


'''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	)









'''simple docstring'''




def 							a_       (						lowercase    )			->str:






				'''simple docstring'''

				return " ".join(sorted(set(sentence.split()    )    )    )


if __name__ == "__main__":
				import doctest

				doctest.testmod()





