{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/amoux/206bd69f2f9cb6d544ddf05947f2b08e/solving-fizzbuzz-with-simple-net.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6cdb6210",
      "metadata": {
        "id": "6cdb6210"
      },
      "outputs": [],
      "source": [
        "from tqdm.auto import tqdm\n",
        "from collections import namedtuple\n",
        "import numpy as np\n",
        "import nltk\n",
        "\n",
        "import ego\n",
        "import ego.nn as nn\n",
        "import ego.nn.functional as F\n",
        "import ego.data.fizzbuzz as fizzbuzz"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eeee1600",
      "metadata": {
        "id": "eeee1600"
      },
      "outputs": [],
      "source": [
        "Batch = namedtuple('Batch', ['inputs', 'target'])\n",
        "\n",
        "def collator_wrapper(batch) -> Batch:\n",
        "    inputs, target = map(ego.stack, zip(*batch))\n",
        "    return Batch(inputs, target)\n",
        "\n",
        "\n",
        "x_train, y_train = fizzbuzz.load_data(101, 1023)\n",
        "y_train = y_train.squeeze()\n",
        "\n",
        "dataset = ego.utils.data.TensorDataset(x_train, y_train)\n",
        "dataloader = ego.utils.data.DataLoader(\n",
        "    dataset, batch_size=64, shuffle=True, collate_fn=collator_wrapper)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4d22d614",
      "metadata": {
        "id": "4d22d614",
        "outputId": "6a405acc-7946-42a4-d520-ab0ca1b3d7b7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "number of parameters: 3,304\n"
          ]
        }
      ],
      "source": [
        "in_features = 10\n",
        "out_features = 4\n",
        "hidden_size = 50\n",
        "\n",
        "model = nn.Sequential(\n",
        "    nn.Linear(in_features, hidden_size),\n",
        "    nn.Linear(hidden_size, hidden_size),\n",
        "    nn.Tanh(),\n",
        "    nn.Linear(hidden_size, out_features),\n",
        ")\n",
        "model.apply(fizzbuzz.init_weights)\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = ego.optim.Adam(model.parameters(), lr=0.01)\n",
        "\n",
        "print(f\"number of parameters: {model.num_parameters():,}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "84ebe5d1",
      "metadata": {
        "scrolled": false,
        "colab": {
          "referenced_widgets": [
            ""
          ]
        },
        "id": "84ebe5d1",
        "outputId": "0c86fc45-cd34-45aa-e75d-d23dd73ad66f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/200 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\t[epoch: 20]\tavg-loss: 0.00807\tavg-acc: 85.5 %\n",
            "\t[epoch: 40]\tavg-loss: 0.00165\tavg-acc: 98.8 %\n",
            "\t[epoch: 60]\tavg-loss: 0.00071\tavg-acc: 99.3 %\n",
            "\t[epoch: 80]\tavg-loss: 0.00044\tavg-acc: 99.8 %\n",
            "\t[epoch: 100]\tavg-loss: 0.00031\tavg-acc: 99.8 %\n",
            "\t[epoch: 120]\tavg-loss: 0.00022\tavg-acc: 99.9 %\n",
            "\t[epoch: 140]\tavg-loss: 0.00019\tavg-acc: 99.9 %\n",
            "\t[epoch: 160]\tavg-loss: 0.00014\tavg-acc: 100.0 %\n",
            "\t[epoch: 180]\tavg-loss: 0.00012\tavg-acc: 100.0 %\n",
            "\t[epoch: 200]\tavg-loss: 0.00010\tavg-acc: 100.0 %\n"
          ]
        }
      ],
      "source": [
        "steps = 200\n",
        "grad_max_norm = 0.5\n",
        "grad_accumulation_steps = 2\n",
        "epochs = tqdm(range(1, steps + 1), leave=False)\n",
        "for epoch in epochs:\n",
        "    optimizer.zero_grad(set_to_none=True)\n",
        "    total, total_loss, total_acc = 0, 0, 0\n",
        "    for step, (inputs, targets) in enumerate(dataloader):\n",
        "        output = model(inputs)\n",
        "        loss = criterion(output, targets)\n",
        "        (loss / grad_accumulation_steps).backward()\n",
