

'''simple docstring'''

import warnings
from typing import Any, Dict, 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 ...utils import PaddingStrategy, TensorType, logging


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






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     ["""input_values""", """attention_mask"""]



				def __init__(    self,     _lowercase = 1,     _lowercase = 16000,     _lowercase = 0.0,     _lowercase = False,     _lowercase = 80,     _lowercase = 16,     _lowercase = 64,     _lowercase = "hann_window",     _lowercase = 1.0,     _lowercase = 80,     _lowercase = 7600,     _lowercase = 1E-10,     _lowercase = 2,     _lowercase = True,     **_lowercase,     )     ->							Union[str, Any]:
											super().__init__(feature_size=_lowercase,     sampling_rate=_lowercase,     padding_value=_lowercase,     **_lowercase       )
											SCREAMING_SNAKE_CASE_				=				do_normalize
											SCREAMING_SNAKE_CASE_				=				return_attention_mask

											SCREAMING_SNAKE_CASE_				=				num_mel_bins
											SCREAMING_SNAKE_CASE_				=				hop_length
											SCREAMING_SNAKE_CASE_				=				win_length
											SCREAMING_SNAKE_CASE_				=				win_function
											SCREAMING_SNAKE_CASE_				=				frame_signal_scale
											SCREAMING_SNAKE_CASE_				=				fmin
											SCREAMING_SNAKE_CASE_				=				fmax
											SCREAMING_SNAKE_CASE_				=				mel_floor
											SCREAMING_SNAKE_CASE_				=				reduction_factor

											SCREAMING_SNAKE_CASE_				=				win_length * sampling_rate // 1000
											SCREAMING_SNAKE_CASE_				=				hop_length * sampling_rate // 1000
											SCREAMING_SNAKE_CASE_				=				optimal_fft_length(self.sample_size       )
											SCREAMING_SNAKE_CASE_				=				(self.n_fft // 2) + 1

											SCREAMING_SNAKE_CASE_				=				window_function(window_length=self.sample_size,     name=self.win_function,     periodic=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				mel_filter_bank(
											    num_frequency_bins=self.n_freqs,     num_mel_filters=self.num_mel_bins,     min_frequency=self.fmin,     max_frequency=self.fmax,     sampling_rate=self.sampling_rate,     norm='slaney',     mel_scale='slaney',     )

											if frame_signal_scale != 1.0:
																		warnings.warn(
																		    'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers',     _lowercase,     )
											if reduction_factor != 2.0:
																		warnings.warn(
																		    'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers',     _lowercase,     )



				@staticmethod
				# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
				def   a__					(    _lowercase,     _lowercase,     _lowercase = 0.0       )     ->							List[np.ndarray]:
											if attention_mask is not None:
																		SCREAMING_SNAKE_CASE_				=				np.array(_lowercase,     np.intaa       )
																		SCREAMING_SNAKE_CASE_				=				[]

																		for vector, length in zip(_lowercase,     attention_mask.sum(-1       )       ):
																									SCREAMING_SNAKE_CASE_				=				(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7       )
																									if length < normed_slice.shape[0]:
																																SCREAMING_SNAKE_CASE_				=				padding_value

																									normed_input_values.append(_lowercase       )
											else:
																		SCREAMING_SNAKE_CASE_				=				[(x - x.mean()) / np.sqrt(x.var() + 1E-7       ) for x in input_values]

											return normed_input_values



				def   a__					(    self,     _lowercase,     )     ->							np.ndarray:
											SCREAMING_SNAKE_CASE_				=				spectrogram(
											    _lowercase,     window=self.window,     frame_length=self.sample_size,     hop_length=self.sample_stride,     fft_length=self.n_fft,     mel_filters=self.mel_filters,     mel_floor=self.mel_floor,     log_mel='log10',     )
											return log_mel_spec.T



				def __call__(    self,     _lowercase = None,     _lowercase = None,     _lowercase = False,     _lowercase = None,     _lowercase = False,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     **_lowercase,     )     ->							BatchFeature:
											if audio is None and audio_target is None:
																		raise ValueError('You must provide either `audio` or `audio_target` values.'       )

