code: 
  
  
  """simple docstring"""
  import importlib
  import importlib.metadata
  import os
  import warnings
  from distutils.util import strtobool
  from functools import lru_cache
  
  import torch
  from packaging import version
  from packaging.version import parse
  
  from .environment import parse_flag_from_env
  from .versions import compare_versions, is_torch_version
  
  
  try:
   import torch_xla.core.xla_model as xm  # noqa: F401
  
   UpperCAmelCase   =	True
  except ImportError:
   UpperCAmelCase   =	False
  
  
  # Cache this result has it's a C FFI call which can be pretty time-consuming
  UpperCAmelCase   =	torch.distributed.is_available()
  
  def 							_snake_case			(      __snake_case       :			Optional[int]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Optional[int]      =		importlib.util.find_spec(__snake_case      ) is not None
     if package_exists:
        try:
           _lowerCamelCase  :				Optional[int]      =		importlib.metadata.metadata(__snake_case      )
           return True
        except importlib.metadata.PackageNotFoundError:
           return False
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return _torch_distributed_available
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     try:
        pass
     except ImportError:
        print(
            """Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not"""
            """ detected. If you see \"ValueError: Invalid backend: 'ccl'\" error, please install Intel(R) oneCCL"""
            """ Bindings for PyTorch*."""      )
     return (
         importlib.util.find_spec("""torch_ccl"""      ) is not None
         or importlib.util.find_spec("""oneccl_bindings_for_pytorch"""      ) is not None
     )
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return importlib.metadata.version("""oneccl_bind_pt"""      )
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return _is_package_available("""transformer_engine"""      )
  
  @lru_cache
  def 							_snake_case			(      __snake_case       :			int=True      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if torch.cuda.is_available():
        return False
     if _tpu_available and check_device:
        try:
           # Will raise a RuntimeError if no XLA configuration is found
           _lowerCamelCase  :				List[Any]      =		xm.xla_device()
           return True
        except RuntimeError:
           return False
     return _tpu_available
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return _is_package_available("""deepspeed"""      )
  
  def 							_snake_case			(      __snake_case       :			Any=False      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if is_tpu_available():
        return not ignore_tpu
     if torch.cuda.is_available():
        return torch.cuda.is_bfaa_supported()
     if is_npu_available():
        return False
     return True
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Optional[Any]      =		_is_package_available("""bitsandbytes"""      )
     if package_exists:
        _lowerCamelCase  :				Tuple      =		version.parse(importlib.metadata.version("""bitsandbytes"""      )      )
        return compare_versions(__snake_case	,   """>="""	,   """0.39.0"""      )
     return False
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				int      =		_is_package_available("""bitsandbytes"""      )
     if package_exists:
        _lowerCamelCase  :				Dict      =		version.parse(importlib.metadata.version("""bitsandbytes"""      )      )
        return compare_versions(__snake_case	,   """>="""	,   """0.37.2"""      )
     return False
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return _is_package_available("""bitsandbytes"""      )
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if strtobool(os.environ.get("""ACCELERATE_USE_MEGATRON_LM"""	,   """False"""      )      ) == 1:
        _lowerCamelCase  :				List[Any]      =		importlib.util.find_spec("""megatron"""      ) is not None
        if package_exists:
           try:
              _lowerCamelCase  :				Dict      =		parse(importlib.metadata.version("""megatron-lm"""      )      )
              return compare_versions(__snake_case	,   """>="""	,   """2.2.0"""      )
           except Exception as e:
              warnings.warn(F'Parse Megatron version failed. Exception:{e}'      )
              return False
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return _is_package_available("""safetensors"""      )
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return _is_package_available("""transformers"""      )
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return _is_package_available("""datasets"""      )
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return _is_package_available("""aim"""      )
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return _is_package_available("""tensorboard"""      ) or _is_package_available("""tensorboardX"""      )
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return _is_package_available("""wandb"""      )
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return _is_package_available("""comet_ml"""      )
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return _is_package_available("""boto3"""      )
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if _is_package_available("""rich"""      ):
        if "ACCELERATE_DISABLE_RICH" in os.environ:
           warnings.warn(
               """`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`."""      )
           return not parse_flag_from_env("""ACCELERATE_DISABLE_RICH"""	,   __snake_case      )
        return parse_flag_from_env("""ACCELERATE_ENABLE_RICH"""	,   __snake_case      )
     return False
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return _is_package_available("""sagemaker"""      )
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return _is_package_available("""tqdm"""      )
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return _is_package_available("""mlflow"""      )
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return is_torch_version(""">="""	,   """1.12"""      ) and torch.backends.mps.is_available() and torch.backends.mps.is_built()
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     def get_major_and_minor_from_version(__snake_case       :			Optional[int]      ):
        return str(version.parse(__snake_case      ).major      ) + "." + str(version.parse(__snake_case      ).minor      )
  
     _lowerCamelCase  :				Dict      =		importlib.metadata.version("""torch"""      )
     if importlib.util.find_spec("""intel_extension_for_pytorch"""      ) is None:
        return False
     _lowerCamelCase  :				Dict      =		"""N/A"""
     try:
        _lowerCamelCase  :				Optional[int]      =		importlib.metadata.version("""intel_extension_for_pytorch"""      )
     except importlib.metadata.PackageNotFoundError:
        return False
     _lowerCamelCase  :				Optional[int]      =		get_major_and_minor_from_version(_torch_version      )
     _lowerCamelCase  :				Dict      =		get_major_and_minor_from_version(_ipex_version      )
     if torch_major_and_minor != ipex_major_and_minor:
        warnings.warn(
            F'Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,'
            F' but PyTorch {_torch_version} is found. Please switch to the matching version and run again.'      )
        return False
     return True
  
  @lru_cache
  def 							_snake_case			(      __snake_case       :			Optional[int]=False      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if importlib.util.find_spec("""torch"""      ) is None or importlib.util.find_spec("""torch_npu"""      ) is None:
        return False
  
     import torch
     import torch_npu  # noqa: F401
  
     if check_device:
        try:
           # Will raise a RuntimeError if no NPU is found
           _lowerCamelCase  :				List[Any]      =		torch.npu.device_count()
           return torch.npu.is_available()
        except RuntimeError:
           return False
     return hasattr(__snake_case	,   """npu"""      ) and torch.npu.is_available()
  
  
  
  
  
  @lru_cache
  def 							_snake_case			(      __snake_case       :			int=False      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if not parse_flag_from_env("""ACCELERATE_USE_XPU"""	,   default=__snake_case      ):
        return False
     if is_ipex_available():
        import torch
  
        if is_torch_version("""<="""	,   """1.12"""      ):
           return False
     else:
        return False
  
     import intel_extension_for_pytorch  # noqa: F401
  
     if check_device:
        try:
           # Will raise a RuntimeError if no XPU  is found
           _lowerCamelCase  :				Optional[int]      =		torch.xpu.device_count()
           return torch.xpu.is_available()
        except RuntimeError:
           return False
     return hasattr(__snake_case	,   """xpu"""      ) and torch.xpu.is_available()
  
  
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
   )
  
  