






"""simple docstring"""





import functools
import operator

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


__lowerCamelCase							=  logging.get_logger(__name__)

__lowerCamelCase							=  {
    '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		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				"wavlm"

					def __init__(			self  :      List[str]     ,		__snake_case  :      Optional[Any]=3_2     ,		__snake_case  :      List[Any]=7_6_8     ,		__snake_case  :      Any=1_2     ,		__snake_case  :      Dict=1_2     ,		__snake_case  :      str=3_0_7_2     ,		__snake_case  :      str="gelu"     ,		__snake_case  :      Dict=0.1     ,		__snake_case  :      List[str]=0.1     ,		__snake_case  :      str=0.1     ,		__snake_case  :      Any=0.0     ,		__snake_case  :      Optional[int]=0.1     ,		__snake_case  :      int=0.1     ,		__snake_case  :      Optional[int]=0.02     ,		__snake_case  :      str=1E-5     ,		__snake_case  :      str="group"     ,		__snake_case  :      Any="gelu"     ,		__snake_case  :      Any=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)     ,		__snake_case  :      Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)     ,		__snake_case  :      List[Any]=(1_0, 3, 3, 3, 3, 2, 2)     ,		__snake_case  :      Any=False     ,		__snake_case  :      List[str]=1_2_8     ,		__snake_case  :      Union[str, Any]=1_6     ,		__snake_case  :      List[Any]=3_2_0     ,		__snake_case  :      List[Any]=8_0_0     ,		__snake_case  :      Optional[Any]=False     ,		__snake_case  :      List[str]=True     ,		__snake_case  :      List[str]=0.05     ,		__snake_case  :      Tuple=1_0     ,		__snake_case  :      List[Any]=2     ,		__snake_case  :      Any=0.0     ,		__snake_case  :      Optional[Any]=1_0     ,		__snake_case  :      int=3_2_0     ,		__snake_case  :      Optional[int]=2     ,		__snake_case  :      List[Any]=0.1     ,		__snake_case  :      Any=1_0_0     ,		__snake_case  :      Union[str, Any]=2_5_6     ,		__snake_case  :      Any=2_5_6     ,		__snake_case  :      List[Any]=0.1     ,		__snake_case  :      int="mean"     ,		__snake_case  :      int=False     ,		__snake_case  :      Any=False     ,		__snake_case  :      List[str]=2_5_6     ,		__snake_case  :      Optional[int]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)     ,		__snake_case  :      List[str]=(5, 3, 3, 1, 1)     ,		__snake_case  :      Optional[Any]=(1, 2, 3, 1, 1)     ,		__snake_case  :      Dict=5_1_2     ,		__snake_case  :      Tuple=8_0     ,		__snake_case  :      Optional[int]=0     ,		__snake_case  :      Optional[Any]=1     ,		__snake_case  :      Optional[int]=2     ,		__snake_case  :      int=False     ,		__snake_case  :      Optional[Any]=3     ,		__snake_case  :      Optional[int]=2     ,		__snake_case  :      List[str]=3     ,		__snake_case  :      Optional[int]=None     ,		**__snake_case  :      List[str]     ,		)			->							Optional[int]:
											super().__init__(**__snake_case     ,		pad_token_id=__snake_case     ,		bos_token_id=__snake_case     ,		eos_token_id=__snake_case					)
											__magic_name__:	Tuple							=       hidden_size
											__magic_name__:	Any							=       feat_extract_norm
											__magic_name__:	List[str]							=       feat_extract_activation
											__magic_name__:	Optional[Any]							=       list(__snake_case					)
											__magic_name__:	str							=       list(__snake_case					)
											__magic_name__:	List[Any]							=       list(__snake_case					)
											__magic_name__:	Union[str, Any]							=       conv_bias
											__magic_name__:	Union[str, Any]							=       num_buckets
											__magic_name__:	Any							=       max_bucket_distance
											__magic_name__:	Any							=       num_conv_pos_embeddings
											__magic_name__:	List[str]							=       num_conv_pos_embedding_groups
											__magic_name__:	Any							=       len(self.conv_dim					)
											__magic_name__:	List[str]							=       num_hidden_layers
											__magic_name__:	Any							=       intermediate_size
											__magic_name__:	Union[str, Any]							=       hidden_act
											__magic_name__:	Any							=       num_attention_heads
											__magic_name__:	Tuple							=       hidden_dropout
											__magic_name__:	Any							=       attention_dropout
											__magic_name__:	List[str]							=       activation_dropout
											__magic_name__:	Union[str, Any]							=       feat_proj_dropout
											__magic_name__:	Optional[int]							=       final_dropout
											__magic_name__:	Tuple							=       layerdrop
											__magic_name__:	int							=       layer_norm_eps
											__magic_name__:	int							=       initializer_range
											__magic_name__:	List[Any]							=       num_ctc_classes
											__magic_name__:	List[Any]							=       vocab_size
											__magic_name__:	Dict							=       do_stable_layer_norm
											__magic_name__:	int							=       use_weighted_layer_sum
											__magic_name__:	List[str]							=       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
											__magic_name__:	Optional[int]							=       apply_spec_augment
											__magic_name__:	Union[str, Any]							=       mask_time_prob
											__magic_name__:	List[str]							=       mask_time_length
											__magic_name__:	Optional[Any]							=       mask_time_min_masks
											__magic_name__:	Optional[int]							=       mask_feature_prob
											__magic_name__:	Any							=       mask_feature_length

