notebooks/18_Transformers_for_Non_NLP_Tasks.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "source": [
        "# Transformers for Non-NLP Tasks with PyTorch and Hugging Face\n",
        "\n",
        "Transformers have revolutionized the field of natural language processing (NLP). However, their application extends beyond NLP to areas such as computer vision, time series analysis, and more. This tutorial explores how to use transformers for non-NLP tasks using PyTorch and Hugging Face's transformers library.\n",
        "\n",
        "## Prerequisites\n",
        "Before we begin, ensure you have a Google Colab notebook or a similar environment set up. We'll use Python 3.x, PyTorch, and Hugging Face's transformers library.\n",
        "\n",
        "## Installation\n",
        "Start by installing the necessary libraries. Run the following commands in your notebook:"
      ],
      "metadata": {
        "id": "COOJzVsbcuJ0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q torch torchvision torchaudio\n",
        "!pip install -q transformers"
      ],
      "metadata": {
        "id": "HJR6Nnhhcvsa"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing Libraries\n",
        "After installation, import the required libraries:"
      ],
      "metadata": {
        "id": "ymYm3c-uc8rG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import transformers\n",
        "from torch import nn\n",
        "from torchvision import models, transforms\n",
        "from transformers import ViTFeatureExtractor, ViTForImageClassification\n",
        "from PIL import Image\n",
        "import requests\n",
        "from io import BytesIO\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "XpNq3Rjsc6F9"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking Package Versions\n",
        "To ensure reproducibility, let's check the version of the installed packages:"
      ],
      "metadata": {
        "id": "T4HcKtaVdD2H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"PyTorch Version: {torch.__version__}\")\n",
        "print(f\"Transformers Version: {transformers.__version__}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VhSYxfGuc_oc",
        "outputId": "67af62b3-a05d-435a-d671-104ea535a2dc"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PyTorch Version: 2.1.0+cu121\n",
            "Transformers Version: 4.38.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conceptual Overview\n",
        "Transformers are models that leverage self-attention mechanisms to process input data in parallel, significantly improving efficiency and performance in various tasks. While originally designed for NLP, the adaptability of transformers allows them to excel in other domains, such as image classification (ViT - Vision Transformer) and time series forecasting.\n",
        "\n",
        "## Vision Transformers (ViT)\n",
        "Vision Transformers (ViT) apply transformer architecture to image classification tasks. Unlike conventional CNNs that process images through local convolutions, ViT divides an image into patches and processes these patches as a sequence, allowing the model to capture global dependencies.\n",
        "\n",
        "## Example: Image Classification with ViT\n",
        "Let's dive into an example of using a Vision Transformer for image classification.\n",
        "\n",
        "### Step 1: Load and Prepare an Image\n",
        "First, we need to load an image and prepare it for the model. We'll use the ViTFeatureExtractor for this purpose.##"
      ],
      "metadata": {
        "id": "-axt-aSEdJ6X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torchvision import datasets, transforms\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define a transform to convert MNIST images to the format expected by ViT\n",
        "transform = transforms.Compose([\n",
        "    transforms.Resize(224),  # Resize the image to 224x224 pixels\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Lambda(lambda x: x.repeat(3, 1, 1)),  # Repeat the single channel image 3 times to create a 3-channel image\n",
        "    transforms.Normalize(0.5, 0.5),  # Normalize the image\n",
        "])\n",
        "\n",
        "# Load the MNIST dataset\n",
        "mnist_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n",
        "\n",
        "# Select an image from the dataset\n",
        "image, label = mnist_dataset[0]  # Change 0 to any other index to select a different image\n",
        "\n",
        "# Since ViTFeatureExtractor expects a PIL Image, we convert the tensor back to a PIL Image for demonstration\n",
        "image_for_vit = transforms.ToPILImage(mode='RGB')(image)\n",
        "\n",
        "# Display the image\n",
        "plt.imshow(image_for_vit)\n",
        "plt.title(f\"MNIST Sample - Label: {label}\")\n",
        "plt.show()"
      ],
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          "height": 799
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        "id": "zuwy-SuBdGWy",
        "outputId": "6a71aabb-63ae-4f83-d757-259af0851878"
      },
      "execution_count": 4,
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          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "100%|██████████| 9912422/9912422 [00:00<00:00, 133849657.11it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n"
          ]
        },
        {
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          "text": [
            "\n"
          ]
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          "text": [
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "100%|██████████| 28881/28881 [00:00<00:00, 17850824.32it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "100%|██████████| 1648877/1648877 [00:00<00:00, 81122922.59it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4542/4542 [00:00<00:00, 17070366.28it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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/Hj/4wQ8sZ87oKBaL+OyzzxAKhTaJTYqBgYGBwYahVCohkUigpaUFdnvleGmjN/Pm83m8/fbbuOSSS9Sa3W7HAQccUNFuJpfLlU3gXL16NXbaaadhP1cDAwMDg+HFp59+WtW4d6On+zo6OtDX14empqay9aampoozcxYsWIBIJKIuhqAMDAwMNg+EQqGq948KW6RLLrkEc+fOVbfj8biyzzfpPgMDA4PRB1aaBtrDNzpJ1dfXw+FwYO3atWXra9eurej07PF44PF4NsbpGRgYGBiMIGz0dJ/b7cYee+yBZ555Rq0Vi0U888wz2HvvvTf26RgYGBgYjGBsknTf3LlzMXv2bOy55574yle+gt/85jdIpVI49dRTN8XpGBgYGBiMUGwSkjr22GPR3t6Oyy+/HK2trdhtt93w+OOP9xNTGBgYGBhs2RiVtkjxeByRSASAEU4YGBgYjEaQemKxGMLhcMXjjMGsgYGBgcGIhSEpAwMDA4MRC0NSBgYGBgYjFoakDAwMDAxGLAxJGRgYGBiMWBiSMjAwMDAYsTAkZWBgYGAwYmFIysDAwMBgxMKQlIGBgYHBiIUhKQMDAwODEQtDUgYGBgYGIxaGpAwMDAwMRiwMSRkYGBgYjFgYkjIwMDAwGLEwJGVgYGBgMGJhSMrAwMDAYMTCkJSBgYGBwYiFISkDAwMDgxELQ1IGBgYGBiMWhqQMDAwMDEYsDEkZGBgYGIxYGJIyMDAwMBixMCRlYGBgYDBiYUjKwMDAwGDEwpCUgYGBgcGIhSEpAwMDA4MRC0NSBgYGBgYjFoakDAwMDAxGLAxJGRgYGBiMWBiSMjAwMDAYsRhyklqwYAH22msvhEIhNDY2YtasWVi8eHHZMdOmTYPNZiu7nHXWWUN9KgYGBgYGoxxDTlIvvPACvv/97+O1117DU089hUKhgIMOOgipVKrsuDlz5mDNmjXqctVVVw31qRgYGBgYjHI4h/oJH3/88bLbd9xxBxobG/H2229j3333Vet+vx/Nzc1D/fIGBgYGBpsRhr0mFYvFAAC1tbVl63fffTfq6+uxyy674JJLLkE6na74HLlcDvF4vOxiYGBgYLD5Y8gjKYlisYgf/vCH+PrXv45ddtlFrZ9wwgmYOHEiWlpa8N577+FHP/oRFi9ejAcffNDyeRYsWID58+cP56kaGBgYGIxA2EqlUmm4nvzss8/GP//5T7z88ssYN25cxeOeffZZzJgxA0uXLsU222zT7/5cLodcLqdux+NxjB8/HgBgs9mG/sQNDAwMDIYVpJ5YLIZwOFzxuGGLpM455xw8+uijePHFF6sSFABMnToVACqSlMfjgcfjGZbzNDAwMDAYuRhykiqVSvjBD36Ahx56CM8//zwmTZo04GMWLlwIABgzZsxQn46BgYGBwSjGkJPU97//fdxzzz145JFHEAqF0NraCgCIRCLw+XxYtmwZ7rnnHhx66KGoq6vDe++9h/PPPx/77rsvdt1116E+HQMDAwODUYwhr0lVqhHdfvvtOOWUU/Dpp5/ipJNOwvvvv49UKoXx48fj8MMPx6WXXlo1LykRj8cRiUSqvp6BgYGBwcjFutakhlU4MVwwJGVgYGAwurGuJGW8+wwMDAwMRiwMSRkYGBgYjFgYkjIwMDAwGLEwJGVgYGBgMGJhSMrAwMDAYMTCkJSBgYGBwYiFISkDAwMDgxELQ1IGBgYGBiMWhqQMDAwMDEYshnWelIHBloyR5oYiz4fX5V99Tb++Pq8zmLVSqaRcCORf/bpck8+lvxd50Y/RH1ftva/v91jtPazPdfm81e7f3GBIysBgGGC1IW5q2O32suv6Rs41/bjBwuq9Wz2PXONmWywWAXw+MFWu8a+86K8n35PD4YDdbi9b4+vxr9V7l+dttTYY6OevX692n3zPOhHpx/G1NlcYkjIwGAJU+oVuhfXZ+DcU3Ih5cTqdsNvtcDgc6iJv8ziHw1Hx+QZ6f9zk5fVKUQ433UKhgGKxiHw+j76+vrLrfX19KBQK6i/Bc3Y6neq6y+Xqt8b37HK51Oehv1derIiOWFdC4Pn39vaq8yfB8n329fWht7fXck3+5br+fHysISkDA4OKqJZGqnTsxo6yuOly43W73YqsuJHrf+WGXel9WEFGZFaRih6x2Ww2FRXk83n09vYin88rQnI4HIqY7HY7ent7yzZlnq/H41Hnz+tut1uRFomL712Stfwc+BnxNo8l1jXFphNrb2+vIhgSb29vbxnx8hiuyeP4WP01RlrEPtQwJGVgMEgMtCnoKTOiUkppY4Ak5XK54HQ61ebtdrvhdrvhcrngdrvhdDrVhs7rVu+j2vnzfZLgZFQiIzq5xggjnU6jUCggk8kgl8uhUCiotVwuh1wuh3w+DwAq1eXxeOByueDz+dT74HWv1wuXywWPxwOv1wun0wmv16vISL5nEpXL5VIRlyQvoH8UVY2wSCrZbFYRD6/LtVwup9YKhQLy+by6n++X6/qaTtibIwxJGRgMEtVIqlJ9Q69/6OvDDZvNpjZct9utNmyPxwOPxwO32w2Px6M2cW7ebre7Yn2pkuCA759Rip5Ws0o5MupIJBLI5/NIpVLIZrPI5XJwOp3I5/Nlz8c0FwB1/oFAQL0Pv98Pt9sNv9+vCCoQCCgyIxnxPZOoSXIkMEnYQDlJDSRgYPRDks3n88hkMuovCYrklE6nFQHpZJ3P58veNy92u72sPrc5wpCUgcEQoZqSzKomY3XccJ4bU2FyU/Z4PPD5fIq4uInzuiSpSuo5q/tkukyveZFoGLGQpHp7e+F2u5HNZuFyuZBOp5HNZgEAuVwOwBfRUzabVddJTF6vV72fQCAAj8eDYDCoSEteJ/nI6IrEXCgUYLPZ1GfAC4B+AgbCiqQKhYJKUQKf/xtIpVJlhJTNZhVxud1uteZwOJDP58ui0FKpVJYG3RJSfYAhKQODqljXTaBS3cYqapLpsI0FkgI3dJ/PB4/Hg0AgoDZ3buKBQEARF+s3lcjVao1pPl20YCVuIEkx9RWLxZDJZNDT04NkMolMJgOXy6U2bn5+uVxOkQRJNRQKwefzIRgMquvRaLRszePxIBwOlxGby+VSERcAnHjiiViyZEnZZyDTngPJ5AmmK5PJJL797W/jsssuQzKZRC6XQyKRUASVSCTK1tLpNOLxOLLZLDweDzKZDDKZjCJ+KcDQ62WbIwxJGRhUwWBJqpJ6zUruvTE3F5l2YySlRyDcqAOBgLru8XgqkpRUvUkVn05Ssq4jVXckKUYvjGDcbjeKxaIivFwuB5vNhnw+r+o3TqdTkZQkX6/XqyIpn8+HUCgEv9+PUCiEcDgMr9eLSCSiUoQLFy5EMpmE3++H1+uF3W5HV1cX0uk0AJSl1vgedTWdJCkZXcnU3erVq/H6668jmUzC6XRi1113hd/vRzqdVu+RghaHw4FisQin06lqThSVlEollX7UVYebKwxJGRgMEaxISF/bVCQlyYmRFOs0jKYikQh8Ph/C4bCKqrhx61GhPH+dgJmekuILkhQ3YSneIEnl83n4fD6kUiklfkgmkygWi4q4uGm73W6ldPN6vYqIAoEAIpEIampqEAgEUFdXh2AwiHA4jJqaGvj9fkSjUZXmmzNnDt5++20Eg8Gyz2SHHXZAMBhUBC7TnlZNxVZr2WwW+XwePT09WL58Oc4//3zE43GMGzcOzz77LBwOBxKJBGKxGLLZLPx+P1KplCKyTCYD4Av1ItN7JL8thagMSRkYDBIDbQqVGmV1aXYlefdwgBEL61AkINZq/H6/IqloNKo2bZ/PZykpt3p/fB0pc5eKudWrV+PKK69EqVRSog3eR8XeWWedhYaGhrIaGqMMqvv6+vrgdrvR29sLAP3eSzgcRjQaVSQVCoUQiURw2223YeXKlaitrVWEVCgUMHHiRBVxyXoW1/j8lMoD/UlJb64FoEQPXV1dSKVSiMfj6O7uhtPpxM9+9jPk83kkk0mcccYZmDBhgqoHMu3o8XjQ29ur/o3kcjlF0CT+jVXT3JQwJGVgMEgMpO6rdrEiq42xychUn0z3eb3eshQZN3pu0H6/f8CIkNdzuRxSqZTqbZKbOtNof/vb31Qqi9EMBQNOpxPHHnsswuEwwuEw+vr6UCqVkEgk0NfXp6IaHktI2bnP54Pf70cgEFDvw+12o1Ao4OWXX8a7776Luro69X5DoRCampoQDocVScnaFdcGIikrZwiKI7xeL1KplFIfplIpPPHEE0gmk0gmk5g1axbGjh2LQqEAp9OJUCikUpyZTEbV60haMjI1kZSBgcE6oVKkYbWhS0eDjUVSrAeRmEhIjDJCoRBqa2sRCARQW1uLSCSiNnorkrKyHPrjH/+In/zkJ4oQ2b9EQiwWixg7dqyKniRJ5fN55PN5nH/++fjyl7+Ma6+9VkV8TG3xGBIWIykSayQSQTgcRm1tLerr6xEOh9Hc3IyHH34Yv/71r+FwODBx4kQ0Njaq9xaJROD1elXkFQgEVDqQnwGJjyRVyeJIj6Qoo29ra0MikUB3dzc6OjqQTCbVWk9PD6688koUCgW0t7fj5JNPxtlnnw273a5SnfysqXBMpVLIZDIm3WdgMNQYiv9MAz2Hfn+l4yup8fTbg1nTe4KsGlj1/qCNSVJUufEiow1eGF0sXrwYa9euVSRlRVTy/G02G959913E43Gl3isUCmW9R4wSdPk3I6lcLofu7m588skn+Mc//oGJEyeivr4egUAAvb29KqrJ5XLw+XyKpPQIiu/LZrPh6aefxsKFC5FIJNDc3KxImceQkCqRFFOhPp9PkZSVr55OXADUe2NtiX1PpVIJfr9fNfC2t7cjmUyivb1dqRn1RmLdrmpLSPMRhqQMhhTVSGFD/1NZPUelxlj92IEaaK0eOxiSkpu2LrXmmhQUSELbmCRFMmI0UVNToyKpuro6FUktWLAA99577zqTFFFfX6/eO0lIb5qVkRTFFFTupVIprF69GmeddRbmz5+PE088EclkEna7XTX3lkolxGIxRVKMoKLRKCKRCGpra1FbW4vOzk5ceOGFsNlsqK+vR0NDA0KhEBoaGhQh1dTUlJGUFFbwc6HyjyRFCbgVOTFFWSwW1fvOZrOw2WyKpIrFInw+n7rNaFESEy/8nPUfBFsKQQGGpAyGEFb/gSr9x1rX/2jVnmugtJpUn1mlpwZ6DqDcJHWg9ygNTKsZnUrfvI2l8pORVCAQQH19vdqQqearqanB4sWL8dOf/hQffPABGhoaFEnJz0KPDCt9DiQm3YZJqvtIWCRwRk2hUAgPPPAA3njjDZx33nmora1FPp9Xmz+l6DabDQ0NDQiHw2hoaEAkEkFdXR3q6upQKpVQU1MDAIrAKKyQMnWp4JPnwgZa3Z1d9inxwuOkZJz9Tel0esDr9fX1+OlPf4qJEyeqNTb5Sum99PCrNLZkc4MhKYMhg76BW5EAsS4bsxUZVCIaKp0q1UtkI6geBcjHVBMJWL0nuab7wekGp1ZrG4uk7Ha7Sof5/X4lHgiHwygWi6qxdNWqVXjyySeVlDsQCPSTmuu1Nf1z4OfL96mTk/wcSOrcaL1er5Kif/jhh/jwww9x7rnnKnJhE28kElGSbBKtvNBaicIPikCYuqOKjuSkRy/yO5EkRacHEpJOVNKxnARDkpG39fu8Xi++9a1voVAooLu7WzlL8KK7oW8pBAUYkjIYAlgRkdzcJUFUS8NZPaeETjCVLHb02pBUQulrklzkBiXJS3+PlSIxPo+0FNKNXKX0mo2yG4ukKNOmzJwuDT/72c9w1113IRKJwG63Y/z48ap3SvZJye/EqjaifzbyO9EdxmUkyWgql8shGAyiVCqpOg4jpYaGBgCfR4QkHvZJ1dfXIxQKoaWlBeFwGHV1dTjvvPPw8ssvKyKuq6tDbW1tWR2KLhsUk8hoiucGoIwYJDHJ0RpyjURGx4xUKlV2oaovmUwikUiUXXp7e5FKpZBOp5UtFC8kOjnCw5CUgcEgIDerSqo2wHronBX0zdtqc7PaBHUi0jdLmZKTx+m/pOW58/UrkbF8HvrA6eIA6ZfHvzyH4QbTb//973/x0UcfKdeFYDCI5cuXqyjA5XIpAYXsD7L6zgaKMuVnyUhXiknk5w98HrFQtUeBRF9fHx5//HFst9122G233VSKj7Ud4PNUHgURa9euxXPPPYdPPvkEhUJB1dl4IUlLpw0p7LCSdutiCTnXibe5JsdysEGZohDp4i6v77///thuu+2UY7p0OZdRlHydLWXgIWBIymCIYFUP0lNpVhHVupKUTCNZ2e1Us+CRaaeB1nTBw7rUruRG7HQ6lecbPeF0t3FGWiSwjUVSbrcbf/rTn3Dttdeq/iGmwMaOHauiCjby8pz1VG0lktK/M/kZ6ulVPQLlZxgIBFAqlRAKhVQ08vOf/xzTp0/HH//4R9hsX9gm9fX1wW63qwipvr4ezz//PC688ELU1dWhpqZGpQIjkYhKX/K903lDfj/yx0s1kuL8J3m7r69PESuVezSMZTqVfxkp5XI5nHfeedh9992xZs0aywhKjuXQa1Kb+8BDwJCUwRBD38wrjWuoVNfRCUuuybScJBVuMiQcnXTkmlScVTtO1ousCNgqkuLjpe+dz+fD4sWL8T//8z9l95Mg+J42xvficrnw8ccfY7vttlPnQOEAz4drJFTaAenflXzvlb4znbxsts/VcVbPx88hEAgAgHKgcLlc6Ovrw7Jly3DKKafglFNOwd577w273a56iG655Ra8++67aGxsRE9PDxoaGpQdElOB0WgU0WgUfr8f4XC4nwxeyr6toluZztPJQqb4SFJU7pF0KJKQt9PpNJLJZFl6Tx5DUtOjKxlRbQkYcpKaN28e5s+fX7a2/fbb48MPPwTwuZ/VBRdcgHvvvRe5XA4HH3wwfvvb36KpqWmoT8VgI8OqNlGphiR/qQIDb3gA+kU+cmOxkjjLmUg8jr+c5Zr+GD1FaBU1WUUFHo8HfX19iMViiqR6e3vR2tqKN954Q50vIxZGWRuLpPhem5qa1FBA6fQtBwXKQYj697ou35nV6wNQRq3yGBkJ8DNk2q9UKiEQCCCVSuH555/H17/+dXzpS19CZ2enIqmFCxfixRdfVGrE2tpahEIhpWRkmo8XRrLyu9dTfXokxahFj5z0qblysm4loQQvXq8X48aNg8PhqCiwkKRYKd23uWNYIqmdd94ZTz/99BcvIixMzj//fDz22GN44IEHEIlEcM455+CII47Av/71r+E4FYONCLmR6z0eUtwgN38+rlIkJSMWXT0nC91yiJ++JjddbsRWx8nBf0xzkVx1kpLnxYvX68V7772H448/XkVtwWAQDocD22+/vaqB0HaHQ/g2FkkxCtUJXBd06JN5q/1wqJau1a2CWPcipEKN85TYLyT7jOx2OzKZDLxeL2666SbccMMN6OrqQqlUUj8oxo4dqxp/a2pqlHsG1yieoAxfr0HqEb/896kTlCQhqvhIJnLiLqMiXTCRSqWQSCRwwgkn4Mc//rGlgELK0xlJ8TUYTZl034Y8qdOJ5ubmfuuxWAx/+MMfcM8992D//fcHANx+++3Ycccd8dprr+GrX/3qcJyOwTBDJxmryEOq62TO3+qiPy9Q7j2nk5Re59EJSa6RGGQNQhKXJKkPPvgAzz//fEWS0te8Xi8+++wztcnL4rx02SZJ0X1hY5GULibRU6YyQpU1Pj5ePhdhVU+TYyv0plc57lxutPyuvV6vcmRgapCjOUqlEpLJJHp7e2G329X90iCXhETfPbprUAxC+bkUbVT6PnWSsoqg9OhJXqxEEvJ2qVSCx+NBT09PWVpPHs/nkq8jZe9bAoaFpJYsWYKWlhZ4vV7svffeWLBgASZMmIC3334bhUIBBxxwgDp2hx12wIQJE/Dqq69WJCl+aUQ8Hh+O0zZYT1gRjU5KurihmhiB0Iv1Mgog+ZCoZPqK5CNrPzpJybqLJDgpH//oo49w5ZVXqvOy2tBkeohk2NjYWNWsNBQKwev1qtrIxiApfp66AlJGtzLFKY/RvwsJq0hK7xmSZFUoFNSmzw2XEZHD8cXsKIoi3G43AKiao/yume5jrYnRE0d1kLjYY8V5UtLZvVpEL1OUVqk+PbVHYpJj4eVF1phIVnpTr4ycpGhCKv2kwnBzj6KAYSCpqVOn4o477sD222+PNWvWYP78+fjGN76B999/H62trXC73YhGo2WPaWpqQmtra8XnXLBgQb86l8HIgi4s0FN/OklZCSgqKciAL0ZNsK5EwmEkxajESghAOTWVa7yP7tTnnXceEomE+pXNKGjNmjUYN26cpfBD77GSJMVN0+fzKQNTaVYq1zZmJKWLPuQmbZXWrBQ9WT2vhJRoyzqKVMRxiB/XnU6nihhYB5Pu6LlcTs1b4nXg838XkUgEfr9fERMtknw+n1qjRJ0/RCQxVavvyKhPj5by+Tx6e3vLop5sNqtIiqk+/W80GsWvf/1rNDc3K5EE04HyOBIVf6TLdJ8epW7OGHKSmjlzprq+6667YurUqZg4cSLuv/9++Hy+9XrOSy65BHPnzlW34/E4xo8fv8HnarDh0ElGRlBW9Q1JVutKUvK5ZfpORlLV1Goc4Ecyy+Vy+OSTT+Dz+ZBIJPD2228jHo/3IylugNy89bqFHjHydbkxyqZZ/qInSfHvxiapSilV+flXqg9We14JKdGWaTHWoyRpAZ8TgV4PoqqPAw+BLyIau92ufPvsdnvZOA3+m5CSf0bRMo1ZCVbDDKWqT0ZNMnrSIylJLjIyyuVyiEaj+OpXv4pisYiurq6ytKAuntCVhDI63VJcJ4Zdgh6NRrHddtth6dKlOPDAA9WkShlNrV271rKGRfAfnMHGw7oWy7l5s47DjUCvEVkp8OSv+IFISqYMZRqPdR9GSNKLjSk9GUmFQiE8/vjjOOWUU9TzsIYqlV80P9WJV4+qJIFSKSfNSuXIcklSTPfx9TcV1oWIBgtuqNJfT9/45eYrlWzc5K1UbPLfgcvlAlAuXOG/H13oIJV2APpt8LrVkKyV6dJzpusKhQLS6XRZwy7FErwdj8eRTqfR2dmJeDyOzs5OdHV1weVyoaenB8ViET09PYjH45bCCY6e12taMuW4JWDY/3ckk0ksW7YMJ598MvbYYw+4XC4888wzOPLIIwEAixcvxieffIK99957uE/FYB1h9au7Ug0JgNpoZe3HKrKRQga9D0mvSVlFaLz4/f6yuhKFCfqaz+fD6tWrce+996pf1IFAAEuWLCn7dU2y47nKSKpais9KOCGfR/6q1x0NdDn+5gJ985dE1Nvbi0wmo9JlvC43YG7OUtGWSqXK6jw8jhEYxRV2u12lEAEonz+v16uei9+7VYOu1XWdpKTpKwcSykiK7ymbzSqy6e7uRjweRywWw0EHHYQdd9xRvUcq/2RflJVwQnee2FJSfcAwkNSFF16Iww47DBMnTsRnn32GK664Ag6HA8cffzwikQhOP/10zJ07F7W1tQiHw/jBD36Avffe2yj7RgishAxWaTkZBUkC4MbM6IURBYmEmzj7kfSNvtJ5yEiKvU4kP6nK08UU//nPf3DNNdeUkafL5UJdXZ0iJL2JVbp2y/eqqxWtSIoRnm5gKhuFrWo+oxlWGyUjEhKUjDS4AadSKbXZc42bPMlAJzMel06nAXz+76RYLMLj8ShbJW7oHIFBsYSc1VTJGJbEqkdZJFt5XpSb87wYafG+ZDKpIinKzo888kjss88+augh61C8SHGFrH3JJl6rUSGbM4acpFatWoXjjz8enZ2daGhowD777IPXXntNGURee+21sNvtOPLII8uaeQ1GDvRCeqVogps0N2Q2UDK9JecXcWYPU3NUa62r/ZAkKUYlJIVSqYQf/OAHWLFiRVkNgsKIsWPH9iMpSWxWEnQ6dOtEqZO0PEemO/l+ZcqR90nz0s2VqGT0wYiBaatEIqFUbMlkErlcDslkUkVIjE54m8QgyY7PCXz+b5UzmbLZrPqRkEgk4PF4ylLCsi9NpvZ0k1ipOtRJSp4XSUqmK+UaDWbb29sxffp0fO9730NTUxPa29vR0dGBRCKBWCyGrq4uRWLSFskqpaj7+G3uBAUMA0nde++9Ve/3er248cYbceONNw71SxsMEXSisKrLyJqM7FXihNRQKKQcqCm/lkorkkElAUV7ezs+/fTTfpGUVTNvsVjE0qVL8fHHH5c1qrJJNxqNlvVOVWr6lc280g5IJymdsGREydeXFz3Fp9ffNidYuTPIjZxSbLqDk7ikuEBPoXFzlhGZJCnWuIDPU8/cyN1ut0rxcQQIv4tKJMXz1edFMZKREU42my0bySHPtVAooLGxUf1g22GHHbDzzjuju7u7LHqyatrV3SaqmcxuCTDefQaW0AlJduhLGbnD4VCCADlAj9NPafIZDAaVIzWnofJXrb7xA8AzzzyDiy66qF8kxd4Zh8NRNlTP4XBgm222USpCvelX2ieR6Ko5LfD9VYvs9DVpvSQNTPWG4o1lKrupQJIiqbD2kslkEIvF1Abd3d2NbDaLWCymNmk9SiFh6WSQz+fLZlC5XC6kUin1I4EpXCmEkdG7rJmRrGT/k1yTtTUpBee5SnKS3n1XXHEFZs6cie7ubmQyGaxZswaxWAzpdFpFT/F4HD09PUin04jH4/3IWlcSSqXfltLUa0jKoAxWKTdJULpjAcmCs4qY3pPEVFNTo0aWL1++HPfdd59Ku1n15thsNixcuBANDQ2WPUrSLYHnJg1mpQGtjLx4rjxOd13QnRaq1eas0pP8fHTvO/nZWfWIjRasa/1DEoBMVTHtx54gKSzQSUrKr+XGXygU4PF4cNZZZyEUCgEAHnnkEXz44YcoFArqxwn7q2SUTMUm61iVxm7oDbMykiJJuVwunH766QiFQmU1I0moLS0tZe+T6b1MJoN4PF7WG8VoSopDdMulLY2cCENSBmXQN15GDbp1jk4QzPmzu58zfmpra1UjazQaxZo1a3DLLbcoybgUHugEMHbsWEv5N5WBkiilMayUKevHUbVnRbiSpGSEJxuKq8nl+TzcGCXxVYrQRhPWNb1kJf/m5ss0XyKRUBJtnaRkNAOgbPxFb28vgsEg5syZg7Fjx8Jms+H999/He++9p34oSBeLVCpV9uOB35s8R+ALAYZOUnpjMkkjGAzi+OOPx7hx4xShFovFMpLKZrNKds5oMhaLIZvNqvfOqFIfbkjSk5LzSr1SmztspVH4LuPxOCKRCIDh6fPYkiEJSabVGHnoa06nEzU1NQgGgxgzZgzq6+sRjUYxbtw4RCIRNDU14brrrsNLL72EsWPHolgsIp1Oq7qUrPdUUhHqQgW9xiMJSV/Xm4cZIcl1q+OsIiWgvxOGXCMBSbIjcTGCq9TUPBqwLr/cS6WSigzWrl2rpNdtbW1IJpNYu3YtYrEY4vE41q5di2QyiY6ODlWboZtCb28vLrroIhx11FH9vOtsNhu23npr1Su1atUqJBKJss/V7XajtbUVJ510EjKZTD+zXElC22+/Pf7whz/A4XCUkZQ+hZekwfrX1ltvDYfDUUYkso7E+hKJidEUSYqWSIlEQvVVyZSm7tohz4tExeNG4TauzjkWiyEcDlc8zkRSBpbQ5dX6pi8jBNZfKJzgYLlCoYD//Oc/WLJkCVasWIG+vj5Eo1E0NTUhHA5bkpSMWqxISpKBfIy+Jp9Td4qo9JhKdk1WJKVHQvrr6cSn90aNxkiqEvQNUooSdBk6owVGEXa7HZMnT1bpLrkR77DDDth2223LjFzlcxYKBQBAY2Mjmpuby36o0DZrl112QSaTKUvhAl+4YvT19eFLX/oSJkyYALvd3q82ZUVSvD+ZTJYRk+6ywYiItaZMJqMIidETyZmRl5VHn5UCcUuKpAxJGfSDrurTyYhybdkMyzQfxyQ0NjbipZdewve//300NTVh/PjxmDRpEmpra9HS0qLGl+ukoqvpuKab0urEZSVksFLlyeexeoxc0yHXKt2vE66M3KyUkZsbUemNvFKCzjRfT08Purq60NnZiV122QX33HOP8rqTG3KpVEJbW1uZkEGSGKF/1vKHzC233FL2+fMc9dTeqlWryqIhGaVIkpJEaeVOLqf1kqQotefnwB4xmf7kmnwu3f1Cd8UwNSmDLRLVRBOybiMbVPUIKhKJqEmokUgE9fX1qKurw5gxY1BXV4dx48apnqlKvUfVIik95aYTjf4+KtWV1uV59M9G/5ysjhlIBSjf3+YIuZHqUZQkK6YCmQJLp9P93MZl1FDJu07K+vXPXf/BwvPT6zyScCqRlCQrPeKS0R6fn6lB6aLBqEmKJLhGkrZq1tWdPPS1zRmGpAzKoP/H1lN9uoiCUm42TnISKgUV0WgUNTU1qKurQ2NjI+rr69Hc3FxGUnoabqBIitDrQlbvo9Kx63rfQJ+TFayer