


from datetime import datetime

import matplotlib.pyplot as plt
import torch







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				int:
  for param in module.parameters():
    snake_case__						= False







def SCREAMING_SNAKE_CASE	(			)    ->				int:
  snake_case__						= '''cuda''' if torch.cuda.is_available() else '''cpu'''
  if torch.backends.mps.is_available() and torch.backends.mps.is_built():
    snake_case__						= '''mps'''
  if device == "mps":
    print(
        '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
        ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
        ''' with generations.'''				)
  return device







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				List[Any]:
  snake_case__						= plt.imshow(__lowerCAmelCase				)
  fig.axes.get_xaxis().set_visible(__lowerCAmelCase				)
  fig.axes.get_yaxis().set_visible(__lowerCAmelCase				)
  plt.show()







def SCREAMING_SNAKE_CASE	(			)    ->				Optional[Any]:
  snake_case__						= datetime.now()
  snake_case__						= current_time.strftime('''%H:%M:%S'''				)
  return timestamp





from __future__ import annotations

from statistics import mean







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				list[int]:
  snake_case__						= [0] * no_of_processes
  snake_case__						= [0] * no_of_processes

  # Initialize remaining_time to waiting_time.

  for i in range(__lowerCAmelCase				):
    snake_case__						= burst_time[i]
  snake_case__						= []

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

  return waiting_time







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				list[int]:
  snake_case__						= [0] * no_of_processes
  for i in range(__lowerCAmelCase				):
    snake_case__						= burst_time[i] + waiting_time[i]
  return turn_around_time


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

   lowerCamelCase__							:						Tuple							     =   4
   lowerCamelCase__							:						Union[str, Any]							     =   [2, 5, 3, 7]
   lowerCamelCase__							:						Optional[Any]							     =   [0, 0, 0, 0]
   lowerCamelCase__							:						Dict							     =   calculate_waitingtime(arrival_time, burst_time, no_of_processes)
   lowerCamelCase__							:						Union[str, 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}""")

