import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
a_     :   int 				=				{'LayoutLMv2Config', 'LayoutLMv3Config'}






@is_pipeline_test
class 		_lowerCamelCase		(   unittest.TestCase    ):
    __a  =      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
    __a  =      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

    if model_mapping is not None:
        __a  =      {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
    if tf_model_mapping is not None:
        __a  =      {
            config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
        }
    @require_torch
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      pipeline(
         task='''text-classification''' , model='''hf-internal-testing/tiny-random-distilbert''' , framework='''pt'''   )

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      text_classifier('''This is great !'''   )
     self.assertEqual(nested_simplify(lowerCAmelCase   ) , [{'''label''': '''LABEL_0''', '''score''': 0.504}]   )

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      text_classifier('''This is great !''' , top_k=2   )
     self.assertEqual(
         nested_simplify(lowerCAmelCase   ) , [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}]   )

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      text_classifier(['''This is great !''', '''This is bad'''] , top_k=2   )
     self.assertEqual(
         nested_simplify(lowerCAmelCase   ) , [
             [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
             [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
         ] , )

     SCREAMING_SNAKE_CASE__:   int=      text_classifier('''This is great !''' , top_k=1   )
     self.assertEqual(nested_simplify(lowerCAmelCase   ) , [{'''label''': '''LABEL_0''', '''score''': 0.504}]   )

     # Legacy behavior
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      text_classifier('''This is great !''' , return_all_scores=lowerCAmelCase   )
     self.assertEqual(nested_simplify(lowerCAmelCase   ) , [{'''label''': '''LABEL_0''', '''score''': 0.504}]   )

     SCREAMING_SNAKE_CASE__:   Tuple=      text_classifier('''This is great !''' , return_all_scores=lowerCAmelCase   )
     self.assertEqual(
         nested_simplify(lowerCAmelCase   ) , [[{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}]]   )

     SCREAMING_SNAKE_CASE__:   Optional[int]=      text_classifier(['''This is great !''', '''Something else'''] , return_all_scores=lowerCAmelCase   )
     self.assertEqual(
         nested_simplify(lowerCAmelCase   ) , [
             [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
             [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
         ] , )

     SCREAMING_SNAKE_CASE__:   int=      text_classifier(['''This is great !''', '''Something else'''] , return_all_scores=lowerCAmelCase   )
     self.assertEqual(
         nested_simplify(lowerCAmelCase   ) , [
             {'''label''': '''LABEL_0''', '''score''': 0.504},
             {'''label''': '''LABEL_0''', '''score''': 0.504},
         ] , )
    @require_torch
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     import torch

     SCREAMING_SNAKE_CASE__:   Optional[int]=      pipeline(
         task='''text-classification''' , model='''hf-internal-testing/tiny-random-distilbert''' , framework='''pt''' , device=torch.device('''cpu'''   ) , )

     SCREAMING_SNAKE_CASE__:   int=      text_classifier('''This is great !'''   )
     self.assertEqual(nested_simplify(lowerCAmelCase   ) , [{'''label''': '''LABEL_0''', '''score''': 0.504}]   )
    @require_tf
    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     SCREAMING_SNAKE_CASE__:   Dict=      pipeline(
         task='''text-classification''' , model='''hf-internal-testing/tiny-random-distilbert''' , framework='''tf'''   )

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      text_classifier('''This is great !'''   )
     self.assertEqual(nested_simplify(lowerCAmelCase   ) , [{'''label''': '''LABEL_0''', '''score''': 0.504}]   )
    @slow
    @require_torch
    def 						UpperCamelCase_				(  self   )       ->					int:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      pipeline('''text-classification'''   )

     SCREAMING_SNAKE_CASE__:   List[str]=      text_classifier('''This is great !'''   )
     self.assertEqual(nested_simplify(lowerCAmelCase   ) , [{'''label''': '''POSITIVE''', '''score''': 1.0}]   )
     SCREAMING_SNAKE_CASE__:   Any=      text_classifier('''This is bad !'''   )
     self.assertEqual(nested_simplify(lowerCAmelCase   ) , [{'''label''': '''NEGATIVE''', '''score''': 1.0}]   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      text_classifier('''Birds are a type of animal'''   )
     self.assertEqual(nested_simplify(lowerCAmelCase   ) , [{'''label''': '''POSITIVE''', '''score''': 0.988}]   )
    @slow
    @require_tf
    def 						UpperCamelCase_				(  self   )       ->					int:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      pipeline('''text-classification''' , framework='''tf'''   )

