
"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_a														=		logging.get_logger(__name__)






class    _UpperCAmelCase(   lowerCamelCase__     ):
		lowercase__      =				['input_features', 'attention_mask']



		def __init__( self    ,		__a=80    ,		__a=1_60_00    ,		__a=80    ,		__a=0.0    ,		__a=True    ,		__a=True    ,		__a=True    ,		**__a    ,		)	->	Optional[Any]:






							'''simple docstring'''
							super().__init__(feature_size=lowercase__    ,		sampling_rate=lowercase__    ,		padding_value=lowercase__    ,		**lowercase__)
							_UpperCamelCase										=  num_mel_bins
							_UpperCamelCase										=  do_ceptral_normalize
							_UpperCamelCase										=  normalize_means
							_UpperCamelCase										=  normalize_vars
							_UpperCamelCase										=  True



		def 						UpperCAmelCase							( self    ,		__a    ,		)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
							_UpperCamelCase										=  torch.from_numpy(lowercase__).unsqueeze(0)
							_UpperCamelCase										=  ta_kaldi.fbank(lowercase__    ,		num_mel_bins=self.num_mel_bins    ,		sample_frequency=self.sampling_rate)
							return features.numpy()



		@staticmethod
		def 						UpperCAmelCase							( __a    ,		__a    ,		__a = True    ,		__a = True    ,		__a = 0.0    ,		)	->	Dict:






							'''simple docstring'''
							# make sure we normalize float32 arrays
							if normalize_means:
												_UpperCamelCase										=  x[:input_length].mean(axis=0)
												_UpperCamelCase										=  np.subtract(lowercase__    ,		lowercase__)
							if normalize_vars:
												_UpperCamelCase										=  x[:input_length].std(axis=0)
												_UpperCamelCase										=  np.divide(lowercase__    ,		lowercase__)

							if input_length < x.shape[0]:
												_UpperCamelCase										=  padding_value

							# make sure array is in float32
							_UpperCamelCase										=  x.astype(np.floataa)

							return x



		def 						UpperCAmelCase							( self    ,		__a    ,		__a = None)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
							return [
							    self.utterance_cmvn(lowercase__    ,		lowercase__    ,		self.normalize_means    ,		self.normalize_vars    ,		self.padding_value)
							    for x, n in zip(lowercase__    ,		lowercase__)
							]




		def __call__( self    ,		__a    ,		__a = False    ,		__a = None    ,		__a = False    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = None    ,		**__a    ,		)	->	str:






							'''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.''')

							_UpperCamelCase										=  isinstance(lowercase__    ,		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}''')
							_UpperCamelCase										=  is_batched_numpy or (
							    isinstance(lowercase__    ,		(list, tuple)) and (isinstance(raw_speech[0]    ,		(np.ndarray, tuple, list)))
							)

							if is_batched:
												_UpperCamelCase										=  [np.asarray(lowercase__    ,		dtype=np.floataa) for speech in raw_speech]
							elif not is_batched and not isinstance(lowercase__    ,		np.ndarray):
												_UpperCamelCase										=  np.asarray(lowercase__    ,		dtype=np.floataa)
							elif isinstance(lowercase__    ,		np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
												_UpperCamelCase										=  raw_speech.astype(np.floataa)

							# always return batch
							if not is_batched:
												_UpperCamelCase										=  [raw_speech]

							# extract fbank features
							_UpperCamelCase										=  [self._extract_fbank_features(lowercase__) for waveform in raw_speech]

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

							_UpperCamelCase										=  self.pad(
							    lowercase__    ,		padding=lowercase__    ,		max_length=lowercase__    ,		truncation=lowercase__    ,		pad_to_multiple_of=lowercase__    ,		return_attention_mask=lowercase__    ,		**lowercase__    ,		)

							# make sure list is in array format
							_UpperCamelCase										=  padded_inputs.get('''input_features''')
							if isinstance(input_features[0]    ,		lowercase__):
												_UpperCamelCase										=  [np.asarray(lowercase__    ,		dtype=np.floataa) for feature in input_features]

							_UpperCamelCase										=  padded_inputs.get('''attention_mask''')
							if attention_mask is not None:
												_UpperCamelCase										=  [np.asarray(lowercase__    ,		dtype=np.intaa) for array in attention_mask]

							# Utterance-level cepstral mean and variance normalization
							if self.do_ceptral_normalize:
												_UpperCamelCase										=  (
												    np.array(lowercase__    ,		dtype=np.intaa)
												    if self._get_padding_strategies(lowercase__    ,		max_length=lowercase__) is not PaddingStrategy.DO_NOT_PAD
												    else None
												)
												_UpperCamelCase										=  self.normalize(
												    padded_inputs['''input_features''']    ,		attention_mask=lowercase__)

							if return_tensors is not None:
												_UpperCamelCase										=  padded_inputs.convert_to_tensors(lowercase__)

							return padded_inputs




"""simple docstring"""





import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    """The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionInpaintPipeline` instead."""
)






