
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


__lowerCAmelCase					   =       random.Random()


def    a      ( a		,						a=1.0		,						a=None		,						a=None	)     ->List[str]:




			'''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   ,     lowercase      :Any   ,     lowercase      :Optional[int]=7   ,     lowercase      :int=4_0_0   ,     lowercase      :Optional[Any]=2_0_0_0   ,     lowercase      :int=1   ,     lowercase      :Tuple=0.0   ,     lowercase      :Tuple=1_6_0_0_0   ,     lowercase      :Optional[int]=True   ,     lowercase      :Optional[Any]=8_0   ,     lowercase      :Dict=1_6   ,     lowercase      :List[Any]=6_4   ,     lowercase      :Tuple="hann_window"   ,     lowercase      :Optional[Any]=8_0   ,     lowercase      :int=7_6_0_0   ,     lowercase      :Union[str, Any]=1e-10   ,     lowercase      :Any=True   ,     )		->      List[str]:







				"""simple docstring"""






				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  snake_case__     (     self      :Dict			)		->      Any:







				"""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  snake_case__     (     self      :List[str]   ,     lowercase      :List[Any]=False   ,     lowercase      :str=False			)		->      Dict:







				"""simple docstring"""






				def _flatten(lowercase      :Dict			):
							return list(itertools.chain(*lowercase			)			)

				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(lowercase			) for x in speech_inputs]

				return speech_inputs







	def  snake_case__     (     self      :Tuple   ,     lowercase      :Tuple=False   ,     lowercase      :Tuple=False			)		->      str:







				"""simple docstring"""






				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(lowercase			) for x in speech_inputs]

				return speech_inputs





@require_torch
class     lowerCamelCase      (       __lowerCamelCase ,		unittest.TestCase					):
	UpperCamelCase_  :					List[Any]    		=	SpeechTaFeatureExtractor







	def  snake_case__     (     self      :Tuple			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= SpeechTaFeatureExtractionTester(self			)







	def  snake_case__     (     self      :List[str]   ,     lowercase      :Optional[Any]			)		->      Union[str, Any]:







				"""simple docstring"""






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







	def  snake_case__     (     self      :Union[str, Any]			)		->      Union[str, Any]:







				"""simple docstring"""






				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(lowercase			) 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(lowercase   ,     lowercase   ,     atol=1e-3			)			)

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







	def  snake_case__     (     self      :int			)		->      Dict:







				"""simple docstring"""






				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(lowercase   ,     lowercase			):
							SCREAMING_SNAKE_CASE     						= feat_extract(lowercase   ,     padding=lowercase   ,     max_length=lowercase   ,     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  snake_case__     (     self      :List[str]			)		->      Optional[int]:







				"""simple docstring"""






				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(lowercase   ,     lowercase			):
							SCREAMING_SNAKE_CASE     						= feat_extract(lowercase   ,     max_length=lowercase   ,     padding=lowercase			)
							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  snake_case__     (     self      :Optional[int]			)		->      List[Any]:







				"""simple docstring"""






				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(
				    lowercase   ,     truncation=lowercase   ,     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  snake_case__     (     self      :Tuple			)		->      str:







				"""simple docstring"""






				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(
				    lowercase   ,     truncation=lowercase   ,     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(
				    lowercase   ,     truncation=lowercase   ,     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  snake_case__     (     self      :Tuple			)		->      List[Any]:







				"""simple docstring"""






				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  snake_case__     (     self      :Optional[Any]			)		->      Optional[Any]:







				"""simple docstring"""






				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(lowercase			) for speech_input in speech_inputs]

				# Test feature size
				SCREAMING_SNAKE_CASE     						= feature_extractor(audio_target=lowercase   ,     padding=lowercase   ,     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(lowercase   ,     lowercase   ,     atol=1e-3			)			)

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







	def  snake_case__     (     self      :Dict			)		->      Any:







				"""simple docstring"""






				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(lowercase			) == len(lowercase			) for x, y in zip(lowercase   ,     processed_features[input_name]			)			)			)

