


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_video_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import VivitImageProcessor

class        a	(unittest.TestCase   ):




							"""simple docstring"""


							def __init__(							self :						Optional[Any]					,			lowerCamelCase :						Optional[int]					,			lowerCamelCase :						Any=7					,			lowerCamelCase :						Dict=3					,			lowerCamelCase :						Tuple=10					,			lowerCamelCase :						Tuple=18					,			lowerCamelCase :						Optional[int]=30					,			lowerCamelCase :						List[str]=400					,			lowerCamelCase :						List[Any]=True					,			lowerCamelCase :						Optional[Any]=None					,			lowerCamelCase :						Optional[int]=True					,			lowerCamelCase :						List[Any]=[0.5, 0.5, 0.5]					,			lowerCamelCase :						int=[0.5, 0.5, 0.5]					,			lowerCamelCase :						List[str]=None					,			)			->					Any:
														__snake_case	:    Optional[int]											=     size if size is not None else {"shortest_edge": 18}
														__snake_case	:    Tuple											=     crop_size if crop_size is not None else {"height": 18, "width": 18}

														__snake_case	:    Optional[Any]											=     parent
														__snake_case	:    Optional[int]											=     batch_size
														__snake_case	:    List[Any]											=     num_channels
														__snake_case	:    Union[str, Any]											=     num_frames
														__snake_case	:    Any											=     image_size
														__snake_case	:    Tuple											=     min_resolution
														__snake_case	:    Optional[Any]											=     max_resolution
														__snake_case	:    Any											=     do_resize
														__snake_case	:    Tuple											=     size
														__snake_case	:    List[Any]											=     do_normalize
														__snake_case	:    Optional[int]											=     image_mean
														__snake_case	:    Any											=     image_std
														__snake_case	:    str											=     crop_size






							def 					__snake_case      (							self :						Dict       )			->					Union[str, Any]:
														return {
														    "image_mean": self.image_mean,
														    "image_std": self.image_std,
														    "do_normalize": self.do_normalize,
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "crop_size": self.crop_size,
														}

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




							"""simple docstring"""


							__UpperCAmelCase  						=						VivitImageProcessor if is_vision_available() else None

							def 					__snake_case      (							self :						List[Any]       )			->					Optional[Any]:
														__snake_case	:    List[Any]											=     VivitImageProcessingTester(self       )

							@property
							def 					__snake_case      (							self :						int       )			->					List[str]:
														return self.image_processor_tester.prepare_image_processor_dict()

							def 					__snake_case      (							self :						List[Any]       )			->					Optional[Any]:
														__snake_case	:    List[Any]											=     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_center_crop"       )       )
														self.assertTrue(hasattr(A_					,			"size"       )       )

							def 					__snake_case      (							self :						Dict       )			->					Tuple:
														__snake_case	:    Dict											=     self.image_processing_class.from_dict(self.image_processor_dict       )
														self.assertEqual(image_processor.size					,			{"shortest_edge": 18}       )
														self.assertEqual(image_processor.crop_size					,			{"height": 18, "width": 18}       )

														__snake_case	:    Dict											=     self.image_processing_class.from_dict(self.image_processor_dict					,			size=42					,			crop_size=84       )
														self.assertEqual(image_processor.size					,			{"shortest_edge": 42}       )
														self.assertEqual(image_processor.crop_size					,			{"height": 84, "width": 84}       )

							def 					__snake_case      (							self :						Any       )			->					List[Any]:
														__snake_case	:    Optional[Any]											=     self.image_processing_class(**self.image_processor_dict       )
														# create random PIL videos
														__snake_case	:    Union[str, Any]											=     prepare_video_inputs(self.image_processor_tester					,			equal_resolution=A_       )
														for video in video_inputs:
																					self.assertIsInstance(A_					,			A_       )
																					self.assertIsInstance(video[0]					,			Image.Image       )

														# Test not batched input
														__snake_case	:    Any											=     image_processing(video_inputs[0]					,			return_tensors="pt"       ).pixel_values
														self.assertEqual(
														    encoded_videos.shape					,			(
														        1,
														        self.image_processor_tester.num_frames,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["height"],
														        self.image_processor_tester.crop_size["width"],
														    )					,			)

