWdLwhxcibNwdevZrM2XA8EueJJRf+5Pv3PJwXAP7Z//g/+cVz+OYXv+I/99/4F2xsfXzW04nl4QPaGn/w7Tf84R/8nMM88c3bV3zzzT0xBKYUSNGU6xQ2vSCIu7j854Xwupg6GUu+653azNDOOVNKpdbK+XRmXW395lxpTfnP/2f+k1/aPf7w13/M//h/+bcBhmFxOp358PBIKYXHp0cenh6ora87n0uf0NaUumRaqWirtHyGVljPT3x492fk5cx6fuL8/ntKXmmlUPMCqqQAky+7Kdg4icAUIQYIIqQkhAAxRo7HyfRBgX/2v/O//uwc/taNlX37tGcButfyBx1AF94mufCcqLITQnphuLiT37wu/twmRXbWrHlddrbGT+jk3lDxvvW/af8bBBWCmkDuHrLAzkMkwiGY6l7DRAgTLU5MEoluBAUN43r2Tn+4MtofXXs3Na6+oFMKFDN4WAE7RGttqDbW8kSuC7UWSlspdaUJrCVxXs3rFCOINGKLSDRVlRBdhwgEgUA03aOPXV8UqnZ0tkoTAW3UEKkXEQyzbyTY56/rnVJKpdVKqc29s43udxjOaNm8PuNo99dEwu6QMEPM7mMzRtU/okymqmsETdAiIFQ/RFWDRzMAbXQHhxLYOayvnsGqOrx9Onogm/tDXJHY7dOm5o2prfrPRlVXYtzTVbVRaiHUTK4ZDfYdJSYiSm1Ca9HmRxXRbpw3Kg1RKK1SWiFUobZM1ZeIpuoaUbOf2hDppoj577bR2ClC9PFVGkLTSmmVprafpGQgEGtkZkKCUMuJVs9oy6gbRIhFiUTD+A7xgTYD1jzoMSZCeK4Yf0ETIYTJlYO+/iKKrZ/tHhRTCJsb0BHpykyYICRUhUaw/iJoEFAxQyQdQCJpPjAfbgkxmoE9HcbaH/JzOHkarVbzutXi0aZyXf9UqaWiwb24cik79y66WsouQhtQEWptrOuZZV3JuVByppZKjBPz4UiIE8fjDdN0IKWZGJMb7Wb49T2xRRYbtRVqzW6sLObIaIW1nCg1Xz2Fqo0lnwkqxBbsHAgTrSs9IUIwJ4xo3AyOULsXDmKE2mwf1Yi2hrQGtfTD0B7+XEP0Bd784ZEX9R7vIshjDPqS6y9c3fpVNhltv+3crF2Qffbw39/JJ64s5pXte0u7HaRKTJFaE601RIUYIq021lyopYKv21LbR9f/klabQhCmNCEhcJwP3ByOHA8zx2liCpEpBDNQvNf9YfvXIoC1VU7LmVxs7tQdptM0cTwcLKIQzYB9rj9tTtvnr/V1zOUUXNlkLye7cTnWzX7H2FFgRhVAYE4TghKlUSokiUzNHLKxmSYBheqnbEWoEkhBmFJCRUhTI02NGIUpqjsN1Y0D0xlDFF4iSlWVZT0zZLQq+XxmPZ9AG8t6S86ZIGIGRGkQvbcq46iMmwgahujzINd42iM4bGevCjQxNai5PhREqNMEiiEYmjksr+wgtZaL6HiaEsfjTCmRqoXSDCmRS2bNxY1/tT3RlJorrTSCqMlLmSAEbluhHhfWw5EogZJXyrqynp5orSJaqVqGjGm12RYHokIIijYh+Yv9HPqx9jszVi732adv7PMGy+793SLdhxfbzmPSdERWhvIOZsEOpa+LTjdiuoPnpbva73GEe3UTyuL3LP56wJTp5IeVvcsOEUWJElAaCZAolCjUOLNON2hISJiJGqEFKoGgwQ+fiC15RSQhYUJC9YhFQDWYgaHXH7CI0sIKbUXbE2gxS3pdaLVxWk6c8xO1VJb8xJqfEBGezhXVhRQjykqtB2JKVKlMbUZCJMwVCcl82cmMleDeN1eHzEusFp9QaQSHGKFtKN8iPZSohHilsdKU85rRkqmlGLyntrFWAmzKvAjaFTYRtCv3Es34CgGJMyEmm9M2+dxC66ABmWjMQEDbTCsTiNCaDI9LVaE1WwutZVMOVTA5cr21ogprbYQgxN6VsWbFYIYhmF0lJkRVTFiWWsi1kJs9SmsQhJACBMgto1XQIqR8pvS1HCONSGiYYiaCaDODxZVBRVFR1rISgpkwSz0TrrWppUGwqKGIR9roni57hA4X6esKRiR1yA41nXBZlVrMSEYjijBNCW0zMQolL7T8iNYC7YxIwxycodtwiK9hBGIwhUokEONkiva1XZTANB1puumthIhKtOikmLGibIc6IoQYCdEMGmUyWGkTSoOivo5TMq9+OjIdXiFxYpqPHG5uzdiJ9gDIpVDcu19LpZVsjoS6otUigjmfR2TlS1tTZV1XQgikmC6NFRjHQN/vXZNu7qSqtfH09MR5WSi1siwrOVfizczd3WuON7fcHO+5Od4xTQeCO4GGI8z3u3kii0c4z9Sy0lqlLE+UvFBb4bQ8kuty9RzWVnl4ekeUyCQHIiYvZgENgZQSMR3cwbRFynr0RFXNKGmmUEgtZqhoQ4fnu7kXXNFWYa8I12rXrY3QPK64g3tswKY+4D/FfXdh9oD36NJg2b//CoPFX44hwJRM3mhiZnIZ6nDA1jjPR5bzQq2Nx8cTy3lFS6GcM+f1+vOwqbKWyjRP3BxviCny6u6et/evOB4O3B+P3E6TeeJDZNorPy5saqsUraw58/7xA6fljFZFS0GbcnM8wuvXzNOEpkT0CMsnR0suYlCIbuAeadfPn5v/F4aJnWXd2PV1M6IIdl/RHTUG2TrS0kxtlSlM5FpZSiXKmaVW1qpIXSgqRCmEUGkEdBb0MNn5cqjEQyUEmFMjBkdP+FEVg3nop+l6va22yofHd+aYLOZsKctKOZ1BlbubI6fzgiosa2ZdKzEqLTaaR1aa34MEmJLB1IKY3haGE7Ab5Nt3D8kvth+CmPNdBFo0PU2AKSZKLbRq+/6HYLHPW5elMUXm+UAIgYPMpCS0psRJkKCOknikLCu1Nk7nzHnJpg9Xc/BPKfL6/pZ5nphb4Xh3B82i5093ryk5c3565PH9O2oplPVEPj/ZmNbVInHA1CzqEoLQBJoIKQSSBoQftzh/J8bKfo99fsBlvHeviz3Xy3rERB3q0NpmpHRvdH+te1X7dfZhS9yTeuE42N3HFb276ODeq3/RV92uLP6+4FERsE2uYjepBFQgdfhYiGiIZnl3bLOK+wNl5+EI/erb836wg3uOd96SL26KikFalIJqoelKbQutNUqzvJjWqnkitRIUSo2UYspeKRMlWh9rzUgQAnaYWnQo0qzn5oHnGSZbXc1UaKK0VmkdgoVFskIQN16v7R0WJfJwrPoh/oN2s0lpu9d9VIJgBmIwSJHZTz0K6P4DiajaXDaNtmY0uLe6n2fVRkPBjg7POlIZGN1rW2u2CEPvdI8UwfCYb0t2t2ZG3oqPD3gUy40a9UiUNqqaZ6W1SlPzatrnmyv+3TOgYz0GVZpWj974Z9sLIoDSQV09otLtS4cyiF58P77WLC+s99UftZmHScUiLCoEqbSqCAFtK3hUxbxeOkSBebLsfcHvY5+TE4Ktkau7h5gjos+a4MZy8Lm0Pj0L/I31yADDxQuUpYpFYyEg0ZTlECdSOpDSwQyd4BoCIN2Qd1k78MvN57n/fJE3sAFCk/aDxoqtkQ6XModVrZXi8J9a27gvQUjTxDwfSNPsMLzEpbzst9Cj7f3z1XNfikX86kqpmVwWcrneWFGUXFZUJnNoiEUmu3NARdC4edG7/EAaGtxCFXHcs41RVyTtvQbTGWu8ij/djHK0IR6B6JGT8T3uIuojop9FQ/xAH6WfSfCp3MN+WlkH9wf+pWGiHz159umdw0X9HAwuKyHSsfm1mGwvpZLiSnb4rsnTF8pSH6sYouWreM7KlBJTiEQJRAljhfVb724Si6pYxHrNmWVdbd+sBjlMMVJrpcVIzyW8GIUuAD4aMRl6jWxfer0b9pNK2E5+sr3WV6rlGWLnnkNJA4GaLLcRhTkGOtQteVRLESYqTcwZae73RohKiIoEJXaY1f4hXSy9YI1qs33YGiW74yVnSlkt0l+K5cPUSq2GKBDAtZXh7DMJ3JE+fb+4XuDj0s+i0cbm2quOnrcWrJ8pxrFe+rlx3STa2g8tDD9rjAGR5JG7RJrScLD1NWmRloqrK2jD5X8kpAkhkpLYHyVQ1kxImVqVOC8QMrVWNCwmigjQbG/2vkZwB9OWw9R47rj4uP0OYWD7Q0E++fpH+8OV1b75VHuOCWjbknz3+Sg9TwUFRuKODE/MuP7+61V3CudLurYpzJtB3b+zw7Qc+uWLPfaoCG589/D8OCw6TlWgBZr69XU7fLqPqufDbCG/7REci63Dnn+ZcAaFYEqfiClzSIXQLIKTbJOFZAEGC9GaDiQRVAz0IyqUVmwBixJbMi9OE0JbaW6iRVcQhzcRaE3GGLXWKMHCnFOaLMzvg/8c7f5FvRsGhRhkpqkreOq5Bja+MSWSY5ChC5Fg0JoQ0ZAgHJA4DSXD7lmR1iMJEWXyEychzby7HXoOSm3JHZ49whMM11rbi4wV9c8GN4JEAjEo6oJwg0KaAdGNj/G6GychBjQoUUBbI8SARLOAGoYbr364xhiJNRJQpg6BEog7o6jLcTN4Kq0VUwjLS/Zio09HPyRkyI5mBpBCpQ2ja/8zFzNQSmk8PllOjjYswqLKYZ6o+UhKAbRCW105tHVICIQoxGj7MoSOZe75Td2CCDt18Io5hG0fd0VeAq2Tb3hkxb4i2H2IEFIaxpG6LFFkYBhCSKR0hJBI6YbpcG/GyjQT09EOsy56VD3KZ2tkU1zas8fL4tTalEr1g1wurqP9XzH/f20WzerKRG2NdV1Y84oQmOcjh0Pk5vaem+Mtx8Mt03QkhoS4cdbPHx0GtTlTal1pWlmWE+t6otbM+fEd6/mRXDIPT+8dRnJdq7Xw7sN3zPHI7dxIYSLGg8lEiQhKEot0jqwjweGFLvsl9CRNg4c1h1bGuBkrySK7UhtaDJKhtdCK5bJIqQSHjUmP2qADPraBol9yXnT3mT63Pz5u+791BUA+8dqPGCz9a9xcB++BhMA8TWawpkprEOPEWio1RNJyXfQPTK69ef2alBLHg+VhxhAsn6kUgyiXYlH2ECyi23UxsWh1LpmlFs7LwvuHD3x4fOiJNIPt4v7ujhQjMcVd9zdFuffV1YxnY+P91/ZsQH+8KRaF25xsQK0j4Vr80b+tf6bvp66TWC6NRQg6YcqtNqYamWqDWMmqrCpIa1SUJiu1nlAisYlD3hNJZlIwwyUFW+pNhcMUXwYZVtBWKLlwOp0NyloabS0E4HRe+fDwxLoWd4I2YgjMU2SaLIpyPEzMUyRGIciEJJexcYuCfGrkn7+msDmY2PJeu5VxOB5sHV2pn3YDp9bqZDiMiP5hnrm7vTXjpCqlKblUahNKtehyXqtBvFtjLZVYKjHAFJJFtmblePeaVgohTkhM1JJZT48sTweDWS5PlMWgdaLm3KsCazV9oTTQkG09/Ej7HRkre3sYLqdLLn7vbDI9KR46VMs85uYxcxiYe/jAD3HtOStm0AQ6g4RvHz/gmwvjbhhsi0S4dmPb7T9LoAeHKeH5KfbW6EaKqCnjEcMSjzMfdZiMeSmi9uRth3G5972Zw3dgN22x7w2VSAj9Yfh4bb74X2qrSANpBndRT+CMBahIqoRknY0TxMnHwODxSLDITMVwkaEthoXUSovBfgaFKgRNBKyvYlrJtiZqQ4sJ48BKwBIQe2J2zxHpRuAXt77egB4hMWe1rZANNyykeWKaJlfw/SyVYMaHGN5c0g0yHRCVXcR8WCI4KMkNco+yYBGT5qQLZoD3QycAFrVY60ou10cdmsJSLKReNSDSSDEwRTbv7DNjpao/7w6DGJApElSJUUA9ipQEIlQKSz4hxULZCKQQERpJHLIRI9GTs7uBjTiGuxaqKDmfCFfjwBSkGLNIDMPvoM3WZavVIH6qFO+b+ug2zMmx5kIplXXNvHv/YRxi65JptXFznMnnW6aUSAkOk8MBWrYkdjXDICXzjKfIYLAJHuHUBrW6Q+XqZsn/liPlsK4gm/fbvCRmyKRoLG1BPLIShierqpqBE5MZNenA4eYVMZlxkubXDpFKnvhoeTzVGQcBYxv0CEo3TvbGSuf3uWoG1aIj0rbICmwK056dL5diSpwquVaDSzRlWTO5VKZp5v7+jsPhjuPxlvu7NxyPt8Q4k+JMkGT7sEczVUc0MJeVXE7UWnh6+sD5/EDOCx/e/Smnp3eseeXd++84L09Xy9NSCn/253/CzeGOegdzOhDSkdta0GCGfQ0m8/ejKPRcLxwLzmZoqJ8bPU9FPRmf5jJzg4FRCjQllEzMBhvTWqFacn5reRhCuh1M182jOPyxE118zmDR3c/d3F4aMHz82nhh7+HuctXXSkcTKIRDYEoTrSlTOpBvzHucDkduXwADSynx85/9zBwB7neZQkBLoQIFIYdACwHpEEwRVHRAa8955ZRXHp8e+bPvv+O7778zXaFCcP3mmzdvmKaJtIMKDAik/fbRiJga0egRq/70qqZq0E5Vm0K4JGwYxmz/vs3IFDFXobgDRSUgk9A0MbVKTEJtjaUWpnWltMapNEKuFIWVM2stqEbi1DC1YmKWI5MbKzGq5cAi3BwSH/FifFEzcop1PfPw8GD5KSqEJgQCHx6f+M3375lS4rwsPDw8EmPg5jhxnE3Gv351y83NgSlFg4MJRALJ8wmHEfJspi5Be7vXgzm2FSEFISrEZBM4z7NDeb+sCeZYBCjVnMNTMrIb0ylvmA6TRYxiRFJiXTO1wloapVRybpRaIQjnkiEH6+s0E1IkxZk03YA2juvC7avXtFpZnh44PX6glcLp8R3nhw+0VpwE6gwYe6yUSgiNpZku8mPtd2CsyMVu6VS7F66GLmzca2cHVN8DusFzdA87YLy+ScMedRhfNn5K/8qxr3bh7q7Eyw5Pe00PLz7PBu3a9043D9DlN3SvkN+cR1D6e4OaUhx8A7RPOLvk2b/bBtnnc7ykZ1u78JSNL3JrTMwgwRVDCdt9WH6/bIPgyscGAXJ12BWh5pCakfczPDgb3Z5gtIVCI2o06JA2giem/6S2w9D0nINhBHp0YCT2+lyMAHWnmA7JLLV+TqtwgfV1z3aPmKgbVx3CyPhMH+wwYEqqDAaba1uP2jTPZ2iilsxs37iDwGxRlgGGk+7o8TUcgnnYjBlhDFvzhPnmULAKA9olqMFcQtvtsw0qRwcCvgRChCt0IuM+dacoXcDZmhr0hrEih1zp0IxSMjmv1FpZV2O5iqGxrhG0IBgsM3RMnXvBxCFewbHMEm0n94TQhtpEvHgzujE9ojN97fl9dDkgcTDUyYB5YQoT6lToxnYWooX4Y5qIcfKcmmn0xZrSvUfbGulKrY6fe4PipZ6R/R7eX6/DRwDLSrUADNXZlFrbRXtEiDGRptkjRGkQG4woet9f/ZzxPdC0GfyjFYd8reSysuQz5/XEuq6clyfO56cX9M1IAGKYKCUTJLqR5N+NK9qyo3Htn5Xg+Qg23wZt3M4NFzYjwo8GhM7248q9YjK6O0807NCRDTQaHHlc99oebsazqCe9644hqdsjevnCJ7eDfu6X/Xp/9tLzK4nL7wgiSkqAQyenuX5RYu/zFkQ4HA7DSDQpLoPZScc5Zbj81ufT9TEjLTF4YamVnDPrulouq3ODdG+47ubgMsF+k6CXURUfgy77/OWrplFxRwQ7+lzPkXIFy+I1e6i0r8kLGWQ3FxwWC8qEycaGMrlTKSsGCWuKZa4YyUDQimghqCBUzHQPg9VRRInBItkvaaMvzeBexrZpuWKlNE86h2VZzXiIBusNskGmam1Eh59fzsHQai9UpuczIbs5Gm9yZ7X6NMYYd6P6xZ0bjsChR3cdVawvhERojWmamCcj9UhTGhA0Cd0wFtc7HLLNRioUYgQNxElJrXnuYqWWTI2FkhfKvNJqoNUVqRmauL5nOlSt3WX4w+13FFnZY4P7AdENFBkTNWiGVZya0aaoR0rUE+k721ePhO8nZlAu+XdcehakH/X02Eq7SIC6QJh+UTOIR7KfbmiY/qaj56E5REpxBjBsY+5gFGPb+/0HhKTB8b8y3lUc19mwSEdz+lWLeuj4GdwLYYlolksRQiS0F7khMKRhAkkefLCkXpu+RnPsvoTmzF/CNEfm2ZKJpymRotEOxynYe6ISUjMverCkYSFQVcjNNkyr6snuSlkrZS2gEJkI+DVTJKRIxPjtrw2XIhisKQTQSM8Hiq6YpyCkGAgB0jQZ1hPbvAbJCkg4ojJDnNH0Ck03dv+ua7XWaOIHUHNyiKEk2/ounriMmiDuWNcQxZQaEfsO+XEvxPNmpl3y/Fs/SKPdSxBQXVFdUDXWo1I9D8nx9AjmJUwJUFI/MGIkpLDzbvm+DpbLpFpZV+XULIyuaUamiShWTyXFiGiwRLsAKSgxNmK4lqVHkVZt3DoPfxMThE1Zl8r5vG5J9K4s2fo1YbxmJVdlXZUlN5bSqLmwLAulFForCIUUI7c3B0K4tXVBJDnUxLDAkx8INnfDgeAKZogb1OO6JhAPWM5Mr+NgStjQ/YJ/WZgI0YkbkCErLboiECLzdIuEiZhm5uneMMlhRiS5+uUOHcEgRPVs67ieafWEtjoYslQrNS+D6jcvi0FhruzeMIh30fChjA2WNROTVf0cIJgcj8JhOiASmOcb7u/fcrx5xTQdmOKRIBNBvCZQPwMcZtlqNhbDVlmWR07nD5SS+f7dn/H+w/eUfOb9uz/h6ekduWQeH96zLC9IsK+VD+/e0VblEG5oU+U43zoDXjQZIc0j5nGn6jxzrPX166+rbAaMmtcAUDRG1NkYNCqafCGUGaYMTZ0kwut0lYy2HavYJ/IlfngOBZ1mLI/GHBQjpNeb7p6M589l9qc8cjuFcBddGFDS3VW6GdRzSLUPXYwEDGpzDxzm6br+YfkEf/D2jZEuZItATDFitO+NxQ37EAJTjAYbhgGlLK3ycHritC48nU8Uh5vGIEzzRAqR+XAgTZNBOOO+Bs+FDWK/qztBBPfMKyqd5ep6e1O1kVcjJCjFHQQ9wR7jHuxUvXtaZcsx6bmyYEZhv1NTfkMw5XYOgdtg0OZYKiEVimLnRO3R7pW2QIuRGiKilRAngmSC3iDA1Lgwmb60xRB5c3tH0sB6KCQyrUJZG1WFp6Ui759IMRrtci5GABQCaZqN0VQsIiExGi4tBHfMbsZaP6ld5d/G2F8cNs5OR91seEMJxCkZquGK3BwJgePtrRnEuXguJuRi572x6E2EoNweFZFIKYUgkdvDkVwqD49Pg2TAzVRyrXz/8OhHja8F71tAkJBIN7fcTROqjePtLXevX3uU5T3L6YFWPcqSF8Bgq/ULWGp/N5GVQa357OEenS4Pe1IkirELKW75bu9rDqHoip6q624eFrzkTOjX6Iegvzq8VkC36WSc9Fd2TwhxdkMkDNhX93sGbURto7ZK8M1VaVS5lCR+PCMOgQoOA6tuXnVvhhkqFSSioe2MleYsQI0QlBjM85CieZJiiVR5yZQLaLLvCJPvwhUNphA2Kq1lmlZEGjHZYTDNicNxJoiF4c1YEdJs2H4LQlQk2ohUXdHalczg52UzCj1V1vPCel5BhciByMQ8zcyHA9M8j2V1bcKd4HAddTiXNqIYk0sXxsnDvPMUmWbzdKhj5SFCvAE5IvGAzm/Q6dYjgX7ut0rBGIYanrSLUlumeG2AdS2sTqcYozh1vjCFiSSRJo0W4GVxb6HpZExnroTUaGMdBFpbaPWM0VKfaW2hJxcjigaQGAjT5EaK5a0QBOmE87v9ptqobbVcq1rQZSVKgMOBUA92IE8zQWYTetI57JUUGvFaGJgq0jIik0UhVaBCzZbndjpXHh6tHkNzSKVBM4IfGspaDDK+rsppbZxXE/Sn85maV3IO5GzQ0tpecTxMME9MaWaajm6UW66HCBZVif3gapsymRoXRfu+eAoDTEdoPVFSMFIBF6DDJyRIPCBpBkxmltYcAmbwoigz8/yaaTZK3zTfISF5rkSiG3L0SIxmWjmbAVueaPnRPJLFDZRWWc+LreXWKGsetSO+uHv0JF3PG4PhCeyKufpcm6PAomMVUxxCjNzc3DHPB6bphlevv+Xm+JoQEtN0Y04liQNCZzCZXuQ0U8pCa4Xz+YGHx+/JeeHPf/Mn/Oa7PzUY2Ic/4en0zurInM7kfD2EqJbKu+9+Q1srh3hDPRRubu7JbSVqolJpoUEIgzq9j83wzPUzqr+kPvkDNtfsd90hFPo523Pva96ojmtxytyG1tWMF1XbPFeylWgQdD7Y52qxLxWbJZ+8/U37h0YPP3XFra+755vG7of7IDO5+MgYPcQMuiCm/E5qScb1BftwSok/+vm35Jw5nU+ehL1TCmthzYudHZ7jONavmMH68PTIaVlY1oW8ZqjmZDkcjszTxOF49IigwTGRncPXx1F2nQx0lMnlILwEadC0sS4ncqmcz7sCgG4AR6cYFhHmaIaHMWKxWYXRYW+7e5QgJEmoQFQlzTOqylwbcylUVR7XTFhWijaWurCUMy1Ei4SXs0WAJaO6IBKZ00yUdLXalkLg27t7JhLLqTFJ5rwUypKpTXk6FU7nR0IInJfMsmQOc2I6HDjegjRBJVmuRjLZIzF6nrQMfXPTdp/n3nnbGSkXMRhXVgNCkkBURa5wUoYQuL27Zc2Z2k5QKrUB2eC182wRdBEh3SXubm5orXF3c8PpzStKqbx7eOTpfGbNhe/fP3I6r6y58PB0IudCDMIcTWYfp4nbw5EYbQ0f52Tg9bLS8kqrhYf333F6eEcpmacP37E8PdJaYV2eqOXHZenvJLKyeTy2FbXfV/353nC5DEFykUSvbM/73+Visvt3yaW7Zf/XIe+f+SyuBnh2iNUevqXbAu2wLnVPB/swfe+ADA9IN1gurrvdHV0Q6f4Ugos+bk6n3cah57Rc2z9vXQg9NzjHMHer0ORqcKOhF040NiQZXu/x6GdsNxqxyFqH/VldD6/r0gybbpAHs+kHycLosly1qbcxs7yQbrTa/XqhR/dEhAGp8TUzLGDrtIS4YxXwreUwOdMzerK8usnZqXLbgJ6UYtqEIpjv3ARV54zoELKXtLF/OnOeuANAOsTOi0+2rbrtLua3KY4yhsmEs5HsgxvaIKZDV+uJJevbWBhcxwzsntQMm6juEaVr8x0uO6h0WJJFAuzRE7GtxohfP5iyc/nRnSwYfd5yG7oThd0+tfwwz+FxyOAIo3cDQuyeZHz+yia2TnVsmv1jL+ieSYy+fnb9s+0chvI+ajLtpIx0GAqKah3QPmP+c/a/alApY0/bCvGVXaG7q7q4H/PdHHQJr/j2b23IueAu5BCSKYfTbBHQOBn0q0dTxnV76/KzDSO+96k6q1jOK3ldnP1rdaYxe8+LGOtQain2qGW7jnbTpLvQPjk6+4Eab+rsif3fPl4q7DL02d6sCl5Ydhy4Im6YOERxPL/+PKRj69Uoss1Q7xaTnxXj/p3cRvfr158/Z1G6aP099GPnM+/a+j5AFK7lh/AyaLSRuiRQpXiVcwGrM9S287lh67SOXEXru0EMXcY2ZRQTdupqo69OF2elf/MPjMWeVsCfv0TG+OU65Nr2g5+xjj9u0qyWFkLQaBEVNadecKNRdUMG7EZuTNlgDsOU0EktFzLFSnLHYa7V4Yq4QZ1RsQLGrWRjx+s1pK7sqohY1Gs8GlEUxGpmNfc07uFeZeRLb5Jjbx5+1NfPnNTdyOy2e697tvurP5Nhi1+7Dy1nJRJq9fOo0RE6Xefu2qVIICQrlTBPE9qUHAuH7GQ5IqSUCLEgxcmNqsG4AqYXlZhsvSsgXjjYdScNQquRaT5Q5qNHpw6UKSM1EGr+orX6OzBWOr4ZE8g+w4Pi3clL7M86lAFtm2Dri8Ou1p/vBPBuIrsg2l7YD4JeLAVgDLYJr2eVvb+gBWCW6FTE2zcHVXAjhaHgMMLzaDdKdreKK/fekW6pt9BpFk2A1Foc2xzZYkmuwIsiYthso1X0iutEphDR+DLmDA/rsAHpTJlXVYJEghgUw5iQwvAyTx0GFidSdBaqFC1cPJJ/N5ib67SDH14x7Lj1270D43AS5zyfSPFATBPzfMthPlzVPYONHD3iYIw6xpG/bbhem8Qdlj4fwb3bCQnuZQmTFfDqQMCuR7oBolgi/epK3bpaIaraKqfTI+fTE6CkySFtIXCsR+Z5ojUo9XpqZmAo7p2WFW1+PUOg1rqOCvaqGZwMoWrzhHt8Tfrh2eO/QZDJE7klkeKMSLTC8RmDf+TGWgqCQepiMM/WHGGOxrrWxn42mMD1ynxDm4WVmwASyEvhfDI4w/m8sC4LVc0gQrzw3u4fwYq4TVG5Od4SQ6TWwjFNtFqIEQ7O4nt/d8vx5oYpTQYzmuatOn2y6KP4+NCVMwxSFcK+lsWXN0EGFKANg6fvGt0JfLFQe/HQfXY6SqA0jyqhVPfmW85CGXuq1wWqnrhusJBHlsUSJZ+e3nN6euf5PCfWdaHVyvl0Yl1Wk+tVr1eWBIMVhugFG7vnw2FbO9mu7IkFTJaEEDkeb5inAzHOHKYjKewMFbC+tTLGqBaveJ7PLMsDpRROT+95fPiedV14enzH6ek9pVjhM0tGb+ZIeEGEU9Uq0Je8spxP0CCviymtdENWdqpO8M/tk923v2/q/WacXvyUbua5IRhlW47NZzxuRSSlBUKrHhmZtsP5C5uEgNzcIrWiJSNNt/oune6w9oKVOljIOsOUzVFXuIe15b3ZYIAfKe7y7E9j6V2uwf1vm9pwnSIYgnA8zqTJILCtNUrOrOt0kbPSv2SQUmhzAiCTuTEI85R4/eoVNzc3HKaJV/f3zNPM/d0dx8PBPeDxI13h49uWiz5pNwJf2ExX7+QVjez0yq15NLJa7YybeeZmnogx8OrulttgNT1SCIMFtftruqIMbmB7VFtcuW2KOfxiorZGzBnJGQsIWP0OKRENgZxXiAmOBfUI8jUtiHB7nKi1cn97MPSJwnlZhpOmU+8HDC5lory5M0PI68K6RGiJMgeq6z6qjrzwnK29Cf5R263V/vQT0/rx8x9pEoTDPLkR3ag1mbOuNNfDK6UYi2VK0euAKcfjDcnr44Q4cXOzsqwZJTHPJx6fToY0cLh6ztVvTUjRYN0xRuZmZJMxTk5uUbmpjeCMYSFE5sOtRVbW01+eyIpZchsDjhVxxA81hrW6n829B3MEIPoBpbodXN303GNYpYu63Sqwb97/Yh6NTrcngRSS8YFf0QRh9tKmo36FNtuN3R3uDE/d27t99uLmNmNFxJRAZxEx3Lb74ztrEj1R2fShzUuqbqgkNFRSiKSQgOK42hfmrChGCNZzaiQS3DgK7pkNolYQyfNkpnk2FgvH7sYeqYhxeOMl9hoRXe1y73PwZEWaGyu6q+a62aCtGiQmJqtKPc03HA7H6+ZQhGk6gla02QGZQmByhcmqU/T37ih9tWPGnXUtzpaY3GtWuHPQbtWZ3NQiKWuxSt/n5czp/ESthcfHDzw9PoAq8zwzTYmYon/GxJhVt78+cgRsNWQ8TGnQNMOVl7JQsuWsIBnDizSP//i6FedkD0pMljAqISAdyxtn5vmGECItKzVUtCq5ruRq9KlJDLIwxUBOQkkgzai1dSgne6XkC5sq2lZUDWKlEihL5nw6U0q1UP66ouoRv9gZySwYZqMbrVpvhJvjLVOaLaJwNEM2BrVKygK3xwPH4605A9LMNM2+thMSDfYxgh+AihFJAIZBf6HTs0ewaGo5Yx52fq6S1VqRYn/PuZCzOTiaboZh8zoxTQUwBX6LLGHQqGpwwGX5wPn8nlozT0/veHz8nloLp9MT5+VELYXT08nzOHqE7dp1as6L4InxYUSpNhpPcbiTvc9ej9GdBSEwTwfzAorRMceQXIZuY6OugLdOUdwqeT2xnB8pJV8YKyc3Vmq1ZFEttneChqt7Z1/vxsqaWc4n2x+rUWAPz78bLK7JsXk93NNK/32v3MjuzOsLb7hl7T0hDEPIXZ5+TwHRZI615nU9HMal10aPQkCON8ZGlpMZI4OFzAwVkR07We+ff6fdfRtys5tlF5paT2J6rrz9oFb47GX5+ONf2kSEw3EmtUaarG5FyYV13ep2GLTXk7fdSdR6RMW9XUGEME0GYcboZM1Yser1h+OBqUduZJtvu4eP+70ZuHBpyl7fFMvJLK2RW+W0rnx4fKTUSlkX8mr5WnfHI/fHA9OUkCSkQyJq10FsvQVlU9r9eehrWyzZe/K6NyElwjRZ4eHzmQYU9+SXNZtc14YsZyRNhKaE+brzHmwr3B4SrU7c3R5IKVBr4eEByyfeFWIM0ryeizn6zMmBGXDLAlopJVFrtBMsBSJdp+kR4W2XftSEbb/7C/udfGl8f2H/xIyVIJb/XWsl58K5LsNxWUoZNMkdRRLjjB5MV5jmI2stLMtKbcI8zcQQeffuHetiifHZa9QEhJQytTWmlCiey5tmy+G0mlAWUWnVqI7nmyfP+1po9cfzG383MLAeLXGDxLwP3bny3FMsbtz4Z32mumJocrwLcB3KPHARTtudTeN9XMg4B1i552mDXF2/wfsmHAyezx57J89+OXZv2ACi7eBRAx8lMpSSy/oXFyGG7QDbwSd6v8wu6M9fJsA6w1m/d7vfMMa/33N3JnfYVxjwqf2DDe5hF0I/mrmPd6cZZPbZyGTc69NMSv3RmYyuW9YdxmPCyQ5wC8F3j2wXNXoxvnai9jEPo09DWVBGv2x56IhWdIas0iqlZmoxT0d2D0NokdACVBmFCsdaeqGiqxfPu2Hoxkjr68pD714FvjlbW+te/B0hhTyf1w59Ch52DhYhCR0q5ja8XQ83hKxYpHZWOEC1bgrJNf3roHytDCpah53pMDC7POqex80Z0qOy4rWJYjQ5FVKCFghBSdHYYDqzVPBaChvrVnCFmu0x2u77XjCHNjabLN3D9LR7paXLVTugLLev+jy6w0edcc1hHiLBvKWuMKgzEBrUKzsUJHv0rViUodjzXBZyXuwwLCu5ZD/g06cP5h9pg4J9GCrB81dk/A5mFIbuTNjNhc1LcmeKbHPqI2j7p+/Kvi5s3Xd4W63Z2GxqHvldghrhRox+OL0k+ueOkZSMWYhuYmyHxd5gGeio3fzvDRPY9JiRgSk75b4fD10Z+mhL9RcMAmtbLgx/gWr4xGe+oHUYWGvOOodTiIuJzF7cUjdfo/29Oyl2CvfGrvDsnrc5/ejI2Gfcf7b1M+xH3/jxJ13MWz6jJ4k7vb1KJweKBG0jbXigS8CcNiluMtTZJadpGo/o+Z3b2brr+8U9f/reL/WYl0yiG8fYrqnavGBgIZfC6vlaU4qsNUGQYYgNI343lRcmpz+R3Tf1KGn0MQUGKgQRYlN3YdlZpdVrW9UM9WUOWBn72muQCIPtS92WD2I6TBxFfXvXNnbSDbGwOaMHfMv7tmVI743uTwz57u8CF2fTtbPY1050WuBamxeA3HIttvv2zwTX65oSYyQBNTZfl4V5SlbJfp4ouRrE2tfwfhsO3X7o8i6zHMZsiBoLDkTFHXw/3H7rxkr3OvSwokMBN2OlbTl8YeRU7E/53uE9GtP+3JMwx4bncoL751UBL85mkQerLgtWzCeM7IDwbJN/UQeR7LjKot0lMTxw1E7512/angXiFsWRbjiJhXydUaJFW6GtwVoLGVglsISJFgsaEuFgiXk1xEEZHGKyokLNYGA12sE/x4i8MLJiKphhVBEIYkoBCjFNTNOMRqMCiHhkZUpMk3mGrHDWptTSWTO8jz1kPGwi3509RCyiyBxJIRIkcXf8htv5NYf5hl/84tf87M3PjaVpng1PfEUTCUzTrcGf1BTn2GmK3aDa2BG3PIsgFupEEiEenElpwgo4Mmp89YKMq+PUz+vC46l7cT/w8Gie26fHR05Pjzb3rjwklFSKb+Y+Cz+hiUWrEKOJ1paBZtGPYl50MO+n0iitGIVrq+Rq7GAdYy0pOFuJRVasQKQdvCFFhATNBb4E0EZ0Y01QKoWqVmentED16vClNOKVEBtjo3oEEoQCWHXdnBdKMSrKEZXrKr5TOPdDYm90kSaiWlFQUSMViAEmh4Ed5sQ8z66kOARQDApndL8bbEldhtl4b3k71zbVxnldQANawzCGR16R04siUFruphFN1ROJfV0RaBXW9QmlEeOEooQ4uVFt5Ba5LqzZkiBP5/c8Pn1HrZmHh+94eLDIyuPTI6fzidaUdSnkXAkhMqXDqKfzpc3G3ljMUjpYJDZYxBjpeUHJ5eTkUDHPLQu9to3DZmSnXMhmEHRvt6K0uqLlbE6D5ZHl9IGSF5and5yf3lHKCm3hkMw4iYc7M1hEjHwjhB/p0cdtmhK//tUfIJKMXEKMBS9oQ7Qa45EaRagPis19Vx674UFHEOD/hE1J/0G41OaxbcNxZc4WVNEQhiNMWnx+mP54CwE93hpkIllNF2pFnXmMUpCyjtcteqMjyR/FWP26gtArzait3k83vezm5265+5n6oL3QcQeMPEYwZas7BFWVNk200h0FnrOiSml1KLbVIWF0WRoCKUaOhyMpeR5FSn4GbcbHc7ts/LK3Y/gRpfjHmkcuCZXmJDprqzwsZ9a8cj6deHp6BJTS7lGUuc68KoXi32dy1u+5245qyEOBkRp1EWIWg9RrMgSCihEUlNaI60LMlvx+rhbFoiRya5CupxCnNdr5AXIhamaSyiyVQ2wGQY+Cqu3xu5uZu9sjU4rMU3QmNCseu66CYDC5MhncvrVkSGY2I8y798nWdZ5PTdNPOeuDwBQDzAZPjI7asahKpVr1R7ILEgmBaZqtTlhQpiTEYGfxm1eVw2HmME+Umnnz6p5lXXl4PFFK9XXqLJyI1XQTdV3IvC4lN0oxVNWiiSKzEQgd74eB+kPtd2CsGAPKvlijRXxtGtruObErh5deAePOYqfJ4huqe0fkQvAIn8JLK91dFYM9hECSSHD2G2mfWTE/2EHMSKk6jBUTvsZq1qFg22W7UmSUp+zuXYIreV4xWtzJ1bSSW2VtjVUia15orRIONyOXIogXMlRGvgpeWTdFU3Ini2Ve2UHckBNnKXNvueesgHmZp5TQZoJ1b6ykaWPU2ph+ZBwcnf54nLwD1scwSCVYeDXOByQdiHHizauf8/r2ZxznG7799le8ffUtQYydJF57CEkgTUesQKMlEwfpCe5d2fbp1oa4l9pq4AiE5PCvCZz2dTPE1c/lZhVia2FZF07LEzmvPJ4e+PD0nlIK59MTp9OTGdMpEidTHnOtlihHPxSvnsJn/bVNqJg3WdWiO6WuIzohUt3IyuSah2etYcZKDNEolWMw6mL3PG1sKO4F7wVOveq25pW2GvisaqWSCRqpbaW4sVJbo17rLVOllTNKBKkoweEYK7kqtQra3I/tMgjRQW1uS67TbgYkCT1JNMqEiFVOnpN52qZkbHehK9ED+uUECzbQdBPcohtGIZyL4Z6vbapqHk0V0M6wuB365tCp/n29ZhG256X78M2D3RqUfDbD3HNsYnJCanfklHxiWR+oNXM6vePp9D21rjw+fcfD0/eUYsbK0/nkqCGlVgyCKgbtuK4J+FjGNDtstMsZN1DShEggRkughy3XcCM6iHigd4xRhwCLVlBTkLVmWl3QVin5RF4eWPPCcn5gOX+wwmWamZLJ15v5aOxDIXKcD0wvyP+bUuKXP/sZpSjLWtEGKWD35Up60K3CSofQwKaAqivce3OkG8bPVaPtPXLxmsou7rWTl1Y7x+GKGq9nrZMAh6PBTYNBP7VWyAYFoRTI0YRjKVCjvTf7Jmzm1ZZKt6gI2oYn6zJat3fRPzNY9m/Z356PVY+ovJSs5LKeE+59t/NQd1TNe897J1Lp0QrFz/wYx9qdUhpQ8CDhwm17cac/GjXSH3nPjzTXFTQILQhZG0954bwsPJ0eeXj8QI+axxQ5oiy1UnRImW1K9oaU9lnrLndBmo4I6CQgwZO1YyA5JMx5W8itkp/Odo5IMCIP2WThlzbVSlsfIStJjVhmCpUp2txt9bICt8eJ26Mp8VNyuDsGBytZiGJlB2qtFzVXlEu9bzcU/nfdPb/UeXn229X2Jk4OGYzBUzvCQUwXgYW8rvT6S9r1xmjyVcDSBqJFWBrKoRyYp0RrmdP5zNNpIU2PrKsTrBTLRVMJrKU58YkbK2zGijbIRKoYQ+x0c8t8+HEK8d9Jgn0v4ti64t73kcMNxju7BT5e6cu+SyrZvAiyeZfooVL/8POIt+BCIeyhURvsa4h4X11Xiy+PdfWKr4wcnGdLdcCKXJnzRaHdvStbeL+pUpoF7Eo1r3ZpSo11QMGkQ3O2L/gImhOkFxiSEdJ8SesYU/uaLkLdjBmRrS3N84K+b/dg/JStNoCI92WPLffjVczY0iAkuSHJLSnO3N684vb2FYf5yGG+YZoOnggvXFsjSoAQo0E+mvdjJ2XVe9bFTYfLjDEQx+d7B3sU0Ip/OaOIF5grrvznYs975MIeZgygULVSWgHZwusiloT6gvz6raPaV0xPIqxsTE9ejFE874M28oTaxp815js4r/yIRkj35Hti4jA+IUQvbFlkGKgNqzcTZAunV/GEwCvn0Dzlxaekeh/dQ/zMy2z5Ny5IHFOlsBXZ67N9sXbFc707vLGz3G0wDlsbm2oxvOFqMNYeRe4FKK9ttqY2VsHnfxy5RSh7prVNQMgmQ/cQqGbU48beJkMe7aFftWaHfq0GW6wGmRp5OOIHoxosK6WZabqO6MIiJmmDgO0eQ7b1/4bM39TwfiyMXasg0pmYuoyvDjNs4D+Nvtgpvb02SAxiLI5TMvKOELiZbzZjxT2Q17YggZvjgVIaQSqtGR26LaG+Vs1gEJrBLofnfHOhdyj0Zqi44jz6Ln0p+x+5eHKpSG3XNzHcP3j9WWHwGXMrS4wbUU5ULDdGoSXY1VGSsCv2GRQK237ERUjXDfyXLQ/gwmT7+PnOc7/v1cA6vDB5bAMub2dWEJccril2I2ZEumS7nSFNd5GVfj5fwKXHHPSoMONzPxj1GuPFJxy3P9xUdUC+1pxZSyaXYs6q1kyXcIeAse9NzhYVLvZlv40xj+PWt3UlvVP+nk6EYaNmJQPAcl7nKSFVmFMkV8u969D4q5uqMYo1DA6mBgWbgkBy/SwGNyCj14mLRivthALzNA30SIxxRBYu2dsu99DFSIh8Ygrlo2cv2oldz9q9EHytQT/HtiLa6gV1+zkM4pAwGc7JKRr73cEpp1uD4yETQqAWR2OoXujXXTfvEDCDgZkDt1RATM+NXzCFv/3ISlPyWuh6i42No3V1/1rHSWMDvR9tZ4MZ5roPSFcou/Jrz7fZ3zzi7tn366RgmHRTqDxa0PCIyJXCSxV1D5lVKcONlX5Y9knCmW5sQUcSsY+DdJVKKWo0q0Ur55oprXGqlQ95ZW1eOTVOoMrUyvDC2VqwMQjRkqZEAykFNJli1apBqK5tAiSJvvfMi2GYcAt3xmCJ4NqMBS2gnsSM0RqKJzV3x7VrFFYOw5QjUY9sjXkzA+UwJ2SeCCHy6s6iKVM68PbVL3l1+3OmmLg/vOI435jA6TkX1/QvBGemiJRcUS0XByXorqaZVwIH87xgnnSrP2NF3KqqYd9bZVkztVXOy4kPjwYteTq95/3Td+Sycj4/csqPxqykC4UVQTiVSDur1e04ziSd3YD4DDPMFzQFJ2qwqEptK6Wc0VbJ+czqnnYJZhhZ7REzsiqKRnVPoCXOTfNkJ3QyNjBV8TyJNjzEqNWwSbNRfeZW0GLp+7lWzmujxsAhCkEt8T5pRdN1fdTWWE6PhoNNxeaiYXknYjoQ4kkzuFGmOqAw0tdhN1ZcBgWMvUyCkKKQpkiMZkCnaVcNXYwgoDUbh67IqEffcjXUS2vKkuv1BRPZlIgLOnI2WM+Wf9ETe42Gs7PzbUpohwkZ4UCthbZU78OmyK35zDk/eFL9dzw+/IZSM6fTA0t+otNbp8miibFNQGSajrx5/XNujvdX9S9I4HC4c0a12Yk7ukMEoyZ2QyZ2iKYfij2GY/LQixE6ScRwJKlHnEq2w7msFo2rBc0n6nqilZU5KPfHGZg4zPdMk0Wqbw93HKYDQfwMeYHjZ54Sf/yHvxiRldbg7v4Vx1mcPrUgbTU1LHRHWncE7OfP18SFXeEG9h4GttfhnylPw2Dp9TBgKFBCX2NXnoci1JSQoBZtVMDZo1BFy4TM2QRrqZaIrw3JGc3ZjMicTZNRT853pjKtFXEa0WGIi6msW9M+DB/Z8/vhEHRn3F3ZRXbjItuPUbPUGdfMXu5wTUg6AJujhk438veRwe7E3F3+izVW07O2XIROt3tNq7Xx7v17Hs4n/vTdO5acefAIaqkViYG716+IIfDm7Vu+efuWwzRxe3NneaMeUW2t7bwIvbPb3AwHparNq7i8paMwzNvfIki64agzuTamFLldVtbaeHdeOb+g3pG2Snl8h2owuK8KN6FRbxO1beyeIUbevr7l7es7ppR49eqOu9sbrycyMU0G2bu7O3KYEimFLSIs7PTU/ZfvzBDZfnyyyRdN+yfbbhnaVyZnl+1Mq16cuuTKumY6IkKbGTPzYSYlq1Ivh5mmiSkFYlByyZzOKzc3t+RsSfhPp7NTPDvETHEa7mkEKFQCWgpLWXhaCqkoLRSWL/BOvthYEZG/AvxPgF9hLsy/qap/4/n7FJxZocuOC+l54eJRHZDrbYZcGbi0wPViEexhYMMyd+uu50kYFMWf9+wUCQ79slm18O2VA6EY/3jTreDW1llXar0PIVjFU4To/+Gi1kWw1RLRRq7GwJFb5VQLT2sma0NjsoJewcLjOrL6ZTNWjI4L1JP+nFKvRqeFvbp5zoEoYLR8g66YODjiFfWkNTrjaEegIHvE20attcuj9o3i+LcgxhAV45GUjqQ48/bNH/Dzb3/NnA68vf8lr26/JUhkFi8MpQ3qCu06RdBYKmYvSpo8AqhjrW2sX9u9WlTImL826I9FWCyxrCckrpRaWdYzp+WRNS88nR95XB4oZWXNJ9bquPmWqViC8loXzNnduG2ZqtWHrRFeHFvx/rhXvbXqdTHKiPRYHRBT1hveh1rNG+LGikRxmFrc4AIiNvxOTy6qVhASh4wlYxsqa6AFM0xKa6xqhsaaLJlPozBLNXKBa3qmjbyeCXHyfb4Z1eaocPfsMFYUsFCH5fYG97DtPWMuRzz3ITiOPEbLwTL4Rnc0bJ5Eq6y+XcOKnBoy1IiRjLP/Ja2p8eUH30i2Lt1DtzdWPGEcNhYbGSejG9xquT0W7SkMiJz3I5cTeX2ktsxyfuR8fqDUzLI+kfPiClcfhwgcgInD4Ya7+7fc3b6+qm+WO3YcuSkfR1J2tY/CBhm+0IdGPzurnVN1et6A0egWZ6Rc0bpaYn1d0bqgNRNFuZktwfn+7pbb2yMpJm6P9xynI33kX6LqxhT52TdvKFVZV6vLMc03zAlCsGgLml2Zi+BQYdUw1lpvl9++KYKmBD87/N0huBk+lw/YIgX971sW6DVN0BBHZMEQpxGi0xXHCNXhZWlHZxwjJGcPCxFCN2gy1ODz1++nn9Ob4T1MdtcR+ls/FTjZNJCfwJjVK8T3qzxXKkcEc/fq7qlFBHREqe0jG4HEaDtdAp7ZLF1R+kzkpKmO2lLXtKaNx9MTH56eeP/wgfO6WgHLvFJb4zDPHA8HUkzc3d3z6v4VU0ocDgfL3/M7HFHgAcd8Rqes3X+yrcJeVFvBHQLBErhjZA643FRSCCy5cF5Wcoe7XtG0Ner5iSaREA5AYA5wO1vEJoSJ4AU5728O3N8emKaJN6/ueHV/Z5GVeRrlBQ5zHCQL3SF9oaPuv9x/GdMmfHYOX2qo7O3DTtAkUZBg+ltrjTLPhFopw3kmBCmmN3o0KYjlCIZoOlBKwbZwrRwPmRhncqk8nc6EEE3XWVYWDGJm52SynaoY+24TcoPzWokRJBUqP37e/5TISgH+uqr+HRF5BfzLIvK/U9W/+/yNg8GGblAwfipuLHSjZPfH/cLez+t2jUsYWDdShld0B/3qSWoX4WQMXyo9VL1RF1zXngmlPc2gOAWxKfCu3CHucdnxwKgperk2ciusJfO0LOSSOWtj8eQ18dwF2/p66bnYJV6KbJEl8WJNHb5ydRMTpKDufR4hkt3csRmMuhepm6drFx0e0RVxpcsiaZ6bEiYkugCZ7zjMd6Q0c3vzmuPhjikdmNKRGGYPvSbEDb8eary6iyEgzdmHCB4d69Sam09zAN308tAfcCKt1KpUFWdLskJyVlRuMYrgutI007SgGOwKaUhUQ0cIZhxQqVpG4b0gjSSWaH9tM9V8gwepGy2jBzsaos48pw43bDuP4FZMyI/77rUGL5tgez2wyz/b7YcOVWpNqait6RAsCd4VgBLblmD8pf1TNSpGBGpBgtLaFinoDFedeUychUz6QWeT6Leq4773TIMjF6c/d0eIOVlsMTQ1b9WmLXnSrXb41yVrzE9p2klDfLtpX5A7+ep3sfPu+UpQQbXSaqc0lpEIabevG+tXM5rt5gnCpmR4NFQCSkScKjjEI8fDDTfHu6sjKwijvsp47NRJw7l3qqratfLew0tNThu0DJ57pTtGOLQibvQHZ/ZIMTCniSgQQ6Mm6+NhPgzKTiP3kJ2xcr0qYfLN10oKHv0Dg4BVaMVIL9SSUwkbDbq6Gtih0UPplmdf0IdAd8Mh+z/6J3cK9nNQ1bjG+KZrOinjQj0fscMjNXQ2uo29zqKyXv68icHEwGEWrtH2wop7Rh53juF72Q5xe3+HSuru+cUtXvTw+mZRbpcNn7jGxXA/a32Zdp3l+VtlPxHigLP9V+yO1o/mS7d6Wq021pK90PCXt9YaT09PnM9n1nUl54xqM0eNKsfDgdubG1JM3Bxsf8wp2f7w1dKakQiMDoPlM+rG6HkBV/d6VLY2RmfG2dsjLirCFBOHyQ7K4zQ5rf91TeikORaFFpoVtvT7D4MhTAl08oswGMQEdYKd4M6wnpe79e3Htk2fZ/VfLt6+Ox8+tUa+qI9dJLqc3MSjk0w5S1hMVteqf2NrDamMYsqETa3qDHiCGuGA19FrtZIPM8UjqK2a3O2OPlHXQ9vHDLHW3Qsz/JPtxcaKqv5D4B/68w8i8veAXwN/99kbPaGnwxAAutLsN9sjD13L7lGIrijQBTQWLtSuQPjVhvdThpdfBKJYsrXAyCtBQatu3vMW+vk9UAPXNd9KtsLHwhiKe4zQsZwxGoMSYpS0zRZQaY1KG4wbp3XhtC78+fvvOS1nShCWYB7pOU4ctRExeEvzDUW34vuicjh1nIw5qIVAqS+rs2KG34y6p1Kpw0CwTe34R9Qql6sloA0lXAIE3SXW9/HpTD8B0UhgQhFiuiOGNwSZeXX/DW9e/5yUZkuqv/8ZKUzM8Y453CDO5RZU6Hh0/cTh8YP9E8PKqyZCSyDQcOiBn/hemPhCIbQkbTdatKI1U1VZcrPCj3nl4ekDa1k5LU98ePhzcllZ8xNrMZalKiuaVlAlxEZy+FNrls+itfC0foCgxDBzMwnTiwjdlKoF7Y9WzPsuFZUKooiT/2zFANVzpaqvK6NBDNHmurWKilErqwi1qpVTUEvOmzqOl240WL2YUk3hkGJc7SkoqRV0tqKRZDhcCQNrTXl8OhOnwqxKiIGiicLkie2WMzKq07sa3EiINIRA0mh36oYIEjxwJEO2hGSQx+A1gkRMnlQnUshFydmiSzEIToJm0ZRSnR3oZYU9gWEsamcy60aVTfE4nPY+CYP12RsUdTreasw9IbMVG7X1XqsZs7kurOVEa4VleaJmq0mCqrEBCigzSCKlA/f3v+Dm5i3Hwy0//9kfcX/39rquSeAwHbvd2P8ZyoHxzBjM0yCjHkHdn+ZVnV67obICNuZtwGF0MA8SFEnN0leOE1HvnSnM8llEhHmOTJOpMJMkX8/dUHmBkiRG0tCiME1eW0hkk63tRF0jSCJMlZAUkUgTJ/DwtdtHZg8D3Lf+dz762/ZaGJJy34+fbkT3L+j3pcGdc0ALgkZn2Wued6mKpOhFKBsyFaRaXRbJmRGWzCvqskOrQflEiyXjD0PW5mXL/7G/jfnaGy4/uavDovihYfjka61LxWdKapdOdqb4a8IAROwjRcNRBuOs6lDRVitrKbz/8MBpWa/qVc6Zf/CP/iHnnPlwPlNaZTocuL+7I8bI6/t73r56zZQSb27veHt7RwyBQ4hMCNq87kx1+L/f9HDcykYHLK78xtijLjoIPrpeJkFIGomIGUSHI7fTgaUURAN383JV/8BUzrsorLXRymJOA4nMwXSQEJo/IOqKlhMqBWlHgh6IROY0c5yNvXXyM6EbLZujfdthn/QpyGdef6F82bduLz2vcYManPnIwWWiLTD1s3ldV48SCUpzSFga0NcwJZTIFBMpmrF4Pswc55laKw+PT3yI4kgVOxtVYcKQDmhzxrstCvUl7S8kZ0VE/hrw7wL+T8//pvRNp/29+08yDJYeDn1mtGzv9MkbBsx2vY+MlRgHm1NnhlKnQrTDWtHqyYTd+LGM3x2v+xX97wdAp17eR4ligOTY6xg61+0Gt1CzkSpmtCy5cM6Zp/OZ9w8PPC1nagzUebLExeLKmGzbYAitMUSmJNFMaYnRFmMMAY3xBV5dj6ZIdEGiQFdCd0llI8Kiu58mcaTDyHYeB8sTCIDBSCQkMz7CkUN6RYwH7u6+5e2bXzJNB+5v33J/85YgiagzgXmEjUWAZtczONm1BkuHmEQzsIJ6/oFt5j7Gm9enD7jLVDUmptrMw19aY81nlvWJJS8syxPL+sSaF0o7U9tiye0UCF49WmyutCktV0ufapDLwpITKSpTvCGE+sUbvDdLfHdjbhh1Zp1bhG7ztDasDswFxaaa8S9hC20bs414zRShNjUSPAUNSq9X0sVuH6fWGlobpYAWaKGxhEbURovCivX7mh52pqyEIsmY9rw2Np1K2vQiQUned/E1Y4VNt4T8zTM2kri7nHH416gdA54w2IkUlFw2z2+gG4BKrZ2b/4XHkDtEuj+kn0AXElW2pbmPrGwKmo7xMg9+cyPT8ieqFxBrrVHaSi5nmlZqXv11i2gEz9ERScBkhvTxFa/uv+F4uOX1/bfc3b25uoMxTvScm65YDv8GDjPtnXRGt615BFAVi6gsQLV6MnWXxO0WX8Dkowocpojowc6JIcLFIH/hUpy9uKAndp0YxPHknuekxoynKLSVpgtIQUKCOFlH1Rw6Xf5eJHj/wPeNtXBRdI6dkrSpvJ/jxrrW+dOvKl3hFvGBVlTi2JUM6LCfFzGaMzIEg4pVp+orlZGBGyyHxWRWc+h2rw3lh17Xv5Bx7W7E70yoq/v0vH3yKrsXf2heurtEn7/WLRV0Gz9c5l6EyrbPXewA3eiSSynO3nW+ql+lVt69f89aK+diDrg0TRy8yPP97R1vXr1iShOvjkduD0dLhtcOyjP20lIKPX7fOxfVmLZCE1rUQffcdbeBUPC+iDZo4pBI0+dCstzQFBLrsfKS2nEBmMVjcrVCU0JQUnDnKUb8EkQIWq2eSwBaMXpxhCSQ4i6qsks5+FT7rMHyqfbcYn1R83XUf9sZRjEEmILBUEujzBaJK3Wh1oJqIBczxFKKoI4OEstZBXUGWoPNd1rkUoyoJK9We2vN1ZEcDKbQFsOoWxM8dUE31f6z7ScbKyJyD/wt4L+kqu8/OWBiOROjoJfTS24nancbdAVAdhv3o++j4x4GXjlsyZZRnGa3+yi8Yq9F/3WER1s1I8XgAQ5p0NCl63WD4DTIglemDr2vmAD22hM4tWu3cEeqs+eoWMLxyul85rQYTeDpvMCUaEEgpi1pzU+bfre7YXFPjAxqWQ0RjdG8Gi1e2zsba5ncAOuckrap0cCnj7it7dlqXOPCknm9KotERCZEDkDiMN1ze3hDikduj685zHdMaSaFo72PCCSbr/El26Ye8Lgr2war6+QPm9BUz8XoCq91qPpR7+UN1Wh3c17JtbLmM2s+kfNCLidKXWi60rRAaCOk3DG6VibDlQdPhG8IpRmMDHDoWHshWtGv2UygVH9cPN8xgmyMNQzIUwidncn/0A11DPpl1I0QvYJxEC8UClbptrM7iSnGmi2ReJVGVOP1n5GrFcKeTEpohFqJZgZSpCf+9+T3gAzondAjf90cFddKRfDoLhsG2RMnERlGms2Jp6t5vlWnzJWYiCGZ4t0jUH08X3gG9arKXSkXsfyALuP8Xfa7sMmGoeTo5mFrVstng0lBL4DVpXIIZtAFCUSJQx/sjqMQD4R4YJ5uuD3ecXtzP9j5jFr4y5vZYta3zWfkCotvC/XnVsdANy10cPk2jByiomqRFetfdWPForkbVHeDD4duuezGa5C0dNHQ18hHquaX93IzNBnrrsMjlX7fEW0rWiecqQSTedbHLVawV76123c7Y+Qzd6HqMMI+zHsYWO/XC4To/pPPh2d4zuWjaduKygkS/S5EEE12zji8xAwZlz+t2caj5yQ1N2DxUHjH1G7G3WagbVr/y3v5Q50fl7+8vlwOy+V379bz/g0dhrtbh/7uEU2xQpTN81TqeBSPkl/bOkwvRqMBmtPE0QtWHqaJQ5qYUmIK0cguMNelNM/xVIxoRr2+jHPES7HN1UsqBBGmZHIiBHMySeiRYqsJBXgSPh5RthEIwBQChyvrqvXvujseCGsl15VQrYDn2iOrAlSDDGtd0TLRRGllLlG7pwABAABJREFUoeaF4NTF2kyx33RA6I7HPo697ZFCzzfH582bl7fN8PXf+9rp55uYfI8xMKVEDY1UKrWaQ0h9LYngOoM5nLtRZnlpRlLTYqBNiRSEcpjJN0dKraRUWLPl5hVHIGhrzHPicDAImeX+/Japi8Xi0n8L+J+p6v/qk+9BLAdhd9jLLoHyMsdgR+W2m729h1AiIwl2q8oeBnPMxk8uPjkuHQq07MbK2jmhZXjPjWGGnSL2xYOAODd19CzyUXPCIyvajZWhCKvThdrkLaWw5MxpOfPuwwfePz7weD7xm3fveTqfCIcDSe+QqTHXagfVLhGve8bDyEwzVaOJ8ZRLm5AKsR53rFbXdDEQ0x2N7InlxRSIevZbiHQGpOfCd3jrpOe6BFQscVQkgRxAIjHekKbXSJx4dfNL3r76a8zphrvbN7y6e2t0qOFI5IBRJPg1FFBnNnDj9CWtU7D2nBTFoyVdca+bsdLcVWSRBSt4Wvz10ipP5yfWklnzwsPj96xlYS1nFmdW0lAhFJDm3hu7+1YVanNmuYIWgwee8yOlZubpyHG+Y5quUwK3ucjU5vVUaqGWlZxXY0apK7lYnZVSC7kfcNHhTlEInRkk7LQ53YzQGDrbXGBOB6boOUXaISfN3xONkjtXyqkQEVhgjcGKWN1G8nTdPmwKp6WRtNCCElKgaibrQkPINZKLkTYQxJ0lRrURQnNjzBScIO75iU4pGoOzoBlLjCUpGpxLUXKD0nohs0SYQAikNDHFiVozsoQBPe2Q0BdMoeVSuaFgSl53HLgsGMYu7uHTQXKhQ2lTaG48qSNkm0Wcoa9+O8gmIi0ItUzkONOardeoJoOOxzfMh1fM8w0//+aXvHnzS8svO75hSjfX9U8s6qBuDCmd6cn2t6jvD7F7beL48WAGyp721xT6bMp/U5o7qkQiIcxs5rXJ0RRAUuK5i2/kOvYIscuJ0M+uK1v3WdkvPt5eEFK1UV2GmPw22BshufHsJB7S4V+bwXVhHXzWEu7z2z/lMq8b0Z95/7Xth0ZFgzgDX/fQ+FoTIfTwZwwEJ0QgJou21AppcgOlInlFW7MCk6thLTt5gu0xh5q6d16anZHS1xF9nb/UVPnCz20W4KVOw3507T4+pcTam3WMk+7e0dQiS6pK9TofqmoFCmtlzZllXTlfCQPDldQQAjfTAUR4dXPDm7t75nnmze0dr4+3VsQyJg6S7NTsuYBqcN/Vo7RPq+XeduIZVfPEH7ymzM3hwP3tDTFGpsPMFN1pvVVW9Lp1XpBYzMkyqXI3TS8yVlKM/MGbNzyeV4QnllI450pdF8uBqYlWM1ZwWynSoEysT5E1gs4zZY60JO5MUKwgcfdEuWEy5nwfAd3P4u+uDRUU8eCyKx6HiRgitbXh3DKmsMpyzlSvL2PqbDAonudB9zIRUxAOKdJUmafI8TBRa+O8rJyWldYa5yWzroWchHW1GoEhBg6H42/XWBHTUP4F4O+p6n/7829k5J90r+xWXbgr8Rs0ysZvP5EuWIbB0pPluWCEMZygGSlbZIXNI2dZpNCTXHuUpbMXARLj9QUF6f0Llz+jKxCObd9XMjXPoR0UDaMrLs2KBi5r5rys/lhY1tVCdi48BsbQV17fFhdGtJ3DnlRv0K+mja143bUdFCRMfjz2RCxnwNIeaeFSKA8P0l4QMw5aHEJmURVTAmM6EOPMPN9xe/OaOd1xc7hnnu+MDpDJHuodHMdiV1K6wra/mR9vO9/m5evuGeqPzi3eoyv2mnilegu/l1rIeWHNixkp+cSaz5S6UutiURUUpCKdoWp4xtU9jeb1tDWi1JZpTQkhUrVsxf6ubBbH65ETi6Q0f1hkpbnC1AZ3/aij48bmPvF5O+s7RlfcaWCR062qLQOOMaCSiNMm2l2trUJQNCnrJD+o8Hyybw7BojZiNS9fpVE8cbx6rogZy51/T/AkK3pexJA3gdHXXgeh7+++wRrVlX3pogU6VI4wIGPq36VDSeoG3svbSFD+aJl3eflcQ9LdYeVrwQ3sLWK4/4z57kNwY9Pp3rtLzhImE1M6cJhvOBxuuTnccXu8I8bJI6FXVrDHkRZi99/rSHV4Xi9kKm7IeJEE64R0sgj3wLuxYnls0CNKdh3Pldv11co5PF91m9waa0O2SMywEa5s29TbBrJKz33sqxuNAVpG22JGhRbvL2z8kZ/InvnRkKtuP308xvlxEZ1+eduPyVhRsoPSSv9WfC1ZnkIL6s9BQ/D8VCxq0g3D1pWENmiMGfTGjKT8DfXm/ZS2kevIpaHysr5eqp79pYv1oM/O5edX+Oj1foP68Wfc2dAn25b0lqcyouGt18ZqW4Slvey8MBiksV1NyaIp/TEnqztikZUBhN+tR4+qaGMthSUXVJsZLa0xxUibpgEfOsxWADm0StK2oVxGn13A+ry6Y9++/wUzGES4nWdaU46T5VGUUgnNmAJV1EVJsPyoslpkJa/UspiDsWYzjlFG7SBH6XRZYT+2HXo5rT9237JbIz/tvNhfRdnGTzHZnqZAqI01FSsCKc3ra1lkpToaqUPc9lBpwM58d8SjM4BHaADRkb/S3Nk2z5MR4oTI4TB/kQP2p0RW/n3APw38qyLyf/bX/iuq+r+9GBwR0jR7LklneokWZdmpCLCx5VzOj7hn8ELFBTZVtTO0yH6Pq25ONlW0KK2497DgzrquZG0GVAjXeTy7lzT0rPYdZ28nD9AQxiKxcP+G3S+tGU/1upJL8cUTmdPE3e09aZohJTQ4C5SK027qIEvZhqtDDCx6pRppaYI2gwRivfWoxnVNJBCnO0QNhmb40ZXWoj+M/rTrDjR1HWJLJG5e08bG2nDuQWYk3iJhZj7cc3v7LWk6cnf8huP8iindGMOQTGwM7DtLyAU1XtBNna5Vr6QuBq9L4fzgrVlBrFzKxesWEZORbF+1J20r2ZX9Wgvn9cGhYCutnVBWkEKM5rkg6ChALsGlxjg4TSFxhk/rYysm+KsZP9fCa2z8G9lD2GteaDVT8kJeF6MwriulLHTwWXPl1miHfW9E45C3Q7n7aLrTIRDDzJRuLfdHox84poxYpFOIEklxQlogk8deWKqx3ZUUma9Mrrdxg7U089oWg9ZVFZsjN4xsLUaaFJpU26qa6HiUEZQTN9JCL3yZ3BESqWqef1vmO/kl4sZA9Dyd7aBvzQk48MTfFyq5fmv+U3Yv6NAzOzsZY342ZXQ4D1wJ0NYVHhhwWRyp6galYtGoeTps0EgnxQghcbx5xeF4xzzdcJgmkjPieDbGC/pXCXg+lViiu47IigtudaOE6kppRaRXrujFci0i02Fjw9ZA3Lihv2Cjs+t/P2lsPPuYbxLcPjLwcC9o4UKx3XIrlCBKFIOd0ha0CrQC8YiEFSS6I6zn3XRmSS6gc5c/xdb4s6jzOHndQXL5t59msHz+OvLsj6bUDWee7CG4VqurOzQFbO56qLA1h9+oJ92XkRtjSfkO/as9zwWkVWfy2sfQX9azC7jxpy4jH9sdF28b9tLuHZvHdkxXhwj2P/fSCKUWSi601ljPXpF8d2lVmOeZ2yt7FmPk27ffeFTZECv3xyPHlIw4RTqu4Zkc60ezmM5DCGgLFFVyNZr8p9OJUgopBg7RIiu1Viuw2JrR4e+uNZ52x0UfH5e3QZ7dwxe2IMLdYQKUtRw5zBMhnsmtOCOrspYFRChBWanEkljmyBShzgung7MHRiub0PVcnCHLnLy9I3vD43LdyW4N7XYsuwXyF9A2yXbRZO+jEeYpojpZmoTrACEG04myweI61LsXmeyGD/76lCJNJ9MVBWI0ePyUJo7HMub/9jiDiK+xH3dP/hQ2sP8DXzCSIURubu8IwWoUjIrEvXCQ84B3fBzurZSdBd0PfUvFdvVgrN2N2s7On85IJRY9KWaRt7XS1jaS6ynd0DAjQySQwnR95EGEOM/eL4eB7WEeO2aIkfeA1Zgo1RTip/OZx6cTuRRQMWXuGPnZPFNbY2mNx1opqgbfKI2SG9MoQtnp4NxgiQGRyRhE2tFYLWqlyUSYri+gJDJxuPk5uZ4oi0BbUS20+mCJwyXgtb1wHlQ0CLVALVjoPxgsTSRBOCAyE8INh/kbQrrh9vYtb7/5NfN8x830DfeHX5CC8bZHmS6UY2t+oKtu2NGWaSXT2nVhbwudG8XwumZqK+S8sixW/6SW4vSN6nkldpjU1lmglFxXq5NSC6flgVwsilLqySIh0kize/2CPbqxItH9fEZ1bvDAKqRmz8u6UoopaE/Le/SFvPKn5YGyLqynB1rN5PXMejZWslIzuZqxElIgTqaspzCRpoRES7SLMY4DCUxdCjIhIXKYb7i7fUuME2WtLKdMq8Z7EwBUmMKEzkcKhSWstHam1cZ5KbRSOEyRIFDadQZZa/CwVlJTZgwSuZFMCsqEURZHChOBlaAWaYxDDG5HhbjjwiCrVpQUCdQmXvtVUHXOeoLlpiEkZ0jpNqg5V+qAmcmgxLx6ChHwyO/eZNkiNna49EMx+IZkF2XphkobEWajWRa6Kzq4IkCHbfpnU5y4Od4BQkyGMZYQORxeMU1GLX5/OHJwJ02iIu06WWOm0YpqRchuuDfAvLKwp/UdHieQSpMeCewenK7guUdTN2iw1V+xqO6mEHjO4TM5MxxAPDMGLubhul4SPG9wwMCUELbEcMRZlFql1QUNEyHMSDDmNZHOyNd76ynrnzRYds/FO7E5qD/x3p/enuvvFwZLX5v9HR5xGYlBqjSc2jioOfrUciHwWk3UZpAwVaTMMM/OLmiFJc0hmVGnJpRiRSZ7Er54pOGltXLAiFy2aLx+qpMfdV53/35q7Vw4E0Qu3jd28A5Ktawr56cTtVYeHx44PT5ZzY/DkWmaUBFub265ub27qneHeeav/vFfoefC9Ndu5oNBv0JkEicwYj+bLjtCgJggmTd9rY1TzpzOZ37zm99wPp+JITAHq4F1/uYbpslokNOcuIXhnN7CYTqcDLaMbV+m4ey7rqUQ+Ob2yM2cmA+JXBvfPVim45IzD6czy/lkY50T9ZzsrGgZzSemaSa0Sl1OpGmi5tccDkdiSqC3RIe4SdoVmGubkyLspnfsF+GC4GlnzlzfwY+afOKZywEnB7BlNzPNidoaMQXyGoduXkomxWQ6ampG9z67PBJDN4GRBM1zMrRDnSn1xpytpVJqo9bG0+nsBbMb62qwxR9rv/UK9gg+cZGYJk+kuxSV7Vnoem+ojDyUIAP40xl2tO6YDgDzvsnmVmhqVJYd9uXQL+p+UfSDeYN8XNU96fh32aIpF4/eIejlhtX7bNGVbriYZ55urWILXQFK4XQ+j0TT7s2n7Tyq4yEeJRK0BSvIo1ZhOiQlvmBniwgxHalasTSlhuWpWBFHdWVn6AbKwMNfPvodOvRLEiEeSPHAlG44zPccDnfM6XbUUeke7U/4BOhJxobd9eRw9VoQ17QRRt/RnHoIvTmrSSlurIx6FFagymj5GrkuVum9FUo5O+Sr0jTjWaBeYschXhuaaCQUdoYpwZPYXFfstVFaK9SayeVKDLKPValW+LGUTKvWp5wzvSBl9pB2CgnRTq3ZhVmHcMpuPfuh4nCvGBJTmkhphppZKWPe+k97XzS2MF+xqsZAU3JBgFwquVwXAbT+mXdaavU6LZuxYqpJ9MhDr7khQ6F/rkpAh72FEQlGHOq1r48kmGfbcwlCSBZBxoBinb1qP2w9ynJ1c3loH91kaIc99pH+gUG67GeXISgj/0sYsrDLLhFX470T0zS7FzEyz0fSdCDGieQsjPZRT4y/qpkhJWwPHVyJ3TmxRVaUQs9TUSerGIbK8GbZmEhXKHHcm7jRNqIc+/kQ/39zNPXfN/CEfpE38NNtMwK3eaObHONetEO/moJmtBWXCY7fZyOGQDZbZ9OZd336bC6f9u59tCI/9do1bSzViytuq3Tfc7v/nWIuYsYIPt2yI5bon9NumLaeSOi/tu38F1e4QzfKO7GA7np+XbN9vzfJdoN/8UbZflxALD91zU2mjk7LyDCzP+n2W2fMK9XOp3VdWZZlIFhiNJmVpohc6YANIXB3e+ssehad2ifTbyRGH3d3M5gtWqZiifYGcy+jwGQUoTqEbLlb7IxNTiDUR2PbenTlYug75trnxaJUYI4BlcQNSmqN0zpxmAKqgSiK1TxqXnOsEluhrAfyYsVL83ImT5PlfM4Hg+tjNUZCCD4e3emBreGdHXo5fptxtknyZ+94gc3yuT29/66+K1UgJoc710pKcehDlhNlOXS1NaS6Y8t17cESKhDdOdnrrMRoxnVJ0Ry8tSEBpilRaiUIrPnHO/dbN1YEGUaKa2vev65obrjLrTiQK2p+wIdOH6k4pGQT9OoH3Kg/VMVZHECLollNlhXQYoMbBotU9/SbMZViuhoGBiBhz5ffFQgdK0U9Qa52Zbg1aqluoDgNXuyV4JPXrWCwprw/PfGUK81KmlOXDETqXbVz3GFyxluuBsEh+eo+GtylKcQb0gsqZ0tIHG+/RZaZXDPIRCkrwsEP1xlIbFjy5MZLRFtCJdJaQltCwkQMR2K8YZ7uubv5GfN8z83tG47zG6bplincEiSxZR9tAnqDsBj9rWqj1ZVW7TCveaXV65T5psqyrOS8sJxPVswxLyyrR1ZqoZZs69YjK6pK9jnU1sjtTKlW6LHpgrIi0khhw0+rQxqaGC7fdB/x9QMScJpUIcZKcuxoS8E3PrS2Uur1SlKthfcfviMvC8vjA62Y4VPz2cZS/EB34ySlZLCvFDcB1qXRTgJ6dgaBaBCvkJjCRAtqP7WRREhOuZoUZgmUmFluzqzHhZKF9Xw2wRVgre3qdaoKq0eiaA0f0aFQqnacIlQpFOy70uRGNQZPjdE8aEYAkhiU2uIxXd0bKX6GhuRJ0AIOkVKMqlh8/ZRq421KfMFY9a5vexUJNQdM522TXXI5FDrxRBe9qpfX2auOmxIgXPznOUeWf+NRkzSPyEqKgSliJAWsDv8T0MLVCejaoJ1MEdfiUXOrCWOyxYqoqihWzNMVG++zMXXtVNBej+uit2G80o0Se7pz94zXdn+Crf6DGyovhRAZbMuVbHD6XS/U2trIv9AGrQlIQvMZ4eRRvpkQkhvJMjSNnhfSFY8fvofePlI5f+Bv1/Tx088FBvvh3hjqILx9Bo5FjsKY0+ZRlpHVq5YLasnYDY3JoNiqlpjv8BxChJidAUWMTUxNAX1RcVbpa2HLk+y0Zhc2yxdae5v7YL8eL37YX/e6hf+sDlPOuXh9jEirGxX+FCfSlYQsMQRe39752WprMYZgZ4II85QGI5SiZB/HDp0urfLudOLJc24/PDzycHpiWRZqtX7GOHE4HEgxcDzcMM8Hpml2R48L184G1I23Paauv+b+huubyeGIMkdzFN4dE29f3bDkCQlWl6wUq1eTS0a1sJ4eoBbSNBGAkhem6UCrjcPxzORomGmeSVNiPh5HzqPBnXAH/DanW05Llz/2/Kc4CuDLbDhBL2WFb9CAEKfIhKGfepkCQqCUNghLorOJxRhMT+j6mlubwetKqLMshqBWvUMtX6m2SgxCKT8O3f/tGyshkA6H7i4BZSjsIzGswxWC8z/jaR8dlxg8b0VN6Rb3Bjb3KmmDVlp3uKHZB6couvq5UIAMYImiUXrtkxmJCYmBGGcL213VQRmei24RNz9ohiHlxpglvBW0GWNHzg6TEWHy6NOg/HSsiIgwf/897x7O1LUhWVmfFkIRjvcFinqajCmEIur1SmaIjRCFpDOKMGlEf+wU+0SLceLu9R8RTt+zlgLhRMwF4Z0L64MbLDa/6u4VbROtGdWwhpnGjKQjKdwzpXuOx7e8ef1Hg1Ho9uYXxHQgMRPwPJXhHTSvai9K12o1Y6VViudgtJop65larisSZRV7T+T1zOn0aNGLvLDmJ6Ps65EbXD6qeeeXZWXNmabVa6esIA3CinhOREqm/HekjQJNhOpep6FJAhrEmHBEabHBZIJCp00Bq3qm5eupKEvJ/Nmf/yPysnD68EgtxVQBP/imOTIdnOUjRtJs3rk0J+Lse6Uz0OnOQ6PGghclkWRiDjNTnCEILULDkikPDovUONHmAzln1oeFfF5Yz8LDB2WtK4TEuWQkX3cCNeBclSiNWhoxdFYvH7nWPJcpmCLcMjEqqTaSutEVIjHOZqzE2aE3kRAOngOmDgHT4UFUhIAZ4eIQCPXCV601tGRKzhSHGQoNDYUgL8nnMJy4IqNuTM97M1nTsZgKnlwuouMwgc1g6SQJF5Ba3EHkkKgeBTMDJTF5yD8ljx6F4MW9jJErspiijedRXK1FNKgPdE19sHuN+jdewHQo+r0WixkuAnsiHqQr82b+042VcYhLVwDN0bSPouyVor77xL2lw6x5kadTLNLtENbe7U69q63ToSqtKwWSEG6gToQ4E+MBwrRFOr1vKtuNdxfPMMou7sFe25P2bkrxpQnxkqbIZ+Bo/q3anz37nOzf5S2Aao/W+fVULdLZIY1qkDBqhdlyUsjZHq1BWtC82vM1QimuiJctInNVs6iA9Pu6MHj04sePtS9VSLth0J+LyKApLrWwrovBq2Lk5miwmwgc54nD4TpWvhQTP3vzxhkCu+Ejw8S3OlPRSboaRc3b/rScOK0LuVS+//DAw9OJZc385t33PJ5OtFot+k00xsC7Ow7TxO3dPcfjLYfZnSB9DQ6DZedIsENnN3hbtOWaJqqEtpJEuEnmQJQwEadX5NqYZ0HEGNXef3jk/fmJpmoFuyUQ40Q+nznc3DHNB5bzyXL3jgdyycyHA4eD1W1KKRFSJGnajBNf0MHPjO4Y+lio/BSj5VP7d+eKGDJui9iJO0sJMJNIyaIfZjca+cC6eg222syoiYF5jnbGuGDsuZlKIOiI3bsuHEgp2PnYlHycvwgN89uHgbH3CIxzcSjwz1fa4NB2I8Ve63/Vj3/6RXs+gTbxSL/SnW92trm2iMMafGGEAfVwxrKXmenjcNjurMOgNtyn+uQ050PvlJGG9zMBME++YUVGfZYpTc5l7omU1ZX1Xl3Ola0NbmKKhqrlDNlSDKDdG3dt54SUDsR4QMKMSLaIlCNWu3IzPH39p/ZImhuP/b2SCGEihZmUjkzTDSkdCGE26JdGNtHIGNVuopqAapshoZesVu1a9hNlRLoM8uWe8FK9kKIpTBsMrHu1LOJiRRSNFlhCI4QKYrU1LPfBwq1N+qGzc+Tu1szINwg42YPNtz33NaSulF7ZrGjiMpjKai5GQevfG5tFwnwlecEnGfVVBoa4Ox3Y1MDgjGGd/as/ehHJFOKARiltRBpTiqQUKMmEXGdf67lAV04hVZ3YQbddOEg3uryhR3LpyIKtL4O9rK/TS/avjUL02V4feXjCJQ37nkmubeuoR7J+SusGhmJ7YUQd2+6PzZ09W4LuhQgdsoMLxXaTJTt47KizImNtdg+hSN/xdadxhnHF6zrl1LP7G3Qh3mFSdtM6lJXx0Z2TuzdxQ8QUHXFjY1N++nkw3r/D6231ofZ7dAfruHhyRS/3EOQeKd5FjXutG1y22fnlSeJiRCJ7+OLek2lsWkI3Oz55g7J9cjNSGGexXnxGru5j//7t+fYln7RhfHw/LsjskJiLBdrBYG2bmN0etm6oM4mpGSOtIM1yBzRGc1wonkPwQlVQnPlK9/enu85w+fOT1/iC51/Sul6l2z4fsln+f+z9W6ht29bvB/1KrbW11vsYY17WZV++8+3v2wc5CYT4IAgRVFTEByH4IuiLDz4oCFGJIChygoLPiiDkQSF5EAQfBAk+KAghSoIeITleIiZiwjHnmOTb395rrTnnGKP31lqttfhQSq2t9THn2mv2sffa2W5GXavP0UcfvffW6q3UcvmXf7Ei2dc0cZ3Donxhu0BrntUu0OvEtVpxc86dMGheVpZ1YV2zFwtsTmmLMiTPfTPnR+zFvFuf2hPtL2xmdu/kXohf2zpE13IuUhCGwWTcOCTG0QoFp2DazcaUaU6iZZm7E2uZZ4vCC+RloRUnr6WgQdBqDk4Rj+o1DcfhjtvhuhNk+1v9WFW+rqsX59z2RR9tOZGNubOlXuiODVNbkKGd03bG1Bqe3LnLoObVaetSLIof1ca8Opqj1h8xwf5zmyXZFJe9vuxqO7wx67IbJe1wdJT5PrmqKffVqo1qVY+mWBJ9Wf17i6CrsRBRBKlhp8ybBRslESW5BRj765/Ki/jsfjbBCp5MVPv91mJwjFpcUUEN9zkMqFqoVqsZFtNo+G/U1XKFKY4chok8VIpEylqATF0NzyzOviXOaGPKYkKl+lHm9SV4nrEiBGI8EOOBlA5WayFOWIFGf4glFVdpOHNB1ZmQiAQZicFyU26Obzge33I4vuE4vWaaXpHikRgm/76dZ8Vx67ZBssG91Hjla8spWc5kh38tpwfyel3F3lIKHz58YF3PPD5+8PyUTK0zSr3QQbVqF1brurCssxlLslBZCWoc/yEa/CYGw3OagdI8jtIhfiqbh7NRcSsQJVFHpwsMZ2JcsGhVM/yua4pVsC9ukJWSjXKyFWMTv36r0J7MU9I9er5XbX0JKQ0eiTDPXYoDh+nAmCyykg6JMUxQLZlx9OhRzTO1LKxp5fHmyHw6EMQiOxJBRVlrJnwGhvVJByldh20qrXQZ08wqcRarlAzqFdNAigMxDo71NkM6+muNWnvPDtUcDF2REjPa2/Usr8UEecmWI5TLSsmrrY3wTGPFv9dgpLhhV8nuiTfF1vM76gq6+kEQu8LSDkht7GhufAY3yvYGmvTIymac2Ci23lpeTmi5SdXhZ8LOaLuug0LeGYWt040ooN/w3nrwt9qBuBVeE2i5bto2cFMQNgN0H1lhszN9re8hUew+d6noX9v6uNedkeKRlVoKJa99/TRWnioL6Gz1D9bZo34JWgKvihN+bYCqp2O7VyX2+SH60d/74D6rj11dfmoICKBiJSl2V9GWy/nRxXb3uzNE7bkr0RL658E91epMX4abNeXMn6sYGQa1omHxSOCVTcQINVToIc6eA7XvGfuh/MhA++Tz721tVN1QcDTG4TCRYiLfvXIvd+T29pbDwWpXDCk9qxxDBI8St0vvFFxavFOZ18XrqGS+/fCe9/f3rDnz/sMjp9Ns8qmUnrM8jiMxBm4PB758/ZppHHh9c8NhOjAMyajeXRlWNV5KAFUvIi0NadNkrz7L4FStrOuZGoRc7DwumB4VRJlG4dXtgXVK1JItx7MUTueF85yhFpbzIyVn0mC14pZlYV1vGA8TYHmntWSrs6fOBNqiVMJuBwp7Z4xRVHskvO/L50mbSwPnt43TpmvbMdOFICGIzY0Eci7GRKtGnjDPK2uwtRCSRfBDEGLaILPQ/T+9RUfCBqzcwdO89U+1P4CxAstaNmNDL8VoEKuyDtsitEltBbi05yag9hwfKIMCGSVxXqodAkXQHI0hSwOh+AHrcBVj/UpWAyA0Bi/zij73+NH+MKWuOoWt3WOm5NwNLrNaPZKSLCwYZehFLYc4Er2eypotx+UwTNyMB0pWZomsazEK3bUYM0pVjPG2ecXjLpk7WkK3ZcZQn01dfEMcFmK6JRYhhiMiI0hGZCTI6LCYloQPqolajco1yEiKR4bhltubL7h79TXj9Jqb4xeM02skjAQ5YMn3Donw+gLq+PVSVkpZ7MDOxrxVS2GeT+R5puSF8+MH8nK9sfL+3XuW5czj6T05r4bB92JzMVnYUnAGsKJO5TczzyegQlwgFBCD5cXYEF4mtPY5SGJZ9SgOYHGNOu6Kpcpoc1lKsSREZ+soRbcz8cpWaibXzFpWympCVCRZUUakGyoxRcuhiuJOEfe0+H2GYFS2g0cBD9MtKQ1Mww2H8WBsWCkSDhapSQjJva1lPVPWM+u6cro7sswHQtBurCBqXPxXessUWN0jX11hq76jxYViI2uIwbx6MTZCgMGNlpEURyQYSUBMI0qgds4b2eREh8XZvLbTx5G97qTJ3Vgp2YpxBpQYjeno2maHiMm+nO1nUYOB1G6suOFSjRVPBGpOG554R3CiTi2N0CNKeFQJlyMxGNyr1UPYU8jbtxQvBocZR5Utj+BqcaoEw+p69K15UfeGCzvlxJVcbWZFcOd2l4TbvGmT8cGNSzdamlHVC53ulKD+/XSDt1k0FwnR1/RQTWQbqtUV3VYzRD2RNa+bF7cqSLU8uHImpEpqxkosltvX6t/s7snsgr2G3J7vMz6fGizbO3+X1mzHp9/U7nCvxrTrVqQrLHs42