


import warnings

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



class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  str						      =	["image_processor", "tokenizer"]
     UpperCAmelCase__ :  int						      =	"ChineseCLIPImageProcessor"
     UpperCAmelCase__ :  int						      =	("BertTokenizer", "BertTokenizerFast")





     def __init__(    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				List[str]=None    ,	SCREAMING_SNAKE_CASE_					:				Dict=None    ,	**SCREAMING_SNAKE_CASE_					:				Optional[int]							):
        __lowerCamelCase:				Optional[int]     =	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:				List[Any]     =	kwargs.pop("""feature_extractor"""							)

        __lowerCamelCase:				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_							)
        __lowerCamelCase:				Tuple     =	self.image_processor





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

        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:				Any     =	self.tokenizer(SCREAMING_SNAKE_CASE_    ,	return_tensors=SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)

        if images is not None:
           __lowerCamelCase:				int     =	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:				Dict     =	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					:				int    ,	*SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	**SCREAMING_SNAKE_CASE_					:				Tuple							):
        return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]    ,	*SCREAMING_SNAKE_CASE_					:				List[str]    ,	**SCREAMING_SNAKE_CASE_					:				str							):
        return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Dict     =	self.tokenizer.model_input_names
        __lowerCamelCase:				List[Any]     =	self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)







     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, 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






def     __lowerCAmelCase						(							snake_case	:    int = 1000000 )       ->							int:
   __lowerCamelCase:				Union[str, Any]     =	set(range(3						,      snake_case						,      2 ) )
   primes.add(2 )
   for p in range(3						,      snake_case						,      2 ):
      if p not in primes:
         continue
      primes.difference_update(set(range(p * p						,      snake_case						,      snake_case ) ) )

   __lowerCamelCase:				Any     =	[float(snake_case ) for n in range(limit + 1 )]

   for p in primes:
      for n in range(snake_case						,      limit + 1						,      snake_case ):
         phi[n] *= 1 - 1 / p

   return int(sum(phi[2:] ) )


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


