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# Source code for torchvision.datasets.caltech

    import os
    import os.path
    from typing import Any, Callable, List, Optional, Tuple, Union

    from PIL import Image

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

    class Caltech101(VisionDataset):
        """`Caltech 101 <https://data.caltech.edu/records/20086>`_ Dataset.

        .. warning::

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

        Args:
            root (string): Root directory of dataset where directory
                ``caltech101`` exists or will be saved to if download is set to True.
            target_type (string or list, optional): Type of target to use, ``category`` or
                ``annotation``. Can also be a list to output a tuple with all specified
                target types.  ``category`` represents the target class, and
                ``annotation`` is a list of points from a hand-generated outline.
                Defaults to ``category``.
            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.
        """

        def __init__(
            self,
            root: str,
            target_type: Union[List[str], str] = "category",
            transform: Optional[Callable] = None,
            target_transform: Optional[Callable] = None,
            download: bool = False,
        ) -> None:
            super().__init__(os.path.join(root, "caltech101"), transform=transform, target_transform=target_transform)
            os.makedirs(self.root, exist_ok=True)
            if isinstance(target_type, str):
                target_type = [target_type]
            self.target_type = [verify_str_arg(t, "target_type", ("category", "annotation")) for t in target_type]

            if download:
                self.download()

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

            self.categories = sorted(os.listdir(os.path.join(self.root, "101_ObjectCategories")))
            self.categories.remove("BACKGROUND_Google")  # this is not a real class

            # For some reason, the category names in "101_ObjectCategories" and
            # "Annotations" do not always match. This is a manual map between the
            # two. Defaults to using same name, since most names are fine.
            name_map = {
                "Faces": "Faces_2",
                "Faces_easy": "Faces_3",
                "Motorbikes": "Motorbikes_16",
                "airplanes": "Airplanes_Side_2",
            }
            self.annotation_categories = list(map(lambda x: name_map[x] if x in name_map else x, self.categories))

            self.index: List[int] = []
            self.y = []
            for (i, c) in enumerate(self.categories):
                n = len(os.listdir(os.path.join(self.root, "101_ObjectCategories", c)))
                self.index.extend(range(1, n + 1))
                self.y.extend(n * [i])

        def __getitem__(self, index: int) -> Tuple[Any, Any]:
            """
            Args:
                index (int): Index

            Returns:
                tuple: (image, target) where the type of target specified by target_type.
            """
            import scipy.io

            img = Image.open(
                os.path.join(
                    self.root,
                    "101_ObjectCategories",
                    self.categories[self.y[index]],
                    f"image_{self.index[index]:04d}.jpg",
                )
            )

            target: Any = []
            for t in self.target_type:
                if t == "category":
                    target.append(self.y[index])
                elif t == "annotation":
                    data = scipy.io.loadmat(
                        os.path.join(
                            self.root,
                            "Annotations",
                            self.annotation_categories[self.y[index]],
                            f"annotation_{self.index[index]:04d}.mat",
                        )
                    )
                    target.append(data["obj_contour"])
            target = tuple(target) if len(target) > 1 else target[0]

            if self.transform is not None:
                img = self.transform(img)

            if self.target_transform is not None:
                target = self.target_transform(target)

            return img, target

        def _check_integrity(self) -> bool:
            # can be more robust and check hash of files
            return os.path.exists(os.path.join(self.root, "101_ObjectCategories"))

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

        def download(self) -> None:
            if self._check_integrity():
                print("Files already downloaded and verified")
                return

            download_and_extract_archive(
                "https://drive.google.com/file/d/137RyRjvTBkBiIfeYBNZBtViDHQ6_Ewsp",
                self.root,
                filename="101_ObjectCategories.tar.gz",
                md5="b224c7392d521a49829488ab0f1120d9",
            )
            download_and_extract_archive(
                "https://drive.google.com/file/d/175kQy3UsZ0wUEHZjqkUDdNVssr7bgh_m",
                self.root,
                filename="Annotations.tar",
                md5="6f83eeb1f24d99cab4eb377263132c91",
            )

        def extra_repr(self) -> str:
            return "Target type: {target_type}".format(**self.__dict__)

    class Caltech256(VisionDataset):
        """`Caltech 256 <https://data.caltech.edu/records/20087>`_ Dataset.

        Args:
            root (string): Root directory of dataset where directory
                ``caltech256`` exists or will be saved to if download is set to True.
            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.
        """

        def __init__(
            self,
            root: str,
            transform: Optional[Callable] = None,
            target_transform: Optional[Callable] = None,
            download: bool = False,
        ) -> None:
            super().__init__(os.path.join(root, "caltech256"), transform=transform, target_transform=target_transform)
            os.makedirs(self.root, exist_ok=True)

            if download:
                self.download()

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

            self.categories = sorted(os.listdir(os.path.join(self.root, "256_ObjectCategories")))
            self.index: List[int] = []
            self.y = []
            for (i, c) in enumerate(self.categories):
                n = len(
                    [
                        item
                        for item in os.listdir(os.path.join(self.root, "256_ObjectCategories", c))
                        if item.endswith(".jpg")
                    ]
                )
                self.index.extend(range(1, n + 1))
                self.y.extend(n * [i])

        def __getitem__(self, index: int) -> Tuple[Any, Any]:
            """
            Args:
                index (int): Index

            Returns:
                tuple: (image, target) where target is index of the target class.
            """
            img = Image.open(
                os.path.join(
                    self.root,
                    "256_ObjectCategories",
                    self.categories[self.y[index]],
                    f"{self.y[index] + 1:03d}_{self.index[index]:04d}.jpg",
                )
            )

            target = self.y[index]

            if self.transform is not None:
                img = self.transform(img)

            if self.target_transform is not None:
                target = self.target_transform(target)

            return img, target

        def _check_integrity(self) -> bool:
            # can be more robust and check hash of files
            return os.path.exists(os.path.join(self.root, "256_ObjectCategories"))

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

        def download(self) -> None:
            if self._check_integrity():
                print("Files already downloaded and verified")
                return

            download_and_extract_archive(
                "https://drive.google.com/file/d/1r6o0pSROcV1_VwT4oSjA2FBUSCWGuxLK",
                self.root,
                filename="256_ObjectCategories.tar",
                md5="67b4f42ca05d46448c6bb8ecd2220f6d",
            )

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