





'''simple docstring'''






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class 			lowerCamelCase					(						__UpperCAmelCase     ,							__UpperCAmelCase     ,							__UpperCAmelCase  ):


					@register_to_config
					def __init__(					self	:  Any       ,     __snake_case	:  int       ,     __snake_case	:  int       ,     __snake_case	:  int       ,     __snake_case	:  float       ,     __snake_case	:  int       ,     __snake_case	:  int       ,     __snake_case	:  int       ,     __snake_case	:  int       ,     __snake_case	:  str       ,     __snake_case	:  bool = False       ,     ):





											'''simple docstring'''





											super().__init__()

											_snake_case:   Union[str, Any]  					=		nn.Embedding(__snake_case       ,     __snake_case       )

											_snake_case:   List[Any]  					=		nn.Embedding(__snake_case       ,     __snake_case       )
											_snake_case:   Dict  					=		False

											_snake_case:   str  					=		nn.Dropout(p=__snake_case       )

											_snake_case:   Optional[int]  					=		TaConfig(
											    vocab_size=__snake_case       ,     d_model=__snake_case       ,     num_heads=__snake_case       ,     d_kv=__snake_case       ,     d_ff=__snake_case       ,     dropout_rate=__snake_case       ,     feed_forward_proj=__snake_case       ,     is_decoder=__snake_case       ,     is_encoder_decoder=__snake_case       ,     )

											_snake_case:   Dict  					=		nn.ModuleList()
											for lyr_num in range(__snake_case       ):
																	_snake_case:   Optional[Any]  					=		TaBlock(__snake_case       )
																	self.encoders.append(__snake_case       )

											_snake_case:   Tuple  					=		TaLayerNorm(__snake_case       )
											_snake_case:   Union[str, Any]  					=		nn.Dropout(p=__snake_case       )




					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     __snake_case	:  Dict       ,     __snake_case	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   int  					=		self.token_embedder(__snake_case       )

											_snake_case:   Optional[Any]  					=		encoder_input_tokens.shape[1]
											_snake_case:   Optional[int]  					=		torch.arange(__snake_case       ,     device=encoder_input_tokens.device       )
											x += self.position_encoding(__snake_case       )

											_snake_case:   List[str]  					=		self.dropout_pre(__snake_case       )

											# inverted the attention mask
											_snake_case:   int  					=		encoder_input_tokens.size()
											_snake_case:   Dict  					=		self.get_extended_attention_mask(__snake_case       ,     __snake_case       )

											for lyr in self.encoders:
																	_snake_case:   Any  					=		lyr(__snake_case       ,     __snake_case       )[0]
											_snake_case:   Union[str, Any]  					=		self.layer_norm(__snake_case       )

											return self.dropout_post(__snake_case       ), encoder_inputs_mask












'''simple docstring'''






from __future__ import annotations

import os
from collections.abc import Mapping

A   : Optional[Any]             =			tuple[int, int]







class 			lowerCamelCase					:


					def __init__(					self	:  Tuple       ,     __snake_case	:  set[int]       ,     __snake_case	:  Mapping[EdgeT, int]       ):





											'''simple docstring'''





											_snake_case:   set[int]  					=		vertices
											_snake_case:   dict[EdgeT, int]  					=		{
											    (min(__snake_case       ), max(__snake_case       )): weight for edge, weight in edges.items()
											}


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  EdgeT       ,     __snake_case	:  int       ):





											'''simple docstring'''





											self.vertices.add(edge[0]       )
											self.vertices.add(edge[1]       )
											_snake_case:   Dict  					=		weight




					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   Graph  					=		Graph({min(self.vertices       )}       ,     {}       )
											_snake_case:   EdgeT
											_snake_case:   int
											_snake_case:   EdgeT
											_snake_case:   int

											while len(subgraph.vertices       ) < len(self.vertices       ):
																	_snake_case:   List[str]  					=		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:   Optional[Any]  					=		edge
																																			_snake_case:   Optional[int]  					=		weight

																	subgraph.add_edge(__snake_case       ,     __snake_case       )

											return subgraph





def       lowercase_      (		lowercase__ = "p107_network.txt"						)   ->int:
						_snake_case:   str  					=		os.path.abspath(os.path.dirname(lowercase__						)						)
						_snake_case:   str  					=		os.path.join(lowercase__				,  lowercase__						)
						_snake_case:   dict[EdgeT, int]  					=		{}
						_snake_case:   list[str]
						_snake_case:   int
						_snake_case:   int

						with open(lowercase__						) as f:
												_snake_case:   Tuple  					=		f.read().strip().split('\n'						)

						_snake_case:   Tuple  					=		[line.split(','						) for line in data]

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

						_snake_case:   Graph  					=		Graph(set(range(len(lowercase__						)						)						)				,  lowercase__						)

						_snake_case:   Graph  					=		graph.prims_algorithm()

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

						return initial_total - optimal_total


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





