




'''simple docstring'''


import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel






class 						lowerCAmelCase_	(       utils.Parser							):







  '''simple docstring'''

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






def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> Any:
     A     ,    A:		Tuple          =		utils.load_model(__lowercase		,				__lowercase		,				__lowercase		,				epoch=__lowercase		,				device=__lowercase	)
     A:		str          =		TrajectoryTransformerModel(gpt.config	)

     trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()	)
     A:		Tuple          =		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			     =					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

import random






class 						lowerCAmelCase_	:







  '''simple docstring'''

  UpperCamelCase_			:      Optional[int]										=				6



  def __init__( self				:							Dict	)			->		Dict:







       '''simple docstring'''



       A:		Union[str, Any]          =		list(range(1					,  Dice.NUM_SIDES + 1	)	)




  def 					_snake_case     ( self				:							Optional[Any]	)			->		Any:







       '''simple docstring'''



       return random.choice(self.sides	)






def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase = 2	)			-> list[float]:
     A:		Union[str, Any]          =		[Dice() for i in range(__lowercase	)]
     A:		Union[str, Any]          =		[0] * (len(__lowercase	) * Dice.NUM_SIDES + 1)
     for _ in range(__lowercase	):
          count_of_sum[sum(dice.roll() for dice in dices	)] += 1
     A:		Dict          =		[round((count * 1_0_0) / num_throws		,				2	) for count in count_of_sum]
     return probability[num_dice:]  # remove probability of sums that never appear


if __name__ == "__main__":
     import doctest

     doctest.testmod()