											# parameters for pretraining with codevector quantized representations
											__magic_name__:	List[Any]							=       num_codevectors_per_group
											__magic_name__:	int							=       num_codevector_groups
											__magic_name__:	int							=       contrastive_logits_temperature
											__magic_name__:	Any							=       num_negatives
											__magic_name__:	Optional[Any]							=       codevector_dim
											__magic_name__:	str							=       proj_codevector_dim
											__magic_name__:	Tuple							=       diversity_loss_weight

											# ctc loss
											__magic_name__:	Tuple							=       ctc_loss_reduction
											__magic_name__:	List[str]							=       ctc_zero_infinity

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

											# SequenceClassification-specific parameter. Feel free to ignore for other classes.
											__magic_name__:	Union[str, Any]							=       classifier_proj_size

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






					@property
					def   lowerCamelCase__   (			self  :      Dict					)			->							Optional[Any]:
											return functools.reduce(operator.mul     ,		self.conv_stride     ,		1					)









'''simple docstring'''





from __future__ import annotations

from typing import Any

class   _a							:






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     None:
          _snake_case				= num_of_nodes
          _snake_case				= []
          _snake_case				= {}






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     None:
          self.m_edges.append([u_node, v_node, weight]       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     int:

          if self.m_component[u_node] == u_node:
               return u_node
          return self.find_component(self.m_component[u_node]       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     None:

          if self.m_component[u_node] != u_node:
               for k in self.m_component:
                    _snake_case				= self.find_component(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     None:

          if component_size[u_node] <= component_size[v_node]:
               _snake_case				= v_node
               component_size[v_node] += component_size[u_node]
               self.set_component(_SCREAMING_SNAKE_CASE       )

          elif component_size[u_node] >= component_size[v_node]:
               _snake_case				= self.find_component(_SCREAMING_SNAKE_CASE       )
               component_size[u_node] += component_size[v_node]
               self.set_component(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     None:
          _snake_case				= []
          _snake_case				= 0

          _snake_case				= [-1] * self.m_num_of_nodes

          # A list of components (initialized to all of the nodes)
          for node in range(self.m_num_of_nodes       ):
               self.m_component.update({node: node}       )
               component_size.append(1       )

          _snake_case				= self.m_num_of_nodes

          while num_of_components > 1:
               for edge in self.m_edges:
                    _snake_case				,   _snake_case				,   _snake_case				= edge

                    _snake_case				= self.m_component[u]
                    _snake_case				= self.m_component[v]

                    if u_component != v_component:

                         for component in (u_component, v_component):
                              if (
                                  minimum_weight_edge[component] == -1
                                  or minimum_weight_edge[component][2] > w
                              ):
                                   _snake_case				= [u, v, w]

               for edge in minimum_weight_edge:
                    if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
                         _snake_case				,   _snake_case				,   _snake_case				= edge

                         _snake_case				= self.m_component[u]
                         _snake_case				= self.m_component[v]

                         if u_component != v_component:
                              mst_weight += w
                              self.union(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
                              print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""       )
                              num_of_components -= 1

               _snake_case				= [-1] * self.m_num_of_nodes
          print(f"""The total weight of the minimal spanning tree is: {mst_weight}"""       )







def       __a      (  )		->					None:

     """simple docstring"""






if __name__ == "__main__":
   import doctest

   doctest.testmod()


