


"""simple docstring"""


from __future__ import annotations

import os
from collections.abc import Mapping

__lowerCamelCase      :Optional[int]	    =		tuple[int, int]


class 			A__	:


     """simple docstring"""

     def __init__(	self:						Union[str, Any]     ,		__a:						List[str]     ,		__a:						Union[str, Any]	)->    Tuple:
           lowerCamelCase					:					set[int]        =	vertices
           lowerCamelCase					:					dict[EdgeT, int]        =	{
               (min(_UpperCAmelCase	), max(_UpperCAmelCase	)): weight for edge, weight in edges.items()
           }

     def 		a__						(	self:						Any     ,		__a:						Optional[Any]     ,		__a:						str	)->    Optional[Any]:
           self.vertices.add(edge[0]	)
           self.vertices.add(edge[1]	)
           lowerCamelCase					:					Any        =	weight







     def 		a__						(	self:						Optional[Any]	)->    Tuple:
           lowerCamelCase					:					Graph        =	Graph({min(self.vertices	)}     ,		{}	)
           lowerCamelCase					:					EdgeT
           lowerCamelCase					:					int
           lowerCamelCase					:					EdgeT
           lowerCamelCase					:					int

           while len(subgraph.vertices	) < len(self.vertices	):
                 lowerCamelCase					:					List[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:
                                   lowerCamelCase					:					str        =	edge
                                   lowerCamelCase					:					int        =	weight

                 subgraph.add_edge(_UpperCAmelCase     ,		_UpperCAmelCase	)

           return subgraph




def   snake_case		(     UpperCamelCase__       :						str = "p107_network.txt"    )   ->      int:
      lowerCamelCase					:					str        =	os.path.abspath(os.path.dirname(a_    )    )
      lowerCamelCase					:					str        =	os.path.join(a_			,				a_    )
      lowerCamelCase					:					dict[EdgeT, int]        =	{}
      lowerCamelCase					:					list[str]
      lowerCamelCase					:					int
      lowerCamelCase					:					int

      with open(a_    ) as f:
            lowerCamelCase					:					Any        =	f.read().strip().split("""\n"""    )

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

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

      lowerCamelCase					:					Graph        =	Graph(set(range(len(a_    )    )    )			,				a_    )

      lowerCamelCase					:					Graph        =	graph.prims_algorithm()

      lowerCamelCase					:					int        =	sum(graph.edges.values()    )
      lowerCamelCase					:					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,
)


__lowerCamelCase      :Optional[Any]	    =		{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Union[str, Any]	    =		[
        'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
        'EncodecModel',
        'EncodecPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_encodec import (
        ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
        EncodecConfig,
    )
    from .feature_extraction_encodec import EncodecFeatureExtractor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_encodec import (
            ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
            EncodecModel,
            EncodecPreTrainedModel,
        )

else:
    import sys

    __lowerCamelCase      :Dict	    =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




