



from statistics import mean

import numpy as np



def 		lowerCAmelCase__     (       UpperCamelCase_					:							list			,							UpperCamelCase_					:							list			,							UpperCamelCase_					:							list			,							UpperCamelCase_					:							int						)->	list:
    A__					=       0
    # Number of processes finished
    A__					=       0
    # Displays the finished process.
    # If it is 0, the performance is completed if it is 1, before the performance.
    A__					=       [0] * no_of_process
    # List to include calculation results
    A__					=       [0] * no_of_process

    # Sort by arrival time.
    A__					=       [burst_time[i] for i in np.argsort(UpperCamelCase_						)]
    A__					=       [process_name[i] for i in np.argsort(UpperCamelCase_						)]
    arrival_time.sort()

    while no_of_process > finished_process_count:
        A__					=       0
        while finished_process[i] == 1:
            i += 1
        if current_time < arrival_time[i]:
            A__					=       arrival_time[i]

        A__					=       0
        # Index showing the location of the process being performed
        A__					=       0
        # Saves the current response ratio.
        A__					=       0
        for i in range(0			,							UpperCamelCase_						):
            if finished_process[i] == 0 and arrival_time[i] <= current_time:
                A__					=       (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
                    i
                ]
            if response_ratio < temp:
                A__					=       temp
                A__					=       i

        # Calculate the turn around time
        A__					=       current_time + burst_time[loc] - arrival_time[loc]
        current_time += burst_time[loc]
        # Indicates that the process has been performed.
        A__					=       1
        # Increase finished_process_count by 1
        finished_process_count += 1

    return turn_around_time



def 		lowerCAmelCase__     (       UpperCamelCase_					:							list			,							UpperCamelCase_					:							list			,							UpperCamelCase_					:							list			,							UpperCamelCase_					:							int						)->	list:
    A__					=       [0] * no_of_process
    for i in range(0			,							UpperCamelCase_						):
        A__					=       turn_around_time[i] - burst_time[i]
    return waiting_time


if __name__ == "__main__":
   _lowercase    	=		5
   _lowercase    	=		["A", "B", "C", "D", "E"]
   _lowercase    	=		[1, 2, 3, 4, 5]
   _lowercase    	=		[1, 2, 3, 4, 5]

   _lowercase    	=		calculate_turn_around_time(
       process_name, arrival_time, burst_time, no_of_process
   )
   _lowercase    	=		calculate_waiting_time(
       process_name, turn_around_time, burst_time, no_of_process
   )

   print("Process name \tArrival time \tBurst time \tTurn around time \tWaiting time")
   for i in range(0, no_of_process):
      print(
          F"{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t"
          F"{turn_around_time[i]}\t\t\t{waiting_time[i]}"
      )

   print(F"average waiting time : {mean(waiting_time):.5f}")
   print(F"average turn around time : {mean(turn_around_time):.5f}")





import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler



@dataclass
class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					0
     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					3.0







class      _UpperCAmelCase      (							unittest.TestCase  ):




     def 		snake_case_		(					self):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs()			,    {})
         self.assertDictEqual(MockClass(a=2).to_kwargs()			,    {'''a''': 2})
         self.assertDictEqual(MockClass(a=2			,    b=a__).to_kwargs()			,    {'''a''': 2, '''b''': True})
         self.assertDictEqual(MockClass(a=2			,    c=2.2_5).to_kwargs()			,    {'''a''': 2, '''c''': 2.2_5})




     @require_cuda
     def 		snake_case_		(					self):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         A__					=       GradScalerKwargs(init_scale=1_0_2_4			,    growth_factor=2)
         AcceleratorState._reset_state()
         A__					=       Accelerator(mixed_precision='''fp16'''			,    kwargs_handlers=[scaler_handler])
         print(accelerator.use_fpaa)
         A__					=       accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale			,    1_0_2_4.0)
         self.assertEqual(scaler._growth_factor			,    2.0)

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor			,    0.5)
         self.assertEqual(scaler._growth_interval			,    2_0_0_0)
         self.assertEqual(scaler._enabled			,    a__)




     @require_multi_gpu
     def 		snake_case_		(					self):
         A__					=       ['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__)]
         execute_subprocess_async(a__			,    env=os.environ.copy())


if __name__ == "__main__":
   _lowercase    	=		DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
   _lowercase    	=		Accelerator(kwargs_handlers=[ddp_scaler])
   _lowercase    	=		torch.nn.Linear(100, 200)
   _lowercase    	=		accelerator.prepare(model)

   # Check the values changed in kwargs
   _lowercase    	=		""
   _lowercase    	=		model.bucket_bytes_cap // (1024 * 1024)
   if observed_bucket_cap_map != 15:
      error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
   if model.find_unused_parameters is not True:
      error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

   # Check the values of the defaults
   if model.dim != 0:
      error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
   if model.broadcast_buffers is not True:
      error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
   if model.gradient_as_bucket_view is not False:
      error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

   # Raise error at the end to make sure we don't stop at the first failure.
   if len(error_msg) > 0:
      raise ValueError(error_msg)
