
"""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 DeformableDetrImageProcessor



class __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			def __init__(				self						:					Optional[Any]       ,   _snake_case						:					Optional[Any]       ,   _snake_case						:					Optional[Any]=7       ,   _snake_case						:					Dict=3       ,   _snake_case						:					List[Any]=30       ,   _snake_case						:					List[Any]=400       ,   _snake_case						:					Union[str, Any]=True       ,   _snake_case						:					Optional[Any]=None       ,   _snake_case						:					Optional[int]=True       ,   _snake_case						:					Tuple=[0.5, 0.5, 0.5]       ,   _snake_case						:					Dict=[0.5, 0.5, 0.5]       ,   _snake_case						:					List[str]=True       ,   _snake_case						:					int=1 / 255       ,   _snake_case						:					Optional[Any]=True       ,   )      -> int:







								"""simple docstring"""
								A_				 =						size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
								A_				 =						parent
								A_				 =						batch_size
								A_				 =						num_channels
								A_				 =						min_resolution
								A_				 =						max_resolution
								A_				 =						do_resize
								A_				 =						size
								A_				 =						do_normalize
								A_				 =						image_mean
								A_				 =						image_std
								A_				 =						do_rescale
								A_				 =						rescale_factor
								A_				 =						do_pad




			def      lowerCamelCase__     (				self						:					int	)      -> Union[str, Any]:







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






			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					Any       ,   _snake_case						:					str=False	)      -> str:







								"""simple docstring"""
								if not batched:
													A_				 =						image_inputs[0]
													if isinstance(A_       ,   Image.Image	):
																		A_ ,    A_				 =						image.size
													else:
																		A_ ,    A_				 =						image.shape[1], image.shape[2]
													if w < h:
																		A_				 =						int(self.size["shortest_edge"] * h / w	)
																		A_				 =						self.size["shortest_edge"]
													elif w > h:
																		A_				 =						self.size["shortest_edge"]
																		A_				 =						int(self.size["shortest_edge"] * w / h	)
													else:
																		A_				 =						self.size["shortest_edge"]
																		A_				 =						self.size["shortest_edge"]

								else:
													A_				 =						[]
													for image in image_inputs:
																		A_ ,    A_				 =						self.get_expected_values([image]	)
																		expected_values.append((expected_height, expected_width)	)
													A_				 =						max(A_       ,   key=lambda _snake_case	: item[0]	)[0]
													A_				 =						max(A_       ,   key=lambda _snake_case	: item[1]	)[1]

								return expected_height, expected_width





@require_torch
@require_vision
class __lowerCAmelCase				(					_SCREAMING_SNAKE_CASE     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				DeformableDetrImageProcessor if is_vision_available() else None




			def      lowerCamelCase__     (				self						:					Tuple	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						DeformableDetrImageProcessingTester(self	)




			@property
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Any:







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




			def      lowerCamelCase__     (				self						:					int	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						self.image_processing_class(**self.image_processor_dict	)
								self.assertTrue(hasattr(A_       ,   "image_mean"	)	)
								self.assertTrue(hasattr(A_       ,   "image_std"	)	)
								self.assertTrue(hasattr(A_       ,   "do_normalize"	)	)
								self.assertTrue(hasattr(A_       ,   "do_resize"	)	)
								self.assertTrue(hasattr(A_       ,   "do_rescale"	)	)
								self.assertTrue(hasattr(A_       ,   "do_pad"	)	)
								self.assertTrue(hasattr(A_       ,   "size"	)	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						self.image_processing_class.from_dict(self.image_processor_dict	)
								self.assertEqual(image_processor.size       ,   {"shortest_edge": 18, "longest_edge": 1_333}	)
								self.assertEqual(image_processor.do_pad       ,   A_	)

								A_				 =						self.image_processing_class.from_dict(
								    self.image_processor_dict       ,   size=42       ,   max_size=84       ,   pad_and_return_pixel_mask=A_	)
								self.assertEqual(image_processor.size       ,   {"shortest_edge": 42, "longest_edge": 84}	)
								self.assertEqual(image_processor.do_pad       ,   A_	)




