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

from torchdata.datapipes.iter import CSVDictParser, Demultiplexer, Filter, IterDataPipe, Mapper, Zipper
from torchvision.datapoints import BoundingBox
from torchvision.prototype.datapoints import Label
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal import (
    hint_sharding,
    hint_shuffling,
    INFINITE_BUFFER_SIZE,
    path_comparator,
)

from .._api import register_dataset, register_info

NAME = "gtsrb"


@register_info(NAME)
def _info() -> Dict[str, Any]:
    return dict(
        categories=[f"{label:05d}" for label in range(43)],
    )


@register_dataset(NAME)
class GTSRB(Dataset):
    """GTSRB Dataset

    homepage="https://benchmark.ini.rub.de"
    """

    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"})
        self._categories = _info()["categories"]
        super().__init__(root, skip_integrity_check=skip_integrity_check)

    _URL_ROOT = "https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/"
    _URLS = {
        "train": f"{_URL_ROOT}GTSRB-Training_fixed.zip",
        "test": f"{_URL_ROOT}GTSRB_Final_Test_Images.zip",
        "test_ground_truth": f"{_URL_ROOT}GTSRB_Final_Test_GT.zip",
    }
    _CHECKSUMS = {
        "train": "df4144942083645bd60b594de348aa6930126c3e0e5de09e39611630abf8455a",
        "test": "48ba6fab7e877eb64eaf8de99035b0aaecfbc279bee23e35deca4ac1d0a837fa",
        "test_ground_truth": "f94e5a7614d75845c74c04ddb26b8796b9e483f43541dd95dd5b726504e16d6d",
    }

    def _resources(self) -> List[OnlineResource]:
        rsrcs: List[OnlineResource] = [HttpResource(self._URLS[self._split], sha256=self._CHECKSUMS[self._split])]

        if self._split == "test":
            rsrcs.append(
                HttpResource(
                    self._URLS["test_ground_truth"],
                    sha256=self._CHECKSUMS["test_ground_truth"],
                )
            )

        return rsrcs

    def _classify_train_archive(self, data: Tuple[str, Any]) -> Optional[int]:
        path = pathlib.Path(data[0])
        if path.suffix == ".ppm":
            return 0
        elif path.suffix == ".csv":
            return 1
        else:
            return None

    def _prepare_sample(self, data: Tuple[Tuple[str, Any], Dict[str, Any]]) -> Dict[str, Any]:
        (path, buffer), csv_info = data
        label = int(csv_info["ClassId"])

        bounding_box = BoundingBox(
            [int(csv_info[k]) for k in ("Roi.X1", "Roi.Y1", "Roi.X2", "Roi.Y2")],
            format="xyxy",
            spatial_size=(int(csv_info["Height"]), int(csv_info["Width"])),
        )

        return {
            "path": path,
            "image": EncodedImage.from_file(buffer),
            "label": Label(label, categories=self._categories),
            "bounding_box": bounding_box,
        }

    def _datapipe(self, resource_dps: List[IterDataPipe]) -> IterDataPipe[Dict[str, Any]]:
        if self._split == "train":
            images_dp, ann_dp = Demultiplexer(
                resource_dps[0], 2, self._classify_train_archive, drop_none=True, buffer_size=INFINITE_BUFFER_SIZE
            )
        else:
            images_dp, ann_dp = resource_dps
            images_dp = Filter(images_dp, path_comparator("suffix", ".ppm"))

        # The order of the image files in the .zip archives perfectly match the order of the entries in the
        # (possibly concatenated) .csv files. So we're able to use Zipper here instead of a IterKeyZipper.
        ann_dp = CSVDictParser(ann_dp, delimiter=";")
        dp = Zipper(images_dp, ann_dp)

        dp = hint_shuffling(dp)
        dp = hint_sharding(dp)

        return Mapper(dp, self._prepare_sample)

    def __len__(self) -> int:
        return 26_640 if self._split == "train" else 12_630

