





import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import BeitImageProcessor






class 					__A    (    unittest.TestCase ):

			'''simple docstring'''







			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=7       ,  __lowerCAmelCase=3       ,  __lowerCAmelCase=1_8       ,  __lowerCAmelCase=3_0       ,  __lowerCAmelCase=4_0_0       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=[0.5, 0.5, 0.5]       ,  __lowerCAmelCase=[0.5, 0.5, 0.5]       ,  __lowerCAmelCase=False       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		size if size is not None else {'''height''': 2_0, '''width''': 2_0}
				lowerCamelCase__								=		crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
				lowerCamelCase__								=		parent
				lowerCamelCase__								=		batch_size
				lowerCamelCase__								=		num_channels
				lowerCamelCase__								=		image_size
				lowerCamelCase__								=		min_resolution
				lowerCamelCase__								=		max_resolution
				lowerCamelCase__								=		do_resize
				lowerCamelCase__								=		size
				lowerCamelCase__								=		do_center_crop
				lowerCamelCase__								=		crop_size
				lowerCamelCase__								=		do_normalize
				lowerCamelCase__								=		image_mean
				lowerCamelCase__								=		image_std
				lowerCamelCase__								=		do_reduce_labels





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_center_crop": self.do_center_crop,
				    "crop_size": self.crop_size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_reduce_labels": self.do_reduce_labels,
				}





def lowerCAmelCase__()				->      Tuple:







	'''simple docstring'''







	lowerCamelCase__								=		load_dataset('''hf-internal-testing/fixtures_ade20k'''       ,split='''test'''							)

	lowerCamelCase__								=		Image.open(dataset[0]['''file''']							)
	lowerCamelCase__								=		Image.open(dataset[1]['''file''']							)

	return image, map





def lowerCAmelCase__()				->      Optional[int]:







	'''simple docstring'''







	lowerCamelCase__								=		load_dataset('''hf-internal-testing/fixtures_ade20k'''       ,split='''test'''							)

	lowerCamelCase__								=		Image.open(ds[0]['''file''']							)
	lowerCamelCase__								=		Image.open(ds[1]['''file''']							)
	lowerCamelCase__								=		Image.open(ds[2]['''file''']							)
	lowerCamelCase__								=		Image.open(ds[3]['''file''']							)

	return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class 					__A    (    UpperCamelCase_			,				unittest.TestCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						BeitImageProcessor if is_vision_available() else None





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		BeitImageProcessingTester(self							)





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return self.image_processor_tester.prepare_image_processor_dict()





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.image_processing_class(**self.image_processor_dict							)
				self.assertTrue(hasattr(UpperCamelCase__       ,  '''do_resize'''							)							)
				self.assertTrue(hasattr(UpperCamelCase__       ,  '''size'''							)							)
				self.assertTrue(hasattr(UpperCamelCase__       ,  '''do_center_crop'''							)							)
				self.assertTrue(hasattr(UpperCamelCase__       ,  '''center_crop'''							)							)
				self.assertTrue(hasattr(UpperCamelCase__       ,  '''do_normalize'''							)							)
				self.assertTrue(hasattr(UpperCamelCase__       ,  '''image_mean'''							)							)
				self.assertTrue(hasattr(UpperCamelCase__       ,  '''image_std'''							)							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.image_processing_class.from_dict(self.image_processor_dict							)
				self.assertEqual(image_processor.size       ,  {'''height''': 2_0, '''width''': 2_0}							)
				self.assertEqual(image_processor.crop_size       ,  {'''height''': 1_8, '''width''': 1_8}							)
				self.assertEqual(image_processor.do_reduce_labels       ,  UpperCamelCase__							)

				lowerCamelCase__								=		self.image_processing_class.from_dict(
				    self.image_processor_dict       ,  size=4_2       ,  crop_size=8_4       ,  reduce_labels=UpperCamelCase__							)
				self.assertEqual(image_processor.size       ,  {'''height''': 4_2, '''width''': 4_2}							)
				self.assertEqual(image_processor.crop_size       ,  {'''height''': 8_4, '''width''': 8_4}							)
				self.assertEqual(image_processor.do_reduce_labels       ,  UpperCamelCase__							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.image_processing_class(**self.image_processor_dict							)
				# create random PIL images
				lowerCamelCase__								=		prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=UpperCamelCase__							)
				for image in image_inputs:
					self.assertIsInstance(UpperCamelCase__       ,  Image.Image							)

