Exercise_3_images.ipynb

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      "metadata": {
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      },
      "source": [
        "# Creating your own dataset from Google Images\n",
        "\n",
        "*by: Francisco Ingham and Jeremy Howard. Inspired by [Adrian Rosebrock](https://www.pyimagesearch.com/2017/12/04/how-to-create-a-deep-learning-dataset-using-google-images/)*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cm484Hmt6Tu4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "9e5936a3-463e-4101-c30a-4e83398e17e6"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pal9GTKX6qL_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%reload_ext autoreload\n",
        "%autoreload 2\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "hide_input": true,
        "id": "gdhKx_0yiYPy",
        "colab_type": "text"
      },
      "source": [
        "In this tutorial we will see how to easily create an image dataset through Google Images. **Note**: You will have to repeat these steps for any new category you want to Google (e.g once for dogs and once for cats)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "hide_input": false,
        "id": "TRK1RdkjiYPz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from fastai.vision import * # import libraries needed"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EuaToupMiYP3",
        "colab_type": "text"
      },
      "source": [
        "## Get a list of URLs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QsYjQUdeiYP4",
        "colab_type": "text"
      },
      "source": [
        "### Search and scroll"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nUGsWegOiYP5",
        "colab_type": "text"
      },
      "source": [
        "Go to [Google Images](http://images.google.com) and search for the images you are interested in. The more specific you are in your Google Search, the better the results and the less manual pruning you will have to do.\n",
        "\n",
        "Scroll down until you've seen all the images you want to download, or until you see a button that says 'Show more results'. All the images you scrolled past are now available to download. To get more, click on the button, and continue scrolling. The maximum number of images Google Images shows is 700.\n",
        "\n",
        "It is a good idea to put things you want to exclude into the search query, for instance if you are searching for the Eurasian wolf, \"canis lupus lupus\", it might be a good idea to exclude other variants:\n",
        "\n",
        "    \"canis lupus lupus\" -dog -arctos -familiaris -baileyi -occidentalis\n",
        "\n",
        "You can also limit your results to show only photos by clicking on Tools and selecting Photos from the Type dropdown."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FhjVXJiUiYP6",
        "colab_type": "text"
      },
      "source": [
        "### Download into file"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-4TSOXYiYP7",
        "colab_type": "text"
      },
      "source": [
        "Now you must run some Javascript code in your browser which will save the URLs of all the images you want for you dataset.\n",
        "\n",
        "In Google Chrome press <kbd>Ctrl</kbd><kbd>Shift</kbd><kbd>j</kbd> on Windows/Linux and <kbd>Cmd</kbd><kbd>Opt</kbd><kbd>j</kbd> on macOS, and a small window the javascript 'Console' will appear. In Firefox press <kbd>Ctrl</kbd><kbd>Shift</kbd><kbd>k</kbd> on Windows/Linux or <kbd>Cmd</kbd><kbd>Opt</kbd><kbd>k</kbd> on macOS. That is where you will paste the JavaScript commands.\n",
        "\n",
        "You will need to get the urls of each of the images. Before running the following commands, you may want to disable ad blocking extensions (uBlock, AdBlockPlus etc.) in Chrome. Otherwise the window.open() command doesn't work. Then you can run the following commands:\n",
        "\n",
        "```javascript\n",
        "urls=Array.from(document.querySelectorAll('.rg_i')).map(el=> el.hasAttribute('data-src')?el.getAttribute('data-src'):el.getAttribute('data-iurl'));\n",
        "window.open('data:text/csv;charset=utf-8,' + escape(urls.join('\\n')));\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0SD9QkliYP8",
        "colab_type": "text"
      },
      "source": [
        "### Create directory and upload urls file into your server"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3OIyIBYziYP9",
        "colab_type": "text"
      },
      "source": [
        "Choose an appropriate name for your labeled images. You can run these steps multiple times to create different labels."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FlAfrH3MiYP-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Make folders for each category \n",
        "folder = 'electronic' \n",
        "file = 'electronic_piano.txt'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1DZNRdeiYQB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "folder = 'grand'\n",
        "file = 'grand_piano.txt'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqZkaxjEiYQE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "folder = 'upright'\n",
        "file = 'upright_piano.txt'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nv6hbcB0iYQH",
        "colab_type": "text"
      },
      "source": [
        "You will need to run this cell once per each category."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8f4D-_jiYQI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path = Path('drive/My Drive/fastai')\n",
        "dest = path/folder\n",
        "dest.mkdir(parents=True, exist_ok=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sp2K0ML4iYQL",
        "colab_type": "code",
        "outputId": "7ceef42a-f44f-41e5-d254-c1643833f214",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "path.ls() #check the directory to make sure all files and folders are in place"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[PosixPath('drive/My Drive/fastai/lavender.jpg'),\n",
              " PosixPath('drive/My Drive/fastai/대학원생명단2020010ANT6320_01.xls'),\n",
              " PosixPath('drive/My Drive/fastai/학부수강생명단2020010MAS2009_01.xls'),\n",
              " PosixPath('drive/My Drive/fastai/학부수강생명단2020010MAS2009_01 (1).xls.gdoc'),\n",
              " PosixPath('drive/My Drive/fastai/학부수강생명단2020010MAS2009_01.xls.gdoc'),\n",
              " PosixPath('drive/My Drive/fastai/대학원생명단2020010ANT6320_01.xls.gdoc'),\n",
              " PosixPath('drive/My Drive/fastai/7.png'),\n",
              " PosixPath('drive/My Drive/fastai/Copy of 00_notebook_tutorial.ipynb'),\n",
              " PosixPath('drive/My Drive/fastai/Copy of lesson1-pets.ipynb'),\n",
              " PosixPath('drive/My Drive/fastai/Machine Learning 수업.gsheet'),\n",
              " PosixPath('drive/My Drive/fastai/electronic'),\n",
              " PosixPath('drive/My Drive/fastai/grand_piano.txt'),\n",
              " PosixPath('drive/My Drive/fastai/upright_piano.txt'),\n",
              " PosixPath('drive/My Drive/fastai/electronic_piano.txt'),\n",
              " PosixPath('drive/My Drive/fastai/grand'),\n",
              " PosixPath('drive/My Drive/fastai/upright')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0slPRJC4iYQP",
        "colab_type": "text"
      },
      "source": [
        "Finally, upload your urls file. You just need to press 'Upload' in your working directory and select your file, then click 'Upload' for each of the displayed files.\n",
        "\n",
        "![uploaded file](https://github.com/fastai/course-v3/blob/master/nbs/dl1/images/download_images/upload.png?raw=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3RnPakohiYQQ",
        "colab_type": "text"
      },
      "source": [
        "## Download images"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-6WN7ybpiYQR",
        "colab_type": "text"
      },
      "source": [
        "Now you will need to download your images from their respective urls.\n",
        "\n",
        "fast.ai has a function that allows you to do just that. You just have to specify the urls filename as well as the destination folder and this function will download and save all images that can be opened. If they have some problem in being opened, they will not be saved.\n",
        "\n",
        "Let's download our images! Notice you can choose a maximum number of images to be downloaded. In this case we will not download all the urls.\n",
        "\n",
        "You will need to run this line once for every category."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NK9Ds876iYQS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classes = ['electronic','grand','upright'] #labels for our classes"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QeTeWdPviYQV",
        "colab_type": "code",
        "outputId": "2ede0fdb-7aeb-4c8f-a639-8abeac085f24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        }
      },
      "source": [
        "download_images(path/file, dest, max_pics=200) #dl images, you can ignore the invalid urls if there is enough images downloaded"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n",
            "Error  Invalid URL '': No schema supplied. Perhaps you meant http://?\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUNCPwGFiYQa",
        "colab_type": "code",
        "outputId": "89bce534-486d-4b99-95ab-4a4d313b649d",
        "colab": {}
      },
      "source": [
        "# If you have problems download, try with `max_workers=0` to see exceptions:\n",
        "download_images(path/file, dest, max_pics=20, max_workers=0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ""
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "49gOhAcgiYQc",
        "colab_type": "text"
      },
      "source": [
        "Then we can remove any images that can't be opened:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xQVOFbYIiYQd",
        "colab_type": "code",
        "outputId": "a4c80077-71b1-402c-97bc-231d0a9ad713",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "for c in classes:\n",
        "    print(c) #classes\n",
        "    verify_images(path/c, delete=True, max_size=500) #the verifying function deletes corrupted files / anything that cannot be opened"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "electronic\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "grand\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "upright\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kf1XHHntiYQg",
        "colab_type": "text"
      },
      "source": [
        "## View data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJT6yRF0iYQh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.random.seed(42) # makes the random numbers predictable\n",
        "data = ImageDataBunch.from_folder(path, train=\".\", valid_pct=0.2,\n",
        "        ds_tfms=get_transforms(), size=224, num_workers=4).normalize(imagenet_stats) #making the dataset with images of the same size, a train and a valid set"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DLbrQ8iZiYQk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# If you already cleaned your data, run this cell instead of the one before\n",
        "# np.random.seed(42)\n",
        "# data = ImageDataBunch.from_csv(path, folder=\".\", valid_pct=0.2, csv_labels='cleaned.csv',\n",
        "#         ds_tfms=get_transforms(), size=224, num_workers=4).normalize(imagenet_stats)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RlFi4hj6iYQn",
        "colab_type": "text"
      },
      "source": [
        "Good! Let's take a look at some of our pictures then."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GYGqxbl2iYQo",
        "colab_type": "code",
        "outputId": "0b213807-9b71-490d-f905-2b63129cde48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data.classes #checking on our labels"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['electronic', 'fastai', 'grand', 'upright']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6zyASXMxiYQq",
        "colab_type": "code",
        "outputId": "70d3ba2c-c3a8-4d52-8166-93208f926299",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "source": [
        "data.show_batch(rows=3, figsize=(7,8)) #lets see a few examples to make sure images are uploaded and labeled correctly"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ycVexd+/eVKYSUh4phjgPxzPLteT+EkPMbNJTUYSQJQO3C72f5zj2daEz4B0e\nbjhfA757uVBrYDAcUrghK8sXsbi4iCsc/X6Ppp5y59EjTCdjnEvh36aeMh5t0EymlK7icVdfQT0d\ngSrD4YCNk8eppyOMkayO1uJsKqFq/QRrHdaVSQzG2vR3JkJZm0rWrHNEJ4QiKY65Ihv8XKNeFI6i\nKIkhGWFrHWVRzctnTxnl9DMLW1sj+TwG8QXSFrnUyuAyocwZAZKoCzkC0O9XXHbpHvZctJgM9nTK\ntG4JQanrlsm0SXXybSqn8j7Mebsxh+6bzPRXzVOFeV4+G10DXiN109K2ybi3IaUWEhlZc3QhOV8W\nwWVv3ree6JODFkLEh0DbtngfUsRgPlnw+Cap2QVfo+rnBjs5UQ7rLMYKsY3Jw89OVtMEptOYxcJy\nOjaXECqCGIg5PJ/6YKavibBax86A3yfyo4gxEHyqa5xOJtxw44288Y1v5BMf+xhWzNwQzkJWJsun\nnk9uHJgLxrS+TeSTHJKxxhKjcs3VT+R5z3k2h44c4TOf+yzHT57MNjiFa0KMGBEWlxZ5xjOezrc8\n73k885u/mYOXXcaOlZU8yfC4wiHW5VlJSvrM1JRmYaR0v6m9eZhnCzoD3uGxiPM14Ad29zWGBmcM\nIgX9apnFxUWK0tHr9ShcwerqKuPNEcZaDLBj505WVpZZWemxMCyoSsd4PGI82qQoCiajEb5tEEkh\n2GY6BaCoqkSH0hmR7FQEMIlFxlP53BDwocZrm+4nC5SIMadCt+SxLobkwTPA0M/7sxfuMit7luud\nB+oUE8BkQpaQ9s3IV1VVUZZJ5zxFFAtWdhQMFpMnElEEixgLJE/V2iKX12aD6z0h+tMtkDDXDjc5\nlWDsjLSXWOOQPejsVkke22ZebciRVNUCkTLfkMxPkPgCgisKrLHzCUIyzknwyznJ9dtZTjVPVOZ1\n+Cg0NbR1UskDWu+ZTKaMx5M0yfBJgjtNWCKtOppomUzGHD9+nBPHV9OkRSMnJ+GxYMCVLXYJOBUm\nvhAE708zcCFGbr31Vn7jLb/Mhz/8YSbjCWVVMp1OUVWcdacU2c7zMmeXNMvFpx+TRPeHAx531VVY\nZ7nxppsYT1KeTWc1l7McvEba1rO8vMz+fft42tOu4xnPeAZPfeq1PO5xj6fq95nFj8TZ/OKQ6iO2\nPJLtwu2n0j6dAe/w2MP5GvC9C1YlRgY9y2DQx5qCqlewvDxk567dLC8tU1UVVdljcWkR55IXHFUp\nTENVpu7c+kDMeey6rnP5FPP8tsaIWIvPSpGzsKzOxhKVXKXCfAzUpiFMp6nEKSZjEbOYTL7JHC5O\nPmD0gRg05V8z2StqSsipyjLQbZkAACAASURBVLxWPRl+BWlAmnlofhbBbNsGI5aymJVypby7GA/U\nObwtiaVtC2bpeWsLCldiXYGzBYgikse8mbjW3MlI3J8kf5r2l8ZSiMnHyzwCGXNeOpVtbSm/FZ/U\n5XLufpYaSPfN/LhUIhaIWgDpehHBWZcWpDI2rWfhHEVObRRFwVJhWbAmr2UhRDR58iHgQ6Buapqm\nyaI1nlYKGizTumY0mrC+MWZtY8wdh45wy+G77ocBD60+dCIfZyYgMs5lkPVegex8+LkZ4acfTJLw\nU8UVjrZpMdYQY+Tuo3fyoQ98kF/9tV/j+PHjc086xDCfZW25lHud4LRrE+Yv3NaxwiCnZtU5ly1z\nAYUtFymn7i34WRmCYWFhyJ6L9nDVlVfwnOc8hyc/5SlctHsXe/bsoewPcGWSdRRjUhlKzrGcysts\n80y+7j5wlu9ydgpbdAa8w8MK52vAL1usdHHY59KDF3PwwALDBUe/P2AwGFBVJSIGZx1t2zIabSZD\nJyYZwOCxkjy7oiwoXEHhXPLU5yS1HNBOFi7nTCVrSSQjHjUzsUMKPYcQCDHifMBkEtosXahxS8mp\nMXNjb62lDau0fiO/okmNjC2ktpRHJhlxFWqFNiZWdmo71W03TYPNnBrfNKk1EWKQFMYOs/C4TXro\nWVwlTR7SfaS0ZMuc9cWpocM6N0/5zWrfRYSecZRi54tD2fwcyWkBY1I+2lozf8Ymh/9t5g8Ym9IL\nIoIPHiXxg8QIITa0bZM954DOkuKY+fOdP6uoFCg2RnzQXEtviCooBo2Sjg2QpG4dLZ6Ix4jiRFNh\nYgiMpy1v/fCnHi4GfPtc8fmOyuf2qPVeJdD3x4ArynQ6pSiK1NFMZiOGSDOe8NGPfpQ3vfGN3PSV\nm3OnMPjg520ZY2ibRO44q3rbNtc0f11V55rsp7zz01zm07xmVcUHnwknBa33OGuJMdLv93jcVVdx\n+eWXc8UVV3D1Ndewb/8BLr3sUnbt3EVRVafa3l7lhRkLRuNMQIZTmvGn3copEtyFmFnTGfAODzOc\nrwF/2TOepPsu3sXei3aysuixpgZJ3BhnbVqAJKQlK1Oe2KXlP0UopJdKk2a6EFm/PMaYCVE2e3dp\njCnKtJKVdan0qShLXFIJ2WJAst56jATvE3NKJJeXpuhdWRR5Ap8MRMorR5o4xWuNZFd69uprjERN\nJXAxhFyyFml1BU+fmbBLIrhFmmaKtYkN5n2bhGusYTqeMp3W89yyKghZMlRzjtoHgk/XpeJzaJot\ng4zMWd8p+CCncsYkBygR8CzW2LnRNnmyMsvlpzG1xJoyy6e6OZdg9kyDT2Nqr6pSFKUXgDaF4DXO\nPXrrbCYBkyl7aaYTM7kwzL6bvJjNLMfuW48PcX4fkwZikGS81UPweN9SNy3/4Xc/tm1/fIDLyLbE\nhrfb+0CMvec+xdd7OJBySVVZEVVzeCaHuq2hGPZ56Sv+e3Zfspdf+qW38Fef/CRtiFibVrhJ6WZN\nWsi+vaDzb02jzwxl2qRnneHMhCGMMRTOEaPicm24iFDXDX/3D//AP3zhixRlQa8/YPdFF7F37172\nXXIJ1113Hddeey27du9mZWkpkXHKEmvsXH3O+xSFmBltY0+tfZ6vIl/zBT7oDh0e4SiAdrTJnaNV\nxgPLoO8gC4UISeAkMbgTC304HADgY8A5S+EqjHXYIhnrWWQOkewd2rnglIhiIhifSK0uRKz6VCIl\ngopBrRDFpMnAQo+Y07Gz1RJnHnjUiBdPICQBmghS7aE0PcgEthm9Co0pVx5SCVzwLRo9vdhDcHMP\nNxlHkGHSSC+KzIbP5y73eKKmZVWbpk3EsZAm+4ks5uc54bZtwYRcRpVXW8tXFDORbfacZuNmq4Ew\nFz3ZYlBNJMoslpHC5SKCdWPUTlHjiDaH842llbQkbCAiGIxEjCilFFhTAAYzmwwYS2nTynLMuQDp\n3CPvqWds+JnYjVHEJoKfcS0mh9NjjPi6TlojRnCksV2cJeQoxnb4+pYT3S5kfQ4P+YEw4Of2qLf3\n/C6UiHW265x1fEFomoajR47w9re+jXf/3rvZ2NhIixM4Nw9pgyYFnws6OfOXgS0vH2Sv+wzve84m\n3zJzt9bOw++zfamzp1aaNom+DPoDVBNL9corruCyyy7jqquu4tprr+WKK67g4MGDLK+sQJYpnLV7\n2iTjtP9T+50H3uGRjvP1wJ9YiS5Uhp3LPQqfhERcmeVSbTa6hmzMZM7sjtEntrixWQhk5m3brC7m\nKOY51YqiKOhVlqhJfnk4HGQylUttOzfPvc7+jr0CKXJpWeGy/Ghix1dlKkErC4ezKVzeyi5a+nNV\nspDzwWkN8+Sp101DXde0bQPjuwj1OHvN6SeGACEyHo8pnKPf69H6wOJwyMH9ixSF5lpyn4y4j4w2\nR0wmU5qmTWmANjCZTpg0I0JenjV9KZkM1rb4kMfE2dKszhFm3q9IkkbNK76l7zOnDHK1kQKuaLF2\ntlqbmUc6jEnRy7b1meCbvPNCS4hpLBQkh+MtZc6Dzwz6TARGjYKRVBlQpNSIiKHxPnngMdC2Yc6a\nX10d4duAEUViIGbNgPG05s3v+av7Q2J7YA34dsdfKO6fAd/20PsIrd/7WlWVpm3nYZgYI2ura/zR\nBz7AL/7iL3Li+Alc1iEOua4wzGaxZ73ie59XIM3ospcbcx3EmWH0U/fBaR7xVu99uy8oH5XZ88m4\n+7ZFxNDv9xgMBuzdu5drnvhEnvD4x/PtL34hl11+eWaWllhn02pqbpaHSu11BrzDowXna8B3WKO9\nyuGMIhFWqpKLh8Le5QWq2BKmDc4WiC2p28B0PAYB6wpinAIRsczD5WLMnDgmIpA5KmKEwmZv0Bhc\nUSIzQZcsrVyUBUVRzolU0ZAUwrInLyblenv9XlpAoywoqyqFiHsVRX+RoupT9fpUvR69fp+qqhKp\nzDlUbJIxjWlMGvivYbTeUmNNIqz5XO7VNElbwxgaH5k20zmZLuSFPzRE6rqhaZpc/prGsbptaEOT\nWeNx/qO5/Nd7jw8+k+lyiiGGxEkizjh1qIJxLnF9jCEq87YSsU5Q4dQonVcgS97+qQmBGMH4HjEw\nX5nSzEr3corgFF8phdZLFymcwRWOokw8B1dYxOb2bDpHnSczG6sO3wgaA8E3tE16Lo2P/MQvfeBh\nYMAvcGDfDnL6P5xJkLqQ5i90srE1V6UxptAOgveej/3pn/Hrv/7rfO5zn0/5j+yxtqFlu6vSfI7t\ndqRyjGQY52IxZ4nFbw3ZnA9mxtvmWWbUmDV3HWyZoMxqz6uyYrC0wJVXXcnLXvZynv3sZ/LEJ15D\nWRZpRZ5tDfj2UYx7pcpnf5rOgHd4eOF8DfiwsIoqrixo1bN3acgTdhc8ae8OdttIs3qCwlZ4dYxb\npfGBJqTyUS+W0DbEZsywX1FYcDYphKkm9nnd1nm1MouGNPirWJpGibR5EEtrTKdhInnTCvRyxYoY\nm8P0DjE2eacmheiTxy9JSMSmkLWxFuNyNMAaTGZbW1fk7SlX3DM1pcs14E5wNq293a9SymBh0McY\ny8YoldBZGSB52c4Z38cAZU75GZJojEFog6eNfm5sfcjlX7mcTbMhTgzz1NbC1GNUc514MvSBVEMe\nNS3EotnApkVZcrXRzFMXg2bugWKYrTIRNVXvmFDgvTJt2lQPHk5dWzL+Ji/lms5ROsHILHwu2MJg\nnWTtdgWjBI34kPLqzahAcFlAJ5X4pbYNP/KWjz4IOfAc4v1GQk//58HDNvc2W44utCmM1UynlGUK\nb73kJS/h4osu4s1veQt/+md/lhadtwUSt7DKc87mnDON/DXNvO4zt99/bNX4zQSN/ILHqIBnFmUX\nSN41QoyB1ZMn+cxnPss//MMXuGjPHr71W57HS1/6Up7+9Kdx8Z6Lc2iI/PnZxCeF32YLrMxqKzt0\neDShKJKimQiICoVN5KnQNlT9AdFl79CWmNggKlRlH+99rumMuMJiJFJYoWeVHcsLhGaUvFdJxsSa\nArTAugrjSprgmUxG+OAJPtIf9CCCKAipqmShjMTQ4LUmKGAciqENQttGYjAgJYolRvB+knLQ5Pc1\nh5PFuVleLxk+ERBDdEWOHEhSJTOCtYJzBghUZYl1jtbXOGcY9BeSY0IaJySPF6VzaAhYY+iVJYVz\nKBA0orI1opfGzxlj3OY11Wder53USZsc5mONkjxmH3KlTiasFYXBOY+IT165zrgBQmhSlbpmhnmK\nWgqiLcZZbOUw1iHGEdTR+oCP6bOR2f+GqBWIJfHJZ/rpYGIqbUuru2U/XyKNm+DbGmJ6lm3whMkE\n789eovz1eeDbeNT3J8T9UOF+MdqROcPTWptV29K+4APHjt3D29/xDt7+jt9iNBrlcEzqeDMFnqSD\nLtt6qee6orPfxzafONdz1jRLnNVoipHEBM27Z1KKqin/FUXTDDSXhjRNw8ryMs965jN54QtfyAtf\n9CIO7NtHlRdlUSNIYdGY8uspN3+qdvVe19/VgXd4mOF8PfClyulscuxD5NJdSzx+V8mBgfCEvbuY\nrh5DoyGailETqL1HraFta5SK4BsGvYKCQK8ACTUL/RJnSOVEdlbvrTTTFudm61AbVGA6GeN9Q1X2\nGFQD2npKYQsq5yjtBOeEfr+PdULURKr1PtA0Hmv7ONvPy6GC2DSWFa7Iq2ylkPn65ga+9SCWougR\noqbcbSvUbcCHpIyWDJdQVhUhZOKVzJY5NtjC55x/MuJokrHulUWKaGquF9fE1sa4nLfeMsbJKfXM\nmcLZzGXoG7B57EqCMrlGPBv/2frpZVlQOofEFmM0q8vlcrQ8EUlR0DxXkXwfcY3CGcrCIWLmHAFj\n7ZbQfLr2Vg0jXUJNmUVkItOmpsnEZjUyD+uLySqYs3p/Y3DOElWpm5YQle/9pb94EDzwRzrOEpY+\nEzGGTIgghdZDoK1rbK+PsY7pZMJ4PGJhcZG9+/bxL3/kR1hYXORt73gHd915J0WRtNOThrpN+Z8c\nun7A7uO8DtP58bOOqXkiEfKSdtbatMZvSApOZBKeQZIgDFBax2Q05uP/7eN8+lOf5v/9k4/w6le/\nmhe84AUcOHgQsSbp+Mos6pCM93wd8w4dHiWwziZpZUDU4Osa0Qpfe0IbsMbR+IY2KIglSsD7tAa4\nmJQb7fUHDCpLaSDWmyge4wpCO8WHhv5gIQmjEFLwLluWSERNCvVO6glF0cPHSFNv0ljDcCBUtsSG\nSOkcSIGitDHQhEjPGtoQ2dhcJyoMFxMxbubvOWewwI6lBepmSgieQT+tFGZU8JM6aakHjyuqNKkg\nKZh5nwh7VpIu+ngyQoMyGCzOSWLr66s0bcvADhOBLUJZVknGtPY0Pq3cJSJzBToRwVUlsxr5GAOt\nb5MhXcpEslkuGpk7I0ZOD91bMRRSYHNEYKYgN9eBF8mlaLlOXFKawlmDc4a0eFgKslflLI2opKVB\nI5FIqw1BUpVA4QqGZcVCr5fW+JY8HsupcVnbKt9jmoBElNq2qe+cBd9gD/yBYaI/kJit6DO7rKip\nJCwGz9raOl/4whe44Ybr2blrFy9+0YtZXlqGkOo168mUT3/6U/z1X/81t331NnqDAa98xSt49nOf\ngy0K/uLPP8Gb3vgmPveZz87JYQDT6ZTJZDJf6u5e1/Qg3q/CtkZ0TvTc7jO6/Z7ZCxVjZHFxkac/\n/em87rWv5Vu//dvZs3dPEj8IIUcq4px0c+aEo/PAOzzccN5a6EsDbeqGqII1BTud8rQrL2aXbnL1\nvl242LKxsUa0PaaqTJopUZTxeAoU9Pt9lhYWWFkYYrSFdkrhDIZIaBvKsqI/WKBtG7QN85xt42vq\ntpnnsCeb64gKpSvx9QRnDINhwcryDsQovapEJC1/mkhgEdUkKDIeTajrCSpJE70qe4gxVFWPmURq\n69u0rrlzSU9dIU6SqpqPkd5gkbppMNbQ7/eJsaUqLP3KYTQwnYyRaFleWiGEQNOkBV2qqs9wMGQ0\n3ky17a6g9S2T6RQ1VWKLu2JO4h0OhjhX0PrAtJniQ2QyndKGQLGUogMznfTWp7y5cQ6VWV48ZKW4\nSL8iLwRjkjhNTie2Pml6zFTnRAXBopoiBWQynclcJWMcVszcY595/1IMiSRBGMga8kWWzjZbVOSM\nQYyiYZoitrnrGZuY6yFEvvc3//Ib44Gfp1P78IGQaxE1G5dkH8bjCW9/+9t57/vex8kTJ1haWiKG\nwKtf+Z044zh5/Dgf+tCH+N3f+12O3nknrW8pypLrr7+e73v99/P617+eF73oxezetZt//7M/y/U3\n3DBfuN45R1VVTKbT7Yls9wNna2Xb7dt4wmcjrZ8LpxiXho2NDT7xiU/wlZtu4tv+8i/4p//in/OE\nq5+Qc16npGI7dHg0wfuWlHy2RHEETexqcSnM5YoC5wrUubz2ATlkKlS9iqoqSCKOkaaeoL4mBiEG\nT+GKRHrb2EhLBgdP2evjYyK3RVWKsp8myBrZXD1JjB4jhmKwQDUcYKsBPrRg+6goweRyL1HqusGY\ngt5wmYBQ15uIpvuwxiGmAjRJfQaYTD0xTtI6EaL0XYUxBT6A+MDq+noylsYSQ0PrBR8c6mumkxGo\nQ13yMifjTZpmyhCDF8t0mrTfR3VNDIHxZBNbDCjKitD4eYlsr99LFTDWYXNQ1PQKVC0aWgpXUtgK\nIxZi8ridEVRbUrVcIGhJYaE0s3VHLV7Bq1KHyLRVjC3wQBM8QVPVwKC/wKSuaXwqPUsLtqbPhzgT\n0yG11TT40V2IJg6DQl5EBVRT3X3IsrWzFSfR9Axm6QhXOqpqptW+Pb5OA37hLLaHgPd2bujpnqTJ\nEqOf+cyn+chHPsLmxga9fp/VtTV+53fexWUHL2N5sMAHP/hBPvzhD3Pi5EmKopjPhI8cPcrb3v52\nTpw8yate9SokK/VYZ5lMJvNyj7ZtL+QSz2vbBd72vbectdH7npIZYwghcNddd/He976PL15/Pa9/\n/ffzyle+kl27dqVj5N7fvuoF1sl36PAwguaVCGNUVIQ2RibTCbqQok/GFjhXotYivkZiynNWTrhk\n78VYYzh6++2s3tlAUBYXLBoi3itlYTFWiWroVwNKPP2Yy45CSzXoIRimE8/OnRcz6A9ZXz2BNQ5X\nVdQx4MdjYvCMG0/TTKiKirZtU+jYlhRlD2MsfZZY2rETSDKtQlr5EE5VutT1lM3N9SR16lsGZUm/\nv4ArKlSEcZOIddPWJ2sllkINQQ3BlFTlEt4knkx0PVSFaTTENlCHJP5S12NQZbTZ0Os5lsoeMXra\npqZuJoh1tL6hcCWqqRxt5kTU7SZlMUBkSmxTSVpaHjp53IN+xeKwT1U6iA34QFVUOZ8dmTYthJDk\nWDH4oDQ+sfj71rFoJ4zNCK2Efn8IAj56yhICYS7DGkWpfUHd7CDGnIA0DhGHj8KoTqTCNiptjAQF\nVQuxIkaom5bJtM1VAkJdn91WPEg58EeOH65sNeABa0vG400+//nPc/jwYV784hdz7bXfxG/+5ts4\ndOgQ/+k//kdMiBw6dJhpXWOtTbNwI8SQVMo2NzZ55zvfxSc/+UlG4zGHv/pVlpYWefrTnsbtt9/B\nHYcOzUUFZuZspouuenb11QfynuVcqZNttswIfKdvToQNm2vfZ6Q3Yy1f/MIX+U+/8At8+ctf5g1v\neANPfvKT8wINicw30y8OIVDa4gG8uw4dvnGYsc8RIRhLUGVaN8SFksa3BGcIoaGqepTeEQpHiC2G\nQK807Nqxi8p4Vo/dSWgalhcXaesJ9SSJe0QxxOjZWF1lsQeT8TrTJoJTRhPwwbC+ViBPKNmxo8IW\nKf2nOUy/tLSCWMM995wghgll2UMDOFdSuArVgDEhBwITOUuwOWKWysX6vSrnkFPY1/tAVTh6dqY7\nXoKx7NtvGU/GxOjRGOiVjmG/hwZP3dREU9IbDNNzm1aYJofgAco+6luIyZBLpRTDPgsrO9AYqesp\nurlGNALWUodU7uqKZMi9Rsr+YqqyUYM6klRp2xB9jTWCWqU37BFDYDLepDQwkBJnBbA0CIEUEq+9\np21bgkYKqVCN1G1aiMQYm0LpIqAxLS2qLRIlVQ0IGB/ok7TrDTaJwUiBWsugiLSqtBrxpFI31Uhb\nJ6JdtC2x9Ax6A1xR0vrtyb/pG3uMY15skEO8CoxGY+688y6MsbzkJS/hwIH9/MmffIQvffnLXH/D\njdiQFc+yJnHMKv8KWXI0hURuvukrc/GXvZfs41/9Hz/JXXfdxU//m3/Dbbd9FWOz5m4IWJs8WEQf\nsLD6ufBAnWHGVp/B5WcCyskTJ3nf+97PjTfeyE/+5E9y3XVPper15seKEZw85rtgh0cyZKbDYPFi\naIFpq/gQaZqaUDo0xpSTLiusgbreZBoCsanpFbBjcQDNAqGZsGvHDuppn7pf0/qAmDLVXQPLpSSi\nmDW0MdBqoG5bvF9H1VNVC/T7PdbXjtP6gslkyu7dPRYGPSabG2yubzBaP4k1Dm8dIx8pioKqLIkx\nhcqbOsxWHs7VukldrCgd1oF1UFU9qqKgEAVKjO1R9srMyhacq7BGGFQlg35JDB7jKjyWshrkypY2\nGdP4/7P3ps+23Wd95+c3rrX2PsMdNEu2hCQD8oBjkQq4MTFgOh1MD0Wni3ToVJEyVZ03Sd6QSqf/\ngVBJpdPpSpMqqgNvmmraELq6ynESOhACxsgGG9mWjUDWjKUr3eGMe6/hN/aLZ+19r2xdAUbYulXn\nqdr33HPOOnvvtfZa6/k9z/MdZCZtldiW7m6Q5SVhVGG5syMdA+fkYS3WmG3lLfajkgCrEu9vNzu+\nxZCklZ0CMQwURNrUaktUFtt0jMrQ2EbGio3BFEHCp2mAkjHKyKJIwZgrRYtbWtV+RsfL76luq4su\nU/iAreG6XGyxYBqs8XRWUcMkIxatGVMgRIVRu5QciVOPVRVipeaBm6fvP68ELiXea75/S7XNvyrU\nzFU2xsxVZebatWvcdttt3HnnnUKLKAVn7UzkmykMWpFnXWIxPhGnnZwizjlJyEg+HkPgzrvv4sLF\ni9x11108//wL0kZXIpYSQhDuuLWUmN5ylOktD/OrQs3c9q9GmG90nVfrFY899in+4T/8n/n7f/8n\n+YEf+AGWy6XoN986jZqzOIvXDTVzooXvC3Geo4YUiUmYF0ZrakpoKl5fT8JpOiUMx4ThFFUmVA2E\n4ZQcIzkGVBWTD20MjTWM60Ns07LY28dicBS6RcfRYaTWieWiRasLpNjTNp6xD3ijuLi/h65v4+WX\nIMXIom3ZWe6gqGhdZQSoDCll+qEnxiSV5ax0ps3Go3uWZ1ZAGrFOE1Pi6PiAqiHlSr+e8I2ZtTKM\njAGoxKwI1QJSweeURQ3OWbSRFrhzisWyRWuLnwfc0zQRYsTMoDqQBYW1fqZ7zf7btYKqMruf70fO\ne6jddpFQSxLOutHsdgt8s5BqX0uXwyqoOVNrRtWMsxtTlFkVXhmcESnaZdvinCXVzBQnKNJ5NNYC\nhagGYk5UZym6yPybhFOi1lNT3QrslDjSDyNWWxHvmVZkpdB10928OXbo60jg9XX/+zVbvMUy9k3Z\n3rXOnOzr2zjrmKZpFvBPHB8dcXJ8DEpmHKRCiHE2vDeEEEgpiR5vznjvt3Z10zQBcHJ8vOVaHx0d\ng4JHHvl2Vqs1L774Im3Xzi0fuXjMnwvg60+wknqTVg6lZKhygTnn+PKXv8w//af/C6vVih/5b39E\ndON9Q80Z7c9a6Gdxq4aSYkVpoMxez5WUFKV4MQCqyMx4uUNKirg+Jo0jdeFkFlsCzlR00eTYCwir\nbQWJXTI1J2IesXmArKhpmrXSNdZ7Gq+I0xoRTzE0jadrPfp8g1ETcerRJaFrxKmCU+A1eGuoNWFU\npG0cdtkxNEZQ2MwzfK1xzm+dzkS9saJrZH/R0DQLTvqeMWZyVfTDQIwRqzWN8zijyCkyznPuECMx\nijNXrYNoi1dIpZKCJsX1TKOqOC0r/Bt9zmNMhJDxzmJcnRXrHFpZXGNoWyc4plqF/jUL6zjrcK4h\np0SKGW88YRK3MWqkcVa46tYyTQMYjbUeazQ5J5S2dO0OxghifB0jJYxb7/FSNEYZdDFUDEHBZAXP\noHRBozHKEjezddeAcVRlGLRiqoUSR2pOUBLiYO4w2s6fx+vHGybwW42xo77mP6/729dGlZNSz/q3\ngIj9Nw39MPDqq5fZ29vFGMuj73sff/2v/yiro1N+/T/+R37nd393e5JrLZ7hy+WSH/zQh3jHO97B\nFAKf+cxn+I3f+gR33XUXKSWuXb3CNE10bcvf+ls/zsWLt/HP//n/xhe/9MV5lelJMf4p2+h/fGL+\nkz6bepPKYlkZa6Yw4ecbwO8/+fv843/yT6i18iM/8iM0raKetdDP4haOhJVOXEk4FdE108fKZM+x\nKpqAyIvGMNDO13YuME0VNwWMEvUxlQMpJ+I0kggY06Ar5BxIQOsMu8tdqtbkNOKsyJRao2h8Zb2u\nxHEQsxOrCOMplkIcT7F7+zRGs2xa7Ex3GtfXKEbTNJbWdTQqk8Ia4oiaO4fkBNqASmjm6Z6SZOpt\npTMZXScanVC2orTD4Rh1wRnLom1prKHkRMiVWPVWEhXYVpZigRqJKVBReOdZtJ7Gzl4Q1K1nQ4yy\nndJmW2AxK88plVHqehc0z5W4qLApQihMY0KbimsasC0xBKxVUBzLRcN999yONaLCVjUztqlQciJP\nIzEUtK5okyhVlPLEflThXCUXwQoE0xH8PtWUGQlv0LaZRXAqyjiqtsRcSPUcS70Hx5eINeIXHRa1\n9YeP6RsOYnvz4o/PxX+2ZKOUFpcgPVe8tdI0LRcuXCClxKVLl3j0Ox/l7/29v8s999zNI4+8E6M0\nDz34IOM48nuPP759rloK/8Vf+St85CMf4Z5778U1Dd/xyU9SqPxX/81/jTGGL3zhCY6Pj1BK813f\n9d08+NCDnD9/np/+m2VCjwAAIABJREFU6Z/mNz/xCUqpeO+Jc+W+SafbBPynAp99HXHT/P36v7j5\n5vU1vE7nHGR45dIr/It/8b9zzz338MHv++CsGX9WgZ/FrRkFmKaAsaCJGAV9LES/y0nsGUrFUtA1\noGsgxUgohuI866FSisObgjITgwqMRRGrxmpH1ZXCIBKsypCNpwImrygxU/SuMFy0qKRpo/Da0V28\njXF9iq6FcUh03lNj4tzuLgvfsl4dsxpHtHE0xtJasDqTQ0KXiGX2ulZii0raILrZyJLRqEpNgb4/\nIpaK0Q3Ugs4BnUdUNRArWnlUKeicURnMrLimZmEUgKIKRhWoMtdeesvSW4zKiJQpbO4yqmsF+Jaz\ntLdn726t1GyuBDFHUkkCNgNSziht0coSs6jQTamSqmHdn6JVJYSRnHvW6xVd62VfAOtknn+0WjGs\nB46PT1Aq45uKMWoWrNFQxHFOKYu1DQOGQdnZ8c3hrMdaT8qQisL4BRmLsp6iFuwvNbG/AkUWWt45\nchTlzjfKcN/UBP7H5+FvzIC0zCeVNkaABdby0EMP4n7d8vjjj/PjP/7jfPiHP0xOclJoFO9+z3v4\nwAc+wKc+/WkWiwV93/PA/ffzoz/6o9x///3zXF3x/g98gPd956PYxnN0dMTjjz/O4eEh3/3d7+fh\ndzxM07R84AMf4M677uRnf/bn+MWP/iLTONK0DdMUKDnPcorXxWbeun2R6y2vjWodgFWGmopIsdbK\nc888y//6z/4Zd95xB+9897u+qe/4LM7izxIpJRHuMFKwKqAkBUWTq7AuxC5U1MRET1zjmoYYBDAW\n48TsMopYk3lwHSVGqhpRKoMqjFhKLtxuJ7SyHGeNSwVdYFI71O48Nl4W8w7j0QVyWVFKppaIIeNN\ngdbi9R5t6zEaUhpJKVCKuJ3VIj7YRou5iDX2NX7iULAkdAk4rYTnrjS5JDqvsThSDJAG0AWn9XV+\nfCnipa01RonyI0qRrcZjKTmjw0AmgPlqb2+gVrSxtNaBSoIt0BpnLLbKvTznCVMSVluM9Yy1EOOI\nbZZ07YJJR2wsrPqezmh2d/d49fLLlKJxxpBTnOVS65ZjH6aRZbfkwt5dNF5GD0qLp0StipIVOcE0\nJYz22JxpcqDmCSahGkY0qcAUK33IrMdMqhbTLAl7sGdW1JI4Xa3p/AJnHbWAeQNa0p97An/9tvZb\nC7mktb4uM4pY5T300EPs7u7y2c9+lj/6oz/iHd/6DlHFSQljPdZa3v2e93Du3DmGYcA6x8MPP8wD\nDzwg9nVKUVMipUTjPVVrnnzyST7zmc+gteH7vu+Ds054RhvNww89zN/9O3+Hfr3ml37pX5NSllXY\nzK9WSm19wd86Ubf/aqVIKW9R6dZY4cjOtBBjLApFKjLd+exnPsvHP/5x3vb2t3Pu/MVv7m6cxVl8\n3VFpmoZcI7UgI3GrCClRNORSwRiYcTZVXWdt1JpJKeKKfF/K7KBVMjUEhDFlUSXPHgoWXRPetXTL\ncxz2MhPWymKcw1mHiqIm6f0CW2BVj+lHQVSXPDEMAa2qzLytJuYw3/8yyiic7ZjWkySlmjCNoVhE\nw7sqYqo0fkFUkSmG+bq2s276nMxqkXZ40XhjsEpQ9E4hNDCYq2eZTYc4EaYeBbRWZtiWguG14NkN\n5bfEiZgC1lipsrPItWalyDWR8iRIcNXi5n3sp1FUVHwlx0yNGRUnahhJg8ZRKQp0LUxhwBkRkVEo\nSq1YrbA1YYDdtkMpQ4gjU4ooDMa1RMAWg3ctpgRIBXGRKDNZLOGbJWjP8RDJeK6drDHeoHWPcxpN\nA8Xgrai7hRQQB5nXjz81Ukrd+FCv91CvedygBn/D4y0Us6ydViLgUoqsQB988CHuueceTk5O+NjH\nPsZ6tRYRFufmmbnh3nvv5cKFC0Ip05r3vve9LHd3UTOaXRmDdQ605uq1q3zs33yM5557jocffpj3\nv//9cvIrmbEYY7j7nrv5yE98ZJvcp2nagsAq0qJ/sylmr/vJzCYn1ConfdlQ28prHlu/3Cr/N0Zv\nP+rNcVQItcwYI62sWWN4mgIf//i/5bnnnntT9+cszuIbGWZeYKc0a/8jaO31eoXSjphEySvmJPPf\nLHPbaRzQJhFnOVFQxJgZ+lHm5PPif3NPkkW8ZWexS9MsCKmglGhQiP1lZppGMQtRFmPcXN1Kkk8l\nE2JgioEpTOTZrEQrA+jZjrQyhpE4g7a0ccSSmXJgCAPraSCUQtWerBqy2WEVFUdDIOJJOPpUORkj\nR2PiJBmOi+dKtASlUQaUES1z3XiK0SIvWyvrlDgZe/qcUL5BzR7b1hrRHzcaZzRmTrI5DEzjKXFa\no0qkpkCOIzkMlDigSsCqAjlAidhaqHFiXJ/Sr0+I/Smthgs7O+g84XVlp3HsdQsWTSveD7USYmIY\nR1RNmBrxKqHLCKknh1NqXJPDCWk8IfYnlLEnrE7I/QqmNTqcYuIxPh3SpAOaeJVlOeScPmFPHXFe\nH9OML1HDNXGkswrvLN4aFIWcAzmFm55/b5jAXy8xc9Ok/BZMzn+SmNVTNiAJED/Ye+65h+/5nu9h\nuVzyK7/yK3z+85/bmhaIeTu0bctysdgmpocefFDUxmZqVckZBYQp8Kv/4Vf5T7/+GwzDwA99+Ie4\n7777iCFQS50v/Ioxlnc+8gg/8RMf4Z2PPELXdQzDcB3Zrr8+ZPrNPqkbP63ZtlZaeXPSFvCG7MPG\neKXe8Ng+9+wutDmG5/b3ufPOO1gul1t+fYwB5xx2bkkZo3nm2Wd58cUXv659OouzeCuEc1akMTdd\nsiLqbMMYUcYRYiYXCDERc5k1xae5K+WYxrD1B4gxEGMBLNY1lFKIYcJoR+NFuSznzDRNXD24wsHB\nZcI0ztfXyND3rNYrTten9ENPLhXnGqxz7C52ca5BKXHOkmQuidwYu8XBbuoDrTXebUxDpKoPcUIp\n6SaIFzeM08Q0BcYwUSoYbXG+pWk6rG0EPJakWkeb+e8EtR9rZSqFYiyqXRBtw2kpHKbEUQpM1jIa\nw2kpTNoRdEMyHlwLvkU3nmrBeE3WhaQKGBlP+Eb03EvNaK1oGo93QvEqWZL7bttwcXeXvcWOqMp5\ny6Jp2V/uopXct0MMrIeBMA0YMgtn0TUSpxWkgdaALpESelSaMDUR+hXrKbHKhqE6gvIk01CsYygT\ngYD2MKY1rtUUNVLrRC2RnAOlJEpJIhCT42ya8vrxx2SEWzgxv+YtbxJ02VaGIsCStw+QecfG6tN7\nz3/+gz/Iu971Ll555RU++tFf5NKlS0xTEKs7JXzOXCTJnTt3jot33CGvNq+0SylMIfDpxx7jF37h\nF7hy9Qof+tCH+O/+2l+jWyykzT6nwpzSjIh0TOPEweEh1lq+//u/jwsXLhBDoO3arczh1+5ifd0E\nretrk/PNHtvDpoQnqWcXnu0FXMrNZ++1SrUwW+Ht7+/xoe//fv7LD3+YRddRZ4laQeCKz3nOhaHv\nefLJJ/8sn/JZnMU3NVKKUIsgmZG1L0qTqiaVyjBNZDQpG1Ku5JyoiBIaxRBCEY7y3ORquxZjZdzk\nXUu32MV74UaXAtM4ME0jKYtBSIqJSiWEnhQjJydHrFZr1us1Vw+u0vc9Rlv6cSSljDUOhSbEyDAO\nopw4g71EgU2LJ3hKoMQP3GgjI7GZG94P/eweJprh1jWM40BKaeaOyz01pUCIEzXLAsfNKOxaBTRc\ncmGaBvphLQYjWtP4jrZphfOt1PycmTp3CZRxxJxZjz1jmNDWEEpkSpMk77bD+BaMJQNjSkwpkqkY\nZ2hbz2LRcn7HsfCZKZ1SbMbvtEylR6kVCzuysIHOVxpnKVRSHslZ6HFOGzrr8bPADiVRS6T1jv3l\nLl3T0OM4zI6j4jmlZa0X9G6H3u+wdgvGZoejqrgWE4NtwIowTEwTMY2SxGvaFs83i7c8Cv0N4wZ0\nItsKerPDgt2utcwFsfxMbTcvsxONkhXOtqtw3djk/m95gB/+4Q/z9NNP89hjj/Gz/+pfcfsdt+Od\n59xylxAjq9UKlDjNvPjCC1Aru7u77CwW+LblD598kp//+Z/n6S8/zXc++ih/+2//j9x+++2M00gt\nleXOUpSGjMEYiDHy1Jef4rnnn+f2227jJ3/yJ3nyyT/gp//lv+QrX3mJrmulJbbhBs5ob8XXMQ95\nnahU8WaY96nksgWlvQYNv92eravaxvv78PCI45Nj7rztToDt7L7UippNCbTRXLx4kdtuv+1NeNdn\ncRbfnBBQV0WrStrMspFkPMXMmMQ2VBlHLqC0Y7HwhJoZVmsk/8s13DQdqhrGOC+Yc5Q5uHXkNKCd\nw5KgaHyzwxJLCAM4wzBMUJE5OSK8YoynDyuGcULPle8GSKeUplTBrYSYMEYzpsjh4Yrj457d/X18\n47aLc2sarCmkmAjTGm8VbdsAmhCDdBpDRM/e1m2z2Fbr0p0waC10K2WstO5nGlnJggWoKFIMjKNm\n4WHKAY3MlFNK24VE1mZWOCuc2zsHtRBKQCGCMClFofVtRo+bz6rO+641KQ2UWsjaEWtljCPOW7TO\nmJrIaY1yG71z6VKI/LMhTAmNw9SOXBJGV8bSE+uKZDTRBZTaocRIyImqCtpawMxdigZrOyKnrMKA\n9Qu8Ed2MkvNsdmPQqqK1uMbdLG7pBF43y1aF0BxubDFvekFq49163Qhebeze5pSk1HULtxt7w846\nPvjBD/Ibv/GbfPKTn+Rj/+bjWGsY+oHWe5x1DMMAwNWrV/mZn/kZFosFdbabc97T9z3PPfccd999\nN3/zb/4PfNu3fTslZ9pG6BA5icdvCjLLOrh2yIvPv4hCceedd/G2t9/Pt37bI7Rtx//58z/PZz/7\ne3RdO4vEhK0CmtZaDAT+zAd1FrfRMtAzxmwT+M0qcDvPtysyEzxdrfj4v/v3WGXmi3qjnmTmzkfh\nu/7iX+Qf/E//gL/w6Pv+7O/5LM7imxSKPN9rpJemlCIX8QhIuVKsJs1sqFI3YNQ5YZWMtYa9nX0O\npwHnPCkCSoqOGCPoglatJENtMVhyLBSt8a6lEsnKMqwcOVVMyfT9hLWWdqelVE8ImXPLBevTY2LM\neOsxxhBzIObMGAM2F4ZhIIRRqFgxcHR0Des11kHbLvC+nRXNpPJW2lCqIgSxPUVB6xcyPix5u7B3\n1qCqop9NPHKpAvIrRURbtMHoupVGTTEw1jTPvK/TzYZhxBoxGtFaQHNTCFIVu4ZSq8jX1kyp0kWQ\nzoIACDdiNCGM1DxRwgR+QUIRU6BdyEKhcaLvntCkXMg54Y3HaIuZOxHUilYKZd12nJBrwtaEspoS\nEiVNWCteZBqDKpmSAlk5VDYQCiZbCKC7hhAiJXuc74AlIfWMsUi35iZxayfwWYdcay0JZ/uL69X4\nRpdb4rrk56Yir0WqdHWDlSiKWWSgctttt/NDf/Wv8uyzz/LypUuYZIgpkmLE6utSiVprXnzxBZlp\na1HvYa78x3Hk3e95N69eepX/95f/H9q2ERWyWlmv13KhzlX/K6+8wuc+9zlyKcQY+bmf/TmMtaQY\nueP229nZ2eH09ATnBAmfc9pKB75Zoef2ljKKd73zEbz3vPLqq7x65cpWFUjN2xlr0UrRdR1pns9t\njntSso21snpFSXUSU6TrOr7lgQfY3d170973WZzFNzpUrahamQljoESnO4YoLXOjmFKi0ZZcCkpb\nSpnpoc4SwopabgcliTAmTU6VlGbUtFKMY8/CgVYWxVx4KI1WFtssGIomZ4MClotzVK8Bj7EeccAS\nI46UK7VknJUCoKRAzoWcCztdR8mavOPoOityr7O9ZdN05CwQ+2W3RwiFVAJ1mmYfB0ffywIkuzKr\nn1lqDsQ4AlC1gHIrlcKENpGUEmMYZbauNW23xHsvlLUcyChiGkkm07U7DOOak+NDvNMYU+naVpzX\nrEWjxCM9TFQliwatjczbKXMBV+f7PWg0UwoUAtWK4pnWGq0MMWeW3ZIJz1AE5Nf4jrZdYLQh1EqK\nEzFOWOPQ2uBdQywbYKBgBDa4JmdEkrWUQAgRYwvoQoyZcRjZ2WtwzhGDCLbkUlivTxmnEec6nGtu\nev7d0glcqukbuMc3JrEZTCZ5WirzOqM5hyFycnrKOEgbxVnL7u4ezjmcs+RcWK1WXL78Ks8/9zy/\n9/jj9OueGALraYK5zWy0ZtGJItI0DrP2eRXUtTYooxiGkfV6xSc+8Qk+8VufRCnkJC1l2xUQEJwA\nwXJOAlpzji9/+Wn+8KmnhE6Wr29fSmUcBpxzs8f4n9ya9I8NtakiROP9vnvv4/s++Jd54YUX+Ogv\n/zIHR0fYGY1vZxGFXPKsbyyLCQU0Xcv+7j6nx6cM40DO0kKvQMmF1XrNOE5vON85i7O4lUJpI/rl\nSm7OpepZZSzRGEMtab7OFb7pZp/skXEKwoIpZTvrTWke8dVMDBPVOqrQO+Q+UKVraLRFYfCNIcXA\nYndBCIrDoxVmaXBeZtr9IDNq1zTElFCqorUTAJtSTCECaq72xEJTaQ1aWtdl5oBrZUS+dLYLrcrg\nGy9t3pk+WmYRFAG/auEzV+HMr1dr2i7ivWiUpyiuX8udXbQyhDBijbiilQIpZWpRGBMBjbUOaqZp\ndtjd2RVjkyIteipMYWSKI13bsbOzT9d0hJSYYibGxDAMDONAu1zibMuAIueCcQ5tHG3TcXp6DaUt\nfrGkTjL3X8eezsLC+7l4sRidCDFgrcf7dqaNVVLKGNNgyyaZy5gipZFapRuQc6EWTUyapmlQGlKe\noECtoomuEPqafoNb5C2dwF8L47pB6KRWoTBpLSfNes3R4SHHJ8dcvXqVy5cvc3h4SC2VppFKVinY\n3dlhf2+PGCNXLr/KSy+9xPPPv8Af/MEfcHxwjZPDa+ScaZuGGBPOijSiMZrjkxOpuLVI4O3sLLHW\nsu7XlFqYJnEXqlS5MG94v1pvugHS4tdGKuBcMgrFlEe01sQYt50GPQvwT1PeLgjKjQOfr4p6w79b\njMANAvCzzhIV4aFaa6gVfuczv8vlK5e3r3nnHXcAlXGcGMeRtEnaStD7GxMXrTTn9vcJY2C9XsvK\nu1aMFbrK6ckpUwhnCfwsbulQQFUKMQbRUOR8zgjItWQpGqqZrzk1+y7MegkpSdXnfctyuU/qJ6zb\nYZwmVM3oMqFQoh2eZDGg0mxDWTLFVsDQtrA+PSbvneN0NZFz4vT0hDiM7C+WNE1HicItT3nCaPBN\ngzGKXIRaZlSD0ZasKlpbrBP6W6kVrSylTPTjKdOUqLrQmSXOOqiFtl0Swij7WKqoocWAMZq26QCD\nNo6cK4vFDm3TAhXnelgLBL7kiDFWPMtTwfuWYsvsEZFRaNp2SeMcXdtIomcDhRLQX0xBzF98gzVO\nKHMy+J+7qvL5pFSw7QJSZpwmulkOdQpBRhPKsLfc5dLhq3i3oKkFrSwpF8iJlCaxKp0tqKmCeC9J\nOh0gfhAhRjSJnOQ4e99SiqIfTtAG9nY7QcobhzYyXqklyWKvQgrj9t78enGLJ/DrCL1SBGwhbi4T\nV69e5eDggKvXrnJ8eMh6vSLGOCPEhV9JrfTTWk4CpRhOjrh6CUIIjOOIofL2e+/m/vvu4dIrr/Dv\n/79f5fT0lPf9he9g0e1wfHzCZx9/HKUU586dwxnL8ckJIUZOTlcoJSvIUsRlbEOhyjnPJiqziEy5\nTmOz1uCdJcZETHnrkKa0INb1DaA9kJ/FEHHWorQYz21c0HKRv5cZUNnOgdzc0k7bdvh1NPzG3Ucp\nxW0XLuK949IrrwAykztdrbb672puj1eqgEpqZTVNOGvx3rO/v0/rWw6PjkRh6YbXXK3X0jk4S+Bn\ncQuHViK1qcJANZWp2u2cdxpHVHedEVKVTENDjISpx9SCTh2lKtq242Q9MI0TjV1Scpxb3BPeVJIG\nnSImTqiYCTURVcQpyKqgdKVfZblGT4/JWdFYBwj/OzrhpKOl4hS8UCWlQkgBUyNVG0QAfF7Ml7pF\npGttCSGwXvXUanCLhSTGKA5fcRooOTLOmKOSIykFalYMakXVrXDii7SyUy545/C+oYkdMY5oYLHc\nIaYASHfBgMiyookhMA4rlu0C5xpSlu0a50X8BpmpN75Da8U4LyhygVzUbHXq6VrNMA2YCsU0nK5W\npJpZdhcYYsIgjJuDkxWvXj2kbdvZD13NOSYQpp4YJhovTpUhTkwhoowHDClNTGFAkbC6ohuhAcaY\nMFaOwTiOVOUxRuO9ISXPlHsqBWMsKU8iR/8GhqK3bgKvUtEN/cA4DhxevcYrly5xcnzCyckxJycH\nTNPc1q5gzHXDEWlfgwDY1KweVhiHAWMEbOV9g9aK5ULaYHt7u3Rdy9HRMffcfQ+Pvu99fOELX+Lz\nX3iCcZp44O3385+9/7sAxad/53f44pd+H+897370nSilODk95eDoaCvOMo4j4ziKpOCc2FKaFZeU\nEhccbbZz9DhO2HnVrmY0fZ3b3BkR2/e+YbHoWK/762poMz3uWx98mFwKp6enXL58hRADRm/aT4q2\naVkuFywXS5rGY4xcsAcHB4zTRIyRphWgiFJKRCmmSWY9VQwErBFh/42MbKmFawcHW9xBybOcqrWs\nViumKbymC3AWZ3GrhVZmbhcrCpVSFbEaYkms+jWx9cQcKcoRckFZyyoCMVJzpXWBw2FgTzUM04Qm\nEVdXsUAugrQeY0IbS5OkmlRKZqKqBGrWVJUwFVK05CLFTAwRg0KXwsnJCTUGETlxjpwmxikyDAL8\n1brSWEWZEyJ4meOniVQC2hSMqQzjyDhOtO0uzK3hApiiWa1WNM6BF5W1mqOAt5Qmp0woo/iNz3xx\nFyZ2lzukLPgfa73Yc6bEyfEh7WJJyoUSBQtUShUK3RhIWXy/UxKXL+XFvWxzX5cWdQatt14MIQRK\nURjjWbQ7+LZjzHUWrFEiuIV0Q6iVKUYCghEopRLzSPaOECOqys+c81QU4zRS5gq/lAxKQHUuemnP\na0MpkHOl6MoUhO8/9IlcE7fdcYFa85bqbIyl8S2hil31G8GbbpEEXrdyohtK2JVXX+Xx3/s9Xv7K\ny6SUiOPEOAzbZGGcgBeuf6hlS43aJHJpq1yv4G98LUFMV/ph4PDwkGsHh4IWT4mDg0MuX77Cyy+/\nvH29mBKLboG1lmEcCTHy8EMP8cHv/V5BfKbIOE2ygsuZcRyZxolSK0dHh5yersSRJyZWqxWHR0db\nQRitFOuct3QsrdW2Yt8A+awV8Ew/DHgvF+A4TWiluHjHBX7sb/z3PPyt38pv/Pp/4v/6vz/KOI3s\nLHfY2Vmyu7NL2zb0fc/JySmXr1xhHAVw4p2sEJfLfcZJjnHZKMJtWudKoYyRYzkn8+OTE37zE7+F\n1RZr5xWkuv5l3a9ZnZyQYsQ1t8hpeBZn8VVRcqbqinaVSCFSKBqigmQMyVoGKjuuJWOp2rBz+z3U\naU2NAaMqU9WcTpmsLK7tKCmLkEuWa7xf90xZo8NEUZa26+isw8WJWgstE+dsJiUYJodXDcokrKqE\nGGiteF9PtXJyekjjDSDSr9ooqlag3WwBnLb6FjH1YLLM80slTJGSQRsnUs/eYrSoL1rrpEM4C8AY\nBUULsC5kGe9tOOJ9v8YajbcOrZk7emI7utHoSEloVXKPMZQSBUinYBwHrDW4eSwRYpIEvhXkks+m\nlsw4jaCMWIlmSaKpRlrvsUqRKhQUjZ/V8HKmpImYK84plIGYRMFOKXEaK1m6n9bYOb8IdoF5KZfn\nXNJ1C/o+zhgs+TtnhE4W40DTOny7wHtPLdIqV2yEryI5C04h55tn8Fvkzqm2tArrLDkmfu3Xfo1/\n9I9+ijAFlosFi7ZjZ7lkf2+PxaKjXXiaxtF18nOhXgmtSSlZJWoj7jWb9ruzDmtkFRhS4ktfepLn\nnn9eVmAxsF4P1ArPPPscIUSef+HFub2kOT464plnnyXEyKuvvgrAcrng+PgYkFZ517Wc39/f6oVv\nWuvTNM2IclmFnZ6e8pnPfpY/+spLLBeyKOj7nvPnz9O1LcM4MAwjeZZnHIaefhgpBdZ9T06Ztm1p\nvCfEwNWrV/k/fvbn+K6/9JfY3dnhbffdO3uTV9brnstXLnN8cjLzPcUXV2nBu/ZDz+233c673vkI\nX3jiCdZ9f12Rb47NIkYAallMYWrFeYfhektOKyXXJMIZvXr1qrT/m+4bfD6dxVm8OZGrQqsKHkKu\n5JrAWaZaOM2FgzGSx0CfKtZotK143+KMw1jpgB2sA6oEVKk4s0B5hbJeKks03u1SSmKsiRojSUGn\nDUq3szWnwlgHVbEKkKtlyqLtHTNkZQhVU3QDfglOURJUonhlG4ulRVlNiBPr9dGMV3E421KSIsdC\n51tU7lGMJCqqJnIGraBdWhQF7Sqh9IzDCVoh6G3b0rDAaUO0nnESqpqedTestdQiYi/eNTjbcnhw\nilJODEm0wxpH03RoKtM0ojXsLJeYopimCbnlbKyh7dbfQrj5G5UMuefGOJDGHtvssIoT69XI7bc3\nKG3ohxVhWtO0uzMJQJgFKYrBi1aaVMrMOTeijldk0aOURlXpTKQa0bMaZ6lgjUfphloNORe6boem\nQ9D+pTCGXhZupsW7lmmYSDMreBYAfd24RRK4HEjnHUYb1sPI4eGhtHfHicPDQxRsNcWdszhvcNbQ\nNI0k8Z0durZluVyyu7vLoutYLpYsFgvarp1lAzcCCJYYpy314OT0lPVqRUyJruu4cuUqp6cr+n7Y\n8qXXfc8fPvUUMaY5ycHR8TFPPf004zByfHLM3Xfdyfve+1729/cBWK1WaMAohbeWOK8gz+/v865H\nHpmBKDJLf/vb7+M973onu7u7GCPvM4SJfhhm6caRr7z0Mp9/4ve5cvUqq/WK/b19FouOvb09Lpw/\nz6VLl3jq5JScM9eOD5imiTBTF7zzNN7Pwg7SOjJGE1MizujV++67j6N5xv9VH84WO6eNVNsbTuxN\nyeO1cnB4OPsWnlE/AAAgAElEQVT5nsVZ3JrRLRZUHUk6zdiTArqSSuFknLhK4HQsXLkSWHTQLcXN\n0GbxBGis5siv0WTaxuONnruEAWPdNhkpLFQ9L44VJSlp32tBT+9cXGJcg+lavOvI06zn7TO6W5JM\ng9IdGkMho2xDir1U3abB6lY6iVqRrRgQZTQpqVkbAkBTlCbXSuuEDidSjpBKxGhF1hlVM26ZsUZT\nyooG8KPBpExB01lLUeBKFpBcDuSSxENCaVzTstwz+KbDaSfo8iJUM2039LSJECxaifWndFRFrEZr\nI/dIoFQBlaUcZpmMzSw7UnOiHwZy1jjrxVY0Ren2asMYIgWN0/aGekWq/lIqOUlX9zqfXW9paikF\nYhjJJeOMl23RtM0S7SxVKaY0EYtYhmrM9nlK0YQoyHnQxHxzfY9bJoEba7Zgr7br+Bs/9mPcfddd\n/NRP/WOeeeYZnHViu1Yr0xSYJuF/1xva0BVZ9W1MSZqmYblc4J3HOcdtt13k/LlztG2Hc5b9vX2+\n89FHZyRlS4iRJ574Ik986Yus1mtuu3iRixcucHh4xDhNvPLq5VkMQdCUly69wquXLzNNE1ppDq5d\no20a3vued9O0jZzgVahnMcnFoLXoHd999x3k/O184pO/zTBm3vnIt3H77bcJEl0pvBezAgGZRFan\nK1588Y9QSvGuR76dt913H+fPn+eJL34RgMOjI178ylcI00TKGWctG/mJnIUqsZF/LXN3wGG3s+y+\nX3P56nUe+J84Xk++bY7Dw0MBE57FWdyisbO/Ty4DqxvUzhQVSpWKfNFAlwl9Qo3Q7QlORYdCHEdK\n1ZycDKSQaP2I0ZpaIE5Z7lvW0Cwcinmh7zTOSnHi3EyPRWGsx3cZHwvGWbJeYBRUVemLYRqLCKqY\nBUVVjC7EakSIxjjGONuFVkOmo+pKVTPrfDNmpBCNZmk8F5t9lJoTfY5QE85pFq4l50iqE5REiCPZ\nOFaqMpRAThGoON+SlaagSKmQSsEqRy5qVpMLhGnELaxgAQgoU8TMZAZ+FaNIG6pVzVuHMqcdIA5q\nKaYZF1AoBcrs7OWaXfJcsLVdQ9M4KhPaeJpGUSqsx14U4GploV+rIZJzItRC49sZic6sPJdlZEsV\nz3JlMcZTS8FoR9suKVTW45opTmBmPZOYiVPAmIasKrloqnKEDNOt30JnmwQ2s/C93T0+8L1/mYc+\n+ks89dRTMouZwV16/mCU3o5dty3rPD9iTPT9ILNmBITyB099eQZ/Gbquo+tarLHbKnZ3d5eUE8vl\nDn3fs7e7y3e85z0sFwuMNRxcO+CTj32Kl15+md2dJQ/cf/+cbD0KxZN/+CS//alPobXiPe9+F8vF\nQmQI51WcVtK6LnOLpmkamrYhrdZb1TWqVO4np6ccn5xwcHCIMYbGex544IGthvBqteLzX/wizz73\n3NzmN3jv2D93juPjY6YYSVlQq18dWil82zKOI85aDg4P+e1PfWrmZJbXtM+3x/cNPrbXxajVyvHx\n8ZsqQHMWZ/GNjp3dHULQ9OtjXFGYqmYAmsIpWO6cY6kL1a0wMXLbhdu44+I56tgzDWu6tiUMK+I0\n0DYO50UNbBh6UXKriq5bEqeeaegJoRAG0aBwXhNjmUVDoOpKTIqSDcYqjDWi2WDddjzojMI3djsH\n996wcA5lZAZdaqHOYjRKG5wTQKtUnRlcoeTKNKhtGxmcJPME02Co1VCK/IzcMljPoU0Eb9HOkqYB\nkwK7vkEbTzIFVEZrj8ajaqRlwlKwWaFLhDKJKJTXaK0wBowRqVEBymlqVShlAU2KmRCk+s6zLvvG\nDS7WBtMtUbpSjcE3CZQg5Kv1lGwkgVZoXUuZVhhlKKmArXjtKMZTcianTMkVu8mkW6ZPg1nskqKM\nJY1zs4IbnKyOOTw+wrUtzcISQyAMolXvrMNojzIZVWWBFN/AzOSWSeB1Bpdt5FC11jhrOXf+3Nx6\nULNQy4bxXDBVb2VWgVn7nK+mj2/DbGbTs0Laar3ebrNpW9/4PM89/wLHxyfs7e2ilFjfNY2fOeCO\nC+fPc9+997JYLABY9yuefvppfuu3H2NnZ8nDDz6I824rp1pqIYU0K5xJx8EoI5V2SnzlKy9zfHJC\nnIci3nseePAhnPdcvnyZyy+/zMHBC1w7OOD09JSY0oyqn1tASrFcLFitVqzW6y2i/fXS72a2Lwpq\nagaV1OuKdV9F/1K13jSJv2a7G17t+Ph41pI+i7O4NcNaR60eXQ1WMFaYlEkVjDIoHJQE2dCayh17\ne5zzmilGvK/sLi1mZ5+uvQM3i6BMs4VlruBcKwXD6ph+dUItYqjRNFJc9H1PKoWQErZZkouIR9Uq\niTbHTC0wDRN9HESKVLPVErdOYZ0RsFitOK/l+yrjMGM9TefFsnRm7KQCSglbRiuNMx6l5uJqi281\n8/3U0Vc48AqMRueKaTtcgdE2NMqgdYvOBTcboxgGGiP+EG7jpW4F56+1FBhaK4yWBJ6wRN1QjSKh\nGWMlpkzMCpSnkMlArpFUK6lkShxRqlJzwOpKzXFO9JEQZ+13bWhtpZ8SyWkmXSFPuJlJo7Tw5LdE\n3BzRpWJKIZQJ6wTkZ1ym8Q6tHc4BfSYXRWsaGt+gS0XnirdCq6ulygwcDcpS38AP/JZJ4FTxnvaN\n3zrwQOX8ufNy8mk9t2OrwPY3qWJuZ6BuoMPX13x5zTdKaUFVz+ptW1lWJVzqnDMZoUOFkHn5lVd4\n+dIlUGrmeQsiPMbEp3/3M3z+iSdkvtw25JTo2pbT1YrHPvU7nJ6ccvHiRdq2xXuP9x5jLZTCyfqE\n4/WarBS26ThcrdGHR5w7d449a1mv1xyfnPDUs8/y0ksvc3h0dN3ghM2+qNmrWH5+znve/va3sb+/\nz2Of/rQoqanXCthtD0cpWx10vblwU9py5r8m1E2e6A1iNQu8nMVZ3KoRkyiuaddgbIC5U2aQblqM\nhaQqrhRuv7jgzr0FS5cZsqJPEyYWjCq07R66in/3zk4Lu/sUpbGuxShIzQ7+rvPouWXceE/fr7l2\nWCgoQix0O+fQ1olE6dADGqUNMSTiXBgIWDaS0jBjijT90DOOgqVRVIwWpbCxH1lNp2gndLGYM1VB\nCVVm9daKZgQG65woWTYNFcERWWOBSsiF2CnyNBGGNbfv38H+cskUpa1tlcZaRdGWrBzVGLTxAvBS\nFadBUQQhTwUlCptKbf7fioKcjRs1C1AJpZLolFOo1VKJQMboijMCPtM1oypMw5pSotishpHG76CV\nI02RkCPOL0m+I+ZAjRO1BJwx+HZBtIqgKtY25FooVHTIog+gK5WJWCKLZgfXeJSOWFuFeJgqxAlX\nIqWphOmIeKoxuSUZKGWNmfqbnn+3TgJHCVqxitzoxkmsW3RYa0W7e55PXG/b3pBoNvBnXi/PXP+L\nMmujg7TqjdYyE56pZUa7LdoawG8cb0oRmleFGBOhRqwxrPs1FTH8uI7WVnzllVe5enSENRbrLMvF\nEq0Nu7s77J/bZ2e5RGvNe9/7Xi5euEDKmb7veeWVV3j+hRe5fPky40xX27yXjSKR7KMsXkTfV3jb\n1ll2d3d56eVL85jhtQfixrSsZaiz3UZkEW88tl97BF8/bvzNDX+pFEdHRwzDeNO/PIuzeKvHMAxE\nVSlNgymJcQyYCiiheK76HmMrXmf2dz2dzXgVyCqjGk3j5tb1dAp6NmTSGW09tShiGog10RrD+e4C\nRsM0rinTms4obttrqMpQkHlyqZFuuUDtd2wcwKZJNCYWiyXWaIZhTcwB7yUBj9Mg986qGIeB1gsu\naN0PpALrUQqJZrFgPfSMMTOlwjQFYgxiTlJgnRIqVGKs0r5OlTBlfFvwjZXxW86M165yzZ/ilZkf\nisYYWufonEO1DrvcxSkNtdBoI1VySWhV0FRkj8UoxGJFUrbxszWyxtaCn+VgN6PTUsSYxFDwRhPi\nyDCtUCrjjCMrJDmUSutEHraYTINlkVu60pISwifXrah9Zk9NEFKC1pFqYSpibBKT5BytNc44dFbE\nKXP5QLqfS2dR2oKpuMU51nUUxbnqyFVJp4CI0zeXyr5lEvgG0JxT2lq7aW04ODyka1sBisFWUxj1\nBugpeJ3f1Vmn9roq2mY2vflea0mEW7MPrWZzeuTim23fahXFtKKAjcb5JhHOX1POrNb93FmAK1wD\nYG93lwe/5Vv4lvvv54477+CZZ57l8c99jqefeZaDgwOmIOInW7T3RrlAzVapm47B9qDV7Wtfu3bA\nR3/pXwuifwNiK2X7XK894JunULNWu9q22W48en+6GXbdLqS0UhyfnHB8csy9f4pnOIuzeCvFpUuv\nkjpHs/T4xpGs0KpKERDYybjGt5pzi0zTKUo+lRGZBozGW0PVQnmFSqkFRabWKAIoMbJsPI3VlLTG\nGI1VSXSzqXRWk0oiV5jSQIqJKfWy2K4btkoS4ZG6T1QiEOOdxYG0iRee3cUOMURWqrD0HqMMS9UI\nWnqn4XB1QrfwdB6GKZJyIWVHTA0ZARzFkjHOkxGlM6UNFU057XFJ9NWnaWLsB+LxBK4la83pFEiD\nAPhyyqxyIRpo2lZc1VoB7RkNi2WL1UgSNwqjFTsGdozBOIvWZkahW4xxGGtljGhaXGNptcbrSmPE\nHS6XiJ+fO9eM1qJAZ7QlREGk56lnv2lYeEeYBsgJo2AYhVa8Yz1D1WiESdQ7TfTi615F3gdtDKFC\nmgqnU8E3LeuiRImtQkmF0xhp7QJrWmJSxJopNdG5m59/b3ICr1/z5U3TulZqntfmbaLKMfLsM8/M\nfMLZb1rNLfOvHdNunohNa/2GtzpHuf6elSQn+Z4ZWSiJ1858ZmMMea66v/oV5Mm/NrndCEfYdAzU\n/N6tMYQYuXzlCr/yH36VECJ934v7WUrbGbSeRVPqxj50w8Eu1wVvNoI1VfRT/3/23uvZsvu68/v8\n4g4n3dC3Exo5kkgEQAQSzKQ4EmXJVU4venC5XHqdKltV8w+Mq1wuv6hclh8sW2VJLs9Q4zHNsWRT\nQ4qURFFMGBHMoEASGejcN5xzdvglP/z2OX0bRJMECZCg3avq9u2bzt5nn31+67fW+ob85hhMUsTA\nPQSusGC94nKly1dHiisr5zcqmuUy+6lfi2vxKxpZWlPQe9AaVC0JLhACJJnHbl6BMJHJrEbriDGW\nGCr6wQVRa0Vd1/jgcK7PCl8DjWlvfw+zMWNSFcM6FJGCXD0P7/NF22UEs1QYLYihy3TQ3g2qX47g\nIgd7F/AhIpVkNK6zGIrRFNaQ+hbf9xASvXNYZSiUxvmAJpG6Ayg1BEflEwqN0IqgDD4GfAwkpfEp\n0nZLVN8ijaGuJ5QbRyixhBhZdg1yR4OQhBRxwWeluhgpipKu7Vl0ns5HitIM61PWQ+/aeVaAS7Ds\nmoz0dj17rqWWWWg6Y5VkBrNJjS1UNkARxcA8MigZsUogVabvjuoqM5iIVGWJ1ZqIwJEwRQmFJRSK\nYBR6bKmVRBIZx0ShDdZYfMy+4VlGNw55IdH7nj74LEDT97TOYdJJEBqEpixKlk3BbiMJQmDqKWKh\n6JtFFgNykdpcPYP/hAT+GlnuxyzgbyagOOvyZnpWHIxKzp4/z/7+Pj74ofJk8OJenbS44nTzd/Is\nPYmrnaxYJ+7V3FwIgVZZ6F4NyXNVnb9WP/mql2EAyB1udV8+an7hk3Ocv3CBGPLue610Jq4cCoQQ\ncuE/gMzWBi6r85NXJt00VO1573MlJuCNS8mvHVdruy+XS+YH1xL4tfjVjUom8B1i2WLqgiQr2qFl\nq1OiFJEiJsZlweZsgmaB0AplDaWeYLXKPGghseUIaQu0KZDagLEkKZmOx0gJTd9hlUQQCa4npWEz\nkEAZg0CShEQokFYiVEdCoU2F98Oq5ANKS7QpMEahVNbndrInK44LkveEEJEGSJHd3QtZu90a9ue7\nWGcpZYUxFiMA3xH6rGVelRVTqRDVGG1tFoORBiMMreuRA1VWGkWUhv1ln9H1waGNYLIxZjM6SmNw\nIc+0vXOABTYRIlGPxvR9xUoJspIKI7L7aiIxny+JSbBYLtE6q6DN58ucJ0TAR49zHt9HEgKtswul\nMQJjsj2pMVliVmuNUxXSWAprqArD1nSM1RYhEsYYNmezbKUaE0orRkYx0iqr3MWAEolKK4iBZddx\n+y0BF+JaeW0ZK/ZCoI0dZVQszzd8+6mn2V+cySY2xc/oB/5aCfn1L/ZXJRK9/kcakNpK53nK9556\natDaZl0pZ+OZocqWh5NpbnVf2QC+MlYydvlY6Uc+K6UuV7Yr96+rpeur7GYOdyRSTEPNn6/QqvVv\njFnP3+Uwg19V36uW+WpAEFNat7U5lOjToXO74pivfbZvWvy4V79tWpbLqwM0rsW1eKvHqCpIsaPr\ne3RpcakAAVokjG+pcEwKOLV1Ap0STdNSTkymTWmBtAbvIk1MdCG3oKPOaHQnJHY8QRhYNAf4vsMo\nRVmUAzBrmOkqg1WWtu9BShj4zjEJIgKpLFrmta2sM4PGFIbCGkL0OLcgRJjUU2SICBfpu47o85rj\neocQehC5MsRe45NEDTYbAoVCoqWkNpY4iLrUNkuutu0BDoW2llGVHcKQASMN08oiUsF86ZChJTlB\nIRxTXYMexpgWyqLMQjGhZzYp2Z93QF6XbVkO3udZ0jr4FmsLxmODtSVFWZGSoHd9tjGOgS74/BxD\nRAg9FGYQvBuU1RJSQNMv6XuP83l8m1IcErwZOp8KaS3aVihTILRBFApTaIy12da0HjEZ1ZSmQIqS\n0lqESaiBT15IgakFhYEyCCba8/0XT9MfvIyUMUvdXiXe0Bb6T5o6/zwPJgaUd4oZeTjf2+dzn/0c\nFy9cXLeNf/QBeH17h9cEV2ced11VpJSYTCacOHGCr3/963kefZX4cVX46mer6n5VFV/WZV9pnsth\nVxIvV+Cr53mogn691/yNrLh/0mt+xTkO4J5VR2DRLOnc1a/htbgWb/U4cfIEfbvH2bNnUdJmjwOX\nqIoij55SohxpRuMK53pUSpiuZSQzSDQs5ozLiiQymMy3HT7leWxIeUM/N1mz3NYT5vNdmpgrbyU1\nSkiIgRjmpBhxrkf3I/ouM0+sLNG6GBwGJdrYXAGrbFucYks1muC8Y6+Zo6KgEJqyHuXn0juiUDjf\nkzoPwuCUJklNFDKLxShFtBahBE3o8a7FGo1NBYHAwh+QkEzrDbRStKFHioiSUBiDkRUGhy3K7DMe\nHU0zpyorBFBZQ2U1XbdApUDsD5ChYTbJstRohRqSatc72sYwGo2IaUDOpx6hFIUxTEZbBK3ofKDr\nsx68MQVam0wh8z0QsUYjdQZMp37BoD2LQGa5aakzZc8Fmj7gos/tfp3V6KJ3dL7lYH+Pl1zIsqhC\nooxBGztgi7LKZR0dymR6dOkVo6i4uL+fsQUkmra56v33MyTwN66i/pGHWXXsV/zkQ5khxUjyASEl\noev5xpNf528///nBqSa+RgJ/fYdeHXcl7SnFyqlHMhqNuOuOO6jqmvFolKvg++/na08+Se+6H3/s\n1Rwd1rPqyychLj9tkXVzV37ZYnjOrJL4GxBvVuL+0dn5lUdKeacyzOkjYkDH9z5LxV6La/GrGvOD\nfWLfQMp+1Em2oCJe9AjhESphK0VRWlJyGFOQZPYHFwh8t8TqCqsNBZGu7/De5U6glPiUaEVBHxIR\nhU81Qhh8VLiQmS+jekRZlDjXEWjpvaMXjn7wWBgXJvO4U8SHFpUUAk2KEhESqpL0MjL3jrKqaXxA\nxI7Wd3RtTxuhqDdBalLwaNtTmEhhFc71EBPaaMQAPJLSoq0lyDyvdsKDEDRhCT7R+iV4mJkNUkio\n5LEiUhBRhWUZDF4I2qQprKUTia4LWD2iKgx98IhCEqRFaEWKPXiPSAbvegQ9wS2QSqNFxvnkkWIe\nI5pKE5MlRIMQCqOzlHYW+XIoJagKgzXZ3KT1fZ7Th4AUmVGkpGTedrQu0PRukF0FKT1uOSd6h5Qm\nC8MoRUhi2DAI6CUueHy7JHQNLy8iTgaa5QEcOHaqGdPZFihFCgL/Y2bTbyyITbxWkvgxM3Ou3A6s\nP68SVhoQ4HGw0FQK7xynz5zh43/2Z1y6dOkq1fdPOs/X/n1xSCpvTUlLif39fb71ne+wc+QIQuTZ\nys6RI4xGNf1ud7nKHJL1WgBlmHmzAptdZf69uhhCynU1rozO0rGvUj77ueM1j/36a/g1hOCn+NOE\nWI81VtdGCIFzjueef+51HvtaXIu3UCTPqBxl9SxjKStN7D1S9CgZGI8t080pVV2A8zjnOC+XpBCw\n2hKUI6QlIy1o+oY2LknRU5oSow0xCprB2KIoSoysqWydsTEqu4ZJoYlR41yg6wWqVCSyy5i0hlb2\nGciVEoUtQSka12dQq9VUaBQGVWTq2W6zTwoCqywojUtQCI3RJUknpBKURZaubpqAIPts912bbZsp\nKW05MHc0OxubxOApi9zFXJAoTNa9EClLhUprc61CRmhLbWn7FiU184M9uq5hMp5gfRwey9J3iZE0\nmLhExkjwPYnEuDTEFGibOdqWlEVF5xqatkUqhewTCJUxAyhaNGKg1SYC2khCVVAWOm9IilNYrYCE\nlpk5oI1hsq2xVY3QlhgFUilsWeIJNE1D1zmci6QoSCFTCnvXDjruDW07p+uWLFxBE1v6bo5c9rgL\nS7puiSjSAHaTV739fqoELq74z49LJm80HEqsq/EUI2lInv/6X/0ZX/jCF2iahnUGuSIxXYG6++nP\nVEoKY9ZANcjJtOs69vb2mM/nazW4M2fODDdovrgxDNrFQ3JaV6iHWubycMX9qkiky1QtVgj3MLxB\n3zgk/xvyMMO/WX3tpxicpKy3vvJeDzGsde0vXLjwhpzTtbgWv4zY2NxgczQbKGNLMIku9pSFYFIo\ndmrFTm0oi4JCelLsEHaM8x3RWEwxwitLh8RLjbAVSXmaGGlclxkkQaCRlFgQAu27zGlOKTt0iUBs\nHaFZ0hxcQtegjcEKzWhco1SmjDrX04QFIkBRVBihM/+5D0gkKSRidNSmQFpFCgmIGXXtOuoiI+Gj\nVoyrGiElVuu1k+O4rgdHwyzfunewR2ELiqJEiExZ650jiYBWBqM0ZgCMhWCx2hABIxIRT6EESvUE\n46ltgTHgXJPFqWRiPN1AKcWoGhNDn5UnhUJpxbJtqKoSpTRGS3xIGC0wRhFSR4oua3qkMDxmpgWn\nFBBB4EVBH3PXQrctMQlChB5JqwxCGXxSmHKMF5ZFH4nCYOoRwWpUUVDYCltUSGVQQrOxpVBGZfCg\nlkgFSsLi0nlaGny34JXv/oAnz3wF7yO6yJBr5M+YwN8wCtiPO8bq86tzQMziKSuN8JASr7zyMh//\nF/8bn/jEJzh77iwhuLwL9eFqRe1rU7pe3cpeJdkYcZE1R1pKSV3XbG1u4ryn6zqC92uzD+9cNgGJ\nWQpV6XyhQwjrgx8WQvnJs2JxGSwnFVEmMh/lqpy4nzrEq6/BzxGvd4hy2Cd8VYH7kGVeryXwa/Gr\nHOdPv0Rb7oMQdMGRlMdoyfUnj/Hog/eSlvukvQsURjMyY1RRoIyg79rckYsQG89if4kErCoH4aU+\nm2RIgSkcrm8QLqC1QUfDslkMgNsWFyJd3+K9pxaCabVNEiBLiVZmvUbVtmbRzGm7ZkCnGyCQtKZz\nHkHK3OiUkdNSCSgEG+NNUkxIlQG0Y1tSGU2IkaIo6FW2M81dAbI+ugChBEILvArZ81pIpFaUZgQk\n2qZBmzFSaoiSsqxQWuPaBc57RnXuQqAC1hZrq2fnPa7vQWa9dqTC6Jplv08iYI1FF2V2HBOCJEGb\nrDppjKH3krZtUTKDkmNKTMZjhMiW1dYaJqMaiCyaOdovkCIbTnnfZ66+FNRFhRYl867HL1uELhHd\nFAK0UbKIkpA0PipCUriYSFkLNgMYtcYWJePpFuWsYFSovCHqu2zy4iJWpYwLuEr8UoVccrv4NZLB\nQIlKMZGE5Py583z+b/6GT//Vp/n7L32RM2fPErzHGJPbHmqgUEmxznUCcRkMptSadpUOt7EPVcer\n5BZjzDrEQ7IJPs9vvHMorbEmyw+SEmVREGIgxJC9X2NcU78Ykvzh4wiunshXCnKr85NDO/1nva6/\niEjDk/mJ2wKR/dfjoIZkrOXkiZM8+OAD/PZv//Yv4EyvxbV4c6LrGlSKuM4RU6AoDdddt80HH7yH\nt99+I4uL53l69wxnXjrHpRCwRtCrLPtstWZUjTAym3pYqfBZbAJinrMWRqELhxmNIUZ619G5li54\nlBKIkBUeO9fS9x1VUUOfCwljLTJKCmWJIiK8AEbIJOj2GpJ2FGWFU4llu0QbjVGKJBJCCxbLg6xH\nXitC8Li+IUaP7LOV6EqTIsRIEinLQReZFx5ITLam2a1MOZKFJBIpADrjm7Q0lOMiz4adw+tAVJBk\nQFkBOhKEwxQKISMudTnxiaGwUZGkAnutyzoX0lDaAh88UYAapGK1MQR6UgSfJD4plK1RxuTupjQk\nqXEhK+AJZXFB4UOi7RVBmKxZbiVJZFEcqTQtGhUNjV8ADomimQeWvRvyT8B5j0BkR7KuQQ40QCET\nQgp6FAd71+MOxoRJxbJp0WWFUgovWrQSeHd1B8hfrhLbayVvsg7vcj7nhWef40tf/jJ//4Uv8LWv\nPcmFixdwwaGVzDMnkYUSZBQEDiG5E4gBaRlClu9bUbJ65y5Xoysk9/D/RFZyWwmghBBo2nad8H0I\ntG27FlDQWhNjwFrDxsYGUgrmiwXeeYQYjN191iKPIawTXTq8o1rvOHKiJ+Udl1JqOJ9fVDp+fZGG\n0+WnSOIikSuAuua+++7jox/9NR588EHuvPNOtra3f0FnfC2uxRsfk9kElRyjyjKuxtx20ynefttJ\nrpuU7D33Q3yzJOzvcensKxgBnsgltXKsUlRFjRnQ5KUpsEpjpCJPaEH1AhMUWmZZVCVLVCURdRYJ\n8UKQQsKk5+UAACAASURBVIDpCBMjMQmWQTEdTVBGZ/vgJIgR+r4bNtFZocxog4wCuo4qKgplqG2F\np6ddLKiipLYFNsSsQEmiDx4/yE0bY3ExV+zRt8yKLaSWeJcNQ8blmK5vKcsSqeTQQs/yq0JIJAov\nI9JKQBBVNhyRMj82JFIKjEcli2aRv0YSQofUCaUTLvZIoWmcpy5LymrE7sHe2kwGofAo+ghLF4C8\nJhtj0SL7j0chWPae0ha5uyoUHolLWZ89qkSbPCopmtAjpGZzOkEpzbzpkLbAu8B82ZCQuJBd2pQu\niLGnd47CGHoZkUIN11Lldn9KHOt75LJD2AJZFmzefB0jaZAu0DWeC3tvcS30w6jrixcu8qUvfpHP\nfe6veeIrX+GHzzyLdw49SOJButzC9j63dgaU4cpO1McsUI/IkqFxBSZLmZgv4Irq+Eq1l5y4V/Pt\nFZDtcPs5pZRbOS5726YUubR7Cedym11KmRXPRNZvr6u8o4oh3zw5sXv6vicOLfPVccXwtzHGy23n\nQYDlLRuvosLFQRdeCMn29hZ33nEn73rPu3n3u9/Ngw89xHQ6JcWAlOpKwZlrcS1+xWJja8ZkJLjt\n+uu57boTHK8LjGsw8z0unj/N/t4eU2Nw4ylN1xH6nkIqkgSSxLWBLvakGNHaDuucXnsV+Bhp3WDe\nAYBEpNz6rco8s40hYI2mKktKYyhFYq/tMD6bO5EixIQLCastRVVk46Why7cRAiJFlBAYKQlJUVhJ\naTNXvHctXQjEqNDCEgfL4iC4LCEtoPWeGDw+BWJKSJfNQZZ9i5SSqqjoXUffd5S2xCidC6yUDaBi\nzAC1cVkO3hHQuZ5CSZqUqMoyr5ckfHC07ZzCVozHG3gf6FzHQbskAEpnLJMZRFo670HmxxRK0juP\n89mUSussguPjIKYlIshESIIoJMJKlFQZDCcEEUhGsfSeZBXaKFLfEUJ+jrrPtqDj0ZjOOXb394dz\nGRNiFuhCyCwgw7AtkZLeBy7sXeLs+ZcxPmGCxOopy/AaUtdD/NIT+GHt8YO9ff7kj/+YT33qL3np\nxZdYzOeD96taJ0YX3doz2/uMQC8Lw+233srJkyf4xje/ydlz5y+DywZvbTGYeqx1zZVaI7zXdKih\n1b2ipb2ae334+4d52875y+16ISAm+q4HEtFYiLldkmLeeEitKMtyXWVbY7Lmbgh0fU9ZlXRdn2+m\n1ebhdVTibyDRj+EC5MdNK531fAQp1RVfx5itC6213HLLzTz+7nfz/ve/n9tvv53j152kHtV5YTqs\nv/6Gn+y1uBa/uLjp5lu4620nODaq2bGaajknLRt2X34R4x3jFPHKsiskQhqqwjKNl02NECBMFoeS\nSh8apWUXLac0ejQBWI/Y4gAAbVPAhUwXS11ANR1aLjFpH2sLqrJGK00InjCM9oqiogrZAEorw9iW\ntCmiJYgUKIfzSDJBVeCFYNknPNlhLEhFlBYfA13XUtUjApHO9SSdMEWB0SoLwkuJLRUyZu8KLeUw\nF5dEH1j2DdFHtMqjSSmzmqaI4Hxui+MDXmT+vIgRESOVMey3SxbNgtmxCaFvcc5hlEYriZF2cKRM\nlNZSWJuFt2JeY/uuR6is7RFCXGOYYgrDeWSvCyUVdVURZU7+wiik0bRty4WDfZQ2TCdTYhLYvico\niTUWa1cukD0CmI5GWVEuRFLKQOmVi7JK0KVE5zp8r7C2ZOfodUxNSbM759LugtZfvb/5S03gKWU9\nczUgGf/u85/nf/zDP2S5bJDDORul8c5RWMt1J0/y4pmXqEcVN914I7fdehu33XYLb3/bXWxsbDCf\nz/mv/5v/lqZp13QvpWQ2s9f5wgMDME6sk/cVLXUhEOIynSyGbCGqlESrvCnIXtkCJRViZZ4SV2A1\nsR7uxwTeu8wtT6wdcYTMaOwEg0Z5dldb+XeHEPCDkL4eKvnXZxryOl6D4XOmaufjhGGjIsUhOh2X\nOyVCSoSQuZsQAolEXdfcdtutPPzwwzz66CM89uhjHD9+nGo0ypslEn5wTssof0mK+W+v5e9r8asa\n/8FvfxStGvzeJZozp1mcfQWxewF36Twbky1UgrP7u7impY8CawyFzFVxCA4QlGWNUpK+b4bCQ+b5\nqetza52Vm1ZGJAuhEEqhjCWpAqpq6EomUuoQdAPbwxEJeY3SAu8dy0XDxXkaJFgli4XHJ5/Xr5iY\nTDR1qSm0Zmd7C6MkXTsnxUBhDIUwbKgCqQQhCuYeQJJkwdJBqSWFsUip8CkhyZ7eUsjBGzuhVQYd\nW51n1CmEgaMeiLGn844UI0ZLBAbvoLJj+r7H+0BVGjbGW0gM0WW3M231MPoc1pSUQcYoUFFgCZRK\nILUhGoXRmul4Qtf3LLtuXTxJPEYatFYUxqKtoY+Z5912jsIWaF1kxU+p6HpP4xwhJqwpsvOYEAM/\nPhcqWfEubxSEEDmnBU/fO1CGpA3tkEMm0xlaG7ZHEy7Zi5y59AzNWxnEJmU2j/cu8J1vf4cLFy5i\ntMb1Lre/tSb4wNaRLX7j1z7K9MiMm26+gVOnTjKbzvJOUitiTHQbG/zn/9l/ysHBAS++9BJf+coT\nVPWIW26+hfPnz/OZv/rsGuUdYyLEtJYt7ft+sN6UCJEr6RADSLJV3fC1kgoxoNTXHPSrINPUSoRl\nqFzX4LTBxWz187Zp1xS0uqoIMQzGLOIy9WqwDP1Z49VgvVVVLQ79fOX9bXS+LVags8OYACEEMib6\nkLmlJ48f47777+NDH/oQ995zDzfeeCPbR45QFEU+Z7nSoxcD6DAOp5EygvR1Rtu2GdE6PI/DVL4f\nUau7FtfiTY4b2WXWJZ558UXOP/2PyBgxJFoMF1tH6zzLLuDDYCtpDV17KYNfU0QZiyoFsjDELmRb\nSvJ4SdUVlRAUqadpG5xzCKkwRYkShhQ6YopIDAhJUoJEJIoCqTVaG6TWuaImZdGrocuYyFSzeFQz\nDz1d29D3HVIKmhSYd0sWFzPg1PUNMWTskO4DW6mirsYgoGl6YhRMpmMSiqKNlJWnqsrBRlNhB6W5\nAAPoN3tXdD4hkkdJifeJ4DPjSKSsyg4auVr/SAQStqww1qKkxLqstGYBq3WWlh2Mm2KMNN0S53uW\nzWK9jhWmQAjNqKwyQFAYrM7r674IOOfRUmFVyslcaIpihKLHxzhIqCpcjLTOM583uJhpcUJKtBAo\nBCHGQfI2t8i11ox0NocRQ3dA6YSxBcIURBeyKI8pqTcrbjl5khdMzbd+8Dz77eKq998vt4Uusra5\nlhptDPNFvtB9ly0zu76j67ohiQluu/VW7n3gbqZbU8yAIFxVsTFGxuMRH/uNXyelyN7eHh/7Jx9l\nMplRlGP+4R++xv7+HAHMlwuefvr7aJ1b8yEEqqpcz9mbRb5gWY88J5lcdV8WIVkzoFM2R3n184Ih\nAa5oZIJ1VYvIAJWYEml4XCEEo9EoywHOuzVifWVn+nrjan+x3nAcrnsPjQNWf7vabKySeCJTMJRS\nHD26w8233MoDDz7I448/ztve/jZ2juygtbqi26+NxjuPj1k/eKXTvDp6BvMlxGvZmV4lfu/3fg9g\nzdWv65rNzU12dnbY2dlhY2ODra2tdZJfvWbW2vVHURRYa9eJ/1pci581nvrbz2KbnnPnTlOPN0lS\nMe9aDvpIdB2mHFEd2WYyn3Pp0gVE22BCQugCszGj3jrG3EcYTTl3+gwXm31sWXP0yAkW8wXd7hlG\nvsFqg3OeykpKm/W0NYngHVImvO8wyiKVpPeS2DlKaSilIcXEcrkgtkusUiiddbx1TOBALhxJaMaT\nLaTK3gsuuOw7ITWdrPEpa4S71OGlpYlZUhSjaRYL2kv7CDQhdQgyQ6iqxxhriURW3g5aKpSSVEVJ\nVZSQsq93DJGUBEVRUlWaqrBYrYfKuIOUsGUWo5l7jxaSZCfE1NMhCEkghQaVPcBNoZloy8H8gL2D\ng5wf6hFlNcLqjDhv+o6UsgukkLkyjiF3B3IL3BOTzGBlDyHkjrHUecZuhKTUUCud1xOlcM7R9x3T\njS304ObmgkdIhVYaZVOefQ8KospagjIoFC4KXO9YtC0X6wl7iwUL77i4uLpa5S8dhZ5iBJX3W0e2\nt7HW4nu3XnjzDBvarmN3f5fz58+zd3BpmEHDaFSztbmFLSwpgDJZY3hcV7ztrtvRpsR5eN/738vd\nd7+Nqq74wQ9+yFNPfZeiKPncX/81L7/8MtefOsWdd9zBc889z//9qU8NN9vlXZ3WOusHD9Vw8CED\nsNaOnkPWWn/K/EOVssfvKrFJkVvnMcUrAGzGGiaTMYvFkq7v123rw+CwNyJWj7uitq156sMx0gBI\nizG3tADKsmRzY5Nbb72VB95xH+9933u58eZbOXHyJOPJJIvYkNZKcpc7Dhm/MPTn81xvAK+txhWv\nl5v+5JNPZrEIrekHLXpjDLPZLCs7HbpOSimKomA0GjGdTtna2mJjY4PRaMRkMkEPCwTkuf1kMmFj\nY4PJZMJsNmM6zRvFw7G+Xteq/GsB6KZlv++I1uB0rjDbFEhWM5psYsuKzgfsqML0FfgepStsPUaU\nFQHBdHOH506fZ9713HDHPdxw8+3sLzp++MQTXLjQcmxrk77zjMYb3P32e5hOpnzj60+ye+40W5vb\nGClZHuwxqgp06hgbzXg0g6qmJ3fVdKEpUoGSEqMVG7NNtFLM9/dQdChhmAiDCBlfpGTWWpdIutiB\nkuhSs5zCwmQsjzWGGCK6PFhv+LuuJYWAlIKUBIs+0Caf9TOCRyQGelxBXddE71kuDgYGjqDrQFtB\nWWjGo4rJOFfcKcXsXV6WxBgojMKYmr6HRddhtKIuC6qyQJuByiYMxaxE1jOcy85me31CO5db9j7T\nvepSURWGqAJ9crg+C9NYrQkoYgAhDUYVg7y2IgmJ6zsEhtKU+JjoXcBYSzGyebaeEsiESnm274aW\nurRF3izEDIaLQiCSopKWvdNn2b14keACS+dpQ6BPb1UamRAYWxCdRyjF0ePHqesRi3iwpogFAsTM\nc/zWd7+NMImNjQlVWVLXFa7vmB8coLWmqiqqqqIsS8oyzyO8dyhZMB1nR5gYApvTKY889AAA73r0\nnexeusT29jYnT57gX/3v/5rnnnuOzc0Nzp49x7PPPcd4POLozg7T6ZTvfPcplosF2hiM1viQVcVW\nKPjLz21I4illd6ABTLfmRJPAhwF9qtmYbRBj5GA+Xz/Km1EhHq7mV+35y52Eywh7KQTHjh7lpptu\n4r577+HRRx7hgYce5PiJE9SjUZ7HCZlpfKsWuw/rUYHSg/EMAy6AQwn9p1Fvu0pIlV2QVudpjKHv\nO5bNksVigTF62Czl5+acW28EVxuhwyBEay3GGMqypKoqRqPRGmB42DpWqezZvLW1xZEjR9jc3GR7\ne5uNjQ2svWz3J6VcuxWtPrTW16r9/49GWRQ0NqtrtS77PauqZGuyQ1mPM8e5aRFSY8eZo62VpJrM\nKKYbnL64x9nvP02yhnc89ADTzR2+/8wzfOOb36FtG6azGqEEN996Mw/cfz+z6ZS//tzf8NJLzzPb\n2GThPOdOv0xRFBy4yObGhBtuv4WN6Yynvvtdzrz0HJPRlNIa+rZhNtng+PYxznU9i/kewR+gbSCF\nltY5tFRE76iKktlokt87LBERAoJS5spTFwaloW0dVWWyzXNKqFFNjIkICCFxWtFK6Pue3vWs1C0y\nWyXhBIhygiBLkaoZyCIDjHe952DZwqLBuYzUn4zzxjtf+xJFQqYEdNgmUlif5U6NoSqLrEJa1GAH\nfQ/XU+gMdosiI/j7IFk6ibETCjtBqXyWneuHGX6RRV9kxh4kBCElpJWU1jKqanof6J0nKYkXCS3y\nGhdj/lite877PEoQuYMctUBIKESBSRrhE92y5Zw/T5MS+8uGxVuWBw4gJD6BjHDsxEnG0wl7+7tr\n9HWMkcIY2rbliX/4B86eP8NkUjObzdje2mQ6nTKdTphMJtRVTVEMGrtSUpUlVVVjTMF4PKasKqQA\na3PLPqbIjTdexy03X7+mbf3Gr3+EX/+1j6KN5ctffYInn/waN914IzfecANnz53lv/uD/4FEZDad\nsru7R4h+3XbKXYPs+rOiU2VQ2CAdKkClrAGeRG4xa62ZTMZ0fcf+/gErr+43K9agvUPI8hUtT2vN\n1tYWp647xf333cOHP/whHnjgQY4c2cYYizAayLK2q83I2lt8aJGtK+vXOnB+wV/19euLrAGQW4Bl\nZYfZllp3RpRReOdRRmONHVyY8nGbpiXGMADo8mvT9/0Vm5q1FO6h1y/jLPQ6GVtrf8QXfvXz0WjE\n5uYmW1tbTKdTJpPJejSSL7ugKPL9uPpY/Y7W+lpl/ysW+64hFgUX9i7S9k2uugtLFBCICKMYF1N6\n70kKiCNOHT/JbbfcQtSav/nKNzg5nfDQo4+wdeQo/8ef/yUvvvQyJ47PaJsSazS33Hwz77jvPs6e\nPcv/82//kqefepqqNixdQ7ts0HVBMRpz/fU3cu8D93HjzdfzxFe/xjdfOseonrJx4iQ+Ro4f2eHh\n+++nbVr+4s//L9qm5+jRbc6dPocSnp2jNQcX9qhKzYnpFsqOOH/mDF3TMarLDDhbLDh+dJPKGoL3\nqNQzrgqSdwTXM60mWWQFQWENLiXa4JiHJZ3vqcoK5zzWlBS2ZNkkvM+a7kkqliLgiQQibXQYXSCN\nxUlJ23V0ztE6h/Oewgckguj8YAAz0Ij7HiEF1hQZcS4Eo1GNlNAvG1QKjEZ1VrVTCmM01iQKayis\npVIlZWEJos8sIVuAlCSpMm5HZJ/q0cRQFiVSqgzGdj6j9YVct+VjzJx4o/L4se17ut5BCFlLQ0es\nloxVhfECFcE1HW3f00pJ6z3+x8B8f+kJPHg/kN4Vx48fZ3v7CM89+8yhKok10vnZ557jpVdeZjKu\nqOqKUV0znU45sr1FXdfMZlMm4/G6/bm5ucGoHiEEaJXBbrl9Oh5mNBXlaEy7WFAOurlbmxsgLVJI\nPvKRD/DBD7xnAHJ07O3to3RGUxpr+cP/+Y/Y29tnNp3xgx8+y3w+p65H1FXF8ePH+P4Pfsh8Pker\nLJqQUgaSaK0RKj9OWZZ0bceyadbqb29mrBLEStNdSklZ1lx36iT33XsvH/nIh3nowVxpjyeTTPda\nKbasIevy8qjgcMIR4s3cewDQzhcD/x5sUaC0wuNQSlGWFSnkDV9mOPQoKfIbKWSlKO/9WgMgV8vZ\nB9h7f6g9noGMYgBYQqa1OOfWnZXDWvkrCuHqeqzGP4c19VfVflEUlGVJXdeMRiOKoljP5A//fVVV\nbG1trdv+m5ubzGYziqK44vdWuI3DH9c2Ab+42O87JIK9vT1CVHTdLtruIZRGqgqkRCqBMpbNrSPc\neNPN3HLTjdx06jqWITA9tkNSElNW2KLgg+9/FJJgVI/4wTPPQRLc+7a3gRB896nvsrG1wfs/8iFe\nOf3CILEpcK5jZ+coj7/r3cx2jvCJv/hzvvPtb1FNxpjJhKVQ3HbnnbzjHfez2NvnU3/1GU63S268\n+VZOX9inK2ecOnWK87uXOHLTDTz26COcOnaUz3z6r3hmfo6Tx2+lkfDy889z8y3XMbv1FOfPn+d7\nT30bq0u2NwvOnT1DZQtOjDc5d/4ss8mE49MNXPAczDt6EuVojIuRsq6pioJSGyyRrm0oTQkJDlyD\nrgpCjPQqJ0+pFD4IOpku3/d17pgt+sBSSHopQGRluFjWNF2LLUpakVveRVHlDmMpCNHTS5tNVAbQ\noU4BHSAte6RYUhZFHvcRsWbggZclk/GEuh4TYsILkV3YpMIDaIMWCqUMdVlgjCGESOcdnQuEmBDC\nIKWjGN6jjhajodIlpkmIANEFEgqXIkhFPZld9f77pSfwFeUqxcjW9jbXnzrFv3viq+T2Q0YU+hjW\nIDDXO85daBAXWC+SRVFQVxVFWVBXNVubG5erm/GYrY0JW1ubTCeTdcWeUqKuKhjaoxtbW1RlSWEt\n2gaUMhRlgRnVubKTgrIs+c3f/BjBe2xhufvtd2Vd3s7zhb//CmfPneP6U9dxx+13sL+/z3/5z/4Z\nAOPxiK7r8+zWaApbwMBl39/fpx/avGqglr1RsZaNHURtLv9XMNvY5NT1p3j88cd5//vfx7seexcb\nm5uv/Thv4Dn9vLG9ucWyadjb3eX8mbOMxiOUUhwczBmPR/nNWhZ0bUtMCVsUg0BEGhK+zq+ftWil\nqes6J/DgWQnerpJyWLe/LqPcvc+byRW4T0iBFAopLyf0zCnNAMXD1X3XdRwcHFzxvVfjGw633Vf/\nP7ypO9yiH41G6wQ/m82YTCaMx+MrWvrWWsbjMaPRaP1R1/W6DXktfr7YPVgyHRusqrm4t4s2MBqP\nSAouXjpNSlCNNFIV3Hv7XXzg0Uexo4ouJkKKbGzO8CkSEkgRufu2GxAJYkzcdt0OLuRZspCCf/83\nPpxb9MZw9sKFgVscOHPuHCdPnuDWm2/im9/+PpNixHsefQ/j8YRz585x/PhxHnv4ncSU+PQXP8