






'''simple docstring'''







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class _lowercase     (    _lowercase		):
							a    				=	(PNDMScheduler,)
							a    				=	(("""num_inference_steps""", 50),)




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							**UpperCamelCase__:    List[Any]					):
														lowerCamelCase__ :     int	  =	{
														    """num_train_timesteps""": 1_000,
														    """beta_start""": 0.0_001,
														    """beta_end""": 0.02,
														    """beta_schedule""": """linear""",
														}

														config.update(**UpperCamelCase__					)
														return config




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    Any=0      ,							**UpperCamelCase__:    Union[str, Any]					):
														lowerCamelCase__ :     Dict	  =	dict(self.forward_default_kwargs					)
														lowerCamelCase__ :     Tuple	  =	kwargs.pop("""num_inference_steps"""      ,							UpperCamelCase__					)
														lowerCamelCase__ :     Dict	  =	self.dummy_sample
														lowerCamelCase__ :     Union[str, Any]	  =	0.1 * sample
														lowerCamelCase__ :     Tuple	  =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__ :     Optional[int]	  =	self.get_scheduler_config(**UpperCamelCase__					)
																					lowerCamelCase__ :     Any	  =	scheduler_class(**UpperCamelCase__					)
																					scheduler.set_timesteps(UpperCamelCase__					)
																					# copy over dummy past residuals
																					lowerCamelCase__ :     int	  =	dummy_past_residuals[:]

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(UpperCamelCase__					)
																												lowerCamelCase__ :     str	  =	scheduler_class.from_pretrained(UpperCamelCase__					)
																												new_scheduler.set_timesteps(UpperCamelCase__					)
																												# copy over dummy past residuals
																												lowerCamelCase__ :     List[str]	  =	dummy_past_residuals[:]

																					lowerCamelCase__ :     Tuple	  =	scheduler.step_prk(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample
																					lowerCamelCase__ :     List[Any]	  =	new_scheduler.step_prk(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample

																					assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

																					lowerCamelCase__ :     Tuple	  =	scheduler.step_plms(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample
																					lowerCamelCase__ :     int	  =	new_scheduler.step_plms(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample

																					assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"




							def 		lowerCamelCase_    (							self:    List[Any]					):
														pass




							def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    Dict=0      ,							**UpperCamelCase__:    Optional[Any]					):
														lowerCamelCase__ :     Union[str, Any]	  =	dict(self.forward_default_kwargs					)
														lowerCamelCase__ :     Any	  =	kwargs.pop("""num_inference_steps"""      ,							UpperCamelCase__					)
														lowerCamelCase__ :     int	  =	self.dummy_sample
														lowerCamelCase__ :     int	  =	0.1 * sample
														lowerCamelCase__ :     Optional[Any]	  =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__ :     List[Any]	  =	self.get_scheduler_config()
																					lowerCamelCase__ :     List[str]	  =	scheduler_class(**UpperCamelCase__					)
																					scheduler.set_timesteps(UpperCamelCase__					)

																					# copy over dummy past residuals (must be after setting timesteps)
																					lowerCamelCase__ :     List[str]	  =	dummy_past_residuals[:]

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(UpperCamelCase__					)
																												lowerCamelCase__ :     Tuple	  =	scheduler_class.from_pretrained(UpperCamelCase__					)
																												# copy over dummy past residuals
																												new_scheduler.set_timesteps(UpperCamelCase__					)

																												# copy over dummy past residual (must be after setting timesteps)
																												lowerCamelCase__ :     List[str]	  =	dummy_past_residuals[:]

																					lowerCamelCase__ :     Tuple	  =	scheduler.step_prk(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample
																					lowerCamelCase__ :     Union[str, Any]	  =	new_scheduler.step_prk(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample

																					assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

																					lowerCamelCase__ :     str	  =	scheduler.step_plms(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample
																					lowerCamelCase__ :     Optional[Any]	  =	new_scheduler.step_plms(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample

																					assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							**UpperCamelCase__:    List[Any]					):
														lowerCamelCase__ :     int	  =	self.scheduler_classes[0]
														lowerCamelCase__ :     str	  =	self.get_scheduler_config(**UpperCamelCase__					)
														lowerCamelCase__ :     List[str]	  =	scheduler_class(**UpperCamelCase__					)

