
import unittest

import numpy as np



def   __lowerCAmelCase    (				UpperCAmelCase__					:   np.ndarray      ,					UpperCAmelCase__					:   np.ndarray      ,					UpperCAmelCase__					:   np.ndarray      ,					UpperCAmelCase__					:   np.ndarray | None = None      ,					)	->     np.ndarray:
							lowerCamelCase_					=       np.shape(_lowercase							)
							lowerCamelCase_					=       np.shape(_lowercase							)
							lowerCamelCase_					=       np.shape(_lowercase							)

							if shape_a[0] != shape_b[0]:
														lowerCamelCase_					=       (
														    'Expected the same number of rows for A and B. '
														    F'''Instead found A of size {shape_a} and B of size {shape_b}'''
														)
														raise ValueError(_lowercase							)

							if shape_b[1] != shape_c[1]:
														lowerCamelCase_					=       (
														    'Expected the same number of columns for B and C. '
														    F'''Instead found B of size {shape_b} and C of size {shape_c}'''
														)
														raise ValueError(_lowercase							)

							lowerCamelCase_					=       pseudo_inv
							if a_inv is None:
														try:
																					lowerCamelCase_					=       np.linalg.inv(_lowercase							)
														except np.linalg.LinAlgError:
																					raise ValueError(
																					    """Input matrix A is not invertible. Cannot compute Schur complement."""							)

							return mat_c - mat_b.T @ a_inv @ mat_b





class    __A(					unittest.TestCase							):

					def  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
												lowerCamelCase_					=       np.array([[0, 3], [3, 0], [2, 3]] )
												lowerCamelCase_					=       np.array([[2, 1], [6, 3]] )

												lowerCamelCase_					=       schur_complement(lowerCamelCase_ ,       lowerCamelCase_ ,       lowerCamelCase_ )

												lowerCamelCase_					=       np.block([[a, b], [b.T, c]] )

												lowerCamelCase_					=       np.linalg.det(lowerCamelCase_ )
												lowerCamelCase_					=       np.linalg.det(lowerCamelCase_ )
												lowerCamelCase_					=       np.linalg.det(lowerCamelCase_ )

												self.assertAlmostEqual(lowerCamelCase_ ,       det_a * det_s )

					def  lowercase__   (						self       :  Any ):
												lowerCamelCase_					=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
												lowerCamelCase_					=       np.array([[0, 3], [3, 0], [2, 3]] )
												lowerCamelCase_					=       np.array([[2, 1], [6, 3]] )

												with self.assertRaises(lowerCamelCase_ ):
																			schur_complement(lowerCamelCase_ ,       lowerCamelCase_ ,       lowerCamelCase_ )



					def  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_					=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
												lowerCamelCase_					=       np.array([[0, 3], [3, 0], [2, 3]] )
												lowerCamelCase_					=       np.array([[2, 1, 3], [6, 3, 5]] )

												with self.assertRaises(lowerCamelCase_ ):
																			schur_complement(lowerCamelCase_ ,       lowerCamelCase_ ,       lowerCamelCase_ )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		unittest.main()












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       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	def __init__(						self:						Optional[int],					lowerCamelCase_:						Dict,					lowerCamelCase_:						Tuple=7,					lowerCamelCase_:						Dict=3,					lowerCamelCase_:						Optional[Any]=30,					lowerCamelCase_:						str=400,					lowerCamelCase_:						List[Any]=True,					lowerCamelCase_:						List[Any]=None,					lowerCamelCase_:						Dict=True,					lowerCamelCase_:						Optional[Any]=[0.5, 0.5, 0.5],					lowerCamelCase_:						Dict=[0.5, 0.5, 0.5],					lowerCamelCase_:						Dict=True,					lowerCamelCase_:						List[str]=1 / 255,					lowerCamelCase_:						Dict=True,					):
			# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
			lowercase__					:      str      =		size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
			lowercase__					:      Optional[int]      =		parent
			lowercase__					:      List[Any]      =		batch_size
			lowercase__					:      Optional[int]      =		num_channels
			lowercase__					:      Optional[Any]      =		min_resolution
			lowercase__					:      Union[str, Any]      =		max_resolution
			lowercase__					:      Tuple      =		do_resize
			lowercase__					:      str      =		size
			lowercase__					:      Dict      =		do_normalize
			lowercase__					:      Optional[int]      =		image_mean
			lowercase__					:      Dict      =		image_std
			lowercase__					:      int      =		do_rescale
			lowercase__					:      Any      =		rescale_factor
			lowercase__					:      Union[str, Any]      =		do_pad

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

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

			return expected_height, expected_width


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


	'''simple docstring'''





	_A													=				YolosImageProcessor if is_vision_available() else None

	def    snake_case__(						self:						int			):
			lowercase__					:      Optional[int]      =		YolosImageProcessingTester(self			)

