

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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[int] )   ->       Optional[Any]:
				if is_torch_version("<"  ,      "2.0.0" ) or not hasattr(lowerCAmelCase  ,      "_dynamo" ):
								return False
				return isinstance(lowerCAmelCase  ,      torch._dynamo.eval_frame.OptimizedModule )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  bool = True )   ->       str:
				_UpperCAmelCase       :		Union[str, Any] 		=    (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

				_UpperCAmelCase       :		Optional[Any] 		=    is_compiled_module(lowerCAmelCase )
				if is_compiled:
								_UpperCAmelCase       :		Tuple 		=    model
								_UpperCAmelCase       :		Dict 		=    model._orig_mod

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

				while isinstance(lowerCAmelCase  ,      lowerCAmelCase ):
								_UpperCAmelCase       :		List[str] 		=    model.module

				if not keep_fpaa_wrapper:
								_UpperCAmelCase       :		Dict 		=    getattr(lowerCAmelCase  ,      "forward" )
								_UpperCAmelCase       :		Tuple 		=    model.__dict__.pop("_original_forward"  ,      lowerCAmelCase )
								if original_forward is not None:
												while hasattr(lowerCAmelCase  ,      "__wrapped__" ):
																_UpperCAmelCase       :		Tuple 		=    forward.__wrapped__
																if forward == original_forward:
																				break
												_UpperCAmelCase       :		Tuple 		=    forward
								if getattr(lowerCAmelCase  ,      "_converted_to_transformer_engine"  ,      lowerCAmelCase ):
												convert_model(lowerCAmelCase  ,      to_transformer_engine=lowerCAmelCase )

				if is_compiled:
								_UpperCAmelCase       :		Optional[int] 		=    model
								_UpperCAmelCase       :		Union[str, Any] 		=    compiled_model

				return model

def 							__SCREAMING_SNAKE_CASE							(				)   ->       Union[str, Any]:
				PartialState().wait_for_everyone()

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Optional[Any] )   ->       Optional[Any]:
				if PartialState().distributed_type == DistributedType.TPU:
								xm.save(lowerCAmelCase  ,      lowerCAmelCase )
				elif PartialState().local_process_index == 0:
								torch.save(lowerCAmelCase  ,      lowerCAmelCase )

@contextmanager
def 							__SCREAMING_SNAKE_CASE							(				**lowerCAmelCase:  Any )   ->       str:
				for key, value in kwargs.items():
								_UpperCAmelCase       :		Dict 		=    str(lowerCAmelCase )

				yield

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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[Any] )   ->       List[Any]:
				if not hasattr(lowerCAmelCase  ,      "__qualname__" ) and not hasattr(lowerCAmelCase  ,      "__name__" ):
								_UpperCAmelCase       :		Any 		=    getattr(lowerCAmelCase  ,      "__class__"  ,      lowerCAmelCase )
				if hasattr(lowerCAmelCase  ,      "__qualname__" ):
								return obj.__qualname__
				if hasattr(lowerCAmelCase  ,      "__name__" ):
								return obj.__name__
				return str(lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict  ,      lowerCAmelCase:  Union[str, Any] )   ->       List[Any]:
				for key, value in source.items():
								if isinstance(lowerCAmelCase  ,      lowerCAmelCase ):
												_UpperCAmelCase       :		List[Any] 		=    destination.setdefault(lowerCAmelCase  ,      {} )
												merge_dicts(lowerCAmelCase  ,      lowerCAmelCase )
								else:
												_UpperCAmelCase       :		List[str] 		=    value

				return destination





def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int = None )   ->       bool:
				if port is None:
								_UpperCAmelCase       :		Dict 		=    2_9500
				with socket.socket(socket.AF_INET  ,      socket.SOCK_STREAM ) as s:
								return s.connect_ex(("localhost", port) ) == 0





import logging
import os

from .state import PartialState

class 				a  (	logging.LoggerAdapter				):


						@staticmethod
						def        _UpperCAmelCase				(       A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    PartialState()
										return not main_process_only or (main_process_only and state.is_main_process)





						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					*A_						,					**A_	):







										'''simple docstring'''


										if PartialState._shared_state == {}:
														raise RuntimeError(
														    "You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."	)
										_UpperCAmelCase       :		Tuple 		=    kwargs.pop("main_process_only"						,					A_	)
										_UpperCAmelCase       :		int 		=    kwargs.pop("in_order"						,					A_	)

										if self.isEnabledFor(A_	):
														if self._should_log(A_	):
																		_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[int] 		=    self.process(A_						,					A_	)
																		self.logger.log(A_						,					A_						,					*A_						,					**A_	)

														elif in_order:
																		_UpperCAmelCase       :		Dict 		=    PartialState()
																		for i in range(state.num_processes	):
																						if i == state.process_index:
																										_UpperCAmelCase		,							_UpperCAmelCase       :		Union[str, Any] 		=    self.process(A_						,					A_	)
																										self.logger.log(A_						,					A_						,					*A_						,					**A_	)
																						state.wait_for_everyone()







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  str = None )   ->       List[Any]:
				if log_level is None:
								_UpperCAmelCase       :		List[str] 		=    os.environ.get("ACCELERATE_LOG_LEVEL"  ,      lowerCAmelCase )
				_UpperCAmelCase       :		str 		=    logging.getLogger(lowerCAmelCase )
				if log_level is not None:
								logger.setLevel(log_level.upper() )
								logger.root.setLevel(log_level.upper() )
				return MultiProcessAdapter(lowerCAmelCase  ,      {} )


