
'''simple docstring'''

import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel


class 					a_					(utils.Parser      ):
       __lowerCAmelCase      :			str      = "halfcheetah-medium-expert-v2"
       __lowerCAmelCase      :			str      = "config.offline"



def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[int]					, _lowerCamelCase  :    int					, _lowerCamelCase  :    Optional[Any]					, _lowerCamelCase  :    int					, _lowerCamelCase  :    Dict						)      ->  Tuple:
  _lowerCAmelCase			,   _lowerCAmelCase    :					List[Any]							=   utils.load_model(_lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, epoch=_lowerCamelCase					, device=_lowerCamelCase						)
  _lowerCAmelCase    :					List[str]							=   TrajectoryTransformerModel(gpt.config						)

  trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()						)
  _lowerCAmelCase    :					Optional[Any]							=   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_            =    Parser().parse_args("""plan""")
     convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
         args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
     )




'''simple docstring'''

from __future__ import annotations


class 					a_					:




       def __init__(       self				,				snake_case_ = 0					):
         _lowerCAmelCase    :					Optional[Any]							=   value
         _lowerCAmelCase    :					Node | None							=   None
         _lowerCAmelCase    :					Node | None							=   None



def 		_UpperCAmelCase			(					_lowerCamelCase  :    Node | None					, _lowerCamelCase  :    Node | None						)      ->  Node | None:
  if treea is None:
    return treea
  if treea is None:
    return treea

  _lowerCAmelCase    :					Any							=   treea.value + treea.value
  _lowerCAmelCase    :					Tuple							=   merge_two_binary_trees(treea.left					, treea.left						)
  _lowerCAmelCase    :					List[str]							=   merge_two_binary_trees(treea.right					, treea.right						)
  return treea


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Node | None						)      ->  None:
  if root:
    print(root.value						)
    print_preorder(root.left						)
    print_preorder(root.right						)


if __name__ == "__main__":
     UpperCamelCase_            =    Node(1)
     UpperCamelCase_            =    Node(2)
     UpperCamelCase_            =    Node(3)
     UpperCamelCase_            =    Node(4)

     UpperCamelCase_            =    Node(2)
     UpperCamelCase_            =    Node(4)
     UpperCamelCase_            =    Node(6)
     UpperCamelCase_            =    Node(9)
     UpperCamelCase_            =    Node(5)

     print("""Tree1 is: """)
     print_preorder(treea)
     print("""Tree2 is: """)
     print_preorder(treea)
     UpperCamelCase_            =    merge_two_binary_trees(treea, treea)
     print("""Merged Tree is: """)
     print_preorder(merged_tree)


