
import warnings

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




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	['''image_processor''', '''tokenizer''']
     SCREAMING_SNAKE_CASE_		 =	'''ChineseCLIPImageProcessor'''
     SCREAMING_SNAKE_CASE_		 =	('''BertTokenizer''', '''BertTokenizerFast''')






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE=None    ,    **__SCREAMING_SNAKE_CASE	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					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    ,    )
                 UpperCamelCase__     : Union[str, Any]											=					kwargs.pop('''feature_extractor'''	)

           UpperCamelCase__     : List[str]											=					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	)
           UpperCamelCase__     : Optional[Any]											=					self.image_processor






     def __call__(				self    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE=None    ,    **__SCREAMING_SNAKE_CASE	)	-> 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:
                 UpperCamelCase__     : Any											=					self.tokenizer(__SCREAMING_SNAKE_CASE    ,    return_tensors=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)

           if images is not None:
                 UpperCamelCase__     : Tuple											=					self.image_processor(__SCREAMING_SNAKE_CASE    ,    return_tensors=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)

           if text is not None and images is not None:
                 UpperCamelCase__     : Any											=					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  __SCREAMING_SNAKE_CASE      (				self    ,    *__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







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






     def  __SCREAMING_SNAKE_CASE      (				self    ,    *__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







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






     @property
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					self.tokenizer.model_input_names
           UpperCamelCase__     : Tuple											=					self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)






     @property
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           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




def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__ = 2_0_0_0_0_0_0		):
      UpperCamelCase__     : Dict											=					[0 for i in range(n + 1		)]
      UpperCamelCase__     : Any											=					1
      UpperCamelCase__     : int											=					1

      for i in range(2			,		int(n**0.5		) + 1		):
            if primality_list[i] == 0:
                  for j in range(i * i			,		n + 1			,		UpperCamelCase__		):
                        UpperCamelCase__     : Any											=					1
      UpperCamelCase__     : Optional[Any]											=					0
      for i in range(UpperCamelCase__		):
            if primality_list[i] == 0:
                  sum_of_primes += i
      return sum_of_primes


if __name__ == "__main__":
    print(F'''{solution() = }''')


