'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

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


if is_vision_available():
  import PIL


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


class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Tuple   				= ['''pixel_values''']




      def __init__(					self       ,lowerCamelCase_ = True       ,lowerCamelCase_ = None       ,lowerCamelCase_ = PIL.Image.BICUBIC       ,lowerCamelCase_ = True       ,lowerCamelCase_ = None       ,lowerCamelCase_ = 1 / 255       ,lowerCamelCase_ = True       ,lowerCamelCase_ = True       ,lowerCamelCase_ = None       ,lowerCamelCase_ = None       ,**lowerCamelCase_       ,)      ->      None:



         '''simple docstring'''



         super().__init__(**lowerCamelCase_				)
         UpperCAmelCase__				:					List[Any] 					=	size if size is not None else {'''height''': 256, '''width''': 256}
         UpperCAmelCase__				:					Union[str, Any] 					=	get_size_dict(lowerCamelCase_				)
         UpperCAmelCase__				:					List[Any] 					=	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         UpperCAmelCase__				:					Any 					=	get_size_dict(lowerCamelCase_       ,param_name='''crop_size'''				)

         UpperCAmelCase__				:					str 					=	do_resize
         UpperCAmelCase__				:					Optional[int] 					=	size
         UpperCAmelCase__				:					Optional[Any] 					=	resample
         UpperCAmelCase__				:					List[str] 					=	do_center_crop
         UpperCAmelCase__				:					Optional[Any] 					=	crop_size
         UpperCAmelCase__				:					Tuple 					=	do_rescale
         UpperCAmelCase__				:					Dict 					=	rescale_factor
         UpperCAmelCase__				:					Tuple 					=	do_normalize
         UpperCAmelCase__				:					Tuple 					=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCAmelCase__				:					List[Any] 					=	image_std if image_std is not None else IMAGENET_STANDARD_STD




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_ = PIL.Image.BICUBIC       ,lowerCamelCase_ = None       ,**lowerCamelCase_       ,)      ->      np.ndarray:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	get_size_dict(lowerCamelCase_				)
         if "height" not in size or "width" not in size:
            raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''				)
         return resize(
             lowerCamelCase_       ,size=(size['''height'''], size['''width'''])       ,resample=lowerCamelCase_       ,data_format=lowerCamelCase_       ,**lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_ = None       ,**lowerCamelCase_       ,)      ->      np.ndarray:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	get_size_dict(lowerCamelCase_				)
         if "height" not in size or "width" not in size:
            raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''				)
         return center_crop(lowerCamelCase_       ,size=(size['''height'''], size['''width'''])       ,data_format=lowerCamelCase_       ,**lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_ = None       ,**lowerCamelCase_       ,)      ->      Union[str, Any]:



         '''simple docstring'''



         return rescale(lowerCamelCase_       ,scale=lowerCamelCase_       ,data_format=lowerCamelCase_       ,**lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_ = None       ,**lowerCamelCase_       ,)      ->      np.ndarray:



         '''simple docstring'''



         return normalize(lowerCamelCase_       ,mean=lowerCamelCase_       ,std=lowerCamelCase_       ,data_format=lowerCamelCase_       ,**lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_ = None       ,lowerCamelCase_ = None       ,lowerCamelCase_=None       ,lowerCamelCase_ = None       ,lowerCamelCase_ = None       ,lowerCamelCase_ = None       ,lowerCamelCase_ = None       ,lowerCamelCase_ = None       ,lowerCamelCase_ = None       ,lowerCamelCase_ = None       ,lowerCamelCase_ = None       ,lowerCamelCase_ = ChannelDimension.FIRST       ,**lowerCamelCase_       ,)      ->      PIL.Image.Image:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase__				:					int 					=	resample if resample is not None else self.resample
         UpperCAmelCase__				:					List[Any] 					=	do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase__				:					Union[str, Any] 					=	do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase__				:					List[Any] 					=	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__				:					Any 					=	image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase__				:					str 					=	image_std if image_std is not None else self.image_std

         UpperCAmelCase__				:					int 					=	size if size is not None else self.size
         UpperCAmelCase__				:					Union[str, Any] 					=	get_size_dict(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase__				:					List[str] 					=	get_size_dict(lowerCamelCase_       ,param_name='''crop_size'''				)

         UpperCAmelCase__				:					Optional[Any] 					=	make_list_of_images(lowerCamelCase_				)

         if not valid_images(lowerCamelCase_				):
            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 or resample is None:
            raise ValueError('''Size and resample 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__				:					str 					=	[to_numpy_array(lowerCamelCase_				) for image in images]

         if do_resize:
            UpperCAmelCase__				:					str 					=	[self.resize(image=lowerCamelCase_       ,size=lowerCamelCase_       ,resample=lowerCamelCase_				) for image in images]

         if do_center_crop:
            UpperCAmelCase__				:					Tuple 					=	[self.center_crop(image=lowerCamelCase_       ,size=lowerCamelCase_				) for image in images]

         if do_rescale:
            UpperCAmelCase__				:					Tuple 					=	[self.rescale(image=lowerCamelCase_       ,scale=lowerCamelCase_				) for image in images]

         if do_normalize:
            UpperCAmelCase__				:					str 					=	[self.normalize(image=lowerCamelCase_       ,mean=lowerCamelCase_       ,std=lowerCamelCase_				) for image in images]

         UpperCAmelCase__				:					Any 					=	[to_channel_dimension_format(lowerCamelCase_       ,lowerCamelCase_				) for image in images]

         UpperCAmelCase__				:					Dict 					=	{'''pixel_values''': images}
         return BatchFeature(data=lowerCamelCase_       ,tensor_type=lowerCamelCase_				)





'''simple docstring'''

import numpy as np







def __UpperCamelCase( _A       :					np.ndarray	,      _A       :					np.ndarray	,      _A       :					float = 1e-12	,      _A       :					int = 1_00	,      ):
   '''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     )
   UpperCAmelCase__				:					int 					=	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.

   UpperCAmelCase__				:					Any 					=	False
   UpperCAmelCase__				:					str 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	1e12

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

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

      if error <= error_tol or iterations >= max_iterations:
         UpperCAmelCase__				:					Optional[Any] 					=	True

      UpperCAmelCase__				:					Optional[int] 					=	lambda_

   if is_complex:
      UpperCAmelCase__				:					Tuple 					=	np.real(lambda_     )

   return lambda_, vector







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					Optional[int] 					=	np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]     )
   UpperCAmelCase__				:					int 					=	np.array([41, 4, 20]     )
   UpperCAmelCase__				:					str 					=	real_input_matrix.astype(np.complexaaa     )
   UpperCAmelCase__				:					Union[str, Any] 					=	np.triu(1J * complex_input_matrix	,      1     )
   complex_input_matrix += imag_matrix
   complex_input_matrix += -1 * imag_matrix.T
   UpperCAmelCase__				:					Tuple 					=	np.array([41, 4, 20]     ).astype(np.complexaaa     )

   for problem_type in ["real", "complex"]:
      if problem_type == "real":
         UpperCAmelCase__				:					Optional[int] 					=	real_input_matrix
         UpperCAmelCase__				:					int 					=	real_vector
      elif problem_type == "complex":
         UpperCAmelCase__				:					int 					=	complex_input_matrix
         UpperCAmelCase__				:					Tuple 					=	complex_vector

      # Our implementation.
      UpperCAmelCase__					,   UpperCAmelCase__				:					str 					=	power_iteration(_A	,      _A     )

      # Numpy implementation.

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




