

'''simple docstring'''
from __future__ import annotations

from statistics import mean


def 	_lowerCAmelCase (						_UpperCamelCase						:					list[int]							,			_UpperCamelCase						:					list[int]							,			_UpperCamelCase						:					int    )				->      list[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =[0] * no_of_processes
     _SCREAMING_SNAKE_CASE							   =[0] * no_of_processes

     # Initialize remaining_time to waiting_time.

     for i in range(_UpperCamelCase    ):
          _SCREAMING_SNAKE_CASE							   =burst_time[i]
     _SCREAMING_SNAKE_CASE							   =[]

     _SCREAMING_SNAKE_CASE							   =0
     _SCREAMING_SNAKE_CASE							   =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:
          _SCREAMING_SNAKE_CASE							   =[]
          _SCREAMING_SNAKE_CASE							   =-1

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

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

     return waiting_time


def 	_lowerCAmelCase (						_UpperCamelCase						:					list[int]							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					list[int]    )				->      list[int]:
     """simple docstring"""




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


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

     lowerCamelCase			:				Union[str, Any] 			=					4
     lowerCamelCase			:				int 			=					[2, 5, 3, 7]
     lowerCamelCase			:				List[Any] 			=					[0, 0, 0, 0]
     lowerCamelCase			:				Dict 			=					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 os
import sys
import unittest


lowerCamelCase			:				Optional[Any] 			=					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowerCamelCase			:				Optional[int] 			=					os.path.join(git_repo_path, "src", "transformers")

lowerCamelCase			:				Union[str, Any] 			=					"\n{0} = None\n"

lowerCamelCase			:				Optional[Any] 			=					"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


lowerCamelCase			:				List[Any] 			=					"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"







class 							A__   (	unittest.TestCase  ):





  def 						A  (		self				:     Optional[int]			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")'			)
       self.assertIsNone(_a			)

       _SCREAMING_SNAKE_CASE							   =find_backend('    if not is_tokenizers_available():'			)
       self.assertEqual(_a							, 'tokenizers'			)

       _SCREAMING_SNAKE_CASE							   =find_backend('    if not is_tensorflow_text_available():'			)
       self.assertEqual(_a							, 'tensorflow_text'			)

       _SCREAMING_SNAKE_CASE							   =find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):'			)
       self.assertEqual(_a							, 'sentencepiece_and_tokenizers'			)

       _SCREAMING_SNAKE_CASE							   =find_backend(
           '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'			)
       self.assertEqual(_a							, 'sentencepiece_and_tensorflow_text'			)

       _SCREAMING_SNAKE_CASE							   =find_backend(
           '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'			)
       self.assertEqual(_a							, 'sentencepiece_and_tokenizers_and_vision'			)





  def 						A  (		self				:     Optional[int]			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =read_init()
       # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
       self.assertIn('torch'							, _a			)
       self.assertIn('tensorflow_text'							, _a			)
       self.assertIn('sentencepiece_and_tokenizers'							, _a			)

       # Likewise, we can't assert on the exact content of a key
       self.assertIn('BertModel'							, objects['torch']			)
       self.assertIn('TFBertModel'							, objects['tf']			)
       self.assertIn('FlaxBertModel'							, objects['flax']			)
       self.assertIn('BertModel'							, objects['torch']			)
       self.assertIn('TFBertTokenizer'							, objects['tensorflow_text']			)
       self.assertIn('convert_slow_tokenizer'							, objects['sentencepiece_and_tokenizers']			)





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =create_dummy_object('CONSTANT'							, '\'torch\''			)
       self.assertEqual(_a							, '\nCONSTANT = None\n'			)

       _SCREAMING_SNAKE_CASE							   =create_dummy_object('function'							, '\'torch\''			)
       self.assertEqual(
           _a							, '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'			)

       _SCREAMING_SNAKE_CASE							   ='\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
       _SCREAMING_SNAKE_CASE							   =create_dummy_object('FakeClass'							, '\'torch\''			)
       self.assertEqual(_a							, _a			)





  def 						A  (		self				:     Optional[int]			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   ='# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
       _SCREAMING_SNAKE_CASE							   =create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}			)
       self.assertEqual(dummy_files['torch']							, _a			)