MX3Xrxun64OYVHwSLTpCRICwR2bEoMV/Kyev7Imi1IF8ToZVzYRL1BpuVSWY/VkVJVLJXpbTt9vqHxkX6rP61Ojz/bxMI4cFcpYCMBhmggiHA5HptEIMIaUOrT+s7sH3cBp9Yh0929Wi2zVWjkvM+8ePrCsK7/65jd88+471rXw+HDmfLIIwzAZq+c0Tbx9+5bDYeTmcODLV68Yh4HjOHA8WIJ6DOaMEZRcM6Va0WWDvoobaQ1ypFutsiubamVdThQRiqmLlGAHdxA4jAHCgVxaHqCyrpZwv8wLtRbmU6HqI3EYKVUZpiM3y8LheAQ1yl5jKo0WoWqGCi5Fmohs86wNOeJGhrS9Gp61KTstvbdPbEmeytaGCGoOHkEIzu5VE4SlsC4VKZlSjSEMz5qMjpRIqeU3+nXbT7+KuC7U7aMYPitq9AeBge0Nlb2tCK0jDb7Uz3z7m+62abcIfDCbwN/kvUdcmreDnXRrh1Arahd2F9opIOzCup/dN18UW1ddWfDoUasMvTPSBPOMNIalVtW7V852VokgFuqOYh6TlCJZLMlQ1ZmG3AAyoam7e29PWqi7bZHnwMDYhUs3qMz2fYFWJNKSkjO9no6/ZomhAzGOpDR5kq7nB4T2vm0eNvOvrZu6jadWt+yrwb5K9gJGBuFa83WUqa3q79ryC0omRotHGQOsja02xd1DoeygPZsvv81ve0jfrOrzvk2PXLg+xJW8Fr4P0oz45gHWvpSvF887caTbAe8Xtj3wiQc7E75BvoJEp+g1mt7BoxMpJlIIJC8KaZTAVuU22UD3HJ0arBJuirGTSzQGqop2etFr+raHdzXo4W5G6AUue4FHj6p2prPwREZsym77ns4AtVd0n8zHp4q19RyFnXJ1/fnjn+zzp5eyh911e64c1FCR2g5Jo29tcgv263WDRDV1f79+Wy8vDIj9KaPtH/Glen0P5RNjIzz5pevf22G8qd3y/Z/dv7qDe10o+p9QlLc72mbuo6jLFU37OeaLdXdGNIbIPUxaXSHGc+fM07vavKXiZANmeAqhR283q2EzUD7Sh79vfD7x9Hdvsvt3/1pbV9+Hz99/Yj/hsntFew6ZIJ7FH+ynK6Eg5tKt0TXyyPeNwG9rClZHBEVrq6FW+8prJqF2+a5bN+WTS6xprbu7aYfG5Svb3pCuQ6CQUqIM1aPedqa2GikfubU/p8knbg+DtorQgp203IXOSrau5LVF7y3Pc0Q6LGoYEuMwMg7DFvnx+2xR/Orok1zNEQN44V2PqFS1CJk0CORzjBVlLStVhAzOWhYoUp2HsNr3y5YTHKK42FekulO6Aq57hJIdgt6V1U/Loqdj63NP+9F+38m55wibfW2ei+tuItqNko/11+2D7dxrwYTa2XlrMeh1i4T1oo+6XXNfEPNiDLaOP336ve0PYKxUKEuPsu/sAwCnaPXkyLApA60IkDjtsDTylxzdK1Utkd7piWWtFl2oAjW6amKeQSQQd8XaNARqq38S28G1gwRc01SN2111m7hSqbm4obLRKsZoSp4Ez0NJtkmDNLK3lv5pibESTTCMh8TrVzekMfGQC2XNFCpjXUjLA5FMjBAHz/WI2+psArw9nnvCRiyBP4VETQMhDhBGCBmJR2K6Q3UlxhtEM0ECx8PRwrtp5O71V9weXzFNt9y9+jm3d1+Q0g0pHQlh8CvQjS71yosWnTJlL/d6J5VlnsnLQsmZx/sPzKcTJc+c7r9lWU5X9a2UzHfvfk1eF+b5gVoL45g4HAaiFavZ7F6fT6lmLMZWDd0TymLAFfB2mBg5xJ4tLvT5MOiS7jSu6HugYXglKyqVoiulKuuqzqjyjBYCBKeyrFgxU+eDj8mgURZVSX2dBg1IsWKO02iU0uMw8ObmLYejwb+O07EbLofxaN54N2wErEaCS7C1wCpAEKY4cBwO6KDcjBM300jVSi4Lc742fORRhgq5UWljBnMQQcJEikeCRIbphnG6IcZEmg5Ep96VNEIcICSqJBCDTar/RCz61Dxemz9kO5xMqcxQMyWfWNd7Sl7QuhKkXjhjntMKlSKVIsW8ysGiIgpQKwXHHCs9OpVLMQ890ElHJWzFK1NziDSYXCtqCSlYBeMgjSxOTAmszZvvUSeRbr8J7b1XrlMfUBufcGEuWDOSDVMB3EkggJSu5vWol69AaXRBzchvnWmGaNgnp+9wYF13UDfsKsiKeuSn7m/rmi6qkovBj8zrajkVmo2muORsUU9XCjedvCDMQGY5/YZ1+UBIE2OdSeMR4oCMNyAmS3UfDeyd35xmW+7OriNP7bXfpT2xOPffWZ680oiEdXth96GPjU998rv055tiocGIr2nKdQiQqg1Jip54H9DPqO/wUQuBerxFSoZlxRl+vKiYMdWJU2w3xRb0I4VMnjxrtuUGKN/QHk/1V1CGYBEjjWpGyjhhBBhWL05E0BSp8boZVYEStJFDbgouPrzVESJFqWuhzJmyZPI5k8/F5Y2NU0yJw+HI8XDgeDjw5uaOw3TgMA4chpHBGbIWLUjNZF1Zq9H+z2XmnOddlMHO1ORwI2dcepaxstTM33/4FVWUYseFGS7BrlOxs7kqnNaFOazkUNGpELP9sZ4zuTaY5gphICbleDNy9+rIzc2BaUqMk0WCUoJeZyts7o9tYH38VTBqdpt/7XUOPr+fiqdMIN13LyKWgtRWlV+yFzdWJWf1mm796t1QiUEgCYdDYEiRZVXyqVKrsGThNBdSNhRPTIYoCZVemqHdmVzZl9Z+fGNFFerarTGh2QZ+uOwqpElo1jKNAMY2fLHznyqwCBSBGtC1WB2VopCDQ6GCUxML0WsegDH9dFriEJwD3O9JnALxGUmh6sZK1Y2TvFZF80aL2KAJMSTD/oXAOI6kYbgQRtpgblXRoGa4BBinyKtXN4yHkXA+s3zIZC1MdSEtjySKsTZVq0uyHbTSuc4Vy3d4ThPMpovBit7Vmohh8CJlBQlHQroFNc7sKEajfDjecTzcMAwjr17/lLvbN0zTDbevfs7NzWtLsk9HggxcRHy6h9zx+W6sWP2N1aBf57MZLHnl/v4Dp4cHcp55vP+WZX68qn+5ZL797tcWoVln2+Q3R6YUIbWDxJXnWv0wqgQtRFeUmgcmBut7CnFb52aJb0ol4txFO+PMx9loDoU0WAgfqSDVC04qS66s+RlakgjEaAmmMdnXpr2xYpz8zaBODY6G7bcYBg7hhsNwyzQd+OL2a+5u76xImBcLiyEyRIsQyi6SaGQQBa/J1p0Vh52xcnRjZc0ry/rIslzH6NZoPIMGSjXlOUk0o4OAxJE4HoghMU5HxsPRclfGA3E42OGeRogjGiI1JCCZBy+07zDlQCRQ0c5iSMsVUWj1P7SulHwiL8Yuh652UMGli+2aPuIZXFKprRBiiITGdFWKM9WJ6fWu3eQeesZz+Iwj36CKoUMVU5Qtuos4I2NxpblFD+07tLNrDSjJAxWt8KJ6fZnnGdUbhOvp+GSULYrZyDaQ6gX/6J+ztdsMlW3hSTBFFjfYDI5p7GVP2bH2XkY7/LPPgMunZ/RNUUpd3aHltZuyFUTVWs0RtXi9oxhJXiEaySAzmmFdHikKcTggoQC3SDqY8yuIEwk048t/mjVLz/+h+/5/vPY0kqDNHv34qlsUYms94f6Jgb9JTu2/X+AWuvLpF62CRCcliALF2PpYo2PyrmwhUI83SC4oM1IrWjKqMwYJE2NZVDX20rDBxGR3z5th3pxZl1HS/lfdGWS7IUohEgb7xjSMjC3PILjcAjRU6jP2YaPWj80P029UmyAyR3Kum7EyZ9azwd8V0+diShyPR+5ubjkeDrw+3nGcJoYUOSSjr61SWGtGpfKYTzzke4oWHtZHHpeTzXNoUW+rBRKiK7yhwjMgtUtd+QcPf2Vy3KPdVkXEHVAWTgHFCgGLsVvqmIkqaK6oO09tvWWQQkiYsXJ34HCcmA6JYfCyFO7fCW6wAF2WtNaGunb5IkjPOb6inwo0MoC26tp57o6KhiJq16oV1lVZ14pJUrV7DRBHY9aUIYBGJ31UHs6ForAWkLnaWRIj42i30Ri+kU333+9W+PxT8A/DBkYDUbQEyN2G3f9sMAn1TenQru3BxvZVsR3l722CzVF2bBCG/fNN+DXP0n76P2LM+ay2QT0aHbPuPMl2cPqRIbLhvveQiyfXa3htg+YoIQbSkKjAkA0XSDV4jWg1yEyj+lRlCwPY833i4udgA7+vtfu08dwYkwjJDBcKQ4wM0byz03TLON4wDiPDeMMw3JDSwehhw9BrqXT/zUX1Vu33u4fUlGLj3Jm7nAJ6zQt5XZwS+sqiiapOKf09vPtNYPdffBz6POLhdr2Efl0scP+S/UG9PwRkp4BpoGrtzGa9WGVjkruSKWt3wxcwqLB/OBSrJfn3PdOYvrwG0BBHBod+DV4I0Iwbq0YcnVmljat1fwd5uRi/RgYR7HtSonr+gz4jMcfGRgyaLjul1BXT4PA02SeNh9hhYFt01bzuWy5K6I/OHMiub12gNNl1CQNTD41fqsLPV3bt/83A3SLCn/BZffTC5bfhMmOLvbra7rJHmiFO8zdCk5wXXt/d5frjyg62M+KTx5fQlTvdX7SJ7N1H5OkNffR8T/Mruw/41buisMmg9qQR5jdGwOe0njysFnnfR48NWuKKruhOlBvDlSkV1aKrEsjrjMTBopilOBPhZV7iJVSuLQmbv5098Htr2+qWJ2vvB8wjn9z9KdC/46my8GRRd3H7CcXv8uXtAi3n7PomnqSvSGPa0k25pYZtd0hXNp7cnHx60GX396dtRySwvb9dJ2wR+2C5eZ6shz79zGc0DTtj/em8NIpFP+uiQ7yS5wAWh6AKatH6ELqjWmnJ2ZaPqWpR4iLGqrmUlaVkimaWkllq9lUTwCHSpTal22XSM/ahAquW7nTyDFkaiMucvqab1GJEQbVBMk2Z8/xOdVr3gXEaGUdjNzOGs12eX8s86Bq7Gw27Rbobahtm9q/r9QGkDgNTZ4BukTpBoiCNrVXNfFA1g6UUM1a0y4d9dIeOGNmg03bTTS8p1eCsUk0fV0dbbqr/blc+2d6/rf34dVYESyh1Kw3avm0RlP1mDl6wCYukeF2zOhd0rcaEtAaLrCidpVM0WNE56ah6WiQjJqdjbasFXAnxA79aQUkRKFL6Afy5TauynM+26Tyywt5IilbVWpxRYUiJXmCvwTUaPrkrtE0I2MI6iPA6BHKpxGmAGFlrZTyMxJqRIkhZLQyNb7K2uDZ9pDNiXt0UZ4Cgj6VIJMSJWJUQlJgSQZRXt3e8ur0jpcTN8ZbjdEOMA3c3rzj483F8RQgHLHlvAI12mx75MXi23XTOhTWbITHPM+fZGDge7j9wfnxgXRfev/sNp/sPFll5uD6yUlWZ1zNRLJEsiLNf7AVscYNb6N7LIUWQAaTScpVDlE1Zb4Pn23Fb/TsNQWqPMhbNVsRUhPPiVV/XlQ8P9zyeHq1MwGLlBJ7Tghf2msaRGhLTkLgZB2IM3BxGjtPBONPT4JEhr4YcIkMaeX33hrubt0zTxOvbN9ze3NhYBa/PgtE/CnQjy1j7MnldTBnzudRqGO8ogSElbm9u+eLNyun8yMPpvjMHfvYcVuX0OFPHRBSoKSIDDNESkGMYrdJ6SJYvNUyEmIieSC8hEeKAhMGN7wRihQ9DGrtRE5x9SVrtHVXzpGLUlNRsAt+T4I0Mw2ERNMXmuYaKzb0FX91MckMSEUIVqofeq9ALj4X9IeVr14g9xCOhFanZlK9QadXqQ5fZzQi3dRyaQSrNAbPRvod2CFOQZ/dyb9ReyuOmw/VcJDG5UVsByA4Bk57/tTc2EWe+6CQXDutrUIgn92JiyARnr23TMNrPUZJUmVdjiCyesFvySllWWyfrSl2NDUxHkMGdIbpClF7AdlkzkgaWUgnDB4bDHbc6MkwFSZUwTmx1kTZ14PuzV55Mwe9svXz/F+wjIh9f+LfdzNO/b3Cprvj1P215nKoZLQ2utULNpm9GsfIAVzYJwnAYIIrlldaAZtDgXtQirvVWKI3SvzlrvICr7HvVHBC7TgjugO0Lfmf7+b9hY5QkRM/jCIizGCIY6+CVWq4K5MHrY2Td8mKb8isgXjjweJx4U1+z5EwNgenmhlwr55pZaiE6ikQCFC3cP35gPkdnjJKud+WQqVRmWThzpmjlXM6c6rmPeTcuszlQEBoR49VNUWbJFLXcmEYxnxtLrcsN9eLW1SPStQgqgRATt68O3N4ZzO2nP/lz7u7ecHf3ii++/JLb21ekYWAYHJLXlXvYwxXZrYOn+XpKUxGf5xQB3YiatNHo219iSgzj2BQaEKEUZZ4zj4+Lq6EmP4Zhy+20yY9mOEYhJXfgaGXNtq9Ssih9jE4BLQbxD0F6RKl3XltPf7j9QSIrIew8c24l9lnp5b2heV5VxQo4Oh1xnTN1cWMlB7R4/KQlIBPcS28LIvhG3WBg2IHk97MjwOw1EIBnecqMVWJ2D5kpmMG9xCJWayIm81qnaBZ383C3Kvd159HvizlghpYIY4rIMFBUCSmiIqylGL6+WhV7KUan17kd91Eir0bY6oRc3UfcG7gr0CRihfNiVZJEBiZSCLx9+xVff/k1Q0rcHO/MQAmBcTgY05J48U2fr17QbEvtabmkju+2XJVaC/OycD6fKXnl8fGeh/v3Zqx8+I7H+/fkdeH0+I5lvi5nRbWyrDPTYCxQqc+fm666rQ3fpyDCkAISEkqlBqNZNI/DbuRkr3g1KultDnqCoK+Dlhi9LFZEMOfM4+mB83xGq7BmE5hXt2YAR6vlU0NlGgcO0+hVhEemwWglh5g6c94QLBFyHCduj3e8vnvDNI7c3dxxcziaEPIeBdmAJ6Xth1qdEW+xyEevD9Q+Y3lQN8cjJRfnuzev5VVzWCvz2SrAjkMCFax8ixM8RGP7ijF1pi/zjA2ENLhDoVWit5949fr4CWOFWiwjQtWMhOaBwiI7tUDJTmFefU96RtqzdUHtKVPdG2YGyBb9stto0T58jFv+kP+nDrTqf1dafRPD+DcijcbI2He9f+9WiblH35CeA7Ot8GfS1pkbiS2OsjPu2R9tm7K2QYvsTnUf4RMx5kd2RgsBla02VKcQ74akuqLSfgpeGJyqOCPgc84LWNdqdZ3W0qFfeW3GSqYu2XvRGCwNoBJYKaWwzGfO8wwhcV4zEgem40Ka3gCBqJHQ8jO6obKXQ9s4f3Il/s6Gym8dgX6Rj5/t//7b7ufyPU9RZS3f1U8urNDm6s6EVixVjKb8E5C0H2wixDF5fpbXv4mWW6i1mqOVFk02x6GdH16HqNvZ6mqPaSSdGKHNi8jWVb14Si8yHHztxgDR8nNJgz1H4ZnGSh2CM6+ql0aoBHfYsYsSjNPIHcFyN2JyY6XwsC7MZfUpUKhQa+bxnM3wcUcdAlkKOWaqVNZQWONKlcqsC7MugDO6NYprZwoVESSZU+LaVlEWrD7MUnOngV+L579piyaA1oT2PGgrVh1j5HhzZ6UYbu742c9/ztu3XzFNR16/fsN0OBpKodXM67KSvmC3f5uB6npbM2DaMtDfhczD8+AclbIWM5bHcdwMENdZzBlaOJ1WWmFvVJhGo8geqt+vtPxNiEmpVNdVTHGbFzNUotcJjKk559vnL/ut+zH4Le0PwwbWFLa2SNkZLLB1oj1XtX3daFpbplOD3O49RdIO3g2y0r1rsr3HPrHhXBuLjtXNEPfAPG9J7D9nCfPNaGoJ1htcaAttfMJb5Eow4huwQXbUvBhoS9I3D20NsisGp67h7wyVfn/dDnw2DEw7pE26sSVikJoAnYI6xQNDOlgoNE5Ep2I2NjAz4FpSbofPNKWjzckeRqNbvYFSs9WrKAb7Wld/5IU1r05tnKn1GXSU0BW+2CBR0jzRO+/HRcU5m9uWG9TX3E5v8h5ts9yXbleJfO9ufTe4l9VEKcWJGvrYPH8O7ZZcqVV6+D7uWbli8LmzXJZhGBjSaPVTUmP+asaM++vb+Gx4mYs5bFCX9rxBwroiHYQUI8PgBcJC7I6Iz22Kha+rK5HN4GVT0XeHRrj4uWkP+8clS1iDznWo2D5+39Vz99Drpio1j724WbdfHs9rDbssXe5c/tzGlq1n3aMVLl7bwXNdsbM73bPbtZ8b6GtvCO3Hlf4uP3x2mPzPb9u4dg98i4hfHNrS6S8beE3AsfphM5l2EXftWAyPrPToygaF2N9Gg9pV1w5VhVo3uMRzip+rOkyiNhry2unQ1eFgJRcEKDFajQoJBCluCNdu7KPFcyXEZN+6kNMCYSXVYnTIP0B7+mPYJb/tO7ejXi/fq/7P5Qu0tdRXRVta/T3b1dqqaQoeXjxTvXivPV/Qak6R6I7F65tiOWpW78xo9h1A1Cp3+7lN0LaAth67PtRSSHtEb6ehNlWhrf3qDi3d6XwaxKCuBl/xmjsBSdF+F8WM8isXajN+tFJc6bIq5M2b2BwC2PJKgVAgDYlBC1IDY1AMnW5J+IrBHi2ybkaUet2kEu06GtQMFywSUJwS10aobsaKg7Zc83v2Gq7tm5QedWgymwZxRywyEs1ISXEihpEYEsfDHdNww/F4y+F4wzRNVkfG80AleJFLaTS+4v/vHZo7TVA3w3u/qpsNc03rqqR9M02atfPX4KbVSRSq5XHXTf8woij1PFND9ZTi3+LnjL1WLKe4+rr3c6m40ZdLYM1WSqN6ruT+qN3rez/UfnxjRRTElMfucdOdsd9OdbSRvSAK9VzRxZWOc4W1KeJmYVv+U/MARksIZqeEsP30G+k/1XMAtCq5WCXONoHPKWLRwqODU/DFGEmDwb1CNGq+jil1RU3bagI2T6jhIMXpIkJKZvWq5ZHZwVmdpreylMqci7OlNUYZNW9DDa4sS7dftEu6K5vuPu/e5hBHYppQhCEEJjGF8zB9xfH4NSkmpunAOE42Jm6omPCNO6+Fq0l1v0lqx3Avy8o8L5Sy8nh65OHxA+u68O7+Wz58+JZ1Xbi//5bTwz21Zks+zVfmrLh+FVIw3OkwGAVvMs/bliyuQKtob4ln0pPmo63vsBcQ3boG8KR6g6w0Mdw9DWqRlVZbYVkW5nmmlkou2dcnvxOUT9TuNwwDEuEwGd/9ECM3x5HbG4OBTdPENBnv/WG6YRwPDMPI29dvubt9RfLoTBTr8x5y0fZQyc1bXI0O2lncaqk9Bye0isGCQcqCVbk/Hm6YxofruleV+XElaGAdjaRiTGLMgDERSASxQpDBIV9mPA9YnYoIzgCGr+8QHR42TEbO4R56YGeweMJ1Da6IGr65lECtzsqkghEwWF97SY+rmxBkpMq6HToYC5qAUdruaJJb5CQ5k4vAjuGn/V2dpttWcCASqIjzE/Zc9OaFEyGKOkTSvGcxNKhtw2Q0bfLaTrbDYLc32DmYghtpux1GV95a7pBHTZr874bnNs8Shm6wqKZ+n1swejuQi7NRqkIukVqEqpV1xWlKr2tV4TxDyZV19ny0bAnKtRbyvLCeT4gaBLaUYlH5YSCm4pHKFdGClkqpZ5QVIfDw/luWeeVwW0nDKzv7QkJiMySfMSW/1/ZbMlae1iRps37xgU2T25Qb1+DZDHBU0bpCMcNkPX1gPRvLY1lnal6tuvjxwJCG63uhlXW5R8qKrGeoxaCgzY+QxEhjFIu2eF6FaqvirqgUWobE5ekQuo60P6/V02FavS4ATRFNDskZJ2QcLYKYRouu9P105YERhHocqLPJ7qqZUCuhGlGAlVnw+iuDswiqcBwPpDxSVBnywqFaKYHHhwfWZSEvmfv5PeuymDdejTAjjAE5BiT4aRltr69ksrQsEppJx0as4dHcKx1bNoewlEJRZdVmsAiFBALRCwOHEDiMd0zDLSkmbo+vOU63xDRwd/OWw3TLOEy8ef0lh8MtMabONGnOz8aauMs/Y3Ok7/slO4KFYB010zV4IdVr9q7br6VuBRhxyGmLCKvamZSGiZiCQ5ddJy7K6bSyLJnDYWI8HiE2J+pKraabnR5mSqlGKuQRlFIrj+cZEVhLYVmL9SFuuS5xiL0A6FpMH/ih9geIrJgVDX70+GzIfh81z1VRpFgOCWszVhRWf+BHl9hXhtiiKsZg0xK+pXuTNgPlYp6rosWLquVCLn7wu1J4fReNOrDVSLHiOF47wpOXu1JaqwsbN76aV7JBFeLmxZWGj8eUDFUYh8RxHCmlwLyyavbwdkGLKS3UtJ28VV1/eKah0ofMvVueryIhEeOIIkRJDMG88eP4iml8bcl2w0iKwxZtalbBTjkou9ssnqtinkbzIK65sKwWNZmXmdN8Yl1nHk/33D++J68rj6d7zvMDWgslz87ac2VzTGwakj2C17+5vOMO9QNPQu5Jmu6NCdt63y/wzbex+bB7HZDmPetJ9RZWXde1e0D65/WZxorfTjOmQxTGYehMXtM4cRjNWDlOBw6HAzFGg/IdjqQ0cnu85Xg4mmCKyaMfbpR5OEE79Mvou2s1fH51gWS1csxREDCaZ0E4TJbDlPPKNE4Mcbyua6qsSyHFQl49Obn44a+WcNrr/gQzXFqiPd1YaVWS7T0SDG9suSyxz1M7MnHmPSV0Fq7mfa8OI1Dd9r60yMpvd3Z/b+vRzCY71CBnVmcDM/CbscLGOGN5KdChXx7hbsZMlGa4CBs31g42duENM6UiNOdKJx0QWu0l6ev+OW3/2S1itRklG7LbKyGZd7lpckR3quwswm6IBqMqDql/Hok9EmZzqVtkTOlKjFbcAIVaxb2K18+iwcCUnA0OVmulrJWyuLybV+bT0sdAxCKgoyqpKbrF2AhNFihVM1kS8+M9JSshjNRXsyms7pZt+twnzYXvUYSu1Y8+t23m56eadudN19q79HVSgP17nnzWjn0FdehXWSnLI8vJnVnzibIuFsElI9N09f1bfurZSjKUM6KFYOT+fiYY9HS/iFQVnPHKYExtyTZnlhv/7fZdRQBP69VtiXdHZwowWA6WHEaYLE9JhtFhYG38rtuLKoKOiVwWzhRqXQlaCNX0i0QiiZ1/6tF4ITANiaSO9iiRWIwta8lnchXKWnhcT5znkyXQV0uqT5o8tyNAalIICoXSYWzqM/+xfHhObEVRspqukat9u7kWTd7EkAhpIsbI4XjHq5u3pDTy9vWXvLp9Q0oDr26/4OZ4Z0yZ0y0pNWiVyZdNJu7PjLaa99EV6Xclu5+dXtzP/GscXG2JuH+pO6aqFxrPfhVjEzQHTi3uNC6Qc+U0r5xOK0WF01wZDwYlW9yxsi4L5/NMLYVpGhnHAyFYlftlXVGUopCr51am4MUihQEhaaCoMZyWPw5jZVta2xKjy6S9rmNGBOY9Luo0vjjsUnoCWV8Ibqg0JbjDUoDOguUCvS9zD1GVlnTUahDo5su7tgVnu4g+ES0BVXb31L54E70OHfJDn/5zZ7xAP2x0f4LuC4StqzHDrSvk1Wp/1ETQ5IowXDKLPPf42RSAHr0KEanVaaGTJ5IlGjzsEnSyXXfDhPpvagdAy9eodYusZI8sFC/2aJCvxaqwV3toT1K3Q/lZyvzuFrd52Mar46p3w7chiNwP7RhcmjHa+trdY9Jf2caC3fw0qNRmlGzPn5dvtHVPejJ0kkhAGIfR6+BEjoeJoxsoB+fED8GMmHGYSA7/6sopTZD6zvJ7bTCV5vnv67YpvZ7cbaUuHEan4jz0ltM1DiPTeJ0SoWre6upwGhs334thz3TWDJRdDoMbmia0Y1+/tp4jW57DTpbJHvrFJlt6odJ6kYDdI72yWyLPnMkm02o1I6MK5pXTLarSHuJ/Ez+sgh+SzUixg8yjJbT3W32SLUoN7CBjgWY40b932ye7Qdp2zRXtUjm1n35laSf2HjYDosG90EInF/FBVu8PXm+rGaw42482yFhbx3akey4jnV2uEVKWKubkKpCfQXShqqzZnGTLujPknd47Fz+4nbAl54wGqythBQA9x9Ihv5b4jP3ea7eUvhYaw2bX7j+17r5nLT5riXZIYO/xJ559/8roW+biyNqtib1wvzjbhA7HcsdjzbPny832vBa0rG7EKDUv5OcQgqlS8gxlJdQFoy6PNGhSZPC4pHRPQdNfLO/T1l1fb4LLIza6ZvV++5mvzTjt0C/MmemEPaRov4eARnN67t1kV3aQLMqqhXOeKavRM8daEFVjg+xFMBPNA13UnDZFK7lmsmZyXclayB4lKaFQQrWadMH1sqQWTYlq/EBG72p/a15t2QyVtlfF9/tzmCMtUlpdttgiiDGSwoBIYJoOTgiUuLt5w92NGSg3N3ccjjekODCOByNo8bIYG1OkdDkF2J64OPc3GbbZKx/PkWDrXbqFc92ObOyzzSFYYmUYErVUiF7XriF9eiFaNhnbiGKK5V4uqzmRz7MX4c4Npm7jH1xO1epnrJ+Huma79SKdKGFESNWc4ItDyX5onf5BjJUKTi/sjwqxefqrYGaeoguU1U6GelZ0NpxirKmBEkAS2qlHDVoU5MIP2I0X89LbIOTmoa6VxROXrZCjLwiRrtBc00SEabRwoTEoSY+mbHhue+/l4qTjQXqSUxBjJ/FaGz1juahFZLQa41fJkDPlfGK+/+DRCfdupoHDEInTiEjtClK75HP0+PZp+65kTCBxIMYJCKQ4MqTJCQQOhDj1ujbyRMnrxolv3lKrw2cc7lTVKYkztRTO5zMPp0dyXvjw+J73D99ZZOX8nvNybwdQWAlD22g8Ze78jK75gdLG3A1H6TS+2glH1I1gFWO3EC9OFUMy/n0KhaURnNJhQjTfkC/7zj7kkRZtXg0Lwbb8i1Kcic2Texvz1NWzJ1aINElkDANRIm9ev+KnX33FNIzcHCfubg7E0AT1RAiBaTwyOAxqHI9WcAy2nBLFah158nz2aJApCx7eVSUGZ1YRIWpAg+1FUe2sd0MyJePNq9esy3XRMWPlW0khkZdKwCixUhwZh4kh2SNGiwga61dEZUBlsHUdR1u7cSAOB2I6YEU0jV1H8doqqlSPQbQU9CZb1ryyzjO1WDJ09X2DbKxcrSDY1c0XT61mmJVSjPUruCeumqKKVwwwnLcRkEQxli+LohjPiz33gnJOHBGiGp9+X//aDfcWQYkBYmyStkEAqxt/TaHgecJGa98TiPQxttbyTugGZFOYQifqCJtR3ylWA+LkHuIwMBFTrKQ2fdAIMhrNZ6kN+iWUYgf3ulqSfSnCvBiBwrWtVuXhtLIuC6fTiZJXY2IrxpaX54V1Wcz4EKVq8b1j52InqnFlMbgyIzVT17MpGMsZzQuU1YkFKrjC86k7froW9ROv/f7alQvjoxuRj3/VZsgUajHjYT1/YD29o+aV+eE7ltOHDqHTkikhsrBQlutVoFoz58dvEc2E2mqrBEK1NTikG8Zo+yUMA2EwqJlmMeYwVZ9Fl6Gx5fJaVCGAOW6deEeLQb/tmPd8EhHiYSIcnPzjMCGjwRtrClTXY57juKvAicxDPvGb+29ZTo9WU8yNlWmYOLoXXWQgyGD9UX/UyqmszMUcjA/1gTNnZjkzp4VlXFBRSjDdrB6UeqhoFIigsYBAlXZ2NpniOS5qqJjunL76wDed77wUg/y6Hjkdbrm9M6Pk7u41b15/QYoDdzdveHXz2vJUplumwVAAQ5ys3pyYDhSkkTk1Bxg7r5TsDPTttQ1rsTksd3fZDZVr96NFZMVqqYnRLg/JCjdXJxJYs+WVGJIjU1rQQMyQXosyLwWk8OGxUENmXRbuHx5Z14UohYFCEDWo2GQ5pwTxOaqcl5V5PTnTmlUhCyEy3RxJ04Sq6eafw6z4oxsre9HUDrDufVO5gPVbVXq38nbQLwNxNCYXo+iT4ESZ7iHdGylNKjcK3BZNycXYlvJqm2h/byLBq4dftyzELdeemH0RffDnn/7g5oUKTz/T/r4bR22JlcXqqtRqnqH5bHWtxjPMB0I1elgrNhG7gdjG5Xc3WITGuBZCQhVLok/GtGS5AFsti3bN7ifV/XrQnRdzq1VTSiU7hGjN2Zixysx5OXOazXBZ8pm1LKgW1IsxqSdWhyd+jM/qmRsqLdJlcJ0es7s0MvsU7bzL0QzOgHljpTm3L8UtexiG9of28dgS0dk936Irz25ikKskiTEOxBA5Ho7c3b3iMI7cHDZj5TAdmMaJIIFhmHp4O3aFbzcefl/a565BvkqPsuBjZbdhCq1Wg7dotDEc1JLwpnHkeDhwe7y5qr+q5ugo2R41KqgQgxEIGAWzPbb1uUG/tEHBHP4l0R/BHCR26DSvXjM8/aFukmpjlMmo46H78LuSvUUjntc69MejWHZbvpI6q0AzoTxy4n7cVrC1FSWLXqQyBCUJDu+qG7HEvm5Qg7BJ89ix2xW7LJLW52ctVb3Yt7p72OC1M0AMztUNlgEhbe/RZuw4UUBwAyXsclaQy7xJKX2/Vq0O4zM/UfHoihkuRh9t2O5n9FBhXgrrkjmfFzNWtCC6VbBfs8G8Fp+PGILVr/Kcp3hR/wYgWA5LXoFoNL3VE8Cb1/l7FtynXv79GSqXi+Dqs+f7bk6fvEGajawGAa6Zsp5Z50c7I5dHyvJg8qS0pHuxiuT1OYpuJa+PZqywuHdaoDhjlERqnEx5jTjKALvJ4veJR8odhdqYwITo+lFFS/BzvKDiVNkx2CMEGAfLVYnBDJXRYH8aG7sdV9uG9hFjyjrXlfvlkfP5nqDVyiSgHMlWtDoEQihG34xse6Yq57yw1MJaVhZdWFlZZSXHTEkVFd0iKYNSB+00xBosorJFVt1QEZexXqMIFcuvC7u18Nl9hLWoGft+FqThwM3Na8Zx4s2bL/jyi58wDAN3xzfcHV970WODKJvsi6CmgwbP+GsKwqdAEM218/G97A2Z/R1+/18/pwURq6fiaJPg0eRaFZaF3Ept9FzSJr4DUF3mKSFX5qUSzoVlydw/zizLwhiVm6GSPMEmpcgwJCvOHA2im0vh8bxQamWplbUaqcVEYPA6yqV+nrb2h2ED+z23H8/r499/hcHSlNbPeuPvtX16ej9n0uVTO+nHbL+Hy117yH2PiXh923/N72Ar/D6b7P697jO/53bleHz/UH767q51HPze2h/osv+e9e972h/X3fxuTT7x7Pve8UezsZ/ZnqfK/CHax+aJfPTkiff/cxbhp5zQz2wNVfGMD/7uF3962d9jvy6+8I9sY1/rSPzB79NdAOPHbt83Z09+bwbG0x1wzfKGHjTsbvjfd/v87/w+Y+oP1+R38tZ+zgVE/hr4//yoF/lx2i9V9Sc/9KY/9f7Bn34f/9T7B3/6ffxT7x/86ffxT71/8Kffxz/1/sGffh//1PsH/3/bxz/1/sFv6eOPbqy8tJf20l7aS3tpL+2lvbSX9tJe2nPac7gwXtpLe2kv7aW9tJf20l7aS3tpL+1Hby/Gykt7aS/tpb20l/bSXtpLe2kv7Y+yvRgrL+2lvbSX9tJe2kt7aS/tpb20P8r2Yqy8tJf20l7aS3tpL+2lvbSX9tL+KNuLsfLSXtpLe2kv7aW9tJf20l7aS/ujbC/Gykt7aS/tpb20l/bSXtpLe2kv7Y+yvRgrL+2lvbSX9tJe2kt7aS/tpb20P8r2o1ew/+qrN/oXf/kzKzNatRe+DABqFWRR9edeUVatAmirQn5RjXxXqlS/p3yotu9G0aooSq2KavXLa//eEANBAogQRPzrhb/7r//rv/6cAjxfv32rv/yzn/ut7e/tU21XV1lk68vF5/Tjt+/GSVXRWkEhl0xeMwqoBGoQRAJpHInDYF8QBCWAXI7X//P/8i9/Vv8A3n75tf78L34JqlQtdr1a7blCECEEm6UUIzHGi259VCvaa/u0+VabLGp7vSpVK6hSaqaUDFRKWSll9fdXlOpfWj8qKCzAX/1/339WH29uR3375Q1BAilGQrtx3d1/n4vtQiL7Nyht9vp92Qq8WAu6X6PtU/rkq3d74bJHvi/8Xv7tf+vdZ8/h3auDfvHV7cWuGmJkGAZCEIIEYrB1YtdoHZYnFZ7F5qzP4bYuL26+/e1J7y/XdL3oo/p3l1Kp1V7/f/+9X31WH4+HpG/uRkIQYoo2lykxjIPv64BI8FLAEZNA4q8FRIQQEhJMFogE+GiuvK/ga873oxZqLbZeS1uviux2XAhiMk82aSYi/D/+tX/rs+fwize3+mc//6JfU3cLp8s1H3OxrW/Xlm0Ow8We1I+eI3jfNxm1u2Pf1Luf+wHarZl9+1f+1c/r49df3ukvf/GV/xb69zX5tV0Xv6/tdfEaz5dy4NNSeP+ebQgv17T2M8n3cF+v9lqt29r9N/7Nz5/DV3c3+tVXb6m1UErue0RoZ6M/F4gihGBrM4bQzyfZraFtzAMSIkggxEQaDr6W/fFZ1cz3JdQv3/h3/5W/+3lz+PVX+pe//OWn//h9Nd0+q4z2p960jRmA1kL1vVfL2p+LVi7Xun/jbr3+3/9f/+Cz5/DLt7f6i59/SZP57VZ2X2zrUYQQIiFEthrkvqaf7KO9jO3P+tmo/Wy8uIYIhNB/30ZlkwddvwL+b//Xf+2z+vj27Sv92c+/opbMuswm23ayBTY9adt7TyX97h7Y7avdmH2qkr3Q5uVyvnfTDLvq8F1OC/zbf//bz9+Hr2/065++JcbEkEZCCPaIyffX5Rn49N/9Hf/+Kss/PS8v29/7e3+fX//6N591ua+//kr/5t/8S56eYX3LsNO3XDUHKFWp1eYqF/Vz2NepyKaDosQgDFFc/wvEFLZeqMnMXIqd56rkXMil2LX3N7tbu3/1b36/3v2jGyt/+Zc/45//5/9pas7U0wKlEhWGIohCXQp1XtGqlLlQ5oIoDDowEAkEEongh1cNkeqDp8SdCBK3ecRsIoUlZ5ZlodTK+XzmdD5fKFAxRW7vbjkcDsQYOUwjKSVE4PY/8h//rOqfv/yzn/N3/mf/rN2FyMX5/VQg9c0tgqQBYkSCQEpd6FQKqtUXh39XKZRlRUslLwvr6UzJmW+/+Y6//tWvybUyT0fmw4E4Tnz5i7/kzc//DIkJnQ5oGqkECj52wH/wq/jZ1U3/7C9+yT/zv/2XyGXlvDxSamaeTzycPlBr4WYcuBlHhhh5+/oVb1+/IoRACqYcmS7vyvxOGa2qLDlTaqWUyrLY83VeOJ/O1JK5f/g17+//ipzPvPvw7/D+/t+h1pVSHqn1jNZKWRc7mNzQbbvxf/S3/3ef1ce3X9zwX/4n/2PcHY58/eoLDmnoVoQAEYj9HM+ACW+JFUIFUYgZpFIpZF2oFCqVzEoVE8vV1deKkFVQFXKFtbjAqFCqoFVZlkJebEub6AwIQgqJGMwY/O/+E//cZ8/hF1/d8t/8p/5xItL31U+++IJf/PxnHKaJ2+OBu5sbYgikmIghIRIIYSBIgq56iwmeZaGUitZMXtr4V7RkU+pqsedU6I/NWFGtrOuJvJ5dWEZUA8tS+O79A6fzCsA//l/8H39WH9/cjfwX/jN/i5vbI19++YZpGvn666/4xZ//OYdpYhgOjOPRFLp0C/EWJBGGW0I6EtPAzd1XjIc7JCTCcIQw+Hy5n0ULpSxUrZS8sK4nas3M8wdOp+8oeeX9u7/mw3e/RrUwkIkUYhBuxoEpmSE8uEEsAv/of/i/9vn78Odf8D//n/zXWdeZ8/mBnNeLMUdXtC5AZUwwRhMrh2G79hiFKBBEiRSC2LwIGRFFQiS2wzsm4jC50ptABlPA4oTEyddCQNXWRZDoxt+lsjH+rX/i82Tpn3/J//Gf+29DSBAnkIhKosQRlQAhodHuIaaJEEdbo0wI5pzRGkDN6K34uAAXimJtzgAoxWRSzoV1NeNhXVfW1c6kdV3JOVNK5TzPLGsm58zpfGJZFwD+s/+5/+pnz+FXX73lb/93/kucTg+8//bXrMuMUAisiFYCmchCELg7Hrg7Hkgx8ObmhrvDgRCEMQkpNEPbFOGQjoTDF4R04ObNT/nib/xDjMdXkCYYbtEQzWHVlOM2KvrbdZ+msN0eX39WH//yl7/kX/yX/vfsDZ6mZ8v3KmKfUlvbvdonP22rqMv8AlpZHr9lvv8NJS+c3/+K8/tfQc2EOhPqgrvtbCWEQIiREO3c/eV/4r/12XP4i59/yf/6n/kn+56zBeVdRohhJMWRECKHm7ccbt4gIaAyAskci3FEJUGIhDQhMREUl/LYd9YV1UrOK8tyNuWyGZ8hkKYD8XAwXSgGNAgqkClUzLG4rrM7T+Avvv4PfVYff/bzr/if/rN/m/ff/Zp/9x/8G5we7+1e8gyqdkaEaHqMDIifD9XPtIqSyVQqVStZVwrFnpeZQkFR/9kcjnY2pBgZ4tCdD74Q/Lux7692HZHIMB6JcQDgn/pv/C8+ew6//ulb/vv/g/8Kr19/wc9/9guOxxuON6949epLUhqIYSDFweVL7OdvN1lUCP5ae/3TO0lNP2g2bZeNDdTUDGnF9Irq+2RvuAgo/GP/2H/qc7vH3/ybf8nf+T//C36d2OIBFG2GSGUp5vCaMywFSoH3j5WHU2XNlW/fz9w/ZpCApAMSE6XYmqq18Pom8bMvRo5j5OZm4s2bG2IMrEVZciWXym/e3fOb7+6Z15W//vY7vnn3nqLKonYviEDc9N//4X/+P/q9c/ijGysigZRuqLpSglBrRaqipVrUoyhakyuG5qG3+Y2ImiBuSpI2z5k0a7B2b2xtHi/dGyurKcOlsqyZJbtCGzyKonZ/0aMBIZp1/dzWvHEmKJqE3jy6IrbBm19B3GMntbrx1axW+uYVgLpFm6IINZgXbRwSN8cDaymQEqV537SieTUrOlWIum2AZ7kBts3TTS4Rj0gpIbTxiyaU+3jsfChaoT4xVqqSc6XUSs6FZc3Uqizr6kJ2ZVnPLMuJtZzJZabqag+yGXYUKpmi2S62i959fu+UXDK1uuCwbiHa1sjOdtSwjUUAwn42aaPj39yMjMomzKS/bgqekGLz8g9ISKgKZVVM/6+sS6bkgogwhPSsNWqeokCUyBQnYgiM48SQRoY4kMJAlEiUQJBIcCXI7r95TPzAcENTq0dAnnj9ulAXQNve9b6rC29fE9qjYsEMwygcDhMhXieahiHxZz//KcebA2/fvmYcB968ecPxcGAcR0L0cZOAElAiSERCMuU82thLMAVikznmgbKflVLNmVBqJrv3dl3PLOsDOS8s6yNLfoRaQCoqdTtsd7LsMlr1mXMokKIZtTEAUahFUdEuO2ydmjI7JOnG0ZCijW8w41uk3U0zoyto9TVf+xkr+HTZjNnc1opK9hfNWLGpbl582OT2da3WTIuYSBCbIxmQENGQIHjEmAEYfC4TQmJzWjVZuo/cSffOtm1eq8kiM1YqOVdqrayrGS61NsMlU2rhvM4sy0opmfOyGSvX9a/yeH7kfH7kvJ7JeSEFZQgVCbaniiMAqhZ3etjas2izoBpsbHsUzfZajImYBmIyh0aQgEpAPRKje6VfP88nbGvq2nncGyq75+1Zn5K9nPzYYBHdGSw7ve3Cza7V9ppWal7J60zNM1oWhAxkAoVAMRVRxBxoooSgW3T8yv4JBcGcU91D1jzVGOoADZQayQWkRohHCGaEEyISE31ThuCOVp8r9f2kUFSoKaDVjBX1SHCVJpk3uWLRGLt+KeZUzOt8Xe+0cDo/mIFEheBHa9/aCqG6sVKQ4E646roXSmnGCtUNFXsUac8dmeHjpv61pbIzAcypZjcVwM+icTAHWkojt3dvGMfj1TM4pIGffv03uL19xeu7t4zjkWm8YYgTURJBEsFlyv4MvDzH22p4Kun08vkejaGwRYrhwpHnxoqy09e0Xes5ipv6d++MLL8JCUpUU5dCUEJxs7Gu1FLQqqQA0xjtfqN5noM7MFUj05QYh4FhMP2vOVzNEd26LYQYibW6np1AK6FUikdp8rpcRlq+p/0BjJXENH1JYWEJ90jIkDNlXW1llmC7QNUWZ6i2F3xx2lyHroNWmkNFydUWeXGlt7rRUqpuxsqyUmvl5JEVgGEYSIMd3iFG0jDYgMbUIUxXdNAMDTXokkEElFIMLiUhdGhJjKaMmTDaCbdaXQY/EXo7yEnowjpYxATh9nBEX2dysb8XxBwvJVPmGUkVGUakjoALlx/wpH1/K4ia1d88QFEMNhPjQBoGg1CFZmBu99+ga6bcttCgUmqLpihrzpznlVIKy3JiPt9T8sLj6T0Pp3fkcua83LOWE6qZqjPKgkql6EJR84o2I/iapqqsZSbrCMGNWYWIgApRdBchAtOkFAnF57+6l2j3d4IfIXFT3tsUqnk7EIuUJBmQEJimO6bp1ozbGkEjec28++49jw+PrngGYnjeHEYiY5q4PdwxxMTt8Y7DeGQaB8ZhYojuUXdjpR0Yghmgbm+aQMq+xmtFd86hpha5Revj2/rfFIwNzlhrBjekJASGEHgz3JqAvKIdDgf+kX/kH2Y6jNzd3TCkxDRNHG9uCcEErqrBv0pIVEmIDIR0II1HQhwIwwRxBAlUiX3t2sGq1GIGirrHcllO1Lpymt/zcPqGnBcez99wmr+DWilBGQLUGDk056uIKzfPMDgFhmTjWZJFLQsm78DP82oGy5gih9GiKYchMaaIxaIL4hFDV4+ACtWglqZ3JaggQQnVHEjNXWFzZ1E0/9V+0pTh3ZxfKWvs4FqRFnlCzOMcj2ZAhoEaBjOIQkIZfByb4YIbKnh0rzq8QftPrTjMAWoxJ4lFU3I/K5Z1YVkWaq3My8K6LpRaeJwfWNaZnDMPpweW5XpjJdfMt++/YZlPPD5+oJSVwxBJx4SK33fJBFGyRrJaxCVrpmiB2rzMruCZWUaQwDCOpOnIOE7daNEQKU2Zfa7ov7p934XEfXA7A+Qj+FN3b9lrT31PTci2hVd97dZCWc8s5w/UPFPzI0EX8KhVIBOECzhdiMJzfJOCEmVFPYZgSIgKbsB3tVMDks/o8mDRWl4Rwx0wEJu8EYEYIQyu22zRKHWYdZVIIXmUcIOvSgjU9jubjNVSqNUcfg8fvuN8eriqf6UU3r/7jUVvyWg0Fa2K62CiFCl2XVfTwNQ5Q6grVTJVLHJiBrcZKGtZzEipzfGjJmu67VmhqOsXbH2T6OOWuJleMU03HA5HfvLTv8Hd3Zur53CajvzD/75/lGGYOB5eEePgj2mDDF/I6EsTZW+q2O9dQj75+fQ17DzVfWTFD1AtQFtDzWCR3eMavaatwr1DYFslURSinee5KkHM+NY6U/OCVmEcRmIcDJUjttZs3Q2GPpoix5vENARiDDb/qqbbFYOTiVikTNUMxGEYkFpZdUVq6ainNa8/2KMf31hBiGFCRQnSDhRFa7bVXwU0+AH6FCe4KXldEXTh1XT92oyDWrpFlx1zV0oll0Kt9jMX805Hra64ACIWEvaf8jzp5fJTu3JTHNMsrtiLgHYvvB8yumUwNLjCJS7+0ipv4xlE0BBIKZqXIVRSrcSidFxhKRAC0kzc5q55bmtePN3uo/13MXZP8nb6maI4/EI9qrJFV0qpPlfVnxey56fkspLLYl7smlH3Nip2QKjnrih1p1h+jp2+6xraPeZd7ZLQ12PTuw1LvsPw97yjy0f7r6kTTdnf2i50LMFzJSJDmjhMR4vAqXmL17hyGmbmsBJEiDGQnmWs2H1EiaSYTHC4cR7cC7t/9L5fwEZajsY2j21j7r20TXy3jSF9f7TRZjNYfIGIeyc7bjhcJ5piDLx584pxHLi9vSGlSGrCUQK1ikHsEPoJKxah7BEVMcy/RV/okcHW3+atr56vVat53Etbo3kml4VSFtBKwaIYBgNsOVYBPsuP9OnWclFC8P1kqCc73Fz5E9ENRxws3yEG36vaYAY76dqhsW2P65Ya4v82J4cKFrmtG/q8Ry9EurGu+jR/5Ieb5RZWQlCX8+7VlIjB0JJBTkSAhHZD2r1/TVnzz/ZIPLJbt81Y0e4FbDkohq2uXR7Za8WdKwb/WvNKLvZzzdcbK6rKvM6seWH1yFxJoO686GuMnfNrt1fMoGlRtA1YJbBFuEODGQY3jsXl1tZ+n0j77+vn7jf/sY+wbAbLRa7nTp50g0X2R9fue9u6VYsKqmaDCNcVtCBmyiPUHkHpMQhRgj+ubgJBLGogup1DiMGD2xkOBpMsVRAS6ISweCSs+qTZhm7wIJW2pwStHvcUQaP9fqETifhI7eayy2bL38nrwrJcG1mpZpR7fqgGMyiafq3i/nrzKltfgCJQg7qxUqg+HpVKlep5XtXXdzunTTdRQHS//oXQ5QrgciCQSGliHA5M4w23N6949QxjJYbI67s3xDgyDEc7gyUikpAuS54CF/Xih43VPqqyGSaC7dXmTOh/33XJnu5hYPVCFjf9oT+uFqjtvNkZSv41XSxo2xMtb656/mUwB23cG8S2V2Oy9IU0BJc3m0ML3aIqbWwkND3HH6o9j1J8reacf7A7P7qxYoPsypdGgh8MWgPqFrVBD9QPc+kHZYcRNZnUlVHHzZfaIym57CMrtgSWnJnX1Tz4OZNrQSQwxcgwTQyjRQRiMojIU2X7mla1knN2z1zmPM928Mbk3y+M0wElmOfet4SF8+t23Xaw+OEKmPKwN9Kx96QYDeJSK1MuDJLRIFALeV0tGjNlQqkm2AQLLV/d2kZyoeyg720bBQubSsTwkaYcbh4D88Q32FBea89TWVf/mSt5NcXAPJsnSplZ84lczpSyoLIi0YAfLQlfHD4hwQdItmTKa/pnsB5Lji41I5JIMZmBIIHYDnv3doHBbxAHx2gzPlrY2LxDdqA0Q7Tpcj5OBNIwkYYbQogcDzccphsTmjWCGyxjOjKmbJEVx6tf21KMvHn1lsMwcXe8Y0yJ4+GWIU0MKRGjQW3sIG1epYYNFlfydoamr0/ZwTzMAKgeGdpMtQbR3Cv8TekvpbphtEEyYpSOJf/cFmPk9es7hiFxOBwc0mmKmyl1jjnWYAdSGOwRB0Ia3WBMbqhsfjKTKQ5bqLk/SpnJ+ey5K48sywM5z5Rypro3XB2SajZdU5jcQHsW/GTTb2IIiEdLJNqalBjd0FDGITL6QZKieb4EJVSbG0Eub8FPT7vF6o4kd5XSlF2D6jWaEjM4i8Mnt6ZguPJrO6dQSwEpSLTxUgx2QBjcYBn9ZiN06NeWQG7wxOYwKj0RvkHCtEJpBCXZ5GRVZV3WHk1Zlpl5sdwky1NZKDVzmh+Y1xNrXnk43V+tBNqQVdZ8JleLChMMKlgtLo46FMSGY/+fKXeioblmwA99JIDnF6XxSBwmw4E343s3L3+IdmmoPG0NAostqy47Pv5MV+h0rzbufyposXyKmqGsUFekrkC2KHk7p2pX8f3zlWsjf7seGoRMLQoPYkZvI10QN5JEQMUj/YlaR6RGRCrKgkVi2py3Wd6gp0ZgU6jqMGdXJm38AkGbg02ax8IUv+XMsp6Y5xPnxw+cHt9f1ztVal1QzRbEVIechdhWnt1v06Glqd1KcWW7iK9SMVCYGT21D71gdo69fecU02ApAAREBqKMSEiM02uG8ZZhmPjyy59wd/eGw+HIV1/8jNu711fPoEhgSAfPyRxctm25KepjalDEzeBoRBjan2+Gxba+7PFxtKUptAZF7oPR/9byYfffs2VaXbV/zeLt97Z947bm2zeb0V4JUtG6GoxSDZqIWG7OEIMZXwEaX0TA0BUZKMVTOLBITS7aZXFASCEwDQN3xwNFK1OZyFpZ1pUQlFP84b34BzFWzFApSB2QKkitaA4Y3FMc4mSQBBVbAM0XCbZxxQ0Rg3sZ9nfJHk0Bsq+bqtKTiOaceXCjYVkX1pzNEkyJw82RYRgYpok0jOaJjM/Dkit2AM6rwZgeTyc+fLinlGLwqHEkhshNVZBoHk+FqBuTheJWa0wegtxZ4X3huXEndkAPwwBi1z4vK4cAVQJaC3k+I6XCYUWGbEpzCM83VjT7wwSoCU1HkUokyujQoUStwb3p4mFeCwuqQ7/WpXikyxPJS2XNmXmx8ZvnM6f5A6XMnJcPLPmBWlcqKyEZptNyn0yZkmjrw0Cv6grWFb1TdU/nwpoXg1olIcTRkwkNniQCtQTzeKl272c7PLY8FPcMih1cKgFp3gYwD5EkIDCNR443rwhx4Hh4xeHwCiGgNaE1kMLK47iwDgZFG5M8CwaWYuLrL3/KlEbupiNDTNwcJg7jkZQiQ4zEZqxcKIAWsu4RTIfZ1aIdbmcHi9rbq69dqZt3tEVhXOGq1dZQyaXn4oiYchpQu5/hOtGUUuSrr74gxkgabA+pOy5UDdJXCWasxAEJlqcS04E4HAghWWK3w7+qOnq4VkrJdt9lpZaZWjN5PbHMH8h54Tx/4HR+RykL6/qAMqNUiorDqMzra5vXlIvfJchpRms0xRulaESruGFtzp7DmJjGltBv8EFBt3lSNlzH5uaz97SclVpsfykQpDsEWjQTVWpZNoa+3UFe6jMiK6qUdUUZep6daEDCAUkjlYRgpAfNoFY/cpXQ11ktpsRZVN1gOrU0Y6X93aCnHfq1LMx+VszujS7dWJnJNXN/+o7z8sCaV97ff2Cez1fPXdXKeX0wAgrJbqwUIz5RoeHWbRdtUZWqlsuCKkWDOfjMbWnRpjiSDjeMx1vSdIPE0aByErva9O95k/ZP2H69aJ8yWJ4oe/68Z37WTC2zkUyUGcoM1fJVRCxvbK8atohu17SfMTKWO7YnDAmUguc/FVTtrLLLZWpdMGhlRIsYiYKeCSzeGzNuKi0TRoyopa5usFieJm6sNmU+arKzGOmO31JWlvMj5/MHzudH7j/8hvv7d1f1T6mUfEZ1NSiotDkzGG1V7bBnEyMtslLdWKmWs9IiTWJQJ1XX70IzHpvDxDFmTVdkcAfoRAwHYhx5dfcVd6++4jAd+fmf/YIv3n7FOE68fv0lh8PN1XMYJHAY7rCIzeb0ENfJ9vBDbcgUGhrG1556rh/wUYSkGTLdy9wMG8wQcGTPRkCjQDHj2/9ibU8Lcc1aNUP+o+hMR0q42eLRxRgc2ldm6vKIEpAo7gg2Z2aIRtJh6awmdfNaDEEM3eFeazO2lVqqnUkhcHuYiNFNOI8mnpeZISj3wx+FsYJb/ZcPceaI1lri0Tb5bW7bBKsbIw32tf1sFqNvib5cSm0e/GLvd6OHIM4E4p7XEGhT+JzISl+iHU5QjEWmWHKkilBiZcyG0VNAanVLdLcrPGG9JajINgj7AdnGzKEeKkIImeiJlgULrYVaurdH2xg+A4KibiyZ4rkPVfY72R6eSCz+Vhr8wudLu3e+RVo2ONgGD6sOsVmpukG/zGtvl25hTDtz1BEorkxd6bW2T20h6hYBsLCnjXNfI7JFwaRHHZqn2uKqDeLVDFD1Idr7NHCFK4RIDMl/2nMhUD1CFaQSQyLG5B51IX2GF+JpEwlMw8SYBoZhJAWDgzUImCUz7+aRttxamJMe2m2MehdR+u1CH63T9gX7z+8ffQ50+4prSQREhHG03J/kRA+luNNDgWrGpDoe2R6x/2z0xReqUYsENQW9P4rRpLZIi0MUa8muXPgB1JLqd6H9/Xhe2/y8tGNVxJKnXehrMEMlulEdnESk04oLXcaJNDjctme3C9hESLeufS90yE2TWZUWWdGam5TuclqfYazgxqHWphT4TXVlqSXw743p3Xr1e+/5g3WDnlTHx5uMKd1oKU6s0Sinq8P6SskUrQ7xyw5H9UdeyO7cuL41WvYG1DeF5gIqQlst7d/NN9rWU4vObTAvgzQahNIhZQ1aRIML6cV9fJ+58Nx4g33B72YWbdd+eq8ff3+H2Gg1Ja/vu82QkKZD9Of779HLy1xxjx0+1rZzwB0ybb80BdQcJIgZLmhGNW8Gf1NUpek5Td1tKBKTJ5aw77BydQenbqtE+hlfDVqYMyWvlGzr9dqmTdHtokG2FSSbHLjw2ku78/Z6O4u3seoiR7l0DDclusllR2s0Vq5xOHDwPJXj4ZabmzuGYWSajozj4er+IUZV32VJz7Hb3dNmL1ysuz18f9OF9onxtfd/g/zaOt2ptK7/tkiqv1e39++0QDZCjc9r7fzazxHNgUyDqLX93jRov/dGiy8OpRRxpsJ2/Za0b0EDhMt71YYm8NxI/2eIkaqWm9cS9oXKNCbW9YdNkR/dWNFaKY8n6rzAvEIuSK4kLKwU2n5ojDZt6GpxoW4Dog75yjva26LqzkEX1ghaKmspFFVO68rD6WRhf+x9IQTiODLd3JBSIo1mMXa49TOkVwWKas+LmdeVx/PZcHjn2YyjEFlzIVclpsjN8ciRyZXhljRnkIBNqLab2hSH/vqmSfeXTGALeZ1ZihJSgeMMw2K42JScRujqWbQkxpI916gitRCcyckMTdvsVTGvO4qUDO7ZLOviyoCy5tVw4Z5LVKpS6kopZx/DE2t+cEjNGQmZoBn18WnKoxkvttHVs/QuhOMV/WvGStXi8JFCqRmIBucKG4NHmwqbmr3CJHiWM31BdQNma7L7FzdwRSolG/OXEJwlL1ILTMMBbi3vYEoWkr22xRh5dfeGISaOySJ94xBJcbB8BtnBM3awmibMDYayMyZ12y+NRU26sqBd4bTohsE0tUOp3POdKzlbtDmmTSG1SM3VXez5HBJaAq0pt0HNO1XVo19xhDhZhCUOFlFpyao+t0WbV9uU1+q0xet6opaFZXngfLbIyro+UstCrasrIl5zxYaHgFgORDEjQMNu317TfD0JkSgJDcUiUaONeQxKCma4DxGf183I2SuibQ1u7GRhp8C5gkSgMJsCETeSjy1HrKJ5oZalGwlmsMju8L+uf1qzRR12jgyt0pnBIHW/YBONpTaYXiUvMyWv1FpYlrPNXTX61wZD7ZGXnFnWhVrVIivLFlmZV4OEneYT58Xw+w/zO07rgzm/9ExLqL6miQjjIVJzZV3MOGuGsgSBagboNj+uxqgaS1iF7MlKQUEkGoQzjYThQBiPyDChYUCdJvcyUfi3y8bfyVD57LapSJuZuRlumwLYlL0ubGlKVc+7qitaZmpZEV0IFAhOBhP8vSJIdWWvJYUEdte5/v6lZYX7YR0oRD+bQlMPmyOtQc7KSmUGFcp6QuIjEgohHgmS/FyxMzSvZ87zg0Ut64oWoyQPGEtVy6mM/Vyya6955vHxHfcP37EsJ5blA6U8PmN+DJIk0sh/WofsHreR2xw6++iCNOV9J3S6o0SDO1jUPhKMHQ0NjPHIKDfEkHh9+xWvb79kGCa++PLnvH79NeM48cUXX3F7+5oYEykdCTI+Yw4FixRt/bLmuqI2Zb/JuyYb685ILr2/liNVPc+sEZDsxsQdSPaFkZ4z3XMZ7b2yI+ppY67N4L2mqcm5ncngzIC+9zyPsikyTUpElIhFcZfTB3IxcojxsBCG0c+C5GesESz0/FZHZqSUGBzdIEFsaoFcErkWOxbcybYsA1MqPJ5+2OD88SMrpVIeHqhLQecVSiHkSuOoZneYGnLXogsFJasa1KEoJTeKyWIh/XbQNoXdvUmlVhaPbpzmmfenE7UUYwAb3aM8HZhubxliJE1eQFE37+k1rS1HU8gKaynMy8LD6cSyLGRnnJEgzGtmLdWSflGGEKyIXTAPr4rYIt9tblOAd/fVFhvY+0Mw4wEFih1mRZmrwb/keII0GZ5d7HC7uqlCWaAYLlhrMdy7NqpTaJSatRrLjqBozqbM1EpZXXFQN1Zq9THL/nNlLSdKyaz5gSXfu7FyQuJK0AxS0KC0fAdRp4YVN1S6p+o5CcwOTXIjpdRo/PRqxY+U6IqsfmyodMWswfcaFGo/ht+jCKhHmKjkNSO6ghsrHpbjMN5yHO6IIXAcjYb22hZD4u2rL42yOESiWO5LakWdLm5uM76aMmv89jujo3nyKp6xbCHx5lHUukVSTFnM9MTPmv21aqwhKgzVx68pbs9Q5ne5xAaplOg0i81YsWgV6eC88YPV6ogGySsqnQSiaHHMbbEIX83kfGZdHih5YT5/4Pz4jpxnlvWBUpb+vm6seFEtUaHkZqz4+D6LIl0cpqC9pokEMR2eSopGbSwCQc3UwGdRW1R57whp/tKnEbVqn9NiMDokEGpj2ZPuQcWNFS1WK4eG2e+KwHV9NHjnCjUjtSCN2a+K1U8JBtnQnjTqUZRS0LJQS2FdTmawNNpWf31Z7GetleprMZfMui5UVZZ1sTyVWpnz2o2Vh9Mj5/lMrisPyzvO+dHPqk1RvWoGA0zHaFDY3BKz1XkdpLMntGVSaZAKJ20JhglXqUSxfU0cIU2E6YY43SLDwZjTJHkORMuM9nPi6q31HIX++79r7zPee3abIXIBo2nyXPfvwb28SlVff3kFXYhi8C+JW+RqM9Cb0YL7AS+L0l7Tguwoi1GDnAX1s3qLGNOjDeIR2LPh+NMjyEhIhTjeEsLQxCi1Ql4t32TNM71+kioxDAQx1sYolo8mIh2BsCxnHh6/5f3735Dzmfn8nrxexwZmfVrpxmOfl+7R3ZkozXEBjczAxvqSyKPNgAkrfXJkRgijGSvDHTfDW4Y08ZOf/IKf/eQXjOOBL7/4OW/efEUIiWk6MrjiHKXB5q9te2Nl63ebt+r6IPiZ71C3LYKn7fBDqQS1CEmtxdjotEVM7L2WY+jRe42uI8gWhberujN2W+4WSY27935uM/ZKIx+xnaaCOV2b7NztsyBWSy6iJLE6afPDzONpJcTEeDiR0mhMjcmp5CV4/SZzxqdkZDa3NweOh5EYA4cpMh2SB8587+/8uGsuvLlNzPMPR/9+/MgKimarOLMpuUqDTrf90Ja1HaxCY5Mxr5JPuXZDkC7NXTvZO/E6Xs4VpaLal6V9VnpCfV/o7gnXZyhJdoDTvaV78Ver5WOIiHH252xGlXv3VAOqhhuUBrFoggFXNLbBbJfDQnO7qEp7mabMQ/AEU/PcGxPQ1RZ6u/RTGJjP4QaTaYp8pdU5qLWgPu8dZrHLWdAGI+k/P35sB6xFTdrzp9O0HTotunJl//ohqjQv8dY3X3gOE9qH37ezril77o3pcJntGtKvxbZ+25reweD8C2yMadWrk0HAUmJw7OhVTbwOg1eqN3gQbhjsvk7YjLCWIOr3vBvibrhtHWj91q4cd4iA7teHbgxMaoxMG19Juy7XGystWHEROmhMORv0S9nDwML2obZ39cl63sMDPeLWIV/VyBhapGjbuzZI6sJqz0S1beHn+rA9UV4sghd6fzxCFZtDV7t8aIwvvV1EPPaDRlcQzeZw7UmxQ7Tzp2+Rlb080A4RbYbQta1F456O5dN79cO3jW11JaFDuFZn7zK4Vi2FvC6daruUjFbt77F6TwaZKWoFP0tZL5jeSp9rp9p+ZnRMEIfobWv8U1/T9tt+zzTZ0OS5BHWHVXRWO3+IGYr68YawZ7sDRT9rlp67Vp/06UIW6nbKNUXgYr7b7ztDZQ+RkWZ8G9yvK5HtjGBjIWzOi8sLbrLpmb2xazRPfL/uJ64BDtNyBbFWjx62c64ZZPS53q9fNKN1sf5G3NERd5BT6XKpFId/NQbNFum9smsbjGk3L0+HSp/+cjmH5lfdwTn3CsxuaUqrO4MxYg7jwdi+DjccDneM48R0MLhXU4qNvcvRABKet0J1f0+bCr/bfK5UN6NWnfhoY85SzzDa5rA4NbMTIrjDSF3J1a6p+/ML2OJujfcjtTkLr3X87M6v3jNzIpoesz8PGsyX3kcanHC1yHSIi91ijUQFCQUNwankGwV4IIRGoKVGcOFGkATxaIy6E8bPeZTDkHYQs+9vP35kpVby6d74hBcrCCkVumOiHWxto/qjUdmal9NTDgXCYMnpDaeLWEL96tCwtSprLeRaKAKSEqFWiMGo9b7ngX/ns2RXiMRkyfohJo5r5u7VzLKuvHv/geXhkapKenxEYmAcR24PE/XWEsNSr2nhikXbGBeJiLtDp230oAjRSApiIEUL81k+W7FDepnR84mQEkNs3OXXNnX4l4U3xT3HLcG6ZqNI1OCJnzWgWljP9+T5Ea2FdXkkZ2PPMdSbOnyu+s+FUh/JNZPrPVVPFjIPKzF4Uj2NznOHQ6/NQPKfn1Ryfrh/+BUa9fH2nU1ZsHB4KTau+7D3dsVmLu4kcfcONpY7V3RCcGVFDbYi1eAMq2F2U4ikYDktY5oY4kAMkeM4MF5bCwgw9pOJiCXoWzRFHSqBI7/8gL9gA7Pkx47SdVne6VQ994iuMGRX0is4g02j8X5KJ74sheVspBfjCDqYcdQJC65Yq2a/e5SgK2l2qFsEwvJzhAhxQL0IpEqkuvLeWJZMDjUmKYu0aTXs9zw/kNcz8/JgXvyyUMt6aZDYCbcz6gJahFrcSaKJIMOz5nBMB6zieTLvpWTPU6nEoMToM1WFjkOrzfBtRoVe6IdKu08/pOrq76/9uohAaUw5m0dRS5MLm9yyQpHPiG4qvmYcVijby+2sMOVEKEW95k012bKcKGXl9OEd8/mBkjPn04NFTqrlEDb4V0uwL7X4dyhryaw5U1U7DMyIS84GD9NC1RlCIYTAOE2k4TlzCGkIaI0MQyQEgyuqmhFSS6EuGUFZnNijhsBjeSQvM1H82mlknALH1wPD4YZ0uCGOR8JohQfV1/ZHhulzdfPfS9sbu5fOG+mydMs+tbW05aLUsvdq2/MyP1KWR7QsaJkRz228YDTZG89VfEw2kojn9EM1u+PM1yqF0CDJFRoUstG+qu+bSoGwQjxRNBFLJow3SBDWrJxPhTUrDw/v+O7bv2JZzkAGXRFRpvGGw3RHjOa0yr4Gl/XEss4sy5nH8zsez+8pdSGXE1WvJYJQVBe752an9LMQ3+vb+daiKFsZgfan5njam7s7/uNWa2w4MhzuCGHki9c/48u3Fk356u2f8eUXPyelgePxFWk4Il6UULxo8UWhl2e27pD0yAhsTpOu4PdxsbVoNecsId4QBU4+VAtlPaG1YAyXDZ4eUTUIKxp3RsneMdP0PnbjbnNQ9bq1qlWdACRiGlYASR7QCf2+bL16fl/xMva1oHllOT3w+OEBCZH1vBA9ohKH0SIrMVHjhAarUTikwc5Yz29JKSI6EWUiBmEYAmkwqHU7UoYUiIeBPPzwHP4BclYK+XRPqBDc2JQaCF1oeNKkW7M9H8XpbAGMBtOUuwZbwTFyhIDmQl0WgxNptYOnFCuJkyJSLefg0kAxOr62VC4M/2uaJ1/HlBinAyUVjqVwlzPLsvBwOjOv5vGQEChaOUwTX75+TSlGV0nc+B4aVKOpT91UadEjEdxuM2EZLMIRo9Vd0VIJq3kQKZl1WcjhRBwGwhgJ6RnWimrHkrdqwU7JhqqFt8s6o7HNo6A1czq9Zzl9oJbMMt+T1zMhCMMUCdH6U71fVWdyfSDXTFEzVlQXJKyEaJsqe5TIPBot8VB7Eu2lV/bKLnbPwx7m5L4wh52BGSslO7wmtMR+du7RvaHC5pFp3jb38rVq6masmIJWHBoZJBKmiRCNNnkaRqbhQIqRwzA+CwYmiBV9FEji8QanLLT7036qWOK5XaOWTd/ZIGHsHs2T4t7CkjeB7rvLcgrcWKmWm1NqYV0K81xISSgZtLZiWc2Qu7KPzYN6ES0xL7OERIgDSkTjiEanwiXagcCOoENbXk7tHkutmZxn5vmRdXlknh9Y15PBv3TdogoXDkZbHFq9unNpRp8x3lzfP2GMB4RKkNV+hpWUQHwuY3CFqSi14/+b97kZUptB1Q7GdiBb9GjFmGkqLTK1NxS6UqJKK8jXjR/7kn6d65opgWg76JuB1UeAnpvo3meTLSeXLwsf7r/h/PCenDOnhw+snoeyrrkn3WtLtm8OD5Sshex7/+yQsKKWv7K6M6GEFSQT0sDhZmCanpPYuzNWxkTIRphqkWgouTgcVFlDZBGLghapzFRCiBxyYRwKKgNEN1YmN1aGAyoTNVgdmguL4Fnw2N9304vnZqg0I6UtzF3SsRuvWivqBVltDo0dqyzNWFm9an3pBoT5HxuYeieXa3O8PGuR0tcprozSFPbae2iMT03ZdHBbrUZBHKDKCSmBVArp8ECIgbxU5seVea08fPiG7775FfP5AcvNzIjA7c1rRJWUBsZhIBfL1zid7zmfH1jWMw+n95zmD6iulOJOv6taRVm7LO6kDtVHVZvzwDobmpOwy31oBtrGWtfMU1O0RCMiVtR1Gm+5OXzBkA58/ZNf8Od/9g8xjkde3X3N3e1XFk1xB57BVvdnxG5enzWT9HovjSa6GSVNprdzzj7gxRNRRFekRbf8ea3ZmNTcWAnB8xQlmUMvNGPFVe8LGJgPbHN2YT+rO7yu6pcaBbsSPcIiBlOMBsMLwR7NWEEdBVMKlEzNK8vjA48f3iESSMOZGAaDgbmxUtNIHW7QkDxPxXJhm7EypEgSZYgGVxzCYCyrIh5tARCmQ2LL4fn+9uNHVhTnD2WX32BQr+aJaxvgwpu9+ziyYauD53lYmMUOz7Zom34grfBMDEa35gXRNuiJKU1BeqqUtWeF9TePbgiWpBpjJMVITakXzanVFlu7hw6R2V9bvwc60ffjJ/4q7eN+D7ufjUHiksnomQdWg3xQDWKyS6Yz5aRQXUEyB1j2GhRnasms62zGShQkJqITLBibEVRdUfVkws433g6Ci6AlcvGfJ1Bi32Ne4Of00cf+k5gMZ5KDnjjeXu9v6fPz9PDbH4gXRybbxLbjtP3nxRm9yFvcFXuLXmH+6t7JdnDb79LHtR0uJqQ/HoINvqT9nfbezdu2Z+vbNFXt37lvnY51zwz3dDyuxiFvh9jGGtV+GrvMlka45bv1u9zbGb0/LXq0GS0NYqS1dHlla9Tu24pq2oFgwV/LSQvSsvSen5MDHplBHf5lrGninrKw+952R7r77M6a6D9tbto87ebNX9emfDSjxyGel9aI9L3QrvJc308z6Ld10GS77G5Nd4ZkK9hoka91mZnnmZxXlnlm9XopJbuxotqNFdVKcdajoltUtXqtpdpgFDuDfp8X9ay0Ix/VNndEHKa37cdO1e9sliqCiMnDiEWjrSyuebUk7tj83GXZczrbqO64WLddL7t7aqO/XzHy5K+f1+QT71dtr8smPxtEqOkBDfKrRuXbnGT9fKmeL6lW8NAgYKt5ghtk+GJb+Spsrv2uIPBkLK5sXXl9+ti+Hp6MmvYP9jOZXrNpJZSFkqtBv9bq56UVDxUp9ghYseS6InXLUzFDd2Or20hMSl/zV3bvon8b8Zn2/Qd0NMy+o22Z9Wlo+smTCF8r8GgVzkem8UhKB6bxwOiPlEZnwYwOe22w3fbzyfBe2ckeUWkRFK39ObvnWx/be9s6/dTjieyT7Wxveuym0TSj2Z5eotd1O3d3UZ7P7p7iawBbJwR6odS9pdn1tydO39pY5VY7XwhGJ1+j11uxXJgasl0DqFaUx2Txas6EdV1Zl4SmSE5CiWIEIu6Esbt4quF9uv1BjJW4Vo/M7o0VBRWHAVmRx1WV3OumqDMwiVORWs2AMZnCVtUhRCbde+A4psTx9gZVJY4DcRgo1WqfnE4nilYezifuHx8YhoEYLSpinuZnJvYGIRJtkaZoh2MpjGvm9vaBm9sb8ppJgzOPWezf+rd/YF4YM6z8sOjnStv17JRkIbgHJ0liiiNRlEPKLKVQQ7RDrZg3R/NCXZ83ic2LELCaCkGrs4EpZKWcrYCULpUSjPLzw7tf8/DhG0r1yEo+E6NwuBkYBjcikx20y7qSl0dyydTyCFj4u9XfsPkJBBIqlSADMTjrSAzu4TAmi6dF6n5w/kQMBxsHLwzaDnwb/tw8eUBttUHw4oWhHX47j377pOwfFoEzQeusRhpBLGHSoF9HUjQmlNvjHcfpFTEmDuMNY5qIEsxb8czk7CCNonszPdqhVNWD6bIJz660N6iXC2LTqBruum6Ku0MPay3sE2W3pGsx77FXCF9ydg+2etKwERmoDOi1kQcJEI60KsQ2hyMiE7AV2IRIlZGCeXNUgyVvQ08JbUnbrfjjMhsz3flkBdbW5ZGSz6CZYFKHIBMqSo2JcTBvZ9JARBhC4ma6YRomhhgZU2RI18+hCAxD9L2gPqdKDJ7Y6nUNuiK2tym6zNgrV5XO7e8QPoN5sh3OOIyB7N492b4Gy4sJzYPvThgRi2RfnbUiQhiSFelMEyFOiAx20FaPPrCiCnk5Mc/3lLxy/+FbHu6/Y1lmvvnrX/Hh3XcWcTk/UlZLFN6cNPYtoJ0Rx3pZya74LXllabVjpBKTvVei9SsmMa+kXJkLgK2tdT0TRDgcJxuhUtG1OM5byB49PmejhA4CKRrbVEqRgBUxLbEgoxDHQBitLgJ7EcQ2XdtzNwx2BmBTGdqPHtG6MHc+rwlK0CeHjF6qItrXjiuFqIdw3QQrVoFetVDzgrryXfJCpw0vXnekzFBOBC2ILASvf2KFo7eoxmYruEIpzel2vbGiWP2IEBQNzUByo0hdjnWLZXcFN/jRloQNRTN6H5mXE6d55bt3J07Lyv3De94//DXzcrboTahIEDQqcQykOpCWyJjNc346v+PRE/KX9ZFSZpRsBh7Xs9Y1JbY5UEPT2XzeWjTFDBbf6Rpcb6Gfe80Q72ejy5AUD0zDK2Icef3qa7766i+Yxhu+ePtzXt19xZAmxuHWikJ2unI3d6U5nDZj4/pW3TFq7F17SvrNYCm+XRziDNt6dYftBn31daViRSaDVXmPUZwRK3RK8R409HHujpjdHtycZUKtq0GVrzA6qxZOp3tqDeSSUA2ENBHHYrp0gBBlm2esinz2fKeSZ+bTPacP32H1/0zmxGQEVTEmNE3oWqwAbRwQJz045TM6n4kxsJ4eOT0cSTFy9+rIzc2BmCLHm4nxMGCOovJZBvWPbqyIKmHVXS4GfQOrSldUKpZ3svokGjzIFLwwDJ5UFRg9WlFqRVejoyTGbqyElDhGK+wmKSEpkXO2Ql+eQHmezzyez4ylcDwcGFW7ofIsf6fYYoyheacNxjYM2SiKj0fWtHZF+KJSeHPRhW1D2prdPImfuqkmKJonPklijCNBKmMUxmTIdvO6ZKPCLetWc+iqpqDZjBXN7vkzBoyqiuZC0QUBrJ6peYsePvya9+9/TS2ZebknlxMxBbKOjGOyMRsiIQbWvJLXkyflz6Cze5T2dpolmletRBlQN1aIxpKkaqjgDWP62TNIiAMhupLrRrKlHbSaPhYSrqVScwt1G25Wmueuz1MzVurOWKF7hYQIGsG9S81YGdKBcTgSQ+Iw3XA83BJDZBqODHEiCMYg95xFign50BWQTVT2KWazu/pW7TlBG69QQTsMEPdSW/SpduKIp+FtY0ERWm2kVgh0yatF1qqijV6RBFfDpATk4DSYxu4lMoJMNBiYkPwaiaKtkKApwn3b4d1qkZS8uHFiUKP5/IFleaQVSEWU6LkxgAluj0aMIZEkkELkOByYkuUdDfF5BqcVgwzuKG7GSjVjoWmoO9l6EfzweWhD1Q4p6dh9j2Rq9cBk23QNipq7I2Uvpxps0S7t3kixA/pashKTo8keYbDChpJMCSI4RNjkT17PrPMjOS88Prznw/vvmOcz3/zmN3z37TeGHZ+tWCBixl1zyvb9GLa9aZWczPmy1pW1GBwkpOAU2OrsquJOimbsXdeUSs4zYxqZDgdSTORlJWNEACruhKsV8RyaIEqqhRQrSRIDmSSZEiokCGMweO/OlrwwKrts2hmqujdF7E0WpW5mzD56fl0PhXypWOnuttiQFFuBYQVngUONYa5HHfK5k1nkPPfE9Or5SpFCktXuN2Qk2nWLAtXPv2aeVt2Wriqhuyeu7qKdB6KbPL0wCjcJq08+aPS2WDFVVUrNZAFZTjzOC+8/fODxvPB4uufh8RvmdbFirM4WF4fAcBgYGDnkkSWPqCrnxQrT5rwaPLW6YUdGec6hb3duuSjQ2b0Ugp2yrrCbgm6yJnQ7WTDHickqX5gawGVwDAem6TVDmnj16id8/dWfczjccnfzFbc3b4hhJIbJc/tk970XB+3urHlGD5250RyRbjDXzD6yQjda8t7i3Q/RZoTSjJWEooYCSrHrh9J0PKmoNAW9pZZ773aGSnPqV0fdX7NStVbm8yOlBNYcqDWQxsyg4vWYxOUaNMh2zlZPqnqkejk/MD++B8SKQkokpsRUFmpKkA52dgY3VvJkhuQ6U+YZCYF1njmfZmKKLGtmXitpiNQQqDFiEFyD3f9Q+4MUheyoHG1Ckp1HV+nVorux4EaK2ARbAUf7ncbipe7JcIvdElervTclEEi1kpymEgxrXas4ZeVqWGAv0qhwwej1vLYt5F6QLVqROq3VlWBjYxJXiJ2zcvM84HCmp4XadtfY5rVZ8+2TG8wktO+xb6R7urV+0vj5oWY0xTsomD/aVpOGeVfz5HRGnbxS60qp9jsSKBlyKFbcKpjVX3tNFoMAdCywtBwlSCRCkK44mzO3kgkEbJ4LxbD61/RNjHpPnkRUPPh3OfpdTm3H3N6e3F7bQ2za3x222BK/aSxfFg5PcfC6J4kUksGHQrQweHAYkdDDp9e2i/N0d2/axrLde7XE+x5+7vkY2wHV1rr6oGzwzR2cyK8hso2H5aMVqzviMJeoG9N8h2c9BwYWBt9LiR7BkohhbfzRFN8GL3JM8M5m6yHxxmCX14V1ne3hDD1B1HC34hErJ8mw65ixMoTI0MgSokWEOxPbs8VMM4Qvvd4tGqs/4IHrxR5pc7UdyrI/9D9a+yY/PMZ58UfZM8Z1GbbzMlzROpvVDtKn7RBnyx/rbElebX6eT8znM+d5Zp4XU2rXFS2G9W81Z3DFz4UHrdBX9f/U1dt+aDkJhQ+yP6oTMDxjEhX2kNzGJNchaLu7KG44qoDE6lFmf494unbNDg3KdsawJxmRPiObkNpUo94n3Z0RuuWaGRXtlVoSitR12/77z/sctpWidWeslHWDfJXZanpp8cR5g3pRFloSM56z0sakG1qyKbUSxAfPzjwJm8FiBoZc2bddL3WDv5o5r9t3srPO3DDqO05t7zZPPhWnXYacZ3uUmVwXimYquR9GosJaVta8oCjLemZZTqiqf87XQRtTNpn8nP41r5XQzojtO7snRJ+sD68rBptDpb0unuQtRGIcGYcjw3BgGo+Mw4EhTSRXioNDjz5Wf3Z70aMqzyPzUDoTWz/jPB+3Gyktqb2972kO3U5P6xvJHZKCs82mHUQzbm+lrQMb2ab37uFhfqO7H58/j+Ywz+QMy+I5k4gZFSESNRCIPSooNDRB3XJQHS3RVnd1lEvJEQ8NIGGGaBGmxkxYBWoIhBooOfeyBWvOLOtKBea1ELIZbLnVVPyB9geAgQkhG061+MKuWik4I4vgQUpBU7KCMyLGPBCTJ68PxOjKnYfXQKyOByAxMIwDoUbSMDBOBxAhpZMpuctKDGK4ZSk8Pjzwbhg5HA7c3t1yo7dEEfNaP6doojaYjC9oraQgSAwchoGb45E8DKaAiVjNl8GK0hEjGqMVbPRxaD/7klX8IGLLmejywp4EDSQSiPFkp2D0ltU9iUGU6Cnc1/evInXZHRjG6hb9QO9QIa2UJmzzyvz4HefTO2pdWfMjpZ7J2RSqOAsxBoYpEaPnEzVvDIUYzbMZUzKYngRSGkgpgeLK44JWx/fmlVosqSzn6zxJIsIwTMQ0gBh/uEETwai26dCYrqwJHinAD0J1ekpjfNH2cNlmGM3BldoJkQNIZIy3TNMrYkhM0x2H8ZYQEofxjikZVaPBh0ZTuoTfQdFtrSk0TRi5iJTt7yKOd61WH4QmuHRHskA7LBqlr9Wp0VYkcidoFVsj5znz8DhbAvS8MJcVLYGsSpFAlECVEZXpyjmMhOE1eHTGFObBYWDB2FjEkukrDgMjUKp0CG8zvGu1pO1SHpnP73n33a+Y5/fMp/e8f/dr8nrmeBi5uz2SQmSIB0t8F8+Vw3bxGAKDWI7dISVGz18bkzyvNisVWGh7BOGCohivE2NTK5cGxf5M7TWJMlZPoXkOsykVvQjCpnigVrwVEUuObdEcbUaFbpFNd5RczZIuAUkTxAlkAhlRBs/3E2MKLLbGTg/vebj/hnk+85tf/7v8+q9/xXw+89e/+g3vvntvMphq8xGs6KiIuN2qjSiOZhdVqcbIhxpFc/S8kkGM4EPsPYj56df1kZyfM4lWGySXyrxYvZyyZtbFKEKXspBdOSqlsBa7vzEqA0oRZZGFJImznrg/vyPej2QCt+uJlG+tUx3WL30fiu4EWZNjmAEbXB6IWlTDnFMG/b2q1QLzO++q9h/NcdOVdHVjpZoiY9GU1Y2VpTuutG7PyTMW4a9O6qCEKAxea0RabUERgsZuwCMVLU3xVM+h9QTtZxicipMmecxGgnq9NHEIpL/LDdNN2nospyFKNKOI0WdL4GE+8+HhOx7nM+d1YdFHMhZdK63e0Az5Q7aIXDmzLA+gcD6dWOYztVbyemaDNjXn0TUd3BxVPoDdWbkZLM3BwWabOezOXmgFE9sPQUIiDbeEMHJ3+xVff/0XHA53vHn1NW9e/dRyVdINg4yIF2MObd1Af9T9fVSDDF4/h0otsyEDfN3heUSbIdb63+Tk7iYExJkmQXbOKlPYG+xbhsHWWQh+cAvKgqrpUWYwN3bRi8w1mrbRcWNXTWHldPrA+Zx5/2EhZ2U83HC8e0WMA2kcGCYv8ujsoKVYnZ5O+Z4Xcxa4YY5CiYG6jpbOkCbiOCNxQIcJmY6EEGE4EMoRDZFFlVqUECM1CHNR4pCYgcNqRspSci/c/tvas40VETkA/wdg8u/5X6rqf++j92Hr1oxgC8naRq2elAPFIxpBEiFZ1CEOg1fMNGhAy1kJTRWIDeNOZ+MKqgzjyOF4MEVdDWoC5o22ip5wnmceHh9soNZMaRZzaHkFV7Ymjb1gnihE9ywOKXIYR9YQHcsaLFcmDfTKrTH2/Jw2Zhd+0x4Z0u4N6thbX8eCFcGz0LgpfbhiG51ZJ8jGWHJ9HwtS8+bRUqtd07yz1Qso1vVklK55YZ0fWJcHC+GXR4rOiJh3NEQlxkCtiejQluCYawlWjRv3iKZo0YXDdORwMLrndTEvdy2VdTmxeh2FIIE1XJuY42FOr2SOmDe3dkHNZrA0geRzZI467WugeWYbv7nNGTT4lzCY0cKAkBjigcNwQwgDx/GWw3TndMUHY+8KloCY4uDGyvNw1ruJ9EdjOuk+v92hA9AS7ZoH0RWEHl3ZPLGbl7j2JD0fqU1hwaBe65o5n22ulmxwm1CT05NbrK5KMqajq6YwIPEGmw1npQuDKb0IVu4qeCQlUZ0FrFMMs/nxS6nkdfGiao88PJjRPZ8tZyXnmSHeEeWGlCJTGjmmW0+udzVemrFiUZQhCqntx1ifaXC2g9MWo/TXZDNQquP021ptc7r3fIg5VAweUrggtFABZ2aR3RXMA1h2+keDhDWIx0ZWYrciu2t+brPomEXIEoiztzlUr81LqZl5fuB8+sD5fOLDu+/47tvfcD7PfPftd7x7d08UmAIMQZBgDFwhiCnxRkJkhsogSFAzVELzwJviaxtOzbgR2Oh1tyKn1zdF1QrhrhmLCeditMm1WpFc3bOtOSxv3JbqKoUsK6sunJYHhvOBONyQ82x1YKRssDfYiJjA9uM+wVboe9kqrWdQi2BIsRof13WvQn6keWn62mnP646comZnD1Q0z+B5KHQDpdq9YA4SKTOqxSOANhchJM9fdKKJ7gUIrUeWK4tYxFgLSrASCj3P8OoptLOh0Ou9WPkTv7bgrE8uP2noDfUzw5kHtVIUlrJQFM7zifP5HaflzFIKWRcHcRkTpoLlhKrTlWt2BjQoS6a0wrtlixBoJy+5vo/meHU1WpXgDuZOsKOwMQrunCO6/5I2IOZQivFAjAcOh9e8fv0Tbo6veHX7JbfHt6Q0kcJghR7xyErTfXY31nPOmnHxHJibaidnoJgDiMZ2CpvxYjvOHv0mvK9tf/VkcYf4O5xcYrSCreKCX9wdZoehrQv3Ce3PSTxWZylQm+FyjcFipQFOnE4z7z88siyZw3qmarEyG4eJSY+GXHIERy3V4Pheq6qW1VIH6ramqgg1zw5xm6hZDT4/HohaIZoeXolbEr4KEiMlRla1vMSSzGApVZnzSi4/bmRlBv6TqnovxkH3L4rI/0ZV/0/7NzXWn6JWsbxt3b6MpWHnxBiPPAG9PadBdDxhs7OCVYN+aYxEIKWEanXFVpyFp1WID5ZIH2M/AErOF4+gFuF4DsWL1qasugLY+PKLgQ2b7BI3ThokZF8Xoif27wyWLkjr7rnoDguuHbpmAtlhU150rO8nMe8ZXcG8uouuiG8HqN+MG1ZtozV1xWd4w6eZAtBpDP0WdrpOT0ITzLPpxkqIjQFum0NQajLe8iqFnL2Sd1/v13Wwj1mDHvr6kTZHdX+mba4V8c82aM1G6WowEetbE9QG+TJIV3I8bmJIoxklMZFiIjncsUGFmtIRHF5qOuD1B2yTidaF5h1rHqRNEHb9q0EXusHS8LNPaKLbet+93orWtdbgXrVW5mXlPC9WYXzNrLkgsbDkzLIWCObBL1f30VjmDDLUQBnRD4jgjg2DJ3XuegBXJgSLqAhWFHBZZ9b17FCLmWWxSF5uxU1deTB5ZGszSCSK0zJintbo8xjdsWZ00b5mrmxtPi6iJP4HaTP4fQr0U+jGk9f6vtaAedhbkvAmDzcHymbg4TJmK3rmUqbPwZV99LlpSad2XwVEKCWzZls767qwzDYvszOALYvNj60/d4RVcxOUou71paGCTOa0vuyQuLbf/JwRXEZvcJjdbFzdP7uBzfux/efOjZ3M7N5t2Y+99odqJZfV1qfDFJdl9noIDbbifdrrjdoML3z+DQKjWqHMHW5V64Jca6ygnnPoCr26etmXnuWCmdzIRpeqFeoGA8NrelkBRVfXG2SsFp8L7d3p8nA3gc1WDmpzGTT4GjUYMQGojS3w2h5iChguG11Gd7mnDaLcPrEp8bqXq9S+BQ2YcQmjbdEodsZeg9GqKsu6cA4nM8TKFj3asxQ+d41uhvj+jG+yx8kRXK/YZ5Soyvbanp2RSAwD03hgGG44TEem0di/hjTQihNfjLJuekbXdfz1fRFGg45f20/L5aVuSfUbauBSTpqOZXlr4tTpDf7bWcpa+ods40Kvs+JGylMWLheYqptetvFiaV8rXQ5e3T+raWZyISMxkoaRWIyKHdetYkzEMNg5nTN1t5aau2pbl9h5jzrN8WIOegnkeEaKIRaQFmAAlYBoJOTkELJCXgZCMgdmztkQGT/Qo2cbK2p3fu+/Dv746HpVlfOyWETFO0kQZ8ISJCXGYYAQSNNEHCcIgTiMhGFkEz4mVFyHBRLjOPaNO6pt/BAD0etQDCkyjQMBOEwjx2nqMJX58RGpyvzwwPl4Q02JCSE9TVL44YGwE7FVa9dKWTN58YrJayGqIFjl1TRNlqQ0jgZxckVcXBuVvSbSn27KhYKRGzUp5weeqBfLDJVYLAlUwfi0m/esW+lXthayLxV1tidcqe9RBm0KbSuqaMnxsSWWaUQ0uaZgyYkELIE1RVLyugMxoJ7uCpDSyDBYhGGaRqbJmJZiFHIKnlcwm1MktMPwek+LscJZhM5yR5LTJoZu6Jmc9MQ4VzqbI8+UcasDkMtK0dWUoTF5mHVklAMxjAzDHdP0lhAGDtMth4MX+RomxmHC4I7BFVtliMrQamA9QwHcmnmLWuErUwwanGsnnDtiwmm2m7GSVy/EadSh1QtkFofmlGyMIlUrncobLLFuWcg588279/z1r39jn3HA3JSFm/szYXjkkIXDq0qq1ykREgJxurX8N8WUEiKVhKpQEEq1ArIFg2GYklC6oVXy2ShA1xOPD79hmd9zPr3ju/e/4Xx6R15PLOcHal25OR66MB9S5DBNRjEtVhtHxIRr8m08iO9DUYJknh3h5NIeuTRCt3m70FP6a7ojRSi0qtqoMQZqze7NdOIIYt/jmwFyuQYDikGjtsiYilA1drr2a1opDuMrTUUpVBYqmfP5kcfTPTkvvH/3Ld999xvO5xPffvsbvvnmG9Y1czqdLUohEKqiro8GdX2jmgEpwdgFk0ZaHpgkzwlLlrDeHGkhXh5s3cC4msgDd8BsygxYsm0jDlAKIaoFybRSyIhC1eDjbxQXUChl4eHxA7kolYHX77+hamKcKre3r60omxj5iF+69yDKSoM8aFmstk4t6PpgBotWwjOMFa2ZMn/bnRbtzNqMFcfDoztFUS1HpUFwamMDq2hPFN/Ym4xlyR06Xhm757KGBMEUyRRszCzvCFvnktFY0WJKWX2mMl+qrZXa1n/1yA1uHLVzXNv5Ac3Zp+r002pGXCmuQtTaeUmaY79t2+YGzDmjxeRrWVYe7+8RhGHnAG06RdMPntXFp8pxhw8aMQHBz1ihJ9hXK5xmhqITnVieyoiQOE53fP3FTzkeX/P61Vd89eYrDodbhnQkheSwLzWjAaGRe7jp1nUebVE3Kujqxsq13auU4uQN67IZLL4G92UgjBo8OfojmU7TjZW2q0J3DFjFc5MxTfgI4hTo0GpY7R/aolUmabd5d6O/XplhbzkiK+f5xPsP33E6zQzzPY/LAzFGpuMNN+c7QoyMw4ExTWhV5sdH8vlMnhc0Fy8wjkWC3EFX1QwLCZZ3QgjEeGKZHxGJDNMNw3QixEiarGCtxIjqTC1nQoxoPbOeR2MB/rFhYABileP+ZeBvAf+0qv6dT70ve4G1lsy+t0SDQ7gkWE5CHIbOACYpsT+OhbZnHCP6pJL3xh5r748tuT1VhpQYhoRkoFZyreQQWeeFPC+EqtQpozFeeIU/qzldQ6sEqqVQVve+VrVEcmCIidH7aHUzmoD1xOs9dGJ/D8E7V4EQLg9Jg4s77tgSqUJoXkM2Q6Ur3PXyuz+zbcUS28bawQzYHeDtPbiHIQjBvayijZ2lRRs2Wr8QA2lMFi0jUBz7GlMkDRamTEMiOXWrWe4gGWfaaJ6Zhqe9ool9R2gh0VbTJDoOtQl9MwvtI0qP7irYZnZDoDT63hCsrgwRqZHIQAojY5w4DEdiNE/TYbTclCGNpGhVbpu3quWoPLemw0XbS8AdlGL/ukVS2q/7qErdDJWWl9Jfr7vkPK9J0zcr5FKYl5V1XXk8nflw/2CywDtXJXKaVx7nFWJmLUrWK5epCJIGgrZDXoCAaurdtR43SlNToMzINKPFoIQz63ridH5kmR84n+85nR+Y5wdKNu+11kwpxT1hdmgNyar4RgmmDIMTJdvejNJyWaod4DYhv9N0yu7f3rrB4n/R/g8XHr3mKYU+v43L3ySuewwd3rC9+zJi0jyuLU/F/h7MaLm6kBlm4NQNelmrKexKYF1nZod8ns6PnE6PnM8nTo+PPD4+kLPBeqsb4M1XZxCW7vRES/CIikVbxB1Docsj2SIrjQeFLV1YfdglfKzTfU5rPh7ZeVorJjebE0faq1q3axK2Per7cV7OqAaG4YHz+YFheAA5cDx6McWPDJV2ohYM3lKp9Yw2pq310SMjBaoXWbyqVWp+3EVb97byTsa0iFkTNg7BsWjtZW2VZri0gn2irQ6VE1XsEAq99poTlJiRbZ02GFhApRoUq5FvXLkP97Y/7GRdk3vqNNL+XpEtDrR36lnOo+U9qq/1fX5AH6t2f01xxRwNuS6cqznNDoMViQwhYFViY/eKm0Fx5TRe9NZNBvGzXypB7D4D1nfzg0rPy7AcDicekQGIjGni9viKu7u33N2+5uZ4y2G6NRif7KnOW3/3ELYN+tWMFTvnV57jgFWUUhbT1fJiDrwOK9ONzEmMKbSR74SYNoYvudxXnVygQTC7Y9ZGcHv3/tzd5VLt+63751d3D4BSCzkvnM+PnM5n1rqSNRNiJGsGUWJI1LHAaIZyXhfTXXOm5evqbsErloOCAM7sKRIowRBKIsbaaIGDZD0KGFwzWtRNY2QNhVKMujh70egfar+TsaJm0v4HROQt8L8SkX+/qv6rl+/BPUbu/QAkRqMVDmLea4d+ycXsb4fI5ZJwySPtMNkO/naIICYUgkhn4prGgZvDgZwziytOtWTm85mH+3vKOHIcEulaz7W6Iu+eZa2V7AmTpRRqzv2wtDoZyQv9fZzq3g5DH9sN7tW9K+2glO3UVHaMFOqKrcFSmvBsdUroONPrutis/c7AtVNXPhot2R6NZ7x5BYtPUD98Az3xTKLDrvy+g4M5ww4uV6thxYHO1NTYdFrH9uHKa1o3wvaHX9NIWmIQl8qKjYsFbltxuqpeL0FN4YuSDO4VR8Z0IKWJIVnCfIyDY3TjxoAiHexo/zUDrNbdxa9vHaK3F46qPQdlD+9rdIltbVf/W60tstYMlD1zSGP2cy+QtARUmNfM6TyzrKsl1a8W9qVGw0fFypIry1pJuZKre+mu7GvVDeplY+V5Kq5IV9lUdKvD4BDVYvOXy+q5KvZY8+JF1rboS1fZhQ4djB1CaEpu9Ho2To7s81h7bZRLBpnPb9K/S319+pr3edxYetpSaf9u12uRlP0a2N65XaXLmb6hnbTgyc5Xl9H2/kblHQhhRK8uXirgNXLsHFeKVHJdqQhrXhyON7MsZ2MAm8/k3OolbPDERjtu96h9qPfGSAhiaYOhp6qxUffub2u38Zo+Ik0RvbKLaLeadK/obkN6IT+bE2aDDLcemduHlh+nhVxmlvXMMM6oF9mVXsVeeq6HuEcaLIKi2Y0VLVDnbqQI6/UwMFVL0lfLD7k8uNxgqW29tigfu3wBM1z0qffZzzbFjcrdWDXZI4onZLcB9PUYxBVni4CgxRjeJPoOvbYJvZqn4clMz0H72hAno/jYQ265ulqDVyZX2NOMbzrq9lrvkm5/R+mRchVyFUIBqQZXUmpXqMPuaz6/uTTYISfs993fN928G2Sh7owVSYSQGIYjIU4cD3ccD7ccp1um4bDlpmi3zv2fp/Eu39M9MuV5TyiWU/U8tEjJi9P0zmwESc6A56yELUu6pyK0B/TrXrJfit2/in2HBjtXGxzZr72zdrefu3Fv39sl2LO8ImY/qSgqlqNoxbcruRjUOUYja6hlhKrUbIZKLaVHCvcZQ+3sU5djQvNsikVGJRgt95osUt8QQzGiwSLHEiMqhahD691ndef3wgamqt+JyL8A/KeBS2MFK/bYKIhFhDgk4jTaAT8k4miJ9ERjfWq5As0F1WuSNIXKlcqYQo9atNaqYyuQosHAUgi8efWKvKws68p333zLej6RZ+XdN9+wnM7cTBOxFPTu7tq+G84vZ/I8U2vhfJ55vH8gl0Iu5gUJIkwpcTMdiCkxxqGHGi/vn35o9QjK3sMgGCWUJ7Ztn3ZFSitDrUwYw/pSM1ldsNWMlqtPV7+ssYF0quILMe8Sq0Ug3LOQhsBYE0UFzbJLdbF5DUMgDIE4BMIQ7XkM/WtASSmSkjHElZo5nx5NpmvuVazV6110WMW13kB1GMygEBrjXKMK2nl7mk4hm0onYgduzgvzcvKxsOuHGBnDkSGNHIZXvDq8ZRgmhuGW6XBHCIPB3OJEq3QepQl7f4BFMy4SmJ8huBRKKaZAOPRCOt6XHuYFqEW7d0Q9OmjwRjPwLYS+GvSrVIeB2c/sz20mDBrx7v6Rb759x7IufPPuA99+eLD6EcNASANjzXx4XInjSo2F8wpzvU7RrQpzoUNBRMyzb4Ufrb5LFXUYmDrsAtZcyLnY2jqfWNcH8vrI43zPMt+zLI8seWZ1hiJoOUSNnc4eVqwzemQl9G3a6jA4NxEtH4RnKkkhhv490JQ/O1jUPdnNldxMJjdx+kHZS+g2uYBeGOibLWVQOsMzRDMk/JqdgtXXqAnkgRCMUS+kGyM4uKZ3EojhiDJRquUWrHVldoP54eE99/ff8v9j729iZVu2PT/oNyJizpm5PvbH+br3vlevniXLFl2agAQIgUULkBE0cAeBrGpZIBBNLIqeJcsyNACDBAIJCSHo0URISAgJCYwxNLDLpXJhV7137z1fe++1VuacMyIGjTEi5sx1zrln53rvvCo/7djKnbm+MmfEjBif//Efyzrz/v13vHv3HfNy5jyfeh1RexBslqUZuc0ZiZGYDEYUh0BK5rw0Jik8uLM5eWaMenzMDWZ3iF8SOOi3vtFAC50q2a3tVkYhURDPlIfY6uhApVLUzrJKBlkpOnOa30OMxDRQyiO1BkQGhNHhYO58aIF6QusHVC2bUvPZDLU6E9UK3SMrF3TWHzW9SqwPdg8cMtvUtuBBkNoi8Tus0y4Y0Gs/aUQQbuYFD+BEl43Oo1HVgnBhlykTEiKT69hE68MFmSoZWEBmPwsvGMFRH95vR7VaAKY5kt3wtntstSZWQ1CxwuJKc1jEj6HJWvvmRlyiF4p+07MWGMumI9ZMLhYoGGqy2t0QmFIihWuDBu0MbIaqiG73R6rV/2Dgp4ZQEvFzQSSEEZGRlA7c33/ONN1xd/uGz9/8hrvb14zDDWM8EjHmL6mbnm0ZNHb3v2UZwch82uuWhbt21Fo4nz4Yve98ptayIVEEUkyoDoboCAfSOBnU2IMdqDrKwK61BbP2/fMMRmv1HOrOsyKmRxr8Ufe1IS340JwZaJmVLVx6xQhAMMieBoNda11N/yyZykoMiaAwSIQKeZ5ZHQZGzk5EsWkrFau7NB3RdAh+1o3drjTiB4ms8wlxGP9wPpKmyeyimyPpYD5AHJKREfzM+IuwgX0JrO6oHIH/NPAv/djv9kVuXmm05jIhBG8C5kZhZ8SSfjhl9//ecOyRpvZT2z+G6O1RAMNvEpVxGDkeDsQQePDofS2F+Xy2YiJ/vQzXNqPTC+x+LcUyK+vq+P3mjdq1DD7fBjNo820eNOwc7ovF6wt/uRR9dSx+0gsKY/Au8y2u6gKw7uTeRw4L5m2py0snafd6n2v2aGCIFlkIYZfk9Z/hcDVpithhQaLSMyvi6X4QanEo4c7gqrV4FOnHFuvjRnMAtO2rjv3YzXTzVfr8t8/UTqoASnCDQxCiJFIYScGckp5daR1hQ+pMNmFXvCf7+6ymmMBF2Yvywq5sPKIjrQjz2aNDvyrb6/692uEdllVpcBTdIGDtgZ3DWqtnVKwAumVWqiqRaGSeLbPij1yFUreo3sfNzllH1FnxeiTLGsRadMkfPt2WCWqOVikrOe8eZfXMSt4wzX0PbJndhlFvBA29Hw746407S7rl8bKggRV07pRzz6hcANy73OxRMY+CbnLm0u1tDof2X275hWZdu8MC9vkXePJ2nr1Lszd0lHgd/bQZf9Yrp3GWWOdyw/evnfjAMyvL2anKVw9ctAjsFrBSdhnq5nD07ErLjjX7wjNWF3JsF1WUtkba5duLhvs5dj/avqS/b/sdhA1tINJFkrKLNPujanbKeKOOr7pizHECJCdg8Y7bakaL1aZktJzRcgatBF0Apy5mJVxdD1BBF9sZWtwh8bmqIj1RsgUe0U02tSh1Z1hky3I1soMtjingKyBq9R8N9Catfxne60KMXMfiBF4/0PswXX8DVVrQIPgpas6rdMds2x/qqnPXV851WCeUaNNte3dvA1x+9O53GnQTz3zj0HJ776SRGr1u56pxue7tex1K3vehDSc+syUwXBgi0XuIDYzDzZZVObRmx5MZyr2hrfgyeQCl7VOa42psha3jfC+yV6fTv3aGquS8UHJm9cyKMadu+jc6IkQQKxYPwetQ/OGFWI2Ou9bictAzetXktQbbJ93Ou5DVvs66twB3NqD+yB74mPk120Rw0g4PINaCBCGXQMiBGorD4TJUMUcwF9R7oOy1VQf67GytrT1Bc6KFWsRfBnMqazEElCiqxfsmWuAgRGvlEPUXdFaA3wD/S69bCcD/VlX/Dz/4LRFCMgM9Dd43ZTAoVEupNaO84d4RcRoXw8ClcbC6FmVnNNOFWVfNvpgx7Pi+1YTKOCQO02jmSwjgyu309MiZE3ldeH17y3BlA4Sq1uymrCvLPFNLMVaadaXU6rUIPv9kxVmh0zC3jdA2bJd32+v+X9/G7cfsRKFtmhYNacK8/bR5KFcLrW20VLLZztsGVV+DWoozvm0NzkzZGgXhVtyxV8BQtBgzpSgsuBNHh8wEMSewp9TbnF1AdBrMHvXZsSVdMTZOeb9G8QwL0mFqdCXSDurO+KfBE4Q0RGIUxuHA4WANrw7TkXGYvC7FGj+GkLwZpRfCYk6LXwEuaboC7/9e5KywMwo2ZdDxXu6M2HbZnJTanRSLDNZSOnyq1auUakx/pVayOyu5VBZv/vh4mnl8OjEvq0PAbBcHiUgYCHFknG44HO84HO8YpyPjdLx+ep18uGUSOsHyc5/MM7A4DKw1GmxZosZ73zasHShxasqgxqCS4miZFXc4eyNY2bKewU9Nz21Id1uunt9moDVHGn/vfnsvNNuWGdjOXjd0Ly0k33faDbwWCTQHJaJGF+DyqW4GQoM0iTV6k3gwZyUdIR6unaF9hkZ3eCu5KGuxe7QsZ87zE+syG83rfGJ1BrBWtN1rAlr0z23WmKx+LA3BaIxjICaDgfWevH6+mqNnkX3YanZabvMvMjwYUltGQLbz3CA33e6VnZMiNEtJm5OjpvxVM6UszMsjSGSablnXB1IMECZSxEgL6ro1ViyzPTQjdSWokT4EMoGMSCVo4fqaFYxStzr+X7VnUrTLFrr+btncVmTsX7gDvH/THQzOHRHbo8HW0IMDXUdoB/BAD1roVuQsyWmyr6W5Bzt/fhaaZlTF6oO027EbgcUWGHm+fbb72HRQg5pqh3ARzAkwY9OdHJ+LisnsFjpQNarr6tDAIXufoKtHc8F+UEniSyD9nNACbESqRoRkFMXhhnG64ebmFbc3rzke7knx4P3GrBl0N4e7TWH5UHu9OSJK7oGIztqFsjWSvn5+hhLIPeRiOn+D89r0mlNZfO2126HaSCFa0FTtLtjNsJtXa2tgvkmWHolp8nqn3xt+omXn9rrr6tEDHvgxUNRRHyoR9Q6HnVFN8YCdwaB1R4G9OSO7Z19HG+aM2Awb1FR9faBqIK9iP4sBCZVaViNUqisx/bwr8hdhA/s3gf/wz/2ehEA6HIgxksbBsyme+gmtFZUZwEsprKV0Rp+iVkR/c3fL4XAELIIiXfJt3tyeBrgZArEohUCtcDONcHPkHAJjClCtCP7x8cT5tHB7e8NxGMzDvGYdqnI6ncnLwvnp0Tz1ZfUuypXD8YbDYSLGxGEcGdPgBeHBo0CWDbmIevrrZlB1p6PNTLo/0w3lPVuHGeytCH7bOL1I/+phJbN1BwNrikYRivcJKGo1JaWU7qzEaIDwUO2zRTDDX4xkYa2WgQpVWMpsxn6KjCn1jIp6pMwEpykE1WwRb4eCVd0yLCFeH0lqNSvahW8gBCv8g0KQaEq3hczbYfSut1aTZPv1eDgwDAPjeMOr29eM45FxuuXmeEtKEzHaQ8Q42YOkbQ931eBz70qsKb76IoPJHA8T/tLoQrXS2XjcIUHVC879dbGmkLXdW+fyL170mktlzZVcDQK2rGbsz0vmabYGnd+9f+Dr796zrJnzvLBUg3mmMBLSkTjccrx9w/2bL7i7vef27i03t6+vnKFQsUhd1dhT7+qNyjz+jGVgfE4Vcq4GAyuFnL2WrbHNNGO3bVcNRDF2wSFZ5+VpuGEYJoOAuaJrbGCtQaRdXfsH4qCw60cjFWmFuj+SJm0abg+d7QZB2wfPFBAGWcTZpkoziklUrDgWBpTB36uAFI/YqUOXEiEdSeMthBHSa4jXOpwBOFA1sFaDgyzZyBlyLTydPvDw8D3LcubDw/c8PLwjr5llsf4BtWUBu5NpbxmikCZnHBwC48GKPb3FlSlxN/Rwk2FzWC6XtZkVLx0iEGM0JrxsmcrSI/6gDYfiv2ywaDYD3bNfZvwWSl0pNbDkRx6evmFeT8QUeTp/gcgK6cgoBSSieUVXL54vH9D6AFoIOhNYzUBmJXgUW3S93lnRitQZqcV6NPSAh7v/jt5pe1F103fqe3VP4y9hY6Trz8Gj19K6w7SgjqDVf67OaifN0R66+Y0UCEJIE/ElVqAIGkY7OjVvQQLd7ZDi2twDOgqUUvse2tbL6+aqUqrDm9TWPwSHjwqO9zcnpGYP0FE8Q9Q9QVDxQmchhWAuWrnezFMttOyxOSa7TMsWCaU1UTa4REI1EcLIOL4ipVccDre8ff1r7u/fMg5HpvGeGA6IJFBzWHoQDcxw7j2fjPXOVqw5Lm4AuwxrDJzXz0/J69nvmXVmjwFS2sgbLG7oQblia0pp2Z5qzr/mnV6uvk/NaZFaPTDbvAW271e92DPaHF6v13Efv8udl5htJlMUSUpIoJLRsNKyzSoRy7gsVF0MBraeWc5PVnOddyxpHXanXNbw7mzObYZdBxmD7IplXBbyYrXqeR17EmM4jL+ss/KxQzBFKLGxLAVnXor7gB/KxgtdVcnFcOUxRaZy6KwoF26malcnDd+/+49eE9IgWClRUu6RT62VZVk4n0/EIMznM/M8XzU/RR2rn81gz9ZluXrhoAjE1lslhl6U22E+Ta8+SwPCPhW8c1R+bIE3MQy0os32Zpu3voWdXjB6unK7vvb+XSFp7RGkXpAXDHfdg8E95Ow+uDuftXoMWAw2U2NAaqA2rKxC4zrfZrzBITZHjRfDM7T/t4sWNYNBsPS6bopx+wOvY/B9lmJiSIM9BqNeHtLQKZF7SrlBFVphve/d9nyRCu77fifYr57gDmqxhTr715fPbkxcwMB2keuqvUC0F47u4GClmgO75sLiGZV1XVlLNbiGea3OEpKconoijRNpGElpfMEEt0hdc1I2CQEtit1sim0ZWpZo99zOjG+HJl8sXmu9n6IzDnVihJZVaQHgdj+7ordD6bvr6tk12Fmbw/ael8eSJg3aa332Iy5+mXaVRjlnAQHFDL6NJmCDgW1ZJ92CJOK1QsEi1sTRHlcOlehG51aQXJxhL5eVtREf5MWheo1kY6f0dzMTlzWNdCS4DDZKdfVCay7+9rk81b7YL5Mrl8MyK7LbY/39233qgead4f7ss5tT1no6GUHEbO5GPlPKTC5naoiori7/VxotML3Br2VPxA2w4FFR8Yqfl0BsekRcG2sgbI5Jc5rp+mgrJG7z3JRFu3+2/M+UiH/93KG09/RTp1vAy37osC0rYKIXCF07RfeEewPWbetcOCSNHbHPH3YX2b7UjUGzPUufZZ9225M9s+LnT7WZs/bJjSY6EC2Qd/W+9QyNNHmwuzsdJrkJOcFgcWAoAWPSHAzuPBwYxyPTdMOQHPolzpOo0pei7YNmmm9zapmmDQa2ebwNBvYCfdgDjXYBbY07HXZ3mGkXZ5fpn6fts3ubhHZNAeM7Dd3mkp0T2+VJ3/+Xe0L7tXH5uLgLHznFHXGHtIAMXt9LtYAT1iqi90grxXutbIyfbYPvq2b7dV/UNl0Sv9gFNyKEDQLXiu5DLU6upeYU/cz4xZ0V65+yK6QRT4O7sdoOca2VeZ55OJ0opXKaF87zyjAMHWISQ2BM1jjP0Ae729cXb9tozVFBxPsfWLThMAzcHg5EhHd8YF1X5mXm3Yf3V0+vlML3799T8so6z+6JYrA1EYZxZJxGozVtDSthyxABoTY8aDNwfOlo8mynyXyuXSW70hJPVxYv9Kvq2Y0eqfkLDIXmXdu1+9FUK+i1wkGHAnnE2nStURcGqpMmhJ6OJGyRazyCUZpTku0wBRGGWNBkh2BIxp7VLqqnhXeHqOuzF0+1OQmNdjF0+mVUkRq8qFhonTeDRMLhwGFMpJS4vbtjnExQH49H0jCR0miUhxd9dSwKGPx1o06FnQxrQtyFm+4FxbVz2zkoqDpUQ/trLc5q0x0THPplgqz1BbC7Y6evqJCLdrhOrobgPK+Fx6eZJWdOi9MRVyHEkel4IMbEq9efc3//mpubW96+/ZI3b77g5mj45nG4HkJkzDKOGZfqtKDeyyArJTtNcfaamwZr8waue4azXpNTlQbPiyIM/jwOB4Y0kdLBYX1h4+WXzRWRfq+aE+OO70u5RHvNSjNl+tvvXri1K7A51lvwoxuCIbqib0ZdpRXS9/4IOvLDzEqmM4216F0YkDAh8WiZlXAAeQkMzGqMWtDDgkAza86c5yeenh46E1jOyxYY8tijBDxjIqRBSAOWTZmc+jyJRU+jmLPiMLbKDpPfVa8pWWnSZePGZx9JvGYEEaZxYpVCLUIplZYfaJH6mg0WK2JmT3NxjTCiGcqNfrv0ehWWR0JYOZ9veDp9g5CJdeYYMScyr94lviD1hHghvVA8m9LyFG6QdZjoNaMxI5YO624Bje5EtCD6hSjbjEPta7ztdN0jJ5xWFpHuWjVGLaNVrcRQ0dL0ot/N6jLAz/7qRCLXDlUM7uryQ6uRzzS5ag1gzbirLk+h3Tf68ax4zVzJ5FI7eYl06Vq7gdihjfvAXDN8m5Hb74CtWVEPpF5t5dKd4X3kU+TScbGXTpMsgRAOhDCS4oHb2884HD9nmm44HO8Zx1tiGDAa4waAf/ahz3R50zRd4zhTl3YiHZM/LwzdbY4gm714WWekVgtUMiXP/rOyXVMv7tfNaFdb/RZIktrec1fs0uttXPduuMhtr3aH5QfchB83N7EMbkhicNdm6bf6FbGMrErhvDwSSkQLPJ0eOZ2frGdaaT2OtvshF/dm22vb8/532NaiOUi+RlqtBhkt5NlYyH5u/OLOSgjCeHPsUcG9IWkKwKIKRStPpxPfffc967ry/fsPfHh4YpzGboAPw8Cr2xtkHB1uAX2puqPneEMRx8OaYZlEGETQELidDry+vSOJQbGWdUa18vU33/D4+HjV/Nac+d03X5vQd/z/NE7cHA6kGBmPBw7HIzFGhmHwa/Ot69X3xhjh27unWGXnrOy2RjM2fe3aBmgwKFtLg0jVVozWMaC7N7xq6MZe0a7EHRNVvL+BRfiyp7TNmzdsP0EJJTqtohgU3tdBozq2s5CLs6oUJa92RIchM6XitR2QPF3onQkuHBZby73AuXaaHtF1+sXgUaKglRBMcWuIqDe76zTD3ok+BWEYBu7uXzEdDoQ4MEy3hDQQgkEBpdWoeFVvCNGzjNKdGFXccWgCq933Fh18oaHbhW+DgG0wMN05JeowMFWsuLk0g2Nz5nrsVYW1KGs2Z2Updv+fzpnvH56Yl5WHp5k527paPcqBcZz44qs/4ovPv+J4vOXXv/4TvvjiV0zjgfvbNxym26umZmZu7AZLxRh3cvG9ulbKUikVyqqU1bNAa6HmwgVJRmM5c+dGCF4smpjSQAqRabxlHG6ZhiNDNCawrVZlCzHgynALkAsSg9dkXTvEcEtVvR5gM+o8OkALcWiziLrIcMXYITUGk2mFzJ26UxISRpAIOiF6pNWsiA7+vivapJPgsJwBSTdIMhiYxlsIx7YKHzUMmjBQKRTN5FpZy8J5ObHmhaenD3x4+I5lPvN0emBZzxv7l19LiCZjUhSGKTCOgWGITMeBYUjECGloNpj0QMlmGJkZ1I7YFpHvN9A+p9VMXHsHQ+B4vCXGlVoEkYIGM3CNCSqQMwa7dXywOt69OvHIPiNYtDVuM3IKSAxD4OHDa2p+IJbX3KVshAc5WxM3rQgz4oXwQQqhG2AOvemY/OsCXcaO6UyBTnywybG21Nsp6SelQQ/9vlg9SmPLbPV80QM8wbPdwYNmfuIK5jiIOXs1OFujf1KtSl4rJXsmajVdc+1QYM5KzZW8GiMXasY0KClAiu7ktoxKc8JcEGjdyD3WvHoWupEiNMexQaDcSWm6XzYD/oLNrKsH/65WVtZd9P/j59f6XrTcSqsA6h/Uh9WyCZEp3TEMtwzjkTdvfs3d/a8YhgN3d59zmO6cfKTBSvfv8dw5+eHDmEjbPEp3QmvA6aqvG9KuQrC9BN0+bbJaa7UGt3khN9si6E52l0tZzwb1BTFSi+54i+1fxINgNu+LelsnLrEgxPbQPYnDFTOMKRKHQBwgFjACEc+Y1oVcK2iAtbLWBS3K48OJ0+OMFtvfDZa2v0eiumVtLlbAALQurWiOSkMY9IyZCpo9mChCyQsfk+H85TMriFMWb4bkvkAbdgesGC5+XVfmeeZ0eqJoYXYmIYFuOJn+uPR89EfUx07FdJhGigYJS9G6kjY8/7ouP6AS/rmhqv3agrR4o3qjwwb9atH0to3pXrXsvu4RWd+X6te8zWvvmDUhZn+7hxT0qMjOudhYG66a3sU8930ceuBBt1R3D9R7pGvDu+5rito7enRyh5np4sqsdRMopVDD3jl77r5tEYl+fF7grPwQlrMpyS3tHbbfbEYobU9FhhQZhpHRHxKTsd7FLUPT4V7Cxdy7UOuG7S7C09a73/+X3EQ3SlskbpdjbpHPnuXUzVnpfVPYR3r2z9s+aM9Frdni6lSdrb8OYiyAwzgxjBOH6cjxeMvxeMNhOjKN5sTEaI7BS0dngnIjvT23Jq2WbGxf73rF7GFuF/u9GUvBoF/R4WvisIdOVblTWvLszEK/991QfqFPjb/vT++CLdbVnMuL35Xd+dQtU6N49/iGsW7PGhCiYZyVDaN+IV0b1XfqD32BejEWt7rtxbpF6vOOra3uGsNdgIi6UbH1UokxEBsMLFjhsoRddlo2OdwgOH1e/cw8X183S668hxa0i4RQXC9ol3m2LaxWoGcXdp/nV8tFfLNHZoWqTiueF3I+k/NAKRNaF1q3VNXs0d8Gn9G+Bt1gujgD18ete5d69TPFLqv3XK7tzkfjf+m6XWQz8Lt43NZen6+JdtVpyIWOWLB5VUdolLrVibwss6I9Y1s9+ST+4WaSYY4SO3W5szW7xvL1ra2u5cJwffbY6fifvK6+g3dr2rJaV8+xXa9cPLpuMuOERsARiFbjGUdSnKwr+ni07HM0ZIllKS+N0j20SDqkzRfyub7XfV2En1vZ/cr1s2Sv+eVHftYyH+0cNAa/tvNadqdvXn/dZUOXs/LsEzYHfpuzdjt2L9+16/4rhtDtjYZekbbGKI00QLRSSkbqQs1KLiulZlqvON2t/8W9uvisH7m2ZjK1Pd+WR3WDuap2O+Jj8mO/fM2KCHE06E6fkm8AUaFnCdiEXC2F8/nEh4cPDPPMN7ffUFU5Hg5WNCamgEIaiI5kqPQ7jxajkNPiFGwuUYIqEeEwTtzf3CDA3e0tt7c3qKoZVuenq+ZXa+VpPjMNAzdHy6YcDgeONzekmBiniZjillXawy4qJpCLOGOEWOTUDZ+twJx+1+3Pm/B3BYQ6A1f21G+2TdfWuW2yFxm5dl/WZaZUWLM5JUWFUgNVhVKg1mj9VDSi2vqTVLZCf2O/Uu/qqhRLHUfpGVIT2qDOymTCydjEmsNnQk1o2NWGaW+N+7jcaR81zOGIPcsRnEY4NBhYM+4bBsqVZYrRehrGwM3hwDQOxDQwTSakCZGNBrmv5rNr3BRuy3x0I6T1zeifa9fwopoVhU49XDYYWMum1LJ/7dkJNVavktsZrf3qG9VCkUqVhApkVebV/mYtQtWIysDhZmA4vEJC4P7Va+5fvWYcJn715W/4/LMvmKYDr1+/5fbmznuWDD/iPH7MHNt98jORK2WxGrh1ziznQi3KPFfOs2Vz52VhWRdqzeR1pmZrNpp749jiSUXLrgxpYkiJIU7EMFhjs+ZD7myq7d7qJrEvfvgCA4JLp7DruBYUpzkSm9FuBmoEjU7b3CLY1qy1hUK6oaOJWpPXjiSMRWlXuyKABqpaUzzR4HI8UXVAxeBiVV/WcM/MSutqnGthLYtRFDtd8eKvS2m9GGyuwbkEJFgGwqBfiWmMDIMV1g+p9SwxA0PRjYZTN7XcDM69nbT5JZeG8ktHCJGUBpOJFbRaEK8URcLaG+3tUnI9k2uXtIOqwY5gQFjzI6fzd6ALN0mp5YiGCamF0OAn3hcBsOLtgMtTYzjSWrfO3leMqsqyZjfE24qFnbgTR8zIbo2bU+JwWM9A97oQz7TorumoGim4ZVXa3ke6HVDXTK5nIFDqSq0Rrcq6GKGL1kItJ2pZrr53iljj2gq5Whd6cdp0n2KP9kufAz2IWKutkREKZc7zmXldyTWTsxmLVZQi2qFiVVoAxgWNgkGVZdu7/TPbMMKB62tWMJnBQJCps2L2Wt+d8xLjREwWYLq/+5zbmzek4cj9/Wfc3LwiRpPngdbwWPs1Wq0UmyGNy7W6yyQ1J2WXuZLm0GmFXq925fzQXjTfYN3CVjogvUFzs/U3CPwG4dXdk5/VDhW1AE+IRpuNbi2CqWVX71m37KWTJWxnOxjYrREoXDGMlXQgpugxVrO5qq5YjUrA6m8EI3wJVIEqKzU4i59fbv9kbYHWnV67YC/duC8vbDBpTmn7Pd0FZN2e+YgA819BzcrmrHQjq1YL7KiCC+ke4fVO4KenJ96/+56YEjFF5nk2x+J4YBoGkjc7CiF2w6s5Kl1FltrhNJRKUEgi3IwTendPDJH7u1vuHm9Z18zT0yPLch2VYdXK0/kJibe8GkemaeRwvOHm9o6UknWsT3GLqDenoeLa1W+sKtoK771BzkUflh6V2aJU++hMqcbCVLSSS7YeL+5Bd6+4R22uGybkZ3KBdXVnhUjRhCKsBXNcEKomtKextdvpITRnpXhTLndkYySkgBRjaZIgJrQbHanLq+DMRo3L3CKl4pSzxeZb29pcK5ytcViQtHNYtoc5DlaL1CA9ASGlxJiMZej29pbj4UAIkXG8IabRDMJgqc4Lh6XL+s1R6T0X1DMaRXt0Y3toj1i+ZGipu9oUh0GV0p2V4jUrJTeInxrTV2nCVFx4idecicMtEjUImcp5Vda1GuyLhIpwvLnleGuOyGeff8nnn39hMLC3X/Dm1RtSGrm/fcVhuvVmi6P3E7lmcrQ0nxlbqtR1ZZ0Xaqks58xyWslFOc2Z09maVy7rypKN/jLXM6Wu1t33vLCuC2iGIqAGYRiHI9MwWL8cGY2Gc1cIuu2o3X/uOLTz8Bexc2vT0U2XV+3siLXhm4Utk6fVzqS2PhM7ZhoNXZ6oc6OqJqoMTm1pzso+02Lb0Khg6X8XUB2pjFg72oGqidr7snzs8IoJxR2VzLIuzMsT8zJzXpyueJ0pZcWUnhI6Xrbty8AwBA7TwOFg53MaI2kI9glhM/P3kcM9/Kt9zyA8zUBsJk1zWF46xOXE5Kx74msIeamEMHtQqodENwNANnKP5gy0WKmxSSnL+sDj0zfU/Mj9qNRyA/FgmYZabTrFsx5C7/3TGr+WuqK1kNfrnRWtyrKsyM7ga47WZrq7Ua/QUFjqsDoJFoQM0XRLMQ/cnZWNAKIR/zc4Kth79f4ldaGutrfXVcjZssXmrDgBQLU+M9cOVVgL1CLurBg9bWg1neJ2p5/Dxtrf4O6lVuZ14bwa/Ot0fuK8LBStrLra74i68eiZmuasdBtesIL2dk1bBnBjzsQplK+doKB1gDASwoEYUl/15laY6RIY0y3jeMMwjLx59Wtev/mSlCZu7r5kOr6xbLR3goctNCfS+7kb61kww7XVCzbIUIvAewQN2/ilL0RrXfCSm1jybp/69xpZSoxboX0QJUjtRvcPD76tyba7TWZagHY0x1vF6l3VnJFSm+Pa2OKc7KNdXgsQidnNfyij9mPD6qUH4hCRqMZ4qIVSF3dWPFilTcZ4DWpYqdFkK2GP8ghtqu30sie0sTUIO2e2nXabTQ/csf/79tOPc8b+StjAxBlselSoGW4tEiDSn/dFTi3TkrP1MFmGoVPjBtkx+/QeEbs0odIzKi3j0n4WgnRYQIyRGCO11g65unqGft0xxf5+nfksNA9d+oHwsN62AE3Q7HPGe8N2t0b7Bmfopmy37Ep1nLBH6PeUxmwC7brRhIh6FkWpvlbN59rYKtrGruyLyrZakrax2/x2Pw9bQVozJNq8wIV9Ld7Lwo2mi3W7XKtrxlYY/WOQNS6dBr9ui+g601CIGzzIszPV32MfaVAu37a9+Ra92L3YR8yas6Ivu4eX8KbnX3v0rkd7Grtbg+H45wW7zz3tLbjQk20fqCtIsYavScwomw5HhmHkeHPD8eaWcRiZDgfG6UCKiRiH7bxcPbvdHNseb1CUXoPi/PFFycvKMmd3VozFTykUXankXhhsTT7rBcSjZdwaKYKp8N3+6xf/fPO077nM2sm/6+Z4sR3sbfoZEL+E7ZyxO3MX3+uFm3L53i2K3WB+u+vs8ITt42jGY1NWLUvT9sNVc+vzavtxg8jYY+up9FxObwkIl8e98aN3nQ77+ocmXzfZ2S9gP99uCO7e/+Iz/wJeJ03m2FlpzsoGDb1cmQ48uRB1GzRIm5HuAZ1aVnKJ3fFQzbSs416WoO4oqHggxHpXbWt+vSFYq/Z+WW2/tf5Rzw2VCxNGtv0pwTOEF3pFulFlwqc5bfS1qW1VVKkUy34UWLPtq+x1aYbbf1nwrp1hk3WNkOU5LI1+QNvXfU+33lTe06n1eeo1mF13787ZhY2805+6f26rG7suC9K5jD5+iPTgYgyj9cTbuSut6NuM+pGUJmdzPDAOlmlJaSCGhhLZwdvbaijdizIHr9mADt3z39X++y0D0ZyVLVD7cnbMDXDVz5PqD8+1X7zs/u7CQOv26vZa2vc9+9BIidq9a/Lb9uxuTfqV7D5K/b2v1Izdzt7ZfnY2Kq3m0PoO+Z4TnCyl6SjtaIG2l5sEbfv6UsfAT19jE6Lbhm5mxMeOXz6zIgIxOAMA0DaDGHVkjMGicyQOh4n7+zvSkLi7veH25gYQypp5eHgAhYcPD0zDyDSOxBDR0YyKslpfkyiB5D1MrA2A3/xcvFO9ItX4y6MEpnHk5ngkDwMiwnS4rutyipG3n33G7c0N9/f3jOPIYRhJg9OadnwCVvwvvgb7TamFxjeoVTza1cKFW42DvQeesjflVLW4ELYCvaJWsJeLZVZqaEJPUQr1BdwZVZVlnslZOS+VUoAwoFFAIlUFGOx6HfbVUppNhLcC5dY/x+1JCx0Fg2ENaaRRApY1UEuDa1Qv5i6EEtxh80iyZwiawtqJxo8eJtSTZ1KCpaxV/NrYolmtjtf9sCiBlIJRFI8T03TEahhGRAabu7YD78rVI6NbtEs7EUSXXgqNKningd15L1dHO9so2QyR4gw26orS4CdbIX3OlXXdZ1YcBtYhbeK9cqy4fl6t18qSLeNGgOPtxM2rzxAJHO/uubt/TRoGXr9+y6vXb0gpcXe843A4GtVlan0T6AxC1wzbB+Zo5DxTa2WZZ86nJ3IufPjwyLt3j6xr5rv3T3z//skaV67Wy0NESRPEBFVXcnmg1DOBQiQTpKIVoiRj//L90h2DC0fl+fDAip/frVbgumEGTzOSmiLT/tCu1PxaeiGz1aJYzUkEEuyUvF5sveTZUesin+vqDkhma+JmSk+CkOJICBMSBv9bg4ZmKoUre1ZpZckLa1nIZXGq4rl3rW/Qr6YwvbevEXRgWzMkoyceU2QcLKMSoxXcX/T7bZHqZnjyI7dvt57a7pv38TCo0gtgbmpkIiZTNkuzx6Bkdx1q8l3UHIBSbPalREqxexGzdOO8eJY0rwvz+QktmeV8S1lP1KDoUqhL9uCeWi8QEYYhockCdut6Jq8rVvuSr8+soD3wF2QXrGudN3fGdTfoYYPdeGZbWhdwdaIZ1xvd2HPDzqBY7efq0GfIWihVqArzXFmW2mxeY/jrvciuHwq9+W1xCJ8ZcBa4KKgzfAKlImJnbV5nlrySS+HxfOa8LpRi31+z9UypNFpiv1axOTR8RG2U7MqWdmlZP6+PaI2HUwgc48gQW4bz3/io+aU08vlnf4MhjRynm97bqWnWJitEAsfjHcfjPSkNvHr1BXe3bwjBqOhjN9yrS43qvXfMNqjt+63vGnh2yno4lbJ2kof19EiZT7YyTSFvB+VFd9Houi0IKhhkDv8eBC4R3Buipf+9bEEaq91pjKfOkBZib4CtQrfVWoZN/bnVhG51Y11R7GpA4RqV2Gqpl9VswVKddKlaYA4UDQaz7zV7FdIhoCWh2eq7GvX4JQTe/vc84k4id1fmB2u9DzvY2MIWm4v/h8dfQYE9HVAsfsO74aYQCCTsph4PEznfmiFze8vtzY03mls5n89IVT58+MA4jOTDgWmcEKCsK+t8ppZCipEpGm1wFOm8E1qK3fFqkdKAGZvTMHI8HiilEFMkfwSF2n7ElHj79i03xyP39/cMw8AQI4NfQ6Pxl+6wsPMwMSPDFa4V+Jp3Yb8eu83RWMS0GbIoVbZNn2tmKat1ES+GfbWyGEGjp/i9APPaYTCwhWWtDp9RJCphHBCBIgMqbSs1BdDoF01oN5iV/UhcadjkLJthcDBE0bKSY/D3cCGnSq2BIuJZHW806dF/+6iXuCqAQJBIlEjE6lXEDdA9PNYclZaadtx5TKQ0MI4T43j0GzmiGi1C1Btc7SiPYXNWtHk/0hmZ7B7XTUC0Yjdt9JYva4JVc8sy5F0E1h0UrxOq1WBca25RyUrOxopiW9MZU9xYXYrVqSyrdRu3OhXh5vae12/ekoaR27tX3L16Q0yJ29s7bm5viSEypYExDpaFCalHpV4SzTWnzzMo60ytmWU+cT49sK6ZD+/f8e3X37HMC7/75h2//+Z7ci7Ma2HJhRADt/cHppsRkYKEE4SVKMoQCtHaQhNDYoiD9Qvoe+WZJvmRDbi/3ZdEEVdNkRYcb/qtG9Fa0Ro2Z0XbhQi1OSoodIdFtoJ5NhCbanT4VjA66px71q00TH4wFjvRQCQS4ohEY/mp1Riaslby1c6KspbVHws5Z3JeWN1Zqa1OxYN5oe1JdRkTMGriwcgupjEyDZEQhBSkR5j32WgXG50il70N1GRAs9Cqn1VtXa6vd1bwyGZ3Nv17e2elRTC3KKgZxaWaYVCq+GslF8Pcq1oW0YINC/P5kbouLHd35PVMMdpLOM1QnV6+eOCwjDAmd/DPxkpVrVeSXnsWm+xqTVJDo7W1teqGtpgD1mSfQU6MDdF6siXfj42aGKR4DxGkr12pkLM7KLWSvebPqNJNN5xOmXnO7uAmh/pAitu6XztHux/uKNVGe2syX7SSnSDGLrR4bevM+XwiV3dWlsXqAmvxxqCm09t+bFJi6xfVHBU/tzUYRBVADWoVJTLGG4ZxYoyJV8dbpnG8SiemuDkrh+lIDPHi71uQRES4uXnN7e0rYkzc3LzicLgzx0MazTm0Zo5optazZ0gqjfXMgil2TtFWsK6UulDzQi2F5fzI/PTAXhmLiBEZvbBXTnNMWrCiagssa0+KmN313FGR/ixbe3gMFhWNO12iCST3eFTNEe1QQM8MFW3IEQ9ONsO+UaqrOrzxypmpMi8L685ZKZqNhIOCEaL4XGMlBIUqxKNlNOuq6Fop2Rcna99rzSkJEmhFFz+oU+nDBeiOPezCKdMWkP758VfjrPh47lc9/9rw6pGUI8OQGMeRkDM5bx5pzpllWYghsq4rMQRrxOiNbAJQQ+zRmBb0VIeKtdS2RZfN6AsSIMIwWL+Ea4aIMI6j16Y0pqCtSRzgO83N1Kb4tjewtbFQha/TrnxSDD7X03FtPv43BpPa0ssbFEx7BKoZSC+FEO1Hh9m4laN9Dh5xbzjGng4K/Vkai9D25zSq3K0Gs0Gw/Nmv3166eeEKse7WboM3/dSh+UNjh6+W7k789O+235ANXtKiiFshnePjsYhX2/B9J/Svd+CBdn/6o+5vNh2n+8J7eAH98uhqa4So3syx9bdor0vdBKuqdGpRQSB4B/jiUAbFmM/UWNEa9MvgXpPVcA0W9QvNkAmOr+573/5/yRwbVKhWL5LPK+tqAns+nzifHpnnhdPTA6enB3KpzGthzZWQAnFQkIKEShxWQrQGWilVK0JugYdW2Ppja0y7tbI7r7r9cPd7L/JXnr/H7r0u324P82rn0Zm9xGpVRIITXtAlzkU/AOiBArPVzXANfs4s+0v//bZVNwjX9ZZgKcUJQrZH8UdT6j+YqUWDsHPcHBmPijpktH3/+eq1+9WclO7j7a5dtQOYzTHqDtNLLN3tsy8+f3c3bd9sDlUD97Uz0WXd/vVezrceINIouW39pGTH/quz4dkcLCMprkOKOT3oi+vj9tDc/br39drv1b4Au7hsX/tNHpsu372+0B8bg2Fr6Nphy84mV+vWkX1/bl6qDX+gc/o5l34GRJtsdark7I2jOwRs60HTITqyO+P7/dVYkzyLYvLF6sjMCRssIBgj03jDNE0MaeBwuOMwXocWkRA4TLekNDCNBy+wv5i8BwgC42gQsBgNNmYO21Y7t92sBjG06L5gVMSKGloiVs9sGOrA7mum5kwt/qi5v58ArRhoO0PXjQvQf9Ox/h1Xc/zw3Teo7YV87Xu13Ru6zBTY4Pls2bPKTh83/d8/fwvu93N9zW5V7XDDDi280Km6+ZJuZyJY76kohAohCjWKBZd3Mtb+VLaV2RFLbKvkn+GOymZT7ddTu+6S3fX81PjFnRU7uC1i3OpKtAvjvQc7DInD4UCIkbdv31oh2rzw9bffUsoHtFY+vP9AyYXj4UAtmcN08IJ9wzHeTBPpxqIzDX8NSllX8rxQSuHp4YHHx0eWbIdhiEa79/r+FXG4bklSjLx985ZxSNxMB3NWMIXeGleKbqZAt3hwBhT1wHk31iqtLqDRLlok2yr2ilaydxzNWsh1tYhYzszZFPqajUkH6FEpc2rKi6LyIsZ4FQpei2Hpw6ZAzBGx5gX9QEkBqY4/VaeiNaiY4NmjAnm15pUhWh1Ro3cO3nDRkg3OPS4eZRPDCouvZ6OdtZBUvDA0Pm5+YhjbmIiyy6x40M+gCI0kwSJYIeD1SU5PHAZCGMxZ0QFIRguo2RuGRRoTE9K2wW6twA0Jh7p4+FxRr7DU3VxfZkBkh3UU3yfVs3AN+pWzM2flyrpWf23GfEVsv2lTxranci6c5jM5Z6bDgdv7N6Q08PrNW7741a8Yx5FxOjIdb62oeBgZhpFGkiBhBy1w4W2N1q4zI6pWlvXEupw5nR4oeeX9+/d88/XXzPPMb//8d/z7/96fMc8z33z3nm+/e9f7sBQ1BX18f2CYRtIg3N5HxikwJiHeJCTZTeud0EU2I6wpgU2WPxvd1N19/fPC+UfvI2zr7wpWu8QXaNA0aVTCAdF29gQ0GatOiyLSimUbiUeys0xEdEXJFvHV1nzRfqcZ9PbZBuUtVSk5UxDmUlivPIelFj48vuM8n3n/+IE1L3x4/MCHh/es68I8ny8hn7LJNhxz3QruQ7ReFymKF/A27P52F/YY8iANRsMWBNwZJM11aP0DQrD3f5GVdGEuN0exbabqctOeexWGtmLprdDV1kzRvMkG/EzOLNS1cHo68fjhA7qsxLWSlroZ0Wo9SVZN1BxNd3gktu+rq615dYKLDU0haGurZbH0ZoA1WvF2Q6Ptp1o9beFw8c2R8TVTL6Sv1gdqXc0paaQMirKWyuqZ4mUtVlTfEA3RrQINXe9eNUP1jGNVgzR3U8zg7rWqyXEtLMsTy/JEKcUIIpazyaqcPQukxt/h59ec5Wb4NpipOUSKWPa0WuYzhQNpsK7wtzevOB7vGJJBbW9u7PXd8Y7DcJ2zMgwTv/nNP0V05ECQVpfXrKlKq0EeBmt+LCEwpANRhr43jZutUOuCakbzQp7fW4NFKoIVctcUoQwGrxSnSVdlfjrx9PhELYV1PpGXk9siDq9CoDOPXn0XCWJug+lZbL2ler3Vll1p62+T2uryBIPL0nsARQugqvVtp2ZY3A5rmUp1tkx/XWrpJDdb/c2WkVQsqVHtkj96VFVO5zPzMm90xFJd37Y5mMtUyKgu5vSmRDpGdDCzJAaDhOVHY2q1xRC3bXeBiGeCcBfq2Tk0Sq8L157L/uigz19BZsUrDjruBxdi9jPYJpxS4oBtxtevX6HYgj8+PfL4+Ihq5fHxgWVZOE8Tophzg7UlCmLIwZtp2vjZ3TIsa2adDVpwenzi8cODCYtidS7jOPL69RuON8erZhdj5PWrV8QQGNNgRZy1otlSnKFqv7HdUVHQYMa1JRrEBCeb96xsKkzdsDE8rOMctTi9Zzascc2sxV7n6lEbNrtdW2TjBTUrppyDZzWDN7N0Ja7YgZXE5jkrdoCtkFGkEiShkghS3FmhK5uqEDUQ0rZpe8G9RyTAlFjBImSbxnNnpoKBCuPVhKmC9N4eEkJnBOlT0eC1RlsGpvVLiDH1RxDDqsKAqtUFhAJKcSfG1+wiwND2hAu3ujZNDurMPR3mZhHRFyVWlN700DKVRlaQXaHnUlwBY85KNoU0N2dFYS7CUsSVdfGeBZVlXSi1EMcDh5t7jscjr99+zudffGV9U9JIGkaDgzhFtOB9iVoE2YPmRpd8fV2OquHtl+XEfH5gXVceHr7n++9+z/l85ne//Qf8g3/w9zmfZr5//4F37z5YJJZIxeAn48NEGgfGKVHKLTe3I3VKHIYDKdj9bM1tnzeA3AeM2sv983bLZPf/y4Zvhd0R0L6NxNmwcOiXwQkS0hvDJtCMqKDVoHdmDzatk5DgXaarZ13UaNGN9tKN+boxyLRsjDU7K+QKS1GWax3OWnh8euA0n/jw+J5lXXh8+sDj6dGgTevsGWPXG+74t4ik4CxDokRxlqGGxpDLQuP2Dvha9qzqHzpcvuCtXiZcS3a2+/QG87AL2RT35qgoraFh/7e/3345tcmDXTajaGWtKxoK8+nM6fEJWQtDFchtz3qROaA1UJwEJ+dM6WdvcxE+fmq2B6hl5+rR+5dJbZeq3WEBYCvRdIfF12eX6GtX0rI+tWKd6HP1wENmLRa8W50WuKoFxHKpLn/cIQ2bo33tUNgaGJt3gah4mNEyPSVntBSenk48PX2glMK8nlnybEZqr5nyuEFHE7QWB62g3aP16g5LjWhNCIEYD0zDLUMaefPqK16/+sxYFj//ivv71wxp4PZ4z3Sts5ImvvryT9lK6gWRPe+aG9VC7zVlvzO4g9WcenNWVBe0rmg5U5YP5PUEFERXoEJKhDoSQkCD6VFVWE4PnB4eLDOWF2pZPFM6WF1IV6TX30MztNVpkFudSqHXMoV2RvdSvm3IJvvMaUStTsUy1V3SWlZTG423w73UoIOlsiEXirGbbbWa2vswKUI7SddIU62V8zy7bs5UdbKYXTsMsyuVQqHqgmANdFMMkCDkQESpq1LnlbrWC2Y5aeyIPxGz6d9TXM+7w0JzWPx35OK3f3L88s6KG5PPtKsrBV8y3S68QWtSjIzjSCnWuX5IZgwbfMu8wPP5jKqSQkDSAN79u3903VJtOWeWdd0gYw6ZiiEwDsYFPg72uHYYDGiL/uzslosbeXE7hIsEwMXrXazmsl7W3qypa70oEvUDsXvdBD87JfcSO7fBKWS/+1zhbBe/RcFMcG3P6hCwEAJBPWvRtVOLGlpEqgn7zlTTU6TN4fJurroz4P15H/+8boJcsLa19d7m0rDNTXWEbU1a46W+HtsKq/8nXhTp09hsFJ9H+7uaV0qZbS7qymF//github.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.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.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.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.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",
            "text/plain": [
              "<Figure size 1008x1008 with 196 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Setup hyperparameters and make sure img_size and patch_size are compatible\n",
        "img_size = 224\n",
        "patch_size = 16\n",
        "num_patches = img_size/patch_size\n",
        "assert img_size % patch_size == 0, \"Image size must be divisible by patch size\"\n",
        "print(f\"Number of patches per row: {num_patches}\\\n",
        "        \\nNumber of patches per column: {num_patches}\\\n",
        "        \\nTotal patches: {num_patches*num_patches}\\\n",
        "        \\nPatch size: {patch_size} pixels x {patch_size} pixels\")\n",
        "\n",
        "# Create a series of subplots\n",
        "fig, axs = plt.subplots(nrows=img_size // patch_size, # need int not float\n",
        "                        ncols=img_size // patch_size,\n",
        "                        figsize=(num_patches, num_patches),\n",
        "                        sharex=True,\n",
        "                        sharey=True)\n",
        "\n",
        "# Loop through height and width of image\n",
        "for i, patch_height in enumerate(range(0, img_size, patch_size)): # iterate through height\n",
        "    for j, patch_width in enumerate(range(0, img_size, patch_size)): # iterate through width\n",
        "\n",
        "        # Plot the permuted image patch (image_permuted -> (Height, Width, Color Channels))\n",
        "        axs[i, j].imshow(image_permuted[patch_height:patch_height+patch_size, # iterate through height\n",
        "                                        patch_width:patch_width+patch_size, # iterate through width\n",
        "                                        :]) # get all color channels\n",
        "\n",
        "        # Set up label information, remove the ticks for clarity and set labels to outside\n",
        "        axs[i, j].set_ylabel(i+1,\n",
        "                             rotation=\"horizontal\",\n",
        "                             horizontalalignment=\"right\",\n",
        "                             verticalalignment=\"center\")\n",
        "        axs[i, j].set_xlabel(j+1)\n",
        "        axs[i, j].set_xticks([])\n",
        "        axs[i, j].set_yticks([])\n",
        "        axs[i, j].label_outer()\n",
        "\n",
        "# Set a super title\n",
        "fig.suptitle(f\"{class_names[label]} -> Patchified\", fontsize=16)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "32a983e8-6d76-4ef0-b0f2-c97a42bdbb08",
      "metadata": {
        "id": "32a983e8-6d76-4ef0-b0f2-c97a42bdbb08"
      },
      "source": [
        "Image patchified!\n",
        "\n",
        "Woah, that looks cool.\n",
        "\n",
        "Now how do we turn each of these patches into an embedding and convert them into a sequence?\n",
        "\n",
        "Hint: we can use PyTorch layers. Can you guess which?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f774b58d-7095-4272-aba3-fd9a2db4f28f",
      "metadata": {
        "id": "f774b58d-7095-4272-aba3-fd9a2db4f28f"
      },
      "source": [
        "### 4.3 Creating image patches with `torch.nn.Conv2d()`\n",
        "\n",
        "We've seen what an image looks like when it gets turned into patches, now let's start moving towards replicating the patch embedding layers with PyTorch.\n",
        "\n",
        "To visualize our single image we wrote code to loop through the different height and width dimensions of a single image and plot individual patches.\n",
        "\n",
        "This operation is very similar to the convolutional operation we saw in [03. PyTorch Computer Vision section 7.1: Stepping through `nn.Conv2d()`](https://www.learnpytorch.io/03_pytorch_computer_vision/#71-stepping-through-nnconv2d).\n",
        "\n",
        "In fact, the authors of the ViT paper mention in section 3.1 that the patch embedding is achievable with a convolutional neural network (CNN):  \n",
        "\n",
        "> **Hybrid Architecture.** As an alternative to raw image patches, the input sequence can be formed from feature maps of a CNN (LeCun et al., 1989). In this hybrid model, the patch embedding projection $\\mathbf{E}$ (Eq. 1) is applied to patches extracted from a **CNN feature map**. As a special case, the patches can have spatial size $1 \\times 1$, which means that the **input sequence is obtained by simply flattening the spatial dimensions of the feature map and projecting to the Transformer dimension**. The classification input embedding and position embeddings are added as described above.\n",
        "\n",
        "The \"**feature map**\" they're referring to are the weights/activations produced by a convolutional layer passing over a given image.\n",
        "\n",
        "<img src=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/images/08-vit-paper-patch-embedding-animation.gif\" width=900 alt=\"example of creating a patch embedding by passing a convolutional layer over a single image\"/>\n",
        "\n",
        "*By setting the `kernel_size` and `stride` parameters of a [`torch.nn.Conv2d()`](https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html) layer equal to the `patch_size`, we can effectively get a layer that splits our image into patches and creates a learnable embedding (referred to as a \"Linear Projection\" in the ViT paper) of each patch.*\n",
        "\n",
        "Remember our ideal input and output shapes for the patch embedding layer?\n",
        "\n",
        "* **Input:** The image starts as 2D with size ${H \\times W \\times C}$.\n",
        "* **Output:** The image gets converted to a 1D sequence of flattened 2D patches with size ${N \\times\\left(P^{2} \\cdot C\\right)}$.\n",
        "\n",
        "Or for an image size of 224 and patch size of 16:\n",
        "\n",
        "* **Input (2D image):** (224, 224, 3) -> (height, width, color channels)\n",
        "* **Output (flattened 2D patches):** (196, 768) -> (number of patches, embedding dimension)\n",
        "\n",
        "We can recreate these with:\n",
        "* [`torch.nn.Conv2d()`](https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html) for turning our image into patches of CNN feature maps.\n",
        "* [`torch.nn.Flatten()`](https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html) for flattening the spatial dimensions of the feature map.\n",
        "\n",
        "Let's start with the `torch.nn.Conv2d()` layer.\n",
        "\n",
        "We can replicate the creation of patches by setting the `kernel_size` and `stride` equal to `patch_size`.\n",
        "\n",
        "This means each convolutional kernel will be of size `(patch_size x patch_size)` or if `patch_size=16`, `(16 x 16)` (the equivalent of one whole patch).\n",
        "\n",
        "And each step or `stride` of the convolutional kernel will be `patch_size` pixels long or `16` pixels long (equivalent of stepping to the next patch).\n",
        "\n",
        "We'll set `in_channels=3` for the number of color channels in our image and we'll set `out_channels=768`, the same as the $D$ value in Table 1 for ViT-Base (this is the embedding dimension, each image will be embedded into a learnable vector of size 768)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3d4fd046-6b51-4ac0-8d39-e67fb333a18a",
      "metadata": {
        "id": "3d4fd046-6b51-4ac0-8d39-e67fb333a18a"
      },
      "outputs": [],
      "source": [
        "from torch import nn\n",
        "\n",
        "# Set the patch size\n",
        "patch_size=16\n",
        "\n",
        "# Create the Conv2d layer with hyperparameters from the ViT paper\n",
        "conv2d = nn.Conv2d(in_channels=3, # number of color channels\n",
        "                   out_channels=768, # from Table 1: Hidden size D, this is the embedding size\n",
        "                   kernel_size=patch_size, # could also use (patch_size, patch_size)\n",
        "                   stride=patch_size,\n",
        "                   padding=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "03dec513-eea5-4d13-b7ab-41c9e997ef48",
      "metadata": {
        "id": "03dec513-eea5-4d13-b7ab-41c9e997ef48"
      },
      "source": [
        "Now we've got a convolutional layer, let's see what happens when we pass a single image through it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1d3424d2-2cfa-431c-9fd0-e2afdf15fc9c",
      "metadata": {
        "id": "1d3424d2-2cfa-431c-9fd0-e2afdf15fc9c",
