






import math





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	[]
	__lowercase   							=	2
	__lowercase   							=	int(math.sqrt(lowerCamelCase       )       )  # Size of every segment
	__lowercase   							=	[True] * (end + 1)
	__lowercase   							=	[]

	while start <= end:
		if temp[start] is True:
			in_prime.append(lowerCamelCase       )
			for i in range(start * start					,	end + 1					,	lowerCamelCase       ):
				__lowercase   							=	False
		start += 1
	prime += in_prime

	__lowercase   							=	end + 1
	__lowercase   							=	min(2 * end					,	lowerCamelCase       )

	while low <= n:
		__lowercase   							=	[True] * (high - low + 1)
		for each in in_prime:
			__lowercase   							=	math.floor(low / each       ) * each
			if t < low:
				t += each

			for j in range(lowerCamelCase					,	high + 1					,	lowerCamelCase       ):
				__lowercase   							=	False

		for j in range(len(lowerCamelCase       )       ):
			if temp[j] is True:
				prime.append(j + low       )

		__lowercase   							=	high + 1
		__lowercase   							=	min(high + end					,	lowerCamelCase       )

	return prime




print(sieve(10**6))








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




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       ["image_processor", "tokenizer"]
							lowerCamelCase_							     =       "AutoImageProcessor"
							lowerCamelCase_							     =       "AutoTokenizer"


							def __init__(				self     :Optional[int]				,					__A     :Optional[Any]				,					__A     :Dict		) -> Dict:



									"""simple docstring"""



									super().__init__(__A				,					__A		)
									SCREAMING_SNAKE_CASE__								=   self.image_processor


							def __call__(				self     :int				,					__A     :str=None				,					__A     :int=None				,					__A     :Union[str, Any]=None				,					**__A     :str		) -> Optional[Any]:



									"""simple docstring"""



									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:
											SCREAMING_SNAKE_CASE__								=   self.tokenizer(__A				,					return_tensors=__A				,					**__A		)

									if images is not None:
											SCREAMING_SNAKE_CASE__								=   self.image_processor(__A				,					return_tensors=__A				,					**__A		)

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


							def   _snake_case	(				self     :str				,					*__A     :List[str]				,					**__A     :List[str]		) -> List[Any]:



									"""simple docstring"""



									return self.tokenizer.batch_decode(*__A				,					**__A		)


							def   _snake_case	(				self     :List[str]				,					*__A     :Any				,					**__A     :Any		) -> Tuple:



									"""simple docstring"""



									return self.tokenizer.decode(*__A				,					**__A		)


							@property
							def   _snake_case	(				self     :Dict		) -> List[Any]:



									"""simple docstring"""



									return ["input_ids", "attention_mask", "pixel_values"]