# coding=utf-8

import pandas as pd

import datasets
from datasets.tasks import AutomaticSpeechRecognition


_CITATION = """\

"""

_DESCRIPTION = """\

"""

_HOMEPAGE = "https://zenodo.org/record/5541827"

_LICENSE = "Creative Commons Attribution 4.0 International"

_INDEX_REPO = "https://huggingface.co/datasets/proxectonos/Nos_Parlaspeech-GL/tree/main/"
#_INDEX_REPO = "https://huggingface.co/datasets/proxectonos/Nos_Parlaspeech-GL/resolve/main/"

_URLS = {
    "index": _INDEX_REPO + "data/{config}/{split}/{config}_{split}.tsv",
    "audio": "audio/{config}/{split}/{config}_{split}.tar",
    #"audio": "audio/{config}/{split}/{config}_{split}.tar?download=1",
}
_SPLITS = {datasets.Split.TRAIN: "train", datasets.Split.VALIDATION: "dev", datasets.Split.TEST: "test"}


class ParlaSpeech(datasets.GeneratorBasedBuilder):
    """Nos-ParlaSpeech."""

    VERSION = datasets.Version("1.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="clean", version=VERSION, description="XX hours of clean quality segments."),
        datasets.BuilderConfig(name="other", version=VERSION, description="XX hours of other quality segments."),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "path": datasets.Value("string"), #path to wav file
                    "audio": datasets.features.Audio(), 
                    "speaker_id": datasets.Value("int64"),
                    "sentence": datasets.Value("string"),
                    "gender": datasets.ClassLabel(names=["F", "M"]),
                    "duration": datasets.Value("float64"),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
            task_templates=[
                AutomaticSpeechRecognition(transcription_column="sentence")
            ],
        )

    def _split_generators(self, dl_manager):
        urls = {
            split: {key: url.format(config=self.config.name, split=_SPLITS[split]) for key, url in _URLS.items()}
            for split in _SPLITS
        }
        dl_dir = dl_manager.download(urls)
        return [
            datasets.SplitGenerator(
                name=split,
                gen_kwargs={
                    "index_path": dl_dir[split]["index"],
                    "audio_files": dl_manager.iter_archive(dl_dir[split]["audio"]),
                },
            )
            for split in _SPLITS
        ]

    def _generate_examples(self, index_path, audio_files):
        with open(index_path, encoding="utf-8") as index_file:
            index = pd.read_csv(index_file, delimiter="\t", index_col="path").to_dict(orient="index")
        # clean: 83568 = 79269 + 2155 + 2144 ; other: 146669 = 142813 + 1957 + 1899
        for key, (path, file) in enumerate(audio_files):
            if path.endswith(".wav"):
                data = index.pop(path)
                audio = {"path": path, "bytes": file.read()}
                yield key, {"path": path, "audio": audio, **data}
            else:
                path = path + ".wav"
                data = index.pop(path)
                audio = {"path": path, "bytes": file.read()}
                yield key, {"path": path, "audio": audio, **data}