

'''simple docstring'''
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class      a__						(							lowerCamelCase_						):
   _SCREAMING_SNAKE_CASE					:  Dict			 =							'AutoFeatureExtractor'
   _SCREAMING_SNAKE_CASE					:  str			 =							'Speech2Text2Tokenizer'


   def __init__(				self    ,       _UpperCamelCase    ,       _UpperCamelCase						):





      """simple docstring"""





      super().__init__(_UpperCamelCase    ,       _UpperCamelCase						)
      _lowercase    :   Union[str, Any]      =     self.feature_extractor
      _lowercase    :   Optional[int]      =     False


   def __call__(				self    ,       *_UpperCamelCase    ,       **_UpperCamelCase						):





      """simple docstring"""





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

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

      if text is None:
         return inputs
      elif audio is None:
         return encodings
      else:
         _lowercase    :   Any      =     encodings["input_ids"]
         return inputs


   def 		_lowerCamelCase     (				self    ,       *_UpperCamelCase    ,       **_UpperCamelCase						):





      """simple docstring"""





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


   def 		_lowerCamelCase     (				self    ,       *_UpperCamelCase    ,       **_UpperCamelCase						):





      """simple docstring"""





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






   @contextmanager
   def 		_lowerCamelCase     (				self						):





      """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."						)
      _lowercase    :   int      =     True
      _lowercase    :   List[Any]      =     self.tokenizer
      yield
      _lowercase    :   Optional[int]      =     self.feature_extractor
      _lowercase    :   Union[str, Any]      =     False









'''simple docstring'''
def       _A	(			snake_case  )    ->			int:

   # validate:
   if not isinstance(snake_case	,							snake_case  ) or not all(
       isinstance(snake_case	,							snake_case  ) and item >= 0 for item in citations  ):
      raise ValueError("The citations should be a list of non negative integers."  )

   citations.sort()
   _lowercase    :   Tuple      =     len(snake_case  )

   for i in range(snake_case  ):
      if citations[len_citations - 1 - i] <= i:
         return i

   return len_citations


if __name__ == "__main__":
    import doctest

    doctest.testmod()






