09: docTR_OCR_Demo.ipynb

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            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "069f29bc5aca4a688cb27709e136842f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/OCR/blob/main/09%3A%20docTR_OCR_Demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "43tpfKq1IxQg"
      },
      "source": [
        "# TensorFlow\n",
        "# !pip install python-doctr[tf]\n",
        "# PyTorch\n",
        "!pip install python-doctr[torch]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zUbhZiRiIxbN"
      },
      "source": [
        "## From source\n",
        "\n",
        "Before being staged for a stable release, we constantly iterate on the community feedback to improve the library. Bug fixes and performance improvements are regularly pushed to the project Git repository. Using this installation method, you will access all the latest features that have not yet made their way to a pypi release!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJZgLM_CIzKf"
      },
      "source": [
        "# Install the most up-to-date version from GitHub\n",
        "# TensorFlow\n",
        "# !pip install -e git+https://github.com/mindee/doctr.git#egg=python-doctr[tf]\n",
        "# PyTorch\n",
        "!pip install -e git+https://github.com/mindee/doctr.git#egg=python-doctr[torch]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RvJ3PzAQI0zK"
      },
      "source": [
        "Now go to  `Runtime/Restart runtime` for your changes to take effect!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2mgRuCaJY4F"
      },
      "source": [
        "# Basic usage\n",
        "\n",
        "We're going to review the main features of docTR 🎁\n",
        "And for you to have a proper overview of its capabilities, we will need some free fonts for a proper output visualization:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Qhrp88TPDZi"
      },
      "source": [
        "# Install some free fonts for result rendering\n",
        "!sudo apt-get install fonts-freefont-ttf -y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fe7KuocKSWX9"
      },
      "source": [
        "Let's take care of all the imports directly"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xSoeo0hRJbnU"
      },
      "source": [
        "%matplotlib inline\n",
        "import os\n",
        "\n",
        "# Let's pick the desired backend\n",
        "# os.environ['USE_TF'] = '1'\n",
        "os.environ['USE_TORCH'] = '1'\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from doctr.io import DocumentFile\n",
        "from doctr.models import ocr_predictor"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7wwEmHqZSaLF"
      },
      "source": [
        "For the next steps, we will need a proper PDF document that will be used to showcase the library features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UIBwFsVuJocy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "29af4540-f08e-468e-8023-e5793e6b0dfb"
      },
      "source": [
        "# Download a sample\n",
        "!wget https://eforms.com/download/2019/01/Cash-Payment-Receipt-Template.pdf\n",
        "# Read the file\n",
        "doc = DocumentFile.from_pdf(\"/content/Cash-Payment-Receipt-Template.pdf\")#.as_images()\n",
        "print(f\"Number of pages: {len(doc)}\")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-05-26 09:10:07--  https://eforms.com/download/2019/01/Cash-Payment-Receipt-Template.pdf\n",
            "Resolving eforms.com (eforms.com)... 52.206.2.160\n",
            "Connecting to eforms.com (eforms.com)|52.206.2.160|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 16080 (16K) [application/pdf]\n",
            "Saving to: ‘Cash-Payment-Receipt-Template.pdf.2’\n",
            "\n",
            "\r          Cash-Paym   0%[                    ]       0  --.-KB/s               \rCash-Payment-Receip 100%[===================>]  15.70K  --.-KB/s    in 0s      \n",
            "\n",
            "2022-05-26 09:10:07 (232 MB/s) - ‘Cash-Payment-Receipt-Template.pdf.2’ saved [16080/16080]\n",
            "\n",
            "Number of pages: 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cM6PPiWpSmb0"
      },
      "source": [
        "docTR is, under the hood, running Deep Learning models to perform the different tasks it supports. Those models were built and trained with very popular frameworks for maximum compatibility (you will be pleased to know that you can switch from [PyTorch](https://pytorch.org/) to [TensorFlow](https://www.tensorflow.org/) without noticing any difference for you). By default, our high-level API sets the best default values so that you get high performing models without having to know anything about it. All of this is wrapper in a `Predictor` object, which will take care of pre-processing, model inference and post-processing for you ⚡\n",
        "\n",
        "Let's instantiate one!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WAoUcVQbKIfT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
          "referenced_widgets": [
            "0075a56ef2134889a76bfd8831cce335",
            "72c4e0697845401098522156adac2d8c",
            "168a8f659c624148a4c768f91c339b70",
            "b9030b8cdf1849ebb79c4ffc6b374829",
            "cfba2c8b98ca4c13964635a5b1dea3bc",
            "54d9ad0c42db4c90811ab4689043d960",
            "0394e53b414e498e93a2170f71f72ef6",
            "b8d67fce98f14fb3b747937eb9da47e9",
            "6a2c5c11973d48d2ae7a72b95b03eacd",
            "04049f4ee1354ae79fdfeddd3069a7aa",
            "aa0100a848474bcb958aba5d970a0682",
            "c09bf9f9ad584405acce0b82a4be7a02",
            "cea9d8e7017346e58c668d60181c1ba0",
            "528651482bc0402981f204a2f05ae5b6",
            "ed734a7d73bf4b37aa48d6ddf785be34",
            "2bfeafc1b7c6432baa66fdb034be4a7f",
            "c3d85a6d14004ed69ede5e59ae31c3a6",
            "37cda7a45ce24e32bf0c253cf47b4e13",
            "7dfdadb3bfb74215b911ca6199305394",
            "c3521a2abfa847a9a2c34108a26fce52",
            "3f8bbe838bf54f14bf547b668fb7afe4",
            "069f29bc5aca4a688cb27709e136842f"
          ]
        },
        "outputId": "28310606-7695-4c75-daaf-91086cf6c178"
      },
      "source": [
        "# Instantiate a pretrained model\n",
        "predictor = ocr_predictor(pretrained=True)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://github.com/mindee/doctr/releases/download/v0.3.1/db_resnet50-ac60cadc.pt to /root/.cache/doctr/models/db_resnet50-ac60cadc.pt\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/101971449 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0075a56ef2134889a76bfd8831cce335"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://github.com/mindee/doctr/releases/download/v0.3.1/crnn_vgg16_bn-9762b0b0.pt to /root/.cache/doctr/models/crnn_vgg16_bn-9762b0b0.pt\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/63286381 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c09bf9f9ad584405acce0b82a4be7a02"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PKKYSs0ET0XQ"
      },
      "source": [
        "By default, PyTorch model provides a nice visual description of a model, which is handy when it comes to debugging or knowing what you just created. We also added a similar feature for TensorFlow backend so that you don't miss on this nice assistance.\n",
        "\n",
        "Let's dive into this model 🕵"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "urR0KyUYOT-B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13fe76cd-c625-445b-bbc4-d643d4cae03f"
      },
      "source": [
        "# Display the architecture\n",
        "print(predictor)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "OCRPredictor(\n",
            "  (det_predictor): DetectionPredictor(\n",
            "    (pre_processor): PreProcessor(\n",
            "      (resize): Resize(output_size=(1024, 1024), interpolation='bilinear')\n",
            "      (normalize): Normalize(mean=(0.798, 0.785, 0.772), std=(0.264, 0.2749, 0.287))\n",
            "    )\n",
            "    (model): DBNet(\n",
            "      (feat_extractor): IntermediateLayerGetter(\n",
            "        (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
            "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu): ReLU(inplace=True)\n",
            "        (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
            "        (layer1): Sequential(\n",
            "          (0): Bottleneck(\n",
            "            (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "            (downsample): Sequential(\n",
            "              (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (1): Bottleneck(\n",
            "            (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "          (2): Bottleneck(\n",
            "            (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "        )\n",
            "        (layer2): Sequential(\n",
            "          (0): Bottleneck(\n",
            "            (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "            (downsample): Sequential(\n",
            "              (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (1): Bottleneck(\n",
            "            (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "          (2): Bottleneck(\n",
            "            (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "          (3): Bottleneck(\n",
            "            (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "        )\n",
            "        (layer3): Sequential(\n",
            "          (0): Bottleneck(\n",
            "            (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "            (downsample): Sequential(\n",
            "              (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (1): Bottleneck(\n",
            "            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "          (2): Bottleneck(\n",
            "            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "          (3): Bottleneck(\n",
            "            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "          (4): Bottleneck(\n",
            "            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "          (5): Bottleneck(\n",
            "            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "        )\n",
            "        (layer4): Sequential(\n",
            "          (0): Bottleneck(\n",
            "            (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "            (downsample): Sequential(\n",
            "              (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "              (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (1): Bottleneck(\n",
            "            (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "          (2): Bottleneck(\n",
            "            (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (fpn): FeaturePyramidNetwork(\n",
            "        (in_branches): ModuleList(\n",
            "          (0): Sequential(\n",
            "            (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (1): Sequential(\n",
            "            (0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (2): Sequential(\n",
            "            (0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (3): Sequential(\n",
            "            (0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "        )\n",
            "        (upsample): Upsample(scale_factor=2.0, mode=bilinear)\n",
            "        (out_branches): ModuleList(\n",
            "          (0): Sequential(\n",
            "            (0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "            (3): Upsample(scale_factor=1.0, mode=bilinear)\n",
            "          )\n",
            "          (1): Sequential(\n",
            "            (0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "            (3): Upsample(scale_factor=2.0, mode=bilinear)\n",
            "          )\n",
            "          (2): Sequential(\n",
            "            (0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "            (3): Upsample(scale_factor=4.0, mode=bilinear)\n",
            "          )\n",
            "          (3): Sequential(\n",
            "            (0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "            (3): Upsample(scale_factor=8.0, mode=bilinear)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (prob_head): Sequential(\n",
            "        (0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (2): ReLU(inplace=True)\n",
            "        (3): ConvTranspose2d(64, 64, kernel_size=(2, 2), stride=(2, 2), bias=False)\n",
            "        (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (5): ReLU(inplace=True)\n",
            "        (6): ConvTranspose2d(64, 1, kernel_size=(2, 2), stride=(2, 2))\n",
            "      )\n",
            "      (thresh_head): Sequential(\n",
            "        (0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (2): ReLU(inplace=True)\n",
            "        (3): ConvTranspose2d(64, 64, kernel_size=(2, 2), stride=(2, 2), bias=False)\n",
            "        (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (5): ReLU(inplace=True)\n",
            "        (6): ConvTranspose2d(64, 1, kernel_size=(2, 2), stride=(2, 2))\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (reco_predictor): RecognitionPredictor(\n",
            "    (pre_processor): PreProcessor(\n",
            "      (resize): Resize(output_size=(32, 128), interpolation='bilinear', preserve_aspect_ratio=True, symmetric_pad=False)\n",
            "      (normalize): Normalize(mean=(0.694, 0.695, 0.693), std=(0.299, 0.296, 0.301))\n",
            "    )\n",
            "    (model): CRNN(\n",
            "      (feat_extractor): Sequential(\n",
            "        (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (2): ReLU(inplace=True)\n",
            "        (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (5): ReLU(inplace=True)\n",
            "        (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "        (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (9): ReLU(inplace=True)\n",
            "        (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (12): ReLU(inplace=True)\n",
            "        (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "        (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (16): ReLU(inplace=True)\n",
            "        (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (19): ReLU(inplace=True)\n",
