Unit_1_Introduction_to_Diffusion_Models.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_TbdpKkYfwvh"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.0 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.2/2.0 MB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m39.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m28.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m17.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "%pip install -qq -U diffusers datasets transformers accelerate ftfy pyarrow==9.0.0"
      ]
    },
    {
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      "execution_count": 2,
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          ]
        },
        "id": "HDVVB9-VeOSL",
        "outputId": "37729afe-c935-4445-cda9-89a7ba06038a"
      },
      "outputs": [
        {
          "data": {
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              "model_id": "d4068d39e39248c49b9d0b814af90c3c",
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            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "q8boQ8nyfvTf"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "from matplotlib import pyplot as plt\n",
        "from PIL import Image\n",
        "import torchvision\n",
        "\n",
        "\n",
        "def show_images(x):\n",
        "    \"\"\"Given a batch of images x, make a grid and convert to PIL\"\"\"\n",
        "    x = x * 0.5 + 0.5  # Map from (-1, 1) back to (0, 1)\n",
        "    grid    = torchvision.utils.make_grid(x)\n",
        "    grid_im = grid.detach().cpu().permute(1, 2, 0).clip(0, 1) * 255\n",
        "    grid_im = Image.fromarray(np.array(grid_im).astype(np.uint8))\n",
        "\n",
        "    return grid_im\n",
        "\n",
        "def make_grid(images, size=64):\n",
        "    \"\"\"Given a list of PIL images, stack them together into a line for easy viewing\"\"\"\n",
        "    output_im = Image.new(\"RGB\", (size * len(images), size))\n",
        "    for i, im in enumerate(images):\n",
        "        output_im.paste(im.resize((size, size)), (i * size, 0))\n",
        "\n",
        "    return output_im\n",
        "\n",
        "# Mac users may need device = 'mps' (untested)\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DiGNj4WBbEkF"
      },
      "source": [
        "## Different ways of Generating Images\n",
        "\n",
        "Existing Approaches & Year of Innovations\n",
        "1. VAEs  (2013)\n",
        "2. GANs  (2014)\n",
        "3. DDPMs (2020)\n",
        "\n",
        "Any other approaches you can think of?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zDT59_A0dzir"
      },
      "source": [
        "## Introduction to Diffusers\n",
        "\n",
        "The core API of 🤗 Diffusers is divided into three main components:\n",
        "\n",
        "1. **Pipelines**: high-level classes designed to rapidly generate samples from popular trained diffusion models in a user-friendly fashion.\n",
        "2. **Models**: popular architectures for training new diffusion models, e.g. UNet.\n",
        "3. **Schedulers**: various techniques for generating images from noise during inference as well as to generate noisy images for training."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I4W0hnBCiBVM"
      },
      "source": [
        "### Pipeline"
      ]
    },
    {
      "cell_type": "code",
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      "outputs": [
        {
          "name": "stderr",
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          "text": [
            "diffusion_pytorch_model.safetensors not found\n"
          ]
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",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=512x64>"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import diffusers\n",
        "\n",
        "butterfly_pipeline  = diffusers.DDPMPipeline.from_pretrained(\"johnowhitaker/ddpm-butterflies-32px\").to(device)\n",
        "\n",
        "images              = butterfly_pipeline(batch_size=8).images\n",
        "\n",
        "make_grid(images)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261,
          "referenced_widgets": [
            "3200586ab0ca40d9afa1b342ccf342cd",
            "990010b8ae5746ffaa80bdf66f0742cb",
            "28fa2147b8444b2ba0b3ce7766155310",
            "0939a6081bc949f39b27333ea346f056",
            "2c7757e32ac64be8b87a01a18d29f451",
            "fb2e2ca6172944628881be6371e1ea79",
            "78fdb25a013347c984d0b5315ae81e8a",
            "8d586d5c95e14b8ca1c99c9630f439bd",
            "3e829a4b9ac34a7491be86a9dafc2d38",
            "b210d7f2fc5a43b9b5edaf5df50611e8",
            "b825f08ac43c4003acaef647033089ed",
            "eb370c9f7ef24129929736f4c8467a0c",
            "c7e3aa6895884d12ac1dd7578353d89e",
            "34d4e1917d4c427fbd275056c8c74703",
            "60cc8d0d2dff47a08ceaf9fa94049f33",
            "2b9f59a3f3814c0db5ef21f023d390fd",
            "e7301eaad9db4982a18f659a60f97753",
            "ad8695464c8f4bcabe5196af35922652",
            "dcb23c4ab52743998d64b6763f8f03d6",
            "7904451ddf6548ceb7ee3264e2790d65",
            "35484cff47ff438daf19b12d0fd27c48",
            "815ae87a42484c299d6479d853408a55",
            "f37e8f58fe9c4b03871fcda00ec4a6b0",
            "4859dbe95fe943c0a892fc7c4fdb413f",
            "84410a29c22d40c599408e2ec3872257",
            "eb27d21bc41a43c6b6823c86b7b00d49",
            "d1449c8029ec46ec98fe0e858dec8257",
            "a61cbbd39d5f415c9875afadc47c7088",
            "84b3327980624799a2e7e140af467fc8",
            "572b6ee205bd4582ad2745b1fe480183",
            "f6e1ad059f5e4b83afc743bc5a91f7cb",
            "bfac7135078841349d9d11db8512366a",
            "86821ae6bd184d61b0b2ba4a74885f12",
            "b41b9b1e976c4456abdbab157dbbce1b",
            "e596eebec3c7434fba17fd2f13d46a52",
            "c0ba22425be24ca9832cbf062ff146d5",
            "88fb25bd2727439ebbe1db636c822b0d",
            "cc9978c818064da8a04cb54a41dd7763",
            "043b46edd7f64514a26abbb2deda9c3e",
            "bca1bfd9c2bc445ea5d1dfedbac22756",
            "329c4a7a78c04ae68f3e9e778b25779d",
            "1f6f771e66fd42e39057db532dc6daf3",
            "49969b7796c2459db4d0e78ac69faa15",
            "dc4942054b94496cafa460eb2f9bf6d2"
          ]
        },
        "id": "bPE7Jay3gzbJ",
        "outputId": "56a25de8-b913-444e-8c9f-928d279edac9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Repo card metadata block was not found. Setting CardData to empty.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "X shape: torch.Size([8, 3, 32, 32])\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=512x64>"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import torchvision\n",
        "import datasets\n",
        "\n",
        "dataset = datasets.load_dataset(\"huggan/smithsonian_butterflies_subset\", split=\"train\")\n",
        "\n",
        "# We'll train on 32-pixel square images, but you can try larger sizes too\n",
        "image_size = 32\n",
        "# You can lower your batch size if you're running out of GPU memory\n",
        "batch_size = 64\n",
        "\n",
        "preprocess = torchvision.transforms.Compose(\n",
        "    [\n",
        "        torchvision.transforms.Resize((image_size, image_size)),  # Resize\n",
        "        torchvision.transforms.RandomHorizontalFlip(),  # Randomly flip (data augmentation)\n",
        "        torchvision.transforms.ToTensor(),  # Convert to tensor (0, 1)\n",
        "        torchvision.transforms.Normalize([0.5], [0.5]),  # Map to (-1, 1)\n",
        "    ]\n",
        ")\n",
        "\n",
        "def transform(examples):\n",
        "    images = [preprocess(image.convert(\"RGB\")) for image in examples[\"image\"]]\n",
        "    return {\"images\": images}\n",
        "\n",
        "\n",
        "dataset.set_transform(transform)\n",
        "\n",
        "# Create a dataloader from the dataset to serve up the transformed images in batches\n",
        "train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)\n",
        "\n",
        "single_batch = next(iter(train_dataloader))[\"images\"].to(device)[:8]\n",
        "print(\"X shape:\", single_batch.shape)\n",
        "show_images(single_batch).resize( (8 * 64, 64) )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aAt8MsMBiKyh"
      },
      "source": [
        "## Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3aYHc398cy_q",
        "outputId": "54737b5b-f0c5-4be9-e76a-771c94547807"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "UNet2DModel(\n",
              "  (conv_in): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "  (time_proj): Timesteps()\n",
              "  (time_embedding): TimestepEmbedding(\n",
              "    (linear_1): Linear(in_features=64, out_features=256, bias=True)\n",
              "    (act): SiLU()\n",
              "    (linear_2): Linear(in_features=256, out_features=256, bias=True)\n",
              "  )\n",
              "  (down_blocks): ModuleList(\n",
              "    (0): DownBlock2D(\n",
              "      (resnets): ModuleList(\n",
              "        (0-1): 2 x ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 64, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
              "          (norm2): GroupNorm(32, 64, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "        )\n",
              "      )\n",
              "      (downsamplers): ModuleList(\n",
              "        (0): Downsample2D(\n",
              "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (1): DownBlock2D(\n",
