





"""simple docstring"""




import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment


class 			__UpperCamelCase							(		unittest.TestCase		):






							def        __a  (  self )      ->					str:
								a :						List[Any]      =							inspect.getfile(accelerate.test_utils )
								a :						Union[str, Any]      =							os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_script.py"] )
								a :						Dict      =							os.path.sep.join(
								    mod_file.split(os.path.sep )[:-1] + ["scripts", "test_distributed_data_loop.py"] )
								a :						List[Any]      =							os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_ops.py"] )






							@require_multi_gpu
							def        __a  (  self )      ->					Optional[int]:
								print(f"""Found {torch.cuda.device_count()} devices.""" )
								a :						int      =							["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
								with patch_environment(omp_num_threads=1 ):
									execute_subprocess_async(lowerCAmelCase__ ,   env=os.environ.copy() )






							@require_multi_gpu
							def        __a  (  self )      ->					List[str]:
								print(f"""Found {torch.cuda.device_count()} devices.""" )
								a :						Any      =							["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
								print(f"""Command: {cmd}""" )
								with patch_environment(omp_num_threads=1 ):
									execute_subprocess_async(lowerCAmelCase__ ,   env=os.environ.copy() )






							@require_multi_gpu
							def        __a  (  self )      ->					Any:
								a :						List[Any]      =							["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
								with patch_environment(omp_num_threads=1 ):
									execute_subprocess_async(lowerCAmelCase__ ,   env=os.environ.copy() )






							@require_multi_gpu
							def        __a  (  self )      ->					List[Any]:
								print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only""" )
								a :						Optional[int]      =							["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
								with patch_environment(omp_num_threads=1 ,   cuda_visible_devices="0,1" ):
									execute_subprocess_async(lowerCAmelCase__ ,   env=os.environ.copy() )


if __name__ == "__main__":
		a		:					List[Any]	      =     Accelerator()
		a		:					Dict	      =     (accelerator.state.process_index + 2, 10)
		a		:					List[Any]	      =     torch.randint(0, 10, shape).to(accelerator.device)

		a		:					Tuple	      =     ''''''

		a		:					Dict	      =     accelerator.pad_across_processes(tensor)
		if tensora.shape[0] != accelerator.state.num_processes + 1:
				error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
		if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
				error_msg += "Tensors have different values."
		if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
				error_msg += "Padding was not done with the right value (0)."

		a		:					int	      =     accelerator.pad_across_processes(tensor, pad_first=True)
		if tensora.shape[0] != accelerator.state.num_processes + 1:
				error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
		a		:					Tuple	      =     accelerator.state.num_processes - accelerator.state.process_index - 1
		if not torch.equal(tensora[index:], tensor):
				error_msg += "Tensors have different values."
		if not torch.all(tensora[:index] == 0):
				error_msg += "Padding was not done with the right value (0)."

		# Raise error at the end to make sure we don't stop at the first failure.
		if len(error_msg) > 0:
				raise ValueError(error_msg)









"""simple docstring"""




from __future__ import annotations

from statistics import mean






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[int]       ,			_lowercase		:       list[int]       ,			_lowercase		:       int   )					->list[int]:






	'''simple docstring'''






	a :						Union[str, Any]      =							[0] * no_of_processes
	a :						int      =							[0] * no_of_processes

	# Initialize remaining_time to waiting_time.

	for i in range(_lowercase   ):
		a :						List[Any]      =							burst_time[i]
	a :						list[int]      =							[]

	a :						List[Any]      =							0
	a :						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:
		a :						str      =							[]
		a :						Tuple      =							-1

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

		if len(_lowercase   ) > 0:
			a :						List[Any]      =							ready_process[0]
			for i in ready_process:
				if remaining_time[i] < remaining_time[target_process]:
					a :						Optional[Any]      =							i
			total_time += burst_time[target_process]
			completed += 1
			a :						str      =							0
			a :						Union[str, Any]      =							(
			    total_time - arrival_time[target_process] - burst_time[target_process]
			)
		else:
			total_time += 1

	return waiting_time






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[int]       ,			_lowercase		:       int       ,			_lowercase		:       list[int]   )					->list[int]:






	'''simple docstring'''






	a :						List[str]      =							[0] * no_of_processes
	for i in range(_lowercase   ):
		a :						Optional[int]      =							burst_time[i] + waiting_time[i]
	return turn_around_time


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

		a		:					Dict	      =     4
		a		:					Tuple	      =     [2, 5, 3, 7]
		a		:					List[Any]	      =     [0, 0, 0, 0]
		a		:					int	      =     calculate_waitingtime(arrival_time, burst_time, no_of_processes)
		a		:					List[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}''')


