




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


lowerCAmelCase_					    = logging.get_logger(__name__)






class  _lowerCAmelCase     (  A_	):
						A__		     =		['''input_features''']

						def __init__(   self      ,				__UpperCAmelCase=80      ,				__UpperCAmelCase=1_6000      ,				__UpperCAmelCase=160      ,				__UpperCAmelCase=30      ,				__UpperCAmelCase=400      ,				__UpperCAmelCase=0.0      ,				__UpperCAmelCase=False      ,				**__UpperCAmelCase      ,				):
								super().__init__(
								    feature_size=__UpperCAmelCase      ,				sampling_rate=__UpperCAmelCase      ,				padding_value=__UpperCAmelCase      ,				return_attention_mask=__UpperCAmelCase      ,				**__UpperCAmelCase      ,				)
								lowerCAmelCase__				:  Tuple		       =     n_fft
								lowerCAmelCase__				:  List[Any]		       =     hop_length
								lowerCAmelCase__				:  List[str]		       =     chunk_length
								lowerCAmelCase__				:  Tuple		       =     chunk_length * sampling_rate
								lowerCAmelCase__				:  Any		       =     self.n_samples // hop_length
								lowerCAmelCase__				:  Dict		       =     sampling_rate
								lowerCAmelCase__				:  Tuple		       =     mel_filter_bank(
								    num_frequency_bins=1 + n_fft // 2      ,				num_mel_filters=__UpperCAmelCase      ,				min_frequency=0.0      ,				max_frequency=8000.0      ,				sampling_rate=__UpperCAmelCase      ,				norm='''slaney'''      ,				mel_scale='''slaney'''      ,				)

						def 	__magic_name__(   self      ,				__UpperCAmelCase		):
								lowerCAmelCase__				:  Tuple		       =     spectrogram(
								    __UpperCAmelCase      ,				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__				:  List[Any]		       =     log_spec[:, :-1]
								lowerCAmelCase__				:  Tuple		       =     np.maximum(__UpperCAmelCase      ,				log_spec.max() - 8.0		)
								lowerCAmelCase__				:  List[Any]		       =     (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 	__magic_name__(   __UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase = 0.0		):
								if attention_mask is not None:
										lowerCAmelCase__				:  Optional[Any]		       =     np.array(__UpperCAmelCase      ,				np.intaa		)
										lowerCAmelCase__				:  Tuple		       =     []

										for vector, length in zip(__UpperCAmelCase      ,				attention_mask.sum(-1		)		):
												lowerCAmelCase__				:  Optional[Any]		       =     (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7		)
												if length < normed_slice.shape[0]:
														lowerCAmelCase__				:  Dict		       =     padding_value

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

								return normed_input_values

						def __call__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = True      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = "max_length"      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				**__UpperCAmelCase      ,				):

								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__				:  Optional[Any]		       =     isinstance(__UpperCAmelCase      ,				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__				:  Union[str, Any]		       =     is_batched_numpy or (
								    isinstance(__UpperCAmelCase      ,				(list, tuple)		) and (isinstance(raw_speech[0]      ,				(np.ndarray, tuple, list)		))
								)

								if is_batched:
										lowerCAmelCase__				:  Optional[Any]		       =     [np.asarray([speech]      ,				dtype=np.floataa		).T for speech in raw_speech]
								elif not is_batched and not isinstance(__UpperCAmelCase      ,				np.ndarray		):
										lowerCAmelCase__				:  List[Any]		       =     np.asarray(__UpperCAmelCase      ,				dtype=np.floataa		)
								elif isinstance(__UpperCAmelCase      ,				np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
										lowerCAmelCase__				:  Optional[Any]		       =     raw_speech.astype(np.floataa		)

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

								lowerCAmelCase__				:  int		       =     BatchFeature({'''input_features''': raw_speech}		)

								# convert into correct format for padding

								lowerCAmelCase__				:  Optional[Any]		       =     self.pad(
								    __UpperCAmelCase      ,				padding=__UpperCAmelCase      ,				max_length=max_length if max_length else self.n_samples      ,				truncation=__UpperCAmelCase      ,				pad_to_multiple_of=__UpperCAmelCase      ,				return_attention_mask=return_attention_mask or do_normalize      ,				)

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

								# make sure list is in array format
								lowerCAmelCase__				:  Union[str, Any]		       =     padded_inputs.get('''input_features'''		).transpose(2      ,				0      ,				1		)

								lowerCAmelCase__				:  Optional[int]		       =     [self._np_extract_fbank_features(__UpperCAmelCase		) for waveform in input_features[0]]

								if isinstance(input_features[0]      ,				__UpperCAmelCase		):
										lowerCAmelCase__				:  List[str]		       =     [np.asarray(__UpperCAmelCase      ,				dtype=np.floataa		) for feature in input_features]
								else:
										lowerCAmelCase__				:  str		       =     input_features

								if return_attention_mask:
										# rescale from sample (48000) to feature (3000)
										lowerCAmelCase__				:  List[Any]		       =     padded_inputs["""attention_mask"""][:, :: self.hop_length]

								if return_tensors is not None:
										lowerCAmelCase__				:  int		       =     padded_inputs.convert_to_tensors(__UpperCAmelCase		)

								return padded_inputs







						def 	__magic_name__(   self		):
								lowerCAmelCase__				:  Union[str, Any]		       =     copy.deepcopy(self.__dict__		)
								lowerCAmelCase__				:  Dict		       =     self.__class__.__name__
								if "mel_filters" in output:
										del output["mel_filters"]
								return output







import math



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	float:
  if (
      not isinstance(UpperCamelCase	,      (int, float)      )
      or power_factor < -1
      or power_factor > 1
  ):
    raise ValueError('''power_factor must be a valid float value between -1 and 1.'''      )
  return apparent_power * power_factor



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	float:
  if (
      not isinstance(UpperCamelCase	,      (int, float)      )
      or power_factor < -1
      or power_factor > 1
  ):
    raise ValueError('''power_factor must be a valid float value between -1 and 1.'''      )
  return apparent_power * math.sqrt(1 - power_factor**2      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

