




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

class 			_lowerCamelCase    (      utils.Parser ):

      """simple docstring"""

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







def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						int:

    """simple docstring"""


    __UpperCamelCase		,   __UpperCamelCase			=    utils.load_model(lowercase_       ,  lowercase_       ,  lowercase_       ,  epoch=lowercase_       ,  device=lowercase_   )
    __UpperCamelCase			=    TrajectoryTransformerModel(gpt.config   )

    trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()   )
    __UpperCamelCase			=    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
 )













'''simple docstring'''

from __future__ import annotations

lowerCAmelCase__      :   Tuple      	=			{1: 1}


def 						__UpperCamelCase						(			_UpperCAmelCase  ):
		if n in COLLATZ_SEQUENCE_LENGTHS:
				return COLLATZ_SEQUENCE_LENGTHS[n]
		__UpperCAmelCase	:							int     =     n // 2 if n % 2 == 0 else 3 * n + 1
		__UpperCAmelCase	:							str     =     collatz_sequence_length(_UpperCAmelCase  ) + 1
		__UpperCAmelCase	:							Union[str, Any]     =     sequence_length
		return sequence_length


def 						__UpperCamelCase						(			_UpperCAmelCase = 1000000  ):
		__UpperCAmelCase	:							Union[str, Any]     =     max((collatz_sequence_length(_UpperCAmelCase  ), i) for i in range(1,       _UpperCAmelCase  )  )
		return result[1]


if __name__ == "__main__":
			print(solution(int(input().strip())))




