from pathlib import Path

import datasets
import pandas as pd

_CITATION = """\
@misc{,
author    = "",
title     = "",
url       = "",
publisher = "",
year      = ""
}
"""


_DESCRIPTION = """\
BanglaBeats is a dataset for musical genre classification of audio signals. The dataset consists of 800 Bangla audio tracks, each of 3 seconds long. It contains 8 genres, each represented by 100 tracks. The tracks are all 22,050Hz Dual 16-bit audio files in WAV format. The genres are: adhunik, folk, hiphop, islamic, indie, metal, pop, and rock.
"""

_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

_URL = ""

GENRES = ["Adhunik", "Folk", "Hiphop", "Indie", "Islamic", "Metal", "Pop", "Rock"]
CORRUPTED_FILES = ["abcd.wav"]


class BanglaBeats(datasets.GeneratorBasedBuilder):
    """The BanglaBeats dataset"""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "file": datasets.Value("string"),
                    "audio": datasets.Audio(sampling_rate=22_050),
                    "genre": datasets.ClassLabel(names=GENRES),
                }
            ),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        local_extracted_archive = dl_manager.download_and_extract("data/data.zip")
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "local_extracted_archive": local_extracted_archive,
                },
            )
        ]

    def _generate_examples(self, local_extracted_archive):
        paths = list(Path(local_extracted_archive).glob("**/*.wav"))
        paths = [p for p in paths if "._" not in p.name]
        data = []

        for path in paths:
            label = str(path).split("/")[-2]
            name = str(path).split("/")[-1]
            if name in CORRUPTED_FILES:
                continue

            data.append({"file": str(path), "genre": label})
        df = pd.DataFrame(data)
        df.sort_values("file", inplace=True)

        for idx_, row in df.iterrows():
            yield idx_, {"file": row["file"], "audio": row["file"], "genre": row["genre"]}