        "        loss.detach_()\n",
        "        output.detach_()\n",
        "        nn.utils.clip_grad_norm_(model.parameters(), grad_max_norm)\n",
        "        if (step + 1) % grad_accumulation_steps == 0:\n",
        "            optimizer.step()\n",
        "            optimizer.zero_grad(set_to_none=True)\n",
        "        n_pred = (output.argmax(-1) == targets.view(-1)).sum().item()\n",
        "        N = targets.size(0)\n",
        "        loss = loss.item()\n",
        "        epochs.set_description(\n",
        "            f'[{epoch}/{steps}] loss: {loss:.3f} acc: {100*n_pred/N:.1f}% ')\n",
        "        total += N\n",
        "        total_loss += loss\n",
        "        total_acc += n_pred\n",
        "    if epoch % 20 == 0:\n",
        "        avg_loss, avg_acc = (total_loss / total), (100*(total_acc/total))\n",
        "        print(f\"\\t[epoch: {epoch}]\\tavg-loss: {avg_loss:.5f}\\tavg-acc: {avg_acc:.1f} %\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f2b5cadb",
      "metadata": {
        "id": "f2b5cadb",
        "outputId": "8ea45ea0-d818-4e97-8149-f39b0b83ebd6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "test accuracy: 99.01%\n"
          ]
        }
      ],
      "source": [
        "(x_test, y_test), test_digits = fizzbuzz.load_data(split=\"test\", return_digits=True)\n",
        "\n",
        "with ego.no_grad():\n",
        "    logits = model(x_test)\n",
        "scores, preds = logits.softmax(-1).topk(1)\n",
        "total_correct = (preds.view(-1) == y_test.view(-1)).sum().item()\n",
        "test_accuracy = 100 * total_correct / len(test_digits)\n",
        "print(f\"test accuracy: {test_accuracy:.2f}%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f48813b0",
      "metadata": {
        "id": "f48813b0",
        "outputId": "854a5472-77de-4d98-d200-1ef5e3516e59"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([68])\n",
            "tensor([0.8298])\n"
          ]
        }
      ],
      "source": [
        "mask = preds.view(-1).ne(y_test.view(-1))\n",
        "all_incorrect = test_digits[mask]\n",
        "print(all_incorrect)\n",
        "print(scores[mask].flatten())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b299ebbb",
      "metadata": {
        "id": "b299ebbb",
        "outputId": "f54f8345-c816-44bd-c3d9-982a4d83f142"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,  15,\n",
              "         16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,  29,  30,  31,\n",
              "         32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,  43,  44,  45,  46,  47,\n",
              "         48,  49,  50,  51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,\n",
              "         64,  65,  66,  67,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,\n",
              "         81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,\n",
              "         97,  98,  99, 100])"
            ]
          },
          "execution_count": 116,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "all_correct = test_digits[~mask]\n",
        "all_correct"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "46f412e6",
      "metadata": {
        "scrolled": true,
        "id": "46f412e6",
        "outputId": "92d09be1-12fc-47a4-93bd-010e5581a47d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([0.9957, 0.9993, 1.0000, 0.9942, 0.9995, 0.9999, 0.9999, 1.0000, 0.9954, 0.9975,\n",
              "        0.9999, 0.9995, 1.0000, 0.9915, 0.9996, 0.9994, 0.9999, 1.0000, 0.9997, 0.9975,\n",
              "        0.9994, 0.9889, 0.9997, 0.9815, 0.9999, 0.9991, 0.9984, 0.9998, 0.9991, 0.9985,\n",
              "        0.9988, 0.9999, 1.0000, 1.0000, 0.9982, 0.9999, 0.9949, 1.0000, 0.9994, 0.9997,\n",