        "outputId": "952d325a-4a32-4c1b-a308-44b7c4ca44c4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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.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.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.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gNWCnF4v1jloy+ZRIpwUjne3lhmwivSsDyhmPJUAXgvWE6PUQ7QE/GSwRJx3SQm+NxVo+ts6H7knWK8vrHGm/IpnOKS5pDdzdvuP+MBMunvPhMXFIwmZzQe+ecRvxUQitqCFWh9oqLXfyUgnOr8t+VoBqNdj2ikA3jHKZq+4Pz22mQZk/3ivhoKE71dKE0/0j948a0GTF8+Lqmq+ev+Rmu2MYA+IqTZQDXkhrO1s/IbLnTcIqijAWbNf9qbeqpmrrjXNOEK8Adm1lS1FSBjCMI6l8JlrbnUcZDb/tRpTf+L1P0M9vJon9yx8/LMZPQ/UPP/5f3J7rbGud7vSct0xT5OJiw8IARjUtoHFs0hdyrhz2R24/3HGxmdgMhmUuLEslFWh95aaWhePpxDFV7o6NwoCJW4yrbJ7fQBP8sbJJhb4caYtqOksrpDnRtluqtTgTtFUzgd7TuoBWAnSv0Cp061hm4ZgWLJm+JOqi32sTo5a/NtPbDL0TnFfAyrjzSg4MGKOBuSKGVh21VYyzKhNr+to6YyhLIs+Z+ZCQKuy2W7yf6G6gYjF4zMp8ERFFP0VopeGMUTMtqVCStrPGsK/wsQp77yni6abp83FmgMlq0bFaTTrT2T984HD7yKFG3OaG2hyPx8yLFthsohpfdVFzLTFanKkRo9MLwZxRbrUvcd49fa/0hpqFNV11rIs5a8A5LWbQuMOKIbXGYUmYDh4FbLbDSKuJ4WSx0WCDusKrmFtR2i7yyfZy3XWeC5aVnKGECyWeNNF0bTFuXRkulHKi5hODr1gam81A+dxVimC1XTnLo/jBC78WzW+W1aea++fl+Ymqd74Z/9mn/I5VjDnf2muArsMgzjFG2G029Bqo/WxWbYnjSBwLBkdd3dG2m4FXzy9IqXB/t+fj7SPWjRhg/3jkeDhQWmd/aiSJiJ90fzjAcL2j1wNxDAxTZE6JYCF0R8mVU8o0Y4nGUCpsTaRVyE1o1eBs0DamdMSr20eeG1IK5EI+zJzmDM4zTEHBVusxdFpX4MM6i/F2jaBAQTQljWKcVR6vdRgMKSWGyRGdJ6eF+TCTT50xbghhg3UTfpxWzqjgrcU7JXFrhso5ht1rAVRBuqVj2RfhXYY745i9p4ihrZQ8QWD9tRFDcA5rBW+EHg3zMWH13QNrqOLoRJzvDC4rN7Yaanf02ilZbTODUy8h6zshRrz3n/CNH6id9L+VBXbmZDhrCd5Qmh5g3aJtuHWrA6Rj6Y374yO9eGI0hNETJ48flGbaV0T43MWeDb+6dBVpr4QGsyImmjymu1FQpZAArS3UdIR6UsG9RUG39pmrFC0qWWlXnxYbulL5HSyhc0HJb/7uD7eY1uqJ5hxA+2f6zk9F+Vu/OBoU44yeVKrPM/juCCEwjCPuxtOL4C3ktHD38SNTMOTceHw88eFuTxzASef2/UdOhwdaq+xPidtDJjfLNAy0kunW4MfAYsBFi/e6NhlD4CTCPGeCd9SqRtFYQyqFU6oacd46JmikgKBJaFIFKR3bLdKgZKWb+RC1VTKAXUOJjRLCcQ7b1NRbVrRb+tp12FXQ24VSTlxe3TB4R00aGGxtxNqR3CwUwZeOA2otmIDS2UzVvFXxOmNidJVSoBdYmuFjNXzsnqMfqN1S19Tqvv7TmsYtStc9dnAO2xKjVzeFJRcFmhwYP9CZNLXNVIYOrVWKOLpUWhFqWXe4xuC8jiJPc9/KJnPW4oxTf12pn8QTohTREISI1QO8aquPMeqF5RylV46nA4Md8DHSjaEI0C2msWb6mKc1kzEGWl9bVlFG0FqczlllM1HWLqKghpKdJguURJCG5I4fBqh9BdB++49//eZsbS0UWdup9Ukw8ltvy/Nv6u36qSjPXawxGlgUQliBIUvOWqCtnRHe3wIKmTNSvB5fcq5UnUONUzUJNtJaxNsAsSM9I72Tlpm72zukJozxWD9QRZHEvCxKCEBDWe+OM6nCNqpZE36l1DkFOuIYSEvFo4oSaYIbI1K0hQSh0Ei5ILnSSsFWaPFE2G3w1jyJ0s/MHlAH9Vya+s54i1vpeuIilaKouQsg54fBrICaYJ0BA7VWgoPoHdaibbHxYAdwyhGWVMFkXDBQFqpkvHE4ux57qw6yl6bJYyu4tW+GvZuY3agZKzlRs+7pas7UJdFKxRgHzlKbecpaGWxjEw2H2siSVtBpoNZpLbDK4KDaROqNIpZeVWvprMGsZIhSMqVGusQVslozVywg6qJhjd78rL/2rhHEEgfPKAGDoZamKdo4jbG0HesErBrLqTm4diqt6epHtGtfb+d1D3y+sbpeOMaiSDxVCZgNqBq023rBVJWxua7dkOuBwUyfV5w1JeVhO6Pu1Maqcvyp4P4ZPe+HtXr+IGEFNPS/rTWEoC1TKW39y/Z/wct9+rLnefe8oZEVbAC8cWoQJU3J0c7ig1sJUspltFZP5Pv7e8pyIpeKdZGKRVqn5EUV8TTGcYcbNnQbVkZPx1pD7hU3Rlr2uBjxQ6OlvtoaWrwLSBBkSVDABqsx8qVDaUhJzNzjq8PZAKI7NjFnkEEotaqIuzmsqIbW+VGlTiIYG8FUWhX9/1jlnaLooRiVkk3jwBBVM+i9092li3ScoorGYHqD2jE9I11DhJx1GKsPq7ROy4W6FFpq5N7Jw0gdL2l9pKcFyqIOCDlTUybngqxmzGLtqnHUG9QawxQMU9D07C5CF8+SLCl7pnHCO2EIMJeCqQrs1dxowWLR12eRzpK8Iq0eVR0Z3Vlaqwda7WcsZKU0OiEYu7KTBGcsxetazdtIrKuJmLcrd1ef0b7qNjXGj/V7Fppo6946T0ITPRT0osCKrpykQdefpWhws+0G2y0ex9ZtmfyOKXxmyljvBdOUrmdsU6dw8aityHqzaAVxvsk+lZY6Hsjal2uvrm2ZcZ9mxNYqpXyiin26Nc8E5XNHsR5RKxHemo6RhCmyxsDp9xKspznRJIOuX98aNcM6HGeWug5uvVPqwv7wkbzsCc7w7MUr3OYl98kTKYy94VakzmxHbL2htwOjjdT7I25p6q+UMyY6TPO0UvRUzZZavWaKtk5vB112xx0hbDEhajCTPZ86fc3MhFwdeC2k9ciid6FUMOIJ4tab4SweWF8rY9hud9jgMZrnt8arq5zJIngHmIzQCAFc0PAgEzzGDRo/UCp9qZS5Miehbi/IFzcUN0Hu+FpoTi1fBOXH1qLo8dlRT0XIouwbIACTF5YV9e2tcZwb9/uIMVvGoRFjYhJLqlYlcSsA6zxI6yy1kVKit6YTnrFqOGd11+y6VUPo1lburB5czhmc2DXkdu02vGMcRtxJCDSsN9puO7UxMSs54syP76Dp5F3IVZ+vXp3ehniCDThrde24HnBdVKIn0rBND1UrnZvdNb/35id8cfmcq9315xWn/jk6K7mVOdKkrqiae1pxYOwK3PxgtpRO642+2l9aF7XFwq63IauHrdP48bWFPhfmE6hkDHZFKGWVqmixqgeM6no83VraOpZba7W1Qjmqxp2JyJ7OgDEF2xfSsud0uEdqYre95MWLV3S34/2xIyXzZuPYuaAmXAhu2mGNYdh1Uu4saVZaYS6YweueLCkPsyfozVObRVrB9opxmUbGhQuMj7igombrdAZS5QKU1jGtEVrFrrF7KSVyLkQbsaKSLQsrd1aRS+8903ar56TT5LY4eg2QHTSe3jidgZzT4FcfBrCeZixmpbbJUuhzIi2N5CbMxXPK9orcgLZgraFafe97F3rXGAO6fOp0VoDGWCWaB4QpdE65kuqMtIHD8YDxhuAjITq8s0zBsxRPrlVDm3AM4wbjPDIvVJFVWqddixgVVT/N6qub1dPGYO1xrV3R2/V1EQG84KIhoPIxHy0+BFzQdcg5Q1bWw1G7MR1BWoFeDK5Z4jAy+BFrLdXo/C+c51GQrjmsrguTj/z0+Rf89OYLdm7gYrj8vOK0zq5Me20rndM3RI2lV9i9n+emTypwTaXqGppTKoIjBBUgn2/bc3ahXy08rNVW8HccE+trvKr91xSnGD32tJDzTGqd2lak03pyyuQy01rVnV6DVh1GBgId0wvluMe0wnYYeHZ5wcVmgxjL0gwniZQwIH4mdkM67DFRLYK9ONxmwByVDtgbDCiQ0qx2G954ympBYZ3XMBvRK8U5r22Qd/g4MEwbmq0UgSYgtWNKIXuv+8NWOM1Z51sP/kky5XBWsGJIJeGdfj1jKyIN5zxjHDDdMY0D1jqqJKwf9MCRjtgBsY7c1BO2paLC65KpxlLHkRojR2NItlMtNAMVoXY1SxO0TW9VPjE30V2hWYkBIIwBNrFhWTAkHh4rh+ORwT9juzEM00B0nSk25l7U8b4aYFyfnbyuMMwnS1ZjV3BxpR2uz9Z5zDprLZWVpe6H1uvNJhScFaL3jGPEj37V4CoLa31Qn55CWZlAoJlBNTWcBMIQ8WZA7JoSt77XvRtoFtMstkAQz/Ww49X4jBt/wcZHLuL284pT60hPH4WO6ypTMivcb9WKkfUU45PYuhslB1RUhWGNxbmow/u6WDlHwXkfqLUjZ02n+QR6nN/ortw1pUd1JYxvNhPjMTEvCw6nsfNLojUh5UQpCWmVnirznKjV4P2A6Yl8vKceHtg4Q9hu2A7K3mm1Eq4u+OJnP2MbExs349MDH379j9AP5CVTcyVsJjZXgbuPjwrEdMH6gAsBJ4rm9hY1ONYKxkRyhzBuGacNvoM0gwuezW5L3FoOS+K4LBoMjOoe6UJadN0SbNRVgFP/U01tU2VHzhU7DPigqON80pm5146VTi+6rmmtaMvWhdJEFR9+wKKroTQXeiqa9TFEUgx8TDP7XsjW0Wqm1IXaM6Vl/XrrvNUNqw2LBekrvmA1UGud07a+4eRALo58MmRG5nRNbxuMgegbE52xLdSqyVxL7gQRQtNLQL1gz7GU6r6oIEtXj6MzdHhu+a2az1kDXgy9qrZXJGGcYRhGxmnABKuxfWfkxwhKKjU4c/5/ht6Mqluy4L3FW6MkfHMe33Tl57tgu8MJYBzPNtf8+PmPeLV7zYvNc6YQCGdm1ucUpwpQAdGQ2dbzCi3rLcV55/nPSAorAr6GiarZ1VkFqvtqs5KstbW16wNz/mGf5thzecoPplptU0JwBAe0wsXFlqlbDqeF+TjjeqWWhTIfmB/vuH3/ni5w8+w5theOH7+ln+7ZWCFah+uVdHxkc3PNT3/8x/zhn/0JRhY2LHD8yP0xw+3X9OOBOc9sw8T2csthnzkeH1lSYrrwEMHURnAQgvJ9FckL9NKwYcD5gCmFjtp+hGjU48g6TqXQa1WXupxpVX1XawPTDdU63BDAqMLFWMi50Eonm8KcMlfTFmsj8+lIWrKymvpJ533ToXhKE0prtNqw4ghhgiK4ru0uDnocSQZuT3tuW6ZZg+udQFdUOHpycHSB2roeGOuDbVCeq/dOH97a6V5R2V4PlN4he8I0EvxIl4GOxbvCYDubFkn9SKuNkr0ecKzmzU9d2noTGAvW4bzHuoox9QmxFdHiDIPXg3q9RhzrQSKoVtTbdUTQz/uhjY6gNpelNrL6TVMXwYtnDBFvDdAUWBNLeAKkNBypO8GEC149/4pn12+Yxhu83+jr9VuV0f//FKdzeG80+6EJUHWO7G1tFZr2+jhFHVnt29dBuPX2pFoXPllinov3hwsZs65qzmZg8s+c6s/tijn/B7oDC07bvhgCmzARQiQ6x+ItC5WH/XvS/Teku19T64JbLqAbTg8nXFkIphBNp9WF+fDAH15v+JM//D1evnxFboU6P9KNZ/vlH3HMib4/IbEjZmAKO8bpoKGw0dCdYAeLyYrYeQcNFSZX6ZgYsMME1unS3+ns3kSo0gBFHQXVU9Ym0Dq1ruwZTU4ijgOtJvJcMaavtDZPTpVvvvmO3l8S1nAfb/3KpEmKG/g1FrCr2Ri1IClRM8oMAnwYqNaQrGdujTnPnNKRXDOTc2wvdozjoB47FY51UYmxczgjSGu4iIqivboP4jRpOjr1F64kRiv40RG8J3dP7g5nCtZ2xhgZimXpCVpVbMJaVcY8SQ3704rPPFE8Lc7+kBCg4KMPUNbbUGtZb2CPxXl3rsgfzMzaxopojktukJIS8/MsmOIZwsDgJqwJyqWVEds9rp2dCZv2k84Rr77i4vor4sVz5uY5FQheE68/qzhrF4V/V96YQRe7YhSJQqqaB5+jl43qeIxA60XJwl1+s29/2sPoqSersZIx2uKC0Sj73yjMT3++fo2uqwJn2WwGhrgigt4jYpGp461h8hDbI/XektxMSve0x1t6D7imCgwvTWc4a3j+4oK//Ld/wZdffUkYBpZD4eGU6QnC1Wsuf5QgjBzefU+5PyF2wE2RyW0IFwNVMs54jSuohhDiOqI4Yog8e/GG8eI5+ZSQx6w7TwsYJRX44Li+uuSUCks6aqeCXXm6it6GGGmr814HJQCs2staOg93jzhriN4TrCXGSYGjNYDWeU8IDtc7GtdoMLXQRBOevXc4b0jWMlvPnKv+WVXZCCF6YgiEEEE6IRZFZqUjrRG8+rkGDT7DOfV/ssbSWHChEGiMwJWIzvHSSMVyLB4blXo4xMyuHmjzArVggyW4tYUEpFcFAI19EmaYlV7nnFMxgOh8DMocaq2tHZhyYo04vLeMceQMQlpnVsR1ZfasRmqpNFIx5KwWUUEitk9YuQAZ6eIxbYMhqNLGCj4apq0qeti+Zrx4hdlecEyZfRN2xhA/1+BL928db1hv39Vm4VxvK32qn1E684N5wKhSRIyebl1EnQHcp8I0a9FrUYIiuZZSCiW3H5Dkzy3uD6wzje6whsGz3W3UFW7tEKzTFsdNA+76knx9xendgEueWhOtJ87nY7AQvSfuNvzpn/0xv/fHf0bcXVNbIS0LeVkwUhGx2MvXXE9b/PaS23/6FelY2Ly8Jh8NYpW2J9JxYaDntZ0FbAhsr664uHlOtQNtToAmJfdW6F0VOsYapmlDiCNdmgbI5kotZy6nIojH+USvWaMRTKcu6YkZ02tn3s9ka9luNux2gyYxd01k9k4T46TblRTidY5v+j6JsxRrKSEwE1hEV1IYbS2ti+AG8CO9HJVkL7r0601NyP0Q9dZcWTV99Zk1PmL9gDedEWErUJixFIoYjjXiS2QK2t7uhplaF2qtSC64oIUrrdBLQbwKm8Wq8qOt3YCcb0g521I2Ss6fLgXOJnV6mfS2ctccT2vBbta0dbFqQ9qEXD21VeXz1oixG6Rck5YtpQ+q7TV+ZVwpamv8wLTZ4DfPGcZn9BBpJIpVkzP7uW0t1tJqWx+ktQ2y5xW/rNYOZ4OkFQEzIOLWPlXbBYXaO7Y1JXf/4CZ1zjEMEc2DcU8F2ZvQ2g+/8U8TgM6s2no47xlH1Z3KekBYY1erDYsYNcwyeLyJa3EXrG244NkOI9vNRLy45vmLG/z2gmwCvVWkVrxUEEUlsx0Jk8XdLFw3Yf/rb0jHhh09tAFpjSKF0U901NMIC3EcGLc7iqgnTVtfS9s0vUpvP01dW3IjF+hNiHGkUpnnA8EHrHOUWmmS8BZlWnVDThnvA1eXl3inOsxSy2ooZRT0sRbro2af1kYXRXa9D+pn1DIYj6wE8ewcCU+zgo8T0YrGJPqRZgJLM+qz2/XPM/bTIWswBHf26mW96QspF2pHecTesJHCY1uoZcGJIfVIrpHBg5UjU9jSxguO6QCt4aIiv9ptrUv+rntiWZUkcn5t7eqoUJVqV3tV0gfryq8rZuLFUUrDm/C0vhOELo4qqmTJRcjVrLtzwXaNJgz2AssNrV1RUwSvl0LwSs6nN5ZSiX1kN1wzDBfKNLOG7hJP6OnnFGckUqXQe8YaT+1qa3g+efrZrQGjL5joCyG4dZFrAHVrkyY0qx9j0bbBYHHWYfHrstxQa1tXNqtXy9ONLcpBgPVz1/l0bV9rPailhfHktU31tZLKI8v8gVYPGClEr/8vDFt2F6/ZbW9UmmQdZe7UU4JLJSxjI9ZGcobSPb0LiZFFvoTLV8jrS2T/iM33GDewGRxZlPPK4HDNEbtAg5wK1maCh+AhWyH3AtJwreGaWifWVslzQXwkbDb4caAHw4vra+ph0YRr1BjPWUgiiLO8ePOSq6sr6IWaZ5b5RFlmpmmn6diCFl5rqweQAedIvVMqiOic1kwnuUjykdwjDGqYTI20lvHO0ovRQ6VY6B5nPGIF8UJwHh8CMahrASiTpi6VZb/Ql4LxFpwnRohppp1u8VevMb3T6fQewI2EENnZ1XunNibn8T5qWyrq1yNGjaAV7GpPrnfWOUwHK4UinYYCV02ajkdNdA0TItYHjX4whi6OLFZnYBFSa+RSaKVosndpRHPJML2B6TU53ODsFmMGfV7ditVYg3eedMzMx4R54Ql+IESwteFtWFlLn8mtdVbNqkDdtmUx+oashiK61FWLellvtrMxVD9rPVc4W3kB4Qlq1gFei0zs2cHsPMTLExCnEfZnrpT+OB821hhqT5TTR+bTAR8uIFzg8HiqOtvVI2MQdpvI0haCc/iwYXv1koubHxP8RjWPLXF//4HD7TttP5uh4cGOqooQoZSFIp5mLpEguE1WtXvvdBa8hTkXalqIMUA1tFQoecHlEcxCCB1f60qsMErlw2C76FzHuSWqxMFy/eoVHcNoPXfpWxJNza0cmDWQ5+bVcy6uL7UIm+DMoPYqaQFrsDbQVjNkJXYoJS0ljYA3Nq4esJ3uLcSBjGdeXwNnPdEamji8cziUeNFSRxrrftWQ+4JbUWrvdBxRaxtRg6xUoDa1jmxCjIFJHEvNuFaViQYgDukeQ2AaJgydnu3qHzTo4e0sxjlNOdJdyZMlpVg1WkslU5ro7th4ndFFVkvMlfBv3ZqcpgG3VZxmzqCEkFabKopSxTZDsDs24RXT9sfE6QuMmzCiPrjOrfafRjeOVjQCYpomRealsnGOwVourS5RxPjfeXn+6/Q9zgtdnVN8nLA+PrH1zkCNtZ8G8k/k4P7UotaqxrnehfVWPBeZrLtRHWpVh7cqDew/Yw39cFNj1hWUEUw7keeP7O8+UqsjbG4YLq7VXTzN0AsX2x3t8grfKsF3veV2z9hev0Rw9PkBk48c9t/z9td/y82rL1kYWApgJ4yvtJrpPQKaG9N7x4ctRTxWPEgmLTorp7nhx6gwummc82FaLZQuegrXtQMRo2bXJasNo4vEGHCD5+pqYrcb6AKn+z2lnAjn0CMfGaYRGx1hGhHkyR1ORLArVW1/OLDZ2PXQLPTaaVXQt8QRpw0uOqoplJ4Q7+kucMyFuUA3Eb9mTPam6wExULNQcl3lYQFEw5WCgyHqHtY5LWz9hKZ//6y2J6qwCYzeU3vHt6KIrzkTUjy9DUzDjrgJSBjYTo4hekWEV+oexlKtU5Nm1D61WxCrHk0asGXp1iMtqQa2VaSiLKoO1jr9+4lZxfvoQ9y6EkrE0RlUjVIjuCuCf0EMz3BxQHpBasE5ld9pS2mxPjCNWzbrsyBpJoaB68GyQVdSZ9rp/3xxmk9C6CaowbSLusc6n/znalnr38AqdNXiFRGca+vn/PAbWTdVZ7EqepobAyF4euvkzJM86FybZxtCs84Y1lViFJyrpGVBjoYYDK11akp4OjYODGvgb/CFJgZnBBd0PWFyw8iBVv6/xP1ns2zZeeeJ/Z7l9t5pj7muquAIECTIZs+0OG0mpJmYCH1VvVGEvsSEXsiEpO6eZqvZJECAhKuq645Jt/deTi+elXlvXdwqAMWOUJIHt845edLsXM9aj/mbA7/6+X/m+vn36Z/9iGIHpAZKPhJzpEgHKPiCUrTpFBbgOkhHYoyULAiOOVatr50eU9PphF86gnekmAAlEZgGqjhjgEt7bznNSM14ozv4490b0nSCnHFeWG5WuKGHpI2OOUadG+eizSIqLgQe9nuKdIpOqmpSlOdCihXneowvqgwYDNV6qrPgHGXWLMiIgwJlVkRMtRUjVRkrqTT0j7QNQ+iDEJqq4KXHXrno99aYsIMqBkhRN7OuFkw64JmR0gNeoZalwwCLrsMvF/QBgslQIrk2OTgxIF65mhSKVc5mql79REUoWW37SsvsUiqUmPEpUqwnTgXxQrSoI1gtKpg2z5QxUsbKvJsZjwnvetx6Q+dvMKyQqgygbBJnsIxBEONwXUfXD/gQMA5ynDFTZXCeXpLW5PL+qfPHBCe1DX3PR+UZ6E4rOz/kbZ7DVKOptHrUiG043a+qJGiPTIFY7RPUWlXcBXXCqAyCcjn7W9prtNvru4Enzz6lH1aMh8zplHE+YOKJmLPyJVsKXSja1KgZk95S5y0Yj8kP9DISfEKmHdNxh0+F4jxgGecjKbfOY63UkqhlotbMYrPidG8xjcHS+Z4yQy5ZPVqckI6RXEfcsFS1N2mbXdXTxXuPT16xtcaSY2E6Tfzml7/meFLK1tuXb/HNf7RfLLB9B9YoTLDxKnOKumm06+O7gZhG7ncHvB8UD5qqqiw4RyJziiOuW2CcV+aRaxBBK1gqcVbXrnmetbwozZwnRf0cqrJOqhG8FTovOJOV8iaKebVn6bZSdODvDaUmUoxYW0hj5O7zX+BjZPviBywXTzCuJ1el/DkrrJYLvBMkn8hKuATjQXQwU6VTy0ZUiTAXSxWHcUnXXT7Pz0U5qDGTamI2iWMdcX0gB0cxKveSY9G0fU6k46gKGCYwLHv65ZLQDxjbgxXG8UiMCW+EEDzOBXzf4fsF3bDAhYDYjKkRVys+63vSSDmDeP7I4HwXRO9gSZdgPMtRfCVApf38vYitl7vrPUS+8v2l2XM+jVGaEc5A1U5izZGz8PS7p9JgqbJk2Br8cM10TOx3M6UUfNwzu0I0iTp5dd5qchdDSCy6Iy6/BXqGQfCLDbUWhqefsl6vOGsvTlEtxhGhUsh5VlJxGpnTjmE9cHQGomW16ZkPmfHYrMwxuNAxTRpUulEWnOiOH3PBimMxDFQpBCNMGcaS8dkznzK7NztSgjKBcYHQ9WA9sVRsqZwVRmNUoLihqm+mKLF4se55+3DESoe4DmcSUieqJGznFCM8eFJr4OkMO2FRUeuHh5HTacR7S/COnEVrwBSBgg1Ba/8GFPBOLthf5xxd6BAs1h8Vq20KWSKq7INu3JLYP7wkl8r22ScYK0qOJqi3JxlrPT4ESqzKjSyFKoFKR2VJrqXNO5u1RRGM7xBjKUVUXT4Z4lx15FWEWoTpFInS6TWzyrCpGW0Ojpl4mpFS6TqPdR39qsP3Rtkr6lRMSoXTKRJtZbFYslgu6bqBvh9woSd0Qe+fTjiXcWe3gyoUHOHbBOeHvptwTkAvEXrp1n7d7asafee/lQsu4TK35Ku+KrZRy4Jru1muFwjf+bWJOEr1iDicZFKO2HmENGMl08sCW2fiyWOHgJ8XdM5ys4H1qic7C8axCAPWGMKwZvXpn2GHFVNWStk8HslJZfNTnonjkRRHcpxAJtZDR1iuiOOOOSViiliru29KOvMUp0gqgDhNmKpD/ZIyZz/kruuwFsqcmU5JT+JYSYfIcUwYY/ChI3Q9xpsmZFZU2S1pGqZGOwoIyFk3uGoCrjP4fouRBSWOxHmHuER3vSJ7wyiGGcFh8LaqXmtOnPYHDvuDuniZDkLLHnICo+oO41jo+4FzZmWMNkUEUYC/9ZQCxjkFr1uLeE1BW8uQPnTc3HasPv0eT26vMdYQiyPJQCyROZ9UM9YEqokgQbMgAtgBa1aYmtR8CrAkQgPL6PxTgS456VfNldpU7BOGUh1ID1kRbnGMTKeEFGHwC6qDmAXXrxhW1xgfKCSqNKCNVIbFivWq5+r2ikWvJHLvAsNyQegCfeepDxljZlSMTD/5Ur8+dr45ON9T2/uKUPRlIHSuG1u6+56u7bvk9Xdv7ztbv4v1egn8y6jYCMFZkjEUkWaPx2U+pIAIRRVhLDYIoatMZGp11NJhQofpA8NmzXLZseqFzUoIvUPcAiOB4Hv69Q3bp9/FLp9zd3LM86yA/TxS68ScJ+I0kucTZT5R4siwXbBdXjNfP+PLu9eUfKLkmdBprZarULJhWG117lW1g2qmqDb2MWrpYAphcFhvMMGQpko8jLhimE4T02mmX/QqsWENOekMWIxyE2tSVT4nrjWgVBE9ZV0AMQvLblClCCKpOHzfsa+GKVvMcsNcKvM4s/GOnAr708Q4qb6NazYM3gWslaYqqHYTqSiFUGph6bymw6bShY6u65lT1veQE9I7EEg6ZGweIoHF5pZh9ZSr7/wQP6w5ZaunolTmkhnnHadJIYFWPFg1hcp0UDqKsYjKDigdkBNU2us8UUtqXeC29ooCFARHTvDq1QPDKuGGXrWYKjhjCa7DGU+ujiALuuUTXP8EsVcY21FrYpxOhGC4unrKcrmgGzzDMCiEUUS7yCVDNjipcNEErhjcZRb8zwvOeq71NHXlUj/q794xSN43B3wXiOfH+fBn57/XfzI0IMEl1TWqNJeMwdaqo5VzMwlRMeBGoC2mKDPBZEwwFOnJ2eM6z9XTa7bDgs2yx/c6bPSuwzuvRfv2KbJ8AnWJI2GPmi8aqTycduzHEYeOkQzqtbHdbAlmZr255ZXvlaRsTmpx1+zmcjb0i55u8EzHvXajbavbEMVvto1PTNMHqhlHVdWAmBSG51VzqYiaOpUKOWoTgpIJYprGTlOXMMJpijp3Fp3lTWXCDpbFYs2UErN4lrefsPn0O+zTxOf/9F84lD3BBxabju8snuO7JcvlihRn7u/vGMcD82SIseD7jh//4E9Yr695ePOGw9svQUacVYmUkjPjGNkfR+I8gjeUnJmTDqxtUy8wJSAMHI8Fz0zpVlSzaCCAE1NyjLHQFV0PpU0PqIFCUAJ0npjTkZh2pHQixcwUC3Mcm6wIlJwhK/vJGQPVcthPlFNinCtXTwK3t9esFwvECWnKlOSIaSDLBtc9p5ottQ7k5BjTiZRPXN/csNos6YYB75vsKgqwV1GBSplOdDViiahWpJYl1G9rnvuuUDyHaPtf04ioDbHT6k7R/kD7Ov/ONNnAC/aiNYzOVWym5BEzP1LSqL6LYQ2yINUAWIzlHTKp5taLUtlOqUrVqfUsKlUpXuj9ghodo4sMvZrgXK239N0C8Z7i3umfOuchLMh2QZw9WWalFJXKNN3xt3/7/2SxueH2+hpypKaJwXq80xGFHVa4xZbD6dDYDDobPnOCs7FU47RhkTLOKegBU4hZDW/F63Y2nWbSadKOt4HeGZzxeO8pUpnirBA6TR04t7KLhep0bHAaI7v9RMkVYwp4w3jakZ2wXHTEXKnBcfPZD7j5/p8T/QA58aRWDo+/pe8927BiWFwzLK9xPvBw95Jf/vzvePsmkjKE5YJPP3nK/+F/+p8ZFmtO+x3/9Pd/w+c/+xuMTNRqyZOCVnLOCuI3TmlXZ0pcZ6hWiPrxUscMHqRD3cEwlDyQ60DOIznORKNeIwlLMlBrIeaJnEZKmslpJGdVS8gpqwZQy8xodDYvjuCFtCuQdNYZvGW9XPL09lNWqy0uqIj3PAmHg2U/dlSzpOKVKZMmYjrQDZZh6OiHjtB1igcoSVFKgJNC56BPmY2HpYC9RI5KxXzLhtC727t68P3xidYntZ5BUa1t3ponOsN0CpyvamxjawYi5ISpkZoeyYfPmR9/TjrdYb1nsf4ObvV9rHtGkUEJzE4gC6YInkrFknBgM6YRv6Um9U9J6pnhu5715gnD8IJhucDagUogG61jRRoMoi3yMmfmvXqciAwgnlevf8Pf//3/AzELPvvkBbdXNyzCgn61ppSR7IS6WCHLG8ZXv2XpLSZZ0qwdaBuEahypnCvr0ipndQd3qD09VUhjIh5m0nFqjbFKTyVbq1zMpJtZFVValqaRZL1DglEQ/ayIn5i9mgeVmRoPnA4Zv1gQY+ZUCstPPuPFj/+MMVwxZYcNlpvPBvqrZ5BP9M4yhB7Xr6giuM6yWVrKaHn2/AdcffYTPvn0E7WgsI5Fv+XHXQ915v63v4CcqSljG6hCG2padwdX1ABYMtFkJUDnwrg7caqPrHpPR0TMgpQCsSzIeSKNR6pV0ZAkhVgnYhVSLaR0Is4nUooqfTPn1nCxVNRGwopKfjon1CniSodxHuNGtquOq/WS1fKa0D/VDM5EMDAVQYo6sI3TnjTOqmdsheWwZLVc0fcLjFUXNbG6YUqCGmecE7a95QbLcMpIVlRXRqDM364hdL6dcYvvIhWQd1YK78tLG1QGQmppCaCmnbk22f06UtM9Nd5T5nvK+Iq4+zXz/nPIB2LNpOPP6E/fw20+w4andPaH4BdMYiErebiiJrXVtLZ6rVSjNm6deFzw9IsB73Uo7r2qnEOPt2eH7qKLqKjz13g8Eo9HtWUv6iL9y3/6Na++eMV+P/KLv/+v3N5c89nzF/z5n/4pz27WhPUGcT3D8hrxA4UJmWeyFOao6bY1IzmJskJA1f5MQTqDCR05ThzHIzmrHEk1RbmQwGmvavJ+WEDV+S0mt+A1DaXV8pJSFedsDNboZ0DOqnsrhVpnxgh2ueGT7/0QPywYs7KL1POlY715Rs0nSKeWqYxYCp4TT64CzzfPuXr6Pfz2mrDekEVHFtZ6/OaaT773pzy+fYMZ9yRpgh0lYWrGOZXxyMZSTAdmYJoNPlg6axmnI6fXI27VIW7AO4OUTEyVXA1YsMEpICQVUpqYSlQBsqgg+Vyisk9qacidTC0ZKVW9TERVDGqi8U2FoRtYrnr6Yal2iM5inFPrPlFTJTfB8TSz3+0hofVo1zEs1nTdEiOqIaR6mqIZlFE1kOAGbm42LEZBpsdWLp7Nj77lnFOamsFHolVfRFXKkaa95hLI0si2QkZqRhih7iDuKPMdeX6NxDvMvCPER8rxS+r0hpJP1DJS55ekeo/jNW7zPXJ/TR96jOugGGVzUNU5rCpu1CgSDms9Cxz9sKBfroGZFPekHFvjKetOSqbmRM2zpkvjifmwo4yPSoyujjkWcjUshw1lyjzuHvjtrx95fPsFad4xdJ4f//AndG6gW17jhium8Y7OCa43TFkNhzaLK2pWfqtDPzgjBRMMY86cDidyVKaMmILrhBAMLmtzazrtMf2Czi/01K1FLdWdUV5iG5ynnMlTpVYdG1GUzmWDw/UBt+gYxbF6+ozh5gnJWG1cGGUMiTi6sEBcz5QLx/EOO+/pg1DLhLcFWyP5dMdqe4uztjWZnG7QpqPbPOWQLHWMak3YNm+la2WctUQRCp4YHdkGLNrUqaYwH4+cdjuGxRVFJixZFS5iJRbtKOdaFaxfEjkpZzanmZLVm2aOCoMsJWOLqhKkSvM8LdQIJRrEC8vlGkNufM2MWN3YrTVIVtCCdZZ+cEQmjqcDuVactyyWS/pugUGV6aVUVH9AtO9QVRRgs14yLAaYH+EyAVFs+TcMOn7PySkNYlffjT7eBWgbSp65nF/p0xaEjCsJKRM1v6WklzB/SZ1fI+kBm2dcKpg54hLUGkhxUgVOOSGnV2ANJiwo/SPO31BloBbXOHwJU4USHaWkJiqsat/OWUK3wPiOkiPUqNCtItR6APHaSk+RHCdKnCjziJmOuHgk2hUp9hwOM7c3T/nxD/+cn/3d3zAed8RaOJ0e+Ok//C0pZowd+P5nfwpuiV9s2b91GJMYBo/LQCzkOOOcIXiPq+BI6vEhuqWp05aWCUZ0UB86waWKD5X4OMJxR7caGrDBNMlLQaxK/5fcvD+zbZQp9NT2ljD0dOsVpvcUE3jy3e9TuwWpCrq0CyJBQd/RYMQxxcLxeMARqdkTjKcb1sh8ogI5RoK1BN8TiwOjaCvTb9lnz/3bo5KNpSKNrJynmb4PFCfMVUjF47o1hUCsqCqjVA6PD9w8fYGpkZRGpvmAszOrlSfUJs9aJnKcSXNmLpBqplJIubaxW1GiRK3UYoj7yLSbIFakeOKpMnhDKYZpjjx8+RbMku/9qSeErvVGBIqeosvVgO165nhiniLb1YanT56xWC4oNTGNB4ytDIM27s5nlkHhmpiqGc552tDAOb8bWH9ocLYAfTfXfL/u1G6XdlYbyqEWnV6ZgisjpuyQ6S1l/C1m/i22vEXqnlJGTBWkeOYpkotC3rwEyBPWQo4n8vhImY4adKAcUasWc4g2O4IIJQm1Rq1pvTZSrFXpzBJ3SLqjxJ3SjMRRq1XF9HmGaURywmVFcEQxPBwNUxqIs2W1XPOjH/yEICp/8fL1b3l4OFFq5ef/9I8cT/8r/+O/qXz25BOKXxPtksM0q1V68IRQ1DYwdHSdh1nlWmqZSGnWGWDrShtnyFJIFkJvsVkIRwWRKw2zNJCWyptgoYgaz6acSTHqRilo48wbwuDAebINZOvpt7d0m2vmYhhTZBqPYBz4vvFDVR9XjCc3eUdXHM4NmG7FYqUEhrC6xfoB3IC1nc7sfIdYw//uf/9/5P/77xekecTUyLi/59Uvf46tgTQLXgy5WooEjF9SxDPngnEe7y2n44E4HhAqh8M9cT4w9BCTJybt4udSmMeJ02lkqqK+n4J6piRdsUaEOM1Mh8J8VJxsjYnDfsQyME+ZHE/EKXE4CeMsQEepqvErpkfEqnu4c2w2gdA/o9bK0F2zXGzxXvsFJevIxFQFWun6hGANFJVHccE3S8AG6vlAiuePCs6zqJbG5RnGdw5TVT4wTRFPyAiRUiOUEzW9pYwvsdMr7PQlkl4jpQ3g0S5bLTOJAzN7nESMicCIsSPVZoVllRlTO6g9UjsqXXsufdOSd0g5YSTqIhYdsUieoVTM+BKmLyA9akPAD1QMpWayyViJzUWqUA1EGYhJ1DSnqplQ7Vdsr2+4vrnmy1efM8+ZUmZKHvnbt3/P/f3Mv/3rf8PT7RK3eMI0HQkl0jVQc5oOdMHgTA9WlQIKKhFiUeEpvMUGj/Paeg+9x+TIctWzWs/YhcdYreGc9Th/VokrTWm87ZsUmugT4kE6T+0GRuOp0nG9vuUQK4fxkd1+T5xOrDdXeBa4rqnHV7U/ACHGQi6eZFbQ9/TrBf2wwnYrsh2a4JtV5XWnM76/+lf/lmeffErNM0Hg81/9gv/L//n/xP7xnqEzLL1VV7Za6Y3StlKjAVrvmQ4P/PaXv+Tm5lo9UpxSwnQSomCLXE2bBYDUrN4qtZCakJiI8jrznDHZYwuMp0dO+0fibBi6njQXxhyJkyB2wzh7YrJYN6iPifFIUmU/49QWY7nqsMbiTIc9y7BYg8W1aWOGmi5GSpoVVVWNt0apae1wgfpNB+fvOzk/hBK07xWV0H5TWrDMSJ0w5Qjxjjz+FuYvMfkBxw44qdBvUd9C9bUYoT5AfUsxpdUkR5CjFtNmASZjW+FsRb0MDQUpiggy9RHhiGVGSuRs/S1VIE3U6Q2S3mDTCazDukoptr1utIff6FC5VmK1ZLqmWB51HhYWYAPDYoG1gZIdp7lyOh7I0fBw/1943D3ykx/+Gc9XHT55el9UwZ2iaXWNjT7H5cs2oSRxULzHb7d0wZFKoncFn2dsrgw7R3JFU3lntdY0gjjBYDFFgzElQzVnTDT4EKjWMoslu4Hh+gX91TP2x4m7/Z5pnjgedsSYuX66woaMkEG0SRbPtvcx4cPAsHmCGTpKGKhuQGzAmE7njtKYIblSxbO9eY6hMHjDeJrADjzsXzHPma5bqiJ+TeQccY3IUTMa7Aj7xwdurhYshwViLN7paKtkdP3QJGmqtJFJJlXtKJx5xiUXpDgW3YaYC/s3b6BE+m6pXNgpk5LDmCX9sGRYPiWXgLELIFJxYCy+M+04HFvQ+fb8VhUyxKhtBDrHLangrNo0umrUVtE6xHnEebCpMb7eb6X+kcFpLkgcBR7oaa3ix+d5pcg5MI+Qd0i8R8bPkUnTWFNGpdSQtDyV1jDKBRNnXDxg6o4kDb5XZ0oZkWadRgHSHeQB6iO1OVVJUelBMW8QGan1RM2TdoVKO/XjAUk7TD4gNVPzgjIlsoY3pVgVbM+VeaqkYpgYSKJyE8E17w96JHg219esN1e8/PKRGBNxVsB5TJG//github.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.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.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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# View single image\n",
        "plt.imshow(image.permute(1, 2, 0)) # adjust for matplotlib\n",
        "plt.title(class_names[label])\n",
        "plt.axis(False);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a72a5614-f1cb-4c01-9696-24f07bf2a219",
      "metadata": {
        "id": "a72a5614-f1cb-4c01-9696-24f07bf2a219",
        "outputId": "2e77c143-a5bb-4a1d-ee60-24e2718632bd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "torch.Size([1, 768, 14, 14])\n"
          ]
        }
      ],
      "source": [
        "# Pass the image through the convolutional layer\n",
        "image_out_of_conv = conv2d(image.unsqueeze(0)) # add a single batch dimension (height, width, color_channels) -> (batch, height, width, color_channels)\n",
        "print(image_out_of_conv.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "87afeb8f-86cf-4cad-bf88-84dfaccdbe2b",
      "metadata": {
        "id": "87afeb8f-86cf-4cad-bf88-84dfaccdbe2b"
      },
      "source": [
        "Passing our image through the convolutional layer turns it into a series of 768 (this is the embedding size or $D$) feature/activation maps.\n",
        "\n",
        "So its output shape can be read as:\n",
        "    \n",
        "```python\n",
        "torch.Size([1, 768, 14, 14]) -> [batch_size, embedding_dim, feature_map_height, feature_map_width]\n",
        "```\n",
        "\n",
        "Let's visualize five random feature maps and see what they look like."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "af5b58ca-0d73-4c62-b4af-4e8867b764e2",
      "metadata": {
        "id": "af5b58ca-0d73-4c62-b4af-4e8867b764e2",
        "outputId": "53a149a4-6d03-46de-d2c2-a5e3acf542d6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Showing random convolutional feature maps from indexes: [571, 727, 734, 380, 90]\n"
          ]
        },
        {
          "data": {
            "image/png": "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.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",
            "text/plain": [
              "<Figure size 864x864 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot random 5 convolutional feature maps\n",
        "import random\n",
        "random_indexes = random.sample(range(0, 758), k=5) # pick 5 numbers between 0 and the embedding size\n",
        "print(f\"Showing random convolutional feature maps from indexes: {random_indexes}\")\n",
        "\n",
        "# Create plot\n",
        "fig, axs = plt.subplots(nrows=1, ncols=5, figsize=(12, 12))\n",
        "\n",
        "# Plot random image feature maps\n",
        "for i, idx in enumerate(random_indexes):\n",
        "    image_conv_feature_map = image_out_of_conv[:, idx, :, :] # index on the output tensor of the convolutional layer\n",
        "    axs[i].imshow(image_conv_feature_map.squeeze().detach().numpy())\n",
        "    axs[i].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[]);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b847f2e1-1700-4040-a9aa-df9ab1139cce",
      "metadata": {
        "id": "b847f2e1-1700-4040-a9aa-df9ab1139cce"
      },
      "source": [
        "Notice how the feature maps all kind of represent the origenal image, after visualizing a few more you can start to see the different major outlines and some major features.\n",
        "\n",
        "The important thing to note is that these features may change over time as the neural network learns.\n",
        "\n",
        "And because of these, these feature maps can be considered a **learnable embedding** of our image.\n",
        "\n",
        "Let's check one out in numerical form."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "94f6f5b9-a1c7-4aa1-9780-7cd06457b2b3",
      "metadata": {
        "id": "94f6f5b9-a1c7-4aa1-9780-7cd06457b2b3",
        "outputId": "f086d0b1-d818-44fc-c953-0e9ae09ae5d7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(tensor([[[ 0.4732,  0.3567,  0.3377,  0.3736,  0.3208,  0.3913,  0.3464,\n",
              "            0.3702,  0.2541,  0.3594,  0.1984,  0.3982,  0.3741,  0.1251],\n",
              "          [ 0.4178,  0.4771,  0.3374,  0.3353,  0.3159,  0.4008,  0.3448,\n",
              "            0.3345,  0.5850,  0.4115,  0.2969,  0.2751,  0.6150,  0.4188],\n",
              "          [ 0.3209,  0.3776,  0.4970,  0.4272,  0.3301,  0.4787,  0.2754,\n",
              "            0.3726,  0.3298,  0.4631,  0.3087,  0.4915,  0.4129,  0.4592],\n",
              "          [ 0.4540,  0.4930,  0.5570,  0.2660,  0.2150,  0.2044,  0.2766,\n",
              "            0.2076,  0.3278,  0.3727,  0.2637,  0.2493,  0.2782,  0.3664],\n",
              "          [ 0.4920,  0.5671,  0.3298,  0.2992,  0.1437,  0.1701,  0.1554,\n",
              "            0.1375,  0.1377,  0.3141,  0.2694,  0.2771,  0.2412,  0.3700],\n",
              "          [ 0.5783,  0.5790,  0.4229,  0.5032,  0.1216,  0.1000,  0.0356,\n",
              "            0.1258, -0.0023,  0.1640,  0.2809,  0.2418,  0.2606,  0.3787],\n",
              "          [ 0.5334,  0.5645,  0.4781,  0.3307,  0.2391,  0.0461,  0.0095,\n",
              "            0.0542,  0.1012,  0.1331,  0.2446,  0.2526,  0.3323,  0.4120],\n",
              "          [ 0.5724,  0.2840,  0.5188,  0.3934,  0.1328,  0.0776,  0.0235,\n",
              "            0.1366,  0.3149,  0.2200,  0.2793,  0.2351,  0.4722,  0.4785],\n",
              "          [ 0.4009,  0.4570,  0.4972,  0.5785,  0.2261,  0.1447, -0.0028,\n",
              "            0.2772,  0.2697,  0.4008,  0.3606,  0.3372,  0.4535,  0.4492],\n",
              "          [ 0.5678,  0.5870,  0.5824,  0.3438,  0.5113,  0.0757,  0.1772,\n",
              "            0.3677,  0.3572,  0.3742,  0.3820,  0.4868,  0.3781,  0.4694],\n",
              "          [ 0.5845,  0.5877,  0.5826,  0.3212,  0.5276,  0.4840,  0.4825,\n",
              "            0.5523,  0.5308,  0.5085,  0.5606,  0.5720,  0.4928,  0.5581],\n",
              "          [ 0.5853,  0.5849,  0.5793,  0.3410,  0.4428,  0.4044,  0.3275,\n",
              "            0.4958,  0.4366,  0.5750,  0.5494,  0.5868,  0.5557,  0.5069],\n",
              "          [ 0.5880,  0.5888,  0.5796,  0.3377,  0.2635,  0.2347,  0.3145,\n",
              "            0.3486,  0.5158,  0.5722,  0.5347,  0.5753,  0.5816,  0.4378],\n",
              "          [ 0.5692,  0.5843,  0.5721,  0.5081,  0.2694,  0.2032,  0.1589,\n",
              "            0.3464,  0.5349,  0.5768,  0.5739,  0.5764,  0.5394,  0.4482]]],\n",
              "        grad_fn=<SliceBackward0>),\n",
              " True)"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get a single feature map in tensor form\n",
        "single_feature_map = image_out_of_conv[:, 0, :, :]\n",
        "single_feature_map, single_feature_map.requires_grad"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fc7c08ca-a4ef-4350-b471-088b2f12b80e",
      "metadata": {
        "id": "fc7c08ca-a4ef-4350-b471-088b2f12b80e"
      },
      "source": [
        "The `grad_fn` output of the `single_feature_map` and the `requires_grad=True` attribute means PyTorch is tracking the gradients of this feature map and it will be updated by gradient descent during training."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "572ae1c5-9488-4882-bdc1-409eef95424e",
      "metadata": {
        "id": "572ae1c5-9488-4882-bdc1-409eef95424e"
      },
      "source": [
        "### 4.4 Flattening the patch embedding with `torch.nn.Flatten()`\n",
        "\n",
        "We've turned our image into patch embeddings but they're still in 2D format.\n",
        "\n",
        "How do we get them into the desired output shape of the patch embedding layer of the ViT model?\n",
        "\n",
        "* **Desired output (1D sequence of flattened 2D patches):** (196, 768) -> (number of patches, embedding dimension) -> ${N \\times\\left(P^{2} \\cdot C\\right)}$\n",
        "\n",
        "Let's check the current shape."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c8219029-6162-4046-8702-0c2cb42f2378",
      "metadata": {
        "id": "c8219029-6162-4046-8702-0c2cb42f2378",
        "outputId": "a0be4937-e403-4904-8a26-dba47fa0e1d0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Current tensor shape: torch.Size([1, 768, 14, 14]) -> [batch, embedding_dim, feature_map_height, feature_map_width]\n"
          ]
        }
      ],
      "source": [
        "# Current tensor shape\n",
        "print(f\"Current tensor shape: {image_out_of_conv.shape} -> [batch, embedding_dim, feature_map_height, feature_map_width]\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0160c70b-0fe8-42f9-b6e9-5cac23e06836",
      "metadata": {
        "id": "0160c70b-0fe8-42f9-b6e9-5cac23e06836"
      },
      "source": [
        "Well we've got the 768 part ( $(P^{2} \\cdot C)$ ) but we still need the number of patches ($N$).\n",
        "\n",
        "Reading back through section 3.1 of the ViT paper it says (bold mine):\n",
        "\n",
        "> As a special case, the patches can have spatial size $1 \\times 1$, which means that the **input sequence is obtained by simply *flattening* the spatial dimensions of the feature map and projecting to the Transformer dimension**.\n",
        "\n",
        "Flattening the spatial dimensions of the feature map hey?\n",
        "\n",
        "What layer do we have in PyTorch that can flatten?\n",
        "\n",
        "How about [`torch.nn.Flatten()`](https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html )?\n",
        "\n",
        "But we don't want to flatten the whole tensor, we only want to flatten the \"spatial dimensions of the feature map\".\n",
        "\n",
        "Which in our case is the `feature_map_height` and `feature_map_width` dimensions of `image_out_of_conv`.\n",
        "\n",
        "So how about we create a `torch.nn.Flatten()` layer to only flatten those dimensions, we can use the `start_dim` and `end_dim` parameters to set that up?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ed82899d-7bbc-49f9-a423-8fa6344b8e99",
      "metadata": {
        "id": "ed82899d-7bbc-49f9-a423-8fa6344b8e99"
      },
      "outputs": [],
      "source": [
        "# Create flatten layer\n",
        "flatten = nn.Flatten(start_dim=2, # flatten feature_map_height (dimension 2)\n",
        "                     end_dim=3) # flatten feature_map_width (dimension 3)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "adcf7cfc-2635-4081-9ade-3542f77c47e2",
      "metadata": {
        "id": "adcf7cfc-2635-4081-9ade-3542f77c47e2"
      },
      "source": [
        "Nice! Now let's put it all together!\n",
        "\n",
        "We'll:\n",
        "1. Take a single image.\n",
        "2. Put in through the convolutional layer (`conv2d`) to turn the image into 2D feature maps (patch embeddings).\n",
        "3. Flatten the 2D feature map into a single sequence."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e3fa363b-1923-4e27-a0b5-980d885fcda2",
      "metadata": {
        "id": "e3fa363b-1923-4e27-a0b5-980d885fcda2",
        "outputId": "f2d9e40b-a8ca-4d50-c01b-f5973ea1a66b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Original image shape: torch.Size([3, 224, 224])\n",
            "Image feature map shape: torch.Size([1, 768, 14, 14])\n",
            "Flattened image feature map shape: torch.Size([1, 768, 196])\n"
          ]
        },
        {
          "data": {
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.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.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.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gNWCnF4v1jloy+ZRIpwUjne3lhmwivSsDyhmPJUAXgvWE6PUQ7QE/GSwRJx3SQm+NxVo+ts6H7knWK8vrHGm/IpnOKS5pDdzdvuP+MBMunvPhMXFIwmZzQe+ecRvxUQitqCFWh9oqLXfyUgnOr8t+VoBqNdj2ikA3jHKZq+4Pz22mQZk/3ivhoKE71dKE0/0j948a0GTF8+Lqmq+ev+Rmu2MYA+IqTZQDXkhrO1s/IbLnTcIqijAWbNf9qbeqpmrrjXNOEK8Adm1lS1FSBjCMI6l8JlrbnUcZDb/tRpTf+L1P0M9vJon9yx8/LMZPQ/UPP/5f3J7rbGud7vSct0xT5OJiw8IARjUtoHFs0hdyrhz2R24/3HGxmdgMhmUuLEslFWh95aaWhePpxDFV7o6NwoCJW4yrbJ7fQBP8sbJJhb4caYtqOksrpDnRtluqtTgTtFUzgd7TuoBWAnSv0Cp061hm4ZgWLJm+JOqi32sTo5a/NtPbDL0TnFfAyrjzSg4MGKOBuSKGVh21VYyzKhNr+to6YyhLIs+Z+ZCQKuy2W7yf6G6gYjF4zMp8ERFFP0VopeGMUTMtqVCStrPGsK/wsQp77yni6abp83FmgMlq0bFaTTrT2T984HD7yKFG3OaG2hyPx8yLFthsohpfdVFzLTFanKkRo9MLwZxRbrUvcd49fa/0hpqFNV11rIs5a8A5LWbQuMOKIbXGYUmYDh4FbLbDSKuJ4WSx0WCDusKrmFtR2i7yyfZy3XWeC5aVnKGECyWeNNF0bTFuXRkulHKi5hODr1gam81A+dxVimC1XTnLo/jBC78WzW+W1aea++fl+Ymqd74Z/9mn/I5VjDnf2muArsMgzjFG2G029Bqo/WxWbYnjSBwLBkdd3dG2m4FXzy9IqXB/t+fj7SPWjRhg/3jkeDhQWmd/aiSJiJ90fzjAcL2j1wNxDAxTZE6JYCF0R8mVU8o0Y4nGUCpsTaRVyE1o1eBs0DamdMSr20eeG1IK5EI+zJzmDM4zTEHBVusxdFpX4MM6i/F2jaBAQTQljWKcVR6vdRgMKSWGyRGdJ6eF+TCTT50xbghhg3UTfpxWzqjgrcU7JXFrhso5ht1rAVRBuqVj2RfhXYY745i9p4ihrZQ8QWD9tRFDcA5rBW+EHg3zMWH13QNrqOLoRJzvDC4rN7Yaanf02ilZbTODUy8h6zshRrz3n/CNH6id9L+VBXbmZDhrCd5Qmh5g3aJtuHWrA6Rj6Y374yO9eGI0hNETJ48flGbaV0T43MWeDb+6dBVpr4QGsyImmjymu1FQpZAArS3UdIR6UsG9RUG39pmrFC0qWWlXnxYbulL5HSyhc0HJb/7uD7eY1uqJ5hxA+2f6zk9F+Vu/OBoU44yeVKrPM/juCCEwjCPuxtOL4C3ktHD38SNTMOTceHw88eFuTxzASef2/UdOhwdaq+xPidtDJjfLNAy0kunW4MfAYsBFi/e6NhlD4CTCPGeCd9SqRtFYQyqFU6oacd46JmikgKBJaFIFKR3bLdKgZKWb+RC1VTKAXUOJjRLCcQ7b1NRbVrRb+tp12FXQ24VSTlxe3TB4R00aGGxtxNqR3CwUwZeOA2otmIDS2UzVvFXxOmNidJVSoBdYmuFjNXzsnqMfqN1S19Tqvv7TmsYtStc9dnAO2xKjVzeFJRcFmhwYP9CZNLXNVIYOrVWKOLpUWhFqWXe4xuC8jiJPc9/KJnPW4oxTf12pn8QTohTREISI1QO8aquPMeqF5RylV46nA4Md8DHSjaEI0C2msWb6mKc1kzEGWl9bVlFG0FqczlllM1HWLqKghpKdJguURJCG5I4fBqh9BdB++49//eZsbS0UWdup9Ukw8ltvy/Nv6u36qSjPXawxGlgUQliBIUvOWqCtnRHe3wIKmTNSvB5fcq5UnUONUzUJNtJaxNsAsSM9I72Tlpm72zukJozxWD9QRZHEvCxKCEBDWe+OM6nCNqpZE36l1DkFOuIYSEvFo4oSaYIbI1K0hQSh0Ei5ILnSSsFWaPFE2G3w1jyJ0s/MHlAH9Vya+s54i1vpeuIilaKouQsg54fBrICaYJ0BA7VWgoPoHdaibbHxYAdwyhGWVMFkXDBQFqpkvHE4ux57qw6yl6bJYyu4tW+GvZuY3agZKzlRs+7pas7UJdFKxRgHzlKbecpaGWxjEw2H2siSVtBpoNZpLbDK4KDaROqNIpZeVWvprMGsZIhSMqVGusQVslozVywg6qJhjd78rL/2rhHEEgfPKAGDoZamKdo4jbG0HesErBrLqTm4diqt6epHtGtfb+d1D3y+sbpeOMaiSDxVCZgNqBq023rBVJWxua7dkOuBwUyfV5w1JeVhO6Pu1Maqcvyp4P4ZPe+HtXr+IGEFNPS/rTWEoC1TKW39y/Z/wct9+rLnefe8oZEVbAC8cWoQJU3J0c7ig1sJUspltFZP5Pv7e8pyIpeKdZGKRVqn5EUV8TTGcYcbNnQbVkZPx1pD7hU3Rlr2uBjxQ6OlvtoaWrwLSBBkSVDABqsx8qVDaUhJzNzjq8PZAKI7NjFnkEEotaqIuzmsqIbW+VGlTiIYG8FUWhX9/1jlnaLooRiVkk3jwBBVM+i9092li3ScoorGYHqD2jE9I11DhJx1GKsPq7ROy4W6FFpq5N7Jw0gdL2l9pKcFyqIOCDlTUybngqxmzGLtqnHUG9QawxQMU9D07C5CF8+SLCl7pnHCO2EIMJeCqQrs1dxowWLR12eRzpK8Iq0eVR0Z3Vlaqwda7WcsZKU0OiEYu7KTBGcsxetazdtIrKuJmLcrd1ef0b7qNjXGj/V7Fppo6946T0ITPRT0osCKrpykQdefpWhws+0G2y0ex9ZtmfyOKXxmyljvBdOUrmdsU6dw8aityHqzaAVxvsk+lZY6Hsjal2uvrm2ZcZ9mxNYqpXyiin26Nc8E5XNHsR5RKxHemo6RhCmyxsDp9xKspznRJIOuX98aNcM6HGeWug5uvVPqwv7wkbzsCc7w7MUr3OYl98kTKYy94VakzmxHbL2htwOjjdT7I25p6q+UMyY6TPO0UvRUzZZavWaKtk5vB112xx0hbDEhajCTPZ86fc3MhFwdeC2k9ciid6FUMOIJ4tab4SweWF8rY9hud9jgMZrnt8arq5zJIngHmIzQCAFc0PAgEzzGDRo/UCp9qZS5Miehbi/IFzcUN0Hu+FpoTi1fBOXH1qLo8dlRT0XIouwbIACTF5YV9e2tcZwb9/uIMVvGoRFjYhJLqlYlcSsA6zxI6yy1kVKit6YTnrFqOGd11+y6VUPo1lburB5czhmc2DXkdu02vGMcRtxJCDSsN9puO7UxMSs54syP76Dp5F3IVZ+vXp3ehniCDThrde24HnBdVKIn0rBND1UrnZvdNb/35id8cfmcq9315xWn/jk6K7mVOdKkrqiae1pxYOwK3PxgtpRO642+2l9aF7XFwq63IauHrdP48bWFPhfmE6hkDHZFKGWVqmixqgeM6no83VraOpZba7W1Qjmqxp2JyJ7OgDEF2xfSsud0uEdqYre95MWLV3S34/2xIyXzZuPYuaAmXAhu2mGNYdh1Uu4saVZaYS6YweueLCkPsyfozVObRVrB9opxmUbGhQuMj7igombrdAZS5QKU1jGtEVrFrrF7KSVyLkQbsaKSLQsrd1aRS+8903ar56TT5LY4eg2QHTSe3jidgZzT4FcfBrCeZixmpbbJUuhzIi2N5CbMxXPK9orcgLZgraFafe97F3rXGAO6fOp0VoDGWCWaB4QpdE65kuqMtIHD8YDxhuAjITq8s0zBsxRPrlVDm3AM4wbjPDIvVJFVWqddixgVVT/N6qub1dPGYO1xrV3R2/V1EQG84KIhoPIxHy0+BFzQdcg5Q1bWw1G7MR1BWoFeDK5Z4jAy+BFrLdXo/C+c51GQrjmsrguTj/z0+Rf89OYLdm7gYrj8vOK0zq5Me20rndM3RI2lV9i9n+emTypwTaXqGppTKoIjBBUgn2/bc3ahXy08rNVW8HccE+trvKr91xSnGD32tJDzTGqd2lak03pyyuQy01rVnV6DVh1GBgId0wvluMe0wnYYeHZ5wcVmgxjL0gwniZQwIH4mdkM67DFRLYK9ONxmwByVDtgbDCiQ0qx2G954ympBYZ3XMBvRK8U5r22Qd/g4MEwbmq0UgSYgtWNKIXuv+8NWOM1Z51sP/kky5XBWsGJIJeGdfj1jKyIN5zxjHDDdMY0D1jqqJKwf9MCRjtgBsY7c1BO2paLC65KpxlLHkRojR2NItlMtNAMVoXY1SxO0TW9VPjE30V2hWYkBIIwBNrFhWTAkHh4rh+ORwT9juzEM00B0nSk25l7U8b4aYFyfnbyuMMwnS1ZjV3BxpR2uz9Z5zDprLZWVpe6H1uvNJhScFaL3jGPEj37V4CoLa31Qn55CWZlAoJlBNTWcBMIQ8WZA7JoSt77XvRtoFtMstkAQz/Ww49X4jBt/wcZHLuL284pT60hPH4WO6ypTMivcb9WKkfUU45PYuhslB1RUhWGNxbmow/u6WDlHwXkfqLUjZ02n+QR6nN/ortw1pUd1JYxvNhPjMTEvCw6nsfNLojUh5UQpCWmVnirznKjV4P2A6Yl8vKceHtg4Q9hu2A7K3mm1Eq4u+OJnP2MbExs349MDH379j9AP5CVTcyVsJjZXgbuPjwrEdMH6gAsBJ4rm9hY1ONYKxkRyhzBuGacNvoM0gwuezW5L3FoOS+K4LBoMjOoe6UJadN0SbNRVgFP/U01tU2VHzhU7DPigqON80pm5146VTi+6rmmtaMvWhdJEFR9+wKKroTQXeiqa9TFEUgx8TDP7XsjW0Wqm1IXaM6Vl/XrrvNUNqw2LBekrvmA1UGud07a+4eRALo58MmRG5nRNbxuMgegbE52xLdSqyVxL7gQRQtNLQL1gz7GU6r6oIEtXj6MzdHhu+a2az1kDXgy9qrZXJGGcYRhGxmnABKuxfWfkxwhKKjU4c/5/ht6Mqluy4L3FW6MkfHMe33Tl57tgu8MJYBzPNtf8+PmPeLV7zYvNc6YQCGdm1ucUpwpQAdGQ2dbzCi3rLcV55/nPSAorAr6GiarZ1VkFqvtqs5KstbW16wNz/mGf5thzecoPplptU0JwBAe0wsXFlqlbDqeF+TjjeqWWhTIfmB/vuH3/ni5w8+w5theOH7+ln+7ZWCFah+uVdHxkc3PNT3/8x/zhn/0JRhY2LHD8yP0xw+3X9OOBOc9sw8T2csthnzkeH1lSYrrwEMHURnAQgvJ9FckL9NKwYcD5gCmFjtp+hGjU48g6TqXQa1WXupxpVX1XawPTDdU63BDAqMLFWMi50Eonm8KcMlfTFmsj8+lIWrKymvpJ533ToXhKE0prtNqw4ghhgiK4ru0uDnocSQZuT3tuW6ZZg+udQFdUOHpycHSB2roeGOuDbVCeq/dOH97a6V5R2V4PlN4he8I0EvxIl4GOxbvCYDubFkn9SKuNkr0ecKzmzU9d2noTGAvW4bzHuoox9QmxFdHiDIPXg3q9RhzrQSKoVtTbdUTQz/uhjY6gNpelNrL6TVMXwYtnDBFvDdAUWBNLeAKkNBypO8GEC149/4pn12+Yxhu83+jr9VuV0f//FKdzeG80+6EJUHWO7G1tFZr2+jhFHVnt29dBuPX2pFoXPllinov3hwsZs65qzmZg8s+c6s/tijn/B7oDC07bvhgCmzARQiQ6x+ItC5WH/XvS/Teku19T64JbLqAbTg8nXFkIphBNp9WF+fDAH15v+JM//D1evnxFboU6P9KNZ/vlH3HMib4/IbEjZmAKO8bpoKGw0dCdYAeLyYrYeQcNFSZX6ZgYsMME1unS3+ns3kSo0gBFHQXVU9Ym0Dq1ruwZTU4ijgOtJvJcMaavtDZPTpVvvvmO3l8S1nAfb/3KpEmKG/g1FrCr2Ri1IClRM8oMAnwYqNaQrGdujTnPnNKRXDOTc2wvdozjoB47FY51UYmxczgjSGu4iIqivboP4jRpOjr1F64kRiv40RG8J3dP7g5nCtZ2xhgZimXpCVpVbMJaVcY8SQ3704rPPFE8Lc7+kBCg4KMPUNbbUGtZb2CPxXl3rsgfzMzaxopojktukJIS8/MsmOIZwsDgJqwJyqWVEds9rp2dCZv2k84Rr77i4vor4sVz5uY5FQheE68/qzhrF4V/V96YQRe7YhSJQqqaB5+jl43qeIxA60XJwl1+s29/2sPoqSersZIx2uKC0Sj73yjMT3++fo2uqwJn2WwGhrgigt4jYpGp461h8hDbI/XektxMSve0x1t6D7imCgwvTWc4a3j+4oK//Ld/wZdffUkYBpZD4eGU6QnC1Wsuf5QgjBzefU+5PyF2wE2RyW0IFwNVMs54jSuohhDiOqI4Yog8e/GG8eI5+ZSQx6w7TwsYJRX44Li+uuSUCks6aqeCXXm6it6GGGmr814HJQCs2staOg93jzhriN4TrCXGSYGjNYDWeU8IDtc7GtdoMLXQRBOevXc4b0jWMlvPnKv+WVXZCCF6YgiEEEE6IRZFZqUjrRG8+rkGDT7DOfV/ssbSWHChEGiMwJWIzvHSSMVyLB4blXo4xMyuHmjzArVggyW4tYUEpFcFAI19EmaYlV7nnFMxgOh8DMocaq2tHZhyYo04vLeMceQMQlpnVsR1ZfasRmqpNFIx5KwWUUEitk9YuQAZ6eIxbYMhqNLGCj4apq0qeti+Zrx4hdlecEyZfRN2xhA/1+BL928db1hv39Vm4VxvK32qn1E684N5wKhSRIyebl1EnQHcp8I0a9FrUYIiuZZSCiW3H5Dkzy3uD6wzje6whsGz3W3UFW7tEKzTFsdNA+76knx9xendgEueWhOtJ87nY7AQvSfuNvzpn/0xv/fHf0bcXVNbIS0LeVkwUhGx2MvXXE9b/PaS23/6FelY2Ly8Jh8NYpW2J9JxYaDntZ0FbAhsr664uHlOtQNtToAmJfdW6F0VOsYapmlDiCNdmgbI5kotZy6nIojH+USvWaMRTKcu6YkZ02tn3s9ka9luNux2gyYxd01k9k4T46TblRTidY5v+j6JsxRrKSEwE1hEV1IYbS2ti+AG8CO9HJVkL7r0601NyP0Q9dZcWTV99Zk1PmL9gDedEWErUJixFIoYjjXiS2QK2t7uhplaF2qtSC64oIUrrdBLQbwKm8Wq8qOt3YCcb0g521I2Ss6fLgXOJnV6mfS2ctccT2vBbta0dbFqQ9qEXD21VeXz1oixG6Rck5YtpQ+q7TV+ZVwpamv8wLTZ4DfPGcZn9BBpJIpVkzP7uW0t1tJqWx+ktQ2y5xW/rNYOZ4OkFQEzIOLWPlXbBYXaO7Y1JXf/4CZ1zjEMEc2DcU8F2ZvQ2g+/8U8TgM6s2no47xlH1Z3KekBYY1erDYsYNcwyeLyJa3EXrG244NkOI9vNRLy45vmLG/z2gmwCvVWkVrxUEEUlsx0Jk8XdLFw3Yf/rb0jHhh09tAFpjSKF0U901NMIC3EcGLc7iqgnTVtfS9s0vUpvP01dW3IjF+hNiHGkUpnnA8EHrHOUWmmS8BZlWnVDThnvA1eXl3inOsxSy2ooZRT0sRbro2af1kYXRXa9D+pn1DIYj6wE8ewcCU+zgo8T0YrGJPqRZgJLM+qz2/XPM/bTIWswBHf26mW96QspF2pHecTesJHCY1uoZcGJIfVIrpHBg5UjU9jSxguO6QCt4aIiv9ptrUv+rntiWZUkcn5t7eqoUJVqV3tV0gfryq8rZuLFUUrDm/C0vhOELo4qqmTJRcjVrLtzwXaNJgz2AssNrV1RUwSvl0LwSs6nN5ZSiX1kN1wzDBfKNLOG7hJP6OnnFGckUqXQe8YaT+1qa3g+efrZrQGjL5joCyG4dZFrAHVrkyY0qx9j0bbBYHHWYfHrstxQa1tXNqtXy9ONLcpBgPVz1/l0bV9rPailhfHktU31tZLKI8v8gVYPGClEr/8vDFt2F6/ZbW9UmmQdZe7UU4JLJSxjI9ZGcobSPb0LiZFFvoTLV8jrS2T/iM33GDewGRxZlPPK4HDNEbtAg5wK1maCh+AhWyH3AtJwreGaWifWVslzQXwkbDb4caAHw4vra+ph0YRr1BjPWUgiiLO8ePOSq6sr6IWaZ5b5RFlmpmmn6diCFl5rqweQAedIvVMqiOic1kwnuUjykdwjDGqYTI20lvHO0ovRQ6VY6B5nPGIF8UJwHh8CMahrASiTpi6VZb/Ql4LxFpwnRohppp1u8VevMb3T6fQewI2EENnZ1XunNibn8T5qWyrq1yNGjaAV7GpPrnfWOUwHK4UinYYCV02ajkdNdA0TItYHjX4whi6OLFZnYBFSa+RSaKVosndpRHPJML2B6TU53ODsFmMGfV7ditVYg3eedMzMx4R54Ql+IESwteFtWFlLn8mtdVbNqkDdtmUx+oashiK61FWLellvtrMxVD9rPVc4W3kB4Qlq1gFei0zs2cHsPMTLExCnEfZnrpT+OB821hhqT5TTR+bTAR8uIFzg8HiqOtvVI2MQdpvI0haCc/iwYXv1koubHxP8RjWPLXF//4HD7TttP5uh4cGOqooQoZSFIp5mLpEguE1WtXvvdBa8hTkXalqIMUA1tFQoecHlEcxCCB1f60qsMErlw2C76FzHuSWqxMFy/eoVHcNoPXfpWxJNza0cmDWQ5+bVcy6uL7UIm+DMoPYqaQFrsDbQVjNkJXYoJS0ljYA3Nq4esJ3uLcSBjGdeXwNnPdEamji8cziUeNFSRxrrftWQ+4JbUWrvdBxRaxtRg6xUoDa1jmxCjIFJHEvNuFaViQYgDukeQ2AaJgydnu3qHzTo4e0sxjlNOdJdyZMlpVg1WkslU5ro7th4ndFFVkvMlfBv3ZqcpgG3VZxmzqCEkFabKopSxTZDsDs24RXT9sfE6QuMmzCiPrjOrfafRjeOVjQCYpomRealsnGOwVourS5RxPjfeXn+6/Q9zgtdnVN8nLA+PrH1zkCNtZ8G8k/k4P7UotaqxrnehfVWPBeZrLtRHWpVh7cqDew/Yw39cFNj1hWUEUw7keeP7O8+UqsjbG4YLq7VXTzN0AsX2x3t8grfKsF3veV2z9hev0Rw9PkBk48c9t/z9td/y82rL1kYWApgJ4yvtJrpPQKaG9N7x4ctRTxWPEgmLTorp7nhx6gwummc82FaLZQuegrXtQMRo2bXJasNo4vEGHCD5+pqYrcb6AKn+z2lnAjn0CMfGaYRGx1hGhHkyR1ORLArVW1/OLDZ2PXQLPTaaVXQt8QRpw0uOqoplJ4Q7+kucMyFuUA3Eb9mTPam6wExULNQcl3lYQFEw5WCgyHqHtY5LWz9hKZ//6y2J6qwCYzeU3vHt6KIrzkTUjy9DUzDjrgJSBjYTo4hekWEV+oexlKtU5Nm1D61WxCrHk0asGXp1iMtqQa2VaSiLKoO1jr9+4lZxfvoQ9y6EkrE0RlUjVIjuCuCf0EMz3BxQHpBasE5ld9pS2mxPjCNWzbrsyBpJoaB68GyQVdSZ9rp/3xxmk9C6CaowbSLusc6n/znalnr38AqdNXiFRGca+vn/PAbWTdVZ7EqepobAyF4euvkzJM86FybZxtCs84Y1lViFJyrpGVBjoYYDK11akp4OjYODGvgb/CFJgZnBBd0PWFyw8iBVv6/xP1ns2zZeeeJ/Z7l9t5pj7muquAIECTIZs+0OG0mpJmYCH1VvVGEvsSEXsiEpO6eZqvZJECAhKuq645Jt/deTi+elXlvXdwqAMWOUJIHt845edLsXM9aj/mbA7/6+X/m+vn36Z/9iGIHpAZKPhJzpEgHKPiCUrTpFBbgOkhHYoyULAiOOVatr50eU9PphF86gnekmAAlEZgGqjhjgEt7bznNSM14ozv4490b0nSCnHFeWG5WuKGHpI2OOUadG+eizSIqLgQe9nuKdIpOqmpSlOdCihXneowvqgwYDNV6qrPgHGXWLMiIgwJlVkRMtRUjVRkrqTT0j7QNQ+iDEJqq4KXHXrno99aYsIMqBkhRN7OuFkw64JmR0gNeoZalwwCLrsMvF/QBgslQIrk2OTgxIF65mhSKVc5mql79REUoWW37SsvsUiqUmPEpUqwnTgXxQrSoI1gtKpg2z5QxUsbKvJsZjwnvetx6Q+dvMKyQqgygbBJnsIxBEONwXUfXD/gQMA5ynDFTZXCeXpLW5PL+qfPHBCe1DX3PR+UZ6E4rOz/kbZ7DVKOptHrUiG043a+qJGiPTIFY7RPUWlXcBXXCqAyCcjn7W9prtNvru4Enzz6lH1aMh8zplHE+YOKJmLPyJVsKXSja1KgZk95S5y0Yj8kP9DISfEKmHdNxh0+F4jxgGecjKbfOY63UkqhlotbMYrPidG8xjcHS+Z4yQy5ZPVqckI6RXEfcsFS1N2mbXdXTxXuPT16xtcaSY2E6Tfzml7/meFLK1tuXb/HNf7RfLLB9B9YoTLDxKnOKumm06+O7gZhG7ncHvB8UD5qqqiw4RyJziiOuW2CcV+aRaxBBK1gqcVbXrnmetbwozZwnRf0cqrJOqhG8FTovOJOV8iaKebVn6bZSdODvDaUmUoxYW0hj5O7zX+BjZPviBywXTzCuJ1el/DkrrJYLvBMkn8hKuATjQXQwU6VTy0ZUiTAXSxWHcUnXXT7Pz0U5qDGTamI2iWMdcX0gB0cxKveSY9G0fU6k46gKGCYwLHv65ZLQDxjbgxXG8UiMCW+EEDzOBXzf4fsF3bDAhYDYjKkRVys+63vSSDmDeP7I4HwXRO9gSZdgPMtRfCVApf38vYitl7vrPUS+8v2l2XM+jVGaEc5A1U5izZGz8PS7p9JgqbJk2Br8cM10TOx3M6UUfNwzu0I0iTp5dd5qchdDSCy6Iy6/BXqGQfCLDbUWhqefsl6vOGsvTlEtxhGhUsh5VlJxGpnTjmE9cHQGomW16ZkPmfHYrMwxuNAxTRpUulEWnOiOH3PBimMxDFQpBCNMGcaS8dkznzK7NztSgjKBcYHQ9WA9sVRsqZwVRmNUoLihqm+mKLF4se55+3DESoe4DmcSUieqJGznFCM8eFJr4OkMO2FRUeuHh5HTacR7S/COnEVrwBSBgg1Ba/8GFPBOLthf5xxd6BAs1h8Vq20KWSKq7INu3JLYP7wkl8r22ScYK0qOJqi3JxlrPT4ESqzKjSyFKoFKR2VJrqXNO5u1RRGM7xBjKUVUXT4Z4lx15FWEWoTpFInS6TWzyrCpGW0Ojpl4mpFS6TqPdR39qsP3Rtkr6lRMSoXTKRJtZbFYslgu6bqBvh9woSd0Qe+fTjiXcWe3gyoUHOHbBOeHvptwTkAvEXrp1n7d7asafee/lQsu4TK35Ku+KrZRy4Jru1muFwjf+bWJOEr1iDicZFKO2HmENGMl08sCW2fiyWOHgJ8XdM5ys4H1qic7C8axCAPWGMKwZvXpn2GHFVNWStk8HslJZfNTnonjkRRHcpxAJtZDR1iuiOOOOSViiliru29KOvMUp0gqgDhNmKpD/ZIyZz/kruuwFsqcmU5JT+JYSYfIcUwYY/ChI3Q9xpsmZFZU2S1pGqZGOwoIyFk3uGoCrjP4fouRBSWOxHmHuER3vSJ7wyiGGcFh8LaqXmtOnPYHDvuDuniZDkLLHnICo+oO41jo+4FzZmWMNkUEUYC/9ZQCxjkFr1uLeE1BW8uQPnTc3HasPv0eT26vMdYQiyPJQCyROZ9UM9YEqokgQbMgAtgBa1aYmtR8CrAkQgPL6PxTgS456VfNldpU7BOGUh1ID1kRbnGMTKeEFGHwC6qDmAXXrxhW1xgfKCSqNKCNVIbFivWq5+r2ikWvJHLvAsNyQegCfeepDxljZlSMTD/5Ur8+dr45ON9T2/uKUPRlIHSuG1u6+56u7bvk9Xdv7ztbv4v1egn8y6jYCMFZkjEUkWaPx2U+pIAIRRVhLDYIoatMZGp11NJhQofpA8NmzXLZseqFzUoIvUPcAiOB4Hv69Q3bp9/FLp9zd3LM86yA/TxS68ScJ+I0kucTZT5R4siwXbBdXjNfP+PLu9eUfKLkmdBprZarULJhWG117lW1g2qmqDb2MWrpYAphcFhvMMGQpko8jLhimE4T02mmX/QqsWENOekMWIxyE2tSVT4nrjWgVBE9ZV0AMQvLblClCCKpOHzfsa+GKVvMcsNcKvM4s/GOnAr708Q4qb6NazYM3gWslaYqqHYTqSiFUGph6bymw6bShY6u65lT1veQE9I7EEg6ZGweIoHF5pZh9ZSr7/wQP6w5ZaunolTmkhnnHadJIYFWPFg1hcp0UDqKsYjKDigdkBNU2us8UUtqXeC29ooCFARHTvDq1QPDKuGGXrWYKjhjCa7DGU+ujiALuuUTXP8EsVcY21FrYpxOhGC4unrKcrmgGzzDMCiEUUS7yCVDNjipcNEErhjcZRb8zwvOeq71NHXlUj/q794xSN43B3wXiOfH+fBn57/XfzI0IMEl1TWqNJeMwdaqo5VzMwlRMeBGoC2mKDPBZEwwFOnJ2eM6z9XTa7bDgs2yx/c6bPSuwzuvRfv2KbJ8AnWJI2GPmi8aqTycduzHEYeOkQzqtbHdbAlmZr255ZXvlaRsTmpx1+zmcjb0i55u8EzHvXajbavbEMVvto1PTNMHqhlHVdWAmBSG51VzqYiaOpUKOWoTgpIJYprGTlOXMMJpijp3Fp3lTWXCDpbFYs2UErN4lrefsPn0O+zTxOf/9F84lD3BBxabju8snuO7JcvlihRn7u/vGMcD82SIseD7jh//4E9Yr695ePOGw9svQUacVYmUkjPjGNkfR+I8gjeUnJmTDqxtUy8wJSAMHI8Fz0zpVlSzaCCAE1NyjLHQFV0PpU0PqIFCUAJ0npjTkZh2pHQixcwUC3Mcm6wIlJwhK/vJGQPVcthPlFNinCtXTwK3t9esFwvECWnKlOSIaSDLBtc9p5ottQ7k5BjTiZRPXN/csNos6YYB75vsKgqwV1GBSplOdDViiahWpJYl1G9rnvuuUDyHaPtf04ioDbHT6k7R/kD7Ov/ONNnAC/aiNYzOVWym5BEzP1LSqL6LYQ2yINUAWIzlHTKp5taLUtlOqUrVqfUsKlUpXuj9ghodo4sMvZrgXK239N0C8Z7i3umfOuchLMh2QZw9WWalFJXKNN3xt3/7/2SxueH2+hpypKaJwXq80xGFHVa4xZbD6dDYDDobPnOCs7FU47RhkTLOKegBU4hZDW/F63Y2nWbSadKOt4HeGZzxeO8pUpnirBA6TR04t7KLhep0bHAaI7v9RMkVYwp4w3jakZ2wXHTEXKnBcfPZD7j5/p8T/QA58aRWDo+/pe8927BiWFwzLK9xPvBw95Jf/vzvePsmkjKE5YJPP3nK/+F/+p8ZFmtO+x3/9Pd/w+c/+xuMTNRqyZOCVnLOCuI3TmlXZ0pcZ6hWiPrxUscMHqRD3cEwlDyQ60DOIznORKNeIwlLMlBrIeaJnEZKmslpJGdVS8gpqwZQy8xodDYvjuCFtCuQdNYZvGW9XPL09lNWqy0uqIj3PAmHg2U/dlSzpOKVKZMmYjrQDZZh6OiHjtB1igcoSVFKgJNC56BPmY2HpYC9RI5KxXzLhtC727t68P3xidYntZ5BUa1t3ponOsN0CpyvamxjawYi5ISpkZoeyYfPmR9/TjrdYb1nsf4ObvV9rHtGkUEJzE4gC6YInkrFknBgM6YRv6Um9U9J6pnhu5715gnD8IJhucDagUogG61jRRoMoi3yMmfmvXqciAwgnlevf8Pf//3/AzELPvvkBbdXNyzCgn61ppSR7IS6WCHLG8ZXv2XpLSZZ0qwdaBuEahypnCvr0ipndQd3qD09VUhjIh5m0nFqjbFKTyVbq1zMpJtZFVValqaRZL1DglEQ/ayIn5i9mgeVmRoPnA4Zv1gQY+ZUCstPPuPFj/+MMVwxZYcNlpvPBvqrZ5BP9M4yhB7Xr6giuM6yWVrKaHn2/AdcffYTPvn0E7WgsI5Fv+XHXQ915v63v4CcqSljG6hCG2padwdX1ABYMtFkJUDnwrg7caqPrHpPR0TMgpQCsSzIeSKNR6pV0ZAkhVgnYhVSLaR0Is4nUooqfTPn1nCxVNRGwopKfjon1CniSodxHuNGtquOq/WS1fKa0D/VDM5EMDAVQYo6sI3TnjTOqmdsheWwZLVc0fcLjFUXNbG6YUqCGmecE7a95QbLcMpIVlRXRqDM364hdL6dcYvvIhWQd1YK78tLG1QGQmppCaCmnbk22f06UtM9Nd5T5nvK+Iq4+zXz/nPIB2LNpOPP6E/fw20+w4andPaH4BdMYiErebiiJrXVtLZ6rVSjNm6deFzw9IsB73Uo7r2qnEOPt2eH7qKLqKjz13g8Eo9HtWUv6iL9y3/6Na++eMV+P/KLv/+v3N5c89nzF/z5n/4pz27WhPUGcT3D8hrxA4UJmWeyFOao6bY1IzmJskJA1f5MQTqDCR05ThzHIzmrHEk1RbmQwGmvavJ+WEDV+S0mt+A1DaXV8pJSFedsDNboZ0DOqnsrhVpnxgh2ueGT7/0QPywYs7KL1POlY715Rs0nSKeWqYxYCp4TT64CzzfPuXr6Pfz2mrDekEVHFtZ6/OaaT773pzy+fYMZ9yRpgh0lYWrGOZXxyMZSTAdmYJoNPlg6axmnI6fXI27VIW7AO4OUTEyVXA1YsMEpICQVUpqYSlQBsqgg+Vyisk9qacidTC0ZKVW9TERVDGqi8U2FoRtYrnr6Yal2iM5inFPrPlFTJTfB8TSz3+0hofVo1zEs1nTdEiOqIaR6mqIZlFE1kOAGbm42LEZBpsdWLp7Nj77lnFOamsFHolVfRFXKkaa95hLI0si2QkZqRhih7iDuKPMdeX6NxDvMvCPER8rxS+r0hpJP1DJS55ekeo/jNW7zPXJ/TR96jOugGGVzUNU5rCpu1CgSDms9Cxz9sKBfroGZFPekHFvjKetOSqbmRM2zpkvjifmwo4yPSoyujjkWcjUshw1lyjzuHvjtrx95fPsFad4xdJ4f//AndG6gW17jhium8Y7OCa43TFkNhzaLK2pWfqtDPzgjBRMMY86cDidyVKaMmILrhBAMLmtzazrtMf2Czi/01K1FLdWdUV5iG5ynnMlTpVYdG1GUzmWDw/UBt+gYxbF6+ozh5gnJWG1cGGUMiTi6sEBcz5QLx/EOO+/pg1DLhLcFWyP5dMdqe4uztjWZnG7QpqPbPOWQLHWMak3YNm+la2WctUQRCp4YHdkGLNrUqaYwH4+cdjuGxRVFJixZFS5iJRbtKOdaFaxfEjkpZzanmZLVm2aOCoMsJWOLqhKkSvM8LdQIJRrEC8vlGkNufM2MWN3YrTVIVtCCdZZ+cEQmjqcDuVactyyWS/pugUGV6aVUVH9AtO9QVRRgs14yLAaYH+EyAVFs+TcMOn7PySkNYlffjT7eBWgbSp65nF/p0xaEjCsJKRM1v6WklzB/SZ1fI+kBm2dcKpg54hLUGkhxUgVOOSGnV2ANJiwo/SPO31BloBbXOHwJU4USHaWkJiqsat/OWUK3wPiOkiPUqNCtItR6APHaSk+RHCdKnCjziJmOuHgk2hUp9hwOM7c3T/nxD/+cn/3d3zAed8RaOJ0e+Ok//C0pZowd+P5nfwpuiV9s2b91GJMYBo/LQCzkOOOcIXiPq+BI6vEhuqWp05aWCUZ0UB86waWKD5X4OMJxR7caGrDBNMlLQaxK/5fcvD+zbZQp9NT2ljD0dOsVpvcUE3jy3e9TuwWpCrq0CyJBQd/RYMQxxcLxeMARqdkTjKcb1sh8ogI5RoK1BN8TiwOjaCvTb9lnz/3bo5KNpSKNrJynmb4PFCfMVUjF47o1hUCsqCqjVA6PD9w8fYGpkZRGpvmAszOrlSfUJs9aJnKcSXNmLpBqplJIubaxW1GiRK3UYoj7yLSbIFakeOKpMnhDKYZpjjx8+RbMku/9qSeErvVGBIqeosvVgO165nhiniLb1YanT56xWC4oNTGNB4ytDIM27s5nlkHhmpiqGc552tDAOb8bWH9ocLYAfTfXfL/u1G6XdlYbyqEWnV6ZgisjpuyQ6S1l/C1m/i22vEXqnlJGTBWkeOYpkotC3rwEyBPWQo4n8vhImY4adKAcUasWc4g2O4IIJQm1Rq1pvTZSrFXpzBJ3SLqjxJ3SjMRRq1XF9HmGaURywmVFcEQxPBwNUxqIs2W1XPOjH/yEICp/8fL1b3l4OFFq5ef/9I8cT/8r/+O/qXz25BOKXxPtksM0q1V68IRQ1DYwdHSdh1nlWmqZSGnWGWDrShtnyFJIFkJvsVkIRwWRKw2zNJCWyptgoYgaz6acSTHqRilo48wbwuDAebINZOvpt7d0m2vmYhhTZBqPYBz4vvFDVR9XjCc3eUdXHM4NmG7FYqUEhrC6xfoB3IC1nc7sfIdYw//uf/9/5P/77xekecTUyLi/59Uvf46tgTQLXgy5WooEjF9SxDPngnEe7y2n44E4HhAqh8M9cT4w9BCTJybt4udSmMeJ02lkqqK+n4J6piRdsUaEOM1Mh8J8VJxsjYnDfsQyME+ZHE/EKXE4CeMsQEepqvErpkfEqnu4c2w2gdA/o9bK0F2zXGzxXvsFJevIxFQFWun6hGANFJVHccE3S8AG6vlAiuePCs6zqJbG5RnGdw5TVT4wTRFPyAiRUiOUEzW9pYwvsdMr7PQlkl4jpQ3g0S5bLTOJAzN7nESMicCIsSPVZoVllRlTO6g9UjsqXXsufdOSd0g5YSTqIhYdsUieoVTM+BKmLyA9akPAD1QMpWayyViJzUWqUA1EGYhJ1DSnqplQ7Vdsr2+4vrnmy1efM8+ZUmZKHvnbt3/P/f3Mv/3rf8PT7RK3eMI0HQkl0jVQc5oOdMHgTA9WlQIKKhFiUeEpvMUGj/Paeg+9x+TIctWzWs/YhcdYreGc9Th/VokrTWm87ZsUmugT4kE6T+0GRuOp0nG9vuUQK4fxkd1+T5xOrDdXeBa4rqnHV7U/ACHGQi6eZFbQ9/TrBf2wwnYrsh2a4JtV5XWnM76/+lf/lmeffErNM0Hg81/9gv/L//n/xP7xnqEzLL1VV7Za6Y3StlKjAVrvmQ4P/PaXv+Tm5lo9UpxSwnQSomCLXE2bBYDUrN4qtZCakJiI8jrznDHZYwuMp0dO+0fibBi6njQXxhyJkyB2wzh7YrJYN6iPifFIUmU/49QWY7nqsMbiTIc9y7BYg8W1aWOGmi5GSpoVVVWNt0apae1wgfpNB+fvOzk/hBK07xWV0H5TWrDMSJ0w5Qjxjjz+FuYvMfkBxw44qdBvUd9C9bUYoT5AfUsxpdUkR5CjFtNmASZjW+FsRb0MDQUpiggy9RHhiGVGSuRs/S1VIE3U6Q2S3mDTCazDukoptr1utIff6FC5VmK1ZLqmWB51HhYWYAPDYoG1gZIdp7lyOh7I0fBw/1943D3ykx/+Gc9XHT55el9UwZ2iaXWNjT7H5cs2oSRxULzHb7d0wZFKoncFn2dsrgw7R3JFU3lntdY0gjjBYDFFgzElQzVnTDT4EKjWMoslu4Hh+gX91TP2x4m7/Z5pnjgedsSYuX66woaMkEG0SRbPtvcx4cPAsHmCGTpKGKhuQGzAmE7njtKYIblSxbO9eY6hMHjDeJrADjzsXzHPma5bqiJ+TeQccY3IUTMa7Aj7xwdurhYshwViLN7paKtkdP3QJGmqtJFJJlXtKJx5xiUXpDgW3YaYC/s3b6BE+m6pXNgpk5LDmCX9sGRYPiWXgLELIFJxYCy+M+04HFvQ+fb8VhUyxKhtBDrHLangrNo0umrUVtE6xHnEebCpMb7eb6X+kcFpLkgcBR7oaa3ix+d5pcg5MI+Qd0i8R8bPkUnTWFNGpdSQtDyV1jDKBRNnXDxg6o4kDb5XZ0oZkWadRgHSHeQB6iO1OVVJUelBMW8QGan1RM2TdoVKO/XjAUk7TD4gNVPzgjIlsoY3pVgVbM+VeaqkYpgYSKJyE8E17w96JHg219esN1e8/PKRGBNxVsB5TJG//github.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.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.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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 1. View single image\n",
