



import functools
import operator

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


_lowercase    	=		logging.get_logger(__name__)

_lowercase    	=		{
    "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      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''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__=0.0_2			,    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__=0.0_5			,    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__					=       hidden_size
         A__					=       feat_extract_norm
         A__					=       feat_extract_activation
         A__					=       list(a__)
         A__					=       list(a__)
         A__					=       list(a__)
         A__					=       conv_bias
         A__					=       num_buckets
         A__					=       max_bucket_distance
         A__					=       num_conv_pos_embeddings
         A__					=       num_conv_pos_embedding_groups
         A__					=       len(self.conv_dim)
         A__					=       num_hidden_layers
         A__					=       intermediate_size
         A__					=       hidden_act
         A__					=       num_attention_heads
         A__					=       hidden_dropout
         A__					=       attention_dropout
         A__					=       activation_dropout
         A__					=       feat_proj_dropout
         A__					=       final_dropout
         A__					=       layerdrop
         A__					=       layer_norm_eps
         A__					=       initializer_range
         A__					=       num_ctc_classes
         A__					=       vocab_size
         A__					=       do_stable_layer_norm
         A__					=       use_weighted_layer_sum
         A__					=       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__					=       apply_spec_augment
         A__					=       mask_time_prob
         A__					=       mask_time_length
         A__					=       mask_time_min_masks
         A__					=       mask_feature_prob
         A__					=       mask_feature_length

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

         # ctc loss
         A__					=       ctc_loss_reduction
         A__					=       ctc_zero_infinity

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

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

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




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





from __future__ import annotations



def 		lowerCAmelCase__     (       UpperCamelCase_					:							dict			,							UpperCamelCase_					:							str						)->	set[str]:
    A__			,						A__					=       set(UpperCamelCase_						), [start]

    while stack:
        A__					=       stack.pop()
        explored.add(UpperCamelCase_						)
        # Differences from BFS:
        # 1) pop last element instead of first one
        # 2) add adjacent elements to stack without exploring them
        for adj in reversed(graph[v]						):
            if adj not in explored:
                stack.append(UpperCamelCase_						)
    return explored


_lowercase    	=		{
    "A": ["B", "C", "D"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B", "D"],
    "E": ["B", "F"],
    "F": ["C", "E", "G"],
    "G": ["F"],
}

if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(depth_first_search(G, "A"))
