code: 
  
  
  """simple docstring"""
  import importlib
  import inspect
  from functools import wraps
  from typing import TYPE_CHECKING, Optional, Union
  
  from .download.streaming_download_manager import (
      xbasename,
      xdirname,
      xet_parse,
      xexists,
      xgetsize,
      xglob,
      xgzip_open,
      xisdir,
      xisfile,
      xjoin,
      xlistdir,
      xnumpy_load,
      xopen,
      xpandas_read_csv,
      xpandas_read_excel,
      xPath,
      xrelpath,
      xsio_loadmat,
      xsplit,
      xsplitext,
      xwalk,
      xxml_dom_minidom_parse,
  )
  from .utils.logging import get_logger
  from .utils.patching import patch_submodule
  from .utils.py_utils import get_imports
  
  
  UpperCAmelCase   =	get_logger(__name__)
  
  
  if TYPE_CHECKING:
   from .builder import DatasetBuilder
  
  def 							_snake_case			(      __snake_case       :			str	,   __snake_case       :			Optional[Union[str, bool]] = None      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				str      =		importlib.import_module(__snake_case      )
  
     # TODO(QL): always update the module to add subsequent new authentication
     if hasattr(__snake_case	,   """_patched_for_streaming"""      ) and module._patched_for_streaming:
        return
  
     def wrap_auth(__snake_case       :			Optional[Any]      ):
        @wraps(__snake_case      )
        def wrapper(*__snake_case       :			List[str]	,   **__snake_case       :			Optional[int]      ):
           return function(*__snake_case	,   use_auth_token=__snake_case	,   **__snake_case      )
  
        _lowerCamelCase  :				str      =		"""wrap_auth"""
        return wrapper
  
     # open files in a streaming fashion
     patch_submodule(__snake_case	,   """open"""	,   wrap_auth(__snake_case      )      ).start()
     patch_submodule(__snake_case	,   """os.listdir"""	,   wrap_auth(__snake_case      )      ).start()
     patch_submodule(__snake_case	,   """os.walk"""	,   wrap_auth(__snake_case      )      ).start()
     patch_submodule(__snake_case	,   """glob.glob"""	,   wrap_auth(__snake_case      )      ).start()
     # allow to navigate in remote zip files
     patch_submodule(__snake_case	,   """os.path.join"""	,   __snake_case      ).start()
     patch_submodule(__snake_case	,   """os.path.dirname"""	,   __snake_case      ).start()
     patch_submodule(__snake_case	,   """os.path.basename"""	,   __snake_case      ).start()
     patch_submodule(__snake_case	,   """os.path.relpath"""	,   __snake_case      ).start()
     patch_submodule(__snake_case	,   """os.path.split"""	,   __snake_case      ).start()
     patch_submodule(__snake_case	,   """os.path.splitext"""	,   __snake_case      ).start()
     # allow checks on paths
     patch_submodule(__snake_case	,   """os.path.exists"""	,   wrap_auth(__snake_case      )      ).start()
     patch_submodule(__snake_case	,   """os.path.isdir"""	,   wrap_auth(__snake_case      )      ).start()
     patch_submodule(__snake_case	,   """os.path.isfile"""	,   wrap_auth(__snake_case      )      ).start()
     patch_submodule(__snake_case	,   """os.path.getsize"""	,   wrap_auth(__snake_case      )      ).start()
     patch_submodule(__snake_case	,   """pathlib.Path"""	,   __snake_case      ).start()
     # file readers
     patch_submodule(__snake_case	,   """gzip.open"""	,   wrap_auth(__snake_case      )      ).start()
     patch_submodule(__snake_case	,   """numpy.load"""	,   wrap_auth(__snake_case      )      ).start()
     patch_submodule(__snake_case	,   """pandas.read_csv"""	,   wrap_auth(__snake_case      )	,   attrs=["""__version__"""]      ).start()
     patch_submodule(__snake_case	,   """pandas.read_excel"""	,   wrap_auth(__snake_case      )	,   attrs=["""__version__"""]      ).start()
     patch_submodule(__snake_case	,   """scipy.io.loadmat"""	,   wrap_auth(__snake_case      )	,   attrs=["""__version__"""]      ).start()
     patch_submodule(__snake_case	,   """xml.etree.ElementTree.parse"""	,   wrap_auth(__snake_case      )      ).start()
     patch_submodule(__snake_case	,   """xml.dom.minidom.parse"""	,   wrap_auth(__snake_case      )      ).start()
     _lowerCamelCase  :				List[Any]      =		True
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			"DatasetBuilder"      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     extend_module_for_streaming(builder.__module__	,   use_auth_token=builder.use_auth_token      )
     # if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
     if not builder.__module__.startswith("""datasets."""      ):  # check that it's not a packaged builder like csv
        for imports in get_imports(inspect.getfile(builder.__class__      )      ):
           if imports[0] == "internal":
              _lowerCamelCase  :				Dict      =		imports[1]
              _lowerCamelCase  :				Optional[int]      =		""".""".join(builder.__module__.split("""."""      )[:-1] + [internal_import_name]      )
              extend_module_for_streaming(__snake_case	,   use_auth_token=builder.use_auth_token      )
  
      # builders can inherit from other builders that might use streaming functionality
      # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
      # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
     from .builder import DatasetBuilder
  
     _lowerCamelCase  :				Union[str, Any]      =		[
         cls.__module__
         for cls in type(__snake_case      ).__mro__[1:]  # make sure it's not the same module we've already patched
         if issubclass(cls	,   __snake_case      ) and cls.__module__ != DatasetBuilder.__module__
     ]  # check it's not a standard builder from datasets.builder
     for module in parent_builder_modules:
        extend_module_for_streaming(__snake_case	,   use_auth_token=builder.use_auth_token      )
  
  
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
   )
  
  