

'''simple docstring'''



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import DetrImageProcessor
class 	lowerCamelCase_							(					unittest.TestCase			):







						def __init__(  self   :      Tuple				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Dict=7				,    lowerCAmelCase__   :      int=3				,    lowerCAmelCase__   :      Any=30				,    lowerCAmelCase__   :      int=4_00				,    lowerCAmelCase__   :      Union[str, Any]=True				,    lowerCAmelCase__   :      Union[str, Any]=None				,    lowerCAmelCase__   :      Union[str, Any]=True				,    lowerCAmelCase__   :      Dict=1 / 2_55				,    lowerCAmelCase__   :      List[str]=True				,    lowerCAmelCase__   :      Optional[Any]=[0.5, 0.5, 0.5]				,    lowerCAmelCase__   :      Optional[int]=[0.5, 0.5, 0.5]				,    lowerCAmelCase__   :      Optional[int]=True				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
													SCREAMING_SNAKE_CASE					:      int     					=			parent
													SCREAMING_SNAKE_CASE					:      str     					=			batch_size
													SCREAMING_SNAKE_CASE					:      int     					=			num_channels
													SCREAMING_SNAKE_CASE					:      str     					=			min_resolution
													SCREAMING_SNAKE_CASE					:      int     					=			max_resolution
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			do_resize
													SCREAMING_SNAKE_CASE					:      Dict     					=			size
													SCREAMING_SNAKE_CASE					:      List[str]     					=			do_rescale
													SCREAMING_SNAKE_CASE					:      Dict     					=			rescale_factor
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			do_normalize
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			image_mean
													SCREAMING_SNAKE_CASE					:      Tuple     					=			image_std
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			do_pad







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													return {
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "do_rescale": self.do_rescale,
													    "rescale_factor": self.rescale_factor,
													    "do_normalize": self.do_normalize,
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_pad": self.do_pad,
													}







						def 					__lowercase    (  self   :      List[str]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Optional[int]=False				):

													"""simple docstring"""
													if not batched:
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			image_inputs[0]
																				if isinstance(lowerCAmelCase__				,    Image.Image				):
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			image.size
																				else:
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			image.shape[1], image.shape[2]
																				if w < h:
																											SCREAMING_SNAKE_CASE					:      str     					=			int(self.size['''shortest_edge'''] * h / w				)
																											SCREAMING_SNAKE_CASE					:      List[str]     					=			self.size['''shortest_edge''']
																				elif w > h:
																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.size['''shortest_edge''']
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			int(self.size['''shortest_edge'''] * w / h				)
																				else:
																											SCREAMING_SNAKE_CASE					:      List[str]     					=			self.size['''shortest_edge''']
																											SCREAMING_SNAKE_CASE					:      str     					=			self.size['''shortest_edge''']

													else:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			[]
																				for image in image_inputs:
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_expected_values([image]				)
																											expected_values.append((expected_height, expected_width)				)
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			max(lowerCAmelCase__				,    key=lambda lowerCAmelCase__				: item[0]				)[0]
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			max(lowerCAmelCase__				,    key=lambda lowerCAmelCase__				: item[1]				)[1]

													return expected_height, expected_width


@require_torch
@require_vision
class 	lowerCamelCase_							(					snake_case_    ,				unittest.TestCase			):
						_lowerCAmelCase     :   Tuple	     =				DetrImageProcessor if is_vision_available() else None







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			DetrImageProcessingTester(self				)







						@property
						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													return self.image_processor_tester.prepare_image_processor_dict()







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.image_processing_class(**self.image_processor_dict				)
													self.assertTrue(hasattr(lowerCAmelCase__				,    '''image_mean'''				)				)
													self.assertTrue(hasattr(lowerCAmelCase__				,    '''image_std'''				)				)
													self.assertTrue(hasattr(lowerCAmelCase__				,    '''do_normalize'''				)				)
													self.assertTrue(hasattr(lowerCAmelCase__				,    '''do_rescale'''				)				)
													self.assertTrue(hasattr(lowerCAmelCase__				,    '''rescale_factor'''				)				)
													self.assertTrue(hasattr(lowerCAmelCase__				,    '''do_resize'''				)				)
													self.assertTrue(hasattr(lowerCAmelCase__				,    '''size'''				)				)
													self.assertTrue(hasattr(lowerCAmelCase__				,    '''do_pad'''				)				)







