
from __future__ import annotations

from typing import Any






class   UpperCAmelCase		:



   def __init__(self		:					int    ,						snake_case__		:					Tuple )		->	None:


          '''simple docstring'''


          snake_case       :   Tuple							=      num_of_nodes
          snake_case       :   list[list[int]]							=      []
          snake_case       :   dict[int, int]							=      {}



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					List[Any]    ,						snake_case__		:					Any    ,						snake_case__		:					int )		->	None:


          '''simple docstring'''


          self.m_edges.append([u_node, v_node, weight] )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					Optional[Any] )		->	int:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Dict )		->	None:


          '''simple docstring'''


          if self.m_component[u_node] != u_node:
                 for k in self.m_component:
                        snake_case       :   str							=      self.find_component(lowerCAmelCase__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Dict    ,						snake_case__		:					List[str]    ,						snake_case__		:					Dict )		->	None:


          '''simple docstring'''


          if component_size[u_node] <= component_size[v_node]:
                 snake_case       :   Union[str, Any]							=      v_node
                 component_size[v_node] += component_size[u_node]
                 self.set_component(lowerCAmelCase__ )

          elif component_size[u_node] >= component_size[v_node]:
                 snake_case       :   List[str]							=      self.find_component(lowerCAmelCase__ )
                 component_size[u_node] += component_size[v_node]
                 self.set_component(lowerCAmelCase__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	None:


          '''simple docstring'''


          snake_case       :   Tuple							=      []
          snake_case       :   int							=      0

          snake_case       :   list[Any]							=      [-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       :   int							=      self.m_num_of_nodes

          while num_of_components > 1:
                 for edge in self.m_edges:
                        snake_case       :   Optional[int]							=      edge

                        snake_case       :   Optional[int]							=      self.m_component[u]
                        snake_case       :   Dict							=      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       :   Optional[int]							=      [u, v, w]

                 for edge in minimum_weight_edge:
                        if isinstance(lowerCAmelCase__    ,						lowerCAmelCase__ ):
                               snake_case       :   int							=      edge

                               snake_case       :   str							=      self.m_component[u]
                               snake_case       :   Optional[int]							=      self.m_component[v]

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

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



def 			UpperCamelCase					(    ):
       pass


if __name__ == "__main__":
   import doctest

   doctest.testmod()









import math

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


__lowerCAmelCase		       =							logging.get_logger(__name__)

__lowerCAmelCase		       =							{
    '''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json''',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}





class   __a			(    __UpperCamelCase      ):
 __lowercase :							Optional[int]        = 'data2vec-audio'
 def __init__(   self				, lowerCAmelCase__=32				, lowerCAmelCase__=768				, lowerCAmelCase__=12				, lowerCAmelCase__=12				, lowerCAmelCase__=3_072				, lowerCAmelCase__="gelu"				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.0				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.0_2				, lowerCAmelCase__=1E-5				, lowerCAmelCase__="gelu"				, lowerCAmelCase__=(512, 512, 512, 512, 512, 512, 512)				, lowerCAmelCase__=(5, 2, 2, 2, 2, 2, 2)				, lowerCAmelCase__=(10, 3, 3, 3, 3, 2, 2)				, lowerCAmelCase__=False				, lowerCAmelCase__=16				, lowerCAmelCase__=19				, lowerCAmelCase__=5				, lowerCAmelCase__=0.0_5				, lowerCAmelCase__=10				, lowerCAmelCase__=2				, lowerCAmelCase__=0.0				, lowerCAmelCase__=10				, lowerCAmelCase__=0				, lowerCAmelCase__="sum"				, lowerCAmelCase__=False				, lowerCAmelCase__=False				, lowerCAmelCase__=256				, lowerCAmelCase__=(512, 512, 512, 512, 1_500)				, lowerCAmelCase__=(5, 3, 3, 1, 1)				, lowerCAmelCase__=(1, 2, 3, 1, 1)				, lowerCAmelCase__=512				, lowerCAmelCase__=0				, lowerCAmelCase__=1				, lowerCAmelCase__=2				, lowerCAmelCase__=False				, lowerCAmelCase__=3				, lowerCAmelCase__=2				, lowerCAmelCase__=3				, lowerCAmelCase__=None				, **lowerCAmelCase__				, )					->				Optional[int]:




        '''simple docstring'''
        super().__init__(**lowerCAmelCase__				, pad_token_id=lowerCAmelCase__				, bos_token_id=lowerCAmelCase__				, eos_token_id=lowerCAmelCase__ )
        lowercase__:   int  			=   hidden_size
        lowercase__:   str  			=   feat_extract_activation
        lowercase__:   List[Any]  			=   list(lowerCAmelCase__ )
        lowercase__:   Optional[int]  			=   list(lowerCAmelCase__ )
        lowercase__:   int  			=   list(lowerCAmelCase__ )
        lowercase__:   Union[str, Any]  			=   conv_bias
        lowercase__:   int  			=   num_conv_pos_embeddings
        lowercase__:   List[str]  			=   num_conv_pos_embedding_groups
        lowercase__:   List[Any]  			=   conv_pos_kernel_size
        lowercase__:   Optional[Any]  			=   len(self.conv_dim )
        lowercase__:   List[str]  			=   num_hidden_layers
        lowercase__:   List[str]  			=   intermediate_size
        lowercase__:   Tuple  			=   hidden_act
        lowercase__:   Any  			=   num_attention_heads
        lowercase__:   Optional[int]  			=   hidden_dropout
        lowercase__:   List[str]  			=   attention_dropout
        lowercase__:   int  			=   activation_dropout
        lowercase__:   Dict  			=   feat_proj_dropout
        lowercase__:   str  			=   final_dropout
        lowercase__:   List[str]  			=   layerdrop
        lowercase__:   str  			=   layer_norm_eps
        lowercase__:   Union[str, Any]  			=   initializer_range
        lowercase__:   Union[str, Any]  			=   vocab_size
        lowercase__:   Any  			=   use_weighted_layer_sum

        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__:   List[str]  			=   mask_time_prob
        lowercase__:   Tuple  			=   mask_time_length
        lowercase__:   List[Any]  			=   mask_time_min_masks
        lowercase__:   Optional[int]  			=   mask_feature_prob
        lowercase__:   Union[str, Any]  			=   mask_feature_length
        lowercase__:   List[str]  			=   mask_feature_min_masks

        # ctc loss
        lowercase__:   Union[str, Any]  			=   ctc_loss_reduction
        lowercase__:   str  			=   ctc_zero_infinity

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

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        lowercase__:   List[Any]  			=   classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        lowercase__:   int  			=   list(lowerCAmelCase__ )
        lowercase__:   Dict  			=   list(lowerCAmelCase__ )
        lowercase__:   int  			=   list(lowerCAmelCase__ )
        lowercase__:   str  			=   xvector_output_dim
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[int]:




        '''simple docstring'''
        return math.prod(self.conv_stride )


