





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase 							=       {
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase 							=       [
					    """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TapasForMaskedLM""",
					    """TapasForQuestionAnswering""",
					    """TapasForSequenceClassification""",
					    """TapasModel""",
					    """TapasPreTrainedModel""",
					    """load_tf_weights_in_tapas""",
					]
try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase 							=       [
					    """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFTapasForMaskedLM""",
					    """TFTapasForQuestionAnswering""",
					    """TFTapasForSequenceClassification""",
					    """TFTapasModel""",
					    """TFTapasPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
					from .tokenization_tapas import TapasTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tapas import (
										    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TapasForMaskedLM,
										    TapasForQuestionAnswering,
										    TapasForSequenceClassification,
										    TapasModel,
										    TapasPreTrainedModel,
										    load_tf_weights_in_tapas,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_tapas import (
										    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFTapasForMaskedLM,
										    TFTapasForQuestionAnswering,
										    TFTapasForSequenceClassification,
										    TFTapasModel,
										    TFTapasPreTrainedModel,
										)


else:
					import sys

					_lowercase 							=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










def   lowerCamelCase__       (       )     ->	int:



					'''simple docstring'''




					return 1





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




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





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




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





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




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





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




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





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




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





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




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





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




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





def   lowerCamelCase__       (       UpperCamelCase__     :    int = 200  )     ->	int:



					'''simple docstring'''




					return two_pound(UpperCamelCase__  )


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



