code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import torch
  import trajectory.utils as utils
  
  from transformers import TrajectoryTransformerModel
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       utils.Parser		):
        __SCREAMING_SNAKE_CASE		:     str				  =				"halfcheetah-medium-expert-v2"
        __SCREAMING_SNAKE_CASE		:     str				  =				"config.offline"
  
  
  
  
  
  
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]		,						SCREAMING_SNAKE_CASE__			:		Dict		,						SCREAMING_SNAKE_CASE__			:		Any		,						SCREAMING_SNAKE_CASE__			:		str		,						SCREAMING_SNAKE_CASE__			:		int       )						->     List[str]:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase       , _UpperCAmelCase    :			Dict              =					utils.load_model(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						epoch=SCREAMING_SNAKE_CASE__		,						device=SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			Tuple              =					TrajectoryTransformerModel(gpt.config       )
  
       trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()       )
       _UpperCAmelCase    :			Optional[int]              =					gpt.pos_emb
       trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()       )
       trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()       )
       trajectory_transformer.head.load_state_dict(gpt.head.state_dict()       )
  
       for i, block in enumerate(gpt.blocks       ):
            trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()       )
            trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()       )
            trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()       )
  
            trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()       )
            trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()       )
            trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()       )
            trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()       )
  
       torch.save(trajectory_transformer.state_dict()		,						"pytorch_model.bin"       )
  
  
  if __name__ == "__main__":
   _lowerCAmelCase       :      List[str]							=							Parser().parse_args("plan")
   convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
       args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
   )
  
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
   )
  