



"""simple docstring"""







import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

class       UpperCamelCase  (					utils.Parser				):





     """simple docstring"""






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



def   a_	(			__a		,							__a		,							__a		,							__a		,							__a      ):
     A__	,     A__				=   utils.load_model(__a		,							__a		,							__a		,							epoch=__a		,							device=__a      )
     A__				=   TrajectoryTransformerModel(gpt.config      )

     trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()      )
     A__				=   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__":
 __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"""







import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__snake_case	:				Optional[Any]  					=			500_000

__snake_case					,  __snake_case	:				Any  					=			os.path.split(__file__)
__snake_case	:				Optional[Any]  					=			os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))



@get_duration
def   a_	(			__a      ):
     A__				=   dataset.select(range(0		,							len(__a      )		,							2      )      )



@get_duration
def   a_	(			__a      ):
     A__				=   dataset.sort('''numbers'''      )



@get_duration
def   a_	(			__a      ):
     A__				=   dataset.shuffle()



@get_duration
def   a_	(			__a      ):
     A__				=   dataset.train_test_split(0.1      )



@get_duration
def   a_	(			__a		,							__a=10      ):
     for shard_id in range(__a      ):
          A__				=   dataset.shard(__a		,							__a      )



def   a_	(			):
     A__				=   {'''num examples''': SPEED_TEST_N_EXAMPLES}
     A__				=   (select, sort, shuffle, train_test_split, shard)
     with tempfile.TemporaryDirectory() as tmp_dir:
          print('''generating dataset'''      )
          A__				=   datasets.Features({'''text''': datasets.Value('''string'''      ), '''numbers''': datasets.Value('''float32'''      )}      )
          A__				=   generate_example_dataset(
              os.path.join(__a		,							'''dataset.arrow'''      )		,							__a		,							num_examples=__a      )
          print('''Functions'''      )
          for func in functions:
               print(func.__name__      )
               A__				=   func(__a      )

     with open(__a		,							'''wb'''      ) as f:
          f.write(json.dumps(__a      ).encode('''utf-8'''      )      )


if __name__ == "__main__":  # useful to run the profiler
 benchmark_indices_mapping()



