generate_demakeup_data.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mJnsnzWiPoAi",
        "outputId": "691c2962-4b75-4901-b6be-b2233d3bc59e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BBne1eqj-T9D"
      },
      "source": [
        "### install miniconda"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "du8s735AiLXi",
        "outputId": "3aa070bc-8859-4bef-96d6-489e770d9b8a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/bin/python\n",
            "Python 3.10.11\n"
          ]
        }
      ],
      "source": [
        "#Path and version numbers for python\n",
        "!which python\n",
        "!python --version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FPpwZf-AiLXk",
        "outputId": "61fa0955-1bd0-43c3-a6b3-51516d5e528d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: PYTHONPATH=\n"
          ]
        }
      ],
      "source": [
        "%env PYTHONPATH="
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wWwRqGxbiLXk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a50cccf-f33b-4699-cbe1-772226717f19"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PREFIX=/usr/local\n",
            "Unpacking payload ...\n",
            "\n",
            "Installing base environment...\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "\r\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "\r\n",
            "Preparing transaction: ...working... done\n",
            "Executing transaction: ...working... done\n",
            "installation finished.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "--2023-05-25 06:38:54--  https://repo.anaconda.com/miniconda/Miniconda3-py37_23.1.0-1-Linux-x86_64.sh\n",
            "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.130.3, 104.16.131.3, 2606:4700::6810:8203, ...\n",
            "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.130.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 90665082 (86M) [application/x-sh]\n",
            "Saving to: ‘Miniconda3-py37_23.1.0-1-Linux-x86_64.sh’\n",
            "\n",
            "     0K .......... .......... .......... .......... ..........  0% 36.5M 2s\n",
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            " 10150K .......... .......... .......... .......... .......... 11%  218M 0s\n",
            " 10200K .......... .......... .......... .......... .......... 11%  261M 0s\n",
            " 10250K .......... .......... .......... .......... .......... 11%  252M 0s\n",
            " 10300K .......... .......... .......... .......... .......... 11%  258M 0s\n",
            " 10350K .......... .......... .......... .......... .......... 11%  199M 0s\n",
            " 10400K .......... .......... .......... .......... .......... 11%  222M 0s\n",
            " 10450K .......... .......... .......... .......... .......... 11%  273M 0s\n",
            " 10500K .......... .......... .......... .......... .......... 11%  271M 0s\n",
            " 10550K .......... .......... .......... .......... .......... 11%  240M 0s\n",
            " 10600K .......... .......... .......... .......... .......... 12%  235M 0s\n",
            " 10650K .......... .......... .......... .......... .......... 12%  251M 0s\n",
            " 10700K .......... .......... .......... .......... .......... 12%  226M 0s\n",
            " 10750K .......... .......... .......... .......... .......... 12% 82.5M 0s\n",
            " 10800K .......... .......... .......... .......... .......... 12%  173M 0s\n",
            " 10850K .......... .......... .......... .......... .......... 12%  157M 0s\n",
            " 10900K .......... .......... .......... .......... .......... 12%  167M 0s\n",
            " 10950K .......... .......... .......... .......... .......... 12%  147M 0s\n",
            " 11000K .......... .......... .......... .......... .......... 12%  177M 0s\n",
            " 11050K .......... .......... .......... .......... .......... 12%  178M 0s\n",
            " 11100K .......... .......... .......... .......... .......... 12%  229M 0s\n",
            " 11150K .......... .......... .......... .......... .......... 12%  210M 0s\n",
            " 11200K .......... .......... .......... .......... .......... 12%  250M 0s\n",
            " 11250K .......... .......... .......... .......... .......... 12%  242M 0s\n",
            " 11300K .......... .......... .......... .......... .......... 12%  128M 0s\n",
            " 11350K .......... .......... .......... .......... .......... 12%  123M 0s\n",
            " 11400K .......... .......... .......... .......... .......... 12%  170M 0s\n",
            " 11450K .......... .......... .......... .......... .......... 12%  154M 0s\n",
            " 11500K .......... .......... .......... .......... .......... 13%  161M 0s\n",
            " 11550K .......... .......... .......... .......... .......... 13%  154M 0s\n",
            " 11600K .......... .......... .......... .......... .......... 13%  191M 0s\n",
            " 11650K .......... .......... .......... .......... .......... 13%  269M 0s\n",
            " 11700K .......... .......... .......... .......... .......... 13%  421M 0s\n",
            " 11750K .......... .......... .......... .......... .......... 13%  125M 0s\n",
            " 11800K .......... .......... .......... .......... .......... 13%  174M 0s\n",
            " 11850K .......... .......... .......... .......... .......... 13%  152M 0s\n",
            " 11900K .......... .......... .......... .......... .......... 13%  226M 0s\n",
            " 11950K .......... .......... .......... .......... .......... 13%  137M 0s\n",
            " 12000K .......... .......... .......... .......... .......... 13%  246M 0s\n",
            " 12050K .......... .......... .......... .......... .......... 13%  267M 0s\n",
            " 12100K .......... .......... .......... .......... .......... 13%  184M 0s\n",
            " 12150K .......... .......... .......... .......... .......... 13%  152M 0s\n",
            " 12200K .......... .......... .......... .......... .......... 13%  240M 0s\n",
            " 12250K .......... .......... .......... .......... .......... 13%  211M 0s\n",
            " 12300K .......... .......... .......... .......... .......... 13%  182M 0s\n",
            " 12350K .......... .......... .......... .......... .......... 14%  109M 0s\n",
            " 12400K .......... .......... .......... .......... .......... 14%  142M 0s\n",
            " 12450K .......... .......... .......... .......... .......... 14%  183M 0s\n",
            " 12500K .......... .......... .......... .......... .......... 14%  238M 0s\n",
            " 12550K .......... .......... .......... .......... .......... 14%  148M 0s\n",
            " 12600K .......... .......... .......... .......... .......... 14%  208M 0s\n",
            " 12650K .......... .......... .......... .......... .......... 14%  263M 0s\n",
            " 12700K .......... .......... .......... .......... .......... 14%  184M 0s\n",
            " 12750K .......... .......... .......... .......... .......... 14%  170M 0s\n",
            " 12800K .......... .......... .......... .......... .......... 14%  272M 0s\n",
            " 12850K .......... .......... .......... .......... .......... 14%  191M 0s\n",
            " 12900K .......... .......... .......... .......... .......... 14%  259M 0s\n",
            " 12950K .......... .......... .......... .......... .......... 14%  190M 0s\n",
            " 13000K .......... .......... .......... .......... .......... 14%  246M 0s\n",
            " 13050K .......... .......... .......... .......... .......... 14%  262M 0s\n",
            " 13100K .......... .......... .......... .......... .......... 14%  191M 0s\n",
            " 13150K .......... .......... .......... .......... .......... 14%  215M 0s\n",
            " 13200K .......... .......... .......... .......... .......... 14%  163M 0s\n",
            " 13250K .......... .......... .......... .......... .......... 15%  180M 0s\n",
            " 13300K .......... .......... .......... .......... .......... 15%  255M 0s\n",
            " 13350K .......... .......... .......... .......... .......... 15%  222M 0s\n",
            " 13400K .......... .......... .......... .......... .......... 15%  222M 0s\n",
            " 13450K .......... .......... .......... .......... .......... 15%  243M 0s\n",
            " 13500K .......... .......... .......... .......... .......... 15%  175M 0s\n",
            " 13550K .......... .......... .......... .......... .......... 15%  135M 0s\n",
            " 13600K .......... .......... .......... .......... .......... 15%  189M 0s\n",
            " 13650K .......... .......... .......... .......... .......... 15%  261M 0s\n",
            " 13700K .......... .......... .......... .......... .......... 15%  265M 0s\n",
            " 13750K .......... .......... .......... .......... .......... 15%  216M 0s\n",
            " 13800K .......... .......... .......... .......... .......... 15%  149M 0s\n",
            " 13850K .......... .......... .......... .......... .......... 15%  202M 0s\n",
            " 13900K .......... .......... .......... .......... .......... 15%  184M 0s\n",
            " 13950K .......... .......... .......... .......... .......... 15%  156M 0s\n",
            " 14000K .......... .......... .......... .......... .......... 15%  238M 0s\n",
            " 14050K .......... .......... .......... .......... .......... 15%  249M 0s\n",
            " 14100K .......... .......... .......... .......... .......... 15%  257M 0s\n",
            " 14150K .......... .......... .......... .......... .......... 16%  160M 0s\n",
            " 14200K .......... .......... .......... .......... .......... 16%  182M 0s\n",
            " 14250K .......... .......... .......... .......... .......... 16%  202M 0s\n",
            " 14300K .......... .......... .......... .......... .......... 16%  218M 0s\n",
            " 14350K .......... .......... .......... .......... .......... 16%  216M 0s\n",
            " 14400K .......... .......... .......... .......... .......... 16%  280M 0s\n",
            " 14450K .......... .......... .......... .......... .......... 16%  184M 0s\n",
            " 14500K .......... .......... .......... .......... .......... 16%  200M 0s\n",
            " 14550K .......... .......... .......... .......... .......... 16%  227M 0s\n",
            " 14600K .......... .......... .......... .......... .......... 16%  195M 0s\n",
            " 14650K .......... .......... .......... .......... .......... 16%  258M 0s\n",
            " 14700K .......... .......... .......... .......... .......... 16%  276M 0s\n",
            " 14750K .......... .......... .......... .......... .......... 16%  229M 0s\n",
            " 14800K .......... .......... .......... .......... .......... 16%  154M 0s\n",
            " 14850K .......... .......... .......... .......... .......... 16%  169M 0s\n",
            " 14900K .......... .......... .......... .......... .......... 16%  190M 0s\n",
            " 14950K .......... .......... .......... .......... .......... 16%  215M 0s\n",
            " 15000K .......... .......... .......... .......... .......... 16%  259M 0s\n",
            " 15050K .......... .......... .......... .......... .......... 17%  267M 0s\n",
            " 15100K .......... .......... .......... .......... .......... 17%  155M 0s\n",
            " 15150K .......... .......... .......... .......... .......... 17%  140M 0s\n",
            " 15200K .......... .......... .......... .......... .......... 17%  186M 0s\n",
            " 15250K .......... .......... .......... .......... .......... 17%  240M 0s\n",
            " 15300K .......... .......... .......... .......... .......... 17%  266M 0s\n",
            " 15350K .......... .......... .......... .......... .......... 17%  209M 0s\n",
            " 15400K .......... .......... .......... .......... .......... 17%  118M 0s\n",
            " 15450K .......... .......... .......... .......... .......... 17%  192M 0s\n",
            " 15500K .......... .......... .......... .......... .......... 17%  199M 0s\n",
            " 15550K .......... .......... .......... .......... .......... 17%  221M 0s\n",
            " 15600K .......... .......... .......... .......... .......... 17%  256M 0s\n",
            " 15650K .......... .......... .......... .......... .......... 17%  178M 0s\n",
            " 15700K .......... .......... .......... .......... .......... 17%  146M 0s\n",
            " 15750K .......... .......... .......... .......... .......... 17%  183M 0s\n",
            " 15800K .......... .......... .......... .......... .......... 17%  221M 0s\n",
            " 15850K .......... .......... .......... .......... .......... 17%  227M 0s\n",
            " 15900K .......... .......... .......... .......... .......... 18%  277M 0s\n",
            " 15950K .......... .......... .......... .......... .......... 18%  142M 0s\n",
            " 16000K .......... .......... .......... .......... .......... 18%  182M 0s\n",
            " 16050K .......... .......... .......... .......... .......... 18%  152M 0s\n",
            " 16100K .......... .......... .......... .......... .......... 18%  248M 0s\n",
            " 16150K .......... .......... .......... .......... .......... 18%  205M 0s\n",
            " 16200K .......... .......... .......... .......... .......... 18%  263M 0s\n",
            " 16250K .......... .......... .......... .......... .......... 18%  180M 0s\n",
            " 16300K .......... .......... .......... .......... .......... 18%  223M 0s\n",
            " 16350K .......... .......... .......... .......... .......... 18%  160M 0s\n",
            " 16400K .......... .......... .......... .......... .......... 18%  184M 0s\n",
            " 16450K .......... .......... .......... .......... .......... 18%  219M 0s\n",
            " 16500K .......... .......... .......... .......... .......... 18%  256M 0s\n",
            " 16550K .......... .......... .......... .......... .......... 18%  231M 0s\n",
            " 16600K .......... .......... .......... .......... .......... 18%  200M 0s\n",
            " 16650K .......... .......... .......... .......... .......... 18%  163M 0s\n",
            " 16700K .......... .......... .......... .......... .......... 18%  213M 0s\n",
            " 16750K .......... .......... .......... .......... .......... 18%  184M 0s\n",
            " 16800K .......... .......... .......... .......... .......... 19%  236M 0s\n",
            " 16850K .......... .......... .......... .......... .......... 19%  266M 0s\n",
            " 16900K .......... .......... .......... .......... .......... 19%  166M 0s\n",
            " 16950K .......... .......... .......... .......... .......... 19%  191M 0s\n",
            " 17000K .......... .......... .......... .......... .......... 19%  196M 0s\n",
            " 17050K .......... .......... .......... .......... .......... 19%  181M 0s\n",
            " 17100K .......... .......... .......... .......... .......... 19%  265M 0s\n",
            " 17150K .......... .......... .......... .......... .......... 19%  230M 0s\n",
            " 17200K .......... .......... .......... .......... .......... 19%  178M 0s\n",
            " 17250K .......... .......... .......... .......... .......... 19%  264M 0s\n",
            " 17300K .......... .......... .......... .......... .......... 19%  222M 0s\n",
            " 17350K .......... .......... .......... .......... .......... 19%  189M 0s\n",
            " 17400K .......... .......... .......... .......... .......... 19%  191M 0s\n",
            " 17450K .......... .......... .......... .......... .......... 19%  248M 0s\n",
            " 17500K .......... .......... .......... .......... .......... 19%  247M 0s\n",
            " 17550K .......... .......... .......... .......... .......... 19%  149M 0s\n",
            " 17600K .......... .......... .......... .......... .......... 19%  261M 0s\n",
            " 17650K .......... .......... .......... .......... .......... 19%  250M 0s\n",
            " 17700K .......... .......... .......... .......... .......... 20%  262M 0s\n",
            " 17750K .......... .......... .......... .......... .......... 20%  240M 0s\n",
            " 17800K .......... .......... .......... .......... .......... 20%  284M 0s\n",
            " 17850K .......... .......... .......... .......... .......... 20%  238M 0s\n",
            " 17900K .......... .......... .......... .......... .......... 20%  253M 0s\n",
            " 17950K .......... .......... .......... .......... .......... 20%  222M 0s\n",
            " 18000K .......... .......... .......... .......... .......... 20%  250M 0s\n",
            " 18050K .......... .......... .......... .......... .......... 20%  255M 0s\n",
            " 18100K .......... .......... .......... .......... .......... 20%  232M 0s\n",
            " 18150K .......... .......... .......... .......... .......... 20%  214M 0s\n",
            " 18200K .......... .......... .......... .......... .......... 20%  210M 0s\n",
            " 18250K .......... .......... .......... .......... .......... 20%  147M 0s\n",
            " 18300K .......... .......... .......... .......... .......... 20%  182M 0s\n",
            " 18350K .......... .......... .......... .......... .......... 20%  139M 0s\n",
            " 18400K .......... .......... .......... .......... .......... 20%  175M 0s\n",
            " 18450K .......... .......... .......... .......... .......... 20%  237M 0s\n",
            " 18500K .......... .......... .......... .......... .......... 20%  159M 0s\n",
            " 18550K .......... .......... .......... .......... .......... 21%  150M 0s\n",
            " 18600K .......... .......... .......... .......... .......... 21%  203M 0s\n",
            " 18650K .......... .......... .......... .......... .......... 21%  195M 0s\n",
            " 18700K .......... .......... .......... .......... .......... 21%  212M 0s\n",
            " 18750K .......... .......... .......... .......... .......... 21%  232M 0s\n",
            " 18800K .......... .......... .......... .......... .......... 21%  186M 0s\n",
            " 18850K .......... .......... .......... .......... .......... 21%  160M 0s\n",
            " 18900K .......... .......... .......... .......... .......... 21%  174M 0s\n",
            " 18950K .......... .......... .......... .......... .......... 21%  156M 0s\n",
            " 19000K .......... .......... .......... .......... .......... 21%  184M 0s\n",
            " 19050K .......... .......... .......... .......... .......... 21%  177M 0s\n",
            " 19100K .......... .......... .......... .......... .......... 21%  210M 0s\n",
            " 19150K .......... .......... .......... .......... .......... 21%  177M 0s\n",
            " 19200K .......... .......... .......... .......... .......... 21%  256M 0s\n",
            " 19250K .......... .......... .......... .......... .......... 21%  154M 0s\n",
            " 19300K .......... .......... .......... .......... .......... 21%  165M 0s\n",
            " 19350K .......... .......... .......... .......... .......... 21%  162M 0s\n",
            " 19400K .......... .......... .......... .......... .......... 21%  188M 0s\n",
            " 19450K .......... .......... .......... .......... .......... 22%  229M 0s\n",
            " 19500K .......... .......... .......... .......... .......... 22%  255M 0s\n",
            " 19550K .......... .......... .......... .......... .......... 22%  140M 0s\n",
            " 19600K .......... .......... .......... .......... .......... 22%  260M 0s\n",
            " 19650K .......... .......... .......... .......... .......... 22%  201M 0s\n",
            " 19700K .......... .......... .......... .......... .......... 22%  264M 0s\n",
            " 19750K .......... .......... .......... .......... .......... 22%  222M 0s\n",
            " 19800K .......... .......... .......... .......... .......... 22%  272M 0s\n",
            " 19850K .......... .......... .......... .......... .......... 22%  185M 0s\n",
            " 19900K .......... .......... .......... .......... .......... 22%  173M 0s\n",
            " 19950K .......... .......... .......... .......... .......... 22%  147M 0s\n",
            " 20000K .......... .......... .......... .......... .......... 22%  199M 0s\n",
            " 20050K .......... .......... .......... .......... .......... 22%  270M 0s\n",
            " 20100K .......... .......... .......... .......... .......... 22%  260M 0s\n",
            " 20150K .......... .......... .......... .......... .......... 22%  226M 0s\n",
            " 20200K .......... .......... .......... .......... .......... 22%  268M 0s\n",
            " 20250K .......... .......... .......... .......... .......... 22%  271M 0s\n",
            " 20300K .......... .......... .......... .......... .......... 22%  278M 0s\n",
            " 20350K .......... .......... .......... .......... .......... 23%  124M 0s\n",
            " 20400K .......... .......... .......... .......... .......... 23%  221M 0s\n",
            " 20450K .......... .......... .......... .......... .......... 23%  252M 0s\n",
            " 20500K .......... .......... .......... .......... .......... 23%  247M 0s\n",
            " 20550K .......... .......... .......... .......... .......... 23%  166M 0s\n",
            " 20600K .......... .......... .......... .......... .......... 23%  241M 0s\n",
            " 20650K .......... .......... .......... .......... .......... 23%  257M 0s\n",
            " 20700K .......... .......... .......... .......... .......... 23%  265M 0s\n",
            " 20750K .......... .......... .......... .......... .......... 23%  124M 0s\n",
            " 20800K .......... .......... .......... .......... .......... 23%  177M 0s\n",
            " 20850K .......... .......... .......... .......... .......... 23%  197M 0s\n",
            " 20900K .......... .......... .......... .......... .......... 23%  205M 0s\n",
            " 20950K .......... .......... .......... .......... .......... 23%  211M 0s\n",
            " 21000K .......... .......... .......... .......... .......... 23%  228M 0s\n",
            " 21050K .......... .......... .......... .......... .......... 23%  172M 0s\n",
            " 21100K .......... .......... .......... .......... .......... 23%  156M 0s\n",
            " 21150K .......... .......... .......... .......... .......... 23%  141M 0s\n",
            " 21200K .......... .......... .......... .......... .......... 24%  138M 0s\n",
            " 21250K .......... .......... .......... .......... .......... 24%  234M 0s\n",
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            " 21450K .......... .......... .......... .......... .......... 24%  247M 0s\n",
            " 21500K .......... .......... .......... .......... .......... 24%  210M 0s\n",