              "      (resnets): ModuleList(\n",
              "        (0): ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 64, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=256, out_features=128, bias=True)\n",
              "          (norm2): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (1): ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=256, out_features=128, bias=True)\n",
              "          (norm2): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "        )\n",
              "      )\n",
              "      (downsamplers): ModuleList(\n",
              "        (0): Downsample2D(\n",
              "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (2): AttnDownBlock2D(\n",
              "      (attentions): ModuleList(\n",
              "        (0-1): 2 x Attention(\n",
              "          (group_norm): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
              "          (to_q): Linear(in_features=128, out_features=128, bias=True)\n",
              "          (to_k): Linear(in_features=128, out_features=128, bias=True)\n",
              "          (to_v): Linear(in_features=128, out_features=128, bias=True)\n",
              "          (to_out): ModuleList(\n",
              "            (0): Linear(in_features=128, out_features=128, bias=True)\n",
              "            (1): Dropout(p=0.0, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (resnets): ModuleList(\n",
              "        (0-1): 2 x ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=256, out_features=128, bias=True)\n",
              "          (norm2): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "        )\n",
              "      )\n",
              "      (downsamplers): ModuleList(\n",
              "        (0): Downsample2D(\n",
              "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (3): AttnDownBlock2D(\n",
              "      (attentions): ModuleList(\n",
              "        (0-1): 2 x Attention(\n",
              "          (group_norm): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "          (to_q): Linear(in_features=256, out_features=256, bias=True)\n",
              "          (to_k): Linear(in_features=256, out_features=256, bias=True)\n",
              "          (to_v): Linear(in_features=256, out_features=256, bias=True)\n",
              "          (to_out): ModuleList(\n",
              "            (0): Linear(in_features=256, out_features=256, bias=True)\n",
              "            (1): Dropout(p=0.0, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (resnets): ModuleList(\n",
              "        (0): ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=256, out_features=256, bias=True)\n",
              "          (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (1): ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=256, out_features=256, bias=True)\n",
              "          (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (up_blocks): ModuleList(\n",
              "    (0): AttnUpBlock2D(\n",
              "      (attentions): ModuleList(\n",
              "        (0-2): 3 x Attention(\n",
              "          (group_norm): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "          (to_q): Linear(in_features=256, out_features=256, bias=True)\n",
              "          (to_k): Linear(in_features=256, out_features=256, bias=True)\n",
              "          (to_v): Linear(in_features=256, out_features=256, bias=True)\n",
              "          (to_out): ModuleList(\n",
              "            (0): Linear(in_features=256, out_features=256, bias=True)\n",
              "            (1): Dropout(p=0.0, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (resnets): ModuleList(\n",
              "        (0-1): 2 x ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 512, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=256, out_features=256, bias=True)\n",
              "          (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (2): ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 384, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=256, out_features=256, bias=True)\n",
              "          (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(384, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "      )\n",
              "      (upsamplers): ModuleList(\n",
              "        (0): Upsample2D(\n",
              "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (1): AttnUpBlock2D(\n",
              "      (attentions): ModuleList(\n",
              "        (0-2): 3 x Attention(\n",
              "          (group_norm): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
              "          (to_q): Linear(in_features=128, out_features=128, bias=True)\n",
              "          (to_k): Linear(in_features=128, out_features=128, bias=True)\n",
              "          (to_v): Linear(in_features=128, out_features=128, bias=True)\n",
              "          (to_out): ModuleList(\n",
              "            (0): Linear(in_features=128, out_features=128, bias=True)\n",
              "            (1): Dropout(p=0.0, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (resnets): ModuleList(\n",
              "        (0): ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 384, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=256, out_features=128, bias=True)\n",
              "          (norm2): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (1-2): 2 x ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=256, out_features=128, bias=True)\n",
              "          (norm2): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "      )\n",
              "      (upsamplers): ModuleList(\n",
              "        (0): Upsample2D(\n",
              "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (2): UpBlock2D(\n",
              "      (resnets): ModuleList(\n",
              "        (0-1): 2 x ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=256, out_features=128, bias=True)\n",
              "          (norm2): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (2): ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 192, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(192, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=256, out_features=128, bias=True)\n",
              "          (norm2): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "      )\n",
              "      (upsamplers): ModuleList(\n",
              "        (0): Upsample2D(\n",
              "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (3): UpBlock2D(\n",
              "      (resnets): ModuleList(\n",
              "        (0): ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 192, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
              "          (norm2): GroupNorm(32, 64, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (1-2): 2 x ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 128, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
              "          (norm2): GroupNorm(32, 64, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (mid_block): UNetMidBlock2D(\n",
              "    (attentions): ModuleList(\n",
              "      (0): Attention(\n",
              "        (group_norm): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "        (to_q): Linear(in_features=256, out_features=256, bias=True)\n",
              "        (to_k): Linear(in_features=256, out_features=256, bias=True)\n",
              "        (to_v): Linear(in_features=256, out_features=256, bias=True)\n",
              "        (to_out): ModuleList(\n",
              "          (0): Linear(in_features=256, out_features=256, bias=True)\n",
              "          (1): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (resnets): ModuleList(\n",
              "      (0-1): 2 x ResnetBlock2D(\n",
              "        (norm1): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (time_emb_proj): Linear(in_features=256, out_features=256, bias=True)\n",
              "        (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "        (dropout): Dropout(p=0.0, inplace=False)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (nonlinearity): SiLU()\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (conv_norm_out): GroupNorm(32, 64, eps=1e-05, affine=True)\n",
              "  (conv_act): SiLU()\n",
              "  (conv_out): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              ")"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from diffusers import UNet2DModel\n",
        "\n",
        "# Create a model\n",
        "model = UNet2DModel(\n",
        "    sample_size=image_size,  # the target image resolution\n",
        "    in_channels=3,  # the number of input channels, 3 for RGB images\n",
        "    out_channels=3,  # the number of output channels\n",
        "    layers_per_block=2,  # how many ResNet layers to use per UNet block\n",
        "    block_out_channels=(64, 128, 128, 256),  # More channels -> more parameters\n",
        "    down_block_types=(\n",
        "        \"DownBlock2D\",  # a regular ResNet downsampling block\n",
        "        \"DownBlock2D\",\n",
        "        \"AttnDownBlock2D\",  # a ResNet downsampling block with spatial self-attention\n",
        "        \"AttnDownBlock2D\",\n",
        "    ),\n",
        "    up_block_types=(\n",
        "        \"AttnUpBlock2D\",\n",
        "        \"AttnUpBlock2D\",  # a ResNet upsampling block with spatial self-attention\n",
