import io
import os
import wave
import zipfile
import datasets
import requests
from datasets.tasks import AudioClassification


# Once upload a new piano brand, please register its name here
_NAMES = [
    "1_PearlRiver",
    "2_YoungChang",
    "3_Steinway-T",
    "4_Hsinghai",
    "5_Kawai",
    "6_Steinway",
    "7_Kawai-G",
    "8_Yamaha",
]

_DBNAME = os.path.basename(__file__).split('.')[0]

_HOMEPAGE = "https://huggingface.co/datasets/ccmusic-database/" + _DBNAME

_CITATION = """\
@dataset{zhaorui_liu_2021_5676893,
  author       = {Zhaorui Liu and Zijin Li},
  title        = {{Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)}},
  month        = nov,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {1.1},
  doi          = {10.5281/zenodo.5676893},
  url          = {https://doi.org/10.5281/zenodo.5676893}
}
"""

_DESCRIPTION = """\
Piano-Sound-Quality-Database is a dataset of piano sound. 
It consists of 8 kinds of pianos including
PearlRiver, YoungChang, Steinway-T, Hsinghai, Kawai, Steinway, Kawai-G, Yamaha. 
Data was annotated by students from the China Conservatory of Music (CCMUSIC) in Beijing
and collected by George Chou.
"""

_URLS = {piano: _HOMEPAGE + "/resolve/main/data/" +
         piano + ".zip" for piano in _NAMES}


_PITCHES = {"009": "A2", "010": "A2#/B2b", "011": "B2", "100": "C1", "101": "C1#/D1b", "102": "D1", "103": "D1#/E1b",
            "104": "E1", "105": "F1", "106": "F1#/G1b", "107": "G1", "108": "G1#/A1b", "109": "A1", "110": "A1#/B1b",
            "111": "B1", "200": "C", "201": "C#/Db", "202": "D", "203": "D#/Eb", "204": "E", "205": "F", "206": "F#/Gb",
            "207": "G", "208": "G#/Ab", "209": "A", "210": "A#/Bb", "211": "B", "300": "c", "301": "c#/db", "302": "d",
            "303": "d#/eb", "304": "e", "305": "f", "306": "f#/gb", "307": "g", "308": "g#/ab", "309": "a", "310": "a#/bb",
            "311": "b", "400": "c1", "401": "c1#/d1b", "402": "d1", "403": "d1#/e1b", "404": "e1", "405": "f1",
            "406": "f1#/g1b", "407": "g1", "408": "g1#/a1b", "409": "a1", "410": "a1#/b1b", "411": "b1", "500": "c2",
            "501": "c2#/d2b", "502": "d2", "503": "d2#/e2b", "504": "e2", "505": "f2", "506": "f2#/g2b", "507": "g2",
            "508": "g2#/a2b", "509": "a2", "510": "a2#/b2b", "511": "b2", "600": "c3", "601": "c3#/d3b", "602": "d3",
            "603": "d3#/e3b", "604": "e3", "605": "f3", "606": "f3#/g3b", "607": "g3", "608": "g3#/a3b", "609": "a3",
            "610": "a3#/b3b", "611": "b3", "700": "c4", "701": "c4#/d4b", "702": "d4", "703": "d4#/e4b", "704": "e4",
            "705": "f4", "706": "f4#/g4b", "707": "g4", "708": "g4#/a4b", "709": "a4", "710": "a4#/b4b", "711": "b4",
            "800": "c5"}


class piano_sound_quality(datasets.GeneratorBasedBuilder):

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "audio": datasets.Audio(sampling_rate=44_100),
                    "label": datasets.features.ClassLabel(names=_NAMES),
                    "pitch": datasets.Value("string"),
                    "duration": datasets.Value("float64"),
                }
            ),
            supervised_keys=("audio", "label"),
            homepage=_HOMEPAGE,
            license="mit",
            citation=_CITATION,
            task_templates=[
                AudioClassification(
                    task="audio-classification",
                    audio_column="audio",
                    label_column="label",
                )
            ],
        )

    def _get_wav_duration(self, file_bytes):
        with wave.open(io.BytesIO(file_bytes), 'r') as wav_file:
            frames = wav_file.getnframes()
            rate = wav_file.getframerate()
            duration = frames / float(rate)
        return round(duration, 3)

    def _read_zip(self, zip_url, wav_file_path):
        resp = requests.get(zip_url)
        with zipfile.ZipFile(io.BytesIO(resp.content)) as zip_file:
            with zip_file.open(wav_file_path) as file:
                file_data = file.read()
                return self._get_wav_duration(file_data)

    def _split_generators(self, dl_manager):
        data_files = dl_manager.download_and_extract(_URLS)
        split_generator = []

        for index in _URLS.keys():
            split_generator.append(
                datasets.SplitGenerator(
                    name=index.replace('-', '_'),
                    gen_kwargs={
                        "files": dl_manager.iter_files([data_files[index]]),
                    },
                )
            )

        return split_generator

    def _generate_examples(self, files):
        for i, path in enumerate(files):
            file_name = os.path.basename(path)
            if file_name.endswith(".wav"):
                yield i, {
                    "audio": path,
                    "label": os.path.basename(os.path.dirname(path)),
                    "pitch": _PITCHES[file_name[1:4]],
                    "duration": self._read_zip(path.split('::')[1], path.split('::')[0].split('//')[1]),
                }