
'''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


_lowercase			:  int   							=	logging.get_logger(__name__)






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					['''input_features''', '''attention_mask''']






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE=80    ,							__SCREAMING_SNAKE_CASE=1_60_00    ,							__SCREAMING_SNAKE_CASE=80    ,							__SCREAMING_SNAKE_CASE=0.0    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=True    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											super().__init__(feature_size=__SCREAMING_SNAKE_CASE    ,							sampling_rate=__SCREAMING_SNAKE_CASE    ,							padding_value=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)
											lowercase_     :					str													=	num_mel_bins
											lowercase_     :					Union[str, Any]													=	do_ceptral_normalize
											lowercase_     :					Dict													=	normalize_means
											lowercase_     :					Dict													=	normalize_vars
											lowercase_     :					List[Any]													=	True






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											lowercase_     :					Dict													=	waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
											lowercase_     :					Union[str, Any]													=	torch.from_numpy(__SCREAMING_SNAKE_CASE		).unsqueeze(0		)
											lowercase_     :					str													=	ta_kaldi.fbank(__SCREAMING_SNAKE_CASE    ,							num_mel_bins=self.num_mel_bins    ,							sample_frequency=self.sampling_rate		)
											return features.numpy()






					@staticmethod
					def      _snake_case   (       __SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = True    ,							__SCREAMING_SNAKE_CASE = True    ,							__SCREAMING_SNAKE_CASE = 0.0    ,							):


											"""simple docstring"""





											if normalize_means:
																	lowercase_     :					int													=	x[:input_length].mean(axis=0		)
																	lowercase_     :					Tuple													=	np.subtract(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
											if normalize_vars:
																	lowercase_     :					List[str]													=	x[:input_length].std(axis=0		)
																	lowercase_     :					Union[str, Any]													=	np.divide(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

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

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

											return x






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	attention_mask.sum(-1		) if attention_mask is not None else [x.shape[0] for x in input_features]
											return [
											    self.utterance_cmvn(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							self.normalize_means    ,							self.normalize_vars    ,							self.padding_value		)
											    for x, n in zip(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
											]






					def __call__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = False    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = False    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""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.'''		)

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

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

											# always return batch
											if not is_batched:
																	lowercase_     :					Optional[int]													=	[raw_speech]

											# extract fbank features
											lowercase_     :					Dict													=	[self._extract_fbank_features(__SCREAMING_SNAKE_CASE		) for waveform in raw_speech]

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

											lowercase_     :					List[str]													=	self.pad(
											    __SCREAMING_SNAKE_CASE    ,							padding=__SCREAMING_SNAKE_CASE    ,							max_length=__SCREAMING_SNAKE_CASE    ,							truncation=__SCREAMING_SNAKE_CASE    ,							pad_to_multiple_of=__SCREAMING_SNAKE_CASE    ,							return_attention_mask=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE    ,							)

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

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

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

											if return_tensors is not None:
																	lowercase_     :					List[str]													=	padded_inputs.convert_to_tensors(__SCREAMING_SNAKE_CASE		)

											return padded_inputs





'''simple docstring'''







from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					Tuple													=	prime_factors(__SCREAMING_SNAKE_CASE							)
						if is_square_free(__SCREAMING_SNAKE_CASE							):
												return -1 if len(__SCREAMING_SNAKE_CASE							) % 2 else 1
						return 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()



