




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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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       __lowerCAmelCase				(							lowerCAmelCase):
       _a           =  ['''pixel_values''']






       def __init__(      self:    Any				,    _lowerCAmelCase:    bool = True				,    _lowerCAmelCase:    Dict[str, int] = None				,    _lowerCAmelCase:    PILImageResampling = PILImageResampling.BICUBIC				,    _lowerCAmelCase:    bool = True				,    _lowerCAmelCase:    Union[int, float] = 1 / 2_55				,    _lowerCAmelCase:    bool = True				,    _lowerCAmelCase:    Optional[Union[float, List[float]]] = None				,    _lowerCAmelCase:    Optional[Union[float, List[float]]] = None				,    _lowerCAmelCase:    bool = True				,    **_lowerCAmelCase:    Optional[Any]				,    ):
         super().__init__(**__SCREAMING_SNAKE_CASE )
         lowercase			:List[Any]							=  size if size is not None else {"height": 3_84, "width": 3_84}
         lowercase			:List[str]							=  get_size_dict(__SCREAMING_SNAKE_CASE				,    default_to_square=__SCREAMING_SNAKE_CASE )

         lowercase			:Optional[Any]							=  do_resize
         lowercase			:Dict							=  size
         lowercase			:int							=  resample
         lowercase			:Optional[int]							=  do_rescale
         lowercase			:List[str]							=  rescale_factor
         lowercase			:Any							=  do_normalize
         lowercase			:Optional[int]							=  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         lowercase			:Union[str, Any]							=  image_std if image_std is not None else OPENAI_CLIP_STD
         lowercase			:Optional[Any]							=  do_convert_rgb






       def     SCREAMING_SNAKE_CASE			(      self:    str				,    _lowerCAmelCase:    np.ndarray				,    _lowerCAmelCase:    Dict[str, int]				,    _lowerCAmelCase:    PILImageResampling = PILImageResampling.BICUBIC				,    _lowerCAmelCase:    Optional[Union[str, ChannelDimension]] = None				,    **_lowerCAmelCase:    Union[str, Any]				,    ):
         lowercase			:Dict							=  get_size_dict(__SCREAMING_SNAKE_CASE				,    default_to_square=__SCREAMING_SNAKE_CASE )
         if "height" not in size or "width" not in size:
           raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}" )
         lowercase			:str							=  (size["height"], size["width"])
         return resize(__SCREAMING_SNAKE_CASE				,    size=__SCREAMING_SNAKE_CASE				,    resample=__SCREAMING_SNAKE_CASE				,    data_format=__SCREAMING_SNAKE_CASE				,    **__SCREAMING_SNAKE_CASE )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int]				,    _lowerCAmelCase:    np.ndarray				,    _lowerCAmelCase:    Union[int, float]				,    _lowerCAmelCase:    Optional[Union[str, ChannelDimension]] = None				,    **_lowerCAmelCase:    Any				,    ):
         return rescale(__SCREAMING_SNAKE_CASE				,    scale=__SCREAMING_SNAKE_CASE				,    data_format=__SCREAMING_SNAKE_CASE				,    **__SCREAMING_SNAKE_CASE )






