





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    : int    	=  logging.get_logger(__name__)

class  __A(						__lowerCamelCase ):
     __A	  =       ['''input_values''', '''attention_mask''']

     def __init__(      self,       A = 1,       A = 1_6000,       A = 0.0,       A = False,       A = 80,       A = 16,       A = 64,       A = "hann_window",       A = 1.0,       A = 80,       A = 7600,       A = 1E-1_0,       A = 2,       A = True,       **A,       ):





            """simple docstring"""

            super().__init__(feature_size=a_,       sampling_rate=a_,       padding_value=a_,       **a_      )
            _UpperCamelCase              =						do_normalize
            _UpperCamelCase              =						return_attention_mask

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

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

            _UpperCamelCase              =						window_function(window_length=self.sample_size,       name=self.win_function,       periodic=a_      )

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

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





            """simple docstring"""

            if attention_mask is not None:
                   _UpperCamelCase              =						np.array(a_,       np.intaa      )
                   _UpperCamelCase              =						[]

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

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

            return normed_input_values

     def 	_UpperCamelCase     (      self,       A,       ):





            """simple docstring"""

            _UpperCamelCase              =						spectrogram(
                a_,       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,       A = None,       A = None,       A = False,       A = None,       A = False,       A = None,       A = None,       A = None,       A = None,       **A,       ):





            """simple docstring"""

            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:
                   _UpperCamelCase              =						self._process_audio(
                       a_,       a_,       a_,       a_,       a_,       a_,       a_,       a_,       **a_,       )
            else:
                   _UpperCamelCase              =						None

            if audio_target is not None:
                   _UpperCamelCase              =						self._process_audio(
                       a_,       a_,       a_,       a_,       a_,       a_,       a_,       a_,       **a_,       )

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

            return inputs

     def 	_UpperCamelCase     (      self,       A,       A = False,       A = False,       A = None,       A = False,       A = None,       A = None,       A = None,       **A,       ):





            """simple docstring"""

            _UpperCamelCase              =						isinstance(a_,       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}'''      )
            _UpperCamelCase              =						is_batched_numpy or (
                isinstance(a_,       (list, tuple)      ) and (isinstance(speech[0],       (np.ndarray, tuple, list)      ))
            )

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

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

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

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

            _UpperCamelCase              =						self.pad(
                a_,       padding=a_,       max_length=a_,       truncation=a_,       pad_to_multiple_of=a_,       return_attention_mask=a_,       **a_,       )

            _UpperCamelCase              =						feature_size_hack

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

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

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

            if return_tensors is not None:
                   _UpperCamelCase              =						padded_inputs.convert_to_tensors(a_      )

            return padded_inputs



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						super().to_dict()

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

            return output













from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       return math.sqrt(sum(pow(a - b    ,       2					) for a, b in zip(lowerCAmelCase    ,       lowerCAmelCase					)					)					)


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):

       if dataset.ndim != value_array.ndim:
              _UpperCamelCase              =						(
                  '''Wrong input data\'s dimensions... '''
                  f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
              )
              raise ValueError(lowerCAmelCase					)

       try:
              if dataset.shape[1] != value_array.shape[1]:
                     _UpperCamelCase              =						(
                         '''Wrong input data\'s shape... '''
                         f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
                     )
                     raise ValueError(lowerCAmelCase					)
       except IndexError:
              if dataset.ndim != value_array.ndim:
                     raise TypeError('''Wrong shape'''					)

       if dataset.dtype != value_array.dtype:
              _UpperCamelCase              =						(
                  '''Input data have different datatype... '''
                  f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
              )
              raise TypeError(lowerCAmelCase					)

       _UpperCamelCase              =						[]

       for value in value_array:
              _UpperCamelCase              =						euclidean(lowerCAmelCase    ,       dataset[0]					)
              _UpperCamelCase              =						dataset[0].tolist()

              for dataset_value in dataset[1:]:
                     _UpperCamelCase              =						euclidean(lowerCAmelCase    ,       lowerCAmelCase					)

                     if dist > temp_dist:
                            _UpperCamelCase              =						temp_dist
                            _UpperCamelCase              =						dataset_value.tolist()

              answer.append([vector, dist]					)

       return answer


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       return np.dot(lowerCAmelCase    ,       lowerCAmelCase					) / (norm(lowerCAmelCase					) * norm(lowerCAmelCase					))


if __name__ == "__main__":
       import doctest

       doctest.testmod()






