






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_       :Any       =						random.Random()






def 							A     (       a_							,a_=1.0							,a_=None							,a_=None					)     ->						int:
					if rng is None:
										__UpperCamelCase      :					Union[str, Any]       		=global_rng

					__UpperCamelCase      :					Optional[Any]       		=[]
					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 				__A     (			unittest.TestCase ):





				"""simple docstring"""







				def __init__( self							,  lowerCamelCase__							,  lowerCamelCase__=7							,  lowerCamelCase__=400							,  lowerCamelCase__=2000							,  lowerCamelCase__=1							,  lowerCamelCase__=0.0							,  lowerCamelCase__=16000							,  lowerCamelCase__=True							,  lowerCamelCase__=80							,  lowerCamelCase__=16							,  lowerCamelCase__=64							,  lowerCamelCase__="hann_window"							,  lowerCamelCase__=80							,  lowerCamelCase__=7600							,  lowerCamelCase__=1E-10							,  lowerCamelCase__=True							,  ):




									"""simple docstring"""

									__UpperCamelCase      :					Any       		=parent
									__UpperCamelCase      :					List[str]       		=batch_size
									__UpperCamelCase      :					List[str]       		=min_seq_length
									__UpperCamelCase      :					int       		=max_seq_length
									__UpperCamelCase      :					int       		=(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									__UpperCamelCase      :					List[str]       		=feature_size
									__UpperCamelCase      :					List[Any]       		=padding_value
									__UpperCamelCase      :					Optional[int]       		=sampling_rate
									__UpperCamelCase      :					Optional[int]       		=do_normalize
									__UpperCamelCase      :					Dict       		=num_mel_bins
									__UpperCamelCase      :					Optional[int]       		=hop_length
									__UpperCamelCase      :					Union[str, Any]       		=win_length
									__UpperCamelCase      :					Dict       		=win_function
									__UpperCamelCase      :					str       		=fmin
									__UpperCamelCase      :					Tuple       		=fmax
									__UpperCamelCase      :					Union[str, Any]       		=mel_floor
									__UpperCamelCase      :					int       		=return_attention_mask


				def       __lowercase							( self    ):




									"""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       __lowercase							( self							,  lowerCamelCase__=False							,  lowerCamelCase__=False    ):




									"""simple docstring"""

									def _flatten(lowerCamelCase__    ):
														return list(itertools.chain(*_SCREAMING_SNAKE_CASE    )    )

									if equal_length:
														__UpperCamelCase      :					Union[str, Any]       		=floats_list((self.batch_size, self.max_seq_length)    )
									else:
														# make sure that inputs increase in size
														__UpperCamelCase      :					List[str]       		=[
														    _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:
														__UpperCamelCase      :					Union[str, Any]       		=[np.asarray(_SCREAMING_SNAKE_CASE    ) for x in speech_inputs]

									return speech_inputs







				def       __lowercase							( self							,  lowerCamelCase__=False							,  lowerCamelCase__=False    ):




									"""simple docstring"""

									if equal_length:
														__UpperCamelCase      :					Tuple       		=[floats_list((self.max_seq_length, self.num_mel_bins)    ) for _ in range(self.batch_size    )]
									else:
														# make sure that inputs increase in size
														__UpperCamelCase      :					List[Any]       		=[
														    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:
														__UpperCamelCase      :					str       		=[np.asarray(_SCREAMING_SNAKE_CASE    ) for x in speech_inputs]

									return speech_inputs




@require_torch
class 				__A     (			__SCREAMING_SNAKE_CASE      ,     unittest.TestCase ):





				"""simple docstring"""







				UpperCamelCase__ :      Optional[int]			 =SpeechTaFeatureExtractor


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Dict       		=SpeechTaFeatureExtractionTester(self    )


				def       __lowercase							( self							,  lowerCamelCase__    ):




									"""simple docstring"""

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


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Any       		=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									# create three inputs of length 800, 1000, and 1200
									__UpperCamelCase      :					Optional[int]       		=[floats_list((1, x)    )[0] for x in range(800							,  1400							,  200    )]
									__UpperCamelCase      :					Any       		=[np.asarray(_SCREAMING_SNAKE_CASE    ) for speech_input in speech_inputs]

