import datasets
import pandas as pd

_CITATION = """\
@InProceedings{huggingface:dataset,
title = {RSNA-ATD2023},
author = {Yeow Zi Qin},
year = {2023}
}
"""

_DESCRIPTION = """\
The dataset is the processed version of Kaggle Competition: RSNA 2023 Abdominal Trauma Detection.
It comprises of segmentation of 205 series of CT scans with 5 classes (liver, spleen, right_kidney, 
left_kidney, bowel).
"""

_NAME = "RSNA-ATD2023"

_HOMEPAGE = f"https://huggingface.co/datasets/ziq/{_NAME}"

_LICENSE = "MIT"

_DATA = f"https://huggingface.co/datasets/ziq/{_NAME}/resolve/main/data/"


class RSNAATD(datasets.GeneratorBasedBuilder):
    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    # "image_path": datasets.Value("string"),
                    "patient_id": datasets.Value("int64"),
                    "series_id": datasets.Value("int64"),
                    "frame_id": datasets.Value("int64"),
                    "image": datasets.Image(),
                    "mask": datasets.Image(),
                    "liver": datasets.Value("int16"),
                    "spleen": datasets.Value("int16"),
                    "right_kidney": datasets.Value("int16"),
                    "left_kidney": datasets.Value("int16"),
                    "bowel": datasets.Value("int16"),
                    "aortic_hu": datasets.Value("int16"),
                    "incomplete_organ": datasets.Value("int16"),
                    "bowel_healthy": datasets.Value("int16"),
                    "bowel_injury": datasets.Value("int16"),
                    "extravasation_healthy": datasets.Value("int16"),
                    "extravasation_injury": datasets.Value("int16"),
                    "kidney_healthy": datasets.Value("int16"),
                    "kidney_low": datasets.Value("int16"),
                    "kidney_high": datasets.Value("int16"),
                    "liver_healthy": datasets.Value("int16"),
                    "liver_low": datasets.Value("int16"),
                    "liver_high": datasets.Value("int16"),
                    "spleen_healthy": datasets.Value("int16"),
                    "spleen_low": datasets.Value("int16"),
                    "spleen_high": datasets.Value("int16"),
                    "any_injury": datasets.Value("int16"),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        train_images = dl_manager.download(f"{_DATA}images.tar.gz")
        train_masks = dl_manager.download(f"{_DATA}masks.tar.gz")

        metadata = dl_manager.download(f"{_DATA}metadata.csv")
        train_images = dl_manager.iter_archive(train_images)
        train_masks = dl_manager.iter_archive(train_masks)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "images": train_images,
                    "masks": train_masks,
                    "metadata": metadata,
                },
            ),
        ]

    # def sort_key(self, x):
    #     patient_id, series_id, frame_id = (
    #         x[0][0].replace("images/", "").replace(".png", "").split("_")
    #     )
    #     return int(patient_id), int(series_id), int(frame_id)

    def _generate_examples(self, images, masks, metadata):
        df = pd.read_csv(metadata)

        for idx, ((image_path, image), (mask_path, mask)) in enumerate(
            zip(images, masks)
        ):
            row = df.loc[df["path"] == image_path.lower().replace("images/", "")]
            (
                liver,
                spleen,
                right_kidney,
                left_kidney,
                bowel,
                aortic_hu,
                incomplete_organ,
                bowel_healthy,
                bowel_injury,
                extravasation_healthy,
                extravasation_injury,
                kidney_healthy,
                kidney_low,
                kidney_high,
                liver_healthy,
                liver_low,
                liver_high,
                spleen_healthy,
                spleen_low,
                spleen_high,
                any_injury,
            ) = row.to_numpy()[0][4:]

            yield idx, {
                "patient_id": row["patient_id"].values[0],
                "series_id": row["series_id"].values[0],
                "frame_id": row["frame_id"].values[0],
                "image": {"path": image_path, "bytes": image.read()},
                "mask": {"path": mask_path, "bytes": mask.read()},
                "liver": liver,
                "spleen": spleen,
                "right_kidney": right_kidney,
                "left_kidney": left_kidney,
                "bowel": bowel,
                "aortic_hu": aortic_hu,
                "incomplete_organ": incomplete_organ,
                "bowel_healthy": bowel_healthy,
                "bowel_injury": bowel_injury,
                "extravasation_healthy": extravasation_healthy,
                "extravasation_injury": extravasation_injury,
                "kidney_healthy": kidney_healthy,
                "kidney_low": kidney_low,
                "kidney_high": kidney_high,
                "liver_healthy": liver_healthy,
                "liver_low": liver_low,
                "liver_high": liver_high,
                "spleen_healthy": spleen_healthy,
                "spleen_low": spleen_low,
                "spleen_high": spleen_high,
                "any_injury": any_injury,
            }