


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 ViTImageProcessor


class 					lowerCAmelCase_ (       unittest.TestCase):




						def __init__(	self						:   Any   ,     __A						:   Dict   ,     __A						:   int=13   ,     __A						:   Optional[int]=3   ,     __A						:   Optional[int]=224   ,     __A						:   Optional[int]=30   ,     __A						:   List[Any]=400   ,     __A						:   List[str]=True   ,     __A						:   Tuple=None   ,     __A						:   List[str]=True   ,     __A						:   Optional[Any]=[0.5, 0.5, 0.5]   ,     __A						:   int=[0.5, 0.5, 0.5]   ,     )      ->Union[str, Any]:






									"""simple docstring"""






									a__ :List[str]						  =    size if size is not None else {"height": 18, "width": 18}
									a__ :Any						  =    parent
									a__ :int						  =    batch_size
									a__ :List[str]						  =    num_channels
									a__ :Any						  =    image_size
									a__ :Any						  =    min_resolution
									a__ :Optional[int]						  =    max_resolution
									a__ :Optional[Any]						  =    do_resize
									a__ :List[str]						  =    size
									a__ :Any						  =    do_normalize
									a__ :Union[str, Any]						  =    image_mean
									a__ :Optional[Any]						  =    image_std






						def  _snake_case					(	self						:   str						)      ->List[Any]:






									"""simple docstring"""






									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 					lowerCAmelCase_ (       _a			,unittest.TestCase):
						lowerCamelCase_         =     ViTImageProcessor if is_vision_available() else None




						def  _snake_case					(	self						:   List[Any]						)      ->Any:






									"""simple docstring"""






									a__ :Dict						  =    EfficientFormerImageProcessorTester(self						)




						@property
						def  _snake_case					(	self						:   List[Any]						)      ->Any:






									"""simple docstring"""






									return self.image_proc_tester.prepare_image_processor_dict()




						def  _snake_case					(	self						:   List[Any]						)      ->List[Any]:






									"""simple docstring"""






									a__ :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						:   Tuple						)      ->Optional[int]:






									"""simple docstring"""






									pass




						def  _snake_case					(	self						:   Dict						)      ->Tuple:






									"""simple docstring"""






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

									# Test not batched input
									a__ :Optional[int]						  =    image_processor(image_inputs[0]   ,     return_tensors="pt"						).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,     (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["height"],
									        self.image_proc_tester.size["width"],
									    )   ,     )

									# Test batched
									a__ :Optional[int]						  =    image_processor(_A   ,     return_tensors="pt"						).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,     (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["height"],
									        self.image_proc_tester.size["width"],
									    )   ,     )




						def  _snake_case					(	self						:   str						)      ->Union[str, Any]:






									"""simple docstring"""






									a__ :Optional[int]						  =    self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									a__ :List[Any]						  =    prepare_image_inputs(self.image_proc_tester   ,     equal_resolution=_A   ,     numpify=_A						)
									for image in image_inputs:
												self.assertIsInstance(_A   ,     np.ndarray						)

									# Test not batched input
									a__ :Any						  =    image_processor(image_inputs[0]   ,     return_tensors="pt"						).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,     (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["height"],
									        self.image_proc_tester.size["width"],
									    )   ,     )

									# Test batched
									a__ :List[Any]						  =    image_processor(_A   ,     return_tensors="pt"						).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,     (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["height"],
									        self.image_proc_tester.size["width"],
									    )   ,     )






						def  _snake_case					(	self						:   str						)      ->Tuple:






									"""simple docstring"""






									a__ :Optional[Any]						  =    self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									a__ :str						  =    prepare_image_inputs(self.image_proc_tester   ,     equal_resolution=_A   ,     torchify=_A						)
									for image in image_inputs:
												self.assertIsInstance(_A   ,     torch.Tensor						)

									# Test not batched input
									a__ :Any						  =    image_processor(image_inputs[0]   ,     return_tensors="pt"						).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,     (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["height"],
									        self.image_proc_tester.size["width"],
									    )   ,     )

