from __future__ import annotations

import os
from collections.abc import Mapping

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





class 					__magic_name__		:







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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						str       ,    lowercase_       :						str					):
										self.vertices.add(edge[0]					)
										self.vertices.add(edge[1]					)
										lowercase_      :    Union[str, Any]       		= weight







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										lowercase_      :    Graph       		= Graph({min(self.vertices					)}       ,    {}					)
										lowercase_      :    EdgeT
										lowercase_      :    int
										lowercase_      :    EdgeT
										lowercase_      :    int

										while len(subgraph.vertices					) < len(self.vertices					):
																	lowercase_      :    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:
																																						lowercase_      :    int       		= edge
																																						lowercase_      :    Optional[Any]       		= weight

																	subgraph.add_edge(lowercase_       ,    lowercase_					)

										return subgraph




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

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

							lowercase_      :    Dict       		= [line.split(""","""				) for line in data]

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

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

							lowercase_      :    Graph       		= graph.prims_algorithm()

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

							return initial_total - optimal_total


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






'''simple docstring'''




from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase		:				List[Any]	       =		{"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				int	       =		[
						    "FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "FocalNetForImageClassification",
						    "FocalNetForMaskedImageModeling",
						    "FocalNetBackbone",
						    "FocalNetModel",
						    "FocalNetPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_focalnet import (
												    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FocalNetBackbone,
												    FocalNetForImageClassification,
												    FocalNetForMaskedImageModeling,
												    FocalNetModel,
												    FocalNetPreTrainedModel,
												)

else:
						import sys

						_lowercase		:				Union[str, Any]	       =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





