




import functools
import operator

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


_lowerCamelCase      			=				logging.get_logger(__name__)

_lowerCamelCase      			=				{
    'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}



class       a					(       _lowerCamelCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	str       		=  'wav2vec2'
     def __init__(     self       :      List[str] ,   __snake_case       :      List[str]=32 ,   __snake_case       :      Union[str, Any]=7_68 ,   __snake_case       :      List[str]=12 ,   __snake_case       :      Tuple=12 ,   __snake_case       :      Union[str, Any]=30_72 ,   __snake_case       :      List[Any]="gelu" ,   __snake_case       :      Union[str, Any]=0.1 ,   __snake_case       :      Optional[int]=0.1 ,   __snake_case       :      List[Any]=0.1 ,   __snake_case       :      int=0.0 ,   __snake_case       :      Dict=0.0 ,   __snake_case       :      Optional[int]=0.1 ,   __snake_case       :      Dict=0.1 ,   __snake_case       :      str=0.02 ,   __snake_case       :      Any=1E-5 ,   __snake_case       :      List[str]="group" ,   __snake_case       :      Optional[Any]="gelu" ,   __snake_case       :      int=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) ,   __snake_case       :      str=(5, 2, 2, 2, 2, 2, 2) ,   __snake_case       :      Union[str, Any]=(10, 3, 3, 3, 3, 2, 2) ,   __snake_case       :      Any=False ,   __snake_case       :      Tuple=1_28 ,   __snake_case       :      Union[str, Any]=16 ,   __snake_case       :      List[str]=False ,   __snake_case       :      str=True ,   __snake_case       :      int=0.05 ,   __snake_case       :      List[str]=10 ,   __snake_case       :      Any=2 ,   __snake_case       :      int=0.0 ,   __snake_case       :      List[str]=10 ,   __snake_case       :      List[str]=0 ,   __snake_case       :      Optional[int]=3_20 ,   __snake_case       :      Any=2 ,   __snake_case       :      List[Any]=0.1 ,   __snake_case       :      int=1_00 ,   __snake_case       :      Any=2_56 ,   __snake_case       :      List[Any]=2_56 ,   __snake_case       :      List[str]=0.1 ,   __snake_case       :      Union[str, Any]="sum" ,   __snake_case       :      int=False ,   __snake_case       :      str=False ,   __snake_case       :      List[Any]=2_56 ,   __snake_case       :      Optional[Any]=(5_12, 5_12, 5_12, 5_12, 15_00) ,   __snake_case       :      int=(5, 3, 3, 1, 1) ,   __snake_case       :      Union[str, Any]=(1, 2, 3, 1, 1) ,   __snake_case       :      List[str]=5_12 ,   __snake_case       :      Tuple=0 ,   __snake_case       :      int=1 ,   __snake_case       :      Dict=2 ,   __snake_case       :      Optional[int]=False ,   __snake_case       :      Tuple=3 ,   __snake_case       :      Any=2 ,   __snake_case       :      List[Any]=3 ,   __snake_case       :      int=None ,   __snake_case       :      List[str]=None ,   **__snake_case       :      List[Any] ,   ):
         super().__init__(**__snake_case ,   pad_token_id=__snake_case ,   bos_token_id=__snake_case ,   eos_token_id=__snake_case    )
         UpperCAmelCase_										=					hidden_size
         UpperCAmelCase_										=					feat_extract_norm
         UpperCAmelCase_										=					feat_extract_activation
         UpperCAmelCase_										=					list(__snake_case    )
         UpperCAmelCase_										=					list(__snake_case    )
         UpperCAmelCase_										=					list(__snake_case    )
         UpperCAmelCase_										=					conv_bias
         UpperCAmelCase_										=					num_conv_pos_embeddings
         UpperCAmelCase_										=					num_conv_pos_embedding_groups
         UpperCAmelCase_										=					len(self.conv_dim    )
         UpperCAmelCase_										=					num_hidden_layers
         UpperCAmelCase_										=					intermediate_size
         UpperCAmelCase_										=					hidden_act
         UpperCAmelCase_										=					num_attention_heads
         UpperCAmelCase_										=					hidden_dropout
         UpperCAmelCase_										=					attention_dropout
         UpperCAmelCase_										=					activation_dropout
         UpperCAmelCase_										=					feat_proj_dropout
         UpperCAmelCase_										=					final_dropout
         UpperCAmelCase_										=					layerdrop
         UpperCAmelCase_										=					layer_norm_eps
         UpperCAmelCase_										=					initializer_range
         UpperCAmelCase_										=					vocab_size
         UpperCAmelCase_										=					do_stable_layer_norm
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					apply_spec_augment
         UpperCAmelCase_										=					mask_time_prob
         UpperCAmelCase_										=					mask_time_length
         UpperCAmelCase_										=					mask_time_min_masks
         UpperCAmelCase_										=					mask_feature_prob
         UpperCAmelCase_										=					mask_feature_length
         UpperCAmelCase_										=					mask_feature_min_masks

