


def        _UpperCamelCase							(      lowercase__ = 1000000      ):
						__SCREAMING_SNAKE_CASE						:     List[str]					=		set(range(3    ,       lowercase__    ,       2      )      )
						primes.add(2      )
						for p in range(3    ,       lowercase__    ,       2      ):
												if p not in primes:
																		continue
												primes.difference_update(set(range(p * p    ,       lowercase__    ,       lowercase__      )      )      )

						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[float(lowercase__      ) for n in range(limit + 1      )]

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

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


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




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
 from .camera import create_pan_cameras
 from .pipeline_shap_e import ShapEPipeline
 from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
 from .renderer import (
     BoundingBoxVolume,
     ImportanceRaySampler,
     MLPNeRFModelOutput,
     MLPNeRSTFModel,
     ShapEParamsProjModel,
     ShapERenderer,
     StratifiedRaySampler,
     VoidNeRFModel,
 )