              "        0.9989, 0.9989, 0.9998, 0.9933, 0.9997, 0.9991, 0.9999, 0.9999, 0.9730, 0.9997,\n",
              "        0.9987, 0.9997, 0.9999, 0.9916, 0.9927, 0.9944, 0.9995, 0.9998, 0.9998, 0.9984,\n",
              "        0.9973, 0.9999, 0.9968, 0.9998, 0.9993, 0.9980, 0.9999, 1.0000, 0.9953, 0.9955,\n",
              "        0.9998, 0.9853, 0.9961, 0.9994, 1.0000, 0.9963, 0.9190, 0.9993, 0.9998, 0.9999,\n",
              "        0.9981, 1.0000, 0.9999, 0.9070, 0.9985, 0.9702, 0.9931, 0.9310, 0.9997, 0.9959,\n",
              "        1.0000, 0.9950, 0.9987, 1.0000, 0.9999, 0.9989, 1.0000, 0.9999, 1.0000, 0.9979])"
            ]
          },
          "execution_count": 117,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "scores[~mask].flatten() # probability score for all correct predictions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "029555c2",
      "metadata": {
        "scrolled": true,
        "id": "029555c2",
        "outputId": "db2e37e0-5da0-4dfe-e40b-c28c55d352ca"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Parameter containing:\n",
              "tensor([ 0.0713, -0.0157,  0.1172, -0.1476], requires_grad=True)"
            ]
          },
          "execution_count": 118,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model[-1].bias"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "df96f566",
      "metadata": {
        "id": "df96f566",
        "outputId": "bb5d013c-4921-4f53-8dbb-7b2e7cd4b231"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[0., 0., 1., 0., 0., 0., 1., 0., 0., 0.]])"
            ]
          },
          "execution_count": 119,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "all_incorrect_features = ego.FloatTensor([\n",
        "    fizzbuzz.encode_binary(x.item()) for x in all_incorrect])\n",
        "all_incorrect_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ce53a5fb",
      "metadata": {
        "id": "ce53a5fb",
        "outputId": "fde3fc09-8e75-4e11-d27d-2f919ba6dd5a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[2]])"
            ]
          },
          "execution_count": 120,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "all_incorrect_features.argmax(1, keepdim=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "62b2000a",
      "metadata": {
        "id": "62b2000a",
        "outputId": "3aaf4676-3833-4ef9-da02-8553796ed46f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">05/23/2022 03:05:53 </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">05</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">23</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2022</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">03:05:53</span> - INFO - numexpr.utils -        <a href=\"file:///home/ego/anaconda3/envs/py310/lib/python3.10/site-packages/numexpr/utils.py\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">utils.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/ego/anaconda3/envs/py310/lib/python3.10/site-packages/numexpr/utils.py#157\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">157</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         NumExpr defaulting to <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span> threads.                    <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[2;36m05/23/2022 03:05:53\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m \u001b[1;36m05\u001b[0m/\u001b[1;36m23\u001b[0m/\u001b[1;36m2022\u001b[0m \u001b[1;92m03:05:53\u001b[0m - INFO - numexpr.utils -        \u001b]8;id=947136;file:///home/ego/anaconda3/envs/py310/lib/python3.10/site-packages/numexpr/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=813157;file:///home/ego/anaconda3/envs/py310/lib/python3.10/site-packages/numexpr/utils.py#157\u001b\\\u001b[2m157\u001b[0m\u001b]8;;\u001b\\\n",