	@property
	def    snake_case__(						self:						Union[str, Any]			):
			return self.image_processor_tester.prepare_image_processor_dict()

	def    snake_case__(						self:						Dict			):
			lowercase__					:      Optional[Any]      =		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    snake_case__(						self:						List[Any]			):
			lowercase__					:      Tuple      =		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_			)

			lowercase__					:      Optional[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    snake_case__(						self:						str			):
			pass

	def    snake_case__(						self:						Optional[Any]			):
			# Initialize image_processing
			lowercase__					:      Optional[int]      =		self.image_processing_class(**self.image_processor_dict			)
			# create random PIL images
			lowercase__					:      Union[str, 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
			lowercase__					:      Tuple      =		image_processing(image_inputs[0],					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      List[Any]      =		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
			lowercase__   ,				lowercase__					:      Optional[Any]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_,					batched=lowerCamelCase_			)

			lowercase__					:      Dict      =		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    snake_case__(						self:						str			):
			# Initialize image_processing
			lowercase__					:      str      =		self.image_processing_class(**self.image_processor_dict			)
			# create random numpy tensors
			lowercase__					:      int      =		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
			lowercase__					:      List[Any]      =		image_processing(image_inputs[0],					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      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
			lowercase__					:      Any      =		image_processing(lowerCamelCase_,					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      Optional[int]      =		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    snake_case__(						self:						Union[str, Any]			):
			# Initialize image_processing
			lowercase__					:      Optional[int]      =		self.image_processing_class(**self.image_processor_dict			)
			# create random PyTorch tensors
			lowercase__					:      Dict      =		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
			lowercase__					:      List[str]      =		image_processing(image_inputs[0],					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      Union[str, Any]      =		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
			lowercase__					:      List[Any]      =		image_processing(lowerCamelCase_,					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      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,
			    ),					)

	def    snake_case__(						self:						int			):
			# Initialize image_processings
			lowercase__					:      Tuple      =		self.image_processing_class(**self.image_processor_dict			)
			lowercase__					:      List[str]      =		self.image_processing_class(do_resize=lowerCamelCase_,					do_normalize=lowerCamelCase_,					do_rescale=lowerCamelCase_			)
			# create random PyTorch tensors
			lowercase__					:      Tuple      =		prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowerCamelCase_,					torchify=lowerCamelCase_			)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,					torch.Tensor			)

			# Test whether the method "pad" and calling the image processor return the same tensors
			lowercase__					:      Optional[int]      =		image_processing_a.pad(lowerCamelCase_,					return_tensors='pt'			)
			lowercase__					:      int      =		image_processing_a(lowerCamelCase_,					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:						List[str]			):
			# prepare image and target
			lowercase__					:      Any      =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
			with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt',					'r'			) as f:
					lowercase__					:      List[str]      =		json.loads(f.read()			)

			lowercase__					:      List[str]      =		{'image_id': 39769, 'annotations': target}

			# encode them
			lowercase__					:      Any      =		YolosImageProcessor.from_pretrained('hustvl/yolos-small'			)
			lowercase__					:      Optional[Any]      =		image_processing(images=lowerCamelCase_,					annotations=lowerCamelCase_,					return_tensors='pt'			)

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

			lowercase__					:      int      =		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
			lowercase__					:      Optional[int]      =		torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['area'],					lowerCamelCase_			)			)
			# verify boxes
			lowercase__					:      int      =		torch.Size([6, 4]			)
			self.assertEqual(encoding['labels'][0]['boxes'].shape,					lowerCamelCase_			)
			lowercase__					:      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
			lowercase__					:      Tuple      =		torch.tensor([39769]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],					lowerCamelCase_			)			)
			# verify is_crowd
			lowercase__					:      Union[str, Any]      =		torch.tensor([0, 0, 0, 0, 0, 0]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],					lowerCamelCase_			)			)
			# verify class_labels
			lowercase__					:      Tuple      =		torch.tensor([75, 75, 63, 65, 17, 17]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],					lowerCamelCase_			)			)
			# verify orig_size
			lowercase__					:      Optional[int]      =		torch.tensor([480, 640]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],					lowerCamelCase_			)			)
			# verify size
			lowercase__					:      List[Any]      =		torch.tensor([800, 1066]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['size'],					lowerCamelCase_			)			)





	@slow
	def    snake_case__(						self:						Dict			):
			# prepare image, target and masks_path
			lowercase__					:      Optional[int]      =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
			with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt',					'r'			) as f:
					lowercase__					:      Optional[Any]      =		json.loads(f.read()			)

			lowercase__					:      List[Any]      =		{'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

			lowercase__					:      Optional[int]      =		pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'			)

			# encode them
			lowercase__					:      List[Any]      =		YolosImageProcessor(format='coco_panoptic'			)
			lowercase__					:      Dict      =		image_processing(images=lowerCamelCase_,					annotations=lowerCamelCase_,					masks_path=lowerCamelCase_,					return_tensors='pt'			)

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

			lowercase__					:      List[str]      =		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
			lowercase__					:      Optional[int]      =		torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['area'],					lowerCamelCase_			)			)
			# verify boxes
			lowercase__					:      Dict      =		torch.Size([6, 4]			)
			self.assertEqual(encoding['labels'][0]['boxes'].shape,					lowerCamelCase_			)
			lowercase__					:      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
			lowercase__					:      Any      =		torch.tensor([39769]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],					lowerCamelCase_			)			)
			# verify is_crowd
			lowercase__					:      List[str]      =		torch.tensor([0, 0, 0, 0, 0, 0]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],					lowerCamelCase_			)			)
			# verify class_labels
			lowercase__					:      List[str]      =		torch.tensor([17, 17, 63, 75, 75, 93]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],					lowerCamelCase_			)			)
			# verify masks
			lowercase__					:      Optional[Any]      =		822873
			self.assertEqual(encoding['labels'][0]['masks'].sum().item(),					lowerCamelCase_			)
			# verify orig_size
			lowercase__					:      Optional[int]      =		torch.tensor([480, 640]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],					lowerCamelCase_			)			)
			# verify size
			lowercase__					:      Dict      =		torch.tensor([800, 1066]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['size'],					lowerCamelCase_			)			)


