from __future__ import annotations

from statistics import mean







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					list[int]   ):







    '''simple docstring'''
    __snake_case       :				int 			=    2
    __snake_case       :				Dict 			=    list(__SCREAMING_SNAKE_CASE   )
    __snake_case       :				Optional[int] 			=    [0] * len(__SCREAMING_SNAKE_CASE   )
    __snake_case       :				str 			=    0
    while True:
        __snake_case       :				str 			=    True
        for i, burst_time in enumerate(__SCREAMING_SNAKE_CASE   ):
            if rem_burst_times[i] > 0:
                __snake_case       :				int 			=    False
                if rem_burst_times[i] > quantum:
                    t += quantum
                    rem_burst_times[i] -= quantum
                else:
                    t += rem_burst_times[i]
                    __snake_case       :				Tuple 			=    t - burst_time
                    __snake_case       :				Dict 			=    0
        if done is True:
            return waiting_times







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					list[int]		,							__SCREAMING_SNAKE_CASE       :					list[int]   ):







    '''simple docstring'''
    return [burst + waiting for burst, waiting in zip(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )]


if __name__ == "__main__":
     lowercase_  							=			[3, 5, 7]
     lowercase_  							=			calculate_waiting_times(burst_times)
     lowercase_  							=			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 torch import autograd







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			,			_lowerCAmelCase					:     bool			):
           __snake_case       :				Optional[int] 			=    inputs.numel()
           __snake_case       :				str 			=    int(0.005 * nb_elems			) + 1
           if sigmoid:
               __snake_case       :				Union[str, Any] 			=    (torch.sigmoid(_lowerCAmelCase			) > threshold).type(inputs.type()			)
           else:
               __snake_case       :				Any 			=    (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
               __snake_case       :				Dict 			=    inputs.flatten().kthvalue(max(nb_elems - nb_min			,			1			)			).values
               __snake_case       :				List[str] 			=    (inputs > k_threshold).type(inputs.type()			)
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     str			,			_lowerCAmelCase					:     Tuple			):
           return gradOutput, None, None







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				str 			=    inputs.clone()
           __snake_case			, __snake_case       :				Dict 			=    inputs.flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    int(threshold * inputs.numel()			)

           # flat_out and mask access the same memory.
           __snake_case       :				Optional[int] 			=    mask.flatten()
           __snake_case       :				Dict 			=    0
           __snake_case       :				Optional[Any] 			=    1
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     Optional[Any]			):
           return gradOutput, None







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				List[str] 			=    inputs.clone()
           __snake_case			, __snake_case       :				str 			=    inputs.abs().flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				List[Any] 			=    int(threshold * inputs.numel()			)

           # flat_out and mask access the same memory.
           __snake_case       :				List[Any] 			=    mask.flatten()
           __snake_case       :				int 			=    0
           __snake_case       :				Optional[int] 			=    1
           return mask



