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

_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",
    "eval": f"{_DOMAIN}/eval.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())
                        ),
                        "name": datasets.Value("string"),
                        "cname": datasets.Value("string"),
                        "pinyin": datasets.Value("string"),
                    }
                )
                if self.config.name == "default"
                else (
                    datasets.Features(
                        {
                            "mel": datasets.Image(),
                            "cqt": datasets.Image(),
                            "chroma": datasets.Image(),
                            "label": datasets.features.ClassLabel(
                                names=list(_NAMES.keys())
                            ),
                        }
                    )
                )
            ),
            supervised_keys=("mel", "label"),
            homepage=_HOMEPAGE,
            license="CC-BY-NC-ND",
            version="1.2.0",
            task_templates=[
                ImageClassification(
                    task="image-classification",
                    image_column="image",
                    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):
        if self.config.name == "default":
            audio_files = dl_manager.download_and_extract(_URLS["audio"])
            mel_files = dl_manager.download_and_extract(_URLS["mel"])
            train_files, 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:
                        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:
                        files[item_id]["mel"] = path

            trainset = list(train_files.values())
            testset = list(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},
                ),
            ]

        else:
            data_files = dl_manager.download_and_extract(_URLS["eval"])
            trainset, validset, testset = [], [], []
            files = {key: [] for key in _NAMES}
            for path in dl_manager.iter_files([data_files]):
                clsdir = os.path.dirname(path)
                cls = os.path.basename(clsdir)
                splt = os.path.basename(os.path.dirname(clsdir))
                if path.endswith(".jpg") and "mel" in path:
                    if splt == "train":
                        trainset.append(path)
                    else:
                        files[cls].append(path)

            for cls in _NAMES:
                count = len(files[cls])
                if count < 2:
                    raise ValueError(f"Class {cls} in test data has items < 2 !")

                random.shuffle(files[cls])
                half = max(count // 2, 1)
                validset += files[cls][:half]
                testset += files[cls][half:]

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

        else:
            for i, path in enumerate(files):
                yield i, {
                    "mel": path,
                    "cqt": path.replace("mel", "cqt"),
                    "chroma": path.replace("mel", "chroma"),
                    "label": os.path.basename(os.path.dirname(path)),
                }