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		):
			A__    :       Dict        =      MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
			A__    :       Optional[int]        =      TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   List[Any]:
								__a		 =       AudioClassificationPipeline(model=UpperCAmelCase				,						feature_extractor=UpperCAmelCase   )

								# test with a raw waveform
								__a		 =       np.zeros((3_4_0_0_0,)   )
								__a		 =       np.zeros((1_4_0_0_0,)   )
								return audio_classifier, [audioa, audio]



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Tuple:
								__a ,				__a		 =       examples
								__a		 =       audio_classifier(UpperCAmelCase   )
								# by default a model is initialized with num_labels=2
								self.assertEqual(
								    UpperCAmelCase				,						[
								        {'score': ANY(UpperCAmelCase   ), 'label': ANY(UpperCAmelCase   )},
								        {'score': ANY(UpperCAmelCase   ), 'label': ANY(UpperCAmelCase   )},
								    ]				,						)
								__a		 =       audio_classifier(UpperCAmelCase				,						top_k=1   )
								self.assertEqual(
								    UpperCAmelCase				,						[
								        {'score': ANY(UpperCAmelCase   ), 'label': ANY(UpperCAmelCase   )},
								    ]				,						)

								self.run_torchaudio(UpperCAmelCase   )



			@require_torchaudio
			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   List[Any]:
								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(UpperCAmelCase   )
								self.assertEqual(
								    UpperCAmelCase				,						[
								        {'score': ANY(UpperCAmelCase   ), 'label': ANY(UpperCAmelCase   )},
								        {'score': ANY(UpperCAmelCase   ), 'label': ANY(UpperCAmelCase   )},
								    ]				,						)



			@require_torch
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       'anton-l/wav2vec2-random-tiny-classifier'

								__a		 =       pipeline('audio-classification'				,						model=UpperCAmelCase   )

								__a		 =       np.ones((8_0_0_0,)   )
								__a		 =       audio_classifier(UpperCAmelCase				,						top_k=4   )

								__a		 =       [
								    {'score': 0.0_842, 'label': 'no'},
								    {'score': 0.0_838, 'label': 'up'},
								    {'score': 0.0_837, 'label': 'go'},
								    {'score': 0.0_834, 'label': 'right'},
								]
								__a		 =       [
								    {'score': 0.0_845, 'label': 'stop'},
								    {'score': 0.0_844, 'label': 'on'},
								    {'score': 0.0_841, 'label': 'right'},
								    {'score': 0.0_834, 'label': 'left'},
								]
								self.assertIn(nested_simplify(UpperCAmelCase				,						decimals=4   )				,						[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]   )

								__a		 =       {'array': np.ones((8_0_0_0,)   ), 'sampling_rate': audio_classifier.feature_extractor.sampling_rate}
								__a		 =       audio_classifier(UpperCAmelCase				,						top_k=4   )
								self.assertIn(nested_simplify(UpperCAmelCase				,						decimals=4   )				,						[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]   )



			@require_torch
			@slow
			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								import datasets

								__a		 =       'superb/wav2vec2-base-superb-ks'

								__a		 =       pipeline('audio-classification'				,						model=UpperCAmelCase   )
								__a		 =       datasets.load_dataset('anton-l/superb_dummy'				,						'ks'				,						split='test'   )

								__a		 =       np.array(dataset[3]['speech']				,						dtype=np.floataa   )
								__a		 =       audio_classifier(UpperCAmelCase				,						top_k=4   )
								self.assertEqual(
								    nested_simplify(UpperCAmelCase				,						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 __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								pass





def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					# 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 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					# 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_    :		str							=	[
    [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_    :		Union[str, Any]							=	0, 5



print(ford_fulkerson(graph, source, sink))




