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  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "# Crafter Human Dataset\n",
    "\n",
    "This dataset was created using the data available at https://archive.org/details/crafter_human_dataset. We only made it available to users using the following script. \n",
    "\n",
    "## Using this dataset\n",
    "To use this dataset, be sure to check our [IL-Datasets](https://github.com/NathanGavenski/IL-Datasets) toolkit.\n",
    "\n",
    "## Original citation\n",
    "Please, if using this dataset, don't forget to cite Hafner's original work:\n",
    "```\n",
    "@article{hafner2021crafter,\n",
    "  title={Benchmarking the Spectrum of Agent Capabilities},\n",
    "  author={Danijar Hafner},\n",
    "  year={2021},\n",
    "  journal={arXiv preprint arXiv:2109.06780},\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a07ce4e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "from PIL import Image\n",
    "from glob import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c48dd39c",
   "metadata": {},
   "outputs": [],
   "source": [
    "files = glob(\"./files/*.npz\")\n",
    "print(f\"Files found: {len(files)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3342b01a",
   "metadata": {},
   "outputs": [],
   "source": [
    "f = np.load(files[0])\n",
    "for _file in files:\n",
    "    f = np.load(_file)\n",
    "    achieve_goal = 0\n",
    "    for k in [k for k in f.keys() if \"achivement\" in k]:\n",
    "        achieve_goal += f[k].sum()\n",
    "\n",
    "    if achieve_goal == 0:\n",
    "        print(_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52c8ca21-bddc-43e0-bd0d-994f9f2bd048",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "from tqdm import tqdm\n",
    "\n",
    "count = 0\n",
    "\n",
    "os.makedirs(\"./images/\", exist_ok=True)\n",
    "\n",
    "dataset = defaultdict(list)\n",
    "for _file in tqdm(files):\n",
    "    f = np.load(_file)\n",
    "\n",
    "    for idx, image in enumerate(f['image']):\n",
    "        Image.fromarray(image).save(f\"images/{count}.png\")\n",
    "        dataset['obs'].append(f\"images/{count}.png\")\n",
    "        dataset['actions'].append(f['action'][idx].item())\n",
    "        dataset['rewards'].append(f['reward'][idx].item())\n",
    "        dataset['episode_starts'].append(f['done'][idx].item())\n",
    "        count += 1\n",
    "    dataset['episode_returns'].append(sum(dataset['rewards']))\n",
    "\n",
    "for k, v in dataset.items():\n",
    "    dataset[k] = np.array(dataset[k])\n",
    "\n",
    "np.savez(\"crafter\", **dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a0eee2d-9cab-4a0c-b221-b4d1f0155cc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imitation_datasets.dataset.huggingface import baseline_to_huggingface\n",
    "\n",
    "baseline_to_huggingface(\"./crafter.npz\", \"./crafter.jsonl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a506b25-760e-4ffe-afa2-6d6d73058bf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tarfile\n",
    "\n",
    "with tarfile.open(\"dataset.tar.gz\", \"w:gz\") as tar_file:\n",
    "    tar_file.add(\"./crafter.jsonl\", \"crafter.jsonl\")\n",
    "\n",
    "with tarfile.open(\"images.tar.gz\", \"w:gz\") as tar_file:\n",
    "    tar_file.add(\"images/\", \"images/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30224224-fd9c-453c-b202-7fad802026f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import shutil\n",
    "\n",
    "shutil.rmtree(\"images/\")\n",
    "os.remove(\"crafter.npz\")\n",
    "os.remove(\"crafter.jsonl\")"
   ]
  }
 ],
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