



'''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        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):
 snake_case_												=							MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
 snake_case_												=							TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING




 def        __magic_name__		( self		:							str   , __lowercase		:							List[Any]   , __lowercase		:							Optional[Any]   , __lowercase		:							Union[str, Any] )		->  int:
       SCREAMING_SNAKE_CASE__       :			int													=AudioClassificationPipeline(model=__lowercase   , feature_extractor=__lowercase )

       # test with a raw waveform
       SCREAMING_SNAKE_CASE__       :			Tuple													=np.zeros((3_40_00,) )
       SCREAMING_SNAKE_CASE__       :			List[str]													=np.zeros((1_40_00,) )
       return audio_classifier, [audioa, audio]




 def        __magic_name__		( self		:							Union[str, Any]   , __lowercase		:							Tuple   , __lowercase		:							Dict )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=examples
       SCREAMING_SNAKE_CASE__       :			str													=audio_classifier(__lowercase )
       # by default a model is initialized with num_labels=2
       self.assertEqual(
           __lowercase   , [
               {'''score''': ANY(__lowercase ), '''label''': ANY(__lowercase )},
               {'''score''': ANY(__lowercase ), '''label''': ANY(__lowercase )},
           ]   , )
       SCREAMING_SNAKE_CASE__       :			Tuple													=audio_classifier(__lowercase   , top_k=1 )
       self.assertEqual(
           __lowercase   , [
               {'''score''': ANY(__lowercase ), '''label''': ANY(__lowercase )},
           ]   , )

       self.run_torchaudio(__lowercase )




 @require_torchaudio
 def        __magic_name__		( self		:							Union[str, Any]   , __lowercase		:							List[str] )		->  Any:
       import datasets

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




 @require_torch
 def        __magic_name__		( self		:							str )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			int													='''anton-l/wav2vec2-random-tiny-classifier'''

       SCREAMING_SNAKE_CASE__       :			List[str]													=pipeline('''audio-classification'''   , model=__lowercase )

       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=np.ones((80_00,) )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=audio_classifier(__lowercase   , top_k=4 )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=[
           {'''score''': 0.0842, '''label''': '''no'''},
           {'''score''': 0.0838, '''label''': '''up'''},
           {'''score''': 0.0837, '''label''': '''go'''},
           {'''score''': 0.0834, '''label''': '''right'''},
       ]
       SCREAMING_SNAKE_CASE__       :			str													=[
           {'''score''': 0.0845, '''label''': '''stop'''},
           {'''score''': 0.0844, '''label''': '''on'''},
           {'''score''': 0.0841, '''label''': '''right'''},
           {'''score''': 0.0834, '''label''': '''left'''},
       ]
       self.assertIn(nested_simplify(__lowercase   , decimals=4 )   , [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													={'''array''': np.ones((80_00,) ), '''sampling_rate''': audio_classifier.feature_extractor.sampling_rate}
       SCREAMING_SNAKE_CASE__       :			Tuple													=audio_classifier(__lowercase   , top_k=4 )
       self.assertIn(nested_simplify(__lowercase   , decimals=4 )   , [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )




 @require_torch
 @slow
 def        __magic_name__		( self		:							str )		->  Dict:
       import datasets

       SCREAMING_SNAKE_CASE__       :			int													='''superb/wav2vec2-base-superb-ks'''

       SCREAMING_SNAKE_CASE__       :			Dict													=pipeline('''audio-classification'''   , model=__lowercase )
       SCREAMING_SNAKE_CASE__       :			int													=datasets.load_dataset('''anton-l/superb_dummy'''   , '''ks'''   , split='''test''' )

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=np.array(dataset[3]['''speech''']   , dtype=np.floataa )
       SCREAMING_SNAKE_CASE__       :			Tuple													=audio_classifier(__lowercase   , top_k=4 )
       self.assertEqual(
           nested_simplify(__lowercase   , decimals=3 )   , [
               {'''score''': 0.981, '''label''': '''go'''},
               {'''score''': 0.007, '''label''': '''up'''},
               {'''score''': 0.006, '''label''': '''_unknown_'''},
               {'''score''': 0.001, '''label''': '''down'''},
           ]   , )




 @require_tf
 @unittest.skip('''Audio classification is not implemented for TF''' )
 def        __magic_name__		( self		:							str )		->  int:
       pass







'''simple docstring'''
from __future__ import annotations

import os
from collections.abc import Mapping

a_					=	tuple[int, int]






class        __SCREAMING_SNAKE_CASE :




 def __init__( self		:							Any   , __lowercase		:							set[int]   , __lowercase		:							Mapping[EdgeT, int] )		->  None:
       SCREAMING_SNAKE_CASE__       :			set[int]													=vertices
       SCREAMING_SNAKE_CASE__       :			dict[EdgeT, int]													={
           (min(__lowercase ), max(__lowercase )): weight for edge, weight in edges.items()
       }




 def        __magic_name__		( self		:							Union[str, Any]   , __lowercase		:							EdgeT   , __lowercase		:							int )		->  None:
       self.vertices.add(edge[0] )
       self.vertices.add(edge[1] )
       SCREAMING_SNAKE_CASE__       :			List[str]													=weight




 def        __magic_name__		( self		:							Optional[Any] )		->  Graph:
       SCREAMING_SNAKE_CASE__       :			Graph													=Graph({min(self.vertices )}   , {} )
       SCREAMING_SNAKE_CASE__       :			EdgeT
       SCREAMING_SNAKE_CASE__       :			int
       SCREAMING_SNAKE_CASE__       :			EdgeT
       SCREAMING_SNAKE_CASE__       :			int

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

             subgraph.add_edge(__lowercase   , __lowercase )

       return subgraph





def 	_a(					UpperCamelCase__			:  str = "p107_network.txt"							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			str													=os.path.abspath(os.path.dirname(UpperCamelCase__							)							)
      SCREAMING_SNAKE_CASE__       :			str													=os.path.join(UpperCamelCase__,		UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			dict[EdgeT, int]													={}
      SCREAMING_SNAKE_CASE__       :			list[str]
      SCREAMING_SNAKE_CASE__       :			int
      SCREAMING_SNAKE_CASE__       :			int

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

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

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

      SCREAMING_SNAKE_CASE__       :			Graph													=Graph(set(range(len(UpperCamelCase__							)							)							),		UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__       :			Graph													=graph.prims_algorithm()

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

      return initial_total - optimal_total


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