
"""simple docstring"""
import math

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


__magic_name__									=					logging.get_logger(__name__)

__magic_name__									=					{
    '''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json''',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}


class  _lowerCAmelCase    (    a_    ):
							lowercase_  :  Any      =				'''data2vec-audio'''



							def __init__(   self			,    a_=32			,    a_=768			,    a_=12			,    a_=12			,    a_=3072			,    a_="gelu"			,    a_=0.1			,    a_=0.1			,    a_=0.1			,    a_=0.0			,    a_=0.1			,    a_=0.1			,    a_=0.02			,    a_=1e-5			,    a_="gelu"			,    a_=(512, 512, 512, 512, 512, 512, 512)			,    a_=(5, 2, 2, 2, 2, 2, 2)			,    a_=(10, 3, 3, 3, 3, 2, 2)			,    a_=False			,    a_=16			,    a_=19			,    a_=5			,    a_=0.05			,    a_=10			,    a_=2			,    a_=0.0			,    a_=10			,    a_=0			,    a_="sum"			,    a_=False			,    a_=False			,    a_=256			,    a_=(512, 512, 512, 512, 1500)			,    a_=(5, 3, 3, 1, 1)			,    a_=(1, 2, 3, 1, 1)			,    a_=512			,    a_=0			,    a_=1			,    a_=2			,    a_=False			,    a_=3			,    a_=2			,    a_=3			,    a_=None			,    **a_			,    )				->				str:
								super().__init__(**__UpperCamelCase			,    pad_token_id=__UpperCamelCase			,    bos_token_id=__UpperCamelCase			,    eos_token_id=__UpperCamelCase )
								_UpperCAmelCase			=    hidden_size
								_UpperCAmelCase			=    feat_extract_activation
								_UpperCAmelCase			=    list(__UpperCamelCase )
								_UpperCAmelCase			=    list(__UpperCamelCase )
								_UpperCAmelCase			=    list(__UpperCamelCase )
								_UpperCAmelCase			=    conv_bias
								_UpperCAmelCase			=    num_conv_pos_embeddings
								_UpperCAmelCase			=    num_conv_pos_embedding_groups
								_UpperCAmelCase			=    conv_pos_kernel_size
								_UpperCAmelCase			=    len(self.conv_dim )
								_UpperCAmelCase			=    num_hidden_layers
								_UpperCAmelCase			=    intermediate_size
								_UpperCAmelCase			=    hidden_act
								_UpperCAmelCase			=    num_attention_heads
								_UpperCAmelCase			=    hidden_dropout
								_UpperCAmelCase			=    attention_dropout
								_UpperCAmelCase			=    activation_dropout
								_UpperCAmelCase			=    feat_proj_dropout
								_UpperCAmelCase			=    final_dropout
								_UpperCAmelCase			=    layerdrop
								_UpperCAmelCase			=    layer_norm_eps
								_UpperCAmelCase			=    initializer_range
								_UpperCAmelCase			=    vocab_size
								_UpperCAmelCase			=    use_weighted_layer_sum

								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)`, but is `len(config.conv_dim) ="
									    f" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"
									    f" `len(config.conv_kernel) = {len(self.conv_kernel )}`." )

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

								# ctc loss
								_UpperCAmelCase			=    ctc_loss_reduction
								_UpperCAmelCase			=    ctc_zero_infinity

								# adapter
								_UpperCAmelCase			=    add_adapter
								_UpperCAmelCase			=    adapter_kernel_size
								_UpperCAmelCase			=    adapter_stride
								_UpperCAmelCase			=    num_adapter_layers
								_UpperCAmelCase			=    output_hidden_size or hidden_size

								# SequenceClassification-specific parameter. Feel free to ignore for other classes.
								_UpperCAmelCase			=    classifier_proj_size

								# XVector-specific parameters. Feel free to ignore for other classes.
								_UpperCAmelCase			=    list(__UpperCamelCase )
								_UpperCAmelCase			=    list(__UpperCamelCase )
								_UpperCAmelCase			=    list(__UpperCamelCase )
								_UpperCAmelCase			=    xvector_output_dim



							@property
							def    _a (   self )				->				Tuple:
								return math.prod(self.conv_stride )












import numpy as np





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case = 1E-12       ,			__snake_case = 100       ,			):
     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							)
     __lowerCAmelCase             =       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.

     __lowerCAmelCase             =       False
     __lowerCAmelCase             =       0
     __lowerCAmelCase             =       0
     __lowerCAmelCase             =       1E12

     while not convergence:
          # Multiple matrix by the vector.
          __lowerCAmelCase             =       np.dot(__snake_case       ,			__snake_case							)
          # Normalize the resulting output vector.
          __lowerCAmelCase             =       w / np.linalg.norm(__snake_case							)
          # 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(__snake_case       ,			np.dot(__snake_case       ,			__snake_case							)							)

          # 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   (    ):
     __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(__snake_case       ,			__snake_case							)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          __lowerCAmelCase      ,			__lowerCAmelCase             =       np.linalg.eigh(__snake_case							)
          # 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(__snake_case							) - np.abs(__snake_case							)							) <= 1E-6


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 test_power_iteration()






