

"""simple docstring"""




import warnings

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


class 		_A							(      __a       ):
      __a             =						['image_processor', 'tokenizer']
      __a             =						'AutoImageProcessor'
      __a             =						'AutoTokenizer'





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__=None     ,    SCREAMING_SNAKE_CASE__=None     ,    **SCREAMING_SNAKE_CASE__		) ->       Optional[Any]:
         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 have to specify an image_processor."		)
         if tokenizer is None:
            raise ValueError("You have 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__		) ->       Union[str, 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:
            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__		) ->       str:
         return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self     ,    *SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__		) ->       List[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		) ->       List[str]:
         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





      @property
      def        _lowerCamelCase	(	self		) ->       str:
         warnings.warn(
             "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,    SCREAMING_SNAKE_CASE__     ,    )
         return self.image_processor







"""simple docstring"""




from __future__ import annotations

import math
from itertools import permutations



def 						UpperCAmelCase__ (					A__ )   ->   bool:




   """simple docstring"""

   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5						,     int(math.sqrt(A__ ) + 1 )						,     6 ):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True



def 						UpperCAmelCase__ (					A__ = 7 )   ->   int:




   """simple docstring"""
   lowerCamelCase__						       =      "".join(str(A__ ) for i in range(1						,     n + 1 ) )
   lowerCamelCase__						       =      [int("".join(A__ ) ) for i in permutations(A__						,     A__ )]
   lowerCamelCase__						       =      [num for num in perm_list if is_prime(A__ )]
   return max(A__ ) if pandigitals else 0


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