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    np.ndarray				,    _lowerCAmelCase:    Union[float, List[float]]				,    _lowerCAmelCase:    Union[float, List[float]]				,    _lowerCAmelCase:    Optional[Union[str, ChannelDimension]] = None				,    **_lowerCAmelCase:    Dict				,    ):
         return normalize(__SCREAMING_SNAKE_CASE				,    mean=__SCREAMING_SNAKE_CASE				,    std=__SCREAMING_SNAKE_CASE				,    data_format=__SCREAMING_SNAKE_CASE				,    **__SCREAMING_SNAKE_CASE )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any]				,    _lowerCAmelCase:    ImageInput				,    _lowerCAmelCase:    Optional[bool] = None				,    _lowerCAmelCase:    Optional[Dict[str, int]] = None				,    _lowerCAmelCase:    PILImageResampling = None				,    _lowerCAmelCase:    Optional[bool] = None				,    _lowerCAmelCase:    Optional[float] = None				,    _lowerCAmelCase:    Optional[bool] = None				,    _lowerCAmelCase:    Optional[Union[float, List[float]]] = None				,    _lowerCAmelCase:    Optional[Union[float, List[float]]] = None				,    _lowerCAmelCase:    Optional[Union[str, TensorType]] = None				,    _lowerCAmelCase:    bool = None				,    _lowerCAmelCase:    ChannelDimension = ChannelDimension.FIRST				,    **_lowerCAmelCase:    str				,    ):
         lowercase			:int							=  do_resize if do_resize is not None else self.do_resize
         lowercase			:Optional[int]							=  resample if resample is not None else self.resample
         lowercase			:Tuple							=  do_rescale if do_rescale is not None else self.do_rescale
         lowercase			:str							=  rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase			:str							=  do_normalize if do_normalize is not None else self.do_normalize
         lowercase			:Dict							=  image_mean if image_mean is not None else self.image_mean
         lowercase			:int							=  image_std if image_std is not None else self.image_std
         lowercase			:Tuple							=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         lowercase			:Dict							=  size if size is not None else self.size
         lowercase			:str							=  get_size_dict(__SCREAMING_SNAKE_CASE				,    default_to_square=__SCREAMING_SNAKE_CASE )

         lowercase			:int							=  make_list_of_images(__SCREAMING_SNAKE_CASE )

         if not valid_images(__SCREAMING_SNAKE_CASE ):
           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_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." )

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
           lowercase			:int							=  [convert_to_rgb(__SCREAMING_SNAKE_CASE ) for image in images]

         # All transformations expect numpy arrays.
         lowercase			:Optional[Any]							=  [to_numpy_array(__SCREAMING_SNAKE_CASE ) for image in images]

         if do_resize:
           lowercase			:List[Any]							=  [self.resize(image=__SCREAMING_SNAKE_CASE				,    size=__SCREAMING_SNAKE_CASE				,    resample=__SCREAMING_SNAKE_CASE ) for image in images]

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

         if do_normalize:
           lowercase			:Tuple							=  [self.normalize(image=__SCREAMING_SNAKE_CASE				,    mean=__SCREAMING_SNAKE_CASE				,    std=__SCREAMING_SNAKE_CASE ) for image in images]

         lowercase			:Tuple							=  [to_channel_dimension_format(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE ) for image in images]

         lowercase			:Union[str, Any]							=  BatchFeature(data={"pixel_values": images}				,    tensor_type=__SCREAMING_SNAKE_CASE )

         return encoded_outputs






'''simple docstring'''



import numpy as np







def    a__       (  a__      ,    a__      ,    a__ = 1E-1_2      ,    a__ = 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__						)
     __SCREAMING_SNAKE_CASE     =      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.

     __SCREAMING_SNAKE_CASE     =      False
     __SCREAMING_SNAKE_CASE     =      0
     __SCREAMING_SNAKE_CASE     =      0
     __SCREAMING_SNAKE_CASE     =      1E1_2

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

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

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

          __SCREAMING_SNAKE_CASE     =      lambda_

     if is_complex:
          __SCREAMING_SNAKE_CASE     =      np.real(lambda_						)

     return lambda_, vector







def    a__       (  ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]						)
     __SCREAMING_SNAKE_CASE     =      np.array([41, 4, 20]						)
     __SCREAMING_SNAKE_CASE     =      real_input_matrix.astype(np.complexaaa						)
     __SCREAMING_SNAKE_CASE     =      np.triu(1J * complex_input_matrix      ,    1						)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     __SCREAMING_SNAKE_CASE     =      np.array([41, 4, 20]						).astype(np.complexaaa						)

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

          # Our implementation.
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      power_iteration(a__      ,    a__						)

          # Numpy implementation.

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






