



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

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


SCREAMING_SNAKE_CASE_      						=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      						=			{
    "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       snake_case_    (    a_					):
							__lowerCAmelCase            =       "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__(**a_      ,  pad_token_id=a_      ,  bos_token_id=a_      ,  eos_token_id=a_   )
									a_  :   Any			    =  hidden_size
									a_  :   Any			    =  feat_extract_norm
									a_  :   Any			    =  feat_extract_activation
									a_  :   Dict			    =  list(a_   )
									a_  :   Tuple			    =  list(a_   )
									a_  :   Dict			    =  list(a_   )
									a_  :   Any			    =  conv_bias
									a_  :   List[str]			    =  num_buckets
									a_  :   Optional[Any]			    =  max_bucket_distance
									a_  :   List[str]			    =  num_conv_pos_embeddings
									a_  :   List[str]			    =  num_conv_pos_embedding_groups
									a_  :   int			    =  len(self.conv_dim   )
									a_  :   Optional[int]			    =  num_hidden_layers
									a_  :   List[str]			    =  intermediate_size
									a_  :   Any			    =  hidden_act
									a_  :   List[Any]			    =  num_attention_heads
									a_  :   int			    =  hidden_dropout
									a_  :   Tuple			    =  attention_dropout
									a_  :   Optional[int]			    =  activation_dropout
									a_  :   Tuple			    =  feat_proj_dropout
									a_  :   Optional[int]			    =  final_dropout
									a_  :   List[str]			    =  layerdrop
									a_  :   Optional[int]			    =  layer_norm_eps
									a_  :   Optional[int]			    =  initializer_range
									a_  :   Dict			    =  num_ctc_classes
									a_  :   List[Any]			    =  vocab_size
									a_  :   Any			    =  do_stable_layer_norm
									a_  :   Optional[Any]			    =  use_weighted_layer_sum
									a_  :   Optional[int]			    =  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
									a_  :   Optional[Any]			    =  apply_spec_augment
									a_  :   Dict			    =  mask_time_prob
									a_  :   List[str]			    =  mask_time_length
									a_  :   int			    =  mask_time_min_masks
									a_  :   Optional[Any]			    =  mask_feature_prob
									a_  :   int			    =  mask_feature_length

									# parameters for pretraining with codevector quantized representations
									a_  :   Tuple			    =  num_codevectors_per_group
									a_  :   Tuple			    =  num_codevector_groups
									a_  :   Union[str, Any]			    =  contrastive_logits_temperature
									a_  :   Union[str, Any]			    =  num_negatives
									a_  :   Union[str, Any]			    =  codevector_dim
									a_  :   Tuple			    =  proj_codevector_dim
									a_  :   Any			    =  diversity_loss_weight

									# ctc loss
									a_  :   Optional[Any]			    =  ctc_loss_reduction
									a_  :   Optional[Any]			    =  ctc_zero_infinity

									# adapter
									a_  :   str			    =  add_adapter
									a_  :   List[str]			    =  adapter_kernel_size
									a_  :   str			    =  adapter_stride
									a_  :   List[Any]			    =  num_adapter_layers
									a_  :   Dict			    =  output_hidden_size or hidden_size

									# SequenceClassification-specific parameter. Feel free to ignore for other classes.
									a_  :   int			    =  classifier_proj_size

									# XVector-specific parameters. Feel free to ignore for other classes.
									a_  :   Optional[Any]			    =  list(a_   )
									a_  :   Optional[Any]			    =  list(a_   )
									a_  :   int			    =  list(a_   )
									a_  :   int			    =  xvector_output_dim






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




from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

_lowerCAmelCase	:   List[str]    =   TypeVar("T")


class 	__snake_case   ( Generic[T] ):
		def __init__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  data
						lowerCAmelCase__		  =  None



		def __str__(							self						):







						"""simple docstring"""
						return f'{self.data}'



class 	__snake_case   ( Generic[T] ):
		def __init__(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  None
		def __iter__(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.top
						while node:
										yield node.data
										lowerCAmelCase__		  =  node.next
		def __str__(							self						):







						"""simple docstring"""
						return "->".join([str(a_						) for item in self]						)
		def __len__(							self						):







						"""simple docstring"""
						return len(tuple(iter(self						)						)						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.top is None
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  Node(a_						)
						if not self.is_empty():
										lowerCAmelCase__		  =  self.top
						lowerCAmelCase__		  =  node
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						if self.is_empty():
										raise IndexError('pop from empty stack'						)
						assert isinstance(self.top    ,a_						)
						lowerCAmelCase__		  =  self.top
						lowerCAmelCase__		  =  self.top.next
						return pop_node.data
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						if self.is_empty():
										raise IndexError('peek from empty stack'						)

						assert self.top is not None
						return self.top.data



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  None


if __name__ == "__main__":
	from doctest import testmod

	testmod()
