





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import numpy
		import tensorflow as tf

		from transformers import (
		    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BertConfig,
		    DPRConfig,
		    TFDPRContextEncoder,
		    TFDPRQuestionEncoder,
		    TFDPRReader,
		)






class 						UpperCamelCase_  :



					def __init__(		self     :     Optional[int]       ,      lowerCamelCase     :     Dict       ,      lowerCamelCase     :     Dict=13       ,      lowerCamelCase     :     Optional[Any]=7       ,      lowerCamelCase     :     List[str]=True       ,      lowerCamelCase     :     Union[str, Any]=True       ,      lowerCamelCase     :     Optional[int]=True       ,      lowerCamelCase     :     Optional[Any]=True       ,      lowerCamelCase     :     Dict=99       ,      lowerCamelCase     :     Dict=32       ,      lowerCamelCase     :     Any=2       ,      lowerCamelCase     :     Optional[int]=4       ,      lowerCamelCase     :     Tuple=37       ,      lowerCamelCase     :     Union[str, Any]="gelu"       ,      lowerCamelCase     :     Optional[Any]=0.1       ,      lowerCamelCase     :     Any=0.1       ,      lowerCamelCase     :     Union[str, Any]=5_12       ,      lowerCamelCase     :     Optional[Any]=16       ,      lowerCamelCase     :     List[str]=2       ,      lowerCamelCase     :     List[Any]=0.02       ,      lowerCamelCase     :     List[str]=3       ,      lowerCamelCase     :     Optional[Any]=4       ,      lowerCamelCase     :     Optional[Any]=None       ,      lowerCamelCase     :     Union[str, Any]=0       ,      ):
							lowerCamelCase_	:     Union[str, Any] 						=      parent
							lowerCamelCase_	:     Optional[Any] 						=      batch_size
							lowerCamelCase_	:     Dict 						=      seq_length
							lowerCamelCase_	:     Dict 						=      is_training
							lowerCamelCase_	:     Any 						=      use_input_mask
							lowerCamelCase_	:     Union[str, Any] 						=      use_token_type_ids
							lowerCamelCase_	:     Dict 						=      use_labels
							lowerCamelCase_	:     Optional[int] 						=      vocab_size
							lowerCamelCase_	:     List[Any] 						=      hidden_size
							lowerCamelCase_	:     str 						=      num_hidden_layers
							lowerCamelCase_	:     Optional[Any] 						=      num_attention_heads
							lowerCamelCase_	:     Optional[int] 						=      intermediate_size
							lowerCamelCase_	:     Any 						=      hidden_act
							lowerCamelCase_	:     str 						=      hidden_dropout_prob
							lowerCamelCase_	:     Dict 						=      attention_probs_dropout_prob
							lowerCamelCase_	:     int 						=      max_position_embeddings
							lowerCamelCase_	:     List[str] 						=      type_vocab_size
							lowerCamelCase_	:     Optional[Any] 						=      type_sequence_label_size
							lowerCamelCase_	:     List[str] 						=      initializer_range
							lowerCamelCase_	:     Any 						=      num_labels
							lowerCamelCase_	:     List[Any] 						=      num_choices
							lowerCamelCase_	:     Tuple 						=      scope
							lowerCamelCase_	:     List[str] 						=      projection_dim



					def   __a     (		self     :     Any						):
							lowerCamelCase_	:     int 						=      ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size						)

							lowerCamelCase_	:     List[Any] 						=      None
							if self.use_input_mask:
									# follow test_modeling_tf_ctrl.py
									lowerCamelCase_	:     Tuple 						=      random_attention_mask([self.batch_size, self.seq_length]						)

							lowerCamelCase_	:     Any 						=      None
							if self.use_token_type_ids:
									lowerCamelCase_	:     Optional[int] 						=      ids_tensor([self.batch_size, self.seq_length]       ,      self.type_vocab_size						)

							lowerCamelCase_	:     Union[str, Any] 						=      None
							lowerCamelCase_	:     List[str] 						=      None
							lowerCamelCase_	:     Optional[Any] 						=      None
							if self.use_labels:
									lowerCamelCase_	:     Tuple 						=      ids_tensor([self.batch_size]       ,      self.type_sequence_label_size						)
									lowerCamelCase_	:     Optional[int] 						=      ids_tensor([self.batch_size, self.seq_length]       ,      self.num_labels						)
									lowerCamelCase_	:     List[Any] 						=      ids_tensor([self.batch_size]       ,      self.num_choices						)

