





from typing import 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 ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


UpperCamelCase__     =	logging.get_logger(__name__)

class     __SCREAMING_SNAKE_CASE   (       _a    ):
							snake_case       : Any         =    ["""input_features""", """attention_mask"""]






							def __init__(  self					,	__lowerCAmelCase=80					,	__lowerCAmelCase=16000					,	__lowerCAmelCase=0.0					,	__lowerCAmelCase=10					,	__lowerCAmelCase=25					,	__lowerCAmelCase="hamming_window"					,	__lowerCAmelCase=32768.0					,	__lowerCAmelCase=0.97					,	__lowerCAmelCase=1.0					,	__lowerCAmelCase=True					,	__lowerCAmelCase=True					,	__lowerCAmelCase=False					,	**__lowerCAmelCase					,	):
														super().__init__(feature_size=__lowerCAmelCase					,	sampling_rate=__lowerCAmelCase					,	padding_value=__lowerCAmelCase					,	**__lowerCAmelCase					)

														UpperCamelCase__			       =     feature_size
														UpperCamelCase__			       =     sampling_rate
														UpperCamelCase__			       =     padding_value
														UpperCamelCase__			       =     hop_length
														UpperCamelCase__			       =     win_length
														UpperCamelCase__			       =     frame_signal_scale
														UpperCamelCase__			       =     preemphasis_coeff
														UpperCamelCase__			       =     mel_floor
														UpperCamelCase__			       =     normalize_means
														UpperCamelCase__			       =     normalize_vars
														UpperCamelCase__			       =     win_function
														UpperCamelCase__			       =     return_attention_mask

														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






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														if self.win_function == "hamming_window":
																					UpperCamelCase__			       =     window_function(window_length=self.sample_size					,	name=self.win_function					,	periodic=__lowerCAmelCase					)
														else:
																					UpperCamelCase__			       =     window_function(window_length=self.sample_size					,	name=self.win_function					)

														UpperCamelCase__			       =     mel_filter_bank(
														    num_frequency_bins=self.n_freqs					,	num_mel_filters=self.feature_size					,	min_frequency=0.0					,	max_frequency=self.sampling_rate / 2.0					,	sampling_rate=self.sampling_rate					,	)

														UpperCamelCase__			       =     spectrogram(
														    one_waveform * self.frame_signal_scale					,	window=__lowerCAmelCase					,	frame_length=self.sample_size					,	hop_length=self.sample_stride					,	fft_length=self.n_fft					,	center=__lowerCAmelCase					,	preemphasis=self.preemphasis_coeff					,	mel_filters=__lowerCAmelCase					,	mel_floor=self.mel_floor					,	log_mel="""log"""					,	)
														return msfc_features.T






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														# make sure we normalize float32 arrays
														if self.normalize_means:
																					UpperCamelCase__			       =     x[:input_length].mean(axis=0					)
																					UpperCamelCase__			       =     np.subtract(__lowerCAmelCase					,	__lowerCAmelCase					)
														if self.normalize_vars:
																					UpperCamelCase__			       =     x[:input_length].std(axis=0					)
																					UpperCamelCase__			       =     np.divide(__lowerCAmelCase					,	__lowerCAmelCase					)

														if input_length < x.shape[0]:
																					UpperCamelCase__			       =     padding_value

														# make sure array is in float32
														UpperCamelCase__			       =     x.astype(np.floataa					)

														return x






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase = None					):
														UpperCamelCase__			       =     attention_mask.sum(-1					) if attention_mask is not None else [x.shape[0] for x in input_features]
														return [self._normalize_one(__lowerCAmelCase					,	__lowerCAmelCase					,	self.padding_value					) for x, n in zip(__lowerCAmelCase					,	__lowerCAmelCase					)]







							def __call__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase = False					,	__lowerCAmelCase = None					,	__lowerCAmelCase = False					,	__lowerCAmelCase = None					,	__lowerCAmelCase = None					,	__lowerCAmelCase = None					,	__lowerCAmelCase = None					,	**__lowerCAmelCase					,	):

														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 `raw_speech` 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."""					)