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> int:







								"""simple docstring"""
								pass




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Tuple:







								"""simple docstring"""
								A_				 =						self.image_processing_class(**self.image_processor_dict	)
								# create random PIL images
								A_				 =						prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=A_	)
								for image in image_inputs:
													self.assertIsInstance(A_       ,   Image.Image	)

								# Test not batched input
								A_				 =						image_processing(image_inputs[0]       ,   return_tensors="pt"	).pixel_values

								A_ ,    A_				 =						self.image_processor_tester.get_expected_values(A_	)

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

								# Test batched
								A_ ,    A_				 =						self.image_processor_tester.get_expected_values(A_       ,   batched=A_	)

								A_				 =						image_processing(A_       ,   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      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Any:







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

								# Test not batched input
								A_				 =						image_processing(image_inputs[0]       ,   return_tensors="pt"	).pixel_values

								A_ ,    A_				 =						self.image_processor_tester.get_expected_values(A_	)

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

								# Test batched
								A_				 =						image_processing(A_       ,   return_tensors="pt"	).pixel_values

								A_ ,    A_				 =						self.image_processor_tester.get_expected_values(A_       ,   batched=A_	)

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




			def      lowerCamelCase__     (				self						:					Tuple	)      -> int:







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

								# Test not batched input
								A_				 =						image_processing(image_inputs[0]       ,   return_tensors="pt"	).pixel_values

								A_ ,    A_				 =						self.image_processor_tester.get_expected_values(A_	)

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

								# Test batched
								A_				 =						image_processing(A_       ,   return_tensors="pt"	).pixel_values

								A_ ,    A_				 =						self.image_processor_tester.get_expected_values(A_       ,   batched=A_	)

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




			@slow
			def      lowerCamelCase__     (				self						:					Any	)      -> Tuple:







								"""simple docstring"""
								A_				 =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
								with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"       ,   "r"	) as f:
													A_				 =						json.loads(f.read()	)

								A_				 =						{"image_id": 39_769, "annotations": target}

								# encode them
								A_				 =						DeformableDetrImageProcessor()
								A_				 =						image_processing(images=A_       ,   annotations=A_       ,   return_tensors="pt"	)

								# verify pixel values
								A_				 =						torch.Size([1, 3, 800, 1_066]	)
								self.assertEqual(encoding["pixel_values"].shape       ,   A_	)

								A_				 =						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]	)
								self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,   A_       ,   atol=1e-4	)	)

								# verify area
								A_				 =						torch.tensor([5_8_8_7.9_6_0_0, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,   A_	)	)
								# verify boxes
								A_				 =						torch.Size([6, 4]	)
								self.assertEqual(encoding["labels"][0]["boxes"].shape       ,   A_	)
								A_				 =						torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,   A_       ,   atol=1e-3	)	)
								# verify image_id
								A_				 =						torch.tensor([39_769]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,   A_	)	)
								# verify is_crowd
								A_				 =						torch.tensor([0, 0, 0, 0, 0, 0]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,   A_	)	)
								# verify class_labels
								A_				 =						torch.tensor([75, 75, 63, 65, 17, 17]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,   A_	)	)
								# verify orig_size
								A_				 =						torch.tensor([480, 640]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,   A_	)	)
								# verify size
								A_				 =						torch.tensor([800, 1_066]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,   A_	)	)






			@slow
			def      lowerCamelCase__     (				self						:					int	)      -> Tuple:







								"""simple docstring"""
								A_				 =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
								with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"       ,   "r"	) as f:
													A_				 =						json.loads(f.read()	)

								A_				 =						{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

								A_				 =						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"	)

								# encode them
								A_				 =						DeformableDetrImageProcessor(format="coco_panoptic"	)
								A_				 =						image_processing(images=A_       ,   annotations=A_       ,   masks_path=A_       ,   return_tensors="pt"	)

								# verify pixel values
								A_				 =						torch.Size([1, 3, 800, 1_066]	)
								self.assertEqual(encoding["pixel_values"].shape       ,   A_	)