            "        (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (22): ReLU(inplace=True)\n",
            "        (23): MaxPool2d(kernel_size=(2, 1), stride=(2, 1), padding=0, dilation=1, ceil_mode=False)\n",
            "        (24): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (25): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (26): ReLU(inplace=True)\n",
            "        (27): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (29): ReLU(inplace=True)\n",
            "        (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (32): ReLU(inplace=True)\n",
            "        (33): MaxPool2d(kernel_size=(2, 1), stride=(2, 1), padding=0, dilation=1, ceil_mode=False)\n",
            "        (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (35): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (36): ReLU(inplace=True)\n",
            "        (37): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (38): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (39): ReLU(inplace=True)\n",
            "        (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (42): ReLU(inplace=True)\n",
            "        (43): MaxPool2d(kernel_size=(2, 1), stride=(2, 1), padding=0, dilation=1, ceil_mode=False)\n",
            "      )\n",
            "      (decoder): LSTM(512, 128, num_layers=2, batch_first=True, bidirectional=True)\n",
            "      (linear): Linear(in_features=256, out_features=124, bias=True)\n",
            "      (postprocessor): CTCPostProcessor(vocab_size=123)\n",
            "    )\n",
            "  )\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dlrtkNzTURAo"
      },
      "source": [
        "Here we are inspecting the most complex (and high-level) object of docTR API: an OCR predictor. Since docTR achieves Optical Character Recognition by first localizing textual elements (Text Detection), then extracting the corresponding text from each location (Text Recognition), the OCR Predictor wraps two sub-predictors: one for text detection, and the other for text recognition."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uyVfbzhUL-Rw"
      },
      "source": [
        "## Basic inference\n",
        "\n",
        "It looks quite complex, isn't it?\n",
        "Well that will not prevent you from easily get nice results. See for yourself:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RLPsdCj3PWzI"
      },
      "source": [
        "result = predictor(doc)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eFAwQaKuPYXG"
      },
      "source": [
        "## Prediction visualization\n",
        "\n",
        "If you rightfully prefer to see the results with your eyes, docTR includes a few visualization features. We will first overlay our predictions on the original document:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sOcqPNnPMAE7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 751
        },
        "outputId": "b4f5f429-3edd-4134-b185-d6905b36df5a"
      },
      "source": [
        "result.show(doc)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 556.364x720 with 1 Axes>"
            ],
            "image/png": 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MAH6/j7KysgHnSU5OprS0FKPRiN0eGBMVFRWF0di/yy3cz11Qb28vW7duHbB96tSpzJs3b9hA5+DBQ9TWngECAXFnZyd+v4+cnBFkZ2cPeawQkUwCHRGRgg8UTdNCD6++AtN/tX4PHp/PR3l5OW63i+zsbLxeL36/nxEjRnDxYhPl5eXMnTs31Ow/ZswY/umf/onq6mqOHj1KdXU19fX12O123n//feLj40NjRSDQXbBo0eJ+XRONjY3s3r37mu/L6XSGulN0Oh2ZmZkUFRVx7733hgaU+v1+9u7dS2+vk7y8kSiKgtPpJDc3j7Nnz1JeXs6CBQtCn/KD3RerV6+mqqoSt9vDypUrSUpKGvShnJubR3R0FGfPnuXIkSN0dHSQn59PQkJi2NdDYGzPkiVLiIuL67c9Pj5+QEABMGnSJEpLS9myZQvvv/9+vxarG0VRVHJzczEajdTX1+Pz+Zg1axZr1qwZMIsNAtPPS0tLycjI6LfdarWGgtpgF5rf7+fq3qZwP3d/PkcUS5cuISqqf+tLamrqsEEOEFpKIDi9PCUlhfz8fL75zW+GllIQ4utIAh0RkSwWM9HR0XR0tNPS0oLf7+/3sGhtbWXnzp2MHj2agoICTCYTra2XQ9N0Kyoq+NnP/h+A0LT06upqGhvPMXLkKBoaGmhubiY+Pp5JkyYxefJknM5eGhsbeeGFFzl1qoaDBw+yfPny0DXNZgsrVqwIPSQ1TWP//v3s2bPnmu9r+fLlrFq1KvTQMhgMREVFodf/eV2Wjo4ODh8+BGicPn2af/7nnwGBaeoAZ86cob6+nvHjx/dr1UlKSsJkMuH3a6Ep8INJTEwgPT2DU6dOsX37djweD6NGjQoFW+EYDAYWL15MXl5e2P1Xj0ExGAysWrWK48ePc/bsWTZv3ozb7Q4bFF0vvV7P/fffz+TJkzl48CAvvhiYbl9ZWRk2wFBVlTvuuIPi4uKwgYPP57sy80mhs7OTnp6efq16wXFHJpOJoqKifrOcrFYry5cvJzU1bcB5YfgxOtOnT+exxx4LrT1kMOixWqMwGAwS5IivNVksQUQkk8lMYWEBoHDixAlaWlr6THf28fnnn7N27Vr+4z/+g6qqKgCOHz/BpUuXMBiM2Gw29HpDaF0Vo9GE3W5n3779eL1e3n33XX7xi1/w+uuvhxbVs1is5OWNJCcnBwi0voRrTfoyoqKiSE5ODn0FFxnsu9BfVVUlTU0XQ2UP3kd0dDQmk5menh727t37pcpmNlsYPXo0Xq+X2traK91Zhf1mZ90IaWlp3HvvvRiNRs6fb8TjCT+j68vQ6/VYLBZmzZrFypUrcTqd/OEP62loaBg2uLiaqqqMHz8eo9HI5cuBwDnYAqdpGg0NDaxfv57//u//5t13372hPx8Wi4Xk5GRSUlKu/GzEh53NJsTXjbToiIik0+lYuHARBw4c5Pz58zz33HPcddcq4uLiOXHiBO+++w4+n5fMzEzy8vJwuVxXHv5e5s6dH/pkDIEH1B/+8Ac++OADDhw4wMqVKxk/fjy7d++msrKStWvXsnDhQkwmE8eOHWP//v2AwogRI25o68O18Hg8lJfvxeNxM3NmCU8++WS/lZg3btzI22+/zaFDh7j77rtJSEi4rgehqqqMGTOGLVu24PN5r4zPGUFPT8+gx3i9Xvbu3Uttbe2AfWlpaaG1iK6+zqxZs9i/fz+7d+/6wuX8InQ6HUuWLAmV8b333uP73/9+v1YqTdM4evRoaIXmvmJjY5k8eTJFRUWMHTuWo0eP8Prrr+NwOBgzZgytra288847XL58iaioaIqLi/u1GDmdTsrKyoiJsQ04d3Z2dmgKuBDii5FAR0Sk4Biab33rW6xdu5ajR49w7NgxVFW98ilaIzU1jUceeYTY2Fjq6upCa87MnDkztLw+BB5uJSUlbN++ncbGwEyjOXPmcOTIEcrLy9m69SO2b99+ZUXhQItDXl4eK1asuKaxFTeKpmm0tLRQWVmJquooKZnZL5AJ3sfWrVu5ePEiJ06c4M4777zu6+Xm5hITE01HRwfp6ekkJiYOGei4XE7WrVsXdt+dd95JYWFh2H0mk4n77ruP6upqLl1que7yDkdRAmk4li1bxosvvsjeveXMmTOHGX1ycvh8XjZufA8YGByOHTuWcePGERMTw+OPP86zz/4Pp06d5uWXX0an010Zs+PHZDJz11139es6BLDbu3nlld+HLduqVasYPXr0jb5lIb4WJNAREUuv17N48WJycnLYtm0bZ86cobe3l6goK4WFY1i8eHFo8TqHw8G0acVYLFbGjRvX7wGkKAqjR49m+fLltLW14XQ6iYqK4qmnnqKwsIADBw7S1tYGEJqttWDBgtD6PBkZGZSWlmK1WgcMqE1MTAzNqLHZbHR2DrwPg0HP9OkzGDly1LALENrtdiZNmoTJZGTixEn99ilKoJVpxYoVoVWE+45dslqtzJlzB263e8CAYQgMii0tLSUxMRG9Xk9SUhJLly6jubmZoqIizGYzNpuNO++8E71eF5pGH5iRNA+fb/A8UWPGBLq9RowYQWnpfDIzM0PlCpb7kUce4ciRI5hMJmy2/mvNmEwmZs2aRVdXV9gFCa+m1xsoKSnBbrf3WxAw2IJ04cIF2tvbuXTpEj6fD5sthjvuuCO0MnM4wVWNFUUhLy+Pn/70H9m+fXtosHZw5ePS0lKmTZsWau3LyMhg/vz5Q3aTjR49OnTe0tL5xMbGYjKZUFWVSZMmERcXN2BdnqEkJSVRWjoPVdUNmEIvRKRRhumD/mId1ELcBjZv/nNSzyC/34fL5cbv911J3GgMmwjziwomAA0mSzQYAuNhvsxYlRkz+mfavvpexBd3q+q078+HoqgYjYZ+CT1vV33rS4i/IGF/saRFR0SkqzOAgw648dOTA79XpitfX42B9yK+rJtXp1/9z4cQYmjSoiOEEEKISBC2RUemlwshhBAiYkmgI4QQQoiIJYGOEEIIISKWBDpCCCGEiFgS6AghhBAiYkmgI4QQQoiIJYGOEEIIISKWBDpCCCGEiFgS6AghhBAiYkmgI4QQQoiIJYGOEEIIISKWBDpCCCGEiFgS6AghhBAiYkmgI4QQQoiIJYGOEEIIISKWBDpCCCGEiFgS6AghhBAiYkmgI4QQQoiIJYGOEEIIISKW/lYX4Ha3efOtLsGNt28fzJhxq0sx0KpVt7oEQgghIo0EOtdg3z7QNA1N0/D5fCiKgqqqKIqCoii3unjXZe/ewP0Ey68oCpqmAYS238x7ux0DLyGEEH/5JNC5Bn6/n+TkOj788EMaGhpQVZX8/HyWL19Oenr6X1Sws29f4F+Px01NzRvk5eVx5513otPp0DRoaGhg9+7d3HvvvVgslptaJiGEEOJGkzE6w9A0jebmZp577lliY2N58skneeyxx+ju7ua5556jra3tVhfxuvh8Po4cOcK6dW9QV1cXas3p7Ozk+PHj+Hy+W1xCIYQQ4suTFp1heL1eDh8+TG5uHg8++CBGoxFN00hNTeX3v/89zc3NJCQk0NnZwd69+7h8+TJZWVkUFxdjtVqpr6/H7XZTU1OD0+lk6tSpuN1uDh8+TFpaGrNmzcJoNHL48CF0Oj01NTVYLBZKSkpISUnB7/dTXV3NsWPH8Hg85ObmMm3aNMxmM2fPnsXtdtHYeJ6mpiYmTZrEmDFjOHPmDC6Xi3HjxqEoCt3d3VRWVjJp0iTgz600er2ehIRENmzYwNNPP01UVNSAe6+srKSiogKv18uoUaMoLi5Gp9NRWVmJXq/n2LFjxMTEMG1aMdXVNZw7d47x48czYcIEVFWlt7eXAwcO0NDQQFpaGiUlJcTExNDZ2cnp06cZN24sYL25b6oQQoivDWnRGUZvby/NzReZOHEiBoMBCIxniY+P5+mnn6awsBCHw8FLL73MyZNVpKWlsWfPHl599VV6e3vZuXMnzz77LF1dXdjtdv7zP/+Tt956i9jYWMrKPufDDz/E43HzwQdbeOONN4iLi6Orq4vnnnuOlpZmqqqqeOmll7BYLGRkZPDhhx+ybds2NE2jvLycX/3q19TX12O1Wvn9739PTU0Nra2t/PGPf6SnpweAI0eOsHPnzgFdbDqdjuXLl9PV1cWnn36K3+/vt//YsWO88sorREdHk5aWxsaNG9m1axdut5t169bx+uuvY7FYOHz4MP/6r/8vhw8fJjo6mnXr1lFdXY3X6+UPf1jPZ5/tIDk5mRMnTvDaa6/hdDppaWlmy5Yt2O2Om/NGCiGE+FqSQGcYHo8Hj8dLTExMv+2KomAwGFBVlZMnT9Le3s63v/0YCxYs4Dvf+Q5nztRSX1+Pz+cjMzOTBx54gLvvvhuj0ciCBQtYsWIFCxYs5Pjx47hcbgAWLlzAkiVLePDBB0lMTGT37s+xWq08/PDDrFixgjvuuIOJEydSU1MTGhydlJTEQw89yKpVqygsLOTw4cMUFhbi8Xior6/H6/Vy8OBBpk6dislkGnB/iYmJ3HfffWzbto3Tp0+HtmuaRkxMDI888gjLly9n7ty5jB07lurqavx+Pz6fjzlz5rBq1Sq+8Y1v4PF4eOCBB1ixYgUjRoygsrKS8+fPc+zYcb71rUdYuHAha9as4fz58zQ0NDBy5Ch+9KMfkZiY+NW+gUIIIb7WpOtqGAaDAZ1Opb29vd92TdPo7OzEYrHQ0tJCQkICUVFRKIpCbGwsMTE2Ll26BEBGRgZ6vR69Xo/ZbCYpKQlFUTAajfh8PjRNw2AwkJWVjaIo6PV6cnJyOHv2LIsWLeLUqVP86le/wm6309bWRkZGeqgcqampGI0mVFUlOjqa7u5uoqOjGTduHEeOHCEuLo7W1lYmTJgw6KDpiRMnMm3aNP74xz8wf/4CIBDIpaamcupUDb/85S/p6enh0qVLjB49GgBVVcnIyEBVVYxGIzabjZiYmNB9eTweLl++zOXLl/nNb35zZbCzRltbG62trRQWFt60wc5CCCG+viTQGYbFYiE5OZlDhw5xxx13hFpFHA4Hzz//PNOmTcNiseBwOPB6Pej1ejweD729vVgsFhRFQafT9QsywgUcfr8PhyPQjaNpGna7HYvFwkcffURlZSX33XcfaWlpbN++nYaGhtBxqtq/UU7TNFRVZfr06bz55puYzWZGjBgxZMuJwWBg1apV/Nd//VeoW8zv97Nx40YaGhq45557SElJYcuWLXR0dITuoe+1w92fwWAgOTmZv/7rvyY6Ohq/309LSwvZ2dnXXP9CCCHElyFdV8PQ6/VMnDiJ06dP8+6773Lx4kWampp47733uHz5EuPHjyc/P5+uri7KyvbQ3t5OWVkZfr+f3NwR13wdj8fL9u3bOXfuLCdOnKCiooLi4mLa29vJyMhgxIgR9PT0cOLECZxO54DxNH0pisKIETkYjUa2b9/OjBkz0Ol0Q14/Pj6eBx54gNraWtxuN36/n9bWVrKzs8nOzg4NaHY6naEZWsPJysrCYDBQVVWFTqfj3LlzrF27ls7OTlpbW9m9e1couBNCCCG+CtKiMwxFUcjJyeaJJ55g48aN7N27F1UNdOt873tPkp6ejt/v58EHH2TLli189tln6HQ6Hn74YZKSkklMTMRoNAKB1pesrCzMZjMAUVFRZGRkhFp9LBYLr776KkajiVWrVjFx4kRsNhvr16/n2WefxWAwMHXqVGpra7Hb7SQkJGAyGUNljY+PD7U4mc0WioqKcDgcjBo1akArkqIEytK3+2j8+PGsXr2ahoYGjEYjS5cu5a23NvDss89iMpkoKSmhrq4Op9NJZmYmVmtgtpTFYiEzMzPUwpOYmIjFYiE+Pp5vfetbvPfee5SXlwOwcuVKsrKyqKurY9eu3RQWjgECs72uNYAS4i/BX9L6WkJEMmWYh8vX/smzeXNgQbvp0zXcbjcdHR2oqkp8fHyoSypYh263m56eHqxWayi4Ce4L/tHru+pwcECx2+3mF7/4/1i4cBHTp09HVdXQysvB/T09PURHR6PT6fD5fP1aaPqeO/i91+tl7dq1ZGRksHTp0tBrgovzaZrG9On9V0C+emVkAJfLhdPZS1TUwGtffR/hyhGsF4fDgcVixmy2DDhm/36FGTNg5UqNymjZ1KoAACAASURBVMpK6urqBrwPwfoIt76PxWIJ29IUDCC9Xu+AY8xmc6jl6moGgwGPxzNge3BpgXD7BjtmuHK7XK4BZRiq3CaTCZ/PF3bfYGXQ6/XodDpcLtc1HxPsmhys3G63O+w+vV4ftmzB3we3233Nx+h0OoxGI729vWHLF+5v11Dltlqtg7aGDlYGk8mE3+8ftF4Hex9mzpxJXFzcgH1CiK9U2E8X0qJzjRRFwWQykZqaGnYfBP4oXj2zaWBLSv+xLMHvVVWHqqro9foBr7n6vFePy7n63BcvXmT9+vX09vbyzW9+c9DXHjhw9c+EctW/AOYrX6GrhzvbVcdcfV7Tla+hjgnw+XyDPngVRRn0IeXxeMIGOl6vN+wxOp0Oj8cTdl9wVlm4Mvj9/rAPt6GOgfCtVcFyhwt0Biv39ZQhOOYqXL0OVe7B6lun0w0aJPp8vkHLPViQONgxwW2DHRMMIq++DoSvb7fbPeh7fiPLPVgQJoS4NaRFZwjB1pyaGigo+Oqu4/f7aGpqwmaLJTo6etAm72vNB+VwOKioqCAvLy80wyso2KJzO+aWkqSeQgghvgRp0bkeNTVw4cJXfRUdkMWNG5cbBcygrg6u7gX6qoO2oUggI4QQ4maTQOcapKdreL1euru78XjcmExmoqOjUVXdgGbqGzUAse84l2CgFfxX03y43d6wXV19BcZyeDAaDSjKn8f0aJqGw+FAVXU3bS2b27EFSQghROSTQGcYgbxWnXzyyce0tbVjNBpxuVykpaWxaNEioqKisNvteDwe4uPj+bJxjqZpOJ1O7HY7iYmJqGr/ExYUQEdHN++++w4ZGRnMnbsIvT7829jU1MKf/vQn7r777tAMKQC/X+PEifVkZWUxb96yL1fgayDZyYUQQtwqEugMIz9f49y5T7DZqvnxj7+HzRZY8fj555/H4dAxb95yfve79aSlpTF9+go0zY+mafh8PvR6PaqqhgaPBlc9Dg4m1jQtNOhUp9OFVg/+8MPtXLhwgSVLHkOv7z9od/p0jT/96RA5Oc10dZ0iN3ciGRkZof3BcwLExnaxa9chpkxZgc2mhQaeqqrKvn09uFyufq/3+/0YDIZ+g2GD5Qq2XAW3921NCrc9eL5AmVQG6ToVQgghvlIS6AxLw+VyYbVaMZvNGAwGMjMzefTRR1EUherqak6cOEFjYyN5eXlcuHCBM2dqaWw8z7Jly5gwYQKbNm2iqqoKo9HInXfeydy5c9HpdBw/fpwtW7bQ1dVFVlYW99xzD36/n71792K329m3bx8lJSUExvAEBDOfz58/n3379oWyoAen1O7fv5+dO3cCkJaWhqYFgo329nbef/99zp49S1paGm1tbaFFCNeufQ2v10dHRwff+973qKur4+OPP8bhcJCbm8s3v/lNkpKSqK6u5r333qOrq4vo6GhWrVrFuHHjOH78OB988AEOh4O4uFjuueceRo/OZ9++fZw/f56MjLuRHzUhhBC3gqyMPAxFUZkzZw6XLl3i5z//OS+//BK7du0iJSWFgoIC4uLiiI2NJSUlhfj4eFpaWqisrGL16tUUFBSwefNmmpqa+Ju/+RseeOABtm/fTlVVFRcvXuT1119n5syZPPPMMyQlJbF+/XoMBgOJiYnExcWRmprab/yNpmlcuHCB9vZ2CgoKmDp1KgcOHAitM1JbW8u7777L/PmlLFu2jOrqalwuN16vl7fffpve3l7+6q/+iqSkJE6dOgUExvFUVFRiMpm49957aWtrY8OGDSxYsIC///u/x2w289Zbb+FwOHjvvfcoKCjgxz/+MZMnT2LXrl10dXXxzjvvUFxczI9+9CNGjhzF7t278fl8REVFER8va4kIIYS4deRj9jAURSE3N5ef/vSnnDhxguPHj7Nx40Y2bdrIY489zrhx40hKSiI7O5v09ECyzfz8fKZOnYrD4eDo0aPMmTMHu90eSpR55MgR0tLSAEhKSgq1rpSXl+PxeMjMzMRkMpGbm9tvzRxN0zhy5DAxMTH09PQQFxfH5cuXqaurY/z48Rw+fJi8vFymTi1GVVVcLifr1r1JZ2cnNTU1PPnkk4wcOZKsrCwqKipC5zUaDcyYMYPCwkI2b96MxWLBZrPR3t5OXl4eGzcGWnFsNhtVVVWkpCQzadJk5s9fgKIoxMTEcOLECWJjY5k9ezaJiYnodDomTJiApmns3y/dVkIIIW4NCXSG4fP5qKqqJj09nTlz5jB79mx6enp45513eO+998jPzx9wjNlsRlVV3G43drudI0eOcPr0aSAQrIwaNZKuri46OjrYunVrqNXm6sDmai6Xi4MHD9HT08PLL78cWjW5vLycwsJCOjo6iImxhRZSS05OwWw243Q68Xq9oezqBoOh38KHqqoLLUjY2dnJ5cuX+fDDD/uUKw+DwcDDD/8VZWVlfPbZTt56622Ki4u5//77efzxx9m9ezeffPIJ7e3tzJkzh9WrV4fG+8hK+EIIIW4VCXSG4fP5eOeddxg5ciT3338/er0eq9VKZmYmNTU1fQbcDpxibjabiY+PZ+nSpZSUlOD3+zl48CDR0dG0tLSQlpbG3/zN32C1Wmlra+PgwYPYbLawK6tqmkZLSwsul4sf/vCHxMbGAnDixAk2bPgjnZ2dJCUl0djYiNfrRa/Xc/HiRZxOJ1arFb1eT3t7O6mpqbhcLpqaLpCcnNyvzIqikJiYSE5ODk8//TQmk4mWlhaOHTuGwWDg8OHDFBdPY8mSpZw8eZIXXniB4uJiLly4wJ133smKFSs4cuQIa9euZe7cO0hKSr6S6kGHDEYWQghxK0igMwy9Xk9paSmvvfYaly5doqCggPb2dvbt28eSJYsxmUxER0dz6NAhJkyY0O9Yi8XCrFmz2LhxIz09PfT09LBjxw7WrFlDUVERn376KW+++SZjx45l7969KIrC3LlziY6O5vPPP+fEiRNMnDgRUNE0P6dPnyY/P5/U1NTQzKZx48ZhMBg5fvw406dPY//+/WzZsgWLxcL27dvx+XzExsZSXFzMxo0baW29TE3NKWprzzBx4qQB9ztlyhR27drFhg0bGDlyJLt27SIuLpaSkhJOnDjB4cOHmT17NmfOnCEmJoaYmBgOHDhAdXU1xcXFVFVVER8fj8ViZffu3Zw9e5ZRox4CDDfh3RJCCCH6kxQQQwimgJg61UtVVRUHDhygvb2d6Ohopk6dyqRJk9Dr9TQ1NVFWVsaECRNCU8mLiopQFOVKd9PB0Kyr4uJiCgsLUVU1dFx7ezuZmRnMnj2H2NhYOjs7+OyznWRkZFBcXMyBA4EZVadOnWLePAs5OTmhViOfz8exY8fQ6XSMHz+e+vp69uzZg6ZpjBs3jp6eHmbMmIHP52PPnjLq6urJysoiPj6epKQksrKy2Lt3L0VFRcTHx6NpGo2N59izp5zu7m5ycnKYNWsW0dHRtLe3s3v3bi5evIjNZqOkpIScnBxaW1vZvXsXly5dJiEhgVmzZpGRkUF1dTVtbW0oykxmzdKxahWhZJ6DJWQcbDuEz190PccM5XryFN3o8g11zGBlu97r3IxyD3e+L3rMUG5WGYItoJKhXIjbSthfSAl0hhAMdGbM+HNixGDG7b4JBftm4g73hy94LBD2OE3TQmNzwv1h/SKDeQPn8185V/+kh8F9ivLnaw1+Dg3QBj0H/PkPfd/X993e14wZgRQQHR0dvPTSS3R0dPTbrygKRqMRt9s94P6NRiNms5murq4BZTUajWETYKqqSlxcHG1tbQOOCbaGXZ0YM9h119raOqAMOl0g6Wq45I7x8fF0dXUNSI4ZvKdwWcNjYmLweDw4nc6wx4Srh+CaRuEygMfFxdHd3R22DCaTacB1gmVwuVxhz2exWMJmDbdYAtnne3p6Buwzm82htZn6MhgMWCyWQd8/n8/3hcptMBgGTUY6WLmHuk5cXBwdHR0Dyh1cJyrcez5+/HgeeOABDAZpqRTiNiKBzhf1z/8cyA11swyXg+qrTqPwVa9gPGMGLF8eWK8nXNbnwQyVSXsowcUab8djrrfF6UaWQVXVQVvXBnO95b6e8t0s11M2k8lETEyMtOgIcXuRpJ63s4KC4QOZm5EU86sOdnQ6HYmJiV/tRYQQQogrJNAZQjDwCP47XBfV9bo6uOj7KbvvtTZvvmGXHLIcX0XLkeS7EkIIcStIoHMNNE2ju7ub/fv3cfp0LXq9nsLCQqZOnYrFYsHhcHD48GGmTp2K1WoN5Xz6osHQvn3Q29vLqVM1nD9/AUVRyMjIID8/H7PZ3G88zHDBlt/vv+6AzOVyUVZWht1u77ddr9dTUlJCY2MjZrOJ/Pxh+tqEEEKIW0wCnWFomkZbWxsvvPACPp+P4uJi3G4XGzdu5OjRo3znO4/jdDqpqqqiqKiI5uZmGhoamDdv3hcOMrxeD1VVv+fMmTqKi4vRNI2DB7fhcuXyxBNPoNfr2bOnjNTUNEaPHj3oQnwOh4MdO3Ywd+5cbDbbNV27b4uL3++nqamJ9vb2UB2cO3eOS5cuMW7cOBoaGrDZbBLoCCGEuO1JoDMMTfPzySef4PF4+MEPfkBMTAwAU6ZM5de//jWnTp1m7Nix3HPPPVitVj766CNaWpqZMWMGdrsdi8USOqa3t5euri6Sk5PR6XQDrmW3O6isrOKpp55izJgxAEybNo3f/va3tLa2EhUVxdatH7N48WJyc3NRFIXz58/T0dGB0WgkOzsbq9XK+fPn2bZtGxMnTiQqKgog9LqEhATS09OHbHEym8089NBDoe8vXrzIr3/9a1auXElmZiZxcXHodDo8Hg8tLS3o9XpaWlqw2WxkZmaGZgZ1dXWRkJAQ9l6FEEKIm0ECnWG43R6OHTvGnXfe2W+WRXZ2Nj/96U+Jjo6itbWVF198kaVLl1JeXk5Pj4OPP/441PXz8MMPo6oqn332GY2NjTz22GNhH/5GoxGLxcL+/fuxWq2kpqYyYsQIfvSjHxEdHc22bdtoaKjngw8+IDExkZqaGvbu3UtmZiZtbW1YrVYef/xxtm7dSlNTE3/84x957LHH2L17N3v27CExMZG2tjYWLVpIaen8QQOQvtPGHQ4H69atIysri298Yxk6nY5NmzYRFxfH7Nmz+dWvfoXRaCQpKYnW1lYmTJjA6tWrOXXqFOvXr+eZZ56RwcdCCCFuGQl0huHxBPJV9U2XAIHZQ8FtbW3tdHV1kpWVxYwZM2hpaWHJkiWcPn2a9evX093djdls5tChQyxYsCC0jsvVzGYza9as4Z133uHf//3fsdlsjBs3joULFxIfH8/s2bMpKytj0aJF5OTkcPDgQdasWcOYMWNobm7mF7/4BZcvX2bx4sWcOXOG++67j87OTj777DO++93vMnr0aGpqali7di0