



"""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    __lowercase     (					unittest.TestCase):







						"""simple docstring"""







						_A		:							List[Any]							       =		MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
						_A		:							List[str]							       =		TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		str 					=					AudioClassificationPipeline(model=lowercase__   ,		feature_extractor=lowercase__       )

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







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											snake_case_					,			snake_case_					:		int 					=					examples
											snake_case_					:		Dict 					=					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__       )},
											    ]   ,		)
											snake_case_					:		List[str] 					=					audio_classifier(lowercase__   ,		top_k=1       )
											self.assertEqual(
											    lowercase__   ,		[
											        {"""score""": ANY(lowercase__       ), """label""": ANY(lowercase__       )},
											    ]   ,		)

											self.run_torchaudio(lowercase__       )







						@require_torchaudio
						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											import datasets

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







						@require_torch
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[str] 					=					"""anton-l/wav2vec2-random-tiny-classifier"""

											snake_case_					:		List[str] 					=					pipeline("""audio-classification"""   ,		model=lowercase__       )

											snake_case_					:		Dict 					=					np.ones((80_00,)       )
											snake_case_					:		Tuple 					=					audio_classifier(lowercase__   ,		top_k=4       )

											snake_case_					:		Dict 					=					[
											    {"""score""": 0.0842, """label""": """no"""},
											    {"""score""": 0.0838, """label""": """up"""},
											    {"""score""": 0.0837, """label""": """go"""},
											    {"""score""": 0.0834, """label""": """right"""},
											]
											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]       )

											snake_case_					:		Union[str, Any] 					=					{"""array""": np.ones((80_00,)       ), """sampling_rate""": audio_classifier.feature_extractor.sampling_rate}
											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 						__UpperCamelCase							(self       ):
											import datasets

											snake_case_					:		Union[str, Any] 					=					"""superb/wav2vec2-base-superb-ks"""

											snake_case_					:		List[Any] 					=					pipeline("""audio-classification"""   ,		model=lowercase__       )
											snake_case_					:		Optional[int] 					=					datasets.load_dataset("""anton-l/superb_dummy"""   ,		"""ks"""   ,		split="""test"""       )

											snake_case_					:		str 					=					np.array(dataset[3]["""speech"""]   ,		dtype=np.floataa       )
											snake_case_					:		Dict 					=					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 						__UpperCamelCase							(self       ):
											pass









"""simple docstring"""

import random







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  float				,    SCREAMING_SNAKE_CASE__ :  bool = False			):



					"""simple docstring"""





					snake_case_					:		dict 					=					{i: [] for i in range(SCREAMING_SNAKE_CASE__			)}

					# if probability is greater or equal than 1, then generate a complete graph
					if probability >= 1:
										return complete_graph(SCREAMING_SNAKE_CASE__			)
					# if probability is lower or equal than 0, then return a graph without edges
					if probability <= 0:
										return graph

					# for each couple of nodes, add an edge from u to v
					# if the number randomly generated is greater than probability probability
					for i in range(SCREAMING_SNAKE_CASE__			):
										for j in range(i + 1				,    SCREAMING_SNAKE_CASE__			):
															if random.random() < probability:
																				graph[i].append(SCREAMING_SNAKE_CASE__			)
																				if not directed:
																									# if the graph is undirected, add an edge in from j to i, either
																									graph[j].append(SCREAMING_SNAKE_CASE__			)
					return graph







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					return {
					    i: [j for j in range(SCREAMING_SNAKE_CASE__			) if i != j] for i in range(SCREAMING_SNAKE_CASE__			)
					}


if __name__ == "__main__":
			import doctest

			doctest.testmod()