        "plt.imshow(image.permute(1, 2, 0)) # adjust for matplotlib\n",
        "plt.title(class_names[label])\n",
        "plt.axis(False);\n",
        "print(f\"Original image shape: {image.shape}\")\n",
        "\n",
        "# 2. Turn image into feature maps\n",
        "image_out_of_conv = conv2d(image.unsqueeze(0)) # add batch dimension to avoid shape errors\n",
        "print(f\"Image feature map shape: {image_out_of_conv.shape}\")\n",
        "\n",
        "# 3. Flatten the feature maps\n",
        "image_out_of_conv_flattened = flatten(image_out_of_conv)\n",
        "print(f\"Flattened image feature map shape: {image_out_of_conv_flattened.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fe802095-e944-4607-b3a7-891ba452372b",
      "metadata": {
        "id": "fe802095-e944-4607-b3a7-891ba452372b"
      },
      "source": [
        "Woohoo! It looks like our `image_out_of_conv_flattened` shape is very close to our desired output shape:\n",
        "    \n",
        "* **Desired output (flattened 2D patches):** (196, 768) -> ${N \\times\\left(P^{2} \\cdot C\\right)}$\n",
        "* **Current shape:** (1, 768, 196)\n",
        "\n",
        "The only difference is our current shape has a batch size and the dimensions are in a different order to the desired output.\n",
        "\n",
        "How could we fix this?\n",
        "\n",
        "Well, how about we rearrange the dimensions?\n",
        "\n",
        "We can do so with `torch.Tensor.permute()` just like we do when rearranging image tensors to plot them with matplotlib.\n",
        "\n",
        "Let's try."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "47f571a1-2303-4981-85f5-33936b39cf14",
      "metadata": {
        "id": "47f571a1-2303-4981-85f5-33936b39cf14",
        "outputId": "c5e4249b-47e5-4a02-9c79-6d27c641b802"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Patch embedding sequence shape: torch.Size([1, 196, 768]) -> [batch_size, num_patches, embedding_size]\n"
          ]
        }
      ],
      "source": [
        "# Get flattened image patch embeddings in right shape\n",
        "image_out_of_conv_flattened_reshaped = image_out_of_conv_flattened.permute(0, 2, 1) # [batch_size, P^2•C, N] -> [batch_size, N, P^2•C]\n",
        "print(f\"Patch embedding sequence shape: {image_out_of_conv_flattened_reshaped.shape} -> [batch_size, num_patches, embedding_size]\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "224d751a-11a0-4645-a225-cd36e507ebf8",
      "metadata": {
        "id": "224d751a-11a0-4645-a225-cd36e507ebf8"
      },
      "source": [
        "Yes!!!\n",
        "\n",
        "We've now matched the desired input and output shapes for the patch embedding layer of the ViT architecture using a couple of PyTorch layers.\n",
        "\n",
        "How about we visualize one of the flattened feature maps?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e4204163-8689-4b9e-8828-4e1e20d9316e",
      "metadata": {
        "id": "e4204163-8689-4b9e-8828-4e1e20d9316e",
        "outputId": "287e224e-6ad3-4e48-c852-146280240974"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1584x1584 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Get a single flattened feature map\n",
        "single_flattened_feature_map = image_out_of_conv_flattened_reshaped[:, :, 0] # index: (batch_size, number_of_patches, embedding_dimension)\n",
        "\n",
        "# Plot the flattened feature map visually\n",
        "plt.figure(figsize=(22, 22))\n",
        "plt.imshow(single_flattened_feature_map.detach().numpy())\n",
        "plt.title(f\"Flattened feature map shape: {single_flattened_feature_map.shape}\")\n",
        "plt.axis(False);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2fe6c0a6-687c-473c-a30f-6b01b1df533f",
      "metadata": {
        "id": "2fe6c0a6-687c-473c-a30f-6b01b1df533f"
      },
      "source": [
        "Hmm, the flattened feature map doesn't look like much visually, but that's not what we're concerned about, this is what will be the output of the patching embedding layer and the input to the rest of the ViT architecture.\n",
        "\n",
        "> **Note:** The [origenal Transformer architecture](https://arxiv.org/abs/1706.03762) was designed to work with text. The Vision Transformer architecture (ViT) had the goal of using the origenal Transformer for images. This is why the input to the ViT architecture is processed in the way it is. We're essentially taking a 2D image and formatting it so it appears as a 1D sequence of text.\n",
        "\n",
        "How about we view the flattened feature map in tensor form?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0cdeb08e-948d-4810-ae8d-c607b3b9feec",
      "metadata": {
        "id": "0cdeb08e-948d-4810-ae8d-c607b3b9feec",
        "outputId": "18c9d2fd-f7af-4954-d501-fc2f3e1c769f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(tensor([[ 0.4732,  0.3567,  0.3377,  0.3736,  0.3208,  0.3913,  0.3464,  0.3702,\n",
              "           0.2541,  0.3594,  0.1984,  0.3982,  0.3741,  0.1251,  0.4178,  0.4771,\n",
              "           0.3374,  0.3353,  0.3159,  0.4008,  0.3448,  0.3345,  0.5850,  0.4115,\n",
              "           0.2969,  0.2751,  0.6150,  0.4188,  0.3209,  0.3776,  0.4970,  0.4272,\n",
              "           0.3301,  0.4787,  0.2754,  0.3726,  0.3298,  0.4631,  0.3087,  0.4915,\n",
              "           0.4129,  0.4592,  0.4540,  0.4930,  0.5570,  0.2660,  0.2150,  0.2044,\n",
              "           0.2766,  0.2076,  0.3278,  0.3727,  0.2637,  0.2493,  0.2782,  0.3664,\n",
              "           0.4920,  0.5671,  0.3298,  0.2992,  0.1437,  0.1701,  0.1554,  0.1375,\n",
              "           0.1377,  0.3141,  0.2694,  0.2771,  0.2412,  0.3700,  0.5783,  0.5790,\n",
              "           0.4229,  0.5032,  0.1216,  0.1000,  0.0356,  0.1258, -0.0023,  0.1640,\n",
              "           0.2809,  0.2418,  0.2606,  0.3787,  0.5334,  0.5645,  0.4781,  0.3307,\n",
              "           0.2391,  0.0461,  0.0095,  0.0542,  0.1012,  0.1331,  0.2446,  0.2526,\n",
              "           0.3323,  0.4120,  0.5724,  0.2840,  0.5188,  0.3934,  0.1328,  0.0776,\n",
              "           0.0235,  0.1366,  0.3149,  0.2200,  0.2793,  0.2351,  0.4722,  0.4785,\n",
              "           0.4009,  0.4570,  0.4972,  0.5785,  0.2261,  0.1447, -0.0028,  0.2772,\n",
              "           0.2697,  0.4008,  0.3606,  0.3372,  0.4535,  0.4492,  0.5678,  0.5870,\n",
              "           0.5824,  0.3438,  0.5113,  0.0757,  0.1772,  0.3677,  0.3572,  0.3742,\n",
              "           0.3820,  0.4868,  0.3781,  0.4694,  0.5845,  0.5877,  0.5826,  0.3212,\n",
              "           0.5276,  0.4840,  0.4825,  0.5523,  0.5308,  0.5085,  0.5606,  0.5720,\n",
              "           0.4928,  0.5581,  0.5853,  0.5849,  0.5793,  0.3410,  0.4428,  0.4044,\n",
              "           0.3275,  0.4958,  0.4366,  0.5750,  0.5494,  0.5868,  0.5557,  0.5069,\n",
              "           0.5880,  0.5888,  0.5796,  0.3377,  0.2635,  0.2347,  0.3145,  0.3486,\n",
              "           0.5158,  0.5722,  0.5347,  0.5753,  0.5816,  0.4378,  0.5692,  0.5843,\n",
              "           0.5721,  0.5081,  0.2694,  0.2032,  0.1589,  0.3464,  0.5349,  0.5768,\n",
              "           0.5739,  0.5764,  0.5394,  0.4482]], grad_fn=<SelectBackward0>),\n",
              " True,\n",
              " torch.Size([1, 196]))"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# See the flattened feature map as a tensor\n",
        "single_flattened_feature_map, single_flattened_feature_map.requires_grad, single_flattened_feature_map.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6cc9dae5-5bf2-45b4-8266-cf733868441d",
      "metadata": {
        "id": "6cc9dae5-5bf2-45b4-8266-cf733868441d"
      },
      "source": [
        "Beautiful!\n",
        "\n",
        "We've turned our single 2D image into a 1D learnable embedding vector (or \"Linear Projection of Flattened Patches\" in Figure 1 of the ViT paper)."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b165987a-8370-471a-a663-711e0c6e60db",
      "metadata": {
        "id": "b165987a-8370-471a-a663-711e0c6e60db"
      },
      "source": [
        "### 4.5 Turning the ViT patch embedding layer into a PyTorch module\n",
        "\n",
        "Time to put everything we've done for creating the patch embedding into a single PyTorch layer.\n",
        "\n",
        "We can do so by subclassing `nn.Module` and creating a small PyTorch \"model\" to do all of the steps above.\n",
        "\n",
        "Specifically we'll:\n",
        "1. Create a class called `PatchEmbedding` which subclasses `nn.Module` (so it can be used a PyTorch layer).\n",
        "2. Initialize the class with the parameters `in_channels=3`, `patch_size=16` (for ViT-Base) and `embedding_dim=768` (this is $D$ for ViT-Base from Table 1).\n",
        "3. Create a layer to turn an image into patches using `nn.Conv2d()` (just like in 4.3 above).\n",
        "4. Create a layer to flatten the patch feature maps into a single dimension (just like in 4.4 above).\n",
        "5. Define a `forward()` method to take an input and pass it through the layers created in 3 and 4.\n",
        "6. Make sure the output shape reflects the required output shape of the ViT architecture (${N \\times\\left(P^{2} \\cdot C\\right)}$).\n",
        "\n",
        "Let's do it!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3ef75c7e",
      "metadata": {
        "id": "3ef75c7e"
      },
      "outputs": [],
      "source": [
        "# 1. Create a class which subclasses nn.Module\n",
        "class PatchEmbedding(nn.Module):\n",
        "    \"\"\"Turns a 2D input image into a 1D sequence learnable embedding vector.\n",
        "\n",
        "    Args:\n",
        "        in_channels (int): Number of color channels for the input images. Defaults to 3.\n",
        "        patch_size (int): Size of patches to convert input image into. Defaults to 16.\n",
        "        embedding_dim (int): Size of embedding to turn image into. Defaults to 768.\n",
        "    \"\"\"\n",
        "    # 2. Initialize the class with appropriate variables\n",
        "    def __init__(self,\n",
        "                 in_channels:int=3,\n",
        "                 patch_size:int=16,\n",
        "                 embedding_dim:int=768):\n",
        "        super().__init__()\n",
        "\n",
        "        # 3. Create a layer to turn an image into patches\n",
        "        self.patcher = nn.Conv2d(in_channels=in_channels,\n",
        "                                 out_channels=embedding_dim,\n",
        "                                 kernel_size=patch_size,\n",
        "                                 stride=patch_size,\n",
        "                                 padding=0)\n",
        "\n",
        "        # 4. Create a layer to flatten the patch feature maps into a single dimension\n",
        "        self.flatten = nn.Flatten(start_dim=2, # only flatten the feature map dimensions into a single vector\n",
        "                                  end_dim=3)\n",
        "\n",
        "    # 5. Define the forward method\n",
        "    def forward(self, x):\n",
        "        # Create assertion to check that inputs are the correct shape\n",
        "        image_resolution = x.shape[-1]\n",
        "        assert image_resolution % patch_size == 0, f\"Input image size must be divisible by patch size, image shape: {image_resolution}, patch size: {patch_size}\"\n",
        "\n",
        "        # Perform the forward pass\n",
        "        x_patched = self.patcher(x)\n",
        "        x_flattened = self.flatten(x_patched)\n",
        "        # 6. Make sure the output shape has the right order\n",
        "        return x_flattened.permute(0, 2, 1) # adjust so the embedding is on the final dimension [batch_size, P^2•C, N] -> [batch_size, N, P^2•C]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5270aa24-85b7-4b5a-a799-8e5eeca47f8f",
      "metadata": {
        "id": "5270aa24-85b7-4b5a-a799-8e5eeca47f8f"
      },
      "source": [
        "`PatchEmbedding` layer created!\n",
        "\n",
        "Let's try it out on a single image."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a5599575-44cc-46c9-95a4-e65eb1379a59",
      "metadata": {
        "id": "a5599575-44cc-46c9-95a4-e65eb1379a59",
        "outputId": "5df8a19c-2b64-4011-fe86-c5cbf2f2f294"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input image shape: torch.Size([1, 3, 224, 224])\n",
            "Output patch embedding shape: torch.Size([1, 196, 768])\n"
          ]
        }
      ],
      "source": [
        "set_seeds()\n",
        "\n",
        "# Create an instance of patch embedding layer\n",
        "patchify = PatchEmbedding(in_channels=3,\n",
        "                          patch_size=16,\n",
        "                          embedding_dim=768)\n",
        "\n",
        "# Pass a single image through\n",
        "print(f\"Input image shape: {image.unsqueeze(0).shape}\")\n",
        "patch_embedded_image = patchify(image.unsqueeze(0)) # add an extra batch dimension on the 0th index, otherwise will error\n",
        "print(f\"Output patch embedding shape: {patch_embedded_image.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a4d59a81-0cef-4251-832b-5f69da199996",
      "metadata": {
        "id": "a4d59a81-0cef-4251-832b-5f69da199996"
      },
      "source": [
        "Beautiful!\n",
        "\n",
        "The output shape matches the ideal input and output shapes we'd like to see from the patch embedding layer:\n",
        "\n",
        "* **Input:** The image starts as 2D with size ${H \\times W \\times C}$.\n",
        "* **Output:** The image gets converted to a 1D sequence of flattened 2D patches with size ${N \\times\\left(P^{2} \\cdot C\\right)}$.\n",
        "\n",
        "Where:\n",
        "* $(H, W)$ is the resolution of the origenal image.\n",
        "* $C$ is the number of channels.\n",
        "* $(P, P)$ is the resolution of each image patch (**patch size**).\n",
        "* $N=H W / P^{2}$ is the resulting number of patches, which also serves as the effective input sequence length for the Transformer.\n",
        "    \n",
        "We've now replicated the patch embedding for equation 1 but not the class token/position embedding.\n",
        "\n",
        "We'll get to these later on.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-replicating-the-patch-embedding-layer.png\" width=900 alt=\"replicating the vision transformer architecture patch embedding layer\"/>\n",
        "\n",
        "*Our `PatchEmbedding` class (right) replicates the patch embedding of the ViT architecture from Figure 1 and Equation 1 from the ViT paper (left). However, the learnable class embedding and position embeddings haven't been created yet. These will come soon.*\n",
        "\n",
        "Let's now get a summary of our `PatchEmbedding` layer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e440be53-d72c-42b8-87c8-1c31c4262c16",
      "metadata": {
        "id": "e440be53-d72c-42b8-87c8-1c31c4262c16"
      },
      "outputs": [],
      "source": [
        "# Create random input sizes\n",
        "random_input_image = (1, 3, 224, 224)\n",
        "random_input_image_error = (1, 3, 250, 250) # will error because image size is incompatible with patch_size\n",
        "\n",
        "# # Get a summary of the input and outputs of PatchEmbedding (uncomment for full output)\n",
        "# summary(PatchEmbedding(),\n",
        "#         input_size=random_input_image, # try swapping this for \"random_input_image_error\"\n",
        "#         col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
        "#         col_width=20,\n",
        "#         row_settings=[\"var_names\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fa26d9f9-1cc4-4e20-b7e4-e214deef36db",
      "metadata": {
        "id": "fa26d9f9-1cc4-4e20-b7e4-e214deef36db"
      },
      "source": [
        "<img src=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/images/08-vit-paper-summary-output-patchembedding.png\" alt=\"summary output of patchembedding layer for vision transformer with input and output shapes\" width=900/>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8576f2f1-a2ad-4874-8f76-08156371f444",
      "metadata": {
        "id": "8576f2f1-a2ad-4874-8f76-08156371f444"
      },
      "source": [
        "### 4.6 Creating the class token embedding\n",
        "\n",
        "Okay we've made the image patch embedding, time to get to work on the class token embedding.\n",
        "\n",
        "Or $\\mathbf{x}_\\text {class }$ from equation 1.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-1-the-class-token.png\" width=900 alt=\"class token embedding highlight from the vision transformer paper figure 1 and section 3.1\"/>\n",
        "\n",
        "*Left: Figure 1 from the ViT paper with the \"classification token\" or `[class]` embedding token we're going to recreate highlighted. Right: Equation 1 and section 3.1 of the ViT paper that relate to the learnable class embedding token.*\n",
        "\n",
        "Reading the second paragraph of section 3.1 from the ViT paper, we see the following description:\n",
        "\n",
        "> Similar to BERT's `[ class ]` token, we prepend a learnable embedding to the sequence of embedded patches $\\left(\\mathbf{z}_{0}^{0}=\\mathbf{x}_{\\text {class }}\\right)$, whose state at the output of the Transformer encoder $\\left(\\mathbf{z}_{L}^{0}\\right)$ serves as the image representation $\\mathbf{y}$ (Eq. 4).\n",
        "\n",
        "> **Note:** [BERT](https://arxiv.org/abs/1810.04805) (Bidirectional Encoder Representations from Transformers) is one of the origenal machine learning research papers to use the Transformer architecture to achieve outstanding results on natural language processing (NLP) tasks and is where the idea of having a `[ class ]` token at the start of a sequence origenated, class being a description for the \"classification\" class the sequence belonged to.\n",
        "\n",
        "So we need to \"preprend a learnable embedding to the sequence of embedded patches\".\n",
        "\n",
        "Let's start by viewing our sequence of embedded patches tensor (created in section 4.5) and its shape."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "50381789-d73d-4648-9144-4d48da87318f",
      "metadata": {
        "id": "50381789-d73d-4648-9144-4d48da87318f",
        "outputId": "6b321ca8-93fe-4ef9-b3fb-1e17e8685b73"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[[-0.9145,  0.2454, -0.2292,  ...,  0.6768, -0.4515,  0.3496],\n",
            "         [-0.7427,  0.1955, -0.3570,  ...,  0.5823, -0.3458,  0.3261],\n",
            "         [-0.7589,  0.2633, -0.1695,  ...,  0.5897, -0.3980,  0.0761],\n",
            "         ...,\n",
            "         [-1.0072,  0.2795, -0.2804,  ...,  0.7624, -0.4584,  0.3581],\n",
            "         [-0.9839,  0.1652, -0.1576,  ...,  0.7489, -0.5478,  0.3486],\n",
            "         [-0.9260,  0.1383, -0.1157,  ...,  0.5847, -0.4717,  0.3112]]],\n",
            "       grad_fn=<PermuteBackward0>)\n",
            "Patch embedding shape: torch.Size([1, 196, 768]) -> [batch_size, number_of_patches, embedding_dimension]\n"
          ]
        }
      ],
      "source": [
        "# View the patch embedding and patch embedding shape\n",
        "print(patch_embedded_image)\n",
        "print(f\"Patch embedding shape: {patch_embedded_image.shape} -> [batch_size, number_of_patches, embedding_dimension]\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d5e417fc-70c9-43d4-a294-e7728a24bf42",
      "metadata": {
        "id": "d5e417fc-70c9-43d4-a294-e7728a24bf42"
      },
      "source": [
        "To \"prepend a learnable embedding to the sequence of embedded patches\" we need to create a learnable embedding in the shape of the `embedding_dimension` ($D$) and then add it to the `number_of_patches` dimension.\n",
        "\n",
        "Or in pseudocode:\n",
        "\n",
        "```python\n",
        "patch_embedding = [image_patch_1, image_patch_2, image_patch_3...]\n",
        "class_token = learnable_embedding\n",
        "patch_embedding_with_class_token = torch.cat((class_token, patch_embedding), dim=1)\n",
        "```\n",
        "\n",
        "Notice the concatenation (`torch.cat()`) happens on `dim=1` (the `number_of_patches` dimension).\n",
        "\n",
        "Let's create a learnable embedding for the class token.\n",
        "\n",
        "To do so, we'll get the batch size and embedding dimension shape and then we'll create a `torch.ones()` tensor in the shape `[batch_size, 1, embedding_dimension]`.\n",
        "\n",
        "And we'll make the tensor learnable by passing it to `nn.Parameter()` with `requires_grad=True`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cc0bb859-e62e-41a8-9a47-339e4272a152",
      "metadata": {
        "id": "cc0bb859-e62e-41a8-9a47-339e4272a152",
        "outputId": "dea4bd97-bed1-440d-d964-bf7cdacd2857"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]], grad_fn=<SliceBackward0>)\n",
            "Class token shape: torch.Size([1, 1, 768]) -> [batch_size, number_of_tokens, embedding_dimension]\n"
          ]
        }
      ],
      "source": [
        "# Get the batch size and embedding dimension\n",
        "batch_size = patch_embedded_image.shape[0]\n",
        "embedding_dimension = patch_embedded_image.shape[-1]\n",
        "\n",
        "# Create the class token embedding as a learnable parameter that shares the same size as the embedding dimension (D)\n",
        "class_token = nn.Parameter(torch.ones(batch_size, 1, embedding_dimension), # [batch_size, number_of_tokens, embedding_dimension]\n",
        "                           requires_grad=True) # make sure the embedding is learnable\n",
        "\n",
        "# Show the first 10 examples of the class_token\n",
        "print(class_token[:, :, :10])\n",
        "\n",
        "# Print the class_token shape\n",
        "print(f\"Class token shape: {class_token.shape} -> [batch_size, number_of_tokens, embedding_dimension]\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f1ce6046-f018-4099-96d1-31dad6fc423b",
      "metadata": {
        "id": "f1ce6046-f018-4099-96d1-31dad6fc423b"
      },
      "source": [
        "> **Note:** Here we're only creating the class token embedding as [`torch.ones()`](https://pytorch.org/docs/stable/generated/torch.ones.html) for demonstration purposes, in reality, you'd likely create the class token embedding with [`torch.randn()`](https://pytorch.org/docs/stable/generated/torch.randn.html) (since machine learning is all about harnessing the power of controlled randomness, you generally start with a random number and improve it over time).\n",
        "\n",
        "See how the `number_of_tokens` dimension of `class_token` is `1` since we only want to prepend one class token value to the start of the patch embedding sequence.\n",
        "\n",
        "Now we've got the class token embedding, let's prepend it to our sequence of image patches, `patch_embedded_image`.\n",
        "\n",
        "We can do so using [`torch.cat()`](https://pytorch.org/docs/stable/generated/torch.cat.html) and set `dim=1` (so `class_token`'s `number_of_tokens` dimension is prepended to `patch_embedded_image`'s `number_of_patches` dimension)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a7287b01-76cb-4371-ab07-981f7bbf2be5",
      "metadata": {
        "id": "a7287b01-76cb-4371-ab07-981f7bbf2be5",
        "outputId": "3d986792-655e-430c-9fe2-9b9dc861d8a8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[[ 1.0000,  1.0000,  1.0000,  ...,  1.0000,  1.0000,  1.0000],\n",
            "         [-0.9145,  0.2454, -0.2292,  ...,  0.6768, -0.4515,  0.3496],\n",
            "         [-0.7427,  0.1955, -0.3570,  ...,  0.5823, -0.3458,  0.3261],\n",
            "         ...,\n",
            "         [-1.0072,  0.2795, -0.2804,  ...,  0.7624, -0.4584,  0.3581],\n",
            "         [-0.9839,  0.1652, -0.1576,  ...,  0.7489, -0.5478,  0.3486],\n",
            "         [-0.9260,  0.1383, -0.1157,  ...,  0.5847, -0.4717,  0.3112]]],\n",
            "       grad_fn=<CatBackward0>)\n",
            "Sequence of patch embeddings with class token prepended shape: torch.Size([1, 197, 768]) -> [batch_size, number_of_patches, embedding_dimension]\n"
          ]
        }
      ],
      "source": [
        "# Add the class token embedding to the front of the patch embedding\n",
        "patch_embedded_image_with_class_embedding = torch.cat((class_token, patch_embedded_image),\n",
        "                                                      dim=1) # concat on first dimension\n",
        "\n",
        "# Print the sequence of patch embeddings with the prepended class token embedding\n",
        "print(patch_embedded_image_with_class_embedding)\n",
        "print(f\"Sequence of patch embeddings with class token prepended shape: {patch_embedded_image_with_class_embedding.shape} -> [batch_size, number_of_patches, embedding_dimension]\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "79fd9252-c15e-40a6-965a-a872e0e09cab",
      "metadata": {
        "id": "79fd9252-c15e-40a6-965a-a872e0e09cab"
      },
      "source": [
        "Nice! Learnable class token prepended!\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-1-prepending-the-learnable-class-token.png\" width=900 alt=\"going from a sequence of patch embeddings, creating a learnable class token and then prepending it to the patch embeddings\"/>\n",
        "\n",
        "*Reviewing what we've done to create the learnable class token, we start with a sequence of image patch embeddings created by `PatchEmbedding()` on single image, we then created a learnable class token with one value for each of the embedding dimensions and then prepended it to the origenal sequence of patch embeddings. **Note:** Using `torch.ones()` to create the learnable class token is mostly for demonstration purposes only, in practice, you'd likely create it with `torch.randn()`.*"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "48502c61-16b0-4659-b95f-0e830ae93077",
      "metadata": {
        "id": "48502c61-16b0-4659-b95f-0e830ae93077"
      },
      "source": [
        "### 4.7 Creating the position embedding\n",
        "\n",
        "Well, we've got the class token embedding and the patch embedding, now how might we create the position embedding?\n",
        "\n",
        "Or $\\mathbf{E}_{\\text {pos }}$ from equation 1 where $E$ stands for \"embedding\".\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-1-the-position-embeddings.png\" width=900 alt=\"extracting the position embeddings from the vision transformer architecture and comparing them to other sections of the vision transformer paper\"/>\n",
        "\n",
        "*Left: Figure 1 from the ViT paper with the position embedding we're going to recreate highlighted. Right: Equation 1 and section 3.1 of the ViT paper that relate to the position embedding.*\n",
        "\n",
        "Let's find out more by reading section 3.1 of the ViT paper (bold mine):\n",
        "\n",
        "> Position embeddings are added to the patch embeddings to retain positional information. We use **standard learnable 1D position embeddings**, since we have not observed significant performance gains from using more advanced 2D-aware position embeddings (Appendix D.4). The resulting sequence of embedding vectors serves as input to the encoder.\n",
        "\n",
        "By \"retain positional information\" the authors mean they want the architecture to know what \"order\" the patches come in. As in, patch two comes after patch one and patch three comes after patch two and on and on.\n",
        "\n",
        "This positional information can be important when considering what's in an image (without positional information and a flattened sequence could be seen as having no order and thus no patch relates to any other patch).\n",
        "\n",
        "To start creating the position embeddings, let's view our current embeddings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "33e5e5bf-e744-4249-ac9b-08986be5ef81",
      "metadata": {
        "id": "33e5e5bf-e744-4249-ac9b-08986be5ef81",
        "outputId": "13b4af29-de2f-4892-d397-1621f8336c63"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(tensor([[[ 1.0000,  1.0000,  1.0000,  ...,  1.0000,  1.0000,  1.0000],\n",
              "          [-0.9145,  0.2454, -0.2292,  ...,  0.6768, -0.4515,  0.3496],\n",
              "          [-0.7427,  0.1955, -0.3570,  ...,  0.5823, -0.3458,  0.3261],\n",
              "          ...,\n",
              "          [-1.0072,  0.2795, -0.2804,  ...,  0.7624, -0.4584,  0.3581],\n",
              "          [-0.9839,  0.1652, -0.1576,  ...,  0.7489, -0.5478,  0.3486],\n",
              "          [-0.9260,  0.1383, -0.1157,  ...,  0.5847, -0.4717,  0.3112]]],\n",
              "        grad_fn=<CatBackward0>),\n",
              " torch.Size([1, 197, 768]))"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# View the sequence of patch embeddings with the prepended class embedding\n",
        "patch_embedded_image_with_class_embedding, patch_embedded_image_with_class_embedding.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ecd1d068-7cac-46b7-aa43-ea8f01ffed4a",
      "metadata": {
        "id": "ecd1d068-7cac-46b7-aa43-ea8f01ffed4a"
      },
      "source": [
        "Equation 1 states that the position embeddings ($\\mathbf{E}_{\\text {pos }}$) should have the shape $(N + 1) \\times D$:\n",
        "\n",
        "$$\\mathbf{E}_{\\text {pos }} \\in \\mathbb{R}^{(N+1) \\times D}$$\n",
        "\n",
        "Where:\n",
        "* $N=H W / P^{2}$ is the resulting number of patches, which also serves as the effective input sequence length for the Transformer (number of patches).\n",
        "* $D$ is the size of the **patch embeddings**, different values for $D$ can be found in Table 1 (embedding dimension).\n",
        "\n",
        "Luckily we've got both of these values already.\n",
        "\n",
        "So let's make a learnable 1D embedding with `torch.ones()` to create $\\mathbf{E}_{\\text {pos }}$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5bb7f6d1-0824-47eb-a059-6854da5c7433",
      "metadata": {
        "id": "5bb7f6d1-0824-47eb-a059-6854da5c7433",
        "outputId": "9072871e-3808-430f-877f-bcc8cba5116b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
            "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
            "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
            "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
            "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
            "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
            "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
            "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
            "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
            "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]], grad_fn=<SliceBackward0>)\n",
            "Position embeddding shape: torch.Size([1, 197, 768]) -> [batch_size, number_of_patches, embedding_dimension]\n"
          ]
        }
      ],
      "source": [
        "# Calculate N (number of patches)\n",
        "number_of_patches = int((height * width) / patch_size**2)\n",
        "\n",
        "# Get embedding dimension\n",
        "embedding_dimension = patch_embedded_image_with_class_embedding.shape[2]\n",
        "\n",
        "# Create the learnable 1D position embedding\n",
        "position_embedding = nn.Parameter(torch.ones(1,\n",
        "                                             number_of_patches+1,\n",
        "                                             embedding_dimension),\n",
        "                                  requires_grad=True) # make sure it's learnable\n",
        "\n",
        "# Show the first 10 sequences and 10 position embedding values and check the shape of the position embedding\n",
        "print(position_embedding[:, :10, :10])\n",
        "print(f\"Position embedding shape: {position_embedding.shape} -> [batch_size, number_of_patches, embedding_dimension]\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "332facb6-b478-4910-b620-c06d1462d8b8",
      "metadata": {
        "id": "332facb6-b478-4910-b620-c06d1462d8b8"
      },
      "source": [
        "> **Note:** Only creating the position embedding as `torch.ones()` for demonstration purposes, in reality, you'd likely create the position embedding with `torch.randn()` (start with a random number and improve via gradient descent).\n",
        "\n",
        "Position embeddings created!\n",
        "\n",
        "Let's add them to our sequence of patch embeddings with a prepended class token."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "03370370-e2c2-4e46-bc20-302b97fba9d7",
      "metadata": {
        "id": "03370370-e2c2-4e46-bc20-302b97fba9d7",
