code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import re
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      def docstring_decorator(lowercase	):
          a					=fn.__doc__
          a					=func_doc.split('''\n'''	)
          a					=0
          while i < len(lowercase	) and re.search(R'''^\s*Examples?:\s*$''' ,					lines[i]	) is None:
              i += 1
          if i < len(lowercase	):
              a					=example_docstring
              a					="\n".join(lowercase	)
          else:
              raise ValueError(
                  f'''The function {fn} should have an empty \'Examples:\' in its docstring as placeholder, '''
                  f'''current docstring is:\n{func_doc}'''	)
          a					=func_doc
          return fn
  
      return docstring_decorator
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