





"""simple docstring"""




from __future__ import annotations

from statistics import mean


def _A       (   __magic_name__  , __magic_name__  , __magic_name__	):
   lowercase__			       =  [0] * no_of_processes
   lowercase__			       =  [0] * no_of_processes

   # Initialize remaining_time to waiting_time.

   for i in range(lowerCamelCase__	):
      lowercase__			       =  burst_time[i]
   lowercase__			       =  []

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

   return waiting_time


def _A       (   __magic_name__  , __magic_name__  , __magic_name__	):
   lowercase__			       =  [0] * no_of_processes
   for i in range(lowerCamelCase__	):
      lowercase__			       =  burst_time[i] + waiting_time[i]
   return turn_around_time


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

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













import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
  from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm


def _A       (   __magic_name__	):
   if is_torch_version("<"  , "2.0.0"	) or not hasattr(__magic_name__  , "_dynamo"	):
      return False
   return isinstance(__magic_name__  , torch._dynamo.eval_frame.OptimizedModule	)


def _A       (   __magic_name__  , __magic_name__ = True	):
   lowercase__			       =  (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

   lowercase__			       =  is_compiled_module(__magic_name__	)
   if is_compiled:
      lowercase__			       =  model
      lowercase__			       =  model._orig_mod

   if is_deepspeed_available():
      options += (DeepSpeedEngine,)

   while isinstance(__magic_name__  , __magic_name__	):
      lowercase__			       =  model.module

   if not keep_fpaa_wrapper:
      lowercase__			       =  getattr(__magic_name__  , "forward"	)
      lowercase__			       =  model.__dict__.pop("_original_forward"  , __magic_name__	)
      if original_forward is not None:
         while hasattr(__magic_name__  , "__wrapped__"	):
            lowercase__			       =  forward.__wrapped__
            if forward == original_forward:
               break
         lowercase__			       =  forward
      if getattr(__magic_name__  , "_converted_to_transformer_engine"  , __magic_name__	):
         convert_model(__magic_name__  , to_transformer_engine=__magic_name__	)

   if is_compiled:
      lowercase__			       =  model
      lowercase__			       =  compiled_model

   return model


def _A       (   ):
   PartialState().wait_for_everyone()


def _A       (   __magic_name__  , __magic_name__	):
   if PartialState().distributed_type == DistributedType.TPU:
      xm.save(__magic_name__  , __magic_name__	)
   elif PartialState().local_process_index == 0:
      torch.save(__magic_name__  , __magic_name__	)


@contextmanager
def _A       (   **__magic_name__	):
   for key, value in kwargs.items():
      lowercase__			       =  str(__magic_name__	)

   yield

   for key in kwargs:
      if key.upper() in os.environ:
         del os.environ[key.upper()]


def _A       (   __magic_name__	):
   if not hasattr(__magic_name__  , "__qualname__"	) and not hasattr(__magic_name__  , "__name__"	):
      lowercase__			       =  getattr(__magic_name__  , "__class__"  , __magic_name__	)
   if hasattr(__magic_name__  , "__qualname__"	):
      return obj.__qualname__
   if hasattr(__magic_name__  , "__name__"	):
      return obj.__name__
   return str(__magic_name__	)


def _A       (   __magic_name__  , __magic_name__	):
   for key, value in source.items():
      if isinstance(__magic_name__  , __magic_name__	):
         lowercase__			       =  destination.setdefault(__magic_name__  , {}	)
         merge_dicts(__magic_name__  , __magic_name__	)
      else:
         lowercase__			       =  value

   return destination







def _A       (   __magic_name__ = None	):
   if port is None:
      lowercase__			       =  2_9500
   with socket.socket(socket.AF_INET  , socket.SOCK_STREAM	) as s:
      return s.connect_ex(("localhost", port)	) == 0






