

'''simple docstring'''



from __future__ import annotations







class 		lowerCAmelCase__     :



 """simple docstring"""



 def __init__(	self      : int      ,							__SCREAMING_SNAKE_CASE      : int = 0    )						->				None:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   value
      __SCREAMING_SNAKE_CASE						     =   None
      __SCREAMING_SNAKE_CASE						     =   None







def      a__ (						a__   ,					a__		):




     """simple docstring"""


     if treea is None:
          return treea
     if treea is None:
          return treea

     __SCREAMING_SNAKE_CASE						     =   treea.value + treea.value
     __SCREAMING_SNAKE_CASE						     =   merge_two_binary_trees(treea.left   ,					treea.left		)
     __SCREAMING_SNAKE_CASE						     =   merge_two_binary_trees(treea.right   ,					treea.right		)
     return treea







def      a__ (						a__		):




     """simple docstring"""


     if root:
          print(root.value		)
          print_preorder(root.left		)
          print_preorder(root.right		)


if __name__ == "__main__":
  UpperCAmelCase				: Dict         =  Node(1)
  UpperCAmelCase				: List[Any]         =  Node(2)
  UpperCAmelCase				: List[Any]         =  Node(3)
  UpperCAmelCase				: Optional[int]         =  Node(4)

  UpperCAmelCase				: Optional[int]         =  Node(2)
  UpperCAmelCase				: str         =  Node(4)
  UpperCAmelCase				: Any         =  Node(6)
  UpperCAmelCase				: Tuple         =  Node(9)
  UpperCAmelCase				: int         =  Node(5)

  print('Tree1 is: ')
  print_preorder(treea)
  print('Tree2 is: ')
  print_preorder(treea)
  UpperCAmelCase				: Optional[Any]         =  merge_two_binary_trees(treea, treea)
  print('Merged Tree is: ')
  print_preorder(merged_tree)









'''simple docstring'''



import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel







class 		lowerCAmelCase__     (   utils.Parser      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "halfcheetah-medium-expert-v2"
 lowerCAmelCase__						      =   "config.offline"







def      a__ (						a__   ,					a__   ,					a__   ,					a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   utils.load_model(a__   ,					a__   ,					a__   ,					epoch=a__   ,					device=a__		)
     __SCREAMING_SNAKE_CASE						     =   TrajectoryTransformerModel(gpt.config		)

     trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()		)
     __SCREAMING_SNAKE_CASE						     =   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__":
  UpperCAmelCase				: Dict         =  Parser().parse_args('plan')
  convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
      args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
  )






