code: 
  
  
  import torch
  import trajectory.utils as utils
  
  from transformers import TrajectoryTransformerModel
  
  
  
  class   __lowerCamelCase (utils.Parser	):
         _lowercase       							=    "halfcheetah-medium-expert-v2"
         _lowercase       							=    "config.offline"
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase   )				->   Optional[Any]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = utils.load_model(_lowercase				,     _lowercase				,     _lowercase				,     epoch=_lowercase				,     device=_lowercase   )
       __UpperCamelCase   = TrajectoryTransformerModel(gpt.config   )
  
       trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()   )
       __UpperCamelCase   = 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__":
      __snake_case             =	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: 
  
  
  from math import cos, sin
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = float(_lowercase   )
       return [[scaling_factor * int(x == y   ) for x in range(2   )] for y in range(2   )]
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
       return [[c, -s], [s, c]]
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
       __UpperCamelCase   = c * s
       return [[c * c, cs], [cs, s * s]]
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
       __UpperCamelCase   = c * s
       return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]
  
  
  print(f"""    {scaling(5) = }""")
  print(f"""  {rotation(4_5) = }""")
  print(f"""{projection(4_5) = }""")
  
  
  print(f"""{reflection(4_5) = }""")
  
  
  