        "        \"UpBlock2D\",\n",
        "        \"UpBlock2D\",  # a regular ResNet upsampling block\n",
        "    ),\n",
        ")\n",
        "model.to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XVLdYdKTiE0H"
      },
      "source": [
        "### Scheduler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "b7vmKytgiJa3"
      },
      "outputs": [],
      "source": [
        "noise_scheduler = diffusers.DDPMScheduler(num_train_timesteps = 1000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "787qB6JJiZwc",
        "outputId": "26349bde-45fb-4805-c243-ac641f136120"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=512x64>"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "timesteps                   = torch.linspace(0, 999, 8).long().to(device)\n",
        "noise                       = torch.randn_like( single_batch )\n",
        "increamental_noise_images   = noise_scheduler.add_noise(single_batch, noise, timesteps)\n",
        "\n",
        "show_images(increamental_noise_images).resize((8 * 64, 64))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EPVcgukPd1pJ"
      },
      "source": [
        "## Diffusion Models from Scratch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C9RWJAyId3VR",
        "outputId": "42d1452f-b07a-4792-d6cb-ec4fc322a4de"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Users/ajinkya/anaconda3/lib/python3.11/site-packages/diffusers/configuration_utils.py:139: FutureWarning: Accessing config attribute `num_train_timesteps` directly via 'DDPMScheduler' object attribute is deprecated. Please access 'num_train_timesteps' over 'DDPMScheduler's config object instead, e.g. 'scheduler.config.num_train_timesteps'.\n",
            "  deprecate(\"direct config name access\", \"1.0.0\", deprecation_message, standard_warn=False)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch:5, loss: 0.15873365942388773\n",
            "Epoch:10, loss: 0.12924087885767221\n",
            "Epoch:15, loss: 0.10738829337060452\n",
            "Epoch:20, loss: 0.09202650608494878\n",
            "Epoch:25, loss: 0.08898070780560374\n"
          ]
        }
      ],
      "source": [
        "# Set the noise scheduler\n",
        "noise_scheduler = diffusers.DDPMScheduler(num_train_timesteps=1000, beta_schedule=\"squaredcos_cap_v2\")\n",
        "\n",
        "# Training loop\n",
        "optimizer = torch.optim.AdamW(model.parameters(), lr=4e-4)\n",
        "\n",
        "losses = []\n",
        "\n",
        "for epoch in range(30):\n",
        "    for step, batch in enumerate(train_dataloader):\n",
        "\n",
        "        clean_images    = batch[\"images\"].to(device)\n",
        "        noise           = torch.randn(clean_images.shape).to(clean_images.device) # Sample noise to add to the images\n",
        "        bs              = clean_images.shape[0]\n",
        "\n",
        "\n",
        "        timesteps       = torch.randint(0, noise_scheduler.num_train_timesteps, (bs,), device=clean_images.device).long() # Sample a random timestep for each image\n",
        "\n",
        "\n",
        "        noisy_images    = noise_scheduler.add_noise(clean_images, noise, timesteps) # Add noise to the clean images according to the noise magnitude at each timestep\n",
        "\n",
        "\n",
        "        noise_pred      = model(noisy_images, timesteps, return_dict=False)[0] # Get the model prediction\n",
        "\n",
        "        # Calculate ERROR\n",
        "        loss            = F.mse_loss(noise_pred, noise)\n",
        "\n",
        "        # Calculate dError / dW. Rate of change of Error with respect to all W\n",
        "        loss.backward(loss)\n",
        "        losses.append(loss.item())\n",
        "\n",
        "        # Update the model parameters with the optimizer\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "    if (epoch + 1) % 5 == 0:\n",
        "        loss_last_epoch = sum(losses[-len(train_dataloader) :]) / len(train_dataloader)\n",
        "        print(f\"Epoch:{epoch+1}, loss: {loss_last_epoch}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "PioD9taRi3my",
        "outputId": "26ac597c-bb22-44a8-9749-6d35cc0db9dd"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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jNiIiagPCLgA3m1iCTkREFA5MRoMchFexERsREbUB4ReAG9mEjYiIKFzI68DZiI2IiNqA8AvAfV3QHS6uBSMiImrvYqzeDHg1A3AiImoDwi8A93VBZwaciIio/Yu2eDPglSxBJyKiNiD8AnBmwImIiMJGrJQBZxM2IiJqA8IvAOcacCIiorAhZcCr7MyAExFR6wu/ANzXBd3BAJyIiKjdi/U1YeMacCIiagvCLwBnBpyIiChsSE3YKmoZgBMRUetrUAD+4osvolu3boiMjMSYMWOwfv36oPs/99xz6Nu3L6KiopCVlYW77roLtbW1DTrhxhIDcI+HQTgREVF7lhRtAQCcrLK38pkQERE1IAD/6KOPMGfOHCxYsACbN2/G0KFDMXnyZJw4cUJz//fffx9z587FggULsGvXLrz++uv46KOPcO+99zb65BtC6oIOAE5mwYmIiNq1RCkAr3a08pkQERE1IAB/5plnMGvWLMyYMQMDBgzAokWLEB0djTfeeENz/9WrV+P000/H1VdfjW7duuG8887DVVddVWfWvLlIXdABwOliAE5ERNSeJUVHAABKq5kBJyKi1levANxut2PTpk3Izs7234HRiOzsbKxZs0bzmPHjx2PTpk1ywH3w4EF89913uOCCC3Qfx2azoby8XPHRVExGIQB3cxQZERFReyZlwEsYgBMRURtgrs/ORUVFcLlcSE9PV2xPT0/H7t27NY+5+uqrUVRUhDPOOAMejwdOpxM333xz0BL0hQsX4qGHHqrPqYUswiSUoDMDTkRE1K75M+AsQSciotbX7F3QV6xYgccffxwvvfQSNm/ejM8//xzffvstHnnkEd1j5s2bh7KyMvnjyJEjTXY+QgKca8CJiIjauaQYaQ04M+BERNT66pUBT0lJgclkQkFBgWJ7QUEBMjIyNI954IEHcO211+KGG24AAAwePBhVVVW48cYbcd9998FoDLwGYLVaYbVa63NqITMYDIgwGeBweViCTkRE1M4l+jLgZTUOuNwexVI0IiKillavDLjFYsHIkSOxbNkyeZvb7cayZcswbtw4zWOqq6sDgmyTyTuTs7XGgEl/fFmCTkRE1L4lRnkz4B4PUF7DMnQiImpd9cqAA8CcOXNw3XXXYdSoURg9ejSee+45VFVVYcaMGQCA6dOno1OnTli4cCEA4OKLL8YzzzyD4cOHY8yYMdi/fz8eeOABXHzxxXIg3tIijEbUws0SdCIionbOYjYi1mpGpc2Jk9V2uSSdiIioNdQ7AL/yyitRWFiI+fPnIz8/H8OGDcPixYvlxmy5ubmKjPf9998Pg8GA+++/H3l5eUhNTcXFF1+Mxx57rOmeRT2ZfKPIXCxBJyIiavcSoyPkAJyIiKg11TsAB4DZs2dj9uzZmretWLFC+QBmMxYsWIAFCxY05KGahdl3gcDBEnQiIqJ2LyEqAkdP1qC8xtnap0JERGGu2bugt0Vmo5QBZwBORETU3sVYvfmGShsDcCIial3hGYD7StAdLpagExERtXdxvgC8igE4ERG1svAMwJkBJyIiChvMgBMRUVsRngG4iWvAiYiIwkWMnAF3tfKZEBFRuAvPAJwZcCIiIk2PPfYYxo8fj+joaCQmJoZ0jMfjwfz585GZmYmoqChkZ2dj3759zXui9RBr9Y49rbRxDjgREbWu8AzApTXgHENGRESkYLfbMW3aNNxyyy0hH/PPf/4Tzz//PBYtWoR169YhJiYGkydPRm1tbTOeaehirREAgEpmwImIqJU1aAzZqc7kG0PmYgk6ERGRwkMPPQQAeOutt0La3+Px4LnnnsP999+PSy+9FADwzjvvID09HV9++SX+9Kc/NdephizGlwFnEzYiImptYZkBj/CVoDuZASciImqUQ4cOIT8/H9nZ2fK2hIQEjBkzBmvWrNE9zmazoby8XPHRXGJ9a8C/+u0Yft1f1GyPQ0REVJewDMBNcgDODDgREVFj5OfnAwDS09MV29PT0+XbtCxcuBAJCQnyR1ZWVrOdo9SEDQCueW1dsz0OERFRXcIyAI/wdUF3sgSdiIjCwNy5c2EwGIJ+7N69u0XPad68eSgrK5M/jhw50myPFRsZlivuiIioDQrLv0jMgBMRUTi5++67cf311wfdp0ePHg2674yMDABAQUEBMjMz5e0FBQUYNmyY7nFWqxVWq7VBj1lfsdawfLtDRERtUFj+RYrwdUF3urgGnIiI2r/U1FSkpqY2y313794dGRkZWLZsmRxwl5eXY926dfXqpN6cYizKtztutwdG38V4IiKilhSWJejMgBMREWnLzc3F1q1bkZubC5fLha1bt2Lr1q2orKyU9+nXrx+++OILAIDBYMCdd96JRx99FF999RW2bduG6dOno2PHjpg6dWorPQsldQa8xsFxZERE1DrCMgNulteAMwNOREQkmj9/Pt5++2356+HDhwMAli9fjgkTJgAA9uzZg7KyMnmff/zjH6iqqsKNN96I0tJSnHHGGVi8eDEiIyNb9Nz1qNeAV9mdisZsRERELSUs//qYmQEnIiLS9NZbb9U5A9zjUf79NBgMePjhh/Hwww8345k1XEAG3M4MOBERtY6wLEE3G30ZcAbgRERE7Z7FbMSTVwyWv65mAE5ERK0kTANwbwbcxQCciIgoLFx5Whd0SY4GAFTbna18NkREFK7CMwD3dUF3cA04ERFR2Ii2mAAwA05ERK0nPANwZsCJiIjCDgNwIiJqbeEZgPu6oDtcDMCJiIjCRbRvHnhFLUvQiYiodYRnAC5nwFmCTkREFC6ifBnwv33yG37dX9TKZ0NEROEoPANweQ04M+BEREThIsYXgAPAA19ub8UzISKicBWeAbg8howZcCIionARZTELn5uC7ElERNQ8wjIAt5h9ATgz4ERERGHDava/7UmLs7bimRARUbgKywBcWgNu5xgyIiKisHGy2i5/Hh8V0YpnQkRE4SosA/AIdkEnIiIKOyfKbfLnNRxFRkRErSA8A3BfCZrDyQw4ERFRuBialSh/XuNgAE5ERC0vLANwi9wFnQE4ERFRuLjtnF4Y3iURAFBl4yxwIiJqeWEZgMsl6G6WoBMREYWLGKsZd5/bFwBQzRJ0IiJqBeEdgLMEnYiIKKxEW73jx6rszIATEVHLC+8AnCXoREREYSXaN/+72sYMOBERtbywDMAtZq4BJyIiCkcxFjMAZsCJiKh1hGUAbjZ6n7adY8iIiIjCipQBr3W44WIvGCIiamFhGYCzBJ2IiCg8xVjN8uccRUZERC0tLANwlqATERGFJ6vZCKP3bQCqfaPI8kprsGxXATweZsSJiKh5hWUAzi7oRERE4clgMAjrwL0Z8NOf+Akz396IH3edaM1TIyKiMBDWATjXgBMREYWfKN868MXb83GoqErevvpAUWudEhERhYmwDsBZgk5ERBR+pHXgTy7ejYlPrZC3m6XadCIiomYSlgG4hQE4ERFR2EqIitDcbmQATkREzSwsA/AIXxM2J0vQiYiIwk7/zDjN7cyAExFRcwvLANw/B9zNjqdERERhZkBmvOZ2k4EBOBERNa+wDMClEnQAcLoZgBMREYWTAR21A3C+JyAioubWoAD8xRdfRLdu3RAZGYkxY8Zg/fr1QfcvLS3FrbfeiszMTFitVvTp0wffffddg064KUgl6ADXgRMREYWbfhnaAXitg+8JiIioedU7AP/oo48wZ84cLFiwAJs3b8bQoUMxefJknDihPTvTbrfj3HPPRU5ODj799FPs2bMHr776Kjp16tTok2+oCCED7nDyajcREVE4ibGa8eyVQwO21zhcrXA2REQUTuodgD/zzDOYNWsWZsyYgQEDBmDRokWIjo7GG2+8obn/G2+8gZKSEnz55Zc4/fTT0a1bN5x99tkYOjTwD5/EZrOhvLxc8dGUxCYrdmbAiYiIws75gzIDttUyACciomZWrwDcbrdj06ZNyM7O9t+B0Yjs7GysWbNG85ivvvoK48aNw6233or09HQMGjQIjz/+OFwu/T9yCxcuREJCgvyRlZVVn9Osk8Fg4CgyIiKiMCb2g5HU2P3vTVxuD+xOvkcgIqKmVa8AvKioCC6XC+np6Yrt6enpyM/P1zzm4MGD+PTTT+FyufDdd9/hgQcewNNPP41HH31U93HmzZuHsrIy+ePIkSP1Oc2QRJi8WXAG4EREROHHaDQEjB0TS9CnvvgrJj2zgkE4ERE1KXNzP4Db7UZaWhpeeeUVmEwmjBw5Enl5efjXv/6FBQsWaB5jtVphtVqb9bwizEbA7mIATkREFKYsZiOcQtZbyoDX2F3YllcGADhYVKnbtI2IiKi+6pUBT0lJgclkQkFBgWJ7QUEBMjIyNI/JzMxEnz59YDKZ5G39+/dHfn4+7HZ7A065acizwNmEjYiISPbYY49h/PjxiI6ORmJiYkjHXH/99TAYDIqPKVOmNO+JNoEIVRn6+pwSbMk9ieIqm7xNer9ARETUFOr1V8VisWDkyJFYtmyZvM3tdmPZsmUYN26c5jGnn3469u/fD7fbn2neu3cvMjMzYbFYGnjajWfxlaA73cyAExERSex2O6ZNm4ZbbrmlXsdNmTIFx48flz8++OCDZjrDpmMxB74Nuuyl1Sip8icInvh+N3Yd124G+86aHCzfoz0FhoiISEu9L+vOmTMHr776Kt5++23s2rULt9xyC6qqqjBjxgwAwPTp0zFv3jx5/1tuuQUlJSW44447sHfvXnz77bd4/PHHceuttzbds2iACDObsBEREak99NBDuOuuuzB48OB6HWe1WpGRkSF/JCUlNdMZNh2tRmwAsDu/Qv78x10FOP/fKwP22ZhTgvn/twMz3tzQbOdHRETtT73XgF955ZUoLCzE/PnzkZ+fj2HDhmHx4sVyY7bc3FwYhXKtrKwsLFmyBHfddReGDBmCTp064Y477sA999zTdM+iAaSyM5agExERNd6KFSuQlpaGpKQknHPOOXj00UfRoUMH3f1tNhtsNn+pd1OPHA2FVgYcAFbtK6rzWDFIJyIiClWDmrDNnj0bs2fP1rxtxYoVAdvGjRuHtWvXNuShmk0Ex5ARERE1iSlTpuDyyy9H9+7dceDAAdx77704//zzsWbNGkUPGNHChQvx0EMPtfCZKkkTUdRW7isM2FZYYUNqnL9BbGm1v0zd5fbAZNS+LyIiIlHYdhaxcAwZERGFiblz5wY0SVN/7N69u8H3/6c//QmXXHIJBg8ejKlTp+Kbb77Bhg0bNC/KS1pi5Ghd9DLgJ6sdAdv2qDLe4j61wvgyIiKiYJp9DFlbxQw4ERGFi7vvvhvXX3990H169OjRZI/Xo0cPpKSkYP/+/Zg0aZLmPi0xcrQu6i7owezOL8cZvVPkr08KjdpqHC7EWMP2LRUREdVD2P61MPsy4HYX14ATEVH7lpqaitTU1BZ7vKNHj6K4uBiZmZkt9pgNodeETcvm3JOKr4+X1cqf19iZASciotCEbQm6nAF3MgNOREQkyc3NxdatW5GbmwuXy4WtW7di69atqKyslPfp168fvvjiCwBAZWUl/v73v2Pt2rXIycnBsmXLcOmll6JXr16YPHlyaz2NkIgl6EM6J2B8T/2mcesPlcDj8V60f2bpXqw5WCzfxhJ0IiIKVdhmwK2+P7p2lqATERHJ5s+fj7ffflv+evjw4QCA5cuXY8KECQCAPXv2oKysDABgMpnw+++/4+2330ZpaSk6duyI8847D4888kirl5jXRcyAP37ZYPTLiEOf+7+HW6M4rqjSjoNFVeiZGotvfjumuK2GATgREYUojANwb1dWG/9oEhERyd566y289dZbQfeRMsEAEBUVhSVLljTzWTUPcQ14jNUMs8moGXxL9uRXoGdqbEDGu8buwkcbcnGkpAZ3n9cHBgM7ohMRkbYwDsC9f3RtLEEnIiIKSxFCCXq0RXtcmujX/UUY0jkBpTXeDuixVjMqbU5UO1y457NtAIApgzIwqFNC85wwERGd8sJ2Dbg1ggE4EREReUUFCcDT472l9O+ty8UZTy5Hta/pWmZCJADgWGmNvG81G7IREVEQ4RuASyXoTv6hJCIiCkcut/8ifHSE933BH0d1DtgvMyEqYJvRAKT5AvMlOwrk7VwPTkREwYRvAO7LgNc6mAEnIiIKRw5hFKnZtx78wUsG4s0Zp+G+C/rLt3VMjAw4NiEqAtEW70q+X/YWytvLfOXpREREWsI3AGcGnIiIKKw5NSahRFvMmNg3DXGR/jY5WhnwpGgLoiICy9YZgBMRUTBhG4BHSmvAmQEnIiIKS84gLc8jheBaWustiouK0AzAyxmAExFREGEbgPsz4AzAiYiIwpFDIwMuMRn9o8S0MuDVNqdm47ZQAvCdx8rx7NK9qLY7QzxTbQ6XGw9/vRPLd59o1P0QEVHLCeMAXFoDzhJ0IiKicOR06WfA3cKs80yNNeDFVXZFllwSSgn6Bc+vxL+X7cMbqw7J2/afqMTKfYVBjgr08cYjeOPXQ5jx1gbFdi6vIyJqu8I+AGcGnIiIKDwFK0EX4m+kxloDj3W5ISTJZfVZA36oqFr+PPuZn3Ht6+uxr6Ai5OOPnqwJ2PbL3kIMmL8Eb/16SOMIIiJqbWEbgEtXrXmVmIiIKDx5PKFlwBOiI+TPrxnTBdEWE569chgqbf4S8mevHAoAKK8NHoCLZedSd3WXcCHgUFFViGevVFBei02HT2L6G+vhcnvw4Nc7G3Q/RETUvMx179I+MQNOREQU3h67bDCuemUt7jy3T8BtZ/RKAQB0TopCnNX/dmn6uG54+NJBMBkN+Pq3Y/L2xCgLgLoz4DlC1ltKBpystsvbYiNDe2vm8XhgF97DjHl8meJ2s1Z6noiIWl34BuC+P3qcA05ERBSeBnVKwNYF5ykarknS4iOx6f5sxFjNMBgMeOyyQThRbkPfjDh5H6PBf1x8lDdLXlcALma4pT40hRU2eZtb423JySo7Nh0+iQl9U+V55Tf/bxOW7CjQfZw+6XG6txERUesJ2wA8Us6AswSdiIgoXGkF35IOwtrva8Z0Dbj9zuw+2HKkFDNO74YEKQCvrisAr5Q/1wrAtd6XTHt5DfafqMTDlw7E9HHdACBo8A0ATq1InoiIWl3YrgGXMuCcA05EREQN0aVDNJb/bQKmj+uGOF/peJXdFXRt+UFFBtz7HqSoUgzAA9+X7D/hDdp/8AXdziDj0ySVtY0bcUZERM0jfANwZsCJiIioicT61om73J6gy9vqKkG3B+lN0yHWu878ZB1ZdgCosDEAJyJqi8I2AI9kBpyIiIiaSLTFBGlJ+Gebj+LeL7ahyhcEl9U4sPZgMTwejzIA9wXbwUrQxa7pHWK8JfElVXbUpdLmDJqJJyKi1hG2a8DZBZ2IiIiaisFgQKzVjIpaJ+7/cjsAwGIyYuYZ3XH3J79h/aESLLh4AEqF7LWcAdcoQc8trsaxshp0iLHIt0WYvRF+cZV/fz0eD1Btd6HW4cILP+3HladloX9mfOOfKBERNUrYB+B2lxtutwdGjusgIiKiRojzBeCSt1bn4OvfjqHYl7F+SDWbWwrAxYy2VJl31r+WAwDuu6C/f3974P7BVNqceODL7fhhZwHeXXsYBx6/oL5PiYiImljYlqBLTdgAZsGJiIio8bRmeBcHCZarbE68sGwf1h4slrfZXW6U1/qz5N9uOy5/Lq0tDzUAr6h1YnNuKQDv2nQiImp9YZsBl8aQAd71VlEWU5C9iYiIiIKTGrHVZXS3ZKzPKcHm3FI5QJbYHC7sK6iQv955rFz+vNa3Pry4MrQAvMrm5DgyIqI2Jmwz4GaTUZ79yQw4ERERNVZsZERI+2UPSNO9zeZ0Y2+Bf1a4XRg5plWyHkylzQkH3+MQEbUpYRuAA0IjNnZCJyIiokaKEzLg790wBn3T4wL26ZQYhRFdknTvw+Z0Y09+heZtUgn6kZPVIZ3PNa+tQ5Wd41aJiNqSsC1BB7yjyKrtLrmki4iIiKihYqz+5WzdU2IwvEsi9gjl5PPO74fLhnfCiQr9LuZvrc7Rva3W4UJhhQ2r9hU1+ByrbE5ERZjYfFaDy+1BRa0DidGWuncmImqgsM6AR/kasVXz6jARERE1kgH+oDYp2oKMhEj56x4pMbjp7J5Ii49EZETD+s7UOt34fvtxOOtoqBan0QwOAPLLajFwwRJc+8a6Bj1+cztYWIn1h0qC7uNye/DJxiPYmBN8v4aY/sY6DHt4KQ4UVta9MxFRA4V1AC79gaoQuo0SERERNYRDWK8dZTGhY0KU/HVitH99eGREw95+2Rwu5BZ7y8/P7J2iu9/T04bi+vHdYFAlub/+7RgA4Nf9xRpH6SurduD/tuahpokSFsdKa7Dw+13IK61RbD/n6Z/xx5fXYP8J/QB4Y04J/v7p7/jDojV4f11uk5yPRHpdPt54pEnvl4hIxAAcUMzsJCIiImoIsWEaAAzunCB/bjX7s96hZsAnD0xXfF3rcKGo0lu+Lgb3GfGRuHxEJ/nr0d2T8eAlA9EnTbkGvaGB/03/24g7PtyKJ77f1aDj1W54eyNe/vkgZr61QfP23fnlmtsB4GS1P2myoRmy4IB/3joRUXMI8wDcezWaGXAiIiJqLIcqAO+X4Q+At+WVyZ+HGoA/dtlgjOmejD+O6gzA24StyDeCrK9w36vumYgnLh8CADAbDfL7m+QY5Vpm8SKAux5zwdce9Aa6X2zJ092n1uEKuXR753FvgL1bp9lcsJnl4kWO0urQusHXV42DATgRNZ+wbsLGDDgRERE1lctHdMaSHQUY4st8GwwGXDgkE9/+fhzTx3WV94s0h5b/SIm14qObxmH/iQp8vPEoanxN2ACgV1os/jdzDCxmI8wm7/39/uB5MBoM8pjV5FhVAC5kwCvtTsSHODZNkhCt3D+nqApVdicGdkzAlS+vwW9Hy/DBrLEY17NDve5Xze3RD8DFsWpiNrwp1XA6DhE1IwbgAMoZgBMREVEjnTcgHV/NPh09UmPlbc9dOQyXDO2oWLMtBczBiI3UpMy1WIKeEmvFgI7ximPUAbU4Fk2torYBAXiUcv8JT60AAGy8Pxu/HfVm+L/YcrRBAbiY9XYFiX/FDHhZTTMF4CxBJ6JmFOYBOEvQiYiIqGkYDAYM6Zyo2BZhMmLywIx639f/Zo6RP5dK1m1ON2xOb9l1Slzdo7Isqkx7lc0fWFaGkHxQl4KLAbhYbn/0pL+ZmvoxQyXeX7DyeHG/k81Ugl7LEnQiakZhvgacJehERETU8tQdykWvXzcKQ7MS5a/VzdMMBiA5hFnVFlWmvdLmTzhU1DrgdnvwxPe7sWRHfsCxj36zEyMfXYqdx/wN0WIs/ryN3ghXcZ05ANicLry28iD2FWiv95aImW1XkBJ0u1OZAa/PWvZQcQ04ETWnMA/AmQEnIiKilrd67jn45rYzNG9Tl3qrm7YlRVtCKmO3qgJ3MetdYXPi532FWPTzAdz07iZ4PB7M+/x33PbBFng8Hry26hBKqx149Nud8jFi9rnaLtyX8D5KnQF/fdUhPPrtLpz77C9Bz1Vc2x1szrkYqHs8QHkzvIdjCToRNaewDsDjmQEnIiKiVpCZEIVBnRI0b1MH4BGqYLtTYhRCMbCj8v4rbGLQ7ES5sIZ67cESfLD+CL7+7RgKym3y9sJK/+e1QnMysZz9mDDP26jK7G/JLQ3pXB0uf9BtC5KBFjPgAFDaDI3YWIJORM0prANwlqATERH55eTkYObMmejevTuioqLQs2dPLFiwAHZ78LW2tbW1uPXWW9GhQwfExsbiiiuuQEFBQQud9antzuzeAIAzevmbtKm7jastuHhASPd9/qAMzL/Iv6+YAa+sdSqC6NdWHpQ/33XcX3ZeXOn/3tc6/fuLGfC80lphuzJ4VZfB6xGz6zZfkO12e/CXtzZg3ufb5NsCAnCNRmzVdic8QcrY68IAnIiaU5gH4CxBJyIikuzevRtutxsvv/wyduzYgWeffRaLFi3CvffeG/S4u+66C19//TU++eQT/Pzzzzh27Bguv/zyFjrrU9vt5/TGN7edgXum9JO3qTPgoitGdMaobskh3bfBYMBfzuiOzIRIAMqEQ0WtAyVV/uz2st0n5M9nvLVB/lxcD70ltxQvLt+PPfkVuPndTfL2PKEJW43dhfWHSnDJf1Zhc+5JmE1BFrsLxNLyWocL6w4W44LnV+Kn3Sfwwfpc/LAjH1U2Z8CsdXUjtiMl1Rgwfwlmf7AlpMfVUs0AnIiaUYMC8BdffBHdunVDZGQkxowZg/Xr14d03IcffgiDwYCpU6c25GGbHDPgREREflOmTMGbb76J8847Dz169MAll1yCv/3tb/j88891jykrK8Prr7+OZ555Bueccw5GjhyJN998E6tXr8batWtb8OxPTUajAYM6JaBXmnd0WUJUREAjMwDolxEHAJhxerd6P4bVty57sdBsrdLmRHFV3V3E1Rnnfy3Zg8nP/YJjZf6st1iCXl7rwG0fbMbvR8tw+Uur5ZnkdXGoAvArX1mL3fn+xm03vrsJcz7eGnA+ZaoS9P+tPQwA+Pb34yE9rkTMmHMNOBE1p3oH4B999BHmzJmDBQsWYPPmzRg6dCgmT56MEydOBD0uJycHf/vb33DmmWc2+GSbmj8DzgCciIhIS1lZGZKT9TOumzZtgsPhQHZ2trytX79+6NKlC9asWaN7nM1mQ3l5ueIjnEVZTPj9wfOw7t5Jmre/P2ssltx5lu668WC0AvqKWidKQgjAQ3G8zB+Af7ctX7GGPMKofKupVxrucPq35xRXa+6zZEcB7C7l8eoqRkOQ9vJlNQ7dbuziyDWbM8ggciKiRqp3AP7MM89g1qxZmDFjBgYMGIBFixYhOjoab7zxhu4xLpcL11xzDR566CH06NGjzsdoqT/KsVZvBtzucgdcUSUiIgp3+/fvxwsvvICbbrpJd5/8/HxYLBYkJiYqtqenpyM/P3C8lWThwoVISEiQP7KysprqtE9Z8ZERAR3PJckxFvT1ZcHrSysLXV7raLIA/JiwBlxNHQ+Lwa14WmIJ+tKd+v0D1O/XxMZw6vtUO+PJn3Dus79gx7GygNvUndfv+HALNh0+qXtfDpcb2/PKmmUMGhG1b/UKwO12OzZt2qS4ym00GpGdnR30KvfDDz+MtLQ0zJw5M6THaak/ytEW/x85lhsREVF7NXfuXBgMhqAfu3fvVhyTl5eHKVOmYNq0aZg1a1aTn9O8efNQVlYmfxw5cqTJH4O8ioRO5pLSaoeiwVpj2F36SYyjwvrw55ftw63vbZa/NgrRuXpttx71fuqGaWLAL2XbbU4XFm/Plysef9lbBAD4eOMR3PreZticLkUGHAD+b+sxXPHf1brnce/n23DRC6vw8i8HdfdRa46RaUR06jHXZ+eioiK4XC6kp6crtqenpwf84ZasWrUKr7/+OrZu3Rry48ybNw9z5syRvy4vL2+WIDzCZITFbITd6UaV3Vln11EiIqJT0d13343rr78+6D5ihdqxY8cwceJEjB8/Hq+88krQ4zIyMmC321FaWqrIghcUFCAjI0P3OKvVCqvVGtL5U+OcqAgMwAsrbE2WAQ9GXMf9zNK9itucbg/cbg+MRkPIAbiUAY8wGeBweVDrdGHb0TJ0SY5GQnQEDPBH4PnltSirceCLLXl4+efAQPkfn/4OABjfqwMuGtJR8/Gk81P7ZNNRAMBLK/bjlgk96zzvt1fnYMFXO/CvPwzBtFGs9iAKZ/UKwOuroqIC1157LV599VWkpKTUfYBPS/5RjrGYvAG4jevAiYiofUpNTUVqampI++bl5WHixIlyMzWjMXix3MiRIxEREYFly5bhiiuuAADs2bMHubm5GDduXKPPnZrHiYpanKxq/oysVvZdZHe5EWk01TsDHh8ZgeIqO1bsKcSLyw8gPd6Klf84R9EVfdzCn2A0AHVViVfWOgMy4JIDhZXona5f+h9nDe2t9IKvdgAA/v7p7wzAicJcvQLwlJQUmEymgNmeele5Dxw4gJycHFx88cXyNrfb+4vTbDZjz5496Nmz7quGzSnaYsbJageqWIJORERhLi8vDxMmTEDXrl3x1FNPobCwUL5N+jufl5eHSZMm4Z133sHo0aORkJCAmTNnYs6cOUhOTkZ8fDxuu+02jBs3DmPHjm2tp0J1EBultSabw43ICBPsztDWUkvl7nGRZhRX2bHjmLdPUEG5DZe99Kv8tUQv+BabwVnMRjjd2hcAthwpDRqAx0Y2ay6LiNqheq0Bt1gsGDlyJJYtWyZvc7vdWLZsmeZV7n79+mHbtm3YunWr/HHJJZdg4sSJ2Lp1a5touBJj9a4Dr2YGnIiIwtzSpUuxf/9+LFu2DJ07d0ZmZqb8IXE4HNizZw+qq/2dqp999llcdNFFuOKKK3DWWWchIyMj6Ogyall3Zfdp7VPQVev0JkDqW4IerzErXR18ByM2g7OYjYoM+IS+/mqRoyXaHdkl0kSdxrI5XVhzoBg256mbEKqodWDpzoKAdfltQbXdiVv+twn/tzWvtU+FqP5d0OfMmYNXX30Vb7/9Nnbt2oVbbrkFVVVVmDFjBgBg+vTpmDdvHgAgMjISgwYNUnwkJiYiLi4OgwYNgsViadpn0wDRFu+VS2bAiYgo3F1//fXweDyaH5Ju3brB4/FgwoQJ8rbIyEi8+OKLKCkpQVVVFT7//POg67+pZd0+qRemDNT+fqTGKZf8ZSZEtsQpyWy+LubqAHxQp3jN/cUMeGOIDdHMRoMcgEdGGPHWjNG4/ZxeAICS6uDr5GNDLEGX6M1Ff+jrnbjq1bV48Kud9bq/tuSv723GrHc24l9L9rT2qQR4feUhfL89H3d8uLW1T4Wo/gH4lVdeiaeeegrz58/HsGHDsHXrVixevFhuzJabm4vjx483+Yk2FzkDbmcGnIiIiNofg8GArOQozdt6pMTIn8dYTPjp7gl13t8LVw1vqlOTM77qAHxYVmLAvhEmgz8D3sjMs9QRHQDsLo8cgJt9PQ+SYrxJIq118uK51rcE3awTgL+/LhcA8MH63HrdX1uycp+vu/yGtjfRoLgFGg4ShapBlw9nz56N2bNna962YsWKoMe+9dZbDXnIZiNnwG3MgBMREVH7pDdfvGdaLNYdKgHgDdSt5uC5mYuHdsTFQzvitg+2NMl5SaXgdpdysfbQzon4H5TBqEnolt7YDLgYgNscLnkOuJShTvYF4Fqd4kur/UF5tM7rqifCVO/c1ynHWs/XpLUVV9pwosKG/pnaVRdETa39/xaoQ4xvFvgzS/fieFlNHXsTERERnXrEADwywv/2r1uHaMV+WiO3RBZfAHnvBf2a5Lyk9cIOpzIDPrxLYsC+TpenCTPg/iDa5nQLGXDv80+K9mXANUrQy2r82+pqHVdtd6JUuA+zyf/67iuowJu/HpKfU3tR10WctmbswmU4/98rsf9ERd07EzWBU+t/SDOI9q3dKaq04eb/bW7lsyEiIiJqemJQ9IeRneXP1WvA65IW793/xrN64te55yhu+6tqHnYo66OlDLi6BL17SmzAvk63BzW+gL0xzc9cbndgBtwVegb8pJABdwrnvWpfER78aoeiCdm5z/yCYQ8vlb8WS9DPffYXPPT1Tny0oWXKzt1uD/JKmz/ZJF7gaSsMOteVPB4PHL7v/ZbcUt3jax0u3PjORry37nAznB2Fm7b3P6SFSRlwAPjtSGnrnQgRERFRMxEz4JP6pePcAemIjDBiTPcO8vZgue/bJ/XGGb1ScIsQZKvLwC8f0UmRuU7Q6FSupl4DHmc1470bxug2Kyuv8QbO8VENL0G3uzy6GXDpceU14NV2eDwerD9UgmtfX4f9JyoVJehOoXv6n19fh7dW5+D1VYcAeIM2dcArrTEX7S2obPBzqY+/f/o7Tn/iJ3z927FmfRyr2f+z9uv+Ijz09Y5W7+5u0PnpLqvxfy+liy5aPt10FD/sLMB9X2xv8nOj8BP2Abi0BpyIiIiovYoSAnCL2YhFfx6JrfPPQ8dE7eZsateN64r/3TBGUfodo3oPFRlhUqxx1hoVplbrUK4Bv2xEJ5zeKwUAsPjOM5HdP12xv5QBDyW412N3qjLgTrc8B1zOgPtK0B0uD6rsLlz96lqs3FeEOz/aoigpd7oCi9D3FXhLmbUaf0n37xYC91CrEDbklODeL7Ypgsb6+GzzUQDA88v2Nej4UIkZ8GteW4c3f83BG6tymvUx66KXAT9eVlvnPoCyZwBRY4V9AC51QQeAaMup1TSCiIiIKBRiBjzCZITJaNBtzFbX8RKT0aB47xQZYVLkGROCZKmlCwLyGnBfBlwM4PtlxOPla0figYsGBBzfmBJ0h8uNckUA7oLbo1wDHmUxyed4ssouZ7q355UrgjExAy6RLioUV9oCbpNK7ouE25KEzGuwIHDaojV4f10uXtAIoDfmeDP0mw6X6N+BjzHYgzSQOKpQ62flQGHzZ/lPVNTi6R/24OhJ/+z2jzccwVn/XI5DRVXyNvFc84UAPNhafL3u9W1dflktHv1mJw4XV9W9M7WYsA/AxTIZqeEGERERUXsiZiUjTPUPJvSCdYuwtjwqwqQIIINlqaWxaFKwKjVhU3cJNxkNmHlGdyRFK+8rvhFd0L0ZcKEE3eEOWAMO+EuS1ZlscXStlDk/UuIP+mp95dZaGXDpgoOiNF0ICNWBntPlxp9eWYN7Pv1d3na8vFaxz4nyWvxh0Rqs3FeEt1fXvUa5GeJvVNn9JeZaTdicruZpNLfmQDH+9slvKKt24O6Pf8MLP+3H1a+uk2//x2e/I7ekGj/tPuE/F+GiiZgBtwUJwPWWRLRFX/92DH9ctAYF5bW45b1NeG3VIVz58trWPi0ShH39dblQxsMMOBEREbVH6gx4fVjMRt0ARFzT7M2A