





"""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 ConditionalDetrImageProcessor






class 						__snake_case   (						unittest.TestCase	):





 """simple docstring"""


 def __init__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase=7   ,		__lowerCamelCase=3   ,		__lowerCamelCase=30   ,		__lowerCamelCase=400   ,		__lowerCamelCase=True   ,		__lowerCamelCase=None   ,		__lowerCamelCase=True   ,		__lowerCamelCase=[0.5, 0.5, 0.5]   ,		__lowerCamelCase=[0.5, 0.5, 0.5]   ,		__lowerCamelCase=True   ,		__lowerCamelCase=1 / 255   ,		__lowerCamelCase=True   ,		):



     '''simple docstring'''




     __A       :		str							=	size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
     __A       :		Union[str, Any]							=	parent
     __A       :		Any							=	batch_size
     __A       :		Any							=	num_channels
     __A       :		Tuple							=	min_resolution
     __A       :		Tuple							=	max_resolution
     __A       :		Optional[int]							=	do_resize
     __A       :		str							=	size
     __A       :		Any							=	do_normalize
     __A       :		List[str]							=	image_mean
     __A       :		int							=	image_std
     __A       :		List[Any]							=	do_rescale
     __A       :		Optional[int]							=	rescale_factor
     __A       :		List[str]							=	do_pad

 def    UpperCamelCase__(       self    ):



     '''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    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase=False    ):



     '''simple docstring'''




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

     else:
         __A       :		Optional[int]							=	[]
         for image in image_inputs:
             __A       :		List[str]							=	self.get_expected_values([image]    )
             expected_values.append((expected_height, expected_width)    )
         __A       :		int							=	max(lowerCamelCase_   ,		key=lambda __lowerCamelCase    : item[0]    )[0]
         __A       :		Tuple							=	max(lowerCamelCase_   ,		key=lambda __lowerCamelCase    : item[1]    )[1]

     return expected_height, expected_width






@require_torch
@require_vision
class 						__snake_case   (						lowercase_				,				unittest.TestCase	):





 """simple docstring"""


 _lowerCamelCase								=	ConditionalDetrImageProcessor if is_vision_available() else None

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Any							=	ConditionalDetrImageProcessingTester(self    )

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return self.image_processor_tester.prepare_image_processor_dict()

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Tuple							=	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_resize'''    )    )
     self.assertTrue(hasattr(lowerCamelCase_   ,		'''size'''    )    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Optional[int]							=	self.image_processing_class.from_dict(self.image_processor_dict    )
     self.assertEqual(image_processor.size   ,		{'''shortest_edge''': 18, '''longest_edge''': 1333}    )
     self.assertEqual(image_processor.do_pad   ,		lowerCamelCase_    )

     __A       :		int							=	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    UpperCamelCase__(       self    ):



     '''simple docstring'''




     pass

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		int							=	self.image_processing_class(**self.image_processor_dict    )
     # create random PIL images
     __A       :		str							=	prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=lowerCamelCase_    )
     for image in image_inputs:
         self.assertIsInstance(lowerCamelCase_   ,		Image.Image    )

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

     __A       :		int							=	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
     __A       :		Any							=	self.image_processor_tester.get_expected_values(lowerCamelCase_   ,		batched=lowerCamelCase_    )

     __A       :		Any							=	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    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Tuple							=	self.image_processing_class(**self.image_processor_dict    )
     # create random numpy tensors
     __A       :		Dict							=	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
     __A       :		str							=	image_processing(image_inputs[0]   ,		return_tensors='''pt'''    ).pixel_values

     __A       :		Optional[int]							=	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
     __A       :		int							=	image_processing(lowerCamelCase_   ,		return_tensors='''pt'''    ).pixel_values

     __A       :		Any							=	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    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Any							=	self.image_processing_class(**self.image_processor_dict    )
     # create random PyTorch tensors
     __A       :		Optional[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
     __A       :		Any							=	image_processing(image_inputs[0]   ,		return_tensors='''pt'''    ).pixel_values

     __A       :		Tuple							=	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
     __A       :		List[str]							=	image_processing(lowerCamelCase_   ,		return_tensors='''pt'''    ).pixel_values

     __A       :		Union[str, Any]							=	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    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[Any]							=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
     with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''   ,		'''r'''    ) as f:
         __A       :		Optional[Any]							=	json.loads(f.read()    )

     __A       :		List[Any]							=	{"""image_id""": 3_9769, """annotations""": target}

     # encode them
     __A       :		str							=	ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''    )
     __A       :		Optional[int]							=	image_processing(images=lowerCamelCase_   ,		annotations=lowerCamelCase_   ,		return_tensors='''pt'''    )

