




'''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        UpperCAmelCase     (	unittest.TestCase    ):
 __lowercase       		=    MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
 __lowercase       		=    TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	lowercase_				:Optional[int]						,	lowercase_				:List[str]						,	lowercase_				:Union[str, Any]							)->							str:
     A__    =      AudioClassificationPipeline(model=lowercase_						,	feature_extractor=lowercase_							)

     # test with a raw waveform
     A__    =      np.zeros((3_40_00,)							)
     A__    =      np.zeros((1_40_00,)							)
     return audio_classifier, [audioa, audio]
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:int						,	lowercase_				:int							)->							int:
     A__,     A__    =      examples
     A__    =      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_							)},
         ]						,	)
     A__    =      audio_classifier(lowercase_						,	top_k=1							)
     self.assertEqual(
         lowercase_						,	[
             {"score": ANY(lowercase_							), "label": ANY(lowercase_							)},
         ]						,	)

     self.run_torchaudio(lowercase_							)
 @require_torchaudio
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:Tuple							)->							Dict:
     import datasets

     # test with a local file
     A__    =      datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy"						,	"clean"						,	split="validation"							)
     A__    =      dataset[0]["audio"]["array"]
     A__    =      audio_classifier(lowercase_							)
     self.assertEqual(
         lowercase_						,	[
             {"score": ANY(lowercase_							), "label": ANY(lowercase_							)},
             {"score": ANY(lowercase_							), "label": ANY(lowercase_							)},
         ]						,	)
 @require_torch
 def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[int]:
     A__    =      "anton-l/wav2vec2-random-tiny-classifier"

     A__    =      pipeline("audio-classification"						,	model=lowercase_							)

     A__    =      np.ones((80_00,)							)
     A__    =      audio_classifier(lowercase_						,	top_k=4							)

     A__    =      [
         {"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"},
     ]
     A__    =      [
         {"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(lowercase_						,	decimals=4							)						,	[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]							)

     A__    =      {"array": np.ones((80_00,)							), "sampling_rate": audio_classifier.feature_extractor.sampling_rate}
     A__    =      audio_classifier(lowercase_						,	top_k=4							)
     self.assertIn(nested_simplify(lowercase_						,	decimals=4							)						,	[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]							)
 @require_torch
 @slow
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Dict:
     import datasets

     A__    =      "superb/wav2vec2-base-superb-ks"

     A__    =      pipeline("audio-classification"						,	model=lowercase_							)
     A__    =      datasets.load_dataset("anton-l/superb_dummy"						,	"ks"						,	split="test"							)

     A__    =      np.array(dataset[3]["speech"]						,	dtype=np.floataa							)
     A__    =      audio_classifier(lowercase_						,	top_k=4							)
     self.assertEqual(
         nested_simplify(lowercase_						,	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 					UpperCAmelCase_							(    self				:Any							)->							str:
     pass










'''simple docstring'''

def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : List[str]						,	_lowerCamelCase : List[str]						,	_lowerCamelCase : Tuple	):
    # Return True if there is node that has not iterated.
    A__    =      [False] * len(_lowerCamelCase	)
    A__    =      []
    queue.append(_lowerCamelCase	)
    A__    =      True

    while queue:
        A__    =      queue.pop(0	)
        for ind in range(len(graph[u]	)	):
            if visited[ind] is False and graph[u][ind] > 0:
                queue.append(_lowerCamelCase	)
                A__    =      True
                A__    =      u

    return visited[t]





def 						UpperCamelCase    (							_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[int]	):
    # This array is filled by BFS and to store path
    A__    =      [-1] * (len(_lowerCamelCase	))
    A__    =      0
    while bfs(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	):
        A__    =      float("Inf"	)
        A__    =      sink

        while s != source:
            # Find the minimum value in select path
            A__    =      min(_lowerCamelCase						,	graph[parent[s]][s]	)
            A__    =      parent[s]

        max_flow += path_flow
        A__    =      sink

        while v != source:
            A__    =      parent[v]
            graph[u][v] -= path_flow
            graph[v][u] += path_flow
            A__    =      parent[v]
    return max_flow


__lowerCAmelCase							:     Any		     =[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

__lowerCAmelCase     ,					__lowerCAmelCase							:     int		     =0, 5



print(ford_fulkerson(graph, source, sink))



