

"""simple docstring"""




from scipy.stats import spearmanr

import datasets


A_							:			List[str]											= "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n"

A_							:			Union[str, Any]											= "\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {'spearmanr': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results['spearmanr'])\n        -0.7\n        >>> print(round(results['spearmanr_pvalue'], 2))\n        0.19\n"

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





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,_KWARGS_DESCRIPTION			)
class    lowerCamelCase						(datasets.Metric			):

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Optional[int]:
						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 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						Optional[Any]=False       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      spearmanr(__UpperCAmelCase    ,				__UpperCAmelCase       )
						if return_pvalue:
									return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
						else:
									return {"spearmanr": results[0]}





"""simple docstring"""




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[str]											= random.Random()

def 	A       (     snake_case__		,				snake_case__=1.0		,				snake_case__=None		,				snake_case__=None    ):




			'''simple docstring'''


			if rng is None:
						SCREAMING_SNAKE_CASE__								=      global_rng

			SCREAMING_SNAKE_CASE__								=      []
			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			):

			def __init__(					self				:						Tuple    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						List[str]=7    ,				__UpperCAmelCase				:						List[str]=4_0_0    ,				__UpperCAmelCase				:						Tuple=2_0_0_0    ,				__UpperCAmelCase				:						Any=1    ,				__UpperCAmelCase				:						str=0.0    ,				__UpperCAmelCase				:						int=1_6_0_0_0    ,				__UpperCAmelCase				:						str=True    ,				__UpperCAmelCase				:						List[str]=8_0    ,				__UpperCAmelCase				:						Dict=1_6    ,				__UpperCAmelCase				:						int=6_4    ,				__UpperCAmelCase				:						Any="hann_window"    ,				__UpperCAmelCase				:						Tuple=8_0    ,				__UpperCAmelCase				:						Tuple=7_6_0_0    ,				__UpperCAmelCase				:						List[Any]=1e-10    ,				__UpperCAmelCase				:						Tuple=True    ,				)   ->    int:
						SCREAMING_SNAKE_CASE__								=      parent
						SCREAMING_SNAKE_CASE__								=      batch_size
						SCREAMING_SNAKE_CASE__								=      min_seq_length
						SCREAMING_SNAKE_CASE__								=      max_seq_length
						SCREAMING_SNAKE_CASE__								=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						SCREAMING_SNAKE_CASE__								=      feature_size
						SCREAMING_SNAKE_CASE__								=      padding_value
						SCREAMING_SNAKE_CASE__								=      sampling_rate
						SCREAMING_SNAKE_CASE__								=      do_normalize
						SCREAMING_SNAKE_CASE__								=      num_mel_bins
						SCREAMING_SNAKE_CASE__								=      hop_length
						SCREAMING_SNAKE_CASE__								=      win_length
						SCREAMING_SNAKE_CASE__								=      win_function
						SCREAMING_SNAKE_CASE__								=      fmin
						SCREAMING_SNAKE_CASE__								=      fmax
						SCREAMING_SNAKE_CASE__								=      mel_floor
						SCREAMING_SNAKE_CASE__								=      return_attention_mask

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

						if equal_length:
									SCREAMING_SNAKE_CASE__								=      floats_list((self.batch_size, self.max_seq_length)       )
						else:
									# make sure that inputs increase in size
									SCREAMING_SNAKE_CASE__								=      [
									    _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:
									SCREAMING_SNAKE_CASE__								=      [np.asarray(__UpperCAmelCase       ) for x in speech_inputs]

						return speech_inputs





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Union[str, Any]=False    ,				__UpperCAmelCase				:						Optional[int]=False       )   ->    str:
						if equal_length:
									SCREAMING_SNAKE_CASE__								=      [floats_list((self.max_seq_length, self.num_mel_bins)       ) for _ in range(self.batch_size       )]
						else:
									# make sure that inputs increase in size
									SCREAMING_SNAKE_CASE__								=      [
									    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:
									SCREAMING_SNAKE_CASE__								=      [np.asarray(__UpperCAmelCase       ) for x in speech_inputs]

						return speech_inputs





@require_torch
class    lowerCamelCase						(A__			,unittest.TestCase			):
			lowerCamelCase__   :		Any							=							SpeechTaFeatureExtractor

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      SpeechTaFeatureExtractionTester(self       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						Any       )   ->    Tuple:
						self.assertTrue(np.all(np.mean(__UpperCAmelCase    ,				axis=0       ) < 1e-3       )       )
						self.assertTrue(np.all(np.abs(np.var(__UpperCAmelCase    ,				axis=0       ) - 1       ) < 1e-3       )       )

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

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

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
						SCREAMING_SNAKE_CASE__								=      [floats_list((1, x)       )[0] for x in range(8_0_0    ,				1_4_0_0    ,				2_0_0       )]

