
import math


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      UpperCamelCase__     : Dict											=					[]
      UpperCamelCase__     : Tuple											=					2
      UpperCamelCase__     : Any											=					int(math.sqrt(_UpperCamelCase		)		)  # Size of every segment
      UpperCamelCase__     : str											=					[True] * (end + 1)
      UpperCamelCase__     : Dict											=					[]

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

      UpperCamelCase__     : Optional[int]											=					end + 1
      UpperCamelCase__     : Optional[Any]											=					min(2 * end			,		_UpperCamelCase		)

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

                  for j in range(_UpperCamelCase			,		high + 1			,		_UpperCamelCase		):
                        UpperCamelCase__     : Tuple											=					False

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

            UpperCamelCase__     : List[str]											=					high + 1
            UpperCamelCase__     : Optional[int]											=					min(high + end			,		_UpperCamelCase		)

      return prime
print(sieve(1_0**6))





import warnings

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




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    =["image_processor", "tokenizer"]
    __UpperCamelCase    ="CLIPImageProcessor"
    __UpperCamelCase    =("CLIPTokenizer", "CLIPTokenizerFast")

    def __init__(      self			:   int				,     snake_case__			:   List[Any]=None				,     snake_case__			:   Any=None				,     **snake_case__			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      None
     if "feature_extractor" in kwargs:
      warnings.warn(
          'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
          ' instead.'				,     snake_case__				,     )
      SCREAMING_SNAKE_CASE		    =      kwargs.pop('feature_extractor'     )

     SCREAMING_SNAKE_CASE		    =      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__(snake_case__				,     snake_case__     )

    def __call__(      self			:   List[Any]				,     snake_case__			:   Tuple=None				,     snake_case__			:   Optional[int]=None				,     snake_case__			:   List[str]=None				,     **snake_case__			:   Dict     ):

     """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(snake_case__				,     return_tensors=snake_case__				,     **snake_case__     )

     if images is not None:
      SCREAMING_SNAKE_CASE		    =      self.image_processor(snake_case__				,     return_tensors=snake_case__				,     **snake_case__     )

     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(**snake_case__     )				,     tensor_type=snake_case__     )

    def UpperCamelCase      (      self			:   Union[str, Any]				,     *snake_case__			:   Optional[Any]				,     **snake_case__			:   Optional[int]     ):

     """simple docstring"""






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

    def UpperCamelCase      (      self			:   Dict				,     *snake_case__			:   Optional[Any]				,     **snake_case__			:   Dict     ):

     """simple docstring"""






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

    @property
    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.tokenizer.model_input_names
     SCREAMING_SNAKE_CASE		    =      self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )

    @property
    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     warnings.warn(
         '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,     snake_case__				,     )
     return self.image_processor_class




    @property
    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     warnings.warn(
         '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,     snake_case__				,     )
     return self.image_processor

