




"""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 						UpperCAmelCase				(			A__:    Optional[int]							,				A__:    Optional[Any]=()							,				A__:    List[str]=None							,				A__:    Any="no"							,				A__:    Any="29500"     )  ->     List[Any]:
			__lowerCamelCase					:	Any	       =				False
			__lowerCamelCase					:	Any	       =				False
			if any(key.startswith('KAGGLE'     ) for key in os.environ.keys()     ):
						__lowerCamelCase					:	Optional[Any]	       =				True
			elif "IPython" in sys.modules:
						__lowerCamelCase					:	Tuple	       =				'google.colab' in str(sys.modules['IPython'].get_ipython()     )

			try:
						__lowerCamelCase					:	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:
									__lowerCamelCase					:	Any	       =				8

						__lowerCamelCase					:	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__     ):
												__lowerCamelCase					:	Dict	       =				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():
												__lowerCamelCase					:	List[str]	       =				'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 						UpperCAmelCase				(			A__:    Dict							,				A__:    Dict=()							,				A__:    List[Any]=2     )  ->     str:
			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'							,				):
									__lowerCamelCase					:	List[Any]	       =				PrepareForLaunch(A__							,				debug=A__     )
									start_processes(A__							,				args=A__							,				nprocs=A__							,				start_method='fork'     )









"""simple docstring"""
from __future__ import annotations

import pandas as pd





def 						UpperCAmelCase				(			A__:    list[int]							,				A__:    list[int]							,				A__:    int     )  ->     list[int]:
			__lowerCamelCase					:	List[Any]	       =				[0] * no_of_processes
			__lowerCamelCase					:	Any	       =				[0] * no_of_processes
			# Copy the burst time into remaining_time[]
			for i in range(A__     ):
						__lowerCamelCase					:	Dict	       =				burst_time[i]

			__lowerCamelCase					:	Any	       =				0
			__lowerCamelCase					:	Tuple	       =				0
			__lowerCamelCase					:	Union[str, Any]	       =				999999999
			__lowerCamelCase					:	str	       =				0
			__lowerCamelCase					:	Optional[int]	       =				False

			# Process until all processes are completed
			while complete != no_of_processes:
						for j in range(A__     ):
									if arrival_time[j] <= increment_time and remaining_time[j] > 0:
												if remaining_time[j] < minm:
															__lowerCamelCase					:	List[Any]	       =				remaining_time[j]
															__lowerCamelCase					:	List[str]	       =				j
															__lowerCamelCase					:	Optional[int]	       =				True

						if not check:
									increment_time += 1
									continue
						remaining_time[short] -= 1

						__lowerCamelCase					:	Optional[Any]	       =				remaining_time[short]
						if minm == 0:
									__lowerCamelCase					:	Optional[int]	       =				999999999

						if remaining_time[short] == 0:
									complete += 1
									__lowerCamelCase					:	List[str]	       =				False

									# Find finish time of current process
									__lowerCamelCase					:	Dict	       =				increment_time + 1

									# Calculate waiting time
									__lowerCamelCase					:	Any	       =				finish_time - arrival_time[short]
									__lowerCamelCase					:	Dict	       =				finar - burst_time[short]

									if waiting_time[short] < 0:
												__lowerCamelCase					:	Optional[Any]	       =				0

        # Increment time
						increment_time += 1
			return waiting_time





def 						UpperCAmelCase				(			A__:    list[int]							,				A__:    int							,				A__:    list[int]     )  ->     list[int]:
			__lowerCamelCase					:	List[Any]	       =				[0] * no_of_processes
			for i in range(A__     ):
						__lowerCamelCase					:	Tuple	       =				burst_time[i] + waiting_time[i]
			return turn_around_time





def 						UpperCAmelCase				(			A__:    list[int]							,				A__:    list[int]							,				A__:    int     )  ->     None:
			__lowerCamelCase					:	int	       =				0
			__lowerCamelCase					:	Dict	       =				0
			for i in range(A__     ):
						__lowerCamelCase					:	str	       =				total_waiting_time + waiting_time[i]
						__lowerCamelCase					:	Union[str, Any]	       =				total_turn_around_time + turn_around_time[i]
			print(f'''Average waiting time = {total_waiting_time / no_of_processes:.5f}'''     )
			print('Average turn around time ='							,				total_turn_around_time / no_of_processes     )


if __name__ == "__main__":
					print('''Enter how many process you want to analyze''')
					a_				:    int										=     int(input())
					a_				:    List[str]										=     [0] * no_of_processes
					a_				:    int										=     [0] * no_of_processes
					a_				:    int										=     list(range(1, no_of_processes + 1))

					for i in range(no_of_processes):
										print('''Enter the arrival time and burst time for process:--''' + str(i + 1))
										a_						,							a_				:    Union[str, Any]										=     map(int, input().split())

					a_				:    Any										=     calculate_waitingtime(arrival_time, burst_time, no_of_processes)

					a_				:    List[str]										=     burst_time
					a_				:    List[Any]										=     no_of_processes
					a_				:    Tuple										=     waiting_time
					a_				:    Optional[Any]										=     calculate_turnaroundtime(bt, n, wt)

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					a_				:    List[Any]										=     pd.DataFrame(
					    list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
					    columns=[
					        '''Process''',
					        '''BurstTime''',
					        '''ArrivalTime''',
					        '''WaitingTime''',
					        '''TurnAroundTime''',
					    ],
					)

					# Printing the dataFrame
					pd.set_option('''display.max_rows''', fcfs.shape[0] + 1)
					print(fcfs)



