

'''simple docstring'''





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 	__A				(			UpperCamelCase__     ):
    a__	:						Optional[int]	       =  """Speech2TextFeatureExtractor"""
    a__	:						Optional[int]	       =  """Speech2TextTokenizer"""



    def __init__(self   :   str       ,      __a   :   Optional[Any]       ,      __a   :   Dict			):
         super().__init__(__a       ,      __a			)
         UpperCAmelCase_	   =       self.feature_extractor
         UpperCAmelCase_	   =       False



    def __call__(self   :   str       ,      *__a   :   Any       ,      **__a   :   Union[str, Any]			):
         # For backward compatibility
         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."			)
              UpperCAmelCase_	   =       kwargs.pop("raw_speech"			)
         else:
              UpperCAmelCase_	   =       kwargs.pop("audio"       ,      __a			)
         UpperCAmelCase_	   =       kwargs.pop("sampling_rate"       ,      __a			)
         UpperCAmelCase_	   =       kwargs.pop("text"       ,      __a			)
         if len(__a			) > 0:
              UpperCAmelCase_	   =       args[0]
              UpperCAmelCase_	   =       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:
              UpperCAmelCase_	   =       self.feature_extractor(__a       ,      *__a       ,      sampling_rate=__a       ,      **__a			)
         if text is not None:
              UpperCAmelCase_	   =       self.tokenizer(__a       ,      **__a			)

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



    def  _lowercase						(self   :   Any       ,      *__a   :   Dict       ,      **__a   :   Tuple			):
         return self.tokenizer.batch_decode(*__a       ,      **__a			)



    def  _lowercase						(self   :   List[Any]       ,      *__a   :   Tuple       ,      **__a   :   int			):
         return self.tokenizer.decode(*__a       ,      **__a			)






    @contextmanager
    def  _lowercase						(self   :   List[Any]			):
         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."			)
         UpperCAmelCase_	   =       True
         UpperCAmelCase_	   =       self.tokenizer
         yield
         UpperCAmelCase_	   =       self.feature_extractor
         UpperCAmelCase_	   =       False





'''simple docstring'''





def      lowerCAmelCase_    ( snake_case_	:			int = 1_00_00_00      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       limit + 1
     UpperCAmelCase_	   =       [0] * limit
     for first_term in range(1 ,   snake_case_      ):
          for n in range(snake_case_ ,   snake_case_ ,   snake_case_      ):
               UpperCAmelCase_	   =       first_term + n / first_term
               if common_difference % 4:  # d must be divisble by 4
                    continue
               else:
                    common_difference /= 4
                    if (
                        first_term > common_difference
                        and first_term < 4 * common_difference
                    ):  # since x,y,z are positive integers
                         frequency[n] += 1  # so z>0 and a>d ,also 4d<a

     UpperCAmelCase_	   =       sum(1 for x in frequency[1:limit] if x == 10      )

     return count


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