									# Test not batched input
									__UpperCamelCase      :					str       		=feat_extract(speech_inputs[0]							,  return_tensors='np'    ).input_values
									__UpperCamelCase      :					Dict       		=feat_extract(np_speech_inputs[0]							,  return_tensors='np'    ).input_values
									self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE							,  atol=1E-3    )    )

									# Test batched
									__UpperCamelCase      :					List[str]       		=feat_extract(_SCREAMING_SNAKE_CASE							,  return_tensors='np'    ).input_values
									__UpperCamelCase      :					Optional[int]       		=feat_extract(_SCREAMING_SNAKE_CASE							,  return_tensors='np'    ).input_values
									for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE    ):
														self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE							,  atol=1E-3    )    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Dict       		=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									__UpperCamelCase      :					Any       		=[floats_list((1, x)    )[0] for x in range(800							,  1400							,  200    )]

									__UpperCamelCase      :					str       		=["longest", "max_length", "do_not_pad"]
									__UpperCamelCase      :					Optional[int]       		=[None, 1600, None]
									for max_length, padding in zip(_SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE    ):
														__UpperCamelCase      :					Any       		=feat_extract(_SCREAMING_SNAKE_CASE							,  padding=_SCREAMING_SNAKE_CASE							,  max_length=_SCREAMING_SNAKE_CASE							,  return_tensors='np'    )
														__UpperCamelCase      :					List[str]       		=processed.input_values

														self._check_zero_mean_unit_variance(input_values[0][:800]    )
														self.assertTrue(input_values[0][800:].sum() < 1E-6    )
														self._check_zero_mean_unit_variance(input_values[1][:1000]    )
														self.assertTrue(input_values[0][1000:].sum() < 1E-6    )
														self._check_zero_mean_unit_variance(input_values[2][:1200]    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[str]       		=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									__UpperCamelCase      :					Optional[int]       		=range(800							,  1400							,  200    )
									__UpperCamelCase      :					Dict       		=[floats_list((1, x)    )[0] for x in lengths]

									__UpperCamelCase      :					Any       		=["longest", "max_length", "do_not_pad"]
									__UpperCamelCase      :					Dict       		=[None, 1600, None]

									for max_length, padding in zip(_SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE    ):
														__UpperCamelCase      :					List[str]       		=feat_extract(_SCREAMING_SNAKE_CASE							,  max_length=_SCREAMING_SNAKE_CASE							,  padding=_SCREAMING_SNAKE_CASE    )
														__UpperCamelCase      :					str       		=processed.input_values

														self._check_zero_mean_unit_variance(input_values[0][:800]    )
														self._check_zero_mean_unit_variance(input_values[1][:1000]    )
														self._check_zero_mean_unit_variance(input_values[2][:1200]    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					int       		=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									__UpperCamelCase      :					Optional[Any]       		=[floats_list((1, x)    )[0] for x in range(800							,  1400							,  200    )]
									__UpperCamelCase      :					str       		=feat_extract(
									    _SCREAMING_SNAKE_CASE							,  truncation=_SCREAMING_SNAKE_CASE							,  max_length=1000							,  padding='max_length'							,  return_tensors='np'    )
									__UpperCamelCase      :					Optional[int]       		=processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]    )
									self._check_zero_mean_unit_variance(input_values[1]    )
									self._check_zero_mean_unit_variance(input_values[2]    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					int       		=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									__UpperCamelCase      :					List[Any]       		=[floats_list((1, x)    )[0] for x in range(800							,  1400							,  200    )]
									__UpperCamelCase      :					List[Any]       		=feat_extract(
									    _SCREAMING_SNAKE_CASE							,  truncation=_SCREAMING_SNAKE_CASE							,  max_length=1000							,  padding='longest'							,  return_tensors='np'    )
									__UpperCamelCase      :					Optional[int]       		=processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]    )
									self._check_zero_mean_unit_variance(input_values[1, :1000]    )
									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, 1000)    )

									__UpperCamelCase      :					Dict       		=[floats_list((1, x)    )[0] for x in range(800							,  1400							,  200    )]
									__UpperCamelCase      :					Optional[int]       		=feat_extract(
									    _SCREAMING_SNAKE_CASE							,  truncation=_SCREAMING_SNAKE_CASE							,  max_length=2000							,  padding='longest'							,  return_tensors='np'    )
									__UpperCamelCase      :					str       		=processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]    )
									self._check_zero_mean_unit_variance(input_values[1, :1000]    )
									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, 1200)    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Optional[Any]       		=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									__UpperCamelCase      :					Optional[int]       		=np.random.rand(100    ).astype(np.floataa    )
									__UpperCamelCase      :					Any       		=np_speech_inputs.tolist()

