



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

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


_lowerCAmelCase    :Dict     =					logging.get_logger(__name__)

_lowerCAmelCase    :Union[str, Any]     =					{
    '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      ):





     '''simple docstring'''



     a__									='''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       ,	)							->					List[str]:
         super().__init__(**A       ,	pad_token_id=A       ,	bos_token_id=A       ,	eos_token_id=A		)
         _UpperCAmelCase							:  List[Any]											=    hidden_size
         _UpperCAmelCase							:  Union[str, Any]											=    feat_extract_norm
         _UpperCAmelCase							:  Optional[int]											=    feat_extract_activation
         _UpperCAmelCase							:  Optional[int]											=    list(A		)
         _UpperCAmelCase							:  Tuple											=    list(A		)
         _UpperCAmelCase							:  Optional[Any]											=    list(A		)
         _UpperCAmelCase							:  Optional[Any]											=    conv_bias
         _UpperCAmelCase							:  Union[str, Any]											=    num_buckets
         _UpperCAmelCase							:  Optional[Any]											=    max_bucket_distance
         _UpperCAmelCase							:  List[str]											=    num_conv_pos_embeddings
         _UpperCAmelCase							:  Tuple											=    num_conv_pos_embedding_groups
         _UpperCAmelCase							:  str											=    len(self.conv_dim		)
         _UpperCAmelCase							:  Union[str, Any]											=    num_hidden_layers
         _UpperCAmelCase							:  List[str]											=    intermediate_size
         _UpperCAmelCase							:  Dict											=    hidden_act
         _UpperCAmelCase							:  Tuple											=    num_attention_heads
         _UpperCAmelCase							:  Optional[int]											=    hidden_dropout
         _UpperCAmelCase							:  Dict											=    attention_dropout
         _UpperCAmelCase							:  List[Any]											=    activation_dropout
         _UpperCAmelCase							:  Optional[Any]											=    feat_proj_dropout
         _UpperCAmelCase							:  Union[str, Any]											=    final_dropout
         _UpperCAmelCase							:  Optional[Any]											=    layerdrop
         _UpperCAmelCase							:  int											=    layer_norm_eps
         _UpperCAmelCase							:  List[Any]											=    initializer_range
         _UpperCAmelCase							:  Tuple											=    num_ctc_classes
         _UpperCAmelCase							:  List[str]											=    vocab_size
         _UpperCAmelCase							:  Tuple											=    do_stable_layer_norm
         _UpperCAmelCase							:  int											=    use_weighted_layer_sum
         _UpperCAmelCase							:  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
         _UpperCAmelCase							:  str											=    apply_spec_augment
         _UpperCAmelCase							:  Union[str, Any]											=    mask_time_prob
         _UpperCAmelCase							:  Optional[Any]											=    mask_time_length
         _UpperCAmelCase							:  str											=    mask_time_min_masks
         _UpperCAmelCase							:  List[Any]											=    mask_feature_prob
         _UpperCAmelCase							:  int											=    mask_feature_length

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

         # ctc loss
         _UpperCAmelCase							:  Tuple											=    ctc_loss_reduction
         _UpperCAmelCase							:  Dict											=    ctc_zero_infinity

         # adapter
         _UpperCAmelCase							:  Optional[Any]											=    add_adapter
         _UpperCAmelCase							:  Tuple											=    adapter_kernel_size
         _UpperCAmelCase							:  Tuple											=    adapter_stride
         _UpperCAmelCase							:  int											=    num_adapter_layers
         _UpperCAmelCase							:  List[Any]											=    output_hidden_size or hidden_size

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

         # XVector-specific parameters. Feel free to ignore for other classes.
         _UpperCAmelCase							:  Union[str, Any]											=    list(A		)
         _UpperCAmelCase							:  Optional[Any]											=    list(A		)
         _UpperCAmelCase							:  Tuple											=    list(A		)
         _UpperCAmelCase							:  Optional[Any]											=    xvector_output_dim





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







"""simple docstring"""
from __future__ import annotations

import os
from collections.abc import Mapping

_lowerCAmelCase    :Tuple     =					tuple[int, int]
class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(					self       ,	A       ,	A		)							->					None:
         _UpperCAmelCase							:  set[int]											=    vertices
         _UpperCAmelCase							:  dict[EdgeT, int]											=    {
             (min(A		), max(A		)): weight for edge, weight in edges.items()
         }





     def  __lowerCAmelCase	(					self       ,	A       ,	A		)							->					None:
         self.vertices.add(edge[0]		)
         self.vertices.add(edge[1]		)
         _UpperCAmelCase							:  List[Any]											=    weight





     def  __lowerCAmelCase	(					self		)							->					Graph:
         _UpperCAmelCase							:  Graph											=    Graph({min(self.vertices		)}       ,	{}		)
         _UpperCAmelCase							:  EdgeT
         _UpperCAmelCase							:  int
         _UpperCAmelCase							:  EdgeT
         _UpperCAmelCase							:  int

         while len(subgraph.vertices		) < len(self.vertices		):
             _UpperCAmelCase							:  Any											=    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							:  Tuple											=    edge
                         _UpperCAmelCase							:  Optional[int]											=    weight

             subgraph.add_edge(A       ,	A		)

         return subgraph





def  lowerCamelCase_						(UpperCamelCase__		:		str = "p107_network.txt"							):
    _UpperCAmelCase							:  str											=    os.path.abspath(os.path.dirname(UpperCamelCase__							)							)
    _UpperCAmelCase							:  str											=    os.path.join(UpperCamelCase__     ,       UpperCamelCase__							)
    _UpperCAmelCase							:  dict[EdgeT, int]											=    {}
    _UpperCAmelCase							:  list[str]
    _UpperCAmelCase							:  int
    _UpperCAmelCase							:  int

    with open(UpperCamelCase__							) as f:
        _UpperCAmelCase							:  str											=    f.read().strip().split('''\n'''							)

    _UpperCAmelCase							:  List[Any]											=    [line.split(''','''							) for line in data]

    for edgea in range(1     ,       len(UpperCamelCase__							)							):
        for edgea in range(UpperCamelCase__							):
            if adjaceny_matrix[edgea][edgea] != "-":
                _UpperCAmelCase							:  Optional[Any]											=    int(adjaceny_matrix[edgea][edgea]							)

    _UpperCAmelCase							:  Graph											=    Graph(set(range(len(UpperCamelCase__							)							)							)     ,       UpperCamelCase__							)

    _UpperCAmelCase							:  Graph											=    graph.prims_algorithm()

    _UpperCAmelCase							:  int											=    sum(graph.edges.values()							)
    _UpperCAmelCase							:  int											=    sum(subgraph.edges.values()							)

    return initial_total - optimal_total


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