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.image_processing_class.from_dict(self.image_processor_dict				)
													self.assertEqual(image_processor.size				,    {'''shortest_edge''': 18, '''longest_edge''': 13_33}				)
													self.assertEqual(image_processor.do_pad				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Any     					=			self.image_processing_class.from_dict(
													    self.image_processor_dict				,    size=42				,    max_size=84				,    pad_and_return_pixel_mask=lowerCAmelCase__				)
													self.assertEqual(image_processor.size				,    {'''shortest_edge''': 42, '''longest_edge''': 84}				)
													self.assertEqual(image_processor.do_pad				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													pass







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.image_processing_class(**self.image_processor_dict				)
													# create random PIL images
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			prepare_image_inputs(self.image_processor_tester				,    equal_resolution=lowerCAmelCase__				)
													for image in image_inputs:
																				self.assertIsInstance(lowerCAmelCase__				,    Image.Image				)

													# Test not batched input
													SCREAMING_SNAKE_CASE					:      Tuple     					=			image_processing(image_inputs[0]				,    return_tensors='''pt'''				).pixel_values

													SCREAMING_SNAKE_CASE					:      str     					=			self.image_processor_tester.get_expected_values(lowerCAmelCase__				)

													self.assertEqual(
													    encoded_images.shape				,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,    )

													# Test batched
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.image_processor_tester.get_expected_values(lowerCAmelCase__				,    batched=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			image_processing(lowerCAmelCase__				,    return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape				,    (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )				,    )







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.image_processing_class(**self.image_processor_dict				)
													# create random numpy tensors
													SCREAMING_SNAKE_CASE					:      str     					=			prepare_image_inputs(self.image_processor_tester				,    equal_resolution=lowerCAmelCase__				,    numpify=lowerCAmelCase__				)
													for image in image_inputs:
																				self.assertIsInstance(lowerCAmelCase__				,    np.ndarray				)

													# Test not batched input
													SCREAMING_SNAKE_CASE					:      List[str]     					=			image_processing(image_inputs[0]				,    return_tensors='''pt'''				).pixel_values

													SCREAMING_SNAKE_CASE					:      str     					=			self.image_processor_tester.get_expected_values(lowerCAmelCase__				)

													self.assertEqual(
													    encoded_images.shape				,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,    )

													# Test batched
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			image_processing(lowerCAmelCase__				,    return_tensors='''pt'''				).pixel_values

													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.image_processor_tester.get_expected_values(lowerCAmelCase__				,    batched=lowerCAmelCase__				)

													self.assertEqual(
													    encoded_images.shape				,    (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )				,    )







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.image_processing_class(**self.image_processor_dict				)
													# create random PyTorch tensors
													SCREAMING_SNAKE_CASE					:      Any     					=			prepare_image_inputs(self.image_processor_tester				,    equal_resolution=lowerCAmelCase__				,    torchify=lowerCAmelCase__				)
													for image in image_inputs:
																				self.assertIsInstance(lowerCAmelCase__				,    torch.Tensor				)

													# Test not batched input
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			image_processing(image_inputs[0]				,    return_tensors='''pt'''				).pixel_values

													SCREAMING_SNAKE_CASE					:      Dict     					=			self.image_processor_tester.get_expected_values(lowerCAmelCase__				)

													self.assertEqual(
													    encoded_images.shape				,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,    )

													# Test batched
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			image_processing(lowerCAmelCase__				,    return_tensors='''pt'''				).pixel_values

													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.image_processor_tester.get_expected_values(lowerCAmelCase__				,    batched=lowerCAmelCase__				)

													self.assertEqual(
													    encoded_images.shape				,    (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )				,    )







						@slow
						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
													with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''				,    '''r'''				) as f:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			json.loads(f.read()				)

													SCREAMING_SNAKE_CASE					:      Any     					=			{'''image_id''': 3_97_69, '''annotations''': target}