							lowerCamelCase_	:     Any 						=      BertConfig(
							    vocab_size=self.vocab_size       ,      hidden_size=self.hidden_size       ,      num_hidden_layers=self.num_hidden_layers       ,      num_attention_heads=self.num_attention_heads       ,      intermediate_size=self.intermediate_size       ,      hidden_act=self.hidden_act       ,      hidden_dropout_prob=self.hidden_dropout_prob       ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,      max_position_embeddings=self.max_position_embeddings       ,      type_vocab_size=self.type_vocab_size       ,      is_decoder=UpperCamelCase__       ,      initializer_range=self.initializer_range       ,      )
							lowerCamelCase_	:     Union[str, Any] 						=      DPRConfig(projection_dim=self.projection_dim       ,      **config.to_dict()						)

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



					def   __a     (		self     :     Tuple       ,      lowerCamelCase     :     Any       ,      lowerCamelCase     :     Any       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     List[Any]       ,      lowerCamelCase     :     Any       ,      lowerCamelCase     :     List[Any]       ,      lowerCamelCase     :     Union[str, Any]						):
							lowerCamelCase_	:     Dict 						=      TFDPRContextEncoder(config=UpperCamelCase__						)
							lowerCamelCase_	:     Optional[int] 						=      model(UpperCamelCase__       ,      attention_mask=UpperCamelCase__       ,      token_type_ids=UpperCamelCase__						)
							lowerCamelCase_	:     Any 						=      model(UpperCamelCase__       ,      token_type_ids=UpperCamelCase__						)
							lowerCamelCase_	:     Any 						=      model(UpperCamelCase__						)
							self.parent.assertEqual(result.pooler_output.shape       ,      (self.batch_size, self.projection_dim or self.hidden_size)						)



					def   __a     (		self     :     Dict       ,      lowerCamelCase     :     int       ,      lowerCamelCase     :     List[Any]       ,      lowerCamelCase     :     List[str]       ,      lowerCamelCase     :     List[Any]       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     str						):
							lowerCamelCase_	:     Union[str, Any] 						=      TFDPRQuestionEncoder(config=UpperCamelCase__						)
							lowerCamelCase_	:     Optional[int] 						=      model(UpperCamelCase__       ,      attention_mask=UpperCamelCase__       ,      token_type_ids=UpperCamelCase__						)
							lowerCamelCase_	:     Dict 						=      model(UpperCamelCase__       ,      token_type_ids=UpperCamelCase__						)
							lowerCamelCase_	:     List[Any] 						=      model(UpperCamelCase__						)
							self.parent.assertEqual(result.pooler_output.shape       ,      (self.batch_size, self.projection_dim or self.hidden_size)						)



					def   __a     (		self     :     int       ,      lowerCamelCase     :     Union[str, Any]       ,      lowerCamelCase     :     List[str]       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     List[str]       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     Optional[int]       ,      lowerCamelCase     :     Optional[int]						):
							lowerCamelCase_	:     Any 						=      TFDPRReader(config=UpperCamelCase__						)
							lowerCamelCase_	:     Optional[Any] 						=      model(UpperCamelCase__       ,      attention_mask=UpperCamelCase__						)

							self.parent.assertEqual(result.start_logits.shape       ,      (self.batch_size, self.seq_length)						)
							self.parent.assertEqual(result.end_logits.shape       ,      (self.batch_size, self.seq_length)						)
							self.parent.assertEqual(result.relevance_logits.shape       ,      (self.batch_size,)						)





					def   __a     (		self     :     Dict						):
							lowerCamelCase_	:     Optional[Any] 						=      self.prepare_config_and_inputs()
							(
							    (
							    lowerCamelCase_       
							)						,      (
							    lowerCamelCase_       
							)						,      (
							    lowerCamelCase_       
							)						,      (
							    lowerCamelCase_       
							)						,      (
							    lowerCamelCase_       
							)						,      (
							    lowerCamelCase_       
							)						,      (
							    lowerCamelCase_       
							)						,             
							)	:     Tuple 						=      config_and_inputs
							lowerCamelCase_	:     Tuple 						=      {'input_ids': input_ids}
							return config, inputs_dict






