import pandas as pd
import urllib

import datasets


_CITATION = """\
@InProceedings{huggingface:dataset,
title = {RSNA 2023 Abdominal Trauma Detection Dataset},
author={Hong Jia Herng},
year={2023}
}
@misc{rsna-2023-abdominal-trauma-detection,
    author = {Errol Colak, Hui-Ming Lin, Robyn Ball, Melissa Davis, Adam Flanders, Sabeena Jalal, Kirti Magudia, Brett Marinelli, Savvas Nicolaou, Luciano Prevedello, Jeff Rudie, George Shih, Maryam Vazirabad, John Mongan},
    title = {RSNA 2023 Abdominal Trauma Detection},
    publisher = {Kaggle},
    year = {2023},
    url = {https://kaggle.com/competitions/rsna-2023-abdominal-trauma-detection}
}
"""

_DESCRIPTION = """\
This dataset is the preprocessed version of the dataset from RSNA 2023 Abdominal Trauma Detection Kaggle Competition. 
It is tailored for segmentation and classification tasks. It contains 3 different configs as described below:
- segmentation: 206 instances where each instance includes a CT scan in NIfTI format, a segmentation mask in NIfTI format, and its relevant metadata (e.g., patient_id, series_id, incomplete_organ, aortic_hu, pixel_representation, bits_allocated, bits_stored)
- classification: 4711 instances where each instance includes a CT scan in NIfTI format, target labels (e.g., extravasation, bowel, kidney, liver, spleen, any_injury), and its relevant metadata (e.g., patient_id, series_id, incomplete_organ, aortic_hu, pixel_representation, bits_allocated, bits_stored)
- classification-with-mask: 206 instances where each instance includes a CT scan in NIfTI format, a segmentation mask in NIfTI format, target labels (e.g., extravasation, bowel, kidney, liver, spleen, any_injury), and its relevant metadata (e.g., patient_id, series_id, incomplete_organ, aortic_hu, pixel_representation, bits_allocated, bits_stored)

All CT scans and segmentation masks had already been resampled with voxel spacing (2.0, 2.0, 3.0) and thus its reduced file size.
"""

_NAME = "rsna-2023-abdominal-trauma-detection"

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

_LICENSE = "MIT"

_URL = f"https://huggingface.co/datasets/jherng/{_NAME}/resolve/main/"


class RSNA2023AbdominalTraumaDetectionSegmentation(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="segmentation",
            version=VERSION,
            description="This part of the dataset loads the CT scans, segmentation masks, and metadata.",
        ),
        datasets.BuilderConfig(
            name="classification",
            version=VERSION,
            description="This part of the dataset loads the CT scans, target labels, and metadata.",
        ),
        datasets.BuilderConfig(
            name="classification-with-mask",
            version=VERSION,
            description="This part of the dataset loads the CT scans, segmentation masks, target labels, and metadata.",
        ),
    ]

    DEFAULT_CONFIG_NAME = "classification"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        if self.config.name == "segmentation":
            features = datasets.Features(
                {
                    "img_path": datasets.Value("string"),
                    "seg_path": datasets.Value("string"),
                    "metadata": {
                        "series_id": datasets.Value("int32"),
                        "patient_id": datasets.Value("int32"),
                        "incomplete_organ": datasets.Value("bool"),
                        "aortic_hu": datasets.Value("float32"),
                        "pixel_representation": datasets.Value("int32"),
                        "bits_allocated": datasets.Value("int32"),
                        "bits_stored": datasets.Value("int32"),
                    },
                }
            )
        elif self.config.name == "classification-with-mask":
            features = datasets.Features(
                {
                    "img_path": datasets.Value("string"),
                    "seg_path": datasets.Value("string"),
                    "bowel": datasets.ClassLabel(
                        num_classes=2, names=["healthy", "injury"]
                    ),
                    "extravasation": datasets.ClassLabel(
                        num_classes=2, names=["healthy", "injury"]
                    ),
                    "kidney": datasets.ClassLabel(
                        num_classes=3, names=["healthy", "low", "high"]
                    ),
                    "liver": datasets.ClassLabel(
                        num_classes=3, names=["healthy", "low", "high"]
                    ),
                    "spleen": datasets.ClassLabel(
                        num_classes=3, names=["healthy", "low", "high"]
                    ),
                    "any_injury": datasets.Value("bool"),
                    "metadata": {
                        "series_id": datasets.Value("int32"),
                        "patient_id": datasets.Value("int32"),
                        "incomplete_organ": datasets.Value("bool"),
                        "aortic_hu": datasets.Value("float32"),
                        "pixel_representation": datasets.Value("int32"),
                        "bits_allocated": datasets.Value("int32"),
                        "bits_stored": datasets.Value("int32"),
                    },
                }
            )

