





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 YolosImageProcessor







class 			__A       (      unittest.TestCase  ):




						"""simple docstring"""







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



													"""simple docstring"""





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


						def 				__snake_case			(		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 				__snake_case			(		self      ,							a__      ,							a__=False):



													"""simple docstring"""





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

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

													return expected_height, expected_width







@require_torch
@require_vision
class 			__A       (      lowerCamelCase__ ,unittest.TestCase  ):




						"""simple docstring"""







						UpperCAmelCase__						=      YolosImageProcessor if is_vision_available() else None


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Optional[int]	=		YolosImageProcessingTester(self)


						@property
						def 				__snake_case			(		self):



													"""simple docstring"""





													return self.image_processor_tester.prepare_image_processor_dict()


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Optional[int]	=		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__      ,							'''size'''))


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     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      ,							a__)

													_lowerCamelCase							:     str	=		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 				__snake_case			(		self):



													"""simple docstring"""





													pass


						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     int	=		self.image_processing_class(**self.image_processor_dict)
													# create random PIL images
													_lowerCamelCase							:     Optional[Any]	=		prepare_image_inputs(self.image_processor_tester      ,							equal_resolution=a__)
													for image in image_inputs:
																				self.assertIsInstance(a__      ,							Image.Image)

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

													_lowerCamelCase							:     Union[str, Any]	=		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
													_lowerCamelCase							:     List[str]	=		self.image_processor_tester.get_expected_values(a__      ,							batched=a__)

													_lowerCamelCase							:     Optional[Any]	=		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 				__snake_case			(		self):



													"""simple docstring"""





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

													_lowerCamelCase							:     List[Any]	=		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
													_lowerCamelCase							:     str	=		image_processing(a__      ,							return_tensors='''pt''').pixel_values

													_lowerCamelCase							:     Any	=		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 				__snake_case			(		self):



													"""simple docstring"""





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

													_lowerCamelCase							:     Optional[int]	=		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
													_lowerCamelCase							:     int	=		image_processing(a__      ,							return_tensors='''pt''').pixel_values

													_lowerCamelCase							:     Dict	=		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 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Dict	=		self.image_processing_class(**self.image_processor_dict)
													_lowerCamelCase							:     int	=		self.image_processing_class(do_resize=a__      ,							do_normalize=a__      ,							do_rescale=a__)
													# create random PyTorch tensors
													_lowerCamelCase							:     Tuple	=		prepare_image_inputs(self.image_processor_tester      ,							equal_resolution=a__      ,							torchify=a__)
													for image in image_inputs:
																				self.assertIsInstance(a__      ,							torch.Tensor)

													# Test whether the method "pad" and calling the image processor return the same tensors
													_lowerCamelCase							:     str	=		image_processing_a.pad(a__      ,							return_tensors='''pt''')
													_lowerCamelCase							:     int	=		image_processing_a(a__      ,							return_tensors='''pt''')

													self.assertTrue(
													    torch.allclose(encoded_images_with_method['''pixel_values''']      ,							encoded_images['''pixel_values''']      ,							atol=1e-4))


						@slow
						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     Tuple	=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
													with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''      ,							'''r''') as f:
																				_lowerCamelCase							:     Any	=		json.loads(f.read())

													_lowerCamelCase							:     Dict	=		{'''image_id''': 3_9769, '''annotations''': target}

													# encode them
													_lowerCamelCase							:     Optional[int]	=		YolosImageProcessor.from_pretrained('''hustvl/yolos-small''')
													_lowerCamelCase							:     str	=		image_processing(images=a__      ,							annotations=a__      ,							return_tensors='''pt''')

