code: 
  '''simple docstring'''
  
  import inspect
  import warnings
  
  import torch
  
  from .state import AcceleratorState, GradientState
  from .utils import DistributedType, honor_type, is_tpu_available
  
  
  if is_tpu_available(check_device=False):
  	import torch_xla.core.xla_model as xm
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase	) ->      str:
  
  
  
  
  
  
  
  		if isinstance(UpperCamelCase			,(list, tuple)	):
  				return honor_type(UpperCamelCase			,(move_to_device(UpperCamelCase			,UpperCamelCase	) for t in state)	)
  		elif isinstance(UpperCamelCase			,UpperCamelCase	):
  				return type(UpperCamelCase	)({k: move_to_device(UpperCamelCase			,UpperCamelCase	) for k, v in state.items()}	)
  		elif isinstance(UpperCamelCase			,torch.Tensor	):
  				return state.to(UpperCamelCase	)
  		return state
  
  
  class 			UpperCAmelCase				(					torch.optim.Optimizer ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		def __init__(    self  ,					_snake_case  ,					_snake_case=True  ,					_snake_case=None     )				->    str:
  				_UpperCamelCase       : Dict    =      optimizer
  				_UpperCamelCase       : str    =      scaler
  				_UpperCamelCase       : str    =      AcceleratorState()
  				_UpperCamelCase       : int    =      GradientState()
  				_UpperCamelCase       : List[Any]    =      device_placement
  				_UpperCamelCase       : Optional[Any]    =      False
  				_UpperCamelCase       : Optional[Any]    =      None
  
  				# Handle device placement
  				if device_placement:
  						_UpperCamelCase       : str    =      self.optimizer.state_dict()
  						if self.accelerator_state.distributed_type == DistributedType.TPU:
  								xm.send_cpu_data_to_device(_snake_case  ,					self.accelerator_state.device     )
  						else:
  								_UpperCamelCase       : Union[str, Any]    =      move_to_device(_snake_case  ,					self.accelerator_state.device     )
  						self.optimizer.load_state_dict(_snake_case     )
  		@property
  		def _lowercase  (    self     )				->    Tuple:
  				return self.optimizer.state
  		@state.setter
  		def _lowercase  (    self  ,					_snake_case     )				->    List[Any]:
  				_UpperCamelCase       : List[str]    =      state
  		@property
  		def _lowercase  (    self     )				->    List[Any]:
  				return self.optimizer.param_groups
  		@param_groups.setter
  		def _lowercase  (    self  ,					_snake_case     )				->    List[Any]:
  				_UpperCamelCase       : int    =      param_groups
  		@property
  		def _lowercase  (    self     )				->    Dict:
  				return self.optimizer.defaults
  		@defaults.setter
  		def _lowercase  (    self  ,					_snake_case     )				->    List[Any]:
  				_UpperCamelCase       : int    =      defaults
  		def _lowercase  (    self  ,					_snake_case     )				->    List[Any]:
  				self.optimizer.add_param_group(_snake_case     )
  		def _lowercase  (    self  ,					_snake_case     )				->    Optional[int]:
  
  
  
  
  
  
  
  				if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
  						xm.send_cpu_data_to_device(_snake_case  ,					self.accelerator_state.device     )
  				self.optimizer.load_state_dict(_snake_case     )
  		def _lowercase  (    self     )				->    Union[str, Any]:
  				return self.optimizer.state_dict()
  		def _lowercase  (    self  ,					_snake_case=None     )				->    Dict:
  
  
  
  
  
  
  
  				if self.gradient_state.sync_gradients:
  						_UpperCamelCase       : Union[str, Any]    =      '''set_to_none''' in inspect.signature(self.optimizer.zero_grad     ).parameters
  						if accept_arg:
  								if set_to_none is None:
  										_UpperCamelCase       : Optional[Any]    =      False
  								self.optimizer.zero_grad(set_to_none=_snake_case     )
  						else:
  								if set_to_none is not None:
  										raise ValueError('''`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.'''     )
  								self.optimizer.zero_grad()
  		def _lowercase  (    self  ,					_snake_case=None     )				->    Any:
  
  
  
  
  
  
  
  				if self.gradient_state.sync_gradients:
  						if self.accelerator_state.distributed_type == DistributedType.TPU:
  								_UpperCamelCase       : Optional[int]    =      {'''closure''': closure} if closure is not None else {}
  								xm.optimizer_step(self.optimizer  ,					optimizer_args=_snake_case     )
  						elif self.scaler is not None:
  								_UpperCamelCase       : Any    =      False
  								if self._last_scale is None:
  										# `get_scale` is an async operation requiring full synchronization
  										# on CPU and GPUs before finishing. As a result, we store away
  										# the prior one to reduce the call overhead
  										_UpperCamelCase       : Tuple    =      self.scaler.get_scale()
  										_UpperCamelCase       : List[str]    =      True
  								self.scaler.step(self.optimizer  ,					_snake_case     )
  								self.scaler.update()
  								_UpperCamelCase       : List[str]    =      self.scaler.get_scale()
  								if not new_scale:
  										# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
  										_UpperCamelCase       : Dict    =      scale_after < self._last_scale
  								_UpperCamelCase       : Union[str, Any]    =      scale_after
  
  						else:
  								self.optimizer.step(_snake_case     )
  		def _lowercase  (    self  ,					_snake_case     )				->    Optional[Any]:
  
  
  
  
  
  
  
  				for param_group in self.optimizer.param_groups:
  						_UpperCamelCase       : Union[str, Any]    =      [parameters_map.get(_snake_case  ,					_snake_case     ) for p in param_group['''params''']]
  		@property
  		def _lowercase  (    self     )				->    Union[str, Any]:
  				warnings.warn(
  				    '''The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '''
  				    '''`optimizer.step_was_skipped` instead.'''  ,					_snake_case  ,					)
  				return self._is_overflow
  		@property
  		def _lowercase  (    self     )				->    int:
  				return self._is_overflow
  		def __getstate__(    self     )				->    int:
  				return self.__dict__.copy()
  
  
  
  
  		def __setstate__(    self  ,					_snake_case     )				->    Dict:
  				self.__dict__.update(_snake_case     )
  
  
  
  
style_context: 
  '''simple docstring'''
  
  _UpperCAmelCase   :      Optional[int] 						=  """
  # Transformers installation
  ! pip install transformers datasets evaluate
  # To install from source instead of the last release, comment the command above and uncomment the following one.
  # ! pip install git+https://github.com/huggingface/transformers.git
  """
  
  _UpperCAmelCase   :      Union[str, Any] 						=  [{"""type""": """code""", """content""": INSTALL_CONTENT}]
  _UpperCAmelCase   :      Union[str, Any] 						=  {
      """{processor_class}""": """FakeProcessorClass""",
      """{model_class}""": """FakeModelClass""",
      """{object_class}""": """FakeObjectClass""",
  }
  
  
  
  
  
  