TJ04acE8D793Du+++i8Ph4PHHH8dstgDgcNgxm81X/u9g6dKlzJs3j6amJn79618zfvx48vLyeOKJJ66560wIIYT4KkigMwxVVVFVdcDCZZqm4fV6+7WKGAx6TCZTqGVm1KhRmM1mamtriY2NxeVyMXbsmEG7jBRFYcyYMfzkJz+hubmZ06dPs2/fPn75y1/ygx/8gISEhFCurejoaFasWEF1dTUbNmygubmZtrY2PB4PNpsNnU5HVFQUFRUVdHZ2smPHDj7//HM8Hg9tbW00NzcPGej4fD527NjBkSNH+Nu//VsSEhLCLmYYHR3NmDFjUFWV1NRUMjLSOXXqFGPGjCE3N/f6K14IIYS4AQZPlS0AMBpNJCYmUlVV1W9RMY/Hw/r16ykvLx/0WKvVyqRJkzh48CCHDh1izJgxxMQM3sJx4cJ51q1bh6Zp5OTkMH/+fP72b/8Wi8VCZWVlv9fa7XZeeOEFysvLSUpKoqSkJJTosy+/34/NZqOoaDxFRUVMnjyZRx99hIyMjEHLoWkaJ06c4IMPPuChhx5ixIgRQw6svjr4kaXxhRBC3C4k0BmGTqfjjjvuYO/evRw6dAiPx4PH4+HAgQPs2bOH+Pj4q16v4nK58Pl8KIrC1KlTOXXqFAcPHmT69Omoqordbqeurm7A0vJ6vZ7PP/+cnTt30tvbi9/v5/LlyzgcjlBWcwhM/758+TJNTRe4//77KS0tJSoqiq6uLrxeL4qi4PP5cLvdZGVlomka6ekZTJs2jZSUFMrKynC73fT29nLmzJl+KQo0TePChQu89tprzJo1i/z8fOx2O11dXaHz99Xd3c3Ro0fxer00NjZy4UITBQUFOJ29nD9/Puzy+UIIIcTNIl1Xw1BVlVmzZnHx4kV+97vfkZqaCkBnZyd33303BQUFNDc3YzAYAYWsrCw+/PAj3n//fe666y5SU1NJTk7G5XKSnZ2NoijU1NTw5ptv8k//9E/9AqWkpGRWrFjB22+/zSeffILZbKajo4NJkyYxceIEdDodmZmZvPXWWzz22GPk5uaxbt06YmJicDgcpKSkcOHCBXJzczGbzbzyyis8+eSTlJSU8MILL5CZmUlTUxPFxcUkJiZy7tw5/ud//odnnnkGyA6Vo6qqiqamJnbu3ElZWVlou16vvzLN3RDqslNVlbKyMg4cOIDH42HevHmMGjWK6upqXn/9dX784x/LYGQhhBC3jOS6GkLfpJ5er5eLFy/S2NiITqcjNzc3NHXa6/XS2dlJbGwsmqbR3NyMyWQiKSkJj8fD888/z6RJk0Jr63R2drJhwwYeeOABbDZbvzVsiot9tLS00Nh4Do/HS3p6OpmZmaHkgT09PbS0tJCamorf76ehoQGdTkdGRkYo0WF8fDzt7e04HHYyMgItOk1NF2htbSMxMZH09HT0ej1Op5M//vGPLFu2lIaGNCDQojNhQi/d3QMTMALExcXjcrlQ1UDL1b/927/xne98h7i4OMxmM7Gxseh0OlwuV7/p5fv2wfTpGrNnt4ZNcqnT6UJJU682WAbp4BT5cMkdh8o6HXzPrvWY4LXC5UMKJmMdbF+47cGksF+k3BCoo8GOgfD5pwY7ZqhrDfVeDHVPg5VBr9cPWt9DlXuwMgzmRtf3YOVWVZWkpCRJ6CnE7UdyXX0Zer2erKwssrKywu7r22qRlZWFpmmcP3+e3bt309vby9SpU0N/2J3OXgoKCkJr3PSl0+lIT08nPT09bDmioqLIy8sLfT9+/Piwr0tMTOxXppycEeTkjOj3Go/HQ1ZWFvHxCTQ0BLYpioLVasVqtQ5SE4GEhkAoYLFYLKSnp/cbl2MymQYMdvb7/Xz00UecOXNmwDkHy6StKErogXP1A8lsNqPT6XA4HAPOZzAYwh6j0+mIjo6ms7NzwDF6vR6/3z/gYTlUFvLgStjhsmwbjUY8Hk/YbOxGo3HAytPBcvt8vgFlCJa7q6srbJZtCJ+N3Waz0d3dHTa7e3AtpKvZbDZ6enrCnm+wehis3IqiEBsbS2dnZ9hya5oWNqN4sJUyXHASXBn8asGs71cLDuAPlz19sJ8TVVWx2Wxhy221WlmzZg0pKSkDzieEuP1IoDOM681ermnQ0qLn7NlUxo+fT1VVTGglY78/BaMxmQMHVBTly2VI/zJ5qWJiYigtLQ21SnxRZrOZ0tLSsIOgw1FVlYcffviGZewW4la53t8ZIcTNJ4HONbi+QERB01JJT0+lowM6O/t+Au3/R/LChfD/H84QE6eukQJc/6rFVquVFStWBM50DbOsAl0IMhtLCCHEzSOBzjA0TaOuzonVGujqUJRAc3dUVDRGowHQ6OjowGqNDnXpBF3rFOtgwFLwBVJH1dQEXn8jM40Hxwpde6ZxCVqEEELc3iTQGUZ+vkZ1dT2nT39KQkICmgZutxtFUSgtnUdGRgZvvfURJSWzGDly5Je61hcNWmbMgFWrvtQlhRBCiIgmgc4QVq0Cvx/Kyi4TE9PD00//LxRFwePx8Pbbb1FdvZalS/+OLVsqyM8fw6hRHSiKQnR0NDqdDk3T8Hg8oYGvNpsNo9GIpmk4nU70ej2dnZ3odLor41x0oRWXu7q68Pl82Gy2UEuRx+PB5/NdSb9w41tTJGgSQggRaSTQuUY6nQ6TyYROp8NsNpOTM4LKyiq8Xi9+v59t2z5l48aN+Hw+Zs2axbJly7Db7axbt47GxkZUVSUhIYFvfethLBYrr7zyOywWK2fPnsXr9bJkyRLmzZuH2+3m7bff5vjx4yiKQkpKCo8++iiJiYns3LmT2tpavvOd7wAytVUIIYQYjgQ616irq4uamprQOjiffvop+fn5mEwmfD4fmgZPPfUUHR0d/OY3v6GoqIgDBw5gt9v5yU9+jF5v4PXX1/Luu+9x7733Ul1dw8yZM/nHf/xHTp8+zZtvvsmkSZM4fvw4lZWV/N3f/R02m40NGzbw/vvvs2bNGgoK8klJSZEZH0IIIcQ1kkDnGtXX1/Paa6+hqioGg4FJkyaxbNkydDoVvV7P3LlzSUtLC3VPXbrUQlVVFXfccQexsXEAzJ49hzfeeIPe3h5MJhPTp0/HarWSnZ2Nz+ejo6ODo0ePYrFYqKioCC10VllZSW9v75W1cG5xRQghhBB/QSTQuUZjx47l+9//fijQMRgMqKpKb28PEFg0L5jMMrBab2CV4uBibvDnBel8Pj+qqobG3gRnZ/l8PlwuJ5qm0d7eDgQW3ps7d26/LOlCCCGEuDYS6FyjwJTyqLABR7hp5AaDgYyMDKqqqpgxYwZ6vZ6amhri4uKwWCxhrxFI5ZBJa+tlVq9ejcFg4PDhw9TV1YVSNni93isrKsvUbiGEEGI4MtjjK6KqKgsWLODUqVO89NJLvPrqq+zatZMlSxZjNBoHPW7evHlcvNjMiy++yBtvvMGbb75JQkICqqqyY8cOXnnllbD5d4QQQggxkCT1HMamTRqffNJBYWEXOTk5A1pvfD4fdXV1pKSkYLPZ+n0fHR1NS0tgrI7H46GwsJCsrCz8fj/19XVkZWVhNlvwer39vr906RIVFRU4nU5Gjx7NyJEjUVWVlpZm7HYHeXl5HDigMmMGrFyphc2n9FUYKgHijTzmdif1EHCj6yHYHSyEENcpbFeHBDrD6JvB/HYSLNPcuR08++yzoTE9fZnN5rDJJk0mE0ajke7u7rD7PB5P2KSSMTExdHR0DDhmqKSScXFxdHZ2hk0qqdfrwyZhjIuLw263hz2fyWQKe0/R0dF4vd6w+4aqB4PBEDa55vXUg8FgCK2DdHW5b2Y9eDyesIk3b+d6MJvNPPHEE+Tm5g44nxBCXCPJXv5lXHtahJvLZrPxgx/84At9sg62St2MVombeQx8sXuSerj+Y4LH3ahjgpnRhRDiRpNAZxg3K8C53hYjVVUHHdwshBBCfN1JoDOM6dO1IYOda03cORTJWSWEEEJ8NSTQuQa1tbV0dHzcb5uiKMyePZuJEyd+qWDndu0SE0IIISKBBDrD0DSNixebOHu2hnnz7kRRArNCgsk7hRBCCHH7kkDnGmVlZXHXXav7LRioaRp+v58zZ2o5deo0mqaRl5dHQUEBdns39fUNFBYWYDZbaGpqoqOjg/z8fBobG7FYLKSmpiIL/wkhhBBfHVm04hq53W46Ojpob2+no6M9lPrh8OHDvPDCi3R1deFwOPjtb3/D4cOHAIVNmzbxpz/9ic7ODn77299SX1+P3+9ny5Yt7JM+KyGEEOIrJy0616iyspJ///d/BwLdVqWl81i27Bvo9Xruv/9+pk+fjtfrpa2tlYqKSoqLp7F69Wpef/11amvPYLFYKC0txWg08u1vf1tyVwkhhBA3gQQ616igoIA1a9aEVm612WJQFIXRo0ezf/9+XnjhBbq6ujh79ixTpkxBURTGjRvHqFEjKSvbwz//889CiT9lbI8QQghxc0igc42sVivZ2dn9WmJ8Ph9vv/02DQ0NfOMb3yAtLY33338/tL+jo4PGxvPExsZy5MhRRo4cJS05QgghxE0kY3S+BL/fT2NjI0VFRRQVFaEoCmfOnMHtduNyudi4cSPp6ek89dRTlJeXU1FRgc/n4+TJk5w7dy7ich8JIYQQtxtp0bkGZrMZozFuwHa9Xs/ChQt5//33qa2tRVEUioqKuHTpEsePH+fixYs8/vjjpKWlsXDhAnbs2EFOTjY7dvyJzMwssrKybsHdCCGEEF8fktRzGJs2aZSX+5k2zY9er++3OKCmaWiaRnd3N729vcTGxmI0GnG7Xej1BoDQMT6fD5/Ph16vx+fzoSgKOp2O/fsVpk/XmDGjmTNnzgxo5VFVFYPBEDZJo06nC01x7yuY9NHlcoU9X7A8VzObzWGPCZb16iSNAEajMXRvV9Pr9WGPGSzpY/AYn883oAw6nQ6dThc2+aVOp8Pv94ct92DJL290PQyW/DJ4T9dTD+G2D1UPg9Xd9dbDYHmphip3WloaI0eOvCErhgshxBckST2vR/DhdvhwuLE1ypWv2CtfQdYwr9Vd+YJwPYbt7e2cPHlywINFr9djNBrp6ekZeMYhHvBWq5Wenp4vFOhERUXR29sbNnAa7AFvtVpxuVxfKNAxmUxomjbow3qwB7/BYKC3t3fAvsHqIZgH7IvWQ3R0ND09PRFXDw6HY8AxwcH14QI0v98f2t+X2WzG5/Ph8XgG7AMYOXJk2O1CCHErSIvOEDZvvr2Seko+LCGEEGJQ0qJzOxsuoLre7OZCCCHE15kEOsPQNI2JE51huwqsVismk+lLjUfQNA2Xy4XX68VqtWK327FYLBgMhtBrZBFlIYQQ4vpIoDMMTdPYvn07W7ZswWw2h7arqsqqVauYO3ful77G559/TlNTE6tXr+aFF55n1aq7GDt27Jc+rxBCCPF1J4HONXA4HKSmpvLYY9/ul708NjYwANnv96MoSmhAp6qqoRlZiqL0a/HpO+gzONBz8uTJjB07FkVRaG1tw+129zteEn8KIYQQ10cCnWtksVjIzMwasLKx2+1m69at6HQqBw4cxGq1UlpayvHjx6mrmkZcjwAAIABJREFUq/v/2bvz4CjuO///z+6eSzOjGd0HEpIAcSNuYQwBjA0GY8DGNiFgx4njK443qd8mu/km+dZ3N7W1V7ZSu5W4Esf4itd24viIjTE+iLnNfctYIEAIhJDQgY7RaDSao/v3h5gxQqODwxzj96PKZTQzn+5P98z0vPrTn/58GDt2LHffvQCr1UZJSQmffvopHo8Hp9PJvHnzKCoqorq6mqamJsaPHx9dbm1tLRs3bmTevHlA8jXeWiGEECI+SNDpp7q6OjZu3BhthVFVlaKiIhISbGzZsgWn08nSpUvZtWsXzzzzDAsXLmTFihX86U9/Iisri1GjRvH2228zc+ZMxo4dy759e3njjTcoKCjgxIkTVFZWMm7cuOj6dF2PtuwIIYQQ4vJI0Omn5uYm9u/fH/3bbDaRn59PQoINRVGYPn06I0aMIBwOs3fvXm6//XaSkpLIy8vj1KlTFBcX8+ijj+JyuQgGgyQk2PF4PDHHxwHIzs7mwQcfjDmOiRBCCCH6R4JOPw0dOowf/ehHXYKHqqp0dPhRVZWkpCQURYkOzmaxWADQNBVd19F1nd27d1NaWoqqqtjtdkKhUI8tNpHB6YQQQghx+STo9FMkeMRqYYkEnN6UlZWxbds2fvjDH5KXl0dFRQX/8z//0+PrpTOyEEIIceUk6PRTVVUV77zzTpdAM27c2H5PzBlp4WlqasLr9bJu3Tra29tpa/PGfH11dTVr137C4sX3AKlXXH8hhBDi60iCTh8URWH48OH4fL4uoURRVAKBICaTmRkzZpCRkQFAamoKs2bNxGKxnJ/NvAhN0xg6dCiLFy9mx44dOBx2Zs2axfDhwwmHdQoLC0lLS8NqtTJr1kzS09PRNJXERBea9mWwilzmut4dlHua7PFql7kR6nC16y06XTzsghBCfFVkrqteROa6ulrTL0RmfFZVtUvLUF8H/Egd7rorxPvvv8/hw4e7vcZut2MYRswRnG02W8yZq81mMzabjdbW1phlepr93OVy0dzc3K1MTzOZR8YcamlpiTlpqaIoMSeItFgsBIPBmGUcDgctLS0xt0nX9Zh1SE5OpqmpKebM6IqixJxEMyUlhebm5m6TXqqqislkijkhp8vlor29vds2KYqCxWKJORO9ydR5znFxHSL7zuPxxKxDTzPbu1wufD5fzOX1NJO53W4nHA7HXF5PnyGLxYLZbI45YWhPM7qbTCYWLVoUHTtKCCGuEpnr6nJdvSkYFMDc56t6omkaM2bMYOLEid2e620W6h5rc75vUawZt69mGeis+6WWudp16GlG8KtdpqdZxC/X5ey7y6nD5X6GLhwss79l0tLS+v16IYS4EhJ0+nAjTaapKArp6emkp6df76oIIYQQNwUJOjeR1asvvcyiRVe/HkIIIcTNQoJOH9av91JXV0teXj4mkwld1zl79ix+f3uXx6qrq7HZbKSmpsbsd6DrYU6friIpKQmXy9WvvgltbV7q6uoZOHAgmqbFnDurNzdSa5QQQghxPUjQ6UN7u4/du3/PzJk/ITs7m0AgyK9//TI1NdX8v//3T2RkZOD3+/n44xeYP38+U6akEiuHBIM669e/zsyZM5gy5RsxX3Ox48fPcvjwWhYvfoTTp0/g9/spKirqV9mr169ICCGEuHnJ/AJ9SEx04XA4qKqqAqCxsZFgMEhaWjqnT58G4Ny5Rtra2sjLywMgHA5TW3uWioqKLnf5hMNhgsEQdXV11NXVRUdGNgwDr9dLRUUFp06dwufzYRgGeXl5LF++HE3TWLt2LUeOHImWaW/3cerUKU6fPh29O0rXdTweT8y7Y4QQQoivI2nR6YPFYmbQoM6JNydPnkxFRQU5OTkkJydz9OhRJk6cSGVlJSkpKaSkpBAKhVizZg3bt2/HZrMRDodZunQpI0eORNd1Pv30UzZt2kQ4HGbYsGE88MADeDwenn/+eUKhEOFwGLvdzpNPPkldXR0ffPABU6dO5eDBgyQkJDBgQDaFhUN5+eWXo7f0ZmVl8fDDD2OxWFi5ciWTJk3C4Zh9XfebEEIIcSOQoNMHRVEZPnwEW7ZsIRgMcuzYMQoLC8nMzGTVqlV0dHRQXl5OYWEhFouFo0ePsnnzZp566ikKCgrYuXMHq1atIj8/D8MwcDgcPPnkk4TDYX73u9+xY8cOVFXFMHR+/OMfA7BmzRo8Hg+BQICGhgaGDx/OiBEjyMjIoKhoLG+99RZut5sf/ehHhMNhXnzxRbZt28acOXNYvHgxycnJVFRc5x0nhBBC3ADk0lUfFEWhoKCA1tZW6uvrOXPmDIMHD2bAgAEEg0Hq6uo4ffo0w4cPR1EUjh07RiDQwf79+1m1ahUVFSepra2loeEcmqbxjW98g5SUFNLT0xk3bhyff/45ubm5tLX5+P3vf8+GDRu49dZbGThwYLQOCQkJ2Gw27HY7mqZx9OhR/H4/H330EWvXrsXv93P48GEURWHYsGFy+7kQQghxnrTo9ENycjKJiYl8/vnnqKoana4hIyOdAwcOEAgEonNeBQIBEhLsuFwuVFUlMTGRxYsX43a7URSFhISE6HIjI+vm5OTw93///3H06DE+//xz1q1bx4MPPojNZutWl8iov263G5fLBcD48eNlADYhhBAiBmnR6Qer1Up+fj4bN25kwIAB2Gy28/NXDWPLli2kpaWRmJgIQF5eZ0vMpEmTmDt3LoMHD6a8vBzo7IxcVlZGKBTC7/dTVlZGQUEB+/btY9OmzUydOpXHH3+cESNGUFr6RZdRbSPTJFgsFrKzs3E4HNxxxx3Mnj0bv99PU1MT0NlZ2ufzXeM9JIQQQtyYpEWnHyITe65evZolS5agqiqKolBYWMi5c+eYO3dudM6mUaNGM3DgDn7zm9+Ql5dHeXk5xcXFOJ1OVFWlpKSEc+fO0dHhJxgMMWvWLLxeL6tWraKmpgar1UJ5eTkPPfRQdD0A2dnZrF27loKCAu666y5efvllzp49C0BtbS2PPfYYgUCA5557juLiYlyuOddzlwkhhBA3BJnUsxeRST0BgsEgzc1NuN1JWCwWoLOFpqmpEaczMXqZyTAMgsEANTVnaW1tJTU1hfT0DFRVpaWlBU3TqKurAyAnJydarqWlhZqaGgzDIDMzk+TkJEKhEF6vl6SkJILBELW1tbjdbhITE/F4WqipOYuqKmRnDyAxMRHD0GlqasZms+FwOJgyBRYuNAgGg7S3t3eb9ygSomJ9Bnp6rrd5pvoqE2vupd7q0NN8Tb2V6W2+ps5O30a/l3ej1Pt6vxe9zWfV13OXs7yeylitVqxWq0wEKoToScyDgwSdXlzOlAs3moULDQ4ePMjbb7/d7Qext9mvrVYrgUAg5kzfLpcreqnsQhaLhVAo1O0HTFEUkpKSaG5uvqSZw91uN21tbd2e623mcIfDga7rMWdxt1qtMbf1cmcv1zQt5qzrbrcbn88Xc/Zys9kcs952ux1FUWLOAt7bLO5OpzPmTPJms5lwOBzzvUhJSaGxsfGS3ouePg9WqxWz2YzX641Z71ifB03TcDqdeDyemNsEsWdxnz17NnPmzJGgI4ToiQSdr6NIC8alzsDdG0VRLnlm7hu5zI1eh6u9rpt1H2ma1uVyrhBCXESCjhBCCCHiVsygI3ddCSGEECJuSdARQgghRNySoCOEEEKIuCVBRwghhBBxS4KOEEIIIeKWBB0hhBBCxC0JOkIIIYSIWxJ0hBBCCBG3JOgIIYQQIm5J0BFCCCFE3JKgI4QQQoi4JUFHCCGEEHFLgo4QQggh4pYEHSGEEELELQk6QgghhIhbEnSEEEIIEbck6AghhBAibknQEUIIIUTckqAjhBBCiLglQUcIIYQQcUuCjhBCCCHilgQdIYQQQsQtCTpCCCGEiFsSdIQQQggRtyToCCGEECJuSdARQgghRNySoCOEEEKIuCVBRwghhBBxy3S9K3AjW736etcAFi263jUQQgghbl4SdPqwa9f1W/eUKddv3UKI6+9GONnqS+QYeTnHKzmRE9eCBJ0+GIZBYWEjzc3NGIaBoig4nQ5SUlIxmUwoitJn+Yi+Xnuh6xmwhBA3jq1bgzQ2nkPXdUBB0zQcDgcJCQkoinJJx5W+GIaBx+NBVVUSExP7fC3AsWNKt8cjx8rI37HqKSdy4lqRoNMHw9D55JNP2LBhAykpKei6jt/vJycnh2XLllFQUNDjgcYwDNra2qisrGT48OFomnaNay+EuNk1NzezZs0vSUlJwWKxEAqF8Pv9zJw5k7vuuqtfJ1z9ZRjwl798hMuVyB133N3j63w+HydPnmTYsGEcO9b5MxIJLuGwzrp168jJySErK4uPP/6Y+++/H5vNFi0vJ3LiWpKg0w/BYJCRI0fy6KOPAtDQ0MAbb7zBc889x89+9n9wu5PQdZ329nZ0XcdkMkW/1Lt372bPnj0UFhaiqmqX1yUkJGAydb4FoVAI4KoetIQQNz9d19E0je9973tkZ2cTDocpLS3llVdeYcSIEQwdOhTDMAiFQrS3t6OqKgkJCdETq8jJWSgUwmq1YrFYUBQleiwKh8PRxwHmz5+PqqqEQiFCoRCGYRAMBru85uDBg6xbt46f/OTHGIbW5ZgVDoc5fLiUIUOGUFNTQ0tLC2azGV3XCQaD5495ctInrh0JOv1ksVhITExEVVVcLhcPP/ww//Zv/0Zp6WGKi4tZs2YNu3fvxjAMVFVlwYIFDB48mM2bN1NdXc0nn3zCnDlzWLduHdu3bycUCpGXl8eyZctITk7mb39bi8/Xzn333SdBRwjRhaKAzWbD4XBgGAYFBQVomkpbWxsA586d48033+TUqVNomsatt97KvHnzUFWVzz77jPXr1xMIBEhJSWHp0qXk5uayefNmNmzYQDAYJCsri2XLlpGdnc22bVtxOJykpaWxZs0awuEwbW1t0bIJCQls2LCBiooKPv74Y3R9MZpmwjAMSkpKOHz4MOXlJ9ixYwc1NTW0trZSWVmJy+XitddeY/HixcCg67tDxdeK3F5+GRRFISMjg+zsbKqqqqitraWkpITvfOc7/NM//ROzZ9/Ghx9+iKZpFBYWkp6ezpgxYzh06BCbN2/m4Ycf5uc//xk2m43Vq99H13Xy8vIZNGiQhBwhRDcdHQE+++wzPvroI957711WrlxJenoGgwYVEA6Hee+99wiHw/z0pz/l8ccfZ9euXXzxxRecOnWKDz74gPvvv5+f/ez/kJ6eztq1azly5Ahr1qxh2bJl/OIXvyA7O5u//vWvBINBqqrOcPbsWXw+H0eOHGHOnDv42c9+Rm5uLn/+85/QNI1hw4aRmppKUdFYVPXLY5bT6QQgPT2dnJwcOjo6GD58ODabDavVSlFRUZ99f4S42qRF5zIpioLZbAYgMzOTp556ira2No4cOUJV1Rk8Hg9ms4mMjAyqqqrIzc1l3bp1uFwumpub8XhaSE1NZdu2bbS3tzN69OjrvEVCiBtVKBSivLycQCBAeXk5c+bMYfHixbjdSXg8HkpLS7nllls4caIcALfbzYEDB8jIyCArK4tRo0ZhMplYtmwZPp+PdevWYbfb8fl8HD1ahtvtZufOnbS0tHRZb15eHkVFY7Fardx222386lc7aW5uJisrE6fTycCBA9mx48vLUIMGDeL06dMkJCRQXFzMwYMHmTlzJllZWQDMnj0bgBMnrtGOEwIJOpfN7/fT1NREZmYmLS0tvPLKK7S3t5OenobJZEZVuzeWtbV58Xg8HDx4MPrYuHHjUFVVWnKEED1yOOx85zvfISMjg08//ZRNmzZxxx13kJycTCAQwO/3U1lZGQ0qiYmJFBQUUF9fT0JCQvR4ZLVa0TSNtrY2vF5vl2PRhAkTon0GIyJ9fRRFwWKxoKoqHR0dPdZz06ZNrFq1ioSEhPOXsI7T0dHB/PnzGTt27FewZ4TomwSdfjIMg3A4TDgcJhQKsnXrVgzDYOTIkRw8eJDm5mZ+8pOf4HK52LFjBwcPHkTXjWinP4DMzM6zmu985ztYLBaOHz9GWdlRzGYzgUAAwzCiHQWFEOJiFouF22+/nYqKCt58802eeuop7HY7ycnJTJ06lenTp6PrOps2bSIpKQlVVSktLaWjo4OEhAR27txJeXk5qamppKen8+1vfxubzcbp06fZv38/Vqu1y/rOnj1Lc3MzaWlpVFdXo6oqqampNDQ0oOt6l+EzAMaOHcvWrVu5++67aWw8h8PhYOnSpbjdLnRdJxAInG8Jl87I4tqRoNNPhw4d4je/+Q2KouD1euno6OChhx4iLS2N9PR02tvb2blzJ4FAgP379xMIBGhtbcXtdnHq1Ck2bdrE1KlTefbZZ3n11VfJzMxk27ZtTJlSjKqqfPDBB/h8PpYvXy5BRwjRI6vVyj333MOvf/1r1q9fz1133cWsWbN4//33aWiox+drZ//+/Tz++OPk5eWxYcMGXnvtNbKzs9m5cyfz58+nsLCQ3bt38/LLL1NQkM/OnbsYOnRo9HJ8RGNjIy+//BI5ObmUlZUxe/ZsUlJSSExMpKamhvXr16Pr81DVzuBiGAZms5nBgwdTWVlJYWEhmZmZKIpCfX09L730Eg888AAw5DrsOfF1pVycyC/S65PxbvVq2LnTID29gtOnTwNEB9IaNGgQLpcL6Lyd8tChQxw9epTk5GRGjx5NdXU1Awfm4nK52bt3DwkJdsaPH09NTQ379+/H5/NRWFhIUVERJpOJ8vLy6G3siqKwaxcUFxvcfXf3sybo7CPU0+PAV17mSpYX67mvooxcEhQ3u9WrYdOmduz2Q4wePRq73Q50HnOOHz+O3+9n9OjRGIZBaWkpZWVlWCwWxo8fT15eHoqiUFdXx549e/D5fIwePZphw4ahaRp1dbXs3bsXj6eVgoICxo8fj9Vq5fjx41gsFurq6njvvfd44IEH8Hg85Obmkp+fj6ZpBAIB9u7di8lkoqxsEoqi8eCDneOGVVVVUVhYGL3TKjU1FUVRaG/38fnnhxg2bChHjyYzZQosXNg5uGCk1ftCX8WxLNZzl1OmtzpcSf0utUzkGCfHuaiYO0KCTi9Wr+4c2Ko/I3j25wMf64Pc0wd01y4YOrSJ48f/B5/P1+15m82G3+/v9nhktNSeynR0dHSrg9lsxm63d+uICJ1N5ZFLdhfXOykpKTpi9IU0TYseDC+WnJyMx+OJuTyLxRLz+r/L5cLv98dcXk/7we1284Mf/IDU1NRuzwlxs7iSY9Cl/rBfeCwyDIM9e/awevVqfv7zn2Oz2bodqyLlX3uts+xDD/Vvm+DLbbrrrhCvv/46n3/+ebfXOByO6Fg/F+vpe2+xWLDZbHg8nphlYh3/NE3D5XLR1NQUc3mhUKhbEFMUheTkZJqamrotLzIWWjAY7La8pKQkvF5vdNy0C5fX0/Gvt/0wc+ZMFi5cGLNP6NdUzB9UuXTVD/0bxbO3RK308O/euVwu/vEf/zFmiLpwiPVua+vlTKPHGt4AZXrapsst43A4LqluQtzMejseXMrjEU6nk7y8vB5bRiOPRZ66nNGONU1j6dKlLFmyJObyL/V731e5ntzox7Keykifzv6RoNOH6zsfiyZjTgghrsOUCQq6PowRI4Zw4ICZvn5Lhw27vGOloijRy3FCfFXk0pUQQtygbobZyyNkJnJxA5A+OkIIIYSIWzGDjvRgEkIIIUTckqAjhBBCiLglQUcIIYQQcUuCjhBCCCHilgQdIYQQQsQtCTpCCCGEiFsSdIQQQggRtyToCCGEECJuSdARQgghRNySoCOEEEKIuCVBRwghhBBxS4KOEEIIIeKWBB0hhBBCxC0JOkIIIYSIWxJ0hBBCCBG3JOgIIYQQIm5J0BFCCCFE3JKgI4QQQoi4JUFHCCGEEHFLgo4QQggh4pYEHSGEEELELQk6QgghhIhbEnSEEEIIEbck6AghhBAibknQEUIIIUTckqAjhBBCiLglQUcIIYQQcUuCjhBCCCHilgQdIYQQQsQtCTpCCCGEiFsSdIQQQggRtyToCCGEECJuSdARQgghRNySoCOEEEKIuCVBRwghhBBxS4KOEEIIIeKWBB0hhBBCxC0JOkIIIYSIWxJ0hBBCCBG3JOgIIYQQIm5J0BFCCCFE3JKgI4QQQoi4JUFHCCGEEHFLgo4QQggh4pbpeldAiHjz/vtGr88rinKNanLtLVrU+f+L90F/ttkwOssEAgFUVcVkMt3w+yocDkfra7FYrmp9I/tSCHFlJOgIcZX5fD5+9av1tLR4oo9pmobdbic/P59hw4Zhs9lu+B/xnhiGgWEYhEJBzOYvf9ynTPny+c2bN/PXv1YxYMAAZsyYgcnU96EmFAqxb99+SktLSUiwMW/efJKSkr7KTblkhmEQDAbRNA1N06ioqGTjxg1kZWUzd+7cfm1nf0T2pRDiyknQEeIqC4VCVFVV0dDwIQkJdjRNIxQKEQgE+OILhXB4Ho8++ihms/l6V/WSGYaB1+vlk08+wev18u1vfxtN09i1q+vramtrqag4QUrKcSZPno7F0vdyv/iijM8++29sNhvDh49nyhS4kXJOZxDbx7ZtG3nkkUdIT09HUer5+OMtJCSMYPLkO/rczv64eF8KIa6MBB0hviIWi5Wnn36akSNH0tHRwfr163n33XfZtWsXS5bcS0ZGJoFAgNraWpqbm7FYLGRmZuJyuVAUhdbWVnRdx+l0RkNRR0cHbW1tWM7/ora1tZGYmEggEKC6uhqn00F29gAMw+DMmTP4/X4GDhyIw+FAUZTzLTEh6urqaGpqwuVKJDMzK7o8v99Pa2srCQkJmEwmqqurCYfDDByYi82WAMAXX3zBG2+8wdSpU/u9L9rb2/F4PNjtdkwmjerqmi7LDYfDnDp1Cr/fzx133MGKFStISOhcXygUoqmpifr6eiwWC1lZWdHtCYfDNDc3R//d2NhIbm4uFouF5uZmzGYzVquVqqoqdF1n4MCBWK1WGhoaaGhoICMjg9TUVFRVjbbW1NfX09jYiKZpZGRkkJycjKZptLa28sc//pH29nZ0PQzAqFGj+eUvf4nNZo225hiGgc/n4+zZs/j9ftLS0khJSYk+39HRgcfjwWazYTabqa6uJhgMkpeXd36bb86WPiFuVBJ0hPiKKIqC2+0mLS0NRVG49dZb+eijj/D