				# Test not batched input
				lowerCamelCase__								=		image_processing(image_inputs[0]       ,  return_tensors='''pt'''							).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,  (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,  )

				# Test batched
				lowerCamelCase__								=		image_processing(UpperCamelCase__       ,  return_tensors='''pt'''							).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,  )





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.image_processing_class(**self.image_processor_dict							)
				# create random numpy tensors
				lowerCamelCase__								=		prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=UpperCamelCase__       ,  numpify=UpperCamelCase__							)
				for image in image_inputs:
					self.assertIsInstance(UpperCamelCase__       ,  np.ndarray							)

				# Test not batched input
				lowerCamelCase__								=		image_processing(image_inputs[0]       ,  return_tensors='''pt'''							).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,  (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,  )

				# Test batched
				lowerCamelCase__								=		image_processing(UpperCamelCase__       ,  return_tensors='''pt'''							).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,  )





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.image_processing_class(**self.image_processor_dict							)
				# create random PyTorch tensors
				lowerCamelCase__								=		prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=UpperCamelCase__       ,  torchify=UpperCamelCase__							)
				for image in image_inputs:
					self.assertIsInstance(UpperCamelCase__       ,  torch.Tensor							)

				# Test not batched input
				lowerCamelCase__								=		image_processing(image_inputs[0]       ,  return_tensors='''pt'''							).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,  (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,  )

				# Test batched
				lowerCamelCase__								=		image_processing(UpperCamelCase__       ,  return_tensors='''pt'''							).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,  )





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.image_processing_class(**self.image_processor_dict							)
				# create random PyTorch tensors
				lowerCamelCase__								=		prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=UpperCamelCase__       ,  torchify=UpperCamelCase__							)
				lowerCamelCase__								=		[]
				for image in image_inputs:
					self.assertIsInstance(UpperCamelCase__       ,  torch.Tensor							)
					maps.append(torch.zeros(image.shape[-2:]							).long()							)

				# Test not batched input
				lowerCamelCase__								=		image_processing(image_inputs[0]       ,  maps[0]       ,  return_tensors='''pt'''							)
				self.assertEqual(
				    encoding['''pixel_values'''].shape       ,  (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,  )
				self.assertEqual(
				    encoding['''labels'''].shape       ,  (
				        1,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,  )
				self.assertEqual(encoding['''labels'''].dtype       ,  torch.long							)
				self.assertTrue(encoding['''labels'''].min().item() >= 0							)
				self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5							)

				# Test batched
				lowerCamelCase__								=		image_processing(UpperCamelCase__       ,  UpperCamelCase__       ,  return_tensors='''pt'''							)
				self.assertEqual(
				    encoding['''pixel_values'''].shape       ,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,  )
				self.assertEqual(
				    encoding['''labels'''].shape       ,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,  )
				self.assertEqual(encoding['''labels'''].dtype       ,  torch.long							)
				self.assertTrue(encoding['''labels'''].min().item() >= 0							)
				self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5							)

				# Test not batched input (PIL images)
				lowerCamelCase__								=		prepare_semantic_single_inputs()

				lowerCamelCase__								=		image_processing(UpperCamelCase__       ,  UpperCamelCase__       ,  return_tensors='''pt'''							)
				self.assertEqual(
				    encoding['''pixel_values'''].shape       ,  (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,  )
				self.assertEqual(
				    encoding['''labels'''].shape       ,  (
				        1,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,  )
				self.assertEqual(encoding['''labels'''].dtype       ,  torch.long							)
				self.assertTrue(encoding['''labels'''].min().item() >= 0							)
				self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5							)

				# Test batched input (PIL images)
				lowerCamelCase__								=		prepare_semantic_batch_inputs()

				lowerCamelCase__								=		image_processing(UpperCamelCase__       ,  UpperCamelCase__       ,  return_tensors='''pt'''							)
				self.assertEqual(
				    encoding['''pixel_values'''].shape       ,  (
				        2,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,  )
				self.assertEqual(
				    encoding['''labels'''].shape       ,  (
				        2,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )       ,  )
				self.assertEqual(encoding['''labels'''].dtype       ,  torch.long							)
				self.assertTrue(encoding['''labels'''].min().item() >= 0							)
				self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.image_processing_class(**self.image_processor_dict							)

				# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
				lowerCamelCase__								=		prepare_semantic_single_inputs()
				lowerCamelCase__								=		image_processing(UpperCamelCase__       ,  UpperCamelCase__       ,  return_tensors='''pt'''							)
				self.assertTrue(encoding['''labels'''].min().item() >= 0							)
				self.assertTrue(encoding['''labels'''].max().item() <= 1_5_0							)

				lowerCamelCase__								=		True
				lowerCamelCase__								=		image_processing(UpperCamelCase__       ,  UpperCamelCase__       ,  return_tensors='''pt'''							)
				self.assertTrue(encoding['''labels'''].min().item() >= 0							)
				self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5							)




from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


__UpperCamelCase		:       Optional[Any]									=     logging.get_logger(__name__)





class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		["""input_features""", """attention_mask"""]


						def __init__(       self	:				Any    ,		UpperCamelCase__	:				List[str]=80    ,		UpperCamelCase__	:				Tuple=1_6000    ,		UpperCamelCase__	:				Dict=0.0    ,		UpperCamelCase__	:				Tuple=10    ,		UpperCamelCase__	:				int=25    ,		UpperCamelCase__	:				Optional[Any]="hamming_window"    ,		UpperCamelCase__	:				Tuple=3_2768.0    ,		UpperCamelCase__	:				str=0.97    ,		UpperCamelCase__	:				List[str]=1.0    ,		UpperCamelCase__	:				Optional[int]=True    ,		UpperCamelCase__	:				Dict=True    ,		UpperCamelCase__	:				List[str]=False    ,		**UpperCamelCase__	:				List[str]    ,		):





												'''simple docstring'''





												super().__init__(feature_size=UpperCamelCase__    ,		sampling_rate=UpperCamelCase__    ,		padding_value=UpperCamelCase__    ,		**UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Dict							=							feature_size
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							sampling_rate
												SCREAMING_SNAKE_CASE							:    int							=							padding_value
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							hop_length
												SCREAMING_SNAKE_CASE							:    Tuple							=							win_length
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							frame_signal_scale
												SCREAMING_SNAKE_CASE							:    int							=							preemphasis_coeff
												SCREAMING_SNAKE_CASE							:    List[Any]							=							mel_floor
												SCREAMING_SNAKE_CASE							:    int							=							normalize_means
												SCREAMING_SNAKE_CASE							:    List[str]							=							normalize_vars
												SCREAMING_SNAKE_CASE							:    Any							=							win_function
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							return_attention_mask

												SCREAMING_SNAKE_CASE							:    int							=							win_length * sampling_rate // 1000
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							hop_length * sampling_rate // 1000

												SCREAMING_SNAKE_CASE							:    int							=							optimal_fft_length(self.sample_size						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							(self.n_fft // 2) + 1


						def        __A       (       self	:				str    ,		UpperCamelCase__	:				np.array						):





												'''simple docstring'''





												if self.win_function == "hamming_window":
																		SCREAMING_SNAKE_CASE							:    List[str]							=							window_function(window_length=self.sample_size    ,		name=self.win_function    ,		periodic=UpperCamelCase__						)
												else:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							window_function(window_length=self.sample_size    ,		name=self.win_function						)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							mel_filter_bank(
												    num_frequency_bins=self.n_freqs    ,		num_mel_filters=self.feature_size    ,		min_frequency=0.0    ,		max_frequency=self.sampling_rate / 2.0    ,		sampling_rate=self.sampling_rate    ,		)

												SCREAMING_SNAKE_CASE							:    Tuple							=							spectrogram(
												    one_waveform * self.frame_signal_scale    ,		window=UpperCamelCase__    ,		frame_length=self.sample_size    ,		hop_length=self.sample_stride    ,		fft_length=self.n_fft    ,		center=UpperCamelCase__    ,		preemphasis=self.preemphasis_coeff    ,		mel_filters=UpperCamelCase__    ,		mel_floor=self.mel_floor    ,		log_mel='''log'''    ,		)
												return msfc_features.T


						def        __A       (       self	:				int    ,		UpperCamelCase__	:				Optional[Any]    ,		UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				Optional[int]						):





												'''simple docstring'''





												if self.normalize_means:
																		SCREAMING_SNAKE_CASE							:    str							=							x[:input_length].mean(axis=0						)
																		SCREAMING_SNAKE_CASE							:    List[str]							=							np.subtract(UpperCamelCase__    ,		UpperCamelCase__						)
												if self.normalize_vars:
																		SCREAMING_SNAKE_CASE							:    str							=							x[:input_length].std(axis=0						)
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							np.divide(UpperCamelCase__    ,		UpperCamelCase__						)

												if input_length < x.shape[0]:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							padding_value