								A_				 =						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]	)
								self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,   A_       ,   atol=1e-4	)	)

								# verify area
								A_				 =						torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,   A_	)	)
								# verify boxes
								A_				 =						torch.Size([6, 4]	)
								self.assertEqual(encoding["labels"][0]["boxes"].shape       ,   A_	)
								A_				 =						torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,   A_       ,   atol=1e-3	)	)
								# verify image_id
								A_				 =						torch.tensor([39_769]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,   A_	)	)
								# verify is_crowd
								A_				 =						torch.tensor([0, 0, 0, 0, 0, 0]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,   A_	)	)
								# verify class_labels
								A_				 =						torch.tensor([17, 17, 63, 75, 75, 93]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,   A_	)	)
								# verify masks
								A_				 =						822_873
								self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,   A_	)
								# verify orig_size
								A_				 =						torch.tensor([480, 640]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,   A_	)	)
								# verify size
								A_				 =						torch.tensor([800, 1_066]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,   A_	)	)






"""simple docstring"""





from collections import deque



class __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					Optional[Any]       ,   _snake_case						:					str       ,   _snake_case						:					int       ,   _snake_case						:					int	)      -> None:







								"""simple docstring"""
								A_				 =						process_name  # process name
								A_				 =						arrival_time  # arrival time of the process
								# completion time of finished process or last interrupted time
								A_				 =						arrival_time
								A_				 =						burst_time  # remaining burst time
								A_				 =						0  # total time of the process wait in ready queue
								A_				 =						0  # time from arrival time to completion time





class __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					str       ,   _snake_case						:					int       ,   _snake_case						:					list[int]       ,   _snake_case						:					deque[Process]       ,   _snake_case						:					int       ,   )      -> None:







								"""simple docstring"""
								# total number of mlfq's queues
								A_				 =						number_of_queues
								# time slice of queues that round robin algorithm applied
								A_				 =						time_slices
								# unfinished process is in this ready_queue
								A_				 =						queue
								# current time
								A_				 =						current_time
								# finished process is in this sequence queue
								A_				 =						deque()




			def      lowerCamelCase__     (				self						:					List[str]	)      -> list[str]:







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




			def      lowerCamelCase__     (				self						:					str       ,   _snake_case						:					list[Process]	)      -> list[int]:







								"""simple docstring"""
								A_				 =						[]
								for i in range(len(_snake_case	)	):
													waiting_times.append(queue[i].waiting_time	)
								return waiting_times




			def      lowerCamelCase__     (				self						:					Tuple       ,   _snake_case						:					list[Process]	)      -> list[int]:







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




			def      lowerCamelCase__     (				self						:					Tuple       ,   _snake_case						:					list[Process]	)      -> list[int]:







								"""simple docstring"""
								A_				 =						[]
								for i in range(len(_snake_case	)	):
													completion_times.append(queue[i].stop_time	)
								return completion_times




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					deque[Process]	)      -> list[int]:







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




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					Process	)      -> int:







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




			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					deque[Process]	)      -> deque[Process]:







								"""simple docstring"""
								A_				 =						deque()  # sequence deque of finished process
								while len(_snake_case	) != 0:
													A_				 =						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(_snake_case	)
													# update current time
													self.current_time += cp.burst_time
													# finish the process and set the process's burst-time 0
													A_				 =						0
													# set the process's turnaround time because it is finished
													A_				 =						self.current_time - cp.arrival_time
													# set the completion time
													A_				 =						self.current_time
													# add the process to queue that has finished queue
													finished.append(_snake_case	)

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




			def      lowerCamelCase__     (				self						:					List[str]       ,   _snake_case						:					deque[Process]       ,   _snake_case						:					int	)      -> tuple[deque[Process], deque[Process]]:







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

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






			def      lowerCamelCase__     (				self						:					Any	)      -> deque[Process]:







								"""simple docstring"""

								#  all queues except last one have round_robin algorithm
								for i in range(self.number_of_queues - 1	):
													A_ ,    A_				 =						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

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

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




