import datasets
import json

import json
import os

import datasets

_DESCRIPTION = """\
Dataset to perform Aesthetic evaluation from Table 2 in ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation.

@misc{xu2023imagereward,
      title={ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation}, 
      author={Jiazheng Xu and Xiao Liu and Yuchen Wu and Yuxuan Tong and Qinkai Li and Ming Ding and Jie Tang and Yuxiao Dong},
      year={2023},
      eprint={2304.05977},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
"""

class ImagerewardEvaluation(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    def _info(self):
        features = datasets.Features(
            {
                "id": datasets.Value("string"),
                "prompt": datasets.Value("string"),
                "generations": datasets.Sequence(datasets.Image()),
                "ranking": datasets.Sequence(datasets.Value("int32")),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download_and_extract("./test.json")
        images = dl_manager.download_and_extract("./images.zip")
        print(downloaded_files, images)
        return [
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"metadata_path": downloaded_files, "images_path": os.path.join(images, "test_images")}),
        ]

    def _generate_examples(self, metadata_path, images_path):
        with open(metadata_path, encoding="utf-8") as f:
            data = json.load(f)
            for item in data:
                yield item["id"], {
                    "id": item["id"],
                    "prompt": item["prompt"],
                    "generations": [os.path.join(images_path, x) for x in item["generations"]],
                    "ranking": item["ranking"],
                }