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# Source code for torchvision.datasets.cifar

    import os.path
    import pickle
    from typing import Any, Callable, Optional, Tuple

    import numpy as np
    from PIL import Image

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

    class CIFAR10(VisionDataset):
        """`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.

        Args:
            root (string): Root directory of dataset where directory
                ``cifar-10-batches-py`` exists or will be saved to if download is set to True.
            train (bool, optional): If True, creates dataset from training set, otherwise
                creates from test set.
            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.

        """

        base_folder = "cifar-10-batches-py"
        url = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
        filename = "cifar-10-python.tar.gz"
        tgz_md5 = "c58f30108f718f92721af3b95e74349a"
        train_list = [
            ["data_batch_1", "c99cafc152244af753f735de768cd75f"],
            ["data_batch_2", "d4bba439e000b95fd0a9bffe97cbabec"],
            ["data_batch_3", "54ebc095f3ab1f0389bbae665268c751"],
            ["data_batch_4", "634d18415352ddfa80567beed471001a"],
            ["data_batch_5", "482c414d41f54cd18b22e5b47cb7c3cb"],
        ]

        test_list = [
            ["test_batch", "40351d587109b95175f43aff81a1287e"],
        ]
        meta = {
            "filename": "batches.meta",
            "key": "label_names",
            "md5": "5ff9c542aee3614f3951f8cda6e48888",
        }

        def __init__(
            self,
            root: str,
            train: bool = True,
            transform: Optional[Callable] = None,
            target_transform: Optional[Callable] = None,
            download: bool = False,
        ) -> None:

            super().__init__(root, transform=transform, target_transform=target_transform)

            self.train = train  # training set or test set

            if download:
                self.download()

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

            if self.train:
                downloaded_list = self.train_list
            else:
                downloaded_list = self.test_list

            self.data: Any = []
            self.targets = []

            # now load the picked numpy arrays
            for file_name, checksum in downloaded_list:
                file_path = os.path.join(self.root, self.base_folder, file_name)
                with open(file_path, "rb") as f:
                    entry = pickle.load(f, encoding="latin1")
                    self.data.append(entry["data"])
                    if "labels" in entry:
                        self.targets.extend(entry["labels"])
                    else:
                        self.targets.extend(entry["fine_labels"])

            self.data = np.vstack(self.data).reshape(-1, 3, 32, 32)
            self.data = self.data.transpose((0, 2, 3, 1))  # convert to HWC

            self._load_meta()

        def _load_meta(self) -> None:
            path = os.path.join(self.root, self.base_folder, self.meta["filename"])
            if not check_integrity(path, self.meta["md5"]):
                raise RuntimeError("Dataset metadata file not found or corrupted. You can use download=True to download it")
            with open(path, "rb") as infile:
                data = pickle.load(infile, encoding="latin1")
                self.classes = data[self.meta["key"]]
            self.class_to_idx = {_class: i for i, _class in enumerate(self.classes)}

        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, target = self.data[index], self.targets[index]

            # doing this so that it is consistent with all other datasets
            # to return a PIL Image
            img = Image.fromarray(img)

            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 __len__(self) -> int:
            return len(self.data)

        def _check_integrity(self) -> bool:
            for filename, md5 in self.train_list + self.test_list:
                fpath = os.path.join(self.root, self.base_folder, filename)
                if not check_integrity(fpath, md5):
                    return False
            return True

        def download(self) -> None:
            if self._check_integrity():
                print("Files already downloaded and verified")
                return
            download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.tgz_md5)

        def extra_repr(self) -> str:
            split = "Train" if self.train is True else "Test"
            return f"Split: {split}"

    class CIFAR100(CIFAR10):
        """`CIFAR100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.

        This is a subclass of the `CIFAR10` Dataset.
        """

        base_folder = "cifar-100-python"
        url = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz"
        filename = "cifar-100-python.tar.gz"
        tgz_md5 = "eb9058c3a382ffc7106e4002c42a8d85"
        train_list = [
            ["train", "16019d7e3df5f24257cddd939b257f8d"],
        ]

        test_list = [
            ["test", "f0ef6b0ae62326f3e7ffdfab6717acfc"],
        ]
        meta = {
            "filename": "meta",
            "key": "fine_label_names",
            "md5": "7973b15100ade9c7d40fb424638fde48",
        }

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