



'''simple docstring'''

from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


__lowerCAmelCase									=   logging.get_logger(__name__)

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    str   				=					['audio_values', 'audio_mask']







							def __init__(							self					:       Tuple	,_UpperCAmelCase					:       str=2048	,_UpperCAmelCase					:       List[str]=1	,_UpperCAmelCase					:       Any=[16, 16]	,_UpperCAmelCase					:       Dict=128	,_UpperCAmelCase					:       Tuple=44100	,_UpperCAmelCase					:       Optional[Any]=86	,_UpperCAmelCase					:       str=2048	,_UpperCAmelCase					:       Union[str, Any]=0.0	,**_UpperCAmelCase					:       List[str]	,):
									super().__init__(
									    feature_size=_UpperCAmelCase	,sampling_rate=_UpperCAmelCase	,padding_value=_UpperCAmelCase	,**_UpperCAmelCase	,)

									_a     :      List[Any]        =							spectrogram_length
									_a     :      Tuple        =							num_channels
									_a     :      Dict        =							patch_size
									_a     :      Optional[Any]        =							feature_size // self.patch_size[1]
									_a     :      Tuple        =							n_fft
									_a     :      List[Any]        =							sampling_rate // hop_length_to_sampling_rate
									_a     :      List[str]        =							sampling_rate
									_a     :      Union[str, Any]        =							padding_value
									_a     :      Any        =							mel_filter_bank(
									    num_frequency_bins=1 + n_fft // 2	,num_mel_filters=_UpperCAmelCase	,min_frequency=0.0	,max_frequency=2_20_50.0	,sampling_rate=_UpperCAmelCase	,norm='slaney'	,mel_scale='slaney'	,).T







							def 							__lowercase			(							self					:       Tuple	,_UpperCAmelCase					:       np.array		):
									_a     :      List[str]        =							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.T	,log_mel='dB'	,db_range=80.0	,)
									_a     :      List[Any]        =							log_spec[:, :-1]
									_a     :      Optional[Any]        =							log_spec - 20.0
									_a     :      Dict        =							np.clip(log_spec / 40.0	,-2.0	,0.0		) + 1.0
									return log_spec







							def __call__(							self					:       int	,_UpperCAmelCase					:       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,_UpperCAmelCase					:       Optional[Union[str, TensorType]] = None	,_UpperCAmelCase					:       Optional[bool] = True	,_UpperCAmelCase					:       Optional[int] = None	,_UpperCAmelCase					:       bool = False	,_UpperCAmelCase					:       bool = False	,**_UpperCAmelCase					:       Any	,):

									if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
													raise ValueError(
													    'This feature extractor is set to support sampling rate'
													    F""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
													    F""" 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     :      Optional[Any]        =							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}"""		)
									_a     :      Optional[int]        =							is_batched_numpy or (
									    isinstance(_UpperCAmelCase	,(list, tuple)		) and (isinstance(raw_speech[0]	,(np.ndarray, tuple, list)		))
									)
									if is_batched:
											_a     :      Union[str, Any]        =							[np.asarray([speech]	,dtype=np.floataa		).T for speech in raw_speech]
									elif not is_batched and not isinstance(_UpperCAmelCase	,np.ndarray		):
											_a     :      Optional[int]        =							np.asarray(_UpperCAmelCase	,dtype=np.floataa		)
									elif isinstance(_UpperCAmelCase	,np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
											_a     :      Optional[Any]        =							raw_speech.astype(np.floataa		)
									# always return batch
									if not is_batched:
											_a     :      Dict        =							[np.asarray([raw_speech]		).T]

									# Convert audio signals to log mel spectrograms, truncate by time axis
									_a     :      Any        =							[
									    self._np_extract_fbank_features(waveform.squeeze()		).T[: self.spectrogram_length] for waveform in raw_speech
									]
									if isinstance(audio_features[0]	,_UpperCAmelCase		):
											_a     :      Tuple        =							[np.asarray(_UpperCAmelCase	,dtype=np.floataa		) for feature in audio_features]

									# Create audio attention mask
									_a     :      List[Any]        =							max(
									    [ceil(feature.shape[0] / self.patch_size[0]		) * self.freq_len for feature in audio_features]		)  # The maximum number of audio patches in a batch
									if return_attention_mask:
											_a     :      Dict        =							[
											    (ceil(feature.shape[0] / self.patch_size[0]		) * self.freq_len) * [1]
											    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]		) * self.freq_len) * [0]
											    for feature in audio_features
											]
											_a     :      Union[str, Any]        =							np.array(_UpperCAmelCase		).astype(np.floataa		)

									# convert into correct format for padding
									_a     :      Dict        =							max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
									_a     :      Union[str, Any]        =							np.ones([len(_UpperCAmelCase		), 1, max_time_len, self.feature_size]		).astype(np.floataa		)
									_a     :      List[Any]        =							padded_audio_features * self.padding_value
									for i in range(len(_UpperCAmelCase		)		):
											_a     :      Any        =							audio_features[i]
											_a     :      List[Any]        =							feature

									# return as BatchFeature
									if return_attention_mask:
											_a     :      Tuple        =							{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
									else:
											_a     :      Tuple        =							{'audio_values': padded_audio_features}

									_a     :      Optional[Any]        =							BatchFeature(data=_UpperCAmelCase	,tensor_type=_UpperCAmelCase		)
									return encoded_inputs









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase									=   {
    '''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''LILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LiltForQuestionAnswering''',
							    '''LiltForSequenceClassification''',
							    '''LiltForTokenClassification''',
							    '''LiltModel''',
							    '''LiltPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_lilt import (
														    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LiltForQuestionAnswering,
														    LiltForSequenceClassification,
														    LiltForTokenClassification,
														    LiltModel,
														    LiltPreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase									=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




