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 							a_       (						lowercase							,						lowercase    )			->Dict:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				if isinstance(lowercase							,						(list, tuple)    ):
  								return honor_type(lowercase							,						(move_to_device(lowercase							,						lowercase    ) for t in state)    )
  				elif isinstance(lowercase							,						lowercase    ):
  								return type(lowercase    )({k: move_to_device(lowercase							,						lowercase    ) for k, v in state.items()}    )
  				elif isinstance(lowercase							,						torch.Tensor    ):
  								return state.to(lowercase    )
  				return state
  
  
  
  
  
  class _snake_case   ( torch.optim.Optimizer   ):
  							"""simple docstring"""
  
  
  
  
  
  							def __init__(   self			,				__A			,				__A=True			,				__A=None	)     ->List[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      str											=					optimizer
  											__lowerCamelCase		:      Tuple											=					scaler
  											__lowerCamelCase		:      Any											=					AcceleratorState()
  											__lowerCamelCase		:      Optional[Any]											=					GradientState()
  											__lowerCamelCase		:      Any											=					device_placement
  											__lowerCamelCase		:      List[Any]											=					False
  											__lowerCamelCase		:      int											=					None
  
  											# Handle device placement
  											if device_placement:
  															__lowerCamelCase		:      Dict											=					self.optimizer.state_dict()
  															if self.accelerator_state.distributed_type == DistributedType.TPU:
  																			xm.send_cpu_data_to_device(__A			,				self.accelerator_state.device	)
  															else:
  																			__lowerCamelCase		:      int											=					move_to_device(__A			,				self.accelerator_state.device	)
  															self.optimizer.load_state_dict(__A	)
  
  
  
  
  
  
  
  							@property
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return self.optimizer.state
  
  
  
  
  
  
  
  							@state.setter
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->Optional[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      str											=					state
  
  
  
  
  
  
  
  							@property
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return self.optimizer.param_groups
  
  
  
  
  
  
  
  							@param_groups.setter
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->List[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Dict											=					param_groups
  
  
  
  
  
  
  
  							@property
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return self.optimizer.defaults
  
  
  
  
  
  
  
  							@defaults.setter
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      str											=					defaults
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											self.optimizer.add_param_group(__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->Union[str, Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
  															xm.send_cpu_data_to_device(__A			,				self.accelerator_state.device	)
  											self.optimizer.load_state_dict(__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return self.optimizer.state_dict()
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A=None	)     ->Any:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if self.gradient_state.sync_gradients:
  															__lowerCamelCase		:      Optional[Any]											=					"set_to_none" in inspect.signature(self.optimizer.zero_grad	).parameters
  															if accept_arg:
  																			if set_to_none is None:
  																							__lowerCamelCase		:      Optional[int]											=					False
  																			self.optimizer.zero_grad(set_to_none=__A	)
  															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 		__SCREAMING_SNAKE_CASE(   self			,				__A=None	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if self.gradient_state.sync_gradients:
  															if self.accelerator_state.distributed_type == DistributedType.TPU:
  																			__lowerCamelCase		:      Optional[Any]											=					{"closure": closure} if closure is not None else {}
  																			xm.optimizer_step(self.optimizer			,				optimizer_args=__A	)
  															elif self.scaler is not None:
  																			__lowerCamelCase		:      Dict											=					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
  																							__lowerCamelCase		:      Dict											=					self.scaler.get_scale()
  																							__lowerCamelCase		:      List[Any]											=					True
  																			self.scaler.step(self.optimizer			,				__A	)
  																			self.scaler.update()
  																			__lowerCamelCase		:      List[Any]											=					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.
  																							__lowerCamelCase		:      List[Any]											=					scale_after < self._last_scale
  																			__lowerCamelCase		:      List[Any]											=					scale_after
  
  															else:
  																			self.optimizer.step(__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->List[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											for param_group in self.optimizer.param_groups:
  															__lowerCamelCase		:      str											=					[parameters_map.get(__A			,				__A	) for p in param_group["params"]]
  
  
  
  
  
  
  
  							@property
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											warnings.warn(
  											    "The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use "
  											    "`optimizer.step_was_skipped` instead."			,				__A			,				)
  											return self._is_overflow
  
  
  
  
  
  
  
  							@property
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->str:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return self._is_overflow
  
  
  
  
  
  
  
  							def __getstate__(   self	)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return self.__dict__.copy()
  
  
  
  
  
  
  
  							def __setstate__(   self			,				__A	)     ->Any:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											self.__dict__.update(__A	)
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  def 							a_       (						lowercase    )			->str:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				return " ".join(sorted(set(sentence.split()    )    )    )
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  
  
  
  
  