





import warnings

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





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    ['image_processor', 'tokenizer']
       __snake_case 			=    'CLIPImageProcessor'
       __snake_case 			=    ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')




       def __init__(      self    ,						__lowerCamelCase=None    ,						__lowerCamelCase=None    ,						**__lowerCamelCase							)			->     Any:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							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       :	Union[str, Any]						    =							kwargs.pop("feature_extractor"							)

            _SCREAMING_SNAKE_CASE       :	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__(__lowerCamelCase    ,						__lowerCamelCase							)




       def __call__(      self    ,						__lowerCamelCase=None    ,						__lowerCamelCase=None    ,						__lowerCamelCase=None    ,						**__lowerCamelCase							)			->     Any:

            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       :	List[str]						    =							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       :	str						    =							image_features.pixel_values
                 return encoding
            elif text is not None:
                 return encoding
            else:
                 return BatchEncoding(data=dict(**__lowerCamelCase							)    ,						tensor_type=__lowerCamelCase							)




       def     UpperCamelCase_			(      self    ,						*__lowerCamelCase    ,						**__lowerCamelCase							)			->     str:
            return self.tokenizer.batch_decode(*__lowerCamelCase    ,						**__lowerCamelCase							)




       def     UpperCamelCase_			(      self    ,						*__lowerCamelCase    ,						**__lowerCamelCase							)			->     List[Any]:
            return self.tokenizer.decode(*__lowerCamelCase    ,						**__lowerCamelCase							)







       @property
       def     UpperCamelCase_			(      self							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.tokenizer.model_input_names
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)






from __future__ import annotations

UpperCamelCase__      =[True] * 100_0001
UpperCamelCase__      =2
while i * i <= 100_0000:
       if seive[i]:
              for j in range(i * i, 100_0001, i):
                     UpperCamelCase__      =False
       i += 1






def     lowerCamelCase__			(__lowerCamelCase	):
     return seive[n]






def     lowerCamelCase__			(__lowerCamelCase	):
     return any(digit in "02468" for digit in str(__lowerCamelCase	)	)






def     lowerCamelCase__			(__lowerCamelCase = 1000000	):
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							[2]  # result already includes the number 2.
     for num in range(3,       limit + 1,       2	):
          if is_prime(__lowerCamelCase	) and not contains_an_even_digit(__lowerCamelCase	):
               _SCREAMING_SNAKE_CASE       :	List[Any]						    =							str(__lowerCamelCase	)
               _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							[int(str_num[j:] + str_num[:j]	) for j in range(len(__lowerCamelCase	)	)]
               if all(is_prime(__lowerCamelCase	) for i in list_nums	):
                    result.append(__lowerCamelCase	)
     return result






def     lowerCamelCase__			():
     return len(find_circular_primes()	)


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