import datasets
import json


_CITATION = ""

_DESCRIPTION = "Dataset for training agents with Maze-v0 environment."

_HOMEPAGE = "https://huggingface.co/datasets/NathanGavenski/imagetest"

_LICENSE = ""

_REPO = "https://huggingface.co/datasets/NathanGavenski/imagetest"


class ImageSet(datasets.GeneratorBasedBuilder):

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                "obs": datasets.Image(),
                "actions": datasets.Value("int32"),
                "rewards": datasets.Value("float32"),
                "episode_starts": datasets.Value("bool"),
                "maze": datasets.Value("string"),
                "image": datasets.Image()
            }),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        image_path = dl_manager.download_and_extract(f"{_REPO}/resolve/main/images.tar.gz")        
        info_path = dl_manager.download_and_extract(f"{_REPO}/resolve/main/dataset.tar.gz")
        
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "images": f"{image_path}/images",
                    "infos": info_path
                }
            ),
        ]

    def _generate_examples(self, images, infos):
        print(images)
        with open(f"{infos}/dataset.jsonl", encoding="utf-8") as data:
            for idx, line in enumerate(data):
                record = json.loads(line)
                image = record["obs"].split(".")[0]
                yield idx, {
                    "obs": 
                }
            
        # for idx, ((filepath, image), info) in enumerate(zip(images, infos)):
        # for idx, info in enumerate(infos):
        #     print(idx, info)
        #     yield idx, {
        #         # "image": {"path": filepath, "bytes": image.read()},
        #     }