



from typing import Dict

from .base import GenericTensor, Pipeline







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			Union[str, Any]				,		lowerCAmelCase							:			Optional[Any]=None				,		lowerCAmelCase							:			Any=None				,		lowerCAmelCase							:			Optional[int]=None				,		**lowerCAmelCase							:			Union[str, Any])				->				int:

    """simple docstring"""







    if tokenize_kwargs is None:
     _snake_case    :  Optional[Any]					=					{}

    if truncation is not None:
     if "truncation" in tokenize_kwargs:
      raise ValueError(
          """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)""")
     _snake_case    :  Union[str, Any]					=					truncation

    _snake_case    :  Dict					=					tokenize_kwargs

    _snake_case    :  int					=					{}
    if return_tensors is not None:
     _snake_case    :  Optional[Any]					=					return_tensors

    return preprocess_params, {}, postprocess_params

   def      UpperCamelCase_  (					self							:			Any				,		lowerCAmelCase							:			List[str]				,		**lowerCAmelCase							:			Union[str, Any])				->				Dict[str, GenericTensor]:

    """simple docstring"""







    _snake_case    :  int					=					self.framework
    _snake_case    :  List[Any]					=					self.tokenizer(lowerCAmelCase				,		return_tensors=lowerCAmelCase				,		**lowerCAmelCase)
    return model_inputs

   def      UpperCamelCase_  (					self							:			Tuple				,		lowerCAmelCase							:			Tuple)				->				Optional[int]:

    """simple docstring"""







    _snake_case    :  List[Any]					=					self.model(**lowerCAmelCase)
    return model_outputs

   def      UpperCamelCase_  (					self							:			int				,		lowerCAmelCase							:			Any				,		lowerCAmelCase							:			List[Any]=False)				->				str:

    """simple docstring"""







    if return_tensors:
     return model_outputs[0]
    if self.framework == "pt":
     return model_outputs[0].tolist()
    elif self.framework == "tf":
     return model_outputs[0].numpy().tolist()


   def __call__(					self							:			List[Any]				,		*lowerCAmelCase							:			Dict				,		**lowerCAmelCase							:			str)				->				Dict:

    """simple docstring"""







    return super().__call__(*lowerCAmelCase				,		**lowerCAmelCase)





from math import ceil, sqrt



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int = 1_000_000				)  ->     int:
 _snake_case    :  Tuple					=					0

 for outer_width in range(3				,       (limit // 4) + 2				):
  if outer_width**2 > limit:
   _snake_case    :  int					=					max(ceil(sqrt(outer_width**2 - limit				)				)				,       1				)
  else:
   _snake_case    :  Any					=					1
  if (outer_width - hole_width_lower_bound) % 2:
   hole_width_lower_bound += 1

  answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

 return answer


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