											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 audio 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.'       )

											if audio is not None:
																		SCREAMING_SNAKE_CASE_				=				self._process_audio(
																		    _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     **_lowercase,     )
											else:
																		SCREAMING_SNAKE_CASE_				=				None

											if audio_target is not None:
																		SCREAMING_SNAKE_CASE_				=				self._process_audio(
																		    _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     **_lowercase,     )

																		if inputs is None:
																									return inputs_target
																		else:
																									SCREAMING_SNAKE_CASE_				=				inputs_target['input_values']
																									SCREAMING_SNAKE_CASE_				=				inputs_target.get('attention_mask'       )
																									if decoder_attention_mask is not None:
																																SCREAMING_SNAKE_CASE_				=				decoder_attention_mask

											return inputs



				def   a__					(    self,     _lowercase,     _lowercase = False,     _lowercase = False,     _lowercase = None,     _lowercase = False,     _lowercase = None,     _lowercase = None,     _lowercase = None,     **_lowercase,     )     ->							BatchFeature:
											SCREAMING_SNAKE_CASE_				=				isinstance(_lowercase,     np.ndarray       ) and len(speech.shape       ) > 1
											if is_batched_numpy and len(speech.shape       ) > 2:
																		raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""       )
											SCREAMING_SNAKE_CASE_				=				is_batched_numpy or (
											    isinstance(_lowercase,     (list, tuple)       ) and (isinstance(speech[0],     (np.ndarray, tuple, list)       ))
											)

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

											# always return batch
											if not is_batched:
																		SCREAMING_SNAKE_CASE_				=				[speech]

											# needed to make pad() work on spectrogram inputs
											SCREAMING_SNAKE_CASE_				=				self.feature_size

											# convert into correct format for padding
											if is_target:
																		SCREAMING_SNAKE_CASE_				=				[self._extract_mel_features(_lowercase       ) for waveform in speech]
																		SCREAMING_SNAKE_CASE_				=				BatchFeature({'input_values': features}       )
																		SCREAMING_SNAKE_CASE_				=				self.num_mel_bins
											else:
																		SCREAMING_SNAKE_CASE_				=				BatchFeature({'input_values': speech}       )

											SCREAMING_SNAKE_CASE_				=				self.pad(
											    _lowercase,     padding=_lowercase,     max_length=_lowercase,     truncation=_lowercase,     pad_to_multiple_of=_lowercase,     return_attention_mask=_lowercase,     **_lowercase,     )

											SCREAMING_SNAKE_CASE_				=				feature_size_hack

											# convert input values to correct format
											SCREAMING_SNAKE_CASE_				=				padded_inputs['input_values']
											if not isinstance(input_values[0],     np.ndarray       ):
																		SCREAMING_SNAKE_CASE_				=				[np.asarray(_lowercase,     dtype=np.floataa       ) for array in input_values]
											elif (
											    not isinstance(_lowercase,     np.ndarray       )
											    and isinstance(input_values[0],     np.ndarray       )
											    and input_values[0].dtype is np.dtype(np.floataa       )
											):
																		SCREAMING_SNAKE_CASE_				=				[array.astype(np.floataa       ) for array in input_values]
											elif isinstance(_lowercase,     np.ndarray       ) and input_values.dtype is np.dtype(np.floataa       ):
																		SCREAMING_SNAKE_CASE_				=				input_values.astype(np.floataa       )

											# convert attention_mask to correct format
											SCREAMING_SNAKE_CASE_				=				padded_inputs.get('attention_mask'       )
											if attention_mask is not None:
																		SCREAMING_SNAKE_CASE_				=				[np.asarray(_lowercase,     dtype=np.intaa       ) for array in attention_mask]

											# zero-mean and unit-variance normalization
											if not is_target and self.do_normalize:
																		SCREAMING_SNAKE_CASE_				=				(
																		    attention_mask
																		    if self._get_padding_strategies(_lowercase,     max_length=_lowercase       ) is not PaddingStrategy.DO_NOT_PAD
																		    else None
																		)
																		SCREAMING_SNAKE_CASE_				=				self.zero_mean_unit_var_norm(
																		    padded_inputs['input_values'],     attention_mask=_lowercase,     padding_value=self.padding_value       )

											if return_tensors is not None:
																		SCREAMING_SNAKE_CASE_				=				padded_inputs.convert_to_tensors(_lowercase       )

											return padded_inputs





				def   a__					(    self       )     ->							Dict[str, Any]:
											SCREAMING_SNAKE_CASE_				=				super().to_dict()

											# Don't serialize these as they are derived from the other properties.
											SCREAMING_SNAKE_CASE_				=				['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
											for name in names:
																		if name in output:
																									del output[name]

											return output








'''simple docstring'''

def 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					int   )      -> str:
							if a < 0 or b < 0:
														raise ValueError('the value of both inputs must be positive'   )
							SCREAMING_SNAKE_CASE_				=				str(bin(lowerCAmelCase__   )   )[2:]  # remove the leading "0b"
							SCREAMING_SNAKE_CASE_				=				str(bin(lowerCAmelCase__   )   )[2:]
							SCREAMING_SNAKE_CASE_				=				max(len(lowerCAmelCase__   )		,len(lowerCAmelCase__   )   )
							return "0b" + "".join(
							    str(int('1' in (char_a, char_b)   )   )
							    for char_a, char_b in zip(a_binary.zfill(lowerCAmelCase__   )		,b_binary.zfill(lowerCAmelCase__   )   )   )


if __name__ == "__main__":
							import doctest

							doctest.testmod()