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

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

													# verify area
													_lowerCamelCase							:     Dict	=		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''']      ,							a__))
													# verify boxes
													_lowerCamelCase							:     Dict	=		torch.Size([6, 4])
													self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape      ,							a__)
													_lowerCamelCase							:     int	=		torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]      ,							a__      ,							atol=1e-3))
													# verify image_id
													_lowerCamelCase							:     Optional[Any]	=		torch.tensor([3_9769])
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']      ,							a__))
													# verify is_crowd
													_lowerCamelCase							:     str	=		torch.tensor([0, 0, 0, 0, 0, 0])
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']      ,							a__))
													# verify class_labels
													_lowerCamelCase							:     Optional[Any]	=		torch.tensor([75, 75, 63, 65, 17, 17])
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']      ,							a__))
													# verify orig_size
													_lowerCamelCase							:     Any	=		torch.tensor([480, 640])
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']      ,							a__))
													# verify size
													_lowerCamelCase							:     List[str]	=		torch.tensor([800, 1066])
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']      ,							a__))



						@slow
						def 				__snake_case			(		self):



													"""simple docstring"""





													_lowerCamelCase							:     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:
																				_lowerCamelCase							:     str	=		json.loads(f.read())

													_lowerCamelCase							:     str	=		{'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

													_lowerCamelCase							:     List[str]	=		pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''')

													# encode them
													_lowerCamelCase							:     Tuple	=		YolosImageProcessor(format='''coco_panoptic''')
													_lowerCamelCase							:     str	=		image_processing(images=a__      ,							annotations=a__      ,							masks_path=a__      ,							return_tensors='''pt''')

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

													_lowerCamelCase							:     List[str]	=		torch.tensor([0.2796, 0.3138, 0.3481])
													self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]      ,							a__      ,							atol=1e-4))

													# verify area
													_lowerCamelCase							:     List[str]	=		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''']      ,							a__))
													# verify boxes
													_lowerCamelCase							:     Union[str, Any]	=		torch.Size([6, 4])
													self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape      ,							a__)
													_lowerCamelCase							:     Dict	=		torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]      ,							a__      ,							atol=1e-3))
													# verify image_id
													_lowerCamelCase							:     str	=		torch.tensor([3_9769])
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']      ,							a__))
													# verify is_crowd
													_lowerCamelCase							:     Any	=		torch.tensor([0, 0, 0, 0, 0, 0])
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']      ,							a__))
													# verify class_labels
													_lowerCamelCase							:     int	=		torch.tensor([17, 17, 63, 75, 75, 93])
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']      ,							a__))
													# verify masks
													_lowerCamelCase							:     Dict	=		82_2873
													self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()      ,							a__)
													# verify orig_size
													_lowerCamelCase							:     Union[str, Any]	=		torch.tensor([480, 640])
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']      ,							a__))
													# verify size
													_lowerCamelCase							:     Optional[Any]	=		torch.tensor([800, 1066])
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']      ,							a__))








import unittest

from knapsack import knapsack as k







class 			__A       (      unittest.TestCase  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		0
             _lowerCamelCase							:     int	=		[0]
             _lowerCamelCase							:     Optional[int]	=		[0]
             _lowerCamelCase							:     Dict	=		len(a__)
             self.assertEqual(k.knapsack(a__      ,							a__      ,							a__      ,							a__)      ,							0)

             _lowerCamelCase							:     str	=		[60]
             _lowerCamelCase							:     List[str]	=		[10]
             _lowerCamelCase							:     List[Any]	=		len(a__)
             self.assertEqual(k.knapsack(a__      ,							a__      ,							a__      ,							a__)      ,							0)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		3
             _lowerCamelCase							:     List[Any]	=		[1, 2, 3]
             _lowerCamelCase							:     Dict	=		[3, 2, 1]
             _lowerCamelCase							:     Any	=		len(a__)
             self.assertEqual(k.knapsack(a__      ,							a__      ,							a__      ,							a__)      ,							5)



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		50
             _lowerCamelCase							:     Dict	=		[60, 100, 120]
             _lowerCamelCase							:     Dict	=		[10, 20, 30]
             _lowerCamelCase							:     Union[str, Any]	=		len(a__)
             self.assertEqual(k.knapsack(a__      ,							a__      ,							a__      ,							a__)      ,							220)


if __name__ == "__main__":
       unittest.main()

