import os
import random
import hashlib
import datasets
from datasets.tasks import AudioClassification

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

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

_NAMES = {
    "vibrato": ["颤音", "chan4_yin1"],
    "upward_portamento": ["上滑音", "shang4_hua2_yin1"],
    "downward_portamento": ["下滑音", "xia4_hua2_yin1"],
    "returning_portamento": ["回滑音", "hui2_hua2_yin1"],
    "glissando": ["刮奏, 花指", "gua1_zou4/hua1_zhi3"],
    "tremolo": ["摇指", "yao2_zhi3"],
    "harmonics": ["泛音", "fan4_yin1"],
    "plucks": ["勾, 打, 抹, 托, ...", "gou1/da3/mo3/tuo1/etc"],
}

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


class GZ_IsoTech(datasets.GeneratorBasedBuilder):
    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features(
                {
                    "audio": datasets.Audio(sampling_rate=44100),
                    "mel": datasets.Image(),
                    "label": datasets.features.ClassLabel(names=list(_NAMES.keys())),
                    "cname": datasets.Value("string"),
                    "pinyin": datasets.Value("string"),
                }
            ),
            supervised_keys=("audio", "label"),
            homepage=_HOMEPAGE,
            license="CC-BY-NC-ND",
            version="1.2.0",
            task_templates=[
                AudioClassification(
                    task="audio-classification",
                    audio_column="audio",
                    label_column="label",
                )
            ],
        )

    def _str2md5(self, original_string: str):
        md5_obj = hashlib.md5()
        md5_obj.update(original_string.encode("utf-8"))
        return md5_obj.hexdigest()

    def _split_generators(self, dl_manager):
        audio_files = dl_manager.download_and_extract(_URLS["audio"])
        mel_files = dl_manager.download_and_extract(_URLS["mel"])
        train_files, test_files = {}, {}
        for path in dl_manager.iter_files([audio_files]):
            fname: str = os.path.basename(path)
            dirname = os.path.dirname(path)
            splt = os.path.basename(os.path.dirname(dirname))
            if fname.endswith(".wav"):
                cls = f"{splt}/{os.path.basename(dirname)}/"
                item_id = self._str2md5(cls + fname.split(".wa")[0])
                if splt == "train":
                    train_files[item_id] = {"audio": path}

                else:
                    test_files[item_id] = {"audio": path}

        for path in dl_manager.iter_files([mel_files]):
            fname = os.path.basename(path)
            dirname = os.path.dirname(path)
            splt = os.path.basename(os.path.dirname(dirname))
            if fname.endswith(".jpg"):
                cls = f"{splt}/{os.path.basename(dirname)}/"
                item_id = self._str2md5(cls + fname.split(".jp")[0])
                if splt == "train":
                    train_files[item_id]["mel"] = path

                else:
                    test_files[item_id]["mel"] = path

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

    def _generate_examples(self, files):
        for i, path in enumerate(files):
            pt = os.path.basename(os.path.dirname(path["audio"]))
            yield i, {
                "audio": path["audio"],
                "mel": path["mel"],
                "label": pt,
                "cname": _NAMES[pt][0],
                "pinyin": _NAMES[pt][1],
            }