




'''simple docstring'''

import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)






class 		a							(  unittest.TestCase	):


				def     A_					(						self       :   Any   ):
									snake_case_		     =							Vector([1, 2, 3]   )
									self.assertEqual(x.component(0   )			,							1   )
									self.assertEqual(x.component(2   )			,							3   )
									snake_case_		     =							Vector()


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							Vector([0, 0, 0, 0, 0, 1]   )
									self.assertEqual(str(lowercase_   )			,							'''(0,0,0,0,0,1)'''   )


				def     A_					(						self       :   int   ):
									snake_case_		     =							Vector([1, 2, 3, 4]   )
									self.assertEqual(len(lowercase_   )			,							4   )


				def     A_					(						self       :   Optional[int]   ):
									snake_case_		     =							Vector([1, 2]   )
									snake_case_		     =							Vector([1, 2, 3, 4, 5]   )
									snake_case_		     =							Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   )
									snake_case_		     =							Vector([1, -1, 1, -1, 2, -3, 4, -5]   )
									self.assertAlmostEqual(x.euclidean_length()			,							2.236			,							3   )
									self.assertAlmostEqual(y.euclidean_length()			,							7.416			,							3   )
									self.assertEqual(z.euclidean_length()			,							0   )
									self.assertAlmostEqual(w.euclidean_length()			,							7.616			,							3   )


				def     A_					(						self       :   Any   ):
									snake_case_		     =							Vector([1, 2, 3]   )
									snake_case_		     =							Vector([1, 1, 1]   )
									self.assertEqual((x + y).component(0   )			,							2   )
									self.assertEqual((x + y).component(1   )			,							3   )
									self.assertEqual((x + y).component(2   )			,							4   )


				def     A_					(						self       :   str   ):
									snake_case_		     =							Vector([1, 2, 3]   )
									snake_case_		     =							Vector([1, 1, 1]   )
									self.assertEqual((x - y).component(0   )			,							0   )
									self.assertEqual((x - y).component(1   )			,							1   )
									self.assertEqual((x - y).component(2   )			,							2   )


				def     A_					(						self       :   Any   ):
									snake_case_		     =							Vector([1, 2, 3]   )
									snake_case_		     =							Vector([2, -1, 4]   )  # for test of dot product
									snake_case_		     =							Vector([1, -2, -1]   )
									self.assertEqual(str(x * 3.0   )			,							'''(3.0,6.0,9.0)'''   )
									self.assertEqual((a * b)			,							0   )


				def     A_					(						self       :   Optional[int]   ):
									self.assertEqual(str(zero_vector(10   )   ).count('''0'''   )			,							10   )


				def     A_					(						self       :   str   ):
									self.assertEqual(str(unit_basis_vector(3			,							1   )   )			,							'''(0,1,0)'''   )


				def     A_					(						self       :   int   ):
									snake_case_		     =							Vector([1, 2, 3]   )
									snake_case_		     =							Vector([1, 0, 1]   )
									self.assertEqual(str(axpy(2			,							lowercase_			,							lowercase_   )   )			,							'''(3,4,7)'''   )


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							Vector([1, 0, 0, 0, 0, 0]   )
									snake_case_		     =							x.copy()
									self.assertEqual(str(lowercase_   )			,							str(lowercase_   )   )


				def     A_					(						self       :   Optional[int]   ):
									snake_case_		     =							Vector([1, 0, 0]   )
									x.change_component(0			,							0   )
									x.change_component(1			,							1   )
									self.assertEqual(str(lowercase_   )			,							'''(0,1,0)'''   )


				def     A_					(						self       :   int   ):
									snake_case_		     =							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]			,							3			,							3   )
									self.assertEqual('''|1,2,3|\n|2,4,5|\n|6,7,8|\n'''			,							str(lowercase_   )   )


				def     A_					(						self       :   Tuple   ):
									snake_case_		     =							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]			,							3			,							3   )
									snake_case_		     =							[[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
									for x in range(a.height()   ):
														for y in range(a.width()   ):
																			self.assertEqual(minors[x][y]			,							a.minor(lowercase_			,							lowercase_   )   )


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]			,							3			,							3   )
									snake_case_		     =							[[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
									for x in range(a.height()   ):
														for y in range(a.width()   ):
																			self.assertEqual(cofactors[x][y]			,							a.cofactor(lowercase_			,							lowercase_   )   )


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]			,							3			,							3   )
									self.assertEqual(-5			,							a.determinant()   )


				def     A_					(						self       :   int   ):
									snake_case_		     =							Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]			,							3			,							3   )
									snake_case_		     =							Vector([1, 2, 3]   )
									self.assertEqual('''(14,32,50)'''			,							str(a * x   )   )
									self.assertEqual('''|2,4,6|\n|8,10,12|\n|14,16,18|\n'''			,							str(a * 2   )   )


				def     A_					(						self       :   Optional[int]   ):
									snake_case_		     =							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]			,							3			,							3   )
									a.change_component(0			,							2			,							5   )
									self.assertEqual('''|1,2,5|\n|2,4,5|\n|6,7,8|\n'''			,							str(lowercase_   )   )


				def     A_					(						self       :   Optional[int]   ):
									snake_case_		     =							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]			,							3			,							3   )
									self.assertEqual(7			,							a.component(2			,							1   )			,							0.01   )


				def     A_					(						self       :   int   ):
									snake_case_		     =							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]			,							3			,							3   )
									snake_case_		     =							Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]			,							3			,							3   )
									self.assertEqual('''|2,4,10|\n|4,8,10|\n|12,14,18|\n'''			,							str(a + b   )   )


				def     A_					(						self       :   List[str]   ):
									snake_case_		     =							Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]			,							3			,							3   )
									snake_case_		     =							Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]			,							3			,							3   )
									self.assertEqual('''|0,0,-4|\n|0,0,0|\n|0,0,-2|\n'''			,							str(a - b   )   )


				def     A_					(						self       :   Any   ):
									self.assertEqual(
									    '''|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n'''			,							str(square_zero_matrix(5   )   )			,							)


if __name__ == "__main__":
							unittest.main()







