

"""simple docstring"""




import warnings

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


class 		_A       (			__a					):
      __a								=	['image_processor', 'tokenizer']
      __a								=	'ChineseCLIPImageProcessor'
      __a								=	('BertTokenizer', 'BertTokenizerFast')





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						**SCREAMING_SNAKE_CASE__		)   ->					Dict:
         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





      def __call__(  self    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						**SCREAMING_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:
            lowerCamelCase__						=			self.tokenizer(SCREAMING_SNAKE_CASE__    ,						return_tensors=SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)

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

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





      def 					_lowerCamelCase  (  self    ,						*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					Union[str, Any]:
         return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					str:
         return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)





      @property
      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__						=			self.tokenizer.model_input_names
         lowerCamelCase__						=			self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)





      @property
      def 					_lowerCamelCase  (  self		)   ->					Optional[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







"""simple docstring"""




def 	UpperCAmelCase__   (				A__   )      ->      list[int]:




   """simple docstring"""

   if num <= 0:
      raise ValueError("Input must be a positive integer"   )

   lowerCamelCase__						=			[True] * (num + 1)

   lowerCamelCase__						=			2
   while p * p <= num:
      if primes[p]:
         for i in range(p * p	,    num + 1	,    A__   ):
            lowerCamelCase__						=			False
      p += 1

   return [prime for prime in range(2	,    num + 1   ) if primes[prime]]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			int(input('''Enter a positive integer: ''').strip())
     print(prime_sieve_eratosthenes(user_num))




