


from scipy.stats import spearmanr

import datasets


a			:			Optional[int]												=					'''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''

a			:			Dict												=					'''
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {\'spearmanr\': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results[\'spearmanr\'])
        -0.7
        >>> print(round(results[\'spearmanr_pvalue\'], 2))
        0.19
'''

a			:			List[str]												=					R'''\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,  _KWARGS_DESCRIPTION					)
class 						lowerCamelCase_							(					datasets.Metric					):



					'''simple docstring'''







					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								return datasets.MetricInfo(
								    description=_DESCRIPTION			,							citation=_CITATION			,							inputs_description=_KWARGS_DESCRIPTION			,							features=datasets.Features(
								        {
								            '''predictions''': datasets.Value('''float'''    ),
								            '''references''': datasets.Value('''float'''    ),
								        }    )			,							reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html''']			,							)






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_=False    ) ->      Any:







								'''simple docstring'''

								__lowercase			       = spearmanr(snake_case_			,							snake_case_    )
								if return_pvalue:
											return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
								else:
											return {"spearmanr": results[0]}






import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch


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


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase=1.0							,      _UpperCamelCase=None							,      _UpperCamelCase=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
class 						lowerCamelCase_							(					unittest.TestCase					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_=7			,							snake_case_=4_0_0			,							snake_case_=2_0_0_0			,							snake_case_=1			,							snake_case_=0.0			,							snake_case_=1_6_0_0_0			,							snake_case_=True			,							snake_case_=8_0			,							snake_case_=1_6			,							snake_case_=6_4			,							snake_case_="hann_window"			,							snake_case_=8_0			,							snake_case_=7_6_0_0			,							snake_case_=1e-1_0			,							snake_case_=True			,							) ->      Tuple:







								'''simple docstring'''

								__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			       = feature_size
								__lowercase			       = padding_value
								__lowercase			       = sampling_rate
								__lowercase			       = do_normalize
								__lowercase			       = num_mel_bins
								__lowercase			       = hop_length
								__lowercase			       = win_length
								__lowercase			       = win_function
								__lowercase			       = fmin
								__lowercase			       = fmax
								__lowercase			       = mel_floor
								__lowercase			       = return_attention_mask






					def A   (					self    ) ->      str:







								'''simple docstring'''

								return {
								    "feature_size": self.feature_size,
								    "padding_value": self.padding_value,
								    "sampling_rate": self.sampling_rate,
								    "do_normalize": self.do_normalize,
								    "num_mel_bins": self.num_mel_bins,
								    "hop_length": self.hop_length,
								    "win_length": self.win_length,
								    "win_function": self.win_function,
								    "fmin": self.fmin,
								    "fmax": self.fmax,
								    "mel_floor": self.mel_floor,
								    "return_attention_mask": self.return_attention_mask,
								}






					def A   (					self			,							snake_case_=False			,							snake_case_=False    ) ->      Tuple:







								'''simple docstring'''

								def _flatten(snake_case_    ):
											return list(itertools.chain(*snake_case_    )    )

								if equal_length:
											__lowercase			       = floats_list((self.batch_size, self.max_seq_length)    )
								else:
											# make sure that inputs increase in size
											__lowercase			       = [
											    _flatten(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(snake_case_    ) for x in speech_inputs]

								return speech_inputs






					def A   (					self			,							snake_case_=False			,							snake_case_=False    ) ->      Any:







								'''simple docstring'''

								if equal_length:
											__lowercase			       = [floats_list((self.max_seq_length, self.num_mel_bins)    ) for _ in range(self.batch_size    )]
								else:
											# make sure that inputs increase in size
											__lowercase			       = [
											    floats_list((x, self.num_mel_bins)    )
											    for x in range(self.min_seq_length			,							self.max_seq_length			,							self.seq_length_diff    )
											]

								if numpify:
											__lowercase			       = [np.asarray(snake_case_    ) for x in speech_inputs]

								return speech_inputs




@require_torch
class 						lowerCamelCase_							(					lowerCAmelCase__  ,  unittest.TestCase					):



					'''simple docstring'''







					__UpperCAmelCase							  =					SpeechTaFeatureExtractor






					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = SpeechTaFeatureExtractionTester(self    )






					def A   (					self			,							snake_case_    ) ->      str:







								'''simple docstring'''

								self.assertTrue(np.all(np.mean(snake_case_			,							axis=0    ) < 1e-3    )    )
								self.assertTrue(np.all(np.abs(np.var(snake_case_			,							axis=0    ) - 1    ) < 1e-3    )    )






					def A   (					self    ) ->      int:







								'''simple docstring'''

								__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(snake_case_    ) for speech_input in speech_inputs]

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

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






					def A   (					self    ) ->      Any:







