code: 
  
  
  '''simple docstring'''
  
  import torch
  import trajectory.utils as utils
  
  from transformers import TrajectoryTransformerModel
  class 						__lowerCAmelCase		(utils.Parser  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  str						      =							"halfcheetah-medium-expert-v2"
        lowerCAmelCase__      :  str						      =							"config.offline"
  
  
  
  
  
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A						,				A						,				A ) -> Optional[int]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__    ,lowercase__       =				utils.load_model(A						,				A						,				A						,				epoch=A						,				device=A )
         lowercase__       =				TrajectoryTransformerModel(gpt.config )
  
         trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict() )
         lowercase__       =				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__":
   lowerCamelCase							:     int								=			Parser().parse_args('plan')
   convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
       args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
   )
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  import argparse
  
  import torch
  
  from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A						,				A ) -> Dict:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				ElectraConfig.from_json_file(A )
         print(f"Building PyTorch model from configuration: {config}" )
  
         if discriminator_or_generator == "discriminator":
                lowercase__       =				ElectraForPreTraining(A )
         elif discriminator_or_generator == "generator":
                lowercase__       =				ElectraForMaskedLM(A )
         else:
                raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\'''' )
  
         # Load weights from tf checkpoint
         load_tf_weights_in_electra(
             A						,				A						,				A						,				discriminator_or_generator=A )
  
         # Save pytorch-model
         print(f"Save PyTorch model to {pytorch_dump_path}" )
         torch.save(model.state_dict()						,				A )
  
  
  if __name__ == "__main__":
   lowerCamelCase							:     Tuple								=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   parser.add_argument(
       '--discriminator_or_generator',
       default=None,
       type=str,
       required=True,
       help=(
           'Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or '
           '\'generator\'.'
       ),
   )
   lowerCamelCase							:     Optional[int]								=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
   )
  
  
  
  
  