

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):
						_UpperCamelCase:Union[str, Any]	=		["image_processor", "tokenizer"]
						_UpperCamelCase:Optional[int]	=		"AutoImageProcessor"
						_UpperCamelCase:Optional[int]	=		"AutoTokenizer"







						def __init__(			self     ,						_SCREAMING_SNAKE_CASE=None     ,						_SCREAMING_SNAKE_CASE=None     ,						**_SCREAMING_SNAKE_CASE							)->							int:
													lowerCamelCase_								=None
													if "feature_extractor" in kwargs:
																				warnings.warn(
																				    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																				    """ instead."""     ,						_SCREAMING_SNAKE_CASE     ,						)
																				lowerCamelCase_								=kwargs.pop("""feature_extractor"""							)

													lowerCamelCase_								=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__(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=self.image_processor
													lowerCamelCase_								=False







						def __call__(			self     ,						*_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)->							int:
													# For backward compatibility
													if self._in_target_context_manager:
																				return self.current_processor(*_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=kwargs.pop("""images"""     ,						_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=kwargs.pop("""text"""     ,						_SCREAMING_SNAKE_CASE							)
													if len(_SCREAMING_SNAKE_CASE							) > 0:
																				lowerCamelCase_								=args[0]
																				lowerCamelCase_								=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:
																				lowerCamelCase_								=self.image_processor(_SCREAMING_SNAKE_CASE     ,						*_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)
													if text is not None:
																				lowerCamelCase_								=self.tokenizer(_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)

													if text is None:
																				return inputs
													elif images is None:
																				return encodings
													else:
																				lowerCamelCase_								=encodings["""input_ids"""]
																				return inputs







						def        _snake_case				(			self     ,						*_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)->							Tuple:
													return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self     ,						*_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)->							Dict:
													return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)







						@contextmanager
						def        _snake_case				(			self							)->							Optional[int]:
													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."""							)
													lowerCamelCase_								=True
													lowerCamelCase_								=self.tokenizer
													yield
													lowerCamelCase_								=self.image_processor
													lowerCamelCase_								=False







						@property
						def        _snake_case				(			self							)->							Any:
													warnings.warn(
													    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,						_SCREAMING_SNAKE_CASE     ,						)
													return self.image_processor_class







						@property
						def        _snake_case				(			self							)->							List[Any]:
													warnings.warn(
													    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,						_SCREAMING_SNAKE_CASE     ,						)
													return self.image_processor



import math
from collections.abc import Generator




def        __UpperCamelCase     (      _A					:							int		)				->Generator[int, None, None]:



							"""simple docstring"""
							lowerCamelCase_								=(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        __UpperCamelCase     (      _A					:							int		)				->Generator[int, None, None]:



							"""simple docstring"""
							lowerCamelCase_								=(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)
														lowerCamelCase_								=int(math.sqrt(_A		)		) + 1
														for j in range(2      ,  _A		):
																					if (i % j) == 0:
																												break
														else:
																					yield i




def        __UpperCamelCase     (      _A					:							int		)				->Generator[int, None, None]:



							"""simple docstring"""
							lowerCamelCase_								=(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):
														lowerCamelCase_								=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        __UpperCamelCase     (      )				->Dict:



							"""simple docstring"""
							from timeit import timeit

							lowerCamelCase_								="""from __main__ import slow_primes, primes, fast_primes"""
							print(timeit("""slow_primes(1_000_000_000_000)"""      ,  setup=_A      ,  number=1000000		)		)
							print(timeit("""primes(1_000_000_000_000)"""      ,  setup=_A      ,  number=1000000		)		)
							print(timeit("""fast_primes(1_000_000_000_000)"""      ,  setup=_A      ,  number=1000000		)		)


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