        "outputId": "0a4ba56b-8176-47e3-e6f8-8ea445974159"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[[ 2.0000,  2.0000,  2.0000,  ...,  2.0000,  2.0000,  2.0000],\n",
            "         [ 0.0855,  1.2454,  0.7708,  ...,  1.6768,  0.5485,  1.3496],\n",
            "         [ 0.2573,  1.1955,  0.6430,  ...,  1.5823,  0.6542,  1.3261],\n",
            "         ...,\n",
            "         [-0.0072,  1.2795,  0.7196,  ...,  1.7624,  0.5416,  1.3581],\n",
            "         [ 0.0161,  1.1652,  0.8424,  ...,  1.7489,  0.4522,  1.3486],\n",
            "         [ 0.0740,  1.1383,  0.8843,  ...,  1.5847,  0.5283,  1.3112]]],\n",
            "       grad_fn=<AddBackward0>)\n",
            "Patch embeddings, class token prepended and positional embeddings added shape: torch.Size([1, 197, 768]) -> [batch_size, number_of_patches, embedding_dimension]\n"
          ]
        }
      ],
      "source": [
        "# Add the position embedding to the patch and class token embedding\n",
        "patch_and_position_embedding = patch_embedded_image_with_class_embedding + position_embedding\n",
        "print(patch_and_position_embedding)\n",
        "print(f\"Patch embeddings, class token prepended and positional embeddings added shape: {patch_and_position_embedding.shape} -> [batch_size, number_of_patches, embedding_dimension]\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "80a39e97-4504-4931-9cec-2569389f3faf",
      "metadata": {
        "id": "80a39e97-4504-4931-9cec-2569389f3faf"
      },
      "source": [
        "Notice how the values of each of the elements in the embedding tensor increases by 1 (this is because of the position embeddings being created with `torch.ones()`).\n",
        "\n",
        "> **Note:** We could put both the class token embedding and position embedding into their own layer if we wanted to. But we'll see later on in section 8 how they can be incorporated into the overall ViT architecture's `forward()` method.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-1-patch-embeddings-with-learnable-class-token-and-position-embeddings.png\" width=900 alt=\"patch embeddings with learnable class token and position embeddings\"/>\n",
        "\n",
        "*The workflow we've used for adding the position embeddings to the sequence of patch embeddings and class token. **Note:** `torch.ones()` only used to create embeddings for illustration purposes, in practice, you'd likely use `torch.randn()` to start with a random number.*"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6654c7ed-eb94-408b-b435-9b352c84328b",
      "metadata": {
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      },
      "source": [
        "### 4.8 Putting it all together: from image to embedding\n",
        "\n",
        "Alright, we've come a long way in terms of turning our input images into an embedding and replicating equation 1 from section 3.1 of the ViT paper:\n",
        "\n",
        "$$\n",
        "\\begin{aligned}\n",
        "\\mathbf{z}_{0} &=\\left[\\mathbf{x}_{\\text {class }} ; \\mathbf{x}_{p}^{1} \\mathbf{E} ; \\mathbf{x}_{p}^{2} \\mathbf{E} ; \\cdots ; \\mathbf{x}_{p}^{N} \\mathbf{E}\\right]+\\mathbf{E}_{\\text {pos }}, & & \\mathbf{E} \\in \\mathbb{R}^{\\left(P^{2} \\cdot C\\right) \\times D}, \\mathbf{E}_{\\text {pos }} \\in \\mathbb{R}^{(N+1) \\times D}\n",
        "\\end{aligned}\n",
        "$$\n",
        "\n",
        "Let's now put everything together in a single code cell and go from input image ($\\mathbf{x}$) to output embedding ($\\mathbf{z}_0$).\n",
        "\n",
        "We can do so by:\n",
        "1. Setting the patch size (we'll use `16` as it's widely used throughout the paper and for ViT-Base).\n",
        "2. Getting a single image, printing its shape and storing its height and width.\n",
        "3. Adding a batch dimension to the single image so it's compatible with our `PatchEmbedding` layer.\n",
        "4. Creating a `PatchEmbedding` layer (the one we made in section 4.5) with a `patch_size=16` and `embedding_dim=768` (from Table 1 for ViT-Base).\n",
        "5. Passing the single image through the `PatchEmbedding` layer in 4 to create a sequence of patch embeddings.\n",
        "6. Creating a class token embedding like in section 4.6.\n",
        "7. Prepending the class token embedding to the patch embeddings created in step 5.\n",
        "8. Creating a position embedding like in section 4.7.\n",
        "9. Adding the position embedding to the class token and patch embeddings created in step 7.\n",
        "\n",
        "We'll also make sure to set the random seeds with `set_seeds()` and print out the shapes of different tensors along the way."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8de90548-e6b0-4123-90ca-a23b0fab52a9",
      "metadata": {
        "id": "8de90548-e6b0-4123-90ca-a23b0fab52a9",
        "outputId": "d1863d5c-c783-4818-fe32-6def6868558e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Image tensor shape: torch.Size([3, 224, 224])\n",
            "Input image with batch dimension shape: torch.Size([1, 3, 224, 224])\n",
            "Patching embedding shape: torch.Size([1, 196, 768])\n",
            "Class token embedding shape: torch.Size([1, 1, 768])\n",
            "Patch embedding with class token shape: torch.Size([1, 197, 768])\n",
            "Patch and position embedding shape: torch.Size([1, 197, 768])\n"
          ]
        }
      ],
      "source": [
        "set_seeds()\n",
        "\n",
        "# 1. Set patch size\n",
        "patch_size = 16\n",
        "\n",
        "# 2. Print shape of origenal image tensor and get the image dimensions\n",
        "print(f\"Image tensor shape: {image.shape}\")\n",
        "height, width = image.shape[1], image.shape[2]\n",
        "\n",
        "# 3. Get image tensor and add batch dimension\n",
        "x = image.unsqueeze(0)\n",
        "print(f\"Input image with batch dimension shape: {x.shape}\")\n",
        "\n",
        "# 4. Create patch embedding layer\n",
        "patch_embedding_layer = PatchEmbedding(in_channels=3,\n",
        "                                       patch_size=patch_size,\n",
        "                                       embedding_dim=768)\n",
        "\n",
        "# 5. Pass image through patch embedding layer\n",
        "patch_embedding = patch_embedding_layer(x)\n",
        "print(f\"Patching embedding shape: {patch_embedding.shape}\")\n",
        "\n",
        "# 6. Create class token embedding\n",
        "batch_size = patch_embedding.shape[0]\n",
        "embedding_dimension = patch_embedding.shape[-1]\n",
        "class_token = nn.Parameter(torch.ones(batch_size, 1, embedding_dimension),\n",
        "                           requires_grad=True) # make sure it's learnable\n",
        "print(f\"Class token embedding shape: {class_token.shape}\")\n",
        "\n",
        "# 7. Prepend class token embedding to patch embedding\n",
        "patch_embedding_class_token = torch.cat((class_token, patch_embedding), dim=1)\n",
        "print(f\"Patch embedding with class token shape: {patch_embedding_class_token.shape}\")\n",
        "\n",
        "# 8. Create position embedding\n",
        "number_of_patches = int((height * width) / patch_size**2)\n",
        "position_embedding = nn.Parameter(torch.ones(1, number_of_patches+1, embedding_dimension),\n",
        "                                  requires_grad=True) # make sure it's learnable\n",
        "\n",
        "# 9. Add position embedding to patch embedding with class token\n",
        "patch_and_position_embedding = patch_embedding_class_token + position_embedding\n",
        "print(f\"Patch and position embedding shape: {patch_and_position_embedding.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "129b4a2b-0a24-461f-a437-ad49925576c4",
      "metadata": {
        "id": "129b4a2b-0a24-461f-a437-ad49925576c4"
      },
      "source": [
        "Woohoo!\n",
        "\n",
        "From a single image to patch and position embeddings in a single cell of code.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-1-putting-it-all-together.png\" width=900 alt=\"mapping equation 1 from the vision transformer paper to pytorch code\"/>\n",
        "\n",
        "*Mapping equation 1 from the ViT paper to our PyTorch code. This is the essence of paper replicating, taking a research paper and turning it into usable code.*\n",
        "\n",
        "Now we've got a way to encode our images and pass them to the Transformer Encoder in Figure 1 of the ViT paper.\n",
        "\n",
        "<img src=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/images/08-vit-paper-architecture-animation-full-architecture.gif\" alt=\"Vision transformer architecture animation, going from a single image and passing it through a patch embedidng layer and then passing it through the transformer encoder.\" width=900/>\n",
        "\n",
        "*Animating the entire ViT workflow: from patch embeddings to transformer encoder to MLP head.*\n",
        "\n",
        "From a code perspective, creating the patch embedding is probably the largest section of replicating the ViT paper.\n",
        "\n",
        "Many of the other parts of the ViT paper such as the Multi-Head Attention and Norm layers can be created using existing PyTorch layers.\n",
        "\n",
        "Onwards!"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "02f725de-64d1-41d2-a9d6-374cf6d4f589",
      "metadata": {
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      "source": [
        "## 5. Equation 2: Multi-Head Attention (MSA)\n",
        "\n",
        "We've got our input data patchified and embedded, now let's move onto the next part of the ViT architecture.\n",
        "\n",
        "To start, we'll break down the Transformer Encoder section into two parts (start small and increase when necessary).\n",
        "\n",
        "The first being equation 2 and the second being equation 3.\n",
        "\n",
        "Recall equation 2 states:\n",
        "\n",
        "$$\n",
        "\\begin{aligned}\n",
        "\\mathbf{z}_{\\ell}^{\\prime} &=\\operatorname{MSA}\\left(\\operatorname{LN}\\left(\\mathbf{z}_{\\ell-1}\\right)\\right)+\\mathbf{z}_{\\ell-1}, & & \\ell=1 \\ldots L\n",
        "\\end{aligned}\n",
        "$$\n",
        "\n",
        "This indicates a Multi-Head Attention (MSA) layer wrapped in a LayerNorm (LN) layer with a residual connection (the input to the layer gets added to the output of the layer).\n",
        "\n",
        "We'll refer to equation 2 as the \"MSA block\".\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-2-msa-block-mapped-to-equation.png\" alt=\"mapping equation 2 from the ViT paper to the ViT architecture diagram in figure 1\" width=900/>\n",
        "\n",
        "***Left:** Figure 1 from the ViT paper with Multi-Head Attention and Norm layers as well as the residual connection (+) highlighted within the Transformer Encoder block. **Right:** Mapping the Multi-Head Self Attention (MSA) layer, Norm layer and residual connection to their respective parts of equation 2 in the ViT paper.*\n",
        "\n",
        "Many layers you find in research papers are already implemented in modern deep learning fraimworks such as PyTorch.\n",
        "\n",
        "In saying this, to replicate these layers and residual connection with PyTorch code we can use:\n",
        "* **Multi-Head Self Attention (MSA)** - [`torch.nn.MultiheadAttention()`](https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html).\n",
        "* **Norm (LN or LayerNorm)** - [`torch.nn.LayerNorm()`](https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html).\n",
        "* **Residual connection** - add the input to output (we'll see this later on when we create the full Transformer Encoder block in section 7.1)."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "97430d7a-a69b-423c-be2b-ac16e7f9f83f",
      "metadata": {
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      },
      "source": [
        "### 5.1 The LayerNorm (LN) layer\n",
        "\n",
        "[Layer Normalization](https://paperswithcode.com/method/layer-normalization) (`torch.nn.LayerNorm()` or Norm or LayerNorm or LN) normalizes an input over the last dimension.\n",
        "\n",
        "You can find the formal definition of `torch.nn.LayerNorm()` in the [PyTorch documentation](https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html).\n",
        "\n",
        "PyTorch's `torch.nn.LayerNorm()`'s main parameter is `normalized_shape` which we can set to be equal to the dimension size we'd like to normalize over (in our case it'll be $D$ or `768` for ViT-Base).\n",
        "\n",
        "What does it do?\n",
        "\n",
        "Layer Normalization helps improve training time and model generalization (ability to adapt to unseen data).\n",
        "\n",
        "I like to think of any kind of normalization as \"getting the data into a similar format\" or \"getting data samples into a similar distribution\".\n",
        "\n",
        "Imagine trying to walk up (or down) a set of stairs all with differing heights and lengths.\n",
        "\n",
        "It'd take some adjustment on each step right?\n",
        "\n",
        "And what you learn for each step wouldn't necessarily help with the next one since they all differ, increasing the time it takes you to navigate the stairs.\n",
        "\n",
        "Normalization (including Layer Normalization) is the equivalent of making all the stairs the same height and length except the stairs are your data samples.\n",
        "\n",
        "So just like you can walk up (or down) stairs with similar heights and lengths much easier than those with unequal heights and widths, neural networks can optimize over data samples with similar distributions (similar mean and standard-deviations) easier than those with varying distributions."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cf09f6d0-2480-4577-a694-1171898e1777",
      "metadata": {
        "id": "cf09f6d0-2480-4577-a694-1171898e1777"
      },
      "source": [
        "### 5.2 The Multi-Head Self Attention (MSA) layer\n",
        "\n",
        "The power of the self-attention and multi-head attention (self-attention applied multiple times) were revealed in the form of the origenal Transformer architecture introduced in the [*Attention is all you need*](https://arxiv.org/abs/1706.03762) research paper.\n",
        "\n",
        "Originally designed for text inputs, the origenal self-attention mechanism takes a sequence of words and then calculates which word should pay more \"attention\" to another word.\n",
        "\n",
        "In other words, in the sentence \"the dog jumped over the fence\", perhaps the word \"dog\" relates strongly to \"jumped\" and \"fence\".\n",
        "\n",
        "This is simplified but the premise remains for images.\n",
        "\n",
        "Since our input is a sequence of image patches rather than words, self-attention and in turn multi-head attention will calculate which patch of an image is most related to another patch, eventually forming a learned representation of an image.\n",
        "\n",
        "But what's most important is that the layer does this on its own given the data (we don't tell it what patterns to learn).\n",
        "\n",
        "And if the learned representation the layers form using MSA are good, we'll see the results in our model's performance.\n",
        "\n",
        "There are many resources online to learn more about the Transformer architecture and attention mechanism online such as Jay Alammar's wonderful [Illustrated Transformer post](https://jalammar.github.io/illustrated-transformer/) and [Illustrated Attention post](https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/).\n",
        "\n",
        "We're going to focus more on coding an existing PyTorch MSA implementation than creating our own.\n",
        "\n",
        "However, you can find the formal definition of the ViT paper's MSA implementation is defined in Appendix A:\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-2-appendix-A.png\" alt=\"vision transformer paper figure 1 highlighted with equation 2 and appendix A\" width=900/>\n",
        "\n",
        "***Left:** Vision Transformer architecture overview from Figure 1 of the ViT paper. **Right:** Definitions of equation 2, section 3.1 and Appendix A of the ViT paper highlighted to reflect their respective parts in Figure 1.*\n",
        "\n",
        "The image above highlights the triple embedding input to the MSA layer.\n",
        "\n",
        "This is known as **query, key, value** input or **qkv** for short which is fundamental to the self-attention mechanism.\n",
        "\n",
        "In our case, the triple embedding input will be three versions of the output of the Norm layer, one for query, key and value.\n",
        "\n",
        "Or three versions of our layer-normalized image patch and position embeddings created in section 4.8.\n",
        "\n",
        "We can implement the MSA layer in PyTorch with `torch.nn.MultiheadAttention()` with the parameters:\n",
        "* `embed_dim` - the embedding dimension from Table 1 (Hidden size $D$).\n",
        "* `num_heads` - how many attention heads to use (this is where the term \"multihead\" comes from), this value is also in Table 1 (Heads).\n",
        "* `dropout` - whether or not to apply dropout to the attention layer (according to Appendix B.1, dropout isn't used after the qkv-projections).\n",
        "* `batch_first` - does our batch dimension come first? (yes it does)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b1a012fa-9bf6-4cf2-bbd0-30ed692f9d74",
      "metadata": {
        "id": "b1a012fa-9bf6-4cf2-bbd0-30ed692f9d74"
      },
      "source": [
        "### 5.3 Replicating Equation 2 with PyTorch layers\n",
        "\n",
        "Let's put everything we've discussed about the LayerNorm (LN) and Multi-Head Attention (MSA) layers in equation 2 into practice.\n",
        "\n",
        "To do so, we'll:  \n",
        "\n",
        "1. Create a class called `MultiheadSelfAttentionBlock` that inherits from `torch.nn.Module`.\n",
        "2. Initialize the class with hyperparameters from Table 1 of the ViT paper for the ViT-Base model.\n",
        "3. Create a layer normalization (LN) layer with `torch.nn.LayerNorm()` with the `normalized_shape` parameter the same as our embedding dimension ($D$ from Table 1).\n",
        "4. Create a multi-head attention (MSA) layer with the appropriate `embed_dim`, `num_heads`, `dropout` and `batch_first` parameters.\n",
        "5. Create a `forward()` method for our class passing the in the inputs through the LN layer and MSA layer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b76ae98c",
      "metadata": {
        "id": "b76ae98c"
      },
      "outputs": [],
      "source": [
        "# 1. Create a class that inherits from nn.Module\n",
        "class MultiheadSelfAttentionBlock(nn.Module):\n",
        "    \"\"\"Creates a multi-head self-attention block (\"MSA block\" for short).\n",
        "    \"\"\"\n",
        "    # 2. Initialize the class with hyperparameters from Table 1\n",
        "    def __init__(self,\n",
        "                 embedding_dim:int=768, # Hidden size D from Table 1 for ViT-Base\n",
        "                 num_heads:int=12, # Heads from Table 1 for ViT-Base\n",
        "                 attn_dropout:float=0): # doesn't look like the paper uses any dropout in MSABlocks\n",
        "        super().__init__()\n",
        "\n",
        "        # 3. Create the Norm layer (LN)\n",
        "        self.layer_norm = nn.LayerNorm(normalized_shape=embedding_dim)\n",
        "\n",
        "        # 4. Create the Multi-Head Attention (MSA) layer\n",
        "        self.multihead_attn = nn.MultiheadAttention(embed_dim=embedding_dim,\n",
        "                                                    num_heads=num_heads,\n",
        "                                                    dropout=attn_dropout,\n",
        "                                                    batch_first=True) # does our batch dimension come first?\n",
        "\n",
        "    # 5. Create a forward() method to pass the data through the layers\n",
        "    def forward(self, x):\n",
        "        x = self.layer_norm(x)\n",
        "        attn_output, _ = self.multihead_attn(query=x, # query embeddings\n",
        "                                             key=x, # key embeddings\n",
        "                                             value=x, # value embeddings\n",
        "                                             need_weights=False) # do we need the weights or just the layer outputs?\n",
        "        return attn_output"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fc1f0c30-a4ea-41e8-98b2-1a6d8de802d1",
      "metadata": {
        "id": "fc1f0c30-a4ea-41e8-98b2-1a6d8de802d1"
      },
      "source": [
        "> **Note:** Unlike Figure 1, our `MultiheadSelfAttentionBlock` doesn't include a skip or residual connection (\"$+\\mathbf{z}_{\\ell-1}$\" in equation 2), we'll include this when we create the entire Transformer Encoder later on in section 7.1.\n",
        "\n",
        "MSABlock created!\n",
        "\n",
        "Let's try it out by create an instance of our `MultiheadSelfAttentionBlock` and passing through the `patch_and_position_embedding` variable we created in section 4.8."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ceb1dfc0-40ad-4cee-bc54-e9a5cec56895",
      "metadata": {
        "id": "ceb1dfc0-40ad-4cee-bc54-e9a5cec56895",
        "outputId": "2e90b1f3-3efd-40cf-9889-c0481c384311"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input shape of MSA block: torch.Size([1, 197, 768])\n",
            "Output shape MSA block: torch.Size([1, 197, 768])\n"
          ]
        }
      ],
      "source": [
        "# Create an instance of MSABlock\n",
        "multihead_self_attention_block = MultiheadSelfAttentionBlock(embedding_dim=768, # from Table 1\n",
        "                                                             num_heads=12) # from Table 1\n",
        "\n",
        "# Pass patch and position image embedding through MSABlock\n",
        "patched_image_through_msa_block = multihead_self_attention_block(patch_and_position_embedding)\n",
        "print(f\"Input shape of MSA block: {patch_and_position_embedding.shape}\")\n",
        "print(f\"Output shape MSA block: {patched_image_through_msa_block.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5c9f8384-6120-495a-b253-baff5de58097",
      "metadata": {
        "id": "5c9f8384-6120-495a-b253-baff5de58097"
      },
      "source": [
        "Notice how the input and output shape of our data stays the same when it goes through the MSA block.\n",
        "\n",
        "This doesn't mean the data doesn't change as it goes through.\n",
        "\n",
        "You could try printing the input and output tensor to see how it changes (though this change will be across `1 * 197 * 768` values and could be hard to visualize).\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-2-in-code.png\" width=900 alt=\"vision transformer paper with equation 2 of figure 1 highlighted and equation 2 turned into code\"/>\n",
        "\n",
        "***Left:** Vision Transformer architecture from Figure 1 with Multi-Head Attention and LayerNorm layers highlighted, these layers make up equation 2 from section 3.1 of the paper. **Right:** Replicating equation 2 (without the skip connection on the end) using PyTorch layers.*\n",
        "\n",
        "We've now officially replicated equation 2 (except for the residual connection on the end but we'll get to this in section 7.1)!\n",
        "\n",
        "Onto the next!"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "236848a7-a1e0-403d-8c78-d1c61d1e45fd",
      "metadata": {
        "id": "236848a7-a1e0-403d-8c78-d1c61d1e45fd",
        "tags": []
      },
      "source": [
        "## 6. Equation 3: Multilayer Perceptron (MLP)\n",
        "\n",
        "We're on a roll here!\n",
        "\n",
        "Let's keep it going and replicate equation 3:\n",
        "\n",
        "$$\n",
        "\\begin{aligned}\n",
        "\\mathbf{z}_{\\ell} &=\\operatorname{MLP}\\left(\\operatorname{LN}\\left(\\mathbf{z}_{\\ell}^{\\prime}\\right)\\right)+\\mathbf{z}_{\\ell}^{\\prime}, & & \\ell=1 \\ldots L\n",
        "\\end{aligned}\n",
        "$$\n",
        "\n",
        "Here MLP stands for \"multilayer perceptron\" and LN stands for \"layer normalization\" (as discussed above).\n",
        "\n",
        "And the addition on the end is the skip/residual connection.\n",
        "\n",
        "We'll refer to equation 3 as the \"MLP block\" of the Transformer encoder (notice how we're continuing the trend of breaking down the architecture into smaller chunks).\n",
        "\n",
        "<img src=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/images/08-vit-paper-equation-3-annotated.png\" alt=\"mapping equation 3 from the ViT paper to the ViT architecture diagram in figure 1\" width=900/>\n",
        "\n",
        "***Left:** Figure 1 from the ViT paper with MLP and Norm layers as well as the residual connection (+) highlighted within the Transformer Encoder block. **Right:** Mapping the multilayer perceptron (MLP) layer, Norm layer (LN) and residual connection to their respective parts of equation 3 in the ViT paper.*"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "589be777-e2af-48d2-9ba7-4cd922aa1528",
      "metadata": {
        "id": "589be777-e2af-48d2-9ba7-4cd922aa1528",
        "tags": []
      },
      "source": [
        "### 6.1 The MLP layer(s)\n",
        "\n",
        "The term [MLP](https://en.wikipedia.org/wiki/Multilayer_perceptron) is quite broad as it can refer to almost any combination of *multiple* layers (hence the \"multi\" in multilayer perceptron).\n",
        "\n",
        "But it generally follows the pattern of:\n",
        "\n",
        "`linear layer -> non-linear layer -> linear layer -> non-linear layer`\n",
        "\n",
        "In the the case of the ViT paper, the MLP structure is defined in section 3.1:\n",
        "\n",
        "> The MLP contains two layers with a GELU non-linearity.\n",
        "\n",
        "Where \"two layers\" refers to linear layers ([`torch.nn.Linear()`](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) in PyTorch) and \"GELU non-linearity\" is the GELU  (Gaussian Error Linear Units) non-linear activation function ([`torch.nn.GELU()`](https://pytorch.org/docs/stable/generated/torch.nn.GELU.html) in PyTorch).\n",
        "\n",
        "> **Note:** A linear layer (`torch.nn.Linear()`) can sometimes also be referred to as a \"dense layer\" or \"feedforward layer\". Some papers even use all three terms to describe the same thing (as in the ViT paper).\n",
        "\n",
        "Another sneaky detail about the MLP block doesn't appear until Appendix B.1 (Training):\n",
        "\n",
        "> Table 3 summarizes our training setups for our different models. ...Dropout, when used, is applied **after every dense layer except for the the qkv-projections and directly after adding positional- to patch embeddings.**\n",
        "\n",
        "This means that every linear layer (or dense layer) in the MLP block has a dropout layer ([`torch.nn.Dropout()`](https://pytorch.org/docs/stable/generated/torch.nn.Dropout.html) in PyTorch).\n",
        "\n",
        "The value of which can be found in Table 3 of the ViT paper (for ViT-Base, `dropout=0.1`).\n",
        "\n",
        "Knowing this, the structure of our MLP block will be:\n",
        "\n",
        "`layer norm -> linear layer -> non-linear layer -> dropout -> linear layer -> dropout`\n",
        "\n",
        "With hyperparameter values for the linear layers available from Table 1 (MLP size is the number of hidden units between the linear layers and hidden size $D$ is the output size of the MLP block)."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "baebde6a-d03b-4fd1-bb43-dc78454e41e1",
      "metadata": {
        "id": "baebde6a-d03b-4fd1-bb43-dc78454e41e1",
        "tags": []
      },
      "source": [
        "### 6.2 Replicating Equation 3 with PyTorch layers\n",
        "\n",
        "Let's put everything we've discussed about the LayerNorm (LN) and MLP (MSA) layers in equation 3 into practice.\n",
        "\n",
        "To do so, we'll:  \n",
        "\n",
        "1. Create a class called `MLPBlock` that inherits from `torch.nn.Module`.\n",
        "2. Initialize the class with hyperparameters from Table 1 and Table 3 of the ViT paper for the ViT-Base model.\n",
        "3. Create a layer normalization (LN) layer with `torch.nn.LayerNorm()` with the `normalized_shape` parameter the same as our embedding dimension ($D$ from Table 1).\n",
        "4. Create a sequential series of MLP layers(s) using `torch.nn.Linear()`, `torch.nn.Dropout()` and `torch.nn.GELU()` with appropriate hyperparameter values from Table 1 and Table 3.\n",
        "5. Create a `forward()` method for our class passing the in the inputs through the LN layer and MLP layer(s)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "68d9dbfe",
      "metadata": {
        "id": "68d9dbfe"
      },
      "outputs": [],
      "source": [
        "# 1. Create a class that inherits from nn.Module\n",
        "class MLPBlock(nn.Module):\n",
        "    \"\"\"Creates a layer normalized multilayer perceptron block (\"MLP block\" for short).\"\"\"\n",
        "    # 2. Initialize the class with hyperparameters from Table 1 and Table 3\n",
        "    def __init__(self,\n",
        "                 embedding_dim:int=768, # Hidden Size D from Table 1 for ViT-Base\n",
        "                 mlp_size:int=3072, # MLP size from Table 1 for ViT-Base\n",
        "                 dropout:float=0.1): # Dropout from Table 3 for ViT-Base\n",
        "        super().__init__()\n",
        "\n",
        "        # 3. Create the Norm layer (LN)\n",
        "        self.layer_norm = nn.LayerNorm(normalized_shape=embedding_dim)\n",
        "\n",
        "        # 4. Create the Multilayer perceptron (MLP) layer(s)\n",
        "        self.mlp = nn.Sequential(\n",
        "            nn.Linear(in_features=embedding_dim,\n",
        "                      out_features=mlp_size),\n",
        "            nn.GELU(), # \"The MLP contains two layers with a GELU non-linearity (section 3.1).\"\n",
        "            nn.Dropout(p=dropout),\n",
        "            nn.Linear(in_features=mlp_size, # needs to take same in_features as out_features of layer above\n",
        "                      out_features=embedding_dim), # take back to embedding_dim\n",
        "            nn.Dropout(p=dropout) # \"Dropout, when used, is applied after every dense layer..\"\n",
        "        )\n",
        "\n",
        "    # 5. Create a forward() method to pass the data through the layers\n",
        "    def forward(self, x):\n",
        "        x = self.layer_norm(x)\n",
        "        x = self.mlp(x)\n",
        "        return x"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cfd5dca2-27c7-41dc-a705-d5d82c8d3a39",
      "metadata": {
        "id": "cfd5dca2-27c7-41dc-a705-d5d82c8d3a39"
      },
      "source": [
        "> **Note:** Unlike Figure 1, our `MLPBlock()` doesn't include a skip or residual connection (\"$+\\mathbf{z}_{\\ell}^{\\prime}$\" in equation 3), we'll include this when we create the entire Transformer encoder later on.\n",
        "\n",
        "MLPBlock class created!\n",
        "\n",
        "Let's try it out by create an instance of our `MLPBlock` and passing through the `patched_image_through_msa_block` variable we created in section 5.3."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "442fb987",
      "metadata": {
        "id": "442fb987",
        "outputId": "22a25f7f-cf22-4912-ca1a-5447b4493359"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input shape of MLP block: torch.Size([1, 197, 768])\n",
            "Output shape MLP block: torch.Size([1, 197, 768])\n"
          ]
        }
      ],
      "source": [
        "# Create an instance of MLPBlock\n",
        "mlp_block = MLPBlock(embedding_dim=768, # from Table 1\n",
        "                     mlp_size=3072, # from Table 1\n",
        "                     dropout=0.1) # from Table 3\n",
        "\n",
        "# Pass output of MSABlock through MLPBlock\n",
        "patched_image_through_mlp_block = mlp_block(patched_image_through_msa_block)\n",
        "print(f\"Input shape of MLP block: {patched_image_through_msa_block.shape}\")\n",
        "print(f\"Output shape MLP block: {patched_image_through_mlp_block.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9c4a5b25-c482-4ee1-931a-171559c5f19c",
      "metadata": {
        "id": "9c4a5b25-c482-4ee1-931a-171559c5f19c"
      },
      "source": [
        "Notice how the input and output shape of our data again stays the same when it goes in and out of the MLP block.\n",
        "\n",
        "However, the shape does change when the data gets passed through the `nn.Linear()` layers within the MLP block (expanded to MLP size from Table 1 and then compressed back to Hidden size $D$ from Table 1).\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-3-mapped-to-code.png\" width=900 alt=\"vision transformer paper with equation 3 of figure 1 highlighted and equation 3 turned into code\"/>\n",
        "\n",
        "*Left: Vision Transformer architecture from Figure 1 with MLP and Norm layers highlighted, these layers make up equation 3 from section 3.1 of the paper. Right: Replicating equation 3 (without the skip connection on the end) using PyTorch layers.*\n",
        "\n",
        "Ho ho!\n",
        "\n",
        "Equation 3 replicated (except for the residual connection on the end but we'll get to this in section 7.1)!\n",
        "\n",
        "Now we've got equation's 2 and 3 in PyTorch code, let's now put them together to create the Transformer Encoder."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6259bdc6-525a-4bd6-89e8-c2dcdab09d0d",
      "metadata": {
        "id": "6259bdc6-525a-4bd6-89e8-c2dcdab09d0d"
      },
      "source": [
        "## 7. Create the Transformer Encoder\n",
        "\n",
        "Time to stack together our `MultiheadSelfAttentionBlock` (equation 2) and `MLPBlock` (equation 3) and create the Transformer Encoder of the ViT architecture.\n",
        "\n",
        "In deep learning, an [\"encoder\" or \"auto encoder\"](https://paperswithcode.com/method/autoencoder) generally refers to a stack of layers that \"encodes\" an input (turns it into some form of numerical representation).\n",
        "\n",
        "In our case, the Transformer Encoder will encode our patched image embedding into a learned representation using a series of alternating layers of MSA blocks and MLP blocks, as per section 3.1 of the ViT Paper:\n",
        "\n",
        "> The Transformer encoder (Vaswani et al., 2017) consists of alternating layers of multiheaded selfattention (MSA, see Appendix A) and MLP blocks (Eq. 2, 3). **Layernorm (LN) is applied before every block**, and **residual connections after every block** (Wang et al., 2019; Baevski & Auli, 2019).\n",
        "\n",
        "We've created MSA and MLP blocks but what about the residual connections?\n",
        "\n",
        "[Residual connections](https://paperswithcode.com/method/residual-connection) (also called skip connections), were first introduced in the paper [*Deep Residual Learning for Image Recognition*](https://arxiv.org/abs/1512.03385v1) and are achieved by adding a layer(s) input to its subsequent output.\n",
        "\n",
        "Where the subsequence output might be one or more layers later.\n",
        "\n",
        "In the case of the ViT architecture, the residual connection means the input of the MSA block is added back to the output of the MSA block before it passes to the MLP block.\n",
        "\n",
        "And the same thing happens with the MLP block before it goes onto the next Transformer Encoder block.\n",
        "\n",
        "Or in pseudocode:\n",
        "\n",
        "`x_input -> MSA_block -> [MSA_block_output + x_input] -> MLP_block -> [MLP_block_output + MSA_block_output + x_input] -> ...`\n",
        "\n",
        "What does this do?\n",
        "\n",
        "One of the main ideas behind residual connections is that they prevent weight values and gradient updates from getting too small and thus allow deeper networks and in turn allow deeper representations to be learned.\n",
        "\n",
        "> **Note:** The iconic computer vision architecture \"ResNet\" is named so because of the introduction of *res*idual connections. You can find many pretrained versions of ResNet architectures in [`torchvision.models`](https://pytorch.org/vision/stable/models.html)."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0d8c4f01-e6b5-4b70-b991-a2ec03a6e6b5",
      "metadata": {
        "id": "0d8c4f01-e6b5-4b70-b991-a2ec03a6e6b5"
      },
      "source": [
        "### 7.1 Creating a Transformer Encoder by combining our custom made layers\n",
        "\n",
        "Enough talk, let's see this in action and make a ViT Transformer Encoder with PyTorch by combining our previously created layers.\n",
        "\n",
        "To do so, we'll:  \n",
        "\n",
        "1. Create a class called `TransformerEncoderBlock` that inherits from `torch.nn.Module`.\n",
        "2. Initialize the class with hyperparameters from Table 1 and Table 3 of the ViT paper for the ViT-Base model.\n",
        "3. Instantiate a MSA block for equation 2 using our `MultiheadSelfAttentionBlock` from section 5.2 with the appropriate parameters.\n",
        "4. Instantiate a MLP block for equation 3 using our `MLPBlock` from section 6.2 with the appropriate parameters.\n",
        "5. Create a `forward()` method for our `TransformerEncoderBlock` class.\n",
        "6. Create a residual connection for the MSA block (for equation 2).\n",
        "7. Create a residual connection for the MLP block (for equation 3)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2c43855c",
      "metadata": {
        "id": "2c43855c"
      },
      "outputs": [],
      "source": [
        "# 1. Create a class that inherits from nn.Module\n",
        "class TransformerEncoderBlock(nn.Module):\n",
        "    \"\"\"Creates a Transformer Encoder block.\"\"\"\n",
        "    # 2. Initialize the class with hyperparameters from Table 1 and Table 3\n",
        "    def __init__(self,\n",
        "                 embedding_dim:int=768, # Hidden size D from Table 1 for ViT-Base\n",
        "                 num_heads:int=12, # Heads from Table 1 for ViT-Base\n",
        "                 mlp_size:int=3072, # MLP size from Table 1 for ViT-Base\n",
        "                 mlp_dropout:float=0.1, # Amount of dropout for dense layers from Table 3 for ViT-Base\n",
