






"""simple docstring"""



import numpy as np
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  np.ndarray		,					__snake_case			:  np.ndarray		,					__snake_case			:  float = 1e-12		,					__snake_case			:  int = 1_00		,					):





  '''simple docstring'''





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

  lowercase							    =       False
  lowercase							    =       0
  lowercase							    =       0
  lowercase							    =       1e12

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

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

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

    lowercase							    =       lambda_

  if is_complex:
    lowercase							    =       np.real(lambda_    )

  return lambda_, vector
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





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

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

    # Our implementation.
    lowercase    ,     lowercase							    =       power_iteration(__snake_case		,					__snake_case    )

    # Numpy implementation.

    # Get eigenvalues and eigenvectors using built-in numpy
    # eigh (eigh used for symmetric or hermetian matrices).
    lowercase    ,     lowercase							    =       np.linalg.eigh(__snake_case    )
    # Last eigenvalue is the maximum one.
    lowercase							    =       eigen_values[-1]
    # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
    lowercase							    =       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(__snake_case    ) - np.abs(__snake_case    )    ) <= 1e-6


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    test_power_iteration()










"""simple docstring"""



import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs

class        a     (   a_	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase=None							,		_lowerCamelCase=True							,		_lowerCamelCase=None							,		**_lowerCamelCase       ):
      lowercase							    =       parent
      lowercase							    =       config_class
      lowercase							    =       has_text_modality
      lowercase							    =       kwargs
      lowercase							    =       common_properties







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.config_class(**self.inputs_dict       )
      lowercase							    =       (
          ['hidden_size', 'num_attention_heads', 'num_hidden_layers']
          if self.common_properties is None
          else self.common_properties
      )

      # Add common fields for text models
      if self.has_text_modality:
        common_properties.extend(['vocab_size']       )

      # Test that config has the common properties as getters
      for prop in common_properties:
        self.parent.assertTrue(hasattr(_lowerCamelCase							,		_lowerCamelCase       )							,		msg=F'`{prop}` does not exist'       )

      # Test that config has the common properties as setter
      for idx, name in enumerate(_lowerCamelCase       ):
        try:
          setattr(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       )
          self.parent.assertEqual(
              getattr(_lowerCamelCase							,		_lowerCamelCase       )							,		_lowerCamelCase							,		msg=F'`{name} value {idx} expected, but was {getattr(_lowerCamelCase							,		_lowerCamelCase       )}'       )
        except NotImplementedError:
          # Some models might not be able to implement setters for common_properties
          # In that case, a NotImplementedError is raised
          pass

        # Test if config class can be called with Config(prop_name=..)
      for idx, name in enumerate(_lowerCamelCase       ):
        try:
          lowercase							    =       self.config_class(**{name: idx}       )
          self.parent.assertEqual(
              getattr(_lowerCamelCase							,		_lowerCamelCase       )							,		_lowerCamelCase							,		msg=F'`{name} value {idx} expected, but was {getattr(_lowerCamelCase							,		_lowerCamelCase       )}'       )
        except NotImplementedError:
          # Some models might not be able to implement setters for common_properties
          # In that case, a NotImplementedError is raised
          pass







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.config_class(**self.inputs_dict       )
      lowercase							    =       json.loads(config.to_json_string()       )
      for key, value in self.inputs_dict.items():
        self.parent.assertEqual(obj[key]							,		_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.config_class(**self.inputs_dict       )

      with tempfile.TemporaryDirectory() as tmpdirname:
        lowercase							    =       os.path.join(_lowerCamelCase							,		'config.json'       )
        config_first.to_json_file(_lowerCamelCase       )
        lowercase							    =       self.config_class.from_json_file(_lowerCamelCase       )

      self.parent.assertEqual(config_second.to_dict()							,		config_first.to_dict()       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.config_class(**self.inputs_dict       )

      with tempfile.TemporaryDirectory() as tmpdirname:
        config_first.save_pretrained(_lowerCamelCase       )
        lowercase							    =       self.config_class.from_pretrained(_lowerCamelCase       )

      self.parent.assertEqual(config_second.to_dict()							,		config_first.to_dict()       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.config_class(**self.inputs_dict       )

      lowercase							    =       'test'
      with tempfile.TemporaryDirectory() as tmpdirname:
        lowercase							    =       os.path.join(_lowerCamelCase							,		_lowerCamelCase       )
        config_first.save_pretrained(_lowerCamelCase       )
        lowercase							    =       self.config_class.from_pretrained(_lowerCamelCase							,		subfolder=_lowerCamelCase       )

      self.parent.assertEqual(config_second.to_dict()							,		config_first.to_dict()       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.config_class(**self.inputs_dict							,		num_labels=5       )
      self.parent.assertEqual(len(config.idalabel       )							,		5       )
      self.parent.assertEqual(len(config.labelaid       )							,		5       )

      lowercase							    =       3
      self.parent.assertEqual(len(config.idalabel       )							,		3       )
      self.parent.assertEqual(len(config.labelaid       )							,		3       )







    def 			UpperCamelCase_      (			self       ):
      if self.config_class.is_composition:
        return
      lowercase							    =       self.config_class()
      self.parent.assertIsNotNone(_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       copy.deepcopy(_lowerCamelCase       )
      lowercase							    =       self.config_class(**_lowerCamelCase       )
      lowercase							    =       []
      for key, value in config_common_kwargs.items():
        if key == "torch_dtype":
          if not is_torch_available():
            continue
          else:
            import torch

            if config.torch_dtype != torch.floataa:
              wrong_values.append(('torch_dtype', config.torch_dtype, torch.floataa)       )
        elif getattr(_lowerCamelCase							,		_lowerCamelCase       ) != value:
          wrong_values.append((key, getattr(_lowerCamelCase							,		_lowerCamelCase       ), value)       )

      if len(_lowerCamelCase       ) > 0:
        lowercase							    =       '\n'.join([F'- {v[0]}: got {v[1]} instead of {v[2]}' for v in wrong_values]       )
        raise ValueError(F'The following keys were not properly set in the config:\n{errors}'       )







    def 			UpperCamelCase_      (			self       ):
      self.create_and_test_config_common_properties()
      self.create_and_test_config_to_json_string()
      self.create_and_test_config_to_json_file()
      self.create_and_test_config_from_and_save_pretrained()
      self.create_and_test_config_from_and_save_pretrained_subfolder()
      self.create_and_test_config_with_num_labels()
      self.check_config_can_be_init_without_params()
      self.check_config_arguments_init()