     # verify pixel values
     __A       :		Any							=	torch.Size([1, 3, 800, 1066]    )
     self.assertEqual(encoding['''pixel_values'''].shape   ,		lowerCamelCase_    )

     __A       :		Tuple							=	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]   ,		lowerCamelCase_   ,		atol=1e-4    )    )

     # verify area
     __A       :		Optional[Any]							=	torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]    )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,		lowerCamelCase_    )    )
     # verify boxes
     __A       :		Dict							=	torch.Size([6, 4]    )
     self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,		lowerCamelCase_    )
     __A       :		Tuple							=	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]   ,		lowerCamelCase_   ,		atol=1e-3    )    )
     # verify image_id
     __A       :		str							=	torch.tensor([3_9769]    )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,		lowerCamelCase_    )    )
     # verify is_crowd
     __A       :		str							=	torch.tensor([0, 0, 0, 0, 0, 0]    )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,		lowerCamelCase_    )    )
     # verify class_labels
     __A       :		str							=	torch.tensor([75, 75, 63, 65, 17, 17]    )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,		lowerCamelCase_    )    )
     # verify orig_size
     __A       :		Tuple							=	torch.tensor([480, 640]    )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,		lowerCamelCase_    )    )
     # verify size
     __A       :		List[str]							=	torch.tensor([800, 1066]    )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,		lowerCamelCase_    )    )







 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[Any]							=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
     with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''   ,		'''r'''    ) as f:
         __A       :		List[str]							=	json.loads(f.read()    )

     __A       :		str							=	{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

     __A       :		Tuple							=	pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''    )

     # encode them
     __A       :		int							=	ConditionalDetrImageProcessor(format='''coco_panoptic'''    )
     __A       :		Dict							=	image_processing(images=lowerCamelCase_   ,		annotations=lowerCamelCase_   ,		masks_path=lowerCamelCase_   ,		return_tensors='''pt'''    )

     # verify pixel values
     __A       :		Union[str, Any]							=	torch.Size([1, 3, 800, 1066]    )
     self.assertEqual(encoding['''pixel_values'''].shape   ,		lowerCamelCase_    )

     __A       :		Optional[Any]							=	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]   ,		lowerCamelCase_   ,		atol=1e-4    )    )

     # verify area
     __A       :		Optional[int]							=	torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]    )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,		lowerCamelCase_    )    )
     # verify boxes
     __A       :		str							=	torch.Size([6, 4]    )
     self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,		lowerCamelCase_    )
     __A       :		List[str]							=	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]   ,		lowerCamelCase_   ,		atol=1e-3    )    )
     # verify image_id
     __A       :		Dict							=	torch.tensor([3_9769]    )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,		lowerCamelCase_    )    )
     # verify is_crowd
     __A       :		Any							=	torch.tensor([0, 0, 0, 0, 0, 0]    )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,		lowerCamelCase_    )    )
     # verify class_labels
     __A       :		Dict							=	torch.tensor([17, 17, 63, 75, 75, 93]    )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,		lowerCamelCase_    )    )
     # verify masks
     __A       :		List[str]							=	82_2873
     self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()   ,		lowerCamelCase_    )
     # verify orig_size
     __A       :		Optional[Any]							=	torch.tensor([480, 640]    )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,		lowerCamelCase_    )    )
     # verify size
     __A       :		Optional[Any]							=	torch.tensor([800, 1066]    )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,		lowerCamelCase_    )    )











'''simple docstring'''




from __future__ import annotations

from statistics import mean



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				List[Any]		=			[0] * no_of_processes
		SCREAMING_SNAKE_CASE :				int		=			[0] * no_of_processes

		# Initialize remaining_time to waiting_time.

		for i in range(lowerCamelCase_	):
				SCREAMING_SNAKE_CASE :				Optional[int]		=			burst_time[i]
		SCREAMING_SNAKE_CASE :				list[int]		=			[]

		SCREAMING_SNAKE_CASE :				List[str]		=			0
		SCREAMING_SNAKE_CASE :				List[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:
				SCREAMING_SNAKE_CASE :				Optional[int]		=			[]
				SCREAMING_SNAKE_CASE :				Any		=			-1

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

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

		return waiting_time



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Any		=			[0] * no_of_processes
		for i in range(lowerCamelCase_	):
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			burst_time[i] + waiting_time[i]
		return turn_around_time


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

							__UpperCAmelCase            =       4
							__UpperCAmelCase            =       [2, 5, 3, 7]
							__UpperCAmelCase            =       [0, 0, 0, 0]
							__UpperCAmelCase            =       calculate_waitingtime(arrival_time, burst_time, no_of_processes)
							__UpperCAmelCase            =       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}''')