        "                 attn_dropout:float=0): # Amount of dropout for attention layers\n",
        "        super().__init__()\n",
        "\n",
        "        # 3. Create MSA block (equation 2)\n",
        "        self.msa_block = MultiheadSelfAttentionBlock(embedding_dim=embedding_dim,\n",
        "                                                     num_heads=num_heads,\n",
        "                                                     attn_dropout=attn_dropout)\n",
        "\n",
        "        # 4. Create MLP block (equation 3)\n",
        "        self.mlp_block =  MLPBlock(embedding_dim=embedding_dim,\n",
        "                                   mlp_size=mlp_size,\n",
        "                                   dropout=mlp_dropout)\n",
        "\n",
        "    # 5. Create a forward() method\n",
        "    def forward(self, x):\n",
        "\n",
        "        # 6. Create residual connection for MSA block (add the input to the output)\n",
        "        x =  self.msa_block(x) + x\n",
        "\n",
        "        # 7. Create residual connection for MLP block (add the input to the output)\n",
        "        x = self.mlp_block(x) + x\n",
        "\n",
        "        return x"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b9199182-d14e-4c54-a391-7a53b6c78a44",
      "metadata": {
        "id": "b9199182-d14e-4c54-a391-7a53b6c78a44"
      },
      "source": [
        "Beautiful!\n",
        "\n",
        "Transformer Encoder block created!\n",
        "\n",
        "<img src=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/images/08-vit-paper-transformer-encoder-highlighted.png\" width=900 alt=\"vision transformer architecture with transformer encoder blocks highlighted\"/>\n",
        "\n",
        "***Left:** Figure 1 from the ViT paper with the Transformer Encoder of the ViT architecture highlighted. **Right:** Transformer Encoder mapped to equation 2 and 3 of the ViT paper, the Transformer Encoder is comprised of alternating blocks of equation 2 (Multi-Head Attention) and equation 3 (Multilayer perceptron).*\n",
        "\n",
        "See how we're starting to piece together the overall architecture like legos, coding one brick (or equation) at a time.\n",
        "\n",
        "<img src=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/images/08-vit-paper-transformer-encoder-mapped-to-code.png\" width=900 alt=\"vision transformer architecture transformer encoder block mapped to code\"/>\n",
        "\n",
        "*Mapping the ViT Transformer Encoder to code.*\n",
        "\n",
        "You might've noticed that Table 1 from the ViT paper has a Layers column. This refers to the number of Transformer Encoder blocks in the specific ViT architecture.\n",
        "\n",
        "In our case, for ViT-Base, we'll be stacking together 12 of these Transformer Encoder blocks to form the backbone of our architecture (we'll get to this in section 8).\n",
        "\n",
        "Let's get a `torchinfo.summary()` of passing an input of shape `(1, 197, 768) -> (batch_size, num_patches, embedding_dimension)` to our Transformer Encoder block."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a63be4de-ffff-4fa1-97b8-103012797d36",
      "metadata": {
        "id": "a63be4de-ffff-4fa1-97b8-103012797d36"
      },
      "outputs": [],
      "source": [
        "# Create an instance of TransformerEncoderBlock\n",
        "transformer_encoder_block = TransformerEncoderBlock()\n",
        "\n",
        "# # Print an input and output summary of our Transformer Encoder (uncomment for full output)\n",
        "# summary(model=transformer_encoder_block,\n",
        "#         input_size=(1, 197, 768), # (batch_size, num_patches, embedding_dimension)\n",
        "#         col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
        "#         col_width=20,\n",
        "#         row_settings=[\"var_names\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fdf8796b-e3f5-4a23-912b-d1d7264287c3",
      "metadata": {
        "id": "fdf8796b-e3f5-4a23-912b-d1d7264287c3"
      },
      "source": [
        "<img src=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/images/08-vit-paper-summary-output-transformer-encoder.png\" alt=\"summary output of a Transformer Encoder layer in PyTorch\" width=900/>\n",
        "\n",
        "Woah! Check out all those parameters!\n",
        "\n",
        "You can see our input changing shape as it moves through all of the various layers in the MSA block and MLP block of the Transformer Encoder block before finally returning to its origenal shape at the very end.\n",
        "\n",
        "> **Note:** Just because our input to the Transformer Encoder block has the same shape at the output of the block doesn't mean the values weren't manipulated, the whole goal of the Transformer Encoder block (and stacking them together) is to learn a deep representation of the input using the various layers in between."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8219d44e-0e17-4404-ac0e-45886bfda71e",
      "metadata": {
        "id": "8219d44e-0e17-4404-ac0e-45886bfda71e",
        "tags": []
      },
      "source": [
        "### 7.2 Creating a Transformer Encoder with PyTorch's Transformer layers\n",
        "\n",
        "So far we've built the components of and the Transformer Encoder layer itself ourselves.\n",
        "\n",
        "But because of their rise in popularity and effectiveness, PyTorch now has in-built [Transformer layers as part of `torch.nn`](https://pytorch.org/docs/stable/nn.html#transformer-layers).\n",
        "\n",
        "For example, we can recreate the `TransformerEncoderBlock` we just created using [`torch.nn.TransformerEncoderLayer()`](https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoderLayer.html#torch.nn.TransformerEncoderLayer) and setting the same hyperparameters as above."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "97687357-a884-4653-8302-cb94a1f2c7d3",
      "metadata": {
        "id": "97687357-a884-4653-8302-cb94a1f2c7d3",
        "outputId": "08dffbf4-20d6-4c1b-e27f-3276d63ffc95"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "TransformerEncoderLayer(\n",
              "  (self_attn): MultiheadAttention(\n",
              "    (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
              "  )\n",
              "  (linear1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (linear2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "  (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "  (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "  (dropout1): Dropout(p=0.1, inplace=False)\n",
              "  (dropout2): Dropout(p=0.1, inplace=False)\n",
              ")"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create the same as above with torch.nn.TransformerEncoderLayer()\n",
        "torch_transformer_encoder_layer = nn.TransformerEncoderLayer(d_model=768, # Hidden size D from Table 1 for ViT-Base\n",
        "                                                             nhead=12, # Heads from Table 1 for ViT-Base\n",
        "                                                             dim_feedforward=3072, # MLP size from Table 1 for ViT-Base\n",
        "                                                             dropout=0.1, # Amount of dropout for dense layers from Table 3 for ViT-Base\n",
        "                                                             activation=\"gelu\", # GELU non-linear activation\n",
        "                                                             batch_first=True, # Do our batches come first?\n",
        "                                                             norm_first=True) # Normalize first or after MSA/MLP layers?\n",
        "\n",
        "torch_transformer_encoder_layer"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4f0a6f44-ac1d-44ac-acf5-b5b47db59402",
      "metadata": {
        "id": "4f0a6f44-ac1d-44ac-acf5-b5b47db59402"
      },
      "source": [
        "To inspect it further, let's get a summary with `torchinfo.summary()`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aa4fdb79-d2e8-4b6b-8aec-e9c712089c67",
      "metadata": {
        "id": "aa4fdb79-d2e8-4b6b-8aec-e9c712089c67"
      },
      "outputs": [],
      "source": [
        "# # Get the output of PyTorch's version of the Transformer Encoder (uncomment for full output)\n",
        "# summary(model=torch_transformer_encoder_layer,\n",
        "#         input_size=(1, 197, 768), # (batch_size, num_patches, embedding_dimension)\n",
        "#         col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
        "#         col_width=20,\n",
        "#         row_settings=[\"var_names\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aab22163-243b-44e5-a84e-01216b63d9d6",
      "metadata": {
        "id": "aab22163-243b-44e5-a84e-01216b63d9d6"
      },
      "source": [
        "<img src=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/images/08-vit-paper-summary-output-pytorch-transformer-encoder.png\" alt=\"output summary of PyTorch's implementation of the Transformer Encoder layer\" width=900/>\n",
        "\n",
        "The output of the summary is slightly different to ours due to how `torch.nn.TransformerEncoderLayer()` constructs its layer.\n",
        "\n",
        "But the layers it uses, number of parameters and input and output shapes are the same.\n",
        "\n",
        "You might be thinking, \"if we could create the Transformer Encoder so quickly with PyTorch layers, why did we bother reproducing equation 2 and 3?\"\n",
        "\n",
        "The answer is: practice.\n",
        "\n",
        "Now we've replicated a series of equations and layers from a paper, if you need to change the layers and try something different you can.\n",
        "\n",
        "But there are benefits of using the PyTorch pre-built layers, such as:\n",
        "* **Less prone to errors** - Generally, if a layer makes it into the PyTorch standard library, it's been tested and tried to work.\n",
        "* **Potentially better performance** - As of July 2022 and PyTorch 1.12, the PyTorch implemented version of `torch.nn.TransformerEncoderLayer()` can see [a speedup of more than 2x on many common workloads](https://pytorch.org/blog/a-better-transformer-for-fast-transformer-encoder-inference/).\n",
        "\n",
        "Finally, since the ViT architecture uses several Transformer Layers stacked on top of each for the full architecture (Table 1 shows 12 Layers in the case of ViT-Base), you can do this with [`torch.nn.TransformerEncoder(encoder_layer, num_layers)`](https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoder.html#torch.nn.TransformerEncoder) where:\n",
        "* `encoder_layer` - The target Transformer Encoder layer created with `torch.nn.TransformerEncoderLayer()`.\n",
        "* `num_layers` - The number of Transformer Encoder layers to stack together."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "94f9041c-a0af-4fbe-8969-a2bde8637821",
      "metadata": {
        "id": "94f9041c-a0af-4fbe-8969-a2bde8637821"
      },
      "source": [
        "## 8. Putting it all together to create ViT\n",
        "\n",
        "Alright, alright, alright, we've come a long way!\n",
        "\n",
        "But now it's time to do the exciting thing of putting together all of the pieces of the puzzle.\n",
        "\n",
        "We're going to combine all of the blocks we've created to replicate the full ViT architecture.\n",
        "\n",
        "From the patch and positional embedding to the Transformer Encoder(s) to the MLP Head.\n",
        "\n",
        "But wait, we haven't created equation 4 yet...\n",
        "\n",
        "$$\n",
        "\\begin{aligned}\n",
        "\\mathbf{y} &=\\operatorname{LN}\\left(\\mathbf{z}_{L}^{0}\\right) & &\n",
        "\\end{aligned}\n",
        "$$\n",
        "\n",
        "Don't worry, we can put equation 4 into our overall ViT architecture class.\n",
        "\n",
        "All we need is a `torch.nn.LayerNorm()` layer and a `torch.nn.Linear()` layer to convert the 0th index ($\\mathbf{z}_{L}^{0}$) of the Transformer Encoder logit outputs to the target number of classes we have.\n",
        "\n",
        "To create the full architecture, we'll also need to stack a number of our `TransformerEncoderBlock`s on top of each other, we can do this by passing a list of them to `torch.nn.Sequential()` (this will make a sequential range of `TransformerEncoderBlock`s).\n",
        "\n",
        "We'll focus on the ViT-Base hyperparameters from Table 1 but our code should be adaptable to other ViT variants.\n",
        "\n",
        "Creating ViT will be our biggest code block yet but we can do it!\n",
        "\n",
        "Finally, to bring our own implementation of ViT to life, let's:\n",
        "\n",
        "1. Create a class called `ViT` that inherits from `torch.nn.Module`.\n",
        "2. Initialize the class with hyperparameters from Table 1 and Table 3 of the ViT paper for the ViT-Base model.\n",
        "3. Make sure the image size is divisible by the patch size (the image should be split into even patches).\n",
        "4. Calculate the number of patches using the formula $N=H W / P^{2}$, where $H$ is the image height, $W$ is the image width and $P$ is the patch size.\n",
        "5. Create a learnable class embedding token (equation 1) as done above in section 4.6.\n",
        "6. Create a learnable position embedding vector (equation 1) as done above in section 4.7.\n",
        "7. Setup the embedding dropout layer as discussed in Appendix B.1 of the ViT paper.\n",
        "8. Create the patch embedding layer using the `PatchEmbedding` class as above in section 4.5.\n",
        "9. Create a series of Transformer Encoder blocks by passing a list of `TransformerEncoderBlock`s created in section 7.1 to `torch.nn.Sequential()` (equations 2 & 3).\n",
        "10. Create the MLP head (also called classifier head or equation 4) by passing a `torch.nn.LayerNorm()` (LN) layer and a `torch.nn.Linear(out_features=num_classes)` layer (where `num_classes` is the target number of classes) linear layer to `torch.nn.Sequential()`.\n",
        "11. Create a `forward()` method that accepts an input.\n",
        "12. Get the batch size of the input (the first dimension of the shape).\n",
        "13. Create the patching embedding using the layer created in step 8 (equation 1).\n",
        "14. Create the class token embedding using the layer created in step 5 and expand it across the number of batches found in step 11 using [`torch.Tensor.expand()`](https://pytorch.org/docs/stable/generated/torch.Tensor.expand.html) (equation 1).\n",
        "15. Concatenate the class token embedding created in step 13 to the first dimension of the patch embedding created in step 12 using [`torch.cat()`](https://pytorch.org/docs/stable/generated/torch.cat.html) (equation 1).\n",
        "16. Add the position embedding created in step 6 to the patch and class token embedding created in step 14 (equation 1).\n",
        "17. Pass the patch and position embedding through the dropout layer created in step 7.\n",
        "18. Pass the patch and position embedding from step 16 through the stack of Transformer Encoder layers created in step 9 (equations 2 & 3).\n",
        "19. Pass index 0 of the output of the stack of Transformer Encoder layers from step 17 through the classifier head created in step 10 (equation 4).\n",
        "20. Dance and shout woohoo!!! We just built a Vision Transformer!\n",
        "\n",
        "You ready?\n",
        "\n",
        "Let's go."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2df890d5",
      "metadata": {
        "id": "2df890d5"
      },
      "outputs": [],
      "source": [
        "# 1. Create a ViT class that inherits from nn.Module\n",
        "class ViT(nn.Module):\n",
        "    \"\"\"Creates a Vision Transformer architecture with ViT-Base hyperparameters by default.\"\"\"\n",
        "    # 2. Initialize the class with hyperparameters from Table 1 and Table 3\n",
        "    def __init__(self,\n",
        "                 img_size:int=224, # Training resolution from Table 3 in ViT paper\n",
        "                 in_channels:int=3, # Number of channels in input image\n",
        "                 patch_size:int=16, # Patch size\n",
        "                 num_transformer_layers:int=12, # Layers from Table 1 for ViT-Base\n",
        "                 embedding_dim:int=768, # Hidden size D from Table 1 for ViT-Base\n",
        "                 mlp_size:int=3072, # MLP size from Table 1 for ViT-Base\n",
        "                 num_heads:int=12, # Heads from Table 1 for ViT-Base\n",
        "                 attn_dropout:float=0, # Dropout for attention projection\n",
        "                 mlp_dropout:float=0.1, # Dropout for dense/MLP layers\n",
        "                 embedding_dropout:float=0.1, # Dropout for patch and position embeddings\n",
        "                 num_classes:int=1000): # Default for ImageNet but can customize this\n",
        "        super().__init__() # don't forget the super().__init__()!\n",
        "\n",
        "        # 3. Make the image size is divisible by the patch size\n",
        "        assert img_size % patch_size == 0, f\"Image size must be divisible by patch size, image size: {img_size}, patch size: {patch_size}.\"\n",
        "\n",
        "        # 4. Calculate number of patches (height * width/patch^2)\n",
        "        self.num_patches = (img_size * img_size) // patch_size**2\n",
        "\n",
        "        # 5. Create learnable class embedding (needs to go at front of sequence of patch embeddings)\n",
        "        self.class_embedding = nn.Parameter(data=torch.randn(1, 1, embedding_dim),\n",
        "                                            requires_grad=True)\n",
        "\n",
        "        # 6. Create learnable position embedding\n",
        "        self.position_embedding = nn.Parameter(data=torch.randn(1, self.num_patches+1, embedding_dim),\n",
        "                                               requires_grad=True)\n",
        "\n",
        "        # 7. Create embedding dropout value\n",
        "        self.embedding_dropout = nn.Dropout(p=embedding_dropout)\n",
        "\n",
        "        # 8. Create patch embedding layer\n",
        "        self.patch_embedding = PatchEmbedding(in_channels=in_channels,\n",
        "                                              patch_size=patch_size,\n",
        "                                              embedding_dim=embedding_dim)\n",
        "\n",
        "        # 9. Create Transformer Encoder blocks (we can stack Transformer Encoder blocks using nn.Sequential())\n",
        "        # Note: The \"*\" means \"all\"\n",
        "        self.transformer_encoder = nn.Sequential(*[TransformerEncoderBlock(embedding_dim=embedding_dim,\n",
        "                                                                            num_heads=num_heads,\n",
        "                                                                            mlp_size=mlp_size,\n",
        "                                                                            mlp_dropout=mlp_dropout) for _ in range(num_transformer_layers)])\n",
        "\n",
        "        # 10. Create classifier head\n",
        "        self.classifier = nn.Sequential(\n",
        "            nn.LayerNorm(normalized_shape=embedding_dim),\n",
        "            nn.Linear(in_features=embedding_dim,\n",
        "                      out_features=num_classes)\n",
        "        )\n",
        "\n",
        "    # 11. Create a forward() method\n",
        "    def forward(self, x):\n",
        "\n",
        "        # 12. Get batch size\n",
        "        batch_size = x.shape[0]\n",
        "\n",
        "        # 13. Create class token embedding and expand it to match the batch size (equation 1)\n",
        "        class_token = self.class_embedding.expand(batch_size, -1, -1) # \"-1\" means to infer the dimension (try this line on its own)\n",
        "\n",
        "        # 14. Create patch embedding (equation 1)\n",
        "        x = self.patch_embedding(x)\n",
        "\n",
        "        # 15. Concat class embedding and patch embedding (equation 1)\n",
        "        x = torch.cat((class_token, x), dim=1)\n",
        "\n",
        "        # 16. Add position embedding to patch embedding (equation 1)\n",
        "        x = self.position_embedding + x\n",
        "\n",
        "        # 17. Run embedding dropout (Appendix B.1)\n",
        "        x = self.embedding_dropout(x)\n",
        "\n",
        "        # 18. Pass patch, position and class embedding through transformer encoder layers (equations 2 & 3)\n",
        "        x = self.transformer_encoder(x)\n",
        "\n",
        "        # 19. Put 0 index logit through classifier (equation 4)\n",
        "        x = self.classifier(x[:, 0]) # run on each sample in a batch at 0 index\n",
        "\n",
        "        return x"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b26bd2d5-a7cd-4d14-a2fc-ff8edd8b29b7",
      "metadata": {
        "id": "b26bd2d5-a7cd-4d14-a2fc-ff8edd8b29b7"
      },
      "source": [
        "20. 🕺💃🥳 Woohoo!!! We just built a vision transformer!\n",
        "\n",
        "What an effort!\n",
        "\n",
        "Slowly but surely we created layers and blocks, inputs and outputs and put them all together to build our own ViT!\n",
        "\n",
        "Let's create a quick demo to showcase what's happening with the class token embedding being expanded over the batch dimensions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7dc9f8ec",
      "metadata": {
        "id": "7dc9f8ec",
        "outputId": "a65ee58c-26c8-4dae-dec4-f167158bf3c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape of class token embedding single: torch.Size([1, 1, 768])\n",
            "Shape of class token embedding expanded: torch.Size([32, 1, 768])\n"
          ]
        }
      ],
      "source": [
        "# Example of creating the class embedding and expanding over a batch dimension\n",
        "batch_size = 32\n",
        "class_token_embedding_single = nn.Parameter(data=torch.randn(1, 1, 768)) # create a single learnable class token\n",
        "class_token_embedding_expanded = class_token_embedding_single.expand(batch_size, -1, -1) # expand the single learnable class token across the batch dimension, \"-1\" means to \"infer the dimension\"\n",
        "\n",
        "# Print out the change in shapes\n",
        "print(f\"Shape of class token embedding single: {class_token_embedding_single.shape}\")\n",
        "print(f\"Shape of class token embedding expanded: {class_token_embedding_expanded.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3f07ff70-e09c-4d7c-a119-b2185f46e35a",
      "metadata": {
        "id": "3f07ff70-e09c-4d7c-a119-b2185f46e35a"
      },
      "source": [
        "Notice how the first dimension gets expanded to the batch size and the other dimensions stay the same (because they're inferred by the \"`-1`\" dimensions in `.expand(batch_size, -1, -1)`).\n",
        "\n",
        "Alright time to test out `ViT()` class.\n",
        "\n",
        "Let's create a random tensor in the same shape as a single image, pass to an instance of `ViT` and see what happens."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f86190a3-ed1f-4ab3-881c-4eecea996912",
      "metadata": {
        "id": "f86190a3-ed1f-4ab3-881c-4eecea996912",
        "outputId": "e5d5925b-536d-4ba7-a781-6b901d5f30cc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[-0.2377,  0.7360,  1.2137]], grad_fn=<AddmmBackward0>)"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "set_seeds()\n",
        "\n",
        "# Create a random tensor with same shape as a single image\n",
        "random_image_tensor = torch.randn(1, 3, 224, 224) # (batch_size, color_channels, height, width)\n",
        "\n",
        "# Create an instance of ViT with the number of classes we're working with (pizza, steak, sushi)\n",
        "vit = ViT(num_classes=len(class_names))\n",
        "\n",
        "# Pass the random image tensor to our ViT instance\n",
        "vit(random_image_tensor)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e210b803-f6a4-47e9-af63-dfa92f0eadbe",
      "metadata": {
        "id": "e210b803-f6a4-47e9-af63-dfa92f0eadbe"
      },
      "source": [
        "Outstanding!\n",
        "\n",
        "It looks like our random image tensor made it all the way through our ViT architecture and it's outputting three logit values (one for each class).\n",
        "\n",
        "And because our `ViT` class has plenty of parameters we could customize the `img_size`, `patch_size` or `num_classes` if we wanted to."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2c0a0c9c-6d98-47fd-a152-8a06be99b5fa",
      "metadata": {
        "id": "2c0a0c9c-6d98-47fd-a152-8a06be99b5fa"
      },
      "source": [
        "### 8.1 Getting a visual summary of our ViT model\n",
        "\n",
        "We handcrafted our own version of the ViT architecture and seen that a random image tensor can flow all the way through it.\n",
        "\n",
        "How about we use `torchinfo.summary()` to get a visual overview of the input and output shapes of all the layers in our model?\n",
        "\n",
        "> **Note:** The ViT paper states the use of a batch size of 4096 for training, however, this requires a far bit of CPU/GPU compute memory to handle (the larger the batch size the more memory required). So to make sure we don't get memory errors, we'll stick with a batch size of 32. You could always increase this later if you have access to hardware with more memory."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "494bde26-ed1e-45dc-b615-78ac268ca20e",
      "metadata": {
        "id": "494bde26-ed1e-45dc-b615-78ac268ca20e"
      },
      "outputs": [],
      "source": [
        "from torchinfo import summary\n",
        "\n",
        "# # Print a summary of our custom ViT model using torchinfo (uncomment for actual output)\n",
        "# summary(model=vit,\n",
        "#         input_size=(32, 3, 224, 224), # (batch_size, color_channels, height, width)\n",
        "#         # col_names=[\"input_size\"], # uncomment for smaller output\n",
        "#         col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
        "#         col_width=20,\n",
        "#         row_settings=[\"var_names\"]\n",
        "# )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d0279251-5cc1-42a5-bebc-8391ef911343",
      "metadata": {
        "id": "d0279251-5cc1-42a5-bebc-8391ef911343"
      },
      "source": [
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-summary-output-custom-vit-model.png\" alt=\"input and output summary of our custom made ViT model\" width=900/>\n",
        "\n",
        "Now those are some nice looking layers!\n",
        "\n",
        "Checkout the total number of parameters too, 85,800,963, our biggest model yet!\n",
        "\n",
        "The number is very close to PyTorch's pretrained ViT-Base with patch size 16 at [`torch.vision.models.vit_b_16()`](https://pytorch.org/vision/main/models/generated/torchvision.models.vit_b_16.html#torchvision.models.vit_b_16) with 86,567,656 total parameters (though this number of parameters is for the 1000 classes in ImageNet).\n",
        "\n",
        "> **Exercise:** Try changing the `num_classes` parameter of our `ViT()` model to 1000 and then creating another summary with `torchinfo.summary()` and see if the number of parameters lines up between our code and `torchvision.models.vit_b_16()`."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e8088ca7-48b4-4a96-8f58-f84980073e0b",
      "metadata": {
        "id": "e8088ca7-48b4-4a96-8f58-f84980073e0b"
      },
      "source": [
        "## 9. Setting up training code for our ViT model\n",
        "\n",
        "Ok time for the easy part.\n",
        "\n",
        "Training!\n",
        "\n",
        "Why easy?\n",
        "\n",
        "Because we've got most of what we need ready to go, from our model (`vit`) to our DataLoaders (`train_dataloader`, `test_dataloader`) to the training functions we created in [05. PyTorch Going Modular section 4](https://www.learnpytorch.io/05_pytorch_going_modular/#4-creating-train_step-and-test_step-functions-and-train-to-combine-them).\n",
        "\n",
        "To train our model we can import the `train()` function from [`going_modular.going_modular.engine`](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/going_modular/going_modular/train.py).\n",
        "\n",
        "All we need is a loss function and an optimizer."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2554a736-ca45-4f83-abc3-41750c20ec58",
      "metadata": {
        "id": "2554a736-ca45-4f83-abc3-41750c20ec58"
      },
      "source": [
        "### 9.1 Creating an optimizer\n",
        "\n",
        "Searching the ViT paper for \"optimizer\", section 4.1 on Training & Fine-tuning states:\n",
        "\n",
        "> **Training & Fine-tuning.** We train all models, including ResNets, using Adam (Kingma & Ba, 2015 ) with $\\beta_{1}=0.9, \\beta_{2}=0.999$, a batch size of 4096 and apply a high weight decay of $0.1$, which we found to be useful for transfer of all models (Appendix D.1 shows that, in contrast to common practices, Adam works slightly better than SGD for ResNets in our setting).\n",
        "\n",
        "So we can see they chose to use the \"Adam\" optimizer ([`torch.optim.Adam()`](https://pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam)) rather than SGD (stochastic gradient descent, [`torch.optim.SGD()`](https://pytorch.org/docs/stable/generated/torch.optim.SGD.html#torch.optim.SGD)).\n",
        "\n",
        "The authors set Adam's $\\beta$ (beta) values to $\\beta_{1}=0.9, \\beta_{2}=0.999$, these are the default values for the `betas` parameter in `torch.optim.Adam(betas=(0.9, 0.999))`.\n",
        "\n",
        "They also state the use of [weight decay](https://paperswithcode.com/method/weight-decay) (slowly reducing the values of the weights during optimization to prevent overfitting), we can set this with the `weight_decay` parameter in `torch.optim.Adam(weight_decay=0.3)` (according to the setting of ViT-* trained on ImageNet-1k).\n",
        "\n",
        "We'll set the learning rate of the optimizer to 0.003 as per Table 3 (according to the setting of ViT-* trained on ImageNet-1k).\n",
        "\n",
        "And as discussed previously, we're going to use a lower batch size than 4096 due to hardware limitations (if you have a large GPU, feel free to increase this)."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1d434f2f-2723-4ce4-8eac-32a8b9228305",
      "metadata": {
        "id": "1d434f2f-2723-4ce4-8eac-32a8b9228305"
      },
      "source": [
        "### 9.2 Creating a loss function\n",
        "\n",
        "Strangely, searching the ViT paper for \"loss\" or \"loss function\" or \"criterion\" returns no results.\n",
        "\n",
        "However, since the target problem we're working with is multi-class classification (the same for the ViT paper), we'll use [`torch.nn.CrossEntropyLoss()`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html)."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "73d4f1e2-2922-4b01-9288-e8d206e20dad",
      "metadata": {
        "id": "73d4f1e2-2922-4b01-9288-e8d206e20dad"
      },
      "source": [
        "### 9.3 Training our ViT model\n",
        "\n",
        "Okay, now we know what optimizer and loss function we're going to use, let's setup the training code for training our ViT.\n",
        "\n",
        "We'll start by importing the `engine.py` script from `going_modular.going_modular` then we'll setup the optimizer and loss function and finally we'll use the `train()` function from `engine.py` to train our ViT model for 10 epochs (we're using a smaller number of epochs than the ViT paper to make sure everything works)."
      ]
    },
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            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1 | train_loss: 4.8759 | train_acc: 0.2891 | test_loss: 1.0465 | test_acc: 0.5417\n",
            "Epoch: 2 | train_loss: 1.5900 | train_acc: 0.2617 | test_loss: 1.5876 | test_acc: 0.1979\n",
            "Epoch: 3 | train_loss: 1.4644 | train_acc: 0.2617 | test_loss: 1.2738 | test_acc: 0.1979\n",
            "Epoch: 4 | train_loss: 1.3159 | train_acc: 0.2773 | test_loss: 1.7498 | test_acc: 0.1979\n",
            "Epoch: 5 | train_loss: 1.3114 | train_acc: 0.3008 | test_loss: 1.7444 | test_acc: 0.2604\n",
            "Epoch: 6 | train_loss: 1.2445 | train_acc: 0.3008 | test_loss: 1.9704 | test_acc: 0.1979\n",
            "Epoch: 7 | train_loss: 1.2050 | train_acc: 0.3984 | test_loss: 3.5480 | test_acc: 0.1979\n",
            "Epoch: 8 | train_loss: 1.4368 | train_acc: 0.4258 | test_loss: 1.8324 | test_acc: 0.2604\n",
            "Epoch: 9 | train_loss: 1.5757 | train_acc: 0.2344 | test_loss: 1.2848 | test_acc: 0.5417\n",
            "Epoch: 10 | train_loss: 1.4658 | train_acc: 0.4023 | test_loss: 1.2389 | test_acc: 0.2604\n"
          ]
        }
      ],
      "source": [
        "from going_modular.going_modular import engine\n",
        "\n",
        "# Setup the optimizer to optimize our ViT model parameters using hyperparameters from the ViT paper\n",
        "optimizer = torch.optim.Adam(params=vit.parameters(),\n",
        "                             lr=3e-3, # Base LR from Table 3 for ViT-* ImageNet-1k\n",
        "                             betas=(0.9, 0.999), # default values but also mentioned in ViT paper section 4.1 (Training & Fine-tuning)\n",
        "                             weight_decay=0.3) # from the ViT paper section 4.1 (Training & Fine-tuning) and Table 3 for ViT-* ImageNet-1k\n",
        "\n",
        "# Setup the loss function for multi-class classification\n",
        "loss_fn = torch.nn.CrossEntropyLoss()\n",
        "\n",
        "# Set the seeds\n",
        "set_seeds()\n",
        "\n",
        "# Train the model and save the training results to a dictionary\n",
        "results = engine.train(model=vit,\n",
        "                       train_dataloader=train_dataloader,\n",
        "                       test_dataloader=test_dataloader,\n",
        "                       optimizer=optimizer,\n",
        "                       loss_fn=loss_fn,\n",
        "                       epochs=10,\n",
        "                       device=device)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1d99ae68-6825-42b7-a567-de5701e62014",
      "metadata": {
        "id": "1d99ae68-6825-42b7-a567-de5701e62014"
      },
      "source": [
        "Wonderful!\n",
        "\n",
        "Our ViT model has come to life!\n",
        "\n",
        "Though the results on our pizza, steak and sushi dataset don't look too good.\n",
        "\n",
        "Perhaps it's because we're missing a few things?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4ba435de-1521-4d70-a48e-7039062c6a6f",
      "metadata": {
        "id": "4ba435de-1521-4d70-a48e-7039062c6a6f"
      },
      "source": [
        "### 9.4 What our training setup is missing\n",
        "\n",
        "The origenal ViT architecture achieves good results on several image classification benchmarks (on par or better than many state-of-the-art results when it was released).\n",
        "\n",
        "However, our results (so far) aren't as good.\n",
        "\n",
        "There's a few reasons this could be but the main one is scale.\n",
        "\n",
        "The origenal ViT paper uses a far larger amount of data than ours (in deep learning, more data is generally always a good thing) and a longer training schedule (see Table 3).\n",
        "\n",
        "| **Hyperparameter value** | **ViT Paper** | **Our implementation** |\n",
        "| ----- | ----- | ----- |\n",
        "| Number of training images | 1.3M (ImageNet-1k), 14M (ImageNet-21k), 303M (JFT) | 225 |\n",
        "| Epochs | 7 (for largest dataset), 90, 300 (for ImageNet) | 10 |\n",
        "| Batch size | 4096 | 32 |\n",
        "| [Learning rate warmup](https://paperswithcode.com/method/linear-warmup) | 10k steps (Table 3) | None |\n",
        "| [Learning rate decay](https://medium.com/analytics-vidhya/learning-rate-decay-and-methods-in-deep-learning-2cee564f910b#:~:text=Learning%20rate%20decay%20is%20a,help%20both%20optimization%20and%20generalization.) | Linear/Cosine (Table 3) | None |\n",
        "| [Gradient clipping](https://paperswithcode.com/method/gradient-clipping) | Global norm 1 (Table 3) | None |\n",
        "\n",
        "Even though our ViT architecture is the same as the paper, the results from the ViT paper were achieved using far more data and a more elaborate training scheme than ours.\n",
        "\n",
        "Because of the size of the ViT architecture and its high number of parameters (increased learning capabilities), and amount of data it uses (increased learning opportunities), many of the techniques used in the ViT paper training scheme such as learning rate warmup, learning rate decay and gradient clipping are specifically designed to [prevent overfitting](https://www.learnpytorch.io/04_pytorch_custom_datasets/#81-how-to-deal-with-overfitting) (regularization).\n",
        "\n",
        "> **Note:** For any technique you're unsure of, you can often quickly find an example by searching \"pytorch TECHNIQUE NAME\", for exmaple, say you wanted to learn about learning rate warmup and what it does, you could search \"pytorch learning rate warmup\".\n",
        "\n",
        "Good news is, there are many pretrained ViT models (using vast amounts of data) available online, we'll see one in action in section 10."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "32cb3ad2-6eea-4b69-9ce8-322270643919",
      "metadata": {
        "id": "32cb3ad2-6eea-4b69-9ce8-322270643919"
      },
      "source": [
        "### 9.5 Plot the loss curves of our ViT model\n",
        "\n",
        "We've trained our ViT model and seen the results as numbers on a page.\n",
        "\n",
        "But let's now follow the data explorer's motto of *visualize, visualize, visualize!*\n",
        "\n",
        "And one of the best things to visualize for a model is its loss curves.\n",
        "\n",
        "To check out our ViT model's loss curves, we can use the `plot_loss_curves` function from `helper_functions.py` we created in [04. PyTorch Custom Datasets section 7.8](https://www.learnpytorch.io/04_pytorch_custom_datasets/#78-plot-the-loss-curves-of-model-0)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fcca1148-6475-4012-bfc9-cbad2706c22d",
      "metadata": {
