






"""simple docstring"""


import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel


class    snake_case						(				utils.Parser		):


   '''simple docstring'''

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





def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A     ,       __A		     =					utils.load_model(__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			epoch=__UpperCamelCase						,			device=__UpperCamelCase					)
  __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__":
       lowercase_								=	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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A      :     Optional[int]       	={'''configuration_visual_bert''': ['''VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VisualBertConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     Optional[int]       	=[
			    '''VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''VisualBertForMultipleChoice''',
			    '''VisualBertForPreTraining''',
			    '''VisualBertForQuestionAnswering''',
			    '''VisualBertForRegionToPhraseAlignment''',
			    '''VisualBertForVisualReasoning''',
			    '''VisualBertLayer''',
			    '''VisualBertModel''',
			    '''VisualBertPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_visual_bert import (
						    VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    VisualBertForMultipleChoice,
						    VisualBertForPreTraining,
						    VisualBertForQuestionAnswering,
						    VisualBertForRegionToPhraseAlignment,
						    VisualBertForVisualReasoning,
						    VisualBertLayer,
						    VisualBertModel,
						    VisualBertPreTrainedModel,
						)


else:
			import sys

			_A      :     int       	=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

