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# Source code for torchvision.datasets.food101

    import json
    from pathlib import Path
    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 Food101(VisionDataset):
        """`The Food-101 Data Set <https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/>`_.

        The Food-101 is a challenging data set of 101 food categories with 101,000 images.
        For each class, 250 manually reviewed test images are provided as well as 750 training images.
        On purpose, the training images were not cleaned, and thus still contain some amount of noise.
        This comes mostly in the form of intense colors and sometimes wrong labels. All images were
        rescaled to have a maximum side length of 512 pixels.

        Args:
            root (string): Root directory of the dataset.
            split (string, optional): The dataset split, supports ``"train"`` (default) and ``"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. Default is False.
        """

        _URL = "http://data.vision.ee.ethz.ch/cvl/food-101.tar.gz"
        _MD5 = "85eeb15f3717b99a5da872d97d918f87"

        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", "test"))
            self._base_folder = Path(self.root) / "food-101"
            self._meta_folder = self._base_folder / "meta"
            self._images_folder = self._base_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._labels = []
            self._image_files = []
            with open(self._meta_folder / f"{split}.json") as f:
                metadata = json.loads(f.read())

            self.classes = sorted(metadata.keys())
            self.class_to_idx = dict(zip(self.classes, range(len(self.classes))))

            for class_label, im_rel_paths in metadata.items():
                self._labels += [self.class_to_idx[class_label]] * len(im_rel_paths)
                self._image_files += [
                    self._images_folder.joinpath(*f"{im_rel_path}.jpg".split("/")) for im_rel_path in im_rel_paths
                ]

        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_exists(self) -> bool:
            return all(folder.exists() and folder.is_dir() for folder in (self._meta_folder, self._images_folder))

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

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