
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      :				Union[str, Any]          = logging.get_logger(__name__)


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Tuple			  =       ["input_features", "attention_mask"]



		def __init__(				self       ,   A_=80       ,   A_=16_000       ,   A_=0.0       ,   A_=10       ,   A_=25       ,   A_="hamming_window"       ,   A_=3_2768.0       ,   A_=0.97       ,   A_=1.0       ,   A_=True       ,   A_=True       ,   A_=False       ,   **A_       ,   )						->      Optional[int]:
				"""simple docstring"""
				super().__init__(feature_size=A_       ,   sampling_rate=A_       ,   padding_value=A_       ,   **A_ )

				UpperCamelCase						=  feature_size
				UpperCamelCase						=  sampling_rate
				UpperCamelCase						=  padding_value
				UpperCamelCase						=  hop_length
				UpperCamelCase						=  win_length
				UpperCamelCase						=  frame_signal_scale
				UpperCamelCase						=  preemphasis_coeff
				UpperCamelCase						=  mel_floor
				UpperCamelCase						=  normalize_means
				UpperCamelCase						=  normalize_vars
				UpperCamelCase						=  win_function
				UpperCamelCase						=  return_attention_mask

				UpperCamelCase						=  win_length * sampling_rate // 1_000
				UpperCamelCase						=  hop_length * sampling_rate // 1_000

				UpperCamelCase						=  optimal_fft_length(self.sample_size )
				UpperCamelCase						=  (self.n_fft // 2) + 1



		def __UpperCamelCase				(				self       ,   A_ )						->      np.ndarray:
				"""simple docstring"""
				if self.win_function == "hamming_window":
						UpperCamelCase						=  window_function(window_length=self.sample_size       ,   name=self.win_function       ,   periodic=A_ )
				else:
						UpperCamelCase						=  window_function(window_length=self.sample_size       ,   name=self.win_function )

				UpperCamelCase						=  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       ,   )

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



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      Union[str, Any]:
				"""simple docstring"""
				# make sure we normalize float32 arrays
				if self.normalize_means:
						UpperCamelCase						=  x[:input_length].mean(axis=0 )
						UpperCamelCase						=  np.subtract(A_       ,   A_ )
				if self.normalize_vars:
						UpperCamelCase						=  x[:input_length].std(axis=0 )
						UpperCamelCase						=  np.divide(A_       ,   A_ )

				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 )						->      List[np.ndarray]:
				"""simple docstring"""
				UpperCamelCase						=  attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
				return [self._normalize_one(A_       ,   A_       ,   self.padding_value ) for x, n in zip(A_       ,   A_ )]



		def __call__(				self       ,   A_       ,   A_ = False       ,   A_ = None       ,   A_ = False       ,   A_ = None       ,   A_ = None       ,   A_ = None       ,   A_ = None       ,   **A_       ,   )						->      BatchFeature:
				"""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(A_       ,   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(A_       ,   (list, tuple) ) and (isinstance(raw_speech[0]       ,   (np.ndarray, tuple, list) ))
				)

				if is_batched:
						UpperCamelCase						=  [np.asarray(A_       ,   dtype=np.floataa ) for speech in raw_speech]
				elif not is_batched and not isinstance(A_       ,   np.ndarray ):
						UpperCamelCase						=  np.asarray(A_       ,   dtype=np.floataa )
				elif isinstance(A_       ,   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_mfsc_features(A_ ) for one_waveform in raw_speech]

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

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

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

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

				if return_tensors is not None:
						UpperCamelCase						=  padded_inputs.convert_to_tensors(A_ )

				return padded_inputs


def    A (      lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

		if a < 0 or b < 0:
				raise ValueError('the value of both inputs must be positive'				)

		UpperCamelCase						=  str(bin(lowercase				)				)[2:]  # remove the leading "0b"
		UpperCamelCase						=  str(bin(lowercase				)				)[2:]  # remove the leading "0b"

		UpperCamelCase						=  max(len(lowercase				)					, len(lowercase				)				)

		return "0b" + "".join(
		    str(int(char_a != char_b				)				)
		    for char_a, char_b in zip(a_binary.zfill(lowercase				)					, b_binary.zfill(lowercase				)				)				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