              "\u001b[2;36m                    \u001b[0m         NumExpr defaulting to \u001b[1;36m8\u001b[0m threads.                    \u001b[2m            \u001b[0m\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import mplcyberpunk\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('cyberpunk')\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "19d331b4",
      "metadata": {
        "scrolled": false,
        "id": "19d331b4",
        "outputId": "c3ddbe8d-e47d-42f9-eca7-a4cae0f46e7c"
      },
      "outputs": [
        {
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\n",
            "text/plain": [
              "<Figure size 1836x612 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "grads = {name: params.grad.data.view(-1).clone().numpy()\n",
        "         for name, params in model.named_parameters()\n",
        "         if \"weight\" in name}\n",
        "# Plotting\n",
        "color=\"C0\"\n",
        "columns = len(grads)\n",
        "fig, ax = plt.subplots(1, columns, figsize=(columns * 8.5, 8.5))\n",
        "fig_index = 0\n",
        "for key in grads:\n",
        "    key_ax = ax[fig_index % columns]\n",
        "    sns.histplot(data=grads[key], bins=30, ax=key_ax, color=color, kde=True)\n",
        "    key_ax.set_title(str(key))\n",
        "    key_ax.set_xlabel(\"Grad magnitude\")\n",
        "    fig_index += 1\n",
        "fig.suptitle(\n",
        "    \"Gradient magnitude distribution\",\n",
        "    fontsize=14, y=1.05)\n",
        "fig.subplots_adjust(wspace=0.45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c0b07205",
      "metadata": {
        "scrolled": true,
        "id": "c0b07205",
        "outputId": "1c29fa09-b937-49c2-eae5-09a82a9fb267"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "OrderedDict([('0.weight',\n",
              "              tensor([[-0.8873, -0.7598, -0.5395,  0.3363,  0.8644, -0.8144,  0.4167,  0.2676,\n",
              "                       -0.2501, -0.7625],\n",
              "                      [-0.2615,  1.1428, -0.3051,  0.2539, -1.5375, -0.2325,  0.6569, -1.2578,\n",
              "                       -1.0124, -0.6312],\n",
              "                      [-1.0963, -0.3108, -1.1028, -0.3593, -0.9839, -0.0103,  1.2113,  0.3423,\n",
              "                       -0.8575, -0.9451],\n",
              "                      [ 1.1700,  0.2604, -0.8866,  1.2129, -0.3495, -0.2631, -1.1626, -0.5842,\n",
              "                       -0.3009,  1.1259],\n",
              "                      [ 1.0666,  0.7672, -0.2228, -1.4403, -0.6929, -1.0576,  0.6120, -0.3722,\n",
              "                        0.1846,  0.1942],\n",
              "                      [ 0.5874,  0.8380,  0.7130, -0.8360,  1.3254,  0.1495,  0.9431,  1.3517,\n",
              "                       -0.5057,  0.1542],\n",
              "                      [-0.1642,  0.8967, -0.6672,  0.7864, -1.0471,  0.3892, -1.4234,  0.6153,\n",
              "                       -0.7046, -0.5053],\n",
              "                      [ 0.0632, -0.3221,  1.1098, -0.2196,  0.5387, -0.3290, -0.7348,  0.7206,\n",
              "                       -1.1093, -1.0446],\n",
              "                      [-0.2728, -0.9304,  1.1155,  0.6788,  0.7857,  0.1241, -1.0928,  0.0591,\n",
              "                        0.4859, -1.4257],\n",
              "                      [-0.4705, -0.3204,  0.4758, -1.5289,  0.8886,  0.6380, -1.3124, -0.5610,\n",
              "                        0.4914,  1.0294],\n",
              "                      [ 0.1349,  0.8357, -0.6116,  0.0773,  1.4448,  0.7550, -0.5187, -1.2984,\n",
              "                       -0.0423, -0.6986],\n",
              "                      [-0.3716,  0.7807, -0.0760, -0.2108,  0.2433,  1.3667,  0.8230,  0.1846,\n",
