"""simple docstring"""






def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase = 1_000_000       )							->   int:
				SCREAMING_SNAKE_CASE__		  =				set(range(3  ,					__UpperCAmelCase  ,					2       )       )
				primes.add(2       )
				for p in range(3  ,					__UpperCAmelCase  ,					2       ):
								if p not in primes:
												continue
								primes.difference_update(set(range(p * p  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )       )       )

				SCREAMING_SNAKE_CASE__		  =				[float(__UpperCAmelCase       ) for n in range(limit + 1       )]

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

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


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





"""simple docstring"""






from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				List[str]   , _snake_case			:				Callable   , _snake_case			:				Optional[Features] = None   , _snake_case			:				str = None   , _snake_case			:				bool = False   , _snake_case			:				bool = False   , _snake_case			:				Optional[dict] = None   , _snake_case			:				Optional[int] = None   , **_snake_case			:				Dict   , )							->       Any:
									super().__init__(
									    features=_snake_case   , cache_dir=_snake_case   , keep_in_memory=_snake_case   , streaming=_snake_case   , num_proc=_snake_case   , **_snake_case   , )
									SCREAMING_SNAKE_CASE__		  =				Generator(
									    cache_dir=_snake_case   , features=_snake_case   , generator=_snake_case   , gen_kwargs=_snake_case   , **_snake_case   , )



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Optional[Any]:
									# Build iterable dataset
									if self.streaming:
													SCREAMING_SNAKE_CASE__		  =				self.builder.as_streaming_dataset(split="train"		)
									# Build regular (map-style) dataset
									else:
													SCREAMING_SNAKE_CASE__		  =				None
													SCREAMING_SNAKE_CASE__		  =				None
													SCREAMING_SNAKE_CASE__		  =				None
													SCREAMING_SNAKE_CASE__		  =				None

													self.builder.download_and_prepare(
													    download_config=_snake_case   , download_mode=_snake_case   , verification_mode=_snake_case   , base_path=_snake_case   , num_proc=self.num_proc   , )
													SCREAMING_SNAKE_CASE__		  =				self.builder.as_dataset(
													    split="train"   , verification_mode=_snake_case   , in_memory=self.keep_in_memory		)
									return dataset




