


"""simple docstring"""
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch







def   _a (					_SCREAMING_SNAKE_CASE )			-> str:  # picklable for multiprocessing
							return x.sum()







def   _a (					_SCREAMING_SNAKE_CASE )			-> str:  # picklable for multiprocessing
							return i + 1





@dataclass
class    __A   :





							'''simple docstring'''

							__lowercase:						int
							__lowercase:						str





class    __A   (lowerCamelCase__):





							'''simple docstring'''

							def 						lowerCAmelCase				( self							:					Dict				)    ->Dict:



														"""simple docstring"""



														snake_case_							=						{}
														snake_case_							=						[]
														snake_case_							=						1
														snake_case_							=						[1, 2]
														snake_case_							=						{"""a""": 1, """b""": 2}
														snake_case_							=						{"""a""": [1, 2], """b""": [3, 4]}
														snake_case_							=						{"""a""": {"""1""": 1}, """b""": 2}
														snake_case_							=						{"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
														snake_case_							=						{}
														snake_case_							=						[]
														snake_case_							=						2
														snake_case_							=						[2, 3]
														snake_case_							=						{"""a""": 2, """b""": 3}
														snake_case_							=						{"""a""": [2, 3], """b""": [4, 5]}
														snake_case_							=						{"""a""": {"""1""": 2}, """b""": 3}
														snake_case_							=						{"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
														self.assertEqual(map_nested(lowercase__						,				lowercase__				)						,				lowercase__				)
														self.assertEqual(map_nested(lowercase__						,				lowercase__				)						,				lowercase__				)
														self.assertEqual(map_nested(lowercase__						,				lowercase__				)						,				lowercase__				)
														self.assertEqual(map_nested(lowercase__						,				lowercase__				)						,				lowercase__				)
														self.assertEqual(map_nested(lowercase__						,				lowercase__				)						,				lowercase__				)
														self.assertEqual(map_nested(lowercase__						,				lowercase__				)						,				lowercase__				)
														self.assertEqual(map_nested(lowercase__						,				lowercase__				)						,				lowercase__				)
														self.assertEqual(map_nested(lowercase__						,				lowercase__				)						,				lowercase__				)

														snake_case_							=						2
														self.assertEqual(map_nested(lowercase__						,				lowercase__						,				num_proc=lowercase__				)						,				lowercase__				)
														self.assertEqual(map_nested(lowercase__						,				lowercase__						,				num_proc=lowercase__				)						,				lowercase__				)
														self.assertEqual(map_nested(lowercase__						,				lowercase__						,				num_proc=lowercase__				)						,				lowercase__				)
														self.assertEqual(map_nested(lowercase__						,				lowercase__						,				num_proc=lowercase__				)						,				lowercase__				)
														self.assertEqual(map_nested(lowercase__						,				lowercase__						,				num_proc=lowercase__				)						,				lowercase__				)
														self.assertEqual(map_nested(lowercase__						,				lowercase__						,				num_proc=lowercase__				)						,				lowercase__				)
														self.assertEqual(map_nested(lowercase__						,				lowercase__						,				num_proc=lowercase__				)						,				lowercase__				)
														self.assertEqual(map_nested(lowercase__						,				lowercase__						,				num_proc=lowercase__				)						,				lowercase__				)

														snake_case_							=						{"""a""": np.eye(2				), """b""": np.zeros(3				), """c""": np.ones(2				)}
														snake_case_							=						{"""a""": 2, """b""": 0, """c""": 2}
														snake_case_							=						{
														    """a""": np.eye(2				).astype(lowercase__				),
														    """b""": np.zeros(3				).astype(lowercase__				),
														    """c""": np.ones(2				).astype(lowercase__				),
														}
														self.assertEqual(map_nested(lowercase__						,				lowercase__						,				map_numpy=lowercase__				)						,				lowercase__				)
														self.assertEqual(
														    {k: v.tolist() for k, v in map_nested(lowercase__						,				lowercase__						,				map_numpy=lowercase__				).items()}						,				{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}						,				)
														self.assertEqual(map_nested(lowercase__						,				lowercase__						,				map_numpy=lowercase__						,				num_proc=lowercase__				)						,				lowercase__				)
														self.assertEqual(
														    {k: v.tolist() for k, v in map_nested(lowercase__						,				lowercase__						,				map_numpy=lowercase__						,				num_proc=lowercase__				).items()}						,				{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}						,				)
														with self.assertRaises(lowercase__				):  # can't pickle a local lambda
																					map_nested(lambda UpperCAmelCase_				: x + 1						,				lowercase__						,				num_proc=lowercase__				)



							def 						lowerCAmelCase				( self							:					int				)    ->Any:



