



from __future__ import annotations

from statistics import mean


def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       2
					_lowerCAmelCase         =       list(snake_case     )
					_lowerCAmelCase         =       [0] * len(snake_case     )
					_lowerCAmelCase         =       0
					while True:
										_lowerCAmelCase         =       True
										for i, burst_time in enumerate(snake_case     ):
															if rem_burst_times[i] > 0:
																				_lowerCAmelCase         =       False
																				if rem_burst_times[i] > quantum:
																									t += quantum
																									rem_burst_times[i] -= quantum
																				else:
																									t += rem_burst_times[i]
																									_lowerCAmelCase         =       t - burst_time
																									_lowerCAmelCase         =       0
										if done is True:
															return waiting_times


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					return [burst + waiting for burst, waiting in zip(snake_case      ,		snake_case     )]


if __name__ == "__main__":
	A__       =			[3, 5, 7]
	A__       =			calculate_waiting_times(burst_times)
	A__       =			calculate_turn_around_times(burst_times, waiting_times)
	print("""Process ID \tBurst Time \tWaiting Time \tTurnaround Time""")
	for i, burst_time in enumerate(burst_times):
		print(
		    f"  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  "
		    f"{turn_around_times[i]}"
		)
	print(f"\nAverage waiting time = {mean(waiting_times):.5f}")
	print(f"Average turn around time = {mean(turn_around_times):.5f}")





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'  )