



import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
		from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm

def 	lowerCAmelCase_     (  _lowercase				:   int)							->       List[Any]:







			"""simple docstring"""


			if is_torch_version("""<"""						,   """2.0.0""") or not hasattr(__A						,   """_dynamo"""):
						return False
			return isinstance(__A						,   torch._dynamo.eval_frame.OptimizedModule)

def 	lowerCAmelCase_     (  _lowercase				:   List[Any]						,   _lowercase				:   bool = True)							->       Tuple:







			"""simple docstring"""
			a__   :						Any						       =  (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

			a__   :						Dict						       =  is_compiled_module(__A)
			if is_compiled:
						a__   :						str						       =  model
						a__   :						Tuple						       =  model._orig_mod

			if is_deepspeed_available():
						options += (DeepSpeedEngine,)

			while isinstance(__A						,   __A):
						a__   :						List[Any]						       =  model.module

			if not keep_fpaa_wrapper:
						a__   :						int						       =  getattr(__A						,   """forward""")
						a__   :						Dict						       =  model.__dict__.pop("""_original_forward"""						,   __A)
						if original_forward is not None:
									while hasattr(__A						,   """__wrapped__"""):
												a__   :						Union[str, Any]						       =  forward.__wrapped__
												if forward == original_forward:
															break
									a__   :						Optional[Any]						       =  forward
						if getattr(__A						,   """_converted_to_transformer_engine"""						,   __A):
									convert_model(__A						,   to_transformer_engine=__A)

			if is_compiled:
						a__   :						int						       =  model
						a__   :						Optional[Any]						       =  compiled_model

			return model

def 	lowerCAmelCase_     (  )							->       List[str]:







			"""simple docstring"""
			PartialState().wait_for_everyone()

def 	lowerCAmelCase_     (  _lowercase				:   Dict						,   _lowercase				:   Union[str, Any])							->       List[str]:







			"""simple docstring"""


			if PartialState().distributed_type == DistributedType.TPU:
						xm.save(__A						,   __A)
			elif PartialState().local_process_index == 0:
						torch.save(__A						,   __A)

@contextmanager
def 	lowerCAmelCase_     (  **_lowercase				:   Union[str, Any])							->       Dict:







			"""simple docstring"""


			for key, value in kwargs.items():
						a__   :						int						       =  str(__A)

			yield

			for key in kwargs:
						if key.upper() in os.environ:
									del os.environ[key.upper()]

def 	lowerCAmelCase_     (  _lowercase				:   List[Any])							->       Any:







			"""simple docstring"""


			if not hasattr(__A						,   """__qualname__""") and not hasattr(__A						,   """__name__"""):
						a__   :						int						       =  getattr(__A						,   """__class__"""						,   __A)
			if hasattr(__A						,   """__qualname__"""):
						return obj.__qualname__
			if hasattr(__A						,   """__name__"""):
						return obj.__name__
			return str(__A)

def 	lowerCAmelCase_     (  _lowercase				:   Tuple						,   _lowercase				:   str)							->       List[str]:







			"""simple docstring"""


			for key, value in source.items():
						if isinstance(__A						,   __A):
									a__   :						Dict						       =  destination.setdefault(__A						,   {})
									merge_dicts(__A						,   __A)
						else:
									a__   :						Dict						       =  value

			return destination





def 	lowerCAmelCase_     (  _lowercase				:   int = None)							->       bool:







			"""simple docstring"""


			if port is None:
						a__   :						Any						       =  2_9500
			with socket.socket(socket.AF_INET						,   socket.SOCK_STREAM) as s:
						return s.connect_ex(("""localhost""", port)) == 0










from diffusers.utils.testing_utils import require_onnxruntime







@require_onnxruntime
class __snake_case			:
   pass