														lowerCamelCase__ :     Tuple	  =	10
														lowerCamelCase__ :     List[Any]	  =	self.dummy_model()
														lowerCamelCase__ :     List[str]	  =	self.dummy_sample_deter
														scheduler.set_timesteps(UpperCamelCase__					)

														for i, t in enumerate(scheduler.prk_timesteps					):
																					lowerCamelCase__ :     Union[str, Any]	  =	model(UpperCamelCase__      ,							UpperCamelCase__					)
																					lowerCamelCase__ :     Tuple	  =	scheduler.step_prk(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					).prev_sample

														for i, t in enumerate(scheduler.plms_timesteps					):
																					lowerCamelCase__ :     List[Any]	  =	model(UpperCamelCase__      ,							UpperCamelCase__					)
																					lowerCamelCase__ :     Optional[Any]	  =	scheduler.step_plms(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					).prev_sample

														return sample




							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     List[Any]	  =	dict(self.forward_default_kwargs					)

														lowerCamelCase__ :     str	  =	kwargs.pop("""num_inference_steps"""      ,							UpperCamelCase__					)

														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__ :     Optional[int]	  =	self.get_scheduler_config()
																					lowerCamelCase__ :     str	  =	scheduler_class(**UpperCamelCase__					)

																					lowerCamelCase__ :     int	  =	self.dummy_sample
																					lowerCamelCase__ :     Tuple	  =	0.1 * sample

																					if num_inference_steps is not None and hasattr(UpperCamelCase__      ,							"""set_timesteps"""					):
																												scheduler.set_timesteps(UpperCamelCase__					)
																					elif num_inference_steps is not None and not hasattr(UpperCamelCase__      ,							"""set_timesteps"""					):
																												lowerCamelCase__ :     List[Any]	  =	num_inference_steps

																					# copy over dummy past residuals (must be done after set_timesteps)
																					lowerCamelCase__ :     Dict	  =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
																					lowerCamelCase__ :     Tuple	  =	dummy_past_residuals[:]

																					lowerCamelCase__ :     Tuple	  =	scheduler.step_prk(UpperCamelCase__      ,							0      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample
																					lowerCamelCase__ :     List[Any]	  =	scheduler.step_prk(UpperCamelCase__      ,							1      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample

																					self.assertEqual(output_a.shape      ,							sample.shape					)
																					self.assertEqual(output_a.shape      ,							output_a.shape					)

																					lowerCamelCase__ :     int	  =	scheduler.step_plms(UpperCamelCase__      ,							0      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample
																					lowerCamelCase__ :     Optional[int]	  =	scheduler.step_plms(UpperCamelCase__      ,							1      ,							UpperCamelCase__      ,							**UpperCamelCase__					).prev_sample

																					self.assertEqual(output_a.shape      ,							sample.shape					)
																					self.assertEqual(output_a.shape      ,							output_a.shape					)




							def 		lowerCamelCase_    (							self:    Any					):
														for timesteps in [100, 1_000]:
																					self.check_over_configs(num_train_timesteps=UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    str					):
														for steps_offset in [0, 1]:
																					self.check_over_configs(steps_offset=UpperCamelCase__					)

														lowerCamelCase__ :     Tuple	  =	self.scheduler_classes[0]
														lowerCamelCase__ :     Optional[Any]	  =	self.get_scheduler_config(steps_offset=1					)
														lowerCamelCase__ :     Dict	  =	scheduler_class(**UpperCamelCase__					)
														scheduler.set_timesteps(10					)
														assert torch.equal(
														    scheduler.timesteps      ,							torch.LongTensor(
														        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]					)      ,							)




							def 		lowerCamelCase_    (							self:    int					):
														for beta_start, beta_end in zip([0.0_001, 0.001]      ,							[0.002, 0.02]					):
																					self.check_over_configs(beta_start=UpperCamelCase__      ,							beta_end=UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Dict					):
														for schedule in ["linear", "squaredcos_cap_v2"]:
																					self.check_over_configs(beta_schedule=UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Any					):
														for prediction_type in ["epsilon", "v_prediction"]:
																					self.check_over_configs(prediction_type=UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														for t in [1, 5, 10]:
																					self.check_over_forward(time_step=UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    List[str]					):
														for t, num_inference_steps in zip([1, 5, 10]      ,							[10, 50, 100]					):
																					self.check_over_forward(num_inference_steps=UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Dict					):
														# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
														lowerCamelCase__ :     Any	  =	27

