





from __future__ import annotations

import os
from collections.abc import Mapping

lowercase_          =		tuple[int, int]
class     SCREAMING_SNAKE_CASE       :


   def __init__(   self      :    Dict      ,					a      :    set[int]      ,					a      :    Mapping[EdgeT, int]	)->       None:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    EdgeT      ,					a      :    int	)->       None:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       Graph:





     """simple docstring"""







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

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

       subgraph.add_edge(a      ,					a	)

     return subgraph






def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE = "p107_network.txt"		)    ->       int:
  lowercase__           =  os.path.abspath(os.path.dirname(_SCREAMING_SNAKE_CASE		)		)
  lowercase__           =  os.path.join(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)
  lowercase__           =  {}
  lowercase__           =  42
  lowercase__           =  42
  lowercase__           =  42

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

  lowercase__           =  [line.split(','		) for line in data]

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

  lowercase__           =  Graph(set(range(len(_SCREAMING_SNAKE_CASE		)		)		)		,						_SCREAMING_SNAKE_CASE		)

  lowercase__           =  graph.prims_algorithm()

  lowercase__           =  sum(graph.edges.values()		)
  lowercase__           =  sum(subgraph.edges.values()		)

  return initial_total - optimal_total


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









from typing import TYPE_CHECKING

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


lowercase_          =		{
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_          =		["""EfficientFormerImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_          =		[
          """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """EfficientFormerForImageClassification""",
          """EfficientFormerForImageClassificationWithTeacher""",
          """EfficientFormerModel""",
          """EfficientFormerPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_          =		[
          """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFEfficientFormerForImageClassification""",
          """TFEfficientFormerForImageClassificationWithTeacher""",
          """TFEfficientFormerModel""",
          """TFEfficientFormerPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_efficientformer import EfficientFormerImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_efficientformer import (
                EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                EfficientFormerForImageClassification,
                EfficientFormerForImageClassificationWithTeacher,
                EfficientFormerModel,
                EfficientFormerPreTrainedModel,
            )
      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_efficientformer import (
                TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFEfficientFormerForImageClassification,
                TFEfficientFormerForImageClassificationWithTeacher,
                TFEfficientFormerModel,
                TFEfficientFormerPreTrainedModel,
            )

else:
      import sys

      lowercase_          =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