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


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Any       		=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									# create three inputs of length 800, 1000, and 1200
									__UpperCamelCase      :					Optional[int]       		=[floats_list((1, x)    )[0] for x in range(800							,  1400							,  200    )]
									__UpperCamelCase      :					Union[str, Any]       		=[np.asarray(_SCREAMING_SNAKE_CASE    ) for speech_input in speech_inputs]

									# Test feature size
									__UpperCamelCase      :					List[str]       		=feature_extractor(audio_target=_SCREAMING_SNAKE_CASE							,  padding=_SCREAMING_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
									__UpperCamelCase      :					Any       		=feature_extractor(speech_inputs[0]							,  return_tensors='np'    ).input_values
									__UpperCamelCase      :					Union[str, Any]       		=feature_extractor(np_speech_inputs[0]							,  return_tensors='np'    ).input_values
									self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE							,  atol=1E-3    )    )

									# Test batched
									__UpperCamelCase      :					List[str]       		=feature_extractor(_SCREAMING_SNAKE_CASE							,  return_tensors='np'    ).input_values
									__UpperCamelCase      :					List[str]       		=feature_extractor(_SCREAMING_SNAKE_CASE							,  return_tensors='np'    ).input_values
									for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE    ):
														self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE							,  atol=1E-3    )    )

									# Test 2-D numpy arrays are batched.
									__UpperCamelCase      :					Optional[Any]       		=[floats_list((1, x)    )[0] for x in (800, 800, 800)]
									__UpperCamelCase      :					Optional[int]       		=np.asarray(_SCREAMING_SNAKE_CASE    )
									__UpperCamelCase      :					Optional[Any]       		=feature_extractor(_SCREAMING_SNAKE_CASE							,  return_tensors='np'    ).input_values
									__UpperCamelCase      :					Any       		=feature_extractor(_SCREAMING_SNAKE_CASE							,  return_tensors='np'    ).input_values
									for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE    ):
														self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE							,  atol=1E-3    )    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Optional[Any]       		=self.feat_extract_tester.prepare_inputs_for_target()
									__UpperCamelCase      :					List[Any]       		=self.feature_extraction_class(**self.feat_extract_dict    )
									__UpperCamelCase      :					str       		=feat_extract.model_input_names[0]

									__UpperCamelCase      :					Optional[int]       		=BatchFeature({input_name: speech_inputs}    )

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

									__UpperCamelCase      :					Union[str, Any]       		=self.feat_extract_tester.prepare_inputs_for_target(equal_length=_SCREAMING_SNAKE_CASE    )
									__UpperCamelCase      :					Union[str, Any]       		=BatchFeature({input_name: speech_inputs}							,  tensor_type='np'    )

									__UpperCamelCase      :					List[str]       		=processed_features[input_name]

									if len(batch_features_input.shape    ) < 3:
														__UpperCamelCase      :					Optional[Any]       		=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       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					str       		=self.feat_extract_tester.prepare_inputs_for_target(equal_length=_SCREAMING_SNAKE_CASE    )
									__UpperCamelCase      :					List[Any]       		=self.feature_extraction_class(**self.feat_extract_dict    )
									__UpperCamelCase      :					int       		=feat_extract.model_input_names[0]

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

									__UpperCamelCase      :					Any       		=processed_features[input_name]

									if len(batch_features_input.shape    ) < 3:
														__UpperCamelCase      :					Tuple       		=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       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					int       		=self.feature_extraction_class(**self.feat_extract_dict    )
									__UpperCamelCase      :					Dict       		=self.feat_extract_tester.prepare_inputs_for_target()
									__UpperCamelCase      :					Optional[Any]       		=feat_extract.model_input_names[0]