								'''simple docstring'''

								__lowercase			       = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
								__lowercase			       = [floats_list((1, x)    )[0] for x in range(8_0_0			,							1_4_0_0			,							2_0_0    )]

								__lowercase			       = ['''longest''', '''max_length''', '''do_not_pad''']
								__lowercase			       = [None, 1_6_0_0, None]
								for max_length, padding in zip(snake_case_			,							snake_case_    ):
											__lowercase			       = feat_extract(snake_case_			,							padding=snake_case_			,							max_length=snake_case_			,							return_tensors='''np'''    )
											__lowercase			       = processed.input_values

											self._check_zero_mean_unit_variance(input_values[0][:8_0_0]    )
											self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6    )
											self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]    )
											self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6    )
											self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]    )






					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
								__lowercase			       = range(8_0_0			,							1_4_0_0			,							2_0_0    )
								__lowercase			       = [floats_list((1, x)    )[0] for x in lengths]

								__lowercase			       = ['''longest''', '''max_length''', '''do_not_pad''']
								__lowercase			       = [None, 1_6_0_0, None]

								for max_length, padding in zip(snake_case_			,							snake_case_    ):
											__lowercase			       = feat_extract(snake_case_			,							max_length=snake_case_			,							padding=snake_case_    )
											__lowercase			       = processed.input_values

											self._check_zero_mean_unit_variance(input_values[0][:8_0_0]    )
											self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]    )
											self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]    )






					def A   (					self    ) ->      List[Any]:







								'''simple docstring'''

								__lowercase			       = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
								__lowercase			       = [floats_list((1, x)    )[0] for x in range(8_0_0			,							1_4_0_0			,							2_0_0    )]
								__lowercase			       = feat_extract(
								    snake_case_			,							truncation=snake_case_			,							max_length=1_0_0_0			,							padding='''max_length'''			,							return_tensors='''np'''    )
								__lowercase			       = processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :8_0_0]    )
								self._check_zero_mean_unit_variance(input_values[1]    )
								self._check_zero_mean_unit_variance(input_values[2]    )






					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
								__lowercase			       = [floats_list((1, x)    )[0] for x in range(8_0_0			,							1_4_0_0			,							2_0_0    )]
								__lowercase			       = feat_extract(
								    snake_case_			,							truncation=snake_case_			,							max_length=1_0_0_0			,							padding='''longest'''			,							return_tensors='''np'''    )
								__lowercase			       = processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :8_0_0]    )
								self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]    )
								self._check_zero_mean_unit_variance(input_values[2]    )

								# make sure that if max_length < longest -> then pad to max_length
								self.assertTrue(input_values.shape == (3, 1_0_0_0)    )

								__lowercase			       = [floats_list((1, x)    )[0] for x in range(8_0_0			,							1_4_0_0			,							2_0_0    )]
								__lowercase			       = feat_extract(
								    snake_case_			,							truncation=snake_case_			,							max_length=2_0_0_0			,							padding='''longest'''			,							return_tensors='''np'''    )
								__lowercase			       = processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :8_0_0]    )
								self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]    )
								self._check_zero_mean_unit_variance(input_values[2]    )

								# make sure that if max_length > longest -> then pad to longest
								self.assertTrue(input_values.shape == (3, 1_2_0_0)    )






					def A   (					self    ) ->      str:







								'''simple docstring'''

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

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






					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								__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(snake_case_    ) for speech_input in speech_inputs]

								# Test feature size
								__lowercase			       = feature_extractor(audio_target=snake_case_			,							padding=snake_case_			,							return_tensors='''np'''    ).input_values
								self.assertTrue(input_values.ndim == 3    )
								self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins    )

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

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






					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

								__lowercase			       = self.feat_extract_tester.prepare_inputs_for_target()
								__lowercase			       = self.feature_extraction_class(**self.feat_extract_dict    )
								__lowercase			       = feat_extract.model_input_names[0]

								__lowercase			       = BatchFeature({input_name: speech_inputs}    )

								self.assertTrue(all(len(snake_case_    ) == len(snake_case_    ) for x, y in zip(snake_case_			,							processed_features[input_name]    )    )    )

								__lowercase			       = self.feat_extract_tester.prepare_inputs_for_target(equal_length=snake_case_    )
								__lowercase			       = BatchFeature({input_name: speech_inputs}			,							tensor_type='''np'''    )

								__lowercase			       = processed_features[input_name]

								if len(batch_features_input.shape    ) < 3:
											__lowercase			       = batch_features_input[:, :, None]

								self.assertTrue(
								    batch_features_input.shape
								    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.num_mel_bins)    )






					@require_torch
					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = self.feat_extract_tester.prepare_inputs_for_target(equal_length=snake_case_    )
								__lowercase			       = self.feature_extraction_class(**self.feat_extract_dict    )
								__lowercase			       = feat_extract.model_input_names[0]

