




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class  lowerCamelCase     (		_lowerCamelCase    ):






			'''simple docstring'''





			UpperCamelCase__										=['''image_processor''', '''tokenizer''']
			UpperCamelCase__										='''AutoImageProcessor'''
			UpperCamelCase__										='''AutoTokenizer'''

			def __init__(			self      : Optional[int]							,    lowerCamelCase_      : int=None							,    lowerCamelCase_      : Union[str, Any]=None							,    **lowerCamelCase_      : List[Any]	)    -> str:
								__magic_name__				:						Any     				=				None
								if "feature_extractor" in kwargs:
													warnings.warn(
													    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
													    ''' instead.'''							,    lowerCamelCase_							,    )
													__magic_name__				:						Any     				=				kwargs.pop('''feature_extractor'''	)

								__magic_name__				:						Tuple     				=				image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
													raise ValueError('''You need to specify an `image_processor`.'''	)
								if tokenizer is None:
													raise ValueError('''You need to specify a `tokenizer`.'''	)

								super().__init__(lowerCamelCase_							,    lowerCamelCase_	)
								__magic_name__				:						Any     				=				self.image_processor
								__magic_name__				:						Optional[int]     				=				False

			def __call__(			self      : Union[str, Any]							,    *lowerCamelCase_      : List[str]							,    **lowerCamelCase_      : Dict	)    -> Union[str, Any]:
								# For backward compatibility
								if self._in_target_context_manager:
													return self.current_processor(*lowerCamelCase_							,    **lowerCamelCase_	)

								__magic_name__				:						Optional[Any]     				=				kwargs.pop('''images'''							,    lowerCamelCase_	)
								__magic_name__				:						Union[str, Any]     				=				kwargs.pop('''text'''							,    lowerCamelCase_	)
								if len(lowerCamelCase_	) > 0:
													__magic_name__				:						List[str]     				=				args[0]
													__magic_name__				:						Optional[int]     				=				args[1:]

								if images is None and text is None:
													raise ValueError('''You need to specify either an `images` or `text` input to process.'''	)

								if images is not None:
													__magic_name__				:						List[Any]     				=				self.image_processor(lowerCamelCase_							,    *lowerCamelCase_							,    **lowerCamelCase_	)
								if text is not None:
													__magic_name__				:						List[Any]     				=				self.tokenizer(lowerCamelCase_							,    **lowerCamelCase_	)

								if text is None:
													return inputs
								elif images is None:
													return encodings
								else:
													__magic_name__				:						Optional[int]     				=				encodings['''input_ids''']
													return inputs

			def 				UpperCAmelCase__							(			self      : Union[str, Any]							,    *lowerCamelCase_      : Tuple							,    **lowerCamelCase_      : int	)    -> int:
								return self.tokenizer.batch_decode(*lowerCamelCase_							,    **lowerCamelCase_	)

			def 				UpperCAmelCase__							(			self      : Optional[int]							,    *lowerCamelCase_      : List[Any]							,    **lowerCamelCase_      : int	)    -> Optional[Any]:
								return self.tokenizer.decode(*lowerCamelCase_							,    **lowerCamelCase_	)

			@contextmanager
			def 				UpperCAmelCase__							(			self      : Optional[int]	)    -> Any:
								warnings.warn(
								    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
								    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
								    '''your images inputs, or in a separate call.'''	)
								__magic_name__				:						Tuple     				=				True
								__magic_name__				:						List[Any]     				=				self.tokenizer
								yield
								__magic_name__				:						Union[str, Any]     				=				self.image_processor
								__magic_name__				:						int     				=				False

			@property
			def 				UpperCAmelCase__							(			self      : List[Any]	)    -> Optional[int]:
								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,    lowerCamelCase_							,    )
								return self.image_processor_class


			@property
			def 				UpperCAmelCase__							(			self      : Optional[int]	)    -> int:
								warnings.warn(
								    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,    lowerCamelCase_							,    )
								return self.image_processor









import math
from collections.abc import Generator







def 		lowercase__ (		__A:						int					):





					'''simple docstring'''






					__magic_name__				:						Generator     				=				(i for i in range(1				,(max_n + 1)					))
					for i in (n for n in numbers if n > 1):
										for j in range(2				,__A					):
															if (i % j) == 0:
																				break
										else:
															yield i







def 		lowercase__ (		__A:						int					):





					'''simple docstring'''






					__magic_name__				:						Generator     				=				(i for i in range(1				,(max_n + 1)					))
					for i in (n for n in numbers if n > 1):
										# only need to check for factors up to sqrt(i)
										__magic_name__				:						Tuple     				=				int(math.sqrt(__A					)					) + 1
										for j in range(2				,__A					):
															if (i % j) == 0:
																				break
										else:
															yield i







def 		lowercase__ (		__A:						int					):





					'''simple docstring'''






					__magic_name__				:						Generator     				=				(i for i in range(1				,(max_n + 1)				,2					))
					# It's useless to test even numbers as they will not be prime
					if max_n > 2:
										yield 2  # Because 2 will not be tested, it's necessary to yield it now
					for i in (n for n in numbers if n > 1):
										__magic_name__				:						str     				=				int(math.sqrt(__A					)					) + 1
										for j in range(3				,__A				,2					):
															# As we removed the even numbers, we don't need them now
															if (i % j) == 0:
																				break
										else:
															yield i







def 		lowercase__ (		):





					'''simple docstring'''






					from timeit import timeit

					__magic_name__				:						Dict     				=				'''from __main__ import slow_primes, primes, fast_primes'''
					print(timeit('''slow_primes(1_000_000_000_000)'''				,setup=__A				,number=1_0_0_0_0_0_0					)					)
					print(timeit('''primes(1_000_000_000_000)'''				,setup=__A				,number=1_0_0_0_0_0_0					)					)
					print(timeit('''fast_primes(1_000_000_000_000)'''				,setup=__A				,number=1_0_0_0_0_0_0					)					)


if __name__ == "__main__":
	__lowerCamelCase :    Union[str, Any]      						=      int(input('''Calculate primes up to:\n>> ''').strip())
	for ret in primes(number):
		print(ret)
	benchmark()



