import os
import random
import datasets
from datasets.tasks import ImageClassification


_NAMES = {
    "all": ["m_bel", "f_bel", "m_folk", "f_folk"],
    "gender": ["female", "male"],
    "singing_method": ["Folk_Singing", "Bel_Canto"],
}

_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic-database/{os.path.basename(__file__)[:-3]}"

_DOMAIN = f"{_HOMEPAGE}/resolve/master/data"

_URLS = {
    "mel": f"{_DOMAIN}/mel.zip",
    "eval": f"{_DOMAIN}/eval.zip",
}


class bel_canto(datasets.GeneratorBasedBuilder):
    def _info(self):
        return datasets.DatasetInfo(
            features=(
                datasets.Features(
                    {
                        "audio": datasets.Audio(sampling_rate=22050),
                        "mel": datasets.Image(),
                        "label": datasets.features.ClassLabel(names=_NAMES["all"]),
                        "gender": datasets.features.ClassLabel(names=_NAMES["gender"]),
                        "singing_method": datasets.features.ClassLabel(
                            names=_NAMES["singing_method"]
                        ),
                    }
                )
                if self.config.name == "default"
                else (
                    datasets.Features(
                        {
                            "mel": datasets.Image(),
                            "cqt": datasets.Image(),
                            "chroma": datasets.Image(),
                            "label": datasets.features.ClassLabel(names=_NAMES["all"]),
                            "gender": datasets.features.ClassLabel(
                                names=_NAMES["gender"]
                            ),
                            "singing_method": datasets.features.ClassLabel(
                                names=_NAMES["singing_method"]
                            ),
                        }
                    )
                )
            ),
            supervised_keys=("mel", "label"),
            homepage=_HOMEPAGE,
            license="CC-BY-NC-ND",
            version="1.2.0",
            task_templates=[
                ImageClassification(
                    task="image-classification",
                    image_column="mel",
                    label_column="label",
                )
            ],
        )

    def _split_generators(self, dl_manager):
        dataset = []
        if self.config.name == "default":
            data_files = dl_manager.download_and_extract(_URLS["mel"])
            for fpath in dl_manager.iter_files([data_files]):
                if fpath.endswith(".wav"):
                    dataset.append(fpath)

            random.shuffle(dataset)
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN, gen_kwargs={"files": dataset}
                ),
            ]

        else:
            data_files = dl_manager.download_and_extract(_URLS["eval"])
            for fpath in dl_manager.iter_files([data_files]):
                fname = os.path.basename(fpath)
                if "mel" in fpath and fname.endswith(".jpg"):
                    dataset.append(fpath)

            categories = {}
            for name in _NAMES["all"]:
                categories[name] = []

            for fpath in dataset:
                label = os.path.basename(os.path.dirname(fpath))
                categories[label].append(fpath)

            testset, validset, trainset = [], [], []
            for cls in categories:
                random.shuffle(categories[cls])
                count = len(categories[cls])
                p80 = int(count * 0.8)
                p90 = int(count * 0.9)
                trainset += categories[cls][:p80]
                validset += categories[cls][p80:p90]
                testset += categories[cls][p90:]

            random.shuffle(trainset)
            random.shuffle(validset)
            random.shuffle(testset)
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN, gen_kwargs={"files": trainset}
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION, gen_kwargs={"files": validset}
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.TEST, gen_kwargs={"files": testset}
                ),
            ]

    def _generate_examples(self, files):
        if self.config.name == "default":
            for i, item in enumerate(files):
                label: str = os.path.basename(os.path.dirname(item))
                yield i, {
                    "audio": item,
                    "mel": item.replace(".wav", ".jpg"),
                    "label": label,
                    "gender": ("male" if label.split("_")[0] == "m" else "female"),
                    "singing_method": (
                        "Bel_Canto" if label.split("_")[1] == "bel" else "Folk_Singing"
                    ),
                }

        else:
            for i, item in enumerate(files):
                label = os.path.basename(os.path.dirname(item))
                yield i, {
                    "mel": item,
                    "cqt": item.replace("mel", "cqt"),
                    "chroma": item.replace("mel", "chroma"),
                    "label": label,
                    "gender": ("male" if label.split("_")[0] == "m" else "female"),
                    "singing_method": (
                        "Bel_Canto" if label.split("_")[1] == "bel" else "Folk_Singing"
                    ),
                }