





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			'Wav2Vec2FeatureExtractor'
     __magic_name__   =			'AutoTokenizer'




     def __init__(    self				,     snake_case_				,     snake_case_							):
         super().__init__(snake_case_				,     snake_case_							)
         _A     =							self.feature_extractor
         _A     =							False




     @classmethod
     def        lowerCAmelCase__		(    cls				,     snake_case_				,     **snake_case_							):
         try:
             return super().from_pretrained(snake_case_				,     **snake_case_							)
         except OSError:
             warnings.warn(
                 F"Loading a tokenizer inside {cls.__name__} from a config that does not"
                 ' include a `tokenizer_class` attribute is deprecated and will be '
                 'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
                 ' attribute to either your `config.json` or `tokenizer_config.json` '
                 'file to suppress this warning: '				,     snake_case_				,     )

             _A     =							WavaVecaFeatureExtractor.from_pretrained(snake_case_				,     **snake_case_							)
             _A     =							WavaVecaCTCTokenizer.from_pretrained(snake_case_				,     **snake_case_							)

             return cls(feature_extractor=snake_case_				,     tokenizer=snake_case_							)




     def __call__(    self				,     *snake_case_				,     **snake_case_							):
         # For backward compatibility
         if self._in_target_context_manager:
             return self.current_processor(*snake_case_				,     **snake_case_							)

         if "raw_speech" in kwargs:
             warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'							)
             _A     =							kwargs.pop('raw_speech'							)
         else:
             _A     =							kwargs.pop('audio'				,     snake_case_							)
         _A     =							kwargs.pop('sampling_rate'				,     snake_case_							)
         _A     =							kwargs.pop('text'				,     snake_case_							)
         if len(snake_case_							) > 0:
             _A     =							args[0]
             _A     =							args[1:]

         if audio is None and text is None:
             raise ValueError('You need to specify either an `audio` or `text` input to process.'							)

         if audio is not None:
             _A     =							self.feature_extractor(snake_case_				,     *snake_case_				,     sampling_rate=snake_case_				,     **snake_case_							)
         if text is not None:
             _A     =							self.tokenizer(snake_case_				,     **snake_case_							)

         if text is None:
             return inputs
         elif audio is None:
             return encodings
         else:
             _A     =							encodings['input_ids']
             return inputs




     def        lowerCAmelCase__		(    self				,     *snake_case_				,     **snake_case_							):
         # For backward compatibility
         if self._in_target_context_manager:
             return self.current_processor.pad(*snake_case_				,     **snake_case_							)

         _A     =							kwargs.pop('input_features'				,     snake_case_							)
         _A     =							kwargs.pop('labels'				,     snake_case_							)
         if len(snake_case_							) > 0:
             _A     =							args[0]
             _A     =							args[1:]

         if input_features is not None:
             _A     =							self.feature_extractor.pad(snake_case_				,     *snake_case_				,     **snake_case_							)
         if labels is not None:
             _A     =							self.tokenizer.pad(snake_case_				,     **snake_case_							)

         if labels is None:
             return input_features
         elif input_features is None:
             return labels
         else:
             _A     =							labels['input_ids']
             return input_features




     def        lowerCAmelCase__		(    self				,     *snake_case_				,     **snake_case_							):
         return self.tokenizer.batch_decode(*snake_case_				,     **snake_case_							)




     def        lowerCAmelCase__		(    self				,     *snake_case_				,     **snake_case_							):
         return self.tokenizer.decode(*snake_case_				,     **snake_case_							)




     @contextmanager
     def        lowerCAmelCase__		(    self							):
         warnings.warn(
             '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
             'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
             'your audio inputs, or in a separate call.'							)
         _A     =							True
         _A     =							self.tokenizer
         yield
         _A     =							self.feature_extractor
         _A     =							False












def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




    return price * (1 + tax_rate)


if __name__ == "__main__":
 print(f"{price_plus_tax(100, 0.2_5) = }")
 print(f"{price_plus_tax(1_2_5.5_0, 0.0_5) = }")