									__UpperCamelCase      :					Tuple       		=BatchFeature({input_name: speech_inputs}    )

									__UpperCamelCase      :					Optional[int]       		=feat_extract.num_mel_bins  # hack!

									__UpperCamelCase      :					Tuple       		=feat_extract.pad(_SCREAMING_SNAKE_CASE							,  padding='longest'							,  return_tensors='np'    )[input_name]
									__UpperCamelCase      :					List[str]       		=feat_extract.pad(_SCREAMING_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       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					int       		=self.feat_extract_dict
									__UpperCamelCase      :					Optional[int]       		=True
									__UpperCamelCase      :					Any       		=self.feature_extraction_class(**_SCREAMING_SNAKE_CASE    )
									__UpperCamelCase      :					List[Any]       		=self.feat_extract_tester.prepare_inputs_for_target()
									__UpperCamelCase      :					Dict       		=[len(_SCREAMING_SNAKE_CASE    ) for x in speech_inputs]
									__UpperCamelCase      :					Optional[int]       		=feat_extract.model_input_names[0]

									__UpperCamelCase      :					List[Any]       		=BatchFeature({input_name: speech_inputs}    )

									__UpperCamelCase      :					Union[str, Any]       		=feat_extract.num_mel_bins  # hack!

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


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					int       		=self.feat_extract_dict
									__UpperCamelCase      :					Any       		=True
									__UpperCamelCase      :					Optional[int]       		=self.feature_extraction_class(**_SCREAMING_SNAKE_CASE    )
									__UpperCamelCase      :					Optional[Any]       		=self.feat_extract_tester.prepare_inputs_for_target()
									__UpperCamelCase      :					str       		=[len(_SCREAMING_SNAKE_CASE    ) for x in speech_inputs]
									__UpperCamelCase      :					str       		=feat_extract.model_input_names[0]

									__UpperCamelCase      :					Optional[int]       		=BatchFeature({input_name: speech_inputs}    )
									__UpperCamelCase      :					List[str]       		=min(_SCREAMING_SNAKE_CASE    )

									__UpperCamelCase      :					Dict       		=feat_extract.num_mel_bins  # hack!

									__UpperCamelCase      :					List[Any]       		=feat_extract.pad(
									    _SCREAMING_SNAKE_CASE							,  padding='max_length'							,  max_length=_SCREAMING_SNAKE_CASE							,  truncation=_SCREAMING_SNAKE_CASE							,  return_tensors='np'    )
									self.assertIn('attention_mask'							,  _SCREAMING_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       __lowercase							( self							,  lowerCamelCase__    ):




									"""simple docstring"""

									from datasets import load_dataset

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

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


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Union[str, Any]       		=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

									__UpperCamelCase      :					str       		=self._load_datasamples(1    )
									__UpperCamelCase      :					Optional[int]       		=SpeechTaFeatureExtractor()
									__UpperCamelCase      :					str       		=feature_extractor(_SCREAMING_SNAKE_CASE							,  return_tensors='pt'    ).input_values
									self.assertEquals(input_values.shape							,  (1, 93680)    )
									self.assertTrue(torch.allclose(input_values[0, :30]							,  _SCREAMING_SNAKE_CASE							,  atol=1E-6    )    )







				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Union[str, Any]       		=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

									__UpperCamelCase      :					int       		=self._load_datasamples(1    )
									__UpperCamelCase      :					str       		=SpeechTaFeatureExtractor()
									__UpperCamelCase      :					List[str]       		=feature_extractor(audio_target=_SCREAMING_SNAKE_CASE							,  return_tensors='pt'    ).input_values
									self.assertEquals(input_values.shape							,  (1, 366, 80)    )
									self.assertTrue(torch.allclose(input_values[0, 0, :30]							,  _SCREAMING_SNAKE_CASE							,  atol=1E-4    )    )






"""simple docstring"""






from typing import TYPE_CHECKING

from ..utils import _LazyModule


__snake_case							:   Optional[int]      =    {
    'config': [
        'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
        'OnnxConfig',
        'OnnxConfigWithPast',
        'OnnxSeq2SeqConfigWithPast',
        'PatchingSpec',
    ],
    'convert': ['export', 'validate_model_outputs'],
    'features': ['FeaturesManager'],
    'utils': ['ParameterFormat', 'compute_serialized_parameters_size'],
}


if TYPE_CHECKING:
	from .config import (
	    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
	    OnnxConfig,
	    OnnxConfigWithPast,
	    OnnxSeqaSeqConfigWithPast,
	    PatchingSpec,
	)
	from .convert import export, validate_model_outputs
	from .features import FeaturesManager
	from .utils import ParameterFormat, compute_serialized_parameters_size

else:
	import sys

	__snake_case							:   Any      =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)