main notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in /root/anaconda3/envs/myenv/lib/python3.11/site-packages (3.7.1)\n",
      "Collecting tensorflow-datasets\n",
      "  Downloading tensorflow_datasets-4.9.2-py3-none-any.whl (5.4 MB)\n",
      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.4/5.4 MB\u001b[0m \u001b[31m400.6 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m[36m0:00:01\u001b[0m\n",
      "\u001b[?25hCollecting ipywidgets\n",
      "  Using cached ipywidgets-8.0.7-py3-none-any.whl (138 kB)\n",
      "Collecting tensorflow\n",
      "  Downloading tensorflow-2.13.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (524.2 MB)\n",
      "\u001b[2K     \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/524.2 MB\u001b[0m \u001b[31m358.8 kB/s\u001b[0m eta \u001b[36m0:24:15\u001b[0m"
     ]
    }
   ],
   "source": [
    "pip install matplotlib tensorflow-datasets ipywidgets tensorflow matplotlib \"protobuf<=3.20.1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): \\ "
     ]
    }
   ],
   "source": [
    "conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JC-P_POyufWp",
    "outputId": "1e164f9e-23cd-424b-8387-8ba82d50d84a"
   },
   "outputs": [],
   "source": [
    "pip install tensorflow_graphics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J7SHRGBdm-jW",
    "outputId": "ff2952bd-3eee-46ec-e4ed-634407e77110",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting opencv-python\n",
      "  Downloading opencv_python-4.8.0.74-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (61.7 MB)\n",
      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.7/61.7 MB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m[36m0:00:02\u001b[0mm\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.21.2 in /root/anaconda3/envs/myenv/lib/python3.11/site-packages (from opencv-python) (1.25.0)\n",
      "Installing collected packages: opencv-python\n",
      "Successfully installed opencv-python-4.8.0.74\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[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting skimage\n",
      "  Using cached skimage-0.0.tar.gz (757 bytes)\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'error'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\xxalh\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\xxalh\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages)\n",
      "  error: subprocess-exited-with-error\n",
      "  \n",
      "  python setup.py egg_info did not run successfully.\n",
      "  exit code: 1\n",
      "  \n",
      "  [3 lines of output]\n",
      "  \n",
      "  *** Please install the `scikit-image` package (instead of `skimage`) ***\n",
      "  \n",
      "  [end of output]\n",
      "  \n",
      "  note: This error originates from a subprocess, and is likely not a problem with pip.\n",
      "error: metadata-generation-failed\n",
      "\n",
      "Encountered error while generating package metadata.\n",
      "\n",
      "See above for output.\n",
      "\n",
      "note: This is an issue with the package mentioned above, not pip.\n",
      "hint: See above for details.\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\xxalh\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages)\n",
      "\n",
      "[notice] A new release of pip is available: 23.1.1 -> 23.1.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "!pip3 install skimage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): ...working... done\n",
      "Solving environment: ...working... done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "conda install -c conda-forge scikit-image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): ...working... done\n",
      "Solving environment: ...working... done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "conda install -c conda-forge trimesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pyglet<2\n",
      "  Downloading pyglet-1.5.27-py3-none-any.whl (1.1 MB)\n",
      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m0:01\u001b[0m:01\u001b[0m0m\n",
      "\u001b[?25hInstalling collected packages: pyglet\n",
      "Successfully installed pyglet-1.5.27\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[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install \"pyglet<2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: failed with initial frozen solve. Retrying with flexible solve.\n",
      "Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.\n",
      "Collecting package metadata (repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 23.3.1\n",
      "  latest version: 23.5.1\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=23.5.1\n",
      "\n",
      "\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /root/anaconda3/envs/myenv\n",
      "\n",
      "  added / updated specs:\n",
      "    - pydot\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    atk-1.0-2.36.0             |       ha1a6a79_0         466 KB\n",
      "    boost-cpp-1.73.0           |      h7f8727e_12          16 KB\n",
      "    font-ttf-dejavu-sans-mono-2.37|       hd3eb1b0_0         335 KB\n",
      "    font-ttf-inconsolata-2.001 |       hcb22688_0          83 KB\n",
      "    font-ttf-source-code-pro-2.030|       hd3eb1b0_0         654 KB\n",
      "    font-ttf-ubuntu-0.83       |       h8b1ccd4_0         1.5 MB\n",
      "    fonts-anaconda-1           |       h8fa9717_0           3 KB\n",
      "    fonts-conda-ecosystem-1    |       hd3eb1b0_0           5 KB\n",
      "    fribidi-1.0.10             |       h7b6447c_0         103 KB\n",
      "    gdk-pixbuf-2.42.10         |       h5eee18b_0         600 KB\n",
      "    gobject-introspection-1.72.0|  py311hbb6d50b_2         1.5 MB\n",
      "    graphviz-2.50.0            |       h1b29801_1         2.6 MB\n",
      "    gtk2-2.24.33               |       h73c1081_2         3.3 MB\n",
      "    gts-0.7.6                  |       hb67d8dd_3         310 KB\n",
      "    libboost-1.73.0            |      h28710b8_12        13.8 MB\n",
      "    libgd-2.3.3                |       h6a678d5_2         240 KB\n",
      "    librsvg-2.54.4             |       h36cc946_2         6.0 MB\n",
      "    libtool-2.4.6              |    h6a678d5_1009         421 KB\n",
      "    pango-1.50.7               |       h05da053_0         427 KB\n",
      "    poppler-22.12.0            |       h381b16e_0        17.3 MB\n",
      "    poppler-data-0.4.11        |       h06a4308_1         2.6 MB\n",
      "    pydot-1.4.2                |  py311h06a4308_0          56 KB\n",
      "    pyparsing-2.4.7            |     pyhd3eb1b0_0          59 KB\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:        52.2 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "  atk-1.0            pkgs/main/linux-64::atk-1.0-2.36.0-ha1a6a79_0 \n",
      "  boost-cpp          pkgs/main/linux-64::boost-cpp-1.73.0-h7f8727e_12 \n",
      "  font-ttf-dejavu-s~ pkgs/main/noarch::font-ttf-dejavu-sans-mono-2.37-hd3eb1b0_0 \n",
      "  font-ttf-inconsol~ pkgs/main/noarch::font-ttf-inconsolata-2.001-hcb22688_0 \n",
      "  font-ttf-source-c~ pkgs/main/noarch::font-ttf-source-code-pro-2.030-hd3eb1b0_0 \n",
      "  font-ttf-ubuntu    pkgs/main/noarch::font-ttf-ubuntu-0.83-h8b1ccd4_0 \n",
      "  fonts-anaconda     pkgs/main/noarch::fonts-anaconda-1-h8fa9717_0 \n",
      "  fonts-conda-ecosy~ pkgs/main/noarch::fonts-conda-ecosystem-1-hd3eb1b0_0 \n",
      "  fribidi            pkgs/main/linux-64::fribidi-1.0.10-h7b6447c_0 \n",
      "  gdk-pixbuf         pkgs/main/linux-64::gdk-pixbuf-2.42.10-h5eee18b_0 \n",
      "  gobject-introspec~ pkgs/main/linux-64::gobject-introspection-1.72.0-py311hbb6d50b_2 \n",
      "  graphviz           pkgs/main/linux-64::graphviz-2.50.0-h1b29801_1 \n",
      "  gtk2               pkgs/main/linux-64::gtk2-2.24.33-h73c1081_2 \n",
      "  gts                pkgs/main/linux-64::gts-0.7.6-hb67d8dd_3 \n",
      "  libboost           pkgs/main/linux-64::libboost-1.73.0-h28710b8_12 \n",
      "  libgd              pkgs/main/linux-64::libgd-2.3.3-h6a678d5_2 \n",
      "  librsvg            pkgs/main/linux-64::librsvg-2.54.4-h36cc946_2 \n",
      "  libtool            pkgs/main/linux-64::libtool-2.4.6-h6a678d5_1009 \n",
      "  pango              pkgs/main/linux-64::pango-1.50.7-h05da053_0 \n",
      "  poppler            pkgs/main/linux-64::poppler-22.12.0-h381b16e_0 \n",
      "  poppler-data       pkgs/main/linux-64::poppler-data-0.4.11-h06a4308_1 \n",
      "  pydot              pkgs/main/linux-64::pydot-1.4.2-py311h06a4308_0 \n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "  ca-certificates    conda-forge::ca-certificates-2023.5.7~ --> pkgs/main::ca-certificates-2023.05.30-h06a4308_0 \n",
      "\n",
      "The following packages will be SUPERSEDED by a higher-priority channel:\n",
      "\n",
      "  certifi            conda-forge/noarch::certifi-2023.5.7-~ --> pkgs/main/linux-64::certifi-2023.5.7-py311h06a4308_0 \n",
      "  pyparsing          pkgs/main/linux-64::pyparsing-3.0.9-p~ --> pkgs/main/noarch::pyparsing-2.4.7-pyhd3eb1b0_0 \n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "libboost-1.73.0      | 13.8 MB   |                                       |   0% \n",
      "librsvg-2.54.4       | 6.0 MB    |                                       |   0% \u001b[A\n",
      "\n",
      "fonts-anaconda-1     | 3 KB      |                                       |   0% \u001b[A\u001b[A\n",
      "\n",
      "\n",
      "font-ttf-inconsolata | 83 KB     |                                       |   0% \u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    |                                       |   0% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "fribidi-1.0.10       | 103 KB    |                                       |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "gts-0.7.6            | 310 KB    |                                       |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "fonts-conda-ecosyste | 5 KB      |                                       |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   |                                       |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "poppler-data-0.4.11  | 2.6 MB    |                                       |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "libtool-2.4.6        | 421 KB    |                                       |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "gtk2-2.24.33         | 3.3 MB    |                                       |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "font-ttf-source-code | 654 KB    |                                       |   0% \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",
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      "pango-1.50.7         | 427 KB    |                                       |   0% \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",
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      "font-ttf-ubuntu-0.83 | 1.5 MB    |                                       |   0% \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",
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      "gdk-pixbuf-2.42.10   | 600 KB    |                                       |   0% \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",
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      "pydot-1.4.2          | 56 KB     |                                       |   0% \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",
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      "pyparsing-2.4.7      | 59 KB     |                                       |   0% \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",
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      "\n",
      "\n",
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      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "atk-1.0-2.36.0       | 466 KB    |                                       |   0% \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\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "gobject-introspectio | 1.5 MB    |                                       |   0% \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\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libgd-2.3.3          | 240 KB    |                                       |   0% \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\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "font-ttf-dejavu-sans | 335 KB    |                                       |   0% \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\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",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "boost-cpp-1.73.0     | 16 KB     |                                       |   0% \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\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   |                                       |   0% \u001b[A\u001b[A\n",
      "\n",
      "fonts-anaconda-1     | 3 KB      | ##################################### | 100% \u001b[A\u001b[A\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | 2                                     |   1% \u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "fribidi-1.0.10       | 103 KB    | #####7                                |  16% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "font-ttf-inconsolata | 83 KB     | ###################################4  |  96% \u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "font-ttf-inconsolata | 83 KB     | ##################################### | 100% \u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | 2                                     |   1% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | 4                                     |   1% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "fribidi-1.0.10       | 103 KB    | ######################9               |  62% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "fribidi-1.0.10       | 103 KB    | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | 8                                     |   2% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | 2                                     |   1% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | 6                                     |   2% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | #3                                    |   4% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | 5                                     |   2% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "gts-0.7.6            | 310 KB    | #############3                        |  36% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ##                                    |   5% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "fonts-conda-ecosyste | 5 KB      | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | 9                                     |   3% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | 9                                     |   3% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #2                                    |   3% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ##4                                   |   7% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "gts-0.7.6            | 310 KB    | ########################8             |  67% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | #4                                    |   4% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   |                                       |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | 1                                     |   0% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #6                                    |   5% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ###1                                  |   8% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "gts-0.7.6            | 310 KB    | ####################################3 |  98% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "gts-0.7.6            | 310 KB    | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ###5                                  |  10% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | #9                                    |   5% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | 2                                     |   1% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ####                                  |  11% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | ##2                                   |   6% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | 3                                     |   1% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #9                                    |   5% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ##4                                   |   7% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ##6                                   |   7% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | 7                                     |   2% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | 8                                     |   2% \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",
