code: 
  
  
  
  """simple docstring"""
  
  
  
  
  from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments
  
  def 		__snake_case		(   )						->     Tuple:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Any              =					HfArgumentParser(SCREAMING_SNAKE_CASE__       )
       try:
            _UpperCAmelCase    :			List[str]              =					parser.parse_args_into_dataclasses()[0]
       except ValueError as e:
            _UpperCAmelCase    :			List[str]              =					"Arg --no_{0} is no longer used, please use --no-{0} instead."
            _UpperCAmelCase    :			str              =					" ".join(str(SCREAMING_SNAKE_CASE__       ).split(" "       )[:-1]       )
            _UpperCAmelCase    :			List[Any]              =					""
            _UpperCAmelCase    :			Any              =					eval(str(SCREAMING_SNAKE_CASE__       ).split(" "       )[-1]       )
            _UpperCAmelCase    :			int              =					[]
            for arg in depreciated_args:
                 # arg[2:] removes '--'
                 if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
                      # arg[5:] removes '--no_'
                      full_error_msg += arg_error_msg.format(arg[5:]       )
                 else:
                      wrong_args.append(SCREAMING_SNAKE_CASE__       )
            if len(SCREAMING_SNAKE_CASE__       ) > 0:
                 _UpperCAmelCase    :			int              =					full_error_msg + begin_error_msg + str(SCREAMING_SNAKE_CASE__       )
            raise ValueError(SCREAMING_SNAKE_CASE__       )
  
       _UpperCAmelCase    :			Union[str, Any]              =					PyTorchBenchmark(args=SCREAMING_SNAKE_CASE__       )
       benchmark.run()
  
  
  if __name__ == "__main__":
   main()
  
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
   )
  