



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     ):
	lowerCAmelCase	:   List[Any]				=	MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
	lowerCAmelCase	:   str				=	TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       AudioClassificationPipeline(model=UpperCAmelCase__			,	feature_extractor=UpperCAmelCase__		)

							# test with a raw waveform
							A__							=       np.zeros((34_000,)		)
							A__							=       np.zeros((14_000,)		)
							return audio_classifier, [audioa, audio]



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							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 			__A							(       self			,	UpperCAmelCase__		):
							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 			__A							(       self		):
							A__							=       "anton-l/wav2vec2-random-tiny-classifier"

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

							A__							=       np.ones((8_000,)		)
							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_000,)		), "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 			__A							(       self		):
							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 			__A							(       self		):
							pass










import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout
def  UpperCamelCase						(       _A			:				Optional[int]    )->       List[Any]:





						"""simple docstring"""





						A__							=       FileLock(str(tmpdir / "foo.lock"    )    )
						A__							=       FileLock(str(tmpdir / "foo.lock"    )    )
						A__							=       0.01
						with locka.acquire():
												with pytest.raises(_A    ):
																		A__							=       time.time()
																		locka.acquire(_A    )
																		assert time.time() - _start > timeout
def  UpperCamelCase						(       _A			:				str    )->       List[Any]:





						"""simple docstring"""





						A__							=       "a" * 1000 + ".lock"
						A__							=       FileLock(str(tmpdir / filename    )    )
						assert locka._lock_file.endswith(".lock"    )
						assert not locka._lock_file.endswith(_A    )
						assert len(os.path.basename(locka._lock_file    )    ) <= 255
						A__							=       FileLock(tmpdir / filename    )
						with locka.acquire():
												with pytest.raises(_A    ):
																		locka.acquire(0    )