+/dTl6iLuiRHY29BpVxSLmXALToXB7zn73/PVlcG/MLBmUiIjpDna4scLjdqHP4gTNkF3f98EqIikFdag735yu7YeaX+oM3l9uCHHfm48d1N/vuTM+D6AbgY+DmEMnb167w5txRrD5Zg7UF/ZjtZlTDaLDQP0+rartYcwaR4QUMrw+7QqBRoCle96g0sLWYj1h4sBgDkChdDtNidbvn/QL4wASnUANzt9tQ5LaA1SSMCH/lmp9xYLl910YZaV9hnwF3CVcfaVm4QQURERNQclBnw+r39iwpSqi5m09WBnTVIN2wpuywFpNIacL1zU2fg62rC9tS0oRjaOUHzNrvTjRohY2tzBs4BB/xd3LcfK1McvyX3pPy50+XBSysOKG6XM+AaJehOtwcOlxvHhAy4lEUH/BcAth0tw7RFq/Hr/qKA+1BXA4gZ+Wp73e9lmyMDXimU5WsF282VAZfsP1EZ8pIKsdT8eIgl6OLPxakSLzS0VwA1v7APwP88tqv8ebXt1PgPRURERFQfygy4TgSmsznYWCmz6r7E4E5c5ic5q08qXps+Sn8NuM4MaXVwFSwD3q1DNCIjjLoBmd3lljPv3nMQMuDC85H6BO04Vq44Pk8VPItrigEhA65Rgu59PBcKK/zBuVYG/No31mFDzkn8W2O9d5SqskAMukPJgDdmDbj0/fpk4xHF9CBxTb16pjug3ayuKbndnpArWe3CxYBSIUgNNQCvCeEiR1tgNBhOqdL5xjheVoPvth2X/x+3dWFfgp4Sa8XKf0zEmf9cjio7OxwSERFR+9OYEvRgPXIiNMZqaT3muzNHAwBO75kCo9GADTnekupgTdiU96Xcrjdj/OFLB+JPp3WBwaDfZM7udCtmdYsZcDE4jfE9xjHVKDF1Ezb1e34pQyo2Wju9Vwf8ut9bIl3rcCvKncX7k9aAi6PO1FxuZaAoZsCDHScxNDAAf+zbnXjz1xzccGYPLPrZm/XPeeJCAMoSdOl7KV6YaKoS9CqbU/6+iFweT9BKDZEYaCs+D5KlFwO72iCBeltiNAAmgwEunBpBaWOc9+wvqKh14rHLBuGaMV3rPqCVhX0GHPCv/RavgBIRERG1F5GqMWSis/qkAvBXBb454zTF7Smx+mOygmXAxaA5MyESZ/ZOldfOSudTVuPA9rwyuVxWbw24GFylxFp1M3tJ0Rb5+ekFZA6XOgB3y0GtWaMEPdj6WafLE/DeUb0G/F9/GIL3bhgrvx61Dpci2CsXgtdQgmO7KpssZsBLq+1wuz2wOV0BmXlJQ5Kiq/YV4dWVh+B0e/CGb865qNImZMBd0mg5/3OUStBX7SvCA19uV7z+aptzT+KHHfkB2z/ecAQDFyzB/9YGNppzuT0hl6CLFz/E+eTS902LQ3guzZUBr7I5Mfez37H+UN2d7ENhNBgQ5PpYuyJdxPpp14k69mwbwuTbEpx4Ja0myC8EIiIiolOR2JlaXYL80jUj8OaM03BXdh8AwMS+afhxztny7R1i9TPgZtU7fLEJm1iCrt4vypf8+HjjUVz0wiqs2FMIQD8DHi2UXQ/oGK97PmJmPEqnJNlbgi4EYQ43pPhKDOyl94c6cSwAKQOuCsCdblTZnNh82LtWvEtyNAD/RQeb06Uo0xYbAjvddWdX1eupxQDc7fGWzA9e8APu/3K7vF0MIDWbpLnc+Gl3geJigGh3vr8MX/18AeUacOkCQa3wGksVBn9+fR3eXXsYr/5yUPvJAbj8pdW48d1NitFhgLejOQD5eYnPye3x6H6/1fQz4PoxgPhYwS4eNMZ76w7jww1H8MeX1+hePKkPg8EAU3Ms+G/DglUxtCUMwOH9oyT9vq1mGToRERG1M2IArh51FWs1Y2LfNEVmXNw/WAY82EgzReM3VdY9Umett14APrxLovz5gEz9AFxMqkRqrEEHAIfTA1tACbovA24KDMCDcbrcAQG6zeHCl1vzUGFzontKDE7rlqw4nxq7WxHQiSXowdYhy+cfEIAr37s+sXgX7C433hM6wItBo1YG/JVfDuIvb23EzLc2aD6mIpstZPylQFG8f+n8FK+xKmjNKa5GXmkNnlqyBx9tyMXHG44EPGbeyZqAbSIx6He6lGvAgwXJ4nOx6QTjauI6/eYKwMVCil3HK/R3DJHB0PLj0zweD7bnlTXba1SX5u410FQYgMN7hUi6sjrhXysYhBMREVG7EhcZgdvP6YVbJ/ZEUox+RlsiBsJBM+CqgFlZgq7f+E0vWzlMCLRFk/qnyZ/3SIkBALxy7Ujcld0Hgzv5u52LQViURfttrl2zBF3qgu4/JtaqPEetknaX26OYqAMA1Q4Xtud5O6dfNCRTKLv3laA7XYqATsw6251uzc7nIoe6BF3VRLigPLD7uljhqZVc/XzzUQDAhpyTgTdCPziVAlgxkN1/ohL3frFNkQEXS9QBwAMPrn9jPf6zfD/u+Wwb/vHZ79h/okJRzl9X8la8zyq7U/EzWx6kA7jdWf8AXLwtlGpZm9OF6W+sxwsaTfT0iOe/RKMEv76MdQTgbrcHH288gv0nGh/sS/5v6zFc9MIq3Pnh1ia7z/oIpYKkLWAA7iP9kai2u/DL3sJWPhsiIiKipjXnvL74++R+Ie0rBswdggTsw7MSFV+nCtlyMaCwqAJ1vfW6PVNjNbf3TY9D1w7RsJiMOL13CgDgvIEZuCO7tyLTLpaga3VhB3xjyFQBuFOeA66fAU+PD6wEcLjdASXZLrdHDoLFBnaRQud3xRpwRQm6B9e8tk7zvOXHDFKCDgAFZYFr1mvt2kGnJLmOizJ6wam0Hlp9+/vrcrHzuL9sPSAA9wD7TlQqtuWX2RT3s/5QCUqDdHUXL1yUVjsU2U+9Unr1uYbahK2+a8A35ZzEL3sL8fTSvaiyhZbYE9ej/3a0NKRjgjEZg3dB//r3Y/jHp78j+5lfGv1YkrfX5AAAFjfBBYSGUF+caqvCvgu6RPwPrm5OQkRERBROxJLxhCj9kV9zzusDa4QR5w/KBADMu6A/CittuGZMF0VgqC4tV2fHuyRH46FLBuk+jsFgwOe3jEdFrROdEqOU5yrcd0wIa8C9Tdj8AVVJlR33f+FdV6xYA64a95UWF4mc4mrFNpfLA623jVLn9LhIoSTe95wX/XxAma2trV/lpToAV0/xqdAI+Kod/m021Rxrj8cTtNM9ECQAd7iQBO2gXuyMXhXCqF+H2604t38v24cvtuThl39M1NxfLN2vqHUqqhqCzcB2KErQ69+ELZQu6GIsseZAMbIHpNd5jPgabztaBo/HU6+O9R6PB2sP+hu4GWAIOnLu96Nlurc1VFZSNLbklgLwLs9QV8g0tfWHShQXj9T/N9oqBuA+4nqWGvup8c0jIiIiag5ixjo+yMztaItZkVVPjbPi3ZljAAD/tzVP3q4OwMVy7rP7pOG160bVeU4dYq3ooLEeXXwPJ2bA1QG0xO5yy6PCxG0AFE2r1BnwNM0MuAcRGjXdRb4O6OK88lFdk7D1SCl+3V8sl9EDwcultajXuUoZ2dQ4q2K+uNY+gDJYfvjrnVi2u0Cxrr7S5gwY86aXHZYqCbRuFzO/lTYn3Bprx0VOlycgkM8tqQ7YT75P1YULce56eY3+RQ2bTgm6LWgGXFgDHkIGXCyl/3lvYUgBuHguxVV25JXWoHNSdJ3HSb7fno+/vrdZ/rquNeDWZkg4JkX7f94PFlWhT3pckz+G5EhJNf748hrFNq4BP4VV2ur3i5CIiIioPRED8MQ6sqN6xOybeg24mAGPj2xcPkjMeonl6JERxoCGcwBwvLRWt7O5SdGETZlBT4+PDNjf5Q4cQwYAJVXeQFh8bvdd2F9e1yzOCNfKHgcjBbs5RVV4fdUhlPiqOEforJ8HlOuWxUzxG78ewuHiany/3V8ynK9Rwl5XCbq6yRoAlKhmklcKmXqtqb8nq+xBs9BqFar36yeEcXFlNQ7dTuLixQK9cnQ1ZQa8fgF4TnFVkD391M99e165zp7a1OvGjYbgGfBQx7bpqbI5MW3RankmPABUCRcnth4pbdT91+W4xs/pqZIBZwCuoaKepUBERERE7YnRaMCfTsvClIEZ6J/ZsCyWmH1Tl9KKGfD4ICXuoRDfdIuP422yGxhkBFvrqzUHXKK1BtzpcisyuxJpk5gBNxgM8kiy+padKx/Te+cXPL8Sj3yzEwcLvQHeyK5Juseom8653R7sPKYd4BUIgaxURh6sBB3Qfk1PVtmDfq32j89+x2Pf7Qy6j0idAa9SZPldisoIkd3pRkmVHec8vUKxTCLUAFy66HCgsFK31N0hPHZpdWiJPfXSAPUYNsm6g8V4cfn+gE7j6mC7Phlw9WOH4qMNR7Ah5ySe+H63vE1sZP3oNzvr/J43hvoCGeBdxnAqYADuc/uk3vLn6kYRREREROHmiSuGYNG1I+u1DlUULPsmdiiPa2QGPFjgFGw2tNEQ2Glbaw64RCsD7nR7gq4JVj+3aJ2y+PqQgkF187VhWUm6ncPF5ZU2hxvvrTuMC55fqbmvlFn894/7MPjBH7Bizwnd8mx/BlwjAFc1UBMDUa1Z4gCwZEdBwDa972+wixhOt0c3G2p3uvHmr4fkCxeSYEGoWIJe43Bhb0EFJj39M87653LN/V1CIFhaE1oQKlVCSOvHczQC8O+2HceVr6zFv5bsCWgarf7eGw3Bm7CJy0IaknzU6jheKaz1L691YofORZ6moFVuzhL0U8xd2b1x9ZguAAKvqBERERFR/XROitK9TexQ3tgAPFjnY63RYeJt0arbg2XA0+L8AbgUJDndnqDja9XPTT3arCEOFFbi4hdWBWxPjbMGNKiTiOdoc7rwyDe7dO//RIU3AH/2x70AgPn/t0M3CJYuAmgF6AEBuJAtrk+psF4AGyxh5nJ74HDqZcC1s+NBx5CJJegOtxz86mXAxUCwtCq0DLj0+P0yvBUnh1Sl6yv3FeLdNYflr9XPX33By1uCrv94Ypl8QwJw9WQDAAEd39Uj+pqS1vfwVOmCzgDcx2AwINN3ZZMZcCIiIqLGGdQpAY9MHYQ3Z5wWcJuYmY61Nq4EPVjgFGydq8vjQbQqyBbngKsz4CnCPPT+vqZl6o7qanGqBnZNkQHPKa7GtrzADtbRFhMyE5RZemkdtFiu7PYEL8NXl0wnx1h0X2PpfrUy4CWqwFMMVtXZ+2DKVOcjxZnBxoE5XR4UVgauEQa8z12rN0DQMWROMQB3wVrH+mlFcGtzKi44HCqqwuebjwasUZcy8H19jcvEDPjBwkpc+/p6rDlYLG9TZ3vVz0hdgq5eKiE+34ogY9v0WISLaNJzCQjAm7Ek3Knx/TpV1oCzC7og1neVUmt8AxERERHVz7Vju2puFzPTjR3/qlUKKz9OkBL0WocbGfEmiIW8YlIvRnWsGJAP6hiP346U6jZzA7xrbNXPTWvdalOJtpiQpiqTd7g8sJgNQcdyqRVXKjPOyTEW3WC3PmvAy4SMuHr9cjAnVQG41Kk+2HztHcfK8fA32uvJ7U43zMbAn7n6rAFXr59Wz5xXZ2dLqx1IjfP2EJj41AoA3gshfxjZWbgf72P09WXAT1TYUGVzIsZq1lwPrl7vHFiCrrygZHe5EWn0n6fY/K9BGXDFa+BGZIRJTmIaDN5Z742Nhz/ecARdO0RjTI8OAbdpZcBDmdHeFjADLpBKjViCTkRERNR81HPAGyPoGvA6MpVRqoy0mFU0GAy4fnw3+WtxrJkUJAWjzn4DTZMB1xNtMSNDFYA/8f1ulNU48NnmPJ2jAp2stitK1pOiLbrZ4eqgXdD114DXOFxBy6OVxynvx+g7sDpIEP/Z5qO6t9mdbpg1fuaCB+D+n4tqh0tRfi0Fr8WVNvnCgrozfplGGf1PuwuU4+F8VQRp8ZFyN3/pYohR48WSsvKfbjqKqS/+isOqGfXeNeD+r9Xd9u3OxmbA/XcuPQ/poog0urAxGfAtuSfxj89+x5WvrNW8XSvbbXe5g34f2wpmwAXSOh2WoBMRERE1H7E0VisbWR+hrgH/5OZxKKt24IZ3Nsrb1FnuGlVQN/d874zz4V0SES1kr3un1R2Aa41XUz9eU+kQY4HJaAjo1P7Gr4ewYs8JHCqqQkJUREiZ8OIqOzbmnJS/tpiNdZagawXo6mPUJehmozFo2bdEXRIvZcAbmu20udwBywuA4OPgxPMsr3Eogr+KWidcbg/OfHI5eqfH4tvbzwzIzqqz+ACwbNcJ9J+/GLPO7I77LhwgP4bVbITVbEKtwy1v07pWIT3GvZ9v03wdDQYDDMKR6u+H+BzEhnaHi6uwZEc+/jy2a8gXjKrsTiTFWORO9PFRZpTVOHQ70Yci2Ax4IPAih6Ta7oTF3LDRiS2FGXCBtAZJzIDvzi/H/hOVrXVKRERERO1aVrJ+s7ZQSEGymK2WRAoB72ndkpE9IF1xu3oNuDqoi4ww4cFLBuLSYZ0QYTLi+vHdcMWIzhgeZOa2RKu5nPrxmkrPtFgA2p3aD/rKlwd2jA/pvn47Uorpb6yXv7Y5XSHMAQ8hkBYC8Fq7K2iHbpG6mZt0nJRtre8ShtIqh+bzCZY5Fdcbl9Y4VOXbDvx2pBR2lxs7jpWjpMoesD5ZaxyXdB+vrjzk+9r7WlrMRrlDuRQka00UkC48xeo0MTQalEGqOki365SgT37uFzz+3W78c/EezfuViNntGrsLDiH7nBAlZcCbryma3oW3UyGRygy4IFaVAa+odWDKc94RDQcevyDkXxREREREFNybM07D0ZJqDOmc2Kj7+fPYrji7T6pm1/W6StDVXdDVGXC1By8ZCCC0wEIrMGquDHjP1BgAQEps4KxySXKMPyt4Zu8UrNxXFNJ925zuukvQQ5gjrS5BD/V9dbEqeJWbsPm+V8nRFuSXazdc0/LRxiOaM9P1AvCNOSXYnFsqf12uCsDLa5yKXgNrDxYHrgEPofJAuojhzYAbFeekFYBLQX5GfCRKdOZtKwLwEEvQpaaCa4WGb1rE5RrVdpdiTb6/BL3hAXhd4w/1ej8Ea4rYVjADLpDWgOeV1uD6N9fj29+Py7dVBRkxQURERET1M7FvGq4d161J7isrOVrzDbtWAB4tBEvRqqZooTYHMxkNujO3JTEa5bvBSnonD0xHn/RYdE+JUWzv2iG6zvNJ9QXe8RrrziVicD51WCf09mXN62Jz6K+rDdaETa1cVYIeal5rx7EyRUZZCtyl4F+8sBCqTYdPBmzTm3X+h0VrFF+X1TgUFxwqah2KCoDVB4oCAk/1OnYtUlBvNZvkvgjBuno7fI+hd/HD7fEoglT1fdld2hlwSV0TxMTnWG13yQlMi9ko93hoTAl6XfRmfp8Ka8AZgAvEUqEVewox9/Nt8tcN6Q5IRERERK1Hqwt6R2FWtjpIrk/2TGuUlSha47HVs8VFM8/ogR/uOluRyV91z0Sc0SulznPJ9D2n/plxGJaVqLlPcowF788ag/sv7I/LR3TCe7PG4OlpQ9FBI4Ad3T0ZD/my/TanS3d99Afrc3H9m+txMoRZ1+IacJvTHXJwtjW3VFGZIK0Br/YFfB1iQw/AP5g1Vvc2u9MdMBpMS1mNQ/FzUl6rzIj/frQs4LlV2bznrx4FJpICaatQgi4Fk+qO54A/A673vXG5PRAfLlgGPFhHeT3ic6xxOOXnGGMxyRdJGpUBFz7X+r7o/fyEUo3R2hiAC1JjrUiM1r5yyM7oRERERKeWab4xT2LJ8X+uHo6+6XFY9OeRARnwukrQRXU1j9MK/tWPp9jflzUUy407J0WjrhhmUKd4XDqso/ecTEZ88dfxmlnhDrEWjO+ZghvO7AGDwYC0uEhcMbKzZgDbrUO0nDG3Od2wBwlqVuwpDGndbamqE7g4C3xApv769Cq7C9vzyuWv1V3Q65MBH9I5IWjPAfUFGK2KCJfbg5Iqm/x1Ra1TEfTtPl4RcFytUyrVD9LozXdbZIR/fJ2UpdbK9jrlDLj2fbo9yjLtgC7oQgZcK2vsQfAfPL0MeIzVLF+caqoMuFYgrzUHHDg1MuBcAy4wGg0YnpWI5XsKA26rtNW/PT8RERERtZ7e6XHYcF+2IsHSLyMeS+46CwCw63i5Yv/6dNauKwMeFaG1Blz/rbcUsKvvNljWFAC+ue1MxdcGg0FztJtWphtQZuVT46worLDhz2O74kS5N8jUWgMerDO6noJym+5tdY2i+8/yffLnLrcH1XZng0rQI0xGX5l+jWK72WiA0+1BWY1DceGkqFL7nAsr/NvLa52Krup2lxt78isU+7/880FsyjmJl68dqXtuUoBsMZnkDHhxpR2fbToKt0YGWCop1xoBB3h/blwu/TXg4kUCmy/7H2pfAEAZXFfbXPISg/jICPkiSV0/u8GISzycbg9Uo9blEny1YBc52gpmwFW6qdbdSMqZAScionYuJycHM2fORPfu3REVFYWePXtiwYIFsNuDr1+cMGGCd+SN8HHzzTe30FkTBZcaZ5UDGjX1Ou4Zp3cL+X5NdQSNUZbAx9QqS1ffpm64pRV81UUrO99Bp0GbGDx+dONYLLnzLAzpnAirbxa1zRHYBT01SLO3wHOpe7G3XkM2aXnor/v9DcFKquwYMH+JfE56Fxa0RJgMmssApAs06o7rRZXav/tOiAF4jSMgCF6x50TAMRsPn8SOY+UB2yXyGnAhA373J7/h7k9+w98//T1gfykrrluC7vEogmTxIspdH21VBNs2pwtP/bBH0f2+7jXg/vurtjvlCoekmIgmz4Br3Q8z4O3ILWf3xJu/5gRsZwk6ERG1d7t374bb7cbLL7+MXr16Yfv27Zg1axaqqqrw1FNPBT121qxZePjhh+Wvo6PrbhxF1NrEYPfLW0/HkE4JIR8rBrkXDcnEdeO7YfH2fLy+yjtWSqvhmtb8aYkcgBvVAXjIpyTTGs2llym2CvumxlkR52vkZvWlHKvtroBzSIu3Iq9UmUUGgPdnjcHnm/Pw6aaj8rZoi6nORJZZ5wJJl+TooEErACSFEIDPntgLcZFmGAwGzfFwCVERKKq0Kzq125wuFKsy4EaD9/txolzMgDsCgmC956vX5M/pcstl1lazERad10NxjNubtQ62BlyvC/oXW/IU+5bVOPDi8gOKbXX92Cky4A4X3N5pd0iMtghrwAPPzeZ0YdfxCgzulBC0E744w9ylUYKvt778VMiAMwBXSYuPxDt/Ga24AgScGjPliIiIGmPKlCmYMmWK/HWPHj2wZ88e/Pe//60zAI+OjkZGRkZznyJRkxLf/+s1L9MjZnYToyNwWrdk/CwsY4zU6MAeLADXK0FvCK2sc0qMdtZaDNbFz6XAXBxRFWc1o8Lm1M2AD8tKRHKMRRGAx1rNdQbget3bs5KCB+Amo0GeOR3M3yb3lT/X+h4kRlsAVKHMl8XdeawcV726VtE4DvB2kj9RYcOJCv/YsxPlNnTr4K2g/dNpWdiQU4IDhVXy+bkUzcq0A3AxO201m0KabX70ZE3AiDaR2+OByxMYgFdrTHbKOxl4McXj8cDj8eiOAxOD4hq7C7W+JQFJ0RHyY2llrh/4cjs+3ngUD1w0ADPP6K57/uIadK0mdHpzwO0uNmE7JWk1zZB++ZyssuO8Z3/Gs0v3tvRpERERtbiysjIkJyfXud97772HlJQUDBo0CPPmzUN1dXXQ/W02G8rLyxUfRC2trlnDwZiFEnQp2y0GTlrl5snR2tlak9EgZz3VWcE7s3sjKToC14/vVo9zU77F75Eag/go7eBfLM8XM69SCboYPEvZ49Q4nWDeZAwofxcD3p6pMQENj/tlxOFvk/to3l+whmmAd457XbPe1TRL0H1BfGm1A1/9dgwXPL8yIPgG/M9bDP4Ol1TJme0oi8kXzPvOT/UzoHWfABRjzCxmY51r4gFg5b4ijHr0R93b3W5lkCwFpkUVgUF7kUYgX1hhw+jHl+Hhr3dq3r9D1YRNmnWeGOXPgEtrwD0efzb+443eizOPfLMTd320VbfzvHjhoj5N2GycA35q0vqPfLi4Gh6PB6+tOoi9BZX497J9GkcSERG1H/v378cLL7yAm266Keh+V199Nf73v/9h+fLlmDdvHt599138+c9/DnrMwoULkZCQIH9kZWU15akTNTsxyyxlu8VgVuv9pFb5M+ANJKWLAVeN7gIAGOXr3J6VHI1N95+Lf0zpG3Cc3v2JQc26eyfhu9vP1L3YIAbd4j5SCboU/BgM/mBaKwA3GQ0wmwKDx2gh4L16TFdFtttqNmLxnWchM0E70O6SHHwpS5SlAQG46jV7c8ZpctB8osKG+77YpnUYAGjOTj9WWiuP8bKaTYrXUx3sl1XrBOC+jLHZaPBejFF3HGsA9RrwSt+YsMLK2oB9tdZNl9c6UVhhwxu/Hgq47cGvduB5IRbKLanGT7u9694ToyPkANzp9mbR53z8G0Y8slRROQB4S+FzS/wXa11uD5bsyMdj3+7EHR9ulberZ5gXV9p0G8aFMpO+tTEA16BVMvTeulws+vkg8sv8az5eWLZPc25epc2JF5fvx+HiqmY9TyIiolDMnTs3oEma+mP37t2KY/Ly8jBlyhRMmzYNs2bNCnr/N954IyZPnozBgwfjmmuuwTvvvIMvvvgCBw4c0D1m3rx5KCsrkz+OHDnSJM+VqD6mjeqMyAgjLhySWe9jxSyzlOkUM+BaFZXq9d2SZGEU2Jm9U7HibxPw3qwxiuPE4P7asV1x6bCO+PimcZr3JzZuS4yO0HxvK4nQKXeOjFBut5iMuGJkZwzulIDTNWaT62XwY4XRa4lREcqSd98xehnfznUE4DFWM6z1DcCFtfkfzBqLiX3T5Kz899vzURGkXP7u8wIvgrjcHhws8r7nt5qNitdTXe5eqpMBlzLoUtl/KBnwurhVa8CLfI3jCjUy4HWR7qfK5sTbq3Pw1uocxe1LdxbgqK+MPSnaIldBuNwe/LjrBL7YkoeyGgfWHSwJuG9xHN3rqw7ipnc34dWVyqBfnQG/4PmVWJ8TeF/AqZEB5xpwDVq/MAHgycW7ce6AdPnrp5fuxd4TlXjhquGK/R78agc+3XQUn20+ip/untCcp0pERFSnu+++G9dff33QfXr06CF/fuzYMUycOBHjx4/HK6+8Uu/HGzPGGzjs378fPXv21NzHarXCag29kzJRc0iLi8TW+ecpGpGFSsyAawbg9QgMU1RrqrWm8oiP1zM1Btefrr9+VoxX6mropXe7VZWFtZiNuPnsnrj57J7IKQpMMkkl6+qO8+LotcToCMXjSa+XXjOuzonBS9CjGlKCLmTApXOWStClsXRXje6CjTkl2HeiUnFsWrwVFw7OxLfbjiu27y2okO9PfH7qAFyvBF0KQqXXoyE/j2reDLg/GJVGqhXqjFYLprjShrT4SCz8fhf+tzY36L6J0RFyc0OX24OlO/Pl2w4WBv7ciK/Ju2sPa96ner13sJF2p0IGnAG4hmD/kbcdLVN8/fVvxwICcKnxhNYPGRERUUtLTU1FampqSPvm5eVh4sSJGDlyJN58800YNcYZ1WXr1q0AgMzM+mcViVpasOxwMOIacOk+LKbAoDwUoYz1EsvD9bLWEnH+cl3r3PWCPfV28Wut10wKPNUN4MQgNCEqQnHuUrAeofN7JsZqRr+MOOxWzdWWxFrNAZl6tdsn9Q44RiI9p0RVJ/XhWYnYne/vS/H5X8cjISoCVrNJsYbdYjLC7nLLAaG3gZr/+ceofgZKdUrQy329pqReAnpj8+rD5fYoLsQUyhnw+gfgeaU1iLSYsHx3YZ37JkZb5P8bLrcHNUJGek9BYK+PB7/agX//aTj6ZsQpusuLXIr15sEb+unNRW9LWIKuIVgAnl8euG6CHdKJiKg9yMvLw4QJE9ClSxc89dRTKCwsRH5+PvLz8xX79OvXD+vXe6eFHDhwAI888gg2bdqEnJwcfPXVV5g+fTrOOussDBkypLWeClGzS9JotmWpI0gFvJ3E1VLiQp9lDegHrJL6zA7XK+FWB+CKBm1BxpypG8DFiCXo0RGwamTAjUZDwEx26fYvbz0dG+/Pxr0X9Au4vVd6LDrEWOXO8bef00tx+0OXDMRd2foBuPQ9SlR1Uu8Qa1FkZkd0SULP1FjF8wSAjomRiuMiVRlw9Si6cp0MuPRY0sWEUMaQ1UW9blrOgDcgAL/spdUY8uAPKK6q+9gkYQ344h35clUBAOxXVRQAwO78Clz8n1UA9EeIiZn8nCJlg8+bzuqB/80cg+njunrvgxnwU1N9yz6W7SrApcM6AQByi/0/FMFm2xEREbU1S5cuxf79+7F//3507txZcZvU1MnhcGDPnj1yl3OLxYIff/wRzz33HKqqqpCVlYUrrrgC999/f4ufP1FL6pzkX58sB+Amf7Cpt6QxNtI7ykukLkGvi7mONcKuegTg143rig/W5+LCwcqKFbPJqBijJV5Q0Lq40LWD9/UIaMImBKHxqjXg4r4RRmNA+bDFbERkhAmRESbMOrMHHv9O2aticKcEJERH4KObxiHGYsa+E8pMeVZyVEAFgKIE3XcuSaru9MkxFgztnIiDhVUBwbDY5Twh2gII7/3VI8TE9e8AUFqjvf5aas4mZ8CboARd3VitqNL72AUaycRQ1Yawvjox2iJXQRwuVgbLhzSWLgDaTeBETqEEPUfVYyvaYsYZvVOw5qC3KdupsAa8Qd/dF198Ed26dUNkZCTGjBkjXwXX8uqrr+LMM89EUlISkpKSkJ2dHXT/tsBoNCjKWTolRiFNZ9wCAHzia6dvc7pw1r+Wy9v1OlMSERG1Rddff708+1X9IenWrRs8Hg8mTJgAAMjKysLPP/+M4uJi1NbWYt++ffjnP/+J+Pj4VnoWRC2jc5J/fbK/C3rdJehaY7DqG4D3SY8LervG2GRdHWKtWDdvEh68ZGDAbWJSKj4qQnO7pKtvFrZ6DJlYPpwQFaEMuoXgVuuigl6HdsmgjgkAgNO6JWNAx/iAx1avYweUJfHS7RkJyte/Q4wV917QH9PHdcX/zT5dcVuSUIKuzpxbzUbFc4pWfa/3FgRmgAF/BjxKvpDTBAG46mKGlPmW1qs3h2kjOyM5xiKvAVfTm90NKOfNq4nd3NVBvPRzI138OhXWgNf7u/vRRx9hzpw5WLBgATZv3oyhQ4di8uTJOHHihOb+K1aswFVXXYXly5djzZo1yMrKwnnnnYe8vLxGn3xzksrQuyRHY9U9E/HG9afp7vvrgSKcrLLjWKnyilJFrROVNieu+O9q/PtHji0jIiIiai+yksUMuDfQEgMPvSWN6sZcANApKXizMck3t52Bl68diUGdEoLuV58SdEC/O7sYaItrn7X2l0aGqTPg4rxmdYZYvH+tytG6AtE+GcqxYOog3qJxoUBr1rl6DFpyrAWpcVY8fOkg9M9UXkwUs+UJGgG4MgMeWjJOCsC1ljI0lMOp/BmocbhworxW7lbe1FLjrPjXtKEAAvsAhCLYeYk/Q+r9pJ836Xv5/rpcjHr0Rzy7dG+9z6Gl1Pu7+8wzz2DWrFmYMWMGBgwYgEWLFiE6OhpvvPGG5v7vvfce/vrXv2LYsGHo168fXnvtNbjdbixbtkz3MWw2G8rLyxUfLU36pZkUHQGDwYBeGnP/AO9/LI8HGP7IUvz9k98A+K+IutwevLHqEDYdPolnf2y7PwREREREVD9iBlwKnMRMnV4JulSqDQC3ndMLFw7OxFm9Q2uSOKhTAiYPzKhzP/XYpoYSM8jqbK+a9HoYDAZFMD2uZwcA/rXTYoZY73OJ3oUBAHj2yqEBGW514KcVwGtdAFBfFFE3TxOJFyICAvAIk2oNeGiN+KTS9OhmyoBLz1NvdnZTEF95UwPGqBUF6c4u/Ty73R4UqmaJS1UP4mtWVGlr05nwen137XY7Nm3ahOzsbP8dGI3Izs7GmjVrQrqP6upqOBwOJCcn6+6zcOFCJCQkyB9ZWVn1Oc0mEen7DyCt84iMMKFnauBICPHq58bDJwEAvdJi5asxzwhXX2pPga58RERERFQ3MQCXgkcx86wXRN1/4QCM79kB/7l6OO4+ry9evGZEk/cNqm8GXI9VWJKpDjbVxCyyGAh3SozG2nmTsPIfEwEoA2BLHRlw/ceKxGXDOwdsV9+HVia5e0oMOidFYWDHeN3vUbDO8WIGXAzGgcZkwL09AaIipC7ojf95kNZVGwz+JQ5rDxYDADLiI3WPawqmOjrvL7v77ICeAyVV+vPJHW4Pqu1OnP3Ucvy4S1l1bVZlwCWhvvatoV4BeFFREVwuF9LT0xXb09PTFR1Sg7nnnnvQsWNHRRCvNm/ePJSVlckfR44cqc9pNgkpAy7+x3rvhrH4evYZ6CTMJOysUTKUlRSt+Usqv6zhTQ+IiIiIqO0QR4el+noFOUMY/5UaZ8X7s8bioiEdm+3cmioDLjZQS4jW79R+zZgu6JPurxYVs9kmowEZCZFyltmqE4BHKIL2KFw1Wj8Bpxesq9eAawXgESYjlv9tAr6afUadI9q0iD2e1Jlzqzn4HHDJ6b06KL4OLEFv2Gg8kZQBNhsNcjD6iW9U8tVjuuDSYR3xl9O7NzrbLp3zAxcNkLcFu5jSIcaCnqmxOHeAP56MjzTLTeK0uNxurD1YjCMlgWXqWhlwoG0H4C16Zk888QQ+/PBDrFixApGR+lderFYrrNb6NaNoav4SdP8vm4yESGQkRCp+qYnBuKRzUpTiF7DkWFkNuqUEZtFFL/98ABW1Tvxtcl95W63DhQhfJ8pQbc8rw8Lvd2HulP4Y3Dn4OiEiIiIiqh+DwYB1906CzeGW3+z3qON9XktpqgA8MyFSHiOllwGPtZrx2GWDFdvMikZryvevYnAeIwT4Ytnyt7efoeg2rqa3xjggA64TXGqVu1vMxjq7cQPeUvpLhnaE0+1GR1UcYDWbFB3MtWaUzzu/H6xmI37dXyxvUwfgTZkBNxoMiosGFpMRfxqdhbQ4byz2ycYjjSrXvv/CAZjQN1XxWgRbAy79HInfA6PRgJIgI84cLo9uVt2fAVdetGjLAXi9LnmkpKTAZDKhoKBAsb2goAAZGcHXozz11FN44okn8MMPP5wSc0GjLIEZcIkYXKdqdEePtppRWh3Yye94afAMeH5ZLRZ+vxv/Wb4fx8u8V3hKquwYu3AZ/vLWhnqd/3VvrMev+4txzWtr5e6176zJwbnP/Ixjpc3TfIGIiIgonKTHR6KLsKZ7UKcELPrzCHxz2xmteFZAE1WgKxJNda0BF4mZ6GBBsbhGWpxtrtW9XKSbAVcFruqy5GDO7uNdh19XR3qDwYDnrxqOl64ZiWhV0GdVzQFXZ+QBb3JPPWbstyOlAPzd9JukCZuYARcC8GFdEuXguykey2wyBFyIMAXJqkvP8dwB6XKPrVqHK2gJusvt0Z1BHiF3QVfPn28nAbjFYsHIkSMVDdSkhmrjxo3TPe6f//wnHnnkESxevBijRo1q+Nm2IOmqSYeYwKtv3YRftOLFmCkDMxBtMSG7f5pifymbLgXVen7ZWyh/frLKG8B/vvkoSqsd+Fm4rS67jpej2PdDXF7rxIXPr8LVr67F/P/bgX0nKvGZr/yEiIiIiJrWlEGZdXYpb271mQMejNidXSsppUfM4KqDUDH4FIMkMaiuKyjUymCr7wMArKbQS7mfuHwwZp3ZHR/eODbkY9SN9qxmo+L5aWWC46PMumO6mrQJmy8DbjIaEBfp/96pY5uaRvao0nqOwdaAS1UBFrMR788aA8A7X7ywIsgacJdbt0Td5Pv5Ul9sacvjoOt9ZnPmzMF1112HUaNGYfTo0XjuuedQVVWFGTNmAACmT5+OTp06YeHChQCAJ598EvPnz8f777+Pbt26yWvFY2NjERur3Vm8LbjhzB6ItZpxgapBAAA888dhePDrHbjprB7onhqDV345iPMGpOPRqYPhcLkRYzUjzmpGhc2JkV2TcHqvFDy/bB/y6siAi0G21A1RHG3mdLlhVv2HnPPxVhwursaHN45FhMmIvNIanP/vlYp9dh5XdpGvdbIZHBEREVF71VQt3cQMeF1N2ERiJjpgNJhijbRJ2M+7PcJkqHPZpf4a8LqbsOnpEGvFfRcOqHtHgbrE3Go2wWrSz/4DQHxkhGalLNDwMWSREUbUOpRl5FLFrtlkVJRjqy+kVNv144LEaP1zlWg9x2Al6OJFi0ihgqCgXD9Ocrk9ul3Spd4B6tn0bbkEvd5nduWVV6KwsBDz589Hfn4+hg0bhsWLF8uN2XJzc2EUrnT997//hd1uxx/+8AfF/SxYsAAPPvhg486+GY3smoSRXZM0b+vSIVoxF3zjfdnyLw3pP8y7N4zBG6sOYe75/eTMdrAMuNPlxsp9/gC8zPfDLpaLV9Q6kSRctSqqtOHzzd556nsLKjCwYwI25pTU+dzyy/TXWBARERHRqe3V60bhpnc34ZFLBzXqfkLJgGuFWmLWO1hQLDZ5M8uBVN1Z65DXgDdBKXcwkaoS9CiLcs65+uIDAMRHRUAvQRxlkbqg1++8E6IiUOvQfn9vMhoQL2SDE6L019YbDMrlCxnxkZoB+OXDO+HzLd4YRKvMPtgFlCjhNYsUvtfBlsg6XR4U62TApRhMHYC35RL0Bp3Z7NmzMXv2bM3bVqxYofg6JyenIQ9xSlFnpQFgWFYinr9qOADI6yJW7CnEuIXLkBhtwV3ZvTGmewc8/9M+lFY78MdRnVFe65SPP+n7YT9QWClvK691ICnGgi25J5EcY8Hu/Ar5tiqb9+pVKJ3W88u9P+CHiqqwYs8JXD2mS0i/7IiIiIio7RvfMwW/zT8v6BztUIgZ8FirdgCuVewuBmDB1oCL87blZlohBM2hdEE3G+vOpDeWGExaTEbEWEyqDvCBzyU+MkK3+3pDM+BJ0RYUlHsD8KnDOuLLrcf852AwBM2AiwxQfj/T4iMV8YYkXqiG0MyAB2kiJ160iDAZ5KC/OMgacKewBnx4l0RcOSoLcz/fpngsdSzTrkrQqf46JvobHRwvq8Xxslrc+O4mZPdPk2fZ7c5XlonnllTjnTU52HdCCMBrnNh1vByXvbQaaXFWXDrMP75CalywX9hfjxSkn/fsz3C4PKh1uHHLhJ4Nf4JERERE1KY0NvgGgLQ4K0Z3T4bD5UaaRuNhPeIjqxNVigy4NTADHkrwqZV1BZTBYLSl+ZNLYjCZHGOBwWBQZsCNBnTtEI3DxdXytvgos+4SgSi5C7r+azC6ezLWH1JWvIpTm84bmKEMwI3KLuhJOgG41WyEy+1RzJBP1/meixl1rWoEvTXugPI1MxgMiDSb6lyH7nS7UeRbIz7n3D6KCwp6PzdtuQS9eesyCACQmRA4qgyAYpD8jmPeALxLsrfB26KfD2D+/+1Q7F9e68CnvgZqJypseHvNYfm20mrvD+W+EALwA4VVOFFRC4fL+x9s/aHiOo4gIiIionBjMBjw0Y1j8fkt4wMC+hvO6A4AuPeC/hrH+T9XB2hicKkMpLRLibWEsga8JUqQxQy4lFlWNqAz4Nvbz8Tt5/SSt8VazZpTlADIXdUdOmPBHp06CC//eWTA9qQYf1Ctfv3MJgNihSZseiXoCVERAYFzerz22GixqZtJI9sddA24qmxfXEcfo3PRxOnyZ8A7xFgVQbz0c6O+35a4ANNQDMBbgPoXQN/0OM39RnZNwh9Hdda9n1vf34y3V+fIX4uzCkuq7fjtSCl2HCsL6ZxGP+bvZB9lMaHW4cLSnQWosjmDHNV6Qp1naXe65bFrTWnT4RJ5DiYRERFRuDAYDJol0/dd2B+/zj0HV4/pEvT4YOuyozVL0ENYA65T4tzSGXCxoZhW+bjZ5C3/7tohRthmxNl9UjHrzO4B9yetiXfrvJdNi7MGjDADoJiZrs4EqzPgeiXoKbFWxYWTlFgrhmYlau4rPm/NLughNmEDlN/vzETtpGVBea3cBb1jYqTiIoN0wUM9Ck2vzL8tYADeCpbcdRZGdEmUvzYZDfjHlL544/rTFP+B1EqrHYoZ5Orbnly8W85q69GabRgZYcIzS/di1jsbMefjrSE9B8nKfYV4cfn+Jgt6l+0qwANfbldcXKiodeD0J37C7R9sCXrsV78dQ78Hvsekp3+uc+RbfZRU2XHFf9fg/H+vhDvECwFERERE7ZnBYFCsEVfcJhRZq8upLTpjyOQmbCHM7h7bo4PmdjEwb4kSZKtGQzlxm7QGvF+mMvlmMBg0O65HmL3nP6Z7B1w0JBM3n61cIhphMmqPNhMy0uoRZmajAXFWsQRdGWs8culAJMdY8NS0oYoAfO28c5ASqx2XWOro9B4sAI9UXSAQM+CZCdoZd2lS1IDMeCRGW5QZcN+5NPd6/6bEALyFST8bk/qny9sevHgA/jqhFxKiIkKasTg0KzHgl0pRhQ1bj5QCAEbpdG8HgH//aRjuyu6j2FZjd+FdXzn7kh0FoTwNAIDH48G1r6/Hv5bsweoDyjL2IyXVqPGNNfhgfS7eX5cb0n3OfHsj3l17GB9u8O//w44C5JfX4qvfjgU5Evh1XxHcHuBgURWW7w59bnpdSoSmEIU6IxCIiIiIyEsM5NRxkcWknaXW62YtWvG3CXhk6iDMOrOH5u3i2nB1prU5iFlWrfXbUrA8sGMC3v7LaCy7++yg9yd1BTcZDfjP1SNwZ3Zvxe1mk0FzfbiY4VZnwI0Gg+JChzrWuHZcN2y6PxsDOsZjnO/CRkqsFWaTUXctunKde/26oEda1CXoQgZcJwCXGsGd0Tsl4BjxofSOb2sYgLcQ6T/QU9OGAgBuOLM7+mfGw2AARnf3X8UTr2DpGds9GT/cdRbuzO6Nv53nDaY3Hj6JarsL0RYTXvrzCJzeqwP+/adh2PnwZOx4aDKuHJWFd/4yGqf3SsEdqv/MJ6vtmg0Z9Mq+iyptOFllR47QUEKczbevoAJn/nM5/vTqWpRU2THv822494ttKK8NHGOglznPE0YRiP+Ja4M0aRDvf29BYMfGhhKz8UdPVgfZk4iIiIhE6lJgRQZcYwxZsCZs3VJicO3Yrrr7iNlh9Zrg5hajUYIuvoc9u08qeqbGKo65z7d+/rRuSZh3fj90S4lR3K7OdpuNRs3gVkzMqUv4zb5O4xKtee7S9+ipaUNx68Se+PTmcfKxWiLqyIDrNckDAr8vVkUArl1RITl3gDeBKWbNxXhFXYbeVrXd9nDtzO3n9MYVIzqjs2+eotVswue3jEd+eS26C//Z9JodSEZ1TcJNZ/dEcowFd2b3weLt+QC8XdMBYFCnBKTFReK9G8YqjnvyD0N077O02oGEaAuO+bqju90e/LKvELPe2Yj5Fw/E1GEd8fh3uzChbxrO6p2KUY/+6L3PKwbL9yGOUPtyq3cu4G9HShWBcH5ZreICw6ebjuLhr3fg1emjMKZHB0VwHaHzH7ekyq77n6usxh+A7zvRdAF4jcP/3I6U1GBk1ya7ayIiIqKwIgZnihJ0OQPe8MBZbAjW0iN2tWZ4B2tGBgCzzuqBy0Z00lwiCniDW3E2d4ROQCxmwNUl/CajEb3SvIF/hxhLwOxyUYdYK/4+uZ9w/trvx9WN5rTOW4/68cWKh46JkXjj+lH4y1sbA467dFhHnNYt2XeM/z7EAPy+C/vj8pdW489jg/claG0MwFuI0WhAlq/DuSTKYlIE3wDQNyMOz181HF2SozH1xV8D7ufTW8YrvlZnrkd00S8/F100JBPf/H4cgDcD3jXZfx6FlTa8uHw/HC4PHvhyO5Zsz8eq/UX4YP0R+coTAPyyt0j+/Outx3BOvzRU25z4QShj/81XFg94A3ADgD0FFbhwcCb+9slvAIC/vrcZmx44FyfK/Vl0sftjhdAYrqjShuJKO3bll+P1lYdw5WlZGN09GYM6JSgC8L0Fgd3g88tq8d66w7hmTFdk1KNEpdruvzDADDgRERFRcMEaYLmE6scYa2Azr1C6oOsRg8HIENaSNyW5CVsd2WE1veAb8L6OEUYj7L73xeqRbhKxK7k6iWU2GhBtMeP3B88LWB9eFzHQ7pwUhfsu6I9+mfHIKa6St9d3DXhgF3T/11lJ0RjfKwVvzjgNM97coNhvdPdkzfsX+1+N6JKEzQ+ci0SNLH9bwgC8DbpkqHe+9wtXDce3vx9H99QY/HfFAc19xYz5H0d1xi2qZg16Hr98MLokR+OlFQdQUG5DgRD85pZU42S1P5hdtd8faC/d6Q+uf88rlT9fn1OC05/4KeBxpKYJADD9jfXy5yWX+tdVF/vWWBdU1PpvF9ZdlwuB9cp9RfjXkj3y1w9/sxMA8OGNYxUBeGGFDaXVdkVTu9s/2IL1OSVYfaAYn6kuZPy8txCJURGa3R7FAPxgURX2n6iUryTqcbk9p1QzCCIiIqKW4BQCpkhz0wbg4nuvlsqATxmYgcU78nHt2K6+xw2+Prq+zCYDpLei0muU3T8dP+7yvycXg1r1Q0q3hbLMNfCxxW7jRpw/OBMAcExYKqpVpl6fDLhYATuwY4L3sTRet0TV+LRz+qXhUFEVRnRNVGxPjtFvaN1WMABvwy4e2hEXD+2IGrsLcZFmnCs0bpN0S4nBgxcPQGZiFCYPzAj5vuMjI3DrxF54SSOw355XhoOFdc8TP1JSd6dxdXM2ySO+wFlyzWtrcbLKH0ArAnBhbbcYfIv+b+sxRQAOAIeLq3GoqAqDOyXAbDJifU4JAGDT4ZPYV1CBuMgIHCysxH+W75fPM+eJC+Fye3DbB5uRER+F+RcPkJvJAcDnm/Pw+eY8PHvlUFw2XHtk3J0fbsGq/UX4cc7ZQbvaS1YfKMKGQycx+5xeDNqJiIjolBfs3YxY5SjOFq/PGDI9YsAbSjf1pvDSNSNQaXfKAa5Ygt4Uk7C89+dS3Per00fivi+3y02O0+L9WXT1LO9gJed1Prbw/RHfo9ZVZh+s9F5dmbBPWK6a4Kvs1Qrq1c3jXr9uFNyeU6v7uYQB+CkgymLCXyf00r39+tMD5wiGQm8+4turc9Dc07bU49J+3a8M1IsVGfC6Z5NHW0yo8K1DT4+3oqDchjkfb8WBwircfk4vHC5Rlo6f++wv6JIcLa+dl9Q6XNiWV4bvtnnX1s+7oJ8iAy65/4vtOH9QZsAvNbvTjS+3eru1/7KvSK5mCObqV9cBAHqmxeCiIYH7O11ulNc6T4krenpqHS5U1DqRGqdfZtVaah0ubD58EqO6JQdt/EJEREShCRZ4StN6xHXLgD94bkzgLMZijcmk1+sxjQblGDDhcfXmedeHYr2173ODwQCX8F66T3ocHrpkINLjrQEBaWNK8cX7MgnfVIvGqDW949TUJehi1a1Eq/u6unmcwWCAzpL4No/vNsOYwWDA4E4JAdvF7uatRS8Drie/zF++3jvNO2vxQKF3fcrzP+3H/20NHGGmDr4B73p4saympMqOanvgBYAquwvrDpUEbBebv6nnHGqxOf3BvV5FwU3vbsKIR5biUFGV5u2ngmmL1uC0x35EQXlt3Tu3sIe+3omrX1uHJ77fHXBbU823JyIiIq+0+Eisu3cS1s6bpNguBW31XacsEteeNybz2xh6o7saSszqi6+NWEkAANeN74YpgzIDMuCN6QZv1snmN6YJm3o83D+m9AUA3H9hf837l4QyqvlUwQA8zL054zTdTPgfRvpLrC8e2hGjuyVr7ieJtpgUndEbQ28NOADcdk5gNYDUbT0qwoROjRhBcLLKIQfugHctuVSCftXoLPzy94mYOsybpd6SexKAd456sW8M2/a8MvnYSqF53PI9J7Byn389vMvtwVNL9uDNX3PkbeJV0md+2IN/LfEGhMt2nwAAfLThSIOfV13ySmuw81h5s9y33enGNt/r8vMe/2tQY3cFHSvXUj5Y7y3feuPXQ4rt5bUOTHxqBW54e4PWYbKSKjte/vkATrTBiwtERERtUXp8pKIDOgAM7BgPwDvRpym0VAZczWI24vLhnZDdPw1dVA2YG0J8P6moJNSJcQMz4I0oQddJMdfVaE4dlCuDd+X35Zaze2LF3yZg5hn+il6ttfOhLOs8VTAAD3MpsVYMyIyXv/7jKG/QHW0x4U+nZcnbe6XGYqFGcC0OvN/8wLm48rQumHt+P/mXKOAtMRK7vYdSSl1pc8rZ4QphxNm5A9Jxeq+UgP33nfCuWU+IikB6fMPLnE9W27FfyGIXVthQ7QsSoyLM6NIhGiN8pVNbckvh8XhwzWtrccaTy7GvoALb8/xBrPQLs9LmxIw3N+Da19ejwpfN/+b3Y/jP8v2KrKuUxd9bUIHnf9qPF5cfUHRdj9G4UHL0ZDVcbg+eX7YPj3yzMyBj63Z7sGxXAT7ecCRoNvf0J37CBc+vxPGyutf119dhoVOmVFbmcLmR/czPmPLcL7rz5hvL6XLLM9yPlFTju23HFa/B8j0n8Nmmo7rHf7g+FznF1fhx14mg53jv59uw8PvdmPXupqY7+WZSY3fJFSVHSqrx9W/HmOUnIqIm15C1z9NGZeG3Bedh6vBOTXIOLT2GTPTMlcPw2nWnBe0GHyoxABeD6buy+yA93op7pvRT7G9SZ8B1Em2h0B9DFnwNuBiUZ/dPw5I7z9K8DfBWLXRLiVG8VhZz4H1qvQ8+VXENOCn+Yz55xRBMGZSB5Bir4qpdckyE5ozy7ikxeHX6KJhNBvmXws1n98RZvVNxwfMrAQC902Lx+V9PR7e53wKo+4qk2WiA0+3BiXIbspKj5YDhscsG4Y+jsmA2GvDARQNwqKgS/1ubqzg2ISoCqXXMUg/GG4D7G9AVVthQ7fvFJ1UKDM+SAvCT+HV/MTbnlgIAnly8R1EuX1HrxCcbj+Dvn/4ub9tbUIGRXZOx6fDJgMc+7gvAv/WNhwOUGXWp1Gh3fjk2HT6JjPhIzHx7I84bkI4ffN3prx/fTTHu7tWVB7HQF+THRZrl7pWiCuGcdx+vQGZCwysItIgj4Up963zyTtYgz1fqn1tSHTCOr7E8Hg8u/+9qnKy244c7z8YfX16D42W1eOaPQ3H5iM7weDwB4y3U1h30LzEoqbLrrl9fvMPbL0AcudcWeTwejHn8R5TXOrH7kSk45+kVcLg8cHs8uHRY07zZaYvcvosnxlOwSQsR0anKELQNmz71Ot/GaKkmbK0lKzkaa+dNCgjy1TFzKEsi9YgZcPF6vdgkra4xZDec2QPdOvjf59U1G927T+A5N8XFjLaiff9kUkjEtSEGgwHn9EvHsKxExYxxo9GAWGvg9Zr0+EgM6pSAfhnxiu09Uv3/0dT/YYI1pMjun4bOSd4A8Istefh+23EUV3rL0YdnJSHCZITBYMDMM7rj0amDseG+bMXxCVERSK+j0df147vhYp3maCVVdhwW1sAXVtrkJmzShYq+GXEwGQ0or3Xi+WX75H2X7S7ADiFgrqh1KoJvANh5rBwejwcrhFJsibQ+evmeE/K2bcL9FfnK8qc8txL3fbEdM9/eCABy8A34A1zJ5lx/oL/mYDHsTjc2HT4Jp7Bu6OhJf9ZbnLkucgfJAG89Uoor/rsaf3x5DX7cWYDe932Hzzf7M8t7he6WJ6u9zyFfKNeWul/+vLcQv+wNfF1Ey3YV4IBGh/41B4rxryW75YsUx8tq8fvRMhwpqcHGwyXyxY1PfRlvvecp8Xg8ijX+hRU23X0bs7YqVMfLavDOmpyATv/1UevwNvMDvBMCpEaIy3efCHbYKc3j8eAPi1bjgudXBqyVIyKi9q21StCbi9YaaK2gNKALeiMyx3pBr0mnO7rWtgiTERazERcOzsSZvVNCSrpEtLPvnRoz4KR7tdFgMKBzUhSOnqzBWb1TAfhnHUqGd0nUPFYskVGv4fZ4vOtjpPLgZ68cCo/HeyVvYMd43PjOJuQUV+OZpXsVx8VHBf64doixKO6rT0Ys0nQy4H+f3BdTBmWgZ2osnvlBe5zZvoJK2Jz+N+piCbqUAbeYjejWIRoHCqvk0WbS86oSOqZXaDSP23m8HI98s0tuADeiSyJKqx04WFQll3+LDeW2CSXtJ8ptQUumAaCk2q74Wmzctv5QCR7/bhfeWp2Du8/tg9sm9QbgLUWWFJQFrmN+Ydk+LPr5AD68cRwGdw5cl/X8sn1yRv9YaQ0cLg++356Py0d4lzPsFwLm537ch/E9UxSl7vtOVGJ8Lyeu882J3/7QZHy68Qi+254Pk8GAcwek4y9ndMfGnBL5osOOhyYr1o5d9epaAECnxGhcPaYLduf7X7ffj/ovYkiBuHRRR0+Fzako+Sqq1A/Aoy0m1NRjLXtxpQ2vrzqEy0d0Qi9fw8C6PLd0Hz7aeARPLdmDlfec06AMQWmN9nOudQQGpiVVdnz92zFMHdZJHgnSWKsPFOHTjUcx9/x+uv9Hm1pJlV2uUNlbUCHPFyUioubVmslKi8kIu8uNcT0ClyyeyjJC/NsZsAa8GUrxxTJ3rWoHrcZxL14zIuT7j2jnVWvt+/ICheSuc/sgLc6KO3wBmej7O87Eyn9MlMua//vnEXjw4gHy7VJgrqWDb633uJ4dAPjXbpzWLVmRNbxseGdcPqIzTuuWjGiLWbOJWp/0WM3SaKPRgFvO7inf/23n9Ea/jDj0z4zHmb39v3g7JUbh1om90DM1FgCQqdOo7fvt+Yqv31qdI5eEi83q+qQrA6duHQKbbORrBLM7jpXj443eZmr3XtAPn//1dHx00zgAQFGlHY9+s1PRgG7b0VL58x93FeDuT37TPG/JSd+x+WW1OFRUpehov6egAm+tzgEAPC1c3DgiZMClzLTH48GTi3fjzV8P4emle1Fld+EvOs3IxK7xUjZdXPd9vFS5rlwqB5fsLahQXATILa7Gg1/vxPpDJVhzsBgPf7MThRU2xcWOgQuWYNmuAqgdLvE+7u58f9Z9jTCLPq+0Bh6PR26apyb1HdgrHA8Ez4CLF5tCyVB/vPEoXlpxANnP/KJY4x/Mb76fg/JaJx7+eqfufnrruVcfKMK5z/wif10ldPavdQZePLjvi21Y8NWOOn/e9FTbnfj6t2OKi1D3fPY7Pt+ShwueX6W4uNEc8kprMG3RanwoNC7MKWr96Q5EROGiNcOnDfdlY/nfJqCLxnuzU5lUIVqXgC7oTbR22gP/e4xYYYScVqm/IgOusZ67LmZVozaxt1R7wAw4oWNiFNbdG7iGBADiIiMQJ8w2NBgM6Cqs4+gWpIzk01vG46utx3D96d0AAF/eejo+3HAEf53QExc+v0o3WBF/wVw2vBOSoi24dWJP3ZEGd2b3xqBOCchKjpLXqX9/x5kAgGEP/4DSagf6pMcqjhHXooiCZzr9/116p8Xie9/nyTEWjO3RIWB8229C5lUiZWMjI4z4i29+e0qsBd1TYnCoqAqvrVJ24taajRjM0l0FyCutwUvL98vZ+AiTd9yclAmUHlMiBr/5ZbV49ZeDeOy7XQH3XVhhQ63DhcgIExZ+tws7j5fjtetGaQanh4ur4XZ7YDQaNC9EiI+5r6BSMRJOa9zaaY/9GFBtMfPtjch54kJFJ3XpKu8eIYBetb9I/tzudKOwwoaCcu3vc0mVHb/sLcQ9n21TbA/2cyE+ft7JGuQUVeGuj7ZiwSUDcXafwAtUeaX+5/rxxqOYc24f+Wub0wW3O/CPpRgwf7b5KK4b3xVDOifKz+mHnfnYnleOd9bkoE96HD67Zbzi/4s0a14i/t/T6kQvXYj6UeMiRyj+uXgP3lqdg+z+aXjtutPg8XjkMXtFlTZc9uKvWDrn7Abddyj+89M+bMg5iQ05/iUYewsqcCECeyAQEVH7khAd0WTVW23B3PP74a1fc7Dg4oEh7d+Uc8D1RFvM+OTmcTBAu8u6eA6hrPlWE9eeP3TJQFwhTGZqD5gBJwD1a2wwoW8qHp06CN/efkbQ/bqnxOCO7N5yuWzv9Dg8cNEAdIi14oYzu8v3pZYudFa/fVJvzL/Ye0ywcz93QHrAOnTA33G9T4YyYz1Eo5Ra1Dc9sDRYzID3Em7vmx6nWM+Sley9gBAsaBvYMUG+umcwGLD4zjODnk+ovv39OP61ZI+iFL5rhxg8MnWQYr+iSjveXp2DgvJaxXizb7cd1wy+JUt25OPzzUfx8i8HsXJfEdYdLEFxVWBps83pxvHyWrjcHpzQCNDFBmdHSqoVAfnveaWaj70lN3C7x+NRBPhSf4HdxysC9pW8/ush3Pr+Zs3biivtAcE3oJ8Bd7s98rp2wNuV/q6Pt+JgUZVcUl9W48Drqw7JZffVwvdmteriwLnP/IILn1+JQ0VVeGdNDmrsLrjd/uc4prt3FOB/ftovH/fqyoOY/f4WLPr5AKrtLmw9UipXMvx3xQE88OX2gPMWlxrUaJSgi51GxfX/R0qqFY0B9Uij3X7c5V1fnq8a0bbvRGWjRtDtya/ABf9eiaU7tS8QqK/+A1A0VyQiombWjhpmtbabz+6JtfdOUjTZDUYd7zZVrxp1kd1p3ZIxSmdEsRiAa/1NrovYZX14l0TNPlSnMgbgVG8GgwF/Htu1UespZ5zeHR/MGosXrw5cD9JBGFPWtZHzE9N8Ddn6qQJw9exJtf6ZgQG4mJU8b0C6nJEd0yNZMbPyb+f1rfO8BqtmXFrNpoAsfVPpnxmPgR0TAmY5LvhqB6a++Gu91i/f8eFWzPnYX5a8LUgw9uPOAmzMKYFTo4HbQSHLXWFzYocwg3yD0PysLhtyTiqCu7IaB+xOt2ajNsnLPx/Uve2iF1Zpbte6mOJ0uXHLe5sgPr3tx8pRbfO/nh6PB/9bexiPfLMT4xb+hPyyWkVPhK1HSlFlkxqjVSG3pBoHi6ow8akVmP9/O/Dsj3tRXGWHw+WBweDtYwBAUc3wxZa8gHMrqbSjxu7Ck4t34921hwNuF5cAlFYHXkCJF9aYi9+rM/+5HBe9sEqx7ECLumO8dEGkV1qs/MZA3RuiPv72yW/Yebwcs97ZqHm70xX4Myc2AyQioubF8Lv1GAwGRRBubYFmsWrmOpq01UUMwNvjtNT2dTmBThkmo0FeG642oW8arh7TBcOzEhs9Oujvk/vhh535OH9QYOmp1GBuWFYiPrxxLGxON4Y+9AMAb4b6y63HFPvbheZskREmfHbzeBworETP1FgYDMCjUwdhRJckeR1xMFprWfpmxCtGdklirWb0TItFabWyQ3t6vFW3lFp0+zm9AACPXDoIcz9XZnelQOypaUPxwJfbUet04Q8jOmPJjny5Y3YwmzXGqUkWfLWjzuMlYhC5WSPTreePL69RfP3OmsPoEGOF0+1BXKQZvdJiNTPnoUqMjkBptUMemQZ4M8InKmzYebwMS3YoM7BrDhQhPsoMqQfcwaIq7Dzuv7hwzWtrFQGt0+3BwAVLcO8F/TSXRSzeno+Lhnh/dlNjrXKlRXGVDV9uycO6QyUBV7oBoKjKButJ/eurBcJFCzG773Z7sOiXA4oA/fejpfhxVwHWHfSvpd95rBwddfooAEBKrFXuB1BW45Bfg/6Z8SiqtKG02oGyGkdAM7ZKmxNVNqfmyEPRiYrAZQ0idTNCADhyshoej6ddjTEhIiLSYjQY5KrApsqA1+fPp5j1bkgGXAzag01POlUxAKc2x2Q04PHLBjfJfY3smoSRXZM0b3vvhjH474oDuPGsHoiMMCEywoQld56Fxdvzcd34bgGl2OqstdFoQG+hFP3PY7sC0C91TYqOkNd0980IzLD3TY/F1xrH9UiNwZd/HQ+n24Pe93lXnpuNBiy7ewIGLVii/cR9Fv15pHyOfxrdBbGRZsx+f4tinw4xFlw2vBPG9khGrNWMxGgLHrh4AGrsLox5fBkA7/dkeFYi4iLNePGaEXjmh714bdUhbPKNORM70Y/unoz1qix2v4w4RWM0SWZCpCLY03LugHTNUuOOCZE4pnHssz96G8z1TY9Dl+TokALwK0Z0xo1n9cDk535RbO+TFof1OSXYkluKPfkV+NeSPdh65CSKdLqoi2uOAW8ALZZ7Hyj0B99Sl1YAePy73bj3gn4B92cyGuTXJzMxCskxFvm4Oz/aqvt8Zry5AecOSNe9XawaqLa7UGlzItZqxg878/HPxcoJAS+tOBDwM71kRz6eWLwbD10yEKf38jY7rKh14HBxNdYdKsFWYSb6S8v34+VfvFUHo7om4fejpXIAfqioCu+vO4w+6XG4dFgn/OG/q7E7vwKdEqNww5ndMcPXJwHw9if4bttxON3uoBeeft5bqPnzUutwo6hSf547ERE1nanDOmLrkVL0Tmue6j4Kzmg0QCrR01qj3RD1iYMtwhixlCDLSIMZ2DEeBeW1GNDOGrABDMApjHXtEIMnrhii2NY3I04Ojq8f3w1vrc7BuQPS8cilg4KuQxfFRSr/Wy2+80zERUbgjg+2YKMvY9xL4w/SuJ4pALzBY4zFJK/j7p8RD4PBgAiTAbdP6o3nl+3Dv6YNQazVjLdmnIaF3+3GeQPT8YJvXXBWchSOlNTg45vGYXR35docbyWAPwCXAh2T0YDOSf5y//jICMQLzfdcbg8+vWW8/LX0y1CaOz6qaxI2HT6JaIsJ5w/KCAjAu6fE4LXrRuGLzXmKDuxdkqPlALNHSowiOwx4m/BdNryTZkB17bhueHLx7oDtkr4ZcXVmUm+f1BsT+qaiV1os4iMjkBpnlTPCESYDHrp0IP763mYcKqoKCM7VtC4y/HfFAXn+9D//MAT/EObCD81KUATsq/YXQ81o8DZ2A4DM+EgYDAakJ1jlhmbB6K2PBgI79K/aV4QpgzLw+ebAcnatC0qf+MbhXfPaOtx4Vg9kJkRiW16Z5vFS8A0Ak/qn4TPfjPj7v9yOvQUVcHu8V9VPVNjk1y+vtAYPfb0TX27Jw3+uHoGOiVGY8NRyzZFpok2HT8pr77VsyCnBOf3SmuzNCBERabt2XDf0TIvFkE6JrX0qYUkcE9ZUGfB6Pb7RgNVzz4HL7WlwF/avZ58Bp9ujCObbi/b3jIiayD1T+uGla0bgmT8ORUZCaLMXAeUadgDonRaHTolRis7TYkd1yfCsRPlzsYna9PFd5c/vyu6NX+eeg8uGe7tBTuibhiV3nYUzhXFw38w+E9/dfmZA8A0oS3pGdk3Cr3PPUWQZ1W48qwcA4JYJPRXbxWAdAPplxOOzW8bjk5vHa15cGNw5AZ2TohVzxGOtZogXU3+cc7bil+ysM7vj2SuHyY301K4Z2wXjenTQHFsHeDPnp/fyLnNIiIrAUOH1BbxXVv88tgtGdEmSLzakCdnRrfPPQ//MeEzqlyZvi4ow4c0Zp8lj5y4akom/nN4d788aI4+Tk3TtEI1Km1OeK3/egHR0FUaiDO2sPJ9f9hYGPIdahxu7fOXbvX09AjLjQxtDEoy6KdoH63Px9uoc/BAkaNfzyi8H8dDXOzWDb9GAzHh0ToqWmzLuzq+Q1897PMC/luwJOOa3o2V4beVBfLElTzf4drs9KKt24LFvd+Jq3zx4yYWDM9E3PU7OwPz1vc0Y8uAP+Pq3Y1p3RT6XXHIJunTpgsjISGRmZuLaa6/FsWPBX7Pa2lrceuut6NChA2JjY3HFFVegoKBhXfSJ6NRnMhpwZu/UdtWN/FQivt9rji7ooeiYGBVy4zgtRqOhXQbfAANwIl1RFhMuGJypGMMWCrPJiLnne8uJ+2fGy78Eg41sA7y/aO7M9s5if+maERjcKQHXj++maHZnMBg0A87TuiXhjkm98d9rRiAhOiKkcp26GtEB3oZyH8waK5+XRD33fELfVAzqlIBeabEBa5mToiMwY7w3yE+L81/ISI6xYO75/RBjMeGJywfDaFQ+Nyl7rRWAD+wYj/jICHxw41j8OvccLLtbOdLq/gv7Y0LfNIzsmoyN92dj3b2TFIF0l+RofHv7mYrz8R7nnXF/zZgu8uszfVw3+fZzB6RjYt80vDtzDP75hyF45o/DMP/iARjfMwUJURG4zbfeHoA8JgzwVkUkREUoLs6oLwhoySutkbPNUqO/+lwM0iNVLvTP9P6c/Ly3UF6zLzXra8or5lOHdcRzfxoGAHIArsX786P82SqstGk2kpOU1jjw7I978erKQ/LFDslDlw7EkrvOUiwfsbvc6JnKkshgJk6ciI8//hh79uzBZ599hgMHDuAPf/hD0GPuuusufP311/jkk0/w888/49ixY7j88stb6IyJiEgkLrtusi7oTXIvBLAEnahZ3HRWD2TERyoC4QUXD0CEyYCZZ+hnnO+Y1BvXju2KDrFWXDA49JnFBoMBdwnzpIOJijChxuFCdv+0Ove1mI2azfLS4iMxqmuSXFI/tod/H7E513kD0vHI1EFy+VFavD/DHB9lxoguSdjx8BTh2Eh5DvgwX4CaFO0PWk/v1QFXje6ieDz1Pm//ZbRi/ra09ihRuAqfqRPEjuvZAb/OPUcRKHfpEI27svvgg/W5uH2SN8DOSo7WvKp7+6TesJiMGN8rBesOFeNrX8P4bh1iYDAYECtczJGCX9FzVw7DfV9sU1RASKTReXZn8DLs+hjSKQHVdqfc3O9Pp2Xh0amDsPpAMdLirZjy3EoAwMS+qVi+R5mh75QYpWhOp+WLv45HcaUd2cJ69GAB+J9Oy8JfTu+OA4WVOPdZb8n/d9vygz7GuoPFeGt1juZtib7H6ix8rzrEWAKmIpDSXXfdJX/etWtXzJ07F1OnToXD4UBEROD3r6ysDK+//jref/99nHPOOQCAN998E/3798fatWsxduxYzcex2Wyw2fzr+cvLyzX3IyKi+hFHiLZGF3QKjhlwomZgMBgwdXgn9BGatHVOisZL14zEyK7aMxOl40Jda95Q391xJh6/bDCuGdO17p2D+PdVwzGiSyIeuGiAokTIZDRgQt9UxFrNeOyywYp12MlCoKzVzMMmlBlL5eri2qGUWCsuGtIxoKFHvLDuvodOpUGi8NiDVA31RJ0SowLWCN+R3Rtr752EXmnBA7cIkxG3TeqNkV2TFAH2lEEZAIA4q/I8z+ydonjcqcM74aFLlTPbJRm+13Fiv1TN27unxODasYHf047CxYaeqcrXJi7SrJhh/+AlA2E2GXFWn1T0TY+Tx4jcdLZyCUJWchSuHtNF8xw7J/kvwAzvkqQIvoHgAfjgTglyc8OV/5iouO3y4Z00j7nlPe9Md5PRgFsm9MQfR3WWbzP7xpiIyztGdE1q9HSFcFJSUoL33nsP48eP1wy+AWDTpk1wOBzIzs6Wt/Xr1w9dunTBmjVrNI8BgIULFyIhIUH+yMrKavLzJyIKRynCkrrWWANOwTEDThRmuqfEKIKuhuqUGIXP/3q65m2vX3caah2ugDJ3MfBxacwH7xDrD5Kt5sA/GIk6wZvZZMRL14xAtd2lu95IPFZaG96cBglLB64a7Q1WY4XXw2g04N2ZY1Blc+LtNTn4wwhv4HjFiE4Y0SURi3fkY8n2fCREW3DR4Ex5fNblIzojMsKEDjFW/Pn1dQC8FyaW/20CfjtSGlCuPaZHB3nM253ZfXDbB/4mfClxVlw6rBNW7y/GTWf3UFx4MBgM+PSW8SittmNsjw747zUj8Nnmo7h9Um9kJERizQF/0zipqmLOuX3w9e/H5BFkWqKDNGMZKFwYUS+1uOf8foi2mvC/tbkBx43okojnrhyOLh2isfpAET7eeFRx+4S+qbjprB54bdUhXDWaQV4o7rnnHvznP/9BdXU1xo4di2+++UZ33/z8fFgsFiQmJiq2p6enIz9fv4Jh3rx5mDNnjvx1eXk5g3AioiZwxYjOeMbX9FZaWtZYnnY4Dqy1MAAnoiZnMhrqXGOuFYAvuHggahxu3KLKuF4wOAPfbcvH9UEaxtVVsm8VsvSjuzd/AJ4aZ8VHN45FtMUsr2O/ZUJPfLElD5cM6yjvF2M1468T/GvHDQYDeqTG4q8Teim2SyJMRlw6rBNOCI3UpPL0oVmJeP26Ufh8cx6+3XYc887vh2mjsmAyGnDlaVmoVM12H5aViMGdE7D7kSma8z2HCZnj8wdn4nzhNc5M8AfIf5/cF9NGdUZcZAR+3hfYTE5UaVOW108ZmIHFO7xBmvoCxTVjumDFnkI8f9UwpMdH4tGpg2E1m/D6qkOK+xAvBI3vmYJXp49CDyHbbzAYMO+C/phzXh/NCzvhYO7cuXjyySeD7rNr1y706+ftX/H3v/8dM2fOxOHDh/HQQw9h+vTp+Oabb5p0jrrVaoXVyrFwRERN7YYzu+OXvYXo0iG6SX9vU9NgAE5ErUKrmVjHxCi885fRAdtfuGoEHr/MoSgjr68RXZNwdp9UDO6UoAj0mtMY1Vr1bikx2DL/3KBZ4FClxFrRIyUG1XYXHrvMX7Y+qX86zuidghvP6oEhnRNgMBjw1LShALzrpSUmo0HuxN6QkuwMYWlBWrxVblb49/P64uc9hbhmbGCJOuAdsSdZ8bcJ6JgYhaeX7sFZvQNL6x+7bDA8Ho/izYPYYyAhKgKvTh8VcJzeDPRwDb4B4O6778b1118fdJ8ePXrIn6ekpCAlJQV9+vRB//79kZWVhbVr12LcuHEBx2VkZMBut6O0tFSRBS8oKEBGRkZTPQUiIgpRtMWsGB9LbQsDcCJqUa9fNwov/3wQj07VXuusxWQ0NCr4BryZ47c1gvuWFkr3+VAYjQYsvvMsON3ugLF2VrNJs8t6ktBcbmDH+AbP5gSUDfUihcC2W0oMts4/V15/rTZ9fDeszynBxUM6ypMB5p3fX/dx1FfurxnTBesPFWNi3zRceVoWr+yHKDU1Famp2v0D6uJ2e3sziA3TRCNHjkRERASWLVuGK664AgCwZ88e5ObmagbsRERE4YwBOBG1qEn90zGpv3aGkurHYjbCUo9emr3TYjH3/H74afeJoN34QyGuF1c3tdMLvgFv1vrdmWMa9bgvXxuY9aamsW7dOmzYsAFnnHEGkpKScODAATzwwAPo2bOnHEzn5eVh0qRJeOeddzB69GgkJCRg5syZmDNnDpKTkxEfH4/bbrsN48aN0+2ATkREp4Yze6dg5b4izUav1DAMwImIwoTBYMDNZ/fEzao19g21/t5JKKtxNMlscmoboqOj8fnnn2PBggWoqqpCZmYmpkyZgvvvv19er+1wOLBnzx5UV1fLxz377LMwGo244oorYLPZMHnyZLz00kut9TSIiKiJvDp9FHbnV2BIkAkyVD8GzynQ0q68vBwJCQkoKytDfHzg7FwiIqKWxL9LTY+vKRERtTXN8beJc8CJiIiIiIiIWgADcCIiIiIiIqIWwACciIiIiIiIqAUwACciIiIiIiJqAQzAiYiIiIiIiFoAA3AiIiIiIiKiFtCgAPzFF19Et27dEBkZiTFjxmD9+vVB9//kk0/Qr18/REZGYvDgwfjuu+8adLJEREREREREp6p6B+AfffQR5syZgwULFmDz5s0YOnQoJk+ejBMnTmjuv3r1alx11VWYOXMmtmzZgqlTp2Lq1KnYvn17o0+eiIiIiIiI6FRh8Hg8nvocMGbMGJx22mn4z3/+AwBwu93IysrCbbfdhrlz5wbsf+WVV6KqqgrffPONvG3s2LEYNmwYFi1aFNJjNscAdCIioobi36Wmx9eUiIjamub421SvDLjdbsemTZuQnZ3tvwOjEdnZ2VizZo3mMWvWrFHsDwCTJ0/W3R8AbDYbysvLFR9EREREREREpzJzfXYuKiqCy+VCenq6Ynt6ejp2796teUx+fr7m/vn5+bqPs3DhQjz00EMB2xmIExFRWyD9PapnERkFIb2W/FtPRERtRXP8va9XAN5S5s2bhzlz5shf5+XlYcCAAcjKymrFsyIiIlKqqKhAQkJCa59Gu1BRUQEA/FtPRERtTlP+va9XAJ6SkgKTyYSCggLF9oKCAmRkZGgek5GRUa/9AcBqtcJqtcpfx8bG4siRI4iLi4PBYKjPKQcoLy9HVlYWjhw5wjVmDcDXr3H4+jUeX8PG4evXONLrl5ubC4PBgI4dO7b2KbUbHTt25N/6NoKvX+PxNWwcvn6Nw9evccTXLy4uDhUVFU36975eAbjFYsHIkSOxbNkyTJ06FYC3CduyZcswe/ZszWPGjRuHZcuW4c4775S3LV26FOPGjQv5cY1GIzp37lyfU61TfHw8fyAbga9f4/D1azy+ho3D169xEhIS+Po1Mf6tb3v4+jUeX8PG4evXOHz9Gkd6/Zq60q3eJehz5szBddddh1GjRmH06NF47rnnUFVVhRkzZgAApk+fjk6dOmHhwoUAgDvuuANnn302nn76aVx44YX48MMPsXHjRrzyyitN+kSIiIiIiIiI2rJ6B+BXXnklCgsLMX/+fOTn52PYsGFYvHix3GgtNzcXRqO/ufr48ePx/vvv4/7778e9996L3r1748svv8SgQYOa7lkQERERERERtXENasI2e/Zs3ZLzFStWBGybNm0apk2b1pCHanJWqxULFixQrDGn0PH1axy+fo3H17Bx+Po1Dl+/UwO/T43D16/x+Bo2Dl+/xuHr1zjN/foZPJyhQkRERERERNTsjHXvQkRERERERESNxQCc6P/bu/+QqO8/DuDPc+eZ4c7LTE/XLKMfUqY0W9ctoj88+iW1oj8i/CMqNiqFAhm0xar9ZTAYrIj+Gav/Jm1kG+sHiZblMCvzUvthNtyM8Ec/MLVV/rjn94/ow/eaSz3lc3fc8wEH9nm/kffnycHTF9p9RERERERETKABXERERERERMQEGsBFRERERERETKABXERERERERMQEETWAHzlyBNOnT8eECRPgcrlw9erVYB8pZFy6dAlr1qxBamoqLBYLTp065bdOEvv27UNKSgpiY2Ph8XjQ3Nzst+fp06fIz8+H3W6Hw+HAtm3b0Nvba+JdBEdxcTE+/vhjvP/++0hKSsK6devQ1NTkt+fly5coKCjA5MmTERcXhw0bNqCjo8NvT2trK/Ly8jBx4kQkJSXhiy++wMDAgJm3EjRHjx5FVlYW7HY77HY73G43zp49a6wrv5E7ePAgLBYLdu/ebVxTfu924MABWCwWv1dGRoaxrvzCj/p+aOr6sVHfj426fnyp70cnpLqeEaKkpIQ2m40//vgjb926xc8++4wOh4MdHR3BPlpIOHPmDPfu3cuTJ08SAEtLS/3WDx48yPj4eJ46dYo3b97k2rVrmZ6ezhcvXhh7Vq5cyezsbF65coWXL1/mzJkzuWnTJpPvxHwrVqzgsWPH2NjYSK/Xy9WrVzMtLY29vb3Gnu3bt/PDDz9keXk5r1+/zsWLF/OTTz4x1gcGBpiZmUmPx8O6ujqeOXOGiYmJ/PLLL4NxS6b77bffePr0ad67d49NTU386quvGB0dzcbGRpLKb6SuXr3K6dOnMysri7t27TKuK793279/P+fNm8e2tjbj9ejRI2Nd+YUX9f1/U9ePjfp+bNT140d9P3qh1PURM4AvWrSIBQUFxr8HBweZmprK4uLiIJ4qNL1dyj6fj06nk99++61xraurizExMfzpp59Ikrdv3yYAXrt2zdhz9uxZWiwWPnz40LSzh4LOzk4CYGVlJcnXWUVHR/Pnn3829ty5c4cAWF1dTfL1D0VRUVFsb2839hw9epR2u52vXr0y9wZCxKRJk/jDDz8ovxHq6enhrFmzWFZWxmXLlhmFrPyGt3//fmZnZw+5pvzCj/p+ZNT1Y6e+Hzt1/eip7wMTSl0fEX+C3tfXh9raWng8HuNaVFQUPB4Pqqurg3iy8NDS0oL29na//OLj4+FyuYz8qqur4XA4sHDhQmOPx+NBVFQUampqTD9zMD179gwAkJCQAACora1Ff3+/X34ZGRlIS0vzy2/+/PlITk429qxYsQLd3d24deuWiacPvsHBQZSUlOD58+dwu93Kb4QKCgqQl5fnlxOg999INTc3IzU1FTNmzEB+fj5aW1sBKL9wo74PnLp+9NT3gVPXB059H7hQ6XrrONxLyHv8+DEGBwf9AgOA5ORk3L17N0inCh/t7e0AMGR+b9ba29uRlJTkt261WpGQkGDsiQQ+nw+7d+/GkiVLkJmZCeB1NjabDQ6Hw2/v2/kNle+btUjQ0NAAt9uNly9fIi4uDqWlpZg7dy68Xq/yG0ZJSQlu3LiBa9eu/WtN77/huVwuHD9+HHPmzEFbWxu++eYbLF26FI2NjcovzKjvA6euHx31fWDU9WOjvg9cKHV9RAzgImYpKChAY2Mjqqqqgn2UsDNnzhx4vV48e/YMv/zyCzZv3ozKyspgHyvkPXjwALt27UJZWRkmTJgQ7OOEpVWrVhlfZ2VlweVyYdq0aThx4gRiY2ODeDIRCVXq+8Co6wOnvh+bUOr6iPgT9MTERLz33nv/+iS7jo4OOJ3OIJ0qfLzJ6F35OZ1OdHZ2+q0PDAzg6dOnEZNxYWEhfv/9d1y4cAFTp041rjudTvT19aGrq8tv/9v5DZXvm7VIYLPZMHPmTOTk5KC4uBjZ2dn4/vvvld8wamtr0dnZiY8++ghWqxVWqxWVlZU4dOgQrFYrkpOTld8oORwOzJ49G/fv39f7L8yo7wOnrh859X3g1PWBU9+Pr2B2fUQM4DabDTk5OSgvLzeu+Xw+lJeXw+12B/Fk4SE9PR1Op9Mvv+7ubtTU1Bj5ud1udHV1oba21thTUVEBn88Hl8tl+pnNRBKFhYUoLS1FRUUF0tPT/dZzcnIQHR3tl19TUxNaW1v98mtoaPD7waasrAx2ux1z584150ZCjM/nw6tXr5TfMHJzc9HQ0ACv12u8Fi5ciPz8fONr5Tc6vb29+PPPP5GSkqL3X5hR3wdOXT889f34U9ePnPp+fAW160f7CXLhqqSkhDExMTx+/Dhv377Nzz//nA6Hw++T7CJZT08P6+rqWFdXRwD87rvvWFdXx7///pvk60eTOBwO/vrrr6yvr+enn3465KNJFixYwJqaGlZVVXHWrFkR8WiSHTt2MD4+nhcvXvR7tME///xj7Nm+fTvT0tJYUVHB69ev0+120+12G+tvHm2wfPlyer1enjt3jlOmTImIx0KQ5J49e1hZWcmWlhbW19dzz549tFgsPH/+PEnlN1r//6mopPIbTlFRES9evMiWlhb+8ccf9Hg8TExMZGdnJ0nlF27U9/9NXT826vuxUdePP/X9yIVS10fMAE6Shw8fZlpaGm02GxctWsQrV64E+0gh48KFCwTwr9fmzZtJvn48yddff83k5GTGxMQwNzeXTU1Nft/jyZMn3LRpE+Pi4mi327llyxb29PQE4W7MNVRuAHjs2DFjz4sXL7hz505OmjSJEydO5Pr169nW1ub3ff766y+uWrWKsbGxTExMZFFREfv7+02+m+DYunUrp02bRpvNxilTpjA3N9coZFL5jdbbhaz83m3jxo1MSUmhzWbjBx98wI0bN/L+/fvGuvILP+r7oanrx0Z9Pzbq+vGnvh+5UOp6C0mO7nfmIiIiIiIiIjJaEfF/wEVERERERESCTQO4iIiIiIiIiAk0gIuIiIiIiIiYQAO4iIiIiIiIiAk0gIuIiIiIiIiYQAO4iIiIiIiIiAk0gIuIiIiIiIiYQAO4iIiIiIiIiAk0gIuIiIiIiIiYQAO4iIiIiIiIiAk0gIuIiIiIiIiY4H9BLurfOxzbuAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axs = plt.subplots(1, 2, figsize=(12, 4))\n",
        "axs[0].plot(losses)\n",
        "axs[1].plot(np.log(losses))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "61390Sjpi4Do"
      },
      "outputs": [],
      "source": [
        "image_generation_pipeline = diffusers.DDPMPipeline(  unet= model,   scheduler= noise_scheduler)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "RbfrlJt5lBVj",
        "outputId": "bc0920e3-3d7e-4ba5-8783-66746a41e685"
      },
      "outputs": [],
      "source": [
        "pipeline_output    = image_generation_pipeline()\n",
        "pipeline_output.images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tHKUvhIIlBp8"
      },
      "outputs": [],
      "source": [
        "pipeline_output   = image_generation_pipeline()\n",
        "pipeline_output.images[0]"
      ]
    }
  ],
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