									# Test batched
									a__ :List[Any]						  =    image_processor(_A   ,     return_tensors="pt"						).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,     (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["height"],
									        self.image_proc_tester.size["width"],
									    )   ,     )






import numpy as np


def 				lowerCamelCase__	(      a	:   np.ndarray	,  a	:   np.ndarray	,  a	:   float = 1e-12	,  a	:   int = 100	,  )    ->	tuple[float, np.ndarray]:





			"""simple docstring"""
			assert np.shape(a				)[0] == np.shape(a				)[1]
			# Ensure proper dimensionality.
			assert np.shape(a				)[0] == np.shape(a				)[0]
			# Ensure inputs are either both complex or both real
			assert np.iscomplexobj(a				) == np.iscomplexobj(a				)
			a__ :List[str]						  =    np.iscomplexobj(a				)
			if is_complex:
						# Ensure complex input_matrix is Hermitian
						assert np.array_equal(a	,  input_matrix.conj().T				)

			# Set convergence to False. Will define convergence when we exceed max_iterations
			# or when we have small changes from one iteration to next.

			a__ :List[str]						  =    False
			a__ :str						  =    0
			a__ :Dict						  =    0
			a__ :Tuple						  =    1e12

			while not convergence:
						# Multiple matrix by the vector.
						a__ :Tuple						  =    np.dot(a	,  a				)
						# Normalize the resulting output vector.
						a__ :List[Any]						  =    w / np.linalg.norm(a				)
						# Find rayleigh quotient
						# (faster than usual b/c we know vector is normalized already)
						a__ :List[str]						  =    vector.conj().T if is_complex else vector.T
						a__ :Any						  =    np.dot(a	,  np.dot(a	,  a				)				)

						# Check convergence.
						a__ :Optional[Any]						  =    np.abs(lambda_ - lambda_previous				) / lambda_
						iterations += 1

						if error <= error_tol or iterations >= max_iterations:
									a__ :List[str]						  =    True

						a__ :Optional[int]						  =    lambda_

			if is_complex:
						a__ :Union[str, Any]						  =    np.real(lambda_				)

			return lambda_, vector


def 				lowerCamelCase__	(      )    ->	None:





			"""simple docstring"""
			a__ :List[str]						  =    np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]				)
			a__ :Dict						  =    np.array([41, 4, 20]				)
			a__ :Union[str, Any]						  =    real_input_matrix.astype(np.complexaaa				)
			a__ :Optional[Any]						  =    np.triu(1j * complex_input_matrix	,  1				)
			complex_input_matrix += imag_matrix
			complex_input_matrix += -1 * imag_matrix.T
			a__ :Union[str, Any]						  =    np.array([41, 4, 20]				).astype(np.complexaaa				)

			for problem_type in ["real", "complex"]:
						if problem_type == "real":
									a__ :Union[str, Any]						  =    real_input_matrix
									a__ :int						  =    real_vector
						elif problem_type == "complex":
									a__ :Optional[Any]						  =    complex_input_matrix
									a__ :List[Any]						  =    complex_vector

						# Our implementation.
						a__  ,  a__ :Any						  =    power_iteration(a	,  a				)

						# Numpy implementation.

						# Get eigenvalues and eigenvectors using built-in numpy
						# eigh (eigh used for symmetric or hermetian matrices).
						a__  ,  a__ :int						  =    np.linalg.eigh(a				)
						# Last eigenvalue is the maximum one.
						a__ :int						  =    eigen_values[-1]
						# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
						a__ :Optional[Any]						  =    eigen_vectors[:, -1]

						# Check our implementation and numpy gives close answers.
						assert np.abs(eigen_value - eigen_value_max				) <= 1e-6
						# Take absolute values element wise of each eigenvector.
						# as they are only unique to a minus sign.
						assert np.linalg.norm(np.abs(a				) - np.abs(a				)				) <= 1e-6


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		test_power_iteration()