														UpperCamelCase__			       =     isinstance(__lowerCAmelCase					,	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}"""					)
														UpperCamelCase__			       =     is_batched_numpy or (
														    isinstance(__lowerCAmelCase					,	(list, tuple)					) and (isinstance(raw_speech[0]					,	(np.ndarray, tuple, list)					))
														)

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

														# always return batch
														if not is_batched:
																					UpperCamelCase__			       =     [raw_speech]

														# extract fbank features
														UpperCamelCase__			       =     [self._extract_mfsc_features(__lowerCAmelCase					) for one_waveform in raw_speech]

														# convert into correct format for padding
														UpperCamelCase__			       =     BatchFeature({"""input_features""": features}					)

														UpperCamelCase__			       =     self.pad(
														    __lowerCAmelCase					,	padding=__lowerCAmelCase					,	max_length=__lowerCAmelCase					,	truncation=__lowerCAmelCase					,	pad_to_multiple_of=__lowerCAmelCase					,	return_attention_mask=__lowerCAmelCase					,	**__lowerCAmelCase					,	)
														# make sure list is in array format
														UpperCamelCase__			       =     padded_inputs.get("""input_features"""					)
														if isinstance(input_features[0]					,	__lowerCAmelCase					):
																					UpperCamelCase__			       =     [np.asarray(__lowerCAmelCase					,	dtype=np.floataa					) for feature in input_features]

														UpperCamelCase__			       =     padded_inputs.get("""attention_mask"""					)
														if attention_mask is not None:
																					UpperCamelCase__			       =     [np.asarray(__lowerCAmelCase					,	dtype=np.intaa					) for array in attention_mask]

														if self.normalize_means or self.normalize_vars:
																					UpperCamelCase__			       =     (
																					    np.array(__lowerCAmelCase					,	dtype=np.intaa					)
																					    if self._get_padding_strategies(__lowerCAmelCase					,	max_length=__lowerCAmelCase					) is not PaddingStrategy.DO_NOT_PAD
																					    and padding
																					    else None
																					)
																					UpperCamelCase__			       =     self.normalize(
																					    padded_inputs["""input_features"""]					,	attention_mask=__lowerCAmelCase					)

														if return_tensors is not None:
																					UpperCamelCase__			       =     padded_inputs.convert_to_tensors(__lowerCAmelCase					)

														return padded_inputs











import logging

from transformers.configuration_utils import PretrainedConfig


UpperCamelCase__     =	logging.getLogger(__name__)

class     __SCREAMING_SNAKE_CASE   (       _a    ):
							snake_case       : Optional[Any]         =    """masked_bert"""






							def __init__(  self					,	__lowerCAmelCase=30522					,	__lowerCAmelCase=768					,	__lowerCAmelCase=12					,	__lowerCAmelCase=12					,	__lowerCAmelCase=3072					,	__lowerCAmelCase="gelu"					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=512					,	__lowerCAmelCase=2					,	__lowerCAmelCase=0.02					,	__lowerCAmelCase=1E-12					,	__lowerCAmelCase=0					,	__lowerCAmelCase="topK"					,	__lowerCAmelCase="constant"					,	__lowerCAmelCase=0.0					,	**__lowerCAmelCase					,	):
														super().__init__(pad_token_id=__lowerCAmelCase					,	**__lowerCAmelCase					)

														UpperCamelCase__			       =     vocab_size
														UpperCamelCase__			       =     hidden_size
														UpperCamelCase__			       =     num_hidden_layers
														UpperCamelCase__			       =     num_attention_heads
														UpperCamelCase__			       =     hidden_act
														UpperCamelCase__			       =     intermediate_size
														UpperCamelCase__			       =     hidden_dropout_prob
														UpperCamelCase__			       =     attention_probs_dropout_prob
														UpperCamelCase__			       =     max_position_embeddings
														UpperCamelCase__			       =     type_vocab_size
														UpperCamelCase__			       =     initializer_range
														UpperCamelCase__			       =     layer_norm_eps
														UpperCamelCase__			       =     pruning_method
														UpperCamelCase__			       =     mask_init
														UpperCamelCase__			       =     mask_scale




