



from math import gcd





def     __lowerCamelCase							(   __magic_name__						:	str = 12_000    ):
							a__:							Tuple									=0
							for d in range(max_d + 1    ):
														for n in range(d // 3 + 1  ,			(d + 1) // 2    ):
																					if gcd(__magic_name__  ,			__magic_name__    ) == 1:
																												fractions_number += 1
							return fractions_number


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













"""simple docstring"""





import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig
class  __lowerCamelCase     (	_a	):
   a   :					List[Any]			    =CustomConfig

   def __init__(       self			,					snake_case_   )		->							str:
       super().__init__(snake_case_   )
       UpperCamelCase__								=     torch.nn.Linear(config.hidden_size			,					config.hidden_size   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Optional[int]:
       return self.linear(snake_case_   )







   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Union[str, Any]:
       pass







class  __lowerCamelCase     (	_a	):
   a   :					Optional[int]			    =NoSuperInitConfig

   def __init__(       self			,					snake_case_   )		->							Optional[int]:
       super().__init__(snake_case_   )
       UpperCamelCase__								=     torch.nn.Linear(config.attribute			,					config.attribute   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							int:
       return self.linear(snake_case_   )







   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Union[str, Any]:
       pass