      "poppler-data-0.4.11  | 2.6 MB    | 2                                     |   1% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ###6                                  |  10% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | #8                                    |   5% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ######5                               |  18% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ##1                                   |   6% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ######7                               |  18% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ##8                                   |   8% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ###7                                  |  10% \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",
      "poppler-22.12.0      | 17.3 MB   | 9                                     |   2% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ###3                                  |   9% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | #######1                              |  19% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ####3                                 |  12% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ########                              |  22% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ###6                                  |  10% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #2                                    |   3% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ####5                                 |  12% \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",
      "poppler-data-0.4.11  | 2.6 MB    | #######6                              |  21% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | #########6                            |  26% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #5                                    |   4% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ###8                                  |  10% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ##########                            |  27% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #6                                    |   4% \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",
      "libboost-1.73.0      | 13.8 MB   | #####2                                |  14% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ####1                                 |  11% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #6                                    |   5% \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",
      "poppler-data-0.4.11  | 2.6 MB    | #########4                            |  26% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ##########5                           |  28% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | #####7                                |  16% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #####9                                |  16% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | ##########1                           |  27% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ####5                                 |  12% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #7                                    |   5% \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",
      "poppler-data-0.4.11  | 2.6 MB    | ##########6                           |  29% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | ######1                               |  17% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #8                                    |   5% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | ######2                               |  17% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ###########4                          |  31% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ######3                               |  17% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ###########6                          |  32% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #8                                    |   5% \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",
      "poppler-data-0.4.11  | 2.6 MB    | ###########5                          |  31% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #####1                                |  14% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ######4                               |  18% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ######6                               |  18% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ############1                         |  33% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #9                                    |   5% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #####3                                |  15% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | ############4                         |  34% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #####5                                |  15% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ######7                               |  18% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##                                    |   5% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #####7                                |  16% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ############5                         |  34% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ######8                               |  19% \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",
      "poppler-22.12.0      | 17.3 MB   | ##                                    |   6% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #####9                                |  16% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##                                    |   6% \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",
      "poppler-data-0.4.11  | 2.6 MB    | #############3                        |  36% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | ######9                               |  19% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##1                                   |   6% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #######                               |  19% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ######3                               |  17% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | #############2                        |  36% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | #############7                        |  37% \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",
      "poppler-22.12.0      | 17.3 MB   | ##1                                   |   6% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | #######1                              |  19% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | ##############2                       |  38% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #######2                              |  20% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ######7                               |  18% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##2                                   |   6% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | #############6                        |  37% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #######3                              |  20% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ######9                               |  19% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##2                                   |   6% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | #######4                              |  20% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | #############8                        |  38% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | ##############9                       |  40% \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",
      "libboost-1.73.0      | 13.8 MB   | #######4                              |  20% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ##############1                       |  38% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | ###############1                      |  41% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #######3                              |  20% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #######5                              |  20% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #######5                              |  20% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ##############3                       |  39% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #######6                              |  21% \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",
      "poppler-22.12.0      | 17.3 MB   | ##3                                   |   6% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #######6                              |  21% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ##############5                       |  39% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | ###############8                      |  43% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #######7                              |  21% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #######8                              |  21% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##4                                   |   7% \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",
      "poppler-data-0.4.11  | 2.6 MB    | ################2                     |  44% \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",
      "libboost-1.73.0      | 13.8 MB   | #######8                              |  21% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ########                              |  22% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #######9                              |  21% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | ################7                     |  45% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ###############2                      |  41% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ########2                             |  22% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ########                              |  22% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##5                                   |   7% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ########4                             |  23% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ###############4                      |  42% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ########1                             |  22% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ###############6                      |  42% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##5                                   |   7% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | ########1                             |  22% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | #################6                    |  48% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ###############9                      |  43% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##5                                   |   7% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | ########2                             |  22% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##6                                   |   7% \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",
      "libboost-1.73.0      | 13.8 MB   | ########4                             |  23% \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",
      "poppler-data-0.4.11  | 2.6 MB    | ##################5                   |  50% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ################1                     |  44% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##6                                   |   7% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #########                             |  24% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ################3                     |  44% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #########2                            |  25% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ########5                             |  23% \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",
      "poppler-data-0.4.11  | 2.6 MB    | ##################9                   |  51% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ################5                     |  45% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##7                                   |   7% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | ########6                             |  23% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | ###################1                  |  52% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ################8                     |  45% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##7                                   |   7% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #########6                            |  26% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ########7                             |  24% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | #################                     |  46% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ########8                             |  24% \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",
      "poppler-data-0.4.11  | 2.6 MB    | ###################8                  |  54% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | ########9                             |  24% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##8                                   |   8% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | #################2                    |  47% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ##########                            |  27% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | ####################3                 |  55% \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",
      "poppler-22.12.0      | 17.3 MB   | ##8                                   |   8% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #########                             |  24% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ##########2                           |  28% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##8                                   |   8% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #########1                            |  25% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | ####################7                 |  56% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ##########3                           |  28% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #########2                            |  25% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | #################9                    |  48% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | #####################2                |  57% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | #########2                            |  25% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##9                                   |   8% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ##################1                   |  49% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##9                                   |   8% \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",
      "poppler-data-0.4.11  | 2.6 MB    | #####################6                |  59% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | #########3                            |  25% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #########4                            |  26% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###                                   |   8% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ##########9                           |  30% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ##################6                   |  50% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #########5                            |  26% \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",
      "poppler-22.12.0      | 17.3 MB   | ###                                   |   8% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ###########1                          |  30% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #########6                            |  26% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | ######################5               |  61% \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",
      "poppler-22.12.0      | 17.3 MB   | ###                                   |   8% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ###########3                          |  31% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #########7                            |  26% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###1                                  |   8% \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",
