






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_      (__A							,					__A			)							->	Dict:



					"""simple docstring"""
					if isinstance(__A							,					(list, tuple)			):
										return honor_type(__A							,					(move_to_device(__A							,					__A			) for t in state)			)
					elif isinstance(__A							,					__A			):
										return type(__A			)({k: move_to_device(__A							,					__A			) for k, v in state.items()}			)
					elif isinstance(__A							,					torch.Tensor			):
										return state.to(__A			)
					return state




class        snake_case_					(						torch.optim.Optimizer			):

						"""simple docstring"""



						def __init__(self:				List[str]   ,				__UpperCAmelCase:				Union[str, Any]   ,				__UpperCAmelCase:				List[Any]=True   ,				__UpperCAmelCase:				List[str]=None      )						->    Tuple:




											'''simple docstring'''



											__a				:	Dict			=				optimizer
											__a				:	Union[str, Any]			=				scaler
											__a				:	List[Any]			=				AcceleratorState()
											__a				:	Optional[int]			=				GradientState()
											__a				:	Optional[Any]			=				device_placement
											__a				:	int			=				False
											__a				:	Tuple			=				None

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



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




											'''simple docstring'''



											return self.optimizer.state



						@state.setter
						def 					UpperCAmelCase__				(self:				Union[str, Any]   ,				__UpperCAmelCase:				Tuple      )						->    Union[str, Any]:




											'''simple docstring'''



											__a				:	Optional[Any]			=				state



						@property
						def 					UpperCAmelCase__				(self:				str      )						->    Any:




											'''simple docstring'''



											return self.optimizer.param_groups



						@param_groups.setter
						def 					UpperCAmelCase__				(self:				Optional[int]   ,				__UpperCAmelCase:				Union[str, Any]      )						->    Tuple:




											'''simple docstring'''



											__a				:	Union[str, Any]			=				param_groups



						@property
						def 					UpperCAmelCase__				(self:				Optional[int]      )						->    int:




											'''simple docstring'''



											return self.optimizer.defaults



						@defaults.setter
						def 					UpperCAmelCase__				(self:				List[Any]   ,				__UpperCAmelCase:				Union[str, Any]      )						->    Tuple:




											'''simple docstring'''



											__a				:	Tuple			=				defaults



						def 					UpperCAmelCase__				(self:				str   ,				__UpperCAmelCase:				List[Any]      )						->    Dict:




											'''simple docstring'''



											self.optimizer.add_param_group(__UpperCAmelCase      )



						def 					UpperCAmelCase__				(self:				Any   ,				__UpperCAmelCase:				Optional[Any]      )						->    Dict:




											'''simple docstring'''



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



						def 					UpperCAmelCase__				(self:				List[str]      )						->    List[Any]:




											'''simple docstring'''



											return self.optimizer.state_dict()



						def 					UpperCAmelCase__				(self:				Union[str, Any]   ,				__UpperCAmelCase:				List[str]=None      )						->    Optional[int]:




											'''simple docstring'''



											if self.gradient_state.sync_gradients:
																__a				:	Tuple			=				"set_to_none" in inspect.signature(self.optimizer.zero_grad      ).parameters
																if accept_arg:
																					if set_to_none is None:
																										__a				:	Optional[Any]			=				False
																					self.optimizer.zero_grad(set_to_none=__UpperCAmelCase      )
																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 					UpperCAmelCase__				(self:				Optional[int]   ,				__UpperCAmelCase:				int=None      )						->    List[Any]:




											'''simple docstring'''



											if self.gradient_state.sync_gradients:
																if self.accelerator_state.distributed_type == DistributedType.TPU:
																					__a				:	List[Any]			=				{"closure": closure} if closure is not None else {}
																					xm.optimizer_step(self.optimizer   ,				optimizer_args=__UpperCAmelCase      )
																elif self.scaler is not None:
																					__a				:	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
																										__a				:	Dict			=				self.scaler.get_scale()
																										__a				:	Dict			=				True
																					self.scaler.step(self.optimizer   ,				__UpperCAmelCase      )
																					self.scaler.update()
																					__a				:	Optional[int]			=				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.
																										__a				:	List[Any]			=				scale_after < self._last_scale
																					__a				:	List[str]			=				scale_after

																else:
																					self.optimizer.step(__UpperCAmelCase      )



						def 					UpperCAmelCase__				(self:				Union[str, Any]   ,				__UpperCAmelCase:				Optional[Any]      )						->    Optional[int]:




											'''simple docstring'''



											for param_group in self.optimizer.param_groups:
																__a				:	Tuple			=				[parameters_map.get(__UpperCAmelCase   ,				__UpperCAmelCase      ) for p in param_group["params"]]



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




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



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




											'''simple docstring'''



											return self._is_overflow



						def __getstate__(self:				Tuple      )						->    Dict:




											'''simple docstring'''



											return self.__dict__.copy()



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




											'''simple docstring'''



											self.__dict__.update(__UpperCAmelCase      )













def 						a_      (__A			)							->	list:



					"""simple docstring"""
					if len(__A			) <= 1:
										return data
					else:
										return [
										    *quick_sort([e for e in data[1:] if e <= data[0]]			),
										    data[0],
										    *quick_sort([e for e in data[1:] if e > data[0]]			),
										]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





