




"""simple docstring"""

def    snake_case__						(							lowerCAmelCase :		list[int]		):
					if len(lowerCAmelCase		) > 1:
										__snake_case        =						len(lowerCAmelCase		) // 2  # Finds the middle of the array
										__snake_case        =						arr[
										    :middle_length
										]  # Creates an array of the elements in the first half.
										__snake_case        =						arr[
										    middle_length:
										]  # Creates an array of the elements in the second half.
										__snake_case        =						len(lowerCAmelCase		)
										__snake_case        =						len(lowerCAmelCase		)
										merge(lowerCAmelCase		)  # Starts sorting the left.
										merge(lowerCAmelCase		)  # Starts sorting the right
										__snake_case        =						0  # Left Counter
										__snake_case        =						0  # Right Counter
										__snake_case        =						0  # Position Counter
										while (
										    left_index < left_size and right_index < right_size
										):  # Runs until the lowers size of the left and right are sorted.
															if left_array[left_index] < right_array[right_index]:
																				__snake_case        =						left_array[left_index]
																				left_index += 1
															else:
																				__snake_case        =						right_array[right_index]
																				right_index += 1
															index += 1
										while (
										    left_index < left_size
										):  # Adds the left over elements in the left half of the array
															__snake_case        =						left_array[left_index]
															left_index += 1
															index += 1
										while (
										    right_index < right_size
										):  # Adds the left over elements in the right half of the array
															__snake_case        =						right_array[right_index]
															right_index += 1
															index += 1
					return arr


if __name__ == "__main__":
		import doctest

		doctest.testmod()








"""simple docstring"""

import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel
class      A__   (   utils.Parser  ):
	A_				:					str      =   "halfcheetah-medium-expert-v2"
	A_				:					str      =   "config.offline"




def    snake_case__						(							lowerCAmelCase :		Optional[int]					,       lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		List[str]					,       lowerCAmelCase :		Optional[int]					,       lowerCAmelCase :		List[Any]		):
					__snake_case   ,   __snake_case        =						utils.load_model(lowerCAmelCase					,       lowerCAmelCase					,       lowerCAmelCase					,       epoch=lowerCAmelCase					,       device=lowerCAmelCase		)
					__snake_case        =						TrajectoryTransformerModel(gpt.config		)

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


