import os
from pandas import read_csv, NA

from datasets import GeneratorBasedBuilder, Value, Version, BuilderConfig, Features, DatasetInfo, SplitGenerator, Split, Audio

_DESCRIPTION = '''
This dataset contains transcribed sounds emitted by characters of the League of Legends game.
The data can be useful for building text classification models, fine-tuning language generation models, speech synthesis and speech recognition models.
The underlying web dump for the dataset construction has been last refreshed **20.10.2023**
'''

_HOMEPAGE = 'https://huggingface.co/datasets/zeio/pale'

_LICENSE = 'Apache License Version 2.0'

_URLS = {
    'vanilla': 'https://huggingface.co/datasets/zeio/pale/resolve/main/pale.tsv',
    'quotes': 'https://huggingface.co/datasets/zeio/pale/resolve/main/quotes.tsv',
    'annotated': 'https://huggingface.co/datasets/zeio/pale/resolve/main/annotated.tsv',
    'pulled': 'https://huggingface.co/datasets/zeio/pale/resolve/main/pulled.tsv'
}

_SOUND_URL = 'https://huggingface.co/datasets/zeio/pale/resolve/main/sound.tar.xz'


class Pale(GeneratorBasedBuilder):

    VERSION = Version('30.10.2023')

    BUILDER_CONFIGS = [
        BuilderConfig(name = 'quotes', version = VERSION, description = 'Truncated version of the corpus, which does\'t contain sound effects'),
        BuilderConfig(name = 'annotated', version = VERSION, description = 'An extended version of the full configuration with a couple of additional columns with labels'),
        BuilderConfig(name = 'vanilla', version = VERSION, description = 'All data pulled from the website without significant modifications apart from the web page structure parsing'),
        BuilderConfig(name = 'pulled', version = VERSION, description = 'Same as vanilla, but sound files have been pulled from the website, and "source" column is replaced with "sound" column')
    ]

    DEFAULT_CONFIG_NAME = 'quotes'

    def _info(self):
        if self.config.name == 'vanilla':
            features = Features({
                'header': Value('string'),
                'subheader': Value('string'),
                'text': Value('string'),
                'source': Value('string'),
                'champion': Value('string')
            })
        elif self.config.name == 'annotated':
            features = Features({
                'header': Value('string'),
                'subheader': Value('string'),
                'text': Value('string'),
                'source': Value('string'),
                'champion': Value('string'),
                'quote': Value('bool')
            })
        elif self.config.name == 'quotes':
            features = Features({
                'header': Value('string'),
                'subheader': Value('string'),
                'text': Value('string'),
                'champion': Value('string')
            })
        elif self.config.name == 'pulled':
            features = Features({
                'header': Value('string'),
                'subheader': Value('string'),
                'text': Value('string'),
                'sound': Audio(sampling_rate = 44_100),
                'champion': Value('string')
            })
        else:
            raise ValueError(f'Unknown config: {self.config.name}')

        return DatasetInfo(
            description=_DESCRIPTION,
            features = features,
            homepage=_HOMEPAGE,
            license=_LICENSE
        )

    def _split_generators(self, dl_manager):
        name = self.config.name

        url = _URLS[name]

        return [
            SplitGenerator(
                name = Split.TRAIN,
                gen_kwargs = {
                    "path": dl_manager.download_and_extract(url),
                    'sound': dl_manager.download_and_extract(_SOUND_URL) if name == 'pulled' else None
                }
            )
        ]

    def _generate_examples(self, path: str, sound: str):
        for i, row in read_csv(path, sep = '\t').iterrows():
            if sound is None:
                yield i, dict(row)
            else:
                data = dict(row)

                folder = data['folder']
                filename = data['filename']

                if folder == folder and filename == filename:  # if folder and filename are not nan
                    data['sound'] = os.path.join(sound, folder, f'{filename}.ogg')
                else:
                    data['sound'] = NA

                data.pop('folder')
                data.pop('filename')

                yield i, data