





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


_SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_  (    A__    ):
						__magic_name__:	Union[str, Any]       					=				['''audio_values''', '''audio_mask''']






						def __init__(					self					:					Optional[Any]						,      _A					:					str=2048						,      _A					:					str=1						,      _A					:					List[Any]=[16, 16]						,      _A					:					List[str]=128						,      _A					:					Dict=44100						,      _A					:					Tuple=86						,      _A					:					List[str]=2048						,      _A					:					Union[str, Any]=0.0						,      **_A					:					int						,      )							->			str:







							"""simple docstring"""


							super().__init__(
							    feature_size=lowerCAmelCase__						,      sampling_rate=lowerCAmelCase__						,      padding_value=lowerCAmelCase__						,      **lowerCAmelCase__						,      )

							snake_case_						:	Union[str, Any]      =		spectrogram_length
							snake_case_						:	Dict      =		num_channels
							snake_case_						:	List[Any]      =		patch_size
							snake_case_						:	Optional[int]      =		feature_size // self.patch_size[1]
							snake_case_						:	Optional[Any]      =		n_fft
							snake_case_						:	int      =		sampling_rate // hop_length_to_sampling_rate
							snake_case_						:	Optional[int]      =		sampling_rate
							snake_case_						:	Tuple      =		padding_value
							snake_case_						:	Dict      =		mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2						,      num_mel_filters=lowerCAmelCase__						,      min_frequency=0.0						,      max_frequency=22050.0						,      sampling_rate=lowerCAmelCase__						,      norm='slaney'						,      mel_scale='slaney'						,      ).T






						def UpperCAmelCase_ (					self					:					Union[str, Any]						,      _A					:					np.array		)							->			np.ndarray:







							"""simple docstring"""


							snake_case_						:	Union[str, Any]      =		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.T						,      log_mel='dB'						,      db_range=8_0.0						,      )
							snake_case_						:	Tuple      =		log_spec[:, :-1]
							snake_case_						:	Tuple      =		log_spec - 2_0.0
							snake_case_						:	List[str]      =		np.clip(log_spec / 4_0.0						,      -2.0						,      0.0		) + 1.0
							return log_spec






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







							"""simple docstring"""


							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.'		)

							snake_case_						:	Any      =		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}"""		)
							snake_case_						:	str      =		is_batched_numpy or (
							    isinstance(lowerCAmelCase__						,      (list, tuple)		) and (isinstance(raw_speech[0]						,      (np.ndarray, tuple, list)		))
							)
							if is_batched:
								snake_case_						:	Union[str, Any]      =		[np.asarray([speech]						,      dtype=np.floataa		).T for speech in raw_speech]
							elif not is_batched and not isinstance(lowerCAmelCase__						,      np.ndarray		):
								snake_case_						:	Union[str, Any]      =		np.asarray(lowerCAmelCase__						,      dtype=np.floataa		)
							elif isinstance(lowerCAmelCase__						,      np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
								snake_case_						:	Optional[Any]      =		raw_speech.astype(np.floataa		)
							# always return batch
							if not is_batched:
								snake_case_						:	str      =		[np.asarray([raw_speech]		).T]

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

							# Create audio attention mask
							snake_case_						:	Tuple      =		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:
								snake_case_						:	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
								]
								snake_case_						:	Optional[int]      =		np.array(lowerCAmelCase__		).astype(np.floataa		)

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

							# return as BatchFeature
							if return_attention_mask:
								snake_case_						:	Dict      =		{'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
							else:
								snake_case_						:	List[str]      =		{'''audio_values''': padded_audio_features}

							snake_case_						:	Any      =		BatchFeature(data=lowerCAmelCase__						,      tensor_type=lowerCAmelCase__		)
							return encoded_inputs





from ..utils import DummyObject, requires_backends
class        _lowercase      (			metaclass=A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Optional[int]						=		['''keras_nlp''']


				def __init__(       self			:Tuple			,			*lowerCAmelCase__			:Optional[Any]			,			**lowerCAmelCase__			:Dict	)		->			Dict:
										requires_backends(self			,			['''keras_nlp''']	)