				SCREAMING_SNAKE_CASE     						= self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase			)
				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  snake_case__     (     self      :Optional[Any]			)		->      str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase			)
				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  snake_case__     (     self      :Tuple			)		->      Union[str, Any]:







				"""simple docstring"""






				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(lowercase   ,     padding='''longest'''   ,     return_tensors='''np'''			)[input_name]
				SCREAMING_SNAKE_CASE     						= feat_extract.pad(lowercase   ,     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  snake_case__     (     self      :Optional[int]			)		->      Optional[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.feat_extract_dict
				SCREAMING_SNAKE_CASE     						= True
				SCREAMING_SNAKE_CASE     						= self.feature_extraction_class(**lowercase			)
				SCREAMING_SNAKE_CASE     						= self.feat_extract_tester.prepare_inputs_for_target()
				SCREAMING_SNAKE_CASE     						= [len(lowercase			) 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(lowercase   ,     padding='''longest'''   ,     return_tensors='''np'''			)
				self.assertIn('''attention_mask'''   ,     lowercase			)
				self.assertListEqual(list(processed.attention_mask.shape			)   ,     list(processed[input_name].shape[:2]			)			)
				self.assertListEqual(processed.attention_mask.sum(-1			).tolist()   ,     lowercase			)







	def  snake_case__     (     self      :int			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.feat_extract_dict
				SCREAMING_SNAKE_CASE     						= True
				SCREAMING_SNAKE_CASE     						= self.feature_extraction_class(**lowercase			)
				SCREAMING_SNAKE_CASE     						= self.feat_extract_tester.prepare_inputs_for_target()
				SCREAMING_SNAKE_CASE     						= [len(lowercase			) 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(lowercase			)

				SCREAMING_SNAKE_CASE     						= feat_extract.num_mel_bins  # hack!

				SCREAMING_SNAKE_CASE     						= feat_extract.pad(
				    lowercase   ,     padding='''max_length'''   ,     max_length=lowercase   ,     truncation=lowercase   ,     return_tensors='''np'''			)
				self.assertIn('''attention_mask'''   ,     lowercase			)
				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  snake_case__     (     self      :Any   ,     lowercase      :Tuple			)		->      Optional[Any]:







				"""simple docstring"""






				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(lowercase			)			)[:num_samples]['''audio''']

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







	def  snake_case__     (     self      :Union[str, Any]			)		->      Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= torch.tensor(
				    [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
				     3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
				     2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
				     4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
				     7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
				     4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03]			)
				# fmt: on

				SCREAMING_SNAKE_CASE     						= self._load_datasamples(1			)
				SCREAMING_SNAKE_CASE     						= SpeechTaFeatureExtractor()
				SCREAMING_SNAKE_CASE     						= feature_extractor(lowercase   ,     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]   ,     lowercase   ,     atol=1e-6			)			)







	def  snake_case__     (     self      :Optional[int]			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= torch.tensor(
				    [-2.68_70, -3.01_04, -3.13_56, -3.53_52, -3.00_44, -3.03_53, -3.47_19, -3.67_77,
				     -3.15_20, -2.94_35, -2.65_53, -2.87_95, -2.99_44, -2.59_21, -3.02_79, -3.03_86,
				     -3.08_64, -3.12_91, -3.23_53, -2.74_44, -2.68_31, -2.72_87, -3.17_61, -3.15_71,
				     -3.27_26, -3.05_82, -3.10_07, -3.45_33, -3.46_95, -3.09_98]			)
				# fmt: on

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

import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


__lowerCAmelCase					   =       logging.get_logger(__name__)





class     lowerCamelCase      (       __lowerCamelCase					):







	def __init__(     self      :List[Any]   ,     *lowercase      :List[str]   ,     **lowercase      :List[Any]			)		->      None:







				"""simple docstring"""






				warnings.warn(
				    '''The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
				    ''' use YolosImageProcessor instead.'''   ,     lowercase   ,     )
				super().__init__(*lowercase   ,     **lowercase			)