


"""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()





"""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
 )