														"""simple docstring"""



														snake_case_							=						{"""a""": 1, """b""": 2}
														snake_case_							=						{"""a""": 3, """b""": 4}
														snake_case_							=						{"""a""": 5, """b""": 6}
														snake_case_							=						sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))]				)
														self.assertEqual(sorted(zip_dict(lowercase__						,				lowercase__						,				lowercase__				)				)						,				lowercase__				)



							def 						lowerCAmelCase				( self							:					Any				)    ->int:



														"""simple docstring"""



														class    __A   :





																					'''simple docstring'''

																					__lowercase:						Optional[int]												=			'bar'


														snake_case_							=						Foo()
														self.assertEqual(foo.my_attr						,				"""bar"""				)
														with temporary_assignment(lowercase__						,				"""my_attr"""						,				"""BAR"""				):
																					self.assertEqual(foo.my_attr						,				"""BAR"""				)
														self.assertEqual(foo.my_attr						,				"""bar"""				)







@pytest.mark.parametrize(
    """iterable_length, num_proc, expected_num_proc""" ,       [
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ] ,       )
def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE )			-> str:



							with patch("""datasets.utils.py_utils._single_map_nested""" ) as mock_single_map_nested, patch(
							    """datasets.parallel.parallel.Pool""" ) as mock_multiprocessing_pool:
														snake_case_							=						{f"""{i}""": i for i in range(A__ )}
														snake_case_							=						map_nested(lambda _SCREAMING_SNAKE_CASE : x + 10 ,       A__ ,       num_proc=A__ ,       parallel_min_length=16 )
														if expected_num_proc == 1:
																					assert mock_single_map_nested.called
																					assert not mock_multiprocessing_pool.called
														else:
																					assert not mock_single_map_nested.called
																					assert mock_multiprocessing_pool.called
																					assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc





class    __A   (lowerCamelCase__):





							'''simple docstring'''

							@require_tf
							def 						lowerCAmelCase				( self							:					Any				)    ->Union[str, Any]:



														"""simple docstring"""



														import tensorflow as tf
														from tensorflow.keras import layers

														snake_case_							=						layers.Dense(2				)

														def gen_random_output():
																					snake_case_							=						tf.random.uniform((1, 3)				)
																					return model(lowercase__				).numpy()

														with temp_seed(42						,				set_tensorflow=lowercase__				):
																					snake_case_							=						gen_random_output()
														with temp_seed(42						,				set_tensorflow=lowercase__				):
																					snake_case_							=						gen_random_output()
														snake_case_							=						gen_random_output()

														np.testing.assert_equal(lowercase__						,				lowercase__				)
														self.assertGreater(np.abs(outa - outa				).sum()						,				0				)



							@require_torch
							def 						lowerCAmelCase				( self							:					Optional[Any]				)    ->List[str]:



														"""simple docstring"""



														import torch

														def gen_random_output():
																					snake_case_							=						torch.nn.Linear(3						,				2				)
																					snake_case_							=						torch.rand(1						,				3				)
																					return model(lowercase__				).detach().numpy()

														with temp_seed(42						,				set_pytorch=lowercase__				):
																					snake_case_							=						gen_random_output()
														with temp_seed(42						,				set_pytorch=lowercase__				):
																					snake_case_							=						gen_random_output()
														snake_case_							=						gen_random_output()

														np.testing.assert_equal(lowercase__						,				lowercase__				)
														self.assertGreater(np.abs(outa - outa				).sum()						,				0				)



							def 						lowerCAmelCase				( self							:					Tuple				)    ->List[str]:



														"""simple docstring"""



														def gen_random_output():
																					return np.random.rand(1						,				3				)

														with temp_seed(42				):
																					snake_case_							=						gen_random_output()
														with temp_seed(42				):
																					snake_case_							=						gen_random_output()
														snake_case_							=						gen_random_output()

														np.testing.assert_equal(lowercase__						,				lowercase__				)
														self.assertGreater(np.abs(outa - outa				).sum()						,				0				)







