"""simple docstring"""


class        __UpperCAmelCase						:

				def __init__(				self     ,			_lowerCamelCase				):
										lowerCamelCase__ =data
										lowerCamelCase__ =None
										lowerCamelCase__ =None





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> BinaryTreeNode | None:




						'''simple docstring'''



						if node is None:
												lowerCamelCase__ =BinaryTreeNode(__lowerCAmelCase    )
												return node

						# binary search tree is not empty,
						# so we will insert it into the tree
						# if new_value is less than value of data in node,
						#  add it to left subtree and proceed recursively
						if new_value < node.data:
												lowerCamelCase__ =insert(node.left_child       ,   __lowerCAmelCase    )
						else:
												# if new_value is greater than value of data in node,
												#  add it to right subtree and proceed recursively
												lowerCamelCase__ =insert(node.right_child       ,   __lowerCAmelCase    )
						return node





def       lowerCamelCase_			( __lowerCAmelCase    )							-> list[int]:  # if node is None,return




						'''simple docstring'''



						if node:
												lowerCamelCase__ =inorder(node.left_child    )
												lowerCamelCase__ =[*inorder_array, node.data]
												lowerCamelCase__ =inorder_array + inorder(node.right_child    )
						else:
												lowerCamelCase__ =[]
						return inorder_array





def       lowerCamelCase_			( )							-> BinaryTreeNode | None:




						'''simple docstring'''



						lowerCamelCase__ =insert(__lowerCAmelCase       ,   15    )
						insert(__lowerCAmelCase       ,   10    )
						insert(__lowerCAmelCase       ,   25    )
						insert(__lowerCAmelCase       ,   6    )
						insert(__lowerCAmelCase       ,   14    )
						insert(__lowerCAmelCase       ,   20    )
						insert(__lowerCAmelCase       ,   60    )
						return root





def       lowerCamelCase_			( )							-> None:




						'''simple docstring'''



						lowerCamelCase__ =make_tree()
						print("Printing values of binary search tree in Inorder Traversal."    )
						inorder(__lowerCAmelCase    )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()



"""simple docstring"""


import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel






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





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						lowerCamelCase__ ,				lowerCamelCase__ =utils.load_model(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   epoch=__lowerCAmelCase       ,   device=__lowerCAmelCase    )
						lowerCamelCase__ =TrajectoryTransformerModel(gpt.config    )

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


