




"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = "Speech2TextFeatureExtractor"
 lowercase__			 = "Speech2TextTokenizer"


 def __init__(    self:		Optional[Any],				a_:		Any,				a_:		Optional[int]		):




   '''simple docstring'''



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


 def __call__(    self:		str,				*a_:		Optional[Any],				**a_:		Dict		):




   '''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."""		)
     _snake_case							:		Tuple								=  kwargs.pop("""raw_speech"""		)
   else:
     _snake_case							:		Optional[int]								=  kwargs.pop("""audio""",				a_		)
   _snake_case							:		List[Any]								=  kwargs.pop("""sampling_rate""",				a_		)
   _snake_case							:		Optional[int]								=  kwargs.pop("""text""",				a_		)
   if len(a_		) > 0:
     _snake_case							:		str								=  args[0]
     _snake_case							:		Any								=  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:
     _snake_case							:		Union[str, Any]								=  self.feature_extractor(a_,				*a_,				sampling_rate=a_,				**a_		)
   if text is not None:
     _snake_case							:		Optional[Any]								=  self.tokenizer(a_,				**a_		)

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


 def 			UpperCamelCase_       (    self:		List[str],				*a_:		List[str],				**a_:		Tuple		):




   '''simple docstring'''



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


 def 			UpperCamelCase_       (    self:		Tuple,				*a_:		List[str],				**a_:		List[str]		):




   '''simple docstring'''



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


 @contextmanager
 def 			UpperCamelCase_       (    self:		Union[str, Any]		):




   '''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."""		)
   _snake_case							:		Dict								=  True
   _snake_case							:		Optional[int]								=  self.tokenizer
   yield
   _snake_case							:		Optional[Any]								=  self.feature_extractor
   _snake_case							:		int								=  False








"""simple docstring"""


def  UpperCAmelCase__	(snake_case__							: int = 1_00_00_00   ):
  """simple docstring"""

  _snake_case							:		Dict								=  [i - 1 for i in range(limit + 1   )]

  for i in range(2		,       limit + 1   ):
    if phi[i] == i - 1:
      for j in range(2 * i		,       limit + 1		,       snake_case__   ):
        phi[j] -= phi[j] // i

  return sum(phi[2 : limit + 1]   )


if __name__ == "__main__":
     print(solution())


