





"""simple docstring"""


import numpy as np




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_ = 1e-12		,lowercase_ = 100		,)       ->		tuple[float, np.ndarray]:




      """simple docstring"""

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

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

            # Check convergence.
            _UpperCamelCase   :						Optional[int]    =							np.abs(lambda_ - lambda_previous      ) / lambda_
            iterations += 1

            if error <= error_tol or iterations >= max_iterations:
                  _UpperCamelCase   :						List[Any]    =							True

            _UpperCamelCase   :						Tuple    =							lambda_

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

      return lambda_, vector




def        lowercase__     (       )       ->		None:




      """simple docstring"""

      _UpperCamelCase   :						Dict    =							np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]      )
      _UpperCamelCase   :						Optional[int]    =							np.array([41, 4, 20]      )
      _UpperCamelCase   :						Union[str, Any]    =							real_input_matrix.astype(np.complexaaa      )
      _UpperCamelCase   :						int    =							np.triu(1J * complex_input_matrix		,1      )
      complex_input_matrix += imag_matrix
      complex_input_matrix += -1 * imag_matrix.T
      _UpperCamelCase   :						Union[str, Any]    =							np.array([41, 4, 20]      ).astype(np.complexaaa      )

      for problem_type in ["real", "complex"]:
            if problem_type == "real":
                  _UpperCamelCase   :						Dict    =							real_input_matrix
                  _UpperCamelCase   :						Any    =							real_vector
            elif problem_type == "complex":
                  _UpperCamelCase   :						int    =							complex_input_matrix
                  _UpperCamelCase   :						Union[str, Any]    =							complex_vector

            # Our implementation.
            _UpperCamelCase,	_UpperCamelCase   :						int    =							power_iteration(lowercase_		,lowercase_      )

            # Numpy implementation.

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


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()







"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__						= logging.get_logger(__name__)

lowerCamelCase__						= {
    "google/mobilenet_v1_1.0_224": "https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json",
    "google/mobilenet_v1_0.75_192": "https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:int						  =       "mobilenet_v1"

  def __init__(		self :							Optional[int]       , __a :							List[str]=3       , __a :							Union[str, Any]=224       , __a :							Tuple=1.0       , __a :							List[Any]=8       , __a :							Union[str, Any]="relu6"       , __a :							Dict=True       , __a :							Tuple=0.9_99       , __a :							Dict=0.02       , __a :							Any=0.0_01       , **__a :							Any       , )		->					int:
        super().__init__(**__a						)

        if depth_multiplier <= 0:
              raise ValueError("depth_multiplier must be greater than zero."						)

        _UpperCamelCase   :						Any    =							num_channels
        _UpperCamelCase   :						Optional[Any]    =							image_size
        _UpperCamelCase   :						Optional[Any]    =							depth_multiplier
        _UpperCamelCase   :						int    =							min_depth
        _UpperCamelCase   :						List[str]    =							hidden_act
        _UpperCamelCase   :						Union[str, Any]    =							tf_padding
        _UpperCamelCase   :						int    =							classifier_dropout_prob
        _UpperCamelCase   :						Optional[int]    =							initializer_range
        _UpperCamelCase   :						Union[str, Any]    =							layer_norm_eps





class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:List[Any]						  =       version.parse("1.11" )

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					Mapping[str, Mapping[int, str]]:
        return OrderedDict([("pixel_values", {0: "batch"})]						)

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					Mapping[str, Mapping[int, str]]:
        if self.task == "image-classification":
              return OrderedDict([("logits", {0: "batch"})]						)
        else:
              return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]						)


  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple						)		->					float:
        return 1e-4