PF\nS/vcdtd9XNq9xF6z5OSpmzi/WDCabvDQe97LPffczef//ot847mXufX2uyjrEc/88DluuO8h3vv+\nB9iaWT7/p/+KC7Hi/Y8+zvee+gEvdZK7brqJ757do3OG4zfdhpxt8o/f+RYvvHCaI9sbqODYvbDP\njTecYKOoIQTOHxxglGYkFPP5HlZJNpVl3uwjkViTxVsW7ZLgPGVR0PeeMxdfYXt7B6lLFnu7hBjZ\nmG1ipMpzeOeYVjXB6KFKzmO0g1Iwj1ln3TmPEBKtEzFlxTrX9yil6cIiA6iNICY/eK07ynKElHrg\ndAvGk1neHCSoqprZZIvClJimzaA1lSt2IWX2cFeZWhdTyowDlTEJVhmU8IiQu7RIRSIn8KKsr3r/\n/dLfxUopksxgptl0ysc+9jGWizkXLpzn4sWL7O5eom0yhUIPKL9MgdDrOcpyma3i0jBreP4FjbWG\nuqqw1lKVlvGoZnNjg63NTba2NpkNiXw6nbK5MRtEYyRlUWLLCmUMZVXl35ltIFSWLDTWoJXAecex\n40eJISKl4cSJ64dZqcR7z9mz5/gv/uk/Zblccml3l0988pMcHMzpug7nHf2AQgQG1aG0po791JHR\naFdwzy9XhhB9BpatQwg2NzZ5/PF385v/3m/y9nvuYefoDtPJlKIs3qiX9E2N3/md3+GTn/wkR3d2\nePjhh3n22Wd59tlnuefuba47dYrz586xuzuAHff3IcSMVUiBrmny5knAfD+LXQTvcK5nsVwSY2Q6\nmbBsMh93VI/XgjMrNzWt1PC9lCt1mavxFSAuxLgW5Vl1VJSSq5cqJ/8Q13S6zEe4rEW7Qr+mH6NP\nu9q4rtr9r+7aKKUwxuR7v6rWLfqqqjDGUBTFFSOAVbW/QvDPZjM2NjbW1T4cwksc6jCsvv7/c/RN\nRyM76nqMMZYQPN71WFNwZGOwo5SDkEnIuhZCKoLrqG3B0jsqY1m0LRqBVZrloqUyBkIPyQzjPijq\nkqkosdYwG5cYa+hD4JZbrh9el8jDd93K/TfcgJASFwKnL15iczLh6NYmF3b3uO/227jvtlvZ3Njg\ny098lfvffgfLruWV0y3vevR+HrznBl585ft89Ym/5brrNjh6fMZT3/0eWzszHnznPRw9doxP//Xf\n8MyLF3nk0YfZbwUvnN3jrvseo6wq9l464PH3fZCPvPddvHL6DJ/56pOMjl/PDXfczve+94+YnRF3\nPPQYVVHyj0//I98+/S3uu/d+zswXPHf2PLffcCN943j5lQtcd+IYZVHTNS0vnN9lNp0xGm3SzA+Y\nHL2eerqR5/Y+EbqWoAxSG1ICrQ1eKnyIFCZv3Of7uxA8E2WxnWCkKsSwOe9cGGbR+f2egXIaURs8\nnqqq8SEMvuGD0pzM2Ach80ZA6ZZO7JO0o42e2HtUyi31ECIhQO8jTevxEZZtwpjIrCrY1WNsJ2h7\nh9TZzaySAmU0ob06hfiXnsAhtyRXM+Tf+q3f4td+7cMoKTn9yit89Ymv8M0nn+QT/+cnmc/nw+KZ\nK/Mw8PJCCOuKQg/uMjGkTBsTEqMFLw6I5cl4hB0q9tl0ynRAHB85coQj29uMRyPGswnj6ZiyKCkK\ni1Kauq6Gyh3KskApTVnXeaHUmrKawiBWIo1hNJ2xc/wYH/8X/5K/+uznCCHStA3eZ0BCiDHzBgc1\ntlVLO1+P17hGYgWSS4fW+7Ru56bhhvIhG6tIIRF6tbgPHHateeSRh/mv/vk/5+T114O4LEoTvM+e\nwa/5+rx1qvBH3v0Y/8uf/gkf+9jH+N3f/V1+//d/nzPnzvKBD36Qx971GAcHB3z5S1/i4x//OL/+\n6/+E973vffzpn/wpCMlDD72T5559lm9961vsHNlhe3ublCJt23D+/Hmcc2xubjJfLJgfHOD6LpsK\n9IK2bbLwArmqbbtMQamqijA4040nE6azKUZIlIR+9Vog0Wtw3SFFrTW3Pg0JfbAPHDoAK75uiimD\nfwagIQJc8OS+3WC3y5X3zapDAFeOOVYV/+GZ/irZH672D7fotdZrFP+qlT+dTpnNZleg9IuiWANJ\nD3+o10ET/FWL5aJn2V5CJEFd5+cqhUUkgdWZyphintWeP3uRJ77xFMVsAgLquiJJGNUlMUWsrBDB\noX2gXiWiugAEPoZMEQueSgoCCUOkNIqDmDd8Mngsgu2tKmNupODG4zOSC4TgKTcqrvvAI9jhsd/3\nwNvQdeTC/i57Bwt2trew2tFMIu97973sbG7mNcFf4pabb+K+O49y7vwlXnzuBe69+3buueNO/uWf\nfZxjO0e57sRJvvyVr3Dq+ut5+KGHab3ky//wTZZt5F0PPMD+/j4Xdh0f/MB7eOdDj9E0LX/xmS9g\nxtdx3zsf5y//7aepj9zEre94iH/31a/QVxu844Mf5shkxtef/DrPf+3b3LF1grmwfPvZFzl+7BhH\nG+dHEwAAIABJREFUrz9C8o7nL1yia3vs1HLu0i7NcsHW1hEWTca3yCgQuiDaEWoJZVRUlCSfiC5v\nzGObaLslIQa00kiZsUrRwMFyHz+e5FGqyOM0KSVVXRO0QajM73cx0uuWvRBomgM2JmNmdUlIecNu\ntEEh6V3DYtkwP+hwrudshMUBXDy7YHtzi1FdMZ0oND3XzQTONVe9/94SCRyRZ91ZL1ZTlgWQmG5s\ncMvtt/L2O+/ks5/9HLu7u0N1YanrirbtsNYQ4wpdvOIuX5535oePg4Rnom0dbevY319w5uyFoUIv\nsSbPJ7e3ttjY2mAyG3F05wiTyYTCWibTCUeOHEEOICQpJUVRMJ1OkVJRllPKoqSs61ydGMG3vvkt\n/vhP/1cuXrzIwWKOcx4GJLaQeTF13rPyD19Lva6vy5ViKyvK1eHqLA2iMzFFnHf5V6SktJoUWSu+\nRZfW4wZbFkP3IqwR0ofnuD9XvMnSqM++8DyXDvY5cvwonXc89fTTzJuGE9ef4vh1JzkO/N0X/56D\npuH2u+7knY88wv/0R3/ELTffwgc/9EG++MUv8rd/93n+w//kP+a973kP1hq+/uST/MEf/Pe8/e67\neezRR/nUpz6FUpL3vvc9dF1PDIFnn3uO06+c5ujRoyiluXDhIru7uxkJLxVFZeiallarNTguhCwM\nYYwhpuwrv0qGK337ZdPgXMdoPMYYncdGSlHaIr9upEHcYkjiApIQeJGtKMNglrOSuV1R+bTOyXjF\nDFhv/IZ7KMa4NuZZvWzrjcOrqHKriv/VM/ZVgl8l//F4zHQ6zYDRslzP9w9X+yuK3qrtv/p8uO1/\nGDdwuPJ/K0bjE65vcU1L2SyAgEgJLbOeubVZ11sKzyuvvMLuV59A1zVJgC0LlNFUdUVhDdPxGKsV\nMiVmVY3WinI6RUuJKWx+36aElxLrPYWS2NLilg3lqMKHgJCO1s9p+55RXRH7lrbvs9BV7zKffNBS\nr+uCg25JWWrqahNrJDF6jh+ZsPXovZRFFky546YdtDUYkyhPbPEf/fZvgJQcO36M06ffw/FjRyls\nySvXn+Ttd93F9saY02fP8/Lp0+wc2WJrNuX7T3+PUye2uePmG0gJvveDZzh9+kXe9ejjNMuWS5d2\nuf/+d0A14kLX8Y6HH+Tudz6EConPffUJzNYGdz/8KGdOn2Y/SB656x4eeddjHLiOL33zG6SYuO/m\nWznz9Se51DmOb2zx7A+fzg5tWzsYXTCPB7SX9qm1ZcfU7O/torViPBqjdY0JktR1FLrK+QhPIBJl\nwnjLqBjlwqtLtF1Dt4QmRfquyeYwxqKKvIlTrsU3kYvxAq5r8ph3NEFqQyU0OpXMJhVLGqKwVNfv\n8D35PK5psbZGqYRJic3ZhN32LayFnpcOuZ5xAwiRq2xiyjev1nmWmTJlYGtzkyNHjvDMs8+wu7u3\nbhnqwRx+xRNeLTjBe7TSl4U9hFwLrKQYaZuOdtmR2OeVl0+DhNG0ztSismA8GRam0Wg49jbHjh4d\nEOQTCltgdTlsPkpGoxEuRP7NJz85VHaeEBKIodpOYr3hOHwV1t84hERfIcbTwCVc/8mKrz0kfDP4\n8d5+2628cvo0i8USpfNOMrf58zHHo/GgfOfybtOYzE1U8seov1wlJ1+9y/umxcHBnLIsmM6m7B3s\nsXewx3g6ZvvoNj7mLsYLL73EaDJmtrWJtJpnX3iehx55hMnmBhf2dolScPOttzCezdjcnGGe+i4X\n/l/q3vxHkvO88/y8R5wZeVRlnX1UF2822U1SFimRoqTVZcnyyPI1q6VEe7TWrgED1mqEHQvwXzCD\nmQH8wwK7A3gBz/w6u5gdwPCudzSzOmhSa69gWQd18exm33VXZeURx/u++8MbEVVNdstDi9LQbyO7\nMiuzMiMi34jnfZ7ne+ztce/99/H+D36AP/0//4xTa6f5x//jFxkdjAD4kz/5Eza3NvjEJz+BlIp/\n97//HwzmBjz11FP8xy//R776ta/xD3/zN1laGrK7u8vm1hYvvfgSZVWSZRmz2YzD0WEb2JqsXQuB\nk4qD3d02K3e15apzjrzyMsK9TkaoApyA3JRYIE06aCnbHlsU+2BZ1Ra4AIXwnNemf38chOfBfP4c\nqI5x+Y9aMUcLxqqq2gXxcf8C97qg32TwxxcBxzP846C94yX5xt1Pa92C9obDIf1+v70dD/LN+7z+\n9vPO9nMVQuB70TMpa4cpgQw0ZZHjjKUwlqrM2R+PKG5cwThBURaeiqQDb2upNL1eRpZ2CJQiDjxn\nO+6mhPW+JXFMVFNk0zSh2+mQdTrkpqKbdaicJR3EOOkojaWTeg/sMBGIIMDNHGkcUpQlKlAYVZHP\noBN3mMxyrPX0zMJYhA4ZVZZAC8Je1wOJA03QCbmzs0oQBhjgIx97sqaiCu64/xRJHKGE4MSZBX71\nNz+OKQ1rp08SJh5gtra+grElucs5c8+dPPSOs7z0ygXSXsIdd6/xgwsXyHXMXeceZGxg4/omP7x0\nmblTa/RWV/n2iy8RDxe4+9xDRMMhB4cjtidTlpZWWLzrLmbPf4/F9bt44PH3cGV3l9A5PvBLHyNN\nEr7xV9/kLy9e5NT8kO4dd/CdZ79OGic89sCDVGXF1sULvPTqS9x91/3EccyN61fJsgwZZWzs77EY\ndEiSDpGKmZQ7OBF6Opr1WJUojDC2wlSWfJLjphWBFIwODgh1gMkco8NDZnmOUCFoxSw2lDJAyQNe\n/tErpFHAysK9dJKAWWUYqIATonvb+fc2CODNuEWhVgio+YFZp9Ou5PcPDjDWMp16bfOqdvnyzjJH\nGWW32yVJEsq8QDrPRdTCv4ezRyXrOIwB/7wMfHY8HnsQw+6uw1y+SlIHZq18kF5cWCAMQwb9Pgvz\nQ+a7vsfuS41D8qLiypWrlGXpueI1ktu3qq0vev4tScVxutfxX4qmRC68e5DWfnGSxDEPnD2LUooX\nXnzJa5kfK8vinN+HMCSIQnzxs0nl/9YtucWv3xjBf8YJOAf7fsG2srzE3u4OURgwNzdgYTgPOJx1\njA4OmJ+bI8u67O8fsLe/z3BxHiEd165fYbg4JO4knuajFDe2NjmcToizlHE+ZXe0z/LJVXQUUDlD\nkiQgvRTk2XMPMJvm7I4OOHv2LB/88If5+l88w/WN65x/+CHOnz/LdDplc3OTP/qjP+L8Q+d4+unf\n4rvf/Q7//J//C37r6adZWlrixo0bOOv4m2//Ddev3+Ch8w8RRREXLlzAOcf8/Dw7u7uUpmJ8eEg1\nKxDakpclBosBpgeHbWAWQhAnMXGcIKRfxE4m3oYwyzLKsqAsK9KOb/tYaz3VRQhQmkC7m3jytgnQ\nDWDPWBz1axw1Fc+X/h0OKVS7KHWu6fP71+e5V1Rsp80xih4clfWbQN4o/jVtseO/b5D8Hozqy/gN\nh/94uV5K2T5/8uRJhsMh3W633b+3ahwWhrQTUmLJ85I4igmUwlRlrY1Qonx2glKaSPi+uBKCylRe\nicsaqskhh0XOTO15DW0nPTgyssRp1FbT4iQGJ9DaVw7jOCHtpERRjNSKsJcgAn+8ur0uUiuyboaq\nKZeDQQ8HZN1a9XFk6Yc9ZFWRpB209gmP04rxdIZQAisl03yGigJG+ZjSVPTDDofjGVE3YDrzxktR\nN2RiZkRhQBgG3PPwGRKtqYTgsf55hJDM8hLpBA++8x4WzyyydnqVdKnDwtoCd9+1jh50OHnmJIvL\nyzitqJRifuUEc/0ByfwcJghYXj/DYHmRXCmub+1QKMXy+jo6ChlVhpPLK4gwZGIdw+GQ4YlVIqkg\nihiFmuDUKv171jn89l/RWznBg//VkwjnMN/u8d3Nq/Tvu4ckjvnx7ha95UUGvS4vffOvAMO5O9cp\npjOuzqZc2d6km/WQUnKwdYPFxVW08vOrkIpJWZIlCTKbwziHSLooq3D2AB3EpFmG4BBbVOxsbbJ9\n4wA3p5iMD1gaLiJUhKksi/Pxbeff2ySA3+ayLwAkadZhbjDwpWJrOTgYMZ1Oa4SwvqksWJTeIMBa\ny6O/8E6+8D98nixJ+cHzP+CVV17l0pXLXLt+ne2dHWazmV8N1XQqwiakORKpfeYq6mzIOA72Rl66\nVSmuXL7mT5JuhpSSbhKxMD9XX0g6DObmGB0cYIylrCr/XnW/+ydFOQGtGlqTZdPcF/7CKaVofcul\n8hQHU/uN53lOp9OpEfzUF906fgOdNCUKwxYA5z/QHvv0NzN+/t3xpuy7urLKSy+/xGw2JY5Xag41\nlGXJq6++yunTpxn0++zv7XlcQ6dDWeYcHh6wurJIHGkEhsl4xM72DgLheeNCUBSlD9pCYPFZ8bWN\n66AkKtRsXbvO7mifuJNSWMP23h4VjrSX0el3CTsJs6pg5+CA3vyAO+65kx/86Afs7O1w7/338fjj\n72Zvb59ut8ulf3qZ57//ff7gS18iCAL+5b/4l5w6dZJf+/Vf55mvP8P3f/RDHnroIT70/g9gjaEs\nS77yzNe4cv0666dPY43htdcu+cVrEBAGXtKxyEuCel4c7O555awoYm9nF1vjLaIoIq6BbdZaiqIg\nL4pWFElK2Wr2B1phTNOX9438IArbXruxBoGoy7iyDVpArczns31f0rdeYMPVVSbXBFWvmdCAdm6V\n6TeOgMf5+s3535Tzm2y/Udw7c+YM99xzD+95z3tYW1t7g7reTzPSsCCLIxLVo8hzQl07YVUVGIc1\nvv8tlW9plLO6n2krAuHlO51UyDDxdsNFAcYreiVJh5J9lPOSw5iK8rCkzGeAYN9UNb0pJe30GnCF\nlyaWgiiOcVISpwk6CrE4Or0uKgjIur7NUeWwORxSWstwOE+UxCit0FmKc44giVBxSOI0oYqoqrw2\niBJoYcm6MdaW6DACJdnY3CfuzjGajSmKkiyOmYynSO3VF2d5QZomWAlL60OCrmYYztE/2aOXdRgu\nLqDw321eGc4+eDdpGhEGAUvLi3yw+CBYy9zqEGsd/aV5zj7yCHfcdw9RlrBw8gSLJ1fRSUS2sMDc\n8gokkRdakZLO8gr9Eyeo0pgijgkWh0zTECkE8Yll4tVVGPZRnQ7VoEc16LH28Dl+tHmDKtQ89MEn\nCYUg+8Epvvyf/hP9M3dw1/o6X3vma2xLwWOPPIRycOP6NV595SWKQDK/uMqlC6+yOx3R7w+4vreJ\nMCUn5vpoYhJn6cSKNJQU05wbVy+zOB+TZhGREm21+FbjbRLAbz0arexO2mFpaZHjTlmuVtsytdSq\n1soL+wsv6GGdozsY8MijjxJHEY+8693kec7GxgY3Njd45dVXuHDhIhcvvcaFCxe5evVKbTnny4XC\nCu+IV5fhnfNUI1mrjyntUcD7+wc4HPt7cO36dTpZhlbeRGCa50RRQF7kPlNxXuSljs7cMvgJgXi9\nmMuxHrlsAngt6ScEtTypbANYFEV1u0DchA8ItPbc4zCkqcc3c6PdpLf5+PjHP879Z+8n62Z0sy4f\n/ehHWVxcJI7jmsoRc++997K2tkav1+PixYto7bOyyWTCj3/8At1ul27WRQiY5TMOD0cEgSbLMi8N\nqRTdrMtsOiMMPJZif2+fNI1J4oTJ4cSXuYMAaw37Bwde3EVrr0ClfMZisYRxhBOOrb1tRtMxFQYR\nKMI0Jkwj9scjnJREaUK/3+dwNiGIYx5+xyNc39zgK88+w/nz5/nox3+JMi9AwLd/8DxXblzjk7/6\nKwyHQ/7XP/5jdnZ2eeqpp1haWqIsK1577TJ//n/9eSsHu7m5iTGG4XBInk+9MlZR0Ol0yPOcyWTC\npChqgJPjoO7vNwGxqqqaAeLaxaDAG2I0ZXsh/CKiEfQJQ6/QJaQE4YE81TG8hXPUkpvHAH144Qtq\nwSDahUCt1FUvJBpsR1MVaHAHeZ7fBOAD+MpXvkJRFDz11FP84R/+ISsrK29ZFi6soZxOEA4C4dBA\nUFtPqkChpSKQNR7BVky1xDrjAy319YBanVEID3iSAikcosqZCstsOvXSzsoLj+gk82ZL+cwLmVhD\nmnQweY7Zm+BMgalKTJxTWcNBPkXHEaWpkIEHXUmtCKMQJXzmLaQi7XZROkBojQ5DhPZA3SiJkUqT\ndjvIKCBOE9IsBQHyYEZZFnTm+2SDLi5MyFRIR4FNAoIoRI5nRFnqjUKMpZdGHE6mVFWBcgEH+/t0\nux2kMOTjnEGWMZ3mOGuJYsXaypAwUIhYcvaBdZIooixKprOSO+8+ze/8zm95xLgQ/OZv/ipZN0NL\nybvf8266WQcZSGam4sTaSe4cjVheWKTf7bI4XCbQEcYJOmlClnVRQcRkVrC0vIIMQoqqIul0CNKU\nsiw8blQpsuE8/aUVXBwzd2KFk3ffy+bmBmfuv5u5bsbq9hrbZY6pKs489BCzMOTa9Svce9/d0M+4\nePECneVFVsMAVzk29w+5enmL7eub7O8dsLe7TxzPE2rNtJzddv69jQN4E10c3X6Pe++9jzCKMNMZ\nSss6OFlsZTyoxwqc8D1haoGOhZVlssHAB3QhiLKEte4dnL5znYd/4R0YYxiPD9nd22N/b5/nn3+e\n559/nuvXrrF59SqHdaafF2Wb/Ta96gY53/QqnTWUVcl4MsMxxdZlxqYXb6zvMYJtg6293cLqGIin\nPhK+D24dVlokGlMrkLX+5tJ70BZFQb/Xu0lhzdWCD1Ec08myn0Of8GdXSO90Ojxw9ixBEHBm/Qxr\nZ9baBUtZeiT97/3e77XBCuALX/jHPPHEE2it+PznP0+WdVheWWZ/fx9V06EaqlWTzZ04sdqCziaT\nCXt7e6RpShCEjMdjTzcMAsrC+zH3e33iKAbrFaAmhzMCHSLRBCpiPJqQdTKU1iit0YFGBRqpJUEU\n1U5Lca34JJjkOVGaMJqMyauizf6RgiTrUFnL2p3rLC0tEXVSRpcvcebOdc6fO09VGc7cscHXvv51\nTp4+xa//+m/wZ3/2Zzz77F/wix/7RdI05tm/eIYf//jHPPXp/4bx2LM1Ll++zDe/+TfEccSjjz7K\nxsYGP/zBD332HoZMJofMZjPyoiDQAVIKDg5GTCZj5uaHTKdT9ra3sc75Y4Hz+6o1znmKXVkUbdBX\nNWOkRcLL2gxH1gC21pvZtSyVoixqoxpbLyQcSNBStXgPgV/INmX6Xq/HdDrlmWee4SMf+Qi//Mu/\n/JadA6FKcZU/v3CSvCwphcUZi3D47Ft4XwUhBcgSKX05vVECU8K3C4QDZwym8v7dlSlJjaaqCqzJ\nsdpCLFFRjJIKp6EsZ5TGcX130x9PNUGHmlD7NlksNbpKmI4PCXQDQpR+2/ISXR5ixocorRjv3MA4\nC7XhiQwChG5wOw6ERkU94tQzeaIkRochTgqcFGT9Lk4Ikm7mkwyt6HW7aCGQHd+7D0MvZBLPvNNj\nfFAxV0qWsh62NLV1s4Bi5heGGA7HBwzme9jpmM2NTRYX5xiNZx47EADSEkSe6bGyOqQ76KJxdPsP\no4TAOtCh5L1PPML5O9dxDgYLAz77X/8GZVGy2usiheD00hKPnjtPv9/n1PISd5w6TRJo+nGHlbkF\n9vb3KQ4mpIM+C1mP+V6ffDIjlJrVxSUO9vbY3txmdTjPqdVV7r3vAV65cBEZxjz0C48y+n9Lru3s\n8ehjjzEzHv0/n3VJo4Q4Dplb7LF1YxsnLNvb2ywtzhFowaDz96GEfqvrvfSZdpRmPPrEE6ycPMkr\nr75Kaa03eAek8hQa645W3NY5AiW58847W7UxUQttVKZCIAgCb07S1wMGAy9g8o53vIM8z9nc3ODa\npctcu3SZV159lYsXL3D50mWuXL3KtC7hGlPVvTmPAK9MXUKsKwR+r5zXTA8CKlPhucOe+Xu89Afc\nlHHfsvdd9yOVUDxw9n5efuXVY/KBjfiIz0K63W4NSjvWw6z7mH4xYT34iaM6wM+6d/1WDlXz5huN\n8kZGtaFHnTlzhqqqSJKEEydO8MEPfoC5+TmqquT9739fK3YyGo1YXFzks5/9LL/xG7/B8vIyUkq+\n9KUvsbq6SmM9q7XmkUceIUkSer0eURSytnaa5eUlyrL0YjxFcVSadbC7u4tp/NIdHI7HpGnasiFw\nvnSshKbTyZAolPRStVHkVQGTOAXnvZt1GECgmUwnzA3nfY9aQGlKZKA4mEywEgiEN0mQkjBN2NjZ\nJkxiSlsxLXKCOOLes/fx7DeeZWN7hyff/15PhasML7zwAt/53g+Ik4RP/MqvcPXqVa5cuYq1lt/5\nnf+WLEvI85wLFy7w53/+fwOOD3/kQ1x67RKbm9uMRoecP3feax/s7tLpdOpzxTAej9ja2mJuMAAH\nW9se3JkkMSpNGU99abnpvZvKtCBLpY5kaBuVOmMc1pralVB6wKaSOE075xujmobrfuXKFV544QV+\n8Rd/8Sa63E8zihykDAhqUJ411pv71JhQW/P+SgfCGDQW4SzCVIhaxU3UaFAlJaEOPSMmSYmFIKvM\nkRGIwM8FpZECSgyl074CNy1wEmbkWFdhhKMsgTDyGhbSO2KhDAh/jCsrkKJLEEZIIJ8eIoUHCNpp\niSoMSvnKiahKyqKkqjY4sN7aUyiNlQoVp6T9ATtaY6RAKg1aIZRXmgzDEKQk63bJOilx6vXdgzCk\nkyWM84L8yh46CikVlLMZAgi6CZWtCBVIadGhIoklg35MSUWnkxBICAOJcQZXWowtqcrcLzpsBVJS\nlhWl8YakaRogpCJSsLQ8hykNnW6MmeasLM7xSx96L1hLNujR/aUPM90fsbowzwfe8zhbWzssDec8\nFVkqzt55FzdubJBKxdrSEtdeu0Q1ntBTIYV1nBgu8tILr3Dj2ibvOPcgK8MlxocjOkHMfXfcxTef\n+wuu7u0TRBFWazq9iM5ch+n0gNFon73dA4aLc5SmuO38e/sE8Jt+1kOIGkELp8+s8eC5c1y4+Jpf\nDd6u9yrAGUtZlNx99114sw2Ns64V11BSolSIB/D6E7ih7yRJwunTp1k7dYrqnT67mkwmbG9vs7u1\nxWuXLvHtb3+H1y5fYmdnt+3HC2WRUMsbOm9DKhpU7jEv8HqfrMHba75x898YvJuj4xxzc3N85qmn\n+J/+5/+FjY2NFl0OTQAvfXlXB4h69W/wfceiyJF4reC3Sonr590Fv3r1qqe/BT6Ip2lKmqa1x7ut\n7fyKm7QBmopHc/GPoqgNuA7Xopx9YDC8853vJEliRocHGGMYDAZ88Ytf5Pr16wSB5rHHHiPLMu64\n4w6yLOW3f/tpOp0Op0+fbMFWp06d4uGHH2Zubg4hBIejQ0C2uulpmhKGIXt7e4RB2Gb/nbTD3GAO\nJRVaKUxRktXPNYsBiaDIS4pZiRQBp0+d4RvP/iVJlIKTCCEpK19u3zvYp9vv0p8bMJ5N2d7dZjCc\nw0qYFDO29nY4kaxiJSRZStrNuHzpEoPhkChJUGHA+GCf4dIi6+unPHUsDLjjxz/ihz/8ER/44AeY\nTqf8q3/1xxxOxvyTL/0BL734Iv/6X/8bzqyf4X3vex+T8QTrDBcuvMp3v/tddnd2+d3//nfZ2dnh\ne9/7HkDtMuWVCff39xGxp2vOZjMODg5RStEf9JnNZmxvbno9hiRGI1BCefWySnhDkSJvy+3WHC3s\nnXNsbm7WpjfBWwJo2zms6PcSjFFgfK+7cp4uSnNuNi0wPJhVtkBUj7Hwz/nFS1UZptUUwcz3mgOJ\nw+KEB74WxqBRKClAWIgUoQwJtEYpSWYTHE3bTCKNAQf90GM6cA5jKooip3QCk/WQnS75bMok95oW\ngdJYV3t5a2/aJIDAlcjqAClqbQljKCtD5XKmh9tQa4Ub62rdhBwZ1EJFeNtOKQVBEBLGKVGcEMax\nt9eMIlQYUihH0ukgw5Agjok6CVJrBovzuMD7cIuqZFIUJMuL2Mgj/OO443U1SkGoBdNJjpKOThax\nv18Rxxq0YBY4Ot0IpyXTaQXaUWjL1JVoa4m7IbNZzrSYMDffIYkVUerR80snF+hmKWVekEvDu5/8\nBWajMaFUnDq5SL+X0gklnUFGMZpycv005w7GGGNZWV7irrvu5vqVK4SB5ty99/DS975DsbNJ5Sps\nEBDEmvnVjCsvTqgqh9YJWoQcjrdvO/9+hgHc3XTX0cSl250wbwwDQviSmLU+cL33vU/yzDN/wd7e\n7u1TxvqDlFa+/+brcZiaAtMYnfgatn8P79vtkZ9+4vvyWhBKAh3QybosrqziHDwhBP/wqZIb165x\n6coVbty4wY9//GNefPFFNjdvcO3aNSYTzwv0piWi7aP7zWuAY+5NBz/nHKurq7z/fe/j3/37f8/2\n9hZSqrrfVyN+C48IDUPvH9wA3aRVbcZ6BC56/TH8SQf15rs/cbt/hun8ZDqqaYCa2WzGZDImDEOv\nYFZWBKE3MADodnutQpp1pm0xzGYzqqryXsN50QbVZhGg6zI3HCGgtdZt5r6yukSSRszV0rMf/vAH\nGQ4XvNrT6BBjDGfPnuX3f//3CcOQsiz53H/3OX77H/0WKysrbWZYFAUf+MAH2LixxeLiAkEQcN99\n93Ly5Al6vS5JktZ9fZ/5CiHI85ydnV2a5orALw7SNCWOE6zxvHElJf1el5dfepH5uT5pEkO9SLUV\nVKVXqLt+7QYnVk+itCDpZGTdDi++/BKbO1sopVhcXWZzZ5txPsVKcFj6C3N0Bj0uXLnMaDbh9OlT\nlNagwoA4TRkuLZL1eyAlZ8+dY2lxgTgOuXHjBv/sn/4zrl2/wZPvey/7+/tcu3Gd3Z1dfvkf/APu\nu/8+BDCeTPjqV77Kc88+y7ve9W5OnjzFt771Lfb39zmxeoIwDLl48aJ3HlSalYUFJvmMoiq5vr3F\nZFr5rBMvQUotZNXpdPjKV77CF77whZsqVz9NEN+cQjTvFR+NA2MqNrZ3ODwcI7VChYHPmgNNqASp\ncLWBhiLQAUGgvXNVnZQI5+qM3NUa3V4jw4PX/bZa4fUBkAAOqwRGQOAgNPWp5xxgcFQt6l33i/hP\nAAAgAElEQVQpXeNzIlzUwQhDqQ6hGiOlJcosUGGdwQX4a4vQFM4nIw6L09b/BJwCq6GyDuNK70gl\nLBUVM5njVEnk7QO8panUhNq3nlxVUoxKqvGIKAyp6utS4UrGQBh4ty7w1dT9MMQJgQ5DdqMIHYYc\nDOap4gwjBFGSEGcdgjDgIIkx1pFmKXpxDpGXBElElEZUUch0OiXspqAcKgwwyjERhjiQGO04LAzD\nQYe93RGlLJFBiDWW3FaYagICZqJkkGnCMMUYSxJHrM+tU00nuG5IosAZeNf7H2M8nhFlHc498gAr\nJxY5ubpALCyrp06wsbeFChWVlkQqpDvoE6UHHOxPGXQXOLm8xCDPbjv/fnYB/C3iCDtASV9WfPiR\nd3D69Cn29vb8KvM2UUIgWBgutJSyBlzj39C1E7yRyZQA1rVZeBtZpTzKkgXtRVOFASfW1lg9fZq8\nyHn/B3wWsrFxna3NLa5cvsy3vvXXXL58mQsXLrK1vQVCoJWkMhW6/tzSvPEI+dh36whYVoYnHn83\n84sL3HHHHXz3e8+3Zd4mKJSlVx8KdOCVw5wHyDjlX+e54bfp/7UQ+WMP32bj5ZdfYTDok6adFpEe\nxw1oz7dKrPMVh7yYMp1ZqqoiLybevrAsiaKQqvJI7HxWMJvlaK0Iw6gVRcm6GbPZpC3NNyhon+Hn\n7SIgCHTt7W4Q9sh6NAxDVldXW/rU/Pw8URQSxxGHh2OstcRxzNNPf4aNjS1WVpYRAj772d9mfn5I\nmiacO/cAf/AH/4T3ve99pGna9uHPnz/HwcFBixTf3d3FWsdsNrsJkX3p0mVu3NjAGFu3FGKK3Fcn\nwiBkfm7I/t4BZVF5q9TBPKODEVEYcLC/x6DfZ34wAGepyqLWVDB0u12QgjiJmBUzdBTSG/Q5GI/5\n0YsvsLy8xMm103zjuef4XQGi9jxOu10q57ACrm3cYH19nbU71vnhCz/GKcF9Z8+itWI6nfLKK6/w\nH778H+j2e/zyJz5BZQ3PPfcca+tn+MynP82f/umf8q1vfYuTi4v82id/hf3RiNIaRpMxX3/2Wbb3\ndtne3fVWv8cC9csvv8ykls19K6pQm3tjRuVlokSDVCRZlyrtsHuYMz6Ygsyx+DnRTWPissKUVVs5\n82VzTagVoZZEkSIMfRUnwVGZiqB2zApqdLmUvuyupPBB3QiE9aD+qjEfqt0VvXOgw+BL+MKWGFNh\nqhKpHEHqW1JREIIwNSBYYozv1etaOCvQGgjIKw+GMzXQkMDbazYuYUKCCGKCyjLOCyZV6a04lcLa\nirLIiYQjiROEc6ggQAQBRVlSVDlWOqriCOdgqrIG74qamleRRIlnWwQhOu1inAdW6iAkjBOiOEYF\nAVGUkHRShJTEcUKn1yUHkIJxv4dTCt1JcN2MREqSSKMLRygCQitJgoBIQxxpyrLCIdChIK8sLgYX\nWsZFzrQsmKmKYlbgigJTag6risO8YnFxAR0p9oqS3rDDYurL/RNjOffw/fzlKy+yePIEJBEHs4Kk\n0ydSGS9+60eMJyWmgjR6G2uh/21DKY+2RgjOrK1x/vx5nv/+98HeJnjXvx4MBgz6fX/hrao2o6Je\n1TWUi4ZXrdSxQ+Fe92b+Qf2cwzranrPWEb1+TKfbY3F5uU6uDb/12X/E5sYNvv7Vr/LlL3+Z5557\njtHBAYHUOHPEgb1lqnqr+C28Jd573/deur0+d911l+dx1gFCCu9RWxald14LA+zYL1Zsjd5X0mfh\nzQLiVh/7dh//9t/+bzQudbZGgmdZ1op/zM15Kh/43jF4g5BuNyNNOzUyWrYBfJZPmEwnaK2xrqrR\n1JL9vZK88O2Tra0trDXMZjN2d3facjZ4xb/x2IvLCCExlWufn81mN6mXNXOmyfR9CyBhMOihtO/f\nnzp9krm5OaSUDBfm+dSnPkW/38fzvSsGgwFPPvkk999/P8PhkDRN+dznPsfnPgf33XcfjU/6YDDg\niSceJ449AGZxcZH1dQ96a1oOk8mEjY0NX5USgjRNGY0OmJubY2d7m6XFRYypuHjxgi/RSkVhCh8U\nHfR7PQ72vEjFuQcf4Dvf/jbf//73uPeeX6Pb7XAwOkAqL+MrnWA6m3LP/fdw8fJrvPDSi7zzXY/S\nn59DKEVRVUyLGSERQRSR9rosnzzBN/6/v+JTn/4MJ06fYm5hyPXNDeJOh3vvv59v/vVfs3Wwy30P\nn2Nufp4g0ExmM9J+l//nq1/xC2d3dCIppej1enzjG99gfX39p86+AfaMwR0Y5ARKA2EyIUhiJpTk\nARjr0FFAr9en0x9gdg443N31hhXWez+7Gs0qAalAa+EdDKUgkYJACWLlA3igJZGuFSsDTRRq4lAR\naOWtdoX07BmJL9U7598XUb/GK72JmkpYOkdlHWXhsKZECEeoNdaW2KrA2golpx5spzUyCAh0QKSj\npnuPEoosjrFhDSx0jjwQzNKAqbWUZYExFcoptNQkKkIYx3R6yBS8RGmZU9mKpNsjSrvEccR0Oq5x\nDiECX09QIkBFCXlVMdvbQuxsMJ2Omc1mnsqoQKgAhPYOb3GKkIowjOkkHRKpCcKIOEk9YFRIgjj2\n14YkQWhN0unQ7/eolKAIFOWgixMglCDspeAsmYJUWmwkiMOArBsx2jGUxgMHS2FwEkJTkFc5whoE\nitKWFFVFHCqyuT733n0nWa+L7PcpKiBI2D95gJs6RtMJL118ldlsm/feZv697QO4VBpnLVIIsizj\nwQcf8NSewhxxoLi5Fyulp7B0u90arXqsx/76KHXbqCW4ZXBtSm/WepvABlCFDygCUCqgLAt6gwGf\n/LVfY3l5ieeff57trS2U8ibwWZaxezAiL25h1n6ba8odd97J6topRKA4dWaNOI4wZQXWK2hJRN3b\nrZG/TZWiLs9JKW4S1Pj7OK5evXbTAkc1al5K1fQlTz8UUhCGEUnirTt7vV7ryx1FUVuxaGQ/h8Mh\ni4sLpOmRYJA3I/FKYo0NqTc18d/14eEhzvnMt6EtVZVFa0+l8splgsPDEZPpBFk72h0H4HkKVtlu\nT2Nm4qsqVQvSa94/CAIWFhba7DsMQ+6+++5a015S1s52g8GAp59+mo997GPEccxHP/pR7r77bk6c\nOIGUkg996EOsr6/zjne8A6VUiwn40Ic+xOXLl1vJ12vXrrG5ucn+/v4R57uuSuzs+MWMkqqWE47p\ndbuEYUiR53TShI0bN1haWmA2neCcYWnR37fWkMQR1viKx49+9EM+/vGPU9TZ18LiIvPDIdeu32Bv\ntM9gfo4oTbh29Sob21ucvnOdMI3Z2N3itetXGSwNqfDBcnFpAVMr2JVFzTuvAY4Azz33HJ/5zGfe\nkvno4sDrPDgorGM8873UCgdSoeOY/sI8w/khgQiwMyA3GMBYQ26mLT1PAFgQJYjSg818guH75lKA\nwlcklfCBPdKSKPR930SHpCryGX0YEAX+FtZ+DaHThCLwaPRQEDlHbIwP1tYSKIm1hrwsUSqhtAWm\nLHGYmrpqQE18i0ZJpK9d1o+VR9srr4iZpB26SRdhaiEp6laA8OyhyXSMLsE6g7IVqZPkUlMUMybF\njGIW1FihClOzfkzl+eYN80ZJjSlmKCRJECGkQgdeKKc0FTiLKGYIKTFVweFkn9xWvrWKX1BXpsRa\nTRhlBFGEUJo4jcmyDKcibJSSpKmn3gWKKIkx0rcPrg566FATJBEHnYS8NCgdowuDqQyxFETTnCov\nQAp6+MqIkZJYKA5mM65uXmb0wz2kUZQzQ9DpsD0uuHrlKuLMPGbkYHpw2/n39g7gws9h6zzUI4oi\n3v3udzM/P8f1q1OUaF/G8VAupPAr1Nrj2483W9N3uNuszm0dMB2uFqqvEeKqkS6tAVTOl6K87nSN\njDSWfr/H/NyA0WRK8foALmhV1qpjKFoBrK2dZmV1FYfjzPoZev0+e7ueCqXqRUo+y1vFqibQNdrp\nTQZ+O5DcWzV+poh2d4SeBx8wj9DG1MHRH7+JmHGwf4C1rs3aXV11kVK0+vdRFBFGUctJbsRywjBk\nfn6OhYXFI3vabrfNsD0QLagldXt1cLZUlcUpRZ7nVKbCOg+s84s9S1Wj0w8PvZLadDphZ2envj8j\nz2d14Fa4utJUVRXj8ZjDw0PA988b+pyqP6vp1zeyp0mSsLi4iJSSTqfDXXfdRb/fp6oqHnvsMR5/\n/PFWnrQsS4Ig4BOf+ARbW1sMBgPSNOVTn/oUn/zkJ3nggbP1MfYL6fPnz/Paa68xHA7Jul263S7L\ny8tkWdYGSiEEly5d4vxD53DOEUVRjT2oGA7nKYqChYUFVldW2NrcopOm/jhJydxgQBgEdLMOly5e\nYG1tjTgMKIucy5df4+zZsywsLHDl2iWub27woPPVukBq+r0+g/6Afn/A1uaO54zXLZbxeMz29jb7\n+/stwPCnmo5C4hBIJdHOYqRAqRBTGcIoYeXEKoO5eT9hywodR+g48rK2TmKD0iOkvbwdDecdARGC\nxNUeDzXSzW+twTiLrSx5VSFyAIezObYc+SRCCAIButGLUMqbLgWaqM7c5wPJghaEgb9FdZk+Cjx3\nP04iZBp5RUcp/eKBhppqPBWtbi3a0ruwGTcDpciNIRcVVeDL31pJoiAiDkKkcASRJsvm0ULVrXxL\nLixV5AHHRemvY6aqKMocGjMQAWY6ZjadYKwEESN0jNI1cK8GDWthccLjQYIgrBkBJaVWFPjtN7ai\nsgXGTmB6iPPChcRxSrAbIEuNrLwfuda+qhmEAToMEEqxG4fecjoMEYGikJKw32MrTTE1PbLXzahq\nTFKedZiVFSoOEf0e08MxxSzHGYN2XmXPAKPdfWaTQ4TsU5QzIvtfwo2sjqri+OM3O+pI0GjtImA4\nP2Rt7QybN26Ard74YrycZpIkhGHUfratxR/e1Pbz+ph/tGJoeqJCCo84h1rOVdE6gFUGZywH+wft\nBQTnGB1OqIxHjN8qmDZBBmhpcEopzpw50waQ5eVllpeXufzapaOSuLPM8hytNEkcN5D2o44Brs3m\npHoTx+JtOUT7symwuJqyI+qLoA+07nWv89UTYx2TMmcymd38lu7oQhAEAZdeu9yK9jQZeWPi0WT1\njQ93g2xuQG9xHNPr9TxXO/LVAKANsE3pulmAgJ8OTUZfliVCKEaj/RZw5+r5PZvN6NTywuD/Zjqd\ntrK6TbWhedwEqkbPHGiBe8fvLy4u1i0IP/ceeOCBGlmvsc4LBsVxzOOPP876+jqDwQCtFZ/4xCe4\n7777WFlZIY5jHnvsMdI0ZW1trc28kiRhbm6Ou+++m5WVFZRSzA3muHTpEvPz8+R53lLxGjW4LMsQ\nQrTSqbIO7r1ul9OnTvL957/HjSs3sGUtgKIUadpldfUkly5dbfe9mfdaa8bjMS+88AJPPPHETz0L\nXWFwlfca0EKinESU0Is7zC8sMsgGhDLwPvKlAVuhMVhKhBRoaZGiwgnqWx2thUCIEEHoS+B1bBeA\nNSXOlF7Bzs94wFEJRxX4OVzhKIRDYME4KCqEm9Xv5TN6LSRaCJRwKAlaCQKlSKKAJAyIAk0catIg\nIAo1/SSmnySEOiIMFZHSvgJWGaSqCEOHEr5cL4RjpHL2dY6xhsJZppRIW9NpsUhTi2UJ4Uv/RUVY\nKOIwJnIQOhA6oBIQ6gCt60qUc5g047AUFETtgjovc8oyR+DZAMZUvuyOVxpsjH0cEqlDinJKkReE\ncYpSmsp6q2qCCCsVwlns9ND3/HVAjqU0FaLmuIsa6yKkb1/mwiI7Xi9dqMZ0y5+jsr4hBHGSkKQp\n+XiPLEmJnUIZhZYBuXWEoSKOwFUeq+C4/bX6Z5iBi7ck0XP22MrUOXr9Pg8//BDf++53KKZvLD/7\nHqCl3+vWvUbjkeh/xw7vrYByzjbe0I2Xs78ABwo8tP0ISCSs8W5kh4eeAiQEo/GYWZ7XF+43fjkO\ncOZINhLnSJKUcw8+gA48CC2OIhaGQxpfZudVLcjzWVuGbO1H/YGhqsvtxhjPK3ZvPCai/e+mO3iU\n+5s5cj+bPLx13aon1+v7mMelN4+/5uZxC2xDs8n1fjrjyE3hQS81wr9FMtbvt7mx3fxR2+OWShKG\nnvsfx0m9kAzb7L4piXe7XRYXF1trzsFgAPjvr9Pp1Oj1qs36waEDSVn64D6djUFYtFaURVnzp72c\nrgfXhbh6EaB10IoJdbOMJEl92bbulzvnPBWyDnRFUbQl+jRNGY/HRHHoxUnqEmgcx20QlkIyNzfH\n+fPniSLvdPfEE0/w6KOP0u1mWOfBoUpKHnvsMYbDoc+o45hHH32UL37xiwwGA7Isa6mecRxz+vRp\n9vf3mZ+f58SJE0ynU6bTKcYY5ubmSNOUJEkYjUbHaHaOU6dOoZQmz/15kNfWr83i4/r162xtbb0V\n05FIaF9iRoIVWKCTdFhaXqGTZVS5pSpmnl5ajKEYI8oJsipRUYgRFoPH1fhiyxHNwynFVBmvvljH\ndS8Mo3FWYWzl1SNr0RilQwKl/DXTWqw1HqcTaZRQ4PxrjbVU1rIrJGOpcMJ7VisHsnLI0iKYoZxF\nOp91KyCLoBN5T/Mo0ERBQBIEdDsZ3SQhlJJIKUIlCZUiQxM5UQvz1CmE8/a5CId1FThqbX9LaL1o\nz7ScAo6cKTi/KCiEB/eKOuBLqYiiDnEYIlVDz80w1s8BISVlVVJVJdbBbDZF5BZZOlQYEqmYw6LC\nHlpU5ZDSoKxFaU2ivByxExWFnlFREiUxQmpcBWWRg7Ao4SiN8a6AxuCcwZZTpqM9rPXn4oEOQQqK\nPKcyFqU9K0GqEIch0JZOGBPJmElRkFtHEJSkqWM6HhEpiZW3D9Nv7xJ6M5pYILxhw4MPPkgURxTT\n8S1fbq2h08mAxl0JL2LwJj9T1Hnr659Q0rUB86YhPMcbxxH/U8q6b+qpbViLrSoqQGlNdQskehN2\nrbWEtU71/Pwcd9x1F+ArEkEYMl8LelQ1tx08YG069fZ1bcFA+D6U14ovfrJ16G1XXTc3Kv5Ljd3a\ntaupShz/CUfB+jjHtwlMzXhjQD8+brX/zb67Wja+rvTU7+Pc0TwxxlAWFTAFDqgnQ/sOuu57a629\nlGXTt6/3Sddyt/1+v83smyy0AfQkSYLWmoWFIQsLi+2+2dAHv9HokG7XU08cFoSkrKoapW6YTicc\njsfEUYSoM9OyKOuM3tMfs1qxzznH4eEh/UGPINCtb32zMKmqql73uJs0xhu7Xa0DrKsoCn8+rK6u\nsry8zHg8RilF1u3ykY985Mg50BiUUpw4cYKnn36azc1NlpeX6Xa7PP3007zrXe9qAWgPPPAAe3t7\nrK+vt3O60UFvcA3T6axdrDjnQYe7u7u88sorzGazFuT3dx2pLTEKZspSSU2302fQWyAUEdWopLKG\nEkdhK7SZoO0MbIWwBuUMWli8Kr8Pku3MFBCWhqCs/HRrOOWyTgyUwAjpiWLOi94oJAHSl+OlwNX3\nhXMIZ+pLqK/uOSmJbYWsch/AlU8MhPRS0dbWgi84CuewCPICdkt8z9yBs77FqeUWgZYEQqKVv4VK\nclIIVpQkjhWdRNNJY5IwIuukRFFYg+1qjwccRhis8qh5QUOps3UL0NZ4JoMzBiqDKHcRcrc910V9\njGSNicnCkCD1Ajuu22WadbCVx0IoqZjLEvK5vj9LarCdsxaFBOMY2YIZMwwVrsKDVI3BupJQBghd\nY6GkAgmhlUgs0lXeQ8EYhDNIpRGuIpICXMl0f9e7QUqB1IJDFRIGKUVZIYMYU5XejQ6JkiHG3e6a\n/PcggPvgczS00tx3332kScrB7s4bwdo0F+taa7jJmpoXvuks8pZbdcvnPLLYk8tVTecwTTYIlKVX\nVGpMWaS7PRe8oZhQ7//pU6c4c3qtPR5hFHHixAmvAV/5TF5Kr/I1nU1bTWu///4AVKbyWtGvC2D/\nuaH57RDCP/3pT7O1tcWNGzfY2tpib2+vzbyAlt99PAs/7mHdnOxN5eJ2XtO3yuqb+02J/vbjeHXA\nLyC9hj9UdY88z0smk2mdzB+z7xSixioc2446wIdBQBRHnhJTZ+laH+2bUp4u0+t1WVxcYjDo0+32\nvCNfWXrN6k5GFIXMZjPm5uYIw8iDvKSjMgXGlozHY4wtW2DmLM8pK99jz/P8JivQyWRCkqQE2mum\nN2JIzYJkOpsCtuWxN9WiZhHjrGj78M3fFLXQkFe9i9ry99mzZzl58qRXGpSSJ598kvPnz7d67U1F\nQQjBvffey7e//Z32s6y1bfug2+3y2muvURTFTx3AA2nQAkphsFGATGMsgulhjjY+qy6UI1cOawTW\nKKTqIIWlrCxOSdAOW5XHp47PqB3oGhjrM+9axZHCl8GlR50LkYCAqTWUrrw5oCGpyrIOJqCbvKOe\nm5EMfKJiQTgLVQMOdl7oSjiE9I+rKsIaTf1i/51RJwPG4VxFURhy4BDYUvAdAXLXf26Ad0+MIl+S\nj7Um0ZosDohDTTfUpKEmijRx5PvlTb9eS1H39v01UzhLYA5QdgIcBX2JxdoSYS0uhyL3UtLOWWYk\nWB0ToAlUiA6kt1xt/lb4SlWo/SJwUjomRUVZFZRVSVGWfn7hAcwNVbUsixbY7KW868RC++9ACtCh\n1yRxziFsiA0UTioMDik1QRhQ1HTXfDYlijpEcYZzgrJ82yux/YQh/FxppEuFEJw4scr8cMj1q5dv\n+XpfZvYCJlVNIRM/r/DjmkntA7g3ZKAthyvlqRxHQeEWJfr6p1KqfXZpeYmFhaE3Pah7psvLy3V7\nwLYcdgfMZjlhGLS99CbcWOuzc45n4G/YhJ8pBO2nHp///OfZ29tjd3eX8XjcHteyLNnZ2eHq1atc\nvHiRjY0NRqNRi5gGb2YyHo/bny3vn6PA3mSCTVbf/G3zOccXAj9pHA/6R7K54nULg+besT5+k9rw\n+kqBoyoN4/EU2DvK2o9drAMdtJm9B1E24ErVKsQ15fos8x73TbBtyuLdboYQkoWFhdZ+05gK5zwC\nP89nwFG1oKq833hRzhhPRlSV8Sj/qvKSxlqjtGI2m5HUzmdNxWAymWCMl01t1OmCIDjCi+DPgbIs\n20VYFEU3HZtGXMfUTm3N8b7//vuZTmceEV8U7fYaY5hOp7z88svs7e21+/h3HVEnQhUWZSsiHRIg\nvRMZGue8P8MRCqP+J6QPjIA8erL+lo8eGwGlPKogyWb+uOacFqimyecEVnkTlOPD4Q2ZhFR1It9Y\nDIOziqoWaZHgmSyiYa7UGW9dMcRZjOrgtG8HSdVQ1RxlkWNNAQ03vBaWOgw0k1DiHAjrkNYhHUhZ\nIU0JJUgDyvku79AKUjy/PVCaQCuiMCCJQ5I4IoliOklMmsTEUUBfB6QqJVSCQEtCBVqCdCXWeuqa\nwPjzyTm0C3FI39supxTOi9JI4cBVGFsCttWpl6KHdBGJkCQqAh0h6sqbVBIpBGVVUFUFQkBpDaUp\ncK6RBD72ZeKlf42x9RxX2HqRVjnLdDbySyEhwIJyElsYDIJpXt52/r39A7ijzmqPspF+f8DpU6f4\n0fe/Vxt13DwaEFnTq6ZBcf2MhxD4We2aso8P4OGxi5JAtMA0Kb0u+a0qugKftTVlzFOnTkFjRFD3\ngpYWFz3quAbDNWXEPM/Jutmx7apPWGcp8uLNgfneZqMB771+NHaYza3p7TZZ4sbGBleuXOHKlSts\nbm62ZVXwCOzt7W22t7fZ2tpif3+/RXXDzcEYeENW3/zu+K35u+a5/7xj/vpAb28R5I+u9KLdNrwQ\nkfGiNBxOPGSEOltpt0si5TV/wW63x7WshTiKybpdosij849T2BpAXpqmLC4ukGUeed4o2znnjunB\nd9ugG4Zeq7socpRSzGbTtl8dRRHWOMqaex+Fvn9elAVhENA4dVlr6fV6bbbcIOyb79z3/IP2OxFC\n0Ol0WFtbYzwet3Oi9UUQnlN/+fJlf17BTYuGNzPiTgdtp8hphbYQCoWsS55O0gbwugpebx+eQVPX\noGX9PR0B2eq2SlN9FEfPHS8k+hnS1nv8Yv0Wu9DYvzrwlCzh31SLEuWK178Y0Uo+O5yobw50WOKk\nbd/TCIEBbGCxSmIBa0UtbORI8oh0qv02NTfw1EFsjbyvP1fASAnG1oIxmFmJNQVCTAmkIKgzWSV9\nZVJLQRoIkgDiUBBHAWnkM/nlxSFp1EEJh5aOoA7sPQJCJIoaxKfw7VBb4qoZxhXganS9FVi8Mp0v\nYkpfkcDhKq+xLqUidJCIkDCMGAkP1vPHpy111ImY10MwVVXTRitsaShNxdQUXpLWOkwlwYItCsrc\nUCCZVH+fAzhtRYdmyoZhwNqZM+ggoKx5zcfza4EgiaO6TOSzGif8AX2rgtfr83nRfLIUzQK0DSD9\nwaDuhRu08h7LwjWgjtvt8xFq3VrH+voZZPMptZvR0tISnazDdDJB0QDZ/IU0jmIvTmJdm4F7ekbp\n0ZBvDpH2uv08fodbr49+zol8A1C6XUn0zjvvvOXvvTjLLltbW+zu7nJwcMS5LP9/9t7sybLsOu/7\n7emcc4ccKrOqu6rnbnRDaBASCFIgQQLyC2SSYYX4QEkvfGHY/ldsP1nym8P+E2w/yXLI4RDhCJkP\nFs2ZABtooLurhxozs3K8wxn24Ie1z7k3s7IKPWFkLkSjMvNO55577l57fetb39d1nJycZFOPu9y/\nf5/ZbJbZ4Wogfy0WC3Gty1aW617VffLuVQHXb+tj/efzm4aP884vVvTq3M+J9R79ZeMoiabugEW+\nXu9LxZQ3AAOjvizFSc0YisKdQyR6MZ3NzU22trYyM93mfrQlJamkRUFvPBD8jAlSuXRNPp/CzF8u\nZ8Qo/XNQ1MslPnSkjMSlxNC/78fzRtVocDUTqL9le3ub09NTGQGylrZtmc/ndF3H9evXB7OV9fP+\nSaMsKzZiYl43pNZjYsLqXHFJMQW5nysmRhqtYq7AE1ElTIoYIOug5QSuSSpD1QqSTtdfIkoAACAA\nSURBVKtqPvatt1V7DHJ/+8dcJnL3rM1uzdo6tvoSy/XSF0YJZWRdweSk2/M/Ut4kpoAZ1rKc8IHk\nWlIm+J7jy+d1auAR5Qt9Hjt8SjLJYy26ENShU8KqTzFKdR9EYbJbjoiYDJ8HNB1OJ0buDKeF/FZY\nzag0jCvDDWuZFJZRWbI5nbA5GbM5GTEuHbZMqORz0peET2ggRtk49Qhd3olp+ikXYaZ3MRALEXOK\nGa3qJbpTaoZrTGuNdSVlNWakLAlYJM8yehofCF2iWTSo5PAYzrpI2yyf+LH+FBL4pR3eT/QMSgn8\nS17UjLG8+uqrVFU1JPCLrzkajzBG54u136N+jmW4ulzIdfA67vuZVhKt0bILy6i2QCwhQ06Xweg5\ngYcQGI8qnn3mGZzNNJdMiNvY3GB7a4v9B3skQ4ZMJSmVGUbt2o6+Dx5iEBnJEOSLsLZofaxP5ElQ\no/rpIBw/iaiqilu3bnHr1q3HbuvV1Prk3LaramW5XPLgwQPu3bvHnTt32NvbG6q8vhd7enrKo0eP\nOD4+5vj4eGB6X2TQr0P367D9+s8fF7q/PJ72GLW2UVCrRThvAIJPBN/SZhhvmKeHFXyfhT2kHZE1\nErJKnitEOGc6nTIejWVuNo+3rSMZu7s77Ozssr29TVG4zIJ3xBDovMcHQbJE5EaLuhsJn5nG4swn\nGgo+BI6PTgbjkp4Z3wvReO+5desWzz///GeCzwGUUVzbusZ81jBfdqiuQ/U9fbVKjmpAVrKOQYpo\nIiEFTEyY/nxjSMpJAtettOP6qnm9IicXtGtIy5M+6Z7EqtZaOP3G8rK3H0IY2jh9hk+s0IOUx9eG\nogiVNTMuLgJqrdLJiX/t+Psip0eqxtb1B7z2j5K1H3ld0Hn2HpwShzGSoBYKQRjmXSRFL0Q8VuhH\nqZNwCbQWiVhjGFclo8oxKiyjwjKtCjamFdOR4xnrGdtEWSicVTgtCEBVOApjIEHwmhT7FoUTJr8C\nYwQfF7EYP3CjBK2XSal5e0YgUptIo7LcLZrKOVQqUW6MwuSWzOXxE07gj1cRavi/jx/SM4pDclRa\n8fLLLzOZTDk7OTn33AmZ+VZKy+xdhtPl9p98opELfHXcWsPzzz/P9vY1jk+O6VWhhr7oExaQfhGM\nMfLss8+wub09fPl7WKYajdjd3eHt3OPpi97ZbA6JIYH3G/UYE8fHx7kfWa0O+CdQKf+UC/CfSBhj\nmEwmTCaTS29/800RN7nIau/lVx89esT+/j5nZ2dDxdo0DbPZjIcPH3L37l329vY4Pj4+tzlomoam\nEb31tm2Hzdy6BeY6CW8dOu5vX49PnqQu3v/yJB+HhTmIMAnrUF9CqTyEl+T72K/n8q8Sgw/nZHRn\nVOWZdZkiERKooCq9RO7GxgZVVTEajc5tcsTIpaIsSzY3NzDWsre/z+HhIV0mHl1sg9y8eZPp9Mkm\nER83ovcUpqKIoG2JSwoVV9V3VLLu9LTamKRCN8qgQkcMEW80dIEuSRUekxkSEusCL/ljSQOBLH8i\nSj4Tvb6+XfgIJXmvkuKqcD8/7tsnalGNSaikVkz2fAxp2A7I42M/TatWlbUoxeZrRT2+7Kq0SuYq\n/6AvkE37Iz+vprk60PW/DcXZgDxZSKv0llKiiX5obdJ5+a9u5PeUBmEa00PuGioLoxKqEiYjw6Qs\nmFQFk7KiKgtRwnOWcVFRkSiMEV17Y4WRrxTWBEJWW4wxIwkkKlfiVcArjycIMhtlQzSfzWjikoPG\nc/IUKO4XAkIHccaRRUGuvBvP3GA8vlzkPcQ4zIuurvrzs8E/uRARgwHNVJrNrS1eeOE5PvjwffHk\nzuMOIRsNXJbE+81ISmJ3efPZZ9d2w/L1KZxjZ2dnYJXLJl3RtI3MnWYSU3/NihLVTNy6skvb5/We\nf2FL8M8QT0qY/ejXK6+88thjuq5jsVgM//UJGmA2m/Ho0SPu3LnD3ex0V9f1IPSSUuLs7Gyo6M/O\nzlgsFpdC8n1iX6/qLzLxL/78Cd/9j719te6YNci1D2ELN7VsXE6OT1b3z7nKGIfWhrt374kqXVrJ\ncfZw+XQ6YTqdUpZS2fejbEdHJ9y5c+cxaDylRFVVvPbaawOB8bNEJBLblrEtwBWQcg9YaaKSKrxv\n9YaoSBFKq5kUJQ5F8mKasbc3Q5HoopCqBGnUkpTzJqBHulKKQ/IcMvGQ39cTmdxn+KTSWgGVGAi2\n6cKYkuoz/dB6kTfRc2mGjUVO7rofXVtbAlQGtge06fyHkDcKrBJ8SqToST1imjcLq+v0QkmgwLfd\nqrIdzsfaHVhtWNafa+3m4ZpLQFSJEBFN8gRnWkEDDNPKEa1qNDXWnGGMWo3NWcO4KBlX8t/mZMzG\nqGJaFWyPSka2pDCKIov3GAWxTMTYEFWHI0DXiMKd0uL5Xo6pbGQRfmYV+OcU/SKTK9aUItd3r7O5\ntTWQbNajZ6T2BLYUJZEFH87Nqv5kDnWFD/XM98lkwn/2T77FW2+9xdHREWvNJknGl6yFCanCZRzB\nsbu7C0pMOLRRhBRRxnDt2o7Atvn5+l7f+edcfYlPTk5ltOenUh7/MtThn2845wb/8YuxLkJzcXRt\nNptxcHDA3t7e0K9fJ+HN53MODg64f/8+Dx8+5PT0dEjuIMSvpmmGirRn4F+E7Pvf16v6z17Rw5Or\n+rzAD9W8JJaEyv3TQHvJFE0Pie/vC/ejb130fAPgnMJdfx5jjDzzzDODBvxn3dQbrdExsbu5Tb3w\nNAmalMRAqIeYE6ikwFpUFGTCGZhYg7KGyiSaSnHWCaEvhhacw+ieIS0JXKW8BmZoOCHuezJCqjLL\n3g/Wyb3zYFpPxOsfx8CzOJd5VzA/SEJdK6NXmwFZ21RaJfU03KQyxK8GcH/9DPfPKct5Go4vBvEO\nWKEADPD/uR1ARnCcO19h558utIHS2jr7OOF52JQk2cgI2tDf2FtArx07Yv7ifRJ+mxILVVQAGjlX\nMaGjtJgKaxk5Q2ktlXOMCysqd4Wj3LTYELkxtjy/7dhyChMV2jlq6zmlYzmZ0j3BuAt+XhN4f7yJ\n1Rezh1fyBzUajdjNSmQ9nNefeKWUzLIOFXga/v7TO/bVaznn+MY3vsG//bf/O4dHR8Nd+rGvi9Vr\n/1uMsiDtXr+OLatcNUtPjBjRxrB9bfv8Y5M4khWuyH66eTObiTWz2Yymrhlwrav8+nMTT6uGd3Z2\n2NnZ4Ytf/OK5v/fs77Ozs0Erva5X8rDz+Zz9/X0ePHjARx99xP7+PovF4hwRb7FYcHJywmw24/T0\n9NzMdh8XhXMuztZf/PfTJnlJsgApUzTWFoO1iHl6I8Z1GVp5/Lpwy3rboe9/7+7uDqYuTxf1+fFh\njMVhqIqSbtZhnaaOkaAFDleAjpLO0AUibSriLePCYqMn2kTYLEjHjeiih04Sie5Wi1p/fgAVYk5i\nEZLOs2iaFIXbolLum6eIop+KuXgOVxA1Km/w+8+sh7fX/s0nfXiW1ap6seLPz5qTvbpkbVstkX0S\nz383Zrh1vdofniGdfxfr1ffq+HthmPVqPK+L2Tjn8VADgpDI3hY9RAAXlkgl5MKkVoUl8muX9RSM\nWaEiIQUWbUtdw1kkK9vJU2sHNsC1Ag5frHjp2V0qY0i64aRuOUuWQ79g/pQ0/VNP4E/PGf1uTF+4\nT8p9tDyzi4g/3Lp5E2MtoQmriw2xy6zrmhhWsqYrgZefdKySZr+TVMbyxS+9yT/86lf54TvvEoLH\nx4jNc95pbREawPBVkc6tW8+htRm0nHt1Lecc29tbw8KlkIWtyVKY618fpaSnfnZ2tnIku0rev/DR\nz0aXZcn169cfu713D+sJXIM8L3ByIkSvvb09Hjx4wMnJyZqvfEfTNEPVv7+/z/HxMYvFYnhd7z1d\n1w3yp+sKeRfH6i4y8J9U1a82DlJZXhpp/YfV86yvzT3ULn+XG6qqwhjDG2+8cS6xf5ZIIeG0wQSF\nTYrWR5JVxAQhifBKXwmGpFBRpDj1yDJ2FUXSqGQx10qx86w1ehHwqj/uvCL0qN5aFaz75JUSKkaI\nAlwP61wSNbCUwoqXM5y5nnjWn4O1z6qfUjmXPRlsTy8/Y2kNj+4Lpv6WvoJeVdJDS39oAfQvtnr2\n4bNRl33GwgB/rNLOP+c94OpxilzQXHL0uTCUDUX/mhAH9c507pIbkKO1ayykhI+KEGUs0KiVe5zW\n+T/yf/nUVlqLpG0L5tCzTDPKwpK0pk2aUFQcLDqW8WcGoV+4An5MyI46obQkKLmYdHZk6pO4Hi6u\nre3tx/pYCdDmPKzeC5r8tOaf0/oGJF9QW9d2+M1vfIPvfOf/Fh3mfC33FqRPWkZCCDz33K0hSfdW\nmWjx0R2PJ4yqavhySQXSsjGdZogpv3el8AjBqql/sS1Fr+Ljxzr57WKMx+NL2fcg18np6Smnp6ec\nnZ2JAFCOvg9///597t27x8OHD+W6WpsIqeua2WzGbDZjPhe/5hXzeZUs1vv064lfeEW9y5zOjPc8\nVaJ4rG22HlKZrb4P/TTHbDbji1/8It/61pPclT95NHXL5qhCo7Da0i6XpGmFpN7U86OAJLLJPqKW\nNWlaURgooxDcis0JbRdQpcH7GY3SdDqzx4dSNBuz5LVREqAekDZUGqrYnqzVm3+c293InXOyyrCx\nkt97otoq167B1+dw8Md+lb53Smt/T8PxryfxVUNvNZM+PGI96eb3u/46q1yuUPZyDsM5zCY/YUwJ\nny4k/OG4V/sLlXothvUhnfPHl9La5q/v2ZNwaGxGTDSJpGKWus469gZMn8yjYnlmsK5gvDlibhXL\nWUKbRONbMJbRpqJVU4L9GVbgl6fwy+vwFYKTBtinv6BiEMhMG/lQnXM88+yzg9tSfiDQz2LHNRQ7\nrV5yDYb/iUTenV2ErKy1fP3rX+e1V19lf39fbo3xyZDE2oa0LEtxxFLiXGXyGIUxmqIsGI1HLGZS\nGUkC95yenTGZTDg8Pl7tFFNiWdfs7e1deJHPHpc+09Phlqv4OY7xeMx4PObmzZuP3dZ7lveytesk\nu76qf/DgAXt7ezx69Ojc+FbbtpycnAy39dB9//j+fn2ffr2q1/m679d8SWYmK4hpeqY7SZSy1iVU\nT09Pmc/nPPvss8P0wOcR85SYOk/EsygX1M2MNnR4VRK1gyRjUCppUqppYiMiHimKgQgJowPFaMLW\npEIXBbPTGq3dQGbLJwfIkqrZFliWMzX0iaNKw+SNzCh7YkZfzsPPUmmmtJZAe9iZfq3NW5ChmlZr\n1sw8/r2+8F3vk2TvBq76ZM5qnV/x73qo/lyxffkTr1fol34iawTN4UX6pX894V+A9td5J0lasj49\nfj95yvW0n+WakxrmumVflEBn7pX2srnS+W9KNPQqN6LaGDG6NsGUmqSzo15IaFOiqymlKil+niD0\np4fK+r252kxRVPDIF2auQmOQ2b/RaMT6/mx1cUofbHXu+6SdPla+Wr/L55l/nn/+ed744hv82Z//\nOSmJK5DMhz/5oMaj0UqcpIcXe2orYtFYVdWQwOVuiod7e0zHEwZ96Pw1UQoeHT46/yKfR6K9JINf\n5e9fzuir5l7WdD12dnZ49dVXH/t7jJHFYsHx8TEnJyecnJwMcHxKifl8zvHxMfv7+9y5c4eDg4Oh\nn98nkX4Eb73X31fxF5n2X/7yl/mN3/gNtra2OD4+HtTavv3tb/P6669/bueibRpaX1CMLKryhHlD\n14ldbdQ9JBuxQea+T0cKNifM1RgVR2i9BHNGkQLPaNgqRhw6S7BKdMuDhyR2nyoprDVoI5M2MWYY\nXEguoqttxOYyJfBK4ZVCn1N77CtS6Pu4cD7Z9Z/JuuCKNDSeBJ6n1fqCpO3eCGW1KJxfHFbVK2v/\nXnrXC39YgwMuPZx0SU7oIfunrEb5AIbzkBI6XLKCqfz+1uD6lCIqamzUA/IScwWecoJXWc47piAt\nUx8pLFSlI5HoEMjeA8oZrCmpg4yzjarHv2d9/JwlcEgxyRhG1o2NeQawaRoKV2SHI4PWSnpa1g7w\nDcgH5jvPcrmkXoodnVbISQtJhvyemjI/5/fD6hKoqhFffvNNxuMx9XI5bFLgsp2nfInG4zGbGxvD\nLno1TysPEBchPexze1eyvf19sXKMEb02MhZD5N7du9kT/LOP0VzFVXyc0FoP43W9fGkf/Saz6zra\nth366n0S6ZXyHj58yL1794b57h4JEAe2s0EO9/DwkN/93d/l93//9wdeQO9Q9rSWwqeJ5WxJW1lU\nOaUsSggKHwI+dAQjil0xJWJIBA0+KAyeZrEg+hGqSEDE4CmMxiXNxsSySAqLAh/RqaMwMj/uHJA6\nOh9ovfTZMYUUAjESvEdFLRLNaJKxNLEFpUhagxJP6gj4mBNNQtwaexSjJ/clgZ5JMskTvV9tBNY2\n7KvkvSKeyaq2UnPr7zxwE9QKEB2er18pz2HzT1YkVI/d+Un3kVfvV8lLY+AbpOFxlyvbrdDcAV1I\nCZUiJq/RcXgtmUaIKmGUQaOJShECWB8oTEdpIqbQdAp8jCKXrY08c/REn2i6XyAp1bpecvfOHT78\n4EP2M8wmFo0tddPwyksv8eaXvsRzLzzP7vY2GxsbHDw6pB8ZI6VhjGw+mxG6lRDGpTOJP5WQY7PO\n8Stf+Qo3btzgzp2PMBiCF3EBsz66MTxOZAV3dq4JfJgi3ne4wg4XfjWqzkFG/c9HR0e89tqrg250\n8vIF88Hz4Ycf4rtO1ISu4ip+xtG3xJxzl2o7PPfcc5c+LqXE6enpYG7Tw/FKKb7xjW+wubl5jk3f\nQ/mfZwJPTaRdtIRxh1GG5BWxi3jr8TGP5BFRIRGVI4SEjR2+q9F6B60SwUeSkomZpAKjUUGxaCmM\nQxuFwuJUpHAGGSYJaB0xRhJ40hajxIs7BNGdV65EW4cPgVK77DWuM3FNZfeSlD3DI0pFtLGgZI2J\nMYHWdK1IL9uipGuEECkbASvJKQHaEELK0qEMC1hM64ldDdDzMKZ2DkpfKWaeO79rMP65GLL/JbDf\nU+LJ6f4CQoD0rR+7NWWdjwFFTxkeB1LIffS8CRhG7cSNTSVQEQgKG8EWHms6jC6JSmOBEBMpecRv\n3GS72Z/lHPgT2qx95dlX13t7D3n/9m3+6k//jNvvvJe1jhV7e/uEEFjUS969fZujkxOeuXGDX//H\nv45zlvl8Lr3kDLFHZEFomjZDdJkBa83AVOh3ipcd6k8s8pO/9NJLvPzSi3zwwfsD2/Zi9b06ZeIB\nbU1vL6llRCFfuErBqKqGHuTARleKk9NTrm1tE4MsWFHJ6EkIkXv37tM2DcVntFK8iqv4WYZSapip\nf/nllx+7vf8ufHJTmY8f0/EUHTx0AayREaH1JCYruqw62qKTx/rIeFIwGhWU2hI6izMWZTRGKUaF\nxZ4u0cpjNYOOulVGzDxsSZEkKUY0UWmcNlRlSQyRuqmxrkAZS13XMlue8mx6D43HiIohM6vUuWya\nCps3O4FGdRgNZaGJxhICoC3KOHxI+CCVvo+JEDUx9v3jhApmgJTlWMX4M6JIWcxqoPpEj87OgX0b\ntb9x6JGvVcdS91+yYq/zBj5RqfY41L/+/E9IY6uHKAgmTxOlRO8+k7IEeG87k0Xr0Dqhioi24r5m\nEU6CUWaYDtApILPrT85Mn1MCXwOK02pURen+cM+TIWKukg8fPeKHP/whf/PXf8O7777L6ekpcdHQ\nLWuKsswMPvlQnbWMxyPu3LvH20dHvP3OjyiLYjUa00NjKaGVputauraVnZTRIkqfRKf5ie2Tz+1c\nPLn7u7uzw1e+8iv8v//pP2X7w3TJ3fudaob+RiNSTMN7FNKKvJ/JZELTtuf6f0ZrTk/PRCxE5YVM\ny847xsj9B/ep65rpJWIinz6eeolfxVX81ONJc+qfZ+zeuEFaHGK0uKcVhcN4cWILRmc3L0VQohxX\nEqgSPHN9h43NEVVsMSlRokhWU5gpUz+nIkIKKB9RBNm4hyDkM6R11sPeEUS+U0ty1GWJNhZtDE6P\n8VnCM8SIzyYbJnpM8NkONmGMw1g3tNVijLRdS5cU2lqKwuS2ppZiyRh8iDSNp/UtUSuigqBkjr/D\nYHvkI+XErSBEsdSRMTa9SlYK7Npma5U+9UDAJfXtflnDZVRsJQQzIK3nSueeCZ+GbcCTF/+1+1xy\npx7lH0rAbA+XgKgjXvUJHFQU3fMobxZ0wmRIPhJINpEKC04+R4OBqFGmAGPwUexNu9DSpstMiCQ+\nWQJXK3KDWhvn6LoW16v+xEgMQpoyzhHXtL+995wcHfHBB+/z1lvf5wc/eJv9vT1mfRWdEiaSfYY9\nXZDqU2tFTAqjDc5arDG0vsPnPhkw9IjlQ1TMZnPef/99vvD660w2piQfxBdcP+VLvJaDUoryBek/\nkc+cm+R1XeH48pe/zObmJgcHB09/2sQw45tYmUfkAxwSeEz9lb2yUG3qmuA9VVmtelVKnvTw8IjT\nkxN2b9x4+vn4FO/w8fdzRWW7il/eGG9tkWwAvMDOxhJTlytuLcxjFVHWoYzGtoHNyrF7bYNRabCt\nolQVeE8wGm0UJnqKlFBGEbqA1lJ964RYbfoO5QpxCctCLb5r0L4gROEFWFeCcaQYMXn0zGpFoS2g\nqFSJS2LHGoIfFB/lmKUH3pCIxmQXOpG+9UaKAaUTXieicSRVElKSvnzT0HUtrSrxyhLyZEHI1X9Q\nUp3L3xMRWbeM1WgrFWuMMZP0ZGzYWkOvCdDLrIZsOzqQnYe8ZNFGiGGrZVtlJ7O0qjP7JUmroWef\n8jHJ/ywJQ5Zooy/reytn+WW1qYg6EXqvqbi6LeUX01rMo1QUydZkEskZkrVgLCraPF3gkE1LIhHw\ncmU98fr7xBV4Qth1SulhzMM5m8EB5CJVdkjI3bJmvpjz0Ucf8f23vs+777zDvfv3V57LSmQAU5QP\nwGix0tNKUeURsc57jDWMxyOMMaL1HQLWuSFxxxiHt2mM4eHeHv/63/wP/Pt//3/yq1/7VX79136N\nF154gWdv3cKV5dCHkYtXhAuU1sJw7y8VlVae4p91957zmNaG1994gxeef4FHB4/oTQYufUgS3Wbr\nLFqvxkV6+cKYRA7TWcsi9S5Rq+f767/9LqUr8DEgpkZShS+XC+7eucvLr76K0T93NIiruIpfmDBV\nhWYDPzvKCUTWL5Ra88BW2KogLlpcu+C1F59lc5zoujP84hRnHSFFGu9xpuLa1phoKrx2YqXqO2Lo\nBPZWlpQCzrhsFyyTOsE3wk/zgViL613ynmY5z14Jeji23hxKWUNVVphqtCKLqSQcGxSVkdGxoiiI\nqabrhP2utUVpcX2TFrgTGD146qWhaaBNFq8MPlf9IUSZw9Y2640HOh+kAteajg4fopw2JVzjpCWJ\nab16zZQRXqst2GIgPacYB08IYlhL4Jmkl+Lw83kCnozkaYTjDMi50H2SX2u3JvGukI90ldRBqmuV\nQHuN8iUxWoKKpCKRTCDgUSmgo8VEh0NjsAKzZ16AwpBSds1TchKUck8tfz7Z6p2E9DDMGWYFMavy\n1qM/fzFyenLKnY8+4r133+Xdd97h9u3bHBw8kt1hFmwojRMoJERSTDImFhNR5ao9V/oxRgyG0WhE\nUTh8kH6J7zqMtVJ5riXelCH6v3vrLX7w9tv8X//hP3Dz2Wd5880v8Wtf+xpf+7Vf5+u/8ZsCzRdF\nlmNM6MQKqjZi46YUeW7v48fTwGRtDNev3+DVV1/hu9/9LsMWLl2yy1Iy82mNHZibKbNEVExoDAQo\nXCkbIJMXjaSwzvG33/1eD8YTU2ajJ6kUPrrzET4EjLtK4FdxFZ829HiEpiXMZG1UWuOqEq8NPjfD\nY5ZP075hd1Rwc3vCpFREP8P4Bh8hGUeyhjZ0WBKlFUJvMz9FG4HfpVhyov/gHEYrUXXTihic8Iqs\npXSyJsYQ2ZhM8CHQNS3WOZwriTHhmzMpBnQCfE6+CxSKshoNHhOmrCTJkVBoNGBUP3ef6HwHEQpX\n4IzBFBYbHZvlGEwhXtleWPM+eEJStF1HHVpastaH1rRAixSGSpvcflV0XUdMHdYolJFcEWKXRwdN\nrrwlHyXIm504gOH932MubtY73YM6W1/x5JZvSrIB6+8/MNlVP0rXc5B6waD8t5TkhaJGRZOfN8js\nd+YdmAQmGCbaUmHRbQDdybyY0cKD0KKxZxF1v5g+tzlwlcUUEkQZLUgxEqz8fblc8OD+Az58/zbf\n/dvvcvv2bY6PjmkWS7FKA5wWOj2phyxEMbgwdjXjraVfUzeNJOickGWMrGJZ1zjrqNvmQr89n+ic\n+AvnaLP707vvvceP3nmHP/7j7zCebPDKKy/zrW9+k29/+9u88MIL7F7fpShKnLWQd3ODEUBMQvT4\n2HEeNh5+y1D3xsaUN15/XVTmsp7vGu9l+DkpIcGIS45HY/C+k6SeDComQuPZnE55+OAh3oe1Xvh5\not5A4Ml9ptu3b4tveFXx5O3GJ4yrNvhV/D2LansLUyTwS+bHJ+giUuqCNghJTGSPEz607Faa15+/\nxa1rE0o3J/kaoyCFSLAObwy+XqIXLSFYkk+0jVTg2lq00pTViNFkIjPgPqCJGDRFadCmkEpVietZ\nb6vsu462aLGmGFDLpgJXOowx4ogHmFJGbJfdHGsdzhUkC11saeqaLsPt/ex9Py8NgRjE/710FYWt\ncFaEpkLS+GDwPuCT8AHazrOsDXXTyibCGIJ2+CzUk/rKGWgIxKRwhUMZWf9axAWO1EnOXIPRrSYn\n377dyyBrG5PKFrj9+irtgKTIcL7o74sqrTonx5pUP+UuIQxzAyrm15J/o0jt5PG8SOpaFB6rEzaC\niS2Fq6hGJa4o0HkMOgRPCCkz3414iJPQRlHoJxtwfXIIfSANiNtNvVzy4MEDbr/zLu+//z4/fPuH\nfPTRR8zOZhTOYbXpJxYyEzISkx8Sje17LhkK75OMYiV9KhKLGmU117a3ODs7f9h9sAAAIABJREFU\nI8WENWZIGpflDe/9anwsiVNX13Xs7+9xdHTI7fdu873vfo8vfOE1Xn7pZd58801u3rrFrVu3mE6n\nvRkNyny+zNWyLHn99S8Mft3SlpCLYwXXrHaUKp/3pl7KDlwrYYIoRd21eTcs5LUY46Vw/0Uiz52P\n7rBcLNja3n7svp84npa4Myx37vcLd7/qkF/FL2rMmiW2bVDGYMqSIhmcT5imE2Z63r6H6NmoHM/f\n2GFsW2JY4IzCGYtRJZ2y4mIWEqap6aKlLKds7Wwzm53RtZ5yMhURnSytGZMX4lds6dqAtlbIaNrk\nliRoJZ7rhbWQwGgDypJCBCuJJogpKsnqrCuRUNYQNXgCXfDUbaBrRHXPWotF1u8YIiG0aNUxGimK\nUvrusZmjNThbUBhN1Doz0CEWlq1JifdBIHnjCBjqpsm/G9qmoWkbRlmD1DmLNhavFW2KNFgCKye6\n3ns8rSfo/HtM4DHE1Aut9P8lQk78UeXzkCJJiZNc7O+1xmxfn4OXH3XWp1cQFTHLrMrqHcAHnEmU\nWnTRnU5ULrcIIG8KVgWX73LFnvlmne/oLkNnc3ziBN5T/2ezM370wx/x1ltv8e6P3uGdH71DVVXc\n2N3FaI2zcuFEH+i3PT1cMUASZKGAsKLfKyRhTyYTtra30VqLDOJyidKK7e1tHjx8iNaK1ssO7EkJ\nwFjZjYY8RqFzUjRaMyoryqJgb2+PGCMP7j/gT//0T9nZ2eFb3/wm//nv/A7TjY28oejZ9J89Uiam\nvfLKK2xMp2L5OHyAeY+XNyQaJUz7IPs6o40INiSGtsFgHajzF3uN2Hf+cwNYkeDu37/PPEtYnr/j\nxQNmQA5WUL96PGk/IYknLv/7VVzFL0PUbcuGMUK6AmxRYImoLjsh5vUphhYbNUXs6BYnJD2jsIbS\nlhAqorK0qRPSWUyMxhNCURKIVOMxSdX4GHBVhS0KnAYCWJ0gaZqmJeQesur7vzHStplnpCRjhCRI\nXFAeqSAVWgnh2LqJrJUxCqEt9/KDl9nvqIzAyEUllXyQtV1rhysKXCUGSnW9ZGNqsRoSAYXokPuu\nRdtiyKwjV6K0Exa2cgQnr6mNYVkvadtGjjkGUEpG47TGdy3HAWofMtSfaJqlrJPZInYQAYuy5tcB\nwhoymjKk3naBmIIgnaR+K4NPkd7DLaaV1Ovwc/88+TpQQrWnH5eDhEoeEyNjBWMrvX2HYjS2RCMz\n3yoxIMgaTUjisBeyKE/bNnh/iZ9ujk+cwLXSLOsF3/nj7/Dv/t3/weHBI7TWdG3LyfExp8fH0kdG\niQhAHhsb3vJ6dXhJokkp0XYd/vQUm72Tr127xksvv8R4MuEv/vqvuHv3HkfHxwPr+kkssH5vo43O\nPryShLTR3Lx5k+efe4679+5lJmbg6PiY927fpu1afvub32S6sSHQfwyfsApf26Zd+DllIYmbN29y\n/foue3v7Qz5cf6hKoLR0n26/8y6FMWxuyrkoyxJSIHYR5wpxXut3omtuUxfPxnq75+DggMPDQ77Q\n74CGa3vFckfJHLqMmfTjJcLyl4NeLRQp8YTXvYqr+OWNo4eHjHc2UKaka09Q2lBogUqtTkQ0Pno2\nQ2BrAm2z5Kyd4cqAUxrtKkIyQlpC03YtE+NIzrFsG5aLOco4yvGEEIQkF4LHGSG2xtjJeJLOa6wW\nGFihSHnDT5K+tdGWFKUdGZqGkG1HfdfifYstSpwrIUZsUeZRNakwi6KCqYzpOutAJbqmJro81hoD\n7XKOMYbRqMKYjtJZgcdjRIWAMRptHE3X0nYNWhVYI4/3dY2vm1xgyTz9RlFgrcviOwZXFJjcaq1C\nYLZcSqILgc7Ie1ZaVOi87/C+E2g6G0aF7I+htEEbObZaCUTfM9t919F5QSfkecKwOZORvQRKk1BZ\n7U7Cppy8U+ZwkTBECmBiYJrRXGcVo5GloSAoA9oIxB8FDeiC2J62bSvTBn2f/gnxqSD00+MT/viP\nv8Nb3/s7bty4Qde2eVwpETqR2zNaZvy6GHHDOFZ+Dh4v2NZ/dsbQdR2Hh4ccn5wQY6QaVYzGY2az\nOTduXGc2n1PFkqZtZSd4yZscBE4yjB568xCT2N97yOnxEXXT4LuG99/zzBcLjDVsb20wm814BqQf\nHhNWu8/ORCdDK0mc1F566SXefvuHwwWm0kqyAICYePjgIf/b//K/cuP6dba3twdLxN1rO9y6+RzL\n5ZLZfI5S0jJ4UhKVHlG/Z4o8OnzEe++9x69//esyJy9nSu6bRRxk0iAbVfTmhWp9tyEVuTBBz6vd\nXcVV/H2Ijz66S5hNuLExJimN72r5jvg5Rlm0tlgcXxiVPLs14mi5QMfIqHUU1XWWZUWkJS1q7GxO\nnHfYzR0emSn7J4f46KiKMdYYCuuI3tN6UU4zRFLncVajbYHWGmudwODofgCb6L30z4uClCLLZUdV\nVJDv3zY1y8UMhabQjqQipckVeIKu7VgsT7FFSVmOUDrinGU0GWHMmBSFUBy8pBNjDJXTGJM5N0ph\nlMxOx5iNZpJD4IlEURaoCL5taJslQWusLfCdeKP3VahWosGfULCxRaXz+JdSJFsNZisixV2KKU4n\n0rxnzZKu84SUMDpRVhWurPAjUavzvhO42kRC1BTTTWKEpqmp6zYb0ChJ7loSeBsCMZHn7cXQJCmx\nFE0ETIhMK82NzRGlkjxlCoc2UEdAm+H+XZTPdb6oJUWZTF4+13l/PD4VBbkoCpw2LBYyjmStMMQn\nozGFtTjrhsq7F25ZhaRvBWtuYeejhynath1Y5rPZnPlySUqR7e1tNjce0fqOwjma3mg+F5NqLcnI\nCIG8ps6lbgqBtl5QWE3pDLOzUwpXsrm5gdGaumlofSfIQtcJU/tzSkx9gnNFwRdef53/+B//HxbL\nZUYL1NpORu6nlWI5X/DQP+TB/QdC1DAG6xxN09E2DYvlAq10vnBltGy1QerRD2GoW2PwIbBYLPi7\n736Xf/77/5zxZJLfXxrmMAWO6nkIecyPfjdrh65Ckjd1fkb9Kq7i70nUXcO9B6fQbjEtC8pyLAnL\n1oTGg1VMypJr1yomWxNUbFFe5sa9V4Sk88K9kH71eJvOlLx7suTgrEXFSNEt0SlSuYKqKCmNpVSW\nkRthR9dkXCmdoZKnCWCCl5ZbAtMnT61IKcumpkipFWVVUY3G+PGIejwCpOgJPlBoTerh67YltjXa\nGXRsWc5Oqcn+6lrLVFEKqCAVLwooN4kIYmCLAkLAty3GOYwqoW1puxbfLMVLu+ug0BAVrW9Q2lKW\nFU2zpEuNjC5bTTIylVQf76G0HcjfKSU8CedKnCukfVqUJCpiCGyEaRaykREzpQ3KaJQe0fmOtkl0\nPhFLTUITlIjSFIVhbCuS0sSkab0kch8itZIRZ21kfdQovAGvk+iOhMSkNGyNChwQowfnWHQNdR0I\nOqJsJCmHTxCCwtoiF3kyaQRP1oKHT9kDL13Bq6++wuRPJxmmCBwfHXN6dMKoqpiMx0zGYwpXAMKk\nzm3bFUTcZ/D1hni+sQt+6IUDgzhLT5ow1vDMjRucnJyiDXglakJpSNqxzywC7yqBZcpSzFBEDEbn\nEbWAsUL88Lliv3XrFkVR0HWZJfmpxE5W/Zbz508yn1aal158UZzEFstsMJaG85AAZy1t03B2djag\nCVprknODO1t/zHUj4yQyrSDjDUrlkQStVwlaThBd53nr+9/nL//8z7m2u4s2hunGlK3t7ewyJRaI\nSq28l5WSPnxKkZi11ZVesdtXqkiPh/p4v1zFVfxCxXMvvsjR/Y84PDnD7mwx3tigC4HOJ0gKoyxW\nGxbdkuN5oCRS4ZkvTvGzGfXWNkZrGq+oRhO8Tzxa1Mw2bmGnO4S2ofYdyXecNA3LRwtSJ2O0ZWGo\nnKMqNGPXUBWa0hWMipLSWpzR6BjQKWJCwgQv88rWUeiI0oGmndH5RtZBYzA6onWHDjVOyfiYKxIl\nO5TVBKUjnUqgPEqLhkZMibZpWc7mkDSj8YjT+QLnCqxNpK4bxsOatiXlefTRZEN4UDGhCrDWkawi\nLCBZCDphRxVd8oQuoIzClhaTQHdtVjZLucpP+K7NIjMAUh33ULqyltI5JlkDRFZIsWOOqaQLVV7n\nhKl+tqhp2pYOmQ9PCJnOF4aEpmk7VGgxqQMlkqdKK1pNlpVNGGDsNGNrKZQQ6Tqt8XVH2yWC0qgA\nUUV80oSoUNbRF7iD+txTSESfogJXVKMRv/d7v8dyseRP/uRPODw8QkVJnHVds1wuOTo6oiorptMp\no9EI02uRq2GtH3qo+WnlHwUpCdxtkMqu855CFyKqgoII08mU3Z0d7j94MMyk694POF8UKQbKqmQ8\nGskxaJPFZyIqz2ZaJxT9Jrsb3bpxnf/2v/tveOaZmygtCVAUfNJnqsL7dD7IApJ4/oUXGI/HnOQ2\nQe+eq9Z6+/PlkrffeQeQHa+1YmRSlhWFdVhjqcqSthHTlqZtV6N1SnHOxyZXymS4/bvf+x7//b/+\nN0w3pozHY0bjMRtbm9y4cZ1nnnmWra1NiqLk2rVtptMNylLGUOR8jgXmwQgXhmF78PgVs95bv4qr\n+CWKl157ha1pyd6HHzDvIlvasVy0zBctdjTB2ApnNE1smHcNMYk1qI6CTJ6dLCgnG0Q7Ya5Kah05\n0VBXO9jCkboOZzXjUQUp0rUN0Xf4tqNrljRtS921LMISuoa0DKhYS8FiNBOXsIDTULmCUVFRWI3r\nlpSuxBpL1BUQ0Qqi74ixkcofsSL1AEVFNIYYPcloimoEmamtlKNrIp0eo82EWG7QuhpvpO2W8toc\nY6BtIkVZoJWmbQJFWdKFlmXrsRlWrjavMZ1Msc4RvKeYTDNJTVqlGphOx2gUbdNkgltFsFZY8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9fNMf/aPcdOMpSAn8U75lFx3FRE0yx48fHwpMLhcnV+DRRx9lsVwym29d8sozZxD7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P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\nDk8o1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G5yZesKG5ubLC0t0e12iMKANBJu8cXJSYoGDvWTLk1baG1otTrEgxiUIW6l/LN/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\n+6TtNloIKmEIwogojkjbb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l+/yu7DHV7/8Y945rnn6Pb6YAzXn3mWS5cv88Uvf5l7d+7w\nyg9/yMsvvcSbb9yidloEs9nMdpOSgrK0zPa6rjkdDhmORghh6LRjNtZX+N73f0AcR6yv2xzZYHGR\nyXTK3v4hRVlZSDecQ27MhcfHDONhwvn0loOmH2vEHWo2d0p+qmGabX2A5y++pwmU5pwFXL4bYdGE\nube8e9z/7kOgz+2PowpikGjfAvVpReCP9shz2xcCIRDCrlcvvHCDKIqYTiZEYcTOzgOSOOHSpU3u\n3LlHFMdcunTJqVdOuXnzY0hppVB3dnYYj8c8//xNAB4+esRgYFstIwR1XbGzs8PCgs2nv/76G6Qu\nWve9LT796c9QV3Zt2tnZoaoq1tbWGI/HHB8fs7y8Yru0YRiORhwcHbG0tkoJ7A6HREuLdK9uce/e\nPaqFPi984fP8p1/8B7TjmH/7p3/GG1/5Cl/+4hdZXVri3//FX6BaLVav9zgdjjja20cDYRw5cZoW\nSSu1NqPVQoYBVaeNXsKJ2AhGozGzLKOqbclbnuVOza2kLmuQwrZbFjgA/gyxtTwOe+XLssK4cs0w\nCG1gWhuCwOrpI2Rz3wjpu1W4EczZvsZFtTM2qANLsDSm8Ro8UuwFZTxc3u/3z+mR/DRz74kGPE1T\nZ4As9DadTjk6OjpnNP0O+sfAkQjtjtnIcDAYkCSpg5ddXWgYkucz6z29y47PQ+UXDdb8a/77PSTu\nT8j8vvnXvfDLaDRintiWpimdTsfmoVykXRRWoaosq3OQt1KaIAjpdnt0Ot13kOIKl5vxBlPKgCiq\nbE9yF9EopZ3Ih3Ge/9m+zhvti1H2xXMwD9dcRDJ8qZx/9PkVzw2o65rT01OKoqDf77+f+fIzjcel\nBD4aA+4yZcbyJLqdNiuDBRYXF9i6colsNuJPvvK/sbG5yWd+5e9x44WbDBYWCEM7lz/92c/y/M0X\n+Adf/jJvvvEmP/zBD3j9Jz9p+gWUZdksOFpZYk+tFGEgybKc22/fJ00T+r0uh4cn3L+3zcLCgtsz\niVYWHpYy4HxqeZ5M8DiDjDPe7pE5Z/jdTvM5g/Z+Tt3jEbBmO3Ovn3v+4jY8vG9MY6ztSxe3b55a\nXvos0poTcuHpfN3x8RHT2ay5ggIrYnL1ytWmMYjEMJtN6XZ7rKys8p3vfJd2q8Xa2ipHR0fMZjMu\nX76MMZYEeXh4SJblbG5uEEURhwcH9Pp9FhYGAJRFye7uLisrq7RaLXYe7NBqtVhcXGRnZ4csy9ja\nuuzEgCS7u7vUdc3q6iqTyYTT01NWV1eIohAMDIcjTk5OuHLlCmVZcnxybBtbpSm7e3tWOGZlhSwM\n0FHI28eHzJKY67/yGUIZMMJw48YN/tl/9V8yOR3yw5df4f/+y/9Ib3WFNIy4f+cOUZIQJQkaCOKI\nKE1J2y1Xx56w0Gqx2O0QRBEaF3wVJVVdM51MKMvKBld5QVVakaOqqpvUk0Ugpe22RuBa42pEEBDG\nMWAaPX9/v0npqrGgMdceXT43V1zELptSF9ecShsCI0ijBIyF4KM05trVa+en44dtwLvdns1ZuDGb\nzdjb23sH1HpuEXb5K9uL1hqewcJCU8fto80wDC2z15w3PE86iHliW/N1xpwzro/LJcy/xxs+b+yr\nqmIymTRGzntJtkxAUlUX8+bWoHsnRSm7nSiy/YSVqq3Wr3ufrSe3te3t9lkUrLU3uufz2WJuMZkn\nKc0fi9/3i7D5/8femz5Jct53fp+8s+6j6+iu6XN6emaAGQxADG6ABARQPESJouS1YnctrzesjbDX\ndtgvbP0F+8LhF35nv7DDoXBo1xve1a64kkguCZIgAYLgAQLEDO4BMFdP313ddZ+Z+fjFk5mVVVPd\nwIBDHWE9ETPdXZmVdz6/6/v7fiepYoNafZRHOKir9fuSGjHIDETBd3/dUfjfzBDYsTi//dWvcrC3\nS7vWwHGGNOqHUglMEdy4/hHvvfsWhUKRe8/fx30XHmC2ckJqxycSnDpzhpXVNZ5+9jl2trf45Wuv\n8bOfvMLVq1epVqs+OM2Tuveeh6rCsC/bHru9AZ3eAer+oeRKiMdI+lmhZCpNf9D323BkHU4escpI\nciqKwh5FAUFUEKTQj4y+IbKdO7lsUyLqsfd/evZschvhezqWLQgmsdudimD5rzZG0X1QHVCClGFY\nhrr7QE7JArYtBV/898txXFZWVsjnCjiug6YZeK7H0tIyhZkipVIRw9BZWloil8uxvr6OYehUKnNh\n2S5Atc/OzuJ5Hlvb2zx08SLZbA7HkYHK1tYWa2trGIbB5sYGyUSCYrHI66+/TrfbZXl5mX5/gG1Z\noQGvVCq0Wi0ODw85f/68xPAoPhXswQEXH3qI4XBIdX+fjI+W39raDpnNBDAYDtnY2kK3TGbm5rh8\n+TJ9TWP29CnspUW0ypDB+g12h32ee+xxSrkcf/Inf8Kp1QVOnTzJ888/TzKVZLZQpN3tUt+r0u51\nsWIxDNMETcO0LEzbIhaLkUkmqeTzOJ7HwHFwPRfXE35tvUuvP6Db6UpSpf4A13GkD+l5DD0XoQgU\nXWJQgmhd+Eba9Z/TIAsU9rcLP1Hvry8xIiJUlAT/efbBpKqf9dM0jWQ8wdLyEpZl3YY9upNxrAFP\nJBJo2qhdzHGckI0tyNtPprs91wv+AEW2yCRiMRKxmGzi9wntNU3D9UZasdFtTBuT9droulFA3bTv\nR73raSNgtQoIPWSa2cS2Y6FASQBuE0KEqfPAoEuD7YSZCvmC2ZimGUpPep4I6f8krauL647q58Fx\nHjWi5xWsF6S+A9BeVD0tIMaRGQGZ3k8kEiGScpTSP/66340x3Uwowcnc0bbuyLkQE79OgJ5URWFu\n7gTFYolBpwd4NBs12q06sVicUqlEPpel22nx0gvf5eUXX+DU2mk+9xvPsXLyFLFEClSdWDzO8uoq\nlfl5HnvyKa5++AGvvPJjrrz/Plubm6gNjV6vh6FqWLpJf9BH1WR7meu59PtDen3ZS26apqRRjceJ\nxRIgPJ9tbVSiGQwks1U0c6NpGk6EBMl1XDwhJwZFHJNwPuq+36kTd4yxnvpx9F/43orx3d5pduDj\nDjH4RYluz5+AfT7uX4fz2mq2qO5XRx8oMnBYWlqSYMihg6HbaKrJbzz9HL1Oh3Q8xYVzF2h32uRz\nOVKpNOfPnyedThOzbZ8hskulUiGdTrO7u4vnujIiNk0Esu7ueR4zMzP0ej3q9TqzPur54OAAwzQp\nlYoELY/VahXLssjlsuzs7NBqtSgWi9LRHzocHh7SaXco+CXEWq3G6dOnMU2T/f09cvk8+XwekHPd\n3u6uFHUqlSRvgfCYq1RkPVxT2a1WaQ2HFE4us7WzQ1W4lM6e4cTpNarf+iYrS8v8/u//Hgh4983L\n/Omf/iknV09RLhS58sEHNFyXdCaDp0ilNsO0sGI2hk9MYxgGuVicbCKOZccYuo4fkA38smmfdqdD\nt9uVlK2OXO75pWBN12Q2jJESDpB/AAAgAElEQVR8blDuUXzjHhh54QOyhRrUwqOwOQXDNMLtep7L\n7FwZy7L8TY6XR+9kfEwEnvRRqCODUd3fRRFDDM1AUQSu742E/Z0qmLoERGmewDRskpZFLplEVxQ8\nx5FoP93Acdwx4BhMn6CjjsK0WkFgwAMDFoxpdfro58GIssEFQC/X7dHr9fzlo+jcskwZJSUTCEFI\ntRrwsQcApKCGLrcNIHxUuoauawgBmhZN899uoINl0eg6mkqPfhYQ2UzW74P0fvQ6RWvo0V73j6vD\nHGfkj3/wxHQjruD3J//NDIEsE2WzWbZurmMYGrpu+gRBHT788AqgkM1mKZfLJJIJrrz/Dj995WUW\nFhd55ImnuffCReYXF7FMC9OyKVfmKJSKnL//fra3tnjrzcu8+eZl3nnnHQ72qwy7PYSioOlGWAsL\nnMehI6Uie/0BeqOJbZrEYzbxWJxsNonnuZLIRRu1WgZiJq4neaNt20JVFCktqSgS6DPRcRCef7T2\nHBlT7+VxEXDwnkW3G2xryjUPvxOJwCObIcDSjFnzSCZdiOgi5fad+Cn6yVLbWATuD09R5BynStW3\nX4sBb7fY298fK2c4rsO9996Lbhg0Gk0syySZSHLy5CpOr0s2neHxRx4FIWVnxcDjwr3nqVTmwuTL\n8tIy6XSGcrnEhx9+xJkzZ1hcXJTlG+Da9evk83kKhQLVapVut8viwiL9fp9ms8n8iROYpoVlmmxs\nblKr1ZidncVxXLa2trAsK+w5HzpDNjY2Qh363d1dBoMh5XKZarXKYDCgMDOD5nN83Ly5TrvdZnFx\nEc/z2N+vEovFKRQKuK4kfNnb20PTdfL5PG+++SauJ8iXijQHA2r9HoXlRbIryxiGzjtbG9xst/jc\nPWd59qmn+MW/+BfM5HJ87StfodVo0mzU+fbz38WOx0im0rQ6bRRNC4Vfgh52Ox7HVBTSySR6Lkd/\nOETgk9P44OZWu0Mn0NcQElQ3HDrS+AqZYpf5sEjJRYCHi+cra0pwptRoVzVNglp9EjPheSwtLmIa\no7Y0+dje5RR6Lp+WQA9Nx3MkkcL+/i6u00NXbQSBErKQ6b3IUBSZTgB8gFASVdX8yDNQLRvVbqe1\nLE3WgQOg2uQ4ykB/3M9gP3C7YyCErFHLFLc0hIFBlw37BpZlY9sWuVyOTCYTMsMFgKbhcIAQgRGX\nUf5IOU0adpmCH6eADLINiqKEtetp9e+gjh04DtE0fJBxiILZJAbBHTv34LMgaj/OiE+7br/q+JtB\noROWL8qzs3zld75KcabAW5deo17fQQjJ32yaUoN7c3Odzc1bZLJZTlQqrK6u0Gq1+A9//md841vf\n4b4L9/PIo4+ydvoMmUwGwzBJZwwSySQLS0s89fQzvPH6a/ziZz/n+kfX2d3doVar+7TBAtP06Vz9\nrI7EYTgM+gNarSaWaZGIx7FsC0M3iMdiDHyWK9eVPNV2zMdy+AI+AfpdeGPwm4nxKa79MRF1YOSj\nz+HR116RNK6h8Y+2N8osluuOHE1ZRpCZhNAwH+X7+dvVdSnDavgcFPhBhvA8hIKsO6sa+IDBWDyB\n4vep382nst1uc1AdReBBsLC6uorrOAydIZZt4ThDHJ/tUVEUqeHgg07n5ubI+DiVw1qNQibP0099\nlna/x8LCAt1uj2effZa1tVMhs6RlWZw7d45KpcLW1hapVIqTJ1fodrvU63WWl5dlzVZTqdfrNBoN\nVlZWUBSFvb09UqkUhUIhfE43NzdJp1LYts2HH36IEJIVbnt7B0VRpLF3XeK6LgFxjsPc3By1Wo1m\ns0EulyUei+E4DvVajVqtRmGmgOu67OzskEwm/HT8FpZlk8vnJRjZ9bh+8yZmPEGiUODQc2loKqsr\ny6xefBBbNzjc2+Pff/s7LMzN8bnHHueb3/gGnVabc/df4MOr16i3mqQyGYaNpuR1VxR0y8SybXTL\nIhaPE7cslHiMfDolHWwh6A0H9Pp9qerW7YV2QOKj/HKx5z8xriPbEXXd51yQ9W+QaHXXddGQRDUL\nC4sYphHO9Z92HGvA8zMzEvjEyENoNJp02h1y2QzTHnPhH7SiSDSbF9KpZjEMnW5PpilSqbgfOY4T\nUgRjEqAW/B5tgYp+Ps24THrg0Z/T9hXdnjSC6th8FdTnhPB87myp2Ryo4ViWSSqVIplM4jhDvz+9\nHxrYwaAfqqINh45fetN8wJIaGt6gxhXU5YHQUAdtb0H0FhzXpBJP9DoE1+woT2+yFBLey0+R0rnT\n8Wvb/sfkXCXDIMQTCe5/8CHO3Hue7VvXefWVH/Daqz/l1s2bDJ0hpqYxk83KqKV2yAfNBqlUEtuO\nkcmXiWWKvPfeu1y+fIlyeZZHH32Ue86dozw7RzwmBUjsWIxnnvs8jz3+FFsbG7zx2utceuMNbt64\nwX61SrfTkWBR10PX5Uvd7/cZDvoyhd6ToBzNj7hjdgzblkZdUzX5T9PwXNdXx0tRO6yxs7tLq932\n63R3eOl+jSWVo0bQi21ZJp2uzCQFnNWu5zvTrouqGmEkNPn4CL9W6fog0ijX9dBxpNHWNHRDlj4S\nySTnz5/jH//hH3LhgQdQ7rJD6XkemxubdLvdsc9y2RwnKifkeSsqhi4pZz3hEbctHM+l2W7JdPVw\nQLvTkUBHITAMnUw6zdzsLEJTiSfiLC4ucO+992JbFvV6HcPQeeD++zlxosLp06cZDofcf//9rK2t\n0Ww2qVQqXLjvPgxTAmur1SqeJ1hYWEAIwc7ODrZtUyqVwvlje3ubdDqNbdvs7u6GRDSXL18GFMrl\nMsPhEN3QuXnzJoP+gKWlJWq1GvVGg7VTp4gnEjiOS70h5ann5uZwnCE7OzukUmlisRibm5vE4xIt\n77guKHD9+nWSyQT5fI6tLdlqWZqdxVVVPNvko71dnJhNbnkZY65MzdA5cfYMDz/zDJuHhwxdl//0\nd7+GEIKNWxv86Ec/olM9YO5EhdrePruOg24YoUSrYVuYloXQFDLxOPlUkqAbwvE8UKDX69PpSv6R\ndruNcExcZ+ij4n3GPf/+Cj/cFZ6LYeosLi1gGEZIuPVp59pjDXixWMT0W8lkYV+h2WzRaDY5wdz0\nLwUFfl9S0HEcX2g9g24YiE6H4WCArmfCiFyi/G43KtMMa6hoMyXiPgrkBtOJU46qmctlMvKWmwsM\n92iZNL4BZ7qshTebI7pL0zR8YJsFKGE7m+s6dLtSLU3TVHTdQNPGewcl2G+krhYcf9QwBxF2lOIv\nGvlMpsWjafNJdrbJDMjkdoLf/1aMX8WuRJ2xCAhM0VRiiQQrZ+9l6dQqz3zhy7z6yktcev0XXHlP\nosulAl0MBPS6XWq1GjsHDTIzbTK+6tzBQZV/9+/+jOz3vstnHrzIZz7zICurq6HqkmVapFJpVk6d\n4rkvfYn3332XV3/+c968fJnqwQGHBwc0mk2GwyFWLEbctvGcUXo9cPyE16Hd6dBoNInH4yTi0hk+\nubLCE089Sa/X5ac/+QmBeJCiSPGMT3yZjngn7uYTMFbq9v8LwJSSq0FiSpLJBAcHhz64aFQu0lQd\nydXO+JaExDYYpqRMtnxsSCKRYPXsWZKpFDMzMywsLHD+wn0SNFYsUigWJFe1cnfP1PM8dna2Q/0F\nmaGQkavpR2C6oYcscKoqZUGHzhBFDdqRFGLxGIqq0Ol0UTWN7qBPp9cllkxIzQZVI5vNSL0GVSVm\nxzh9+jRzlQqZTJpYzGZ5eZnZ2VnW19d55plnuPeee4jHR6IeZ8+eYWFhwSeh0qhUKmQzGbo9CQTb\n39/nwYsXSSQS1Op1TNNktlzmB9UqQngsLCwAEtC7ubmJJzzm5+epVqvUa3XK5TLJZJJ2u02j0eTg\n8JALFy7gOC57e3ssL6+QSMTZ2t4mHo+Tz+dDA7e+vk4imSSfz3P58mU8z6NcLoOioKoaW1tbCASF\ncolas0m91eKBEyfomwbb3Q7ZE3MsPXyRmG6QunGD77z8I4qVOb74xS/xwx/+gKtXPuDBRx5B03V2\nq/vcvHVLlrpMA6GqqLqOacsedt00paSqaZKybbxcVs7JntQz7w/6tNptur2e7GYaDPE8l76QhDCl\nmRmKxZJ8D37FefVYA14uz2LZloxWFIn86HS61OuN29YN61P+A6ogH0jP89A1g1Qq7XuZhCIh0+Q9\nx7apKLcZ3igYLRqlB2Cwo6LJowBb0xDro30FxxQYb5/2UYhRSm/i+4HXH6SddN3AskwfDGehKDaG\nITl9oxFzVDUnmg4PmNyC6wUj1Hq0dh8c+6QxjvKeTzo6kxH5pCc4+fuvY3i/pu1+/FCYVj9VdJvC\niRW+8LUTPPr0F3jvnbf40Ys/4L133qRWO0AFdEOgI+lLdzZusrNxk1g8TuXEPMWZLJ7r8cPvf5cX\nvvttzp2/wMWHHub8hQsUCiU0n5KxUCiQfeIJ7j1/np2tbd55+22+973vyfaeTodGs4nT7aArhJG3\n4wxxXA9FlSC1gHe6Vq+zOD/Ps5//PKfPnuEH3/8e1WqVfn8QEvfc8ZiWkVE+PXnpkbuZ+ENVVRLJ\nJIahkUjE0HSNRqOJoij0+gOEB64n0DUDRdHCLQTbURWFmB3zy+ACqX6o8T/+8R/zhd/+bcnl4IM5\nNV1ywyt+GlmedgCmuztnqqCQTKaIx+P0ej1Mw6Df7/P+lSv8T3/8x5w8eZL5+XnOnztHfmYGzxtC\nLifxKYYBmspw0A+1ygeOQ8y2UTVVyoH6c6gAnKGD47g+iE3S8QYsYZZpceJEhXgszsLCAgsL8yQS\nCRr1BgLBww8/zNzcHMvLyxweHHD+3DnmF+YxTRPXc9nY2GR+fp6TKzIF32l3KJVKDIZDDg4OSKVS\nEmAXi9FuS93zZCIRCrIIBLlcDkPXZSfM4SGu41IoFCSff6tFPp8DFPb39snnc2QzGUDQ63XZ3Nzk\n9OnT5HI5dnZ2EALK5bI/30nAnhBQKhbZ3d2l1WpSLpdCFc21tdPEU0kGgyHVXpfdfpeTi/MkV5Y4\n+JFCN5Xgs7/7O+STSbqNJv/X//F/clCt8huPP8FPX/0525tbLCwt0tzZpd3pomoqVjyOYUkKWcM0\nSdhxEoZBPG5QyORwEQwch4Hj41s6bTqdLouLSxSKhTCj/KuMYw14oVj0e8FVX+xQTlqNRjMkIQkg\n9wEHraKqCE0LkXiBV5nypRZVVfXJ4zW/nUr2VE/r24bxSDxaA48alwAcMEmFeJzROcqIR38f+Q6S\neEEI+VPuQ4vMcaPoXBrmUdQv0+cOQRRvGCamaZDL5UOpUdlaN9pvYGADkFm07h9tOYie66TjElwL\nWUscyaFGxV2Oi8CnGfFPM4IIYpTFEJF7LVOifx1Difw/5SjD/xV8Ri5To1Ce5/HCLOcfeJhrVz/g\nlZd/yNuX32B7cwOhusRsHQ1JCdk42KVdr2LZFoVCibm5Cpquc+Wdy/z4pR9QKs/yyMOP88STT7G6\ndppYIo6pauRyGdKZFCuryzz59JNc++gjXnrpJT788EO21q9Tq1ZptdpoqgKqhqka9PsDNF3B0s3w\nnYonbTqdLj968SW+/70fUK/X5fsSvFtTwWr4NLJ+RkkJ7ouH5zuoMssUXEMlBFD9KnXi274XZMkk\n4oZEIk4um0Hgcnh4KEsKgyGGaaLrBqWZErV6g263IzEUYnT/hBB0e110TccT8nkvZ8s8cPEiSydX\norc7cvfvPrYjGJqu8YXf/E3WTp3io48+Ynd3l+3tbT788EP29va4tXGLVqtFr9cjZseYXzjBwsI8\nxWKR+fkTnDgxj2EYrK2dQjU0hq5D2jYlKxnyng2HQywf+9DutNF8wY9Bf4CmqZiGSaMuJY2HjoOq\nKCSSSb+NV5BKJsmkM6RSSfK5PLlUmvzvfo1cTqqPZRIpyoUiTz3xJGun1/A8j5WVFebmZPuabdvM\nzMwAcv7b3Nyk0WhQLBbRNE2m431A3NAvJ+7u7BCPxyiXy6yvS270fD7vO549KpWKBH9pGh9++BFC\nyIxwr9djb2+PfD5HIpEIy7qyZp/Etm0JjtM00uk0h4eH9Ho9ZmfLslPD6VKtVun1ehRLJfrOkP3D\nQ+x0ivzCPP1+H0fX2FY8UosV7n/ms/z4rcu0XYd/+s/+iP/4V9/ktVdf5bd+8zdxPY92v8e777/P\nXqNJzE6gGpL/QbdNVMPAiFkYtoVtW9gxi3TWZe30Grlc/q48a8eD2HJZsrkct26tM0SmlKUUW80X\nYHfQfYJCD1XyOHsKKALHEX5aTH4vHo+Hk43ryvpAwAamaSqaNupDDkYA0pk04NMM11Fp32jUGU3H\nT4LkpgHcYNwrn6yHB17+yMATAhsC4xkcqlwm6+JCCJLJFK7r+HzbUggjmokI0loBp3q0l3sSuRhc\ny+B7Uc8uQJkHkXpwPaPf/TRj0rAfZ+gDRHG0Zhk4FI47ya39Mfv9FMf68Wc4yh8FhilIsWuaRjaX\n58IDD7Fy8hRXP7zCaz97hbffepONGzdoNeugqiGrUq8/4OrVa1y/cZNMJsPJ1VMsLa+wv7/PD1/4\nHj9+6SXuufcczzz3HGfuuYdUJoNpSd5+wzTI5fOsnTnD9vYW715+g0uvv8aNGzcwTZN6vc729nYo\noCDV5GLYlo3jDvkPX/86h4f1kFlu6PhyuGJ6VspjKPnffSPuCWmgBbKGDKCqkhtaEtHosqFGNrRK\nIBi3Z8lGPBaqLzwyPoSqIFQJKPOET8Oq4HvMAkM3pMQlMHBc8oUiSytrfOnLX+XU6bPYVoz/9X/+\nX/jJT3982zPneR59v24qVeEE8/Pzkp0segkiX1PERObpEz0zn2wIIYjH4tx/4X7uuecev+NAvve7\nuztsbW2xs7PL7u4O29vb7Ff3uHHzJr/4xWvASJthfn6e2dlZEokEDzzwAIuLi6iKQjKZoD/oS/ZH\nTRpzw5A95YPBAEVRGDqOrMX6LFuu6/r4Kg9VkWRWAhGKNtmWRblcJpVMsre/TzKZ4szqKTRFpTI3\nx2HtkC88+yyVSgUB3H/hfkzTJB6LkUwkudq6Ri6XZ2lpEcsyqdVqxOMJSqWir+Io2NzcCmliL1++\njK5rFIslyVExGIQiH0LAxsYGpmlQLJbo+uWrrE+sBLI/vVarkcvlAdjd3SWdlsJWV69eRdd1H4wn\nS5I7OzuYpkk2m5XSu90ua6dOgf8M7O3t0W63OXlyhWqnzUG/S2y2iJJNU1MF5DOce/opKsUiw16f\njf/tf6fdbPPc557m5uYGr196g0K5hMuQrZ1tMLRQ+CUWj5FIJonFYnfl+TrWgKdSkpYvAG8pimTx\nqtXrYfRMiD73DY9fuA+w9dK+CRKJBLF43J+4pTExDCNEak8b00Bs01K60frvJAVq8Ps0vevj/h5f\nJm47xmnrjjsfQTfLOLWp6k8qruuGqXSZzRgHM0wa84ACNeqURI158PmkgZ/MVERLDYEkanQSjDo6\nxxn3SSfq04xg3r7jL93hELf9cvzKHoHIymhnmqaTzRV48KEZzl/4DLtbm7z6k5f56csvcunSJQaD\nPqZlE9cM7FicwWBAo97gl6+/RiqdYXZ2lhOVCgiVD95/j1++9hqLy0s89sQTfObhhynPzckoStWY\nKRTI5fOsrqzwwAMP8MvXfkEsHmd3e4dvfvMb7PsRhuM4tNuyFu44DoOeg6FJquAAza6q6hgynMi9\nchwHwzYRnsLMTA5N1aj6PA/xuM1wMARFTuw20GrLftmTK8shQNMTUup32jscvheTZQpVzhGuELi+\nwpiiqdh+t4sQ0O0PmV9Z4fHnvsh9991PsViWzz8K7WaL0mwR068VD10X27JwXQ8hPFRNYeD0JcJc\nVagsLZCdyd1eLgkeCYUxN+NuxuCe54VtXbYtnS25E4XZ2TL33XdfSGjV6bSpHu7TaDRpNptsbW2x\nvr7OxsYGOzs7vH/lCrXDQ77z/PM0G03S6TSnTklRnNXVVZaWpOjJ4uKCL14iuxM8TwrrBJlK3dBB\nkdLHKGAYOrV6w+f3MBh0uj4HiOavo/jgSZtMKkUiHiedTJHJZNja3uZLz32eVCpFp9MhFYszk8nx\n9Gc/y9zcHIWZguRTyOdZWTnJYNBHURQGgz4zMzOUSkW2treJxWLMzpZ5++236bTbVCoVBoMBpmly\n/fp1PE/iBvr9PoeHh5w5c4ZkUspI12o16vUGS0tLqKrKzs4O6XQa0zTZ2tpC13UJsPNpsre2t7Es\ni3w+z/r6Op22ZLgLSMW2t7fpdDrMzc2G7IrLy8u0Oh12q/tkigWMVAI1k6LW67DR6yBm0iw/coFb\nL9bpKh6f+/yzeJ7H//2v/iVoKp4KXlMQTyQkQdqvgDyPjo/hQjdIpzOM2IlkfTcgJQiAbdEUumAk\nYiLVkLwwAk8mEgChoTEMg263MwbQikbEUdnLSQMeHZN84dNGtK0qGMKva0+itoNlysQkPjLUkowl\nTL2OpdKj/vsobSyCKCckWen5+1TD/U+TK50E5k1mGyY/j0bV0XUnDfNk/XtyHGWgJ+/BtO8fue3J\nVSeu7ycaIvxvfMOfZDMft040VTztSyLAJcSYXzxJebbCY09+jp/95GV++ML3ee/dd3CdIarPghc4\nSe1Wk7ff2kfXTBZOLFKeLZNKJTmo7vP//ut/xQvf/x73feYBHn38cZaXT5JIJSX3fjyOoqi89OKL\nVKtVDg4OuHVrAxi1JgZANeGXZyzDCsUSApanMZzBWJlFxfU8TMPg7JkznDq1xhu//CXNVotut4Np\n+ixXGVnb7PdlKahYLIR95m9cukS73Zn+HISh+OiOBXdcxgQiohwFrpBzQq1Wx3Zd/uhLX+Hk6mn0\nqCaDn11KpVNomspwKEYpeM+VbWb+Jj3hoak6M4UZrJgdZoLGj3HKY3DMPHKnQ1VVX7k1TD0hhPA7\nDJAEH0iqzWw2QzKd8J1/lV6vG0aN3W6X3d09trdlxL6zvc3W1hbVapVLly7xwxdfxPIVGGdmZshm\ns+RyOVZXV1lZWaFSqZDXNFTV9SlAJShQ13WJ2vdbe52h5Bi3bBs8x6/xmnS7XUlsgqDX74cMnbFY\njGQigaqqUg1S0zm7tkapUAidgN/+4pdxXJdMIsmh4yA8wYnKCRRV4gNMw2B5eZl8Pk+z2SSZTFIs\nFgE5b9+4cROAUklSvO7v71MoFEin0ygK1Ot1qtV9stksmqZRrR5w5sxpX8GtGpLJOK6kut7f28Oy\npOjLG29cotfrMjc3F9qcvf19ut0u5XKZZrNJu91hdnaWbrdLtXrA6dNrxOwYqqpSr9VptVpUKrNY\niRi39nbQkjGylTLXrl5nIMDwtc5BoCkGc3MV/1G4PRi903G8nKihh+o3ih9SSj3XzbAn2sXD9dNv\nLoBwQfipMSFfLoHUVU5nMqGBcl0vhNFHTyYYUeMVlVsLAF1B9BhcgGhtNYjyg8hZpvFH3LajiDpq\n3Ebbmtyu/9eU7wQGMZpKH60T/a6sJxLWxyWfuuqnx13/pVVCp+WTjqNAe0ctH2UCxhntjjLK0+7L\ncZ9P/3tUgb7tWO843Jl2rgI/n3vM6tO+p9y2bNIOBc/PaKHi304Fw7KZW1jidyrz/MYXvsy7b73J\nC9/9Dm9evkS9Vhs5T4qCaRh0Wn1u3LjOrVu3yGQzVCoVFuYXOKzV+Ms//3P+6i//gvsuXOA3nv08\nFz7zAMWZAgcHVd5++20ajUbY3TDKskgDLJAoc8/1cFQX13cSg/c2ypMfHZJKd0i702Vjc4sHLz7E\n+fvuwzRNDg4OZOvVcEAmk6Xb62LoFoeHh+zv74ctjV2/lXLaUI94LBUImlQkK5f/TwjQDYN8scBX\nvvb7nDx1RpaAgm4B3/yruk4skQjTXJqqjuYcPy0fzgWqKrtpzE+u6nY36+BHObimbfklJQXNF1YT\nuBFcijSOsVjcLznOyLqw+gAgteyr1arUAG80aTYbrK/f4tatW2xsbLC5ucnGxgavvf46nXabTqdD\noVBgaWmJ+fkF1tZOkclkKBYLNJstdF0jlUyBouAKD8dzZb1cU/GER38wkG1VwMCRaXpV19B0qTA2\n9PvXJRZJlcdu2+i6zpm1NeJ+8GbrJqcWl/mn/9kf0u31SMfiPPHoY6AoFPJ5yuUyn/vc58ik0yQS\nCV8AxuP06dNkMhmuXr1KJpOh4Euquo7sIY/FYhQKBWq1Ov1+L2xpa7dbEjCazVCrSbW1eqMROhfV\n6j6KojA7OxuWSnd3dnBch1KpyMbGJr2eZLxrt9scHh6Sz+eJxaUBP/Aj9JmZGRKJJDs7OxiGQblc\n5uWXXwm5BjxXoAiFuJVg7dTabWDsTzuOlxPVdPIzM1iWRVvTfFCIx87OrpRHTCSQ/MzyRVL8+piu\ngaoJXE2goKMqFul0mpSfOnBcB89zQ+8vehKT0fakUVPVgPJwBKIDaQQ1TffThr7wu39DggxC1C6K\nUZ6f8Ug6avCUcPIeTeSTrVVifJJHps3lchFuMzDswXoSjKaFRCZRMNnHRcefdExuZ7L2PbmvT2Ks\np2VBjvs7WleccoR3bL+PrrWLMNI7bpvjyz/e8w38sdF5RL7jS1pqukomN8PDjz/Fvefu48p77/DD\nF77LSz94gVarFZZAUkkNd+gydF12d7fZ2t4km81SOVHh9OlTuMLjnbff5JVXXmZ+foHPP/sctt8G\nJYQImfYEkhxJVVVURUXVFNk/7kpnwQudZBddG5cEjo7BQBILxWIWiqLy/nvvc+3aNRzXZTgY4Hou\nw6FDLpulVqsxGDoMB0OGzlA6C67jOwdTOkiOuQ/yjZVXX0UacwUYeh4oCtlsjqWllbC1cmxLvqMb\npCEVCMsEnt+fG9xl4b9vQRDyt22M9ZwroKCBUMM5QvG9nKDsBnIeCshCyuUyhUIhnB97vV5IB93t\ndtne3mFzc5Pd3V3W19fZ2dlhd3eXl156ia9//c9D8FkqlSKVSrG6usrJkyfJ5XLcc89Zme5X1RBJ\nbcVsKdDjP8+e8GQJw33rMogAACAASURBVBnieI5UD3MdXM+TYDXh4Q4GCCBm23S73bAUaFsWqVSK\nTDrN5558ing8TiqZ5MlHH0PTNCrlWYaOQ2vocPq01C9PpVL0ej3uuffekP8dCA14MpkMDXI2m6PR\naNBqtTh16hQylhSSka7TZe3UGt2ulCnN5/NhtqrT6VCtVsllcwyHkjrc8o91Z2c33LaqqghPsLMj\nKWJnZmbodju+o1RE13Wq1apfiiMEgGayWUql0l17ho59qoXrkA2VxOSLJIBarUG3O6BYnEUoqtTu\nFQqub7gMIdA9D8/wD11RSZFkppgHFZzBEMd1MUwL4Y0m5cn0cADcCqQuR2xh40bddeWFdl2puRrI\nhMrIWBn7GbwE0UhZGuXoZ9O85lH2MYi4R8ZZhEY7wApIwz4y3KN1g2Xe1L71X8WAT4vGo3V1ee7j\nmYrJuvvkfj+Jcf4k6407MsHnTJ38jz9HOM7wKrfdv3En7egrGjHrgVFSRovGyGCV6d/TdIN0foYH\nH32CeDzO9sYt1m/ekNz5vS59x8EyLQzPw7ZM+sMh3U6Hy5cvY9s28wvzzJ+osLSwQPXwkH/9r/4l\nqqKEXPaDgewNNiJkRjK75GM/vNH9D6LvadiP4Kh13ZBI2naXw4NDfvKTn1E9PEALeAIUqWm+ubkd\nZotARu5umLoPrrncqkLk7ogIh/TEvoMIXIn8k7wQYJo2lm0TlLBkWjww+bJPOplKjTmhQVlO1eSx\nu35rlWkYzM6W71rN8dc9pmWuVDUApPoXLRyjTJ+iKMRsm0QiDooEAC6vLMs50/XodbuSw6PVpFGv\nU60esL29zfXrN7h1S9bZX3rpJb73/e/TajbRdZ3Z2TkqJyqcPXOGfD7P/LyvGzAYsJJaZuhJIRBU\nBW8o0A1DRvCui6IqWJZFf9CXYEUF+n6PugRJeiT8PnjDMMjncnieR6U8K+WaBwMUAXPFEv/JV79G\nMpEgP5Nn+9wmp0+tsbK0hO6j1C3L4uzZsywsLPDqq69SKpVYWJhnb28Px3FYXl7G8RXX9vb26XQ6\nLC0t+h1VDXK5nK8nYVGr1dnb22N+fh7Xddnf3yMWi5NMJnnvvfewbZtisSABwZ7L5uYGmqYxNzfH\n1qYkmCmXS6G+enDvgud0cXHhV24di47jQWwJi7m5MrF4CoGK64FApdXu0Wz1EIpEpQokY5znyd91\n/2/Xk0IeiiKNbjqdkmxsHYHjuMRjsZHnzGTKenTSgV53rycJUMa5vj2EcNjfr/rcu0u+klgQA0RT\n25NjbLo54rNJANvtUfW4oR7N+uMReBCpy+WSJOZ2Pe/gvO/mmBZlH1U/n1x2uyEWU5dNbv+25X7t\nMhDHCdK6hmlwZ2PifkVvTuCkhHzEXmh4w2Px05OKElz3YGIPtKKDdT1C9JUItn1cxC6XK0it5lg8\nge3rx6sKDHpd4jELQzepNxo+HiSIsBQGwwEffPABVz64QrFU4uTKMvfec5b9vX1JiuEDkEYZnFGJ\nCMWnJxWjdjHFvzZHHbEAHHekIXBYawCy7SzYQvBd+Yyq/rUaqegFTIVBzd0w9JBd0fOC6ycIW0r9\na6v5rZaOG8wPvt633+ssVBU7FgvOIvwZONmKooQoXkVRQFXxAkclfL98A26ZZH0grph8Xo67m3f5\nHbw74/a8hqYFLF+BuEbgMsnrrmkamqKipZIkU0kqakWWGzwpktPtdBm6Dr1ul5s319nc2mJra4vt\n7W02NzfZ2tzijTfekMpj2SzlUolEIsHJkyeZnZ0ln89z+vRpSsWiX+/3NTF0DaEoOK4n++398oZp\nGAxdl4E7RDMMhCKFSBRNxXMdWQlTFPq9nnyP/Br7zMwMtmVx7uw9ZNJpdMMIW++eevwJ2u0OCydO\ncP3qNR579DHWTp3i5s2blEpl7rnnHkBS0+7sbOM4ksO90+nQbDZZXV0l5zsQ7Xab/f197j13L4oi\nRV9yuSzZbJb9/X0sy6JcKoXv2e7eHiCzPDdu3JAMcaUynY6M7pWI46iqKouLi3f1iTjWgCsKIT+t\nokriAMWTkfHe3r4Ugtc0XCERpZ4nX8SBr6c6hg73XMnta0ivyxkOURIJEFIQJKBMDEhQAqMd9Eh2\nOp2wjUrS1MkJWLZfyfcyEHaPptunRh9K9D0+emKWvO1B3n3ckI88+sl2sui6ip8GC9LqIpzIglav\nyRThZF160qhOa986+v6NA9oCZbVOpxP2k0czH5PXa3ISO27f0ya8seX+f+POhCQpuZPxyovPS3EW\nISfnWExKcBqm7ROcOLSasibY78nINaz3B/vUdGKxOKl0hlw+TyKZxrJt6S2PKZYFOflPOpn7ql9C\nChh4nsB1HAaDPoeHNWKGTTqlk4jHZPuc8FAVgWlo2HaSFDAYDqgdVHn98IBUIhWR4RWRe3ZHl+zY\n4w2POywpRe+v/Cx41kNHW0o0+euIkCxGagZIY65reij2IIL6tJDgKVWofguZBNi5SDQ6KKiaTjaX\nw7JjBHCDMAqPPDuxWBxVGTm/jhAYui4VDkXAeCYkErpUvoN7+HdsKAF8GJSxyM5/joVAKIIwWy8E\n+GWXeFzWqVXfwM7NzYUlzSC93Gg0ODg4oFarc+PGDW6t3+La9Wu89trr9Po9Br4sMcDs7Cxnzpyh\nXC5z9uxZigUZqebyOYbCQ9E1dMtk0Jdtb5quMRgMiSfiaLpGt+fKerrn0B30pZOrKJIoRdfpD/rE\nYjGy2axkOOv3sQyTiw98BoTAtCw++8QTeK7H7GyZ1sWHKM4UWF1ZkQQywiOdTvPQQw+xtLjIBx9+\nSDyRoFKpyGyBr8jWaDQol4q4rsfe3j4rK8tkMmm2t7fRdVlWVlRFgtr2q+iGLGf8/OevAgoLC/M4\njkOr1cIybWlDFNmBdGrt1F11Do9PoQfo8VRqBCZTpAHf2d2hP+hjGCYu4HoSyOYJUIWH4xtTIaSH\nr2oqtm37z1DAJCbbE1rtNo7j0Ol0aLVaoVxnlKksKnoyLS0se5yVMBpQFG4zNqP1x1Ox0fR6NIUu\na9Ra5POR4Q9Q5ZPfkWNkuIPJcWTk5boB73NADznNiYgiyKPnGl0eLT/AeMp8Wor8KLGSye1OjlHm\nYDrAbtLhiH5HRj++WIUyXiq50/H1f/v/hM9GPB4nk5FcBbJsojAcDqjXa9TrdVrNpqy/xWIkEkmS\nSUn0EDiHQggymSyVE/PMVebJ5mcolGZJZ/Pohgl+3co/Qyajn8C4KBP3TlEUSYWaSPjKcwJ3OKTr\neLRabZKJBJZtkUwm6fV7tLtdFJSwJ76Qz+N6HrVaM3QgDUP2Y8uU+FFX5+5MDEdhDAKhkfF1wbat\nEFjqeSJsjRRCXhnhCSI9GwjPlUZH03CGQ1zPQzNMNNNg+eRJLj78CPFEwse4BMcz/lwFiOggsg6i\neC9wpP3Ee6FYkqqKd+na/N0ZkYzT2McRp1pViDQYybKLOhKNymazBFLIQgh6PjVos9mk1+uxubnF\njRs32NnZYX19nb29PX7xi9fY3Nqk4wO7stksKysrlEolyuUyS0tLFAoFCoUZHM+j3e2gGTqOJzn/\ng2dJKjeaDHxKWUVXGfRlnR1VoT8coGoj7INlWQjPIxlPYNk2mqpxcnmFpYVFMuk07W4X1TR46qmn\nOH/+HOXZMvvVKk88/jgXLlwI56hWq8Xa2mnK5VlqNcl3srS0RKMh0egnTpwgHo/hebKXvdNpk8lk\n0DSNg4MD7JhNoVDg1i2p1W6ZhKWdeCLO8tLyXb3LxxtwIfw2kgyaquGqKsLnIt7b26PfH6CqkQhc\nSGCZ50iQmhCydjgcDvH6knFNUaSowM7ODo1mg263y7WrV4FRi1V0cp9Eoo9qegojPWR8Qzgylqqq\nR1ikbgc+jRuQqHEe0aVGlwefRdPiR1y14MjDTwJnwfMYmwDlg6qMGeCPG5PGetpk+3H16Gn/omOS\n6S26PKpmdtx+p33Xcz3Gv32MTvURw+0PSdpxVD+d3Gm3aLckM6BuyB5307IoFGbIZjJS39eTfPGH\nB1VUVQ376V3XZW93m+r+Lu++fRlFVcnlC5xYWGR+cZnK/BLZ3Ay6YUXOI0hQBkNMMe1gWzbxmESq\nuo7rK2CpMkp0Hfb3W6DAs889x6kzZ7h06Q3ee+99HxQkMwzpVEqigF0P1xwn6vmU/s/4mGzQDs9j\nCvlKuPT2kpMQgm6vh6Zq2LYlM2meYOAO/RR/5DnzU96JeJx8Pie1z5Mp8rk8Dz74GZ5+9jlWz5zB\nMEyZTvfnEGXsuivYdgxd0xhGHFTPj741XZOEUkIwOzvrp9un3aX/P47oNRivxWr6qESi66OsYkD7\nnE6nAEin0z473Fqo1+B5Lnt71ZBUZX9/n+vXr3P9+nU2NjZ449KlsDPCc11SqRTLKysUC0Xuv/8C\ny8tLOI4r5VJVFc2UgiKdTid8r11fGAtgMBwSi9nops6w6RA34iDA6/dkpscPkFLJpGyPazbJZFKc\nPXuGbqdLoVBAUWRKO5/Po2saVibL0tIizz33LCsry1y5coWl5SXOnz9Ps9mkUChw5swZScPr86/3\nej3OnbuXZrPJwcEBSZ+md2NjA0M3QucSFGbLsyE+626NYw24JzypsJVO+yl0xfd0PQ4PDxkOZWuB\n508onvDbQhAgJGtbrV6jur9PrVbj9ddfo16X7TXtTpuO3wMe9MhNRo1AmFYfTwcTphIDmUCJ5lZ8\nVjcN07TGauuqGqkZIsJ2ruAhDeqR0VT3KNomspyIAR2tM56WD9aP/BX5MwB1jWqL49c9GjWPI94n\nMwm3162ngdiiv0ej8KP45yfVy6LHNG0fwZiM7sd+BzzGaWAVRQlRmp90OJ0+AxcSSRnFDl2H3rCH\nwMNxBEOnx9Dt4ok4phEjZsQY9Pu0220GgwGJhBR/CEoGrutimqZEoLZaHB4ccOW9d4jF46ysnmLt\n9D0sLq2QmymSSGXQfD7/oKVp5IQEOWVAkWIaVizhZ6YEiqriOB6ecBm6HoZhsV+rc/rc/fyjf/JP\n+PJBlZ+98jI//vGPePvtt2k1m7JTQ5E83ZIFcXDHDt+nGVOdQiSz2dhz7RtXz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8od9LMbFmYg29bg+jI5qRrgsv/MK+iPZMAG96Uc2Bd559PA/g8l5kZV1jkK7FL0pPhiso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\nV76yir0De3nfb7yPck/UqEqZ34exsJBiejCthVyMtJ5MuTo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ail3nSiks20aFAs/3yWR0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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 504x576 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agwzh0cbiYQu",
        "colab_type": "code",
        "outputId": "519f1518-d12b-482f-93fb-4bfbd8acf183",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data.classes, data.c, len(data.train_ds), len(data.valid_ds) #print the 3 labels, length of the train division, length of the valid division"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(['electronic', 'fastai', 'grand', 'upright'], 4, 386, 96)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lk4kGc7tiYQy",
        "colab_type": "text"
      },
      "source": [
        "## Train model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5JjJP2ItiYQz",
        "colab_type": "code",
        "outputId": "30b0808c-38ff-4177-dc13-963268c9f6d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 82,
          "referenced_widgets": [
            "fbe50ba1d6514bd29e4fe908dabf8a6f",
            "16b4932114a547efafbd4fae882cf3e1",
            "ff0c59c65f2f46dfb4ef1c8eb56205c8",
            "879bb401277f41be8b1cfed4d32416a1",
            "7234908ee1254f009be437e89d71683f",
            "80ad5ea9aae94a85916c0751d1964174",
            "5c21f585f1c24d9e9f0f3e7ec467eabb",
            "b25c0dedfd194c92a5a226e129b4ef7a"
          ]
        }
      },
      "source": [
        "learn = cnn_learner(data, models.resnet34, metrics=error_rate) #loading our model : resnet 34"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet34-333f7ec4.pth\" to /root/.cache/torch/checkpoints/resnet34-333f7ec4.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fbe50ba1d6514bd29e4fe908dabf8a6f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=87306240), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cUAJO_APiYQ2",
        "colab_type": "code",
        "outputId": "3ea89e1a-a6b0-44af-ea49-bcd1cc6511ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "learn.fit_one_cycle(4) #now we train on 4 epochs"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.869040</td>\n",
              "      <td>0.898505</td>\n",
              "      <td>0.208333</td>\n",
              "      <td>00:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.103181</td>\n",
              "      <td>0.597841</td>\n",
              "      <td>0.093750</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.858375</td>\n",
              "      <td>0.477728</td>\n",
              "      <td>0.104167</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.712516</td>\n",
              "      <td>0.471621</td>\n",
              "      <td>0.104167</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5Yxx3bmiYQ5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.save('stage-1') #saving our weights"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8loqY7gAiYQ7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.unfreeze() #enable learner to train from new weights"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9p2pBaCgiYQ9",
        "colab_type": "code",
        "outputId": "f52b88c6-8c6a-4fa1-baff-0d26f5de788b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "learn.lr_find() #find the learning rate"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='10' class='' max='17', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      58.82% [10/17 00:49<00:34]\n",
              "    </div>\n",
              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.222304</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.279031</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.259145</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.223122</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.226621</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.199530</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.178100</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.156925</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.162662</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.386609</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='4' class='' max='6', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      66.67% [4/6 00:03<00:01 0.5914]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJqpH7wViYRA",
        "colab_type": "code",
        "outputId": "55c485e4-3a99-4445-e4a9-4538e1a50093",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "# If the plot is not showing try to give a start and end learning rate\n",
        "# learn.lr_find(start_lr=1e-5, end_lr=1e-1)\n",
        "learn.recorder.plot()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cc/Wcc/XDvOo55454nWNmw81smZmtNLM7w2y/zcwWm9lCM5sarPG0f1uJmc0PviYf3ceL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\nIiISlhKEiIiEpQQhIiJhKUGIiEhY/x9DraJRegPKawAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-Xz_5RviYRD",
        "colab_type": "code",
        "outputId": "f4a2cf46-1717-4b11-f0f6-e37b0e44422c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "learn.fit_one_cycle(2, max_lr=slice(1e-6,1e-4)) #let's train this where the model was most efficient"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.325855</td>\n",
              "      <td>0.459703</td>\n",
              "      <td>0.104167</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.256838</td>\n",
              "      <td>0.453350</td>\n",
              "      <td>0.114583</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oSunPwEFiYRF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.save('stage-2') #let's save our weights"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SnK1BYNuiYRI",
        "colab_type": "text"
      },
      "source": [
        "## Interpretation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXAc13TMiYRI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.load('stage-2'); #let's make sure we have the model loaded at the right stage "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UPfyXpFJiYRL",
        "colab_type": "code",
        "outputId": "5b4119a8-56ea-46d0-dba4-a4f207ac716c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "interp = ClassificationInterpretation.from_learner(learn) "
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KhFt29fZiYRP",
        "colab_type": "code",
        "outputId": "289c97ef-d788-472b-f44f-cfc4a2dc95da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "source": [
        "interp.plot_confusion_matrix() # so we can see a few images were not put in the right category. \n",
        "# for example 3 electronic pianos were classified as grand pianos"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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H9cysoQ/3OedcRgdvHwDeBPYDrgE+AObnMJNzLuEyKZbdzewuoMbMnjOz8wHfWnHObVcm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\nA2O4TIxY8MyMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vuevFi-eiYRS",
        "colab_type": "text"
      },
      "source": [
        "## Cleaning Up\n",
        "\n",
        "Some of our top losses aren't due to bad performance by our model. There are images in our data set that shouldn't be.\n",
        "\n",
        "Using the `ImageCleaner` widget from `fastai.widgets` we can prune our top losses, removing photos that don't belong."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OETktbn2iYRT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from fastai.widgets import * #lets load additional functions "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bIfWJBc7iYRW",
        "colab_type": "text"
      },
      "source": [
        "First we need to get the file paths from our top_losses. We can do this with `.from_toplosses`. We then feed the top losses indexes and corresponding dataset to `ImageCleaner`.\n",
        "\n",
        "Notice that the widget will not delete images directly from disk but it will create a new csv file `cleaned.csv` from where you can create a new ImageDataBunch with the corrected labels to continue training your model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PBoQdzaliYRX",
        "colab_type": "text"
      },
      "source": [
        "In order to clean the entire set of images, we need to create a new dataset without the split. The video lecture demostrated the use of the `ds_type` param which no longer has any effect. See [the thread](https://forums.fast.ai/t/duplicate-widget/30975/10) for more details."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ifsa5qkyiYRX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "db = (ImageList.from_folder(path)\n",
        "                   .split_none()\n",
        "                   .label_from_folder()\n",
        "                   .transform(get_transforms(), size=224)\n",
        "                   .databunch()\n",
        "     ) #this time we're loading the dataset as one, no train/validation sets"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b7V7u05ziYRb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# If you already cleaned your data using indexes from `from_toplosses`,\n",
        "# run this cell instead of the one before to proceed with removing duplicates.\n",
        "# Otherwise all the results of the previous step would be overwritten by\n",
        "# the new run of `ImageCleaner`.\n",
        "\n",
        "# db = (ImageList.from_csv(path, 'cleaned.csv', folder='.')\n",
        "#                    .split_none()\n",
        "#                    .label_from_df()\n",
        "#                    .transform(get_transforms(), size=224)\n",
        "#                    .databunch()\n",
        "#      )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T3BM_DrIiYRe",
        "colab_type": "text"
      },
      "source": [
        "Then we create a new learner to use our new databunch with all the images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y4Hf9kFCiYRf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn_cln = cnn_learner(db, models.resnet34, metrics=error_rate)\n",
        "\n",
        "learn_cln.load('stage-2');"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qz-MNUTfiYRh",
        "colab_type": "code",
        "outputId": "858fa0fc-53cb-4cbf-fba2-dd62f871e419",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "ds, idxs = DatasetFormatter().from_toplosses(learn_cln)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YihSu8X4iYRj",
        "colab_type": "text"
      },
      "source": [
        "Make sure you're running this notebook in Jupyter Notebook, not Jupyter Lab. That is accessible via [/tree](/tree), not [/lab](/lab). Running the `ImageCleaner` widget in Jupyter Lab is [not currently supported](https://github.com/fastai/fastai/issues/1539)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YI2kVxtbiYRk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Don't run this in google colab or any other instances running jupyter lab.\n",
        "# If you do run this on Jupyter Lab, you need to restart your runtime and\n",
        "# runtime state including all local variables will be lost.\n",
        "ImageCleaner(ds, idxs, path)\n",
        "\n",
        "#as this doesn't work in colab, the data was cleaned manually. "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rBLWVOWoiYRm",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "If the code above does not show any GUI(contains images and buttons) rendered by widgets but only text output, that may caused by the configuration problem of ipywidgets. Try the solution in this [link](https://github.com/fastai/fastai/issues/1539#issuecomment-505999861) to solve it.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kM83ZKwPiYRn",
        "colab_type": "text"
      },
      "source": [
        "Flag photos for deletion by clicking 'Delete'. Then click 'Next Batch' to delete flagged photos and keep the rest in that row. `ImageCleaner` will show you a new row of images until there are no more to show. In this case, the widget will show you images until there are none left from `top_losses.ImageCleaner(ds, idxs)`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BmIpiPMYiYRn",
        "colab_type": "text"
      },
      "source": [
        "You can also find duplicates in your dataset and delete them! To do this, you need to run `.from_similars` to get the potential duplicates' ids and then run `ImageCleaner` with `duplicates=True`. The API works in a similar way as with misclassified images: just choose the ones you want to delete and click 'Next Batch' until there are no more images left."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FOejSOV4iYRo",
        "colab_type": "text"
      },
      "source": [
        "Make sure to recreate the databunch and `learn_cln` from the `cleaned.csv` file. Otherwise the file would be overwritten from scratch, losing all the results from cleaning the data from toplosses."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XXZiZgxuiYRo",
        "colab_type": "code",
