


import torch
from torch import autograd







class     __lowerCamelCase     (  autograd.Function     ):


							"""simple docstring"""

							@staticmethod
							def 				A__      (  UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       Dict:






								'''simple docstring'''






								lowercase_			 = inputs.numel()
								lowercase_			 = int(0.005 * nb_elems  ) + 1
								if sigmoid:
									lowercase_			 = (torch.sigmoid(UpperCAmelCase  ) > threshold).type(inputs.type()  )
								else:
									lowercase_			 = (inputs > threshold).type(inputs.type()  )
								if mask.sum() < nb_min:
									# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
									lowercase_			 = inputs.flatten().kthvalue(max(nb_elems - nb_min						,       1  )  ).values
									lowercase_			 = (inputs > k_threshold).type(inputs.type()  )
								return mask




							@staticmethod
							def 				A__      (  UpperCAmelCase						,       UpperCAmelCase  )     ->       Any:






								'''simple docstring'''






								return gradOutput, None, None







class     __lowerCamelCase     (  autograd.Function     ):


							"""simple docstring"""

							@staticmethod
							def 				A__      (  UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       str:






								'''simple docstring'''






								lowercase_			 = inputs.clone()
								lowercase_ ,	lowercase_			 = inputs.flatten().sort(descending=UpperCAmelCase  )
								lowercase_			 = int(threshold * inputs.numel()  )

								# flat_out and mask access the same memory.
								lowercase_			 = mask.flatten()
								lowercase_			 = 0
								lowercase_			 = 1
								return mask




							@staticmethod
							def 				A__      (  UpperCAmelCase						,       UpperCAmelCase  )     ->       Optional[Any]:






								'''simple docstring'''






								return gradOutput, None







class     __lowerCamelCase     (  snake_case_     ):


							"""simple docstring"""

							@staticmethod
							def 				A__      (  UpperCAmelCase						,       UpperCAmelCase  )     ->       str:






								'''simple docstring'''






								lowercase_			 = inputs.clone()
								lowercase_ ,	lowercase_			 = inputs.abs().flatten().sort(descending=UpperCAmelCase  )
								lowercase_			 = int(threshold * inputs.numel()  )

								# flat_out and mask access the same memory.
								lowercase_			 = mask.flatten()
								lowercase_			 = 0
								lowercase_			 = 1
								return mask





from __future__ import annotations

from statistics import mean




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list[int]  ):







	'''simple docstring'''



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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list[int]  ,			__lowerCamelCase:		list[int]  ):







	'''simple docstring'''



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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       			=				[3, 5, 7]
							SCREAMING_SNAKE_CASE__       			=				calculate_waiting_times(burst_times)
							SCREAMING_SNAKE_CASE__       			=				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}""")

