"""simple docstring"""




from __future__ import annotations

import os
from collections.abc import Mapping

_a   :	Union[str, Any] 					=     tuple[int, int]
class 		__A							:






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






     def 					__A						(	self       ,  a__       ,  a__						):
       self.vertices.add(edge[0]						)
       self.vertices.add(edge[1]						)
       _lowerCAmelCase		: Optional[Any]						    =  weight






     def 					__A						(	self						):
       _lowerCAmelCase		: Graph						    =  Graph({min(self.vertices						)}       ,  {}						)
       _lowerCAmelCase		: EdgeT
       _lowerCAmelCase		: int
       _lowerCAmelCase		: EdgeT
       _lowerCAmelCase		: int

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

         subgraph.add_edge(a__       ,  a__						)

       return subgraph







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

  with open(_lowerCamelCase			) as f:
    _lowerCAmelCase		: Optional[Any]						    =  f.read().strip().split("""\n"""			)

  _lowerCAmelCase		: int						    =  [line.split(""","""			) for line in data]

  for edgea in range(1    ,len(_lowerCamelCase			)			):
    for edgea in range(_lowerCamelCase			):
      if adjaceny_matrix[edgea][edgea] != "-":
        _lowerCAmelCase		: Union[str, Any]						    =  int(adjaceny_matrix[edgea][edgea]			)

  _lowerCAmelCase		: Graph						    =  Graph(set(range(len(_lowerCamelCase			)			)			)    ,_lowerCamelCase			)

  _lowerCAmelCase		: Graph						    =  graph.prims_algorithm()

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

  return initial_total - optimal_total


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





"""simple docstring"""




import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY
@is_pipeline_test
class 		__A							(				unittest.TestCase	):
     _UpperCamelCase							:    str	      =	MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
     _UpperCamelCase							:    Any	      =	TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: List[Any]						    =  AudioClassificationPipeline(model=a__       ,  feature_extractor=a__						)

       # test with a raw waveform
       _lowerCAmelCase		: Optional[int]						    =  np.zeros((34000,)						)
       _lowerCAmelCase		: Optional[Any]						    =  np.zeros((14000,)						)
       return audio_classifier, [audioa, audio]






     def 					__A						(	self       ,  a__       ,  a__						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: List[Any]						    =  examples
       _lowerCAmelCase		: List[Any]						    =  audio_classifier(a__						)
       # by default a model is initialized with num_labels=2
       self.assertEqual(
           a__       ,  [
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
           ]       ,  )
       _lowerCAmelCase		: Tuple						    =  audio_classifier(a__       ,  top_k=1						)
       self.assertEqual(
           a__       ,  [
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
           ]       ,  )

       self.run_torchaudio(a__						)






     @require_torchaudio
     def 					__A						(	self       ,  a__						):
       import datasets

       # test with a local file
       _lowerCAmelCase		: int						    =  datasets.load_dataset("""hf-internal-testing/librispeech_asr_dummy"""       ,  """clean"""       ,  split="""validation"""						)
       _lowerCAmelCase		: List[Any]						    =  dataset[0]["""audio"""]["""array"""]
       _lowerCAmelCase		: str						    =  audio_classifier(a__						)
       self.assertEqual(
           a__       ,  [
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
           ]       ,  )






     @require_torch
     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  """anton-l/wav2vec2-random-tiny-classifier"""

       _lowerCAmelCase		: Optional[Any]						    =  pipeline("""audio-classification"""       ,  model=a__						)

       _lowerCAmelCase		: Any						    =  np.ones((8000,)						)
       _lowerCAmelCase		: List[str]						    =  audio_classifier(a__       ,  top_k=4						)

       _lowerCAmelCase		: List[str]						    =  [
           {"""score""": 0.0_8_4_2, """label""": """no"""},
           {"""score""": 0.0_8_3_8, """label""": """up"""},
           {"""score""": 0.0_8_3_7, """label""": """go"""},
           {"""score""": 0.0_8_3_4, """label""": """right"""},
       ]
       _lowerCAmelCase		: str						    =  [
           {"""score""": 0.0_8_4_5, """label""": """stop"""},
           {"""score""": 0.0_8_4_4, """label""": """on"""},
           {"""score""": 0.0_8_4_1, """label""": """right"""},
           {"""score""": 0.0_8_3_4, """label""": """left"""},
       ]
       self.assertIn(nested_simplify(a__       ,  decimals=4						)       ,  [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]						)

       _lowerCAmelCase		: int						    =  {"""array""": np.ones((8000,)						), """sampling_rate""": audio_classifier.feature_extractor.sampling_rate}
       _lowerCAmelCase		: int						    =  audio_classifier(a__       ,  top_k=4						)
       self.assertIn(nested_simplify(a__       ,  decimals=4						)       ,  [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]						)






     @require_torch
     @slow
     def 					__A						(	self						):
       import datasets

       _lowerCAmelCase		: Optional[Any]						    =  """superb/wav2vec2-base-superb-ks"""

       _lowerCAmelCase		: List[str]						    =  pipeline("""audio-classification"""       ,  model=a__						)
       _lowerCAmelCase		: str						    =  datasets.load_dataset("""anton-l/superb_dummy"""       ,  """ks"""       ,  split="""test"""						)

       _lowerCAmelCase		: Optional[Any]						    =  np.array(dataset[3]["""speech"""]       ,  dtype=np.floataa						)
       _lowerCAmelCase		: List[str]						    =  audio_classifier(a__       ,  top_k=4						)
       self.assertEqual(
           nested_simplify(a__       ,  decimals=3						)       ,  [
               {"""score""": 0.9_8_1, """label""": """go"""},
               {"""score""": 0.0_0_7, """label""": """up"""},
               {"""score""": 0.0_0_6, """label""": """_unknown_"""},
               {"""score""": 0.0_0_1, """label""": """down"""},
           ]       ,  )






     @require_tf
     @unittest.skip("""Audio classification is not implemented for TF"""						)
     def 					__A						(	self						):
       pass




