import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


snake_case__				= logging.get_logger(__name__)



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  ['input_features']



     def __init__(					self				,				A_=80				,				A_=1_60_00				,				A_=1_60				,				A_=30				,				A_=4_00				,				A_=0.0				,				A_=False				,				**A_				,				) ->				Union[str, Any]:






           """simple docstring"""
           super().__init__(
               feature_size=A_				,				sampling_rate=A_				,				padding_value=A_				,				return_attention_mask=A_				,				**A_				,				)
           _lowerCamelCase			    =				n_fft
           _lowerCamelCase			    =				hop_length
           _lowerCamelCase			    =				chunk_length
           _lowerCamelCase			    =				chunk_length * sampling_rate
           _lowerCamelCase			    =				self.n_samples // hop_length
           _lowerCamelCase			    =				sampling_rate
           _lowerCamelCase			    =				mel_filter_bank(
               num_frequency_bins=1 + n_fft // 2				,				num_mel_filters=A_				,				min_frequency=0.0				,				max_frequency=8000.0				,				sampling_rate=A_				,				norm='''slaney'''				,				mel_scale='''slaney'''				,				)



     def 		UpperCamelCase_						(					self				,				A_						) ->				np.ndarray:






           """simple docstring"""
           _lowerCamelCase			    =				spectrogram(
               A_				,				window_function(self.n_fft				,				'''hann'''						)				,				frame_length=self.n_fft				,				hop_length=self.hop_length				,				power=2.0				,				mel_filters=self.mel_filters				,				log_mel='''log10'''				,				)
           _lowerCamelCase			    =				log_spec[:, :-1]
           _lowerCamelCase			    =				np.maximum(A_				,				log_spec.max() - 8.0						)
           _lowerCamelCase			    =				(log_spec + 4.0) / 4.0
           return log_spec



     @staticmethod
     # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
     def 		UpperCamelCase_						(					A_				,				A_				,				A_ = 0.0						) ->				List[np.ndarray]:






           """simple docstring"""
           if attention_mask is not None:
                 _lowerCamelCase			    =				np.array(A_				,				np.intaa						)
                 _lowerCamelCase			    =				[]

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

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

           return normed_input_values



     def __call__(					self				,				A_				,				A_ = True				,				A_ = None				,				A_ = None				,				A_ = None				,				A_ = "max_length"				,				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.__class__.__name__} was trained using a'
                           F' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
                           F' was sampled with {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			    =				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}'						)
           _lowerCamelCase			    =				is_batched_numpy or (
               isinstance(A_				,				(list, tuple)						) and (isinstance(raw_speech[0]				,				(np.ndarray, tuple, list)						))
           )

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

           # always return batch
           if not is_batched:
                 _lowerCamelCase			    =				[np.asarray([raw_speech]						).T]

           _lowerCamelCase			    =				BatchFeature({'''input_features''': raw_speech}						)

           # convert into correct format for padding

           _lowerCamelCase			    =				self.pad(
               A_				,				padding=A_				,				max_length=max_length if max_length else self.n_samples				,				truncation=A_				,				pad_to_multiple_of=A_				,				return_attention_mask=return_attention_mask or do_normalize				,				)

           # zero-mean and unit-variance normalization
           if do_normalize:
                 _lowerCamelCase			    =				self.zero_mean_unit_var_norm(
                     padded_inputs['''input_features''']				,				attention_mask=padded_inputs['''attention_mask''']				,				padding_value=self.padding_value				,				)
                 _lowerCamelCase			    =				np.stack(padded_inputs['''input_features''']				,				axis=0						)

           # make sure list is in array format
           _lowerCamelCase			    =				padded_inputs.get('''input_features'''						).transpose(2				,				0				,				1						)

           _lowerCamelCase			    =				[self._np_extract_fbank_features(A_						) for waveform in input_features[0]]

           if isinstance(input_features[0]				,				A_						):
                 _lowerCamelCase			    =				[np.asarray(A_				,				dtype=np.floataa						) for feature in input_features]
           else:
                 _lowerCamelCase			    =				input_features

           if return_attention_mask:
                 # rescale from sample (48000) to feature (3000)
                 _lowerCamelCase			    =				padded_inputs['''attention_mask'''][:, :: self.hop_length]

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

           return padded_inputs





     def 		UpperCamelCase_						(					self						) ->				Dict[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				copy.deepcopy(self.__dict__						)
           _lowerCamelCase			    =				self.__class__.__name__
           if "mel_filters" in output:
                 del output["mel_filters"]
           return output


def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    str:



      '''simple docstring'''

      _lowerCamelCase			    =				0
      while b > 0:
            if b & 1:
                  res += a

            a += a
            b >>= 1

      return res
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    Optional[Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				0
      while b > 0:
            if b & 1:
                  _lowerCamelCase			    =				((res % c) + (a % c)) % c

            a += a
            b >>= 1

      return res