






"""simple docstring"""



def 						lowercase    (				__snake_case				: int = 1_0_0_0_0_0_0	):
		lowercase_ : Union[str, Any]							       =   set(range(3		,					__snake_case		,					2	)	)
		primes.add(2	)
		for p in range(3		,					__snake_case		,					2	):
				if p not in primes:
						continue
				primes.difference_update(set(range(p * p		,					__snake_case		,					__snake_case	)	)	)

		lowercase_ : Dict							       =   [float(__snake_case	) for n in range(limit + 1	)]

		for p in primes:
				for n in range(__snake_case		,					limit + 1		,					__snake_case	):
						phi[n] *= 1 - 1 / p

		return int(sum(phi[2:]	)	)


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











"""simple docstring"""



from typing import TYPE_CHECKING

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


__A		:       List[str]  				=		{
    '''configuration_mobilenet_v2''': [
        '''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV2Config''',
        '''MobileNetV2OnnxConfig''',
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Dict  				=		['''MobileNetV2FeatureExtractor''']
						__A		:       Optional[int]  				=		['''MobileNetV2ImageProcessor''']


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Optional[Any]  				=		[
						    '''MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MobileNetV2ForImageClassification''',
						    '''MobileNetV2ForSemanticSegmentation''',
						    '''MobileNetV2Model''',
						    '''MobileNetV2PreTrainedModel''',
						    '''load_tf_weights_in_mobilenet_v2''',
						]


if TYPE_CHECKING:
						from .configuration_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileNetVaConfig,
						    MobileNetVaOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
												from .image_processing_mobilenet_va import MobileNetVaImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilenet_va import (
												    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileNetVaForImageClassification,
												    MobileNetVaForSemanticSegmentation,
												    MobileNetVaModel,
												    MobileNetVaPreTrainedModel,
												    load_tf_weights_in_mobilenet_va,
												)


else:
						import sys

						__A		:       List[Any]  				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



