'''simple docstring'''




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




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






def __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__       )			->					str:
    __lowerCamelCase			,						__lowerCamelCase						=						utils.load_model(UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__       ,						epoch=UpperCamelCase__       ,						device=UpperCamelCase__       )
    __lowerCamelCase						=						TrajectoryTransformerModel(gpt.config       )

    trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()       )
    __lowerCamelCase						=						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'''




def __lowerCAmelCase				(							UpperCamelCase__       )			->					bool:
    if not isinstance(UpperCamelCase__       ,						UpperCamelCase__       ):
        raise ValueError('''Input series is not valid, valid series - [1, 2/3, 2]'''       )
    if len(UpperCamelCase__       ) == 0:
        raise ValueError('''Input list must be a non empty list'''       )
    if len(UpperCamelCase__       ) == 1 and series[0] != 0:
        return True
    __lowerCamelCase						=						[]
    __lowerCamelCase						=						len(UpperCamelCase__       )
    for i in range(0       ,						UpperCamelCase__       ):
        if series[i] == 0:
            raise ValueError('''Input series cannot have 0 as an element'''       )
        rec_series.append(1 / series[i]       )
    __lowerCamelCase						=						rec_series[1] - rec_series[0]
    for index in range(2       ,						UpperCamelCase__       ):
        if rec_series[index] - rec_series[index - 1] != common_diff:
            return False
    return True






def __lowerCAmelCase				(							UpperCamelCase__       )			->					float:
    if not isinstance(UpperCamelCase__       ,						UpperCamelCase__       ):
        raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''       )
    if len(UpperCamelCase__       ) == 0:
        raise ValueError('''Input list must be a non empty list'''       )
    __lowerCamelCase						=						0
    for val in series:
        answer += 1 / val
    return len(UpperCamelCase__       ) / answer


if __name__ == "__main__":
  import doctest

  doctest.testmod()
