code: 
  
  
  
  import copy
  
  from transformers.configuration_utils import PretrainedConfig
  from transformers.utils import logging
  
  
  __UpperCAmelCase      = logging.get_logger(__name__)
  
  
  
  class        lowerCAmelCase_	(						lowercase__							):
         UpperCAmelCase__       :      int       =						"""hybrid-clip"""
         UpperCAmelCase__       :      Dict       =						True
         def __init__(    self,    SCREAMING_SNAKE_CASE_=512,    **SCREAMING_SNAKE_CASE_ )  ->	Union[str, 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`.' )
  
            UpperCamelCase					:						Optional[int]					=			kwargs.pop('text_config' )
            UpperCamelCase					:						Tuple					=			kwargs.pop('vision_config' )
  
            UpperCamelCase					:						Optional[Any]					=			text_config.pop('model_type' )
            UpperCamelCase					:						Optional[Any]					=			vision_config.pop('model_type' )
  
            from transformers import AutoConfig
  
            UpperCamelCase					:						Any					=			AutoConfig.for_model(_A,    **_A )
  
            if vision_model_type == "clip":
               UpperCamelCase					:						Union[str, Any]					=			AutoConfig.for_model(_A,    **_A ).vision_config
            elif vision_model_type == "clip_vision_model":
               from transformers import CLIPVisionConfig
  
               UpperCamelCase					:						str					=			CLIPVisionConfig(**_A )
            else:
               UpperCamelCase					:						Dict					=			AutoConfig.for_model(_A,    **_A )
  
            UpperCamelCase					:						Dict					=			projection_dim
            UpperCamelCase					:						int					=			1.0
         @classmethod
         def       snake_case_ (    cls,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_ )  ->	Dict:
            return cls(text_config=text_config.to_dict(),    vision_config=vision_config.to_dict(),    **_A )
  
  
  
  
  
  
         def       snake_case_ (    self )  ->	Optional[Any]:
            UpperCamelCase					:						Optional[int]					=			copy.deepcopy(self.__dict__ )
            UpperCamelCase					:						Optional[Any]					=			self.text_config.to_dict()
            UpperCamelCase					:						Union[str, Any]					=			self.vision_config.to_dict()
            UpperCamelCase					:						Tuple					=			self.__class__.model_type
            return output
  
  
  
  
style_context: 
  
  
  
  import json
  import logging
  import os
  import subprocess
  from argparse import ArgumentParser
  
  
  __UpperCAmelCase      = logging.getLogger(__name__)
  def 	UpperCamelCase			(     )			->  List[str]:
     UpperCamelCase					:						Union[str, Any]					=			ArgumentParser()
     UpperCamelCase			,  UpperCamelCase					:						Optional[int]					=			parser.parse_known_args()
     for arg in unknown:
        if arg.startswith(('-', '--')			):
           parser.add_argument(arg.split('='			)[0]			)
  
     return parser.parse_args()
  def 	UpperCamelCase			(     )			->  Optional[Any]:
     UpperCamelCase					:						int					=			parse_args()
     UpperCamelCase					:						Dict					=			8888
     UpperCamelCase					:						Union[str, Any]					=			int(os.environ['SM_NUM_GPUS']			)
     UpperCamelCase					:						Optional[Any]					=			json.loads(os.environ['SM_HOSTS']			)
     UpperCamelCase					:						Dict					=			len(snake_case__			)
     UpperCamelCase					:						int					=			os.environ['SM_CURRENT_HOST']
     UpperCamelCase					:						List[str]					=			hosts.index(snake_case__			)
     UpperCamelCase					:						Optional[Any]					=			'INFO'
  
     if num_nodes > 1:
        UpperCamelCase					:						List[Any]					=			F"""python -m torch.distributed.launch \
                  --nnodes={num_nodes}  \
                  --node_rank={rank}  \
                  --nproc_per_node={num_gpus}  \
                  --master_addr={hosts[0]}  \
                  --master_port={port} \
                  ./run_glue.py \
                  {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}"""
     else:
        UpperCamelCase					:						Any					=			F"""python -m torch.distributed.launch \
              --nproc_per_node={num_gpus}  \
              ./run_glue.py \
              {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}"""
     try:
        subprocess.run(snake_case__ ,  shell=snake_case__			)
     except Exception as e:
        logger.info(snake_case__			)
  
  
  if __name__ == "__main__":
      main()
  
  
  
  