import LANGUAGES as LANGUAGES
import STATS as STATS
import datasets as datasets
from datasets.utils.py_utils import size_str

_HOMEPAGE = "homepage-info"
_CITATION = "citation-info"
_LICENSE = "license-info"
_DESCRIPTION = "description-info"

_PROMPTS_URLS = "....."
_DATA_URL = "...."



"""Configuration class, allows to have multiple configurations if needed"""
class ParlaSpeechDatasetConfig(datasets.BuilderConfig):
    """BuilderConfig for ParlaSpeech"""

    def __init__(self, name, version, **kwargs):
        self.language = kwargs.pop("language", None)
        self.release_date = kwargs.pop("release_date", None)
        self.num_clips = kwargs.pop("num_clips", None)
        self.num_speakers = kwargs.pop("num_speakers", None)
        self.validated_hr = kwargs.pop("validated_hr", None)
        self.total_hr = kwargs.pop("total_hr", None)
        self.size_bytes = kwargs.pop("size_bytes", None)
        self.size_human = size_str(self.size_bytes)
        description = ( ##Update Description in the final version
            f"ParlaSpeech is a dataset in {self.language} released on {self.release_date}. "
        )
        super(ParlaSpeechDatasetConfig, self).__init__(
            name=name,
            version=datasets.Version(version),
            description=description,
            **kwargs,
        )


class ParlaSpeechDataset(datasets.GeneratroBasedBuilder):

    """"
    ### NO TENGO CLARO SI HACE FALTA ESTO ###
    DEFAULT_CONFIG_NAME = "all"

    BUILDER_CONFIGS = [
        ParlaSpeechDatasetConfig(
            name=lang,
            version=STATS["version"],
            language=LANGUAGES[lang],
            release_date=STATS["date"],
            num_clips=lang_stats["clips"],
            num_speakers=lang_stats["users"],
            total_hr=float(lang_stats["totalHrs"]) if lang_stats["totalHrs"] else None,
            size_bytes=int(lang_stats["size"]) if lang_stats["size"] else None,
        )
        for lang, lang_stats in STATS["locales"].items()
    ]
    """

    """ When the dataset is loaded and .info is called, the info defined here is displayed."""
    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    #"speaker_id": datasets.Value("string"),
                    #"path": datasets.Value("string"),
                    "path": datasets.Audio(sampling_rate=16_000),
                    "sentence": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
            version = self.config.version,
        )

    " Used to organize the audio files and sentence prompts in each split, once downloaded the dataset."
    def _split_generators(self, dl_manager):
        """Returns SplitGenerators"""
        prompts_paths = dl_manager.download(_PROMPTS_URLS)
        archive = dl_manager.download(_DATA_URL)
        ## local_extracted_archives = dl_manager.extract(archive)
        train_dir = "vivos/train"
        test_dir = "vivos/test"

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "prompts_path": prompts_paths["train"],
                    "path_to_clips": train_dir + "/waves",
                    "audio_files": dl_manager.iter_archive(archive),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "prompts_path": prompts_paths["test"],
                    "path_to_clips": test_dir + "/waves",
                    "audio_files": dl_manager.iter_archive(archive),
                },
            ),
        ]

    def _generate_examples(self, prompts_path, path_to_clips, audio_files):
        """Yields examples as (key, example) tuples."""
        examples = {}
        with open(prompts_path, encoding="utf-8") as f: ##prompts_path -> transcript.tsv
            for row in f:
                data = row.strip().split(" ", 1)
                #speaker_id = data[0].split("_")[0]
                #audio_path = "/".join([path_to_clips, speaker_id, data[0] + ".wav"])
                audio_path = "/".join([path_to_clips, "DSPG_137_23122015_9873.69_9888.03.wav"])
                examples[audio_path] = {
                    #"speaker_id": speaker_id,
                    "path": audio_path,
                    "sentence": data[1],
                }
        inside_clips_dir = False
        id_ = 0
        for path, f in audio_files:
            if path.startswith(path_to_clips):
                inside_clips_dir = True
                if path in examples:
                    audio = {"path": path, "bytes": f.read()}
                    yield id_, {**examples[path], "audio": audio}
                    id_ += 1
            elif inside_clips_dir:
                break