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# Source code for torchvision.datasets.usps

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

    import numpy as np
    from PIL import Image

    from .utils import download_url
    from .vision import VisionDataset

    class USPS(VisionDataset):
        """`USPS <https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#usps>`_ Dataset.
        The data-format is : [label [index:value ]*256 \\n] * num_lines, where ``label`` lies in ``[1, 10]``.
        The value for each pixel lies in ``[-1, 1]``. Here we transform the ``label`` into ``[0, 9]``
        and make pixel values in ``[0, 255]``.

        Args:
            root (string): Root directory of dataset to store``USPS`` data files.
            train (bool, optional): If True, creates dataset from ``usps.bz2``,
                otherwise from ``usps.t.bz2``.
            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.

        """

        split_list = {
            "train": [
                "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/usps.bz2",
                "usps.bz2",
                "ec16c51db3855ca6c91edd34d0e9b197",
            ],
            "test": [
                "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/usps.t.bz2",
                "usps.t.bz2",
                "8ea070ee2aca1ac39742fdd1ef5ed118",
            ],
        }

        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)
            split = "train" if train else "test"
            url, filename, checksum = self.split_list[split]
            full_path = os.path.join(self.root, filename)

            if download and not os.path.exists(full_path):
                download_url(url, self.root, filename, md5=checksum)

            import bz2

            with bz2.open(full_path) as fp:
                raw_data = [line.decode().split() for line in fp.readlines()]
                tmp_list = [[x.split(":")[-1] for x in data[1:]] for data in raw_data]
                imgs = np.asarray(tmp_list, dtype=np.float32).reshape((-1, 16, 16))
                imgs = ((imgs + 1) / 2 * 255).astype(dtype=np.uint8)
                targets = [int(d[0]) - 1 for d in raw_data]

            self.data = imgs
            self.targets = targets

        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], int(self.targets[index])

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

            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)

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