      "poppler-data-0.4.11  | 2.6 MB    | #######################               |  62% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | #########7                            |  26% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ###################2                  |  52% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###1                                  |   9% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #########8                            |  27% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | #######################4              |  63% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | #########9                            |  27% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ###################7                  |  53% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ###########9                          |  32% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | #######################9              |  65% \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",
      "libboost-1.73.0      | 13.8 MB   | ##########                            |  27% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ###################9                  |  54% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | ##########1                           |  27% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###2                                  |   9% \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",
      "poppler-data-0.4.11  | 2.6 MB    | ########################3             |  66% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ####################1                 |  55% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | ##########2                           |  28% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ####################3                 |  55% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | ########################8             |  67% \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",
      "poppler-22.12.0      | 17.3 MB   | ###3                                  |   9% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | ##########4                           |  28% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ####################8                 |  56% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | #########################5            |  69% \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",
      "libboost-1.73.0      | 13.8 MB   | ##########5                           |  28% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ############9                         |  35% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | #########################9            |  70% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ##########6                           |  29% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###4                                  |   9% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #############2                        |  36% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ##########7                           |  29% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #############4                        |  36% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | #####################7                |  59% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###5                                  |  10% \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",
      "poppler-data-0.4.11  | 2.6 MB    | ##########################8           |  73% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | ##########9                           |  30% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | ###########################3          |  74% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ######################1               |  60% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | ###########                           |  30% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###5                                  |  10% \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",
      "poppler-data-0.4.11  | 2.6 MB    | ###########################7          |  75% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ##############2                       |  38% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ###########2                          |  30% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###6                                  |  10% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ##############5                       |  39% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ###########3                          |  31% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | #######################               |  62% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###7                                  |  10% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ##############8                       |  40% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ###########5                          |  31% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | #######################7              |  64% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###7                                  |  10% \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",
      "libboost-1.73.0      | 13.8 MB   | ###########7                          |  32% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ###############2                      |  41% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ########################2             |  65% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###8                                  |  10% \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",
      "libboost-1.73.0      | 13.8 MB   | ###########9                          |  32% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ###############5                      |  42% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ########################6             |  67% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ############1                         |  33% \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",
      "poppler-22.12.0      | 17.3 MB   | ###9                                  |  11% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ###############9                      |  43% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | #########################1            |  68% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ############3                         |  33% \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",
      "poppler-22.12.0      | 17.3 MB   | ####                                  |  11% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | #########################5            |  69% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | ############5                         |  34% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | ################################5     |  88% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ##########################            |  70% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ####1                                 |  11% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ################6                     |  45% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ############8                         |  35% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ##########################4           |  71% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ####2                                 |  12% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #################                     |  46% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #############                         |  35% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ##########################8           |  73% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ####3                                 |  12% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #################4                    |  47% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #############2                        |  36% \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",
      "poppler-22.12.0      | 17.3 MB   | ####4                                 |  12% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #################8                    |  48% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | ###################################2  |  95% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #############4                        |  36% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ####5                                 |  12% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ############################          |  76% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ##################1                   |  49% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #############6                        |  37% \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",
      "poppler-22.12.0      | 17.3 MB   | ####6                                 |  13% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ############################4         |  77% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #############8                        |  38% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ##################5                   |  50% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-data-0.4.11  | 2.6 MB    | ##################################### | 100% \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",
      "libboost-1.73.0      | 13.8 MB   | ##############                        |  38% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ###################                   |  52% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | #############################1        |  79% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ##############3                       |  39% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ###################5                  |  53% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | #############################8        |  81% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ##############5                       |  39% \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",
      "poppler-22.12.0      | 17.3 MB   | #####                                 |  14% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ###################9                  |  54% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ##############################2       |  82% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #####1                                |  14% \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",
      "libboost-1.73.0      | 13.8 MB   | ##############7                       |  40% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ##############################7       |  83% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "libboost-1.73.0      | 13.8 MB   | ##############9                       |  40% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libtool-2.4.6        | 421 KB    | #########8                            |  27% \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",
      "poppler-22.12.0      | 17.3 MB   | #####2                                |  14% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ###############################1      |  84% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ####################6                 |  56% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libtool-2.4.6        | 421 KB    | ##############                        |  38% \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",
      "libboost-1.73.0      | 13.8 MB   | ###############1                      |  41% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ###############################6      |  85% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #####################                 |  57% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libtool-2.4.6        | 421 KB    | ##################2                   |  49% \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",
      "libboost-1.73.0      | 13.8 MB   | ###############3                      |  42% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ################################      |  87% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #####################4                |  58% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #####5                                |  15% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ###############6                      |  42% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libtool-2.4.6        | 421 KB    | ######################4               |  61% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #####################8                |  59% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #####6                                |  15% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ################################9     |  89% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ###############8                      |  43% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ######################3               |  60% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | #################################4    |  90% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ################                      |  43% \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",
      "libtool-2.4.6        | 421 KB    | ##############################9       |  84% \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",
      "poppler-22.12.0      | 17.3 MB   | #####8                                |  16% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ######################7               |  61% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ##################################    |  92% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ################2                     |  44% \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",
      "libtool-2.4.6        | 421 KB    | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #######################               |  62% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ################4                     |  44% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ##################################7   |  94% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #######################4              |  63% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ################6                     |  45% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "graphviz-2.50.0      | 2.6 MB    | ###################################4  |  96% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ######1                               |  17% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #######################8              |  65% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ################8                     |  46% \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",
      "gtk2-2.24.33         | 3.3 MB    | 7                                     |   2% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ########################2             |  66% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ######2                               |  17% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #################                     |  46% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ########################6             |  67% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ######3                               |  17% \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",
      "gtk2-2.24.33         | 3.3 MB    | #2                                    |   3% \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",
      "libboost-1.73.0      | 13.8 MB   | #################2                    |  47% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #########################             |  68% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ######4                               |  17% \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",
      "gtk2-2.24.33         | 3.3 MB    | #7                                    |   5% \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",
      "libboost-1.73.0      | 13.8 MB   | #################4                    |  47% \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ######5                               |  18% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #########################4            |  69% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #################6                    |  48% \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",
      "poppler-22.12.0      | 17.3 MB   | ######6                               |  18% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #########################7            |  70% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "font-ttf-source-code | 654 KB    | 9                                     |   2% \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",
      "libboost-1.73.0      | 13.8 MB   | #################9                    |  48% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ##########################1           |  71% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "gtk2-2.24.33         | 3.3 MB    | ###3                                  |   9% \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",
      "poppler-22.12.0      | 17.3 MB   | ######8                               |  18% \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",
      "libboost-1.73.0      | 13.8 MB   | ##################1                   |  49% \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",
