




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 GLPNImageProcessor




class       _A				(		unittest.TestCase     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=7						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=18						,		_SCREAMING_SNAKE_CASE=30						,		_SCREAMING_SNAKE_CASE=400						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE=32						,		_SCREAMING_SNAKE_CASE=True						,		):
								_UpperCAmelCase      =			parent
								_UpperCAmelCase      =			batch_size
								_UpperCAmelCase      =			num_channels
								_UpperCAmelCase      =			image_size
								_UpperCAmelCase      =			min_resolution
								_UpperCAmelCase      =			max_resolution
								_UpperCAmelCase      =			do_resize
								_UpperCAmelCase      =			size_divisor
								_UpperCAmelCase      =			do_rescale






		def 				UpperCAmelCase (    self			):
								return {
								    "do_resize": self.do_resize,
								    "size_divisor": self.size_divisor,
								    "do_rescale": self.do_rescale,
								}




@require_torch
@require_vision
class       _A				(		__lowercase       ,     unittest.TestCase     ):
		__a					    =       GLPNImageProcessor if is_vision_available() else None






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			GLPNImageProcessingTester(self			)






		@property
		def 				UpperCAmelCase (    self			):
								return self.image_processor_tester.prepare_image_processor_dict()






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.image_processing_class(**self.image_processor_dict			)
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,		"""do_resize"""			)			)
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,		"""size_divisor"""			)			)
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,		"""resample"""			)			)
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,		"""do_rescale"""			)			)






		def 				UpperCAmelCase (    self			):
								pass






		def 				UpperCAmelCase (    self			):
								# Initialize image_processing
								_UpperCAmelCase      =			self.image_processing_class(**self.image_processor_dict			)
								# create random PIL images
								_UpperCAmelCase      =			prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_SCREAMING_SNAKE_CASE			)
								for image in image_inputs:
														self.assertIsInstance(_SCREAMING_SNAKE_CASE						,		Image.Image			)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_UpperCAmelCase      =			image_processing(image_inputs[0]						,		return_tensors="""pt"""			).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0			)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0			)






		def 				UpperCAmelCase (    self			):
								# Initialize image_processing
								_UpperCAmelCase      =			self.image_processing_class(**self.image_processor_dict			)
								# create random numpy tensors
								_UpperCAmelCase      =			prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_SCREAMING_SNAKE_CASE						,		numpify=_SCREAMING_SNAKE_CASE			)
								for image in image_inputs:
														self.assertIsInstance(_SCREAMING_SNAKE_CASE						,		np.ndarray			)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_UpperCAmelCase      =			image_processing(image_inputs[0]						,		return_tensors="""pt"""			).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0			)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0			)






		def 				UpperCAmelCase (    self			):
								# Initialize image_processing
								_UpperCAmelCase      =			self.image_processing_class(**self.image_processor_dict			)
								# create random PyTorch tensors
								_UpperCAmelCase      =			prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_SCREAMING_SNAKE_CASE						,		torchify=_SCREAMING_SNAKE_CASE			)
								for image in image_inputs:
														self.assertIsInstance(_SCREAMING_SNAKE_CASE						,		torch.Tensor			)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_UpperCAmelCase      =			image_processing(image_inputs[0]						,		return_tensors="""pt"""			).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0			)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0			)





import unittest

import numpy as np




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case = None			,     )						->				np.ndarray:
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )

						if shape_a[0] != shape_b[0]:
												_UpperCAmelCase      =			(
												    """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(snake_case       )

						if shape_b[1] != shape_c[1]:
												_UpperCAmelCase      =			(
												    """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(snake_case       )

						_UpperCAmelCase      =			pseudo_inv
						if a_inv is None:
												try:
																		_UpperCAmelCase      =			np.linalg.inv(snake_case       )
												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 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

								_UpperCAmelCase      =			schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)

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

								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)

								self.assertAlmostEqual(_SCREAMING_SNAKE_CASE						,		det_a * det_s			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

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






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1, 3], [6, 3, 5]]			)

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


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				unittest.main()