            " 21550K .......... .......... .......... .......... .......... 24%  212M 0s\n",
            " 21600K .......... .......... .......... .......... .......... 24%  200M 0s\n",
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            " 21700K .......... .......... .......... .......... .......... 24%  258M 0s\n",
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            " 21800K .......... .......... .......... .......... .......... 24%  262M 0s\n",
            " 21850K .......... .......... .......... .......... .......... 24%  269M 0s\n",
            " 21900K .......... .......... .......... .......... .......... 24%  266M 0s\n",
            " 21950K .......... .......... .......... .......... .......... 24%  162M 0s\n",
            " 22000K .......... .......... .......... .......... .......... 24%  182M 0s\n",
            " 22050K .......... .......... .......... .......... .......... 24%  266M 0s\n",
            " 22100K .......... .......... .......... .......... .......... 25%  146M 0s\n",
            " 22150K .......... .......... .......... .......... .......... 25%  187M 0s\n",
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            " 22350K .......... .......... .......... .......... .......... 25%  199M 0s\n",
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            " 22650K .......... .......... .......... .......... .......... 25%  250M 0s\n",
            " 22700K .......... .......... .......... .......... .......... 25%  269M 0s\n",
            " 22750K .......... .......... .......... .......... .......... 25%  189M 0s\n",
            " 22800K .......... .......... .......... .......... .......... 25%  169M 0s\n",
            " 22850K .......... .......... .......... .......... .......... 25%  175M 0s\n",
            " 22900K .......... .......... .......... .......... .......... 25%  199M 0s\n",
            " 22950K .......... .......... .......... .......... .......... 25%  207M 0s\n",
            " 23000K .......... .......... .......... .......... .......... 26%  229M 0s\n",
            " 23050K .......... .......... .......... .......... .......... 26%  221M 0s\n",
            " 23100K .......... .......... .......... .......... .......... 26%  179M 0s\n",
            " 23150K .......... .......... .......... .......... .......... 26%  164M 0s\n",
            " 23200K .......... .......... .......... .......... .......... 26%  211M 0s\n",
            " 23250K .......... .......... .......... .......... .......... 26%  170M 0s\n",
            " 23300K .......... .......... .......... .......... .......... 26%  262M 0s\n",
            " 23350K .......... .......... .......... .......... .......... 26%  207M 0s\n",
            " 23400K .......... .......... .......... .......... .......... 26%  240M 0s\n",
            " 23450K .......... .......... .......... .......... .......... 26%  147M 0s\n",
            " 23500K .......... .......... .......... .......... .......... 26%  241M 0s\n",
            " 23550K .......... .......... .......... .......... .......... 26%  158M 0s\n",
            " 23600K .......... .......... .......... .......... .......... 26%  252M 0s\n",
            " 23650K .......... .......... .......... .......... .......... 26%  254M 0s\n",
            " 23700K .......... .......... .......... .......... .......... 26%  272M 0s\n",
            " 23750K .......... .......... .......... .......... .......... 26%  167M 0s\n",
            " 23800K .......... .......... .......... .......... .......... 26%  211M 0s\n",
            " 23850K .......... .......... .......... .......... .......... 26%  254M 0s\n",
            " 23900K .......... .......... .......... .......... .......... 27%  214M 0s\n",
            " 23950K .......... .......... .......... .......... .......... 27%  218M 0s\n",
            " 24000K .......... .......... .......... .......... .......... 27%  267M 0s\n",
            " 24050K .......... .......... .......... .......... .......... 27%  268M 0s\n",
            " 24100K .......... .......... .......... .......... .......... 27%  155M 0s\n",
            " 24150K .......... .......... .......... .......... .......... 27%  197M 0s\n",
            " 24200K .......... .......... .......... .......... .......... 27%  258M 0s\n",
            " 24250K .......... .......... .......... .......... .......... 27%  156M 0s\n",
            " 24300K .......... .......... .......... .......... .......... 27%  226M 0s\n",
            " 24350K .......... .......... .......... .......... .......... 27%  225M 0s\n",
            " 24400K .......... .......... .......... .......... .......... 27%  243M 0s\n",
            " 24450K .......... .......... .......... .......... .......... 27%  128M 0s\n",
            " 24500K .......... .......... .......... .......... .......... 27%  209M 0s\n",
            " 24550K .......... .......... .......... .......... .......... 27%  229M 0s\n",
            " 24600K .......... .......... .......... .......... .......... 27%  186M 0s\n",
            " 24650K .......... .......... .......... .......... .......... 27%  275M 0s\n",
            " 24700K .......... .......... .......... .......... .......... 27%  241M 0s\n",
            " 24750K .......... .......... .......... .......... .......... 28%  138M 0s\n",
            " 24800K .......... .......... .......... .......... .......... 28%  199M 0s\n",
            " 24850K .......... .......... .......... .......... .......... 28%  244M 0s\n",
            " 24900K .......... .......... .......... .......... .......... 28%  200M 0s\n",
            " 24950K .......... .......... .......... .......... .......... 28%  219M 0s\n",
            " 25000K .......... .......... .......... .......... .......... 28%  262M 0s\n",
            " 25050K .......... .......... .......... .......... .......... 28%  172M 0s\n",
            " 25100K .......... .......... .......... .......... .......... 28%  149M 0s\n",
            " 25150K .......... .......... .......... .......... .......... 28%  258M 0s\n",
            " 25200K .......... .......... .......... .......... .......... 28%  306M 0s\n",
            " 25250K .......... .......... .......... .......... .......... 28%  239M 0s\n",
            " 25300K .......... .......... .......... .......... .......... 28%  224M 0s\n",
            " 25350K .......... .......... .......... .......... .......... 28%  208M 0s\n",
            " 25400K .......... .......... .......... .......... .......... 28%  328M 0s\n",
            " 25450K .......... .......... .......... .......... .......... 28%  196M 0s\n",
            " 25500K .......... .......... .......... .......... .......... 28%  180M 0s\n",
            " 25550K .......... .......... .......... .......... .......... 28%  105M 0s\n",
            " 25600K .......... .......... .......... .......... .......... 28%  229M 0s\n",
            " 25650K .......... .......... .......... .......... .......... 29%  258M 0s\n",
            " 25700K .......... .......... .......... .......... .......... 29%  207M 0s\n",
            " 25750K .......... .......... .......... .......... .......... 29%  193M 0s\n",
            " 25800K .......... .......... .......... .......... .......... 29%  305M 0s\n",
            " 25850K .......... .......... .......... .......... .......... 29%  319M 0s\n",
            " 25900K .......... .......... .......... .......... .......... 29%  426M 0s\n",
            " 25950K .......... .......... .......... .......... .......... 29%  303M 0s\n",
            " 26000K .......... .......... .......... .......... .......... 29%  307M 0s\n",
            " 26050K .......... .......... .......... .......... .......... 29%  238M 0s\n",
            " 26100K .......... .......... .......... .......... .......... 29%  260M 0s\n",
            " 26150K .......... .......... .......... .......... .......... 29%  178M 0s\n",
            " 26200K .......... .......... .......... .......... .......... 29%  197M 0s\n",
            " 26250K .......... .......... .......... .......... .......... 29%  155M 0s\n",
            " 26300K .......... .......... .......... .......... .......... 29%  194M 0s\n",
            " 26350K .......... .......... .......... .......... .......... 29%  164M 0s\n",
            " 26400K .......... .......... .......... .......... .......... 29%  203M 0s\n",
            " 26450K .......... .......... .......... .......... .......... 29%  150M 0s\n",
            " 26500K .......... .......... .......... .......... .......... 29%  192M 0s\n",
            " 26550K .......... .......... .......... .......... .......... 30%  214M 0s\n",
            " 26600K .......... .......... .......... .......... .......... 30%  152M 0s\n",
            " 26650K .......... .......... .......... .......... .......... 30%  229M 0s\n",
            " 26700K .......... .......... .......... .......... .......... 30%  257M 0s\n",
            " 26750K .......... .......... .......... .......... .......... 30%  167M 0s\n",
            " 26800K .......... .......... .......... .......... .......... 30%  176M 0s\n",
            " 26850K .......... .......... .......... .......... .......... 30%  229M 0s\n",
            " 26900K .......... .......... .......... .......... .......... 30%  190M 0s\n",
            " 26950K .......... .......... .......... .......... .......... 30%  107M 0s\n",
            " 27000K .......... .......... .......... .......... .......... 30%  229M 0s\n",
            " 27050K .......... .......... .......... .......... .......... 30%  218M 0s\n",
            " 27100K .......... .......... .......... .......... .......... 30%  143M 0s\n",
            " 27150K .......... .......... .......... .......... .......... 30%  174M 0s\n",
            " 27200K .......... .......... .......... .......... .......... 30%  253M 0s\n",
            " 27250K .......... .......... .......... .......... .......... 30%  199M 0s\n",
            " 27300K .......... .......... .......... .......... .......... 30%  243M 0s\n",
            " 27350K .......... .......... .......... .......... .......... 30%  237M 0s\n",
            " 27400K .......... .......... .......... .......... .......... 31%  174M 0s\n",
            " 27450K .......... .......... .......... .......... .......... 31%  183M 0s\n",
            " 27500K .......... .......... .......... .......... .......... 31%  273M 0s\n",
            " 27550K .......... .......... .......... .......... .......... 31%  189M 0s\n",
            " 27600K .......... .......... .......... .......... .......... 31%  168M 0s\n",
            " 27650K .......... .......... .......... .......... .......... 31%  211M 0s\n",
            " 27700K .......... .......... .......... .......... .......... 31%  256M 0s\n",
            " 27750K .......... .......... .......... .......... .......... 31%  171M 0s\n",
            " 27800K .......... .......... .......... .......... .......... 31%  153M 0s\n",
            " 27850K .......... .......... .......... .......... .......... 31%  249M 0s\n",
            " 27900K .......... .......... .......... .......... .......... 31%  204M 0s\n",
            " 27950K .......... .......... .......... .......... .......... 31%  219M 0s\n",
            " 28000K .......... .......... .......... .......... .......... 31%  226M 0s\n",
            " 28050K .......... .......... .......... .......... .......... 31%  198M 0s\n",
            " 28100K .......... .......... .......... .......... .......... 31%  178M 0s\n",
            " 28150K .......... .......... .......... .......... .......... 31%  168M 0s\n",
            " 28200K .......... .......... .......... .......... .......... 31%  247M 0s\n",
            " 28250K .......... .......... .......... .......... .......... 31%  260M 0s\n",
            " 28300K .......... .......... .......... .......... .......... 32%  292M 0s\n",
            " 28350K .......... .......... .......... .......... .......... 32%  202M 0s\n",
            " 28400K .......... .......... .......... .......... .......... 32%  196M 0s\n",
            " 28450K .......... .......... .......... .......... .......... 32%  193M 0s\n",
            " 28500K .......... .......... .......... .......... .......... 32%  207M 0s\n",
            " 28550K .......... .......... .......... .......... .......... 32%  136M 0s\n",
            " 28600K .......... .......... .......... .......... .......... 32%  192M 0s\n",
            " 28650K .......... .......... .......... .......... .......... 32%  264M 0s\n",
            " 28700K .......... .......... .......... .......... .......... 32%  172M 0s\n",
            " 28750K .......... .......... .......... .......... .......... 32%  158M 0s\n",
            " 28800K .......... .......... .......... .......... .......... 32%  195M 0s\n",
            " 28850K .......... .......... .......... .......... .......... 32%  206M 0s\n",
            " 28900K .......... .......... .......... .......... .......... 32%  229M 0s\n",
            " 28950K .......... .......... .......... .......... .......... 32%  227M 0s\n",
            " 29000K .......... .......... .......... .......... .......... 32%  174M 0s\n",
            " 29050K .......... .......... .......... .......... .......... 32%  181M 0s\n",
            " 29100K .......... .......... .......... .......... .......... 32%  195M 0s\n",
            " 29150K .......... .......... .......... .......... .......... 32%  215M 0s\n",
            " 29200K .......... .......... .......... .......... .......... 33%  198M 0s\n",
            " 29250K .......... .......... .......... .......... .......... 33%  242M 0s\n",
            " 29300K .......... .......... .......... .......... .......... 33%  164M 0s\n",
            " 29350K .......... .......... .......... .......... .......... 33%  172M 0s\n",
            " 29400K .......... .......... .......... .......... .......... 33%  202M 0s\n",
            " 29450K .......... .......... .......... .......... .......... 33%  229M 0s\n",
            " 29500K .......... .......... .......... .......... .......... 33%  189M 0s\n",
            " 29550K .......... .......... .......... .......... .......... 33%  211M 0s\n",
            " 29600K .......... .......... .......... .......... .......... 33%  235M 0s\n",
            " 29650K .......... .......... .......... .......... .......... 33%  207M 0s\n",
            " 29700K .......... .......... .......... .......... .......... 33%  261M 0s\n",
            " 29750K .......... .......... .......... .......... .......... 33%  179M 0s\n",
            " 29800K .......... .......... .......... .......... .......... 33%  190M 0s\n",
            " 29850K .......... .......... .......... .......... .......... 33%  204M 0s\n",
            " 29900K .......... .......... .......... .......... .......... 33%  242M 0s\n",
            " 29950K .......... .......... .......... .......... .......... 33%  156M 0s\n",
            " 30000K .......... .......... .......... .......... .......... 33%  208M 0s\n",
            " 30050K .......... .......... .......... .......... .......... 33%  257M 0s\n",
            " 30100K .......... .......... .......... .......... .......... 34%  212M 0s\n",
            " 30150K .......... .......... .......... .......... .......... 34%  258M 0s\n",
            " 30200K .......... .......... .......... .......... .......... 34%  269M 0s\n",
            " 30250K .......... .......... .......... .......... .......... 34%  289M 0s\n",
            " 30300K .......... .......... .......... .......... .......... 34%  195M 0s\n",
            " 30350K .......... .......... .......... .......... .......... 34%  176M 0s\n",
            " 30400K .......... .......... .......... .......... .......... 34%  237M 0s\n",
            " 30450K .......... .......... .......... .......... .......... 34%  226M 0s\n",
            " 30500K .......... .......... .......... .......... .......... 34%  255M 0s\n",
            " 30550K .......... .......... .......... .......... .......... 34%  253M 0s\n",
            " 30600K .......... .......... .......... .......... .......... 34%  295M 0s\n",
            " 30650K .......... .......... .......... .......... .......... 34%  216M 0s\n",
            " 30700K .......... .......... .......... .......... .......... 34%  220M 0s\n",
            " 30750K .......... .......... .......... .......... .......... 34%  165M 0s\n",
            " 30800K .......... .......... .......... .......... .......... 34%  189M 0s\n",
            " 30850K .......... .......... .......... .......... .......... 34%  228M 0s\n",
            " 30900K .......... .......... .......... .......... .......... 34%  281M 0s\n",
            " 30950K .......... .......... .......... .......... .......... 35%  260M 0s\n",
            " 31000K .......... .......... .......... .......... .......... 35%  152M 0s\n",
            " 31050K .......... .......... .......... .......... .......... 35%  211M 0s\n",
            " 31100K .......... .......... .......... .......... .......... 35%  270M 0s\n",
            " 31150K .......... .......... .......... .......... .......... 35%  147M 0s\n",
            " 31200K .......... .......... .......... .......... .......... 35%  189M 0s\n",
            " 31250K .......... .......... .......... .......... .......... 35%  265M 0s\n",
            " 31300K .......... .......... .......... .......... .......... 35%  266M 0s\n",
            " 31350K .......... .......... .......... .......... .......... 35%  178M 0s\n",
            " 31400K .......... .......... .......... .......... .......... 35%  259M 0s\n",
            " 31450K .......... .......... .......... .......... .......... 35%  182M 0s\n",
            " 31500K .......... .......... .......... .......... .......... 35%  225M 0s\n",
            " 31550K .......... .......... .......... .......... .......... 35%  189M 0s\n",
            " 31600K .......... .......... .......... .......... .......... 35%  268M 0s\n",
            " 31650K .......... .......... .......... .......... .......... 35%  181M 0s\n",
            " 31700K .......... .......... .......... .......... .......... 35%  182M 0s\n",
            " 31750K .......... .......... .......... .......... .......... 35%  207M 0s\n",
            " 31800K .......... .......... .......... .......... .......... 35%  250M 0s\n",
            " 31850K .......... .......... .......... .......... .......... 36%  182M 0s\n",
            " 31900K .......... .......... .......... .......... .......... 36%  271M 0s\n",
            " 31950K .......... .......... .......... .......... .......... 36%  176M 0s\n",
            " 32000K .......... .......... .......... .......... .......... 36%  167M 0s\n",
            " 32050K .......... .......... .......... .......... .......... 36%  201M 0s\n",
            " 32100K .......... .......... .......... .......... .......... 36%  289M 0s\n",
            " 32150K .......... .......... .......... .......... .......... 36%  215M 0s\n",
            " 32200K .......... .......... .......... .......... .......... 36%  218M 0s\n",
            " 32250K .......... .......... .......... .......... .......... 36%  250M 0s\n",
            " 32300K .......... .......... .......... .......... .......... 36%  232M 0s\n",
            " 32350K .......... .......... .......... .......... .......... 36%  196M 0s\n",
            " 32400K .......... .......... .......... .......... .......... 36%  244M 0s\n",
            " 32450K .......... .......... .......... .......... .......... 36%  278M 0s\n",
            " 32500K .......... .......... .......... .......... .......... 36%  251M 0s\n",
            " 32550K .......... .......... .......... .......... .......... 36%  244M 0s\n",
            " 32600K .......... .......... .......... .......... .......... 36%  260M 0s\n",
            " 32650K .......... .......... .......... .......... .......... 36%  277M 0s\n",
            " 32700K .......... .......... .......... .......... .......... 36%  253M 0s\n",
            " 32750K .......... .......... .......... .......... .......... 37%  226M 0s\n",
            " 32800K .......... .......... .......... .......... .......... 37%  291M 0s\n",
            " 32850K .......... .......... .......... .......... .......... 37%  285M 0s\n",
            " 32900K .......... .......... .......... .......... .......... 37%  293M 0s\n",
            " 32950K .......... .......... .......... .......... .......... 37%  235M 0s\n",
            " 33000K .......... .......... .......... .......... .......... 37%  290M 0s\n",
            " 33050K .......... .......... .......... .......... .......... 37%  276M 0s\n",
            " 33100K .......... .......... .......... .......... .......... 37%  250M 0s\n",
            " 33150K .......... .......... .......... .......... .......... 37%  233M 0s\n",
            " 33200K .......... .......... .......... .......... .......... 37% 87.7M 0s\n",
            " 33250K .......... .......... .......... .......... .......... 37%  166M 0s\n",
            " 33300K .......... .......... .......... .......... .......... 37%  213M 0s\n",
            " 33350K .......... .......... .......... .......... .......... 37%  174M 0s\n",
            " 33400K .......... .......... .......... .......... .......... 37%  196M 0s\n",
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            " 33800K .......... .......... .......... .......... .......... 38%  195M 0s\n",
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            " 33950K .......... .......... .......... .......... .......... 38%  154M 0s\n",
            " 34000K .......... .......... .......... .......... .......... 38%  197M 0s\n",
            " 34050K .......... .......... .......... .......... .......... 38%  235M 0s\n",
            " 34100K .......... .......... .......... .......... .......... 38%  179M 0s\n",
            " 34150K .......... .......... .......... .......... .......... 38%  180M 0s\n",
            " 34200K .......... .......... .......... .......... .......... 38%  165M 0s\n",
            " 34250K .......... .......... .......... .......... .......... 38%  211M 0s\n",
            " 34300K .......... .......... .......... .......... .......... 38%  272M 0s\n",
            " 34350K .......... .......... .......... .......... .......... 38%  165M 0s\n",
            " 34400K .......... .......... .......... .......... .......... 38%  266M 0s\n",
            " 34450K .......... .......... .......... .......... .......... 38%  274M 0s\n",
            " 34500K .......... .......... .......... .......... .......... 39%  264M 0s\n",
            " 34550K .......... .......... .......... .......... .......... 39%  185M 0s\n",
            " 34600K .......... .......... .......... .......... .......... 39%  129M 0s\n",
            " 34650K .......... .......... .......... .......... .......... 39%  176M 0s\n",
            " 34700K .......... .......... .......... .......... .......... 39%  160M 0s\n",
            " 34750K .......... .......... .......... .......... .......... 39%  186M 0s\n",
            " 34800K .......... .......... .......... .......... .......... 39%  222M 0s\n",
