



"""simple docstring"""






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


__snake_case							:     List[Any]   =					logging.get_logger(__name__)



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			['input_features', 'attention_mask']

	def __init__(  self:	Any ,    _SCREAMING_SNAKE_CASE:	Dict=80 ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=1_6000 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=0.0 ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=10 ,    _SCREAMING_SNAKE_CASE:	List[str]=25 ,    _SCREAMING_SNAKE_CASE:	Optional[Any]="hamming_window" ,    _SCREAMING_SNAKE_CASE:	str=3_2768.0 ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=0.97 ,    _SCREAMING_SNAKE_CASE:	Dict=1.0 ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=True ,    _SCREAMING_SNAKE_CASE:	Dict=True ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=False ,    **_SCREAMING_SNAKE_CASE:	List[Any] ,    )       -> Dict:

				"""simple docstring"""

				super().__init__(feature_size=_SCREAMING_SNAKE_CASE ,    sampling_rate=_SCREAMING_SNAKE_CASE ,    padding_value=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Tuple				= feature_size
				__lowerCAmelCase   :						int				= sampling_rate
				__lowerCAmelCase   :						Optional[Any]				= padding_value
				__lowerCAmelCase   :						Tuple				= hop_length
				__lowerCAmelCase   :						int				= win_length
				__lowerCAmelCase   :						Optional[Any]				= frame_signal_scale
				__lowerCAmelCase   :						Optional[Any]				= preemphasis_coeff
				__lowerCAmelCase   :						Optional[Any]				= mel_floor
				__lowerCAmelCase   :						Dict				= normalize_means
				__lowerCAmelCase   :						Dict				= normalize_vars
				__lowerCAmelCase   :						str				= win_function
				__lowerCAmelCase   :						Optional[Any]				= return_attention_mask

				__lowerCAmelCase   :						Any				= win_length * sampling_rate // 1000
				__lowerCAmelCase   :						Dict				= hop_length * sampling_rate // 1000

				__lowerCAmelCase   :						List[str]				= optimal_fft_length(self.sample_size)
				__lowerCAmelCase   :						str				= (self.n_fft // 2) + 1

	def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    _SCREAMING_SNAKE_CASE:	np.array)       -> np.ndarray:

				"""simple docstring"""

				if self.win_function == "hamming_window":
							__lowerCAmelCase   :						List[Any]				= window_function(window_length=self.sample_size ,    name=self.win_function ,    periodic=_SCREAMING_SNAKE_CASE)
				else:
							__lowerCAmelCase   :						Optional[Any]				= window_function(window_length=self.sample_size ,    name=self.win_function)

				__lowerCAmelCase   :						Any				= 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 ,    )

				__lowerCAmelCase   :						Union[str, Any]				= spectrogram(
				    one_waveform * self.frame_signal_scale ,    window=_SCREAMING_SNAKE_CASE ,    frame_length=self.sample_size ,    hop_length=self.sample_stride ,    fft_length=self.n_fft ,    center=_SCREAMING_SNAKE_CASE ,    preemphasis=self.preemphasis_coeff ,    mel_filters=_SCREAMING_SNAKE_CASE ,    mel_floor=self.mel_floor ,    log_mel="log" ,    )
				return msfc_features.T

	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	Union[str, Any])       -> str:

				"""simple docstring"""

				if self.normalize_means:
							__lowerCAmelCase   :						Tuple				= x[:input_length].mean(axis=0)
							__lowerCAmelCase   :						str				= np.subtract(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
				if self.normalize_vars:
							__lowerCAmelCase   :						Dict				= x[:input_length].std(axis=0)
							__lowerCAmelCase   :						Any				= np.divide(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

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

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

				return x

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any] ,    _SCREAMING_SNAKE_CASE:	List[np.ndarray] ,    _SCREAMING_SNAKE_CASE:	Optional[np.ndarray] = None)       -> List[np.ndarray]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
				return [self._normalize_one(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    self.padding_value) for x, n in zip(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)]







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

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

				if is_batched:
							__lowerCAmelCase   :						Union[str, Any]				= [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):
							__lowerCAmelCase   :						Optional[Any]				= np.asarray(_SCREAMING_SNAKE_CASE ,    dtype=np.floataa)
				elif isinstance(_SCREAMING_SNAKE_CASE ,    np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
							__lowerCAmelCase   :						Dict				= raw_speech.astype(np.floataa)

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

				# extract fbank features
				__lowerCAmelCase   :						int				= [self._extract_mfsc_features(_SCREAMING_SNAKE_CASE) for one_waveform in raw_speech]

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

				__lowerCAmelCase   :						Dict				= 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
				__lowerCAmelCase   :						Optional[int]				= padded_inputs.get("input_features")
				if isinstance(input_features[0] ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						Dict				= [np.asarray(_SCREAMING_SNAKE_CASE ,    dtype=np.floataa) for feature in input_features]

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

				if self.normalize_means or self.normalize_vars:
							__lowerCAmelCase   :						List[str]				= (
							    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
							    and padding
							    else None
							)
							__lowerCAmelCase   :						Optional[Any]				= self.normalize(
							    padded_inputs["input_features"] ,    attention_mask=_SCREAMING_SNAKE_CASE)

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

				return padded_inputs




"""simple docstring"""






import math


def     _lowercase       (						__snake_case      ,__snake_case			)		->     float:
			return math.pow(__snake_case      ,2			) - a


def     _lowercase       (						__snake_case			)		->     float:
			return 2 * x


def     _lowercase       (						__snake_case			)		->     float:
			__lowerCAmelCase   :						List[str]				= 2.0

			while start <= a:
						__lowerCAmelCase   :						Union[str, Any]				= math.pow(__snake_case      ,2			)

			return start


def     _lowercase       (						__snake_case      ,__snake_case = 9_999      ,__snake_case = 0.00000000000001			)		->     float:

			if a < 0:
						raise ValueError("math domain error"			)

			__lowerCAmelCase   :						List[Any]				= get_initial_point(__snake_case			)

			for _ in range(__snake_case			):
						__lowerCAmelCase   :						Union[str, Any]				= value
						__lowerCAmelCase   :						List[str]				= value - fx(__snake_case      ,__snake_case			) / fx_derivative(__snake_case			)
						if abs(prev_value - value			) < tolerance:
									return value

			return value


if __name__ == "__main__":
	from doctest import testmod

	testmod()