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


_NAMES = {
    "songs": [f"song{i}" for i in range(1, 7)],
    "singers": [f"singer{i}" for i in range(1, 23)],
}

_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",
}


class acapella(datasets.GeneratorBasedBuilder):
    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features(
                {
                    "audio": datasets.Audio(sampling_rate=48000),
                    "mel": datasets.Image(),
                    "singer_id": datasets.features.ClassLabel(names=_NAMES["singers"]),
                    "pitch": datasets.Value("float32"),
                    "rhythm": datasets.Value("float32"),
                    "vocal_range": datasets.Value("float32"),
                    "timbre": datasets.Value("float32"),
                    "pronunciation": datasets.Value("float32"),
                    "vibrato": datasets.Value("float32"),
                    "dynamic": datasets.Value("float32"),
                    "breath_control": datasets.Value("float32"),
                    "overall_performance": datasets.Value("float32"),
                }
            ),
            supervised_keys=("audio", "singer_id"),
            homepage=_HOMEPAGE,
            license="CC-BY-NC-ND",
            version="1.2.0",
            task_templates=[
                AudioClassification(
                    task="audio-classification",
                    audio_column="audio",
                    label_column="singer_id",
                )
            ],
        )

    def _split_generators(self, dl_manager):
        songs = {}
        for index in _NAMES["songs"]:
            csv_files = dl_manager.download(f"{_DOMAIN}/{index}.csv")
            song_eval = pd.read_csv(csv_files, index_col="singer_id")
            scores = []
            for i in range(len(_NAMES["singers"])):
                scores.append(
                    {
                        "pitch": song_eval.iloc[i]["pitch"],
                        "rhythm": song_eval.iloc[i]["rhythm"],
                        "vocal_range": song_eval.iloc[i]["vocal_range"],
                        "timbre": song_eval.iloc[i]["timbre"],
                        "pronunciation": song_eval.iloc[i]["pronunciation"],
                        "vibrato": song_eval.iloc[i]["vibrato"],
                        "dynamic": song_eval.iloc[i]["dynamic"],
                        "breath_control": song_eval.iloc[i]["breath_control"],
                        "overall_performance": song_eval.iloc[i]["overall_performance"],
                    }
                )

            songs[index] = scores

        audio_files = dl_manager.download_and_extract(_URLS["audio"])
        for fpath in dl_manager.iter_files([audio_files]):
            fname: str = os.path.basename(fpath)
            if fname.endswith(".wav"):
                song_id = os.path.basename(os.path.dirname(fpath))
                singer_id = int(fname.split("(")[1].split(")")[0]) - 1
                songs[song_id][singer_id]["audio"] = fpath

        mel_files = dl_manager.download_and_extract(_URLS["mel"])
        for fpath in dl_manager.iter_files([mel_files]):
            fname = os.path.basename(fpath)
            if fname.endswith(".jpg"):
                song_id = os.path.basename(os.path.dirname(fpath))
                singer_id = int(fname.split("(")[1].split(")")[0]) - 1
                songs[song_id][singer_id]["mel"] = fpath

        split_generator = []
        for key in songs.keys():
            split_generator.append(
                datasets.SplitGenerator(
                    name=key,
                    gen_kwargs={"files": songs[key]},
                )
            )

        return split_generator

    def _generate_examples(self, files):
        for i, item in enumerate(files):
            yield i, {
                "audio": item["audio"],
                "mel": item["mel"],
                "singer_id": i,
                "pitch": item["pitch"],
                "rhythm": item["rhythm"],
                "vocal_range": item["vocal_range"],
                "timbre": item["timbre"],
                "pronunciation": item["pronunciation"],
                "vibrato": item["vibrato"],
                "dynamic": item["dynamic"],
                "breath_control": item["breath_control"],
                "overall_performance": item["overall_performance"],
            }