														# Test batched
														__snake_case	:    str											=     image_processing(A_					,			return_tensors="pt"       ).pixel_values
														self.assertEqual(
														    encoded_videos.shape					,			(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_frames,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["height"],
														        self.image_processor_tester.crop_size["width"],
														    )					,			)

							def 					__snake_case      (							self :						List[str]       )			->					List[Any]:
														__snake_case	:    List[Any]											=     self.image_processing_class(**self.image_processor_dict       )
														# create random numpy tensors
														__snake_case	:    str											=     prepare_video_inputs(self.image_processor_tester					,			equal_resolution=A_					,			numpify=A_       )
														for video in video_inputs:
																					self.assertIsInstance(A_					,			A_       )
																					self.assertIsInstance(video[0]					,			np.ndarray       )

														# Test not batched input
														__snake_case	:    Tuple											=     image_processing(video_inputs[0]					,			return_tensors="pt"       ).pixel_values
														self.assertEqual(
														    encoded_videos.shape					,			(
														        1,
														        self.image_processor_tester.num_frames,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["height"],
														        self.image_processor_tester.crop_size["width"],
														    )					,			)

														# Test batched
														__snake_case	:    Any											=     image_processing(A_					,			return_tensors="pt"       ).pixel_values
														self.assertEqual(
														    encoded_videos.shape					,			(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_frames,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["height"],
														        self.image_processor_tester.crop_size["width"],
														    )					,			)






							def 					__snake_case      (							self :						Any       )			->					Optional[Any]:
														__snake_case	:    str											=     self.image_processing_class(**self.image_processor_dict       )
														# create random PyTorch tensors
														__snake_case	:    Union[str, Any]											=     prepare_video_inputs(self.image_processor_tester					,			equal_resolution=A_					,			torchify=A_       )
														for video in video_inputs:
																					self.assertIsInstance(A_					,			A_       )
																					self.assertIsInstance(video[0]					,			torch.Tensor       )

														# Test not batched input
														__snake_case	:    Tuple											=     image_processing(video_inputs[0]					,			return_tensors="pt"       ).pixel_values
														self.assertEqual(
														    encoded_videos.shape					,			(
														        1,
														        self.image_processor_tester.num_frames,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["height"],
														        self.image_processor_tester.crop_size["width"],
														    )					,			)

														# Test batched
														__snake_case	:    List[Any]											=     image_processing(A_					,			return_tensors="pt"       ).pixel_values
														self.assertEqual(
														    encoded_videos.shape					,			(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_frames,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["height"],
														        self.image_processor_tester.crop_size["width"],
														    )					,			)





import unittest

import numpy as np







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase = None							,			):
							__snake_case	:    List[str]											=     np.shape(__lowerCamelCase			)
							__snake_case	:    Optional[Any]											=     np.shape(__lowerCamelCase			)
							__snake_case	:    List[str]											=     np.shape(__lowerCamelCase			)

							if shape_a[0] != shape_b[0]:
														__snake_case	:    Any											=     (
														    "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(__lowerCamelCase			)

							if shape_b[1] != shape_c[1]:
														__snake_case	:    int											=     (
														    "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(__lowerCamelCase			)

							__snake_case	:    str											=     pseudo_inv
							if a_inv is None:
														try:
																					__snake_case	:    Optional[Any]											=     np.linalg.inv(__lowerCamelCase			)
														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   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Tuple       )			->					None:
														__snake_case	:    str											=     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
														__snake_case	:    str											=     np.array([[0, 3], [3, 0], [2, 3]]       )
														__snake_case	:    Dict											=     np.array([[2, 1], [6, 3]]       )

														__snake_case	:    Dict											=     schur_complement(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

														__snake_case	:    int											=     np.block([[a, b], [b.T, c]]       )

														__snake_case	:    Optional[int]											=     np.linalg.det(lowerCamelCase       )
														__snake_case	:    Any											=     np.linalg.det(lowerCamelCase       )
														__snake_case	:    Tuple											=     np.linalg.det(lowerCamelCase       )

														self.assertAlmostEqual(lowerCamelCase					,			det_a * det_s       )

							def 					__snake_case      (							self :						int       )			->					None:
														__snake_case	:    str											=     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
														__snake_case	:    Dict											=     np.array([[0, 3], [3, 0], [2, 3]]       )
														__snake_case	:    Tuple											=     np.array([[2, 1], [6, 3]]       )

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






							def 					__snake_case      (							self :						List[Any]       )			->					None:
														__snake_case	:    List[Any]											=     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
														__snake_case	:    Tuple											=     np.array([[0, 3], [3, 0], [2, 3]]       )
														__snake_case	:    List[Any]											=     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()