              "                        0.6285,  0.8363],\n",
              "                      [ 1.0625,  1.0139, -0.6693,  0.0615,  1.2086,  1.3678, -0.8461,  0.7202,\n",
              "                        0.6998,  1.1411],\n",
              "                      [-0.3380, -0.4992,  1.0787, -1.1417, -0.2908, -0.3078, -0.6729, -1.0561,\n",
              "                        0.3746, -0.6035],\n",
              "                      [ 1.0892, -0.0897, -0.6640,  0.0787, -1.2722,  0.7039, -1.2758,  0.4980,\n",
              "                        0.1868, -0.9322],\n",
              "                      [ 0.6012, -0.6060, -0.4872,  1.4037, -0.2537, -0.6855,  0.7797, -1.7071,\n",
              "                       -0.8178, -1.1993],\n",
              "                      [-0.3094,  0.8380,  0.7814, -1.3752,  0.0137, -0.4508, -1.2176,  0.3264,\n",
              "                        0.2885, -0.6802],\n",
              "                      [ 0.8883, -1.5091,  0.6076, -0.4242,  0.9141,  0.2421, -0.4430,  1.0078,\n",
              "                        1.0056, -1.1625],\n",
              "                      [-0.9749,  0.4872,  0.5070, -0.9669, -1.3155,  0.8329, -0.6411, -1.4849,\n",
              "                        0.5728,  0.6740],\n",
              "                      [ 0.2450,  0.6863, -1.0707,  0.1130,  1.2180,  1.5414, -1.1568,  0.1290,\n",
              "                        0.2978,  1.2407],\n",
              "                      [-1.6982,  0.8687, -0.8154,  0.3401,  0.4995,  1.1262, -0.7124,  1.1532,\n",
              "                       -1.1394,  0.7674],\n",
              "                      [ 0.8603,  1.0422, -0.9209, -1.0692, -0.4654,  0.1900, -0.7056,  0.1197,\n",
              "                        1.1613, -0.2060],\n",
              "                      [-0.9378, -0.8152, -1.3094,  1.5487,  0.0382, -0.5178, -0.6653,  0.8421,\n",
              "                       -0.1693,  0.5695],\n",
              "                      [-0.8937,  1.0746,  0.2102,  0.2871, -0.8656,  1.3936, -0.1584, -0.1629,\n",
              "                       -1.4615,  1.1283],\n",
              "                      [ 0.1550, -0.1312, -0.3308, -0.8408, -0.1819,  1.0072, -0.9807,  0.7026,\n",
              "                        0.2163,  0.1516],\n",
              "                      [-0.9049,  0.4856,  1.3450, -0.1983,  0.9982, -0.7113,  0.7463, -0.2110,\n",
              "                       -0.8899, -0.3393],\n",
              "                      [ 1.3274,  0.8027, -1.5072,  0.0049, -0.4918, -0.7625, -0.8654,  0.8791,\n",
              "                       -0.4684, -0.3986],\n",
              "                      [ 0.4940,  0.5221, -1.0395, -1.4082,  1.5755,  0.1932,  0.4916,  0.9437,\n",
              "                       -0.4454, -1.2916],\n",
              "                      [ 0.6480,  0.4199, -0.8077,  0.2858,  0.1819, -1.0706, -1.3080, -0.2302,\n",
              "                        1.0285, -1.2038],\n",
              "                      [-0.2551, -0.7907, -0.6750, -0.6733,  1.2839,  0.3117,  0.7503,  1.2793,\n",
              "                       -0.2654, -0.5150],\n",
              "                      [-0.2648, -1.0705, -1.3783,  0.5631,  0.6257,  0.2079,  0.0143, -0.0771,\n",
              "                       -0.2313,  0.5220],\n",
              "                      [-0.5307, -0.9204, -0.8580,  0.3858, -0.0463, -0.2882,  0.4075,  0.2947,\n",
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              "                        2.9409e-01,  6.0170e-01]])),\n",
              "             ('3.bias', tensor([ 0.0713, -0.0157,  0.1172, -0.1476]))])"
            ]
          },
          "execution_count": 124,
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