@pytest.mark.parametrize("""input_data""" ,       [{}] )
def   _a (					_SCREAMING_SNAKE_CASE )			-> Dict:
							snake_case_							=						NestedDataStructure(A__ ).data
							assert output_data == input_data







@pytest.mark.parametrize(
    """data, expected_output""" ,       [
        ({}, []),
        ([], []),
        ("""foo""", ["""foo"""]),
        (["""foo""", """bar"""], ["""foo""", """bar"""]),
        ([["""foo""", """bar"""]], ["""foo""", """bar"""]),
        ([[["""foo"""], ["""bar"""]]], ["""foo""", """bar"""]),
        ([[["""foo"""], """bar"""]], ["""foo""", """bar"""]),
        ({"""a""": 1, """b""": 2}, [1, 2]),
        ({"""a""": [1, 2], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[[3], [4]]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, [4]]}, [1, 2, 3, 4]),
        ({"""a""": {"""1""": 1}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": [2]}, [1, 2]),
    ] ,       )
def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE )			-> Optional[Any]:
							snake_case_							=						NestedDataStructure(A__ ).flatten()
							assert output == expected_output







def   _a (					)			-> List[str]:
							snake_case_							=						A(x=1 ,       y="""foobar""" )
							snake_case_							=						{"""x""": 1, """y""": """foobar"""}
							assert asdict(A__ ) == expected_output

							snake_case_							=						{"""a""": {"""b""": A(x=10 ,       y="""foo""" )}, """c""": [A(x=20 ,       y="""bar""" )]}
							snake_case_							=						{"""a""": {"""b""": {"""x""": 10, """y""": """foo"""}}, """c""": [{"""x""": 20, """y""": """bar"""}]}
							assert asdict(A__ ) == expected_output

							with pytest.raises(A__ ):
														asdict([1, A(x=10 ,       y="""foo""" )] )







def   _a (					_SCREAMING_SNAKE_CASE )			-> int:
							return text.split()







def   _a (					_SCREAMING_SNAKE_CASE )			-> Union[str, Any]:
							yield (time.time(), content)
							time.sleep(2 )
							yield (time.time(), content)







def   _a (					)			-> List[Any]:



							with Pool(2 ) as pool:
														snake_case_							=						list(iflatmap_unordered(A__ ,       _split_text ,       kwargs_iterable=[{"""text""": """hello there"""}] * 10 ) )
														assert out.count("""hello""" ) == 10
														assert out.count("""there""" ) == 10
														assert len(A__ ) == 20

							# check multiprocess from pathos (uses dill for pickling)
							with multiprocess.Pool(2 ) as pool:
														snake_case_							=						list(iflatmap_unordered(A__ ,       _split_text ,       kwargs_iterable=[{"""text""": """hello there"""}] * 10 ) )
														assert out.count("""hello""" ) == 10
														assert out.count("""there""" ) == 10
														assert len(A__ ) == 20

							# check that we get items as fast as possible
							with Pool(2 ) as pool:
														snake_case_							=						[]
														for yield_time, content in iflatmap_unordered(
														    A__ ,       _aseconds_generator_of_aitems_with_timing ,       kwargs_iterable=[{"""content""": """a"""}, {"""content""": """b"""}] ):
																					assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
																					out.append(A__ )
														assert out.count("""a""" ) == 2
														assert out.count("""b""" ) == 2
														assert len(A__ ) == 4






'''simple docstring'''






import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
 from transformers import WhisperFeatureExtractor

if is_torch_available():
 import torch

lowerCAmelCase__											=	random.Random()


def 		_A     (    A__     ,				A__=1.0     ,				A__=None     ,				A__=None      ):






