from dataclasses import dataclass
from pathlib import Path
from typing import Optional

import datasets
import pyarrow as pa
from datasets.features import ClassLabel, Image
from datasets.tasks import ImageClassification

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


@dataclass
class ImageFolderConfig(datasets.BuilderConfig):
    """BuilderConfig for ImageFolder."""

    features: Optional[datasets.Features] = None

    @property
    def schema(self):
        return (
            pa.schema(self.features.type)
            if self.features is not None
            else None
        )


class ImageFolder(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIG_CLASS = ImageFolderConfig

    def _info(self):
        filepaths = None
        if isinstance(self.config.data_files, str):
            filepaths = self.config.data_files
        elif isinstance(self.config.data_files, dict):
            filepaths = self.config.data_files.get("train", None)
        if filepaths is None:
            raise RuntimeError("data_files must be specified")

        classes = sorted(
            [Path(file_path).parent.name.lower() for file_path in filepaths]
        )

        # Remove duplicates
        classes = list(set(classes))

        return datasets.DatasetInfo(
            features=datasets.Features(
                {
                    "image_filepath": Image(),
                    "labels": ClassLabel(names=classes),
                }
            ),
            task_templates=[
                ImageClassification(
                    image_column="image_filepath",
                    label_column="labels",
                )
            ],
        )

    def _split_generators(self, dl_manager):
        if not self.config.data_files:
            raise ValueError(
                f"At least one data file must be specified, but got data_files={self.config.data_files}"
            )

        data_files = self.config.data_files
        if isinstance(data_files, str):
            file_paths = data_files
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={"file_paths": file_paths},
                )
            ]
        splits = []
        for split_name, file_paths in data_files.items():
            splits.append(
                datasets.SplitGenerator(
                    name=split_name, gen_kwargs={"file_paths": file_paths}
                )
            )
        return splits

    def _generate_examples(self, file_paths):
        logger.info("generating examples from = %s", file_paths)
        extensions = {
            ".jpg",
            ".jpeg",
            ".png",
            ".ppm",
            ".bmp",
            ".pgm",
            ".tif",
            ".tiff",
            ".webp",
        }
        for i, path in enumerate(file_paths):
            path = Path(path)
            if path.suffix in extensions:
                yield i, {
                    "image_filepath": path.as_posix(),
                    "labels": path.parent.name.lower(),
                }