      "poppler-22.12.0      | 17.3 MB   | ######9                               |  19% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ##########################5           |  72% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "gtk2-2.24.33         | 3.3 MB    | ###8                                  |  10% \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",
      "libboost-1.73.0      | 13.8 MB   | ##################2                   |  49% \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",
      "poppler-22.12.0      | 17.3 MB   | #######                               |  19% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ##########################9           |  73% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "gtk2-2.24.33         | 3.3 MB    | ####4                                 |  12% \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",
      "font-ttf-source-code | 654 KB    | ########1                             |  22% \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",
      "gtk2-2.24.33         | 3.3 MB    | ####9                                 |  13% \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",
      "libboost-1.73.0      | 13.8 MB   | ##################4                   |  50% \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",
      "font-ttf-source-code | 654 KB    | #########9                            |  27% \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",
      "librsvg-2.54.4       | 6.0 MB    | ###########################2          |  74% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ##################9                   |  51% \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",
      "poppler-22.12.0      | 17.3 MB   | #######4                              |  20% \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",
      "font-ttf-source-code | 654 KB    | ################2                     |  44% \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",
      "librsvg-2.54.4       | 6.0 MB    | ############################2         |  76% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "gtk2-2.24.33         | 3.3 MB    | ######3                               |  17% \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",
      "libboost-1.73.0      | 13.8 MB   | ###################2                  |  52% \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",
      "gtk2-2.24.33         | 3.3 MB    | ######8                               |  19% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ############################7         |  78% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "font-ttf-source-code | 654 KB    | ###################9                  |  54% \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",
      "libboost-1.73.0      | 13.8 MB   | ###################4                  |  53% \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",
      "gtk2-2.24.33         | 3.3 MB    | #######4                              |  20% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #############################1        |  79% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "font-ttf-source-code | 654 KB    | ######################6               |  61% \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",
      "poppler-22.12.0      | 17.3 MB   | #######7                              |  21% \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",
      "gtk2-2.24.33         | 3.3 MB    | #######9                              |  21% \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",
      "libboost-1.73.0      | 13.8 MB   | ###################7                  |  53% \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",
      "librsvg-2.54.4       | 6.0 MB    | #############################5        |  80% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "font-ttf-source-code | 654 KB    | ##################################3   |  93% \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",
      "libboost-1.73.0      | 13.8 MB   | ####################4                 |  55% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ##############################9       |  84% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "gtk2-2.24.33         | 3.3 MB    | ##########                            |  27% \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",
      "font-ttf-source-code | 654 KB    | ##################################### | 100% \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",
      "gtk2-2.24.33         | 3.3 MB    | ##########7                           |  29% \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",
      "libboost-1.73.0      | 13.8 MB   | ####################7                 |  56% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ###############################6      |  86% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "gtk2-2.24.33         | 3.3 MB    | ###########4                          |  31% \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",
      "libboost-1.73.0      | 13.8 MB   | #####################                 |  57% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ################################2     |  87% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #####################2                |  58% \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",
      "poppler-22.12.0      | 17.3 MB   | ########8                             |  24% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ################################7     |  88% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #####################5                |  58% \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",
      "poppler-22.12.0      | 17.3 MB   | ########9                             |  24% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | #################################2    |  90% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "pango-1.50.7         | 427 KB    | #3                                    |   4% \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",
      "gtk2-2.24.33         | 3.3 MB    | #############2                        |  36% \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",
      "libboost-1.73.0      | 13.8 MB   | #####################7                |  59% \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",
      "libboost-1.73.0      | 13.8 MB   | #####################9                |  59% \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",
      "librsvg-2.54.4       | 6.0 MB    | #################################5    |  91% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "gtk2-2.24.33         | 3.3 MB    | #############7                        |  37% \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",
      "libboost-1.73.0      | 13.8 MB   | ######################1               |  60% \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",
      "poppler-22.12.0      | 17.3 MB   | #########1                            |  25% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ##################################1   |  92% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ######################5               |  61% \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",
      "poppler-22.12.0      | 17.3 MB   | #########6                            |  26% \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",
      "pango-1.50.7         | 427 KB    | ##################                    |  49% \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",
      "libboost-1.73.0      | 13.8 MB   | ######################8               |  62% \u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ###################################4  |  96% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #########7                            |  26% \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",
      "gtk2-2.24.33         | 3.3 MB    | ################5                     |  45% \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",
      "libboost-1.73.0      | 13.8 MB   | #######################               |  62% \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",
      "librsvg-2.54.4       | 6.0 MB    | ###################################9  |  97% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "pango-1.50.7         | 427 KB    | ##########################3           |  71% \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",
      "poppler-22.12.0      | 17.3 MB   | #########9                            |  27% \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",
      "libboost-1.73.0      | 13.8 MB   | #######################3              |  63% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ####################################3 |  98% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "pango-1.50.7         | 427 KB    | ##############################4       |  82% \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",
      "poppler-22.12.0      | 17.3 MB   | ##########1                           |  27% \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",
      "libboost-1.73.0      | 13.8 MB   | #######################6              |  64% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ####################################8 | 100% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "pango-1.50.7         | 427 KB    | ##################################6   |  94% \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",
      "poppler-22.12.0      | 17.3 MB   | ##########2                           |  28% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "librsvg-2.54.4       | 6.0 MB    | ##################################### | 100% \u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "gtk2-2.24.33         | 3.3 MB    | ##################6                   |  51% \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",
      "libboost-1.73.0      | 13.8 MB   | #######################9              |  65% \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",
      "poppler-22.12.0      | 17.3 MB   | ##########4                           |  28% \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",
      "libboost-1.73.0      | 13.8 MB   | ########################1             |  65% \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",
      "font-ttf-ubuntu-0.83 | 1.5 MB    | 3                                     |   1% \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",
      "libboost-1.73.0      | 13.8 MB   | ########################4             |  66% \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",
      "font-ttf-ubuntu-0.83 | 1.5 MB    | #9                                    |   5% \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",
      "gtk2-2.24.33         | 3.3 MB    | ####################2                 |  55% \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",
      "libboost-1.73.0      | 13.8 MB   | ########################6             |  67% \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",
      "font-ttf-ubuntu-0.83 | 1.5 MB    | ###4                                  |   9% \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",
      "gtk2-2.24.33         | 3.3 MB    | ####################9                 |  57% \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",
      "gdk-pixbuf-2.42.10   | 600 KB    | 9                                     |   3% \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",
      "libboost-1.73.0      | 13.8 MB   | ########################8             |  67% \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",
      "font-ttf-ubuntu-0.83 | 1.5 MB    | #####                                 |  14% \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",
      "gdk-pixbuf-2.42.10   | 600 KB    | ##9                                   |   8% \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",
      "gtk2-2.24.33         | 3.3 MB    | #####################5                |  58% \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",
      "poppler-22.12.0      | 17.3 MB   | ###########                           |  30% \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",
      "font-ttf-ubuntu-0.83 | 1.5 MB    | ######1                               |  17% \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",
      "libboost-1.73.0      | 13.8 MB   | #########################1            |  68% \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",
      "gtk2-2.24.33         | 3.3 MB    | ######################                |  60% \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",
      "poppler-22.12.0      | 17.3 MB   | ###########1                          |  30% \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",
      "libboost-1.73.0      | 13.8 MB   | #########################2            |  68% \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",
      "gdk-pixbuf-2.42.10   | 600 KB    | ######9                               |  19% \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",
      "gtk2-2.24.33         | 3.3 MB    | ######################5               |  61% \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",
      "libboost-1.73.0      | 13.8 MB   | #########################4            |  69% \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",
      "gdk-pixbuf-2.42.10   | 600 KB    | ########8                             |  24% \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",
      "font-ttf-ubuntu-0.83 | 1.5 MB    | ########5                             |  23% \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",
      "gtk2-2.24.33         | 3.3 MB    | #######################               |  62% \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",
      "libboost-1.73.0      | 13.8 MB   | ##########################            |  70% \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",
      "font-ttf-ubuntu-0.83 | 1.5 MB    | ##########4                           |  28% \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",
      "gtk2-2.24.33         | 3.3 MB    | ########################6             |  67% \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",
      "gdk-pixbuf-2.42.10   | 600 KB    | ##############8                       |  40% \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",
      "libboost-1.73.0      | 13.8 MB   | ##########################2           |  71% \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",
      "font-ttf-ubuntu-0.83 | 1.5 MB    | #############1                        |  36% \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",
      "gtk2-2.24.33         | 3.3 MB    | #########################3            |  69% \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",
      "poppler-22.12.0      | 17.3 MB   | ###########7                          |  32% \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",
      "libboost-1.73.0      | 13.8 MB   | ##########################5           |  72% \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",
      "font-ttf-ubuntu-0.83 | 1.5 MB    | ##############7                       |  40% \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",
      "poppler-22.12.0      | 17.3 MB   | ###########8                          |  32% \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",
      "gtk2-2.24.33         | 3.3 MB    | ##########################            |  71% \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",
      "libboost-1.73.0      | 13.8 MB   | ##########################7           |  72% \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",
      "font-ttf-ubuntu-0.83 | 1.5 MB    | ################2                     |  44% \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",
      "poppler-22.12.0      | 17.3 MB   | ############                          |  32% \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",
      "gdk-pixbuf-2.42.10   | 600 KB    | ######################7               |  61% \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",
      "libboost-1.73.0      | 13.8 MB   | ##########################9           |  73% \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",
      "font-ttf-ubuntu-0.83 | 1.5 MB    | #################8                    |  48% \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",
      "poppler-22.12.0      | 17.3 MB   | ############1                         |  33% \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",
      "gdk-pixbuf-2.42.10   | 600 KB    | ########################6             |  67% \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",
      "libboost-1.73.0      | 13.8 MB   | ###########################1          |  73% \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",
      "font-ttf-ubuntu-0.83 | 1.5 MB    | ##################9                   |  51% \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",
      "poppler-22.12.0      | 17.3 MB   | ############2                         |  33% \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",
      "gtk2-2.24.33         | 3.3 MB    | ###########################8          |  75% \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",
      "gdk-pixbuf-2.42.10   | 600 KB    | ##########################6           |  72% \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",
      "libboost-1.73.0      | 13.8 MB   | ###########################3          |  74% \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",