     SCREAMING_SNAKE_CASE__:   Any=      text_classifier('''This is great !'''   )
     self.assertEqual(nested_simplify(lowerCAmelCase   ) , [{'''label''': '''POSITIVE''', '''score''': 1.0}]   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      text_classifier('''This is bad !'''   )
     self.assertEqual(nested_simplify(lowerCAmelCase   ) , [{'''label''': '''NEGATIVE''', '''score''': 1.0}]   )
     SCREAMING_SNAKE_CASE__:   int=      text_classifier('''Birds are a type of animal'''   )
     self.assertEqual(nested_simplify(lowerCAmelCase   ) , [{'''label''': '''POSITIVE''', '''score''': 0.988}]   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Optional[Any]:
     SCREAMING_SNAKE_CASE__:   Dict=      TextClassificationPipeline(model=lowerCAmelCase , tokenizer=lowerCAmelCase   )
     return text_classifier, ["HuggingFace is in", "This is another test"]



    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase   )       ->					str:
     SCREAMING_SNAKE_CASE__:   List[str]=      text_classifier.model
     # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
     SCREAMING_SNAKE_CASE__:   Tuple=      'HuggingFace is in'
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      text_classifier(lowerCAmelCase   )

     self.assertEqual(nested_simplify(lowerCAmelCase   ) , [{'''label''': ANY(lowerCAmelCase   ), '''score''': ANY(lowerCAmelCase   )}]   )
     self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()   )

     SCREAMING_SNAKE_CASE__:   Any=      ['HuggingFace is in ', 'Paris is in France']
     SCREAMING_SNAKE_CASE__:   Any=      text_classifier(lowerCAmelCase   )
     self.assertEqual(
         nested_simplify(lowerCAmelCase   ) , [{'''label''': ANY(lowerCAmelCase   ), '''score''': ANY(lowerCAmelCase   )}, {'''label''': ANY(lowerCAmelCase   ), '''score''': ANY(lowerCAmelCase   )}] , )
     self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()   )
     self.assertTrue(outputs[1]['''label'''] in model.config.idalabel.values()   )

     # Forcing to get all results with `top_k=None`
     # This is NOT the legacy format
     SCREAMING_SNAKE_CASE__:   List[Any]=      text_classifier(lowerCAmelCase , top_k=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      len(model.config.idalabel.values()   )
     self.assertEqual(
         nested_simplify(lowerCAmelCase   ) , [[{'''label''': ANY(lowerCAmelCase   ), '''score''': ANY(lowerCAmelCase   )}] * N, [{'''label''': ANY(lowerCAmelCase   ), '''score''': ANY(lowerCAmelCase   )}] * N] , )

     SCREAMING_SNAKE_CASE__:   str=      {'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
     SCREAMING_SNAKE_CASE__:   Tuple=      text_classifier(lowerCAmelCase   )
     self.assertEqual(
         nested_simplify(lowerCAmelCase   ) , {'''label''': ANY(lowerCAmelCase   ), '''score''': ANY(lowerCAmelCase   )} , )
     self.assertTrue(outputs['''label'''] in model.config.idalabel.values()   )

     # This might be used a text pair, but tokenizer + pipe interaction
     # makes it hard to understand that it's not using the pair properly
     # https://github.com/huggingface/transformers/issues/17305
     # We disabled this usage instead as it was outputting wrong outputs.
     SCREAMING_SNAKE_CASE__:   Dict=      [['HuggingFace is in ', 'Paris is in France']]
     with self.assertRaises(lowerCAmelCase   ):
      text_classifier(lowerCAmelCase   )

     # This used to be valid for doing text pairs
     # We're keeping it working because of backward compatibility
     SCREAMING_SNAKE_CASE__:   str=      text_classifier([[['''HuggingFace is in ''', '''Paris is in France''']]]   )
     self.assertEqual(
         nested_simplify(lowerCAmelCase   ) , [{'''label''': ANY(lowerCAmelCase   ), '''score''': ANY(lowerCAmelCase   )}] , )
     self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()   )


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

lowercase_     :   Any 				=				random.Random()







def 			A__      (	snake_case_ :		int       ,				snake_case_ :		Tuple=1.0       ,				snake_case_ :		Optional[Any]=None       ,				snake_case_ :		List[Any]=None      ):
 if rng is None:
  SCREAMING_SNAKE_CASE__:   Tuple=      global_rng

