import os
from datasets import DatasetInfo, GeneratorBasedBuilder, Split, Features, Image

class FFHQProxy(GeneratorBasedBuilder):
    """FFHQ Proxy Dataset Loader"""

    VERSION = "1.0.0"

    def _info(self):
        return DatasetInfo(
            description="Proxy repository for FFHQ: facial upscaling and restoration reference dataset.",
            homepage="https://github.com/NVlabs/ffhq-dataset",
            license="creativeml-openrail-m",
            features=Features({
                "low_quality": Image(),
                "high_quality": Image()
            }),
            task_categories=["image-to-image"],
            language=["en"],
        )

    def _split_generators(self, dl_manager):
        """Define splits (train/test)"""
        data_dir = os.path.expanduser(dl_manager.download_and_extract("https://drive.google.com/drive/folders/1u2xu7bSrWxrbUxk-dT-UvEJq8IjdmNTP"))
        return [
            self.SplitGenerator(
                name=Split.TRAIN,
                gen_kwargs={"images_dir": os.path.join(data_dir, "train")}
            ),
        ]

    def _generate_examples(self, images_dir):
        """Yield examples."""
        for idx, fname in enumerate(os.listdir(images_dir)):
            if fname.endswith(".png") or fname.endswith(".jpg"):
                low_quality_path = os.path.join(images_dir, fname)
                high_quality_path = os.path.join(images_dir, "high_quality", fname)
                yield idx, {
                    "low_quality": low_quality_path,
                    "high_quality": high_quality_path
                }