# 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.
""" Common Voice Dataset"""


import csv
import os
import json

import datasets
from datasets.utils.py_utils import size_str
from tqdm import tqdm


# TODO: change "streaming" to "main" after merge!
_BASE_URL = "https://huggingface.co/datasets/leviethoang/VBVLSP/resolve/main/"

_AUDIO_URL = {
        "train": "https://husteduvn-my.sharepoint.com/:u:/g/personal/hoang_lv194767_sis_hust_edu_vn/EYhNns0j8GJEgZvb-G2aRS4Bt7AEdQMrGxYtyO2xjc6Img?e=3PkypA&download=1",
        "test": "https://husteduvn-my.sharepoint.com/:u:/g/personal/hoang_lv194767_sis_hust_edu_vn/Ea0uw5DdlxRKpjay1pm6LIoBI6cU4cxHbpTmhWCCRtvMXw?e=yfN5NR&download=1",
        "validation": "https://husteduvn-my.sharepoint.com/:u:/g/personal/hoang_lv194767_sis_hust_edu_vn/EerG7YTpS8dNgpG5vsnpsm0BBKZYYifqcW4kRX3VzHHO5w?e=uvo7Is&download=1"
} 

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


class CommonVoice(datasets.GeneratorBasedBuilder):
    DEFAULT_WRITER_BATCH_SIZE = 1000

    def _info(self):
        description = ("""

                       """
        )
        features = datasets.Features(
            {
                "file_path": datasets.Value("string"),
                "audio": datasets.features.Audio(sampling_rate=48_000),
                "script": datasets.Value("string"),
            }
        )

        return datasets.DatasetInfo(
            description=description,
            features=features,
            supervised_keys=None,
            version=self.config.version,
        )

    def _split_generators(self, dl_manager):
        splits = ("train", "test", "validation")
        archive_paths = dl_manager.download(_AUDIO_URL)
        local_extracted_archive_paths = dl_manager.extract(archive_paths)

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

        split_generators = []
        split_names = {
            "train": datasets.Split.TRAIN,
            "dev": datasets.Split.VALIDATION,
            "test": datasets.Split.TEST,
        }

        for split in splits:
            split_generators.append(
                datasets.SplitGenerator(
                    name=split_names.get(split, split),
                    gen_kwargs={
                        "local_extracted_archive_path": local_extracted_archive_paths.get(split),
                        "archive": dl_manager.iter_archive(archive_paths.get(split)),
                        "meta_path": meta_paths[split],
                    },
                ),
            )

        return split_generators

    def _generate_examples(self, local_extracted_archive_path, archive, 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 data is incomplete, fill with empty values
                for field in data_fields:
                    if field not in row:
                        row[field] = ""
                metadata[row["file_path"]] = row

        
        for filename, file in 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_path, filename) if local_extracted_archive_path else filename
                result["audio"] = {"file_path": path, "bytes": file.read()}
                # set path to None if the audio file doesn't exist locally (i.e. in streaming mode)
                result["file_path"] = path if local_extracted_archive_path else filename

                yield path, result