




"""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 						_A (  unittest.TestCase				):
  _UpperCamelCase       :      Optional[int]			=      MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
  _UpperCamelCase       :      Union[str, Any]			=      TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING






  def  __a      (  self		:     List[Any]				,						_A		:     str				,						_A		:     Optional[Any]				,						_A		:     Optional[int]				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       Optional[int]       =			AudioClassificationPipeline(model=_A				,						feature_extractor=_A				)

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






  def  __a      (  self		:     List[Any]				,						_A		:     List[Any]				,						_A		:     List[str]				)		->   List[str]:






       """simple docstring"""







       lowercase :       List[str]       =			examples
       lowercase :       int       =			audio_classifier(_A				)
       # by default a model is initialized with num_labels=2
       self.assertEqual(
           _A				,						[
               {'''score''': ANY(_A				), '''label''': ANY(_A				)},
               {'''score''': ANY(_A				), '''label''': ANY(_A				)},
           ]				,						)
       lowercase :       Dict       =			audio_classifier(_A				,						top_k=1				)
       self.assertEqual(
           _A				,						[
               {'''score''': ANY(_A				), '''label''': ANY(_A				)},
           ]				,						)

       self.run_torchaudio(_A				)






  @require_torchaudio
  def  __a      (  self		:     List[str]				,						_A		:     Optional[Any]				)		->   int:






       """simple docstring"""







       import datasets

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






  @require_torch
  def  __a      (  self		:     str				)		->   Union[str, Any]:






       """simple docstring"""







       lowercase :       Any       =			'''anton-l/wav2vec2-random-tiny-classifier'''

       lowercase :       str       =			pipeline('''audio-classification'''				,						model=_A				)

       lowercase :       Optional[Any]       =			np.ones((8_000,)				)
       lowercase :       Tuple       =			audio_classifier(_A				,						top_k=4				)

       lowercase :       Dict       =			[
           {'''score''': 0.0_842, '''label''': '''no'''},
           {'''score''': 0.0_838, '''label''': '''up'''},
           {'''score''': 0.0_837, '''label''': '''go'''},
           {'''score''': 0.0_834, '''label''': '''right'''},
       ]
       lowercase :       int       =			[
           {'''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(_A				,						decimals=4				)				,						[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]				)

       lowercase :       Optional[int]       =			{'''array''': np.ones((8_000,)				), '''sampling_rate''': audio_classifier.feature_extractor.sampling_rate}
       lowercase :       Any       =			audio_classifier(_A				,						top_k=4				)
       self.assertIn(nested_simplify(_A				,						decimals=4				)				,						[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]				)






  @require_torch
  @slow
  def  __a      (  self		:     str				)		->   str:






       """simple docstring"""







       import datasets

       lowercase :       Tuple       =			'''superb/wav2vec2-base-superb-ks'''

       lowercase :       List[str]       =			pipeline('''audio-classification'''				,						model=_A				)
       lowercase :       Optional[int]       =			datasets.load_dataset('''anton-l/superb_dummy'''				,						'''ks'''				,						split='''test'''				)

       lowercase :       int       =			np.array(dataset[3]['''speech''']				,						dtype=np.floataa				)
       lowercase :       Any       =			audio_classifier(_A				,						top_k=4				)
       self.assertEqual(
           nested_simplify(_A				,						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		:     int				)		->   Any:






       """simple docstring"""







       pass





from sklearn.metrics import mean_squared_error

import datasets


lowerCAmelCase_						=			'\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

lowerCAmelCase_						=			'\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


lowerCAmelCase_						=			'\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,       _KWARGS_DESCRIPTION				)
class 						_A (  datasets.Metric				):






  def  __a      (  self		:     List[Any]				)		->   Optional[Any]:






       """simple docstring"""







       return datasets.MetricInfo(
           description=_DESCRIPTION				,						citation=_CITATION				,						inputs_description=_KWARGS_DESCRIPTION				,						features=datasets.Features(self._get_feature_types()				)				,						reference_urls=[
               '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
           ]				,						)






  def  __a      (  self		:     List[Any]				)		->   int:






       """simple docstring"""







       if self.config_name == "multilist":
            return {
                "predictions": datasets.Sequence(datasets.Value('''float'''				)				),
                "references": datasets.Sequence(datasets.Value('''float'''				)				),
            }
       else:
            return {
                "predictions": datasets.Value('''float'''				),
                "references": datasets.Value('''float'''				),
            }






  def  __a      (  self		:     Any				,						_A		:     Dict				,						_A		:     Any				,						_A		:     Any=None				,						_A		:     Any="uniform_average"				,						_A		:     Optional[Any]=True				)		->   Dict:






       """simple docstring"""







       lowercase :       Any       =			mean_squared_error(
           _A				,						_A				,						sample_weight=_A				,						multioutput=_A				,						squared=_A				)

       return {"mse": mse}