import json
import urllib.parse

import datasets


_CITATION = """\
@inproceedings{Wu2020not,
    title={Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision},
    author={Wu, Peng and Liu, jing and Shi, Yujia and Sun, Yujia and Shao, Fangtao and Wu, Zhaoyang and Yang, Zhiwei},
    booktitle={European Conference on Computer Vision (ECCV)},
    year={2020}
}
"""

_DESCRIPTION = """\
Dataset for the paper "Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision". \
The dataset is downloaded from the authors' website (https://roc-ng.github.io/XD-Violence/). Hosting this dataset on HuggingFace \
is just to make it easier for my own project to use this dataset. Please cite the original paper if you use this dataset.
"""

_NAME = "xd-violence"

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

_LICENSE = "MIT"

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


class XDViolenceConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        """BuilderConfig for XD-Violence.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(XDViolenceConfig, self).__init__(**kwargs)


class Squad(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        XDViolenceConfig(
            name="video",
            description="Video dataset",
        ),
        XDViolenceConfig(
            name="rgb",
            description="RGB visual features of the video dataset",
        ),
    ]

    DEFAULT_CONFIG_NAME = "video"
    BUILDER_CONFIG_CLASS = XDViolenceConfig

    def _info(self):
        if self.config.name == "rgb":
            features = datasets.Features(
                {
                    "rgb_feats": datasets.Array3D(
                        shape=(None, 10, 2048),
                        dtype="float32",  # (num_frames, num_crops, feature_dim) use 10 crops by default as of now
                    ),
                    "binary_target": datasets.ClassLabel(
                        names=["non-violence", "violence"]
                    ),
                    "multilabel_targets": datasets.Sequence(
                        datasets.ClassLabel(
                            names=[
                                "Fighting",
                                "Shooting",
                                "Riot",
                                "Abuse",
                                "Car accident",
                                "Explosion",
                            ]
                        )
                    ),
                    "frame_annotations": datasets.Sequence(
                        {
                            "start": datasets.Value("int32"),
                            "end": datasets.Value("int32"),
                        }
                    ),
                }
            )
        else:  # default = "video"
            features = datasets.Features(
                {
                    "video_path": datasets.Value("string"),
                    "binary_target": datasets.ClassLabel(
                        names=["non-violence", "violence"]
                    ),
                    "multilabel_targets": datasets.Sequence(
                        datasets.ClassLabel(
                            names=[
                                "Fighting",
                                "Shooting",
                                "Riot",
                                "Abuse",
                                "Car accident",
                                "Explosion",
                            ]
                        )
                    ),
                    "frame_annotations": datasets.Sequence(
                        {
                            "start": datasets.Value("int32"),
                            "end": datasets.Value("int32"),
                        }
                    ),
                }
            )

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

    def _split_generators(self, dl_manager):
        # Download videos
        # - get URLs
        # - download annotations
        
        train_list_fpath = dl_manager.download_and_extract(urllib.parse.urljoin(_URL, "train_list.txt"))
        test_ann_fpath = dl_manager.download_and_extract(urllib.parse.urljoin(_URL, "test_annotations.txt"))
        print(f"{train_list_fpath=}")
        print(f"{test_ann_fpath=}")
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"filepath": train_list_fpath},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"filepath": test_ann_fpath},
            ),
        ]

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        pass