{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NarMcB0oh2YR",
        "outputId": "71eb5e19-c1e1-40fb-c42a-d9201e7bccc8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting diffusers\n",
            "  Downloading diffusers-0.24.0-py3-none-any.whl (1.8 MB)\n",
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            "\u001b[?25hCollecting accelerate\n",
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            "\u001b[?25hCollecting datasets\n",
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            "Collecting pyarrow-hotfix (from datasets)\n",
            "  Downloading pyarrow_hotfix-0.6-py3-none-any.whl (7.9 kB)\n",
            "Collecting dill<0.3.8,>=0.3.0 (from datasets)\n",
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            "  Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
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            "\u001b[?25hRequirement already satisfied: fsspec[http]<=2023.10.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
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            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.1.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata->diffusers) (3.17.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3.post1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Installing collected packages: pyarrow-hotfix, dill, multiprocess, diffusers, accelerate, datasets\n",
            "Successfully installed accelerate-0.25.0 datasets-2.16.0 diffusers-0.24.0 dill-0.3.7 multiprocess-0.70.15 pyarrow-hotfix-0.6\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers diffusers accelerate datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import Dataset"
      ],
      "metadata": {
        "id": "bQPq9TT6D-mN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XFRgpDItiiwa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180,
          "referenced_widgets": [
            "6d8f89d1eee74e48ba7650100e6b97d4",
            "5d62a1ac7a274b868c89994660c36530",
            "cd28db326d3648a7a2fdbbd15d741fda",
            "11506fbe1f9547578481d9c3b2838b8a",
            "d4b0c4ad465b430c94c36af21d4ecf2e",
            "4d10b974acf4441b893c9be379429c6a",
            "021379988e79476da8f9e225ed42b452",
            "e946f97172f643de93e7c39f340e1d8d",
            "72e6e9214fa24cba8fb29f2ec9c1bb70",
            "e62022988e404658b56b3ad7338e60be",
            "28fd74049b614c56bbd5ec03270e2392",
            "b3843e1999b345f59e7a2f63d769dc76",
            "876decbbac584e288e6eb05352046141",
            "f6ed0a1b6de54f89a31daf331d89bd06",
            "d4c6d4eb7f844c019389eeb9a42a733b",
            "76ab66ec873e4403b7bb86d543ffa173",
            "99a0b89f6c124a18b39462a65419f28c",
            "f16cf87366154e429132396eca4a9e6c",
            "1ccfe7117d06474ba7495584c5f25fdb",
            "367ef9d641be44eaa32df8a0d9a8a7c3",
            "18e3dc755965433aa228fb1d6336901a",
            "2d61c3cefc5a480daf8016968aec8868",
            "956d1596ac344500a489474949903a39",
            "009309a642e14af2829673e38a75c843",
            "47c710eebeb644ea9a16e436bb048a1c",
            "49d5b43bfb844949b06ea02c9083aa4b",
            "1fa69c8f88c84ac9a1316af3d4ef85cb",
            "af700baccf174d9b97ba47052b9193ad",
            "0e3fd8235c0144afbe5c50e89bf492bc",
            "24e8443ce90840f89bdc0d72d19be8e9",
            "68e41f32a0364e969e3726948364bcc2",
            "ad470bf322294e29854b4eea6f72029d",
            "ef7912da29b34509bb26f960732bf026",
            "f170a942571f49ecbba2e79c799f31f1",
            "f8a7155764d04cc3938a0c9848192ac9",
            "4352e78bfd3548ca88f76e4bcb4e736a",
            "5843ae31990d43f99e164616facba7d5",
            "c74dc103630e4112b89e13be7c80d4bd",
            "94ced5dfee0348248b360487d7948c28",
            "5054362df0064e538e705030eda7e0bd",
            "2cf375c36b4a46be8f272f1af3c46b96",
            "dea147f4f36c4880b1d49064565a3432",
            "be8f6285e87e448caa00715d2e4756a5",
            "685dd39b38fe4414948ec07a65fd17a7"
          ]
        },
        "outputId": "fbff3389-fa61-4507-c790-a198320ec4d8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/609 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6d8f89d1eee74e48ba7650100e6b97d4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/repocard.py:105: UserWarning: Repo card metadata block was not found. Setting CardData to empty.\n",
            "  warnings.warn(\"Repo card metadata block was not found. Setting CardData to empty.\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/1.65k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3843e1999b345f59e7a2f63d769dc76"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/237M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "956d1596ac344500a489474949903a39"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f170a942571f49ecbba2e79c799f31f1"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"huggan/smithsonian_butterflies_subset\", split=\"train\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Preprocess dataset"
      ],
      "metadata": {
        "id": "xc4NxspmEDVS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision import transforms\n",
        "\n",
        "image_size = 64\n",
        "preprocess = transforms.Compose([\n",
        "    transforms.Resize((image_size, image_size)),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0.5],[0.5])\n",
        "])"
      ],
      "metadata": {
        "id": "5zAaBWfhxKHj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "batch_size = 32\n",
        "\n",
        "def transform(examples):\n",
        "    images = [preprocess(image.convert(\"RGB\")) for image in examples[\"image\"]]\n",
        "    return {\"images\":images}\n",
        "\n",
        "dataset.set_transform(transform)\n",
        "\n",
        "train_dataloader = torch.utils.data.DataLoader(\n",
        "    dataset, batch_size, shuffle=True\n",
        ")"
      ],
      "metadata": {
        "id": "0O-HrjiWxoyr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch = next(iter(train_dataloader))\n",
        "print('Shape:', batch['images'].shape,\n",
        "      '\\nBounds:', batch['images'].min().item(), 'to', batch['images'].max().item())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zddgfvFVx_uL",
        "outputId": "3ab2437e-d96d-4913-dd95-169297fc7dc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape: torch.Size([32, 3, 64, 64]) \n",
            "Bounds: -0.9843137264251709 to 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check the shape"
