import pathlib
from typing import Any, BinaryIO, Dict, List, Tuple, Union

import numpy as np
from torchdata.datapipes.iter import IterDataPipe, Mapper, UnBatcher
from torchvision.datapoints import Image
from torchvision.prototype.datapoints import Label
from torchvision.prototype.datasets.utils import Dataset, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal import hint_sharding, hint_shuffling, read_mat

from .._api import register_dataset, register_info

NAME = "svhn"


@register_info(NAME)
def _info() -> Dict[str, Any]:
    return dict(categories=[str(c) for c in range(10)])


@register_dataset(NAME)
class SVHN(Dataset):
    """SVHN Dataset.
    homepage="http://ufldl.stanford.edu/housenumbers/",
    dependencies = scipy
    """

    def __init__(
        self,
        root: Union[str, pathlib.Path],
        *,
        split: str = "train",
        skip_integrity_check: bool = False,
    ) -> None:
        self._split = self._verify_str_arg(split, "split", {"train", "test", "extra"})
        self._categories = _info()["categories"]
        super().__init__(root, skip_integrity_check=skip_integrity_check, dependencies=("scipy",))

    _CHECKSUMS = {
        "train": "435e94d69a87fde4fd4d7f3dd208dfc32cb6ae8af2240d066de1df7508d083b8",
        "test": "cdce80dfb2a2c4c6160906d0bd7c68ec5a99d7ca4831afa54f09182025b6a75b",
        "extra": "a133a4beb38a00fcdda90c9489e0c04f900b660ce8a316a5e854838379a71eb3",
    }

    def _resources(self) -> List[OnlineResource]:
        data = HttpResource(
            f"http://ufldl.stanford.edu/housenumbers/{self._split}_32x32.mat",
            sha256=self._CHECKSUMS[self._split],
        )

        return [data]

    def _read_images_and_labels(self, data: Tuple[str, BinaryIO]) -> List[Tuple[np.ndarray, np.ndarray]]:
        _, buffer = data
        content = read_mat(buffer)
        return list(
            zip(
                content["X"].transpose((3, 0, 1, 2)),
                content["y"].squeeze(),
            )
        )

    def _prepare_sample(self, data: Tuple[np.ndarray, np.ndarray]) -> Dict[str, Any]:
        image_array, label_array = data

        return dict(
            image=Image(image_array.transpose((2, 0, 1))),
            label=Label(int(label_array) % 10, categories=self._categories),
        )

    def _datapipe(self, resource_dps: List[IterDataPipe]) -> IterDataPipe[Dict[str, Any]]:
        dp = resource_dps[0]
        dp = Mapper(dp, self._read_images_and_labels)
        dp = UnBatcher(dp)
        dp = hint_shuffling(dp)
        dp = hint_sharding(dp)
        return Mapper(dp, self._prepare_sample)

    def __len__(self) -> int:
        return {
            "train": 73_257,
            "test": 26_032,
            "extra": 531_131,
        }[self._split]

import pathlib
from typing import Any, BinaryIO, Dict, List, Tuple, Union

import numpy as np
from torchdata.datapipes.iter import IterDataPipe, Mapper, UnBatcher
from torchvision.prototype.datapoints import Image, Label
from torchvision.prototype.datasets.utils import Dataset, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal import hint_sharding, hint_shuffling, read_mat

from .._api import register_dataset, register_info

NAME = "svhn"


@register_info(NAME)
def _info() -> Dict[str, Any]:
    return dict(categories=[str(c) for c in range(10)])


@register_dataset(NAME)
class SVHN(Dataset):
    """SVHN Dataset.
    homepage="http://ufldl.stanford.edu/housenumbers/",
    dependencies = scipy
    """

    def __init__(
        self,
        root: Union[str, pathlib.Path],
        *,
        split: str = "train",
        skip_integrity_check: bool = False,
    ) -> None:
        self._split = self._verify_str_arg(split, "split", {"train", "test", "extra"})
        self._categories = _info()["categories"]
        super().__init__(root, skip_integrity_check=skip_integrity_check, dependencies=("scipy",))

    _CHECKSUMS = {
        "train": "435e94d69a87fde4fd4d7f3dd208dfc32cb6ae8af2240d066de1df7508d083b8",
        "test": "cdce80dfb2a2c4c6160906d0bd7c68ec5a99d7ca4831afa54f09182025b6a75b",
        "extra": "a133a4beb38a00fcdda90c9489e0c04f900b660ce8a316a5e854838379a71eb3",
    }

    def _resources(self) -> List[OnlineResource]:
        data = HttpResource(
            f"http://ufldl.stanford.edu/housenumbers/{self._split}_32x32.mat",
            sha256=self._CHECKSUMS[self._split],
        )

        return [data]

    def _read_images_and_labels(self, data: Tuple[str, BinaryIO]) -> List[Tuple[np.ndarray, np.ndarray]]:
        _, buffer = data
        content = read_mat(buffer)
        return list(
            zip(
                content["X"].transpose((3, 0, 1, 2)),
                content["y"].squeeze(),
            )
        )

    def _prepare_sample(self, data: Tuple[np.ndarray, np.ndarray]) -> Dict[str, Any]:
        image_array, label_array = data

        return dict(
            image=Image(image_array.transpose((2, 0, 1))),
            label=Label(int(label_array) % 10, categories=self._categories),
        )

    def _datapipe(self, resource_dps: List[IterDataPipe]) -> IterDataPipe[Dict[str, Any]]:
        dp = resource_dps[0]
        dp = Mapper(dp, self._read_images_and_labels)
        dp = UnBatcher(dp)
        dp = hint_shuffling(dp)
        dp = hint_sharding(dp)
        return Mapper(dp, self._prepare_sample)

    def __len__(self) -> int:
        return {
            "train": 73_257,
            "test": 26_032,
            "extra": 531_131,
        }[self._split]