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            " 34950K .......... .......... .......... .......... .......... 39%  149M 0s\n",
            " 35000K .......... .......... .......... .......... .......... 39%  187M 0s\n",
            " 35050K .......... .......... .......... .......... .......... 39%  241M 0s\n",
            " 35100K .......... .......... .......... .......... .......... 39%  227M 0s\n",
            " 35150K .......... .......... .......... .......... .......... 39%  167M 0s\n",
            " 35200K .......... .......... .......... .......... .......... 39%  202M 0s\n",
            " 35250K .......... .......... .......... .......... .......... 39%  262M 0s\n",
            " 35300K .......... .......... .......... .......... .......... 39%  275M 0s\n",
            " 35350K .......... .......... .......... .......... .......... 39%  151M 0s\n",
            " 35400K .......... .......... .......... .......... .......... 40%  249M 0s\n",
            " 35450K .......... .......... .......... .......... .......... 40%  269M 0s\n",
            " 35500K .......... .......... .......... .......... .......... 40%  197M 0s\n",
            " 35550K .......... .......... .......... .......... .......... 40%  151M 0s\n",
            " 35600K .......... .......... .......... .......... .......... 40%  284M 0s\n",
            " 35650K .......... .......... .......... .......... .......... 40%  294M 0s\n",
            " 35700K .......... .......... .......... .......... .......... 40%  223M 0s\n",
            " 35750K .......... .......... .......... .......... .......... 40%  252M 0s\n",
            " 35800K .......... .......... .......... .......... .......... 40%  257M 0s\n",
            " 35850K .......... .......... .......... .......... .......... 40%  172M 0s\n",
            " 35900K .......... .......... .......... .......... .......... 40%  235M 0s\n",
            " 35950K .......... .......... .......... .......... .......... 40%  197M 0s\n",
            " 36000K .......... .......... .......... .......... .......... 40%  252M 0s\n",
            " 36050K .......... .......... .......... .......... .......... 40%  182M 0s\n",
            " 36100K .......... .......... .......... .......... .......... 40%  198M 0s\n",
            " 36150K .......... .......... .......... .......... .......... 40%  167M 0s\n",
            " 36200K .......... .......... .......... .......... .......... 40%  192M 0s\n",
            " 36250K .......... .......... .......... .......... .......... 40%  248M 0s\n",
            " 36300K .......... .......... .......... .......... .......... 41%  267M 0s\n",
            " 36350K .......... .......... .......... .......... .......... 41%  157M 0s\n",
            " 36400K .......... .......... .......... .......... .......... 41%  204M 0s\n",
            " 36450K .......... .......... .......... .......... .......... 41%  258M 0s\n",
            " 36500K .......... .......... .......... .......... .......... 41%  207M 0s\n",
            " 36550K .......... .......... .......... .......... .......... 41%  238M 0s\n",
            " 36600K .......... .......... .......... .......... .......... 41%  262M 0s\n",
            " 36650K .......... .......... .......... .......... .......... 41%  268M 0s\n",
            " 36700K .......... .......... .......... .......... .......... 41%  178M 0s\n",
            " 36750K .......... .......... .......... .......... .......... 41%  145M 0s\n",
            " 36800K .......... .......... .......... .......... .......... 41%  261M 0s\n",
            " 36850K .......... .......... .......... .......... .......... 41%  275M 0s\n",
            " 36900K .......... .......... .......... .......... .......... 41%  179M 0s\n",
            " 36950K .......... .......... .......... .......... .......... 41%  231M 0s\n",
            " 37000K .......... .......... .......... .......... .......... 41%  275M 0s\n",
            " 37050K .......... .......... .......... .......... .......... 41%  191M 0s\n",
            " 37100K .......... .......... .......... .......... .......... 41%  152M 0s\n",
            " 37150K .......... .......... .......... .......... .......... 42%  240M 0s\n",
            " 37200K .......... .......... .......... .......... .......... 42%  246M 0s\n",
            " 37250K .......... .......... .......... .......... .......... 42%  254M 0s\n",
            " 37300K .......... .......... .......... .......... .......... 42%  286M 0s\n",
            " 37350K .......... .......... .......... .......... .......... 42%  242M 0s\n",
            " 37400K .......... .......... .......... .......... .......... 42%  152M 0s\n",
            " 37450K .......... .......... .......... .......... .......... 42%  265M 0s\n",
            " 37500K .......... .......... .......... .......... .......... 42%  213M 0s\n",
            " 37550K .......... .......... .......... .......... .......... 42%  141M 0s\n",
            " 37600K .......... .......... .......... .......... .......... 42%  212M 0s\n",
            " 37650K .......... .......... .......... .......... .......... 42%  275M 0s\n",
            " 37700K .......... .......... .......... .......... .......... 42%  175M 0s\n",
            " 37750K .......... .......... .......... .......... .......... 42%  185M 0s\n",
            " 37800K .......... .......... .......... .......... .......... 42%  266M 0s\n",
            " 37850K .......... .......... .......... .......... .......... 42%  263M 0s\n",
            " 37900K .......... .......... .......... .......... .......... 42%  224M 0s\n",
            " 37950K .......... .......... .......... .......... .......... 42%  220M 0s\n",
            " 38000K .......... .......... .......... .......... .......... 42%  256M 0s\n",
            " 38050K .......... .......... .......... .......... .......... 43%  185M 0s\n",
            " 38100K .......... .......... .......... .......... .......... 43%  175M 0s\n",
            " 38150K .......... .......... .......... .......... .......... 43%  168M 0s\n",
            " 38200K .......... .......... .......... .......... .......... 43%  248M 0s\n",
            " 38250K .......... .......... .......... .......... .......... 43%  267M 0s\n",
            " 38300K .......... .......... .......... .......... .......... 43%  285M 0s\n",
            " 38350K .......... .......... .......... .......... .......... 43%  234M 0s\n",
            " 38400K .......... .......... .......... .......... .......... 43%  155M 0s\n",
            " 38450K .......... .......... .......... .......... .......... 43%  216M 0s\n",
            " 38500K .......... .......... .......... .......... .......... 43%  216M 0s\n",
            " 38550K .......... .......... .......... .......... .......... 43%  187M 0s\n",
            " 38600K .......... .......... .......... .......... .......... 43%  270M 0s\n",
            " 38650K .......... .......... .......... .......... .......... 43%  253M 0s\n",
            " 38700K .......... .......... .......... .......... .......... 43%  206M 0s\n",
            " 38750K .......... .......... .......... .......... .......... 43%  159M 0s\n",
            " 38800K .......... .......... .......... .......... .......... 43%  196M 0s\n",
            " 38850K .......... .......... .......... .......... .......... 43%  257M 0s\n",
            " 38900K .......... .......... .......... .......... .......... 43%  223M 0s\n",
            " 38950K .......... .......... .......... .......... .......... 44%  160M 0s\n",
            " 39000K .......... .......... .......... .......... .......... 44%  281M 0s\n",
            " 39050K .......... .......... .......... .......... .......... 44%  271M 0s\n",
            " 39100K .......... .......... .......... .......... .......... 44%  208M 0s\n",
            " 39150K .......... .......... .......... .......... .......... 44%  163M 0s\n",
            " 39200K .......... .......... .......... .......... .......... 44%  272M 0s\n",
            " 39250K .......... .......... .......... .......... .......... 44%  287M 0s\n",
            " 39300K .......... .......... .......... .......... .......... 44%  221M 0s\n",
            " 39350K .......... .......... .......... .......... .......... 44%  191M 0s\n",
            " 39400K .......... .......... .......... .......... .......... 44%  252M 0s\n",
            " 39450K .......... .......... .......... .......... .......... 44%  284M 0s\n",
            " 39500K .......... .......... .......... .......... .......... 44%  259M 0s\n",
            " 39550K .......... .......... .......... .......... .......... 44%  178M 0s\n",
            " 39600K .......... .......... .......... .......... .......... 44%  270M 0s\n",
            " 39650K .......... .......... .......... .......... .......... 44%  272M 0s\n",
            " 39700K .......... .......... .......... .......... .......... 44%  154M 0s\n",
            " 39750K .......... .......... .......... .......... .......... 44%  159M 0s\n",
            " 39800K .......... .......... .......... .......... .......... 45%  240M 0s\n",
            " 39850K .......... .......... .......... .......... .......... 45%  254M 0s\n",
            " 39900K .......... .......... .......... .......... .......... 45%  184M 0s\n",
            " 39950K .......... .......... .......... .......... .......... 45%  166M 0s\n",
            " 40000K .......... .......... .......... .......... .......... 45%  184M 0s\n",
            " 40050K .......... .......... .......... .......... .......... 45%  208M 0s\n",
            " 40100K .......... .......... .......... .......... .......... 45%  288M 0s\n",
            " 40150K .......... .......... .......... .......... .......... 45%  221M 0s\n",
            " 40200K .......... .......... .......... .......... .......... 45%  264M 0s\n",
            " 40250K .......... .......... .......... .......... .......... 45%  194M 0s\n",
            " 40300K .......... .......... .......... .......... .......... 45%  192M 0s\n",
            " 40350K .......... .......... .......... .......... .......... 45%  170M 0s\n",
            " 40400K .......... .......... .......... .......... .......... 45%  199M 0s\n",
            " 40450K .......... .......... .......... .......... .......... 45%  254M 0s\n",
            " 40500K .......... .......... .......... .......... .......... 45%  234M 0s\n",
            " 40550K .......... .......... .......... .......... .......... 45%  154M 0s\n",
            " 40600K .......... .......... .......... .......... .......... 45%  198M 0s\n",
            " 40650K .......... .......... .......... .......... .......... 45%  271M 0s\n",
            " 40700K .......... .......... .......... .......... .......... 46%  237M 0s\n",
            " 40750K .......... .......... .......... .......... .......... 46%  164M 0s\n",
            " 40800K .......... .......... .......... .......... .......... 46%  221M 0s\n",
            " 40850K .......... .......... .......... .......... .......... 46%  266M 0s\n",
            " 40900K .......... .......... .......... .......... .......... 46%  277M 0s\n",
            " 40950K .......... .......... .......... .......... .......... 46%  190M 0s\n",
            " 41000K .......... .......... .......... .......... .......... 46%  267M 0s\n",
            " 41050K .......... .......... .......... .......... .......... 46%  209M 0s\n",
            " 41100K .......... .......... .......... .......... .......... 46%  186M 0s\n",
            " 41150K .......... .......... .......... .......... .......... 46%  192M 0s\n",
            " 41200K .......... .......... .......... .......... .......... 46%  271M 0s\n",
            " 41250K .......... .......... .......... .......... .......... 46%  252M 0s\n",
            " 41300K .......... .......... .......... .......... .......... 46%  197M 0s\n",
            " 41350K .......... .......... .......... .......... .......... 46%  201M 0s\n",
            " 41400K .......... .......... .......... .......... .......... 46%  233M 0s\n",
            " 41450K .......... .......... .......... .......... .......... 46%  166M 0s\n",
            " 41500K .......... .......... .......... .......... .......... 46%  258M 0s\n",
            " 41550K .......... .......... .......... .......... .......... 46%  220M 0s\n",
            " 41600K .......... .......... .......... .......... .......... 47%  232M 0s\n",
            " 41650K .......... .......... .......... .......... .......... 47%  237M 0s\n",
            " 41700K .......... .......... .......... .......... .......... 47%  249M 0s\n",
            " 41750K .......... .......... .......... .......... .......... 47%  251M 0s\n",
            " 41800K .......... .......... .......... .......... .......... 47%  283M 0s\n",
            " 41850K .......... .......... .......... .......... .......... 47%  295M 0s\n",
            " 41900K .......... .......... .......... .......... .......... 47%  273M 0s\n",
            " 41950K .......... .......... .......... .......... .......... 47%  227M 0s\n",
            " 42000K .......... .......... .......... .......... .......... 47%  291M 0s\n",
            " 42050K .......... .......... .......... .......... .......... 47%  287M 0s\n",
            " 42100K .......... .......... .......... .......... .......... 47%  282M 0s\n",
            " 42150K .......... .......... .......... .......... .......... 47%  255M 0s\n",
            " 42200K .......... .......... .......... .......... .......... 47%  294M 0s\n",
            " 42250K .......... .......... .......... .......... .......... 47%  277M 0s\n",
            " 42300K .......... .......... .......... .......... .......... 47%  288M 0s\n",
            " 42350K .......... .......... .......... .......... .......... 47%  246M 0s\n",
            " 42400K .......... .......... .......... .......... .......... 47%  289M 0s\n",
            " 42450K .......... .......... .......... .......... .......... 48%  262M 0s\n",
            " 42500K .......... .......... .......... .......... .......... 48%  267M 0s\n",
            " 42550K .......... .......... .......... .......... .......... 48% 23.2M 0s\n",
            " 42600K .......... .......... .......... .......... .......... 48%  205M 0s\n",
            " 42650K .......... .......... .......... .......... .......... 48%  210M 0s\n",
            " 42700K .......... .......... .......... .......... .......... 48%  185M 0s\n",
            " 42750K .......... .......... .......... .......... .......... 48%  161M 0s\n",
            " 42800K .......... .......... .......... .......... .......... 48%  262M 0s\n",
            " 42850K .......... .......... .......... .......... .......... 48%  226M 0s\n",
            " 42900K .......... .......... .......... .......... .......... 48%  286M 0s\n",
            " 42950K .......... .......... .......... .......... .......... 48%  258M 0s\n",
            " 43000K .......... .......... .......... .......... .......... 48%  275M 0s\n",
            " 43050K .......... .......... .......... .......... .......... 48%  204M 0s\n",
            " 43100K .......... .......... .......... .......... .......... 48%  161M 0s\n",
            " 43150K .......... .......... .......... .......... .......... 48%  157M 0s\n",
            " 43200K .......... .......... .......... .......... .......... 48%  210M 0s\n",
            " 43250K .......... .......... .......... .......... .......... 48%  201M 0s\n",
            " 43300K .......... .......... .......... .......... .......... 48%  248M 0s\n",
            " 43350K .......... .......... .......... .......... .......... 49%  170M 0s\n",
            " 43400K .......... .......... .......... .......... .......... 49%  175M 0s\n",
            " 43450K .......... .......... .......... .......... .......... 49%  213M 0s\n",
            " 43500K .......... .......... .......... .......... .......... 49%  288M 0s\n",
            " 43550K .......... .......... .......... .......... .......... 49%  237M 0s\n",
            " 43600K .......... .......... .......... .......... .......... 49%  158M 0s\n",
            " 43650K .......... .......... .......... .......... .......... 49%  275M 0s\n",
            " 43700K .......... .......... .......... .......... .......... 49%  198M 0s\n",
            " 43750K .......... .......... .......... .......... .......... 49%  184M 0s\n",
            " 43800K .......... .......... .......... .......... .......... 49%  167M 0s\n",
            " 43850K .......... .......... .......... .......... .......... 49%  187M 0s\n",
            " 43900K .......... .......... .......... .......... .......... 49%  197M 0s\n",
            " 43950K .......... .......... .......... .......... .......... 49%  138M 0s\n",
            " 44000K .......... .......... .......... .......... .......... 49%  175M 0s\n",
            " 44050K .......... .......... .......... .......... .......... 49%  204M 0s\n",
            " 44100K .......... .......... .......... .......... .......... 49%  200M 0s\n",
            " 44150K .......... .......... .......... .......... .......... 49%  187M 0s\n",
            " 44200K .......... .......... .......... .......... .......... 49%  256M 0s\n",
            " 44250K .......... .......... .......... .......... .......... 50%  189M 0s\n",
            " 44300K .......... .......... .......... .......... .......... 50%  186M 0s\n",
            " 44350K .......... .......... .......... .......... .......... 50%  213M 0s\n",
            " 44400K .......... .......... .......... .......... .......... 50%  271M 0s\n",
            " 44450K .......... .......... .......... .......... .......... 50%  203M 0s\n",
            " 44500K .......... .......... .......... .......... .......... 50%  209M 0s\n",
            " 44550K .......... .......... .......... .......... .......... 50%  170M 0s\n",
            " 44600K .......... .......... .......... .......... .......... 50%  189M 0s\n",
            " 44650K .......... .......... .......... .......... .......... 50%  152M 0s\n",
            " 44700K .......... .......... .......... .......... .......... 50%  159M 0s\n",
            " 44750K .......... .......... .......... .......... .......... 50%  211M 0s\n",
            " 44800K .......... .......... .......... .......... .......... 50%  171M 0s\n",
            " 44850K .......... .......... .......... .......... .......... 50%  265M 0s\n",
            " 44900K .......... .......... .......... .......... .......... 50%  186M 0s\n",
            " 44950K .......... .......... .......... .......... .......... 50%  140M 0s\n",
            " 45000K .......... .......... .......... .......... .......... 50%  250M 0s\n",
            " 45050K .......... .......... .......... .......... .......... 50%  252M 0s\n",
            " 45100K .......... .......... .......... .......... .......... 50%  174M 0s\n",
            " 45150K .......... .......... .......... .......... .......... 51%  136M 0s\n",
            " 45200K .......... .......... .......... .......... .......... 51%  261M 0s\n",
            " 45250K .......... .......... .......... .......... .......... 51%  216M 0s\n",
            " 45300K .......... .......... .......... .......... .......... 51%  254M 0s\n",
            " 45350K .......... .......... .......... .......... .......... 51%  235M 0s\n",
            " 45400K .......... .......... .......... .......... .......... 51%  205M 0s\n",
            " 45450K .......... .......... .......... .......... .......... 51%  208M 0s\n",
            " 45500K .......... .......... .......... .......... .......... 51%  266M 0s\n",
            " 45550K .......... .......... .......... .......... .......... 51%  229M 0s\n",
            " 45600K .......... .......... .......... .......... .......... 51%  283M 0s\n",
            " 45650K .......... .......... .......... .......... .......... 51%  159M 0s\n",
            " 45700K .......... .......... .......... .......... .......... 51%  258M 0s\n",
            " 45750K .......... .......... .......... .......... .......... 51%  128M 0s\n",
            " 45800K .......... .......... .......... .......... .......... 51%  210M 0s\n",
            " 45850K .......... .......... .......... .......... .......... 51%  244M 0s\n",
            " 45900K .......... .......... .......... .......... .......... 51%  267M 0s\n",
            " 45950K .......... .......... .......... .......... .......... 51%  190M 0s\n",
            " 46000K .......... .......... .......... .......... .......... 52%  250M 0s\n",
            " 46050K .......... .......... .......... .......... .......... 52%  264M 0s\n",
            " 46100K .......... .......... .......... .......... .......... 52%  194M 0s\n",
            " 46150K .......... .......... .......... .......... .......... 52%  144M 0s\n",
            " 46200K .......... .......... .......... .......... .......... 52%  228M 0s\n",
            " 46250K .......... .......... .......... .......... .......... 52%  195M 0s\n",
            " 46300K .......... .......... .......... .......... .......... 52%  245M 0s\n",
            " 46350K .......... .......... .......... .......... .......... 52%  196M 0s\n",
            " 46400K .......... .......... .......... .......... .......... 52%  219M 0s\n",
            " 46450K .......... .......... .......... .......... .......... 52%  149M 0s\n",
            " 46500K .......... .......... .......... .......... .......... 52%  222M 0s\n",
            " 46550K .......... .......... .......... .......... .......... 52%  187M 0s\n",
            " 46600K .......... .......... .......... .......... .......... 52%  172M 0s\n",
            " 46650K .......... .......... .......... .......... .......... 52%  247M 0s\n",
            " 46700K .......... .......... .......... .......... .......... 52%  267M 0s\n",
            " 46750K .......... .......... .......... .......... .......... 52%  242M 0s\n",
            " 46800K .......... .......... .......... .......... .......... 52%  154M 0s\n",
            " 46850K .......... .......... .......... .......... .......... 52%  230M 0s\n",
            " 46900K .......... .......... .......... .......... .......... 53%  257M 0s\n",
            " 46950K .......... .......... .......... .......... .......... 53%  184M 0s\n",
            " 47000K .......... .......... .......... .......... .......... 53%  240M 0s\n",
            " 47050K .......... .......... .......... .......... .......... 53%  264M 0s\n",
            " 47100K .......... .......... .......... .......... .......... 53%  221M 0s\n",
            " 47150K .......... .......... .......... .......... .......... 53%  166M 0s\n",
            " 47200K .......... .......... .......... .......... .......... 53%  361M 0s\n",
            " 47250K .......... .......... .......... .......... .......... 53%  468M 0s\n",
            " 47300K .......... .......... .......... .......... .......... 53%  384M 0s\n",
            " 47350K .......... .......... .......... .......... .......... 53%  325M 0s\n",
