





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 EfficientNetImageProcessor
class       UpperCamelCase_		(	unittest.TestCase       ):




					def __init__(		self					,    lowerCAmelCase_					,    lowerCAmelCase_=13					,    lowerCAmelCase_=3					,    lowerCAmelCase_=18					,    lowerCAmelCase_=30					,    lowerCAmelCase_=400					,    lowerCAmelCase_=True					,    lowerCAmelCase_=None					,    lowerCAmelCase_=True					,    lowerCAmelCase_=[0.5, 0.5, 0.5]					,    lowerCAmelCase_=[0.5, 0.5, 0.5]					,    )		->				Any:
										_snake_case						=					size if size is not None else {'height': 18, 'width': 18}
										_snake_case						=					parent
										_snake_case						=					batch_size
										_snake_case						=					num_channels
										_snake_case						=					image_size
										_snake_case						=					min_resolution
										_snake_case						=					max_resolution
										_snake_case						=					do_resize
										_snake_case						=					size
										_snake_case						=					do_normalize
										_snake_case						=					image_mean
										_snake_case						=					image_std






					def 						lowerCAmelCase      (		self	)		->				Optional[int]:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										}





@require_torch
@require_vision
class       UpperCamelCase_		(	_lowerCamelCase				,	unittest.TestCase       ):
					lowerCAmelCase_				=						EfficientNetImageProcessor if is_vision_available() else None




					def 						lowerCAmelCase      (		self	)		->				Tuple:
										_snake_case						=					EfficientNetImageProcessorTester(self	)




					@property
					def 						lowerCAmelCase      (		self	)		->				List[Any]:
										return self.image_processor_tester.prepare_image_processor_dict()




					def 						lowerCAmelCase      (		self	)		->				int:
										_snake_case						=					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 						lowerCAmelCase      (		self	)		->				Tuple:
										_snake_case						=					self.image_processing_class.from_dict(self.image_processor_dict	)
										self.assertEqual(image_processor.size					,    {'height': 18, 'width': 18}	)

										_snake_case						=					self.image_processing_class.from_dict(self.image_processor_dict					,    size=42	)
										self.assertEqual(image_processor.size					,    {'height': 42, 'width': 42}	)




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										# Initialize image_processing
										_snake_case						=					self.image_processing_class(**self.image_processor_dict	)
										# create random PIL images
										_snake_case						=					prepare_image_inputs(self.image_processor_tester					,    equal_resolution=lowerCAmelCase_	)
										for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase_					,    Image.Image	)

										# Test not batched input
										_snake_case						=					image_processing(image_inputs[0]					,    return_tensors='pt'	).pixel_values
										self.assertEqual(
										    encoded_images.shape					,    (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )					,    )

										# Test batched
										_snake_case						=					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,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )					,    )




					def 						lowerCAmelCase      (		self	)		->				Any:
										# Initialize image_processing
										_snake_case						=					self.image_processing_class(**self.image_processor_dict	)
										# create random numpy tensors
										_snake_case						=					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
										_snake_case						=					image_processing(image_inputs[0]					,    return_tensors='pt'	).pixel_values
										self.assertEqual(
										    encoded_images.shape					,    (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )					,    )

										# Test batched
										_snake_case						=					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,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )					,    )






					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										# Initialize image_processing
										_snake_case						=					self.image_processing_class(**self.image_processor_dict	)
										# create random PyTorch tensors
										_snake_case						=					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
										_snake_case						=					image_processing(image_inputs[0]					,    return_tensors='pt'	).pixel_values
										self.assertEqual(
										    encoded_images.shape					,    (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )					,    )

										# Test batched
										_snake_case						=					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,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )					,    )










from typing import Any

import numpy as np





def     lowerCamelCase__  (  UpperCamelCase__      :							np.ndarray    )      ->			bool:



					'''simple docstring'''




					assert np.shape(UpperCamelCase__    )[0] == np.shape(UpperCamelCase__    )[1]

					# If matrix not symmetric, exit right away.
					if np.allclose(UpperCamelCase__							,     matrix.T    ) is False:
										return False