      "poppler-22.12.0      | 17.3 MB   | ############3                         |  33% \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",
      "gdk-pixbuf-2.42.10   | 600 KB    | ##############################5       |  83% \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",
      "libboost-1.73.0      | 13.8 MB   | ###########################7          |  75% \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",
      "font-ttf-ubuntu-0.83 | 1.5 MB    | ####################1                 |  54% \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",
      "gdk-pixbuf-2.42.10   | 600 KB    | #################################5    |  91% \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",
      "poppler-22.12.0      | 17.3 MB   | ############6                         |  34% \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",
      "gdk-pixbuf-2.42.10   | 600 KB    | ###################################5  |  96% \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",
      "libboost-1.73.0      | 13.8 MB   | ###########################9          |  76% \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",
      "gdk-pixbuf-2.42.10   | 600 KB    | ##################################### | 100% \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",
      "poppler-22.12.0      | 17.3 MB   | ############7                         |  34% \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",
      "libboost-1.73.0      | 13.8 MB   | ############################1         |  76% \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",
      "gtk2-2.24.33         | 3.3 MB    | ##############################8       |  83% \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",
      "poppler-22.12.0      | 17.3 MB   | ############8                         |  35% \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",
      "pydot-1.4.2          | 56 KB     | ##########6                           |  29% \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",
      "libboost-1.73.0      | 13.8 MB   | ############################4         |  77% \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",
      "gtk2-2.24.33         | 3.3 MB    | ###############################5      |  85% \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",
      "font-ttf-ubuntu-0.83 | 1.5 MB    | ########################7             |  67% \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",
      "pydot-1.4.2          | 56 KB     | ##################################### | 100% \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",
      "poppler-22.12.0      | 17.3 MB   | #############3                        |  36% \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",
      "libboost-1.73.0      | 13.8 MB   | #############################1        |  79% \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",
      "font-ttf-ubuntu-0.83 | 1.5 MB    | #############################8        |  81% \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",
      "libboost-1.73.0      | 13.8 MB   | #############################4        |  80% \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",
      "gtk2-2.24.33         | 3.3 MB    | ##################################2   |  92% \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",
      "font-ttf-ubuntu-0.83 | 1.5 MB    | ###############################7      |  86% \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",
      "libboost-1.73.0      | 13.8 MB   | #############################7        |  80% \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",
      "gtk2-2.24.33         | 3.3 MB    | ###################################   |  95% \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",
      "font-ttf-ubuntu-0.83 | 1.5 MB    | #################################6    |  91% \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",
      "libboost-1.73.0      | 13.8 MB   | #############################9        |  81% \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",
      "font-ttf-ubuntu-0.83 | 1.5 MB    | ###################################2  |  95% \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",
      "gtk2-2.24.33         | 3.3 MB    | ###################################7  |  97% \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",
      "pyparsing-2.4.7      | 59 KB     | #########9                            |  27% \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",
      "poppler-22.12.0      | 17.3 MB   | ##############                        |  38% \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",
      "font-ttf-ubuntu-0.83 | 1.5 MB    | ####################################7 |  99% \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",
      "libboost-1.73.0      | 13.8 MB   | ##############################2       |  82% \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",
      "gtk2-2.24.33         | 3.3 MB    | ####################################4 |  99% \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",
      "poppler-22.12.0      | 17.3 MB   | ##############2                       |  38% \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",
      "libboost-1.73.0      | 13.8 MB   | ##############################4       |  82% \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",
      "pyparsing-2.4.7      | 59 KB     | ##################################### | 100% \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",
      "gtk2-2.24.33         | 3.3 MB    | ##################################### | 100% \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",
      "poppler-22.12.0      | 17.3 MB   | ##############4                       |  39% \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",
      "\n",
      "atk-1.0-2.36.0       | 466 KB    | #2                                    |   3% \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\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ##############################7       |  83% \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",
      "\n",
      "atk-1.0-2.36.0       | 466 KB    | ##5                                   |   7% \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\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "gobject-introspectio | 1.5 MB    | 3                                     |   1% \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\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libgd-2.3.3          | 240 KB    | ##4                                   |   7% \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\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ###############################1      |  84% \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",
      "\n",
      "atk-1.0-2.36.0       | 466 KB    | #############9                        |  38% \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\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libgd-2.3.3          | 240 KB    | #######3                              |  20% \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\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###############                       |  41% \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",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ###############################5      |  85% \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\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "atk-1.0-2.36.0       | 466 KB    | #################################     |  89% \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\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "gobject-introspectio | 1.5 MB    | ########3                             |  23% \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\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ################################6     |  88% \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",
      "\n",
      "\n",
      "\n",
      "libgd-2.3.3          | 240 KB    | ####################################9 | 100% \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\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libgd-2.3.3          | 240 KB    | ##################################### | 100% \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\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #################################     |  89% \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\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ################                      |  43% \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",
      "\n",
      "atk-1.0-2.36.0       | 466 KB    | ##################################### | 100% \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\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #################################3    |  90% \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\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ################2                     |  44% \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",
      "\n",
      "\n",
      "\n",
      "\n",
      "font-ttf-dejavu-sans | 335 KB    | #7                                    |   5% \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\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",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "boost-cpp-1.73.0     | 16 KB     | ####################################1 |  98% \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\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",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | #################################6    |  91% \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\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ################4                     |  44% \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",
      "\n",
      "\n",
      "gobject-introspectio | 1.5 MB    | ###############1                      |  41% \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\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ##################################    |  92% \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\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ################6                     |  45% \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",
      "\n",
      "\n",
      "gobject-introspectio | 1.5 MB    | #################4                    |  47% \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\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ##################################3   |  93% \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\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ##################################5   |  93% \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",
      "\n",
      "\n",
      "gobject-introspectio | 1.5 MB    | ###################3                  |  52% \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\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #################                     |  46% \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",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ##################################7   |  94% \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\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",
      "\n",
      "\n",
      "gobject-introspectio | 1.5 MB    | ####################8                 |  56% \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\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #################1                    |  46% \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",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ###################################5  |  96% \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\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",
      "\n",
      "\n",
      "gobject-introspectio | 1.5 MB    | ########################6             |  67% \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\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ###################################8  |  97% \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",
      "\n",
      "\n",
      "\n",
      "\n",
      "font-ttf-dejavu-sans | 335 KB    | ###################################3  |  96% \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\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",
      "\n",
      "\n",
      "\n",
      "\n",
      "font-ttf-dejavu-sans | 335 KB    | ##################################### | 100% \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\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #################7                    |  48% \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",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ####################################1 |  98% \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\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #################9                    |  49% \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",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ####################################4 |  99% \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\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##################1                   |  49% \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",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ####################################7 |  99% \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\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##################3                   |  50% \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",
      "\n",
      "\n",
      "gobject-introspectio | 1.5 MB    | ################################6     |  88% \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\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##################5                   |  50% \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",
      "\n",
      "\n",
      "gobject-introspectio | 1.5 MB    | ##################################9   |  94% \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\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##################7                   |  51% \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",
      "\n",
      "\n",
      "gobject-introspectio | 1.5 MB    | ####################################8 | 100% \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\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##################9                   |  51% \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",
      "\n",
      "\n",
      "gobject-introspectio | 1.5 MB    | ##################################### | 100% \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\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###################1                  |  52% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###################3                  |  52% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###################5                  |  53% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###################7                  |  53% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###################9                  |  54% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ####################2                 |  55% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ####################5                 |  55% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ####################7                 |  56% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "libboost-1.73.0      | 13.8 MB   | ##################################### | 100% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #####################3                |  58% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #####################6                |  59% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #####################9                |  59% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ######################2               |  60% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ######################4               |  61% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ######################7               |  62% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #######################               |  62% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #######################3              |  63% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #######################7              |  64% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #######################9              |  65% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ########################3             |  66% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ########################6             |  67% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #########################             |  68% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #########################4            |  69% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #########################8            |  70% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##########################1           |  71% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##########################5           |  72% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##########################9           |  73% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###########################2          |  74% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###########################7          |  75% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ############################1         |  76% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ############################6         |  77% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #############################         |  78% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #############################3        |  79% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###############################       |  84% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###############################9      |  86% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ################################6     |  88% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #################################1    |  90% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | #################################6    |  91% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##################################1   |  92% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ##################################6   |  94% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###################################1  |  95% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ###################################6  |  96% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ####################################1 |  98% \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "poppler-22.12.0      | 17.3 MB   | ####################################5 |  99% \u001b[A\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",
