# coding=utf-8
# Copyright 2023 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.
"""NINJAL Ainu folklore corpus"""

import os
import json

import datasets


_DESCRIPTION = ""
_CITATION = ""
_HOMEPAGE_URL = ""

_BASE_PATH = "data/"
_DATA_URL = _BASE_PATH + "audio/{split}.tar.gz"
_META_URL = _BASE_PATH + "{split}.json"


class AinuFolkloreConfig(datasets.BuilderConfig):
    def __init__(self, name, **kwargs):
        super().__init__(name=name, version=datasets.Version("0.0.0", ""), **kwargs)


class AinuFolklore(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [AinuFolkloreConfig("all")]

    def _info(self):
        task_templates = None
        features = datasets.Features(
            {
                "id": datasets.Value("string"),
                "audio": datasets.features.Audio(sampling_rate=16_000),
                "transcription": datasets.Value("string"),
                "speaker": datasets.Value("string"),
                "surface": datasets.Value("string"),
                "underlying": datasets.Value("string"),
                "gloss": datasets.Value("string"),
                "translation": datasets.Value("string"),
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=("audio", "transcription"),
            homepage=_HOMEPAGE_URL,
            citation=_CITATION,
            task_templates=task_templates,
        )

    def _split_generators(self, dl_manager):
        splits = ["train", "dev", "test"]

        data_urls = {split: [_DATA_URL.format(split=split)] for split in splits}
        meta_urls = {split: [_META_URL.format(split=split)] for split in splits}

        archive_paths = dl_manager.download(data_urls)
        local_extracted_archives = (
            dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
        )
        archive_iters = {
            split: [dl_manager.iter_archive(path) for path in paths]
            for split, paths in archive_paths.items()
        }

        meta_paths = dl_manager.download(meta_urls)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "local_extracted_archives": local_extracted_archives.get(
                        "train", [None] * len(meta_paths.get("train"))
                    ),
                    "archive_iters": archive_iters.get("train"),
                    "text_paths": meta_paths.get("train"),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "local_extracted_archives": local_extracted_archives.get(
                        "dev", [None] * len(meta_paths.get("dev"))
                    ),
                    "archive_iters": archive_iters.get("dev"),
                    "text_paths": meta_paths.get("dev"),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "local_extracted_archives": local_extracted_archives.get(
                        "test", [None] * len(meta_paths.get("test"))
                    ),
                    "archive_iters": archive_iters.get("test"),
                    "text_paths": meta_paths.get("test"),
                },
            ),
        ]

    def _generate_examples(self, local_extracted_archives, archive_iters, text_paths):
        assert len(local_extracted_archives) == len(archive_iters) == len(text_paths)
        key = 0

        for archive, text_path, local_extracted_path in zip(
            archive_iters, text_paths, local_extracted_archives
        ):
            with open(text_path, encoding="utf-8") as fin:
                data = json.load(fin)

            for audio_path, audio_file in archive:
                audio_filename = audio_path.split("/")[-1]
                if audio_filename not in data:
                    continue

                result = data[audio_filename]
                extracted_audio_path = (
                    os.path.join(local_extracted_path, audio_filename)
                    if local_extracted_path is not None
                    else None
                )
                result["audio"] = {"path": audio_path, "bytes": audio_file.read()}
                yield key, result
                key += 1