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# Source code for torchvision.datasets.dtd

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

    import PIL.Image

    from .utils import download_and_extract_archive, verify_str_arg
    from .vision import VisionDataset

    class DTD(VisionDataset):
        """`Describable Textures Dataset (DTD) <https://www.robots.ox.ac.uk/~vgg/data/dtd/>`_.

        Args:
            root (string): Root directory of the dataset.
            split (string, optional): The dataset split, supports ``"train"`` (default), ``"val"``, or ``"test"``.
            partition (int, optional): The dataset partition. Should be ``1 <= partition <= 10``. Defaults to ``1``.

                .. note::

                    The partition only changes which split each image belongs to. Thus, regardless of the selected
                    partition, combining all splits will result in all images.

            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 in root directory. If dataset is already downloaded, it is not
                downloaded again. Default is False.
        """

        _URL = "https://www.robots.ox.ac.uk/~vgg/data/dtd/download/dtd-r1.0.1.tar.gz"
        _MD5 = "fff73e5086ae6bdbea199a49dfb8a4c1"

        def __init__(
            self,
            root: str,
            split: str = "train",
            partition: int = 1,
            transform: Optional[Callable] = None,
            target_transform: Optional[Callable] = None,
            download: bool = False,
        ) -> None:
            self._split = verify_str_arg(split, "split", ("train", "val", "test"))
            if not isinstance(partition, int) and not (1 <= partition <= 10):
                raise ValueError(
                    f"Parameter 'partition' should be an integer with `1 <= partition <= 10`, "
                    f"but got {partition} instead"
                )
            self._partition = partition

            super().__init__(root, transform=transform, target_transform=target_transform)
            self._base_folder = pathlib.Path(self.root) / type(self).__name__.lower()
            self._data_folder = self._base_folder / "dtd"
            self._meta_folder = self._data_folder / "labels"
            self._images_folder = self._data_folder / "images"

            if download:
                self._download()

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

            self._image_files = []
            classes = []
            with open(self._meta_folder / f"{self._split}{self._partition}.txt") as file:
                for line in file:
                    cls, name = line.strip().split("/")
                    self._image_files.append(self._images_folder.joinpath(cls, name))
                    classes.append(cls)

            self.classes = sorted(set(classes))
            self.class_to_idx = dict(zip(self.classes, range(len(self.classes))))
            self._labels = [self.class_to_idx[cls] for cls in classes]

        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 extra_repr(self) -> str:
            return f"split={self._split}, partition={self._partition}"

        def _check_exists(self) -> bool:
            return os.path.exists(self._data_folder) and os.path.isdir(self._data_folder)

        def _download(self) -> None:
            if self._check_exists():
                return
            download_and_extract_archive(self._URL, download_root=str(self._base_folder), md5=self._MD5)

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