												# make sure array is in float32
												SCREAMING_SNAKE_CASE							:    str							=							x.astype(np.floataa						)

												return x


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				List[np.ndarray]    ,		UpperCamelCase__	:				Optional[np.ndarray] = None						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							attention_mask.sum(-1						) if attention_mask is not None else [x.shape[0] for x in input_features]
												return [self._normalize_one(UpperCamelCase__    ,		UpperCamelCase__    ,		self.padding_value						) for x, n in zip(UpperCamelCase__    ,		UpperCamelCase__						)]



						def __call__(       self	:				Dict    ,		UpperCamelCase__	:				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,		UpperCamelCase__	:				Union[bool, str, PaddingStrategy] = False    ,		UpperCamelCase__	:				Optional[int] = None    ,		UpperCamelCase__	:				bool = False    ,		UpperCamelCase__	:				Optional[int] = None    ,		UpperCamelCase__	:				Optional[bool] = None    ,		UpperCamelCase__	:				Optional[Union[str, TensorType]] = None    ,		UpperCamelCase__	:				Optional[int] = None    ,		**UpperCamelCase__	:				Optional[int]    ,		):





												'''simple docstring'''





												if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																								raise ValueError(
																								    f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
																								    f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
																								    f""" {self.sampling_rate} and not {sampling_rate}."""						)
												else:
																		logger.warning(
																		    '''It is strongly recommended to pass the ``sampling_rate`` argument to this function. '''
																		    '''Failing to do so can result in silent errors that might be hard to debug.'''						)

												SCREAMING_SNAKE_CASE							:    Any							=							isinstance(UpperCamelCase__    ,		np.ndarray						) and len(raw_speech.shape						) > 1
												if is_batched_numpy and len(raw_speech.shape						) > 2:
																		raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""						)
												SCREAMING_SNAKE_CASE							:    str							=							is_batched_numpy or (
												    isinstance(UpperCamelCase__    ,		(list, tuple)						) and (isinstance(raw_speech[0]    ,		(np.ndarray, tuple, list)						))
												)

												if is_batched:
																		SCREAMING_SNAKE_CASE							:    int							=							[np.asarray(UpperCamelCase__    ,		dtype=np.floataa						) for speech in raw_speech]
												elif not is_batched and not isinstance(UpperCamelCase__    ,		np.ndarray						):
																		SCREAMING_SNAKE_CASE							:    Dict							=							np.asarray(UpperCamelCase__    ,		dtype=np.floataa						)
												elif isinstance(UpperCamelCase__    ,		np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							raw_speech.astype(np.floataa						)

												# always return batch
												if not is_batched:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							[raw_speech]

												# extract fbank features
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							[self._extract_mfsc_features(UpperCamelCase__						) for one_waveform in raw_speech]

												# convert into correct format for padding
												SCREAMING_SNAKE_CASE							:    Tuple							=							BatchFeature({'''input_features''': features}						)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.pad(
												    UpperCamelCase__    ,		padding=UpperCamelCase__    ,		max_length=UpperCamelCase__    ,		truncation=UpperCamelCase__    ,		pad_to_multiple_of=UpperCamelCase__    ,		return_attention_mask=UpperCamelCase__    ,		**UpperCamelCase__    ,		)
												# make sure list is in array format
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							padded_inputs.get('''input_features'''						)
												if isinstance(input_features[0]    ,		UpperCamelCase__						):
																		SCREAMING_SNAKE_CASE							:    Any							=							[np.asarray(UpperCamelCase__    ,		dtype=np.floataa						) for feature in input_features]

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							padded_inputs.get('''attention_mask'''						)
												if attention_mask is not None:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							[np.asarray(UpperCamelCase__    ,		dtype=np.intaa						) for array in attention_mask]

												if self.normalize_means or self.normalize_vars:
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							(
																		    np.array(UpperCamelCase__    ,		dtype=np.intaa						)
																		    if self._get_padding_strategies(UpperCamelCase__    ,		max_length=UpperCamelCase__						) is not PaddingStrategy.DO_NOT_PAD
																		    and padding
																		    else None
																		)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							self.normalize(
																		    padded_inputs['''input_features''']    ,		attention_mask=UpperCamelCase__						)

												if return_tensors is not None:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							padded_inputs.convert_to_tensors(UpperCamelCase__						)

												return padded_inputs





