import io
import zipfile
from collections.abc import Sequence
from dataclasses import dataclass
from typing import TYPE_CHECKING, Union

import datasets

if TYPE_CHECKING:
    import numpy as np
    import PIL.Image

logger = datasets.utils.logging.get_logger(__name__)


@dataclass
class Comic(datasets.Image):
    """Comic feature that extends Image feature for CBZ files."""

    def encode_example(
        self, value: Sequence[Union[str, bytes, dict, "np.ndarray", "PIL.Image.Image"]]
    ) -> dict:
        """Encode example into a format for Arrow.

        Args:
            value (`Sequence` of `str`, `bytes`, `dict`, `np.ndarray`, or
            `PIL.Image.Image`):
                Sequence of data passed as input to Comic feature. Each element can be:
                - A path to a local image file
                - Raw bytes of an image file
                - A dictionary containing image data
                - A numpy array representing an image
                - A PIL Image object

        Returns:
            `dict` with "path" and "bytes" fields for each image in the sequence
        """
        return [super().encode_example(img) for img in value]

    def decode_example(
        self, value: dict, token_per_repo_id=None
    ) -> list["PIL.Image.Image"]:
        """Decode example CBZ file into image data.

        Args:
            value (`str` or `dict`):
                Either a string with absolute path to CBZ file, or a dictionary
                with keys:
                - `path`: String with absolute path to CBZ file.
                - `bytes`: The bytes of the CBZ file.
            token_per_repo_id (`dict`, *optional*):
                To access and decode files from private repositories on the Hub.

        Returns:
            `List[PIL.Image.Image]`: List of images from CBZ
        """
        if isinstance(value, str):
            zip_file = value
        else:
            zip_file = (
                io.BytesIO(value["bytes"]) if value.get("bytes") else value["path"]
            )

        with zipfile.ZipFile(zip_file, "r") as zip_ref:
            return [
                super().decode_example(img_file)
                for img_file in sorted(zip_ref.namelist())
            ]


class NhentaiConfig(datasets.folder_based_builder.FolderBasedBuilderConfig):
    """BuilderConfig for ImageFolder."""

    drop_labels: bool = None
    drop_metadata: bool = None

    def __post_init__(self):
        super().__post_init__()


class Nhentai(datasets.folder_based_builder.FolderBasedBuilder):
    BASE_COLUMN_NAME = "comic"
    BASE_FEATURE = Comic
    BUILDER_CONFIG_CLASS = NhentaiConfig
    BUILDER_CONFIGS = [
        NhentaiConfig(
            name="nhentai",
            description="Default configuration for nhentai dataset",
            drop_labels=False,
            drop_metadata=False,
        )
    ]
    DEFAULT_CONFIG_NAME = "nhentai"
    EXTENSIONS: list[str] = [".cbz"]
    VERSION = datasets.Version("1.0.0")

    def _info(self):
        """Returns the dataset metadata."""
        return datasets.DatasetInfo(
            description="Dataset of comic books in CBZ format",
            features=datasets.Features(
                {
                    "comic": Comic(),
                    "title": datasets.Value("string"),
                    "media_id": datasets.Value("int64"),
                    "num_favorites": datasets.Value("int64"),
                    "tag": datasets.Sequence(datasets.Value("string")),
                    "language": datasets.Sequence(datasets.Value("string")),
                    "artist": datasets.Sequence(datasets.Value("string")),
                    "category": datasets.Sequence(datasets.Value("string")),
                    "num_pages": datasets.Value("int64"),
                    "scanlator": datasets.Value("string"),
                    "group": datasets.Sequence(datasets.Value("string")),
                    "parody": datasets.Sequence(datasets.Value("string")),
                    "character": datasets.Sequence(datasets.Value("string")),
                    "epos": datasets.Value("int64"),
                    "file_name": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage="https://nhentai.net/",
        )