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


_NAMES = [
    "PearlRiver",
    "YoungChang",
    "Steinway-T",
    "Hsinghai",
    "Kawai",
    "Steinway",
    "Kawai-G",
    "Yamaha"
]

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

_HOMEPAGE = f"https://huggingface.co/datasets/ccmusic-database/{_NAME}"

_CITATION = """\
@dataset{zhaorui_liu_2021_5676893,
  author       = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Yuan Wang, Zhaowen Wang, Wei Li and Zijin Li},
  title        = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research},
  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 is a dataset of piano sound. It consists of 8 kinds of pianos including PearlRiver, YoungChang, Steinway-T, Hsinghai, Kawai, Steinway, Kawai-G, Yamaha(recorded by Shaohua Ji with SONY PCM-D100). Data was annotated by students from the China Conservatory of Music (CCMUSIC) in Beijing and collected by Monan Zhou.
"""

_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 pianos(datasets.GeneratorBasedBuilder):
    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "mel": datasets.Image(),
                    "label": datasets.features.ClassLabel(names=_NAMES),
                    "pitch": datasets.features.ClassLabel(names=list(_PITCHES.values()))
                }
            ),
            supervised_keys=("mel", "label"),
            homepage=_HOMEPAGE,
            license="mit",
            citation=_CITATION,
            task_templates=[
                ImageClassification(
                    task="image-classification",
                    image_column="mel",
                    label_column="label"
                )
            ]
        )

    def _cdn_url(self, ip='127.0.0.1', port=80):
        try:
            # easy for local test
            with socket.create_connection((ip, port), timeout=5):
                return f'http://{ip}/{_NAME}/data/pianos_data.zip'

        except (socket.timeout, socket.error):
            return f"{_HOMEPAGE}/resolve/main/data/pianos_data.zip"

    def _split_generators(self, dl_manager):
        data_files = dl_manager.download_and_extract(self._cdn_url())
        dataset = []

        for path in dl_manager.iter_files([data_files]):
            fname = os.path.basename(path)
            if fname.endswith(".jpg"):
                dataset.append({
                    'mel': path,
                    'label': os.path.basename(os.path.dirname(path)),
                    'pitch': _PITCHES[fname.split('_')[0]]
                })

        random.shuffle(dataset)
        count = len(dataset)
        p80 = int(0.8 * count)
        p90 = int(0.9 * count)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "files": dataset[:p80]
                }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "files": dataset[p80:p90]
                }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "files": dataset[p90:]
                }
            )
        ]

    def _generate_examples(self, files):
        for i, path in enumerate(files):
            yield i, {
                "mel": path['mel'],
                "label": path['label'],
                "pitch": path['pitch']
            }