					# Get eigenvalues and eignevectors for a symmetric matrix.
					_snake_case				,  _snake_case						=					np.linalg.eigh(UpperCamelCase__    )

					# Check sign of all eigenvalues.
					# np.all returns a value of type np.bool_
					return bool(np.all(eigen_values > 0    )    )





def     lowerCamelCase__  (  UpperCamelCase__      :							int    )      ->			Any:



					'''simple docstring'''




					_snake_case						=					np.random.randn(UpperCamelCase__							,     UpperCamelCase__    )
					_snake_case						=					np.dot(UpperCamelCase__							,     random_matrix.T    )
					assert _is_matrix_spd(UpperCamelCase__    )
					return spd_matrix





def     lowerCamelCase__  (  UpperCamelCase__      :							np.ndarray							,     UpperCamelCase__      :							np.ndarray							,     UpperCamelCase__      :							int = 1_000							,     UpperCamelCase__      :							float = 1e-8							,     )      ->			Any:



					'''simple docstring'''




					assert np.shape(UpperCamelCase__    )[0] == np.shape(UpperCamelCase__    )[1]
					assert np.shape(UpperCamelCase__    )[0] == np.shape(UpperCamelCase__    )[0]
					assert _is_matrix_spd(UpperCamelCase__    )

					# Initialize solution guess, residual, search direction.
					_snake_case						=					np.zeros((np.shape(UpperCamelCase__    )[0], 1)    )
					_snake_case						=					np.copy(UpperCamelCase__    )
					_snake_case						=					np.copy(UpperCamelCase__    )

					# Set initial errors in solution guess and residual.
					_snake_case						=					1e9
					_snake_case						=					1e9
					_snake_case						=					1e9

					# Set iteration counter to threshold number of iterations.
					_snake_case						=					0

					while error > tol:
										# Save this value so we only calculate the matrix-vector product once.
										_snake_case						=					np.dot(UpperCamelCase__							,     UpperCamelCase__    )

										# The main algorithm.

										# Update search direction magnitude.
										_snake_case						=					np.dot(ra.T							,     UpperCamelCase__    ) / np.dot(pa.T							,     UpperCamelCase__    )
										# Update solution guess.
										_snake_case						=					xa + alpha * pa
										# Calculate new residual.
										_snake_case						=					ra - alpha * w
										# Calculate new Krylov subspace scale.
										_snake_case						=					np.dot(r.T							,     UpperCamelCase__    ) / np.dot(ra.T							,     UpperCamelCase__    )
										# Calculate new A conjuage search direction.
										_snake_case						=					r + beta * pa

										# Calculate errors.
										_snake_case						=					np.linalg.norm(r - ra    )
										_snake_case						=					np.linalg.norm(x - xa    )
										_snake_case						=					np.maximum(UpperCamelCase__							,     UpperCamelCase__    )

										# Update variables.
										_snake_case						=					np.copy(UpperCamelCase__    )
										_snake_case						=					np.copy(UpperCamelCase__    )
										_snake_case						=					np.copy(UpperCamelCase__    )

										# Update number of iterations.
										iterations += 1
										if iterations > max_iterations:
															break

					return x





def     lowerCamelCase__  (  )      ->			None:



					'''simple docstring'''




					_snake_case						=					3
					_snake_case						=					_create_spd_matrix(UpperCamelCase__    )
					_snake_case						=					np.random.randn(UpperCamelCase__							,     1    )
					_snake_case						=					np.dot(UpperCamelCase__							,     UpperCamelCase__    )

					# Numpy solution.
					_snake_case						=					np.linalg.solve(UpperCamelCase__							,     UpperCamelCase__    )

					# Our implementation.
					_snake_case						=					conjugate_gradient(UpperCamelCase__							,     UpperCamelCase__    )

					# Ensure both solutions are close to x_true (and therefore one another).
					assert np.linalg.norm(x_numpy - x_true    ) <= 1e-6
					assert np.linalg.norm(x_conjugate_gradient - x_true    ) <= 1e-6


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					test_conjugate_gradient()



