



'''simple docstring'''




from __future__ import annotations

from statistics import mean



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Optional[int]     =					[0] * no_of_processes
			A_     :						Optional[Any]     =					[0] * no_of_processes

			# Initialize remaining_time to waiting_time.

			for i in range(lowerCamelCase__   ):
						A_     :						Tuple     =					burst_time[i]
			A_     :						list[int]     =					[]

			A_     :						Optional[Any]     =					0
			A_     :						Optional[Any]     =					0

			# When processes are not completed,
			# A process whose arrival time has passed \
			# and has remaining execution time is put into the ready_process.
			# The shortest process in the ready_process, target_process is executed.

			while completed != no_of_processes:
						A_     :						Optional[Any]     =					[]
						A_     :						int     =					-1

						for i in range(lowerCamelCase__   ):
									if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
												ready_process.append(lowerCamelCase__   )

						if len(lowerCamelCase__   ) > 0:
									A_     :						Dict     =					ready_process[0]
									for i in ready_process:
												if remaining_time[i] < remaining_time[target_process]:
															A_     :						Any     =					i
									total_time += burst_time[target_process]
									completed += 1
									A_     :						List[Any]     =					0
									A_     :						Optional[Any]     =					(
									    total_time - arrival_time[target_process] - burst_time[target_process]
									)
						else:
									total_time += 1

			return waiting_time



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						int     =					[0] * no_of_processes
			for i in range(lowerCamelCase__   ):
						A_     :						Union[str, Any]     =					burst_time[i] + waiting_time[i]
			return turn_around_time


if __name__ == "__main__":
						print('''[TEST CASE 01]''')

						lowerCamelCase							:Tuple       = 4
						lowerCamelCase							:Tuple       = [2, 5, 3, 7]
						lowerCamelCase							:List[str]       = [0, 0, 0, 0]
						lowerCamelCase							:Any       = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
						lowerCamelCase							:Any       = calculate_turnaroundtime(
						    burst_time, no_of_processes, waiting_time
						)

						# Printing the Result
						print('''PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time''')
						for i, process_id in enumerate(list(range(1, 5))):
												print(
												    F"{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"
												    F"{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"
												)
						print(F"\nAverage waiting time = {mean(waiting_time):.5f}")
						print(F"Average turnaround time = {mean(turn_around_time):.5f}")




'''simple docstring'''




import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad







class      _lowerCAmelCase							(      unittest.TestCase			):


	def    _a     (self							):
				A_     :						Dict     =					10


	def    _a     (self							):
				A_     :						str     =					[1, 2, 3, 4]
				A_     :						List[str]     =					[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
				self.assertEqual(truncate_or_pad(lowercase	,				self.block_size	,				0							)	,				lowercase							)


	def    _a     (self							):
				A_     :						str     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				A_     :						Tuple     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(lowercase	,				self.block_size	,				0							)	,				lowercase							)


	def    _a     (self							):
				A_     :						List[Any]     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
				A_     :						str     =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(lowercase	,				self.block_size	,				0							)	,				lowercase							)


	def    _a     (self							):
				A_     :						Any     =					"""It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this."""
				A_,   A_     :						Optional[int]     =					process_story(lowercase							)
				self.assertEqual(lowercase	,				[]							)


	def    _a     (self							):
				A_     :						Union[str, Any]     =					""""""
				A_,   A_     :						Union[str, Any]     =					process_story(lowercase							)
				self.assertEqual(lowercase	,				[]							)
				self.assertEqual(lowercase	,				[]							)


	def    _a     (self							):
				A_     :						List[Any]     =					(
				    """It was the year of Our Lord one thousand seven hundred and """
				    """seventy-five\n\nSpiritual revelations were conceded to England """
				    """at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
				)
				A_,   A_     :						int     =					process_story(lowercase							)

				A_     :						List[Any]     =					[
				    """It was the year of Our Lord one thousand seven hundred and seventy-five.""",
				    """Spiritual revelations were conceded to England at that favoured period, as at this.""",
				]
				self.assertEqual(lowercase	,				lowercase							)

				A_     :						Tuple     =					["""It was the best of times."""]
				self.assertEqual(lowercase	,				lowercase							)


	def    _a     (self							):
				A_     :						Dict     =					torch.tensor([1, 2, 3, 4]							)
				A_     :						Union[str, Any]     =					torch.tensor([1, 1, 1, 1]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				0							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						Union[str, Any]     =					torch.tensor([1, 2, 3, 4, 23, 23, 23]							)
				A_     :						List[str]     =					torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				23							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						Optional[Any]     =					torch.tensor([8, 2, 3, 4, 1, 1, 1]							)
				A_     :						Tuple     =					torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase	,				1							).numpy()	,				expected.numpy()							)


	def    _a     (self							):
				A_     :						List[str]     =					101
				A_     :						str     =					torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]							)
				A_     :						int     =					torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]							)

				A_     :						Optional[Any]     =					compute_token_type_ids(lowercase	,				lowercase							)
				np.testing.assert_array_equal(lowercase	,				lowercase							)