



"""simple docstring"""






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


__UpperCAmelCase   :   Any        =       logging.get_logger(__name__)

class 						__lowerCAmelCase				(A__	):


					'''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     ,							):




										"""simple docstring"""





										super().__init__(
										    feature_size=_UpperCamelCase     ,							sampling_rate=_UpperCamelCase     ,							padding_value=_UpperCamelCase     ,							return_attention_mask=_UpperCamelCase     ,							**_UpperCamelCase     ,							)
										snake_case_    :str										=    n_fft
										snake_case_    :Dict										=    hop_length
										snake_case_    :Union[str, Any]										=    chunk_length
										snake_case_    :List[str]										=    chunk_length * sampling_rate
										snake_case_    :int										=    self.n_samples // hop_length
										snake_case_    :Any										=    sampling_rate
										snake_case_    :Optional[int]										=    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 		_a			(   self     ,							a ):




										"""simple docstring"""





										snake_case_    :Union[str, Any]										=    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"     ,							)
										snake_case_    :Any										=    log_spec[:, :-1]
										snake_case_    :int										=    np.maximum(_UpperCamelCase     ,							log_spec.max() - 8.0 )
										snake_case_    :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 		_a			(   a     ,							a     ,							a = 0.0 ):




										"""simple docstring"""





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

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

																				normed_input_values.append(_UpperCamelCase )
										else:
															snake_case_    :Optional[Any]										=    [(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     ,							):




										"""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." )

										snake_case_    :Optional[int]										=    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}''' )
										snake_case_    :Tuple										=    is_batched_numpy or (
										    isinstance(_UpperCamelCase     ,							(list, tuple) ) and (isinstance(raw_speech[0]     ,							(np.ndarray, tuple, list) ))
										)

										if is_batched:
															snake_case_    :Any										=    [np.asarray([speech]     ,							dtype=np.floataa ).T for speech in raw_speech]
										elif not is_batched and not isinstance(_UpperCamelCase     ,							np.ndarray ):
															snake_case_    :Tuple										=    np.asarray(_UpperCamelCase     ,							dtype=np.floataa )
										elif isinstance(_UpperCamelCase     ,							np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
															snake_case_    :Dict										=    raw_speech.astype(np.floataa )

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

										snake_case_    :List[Any]										=    BatchFeature({"input_features": raw_speech} )

										# convert into correct format for padding

										snake_case_    :int										=    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:
															snake_case_    :Tuple										=    self.zero_mean_unit_var_norm(
															    padded_inputs["input_features"]     ,							attention_mask=padded_inputs["attention_mask"]     ,							padding_value=self.padding_value     ,							)
															snake_case_    :Optional[Any]										=    np.stack(padded_inputs["input_features"]     ,							axis=0 )

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

										snake_case_    :Dict										=    [self._np_extract_fbank_features(_UpperCamelCase ) for waveform in input_features[0]]

										if isinstance(input_features[0]     ,							_UpperCamelCase ):
															snake_case_    :Optional[Any]										=    [np.asarray(_UpperCamelCase     ,							dtype=np.floataa ) for feature in input_features]
										else:
															snake_case_    :Union[str, Any]										=    input_features

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

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

										return padded_inputs







					def 		_a			(   self ):




										"""simple docstring"""





										snake_case_    :Any										=    copy.deepcopy(self.__dict__ )
										snake_case_    :Optional[int]										=    self.__class__.__name__
										if "mel_filters" in output:
															del output["mel_filters"]
										return output








import numpy as np
def      A__       (    SCREAMING_SNAKE_CASE_   :      np.ndarray				,	SCREAMING_SNAKE_CASE_   :      float						)  ->  np.ndarray:






   """simple docstring"""







   return np.where(vector > 0				,	SCREAMING_SNAKE_CASE_				,	(alpha * (np.exp(SCREAMING_SNAKE_CASE_						) - 1))						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()