"""PMC-OA Dataset"""

import os
import jsonlines

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@article{lin2023pmc,
  title={PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents},
  author={Lin, Weixiong and Zhao, Ziheng and Zhang, Xiaoman and Wu, Chaoyi and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
  journal={arXiv preprint arXiv:2303.07240},
  year={2023}
}
"""

_DESCRIPTION = """\
Foundation models trained on large-scale dataset gain a recent surge in CV and NLP. In contrast, development in biomedical domain lags far behind due to data scarcity.
To address this issue, we build and release PMC-OA, a biomedical dataset with 1.6M image-caption pairs collected from PubMedCentral's OpenAccess subset, which is 8 times larger than before.
PMC-OA covers diverse modalities or diseases, with majority of the image-caption samples aligned at finer-grained level, i.e., subfigure and subcaption.
While pretraining a CLIP-style model on PMC-OA, our model named PMC-CLIP achieves state-of-the-art results on various downstream tasks,
including image-text retrieval on ROCO, MedMNIST image classification, Medical VQA, i.e. +8.1% R@10 on image-text retrieval, +3.9% accuracy on image classification.
"""

_HOMEPAGE = "https://weixionglin.github.io/PMC-CLIP/"

_URL = "https://huggingface.co/datasets/axiong/pmc_oa/resolve/main/"
_URLS = {
    "images": _URL + "images.zip",
    "train": _URL + "train.jsonl",
    "valid": _URL + "valid.jsonl",
    "test": _URL + "test.jsonl"
}


class PMC_OA(datasets.GeneratorBasedBuilder):
    """PMC_OA Dataset."""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "caption": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract(_URLS)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir['train'], "image_dir": data_dir['images']}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_dir['valid'], "image_dir": data_dir['images']}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir['test'], "image_dir": data_dir['images']}),
        ]

    def _generate_examples(self, filepath, image_dir):
        """This function returns the examples in the raw (text) form."""
        logger.info("generating examples from = %s", filepath)

        with jsonlines.open(filepath) as reader:
            _id = 0
            for obj in reader:
                relative_image_path = obj['image']
                image_path = os.path.join(image_dir, "caption_T060_filtered_top4_sep_v0_subfigures", relative_image_path)
                caption = obj['caption']
                yield _id, {
                    "image": {
                        "path": image_path,
                        "bytes": open(image_path, "rb").read(),
                    },
                    "caption": caption,
                }
                _id += 1