





"""simple docstring"""


from collections import deque






class 							lowercase__			:

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		int				   =			process_name  # process name
									UpperCamelCase				:		int				   =			arrival_time  # arrival time of the process
									# completion time of finished process or last interrupted time
									UpperCamelCase				:		str				   =			arrival_time
									UpperCamelCase				:		Optional[Any]				   =			burst_time  # remaining burst time
									UpperCamelCase				:		Any				   =			0  # total time of the process wait in ready queue
									UpperCamelCase				:		List[str]				   =			0  # time from arrival time to completion time






class 							lowercase__			:

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A						,   _A						,   _A						,   ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			number_of_queues
									# time slice of queues that round robin algorithm applied
									UpperCamelCase				:		Optional[Any]				   =			time_slices
									# unfinished process is in this ready_queue
									UpperCamelCase				:		List[Any]				   =			queue
									# current time
									UpperCamelCase				:		List[Any]				   =			current_time
									# finished process is in this sequence queue
									UpperCamelCase				:		deque[Process]				   =			deque()







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			[]
									for i in range(len(self.finish_queue    )    ):
															sequence.append(self.finish_queue[i].process_name    )
									return sequence







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			[]
									for i in range(len(_A    )    ):
															waiting_times.append(queue[i].waiting_time    )
									return waiting_times







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			[]
									for i in range(len(_A    )    ):
															turnaround_times.append(queue[i].turnaround_time    )
									return turnaround_times







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			[]
									for i in range(len(_A    )    ):
															completion_times.append(queue[i].stop_time    )
									return completion_times







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									return [q.burst_time for q in queue]







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									process.waiting_time += self.current_time - process.stop_time
									return process.waiting_time







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		deque[Process]				   =			deque()  # sequence deque of finished process
									while len(_A    ) != 0:
															UpperCamelCase				:		int				   =			ready_queue.popleft()  # current process

															# if process's arrival time is later than current time, update current time
															if self.current_time < cp.arrival_time:
																					self.current_time += cp.arrival_time

															# update waiting time of current process
															self.update_waiting_time(_A    )
															# update current time
															self.current_time += cp.burst_time
															# finish the process and set the process's burst-time 0
															UpperCamelCase				:		List[Any]				   =			0
															# set the process's turnaround time because it is finished
															UpperCamelCase				:		Optional[Any]				   =			self.current_time - cp.arrival_time
															# set the completion time
															UpperCamelCase				:		Tuple				   =			self.current_time
															# add the process to queue that has finished queue
															finished.append(_A    )

									self.finish_queue.extend(_A    )  # add finished process to finish queue
									# FCFS will finish all remaining processes
									return finished







			def   _a      (      self						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		deque[Process]				   =			deque()  # sequence deque of terminated process
									# just for 1 cycle and unfinished processes will go back to queue
									for _ in range(len(_A    )    ):
															UpperCamelCase				:		Optional[int]				   =			ready_queue.popleft()  # current process

															# if process's arrival time is later than current time, update current time
															if self.current_time < cp.arrival_time:
																					self.current_time += cp.arrival_time

															# update waiting time of unfinished processes
															self.update_waiting_time(_A    )
															# if the burst time of process is bigger than time-slice
															if cp.burst_time > time_slice:
																					# use CPU for only time-slice
																					self.current_time += time_slice
																					# update remaining burst time
																					cp.burst_time -= time_slice
																					# update end point time
																					UpperCamelCase				:		List[str]				   =			self.current_time
																					# locate the process behind the queue because it is not finished
																					ready_queue.append(_A    )
															else:
																					# use CPU for remaining burst time
																					self.current_time += cp.burst_time
																					# set burst time 0 because the process is finished
																					UpperCamelCase				:		Tuple				   =			0
																					# set the finish time
																					UpperCamelCase				:		List[str]				   =			self.current_time
																					# update the process' turnaround time because it is finished
																					UpperCamelCase				:		Any				   =			self.current_time - cp.arrival_time
																					# add the process to queue that has finished queue
																					finished.append(_A    )

									self.finish_queue.extend(_A    )  # add finished process to finish queue
									# return finished processes queue and remaining processes queue
									return finished, ready_queue







			def   _a      (      self    ):






