code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import copy
  
  from transformers.configuration_utils import PretrainedConfig
  from transformers.utils import logging
  
  
  _lowerCAmelCase       :      Union[str, Any]							=							logging.get_logger(__name__)
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
        __SCREAMING_SNAKE_CASE		:     Any				  =				'hybrid-clip'
        __SCREAMING_SNAKE_CASE		:     List[Any]				  =				True
  
  
  
        def __init__(  self  :			int  ,			A  :			int=5_1_2  ,			**A  :			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    :			Tuple              =					kwargs.pop("text_config"		)
             _UpperCAmelCase    :			Optional[int]              =					kwargs.pop("vision_config"		)
  
             _UpperCAmelCase    :			List[str]              =					text_config.pop("model_type"		)
             _UpperCAmelCase    :			Union[str, Any]              =					vision_config.pop("model_type"		)
  
             from transformers import AutoConfig
  
             _UpperCAmelCase    :			str              =					AutoConfig.for_model(A  ,			**A		)
  
             if vision_model_type == "clip":
                  _UpperCAmelCase    :			List[str]              =					AutoConfig.for_model(A  ,			**A		).vision_config
             elif vision_model_type == "clip_vision_model":
                  from transformers import CLIPVisionConfig
  
                  _UpperCAmelCase    :			int              =					CLIPVisionConfig(**A		)
             else:
                  _UpperCAmelCase    :			Tuple              =					AutoConfig.for_model(A  ,			**A		)
  
             _UpperCAmelCase    :			Union[str, Any]              =					projection_dim
             _UpperCAmelCase    :			Dict              =					1.0
  
  
  
        @classmethod
        def 		snake_case_      (  cls  :			Dict  ,			A  :			PretrainedConfig  ,			A  :			PretrainedConfig  ,			**A  :			Dict		):
             return cls(text_config=text_config.to_dict()  ,			vision_config=vision_config.to_dict()  ,			**A		)
  
  
  
        def 		snake_case_      (  self  :			int		):
             _UpperCAmelCase    :			Any              =					copy.deepcopy(self.__dict__		)
             _UpperCAmelCase    :			List[str]              =					self.text_config.to_dict()
             _UpperCAmelCase    :			str              =					self.vision_config.to_dict()
             _UpperCAmelCase    :			Optional[Any]              =					self.__class__.model_type
             return output
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
  from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
  
  
  logging.set_verbosity_info()
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[Any]		,						SCREAMING_SNAKE_CASE__			:		Tuple		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Dict:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Any              =					{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
       _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase    :			str              =					MODELS[model_size]  # set model hyperparameters
       _UpperCAmelCase    :			Tuple              =					ImageGPTConfig(n_embd=SCREAMING_SNAKE_CASE__		,						n_layer=SCREAMING_SNAKE_CASE__		,						n_head=SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			int              =					ImageGPTForCausalLM(SCREAMING_SNAKE_CASE__       )
  
       # Load weights from numpy
       load_tf_weights_in_imagegpt(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
  
       # Save pytorch-model
       _UpperCAmelCase    :			Union[str, Any]              =					pytorch_dump_folder_path + "/" + WEIGHTS_NAME
       _UpperCAmelCase    :			int              =					pytorch_dump_folder_path + "/" + CONFIG_NAME
       print(f'Save PyTorch model to {pytorch_weights_dump_path}'       )
       torch.save(model.state_dict()		,						SCREAMING_SNAKE_CASE__       )
       print(f'Save configuration file to {pytorch_config_dump_path}'       )
       with open(SCREAMING_SNAKE_CASE__		,						"w"		,						encoding="utf-8"       ) as f:
            f.write(config.to_json_string()       )
  
  
  if __name__ == "__main__":
   _lowerCAmelCase       :      List[str]							=							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--imagegpt_checkpoint_path",
       default=None,
       type=str,
       required=True,
       help="Path to the TensorFlow checkpoint path.",
   )
   parser.add_argument(
       "--model_size",
       default=None,
       type=str,
       required=True,
       help="Size of the model (can be either 'small', 'medium' or 'large').",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   _lowerCAmelCase       :      Optional[int]							=							parser.parse_args()
   convert_imagegpt_checkpoint_to_pytorch(
       args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
   )
  