
"""simple docstring"""



from __future__ import annotations

import os
from collections.abc import Mapping

UpperCamelCase__          =	tuple[int, int]





class 				a			:



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      set[int]     							=					vertices
          UpperCAmelCase__	:      dict[EdgeT, int]     							=					{
              (min(UpperCamelCase_      ), max(UpperCamelCase_      )): weight for edge, weight in edges.items()
          }



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          self.vertices.add(edge[0]      )
          self.vertices.add(edge[1]      )
          UpperCAmelCase__	:      Optional[int]     							=					weight



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Graph     							=					Graph({min(self.vertices      )}     ,	{}      )
          UpperCAmelCase__	:      EdgeT
          UpperCAmelCase__	:      int
          UpperCAmelCase__	:      EdgeT
          UpperCAmelCase__	:      int

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

              subgraph.add_edge(UpperCamelCase_     ,	UpperCamelCase_      )

          return subgraph




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

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

    UpperCAmelCase__	:      int     							=					[line.split(',' ) for line in data]

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

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

    UpperCAmelCase__	:      Graph     							=					graph.prims_algorithm()

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

    return initial_total - optimal_total


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



"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCamelCase__          =	{
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__          =	[
        'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MegaForCausalLM',
        'MegaForMaskedLM',
        'MegaForMultipleChoice',
        'MegaForQuestionAnswering',
        'MegaForSequenceClassification',
        'MegaForTokenClassification',
        'MegaModel',
        'MegaPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mega import (
            MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
            MegaForCausalLM,
            MegaForMaskedLM,
            MegaForMultipleChoice,
            MegaForQuestionAnswering,
            MegaForSequenceClassification,
            MegaForTokenClassification,
            MegaModel,
            MegaPreTrainedModel,
        )

else:
    import sys

    UpperCamelCase__          =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

