




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


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


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      ["input_features", "attention_mask"]


    def __init__(			self      :	List[str]					, UpperCAmelCase__      :	Union[str, Any]=80					, UpperCAmelCase__      :	Dict=16_000					, UpperCAmelCase__      :	List[Any]=0.0					, UpperCAmelCase__      :	Tuple=10					, UpperCAmelCase__      :	str=25					, UpperCAmelCase__      :	int="hamming_window"					, UpperCAmelCase__      :	Union[str, Any]=32_768.0					, UpperCAmelCase__      :	List[str]=0.97					, UpperCAmelCase__      :	List[Any]=1.0					, UpperCAmelCase__      :	Dict=True					, UpperCAmelCase__      :	List[str]=True					, UpperCAmelCase__      :	Optional[Any]=False					, **UpperCAmelCase__      :	Any					, )						->Tuple:





           '''simple docstring'''

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

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

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

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


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





           '''simple docstring'''

           if self.win_function == "hamming_window":
                  A__       =    window_function(window_length=self.sample_size					, name=self.win_function					, periodic=lowerCamelCase__)
           else:
                  A__       =    window_function(window_length=self.sample_size					, name=self.win_function)

           A__       =    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					, )

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


    def        SCREAMING_SNAKE_CASE							(			self      :	str					, UpperCAmelCase__      :	Optional[int]					, UpperCAmelCase__      :	str					, UpperCAmelCase__      :	Any)						->Tuple:





           '''simple docstring'''

           if self.normalize_means:
                  A__       =    x[:input_length].mean(axis=0)
                  A__       =    np.subtract(lowerCamelCase__					, lowerCamelCase__)
           if self.normalize_vars:
                  A__       =    x[:input_length].std(axis=0)
                  A__       =    np.divide(lowerCamelCase__					, lowerCamelCase__)

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

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

           return x


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict					, UpperCAmelCase__      :	List[np.ndarray]					, UpperCAmelCase__      :	Optional[np.ndarray] = None)						->List[np.ndarray]:





           '''simple docstring'''

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





    def __call__(			self      :	Tuple					, UpperCAmelCase__      :	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					, UpperCAmelCase__      :	Union[bool, str, PaddingStrategy] = False					, UpperCAmelCase__      :	Optional[int] = None					, UpperCAmelCase__      :	bool = False					, UpperCAmelCase__      :	Optional[int] = None					, UpperCAmelCase__      :	Optional[bool] = None					, UpperCAmelCase__      :	Optional[Union[str, TensorType]] = None					, UpperCAmelCase__      :	Optional[int] = None					, **UpperCAmelCase__      :	Optional[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.''')

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

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

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

           # extract fbank features
           A__       =    [self._extract_mfsc_features(lowerCamelCase__) for one_waveform in raw_speech]

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

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

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

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

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

           return padded_inputs






'''simple docstring'''


import math






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		return math.sqrt(__lowerCAmelCase      ) * math.sqrt(__lowerCAmelCase      ) == num






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				int	    =					0
		_UpperCAmelCase						:				Tuple	    =					n
		while left <= right:
				_UpperCAmelCase						:				int	    =					(left + right) // 2
				if mid**2 == n:
						return True
				elif mid**2 > n:
						_UpperCAmelCase						:				str	    =					mid - 1
				else:
						_UpperCAmelCase						:				List[str]	    =					mid + 1
		return False


if __name__ == "__main__":
						import doctest

						doctest.testmod()



