





'''simple docstring'''






import numpy as np


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE = 1E-1_2  ,      __SCREAMING_SNAKE_CASE = 100  ,      ):


		"""simple docstring"""

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

		_UpperCamelCase       			=False
		_UpperCamelCase       			=0
		_UpperCamelCase       			=0
		_UpperCamelCase       			=1E1_2

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

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

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

				_UpperCamelCase       			=lambda_

		if is_complex:
				_UpperCamelCase       			=np.real(lambda_   )

		return lambda_, vector


def 					_a   ():


		"""simple docstring"""

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

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

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

				# Numpy implementation.

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

__lowerCamelCase						: int		=   {
    'google/mobilenet_v2_1.4_224': 'https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json',
    'google/mobilenet_v2_1.0_224': 'https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json',
    'google/mobilenet_v2_0.75_160': 'https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json',
    'google/mobilenet_v2_0.35_96': 'https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				"""mobilenet_v2"""


  def __init__(				self    :					Tuple							,	UpperCamelCase__    :					Union[str, Any]=3							,	UpperCamelCase__    :					Dict=224							,	UpperCamelCase__    :					str=1.0							,	UpperCamelCase__    :					List[Any]=8							,	UpperCamelCase__    :					Union[str, Any]=8							,	UpperCamelCase__    :					str=6							,	UpperCamelCase__    :					str=32							,	UpperCamelCase__    :					str=True							,	UpperCamelCase__    :					int=True							,	UpperCamelCase__    :					str="relu6"							,	UpperCamelCase__    :					Tuple=True							,	UpperCamelCase__    :					List[str]=0.8							,	UpperCamelCase__    :					List[str]=0.02							,	UpperCamelCase__    :					str=0.001							,	UpperCamelCase__    :					Dict=255							,	**UpperCamelCase__    :					Tuple							,	)     ->						List[Any]:
    super().__init__(**UpperCamelCase__  )

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

    _UpperCamelCase       			=num_channels
    _UpperCamelCase       			=image_size
    _UpperCamelCase       			=depth_multiplier
    _UpperCamelCase       			=depth_divisible_by
    _UpperCamelCase       			=min_depth
    _UpperCamelCase       			=expand_ratio
    _UpperCamelCase       			=output_stride
    _UpperCamelCase       			=first_layer_is_expansion
    _UpperCamelCase       			=finegrained_output
    _UpperCamelCase       			=hidden_act
    _UpperCamelCase       			=tf_padding
    _UpperCamelCase       			=classifier_dropout_prob
    _UpperCamelCase       			=initializer_range
    _UpperCamelCase       			=layer_norm_eps
    _UpperCamelCase       			=semantic_loss_ignore_index







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				version.parse("""1.11""")


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


  @property
  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						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   UpperCamelCase__      (				self    :					List[Any]  )     ->						float:
    return 1E-4






