






'''simple docstring'''




from __future__ import annotations

from statistics import mean

def 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase       )       ->						list[int]:

      '''simple docstring'''


      _A			     =	[0] * no_of_processes
      _A			     =	[0] * no_of_processes

      # Initialize remaining_time to waiting_time.

      for i in range(__lowercase       ):
            _A			     =	burst_time[i]
      _A			     =	[]

      _A			     =	0
      _A			     =	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			     =	[]
            _A			     =	-1

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

            if len(__lowercase       ) > 0:
                  _A			     =	ready_process[0]
                  for i in ready_process:
                        if remaining_time[i] < remaining_time[target_process]:
                              _A			     =	i
                  total_time += burst_time[target_process]
                  completed += 1
                  _A			     =	0
                  _A			     =	(
                      total_time - arrival_time[target_process] - burst_time[target_process]
                  )
            else:
                  total_time += 1

      return waiting_time

def 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase       )       ->						list[int]:

      '''simple docstring'''


      _A			     =	[0] * no_of_processes
      for i in range(__lowercase       ):
            _A			     =	burst_time[i] + waiting_time[i]
      return turn_around_time


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

    lowerCamelCase_												=					4
    lowerCamelCase_												=					[2, 5, 3, 7]
    lowerCamelCase_												=					[0, 0, 0, 0]
    lowerCamelCase_												=					calculate_waitingtime(arrival_time, burst_time, no_of_processes)
    lowerCamelCase_												=					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

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     @require_torch
     def  __lowerCAmelCase	(					self		)							->					Any:
         _UpperCAmelCase							:  str											=    pipeline(
             task='''zero-shot-audio-classification'''       ,	model='''hf-internal-testing/tiny-clap-htsat-unfused'''		)
         _UpperCAmelCase							:  List[Any]											=    load_dataset('''ashraq/esc50'''		)
         _UpperCAmelCase							:  Optional[int]											=    dataset['''train''']['''audio'''][-1]['''array''']
         _UpperCAmelCase							:  str											=    audio_classifier(A       ,	candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']		)
         self.assertEqual(
             nested_simplify(A		)       ,	[{'''score''': 0.501, '''label''': '''Sound of a dog'''}, {'''score''': 0.499, '''label''': '''Sound of vaccum cleaner'''}]       ,	)





     @unittest.skip('''No models are available in TF'''		)
     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         pass





     @slow
     @require_torch
     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Union[str, Any]											=    pipeline(
             task='''zero-shot-audio-classification'''       ,	model='''laion/clap-htsat-unfused'''       ,	)
         # This is an audio of a dog
         _UpperCAmelCase							:  List[Any]											=    load_dataset('''ashraq/esc50'''		)
         _UpperCAmelCase							:  Optional[int]											=    dataset['''train''']['''audio'''][-1]['''array''']
         _UpperCAmelCase							:  Any											=    audio_classifier(A       ,	candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']		)

         self.assertEqual(
             nested_simplify(A		)       ,	[
                 {'''score''': 0.999, '''label''': '''Sound of a dog'''},
                 {'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
             ]       ,	)

         _UpperCAmelCase							:  List[Any]											=    audio_classifier([audio] * 5       ,	candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']		)
         self.assertEqual(
             nested_simplify(A		)       ,	[
                 [
                     {'''score''': 0.999, '''label''': '''Sound of a dog'''},
                     {'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
                 ],
             ]
             * 5       ,	)
         _UpperCAmelCase							:  Tuple											=    audio_classifier(
             [audio] * 5       ,	candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']       ,	batch_size=5		)
         self.assertEqual(
             nested_simplify(A		)       ,	[
                 [
                     {'''score''': 0.999, '''label''': '''Sound of a dog'''},
                     {'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
                 ],
             ]
             * 5       ,	)





     @unittest.skip('''No models are available in TF'''		)
     def  __lowerCAmelCase	(					self		)							->					int:
         pass


