
import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel





class   __UpperCamelCase				(    utils.Parser   ):






     """simple docstring"""







     lowerCAmelCase_     =	"halfcheetah-medium-expert-v2"
     lowerCAmelCase_     =	"config.offline"






def     a__			(    snake_case   ,	snake_case   ,	snake_case   ,	snake_case   ,	snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						utils.load_model(_UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase   ,	epoch=_UpperCAmelCase   ,	device=_UpperCAmelCase    )
  __SCREAMING_SNAKE_CASE     :					Tuple			=						TrajectoryTransformerModel(gpt.config    )

  trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()    )
  __SCREAMING_SNAKE_CASE     :					List[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__":
  lowercase_											=	Parser().parse_args("""plan""")
  convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
      args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
  )


from collections import defaultdict


def     a__			(    snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					defaultdict[str, int]			=						defaultdict(snake_case    )
  # Creating a dictionary containing count of each word
  for word in sentence.split():
    occurrence[word] += 1
  return occurrence


if __name__ == "__main__":
   for word, count in word_occurrence("""INPUT STRING""").items():
      print(f'''{word}: {count}''')
