



'''simple docstring'''




import numpy as np



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__ = 1E-12						,				lowerCamelCase__ = 1_00						,				):
			'''simple docstring'''





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

			A_     :						Any     =					False
			A_     :						Tuple     =					0
			A_     :						List[Any]     =					0
			A_     :						List[str]     =					1E12

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

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

						if error <= error_tol or iterations >= max_iterations:
									A_     :						Any     =					True

						A_     :						Dict     =					lambda_

			if is_complex:
						A_     :						Union[str, Any]     =					np.real(lambda_   )

			return lambda_, vector



def      a      (			):
			'''simple docstring'''





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

			for problem_type in ["real", "complex"]:
						if problem_type == "real":
									A_     :						Union[str, Any]     =					real_input_matrix
									A_     :						Tuple     =					real_vector
						elif problem_type == "complex":
									A_     :						str     =					complex_input_matrix
									A_     :						Optional[Any]     =					complex_vector

						# Our implementation.
						A_,   A_     :						Any     =					power_iteration(lowerCamelCase__						,				lowerCamelCase__   )

						# Numpy implementation.

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


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						test_power_iteration()




'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase							:Any       = logging.get_logger(__name__)

lowerCamelCase							:List[Any]       = {
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = 'camembert'


	def __init__(self	,				lowercase=30522	,				lowercase=768	,				lowercase=12	,				lowercase=12	,				lowercase=3072	,				lowercase="gelu"	,				lowercase=0.1	,				lowercase=0.1	,				lowercase=512	,				lowercase=2	,				lowercase=0.02	,				lowercase=1E-12	,				lowercase=1	,				lowercase=0	,				lowercase=2	,				lowercase="absolute"	,				lowercase=True	,				lowercase=None	,				**lowercase	,				):
				super().__init__(pad_token_id=lowercase	,				bos_token_id=lowercase	,				eos_token_id=lowercase	,				**lowercase							)

				A_     :						List[Any]     =					vocab_size
				A_     :						int     =					hidden_size
				A_     :						Dict     =					num_hidden_layers
				A_     :						Dict     =					num_attention_heads
				A_     :						Optional[Any]     =					hidden_act
				A_     :						str     =					intermediate_size
				A_     :						int     =					hidden_dropout_prob
				A_     :						List[Any]     =					attention_probs_dropout_prob
				A_     :						Optional[Any]     =					max_position_embeddings
				A_     :						Optional[int]     =					type_vocab_size
				A_     :						int     =					initializer_range
				A_     :						str     =					layer_norm_eps
				A_     :						int     =					position_embedding_type
				A_     :						Dict     =					use_cache
				A_     :						Any     =					classifier_dropout







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	@property
	def    _a     (self							):
				if self.task == "multiple-choice":
							A_     :						Optional[Any]     =					{0: """batch""", 1: """choice""", 2: """sequence"""}
				else:
							A_     :						int     =					{0: """batch""", 1: """sequence"""}
				return OrderedDict(
				    [
				        ("""input_ids""", dynamic_axis),
				        ("""attention_mask""", dynamic_axis),
				    ]							)