        "outputId": "61a62166-3462-4457-aca7-85692bc253c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "ds, idxs = DatasetFormatter().from_similars(learn_cln)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Getting activations...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='8' class='' max='8', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      100.00% [8/8 00:03<00:00]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Computing similarities...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_v0fYkZFiYRs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ImageCleaner(ds, idxs, path, duplicates=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sF6PEVkXiYRv",
        "colab_type": "text"
      },
      "source": [
        "Remember to recreate your ImageDataBunch from your `cleaned.csv` to include the changes you made in your data!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "97wB-_hgiYRw",
        "colab_type": "text"
      },
      "source": [
        "## Putting your model in production"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TqCD73YFiYRw",
        "colab_type": "text"
      },
      "source": [
        "First thing first, let's export the content of our `Learner` object for production:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ypaGtnQGiYRx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.export()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F28jXp5_iYR0",
        "colab_type": "text"
      },
      "source": [
        "This will create a file named 'export.pkl' in the directory where we were working that contains everything we need to deploy our model (the model, the weights but also some metadata like the classes or the transforms/normalization used)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ht19VlGdiYR2",
        "colab_type": "text"
      },
      "source": [
        "You probably want to use CPU for inference, except at massive scale (and you almost certainly don't need to train in real-time). If you don't have a GPU that happens automatically. You can test your model on CPU like so:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OmsoO3dKiYR2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "defaults.device = torch.device('cuda') #change this to CPU if you don't want to use GPU"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sgzBqUDqyUQi",
        "colab_type": "code",
        "outputId": "e54057ec-63a1-448b-f53d-755eb0ccf24f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "img = open_image(path/'electronic'/'00000021.jpg')\n",
        "img"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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4mE984hOEQqFgQRIEIWjw8HYhw9fKzO7atYujR49y8OBB+vv7GRsbY3JykqGh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\nCiEaRrGaV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QzI9fN6vVW4bzablX2Yn59HT0+PdNNEo1FYrZU5yzabTQpPHAvAY+vxeLC4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\nrkJggg==\n",
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BWe15F/faX/dX+pxRHdv9/wCyr/31u2/jwP0NAF4XP9xP8/hSfaU/juIl/wBncB+lLDa6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\ngAooooAKKKKAP//Z\n",
            "text/plain": [
              "Image (3, 156, 323)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MzlkRrsOiYR6",
        "colab_type": "text"
      },
      "source": [
        "We create our `Learner` in production enviromnent like this, just make sure that `path` contains the file 'export.pkl' from before."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JglMQEr-iYR7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn = load_learner(path) #path contains the export file, we can load it"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eOhheyQciYR-",
        "colab_type": "code",
        "outputId": "1491956d-a38d-4511-d381-15cc0e5e5855",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "pred_class,pred_idx,outputs = learn.predict(img) #our learner can predict the class of this image right !\n",
        "pred_class"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Category electronic"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zl6poIdRiYSA",
        "colab_type": "text"
      },
      "source": [
        "So you might create a route something like this ([thanks](https://github.com/simonw/cougar-or-not) to Simon Willison for the structure of this code):\n",
        "\n",
        "```python\n",
        "@app.route(\"/classify-url\", methods=[\"GET\"])\n",
        "async def classify_url(request):\n",
        "    bytes = await get_bytes(request.query_params[\"url\"])\n",
        "    img = open_image(BytesIO(bytes))\n",
        "    _,_,losses = learner.predict(img)\n",
        "    return JSONResponse({\n",
        "        \"predictions\": sorted(\n",
        "            zip(cat_learner.data.classes, map(float, losses)),\n",
        "            key=lambda p: p[1],\n",
        "            reverse=True\n",
        "        )\n",
        "    })\n",
        "```\n",
        "\n",
        "(This example is for the [Starlette](https://www.starlette.io/) web app toolkit.)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qf2DgnSAiYSB",
        "colab_type": "text"
      },
      "source": [
        "## Things that can go wrong"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kQmThnBwiYSB",
        "colab_type": "text"
      },
      "source": [
        "- Most of the time things will train fine with the defaults\n",
        "- There's not much you really need to tune (despite what you've heard!)\n",
        "- Most likely are\n",
        "  - Learning rate\n",
        "  - Number of epochs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zw7QYYpCiYSC",
        "colab_type": "text"
      },
      "source": [
        "### Learning rate (LR) too high"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-mmC3jgBB7y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = ImageDataBunch.from_folder(path, train=\".\", valid_pct=0.2,\n",
        "        ds_tfms=get_transforms(), size=224, num_workers=4).normalize(imagenet_stats)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JYvWruNgiYSC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn = cnn_learner(data, models.resnet34, metrics=error_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1rQNBybJiYSE",
        "colab_type": "code",
        "outputId": "037e0ebd-a92a-4e65-bfd5-135637eaa49c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        }
      },
      "source": [
        "learn.fit_one_cycle(1, max_lr=0.5) # this is not efficient because the learning rate is too high. "
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>20.966042</td>\n",
              "      <td>3155979264.000000</td>\n",
              "      <td>0.659341</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tyUrmWq0iYSH",
        "colab_type": "text"
      },
      "source": [
        "### Learning rate (LR) too low"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SBN7J4eIiYSK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn = cnn_learner(data, models.resnet34, metrics=error_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NzVmg9kYiYSL",
        "colab_type": "text"
      },
      "source": [
        "Previously we had this result:\n",
        "\n",
        "```\n",
        "Total time: 00:57\n",
        "epoch  train_loss  valid_loss  error_rate\n",
        "1      1.030236    0.179226    0.028369    (00:14)\n",
        "2      0.561508    0.055464    0.014184    (00:13)\n",
        "3      0.396103    0.053801    0.014184    (00:13)\n",
        "4      0.316883    0.050197    0.021277    (00:15)\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mTFHHEutiYSM",
        "colab_type": "code",
        "outputId": "b68edcce-d2e2-4e0d-c640-3debfd9d6165",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "learn.fit_one_cycle(5, max_lr=1e-5)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>2.198341</td>\n",
              "      <td>1.810206</td>\n",
              "      <td>0.604396</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>2.093911</td>\n",
              "      <td>1.555547</td>\n",
              "      <td>0.615385</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.078865</td>\n",
              "      <td>1.476163</td>\n",
              "      <td>0.648352</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.059384</td>\n",
              "      <td>1.457297</td>\n",
              "      <td>0.681319</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.051179</td>\n",
              "      <td>1.463849</td>\n",
              "      <td>0.670330</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2MOvgcUyiYSN",
        "colab_type": "code",
        "outputId": "279c24e6-679f-4468-e2ee-4a4e7eefcb83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "learn.recorder.plot_losses() #this is not good because there is a big difference between our train and validation plots. \n",
        "# using more images could help on top of finetuning params"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OTWyyd9HZwPPgtTvg48eCrkhETpCCQI5dTALc+CIMvQze+jeYNz3oikTkBCgI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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LIGJovH8iYSP",
        "colab_type": "text"
      },
      "source": [
        "As well as taking a really long time, it's getting too many looks at each image, so may overfit."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h7jAPbTsiYSP",
        "colab_type": "text"
      },
      "source": [
        "### Too few epochs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "03CaCkd4iYSQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn = cnn_learner(data, models.resnet34, metrics=error_rate, pretrained=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vA7HnNtEiYSR",
        "colab_type": "code",
        "outputId": "55d7c35b-c934-4ed9-b85c-0b73b2e3c015",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        }
      },
      "source": [
        "learn.fit_one_cycle(1) #one epoch provides unsatisfactory results. \n",
        "# The point of deep learning is to go through the data multiple times to learn better, so training on several epochs is a must"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.969787</td>\n",
              "      <td>15.313684</td>\n",
              "      <td>0.648352</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
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              "<IPython.core.display.HTML object>"
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          "metadata": {
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VdlDzArZiYST",
        "colab_type": "text"
      },
      "source": [
        "### Too many epochs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1KVJhrYciYST",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.random.seed(42)\n",
        "data = ImageDataBunch.from_folder(path, train=\".\", valid_pct=0.9, bs=32, \n",
        "        ds_tfms=get_transforms(do_flip=False, max_rotate=0, max_zoom=1, max_lighting=0, max_warp=0\n",
        "                              ),size=224, num_workers=4).normalize(imagenet_stats)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4KKvixPuiYSV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 82,
          "referenced_widgets": [
            "9b34ebc0e2e4416591402d6a1311fe2c",
            "02f510ddb45a43d1b90bf21fa6f2678b",
            "733a7eac2b9c43e5805ff4fa5cc81e21",
            "bdb0522adf214736a790db2cd9ae1332",
            "619f9390277b487ab2674a879c1c415c",
            "c5beec1303d94707b94b7d0d02c68726",
            "1e5f6d65f8754a4e9ff34099828f95b9",
            "f975a99caca845e1bc043ef12505c6a7"
          ]
        },
        "outputId": "c5c18388-31e4-46d2-aa42-c1919e52404c"
      },
      "source": [
        "learn = cnn_learner(data, models.resnet50, metrics=error_rate, ps=0, wd=0)\n",
        "learn.unfreeze()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /root/.cache/torch/checkpoints/resnet50-19c8e357.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9b34ebc0e2e4416591402d6a1311fe2c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=102502400), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a6OrllTLiYSZ",
        "colab_type": "code",
        "outputId": "2f4592f8-dfab-4d6b-a285-c3e2267745db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "learn.fit_one_cycle(40, slice(1e-6,1e-4)) #even if one epoch isn't enough, 40 might be overdoing it. \n",
        "# the model can bring good results before getting to 40 epochs. Best results are around 25 epochs"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>2.153703</td>\n",
              "      <td>3.106705</td>\n",
              "      <td>0.669100</td>\n",
              "      <td>00:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>2.110252</td>\n",
              "      <td>2.661309</td>\n",
              "      <td>0.669100</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.996791</td>\n",
              "      <td>2.295130</td>\n",
              "      <td>0.671533</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.974891</td>\n",
              "      <td>1.953619</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.876131</td>\n",
              "      <td>1.670445</td>\n",
              "      <td>0.644769</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.763682</td>\n",
              "      <td>1.386514</td>\n",
              "      <td>0.600973</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.642349</td>\n",
              "      <td>1.177114</td>\n",
              "      <td>0.518248</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.492676</td>\n",
              "      <td>1.021876</td>\n",
              "      <td>0.450122</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.358472</td>\n",
              "      <td>0.885920</td>\n",
              "      <td>0.384428</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.235644</td>\n",
              "      <td>0.783047</td>\n",
              "      <td>0.326034</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.131476</td>\n",
              "      <td>0.708085</td>\n",
              "      <td>0.289538</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>1.034642</td>\n",
              "      <td>0.638594</td>\n",
              "      <td>0.255474</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.951168</td>\n",
              "      <td>0.586400</td>\n",
              "      <td>0.228710</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.878296</td>\n",
              "      <td>0.539334</td>\n",
              "      <td>0.201946</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.813154</td>\n",
              "      <td>0.497931</td>\n",
              "      <td>0.184915</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.755015</td>\n",
              "      <td>0.474272</td>\n",
              "      <td>0.180049</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.703814</td>\n",
              "      <td>0.459596</td>\n",
              "      <td>0.175182</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.658257</td>\n",
              "      <td>0.441705</td>\n",
              "      <td>0.165450</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.617393</td>\n",
              "      <td>0.424879</td>\n",
              "      <td>0.155718</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.580447</td>\n",
              "      <td>0.413788</td>\n",
              "      <td>0.163017</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.547101</td>\n",
              "      <td>0.407513</td>\n",
              "      <td>0.155718</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.516788</td>\n",
              "      <td>0.403569</td>\n",
              "      <td>0.155718</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.489207</td>\n",
              "      <td>0.395437</td>\n",
              "      <td>0.155718</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.463871</td>\n",
              "      <td>0.390492</td>\n",
              "      <td>0.145985</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.440692</td>\n",
              "      <td>0.381679</td>\n",
              "      <td>0.145985</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.419258</td>\n",
              "      <td>0.380743</td>\n",
              "      <td>0.138686</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.399388</td>\n",
              "      <td>0.379268</td>\n",
              "      <td>0.143552</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.380972</td>\n",
              "      <td>0.382456</td>\n",
              "      <td>0.141119</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>0.363963</td>\n",
              "      <td>0.380038</td>\n",
              "      <td>0.145985</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>0.348009</td>\n",
              "      <td>0.375189</td>\n",
              "      <td>0.138686</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.333159</td>\n",
              "      <td>0.376495</td>\n",
              "      <td>0.143552</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>0.319221</td>\n",
              "      <td>0.377831</td>\n",
              "      <td>0.141119</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>0.306279</td>\n",
              "      <td>0.378338</td>\n",
              "      <td>0.153285</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>0.293984</td>\n",
              "      <td>0.378632</td>\n",
              "      <td>0.148418</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>0.282461</td>\n",
              "      <td>0.382751</td>\n",
              "      <td>0.158151</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>0.271613</td>\n",
              "      <td>0.380803</td>\n",
              "      <td>0.148418</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>0.261357</td>\n",
              "      <td>0.379927</td>\n",
              "      <td>0.143552</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>0.251676</td>\n",
              "      <td>0.382389</td>\n",
              "      <td>0.148418</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>0.242571</td>\n",
              "      <td>0.380788</td>\n",
              "      <td>0.150852</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>0.233909</td>\n",
              "      <td>0.381655</td>\n",
              "      <td>0.153285</td>\n",
              "      <td>00:03</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
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}