

import numpy as np


def        __lowerCAmelCase    (       _UpperCamelCase		:  np.ndarray		,							_UpperCamelCase		:  np.ndarray		,							_UpperCamelCase		:  float = 1e-12		,							_UpperCamelCase		:  int = 1_00		,							)		->							tuple[float, np.ndarray]:
 '''simple docstring'''


 assert np.shape(_UpperCamelCase	)[0] == np.shape(_UpperCamelCase	)[1]
 # Ensure proper dimensionality.
 assert np.shape(_UpperCamelCase	)[0] == np.shape(_UpperCamelCase	)[0]
 # Ensure inputs are either both complex or both real
 assert np.iscomplexobj(_UpperCamelCase	) == np.iscomplexobj(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			np.iscomplexobj(_UpperCamelCase	)
 if is_complex:
  # Ensure complex input_matrix is Hermitian
  assert np.array_equal(_UpperCamelCase		,							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								=			1e12

 while not convergence:
  # Multiple matrix by the vector.
  SCREAMING_SNAKE_CASE								=			np.dot(_UpperCamelCase		,							_UpperCamelCase	)
  # Normalize the resulting output vector.
  SCREAMING_SNAKE_CASE								=			w / np.linalg.norm(_UpperCamelCase	)
  # 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(_UpperCamelCase		,							np.dot(_UpperCamelCase		,							_UpperCamelCase	)	)

  # 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        __lowerCAmelCase    (       )		->							None:
 '''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(_UpperCamelCase		,							_UpperCamelCase	)

  # 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(_UpperCamelCase	)
  # 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(_UpperCamelCase	) - np.abs(_UpperCamelCase	)	) <= 1e-6


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      test_power_iteration()




from ....configuration_utils import PretrainedConfig
from ....utils import logging


a_    :						Any    		=      logging.get_logger(__name__)

a_    :						Dict    		=      {
    "Visual-Attention-Network/van-base": (
        "https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
    ),
}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         ="van"

    def __init__(			self :      Optional[Any]							,    snake_case__ :      Tuple=2_2_4							,    snake_case__ :      Dict=3							,    snake_case__ :      Union[str, Any]=[7, 3, 3, 3]							,    snake_case__ :      str=[4, 2, 2, 2]							,    snake_case__ :      Optional[Any]=[6_4, 1_2_8, 3_2_0, 5_1_2]							,    snake_case__ :      Optional[Any]=[3, 3, 1_2, 3]							,    snake_case__ :      Tuple=[8, 8, 4, 4]							,    snake_case__ :      Any="gelu"							,    snake_case__ :      Dict=0.02							,    snake_case__ :      List[str]=1E-6							,    snake_case__ :      int=1E-2							,    snake_case__ :      Any=0.0							,    snake_case__ :      Tuple=0.0							,    **snake_case__ :      Any							,    ):

     """simple docstring"""






     super().__init__(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			image_size
     SCREAMING_SNAKE_CASE								=			num_channels
     SCREAMING_SNAKE_CASE								=			patch_sizes
     SCREAMING_SNAKE_CASE								=			strides
     SCREAMING_SNAKE_CASE								=			hidden_sizes
     SCREAMING_SNAKE_CASE								=			depths
     SCREAMING_SNAKE_CASE								=			mlp_ratios
     SCREAMING_SNAKE_CASE								=			hidden_act
     SCREAMING_SNAKE_CASE								=			initializer_range
     SCREAMING_SNAKE_CASE								=			layer_norm_eps
     SCREAMING_SNAKE_CASE								=			layer_scale_init_value
     SCREAMING_SNAKE_CASE								=			drop_path_rate
     SCREAMING_SNAKE_CASE								=			dropout_rate

