



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case							:   Union[str, Any]      =    {'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case							:   str      =    ['ReformerTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case							:   List[Any]      =    ['ReformerTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case							:   str      =    [
	    'REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'ReformerAttention',
	    'ReformerForMaskedLM',
	    'ReformerForQuestionAnswering',
	    'ReformerForSequenceClassification',
	    'ReformerLayer',
	    'ReformerModel',
	    'ReformerModelWithLMHead',
	    'ReformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_reformer import ReformerTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_reformer_fast import ReformerTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_reformer import (
		    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ReformerAttention,
		    ReformerForMaskedLM,
		    ReformerForQuestionAnswering,
		    ReformerForSequenceClassification,
		    ReformerLayer,
		    ReformerModel,
		    ReformerModelWithLMHead,
		    ReformerPreTrainedModel,
		)

else:
	import sys

	__snake_case							:   Union[str, Any]      =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




"""simple docstring"""






def 			_lowercase				(  )						->   int:
			return 1


def 			_lowercase				(  __snake_case	)						->   int:
			return 0 if x < 0 else two_pence(x - 2	) + one_pence()


def 			_lowercase				(  __snake_case	)						->   int:
			return 0 if x < 0 else five_pence(x - 5	) + two_pence(__snake_case	)


def 			_lowercase				(  __snake_case	)						->   int:
			return 0 if x < 0 else ten_pence(x - 10	) + five_pence(__snake_case	)


def 			_lowercase				(  __snake_case	)						->   int:
			return 0 if x < 0 else twenty_pence(x - 20	) + ten_pence(__snake_case	)


def 			_lowercase				(  __snake_case	)						->   int:
			return 0 if x < 0 else fifty_pence(x - 50	) + twenty_pence(__snake_case	)


def 			_lowercase				(  __snake_case	)						->   int:
			return 0 if x < 0 else one_pound(x - 100	) + fifty_pence(__snake_case	)


def 			_lowercase				(  __snake_case	)						->   int:
			return 0 if x < 0 else two_pound(x - 200	) + one_pound(__snake_case	)


def 			_lowercase				(  __snake_case = 200	)						->   int:
			return two_pound(__snake_case	)


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