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# Source code for torchvision.datasets.semeion

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

    import numpy as np
    from PIL import Image

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

    class SEMEION(VisionDataset):
        r"""`SEMEION <http://archive.ics.uci.edu/ml/datasets/semeion+handwritten+digit>`_ Dataset.

        Args:
            root (string): Root directory of dataset where directory
                ``semeion.py`` exists.
            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.

        """
        url = "http://archive.ics.uci.edu/ml/machine-learning-databases/semeion/semeion.data"
        filename = "semeion.data"
        md5_checksum = "cb545d371d2ce14ec121470795a77432"

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

            if download:
                self.download()

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

            fp = os.path.join(self.root, self.filename)
            data = np.loadtxt(fp)
            # convert value to 8 bit unsigned integer
            # color (white #255) the pixels
            self.data = (data[:, :256] * 255).astype("uint8")
            self.data = np.reshape(self.data, (-1, 16, 16))
            self.labels = np.nonzero(data[:, 256:])[1]

        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.labels[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)

        def _check_integrity(self) -> bool:
            root = self.root
            fpath = os.path.join(root, self.filename)
            if not check_integrity(fpath, self.md5_checksum):
                return False
            return True

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

            root = self.root
            download_url(self.url, root, self.filename, self.md5_checksum)

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