




'''simple docstring'''





import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel


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






def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	):
    _a					,   _a							:       int       					=			utils.load_model(UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  epoch=UpperCAmelCase_      ,  device=UpperCAmelCase_	)
    _a							:       Tuple       					=			TrajectoryTransformerModel(gpt.config	)

    trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()	)
    _a							:       Optional[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		:  int   			=			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 math import exp





def UpperCAmelCase						(					UpperCAmelCase_	):
    return 8 * x - 2 * exp(-x	)





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	):
    _a							:       Optional[int]       					=			lower_bound
    _a							:       str       					=			upper_bound
    for _ in range(0      ,  UpperCAmelCase_	):
        _a					,   _a							:       List[str]       					=			xa, xa - (f(UpperCAmelCase_	) * (xa - xa)) / (f(UpperCAmelCase_	) - f(UpperCAmelCase_	))
    return xa


if __name__ == "__main__":
  print(F"""Example: {secant_method(1, 3, 2)}""")


