


'''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
 )








'''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
 )