									'''simple docstring'''







									for i in range(self.number_of_queues - 1    ):
															UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			self.round_robin(
															    self.ready_queue						,   self.time_slices[i]    )
									#  the last queue has first_come_first_served algorithm
									self.first_come_first_served(self.ready_queue    )

									return self.finish_queue


if __name__ == "__main__":
		import doctest

		__magic_name__       :					Dict						=			Process("""P1""", 0, 5_3)
		__magic_name__       :					Union[str, Any]						=			Process("""P2""", 0, 1_7)
		__magic_name__       :					int						=			Process("""P3""", 0, 6_8)
		__magic_name__       :					Any						=			Process("""P4""", 0, 2_4)
		__magic_name__       :					List[str]						=			3
		__magic_name__       :					List[Any]						=			[1_7, 2_5]
		__magic_name__       :					Union[str, Any]						=			deque([Pa, Pa, Pa, Pa])

		if len(time_slices) != number_of_queues - 1:
				raise SystemExit(0)

		doctest.testmod(extraglobs={"""queue""": deque([Pa, Pa, Pa, Pa])})

		__magic_name__       :					Dict						=			Process("""P1""", 0, 5_3)
		__magic_name__       :					Tuple						=			Process("""P2""", 0, 1_7)
		__magic_name__       :					Union[str, Any]						=			Process("""P3""", 0, 6_8)
		__magic_name__       :					Any						=			Process("""P4""", 0, 2_4)
		__magic_name__       :					int						=			3
		__magic_name__       :					List[str]						=			[1_7, 2_5]
		__magic_name__       :					Any						=			deque([Pa, Pa, Pa, Pa])
		__magic_name__       :					List[str]						=			MLFQ(number_of_queues, time_slices, queue, 0)
		__magic_name__       :					List[Any]						=			mlfq.multi_level_feedback_queue()

		# print total waiting times of processes(P1, P2, P3, P4)
		print(
		    f'''waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'''
		)
		# print completion times of processes(P1, P2, P3, P4)
		print(
		    f'''completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'''
		)
		# print total turnaround times of processes(P1, P2, P3, P4)
		print(
		    f'''turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'''
		)
		# print sequence of finished processes
		print(
		    f'''sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}'''
		)





import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory

def 		UpperCamelCase						(   )		->						List[Any]:


  '''simple docstring'''






  raise RuntimeError("""CUDA out of memory.""" )



class    _snake_case (						nn.Module				):


 def __init__(       self):


   '''simple docstring'''




   super().__init__()
   lowercase__	: Optional[Any]     		=   nn.Linear(3       ,	4)
   lowercase__	: Union[str, Any]     		=   nn.BatchNormad(4)
   lowercase__	: str     		=   nn.Linear(4       ,	5)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.lineara(self.batchnorm(self.lineara(SCREAMING_SNAKE_CASE_)))



class    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   []

   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     nonlocal batch_sizes
     batch_sizes.append(SCREAMING_SNAKE_CASE_)
     if batch_size != 8:
       raise_fake_out_of_memory()

   mock_training_loop_function()
   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	[1_28, 64, 32, 16, 8])


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   []

   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
     nonlocal batch_sizes
     batch_sizes.append(SCREAMING_SNAKE_CASE_)
     if batch_size != 8:
       raise_fake_out_of_memory()
     return batch_size, arga

   lowercase__		,     lowercase__	: int     		=   mock_training_loop_function("""hello""")
   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	[1_28, 64, 32, 16, 8])
   self.assertListEqual([bs, arga]       ,	[8, """hello"""])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=0)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     pass

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function()
     self.assertIn("""No executable batch size found, reached zero."""       ,	cm.exception.args[0])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=16)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     if batch_size > 0:
       raise_fake_out_of_memory()
     pass

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function()
     self.assertIn("""No executable batch size found, reached zero."""       ,	cm.exception.args[0])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
     if batch_size != 8:
       raise raise_fake_out_of_memory()

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function(1_28       ,	"""hello"""       ,	"""world""")
     self.assertIn("""Batch size was passed into `f`"""       ,	cm.exception.args[0])
     self.assertIn("""`f(arg1='hello', arg2='world')"""       ,	cm.exception.args[0])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=16)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     raise ValueError("""Oops, we had an error!""")

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function()
     self.assertIn("""Oops, we had an error!"""       ,	cm.exception.args[0])



 @require_cuda
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   torch.cuda.memory_allocated()
   lowercase__	: str     		=   ModelForTest()
   model.cuda()
   self.assertGreater(torch.cuda.memory_allocated()       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Tuple     		=   release_memory(SCREAMING_SNAKE_CASE_)
   self.assertEqual(torch.cuda.memory_allocated()       ,	SCREAMING_SNAKE_CASE_)