								__lowercase			       = BatchFeature({input_name: speech_inputs}			,							tensor_type='''pt'''    )

								__lowercase			       = processed_features[input_name]

								if len(batch_features_input.shape    ) < 3:
											__lowercase			       = batch_features_input[:, :, None]

								self.assertTrue(
								    batch_features_input.shape
								    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.num_mel_bins)    )






					@require_torch
					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

								__lowercase			       = self.feature_extraction_class(**self.feat_extract_dict    )
								__lowercase			       = self.feat_extract_tester.prepare_inputs_for_target()
								__lowercase			       = feat_extract.model_input_names[0]

								__lowercase			       = BatchFeature({input_name: speech_inputs}    )

								__lowercase			       = feat_extract.num_mel_bins  # hack!

								__lowercase			       = feat_extract.pad(snake_case_			,							padding='''longest'''			,							return_tensors='''np'''    )[input_name]
								__lowercase			       = feat_extract.pad(snake_case_			,							padding='''longest'''			,							return_tensors='''pt'''    )[input_name]

								self.assertTrue(abs(input_np.astype(np.floataa    ).sum() - input_pt.numpy().astype(np.floataa    ).sum()    ) < 1e-2    )






					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = self.feat_extract_dict
								__lowercase			       = True
								__lowercase			       = self.feature_extraction_class(**snake_case_    )
								__lowercase			       = self.feat_extract_tester.prepare_inputs_for_target()
								__lowercase			       = [len(snake_case_    ) for x in speech_inputs]
								__lowercase			       = feat_extract.model_input_names[0]

								__lowercase			       = BatchFeature({input_name: speech_inputs}    )

								__lowercase			       = feat_extract.num_mel_bins  # hack!

								__lowercase			       = feat_extract.pad(snake_case_			,							padding='''longest'''			,							return_tensors='''np'''    )
								self.assertIn('''attention_mask'''			,							snake_case_    )
								self.assertListEqual(list(processed.attention_mask.shape    )			,							list(processed[input_name].shape[:2]    )    )
								self.assertListEqual(processed.attention_mask.sum(-1    ).tolist()			,							snake_case_    )






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = self.feat_extract_dict
								__lowercase			       = True
								__lowercase			       = self.feature_extraction_class(**snake_case_    )
								__lowercase			       = self.feat_extract_tester.prepare_inputs_for_target()
								__lowercase			       = [len(snake_case_    ) for x in speech_inputs]
								__lowercase			       = feat_extract.model_input_names[0]

								__lowercase			       = BatchFeature({input_name: speech_inputs}    )
								__lowercase			       = min(snake_case_    )

								__lowercase			       = feat_extract.num_mel_bins  # hack!

								__lowercase			       = feat_extract.pad(
								    snake_case_			,							padding='''max_length'''			,							max_length=snake_case_			,							truncation=snake_case_			,							return_tensors='''np'''    )
								self.assertIn('''attention_mask'''			,							snake_case_    )
								self.assertListEqual(
								    list(processed_pad.attention_mask.shape    )			,							[processed_pad[input_name].shape[0], max_length]    )
								self.assertListEqual(
								    processed_pad.attention_mask[:, :max_length].sum(-1    ).tolist()			,							[max_length for x in speech_inputs]    )






					def A   (					self			,							snake_case_    ) ->      str:







								'''simple docstring'''

								from datasets import load_dataset

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

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






					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								__lowercase			       = torch.tensor(
								    [2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
								     3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
								     2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
								     4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
								     7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
								     4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3]    )
								# fmt: on

								__lowercase			       = self._load_datasamples(1    )
								__lowercase			       = SpeechTaFeatureExtractor()
								__lowercase			       = feature_extractor(snake_case_			,							return_tensors='''pt'''    ).input_values
								self.assertEquals(input_values.shape			,							(1, 9_3_6_8_0)    )
								self.assertTrue(torch.allclose(input_values[0, :3_0]			,							snake_case_			,							atol=1e-6    )    )






					def A   (					self    ) ->      str:







								'''simple docstring'''

								__lowercase			       = torch.tensor(
								    [-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
								     -3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
								     -3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
								     -3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8]    )
								# fmt: on

								__lowercase			       = self._load_datasamples(1    )
								__lowercase			       = SpeechTaFeatureExtractor()
								__lowercase			       = feature_extractor(audio_target=snake_case_			,							return_tensors='''pt'''    ).input_values
								self.assertEquals(input_values.shape			,							(1, 3_6_6, 8_0)    )
								self.assertTrue(torch.allclose(input_values[0, 0, :3_0]			,							snake_case_			,							atol=1e-4    )    )