 """simple docstring"""



 if rng is None:
  __lowercase							  = global_rng

 __lowercase							  = []
 for batch_idx in range(shape[0]      ):
  values.append([]      )
  for _ in range(shape[1]      ):
   values[-1].append(rng.random() * scale      )

 return values






@require_torch
@require_torchaudio
class 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  def __init__(      self   :     List[str]					,lowercase__   :     List[str]					,lowercase__   :     List[Any]=7					,lowercase__   :     Dict=4_0_0					,lowercase__   :     Tuple=2_0_0_0					,lowercase__   :     Optional[int]=1_0					,lowercase__   :     Optional[int]=1_6_0					,lowercase__   :     Dict=8					,lowercase__   :     str=0.0					,lowercase__   :     Union[str, Any]=4_0_0_0					,lowercase__   :     Optional[int]=False					,lowercase__   :     List[str]=True					,):
   __lowercase							  = parent
   __lowercase							  = batch_size
   __lowercase							  = min_seq_length
   __lowercase							  = max_seq_length
   __lowercase							  = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
   __lowercase							  = padding_value
   __lowercase							  = sampling_rate
   __lowercase							  = return_attention_mask
   __lowercase							  = do_normalize
   __lowercase							  = feature_size
   __lowercase							  = chunk_length
   __lowercase							  = hop_length






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   return {
       "feature_size": self.feature_size,
       "hop_length": self.hop_length,
       "chunk_length": self.chunk_length,
       "padding_value": self.padding_value,
       "sampling_rate": self.sampling_rate,
       "return_attention_mask": self.return_attention_mask,
       "do_normalize": self.do_normalize,
   }






  def 					SCREAMING_SNAKE_CASE      (      self   :     int					,lowercase__   :     List[str]=False					,lowercase__   :     Dict=False			):
   def _flatten(lowercase__   :     Optional[Any]			):
    return list(itertools.chain(*lowercase__			)			)

   if equal_length:
    __lowercase							  = [floats_list((self.max_seq_length, self.feature_size)			) for _ in range(self.batch_size			)]
   else:
    # make sure that inputs increase in size
    __lowercase							  = [
        floats_list((x, self.feature_size)			)
        for x in range(self.min_seq_length					,self.max_seq_length					,self.seq_length_diff			)
    ]
   if numpify:
    __lowercase							  = [np.asarray(lowercase__			) for x in speech_inputs]
   return speech_inputs






@require_torch
@require_torchaudio
class 			lowercase_	(lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[int]							=			WhisperFeatureExtractor if is_speech_available() else None






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   __lowercase							  = WhisperFeatureExtractionTester(self			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_dict			)

   with tempfile.TemporaryDirectory() as tmpdirname:
    __lowercase							  = feat_extract_first.save_pretrained(lowercase__			)[0]
    check_json_file_has_correct_format(lowercase__			)
    __lowercase							  = self.feature_extraction_class.from_pretrained(lowercase__			)

   __lowercase							  = feat_extract_first.to_dict()
   __lowercase							  = feat_extract_second.to_dict()
   __lowercase							  = feat_extract_first.mel_filters
   __lowercase							  = feat_extract_second.mel_filters
   self.assertTrue(np.allclose(lowercase__					,lowercase__			)			)
   self.assertEqual(lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_dict			)

   with tempfile.TemporaryDirectory() as tmpdirname:
    __lowercase							  = os.path.join(lowercase__					,'''feat_extract.json'''			)
    feat_extract_first.to_json_file(lowercase__			)
    __lowercase							  = self.feature_extraction_class.from_json_file(lowercase__			)

   __lowercase							  = feat_extract_first.to_dict()
   __lowercase							  = feat_extract_second.to_dict()
   __lowercase							  = feat_extract_first.mel_filters
   __lowercase							  = feat_extract_second.mel_filters
   self.assertTrue(np.allclose(lowercase__					,lowercase__			)			)
   self.assertEqual(lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   # Tests that all call wrap to encode_plus and batch_encode_plus
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
   # create three inputs of length 800, 1000, and 1200
   __lowercase							  = [floats_list((1, x)			)[0] for x in range(8_0_0					,1_4_0_0					,2_0_0			)]
   __lowercase							  = [np.asarray(lowercase__			) for speech_input in speech_inputs]

   # Test feature size
   __lowercase							  = feature_extractor(lowercase__					,padding='''max_length'''					,return_tensors='''np'''			).input_features
   self.assertTrue(input_features.ndim == 3			)
   self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames			)
   self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size			)