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

from torchdata.datapipes.iter import CSVDictParser, Demultiplexer, Filter, IterDataPipe, Mapper, Zipper
from torchvision.prototype.datapoints import BoundingBox, Label
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal import (
    hint_sharding,
    hint_shuffling,
    INFINITE_BUFFER_SIZE,
    path_comparator,
)

from .._api import register_dataset, register_info

NAME = "gtsrb"


@register_info(NAME)
def _info() -> Dict[str, Any]:
    return dict(
        categories=[f"{label:05d}" for label in range(43)],
    )


@register_dataset(NAME)
class GTSRB(Dataset):
    """GTSRB Dataset

    homepage="https://benchmark.ini.rub.de"
    """

    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"})
        self._categories = _info()["categories"]
        super().__init__(root, skip_integrity_check=skip_integrity_check)

    _URL_ROOT = "https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/"
    _URLS = {
        "train": f"{_URL_ROOT}GTSRB-Training_fixed.zip",
        "test": f"{_URL_ROOT}GTSRB_Final_Test_Images.zip",
        "test_ground_truth": f"{_URL_ROOT}GTSRB_Final_Test_GT.zip",
    }
    _CHECKSUMS = {
        "train": "df4144942083645bd60b594de348aa6930126c3e0e5de09e39611630abf8455a",
        "test": "48ba6fab7e877eb64eaf8de99035b0aaecfbc279bee23e35deca4ac1d0a837fa",
        "test_ground_truth": "f94e5a7614d75845c74c04ddb26b8796b9e483f43541dd95dd5b726504e16d6d",
    }

    def _resources(self) -> List[OnlineResource]:
        rsrcs: List[OnlineResource] = [HttpResource(self._URLS[self._split], sha256=self._CHECKSUMS[self._split])]

        if self._split == "test":
            rsrcs.append(
                HttpResource(
                    self._URLS["test_ground_truth"],
                    sha256=self._CHECKSUMS["test_ground_truth"],
                )
            )

        return rsrcs

    def _classify_train_archive(self, data: Tuple[str, Any]) -> Optional[int]:
        path = pathlib.Path(data[0])
        if path.suffix == ".ppm":
            return 0
        elif path.suffix == ".csv":
            return 1
        else:
            return None

    def _prepare_sample(self, data: Tuple[Tuple[str, Any], Dict[str, Any]]) -> Dict[str, Any]:
        (path, buffer), csv_info = data
        label = int(csv_info["ClassId"])

        bounding_box = BoundingBox(
            [int(csv_info[k]) for k in ("Roi.X1", "Roi.Y1", "Roi.X2", "Roi.Y2")],
            format="xyxy",
            spatial_size=(int(csv_info["Height"]), int(csv_info["Width"])),
        )

        return {
            "path": path,
            "image": EncodedImage.from_file(buffer),
            "label": Label(label, categories=self._categories),
            "bounding_box": bounding_box,
        }

    def _datapipe(self, resource_dps: List[IterDataPipe]) -> IterDataPipe[Dict[str, Any]]:
        if self._split == "train":
            images_dp, ann_dp = Demultiplexer(
                resource_dps[0], 2, self._classify_train_archive, drop_none=True, buffer_size=INFINITE_BUFFER_SIZE
            )
        else:
            images_dp, ann_dp = resource_dps
            images_dp = Filter(images_dp, path_comparator("suffix", ".ppm"))

        # The order of the image files in the .zip archives perfectly match the order of the entries in the
        # (possibly concatenated) .csv files. So we're able to use Zipper here instead of a IterKeyZipper.
        ann_dp = CSVDictParser(ann_dp, delimiter=";")
        dp = Zipper(images_dp, ann_dp)

        dp = hint_shuffling(dp)
        dp = hint_sharding(dp)

        return Mapper(dp, self._prepare_sample)

    def __len__(self) -> int:
        return 26_640 if self._split == "train" else 12_630
