
import functools
import operator

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


lowercase__	:Any										=					logging.get_logger(__name__)

lowercase__	:Tuple										=					{
    "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        lowercase	(      _UpperCamelCase	):
							lowercase_ : Optional[Any]       ='''wavlm'''




							def __init__(       self     ,A__=3_2     ,A__=7_6_8     ,A__=1_2     ,A__=1_2     ,A__=3_0_7_2     ,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__="group"     ,A__="gelu"     ,A__=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)     ,A__=(5, 2, 2, 2, 2, 2, 2)     ,A__=(1_0, 3, 3, 3, 3, 2, 2)     ,A__=False     ,A__=1_2_8     ,A__=1_6     ,A__=3_2_0     ,A__=8_0_0     ,A__=False     ,A__=True     ,A__=0.05     ,A__=1_0     ,A__=2     ,A__=0.0     ,A__=1_0     ,A__=3_2_0     ,A__=2     ,A__=0.1     ,A__=1_0_0     ,A__=2_5_6     ,A__=2_5_6     ,A__=0.1     ,A__="mean"     ,A__=False     ,A__=False     ,A__=2_5_6     ,A__=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)     ,A__=(5, 3, 3, 1, 1)     ,A__=(1, 2, 3, 1, 1)     ,A__=5_1_2     ,A__=8_0     ,A__=0     ,A__=1     ,A__=2     ,A__=False     ,A__=3     ,A__=2     ,A__=3     ,A__=None     ,**A__     ,):
												super().__init__(**_UpperCAmelCase     ,pad_token_id=_UpperCAmelCase     ,bos_token_id=_UpperCAmelCase     ,eos_token_id=_UpperCAmelCase)
												lowercase					=		hidden_size
												lowercase					=		feat_extract_norm
												lowercase					=		feat_extract_activation
												lowercase					=		list(_UpperCAmelCase)
												lowercase					=		list(_UpperCAmelCase)
												lowercase					=		list(_UpperCAmelCase)
												lowercase					=		conv_bias
												lowercase					=		num_buckets
												lowercase					=		max_bucket_distance
												lowercase					=		num_conv_pos_embeddings
												lowercase					=		num_conv_pos_embedding_groups
												lowercase					=		len(self.conv_dim)
												lowercase					=		num_hidden_layers
												lowercase					=		intermediate_size
												lowercase					=		hidden_act
												lowercase					=		num_attention_heads
												lowercase					=		hidden_dropout
												lowercase					=		attention_dropout
												lowercase					=		activation_dropout
												lowercase					=		feat_proj_dropout
												lowercase					=		final_dropout
												lowercase					=		layerdrop
												lowercase					=		layer_norm_eps
												lowercase					=		initializer_range
												lowercase					=		num_ctc_classes
												lowercase					=		vocab_size
												lowercase					=		do_stable_layer_norm
												lowercase					=		use_weighted_layer_sum
												lowercase					=		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
												lowercase					=		apply_spec_augment
												lowercase					=		mask_time_prob
												lowercase					=		mask_time_length
												lowercase					=		mask_time_min_masks
												lowercase					=		mask_feature_prob
												lowercase					=		mask_feature_length

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

												# ctc loss
												lowercase					=		ctc_loss_reduction
												lowercase					=		ctc_zero_infinity

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

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

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





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






from numpy import exp, pi, sqrt



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__ = 0.0				,    lowerCAmelCase__ = 1.0	):
					'''simple docstring'''
					return 1 / sqrt(2 * pi * sigma**2	) * exp(-((x - mu) ** 2) / (2 * sigma**2)	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




