code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  
  from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      # Initialize PyTorch model
      a					=CanineConfig()
      a					=CanineModel(lowercase	)
      model.eval()
  
      print(f'''Building PyTorch model from configuration: {config}'''	)
  
      # Load weights from tf checkpoint
      load_tf_weights_in_canine(lowercase ,					lowercase ,					lowercase	)
  
      # Save pytorch-model (weights and configuration)
      print(f'''Save PyTorch model to {pytorch_dump_path}'''	)
      model.save_pretrained(lowercase	)
  
      # Save tokenizer files
      a					=CanineTokenizer()
      print(f'''Save tokenizer files to {pytorch_dump_path}'''	)
      tokenizer.save_pretrained(lowercase	)
  
  
  if __name__ == "__main__":
         lowerCamelCase_     :     Optional[Any]    		=						argparse.ArgumentParser()
         # Required parameters
         parser.add_argument(
             """--tf_checkpoint_path""",
             default=None,
             type=str,
             required=True,
             help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
         )
         parser.add_argument(
             """--pytorch_dump_path""",
             default=None,
             type=str,
             required=True,
             help="""Path to a folder where the PyTorch model will be placed.""",
         )
         lowerCamelCase_     :     str    		=						parser.parse_args()
         convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
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