'''simple docstring'''

import functools
import operator

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


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

a    :							List[str]										=    {
    'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}






class 		a							(  _lowerCamelCase	):
				snake_case_           =						"wavlm"


				def __init__(						self       :   Union[str, Any]			,							lowercase_       :   Union[str, Any]=32			,							lowercase_       :   Optional[int]=768			,							lowercase_       :   List[str]=12			,							lowercase_       :   Dict=12			,							lowercase_       :   Any=3072			,							lowercase_       :   List[Any]="gelu"			,							lowercase_       :   int=0.1			,							lowercase_       :   Dict=0.1			,							lowercase_       :   int=0.1			,							lowercase_       :   int=0.0			,							lowercase_       :   List[Any]=0.1			,							lowercase_       :   int=0.1			,							lowercase_       :   Optional[int]=0.02			,							lowercase_       :   Dict=1e-5			,							lowercase_       :   Tuple="group"			,							lowercase_       :   str="gelu"			,							lowercase_       :   Any=(512, 512, 512, 512, 512, 512, 512)			,							lowercase_       :   str=(5, 2, 2, 2, 2, 2, 2)			,							lowercase_       :   Any=(10, 3, 3, 3, 3, 2, 2)			,							lowercase_       :   Tuple=False			,							lowercase_       :   List[Any]=128			,							lowercase_       :   int=16			,							lowercase_       :   Tuple=320			,							lowercase_       :   Union[str, Any]=800			,							lowercase_       :   List[str]=False			,							lowercase_       :   str=True			,							lowercase_       :   List[Any]=0.05			,							lowercase_       :   Optional[Any]=10			,							lowercase_       :   Optional[Any]=2			,							lowercase_       :   Optional[int]=0.0			,							lowercase_       :   List[str]=10			,							lowercase_       :   Union[str, Any]=320			,							lowercase_       :   Optional[Any]=2			,							lowercase_       :   List[str]=0.1			,							lowercase_       :   int=100			,							lowercase_       :   Tuple=256			,							lowercase_       :   Tuple=256			,							lowercase_       :   Dict=0.1			,							lowercase_       :   Any="mean"			,							lowercase_       :   Tuple=False			,							lowercase_       :   Dict=False			,							lowercase_       :   str=256			,							lowercase_       :   Optional[int]=(512, 512, 512, 512, 1500)			,							lowercase_       :   List[str]=(5, 3, 3, 1, 1)			,							lowercase_       :   Any=(1, 2, 3, 1, 1)			,							lowercase_       :   int=512			,							lowercase_       :   List[Any]=80			,							lowercase_       :   Optional[Any]=0			,							lowercase_       :   int=1			,							lowercase_       :   List[str]=2			,							lowercase_       :   List[str]=False			,							lowercase_       :   Dict=3			,							lowercase_       :   List[Any]=2			,							lowercase_       :   Optional[Any]=3			,							lowercase_       :   int=None			,							**lowercase_       :   List[str]			,							):
									super().__init__(**lowercase_			,							pad_token_id=lowercase_			,							bos_token_id=lowercase_			,							eos_token_id=lowercase_   )
									snake_case_		     =							hidden_size
									snake_case_		     =							feat_extract_norm
									snake_case_		     =							feat_extract_activation
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							conv_bias
									snake_case_		     =							num_buckets
									snake_case_		     =							max_bucket_distance
									snake_case_		     =							num_conv_pos_embeddings
									snake_case_		     =							num_conv_pos_embedding_groups
									snake_case_		     =							len(self.conv_dim   )
									snake_case_		     =							num_hidden_layers
									snake_case_		     =							intermediate_size
									snake_case_		     =							hidden_act
									snake_case_		     =							num_attention_heads
									snake_case_		     =							hidden_dropout
									snake_case_		     =							attention_dropout
									snake_case_		     =							activation_dropout
									snake_case_		     =							feat_proj_dropout
									snake_case_		     =							final_dropout
									snake_case_		     =							layerdrop
									snake_case_		     =							layer_norm_eps
									snake_case_		     =							initializer_range
									snake_case_		     =							num_ctc_classes
									snake_case_		     =							vocab_size
									snake_case_		     =							do_stable_layer_norm
									snake_case_		     =							use_weighted_layer_sum
									snake_case_		     =							classifier_proj_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)`, 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
									snake_case_		     =							apply_spec_augment
									snake_case_		     =							mask_time_prob
									snake_case_		     =							mask_time_length
									snake_case_		     =							mask_time_min_masks
									snake_case_		     =							mask_feature_prob
									snake_case_		     =							mask_feature_length

									# parameters for pretraining with codevector quantized representations
									snake_case_		     =							num_codevectors_per_group
									snake_case_		     =							num_codevector_groups
									snake_case_		     =							contrastive_logits_temperature
									snake_case_		     =							num_negatives
									snake_case_		     =							codevector_dim
									snake_case_		     =							proj_codevector_dim
									snake_case_		     =							diversity_loss_weight

									# ctc loss
									snake_case_		     =							ctc_loss_reduction
									snake_case_		     =							ctc_zero_infinity

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

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

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


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

