
from __future__ import annotations

from collections.abc import Generator







def     A__   (  )					->		Generator[int, None, None]:
				UpperCamelCase_:							dict[int, int]		=  {}
				UpperCamelCase_:							int		=  2
				while True:
								UpperCamelCase_:							int		=  factor_map.pop(lowerCamelCase       ,      lowerCamelCase  )
								if factor:
												UpperCamelCase_:							Optional[Any]		=  factor + prime
												while x in factor_map:
																x += factor
												UpperCamelCase_:							List[str]		=  factor
								else:
												UpperCamelCase_:							Dict		=  prime
												yield prime
								prime += 1







def     A__   (  lowerCamelCase = 1E1_0  )					->		int:
				UpperCamelCase_:							Union[str, Any]		=  sieve()

				UpperCamelCase_:							List[Any]		=  1
				while True:
								UpperCamelCase_:							Tuple		=  next(lowerCamelCase  )
								if (2 * prime * n) > limit:
												return n
								# Ignore the next prime as the reminder will be 2.
								next(lowerCamelCase  )
								n += 2


if __name__ == "__main__":
							print(solution())








from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Union[str, Any]    =	["""image_processor""", """tokenizer"""]
			__UpperCamelCase :  List[Any]    =	"""AutoImageProcessor"""
			__UpperCamelCase :  Tuple    =	"""AutoTokenizer"""

			def __init__(    self	:    Any  ,		snake_case_	:    Optional[Any]  ,		snake_case_	:    Any ):
							super().__init__(snake_case_  ,		snake_case_ )
							UpperCamelCase_:							int		=  self.image_processor

			def __call__(    self	:    str  ,		snake_case_	:    Optional[int]=None  ,		snake_case_	:    int=None  ,		snake_case_	:    Dict=None  ,		**snake_case_	:    Optional[int] ):

							if text is None and images is None:
											raise ValueError("""You have to specify either text or images. Both cannot be none.""" )

							if text is not None:
											UpperCamelCase_:							List[Any]		=  self.tokenizer(snake_case_  ,		return_tensors=snake_case_  ,		**snake_case_ )

							if images is not None:
											UpperCamelCase_:							Any		=  self.image_processor(snake_case_  ,		return_tensors=snake_case_  ,		**snake_case_ )

							if text is not None and images is not None:
											UpperCamelCase_:							Tuple		=  image_features.pixel_values
											return encoding
							elif text is not None:
											return encoding
							else:
											return BatchEncoding(data=dict(**snake_case_ )  ,		tensor_type=snake_case_ )

			def  lowerCAmelCase__		(    self	:    List[str]  ,		*snake_case_	:    int  ,		**snake_case_	:    Optional[Any] ):
							return self.tokenizer.batch_decode(*snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    int  ,		*snake_case_	:    Optional[Any]  ,		**snake_case_	:    str ):
							return self.tokenizer.decode(*snake_case_  ,		**snake_case_ )




			@property
			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							return ["input_ids", "attention_mask", "pixel_values"]