						SCREAMING_SNAKE_CASE__								=      ["""longest""", """max_length""", """do_not_pad"""]
						SCREAMING_SNAKE_CASE__								=      [None, 1_6_0_0, None]
						for max_length, padding in zip(__UpperCAmelCase    ,				__UpperCAmelCase       ):
									SCREAMING_SNAKE_CASE__								=      feat_extract(__UpperCAmelCase    ,				padding=__UpperCAmelCase    ,				max_length=__UpperCAmelCase    ,				return_tensors="""np"""       )
									SCREAMING_SNAKE_CASE__								=      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 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
						SCREAMING_SNAKE_CASE__								=      range(8_0_0    ,				1_4_0_0    ,				2_0_0       )
						SCREAMING_SNAKE_CASE__								=      [floats_list((1, x)       )[0] for x in lengths]

						SCREAMING_SNAKE_CASE__								=      ["""longest""", """max_length""", """do_not_pad"""]
						SCREAMING_SNAKE_CASE__								=      [None, 1_6_0_0, None]

						for max_length, padding in zip(__UpperCAmelCase    ,				__UpperCAmelCase       ):
									SCREAMING_SNAKE_CASE__								=      feat_extract(__UpperCAmelCase    ,				max_length=__UpperCAmelCase    ,				padding=__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      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 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
						SCREAMING_SNAKE_CASE__								=      [floats_list((1, x)       )[0] for x in range(8_0_0    ,				1_4_0_0    ,				2_0_0       )]
						SCREAMING_SNAKE_CASE__								=      feat_extract(
						    __UpperCAmelCase    ,				truncation=__UpperCAmelCase    ,				max_length=1_0_0_0    ,				padding="""max_length"""    ,				return_tensors="""np"""       )
						SCREAMING_SNAKE_CASE__								=      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 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
						SCREAMING_SNAKE_CASE__								=      [floats_list((1, x)       )[0] for x in range(8_0_0    ,				1_4_0_0    ,				2_0_0       )]
						SCREAMING_SNAKE_CASE__								=      feat_extract(
						    __UpperCAmelCase    ,				truncation=__UpperCAmelCase    ,				max_length=1_0_0_0    ,				padding="""longest"""    ,				return_tensors="""np"""       )
						SCREAMING_SNAKE_CASE__								=      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)       )

						SCREAMING_SNAKE_CASE__								=      [floats_list((1, x)       )[0] for x in range(8_0_0    ,				1_4_0_0    ,				2_0_0       )]
						SCREAMING_SNAKE_CASE__								=      feat_extract(
						    __UpperCAmelCase    ,				truncation=__UpperCAmelCase    ,				max_length=2_0_0_0    ,				padding="""longest"""    ,				return_tensors="""np"""       )
						SCREAMING_SNAKE_CASE__								=      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 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
						SCREAMING_SNAKE_CASE__								=      np.random.rand(1_0_0       ).astype(np.floataa       )
						SCREAMING_SNAKE_CASE__								=      np_speech_inputs.tolist()

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

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

						# Test feature size
						SCREAMING_SNAKE_CASE__								=      feature_extractor(audio_target=__UpperCAmelCase    ,				padding=__UpperCAmelCase    ,				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
						SCREAMING_SNAKE_CASE__								=      feature_extractor(speech_inputs[0]    ,				return_tensors="""np"""       ).input_values
						SCREAMING_SNAKE_CASE__								=      feature_extractor(np_speech_inputs[0]    ,				return_tensors="""np"""       ).input_values
						self.assertTrue(np.allclose(__UpperCAmelCase    ,				__UpperCAmelCase    ,				atol=1e-3       )       )

						# Test batched
						SCREAMING_SNAKE_CASE__								=      feature_extractor(__UpperCAmelCase    ,				return_tensors="""np"""       ).input_values
						SCREAMING_SNAKE_CASE__								=      feature_extractor(__UpperCAmelCase    ,				return_tensors="""np"""       ).input_values
						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.
						SCREAMING_SNAKE_CASE__								=      [floats_list((1, x)       )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
						SCREAMING_SNAKE_CASE__								=      np.asarray(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      feature_extractor(__UpperCAmelCase    ,				return_tensors="""np"""       ).input_values
						SCREAMING_SNAKE_CASE__								=      feature_extractor(__UpperCAmelCase    ,				return_tensors="""np"""       ).input_values
						for enc_seq_a, enc_seq_a in zip(__UpperCAmelCase    ,				__UpperCAmelCase       ):
									self.assertTrue(np.allclose(__UpperCAmelCase    ,				__UpperCAmelCase    ,				atol=1e-3       )       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      self.feat_extract_tester.prepare_inputs_for_target()
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**self.feat_extract_dict       )
						SCREAMING_SNAKE_CASE__								=      feat_extract.model_input_names[0]

						SCREAMING_SNAKE_CASE__								=      BatchFeature({input_name: speech_inputs}       )

