



'''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     		=   logging.get_logger(__name__)

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   str									=		["input_features"]


						def __init__( self     ,	A_=80     ,	A_=1_6000     ,	A_=160     ,	A_=30     ,	A_=400     ,	A_=0.0     ,	A_=False     ,	**A_     ,	)     ->     Optional[int]:
							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     __snake_case   ( self     ,	A_				)     ->     np.ndarray:
							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     __snake_case   ( A_     ,	A_     ,	A_ = 0.0				)     ->     List[np.ndarray]:
							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:

							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     __snake_case   ( self				)     ->     Dict[str, Any]:
							lowerCAmelCase   =     copy.deepcopy(self.__dict__				)
							lowerCAmelCase   =     self.__class__.__name__
							if "mel_filters" in output:
								del output["mel_filters"]
							return output




'''simple docstring'''

from torch import nn






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[int]							)      ->						Union[str, Any]:







	"""simple docstring"""
	if act_fn in ["swish", "silu"]:
		return nn.SiLU()
	elif act_fn == "mish":
		return nn.Mish()
	elif act_fn == "gelu":
		return nn.GELU()
	else:
		raise ValueError(f'Unsupported activation function: {act_fn}'							)