7/XR0dHDu3DleeulF9u8/QDAYRFVVkpOTWbFiBcXFxfz+97+ntLSUxx9/nBkzZmAYBqtWrWLVqlUsWLAAh8PB22+/zS233EJ5eTlnzpzBYrEwd+5c2tra2Lp1K6FQiFGjRvHDH/6QlJQUPB4Pr7/+Otu2bcPv92M2m5k4cSKPPPIIqamp7Ny5kxdeeIEJEybg9/v5/PPPMQyDKVOm8OSTT+LxeNiwYQOhUIjy8nLefPNNFi5cCCT2ui927tzJ888/z8SJE/H52vjii1IMQ2fKlFt44oknOHLkCB988AG6Hmb79u1YrVaWL19Oe3s77733HuvWraO1tRVVVcjNzeVb3/oWEydOwuv18u///u8EAgEMQ6e+voGHH36YwsJCfvWrX5GTk4PdbufAgQOAwdix4xg+fDgffvghra2tpKWl8fTTTzN69GhaW1t59dVX2bZtGx0dHSiKQmJiIvfddx933HE7n3zyCY2Njei6zltvvc28efPweDy8+OKLDB8+jB/84GkAPv/8c/70pz9x8uRJdF3H4XAwc+ZMHnjgARITE9m3bx/PPvssRUVjCIXClJSUYBg6EydO4vvf/z7g/so/m0J8nchdV0J8RQzDoLa2lsrKSo4dO8bGjRvx+/3YbDasVitbt25lz569DB06lCeeeIJp06ZRW1vLmjVr0HWdMWPG0Nrayp49ewiFQvh8Pvbt20cwGGT06NGEQiFaW1vZsWMHkydPZuHChXR0dPDee+9RUVHB8uXLGTBgACUlJezevZtwOMx7773H3/72N/Lz83nkkUcYPnw4W7du5Z133iEUChEMBvF6vezYsYOEhASWL19OUlIS27Zt4+DBg7S2tlJeXo6uh2loaODAgQMEAoE+90UoFIou1+lMZPny5bjdncstKSmJ7q9Ia4ZhGOi6zocffsg777yDpmnMmzePSZMmU1l5mmef/QMnTpRHW09OnTpFIBBk3LhxDBs2FF3XaWvz8sUXX2CxWFixYgVOZyI7d+5kzZo1zJ8/n1tuuYWamho++ugjQqEQ+/fvZ8uWLWRnZ/PYY48xd+5cmpubWbNmDefONXLkyBH8fj+BQICDBw/Q0NCA3++npqaGc+c6A1BVVRXPPvss5eXlTJgwgXnz5mGz2Vi9ejV//etfCYfDhEIh2tra2LVrNxaLhQcffJCUlM6QuXfv3minbCHE1SEtOkJ8RQKBDlauXImqqoRCIUKhECaTialTp5KSkso3vvENhg4dSlpaGiaTCbPZzPbt22htbSUYDDJ+/HiSkpI4cuQITU1NtLS0UFlZSV5eHoMHD+b48eMAjBkzhmXLltHa2sq2bds4d+4cDzzwAFOmTEHXdV5++WVOnDhBY2Mj27dvx2az8eCDDzJ8+HCKior453/+Z/bs2cOSJUuidc/KyuKRRx7B5XLh8Xh4++23KSsrY8qUKXzzm9/kD3/4A+PHj+f73//+JXUYzs7O5rvf/S5utxuPx8Nbb73F0aNlPPzwd6ivr2flypXccccdLF++nKamJjZs2ICmaTzxxONMmjSZYDDIc889x6effsqmTZu57777ALBarTz22GNMmjQJRVEoKysDICUlhYceeoiUlBTKy8vZuHEj06dP5/777+fEiXL27t1LZWUl7e3tjBs3jn/+53/G7XZjt9tJSUlh8+bNtLV5AXjkkUf4l3/5F0KhEP/wD/9IQUEBe/bsiW6bYRhs27aNs2fPMm3aNH70ox9hsVgoLi7mP//zP/nss8+YP39+9PXp6el897vfJTU1lY6ODl599VWOHj3K+PGlxYaZAAAgAElEQVSzAO2yP3dCiK4k6AjxFVFVjREjRpCUlISqqjgcDkaMGMHkyZMxmUxYrVZOnjzJG2+8QW1tLa2trbS3+6Pls7KyGDVqFDt37qSsrIyGhnp8Ph+TJk3C4XBEX5eamoqmaefDUudyOzvKKjidna9rb2+PhiW/38+zzz6L2WwmHA7j9XrRNI3GxsboMtPS0nA4HKiqSkpKCoqi0NzcHN0O6AwXSUlJaFr/f5TT09Ow2+3R5QI0N7eg63r07i1FUVAUhaamJpqamnC73RQUDIrewj1y5EjWr19PVVUVoVAIgMTERPLy8rrVxel0RrfDarWiqirZ2dmoqorNloDZbCIQCBAMBrHb7Zw9e5a3336L6uoaPB4PXq8Xt9sVXZamaei6TlKSG6vV2mVduq5TWXkKwzAYMWIEVqsVRVHIz88nJSWF+vp6zp071+V9S0zsvOSXnJyEoii0tLSg69KiI8TVJEFHiK+I2Wxm6dKljB07Fug6lkw4HOatt95k9eoPGDJkCEuWLMFut/Pcc8+dfy2YTCZuvfVWdu7cyY4dO2hpacFut0dbLSJUVe12q7qqRq5KK9FOyJqmoSgKVquV4uJiEhOdGEbnD7TFYiE1NZXTp08DdBnDJrKscDjcZR2Xc4nFZDL3udyLtytySS3C7+8MgxeOW2M2m6Mdqi8U6xb+CwPVhdvy8ccf8frrfyI7O5u77rqLjIwMVq5cSSgU6tdQAIoCmmaK1tEwjC711zSty512XfdxZ0CLBDchxNUjfXSEuAYu/qH0+/0cOnQIs9nMgw8+yJ133onZbO7W32XUqFFkZmayZ88ejhw5wpAhQxg4cOBl1SElJYWMjAx0XWfYsGEsXLiIESNGRO+sstvtvZaPBJtIYPL72/H5fD0GlSsVqW9zczOfffYZHo+HM2fOsG3bNgBGjx6N2dwZLFRVRVUv/26lUCjEwYMl6LrOAw88wN13301iYmI0VH25DvV8f6O2bu+VqmoMHjwYRVHYuXMHZ86cwePxsHXrVs6dO8eAAQPIysrqR22kRUeIq0ladIS4Djpvk86mvPwEf/nLX/jss884dOgQoVCIjo6O6F0/SUlJjB8/ng8++ABVVSkuLsZms11Wa0piYiJ33XUXL774IitXrmTQoAJOnjxFY2Mjd999d7/H9XG5XJjNZr74opRf//rXPPXUU0B/fsAvvb4LFizgxRdf4M0332Tr1q34fG3U1zdQVFTEjBkz6Hor9uUHHU3TyMnJ4cCBA6xatYpDhw5FOx9brVba29txu90kJiZy9uxZnnnmGZYsWdJtgMDp06ezbds2jh49yi9/+UsSExOpqqrCZrNx77334nQ6L7uOQojLI0FHiKvMZDKRmzsQq3V8tA9GrNcsX74cRVE4ebKC06d1Fi9eRGXlaerq6mht9ZKRYaCqKhMmjGft2rW4XC4mTJgAdLYQZWVlMmHCBHJzc6PLHDlyFF6vNzoGTUpKCuPHj6egoABVVbn99tuxWCysW7eOs2drSUtLY/HixcyZMwez2UxqaioTJkxgyJAh0UtLaWlpjB8/nkGDBgEwaFABCxcu5NChQ9H+LxfLyspi8OAghYWddY0sd9CgQdHWrbS0NCZOnEhBQQGKokTXk52dDXSGj9mzZ+N0Ovnb3/5GfX0diYkupk2bzsKFC0lOTqatrY1Ro0ZFtz/C4XAwduw40tLSov128vLyGD9+POnpaUBnH6OiorFYLBasViv33nsvHR0dHDlyhIqKCmbPnh29y6y1tZVBgwaxZMkSPvroIwzDwGq1kpiYyIQJE6L7Nz09nb//+7/ngw8+oLS0lFAoyIQJE5g/fz7jx49HUZToexJp/Ym8T5H9LuPoCHF1KX2cGUobqhCXaPXqztFt+xrGPzJ4X3t7O2azGZvNhq7rQGc40HWdYDDIu+++y9tvd47b8uijj17xNAOGYRAIBKKtFZFOs5ciUjeTyYSqquzerTBlypfzM0X2wdVgGAbhcJiOjg40TcNiscQMV1djPYah4/d/uZ7O42Pnbe+RfRS5VBe5hBeLrut0dHRgGAYWi+WS37ML96UQot9ifiGlRUeI60RRFMxmc5dLRpHWB8Mw2LJlC6tWreLMmTNkZGRw5513XtIdTr2tNxJwLlfkLqbeXL35mhQ6D1Vf9eFKofO2bvtFj12sP/VQgYSrUSkhxBWSoCPEDSoy9cMtt9zCggULyM/Pv2kmApXWCCHEjUKCjhBfkSu7dKOg6xNZsmTC+XmvVPbsuTlCjhBC3Egk6AjxFbg6l21kdFwhhLhS0hlZCCGEEPEgZrO3DBgohBBCiLglQUcIIYQQcUuCjhBCCCHilgQdIYQQQsQtCTpCCCGEiFsSdIQQQggRtyToCCGEECJuSdARQgghRNySoCOEEEKIuCVBRwghhBBxS4KOEEIIIeKWBB0hhBBCxC0JOkIIIYSIWxJ0hBBCCBG3JOgIIYQQIm6ZrncFbnSrV1+b9ezaBVOmXN1lLlp0dZcnhBBC3Gwk6PTBMAy2bg3Q0REAQFEUVFXFarWiqiqKovRYLhwOEQwGsdkSurzOMAz8fj+apmE2m6PP7dxp0N7eTjgcBkBVFSwWKyaTqcf19ORqhyYhhBDiZiRBpw+GYXDwYAmlpX8kISEBAE3TyMvL44EH7icjIzNmCDEMOHDgEPv3b+OJJ57AbDZHnwuHdf73f98gL28g06ffwe7dnY+HQiH27fsDJ0+exGKxoGkaiYmJ3HnnnUyaNAlN0/pV5127rny7hRBCiHggQacfOjr8ZGZm8t3vfgdFUfF6vfz1r3/ljTfe4Ac/eBqTyYRhGBiGAYCqdnZ9GjJkCKmpKWiaFn1O13V0XcfjaaGtLbXbupqbm5k2bRrf+MY3CIfDlJWV8dprrzFgwAAGDBgAdLYqKYoSXWfk318Grktr/RFCCCHilQSdfrLb7eTk5EZDy7Rp0/jwww/p6Ojg1KlTfPjhhzQ0NGC1Wpk1axa33nor586do6ysjJycXNra2vjb3/7GiRMnyMnJwePx9Liu5ORkcnNzMQwDh8PBmjVraGpqoqqqiubmZubOnYuqqtTU1LB582bmz5/Hli2fkZeXx9ixY6/hXhFCCCFubBJ0+ikYDNLW1oaqqrS3t1NSUkJaWhq6rvPWW29RWFjI0qVLKS8v55133mHIkCHU1NSwc+dObrvtNt59910aGxuZO3cuJSUllJYeZty48THX9cUXXwAQDoc4cqSMrKws8vPzOXnyJO+99x633HILSUlJ7Nmzh3PnzmE2WwgEAtG+PUIIIYToJEGnn0pLS/nXf/1XoDP0pKQks2LFg9jtdpYvX47T6UTXdaxWKx0dHbS0tETLtrQ0U1JSwpNPPklhYSGFhYUcP3485noMw6Curg6z2UwwGOTs2bPYbDbOnTvH4MGDsdlsHDt2jKKiMZSUlDB//nzsdjv33nvvJXdYFkIIIeKdBJ1+GjJkCA899BCapmG3J5CUlIzFYiEUCnHkyBF27dqFYRg4nU6CwUC0Tw6Az9dOKBTC5UpEURRsNhs5OTkxg4miKNx2223MnTsXwzAIBgO88sr/8u677/L0008zYcIE9u/fj8vlIhQKMWzYMBRF6XdHZSGEEOLrRAYM7Cen08mgQYMYNGgQmZlZWK1WAKqrq1mzZg333HMPP//5z3nwwQexWKxdgk5CQgKaptLa6j0fXoLU19d3ec2FIrewd95+bsFut+P1egGYMGECp0+fZsOGDYwePRqn04lhGOi63qVDtBBCCCGkReeKmUwmNE2jpaWFY8eOsXXrVjweD62trdHXuN1uxowZwwcffMDs2bM5cuQIZWVHKCoqirnMffv20draSigUoqqqivLyclasWIHZbCYzM5OUlBT27dvHL37xC1RVpaOjg1WrVjF48GAmTZp0rTZdCCGEuOFJ0OmDoijk5OSSlaV3u9SkKApZWVmsWLGC3bt3Y7FYGDNmDIWFhaiqSkZGBtOmTcNisXD//Q/w6aefsnHjBrKysnnooW+Tm5vbbXnTpk2jpqaa1tZWTCYTw4cP57777iM/Px9VVbFYLAwdOpRQKEROTk60nNPpxGazdVmetPCIr6vIkAvi6+Navuc3wucr8nskfTP7pvTxZn3tjxSrV/c9PYNhGIRCoS59ZSLj2kQ+hJHQEQ6H0TSty3MXDvDX23qCwSCNjY28/vrrzJo1i4kTJ8b8kO/aBcXFBmlp29m4cWO3L6TFYsFisUQvh13IarUSDAbRdb3L45qm4XQ68Xg83ZZnNpuj++BCiqLgdrvxeDzdlqeqKmazmY6Ojm51cLlc+Hy+mMuzWq34/f5uZex2O+FwOObybDZbzDIWiwWz2UxbW1u353rbDw6HI+bwAJe7H0wmE4FAoNvyLmc/OBwOQqHQJe0Hq7Vz9O1rtR9aWlq6fYY0TUPTtB73Q1tbW8y7Cnv6wUlKSsLr9casg9lsjrkeh8NBOBzu8bMSDAYvqd5utxuv19ut3r29f06nk0AgEHN5Pb1/NpsNVVXx+XzdnrNarQQCgW71NplM2O32mO+fxWIhHA7HrLfFYunxs9VTHXqqd291UFW122cOOve3qqoEg8Fuz7lcLtrb27s911u9I4PAtre397veVquV1NRUqqurr8ryzGYzCQkJMfdDT++fqqosWrSI0aNHS9DpKubOkBadfup9tGEFMMd47OK/Fa6kW1RtbS3PP/88BQX5jBkzps/Xjx49OjrIYJeanO8DdCm3o19OGeg8MF1OmUifo/6KDNIY6+DYk0jYvNQyN/p+uNSWvJt1P0S2M/Le96dMby7nM9Sby6nDtfzsX877d63qoOt6zPe1N9dq3/V2cnKt3gtFUUhN7T7grIhNgk4/XIt5o/ozbUNmZiZPP/00SUlJXebIiiVy9ux2u69iLYUQQoibiwSdPlzLGcB37eor8JiBjGtUGyGEEOLmJ0HnBiIzjgshhBBXl3RGFkIIIUQ8iNmfQwYMFEIIIUTckqAjhBBCiLglQUcIIYQQcUuCjhBCCCHilgQdIYQQQsQtCTpCCCGEiFsSdIQQQggRtyToCCGEECJuSdARQgghRNySoCOEEEKIuCVBRwghhBBxSyb1FEKIm8zq1VdWftGiq1MPIW4GEnSEEOIiVxokvmq7dl1+2SlTrl49hLgZSNDpxY1+sLvYokXXr85yhijizZWEiWtlyhQwDCP6t6IoGIZBIBAgFApht9tRlC8ndL4ZtulGc/H+FTcfCTp9iBwYDMOgqamRU6cqGT58OHa7/brWyzAMDENHUVQURYmepem6zptvnqShoQHo/GI6HA4GDMjG5XKjqrG7ZUW2r76+gcLCQjRNiz4XDoc5fvwYmZmZJCUldysrZ4giHhmGwaBB9ezZs5twWAfA6XRSUFBAbm4uZrO51/Iej4eDBw8yadIkysrKSEpKoqCg4Ip/LC8OK7qus2PHDvLz88nMzOTQoUNs3ryZtrY2br/9diZOnIjFYrmidX6dBQIB3n77bW6//XaysrIk7NyEJOj0w5QpoOsGr776AXv3bmXYsOUUF992XT/w1dU1HDp0iNmzZ7N//5cHXMMwKC39Aq93A4MHD0bXDU6ePMf+/U089thjjBkzJma9DQMOH66loWEfkyYNwmL5Muh0dIT45JM3GDlyIZMnT+5STs4QRTw7c+YMb731NmPHjsVkMtHY2Mif//xnpk+fzje/+U1sNluPx4Hm5iZWrVrFsGHD2Lx5E0OHDqOgoOCq1s8wDCoqKti6dStjxoxh3759/PnPf2bo0KGYTBpvvvkmbW1eZs++vceTHNE7s9lMIBDg+eef5//+3//b5SRQ3Bwk6PRTS0sLFRUVzJ07lz179jBt2jSsViu6rtPU1ISmqZw5U43NZiM3N5eGhgaamprIysoiNTUV6Dzzqq6uprm5GbfbzYABA9A0jdbWVgKBQPR1LS0thMNhkpKSaGpqwmq1UlVVhcViIS8vD1VVOXBgP/v3H2Dq1KkYhgnoerCdOHEiy5cvR1EUgsEgL7zwAhs2bGDkyJGEw2EqKytpa2vD6XQycOBALBYLgwYNIjMzE5PJhK7rNDQ04PF4cLvd51uQDMLhMI2NjSQmJmKz2a712yDENZeUlMQjj3wXl8tNOBxm7969vPDCCxQUFDBz5kx0Xae+vp76+noSEhLIzc2NtqAYRmdLkK4bQOclkPZ2H5WVp/H7/bhcLnJzczGZTLS0NNPe7qelpYWUlBSSk5OpqqrC4/GQlJRETk7O+R/ZL7/ruq6zfv16xowZg8PhYP/+/UycOJFx48ZRU1PDN77hwufzdbn8Ii6NqqrceeedPPHEE5w4cYKhQ4de7yqJSyRBpx8Mw6CsrAyn08n06dPZu3cvVVVVDB48mI6ODlauXEkoFMJkMtHQ0EB2djY+nw+z2YzP5+PJJ58kOzubNWvWsH37djIzM6mvr6eoqIglS5awfft2Tp48yaOPPoqmaWzatImmpibuuecefve735GQkAB0NoUXFRUxdepUNm7cRF1dHWvWrCE/fykXv5WRs0xFUTCZTNjtCTQ2NuH3+3nttVc5c6aajIwMzp49y7Bhw1ixYgWHDh1iy5YtPPXUU+zevZtPPvkEh8OB3++ntrYWgNbWVp555hnuueceJk2adE3fByGuB0VRUBQVVe38b/LkyezZs4e9e/dy6623sm/fPt59910yMzPxeDykp6fz8MMPx1yW1+tl5cqVeL1eUlJSqKqqYurUW1i8+B62bt3Gp59+isViYe7cubS0tLB3714yMzM4c6aamTNnsmDBAi4MOk1NTZSXlzN//jxUVSUzM5PS0lLy8vIAuPXWW+VSy1WQkZHBwIEDWbduHYWFhbJPbzISdPohFAqxe/duxowZQ3p6Ovn5+ezevft8M7RBU1MTo0aNYvny5ezZs4fnn3+en/70pwwePJjnnvsDe/bsYeLEiWzcuJHHH3+cYcOGUVVVxW9/+xvGjh1LIBDA7/dH1xf5O3KmePvtt7No0SJOnz7Ns88+y4wZM5gxYwYlJSUsWLCAI0e6NqUaBpw8eZINGzagKFBTc5Y9e/aydOlS/H4/JpOZp59+moyMDEpLS3n55ZdYuHAhgUAAr9fLuXPn+Oijj/jmN7/JyJEjOXDgAH/4wx8AcDgcPProo9HWJyG+bjRNIz8/n127dtHU1MT777/P3XffzfTp02lpaeG3v/0tu3btorCwsFvZlpaW8y1Ej5CUlMSOHTt4//33mTv3TkKhEBaLhX/4h3/AarXyX//1X8ybN4+pU6dSUVHB6dOn0XWdC4c/q6mpQVEUkpKSUVWV2bNv49SpU7z++uukpaVht9spLi7u9RKb6JvJZGLMmDHs2LGDRx99tM/+WeLGIkGnH+rr6yktLcXhcPDxxx/T1tbG8ePHueuuu7BYzGiaxogRI7BYLCQnJ5Oenh7trJiY6MLr9VJdXY3dbic/Px9N0xgwYAAZGZlUVFT0um6r1cKoUaMwmUykpHR2BG5vbychIQGTyURCQkLMA1htbS0lJSVomobL5eJ73/seY8eORVVVFixYQGlpKevWrePMmSq83jZCoVCXsrquU1hYiM1mY9y4sdFOlGazmfz8/Ku7g4W4yei6jtlsprm5GZ/Px4gRI1BVFbfbzdChQykvL2fIkCHdymVnZ0cvf9fW1nLq1Cna29sJh8PR55OSkqItR6tXr2bv3r2MGTOGyZMnd+sf0tzcjNVqjf7wut1JPPXUU2zZsoXdu3ezZs0aTp8+zbJlyzCZ5HB/uRRFibaABwIBCTo3Gfnk98EwDEpKSnA4HDgcDtrb28nOzubkyZMcPnyYcePGAmC1WqOBQ1XVCy4ddS5DURR0XT9/RtZJ13UUpfNLFDnQAV1Ch6Ko3Q5ufV1vVxQoLi7mW9/6VrQDYqROVVVVPPPMMwwZMoQRI0aQm5tLVdWZLuV1PXx+OZHt0eSLLcR5oVCI8vLyaN82wzC6fa97cuJEOc89t5KioiIKCwtJT09n7dq10e+0pmkoihLtFzJx4gSOHTvOtm3b2L59Oz/+8Y+BxOjyLjzJCYfDnDx5MnqyNWHCBNLS0njjjTdYtGgRLpfr6u+MrxHp53Tzkm74fQgGA+zdu5e5c+eydOlSli5dyrJly5g8eTLbt28nFAr3q0k4JyeHtrY2jh07Rjgc5syZM5w9e5aCgkEkJSXR2NiIz+ejtbWVw4cPE+m42BNFUQgEAoTD4ZhfwEh/Ak3TogdPgMrKSnRdZ/ny5UyfPp1QKITf7+8StNLTM9B1naqqKsLhMBUVFVRWVmIYBqFQiDNnztDW1nZpO1KIm5Su63i9XjweD/X19Xz66aecOnWKGTNmkJqait1u58CBA4TDYVpaWigrK2PIkMExjwvHjh0jMTGRb35zKVOmTMHn8xEIdHT7Dnu9Xp577jm83jZuu+02VqxYQX19Pa2trV1el5KSQiAQIBgMEg6Heffdd1m7dm00bHm9Xkwmk9xxdYUMw6Curo6srCy5Vf8mJC06vegcW6aJlpYWioqKzndKVNA0jSlTpvDCCy9QX1+P2Wzu0ppzYeuHyWTGbDaTlZXFnDlzeO21VxkwIIe6ujqKi4spLCzE52tj8+bN/Pd//zeapmIymTCbLdFLRV8epBQsls7Hs7OzOXPmDO+88w6DB3+LC99KVdV6vAVy0KBBWK1WXnrppWhYcjqd1NfXo2mdLTfp6enMnj2bV155hYyMDBobG7HZbKiqitfbyu9//3vuu+8+6Yws4p6iKDQ1NfEf//EfaJp2/hJyCt/73vcYNGgQiqJw77338uabf6GkpITW1lbS09MoLp5CS0tL9HvcGTY0hg4dyqZNm3nppZej4cRsttDY2Hj+2NH5Pbbb7RQU5PPHP/6RgQMHUltby9ixY0lNTaWq6sv6Re7cbGxsJD8/n7lz5/K///sKhw8fjvb1W7BgwXUf9+tmFwqF+Pzzz5k2bZpcArwJKX00x32t2+ref99g27YQfn87DocDVe06iF5bWxsJCQn4/X5sNhtms5lQKEh7e+frFUXF728HFGw2G7qu09LSTEuLB6fTQXJySjSQ+P3t1NXVY7NZcbk6b+e22azn12FH0zQMQ4/+raoKzc3NqKqKy+Vm6lSVRYs667xxo4/iYuN8HbqeVUbC2+nTp3E6ndE7xCLX+dvb23G73ei6Tk1NDQ0NDdHbX+12OyaTiaamJpxOJzabjV27oLjY4Pbb22hpaem2DyPrj/U5M5lMXS7TXVwuVhlN09B1PeZzPZWJXLa7sNWqrzKRQBurfpezTb2VUVU15uWOyCWMS6k3dO6jnsr0VIeeyvS2rt7qfa32XU96e897q3dKSgo2m43Vq2HnToPx4wM0NzcTORRqmgmn09nlUnVkyIWamhrsdjs5OTnYbDZCoVB0eIa2tjbMZjM2m41z585RXV2N2+2O3qmVmJgYHb4hMbHz0lQoFKKmppr6+gaSkpKit63v3v3ld3ryZJ0333wTl8vF/Pnzgc4+hevXr6ehoYEFCxaQn59PIBCgvb2dAwfMjB8fZO5c/2W/H5Fyl/r97Gm/R07kenruan43Lucz5nQ6aWpq4u/+7u/47W9/y+DBg2MuW9wQYl5ekWjaC0VRmD7dDMTqn6IBkWveFz5/8esvPJPSgNTz/13MDsTq5HvhdXXtor+7LycyErLTGWNRfHkwT0lJ+XLNF5ztRcbGUVWVgQMHMnDgwG7LSEtL6/ZYSUkJ69at63agiFw2i3VwSUpKwuPxdDuQRS65BYPBbmUSExPp6OggEAh0267IQeziOthsNjRNi3m5rTOcdi+jaRpOpzNmeIuMM3RxvRVFITk5maamppjLA2IemC0WC8FgsFsZi8WCxWLB6/XGrHc4HI657xITE/F4PP1+LxRFweVy0draGnN5JpOp2/7urd697bue6q0oCm63m5aWlkv6DEX6v13MarViMplivucWiyXm9phMJpYtWxbtRKwoClarlczMzG6vvbh+6enppKend9vWyN2Jbrc7+vjFr40MH3Exs9lMXl4+eXk9d/5XFIVZs2bxzjvvRMfbycjIYM6cOfh8vuht5seOHePw4cNUVQ2gvr6aysqj0W2OBKyLl9vbZ6Kn76fT6SQQCMT8fkYG3rtYpLXY5/N1e85qtdLR0dHt8ciJl8fj6fZcT5/LzpNC1/ng2n15Pe2HyZMnU1FRwezZs+VGjJuUBJ1e3MzzN13rEYunTp3KLbfccm1XKsRVdnFflht95G9FUcjMzOS2227D5/PhdrtRFKVb6CoqKmLMmDHs3g3FxTf3se1aCwY7W+lnzZolfZ1uUhJ04tC1n3tKQVVljA4RX27kOdwuDGCKojB69OheX//l3ZegaZ3/if5RVZUlS5YAMqnnzUqCTpyRMzUhrtzN8D3atSsSeOTH96sk4ebmJ0FHCCFuQjdyi5MQNxK560oIIYQQ8SBm85v0rBJCCCFE3JKgI4QQQoi4JUFHCCGEEHFLgo4QQggh4pYEHSGEEELELQk6QgghhIhbEnSEEEIIEbck6AghhBAibknQEUIIIUTckqAjhBBCiLglQUcIIYQQcUsm9ezF6tVf3bJvhtmRe/JV7JebeX8IIYS4cUnQ6cOuXde7BjeeXbvg6FEYNuz/Z+++46O67vz/v+6900cajTrqAiEQCEQR1cYG2zQbE+J1IW4hTnOyzmM32Wz+z/exj8fufnc3+SabTfk5sbMmgWDjDq4YF4qMJKoooloFSUioS6PR1Ht/f4gZS6hQLIEtPs/HIw/D3HvOPTMjdN8559xzRqc+2YVZCCHEWJGgcwWGYTBnTpBgMDjomMViwWQyoShDbpg6pLKyvjpLSkKEQuHo66qqYjJpKIp6TfXdDKdP9/03ElAMw8Dn66Wnx4uiKDidTqxWa/R96LpOb28vJpMJi8Uy4P1JkBRCCDGWJOhchY8//pj33kvvWdEAACAASURBVHsPi8USfU1RFNasWcNtt912zfUZhsGBAweort4avfFbrVamTp3Kvffei8vl+lKFnXA4TEdHB263G03TokEHIBgMUlJSwo4dO+ju7sYwDBISErj//vuZM2cOqqpy+PBhXnzxRe655x5WrFhx896IEEKIW44EnSswDIOOjg6cTiePPvpoNIAoikJKSkr0nP4URRn0WuT1iO5uDwkJCTz44IMAdHR08Prrr+P3+3niiSdQ1ZHniUfqGuo6I5UZrl0j1VNdXc0rr7zMM8/8CIfDAXzeU7N3715efPFFHnjgAebNm0cwGKSsrIw///nPqKpKUVER+/btY/LkydFQGLnWlynMCSGEGJ8k6Fwll8vFlClT0DRtwOuhUIgDBw5QUlJyaZhrDrfddhuapvHOO+8QCgWprq5h/fr1pKWlEQkJAG63mylTpqCqKoZh0NraSklJCYFAgMbGRnbs2EFzczOxsbHcfffd5OVNYseOD5g7dy4ZGRnRnqFwuG8ILBwOc/DgQYLBIHfccQednZ2Ul5eTk5PDmjVrcLlcdHd3s2PHDk6fPk1SUhKrVq0iKyuLrq4uysrKsFgslJaWkpKSwpo1a4iJiaG8vJzq6hoOHjzIokWLADMAHo+Hd955h7vuuot77rkHTdMwDIN7772XtrY2tm/fjtls5syZM7jdbk6dOsXUqVP55JNPKC4uZsKECTfq6xNCCHGLksfLr1JHRwfHjh2joqKCiooKLly4EA0aL7/8MgsWLGDZsmXs3LmT999/n3A4TFlZGYcOHaawsJCYmJhBPRi6rhMMBgkEAnR2dnLmzBkSEhLw+/1s3LiR2NhYvvGNbzBx4kQ2btxId7eH8+fPs2fPHgzDwO/38d5776HrOuXl5fztb3+jqKiISZMm8bvf/Y6Kigruu+8+6uvreffdd/H7/WzZsoWqqiruv/9+EhIS+POf/0x7eztdXV289NKLHDlyhFWrVhEMBtmyZQuBQACv10soFKK7uxtd16Ptb2lpoaOjIzpEBX29NCaTiblz59LQ0EA4HMbpdBIfH4/b7SYQCFBdXY3X672h358QQohbk/ToXKW6ujpeeeWV6N+XLFlCUlISe/bsYfHixSxevBhFUQgEArzxxhssWbIERVG4/fbbWbFixZDDNAcPHuRf/uVfAPB6vTgcDjZs2IDdbufhhx8mLS0NTdPIysrC6+3B6/WycOFC3nzzTbxeL/X19QQCAaZOncqBAweYMaOQJUuW0NbWxs6dO1m5ciWFhYW0trZSWlrKhQsXOH78ON/+9rfJyMggKSmJEyeOc+rUKTIyMjCZzKxZs4ZJkyYRExPDH/7wB3Rdp7i4mHPnzrF06VKsVmu0/X6/H1VVcTqdg96fw+FA13USExNJTk5i4sSJTJo0CYCnn376ikNzQgghxGiQoHOVpk2bxo9+9KPoDdpkMqHrOp2dnSQlJUVfT0pKwufz4fP5UFWV2NjYYeucNGkiX/vaOjRNIyYmhuTkZBwOB6FQiAsXLvDmm2/S29uL1WrF5/NjGAb5+flA37yZY8eOMX36dFwu16VrJ6OqKqqqYrVaiYmJGdDWjo4O2tvb2bJlC2azGcMwCAQCBAIBAKxWa3QitNlsIhwOEwqFhm1/bGwsiqLQ2dlJWlragLDT2dl56Skr84AykR4fIYQQ4kaQO85V0jQNm802YI5OKBTC5XLR1NSErusoikJLSwsWi2XAE1rDSUhIZObMmQN6NwzDoKamhjfeeINvfetbFBQU0NLSzP/9v/+BYRjExMQwY8YM9uzZTVPTRR577LFo+Sv1ktjtdhISEvjhD3/IhAkTCIfD0d6c3t7eEScHDzVZOSkpiezsbHbv3k1eXh5mc1+oCQQC7N27l7y8vGgIG6oemYwshBBirMn4wRegaRqLFi1i7969lJaWUllZyTvvvMOcOXNwOp1fqG5FUdA0jaamJt5