"""simple docstring"""


from __future__ import annotations

import os
from collections.abc import Mapping

__UpperCamelCase									=			tuple[int, int]




class  UpperCamelCase					:







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__)      ->      None:
            snake_case_					       =					vertices
            snake_case_					       =					{
                (min(lowerCAmelCase__), max(lowerCAmelCase__)): weight for edge, weight in edges.items()
            }







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__)      ->      None:
            self.vertices.add(edge[0])
            self.vertices.add(edge[1])
            snake_case_					       =					weight







       def 		a_						( self)      ->      Graph:
            snake_case_					       =					Graph({min(self.vertices)},							{})
            snake_case_					       =					42
            snake_case_					       =					42
            snake_case_					       =					42
            snake_case_					       =					42

            while len(subgraph.vertices) < len(self.vertices):
                 snake_case_					       =					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:
                                snake_case_					       =					edge
                                snake_case_					       =					weight

                 subgraph.add_edge(lowerCAmelCase__,							lowerCAmelCase__)

            return subgraph







def 					UpperCAmelCase    (		UpperCAmelCase = "p107_network.txt"					)				->      int:
     snake_case_					       =					os.path.abspath(os.path.dirname(UpperCAmelCase					)					)
     snake_case_					       =					os.path.join(UpperCAmelCase						,					UpperCAmelCase					)
     snake_case_					       =					{}
     snake_case_					       =					42
     snake_case_					       =					42
     snake_case_					       =					42

     with open(UpperCAmelCase					) as f:
          snake_case_					       =					f.read().strip().split('\n'					)

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

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

     snake_case_					       =					Graph(set(range(len(UpperCAmelCase					)					)					)						,					UpperCAmelCase					)

     snake_case_					       =					graph.prims_algorithm()

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

     return initial_total - optimal_total


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







from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy







@add_start_docstrings(
    "The RoBERTa Model transformer with early exiting (DeeRoBERTa). ",UpperCamelCase__,)
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						RobertaConfig
     _a							 =						"roberta"

     def __init__(  self		,     _a				)						->							Optional[int]:
           super().__init__(_a				)

           _A							:     Union[str, Any]			=						RobertaEmbeddings(_a				)
           self.init_weights()







@add_start_docstrings(
    "RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. ",UpperCamelCase__,)
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						RobertaConfig
     _a							 =						"roberta"

     def __init__(  self		,     _a				)						->							str:
           super().__init__(_a				)
           _A							:     Any			=						config.num_labels
           _A							:     Dict			=						config.num_hidden_layers

           _A							:     List[str]			=						DeeRobertaModel(_a				)
           _A							:     int			=						nn.Dropout(config.hidden_dropout_prob				)
           _A							:     int			=						nn.Linear(config.hidden_size		,     self.config.num_labels				)



     @add_start_docstrings_to_model_forward(_a				)
     def 					a__	(  self		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=-1		,     _a=False		,     )						->							Any:
           _A							:     Optional[int]			=						self.num_layers
           try:
                 _A							:     List[str]			=						self.roberta(
                     _a		,     attention_mask=_a		,     token_type_ids=_a		,     position_ids=_a		,     head_mask=_a		,     inputs_embeds=_a		,     )

                 _A							:     List[str]			=						outputs[1]

                 _A							:     List[str]			=						self.dropout(_a				)
                 _A							:     Optional[Any]			=						self.classifier(_a				)
                 _A							:     List[Any]			=						(logits,) + outputs[2:]  # add hidden states and attention if they are here
           except HighwayException as e:
                 _A							:     List[Any]			=						e.message
                 _A							:     Optional[int]			=						e.exit_layer
                 _A							:     Optional[int]			=						outputs[0]

           if not self.training:
                 _A							:     int			=						entropy(_a				)
                 _A							:     int			=						[]
                 _A							:     int			=						[]
           if labels is not None:
                 if self.num_labels == 1:
                       #  We are doing regression
                       _A							:     Union[str, Any]			=						MSELoss()
                       _A							:     Tuple			=						loss_fct(logits.view(-1				)		,     labels.view(-1				)				)
                 else:
                       _A							:     List[Any]			=						CrossEntropyLoss()
                       _A							:     Dict			=						loss_fct(logits.view(-1		,     self.num_labels				)		,     labels.view(-1				)				)

                 # work with highway exits
                 _A							:     Optional[Any]			=						[]
                 for highway_exit in outputs[-1]:
                       _A							:     Tuple			=						highway_exit[0]
                       if not self.training:
                             highway_logits_all.append(_a				)
                             highway_entropy.append(highway_exit[2]				)
                       if self.num_labels == 1:
                             #  We are doing regression
                             _A							:     List[str]			=						MSELoss()
                             _A							:     Optional[int]			=						loss_fct(highway_logits.view(-1				)		,     labels.view(-1				)				)
                       else:
                             _A							:     List[Any]			=						CrossEntropyLoss()
                             _A							:     Tuple			=						loss_fct(highway_logits.view(-1		,     self.num_labels				)		,     labels.view(-1				)				)
                       highway_losses.append(_a				)

                 if train_highway:
                       _A							:     Dict			=						(sum(highway_losses[:-1]				),) + outputs
                       # exclude the final highway, of course
                 else:
                       _A							:     int			=						(loss,) + outputs
           if not self.training:
                 _A							:     Optional[Any]			=						outputs + ((original_entropy, highway_entropy), exit_layer)
                 if output_layer >= 0:
                       _A							:     Union[str, Any]			=						(
                           (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                       )  # use the highway of the last layer

           return outputs  # (loss), logits, (hidden_states), (attentions), entropy

