code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import math
  from itertools import permutations
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """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(lowercase	) + 1	) ,					6	):
          if number % i == 0 or number % (i + 2) == 0:
              return False
      return True
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a	,   a					=0, len(lowercase	) - 1
      while left <= right:
          a					=(left + right) // 2
          if prime_list[middle] == target:
              return True
          elif prime_list[middle] < target:
              a					=middle + 1
          else:
              a					=middle - 1
  
      return False
  def _A		(		):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=[n for n in range(10_01 ,					1_00_00 ,					2	) if is_prime(lowercase	)]
      a					=[]
  
      for number in prime_list:
          a					=[]
  
          for prime_member in permutations(list(str(lowercase	)	)	):
              a					=int(''''''.join(lowercase	)	)
  
              if prime % 2 == 0:
                  continue
  
              if search(lowercase ,					lowercase	):
                  tmp_numbers.append(lowercase	)
  
          tmp_numbers.sort()
          if len(lowercase	) >= 3:
              candidates.append(lowercase	)
  
      a					=[]
      for candidate in candidates:
          a					=len(lowercase	)
          a					=False
  
          for i in range(lowercase	):
              for j in range(i + 1 ,					lowercase	):
                  for k in range(j + 1 ,					lowercase	):
                      if (
                          abs(candidate[i] - candidate[j]	)
                          == abs(candidate[j] - candidate[k]	)
                          and len({candidate[i], candidate[j], candidate[k]}	) == 3
                      ):
                          passed.append(
                              sorted([candidate[i], candidate[j], candidate[k]]	)	)
                          a					=True
  
                      if found:
                          break
                  if found:
                      break
              if found:
                  break
  
      a					=set()
      for seq in passed:
          answer.add(''''''.join([str(lowercase	) for i in seq]	)	)
  
      return max(int(lowercase	) for x in answer	)
  
  
  if __name__ == "__main__":
         print(solution())
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import copy
  
  from transformers.configuration_utils import PretrainedConfig
  from transformers.utils import logging
  
  
  lowerCamelCase_     :     int    		=						logging.get_logger(__name__)
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"hybrid-clip"
   __lowerCAmelCase				      =						True
  
  
   def __init__(	self				,					__A=512				,					**__A		)      ->  Any:
       super().__init__(**__A		)
  
       if "text_config" not in kwargs:
           raise ValueError('''`text_config` can not be `None`.'''		)
  
       if "vision_config" not in kwargs:
           raise ValueError('''`vision_config` can not be `None`.'''		)
  
       a					=kwargs.pop('''text_config'''		)
       a					=kwargs.pop('''vision_config'''		)
  
       a					=text_config.pop('''model_type'''		)
       a					=vision_config.pop('''model_type'''		)
  
       from transformers import AutoConfig
  
       a					=AutoConfig.for_model(__A				,					**__A		)
  
       if vision_model_type == "clip":
           a					=AutoConfig.for_model(__A				,					**__A		).vision_config
       elif vision_model_type == "clip_vision_model":
           from transformers import CLIPVisionConfig
  
           a					=CLIPVisionConfig(**__A		)
       else:
           a					=AutoConfig.for_model(__A				,					**__A		)
  
       a					=projection_dim
       a					=1.0
  
  
   @classmethod
   def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A				,					**__A		)      ->  Tuple:
       return cls(text_config=text_config.to_dict()				,					vision_config=vision_config.to_dict()				,					**__A		)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
       a					=copy.deepcopy(self.__dict__		)
       a					=self.text_config.to_dict()
       a					=self.vision_config.to_dict()
       a					=self.__class__.model_type
       return output