


from statistics import mean

import numpy as np
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				0
		# Number of processes finished
		lowercase__						=				0
		# Displays the finished process.
		# If it is 0, the performance is completed if it is 1, before the performance.
		lowercase__						=				[0] * no_of_process
		# List to include calculation results
		lowercase__						=				[0] * no_of_process

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

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

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

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

		return turn_around_time
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				[0] * no_of_process
		for i in range(0						,    SCREAMING_SNAKE_CASE					):
				lowercase__						=				turn_around_time[i] - burst_time[i]
		return waiting_time


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

			lowerCAmelCase	=							calculate_turn_around_time(
			    process_name, arrival_time, burst_time, no_of_process
			)
			lowerCAmelCase	=							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 unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
			import torch

			from transformers.activations import gelu_new, gelu_python, get_activation


@require_torch
class 	_a							(      unittest.TestCase      ):

							def 				lowerCamelCase_			(							self:     Dict							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]							)
									lowercase__						=				get_activation('''gelu'''							)
									self.assertTrue(torch.allclose(gelu_python(UpperCamelCase_							)   ,						torch_builtin(UpperCamelCase_							)							)							)
									self.assertFalse(torch.allclose(gelu_python(UpperCamelCase_							)   ,						gelu_new(UpperCamelCase_							)							)							)

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       str:





									"""simple docstring"""







									lowercase__						=				torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]							)
									lowercase__						=				get_activation('''gelu'''							)
									lowercase__						=				get_activation('''gelu_10'''							)

									lowercase__						=				torch_builtin(UpperCamelCase_							)
									lowercase__						=				geluaa(UpperCamelCase_							)

									lowercase__						=				torch.where(y_gelu_aa < 10.0   ,						1   ,						0							)

									self.assertTrue(torch.max(UpperCamelCase_							).item() == 10.0							)
									self.assertTrue(torch.allclose(y_gelu * clipped_mask   ,						y_gelu_aa * clipped_mask							)							)

							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       Tuple:





									"""simple docstring"""







									get_activation('''gelu'''							)
									get_activation('''gelu_10'''							)
									get_activation('''gelu_fast'''							)
									get_activation('''gelu_new'''							)
									get_activation('''gelu_python'''							)
									get_activation('''gelu_pytorch_tanh'''							)
									get_activation('''linear'''							)
									get_activation('''mish'''							)
									get_activation('''quick_gelu'''							)
									get_activation('''relu'''							)
									get_activation('''sigmoid'''							)
									get_activation('''silu'''							)
									get_activation('''swish'''							)
									get_activation('''tanh'''							)
									with self.assertRaises(UpperCamelCase_							):
											get_activation('''bogus'''							)
									with self.assertRaises(UpperCamelCase_							):
											get_activation(UpperCamelCase_							)


							def 				lowerCamelCase_			(							self:     Tuple							)      ->       int:





									"""simple docstring"""







									lowercase__						=				get_activation('''gelu'''							)
									lowercase__						=				1
									lowercase__						=				get_activation('''gelu'''							)
									self.assertEqual(acta.a   ,						1							)
									with self.assertRaises(UpperCamelCase_							):
											lowercase__						=				acta.a