        else:
            features = datasets.Features(
                {
                    "img_path": datasets.Value("string"),
                    "bowel": datasets.ClassLabel(
                        num_classes=2, names=["healthy", "injury"]
                    ),
                    "extravasation": datasets.ClassLabel(
                        num_classes=2, names=["healthy", "injury"]
                    ),
                    "kidney": datasets.ClassLabel(
                        num_classes=3, names=["healthy", "low", "high"]
                    ),
                    "liver": datasets.ClassLabel(
                        num_classes=3, names=["healthy", "low", "high"]
                    ),
                    "spleen": datasets.ClassLabel(
                        num_classes=3, names=["healthy", "low", "high"]
                    ),
                    "any_injury": datasets.Value("bool"),
                    "metadata": {
                        "series_id": datasets.Value("int32"),
                        "patient_id": datasets.Value("int32"),
                        "incomplete_organ": datasets.Value("bool"),
                        "aortic_hu": datasets.Value("float32"),
                        "pixel_representation": datasets.Value("int32"),
                        "bits_allocated": datasets.Value("int32"),
                        "bits_stored": datasets.Value("int32"),
                    },
                }
            )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # segmentation: 206 segmentations and the relevant imgs, train_series_meta.csv, train_dicom_tags.parquet
        # classification: 4711 all imgs, train.csv, train_series_meta.csv, train_dicom_tags.parquet
        # classification-with-mask: 206 segmentations and the relevant imgs, train.csv, train_series_meta.csv, train_dicom_tags.parquet
        series_meta_file = dl_manager.download_and_extract(
            urllib.parse.urljoin(_URL, "train_series_meta.csv")
        )
        dicom_tags_file = dl_manager.download_and_extract(
            urllib.parse.urljoin(_URL, "train_dicom_tags.parquet")
        )
        labels_file = (
            dl_manager.download_and_extract(urllib.parse.urljoin(_URL, "train.csv"))
            if self.config.name != "segmentation"
            else None
        )

        series_meta_df = pd.read_csv(series_meta_file)
        if (
            self.config.name == "classification-with-mask"
            or self.config.name == "segmentation"
        ):
            series_meta_df = series_meta_df.loc[series_meta_df["has_segmentation"] == 1]
            img_files = dl_manager.download(
                series_meta_df.apply(
                    lambda x: urllib.parse.urljoin(
                        _URL, f"train_images/{x['patient_id']}/{x['series_id']}.nii.gz"
                    ),
                    axis=1,
                ).tolist()
            )
            seg_files = dl_manager.download(
                series_meta_df.apply(
                    lambda x: urllib.parse.urljoin(
                        _URL, f"segmentations/{x['series_id']}.nii.gz"
                    ),
                    axis=1,
                ).tolist()
            )
        else:
            img_files = dl_manager.download(
                series_meta_df.apply(
                    lambda x: urllib.parse.urljoin(
                        _URL, f"train_images/{x['patient_id']}/{x['series_id']}.nii.gz"
                    ),
                    axis=1,
                ).tolist()
            )
            seg_files = None

