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# Source code for torchvision.datasets.sun397

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

    import PIL.Image

    from .utils import download_and_extract_archive
    from .vision import VisionDataset

    class SUN397(VisionDataset):
        """`The SUN397 Data Set <https://vision.princeton.edu/projects/2010/SUN/>`_.

        The SUN397 or Scene UNderstanding (SUN) is a dataset for scene recognition consisting of
        397 categories with 108'754 images.

        Args:
            root (string): Root directory of the dataset.
            transform (callable, optional): A function/transform that  takes in an 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 in root directory. If dataset is already downloaded, it is not
                downloaded again.
        """

        _DATASET_URL = "http://vision.princeton.edu/projects/2010/SUN/SUN397.tar.gz"
        _DATASET_MD5 = "8ca2778205c41d23104230ba66911c7a"

        def __init__(
            self,
            root: str,
            transform: Optional[Callable] = None,
            target_transform: Optional[Callable] = None,
            download: bool = False,
        ) -> None:
            super().__init__(root, transform=transform, target_transform=target_transform)
            self._data_dir = Path(self.root) / "SUN397"

            if download:
                self._download()

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

            with open(self._data_dir / "ClassName.txt") as f:
                self.classes = [c[3:].strip() for c in f]

            self.class_to_idx = dict(zip(self.classes, range(len(self.classes))))
            self._image_files = list(self._data_dir.rglob("sun_*.jpg"))

            self._labels = [
                self.class_to_idx["/".join(path.relative_to(self._data_dir).parts[1:-1])] for path in self._image_files
            ]

        def __len__(self) -> int:
            return len(self._image_files)

        def __getitem__(self, idx: int) -> Tuple[Any, Any]:
            image_file, label = self._image_files[idx], self._labels[idx]
            image = PIL.Image.open(image_file).convert("RGB")

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

            if self.target_transform:
                label = self.target_transform(label)

            return image, label

        def _check_exists(self) -> bool:
            return self._data_dir.is_dir()

        def _download(self) -> None:
            if self._check_exists():
                return
            download_and_extract_archive(self._DATASET_URL, download_root=self.root, md5=self._DATASET_MD5)

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