														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__ :     Any	  =	self.dummy_sample
																					lowerCamelCase__ :     int	  =	0.1 * sample

																					lowerCamelCase__ :     str	  =	self.get_scheduler_config()
																					lowerCamelCase__ :     Tuple	  =	scheduler_class(**UpperCamelCase__					)

																					scheduler.set_timesteps(UpperCamelCase__					)

																					# before power of 3 fix, would error on first step, so we only need to do two
																					for i, t in enumerate(scheduler.prk_timesteps[:2]					):
																												lowerCamelCase__ :     str	  =	scheduler.step_prk(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					).prev_sample




							def 		lowerCamelCase_    (							self:    str					):
														with self.assertRaises(UpperCamelCase__					):
																					lowerCamelCase__ :     str	  =	self.scheduler_classes[0]
																					lowerCamelCase__ :     Optional[Any]	  =	self.get_scheduler_config()
																					lowerCamelCase__ :     List[Any]	  =	scheduler_class(**UpperCamelCase__					)

																					scheduler.step_plms(self.dummy_sample      ,							1      ,							self.dummy_sample					).prev_sample




							def 		lowerCamelCase_    (							self:    List[str]					):
														lowerCamelCase__ :     Optional[Any]	  =	self.full_loop()
														lowerCamelCase__ :     Optional[Any]	  =	torch.sum(torch.abs(UpperCamelCase__					)					)
														lowerCamelCase__ :     int	  =	torch.mean(torch.abs(UpperCamelCase__					)					)

														assert abs(result_sum.item() - 198.1_318					) < 1e-2
														assert abs(result_mean.item() - 0.2_580					) < 1e-3




							def 		lowerCamelCase_    (							self:    str					):
														lowerCamelCase__ :     int	  =	self.full_loop(prediction_type="""v_prediction"""					)
														lowerCamelCase__ :     Tuple	  =	torch.sum(torch.abs(UpperCamelCase__					)					)
														lowerCamelCase__ :     Optional[int]	  =	torch.mean(torch.abs(UpperCamelCase__					)					)

														assert abs(result_sum.item() - 67.3_986					) < 1e-2
														assert abs(result_mean.item() - 0.0_878					) < 1e-3




							def 		lowerCamelCase_    (							self:    Tuple					):
														# We specify different beta, so that the first alpha is 0.99
														lowerCamelCase__ :     str	  =	self.full_loop(set_alpha_to_one=UpperCamelCase__      ,							beta_start=0.01					)
														lowerCamelCase__ :     Union[str, Any]	  =	torch.sum(torch.abs(UpperCamelCase__					)					)
														lowerCamelCase__ :     int	  =	torch.mean(torch.abs(UpperCamelCase__					)					)

														assert abs(result_sum.item() - 230.0_399					) < 1e-2
														assert abs(result_mean.item() - 0.2_995					) < 1e-3




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														# We specify different beta, so that the first alpha is 0.99
														lowerCamelCase__ :     Dict	  =	self.full_loop(set_alpha_to_one=UpperCamelCase__      ,							beta_start=0.01					)
														lowerCamelCase__ :     List[Any]	  =	torch.sum(torch.abs(UpperCamelCase__					)					)
														lowerCamelCase__ :     str	  =	torch.mean(torch.abs(UpperCamelCase__					)					)

														assert abs(result_sum.item() - 186.9_482					) < 1e-2
														assert abs(result_mean.item() - 0.2_434					) < 1e-3









'''simple docstring'''







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


_A     :      List[str]   					=logging.get_logger(__name__)

class _lowercase     (    _lowercase		):
							a    				=	["""input_features""", """attention_mask"""]




							def __init__(							self:    Optional[int]      ,							UpperCamelCase__:    str=80      ,							UpperCamelCase__:    Union[str, Any]=16_000      ,							UpperCamelCase__:    str=0.0      ,							UpperCamelCase__:    Tuple=10      ,							UpperCamelCase__:    Dict=25      ,							UpperCamelCase__:    List[Any]="hamming_window"      ,							UpperCamelCase__:    Tuple=32_768.0      ,							UpperCamelCase__:    List[Any]=0.97      ,							UpperCamelCase__:    Optional[int]=1.0      ,							UpperCamelCase__:    str=True      ,							UpperCamelCase__:    Optional[Any]=True      ,							UpperCamelCase__:    Optional[int]=False      ,							**UpperCamelCase__:    Optional[int]      ,							):
														super().__init__(feature_size=UpperCamelCase__      ,							sampling_rate=UpperCamelCase__      ,							padding_value=UpperCamelCase__      ,							**UpperCamelCase__					)