   # Test not batched input
   __lowercase							  = feature_extractor(speech_inputs[0]					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(np_speech_inputs[0]					,return_tensors='''np'''			).input_features
   self.assertTrue(np.allclose(lowercase__					,lowercase__					,atol=1e-3			)			)

   # Test batched
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   for enc_seq_a, enc_seq_a in zip(lowercase__					,lowercase__			):
    self.assertTrue(np.allclose(lowercase__					,lowercase__					,atol=1e-3			)			)

   # Test 2-D numpy arrays are batched.
   __lowercase							  = [floats_list((1, x)			)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
   __lowercase							  = np.asarray(lowercase__			)
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   for enc_seq_a, enc_seq_a in zip(lowercase__					,lowercase__			):
    self.assertTrue(np.allclose(lowercase__					,lowercase__					,atol=1e-3			)			)

   # Test truncation required
   __lowercase							  = [floats_list((1, x)			)[0] for x in range(2_0_0					,(feature_extractor.n_samples + 5_0_0)					,2_0_0			)]
   __lowercase							  = [np.asarray(lowercase__			) for speech_input in speech_inputs]

   __lowercase							  = [x[: feature_extractor.n_samples] for x in speech_inputs]
   __lowercase							  = [np.asarray(lowercase__			) for speech_input in speech_inputs_truncated]

   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   for enc_seq_a, enc_seq_a in zip(lowercase__					,lowercase__			):
    self.assertTrue(np.allclose(lowercase__					,lowercase__					,atol=1e-3			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   import torch

   __lowercase							  = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
   __lowercase							  = np.random.rand(1_0_0					,3_2			).astype(np.floataa			)
   __lowercase							  = np_speech_inputs.tolist()

   for inputs in [py_speech_inputs, np_speech_inputs]:
    __lowercase							  = feature_extractor.pad([{'''input_features''': inputs}]					,return_tensors='''np'''			)
    self.assertTrue(np_processed.input_features.dtype == np.floataa			)
    __lowercase							  = feature_extractor.pad([{'''input_features''': inputs}]					,return_tensors='''pt'''			)
    self.assertTrue(pt_processed.input_features.dtype == torch.floataa			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     int			):
   __lowercase							  = load_dataset('''hf-internal-testing/librispeech_asr_dummy'''					,'''clean'''					,split='''validation'''			)
   # automatic decoding with librispeech
   __lowercase							  = ds.sort('''id'''			).select(range(lowercase__			)			)[:num_samples]['''audio''']

   return [x["array"] for x in speech_samples]






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   # fmt: off
   __lowercase							  = torch.tensor(
       [
           0.1_1_9_3, -0.0_9_4_6, -0.1_0_9_8, -0.0_1_9_6, 0.0_2_2_5, -0.0_6_9_0, -0.1_7_3_6, 0.0_9_5_1,
           0.0_9_7_1, -0.0_8_1_7, -0.0_7_0_2, 0.0_1_6_2, 0.0_2_6_0, 0.0_0_1_7, -0.0_1_9_2, -0.1_6_7_8,
           0.0_7_0_9, -0.1_8_6_7, -0.0_6_5_5, -0.0_2_7_4, -0.0_2_3_4, -0.1_8_8_4, -0.0_5_1_6, -0.0_5_5_4,
           -0.0_2_7_4, -0.1_4_2_5, -0.1_4_2_3, 0.0_8_3_7, 0.0_3_7_7, -0.0_8_5_4
       ]			)
   # fmt: on

   __lowercase							  = self._load_datasamples(1			)
   __lowercase							  = WhisperFeatureExtractor()
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''pt'''			).input_features
   self.assertEqual(input_features.shape					,(1, 8_0, 3_0_0_0)			)
   self.assertTrue(torch.allclose(input_features[0, 0, :3_0]					,lowercase__					,atol=1e-4			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
   __lowercase							  = self._load_datasamples(1			)[0]
   __lowercase							  = ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5_5_3_5  # Rescale to [0, 65535] to show issue
   __lowercase							  = feat_extract.zero_mean_unit_var_norm([audio]					,attention_mask=lowercase__			)[0]

   self.assertTrue(np.all(np.mean(lowercase__			) < 1e-3			)			)
   self.assertTrue(np.all(np.abs(np.var(lowercase__			) - 1			) < 1e-3			)			)