 SCREAMING_SNAKE_CASE__:   List[str]=      []
 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 		_lowerCamelCase		(   unittest.TestCase    ):
    def __init__(  self , lowerCAmelCase , lowerCAmelCase=7 , lowerCAmelCase=400 , lowerCAmelCase=2000 , lowerCAmelCase=10 , lowerCAmelCase=160 , lowerCAmelCase=8 , lowerCAmelCase=0.0 , lowerCAmelCase=4000 , lowerCAmelCase=False , lowerCAmelCase=True , )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Any=      parent
     SCREAMING_SNAKE_CASE__:   Dict=      batch_size
     SCREAMING_SNAKE_CASE__:   List[Any]=      min_seq_length
     SCREAMING_SNAKE_CASE__:   Tuple=      max_seq_length
     SCREAMING_SNAKE_CASE__:   Optional[int]=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     SCREAMING_SNAKE_CASE__:   str=      padding_value
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      sampling_rate
     SCREAMING_SNAKE_CASE__:   List[str]=      return_attention_mask
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      do_normalize
     SCREAMING_SNAKE_CASE__:   Optional[int]=      feature_size
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      chunk_length
     SCREAMING_SNAKE_CASE__:   List[Any]=      hop_length
    def 						UpperCamelCase_				(  self   )       ->					str:
     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 						UpperCamelCase_				(  self , lowerCAmelCase=False , lowerCAmelCase=False   )       ->					Optional[Any]:
     def _flatten(lowerCAmelCase   ):
      return list(itertools.chain(*lowerCAmelCase   )   )

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






@require_torch
@require_torchaudio
class 		_lowerCamelCase		(   UpperCamelCase_    ,     unittest.TestCase    ):
    __a  =      WhisperFeatureExtractor if is_speech_available() else None
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   Any=      WhisperFeatureExtractionTester(self   )
    def 						UpperCamelCase_				(  self   )       ->					int:
     SCREAMING_SNAKE_CASE__:   List[str]=      self.feature_extraction_class(**self.feat_extract_dict   )

     with tempfile.TemporaryDirectory() as tmpdirname:
      SCREAMING_SNAKE_CASE__:   List[str]=      feat_extract_first.save_pretrained(lowerCAmelCase   )[0]
      check_json_file_has_correct_format(lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   str=      self.feature_extraction_class.from_pretrained(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   int=      feat_extract_first.to_dict()
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      feat_extract_second.to_dict()
     SCREAMING_SNAKE_CASE__:   int=      feat_extract_first.mel_filters
     SCREAMING_SNAKE_CASE__:   Any=      feat_extract_second.mel_filters
     self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase   )   )
     self.assertEqual(lowerCAmelCase , lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.feature_extraction_class(**self.feat_extract_dict   )

     with tempfile.TemporaryDirectory() as tmpdirname:
      SCREAMING_SNAKE_CASE__:   List[str]=      os.path.join(lowerCAmelCase , '''feat_extract.json'''   )
      feat_extract_first.to_json_file(lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   Tuple=      self.feature_extraction_class.from_json_file(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Tuple=      feat_extract_first.to_dict()
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      feat_extract_second.to_dict()
     SCREAMING_SNAKE_CASE__:   List[str]=      feat_extract_first.mel_filters
     SCREAMING_SNAKE_CASE__:   Optional[int]=      feat_extract_second.mel_filters
     self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase   )   )
     self.assertEqual(lowerCAmelCase , lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     # Tests that all call wrap to encode_plus and batch_encode_plus
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     # create three inputs of length 800, 1000, and 1200
     SCREAMING_SNAKE_CASE__:   str=      [floats_list((1, x)   )[0] for x in range(800 , 1400 , 200   )]
     SCREAMING_SNAKE_CASE__:   Tuple=      [np.asarray(lowerCAmelCase   ) for speech_input in speech_inputs]

     # Test feature size
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      feature_extractor(lowerCAmelCase , 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
     SCREAMING_SNAKE_CASE__:   List[str]=      feature_extractor(speech_inputs[0] , return_tensors='''np'''   ).input_features
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      feature_extractor(np_speech_inputs[0] , return_tensors='''np'''   ).input_features
     self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase , atol=1e-3   )   )