      ],
      "metadata": {
        "id": "EroC9RpwEIVQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from diffusers import DDPMScheduler\n",
        "scheduler = DDPMScheduler(num_train_timesteps=1000, beta_start=0.001, beta_end=0.02)\n",
        "timesteps = torch.linspace(0, 999, 8).long()\n",
        "\n",
        "x = batch['images'][:8]\n",
        "noise = torch.rand_like(x)\n",
        "noised_x = scheduler.add_noise(x, noise, timesteps)"
      ],
      "metadata": {
        "id": "OK-ZKEbIzdxH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Build the Model"
      ],
      "metadata": {
        "id": "tKVpKWi2EKkS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from diffusers import UNet2DModel\n",
        "\n",
        "unet = UNet2DModel(\n",
        "    in_channels=3,\n",
        "    sample_size=64,\n",
        "    block_out_channels=(64,128,256,512),\n",
        "    down_block_types=(\n",
        "        \"DownBlock2D\",\n",
        "        \"DownBlock2D\",\n",
        "        \"AttnDownBlock2D\",\n",
        "        \"AttnDownBlock2D\"\n",
        "    ),\n",
        "    up_block_types=(\n",
        "        \"AttnUpBlock2D\",\n",
        "        \"AttnUpBlock2D\",\n",
        "        \"UpBlock2D\",\n",
        "        \"UpBlock2D\",\n",
        "    )\n",
        ")"
      ],
      "metadata": {
        "id": "adHgSTp9yFa6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad():\n",
        "    out = unet(noised_x, timestep=timesteps).sample\n",
        "\n",
        "out.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bb3iC8FZzlCl",
        "outputId": "e96fc7c4-c3ea-4800-d95b-4743af719140"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([8, 3, 64, 64])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training"
      ],
      "metadata": {
        "id": "UyO847eyENwt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn.functional as F\n",
        "device = \"cuda\"\n",
        "num_epochs = 50 # How many runs through the data should we do?\n",
        "lr = 1e-4 # What learning rate should we use\n",
        "unet = unet.to(device) # The model we're training (defined in the previous section)\n",
        "optimizer = torch.optim.AdamW(unet.parameters(), lr=lr) # The optimizer\n",
        "losses = [] # somewhere to store the loss values for later plotting\n",
        "\n",
        "# Train the model (this takes a while!)\n",
        "for epoch in range(num_epochs):\n",
        "    for step, batch in enumerate(train_dataloader):\n",
        "\n",
        "        # Load the input images\n",
        "        clean_images = batch[\"images\"].to(device)\n",
        "\n",
        "        # Sample noise to add to the images\n",
        "        noise = torch.randn(clean_images.shape).to(clean_images.device)\n",
        "\n",
        "        # Sample a random timestep for each image\n",
        "        timesteps = torch.randint(\n",
        "            0,\n",
        "            scheduler.config.num_train_timesteps,\n",
        "            (clean_images.shape[0],),\n",
        "            device=clean_images.device,\n",
        "        ).long()\n",
        "\n",
        "        # Add noise to the clean images according timestep\n",
        "        noisy_images = scheduler.add_noise(clean_images, noise, timesteps)\n",
        "\n",
        "        # Get the model prediction for the noise\n",
        "        noise_pred = unet(noisy_images, timesteps, return_dict=False)[0]\n",
        "\n",
        "        # Compare the prediction with the actual noise:\n",
        "        loss = F.mse_loss(noise_pred, noise)\n",
        "\n",
        "        # Store the loss for later plotting\n",
        "        losses.append(loss.item())\n",
        "\n",
        "        # Update the model parameters with the optimizer based on this loss\n",
        "        loss.backward(loss)\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "    print(f\"Epoch {epoch}: loss={losses[-1]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t_GTwjUszsoq",
        "outputId": "3f8264e5-8dd1-4f43-9f1c-bbcf0f4d9695"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0: loss=0.17056381702423096\n",
            "Epoch 1: loss=0.10094346851110458\n",
            "Epoch 2: loss=0.10799332708120346\n",
            "Epoch 3: loss=0.12123969197273254\n",
            "Epoch 4: loss=0.07544992864131927\n",
            "Epoch 5: loss=0.1302579641342163\n",
            "Epoch 6: loss=0.08400877565145493\n",
            "Epoch 7: loss=0.07889874279499054\n",
            "Epoch 8: loss=0.08182328939437866\n",
            "Epoch 9: loss=0.049215372651815414\n",
            "Epoch 10: loss=0.05689718574285507\n",
            "Epoch 11: loss=0.07737085223197937\n",
            "Epoch 12: loss=0.07363582402467728\n",
            "Epoch 13: loss=0.10461263358592987\n",
            "Epoch 14: loss=0.04150570556521416\n",
            "Epoch 15: loss=0.03741295635700226\n",
            "Epoch 16: loss=0.10159166902303696\n",
            "Epoch 17: loss=0.04309024661779404\n",
            "Epoch 18: loss=0.06622651219367981\n",
            "Epoch 19: loss=0.06012377142906189\n",
            "Epoch 20: loss=0.03762900084257126\n",
            "Epoch 21: loss=0.03851134330034256\n",
            "Epoch 22: loss=0.10083463042974472\n",
            "Epoch 23: loss=0.07816964387893677\n",
            "Epoch 24: loss=0.030888361856341362\n",
            "Epoch 25: loss=0.036953866481781006\n",
            "Epoch 26: loss=0.06251120567321777\n",
            "Epoch 27: loss=0.10792180150747299\n",
            "Epoch 28: loss=0.02173011749982834\n",
            "Epoch 29: loss=0.02668747305870056\n",
            "Epoch 30: loss=0.02066601812839508\n",
            "Epoch 31: loss=0.03788250312209129\n",
            "Epoch 32: loss=0.040425822138786316\n",
            "Epoch 33: loss=0.020408647134900093\n",
            "Epoch 34: loss=0.09873066842556\n",
            "Epoch 35: loss=0.030844775959849358\n",
            "Epoch 36: loss=0.03695472702383995\n",
            "Epoch 37: loss=0.028101062402129173\n",
            "Epoch 38: loss=0.026577219367027283\n",
            "Epoch 39: loss=0.022410787642002106\n",
            "Epoch 40: loss=0.05230000242590904\n",
            "Epoch 41: loss=0.02884005568921566\n",
            "Epoch 42: loss=0.04662346467375755\n",
            "Epoch 43: loss=0.0319201797246933\n",
            "Epoch 44: loss=0.08381204307079315\n",
            "Epoch 45: loss=0.014857662841677666\n",
