





'''simple docstring'''

from __future__ import annotations

import os
from collections.abc import Mapping

a        =						tuple[int, int]







class __a :





   def __init__(					self				:			List[Any]				,lowerCamelCase				:			set[int]				,lowerCamelCase				:			Mapping[EdgeT, int]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       vertices
      __SCREAMING_SNAKE_CASE      							=       {
          (min(lowerCamelCase    ), max(lowerCamelCase    )): weight for edge, weight in edges.items()
      }





   def      UpperCAmelCase__     (					self				:			Optional[Any]				,lowerCamelCase				:			EdgeT				,lowerCamelCase				:			int    ):

      '''simple docstring'''

      self.vertices.add(edge[0]    )
      self.vertices.add(edge[1]    )
      __SCREAMING_SNAKE_CASE      							=       weight





   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       Graph({min(self.vertices    )}				,{}    )
      __SCREAMING_SNAKE_CASE      							=       42
      __SCREAMING_SNAKE_CASE      							=       42
      __SCREAMING_SNAKE_CASE      							=       42
      __SCREAMING_SNAKE_CASE      							=       42

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

         subgraph.add_edge(lowerCamelCase				,lowerCamelCase    )

      return subgraph





def 						__magic_name__       (							__UpperCAmelCase = "p107_network.txt"      )						->					int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       os.path.abspath(os.path.dirname(__UpperCAmelCase      )      )
   __SCREAMING_SNAKE_CASE      							=       os.path.join(__UpperCAmelCase							,	__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       {}
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42

   with open(__UpperCAmelCase      ) as f:
      __SCREAMING_SNAKE_CASE      							=       f.read().strip().split("""\n"""      )

   __SCREAMING_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] != "-":
            __SCREAMING_SNAKE_CASE      							=       int(adjaceny_matrix[edgea][edgea]      )

   __SCREAMING_SNAKE_CASE      							=       Graph(set(range(len(__UpperCAmelCase      )      )      )							,	__UpperCAmelCase      )

   __SCREAMING_SNAKE_CASE      							=       graph.prims_algorithm()

   __SCREAMING_SNAKE_CASE      							=       sum(graph.edges.values()      )
   __SCREAMING_SNAKE_CASE      							=       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


a        =						logging.get_logger(__name__)

a        =						{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}







class __a (   _snake_case			):
   __UpperCamelCase   : Dict	      =	'deta'
   __UpperCamelCase   : List[str]	      =	{
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
   }





   def __init__(					self				:			Tuple				,lowerCamelCase				:			List[Any]=None				,lowerCamelCase				:			Any=900				,lowerCamelCase				:			int=2048				,lowerCamelCase				:			Any=6				,lowerCamelCase				:			Optional[Any]=2048				,lowerCamelCase				:			str=8				,lowerCamelCase				:			Union[str, Any]=6				,lowerCamelCase				:			List[str]=1024				,lowerCamelCase				:			int=8				,lowerCamelCase				:			Any=0.0				,lowerCamelCase				:			Any=True				,lowerCamelCase				:			Optional[int]="relu"				,lowerCamelCase				:			int=256				,lowerCamelCase				:			Tuple=0.1				,lowerCamelCase				:			Optional[Any]=0.0				,lowerCamelCase				:			Tuple=0.0				,lowerCamelCase				:			List[str]=0.02				,lowerCamelCase				:			Any=1.0				,lowerCamelCase				:			Optional[int]=True				,lowerCamelCase				:			int=False				,lowerCamelCase				:			Optional[Any]="sine"				,lowerCamelCase				:			Dict=5				,lowerCamelCase				:			List[Any]=4				,lowerCamelCase				:			Optional[Any]=4				,lowerCamelCase				:			Any=True				,lowerCamelCase				:			int=300				,lowerCamelCase				:			Any=True				,lowerCamelCase				:			Tuple=True				,lowerCamelCase				:			int=1				,lowerCamelCase				:			Tuple=5				,lowerCamelCase				:			Union[str, Any]=2				,lowerCamelCase				:			Tuple=1				,lowerCamelCase				:			int=1				,lowerCamelCase				:			str=5				,lowerCamelCase				:			Optional[Any]=2				,lowerCamelCase				:			List[Any]=0.1				,lowerCamelCase				:			Union[str, Any]=0.25				,**lowerCamelCase				:			int				,):

      '''simple docstring'''

      if backbone_config is None:
         logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""    )
         __SCREAMING_SNAKE_CASE      							=       CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]    )
      else:
         if isinstance(lowerCamelCase				,lowerCamelCase    ):
            __SCREAMING_SNAKE_CASE      							=       backbone_config.pop("""model_type"""    )
            __SCREAMING_SNAKE_CASE      							=       CONFIG_MAPPING[backbone_model_type]
            __SCREAMING_SNAKE_CASE      							=       config_class.from_dict(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       backbone_config
      __SCREAMING_SNAKE_CASE      							=       num_queries
      __SCREAMING_SNAKE_CASE      							=       max_position_embeddings
      __SCREAMING_SNAKE_CASE      							=       d_model
      __SCREAMING_SNAKE_CASE      							=       encoder_ffn_dim
      __SCREAMING_SNAKE_CASE      							=       encoder_layers
      __SCREAMING_SNAKE_CASE      							=       encoder_attention_heads
      __SCREAMING_SNAKE_CASE      							=       decoder_ffn_dim
      __SCREAMING_SNAKE_CASE      							=       decoder_layers
      __SCREAMING_SNAKE_CASE      							=       decoder_attention_heads
      __SCREAMING_SNAKE_CASE      							=       dropout
      __SCREAMING_SNAKE_CASE      							=       attention_dropout
      __SCREAMING_SNAKE_CASE      							=       activation_dropout
      __SCREAMING_SNAKE_CASE      							=       activation_function
      __SCREAMING_SNAKE_CASE      							=       init_std
      __SCREAMING_SNAKE_CASE      							=       init_xavier_std
      __SCREAMING_SNAKE_CASE      							=       encoder_layerdrop
      __SCREAMING_SNAKE_CASE      							=       auxiliary_loss
      __SCREAMING_SNAKE_CASE      							=       position_embedding_type
      # deformable attributes
      __SCREAMING_SNAKE_CASE      							=       num_feature_levels
      __SCREAMING_SNAKE_CASE      							=       encoder_n_points
      __SCREAMING_SNAKE_CASE      							=       decoder_n_points
      __SCREAMING_SNAKE_CASE      							=       two_stage
      __SCREAMING_SNAKE_CASE      							=       two_stage_num_proposals
      __SCREAMING_SNAKE_CASE      							=       with_box_refine
      __SCREAMING_SNAKE_CASE      							=       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
      __SCREAMING_SNAKE_CASE      							=       class_cost
      __SCREAMING_SNAKE_CASE      							=       bbox_cost
      __SCREAMING_SNAKE_CASE      							=       giou_cost
      # Loss coefficients
      __SCREAMING_SNAKE_CASE      							=       mask_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       dice_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       bbox_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       giou_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       eos_coefficient
      __SCREAMING_SNAKE_CASE      							=       focal_alpha
      super().__init__(is_encoder_decoder=lowerCamelCase				,**lowerCamelCase    )





   @property
   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      return self.encoder_attention_heads





   @property
   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      return self.d_model





   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       copy.deepcopy(self.__dict__    )
      __SCREAMING_SNAKE_CASE      							=       self.backbone_config.to_dict()
      __SCREAMING_SNAKE_CASE      							=       self.__class__.model_type
      return output





