



'''simple docstring'''

from __future__ import annotations

import os
from collections.abc import Mapping

__lowerCAmelCase									=   tuple[int, int]

class 	__magic_name__						:







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







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       Union[str, Any]	,_UpperCAmelCase					:       Optional[int]		):
									self.vertices.add(edge[0]		)
									self.vertices.add(edge[1]		)
									_a     :      Optional[int]        =							weight







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

									while len(subgraph.vertices		) < len(self.vertices		):
											_a     :      Dict        =							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:
																	_a     :      Dict        =							edge
																	_a     :      List[str]        =							weight

											subgraph.add_edge(lowerCAmelCase__	,lowerCAmelCase__		)

									return subgraph



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

		with open(lowerCAmelCase_  ) as f:
				_a     :      List[Any]        =							f.read().strip().split('\n'  )

		_a     :      Any        =							[line.split(','  ) for line in data]

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

		_a     :      Graph        =							Graph(set(range(len(lowerCAmelCase_  )  )  )						,			lowerCAmelCase_  )

		_a     :      Graph        =							graph.prims_algorithm()

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

		return initial_total - optimal_total


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








from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

__lowerCAmelCase		       =							logging.get_logger(__name__)





@add_end_docstrings(__UpperCamelCase      )
class   __a			(    __UpperCamelCase      ):
 def __init__(   self				, **lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        super().__init__(**lowerCAmelCase__ )

        requires_backends(self				, 'vision' )
        self.check_model_type(
            TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
            if self.framework == 'tf'
            else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
 def __call__(   self				, lowerCAmelCase__				, **lowerCAmelCase__ )					->				Optional[Any]:




        '''simple docstring'''
        return super().__call__(lowerCAmelCase__				, **lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, **lowerCAmelCase__ )					->				Dict:




        '''simple docstring'''
        lowercase__:   int  			=   {}
        if "candidate_labels" in kwargs:
               lowercase__:   Dict  			=   kwargs['candidate_labels']
        if "hypothesis_template" in kwargs:
               lowercase__:   List[Any]  			=   kwargs['hypothesis_template']

        return preprocess_params, {}, {}
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__=None				, lowerCAmelCase__="This is a photo of {}." )					->				int:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   load_image(lowerCAmelCase__ )
        lowercase__:   Dict  			=   self.image_processor(images=[image]				, return_tensors=self.framework )
        lowercase__:   Tuple  			=   candidate_labels
        lowercase__:   List[str]  			=   [hypothesis_template.format(lowerCAmelCase__ ) for x in candidate_labels]
        lowercase__:   Optional[Any]  			=   self.tokenizer(lowerCAmelCase__				, return_tensors=self.framework				, padding=lowerCAmelCase__ )
        lowercase__:   str  			=   [text_inputs]
        return inputs
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        lowercase__:   str  			=   model_inputs.pop('candidate_labels' )
        lowercase__:   List[str]  			=   model_inputs.pop('text_inputs' )
        if isinstance(text_inputs[0]				, lowerCAmelCase__ ):
               lowercase__:   Any  			=   text_inputs[0]
        else:
               # Batching case.
               lowercase__:   Optional[int]  			=   text_inputs[0][0]

        lowercase__:   Tuple  			=   self.model(**lowerCAmelCase__				, **lowerCAmelCase__ )

        lowercase__:   Any  			=   {
            'candidate_labels': candidate_labels,
            'logits': outputs.logits_per_image,
        }
        return model_outputs
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Union[str, Any]:




        '''simple docstring'''
        lowercase__:   Tuple  			=   model_outputs.pop('candidate_labels' )
        lowercase__:   Dict  			=   model_outputs['logits'][0]
        if self.framework == "pt":
               lowercase__:   Optional[int]  			=   logits.softmax(dim=-1 ).squeeze(-1 )
               lowercase__:   Dict  			=   probs.tolist()
               if not isinstance(lowerCAmelCase__				, lowerCAmelCase__ ):
                      lowercase__:   Dict  			=   [scores]
        elif self.framework == "tf":
               lowercase__:   Optional[int]  			=   stable_softmax(lowerCAmelCase__				, axis=-1 )
               lowercase__:   Union[str, Any]  			=   probs.numpy().tolist()
        else:
               raise ValueError(F'Unsupported framework: {self.framework}' )

        lowercase__:   List[Any]  			=   [
            {'score': score, 'label': candidate_label}
            for score, candidate_label in sorted(zip(lowerCAmelCase__				, lowerCAmelCase__ )				, key=lambda lowerCAmelCase__ : -x[0] )
        ]
        return result