													# encode them
													SCREAMING_SNAKE_CASE					:      Dict     					=			DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''				)
													SCREAMING_SNAKE_CASE					:      Any     					=			image_processing(images=lowerCAmelCase__				,    annotations=lowerCAmelCase__				,    return_tensors='''pt'''				)

													# verify pixel values
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			torch.Size([1, 3, 8_00, 10_66]				)
													self.assertEqual(encoding['''pixel_values'''].shape				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			torch.tensor([0.2796, 0.3138, 0.3481]				)
													self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]				,    lowerCAmelCase__				,    atol=1e-4				)				)

													# verify area
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.tensor([5887.9600, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,    lowerCAmelCase__				)				)
													# verify boxes
													SCREAMING_SNAKE_CASE					:      Tuple     					=			torch.Size([6, 4]				)
													self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      str     					=			torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,    lowerCAmelCase__				,    atol=1e-3				)				)
													# verify image_id
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.tensor([3_97_69]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,    lowerCAmelCase__				)				)
													# verify is_crowd
													SCREAMING_SNAKE_CASE					:      Tuple     					=			torch.tensor([0, 0, 0, 0, 0, 0]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,    lowerCAmelCase__				)				)
													# verify class_labels
													SCREAMING_SNAKE_CASE					:      Dict     					=			torch.tensor([75, 75, 63, 65, 17, 17]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,    lowerCAmelCase__				)				)
													# verify orig_size
													SCREAMING_SNAKE_CASE					:      List[str]     					=			torch.tensor([4_80, 6_40]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,    lowerCAmelCase__				)				)
													# verify size
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			torch.tensor([8_00, 10_66]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,    lowerCAmelCase__				)				)







						@slow
						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
													with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''				,    '''r'''				) as f:
																				SCREAMING_SNAKE_CASE					:      Dict     					=			json.loads(f.read()				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			{'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

													SCREAMING_SNAKE_CASE					:      str     					=			pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''				)

													# encode them
													SCREAMING_SNAKE_CASE					:      str     					=			DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			image_processing(images=lowerCAmelCase__				,    annotations=lowerCAmelCase__				,    masks_path=lowerCAmelCase__				,    return_tensors='''pt'''				)

													# verify pixel values
													SCREAMING_SNAKE_CASE					:      str     					=			torch.Size([1, 3, 8_00, 10_66]				)
													self.assertEqual(encoding['''pixel_values'''].shape				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.tensor([0.2796, 0.3138, 0.3481]				)
													self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]				,    lowerCAmelCase__				,    atol=1e-4				)				)

													# verify area
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 5879.6562, 7634.1147]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,    lowerCAmelCase__				)				)
													# verify boxes
													SCREAMING_SNAKE_CASE					:      Any     					=			torch.Size([6, 4]				)
													self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      int     					=			torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,    lowerCAmelCase__				,    atol=1e-3				)				)
													# verify image_id
													SCREAMING_SNAKE_CASE					:      Tuple     					=			torch.tensor([3_97_69]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,    lowerCAmelCase__				)				)
													# verify is_crowd
													SCREAMING_SNAKE_CASE					:      Any     					=			torch.tensor([0, 0, 0, 0, 0, 0]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,    lowerCAmelCase__				)				)
													# verify class_labels
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			torch.tensor([17, 17, 63, 75, 75, 93]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,    lowerCAmelCase__				)				)
													# verify masks
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			82_28_73
													self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()				,    lowerCAmelCase__				)
													# verify orig_size
													SCREAMING_SNAKE_CASE					:      Dict     					=			torch.tensor([4_80, 6_40]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,    lowerCAmelCase__				)				)
													# verify size
													SCREAMING_SNAKE_CASE					:      Dict     					=			torch.tensor([8_00, 10_66]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,    lowerCAmelCase__				)				)









'''simple docstring'''



from collections import deque
class 	lowerCamelCase_							:







						def __init__(  self   :      Tuple				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      int				):

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


class 	lowerCamelCase_							:







						def __init__(  self   :      str				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      list[int]				,    lowerCAmelCase__   :      deque[Process]				,    lowerCAmelCase__   :      int				,    ):

