

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class    A__ ( _snake_case     ):
    lowercase		   =						"Speech2TextFeatureExtractor"
    lowercase		   =						"Speech2TextTokenizer"



    def __init__(   self						,			UpperCamelCase__						,			UpperCamelCase__    )  -> List[Any]:





           '''simple docstring'''
           super().__init__(UpperCamelCase__						,			UpperCamelCase__    )
           A_		   = self.feature_extractor
           A_		   = False



    def __call__(   self						,			*UpperCamelCase__						,			**UpperCamelCase__    )  -> Optional[Any]:





           '''simple docstring'''
           # For backward compatibility
           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."""    )
                  A_		   = kwargs.pop("""raw_speech"""    )
           else:
                  A_		   = kwargs.pop("""audio"""						,			UpperCamelCase__    )
           A_		   = kwargs.pop("""sampling_rate"""						,			UpperCamelCase__    )
           A_		   = kwargs.pop("""text"""						,			UpperCamelCase__    )
           if len(UpperCamelCase__    ) > 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(UpperCamelCase__						,			*UpperCamelCase__						,			sampling_rate=UpperCamelCase__						,			**UpperCamelCase__    )
           if text is not None:
                  A_		   = self.tokenizer(UpperCamelCase__						,			**UpperCamelCase__    )

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



    def    snake_case_     (   self						,			*UpperCamelCase__						,			**UpperCamelCase__    )  -> Optional[Any]:





           '''simple docstring'''
           return self.tokenizer.batch_decode(*UpperCamelCase__						,			**UpperCamelCase__    )



    def    snake_case_     (   self						,			*UpperCamelCase__						,			**UpperCamelCase__    )  -> List[str]:





           '''simple docstring'''
           return self.tokenizer.decode(*UpperCamelCase__						,			**UpperCamelCase__    )







    @contextmanager
    def    snake_case_     (   self    )  -> int:





           '''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_		   = True
           A_		   = self.tokenizer
           yield
           A_		   = self.feature_extractor
           A_		   = False



'''simple docstring'''







def  UpperCAmelCase__      ( UpperCAmelCase__ = 1_00			)     ->     int:
       A_		   = n * (n + 1) * (2 * n + 1) / 6
       A_		   = (n * (n + 1) / 2) ** 2
       return int(square_of_sum - sum_of_squares			)


if __name__ == "__main__":
      print(f"""{solution() = }""")
