'''simple docstring'''




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

class      lowerCamelCase_     (utils.Parser   ):
      '''simple docstring'''

      __UpperCamelCase:  str      =						"halfcheetah-medium-expert-v2"
      __UpperCamelCase:  str      =						"config.offline"

def      UpperCamelCase_							( _UpperCAmelCase  :				str      ,     _UpperCAmelCase  :				List[str]      ,     _UpperCAmelCase  :				Optional[int]      ,     _UpperCAmelCase  :				int      ,     _UpperCAmelCase  :				Dict   )   ->   int:


       """simple docstring"""







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

       trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()   )
       _UpperCAmelCase					:  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__":
  __SCREAMING_SNAKE_CASE	:					List[str]					=  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 itertools import compress, repeat
from math import ceil, sqrt

def      UpperCamelCase_							( _UpperCAmelCase  :				int   )   ->   list[int]:


       """simple docstring"""







       if num <= 2:
              return []
       if num == 3:
              return [2]

       # Odd sieve for numbers in range [3, num - 1]
       _UpperCAmelCase					:  Optional[int]											=   bytearray(b"\x01"   ) * ((num >> 1) - 1)

       for i in range(3      ,     int(sqrt(_UpperCAmelCase   )   ) + 1      ,     2   ):
              if sieve[(i >> 1) - 1]:
                     _UpperCAmelCase					:  List[Any]											=   i**2
                     _UpperCAmelCase					:  Optional[int]											=   repeat(
                         0      ,     ceil((num - i_squared) / (i << 1)   )   )

       return [2] + list(compress(range(3      ,     _UpperCAmelCase      ,     2   )      ,     _UpperCAmelCase   )   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

