






def 	_UpperCamelCase   (    )  ->   int:

						"""simple docstring"""



						return 1







def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   int:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   int:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   int:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   int:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   int:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   int:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   int:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					int = 200		)  ->   int:

						"""simple docstring"""



						return two_pound(UpperCamelCase_		)


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













import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()







def 	_UpperCamelCase   (    UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					str    ,       UpperCamelCase_       :					Optional[Any]		)  ->   Any:

						"""simple docstring"""



						lowerCAmelCase__    						=						BertConfig.from_json_file(UpperCamelCase_		)
						print(F"Building PyTorch model from configuration: {config}"		)
						lowerCAmelCase__    						=						BertForPreTraining(UpperCamelCase_		)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)

						# Save pytorch-model
						print(F"Save PyTorch model to {pytorch_dump_path}"		)
						torch.save(model.state_dict()    ,       UpperCamelCase_		)


if __name__ == "__main__":
					__snake_case    :    Union[str, Any]         =     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--bert_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					__snake_case    :    List[str]         =     parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





