'''simple docstring'''





import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment


def     __lowerCAmelCase						(       a_       ,      a_=()       ,      a_=None       ,      a_="no"       ,      a_="29500"		)      ->	Optional[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[str]    	=       False
     SCREAMING_SNAKE_CASE					:			int    	=       False
     if any(key.startswith('KAGGLE'		) for key in os.environ.keys()		):
          SCREAMING_SNAKE_CASE					:			int    	=       True
     elif "IPython" in sys.modules:
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       'google.colab' in str(sys.modules['IPython'].get_ipython()		)

     try:
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       PrecisionType(mixed_precision.lower()		)
     except ValueError:
          raise ValueError(
              f"""Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."""		)

     if (in_colab or in_kaggle) and (os.environ.get('TPU_NAME'       ,      a_		) is not None):
          # TPU launch
          import torch_xla.distributed.xla_multiprocessing as xmp

          if len(AcceleratorState._shared_state		) > 0:
               raise ValueError(
                   'To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '
                   'your training function. Restart your notebook and make sure no cells initializes an '
                   '`Accelerator`.'		)
          if num_processes is None:
               SCREAMING_SNAKE_CASE					:			Any    	=       8

          SCREAMING_SNAKE_CASE					:			List[Any]    	=       PrepareForLaunch(a_       ,      distributed_type='TPU'		)
          print(f"""Launching a training on {num_processes} TPU cores."""		)
          xmp.spawn(a_       ,      args=a_       ,      nprocs=a_       ,      start_method='fork'		)
     elif in_colab:
          # No need for a distributed launch otherwise as it's either CPU or one GPU.
          if torch.cuda.is_available():
               print('Launching training on one GPU.'		)
          else:
               print('Launching training on one CPU.'		)
          function(*a_		)
     else:
          if num_processes is None:
               raise ValueError(
                   'You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.'		)

          if num_processes > 1:
               # Multi-GPU launch
               from torch.multiprocessing import start_processes
               from torch.multiprocessing.spawn import ProcessRaisedException

               if len(AcceleratorState._shared_state		) > 0:
                    raise ValueError(
                        'To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '
                        'inside your training function. Restart your notebook and make sure no cells initializes an '
                        '`Accelerator`.'		)

               if torch.cuda.is_initialized():
                    raise ValueError(
                        'To launch a multi-GPU training from your notebook, you need to avoid running any instruction '
                        'using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '
                        'function.'		)

               # torch.distributed will expect a few environment variable to be here. We set the ones common to each
               # process here (the other ones will be set be the launcher).
               with patch_environment(
                   world_size=a_       ,      master_addr='127.0.01'       ,      master_port=a_       ,      mixed_precision=a_		):
                    SCREAMING_SNAKE_CASE					:			List[str]    	=       PrepareForLaunch(a_       ,      distributed_type='MULTI_GPU'		)
                    print(f"""Launching training on {num_processes} GPUs."""		)
                    try:
                         start_processes(a_       ,      args=a_       ,      nprocs=a_       ,      start_method='fork'		)
                    except ProcessRaisedException as e:
                         if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                              raise RuntimeError(
                                  'CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '
                                  'This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '
                                  'Please review your imports and test them when running the `notebook_launcher()` to identify '
                                  'which one is problematic.'		) from e

          else:
               # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
               if is_mps_available():
                    SCREAMING_SNAKE_CASE					:			Tuple    	=       '1'
                    print('Launching training on MPS.'		)
               elif torch.cuda.is_available():
                    print('Launching training on one GPU.'		)
               else:
                    print('Launching training on CPU.'		)
               function(*a_		)


def     __lowerCAmelCase						(       a_       ,      a_=()       ,      a_=2		)      ->	List[Any]:





     '''simple docstring'''





     from torch.multiprocessing import start_processes

     with tempfile.NamedTemporaryFile() as tmp_file:
          # torch.distributed will expect a few environment variable to be here. We set the ones common to each
          # process here (the other ones will be set be the launcher).
          with patch_environment(
              world_size=a_       ,      master_addr='127.0.01'       ,      master_port='29500'       ,      accelerate_mixed_precision='no'       ,      accelerate_debug_rdv_file=tmp_file.name       ,      accelerate_use_cpu='yes'       ,      ):
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       PrepareForLaunch(a_       ,      debug=a_		)
               start_processes(a_       ,      args=a_       ,      nprocs=a_       ,      start_method='fork'		)




'''simple docstring'''





from __future__ import annotations

from statistics import mean


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	list[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [0] * no_of_processes
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [0] * no_of_processes

     # Initialize remaining_time to waiting_time.

     for i in range(a_		):
          SCREAMING_SNAKE_CASE					:			Tuple    	=       burst_time[i]
     SCREAMING_SNAKE_CASE					:			list[int]    	=       []

     SCREAMING_SNAKE_CASE					:			List[Any]    	=       0
     SCREAMING_SNAKE_CASE					:			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:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       []
          SCREAMING_SNAKE_CASE					:			Dict    	=       -1

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

          if len(a_		) > 0:
               SCREAMING_SNAKE_CASE					:			Dict    	=       ready_process[0]
               for i in ready_process:
                    if remaining_time[i] < remaining_time[target_process]:
                         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       i
               total_time += burst_time[target_process]
               completed += 1
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       0
               SCREAMING_SNAKE_CASE					:			Tuple    	=       (
                   total_time - arrival_time[target_process] - burst_time[target_process]
               )
          else:
               total_time += 1

     return waiting_time


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	list[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       [0] * no_of_processes
     for i in range(a_		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       burst_time[i] + waiting_time[i]
     return turn_around_time


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

 _lowerCAmelCase  :Optional[int]  				=    4
 _lowerCAmelCase  :Optional[int]  				=    [2, 5, 3, 7]
 _lowerCAmelCase  :List[str]  				=    [0, 0, 0, 0]
 _lowerCAmelCase  :Union[str, 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}""")



