


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


snake_case							:	int					      =				logging.get_logger(__name__)







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   str   				=      ['''input_features''']
					def __init__(				self       :Any      ,__snake_case       :Union[str, Any]=80      ,__snake_case       :Any=1_60_00      ,__snake_case       :Any=1_60      ,__snake_case       :Optional[Any]=30      ,__snake_case       :Optional[int]=4_00      ,__snake_case       :List[str]=0.0      ,__snake_case       :Union[str, Any]=False      ,**__snake_case       :str      ,) ->   Tuple:
												super().__init__(
												    feature_size=__snake_case      ,sampling_rate=__snake_case      ,padding_value=__snake_case      ,return_attention_mask=__snake_case      ,**__snake_case      ,)
												a__					=					n_fft
												a__					=					hop_length
												a__					=					chunk_length
												a__					=					chunk_length * sampling_rate
												a__					=					self.n_samples // hop_length
												a__					=					sampling_rate
												a__					=					mel_filter_bank(
												    num_frequency_bins=1 + n_fft // 2      ,num_mel_filters=__snake_case      ,min_frequency=0.0      ,max_frequency=80_00.0      ,sampling_rate=__snake_case      ,norm='slaney'      ,mel_scale='slaney'      ,)
					def 			lowerCamelCase__(				self       :Tuple      ,__snake_case       :np.array				) ->   np.ndarray:
												a__					=					spectrogram(
												    __snake_case      ,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'      ,)
												a__					=					log_spec[:, :-1]
												a__					=					np.maximum(__snake_case      ,log_spec.max() - 8.0				)
												a__					=					(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 			lowerCamelCase__(				__snake_case       :List[np.ndarray]      ,__snake_case       :List[np.ndarray]      ,__snake_case       :float = 0.0				) ->   List[np.ndarray]:
												if attention_mask is not None:
																			a__					=					np.array(__snake_case      ,np.intaa				)
																			a__					=					[]

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

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

												return normed_input_values
					def __call__(				self       :List[Any]      ,__snake_case       :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,__snake_case       :bool = True      ,__snake_case       :Optional[int] = None      ,__snake_case       :Optional[Union[str, TensorType]] = None      ,__snake_case       :Optional[bool] = None      ,__snake_case       :Optional[str] = "max_length"      ,__snake_case       :Optional[int] = None      ,__snake_case       :Optional[int] = None      ,__snake_case       :Optional[bool] = None      ,**__snake_case       :str      ,) ->   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.'				)

												a__					=					isinstance(__snake_case      ,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}'				)
												a__					=					is_batched_numpy or (
												    isinstance(__snake_case      ,(list, tuple)				) and (isinstance(raw_speech[0]      ,(np.ndarray, tuple, list)				))
												)

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

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

												a__					=					BatchFeature({'input_features': raw_speech}				)

												# convert into correct format for padding

												a__					=					self.pad(
												    __snake_case      ,padding=__snake_case      ,max_length=max_length if max_length else self.n_samples      ,truncation=__snake_case      ,pad_to_multiple_of=__snake_case      ,return_attention_mask=return_attention_mask or do_normalize      ,)

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

												# make sure list is in array format
												a__					=					padded_inputs.get('input_features'				).transpose(2      ,0      ,1				)

												a__					=					[self._np_extract_fbank_features(__snake_case				) for waveform in input_features[0]]

												if isinstance(input_features[0]      ,__snake_case				):
																			a__					=					[np.asarray(__snake_case      ,dtype=np.floataa				) for feature in input_features]
												else:
																			a__					=					input_features

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

												if return_tensors is not None:
																			a__					=					padded_inputs.convert_to_tensors(__snake_case				)

												return padded_inputs



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








from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