      "                                                                                \u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "                                                                                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
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      "                                                                                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
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      "\n",
      "\n",
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      "                                                                                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "\n",
      "\n",
      "\n",
      "\n",
      "                                                                                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\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",
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      "\n",
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      "                                                                                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "                                                                                \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",
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      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
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      "\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",
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      "\n",
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      "\n",
      "\n",
      "\n",
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      "\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",
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      "\n",
      "\n",
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      "\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",
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      "\n",
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      "\n",
      "\n",
      "\n",
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      "\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",
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      "\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",
      "\n",
      "\n",
      "\n",
      "\n",
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      "\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\u001b[A\n",
      "\n",
      "\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\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\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\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\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\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",
      "\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\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "conda install pydot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting graphviz\n",
      "  Downloading graphviz-0.20.1-py3-none-any.whl (47 kB)\n",
      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.0/47.0 kB\u001b[0m \u001b[31m203.6 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m kB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: graphviz\n",
      "Successfully installed graphviz-0.20.1\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[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**********************************************************************\n",
      "File \"/home/hedrax/Desktop/Project 3D voxels/Pix2Vox/pretrained/binvox_rw.py\", line 25, in __main__\n",
      "Failed example:\n",
      "    with open('chair.binvox', 'rb') as f:\n",
      "        m1 = binvox_rw.read_as_3d_array(f)\n",
      "Exception raised:\n",
      "    Traceback (most recent call last):\n",
      "      File \"/root/anaconda3/envs/myenv/lib/python3.11/doctest.py\", line 1351, in __run\n",
      "        exec(compile(example.source, filename, \"single\",\n",
      "      File \"<doctest __main__[2]>\", line 1, in <module>\n",
      "        with open('chair.binvox', 'rb') as f:\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "    FileNotFoundError: [Errno 2] No such file or directory: 'chair.binvox'\n",
      "**********************************************************************\n",
      "File \"/home/hedrax/Desktop/Project 3D voxels/Pix2Vox/pretrained/binvox_rw.py\", line 28, in __main__\n",
      "Failed example:\n",
      "    m1.dims\n",
      "Exception raised:\n",
      "    Traceback (most recent call last):\n",
      "      File \"/root/anaconda3/envs/myenv/lib/python3.11/doctest.py\", line 1351, in __run\n",
      "        exec(compile(example.source, filename, \"single\",\n",
      "      File \"<doctest __main__[3]>\", line 1, in <module>\n",
      "        m1.dims\n",
      "        ^^\n",
      "    NameError: name 'm1' is not defined\n",
      "**********************************************************************\n",
      "File \"/home/hedrax/Desktop/Project 3D voxels/Pix2Vox/pretrained/binvox_rw.py\", line 30, in __main__\n",
      "Failed example:\n",
      "    round(m1.scale,3)\n",
      "Exception raised:\n",
      "    Traceback (most recent call last):\n",
      "      File \"/root/anaconda3/envs/myenv/lib/python3.11/doctest.py\", line 1351, in __run\n",
      "        exec(compile(example.source, filename, \"single\",\n",
      "      File \"<doctest __main__[4]>\", line 1, in <module>\n",
      "        round(m1.scale,3)\n",
      "              ^^\n",
      "    NameError: name 'm1' is not defined\n",
      "**********************************************************************\n",
      "File \"/home/hedrax/Desktop/Project 3D voxels/Pix2Vox/pretrained/binvox_rw.py\", line 32, in __main__\n",
      "Failed example:\n",
      "    m1.translate\n",
      "Exception raised:\n",
      "    Traceback (most recent call last):\n",
      "      File \"/root/anaconda3/envs/myenv/lib/python3.11/doctest.py\", line 1351, in __run\n",
      "        exec(compile(example.source, filename, \"single\",\n",
      "      File \"<doctest __main__[5]>\", line 1, in <module>\n",
      "        m1.translate\n",
      "        ^^\n",
      "    NameError: name 'm1' is not defined\n",
      "**********************************************************************\n",
      "File \"/home/hedrax/Desktop/Project 3D voxels/Pix2Vox/pretrained/binvox_rw.py\", line 34, in __main__\n",
      "Failed example:\n",
      "    with open('chair_out.binvox', 'wb') as f:\n",
      "        m1.write(f)\n",
      "Exception raised:\n",
      "    Traceback (most recent call last):\n",
      "      File \"/root/anaconda3/envs/myenv/lib/python3.11/doctest.py\", line 1351, in __run\n",
      "        exec(compile(example.source, filename, \"single\",\n",
      "      File \"<doctest __main__[6]>\", line 2, in <module>\n",
      "        m1.write(f)\n",
      "        ^^\n",
      "    NameError: name 'm1' is not defined\n",
      "**********************************************************************\n",
      "File \"/home/hedrax/Desktop/Project 3D voxels/Pix2Vox/pretrained/binvox_rw.py\", line 37, in __main__\n",
      "Failed example:\n",
      "    with open('chair_out.binvox', 'rb') as f:\n",
      "        m2 = binvox_rw.read_as_3d_array(f)\n",
      "Exception raised:\n",
      "    Traceback (most recent call last):\n",
      "      File \"/root/anaconda3/envs/myenv/lib/python3.11/doctest.py\", line 1351, in __run\n",
      "        exec(compile(example.source, filename, \"single\",\n",
      "      File \"<doctest __main__[7]>\", line 2, in <module>\n",
      "        m2 = binvox_rw.read_as_3d_array(f)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "      File \"/home/hedrax/Desktop/Project 3D voxels/Pix2Vox/pretrained/binvox_rw.py\", line 131, in read_as_3d_array\n",
      "        dims, translate, scale = read_header(fp)\n",
      "                                 ^^^^^^^^^^^^^^^\n",
      "      File \"/home/hedrax/Desktop/Project 3D voxels/Pix2Vox/pretrained/binvox_rw.py\", line 112, in read_header\n",
      "        raise IOError('Not a binvox file')\n",
      "    OSError: Not a binvox file\n",
      "**********************************************************************\n",
      "File \"/home/hedrax/Desktop/Project 3D voxels/Pix2Vox/pretrained/binvox_rw.py\", line 40, in __main__\n",
      "Failed example:\n",
      "    m1.dims==m2.dims\n",
      "Exception raised:\n",
      "    Traceback (most recent call last):\n",
      "      File \"/root/anaconda3/envs/myenv/lib/python3.11/doctest.py\", line 1351, in __run\n",
      "        exec(compile(example.source, filename, \"single\",\n",
      "      File \"<doctest __main__[8]>\", line 1, in <module>\n",
      "        m1.dims==m2.dims\n",
      "        ^^\n",
      "    NameError: name 'm1' is not defined\n",
      "**********************************************************************\n",
      "File \"/home/hedrax/Desktop/Project 3D voxels/Pix2Vox/pretrained/binvox_rw.py\", line 42, in __main__\n",
      "Failed example:\n",
      "    m1.scale==m2.scale\n",
      "Exception raised:\n",
      "    Traceback (most recent call last):\n",
      "      File \"/root/anaconda3/envs/myenv/lib/python3.11/doctest.py\", line 1351, in __run\n",
      "        exec(compile(example.source, filename, \"single\",\n",
      "      File \"<doctest __main__[9]>\", line 1, in <module>\n",
      "        m1.scale==m2.scale\n",
      "        ^^\n",
      "    NameError: name 'm1' is not defined\n",
      "**********************************************************************\n",
      "File \"/home/hedrax/Desktop/Project 3D voxels/Pix2Vox/pretrained/binvox_rw.py\", line 44, in __main__\n",
      "Failed example:\n",
      "    m1.translate==m2.translate\n",
      "Exception raised:\n",
      "    Traceback (most recent call last):\n",
      "      File \"/root/anaconda3/envs/myenv/lib/python3.11/doctest.py\", line 1351, in __run\n",
      "        exec(compile(example.source, filename, \"single\",\n",
      "      File \"<doctest __main__[10]>\", line 1, in <module>\n",
      "        m1.translate==m2.translate\n",
      "        ^^\n",
      "    NameError: name 'm1' is not defined\n",
      "**********************************************************************\n",
      "File \"/home/hedrax/Desktop/Project 3D voxels/Pix2Vox/pretrained/binvox_rw.py\", line 46, in __main__\n",
      "Failed example:\n",
      "    np.all(m1.data==m2.data)\n",
      "Exception raised:\n",
      "    Traceback (most recent call last):\n",
      "      File \"/root/anaconda3/envs/myenv/lib/python3.11/doctest.py\", line 1351, in __run\n",
      "        exec(compile(example.source, filename, \"single\",\n",
      "      File \"<doctest __main__[11]>\", line 1, in <module>\n",
      "        np.all(m1.data==m2.data)\n",
      "               ^^\n",
      "    NameError: name 'm1' is not defined\n",
      "**********************************************************************\n",
      "File \"/home/hedrax/Desktop/Project 3D voxels/Pix2Vox/pretrained/binvox_rw.py\", line 49, in __main__\n",
      "Failed example:\n",
      "    with open('chair.binvox', 'rb') as f:\n",
      "        md = binvox_rw.read_as_3d_array(f)\n",
      "Exception raised:\n",
      "    Traceback (most recent call last):\n",
      "      File \"/root/anaconda3/envs/myenv/lib/python3.11/doctest.py\", line 1351, in __run\n",
      "        exec(compile(example.source, filename, \"single\",\n",
      "      File \"<doctest __main__[12]>\", line 1, in <module>\n",
      "        with open('chair.binvox', 'rb') as f:\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "    FileNotFoundError: [Errno 2] No such file or directory: 'chair.binvox'\n",
      "**********************************************************************\n",
      "File \"/home/hedrax/Desktop/Project 3D voxels/Pix2Vox/pretrained/binvox_rw.py\", line 52, in __main__\n",
      "Failed example:\n",
      "    with open('chair.binvox', 'rb') as f:\n",
      "        ms = binvox_rw.read_as_coord_array(f)\n",
      "Exception raised:\n",
      "    Traceback (most recent call last):\n",
      "      File \"/root/anaconda3/envs/myenv/lib/python3.11/doctest.py\", line 1351, in __run\n",
      "        exec(compile(example.source, filename, \"single\",\n",
      "      File \"<doctest __main__[13]>\", line 1, in <module>\n",
      "        with open('chair.binvox', 'rb') as f:\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "    FileNotFoundError: [Errno 2] No such file or directory: 'chair.binvox'\n",
      "**********************************************************************\n",
      "File \"/home/hedrax/Desktop/Project 3D voxels/Pix2Vox/pretrained/binvox_rw.py\", line 55, in __main__\n",
      "Failed example:\n",
      "    data_ds = binvox_rw.dense_to_sparse(md.data)\n",
      "Exception raised:\n",
      "    Traceback (most recent call last):\n",
      "      File \"/root/anaconda3/envs/myenv/lib/python3.11/doctest.py\", line 1351, in __run\n",
      "        exec(compile(example.source, filename, \"single\",\n",
      "      File \"<doctest __main__[14]>\", line 1, in <module>\n",
      "        data_ds = binvox_rw.dense_to_sparse(md.data)\n",
      "                                            ^^\n",
      "    NameError: name 'md' is not defined\n",
      "**********************************************************************\n",
      "File \"/home/hedrax/Desktop/Project 3D voxels/Pix2Vox/pretrained/binvox_rw.py\", line 56, in __main__\n",
      "Failed example:\n",
      "    data_sd = binvox_rw.sparse_to_dense(ms.data, 32)\n",
      "Exception raised:\n",
      "    Traceback (most recent call last):\n",
      "      File \"/root/anaconda3/envs/myenv/lib/python3.11/doctest.py\", line 1351, in __run\n",
      "        exec(compile(example.source, filename, \"single\",\n",
      "      File \"<doctest __main__[15]>\", line 1, in <module>\n",
      "        data_sd = binvox_rw.sparse_to_dense(ms.data, 32)\n",
      "                                            ^^\n",
      "    NameError: name 'ms' is not defined\n",
      "**********************************************************************\n",
      "File \"/home/hedrax/Desktop/Project 3D voxels/Pix2Vox/pretrained/binvox_rw.py\", line 57, in __main__\n",
      "Failed example:\n",
      "    np.all(data_sd==md.data)\n",
      "Exception raised:\n",
      "    Traceback (most recent call last):\n",
      "      File \"/root/anaconda3/envs/myenv/lib/python3.11/doctest.py\", line 1351, in __run\n",
      "        exec(compile(example.source, filename, \"single\",\n",
      "      File \"<doctest __main__[16]>\", line 1, in <module>\n",
      "        np.all(data_sd==md.data)\n",
      "               ^^^^^^^\n",
      "    NameError: name 'data_sd' is not defined\n",
      "**********************************************************************\n",
      "File \"/home/hedrax/Desktop/Project 3D voxels/Pix2Vox/pretrained/binvox_rw.py\", line 61, in __main__\n",
      "Failed example:\n",
      "    np.all(ms.data[:, np.lexsort(ms.data)] == data_ds[:, np.lexsort(data_ds)])\n",
      "Exception raised:\n",
      "    Traceback (most recent call last):\n",
      "      File \"/root/anaconda3/envs/myenv/lib/python3.11/doctest.py\", line 1351, in __run\n",
      "        exec(compile(example.source, filename, \"single\",\n",
