


import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel



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



def      __lowerCAmelCase    (				snake_case						:     Tuple    ,   snake_case						:     str    ,   snake_case						:     List[Any]    ,   snake_case						:     str    ,   snake_case						:     Any			)							->				Optional[Any]:
   __lowerCamelCase ,  __lowerCamelCase:					Optional[int]	 =						utils.load_model(snake_case    ,   snake_case    ,   snake_case    ,   epoch=snake_case    ,   device=snake_case			)
   __lowerCamelCase:					Optional[Any]	 =						TrajectoryTransformerModel(gpt.config			)

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






_A		:					Union[str, Any]          =							1_000



def      __lowerCAmelCase    (				snake_case						:     float			)							->				float:
   return x * x



def      __lowerCAmelCase    (				snake_case						:     Optional[int]    ,   snake_case						:     float    ,   snake_case						:     float    ,   snake_case						:     int = 4			)							->				float:
   assert callable(
       snake_case			), f'the function(object) passed should be callable your input : {function}'
   assert isinstance(snake_case    ,   (float, int)			), f'a should be float or integer your input : {a}'
   assert isinstance(function(snake_case			)    ,   (float, int)			), (
       "the function should return integer or float return type of your function, "
       f'{type(snake_case			)}'
   )
   assert isinstance(snake_case    ,   (float, int)			), f'b should be float or integer your input : {b}'
   assert (
       isinstance(snake_case    ,   snake_case			) and precision > 0
   ), f'precision should be positive integer your input : {precision}'

   # just applying the formula of simpson for approximate integration written in
   # mentioned article in first comment of this file and above this function

   __lowerCamelCase:					List[str]	 =						(b - a) / N_STEPS
   __lowerCamelCase:					Optional[Any]	 =						function(snake_case			) + function(snake_case			)

   for i in range(1    ,   snake_case			):
      __lowerCamelCase:					Dict	 =						a + h * i
      result += function(snake_case			) * (4 if i % 2 else 2)

   result *= h / 3
   return round(snake_case    ,   snake_case			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()


