



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_													=      logging.get_logger(__name__)






class     __a			(  SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							["input_features", "attention_mask"]



	def __init__(    self				:     Optional[Any]					,						snake_case_				:     List[Any]=80					,						snake_case_				:     List[str]=1_60_00					,						snake_case_				:     Tuple=80					,						snake_case_				:     List[str]=0.0					,						snake_case_				:     int=True					,						snake_case_				:     Any=True					,						snake_case_				:     Any=True					,						**snake_case_				:     Optional[Any]					,						)->				Any:
						super().__init__(feature_size=snake_case_					,						sampling_rate=snake_case_					,						padding_value=snake_case_					,						**snake_case_)
						__lowerCAmelCase						 =num_mel_bins
						__lowerCAmelCase						 =do_ceptral_normalize
						__lowerCAmelCase						 =normalize_means
						__lowerCAmelCase						 =normalize_vars
						__lowerCAmelCase						 =True



	def 		UpperCamelCase				(    self				:     int					,						snake_case_				:     np.ndarray					,						)->				np.ndarray:
						__lowerCAmelCase						 =waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
						__lowerCAmelCase						 =torch.from_numpy(snake_case_).unsqueeze(0)
						__lowerCAmelCase						 =ta_kaldi.fbank(snake_case_					,						num_mel_bins=self.num_mel_bins					,						sample_frequency=self.sampling_rate)
						return features.numpy()



	@staticmethod
	def 		UpperCamelCase				(    snake_case_				:     np.ndarray					,						snake_case_				:     int					,						snake_case_				:     Optional[bool] = True					,						snake_case_				:     Optional[bool] = True					,						snake_case_				:     float = 0.0					,						)->				np.ndarray:
						# make sure we normalize float32 arrays
						if normalize_means:
											__lowerCAmelCase						 =x[:input_length].mean(axis=0)
											__lowerCAmelCase						 =np.subtract(snake_case_					,						snake_case_)
						if normalize_vars:
											__lowerCAmelCase						 =x[:input_length].std(axis=0)
											__lowerCAmelCase						 =np.divide(snake_case_					,						snake_case_)

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

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

						return x



	def 		UpperCamelCase				(    self				:     int					,						snake_case_				:     List[np.ndarray]					,						snake_case_				:     Optional[np.ndarray] = None)->				List[np.ndarray]:
						__lowerCAmelCase						 =attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
						return [
						    self.utterance_cmvn(snake_case_					,						snake_case_					,						self.normalize_means					,						self.normalize_vars					,						self.padding_value)
						    for x, n in zip(snake_case_					,						snake_case_)
						]



	def __call__(    self				:     Any					,						snake_case_				:     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,						snake_case_				:     Union[bool, str, PaddingStrategy] = False					,						snake_case_				:     Optional[int] = None					,						snake_case_				:     bool = False					,						snake_case_				:     Optional[int] = None					,						snake_case_				:     Optional[Union[str, TensorType]] = None					,						snake_case_				:     Optional[int] = None					,						snake_case_				:     Optional[bool] = None					,						**snake_case_				:     Optional[Any]					,						)->				BatchFeature:

						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						 =isinstance(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}""")
						__lowerCAmelCase						 =is_batched_numpy or (
						    isinstance(snake_case_					,						(list, tuple)) and (isinstance(raw_speech[0]					,						(np.ndarray, tuple, list)))
						)

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

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

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

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

						__lowerCAmelCase						 =self.pad(
						    snake_case_					,						padding=snake_case_					,						max_length=snake_case_					,						truncation=snake_case_					,						pad_to_multiple_of=snake_case_					,						return_attention_mask=snake_case_					,						**snake_case_					,						)

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

						__lowerCAmelCase						 =padded_inputs.get("""attention_mask""")
						if attention_mask is not None:
											__lowerCAmelCase						 =[np.asarray(snake_case_					,						dtype=np.intaa) for array in attention_mask]

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

						if return_tensors is not None:
											__lowerCAmelCase						 =padded_inputs.convert_to_tensors(snake_case_)

						return padded_inputs









def        __lowerCAmelCase		(	__lowerCamelCase						:     int						)	->    bool:
					if number < 0:
										raise ValueError("""number must not be negative"""						)
					return number & (number - 1) == 0


if __name__ == "__main__":
				import doctest

				doctest.testmod()