            "Epoch 46: loss=0.020062051713466644\n",
            "Epoch 47: loss=0.02826770208775997\n",
            "Epoch 48: loss=0.017473429441452026\n",
            "Epoch 49: loss=0.06993352621793747\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(losses)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "1edH-hvv0VO8",
        "outputId": "410a3bf3-0bc6-4120-8574-d5ecd3e91b34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x783d02e3a890>]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Testing"
      ],
      "metadata": {
        "id": "A2Nz8uBqERSs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from diffusers import DDPMPipeline\n",
        "\n",
        "pipe = DDPMPipeline(unet, scheduler)\n",
        "image = pipe().images[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "50efa27876e7412c954c36d95c2f9e2d",
            "9491ced0e0f64c8cac48328be461b4f5",
            "be2d25f63f8744caa32ff4a0bb1a6316",
            "c9c3beaf98d447e1bdfc7f8626603d6d",
            "36911a47218f4fc9bfe121f9784e9c7f",
            "3791a687a33643dfbc949fc64c91932f",
            "c1c4c3e4b22946df889892c0aec70a22",
            "906ad68d5be843b7a5c25c7e602bd712",
            "bd16fd344c424dbe93f7b9120cb650b3",
            "24e411bbe7434d6e8c909dcd28015107",
            "077cc5ac43754288b13bfc665c6279a0"
          ]
        },
        "id": "Q4EUWEMK5nKR",
        "outputId": "dc01d90e-fad5-4eb7-90ba-fde1ab794b63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "50efa27876e7412c954c36d95c2f9e2d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "oBtzonDI5x5g",
        "outputId": "d32e8690-afb3-4c0f-a1a9-05cfa8a4ce11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x783d02e99750>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Upload to HuggingFace"
      ],
      "metadata": {
        "id": "SADB70Q0EUOt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install huggingface_hub"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TuYN0IVv6HGg",
        "outputId": "5831f0bc-fc17-466d-824a-28ad9c8c30be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.19.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.13.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.66.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.5.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (23.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2023.11.17)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login"
      ],
      "metadata": {
        "id": "QSHIpezw61VS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "bfbe3dc471cf4a6287f10f0428772946",
            "de16c11a5a1440a39e73714f382d88f6",
            "7e22d5b4a81645ababf63ec6dd900aca",
            "d3de32612b2744a59a26d1832698df3c",
            "abcef5e98a5044f5b3e647b7174cbdbc",
            "0cb689b7ad244db39adce28ba206624f",
            "558cafe47abe4314aef5bf11f83fdae8",
            "7375fe8cc21a4d26ab64cb8edad2c715",
            "b671e2eb72894796b50fd4da583a6062",
            "326cff0f3e2a4a0aa80767f7fd508d29",
            "a31a25d7b14a464bab18e8210ae31c54",
            "d8aacf59d9ad43d98be2bb4ac148a1a9",
            "9038584a581e4200a62adfb7eb1b8ab2",
            "476e2af1a24c4ae58b1b9e11b7291247",
            "2231c68f4f814dc6b08262109b5622dd",
            "2a194ed0a8ac4b31abdb6008659725fd",
            "1bbb4097d25a4393b4c3ae45fc1d96f8",
            "e9461c508309496eb15278b797de5b1b",
            "e0790bb61f964640912f4d4960cb8d63",
            "cd991e8d20364571b67ba447d1950f95",
            "95ddf99fe556412f91e7d948884816b4",
            "9a4dc2f79d734be1b722328987a95d9e",
            "44cbcffeb2844765a9ecdaa3a504612e",
            "2e1c3d0c0c2c48cfa26eba6ceea7c7ba",
            "6ca3930b98bd41fbab8c78201bf4a61a",
            "16995c85c7c943c0b6b3a0b6f0896d68",
            "a06489c47bbc46f897c0de74e8adde0a",
            "01115b16c5224fa1b62a8a34e932d912",
            "2a1839bf98ed45daa338093a24f64868"
          ]
        },
        "id": "Exw5hjWZ67yi",
        "outputId": "3b8c839b-09cc-4ab2-9514-1f16d7d75c3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bfbe3dc471cf4a6287f10f0428772946"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe.push_to_hub(\"ddpm-butterflies-64\", variant=\"fp16\", private=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68,
          "referenced_widgets": [
            "9d1433606e3446cc9014e8278efedac2",
            "3567fd3ace6a427da132e644ca833216",
            "31c0a7a410e74e50a0dcc181106b7f0d",
            "124ae971eb27407784d6b84e37b2b2ea",
            "5f8a02b23b8843d094778dfb8c9ae8ca",
            "cb26479ed1184108856444026d215c52",
            "b4ef8029a51c42b7bde77a611745ec9c",
            "f9956bf207dc4424a1c6da007c39c0b0",
            "4cf171935d01457fba67a6542dddaa02",
            "caa5fb447c9941638e86103d7f8f6670",
            "52cc41d0b751439abf04d0649094ff46"
          ]
        },
        "id": "6k-2d8QA6-PC",
        "outputId": "c74e62e4-c40d-4c6f-df26-ee9eeff50916"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "diffusion_pytorch_model.fp16.safetensors:   0%|          | 0.00/253M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9d1433606e3446cc9014e8278efedac2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/gnokit/ddpm-butterflies-64/tree/main/'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from diffusers import DDPMPipeline\n",
        "\n",
        "pipeline = DDPMPipeline.from_pretrained(\"gnokit/ddpm-butterflies-64\", use_safetensors=True, variant=\"fp16\").to(\"cuda\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "d9b0160ed75d4e91a316c4383bda7f31",
            "2a0fb22826e5458d83a0ec23b77693d2",
            "c8fa803310004524b1acf9deddd95f86",
            "1dc5edd499f64c2ebea97aad98514f36",
            "d03d9470d2da4ac096db7a6ba75c4e2e",
            "86c8ccfebf2047e6b4d2d68aa8c507f1",
            "379de023ddbd4176b58401e787a4f003",
            "542956c4985041c082f0c256b65b085f",
            "d2f55beaea654edabcfa752d8abe9579",
            "4b22dd70bec8476e826bfba1e462fa58",
            "990827a9a5be4a87adcb62e9740e2b05"
          ]
        },
        "id": "qctS_UJw7ZOC",
        "outputId": "9e3f6cd7-1401-4ded-fd1a-d37b02f31558"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading pipeline components...:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d9b0160ed75d4e91a316c4383bda7f31"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "images = pipeline(batch_size=4).images"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "ecd55f2961be472f8ab522848b5d5de9",