        return [
            datasets.SplitGenerator(
                name=datasets.Split.ALL,
                gen_kwargs={
                    "series_ids": series_meta_df["series_id"].tolist(),
                    "dicom_tags_file": dicom_tags_file,
                    "series_meta_file": series_meta_file,
                    "labels_file": labels_file,
                    "img_files": img_files,
                    "seg_files": seg_files,
                },
            ),
        ]

    def _generate_examples(
        self,
        series_ids,
        dicom_tags_file,
        series_meta_file,
        labels_file,
        img_files,
        seg_files,
    ):
        series_meta_df = pd.read_csv(series_meta_file)
        dicom_tags_df = pd.read_parquet(dicom_tags_file)[
            [
                "SeriesInstanceUID",
                "PixelRepresentation",
                "BitsAllocated",
                "BitsStored",
            ]
        ]
        dicom_tags_df["SeriesID"] = dicom_tags_df["SeriesInstanceUID"].apply(
            lambda x: int(x.split(".")[-1])
        )
        dicom_tags_df = dicom_tags_df.drop(labels=["SeriesInstanceUID"], axis=1)
        dicom_tags_df = dicom_tags_df.groupby(by=["SeriesID"], as_index=False).first()
        dicom_tags_df = dicom_tags_df.rename(
            columns={
                "SeriesID": "series_id",
                "PixelRepresentation": "pixel_representation",
                "BitsAllocated": "bits_allocated",
                "BitsStored": "bits_stored",
            }
        )
        series_meta_df = pd.merge(
            left=series_meta_df, right=dicom_tags_df, how="inner", on="series_id"
        )
        labels_df = pd.read_csv(labels_file) if self.config != "segmentation" else None

        if self.config.name == "segmentation":
            for key, (series_id, img_path, seg_path) in enumerate(
                zip(series_ids, img_files, seg_files)
            ):
                series_meta = (
                    series_meta_df.loc[series_meta_df["series_id"] == series_id]
                    .iloc[0]
                    .to_dict()
                )
                yield key, {
                    "img_path": img_path,
                    "seg_path": seg_path,
                    "metadata": {
                        "series_id": series_id,
                        "patient_id": series_meta["patient_id"],
                        "incomplete_organ": series_meta["incomplete_organ"],
                        "aortic_hu": series_meta["aortic_hu"],
                        "pixel_representation": series_meta["pixel_representation"],
                        "bits_allocated": series_meta["bits_allocated"],
                        "bits_stored": series_meta["bits_stored"],
                    },
                }

        elif self.config.name == "classification-with-mask":
            for key, (series_id, img_path, seg_path) in enumerate(
                zip(series_ids, img_files, seg_files)
            ):
                series_meta = (
                    series_meta_df.loc[series_meta_df["series_id"] == series_id]
                    .iloc[0]
                    .to_dict()
                )
                patient_id = series_meta["patient_id"]
                label_data = (
                    labels_df.loc[labels_df["patient_id"] == patient_id]
                    .iloc[0]
                    .to_dict()
                )
                yield key, {
                    "img_path": img_path,
                    "seg_path": seg_path,
                    "bowel": datasets.ClassLabel(
                        num_classes=2, names=["healthy", "injury"]
                    ),
                    "extravasation": datasets.ClassLabel(
                        num_classes=2, names=["healthy", "injury"]
                    ),
                    "kidney": datasets.ClassLabel(
                        num_classes=3, names=["healthy", "low", "high"]
                    ),
                    "liver": datasets.ClassLabel(
                        num_classes=3, names=["healthy", "low", "high"]
                    ),
                    "spleen": datasets.ClassLabel(
                        num_classes=3, names=["healthy", "low", "high"]
                    ),
                    "any_injury": datasets.Value("bool"),
                    "metadata": {
                        "series_id": series_id,
                        "patient_id": series_meta["patient_id"],
                        "incomplete_organ": series_meta["incomplete_organ"],
                        "aortic_hu": series_meta["aortic_hu"],
                        "pixel_representation": series_meta["pixel_representation"],
                        "bits_allocated": series_meta["bits_allocated"],
                        "bits_stored": series_meta["bits_stored"],
                    },
                }
        else:
            pass