




'''simple docstring'''





import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel






class     lowercase  ( utils.Parser):
      __lowercase					=	"halfcheetah-medium-expert-v2"
      __lowercase					=	"config.offline"


def 					UpperCamelCase						(	snake_case						,  snake_case						,  snake_case						,  snake_case						,  snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Tuple							    =			utils.load_model(snake_case						,  snake_case						,  snake_case						,  epoch=snake_case						,  device=snake_case )
   SCREAMING_SNAKE_CASE__      :					List[str]							    =			TrajectoryTransformerModel(gpt.config )

   trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict() )
   SCREAMING_SNAKE_CASE__      :					int							    =			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__":
       lowercase__								=						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'''





import math
from decimal import Decimal, getcontext


def 					UpperCamelCase						(	snake_case = 4_00_00_00 ):




   '''simple docstring'''

   try:
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			int(snake_case )
   except (TypeError, ValueError):
      raise TypeError('''Parameter n must be int or castable to int.''' )
   if n <= 0:
      raise ValueError('''Parameter n must be greater than or equal to one.''' )
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			1_00
   SCREAMING_SNAKE_CASE__      :					int							    =			(Decimal(5 ) ** Decimal(0.5 ) + 1) / Decimal(2 )

   SCREAMING_SNAKE_CASE__      :					int							    =			(math.floor(math.log(n * (phi + 2)						,  snake_case ) - 1 ) // 3) * 3 + 2
   SCREAMING_SNAKE_CASE__      :					int							    =			Decimal(round(phi ** Decimal(index + 1 ) ) ) / (phi + 2)
   SCREAMING_SNAKE_CASE__      :					Any							    =			num // 2
   return int(snake_case )


if __name__ == "__main__":
       print(F"{solution() = }")