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

														lowerCamelCase__ :     Optional[Any]	  =	win_length * sampling_rate // 1_000
														lowerCamelCase__ :     Tuple	  =	hop_length * sampling_rate // 1_000

														lowerCamelCase__ :     List[Any]	  =	optimal_fft_length(self.sample_size					)
														lowerCamelCase__ :     List[str]	  =	(self.n_fft // 2) + 1




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    np.array					):
														if self.win_function == "hamming_window":
																					lowerCamelCase__ :     Dict	  =	window_function(window_length=self.sample_size      ,							name=self.win_function      ,							periodic=UpperCamelCase__					)
														else:
																					lowerCamelCase__ :     Union[str, Any]	  =	window_function(window_length=self.sample_size      ,							name=self.win_function					)

														lowerCamelCase__ :     List[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      ,							)

														lowerCamelCase__ :     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 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    List[str]      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    Union[str, Any]					):
														# make sure we normalize float32 arrays
														if self.normalize_means:
																					lowerCamelCase__ :     List[str]	  =	x[:input_length].mean(axis=0					)
																					lowerCamelCase__ :     List[str]	  =	np.subtract(UpperCamelCase__      ,							UpperCamelCase__					)
														if self.normalize_vars:
																					lowerCamelCase__ :     Union[str, Any]	  =	x[:input_length].std(axis=0					)
																					lowerCamelCase__ :     str	  =	np.divide(UpperCamelCase__      ,							UpperCamelCase__					)

														if input_length < x.shape[0]:
																					lowerCamelCase__ :     List[Any]	  =	padding_value

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

														return x




							def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    List[np.ndarray]      ,							UpperCamelCase__:    Optional[np.ndarray] = None					):
														lowerCamelCase__ :     Optional[int]	  =	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:    List[str]      ,							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__:    Tuple      ,							):

														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."""					)

														lowerCamelCase__ :     List[str]	  =	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}'''					)
														lowerCamelCase__ :     List[str]	  =	is_batched_numpy or (
														    isinstance(UpperCamelCase__      ,							(list, tuple)					) and (isinstance(raw_speech[0]      ,							(np.ndarray, tuple, list)					))
														)

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

														# always return batch
														if not is_batched:
																					lowerCamelCase__ :     Optional[Any]	  =	[raw_speech]

														# extract fbank features
														lowerCamelCase__ :     str	  =	[self._extract_mfsc_features(UpperCamelCase__					) for one_waveform in raw_speech]

														# convert into correct format for padding
														lowerCamelCase__ :     str	  =	BatchFeature({"""input_features""": features}					)

														lowerCamelCase__ :     str	  =	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
														lowerCamelCase__ :     str	  =	padded_inputs.get("""input_features"""					)
														if isinstance(input_features[0]      ,							UpperCamelCase__					):
																					lowerCamelCase__ :     Dict	  =	[np.asarray(UpperCamelCase__      ,							dtype=np.floataa					) for feature in input_features]

														lowerCamelCase__ :     List[str]	  =	padded_inputs.get("""attention_mask"""					)
														if attention_mask is not None:
																					lowerCamelCase__ :     Any	  =	[np.asarray(UpperCamelCase__      ,							dtype=np.intaa					) for array in attention_mask]

														if self.normalize_means or self.normalize_vars:
																					lowerCamelCase__ :     str	  =	(
																					    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
																					)
																					lowerCamelCase__ :     Union[str, Any]	  =	self.normalize(
																					    padded_inputs["""input_features"""]      ,							attention_mask=UpperCamelCase__					)

														if return_tensors is not None:
																					lowerCamelCase__ :     Dict	  =	padded_inputs.convert_to_tensors(UpperCamelCase__					)

														return padded_inputs

