


from __future__ import annotations

import os
from collections.abc import Mapping

__lowerCAmelCase	: Optional[int]  				=tuple[int, int]
class        _lowercase      :






				'''simple docstring'''



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


				def 		__magic_name__(       self			:List[str]			,			lowerCAmelCase__			:EdgeT			,			lowerCAmelCase__			:int	)		->			None:
										self.vertices.add(edge[0]	)
										self.vertices.add(edge[1]	)
										__SCREAMING_SNAKE_CASE						:     List[str]					=		weight



				def 		__magic_name__(       self			:List[str]	)		->			Graph:
										__SCREAMING_SNAKE_CASE						:     Graph					=		Graph({min(self.vertices	)}			,			{}	)
										__SCREAMING_SNAKE_CASE						:     EdgeT
										__SCREAMING_SNAKE_CASE						:     int
										__SCREAMING_SNAKE_CASE						:     EdgeT
										__SCREAMING_SNAKE_CASE						:     int

										while len(subgraph.vertices	) < len(self.vertices	):
																__SCREAMING_SNAKE_CASE						:     int					=		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:
																																		__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		edge
																																		__SCREAMING_SNAKE_CASE						:     Dict					=		weight

																subgraph.add_edge(lowerCAmelCase__			,			lowerCAmelCase__	)

										return subgraph




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

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

						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		[line.split(''','''      ) for line in data]

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

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

						__SCREAMING_SNAKE_CASE						:     Graph					=		graph.prims_algorithm()

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

						return initial_total - optimal_total


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







'''simple docstring'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase_					:    Dict      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    Optional[int]      	=			{
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='deta'
				__a            ={
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				}




				def __init__(  self     :					List[str]					,				__a     :					List[str]=None					,				__a     :					Dict=9_00					,				__a     :					str=20_48					,				__a     :					Tuple=6					,				__a     :					List[str]=20_48					,				__a     :					str=8					,				__a     :					Union[str, Any]=6					,				__a     :					int=10_24					,				__a     :					List[Any]=8					,				__a     :					Dict=0.0					,				__a     :					Tuple=True					,				__a     :					Optional[Any]="relu"					,				__a     :					Tuple=2_56					,				__a     :					Optional[Any]=0.1					,				__a     :					int=0.0					,				__a     :					List[Any]=0.0					,				__a     :					Optional[int]=0.02					,				__a     :					str=1.0					,				__a     :					Dict=True					,				__a     :					Dict=False					,				__a     :					Optional[int]="sine"					,				__a     :					Any=5					,				__a     :					List[str]=4					,				__a     :					Optional[int]=4					,				__a     :					List[str]=True					,				__a     :					str=3_00					,				__a     :					int=True					,				__a     :					int=True					,				__a     :					Tuple=1					,				__a     :					Optional[int]=5					,				__a     :					Tuple=2					,				__a     :					Dict=1					,				__a     :					Optional[int]=1					,				__a     :					Any=5					,				__a     :					Optional[int]=2					,				__a     :					Dict=0.1					,				__a     :					str=0.25					,				**__a     :					Tuple					,				):
								if backbone_config is None:
												logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."					)
												_a								= CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]					)
								else:
												if isinstance(__a					,				__a					):
																_a								= backbone_config.pop("model_type"					)
																_a								= CONFIG_MAPPING[backbone_model_type]
																_a								= config_class.from_dict(__a					)

								_a								= backbone_config
								_a								= num_queries
								_a								= max_position_embeddings
								_a								= d_model
								_a								= encoder_ffn_dim
								_a								= encoder_layers
								_a								= encoder_attention_heads
								_a								= decoder_ffn_dim
								_a								= decoder_layers
								_a								= decoder_attention_heads
								_a								= dropout
								_a								= attention_dropout
								_a								= activation_dropout
								_a								= activation_function
								_a								= init_std
								_a								= init_xavier_std
								_a								= encoder_layerdrop
								_a								= auxiliary_loss
								_a								= position_embedding_type
								# deformable attributes
								_a								= num_feature_levels
								_a								= encoder_n_points
								_a								= decoder_n_points
								_a								= two_stage
								_a								= two_stage_num_proposals
								_a								= with_box_refine
								_a								= assign_first_stage
								if two_stage is True and with_box_refine is False:
												raise ValueError("If two_stage is True, with_box_refine must be True."					)
								# Hungarian matcher
								_a								= class_cost
								_a								= bbox_cost
								_a								= giou_cost
								# Loss coefficients
								_a								= mask_loss_coefficient
								_a								= dice_loss_coefficient
								_a								= bbox_loss_coefficient
								_a								= giou_loss_coefficient
								_a								= eos_coefficient
								_a								= focal_alpha
								super().__init__(is_encoder_decoder=__a					,				**__a					)




				@property
				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								return self.encoder_attention_heads




				@property
				def   UpperCamelCase__   (  self     :					Dict					):
								return self.d_model




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= copy.deepcopy(self.__dict__					)
								_a								= self.backbone_config.to_dict()
								_a								= self.__class__.model_type
								return output