            " 47400K .......... .......... .......... .......... .......... 53%  286M 0s\n",
            " 47450K .......... .......... .......... .......... .......... 53%  224M 0s\n",
            " 47500K .......... .......... .......... .......... .......... 53%  289M 0s\n",
            " 47550K .......... .......... .......... .......... .......... 53%  240M 0s\n",
            " 47600K .......... .......... .......... .......... .......... 53%  214M 0s\n",
            " 47650K .......... .......... .......... .......... .......... 53%  271M 0s\n",
            " 47700K .......... .......... .......... .......... .......... 53%  210M 0s\n",
            " 47750K .......... .......... .......... .......... .......... 53%  168M 0s\n",
            " 47800K .......... .......... .......... .......... .......... 54%  184M 0s\n",
            " 47850K .......... .......... .......... .......... .......... 54%  246M 0s\n",
            " 47900K .......... .......... .......... .......... .......... 54%  288M 0s\n",
            " 47950K .......... .......... .......... .......... .......... 54%  159M 0s\n",
            " 48000K .......... .......... .......... .......... .......... 54%  217M 0s\n",
            " 48050K .......... .......... .......... .......... .......... 54%  209M 0s\n",
            " 48100K .......... .......... .......... .......... .......... 54%  187M 0s\n",
            " 48150K .......... .......... .......... .......... .......... 54%  213M 0s\n",
            " 48200K .......... .......... .......... .......... .......... 54%  267M 0s\n",
            " 48250K .......... .......... .......... .......... .......... 54%  265M 0s\n",
            " 48300K .......... .......... .......... .......... .......... 54%  212M 0s\n",
            " 48350K .......... .......... .......... .......... .......... 54%  145M 0s\n",
            " 48400K .......... .......... .......... .......... .......... 54%  228M 0s\n",
            " 48450K .......... .......... .......... .......... .......... 54%  207M 0s\n",
            " 48500K .......... .......... .......... .......... .......... 54%  260M 0s\n",
            " 48550K .......... .......... .......... .......... .......... 54%  243M 0s\n",
            " 48600K .......... .......... .......... .......... .......... 54%  273M 0s\n",
            " 48650K .......... .......... .......... .......... .......... 55%  171M 0s\n",
            " 48700K .......... .......... .......... .......... .......... 55%  175M 0s\n",
            " 48750K .......... .......... .......... .......... .......... 55%  156M 0s\n",
            " 48800K .......... .......... .......... .......... .......... 55%  279M 0s\n",
            " 48850K .......... .......... .......... .......... .......... 55%  287M 0s\n",
            " 48900K .......... .......... .......... .......... .......... 55%  289M 0s\n",
            " 48950K .......... .......... .......... .......... .......... 55%  209M 0s\n",
            " 49000K .......... .......... .......... .......... .......... 55%  210M 0s\n",
            " 49050K .......... .......... .......... .......... .......... 55%  227M 0s\n",
            " 49100K .......... .......... .......... .......... .......... 55%  203M 0s\n",
            " 49150K .......... .......... .......... .......... .......... 55%  154M 0s\n",
            " 49200K .......... .......... .......... .......... .......... 55%  284M 0s\n",
            " 49250K .......... .......... .......... .......... .......... 55%  284M 0s\n",
            " 49300K .......... .......... .......... .......... .......... 55%  227M 0s\n",
            " 49350K .......... .......... .......... .......... .......... 55%  162M 0s\n",
            " 49400K .......... .......... .......... .......... .......... 55%  191M 0s\n",
            " 49450K .......... .......... .......... .......... .......... 55%  183M 0s\n",
            " 49500K .......... .......... .......... .......... .......... 55%  231M 0s\n",
            " 49550K .......... .......... .......... .......... .......... 56%  220M 0s\n",
            " 49600K .......... .......... .......... .......... .......... 56%  171M 0s\n",
            " 49650K .......... .......... .......... .......... .......... 56%  194M 0s\n",
            " 49700K .......... .......... .......... .......... .......... 56%  210M 0s\n",
            " 49750K .......... .......... .......... .......... .......... 56%  176M 0s\n",
            " 49800K .......... .......... .......... .......... .......... 56%  257M 0s\n",
            " 49850K .......... .......... .......... .......... .......... 56%  250M 0s\n",
            " 49900K .......... .......... .......... .......... .......... 56%  267M 0s\n",
            " 49950K .......... .......... .......... .......... .......... 56%  126M 0s\n",
            " 50000K .......... .......... .......... .......... .......... 56%  152M 0s\n",
            " 50050K .......... .......... .......... .......... .......... 56%  178M 0s\n",
            " 50100K .......... .......... .......... .......... .......... 56%  248M 0s\n",
            " 50150K .......... .......... .......... .......... .......... 56%  205M 0s\n",
            " 50200K .......... .......... .......... .......... .......... 56%  174M 0s\n",
            " 50250K .......... .......... .......... .......... .......... 56%  169M 0s\n",
            " 50300K .......... .......... .......... .......... .......... 56%  208M 0s\n",
            " 50350K .......... .......... .......... .......... .......... 56%  179M 0s\n",
            " 50400K .......... .......... .......... .......... .......... 56%  248M 0s\n",
            " 50450K .......... .......... .......... .......... .......... 57%  273M 0s\n",
            " 50500K .......... .......... .......... .......... .......... 57%  273M 0s\n",
            " 50550K .......... .......... .......... .......... .......... 57%  183M 0s\n",
            " 50600K .......... .......... .......... .......... .......... 57%  172M 0s\n",
            " 50650K .......... .......... .......... .......... .......... 57%  169M 0s\n",
            " 50700K .......... .......... .......... .......... .......... 57%  239M 0s\n",
            " 50750K .......... .......... .......... .......... .......... 57%  235M 0s\n",
            " 50800K .......... .......... .......... .......... .......... 57%  276M 0s\n",
            " 50850K .......... .......... .......... .......... .......... 57%  207M 0s\n",
            " 50900K .......... .......... .......... .......... .......... 57%  278M 0s\n",
            " 50950K .......... .......... .......... .......... .......... 57%  256M 0s\n",
            " 51000K .......... .......... .......... .......... .......... 57%  284M 0s\n",
            " 51050K .......... .......... .......... .......... .......... 57%  296M 0s\n",
            " 51100K .......... .......... .......... .......... .......... 57%  277M 0s\n",
            " 51150K .......... .......... .......... .......... .......... 57%  246M 0s\n",
            " 51200K .......... .......... .......... .......... .......... 57%  287M 0s\n",
            " 51250K .......... .......... .......... .......... .......... 57%  293M 0s\n",
            " 51300K .......... .......... .......... .......... .......... 57%  266M 0s\n",
            " 51350K .......... .......... .......... .......... .......... 58%  260M 0s\n",
            " 51400K .......... .......... .......... .......... .......... 58%  285M 0s\n",
            " 51450K .......... .......... .......... .......... .......... 58%  386M 0s\n",
            " 51500K .......... .......... .......... .......... .......... 58%  509M 0s\n",
            " 51550K .......... .......... .......... .......... .......... 58%  429M 0s\n",
            " 51600K .......... .......... .......... .......... .......... 58%  505M 0s\n",
            " 51650K .......... .......... .......... .......... .......... 58%  461M 0s\n",
            " 51700K .......... .......... .......... .......... .......... 58%  475M 0s\n",
            " 51750K .......... .......... .......... .......... .......... 58%  355M 0s\n",
            " 51800K .......... .......... .......... .......... .......... 58%  214M 0s\n",
            " 51850K .......... .......... .......... .......... .......... 58%  184M 0s\n",
            " 51900K .......... .......... .......... .......... .......... 58%  202M 0s\n",
            " 51950K .......... .......... .......... .......... .......... 58%  182M 0s\n",
            " 52000K .......... .......... .......... .......... .......... 58%  194M 0s\n",
            " 52050K .......... .......... .......... .......... .......... 58%  165M 0s\n",
            " 52100K .......... .......... .......... .......... .......... 58%  186M 0s\n",
            " 52150K .......... .......... .......... .......... .......... 58%  175M 0s\n",
            " 52200K .......... .......... .......... .......... .......... 59%  185M 0s\n",
            " 52250K .......... .......... .......... .......... .......... 59%  202M 0s\n",
            " 52300K .......... .......... .......... .......... .......... 59%  174M 0s\n",
            " 52350K .......... .......... .......... .......... .......... 59%  155M 0s\n",
            " 52400K .......... .......... .......... .......... .......... 59%  189M 0s\n",
            " 52450K .......... .......... .......... .......... .......... 59%  166M 0s\n",
            " 52500K .......... .......... .......... .......... .......... 59%  186M 0s\n",
            " 52550K .......... .......... .......... .......... .......... 59%  118M 0s\n",
            " 52600K .......... .......... .......... .......... .......... 59%  154M 0s\n",
            " 52650K .......... .......... .......... .......... .......... 59%  196M 0s\n",
            " 52700K .......... .......... .......... .......... .......... 59%  208M 0s\n",
            " 52750K .......... .......... .......... .......... .......... 59%  205M 0s\n",
            " 52800K .......... .......... .......... .......... .......... 59%  284M 0s\n",
            " 52850K .......... .......... .......... .......... .......... 59%  187M 0s\n",
            " 52900K .......... .......... .......... .......... .......... 59%  182M 0s\n",
            " 52950K .......... .......... .......... .......... .......... 59%  223M 0s\n",
            " 53000K .......... .......... .......... .......... .......... 59%  479M 0s\n",
            " 53050K .......... .......... .......... .......... .......... 59%  411M 0s\n",
            " 53100K .......... .......... .......... .......... .......... 60%  504M 0s\n",
            " 53150K .......... .......... .......... .......... .......... 60%  378M 0s\n",
            " 53200K .......... .......... .......... .......... .......... 60%  487M 0s\n",
            " 53250K .......... .......... .......... .......... .......... 60%  491M 0s\n",
            " 53300K .......... .......... .......... .......... .......... 60%  272M 0s\n",
            " 53350K .......... .......... .......... .......... .......... 60%  249M 0s\n",
            " 53400K .......... .......... .......... .......... .......... 60%  270M 0s\n",
            " 53450K .......... .......... .......... .......... .......... 60%  284M 0s\n",
            " 53500K .......... .......... .......... .......... .......... 60%  279M 0s\n",
            " 53550K .......... .......... .......... .......... .......... 60%  152M 0s\n",
            " 53600K .......... .......... .......... .......... .......... 60%  164M 0s\n",
            " 53650K .......... .......... .......... .......... .......... 60%  165M 0s\n",
            " 53700K .......... .......... .......... .......... .......... 60%  225M 0s\n",
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            " 53800K .......... .......... .......... .......... .......... 60%  291M 0s\n",
            " 53850K .......... .......... .......... .......... .......... 60%  178M 0s\n",
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            " 54000K .......... .......... .......... .......... .......... 61%  174M 0s\n",
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            " 54100K .......... .......... .......... .......... .......... 61%  228M 0s\n",
            " 54150K .......... .......... .......... .......... .......... 61%  189M 0s\n",
            " 54200K .......... .......... .......... .......... .......... 61%  176M 0s\n",
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            " 54300K .......... .......... .......... .......... .......... 61%  179M 0s\n",
            " 54350K .......... .......... .......... .......... .......... 61%  171M 0s\n",
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            " 54450K .......... .......... .......... .......... .......... 61%  177M 0s\n",
            " 54500K .......... .......... .......... .......... .......... 61%  218M 0s\n",
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            " 54600K .......... .......... .......... .......... .......... 61%  289M 0s\n",
            " 54650K .......... .......... .......... .......... .......... 61%  239M 0s\n",
            " 54700K .......... .......... .......... .......... .......... 61%  185M 0s\n",
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            " 54950K .......... .......... .......... .......... .......... 62%  187M 0s\n",
            " 55000K .......... .......... .......... .......... .......... 62%  197M 0s\n",
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            " 60650K .......... .......... .......... .......... .......... 68%  255M 0s\n",
            " 60700K .......... .......... .......... .......... .......... 68%  218M 0s\n",
            " 60750K .......... .......... .......... .......... .......... 68%  205M 0s\n",
            " 60800K .......... .......... .......... .......... .......... 68%  262M 0s\n",
            " 60850K .......... .......... .......... .......... .......... 68%  276M 0s\n",
            " 60900K .......... .......... .......... .......... .......... 68%  261M 0s\n",
            " 60950K .......... .......... .......... .......... .......... 68%  239M 0s\n",
            " 61000K .......... .......... .......... .......... .......... 68%  273M 0s\n",
            " 61050K .......... .......... .......... .......... .......... 69%  267M 0s\n",
            " 61100K .......... .......... .......... .......... .......... 69%  262M 0s\n",
            " 61150K .......... .......... .......... .......... .......... 69%  241M 0s\n",
            " 61200K .......... .......... .......... .......... .......... 69%  334M 0s\n",
            " 61250K .......... .......... .......... .......... .......... 69%  487M 0s\n",
            " 61300K .......... .......... .......... .......... .......... 69%  508M 0s\n",
            " 61350K .......... .......... .......... .......... .......... 69%  458M 0s\n",
            " 61400K .......... .......... .......... .......... .......... 69%  488M 0s\n",
            " 61450K .......... .......... .......... .......... .......... 69%  463M 0s\n",
            " 61500K .......... .......... .......... .......... .......... 69%  495M 0s\n",
            " 61550K .......... .......... .......... .......... .......... 69%  404M 0s\n",
            " 61600K .......... .......... .......... .......... .......... 69%  259M 0s\n",
            " 61650K .......... .......... .......... .......... .......... 69%  228M 0s\n",
            " 61700K .......... .......... .......... .......... .......... 69%  270M 0s\n",
            " 61750K .......... .......... .......... .......... .......... 69%  246M 0s\n",
            " 61800K .......... .......... .......... .......... .......... 69%  281M 0s\n",
            " 61850K .......... .......... .......... .......... .......... 69%  205M 0s\n",
            " 61900K .......... .......... .......... .......... .......... 69%  225M 0s\n",
            " 61950K .......... .......... .......... .......... .......... 70%  208M 0s\n",
            " 62000K .......... .......... .......... .......... .......... 70%  213M 0s\n",
            " 62050K .......... .......... .......... .......... .......... 70%  221M 0s\n",
            " 62100K .......... .......... .......... .......... .......... 70%  225M 0s\n",
            " 62150K .......... .......... .......... .......... .......... 70%  220M 0s\n",
            " 62200K .......... .......... .......... .......... .......... 70%  167M 0s\n",
            " 62250K .......... .......... .......... .......... .......... 70%  171M 0s\n",
            " 62300K .......... .......... .......... .......... .......... 70%  218M 0s\n",
            " 62350K .......... .......... .......... .......... .......... 70%  183M 0s\n",
            " 62400K .......... .......... .......... .......... .......... 70%  224M 0s\n",
            " 62450K .......... .......... .......... .......... .......... 70%  244M 0s\n",
            " 62500K .......... .......... .......... .......... .......... 70%  210M 0s\n",
            " 62550K .......... .......... .......... .......... .......... 70%  129M 0s\n",
            " 62600K .......... .......... .......... .......... .......... 70%  217M 0s\n",
            " 62650K .......... .......... .......... .......... .......... 70%  214M 0s\n",
            " 62700K .......... .......... .......... .......... .......... 70%  195M 0s\n",
            " 62750K .......... .......... .......... .......... .......... 70%  177M 0s\n",
            " 62800K .......... .......... .......... .......... .......... 70%  264M 0s\n",
            " 62850K .......... .......... .......... .......... .......... 71%  247M 0s\n",
            " 62900K .......... .......... .......... .......... .......... 71%  265M 0s\n",
            " 62950K .......... .......... .......... .......... .......... 71%  225M 0s\n",
            " 63000K .......... .......... .......... .......... .......... 71%  259M 0s\n",
            " 63050K .......... .......... .......... .......... .......... 71%  266M 0s\n",
            " 63100K .......... .......... .......... .......... .......... 71%  221M 0s\n",
            " 63150K .......... .......... .......... .......... .......... 71%  199M 0s\n",
            " 63200K .......... .......... .......... .......... .......... 71%  278M 0s\n",
            " 63250K .......... .......... .......... .......... .......... 71%  154M 0s\n",
            " 63300K .......... .......... .......... .......... .......... 71%  197M 0s\n",
            " 63350K .......... .......... .......... .......... .......... 71%  224M 0s\n",
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            " 63500K .......... .......... .......... .......... .......... 71%  222M 0s\n",
            " 63550K .......... .......... .......... .......... .......... 71%  224M 0s\n",
            " 63600K .......... .......... .......... .......... .......... 71%  209M 0s\n",
            " 63650K .......... .......... .......... .......... .......... 71%  223M 0s\n",
            " 63700K .......... .......... .......... .......... .......... 72%  271M 0s\n",
            " 63750K .......... .......... .......... .......... .......... 72%  233M 0s\n",
            " 63800K .......... .......... .......... .......... .......... 72%  205M 0s\n",
            " 63850K .......... .......... .......... .......... .......... 72%  254M 0s\n",
            " 63900K .......... .......... .......... .......... .......... 72%  282M 0s\n",
            " 63950K .......... .......... .......... .......... .......... 72%  215M 0s\n",
            " 64000K .......... .......... .......... .......... .......... 72%  239M 0s\n",
            " 64050K .......... .......... .......... .......... .......... 72%  257M 0s\n",
            " 64100K .......... .......... .......... .......... .......... 72%  256M 0s\n",
            " 64150K .......... .......... .......... .......... .......... 72%  199M 0s\n",
            " 64200K .......... .......... .......... .......... .......... 72%  184M 0s\n",
            " 64250K .......... .......... .......... .......... .......... 72%  270M 0s\n",
            " 64300K .......... .......... .......... .......... .......... 72%  263M 0s\n",
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            " 64450K .......... .......... .......... .......... .......... 72%  286M 0s\n",
            " 64500K .......... .......... .......... .......... .......... 72%  200M 0s\n",
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            " 67000K .......... .......... .......... .......... .......... 75%  270M 0s\n",
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            " 73150K .......... .......... .......... .......... .......... 82%  237M 0s\n",
            " 73200K .......... .......... .......... .......... .......... 82%  261M 0s\n",
            " 73250K .......... .......... .......... .......... .......... 82%  277M 0s\n",
            " 73300K .......... .......... .......... .......... .......... 82%  233M 0s\n",
            " 73350K .......... .......... .......... .......... .......... 82%  227M 0s\n",
            " 73400K .......... .......... .......... .......... .......... 82%  254M 0s\n",
            " 73450K .......... .......... .......... .......... .......... 83%  243M 0s\n",
            " 73500K .......... .......... .......... .......... .......... 83%  273M 0s\n",
            " 73550K .......... .......... .......... .......... .......... 83%  226M 0s\n",
            " 73600K .......... .......... .......... .......... .......... 83%  290M 0s\n",
            " 73650K .......... .......... .......... .......... .......... 83%  236M 0s\n",
            " 73700K .......... .......... .......... .......... .......... 83%  215M 0s\n",
            " 73750K .......... .......... .......... .......... .......... 83%  205M 0s\n",
            " 73800K .......... .......... .......... .......... .......... 83%  247M 0s\n",
            " 73850K .......... .......... .......... .......... .......... 83%  232M 0s\n",
            " 73900K .......... .......... .......... .......... .......... 83%  268M 0s\n",
            " 73950K .......... .......... .......... .......... .......... 83%  214M 0s\n",
            " 74000K .......... .......... .......... .......... .......... 83%  267M 0s\n",
            " 74050K .......... .......... .......... .......... .......... 83%  240M 0s\n",
            " 74100K .......... .......... .......... .......... .......... 83%  239M 0s\n",
            " 74150K .......... .......... .......... .......... .......... 83%  181M 0s\n",
            " 74200K .......... .......... .......... .......... .......... 83%  223M 0s\n",
            " 74250K .......... .......... .......... .......... .......... 83%  210M 0s\n",
            " 74300K .......... .......... .......... .......... .......... 83%  249M 0s\n",
            " 74350K .......... .......... .......... .......... .......... 84%  224M 0s\n",
