# coding=utf-8
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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""" LibriVox-Indonesia Dataset"""


import csv
import os

import datasets
from datasets.utils.py_utils import size_str

from languages import LANGUAGES
from release_stats import STATS

_CITATION = """\
"""

_HOMEPAGE = "https://huggingface.co/indonesian-nlp/librivox-indonesia"

_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"

_AUDIO_URL = "https://huggingface.co/datasets/cahya/librivox-indonesia/resolve/main/librivox-indonesia.tgz"


class LibriVoxIndonesiaConfig(datasets.BuilderConfig):
    """BuilderConfig for LibriVoxIndonesia."""

    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 = (
            f"LibriVox-Indonesia speech to text dataset in {self.language} released on {self.release_date}. "
            f"The dataset comprises {self.validated_hr} hours of transcribed speech data"
        )
        super(LibriVoxIndonesiaConfig, self).__init__(
            name=name,
            version=datasets.Version(version),
            description=description,
            **kwargs,
        )


class LibriVoxIndonesia(datasets.GeneratorBasedBuilder):
    DEFAULT_CONFIG_NAME = "all"

    BUILDER_CONFIGS = [
        LibriVoxIndonesiaConfig(
            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()
    ]

    def _info(self):
        total_languages = len(STATS["locales"])
        total_hours = self.config.total_hr
        description = (
            "LibriVox-Indonesia is a speech dataset generated from LibriVox with only languages from Indonesia."
            f"The dataset currently consists of {total_hours} hours of speech "
            f" in {total_languages} languages, but more voices and languages are always added."
        )
        features = datasets.Features(
            {
                "path": datasets.Value("string"),
                "language": datasets.Value("string"),
                "reader": datasets.Value("string"),
                "sentence": datasets.Value("string"),
                "audio": datasets.features.Audio(sampling_rate=48_000)
            }
        )

        return datasets.DatasetInfo(
            description=description,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
            version=self.config.version,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        dl_manager.download_config.ignore_url_params = True

        archive_path = dl_manager.download(_AUDIO_URL)
        local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else None
        path_to_clips = "audio"

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "local_extracted_archive": local_extracted_archive,
                    "archive_iterator": dl_manager.iter_archive(archive_path),
                    "metadata_filepath": "audio_transcription.csv",
                    "path_to_clips": path_to_clips,
                },
            ),
        ]

    def _generate_examples(
        self,
        local_extracted_archive,
        archive_iterator,
        metadata_filepath,
        path_to_clips,
    ):
        """Yields examples."""
        data_fields = list(self._info().features.keys())
        metadata = {}
        filepath = local_extracted_archive + "/audio/audio_transcription.csv"
        with open(filepath, "r") as f:
            lines = (line for line in f)
            utterances = csv.DictReader(lines)
            for row in utterances:
                if self.config.name == "all" or self.config.name == row["language"]:
                    row["path"] = os.path.join(path_to_clips, row["path"])
                    # if data is incomplete, fill with empty values
                    for field in data_fields:
                        if field not in row:
                            row[field] = ""
                    metadata[row["path"]] = row
        for path, f in archive_iterator:
            if path in metadata:
                result = dict(metadata[path])
                # set the audio feature and the path to the extracted file
                path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path
                result["audio"] = {"path": path, "bytes": f.read()}
                # set path to None if the audio file doesn't exist locally (i.e. in streaming mode)
                result["path"] = path if local_extracted_archive else None

                yield path, result