



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


_lowercase     							=						logging.get_logger(__name__)
class 				__A					( _UpperCAmelCase      ):
			UpperCamelCase					:str     =			['''input_values''', '''attention_mask''']





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

						lowerCamelCase__ :      Tuple								=		num_mel_bins
						lowerCamelCase__ :      Tuple								=		hop_length
						lowerCamelCase__ :      str								=		win_length
						lowerCamelCase__ :      Any								=		win_function
						lowerCamelCase__ :      int								=		frame_signal_scale
						lowerCamelCase__ :      List[Any]								=		fmin
						lowerCamelCase__ :      Optional[int]								=		fmax
						lowerCamelCase__ :      Any								=		mel_floor
						lowerCamelCase__ :      Optional[Any]								=		reduction_factor

						lowerCamelCase__ :      Dict								=		win_length * sampling_rate // 1000
						lowerCamelCase__ :      List[str]								=		hop_length * sampling_rate // 1000
						lowerCamelCase__ :      str								=		optimal_fft_length(self.sample_size				)
						lowerCamelCase__ :      Optional[int]								=		(self.n_fft // 2) + 1

						lowerCamelCase__ :      Optional[Any]								=		window_function(window_length=self.sample_size							,    name=self.win_function							,    periodic=lowerCamelCase_				)

						lowerCamelCase__ :      Union[str, Any]								=		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"""							,    lowerCamelCase_							,    )
						if reduction_factor != 2.0:
									warnings.warn(
									    """The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"""							,    lowerCamelCase_							,    )





			@staticmethod
			# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
			def        _snake_case    (__magic_name__							,    __magic_name__							,    __magic_name__ = 0.0				):




						if attention_mask is not None:
									lowerCamelCase__ :      Any								=		np.array(lowerCamelCase_							,    np.intaa				)
									lowerCamelCase__ :      Optional[Any]								=		[]

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

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

						return normed_input_values





			def        _snake_case    (self							,    __magic_name__							,    ):
						lowerCamelCase__ :      Tuple								=		spectrogram(
						    lowerCamelCase_							,    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							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = False							,    __magic_name__ = None							,    __magic_name__ = False							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    **__magic_name__							,    ):




						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:
									lowerCamelCase__ :      int								=		self._process_audio(
									    lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_							,    **lowerCamelCase_							,    )
						else:
									lowerCamelCase__ :      int								=		None

						if audio_target is not None:
									lowerCamelCase__ :      Optional[Any]								=		self._process_audio(
									    lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_							,    **lowerCamelCase_							,    )

									if inputs is None:
												return inputs_target
									else:
												lowerCamelCase__ :      Tuple								=		inputs_target['''input_values''']
												lowerCamelCase__ :      Any								=		inputs_target.get("""attention_mask"""				)
												if decoder_attention_mask is not None:
															lowerCamelCase__ :      Optional[Any]								=		decoder_attention_mask

						return inputs





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__ = False							,    __magic_name__ = False							,    __magic_name__ = None							,    __magic_name__ = False							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Optional[Any]								=		isinstance(lowerCamelCase_							,    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}"				)
						lowerCamelCase__ :      Tuple								=		is_batched_numpy or (
						    isinstance(lowerCamelCase_							,    (list, tuple)				) and (isinstance(speech[0]							,    (np.ndarray, tuple, list)				))
						)

						if is_batched:
									lowerCamelCase__ :      int								=		[np.asarray(lowerCamelCase_							,    dtype=np.floataa				) for speech in speech]
						elif not is_batched and not isinstance(lowerCamelCase_							,    np.ndarray				):
									lowerCamelCase__ :      List[str]								=		np.asarray(lowerCamelCase_							,    dtype=np.floataa				)
						elif isinstance(lowerCamelCase_							,    np.ndarray				) and speech.dtype is np.dtype(np.floataa				):
									lowerCamelCase__ :      Optional[int]								=		speech.astype(np.floataa				)

