



from __future__ import annotations

import os
from collections.abc import Mapping

UpperCAmelCase__       =	tuple[int, int]







class       __lowerCAmelCase   :



			def __init__(						self       :  List[Any]			,		A       :  set[int]			,		A       :  Mapping[EdgeT, int])       ->   None:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Tuple			,		A       :  EdgeT			,		A       :  int)       ->   None:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Graph:



						"""simple docstring"""






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

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

									subgraph.add_edge(A			,		A)

						return subgraph





def 							A     (					_UpperCAmelCase    :       str = "p107_network.txt"   )    ->			int:







			'''simple docstring'''
			_UpperCAmelCase			=       os.path.abspath(os.path.dirname(_UpperCAmelCase   )   )
			_UpperCAmelCase			=       os.path.join(_UpperCAmelCase							,       _UpperCAmelCase   )
			_UpperCAmelCase			=       {}
			_UpperCAmelCase			=       42
			_UpperCAmelCase			=       42
			_UpperCAmelCase			=       42

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

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

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

			_UpperCAmelCase			=       Graph(set(range(len(_UpperCAmelCase   )   )   )							,       _UpperCAmelCase   )

			_UpperCAmelCase			=       graph.prims_algorithm()

			_UpperCAmelCase			=       sum(graph.edges.values()   )
			_UpperCAmelCase			=       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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__       =	{
    "configuration_clip": [
        "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPConfig",
        "CLIPOnnxConfig",
        "CLIPTextConfig",
        "CLIPVisionConfig",
    ],
    "processing_clip": ["CLIPProcessor"],
    "tokenization_clip": ["CLIPTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	["CLIPTokenizerFast"]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	["CLIPFeatureExtractor"]
							UpperCAmelCase__       =	["CLIPImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "CLIPModel",
							    "CLIPPreTrainedModel",
							    "CLIPTextModel",
							    "CLIPTextModelWithProjection",
							    "CLIPVisionModel",
							    "CLIPVisionModelWithProjection",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFCLIPModel",
							    "TFCLIPPreTrainedModel",
							    "TFCLIPTextModel",
							    "TFCLIPVisionModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "FlaxCLIPModel",
							    "FlaxCLIPPreTrainedModel",
							    "FlaxCLIPTextModel",
							    "FlaxCLIPTextPreTrainedModel",
							    "FlaxCLIPVisionModel",
							    "FlaxCLIPVisionPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_clip import (
							    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    CLIPConfig,
							    CLIPOnnxConfig,
							    CLIPTextConfig,
							    CLIPVisionConfig,
							)
							from .processing_clip import CLIPProcessor
							from .tokenization_clip import CLIPTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_clip_fast import CLIPTokenizerFast

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_clip import CLIPFeatureExtractor
														from .image_processing_clip import CLIPImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_clip import (
														    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CLIPModel,
														    CLIPPreTrainedModel,
														    CLIPTextModel,
														    CLIPTextModelWithProjection,
														    CLIPVisionModel,
														    CLIPVisionModelWithProjection,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_clip import (
														    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCLIPModel,
														    TFCLIPPreTrainedModel,
														    TFCLIPTextModel,
														    TFCLIPVisionModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_clip import (
														    FlaxCLIPModel,
														    FlaxCLIPPreTrainedModel,
														    FlaxCLIPTextModel,
														    FlaxCLIPTextPreTrainedModel,
														    FlaxCLIPVisionModel,
														    FlaxCLIPVisionPreTrainedModel,
														)


else:
							import sys

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