


"""simple docstring"""
def 		lowercase							(     )     ->int:
	"""simple docstring"""





	return 1






def 		lowercase							(     _snake_case						:      int     )     ->int:
	"""simple docstring"""





	return 0 if x < 0 else two_pence(x - 2     ) + one_pence()






def 		lowercase							(     _snake_case						:      int     )     ->int:
	"""simple docstring"""





	return 0 if x < 0 else five_pence(x - 5     ) + two_pence(_snake_case     )






def 		lowercase							(     _snake_case						:      int     )     ->int:
	"""simple docstring"""





	return 0 if x < 0 else ten_pence(x - 10     ) + five_pence(_snake_case     )






def 		lowercase							(     _snake_case						:      int     )     ->int:
	"""simple docstring"""





	return 0 if x < 0 else twenty_pence(x - 20     ) + ten_pence(_snake_case     )






def 		lowercase							(     _snake_case						:      int     )     ->int:
	"""simple docstring"""





	return 0 if x < 0 else fifty_pence(x - 50     ) + twenty_pence(_snake_case     )






def 		lowercase							(     _snake_case						:      int     )     ->int:
	"""simple docstring"""





	return 0 if x < 0 else one_pound(x - 100     ) + fifty_pence(_snake_case     )






def 		lowercase							(     _snake_case						:      int     )     ->int:
	"""simple docstring"""





	return 0 if x < 0 else two_pound(x - 200     ) + one_pound(_snake_case     )






def 		lowercase							(     _snake_case						:      int = 200     )     ->int:
	"""simple docstring"""





	return two_pound(_snake_case     )


if __name__ == "__main__":
			print(solution(int(input().strip())))









import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Optional[int]     		=		"M-CLIP"






   def __init__(		self						,				__A=1024						,				__A=768						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					str    =				transformerDimSize
     lowerCamelCase :					Any    =				imageDimSize
     super().__init__(**__A )



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Tuple     		=		MCLIPConfig






   def __init__(		self						,				__A						,				*__A						,				**__A ):






     """simple docstring"""





     super().__init__(__A						,				*__A						,				**__A )
     lowerCamelCase :					Tuple    =				XLMRobertaModel(__A )
     lowerCamelCase :					Optional[Any]    =				torch.nn.Linear(
         in_features=config.transformerDimensions						,				out_features=config.numDims )






   def _snake_case    (		self						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Any    =				self.transformer(input_ids=__A						,				attention_mask=__A )[0]
     lowerCamelCase :					int    =				(embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
     return self.LinearTransformation(__A ), embs



