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# Source code for torchvision.datasets.pcam

    import pathlib
    from typing import Any, Callable, Optional, Tuple

    from PIL import Image

    from .utils import _decompress, download_file_from_google_drive, verify_str_arg
    from .vision import VisionDataset

    class PCAM(VisionDataset):
        """`PCAM Dataset   <https://github.com/basveeling/pcam>`_.

        The PatchCamelyon dataset is a binary classification dataset with 327,680
        color images (96px x 96px), extracted from histopathologic scans of lymph node
        sections. Each image is annotated with a binary label indicating presence of
        metastatic tissue.

        This dataset requires the ``h5py`` package which you can install with ``pip install h5py``.

        Args:
             root (string): Root directory of the dataset.
             split (string, optional): The dataset split, supports ``"train"`` (default), ``"test"`` or ``"val"``.
             transform (callable, optional): A function/transform that  takes in a PIL image and returns a transformed
                 version. E.g, ``transforms.RandomCrop``.
             target_transform (callable, optional): A function/transform that takes in the target and transforms it.
             download (bool, optional): If True, downloads the dataset from the internet and puts it into ``root/pcam``. If
                 dataset is already downloaded, it is not downloaded again.
        """

        _FILES = {
            "train": {
                "images": (
                    "camelyonpatch_level_2_split_train_x.h5",  # Data file name
                    "1Ka0XfEMiwgCYPdTI-vv6eUElOBnKFKQ2",  # Google Drive ID
                    "1571f514728f59376b705fc836ff4b63",  # md5 hash
                ),
                "targets": (
                    "camelyonpatch_level_2_split_train_y.h5",
                    "1269yhu3pZDP8UYFQs-NYs3FPwuK-nGSG",
                    "35c2d7259d906cfc8143347bb8e05be7",
                ),
            },
            "test": {
                "images": (
                    "camelyonpatch_level_2_split_test_x.h5",
                    "1qV65ZqZvWzuIVthK8eVDhIwrbnsJdbg_",
                    "d8c2d60d490dbd479f8199bdfa0cf6ec",
                ),
                "targets": (
                    "camelyonpatch_level_2_split_test_y.h5",
                    "17BHrSrwWKjYsOgTMmoqrIjDy6Fa2o_gP",
                    "60a7035772fbdb7f34eb86d4420cf66a",
                ),
            },
            "val": {
                "images": (
                    "camelyonpatch_level_2_split_valid_x.h5",
                    "1hgshYGWK8V-eGRy8LToWJJgDU_rXWVJ3",
                    "d5b63470df7cfa627aeec8b9dc0c066e",
                ),
                "targets": (
                    "camelyonpatch_level_2_split_valid_y.h5",
                    "1bH8ZRbhSVAhScTS0p9-ZzGnX91cHT3uO",
                    "2b85f58b927af9964a4c15b8f7e8f179",
                ),
            },
        }

        def __init__(
            self,
            root: str,
            split: str = "train",
            transform: Optional[Callable] = None,
            target_transform: Optional[Callable] = None,
            download: bool = False,
        ):
            try:
                import h5py

                self.h5py = h5py
            except ImportError:
                raise RuntimeError(
                    "h5py is not found. This dataset needs to have h5py installed: please run pip install h5py"
                )

            self._split = verify_str_arg(split, "split", ("train", "test", "val"))

            super().__init__(root, transform=transform, target_transform=target_transform)
            self._base_folder = pathlib.Path(self.root) / "pcam"

            if download:
                self._download()

            if not self._check_exists():
                raise RuntimeError("Dataset not found. You can use download=True to download it")

        def __len__(self) -> int:
            images_file = self._FILES[self._split]["images"][0]
            with self.h5py.File(self._base_folder / images_file) as images_data:
                return images_data["x"].shape[0]

        def __getitem__(self, idx: int) -> Tuple[Any, Any]:
            images_file = self._FILES[self._split]["images"][0]
            with self.h5py.File(self._base_folder / images_file) as images_data:
                image = Image.fromarray(images_data["x"][idx]).convert("RGB")

            targets_file = self._FILES[self._split]["targets"][0]
            with self.h5py.File(self._base_folder / targets_file) as targets_data:
                target = int(targets_data["y"][idx, 0, 0, 0])  # shape is [num_images, 1, 1, 1]

            if self.transform:
                image = self.transform(image)
            if self.target_transform:
                target = self.target_transform(target)

            return image, target

        def _check_exists(self) -> bool:
            images_file = self._FILES[self._split]["images"][0]
            targets_file = self._FILES[self._split]["targets"][0]
            return all(self._base_folder.joinpath(h5_file).exists() for h5_file in (images_file, targets_file))

        def _download(self) -> None:
            if self._check_exists():
                return

            for file_name, file_id, md5 in self._FILES[self._split].values():
                archive_name = file_name + ".gz"
                download_file_from_google_drive(file_id, str(self._base_folder), filename=archive_name, md5=md5)
                _decompress(str(self._base_folder / archive_name))

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