/fwednZ14vV5UVaW4uJiDBw9htVrJysq66jonTEglLS2Nt99+i/Pnz7Nv3z7+8peNdHV1jRg6LBYL3d3dnD59OjrxGfp6gB544AFOnDjBxo0bOX78OBUVFfz5z89TVVXFAw88gNk8MPB1d3ezdetWGhoarv1DEUIIIa6R9OhcgaIo5OTkEBMzeB4KwMKFCwkGg+zevRtd15k1axYrV65E0zRmzZpFcnLykPWmpU0gK2twr0bkevfeey/vv/8+VquVwsJC4uPj8fl8l8qmkZKSwoIFC7Barei6TkFBAfHx8UBfACkuLo4OXaWkpDBjxgyczhg2bNjA9u3befHFF7HZbDzxxJPk5ubS0dHBvHnzonNwnM4YiouLo2Fq/vz5VFRUMH36dECLtjU/P58f//jHvPfee7z44osYhkFubi4/+clPyMzMvNS2adFH8XVdp6fHM2BIzDAMruEpeSGEEEOQXvKhKVdYh+WWvv1s29a3cu+VtigwDINgMIhhGINW/r1c/5WAR6o3Mn+mb8Vk06UwYBAOh6mqqmLr1q0888wz0XBzLcLhMIFAAJPJdNUrO+u6jmEYqKrKpk195z/xxOdt1XUdv9+PooDFYh30GP5wyspg3jwdi+U9zp49O+i4xWIhFAoNeNoLuDSPyBydX9RfZCJ0JBheXibyXfVnNpux2Wx0d3cPqs9sNhMOhwe1QVVVXC4XnZ2dg+qLzEO6fI6Toii43W46OjoGlVFVFU3ThlyF22q1EggEhryO3W4ftt26rg/ohYu0ITY2NrrAY3+apqFp2pCfq8vloqenZ8j6LBYLfr9/UBmn0xl9svDyMn1zz3yDylitVlRVpbe3d9AxXdeHHKK12WwoijJkGZvNht/vH/I7t1qteDyeIdsw1OcdmXfX2dk55HtNTk6mpqZmyO8p8u+3v5F+HjRNQ1XVQT8PiqLgcrnweDyD6ov8vhjq+3M6nYRCoUHf00j/LiLD8EN9RsP924x8Rl1dXVf9OUTKXV4XQExMzIC5hFfTbqvVislkoqen56rbHZkrOVS7R/q3NNL3pyjKkL8DIkt9DPXZDff9jfQ5FBcXs2jRIgk6/ddv6Ud6dK7ClbcpUIArz8m5FpEbQf+/h8NhPvroIz766CPWrFmD2+2+rro1TcNut19TmZHm/0SG2foWE7x2iqIwffp0MjIyhjx2LYsiAtGQNdQv0+Hqi0ziHmry9UgLLZpMpiGDyUgsFsuQv8hGMlIbNE27pnYD0RvEtVzLbDYTCoWuadHJyET4oW5gw5UZ6fsLBAJDzn/7MnznkbCck5Mz6NhIrufnYaTvYjjX813cyH8X4XB4yP+DNFK7hzPa7R7p39KX4fuL9JiLoUnQuYKx2HDyevd3UlWV+fPnM3PmTFJTU0e3UddhtPapigzXCSGEEKNNgs4I1q4du7rLyq4nKChAAgB1daPdomszZYrsOi6EEOLLT+bo3ATbtt3sFoyOsQyCQgghxDUaco6OBB0hhBBCjAdDBh1ZR0cIIYQQ45YEHSGEEEKMWxJ0hBBCCDFuSdARQgghxLglQUcIIYQQ45YEHSGEEEKMWxJ0hBBCCDFuSdARQgghxLglQUcIIYQQ45YEHSGEEEKMWxJ0hBBCCDFuSdARQgghxLglQUcIIYQQ45YEHSGEEEKMWxJ0hBBCCDFuSdARQgghxLglQUcIIYQQ45YEHSGEEEKMWxJ0hBBCCDFuSdARQgghxLglQUcIIYQQ45YEHSGEEEKMWxJ0hBBCCDFuSdARQgghxLglQUcIIYQQ45YEHSGEEEKMWxJ0hBBCCDFuSdARQgghxLglQUcIIYQQ45YEHSGEEEKMWxJ0hBBCCDFuSdARQgghxLglQUcIIYQQ45YEHSGEEEKMW6ab3QAhhBDj37Zt115m7drRb4e49UjQEUKIL7nrCQlfRmVlff81DAPDMAADVdWGPHfBghvXLjG+SdARQoivgNJSA13X6ejooLu7G7PZjNvtxuFwoCjKsOV0PYzH04PDYcdkMg863hc4GLGOqznPMAy8Xi+qqmC3OwYc6x9a5s836O7upqKiAr/fz7x583C5XAPqjQQiIUaDBB0hhPgKCIdD1NS8zN69e7DbHQSDQSwWCxs2bGD69OnDBpCurh5+9atf8fjjj5OXlxd93TAMAoEADQ0NZGVlYTINfTswDIPe3l6am5vJzMxE04bugTEM2LTpNRwOB3/3d38Xfb1/aDEMg+rqap577jm6u7sJhUJ8+OGHfPe73yU3N/eKYUuI6yFBRwghvgIuXrzI3r17efrpHzB58mQCgQCvv/46r776CpMmTcJisWAYRjSI6LqOYfT1Ank8HoLBIMFgECAaaiorK9m+fTs/+clP0DQNwzAIhUIYhoHJZEJV+55X+fTTTzl+/Dg/+MEPUFUVXdcJhUIAaJoWvabP50NV1eh1AQxDBZRLf9bZuXMnGRkZzJo1i66uLmpraykvLycnJ0eCjhgTEnSEEOIrIBQKo6oqMTExmEwmbDYba9as4cyZM6iqSmVlJRUVFTzyyCNomsbhw4epqqrirrvuQtd19uzZw2uvvYbdbmf58uVkZmaye/duamtr+fDDD1m+/B5KS8vYtWsXwWCQrKws1q37GtAXdJqamti7dy9FRUW8+eabnDt3DsMwSE9P55FHHiEpKalfW0Ns27aN1NRUTKbbLnsfQRwOB6qqYrVaeeKJJzAMQ0KOGDPyeLkQQnwFpKamMnXqVH75y1/yq1/9iu3bt+PxeJg/fz4Wi4W2trZo+ABobW2lqqoKXdfx+/1cuHCBhx56kLlz5/LXv/6V5uaLpKam4nA4yMzMpLLyJNu3b2fdunX8wz/8A5qmsWnTZlRVIykpCZfLRVpaGgcOHKC5+SLPPPMMP/7xj+np6eGTTz6J9uBA3zwet9uN0+kc8B4URWXZsruoqDjC9u3bqampobe3F7vdLkFHjBkJOkII8RVgsVj47ne/y9NPP016ejplZWX8+7//O2+++WZ0GGmksvfffz+TJ+ezaNEiUlJSOHnyFBMn5uJyucjPz6eiooKCggJmzpxJUlISq1atoq6uDr/fT3Z2FomJieTl5bFo0SK++c0N6LpOU1MTuq7T2to64HqaprFs2TJmzZo14HVFUZgyZQp///fPkJKSQmlpKf/+7//OkSNHogFNiNEmQ1dCCPElZxgGra2t1Nd3UVhYSGFhIaFQiMOHD/HCCy9QXFwcOTMaGPr3sFgsFhITE1AUBU3TiI2NpaOjgwkTUqPn9PT0kJiYiKIoKIqCzWZDUcDv9w9oS11dHa+88gpgEBfnpqenB7fbPeCcSB19fx74PoLBINnZ2SxevJipU6fS3d3NG2+8wbRp07DZbKP3oQlxifToCCHEV8Bnn33Gc889R3t7O9A3oTgjIxNV1QgE/FitVnp7fQSDwWhvi2H0hZ1AIEBLS+ulJ638NDc3k5r6echRFIXU1FRqa2sJBAIYhsHFixfRdQOXywUo0ZDy9ttvk5ubyz//8z/zwx/+kPT09AGhCohOag6HwwN6aoLBIM8++yyHDx9CURQsFgvZ2dn09PRcsVdKiOslPTpCCPElpygK+fn51NQE+OUvf8n8+fMBOHToENnZ2aSnZ+ByuaKTgO12O0eOHGHChAkAhMNhXn/9dTo7O6mvr6e3t5fZs2fT3NxMS0sL+/bto7i4mNLSUrZs2UJubi4ff/wxc+bMIT4+npiYGGpqajhy5AhxcXE0NzdTWXmS2tpaKisrSUtLIxwOR9sbCoXYunUraWlpOBzLoq+bTBppaWm8+uprTJ8+Hb/fT0NDA9OnT8dut9/Qz1TcOrSf//znIx0f8aAQQoixd/o0tLbaWLVqBqFQiJqaGrq7u5k1axbr1q0jJiYGh8PJxIm5VFdXoygKK1euJDs7m4yMdOLj3cyYMZMzZ85gt9t58MEHSU1NJTY2NjphuKioiGnTptHY2EhTUxOzZ89mxYoVWK1WEhIS0DQNu93OkiVL6OrqoqamhuTkZFavXo3T6SAzMwOn00lGRgaJiYn09vaSlJSE359EZmbf+FVDg8rtt+diMpk4dOgQFy9eZN68eaxZswar1Rp9JL6+3iAtzSA/f+BQXGRF5c9XVh769SuViTz+fjPLXGt9Ix37fJjwlp/Q/X+GelG5wgQwmR0mhBA32bZtfQvv9d86AfqeYur7b2SdGuPScJUyYJ7McMeG+v0fqV9R1AH1DndOf8PdaCMrI5eV9f3ZMAyOHz9Ob6+X4uJ5KIpCeXk5J0+eBKChIZP09Dry809isVgIh8MDeowi13K73XR2dg4aOous7RMIBAa1xeVy0dvbG11TqH99VqsVn883qIzD0bfSs9frHXTMZrMNWcZsNmO32+nq6hqyjN/vH/S5qqpKXFxcdHiyv5E+h6VLl7J8+fLouke3sCF/ACXoCCHEl9x42usqEnQu5/P5osHk4EETc+eGWLVqcFDpL7J44bW4njLDBb4rlVEU5Ya0z+FwXHErkFuEBB0hhBA3R6RX6motWCC7l4trNmTQkcnIQgghbgjZkVzcDNKjI4QQQojxYMgenVt+5pIQQgghxi8JOkIIIYQYtyToCCGEEGLckqAjhBBCiHFLgo4QQgghxi15vFwIIUbRjVjc70auL3OjFiuUNXPEWJGgI4QQo+xaFsa7VjdjLZqxfD8g6+uIsSVBRwghxsBY3LzLysY+dAzn8q0brma7gas5/2a9H3HrkKAjhBCjzDAMzp37jPr6+kHHFEVhypR8UlJSr3tvopKSIFVVVdG9oTTNhMvlIikpEbPZcsV6++96PdK5l4c1n6+XQ4cOM2fOHFRVpbW1FYfDTmysC03TMAyDzs4OdN0gPj4ewzA4cuQI6enppKSkyF5M4qaQoCOEEGOgurqaAwf2o+sG586dw+l0kp6ehqpquN1uUlJSr7tuv99PWdlvSU5OJj4+nkAgQEVFPampqXz3u9/F7XYPGyp0XefEiROYTCamTp3KcNnj8p4WXdcpKfmUuro6ioqK2Lx5Mz6fj66uLlauXMncuXPx+Xxs2rSZO++8k/j4eBRFIRQKsW3bNp588kmsVut1v2chrpcEHSGEGAPLli1j6dKlBINBfvOb35Cbm8sDDzyAoijRHapbWlrweDxYLBZSUlKwWCx0dnaiqiqxsbEYhkFXVycmkxmn00n/Fe4VRWHNmjUUFRVhGAbNzc38x3/8BwcPHuTuu++O9q60t3cAkJSURExMDH6/j7fffpuZM2cyefJkNE2jp6eHlpYWTCYTKSkpmM1mLl9N3+PxUFKyl0ceWU9TUxPV1dX89Kc/5eDBg+zZs4eioiL279+P2WxmypQp0aBVWFjIBx98QFVVFVOnTkXXdTweDw6H49J1hBhbEnSEEGKUKYqCpmkA0SEiVVUxmUwoikI4HOa9997lk092ERcXR3d3N9nZ2WzYsIFDhw5RUlLCM888Q1dXF88//zzr16+noKBgyOtE/mexWC5ds29ezPHjx9m0aRMOh4NwOAzA97//fc6dO8fJkydpbGwkJSWFlJQU/vd//5dAIEA4HGbixIk89thjgDN6HcMwOHPmDKCQnZ1FXV09uq73e58G7e3t7Nmzh0cffRSLxRIta7fbycvLo7y8nPz8fJqbm/mf//kfnnrqKfLy8sbqKxAiSoKOEELcYH6/n9ra8zzxxBMUFhbS0NDAL37xCy5cuMD8+fPZt28fb731FhcvXiQ/P5/JkycPGooKhUK88847lJeXEwgEOH/+PG63m9mz51yaI3SOJUuWsHLlSoLBIL/4xS+oqKhg0aJFTJo0icLC6eTn5/PCCy+QnZ3NI488jM/n5/e//z379+/HZruTSK+OYRicPXuWCRMmYLFYSU9PJy0tjRdeeAGv18s999zDJ598wtSpU8nIyCAQCGAymVDVvqXacnNzeffddwkEArjdbh599FFSU69/6E6IayFBRwghbjCbzcb69eupra3l/fffp76+nq6uLvx+P06nk4ceepD//M//IiUlhW9961uYTIN/VSuKgsvlwmKxcPjwYQoKCnjyySeJi4sDYPny5dTX17Nr1y6am5tpamrE6/XicDiwWq3ExrrQdZ2zZ88yefJk3nrrbQCCwSCnTp2iqGgJoF26mkFLSwvJyckoioLD4eA73/kObW1t2O122tra2LlzJ+vXr+cvf/kLXV1dpKamsnbtWpxOJy6Xi+7ubvx+P263m8LCwhv0SQshKyMLIcQN19PTw3PPPcc777xDb28vkyZNwuFwRI+Hw33DQsFgkFAoNGQdmqaxZMkSnnzySf7xH/+R6upqjh07imEY6LrOW2+9xcaNG2lubiYtLY2kpOQBj3v3XSeMYRi43W5cLhcul4v58+ezYJhn4yO9SoqiYLfbycjIwOFw8MEHO7jjjjs4ffo0gUCAJ598kvPna6moqIieH3nSS4gbTYKOEELcYBcvXqS2tpYnn3ySdevWkZ6ejs/XSzgcpru7m61bt7J8+XKys7N54403CAaDw9alKAp5eXmsWrWKV199jYaGBnw+HwcOHGD16lU88sgjzJo1C6/XG52roygKgUAAp9NJcnISbrebe+65h2XLltHZ2UlnZycDJyMrJCYm0t3dPSCsGIZBRUUFwWCIuXPn0tnZSWxsLC6Xi9hYFx6PByA6+dhqtRIKhWhpaYk+Gi/EWJOhKyGEGGOXr1eTmJhAYmIir776KjExMTQ1NeFwOKmvr6eyshJVVbn33nvp7Ozk17/+Nfv372fx4sX0Dx+R+S+RPy9ZsoRDhw7x8ssv873vfY+Cgql8+OFHVFfXcOHCBTRNo6mpCcMwSE1N5e233yIjI4P77lvD5s2bqa6uxu/309HRwZIlS2hoGPiEV35+Pjt27CAYDEbXzOnu7ubjjz9m3bp12Gw2pk6dyquvvsqOHe/T0tLM6tWrATh//jw5OTlYrVaampr49a9/zXe/+x3y86eM/YcvbnnKFboSpZ9RCCGuwbZtfWvQREZ/dF2nubkZq9VCXJw7OozT3t7O6dOnMZvN5ObmEgqFoj0udrsdt9t96bHxi6iqRlJSEuXlfeEjHA6TlXWBxMQEbDZ7tM7Ozk66urpIS0sjFApy6tRpent7yc7OJiYmhvb2drKysujt7aWmpoa0tDTcbjeNjY1UVVVhsZiZPDkft9tNebkSfQ9lZVBQ0MVvf/tbHnvsMbKzswFobW2ltraWoqIiTCYT4XCYs2fP0tDQQH5+Punp6fj9fn7zm/9m9ep7mTlzJsFgkMbGCyQnp2C32ykrg/nzDe65xztkz5WqqkMOe/UPjsMdG+r+dqX6rrWMoijouj5kGWDQsUiZa61vuPZFhhHNZrMsyHj5mgiRFyXoCCHE6Lk86IymG71dQv+gM2+ezq5du2hvb+NrX1sXfXx+JJGhrU8/LeFb33oKm8026JyyMiguDtPZ+VdOnjw56HhsbCyBQAC/3z/gdUVRMJvNBIPBQTd/TdNQVXXI4OR2u+nq6hoUJlRVxWw2D7oOgMvlore3d1B9iqJgtVrx+XyDyjgcDgzDoLe3d9Axq9VKIBAY1G6LxYLVaqW7u3tQGYvFQigUGtRuTdNYt24d8+bNk6AzTNCRoSshhBgD421jT1VVWbhwAaWlZfj9/gGTp4ej6zo9PT3cf//aEVdFVlWVxx9/fNiejNGcxHw99Y12G0b7WlcTOm9lEnSEEGKUjXUQWbt2bOvvb9u2z/9ss9m58847r7rnoC8cLURV1RHLKIoy5CP0QowG+ckSQohRdCNDyI12pU1AhzpfehvEzSZBRwghxBXd6PlBQowWCTpCCCFGdDPmBAkxWuSpKyGEEEKMB0OOq8rKyEIIIYQYtyToCCGEEGLckqAjhBBCiHFLJiMLIYQYc/3X47la4/lRfXHjSNARQogvgesJAl811/KIujzpJUaLBB0hhPiSKC298oOuX2Q/o/5P2SpK36adhmFcMYB80T2U+oeW+fP72tDV1UUoFCIhIWHQNWTNHjGaJOgIIcSXRDAYoK7uNdra2oC+EBIJADExMXz961/H5XINKhcJMKFQCJPJNGwwCYd1du/eTVxcHIHAbCJP41640EBj4zZ0XY/WFaljxowZ3HHHHdcddi4PLT6fj507d7J7925CoRDLli1j+fJ7oruwCzHaZDKyEEJ8SSiKSnJyMunp6cTGxlJWVobNZiM9PZ3U1NQRt1M4cuQIb7/99pAbY0YYhsHx48eprq4e0LtjsVhJS0sjPT2d9vZ2Tp06RWpqKunp6cTFxY3a+9N1nY8++og9e/YwZ84cpk2bxu7du/noo49H7RpCXE56dIQQ4kvCbDZz++33AHDhwgVKS0tZunQpeXl5QF9Q6erqoqLiCF1d3WRnZzNlyhRCoRAHDx6kvb2d9vZ23G43Z8+e5bPPzmEYMGnSJPLz84e8pqIoJCUlsWDBOgzDwDB0NE1j7dq1WCwWDMMgFApRWVlJbW0tmqYxdepUcnJyaGlp5uLFZgoKCjCZTFRXVxMOh8nJyaG6uprExEQMI55Iz5FhGJw5c5ri4mJyc3Pp7u5m0aJFhMPhG/L5iluT9OgIIcRXQCTk/P73v6esrJze3l62bNnC66+/Tl1dHSdOnODs2bOUlZWxf/9+/vSnP+Hx9ODxePjjH/9IRUXFdV97586dbNq0iUAgQEtLC7/97f9QXV1NKBRm06ZNHDp0iIaGBp599lna2trw+Xxs3bqVU6dODahHURTy8/OpqKigtraWUCjE9OnTmTVrlgxbiTEjPTpCCPEVcfjwYbxeLz/96U+JjY2lqGgmv/3t71i8eDELFiygtbWVFStWcPToUdavf4S5c4sJhUJcvHiRyspKioqKrvmauq7jdDrZsGEDBQUFeL1eqqurOHfuHMuXL2fFihW8/vrruN1upk6dypw5c9A0jR/96EdYrVYOH/68LlVVWbbsLlpb23j//fcxm810dHSwatUqXC6XhB0xJiToCCHEB/eCSwAAIABJREFUV0R9fT1paRNwOp0oisKECWmYzWba2tpQVTX6v/z8fMrKyvj9739Pd3c3NTU1LFy48LquqaoqhYWF7Nu3jx073sfj6aGqqpq5c4tRFIXFixezd+9eqqqq+Na3vhWdDB2ZNN0/uxiGgd1u57HHHiMnJ4dTp05x4sQJOjo6+Pa3v43JJLckMfrkp0oIIb4irFYrXm8vut43jyYYDBIOhzGbzdFzdF3npZdeorGxkdWrV5OSksLrr7/O9e7RHAgEeP755zGZTNx1113Ex7v53/99ITqZubGxkc7ODkwmE5WVlSQlJY3w1FeYTz8tY+LEiTgcDiZPnszs2bN56aWX6O3tJTY29rraKMRIZI6OEEJ8RRQUFFBbW0tlZSUej4fy8nLMZjMTJkxA0zS6urrweDzU19dTVFTE9OnTMQyd6uoq/P7AgCetrpbf76exsZH58+czZcoUvN5e6uvrCQQC9PT08PLLW5k/fwFPPPEE27dvp66ujmAwyNGjR2lubr7smgaffvop7777Dj5fL36/n+rqalyuWCwWy+h9UEL0Iz06QgjxJWQymUhOTo721iiKwpQpU1i+fDmbN2/GZrMRDof5xje+gdvtJj8/n927d7Nz507uuece3nnnHU6ePImiKMycWURbWxt+vx+XyxUd+hqK0xlDfHx89LjT6eTuu+/m3XffpaSkBLPZTHFxMW1tbZSXl6OqGqtXr8bpdHLq1Ck+/vhj1q5dy1tvvcWyZctQlKRo3ZpmYt26dfzlL3/h2LFjhEJhHA4Hjz76qAQdMWaUKyT86+vrFEIIcU22betbXC+yirCu69EFAFW1r/PdMAx0Xaenpwev10tcnCu60J6u63i9XjRNw2q10tXVhd/vIy7OjdlsJhDwY7X2hSNVVdm/X2Xhwr4wE7lupH5d16NzbSKvdXZ2EgwGcbvdqKoabRsQPTcUCmEYBpqmDXud+fMNvF4vH330ER6Ph9WrV+Nyubh48WJ0ocQTJ2KYPt3DkiXtaJoWbUN/iqJgtVrx+/2DeqpUVUVRlCEfW7darYRCoUHHFEVB0zRCodCgMpGwGQwGBx0zmUxDltE0DZPJhN/vH7JMOBwe1O7Ie/L5fEPWN9znkJGRQXZ2tkzmjqxjcBnp0RFCiC+Rz1cSVoHLezkUQANcl/7XnwrE9Pu7+7Ljjn7nDZwk/Pl1I/X3X5gw8lrfVg01NZHXzQzW/5Yy9HUURcHhcHDnnXcSCoWiCxJ2dHRQV1cHQGtrMnV1zZw8eR5N0was2Ny/HqfTSU9PzzUFHYfDQSAQGBRORgo6VqsVYNjQMlQZk8mExWLB6/VedRlVVXE4HHg8nkHHRvocLBYL2dnZg8qIPtKjI4QQXwK3yqaeV7NZZ+Q82b1cXCPp0RFCiC+r8X5TjwQ52bBT3GgSdIQQQtwQV9ObI8Rok6ErIYQQQowHQw5dyTo6QgghhBi3JOgIIYQQYtySoCOEEEKIcUuCjhBCCCHGLXnqSgghxJj6ImsEjffH7sXYk6AjhBhXbubCe3JTHt71rJ8jj6OL0SBBRwgx7tyMRenkpnxlkc+o/7Imw+3PJAsLitEiQUcIMe4YhkFRkW/QBojw+caJmqYNUfLq6w8EAiiKgtlsprxcGXRjDgaDhEJBrFZbdFPOLxvDMAgGg9HNKiP7PZnN5lFtc//PJvLZ1dbWEAgEmTx5MhaLRTakFGNGgo4QYtwJBgP86U9/oqGhAcMw8Pl8WK1WVFUlNjaW73//+yQlJQ0qF7nxezwe4uPjh7356rrO9u3biYmJYcWKFUTWKYvc0HVd5+DBI5w+fYp7772P+Pj4sXqrV0XXw/T09OBwONE0LdqzUlpqcPBgBUeOHMFs7rsdmM0WcnNzKCoqwuFwjnoAaW5uZtOmTZw9e5ZQKMTUqVN54oknSE5OlrAjxoQEHSHEuGMymVi7di0+n4/Ozk6ef/551qxZQ1ZWFmazGZfLNWgX6IjS0lJOnTrFU089NWKvRltb25A7UC9YAB6Plw8+eJeYGB/hcDzz5q1GVdVhr3k5RVGGPPfy14c773I1Nef529/+xjPPPENlZSz9F5B1u0+TmVnPgw8+BEBdXR1vvfUiwWAJf//3f4/dbr+m611+Tnn559cKh8Ps2LGDYDDIgw8+SFdXF6dPn+Ljjz/moYcekqAjxoQEHSHEuKOqGrm5uUBfD4LFYiErK4uCggKgr+emurqaDz74gPb2NnJzJ7J8+XIADh48QGNjE8eOHWPKlHw+/vgTjh6tQNcNpk6dysqVK7HZbMNe2zAMPvvsMzRNY9WqVezatYtly5bhcDjw+XrZvXsPmqayf/8BkpOTueOOJZSUfEpTUyMLFy7i9ttvR9M0zp8/z/vvv09rays5OTksX76cxMRE6uvrOXLkCCtWrMBsNlNdXc2pU6dYunQp+/fvx263U1ZWht/vZ/ny5UyePJn9+/dTXV3NgQMHsFqXAOYBbXa5XOTn52MymZg2bRqZmZn86le/orKykvz8fHbu3Mldd92F2+2mo6ODTz75hOXLl2M2mykpKaG8vByr1cLdd9/D9OnTMQyDkpIS4uPjMYwZ0QBjGAZtbW1kZ2cTGxuLoih8+9vfQdd1CTlizHw5B46FEGKMGIbBxYsXefbZZ4mPj+drX1tHc3Mzzz//PF1dXXi9vQQCAXp6eigp+ZTdu3ezatVq7r//fvbv38/u3btHrD8cDlNeXs60adOYMWMGHo+HqqoqDMOgt9fHm2++QVlZOStWrKClpYX//M//wuFwcOedS3nrrbc4c+YMbW1tPPvss8TExLBu3Tq6urr44x//SE9PD83NzZSUlER7ky5cuEBpaSmBQICPP/6IrVu3Mnv2bGbOnMmmTZtobGykp6eHcDiMx+NB14fvkVEUBUVRyMvLIz09naqqKnp6eti1axc9PT0AdHd3sWvXLrxeLx999BEffPABd921jKKiWfz1r3/ls8/OYRgGDQ0NtLW1Dahf0zQWLlxIaWkpJSUldHS0ExsbK8NWYkxJj44Q4pZz9OhRHA4Ha9bch93uICEhgX/7t3/D5/NRWFhITU0NCxcupKmpiUmTJpGcnIzX6yUlJZnz58+PWHdnZyenT5/m8ccfR9M0MjMzKSsrjfYmKYrK8uXLmTNnDt3d3TQ3N3PvvfditVrZs2cPNTU1tLS0oKoqa9euxel0kpyczL/+679SXV094rUNAxYtWsjixYvx+/2UlJRw8eJF5s+fz4kTJ1i6dCknT1qu+PmoqkpMTAw+n2/Yc3w+H3v27OH2229n0qQ8AM6ePUtZWTmTJuVFh6L27/+8jKIozJ07l3A4zNtvv83Ro0eprDzJo48+Sm5uroQdMSYk6Aghbjnt7e243W7M5r6bvtPpxGaz0d3dNeA8Xdd57733aGxsxGQy0draGg0sQzEMg8rKEzQ1NfHSSy+haRqdnZ3ous7atV9DVVXMZjMJCQnRJ5ycTicWS187VFVF18O0t7fjcrmirzscDpxOJ11dXQPmzPS/bqR8QkIi0BcqTCYTfr+f2NjYa/p8wuEw3d3dFBQUROfcXD43x+fz0dbWxs6dO9m3bx8AgUCAGTNmYBgGJpPpUjsGt3PBggWoqkpjYyPNzc289NJL/PjHP8ZqtV5TO4W4GhJ0hBC3HLfbzfHjxwkGA2iahtfrpbe3l5iYWBSlCYBQKMTLL7+MzWbjRz/6ETExMbzwwgsjTsYNh0OUl+9n5cqV3HXXXSiKQm9vL3/4wx84duwYRUVFwPBrx/RvX3t7O36/H5PJhNfrpafHEw0sfY+u9w1dRYJUxJXq7mv/0OdE3tvZs2dpb2+noKAg+hh+IBDAMAy6uz0EAgHMZjOxsbE88MADzJ07F4DPPvss+uj+55/T59cKBAJs3bqV4uJiNE3D5XKRmZnJli1bCAQCEnTEmJCgI4S45RQWFvLuu+/w1ltvM3v2bHbv3kVCQgJZWVmcP3+ehoYGamtr0XUds9mMz+fj9OnTHD1awcSJk4Zcn8cwDDo6Ojl//jzr1q1jwoQJKIpCOBxmzpw5fPrpp0ybNnxvUH8FBQVs27aN7du3M2/ePD799FOczhhycnLw+frWB9q3bx/Jycns2rULs9k8Yn1msxmPx8OpU6fQ9bnAwDWEGhousHPnTlRVpbm5mYMHD7Bs2TJycnIIh8MkJSWxd+9efD4f7733HqFQCIfDwezZs3nvvfdwuVz4/X42b97M/fffT2ZmJh9++CFJSUkYxtxo+NI0Db/fz2uvvUZRURG9vb1UVFSQmZk54gRvIb4I7ec///lIx0c8KIQQXzanT0N9PWRk9P09Mgl3+vTp0Sd9nE4nOTk5HDlyhMOHD2O12li/fj2JiYnExMRQX19POBxm6dKlnDhxgiNHjhAIBFi2bBmBQID8/Hx8Ph9JSUlkZGTQ0NB3I+/oaCcjA+bPn99v6EYhLi6O1tZWJk6chN/vZ9q0aTidTnw+H6qqUFBQgKqqdHd3k56ewcSJE5k0aSJHjx7l0KFDaJrG+vXrSU5OxuFwkJycTEnJXqqrq7n99ttISkoiPz8fj8dDTk7OpYBh0N3dTXZ2NllZWXg8Hi5cuEBCwgyys/vaVl8PcXEeOjs7aW5uprW1FZvNxr333sdtt92G2WxG0zSys7M5fPhwtFcqLS2NadOmUVBQQFdXFyUlJVRVfcYdd9zBkiVLUBSF48ePY7PZUNWsaNDJylLJzc2lvr6e0tJSqqqqSElJ4eGHH8blcg34HiPf4ZQpN+gHZxyQOU78n6FeVK6wJsLVLfoghBBfEtu29S3cd6UtGQzDIBwOEwqFoisBR+ajRHpsVFUlFAoRDoejq/fquh49N2IstivomxejEwqFMZk0FEUd8Jh2ONw3dKVppqsarur7XW+gKCoLF36+wOHVbF1hGAahUAhd1wetYqzrenSV6KFWOO7/2USuFXkyrafHw513LsVkMnH8+HGqqqqi59bUpJKT08TcufXouk44HB5Qr6IoxMbGXnqSbGAPm6ZpqKoaXfG5v5iYGPx+/6Bjkfb7/f5BZSK9TUNNzrbZbPj9/kFDmmazGavVisfjGVTGarVGhwL7iyxo2dnZOaiM2Wwe9nMoLi5m0aJFEnSGGZOVoSshxC0pMlk30vPS//X+20OYzeYBQ0MjbR0xuvtdKfQNMQ11PYXL18K5cl1fZE+pka6nAtc27KSqKsXFxQDRzz8tLQ2n09nvLDszZiRz++25w86LMpvNhEKhq16IMXK9cDh8VQsyRkS+88tDxkhlVFWNBuWrLRP5mRwqoI20WGNKSsqQr4s+EnSEEOPSjd4UcsGCr9bu5du23biNSC//LiJ7hPWXmJhIYmJi9O/NzTBxIsyceSNaKMYzCTpCiHFHdhK/shsdymQ3cnGzSNARQowrX6VelVuNhB1xM0jQEUIIMeakl03cLPLUlRBCCCHGgyFn3MumnkIIIYQYtyToCCGEEGLckqAjhBBCiHFLgo4QQgghxi156koIIcSY2rbt2svIMgFitEjQEUKIL+B6buI309q1N77Nl6+fE9nHC0BVB29xIY+ii9EkQUcIIb6gyI3cMAwCgQAdHR0kJCRgMl15w82xFllCRFGUaIAwDIMPPujC6+25dJaC1WohNtZ1xTb7/X48Hg/x8e4BIUXXdTo62rHb7djtjiHLLljQd15jYyMlJSW43W7mzSsmLs4dvaYsKihGmwQdIYQYBX03cYMPPviEbdte5qmnnmLBgkXczJzj9/s5e/YskydP5sgRa/R1wzAoLy+nuvol4uPjAQgEAiQmJrJhwwYyMzOHDTvHj5+lrOxdnnnmGWy2z4NOMBjm//2/P7FkyR0sWHBb9PX+wcUwDMrKyti6dSuRndQ/+eQTfvjDH5KWlnbTQ6EYnyToCCHEKPH7/VRUVDB79mw+/fRT5s2bh9lsxjAMgsEgiqLg8/lQFAW73U4oFMLv92O1WrFYLNEdqv1+P4FAALPZhNVqi+6CHQ6HsVgsAIRCIXRdx2KxEAqFUBQFv9+PYRjY7XZUVaW2toZNmzbxs5/9DMOwEFlPzTAMwuEQs2bN4oknnkBRFLxeLxs3vsAbb7zBD37wA1RVpbe3l3A4jKqq2O12NE0jLy+Pb37zm1gsluj7irw3v99PMBiMvq5pGoahRgNMIBBg586dLFu2DE3TiI+P58CBA5w5c4a0tLSb9bWJcU6CjhBCjALDMKirq6O318sDDzzAs88+S0NDAzk5OQQCAV566SVCoRCnT58GYMGCBVRXV9PY2Ehqairf/OY3SUhI4OTJk7z22mt4vV4sFgt33303t912G4cOHeL48eM8/vjjmEwmSkv3cf58HQ899BCvvPIKAKdOnaK3t5eFCxdy991388EHO6mrq+PNN99k4sT1gG1Am81mM7GxsaiqSmxsLPn5Uzh8+DDBYIB9+0r58MMPCYfDANx5550sX76curo6du/exWOPPU5TUyOvvvoa7e3tJCcn097eAUBvby8bN77AwoWLgNkDrqmqKrquo2kaDoeD733ve6iqPAAsxo4EHSGEGAW6rnPo0CFycyeSmZlJeno6Bw4cICsrC13XOXPmDFarlX/6p3/iyJEjbNy4kccee4ynnvoWzz33PLt372bp0qVs2rSJO+64gzvuuIPTp0/z17/+lQkTJtDV1UVDQ0N0zk17ewcXLlwA4Ny5cwQCAZ5++mn8fj+/+93vmDp1KgUFBZw4cYLZs2fT0zP4173H46G+vh6AtrY29u3bR2FhIb29PkpKSvj6179OYWEhJ06cYNOmTRQXF9PV1cWZM2fxer1s3vw3pkyZwvr16yktLWXPnj0AaJrGtGnTSUxM5FITAbBYLKxatYqNGzeiKAoLFy4kIyODxMREGbYSY0aCjhBCfEGGYeD1ejl8+DCLFy/m/PnzpKamUlZWxvLlyzGbzSiKwvz580lKSiI3N5eEhATmzZtHXJybjIwMLl68yPnz5wkEAixevBiHw8GMGTPIyMjg5MmTOBz2Ya+vqirz5s0jLS2NUCiE2+2mubmZ9PR07HY7EydO5OTJwb/uDx8+TH19Paqqoqoq06dP52tf+xqxsbE8/fTT9Pb2cubMGaqqqvB4PPh8vmjZpqYmmpub+fa3v01ycjIrV66kvLwc6As0d955J8CAoKMoCrNnzyY+Pp5XX32Vffv2ceDAAZ566immTZsmYUeMCQk6QggxCs6ePUtrayuHDx/m2LFjhEJBmpubOXPmDNOnT0dRFGJiYoC+G77JZMJk6pvMq6pqdF6LqqrRJ58URcFqtdLb24vDYb/0WHZfj46u6wOu3z8IqaoaHXIayfz583n88cdRVQWLxYrNZkNRFHp6enj55a00NjaRnJxMTEzMoOGlyFwji8V86b+W6MTm/oGlf3bRdR2Px0NmZiaFhYXceeedHDt2lB073mfq1Klo2uBHzYX4oiToCCHEF2QYOuXl5SxatIj169dfCi46f/nLX9m3bx9Tpky5qnoSExPx+XxcuHCBvLw8vF4vFy5cYObMmZhMJrxebzQMRYacPje4NyQyuVnX9UsBaeA5VqsVt9s9IMQYhsG5c+c4deo0P/vZz0hNTeX06dOUlJRE174BiIuLwzAM2ts7iItz09nZSUNDQ7QOv9+PyWTCMLRo8Onq6uK///u/eeihh4C+OULx8Qk0NjZF5+0IMdok6AghxBfk8fRw6tQpnnrqKex2ezRgLFy4kOeff56WlparqmfChAnMmjWLP//5zyxatIjPPvsMq9VKUdFMAoEgoVCILVu2YDabOXfuLBkZmSPW53A48Hq9vPXWW+TmPgKYr6od8fFuNE2jvLwci8XCkSNHCIVCtLW1R89JSkpi9uzZbNmyhaKiIiorK6Pv0+v18uyzz7JkyRJgXrSM0+kkKyuLLVu2kJiYSG1tLadOneK+++7FZJLbkRgbSqQbdBgjHhRCiFvdtm3w8cdeYmMrKSwsxGbre7Ip0qtx9OhRcnNzaWxsZMKECSQlJeHxeDh79iyFhYWYzWZqa2sJBALk5eXh9/s4fPgI58+fJz4+nrlz55KQkADA+fO1HDhwAIfDyeTJkwkGg+Tn53Py5EkSExNJTU3FMAwqKytJSkokMTGJiooKuru7MZtv47bbzKxdC2++afDGGxeYMcNLXl7eoLkx4XCYM2fOcOzYsUtzhQrp6OjE5XIRGxtLXV0dhYWFBIMBDhw4QF1dPZMmTcJmszFhwgTi4+OpqKggIyODmprUaP3z5/fNZSorK2Pnzp04nU6WLVtGcXHxpd4fg/JymD8f7r//84UOI5/n5SKBcijDHbue+saizFDHrqcM9A1VRoY6b3FDfgASdIQQ4gvYtm30V/O92hvg1dzY+pdZuFCJbgFxpTaPdG+4mhvyUPqvzHzgwAESEhKYOHEiAKWlpZw4cYILF7JISztPXt4JoG94S9f1QXOOFEXB7e4bMrt8vpKmaaiqSjAYHNSGuLi46BDg5fVZLBb8fv+gMg5H30rPXq930DGbzTZgknaE2WzG4XDQ2dk56JjVaiUQCAz6/FRVxe1209bWNqiMxWIhHA4P+TncfffdrFq1Sh7Tl6AjhBCjT/a6urJIqBppD6tAIEAoFOLAAY3i4jD33hu6Yr0j9egYhjFk6LpRZa5UbjjXU8ZisWC1WqVHR4KOEEKIm+Fae70WLJDdy8V1GTLoyOwvIYQQY052JBc3i/ToCCGEEGI8GLJH55afuSSEEEKI8UuCjhBCCCHGLQk6QgghhBi3JOgIIYQQYtySoCOEEEKIcUseLxdCCDGmrnWBQllDR4wmCTpCCCHG3NUuGCjr7YjRJkFHCPGV9FXbekEM3OvK6/ViMpmwWq3R46O9Z5gQIEFHCPEVZRgG77/fwblzZ9H1vrVNrVYrycnJpKQkYzKZx/T6oVCIs2fPkpaWRlxc3JDn6HqYc+c+Iz4+nqSkpOu6TiQclJSEOH36FB5PDwAWi5mEhEQmTJiAxWIZsY6Wlmba29vJy8tDVbXrasf1uryHxufzsX9/OZ98sguXy8Xdd99NQUEBmnZj2yVuHRJ0hBBfWc3NzRw69P+xcOEizGYznZ2dfPjhWRYuXMgjjzyC2Tx2YcfvD3PmTCmTJi0hL2/ooBMKGVRXHyAjYxqzZl170Onfw6HrYSorN2KxWJgwYQJeb5DTp+vIz89nw4YN2Gy2Yev56KNjHDmyj4cf/ikWy40LFJf30ITDYd555x1KS0tJTk4mFArx7LPP8v3vf5/p06czzMK2QnwhEnSEEF9ZhmHgdsezfv16YmJiMAyDgwcP8vzzz3PXXXeRlpaGYRg0N1/k4sVmYmJiyMzMxGQyEQgE8Hg8+P1+WltbSUlJIS4ujpqaGgBycnKwWq0YhkFLSwtNTU0ApKWlkZiYiNls5v777ycmJgaPx0MoFMLr9dLW1kZaWhoJCQlomsbKlSux222EQiE6OjqwWCzU1dVht9vJysqK9mT09PRQW1uL2WwmKSmJYDCIYSRz+c1/+fLl3HHHHQDU1tbyX//1nzQ0NDBx4kRCoRDnz5+nq6sLu91OdnYWNpsdwzDQdR0Av99PXV0d3d3dOJ0OsrKysVqtBINBuru70TSVurp6XC4XGRkZqGrfw7mtra00NTVhs9nIzMzEYrFgGAatra00NjbicDjIysrCbDaj6zrt7e0EArEYhiW6q3YgEODQoUOsXr0an89HcnIyU6ZMwe/3j/nPirh1SdARQnzlhcNhwuEwhmHg9/vRNA1N67tBl5aW8vrrr5OYmEhXVxfTp0/n4Ycf5syZM2zevBmbzYbJZKK5uZnc3By6uz0EAgGys7P55je/SWVlJZs3byY1NZVwOExLSws/+MEPmDBhAs899xz33XcfFy5cYM+ePdhsNhRFwePx8L3vfY/09HQ2b97M3Llzyc/P5ze/+Q0xMU4URaW9vZ3Fixdz33330d7ezh//+Ed0XScYDNLb20tWVhbFxT8EBvbARM4xDIP29nZMJjMWi4VQKMSWLVuorKwkLS2N5uZmUlNT+c53vhMtGwgEeOmll/jss89ITU2lqamJ7OxsNmz4/9u78+iqykPv49+9z5ST4WQgCSGEEAQSkgABGRwBEbAuhioq6HWotld7te3SarvWvbddvcv3fde9q7e3t7b29jrVUm2tChYsqL1akV5BJplkSgmEMCZkJMlJcpIz7P3+EXMEE0YZZPP7rJWlOfs8z372c8I5v/M8z977fo4cOcJzzz1HWloqhmHS1NTEV77yFSZPnsz27dt57bXXSEtLpaWllQEDBvDAAw+we/duFi5cSHp6Gm1t7QwZUsDdd99DJBLhmWeeIT//Dq67bkR8/263m6ysLPbs2UO/ft1BcObMmfEgJHI+KOiIyCWtvr6eF154AcMw4iMqN910E/36ZdLS0sKf/vQnZs+ezdVXX019fT3//d//zd69e4lGo9TV1fH4448zbNgwfvGLnxMMtvHYY4/R3NzMz3/+FHV1ddTV1TF9+nSmTp1KNBrl6aef5pNPPqF///60trYSiUTo7OyksbGRH/zgB2RmZvLyyy+zatUq5s+fTzAYJBwOY1kWNTU1zJs3j6lTp7Jr1y5+97vfMWnS9fzv//4vXq+Xf/iHfyAajfDzn/+CUCjE52+6bFkWb7zxBu+99x7RaJT6+npuuOEGsrKy6OzsJBqN8M1vfpP8/Hz279/P008/TUNDQ7x8T50PP/wwubm57Nmzh2eeeYbm5qNEo1Fqamr46le/yvjx4/n444959913KSsr409/+hOTJk1i+vTpNDQ0sHDh6xw6dIglS5YwdepUpk6dSktLC08//TTl5eWMGjWK+++/n717j5+uc7vd3Hrrrbz44ousXr2awsJCbNtm5MiRuN1uNHUl54OCjohc0vx+P6WlJdTXN7B69WrmzZvHlClT4qM0dXV1bNmyhYqKCizLoqWlhaoUSeL6AAAgAElEQVSqKnJycsjIyCA/Px+v10tqahrJyckkJycTjUYBg3A4zHXXXcfu3btZtmwpjY1NVFVVkZ+ff1wbDMMgPz+frKws3G432dnZVFXtjU8X9UhMTGTEiKL49FQ0GqG1NciuXbsYP348SUlJAEycOIGdO8t7HatpmkyaNInRo0cTjUY5dOgQy5cvZ/DgwUyePJnZs+dQXl7OmjVrqKmpobW1lXA4HC8fCASYPXsW5eV/48MPP6S6uppgMEgkEgUgNTWVYcOGxY+hvb2dhoZ6mpqaKC4uxuPxkJOTw8MPP0Jt7RFqaqrZuXMnBw8exLZtgsEglZWVlJWVkZ+fz5EjcOxgjWEY5OXl8f3vf58333yTuro6XnrpJW655RZuuOGGc/MHIfI5CjoicklLTk5m8uQp+P1+Bg4cyJ///GcKC4czaFA+lmXh8/koKSkmOTkF27YpKiriiiuuoKGhAbfbHV8jYxhGrzN/LMvizTffpLy8nIkTJzJ27FgaGhp6jbRA92hFzxSMYRjxM8GO1b2P4992bdsmFosdN33j8XhPOJ2Tm5v76cJdGDlyJE1NTaxfv54RI0bwzDPPkJ2dTWlpKQUFBVRVVR1Xtr6+nmeffZa8vIEUF5eQn5/Pvn374ttdLjO+JqdHLGZh23a8PZZl0dx8lK6uLjweLyNGjCA9PR2AoqIiBg0a1Ge7ASKRCJWVleTn55OZmUlpaSn79u1j3bp1XH/99cD5PVNOLk+6BYSIOELPaEdBQQELFy4iFAqRmZlJYmIiiYlJjB8/nmHDhvHxxx/T2tp6WnWGw2G2bdvGjTfeyMyZMykoKKCxsZFoNNpn2DkbLpeLYcOGsWXLFjo6OgiFQmzdurXXaFCPzs5OWltbOXr0KBUVFezYsYPs7Czq6upobW3lrrvuYvLkybjdLjo6OojFYvGyhw4dIhQKcdddd30aLOj1nM8LBAIEAgF2795NLBajpqaGX/ziacLhCKmpqfh8PsaPH8+IESPYtGkjTU1NWJZFdXV1r+m3jo4OFixYwKZNG4HukNfW1obXe+JgJ/JFaURHRC5ZpmnEryFjGN3/P3fuXJ566mesWrWKadOmMXv2bN544w3WrFlDU1MTubm5DB48mD179nz6Adtdl8vl+nSdSPd0i9frxefzMW7cOJYvX87u3btpbj5KWloa9fX1RCIRvF4vpmnicrmOO5X92N89Hg8ulyvevmNHfTye7vLTp0/n17/+NT/96U9xuVw0NDQwaNCgXh/+LpeLRYsW8dZbb2GaJoZhUFhYyJw5X8U0Tfr168fLL3efgt7Z2UlGRgZHjhzBNE08Hg/5+fmkpKTw29++hMvloquri/T0NGpra8nKyjpuJKmnjN/vZ86c2Sxa9Abbtm3j6NGjjBkz5tP9zmHx4sVs2rSJ1tZW0tLSGDp0KB0dHTz//PMUFMxn8uTiePuTk5OZNm0aixcviS8Cb29v44EHvq7r6Mh5Y5ziW8m5+coiInKOLV1q89FHEYqLg6SlpcU/KLunVppxuVwEAgEsy6K+vp6amhoCgUD81OhwOEx7eztpaWkYhkEwGMQ0TZKSkuJ1BAIBbNvm4MGDdHR0kJOTQ1JSEsFgK/36ZdLa2kpSUhKxWIxIJEIgEAC6Ry6i0SgpKSm0trbg8yXEr/OTmpqK2+0mGo3Gf6+vrycUChGLxfB6vaxcuZLOzk5Gjvw6V1/dfVxr11oMH36UaDQCgGGYeL0ekpKS8Xg82LZNa2srBw4cICHBx4ABuYTDYUzTxOfz0tXVRSCQSmtrCwcOHCQxMZEBAwbQ2dkZDzQ9YcXlchGJROK/AzQ0NFBTU0NGRjoDBuTidruxLIvGxkaqq6vjp5cnJCTE+6+8PJnrrusOT+vXd188MBqNcvjwYZYtW0Z6ejpTp04lJyeHUCjE2rUxxoyJMGNGZ/x17gleJ5ou7F5P1ZthGH2WcblcWJbV5zbTNPscSeuZzjvRtr4eNwwD0zT7HC07URk48TGdrB9SUlJISUnRqNgJVrMr6IjIJWnZMuIfnpcy27ZZuXIlH3zwAbNmzaSrq4u3336H+fPnEw6P4aqrut+7L9XbI/S8Pse+VrZts3//fpKSksjKyopf/2jFinby8qopKqqIl/d4PMRisV7BwDAMAoEAwWCw17aeEbS+AkNKSgqdnZ1EIpFe9Xk8nuMWb/fw+/1A91lrn+fz+fq8DpDH4yEhIYFgMNhrm9frjV8i4FimaRIIBGhubu6zvhP1w3XXXccNN9zQa33VZajPoKOpKxGRi2zChAlEIhHWrFkbPwV71KhRbNr02fv2pR7ojmUYBgUFBcf9PnbsWCIRmwkTdPfyM2UYhkZzTkJBR0QuaZfqSMdnDGw7geTkG5k4cWr8sY0bPzs1+1L/4O+5AevJXysT0wSXq/tH5FxR0BGRS5ZzRjmMz/3XeZzzWsmlRmt0RERExAn6/KZw2a9cEhEREedS0BERERHHUtARERERx1LQEREREcdS0BERERHHUtARERERx1LQEREREcdS0BERERHHUtARERERx1LQEREREcdS0BERERHHUtARERERx1LQEREREcdS0BERERHHUtARERERx1LQEREREcdS0BERERHHUtARERERx1LQEREREcdS0BERERHHUtARERERx1LQEREREcdS0BERERHHUtARERERx1LQEREREcdS0BERERHHUtARERERx1LQEREREcdS0BERERHHUtARERERx1LQEREREcdS0BERERHHUtARERERx1LQEREREcdS0BERERHHUtARERERx3Jf7AZ82S1bdrFb8OU3Z87FboGIiEjfFHROwbZtVqxoJxgMAjZg4PV6SUlJwePxYBjGOd1fJBKhpaWF9PR0XC7XF6rLsixaWlrw+XwkJiYycWL346tXR2lqaiIWiwIGHo+b5OQUfD7fGR9PT50iIiJfRgo6p2HHjh1s2/Yiubm5AIRCIXw+H3fddRfFxcXnNOwcPlzHyy+/zCOPPEJaWtoXqisSifHLXy7gyiuvBG6IPx4KdfCXv/wrhmHg8/mwbRuXy8XcuXMZP348pnl6M5rr13+h5omIiJx3CjqnwbJi5Ofn8+ijj+Jyuejs7GTJkiW8/vrr/NM//RN+vx/btuns7CQSieDxeEhISMAwDGzbJhqN0tXVBUBCQgIulwvDMIhEInR2dmIYBn6/H9M0yc7O5sEHHyQpKYnOzk68Xi+maWLbNpFIBMMwcLvdWJZFKBTCtm0SEhJwu93x/UUiESKRCKZp0NXVRTQaPe54bBtisRhf//rXGT58OJYVY+3adSxatIjCwsJ4W3rqjMVi8eOKRqN4PJ7TDkMiIiIXk4LOaXK5XCQmJuJ2u/H7/RQUFFBeXk4sFsOyLLZt28bSpX+ira2dQCDA3LlzKS4uJhgM8sc/vsHu3XuwbZvS0lJuvfVWYrEYixYtYvfu3Zimybhx45gzZw7Nzc28+eabzJgxg6VLl3LnnXeSk5NDNBrl9ddfp6ioiFGjRrJs2Vts3rwZ27YZPnw48+bNIyUlhcOHD7N48WIaGxvp168fTU1NfR6PYRgkJiYSCASwLIucnJx4QFq7di1tbW3MnTsX0zSpqKhg5cqVTJ16A2+99Tb33nsvWVlZF/gVEBEROXMKOqeptraW9957FzBobGxk48aNXHfddfj9fhobG3n11VeZMWMGEydOZMOGDSxcuJDHHnuMFStWUFtbx2OPPUYoFGLBggVs3bqVffv20dTUyPe+9z3C4TALFixgwIABDBw4kIqKXXz1q18lGo3yySefkJOTQ11dHdu3b2fq1KmsXbuOLVu28MgjD5OYmMSrr77KX/7yF2bNmsVrr73GoEGDmDdvHhs3bmTNmjV9Hk8kEmHp0qWsXLmSzs5O9uzZw5QpU0hPT2fgwIH8/ve/Z9q0aQQCAdatW0cgECA9PYPRo0eRkJBwYTtfRETkLGn+4TS1t7exc2c57733HitXruSOO+7glltuwTRNqqqq6OjowOPx8Le/leN2u2lubqaqqoodO3ZwzTXXkJ2dzeDBg3n00UcZNmwYW7duJSdnAPv376Omppr09HS2bNmCbdsAeL1eJkyYwJYtWwiHw2zdupW8vDwyMjLYtGkT2dnZ1NbWUlW1l379+rF161aqq6uprq5m0qRJ5OTkMG3aNAoLC/s8HsMwSEtLIzMzk8zMTPr378/WrVupra1lyJAh+P1+du/eTTDYSmVlJePHj6dfv37ceOM0kpOTL2TXi4iInDWN6JymK64Yyne/+11aW1v5zW9+w44dOxg/fjwA7e3tdHV1sWPHjviZUqWlpfj9frq6ukhM9MfrCQQCBINBOjs7OXz4MJ2dnQB4PB4GDx4cf55hGJSWlvLee+9RVVXF5s2bmTZtGi6Xi46OdsLhCJs3bwG6zwwbNWpUfB1Qz9lTXq+XjIz0Po/H7XYzefJkiouLge4F1j/72c9YvXo1t99+O2PHjmXTpo1YlkVqaip5eXnn/AwzERGR801B5wylp6dz55138tRTT/HRR6uYOvVGMjMzCQQC3HHHHWRmZtLS0sz77y8nNTWV9PR0qqtr4ouEX3nlFYqKisjIyGDs2LHcdNNN2LbNmjWr8Xi8x4WJ9PR0hg0bxjvvvENXVxdFRUW4XC6ysrLx+bzcd9/XME2T7du3U1tbS3p6OoZh0NTUSEZGBsFgK4cPV1NaOrLPY7Esi2g0im3btLW10dHRgWEYmKbJ2LFjWbduHUePNnPVVVfh8/mIxWKEw2F8Pp8WI4uIyCVBQecMGYZBbm4uN998M2+++SeuuGIoQ4cOZcCAAbz44ouUlZWxY8cOAL7yla8wadIkFi1ahGVZBINBDhw4wKxZM7FtmyVLFtPW1kY0GmXjxo3ce++9x+3LNE0mTpzIj3/8Y2bNmkVKSgqmaXLDDTfwwgvP8+qrr5KSksKaNWu46aYZ9OvXjwkTJrBw4SLGjBnDrl27qK2t7fM4wuEwCxcuJBAIEI1GqaurJSUlwLXXXgtATk4OmZmZ7N27l1GjRmEYBgcPHuQPf/gDf//3f0///v3Pb0eLiIicA0bPmpATOOnGy8HSpTbvvNNAQUEdxcXF8ZGMUCjEtm3bGDBgAHl5eQSDQTZu3MiRI0fo378/48ePJyUlhVgsxq5du9i5cydJSUmMG3cl2dn9icViVFRUxKe7Ro8ezZAhQwiFQlRUVFBaWorP56Orq4tt27YxZMgQMjIyMAwDy7LYv38/W7ZsIRKJUFxczIgRI3C73XR1dbFp0yYOHDhAQUEBycnJZGZmcuBATvzifh99FMHt3nTctFlaWhqDBw8mMTExvo+FCxfS0dHB/fffj8vlorW1lfLyckaOHElSUhLr18OECTazZ9tYltWr73pOd+/Libb1jGidybazKfNF2nCuynyR+vradqn23anqO8V71Dlrt2mamp4VubT1+Q9YQecUli07vQvjnegD5GTbTlbmbPd3sg+fnqCzfv3Jr2jc0dFBVVUVixf/kXvuuZchQ4b02bb162HcuBjB4B/45JNPem33er3EYjFisViv9qSlpdHc3NyrvS6XC5fLRTgc7lVfeno6ra2tfdbn8/niwe1YKSkpdHV19VlfQkJCn2X8fj+GYdDR0dFnma6url7t9ng8JCYm0tLS0qvMhewHr9cbX6t1rEAgQGdn55eyH0zTJDU19YT9YJomkUikV30nCi2JiYnYtk0oFDrtdvt8Ph566CEKCgp6lRGRS0afH6KaujoNp3ebg5OFlBNt+yLfHs9Hnd1qa2tZvHgxEyZMZPDgwScNYKZpcvvtt3PLLbec0T7O9pv6icrYtt1nO8+mzPlo34Uqczn1g2VZfa4VO5u6AJKSks64jIh8+SnonILTblh5Ojcpzc/P54knnohfxflkei48KCIi8mWkoHOZOvl0nAvQt1sREbn0KehchnTHcRERuVxoMbKIiIg4QZ+LDXXVNxEREXEsBR0RERFxLAUdERERcSwFHREREXEsBR0RERFxLAUdERERcSwFHREREXEsBR0RERFxLAUdERERcSwFHREREXEsBR0RERFxLN3UU05q2bKL3YILY86ci90CERE5HxR05JTWr7/YLTi/dDd3ERHnUtA5hZ4RDdu2CYfDHD16FJ/PR1paGobR541Sezn2DvGRSISmpiYSExNJSUk57Tr6qjMcDlNbW0tycjLp6elnXVdPfZ2dIZqbW0hNTSUxMfG4bRMm2DQ3N1NbW4tlWaSmptK/f3/cbjfRaJTa2lp8Ph/9+vXDNM/PjGhPP36R4/w8p4c4EZHLnYLOaVi/Hmwbyssree+998jOzmbu3Ln4/f5TlrVtm2AwSDDYSm7uQI4caWTx4sWUlY3muuuu52w/s20bjhxpZOHCJZSVjWby5MmYpuvsKvu0voqKA7z77ntMmzaN0tLS+EiHbdts3LiRl176LdXVNdi2TWJiIpMnT+aee+6hvb2df/3Xf2XEiCK+9a1vn/Og0x3COqmsrGT48OH4fL5zWr+IiDiXgs5puvLKKH/96zI6OtZw5EgCaWmljB49GoBYLEYsFsPj8WAYxnG/Nzc38+///u8UFBRwyy3/wJ49Qbq6VpKZmcK4cddgWRYulwvTNDEMA9u2iUajhEIhXC4XCQkJ8eAQjUYBCIfDuN1uQiE/fn8xgwblUFgI0WjXcW02TRO32x0f/ens7MTv9+P1euP7sm2bWCwGQFdXHZ2dH5KfXwqUxuuJRCK8+eYfaWtr5+GHHyYrK4tly5bxwQfLKS0t4corx3HHHXeQkZGBaZq99tezL4/HQzQaxbZtXC4XoVAIwzDw+/3xcrFYjFAoRCwWxe9PxOPxEIvFeOWVV9ixYwdPPvkkXq833hdut/u4vukZYfrsuGx8voT4cYRCIRISEvB6vedt5ElERL48FHROU01NDeXl5YwdO5bKykrWrVvHyJEjMU2T9957l5UrV/Hwww+Tn5/P+++/z4oVK3jwwQeprKxk//79WJbFhg0bCAQCANTX1/Pcc89x8OBBrrzySubMmUNCQgJ79+7l9ddfZ+/evXg8HiZOnMjcuXNJSkri+eefp6mpiYaGBgYPHsytt97Khg0bsG2bjo4OXnnlFSzLire5pKSE+++/n7Vr17J06VJaW1vIzMzizjvvZPTo0YTDYZYvX866devi00596Qkffr+f3NxcCgoKuO+++ygvL2fAgFw6OzvZvHkz+fn5jBw5ksrKSv7whz9w+PAhCgqG4Pf7CYVCPProo7z99tts376dwsJCPv74Y2zb5u677+bqq6+mubmZV155he3btxOLxUhPT+euu+7C7/ezZcsWWltb+Otf/8rkyZP57W9/C8C3vvUtAJ577jmi0Sjf+c53ePPNN9m2bRvhcBiPx8MTTzxBRUUFixcv5ujRJtLTM7jtttuYMGECcPajYCIi8uWnr7SnwbIsNm/eTGdnJzNnzqSoqIjNmzfT1NSEbds0NR2lqqqKrq7uEZXm5mb27auio6ODgwcPEg6Hqa+vZ+/evfEgsn37dgKBAKmpqSxZsoTt27fT1NTEM888w549e5g+fTqlpaX8z//8mTfeeCO+DmbLli3k5uZSVlZGJBJh+/bt1NTUkJaWRllZGSNHjiQcDrN///54cPrNb35DIBBg3rz5RKNRfv3rX9PY2Mjq1at5+eWXMQyDQYMG8fHHH8dHd46VkOBj0qRJNDU18v/+3//ln//5n3nnnXfIy8tj8ODBRCJhdu7cyf79+2lra+Oll15i795KZs+eQ35+PuvWraO6uppYLEZjY2O8zbNnzyYSibBkyRLa29vZtm0bf/vb37jxxhu57ba51NbW8vbbb1NdXU1LSwuhUCfl5eV0dnZy+PBhDh8+jGVZWJbF4cOHqa6u/vT1aGLbtm34fD7Gjx9PY2MjL7zwAm63m3nz5uP1elmwYAE1NTXHrZ8SERHnUdA5DeFwmHXr1pGVlUX//v0ZMWIEDQ0NbN++/aTlPB4Ps2bNIhAIMG7cOG6//fb4VMvYsWP5u7/7O2655RYAKisr2bVrF3v37uXmm29m3rx5PPDAAwwf3j3y0dLSAkD//v158MEHmTp1Kqb52QKfgoIC7r33XkaMKKK1tZWrrrqKW265hU8++YRgMEhxcTH9+/enuLiYmpoaKioq+Pjjj/H5fDzwwAPcc8893HzzzX0u9DUMkzlz5vDEE99jwoSJRCIRPvjgA37yk598Oirz2XMbGhrYt28fZWVjmDVrFnPnzmXQoEHH1efzeZk9ezbTpk1j1KhR1NbW0t7ezrXXXsv3v/99hg4dSktLK5Zl0d7ezrhx4ygqKqJ//2y++c1vxkfFTiYpKYmvfe1rzJkzh127dtHU1BTvg5KSEhobG9m1a9cp6xERkUubpq5OwbZt6urq4qMx//Zv/0Y4HCYcDrN69WquvfbaXuHg2FGCnrU3pmnicn02TZKamoppmvE1OB0dHQSDQSzLIj09HdM08fl8BAIpHDx4kEgkEi+XnJzcZyApLy9nwYLfMmjQIL7+9a+TkpISr/Ojjz5i8+bN2LbN8OHDse3uENG9jwCmaZKdnd1r3UrPtNjatRvIzs7m8ccfJxgMsn79el588UVWrlxJfn5+/PmRSATLskhKSsI0TTweDwkJCYRCofhz3G5P/BgSEhKIRqNEo1G2bNnCSy/9FoArrhgaD4U9fQgGLpcLwzDi637ABnr3RWKin7S0NEzTpK2tDcuyWL9+PTt37sS2bYYNG3bc6yEiIs6koHMKlmWxe3cFtm0xadIkkpOTAZvt23ewa9ffOHToEH6/H9u2aGlpoauri7q6uvgoR8+Hcjgcji+S7Xn8WLZtk52djdfrZevWrVx11VU0NTVRVbWPnJwcUlKSge5Ror4cOHCA559/HsuyuPXWWwE4evQoAwYMwO12M2fOHK6++mq2bNlCS0sLRUUj2Lx5C7t37+bAgQMkJydTWVl53BqfHu3t7Sxa9BtycnJ4/PHHSU9Pjwe1z0JIt55tu3dXUFNTQ0NDA4cOHSIpKem4Oo89/J6FwytWrCAU6uSHP/whKSkpPPnkk/G+Mk2TWCxGOBzG6/WSkJDA0aNNdHSEiEajtLa2HrcPl8sdD209fTB9+nSmTZvGzp07qa2tpaSkhKqqU/wBiIjIJU1B5xRaW1uprNxLQcEQvvGNb5CUlIRt26xatYpf/OIXbNiwgREjRuB2e1iwYAE5OTnU1dXGP/wTEhIIBAJs2LCB3/3ud1x11VUn3Nfw4cO5+uqrWbt2Lfv27SMU6v4Qv++++0hMTDphOdu2WLlyJfv27cPtdvOrX/0XYJCbm8tDDz3E0KFDee2111i1ahV79uxhyJAhXHPNNUyaNImNGzfyX//1XwwYMIC6urpeAcwwDDIyMpgyZQp//vOf4yGkvr6ehIQEpkyZctwoUHp6OjNmzGDRokX88Ic/jPfXqRiGQf/+/dm4cSOvv/46XV1dNDU14XK