# coding=utf-8
# Copyright 2020 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.
import csv
import os
import datasets


_CITATION = """\
@misc{Sofwath_2023, 
    title = "Dhivehi Presidential Speech Dataset", 
    url = "https://huggingface.co./datasets/dash8x/presidential_speech", 
    journal = "Hugging Face",
    author = "Sofwath", 
    year = "2018", 
    month = jul
} 
"""

_DESCRIPTION = """\
Dhivehi Presidential Speech is a Dhivehi speech dataset created from data extracted and 
processed by [Sofwath](https://github.com/Sofwath) as part of a collection of Dhivehi 
datasets found [here](https://github.com/Sofwath/DhivehiDatasets).

The dataset contains around 2.5 hrs (1 GB) of speech collected from Maldives President's Office
consisting of 7 speeches given by President Yaameen Abdhul Gayyoom. 
"""

_HOMEPAGE = 'https://github.com/Sofwath/DhivehiDatasets'

_LICENSE = 'CC BY-NC-SA 4.0'

# Source data: 'https://drive.google.com/file/d/1vhMXoB2L23i4HfAGX7EYa4L-sfE4ThU5/view?usp=sharing'
_DATA_URL = 'data'

_PROMPTS_URLS = {
    'train': 'data/metadata_train.tsv.gz',
    'test': 'data/metadata_test.tsv.gz',
    'validation': 'data/metadata_validation.tsv.gz',
}


class DhivehiPresidentialSpeech(datasets.GeneratorBasedBuilder):
    """Dhivehi Presidential Speech is a free Dhivehi speech corpus consisting of around 2.5 hours of
    recorded speech prepared for Dhivehi Automatic Speech Recognition task."""

    VERSION = datasets.Version('1.0.0')

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    def _info(self):
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    'path': datasets.Value('string'),
                    'audio': datasets.Audio(sampling_rate=16_000),
                    'sentence': datasets.Value('string'),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        dl_manager.download_config.ignore_url_params = True
        audio_path = {}
        local_extracted_archive = {}
        metadata_path = {}

        split_type = {
            'train': datasets.Split.TRAIN, 
            'test': datasets.Split.TEST,
            'validation': datasets.Split.VALIDATION,
        }

        for split in split_type:
            audio_path[split] = dl_manager.download(f'{_DATA_URL}/audio_{split}.tar.gz')
            local_extracted_archive[split] = dl_manager.extract(audio_path[split]) if not dl_manager.is_streaming else None
            metadata_path[split] = dl_manager.download_and_extract(f'{_DATA_URL}/metadata_{split}.csv.gz')

        path_to_clips = 'dv-presidential-speech'

        return [
            datasets.SplitGenerator(
                name=split_type[split],
                gen_kwargs={
                    'local_extracted_archive': local_extracted_archive[split],
                    'audio_files': dl_manager.iter_archive(audio_path[split]),
                    'metadata_path': dl_manager.download_and_extract(metadata_path[split]),
                    'path_to_clips': f'{path_to_clips}-{split}/waves',
                },
            ) for split in split_type
        ]

    def _generate_examples(
        self,
        local_extracted_archive,
        audio_files,
        metadata_path,
        path_to_clips,
    ):
        """Yields examples."""
        data_fields = list(self._info().features.keys())
        metadata = {}
        with open(metadata_path, 'r', encoding='utf-8') as f:
            reader = csv.reader(f)
            row_dict = {}

            for row in reader:
                row_dict['path'] = row[0]
                row_dict['sentence'] = row[1]

                # if data is incomplete, fill with empty values
                for field in data_fields:
                    if field not in row_dict:
                        row_dict[field] = ''

                metadata[row_dict['path']] = row_dict

        id_ = 0
        for path, f in audio_files:
            file_name = os.path.splitext(os.path.basename(path))[0]
            os.path.join(path_to_clips, row[0])

            if file_name in metadata:
                result = dict(metadata[file_name])
                # 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()}
                result['path'] = path
                yield id_, result
                id_ += 1