9Jr2Gy2Eb/JVHqfVmk+/pWRihRPyPHovJ+KN1od6amtSmu8WP14qPadSsKT5CNHbwwUScnnkNGPvE/6/bFGJW2TrNasLI90IpIR1Ej/tzMUMCRl0A+VNmA9ApARkCxY53I5HHXUUejs7EQoFEI4HC6LrmpqalBTU1M2fE4SkhVxyV/GfO3BvqehPG6oX9dqs9E3o0qksKHnZRWBWh1nBZ6DPFdu3nqfVDKZRCwWQ09PD1KpFK6//npsvfXW6O7uRiKRQCqVKtugJUHJ1JceTVR7b1awIhdJUJWipkrOFDpxyXSnbADm52G1xhrWllRrWlcYkjKoCvmfxWrzsPrV19fXh2XLliGVSmHSpEmW0ZkuJrCq2VRS921OkJ+b/AugbLMiCQzFL2o93VnpwmP1fwNW70E/RrcTkk7kVL01NTWhsbERK1euVGnASmktXSywvqkuq8ZdnaT0NaumX5mClAILSaRStq47R1j1PW0p6bvBwpCUQT9UIiP+5wKAQqGgyEO6NdONWqZ6+B9Sv7BHBfg8SuOmq2+Qm/N/2mo1B6vIQX5m6xJp6Wsynar/ILASpBSLRfV9WBXqK/1IsWrkZUqPTgusSfX09FSMpPgaViS1vp+3JBopdOA583Ov1JskU3IyotK/M52k9H/7W5pKb31hSMqgDAMRFEnK6XQqQtL/AwaDQcybNw+LFy/G448/bvkflBc9zaRv1ptb5FQJerQgf73LX+hyE9R/eVe7DpTL5KvJ80ulUlnkWqk2Itckgen/ZmQUlclkMGPGDEydOhV1dXVKREG7pHUhbb6GfG/r+znrThN6ak9P88l0nlWtTJKolSijUkOuiaIqw5CUQRmsNjr+R2Szo81m66eOktedTieOP/54vPLKK3jssceqqrX4C76SmmlLgVVaq9omaVWXqUZewBcDKmVqtVT6YiqufqzD4eiXxqtEUpXSfVZENXnyZHznO9/Bxx9/jM7OzjKrJKv3US2KtIruBoL+GUtC4UWPuKx+JFg9jxVJ6T1P8liT5hsYhqQM+sHqlyIjJvaasJGyVCr1M/7Uh7XJVKA+wE3+UpfSYd7eUmBV42CRXf4AkKklK7KqFIkwKtJ9Del3x+nA/H5l+k+eo9V56xusTkx6qo9+dj09Peju7kZXV5eSolcj3UqkvL6ftSQJPXqSa5Kc9HSkFXHxukwpWgk19Oc0sIYhKYOKsNok9F/1Mt0nfylWigL0DYIbaKVfk1vCL8xqUZTc8IvFonL/rpQqqrQGQEXCJCI9UpIExVSr1fdhdduKWLgZyx8nrFHpdSperAioUkpsQ0hK/wz1mpNOTPIx+mdttSYfJ4UVVpct4d/4hsCQlEFFVNo8gS8iKZvN1m+CKBs4ddGETlgy3ae/lmy85PrmWJ+Sv7r1uoWsadCOhzJlq5pGNdICPv+u2ITNtKx8jHRq0CMpSXb6uVv9kNEJSrdDYjMvR1X09PT0S/dZvYZ+//p+3jJLoEc+8hirtYF+HBDVfqTp/x8MSVWGISkDS/A/E39VsxZF4YSsD8gUnlRIVVL16eomSVaAdTF+c4fcNCU5kfDlnCU9orWKrPRf+QBU1Mvx6UzxEZXqg4B1Wo/nbRVByAiQkVNTUxPOOeccNDc3I51Oq7QfySqZTPZ7DSuCkn/XB9U+p2pRkU6SlT4n/XV0kpJRmv54g/4wJGXQD/p/Ov3CX/0AykQUVgqmauk/GTVZbRJb0n9ePRLRP08Zreq1E6vUkVVK1eFwlP34KJVKZSIKRlhWaahK30e1aEo//8bGRhxzzDFoa2vDqlWrlKksU31U91k9P7GhP1yszpXPW22tWnRXKcKzSiVa/aDYEv+9DwaGpAzKwP9EjKBIQrwPQNk66ySySdNKNCEv8lc2oyeZYuLzb47pvUqoFIHIVJm0zrFSnum/2uUaU3m0sSqVSuov8Pl3KkUxJC9u2JUiW6vNlufG71vWn5LJpIqg2CNF8UQikbD8bKzSjFbXB/NZ689rdb3S/fqaVfQn1wdKD5q6VHUYkjIoQ6Vfizphyb/6pmkVLVXKza/Lr9TN/T+v/jlbRaa8yFERldJIkqRkqtbpdKrPkrOYnE6nIiX5nANFCfLc9fcgo4VKjhMkXkZS/GuFStHThvy7qBahyfdU7RysHmP1GtX+TW8J/743FIakDPrBipwY4XDzY7qoVCr1q0lVq0NZpQK5gco0VCWS2lj/oTdE/j6Yc6yWJtPVcIVCAZlMpkyKrpO/Lp/WP2OuM1KW6T6OTtGJChi4JmVFspVcJ6QMna4THIRY7TMaTgz0GutzDtU+L33NoDIMSRlUhJ7Ckb/+6IZAkpKbKTekyZMn409/+hMeeeQR/Pe//1WjshOJBLxeL4rFourTyefzcLvdyj2dvTss8HNjtZJF85z085ZY30GG6/uZVTs/eRwJnGPDWZ/J5/NqQB6jjFwuh2QyWZY2tYpa+bySxEhSXq8XXq8XmUwGbrcbmUwGfr8fPp8P+XweHo8HgUAA2WxWudtLQUul9yz/ylHog71sKqwLUQwFmRhCGjwMSRlYwiqSkZGOTHXov5xJWg0NDdhzzz3x2muv4d1331WFcm66TB+yYE9ZdKlUKmsw5V9a98haAKGnXuR562NH5G05a8iqgXUwn5fVdXk++v0yHaanwkhYnOKaSqWQy+WQSCTUsfzhIM1PJflZkRSfv6+vD263u0ziXiwW1RoJy+v1VmyurkToPD/9Uqmp28rL0cCAMCRl0A8ydQOUz0fimoyspLMApcXxeBxutxtdXV3o6OhAW1sbwuGwcq3IZrMIBAJl86gYQXm9XjWrite9Xm9ZJGWVZtI3apmqdLlcamijHN5IVZtcA75obF2fz83qvCrdR7FJPB5X48UTiQQymQy6u7v7rfX09JSNfKgUSen1QQBqorLX60UwGFQzwHw+H7xeL0KhkIqkfD6f+vwrOdNLM1ppSksHCbpIpFIp9eOEBCzHYPCcDQysYEjKYEBYRSzy1zU3Qlkcl0XxvfbaC263G//+979VDcLlcqFQKMDj8ajNkCRVKBQUSbGvh71UrKXIGo5ez5FrwOckq6cS2dRKUmKdTfZrbcjnpUvqK51zpXpNOp1GPB5HOp1W9Zp0Oo2urq5BkRQAHHrooSiVSnj88cfVD4J0Og23260Iyev1Ip1OW5IUZ4XJuWHyujSqdTgcKmXJFF4+n8fBBx+MCRMmqOhRnq+BQTUYkjKoCBmNMJqy+tWrF8i52Xo8HsTjcRx11FGYNWsWDj/8cHR1dcHr9aJUKiEQCMDr9arN0Ov1wul0wu/3q800l8vB5XIhn88rEpHigGpd/YwgHA6Hisj8fn9ZlOZ2u9V7lVHCUHxuusrOSujAyIIpPUYgqVQKXV1dSCaT6O7uVoMCOzo6VN1Ppvt08uNrOJ1OnHrqqejr68Mdd9wBh8OhiMjtdqtIimtWJCWnMPPxjDxdLpcSXTBFy8gvlUqplOX555+PbbfdFu+//36Zd6M8bwMDKxiSMqgIWRC3IitC9vXIaMDpdCIYDMLn86FQKKC9vR3ZbFaN+eAG6Xa71a95l8ulNlCmo1wuFzKZjCIQbm66A7uVfBv4vKbFjTgYDKrNube3F263W0WDTP1tyIZZjZCkco/nzxSY7B3q7OxEMplEW1sbEokEurq60NnZiUAggKuvvlp9HlYiCZ2kAKiI8brrrlNpVX6ugUAAd9xxB5544gn1ufPHA+9nBOrxeFTqlaRllaJl5JdIJFRUyHQf05usqZlIymAgGJIysIQs9ldTzuljO2S6T6b8isUi6uvr0dXVhXg8rhy4C4UC3G63uk7LHo/Ho0xV2XQqIynd3UIfXidJyuVyqXOjtZMcT8EobajqI7qkXJKnJFeek+wb0p3CmR71+XxoamrCnnvuqdJ1VvUnqzX6LO61115lJEWyfu655xCNRsuiq1gshu7ubgSDwbJaFu+Xa/x8mZ4l8TJKlE3eulDCRFAGA8GQlEEZKsmndYWf7jjBjZa1jng8DpvNBp/PB6fTiXA4jLvuugtPPPEELr74YqTT6bKivc/nUxsn004s8HONkRR/hcviO73tZLMrHS0Y0fn9fuTzefh8vrImU4okqGxb343TqkYmCYnnK103WL/p6elBPB5HZ2cnOjo6EI/H8dlnnyEej6O9vR3XXnst9ttvPxWNplIpy1RiJeKiuIHRjySkWbNmYdasWeqz9vl8mDt3Lu69996yz59RLwUWXq9XfWdyja4S9OTTU3/8riRxGxhUgiEpg34olUrKmZy3JbjpAeWRlF6XcrlcSKVS8Pl8cDgcCIfD2GqrrXDKKafglVdewerVq1XUxV/aHo9Hqd4KhQJKpRI8Hk9ZJCVJimTDGo0c1U2SovCCPVmUurPYn8/nVTQwXJFUJbLSCV46hMdiMYwZMwYzZ87EmDFjkM1m0dHRoRSUupLRKs0o3w/rSSQdpvO4RgPaUqmEL3/5yygWiwiFQvB6vQgEAnjhhRewcuVK9T2xt4o1Rq/XW0a8mUwGO++8M2bNmoW6urp+0ZQUTpiIyqAShpykttpqK6xcubLf+ve+9z3ceOONmDZtGl544YWy+84880zcdNNNQ30qBhsIGT0B5b0wFCWQpPTUVTqdhsPhQCKRUOIEr9eLrbfeGldccQXmzJmjiuhsKs3lcir15/F4VD+Px+MpS9HJsRWyGZbnIImL4ylYM2M9jCRls9lU+oopw6EgKZ0wSE7yr0yL6gTF8RVf//rXcdlll2H16tVYu3Yt1qxZo1KBlQjKSvHHSIrpPkZFfr+/jLgolthnn30wffp0RCIRFdEuXboUb731lvp+vF4vcrkcfD4f+vr6VO1RDjj82te+hgsuuADLli1DZ2dnPw9HE0UZDIQhJ6k333yzrCnv/fffx4EHHoijjz5arc2ZMwc//elP1W2/3z/Up2EwBOAmaDVaXEZSkqAymYxSeFFdZrPZVN2HRqlnnXUWTjrpJASDQTzzzDO48cYblSIwl8v1+6Wey+UUSZGcGG1xY5Q1MZKSzWaDx+NBJpNBKBSCw+EoUwpSZThUkZSu7NNdzGkSy3OV5BSLxVRfGQD87ne/Q1NTE1avXo1PP/0UnZ2dWL58+YAktS7pPimMoCCCnwMJzOVyIRKJwOfzIRwO4+STT1ZR0QsvvIDrr78e6XQaPp8PuVwOgUAA+XxekRRVflI8wahZqvtMFGVQDUNOUg0NDWW3f/GLX2CbbbbBfvvtp9b8fj+am5uH+qUNhhCSkKptIlIKrkvRHQ6HUvvZbDY1L8jpdGL8+PHw+XwIhUJobW3FxIkTEQgEYLPZ0NHRoQiDXnLSMYKbvEwTZjIZ9dq1tbWIRCLIZDJKHk0H7lQqBbvdDr/fr8hQn3HFzX99+6UkeegyeSvi0oUmqVQKbrcbkydPhsvlwqpVq5RbOJV//BFgRVQDkZRskmZvlNvtRjqd7kdSrOEVCgU0Nzdj6623Rn19Pdra2jB+/Hg1B4rRMhu1ZeOu7jYhPxP5HgwMrDCsNal8Po+77roLc+fOLfsPf/fdd+Ouu+5Cc3MzDjvsMFx22WVVoykqhIhKRpQGQwdZj9L7hir1SWWzWeWuzfQZ76cxqt/vVzUVktRee+2Fv/3tbwiFQli1ahUOOeQQFAoFBAIBRSTZbLYskiIxcRMkSWUyGZx11lk4++yzkclklHjjiiuuwF/+8he18bKh12azIRKJKGXahjSY6s4XVlZRJHBGeoykWIOi3NzpdKK1tRV2ux2rVq3CihUr0N7ejmXLliGZTCIWi1UkKCtZOlBek2L/kyQkpj3ZWO1yuRAOhxEIBFBbW4uamhoEg0Gk02lMmTIFjzzyCM444wy88MIL6OvrK/uhkM1mLWXoJC6rupSBgRWGlaQefvhh9PT04JRTTlFrJ5xwAiZOnIiWlha89957+NGPfoTFixfjwQcfrPg8CxYswPz584fzVA2qYKBfuXrdhRsxox6Xy6XSblJ0Qb84n8+nxtAHAgF897vfVeTy2GOPoa2tTakKdZJiferoo49GNBpFNpvFLrvsUnYOdrtdpZ+SySQcDofabNlzRCKUdTErE1oJqzV+Hnq6T/fm4/mziVdeZsyYgW222QbFYrHsvBlBsW6lWyzpJCVJE4BKdbrdbkXUrAnyR4XL5UI2m1UkRQk7FZ2UmgcCAdjtdhxyyCHYaaedUF9fjyVLluC1115DoVBAMBjEmWeeiS9/+cv9Un2yJiXFHwYGVhhWkvrDH/6AmTNnoqWlRa2dccYZ6vrkyZMxZswYzJgxA8uWLcM222xj+TyXXHIJ5s6dq27H43GMHz9++E7coAzrSlIkKFn3YY0KgBIt8PlIUiSa3t5eeL1ezJ07V/2af+ONN/Dxxx+XkQE3fpKU3W7H7NmzseOOOyqi6erqKoukEomE2vDtdjuCwaBqVGXKixspe4B0w1kZUVq5qvOz0lN7cvAf63ayHiUJKJlM4rDDDsOBBx6IxYsXK1KS9R2dpPi6csOX8nRJUiQkikgkSZG4GG253W5FUnyPtKnid3fYYYchGAyioaEBd911F55++mn09vaisbERF198MXK5HDo6Ovr1Tsm6lCEog2oYNpJauXIlnn766aoREgBMnToVALB06dKKJEVFksHIhNyY5aYMQJm3MhKSKR7Wg+QGRuEEe6d+8pOfIJfLIRgM4u6778att96K6667Dttvvz3S6bSqcwSDQaxdu7bs13o2m1Ukddppp+Hb3/42Lr30UnR0dKjzovMFZelyA5ZGqtKfTl54jBzNLlNeTEky5ZXNZtUIjmQyqcxYu7q60N3dXTZbSfYWyY1d9mIRck2/T/+e5DFUTQJQvojyWEbB8vOimzqjoVQqhUKhgK985Su49957VQ2R76Orq0s1cfN9MTU4VLJ/g80Xw0ZSt99+OxobG/HNb36z6nELFy4EAIwZM2a4TsVgmKGnnUhW0lWC0nEW8Lmpc1YUAJXy4y/2vr4+jBs3TtkZbb/99thuu+2www47YIcddlAbHVNn3PwlSXHjbWpqQktLC3bccUesWbNGpdbcbnfZ31QqpWo3unkqiYsXiisoLrH6DPRZW1JUIFN8jOZ23nlnJeqolBqTm7r8a7Wmf0/8yx8NPFc6bkhHDn4fMk3J1CkAZSPF7zEQCGCHHXZQUXVra6sSq+i2SJKgDAyqYVhIqlgs4vbbb8fs2bNVqgcAli1bhnvuuQeHHnoo6urq8N577+H888/Hvvvui1133XU4TsVgI8CKnPL5PIAvRl7QoojRAHttWOPgJsbrlElLP7n9998f06ZNQ6lUwmeffaZqO1LWzb+8TiWfw+FATU0Nbr31Vrzwwgs455xzVGTAPh+KKZiSpIEqjVXppE4vPDnOg0QlIymm9KgsZMqxp6dHjd1gJNXZ2Yn99tsPv/rVr9DW1oaenh61wTOakmIDPVqSBKnbWOnflZ6SZKpSOswzZUjykjUpm82mokKq/zg8MRAIKJJqb29XqcnOzk6V2iRpkaxMJGVQDcNCUk8//TQ++eQTnHbaaWXrbrcbTz/9NH7zm98glUph/PjxOPLII3HppZcOx2kYbGToEmubzabEC1JOLtNipVKpzLuPJEEhA90g2PPEniu9T0r2TkmSkkTkdDqRzWYxceJEXHzxxXjppZewcuVKJBIJFQnQzkkSFFNcUtrNjZzrlaIoPYJiT1QmkykbnU4C0+tXMs0nU6W6dLtSik+HPE9+Z/xcpUUUAPWdAVAGv1yT5MKma6ZsSVLd3d1ltTaSk0zJ8geBISmDShgWkjrooIMs/9GNHz++n9uEweYBK8EAgLIZUCQMbrKs/bAORJLq6+vrN6pDJylaIPFxco2bH3u16IbgdDoRj8fR0tKCH/7wh/jkk0/wn//8R5FUMBhUwgq+PtOOlNZLs1sA/QiDRKYr+tjgSvk9RRyUafM9SEshqx4jq9fj518pagLK3UIkSUklot4TJ0mKKU9GVTIF6fV6lQSdrQa9vb3o7u5W7zeZTPbrnTLCCYN1gfHuM9hgsL7BDZrpN0YZrFkwIiH5sAAvxz6wmVTOfGI/j/zFL336pEWS7t3ncDiUXLpYLCIQCKjz6OzsVCMwpHCCNRlposraC6M8kpSMPuSGT9IhOUmzVUYYHR0d6OzshM1mw+23347m5mZlNMsmWUYfei1HuroA/etQVqlAfgZSjSil6lLcwnSm7Lmiso9SdZrJSnslthn09fWpacOMHnO5HGKxmCIzvh9jj2RQDYakDIYEelMpN1G5gQNfjJ0ncbH2w82XZMZ0G2XRFF2QpHTpuh5JSUNZAEgmk/B4PEilUsr4dty4cZg8eTI6OjpU7SeVSqnUH8+ZmzsH/DE6k6lL/XPQe6TkOA4SVzKZREtLC5qbm7HddtvB6/Wira2tqlOD3qArX9fqun6MfpF9a1bPwx8f0vlemvYyymRtUZIUoycpmrASThiCMqgGQ1IGQwIpaeZGpyvi2EMlxQdSjECBQqVpsDpJyZSTHH8hr7MORRUhIymXy4WzzjoLp556KmbNmoWuri7VH8TH0TtQRk8kCJIfN2V50d3gSUjSny+RSKCzsxMXXnghjj76aLS1tSkJOkdb6OMt5KDAaqm9dfmuZJqNJMRUnpTe9/X1qTStjH75/fB7lN8ZP4d0Oq3Om5ET/0qyMjUpg2owJGWwwZC1GN4GykmKaSRKuplOIglxg6P3G4/hGvt1AKiNWvrA8br04WMKj3UTh8OBWCymnM85rfeCCy7A+++/j6effhp+v1/1VpFw+T5IlBQOyN6oasIJKZpg6qulpQVnnnkmdtppJ1WbIpGRnGTzqyTkStGHvlapTsXoqZIkndcZOTFy5HvmX6vvUZIUlZa6sEWa7cofNQYGVjAkZbDBkBsfNz+OZJfmsCQpbnAswDPSYjqNEReP432EbtgqN2+5mZOkSqWSqitRJJFIJNTU2RNPPBH/+Mc/8NBDDyEUCsHpdCozXPnaFGIwvaUr/XThBDdlSVRMKTY0NGDOnDno7OxELBZT4gI5ap0qOCvXcKuNvVoKUF/Te7z07433SRm6nL8l+8XYL8UfIDxHzvhiGlZ+LlamvgYGVjAkZbDBkJsmNzhK0KX3nbQS4nVubvI6iUmmnTjyA/ii2G/lps1UIC9M93Gj9fv9ZZEAHSfa29vR3t5eJgyg+pDPyxQfU5KMKmSdTYomJDHJOVHd3d3o6upCe3s7Ojs7y/qIOEdKpvtkjUo29Vb6Dqp9T/ptRk38jgir707aROl2UPJ++dzyc5Hfk5WM3sDACoakDIYMusJMN2DVve7kr3f5q13+Sud9eiQle5bkhidlzRQ7uN1ulTqTKTR5u6amBvvtt58SLjDlSHIgoclf/5UiG5n6sxKT7L333th1113LHNL16EKPECtJz/XPfl2/Iwmr1OG6fHeV7pefAV9P+grq9xuiMqgGQ1IGQ4bBbDbSsFT+UidJ6b/YuQZ84VMnNz65JsmD0Vk6nYbX61X2Q4xyPB4P0uk0vvrVr+Lb3/42zjnnHDz77LNqsjDl8hR+6Eq7wQoWvF4vfv/736O+vh6rV6/ud/6VyM7qvvXF+hKcgcGmgCEpg00KPU2opw7lVGC90K9HFpWcGOTjKx1r9dxWx+jEIUFilaIPOWSQRra//OUvsfPOO+OII45Q9lE0w5UqPo7UkDZMlcaDGBhsrrAPfIiBwfBhoBTQhl6sXqvaZV0ISj9fQtbbpPqNoz88Hg8cDgceeeQRPPXUU4qAAoGAUhtSccjGZklQMpo0MNhSYEjKYMRAT21J0pA1GmmyapUSq0R6QH8bI71mpBf2rdasSAwoj6SkY4bP50MgEEAoFEI4HEY0GsWiRYuw//774/nnn0dtba2afBsOhxEKhRAMBuH3+9VcLRKVFCiYqMpgS4BJ9xmMCAxUb9FdHdYlcpLHyuddl2jKKi2oP58OSRyy90vaPnm9Xni9XuTzebS3t6NYLKooikMfpWch+8RIUFJxV+mzMjDYnGBIymBEQG62rE/RkgforzarFkFVen55/LpEbFZRVLXX0lN99LcrFArw+/2quTUcDqv6U01NDaLRKNLpNEqlElKplJLJ+3y+MrJin5KJngy2JBiSMtgk4EZv1aNDApBjJOS6TN3J59LJZ6DXlopAq1SeVcpvoNqUzWZTtSjaPnk8HpXyKxaLCIVCiqQeeOABvPXWW/je976HUCikeqPy+byqTbGWJdN8EpVIy0RZBpsDTE3KYJPCKjLRIxcrgqjWO7Qum/NAggmr2tW6pBcBlEnpZX1KuqpzQOB7772Hv//972hra0Mul4PX61WKQF04YUVSlfqXTL3KYHOBiaQMRgRIBEyZEbofoDxeTxGuy/NXuzCqGijlV+m1dOEEI6hS6YvBgDTIdbvd6nHxeBzHHnssvvGNb+CnP/0pkslkWYqQSj+T7jPYEmFIymDEoFokZEUM1cQW65LuqyaYGCiCsjrXasIJRlEcwFgqfe4nSFfw1tZWFUnpEnQr4YSBwZYCQ1IGmxw6oQwUFVV6Diui0tV51VKI1WpTA5EaYC2ccLvdKBaL8Pl8KiqUDuocVUHpOXumstmsIixJVNKdo9L7NjDYnGBIymCTwopYZLpvMM8zUPRkRVD6WiWCqiZLJ/R0HyOoUqmkoieSk9vtLrOFCofDeP/99zF79mwcddRR2HHHHdV8K9anGE0ZGGxJMP/iDUYcKqXXBroM9rmtiKm2thZNTU1lqj59NEgl8iKkcIJRFclG9kpRQME+qa6uLjz22GNoa2uzFE7ISIow6T+DzR0mkjIYcRiO9BUJRU7ylZNzOVJ+/vz56OjowLHHHot4PK6mzgJAIBBQA/6y2SxcLpdKy+VyuTLyA6DmLJVKX4wQods7RRAyksrn8wgGgwiFQsp1IpPJIBAIwOfzqfQf5emV0o5A/+ZnA4PRCkNSBlsESFAcj8ER5nRDd7lcSCaT8Pl8iEQiOOKII7BkyRIsWrQIPp8PdrsdqVRKkRRd0jnWg0RWKpUsh/np9SrWwCiSIAFJ4YS8MPpic3AulytTJEpyLJW+GF4o1wwMRiMMSRls9pAERXJiBOVwOJBIJFS9yOFwIBQK4fLLL8d9992Hl156SYkfvF4vSqUSnE4nfD4fACAcDqu0HAlBjvQgkUiCcrvdKmXn8/mU3Fym/nw+X9ka+6oCgYCKBPV0pJWUvpoC0sBgNMCQlMFmD27aHOfucrnUXCmKGnp7e9WxmUwGNpsNe+yxB+666y7ccMMN+Oijj9TxnDNVKpUQCoXKoiM6SehGuJSll0qlMsNYRkSBQADpdBqBQAA33HAD7r//fpx//vkq7SdTf8ViUQ1IpEJQEjHTinw/fG8GBqMRRjhhsFlD1m30dB9TdZlMRo1rTyaTSCQSSCQSiEaj2GeffeD1esvGuXOkuz7pl1N85YTdSuk+aZnEv7x8+OGHeOWVV2Cz2SxTgPJxvPA5ZU+VPtLdwGA0wkRSBps9GEmxlmO321XKrbe3F06nE5lMRt2vu0S0t7ejvb0dbrdb9Tp5vV4Ui0UEg0FFOACUxFwXNTCSIlHx/t7eXhWRZbNZJZpwOp0IBoMq/RgOh5HL5RAKhdTzMmLL5XKKGO12e5lLB9878IV7h4HBaIIhKYPNHjKSYiMt034AkEqlVIqsVCop9RwjmRkzZqClpQUvvviiElekUik4nU6k02lVP2IvkxWkl548hkIIKYzwer1KVcjUIudSsYbFSb56ek8qC00UZbA5wJCUwWYNXXqez+dht9uRzWZV1ONyuZTQoVAoIJPJqBqTy+XCcccdh/b2djz77LOKpJLJJBwOB1KpFHw+n5Kxu1yusgm9VtflGtODJDqKI0hODoejn4BCRoRWJEWZO2tXhqgMRjMMSRls9tDTfcDnkUaxWFTCBbfbrRR/FEUUCgXY7XYV3fzpT3/Ck08+ifvvvx+BQAAAkEgkVKTDtJ90LZemsHKNtSNGPsFgENlsVokkYrEYvvOd72Dq1KmYPXt22f2srTkcDmWrJN0oCoWCihjZAGyIymC0wpCUwWYPXTjBCIbpPYfDUSbnzufz8Hq9cLlcCIVCSCaTcLlc2G233fCf//wH6XQamUwGXq+3n2hCviajMaC/cMLhcJSN8NCFEDabDe+88w4aGxst+6ZYH6OakO+RkZURThhsLjAkZbBZQwoUAChhAYUHTqcTuVyuzBDW5XKpnipGR729vQgGg+jq6kJ3d7eartvT0wO32w2fz6eOYwpRRjiMpDit1+l0wuPxqHNipEThRC6XQzgcVhcprCAZORwO5PN5FItFFTExMmPflkn3GYx2DFqC/uKLL+Kwww5DS0sLbDYbHn744bL7S6USLr/8cowZMwY+nw8HHHAAlixZUnZMV1cXTjzxRITDYUSjUZx++ulIJpMb9EYMDCpBF07obhNSeh6LxRCLxdDT06P+xuNxxONxJBIJ7LTTTjjvvPNQV1enJOlMEzKqYp1Juk7ISEofgrguF9asKJ6QsnRpQiul6HodzMBgNGLQJJVKpTBlyhTceOONlvdfddVVuO6663DTTTfh9ddfRyAQwMEHH4xsNquOOfHEE7Fo0SI89dRTePTRR/Hiiy/ijDPOWP93YWBQBXIMvOyRIrmQpOLxuCImXrq7uxGLxRRRTZ48GT/+8Y9RX19f1jeVyWQUSbEmVK1Pyu129yMheeEaxROSqEhW1YhK9koZkjIYzRh0um/mzJmYOXOm5X2lUgm/+c1vcOmll+Lb3/42AOCPf/wjmpqa8PDDD+O4447DBx98gMcffxxvvvkm9txzTwDA9ddfj0MPPRTXXHMNWlpaNuDtGBiUw2pWFEUT3MCZCuRmLl3LKWJwOp3o7u5WCr6uri50dXUhHA4r4rHb7Up9x79U2kmCIhhVSX++QCCgUn/hcBivvvoqDj30UJx77rmYPHkyuru74Xa7leovm82WiT7cbrdKX5IAKbBg7cpqPIqVfZLV+rq40Bv7JYOhxJDWpJYvX47W1lYccMABai0SiWDq1Kl49dVXcdxxx+HVV19FNBpVBAUABxxwAOx2O15//XUcfvjh/Z6XhWkiHo8P5WkbbOZguo3XZU+UHmXwOjd1K3eKTCaDiRMnKheKRCKhhhWS/PhabPJlvUqmAOXrM03HVCAjrY6ODnz00UfK6JaNxhSAuN1uJZUnUTJrwedk7xfrVzw3+flYEY5uWltp9pZc05/bwGBDMaQk1draCgBoamoqW29qalL3tba2orGxsfwknE7U1taqY3QsWLAA8+fPH8pTNdhCYbWRyj4j3mbKLpvNIpVKweVyIR6Pq8hn3rx5+OSTT3DssceqGlShUFAefMFgUPnskVh4YaOwz+dTry1tknw+H4LBICKRiHKUuPbaaxGNRvGXv/wFtbW1CIVCSCQSyGaziEQiyGazSKfTiMfjyOVySCQSKrqKx+PqvViN99DdL6zWpC+glVegVEfyB4CBwVBgVKj7LrnkEsydO1fdjsfjGD9+/CY8I4PRBn3T1CMe3m+328vk3FJkkU6n4fV6kUgklArv/PPPxzvvvIN33nkHdrsdmUxG9Vj19vbC5XKhr6+vbIAhVYNWLumMoHw+HwKBAAqFgqp5hUIh1NXVqefiHCuPx1N2frlcDl6vV0V9Xq9XvQ8rt3QpLNHvZ/RHEqaqkJ+PtGHiZylnWRkYbCiGlKSam5sBAGvXrsWYMWPU+tq1a7HbbrupY9ra2soe19vbi66uLvV4HczZGxhsCPS0n1VUpRvSsgFYjvegEjAcDuOMM87ALbfcgpdffhnd3d3I5XJl/n0c70GRg8vlQj6fV31OMu0na1SsT/X29irlYCQSUfZLvb29cLvdKBQKZTUpkhQHM2YyGTUoMZvNKgKSEZLVyA9epwCE6UI2CZOggC8aow1BGQwHhpSkJk2ahObmZjzzzDOKlOLxOF5//XWcffbZAIC9994bPT09ePvtt7HHHnsAAJ599lkUi0VMnTp1KE/HwKAq9LoQ8EXqT9aiOHcqFosplR5NaQHg4IMPxj777IMLL7wQK1asUMSSyWRgt9uVTyAVd6FQSEVY0s+P0RHThNFoVJnR2mw25HI5zJ49W6Ugr7nmGuyxxx4q7UeVYi6XQzweVyQVj8dVJMXIiCm7SiM/eMlmsyqizGazyjaK6UO73V6mZDTpPoOhxqBJKplMYunSper28uXLsXDhQtTW1mLChAn44Q9/iJ/97Gf40pe+hEmTJuGyyy5DS0sLZs2aBQDYcccdccghh2DOnDm46aabUCgUcM455+C4444zyj6DYYOMoqzW9BqVjDIqydYBwO/3IxKJoK6uDrvssgucTidaW1uVaCGdTsPpdCKbzZYNQ5TOELo8neIJr9eL3t5eBAIB5HI52Gw2fPbZZ+r1//vf/8LpdKr6UyaTwbbbbouamho4HA6V6mPTLwlHDk3kX/06z5HKQJri0p0DQFlNykjdDYYLgyapt956C9OnT1e3WSuaPXs27rjjDlx88cVIpVI444wz0NPTg3322QePP/64Gq8NAHfffTfOOecczJgxA3a7HUceeSSuu+66IXg7Bgb9YZXmk3ZB+pBAKRTI5/NqCKLH4ykbyZ5Op1EsFpFKpRAOh3HZZZfhgw8+wHHHHac2fIoj2PPkcrmQzWbh8XhQKBTgcrlUZMZUn9/vR6lUUlJy3h8KheDxeFTK8frrr0c+n0csFlMk+Le//Q177bUXksmkiuYSiYSqbZF8ZBqPxJTL5dSaFI7wsZS3A5+LnehwQWsmRmOs61X6LgwMBgNbaRT+q4nH44hEIgBgfrkZrBOspOaV3BjsdrsSLoTDYUQiEfh8PkSjUQSDQYRCIUSjUfh8PtTW1qq1pqYmZDIZvPbaa3jllVfwzjvvYMKECYhGoxg3bhyamppQV1dXtsaep56eHmSzWTVwMZPJoKenB5lMRtXAMpmMIiSr1F42m8VBBx2Euro6JJNJ7Lvvvpg+fXoZSTGSs5pFxetSfp9Op5HL5VT0ls1mEYvFkMvl1DnwvOUASKvalFWPlsGWC/5biMViCIfDFY8bFeo+A4OhgHR/kLetjmO9hhs2+4/o4sA0GGsyjIoikQhOPvlkJBIJvPPOO8hkMnC5XMqRQqb85EYuLZPkWHuZRuOgRqr3qOpjdJbJZPDcc88pCXpNTQ0OPvjgsl4tr9erIp/e3l6VApTXpUjCbrer8SOyNgZ83kvGtKYxszUYLhiSMtgiUImgmPqTkASVzWbhdDpVHYZKP0q/qajz+/3I5XKIRCJwuVw4+uijccQRR+DCCy9ER0eHisL8fj8ymYxypGC6jyk9psUptMjn88qFIp/PI51OqxQkI6lkMlmmPGR09dRTT+Ff//oXksmkesy5556L0047TaXxJHGSpKSrO8/X4/GoXq6+vj6lYOSoECoOTV3KYKhhSMpgi4OVLZAO2azKERzS0ogKQM6k4ngP4PN09JgxYxAOh9Hb21vm68cIRvZIAegnnGC9R86eouycBOlyucoskEgkHIiYzWbR2dmJRCKhSOrDDz/EK6+8UqbU23bbbTF27NgySTul8iRx1qo4e4s9YNLQ1hCUwXDAkJTBFoVKhCQ3Vzaxyom6JBVGGYywvF6vcoro6+tDOp1GOBxWTbs0qE0kEsqQVkYvTPnJ4YgkBcrUpdBBd8Jgao/pPt2FQkZc6XQaTz75JB577DFVn0qlUvjVr36Fs88+G8lkUvWFkVRdLhfS6bQiTaoGAah+L5n2MyRlMNQwJGWwxUMv6LP+QpUfU1rcqHmR0U0+n4fP51P9VIFAAG63GxdccAGWLVuGRx55BLFYDD6fD4lEQo2gJyHJPiM53oMRChVzjGIouGC9TI+kWKeiqjCfzytSy+Vy8Pl8Kvp78sknsWLFCiSTSey4444444wzVNRHImZKlJGcTPfZbDZlucToi83C+mfMtKnVfev6XRlsWTAkZWCgQbpOMO0HoF/tihs1SYYybRrPhsNhHHLIIVi0aBH+/Oc/q3Uq5dhDBaCi8StfhyQl02u8zRoRj+HARfZbkczYr0VHCtbEFi1ahIULFyKVSmHatGk49dRTy6TqbECmywR7xJgupMS+t7cXXq9XnY8kH2n/pBvZSh/BdfluDLYsGJIyMKgCkhVHbjCdJafgcoOlBJuScb/fj+7ubnR1daGtrU1t3Jy863K5EA6H4ff7yyImXifxyFQg1+jfR8slii8YbVEFKFNyPp9PrVEkIVOPmUwGy5Ytw7e+9S2l8Ovp6cG0adPwv//7vwgEAkgmk3A4HOr9eTweJBIJOJ1OBAIBeL1eJJPJMsd3ClFk8y/JXUaP+ucpvwPdW9GQ1ZYDQ1IGBhWgO4LLVB83VCkRl5J1OfnX7/fjkEMOwdq1a9XkX86nkmPsKZzQrzNikgTG6zyO5+d2uxWB8hgAKiKiQo8GtxRI9Pb2qjU6qRcKBcTjcaxYsQJPPfUUtttuO9TU1CCdTiu/vnw+D5vNpupUJHLK7HUPQN3Vgp+jXhPUvRWt3EIMtgwYkjIwsAA3ed1wlgTB1BfvYz1GH0kfi8Uwbtw43HLLLViwYAHuvvtupfpjatDv98Pr9aoeKZKIz+eD0+lUfVNS+cfxHvJcWUuj6o6EyUiKbuhcp9ychMLoiuIJPmb58uU455xz8Nvf/hbf/OY3USwWldEtG5/7+vrUFGHWwNgwLJuHZZ8Y1/h5ylqg/FytHCxMRLXlwJCUgUEFyOiJv+Zlyk/fPNlYy9qTz+dDPB6Hw+FAd3c3vvWtb2HKlCm45ZZbsHbtWtXky4GJbrdbjYZ3u90qMgJQpjTkX6YhSVJSicioiBMEdEcJRjQkI919giRGgYfT6cS9996LN998E+edd57yBASglI1co7hCEiCvU/rOyE72VumCFd62GrxosOXAkJSBQRVwY2R6S46DB6BuA+iX6pOjMlKpFLbbbjvssssu+Otf/4rW1lZ0d3ejVPpi9hS9AblJkwRcLldZipG1Kd7P85KO6iQAPk9fX58iLilpp1qQr6/PjmLE1tvbi0WLFmH58uWYPXs2IpEIIpGIUhem02n1GbAuxmhOphqlb6L8PPXoySrlRxiX9S0LhqQMDCpATz1Z+f9JmyRKwFOplEp9cSYUCSgajeKmm27Ca6+9hgsuuACxWAzBYBA1NTXw+XwIhULIZrPwer0qrUahBElJWijx9ZmOZG+VvMhRHNLxXK7JUR1c6+3tRTgcRjKZhM/ng9/vRyqVwsknn4xvfvObmDdvnnqPAJSQwu/3KwEJSZtOGQ6HQzUg056JUZMkLL4nPYriusGWA0NSBgZVIDdHRjhWSjNGRLpoIpVKwel0IplMKuIKh8MYN24cvvnNb+KDDz5Aa2ur8gBklETvPofDoWo+TDnKY2SajKIKqun0AYa6kEE/jtelEk9GUrwdj8dV79fOO++McDiMdDqt+qMYtQFQPVU8P9knxV4qKfywqjNZiSsMthwYkjIwqAA9BQWUp5pkdEXJt8PhUP1SLpcL8XgcAODz+VS9yOVyobm5GVdffTUuuugivPfeeyiVSmrYIQAlRLDb7ao+xNeTKj9aJ/E8eV2OhZd1HjmF12pNEhvrTIFAQEVsFF988MEHOP/88/H73/8e06ZNU++L50djXQ6N5OcjRR5yzAfPUa4T7EfjZ26wZcGQlIGBBSQRsf7EzVP+2pdFf1mfYXqO86QohODwwlQqhUKhgMMPPxy77747fvnLX6K9vR3AF+PYqe6TAgTpRMHzkpGHHo3Iv7xPikGkw4X8y3W/3696o6g47O3tVeq+3//+93jmmWdw0UUXqf4skhSnF7OhmOPrSVKMquhWIT9Tq++A712m/ExtavOHISkDgwrQN3oZOcneHa7Rw06m/BhFMO0HoMyVYdKkSZgwYQLuuOMOtLW1qfoP+5co2SZpyMiNsOoxskqNyYhQkpQuVJDkxaiN5wEAiUQCwOciif/+979YtWoVTjnlFDVXi7UnpgcBqOvZbFZFaewDk64ZekOv1efM64agtgwYkjIwqAIpmpCwcvymsCGVSpXViXK5HEqlEjKZjBq5QQ+/eDyOUCiE2267Dc8//zx+/OMfq9pNIBCAw+FQlkQUGFA0wfOwciCXa7qdEwBLMrB63xzC6Ha74Xa7EQqFAHw+qI41tkQigaOPPhpHH300LrnkEiUeoRw/mUyqeVhSck53eVorSQLVz1WmOkmiBlsGDEkZGAyASr/Y9XUp35bRFFOAUlLOzZprtbW1mDRpEk488US8//776OjoQCqVKvMClI7kVMfxuuyTkr1UVhd57jJ9aeXqwIgqEAggnU6jr68PwWBQpS7T6TSKxSJisRgWL16Me++9F1OnTkU0GlUNz8AXjhck7FKppIYnZrNZdS403GX9TjZMy3PVU50mqtp8YUjKwGAdYBVp6OCmSpsju91e5hbBY5