code: 
  
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  UpperCAmelCase   =	{
      """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
      """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
  }
  
  try:
        if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        UpperCAmelCase   =	["""DebertaV2TokenizerFast"""]
  
  try:
        if not is_tf_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        UpperCAmelCase   =	[
            """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """TFDebertaV2ForMaskedLM""",
            """TFDebertaV2ForQuestionAnswering""",
            """TFDebertaV2ForSequenceClassification""",
            """TFDebertaV2ForTokenClassification""",
            """TFDebertaV2Model""",
            """TFDebertaV2PreTrainedModel""",
        ]
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        UpperCAmelCase   =	[
            """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """DebertaV2ForMaskedLM""",
            """DebertaV2ForMultipleChoice""",
            """DebertaV2ForQuestionAnswering""",
            """DebertaV2ForSequenceClassification""",
            """DebertaV2ForTokenClassification""",
            """DebertaV2Model""",
            """DebertaV2PreTrainedModel""",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_deberta_va import (
            DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
            DebertaVaConfig,
            DebertaVaOnnxConfig,
        )
        from .tokenization_deberta_va import DebertaVaTokenizer
  
        try:
              if not is_tokenizers_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .tokenization_deberta_va_fast import DebertaVaTokenizerFast
  
        try:
              if not is_tf_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_tf_deberta_va import (
                  TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDebertaVaForMaskedLM,
                  TFDebertaVaForQuestionAnswering,
                  TFDebertaVaForSequenceClassification,
                  TFDebertaVaForTokenClassification,
                  TFDebertaVaModel,
                  TFDebertaVaPreTrainedModel,
              )
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_deberta_va import (
                  DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DebertaVaForMaskedLM,
                  DebertaVaForMultipleChoice,
                  DebertaVaForQuestionAnswering,
                  DebertaVaForSequenceClassification,
                  DebertaVaForTokenClassification,
                  DebertaVaModel,
                  DebertaVaPreTrainedModel,
              )
  
  else:
        import sys
  
        UpperCAmelCase   =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
style_context: 
  
  
  """simple docstring"""
  import torch
  import trajectory.utils as utils
  
  from transformers import TrajectoryTransformerModel
  
  
  
  
  class   lowercase__							(				utils.Parser							):
     __UpperCAmelCase       					=				"halfcheetah-medium-expert-v2"
     __UpperCAmelCase       					=				"config.offline"
  
  
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Any	,   __snake_case       :			Optional[Any]	,   __snake_case       :			Optional[int]	,   __snake_case       :			List[str]	,   __snake_case       :			Union[str, Any]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase      ,						_lowerCamelCase  :				int      =		utils.load_model(__snake_case	,   __snake_case	,   __snake_case	,   epoch=__snake_case	,   device=__snake_case      )
     _lowerCamelCase  :				List[str]      =		TrajectoryTransformerModel(gpt.config      )
  
     trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()      )
     _lowerCamelCase  :				str      =		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   =	Parser().parse_args("""plan""")
   convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
       args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
   )
  
  