



"""simple docstring"""
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				Tuple     						=					['image_processor', 'tokenizer']
  UpperCamelCase	:				Union[str, Any]     						=					'CLIPImageProcessor'
  UpperCamelCase	:				str     						=					('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					Union[str, Any]=None       ,		lowerCAmelCase :					Optional[int]=None       ,		**lowerCAmelCase :					List[str]		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=None
     if "feature_extractor" in kwargs:
        warnings.warn(
            """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
            """ instead."""       ,		lowerCAmelCase       ,		)
        SCREAMING_SNAKE_CASE_: Tuple     			=kwargs.pop("""feature_extractor"""		)

     SCREAMING_SNAKE_CASE_: Any     			=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__(lowerCAmelCase       ,		lowerCAmelCase		)



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


     '''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_: Optional[int]     			=self.tokenizer(lowerCAmelCase       ,		return_tensors=lowerCAmelCase       ,		**lowerCAmelCase		)

     if images is not None:
        SCREAMING_SNAKE_CASE_: str     			=self.image_processor(lowerCAmelCase       ,		return_tensors=lowerCAmelCase       ,		**lowerCAmelCase		)

     if text is not None and images is not None:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=image_features.pixel_values
        return encoding
     elif text is not None:
        return encoding
     else:
        return BatchEncoding(data=dict(**lowerCAmelCase		)       ,		tensor_type=lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					List[Any]       ,		*lowerCAmelCase :					Tuple       ,		**lowerCAmelCase :					Tuple		)     ->					List[Any]:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					Dict       ,		*lowerCAmelCase :					Dict       ,		**lowerCAmelCase :					List[Any]		)     ->					int:


     '''simple docstring'''




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





  @property
  def 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					str:


     '''simple docstring'''




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








"""simple docstring"""
def      __magic_name__				(							lowercase = 200_0000 ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=[0 for i in range(n + 1 )]
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=1
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=1

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


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



