
from statistics import mean

import numpy as np


def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)       ->  list:
		'''simple docstring'''

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

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

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

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

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

		return turn_around_time


def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)       ->  list:
		'''simple docstring'''

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


if __name__ == "__main__":
					_UpperCAmelCase :			List[str]              =	5
					_UpperCAmelCase :			Tuple              =	["A", "B", "C", "D", "E"]
					_UpperCAmelCase :			Union[str, Any]              =	[1, 2, 3, 4, 5]
					_UpperCAmelCase :			int              =	[1, 2, 3, 4, 5]

					_UpperCAmelCase :			str              =	calculate_turn_around_time(
					    process_name, arrival_time, burst_time, no_of_process
					)
					_UpperCAmelCase :			Dict              =	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

import torch
from torch import nn

from diffusers.models.activations import get_activation


class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('swish'	)

				self.assertIsInstance(A_    ,				nn.SiLU	)

				self.assertEqual(act(torch.tensor(-100    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('silu'	)

				self.assertIsInstance(A_    ,				nn.SiLU	)

				self.assertEqual(act(torch.tensor(-100    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('mish'	)

				self.assertIsInstance(A_    ,				nn.Mish	)

				self.assertEqual(act(torch.tensor(-200    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('gelu'	)

				self.assertIsInstance(A_    ,				nn.GELU	)

				self.assertEqual(act(torch.tensor(-100    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)