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

						# needed to make pad() work on spectrogram inputs
						lowerCamelCase__ :      Any								=		self.feature_size

						# convert into correct format for padding
						if is_target:
									lowerCamelCase__ :      Any								=		[self._extract_mel_features(lowerCamelCase_				) for waveform in speech]
									lowerCamelCase__ :      Optional[int]								=		BatchFeature({"""input_values""": features}				)
									lowerCamelCase__ :      Optional[Any]								=		self.num_mel_bins
						else:
									lowerCamelCase__ :      Union[str, Any]								=		BatchFeature({"""input_values""": speech}				)

						lowerCamelCase__ :      Any								=		self.pad(
						    lowerCamelCase_							,    padding=lowerCamelCase_							,    max_length=lowerCamelCase_							,    truncation=lowerCamelCase_							,    pad_to_multiple_of=lowerCamelCase_							,    return_attention_mask=lowerCamelCase_							,    **lowerCamelCase_							,    )

						lowerCamelCase__ :      Any								=		feature_size_hack

						# convert input values to correct format
						lowerCamelCase__ :      Union[str, Any]								=		padded_inputs['''input_values''']
						if not isinstance(input_values[0]							,    np.ndarray				):
									lowerCamelCase__ :      Optional[int]								=		[np.asarray(lowerCamelCase_							,    dtype=np.floataa				) for array in input_values]
						elif (
						    not isinstance(lowerCamelCase_							,    np.ndarray				)
						    and isinstance(input_values[0]							,    np.ndarray				)
						    and input_values[0].dtype is np.dtype(np.floataa				)
						):
									lowerCamelCase__ :      List[str]								=		[array.astype(np.floataa				) for array in input_values]
						elif isinstance(lowerCamelCase_							,    np.ndarray				) and input_values.dtype is np.dtype(np.floataa				):
									lowerCamelCase__ :      List[str]								=		input_values.astype(np.floataa				)

						# convert attention_mask to correct format
						lowerCamelCase__ :      Tuple								=		padded_inputs.get("""attention_mask"""				)
						if attention_mask is not None:
									lowerCamelCase__ :      List[str]								=		[np.asarray(lowerCamelCase_							,    dtype=np.intaa				) for array in attention_mask]

						# zero-mean and unit-variance normalization
						if not is_target and self.do_normalize:
									lowerCamelCase__ :      str								=		(
									    attention_mask
									    if self._get_padding_strategies(lowerCamelCase_							,    max_length=lowerCamelCase_				) is not PaddingStrategy.DO_NOT_PAD
									    else None
									)
									lowerCamelCase__ :      List[Any]								=		self.zero_mean_unit_var_norm(
									    padded_inputs["""input_values"""]							,    attention_mask=lowerCamelCase_							,    padding_value=self.padding_value				)

						if return_tensors is not None:
									lowerCamelCase__ :      Any								=		padded_inputs.convert_to_tensors(lowerCamelCase_				)

						return padded_inputs





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[str]								=		super().to_dict()

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

						return output








def      _A				(UpperCamelCase		:      int      ,			UpperCamelCase		:      int )  ->int:



			'''simple docstring'''







			while b:
						lowerCamelCase__							,lowerCamelCase__ :      int								=		b, a % b
			return a






def      _A				(UpperCamelCase		:      int      ,			UpperCamelCase		:      int )  ->int:



			'''simple docstring'''







			return a if b == 0 else euclidean_gcd_recursive(UpperCamelCase      ,			a % b )






def      _A				()  ->str:



			'''simple docstring'''







			print(f"euclidean_gcd(3, 5) = {euclidean_gcd(3      ,			5 )}" )
			print(f"euclidean_gcd(5, 3) = {euclidean_gcd(5      ,			3 )}" )
			print(f"euclidean_gcd(1, 3) = {euclidean_gcd(1      ,			3 )}" )
			print(f"euclidean_gcd(3, 6) = {euclidean_gcd(3      ,			6 )}" )
			print(f"euclidean_gcd(6, 3) = {euclidean_gcd(6      ,			3 )}" )

			print(f"euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3      ,			5 )}" )
			print(f"euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5      ,			3 )}" )
			print(f"euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1      ,			3 )}" )
			print(f"euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3      ,			6 )}" )
			print(f"euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6      ,			3 )}" )


if __name__ == "__main__":
				main()



