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

from torchdata.datapipes.iter import Demultiplexer, Filter, IterDataPipe, IterKeyZipper, JsonParser, Mapper, UnBatcher
from torchvision.prototype.datasets.utils import Dataset, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal import (
    getitem,
    hint_sharding,
    hint_shuffling,
    INFINITE_BUFFER_SIZE,
    path_accessor,
    path_comparator,
)
from torchvision.prototype.features import EncodedImage, Label

from .._api import register_dataset, register_info

NAME = "clevr"


@register_info(NAME)
def _info() -> Dict[str, Any]:
    return dict()


@register_dataset(NAME)
class CLEVR(Dataset):
    """
    - **homepage**: https://cs.stanford.edu/people/jcjohns/clevr/
    """

    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", "val", "test"))

        super().__init__(root, skip_integrity_check=skip_integrity_check)

    def _resources(self) -> List[OnlineResource]:
        archive = HttpResource(
            "https://dl.fbaipublicfiles.com/clevr/CLEVR_v1.0.zip",
            sha256="5cd61cf1096ed20944df93c9adb31e74d189b8459a94f54ba00090e5c59936d1",
        )
        return [archive]

    def _classify_archive(self, data: Tuple[str, Any]) -> Optional[int]:
        path = pathlib.Path(data[0])
        if path.parents[1].name == "images":
            return 0
        elif path.parent.name == "scenes":
            return 1
        else:
            return None

    def _filter_scene_anns(self, data: Tuple[str, Any]) -> bool:
        key, _ = data
        return key == "scenes"

    def _add_empty_anns(self, data: Tuple[str, BinaryIO]) -> Tuple[Tuple[str, BinaryIO], None]:
        return data, None

    def _prepare_sample(self, data: Tuple[Tuple[str, BinaryIO], Optional[Dict[str, Any]]]) -> Dict[str, Any]:
        image_data, scenes_data = data
        path, buffer = image_data

        return dict(
            path=path,
            image=EncodedImage.from_file(buffer),
            label=Label(len(scenes_data["objects"])) if scenes_data else None,
        )

    def _datapipe(self, resource_dps: List[IterDataPipe]) -> IterDataPipe[Dict[str, Any]]:
        archive_dp = resource_dps[0]
        images_dp, scenes_dp = Demultiplexer(
            archive_dp,
            2,
            self._classify_archive,
            drop_none=True,
            buffer_size=INFINITE_BUFFER_SIZE,
        )

        images_dp = Filter(images_dp, path_comparator("parent.name", self._split))
        images_dp = hint_shuffling(images_dp)
        images_dp = hint_sharding(images_dp)

        if self._split != "test":
            scenes_dp = Filter(scenes_dp, path_comparator("name", f"CLEVR_{self._split}_scenes.json"))
            scenes_dp = JsonParser(scenes_dp)
            scenes_dp = Mapper(scenes_dp, getitem(1, "scenes"))
            scenes_dp = UnBatcher(scenes_dp)

            dp = IterKeyZipper(
                images_dp,
                scenes_dp,
                key_fn=path_accessor("name"),
                ref_key_fn=getitem("image_filename"),
                buffer_size=INFINITE_BUFFER_SIZE,
            )
        else:
            for _, file in scenes_dp:
                file.close()
            dp = Mapper(images_dp, self._add_empty_anns)

        return Mapper(dp, self._prepare_sample)

    def __len__(self) -> int:
        return 70_000 if self._split == "train" else 15_000