j24vW+vj6Ew2FMmjQJv/jFL3DBBRfg448/Rk9PD2w2mxqqaLfbEY/HVYpO+vixb0rWqqRknX/1lJnVdR4rI6xMJqMIhqO+2aTL2VXvvfceXnjhBdxzzz2YNGlSWb8Ur7PHjCQkJxzzvejOFPyMent71bnKSMoq0tW/K0NioxeGpAwMhgiynkPJNoUSjKhIWNxwnU4nisUiQqGQqtOceuqpOOigg/Czn/0M6XRapfri8biKakKhkCInr9erLJO4xqGIck2fWSX7vPQ1KfuWnn8kTMrYs9msSmnytX7zm9/gH//4B+bPn49QKIRgMKiiQBrRBgIB1eTM9CAd2vm5sSbHNV5nw7AccSIjqkqkZIhqdMKQlIHBEELK1mUzLZ0XZI1KEkwqlVL1na222gotLS0YO3YsOjs70dXVpdwgfD4fMpmM8uTjvCeO5WBDMb0B6QeoN//qTuskUBIV8EV0JR9DsuJrUb0YCAQUcXz00UdIJBJYs2YNisUikskk/H6/cqDg8zKSoiqSnoNc54WiFOALwQfTnVbOFNXqbAajD4akDAyGCLIpVhq1AlBCB/7yd7lcauPlyHaSV19fnxJTPPbYY7j44ovVxN9UKoVAIIBIJKKmAnPAotfrVcpARipcI7lw4ydhSTKQVku68IJkR0GHJAiv1wsACAQC8Pl88Hq9yGazOOaYY5BMJpHJZPDnP/8ZkydPVqM9EokEPB5PvxlcdKbIZDJqjAgnBlMpyfQhPz/5w4Dfg05WumGwweiBISkDgyGE7EOSFkSMFgCUiQNIXslkEgBUlFIsFuH1ejFhwgTMnj0br7zyCtauXQu73Y5AIKBGgFBxRzLiQEOm49hbRf8/Of5DbvhSOCEjK9k8zHQeLZ6kLx9l53yPmUxGSfLtdjseffRRrFixAgceeKAiQ2njxHQlJfxyDIn0H2QtioQuCUlvtJbficHohSEpA4Mhhk5QAFRExc2T5CEbd7lZ0yLJ4XBgwoQJ+PGPf4w5c+bg/fffh81mg9/vRyKRQDAYVNEIvfW4xtSgNHwtFouKCIrFoupL0hVzUtwga1KMxuieQZLg87AO5nA4lKycKce77roL22+/PQ4//HB1HAAVhclUJZ07SFJ8DSoNAaiBj7IxGSifPSXraqavavTCkJSBwRBB/rInQTFqAqDWOEojl8upERl0aaCyLZFIIJvNIhgMIpVK4YwzzsDJJ5+Muro6PP3007jlllsQCoXg8/lQU1OjCCocDsPr9aJQKCAQCCgrIqoMGQ2REDkKRJIUU35AuXDC6/WqWpKUvdN9IpfLIRgMqroZPQuDwSDcbjcSiQS+853v4JhjjsFRRx2l0nwulwt+vx/pdBoej0eNPOEoE9agpOEsBSlck2NLJMlK81qD0QlDUgYGQwhZA+GGCUAJBvhrnlEBzVdTqRQAqFoRU1xUATY3N8Pv96O2thYrVqzA2LFjEYlEYLfb0dHRUeYmTtEEgDIZPKMPmRpjhCJdx3WLIimaYHQkCZnrjNL4WkxxsnE3nU7jvffew+TJk7F8+XJ0dnYCAKLRqDpXiiaoaGTNi9Ege8xkGlCHdEs3LhWjH4akDAyGGNLpG/jCi0/aJnHTLxQKKj2XTqeRy+WQyWTg9XrV1N5YLIbu7m54vV50dXVh8uTJuOuuu1BTU4PVq1fjsMMOg81mQyQSQTqdht/vR29vr7IoKpVKKkXHmhfrQTxPEiajDxKQjKRoeAtAiTI8Hg8KhYJKLebzeQSDQeXZl0wmEY/HlQTd7XbjsccewwMPPIDOzk7suuuuuO+++xAKhZBMJuF2u5VUnYpF1tJIXMAXfn+MVmWKVYoojFBi9MOQlIHBEEH2SRHSlogbKZt+GYEwypL9P/T304krm83C7/cjHA4rovvud7+LhQsX4o033lD1LI/HAwDKuRyAiqgYQQEo652SEVWlSEpGYLxwXhaFFDSiJRHKmpWsYXm9XnR2duLGG2/EV7/6VUyePFlFSRwlAnxBSEwH8hin01mmQtRra/zMdccNQ1qjC4akDAyGCNK+h7cBqFoJazgkBW7wTJ/l83lFWhyJ4fV61VwqNr/Soqi3txfBYBBnn302br/9djz77LNK2u71ehUR+v1+5VzB82JaTvZJkTzlzCwZSQFfuFewZsSojCa4lNPTSJYkVSqVykiK6bpEIoHf/OY3uOSSSzB16lQVZfKzIDmR1NjYq5vT8jOWBMzzl55/pjY1+mBIysBgiKA3lbJnyspLD/h8w2dDbjabVUICn8+nJty63W74/f6y6+yTisfjCIfDKBQK+MY3voH7778fl19+OZYtW6Y2b5KDPj6eqT/ZrCu98nj+jFYYobA2pCvrZJqNPU4US1BxSJEEHSekEe1f/vIXPPfcc7j66qvR2NioPh8Sls1mU03MctQ9I0Gp5JPfhe6gYTD6MGiSevHFF3H11Vfj7bffxpo1a/DQQw9h1qxZAD4vDl966aX4xz/+gY8//hiRSAQHHHAAfvGLX6ClpUU9x1ZbbYWVK1eWPe+CBQvw4x//eMPejYHBCIAeUQHoV8QHvijkMxLQp9ZSpEBioRiBaUGm7zh+vqWlBTvttBOKxSK6u7uVrJumtD6fTz2/jKS40dOV3O12q1oPoxI9spKkxfslMUgZem9vrzKR5VqxWEQ2m4XNZkMul0N3dzfa29uxaNEiZLNZ1NfXKwsk1ru8Xq+KMElSVEjKGpo8X32iscHow6BJKpVKYcqUKTjttNNwxBFHlN2XTqfxzjvv4LLLLsOUKVPQ3d2N8847D//v//0/vPXWW2XH/vSnP8WcOXPUbY4AMDAY7RhMsZ5qNooAWLPiVFuXywWv16sGKdJhIpPJAICSejPCuOKKK/Dxxx/j6KOPVps8G4A5rJBNvx6PB8FgENlsVhEBa0tMNVJ2rkeCur2SvFD0QJk4U3GMwKgCBKCMc+lEcf755+MrX/kK7rrrLtVAzM+DKVOueb1eZVBLbz/pQC+HRMrIz2B0YdAkNXPmTMycOdPyvkgkgqeeeqps7YYbbsBXvvIVfPLJJ5gwYYJaD4VCaG5uHuzLGxhsdtAl63KMBwUVJDJGHhyo6HA4kEwmFTF4vV4Eg0HMnTsXb7zxBt588004HI5+JJXJZMpqXCRCEhfXfD5fWbTF+hTXWNPi/TxnKbbguVERSFJhfxgbnpnSW7t2LX75y19i3333xc4776x8Cvn+WduiWjCbzSqz23w+j2w2q4hbCinkEEaD0YNhr0nFYjHYbDbVC0H84he/wJVXXokJEybghBNOwPnnn69+XenI5XKqYx8A4vH4cJ6ygcFGg5XHnJRTs++JKT5u/lT8sa6jk9Tpp5+OfD6PF198UQkP6ELh9/tVJEVRhs/nQzabVc20VBPSGJbRDx3W9TXgC6Ug0F8VyOiHkRqblklSjHAymQy6u7tx0003obGxEbvvvjui0aiKpEhSfX196n1zjAiVkNI8l31mcmy9wejCsJJUNpvFj370Ixx//PFqBg0AnHvuudh9991RW1uLV155BZdccgnWrFmDX//615bPs2DBAsyfP384T9XAYJNCd6uQknSC6StGFGxypVydj0+lUigWi5g5cyamT5+Ouro6LF26FD/4wQ+UrVEkEoHH41EiBp/Pp9wqZCRF81o6nrtcLvWXfVKy30pGUjKCYp+WPuyRKTtpt8TU5h//+Ec8+uij+N3vfodx48bB5/MhGAwimUzC5/MhlUqhp6cHyWQS2WwWsVgMmUxG9Vtls1lFTtL/z2B0YdhIqlAo4JhjjkGpVMLvfve7svvmzp2rru+6665wu90488wzsWDBAtXfIXHJJZeUPSYej2P8+PHDdeoGBhsdeiTFyEeXUDPlRod1RhKs31BM4fV6EQqFUF9fj9raWuTzeUyZMgUejwdutxtLlixBKpVSM5toycR0HGtSlJT7fD4VCQFfNCwzMmHTrT6HSnrwScl6sVhUqj2KRJjK5ATfnp4exONxLFy4EOFwGN3d3WhubkY0GkWhUFDkSJJjnxdTplQisu5noqjRiWEhKRLUypUr8eyzz5ZFUVaYOnUqent7sWLFCmy//fb97vd4PJbkZWCwuUASFDdYrvM661KMpKQHH0UDfX19ynGCbg+pVArBYBC33nor/H4/AOCAAw7AypUrEQ6HVZ0qFospcmMkRYf1YDCIYDBY1r/FSI+WSJTUkwzk/CmPx1PWMCwVhVIcQgNdphWTySQuvPBCpNNpdHd349prr8VRRx2l6mnBYBA9PT3KpSKZTKq0HqX9VAFKl3aD0YMhJykS1JIlS/Dcc8+hrq5uwMcsXLgQdrtd9UcYGGwp0GtSMnqSTbUA+kmt6cXHKIbHMipivSmXy6mmYJrBnnbaaUilUgiHw/jHP/6B119/Hel0Gl6vF8lkUqUAqfzjpFySHAmKaTs6TLC3ympQomxq5uM440oa3/IHKSMkAEpl+Pjjj+PTTz/FnDlz1MRfkhMJWxK9x+NRRGiiqdGJQZNUMpnE0qVL1e3ly5dj4cKFqK2txZgxY3DUUUfhnXfewaOPPoq+vj60trYCAGpra+F2u/Hqq6/i9ddfx/Tp0xEKhfDqq6/i/PPPx0knnYSampqhe2cGBqMEVsIJSVKy+Vf2K3EDT6fTZREXRUZUATJNR5Lx+Xw4+OCDVaT04Ycf4uWXX1YuDxROcOoux8Wzf4s9VvTzk2M09H4qOYuKUnBpFUXlIo+T5rEAykxtAeCdd97BBx98gFNOOUUpDwOBABwOh+rFopktU5UkMp6PweiCrTRII6vnn38e06dP77c+e/ZszJs3D5MmTbJ83HPPPYdp06bhnXfewfe+9z18+OGHyOVymDRpEk4++WTMnTt3nVN68XgckUjk8zdgfhkZjGIwauJGLa/rf6UEnHUit9utHB2k0EGu8XowGFQkRZEETWnz+TzC4bD60cjj6urqEAwGUVtbi5qaGgQCATQ2NiIYDCIajar7uUZBButLVCbSpZ3+fpSgMxWXzWaRy+XQ1dWFTCaDeDyOeDyOdDqt0nmxWAzxeFyNBMlms+jq6sJll12GffbZB5999hl6enrQ1taG1tZWxGIxrFq1CrFYDJ2dnWhtbVUpUINND1JPLBarWhIadCQ1bdq0qo2KA3He7rvvjtdee22wL2tgsNmD/3f0qbJMqzHC0mtUeiTD9B8VbuwzYg8RRRBUBQYCAdTU1CASiWDChAnYe++9VXTywQcflHnlAVBCDdanSDp6JCVraBR9SOUfRQ6y74oGtfwM5BgQ4PNoksSVSqXQ2dmJf//73/B4PJg0aRICgUBZwzJtp2TTscHogvHuMzAYASABSRdyuUbpNtVwJAX68XFAIAUEXq9XqemY8pKRVCQSUWIJ1p8SiQTGjBmDm2++GYFAAKlUCgceeCDa2trU69GeiMQRCASUqa10eCAR6epE9n+xbkQVI2teFDvQu5B1MtbTQqEQMpmMkqJ7PB7ceeedeOCBB/Dwww+jpqYG2WwWqVRKiUjYT2b6pEYnDEkZGGxC6OMlKJwAvnAcl0P7uOHLCb+s61B8QMGBHPnudDrLooxUKtVPJEFCCIfDCAQCsNlsuOiii7Bw4UI8+OCDKqoJBoOqqTYcDqt6F0mH5yrPX4+qdLk9IzLKz1nv8vv9qsHY5/MpSyifz6ecNnp7e9WEYpvNhlQqhXg8rlSIJGkjnBidMCRlYDACoPv9VZp7JD30GI2wD4ikRH879ihROMDaD5V4Xq9Xmb9SJMG1XC5XJrD4+9//jlwuh2QyWWZHxAhIVx5Kv79KLuSSrBiN8TqJm4ILDlxklKiP+6ApLkfRy0ZjXThhZkqNLhiSMjAYAdA3zUqbqJSoU87NERu6+EIKL9j0y2iJabtcLqeiKkYe6XRaRVcAMGXKFDz99NP4+c9/jpdffhnRaBR2u12l0mhOK0e+AyirAVFyLtV1upsGPfyo5JOEROdzyuJp2cQaVXd3N4444gjMmDEDP/zhD9HT06OmBHNaMYdG0vvPqhdNv23IbNPDkJSBwSiD7uzNeg83Xll7kRsuRQnc9JlaI3FwjAbTYqXSF9Nw6+vr1fF0VycxkZz4+npEqEOmLwldzcioUMrPpYchX5dO7S6XC11dXYqEaQDACxuKGV3Jc6124WdosOlgSMrAYJRCVwNy05ezrBhx0SpI1n3oysBjSF4kgXw+r3qREokEUqkUUqkUMpmMGkAo1X2MoqQrxmAge6usSIpEyPQhlYmsU3EgJP9mMhl1P9fZKyatp2QjtSQv3paftcHGhyEpA4NRDlnbYVTE28AX5CXrVxxwKFV3FGRIkqILeiKRwOmnn45DDjkEv//971U9ihEVN/RKaTSm/uRtK+hEJeXnfA2SKsd1+Hy+soskLklagUAAhUIB6XRaqQz5nDSglapDnqsxpd20MCRlYDDKUS1FJSMPOQKEknam/VjHkr1YkgSy2Sx23313TJ48GX/4wx+Umk+m+uTGvr6wGqpIhwvaJgFfpP6YxuPfvr4+dHZ2KkukSheetxyJwr8yetJ70ww2PgxJGRhsBpDRlC5ZZyqPjg8kJKa6GDWx34oqQG7odrsd0WgUiUQChUIByWRSDSikuo/R1Ppu5tI1nYTElKGMEOlZyPeWy+UQCoWQzWYRCoXw7LPP4qGHHsLPf/5z7L777giFQkin0wiFQgiFQujr61ORFN83r/MClKcWDUFtWhiSMjAYhbASJ+gKNQlGUVQDcjOW6UHpF8jj0+k00um0qkPpwwqtIik9zWd1PpUgIylGdnS6kMIQnh+JlGKJeDyOWCwGACq60oUUVDfKib08R6Y+5esYbFoYkjIwGIWQm750pqi0xlqLlITLIYskGzYHM6Ly+/1wu91KOEF3CEYxOlFVE07ItUoKQOllKJ9PN6Hl+8pms/D7/eqi16e4zpoU62wUfVA8Il+fKUUzJHFkwJCUgcFmApmekkIF2VjLhlnaKzEF6HA41F/aEzmdTvh8PjXXKZlMIhqN4pZbbsG7776LRx55pJ+6b33rUrpggs/B82a9iPez7yqXy1kq+QKBgJowLC9ULMrGZ4pFSJYUasiozpDVpoMhKQODzQC6YEJ3fpC1KuCLUfQAVBpQmtXSqYKpPqr5bDYbpk+fjnw+j7/85S8DCicqpSUrqfvkBGISkV5nYx9XX19fP+EEL93d3Whra1PTivULn4/kQwGFdJw3hrQjA4akDAxGOaxSf3Jdyra5AfO6HA2SzWbLmmrpn+dyudDd3a3MaGmNROLiSA6dpAZLUDyO5yCjK9amSIhut1tN9pWRFCcI/+QnP0FtbS3+8Ic/KNFEKBRCb28vUqkUstmsSvNJ13VGmBSbmPlTmx6GpAwMRikGk1bTjV1lpMUaj/TaI0mlUql+0ZRVE69VTQpY93lvMlKSSj7dAorqP76eFEbQfYIjSHK5nJpRJ+/zeDzqHCmaoAsFoylpMaX3eBlsXBiSMjAYxViXjdMq0pJu63paS0ZYrOVIhR+l52zkpfx8QxwnrJzemYbkecv+Jb4WxRKVRBM+nw92u12t0fePIEnxPbDZWc6fMim/TQtDUgYGIwDVPO7kbSsl3EBrMmUl77PagOXjubnTG0+fT8XNfCgHClZKC8p+JalQ1M9XXqRgxGD0wpCUgcEIQDWykLBKhcmNmPdTdFDpMbyt11z055URitfrhd1ux8qVK9He3q4UcjI9Vu09VYMkIN2kVt6vWy7pacwNuVQzmTXYdDAkZWCwCSFJQZIDgH41ET1aYCRBk1gpNpBTcWWPkVyTBGf1Gk6nE01NTWhoaMDEiRMxduxYAMAJJ5yAUqmElpYWJfPmcEF9btO6Qoo89KZa2c8la0nZbLbswlEkNMJ1OBxKJMG1VCqFZDKJfD6vnDXy+bz6K93d9RqewaaBISkDgxGASkRR6To97WTfkIxoXC5XGdnIv5K4rJ5bPq6hoQENDQ2oq6tDbW2tqtnYbDY1c4oKQKu032CJqlpUJMUZ9O7jhWIOXqg6lBeSGcUf1UaODJUXocGGw5CUgcEIQKVISf7VCYTk5Ha7y2YwORwOeDyessm8OnFZPbeMyHhpbGxEfX09GhoaEI1Gkc/nEQ6HYbPZEAwGVTpQ1qmqvb+BIC2ZpBijWCyqRmTeL8lJJyQSj1WkJY+TKkVDUiMThqQMDEYAZH+QHhXJFBpJyOl0qvSa1+tVBOXxeMr+6tetiEuPvnis0+lEfX09ampqMHbsWNx8881444034HQ6EYlEEIlEFFHJaGpDxAoy1aeP08jn84o8CoWCUhrqysNsNot58+Zh1113hc1mUyk+XlKplCK3bDarjGat3DMMUW16GJIyMBgBsEr1yWhHEglJShIS1Xe8TkWeJDHeZvSlPx/v43FOpxO1tbUAgI8//hgffPABPvzwQ2y99dZqHDvdHvjYDZVtV0r56bOfpGu5Vepv2223xeTJk/HRRx+VRVoyJSgjKL3fS5+LZbDpYEjKwGATwirFxwuJxIpA3G43fD4fXC6XSrexodXpdCIQCCi5OI8jgfHxJCeukbBIeC6XC7W1tXj66acxd+5c1NfXo7m5WaX+otEowuEwgsFg2XNL9WC19y0hBQryL9N6kpjoxD6QcEJe6OaeSqWQSCTKnkcf2SH7vowL+qaHISkDgxEAPYKSF27+drtd1X7cbreKluhU7vV6FWEFg0FFUoFAoIykeJ3PzUiIxMXHFQoF3H333Vi0aBFCoZBK8YXDYYRCIaXqkwSle95V6v+qRGDVoiidpKwEE7JGJYlMpgJp48Tn0Z+70tgRg00DQ1IGBiMAUhouL7I+RAIhsdAslQo7r9eryCkUCqljuMbUHNOBjJ5IjjJ96Ha7EY/HcddddyEWi6naFEmKtSg95behxqwygpIkJSMcXtfTeHpKT6r6SFD8K+tcev3LiqgMNh0MSRkYjADIniWZ4iMhyfoSI6FAIACPx4NwOAyv1wu/349QKFS25vV6EQ6HVdqPURJJyuPx4De/+Q3++te/qjUSD2Xm0WgUtbW1qK2tRTAYRH19PYLBIGpqahRhsU+KdSmr97cusCImSUokKqtUn7zI9B4viURCCSdIROyHqiR5NwS16WFIysBgE6NSj1SliEraEzGCkp51Xq9XpeJ8Pp+Kqvx+P1pbW9HW1qZmKnm9XixZsgTLly9XtSuZwguHw/D7/ar+JGczMYKzEk5Uep/rAj3lJwUTA4kmampq0NTUpGZHVbroRGRVDzNR1MiAISkDg00InZwqEZJU7THFxwiGRBIIBFQEVVNTo6bSRiIRlQq85ZZbcO2116o6FSOgiRMnlgkx+Doko0gkglAoBJ/Ph2g0Cr/fr8Zi8Hg95TcYSFKSJKVHVBQ6WDXqZjIZHHLIIbjsssuwfPlydHR0lEVTvJ5Op6uSkX4OBpsWhqQMDNYB62oAa/WX163uZwTi9/vLBBGMfCSRkEA8Ho8iH500dJJyu9344x//iI6ODgSDQbzzzjsqRcdIis/PdKAkKdadGEXJKK2SaKLS57Uu0Puk9AhK/ysdI5gK5ONlilBPHQ7k0WcIauRg0BO9XnzxRRx22GFoaWmBzWbDww8/XHb/Kaec0i99ccghh5Qd09XVhRNPPBHhcBjRaBSnn346ksnkBr0RA4PhQiV/Pf0iU3NSxi1FDjI1x0iHBCOH9gWDQYRCIaWkC4fDCIfDSmEXiUTKZOByPRQKlRHHww8/jFtuuQW33HILFi9ejGg0WvZ4XvTnlmo+nhMjK0lQUnyxPhGUPihRRjm6+4S86Gm/SqlAq+N0kYQxlh25GHQklUqlMGXKFJx22mk44ogjLI855JBDcPvtt6vbHDxGnHjiiVizZg2eeuopFAoFnHrqqTjjjDNwzz33DPZ0DAyGFQMZvsoLazK6Y4TuGuFyudRxTJEFg0EVNUkZuUy/uVwuJZbw+/0qagqHw/D5fAgEAgiFQli5ciVmz54Nu90Or9eLWCyGMWPGqN4pytYpwJBiCp4Dr/NcAoFAmdRdjuzQTWXXl6j0NJuVRZJV9CRvWxGPkZSPbgyapGbOnImZM2dWPcbj8aC5udnyvg8++ACPP/443nzzTey5554AgOuvvx6HHnoorrnmGrS0tAz2lAwMhgWVIijdPVw3bZWScdmEq1sSsTfJ5XIpmbhVr5Os+eiCCJJUe3s7Fi9ejFAohFWrVmH16tVqHlQkElHExshHb/CVLhUySpIkxuN0Wbw+xbYSqpHDuo7g0FOA6zp2Qz73QOdiMLIwLDWp559/Ho2NjaipqcH++++Pn/3sZ6irqwMAvPrqq4hGo4qgAOCAAw6A3W7H66+/jsMPP7zf87E4SsTj8eE4bQMDBau0ntVtK+siuanrDbKyeVYSAwmJhq0ej0ep8iRJUWLu9XoVSQWDQTz22GP4yU9+otwf6urqFJlJCbqUslc6L6YmpeRd1q5kv5W0WlpXA9mB7pejOSql9qzUfVbpPElc8jUMSY0eDDlJHXLIITjiiCMwadIkLFu2DP/zP/+DmTNn4tVXX4XD4UBraysaGxvLT+L/9whrbW21fM4FCxZg/vz5Q32qBgZVUSlSkmk8eV0SgGyaZUTC6ITyb6r0ZNTEmo/b7UY4HFbHcC0UCuHNN9/Eww8/rB4TDAbx4YcforGxUaX0qPwjmcnXpUu6VA9KeyQSl/QHlNGVFUGtb01KQo+eSFRWUZQkKf1+GU1ViqoMRg+GnKSOO+44dX3y5MnYddddsc022+D555/HjBkz1us5L7nkEsydO1fdjsfjGD9+/Aafq4HBQKjWv6STlB6JSIWe7CmSa1xnrUn2H4XDYdjtn49NJ3G43W6sXLkS999/v6oRMQVYV1dXtiZdKEiY0kFd1sSkd58cu0GSksSlE5QVSa0rGQyU5qs2cbdSxDQQQZmU3+jCsEvQt956a9TX12Pp0qWYMWMGmpub0dbWVnZMb28vurq6Ktax+J/ewGBjwqp/Sdad5JrD4Sjz0mOKTqbxONJCNtfyOBk1yf6nf/7zn7j00ksV6YRCIeRyOWy77baK7HTvPimwkKRIwpF1Mb1uJiNCWUuTIglJbHpNSmIwRCUn8+pRkVXPlJVwQs6fktfXpRZmMHIx7CS1atUqdHZ2YsyYMQCAvffeGz09PXj77bexxx57AACeffZZFItFTJ06dbhPx8BgUKgkO5dycymIkKk96atHdZysEXHN6/UqcYPP58NHH32Erq4uRCIRLFq0CD09PSgUCvB6vejr61M1J9nXJN0nSFLSBZ0RnjxvK1cL/X7dGV0nOCvRxIb0SPGvHkFVG90xkHBCPr8hpdGHQZNUMpnE0qVL1e3ly5dj4cKFyttr/vz5OPLII9Hc3Ixly5bh4osvxrbbbouDDz4YALDjjjvikEMOwZw5c3DTTTehUCjgnHPOwXHHHWeUfQYjCtWISY664F+pwCMh0e2BzbBsvmWUxTXpvzd//nw89NBDiEaj8Hg8GD9+vBJMUCzBCEy+rl73kj1a8pz5XqRcvlKtzUqxKPuiJGFxbX1QSYZuNUtKr0npaT8pQbciLoPRhUGT1FtvvYXp06er26wVzZ49G7/73e/w3nvv4c4770RPTw9aWlpw0EEH4corryxL1919990455xzMGPGDNjtdhx55JG47rrrhuDtGBgMDXSJudUIDSk2IAkwGpI2RSShUCgEv9+PmpoaRWDBYBB9fX246qqrkE6nEQwGsXLlSjQ3N6O2tlY5PMgUoIzQ5NBDSUhSgSfHcugSep1g5PuVYhGdsHi/jMgGiqDWhSSq+fZVU/hJ8jIR1eaFQZPUtGnTqn7BTzzxxIDPUVtbaxp3DUY8KvVH6aPdKzlMMDJioy1dGyKRCBwOh6qrZLNZPPHEE+js7EQoFEIwGERdXZ0iKT5OpgWptpMKPKb3ZA2JJCWbiK1SmHrvl96grEeU+v3rO57Dyo6oUp+UnuLTG3ej0SgCgUDVfilDUKMPxrvPwKAK5AatE5JsxpX9TPTQC4VCqKmpQSAQQG1trVqrr6/H66+/jnPPPVeZtnq9Xmy11VYIhUJqrba2VhEc04FMAepycqs0pJy4S3LheyJkek5e5zH6miQzuaZfHwx0V4hqtki6WIKX2tpa/PnPf4bX60Uymayq9DMYXTAkZbDZo1JBv5pBrNz0pbRcjslwOp1ltR89amJUJG97vV68/PLLePvtt5FIJGCz2VAsFpVZLB8j61R8LNfkmHhGTZVGzuuDDfX3q5vgVvrsKj2GsJJ3s4HWSmYuH1csFhXZcHKunBOlz4zK5XJIp9Nl03Y9Hg9qa2ths9nQ3d3dzybJRFGjF4akDEYt1uVXu/z1P9CaTHexhiPTapR6VxIosP7U0NCgoqa6ujoEAgHU1dUhEomgVCph/vz5WLNmDerr68vmNcnUns/nU7Urme6LRqNlQwulAo+kpIs81nd8hoSVI7iUjVsRkT6ryaoHisfRrTwWiyGXy6nxGtlsFolEAtlsFslkEul0GtlsFrFYDMlkErFYDPF4XEVQdrtdHUPCozxdnqfB6IEhKYNRh0qpqErXpUzayr3cSkQgx2bosm65xkZZNt/6fD7U1dWptB0bbGtra/HXv/4Vzz77LHp7e1XNSUZIFElIE1mSpBwuKC/ynCtZOQ0ULVVCNd87q4Zb3k9CkORAQYOsI3FNjtSIxWJlhJTJZJBIJJDL5RCPx5FOp5HL5RCLxZBKpdDV1YVjjjkGu+++O/L5PFKpFLq7uxGPx5FMJlWkVSgUDEmNUhiSMhiVsNp8K9VJrGTWA62RFOgKQUKS4zakswPl4awlhUIhBAIBFAoFpNNpuN1uvPfee3j66acViUkJOht95RgM6TJRyeFBV+bpn8GGRE/EQFGQ1aBCRkeyjlQsFtUa5z319fWpaCefz6Onpwe5XE6NeZeRFEmK14vFIrxeL3bffXd84xvfwKeffopEIqEGGzI1yNSfIanRCUNSBqMKelqOsFKmSRm1bETlX72mI62B5NA/pvlkjxKjKvYvyUiKYgm73Y5jjjkGK1asQCQSgc1mQ0tLixJEyN4pPh/rWtLPjxGVnNDL89SJaig89HTohGTl6iDvk6pFTtPN5XJqTdafOGWXqbl4PK6IiUSjr2WzWfT09OCggw7Cr3/9a2SzWXz66af47LPPEIvF0NHRga6uLsTjcUVY2WxWnavB6IIhKYNRhUrRE1N/elShiwiYMpOpM9lHJAURdIoggciGW2lTJI1ck8kkHn30UVUz6unpUdFEMBgsmwPFaItkKCM3K7fxStGUVXpvqKIoQo+kKjXQyvpSNptFPp9HPp9HJpNBoVBAJpPpt8b6ESMkEhIjqVgshoaGBkybNq2MpCZPngybzYZUKoVEIqHqU3ysHkkZkhqdMCRlMGpRrbdH1pmkbFy6kktC0sdWUKhAZZ7H41HuEXKNhENl3nvvvYfLLrtMiSqi0SiampoUIemOE0zzySGDMtWoO6jL5mGrZmOraHIoIOXg+hRc3QmCxJBKpVSUlE6nVc2IUVMqlUI+n1cREwkpl8v1I6mddtoJP/vZz8rWMpkMVq1ahe7ubqRSKXR0dCCRSKC7u9uSqEikBqMLhqQMRh30JlTZVKo3mjJC0tV4rP/I6bNSEMEISe9Rkn1Lra2t+OEPfwiHw6EipEwmg5aWFvVclIyz/qSvMY3HyEuSJ89FOklIk1vZaGvVfLuh/UvAF+SkE5GsNen1J0ZIjIoYGZF8KB1PJpOq/kSiksKJk08+GTNmzEA8Hkc4HMaKFSvKIik+L2tVXV1dZVFVJpNRx7MmZkhq9MGQlMGog5U4oBJxSRm2TJtJxRyjF324oCQk2hgVCgV0d3ejWCyitbUVr776Kux2uzrO4/GgpqamLAXI+hLrTYzCpFO5nO/EaEk6SVi5jldzihiKJluikrmrlY8eBRD5fF4RVDqdLhNBSNWeLoggcTudTuywww742te+poirs7OzjKRIdFTxxeNxZDIZFUExtWiEE6MbhqQMRiV0YmL6S27gUhhBUpICBUY2VrOcotEo/H6/sjGi596dd96J//3f/0UkEoHb7UZLS4uqW5Gk5Cwn+XoUY0hBhJzRJAlVd5KwMnetJhYZDnWfTkqFQkEJIkgGTN9RJk4iIal0d3cr5R7v7+7uRjqdhs1mw0MPPYStttoKyWQSfX19WLp0qSIfCiFkJEWCYvowl8upCCqfz6toLZ/PK6IyGF0wJGUwamAlmqgUTVk5l0u3CCklJ6kw2qFHHutM0WgU+XweDzzwAN555x1FgCQzNtuSpFhnkkTENSmSYNQme5700R9yTZKxTOdVi6SGSjxhJZqwGuFOJZ+MotLptErpUdiQTCbR3d2NSCSCY489Ful0Gr29vfD7/SoSIwGRzPg4khSJiem8dDqtiJNrJCjTJzV6YUjKYFTBSr2nN+paDSaUBEWFHtNvrD9JZ4eamhpVZ4pGo1i1ahUWLFiAUqmExsZG1NbWquiKtkWcE8UhgxxIKAURco2313Vchl6HIqq5agwVdOGEPnyQxMJIJpvNIpVKIZlMqhoRm28TiQQSiQS6urowbtw4XHrppYrI4vE4Wltby+TjjJpkJNXd3a1eS5e86+dF8jQkNTphSMpg1MEqtaVHTbohrIxiZK2JlkSsFbH+VF9fr4656qqr8Oabb5aN2KCThHSXIElR8EA1oT46g2uMonRV3vqk8fTbQ6Xqk827usmrJAFGLEy1MfIhQXV1dSGZTKKtrQ2zZs3CrFmz0N7eDqfTiVWrVik1nhREMPpiqlBP97Hupc+a0p3S5X2GpEYfDEkZjGhYNezq7hAyapKRiS6SoJiBDg/sSWJUxdqSy+XC6tWrVZT173//G4sWLUJDQ4MiI9apIpGIWiNJSSWeJCR5PvoQQr5XKxJa1yip2gZczXvPCrxPn9sknccZOcmeKGkGS8EEI6re3l6MGzcOO+64I/baay+0tbUhmUyivb1d2RhRJGFFUnq6j+Soj+WQKj7jgj76YUjKYFgwUKrJ6pf+QGvS6Vtu8vqabNyldRHrTUzTRSIRFUnRASIUCiESiaCzsxMnnngicrkcIpEIAoEAmpub0dDQoKItaRwr032yIbjSeZFI6Vi+vlGP1YYr/fMq+e1VMn0l5H0kIYoh2Ouk9z/xLwkklUqhra0N8XgcPT09aGtrwzbbbIP7778ffX19aG1t7UdSjLooS5c1KYokSFKxWEyRlMHmDUNSBkMOq0hA/gXQL3rQ1+R9UlIuoxPWdOR1vc+I8m+SlEzZ6ZNyQ6EQnnnmGbz//vsoFovqeTgOnpEWozBp/mrlCiEFD5Vsi9andlTJ9BX4nJzkfVYmsHoaTL9f3ifTeIyWkslkWWpPKvpIKul0Gu3t7SrlN3PmTOy0007o6+tTyj5KzxltSb896VbB1KIcvWHqS1sODEkZDBkq9eZU692pVF+yup+RCJtwqwkUZP+TJCm6PXCUBmtRJKl//OMfePzxx5VwghJ03k+iYqqQRKj3MkmVnpUJ7IYKGyqNv7Aaj6ETEp3HpVO5HL0uCYFkIWc50SmCPUmc7yRJihEVU3bHHnsspkyZgo6ODsTjcXVJp9P9TGEZkUllnjwnk7rbsmBIymDIMVCDqby/kjpPv08OIZR9TYxk2CBLdR7VetJ/jxEUSSoYDCKdTuOcc85BOp1GKBTCZ599hrFjxypiCgQCisyk44Q0myVZyUm4uohD/zysTHIHC73JVh+JoYsHpOCBggNJAuwlYgTD45jiIxFZkRTTcCSd5uZm3HzzzSgWi0gmk4hEIsrCiGk79kcxPcieKnr8kRRln5OJpLY8GJIyGDJUIySgXPjA+6QHnZSO66kx1nBktMRmXDo78C9JQzbXSkGEw+FQUVE6ncaHH36IVCqlxmewXkWpOslOii2kY4X005PR03BEUBK6UEBGQ7r5K93JKXiQDuRSoSeJSRrCMiWnN82mUikAwLhx49Tx8XgcLS0t2H777RVJkYgYNbGhV1oXyRSfHkHpURTfv8HmD0NSBkOCSmm7Sn1NXJMOCnLgoNUa02pM2VFhx/EYXCN58TgSUiaTwXHHHYe2trYyc9dgMKiEENI4linDaqM6ZDQnhRwykiLJ6vWpDYXuBCFnNMk0nvTXY2TCxldZa9LrS1IkwdoR/ffk2pgxY/Db3/4WXq9XRVL5fB6fffaZIiF9uq5MC9LjT/Y9WZGnJCuDLQeGpAyGHHo6z8oJwqrxVndd0J0WpNUQU3cUNFChF4lElJlrTU0NVq9ejbvvvhs+nw+lUgl2u125RJCkOBNKjuWg554+4FBf0wcTWsnj9cuGotLIDLmJ67JxRk1MoVH8IF3J5Zo8TprAkqR23HFHHHzwwUgmk/D7/SqFx6ZcCiwoR5fPo68xktIjPNmUKx3XTT1qy4IhKYMNRrWaU6Vakxx9LutNes+TTKExtUeFHetKJCQKI2htVFNTg0WLFuHWW28tm57LaIskpXv3URGou6VbCTX0lB/P1eq9DxVJAZWJSkZP3Nhl6o6RUyXysZqKy2hG1p/GjRuHM888U5FOd3e3cpmQYzl0qTpfl8fyvEhKuVxORX2sQ8moUBeMGGz+MCRlMGSwIqlKo8715ls5bVbWeOTUXKbeaEVUX1+vVHoNDQ2qb+nWW2/Fww8/jIaGBvT19WGbbbaxNHfVJ+DKCEkSEpV70qlcyuCle7mVQEIStPxsNhSSpCQh6ZGI9LZjlCSthip55PG4b3zjG5g3b54inXg8Dp/Ph48++kiRFNN5kqSYumOEpA9CZNTE+yQhyUGFXJPCCUNQWw4MSRkMCapJzyuZvlrVb7jhS789OROKkQ3rQqwjhcNhFItFLFq0CEuWLMHKlSuRzWaVdRFJiik7OQGXaxRHUJwhyUcfMy+n+UrRxLrI6YdKQGElnNCtixhFSYcIKVagiCGdTiuSisfjaG5uRigUQk9PD7bbbjuMHz9ekZQcnUFy4pwoEiGjIr4260r8K8fHk1j1dKUujZeKRYMtB4akDIYUOinp5qmSkKQjA5tvOaJCj2LkeI1oNKpcH6LRKKLRKMaMGYN///vfOPnkkxEMBjFx4kQ1RiMajZb1TsnRGTJ1J4ceSkKyEnTo9Sc5DdhK2QhYu5VvCKRwQiclRixSwk11HafXUv5NT7yOjg6k02l0d3fjhz/8IY499lgVcS1fvlxFTXJuE0lKpvsymUxZLYzXdSJlao8kRJm8FIPI3i/Z82Uk6FsODEkZDBkqbdI6YelNr9LbTrpHyCZZRlCMnCgVj0ajCIVCuOOOO/Dhhx8iGo2ipqYG0Wi0H0nJKbyUkTONKMd3yPqYfE/VxBBWLhJWRGTV1DxY6A28VuMzpIxbNuTK0Rl0fqDF0Jlnngmv14uuri5svfXW6OjoQHd3t4qudJuiVCqllHk8jrUsParT+7lkQ7EkISuvPemiYWpSWx4MSRlsEKqRkk5QOjHJWpQkJDnzSVocSSNY+uV5vV4AwEMPPYRPPvkEY8aMQX19PWpqapTXniQpRkpM/0kHC6YTmebTSaYSCVcio2qfF7E+G22lSblWs51kNEXhBFNyHKORy+Xgcrlw9NFHo7GxUZFTe3s7urq6yhzNZe1KklRPT48iP0ZI+nnqDhnyGEIaw8r3a+VBaLBlwJDUFoaBfrlXqqFIKbl+nJWMXBdB6A7lTKfJ9JokEN4maVDVFwwG0dLSgnA4jObmZvzpT3/Cn//8Z/T19aG5uRlNTU2oq6tDTU0NmpqaVCRlNSbDak1Ow12Xz85qTd9gJfRNln911255XT4HN3cKISqp6BjRMOKh4KGnpwexWAxtbW1IpVLo7OzEOeecg8MPPxxOpxOtra2KpBKJBDo7Oy0dyPlacmqudKCw8hC0+pwqkY0hIQPCkNQWhEp1ED1aqBQJybqLvF4pQpI1J7vdbulaXo2kpHmrdH+IRqMAgDfeeAMffPCBsjKirFwawJLodDdyaQYrz1uqEAeTiqvkSE7ysXIZl9f1yEOPQvgcUmJOgYKVrJu1IZ2kmN6LxWJwu934yle+gu222w7Nzc3o6OhQzhJU6bHxls/Fi6x1yfEdsp+J51zpszFEZLAuGDRJvfjii7j66qvx9ttvY82aNXjooYcwa9YsdX+l/9hXXXUVLrroIgDAVltthZUrV5bdv2DBAvz4xz8e7OkYDAD5fVSrkfC6nGSrixus1nQRhL7x64+R6jh93hLTblZDA0lSHJuxePFinH766fD5fGhoaEB9fb0iMGkeS5LSz0snpqEQM1g5j1vVW6yEAKzPSPufSmusOem9R4xkZCTF9B5JiiMxOjs78ZWvfAW33XYbUqkU1q5di87OTiWc4CRdpvOk44TuqUeJuFHeGQwHBk1SqVQKU6ZMwWmnnYYjjjii3/1r1qwpu/3Pf/4Tp59+Oo488siy9Z/+9KeYM2eOuh0KhQZ7KgbrCLn5SrWZVRFf1ogkkZCAuCabWK2iE32Nqj1GWVIQISMpPbqSkdQLL7yAJ598Eo2NjUin06ipqVHTc+k0QQsk3V/PiqD03q2hMH0F0K9eVMkEVtaTZOOqFD/I+6iI432yudaKpKwad2fMmIF99tkH3d3dqKmpUWm9VCrVT/Ena0zSoVxerExfDVkZDCUGTVIzZ87EzJkzK97f3NxcdvuRRx7B9OnTsfXWW5eth0KhfscaDC+qCQF4vyQWkoU+/sLpdCq3BkkukqSkAIERFElKRlw6IdntduTz+X51I5fLhaVLl+Kvf/0r6uvr1bBCmsLqYzQksfK1JClJZwgrE9j1hZUTRKUxGbIfKJPJlPnryZ4i9hv19vYqebeMoCh+ICHpNSkA6rl33nlnHH300aquxHEajJpoVyTNX6kM1Edn6E4QRhZuMBwY1prU2rVr8dhjj+HOO+/sd98vfvELXHnllZgwYQJOOOEEnH/++ZbFagAqtUDE4/FhO+fNFVaEJDdoHiPl3nL8BZtf6c7ANQoapIycgghdWs4+KBlJSecGj8eDVatW4aSTTkIulyuL5jweD7LZLLbZZhvl00cLJCvvPq7LSIqvrfc5DVUEVcnwVZ/RxAhJNrAyAiLZsK5UbY2ExF4lEowkqb6+Ptx5553YfvvtkUgk4PF48PHHH6s0nrRA4poUSXB0ButQFGywQZdrZs6TwXBhWEnqzjvvRCgU6pcWPPfcc7H77rujtrYWr7zyCi655BKsWbMGv/71ry2fZ8GCBZg/f/5wnuoWAz31pyv4ZC2JJEE7Ir/fr8xYOXqdvUs8XhKSFUm1trbizTfftEz3eTweNUq8UCjA7XajUCjA6XSiUCjA4/GoBl7dBZ1zpGQdSkZSuihCJ6ihICqr/iVGTPpwQa7pIzEoUtBVe1wjCcmoiSSVzWYxZcoUhEIhRTKFQkFZRgFQKTxGX3LYICMzOTuKz0FCkhJ3meqrNOPJEJbBhsJW2oB/RTabrZ9wQmKHHXbAgQceiOuvv77q89x2223KrNLj8fS73yqSGj9+vDoHA2tYWfRIDzkrfznZi8Sx6vTKk2tsouWxutmq/Cujofvvvx+nnHJK1eOs1qTiT4/w5OwoqeyLRCLqvKxIShrCWhHXYNV93KitBglyg9frT0yj6eMvqq3pQgau5fN5PPbYY/jKV76iGm+lK7l0PGdEJke2y7HwJMV0Ol3mZlFpYCItjhg5Wo24NzCQ4L+LWCyGcDhc8bhhi6ReeuklLF68GPfdd9+Ax06dOhW9vb1YsWIFtt9++373MxVksP6o1oiqu0GwHsUIKhQKKcVcXV2d8sOjC3koFFJkcM899+DFF18sS+NJ0cWqVaswYcKEqoRUieBYo+J8J9bFpCeflJ1buUfIOpQ+lHBDjV9lLUqKJGT0wZHsUqEnR1hQxCD7kaT9EN0e0uk0vve972G77bYrS/f5/X58+umn/UhKjnjn6+oO5FxjVMbruuGrtDOS6ybdZzAcGDaS+sMf/oA99tgDU6ZMGfDYhQsXwm63o7GxcbhOxwCVR2pYOUOQVKzIig2yDodDbbwOhwPFYhH/+c9/8I9//EMJIiRJkTQaGxsHTVJyjeINCjrovydrW1KwIUUSTqez33sfKi89oL/pq+5Srs93quQGoTfIxuNx5HK5sh8UU6dOxd57713mQJ5MJtHR0VFm+MohhBRBMFrSJ/PKWU4kLr1+xghKkrCU1OtNyoawDDYUgyapZDKJpUuXqtvLly/HwoULUVtbiwkTJgD4PB33wAMP4Fe/+lW/x7/66qt4/fXXMX36dIRCIbz66qs4//zzcdJJJ6GmpmYD3opBNVhFDHqai2TAniRGT7W1taipqVEuDsFgEE1NTbjuuuvw5z//GTU1NYpQMpkMtt12235WQ3qzrl67kr1M1UiKBCrVgrxfd7igWrBS7cnK2mgo5j3JOpR0Aed1GbHI0Rl0cOB19i1xraamBvfddx88Hg9SqRTcbjeWLl2qBA8kNjknio8nSZFwSD6yviQn+Mo16a8nI0U5NkMOJDTyc4OhxKBJ6q233sL06dPV7blz5wIAZs+ejTvuuAMAcO+996JUKuH444/v93iPx4N7770X8+bNQy6Xw6RJk3D++eer5zEYelhFUJWiKatIinWnQCCAUCiEXC6Hf/7zn1iyZImqhQCfEyEdxeVQQGnuKh0ldIGFlKpLmyLZdyXrSLpDue5ILl3LrVSNVp8N19YH1YxfZXpMH50hIynp+BCPx9HY2Kj6mihSsdvtKBQKKoKSDbfVSIoyd9bNdNWhFHTo/U8ylaf3fenGrwYGQ4kNEk5sKsTjcUQiEQBGOFENknyk5Fp67nGz5zF0Ea+vr8e4ceMQiUQwfvx41NXVoba2FmPHjsV//vMfHH/88aipqUFtbS3q6uoUoenjLxhByTEZkqRkzcmqEViSFM/VKgqyGipoRUCE1ZqMotbnvwWFBWyiJfnIniap0KMnXjqdRltbG5LJJLq6ulTv0tq1a3H44Yfj+uuvR2dnp1rTFXrS8JVrMt0nSUq6XugNx1ZrkpjkZ1NpbRRuJwabCJtcOGFQHbpdkdX9VpZGVr1OlY6RqS6SFAlJF0swVca0Xn19Perr6xGNRlFfX6+EEr/85S+xZMkStVZTU6NIiiQkLY5kuk+vGw3U9CuVebxPj4qsPq+BPttq60Bl123d6BX4whi2VCqVCRAoSNCVelJFx3pRKpVCe3s7UqkUurq60NXVBZfLhV/84hfYeuutlRs56018nCQpRlIkQz0FyKjNyoG82pqV9+C63DYwGCoYktpE0J0f9PVKjbf6X/lYfV0nKT1FZmUKyxlNkUgEkUgE4XAY4XAYpVIJ3d3dePnll7Fq1SqMHTsWoVBIXRhJsXYkCUk6V0gxBe+XDb5W9kkkKZ7z+mKgjVQnId1zTzdN1Q1h5Zh23dyV6j0SlhxCyNReMplEPB6Hy+VCfX09pk+fDp/Pp6yLksmkar7lbRKh7tPHybtMI7IuppOu1QgNk74zGEkwJLWRYZWGsho7rvcvUZmm9/dw7pGetpONudJpQa7pJrCMpKLRKGpra9Hc3IxwOIwxY8bg5ptvxi233KIMXhsaGlBbW6vSg3L+U6X+J6ryZGpPjvTQ60pDqboj1mXTlekwKa/WJ8jqo82Z2rMSMtALjzJzkhVrR4ykYrEYrr76ahx66KHI5/PKsby7u1tFWnIelEwlSody6a1nNc/JipyM4MFgJMKQ1CaCVT1FrxfJupGMhqTNj6zV8KKvyeP0+U6SqFiTikQiKuWXy+Xw8MMPY9GiRSgWi2WTcTkaQ6b59FqTXJOEKIlWb6itVFPa0B6mdVnjuj5MUEqvdSUcj5Nu44xgSFixWExFWLr9UF9fHw466CDYbDbE43FMnDgRdrtd1askmVENyEhNmr3q4zIqCR8qXap9JgYGmwqGpDYRdPGCPrNJkhQ3dCu5tq6OY8QiVXq6Yk4Slx5J0UkiEomgsbERixcvxuWXXw6fz4f6+nrU1taqkRiRSEQZvOpkpJvJynPg30ppSKvLhkZTVvWkauks2d8kncdJUFZrsh5EayESVk9PT5ncnLZGiUQCPp8Pc+fORX19vRqlUWkqrlTyMb3ICE72N+lkZaXCqyR2MERlMJJgSGoTQW7AMv2mK+4k0dB+iLUfihG4xnXWg2RvkZVAQXrb8Xo4HMbjjz+OBx54AHV1dejt7VUTbsPhMOrq6hAIBMocJ2g/JCMk2cOkR4BWkZReKxvK0RlWsCIteVtKxqW/Hm/zuoxmKAknIaVSKfT09ChiYj2pp6cHu+yyC6666iokk0n09vYinU4rp4hKE3C5Ru8+6QohJe3ynCVJVVLrWX0GBgYjBYakNgGsak76hi3duvWxFnJMBqMY9jPRv44CBT3i0gUKrIOQpEqlEpYvX4433ngDtbW1/UZihMNh1a9Dnz/ZnCujNKu6mExD6tGj3nA8XBHUQPfJjVz2NpGMKC2XI9yl7JsDAzmbKZPJoLu7G16vF3V1dXA4HBg/fnyZxx5rVyQ3pgOlT580mtVdIayISZ9lVc0VwkRTBiMVhqQ2MqQ0XI8q5EYvx53zQhWdnDpLY1WSBh3KSVzSoJUERZLyer24/fbbcckllyhCC4VCcDqd+NKXvqTcxqPRqHr+2trafmt+v7+MhHTClVGhjBytXMl1AclQoBoZWR0nyYlRiz63STd3lZGPVONR6NDZ2YnLLrsMs2fPRmdnJ/L5PJYtW6ZISvZOyYm7MrVHRSBJiqlGfWCi3pBrleIb7GdjYLCpYEhqE0HvZdLVdpKkZDMs5zcFAgFFUl6vVwkZSCDSiDWXy+HBBx9Eb29vWe3I6/Xi3//+d1k0xKGBtEWSYzn8fj8ikQi8Xq9yQOc5VRJCWBGSVO9Z1aF0Gf1Qp/sGqk9ViqQYvZA4GPnIplmOXyd5HXroofD5fOjs7MSXvvQl9Rw8jiSVSqVU6k46kEsTWmmpJMe2k6R4kerDSsIJ/b0bGIxUGJLaRKhEUNIxXPYZMVry+Xwq9eb3+xGNRuHz+VR9iIP/mPbzeDxoa2vDjTfeiJ6eHsvxF+PHj1c9TCQ6q6GHMnJjNMfz09N2A0VKOiHpknN9bUNhFTVZkRJvy6GFcvCgNIGlJRFvc/w63SGKxSK++93vYtKkSejo6EAsFkNra6tqyO3s7FTPIwcJ6rUmRnL6SAzdzkiXy0uJvC6cqPTZGMIyGGkwJLWJYOWVJ1N70kVczkfy+/2oqalRkQxHZ0SjUYTDYRQKBVx44YVIJBJqMm2pVEJtba1K1cm+JTnY0OVyqRoXSU6OxJA1MLlGI1dJPDohWRm56qQEWDcu8/PaUFSSoeuNq7pzOWtQ9MJLJpNltSaKHUg6HR0dOPbYYzFr1iy43W6sWrUK7e3tiMViSCQSaGtrQyqVUmRGEmJUpM+gkpGSvM7zlr1aunyehLUuisZqn5OBwaaCIamNDCsnCat0n1Td8SINX2n6GgwGUSwW8emnnyISiSCfz+P9999HLBZTUZHb7VZ1KjlniSQkLY24Jq2NZB8URRl6b1QlEqrW82RFUvJz0W2ONrRPqpKjhFUaTG72sj4lIyoZVVFOXiwWMX78eOywww6YMmUK2tvbkUwm0d3drUiKFkeSpJi6k3OnpFO5Va8Wz5PkJEdmWKX5rD4HA4ORDkNSGxl6OkuvQ9HXTtaIWPuJRCIIBoPKFYIjM1588UWceeaZqKmpQSAQQCQSQV1dnUrTMQLSpeocFCijJrpC6HUxK8NX2V9Vzc7J6v0PtEYMRQQlMZDrAo+RqT4KI0hGHJ1Bz71EIoF4PI62tjbsuOOO+POf/4x8Po/Vq1ejtbVVGcN2dXUhHo9j7dq1SCaTKqJiFKVHQNJBXTeBrUSsRCVbI0NSBqMNhqQ2ASrJz2XkRLWdHDrIC0USnIobCATKalqRSETVrnRCkoMC9dQeIySrRmCuSV892d9VySSX4MZodVw11RmPX5+N1YqQZLQh6zR63YbqvFgspnz1GAnF43ElM5f3z5o1CzvttJPqe6KlkXweXhh5kaTk68vISHcntyIo/fOpJogwBGUw2mBIahNBJyiraIqkQpKSlkQ0diVx0SUiFAohGo2qNUZkJCRJUnokRVKUUZ6VDZN+vzR9XdeeG6soYKDIYLCwmoGkp8msRlT09fUpkurp6SnzzWMkRZEE61HpdBonnngidtxxR7S3t6Onp0eRWSKRUIRFktNJSq+JScLUJ9+aaMhgS4IhqY0Mq1SfPqZdKuuo2mMaj2QUiURgt9sxZ84crFy5ssy1nAIL9lWxAVgSoJyQK0dnyEGBcgZVNWn4utR6rNRzOoHotRWrMRKD2ZxlZCLVevp16b/H+g998+LxuOp16ujoUA7kXV1dGDNmDO68807V01RbW4u1a9eq8e2JREKlBfl8lJfzdaym2eoRniEkgy0ZhqQ2MqzcJnTBhBQ48MKIirfb29vR3t6Of//734jFYqirqys7nhd9qKCV4atswtVl47o6T74HwoqkrLzirNJregRjNYSPCjZ9Mx8Iuvihr69P1X9Yb6qknpMkpY9zTyaTaGhowLbbbosddthBTcmVDuV0nNCNYKWnnpWwQX6G1dJ2BgZbCgxJbWTokZQkDpIRffKkWEJGUtFoFNdeey3+9Kc/ob6+Xs2Aojkse6cYjVk5TujmtFIEIT0FrXqedAUfoafQ9ChIJ6VisYhCoaDScNItQXcg53HrQ1KSkKikk7ZCVt530oGcQwVJVtlsFr/61a+w1157qUm57JOiLRLTgJSqM7UnSUtX4OnnbYjKwMCQ1EaHVSSlCyZkFCXrUYFAAGvXrsVtt92GhQsXKkk475PXSXZWozPkDKfBRlN6FCVTflZpKr3or89okjJrRhky2pApOZLbuoKvR1LSjWFJFmyklWv04Usmkzj88MMxfvx4NaY9k8kgHA6jp6dHjeCgTx8bfBlJUWLOSb0kQZnqG6geZ2CwJcOQ1CaAlduEnuaTggl5aWtrw7XXXotwOKyEEzpBsa5FMYQuI7fyCtRtjSrVoHSi0jdWqzSentKTUZPV+Av+lek3WTdaF8goTo6zkFZDXJO+eXxdae66//77Y7/99lPERfJpb29XoziYGqSfH5+fkZccRqhbFq0rSRniMtgSYUhqE0DOiZLRkyQa2h5REBEKhVBbW6t6pLhG94mamhrU1NQgHA6jpqZGRVKVHMgrrUmnCCsbIys/PV0coff4WE2xrZZ+k5NluZbJZMrIb13AY0lKjHJ0t3GupdNpXHnlldhnn32UmWwmk0FtbS0++ugjNfads6CkCaxco2KPr8PnltGUrIPpxGuletTvMzDYUmBIaiNDNrpaSdCtmnrZ67Ro0SIsWbKkn5ii0oU1J6nUk67kUr0n60+VrIsqpfqA/qo+XVnHNB9Jymr8hfSq09fS6XS/WtdA4OuTOFgvYmPu2LFjEQqFytbGjx+PpqYm+P1+RUiMkkhCMvoi+ZGIZLQm04skpkru5AYGBtYwJLWRoaf6KAG3qj9xflM4HIbT6cS5556LTz75RIklKDeXsnSu8bkqGb3qayQs3apIv13NAUKq6Sql9khWJCJGFySD/6+9u49tqnrjAP7tRtd167oBoyvjdRheRF5EYHNO/ZmwsBFUFEiAEB1GIYxBguBLMAr6h46XRBONQGIM0z8AIRGJCCQT2AgwQHBGAZlgkKGsGw66dW/spef3hznX07t263C0t/D9JMu228s492S9z865z3mOepNXK4I3Njb6BLxgyLaoU3C3bt3SRk9vvPEGZs+e3WkH3AsXLsDj8XTa4FC2RT2mBiRZ/FUtAiu/VoOUOuXnL8X8TtaEEd2rGKRCLNAWHfpnUmp9vsOHD+PkyZOoq6vTpvHUqhPqZzVxQlaPCLQFRqCpPf2oKdDzKP1zKX8p5eoWEmqmnnpjVwu4qtNl+r2V1NTtYMh2tLS0oK6uDkIILF26FLGxsairq8PgwYNRXV2tpZbLdHO5CaGa8adOTcqRkT4z0N++TuoxNTFEv42GKtAUH0dcdD9ikAqDrtZJ6ZMn4uLicObMGXz++edwOp3asyr5oS4AVo/Lf69PG1cDkX57DH2QkroLUkDnxIlAz6TUG7Z+jyY1uUF+L6fV5MjmToKUTPu2Wq2YO3cu+vfvrxV8lYtvZXaeTDNXg5RM2lADjjwmR0r65I5A166m3fekvh4DFN2vGKRCTF2DpBZtlaMnORKSZY/k9J1cB2Wz2TqtiZKf5XYdckddq9Xqd9NA/aJc/XOmnhZ89fc8Sq0erqaRq1NoMrFAPg+SyQpqxpzMspNBQ97ggyGnFpuamrBq1So8++yzaGtrw9WrV1FbW6uVOaqpqdF20b1586bWFv0IUA08sh1yClMtuaQva9TVsUDtJqJ/MEiFmPoXsb5auL/vAwUNf0kKgdK//VUdV7/urjis/rh+BKVObclApD6LkSMgdWpMnwquZtvJ3WplkJLPlGSwmzJlChITE7vsW7VtjY2NGDVqFBwOh7Z1hlpLz+12a59lnT6Px+MTlPyNDvXJIP7WPXW1DoqjI6LuMUiFmH5aTF9bTr+wVb+XkdlsRnNzs1aNvLGxESaTSduSw2QyIS4uTptGC8X16NupBil1/ZN6TD53UhfNqqMmGbhkkKqrq0N7eztMJhPefvttpKen++1Xf22TCRPV1dVakFJHUnLrDLfbrW1cKIOUvkyTuv5Kvw6MIyCi3scgFWLqdJi6X5HZbEZDQ4OWCm61WtHR0QGz2Yz//e9/cDgc+Oyzz+ByubSbvM1mw+3btxEfHw+Px4Nbt27BZrOhvr5eS2EPBf10nrogV81mU4+pCRHqXk1yuk+uPVq8eDGGDx+uTb95vV4kJCSgtra2U7/6y5KTz43kaElusdHQ0OAzeguUIq5/duRvyo6jIqK7h0EqDNTRkzpKam5u1ipDeDweCCFgsVgwZswYjB07Fjt37oTb7YbX69VSqjs6OrSRU1NTE+Li4tDS0qLVAgwFNUjpK0Woo0K1uKs+k6+pqQkmk6nTKPOpp55Ceno6GhsbtZ8jhIDb7e7Up/5GUjJIyXVO/oq/qgFKn3UXzPQdEd09DFIhphY9lVltDQ0N8Hq9MJvN2nMOAGhoaEBbWxtsNhtiY2Px0UcfoU+fPkhMTMTGjRuxa9curbqELJNktVrRt29fLSEjFOSNXV3/o26Hrq9Arq6TUtdEbd68GVlZWWhqatL6JiEhAVevXtUCcqB1Uv6Oyb6WfSwz+OSiXrXMkVr4Vb+Gyd+oqavEByLqPT0KUoWFhfj6669x8eJFWK1WPPbYY9iwYQNGjx6tndPS0oLVq1dj586duH37NnJycrB582akpKRo51RWViI/Px9HjhyBzWZDXl4eCgsL0afPvR8z9RlwciQVHR2NlpYWn2oQcrpPBoC+ffsiNjYWCQkJGD16NB5//HEtky8hIQG//fYbqqqq0NTUpKWzh4J6Lf6ClLyOrKwsmM1mn4QKtbhramoqbDabthtwTEyM9npzc3OXQSrQyEYGR1lxQk7v6UdRdzKSIqK7zyR68G7Lzc3F/PnzMXXqVLS3t+Ott97CuXPncOHCBcTHxwMA8vPz8d1336GoqAiJiYlYvnw5oqKicPz4cQD/3DQefvhhOJ1ObNq0CVVVVXjxxRexePFifPDBB0G1o76+XsvuCpSFZlTy5iu305D1+WJjY2G327UCsXa7HbGxsdp5sbGxSExMhMVi0c6xWCw+xwoKCrB9+3YkJCRodQHvNjXo6hfsqtlvycnJOHPmDBwOhzYdqF8M668skpp0IUdhwZZEUgvMyuAkEzHk9hoyKUN/rKWlxe/oqbtjRBQc+X6pq6uD3W4PeF6PgpTejRs34HA4UFpaiieffBJ1dXUYMGAAtm/fjrlz5wIALl68iAcffBBlZWV49NFHceDAATz99NO4fv26NrraunUr3nzzTdy4cSOoG2skBym5VYbc2t1qtSIhIQExMTFaYJLHLBaLNtUXGxuL+Ph4LSDJxAj5dVxcHMrLy3Ht2jXYbDZtHVYoyKw3dZpMn7YdExODnJwcWCwWnxGWzPhTKzvIz/pqD+pWHcEudpVTq+q6LPksTAZDtQq6HLnJ9U/BBicGKKKeCTZI/ae7WF1dHQCgX79+AICzZ8+ira0N2dnZ2jljxozB0KFDtSBVVlaG8ePH+0z/5eTkID8/H+fPn8ekSZM6/T/6dOr6+vr/0uyw0o865E1ZCOFT6dtkMmkVDORf9q2trT4p6DExMWhoaND2lRo+fDhGjhypVT8PRZCSoxU1LVufti0/ZNKHuqeUGpDkdKGagKEGrq72k/IXJGTwaG9v16pZqJ/VUZw61aemmjNxgii87vgu5vV6sXLlSmRlZWHcuHEAAJfLpU1lqVJSUuByubRz1AAlX5ev+VNYWIj33nvvTptqKGqQksFJCKHdmOVISz5XksFIfq3usqt+rT8m6wKG4nr0a7/UNUX6r/VBTAZifzvz+ps+DDZZQW2XGgz1dfjU9VtqCSQ5rahPkGBwIgqtOw5SBQUFOHfuHI4dO9ab7fFrzZo1WLVqlfZ9fX09hgwZctf/37tBvXHK6g3R0dE+U2Yyy69Pnz7aAl75jCk6OlpLU9fvuCv3hLJYLN1WLO/tawL+LTCrjq7Ur/1VxlCfXalThYEqO/Qko07/XErNQFSz+NTApCZO+BsxMUgRhdYdBanly5dj3759OHr0KAYPHqwddzqdaG1thdvt9hlNVVdXw+l0auecPn3a5+dVV1drr/kTyoWpd5s6klL/Uo+OjkZra6tWz6+pqcnvJoVq7T/1mPxeZgZ2VX/vbulpkoE6RajPqFOnCvWBrifUn6UGRXVkph+56Rfv+rtOIgqNHgUpIQRWrFiBPXv2oKSkBGlpaT6vT548GWazGYcOHcKcOXMAABUVFaisrERmZiYAIDMzE++//z5qamrgcDgAAMXFxbDb7Rg7dmxvXJOhqc9J1NRt/c63skK5WsVcv1Ouekx+BuBT3TyU/E2LyVGH+lk97q+agzwvUBp4T/mbjvQ36lOnJtV/6+9nEVFo9Ci7b9myZdi+fTv27t3rszZKbrIH/JOCvn//fhQVFcFut2PFihUAgBMnTgD4NwU9NTUVGzduhMvlwgsvvIBXXnnlvkhBD1Q81t+WGQB8qpgH2im3q601QsXfzTvQDV0fzAIdDzTd9l/aJ/+PO10DxQBF1DuCze7rUZAKdNPbtm0bFi1aBODfxbw7duzwWcyrTuVdvXoV+fn5KCkpQXx8PPLy8rB+/fqgs9EiOUj1xJ1cm9H6w0g3dSO1heh+d1eClFHcL0GKiOheFWyQCk36FxER0R1gkCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsNikCIiIsPqE+4G3AkhhN+viYgosnR3D4/IkZTH4wl3E4iIqBd0dz83iQgcini9XlRUVGDs2LG4du0a7HZ7uJsUserr6zFkyBD2Yy9gX/YO9mPvMXJfCiHg8XiQmpqKqKjA46WInO6LiorCoEGDAAB2u91wnR+J2I+9h33ZO9iPvceofZmYmNjtORE53UdERPcHBikiIjKsiA1SFosF69atg8ViCXdTIhr7sfewL3sH+7H33At9GZGJE0REdH+I2JEUERHd+xikiIjIsBikiIjIsBikiIjIsBikiIjIsCIySH366acYPnw4YmNjkZGRgdOnT4e7SYb37rvvwmQy+XyMGTNGe72lpQUFBQXo378/bDYb5syZg+rq6jC22BiOHj2KZ555BqmpqTCZTPjmm298XhdCYO3atRg4cCCsViuys7Nx6dIln3Nu3ryJhQsXwm63IykpCS+//DIaGhpCeBXG0F1fLlq0qNPvaG5urs857EugsLAQU6dORUJCAhwOB5577jlUVFT4nBPM+7myshIzZ85EXFwcHA4HXn/9dbS3t4fyUoIScUHqq6++wqpVq7Bu3Tr8+OOPmDhxInJyclBTUxPuphneQw89hKqqKu3j2LFj2muvvvoqvv32W+zevRulpaW4fv06Zs+eHcbWGkNjYyMmTpyITz/91O/rGzduxMcff4ytW7fi1KlTiI+PR05ODlpaWrRzFi5ciPPnz6O4uBj79u3D0aNHsWTJklBdgmF015cAkJub6/M7umPHDp/X2ZdAaWkpCgoKcPLkSRQXF6OtrQ3Tp09HY2Ojdk537+eOjg7MnDkTra2tOHHiBL744gsUFRVh7dq14bikrokIk56eLgoKCrTvOzo6RGpqqigsLAxjq4xv3bp1YuLEiX5fc7vdwmw2i927d2vHfv31VwFAlJWVhaiFxgdA7NmzR/ve6/UKp9MpNm3apB1zu93CYrGIHTt2CCGEuHDhggAgfvjhB+2cAwcOCJPJJP7666+Qtd1o9H0phBB5eXli1qxZAf8N+9K/mpoaAUCUlpYKIYJ7P+/fv19ERUUJl8ulnbNlyxZht9vF7du3Q3sB3YiokVRrayvOnj2L7Oxs7VhUVBSys7NRVlYWxpZFhkuXLiE1NRUjRozAwoULUVlZCQA4e/Ys2trafPp1zJgxGDp0KPu1C1euXIHL5fLpt8TERGRkZGj9VlZWhqSkJEyZMkU7Jzs7G1FRUTh16lTI22x0JSUlcDgcGD16NPLz81FbW6u9xr70r66uDgDQr18/AMG9n8vKyjB+/HikpKRo5+Tk5KC+vh7nz58PYeu7F1FB6u+//0ZHR4dPxwJASkoKXC5XmFoVGTIyMlBUVISDBw9iy5YtuHLlCp544gl4PB64XC7ExMQgKSnJ59+wX7sm+6ar30eXywWHw+Hzep8+fdCvXz/2rU5ubi6+/PJLHDp0CBs2bEBpaSlmzJiBjo4OAOxLf7xeL1auXImsrCyMGzcOAIJ6P7tcLr+/t/I1I4nIrTqo52bMmKF9PWHCBGRkZGDYsGHYtWsXrFZrGFtG9I/58+drX48fPx4TJkzAAw88gJKSEkybNi2MLTOugoICnDt3zuf58r0mokZSycnJiI6O7pSlUl1dDafTGaZWRaakpCSMGjUKly9fhtPpRGtrK9xut8857Neuyb7p6vfR6XR2Suppb2/HzZs32bfdGDFiBJKTk3H58mUA7Eu95cuXY9++fThy5AgGDx6sHQ/m/ex0Ov3+3srXjCSiglRMTAwmT56MQ4cOace8Xi8OHTqEzMzMMLYs8jQ0NOD333/HwIEDMXnyZJjNZp9+raioQGVlJfu1C2lpaXA6nT79Vl9fj1OnTmn9lpmZCbfbjbNnz2rnHD58GF6vFxkZGSFvcyT5888/UVtbi4EDBwJgX0pCCCxfvhx79uzB4cOHkZaW5vN6MO/nzMxM/PLLLz5Bv7i4GHa7HWPHjg3NhQQr3JkbPbVz505hsVhEUVGRuHDhgliyZIlISkryyVKhzlavXi1KSkrElStXxPHjx0V2drZITk4WNTU1Qgghli5dKoYOHSoOHz4szpw5IzIzM0VmZmaYWx1+Ho9HlJeXi/LycgFAfPjhh6K8vFxcvXpVCCHE+vXrRVJSkti7d6/4+eefxaxZs0RaWppobm7WfkZubq6YNGmSOHXqlDh27JgYOXKkWLBgQbguKWy66kuPxyNee+01UVZWJq5cuSK+//578cgjj4iRI0eKlpYW7WewL4XIz88XiYmJoqSkRFRVVWkfTU1N2jndvZ/b29vFuHHjxPTp08VPP/0kDh48KAYMGCDWrFkTjkvqUsQFKSGE+OSTT8TQoUNFTEyMSE9PFydPngx3kwxv3rx5YuDAgSImJkYMGjRIzJs3T1y+fFl7vbm5WSxbtkz07dtXxMXFieeff15UVVWFscXGcOTIEQGg00deXp4Q4p809HfeeUekpKQIi8Uipk2bJioqKnx+Rm1trViwYIGw2WzCbreLl156SXg8njBcTXh11ZdNTU1i+vTpYsCAAcJsNothw4aJxYsXd/rjk30p/PYhALFt2zbtnGDez3/88YeYMWOGsFqtIjk5WaxevVq0tbWF+Gq6x/2kiIjIsCLqmRQREd1fGKSIiMiwGKSIiMiwGKSIiMiwGKSIiMiwGKSIiMiwGKSIiMiwGKSIiMiwGKSIiMiwGKSIiMiwGKSIiMiw/g81wr5eGPp59wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 2: Feature Extraction\n",
        "\n",
        "The ViTFeatureExtractor processes the image into the format expected by the transformer model."
      ],
      "metadata": {
        "id": "lOwodlKLd4ff"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')\n",
        "\n",
        "inputs = feature_extractor(images=image_for_vit, return_tensors=\"pt\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188,
          "referenced_widgets": [
            "3aa31805140b41e5ba79de106c2855ab",
            "f46c84ddbf3a48fa8a801509dcd04297",
            "ebfa81b550de4435959b3f2c732007f9",
            "fe59c19e8d054a2ca4ec512558febcaa",
            "7cd8674326f84272b8d6ef8e56104d0b",
            "d2d705c9d9c8417b89f7e9cf82b109c2",
            "99e2aa47812645e8a8113bfe5a0ae128",
            "db920887010f4ee9acd5f2d622b9eb26",
            "45f9faa2ef94451d9a668492978fe622",
            "3ed7107338c8430d9afa914b5de5c09e",
            "0ac43697f7bc454996ebcfe102cce135"
          ]
        },
        "id": "EkEXpLH-dZw1",
        "outputId": "55d7dd3f-8a0d-449b-c7b7-5fc4f9b9725d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/160 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3aa31805140b41e5ba79de106c2855ab"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/vit/feature_extraction_vit.py:28: FutureWarning: The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use ViTImageProcessor instead.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 3: Load the ViT Model\n",
        "\n",
        "We load a pre-trained Vision Transformer model from Hugging Face's model hub."
      ],
      "metadata": {
        "id": "HfYNLwrqeJN3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224-in21k')"
      ],
      "metadata": {
        "id": "kMYQxZO-d-c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116,
          "referenced_widgets": [
            "92f7b5dfd0414ab0834fa768225b5915",
            "0cf4341255a943e3b7e2e6b564231a23",
            "1c84babdd02d40c8b7a959aff43ef623",
            "93bbdbb62bbc46fc8615cc4ac3b6f3c0",
            "86db63130e0842caa5bbcd3ebc685147",
            "ace751a90e1c4e5384bdd80040f74a77",
            "c724dcc993b44e88be508f2d8ad348cd",
            "b74c02e25e10470f838d4cea44257440",
            "33ccb79bb61b4bd0b2644d2cfa28e9c8",
            "9fff2e4db05644ed8fea942748b0bb95",
            "5da7d7026fd1434d9f643401835e7c01",
            "979ef8c344fb4308a8311a50b2c4f1d9",
            "b9b9af512fc44a8b9dc1419c9627dbde",
            "1262b5d1eda949b6bf64bcdb33b4813a",
            "a06330caedd74862b7cab51e29b995f9",
            "fa572803fca647e79f176d7173943662",
            "d16bcde845c548f182babdd8787db709",
            "7d98f456832b4a35a5e79d9dac1ac005",
            "8fa01c08a74e405694da70f03fe99f93",
            "e07b87fd78b4401ca017dbaa934f457b",
            "007902dfb9a34bcba6288c80261be9a8",
            "5bc00210724040159e4e652f8516e09e"
          ]
        },
        "outputId": "67df60cc-b7a4-4d06-b50e-f7fae1d07149"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/502 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "92f7b5dfd0414ab0834fa768225b5915"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/346M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "979ef8c344fb4308a8311a50b2c4f1d9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 4: Image Classification\n",
        "Now, we can classify the image using the transformer model."
      ],
      "metadata": {
        "id": "YC-_xVOKeOGZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "outputs = model(**inputs)\n",
        "logits = outputs.logits\n",
        "predicted_class_idx = logits.argmax(-1).item()\n",
        "print(f\"Predicted class: {model.config.id2label[predicted_class_idx]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "InAIsZlOeRo5",
        "outputId": "2f6848dd-a5ab-4ce1-ade5-bd77ef999dc3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted class: LABEL_1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conclusion\n",
        "In this tutorial, we've explored how transformers can be applied to non-NLP tasks, specifically image classification using Vision Transformers (ViT). The principles learned here can be extended to other domains such as time series analysis, where the sequential nature of transformers can be leveraged.\n",
        "\n",
        "Remember, the field of AI and machine learning is rapidly evolving, and staying updated with the latest advancements is key to leveraging these powerful models effectively."
      ],
      "metadata": {
        "id": "EDf0s8GOeX11"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7UTGPnfreTxU"
      },
      "execution_count": 7,
      "outputs": []
    }
  ]
}