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# Source code for torchvision.datasets.flowers102

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

    import PIL.Image

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

    class Flowers102(VisionDataset):
        """`Oxford 102 Flower <https://www.robots.ox.ac.uk/~vgg/data/flowers/102/>`_ Dataset.

        .. warning::

            This class needs `scipy <https://docs.scipy.org/doc/>`_ to load target files from `.mat` format.

        Oxford 102 Flower is an image classification dataset consisting of 102 flower categories. The
        flowers were chosen to be flowers commonly occurring in the United Kingdom. Each class consists of
        between 40 and 258 images.

        The images have large scale, pose and light variations. In addition, there are categories that
        have large variations within the category, and several very similar categories.

        Args:
            root (string): Root directory of the dataset.
            split (string, optional): The dataset split, supports ``"train"`` (default), ``"val"``, or ``"test"``.
            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.
        """

        _download_url_prefix = "https://www.robots.ox.ac.uk/~vgg/data/flowers/102/"
        _file_dict = {  # filename, md5
            "image": ("102flowers.tgz", "52808999861908f626f3c1f4e79d11fa"),
            "label": ("imagelabels.mat", "e0620be6f572b9609742df49c70aed4d"),
            "setid": ("setid.mat", "a5357ecc9cb78c4bef273ce3793fc85c"),
        }
        _splits_map = {"train": "trnid", "val": "valid", "test": "tstid"}

        def __init__(
            self,
            root: str,
            split: str = "train",
            transform: Optional[Callable] = None,
            target_transform: Optional[Callable] = None,
            download: bool = False,
        ) -> None:
            super().__init__(root, transform=transform, target_transform=target_transform)
            self._split = verify_str_arg(split, "split", ("train", "val", "test"))
            self._base_folder = Path(self.root) / "flowers-102"
            self._images_folder = self._base_folder / "jpg"

            if download:
                self.download()

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

            from scipy.io import loadmat

            set_ids = loadmat(self._base_folder / self._file_dict["setid"][0], squeeze_me=True)
            image_ids = set_ids[self._splits_map[self._split]].tolist()

            labels = loadmat(self._base_folder / self._file_dict["label"][0], squeeze_me=True)
            image_id_to_label = dict(enumerate((labels["labels"] - 1).tolist(), 1))

            self._labels = []
            self._image_files = []
            for image_id in image_ids:
                self._labels.append(image_id_to_label[image_id])
                self._image_files.append(self._images_folder / f"image_{image_id:05d}.jpg")

        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}"

        def _check_integrity(self):
            if not (self._images_folder.exists() and self._images_folder.is_dir()):
                return False

            for id in ["label", "setid"]:
                filename, md5 = self._file_dict[id]
                if not check_integrity(str(self._base_folder / filename), md5):
                    return False
            return True

        def download(self):
            if self._check_integrity():
                return
            download_and_extract_archive(
                f"{self._download_url_prefix}{self._file_dict['image'][0]}",
                str(self._base_folder),
                md5=self._file_dict["image"][1],
            )
            for id in ["label", "setid"]:
                filename, md5 = self._file_dict[id]
                download_url(self._download_url_prefix + filename, str(self._base_folder), md5=md5)

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