            "4ca1d8e6f6e949908bf7ea2cfd953f4b",
            "9a2e7d9e780f4b84a46e1c71fa8976be",
            "0e22bdcc5c644bc4b1d921bfacaa5657",
            "9999713fe05b4da6ac23508c4167ebca",
            "d35681af972d406fa966bd3a1e77eb48",
            "9ae1ccc983a5482b8cf2086383720dad",
            "607c8d9796b4481f8f7061e433756b97",
            "aa9c62281692450799db9f08f89e01e1",
            "7c16dbdb92e34e18bf8e6927004de6f1",
            "d9a229b223374cdf96b71e6c89d12a55"
          ]
        },
        "id": "DtzQa3Dw9Nyp",
        "outputId": "d514f685-864e-44ce-e11f-12783424c439"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ecd55f2961be472f8ab522848b5d5de9"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(images[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "IDlY4Bkl-tz8",
        "outputId": "b8a53706-2ac9-4fc9-9c99-2ce3e702eebc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x783cf0585a50>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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JVY1GgO/DKCj2Z9ewanciUJWoLY011UL4CYgQQkgoMAERQggJBSYgQgghocAERAghJBSYgAghhITCklXBWQ1PrAU+UkENKzZmfVUhVB/H3mGzGu80K4LVR44Pio914WlLTmOVSHcvVkKddZbqwSUiEjfVcU7UsMfTKTkK44efO46v7asFzzq78XjKZaxk+cfvfwfGx6Zx8bX3v/d9Siy14QLYNqFRCFkxvG7dw3jOz/JWKLFXJ9WYiEjTwQqhN57cD+N1S1U3nTuIVVb5AlZ2FTQeZNXgIhhvJFXl3Wy9CNt6M1jVGFuB/QGtiFp0MRrH6rBkEnuHnS5jFZyNxX4y4ar324nnfgTbPv79v4fxYxN4j7fAXulLYBWpE8cK1fXD+HmQTWAVoN2t7sMXX8BK1NOHVIWmiEjVxOrSjEZ1WvTUQpziL+4zRTuCr90XV59BsTTesys35JWY08Tqx4XwExAhhJBQYAIihBASCkxAhBBCQoEJiBBCSCgwARFCCAmFRavgfvjDH8oXvvAFOXjwoIyNjcnDDz8sH/nIR+Z+HwSB3HXXXfK1r31NisWiXHHFFbJ7925Zt27dol7Ha7fEa8/v3ljrGGzrO2rFwHgBq148D6vJxMBeY53ZLiWWTmL1mtuDlR8DOV2VQtyVtq2qzwp2Hra1Dazs6vvXq2G8XBlTYsUxXIWzOFaC8ZqFFUIvHH4VxhNdqhfc+wp4DruTWGUkmgqduSpun86o6zY4jPfgsZfewNdetRHGT554SoltuvIy2LY3txbGXRur5gLBlVKjhqpKG/Xx+lgtrIKrCZ7zAngMOFW8l704rvpruUUYPy3Yr+7gU/+oxA7sxd5uUzNYjWloXjOaKKj968EKrv4cVsFddtVVMJ6oYxVgO1B9HRuj2MPu+YamOquDr12xcd+TtqpqrJbxQ8XTVHy2PRw3QHsLqHNFRKJNta0PYohFfwKq1WpywQUXyK5du+DvP//5z8uXvvQl+cpXviJPPPGEpFIpufbaa6XZfHPmdIQQQn41WPQnoOuuu06uu+46+LsgCOTee++VP/qjP5Lrr79eRET+9m//VgqFgnzzm9+U3/qt31L+jeM44jg/fzdYLuO/3yGEELK8OKNnQEePHpXx8XHZtm3bXCyXy8mWLVtk/378R307d+6UXC439zM0NHQmu0QIIWSJckYT0Pj4uIiIFArzv4MtFApzv1vIjh07pFQqzf2MjIycyS4RQghZooRuxWPbttg2PtwihBCyfDmjCaiv72fVNicmJqS//+dV8iYmJuTCCy9c1LVqlZJ4rflKIU9X1bCtKllcC3+4i3hYUeL5eCpShlp1MNWLFXOXnX0xjLfrqv+aiEijhL2iMqJWGEyswaq2fk3FzSkXv+bp02qyb5Q0Pmar8LXrDvZ8q48ehvGDL6reV+t7sZqqb/2lMO5n8zBejuEzwyjwvJtxsF9ZeXYSxhud2Mjs/LVXKbG+gkblmcPVc51JvA+n2/ibgj6gmjOquOIm8kITEbE1/lxmS71/iiaeK8M6DeP1FvYUO/Qi9nc7+oJabXe8ib/9aDTxOHOaKrTJlOrL9ovPo1/kgrVYvdg/uB5fG9vpieuq95XVXYRtDx7fBOP1Y3jtYx6eF7et+gx2FwZg29lxfA3Lx+smKXU8gwlcsXVg5Uol1mjg589CzuhXcKtXr5a+vj7Zu3fvXKxcLssTTzwhW7duPZMvRQgh5F3Ooj8BVatVOXLkyNz/Hz16VJ577jnp7OyU4eFhuf322+VP//RPZd26dbJ69Wr5zGc+IwMDA/P+VogQQghZdAJ6+umn5X3v+7m9/p133ikiIjfffLM88MAD8gd/8AdSq9Xk4x//uBSLRbnyyivl0UcflXgc2+YTQgj51WTRCeiqq66SIMDfD4uIGIYhd999t9x9991vq2OEEEKWN6Gr4HQk4llJxOcf9reA2EBExG2pw4hEsf2N4+MDzaCEk2o90lZi/Zk+2DbWwJ/y0qZqCyMiUtPMfjumWolEZ/Bhu5fF4+nMY6FEE1wmo7EjKU4VYXx2EosWmgE+/E7U1APQHz2ljlFExE/idbgovw3GoxFcOK3sqevWncLF4SzNNWZP4AP3gUvVccaTeB2ybWyNMjmFhQ+xnHqgKyLiAduZjKb4WGpYtT4SEWkFeMPNgFvFsrAAY/QktkTa/5MnYHzk2edg/DnwZxlmgIUpySQ+/E4m8PhXrFQP4od78Zz0bMJWSQmN2CDewjZUiV51T9htLEy5+v14L88++i0cL2FbsTjQsdgGXre4rd4PIiKlGFYg50tq+1QPfk5ELNUKLRLRVCJcAM1ICSGEhAITECGEkFBgAiKEEBIKTECEEEJCgQmIEEJIKCxZFZw72xLTnt+9qIGVbW1PtZOoaYq9JTT1zjqSeCpWDqhqrXSAVTleAitKcl1YkRZM42JymZTavpzGCqGZl3ARuHoGv7eYqqsT42hsVBoOtm5x2lgF15HD0qF0Z16JNVu4UNnx13C/o7HXYXzVWuye7kTU4l7ZDqxSLBTU/omIfG8UFw7rnVbH6bSLsG01gq1RMjk8t9UIntuRinr9fAarDoe68F7xPLzHy021SGFrFhe78ybwurVOvQLjoxVcYM+vqPeKXcDKSDPACsO4YFVjf0JVNfb34GJ8vYKvHQvwPVHXPYOArVZecNsNa7DS8ZLzV8H4P+7HBQars2qNtVaA1ZV1D9+bXQF+UM5MqfszsR63TaVXKTHTVJVxCH4CIoQQEgpMQIQQQkKBCYgQQkgoMAERQggJBSYgQgghobBkVXDrzxqSZHK+yueFibNg27GJk0os0lYVIiIiQVNVR4mITDax6qU7r6pbevtwsaqOGPZKMioapU0EFyurgb57o6pSSQSro0REpp7HqqRoh6qGKdewv1dtBiue8iuxx9X6s7HiKxdT1WepBF6HyixWu5068iKMd3bjOc93qwoxsxP76fUOrYLx+g+wh98zz6nzcu1leA7tHPaTa2Zwv2dfwaqx+tgbSqzjsvNh28DFfmVOdAbGLSDIm9UU72tO4j0RdOVhfLCyEcbjcbV44WkP9zvexmqyWBLf4zHgbza8bgNs25nCa+z5eH+KpXlk1lSlWtbH48muwFLctSu2wPjoWXhvvfiTQ0rMjGJlpN/EqrSmjee2e0A1mustYCWhGVX3svEmMws/ARFCCAkFJiBCCCGhwARECCEkFJiACCGEhAITECGEkFBYsiq4UldJWqn53kMdSVzVr9Krej/5ZY3XVhUrUxoVrKgZHF6lxPrPwb5KbQ9fY6KElUMzUzUcB+HK1HHY9vAMjns17AmVq6lKlmYMz2sqhSu/FjLYUy0ew+9nBgZVvzazhb3GKhW8JafrJ2D8lWdVNZWIyEVn9yqxbAZ77xW6cb+72lgJ5h99TYm99sOnYdvgPOz75Waw+so/fQrGZ9qqcqpwUrN/0jged/HclkygAHWxCsxLq/MqIrJCU2k4MoN9zCYbqiLPnMFr3ND442VdvJ6mqa7bzCl8n6TWaZSrmvGYPlaN5W3VH3KsjFWHXgmv8VkJ7DGZ2vIbML4y+agSO/Y6VlEereD7JObiez8SqM/JrhUXwrYrelXFXK0KyrUC+AmIEEJIKDABEUIICQUmIEIIIaHABEQIISQUlqwIIWtkJWnMt5WIpHEBqsaoesA228DWOuNFfLC8qjsP42dvWavE+iLYkmLyND50rDr40P545SiMj71yTInZNj4U7kxjQYS4+HC1bKmCiJyJ34fUU3h7+IFGVDGLrYU6Y+rBtWtiwYZv4cNf38cHtPUqtig6PaGOf0rT1jWwfUmhB1umjPiqvc4Lp/bCtm/M4oPygQ3YdsUewvvTbHQrsZpm3YqupmCgj9ezWlEFO6aD91s8gtchbeGiiyuGNEXwaqrApVnHh/MTo7gvxXwdxmtV9d4/Na0R2mSxGGZFJx5nu4Vf07DVtUhrCgZ2ZvthvN6rEXgIFk51DF2mxBKP/hi2bRzGzwnDxxY9mS5VbLGygMdj96t7tl3B+3gh/ARECCEkFJiACCGEhAITECGEkFBgAiKEEBIKTECEEEJCYcmq4JqeLaY3Xz12akotPCciUgUF1RwPK5hcswXjhU1Y2VYcVa0t2kmspsqnsIqldgzbkZw4hsczVX9Jia1b8yH8mhms7pmNY3WPPa4qpCrJPGybjGBlU7wXK4TiDdzetVXlUHsWK3tsA6v3MprCWbUmLvjWBgXCIhpVkil4fbauwwUQY0lV1dix/kLYtjOFLaGS5jiM5xMFGPdcdW8l8ynYtnYSqxGdJFY8BQ1VsRSP4mtnO3FcEli9GC1oFFy9qirLOIrXvtTGyi4HD0eKLdWKqHX8SdjW8DSWQ8N47TM5rGDrBiIzK8B71jXxs6k/iu2ZxitY1eg1VYVlOovVv+kWHqeTw5Y57zlPLeDXN6wqMUVErEm13y1a8RBCCFnKMAERQggJBSYgQgghocAERAghJBSYgAghhITCklXBFQ+PihOfr6oqTWI1iC+qr1RhBVar9EZUxZyISCGPi6+dmFFfsz2GPatsFxeDOvE69nybCbAyJZFX/ecazTdg26Sdh/FYHKuSmmmgzCnjbbBiDQxLVlN4zs9ilUyzrCoPI1Ebtk3GsWrKiOIia5aLrzPhqfH4LG47Vcbr9koJKwy7+gaU2NlZrIBsxrDnndHCSsroDPYNzPWr6jOviPud6sT+c34Nq6/aSVVJ6OdxP6wyVhImNGq3oIjbd+TU6/ckcfG+s3rwOLPr8D3eZ6h7v2Vi9d7xaaxELU7j8Vy4Du+JdqCOs2Xhtsk6VilmVuLnW8rQrLOtKs1m03nYtlrD92xbsLed01ZVwVGNZ6KdBSpXA6s/F8JPQIQQQkKBCYgQQkgoMAERQggJBSYgQgghocAERAghJBSWrApuujsq8eR8xVYjhpUVGckrsUQX9iUb7FfbiohszuOKgT95ab8SO/b0M7BtM6vxn9P4ZPWaWGn0uqN6c52ewKqceHoQxge7sSKtK1D9tqIafys7iccT6caeXUkHz6EZV1UyTQcrgQLBXn3NMvbVslLYc8pKqIqdknMEtj1+6jh+zdO48uuFq9+jxCJZPCevHn4dxmNl7GFnrsRqOjupKqpMA9++LRcr7ywLz2Ekoar6sjWsOixn8GtGXazo9LMaT7CG2v7c9Wo/RETOWn8jjFs2VraZUdUPbexVrDr0ZvBcTTawOm6kjO+JVkl9NpXHy7DthlWrYNxKaRSQThHGU231+uky9mmUJN6f2RSO23l1fVpxvGe7pEOJBW/ysw0/ARFCCAkFJiBCCCGhwARECCEkFJiACCGEhMKiEtDOnTvl0ksvlUwmI729vfKRj3xEDh06NK9Ns9mU7du3S1dXl6TTabnxxhtlYgJbSRBCCPnVZVEquH379sn27dvl0ksvlXa7Lf/5P/9n+cAHPiAvv/yypFI/U6Tccccd8vd///fy0EMPSS6Xk1tvvVVuuOEG+fGPcVVDHb0tRxLu/PwYq2Bl26mW6s12dhErZFauuhbG4z24/VhlSomdnDkM27YbnTBuJHGej3lq1UERES8OKryaeKmMBk7urTj2bcr2qEqjRBdWr9UDrLxzq1jxNJgownjgqnPbEFwRNDuN5yrai9VhroHVWqcmnlVi9SJWgUVN7A+YSuE5rFTU/TY6PQLbnjxyCMb7OnDl04qBx5/z1XHanfh+cGbweLIaxaAXqK9Z9/DYpajeDyIi1R6NT1gD31cJW33NbAe+htNQVVYiItP1SRi3QIXX2Eqs9rJamkquSdzvmXE8/qlp1auxnsFK1OkXn4DxTZNPwbjdg739BlaoVVu7hrGB4+l9P4XxbGsUxidG1XKzrSnsbVcfVPdVw39zqWVRCejRRx+d9/8PPPCA9Pb2ysGDB+XXfu3XpFQqyX333ScPPvigXH311SIicv/998uGDRvkwIEDcvnlly/m5QghhCxj3tYZUKlUEhGRzs6fvfM/ePCgtFot2bZt21yb9evXy/DwsOzfr/49jYiI4zhSLpfn/RBCCFn+vOUE5Pu+3H777XLFFVfIueeeKyIi4+PjEovFJJ/Pz2tbKBRkfBx/5bJz507J5XJzP0NDQ2+1S4QQQt5FvOUEtH37dnnxxRdlz549b6sDO3bskFKpNPczMoK/SyeEELK8eEtWPLfeeqt85zvfkR/+8IcyOPhzK5i+vj5xXVeKxeK8T0ETExPS14cLvtm2LbatFgrryW+U5AIrmO7OV+A13JZ6uHzexefBthdtuBjGgwAXfCseeVmJjZbx4W86iQuedRpYnNDO4wPQnKkexkYt/JpWDh90RmK4fTmqvufIOKp1iYiIn8G2OGYDf0064+FxlluqhUfcx7YjxQDbLaVqRdyX0iyMT5VU+59IAq9PNo0LoVUzWOBx5Ji6V5oNLIYI2lg8ke7QfNL31AN0EZHAUee8WcfvHw0Hiy2anXj8dgXMVRwfOAdpbPXSrmisknwsFKga6v50NaKPZASvMX5Fkaqv7uegjK+RMtXCayIiUR+3L9VwAbdmWxXmzL5yDHdwAN9XT76BR7SqogoCRER8R10ju2cYts1ity2pjGF7nUxOtfiqxfOwbWFWfUZGqvh+WMiiPgEFQSC33nqrPPzww/L9739fVq9ePe/3mzdvlmg0Knv37p2LHTp0SE6cOCFbt25dzEsRQghZ5izqE9D27dvlwQcflG9961uSyWTmznVyuZwkEgnJ5XLysY99TO68807p7OyUbDYrt912m2zdupUKOEIIIfNYVALavXu3iIhcddVV8+L333+//Lt/9+9EROSLX/yimKYpN954oziOI9dee618+ctfPiOdJYQQsnxYVAIKAvz99C8Sj8dl165dsmvXrrfcKUIIIcsfesERQggJhSVbkK5U9KS1QOURtLFCampELfpVO41VcN5WrO4ZOYWLjxUdVZnjaZQ9ApRXIiJTBn5NyeF4t6/atMSimoJS/V0wHmlh+xLLBgXpOrASqDiLFTKugS16bI2CzS6pqqRWGivvrBh+T5SwsaqmicV+YjrqdYIiVu9ZcWxF1N2F7Vimy+r4jQhWmK3uxsqmdBuPs0tjgdNsqqrBhIn3W6uK18doYgWbaajFAdsBVl7FAzxXqaimOF4kj6/jqn1sV0qwbcnCiskpjRWPN6Wuc7tYxP3L4/XJ2NgqqZTCz4n6SVUFF0nj/dacxcUYoy2NJVYPvj9Pt1XLpbyNlbXnDGEV8kgJ24oN2qpSLz6D+13sVRVzVQPbQS2En4AIIYSEAhMQIYSQUGACIoQQEgpMQIQQQkKBCYgQQkgoLFkVnL96ULzUfMVWx0Gs5FhjzSgxw8XKmcnZMRjf2IHNkjZdcLUSOz76EGxbMdIw3hnBfz9VHseqsSDeVGIJCyubhix87apghV0sqsYn6idh21JD43vVwAqpLo2/WyWVV2JDLTUmIiIR/JrlSXVOREQMD89LOqkqh3wfX6OueR9mBrjw3toVqv9ew8X+Xo6HZXqpfjxXVg/eh/E4UCDh7SP2GqyYbNdwQbp6Qh1n28SqPsvH+81z8LqZGgWbEVMLuzmgcKGIiNF0YNwHhedERE60VQ+/5oRaRFBEpDiB56Rcwgq22iRWhjYNdQ6Teaw6LDXweLLYNlD6B7DStRlRFW+RMt6HnSuw9+BQ+n0w3rPqIiUW78M+c1Xg1VeLYpXrQvgJiBBCSCgwARFCCAkFJiBCCCGhwARECCEkFJiACCGEhMKSVcH1lauSWiDY+rX3/ivY9viUqhI59AaucDp7COfcV9ZeCuMrsnklZnvYbymWKMJ4s4n9poI2Vsm4rqoy689jNVErilVTHaex2i/ar1YtjWBxmDR9rBBqmVh5NmPhKpKrga9W1CzCtk5VVTSKiBRj2IcqHsF9TMRVL7xYHKsUXQcrh6Jp7AfWNlUVnDOOPcLSmv51r1kB44GP1YteoO6haDeuQBtr4WsYGdyXdltVZdk+XmM/wHEzipWRzSie23hbvYcCD8v6StMajzjNPqy5qg9ZqY7HXqqPwni5huewZWEFWw3cy4bmOeHZWL3XKGKl3nPP4HsiYqsKy9UrsWIuodmH512KC4X2ZVTFm42UmCISj6v3Q0xbr3Y+/ARECCEkFJiACCGEhAITECGEkFBgAiKEEBIKTECEEEJCYcmq4FasiUs6vUDlMY19tU5m1yqx+BSuyJcYuBjGU3WsNOkaVn21zBhWsbRnsUIm2YnzfEuKMG76avXP2hRWCJ14CiuBEv3YN+8cX/V9izWxki4FVDYiIvEYrrZaLmMllCeqEqrYHoBt2xa+RrKJ44leXEE0Jaoyp92Bfbxy0/g2CKJYUTTWVL3GXI3PXG8OK+9sjdeYH9eomJCIydH442HxlTht3D7aUNcnFsOqSwmwEqoRxe3tFn5NN1DnpWFgJaETwXPVruJrW23VBzCVwEq6YgzfVxEwJyIiDSwmk0hdvWdLmntWBHsMGib2TztewdfpABV7zaP42RQ1cbVVbzX233NsdW5jCdy/qKX2z4tib8CF8BMQIYSQUGACIoQQEgpMQIQQQkKBCYgQQkgoLFkRQqIzJ8nMfHuLRAKfruZ/pMadssbS5cjTMN4GBcxERNLgzLWwahVse+LYszAew+fnYmjEDMmcetjnaA5za0UsfOgcwkXJ/Fn10NHRVDarePiwtKIpSBcXzcF1XI23ouqhrYhI2sOH8H4TV+vyNYX3TtePKLEOF4sqpqKag1jnBIwbE+resqN4HUwD78OxU/hgvbMD73GjH1i9aOxO7LSuyBoW5rRMdZP7Bj5EtiL4MDto4GvXDfwet9FQhRxTdbyWk6fxOozVsXWNOauucyaTh20jZXzfd3fge8K28bwEcXUPNQIsfKid1tgZ2Xj8HTFs2VUDYqWTx7EF2WACPw9KY9hu6py1Vykx28fjCVqq4MloYbHKQvgJiBBCSCgwARFCCAkFJiBCCCGhwARECCEkFJiACCGEhMKSVcE5hifRBdYUXaDQlIjIxRdfoMSOHT8G2z5/8DUYv/BKrD4aiJ6rxLp6sRXNidfwdLpRHDdjWPVSr6vqODuBFVxuFNtjlCtYZfVKaUqJGU2sxhtcixUyQQm/b3E1BcJOTagWOGnNnAQGVh+ZbWy5UwtwX+JgOScFK5jsBo6nxYbxRo9aTC7uYpuf2SZeN13xwm5jBMYTnlogrGnjfeiUsALJ8HFfajW170EU3w9V7zCMZ1J4fXyNI0tFVCXlTAnPYfW0xuKpjtVhTUPdhytyuHhfy8DPlOmTWL3Yk8QqwMQKdS3KAbbDOhZg9Z6nqQyZTmC148lDx5RY4OA9YXbj+y2dw+rShK2q+poeVosmPVWNF3FpxUMIIWQJwwRECCEkFJiACCGEhAITECGEkFBgAiKEEBIKS1YFl5KSpGW+msVLahQbUVWtNLTuHNh25Mlvw/ip53A/grWq8iNeVpVkIiKeYBWYp/MJM7F3WrOFPNiwOiyfx0q1WAQX1HI8Nd5oYXXUyBRWskQC3G9fU6zLmHhViVUN7PmWyeP46iGNAkfjD9iuqHPo+lh1GDHwtYPuPIz3xlWlkVvGbUdbx2HcqqhKOhERo2cNjLeBqLGZ1Pj9CfbZq7dw3Cupiq+ahZVXNRPvlXId+4SlPbz33VJRvUZZ028Dq/qaUbyeqUDdEzUT37O5Dlww0Cjj+KiL78MC8M5rG/geTNbwnNQ8rFx9bgrvoa6FBTtFZMUKtRCjiEhE4w/YP4CVevFu9dqRKu5321TViO03mVn4CYgQQkgoMAERQggJBSYgQgghocAERAghJBSYgAghhITCklXBBX6nBN58dUUlg72SMjOqwmN1DquMHkvhKp+pGr62C1J0xcMeVNEsVgiJh/N83cSqEisKFCu+RjnTxP32saBGEnF1XoxhPB5DU220Kvg1pYl9tZoN1a/OjkzDtjYWU0mxgL3GgihWHvqBOqYEHo5EsfBOMi2sAowbqndesgurpqKlPIy3LE3lU09TSTKnKsRqM9jDr+5ixZdh4L3vWKqqLxnHbeuTRRivOHjtp2ysBGu56n72ahp1qYmVXZk4XtDAUffbG68UcT/kJIznK9iXTsdJoLwzNH56nonXbVqjOjU1eyXbu1KJnf2eDbDtYCfen8nVm2A8Dp4rqRTem2UXrDGKAfgJiBBCSCgwARFCCAkFJiBCCCGhwARECCEkFBYlQti9e7fs3r1bjh07JiIimzZtks9+9rNy3XXXiYhIs9mUT33qU7Jnzx5xHEeuvfZa+fKXvyyFAraL+WXU66fFjMy3vTC9PGzr2qAA1SC2mLjqQ/8Bxh/f93UYP7hXPRi0Y/hQsHfFWhifLY7BeNrDh6iOr47HS+JDRBO7yMhUDB8KRw3VRsdu4qJ2cQsfJHrTWBAhGtFCra0e6BpGP2x7MoLnqnYECx9WduVhfAC41NgFfJgtpVEYrpt4XjLJjUosqBVh21oFHywnIrhAmtHEhdBMUQUumTS2ywnaeRgvm3gOrbK6zmWNKGdKU2EuqM/CuFfERf0qMqHE0hqxQaKOBSturgfGTVsVUPgzuKhdo4yv3YjgA/dAJ5RoqveEb+D1iaXx83DAxnMV36AWIxQRGUyreyhIYlFOqlsVLIiIWC18zzZ71TmMNHExPrepzq2rEUctZFGfgAYHB+Wee+6RgwcPytNPPy1XX321XH/99fLSSy+JiMgdd9whjzzyiDz00EOyb98+GR0dlRtuuGExL0EIIeRXhEV9Avrwhz887///7M/+THbv3i0HDhyQwcFBue++++TBBx+Uq6++WkRE7r//ftmwYYMcOHBALr/88jPXa0IIIe963vIZkOd5smfPHqnVarJ161Y5ePCgtFot2bZt21yb9evXy/DwsOzfv197HcdxpFwuz/shhBCy/Fl0AnrhhRcknU6LbdvyiU98Qh5++GHZuHGjjI+PSywWk3w+P699oVCQ8fFx7fV27twpuVxu7mdoaGjRgyCEEPLuY9EJ6JxzzpHnnntOnnjiCbnlllvk5ptvlpdffvktd2DHjh1SKpXmfkZGRt7ytQghhLx7WLQVTywWk7POOktERDZv3ixPPfWU/OVf/qV89KMfFdd1pVgszvsUNDExIX19WJEmImLbtthA/ZHOdEgmO1/KVGxghYdpqdYWXcNYwXVOcQuMPzH0ExgPXj8EXjAP23bE8Guexq4mUtfY6wSeqjaJxbECJQiwcsar4qXtGlLVPeUpXAis5mEVWDWNVYBDaVwgLVlWJ8BMY/le28CqnMYsVl+NJrHSKJ5T31s5E1iZE9UUquuPYWulZEKdw0Cwgsucwl8pn9QUqus5heelsuCbBRGR2PQp2FaiqrWOiEhTsOLLBMOv1nC/0y6+RsnHa9/WFEaMttW5NU2s3Jz28TWMsdfxa8ZVbyXHx2sf0Sg33TKOtyPYt6kVV5We2Q6s9MzEsK1UIoqVkak4jndl1Hu8+gJ4XomIaeI9kejGhTu9KfXavuB7MGupa2loFLRKv95Uq1+C7/viOI5s3rxZotGo7N27d+53hw4dkhMnTsjWrVvf7ssQQghZZizqE9COHTvkuuuuk+HhYalUKvLggw/K448/Lo899pjkcjn52Mc+Jnfeead0dnZKNpuV2267TbZu3UoFHCGEEIVFJaDJyUn5t//238rY2Jjkcjk5//zz5bHHHpP3v//9IiLyxS9+UUzTlBtvvHHeH6ISQgghC1lUArrvvvt+6e/j8bjs2rVLdu3a9bY6RQghZPlDLzhCCCGhsGQL0lUbVTGs+WojP4b90DxX9c9qz2K1SqIHK226i1gl8g+u6nFlt3DVtIqDC00Fbazeq/lYDdMB/M0iCXztGY3KKpfG7ScmVNWc28b+Y5bGe2/Axl5Wloflfn5ELbAXDbDXlq1R9bk2VqTVJvFaTHrqmOJRrCQ0LI2yaRCvz4m6OudRV/U2ExFxM1hNlXCxkjCdwCo4t6x6kDUd/P4xiGBloN0uwrgHVIAa1zw5FcHr45tY9eQ2sMIyZan34WgRqy4bbawWNU28J6yK6ieYSGPfONHcV7NuEcYDwYXqmqCQoK3xB7QCrCbzizhensEqOL9bXYtVCaw4Trp43SKaIppmQt1bMU0hylhbncMWiMHXeVOtCCGEkDMMExAhhJBQYAIihBASCkxAhBBCQoEJiBBCSCgsWRWc1/ClbS1QjxlYBVeOqPEcqMIpIpIrYVWS0YdVMu0pVa3UTGK1V+00VjbZWTzNMQure+ptVW2Srmn63cIeXKeB/5qISNxSqxdmmvh9SFXjkxXvwErCVhOrr/wxtX0jhVUy7SxWgfWYqpJORCTWgVU8sxV1DrOmWg1WRCS3BisgY1k8nhSofhkk8LWNCl7jjhnNe79zsf6sZarz1ZjQeMHp1FSa95tOUVVpTni4Mm2jOAnjfgvPVUcvVmW5njrOVAxfu1XC6kXfxupNxxtQYhlNReGUi6+dzjdgfKaJFaC9UfVecTRVQcensOFyrIL3cqQHV3N1X1P3nLUe3ycXpLDCUBq4wmsioq4PKKYsIiLNtqoAbLZxPxbCT0CEEEJCgQmIEEJIKDABEUIICQUmIEIIIaHABEQIISQUlqwKLpHNSTKTnRdrmVhRFAPyDKOBc6ubwVKOfAQroXp6VT+w46PYfyyZwsqPdBwruySSx+2BN9dpD187FWClmo8FeVJrqt5cVU0V0h4PK9Uifh7G202NUi+tKpCiUexNlQo0SsIO3D6fwz5Z9SlVORRLYIWZncdKwoil6YurrkVMo7ISU7MQBTxXuapGTZdX1VqtKl6fcQerw9yipvKtq+5np4693Xwf9883sWrM1FQPrnmqUs+v4bkyDI06zsDr6ZiqAtZ7A8+VDyrniohIFO+JVRHs61gFz6aoYOWZHWj67eHnSiGG2wcD6j7s3oD34caLcFHQSBp7MtZbqgem18DjcWrq2Kt1rJRdCD8BEUIICQUmIEIIIaHABEQIISQUmIAIIYSEwpIVIdQ9T0xv/kFtJIYPEu22erhqa8QGfZ24+NjGjdiKp+tx9QBwsjYN2/o+FgoEEc0BrZGFcT+mvi/Ia9rGevB4spVRGB8tqge9sV587SCB+z07ox5QiojgY2uRwUyvErO78Hsf08cWQs06bt9I4EPUaHKl+pomHmfbwbZNaVkB49lBdZ19Fx9aD+bwoXAzgccZ0dTxqtXUXwQlfNDrayyeWk1s0ROLq9eOlbG1TsnE14gIFhCUy1gQ0bRVm5oYrv8n7SYWmgRR/PhKWuo6lzRjr9awMKNXIyrJ9+DnhOGo99vMNH4etAN8p8TS2OanAwhQRERyXeqcn92B92wqhYVQ9Rq+rwroseLjfrdaoOCki8UaC+EnIEIIIaHABEQIISQUmIAIIYSEAhMQIYSQUGACIoQQEgpLVgXnRD2JRudbP6Rr2I4lSKuKt6CIFUL1hsbuIoGVNmvSqhrotVmsMkoVsIynEsPT3BvFSiipq4qVaBTLozIGVqT5WVV5JiJy4cpBJeZlNWqd03iuIlFcIKteLcN41VMVQpEW7p8Rx3OYT2vUOhrVjzer9sX1j+PXLGGFlMTwvEQiapG1aBaP3bCwSrHdxuNJagqHBYa698smblsdGYfxloOVUBVb3VtVwXvcKOE960TxeDqTuI+NlvqaKQvvt3ZMVTSKiGRtfF9Fouo9a/i4bat1DMZLdXxfVU/idY47qjLWamC7pSCJC8+18W0lHcO4qF/fBlWh2x0/G7atRDXKuxgumtesq3vf9PBanmoDK5625tm28JpvqhUhhBByhmECIoQQEgpMQIQQQkKBCYgQQkgoMAERQggJhSWrgksVeyXVnu/p5GaKsC3SZrTjWGkSr2OFUFuj2oj1qIXqEmlcICoZxWqdfB57jdWnsF9S0VflMBsyq2HbaA4rU2pvYI+rPuD75plYeXY6wH5YM3ICxusOVgh1tFWVYsvDbVMO9trq7cJqN8PCc5joH1JiuRpWWbWCERiftfAcythrSqj0Cu6H3Ys94lYODeO+CC6MGKsV1VgTv2Zg4/U0NUqwqKmqxqw2LgI3pSlSmDZwX2Z9TRFJD8SzeI8nevA+NBp4rkxQwC5hY//GmsZ7sFbEqjHLPwnjElHvq2YaK2v9ACvsrCxenyCl8fybUOOpwQ2wbd7FijyvhfenmVLbt2t4Ttam1T1RCTSmhgtf5021IoQQQs4wTECEEEJCgQmIEEJIKDABEUIICQUmIEIIIaGwZFVwyVggqQUeVafjWGmTbKg+WY6F1S1BQlVHiYj0nIe9yc57/Rwl9n++vw+2bbinYDwyg1VJLeDBJSLSDXzCnARW8bgz2B/veBnP1alXn1BiAymsyJIAe6HVNX2ZKmO1zgjw+Op1sUpxBfCNExHp7sFzmLCwOu6sfrWKpBVgX7Z6bQDHi1MwbkVV77h2EvvJjRw7DOOmprpvfx9W3k256p6Y1FQ+jWdw5VdPc0+cLqtVZT0Dq6Pihq66r6ZibRErQKP9qkKs1sTXjnt47e0kVlJOV4Aqq4mVZ7aP+11M4orKfk1T4dZUlavBRBG21b3vj2o84hKaaq6+o6oAzQzun2PgR33Twsq2TvAMiqdwv6cb6vpUfay6Wwg/ARFCCAkFJiBCCCGhwARECCEkFJiACCGEhMKSFSG00660M/MPTdOexqoipxZVSlUjuG0MH/L6tmpHIiKyaeNlSuzSKw/Btk//4B9gvNKFLUY6TXzo6HapccfFh4t+gMeTSOMD3ea0Kgg4UcMHtKkUthKROj6g7UljG51jU6qwYKqKD1Z7V+CD9aCJ16c7pYoNRES8YEaJJeNYbOHb+DDbd7BQ4HRJPXCeLGEByuzkGIz/VHAhsNdHsKii3lYPi2vNImzb1YHXYSapEQpMqfZUQVVT7M1VBQsiIp05bE9VN/AejwFBiGFhEUu7hffyis5+GO+IqNd5qXoMtpUo3j/ZCLbsqiWx6Kc5q96H7To+4Jc83m+ZLO7LTA3fEytAobpIBAttROOM41uaZyooYBc0se1XCxQvbGn290L4CYgQQkgoMAERQggJBSYgQgghocAERAghJBSYgAghhITC21LB3XPPPbJjxw755Cc/Kffee6+IiDSbTfnUpz4le/bsEcdx5Nprr5Uvf/nLUigUFnVt16+Is0B04dawsi1aU1VZfh6rrGKaAkwyjZU2g0OqrcnmFatg2592YNWYGeB+VxO4j1JTrW7cGlbISAqrcvJRbLtS7lLtO2Y1hdpiBlayJEz8vsXJYMXTai+lxOqxPGwbSeB9EilhW5ziaayOGz+lKvsykVfwtX18jUpxAsYbTXVu2y1cqKzawPKj6deOw/hQHq9bPaHu8YiBFZCtFFZfpSxs0RMJ1L3lNLAy0sthlVUkiVVwmay69iIi2bR6nYiN7bAqk1gF5qXx3vfbRSUWOHgvT5Zw3PLxfov7WAXYjqrrHE/itnYePw8iHh7PaBUX3rsyrVpIpZJYAdms4zm0NJZQnqNaeaVS2BLp5Gk1jdSaby61vOVPQE899ZT8zd/8jZx//vnz4nfccYc88sgj8tBDD8m+fftkdHRUbrjhhrf6MoQQQpYpbykBVatVuemmm+RrX/uadHT8PDuXSiW577775C/+4i/k6quvls2bN8v9998vP/nJT+TAgQNnrNOEEELe/bylBLR9+3b54Ac/KNu2bZsXP3jwoLRarXnx9evXy/DwsOzfvx9ey3EcKZfL834IIYQsfxZ9BrRnzx555pln5KmnnlJ+Nz4+LrFYTPL5/Lx4oVCQ8XH8l8U7d+6UP/mTP1lsNwghhLzLWdQnoJGREfnkJz8pf/d3fyfxOLZjWSw7duyQUqk09zMyMnJGrksIIWRps6hPQAcPHpTJyUm5+OKL52Ke58kPf/hD+eu//mt57LHHxHVdKRaL8z4FTUxMSF+f6lskImLbttg2UH+0OsVozVft+GmNaiyi+n6ZoEiSiEhLU0yt08YKrmaHqtY5Z/UFsG2+8U0Yb5i43/koLvg26asKFC/A44lo/JncDPbJSrbVQlF+AithWh4uKtW2sWKw08NvSqwuVd003I3ViFmNN9XEFFaHjb76AxifOaXOYW8vVmTlYlgd1lXAyq6Z46qabszFyqZcEq9xLolVjYaN17k+85oSKzrYH7BT8F7uxyIziRrqa1qrcL/7MuthPDeE56pWwXs/A4qbnduN1+fVGFb1VTTKtvKM+jX+lIvbukDRKCIiAVakNVyNasxU7xUjgtWlXhnfV7OORo0KisOJiFhDW5RYV6dG0VnXKFojeN+20uAZVMOqvjy4ZaNvMrMsKgFdc8018sILL8yL/e7v/q6sX79e/vAP/1CGhoYkGo3K3r175cYbbxQRkUOHDsmJEydk69ati3kpQgghy5xFJaBMJiPnnnvuvFgqlZKurq65+Mc+9jG58847pbOzU7LZrNx2222ydetWufzyy89crwkhhLzrOePlGL74xS+KaZpy4403zvtDVEIIIeQXedsJ6PHHH5/3//F4XHbt2iW7du16u5cmhBCyjKEXHCGEkFBYshVRo2ZZogvSY+Bh5VQMCD88CytnvBZW6zQs7KtlVdXql0OrV8K2l773X8H4c8+8COPJDFawpaZVxUrdV6uKioj4bawcqgRY3VOpqK9paFRwnolVL5EMVl9V26pyRkREwPr0RrESqLsLj2d86mUYnziNVVZTDXX8kWm8J+qdWPGVrkzCuIsqiwL/PhERt4kVdlXB8QSoRCkikhhU1WeV8ROwbaWO1z4Tx+83+zvVOa8GeE6yeOmlGcGPkkwLe6qdOHJUieV73gfbTtaOwPix1/HfFsaaqodfsqEqZUVEXI1XXzXA+9NvYTVZy1f3YdzHc1jVvO9vV7AKLuLg9j11VY1qBXj/mFnclxhQ3IqItBrqayZMzd6MqG29iKYE68J+valWhBBCyBmGCYgQQkgoMAERQggJBSYgQgghocAERAghJBSWrApuLNqW8gIPqIKJq/q1XTWPGhGNgktT5TNuYg+laBt4cw1UYNuP3Pj/wPg5V14C48eefh7Gf/Lc/1ZijVPYmyqqkSV5gstapC1V3VMPNF5TNTwntRb2gjM6cTXGdfaQEuuOYfVN5TRWz0R7cF9Kb2h87MBeqQT4GvkqVh+NuKdh3Kmo1yk18H6rtfAt5vlFGO8exCpNq6720YrgtrZm788cwqq+FZs2KbFCAq9lvI4rojpuEcZn/TEYbzbUyrejJ3AV1qkpvJcbbXztqZKq9HTL+J5t1fAzJWJi1Zgdx/vWB16NvqGpTFvCnomngapNRGTzBuzr6HbklVh5VqM+y2J/QEvznGiL+rypxbAyMFlWVZdeVeOxtwB+AiKEEBIKTECEEEJCgQmIEEJIKDABEUIICYUlK0JYl8xIJjnfrmSyjg8Mk8CiJ2pobC2CThivxbEVTzKmWvF0RbEgoLN7BYwnTHzt3veeBePHjqmH9m4bHyB3pfDBpWjOAI+Dwmn1Nj64TCXwgWtbUwis6alzJSJip9X3ORVNcauyge2J0gG2YYpb+OC6FM0rsainOaDVVPctVvBB9NSsKk7w2ljgYMZUWxgRkVgW76GRl9TCcyIiZpc6/loZr33fitUw3rNyGMabs+r+nBjF9jdrC++B8Y4oLrBXTeMD9EsuUA/tXz6FhQJHXsc2TPUK3uRWoI7H19hEtX38HtzT2M5YmqKLaVO9jit4v5UymucHdmeSNWsvgvFsryoUSWTxayZ8PB4ngfdtPAKeWR4uluim1fu+FeBnxEL4CYgQQkgoMAERQggJBSYgQgghocAERAghJBSYgAghhITCklXBlUuuBP58lUvCwSo4Sas2E66pGVobq6wi01iVVY+oqrGuGFaazBo4Pj42AuMHn3wWxtuOqpyKtLCSrgpsiEREXBfPVSyqWvekBRd1K9Vw3Nco0qoNbF1TqajXMXrx+vRrCgZ63dgaJrcBhsU9qfbldIBVekYVFzaLatbTs9Q90W5i1U8q1YtfM4qvXa9gJZg3ocanfWyN0prQFDwDe1lExGyr1zn0yuuw7diKIoxf3o8teho958O4l1T3UF6w8i5SxorWqWlsI5OKq3vLM/B+S9n4eRD18Xq2q9jSxrTVPd7X3wfb9jWxerEYbITxj1xyGYznLPU6bcHWXBVbo5bF203MrCrJC5wibBvU1fsqaOB7TXmdN9WKEEIIOcMwARFCCAkFJiBCCCGhwARECCEkFJiACCGEhMKSVcFZliOWNV+J0jaxF1HMAkoWLG6RtonVIG4fVhRFZlQ/sHHNtMVsfI1jR0/A+Esv/BjGm67ax0lNQa3IbBHGo1msVJOi6gllJLE6Kp/E/l6VKla7SQsrvqpAvZiu4ralLo031xtY8TTcrVE39aoKpCNTz8C25ckijKct7AWXAAXfjDhWgcWT2K/M13j1lTUFwuym6kFWd7G/V6KN40EGx5tN9TXjnSnYdvoYVgx+Y3QUxnMTT8F47zpVHWflsepytqZ5n2zge7leUfeEr7EBTMQ7YNyJYc/ITAbv/USHqhpLR7CS7lQV+8lddAn28OsunAvjdkR9yNVsPNBcBc9howN7wXWDZ1Azi58Trbp6n7QMfO8shJ+ACCGEhAITECGEkFBgAiKEEBIKTECEEEJCgQmIEEJIKCxZFVzN7BUjMl9ZEhFc/TLiqYqLwMfebpEIVvcky9hrzcmo1446+NqlxgyM+zZuf+w09rhqgkqPsZSmQqOBVTytJn7NeE9eiSUFq3VqLq4iOTyIq1z6vnptEZFoAijvOvBa5lK4amfuQqyqiTTx+KsVtbKo6WIVpZ3UVG808F7xPVXV18poqvWm8C1mO1imWXXxunktdQ5TBl6fVAz7gaU78zA+A4RdScFKrZe8afya+PaRY1msbDs58rwSs0uaOSlidWk0itezkQBrofH1szT3ZjyK160rNohfM6auz6zGfm1wDd5X1227Hsa7V+D9mUiryrsWUK+JiHg5vCcMB89LOa6uRYAFmhKx1PlGMQQ/ARFCCAkFJiBCCCGhwARECCEkFJiACCGEhMKSFSFUamXxF6THDhPbRtRj6oFXSnDbto8Px6oWPtDsBId0M60x2LYRYKFAZUYtMCciEszg6feApY/vaw6ck/g1vQBf28qodhqJAB9QBppCYK1JfHCZKqiHoiIifRn10HVoYBNsW5/GlkOtFLYFajWwNUytMqXE4i18gO438EF0K6qxtImrc7gmvwK2rftYmIKKwImIxBqTMO7Y6nqaCWyNkkzjfmtcZyTTuVaJdZzEczXZi9dn9PVX8cXj+AA9Ciyx3AZe41QGCyKkWsRx01ZCvqvGREQkgp8TpoPnttGL934bLHN/L7Zn+n8/8JswfvHGARiPWLgInmEUlVhdI8rJaYRGCY1FkQVSQyOGn5F+Xf0c47dpxUMIIWQJwwRECCEkFJiACCGEhAITECGEkFBgAiKEEBIKS1YFZ8lpiS5QbtRNrNZKiao2qbaxvKMDC0pEIthnolZWrxPTKEp8jYIrHsVqmOG1WDU2cky9ftnGY29pVjAlWGXlVFV1TzKFFStODCvv7BmsHCoBNaKISNRUJ3321YOwba5rA4yXDz8L44eOHoXxmRnVisdYKKv8v9Q8bAGTjuJ4Z0tVZVUCrJrKAJsoERHXwMq7iqYIXttV1X5GA1u6TMWLMH72FFbHdXSq6zmrKVRWqOZhfCaPVWZNjeo0ANuzrikW2a/Z+7HOAn7N8aoSq7bxfLt1PCddQ9hu6qyCpgjgmrwSu2TTZth227/eAuO1BC4iGdG42hSB0qwLL5sEJr5IzcrDeB7cExlNAcAysN0JLE3FxQXwExAhhJBQYAIihBASCkxAhBBCQoEJiBBCSCgwARFCCAmFRang/viP/1j+5E/+ZF7snHPOkVdf/ZkPVLPZlE996lOyZ88ecRxHrr32Wvnyl78shQJWq/wynNOmWI35+dHW+TABxVfWxG2dGlZ2uQHOxaaoShPH74Rtuzuwgqlw0a/BuHXoCIzLMVXxFFSxAmW2jRVCvokrhBlAxBMb1BTva+HxjLewYjA/iuf8QFlVxKw6uwe2Haj9FMZni/jateZJGC+XgOddRuPt1sKqxpZG2TYeUfuyXrDKKNuJ1VSeg/0Bjx/T+OxZquKt2MB7onoSe/iNFLCHYW9SvU5jHF8jauK9kvKwvNSr4rmti3odI43vwUYbz21Msw9bTVVdGsnjtVzbiT38tl50NoyvWbkOxqsRdW6HCvjaksZ9SbtYuepG8Dq3A1Xy1k7g51vbw3Mb1Vi2tUDRRcvX+OZZ6nhQDP7bN9XqF9i0aZOMjY3N/fzoRz+a+90dd9whjzzyiDz00EOyb98+GR0dlRtuuGGxL0EIIeRXgEX/HZBlWdLX16fES6WS3HffffLggw/K1VdfLSIi999/v2zYsEEOHDggl19+Obye4zjiOD/PtuWypu4rIYSQZcWiPwEdPnxYBgYGZM2aNXLTTTfJiRMnRETk4MGD0mq1ZNu2bXNt169fL8PDw7J//37t9Xbu3Cm5XG7uZ2ho6C0MgxBCyLuNRSWgLVu2yAMPPCCPPvqo7N69W44ePSrvfe97pVKpyPj4uMRiMcnn8/P+TaFQkPFxXLNFRGTHjh1SKpXmfkZGRt7SQAghhLy7WNRXcNddd93cf59//vmyZcsWWblypXzjG9+QRELncfPLsW1bbFtTLIoQQsiy5W15weXzeTn77LPlyJEj8v73v19c15VisTjvU9DExAQ8M/pnr91hSTozv3tlD3tfRQzVt2iyjn3Z7AiWfVhpfO2MpSpTGk1coTG3BoblCjsP4wd68by8GgcVHQ3Nh1UDq6aqGhVKvDWqxCZKWKnVNrA3lbSwKqdh4u1U99XqmsfGcMXNaN95MH56ZhbGvShWASZ61TdETfc0bJtO4TdPSY2SslFXx5+O4fW5ZEM3jD/zND7rtHzsP1erqwqkio+VjpEm9sd77Q2sPHSqqndapYLvn6CJ11inebLy2O9Q2uo4gwZWzNVy2FcsXccei82Y2puhblzNc+MVWKG65Qp8b3Zq1GR+51VKzIzhfTUxgdcta+A+uhZWnyWBINFIaTzYTE2/A9zei6jPONPVrHIDqPdQDHXrTbXSUK1W5fXXX5f+/n7ZvHmzRKNR2bt379zvDx06JCdOnJCtW7e+nZchhBCyDFnUJ6D/9J/+k3z4wx+WlStXyujoqNx1110SiUTkt3/7tyWXy8nHPvYxufPOO6Wzs1Oy2azcdtttsnXrVq0CjhBCyK8ui0pAJ0+elN/+7d+W6elp6enpkSuvvFIOHDggPT0/+2j/xS9+UUzTlBtvvHHeH6ISQgghC1lUAtqzZ88v/X08Hpddu3bJrl273lanCCGELH/oBUcIISQUlmxF1MCMSGDOV13k21g9IlHVmy2RzsGmbVSKUUTcJlas1ERVTqU11/BmsWoqY2L1yLk92FPupR5VBTd5sgjbtjJYvRevaVQoaVXBFY9jhVlZ8jDud6iqKRERo1KHca+lKtVmXexZ9dTUizCey2IPst40Vg6lMuprOpMzsK07g73Gmll8e/REVimxSy4fhG3P6TsXxstTeC+/+Bqel0hZHb+V64Vtxcdqv/rkFIwfqah96UxjhVm9XYRxw9IoQ23sKZfqVBV5DRfPycTEKRi3snkYv3C9el8VVp8D2/76+9fDeCGD1XteHKsuO0AfW7bmfnCxN6aVwfsw1cbXqQDft2odPw8sX1MRNo6fTVkpqm2BH6GISDqmPlMCEEPwExAhhJBQYAIihBASCkxAhBBCQoEJiBBCSCgsWRFCJtsrmez8g8BaEdtGGBFQ3KqKD/TMLM65bcHWI9JUrVR8A1uGeD4+5J3xcV+6+/AhctNaqcQqvmqhIyJSPaHpdycWRESr6viLGpsOx8EHyFLG46zZ2HbGdNT2gY9tYYIcPoRfvQYLNhwH25TEfXWNIlGNHQnWMYitsV257D1q3zcNXIivrSl2l+zBvk3eDH7Ncl09oPc9bNvjavqdSuAieFVQYDFh4XvNTWBxTyGFD6gNweIeK6res56PD/6HNMXk+lZhu5xCQl2fc8/eDNvOnsRzOHAeHudAAwtwZpLqHMYcfBCfTmNBQBtvfWnE8S/surrOySguuliMYQuyrKYIXtVW5zzaxHs5EFU0FYjGEmgB/ARECCEkFJiACCGEhAITECGEkFBgAiKEEBIKTECEEEJCYcmq4AKvLkF7fvfiEVzEzLA7lFg1hqVN8QZWprQTWGkTBQq7Zh2r2iSJlWd2BMcTLlaVXNarqngeOYyVMFYKW6C0NQXSAlDAzi5iKx4npikmZg/juHsShv0qqHibmsTXcPC6HR3F65PxsdomBVRj5ShWAnXHsOJpywVYOXXu+65RYmlgXSIiUvewMvDsPqwa61mJ+zJ9XFUk+hGN7UoSW9oYbVxg0Iqp+7nl4SrFfhvv2dkGVmOWNQX2shl13/b7eDytLFY62uPYcqj3rAElds5KrJjrTeH91j42BuMnM7iQYAnchn01zfrk8fMjHcVzlTKwkjCw1Hu5gQV20qmxvmqk8T2RBIUHfRs/O52k2tZ9k6mFn4AIIYSEAhMQIYSQUGACIoQQEgpMQIQQQkKBCYgQQkgoLFkVnNVoi2XN9zWqaPymIqAIk+ljtUolitVUaeCrJCLStlTvJwtfQtpAjSciEteodQZ7cBGz/jVHlFjulbWwbaRchPFpD6vDvEBVzjR9rJqKNLASytMUt7Iz/TDuJFX1jCFYlRMEeEs2ZnFRsmwCq5vK4DJBHauMJqu44Fe8B69nT0ItStbQ+P0lIngdEoKVhIMrz4PxkWmgVCxj3y/XwHvZNXH7aKBKuGwDe4QFDXxfnYrhPWRG8T0bnVbVVycCfI1UVGOS1oH34bSlrudgHvfD78ZKz64KXk87pfo0iojEY+p95Rj4GisiWI3YdPH6RAI852ZLXbe2xmeuZGr85AxNkU+kgAWqOxGRoKj2L9B4cS6En4AIIYSEAhMQIYSQUGACIoQQEgpMQIQQQkKBCYgQQkgoLFkV3Lgdlao93wMqX9J0N60qLowq9o8y4vgaNY3HFRKTJQ3snxQxsZoqmcDKlOjWc2B8BfD96s88Bds+X8T9jmsKEraAGMgVPFe6yqeWkYdxR7AvXaSqquC8TuzBZXm4Cmvcw32cLWKFoRFVr59t47VPpPAcDtorYDybVt+35bqwb5wzifuX9PAeOveidTD+/LNvqNe2cJVcjcWgpDo1KqumOrfNKPb9iup8yeoaEzITzzmwGpNsHCvvki3sM3f0ML7fLj/7YiU2Ooa93VZ3au5NswDjnqXpo6sqxHoDvMd9wWtfA1ViRURyWHwmdVtVqcY1nyk8A6+n+Nh7sA6ecZaLPSOTou4rD8QQ/ARECCEkFJiACCGEhAITECGEkFBgAiKEEBIKTECEEEJCYcmq4FYkTMkk5+fHksaDLLDUYcSTOLe2AuxRZMawgsv0VA+lQKOCm53F09np4b50NrCiaOOlZymxs4//OmxbaeyB8dOaSqHtiKp6KTewuiWq8wPTqJWsCFYlNRPAg83DHlRtyeN4C6vg3BY25ov7qoefl8KKnw3rVdWUiMi6jZtgvJlWX7OjitVuHVn8msl0HsZ7qufC+PoLX1Bi+3+Kq8omZvH6tKt43yYs9Z5oa1SkEcG+ZPmo5v7R7KFaRVVljbvYI23Sw7K+C4bV+0REpH/9tUps7eBq2Dbhq75+IiIJwXM7rVGXRprqc6IG1JIiInYEx7taWO5WsvHcOqZ6D3W5+JlitvE1XE2l1K4EuH8c/PydCtS5rWv865R+valWhBBCyBmGCYgQQkgoMAERQggJBSYgQgghobBkRQiOE5dYc8HBWQQfRpq+OoxITFN8y8VF1to+zsVNW72O6eGD1XQHPuSVFH5No4TtMXrW9CixD267ALY9euRRGG8ewTY6s4Hax5Xd+ODyWA33u7eKD0uLs3j8VlQ96AVOOT9rm8Sigmwdv+aMqIelIiJdCfVAu2NVHrb9V5svhPF167phvKOgrk+0Pg3blixNMbU2FsOc1XM2jNcvv0aJvXL0MGxbdHSviQ+Gi6BIY8bBe7xu4oPoSAPfm00Tixn8jLrn0hrxRH8B7/3f+Q+3wfj7rlyvxDwLFyOMlLH10xsOHk9WU7jS6lGfQU4Dr0Muh+OO5qZIm7gvhqvOrRPDc5gysQghaOP2Xlt9Npl5fA2ZBVZJEWyfpFzzTbUihBBCzjBMQIQQQkKBCYgQQkgoMAERQggJBSYgQgghobBkVXDSLom05yuf0hoLCxeoQdoWVutkDKyGacawMsVoquorK4rVXoaLFVxtjYWQn8N9yQNxXKwbW4mctfJCGH9jZD+Mv2eVWmjLjWMLlLWaYlWisRhxxrpgfNZU1VfN8gxsW8lhO59CDlumNLGYTBKWqmC7bP35sO2ai7HyLBHDt0eloirvOgysGPTbeI2tGlYfFbqx8u79Gy9VYntXYBum8VHsF1PxsJIw6ql9jLTweBxNQbrCALbF8fJ4r1QbqtWNaWVh2/e//yoY3zKA7wnDV58HGRdbP7lZvPfzE1hF2ihgVV8iMqDEYi62ZyrV8J6I54sw3qpjdZzrqfvTCvDamxa+r6wYHk8jllf752iKPxrqc88w8Osp/XpTrQghhJAzDBMQIYSQUGACIoQQEgpMQIQQQkJh0Qno1KlT8ju/8zvS1dUliURCzjvvPHn66afnfh8EgXz2s5+V/v5+SSQSsm3bNjl8GFuGEEII+dVlUSq42dlZueKKK+R973uffPe735Wenh45fPiwdHR0zLX5/Oc/L1/60pfk61//uqxevVo+85nPyLXXXisvv/yyxOMaLyGAZ8XFW1Dkqu3gfJlKqAoXR5tbcTyhUcF5TVUlEmiKbHkW9j+KaYqmZetYZdZKqjK4zngetr10zTYYX5tX/cpERDLplBI758I1sG2fpkjfiWnss6crHPbysQk19jz2Tqv5r8J4LoNf8/zkZTBud6xSYu89V1UqiYictXYIxi0Lr1uspa5/WVPYzDPwvmpGx2A8WdV4Ffar+/a9694H21ZO/28Yf/VlvCdc4AcWX4WVm1EXX6NnNZ6rVAz7uPUm1Xs234PVXh++SlUAiojkN2IFW2tC3YdtjRdc8eQpGJdsBw63sAowG1WvPx3Hqr44KtAoIrG6xkvRw/FWUlU1VjTXSKex76Rj4r4kQdHJWIDTRbKqPsc8TRHOhSwqAf35n/+5DA0Nyf333z8XW73651LIIAjk3nvvlT/6oz+S66+/XkRE/vZv/1YKhYJ885vflN/6rd9azMsRQghZxizqK7hvf/vbcskll8hv/MZvSG9vr1x00UXyta99be73R48elfHxcdm27efvynO5nGzZskX278d/l+I4jpTL5Xk/hBBClj+LSkBvvPGG7N69W9atWyePPfaY3HLLLfL7v//78vWvf11ERMbHx0VEpFCY/8eOhUJh7ncL2blzp+RyubmfoSH8dQghhJDlxaISkO/7cvHFF8vnPvc5ueiii+TjH/+4/N7v/Z585Stfecsd2LFjh5RKpbmfkZGRt3wtQggh7x4WlYD6+/tl48aN82IbNmyQEydOiIhIX1+fiIhMTMw/dJ6YmJj73UJs25ZsNjvvhxBCyPJnUSKEK664Qg4dOjQv9tprr8nKlStF5GeChL6+Ptm7d69ceOGFIiJSLpfliSeekFtuuWVRHRuPelJZoCxZ6WE/J6OiJi0rgVUfro3VOskWVo/YaTXuYuGIdFaxUqsZw9duJPD0B56qhBrM42v0fAD7mJVqeRjv6h5WYmmNF9xMBc/hCtxccsbrMJ7JqhUqV3Q9Dtu+/CRW3p0GvlciIkMDqrediEh+VafadpPaDxERv4Hfh0WBykhExAvU9bGj2PsqGWgqaKbwtVOZIoyX06pCbP37sAJw/yvP4NfMYP+9aEJVb/abeL7NYTyehlGE8U0FvIfstKqc+rXCStg23oP3vlnE44nF1Bu0jAWaYtjYC60rheNRW6NSjKivmY9jBWTEx+OJxLHCruXiObREvX4hidfN0/hR2oLHGfFUT7l2Fa/9RF1tW2tgT7qFLCoB3XHHHfKe97xHPve5z8lv/uZvypNPPilf/epX5atf/aqIiBiGIbfffrv86Z/+qaxbt25Ohj0wMCAf+chHFvNShBBCljmLSkCXXnqpPPzww7Jjxw65++67ZfXq1XLvvffKTTfdNNfmD/7gD6RWq8nHP/5xKRaLcuWVV8qjjz66qL8BIoQQsvxZdDmGD33oQ/KhD31I+3vDMOTuu++Wu++++211jBBCyPKGXnCEEEJCYckWpOsrTknGm19tzIoMwrbNjHqQ5oFCciIiorF0yWpOKaNRVbQwq7l0NYW/Zoy08T/otfHB9Sw4c8xH8EFkU1OUq9PohXHHV8fja4pYxSKa7ZHCVkT55loYz2ZVm5qItQm2rftYBdnjqUXgRES6C9heJw8OorvjeL6zcVxIr9XG65mz1PlyWpqD4gDby3gW7rfb0vwhdlUdz+aVq2BT5/2/DuPpnqdh/PisOp7zLaxaDXqwfVRz6iUYL/Rg656zcqqSpePsi2Bby8IH/00X31fdQOBha8Qgvk4gpBG9RAz8mglTfX60NM+JiK3aYYmIWI1ZGDeA6EVEpDOm3of1NhYV2Jo5NJwSjFfbah/763gOT9jq/W26WIChtHtTrQghhJAzDBMQIYSQUGACIoQQEgpMQIQQQkKBCYgQQkgoLFkV3L74qCTi85UYV8Zx0a9sTFXmNOLYL6dpY2VTpoXjpbSqNPE0hdqcNlbSZQNsseE0sC1QtFN9X1AqY0lNI48Lu/n1PIzb7mkl1oxgpVIQ1djF5HA8PovHfzqhzld/E9uuOA5W2DUTeRhPtrE6rmmr1iMVWQHbJmys2IlaWNl2PKcqKVMeXmPPwXvWdHG/EzmsVnKBC5W5Ar9m3zUbYfzqHLZjiVmq8nBmALvS57yXYXyo/gEYP5nCysi18W61H114X6UDjcWViT2hmqDIWs3Ca5wUrFI0crp7HCvVzECdQ6+OH6/tHH42GQYugheksUp1rKIq+DIJbE80EWClZzKKVY05R3021fvxfhMHvKaJi3Mqzd5UK0IIIeQMwwRECCEkFJiACCGEhAITECGEkFBYciKEIPjZQWSjotqmVF18sGXE1MPVqmgOs11sVVHRnJlVIup1/Bg+FNSJEAzNIarfwH1sAduQtkaE0Izgjvsa2wwXzGHLxAffgcZ2xI3heKuCx19rqQfAXhX3u17HNZ8cX7NV25r2bXVP1CrY5qaCz+YlqqmhUjXUdQs01i2eg+fKbGERQlvjYNICLkItE++rWgVfu1HHVkSepfa9WcXXiPn4GrU63ssNwdeptdU954L7WEREAizWaQZ4bgNgw1QHe1BExNc8Ah1DU8/Gw/sWuW35NXxtI4lFCK0GtgUKfNyXBtq4Fu5fNcDX9gw8L4arzrnp4WdK1VFfs/Z/H6iBZo3mXif451r8C3Py5EkZGsIKHEIIIe8eRkZGZHAQe3iKLMEE5Pu+jI6OSiaTkUqlIkNDQzIyMrKsS3WXy2WOc5nwqzBGEY5zuXGmxxkEgVQqFRkYGBDT1J/0LLmv4EzTnMuYhvGzj5jZbHZZL/4/wXEuH34VxijCcS43zuQ4czn8N0a/CEUIhBBCQoEJiBBCSCgs6QRk27bcddddYttYpbVc4DiXD78KYxThOJcbYY1zyYkQCCGE/GqwpD8BEUIIWb4wARFCCAkFJiBCCCGhwARECCEkFJiACCGEhMKSTkC7du2SVatWSTwely1btsiTTz4ZdpfeFj/84Q/lwx/+sAwMDIhhGPLNb35z3u+DIJDPfvaz0t/fL4lEQrZt2yaHDx8Op7NvkZ07d8qll14qmUxGent75SMf+YgcOnRoXptmsynbt2+Xrq4uSafTcuONN8rExERIPX5r7N69W84///y5vxzfunWrfPe73537/XIY40LuueceMQxDbr/99rnYchjnH//xH4thGPN+1q9fP/f75TDGf+LUqVPyO7/zO9LV1SWJRELOO+88efrpp+d+/y/9DFqyCeh//s//KXfeeafcdddd8swzz8gFF1wg1157rUxOTobdtbdMrVaTCy64QHbt2gV///nPf16+9KUvyVe+8hV54oknJJVKybXXXivNJnaZXors27dPtm/fLgcOHJDvfe970mq15AMf+IDUaj93rb7jjjvkkUcekYceekj27dsno6OjcsMNN4TY68UzODgo99xzjxw8eFCefvppufrqq+X666+Xl156SUSWxxh/kaeeekr+5m/+Rs4///x58eUyzk2bNsnY2Njcz49+9KO53y2XMc7OzsoVV1wh0WhUvvvd78rLL78s/+W//Bfp6Ph5KfB/8WdQsES57LLLgu3bt8/9v+d5wcDAQLBz584Qe3XmEJHg4Ycfnvt/3/eDvr6+4Atf+MJcrFgsBrZtB//jf/yPEHp4ZpicnAxEJNi3b18QBD8bUzQaDR566KG5Nq+88kogIsH+/fvD6uYZoaOjI/iv//W/LrsxViqVYN26dcH3vve94Nd//deDT37yk0EQLJ+1vOuuu4ILLrgA/m65jDEIguAP//APgyuvvFL7+zCeQUvyE5DrunLw4EHZtm3bXMw0Tdm2bZvs378/xJ69cxw9elTGx8fnjTmXy8mWLVve1WMulUoiItLZ2SkiIgcPHpRWqzVvnOvXr5fh4eF37Tg9z5M9e/ZIrVaTrVu3Lrsxbt++XT74wQ/OG4/I8lrLw4cPy8DAgKxZs0ZuuukmOXHihIgsrzF++9vflksuuUR+4zd+Q3p7e+Wiiy6Sr33ta3O/D+MZtCQT0NTUlHieJ4VCYV68UCjI+Ph4SL16Z/mncS2nMfu+L7fffrtcccUVcu6554rIz8YZi8Ukn8/Pa/tuHOcLL7wg6XRabNuWT3ziE/Lwww/Lxo0bl9UY9+zZI88884zs3LlT+d1yGeeWLVvkgQcekEcffVR2794tR48elfe+971SqVSWzRhFRN544w3ZvXu3rFu3Th577DG55ZZb5Pd///fl61//uoiE8wxacuUYyPJh+/bt8uKLL877Pn05cc4558hzzz0npVJJ/tf/+l9y8803y759+8Lu1hljZGREPvnJT8r3vvc9icfjYXfnHeO6666b++/zzz9ftmzZIitXrpRvfOMbkkgkQuzZmcX3fbnkkkvkc5/7nIiIXHTRRfLiiy/KV77yFbn55ptD6dOS/ATU3d0tkUhEUZpMTExIX19fSL16Z/mncS2XMd96663yne98R37wgx/Mq4jY19cnrutKsVic1/7dOM5YLCZnnXWWbN68WXbu3CkXXHCB/OVf/uWyGePBgwdlcnJSLr74YrEsSyzLkn379smXvvQlsSxLCoXCshjnQvL5vJx99tly5MiRZbOWIiL9/f2ycePGebENGzbMfd0YxjNoSSagWCwmmzdvlr17987FfN+XvXv3ytatW0Ps2TvH6tWrpa+vb96Yy+WyPPHEE++qMQdBILfeeqs8/PDD8v3vf19Wr1497/ebN2+WaDQ6b5yHDh2SEydOvKvGifB9XxzHWTZjvOaaa+SFF16Q5557bu7nkksukZtuumnuv5fDOBdSrVbl9ddfl/7+/mWzliIiV1xxhfInEa+99pqsXLlSREJ6Br0j0oYzwJ49ewLbtoMHHnggePnll4OPf/zjQT6fD8bHx8Pu2lumUqkEzz77bPDss88GIhL8xV/8RfDss88Gx48fD4IgCO65554gn88H3/rWt4Lnn38+uP7664PVq1cHjUYj5J6/eW655ZYgl8sFjz/+eDA2Njb3U6/X59p84hOfCIaHh4Pvf//7wdNPPx1s3bo12Lp1a4i9Xjyf/vSng3379gVHjx4Nnn/++eDTn/50YBhG8A//8A9BECyPMSJ+UQUXBMtjnJ/61KeCxx9/PDh69Gjw4x//ONi2bVvQ3d0dTE5OBkGwPMYYBEHw5JNPBpZlBX/2Z38WHD58OPi7v/u7IJlMBv/9v//3uTb/0s+gJZuAgiAI/uqv/ioYHh4OYrFYcNlllwUHDhwIu0tvix/84AeBiCg/N998cxAEP5NBfuYznwkKhUJg23ZwzTXXBIcOHQq304sEjU9Egvvvv3+uTaPRCP7jf/yPQUdHR5BMJoN/82/+TTA2NhZep98C//7f//tg5cqVQSwWC3p6eoJrrrlmLvkEwfIYI2JhAloO4/zoRz8a9Pf3B7FYLFixYkXw0Y9+NDhy5Mjc75fDGP+JRx55JDj33HMD27aD9evXB1/96lfn/f5f+hnEekCEEEJCYUmeARFCCFn+MAERQggJBSYgQgghocAERAghJBSYgAghhIQCExAhhJBQYAIihBASCkxAhBBCQoEJiBBCSCgwARFCCAkFJiBCCCGh8P8D7TZA6ZM/3zwAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(images[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "A1Qch3Mw-8Fd",
        "outputId": "7291fc2e-c5a1-4ec3-f249-0f83e2eb3f9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x783cf058d090>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(images[2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "LP0ltgEt_rSL",
        "outputId": "103fc813-1eb1-4c8f-d92c-929b61ce3bee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x783cf058c4c0>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(images[3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "OxAWOG_O_r-9",
        "outputId": "f0a7d89c-d53a-4b4e-8905-b230297a38de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x783c91638640>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "images[0].save(\"bf1.png\")"
      ],
      "metadata": {
        "id": "ytJCj5aQ_sq9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for idx, img in enumerate(images):\n",
        "    img.save(f\"bf-{idx}.png\")"
      ],
      "metadata": {
        "id": "ULQGRgld_2wu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JejLixsL__KY"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "50efa27876e7412c954c36d95c2f9e2d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
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