


import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

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



def       _a	(					SCREAMING_SNAKE_CASE						:					Union[str, Any]     ,			SCREAMING_SNAKE_CASE						:					Tuple     ,			SCREAMING_SNAKE_CASE						:					List[Any]     ,			SCREAMING_SNAKE_CASE						:					int     ,			SCREAMING_SNAKE_CASE						:					int  ):







 """simple docstring"""



 UpperCamelCase__							,					UpperCamelCase__       :     Dict 							= utils.load_model(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE     ,			epoch=SCREAMING_SNAKE_CASE     ,			device=SCREAMING_SNAKE_CASE  )
 UpperCamelCase__       :     Optional[Any] 							= 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__":
       __UpperCamelCase							:							List[Any]    							=					Parser().parse_args("plan")
       convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
           args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
       )






import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir

@dataclass
class    __magic_name__						:
     A:		str



class    __magic_name__						(  unittest.TestCase):


     def   UpperCAmelCase__						(   self				:  Any      )	->   List[Any]:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[Any] 							= get_tests_dir('''fixtures/test_sentencepiece.model'''      )
      UpperCamelCase__       :     Any 							= get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model'''      )

      UpperCamelCase__       :     List[str] 							= FakeOriginalTokenizer(vocab_file=lowerCamelCase__      )

      with warnings.catch_warnings(record=lowerCamelCase__      ) as w:
       UpperCamelCase__       :     Optional[int] 							= SpmConverter(lowerCamelCase__      )
      self.assertEqual(len(lowerCamelCase__      )				,						0      )

      UpperCamelCase__       :     Tuple 							= FakeOriginalTokenizer(vocab_file=lowerCamelCase__      )

      with warnings.catch_warnings(record=lowerCamelCase__      ) as w:
       UpperCamelCase__       :     str 							= SpmConverter(lowerCamelCase__      )
      self.assertEqual(len(lowerCamelCase__      )				,						1      )

      self.assertIn(
          '''The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'''
          ''' which is not implemented in the fast tokenizers.'''				,						str(w[0].message      )				,						)