      "      File \"<doctest __main__[17]>\", line 1, in <module>\n",
      "        np.all(ms.data[:, np.lexsort(ms.data)] == data_ds[:, np.lexsort(data_ds)])\n",
      "               ^^\n",
      "    NameError: name 'ms' is not defined\n",
      "**********************************************************************\n",
      "1 items had failures:\n",
      "  16 of  18 in __main__\n",
      "***Test Failed*** 16 failures.\n"
     ]
    }
   ],
   "source": [
    "%run \"binvox_rw.py\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%run \"file_management.py\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "deGpkHjm8vBo"
   },
   "outputs": [],
   "source": [
    "%run utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "#limiting the consumption of the gpu\n",
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "for gpu in gpus: \n",
    "    tf.config.experimental.set_memory_growth(gpu, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128, 128, 128)\n"
     ]
    }
   ],
   "source": [
    "print(load3DFile(str(70),False).data.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CuqAWMJjjYYE"
   },
   "source": [
    "# Taking screenShot from the 3d objects that we have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = pathlib.Path(PATH+\"/Data/images/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_3d_dir = pathlib.Path(PATH+\"/Data/temp/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_dataset = tf.data.Dataset.list_files(os.path.join(data_dir, '*.png'),shuffle=False)\n",
    "binvox_dataset = tf.data.Dataset.list_files(os.path.join(PATH + \"/Data/temp\", '*.binvox'),shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1901\n"
     ]
    }
   ],
   "source": [
    "# Define a dataset of image and binvox pairs\n",
    "dataset = tf.data.Dataset.zip((image_dataset, binvox_dataset))\n",
    "\n",
    "# Apply the load_data function to each image/binvox pair\n",
    "dataset = dataset.map(lambda x, y: tf.numpy_function(load_data, [x, y], [tf.uint8, tf.int32]), num_parallel_calls=tf.data.AUTOTUNE)\n",
    "\n",
    "# Print the size of the dataset\n",
    "print(len(dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.name = \"training Ds\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.shuffle(buffer_size=len(dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "reSeQLSlE2l6",
    "outputId": "a268947d-c3f1-4363-d093-4531c4f996da"
   },
   "outputs": [],
   "source": [
    "sample_image, vox  = next(iter(dataset.take(5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "id": "oFq6jQ0SE5zH",
    "outputId": "cce407a7-6c0a-446f-9388-439876205a55",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e61e2cc5b0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(sample_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128, 128, 1)\n"
     ]
    }
   ],
   "source": [
    "print(sample_image.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 260
    },
    "id": "_f997oVuLS6d",
    "outputId": "f2ea4e07-e50b-4aaf-996b-81032b35891a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(480, 640, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e614232800>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inp = load(str(PATH + '/Data/images/105.png'))\n",
    "# Casting to int for matplotlib to display the images\n",
    "# Loop four times and get images \n",
    "\n",
    "lop = [inp/255]\n",
    "\n",
    "print(inp.shape)\n",
    "plt.figure()\n",
    "plt.imshow(inp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "6LbAVajqYPez"
   },
   "outputs": [],
   "source": [
    "#loading the files = dataset\n",
    "#shuffling\n",
    "dataset = dataset.shuffle(BUFFER_SIZE)\n",
    "#batching\n",
    "dataset = dataset.batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "QB11tuwKZj8C"
   },
   "outputs": [],
   "source": [
    "#will not use it for now\n",
    "#getting the test images\n",
    "# try:\n",
    "#   test_dataset = tf.data.Dataset.list_files(str(PATH / 'testA/*.jpg'))\n",
    "# except tf.errors.InvalidArgumentError:\n",
    "#   test_dataset = tf.data.Dataset.list_files(str(PATH / 'testB/*.jpg'))\n",
    "# test_dataset = test_dataset.map(load_image_test)\n",
    "# test_dataset = test_dataset.batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "kfN3xWHuZy4W"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "q9525d6IdWOr"
   },
   "outputs": [],
   "source": [
    "generator = Generator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 128, 128, 1)]     0         \n",
      "                                                                 \n",
      " block1_conv1 (Conv2D)       (None, 128, 128, 64)      640       \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 128, 128, 64)     256       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, 128, 128, 64)      36928     \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 128, 128, 64)     256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, 64, 64, 64)        0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, 64, 64, 128)       73856     \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 64, 64, 128)      512       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, 64, 64, 128)       147584    \n",
      "                                                                 \n",
      " batch_normalization_3 (Batc  (None, 64, 64, 128)      512       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, 32, 32, 128)       0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, 32, 32, 256)       295168    \n",
      "                                                                 \n",
      " batch_normalization_4 (Batc  (None, 32, 32, 256)      1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, 32, 32, 256)       590080    \n",
      "                                                                 \n",
      " batch_normalization_5 (Batc  (None, 32, 32, 256)      1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, 32, 32, 256)       590080    \n",
      "                                                                 \n",
      " batch_normalization_6 (Batc  (None, 32, 32, 256)      1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, 16, 16, 256)       0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, 16, 16, 512)       1180160   \n",
      "                                                                 \n",
      " batch_normalization_7 (Batc  (None, 16, 16, 512)      2048      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, 16, 16, 512)       2359808   \n",
      "                                                                 \n",
      " batch_normalization_8 (Batc  (None, 16, 16, 512)      2048      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, 16, 16, 512)       2359808   \n",
      "                                                                 \n",
      " batch_normalization_9 (Batc  (None, 16, 16, 512)      2048      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, 8, 8, 512)         0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, 8, 8, 512)         2359808   \n",
      "                                                                 \n",
      " batch_normalization_10 (Bat  (None, 8, 8, 512)        2048      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, 8, 8, 512)         2359808   \n",
      "                                                                 \n",
      " batch_normalization_11 (Bat  (None, 8, 8, 512)        2048      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, 8, 8, 512)         2359808   \n",
      "                                                                 \n",
      " batch_normalization_12 (Bat  (None, 8, 8, 512)        2048      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, 4, 4, 512)         0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 8192)              0         \n",
      "                                                                 \n",
      " fc1 (Dense)                 (None, 8192)              67117056  \n",
      "                                                                 \n",
      " fc2 (Dense)                 (None, 4096)              33558528  \n",
      "                                                                 \n",
      " reshape (Reshape)           (None, 1, 1, 1, 4096)     0         \n",
      "                                                                 \n",
      " tf.reshape (TFOpLambda)     (None, 1, 1, 1, 4096)     0         \n",
      "                                                                 \n",
      " conv3d_transpose (Conv3DTra  (None, 2, 2, 2, 64)      7077952   \n",
      " nspose)                                                         \n",
      "                                                                 \n",
      " batch_normalization_13 (Bat  (None, 2, 2, 2, 64)      256       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " conv3d_transpose_1 (Conv3DT  (None, 4, 4, 4, 64)      110656    \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      " batch_normalization_14 (Bat  (None, 4, 4, 4, 64)      256       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " conv3d_transpose_2 (Conv3DT  (None, 8, 8, 8, 64)      110656    \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      " batch_normalization_15 (Bat  (None, 8, 8, 8, 64)      256       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " conv3d_transpose_3 (Conv3DT  (None, 16, 16, 16, 32)   55328     \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      " batch_normalization_16 (Bat  (None, 16, 16, 16, 32)   128       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " conv3d_transpose_4 (Conv3DT  (None, 32, 32, 32, 32)   27680     \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      " batch_normalization_17 (Bat  (None, 32, 32, 32, 32)   128       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " conv3d_transpose_5 (Conv3DT  (None, 32, 32, 32, 24)   20760     \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      " batch_normalization_18 (Bat  (None, 32, 32, 32, 24)   96        \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " chNormalization)                                                \n",
      "                                                                 \n",
      " conv3d (Conv3D)             (None, 32, 32, 32, 1)     649       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 122,810,817\n",
      "Trainable params: 122,801,809\n",
      "Non-trainable params: 9,008\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "generator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "nu1w1wtXdhnz",
    "outputId": "654a6bd3-a506-472a-d4b1-c842fe3fb839",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model/model_to_dot to work.\n"
     ]
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(generator, show_shapes=True, dpi=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "zesSP8aq2a7A"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(480, 640, 1)\n"
     ]
    }
   ],
   "source": [
    "img = load(str(PATH + '/Data/images/5.png'))\n",
    "print(img.shape)\n",
    "img = resize(img, 128, 128)\n",
    "img = reshape(img)\n",
    "gen_output = generator(img, training=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.python.ops.numpy_ops import np_config\n",
    "np_config.enable_numpy_behavior()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "gen_output = np.squeeze(gen_output)\n",
    "binary_output = (gen_output > 0.5).astype(np.bool_)\n",
    "saveVoxel(binary_output,\"lol\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 413
    },
    "id": "MpwtRohk7Ud7",
    "outputId": "f0560739-f1d4-4b55-ac9a-6fcc7bfb17aa",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# assume output is a 3D numpy array of shape (x,y,z)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "x, y, z = np.indices(binary_output.shape)\n",
    "\n",
    "# Flatten the 3D array into a 1D array\n",
    "voxels = tf.reshape(gen_output,  [32,32,32])\n",
    "\n",
    "# Plot the voxels with a color map\n",
    "ax.scatter(x, y, z, c=voxels, cmap='binary', s=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1G9FMz3IkVHI"
   },
   "source": [
    "#Defining the loss fn of the generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "TSmY30ESZMMG"
   },
   "outputs": [],
   "source": [
    "LAMBDA = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "eMePiyCPkV7l"
   },
   "outputs": [],
   "source": [
    "loss_object = tf.keras.losses.BinaryCrossentropy(from_logits=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "example = load3DFile(\"5\",False).data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "vpn44PXYCp4x"
   },
   "outputs": [],
   "source": [
    "#optimizers for generator and discriminator\n",
    "generator_optimizer = tf.keras.optimizers.Adam()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "JvYSZClnCuOp"
   },
   "outputs": [],
   "source": [
    "#making a checkpoint in case of failure or pausing training\n",
    "checkpoint_dir = './training_checkpoints'\n",
    "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n",
    "checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\n",
    "                                 generator=generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "id": "8VifAmiqC7CR"
   },
   "outputs": [],
   "source": [
    "#The fn should outputs some generated images during the training\n",
    "@tf.function\n",
    "def train_dataset(model, test_input, number):\n",
    "  #save the predicted object in the local dir as a 3d object\n",
    "    prediction = model(test_input, training=True)\n",
    "    prediction, valid = handleGeneratorOut(prediction)\n",
    "    if (valid):\n",
    "        saveVoxel(prediction,str(number))\n",
    "    else:\n",
    "        print(\"not Valid\")\n",
    "    # save array to file\n",
    "    file_name = str(number) + \".npy\"\n",
    "    file_path = os.path.join(dir_name, file_name)\n",
    "    np.save(file_path, prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NHah-mpUHdKT"
   },
   "source": [
    "# Logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "id": "lZYJdpBWFmCo"
   },
   "outputs": [],
   "source": [
    "#logs\n",
    "log_dir=\"logs/\"\n",
    "\n",
    "summary_writer = tf.summary.create_file_writer(\n",
    "  log_dir + \"fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "id": "5XeZOmTfHfqQ"
   },
   "outputs": [],
   "source": [
    "#a little bit stright forward\n",
    "@tf.function\n",
    "def train_step(input_image, target, step):\n",
    "    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n",
    "        genereted = generator(input_image, training=True)\n",
    "        gen_total_loss = generator_loss(genereted, target)\n",
    "\n",
    "    generator_gradients = gen_tape.gradient(gen_total_loss,\n",
    "                                      generator.trainable_variables)\n",
    "\n",
    "    generator_optimizer.apply_gradients(zip(generator_gradients,\n",
    "                                      generator.trainable_variables))\n",
    "\n",
    "    with summary_writer.as_default():\n",
    "        tf.summary.scalar('gen_total_loss', gen_total_loss, step=step//100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_images(model, test_input, tar):\n",
    "    prediction = model(test_input, training=True)\n",
    "    \n",
    "    binary_output, lol = handleGeneratorOut(prediction)\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "    x, y, z = np.indices(binary_output.shape)\n",
    "\n",
    "    # Flatten the 3D array into a 1D array\n",
    "    voxels = tf.reshape(gen_output,  [32,32,32])\n",
    "\n",
    "    # Plot the voxels with a color map\n",
    "    ax.scatter(x, y, z, c=voxels, cmap='binary', s=15)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4Jh8pgrPJ-i7"
   },
   "outputs": [],
   "source": [
    "#Every 10 steps print a dot (.).\n",
    "#Every 100 steps: clear the display and run generate_images to show the progress.\n",
    "#Every 1k steps: save a checkpoint.\n",
    "def fit(train_ds, test_ds, steps):   \n",
    "    example_input, exmpleModel = next(iter(test_ds.take(1)))\n",
    "    start = time.time()\n",
    "    for step, (input_image, target) in train_ds.repeat().take(steps).enumerate():\n",
    "    #the next line fn is to get the name of the image in the dataset\n",
    "        if (step) % 100 == 0:\n",
    "            display.clear_output(wait=True)\n",
    "\n",
    "            if step != 0:\n",
    "                print(f'Time taken for 1000 steps: {time.time()-start:.2f} sec\\n')\n",
    "\n",
    "            start = time.time()\n",
    "            example_input = resize(example_input,128,128)\n",
    "            generate_images(generator, example_input, step)\n",
    "            print(f\"Step: {step//100}k\")\n",
    "        train_step(input_image, target, step)\n",
    "\n",
    "    # Training step\n",
    "    if (step+1) % 10 == 0:\n",
    "        print('.', end='', flush=True)\n",
    "\n",
    "\n",
    "    # Save (checkpoint) the model every 5k steps\n",
    "    if (step + 1) % 100 == 0:\n",
    "        checkpoint.save(file_prefix=checkpoint_prefix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step: 0k\n"
     ]
    },
    {
     "ename": "ResourceExhaustedError",
