
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__		):
						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      A							(    torch.optim.Optimizer			):



			def __init__(   self    ,       lowerCamelCase__    ,       lowerCamelCase__=True    ,       lowerCamelCase__=None    )     ->						List[Any]:




									'''simple docstring'''



									lowercase__								=    optimizer
									lowercase__								=    scaler
									lowercase__								=    AcceleratorState()
									lowercase__								=    GradientState()
									lowercase__								=    device_placement
									lowercase__								=    False
									lowercase__								=    None

									# Handle device placement
									if device_placement:
															lowercase__								=    self.optimizer.state_dict()
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																					xm.send_cpu_data_to_device(lowerCamelCase__    ,       self.accelerator_state.device    )
															else:
																					lowercase__								=    move_to_device(lowerCamelCase__    ,       self.accelerator_state.device    )
															self.optimizer.load_state_dict(lowerCamelCase__    )



			@property
			def A__	(   self    )     ->						Any:




									'''simple docstring'''



									return self.optimizer.state



			@state.setter
			def A__	(   self    ,       lowerCamelCase__    )     ->						List[str]:




									'''simple docstring'''



									lowercase__								=    state



			@property
			def A__	(   self    )     ->						Optional[Any]:




									'''simple docstring'''



									return self.optimizer.param_groups



			@param_groups.setter
			def A__	(   self    ,       lowerCamelCase__    )     ->						str:




									'''simple docstring'''



									lowercase__								=    param_groups



			@property
			def A__	(   self    )     ->						Optional[Any]:




									'''simple docstring'''



									return self.optimizer.defaults



			@defaults.setter
			def A__	(   self    ,       lowerCamelCase__    )     ->						Dict:




									'''simple docstring'''



									lowercase__								=    defaults



			def A__	(   self    ,       lowerCamelCase__    )     ->						List[str]:




									'''simple docstring'''



									self.optimizer.add_param_group(lowerCamelCase__    )



			def A__	(   self    ,       lowerCamelCase__    )     ->						Tuple:




									'''simple docstring'''



									if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
															xm.send_cpu_data_to_device(lowerCamelCase__    ,       self.accelerator_state.device    )
									self.optimizer.load_state_dict(lowerCamelCase__    )



			def A__	(   self    )     ->						Union[str, Any]:




									'''simple docstring'''



									return self.optimizer.state_dict()



			def A__	(   self    ,       lowerCamelCase__=None    )     ->						Optional[Any]:




									'''simple docstring'''



									if self.gradient_state.sync_gradients:
															lowercase__								=    """set_to_none""" in inspect.signature(self.optimizer.zero_grad    ).parameters
															if accept_arg:
																					if set_to_none is None:
																											lowercase__								=    False
																					self.optimizer.zero_grad(set_to_none=lowerCamelCase__    )
															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 A__	(   self    ,       lowerCamelCase__=None    )     ->						Dict:




									'''simple docstring'''



									if self.gradient_state.sync_gradients:
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																					lowercase__								=    {"""closure""": closure} if closure is not None else {}
																					xm.optimizer_step(self.optimizer    ,       optimizer_args=lowerCamelCase__    )
															elif self.scaler is not None:
																					lowercase__								=    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
																											lowercase__								=    self.scaler.get_scale()
																											lowercase__								=    True
																					self.scaler.step(self.optimizer    ,       lowerCamelCase__    )
																					self.scaler.update()
																					lowercase__								=    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.
																											lowercase__								=    scale_after < self._last_scale
																					lowercase__								=    scale_after

															else:
																					self.optimizer.step(lowerCamelCase__    )



			def A__	(   self    ,       lowerCamelCase__    )     ->						List[str]:




									'''simple docstring'''



									for param_group in self.optimizer.param_groups:
															lowercase__								=    [parameters_map.get(lowerCamelCase__    ,       lowerCamelCase__    ) for p in param_group["""params"""]]



			@property
			def A__	(   self    )     ->						Tuple:




									'''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."""    ,       lowerCamelCase__    ,       )
									return self._is_overflow



			@property
			def A__	(   self    )     ->						Union[str, Any]:




									'''simple docstring'''



									return self._is_overflow



			def __getstate__(   self    )     ->						int:




									'''simple docstring'''



									return self.__dict__.copy()



			def __setstate__(   self    ,       lowerCamelCase__    )     ->						List[Any]:




									'''simple docstring'''



									self.__dict__.update(lowerCamelCase__    )








'''simple docstring'''



def 	_A						(							lowercase__		):
						return " ".join(sorted(set(sentence.split()		)		)		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






