






"""simple docstring"""


import warnings
from contextlib import contextmanager

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






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					'''Wav2Vec2FeatureExtractor'''
    __magic_name__   					=					'''AutoTokenizer'''





    def __init__(							self     : Tuple    ,		A     : Union[str, Any]    ,		A     : Dict      ):


      '''simple docstring'''




      super().__init__(A    ,		A      )
      a					:      Union[str, Any]    =       self.feature_extractor
      a					:      int    =       False





    @classmethod
    def     lowerCamelCase__				(							cls     : Optional[Any]    ,		A     : Optional[Any]    ,		**A     : int      ):


      '''simple docstring'''




      try:
        return super().from_pretrained(A    ,		**A      )
      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: '    ,		A    ,		)

        a					:      int    =       WavaVecaFeatureExtractor.from_pretrained(A    ,		**A      )
        a					:      Any    =       WavaVecaCTCTokenizer.from_pretrained(A    ,		**A      )

        return cls(feature_extractor=A    ,		tokenizer=A      )





    def __call__(							self     : int    ,		*A     : List[Any]    ,		**A     : List[str]      ):


      '''simple docstring'''




      if self._in_target_context_manager:
        return self.current_processor(*A    ,		**A      )

      if "raw_speech" in kwargs:
        warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'      )
        a					:      Tuple    =       kwargs.pop('raw_speech'      )
      else:
        a					:      Tuple    =       kwargs.pop('audio'    ,		A      )
      a					:      Tuple    =       kwargs.pop('sampling_rate'    ,		A      )
      a					:      str    =       kwargs.pop('text'    ,		A      )
      if len(A      ) > 0:
        a					:      Optional[int]    =       args[0]
        a					:      str    =       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					:      Tuple    =       self.feature_extractor(A    ,		*A    ,		sampling_rate=A    ,		**A      )
      if text is not None:
        a					:      str    =       self.tokenizer(A    ,		**A      )

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





    def     lowerCamelCase__				(							self     : List[Any]    ,		*A     : Dict    ,		**A     : List[str]      ):


      '''simple docstring'''




      if self._in_target_context_manager:
        return self.current_processor.pad(*A    ,		**A      )

      a					:      Dict    =       kwargs.pop('input_features'    ,		A      )
      a					:      Tuple    =       kwargs.pop('labels'    ,		A      )
      if len(A      ) > 0:
        a					:      Dict    =       args[0]
        a					:      List[str]    =       args[1:]

      if input_features is not None:
        a					:      Any    =       self.feature_extractor.pad(A    ,		*A    ,		**A      )
      if labels is not None:
        a					:      int    =       self.tokenizer.pad(A    ,		**A      )

      if labels is None:
        return input_features
      elif input_features is None:
        return labels
      else:
        a					:      Optional[int]    =       labels['input_ids']
        return input_features





    def     lowerCamelCase__				(							self     : Dict    ,		*A     : Any    ,		**A     : List[Any]      ):


      '''simple docstring'''




      return self.tokenizer.batch_decode(*A    ,		**A      )





    def     lowerCamelCase__				(							self     : Tuple    ,		*A     : Optional[int]    ,		**A     : int      ):


      '''simple docstring'''




      return self.tokenizer.decode(*A    ,		**A      )





    @contextmanager
    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      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					:      List[str]    =       True
      a					:      List[Any]    =       self.tokenizer
      yield
      a					:      Optional[int]    =       self.feature_extractor
      a					:      Dict    =       False












"""simple docstring"""


_UpperCamelCase    :   List[Any]   			=						8.31_44_62  # Unit - J mol-1 K-1




def       snake_case  (A_				:float     ,      A_				:float     ,      A_				:float				):







  '''simple docstring'''

  if moles < 0 or kelvin < 0 or volume < 0:
    raise ValueError('Invalid inputs. Enter positive value.'				)
  return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume




def       snake_case  (A_				:float     ,      A_				:float     ,      A_				:float				):







  '''simple docstring'''

  if moles < 0 or kelvin < 0 or pressure < 0:
    raise ValueError('Invalid inputs. Enter positive value.'				)
  return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
   from doctest import testmod

   testmod()




