


'''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 		_snake_case					(     unittest.TestCase	):
					lowerCAmelCase      :Tuple   =					MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
					lowerCAmelCase      :Optional[Any]   =					TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					Any        =     AudioClassificationPipeline(model=_lowerCamelCase							,							feature_extractor=_lowerCamelCase)

								# test with a raw waveform
								UpperCAmelCase__  :					Dict        =     np.zeros((3_4000,))
								UpperCAmelCase__  :					Any        =     np.zeros((1_4000,))
								return audio_classifier, [audioa, audio]




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__   ,       UpperCAmelCase__  :					List[str]        =     examples
								UpperCAmelCase__  :					Any        =     audio_classifier(_lowerCamelCase)
								# by default a model is initialized with num_labels=2
								self.assertEqual(
								    _lowerCamelCase							,							[
								        {"""score""": ANY(_lowerCamelCase), """label""": ANY(_lowerCamelCase)},
								        {"""score""": ANY(_lowerCamelCase), """label""": ANY(_lowerCamelCase)},
								    ]							,							)
								UpperCAmelCase__  :					Union[str, Any]        =     audio_classifier(_lowerCamelCase							,							top_k=1)
								self.assertEqual(
								    _lowerCamelCase							,							[
								        {"""score""": ANY(_lowerCamelCase), """label""": ANY(_lowerCamelCase)},
								    ]							,							)

								self.run_torchaudio(_lowerCamelCase)




					@require_torchaudio
					def 				snake_case__						(      self							,							_lowerCamelCase):
								import datasets

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




					@require_torch
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[Any]        =     """anton-l/wav2vec2-random-tiny-classifier"""

								UpperCAmelCase__  :					int        =     pipeline("""audio-classification"""							,							model=_lowerCamelCase)

								UpperCAmelCase__  :					Dict        =     np.ones((8000,))
								UpperCAmelCase__  :					Optional[Any]        =     audio_classifier(_lowerCamelCase							,							top_k=4)

								UpperCAmelCase__  :					Dict        =     [
								    {"""score""": 0.0842, """label""": """no"""},
								    {"""score""": 0.0838, """label""": """up"""},
								    {"""score""": 0.0837, """label""": """go"""},
								    {"""score""": 0.0834, """label""": """right"""},
								]
								UpperCAmelCase__  :					List[Any]        =     [
								    {"""score""": 0.0845, """label""": """stop"""},
								    {"""score""": 0.0844, """label""": """on"""},
								    {"""score""": 0.0841, """label""": """right"""},
								    {"""score""": 0.0834, """label""": """left"""},
								]
								self.assertIn(nested_simplify(_lowerCamelCase							,							decimals=4)							,							[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])

								UpperCAmelCase__  :					Tuple        =     {"""array""": np.ones((8000,)), """sampling_rate""": audio_classifier.feature_extractor.sampling_rate}
								UpperCAmelCase__  :					List[Any]        =     audio_classifier(_lowerCamelCase							,							top_k=4)
								self.assertIn(nested_simplify(_lowerCamelCase							,							decimals=4)							,							[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])




					@require_torch
					@slow
					def 				snake_case__						(      self):
								import datasets

								UpperCAmelCase__  :					Dict        =     """superb/wav2vec2-base-superb-ks"""

								UpperCAmelCase__  :					int        =     pipeline("""audio-classification"""							,							model=_lowerCamelCase)
								UpperCAmelCase__  :					List[str]        =     datasets.load_dataset("""anton-l/superb_dummy"""							,							"""ks"""							,							split="""test""")

								UpperCAmelCase__  :					Union[str, Any]        =     np.array(dataset[3]["""speech"""]							,							dtype=np.floataa)
								UpperCAmelCase__  :					Dict        =     audio_classifier(_lowerCamelCase							,							top_k=4)
								self.assertEqual(
								    nested_simplify(_lowerCamelCase							,							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 				snake_case__						(      self):
								pass



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A	={
    'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'NezhaForNextSentencePrediction',
		    'NezhaForMaskedLM',
		    'NezhaForPreTraining',
		    'NezhaForMultipleChoice',
		    'NezhaForQuestionAnswering',
		    'NezhaForSequenceClassification',
		    'NezhaForTokenClassification',
		    'NezhaModel',
		    'NezhaPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_nezha import (
				    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    NezhaForMaskedLM,
				    NezhaForMultipleChoice,
				    NezhaForNextSentencePrediction,
				    NezhaForPreTraining,
				    NezhaForQuestionAnswering,
				    NezhaForSequenceClassification,
				    NezhaForTokenClassification,
				    NezhaModel,
				    NezhaPreTrainedModel,
				)


else:
		import sys

		__A	=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)