# 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.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Elite Voice Project"""

import csv
import json
import os

import datasets

_CITATION = """\
@InProceedings{elitevoiceproject:dataset,
    title = {Elite Voice Project},
    author={Elite35P Server.},
    year={2022}
}
"""

_HOMEPAGE = "https://nyahello.jp/"

_LICENSE = "https://hololive.hololivepro.com/guidelines/"

_BASE_URL = "https://huggingface.co/datasets/Elite35P-Server/EliteVoiceProject/resolve/main/"

_AUDIO_URL = _BASE_URL + "audio/{platform}/{split}/{platform}_{split}_{version}.tar"

_TRANSCRIPT_URL = _BASE_URL + "transcript/{platform}/{split}/{platform}_{split}_{version}.tsv"


class EliteVoiceProjectConfig(datasets.BuilderConfig):
    """BuilderConfig for EliteVoiceProject."""

    def __init__(self, name, version, **kwargs):
        self.language = kwargs.pop("language", None)
        self.release_date = kwargs.pop("release_date", None)
        description = (
            f"Elite Voice Project speech to text dataset in {self.language} released on {self.release_date}. "
        )
        super(EliteVoiceProjectConfig, self).__init__(
            name=name,
            version=datasets.Version(version),
            description=description,
            **kwargs,
        )


class EliteVoiceProject(datasets.GeneratorBasedBuilder):
    DEFAULT_WRITER_BATCH_SIZE = 1000

    BUILDER_CONFIGS = [
        EliteVoiceProjectConfig(
            name=platporm,
            version='0.0.1',
            language='Japanese',
            release_date='2022-12-06',
        )
        for platporm in ["twitter", "youtube", "youtube"]
    ]

    def _info(self):
        description = (
            "Elite Voice Project はホロライブ所属VTuberのさくらみこ氏の声をデータセット化することを目的に"
            "TwitterのSpace配信等のアーカイブから音声を切り出し、センテンスを当てています。"
            "当データセットは、hololive productionの二次創作ガイドラインに沿ってご利用ください。"
        )
        features = datasets.Features(
            {
                "audio": datasets.features.Audio(sampling_rate=48_000),
                "sentence": datasets.Value("string"),
            }
        )

        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):
        platporm = self.config.name
        version = self.config.version
        
        audio_urls = {}
        #splits = ("twitter", "youtube", "twitch", "test")
        splits = {"train", "test"}
        for split in splits:
            audio_urls[split] = [
                _AUDIO_URL.format(platform=platporm, split=split, version=version)
            ]
        archive_paths = dl_manager.download(audio_urls)
        local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}

        meta_urls = {split: _TRANSCRIPT_URL.format(platform=platporm, split=split, version=version) for split in splits}
        meta_paths = dl_manager.download_and_extract(meta_urls)

        split_generators = []
        split_names = {
            "train": datasets.Split.TRAIN,
            "test": datasets.Split.TEST,
        }
        for split in splits:
            split_generators.append(
                datasets.SplitGenerator(
                    name=split_names.get(split, split),
                    gen_kwargs={
                        "local_extracted_archive_paths": local_extracted_archive_paths.get(split),
                        "archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)],
                        "meta_path": meta_paths[split],
                    },
                ),
            )

        return split_generators

    def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
        data_fields = list(self._info().features.keys())
        metadata = {}
        with open(meta_path, encoding="utf-8") as f:
            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
            for row in tqdm(reader, desc="Reading metadata..."):
                if not row["path"].endswith(".mp3"):
                    row["path"] += ".mp3"
                # accent -> accents in CV 8.0
                if "accents" in row:
                    row["accent"] = row["accents"]
                    del row["accents"]
                # 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 i, audio_archive in enumerate(archives):
            for filename, file in audio_archive:
                _, filename = os.path.split(filename)
                if filename in metadata:
                    result = dict(metadata[filename])
                    # set the audio feature and the path to the extracted file
                    path = os.path.join(local_extracted_archive_paths[i], filename) if local_extracted_archive_paths else filename
                    result["audio"] = {"path": path, "bytes": file.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_paths else filename

                    yield path, result