5iMVi9OvXj40bN/KrX/2KRx55mBEjRrBkyRJ++tOf4na76ejo6CNMdYfMsrLRlJaWsmTJEjZv3kxVVRXZ2dlM1JUCRUQcz9XzrfkETrrxcvDJJ500NvqYNWsMeXl58Q/P9PR0srKyyMnJYdSoUeTmDsDt9lBaWsqNN95IXl4excXFpKamkp+fT0ZGBsOGDSM/P5+srExKSkrJysrCNE0yMjIoKSkhNzf307py8Xg8DB8+nPnz5zN69GhM08Tv98fr6B7lcMXLDhgwgIKCAsaMGUNJSQklJSUUFxdTWlrKlVeOxe/vniK79tprmTdvHhkZGWRnZ1NYWIjP5yMnJ4fZs2czePBgSkpKaG9PY+DA7j6orjaYNm0E+fn5mKZJcnIyY8eO5c4776S0tBSXy0Vqamq8HRkZGRQUFFBUVMhNN93EgQMHiEQiTJ8+nZSUFIYMGUJhYSEJCQn4fD4KCgoYMWIEhYWFBAIBbNtm/PjxXH/99fGzvIYMGUJ6ejr5+fkMH17IsGHDSElJxu9PZMqUKZSVlXHFFVcwZMgQfD4fQ4YUxKe/fL4ERo8eTVJSEoZhMGHCBObPn0d2dn+qqw0GDoTCwu5T7S3Lip923/MDnHTbxSxzqvrOdZm+2neqdjuxH86m3RfqbwjO7UU1RS4h/6evB41TfNu+7E9JWbbs9K6e2/1GY2EYx69x+WwtyWe/n25dYMSD1Rfx2ZugHW9fT53HvkEeux/DMOIXDFy/vvs2CT1vuj3b+2pbW1sbP/7xjzl69CgzZ84EYNGiRZSUFPP440+ccOrt2LbYtn3Sa9wcew2gvp7bV38d++HQ83zDMOLHNnVqGy+88AL19fW9yqalpREMBnudkWYYBj6fj87Ozl5lkpOTiUQi8TPxjtVzuv3nJSQk4HK5aG9v73NbV1cXn//36na7SUpKii9WP5bX68WyrPg1ho5td1paGs3Nzb3qc7lcuFwuwuFwr/rOph9SUlLo6urqs74L2Q8917b6fLu/zP3Qcw2qjo6OXttO1A9er5d7772XoUOHKuzI5ajPP3pNXZ2G05vhMDjxNVnO9A3nZHWdDeMkbTjZts890zBOuYDX7/czY8YMlixZwh//+EdM06SgoIDbb78jvrj4ZPWf7pvzmQbAnueeqP2JiYncf//9fZ5eb5pmn2uXTmd/p/gi8YXLwNm170KVUT90u1D9YBjGaZ2VKHI5UdA5hcv9rtZnevdyl8vF5MmTGTduXPxbaiAQiF+N+cuqZwpRREScRUFHTsuZ3fzSAJI//REREbl4FHTklHRykoiIXKq0GFlEREScoM/1EboFhIiIiDiWgo6IiIg4loKOiIiIOJaCjoiIiDiWgo6IiIg4loKOiIiIOJaCjoiIiDiWgo6IiIg4loKOiIiIOJaCjoiIiDiWgo6IiIg4loKOiIiIOJaCjoiIiDiWgo6IiIg4loKOiIiIOB3qmw0AAAkBSURBVJaCjoiIiDiWgo6IiIg4loKOiIiIOJaCjoiIiDiWgo6IiIg4loKOiIiIOJaCjoiIiDiWgo6IiIg4loKOiIiIOJaCjoiIiDiWgo6IiIg4loKOiIiIOJaCjoiIiDiWgo6IiIg4loKOiIiIOJaCjoiIiDiWgo6IiIg4loKOiIiIOJaCjoiIiDiWgo6IiIg4loKOiIiIOJaCjoiIiDiWgo6IiIg4loKOiIiIOJaCjoiIiDiWgo6IiIg4loKOiIiIOJaCjoiIiDiWgo6IiIg4loKOiIiIOJaCjoiIiDiWgo6IiIg4loKOiIiIOJaCjoiIiDiW+2I34FISjUZpamrCsqxe2zweD+np6ZimsqOIiMiXhYLOGTh8+DD33HMPdXV1vbaNGDGCH/3oR4wZMwa3241hGBehhSIiInIsBZ0zEIlE2Lt3LzU1Nb22JSQk8Oyzz1JWVsYjjzyCx+O5CC0UERGRY2me5Rxqa2vjySef5LXXXiMajV7s5oiIiFz2FHTOsaNHj/KDH/yAFStW9LmWR0RERC4cBZ1zxDAM0tPTycvLA+AnP/kJlZWV2LZ9kVsmIiJy+dIanXOkqamJ5ORkbrvtNmzbxjAM3n//ffLz8/H5fBe7eSIich4tW3Z+61+/HiZOPPlz5sw5v224VCnonAMul4u6ujqeeuqp46arSktLufvuuxV0REQuA+vW2dj28UsWDMM8Z2fhrl/f/d9jZwoMw6CiAgoL+y5j2zaxWAyXyxVvR08g6qnH6WcJK+h8AcnJydx6661MmTKFQYMGsX37dt566y1WrVqlxcgiIpcZ27ZoaXmfqqq9uFxuEhMTGT16NCUlJbhcrrOutyfgAEyYYHPw4AFaW4OUlpZiGFBR0f3Tuz02Bw8eZPfuCq699lr8/sT4qFAsFuPDDz8kLy+P4cOHn3XbLgVao3OWEhIS+NGPfsSzzz7LnXfeydChQ/nWt77Fq6++ysyZMy9280RE5AKzbaioqGDgwDwmTZpEQUEBb7zxBocPHyIWi9HS0kJTUxPhcBjbtrEsi2AwSGtrK6FQiGg0im3btLW10dx8tNcXZsuyqKqq4vnnX2DLli299j9+vEVxcZDCwmauvDLKlVdGqat7i3HjLK67zht/XiQS4Z133uG73/0uu3btOu/9crFpROcsTZw4kYceegiA733ve/E/mscff5xHH32UFStWXOQWiojIxZCXl0dRURFdXV2sXr2ao0ebKS//G+Xl5bhcLtLS0pg3bx67du1i+fLluFwmXV1dzJ17G6FQiPfffx+XyyQ/P5+ZM2cB3csfYrEYGzZsYOjQob2mm2zbZtOmTaxcuRKv10tWVhbjxo1j9+7dRCJhrrnmGiAFgJqaGtavX09ZWdkF7pmLQyM6Z8EwDCZOnEggEKCxsRG/38/NN99MV1cXlmVxxRVX0K9fv4vdTBERucAsK8YHH3zA73//e55//nkMwyA3Nxe3280999zD/PnzOXDgAIcPH+Yvf/kLc+bM5t5776OtrZ3m5qP8z//8malTp3L33fdQXV3Dvn374nW73W5uv/12CgsLewWdSCTM8uXLmTt3Lt/4xjc4evQojY2NDB8+nGnTppOcnBx/bl5eHv/yL/9Cfn7+heqWi0ojOmfBMAwyMjIwDIOsrCzuu+8+wuEwABs2bKClpQWv13uKWkRExGkMwyQvbwBDhw4lJSWF3Nxc/H4/LpeLRYsW4XK5aG5uprW1lUgkQv/+OSQmJjJkSAEdHSFqa+s+HZXxEAqFjjvBxTCME671iUajWJZFZmYmPp+PnJwcGhrqMQwD0zx+QbRpmpfVpU8UdM6CZVls3LiRSCRCNBrlP/7jP1i5ciXp6emMHTuWd999l6amJoqLiy92U0VE5AIyDIPCwkLGjh0bf6yhoYEPP/yQr33ta3i9Xp599tn4l+G6ujqys7Opra2juLiEfv36MWPGDHJzc1mzZg2BQIBg8NT7dbu7P85ra2sZMGAAR44coaysjP37D5yX47yUKOicpVWrVrFu3Tquv/56fvnLX7Jjxw4GDhxIZmYm3/72t3nttdcudhNFROQCS0tL6zWin5SUxMCBA1m+fDkAgUAAy7KYPn0aS5cuxe/309jYSEpKCjfeeCNvv/02SUlJeL1err/+eg4fPn4fCQkJpKSkHPeYx+Nh6tSpLF68mMTERBITEykpKaGysrLPey8ahkG/fv1ISko6tx3wJaSgc5Zqa2v59re/zT/+4z8yZcoUJkyYwJEjR3j99ddZt27dxW6eiIhcYKZpctddd2Gaxy9/TUhI4L777iMYDJKYmIjL5cKyLNasWcOtt96Kz+dj4cKFpKamUlxcTGlpKeFwmEAgEB+pOdaYMWMoKys7bjrKMEwmTpzIyJEj6erqipedP39+r/Z0P9/gO9/5Tp/bnEZB5wvYvn07DzzwAHl5eWRlZXHo0CHq6up0jysRkcuQYRgnHD3xer3HnaRiWRY+n49ly5ZhmiZFRUX0798f0zR7jdYcq/uaOn2v09mwwQSSP/3p0bs9J2urEynofEGxWIz9+/ezf//+i90UERG5RJimyTXXXMPYsWOwLBu/33/K0ZVT3QLiVNsvVwo6ZyAlJYV58+bR0tJyWs8fOHDgZZOYRUTkzBiGgd+feFrPnTjxxPey0j2uTk5B5wxkZ2fzn//5n6d9Wt7JTgUUERHnOfZ2DfLloKBzBgzD6HNhmIiIiKaOvpyMU4xOXD5XFBIREZFLWZ+3YXf+eWUiIiJy2VLQEREREcdS0BERERHHUtARERERx1LQEREREcdS0BERERHHUtARERERx1LQEREREcdS0BERERHHUtARERERx1LQEREREcdS0BERERHHUtARERERx1LQEREREcdS0BERERHHUtARERERx1LQEREREcdS0BERERHHUtARERERx1LQEREREcdS0BERERHHUtARERERx1LQEREREcdS0BERERHHUtARERERx1LQEREREcdS0BERERHHcp9iu3FBWiEiIiJyHmhER0RERBxLQUdEREQcS0FHREREHEtBR0RERBxLQUdEREQcS0FHREREHOv/Ax2MXw/N9QGMAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EnJERph1VXgN"
      },
      "source": [
        "Looks accurate!\n",
        "But we can go further: if the extracted information is correctly structured, we should be able to recreate the page entirely. So let's do this 🎨"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QdoSNYzROeRv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "816376ca-adc6-4a55-decf-63c12cc711e9"
      },
      "source": [
        "synthetic_pages = result.synthesize()\n",
        "plt.imshow(synthetic_pages[0]); plt.axis('off'); plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mEabainyPeV8"
      },
      "source": [
        "## Exporting results\n",
        "\n",
        "OK, so the predictions are relevant, but how would you integrate this into your own document processing pipeline? Perhaps you're not using Python at all?\n",
        "\n",
        "Well, if you happen to be using JSON or XML exports, they are already supported 🤗"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mhIolyjQOqLp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13ef6952-c0e8-45cf-9b23-2079a6138f4e"
      },
      "source": [
        "# JSON export\n",
        "json_export = result.export()\n",
        "print(json_export)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'pages': [{'page_idx': 0, 'dimensions': (1584, 1224), 'orientation': {'value': None, 'confidence': None}, 'language': {'value': None, 'confidence': None}, 'blocks': [{'geometry': ((0.314453125, 0.0634765625), (0.6875, 0.083984375)), 'lines': [{'geometry': ((0.314453125, 0.0634765625), (0.6875, 0.083984375)), 'words': [{'value': 'CASH', 'confidence': 0.9991496801376343, 'geometry': ((0.314453125, 0.064453125), (0.3984375, 0.0830078125))}, {'value': 'PAYMENT', 'confidence': 0.9999822378158569, 'geometry': ((0.40625, 0.0634765625), (0.5517578125, 0.083984375))}, {'value': 'RECEIPT', 'confidence': 0.9982283711433411, 'geometry': ((0.55859375, 0.0634765625), (0.6875, 0.083984375))}]}], 'artefacts': []}, {'geometry': ((0.115234375, 0.11328125), (0.2783203125, 0.3037109375)), 'lines': [{'geometry': ((0.1171875, 0.11328125), (0.2783203125, 0.134765625)), 'words': [{'value': 'Company', 'confidence': 0.9983745813369751, 'geometry': ((0.1171875, 0.11328125), (0.2109375, 0.134765625))}, {'value': 'Name:', 'confidence': 0.9669256806373596, 'geometry': ((0.212890625, 0.11328125), (0.2783203125, 0.130859375))}]}, {'geometry': ((0.1162109375, 0.1318359375), (0.263671875, 0.150390625)), 'words': [{'value': 'Street', 'confidence': 0.9996938705444336, 'geometry': ((0.1162109375, 0.1318359375), (0.1767578125, 0.150390625))}, {'value': 'Address:', 'confidence': 0.9818301796913147, 'geometry': ((0.1787109375, 0.1337890625), (0.263671875, 0.1484375))}]}, {'geometry': ((0.115234375, 0.150390625), (0.2646484375, 0.1728515625)), 'words': [{'value': 'City,', 'confidence': 0.9999579191207886, 'geometry': ((0.115234375, 0.150390625), (0.1630859375, 0.1728515625))}, {'value': 'State,', 'confidence': 0.9995167255401611, 'geometry': ((0.1650390625, 0.15234375), (0.22265625, 0.1708984375))}, {'value': 'Zip:', 'confidence': 0.9999488592147827, 'geometry': ((0.2236328125, 0.150390625), (0.2646484375, 0.1728515625))}]}, {'geometry': ((0.1171875, 0.169921875), (0.1865234375, 0.1875)), 'words': [{'value': 'Phone:', 'confidence': 0.9998382329940796, 'geometry': ((0.1171875, 0.169921875), (0.1865234375, 0.1875))}]}, {'geometry': ((0.1171875, 0.189453125), (0.16015625, 0.205078125)), 'words': [{'value': 'Fax:', 'confidence': 0.9999557733535767, 'geometry': ((0.1171875, 0.189453125), (0.16015625, 0.205078125))}]}, {'geometry': ((0.1162109375, 0.20703125), (0.177734375, 0.224609375)), 'words': [{'value': 'Email:', 'confidence': 0.99654620885849, 'geometry': ((0.1162109375, 0.20703125), (0.177734375, 0.224609375))}]}, {'geometry': ((0.1162109375, 0.2275390625), (0.2001953125, 0.2421875)), 'words': [{'value': 'Website:', 'confidence': 0.9696999788284302, 'geometry': ((0.1162109375, 0.2275390625), (0.2001953125, 0.2421875))}]}, {'geometry': ((0.1162109375, 0.2861328125), (0.1708984375, 0.3037109375)), 'words': [{'value': 'Date:', 'confidence': 0.9999265670776367, 'geometry': ((0.1162109375, 0.2861328125), (0.1708984375, 0.3037109375))}]}], 'artefacts': []}, {'geometry': ((0.6357421875, 0.287109375), (0.7333984375, 0.3056640625)), 'lines': [{'geometry': ((0.6357421875, 0.287109375), (0.7333984375, 0.3056640625)), 'words': [{'value': 'Receipt', 'confidence': 0.9998490810394287, 'geometry': ((0.6357421875, 0.287109375), (0.7099609375, 0.3056640625))}, {'value': '#:', 'confidence': 0.9998278617858887, 'geometry': ((0.7099609375, 0.287109375), (0.7333984375, 0.3046875))}]}], 'artefacts': []}, {'geometry': ((0.37109375, 0.3701171875), (0.6298828125, 0.3916015625)), 'lines': [{'geometry': ((0.37109375, 0.3701171875), (0.6298828125, 0.3916015625)), 'words': [{'value': 'Payment', 'confidence': 0.9969426989555359, 'geometry': ((0.37109375, 0.3701171875), (0.48046875, 0.3916015625))}, {'value': 'Information', 'confidence': 0.9889318943023682, 'geometry': ((0.4873046875, 0.3701171875), (0.6298828125, 0.3876953125))}]}], 'artefacts': []}, {'geometry': ((0.1162109375, 0.416015625), (0.2763671875, 0.517578125)), 'lines': [{'geometry': ((0.1171875, 0.416015625), (0.19921875, 0.4365234375)), 'words': [{'value': 'Paid', 'confidence': 0.9952682852745056, 'geometry': ((0.1171875, 0.4169921875), (0.162109375, 0.4326171875))}, {'value': 'By:', 'confidence': 0.999660849571228, 'geometry': ((0.1640625, 0.416015625), (0.19921875, 0.4365234375))}]}, {'geometry': ((0.1162109375, 0.4580078125), (0.2470703125, 0.474609375)), 'words': [{'value': 'Amount', 'confidence': 0.8845924735069275, 'geometry': ((0.1162109375, 0.4580078125), (0.1923828125, 0.47265625))}, {'value': 'Paid:', 'confidence': 0.9999610185623169, 'geometry': ((0.1962890625, 0.4580078125), (0.2470703125, 0.474609375))}]}, {'geometry': ((0.1162109375, 0.4970703125), (0.2763671875, 0.517578125)), 'words': [{'value': 'For', 'confidence': 0.9990000128746033, 'geometry': ((0.1162109375, 0.4970703125), (0.1533203125, 0.515625))}, {'value': 'Payment', 'confidence': 0.9996603727340698, 'geometry': ((0.1552734375, 0.4990234375), (0.2412109375, 0.517578125))}, {'value': 'Of:', 'confidence': 0.9992495179176331, 'geometry': ((0.2421875, 0.4970703125), (0.2763671875, 0.5166015625))}]}], 'artefacts': []}, {'geometry': ((0.5751953125, 0.4560546875), (0.671875, 0.4775390625)), 'lines': [{'geometry': ((0.5751953125, 0.4560546875), (0.671875, 0.4775390625)), 'words': [{'value': 'Dollars', 'confidence': 0.9999568462371826, 'geometry': ((0.5751953125, 0.45703125), (0.64453125, 0.474609375))}, {'value': '($', 'confidence': 0.8891334533691406, 'geometry': ((0.646484375, 0.4560546875), (0.671875, 0.4775390625))}]}], 'artefacts': []}, {'geometry': ((0.6005859375, 0.5693359375), (0.74609375, 0.65234375)), 'lines': [{'geometry': ((0.638671875, 0.5693359375), (0.74609375, 0.5888671875)), 'words': [{'value': 'Subtotal:', 'confidence': 0.9910097122192383, 'geometry': ((0.638671875, 0.5712890625), (0.7236328125, 0.5859375))}, {'value': '$', 'confidence': 0.9905773401260376, 'geometry': ((0.7265625, 0.5693359375), (0.74609375, 0.5888671875))}]}, {'geometry': ((0.6005859375, 0.6005859375), (0.73828125, 0.623046875)), 'words': [{'value': 'Tax', 'confidence': 0.9200722575187683, 'geometry': ((0.6005859375, 0.6005859375), (0.642578125, 0.619140625))}, {'value': 'Rate', 'confidence': 0.9997021555900574, 'geometry': ((0.64453125, 0.6025390625), (0.69140625, 0.6181640625))}, {'value': '(%):', 'confidence': 0.9982444047927856, 'geometry': ((0.6943359375, 0.6005859375), (0.73828125, 0.623046875))}]}, {'geometry': ((0.626953125, 0.6318359375), (0.74609375, 0.65234375)), 'words': [{'value': 'Total', 'confidence': 0.9508046507835388, 'geometry': ((0.626953125, 0.6337890625), (0.677734375, 0.650390625))}, {'value': 'Tax:', 'confidence': 0.9999213218688965, 'geometry': ((0.6796875, 0.6337890625), (0.7236328125, 0.6494140625))}, {'value': '$', 'confidence': 0.9962361454963684, 'geometry': ((0.7265625, 0.6318359375), (0.74609375, 0.65234375))}]}], 'artefacts': []}, {'geometry': ((0.533203125, 0.6630859375), (0.7490234375, 0.7470703125)), 'lines': [{'geometry': ((0.5439453125, 0.6630859375), (0.7490234375, 0.685546875)), 'words': [{'value': 'Total', 'confidence': 0.6785988211631775, 'geometry': ((0.5439453125, 0.6640625), (0.595703125, 0.6826171875))}, {'value': 'Amount', 'confidence': 0.9708536863327026, 'geometry': ((0.59765625, 0.666015625), (0.673828125, 0.6806640625))}, {'value': 'Due:', 'confidence': 0.9908701777458191, 'geometry': ((0.677734375, 0.666015625), (0.7236328125, 0.681640625))}, {'value': '$', 'confidence': 0.9960534572601318, 'geometry': ((0.7255859375, 0.6630859375), (0.7490234375, 0.685546875))}]}, {'geometry': ((0.5947265625, 0.6953125), (0.7451171875, 0.7158203125)), 'words': [{'value': 'Amount', 'confidence': 0.9975470900535583, 'geometry': ((0.5947265625, 0.697265625), (0.669921875, 0.7119140625))}, {'value': 'Paid:', 'confidence': 0.9769517779350281, 'geometry': ((0.6748046875, 0.697265625), (0.7236328125, 0.712890625))}, {'value': '$', 'confidence': 0.6455327868461609, 'geometry': ((0.7255859375, 0.6953125), (0.7451171875, 0.7158203125))}]}, {'geometry': ((0.533203125, 0.7265625), (0.744140625, 0.7470703125)), 'words': [{'value': 'Remaining', 'confidence': 0.9975044131278992, 'geometry': ((0.533203125, 0.7294921875), (0.6357421875, 0.7470703125))}, {'value': 'Balance:', 'confidence': 0.9993085861206055, 'geometry': ((0.6396484375, 0.7294921875), (0.72265625, 0.744140625))}, {'value': '$', 'confidence': 0.9855644106864929, 'geometry': ((0.7255859375, 0.7265625), (0.744140625, 0.7470703125))}]}], 'artefacts': []}, {'geometry': ((0.1171875, 0.80078125), (0.333984375, 0.861328125)), 'lines': [{'geometry': ((0.1181640625, 0.80078125), (0.2451171875, 0.8212890625)), 'words': [{'value': 'Received', 'confidence': 0.999503493309021, 'geometry': ((0.1181640625, 0.8017578125), (0.2080078125, 0.81640625))}, {'value': 'By:', 'confidence': 0.999924898147583, 'geometry': ((0.2109375, 0.80078125), (0.2451171875, 0.8212890625))}]}, {'geometry': ((0.1171875, 0.8408203125), (0.333984375, 0.861328125)), 'words': [{'value': 'Authorized', 'confidence': 0.9200044274330139, 'geometry': ((0.1171875, 0.8427734375), (0.228515625, 0.8564453125))}, {'value': 'Signature', 'confidence': 0.9994805455207825, 'geometry': ((0.2333984375, 0.8408203125), (0.333984375, 0.861328125))}]}], 'artefacts': []}, {'geometry': ((0.8271484375, 0.94140625), (0.9013671875, 0.9599609375)), 'lines': [{'geometry': ((0.8271484375, 0.94140625), (0.9013671875, 0.9599609375)), 'words': [{'value': 'Page', 'confidence': 0.9994620680809021, 'geometry': ((0.8271484375, 0.94140625), (0.869140625, 0.9599609375))}, {'value': '1', 'confidence': 0.9896445274353027, 'geometry': ((0.8701171875, 0.94140625), (0.8828125, 0.9560546875))}, {'value': 'of', 'confidence': 0.9998646974563599, 'geometry': ((0.8828125, 0.9423828125), (0.9013671875, 0.9560546875))}]}], 'artefacts': []}]}]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3RsuKIlYPnNf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e88812b-3b7d-4d95-f1ef-3b9439afdfce"
