"""simple docstring"""




def SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[Any]						,  lowercase_:		Dict = 1000			):
       if (not isinstance(lowercase_						,  lowercase_			)) or (digit_position <= 0):
              raise ValueError('Digit position must be a positive integer'			)
       elif (not isinstance(lowercase_						,  lowercase_			)) or (precision < 0):
              raise ValueError('Precision must be a nonnegative integer'			)

       # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
       # accurate
       _UpperCamelCase       :    List[Any]					  =					(
           4 * _subsum(lowercase_						,  1						,  lowercase_			)
           - 2 * _subsum(lowercase_						,  4						,  lowercase_			)
           - _subsum(lowercase_						,  5						,  lowercase_			)
           - _subsum(lowercase_						,  6						,  lowercase_			)
       )

       # return the first hex digit of the fractional part of the result
       return hex(int((sum_result % 1) * 16			)			)[2:]


def SCREAMING_SNAKE_CASE			(       lowercase_:		Dict						,  lowercase_:		Optional[Any]						,  lowercase_:		Any			):
       _UpperCamelCase       :    List[str]					  =					0.0
       for sum_index in range(digit_pos_to_extract + precision			):
              _UpperCamelCase       :    List[Any]					  =					8 * sum_index + denominator_addend
              if sum_index < digit_pos_to_extract:
                     # if the exponential term is an integer and we mod it by the denominator
                     # before dividing, only the integer part of the sum will change;
                     # the fractional part will not
                     _UpperCamelCase       :    Optional[Any]					  =					pow(
                         16						,  digit_pos_to_extract - 1 - sum_index						,  lowercase_			)
              else:
                     _UpperCamelCase       :    Optional[Any]					  =					pow(16						,  digit_pos_to_extract - 1 - sum_index			)
              total += exponential_term / denominator
       return total


if __name__ == "__main__":
 import doctest

 doctest.testmod()




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_:		Optional[Any]						,  lowercase_:		Tuple						,  lowercase_:		Dict						,  lowercase_:		Union[str, Any]						,  lowercase_:		List[Any]			):
       _UpperCamelCase  , _UpperCamelCase       :    List[Any]					  =					utils.load_model(lowercase_						,  lowercase_						,  lowercase_						,  epoch=lowercase_						,  device=lowercase_			)
       _UpperCamelCase       :    Tuple					  =					TrajectoryTransformerModel(gpt.config			)

       trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
       _UpperCamelCase       :    Any					  =					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
 )



