






'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
			import torch

			from transformers.generation import DisjunctiveConstraint

@require_torch
class _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       Tuple  ):
														# For consistency across different places the DisjunctiveConstraint is called,
														# dc.token_ids is a list of integers. It is also initialized only by integers.

														lowerCamelCase__      :			List[str] 						= [[1, 2, 4], [1, 2, 3, 4]]
														lowerCamelCase__      :			List[str] 						= DisjunctiveConstraint(UpperCamelCase__  )
														self.assertTrue(isinstance(dc.token_ids    ,							UpperCamelCase__  )  )

														with self.assertRaises(UpperCamelCase__  ):
																					DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]  )  )

														with self.assertRaises(UpperCamelCase__  ):
																					DisjunctiveConstraint([torch.LongTensor([1, 2, 4]  ), torch.LongTensor([1, 2, 3, 4, 5]  )]  )




							def 	lowerCamelCase_       (							self:       List[str]  ):
														# We can't have constraints that are complete subsets of another. This leads to a preverse
														# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
														# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
														# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
														# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
														lowerCamelCase__      :			Union[str, Any] 						= [[1, 2], [1, 2, 3, 4]]

														with self.assertRaises(UpperCamelCase__  ):
																					DisjunctiveConstraint(UpperCamelCase__  )  # fails here




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														lowerCamelCase__      :			List[Any] 						= [[1, 2, 3], [1, 2, 4]]

														lowerCamelCase__      :			Union[str, Any] 						= DisjunctiveConstraint(UpperCamelCase__  )

														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			Optional[Any] 						= dc.update(1  )
														lowerCamelCase__      :			Optional[int] 						= stepped is True and completed is False and reset is False
														self.assertTrue(UpperCamelCase__  )
														self.assertTrue(not dc.completed  )
														self.assertTrue(dc.current_seq == [1]  )

														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			int 						= dc.update(2  )
														lowerCamelCase__      :			Any 						= stepped is True and completed is False and reset is False
														self.assertTrue(UpperCamelCase__  )
														self.assertTrue(not dc.completed  )
														self.assertTrue(dc.current_seq == [1, 2]  )

														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			List[Any] 						= dc.update(3  )
														lowerCamelCase__      :			List[str] 						= stepped is True and completed is True and reset is False
														self.assertTrue(UpperCamelCase__  )
														self.assertTrue(dc.completed  )  # Completed!
														self.assertTrue(dc.current_seq == [1, 2, 3]  )




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			List[str] 						= [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

														lowerCamelCase__      :			Optional[int] 						= DisjunctiveConstraint(UpperCamelCase__  )

														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			List[str] 						= dc.update(1  )
														self.assertTrue(not dc.completed  )
														self.assertTrue(dc.current_seq == [1]  )

														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			int 						= dc.update(2  )
														self.assertTrue(not dc.completed  )
														self.assertTrue(dc.current_seq == [1, 2]  )

														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			Dict 						= dc.update(4  )
														self.assertTrue(not dc.completed  )
														self.assertTrue(dc.current_seq == [1, 2, 4]  )

														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			Optional[int] 						= dc.update(5  )
														self.assertTrue(dc.completed  )  # Completed!
														self.assertTrue(dc.current_seq == [1, 2, 4, 5]  )

														dc.reset()

														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			str 						= dc.update(1  )
														self.assertTrue(not dc.completed  )
														self.assertTrue(dc.remaining() == 3  )
														self.assertTrue(dc.current_seq == [1]  )

														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			Any 						= dc.update(2  )
														self.assertTrue(not dc.completed  )
														self.assertTrue(dc.remaining() == 2  )
														self.assertTrue(dc.current_seq == [1, 2]  )

														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			Tuple 						= dc.update(5  )
														self.assertTrue(dc.completed  )  # Completed!
														self.assertTrue(dc.remaining() == 0  )
														self.assertTrue(dc.current_seq == [1, 2, 5]  )









'''simple docstring'''







from statistics import mean

import numpy as np





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> list:
							lowerCamelCase__      :			Optional[int] 						= 0
							# Number of processes finished
							lowerCamelCase__      :			Union[str, Any] 						= 0
							# Displays the finished process.
							# If it is 0, the performance is completed if it is 1, before the performance.
							lowerCamelCase__      :			Tuple 						= [0] * no_of_process
							# List to include calculation results
							lowerCamelCase__      :			List[str] 						= [0] * no_of_process

							# Sort by arrival time.
							lowerCamelCase__      :			Union[str, Any] 						= [burst_time[i] for i in np.argsort(UpperCamelCase   )]
							lowerCamelCase__      :			List[Any] 						= [process_name[i] for i in np.argsort(UpperCamelCase   )]
							arrival_time.sort()

							while no_of_process > finished_process_count:
														lowerCamelCase__      :			str 						= 0
														while finished_process[i] == 1:
																					i += 1
														if current_time < arrival_time[i]:
																					lowerCamelCase__      :			Union[str, Any] 						= arrival_time[i]

														lowerCamelCase__      :			Any 						= 0
														# Index showing the location of the process being performed
														lowerCamelCase__      :			Union[str, Any] 						= 0
														# Saves the current response ratio.
														lowerCamelCase__      :			Any 						= 0
														for i in range(0       ,    UpperCamelCase   ):
																					if finished_process[i] == 0 and arrival_time[i] <= current_time:
																												lowerCamelCase__      :			Optional[int] 						= (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
																												    i
																												]
																					if response_ratio < temp:
																												lowerCamelCase__      :			int 						= temp
																												lowerCamelCase__      :			str 						= i

        # Calculate the turn around time
														lowerCamelCase__      :			Optional[int] 						= current_time + burst_time[loc] - arrival_time[loc]
														current_time += burst_time[loc]
														# Indicates that the process has been performed.
														lowerCamelCase__      :			List[str] 						= 1
														# Increase finished_process_count by 1
														finished_process_count += 1

							return turn_around_time





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> list:
							lowerCamelCase__      :			int 						= [0] * no_of_process
							for i in range(0       ,    UpperCamelCase   ):
														lowerCamelCase__      :			Optional[Any] 						= turn_around_time[i] - burst_time[i]
							return waiting_time


if __name__ == "__main__":
			_A			:    List[str]					  =5
			_A			:    Optional[Any]					  =['''A''', '''B''', '''C''', '''D''', '''E''']
			_A			:    Optional[int]					  =[1, 2, 3, 4, 5]
			_A			:    Dict					  =[1, 2, 3, 4, 5]

			_A			:    Any					  =calculate_turn_around_time(
			    process_name, arrival_time, burst_time, no_of_process
			)
			_A			:    Optional[int]					  =calculate_waiting_time(
			    process_name, turn_around_time, burst_time, no_of_process
			)

			print('''Process name \tArrival time \tBurst time \tTurn around time \tWaiting time''')
			for i in range(0, no_of_process):
						print(
						    F'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
						    F'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
						)

			print(F'average waiting time : {mean(waiting_time):.5f}')
			print(F'average turn around time : {mean(turn_around_time):.5f}')

