
import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




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





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    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__":
     __magic_name__           =					Parser().parse_args('''plan''')
     convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
         args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
     )





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    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()