            " 74400K .......... .......... .......... .......... .......... 84%  251M 0s\n",
            " 74450K .......... .......... .......... .......... .......... 84%  224M 0s\n",
            " 74500K .......... .......... .......... .......... .......... 84%  210M 0s\n",
            " 74550K .......... .......... .......... .......... .......... 84%  226M 0s\n",
            " 74600K .......... .......... .......... .......... .......... 84%  272M 0s\n",
            " 74650K .......... .......... .......... .......... .......... 84%  258M 0s\n",
            " 74700K .......... .......... .......... .......... .......... 84%  271M 0s\n",
            " 74750K .......... .......... .......... .......... .......... 84%  219M 0s\n",
            " 74800K .......... .......... .......... .......... .......... 84%  218M 0s\n",
            " 74850K .......... .......... .......... .......... .......... 84%  232M 0s\n",
            " 74900K .......... .......... .......... .......... .......... 84%  231M 0s\n",
            " 74950K .......... .......... .......... .......... .......... 84%  209M 0s\n",
            " 75000K .......... .......... .......... .......... .......... 84%  231M 0s\n",
            " 75050K .......... .......... .......... .......... .......... 84%  267M 0s\n",
            " 75100K .......... .......... .......... .......... .......... 84%  243M 0s\n",
            " 75150K .......... .......... .......... .......... .......... 84%  190M 0s\n",
            " 75200K .......... .......... .......... .......... .......... 84%  204M 0s\n",
            " 75250K .......... .......... .......... .......... .......... 85%  204M 0s\n",
            " 75300K .......... .......... .......... .......... .......... 85%  226M 0s\n",
            " 75350K .......... .......... .......... .......... .......... 85%  194M 0s\n",
            " 75400K .......... .......... .......... .......... .......... 85%  233M 0s\n",
            " 75450K .......... .......... .......... .......... .......... 85%  243M 0s\n",
            " 75500K .......... .......... .......... .......... .......... 85%  207M 0s\n",
            " 75550K .......... .......... .......... .......... .......... 85%  176M 0s\n",
            " 75600K .......... .......... .......... .......... .......... 85%  139M 0s\n",
            " 75650K .......... .......... .......... .......... .......... 85%  255M 0s\n",
            " 75700K .......... .......... .......... .......... .......... 85%  212M 0s\n",
            " 75750K .......... .......... .......... .......... .......... 85%  204M 0s\n",
            " 75800K .......... .......... .......... .......... .......... 85%  229M 0s\n",
            " 75850K .......... .......... .......... .......... .......... 85%  225M 0s\n",
            " 75900K .......... .......... .......... .......... .......... 85%  232M 0s\n",
            " 75950K .......... .......... .......... .......... .......... 85%  230M 0s\n",
            " 76000K .......... .......... .......... .......... .......... 85%  229M 0s\n",
            " 76050K .......... .......... .......... .......... .......... 85%  192M 0s\n",
            " 76100K .......... .......... .......... .......... .......... 86%  224M 0s\n",
            " 76150K .......... .......... .......... .......... .......... 86%  227M 0s\n",
            " 76200K .......... .......... .......... .......... .......... 86%  216M 0s\n",
            " 76250K .......... .......... .......... .......... .......... 86%  203M 0s\n",
            " 76300K .......... .......... .......... .......... .......... 86%  252M 0s\n",
            " 76350K .......... .......... .......... .......... .......... 86%  215M 0s\n",
            " 76400K .......... .......... .......... .......... .......... 86%  238M 0s\n",
            " 76450K .......... .......... .......... .......... .......... 86%  213M 0s\n",
            " 76500K .......... .......... .......... .......... .......... 86%  212M 0s\n",
            " 76550K .......... .......... .......... .......... .......... 86%  198M 0s\n",
            " 76600K .......... .......... .......... .......... .......... 86%  257M 0s\n",
            " 76650K .......... .......... .......... .......... .......... 86%  262M 0s\n",
            " 76700K .......... .......... .......... .......... .......... 86%  248M 0s\n",
            " 76750K .......... .......... .......... .......... .......... 86%  191M 0s\n",
            " 76800K .......... .......... .......... .......... .......... 86%  217M 0s\n",
            " 76850K .......... .......... .......... .......... .......... 86%  223M 0s\n",
            " 76900K .......... .......... .......... .......... .......... 86%  250M 0s\n",
            " 76950K .......... .......... .......... .......... .......... 86%  221M 0s\n",
            " 77000K .......... .......... .......... .......... .......... 87%  275M 0s\n",
            " 77050K .......... .......... .......... .......... .......... 87%  270M 0s\n",
            " 77100K .......... .......... .......... .......... .......... 87%  278M 0s\n",
            " 77150K .......... .......... .......... .......... .......... 87% 92.5M 0s\n",
            " 77200K .......... .......... .......... .......... .......... 87%  212M 0s\n",
            " 77250K .......... .......... .......... .......... .......... 87%  245M 0s\n",
            " 77300K .......... .......... .......... .......... .......... 87%  256M 0s\n",
            " 77350K .......... .......... .......... .......... .......... 87%  229M 0s\n",
            " 77400K .......... .......... .......... .......... .......... 87%  248M 0s\n",
            " 77450K .......... .......... .......... .......... .......... 87%  213M 0s\n",
            " 77500K .......... .......... .......... .......... .......... 87%  230M 0s\n",
            " 77550K .......... .......... .......... .......... .......... 87%  215M 0s\n",
            " 77600K .......... .......... .......... .......... .......... 87%  231M 0s\n",
            " 77650K .......... .......... .......... .......... .......... 87%  221M 0s\n",
            " 77700K .......... .......... .......... .......... .......... 87%  255M 0s\n",
            " 77750K .......... .......... .......... .......... .......... 87%  261M 0s\n",
            " 77800K .......... .......... .......... .......... .......... 87%  273M 0s\n",
            " 77850K .......... .......... .......... .......... .......... 87%  258M 0s\n",
            " 77900K .......... .......... .......... .......... .......... 88%  254M 0s\n",
            " 77950K .......... .......... .......... .......... .......... 88%  174M 0s\n",
            " 78000K .......... .......... .......... .......... .......... 88%  242M 0s\n",
            " 78050K .......... .......... .......... .......... .......... 88%  250M 0s\n",
            " 78100K .......... .......... .......... .......... .......... 88%  254M 0s\n",
            " 78150K .......... .......... .......... .......... .......... 88%  247M 0s\n",
            " 78200K .......... .......... .......... .......... .......... 88%  231M 0s\n",
            " 78250K .......... .......... .......... .......... .......... 88%  222M 0s\n",
            " 78300K .......... .......... .......... .......... .......... 88%  261M 0s\n",
            " 78350K .......... .......... .......... .......... .......... 88%  170M 0s\n",
            " 78400K .......... .......... .......... .......... .......... 88%  207M 0s\n",
            " 78450K .......... .......... .......... .......... .......... 88%  240M 0s\n",
            " 78500K .......... .......... .......... .......... .......... 88%  260M 0s\n",
            " 78550K .......... .......... .......... .......... .......... 88%  187M 0s\n",
            " 78600K .......... .......... .......... .......... .......... 88%  222M 0s\n",
            " 78650K .......... .......... .......... .......... .......... 88%  217M 0s\n",
            " 78700K .......... .......... .......... .......... .......... 88%  238M 0s\n",
            " 78750K .......... .......... .......... .......... .......... 88%  222M 0s\n",
            " 78800K .......... .......... .......... .......... .......... 89%  263M 0s\n",
            " 78850K .......... .......... .......... .......... .......... 89%  274M 0s\n",
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            " 78950K .......... .......... .......... .......... .......... 89%  194M 0s\n",
            " 79000K .......... .......... .......... .......... .......... 89%  225M 0s\n",
            " 79050K .......... .......... .......... .......... .......... 89%  226M 0s\n",
            " 79100K .......... .......... .......... .......... .......... 89%  236M 0s\n",
            " 79150K .......... .......... .......... .......... .......... 89%  199M 0s\n",
            " 79200K .......... .......... .......... .......... .......... 89%  271M 0s\n",
            " 79250K .......... .......... .......... .......... .......... 89%  266M 0s\n",
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            " 79350K .......... .......... .......... .......... .......... 89%  194M 0s\n",
            " 79400K .......... .......... .......... .......... .......... 89%  240M 0s\n",
            " 79450K .......... .......... .......... .......... .......... 89%  209M 0s\n",
            " 79500K .......... .......... .......... .......... .......... 89%  199M 0s\n",
            " 79550K .......... .......... .......... .......... .......... 89%  178M 0s\n",
            " 79600K .......... .......... .......... .......... .......... 89%  223M 0s\n",
            " 79650K .......... .......... .......... .......... .......... 90%  257M 0s\n",
            " 79700K .......... .......... .......... .......... .......... 90%  198M 0s\n",
            " 79750K .......... .......... .......... .......... .......... 90%  198M 0s\n",
            " 79800K .......... .......... .......... .......... .......... 90%  230M 0s\n",
            " 79850K .......... .......... .......... .......... .......... 90%  243M 0s\n",
            " 79900K .......... .......... .......... .......... .......... 90%  231M 0s\n",
            " 79950K .......... .......... .......... .......... .......... 90%  209M 0s\n",
            " 80000K .......... .......... .......... .......... .......... 90%  277M 0s\n",
            " 80050K .......... .......... .......... .......... .......... 90%  214M 0s\n",
            " 80100K .......... .......... .......... .......... .......... 90%  242M 0s\n",
            " 80150K .......... .......... .......... .......... .......... 90%  226M 0s\n",
            " 80200K .......... .......... .......... .......... .......... 90%  220M 0s\n",
            " 80250K .......... .......... .......... .......... .......... 90%  233M 0s\n",
            " 80300K .......... .......... .......... .......... .......... 90%  253M 0s\n",
            " 80350K .......... .......... .......... .......... .......... 90%  199M 0s\n",
            " 80400K .......... .......... .......... .......... .......... 90%  224M 0s\n",
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            " 80550K .......... .......... .......... .......... .......... 91%  226M 0s\n",
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            " 80700K .......... .......... .......... .......... .......... 91%  255M 0s\n",
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            " 80950K .......... .......... .......... .......... .......... 91%  201M 0s\n",
            " 81000K .......... .......... .......... .......... .......... 91%  244M 0s\n",
            " 81050K .......... .......... .......... .......... .......... 91%  263M 0s\n",
            " 81100K .......... .......... .......... .......... .......... 91%  257M 0s\n",
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            " 81200K .......... .......... .......... .......... .......... 91%  223M 0s\n",
            " 81250K .......... .......... .......... .......... .......... 91%  265M 0s\n",
            " 81300K .......... .......... .......... .......... .......... 91%  234M 0s\n",
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            " 81400K .......... .......... .......... .......... .......... 91%  276M 0s\n",
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            " 81900K .......... .......... .......... .......... .......... 92%  273M 0s\n",
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            " 82150K .......... .......... .......... .......... .......... 92%  221M 0s\n",
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            " 82250K .......... .......... .......... .......... .......... 92%  261M 0s\n",
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            " 83000K .......... .......... .......... .......... .......... 93%  274M 0s\n",
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            " 83100K .......... .......... .......... .......... .......... 93%  201M 0s\n",
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            " 84000K .......... .......... .......... .......... .......... 94%  233M 0s\n",
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            " 85550K .......... .......... .......... .......... .......... 96%  200M 0s\n",
            " 85600K .......... .......... .......... .......... .......... 96%  302M 0s\n",
            " 85650K .......... .......... .......... .......... .......... 96%  309M 0s\n",
            " 85700K .......... .......... .......... .......... .......... 96%  289M 0s\n",
            " 85750K .......... .......... .......... .......... .......... 96%  187M 0s\n",
            " 85800K .......... .......... .......... .......... .......... 96%  232M 0s\n",
            " 85850K .......... .......... .......... .......... .......... 97%  279M 0s\n",
            " 85900K .......... .......... .......... .......... .......... 97%  253M 0s\n",
            " 85950K .......... .......... .......... .......... .......... 97%  230M 0s\n",
            " 86000K .......... .......... .......... .......... .......... 97%  262M 0s\n",
            " 86050K .......... .......... .......... .......... .......... 97%  224M 0s\n",
            " 86100K .......... .......... .......... .......... .......... 97%  207M 0s\n",
            " 86150K .......... .......... .......... .......... .......... 97%  206M 0s\n",
            " 86200K .......... .......... .......... .......... .......... 97%  246M 0s\n",
            " 86250K .......... .......... .......... .......... .......... 97%  207M 0s\n",
            " 86300K .......... .......... .......... .......... .......... 97%  259M 0s\n",
            " 86350K .......... .......... .......... .......... .......... 97%  194M 0s\n",
            " 86400K .......... .......... .......... .......... .......... 97%  258M 0s\n",
            " 86450K .......... .......... .......... .......... .......... 97%  185M 0s\n",
            " 86500K .......... .......... .......... .......... .......... 97%  207M 0s\n",
            " 86550K .......... .......... .......... .......... .......... 97%  199M 0s\n",
            " 86600K .......... .......... .......... .......... .......... 97%  206M 0s\n",
            " 86650K .......... .......... .......... .......... .......... 97%  250M 0s\n",
            " 86700K .......... .......... .......... .......... .......... 97%  254M 0s\n",
            " 86750K .......... .......... .......... .......... .......... 98%  203M 0s\n",
            " 86800K .......... .......... .......... .......... .......... 98%  245M 0s\n",
            " 86850K .......... .......... .......... .......... .......... 98%  269M 0s\n",
            " 86900K .......... .......... .......... .......... .......... 98%  265M 0s\n",
            " 86950K .......... .......... .......... .......... .......... 98%  207M 0s\n",
            " 87000K .......... .......... .......... .......... .......... 98%  252M 0s\n",
            " 87050K .......... .......... .......... .......... .......... 98%  274M 0s\n",
            " 87100K .......... .......... .......... .......... .......... 98%  275M 0s\n",
            " 87150K .......... .......... .......... .......... .......... 98%  168M 0s\n",
            " 87200K .......... .......... .......... .......... .......... 98%  249M 0s\n",
            " 87250K .......... .......... .......... .......... .......... 98%  273M 0s\n",
            " 87300K .......... .......... .......... .......... .......... 98%  274M 0s\n",
            " 87350K .......... .......... .......... .......... .......... 98%  245M 0s\n",
            " 87400K .......... .......... .......... .......... .......... 98%  279M 0s\n",
            " 87450K .......... .......... .......... .......... .......... 98%  249M 0s\n",
            " 87500K .......... .......... .......... .......... .......... 98%  271M 0s\n",
            " 87550K .......... .......... .......... .......... .......... 98%  226M 0s\n",
            " 87600K .......... .......... .......... .......... .......... 98%  273M 0s\n",
            " 87650K .......... .......... .......... .......... .......... 99%  235M 0s\n",
            " 87700K .......... .......... .......... .......... .......... 99%  228M 0s\n",
            " 87750K .......... .......... .......... .......... .......... 99%  233M 0s\n",
            " 87800K .......... .......... .......... .......... .......... 99%  280M 0s\n",
            " 87850K .......... .......... .......... .......... .......... 99%  275M 0s\n",
            " 87900K .......... .......... .......... .......... .......... 99%  278M 0s\n",
            " 87950K .......... .......... .......... .......... .......... 99%  224M 0s\n",
            " 88000K .......... .......... .......... .......... .......... 99%  247M 0s\n",
            " 88050K .......... .......... .......... .......... .......... 99%  285M 0s\n",
            " 88100K .......... .......... .......... .......... .......... 99%  261M 0s\n",
            " 88150K .......... .......... .......... .......... .......... 99%  239M 0s\n",
            " 88200K .......... .......... .......... .......... .......... 99%  268M 0s\n",
            " 88250K .......... .......... .......... .......... .......... 99%  264M 0s\n",
            " 88300K .......... .......... .......... .......... .......... 99%  238M 0s\n",
            " 88350K .......... .......... .......... .......... .......... 99%  217M 0s\n",
            " 88400K .......... .......... .......... .......... .......... 99%  249M 0s\n",
            " 88450K .......... .......... .......... .......... .......... 99%  252M 0s\n",
            " 88500K .......... .......... .......... ..........           100%  244M=0.4s\n",
            "\n",
            "2023-05-25 06:38:55 (210 MB/s) - ‘Miniconda3-py37_23.1.0-1-Linux-x86_64.sh’ saved [90665082/90665082]\n",
            "\n",
            "\r  0%|          | 0/47 [00:00<?, ?it/s]\rExtracting : xz-5.2.10-h5eee18b_1.conda:   0%|          | 0/47 [00:00<?, ?it/s]\rExtracting : urllib3-1.26.14-py37h06a4308_0.conda:   2%|▏         | 1/47 [00:00<00:03, 14.26it/s]\rExtracting : tqdm-4.64.1-py37h06a4308_0.conda:   4%|▍         | 2/47 [00:00<00:01, 22.84it/s]    \rExtracting : wheel-0.37.1-pyhd3eb1b0_0.conda:   6%|▋         | 3/47 [00:00<00:01, 34.20it/s] \rExtracting : ruamel.yaml-0.17.21-py37h5eee18b_0.conda:   9%|▊         | 4/47 [00:00<00:01, 33.00it/s]\rExtracting : ruamel.yaml-0.17.21-py37h5eee18b_0.conda:  11%|█         | 5/47 [00:00<00:01, 41.20it/s]\rExtracting : pluggy-1.0.0-py37h06a4308_1.conda:  11%|█         | 5/47 [00:00<00:01, 41.20it/s]       \rExtracting : typing_extensions-4.4.0-py37h06a4308_0.conda:  13%|█▎        | 6/47 [00:00<00:00, 41.20it/s]\rExtracting : libgomp-11.2.0-h1234567_1.conda:  15%|█▍        | 7/47 [00:00<00:00, 41.20it/s]             \rExtracting : importlib_metadata-4.11.3-hd3eb1b0_0.conda:  17%|█▋        | 8/47 [00:00<00:00, 41.20it/s]\rExtracting : importlib-metadata-4.11.3-py37h06a4308_0.conda:  19%|█▉        | 9/47 [00:00<00:00, 41.20it/s]\rExtracting : conda-content-trust-0.1.3-py37h06a4308_0.conda:  21%|██▏       | 10/47 [00:00<00:00, 41.20it/s]\rExtracting : libgcc-ng-11.2.0-h1234567_1.conda:  23%|██▎       | 11/47 [00:00<00:00, 41.20it/s]             \rExtracting : libgcc-ng-11.2.0-h1234567_1.conda:  26%|██▌       | 12/47 [00:00<00:00, 39.29it/s]\rExtracting : conda-package-streaming-0.7.0-py37h06a4308_0.conda:  26%|██▌       | 12/47 [00:00<00:00, 39.29it/s]\rExtracting : readline-8.2-h5eee18b_0.conda:  28%|██▊       | 13/47 [00:00<00:00, 39.29it/s]                     \rExtracting : libstdcxx-ng-11.2.0-h1234567_1.conda:  30%|██▉       | 14/47 [00:00<00:00, 39.29it/s]\rExtracting : six-1.16.0-pyhd3eb1b0_1.conda:  32%|███▏      | 15/47 [00:00<00:00, 39.29it/s]       \rExtracting : zlib-1.2.13-h5eee18b_0.conda:  34%|███▍      | 16/47 [00:00<00:00, 39.29it/s] \rExtracting : brotlipy-0.7.0-py37h27cfd23_1003.conda:  36%|███▌      | 17/47 [00:00<00:00, 39.29it/s]\rExtracting : ncurses-6.4-h6a678d5_0.conda:  38%|███▊      | 18/47 [00:03<00:00, 39.29it/s]          \rExtracting : ncurses-6.4-h6a678d5_0.conda:  40%|████      | 19/47 [00:03<00:06,  4.21it/s]\rExtracting : conda-package-handling-2.0.2-py37h06a4308_0.conda:  40%|████      | 19/47 [00:03<00:06,  4.21it/s]\rExtracting : idna-3.4-py37h06a4308_0.conda:  43%|████▎     | 20/47 [00:03<00:06,  4.21it/s]                    \rExtracting : tk-8.6.12-h1ccaba5_0.conda:  45%|████▍     | 21/47 [00:03<00:06,  4.21it/s]   \rExtracting : python-3.7.16-h7a1cb2a_0.conda:  47%|████▋     | 22/47 [00:05<00:05,  4.21it/s]\rExtracting : python-3.7.16-h7a1cb2a_0.conda:  49%|████▉     | 23/47 [00:05<00:06,  3.53it/s]\rExtracting : _openmp_mutex-5.1-1_gnu.conda:  49%|████▉     | 23/47 [00:05<00:06,  3.53it/s] \rExtracting : pycparser-2.21-pyhd3eb1b0_0.conda:  51%|█████     | 24/47 [00:05<00:06,  3.53it/s]\rExtracting : setuptools-65.6.3-py37h06a4308_0.conda:  53%|█████▎    | 25/47 [00:05<00:06,  3.53it/s]\rExtracting : pyopenssl-22.0.0-pyhd3eb1b0_0.conda:  55%|█████▌    | 26/47 [00:05<00:05,  3.53it/s]   \rExtracting : flit-core-3.6.0-pyhd3eb1b0_0.conda:  57%|█████▋    | 27/47 [00:05<00:05,  3.53it/s] \rExtracting : toolz-0.12.0-py37h06a4308_0.conda:  60%|█████▉    | 28/47 [00:05<00:05,  3.53it/s] \rExtracting : cryptography-38.0.4-py37h9ce1e76_0.conda:  62%|██████▏   | 29/47 [00:05<00:05,  3.53it/s]\rExtracting : conda-23.1.0-py37h06a4308_0.conda:  64%|██████▍   | 30/47 [00:05<00:04,  3.53it/s]       \rExtracting : _libgcc_mutex-0.1-main.conda:  66%|██████▌   | 31/47 [00:05<00:04,  3.53it/s]     \rExtracting : certifi-2022.12.7-py37h06a4308_0.conda:  68%|██████▊   | 32/47 [00:05<00:04,  3.53it/s]\rExtracting : ca-certificates-2023.01.10-h06a4308_0.conda:  70%|███████   | 33/47 [00:05<00:03,  3.53it/s]\rExtracting : zstandard-0.18.0-py37h5eee18b_0.conda:  72%|███████▏  | 34/47 [00:05<00:03,  3.53it/s]      \rExtracting : zipp-3.11.0-py37h06a4308_0.conda:  74%|███████▍  | 35/47 [00:05<00:03,  3.53it/s]     \rExtracting : pip-22.3.1-py37h06a4308_0.conda:  77%|███████▋  | 36/47 [00:05<00:03,  3.53it/s] \rExtracting : pip-22.3.1-py37h06a4308_0.conda:  79%|███████▊  | 37/47 [00:05<00:01,  7.61it/s]\rExtracting : libffi-3.4.2-h6a678d5_6.conda:  79%|███████▊  | 37/47 [00:05<00:01,  7.61it/s]  \rExtracting : requests-2.28.1-py37h06a4308_0.conda:  81%|████████  | 38/47 [00:05<00:01,  7.61it/s]\rExtracting : ruamel.yaml.clib-0.2.6-py37h5eee18b_1.conda:  83%|████████▎ | 39/47 [00:05<00:01,  7.61it/s]\rExtracting : pysocks-1.7.1-py37_1.conda:  85%|████████▌ | 40/47 [00:05<00:00,  7.61it/s]                 \rExtracting : cffi-1.15.1-py37h5eee18b_3.conda:  87%|████████▋ | 41/47 [00:05<00:00,  7.61it/s]\rExtracting : charset-normalizer-2.0.4-pyhd3eb1b0_0.conda:  89%|████████▉ | 42/47 [00:05<00:00,  7.61it/s]\rExtracting : openssl-1.1.1s-h7f8727e_0.conda:  91%|█████████▏| 43/47 [00:05<00:00,  7.61it/s]            \rExtracting : openssl-1.1.1s-h7f8727e_0.conda:  94%|█████████▎| 44/47 [00:05<00:00, 10.14it/s]\rExtracting : pycosat-0.6.4-py37h5eee18b_0.conda:  94%|█████████▎| 44/47 [00:05<00:00, 10.14it/s]\rExtracting : sqlite-3.40.1-h5082296_0.conda:  96%|█████████▌| 45/47 [00:05<00:00, 10.14it/s]    \rExtracting : ld_impl_linux-64-2.38-h1181459_1.conda:  98%|█████████▊| 46/47 [00:05<00:00, 10.14it/s]\r                                                                                                    \r"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "MINICONDA_INSTALLER_SCRIPT=Miniconda3-py37_23.1.0-1-Linux-x86_64.sh\n",
        "MINICONDA_PREFIX=/usr/local\n",
        "wget https://repo.anaconda.com/miniconda/Miniconda3-py37_23.1.0-1-Linux-x86_64.sh\n",
        "chmod +x $MINICONDA_INSTALLER_SCRIPT\n",
        "./$MINICONDA_INSTALLER_SCRIPT -b -f -p $MINICONDA_PREFIX"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w5jDaZY_iLXl",
        "outputId": "16d38405-355c-4b22-96d6-d23d0e6e1fd9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/bin/conda\n"
          ]
        }
      ],
      "source": [
        "!which conda # should return /usr/local/bin/conda"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mT07XQJ4iLXl",
        "outputId": "229d2d5c-a631-4863-9f33-507bf2c9064f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "conda 23.1.0\n"
          ]
        }
      ],
      "source": [
        "!conda --version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ss-2jRS1iLXl",
        "outputId": "5f0bcb78-bced-4b62-f302-0c2067c47487"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python 3.7.16\n"
          ]
        }
      ],
      "source": [
        "!python --version "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eE-XsQKYiLXm"
      },
      "source": [
        "Update conda"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sQH9P-M-iLXm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b1b95b3-60bc-472d-d8fb-85ab19155c31"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting package metadata (current_repodata.json): ...working... done\n",
            "Solving environment: ...working... done\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - conda\n",
            "    - python=3.7\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    openssl-1.1.1t             |       h7f8727e_0         3.7 MB\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:         3.7 MB\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  openssl                                 1.1.1s-h7f8727e_0 --> 1.1.1t-h7f8727e_0 \n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "\ropenssl-1.1.1t       | 3.7 MB    |            |   0% \ropenssl-1.1.1t       | 3.7 MB    | ########## | 100% \ropenssl-1.1.1t       | 3.7 MB    | ########## | 100% \r                                                     \r\r\n",
            "Preparing transaction: ...working... done\n",
            "Verifying transaction: ...working... done\n",
            "Executing transaction: ...working... done\n",
            "Collecting package metadata (current_repodata.json): ...working... done\n",
            "Solving environment: ...working... done\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    cryptography-39.0.1        |   py37h9ce1e76_0         1.4 MB\n",
            "    libffi-3.4.4               |       h6a678d5_0         142 KB\n",
            "    pyopenssl-23.0.0           |   py37h06a4308_0          96 KB\n",
            "    sqlite-3.41.2              |       h5eee18b_0         1.2 MB\n",
            "    wheel-0.38.4               |   py37h06a4308_0          63 KB\n",
            "    xz-5.4.2                   |       h5eee18b_0         642 KB\n",
            "    zstandard-0.19.0           |   py37h5eee18b_0         473 KB\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:         4.0 MB\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  cryptography                        38.0.4-py37h9ce1e76_0 --> 39.0.1-py37h9ce1e76_0 \n",
            "  libffi                                   3.4.2-h6a678d5_6 --> 3.4.4-h6a678d5_0 \n",
            "  pyopenssl          pkgs/main/noarch::pyopenssl-22.0.0-py~ --> pkgs/main/linux-64::pyopenssl-23.0.0-py37h06a4308_0 \n",
            "  sqlite                                  3.40.1-h5082296_0 --> 3.41.2-h5eee18b_0 \n",
            "  wheel              pkgs/main/noarch::wheel-0.37.1-pyhd3e~ --> pkgs/main/linux-64::wheel-0.38.4-py37h06a4308_0 \n",
            "  xz                                      5.2.10-h5eee18b_1 --> 5.4.2-h5eee18b_0 \n",
            "  zstandard                           0.18.0-py37h5eee18b_0 --> 0.19.0-py37h5eee18b_0 \n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "\rxz-5.4.2             | 642 KB    |            |   0% \n",
            "\rwheel-0.38.4         | 63 KB     |            |   0% \u001b[A\n",
            "\n",
            "\rcryptography-39.0.1  | 1.4 MB    |            |   0% \u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\rsqlite-3.41.2        | 1.2 MB    |            |   0% \u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\rlibffi-3.4.4         | 142 KB    |            |   0% \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\rzstandard-0.19.0     | 473 KB    |            |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\rpyopenssl-23.0.0     | 96 KB     |            |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\rwheel-0.38.4         | 63 KB     | ##5        |  25% \u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\rlibffi-3.4.4         | 142 KB    | #1         |  11% \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\rcryptography-39.0.1  | 1.4 MB    | 1          |   1% \u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\rsqlite-3.41.2        | 1.2 MB    | 1          |   1% \u001b[A\u001b[A\u001b[A\rxz-5.4.2             | 642 KB    | 2          |   2% \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\rzstandard-0.19.0     | 473 KB    | 3          |   3% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\rpyopenssl-23.0.0     | 96 KB     | #6         |  17% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\rwheel-0.38.4         | 63 KB     | ########## | 100% \u001b[A\n",
            "\rwheel-0.38.4         | 63 KB     | ########## | 100% \u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\rlibffi-3.4.4         | 142 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\rlibffi-3.4.4         | 142 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\rpyopenssl-23.0.0     | 96 KB     | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\rpyopenssl-23.0.0     | 96 KB     | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\rzstandard-0.19.0     | 473 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\rzstandard-0.19.0     | 473 KB    | ########## | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\rxz-5.4.2             | 642 KB    | ########## | 100% \rxz-5.4.2             | 642 KB    | ########## | 100% \n",
            "\n",
            "\n",
            "\rsqlite-3.41.2        | 1.2 MB    | ########## | 100% \u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\rsqlite-3.41.2        | 1.2 MB    | ########## | 100% \u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\rcryptography-39.0.1  | 1.4 MB    | ########## | 100% \u001b[A\u001b[A\n",
            "\n",
            "\rcryptography-39.0.1  | 1.4 MB    | ########## | 100% \u001b[A\u001b[A\r                                                     \r\n",
            "\r                                                     \u001b[A\n",
            "\n",
            "\r                                                     \u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\r                                                     \u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\r                                                     \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\r                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\r                                                     \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\r\n",
            "Preparing transaction: ...working... done\n",
            "Verifying transaction: ...working... done\n",
            "Executing transaction: ...working... done\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "\n",
            "==> WARNING: A newer version of conda exists. <==\n",
            "  current version: 23.1.0\n",
            "  latest version: 23.3.1\n",
            "\n",
            "Please update conda by running\n",
            "\n",
            "    $ conda update -n base -c defaults conda\n",
            "\n",
            "Or to minimize the number of packages updated during conda update use\n",
            "\n",
            "     conda install conda=23.3.1\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "==> WARNING: A newer version of conda exists. <==\n",
            "  current version: 23.1.0\n",
            "  latest version: 23.3.1\n",
            "\n",
            "Please update conda by running\n",
            "\n",
            "    $ conda update -n base -c defaults conda\n",
            "\n",
            "Or to minimize the number of packages updated during conda update use\n",
            "\n",
            "     conda install conda=23.3.1\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "conda install --channel defaults conda python=3.7 --yes\n",
        "conda update --channel defaults --all --yes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1E_CAd4-iLXm",
        "outputId": "7c10f492-178e-4566-d219-6c7559f9ddb3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "conda 23.1.0\n"
          ]
        }
      ],
      "source": [
        "!conda --version # updated version of conda"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VmckoiwqiLXm",
        "outputId": "78701622-9642-43a0-cb26-ff0e90193687"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python 3.7.16\n"
          ]
        }
      ],
      "source": [
        "!python --version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lI78YaUdiLXn",
        "outputId": "0a42e4b5-8ba6-4f8f-8a79-5d0919d4cabc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content',\n",
              " '/env/python',\n",
              " '/usr/lib/python310.zip',\n",
              " '/usr/lib/python3.10',\n",
              " '/usr/lib/python3.10/lib-dynload',\n",
              " '',\n",
              " '/usr/local/lib/python3.10/dist-packages',\n",
              " '/usr/lib/python3/dist-packages',\n",
              " '/usr/local/lib/python3.10/dist-packages/IPython/extensions',\n",
              " '/root/.ipython']"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "import sys\n",
        "sys.path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o00OJQRFiLXo"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "_ = (sys.path\n",
        "        .append(\"/usr/local/lib/python3.7/site-packages\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### install packages"
      ],
      "metadata": {
        "id": "PqBIVcdLYpLl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!conda install -c anaconda cmake\n",
        "!conda install -c conda-forge dlib\n",
        "!conda install -c conda-forge blend_modes\n",
        "!conda install -c anaconda protobuf\n",
        "\n",
        "!pip install scipy\n",
        "!pip install matplotlib\n",
        "!pip install scikit-image\n",
        "!pip install albumentations\n",
        "!pip install opencv-python==4.5.5.62\n",
        "\n",
        "!pip install tensorflow-gpu==1.14\n",
        "!pip install segmentation_models_pytorch==0.1.3\n",
        "!pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html\n",
        "!pip install torchtext==0.9.1\n",
        "\n",
        "!conda install cudatoolkit=10.0\n",
        "!conda install cudnn=7.3.1"
      ],
      "metadata": {
        "id": "xM4BHB_RlxLH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "717cfd0b-6664-4a9e-8f6b-dc3f504f2359"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Solving environment: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "\n",
            "\n",
            "==> WARNING: A newer version of conda exists. <==\n",
            "  current version: 23.1.0\n",
            "  latest version: 23.3.1\n",
            "\n",
            "Please update conda by running\n",
            "\n",
            "    $ conda update -n base -c defaults conda\n",
            "\n",
            "Or to minimize the number of packages updated during conda update use\n",
            "\n",
            "     conda install conda=23.3.1\n",
            "\n",
            "\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - cmake\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    bzip2-1.0.8                |       h7b6447c_0         105 KB  anaconda\n",
            "    c-ares-1.18.1              |       h7f8727e_0         119 KB  anaconda\n",
            "    ca-certificates-2023.01.10 |       h06a4308_0         127 KB  anaconda\n",
            "    certifi-2022.12.7          |   py37h06a4308_0         152 KB  anaconda\n",
            "    cmake-3.22.1               |       h1fce559_0        14.9 MB  anaconda\n",
            "    conda-23.1.0               |   py37h06a4308_0         955 KB  anaconda\n",
            "    expat-2.4.9                |       h6a678d5_0         188 KB  anaconda\n",
            "    krb5-1.19.4                |       h568e23c_0         1.4 MB  anaconda\n",
            "    libcurl-7.87.0             |       h91b91d3_0         354 KB  anaconda\n",
            "    libedit-3.1.20221030       |       h5eee18b_0         191 KB  anaconda\n",
            "    libev-4.33                 |       h7f8727e_1         106 KB  anaconda\n",
            "    libnghttp2-1.46.0          |       hce63b2e_0         795 KB  anaconda\n",
            "    libssh2-1.10.0             |       h8f2d780_0         304 KB  anaconda\n",
            "    libuv-1.40.0               |       h7b6447c_0         933 KB  anaconda\n",
            "    lz4-c-1.9.4                |       h6a678d5_0         160 KB  anaconda\n",
            "    openssl-1.1.1s             |       h7f8727e_0         3.8 MB  anaconda\n",
            "    rhash-1.4.1                |       h3c74f83_1         251 KB  anaconda\n",
            "    zstd-1.5.2                 |       ha4553b6_0         808 KB  anaconda\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:        25.5 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  bzip2              anaconda/linux-64::bzip2-1.0.8-h7b6447c_0 \n",
            "  c-ares             anaconda/linux-64::c-ares-1.18.1-h7f8727e_0 \n",
            "  cmake              anaconda/linux-64::cmake-3.22.1-h1fce559_0 \n",
            "  expat              anaconda/linux-64::expat-2.4.9-h6a678d5_0 \n",
            "  krb5               anaconda/linux-64::krb5-1.19.4-h568e23c_0 \n",
            "  libcurl            anaconda/linux-64::libcurl-7.87.0-h91b91d3_0 \n",
            "  libedit            anaconda/linux-64::libedit-3.1.20221030-h5eee18b_0 \n",
            "  libev              anaconda/linux-64::libev-4.33-h7f8727e_1 \n",
            "  libnghttp2         anaconda/linux-64::libnghttp2-1.46.0-hce63b2e_0 \n",
            "  libssh2            anaconda/linux-64::libssh2-1.10.0-h8f2d780_0 \n",
            "  libuv              anaconda/linux-64::libuv-1.40.0-h7b6447c_0 \n",
            "  lz4-c              anaconda/linux-64::lz4-c-1.9.4-h6a678d5_0 \n",
            "  rhash              anaconda/linux-64::rhash-1.4.1-h3c74f83_1 \n",
            "  zstd               anaconda/linux-64::zstd-1.5.2-ha4553b6_0 \n",
            "\n",
            "The following packages will be SUPERSEDED by a higher-priority channel:\n",
            "\n",
            "  ca-certificates                                 pkgs/main --> anaconda \n",
            "  certifi                                         pkgs/main --> anaconda \n",
            "  conda                                           pkgs/main --> anaconda \n",
            "  openssl              pkgs/main::openssl-1.1.1t-h7f8727e_0 --> anaconda::openssl-1.1.1s-h7f8727e_0 \n",
            "\n",
            "\n",
            "Proceed ([y]/n)? y\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "rhash-1.4.1          | 251 KB    | :   0% 0/1 [00:00<?, ?it/s]\n",
            "openssl-1.1.1s       | 3.8 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "\n",
            "libuv-1.40.0         | 933 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "c-ares-1.18.1        | 119 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libedit-3.1.20221030 | 191 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "krb5-1.19.4          | 1.4 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcurl-7.87.0       | 354 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "ca-certificates-2023 | 127 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "zstd-1.5.2           | 808 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "bzip2-1.0.8          | 105 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "conda-23.1.0         | 955 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnghttp2-1.46.0    | 795 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "expat-2.4.9          | 188 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libev-4.33           | 106 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "certifi-2022.12.7    | 152 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "lz4-c-1.9.4          | 160 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "cmake-3.22.1         | 14.9 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "rhash-1.4.1          | 251 KB    | :  51% 0.5102837721569253/1 [00:00<00:00,  5.08it/s]\n",
            "openssl-1.1.1s       | 3.8 MB    | :  23% 0.23406113186891406/1 [00:00<00:00,  2.32it/s]\u001b[A\n",
            "\n",
            "libuv-1.40.0         | 933 KB    | :  63% 0.6342109502995252/1 [00:00<00:00,  6.23it/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcurl-7.87.0       | 354 KB    | :   5% 0.04513884105805179/1 [00:00<00:02,  2.55s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "krb5-1.19.4          | 1.4 MB    | :   1% 0.01130270115517414/1 [00:00<00:10, 10.25s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "ca-certificates-2023 | 127 KB    | :  13% 0.1263573543928924/1 [00:00<00:00,  1.00s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "zstd-1.5.2           | 808 KB    | :   2% 0.019807246860386957/1 [00:00<00:06,  6.79s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "bzip2-1.0.8          | 105 KB    | :  15% 0.1529713832220718/1 [00:00<00:00,  1.02s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "conda-23.1.0         | 955 KB    | :   2% 0.01675540020085209/1 [00:00<00:09,  9.99s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "openssl-1.1.1s       | 3.8 MB    | :  76% 0.7596720946622649/1 [00:00<00:00,  4.03it/s] \u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnghttp2-1.46.0    | 795 KB    | :   2% 0.020124649010104086/1 [00:00<00:09,  9.44s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "expat-2.4.9          | 188 KB    | :   9% 0.085302233560681/1 [00:00<00:02,  2.37s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libev-4.33           | 106 KB    | :  15% 0.1504416652893321/1 [00:00<00:01,  1.37s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "certifi-2022.12.7    | 152 KB    | :  11% 0.10551601996457897/1 [00:00<00:01,  2.23s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "cmake-3.22.1         | 14.9 MB   | :   0% 0.0010462206810447085/1 [00:00<03:50, 230.63s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "lz4-c-1.9.4          | 160 KB    | :  10% 0.10011671320936884/1 [00:00<00:02,  2.43s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libssh2-1.10.0       | 304 KB    | :   5% 0.052617887641026796/1 [00:00<00:04,  4.82s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libedit-3.1.20221030 | 191 KB    | : 100% 1.0/1 [00:00<00:00,  3.33it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libedit-3.1.20221030 | 191 KB    | : 100% 1.0/1 [00:00<00:00,  3.33it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "c-ares-1.18.1        | 119 KB    | : 100% 1.0/1 [00:00<00:00,  2.89it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "c-ares-1.18.1        | 119 KB    | : 100% 1.0/1 [00:00<00:00,  2.89it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "rhash-1.4.1          | 251 KB    | : 100% 1.0/1 [00:00<00:00,  5.08it/s]               \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "cmake-3.22.1         | 14.9 MB   | :  64% 0.6361021740751828/1 [00:00<00:00,  1.83it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "ca-certificates-2023 | 127 KB    | : 100% 1.0/1 [00:00<00:00,  2.02it/s]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "ca-certificates-2023 | 127 KB    | : 100% 1.0/1 [00:00<00:00,  2.02it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "cmake-3.22.1         | 14.9 MB   | :  99% 0.9876323229062048/1 [00:00<00:00,  2.39it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "libuv-1.40.0         | 933 KB    | : 100% 1.0/1 [00:00<00:00,  6.23it/s]               \u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcurl-7.87.0       | 354 KB    | : 100% 1.0/1 [00:00<00:00,  1.72it/s]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcurl-7.87.0       | 354 KB    | : 100% 1.0/1 [00:00<00:00,  1.72it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "bzip2-1.0.8          | 105 KB    | : 100% 1.0/1 [00:00<00:00,  1.57it/s]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "bzip2-1.0.8          | 105 KB    | : 100% 1.0/1 [00:00<00:00,  1.57it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "zstd-1.5.2           | 808 KB    | : 100% 1.0/1 [00:00<00:00,  1.35it/s]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "zstd-1.5.2           | 808 KB    | : 100% 1.0/1 [00:00<00:00,  1.35it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "krb5-1.19.4          | 1.4 MB    | : 100% 1.0/1 [00:01<00:00,  1.05s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "krb5-1.19.4          | 1.4 MB    | : 100% 1.0/1 [00:01<00:00,  1.05s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "expat-2.4.9          | 188 KB    | : 100% 1.0/1 [00:01<00:00,  1.13s/it]              \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "expat-2.4.9          | 188 KB    | : 100% 1.0/1 [00:01<00:00,  1.13s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libev-4.33           | 106 KB    | : 100% 1.0/1 [00:01<00:00,  1.22s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libev-4.33           | 106 KB    | : 100% 1.0/1 [00:01<00:00,  1.22s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "conda-23.1.0         | 955 KB    | : 100% 1.0/1 [00:01<00:00,  1.41s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "conda-23.1.0         | 955 KB    | : 100% 1.0/1 [00:01<00:00,  1.41s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnghttp2-1.46.0    | 795 KB    | : 100% 1.0/1 [00:01<00:00,  1.63s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnghttp2-1.46.0    | 795 KB    | : 100% 1.0/1 [00:01<00:00,  1.63s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "certifi-2022.12.7    | 152 KB    | : 100% 1.0/1 [00:01<00:00,  1.77s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "certifi-2022.12.7    | 152 KB    | : 100% 1.0/1 [00:01<00:00,  1.77s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "lz4-c-1.9.4          | 160 KB    | : 100% 1.0/1 [00:01<00:00,  1.85s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "lz4-c-1.9.4          | 160 KB    | : 100% 1.0/1 [00:01<00:00,  1.85s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libssh2-1.10.0       | 304 KB    | : 100% 1.0/1 [00:02<00:00,  2.11s/it]                 \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libssh2-1.10.0       | 304 KB    | : 100% 1.0/1 [00:02<00:00,  2.11s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "openssl-1.1.1s       | 3.8 MB    | : 100% 1.0/1 [00:02<00:00,  4.03it/s]               \u001b[A\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "                                                                        \n",
            "                                                                        \u001b[A\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "Preparing transaction: / \b\b- \b\b\\ \b\bdone\n",
            "Verifying transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bfailed with initial frozen solve. Retrying with flexible solve.\n",
            "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bfailed with repodata from current_repodata.json, will retry with next repodata source.\n",
            "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \n",
            "Warning: 2 possible package resolutions (only showing differing packages):\n",
            "  - anaconda/linux-64::conda-23.1.0-py37h06a4308_0\n",
            "  - defaults/linux-64::conda-23.1.0-py37h06a4308_0\b\b- \b\b\\ \b\b| \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - dlib\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    ca-certificates-2023.5.7   |       hbcca054_0         145 KB  conda-forge\n",
            "    certifi-2023.5.7           |     pyhd8ed1ab_0         149 KB  conda-forge\n",
            "    dlib-19.24.0               |   py37hd2836c6_0         4.0 MB  conda-forge\n",
            "    jpeg-9e                    |       h166bdaf_1         268 KB  conda-forge\n",
            "    libblas-3.9.0              |15_linux64_openblas          12 KB  conda-forge\n",
            "    libcblas-3.9.0             |15_linux64_openblas          12 KB  conda-forge\n",
            "    libgfortran-ng-12.2.0      |      h69a702a_19          22 KB  conda-forge\n",
            "    libgfortran5-12.2.0        |      h337968e_19         1.8 MB  conda-forge\n",
            "    liblapack-3.9.0            |15_linux64_openblas          12 KB  conda-forge\n",
            "    libopenblas-0.3.20         |pthreads_h78a6416_0        10.1 MB  conda-forge\n",
            "    libpng-1.6.37              |       h21135ba_2         306 KB  conda-forge\n",
            "    numpy-1.21.6               |   py37h976b520_0         6.1 MB  conda-forge\n",
            "    python_abi-3.7             |          2_cp37m           4 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:        22.8 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  dlib               conda-forge/linux-64::dlib-19.24.0-py37hd2836c6_0 \n",
            "  jpeg               conda-forge/linux-64::jpeg-9e-h166bdaf_1 \n",
            "  libblas            conda-forge/linux-64::libblas-3.9.0-15_linux64_openblas \n",
            "  libcblas           conda-forge/linux-64::libcblas-3.9.0-15_linux64_openblas \n",
            "  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-12.2.0-h69a702a_19 \n",
            "  libgfortran5       conda-forge/linux-64::libgfortran5-12.2.0-h337968e_19 \n",
            "  liblapack          conda-forge/linux-64::liblapack-3.9.0-15_linux64_openblas \n",
            "  libopenblas        conda-forge/linux-64::libopenblas-0.3.20-pthreads_h78a6416_0 \n",
            "  libpng             conda-forge/linux-64::libpng-1.6.37-h21135ba_2 \n",
            "  numpy              conda-forge/linux-64::numpy-1.21.6-py37h976b520_0 \n",
            "  python_abi         conda-forge/linux-64::python_abi-3.7-2_cp37m \n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  ca-certificates    anaconda::ca-certificates-2023.01.10-~ --> conda-forge::ca-certificates-2023.5.7-hbcca054_0 \n",
            "  certifi            anaconda/linux-64::certifi-2022.12.7-~ --> conda-forge/noarch::certifi-2023.5.7-pyhd8ed1ab_0 \n",
            "  openssl               anaconda::openssl-1.1.1s-h7f8727e_0 --> pkgs/main::openssl-1.1.1t-h7f8727e_0 \n",
            "\n",
            "\n",
            "Proceed ([y]/n)? y\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "libgfortran5-12.2.0  | 1.8 MB    | :   0% 0/1 [00:00<?, ?it/s]\n",
            "numpy-1.21.6         | 6.1 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "\n",
            "libgfortran-ng-12.2. | 22 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "libopenblas-0.3.20   | 10.1 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libpng-1.6.37        | 306 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "ca-certificates-2023 | 145 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcblas-3.9.0       | 12 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "liblapack-3.9.0      | 12 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "dlib-19.24.0         | 4.0 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python_abi-3.7       | 4 KB      | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "jpeg-9e              | 268 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libblas-3.9.0        | 12 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "certifi-2023.5.7     | 149 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "libgfortran5-12.2.0  | 1.8 MB    | :  39% 0.3919934792455457/1 [00:00<00:00,  3.85it/s]\n",
            "\n",
            "\n",
            "libopenblas-0.3.20   | 10.1 MB   | :   7% 0.06662241639218289/1 [00:00<00:01,  1.51s/it]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcblas-3.9.0       | 12 KB     | : 100% 1.0/1 [00:00<00:00,  8.46it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "liblapack-3.9.0      | 12 KB     | : 100% 1.0/1 [00:00<00:00,  6.98it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "ca-certificates-2023 | 145 KB    | :  11% 0.1104340792666487/1 [00:00<00:01,  1.46s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "libgfortran-ng-12.2. | 22 KB     | : 100% 1.0/1 [00:00<00:00,  5.83it/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "dlib-19.24.0         | 4.0 MB    | :   0% 0.00391736895499449/1 [00:00<00:42, 42.30s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "libgfortran-ng-12.2. | 22 KB     | : 100% 1.0/1 [00:00<00:00,  5.83it/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python_abi-3.7       | 4 KB      | : 100% 1.0/1 [00:00<00:00,  5.69it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "numpy-1.21.6         | 6.1 MB    | :  45% 0.4454248277168059/1 [00:00<00:00,  2.40it/s] \u001b[A\n",
            "\n",
            "\n",
            "libopenblas-0.3.20   | 10.1 MB   | :  29% 0.2866313263384613/1 [00:00<00:00,  1.56it/s] \u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "jpeg-9e              | 268 KB    | :   6% 0.05972673850595663/1 [00:00<00:03,  3.46s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libcblas-3.9.0       | 12 KB     | : 100% 1.0/1 [00:00<00:00,  8.46it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libblas-3.9.0        | 12 KB     | : 100% 1.0/1 [00:00<00:00,  4.08it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "certifi-2023.5.7     | 149 KB    | :  11% 0.10751855521941424/1 [00:00<00:02,  2.45s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "dlib-19.24.0         | 4.0 MB    | :  54% 0.5445142847442341/1 [00:00<00:00,  2.51it/s] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "numpy-1.21.6         | 6.1 MB    | :  76% 0.7628540152851043/1 [00:00<00:00,  2.70it/s]\u001b[A\n",
            "\n",
            "\n",
            "libopenblas-0.3.20   | 10.1 MB   | :  44% 0.4431165369340536/1 [00:00<00:00,  1.51it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libpng-1.6.37        | 306 KB    | : 100% 1.0/1 [00:00<00:00,  2.71it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libpng-1.6.37        | 306 KB    | : 100% 1.0/1 [00:00<00:00,  2.71it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "liblapack-3.9.0      | 12 KB     | : 100% 1.0/1 [00:00<00:00,  6.98it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "libopenblas-0.3.20   | 10.1 MB   | :  83% 0.8258080915589181/1 [00:00<00:00,  2.37it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python_abi-3.7       | 4 KB      | : 100% 1.0/1 [00:00<00:00,  5.69it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "jpeg-9e              | 268 KB    | : 100% 1.0/1 [00:00<00:00,  2.26it/s]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "jpeg-9e              | 268 KB    | : 100% 1.0/1 [00:00<00:00,  2.26it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "ca-certificates-2023 | 145 KB    | : 100% 1.0/1 [00:00<00:00,  2.09it/s]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "ca-certificates-2023 | 145 KB    | : 100% 1.0/1 [00:00<00:00,  2.09it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libblas-3.9.0        | 12 KB     | : 100% 1.0/1 [00:00<00:00,  4.08it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "certifi-2023.5.7     | 149 KB    | : 100% 1.0/1 [00:00<00:00,  2.09it/s]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgfortran5-12.2.0  | 1.8 MB    | : 100% 1.0/1 [00:00<00:00,  1.20it/s]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "dlib-19.24.0         | 4.0 MB    | : 100% 1.0/1 [00:01<00:00,  1.46s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "dlib-19.24.0         | 4.0 MB    | : 100% 1.0/1 [00:01<00:00,  1.46s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "numpy-1.21.6         | 6.1 MB    | : 100% 1.0/1 [00:02<00:00,  2.70it/s]               \u001b[A\n",
            "\n",
            "\n",
            "                                                                        \n",
            "                                                                        \u001b[A\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "Preparing transaction: - \b\bdone\n",
            "Verifying transaction: | \b\b/ \b\b- \b\bdone\n",
            "Executing transaction: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - blend_modes\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    blend_modes-2.1.0          |             py_0          12 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:          12 KB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  blend_modes        conda-forge/noarch::blend_modes-2.1.0-py_0 \n",
            "\n",
            "\n",
            "Proceed ([y]/n)? y\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "                                                                        \n",
            "Preparing transaction: | \b\bdone\n",
            "Verifying transaction: - \b\bdone\n",
            "Executing transaction: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - protobuf\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    libprotobuf-3.20.3         |       he621ea3_0         2.6 MB  anaconda\n",
            "    protobuf-3.20.3            |   py37h6a678d5_0         335 KB  anaconda\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:         2.9 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  libprotobuf        anaconda/linux-64::libprotobuf-3.20.3-he621ea3_0 \n",
            "  protobuf           anaconda/linux-64::protobuf-3.20.3-py37h6a678d5_0 \n",
            "\n",
            "The following packages will be SUPERSEDED by a higher-priority channel:\n",
            "\n",
            "  ca-certificates    conda-forge::ca-certificates-2023.5.7~ --> anaconda::ca-certificates-2023.01.10-h06a4308_0 \n",
            "  certifi            conda-forge/noarch::certifi-2023.5.7-~ --> anaconda/linux-64::certifi-2022.12.7-py37h06a4308_0 \n",
            "  openssl              pkgs/main::openssl-1.1.1t-h7f8727e_0 --> anaconda::openssl-1.1.1s-h7f8727e_0 \n",
            "\n",
            "\n",
            "Proceed ([y]/n)? y\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "libprotobuf-3.20.3   | 2.6 MB    | :   0% 0/1 [00:00<?, ?it/s]\n",
            "protobuf-3.20.3      | 335 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "protobuf-3.20.3      | 335 KB    | : 100% 1.0/1 [00:00<00:00,  5.04it/s]\u001b[A\n",
            "                                                                        \n",
            "                                                                        \u001b[A\n",
            "Preparing transaction: | \b\bdone\n",
            "Verifying transaction: - \b\b\\ \b\b| \b\bdone\n",
            "Executing transaction: - \b\bdone\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting scipy\n",
            "  Downloading scipy-1.7.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (38.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38.1/38.1 MB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Installing collected packages: tensorflow-estimator, wrapt, termcolor, MarkupSafe, keras-preprocessing, h5py, grpcio, google-pasta, gast, astor, absl-py, werkzeug, markdown, keras-applications, tensorboard, tensorflow-gpu\n",
            "Successfully installed MarkupSafe-2.1.2 absl-py-1.4.0 astor-0.8.1 gast-0.5.4 google-pasta-0.2.0 grpcio-1.54.2 h5py-3.8.0 keras-applications-1.0.8 keras-preprocessing-1.1.2 markdown-3.4.3 tensorboard-1.14.0 tensorflow-estimator-1.14.0 tensorflow-gpu-1.14.0 termcolor-2.3.0 werkzeug-2.2.3 wrapt-1.15.0\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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            "Building wheels for collected packages: efficientnet-pytorch, pretrainedmodels\n",
            "  Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.6.3-py3-none-any.whl size=12404 sha256=d5f0c167b774e22c5fc18a1c563a0f115b83c7f7466d1ab4da01940f1f234910\n",
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            "Installing collected packages: nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cublas-cu11, nvidia-cudnn-cu11, munch, torch, torchvision, efficientnet-pytorch, timm, pretrainedmodels, segmentation_models_pytorch\n",
            "Successfully installed efficientnet-pytorch-0.6.3 munch-3.0.0 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 pretrainedmodels-0.7.4 segmentation_models_pytorch-0.1.3 timm-0.3.2 torch-1.13.1 torchvision-0.14.1\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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            "Collecting torch==1.8.1+cu111\n",
            "  Downloading https://download.pytorch.org/whl/lts/1.8/cu111/torch-1.8.1%2Bcu111-cp37-cp37m-linux_x86_64.whl (1982.2 MB)\n",
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            "      Successfully uninstalled torchvision-0.14.1\n",
            "Successfully installed torch-1.8.1+cu111 torchaudio-0.8.1 torchvision-0.9.1+cu111\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting torchtext==0.9.1\n",
            "  Downloading torchtext-0.9.1-cp37-cp37m-manylinux1_x86_64.whl (7.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m12.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/site-packages (from torchtext==0.9.1) (4.64.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/site-packages (from torchtext==0.9.1) (1.21.6)\n",
            "Requirement already satisfied: torch==1.8.1 in /usr/local/lib/python3.7/site-packages (from torchtext==0.9.1) (1.8.1+cu111)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/site-packages (from torchtext==0.9.1) (2.28.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/site-packages (from torch==1.8.1->torchtext==0.9.1) (4.4.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/site-packages (from requests->torchtext==0.9.1) (1.26.14)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/site-packages (from requests->torchtext==0.9.1) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.7/site-packages (from requests->torchtext==0.9.1) (2.0.4)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/site-packages (from requests->torchtext==0.9.1) (3.4)\n",
            "Installing collected packages: torchtext\n",
            "Successfully installed torchtext-0.9.1\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mCollecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - cudatoolkit=10.0\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    ca-certificates-2023.01.10 |       h06a4308_0         120 KB\n",
            "    certifi-2022.12.7          |   py37h06a4308_0         150 KB\n",
            "    cudatoolkit-10.0.130       |                0       261.2 MB\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:       261.5 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  cudatoolkit        pkgs/main/linux-64::cudatoolkit-10.0.130-0 \n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  openssl               anaconda::openssl-1.1.1s-h7f8727e_0 --> pkgs/main::openssl-1.1.1t-h7f8727e_0 \n",
            "\n",
            "The following packages will be SUPERSEDED by a higher-priority channel:\n",
            "\n",
            "  ca-certificates                                  anaconda --> pkgs/main \n",
            "  certifi                                          anaconda --> pkgs/main \n",
            "\n",
            "\n",
            "Proceed ([y]/n)? y\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "cudatoolkit-10.0.130 | 261.2 MB  | :   0% 0/1 [00:00<?, ?it/s]\n",
            "ca-certificates-2023 | 120 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "\n",
            "cudatoolkit-10.0.130 | 261.2 MB  | :   0% 0.0031101591898034728/1 [00:00<00:33, 33.49s/it]\n",
            "ca-certificates-2023 | 120 KB    | : 100% 1.0/1 [00:00<00:00,  7.64it/s]\u001b[A\n",
            "ca-certificates-2023 | 120 KB    | : 100% 1.0/1 [00:00<00:00,  7.64it/s]\u001b[A\n",
            "\n",
            "certifi-2022.12.7    | 150 KB    | : 100% 1.0/1 [00:00<00:00,  6.51it/s]\u001b[A\u001b[A\n",
            "\n",
            "                                                                        \n",
            "                                                                        \u001b[A\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\n",
            "Preparing transaction: / \b\bdone\n",
            "Verifying transaction: \\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "Executing transaction: | \b\bdone\n",
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "Solving environment: | \b\bfailed with initial frozen solve. Retrying with flexible solve.\n",
            "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - cudnn=7.3.1\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    conda-23.1.0               |   py37h06a4308_0         937 KB\n",
            "    cudnn-7.3.1                |       cuda10.0_0       286.1 MB\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:       287.0 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  cudnn              pkgs/main/linux-64::cudnn-7.3.1-cuda10.0_0 \n",
            "\n",
            "The following packages will be SUPERSEDED by a higher-priority channel:\n",
            "\n",
            "  conda                                            anaconda --> pkgs/main \n",
            "\n",
            "\n",
            "Proceed ([y]/n)? y\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "conda-23.1.0         | 937 KB    | :   0% 0/1 [00:00<?, ?it/s]\n",
            "cudnn-7.3.1          | 286.1 MB  | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :   1% 0.010815083243748054/1 [00:00<00:09,  9.25s/it]\u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :   3% 0.02938643830876997/1 [00:00<00:06,  6.51s/it] \u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :   5% 0.04795779337379188/1 [00:00<00:05,  5.95s/it]\u001b[A\n",
            "conda-23.1.0         | 937 KB    | : 100% 1.0/1 [00:00<00:00,  2.37it/s]\n",
            "cudnn-7.3.1          | 286.1 MB  | :  11% 0.10804158917239219/1 [00:00<00:03,  3.84s/it]\u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :  16% 0.15698257193197934/1 [00:00<00:02,  2.96s/it]\u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :  21% 0.21127647468089636/1 [00:00<00:01,  2.47s/it]\u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :  26% 0.2629485390676926/1 [00:00<00:01,  2.27s/it] \u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :  31% 0.31352817080360523/1 [00:00<00:01,  2.17s/it]\u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :  36% 0.3596288286708949/1 [00:01<00:01,  2.25s/it] \u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :  40% 0.4043093240920359/1 [00:01<00:01,  2.45s/it]\u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :  45% 0.4533049284841672/1 [00:01<00:01,  2.32s/it]\u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :  50% 0.5009896136952382/1 [00:01<00:01,  2.25s/it]\u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :  55% 0.5459432172791/1 [00:01<00:01,  2.24s/it]   \u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :  59% 0.5908968208629619/1 [00:01<00:00,  2.38s/it]\u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :  63% 0.633501694247424/1 [00:01<00:00,  2.40s/it] \u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :  68% 0.6780729464034766/1 [00:01<00:00,  2.36s/it]\u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :  73% 0.7307282001760681/1 [00:01<00:00,  2.20s/it]\u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :  78% 0.7826733727255852/1 [00:01<00:00,  2.11s/it]\u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :  83% 0.8328706530336886/1 [00:02<00:00,  2.08s/it]\u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :  89% 0.8882023668009451/1 [00:02<00:00,  1.99s/it]\u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | :  95% 0.9455550809723363/1 [00:02<00:00,  1.91s/it]\u001b[A\n",
            "cudnn-7.3.1          | 286.1 MB  | : 100% 0.9981557131123836/1 [00:02<00:00,  1.91s/it]\u001b[A\n",
