





'''simple docstring'''




import copy

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


_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "upernet"



     def __init__(   self							:			Tuple   ,       __snake_case							:			Dict=None   ,       __snake_case							:			Optional[Any]=5_12   ,       __snake_case							:			Dict=0.02   ,       __snake_case							:			Optional[Any]=[1, 2, 3, 6]   ,       __snake_case							:			Dict=True   ,       __snake_case							:			Any=0.4   ,       __snake_case							:			Any=3_84   ,       __snake_case							:			Union[str, Any]=2_56   ,       __snake_case							:			List[Any]=1   ,       __snake_case							:			str=False   ,       __snake_case							:			Union[str, Any]=2_55   ,       **__snake_case							:			List[Any]   ,       )->	Any:
        super().__init__(**__snake_case						)

        if backbone_config is None:
           logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""						)
           snake_case 						=   CONFIG_MAPPING["""resnet"""](out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]						)
        elif isinstance(__snake_case   ,       __snake_case						):
           snake_case 						=   backbone_config.get("""model_type"""						)
           snake_case 						=   CONFIG_MAPPING[backbone_model_type]
           snake_case 						=   config_class.from_dict(__snake_case						)

        snake_case 						=   backbone_config
        snake_case 						=   hidden_size
        snake_case 						=   initializer_range
        snake_case 						=   pool_scales
        snake_case 						=   use_auxiliary_head
        snake_case 						=   auxiliary_loss_weight
        snake_case 						=   auxiliary_in_channels
        snake_case 						=   auxiliary_channels
        snake_case 						=   auxiliary_num_convs
        snake_case 						=   auxiliary_concat_input
        snake_case 						=   loss_ignore_index



     def      lowerCAmelCase			(   self							:			Dict						)->	int:
        snake_case 						=   copy.deepcopy(self.__dict__						)
        snake_case 						=   self.backbone_config.to_dict()
        snake_case 						=   self.__class__.model_type
        return output








'''simple docstring'''




from __future__ import annotations

import os
from collections.abc import Mapping

_SCREAMING_SNAKE_CASE							     =			tuple[int, int]
class 			_lowerCAmelCase		:






     """simple docstring"""






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



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       __snake_case							:			EdgeT   ,       __snake_case							:			int						)->	None:
        self.vertices.add(edge[0]						)
        self.vertices.add(edge[1]						)
        snake_case 						=   weight



     def      lowerCAmelCase			(   self							:			str						)->	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(__snake_case   ,       __snake_case						)

        return subgraph





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str = "p107_network.txt"		)		->  int:
   snake_case 						=   os.path.abspath(os.path.dirname(__lowerCAmelCase		)		)
   snake_case 						=   os.path.join(__lowerCAmelCase      ,    __lowerCAmelCase		)
   snake_case 						=   {}
   snake_case 						=   42
   snake_case 						=   42
   snake_case 						=   42

   with open(__lowerCAmelCase		) as f:
      snake_case 						=   f.read().strip().split("""\n"""		)

   snake_case 						=   [line.split(""","""		) for line in data]

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

   snake_case 						=   Graph(set(range(len(__lowerCAmelCase		)		)		)      ,    __lowerCAmelCase		)

   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() = }""")