      },
      "source": [
        "# XML export\n",
        "xml_output = result.export_as_xml()\n",
        "print(xml_output[0][0])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "b'<html xml:lang=\"en\" xmlns=\"http://www.w3.org/1999/xhtml\"><head><title>docTR - XML export (hOCR)</title><meta content=\"text/html; charset=utf-8\" http-equiv=\"Content-Type\" /><meta content=\"python-doctr 0.6.0a0\" name=\"ocr-system\" /><meta content=\"ocr_page ocr_carea ocr_par ocr_line ocrx_word\" name=\"ocr-capabilities\" /></head><body><div class=\"ocr_page\" id=\"page_1\" title=\"image; bbox 0 0 1224 1584; ppageno 0\" /><div class=\"ocr_carea\" id=\"block_1\" title=\"bbox 385 101                     842 133\"><p class=\"ocr_par\" id=\"par_1\" title=\"bbox 385 101                     842 133\"><span class=\"ocr_line\" id=\"line_1\" title=\"bbox 385 101                         842 133;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_1\" title=\"bbox 385 102                             488 131;                             x_wconf 100\">CASH</span><span class=\"ocrx_word\" id=\"word_2\" title=\"bbox 497 101                             675 133;                             x_wconf 100\">PAYMENT</span><span class=\"ocrx_word\" id=\"word_3\" title=\"bbox 684 101                             842 133;                             x_wconf 100\">RECEIPT</span></span></p></div><div class=\"ocr_carea\" id=\"block_2\" title=\"bbox 141 179                     341 481\"><p class=\"ocr_par\" id=\"par_2\" title=\"bbox 141 179                     341 481\"><span class=\"ocr_line\" id=\"line_2\" title=\"bbox 143 179                         341 213;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_4\" title=\"bbox 143 179                             258 213;                             x_wconf 100\">Company</span><span class=\"ocrx_word\" id=\"word_5\" title=\"bbox 261 179                             341 207;                             x_wconf 97\">Name:</span></span><span class=\"ocr_line\" id=\"line_3\" title=\"bbox 142 209                         323 238;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_6\" title=\"bbox 142 209                             216 238;                             x_wconf 100\">Street</span><span class=\"ocrx_word\" id=\"word_7\" title=\"bbox 219 212                             323 235;                             x_wconf 98\">Address:</span></span><span class=\"ocr_line\" id=\"line_4\" title=\"bbox 141 238                         324 274;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_8\" title=\"bbox 141 238                             200 274;                             x_wconf 100\">City,</span><span class=\"ocrx_word\" id=\"word_9\" title=\"bbox 202 241                             273 271;                             x_wconf 100\">State,</span><span class=\"ocrx_word\" id=\"word_10\" title=\"bbox 274 238                             324 274;                             x_wconf 100\">Zip:</span></span><span class=\"ocr_line\" id=\"line_5\" title=\"bbox 143 269                         228 297;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_11\" title=\"bbox 143 269                             228 297;                             x_wconf 100\">Phone:</span></span><span class=\"ocr_line\" id=\"line_6\" title=\"bbox 143 300                         196 325;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_12\" title=\"bbox 143 300                             196 325;                             x_wconf 100\">Fax:</span></span><span class=\"ocr_line\" id=\"line_7\" title=\"bbox 142 328                         218 356;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_13\" title=\"bbox 142 328                             218 356;                             x_wconf 100\">Email:</span></span><span class=\"ocr_line\" id=\"line_8\" title=\"bbox 142 360                         245 384;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_14\" title=\"bbox 142 360                             245 384;                             x_wconf 97\">Website:</span></span><span class=\"ocr_line\" id=\"line_9\" title=\"bbox 142 453                         209 481;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_15\" title=\"bbox 142 453                             209 481;                             x_wconf 100\">Date:</span></span></p></div><div class=\"ocr_carea\" id=\"block_3\" title=\"bbox 778 455                     898 484\"><p class=\"ocr_par\" id=\"par_3\" title=\"bbox 778 455                     898 484\"><span class=\"ocr_line\" id=\"line_10\" title=\"bbox 778 455                         898 484;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_16\" title=\"bbox 778 455                             869 484;                             x_wconf 100\">Receipt</span><span class=\"ocrx_word\" id=\"word_17\" title=\"bbox 869 455                             898 483;                             x_wconf 100\">#:</span></span></p></div><div class=\"ocr_carea\" id=\"block_4\" title=\"bbox 454 586                     771 620\"><p class=\"ocr_par\" id=\"par_4\" title=\"bbox 454 586                     771 620\"><span class=\"ocr_line\" id=\"line_11\" title=\"bbox 454 586                         771 620;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_18\" title=\"bbox 454 586                             588 620;                             x_wconf 100\">Payment</span><span class=\"ocrx_word\" id=\"word_19\" title=\"bbox 596 586                             771 614;                             x_wconf 99\">Information</span></span></p></div><div class=\"ocr_carea\" id=\"block_5\" title=\"bbox 142 659                     338 820\"><p class=\"ocr_par\" id=\"par_5\" title=\"bbox 142 659                     338 820\"><span class=\"ocr_line\" id=\"line_12\" title=\"bbox 143 659                         244 691;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_20\" title=\"bbox 143 661                             198 685;                             x_wconf 100\">Paid</span><span class=\"ocrx_word\" id=\"word_21\" title=\"bbox 201 659                             244 691;                             x_wconf 100\">By:</span></span><span class=\"ocr_line\" id=\"line_13\" title=\"bbox 142 725                         302 752;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_22\" title=\"bbox 142 725                             235 749;                             x_wconf 88\">Amount</span><span class=\"ocrx_word\" id=\"word_23\" title=\"bbox 240 725                             302 752;                             x_wconf 100\">Paid:</span></span><span class=\"ocr_line\" id=\"line_14\" title=\"bbox 142 787                         338 820;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_24\" title=\"bbox 142 787                             188 817;                             x_wconf 100\">For</span><span class=\"ocrx_word\" id=\"word_25\" title=\"bbox 190 790                             295 820;                             x_wconf 100\">Payment</span><span class=\"ocrx_word\" id=\"word_26\" title=\"bbox 296 787                             338 818;                             x_wconf 100\">Of:</span></span></p></div><div class=\"ocr_carea\" id=\"block_6\" title=\"bbox 704 722                     822 756\"><p class=\"ocr_par\" id=\"par_6\" title=\"bbox 704 722                     822 756\"><span class=\"ocr_line\" id=\"line_15\" title=\"bbox 704 722                         822 756;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_27\" title=\"bbox 704 724                             789 752;                             x_wconf 100\">Dollars</span><span class=\"ocrx_word\" id=\"word_28\" title=\"bbox 791 722                             822 756;                             x_wconf 89\">($</span></span></p></div><div class=\"ocr_carea\" id=\"block_7\" title=\"bbox 735 902                     913 1033\"><p class=\"ocr_par\" id=\"par_7\" title=\"bbox 735 902                     913 1033\"><span class=\"ocr_line\" id=\"line_16\" title=\"bbox 782 902                         913 933;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_29\" title=\"bbox 782 905                             886 928;                             x_wconf 99\">Subtotal:</span><span class=\"ocrx_word\" id=\"word_30\" title=\"bbox 889 902                             913 933;                             x_wconf 99\">$</span></span><span class=\"ocr_line\" id=\"line_17\" title=\"bbox 735 951                         904 987;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_31\" title=\"bbox 735 951                             787 981;                             x_wconf 92\">Tax</span><span class=\"ocrx_word\" id=\"word_32\" title=\"bbox 789 954                             846 979;                             x_wconf 100\">Rate</span><span class=\"ocrx_word\" id=\"word_33\" title=\"bbox 850 951                             904 987;                             x_wconf 100\">(%):</span></span><span class=\"ocr_line\" id=\"line_18\" title=\"bbox 767 1001                         913 1033;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_34\" title=\"bbox 767 1004                             830 1030;                             x_wconf 95\">Total</span><span class=\"ocrx_word\" id=\"word_35\" title=\"bbox 832 1004                             886 1029;                             x_wconf 100\">Tax:</span><span class=\"ocrx_word\" id=\"word_36\" title=\"bbox 889 1001                             913 1033;                             x_wconf 100\">$</span></span></p></div><div class=\"ocr_carea\" id=\"block_8\" title=\"bbox 653 1050                     917 1183\"><p class=\"ocr_par\" id=\"par_8\" title=\"bbox 653 1050                     917 1183\"><span class=\"ocr_line\" id=\"line_19\" title=\"bbox 666 1050                         917 1086;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_37\" title=\"bbox 666 1052                             729 1081;                             x_wconf 68\">Total</span><span class=\"ocrx_word\" id=\"word_38\" title=\"bbox 732 1055                             825 1078;                             x_wconf 97\">Amount</span><span class=\"ocrx_word\" id=\"word_39\" title=\"bbox 830 1055                             886 1080;                             x_wconf 99\">Due:</span><span class=\"ocrx_word\" id=\"word_40\" title=\"bbox 888 1050                             917 1086;                             x_wconf 100\">$</span></span><span class=\"ocr_line\" id=\"line_20\" title=\"bbox 728 1101                         912 1134;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_41\" title=\"bbox 728 1104                             820 1128;                             x_wconf 100\">Amount</span><span class=\"ocrx_word\" id=\"word_42\" title=\"bbox 826 1104                             886 1129;                             x_wconf 98\">Paid:</span><span class=\"ocrx_word\" id=\"word_43\" title=\"bbox 888 1101                             912 1134;                             x_wconf 65\">$</span></span><span class=\"ocr_line\" id=\"line_21\" title=\"bbox 653 1151                         911 1183;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_44\" title=\"bbox 653 1156                             778 1183;                             x_wconf 100\">Remaining</span><span class=\"ocrx_word\" id=\"word_45\" title=\"bbox 783 1156                             885 1179;                             x_wconf 100\">Balance:</span><span class=\"ocrx_word\" id=\"word_46\" title=\"bbox 888 1151                             911 1183;                             x_wconf 99\">$</span></span></p></div><div class=\"ocr_carea\" id=\"block_9\" title=\"bbox 143 1268                     409 1364\"><p class=\"ocr_par\" id=\"par_9\" title=\"bbox 143 1268                     409 1364\"><span class=\"ocr_line\" id=\"line_22\" title=\"bbox 145 1268                         300 1301;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_47\" title=\"bbox 145 1270                             255 1293;                             x_wconf 100\">Received</span><span class=\"ocrx_word\" id=\"word_48\" title=\"bbox 258 1268                             300 1301;                             x_wconf 100\">By:</span></span><span class=\"ocr_line\" id=\"line_23\" title=\"bbox 143 1332                         409 1364;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_49\" title=\"bbox 143 1335                             280 1357;                             x_wconf 92\">Authorized</span><span class=\"ocrx_word\" id=\"word_50\" title=\"bbox 286 1332                             409 1364;                             x_wconf 100\">Signature</span></span></p></div><div class=\"ocr_carea\" id=\"block_10\" title=\"bbox 1012 1491                     1103 1521\"><p class=\"ocr_par\" id=\"par_10\" title=\"bbox 1012 1491                     1103 1521\"><span class=\"ocr_line\" id=\"line_24\" title=\"bbox 1012 1491                         1103 1521;                         baseline 0 0; x_size 0; x_descenders 0; x_ascenders 0\"><span class=\"ocrx_word\" id=\"word_51\" title=\"bbox 1012 1491                             1064 1521;                             x_wconf 100\">Page</span><span class=\"ocrx_word\" id=\"word_52\" title=\"bbox 1065 1491                             1081 1514;                             x_wconf 99\">1</span><span class=\"ocrx_word\" id=\"word_53\" title=\"bbox 1081 1493                             1103 1514;                             x_wconf 100\">of</span></span></p></div></body></html>'\n"
          ]
        }
      ]
    }
  ]
}