
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						None:
			if target == 0:
						answer.append(path.copy()	)
			else:
						for index in range(SCREAMING_SNAKE_CASE_			,				len(SCREAMING_SNAKE_CASE_	)	):
									if target >= candidates[index]:
												path.append(candidates[index]	)
												backtrack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				target - candidates[index]			,				SCREAMING_SNAKE_CASE_	)
												path.pop(len(SCREAMING_SNAKE_CASE_	) - 1	)
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						list:
			lowerCAmelCase__ : List[Any]   =						[]  # type: list[int]
			lowerCAmelCase__ : Optional[int]   =						[]  # type: list[int]
			backtrack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				0	)
			return answer
def 					lowerCAmelCase__     (    )							->						None:
			print(combination_sum([-8, 2.3, 0]			,				1	)	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()

import torch

from accelerate import Accelerator, DistributedType







class 	A__				:






				def __enter__(		self   : Optional[Any]  ):






							'''simple docstring'''







							if self.enabled:
										lowerCAmelCase__ : Any   =						self.model.no_sync()
										self.model_sync_obj.__enter__()

							return self






				def __exit__(		self   : int       ,	a   : int       ,	a   : Optional[Any]       ,	a   : str  ):






							'''simple docstring'''







							if self.enabled:
										# Average all models on exit
										self._sync_and_avg_model_params()
										self.model_sync_obj.__exit__(a       ,	a       ,	a  )






				def __init__(		self   : List[str]       ,	a   : Accelerator       ,	a   : torch.nn.Module       ,	a   : int       ,	a   : bool = True  ):






							'''simple docstring'''







							if accelerator.distributed_type not in [
							    DistributedType.NO,
							    DistributedType.MULTI_CPU,
							    DistributedType.MULTI_GPU,
							]:
										raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'  )
							lowerCAmelCase__ : Union[str, Any]   =						enabled and accelerator.distributed_type != DistributedType.NO
							lowerCAmelCase__ : str   =						0
							if self.enabled:
										lowerCAmelCase__ : Dict   =						accelerator
										lowerCAmelCase__ : Dict   =						model
										lowerCAmelCase__ : Optional[int]   =						local_sgd_steps






				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







							self.num_steps += 1
							if not self.enabled:
										return

							if self.num_steps % self.local_sgd_steps == 0:
										self._sync_and_avg_model_params()






				def 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							self.accelerator.wait_for_everyone()
							with self.accelerator.autocast():
										for param in self.model.parameters():
													lowerCAmelCase__ : Dict   =						self.accelerator.reduce(param.data       ,	reduction='mean'  )