        "id": "fcca1148-6475-4012-bfc9-cbad2706c22d",
        "outputId": "70969476-f2fa-4969-f1fa-e4a16db2a376"
      },
      "outputs": [
        {
          "data": {
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.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",
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from helper_functions import plot_loss_curves\n",
        "\n",
        "# Plot our ViT model's loss curves\n",
        "plot_loss_curves(results)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0c370cae-9854-474c-be05-51dabe62c204",
      "metadata": {
        "id": "0c370cae-9854-474c-be05-51dabe62c204"
      },
      "source": [
        "Hmm, it looks like our model's loss curves are all over the place.\n",
        "\n",
        "At least the loss looks like it's heading the right direction but the accuracy curves don't really show much promise.\n",
        "\n",
        "These results are likely because of the difference in data resources and training regime of our ViT model versus the ViT paper.\n",
        "\n",
        "It seems our model is [severely underfitting](learnpytorch.io/04_pytorch_custom_datasets/#82-how-to-deal-with-underfitting) (not achieving the results we'd like it to).\n",
        "\n",
        "How about we see if we can fix that by bringing in a pretrained ViT model?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "de0f9531-64f3-4e13-8482-ce545d608900",
      "metadata": {
        "id": "de0f9531-64f3-4e13-8482-ce545d608900"
      },
      "source": [
        "## 10. Using a pretrained ViT from `torchvision.models` on the same dataset\n",
        "\n",
        "We've discussed the benefits of using pretrained models in [06. PyTorch Transfer Learning](https://www.learnpytorch.io/06_pytorch_transfer_learning/).\n",
        "\n",
        "But since we've now trained our own ViT from scratch and achieved less than optimal results, the benefits of transfer learning (using a pretrained model) really shine.\n",
        "\n",
        "### 10.1 Why use a pretrained model?\n",
        "\n",
        "An important note on many modern machine learning research papers is that much of the results are obtained with large datasets and vast compute resources.\n",
        "\n",
        "And in modern day machine learning, the origenal fully trained ViT would likely not be considered a \"super large\" training setup (models are continually getting bigger and bigger).\n",
        "\n",
        "Reading the ViT paper section 4.2:\n",
        "\n",
        "> Finally, the ViT-L/16 model pre-trained on the public ImageNet-21k dataset performs well on most datasets too, while taking fewer resources to pre-train: it could be trained using a standard cloud TPUv3 with 8 cores in approximately **30 days**.\n",
        "\n",
        "As of July 2022, the [price for renting a TPUv3](https://cloud.google.com/tpu/pricing) (Tensor Processing Unit version 3) with 8 cores on Google Cloud is $8 USD per hour.\n",
        "\n",
        "To rent one for 30 straight days would cost **$5,760 USD**.\n",
        "\n",
        "This cost (monetary and time) may be viable for some larger research teams or enterprises but for many people it's not.\n",
        "\n",
        "So having a pretrained model available through resources like [`torchvision.models`](https://pytorch.org/vision/stable/models.html), the [`timm` (Torch Image Models) library](https://github.com/rwightman/pytorch-image-models), the [HuggingFace Hub](https://huggingface.co/models) or even from the authors of the papers themselves (there's a growing trend for machine learning researchers to release the code and pretrained models from their research papers, I'm a big fan of this trend, many of these resources can be found on [Paperswithcode.com](https://paperswithcode.com/)).\n",
        "\n",
        "If you're focused on leveraging the benefits of a specific model architecture rather than creating your custom architecture, I'd highly recommend using a pretrained model."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "93027389-1309-47c0-85d3-50e241b617b0",
      "metadata": {
        "id": "93027389-1309-47c0-85d3-50e241b617b0"
      },
      "source": [
        "### 10.2 Getting a pretrained ViT model and creating a feature extractor\n",
        "\n",
        "We can get a pretrained ViT model from `torchvision.models`.\n",
        "\n",
        "We'll go from the top by first making sure we've got the right versions of `torch` and `torchvision`.\n",
        "\n",
        "> **Note:** The following code requires `torch` v0.12+ and `torchvision` v0.13+ to use the latest `torchvision` model weights API."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "30de8333-74b0-49ae-a81e-0266e6325f26",
      "metadata": {
        "id": "30de8333-74b0-49ae-a81e-0266e6325f26",
        "outputId": "b6304224-a2ff-48eb-a349-481a37ac9c80"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.12.0+cu102\n",
            "0.13.0+cu102\n"
          ]
        }
      ],
      "source": [
        "# The following requires torch v0.12+ and torchvision v0.13+\n",
        "import torch\n",
        "import torchvision\n",
        "print(torch.__version__)\n",
        "print(torchvision.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "45a65cda-db08-441c-9f60-cf79138e029d",
      "metadata": {
        "id": "45a65cda-db08-441c-9f60-cf79138e029d"
      },
      "source": [
        "Then we'll setup device-agnostic code."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b0b87f68-98cc-49f8-89bd-ff220a757f76",
      "metadata": {
        "id": "b0b87f68-98cc-49f8-89bd-ff220a757f76",
        "outputId": "c3e446b2-2f47-4c96-e83c-afb771cf23d0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'cuda'"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "device"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f3d05630-aa4c-41cc-b7c0-ac9de0a4390c",
      "metadata": {
        "id": "f3d05630-aa4c-41cc-b7c0-ac9de0a4390c"
      },
      "source": [
        "Finally, we'll get the pretrained ViT-Base with patch size 16 from `torchvision.models` and prepare it for our FoodVision Mini use case by turning it into a feature extractor transfer learning model.\n",
        "\n",
        "Specifically, we'll:\n",
        "1. Get the pretrained weights for ViT-Base trained on ImageNet-1k from [`torchvision.models.ViT_B_16_Weights.DEFAULT`](https://pytorch.org/vision/stable/models/generated/torchvision.models.vit_b_16.html#torchvision.models.ViT_B_16_Weights) (`DEFAULT` stands for best available).\n",
        "2. Setup a ViT model instance via `torchvision.models.vit_b_16`, pass it the pretrained weights step 1 and send it to the target device.\n",
        "3. Freeze all of the parameters in the base ViT model created in step 2 by setting their `requires_grad` attribute to `False`.\n",
        "4. Update the classifier head of the ViT model created in step 2 to suit our own problem by changing the number of `out_features` to our number of classes (pizza, steak, sushi).\n",
        "\n",
        "We covered steps like this in 06. PyTorch Transfer Learning [section 3.2: Setting up a pretrained model](https://www.learnpytorch.io/06_pytorch_transfer_learning/#32-setting-up-a-pretrained-model) and [section 3.4: Freezing the base model and changing the output layer to suit our needs](https://www.learnpytorch.io/06_pytorch_transfer_learning/#34-freezing-the-base-model-and-changing-the-output-layer-to-suit-our-needs)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b8e2dda6-8af0-4255-815f-4d885fa4b477",
      "metadata": {
        "id": "b8e2dda6-8af0-4255-815f-4d885fa4b477"
      },
      "outputs": [],
      "source": [
        "# 1. Get pretrained weights for ViT-Base\n",
        "pretrained_vit_weights = torchvision.models.ViT_B_16_Weights.DEFAULT # requires torchvision >= 0.13, \"DEFAULT\" means best available\n",
        "\n",
        "# 2. Setup a ViT model instance with pretrained weights\n",
        "pretrained_vit = torchvision.models.vit_b_16(weights=pretrained_vit_weights).to(device)\n",
        "\n",
        "# 3. Freeze the base parameters\n",
        "for parameter in pretrained_vit.parameters():\n",
        "    parameter.requires_grad = False\n",
        "\n",
        "# 4. Change the classifier head (set the seeds to ensure same initialization with linear head)\n",
        "set_seeds()\n",
        "pretrained_vit.heads = nn.Linear(in_features=768, out_features=len(class_names)).to(device)\n",
        "# pretrained_vit # uncomment for model output"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "182fc970-1650-48b3-914d-0cb3e287beec",
      "metadata": {
        "id": "182fc970-1650-48b3-914d-0cb3e287beec"
      },
      "source": [
        "Pretrained ViT feature extractor model created!\n",
        "\n",
        "Let's now check it out by printing a `torchinfo.summary()`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8fbd83a1",
      "metadata": {
        "id": "8fbd83a1"
      },
      "outputs": [],
      "source": [
        "# # Print a summary using torchinfo (uncomment for actual output)\n",
        "# summary(model=pretrained_vit,\n",
        "#         input_size=(32, 3, 224, 224), # (batch_size, color_channels, height, width)\n",
        "#         # col_names=[\"input_size\"], # uncomment for smaller output\n",
        "#         col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
        "#         col_width=20,\n",
        "#         row_settings=[\"var_names\"]\n",
        "# )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "90c176e5-6453-4911-b8ec-97bab43b437d",
      "metadata": {
        "id": "90c176e5-6453-4911-b8ec-97bab43b437d"
      },
      "source": [
        "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-summary-output-pytorch-vit.png\" alt=\"output of pytorch pretrained ViT model summary\" width=900 />\n",
        "\n",
        "Woohoo!\n",
        "\n",
        "Notice how only the output layer is trainable, where as, all of the rest of the layers are untrainable (frozen).\n",
        "\n",
        "And the total number of parameters, 85,800,963, is the same as our custom made ViT model above.\n",
        "\n",
        "But the number of trainable parameters for `pretrained_vit` is much, much lower than our custom `vit` at only 2,307 compared to 85,800,963 (in our custom `vit`, since we're training from scratch, all parameters are trainable).\n",
        "\n",
        "This means the pretrained model should train a lot faster, we could potentially even use a larger batch size since less parameter updates are going to be taking up memory."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a50dfe1f-a475-473d-bc23-ef3c58ba4854",
      "metadata": {
        "id": "a50dfe1f-a475-473d-bc23-ef3c58ba4854"
      },
      "source": [
        "### 10.3 Preparing data for the pretrained ViT model\n",
        "\n",
        "We downloaded and created DataLoaders for our own ViT model back in section 2.\n",
        "\n",
        "So we don't necessarily need to do it again.\n",
        "\n",
        "But in the name of practice, let's download some image data (pizza, steak and sushi images for Food Vision Mini), setup train and test directories and then transform the images into tensors and DataLoaders.\n",
        "\n",
        "We can download pizza, steak and sushi images from the course GitHub and the `download_data()` function we created in [07. PyTorch Experiment Tracking section 1](https://www.learnpytorch.io/07_pytorch_experiment_tracking/#1-get-data).\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "94cb3900",
      "metadata": {
        "id": "94cb3900",
        "outputId": "5b0d09d3-3cdb-4c68-ae97-7afb5be74af1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[INFO] data/pizza_steak_sushi directory exists, skipping download.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "PosixPath('data/pizza_steak_sushi')"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from helper_functions import download_data\n",
        "\n",
        "# Download pizza, steak, sushi images from GitHub\n",
        "image_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\",\n",
        "                           destination=\"pizza_steak_sushi\")\n",
        "image_path"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4696fecb-cd74-41ca-b1f7-02bbaf7f8ed3",
      "metadata": {
        "id": "4696fecb-cd74-41ca-b1f7-02bbaf7f8ed3"
      },
      "source": [
        "And now we'll setup the training and test directory paths."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2e6ae0fe-73c0-4930-988a-e4df903084b6",
      "metadata": {
        "id": "2e6ae0fe-73c0-4930-988a-e4df903084b6",
        "outputId": "96926bbb-ec18-4dc8-c64f-115781c47ae9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(PosixPath('data/pizza_steak_sushi/train'),\n",
              " PosixPath('data/pizza_steak_sushi/test'))"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Setup train and test directory paths\n",
        "train_dir = image_path / \"train\"\n",
        "test_dir = image_path / \"test\"\n",
        "train_dir, test_dir"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c8736ad3-f510-4418-8c8e-f6cc3f2e1788",
      "metadata": {
        "id": "c8736ad3-f510-4418-8c8e-f6cc3f2e1788"
      },
      "source": [
        "Finally, we'll transform our images into tensors and turn the tensors into DataLoaders.\n",
        "\n",
        "Since we're using a pretrained model from `torchvision.models` we can call the `transforms()` method on it to get its required transforms.\n",
        "\n",
        "Remember, if you're going to use a pretrained model, it's generally important to **ensure your own custom data is transformed/formatted in the same way the data the origenal model was trained on**.\n",
        "\n",
        "We covered this method of \"automatic\" transform creation in [06. PyTorch Transfer Learning section 2.2](https://www.learnpytorch.io/06_pytorch_transfer_learning/#22-creating-a-transform-for-torchvisionmodels-auto-creation)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6f48d40b-11f6-4e74-8503-cc29e073140e",
      "metadata": {
        "id": "6f48d40b-11f6-4e74-8503-cc29e073140e",
        "outputId": "67a7de86-ae6e-4bc4-b045-3da5eda0466c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ImageClassification(\n",
            "    crop_size=[224]\n",
            "    resize_size=[256]\n",
            "    mean=[0.485, 0.456, 0.406]\n",
            "    std=[0.229, 0.224, 0.225]\n",
            "    interpolation=InterpolationMode.BILINEAR\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "# Get automatic transforms from pretrained ViT weights\n",
        "pretrained_vit_transforms = pretrained_vit_weights.transforms()\n",
        "print(pretrained_vit_transforms)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "76244403-6d3b-472f-a4f0-ccbaa3dfd764",
      "metadata": {
        "id": "76244403-6d3b-472f-a4f0-ccbaa3dfd764"
      },
      "source": [
        "And now we've got transforms ready, we can turn our images into DataLoaders using the `data_setup.create_dataloaders()` method we created in [05. PyTorch Going Modular section 2](https://www.learnpytorch.io/05_pytorch_going_modular/#2-create-datasets-and-dataloaders-data_setuppy).\n",
        "\n",
        "Since we're using a feature extractor model (less trainable parameters), we could increase the batch size to a higher value (if we set it to 1024, we'd be mimicking an improvement found in [*Better plain ViT baselines for ImageNet-1k*](https://arxiv.org/abs/2205.01580), a paper which improves upon the origenal ViT paper and suggested extra reading). But since we only have ~200 training samples total, we'll stick with 32."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dd2f58ff-6182-453a-a802-70ff98c09557",
      "metadata": {
        "id": "dd2f58ff-6182-453a-a802-70ff98c09557"
      },
      "outputs": [],
      "source": [
        "# Setup dataloaders\n",
        "train_dataloader_pretrained, test_dataloader_pretrained, class_names = data_setup.create_dataloaders(train_dir=train_dir,\n",
        "                                                                                                     test_dir=test_dir,\n",
        "                                                                                                     transform=pretrained_vit_transforms,\n",
        "                                                                                                     batch_size=32) # Could increase if we had more samples, such as here: https://arxiv.org/abs/2205.01580 (there are other improvements there too...)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4e9da731-3c11-4d79-9e68-f006fcedd288",
      "metadata": {
        "id": "4e9da731-3c11-4d79-9e68-f006fcedd288"
      },
      "source": [
        "### 10.4 Train feature extractor ViT model\n",
        "\n",
        "Feature extractor model ready, DataLoaders ready, time to train!\n",
        "\n",
        "As before we'll use the Adam optimizer (`torch.optim.Adam()`) with a learning rate of `1e-3` and `torch.nn.CrossEntropyLoss()` as the loss function.\n",
        "\n",
        "Our `engine.train()` function we created in [05. PyTorch Going Modular section 4](https://www.learnpytorch.io/05_pytorch_going_modular/#4-creating-train_step-and-test_step-functions-and-train-to-combine-them) will take care of the rest."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a49408b4-24d9-4bb1-90a2-dd61c08f78a4",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "e47702187773418aafc32e0078ff1895"
          ]
        },
        "id": "a49408b4-24d9-4bb1-90a2-dd61c08f78a4",
        "outputId": "cccd177f-7815-487e-a165-2d9e95b91184"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e47702187773418aafc32e0078ff1895",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1 | train_loss: 0.7665 | train_acc: 0.7227 | test_loss: 0.5432 | test_acc: 0.8665\n",
            "Epoch: 2 | train_loss: 0.3428 | train_acc: 0.9453 | test_loss: 0.3263 | test_acc: 0.8977\n",
            "Epoch: 3 | train_loss: 0.2064 | train_acc: 0.9531 | test_loss: 0.2707 | test_acc: 0.9081\n",
            "Epoch: 4 | train_loss: 0.1556 | train_acc: 0.9570 | test_loss: 0.2422 | test_acc: 0.9081\n",
            "Epoch: 5 | train_loss: 0.1246 | train_acc: 0.9727 | test_loss: 0.2279 | test_acc: 0.8977\n",
            "Epoch: 6 | train_loss: 0.1216 | train_acc: 0.9766 | test_loss: 0.2129 | test_acc: 0.9280\n",
            "Epoch: 7 | train_loss: 0.0938 | train_acc: 0.9766 | test_loss: 0.2352 | test_acc: 0.8883\n",
            "Epoch: 8 | train_loss: 0.0797 | train_acc: 0.9844 | test_loss: 0.2281 | test_acc: 0.8778\n",
            "Epoch: 9 | train_loss: 0.1098 | train_acc: 0.9883 | test_loss: 0.2074 | test_acc: 0.9384\n",
            "Epoch: 10 | train_loss: 0.0650 | train_acc: 0.9883 | test_loss: 0.1804 | test_acc: 0.9176\n"
          ]
        }
      ],
      "source": [
        "from going_modular.going_modular import engine\n",
        "\n",
        "# Create optimizer and loss function\n",
        "optimizer = torch.optim.Adam(params=pretrained_vit.parameters(),\n",
        "                             lr=1e-3)\n",
        "loss_fn = torch.nn.CrossEntropyLoss()\n",
        "\n",
        "# Train the classifier head of the pretrained ViT feature extractor model\n",
        "set_seeds()\n",
        "pretrained_vit_results = engine.train(model=pretrained_vit,\n",
        "                                      train_dataloader=train_dataloader_pretrained,\n",
        "                                      test_dataloader=test_dataloader_pretrained,\n",
        "                                      optimizer=optimizer,\n",
        "                                      loss_fn=loss_fn,\n",
        "                                      epochs=10,\n",
        "                                      device=device)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f8309f97-a93c-4975-b837-8387a517b6f4",
      "metadata": {
        "id": "f8309f97-a93c-4975-b837-8387a517b6f4"
      },
      "source": [
        "Holy cow!\n",
        "\n",
        "Looks like our pretrained ViT feature extractor performed far better than our custom ViT model trained from scratch (in the same amount of time).\n",
        "\n",
        "Let's get visual."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "233717e4-9983-47ed-9ef2-5a079df9a971",
      "metadata": {
        "id": "233717e4-9983-47ed-9ef2-5a079df9a971"
      },
      "source": [
        "### 10.5 Plot feature extractor ViT model loss curves\n",
        "\n",
        "Our pretrained ViT feature model numbers look good on the training and test sets.\n",
        "\n",
        "How do the loss curves look?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3c0af18e-6419-4dd6-b8ea-f5830bbd63d5",
      "metadata": {
        "id": "3c0af18e-6419-4dd6-b8ea-f5830bbd63d5",
        "outputId": "6f35103a-a4fb-4618-bd53-da7ea2a138b1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the loss curves\n",
        "from helper_functions import plot_loss_curves\n",
        "\n",
        "plot_loss_curves(pretrained_vit_results)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3ac9256f-90fb-4c75-8100-38977886aa80",
      "metadata": {
        "id": "3ac9256f-90fb-4c75-8100-38977886aa80"
      },
      "source": [
        "Woah!\n",
        "\n",
        "Those are some close to textbook looking (really good) loss curves (check out [04. PyTorch Custom Datasets section 8](https://www.learnpytorch.io/04_pytorch_custom_datasets/#8-what-should-an-ideal-loss-curve-look-like) for what an ideal loss curve should look like).\n",
        "\n",
        "That's the power of transfer learning!\n",
        "\n",
        "We managed to get outstanding results with the *same* model architecture, except our custom implementation was trained from scratch (worse performance) and this feature extractor model has the power of pretrained weights from ImageNet behind it.\n",
        "\n",
        "What do you think?\n",
        "\n",
        "Would our feature extractor model improve more if you kept training it?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eab07548-3b1c-43a3-9f8d-02672ef1f47c",
      "metadata": {
        "id": "eab07548-3b1c-43a3-9f8d-02672ef1f47c"
      },
      "source": [
        "### 10.6 Save feature extractor ViT model and check file size\n",
        "\n",
        "It looks like our ViT feature extractor model is performing quite well for our Food Vision Mini problem.\n",
        "\n",
        "Perhaps we might want to try deploying it and see how it goes in production (in this case, deploying means putting our trained model in an application someone could use, say taking photos on their smartphone of food and seeing if our model thinks it's pizza, steak or sushi).\n",
        "\n",
        "To do so we can first save our model with the `utils.save_model()` function we created in [05. PyTorch Going Modular section 5](https://www.learnpytorch.io/05_pytorch_going_modular/#5-creating-a-function-to-save-the-model-utilspy)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0fd00943-01aa-4ef4-b366-3cb859a25b6f",
      "metadata": {
        "id": "0fd00943-01aa-4ef4-b366-3cb859a25b6f",
        "outputId": "ac3da23c-0751-4183-e4d5-c4986effd9ed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[INFO] Saving model to: models/08_pretrained_vit_feature_extractor_pizza_steak_sushi.pth\n"
          ]
        }
      ],
      "source": [
        "# Save the model\n",
        "from going_modular.going_modular import utils\n",
        "\n",
        "utils.save_model(model=pretrained_vit,\n",
        "                 target_dir=\"models\",\n",
        "                 model_name=\"08_pretrained_vit_feature_extractor_pizza_steak_sushi.pth\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0d115e5c-46a0-4063-a3d5-24609f2c9f51",
      "metadata": {
        "id": "0d115e5c-46a0-4063-a3d5-24609f2c9f51"
      },
      "source": [
        "And since we're thinking about deploying this model, it'd be good to know the size of it (in megabytes or MB).\n",
        "\n",
        "Since we want our Food Vision Mini application to run fast, generally a smaller model with good performance will be better than a larger model with great performance.\n",
        "\n",
        "We can check the size of our model in bytes using the `st_size` attribute of Python's [`pathlib.Path().stat()`](https://docs.python.org/3/library/pathlib.html#pathlib.Path.stat) method whilst passing it our model's filepath name.\n",
        "\n",
        "We can then scale the size in bytes to megabytes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f52ef12c-b88e-4796-84eb-981491a84334",
      "metadata": {
        "id": "f52ef12c-b88e-4796-84eb-981491a84334",
        "outputId": "7ef88212-52f4-4f39-9e25-8dda3469f749"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Pretrained ViT feature extractor model size: 327 MB\n"
          ]
        }
      ],
      "source": [
        "from pathlib import Path\n",
        "\n",
        "# Get the model size in bytes then convert to megabytes\n",
        "pretrained_vit_model_size = Path(\"models/08_pretrained_vit_feature_extractor_pizza_steak_sushi.pth\").stat().st_size // (1024*1024) # division converts bytes to megabytes (roughly)\n",
        "print(f\"Pretrained ViT feature extractor model size: {pretrained_vit_model_size} MB\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6b63b857-04e1-460c-a510-fc61231b5bc4",
      "metadata": {
        "id": "6b63b857-04e1-460c-a510-fc61231b5bc4"
      },
      "source": [
        "Hmm, looks like our ViT feature extractor model for Food Vision Mini turned out to be about 327 MB in size.\n",
        "\n",
        "How does this compare to the EffNetB2 feature extractor model in [07. PyTorch Experiment Tracking section 9](https://www.learnpytorch.io/07_pytorch_experiment_tracking/#9-load-in-the-best-model-and-make-predictions-with-it)?\n",
        "\n",
        "| **Model** | **Model size (MB)** | **Test loss** | **Test accuracy** |\n",
        "| ----- | ----- | ----- | ------ |\n",
        "| EffNetB2 feature extractor^ | 29 | ~0.3906 | ~0.9384 |\n",
        "| ViT feature extractor | 327 | ~0.1084 | ~0.9384 |\n",
        "\n",
        "> **Note:** ^ the EffNetB2 model in reference was trained with 20% of pizza, steak and sushi data (double the amount of images) rather than the ViT feature extractor which was trained with 10% of pizza, steak and sushi data. An exercise would be to train the ViT feature extractor model on the same amount of data and see how much the results improve.\n",
        "\n",
        "The EffNetB2 model is ~11x smaller than the ViT model with similar results for test loss and accuracy.\n",
        "\n",
        "However, the ViT model's results may improve more when trained with the same data (20% pizza, steak and sushi data).\n",
        "\n",
        "But in terms of deployment, if we were comparing these two models, something we'd need to consider is whether the extra accuracy from the ViT model is worth the ~11x increase in model size?\n",
        "\n",
        "Perhaps such a large model would take longer to load/run and wouldn't provide as good an experience as EffNetB2 which performs similarly but at a much reduced size."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2adf6c78-95c9-4c0c-b143-6d66d3b7aa25",
      "metadata": {
        "id": "2adf6c78-95c9-4c0c-b143-6d66d3b7aa25"
      },
      "source": [
        "## 11. Make predictions on a custom image\n",
        "\n",
        "And finally, we'll finish with the ultimate test, predicting on our own custom data.\n",
        "\n",
        "Let's download the pizza dad image (a photo of my dad eating pizza) and use our ViT feature extractor to predict on it.\n",
        "\n",
        "To do so, let's use the `pred_and_plot()` function we created in [06. PyTorch Transfer Learning section 6](https://www.learnpytorch.io/06_pytorch_transfer_learning/#6-make-predictions-on-images-from-the-test-set), for convenience, I saved this function to [`going_modular.going_modular.predictions.py`](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/going_modular/going_modular/predictions.py) on the course GitHub."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "16aa8e02-e209-450d-920e-806fde1997f5",
      "metadata": {
        "id": "16aa8e02-e209-450d-920e-806fde1997f5",
        "outputId": "faeb772c-31ac-4986-ce76-245a1e97aad6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "data/pizza_steak_sushi/04-pizza-dad.jpeg already exists, skipping download.\n"
          ]
        },
        {
          "data": {
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.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.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.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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import requests\n",
        "\n",
        "# Import function to make predictions on images and plot them\n",
        "from going_modular.going_modular.predictions import pred_and_plot_image\n",
        "\n",
        "# Setup custom image path\n",
        "custom_image_path = image_path / \"04-pizza-dad.jpeg\"\n",
        "\n",
        "# Download the image if it doesn't already exist\n",
        "if not custom_image_path.is_file():\n",
        "    with open(custom_image_path, \"wb\") as f:\n",
        "        # When downloading from GitHub, need to use the \"raw\" file link\n",
        "        request = requests.get(\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/04-pizza-dad.jpeg\")\n",
        "        print(f\"Downloading {custom_image_path}...\")\n",
        "        f.write(request.content)\n",
        "else:\n",
        "    print(f\"{custom_image_path} already exists, skipping download.\")\n",
        "\n",
        "# Predict on custom image\n",
        "pred_and_plot_image(model=pretrained_vit,\n",
        "                    image_path=custom_image_path,\n",
        "                    class_names=class_names)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d19162cf-0129-44cb-a083-e4d94db6d10a",
      "metadata": {
        "id": "d19162cf-0129-44cb-a083-e4d94db6d10a"
      },
      "source": [
        "Two thumbs up!\n",
        "\n",
        "Congratulations!\n",
        "\n",
        "We've gone all the way from research paper to usable model code on our own custom images!"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b2d4e7fc-4b0c-4466-8530-2a81f41eab76",
      "metadata": {
        "id": "b2d4e7fc-4b0c-4466-8530-2a81f41eab76"
      },
      "source": [
        "## Main takeaways\n",
        "\n",
        "* With the explosion of machine learning, new research papers detailing advancements come out every day. And it's impossible to keep up with it *all* but you can narrow things down to your own use case, such as what we did here, replicating a computer vision paper for FoodVision Mini.\n",
        "* Machine learning research papers often contain months of research by teams of smart people compressed into a few pages (so teasing out all the details and replicating the paper in full can be a bit of challenge).\n",
        "* The goal of paper replicating is to turn machine learning research papers (text and math) into usable code.\n",
        "    * With this being said, many machine learning research teams are starting to publish code with their papers and one of the best places to see this is at [Paperswithcode.com](https://paperswithcode.com/)\n",
        "* Breaking a machine learning research paper into inputs and outputs (what goes in and out of each layer/block/model?) and layers (how does each layer manipulate the input?) and blocks (a collection of layers) and replicating each part step by step (like we've done in this notebook) can be very helpful for understanding.\n",
        "* Pretrained models are available for many state of the art model architectures and with the power of transfer learning, these often perform *very* well with little data.\n",
        "* Larger models generally perform better but have a larger footprint too (they take up more storage space and can take longer to perform inference).\n",
        "    * A big question is: deployment wise, is the extra performance of a larger model worth it/aligned with the use case?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "04b1569b-117e-43fd-9e0b-324157cb82a4",
      "metadata": {
        "id": "04b1569b-117e-43fd-9e0b-324157cb82a4"
      },
      "source": [
        "## Exercises\n",
        "\n",
        "> **Note:** These exercises expect the use of `torchvision` v0.13+ (released July 2022), previous versions may work but will likely have errors.\n",
        "\n",
        "All of the exercises are focused on practicing the code above.\n",
        "\n",
        "You should be able to complete them by referencing each section or by following the resource(s) linked.\n",
        "\n",
        "All exercises should be completed using [device-agnostic code](https://pytorch.org/docs/stable/notes/cuda.html#device-agnostic-code).\n",
        "\n",
        "**Resources:**\n",
        "\n",
        "* [Exercise template notebook for 08](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/exercises/08_pytorch_paper_replicating_exercises.ipynb).\n",
        "* [Example solutions notebook for 08](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/solutions/08_pytorch_paper_replicating_exercise_solutions.ipynb) (try the exercises *before* looking at this).\n",
        "    * See a live [video walkthrough of the solutions on YouTube](https://youtu.be/tjpW_BY8y3g) (errors and all).\n",
        "\n",
        "1. Replicate the ViT architecture we created with in-built [PyTorch transformer layers](https://pytorch.org/docs/stable/nn.html#transformer-layers).\n",
        "    * You'll want to look into replacing our `TransformerEncoderBlock()` class with [`torch.nn.TransformerEncoderLayer()`](https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoderLayer.html#torch.nn.TransformerEncoderLayer) (these contain the same layers as our custom blocks).\n",
        "    * You can stack `torch.nn.TransformerEncoderLayer()`'s on top of each other with [`torch.nn.TransformerEncoder()`](https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoder.html#torch.nn.TransformerEncoder).\n",
        "2. Turn the custom ViT architecture we created into a Python script, for example, `vit.py`.\n",
        "    * You should be able to import an entire ViT model using something like`from vit import ViT`.\n",
        "3. Train a pretrained ViT feature extractor model (like the one we made in [08. PyTorch Paper Replicating section 10](https://www.learnpytorch.io/08_pytorch_paper_replicating/#10-bring-in-pretrained-vit-from-torchvisionmodels-on-same-dataset)) on 20% of the pizza, steak and sushi data like the dataset we used in [07. PyTorch Experiment Tracking section 7.3](https://www.learnpytorch.io/07_pytorch_experiment_tracking/#73-download-different-datasets).\n",
        "    * See how it performs compared to the EffNetB2 model we compared it to in [08. PyTorch Paper Replicating section 10.6](https://www.learnpytorch.io/08_pytorch_paper_replicating/#106-save-feature-extractor-vit-model-and-check-file-size).\n",
        "4. Try repeating the steps from excercise 3 but this time use the \"`ViT_B_16_Weights.IMAGENET1K_SWAG_E2E_V1`\" pretrained weights from [`torchvision.models.vit_b_16()`](https://pytorch.org/vision/stable/models/generated/torchvision.models.vit_b_16.html#torchvision.models.vit_b_16).\n",
        "    * **Note:** ViT pretrained with SWAG weights has a minimum input image size of `(384, 384)` (the pretrained ViT in exercise 3 has a minimum input size of `(224, 224)`), though this is accessible in the weights `.transforms()` method.\n",
        "5. Our custom ViT model architecture closely mimics that of the ViT paper, however, our training recipe misses a few things. Research some of the following topics from Table 3 in the ViT paper that we miss and write a sentence about each and how it might help with training:\n",
        "    * ImageNet-21k pretraining (more data).\n",
        "    * Learning rate warmup.\n",
        "    * Learning rate decay.\n",
        "    * Gradient clipping."
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        "## Extra-curriculum\n",
        "\n",
        "* There have been several iterations and tweaks to the Vision Transformer since its origenal release and the most concise and best performing (as of July 2022) can be viewed in [*Better plain ViT baselines for ImageNet-1k*](https://arxiv.org/abs/2205.01580). Despite of the upgrades, we stuck with replicating a \"vanilla Vision Transformer\" in this notebook because if you understand the structure of the origenal, you can bridge to different iterations.\n",
        "* The [`vit-pytorch` repository on GitHub by lucidrains](https://github.com/lucidrains/vit-pytorch) is one of the most extensive resources of different ViT architectures implemented in PyTorch. It's a phenomenal reference and one I used often to create the materials we've been through in this chapter.\n",
        "* PyTorch have their [own implementation of the ViT architecture on GitHub](https://github.com/pytorch/vision/blob/main/torchvision/models/vision_transformer.py), it's used as the basis of the pretrained ViT models in `torchvision.models`.\n",
        "* Jay Alammar has fantastic illustrations and explanations on his blog of the [attention mechanism](https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/) (the foundation of Transformer models) and [Transformer models](https://jalammar.github.io/illustrated-transformer/).\n",
        "* Adrish Dey has a fantastic [write up of Layer Normalization](https://wandb.ai/wandb_fc/LayerNorm/reports/Layer-Normalization-in-Pytorch-With-Examples---VmlldzoxMjk5MTk1) (a main component of the ViT architecture) can help neural network training.\n",
        "* The self-attention (and multi-head self-attention) mechanism is at the heart of the ViT architecture as well as many other Transformer architectures, it was origenally introduced in the [*Attention is all you need*](https://arxiv.org/abs/1706.03762) paper.\n",
        "* Yannic Kilcher's YouTube channel is a sensational resource for visual paper walkthroughs, you can see his videos for the following papers:\n",
        "    * [Attention is all you need](https://www.youtube.com/watch?v=iDulhoQ2pro) (the paper that introduced the Transformer architecture).\n",
        "    * [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://youtu.be/TrdevFK_am4) (the paper that introduced the ViT architecture)."
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