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

						SCREAMING_SNAKE_CASE__								=      self.feat_extract_tester.prepare_inputs_for_target(equal_length=__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      BatchFeature({input_name: speech_inputs}    ,				tensor_type="""np"""       )

						SCREAMING_SNAKE_CASE__								=      processed_features[input_name]

						if len(batch_features_input.shape       ) < 3:
									SCREAMING_SNAKE_CASE__								=      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 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      self.feat_extract_tester.prepare_inputs_for_target(equal_length=__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**self.feat_extract_dict       )
						SCREAMING_SNAKE_CASE__								=      feat_extract.model_input_names[0]

						SCREAMING_SNAKE_CASE__								=      BatchFeature({input_name: speech_inputs}    ,				tensor_type="""pt"""       )

						SCREAMING_SNAKE_CASE__								=      processed_features[input_name]

						if len(batch_features_input.shape       ) < 3:
									SCREAMING_SNAKE_CASE__								=      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 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**self.feat_extract_dict       )
						SCREAMING_SNAKE_CASE__								=      self.feat_extract_tester.prepare_inputs_for_target()
						SCREAMING_SNAKE_CASE__								=      feat_extract.model_input_names[0]

						SCREAMING_SNAKE_CASE__								=      BatchFeature({input_name: speech_inputs}       )

						SCREAMING_SNAKE_CASE__								=      feat_extract.num_mel_bins  # hack!

						SCREAMING_SNAKE_CASE__								=      feat_extract.pad(__UpperCAmelCase    ,				padding="""longest"""    ,				return_tensors="""np"""       )[input_name]
						SCREAMING_SNAKE_CASE__								=      feat_extract.pad(__UpperCAmelCase    ,				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 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.feat_extract_dict
						SCREAMING_SNAKE_CASE__								=      True
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.feat_extract_tester.prepare_inputs_for_target()
						SCREAMING_SNAKE_CASE__								=      [len(__UpperCAmelCase       ) for x in speech_inputs]
						SCREAMING_SNAKE_CASE__								=      feat_extract.model_input_names[0]

						SCREAMING_SNAKE_CASE__								=      BatchFeature({input_name: speech_inputs}       )

						SCREAMING_SNAKE_CASE__								=      feat_extract.num_mel_bins  # hack!

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      self.feat_extract_dict
						SCREAMING_SNAKE_CASE__								=      True
						SCREAMING_SNAKE_CASE__								=      self.feature_extraction_class(**__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.feat_extract_tester.prepare_inputs_for_target()
						SCREAMING_SNAKE_CASE__								=      [len(__UpperCAmelCase       ) for x in speech_inputs]
						SCREAMING_SNAKE_CASE__								=      feat_extract.model_input_names[0]

						SCREAMING_SNAKE_CASE__								=      BatchFeature({input_name: speech_inputs}       )
						SCREAMING_SNAKE_CASE__								=      min(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      feat_extract.num_mel_bins  # hack!

						SCREAMING_SNAKE_CASE__								=      feat_extract.pad(
						    __UpperCAmelCase    ,				padding="""max_length"""    ,				max_length=__UpperCAmelCase    ,				truncation=__UpperCAmelCase    ,				return_tensors="""np"""       )
						self.assertIn("""attention_mask"""    ,				__UpperCAmelCase       )
						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 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						int       )   ->    str:
						from datasets import load_dataset

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

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Any:
						# fmt: off
						SCREAMING_SNAKE_CASE__								=      torch.tensor(
						    [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
						     3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
						     2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
						     4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
						     7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
						     4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]       )
						# fmt: on

						SCREAMING_SNAKE_CASE__								=      self._load_datasamples(1       )
						SCREAMING_SNAKE_CASE__								=      SpeechTaFeatureExtractor()
						SCREAMING_SNAKE_CASE__								=      feature_extractor(__UpperCAmelCase    ,				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]    ,				__UpperCAmelCase    ,				atol=1e-6       )       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Union[str, Any]:
						# fmt: off
						SCREAMING_SNAKE_CASE__								=      torch.tensor(
						    [-2.6_870, -3.0_104, -3.1_356, -3.5_352, -3.0_044, -3.0_353, -3.4_719, -3.6_777,
						     -3.1_520, -2.9_435, -2.6_553, -2.8_795, -2.9_944, -2.5_921, -3.0_279, -3.0_386,
						     -3.0_864, -3.1_291, -3.2_353, -2.7_444, -2.6_831, -2.7_287, -3.1_761, -3.1_571,
						     -3.2_726, -3.0_582, -3.1_007, -3.4_533, -3.4_695, -3.0_998]       )
						# fmt: on

						SCREAMING_SNAKE_CASE__								=      self._load_datasamples(1       )
						SCREAMING_SNAKE_CASE__								=      SpeechTaFeatureExtractor()
						SCREAMING_SNAKE_CASE__								=      feature_extractor(audio_target=__UpperCAmelCase    ,				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]    ,				__UpperCAmelCase    ,				atol=1e-4       )       )


