

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


a__				=		logging.get_logger(__name__)

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						int	    =    ["audio_values", "audio_mask"]


							def __init__(			self       ,							_a=2_0_4_8       ,							_a=1       ,							_a=[1_6, 1_6]       ,							_a=1_2_8       ,							_a=4_4_1_0_0       ,							_a=8_6       ,							_a=2_0_4_8       ,							_a=0.0       ,							**_a       ,							)    ->    Optional[Any]:
												super().__init__(
												    feature_size=_a       ,							sampling_rate=_a       ,							padding_value=_a       ,							**_a       ,							)

												_a	:    str         =		spectrogram_length
												_a	:    List[Any]         =		num_channels
												_a	:    List[Any]         =		patch_size
												_a	:    Any         =		feature_size // self.patch_size[1]
												_a	:    Optional[int]         =		n_fft
												_a	:    int         =		sampling_rate // hop_length_to_sampling_rate
												_a	:    str         =		sampling_rate
												_a	:    int         =		padding_value
												_a	:    Tuple         =		mel_filter_bank(
												    num_frequency_bins=1 + n_fft // 2       ,							num_mel_filters=_a       ,							min_frequency=0.0       ,							max_frequency=2_2050.0       ,							sampling_rate=_a       ,							norm='''slaney'''       ,							mel_scale='''slaney'''       ,							).T


							def 				__lowercase				(			self       ,							_a       )    ->    np.ndarray:
												_a	:    List[str]         =		spectrogram(
												    _a       ,							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	:    str         =		log_spec[:, :-1]
												_a	:    Optional[int]         =		log_spec - 20.0
												_a	:    List[str]         =		np.clip(log_spec / 40.0       ,							-2.0       ,							0.0       ) + 1.0
												return log_spec




							def __call__(			self       ,							_a       ,							_a = None       ,							_a = True       ,							_a = None       ,							_a = False       ,							_a = False       ,							**_a       ,							)    ->    BatchFeature:

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

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

												# Create audio attention mask
												_a	:    List[str]         =		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	:    str         =		[
																	    (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	:    str         =		np.array(_a       ).astype(np.floataa       )

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

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

												_a	:    str         =		BatchFeature(data=_a       ,							tensor_type=_a       )
												return encoded_inputs






def    __UpperCAmelCase (	__a      :			str	)			->     int:

					"""simple docstring"""

					assert column_title.isupper()
					_a	:    Optional[Any]         =		0
					_a	:    List[Any]         =		len(__a	) - 1
					_a	:    List[str]         =		0

					while index >= 0:
										_a	:    Dict         =		(ord(column_title[index]	) - 64) * pow(26		,__a	)
										answer += value
										power += 1
										index -= 1

					return answer


if __name__ == "__main__":
			from doctest import testmod

			testmod()



