





from __future__ import annotations

import os
from collections.abc import Mapping

UpperCAmelCase_      							=     tuple[int, int]
class 			UpperCamelCase_		:




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							None:
										_snake_case									=				vertices
										_snake_case									=				{
										    (min(lowerCAmelCase_  ), max(lowerCAmelCase_  )): weight for edge, weight in edges.items()
										}




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							None:
										self.vertices.add(edge[0]  )
										self.vertices.add(edge[1]  )
										_snake_case									=				weight






					def 	lowerCAmelCase			(		self  )		->							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(lowerCAmelCase_				,						lowerCAmelCase_  )

										return subgraph





def 						lowerCamelCase__     (     UpperCamelCase__			:       str = "p107_network.txt"						)			-> int:



					'''simple docstring'''




					_snake_case									=				os.path.abspath(os.path.dirname(UpperCamelCase__						)						)
					_snake_case									=				os.path.join(UpperCamelCase__       ,    UpperCamelCase__						)
					_snake_case									=				{}
					_snake_case									=				42
					_snake_case									=				42
					_snake_case									=				42

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

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

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

					_snake_case									=				Graph(set(range(len(UpperCamelCase__						)						)						)       ,    UpperCamelCase__						)

					_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() = }")










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase_      							=     {"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_      							=     [
					    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """SwinForImageClassification""",
					    """SwinForMaskedImageModeling""",
					    """SwinModel""",
					    """SwinPreTrainedModel""",
					    """SwinBackbone""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_      							=     [
					    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFSwinForImageClassification""",
					    """TFSwinForMaskedImageModeling""",
					    """TFSwinModel""",
					    """TFSwinPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swin import (
										    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwinBackbone,
										    SwinForImageClassification,
										    SwinForMaskedImageModeling,
										    SwinModel,
										    SwinPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_swin import (
										    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFSwinForImageClassification,
										    TFSwinForMaskedImageModeling,
										    TFSwinModel,
										    TFSwinPreTrainedModel,
										)

else:
					import sys

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