            "                                                                        \n",
            "                                                                        \u001b[A\n",
            "Preparing transaction: - \b\bdone\n",
            "Verifying transaction: | \b\bdone\n",
            "Executing transaction: - \b\bdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pF8vJuHcs3sv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c342f62-2f25-43fa-d397-1eedb1cc5172"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# packages in environment at /usr/local:\n",
            "#\n",
            "# Name                    Version                   Build  Channel\n",
            "_libgcc_mutex             0.1                        main  \n",
            "_openmp_mutex             5.1                       1_gnu  \n",
            "absl-py                   1.4.0                    pypi_0    pypi\n",
            "albumentations            1.3.0                    pypi_0    pypi\n",
            "astor                     0.8.1                    pypi_0    pypi\n",
            "blend_modes               2.1.0                      py_0    conda-forge\n",
            "brotlipy                  0.7.0           py37h27cfd23_1003  \n",
            "bzip2                     1.0.8                h7b6447c_0    anaconda\n",
            "c-ares                    1.18.1               h7f8727e_0    anaconda\n",
            "ca-certificates           2023.01.10           h06a4308_0  \n",
            "certifi                   2022.12.7        py37h06a4308_0  \n",
            "cffi                      1.15.1           py37h5eee18b_3  \n",
            "charset-normalizer        2.0.4              pyhd3eb1b0_0  \n",
            "cmake                     3.22.1               h1fce559_0    anaconda\n",
            "conda                     23.1.0           py37h06a4308_0  \n",
            "conda-content-trust       0.1.3            py37h06a4308_0  \n",
            "conda-package-handling    2.0.2            py37h06a4308_0  \n",
            "conda-package-streaming   0.7.0            py37h06a4308_0  \n",
            "cryptography              39.0.1           py37h9ce1e76_0  \n",
            "cudatoolkit               10.0.130                      0  \n",
            "cudnn                     7.3.1                cuda10.0_0  \n",
            "cycler                    0.11.0                   pypi_0    pypi\n",
            "dlib                      19.24.0          py37hd2836c6_0    conda-forge\n",
            "efficientnet-pytorch      0.6.3                    pypi_0    pypi\n",
            "expat                     2.4.9                h6a678d5_0    anaconda\n",
            "flit-core                 3.6.0              pyhd3eb1b0_0  \n",
            "fonttools                 4.38.0                   pypi_0    pypi\n",
            "gast                      0.5.4                    pypi_0    pypi\n",
            "google-pasta              0.2.0                    pypi_0    pypi\n",
            "grpcio                    1.54.2                   pypi_0    pypi\n",
            "h5py                      3.8.0                    pypi_0    pypi\n",
            "idna                      3.4              py37h06a4308_0  \n",
            "imageio                   2.29.0                   pypi_0    pypi\n",
            "importlib-metadata        4.11.3           py37h06a4308_0  \n",
            "importlib_metadata        4.11.3               hd3eb1b0_0  \n",
            "joblib                    1.2.0                    pypi_0    pypi\n",
            "jpeg                      9e                   h166bdaf_1    conda-forge\n",
            "keras-applications        1.0.8                    pypi_0    pypi\n",
            "keras-preprocessing       1.1.2                    pypi_0    pypi\n",
            "kiwisolver                1.4.4                    pypi_0    pypi\n",
            "krb5                      1.19.4               h568e23c_0    anaconda\n",
            "ld_impl_linux-64          2.38                 h1181459_1  \n",
            "libblas                   3.9.0           15_linux64_openblas    conda-forge\n",
            "libcblas                  3.9.0           15_linux64_openblas    conda-forge\n",
            "libcurl                   7.87.0               h91b91d3_0    anaconda\n",
            "libedit                   3.1.20221030         h5eee18b_0    anaconda\n",
            "libev                     4.33                 h7f8727e_1    anaconda\n",
            "libffi                    3.4.4                h6a678d5_0  \n",
            "libgcc-ng                 11.2.0               h1234567_1  \n",
            "libgfortran-ng            12.2.0              h69a702a_19    conda-forge\n",
            "libgfortran5              12.2.0              h337968e_19    conda-forge\n",
            "libgomp                   11.2.0               h1234567_1  \n",
            "liblapack                 3.9.0           15_linux64_openblas    conda-forge\n",
            "libnghttp2                1.46.0               hce63b2e_0    anaconda\n",
            "libopenblas               0.3.20          pthreads_h78a6416_0    conda-forge\n",
            "libpng                    1.6.37               h21135ba_2    conda-forge\n",
            "libprotobuf               3.20.3               he621ea3_0    anaconda\n",
            "libssh2                   1.10.0               h8f2d780_0    anaconda\n",
            "libstdcxx-ng              11.2.0               h1234567_1  \n",
            "libuv                     1.40.0               h7b6447c_0    anaconda\n",
            "lz4-c                     1.9.4                h6a678d5_0    anaconda\n",
            "markdown                  3.4.3                    pypi_0    pypi\n",
            "markupsafe                2.1.2                    pypi_0    pypi\n",
            "matplotlib                3.5.3                    pypi_0    pypi\n",
            "munch                     3.0.0                    pypi_0    pypi\n",
            "ncurses                   6.4                  h6a678d5_0  \n",
            "networkx                  2.6.3                    pypi_0    pypi\n",
            "numpy                     1.21.6           py37h976b520_0    conda-forge\n",
            "nvidia-cublas-cu11        11.10.3.66               pypi_0    pypi\n",
            "nvidia-cuda-nvrtc-cu11    11.7.99                  pypi_0    pypi\n",
            "nvidia-cuda-runtime-cu11  11.7.99                  pypi_0    pypi\n",
            "nvidia-cudnn-cu11         8.5.0.96                 pypi_0    pypi\n",
            "opencv-python             4.5.5.62                 pypi_0    pypi\n",
            "opencv-python-headless    4.7.0.72                 pypi_0    pypi\n",
            "openssl                   1.1.1t               h7f8727e_0  \n",
            "packaging                 23.1                     pypi_0    pypi\n",
            "pillow                    9.5.0                    pypi_0    pypi\n",
            "pip                       22.3.1           py37h06a4308_0  \n",
            "pluggy                    1.0.0            py37h06a4308_1  \n",
            "pretrainedmodels          0.7.4                    pypi_0    pypi\n",
            "protobuf                  3.20.3           py37h6a678d5_0    anaconda\n",
            "pycosat                   0.6.4            py37h5eee18b_0  \n",
            "pycparser                 2.21               pyhd3eb1b0_0  \n",
            "pyopenssl                 23.0.0           py37h06a4308_0  \n",
            "pyparsing                 3.0.9                    pypi_0    pypi\n",
            "pysocks                   1.7.1                    py37_1  \n",
            "python                    3.7.16               h7a1cb2a_0  \n",
            "python-dateutil           2.8.2                    pypi_0    pypi\n",
            "python_abi                3.7                     2_cp37m    conda-forge\n",
            "pywavelets                1.3.0                    pypi_0    pypi\n",
            "pyyaml                    6.0                      pypi_0    pypi\n",
            "qudida                    0.0.4                    pypi_0    pypi\n",
            "readline                  8.2                  h5eee18b_0  \n",
            "requests                  2.28.1           py37h06a4308_0  \n",
            "rhash                     1.4.1                h3c74f83_1    anaconda\n",
            "ruamel.yaml               0.17.21          py37h5eee18b_0  \n",
            "ruamel.yaml.clib          0.2.6            py37h5eee18b_1  \n",
            "scikit-image              0.19.3                   pypi_0    pypi\n",
            "scikit-learn              1.0.2                    pypi_0    pypi\n",
            "scipy                     1.7.3                    pypi_0    pypi\n",
            "segmentation-models-pytorch 0.1.3                    pypi_0    pypi\n",
            "setuptools                65.6.3           py37h06a4308_0  \n",
            "six                       1.16.0             pyhd3eb1b0_1  \n",
            "sqlite                    3.41.2               h5eee18b_0  \n",
            "tensorboard               1.14.0                   pypi_0    pypi\n",
            "tensorflow-estimator      1.14.0                   pypi_0    pypi\n",
            "tensorflow-gpu            1.14.0                   pypi_0    pypi\n",
            "termcolor                 2.3.0                    pypi_0    pypi\n",
            "threadpoolctl             3.1.0                    pypi_0    pypi\n",
            "tifffile                  2021.11.2                pypi_0    pypi\n",
            "timm                      0.3.2                    pypi_0    pypi\n",
            "tk                        8.6.12               h1ccaba5_0  \n",
            "toolz                     0.12.0           py37h06a4308_0  \n",
            "torch                     1.8.1+cu111              pypi_0    pypi\n",
            "torchaudio                0.8.1                    pypi_0    pypi\n",
            "torchtext                 0.9.1                    pypi_0    pypi\n",
            "torchvision               0.9.1+cu111              pypi_0    pypi\n",
            "tqdm                      4.64.1           py37h06a4308_0  \n",
            "typing_extensions         4.4.0            py37h06a4308_0  \n",
            "urllib3                   1.26.14          py37h06a4308_0  \n",
            "werkzeug                  2.2.3                    pypi_0    pypi\n",
            "wheel                     0.38.4           py37h06a4308_0  \n",
            "wrapt                     1.15.0                   pypi_0    pypi\n",
            "xz                        5.4.2                h5eee18b_0  \n",
            "zipp                      3.11.0           py37h06a4308_0  \n",
            "zlib                      1.2.13               h5eee18b_0  \n",
            "zstandard                 0.19.0           py37h5eee18b_0  \n",
            "zstd                      1.5.2                ha4553b6_0    anaconda\n"
          ]
        }
      ],
      "source": [
        "!conda list"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### prepare makeup data"
      ],
      "metadata": {
        "id": "O8ONBOtZReWm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown 1-8bNPsiXD5WwVAQDQpRE9OeNMXJNRxwm\n",
        "!unzip -q /content/images.zip -d /content/data"
      ],
      "metadata": {
        "id": "D4z4Wb9kRInM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f7b937fa-fe86-4f56-898c-b96e337c37d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1-8bNPsiXD5WwVAQDQpRE9OeNMXJNRxwm\n",
            "To: /content/images.zip\n",
            "100% 646M/646M [00:05<00:00, 115MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### check module"
      ],
      "metadata": {
        "id": "TpwdCJsKRx7H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/drive/MyDrive/De-makup/CPM"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c5yLe4gE7NiY",
        "outputId": "3dd9dbf6-2c79-4975-d086-b5435b3870b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/.shortcut-targets-by-id/1OGnjk5IbzIV8UyA5ByEDtaT64FHS_-Oz/De-makup/CPM\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check input images\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "original = np.array(Image.open('./imgs/non-makeup.png').resize((256, 256), Image.ANTIALIAS))\n",
        "style = np.array(Image.open('./imgs/style-1.png').resize((256, 256), Image.ANTIALIAS))\n",
        "\n",
        "Image.fromarray(np.concatenate([original, style], axis=1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "Hl6uLIxjR48i",
        "outputId": "5bc0fbbe-3319-4a3f-ace3-a4c2d4f2194c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=512x256 at 0x7F5220149870>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python -W ignore main.py --style imgs/style-1.png\\\n",
        "                                       --input imgs/non-makeup.png"
      ],
      "metadata": {
        "id": "IkNqGtUzRq_0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62952fc2-478c-41f9-d651-00527c4a3c8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:From /content/drive/.shortcut-targets-by-id/1OGnjk5IbzIV8UyA5ByEDtaT64FHS_-Oz/De-makup/CPM/utils/predictor.py:7: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/drive/.shortcut-targets-by-id/1OGnjk5IbzIV8UyA5ByEDtaT64FHS_-Oz/De-makup/CPM/utils/predictor.py:7: The name tf.logging.ERROR is deprecated. Please use tf.compat.v1.logging.ERROR instead.\n",
            "\n",
            "           ⊱ ──────ஓ๑♡๑ஓ ────── ⊰\n",
            "🎵 hhey, arguments are here if you need to check 🎵\n",
            "checkpoint_pattern:      ./checkpoints/pattern.pth\n",
            "checkpoint_color:        ./checkpoints/color.pth\n",
            "         device:                           cuda\n",
            "            prn:                           True\n",
            "     color_only:                          False\n",
            "   pattern_only:                          False\n",
            "          input:            imgs/non-makeup.png\n",
            "          style:               imgs/style-1.png\n",
            "          alpha:                            0.5\n",
            "        savedir:                              .\n",
            "\n",
            "Downloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-19c8e357.pth\n",
            "100% 97.8M/97.8M [00:01<00:00, 62.7MB/s]\n",
            "2023-05-25 06:57:43.139187: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
            "2023-05-25 06:57:43.167530: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2199995000 Hz\n",
            "2023-05-25 06:57:43.167738: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x13dd1ea0 executing computations on platform Host. Devices:\n",
            "2023-05-25 06:57:43.167789: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>\n",
            "2023-05-25 06:57:43.177421: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1\n",
            "2023-05-25 06:57:43.179770: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-05-25 06:57:43.179999: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xa4264b60 executing computations on platform CUDA. Devices:\n",
            "2023-05-25 06:57:43.180018: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n",
            "2023-05-25 06:57:43.181394: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-05-25 06:57:43.181553: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2023-05-25 06:57:43.188767: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2023-05-25 06:57:43.330083: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2023-05-25 06:57:43.409599: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2023-05-25 06:57:43.427724: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2023-05-25 06:57:43.595475: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2023-05-25 06:57:43.693045: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2023-05-25 06:57:43.941374: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2023-05-25 06:57:43.941655: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-05-25 06:57:43.942003: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-05-25 06:57:43.942130: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2023-05-25 06:57:43.944689: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2023-05-25 06:57:43.945181: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2023-05-25 06:57:43.945208: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2023-05-25 06:57:43.945218: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2023-05-25 06:57:43.945386: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-05-25 06:57:43.945701: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-05-25 06:57:43.945904: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 12920 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "2023-05-25 06:57:55.307638: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "Completed 👍 Please check result in: ./result.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Image.open('result.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "CyzFDIuNR8mI",
        "outputId": "e01ad3ad-cdb7-4bf7-cede-46f56e96f796"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=768x256 at 0x7F522014BD90>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### generate data"
      ],
      "metadata": {
        "id": "pn2yaSivSTIE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/drive/MyDrive/De-makup/CPM\n",
        "!python -W ignore generate_data.py"
      ],
      "metadata": {
        "id": "TCHDKf4VSRXn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aaa018a7-2315-4713-9c4b-035a17b4a17e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/.shortcut-targets-by-id/1OGnjk5IbzIV8UyA5ByEDtaT64FHS_-Oz/De-makup/CPM\n",
            "WARNING:tensorflow:From /content/drive/.shortcut-targets-by-id/1OGnjk5IbzIV8UyA5ByEDtaT64FHS_-Oz/De-makup/CPM/utils/predictor.py:7: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/drive/.shortcut-targets-by-id/1OGnjk5IbzIV8UyA5ByEDtaT64FHS_-Oz/De-makup/CPM/utils/predictor.py:7: The name tf.logging.ERROR is deprecated. Please use tf.compat.v1.logging.ERROR instead.\n",
            "\n",
            "           ⊱ ──────ஓ๑♡๑ஓ ────── ⊰\n",
            "🎵 hhey, arguments are here if you need to check 🎵\n",
            "checkpoint_pattern:      ./checkpoints/pattern.pth\n",
            "checkpoint_color:        ./checkpoints/color.pth\n",
            "         device:                           cuda\n",
            "            prn:                           True\n",
            "     color_only:                          False\n",
            "   pattern_only:                          False\n",
            "          input:          ./imgs/non-makeup.png\n",
            "          style:             ./imgs/style-2.png\n",
            "          alpha:                            0.5\n",
            "        savedir:                              .\n",
            "\n",
            "Downloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-19c8e357.pth\n",
            "100% 97.8M/97.8M [00:01<00:00, 94.7MB/s]\n",
            "2023-04-25 15:31:43.389609: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
            "2023-04-25 15:31:43.421924: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2299995000 Hz\n",
            "2023-04-25 15:31:43.422121: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x10ee61f0 executing computations on platform Host. Devices:\n",
            "2023-04-25 15:31:43.422152: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>\n",
            "2023-04-25 15:31:43.434371: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1\n",
            "2023-04-25 15:31:43.437161: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-04-25 15:31:43.437434: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xa6935fa0 executing computations on platform CUDA. Devices:\n",
            "2023-04-25 15:31:43.437459: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n",
            "2023-04-25 15:31:43.438651: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-04-25 15:31:43.438918: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2023-04-25 15:31:43.445156: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2023-04-25 15:31:43.619999: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0\n",
            "2023-04-25 15:31:43.711250: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0\n",
            "2023-04-25 15:31:43.733561: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0\n",
            "2023-04-25 15:31:43.935747: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2023-04-25 15:31:44.065015: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2023-04-25 15:31:44.384623: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "2023-04-25 15:31:44.384971: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-04-25 15:31:44.385254: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-04-25 15:31:44.385370: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0\n",
            "2023-04-25 15:31:44.388713: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0\n",
            "2023-04-25 15:31:44.389185: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2023-04-25 15:31:44.389215: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 \n",
            "2023-04-25 15:31:44.389225: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N \n",
            "2023-04-25 15:31:44.389391: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-04-25 15:31:44.389720: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-04-25 15:31:44.389927: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 12920 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "  0% 0/311 [00:00<?, ?it/s]2023-04-25 15:31:57.542232: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7\n",
            "100% 311/311 [2:04:38<00:00, 24.05s/it]\n",
            "100% 100/100 [40:04<00:00, 24.04s/it]\n",
            "100% 100/100 [39:49<00:00, 23.89s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### reference"
      ],
      "metadata": {
        "id": "orKWL-M9ScMd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nguyen, T., Tran, A., & Hoai, M. (2021). Lipstick ain’t enough: Beyond Color Matching for In-the-Wild Makeup Transfer. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 13300-13309."
      ],
      "metadata": {
        "id": "O-QMZ24xSgAD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Link Github của paper [Lipstick ain’t enough: Beyond Color Matching for In-the-Wild Makeup Transfer](https://github.com/VinAIResearch/CPM)"
      ],
      "metadata": {
        "id": "jL-ccbH1SiYR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://github.com/tensorflow/tensorflow/issues/20271"
      ],
      "metadata": {
        "id": "ns1gRsh6TBkU"
      }
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "qHOdoC1IJQ2C"
      ]
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}