



"""simple docstring"""
import functools
import operator

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


__SCREAMING_SNAKE_CASE  :						List[Any]    							=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      {
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					lowercase__   :       int										= 'sew-d'

					def __init__(    self				,       lowerCamelCase__=3_2				,       lowerCamelCase__=7_6_8				,       lowerCamelCase__=1_2				,       lowerCamelCase__=1_2				,       lowerCamelCase__=3_0_7_2				,       lowerCamelCase__=2				,       lowerCamelCase__=5_1_2				,       lowerCamelCase__=2_5_6				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=("p2c", "c2p")				,       lowerCamelCase__="layer_norm"				,       lowerCamelCase__="gelu_python"				,       lowerCamelCase__=0.1				,       lowerCamelCase__=0.1				,       lowerCamelCase__=0.1				,       lowerCamelCase__=0.0				,       lowerCamelCase__=0.1				,       lowerCamelCase__=0.0_2				,       lowerCamelCase__=1e-7				,       lowerCamelCase__=1e-5				,       lowerCamelCase__="group"				,       lowerCamelCase__="gelu"				,       lowerCamelCase__=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)				,       lowerCamelCase__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,       lowerCamelCase__=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,       lowerCamelCase__=False				,       lowerCamelCase__=1_2_8				,       lowerCamelCase__=1_6				,       lowerCamelCase__=True				,       lowerCamelCase__=0.0_5				,       lowerCamelCase__=1_0				,       lowerCamelCase__=2				,       lowerCamelCase__=0.0				,       lowerCamelCase__=1_0				,       lowerCamelCase__=0				,       lowerCamelCase__="mean"				,       lowerCamelCase__=False				,       lowerCamelCase__=False				,       lowerCamelCase__=2_5_6				,       lowerCamelCase__=0				,       lowerCamelCase__=1				,       lowerCamelCase__=2				,       **lowerCamelCase__				,       ):
									super().__init__(**lowerCamelCase__				,       pad_token_id=lowerCamelCase__				,       bos_token_id=lowerCamelCase__				,       eos_token_id=lowerCamelCase__   )
									_lowerCamelCase        =				hidden_size
									_lowerCamelCase        =				feat_extract_norm
									_lowerCamelCase        =				feat_extract_activation
									_lowerCamelCase        =				list(lowerCamelCase__   )
									_lowerCamelCase        =				list(lowerCamelCase__   )
									_lowerCamelCase        =				list(lowerCamelCase__   )
									_lowerCamelCase        =				conv_bias
									_lowerCamelCase        =				num_conv_pos_embeddings
									_lowerCamelCase        =				num_conv_pos_embedding_groups
									_lowerCamelCase        =				len(self.conv_dim   )
									_lowerCamelCase        =				num_hidden_layers
									_lowerCamelCase        =				intermediate_size
									_lowerCamelCase        =				squeeze_factor
									_lowerCamelCase        =				max_position_embeddings
									_lowerCamelCase        =				position_buckets
									_lowerCamelCase        =				share_att_key
									_lowerCamelCase        =				relative_attention
									_lowerCamelCase        =				norm_rel_ebd
									_lowerCamelCase        =				list(lowerCamelCase__   )
									_lowerCamelCase        =				hidden_act
									_lowerCamelCase        =				num_attention_heads
									_lowerCamelCase        =				hidden_dropout
									_lowerCamelCase        =				attention_dropout
									_lowerCamelCase        =				activation_dropout
									_lowerCamelCase        =				feat_proj_dropout
									_lowerCamelCase        =				final_dropout
									_lowerCamelCase        =				layer_norm_eps
									_lowerCamelCase        =				feature_layer_norm_eps
									_lowerCamelCase        =				initializer_range
									_lowerCamelCase        =				vocab_size

									if (
									    (len(self.conv_stride   ) != self.num_feat_extract_layers)
									    or (len(self.conv_kernel   ) != self.num_feat_extract_layers)
									    or (len(self.conv_dim   ) != self.num_feat_extract_layers)
									):
													raise ValueError(
													    '''Configuration for convolutional layers is incorrect.'''
													    '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
													    F"""but is `len(config.conv_dim) = {len(self.conv_dim   )}`, `len(config.conv_stride)"""
													    F"""= {len(self.conv_stride   )}`, `len(config.conv_kernel) = {len(self.conv_kernel   )}`."""   )

									# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
									_lowerCamelCase        =				apply_spec_augment
									_lowerCamelCase        =				mask_time_prob
									_lowerCamelCase        =				mask_time_length
									_lowerCamelCase        =				mask_time_min_masks
									_lowerCamelCase        =				mask_feature_prob
									_lowerCamelCase        =				mask_feature_length
									_lowerCamelCase        =				mask_feature_min_masks

									# ctc loss
									_lowerCamelCase        =				ctc_loss_reduction
									_lowerCamelCase        =				ctc_zero_infinity

									# sequence classification
									_lowerCamelCase        =				use_weighted_layer_sum
									_lowerCamelCase        =				classifier_proj_size





					@property
					def 			snake_case__      (    self   ):
									return functools.reduce(operator.mul				,       self.conv_stride				,       1   )





"""simple docstring"""
import numpy as np



def        lowerCAmelCase_(   lowercase_     :							np.ndarray						,   lowercase_     :							np.ndarray						,   lowercase_     :							float = 1e-12						,   lowercase_     :							int = 1_00						,   )	->       tuple[float, np.ndarray]:
				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_							)
				_lowerCamelCase        =				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.

				_lowerCamelCase        =				False
				_lowerCamelCase        =				0
				_lowerCamelCase        =				0
				_lowerCamelCase        =				1e12

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

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

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

								_lowerCamelCase        =				lambda_

				if is_complex:
								_lowerCamelCase        =				np.real(lambda_							)

				return lambda_, vector



def        lowerCAmelCase_(   )	->       None:
				_lowerCamelCase        =				np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]							)
				_lowerCamelCase        =				np.array([41, 4, 20]							)
				_lowerCamelCase        =				real_input_matrix.astype(np.complexaaa							)
				_lowerCamelCase        =				np.triu(1j * complex_input_matrix						,   1							)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				_lowerCamelCase        =				np.array([41, 4, 20]							).astype(np.complexaaa							)

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

								# Our implementation.
								_lowerCamelCase  ,     _lowerCamelCase        =				power_iteration(lowercase_						,   lowercase_							)

								# Numpy implementation.

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