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

         # ctc loss
         UpperCAmelCase_										=					ctc_loss_reduction
         UpperCAmelCase_										=					ctc_zero_infinity

         # adapter
         UpperCAmelCase_										=					add_adapter
         UpperCAmelCase_										=					adapter_kernel_size
         UpperCAmelCase_										=					adapter_stride
         UpperCAmelCase_										=					num_adapter_layers
         UpperCAmelCase_										=					output_hidden_size or hidden_size
         UpperCAmelCase_										=					adapter_attn_dim

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

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




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











from __future__ import annotations

import os
from collections.abc import Mapping

_lowerCamelCase      			=						tuple[int, int]



class       a					:

     '''simple docstring'''





     def __init__(     self       :      str ,   __snake_case       :      set[int] ,   __snake_case       :      Mapping[EdgeT, int]    ):
         UpperCAmelCase_										=					vertices
         UpperCAmelCase_										=					{
             (min(__snake_case    ), max(__snake_case    )): weight for edge, weight in edges.items()
         }
     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      EdgeT ,   __snake_case       :      int    ):
         self.vertices.add(edge[0]    )
         self.vertices.add(edge[1]    )
         UpperCAmelCase_										=					weight




     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					Graph({min(self.vertices    )} ,   {}    )
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42

         while len(subgraph.vertices    ) < len(self.vertices    ):
             UpperCAmelCase_										=					max(self.edges.values()    ) + 1
             for edge, weight in self.edges.items():
                 if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                     if weight < min_weight:
                         UpperCAmelCase_										=					edge
                         UpperCAmelCase_										=					weight

             subgraph.add_edge(__snake_case ,   __snake_case    )

         return subgraph




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str = "p107_network.txt"				)  ->							int:
    UpperCAmelCase_										=					os.path.abspath(os.path.dirname(__UpperCamelCase				)				)
    UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					__UpperCamelCase				)
    UpperCAmelCase_										=					{}
    UpperCAmelCase_										=					42
    UpperCAmelCase_										=					42
    UpperCAmelCase_										=					42

    with open(__UpperCamelCase				) as f:
        UpperCAmelCase_										=					f.read().strip().split('''\n'''				)

    UpperCAmelCase_										=					[line.split(''','''				) for line in data]

    for edgea in range(1	,					len(__UpperCamelCase				)				):
        for edgea in range(__UpperCamelCase				):
            if adjaceny_matrix[edgea][edgea] != "-":
                UpperCAmelCase_										=					int(adjaceny_matrix[edgea][edgea]				)

    UpperCAmelCase_										=					Graph(set(range(len(__UpperCamelCase				)				)				)	,					__UpperCamelCase				)

    UpperCAmelCase_										=					graph.prims_algorithm()

    UpperCAmelCase_										=					sum(graph.edges.values()				)
    UpperCAmelCase_										=					sum(subgraph.edges.values()				)

    return initial_total - optimal_total


if __name__ == "__main__":
 print(F"{solution() = }")