     # Test batched
     SCREAMING_SNAKE_CASE__:   Optional[int]=      feature_extractor(lowerCAmelCase , return_tensors='''np'''   ).input_features
     SCREAMING_SNAKE_CASE__:   List[Any]=      feature_extractor(lowerCAmelCase , return_tensors='''np'''   ).input_features
     for enc_seq_a, enc_seq_a in zip(lowerCAmelCase , lowerCAmelCase   ):
      self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase , atol=1e-3   )   )

     # Test 2-D numpy arrays are batched.
     SCREAMING_SNAKE_CASE__:   Optional[int]=      [floats_list((1, x)   )[0] for x in (800, 800, 800)]
     SCREAMING_SNAKE_CASE__:   Any=      np.asarray(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[str]=      feature_extractor(lowerCAmelCase , return_tensors='''np'''   ).input_features
     SCREAMING_SNAKE_CASE__:   List[Any]=      feature_extractor(lowerCAmelCase , return_tensors='''np'''   ).input_features
     for enc_seq_a, enc_seq_a in zip(lowerCAmelCase , lowerCAmelCase   ):
      self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase , atol=1e-3   )   )

     # Test truncation required
     SCREAMING_SNAKE_CASE__:   str=      [floats_list((1, x)   )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200   )]
     SCREAMING_SNAKE_CASE__:   str=      [np.asarray(lowerCAmelCase   ) for speech_input in speech_inputs]

     SCREAMING_SNAKE_CASE__:   str=      [x[: feature_extractor.n_samples] for x in speech_inputs]
     SCREAMING_SNAKE_CASE__:   int=      [np.asarray(lowerCAmelCase   ) for speech_input in speech_inputs_truncated]

     SCREAMING_SNAKE_CASE__:   Dict=      feature_extractor(lowerCAmelCase , return_tensors='''np'''   ).input_features
     SCREAMING_SNAKE_CASE__:   List[str]=      feature_extractor(lowerCAmelCase , return_tensors='''np'''   ).input_features
     for enc_seq_a, enc_seq_a in zip(lowerCAmelCase , lowerCAmelCase   ):
      self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase , atol=1e-3   )   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     import torch

     SCREAMING_SNAKE_CASE__:   List[Any]=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     SCREAMING_SNAKE_CASE__:   Dict=      np.random.rand(100 , 32   ).astype(np.floataa   )
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      np_speech_inputs.tolist()

     for inputs in [py_speech_inputs, np_speech_inputs]:
      SCREAMING_SNAKE_CASE__:   Tuple=      feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''np'''   )
      self.assertTrue(np_processed.input_features.dtype == np.floataa   )
      SCREAMING_SNAKE_CASE__:   Any=      feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''pt'''   )
      self.assertTrue(pt_processed.input_features.dtype == torch.floataa   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					str:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation'''   )
     # automatic decoding with librispeech
     SCREAMING_SNAKE_CASE__:   List[str]=      ds.sort('''id'''   ).select(range(lowerCAmelCase   )   )[:num_samples]['''audio''']

     return [x["array"] for x in speech_samples]
    def 						UpperCamelCase_				(  self   )       ->					int:
     # fmt: off
     SCREAMING_SNAKE_CASE__:   Dict=      torch.tensor(
         [
             0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
             0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
             0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
             -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
         ]   )
     # fmt: on

     SCREAMING_SNAKE_CASE__:   str=      self._load_datasamples(1   )
     SCREAMING_SNAKE_CASE__:   Dict=      WhisperFeatureExtractor()
     SCREAMING_SNAKE_CASE__:   Optional[int]=      feature_extractor(lowerCAmelCase , return_tensors='''pt'''   ).input_features
     self.assertEqual(input_features.shape , (1, 80, 3000)   )
     self.assertTrue(torch.allclose(input_features[0, 0, :30] , lowerCAmelCase , atol=1e-4   )   )



    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     SCREAMING_SNAKE_CASE__:   List[str]=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     SCREAMING_SNAKE_CASE__:   Dict=      self._load_datasamples(1   )[0]
     SCREAMING_SNAKE_CASE__:   str=      ((audio - audio.min()) / (audio.max() - audio.min())) * 65535  # Rescale to [0, 65535] to show issue
     SCREAMING_SNAKE_CASE__:   Optional[int]=      feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=lowerCAmelCase   )[0]

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

