import abc
import functools
import io
import pathlib
import pickle
from typing import Any, Dict, List, Optional, Tuple, Iterator, cast, BinaryIO

import numpy as np
from torchdata.datapipes.iter import (
    IterDataPipe,
    Filter,
    Mapper,
)
from torchvision.prototype.datasets.utils import (
    Dataset,
    DatasetConfig,
    DatasetInfo,
    HttpResource,
    OnlineResource,
)
from torchvision.prototype.datasets.utils._internal import (
    hint_shuffling,
    path_comparator,
    hint_sharding,
)
from torchvision.prototype.features import Label, Image


class CifarFileReader(IterDataPipe[Tuple[np.ndarray, int]]):
    def __init__(self, datapipe: IterDataPipe[Dict[str, Any]], *, labels_key: str) -> None:
        self.datapipe = datapipe
        self.labels_key = labels_key

    def __iter__(self) -> Iterator[Tuple[np.ndarray, int]]:
        for mapping in self.datapipe:
            image_arrays = mapping["data"].reshape((-1, 3, 32, 32))
            category_idcs = mapping[self.labels_key]
            yield from iter(zip(image_arrays, category_idcs))


class _CifarBase(Dataset):
    _FILE_NAME: str
    _SHA256: str
    _LABELS_KEY: str
    _META_FILE_NAME: str
    _CATEGORIES_KEY: str

    @abc.abstractmethod
    def _is_data_file(self, data: Tuple[str, BinaryIO], *, split: str) -> Optional[int]:
        pass

    def _make_info(self) -> DatasetInfo:
        return DatasetInfo(
            type(self).__name__.lower(),
            homepage="https://www.cs.toronto.edu/~kriz/cifar.html",
            valid_options=dict(split=("train", "test")),
        )

    def resources(self, config: DatasetConfig) -> List[OnlineResource]:
        return [
            HttpResource(
                f"https://www.cs.toronto.edu/~kriz/{self._FILE_NAME}",
                sha256=self._SHA256,
            )
        ]

    def _unpickle(self, data: Tuple[str, io.BytesIO]) -> Dict[str, Any]:
        _, file = data
        return cast(Dict[str, Any], pickle.load(file, encoding="latin1"))

    def _prepare_sample(self, data: Tuple[np.ndarray, int]) -> Dict[str, Any]:
        image_array, category_idx = data
        return dict(
            image=Image(image_array),
            label=Label(category_idx, categories=self.categories),
        )

    def _make_datapipe(
        self,
        resource_dps: List[IterDataPipe],
        *,
        config: DatasetConfig,
    ) -> IterDataPipe[Dict[str, Any]]:
        dp = resource_dps[0]
        dp = Filter(dp, functools.partial(self._is_data_file, split=config.split))
        dp = Mapper(dp, self._unpickle)
        dp = CifarFileReader(dp, labels_key=self._LABELS_KEY)
        dp = hint_shuffling(dp)
        dp = hint_sharding(dp)
        return Mapper(dp, self._prepare_sample)

    def _generate_categories(self, root: pathlib.Path) -> List[str]:
        resources = self.resources(self.default_config)

        dp = resources[0].load(root)
        dp = Filter(dp, path_comparator("name", self._META_FILE_NAME))
        dp = Mapper(dp, self._unpickle)

        return cast(List[str], next(iter(dp))[self._CATEGORIES_KEY])


class Cifar10(_CifarBase):
    _FILE_NAME = "cifar-10-python.tar.gz"
    _SHA256 = "6d958be074577803d12ecdefd02955f39262c83c16fe9348329d7fe0b5c001ce"
    _LABELS_KEY = "labels"
    _META_FILE_NAME = "batches.meta"
    _CATEGORIES_KEY = "label_names"

    def _is_data_file(self, data: Tuple[str, Any], *, split: str) -> bool:
        path = pathlib.Path(data[0])
        return path.name.startswith("data" if split == "train" else "test")


class Cifar100(_CifarBase):
    _FILE_NAME = "cifar-100-python.tar.gz"
    _SHA256 = "85cd44d02ba6437773c5bbd22e183051d648de2e7d6b014e1ef29b855ba677a7"
    _LABELS_KEY = "fine_labels"
    _META_FILE_NAME = "meta"
    _CATEGORIES_KEY = "fine_label_names"

    def _is_data_file(self, data: Tuple[str, Any], *, split: str) -> bool:
        path = pathlib.Path(data[0])
        return path.name == split
