import os
import datasets
import pandas as pd
from datasets.tasks import AudioClassification


_NAMES = [
    # Chinese 0-36
    "gao_hu",
    "er_hu",
    "zhong_hu",
    "ge_hu",
    "di_yin_ge_hu",
    "jing_hu",
    "ban_hu",
    "bang_di",
    "qu_di",
    "xin_di",
    "da_di",
    "gao_yin_sheng",
    "zhong_yin_sheng",
    "di_yin_sheng",
    "gao_yin_suo_na",
    "zhong_yin_suo_na",
    "ci_zhong_yin_suo_na",
    "di_yin_suo_na",
    "gao_yin_guan",
    "zhong_yin_guan",
    "di_yin_guan",
    "bei_di_yin_guan",
    "ba_wu",
    "xun",
    "xiao",
    "liu_qin",
    "xiao_ruan",
    "pi_pa",
    "yang_qin",
    "zhong_ruan",
    "da_ruan",
    "gu_zheng",
    "gu_qin",
    "kong_hou",
    "san_xian",
    "yun_luo",
    "bian_zhong",
    # Western 37-60
    "violin",
    "viola",
    "cello",
    "double_bass",
    "piccolo",
    "flute",
    "oboe",
    "clarinet",
    "bassoon",
    "saxophone",
    "trumpet",
    "trombone",
    "horn",
    "tuba",
    "harp",
    "tubular_bells",
    "bells",
    "xylophone",
    "vibraphone",
    "marimba",
    "piano",
    "clavichord",
    "accordion",
    "organ",
]

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

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

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


class instrument_timbre(datasets.GeneratorBasedBuilder):
    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features(
                {
                    "audio": datasets.Audio(sampling_rate=44100),
                    "mel": datasets.Image(),
                    "instrument": datasets.features.ClassLabel(names=_NAMES),
                    "slim": datasets.Value("float32"),
                    "bright": datasets.Value("float32"),
                    "dark": datasets.Value("float32"),
                    "sharp": datasets.Value("float32"),
                    "thick": datasets.Value("float32"),
                    "thin": datasets.Value("float32"),
                    "vigorous": datasets.Value("float32"),
                    "silvery": datasets.Value("float32"),
                    "raspy": datasets.Value("float32"),
                    "full": datasets.Value("float32"),
                    "coarse": datasets.Value("float32"),
                    "pure": datasets.Value("float32"),
                    "hoarse": datasets.Value("float32"),
                    "consonant": datasets.Value("float32"),
                    "mellow": datasets.Value("float32"),
                    "muddy": datasets.Value("float32"),
                }
            ),
            supervised_keys=("audio", "instrument"),
            homepage=_HOMEPAGE,
            license="CC-BY-NC-ND",
            version="1.2.0",
            task_templates=[
                AudioClassification(
                    task="audio-classification",
                    audio_column="audio",
                    label_column="instrument",
                )
            ],
        )

    def _split_generators(self, dl_manager):
        audio_files = dl_manager.download_and_extract(_URLS["audio"])
        mel_files = dl_manager.download_and_extract(_URLS["mel"])
        cn_ins_eval = dl_manager.download(_URLS["Chinese"])
        en_ins_eval = dl_manager.download(_URLS["Western"])
        cn_labels = pd.read_csv(cn_ins_eval, index_col="instrument_id")
        en_labels = pd.read_csv(en_ins_eval, index_col="instrument_id")
        cn_dataset, en_dataset = {}, {}
        for path in dl_manager.iter_files([audio_files]):
            fname: str = os.path.basename(path)
            i = int(fname.split(".wa")[0]) - 1
            if fname.endswith(".wav"):
                region = os.path.basename(os.path.dirname(path))
                labels = cn_labels if region == "Chinese" else en_labels
                data = {
                    "audio": path,
                    "mel": "",
                    "instrument": labels.iloc[i]["instrument_name"],
                    "slim": labels.iloc[i]["slim"],
                    "bright": labels.iloc[i]["bright"],
                    "dark": labels.iloc[i]["dim"],
                    "sharp": labels.iloc[i]["sharp"],
                    "thick": labels.iloc[i]["thick"],
                    "thin": labels.iloc[i]["thin"],
                    "vigorous": labels.iloc[i]["solid"],
                    "silvery": labels.iloc[i]["clear"],
                    "raspy": labels.iloc[i]["dry"],
                    "full": labels.iloc[i]["plump"],
                    "coarse": labels.iloc[i]["rough"],
                    "pure": labels.iloc[i]["pure"],
                    "hoarse": labels.iloc[i]["hoarse"],
                    "consonant": labels.iloc[i]["harmonious"],
                    "mellow": labels.iloc[i]["soft"],
                    "muddy": labels.iloc[i]["turbid"],
                }
                if region == "Chinese":
                    cn_dataset[i] = data

                else:
                    en_dataset[i] = data

        for path in dl_manager.iter_files([mel_files]):
            fname = os.path.basename(path)
            i = int(fname.split(".jp")[0]) - 1
            if fname.endswith(".jpg"):
                if os.path.basename(os.path.dirname(path)) == "Chinese":
                    cn_dataset[i]["mel"] = path

                else:
                    en_dataset[i]["mel"] = path

        return [
            datasets.SplitGenerator(
                name="Chinese",
                gen_kwargs={
                    "files": [cn_dataset[k] for k in sorted(cn_dataset)],
                },
            ),
            datasets.SplitGenerator(
                name="Western",
                gen_kwargs={
                    "files": [en_dataset[k] for k in sorted(en_dataset)],
                },
            ),
        ]

    def _generate_examples(self, files):
        for i, path in enumerate(files):
            yield i, path