
'''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


_lowercase    =		logging.get_logger(__name__)




class   UpperCAmelCase_					(		A_			):






						'''simple docstring'''






						_lowercase		:				Optional[int]									=       ['''input_features''']




						def __init__( self   ,       _lowercase=80   ,       _lowercase=16_000   ,       _lowercase=160   ,       _lowercase=30   ,       _lowercase=400   ,       _lowercase=0.0   ,       _lowercase=False   ,       **_lowercase   ,       ):

												"""simple docstring"""



												super().__init__(
												    feature_size=_lowerCamelCase   ,       sampling_rate=_lowerCamelCase   ,       padding_value=_lowerCamelCase   ,       return_attention_mask=_lowerCamelCase   ,       **_lowerCamelCase   ,       )
												_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=_lowerCamelCase   ,       min_frequency=0.0   ,       max_frequency=8_000.0   ,       sampling_rate=_lowerCamelCase   ,       norm="""slaney"""   ,       mel_scale="""slaney"""   ,       )




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			spectrogram(
												    _lowerCamelCase   ,       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(_lowerCamelCase   ,       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  _lowercase     ( _lowercase   ,       _lowercase   ,       _lowercase = 0.0				):

												"""simple docstring"""



												if attention_mask is not None:
																		_lowerCAmelCase  						=			np.array(_lowerCamelCase   ,       np.intaa				)
																		_lowerCAmelCase  						=			[]

																		for vector, length in zip(_lowerCamelCase   ,       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(_lowerCamelCase				)
												else:
																		_lowerCAmelCase  						=			[(x - x.mean()) / np.sqrt(x.var() + 1e-7				) for x in input_values]

												return normed_input_values




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

												"""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."""				)

												_lowerCAmelCase  						=			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}'				)
												_lowerCAmelCase  						=			is_batched_numpy or (
												    isinstance(_lowerCamelCase   ,       (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(_lowerCamelCase   ,       np.ndarray				):
																		_lowerCAmelCase  						=			np.asarray(_lowerCamelCase   ,       dtype=np.floataa				)
												elif isinstance(_lowerCamelCase   ,       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(
												    _lowerCamelCase   ,       padding=_lowerCamelCase   ,       max_length=max_length if max_length else self.n_samples   ,       truncation=_lowerCamelCase   ,       pad_to_multiple_of=_lowerCamelCase   ,       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(_lowerCamelCase				) for waveform in input_features[0]]

												if isinstance(input_features[0]   ,       _lowerCamelCase				):
																		_lowerCAmelCase  						=			[np.asarray(_lowerCamelCase   ,       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(_lowerCamelCase				)

												return padded_inputs




						def  _lowercase     ( self				):

												"""simple docstring"""



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


'''simple docstring'''



import logging

from transformers import PretrainedConfig


_lowercase    =		logging.getLogger(__name__)


_lowercase    =		{
    """bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json""",
}




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				Optional[Any]									=       '''bertabs'''




						def __init__( self   ,       _lowercase=30_522   ,       _lowercase=512   ,       _lowercase=6   ,       _lowercase=512   ,       _lowercase=8   ,       _lowercase=512   ,       _lowercase=0.2   ,       _lowercase=6   ,       _lowercase=768   ,       _lowercase=8   ,       _lowercase=2_048   ,       _lowercase=0.2   ,       **_lowercase   ,       ):

												"""simple docstring"""



												super().__init__(**_lowercase				)

												_lowerCAmelCase  						=			vocab_size
												_lowerCAmelCase  						=			max_pos

												_lowerCAmelCase  						=			enc_layers
												_lowerCAmelCase  						=			enc_hidden_size
												_lowerCAmelCase  						=			enc_heads
												_lowerCAmelCase  						=			enc_ff_size
												_lowerCAmelCase  						=			enc_dropout

												_lowerCAmelCase  						=			dec_layers
												_lowerCAmelCase  						=			dec_hidden_size
												_lowerCAmelCase  						=			dec_heads
												_lowerCAmelCase  						=			dec_ff_size
												_lowerCAmelCase  						=			dec_dropout
