
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


lowerCamelCase__          =		logging.get_logger(__name__)







class      A__							(     A_	):
				lowercase			    =  ['input_values', 'attention_mask']






				def __init__(						self					:      str    ,     a					:      int = 1    ,     a					:      int = 16_000    ,     a					:      float = 0.0    ,     a					:      bool = False    ,     a					:      int = 80    ,     a					:      int = 16    ,     a					:      int = 64    ,     a					:      str = "hann_window"    ,     a					:      float = 1.0    ,     a					:      float = 80    ,     a					:      float = 7_600    ,     a					:      float = 1E-10    ,     a					:      int = 2    ,     a					:      bool = True    ,     **a					:      Any    ,     ):






							'''simple docstring'''







							super().__init__(feature_size=_lowerCamelCase    ,     sampling_rate=_lowerCamelCase    ,     padding_value=_lowerCamelCase    ,     **_lowerCamelCase   )
							lowerCAmelCase__      :      List[Any]         =		do_normalize
							lowerCAmelCase__      :      Union[str, Any]         =		return_attention_mask

							lowerCAmelCase__      :      Any         =		num_mel_bins
							lowerCAmelCase__      :      Optional[int]         =		hop_length
							lowerCAmelCase__      :      List[Any]         =		win_length
							lowerCAmelCase__      :      Any         =		win_function
							lowerCAmelCase__      :      Any         =		frame_signal_scale
							lowerCAmelCase__      :      Union[str, Any]         =		fmin
							lowerCAmelCase__      :      int         =		fmax
							lowerCAmelCase__      :      int         =		mel_floor
							lowerCAmelCase__      :      Union[str, Any]         =		reduction_factor

							lowerCAmelCase__      :      Union[str, Any]         =		win_length * sampling_rate // 1_000
							lowerCAmelCase__      :      str         =		hop_length * sampling_rate // 1_000
							lowerCAmelCase__      :      List[str]         =		optimal_fft_length(self.sample_size   )
							lowerCAmelCase__      :      str         =		(self.n_fft // 2) + 1

							lowerCAmelCase__      :      int         =		window_function(window_length=self.sample_size    ,     name=self.win_function    ,     periodic=_lowerCamelCase   )

							lowerCAmelCase__      :      List[str]         =		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 							_lowerCamelCase		(						a					:      List[np.ndarray]    ,     a					:      List[np.ndarray]    ,     a					:      float = 0.0   ):






							'''simple docstring'''







							if attention_mask is not None:
										lowerCAmelCase__      :      Optional[Any]         =		np.array(_lowerCamelCase    ,     np.intaa   )
										lowerCAmelCase__      :      List[Any]         =		[]

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

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

							return normed_input_values






				def 							_lowerCamelCase		(						self					:      Union[str, Any]    ,     a					:      np.ndarray    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		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					:      List[str]    ,     a					:      Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None    ,     a					:      Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None    ,     a					:      Union[bool, str, PaddingStrategy] = False    ,     a					:      Optional[int] = None    ,     a					:      bool = False    ,     a					:      Optional[int] = None    ,     a					:      Optional[bool] = None    ,     a					:      Optional[Union[str, TensorType]] = None    ,     a					:      Optional[int] = None    ,     **a					:      Tuple    ,     ):






							'''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:
										lowerCAmelCase__      :      Dict         =		self._process_audio(
										    _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     **_lowerCamelCase    ,     )
							else:
										lowerCAmelCase__      :      Optional[Any]         =		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__      :      Optional[Any]         =		inputs_target.get('attention_mask'   )
													if decoder_attention_mask is not None:
																lowerCAmelCase__      :      Union[str, Any]         =		decoder_attention_mask

							return inputs






				def 							_lowerCamelCase		(						self					:      Union[str, Any]    ,     a					:      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,     a					:      bool = False    ,     a					:      Union[bool, str, PaddingStrategy] = False    ,     a					:      Optional[int] = None    ,     a					:      bool = False    ,     a					:      Optional[int] = None    ,     a					:      Optional[bool] = None    ,     a					:      Optional[Union[str, TensorType]] = None    ,     **a					:      Optional[int]    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      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__      :      Union[str, Any]         =		is_batched_numpy or (
							    isinstance(_lowerCamelCase    ,     (list, tuple)   ) and (isinstance(speech[0]    ,     (np.ndarray, tuple, list)   ))
							)

							if is_batched:
										lowerCAmelCase__      :      str         =		[np.asarray(_lowerCamelCase    ,     dtype=np.floataa   ) for speech in speech]
							elif not is_batched and not isinstance(_lowerCamelCase    ,     np.ndarray   ):
										lowerCAmelCase__      :      Union[str, Any]         =		np.asarray(_lowerCamelCase    ,     dtype=np.floataa   )
							elif isinstance(_lowerCamelCase    ,     np.ndarray   ) and speech.dtype is np.dtype(np.floataa   ):
										lowerCAmelCase__      :      Tuple         =		speech.astype(np.floataa   )

							# always return batch
							if not is_batched:
										lowerCAmelCase__      :      Optional[int]         =		[speech]

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

							# convert into correct format for padding
							if is_target:
										lowerCAmelCase__      :      str         =		[self._extract_mel_features(_lowerCamelCase   ) for waveform in speech]
										lowerCAmelCase__      :      Tuple         =		BatchFeature({'input_values': features}   )
										lowerCAmelCase__      :      List[str]         =		self.num_mel_bins
							else:
										lowerCAmelCase__      :      int         =		BatchFeature({'input_values': speech}   )

							lowerCAmelCase__      :      Dict         =		self.pad(
							    _lowerCamelCase    ,     padding=_lowerCamelCase    ,     max_length=_lowerCamelCase    ,     truncation=_lowerCamelCase    ,     pad_to_multiple_of=_lowerCamelCase    ,     return_attention_mask=_lowerCamelCase    ,     **_lowerCamelCase    ,     )

							lowerCAmelCase__      :      Union[str, Any]         =		feature_size_hack

							# convert input values to correct format
							lowerCAmelCase__      :      List[str]         =		padded_inputs['input_values']
							if not isinstance(input_values[0]    ,     np.ndarray   ):
										lowerCAmelCase__      :      Dict         =		[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__      :      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__      :      Union[str, Any]         =		input_values.astype(np.floataa   )

							# convert attention_mask to correct format
							lowerCAmelCase__      :      str         =		padded_inputs.get('attention_mask'   )
							if attention_mask is not None:
										lowerCAmelCase__      :      Dict         =		[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__      :      List[Any]         =		(
										    attention_mask
										    if self._get_padding_strategies(_lowerCamelCase    ,     max_length=_lowerCamelCase   ) is not PaddingStrategy.DO_NOT_PAD
										    else None
										)
										lowerCAmelCase__      :      Optional[int]         =		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__      :      str         =		padded_inputs.convert_to_tensors(_lowerCamelCase   )

							return padded_inputs






				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		super().to_dict()

							# Don't serialize these as they are derived from the other properties.
							lowerCAmelCase__      :      List[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

"""simple docstring"""



def 					_UpperCAmelCase      ( __lowerCamelCase   :       list ,					__lowerCamelCase   :       int = 0		)     ->					list:
  _snake_case					     =       length or len(__lowerCamelCase		)
  _snake_case					     =       False
  for i in range(length - 1		):
    if list_data[i] > list_data[i + 1]:
      _snake_case     ,					_snake_case					     =       list_data[i + 1], list_data[i]
      _snake_case					     =       True

  return list_data if not swapped else bubble_sort(__lowerCamelCase ,					length - 1		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()