@require_tf
class 						UpperCamelCase_  (  _UpperCamelCase  ,     _UpperCamelCase  ,     unittest.TestCase							):
					_a      :						List[str]						    =					(
					    (
					        TFDPRContextEncoder,
					        TFDPRQuestionEncoder,
					        TFDPRReader,
					    )
					    if is_tf_available()
					    else ()
					)
					_a      :						List[Any]						    =					{'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}

					_a      :						Optional[int]						    =					False
					_a      :						int						    =					False
					_a      :						Dict						    =					False
					_a      :						Optional[Any]						    =					False
					_a      :						str						    =					False



					def   __a     (		self     :     Optional[int]						):
							lowerCamelCase_	:     Optional[Any] 						=      TFDPRModelTester(self						)
							lowerCamelCase_	:     Optional[Any] 						=      ConfigTester(self       ,      config_class=UpperCamelCase__       ,      hidden_size=37						)



					def   __a     (		self     :     List[str]						):
							self.config_tester.run_common_tests()



					def   __a     (		self     :     List[str]						):
							lowerCamelCase_	:     Optional[int] 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_context_encoder(*UpperCamelCase__						)



					def   __a     (		self     :     int						):
							lowerCamelCase_	:     str 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_question_encoder(*UpperCamelCase__						)



					def   __a     (		self     :     int						):
							lowerCamelCase_	:     Any 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_dpr_reader(*UpperCamelCase__						)





					@slow
					def   __a     (		self     :     List[Any]						):
							for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase_	:     Optional[int] 						=      TFDPRContextEncoder.from_pretrained(UpperCamelCase__						)
									self.assertIsNotNone(UpperCamelCase__						)

							for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase_	:     List[str] 						=      TFDPRContextEncoder.from_pretrained(UpperCamelCase__						)
									self.assertIsNotNone(UpperCamelCase__						)

							for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase_	:     Tuple 						=      TFDPRQuestionEncoder.from_pretrained(UpperCamelCase__						)
									self.assertIsNotNone(UpperCamelCase__						)

							for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase_	:     int 						=      TFDPRReader.from_pretrained(UpperCamelCase__						)
									self.assertIsNotNone(UpperCamelCase__						)






@require_tf
class 						UpperCamelCase_  (  unittest.TestCase							):



					@slow
					def   __a     (		self     :     Union[str, Any]						):
							lowerCamelCase_	:     str 						=      TFDPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base'						)

							lowerCamelCase_	:     Optional[int] 						=      tf.constant(
							    [[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]]						)  # [CLS] hello, is my dog cute? [SEP]
							lowerCamelCase_	:     List[Any] 						=      model(UpperCamelCase__						)[0]  # embedding shape = (1, 768)
							# compare the actual values for a slice.
							lowerCamelCase_	:     str 						=      tf.constant(
							    [
							        [
							            0.03_236_253,
							            0.12_753_335,
							            0.16_818_509,
							            0.00_279_786,
							            0.3_896_933,
							            0.24_264_945,
							            0.2_178_971,
							            -0.02_335_227,
							            -0.08_481_959,
							            -0.14_324_117,
							        ]
							    ]						)
							self.assertTrue(numpy.allclose(output[:, :10].numpy()       ,      expected_slice.numpy()       ,      atol=1E-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

_UpperCAmelCase    :     List[Any]         =       random.Random()





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int   ,		__snake_case							:			Dict=1.0   ,		__snake_case							:			List[Any]=None   ,		__snake_case							:			List[str]=None				):
     if rng is None:
          _A    		=  global_rng