     "evalue": "Graph execution error:\n\nDetected at node 'gradient_tape/model/fc1/MatMul/MatMul_1' defined at (most recent call last):\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\runpy.py\", line 196, in _run_module_as_main\n      return _run_code(code, main_globals, None,\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\runpy.py\", line 86, in _run_code\n      exec(code, run_globals)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel_launcher.py\", line 17, in <module>\n      app.launch_new_instance()\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\traitlets\\config\\application.py\", line 1043, in launch_instance\n      app.start()\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelapp.py\", line 725, in start\n      self.io_loop.start()\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\tornado\\platform\\asyncio.py\", line 195, in start\n      self.asyncio_loop.run_forever()\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\asyncio\\base_events.py\", line 603, in run_forever\n      self._run_once()\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\asyncio\\base_events.py\", line 1909, in _run_once\n      handle._run()\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\asyncio\\events.py\", line 80, in _run\n      self._context.run(self._callback, *self._args)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelbase.py\", line 513, in dispatch_queue\n      await self.process_one()\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelbase.py\", line 502, in process_one\n      await dispatch(*args)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelbase.py\", line 409, in dispatch_shell\n      await result\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelbase.py\", line 729, in execute_request\n      reply_content = await reply_content\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\ipkernel.py\", line 422, in do_execute\n      res = shell.run_cell(\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\zmqshell.py\", line 540, in run_cell\n      return super().run_cell(*args, **kwargs)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3006, in run_cell\n      result = self._run_cell(\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3061, in _run_cell\n      result = runner(coro)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n      coro.send(None)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3266, in run_cell_async\n      has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3445, in run_ast_nodes\n      if await self.run_code(code, result, async_=asy):\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3505, in run_code\n      exec(code_obj, self.user_global_ns, self.user_ns)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Temp\\ipykernel_10488\\1647113384.py\", line 1, in <module>\n      fit(dataset, dataset, steps=100)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Temp\\ipykernel_10488\\3481257701.py\", line 20, in fit\n      train_step(input_image, target, step)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Temp\\ipykernel_10488\\2983142405.py\", line 8, in train_step\n      generator_gradients = gen_tape.gradient(gen_total_loss,\nNode: 'gradient_tape/model/fc1/MatMul/MatMul_1'\nOOM when allocating tensor with shape[8192,8192] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[{{node gradient_tape/model/fc1/MatMul/MatMul_1}}]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.\n [Op:__inference_train_step_51952]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mResourceExhaustedError\u001b[0m                    Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[78], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[1;32mIn[77], line 20\u001b[0m, in \u001b[0;36mfit\u001b[1;34m(train_ds, test_ds, steps)\u001b[0m\n\u001b[0;32m     18\u001b[0m         \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStep: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstep\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m100\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     19\u001b[0m     input_image, target \u001b[38;5;241m=\u001b[39m handleIncomingDb(input_image, target)\n\u001b[1;32m---> 20\u001b[0m     \u001b[43mtrain_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_image\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     22\u001b[0m \u001b[38;5;66;03m# Training step\u001b[39;00m\n\u001b[0;32m     23\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (step\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m10\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:153\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m--> 153\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    154\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    155\u001b[0m   \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\tensorflow\\python\\eager\\execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     52\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     53\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 54\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[0;32m     55\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[0;32m     56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     57\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[1;31mResourceExhaustedError\u001b[0m: Graph execution error:\n\nDetected at node 'gradient_tape/model/fc1/MatMul/MatMul_1' defined at (most recent call last):\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\runpy.py\", line 196, in _run_module_as_main\n      return _run_code(code, main_globals, None,\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\runpy.py\", line 86, in _run_code\n      exec(code, run_globals)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel_launcher.py\", line 17, in <module>\n      app.launch_new_instance()\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\traitlets\\config\\application.py\", line 1043, in launch_instance\n      app.start()\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelapp.py\", line 725, in start\n      self.io_loop.start()\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\tornado\\platform\\asyncio.py\", line 195, in start\n      self.asyncio_loop.run_forever()\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\asyncio\\base_events.py\", line 603, in run_forever\n      self._run_once()\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\asyncio\\base_events.py\", line 1909, in _run_once\n      handle._run()\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\asyncio\\events.py\", line 80, in _run\n      self._context.run(self._callback, *self._args)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelbase.py\", line 513, in dispatch_queue\n      await self.process_one()\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelbase.py\", line 502, in process_one\n      await dispatch(*args)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelbase.py\", line 409, in dispatch_shell\n      await result\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelbase.py\", line 729, in execute_request\n      reply_content = await reply_content\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\ipkernel.py\", line 422, in do_execute\n      res = shell.run_cell(\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\zmqshell.py\", line 540, in run_cell\n      return super().run_cell(*args, **kwargs)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3006, in run_cell\n      result = self._run_cell(\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3061, in _run_cell\n      result = runner(coro)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n      coro.send(None)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3266, in run_cell_async\n      has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3445, in run_ast_nodes\n      if await self.run_code(code, result, async_=asy):\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3505, in run_code\n      exec(code_obj, self.user_global_ns, self.user_ns)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Temp\\ipykernel_10488\\1647113384.py\", line 1, in <module>\n      fit(dataset, dataset, steps=100)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Temp\\ipykernel_10488\\3481257701.py\", line 20, in fit\n      train_step(input_image, target, step)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Temp\\ipykernel_10488\\2983142405.py\", line 8, in train_step\n      generator_gradients = gen_tape.gradient(gen_total_loss,\nNode: 'gradient_tape/model/fc1/MatMul/MatMul_1'\nOOM when allocating tensor with shape[8192,8192] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[{{node gradient_tape/model/fc1/MatMul/MatMul_1}}]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.\n [Op:__inference_train_step_51952]"
     ]
    }
   ],
   "source": [
    "fit(dataset, dataset, steps=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Testing the training step fn\n",
    "example_input, exampleModel = next(iter(dataset.take(1)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 128, 128, 1) (1, 32, 32, 32)\n"
     ]
    }
   ],
   "source": [
    "print(example_input.shape, exampleModel.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "example_input, exampleModel = handleIncomingDb(example_input, exampleModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 128, 128, 1) (1, 32, 32, 32)\n"
     ]
    }
   ],
   "source": [
    "print(example_input.shape, exampleModel.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ResourceExhaustedError",
     "evalue": "Graph execution error:\n\nDetected at node 'gradient_tape/model/fc1/MatMul/MatMul_1' defined at (most recent call last):\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\runpy.py\", line 196, in _run_module_as_main\n      return _run_code(code, main_globals, None,\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\runpy.py\", line 86, in _run_code\n      exec(code, run_globals)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel_launcher.py\", line 17, in <module>\n      app.launch_new_instance()\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\traitlets\\config\\application.py\", line 1043, in launch_instance\n      app.start()\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelapp.py\", line 725, in start\n      self.io_loop.start()\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\tornado\\platform\\asyncio.py\", line 195, in start\n      self.asyncio_loop.run_forever()\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\asyncio\\base_events.py\", line 603, in run_forever\n      self._run_once()\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\asyncio\\base_events.py\", line 1909, in _run_once\n      handle._run()\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\asyncio\\events.py\", line 80, in _run\n      self._context.run(self._callback, *self._args)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelbase.py\", line 513, in dispatch_queue\n      await self.process_one()\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelbase.py\", line 502, in process_one\n      await dispatch(*args)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelbase.py\", line 409, in dispatch_shell\n      await result\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelbase.py\", line 729, in execute_request\n      reply_content = await reply_content\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\ipkernel.py\", line 422, in do_execute\n      res = shell.run_cell(\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\zmqshell.py\", line 540, in run_cell\n      return super().run_cell(*args, **kwargs)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3006, in run_cell\n      result = self._run_cell(\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3061, in _run_cell\n      result = runner(coro)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n      coro.send(None)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3266, in run_cell_async\n      has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3445, in run_ast_nodes\n      if await self.run_code(code, result, async_=asy):\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3505, in run_code\n      exec(code_obj, self.user_global_ns, self.user_ns)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Temp\\ipykernel_10488\\2738956576.py\", line 1, in <module>\n      train_step(example_input, exampleModel,0)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Temp\\ipykernel_10488\\2983142405.py\", line 8, in train_step\n      generator_gradients = gen_tape.gradient(gen_total_loss,\nNode: 'gradient_tape/model/fc1/MatMul/MatMul_1'\nOOM when allocating tensor with shape[8192,8192] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[{{node gradient_tape/model/fc1/MatMul/MatMul_1}}]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.\n [Op:__inference_train_step_68671]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mResourceExhaustedError\u001b[0m                    Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[83], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mtrain_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexample_input\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexampleModel\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:153\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m--> 153\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    154\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    155\u001b[0m   \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\tensorflow\\python\\eager\\execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     52\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     53\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 54\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[0;32m     55\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[0;32m     56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     57\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[1;31mResourceExhaustedError\u001b[0m: Graph execution error:\n\nDetected at node 'gradient_tape/model/fc1/MatMul/MatMul_1' defined at (most recent call last):\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\runpy.py\", line 196, in _run_module_as_main\n      return _run_code(code, main_globals, None,\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\runpy.py\", line 86, in _run_code\n      exec(code, run_globals)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel_launcher.py\", line 17, in <module>\n      app.launch_new_instance()\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\traitlets\\config\\application.py\", line 1043, in launch_instance\n      app.start()\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelapp.py\", line 725, in start\n      self.io_loop.start()\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\tornado\\platform\\asyncio.py\", line 195, in start\n      self.asyncio_loop.run_forever()\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\asyncio\\base_events.py\", line 603, in run_forever\n      self._run_once()\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\asyncio\\base_events.py\", line 1909, in _run_once\n      handle._run()\n    File \"C:\\Users\\xxalh\\miniconda3\\envs\\tensorflow\\lib\\asyncio\\events.py\", line 80, in _run\n      self._context.run(self._callback, *self._args)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelbase.py\", line 513, in dispatch_queue\n      await self.process_one()\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelbase.py\", line 502, in process_one\n      await dispatch(*args)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelbase.py\", line 409, in dispatch_shell\n      await result\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\kernelbase.py\", line 729, in execute_request\n      reply_content = await reply_content\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\ipkernel.py\", line 422, in do_execute\n      res = shell.run_cell(\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ipykernel\\zmqshell.py\", line 540, in run_cell\n      return super().run_cell(*args, **kwargs)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3006, in run_cell\n      result = self._run_cell(\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3061, in _run_cell\n      result = runner(coro)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n      coro.send(None)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3266, in run_cell_async\n      has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3445, in run_ast_nodes\n      if await self.run_code(code, result, async_=asy):\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3505, in run_code\n      exec(code_obj, self.user_global_ns, self.user_ns)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Temp\\ipykernel_10488\\2738956576.py\", line 1, in <module>\n      train_step(example_input, exampleModel,0)\n    File \"C:\\Users\\xxalh\\AppData\\Local\\Temp\\ipykernel_10488\\2983142405.py\", line 8, in train_step\n      generator_gradients = gen_tape.gradient(gen_total_loss,\nNode: 'gradient_tape/model/fc1/MatMul/MatMul_1'\nOOM when allocating tensor with shape[8192,8192] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[{{node gradient_tape/model/fc1/MatMul/MatMul_1}}]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.\n [Op:__inference_train_step_68671]"
     ]
    }
   ],
   "source": [
    "train_step(example_input, exampleModel,0)"
   ]
  }
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