



'''simple docstring'''






from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A_							: Optional[Any]						 =	logging.get_logger(__name__)



class 							lowercase       (  __SCREAMING_SNAKE_CASE				):


 """simple docstring"""


 UpperCAmelCase							=  ["""pixel_values"""]







 def __init__(							self  ,a_ = True  ,a_ = None  ,a_ = PILImageResampling.BICUBIC  ,a_ = True  ,a_ = None  ,a_ = True  ,a_ = 1 / 255  ,a_ = True  ,a_ = IMAGENET_DEFAULT_MEAN  ,a_ = IMAGENET_DEFAULT_STD  ,**a_  ,)		->   Dict:
        super().__init__(**a_							)
        _UpperCAmelCase  :						int			     = size if size is not None else {"""shortest_edge""": 224}
        _UpperCAmelCase  :						Tuple			     = get_size_dict(a_  ,default_to_square=a_							)
        _UpperCAmelCase  :						Optional[Any]			     = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        _UpperCAmelCase  :						List[Any]			     = get_size_dict(a_  ,param_name="""crop_size"""							)

        _UpperCAmelCase  :						Union[str, Any]			     = do_resize
        _UpperCAmelCase  :						Optional[Any]			     = size
        _UpperCAmelCase  :						Tuple			     = resample
        _UpperCAmelCase  :						int			     = do_center_crop
        _UpperCAmelCase  :						Tuple			     = crop_size
        _UpperCAmelCase  :						int			     = do_rescale
        _UpperCAmelCase  :						List[Any]			     = rescale_factor
        _UpperCAmelCase  :						List[Any]			     = do_normalize
        _UpperCAmelCase  :						Optional[int]			     = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        _UpperCAmelCase  :						List[Any]			     = image_std if image_std is not None else IMAGENET_DEFAULT_STD







 def _snake_case (							self  ,a_  ,a_  ,a_ = PILImageResampling.BICUBIC  ,a_ = None  ,**a_  ,)		->   Dict:
        _UpperCAmelCase  :						Dict			     = get_size_dict(a_  ,default_to_square=a_							)
        # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
        if "shortest_edge" in size:
               _UpperCAmelCase  :						Dict			     = int((256 / 224) * size["""shortest_edge"""]							)
               _UpperCAmelCase  :						List[str]			     = get_resize_output_image_size(a_  ,size=a_  ,default_to_square=a_							)
               _UpperCAmelCase  :						Union[str, Any]			     = {"""height""": output_size[0], """width""": output_size[1]}
        if "height" not in size_dict or "width" not in size_dict:
               raise ValueError(
                   f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''							)
        return resize(
            a_  ,size=(size_dict["""height"""], size_dict["""width"""])  ,resample=a_  ,data_format=a_  ,**a_							)







 def _snake_case (							self  ,a_  ,a_  ,a_ = None  ,**a_  ,)		->   int:
        _UpperCAmelCase  :						Tuple			     = get_size_dict(a_							)
        if "height" not in size or "width" not in size:
               raise ValueError(f'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''							)
        return center_crop(a_  ,size=(size["""height"""], size["""width"""])  ,data_format=a_  ,**a_							)







 def _snake_case (							self  ,a_  ,a_  ,a_ = None  ,**a_  ,)		->   Dict:
        return rescale(a_  ,scale=a_  ,data_format=a_  ,**a_							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,a_ = None  ,**a_  ,)		->   List[str]:
        return normalize(a_  ,mean=a_  ,std=a_  ,data_format=a_  ,**a_							)







 def _snake_case (							self  ,a_  ,a_ = None  ,a_ = None  ,a_ = None  ,a_ = None  ,a_ = None  ,a_ = None  ,a_ = None  ,a_ = None  ,a_ = None  ,a_ = None  ,a_ = None  ,a_ = ChannelDimension.FIRST  ,**a_  ,)		->   Union[str, Any]:
        _UpperCAmelCase  :						List[str]			     = do_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase  :						Any			     = resample if resample is not None else self.resample
        _UpperCAmelCase  :						Optional[int]			     = do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCAmelCase  :						Optional[int]			     = do_rescale if do_rescale is not None else self.do_rescale
        _UpperCAmelCase  :						Optional[int]			     = rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCAmelCase  :						List[str]			     = do_normalize if do_normalize is not None else self.do_normalize
        _UpperCAmelCase  :						Optional[Any]			     = image_mean if image_mean is not None else self.image_mean
        _UpperCAmelCase  :						int			     = image_std if image_std is not None else self.image_std

        _UpperCAmelCase  :						Union[str, Any]			     = size if size is not None else self.size
        _UpperCAmelCase  :						Any			     = get_size_dict(a_  ,default_to_square=a_							)
        _UpperCAmelCase  :						Tuple			     = crop_size if crop_size is not None else self.crop_size
        _UpperCAmelCase  :						List[Any]			     = get_size_dict(a_  ,param_name="""crop_size"""							)

        _UpperCAmelCase  :						List[str]			     = make_list_of_images(a_							)

        if not valid_images(a_							):
               raise ValueError(
                   """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                   """torch.Tensor, tf.Tensor or jax.ndarray."""							)

        if do_resize and size is None:
               raise ValueError("""Size must be specified if do_resize is True."""							)

        if do_center_crop and crop_size is None:
               raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

        if do_rescale and rescale_factor is None:
               raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

        if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

        # All transformations expect numpy arrays.
        _UpperCAmelCase  :						List[str]			     = [to_numpy_array(a_							) for image in images]

        if do_resize:
               _UpperCAmelCase  :						List[str]			     = [self.resize(a_  ,a_  ,a_							) for image in images]

        if do_center_crop:
               _UpperCAmelCase  :						Optional[int]			     = [self.center_crop(a_  ,a_							) for image in images]

        if do_rescale:
               _UpperCAmelCase  :						Optional[Any]			     = [self.rescale(a_  ,a_							) for image in images]

        if do_normalize:
               _UpperCAmelCase  :						Dict			     = [self.normalize(a_  ,a_  ,a_							) for image in images]

        _UpperCAmelCase  :						str			     = [to_channel_dimension_format(a_  ,a_							) for image in images]

        _UpperCAmelCase  :						Optional[int]			     = {"""pixel_values""": images}
        return BatchFeature(data=a_  ,tensor_type=a_							)





import numpy as np


def 						UpperCAmelCase			(	a_		,   a_		,   a_ = 1E-12		,   a_ = 1_0_0		,   )		->	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   			= 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   			= False
				__A   			= 0
				__A   			= 0
				__A   			= 1E12

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

								# Check convergence.
								__A   			= np.abs(lambda_ - lambda_previous						) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												__A   			= True

								__A   			= lambda_

				if is_complex:
								__A   			= np.real(lambda_						)

				return lambda_, vector


def 						UpperCAmelCase			(	)		->	None:







				"""simple docstring"""



				__A   			= np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]						)
				__A   			= np.array([4_1, 4, 2_0]						)
				__A   			= real_input_matrix.astype(np.complexaaa						)
				__A   			= np.triu(1J * complex_input_matrix		,   1						)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				__A   			= np.array([4_1, 4, 2_0]						).astype(np.complexaaa						)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												__A   			= real_input_matrix
												__A   			= real_vector
								elif problem_type == "complex":
												__A   			= complex_input_matrix
												__A   			= complex_vector

								# Our implementation.
								__A			,							__A   			= power_iteration(a_		,   a_						)

								# Numpy implementation.

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