     _A    		=  []
     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      :			Tuple, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Union[str, Any]=7, UpperCamelCase__      :			List[str]=4_00, UpperCamelCase__      :			Any=20_00, UpperCamelCase__      :			int=10, UpperCamelCase__      :			Tuple=1_60, UpperCamelCase__      :			Tuple=8, UpperCamelCase__      :			List[str]=0.0, UpperCamelCase__      :			Optional[Any]=40_00, UpperCamelCase__      :			List[str]=False, UpperCamelCase__      :			List[Any]=True, )  ->			Dict:
        _A    		=  parent
        _A    		=  batch_size
        _A    		=  min_seq_length
        _A    		=  max_seq_length
        _A    		=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        _A    		=  padding_value
        _A    		=  sampling_rate
        _A    		=  return_attention_mask
        _A    		=  do_normalize
        _A    		=  feature_size
        _A    		=  chunk_length
        _A    		=  hop_length
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			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 	__UpperCAmelCase   (      self      :			Optional[Any], UpperCamelCase__      :			List[Any]=False, UpperCamelCase__      :			str=False     )  ->			Any:
        def _flatten(UpperCamelCase__      :			List[str]     ):
             return list(itertools.chain(*UpperCamelCase__     )     )

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







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







   """simple docstring"""

   __lowerCAmelCase         =   WhisperFeatureExtractor if is_speech_available() else None
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Optional[Any]:
        _A    		=  WhisperFeatureExtractionTester(self     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			int:
        _A    		=  self.feature_extraction_class(**self.feat_extract_dict     )

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

        _A    		=  feat_extract_first.to_dict()
        _A    		=  feat_extract_second.to_dict()
        _A    		=  feat_extract_first.mel_filters
        _A    		=  feat_extract_second.mel_filters
        self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__     )     )
        self.assertEqual(UpperCamelCase__, UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			List[Any]:
        _A    		=  self.feature_extraction_class(**self.feat_extract_dict     )

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

        _A    		=  feat_extract_first.to_dict()
        _A    		=  feat_extract_second.to_dict()
        _A    		=  feat_extract_first.mel_filters
        _A    		=  feat_extract_second.mel_filters
        self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__     )     )
        self.assertEqual(UpperCamelCase__, UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			List[Any]:
        # Tests that all call wrap to encode_plus and batch_encode_plus
        _A    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
        # create three inputs of length 800, 1000, and 1200
        _A    		=  [floats_list((1, x)     )[0] for x in range(8_00, 14_00, 2_00     )]
        _A    		=  [np.asarray(UpperCamelCase__     ) for speech_input in speech_inputs]

        # Test feature size
        _A    		=  feature_extractor(UpperCamelCase__, 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
        _A    		=  feature_extractor(speech_inputs[0], return_tensors='np'     ).input_features
        _A    		=  feature_extractor(np_speech_inputs[0], return_tensors='np'     ).input_features
        self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__, atol=1e-3     )     )

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

        # Test 2-D numpy arrays are batched.
        _A    		=  [floats_list((1, x)     )[0] for x in (8_00, 8_00, 8_00)]
        _A    		=  np.asarray(UpperCamelCase__     )
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        for enc_seq_a, enc_seq_a in zip(UpperCamelCase__, UpperCamelCase__     ):
             self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__, atol=1e-3     )     )

        # Test truncation required
        _A    		=  [floats_list((1, x)     )[0] for x in range(2_00, (feature_extractor.n_samples + 5_00), 2_00     )]
        _A    		=  [np.asarray(UpperCamelCase__     ) for speech_input in speech_inputs]

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

        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        for enc_seq_a, enc_seq_a in zip(UpperCamelCase__, UpperCamelCase__     ):
             self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__, atol=1e-3     )     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Any:
        import torch

        _A    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
        _A    		=  np.random.rand(1_00, 32     ).astype(np.floataa     )
        _A    		=  np_speech_inputs.tolist()

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

        return [x["array"] for x in speech_samples]
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			List[Any]:
        # fmt: off
        _A    		=  torch.tensor(
            [
                0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
                0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
                0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
                -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
            ]     )
        # fmt: on

        _A    		=  self._load_datasamples(1     )
        _A    		=  WhisperFeatureExtractor()
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='pt'     ).input_features
        self.assertEqual(input_features.shape, (1, 80, 30_00)     )
        self.assertTrue(torch.allclose(input_features[0, 0, :30], UpperCamelCase__, atol=1e-4     )     )

   def 	__UpperCAmelCase   (      self      :			int     )  ->			Any:
        _A    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
        _A    		=  self._load_datasamples(1     )[0]
        _A    		=  ((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
        _A    		=  feat_extract.zero_mean_unit_var_norm([audio], attention_mask=UpperCamelCase__     )[0]

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



