



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 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Any									=  ['audio_values', 'audio_mask']



					def __init__(			self    ,	lowerCAmelCase=2048    ,	lowerCAmelCase=1    ,	lowerCAmelCase=[16, 16]    ,	lowerCAmelCase=128    ,	lowerCAmelCase=4_4100    ,	lowerCAmelCase=86    ,	lowerCAmelCase=2048    ,	lowerCAmelCase=0.0    ,	**lowerCAmelCase    ,	):
							super().__init__(
							    feature_size=lowerCAmelCase    ,	sampling_rate=lowerCAmelCase    ,	padding_value=lowerCAmelCase    ,	**lowerCAmelCase    ,	)

							UpperCAmelCase_						=				spectrogram_length
							UpperCAmelCase_						=				num_channels
							UpperCAmelCase_						=				patch_size
							UpperCAmelCase_						=				feature_size // self.patch_size[1]
							UpperCAmelCase_						=				n_fft
							UpperCAmelCase_						=				sampling_rate // hop_length_to_sampling_rate
							UpperCAmelCase_						=				sampling_rate
							UpperCAmelCase_						=				padding_value
							UpperCAmelCase_						=				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    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				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=80.0    ,	)
							UpperCAmelCase_						=				log_spec[:, :-1]
							UpperCAmelCase_						=				log_spec - 20.0
							UpperCAmelCase_						=				np.clip(log_spec / 40.0    ,	-2.0    ,	0.0     ) + 1.0
							return log_spec



					def __call__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = True    ,	lowerCAmelCase = None    ,	lowerCAmelCase = False    ,	lowerCAmelCase = False    ,	**lowerCAmelCase    ,	):

							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."     )

							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([speech]    ,	dtype=np.floataa     ).T 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_						=				[np.asarray([raw_speech]     ).T]

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

							# Create audio attention mask
							UpperCAmelCase_						=				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:
									UpperCAmelCase_						=				[
									    (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
									]
									UpperCAmelCase_						=				np.array(lowerCAmelCase     ).astype(np.floataa     )

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

							# return as BatchFeature
							if return_attention_mask:
									UpperCAmelCase_						=				{"audio_values": padded_audio_features, "audio_mask": audio_mask}
							else:
									UpperCAmelCase_						=				{"audio_values": padded_audio_features}

							UpperCAmelCase_						=				BatchFeature(data=lowerCAmelCase    ,	tensor_type=lowerCAmelCase     )
							return encoded_inputs






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


SCREAMING_SNAKE_CASE     				=						{"tokenization_herbert": ["HerbertTokenizer"]}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["HerbertTokenizerFast"]


if TYPE_CHECKING:
			from .tokenization_herbert import HerbertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_herbert_fast import HerbertTokenizerFast

else:
			import sys

			SCREAMING_SNAKE_CASE     				=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