													"""simple docstring"""
													# total number of mlfq's queues
													SCREAMING_SNAKE_CASE					:      str     					=			number_of_queues
													# time slice of queues that round robin algorithm applied
													SCREAMING_SNAKE_CASE					:      int     					=			time_slices
													# unfinished process is in this ready_queue
													SCREAMING_SNAKE_CASE					:      str     					=			queue
													# current time
													SCREAMING_SNAKE_CASE					:      Any     					=			current_time
													# finished process is in this sequence queue
													SCREAMING_SNAKE_CASE					:      deque[Process]     					=			deque()







						def 					__lowercase    (  self   :      Optional[int]				):

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







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      list[Process]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[]
													for i in range(len(lowerCAmelCase__				)				):
																				waiting_times.append(queue[i].waiting_time				)
													return waiting_times







						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      list[Process]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			[]
													for i in range(len(lowerCAmelCase__				)				):
																				turnaround_times.append(queue[i].turnaround_time				)
													return turnaround_times







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      list[Process]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[]
													for i in range(len(lowerCAmelCase__				)				):
																				completion_times.append(queue[i].stop_time				)
													return completion_times







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      deque[Process]				):

													"""simple docstring"""
													return [q.burst_time for q in queue]







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      Process				):

													"""simple docstring"""
													process.waiting_time += self.current_time - process.stop_time
													return process.waiting_time







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      deque[Process]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      deque[Process]     					=			deque()  # sequence deque of finished process
													while len(lowerCAmelCase__				) != 0:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			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(lowerCAmelCase__				)
																				# update current time
																				self.current_time += cp.burst_time
																				# finish the process and set the process's burst-time 0
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			0
																				# set the process's turnaround time because it is finished
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.current_time - cp.arrival_time
																				# set the completion time
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.current_time
																				# add the process to queue that has finished queue
																				finished.append(lowerCAmelCase__				)

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







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      deque[Process]				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      deque[Process]     					=			deque()  # sequence deque of terminated process
													# just for 1 cycle and unfinished processes will go back to queue
													for _ in range(len(lowerCAmelCase__				)				):
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			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(lowerCAmelCase__				)
																				# 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
																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.current_time
																											# locate the process behind the queue because it is not finished
																											ready_queue.append(lowerCAmelCase__				)
																				else:
																											# use CPU for remaining burst time
																											self.current_time += cp.burst_time
																											# set burst time 0 because the process is finished
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			0
																											# set the finish time
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			self.current_time
																											# update the process' turnaround time because it is finished
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.current_time - cp.arrival_time
																											# add the process to queue that has finished queue
																											finished.append(lowerCAmelCase__				)

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







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""

													#  all queues except last one have round_robin algorithm
													for i in range(self.number_of_queues - 1				):
																				SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Optional[int]     					=			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

							lowerCAmelCase_     :       str								=						Process('P1', 0, 53)
							lowerCAmelCase_     :       Optional[int]								=						Process('P2', 0, 17)
							lowerCAmelCase_     :       Optional[Any]								=						Process('P3', 0, 68)
							lowerCAmelCase_     :       Optional[int]								=						Process('P4', 0, 24)
							lowerCAmelCase_     :       List[str]								=						3
							lowerCAmelCase_     :       str								=						[17, 25]
							lowerCAmelCase_     :       Optional[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])})

							lowerCAmelCase_     :       Tuple								=						Process('P1', 0, 53)
							lowerCAmelCase_     :       int								=						Process('P2', 0, 17)
							lowerCAmelCase_     :       Union[str, Any]								=						Process('P3', 0, 68)
							lowerCAmelCase_     :       Any								=						Process('P4', 0, 24)
							lowerCAmelCase_     :       int								=						3
							lowerCAmelCase_     :       Optional[Any]								=						[17, 25]
							lowerCAmelCase_     :       Dict								=						deque([Pa, Pa, Pa, Pa])
							lowerCAmelCase_     :       Union[str, Any]								=						MLFQ(number_of_queues, time_slices, queue, 0)
							lowerCAmelCase_     :       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()}'
							)




