






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


snake_case    				=		logging.get_logger(__name__)


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
					'''simple docstring'''


					UpperCamelCase_							:		List[Any]			=	['''audio_values''', '''audio_mask''']


					def __init__(				self       :	int					, UpperCAmelCase_       :	Tuple=2048					, UpperCAmelCase_       :	Any=1					, UpperCAmelCase_       :	int=[16, 16]					, UpperCAmelCase_       :	List[Any]=128					, UpperCAmelCase_       :	List[str]=4_4100					, UpperCAmelCase_       :	Optional[Any]=86					, UpperCAmelCase_       :	Tuple=2048					, UpperCAmelCase_       :	Optional[int]=0.0					, **UpperCAmelCase_       :	List[Any]					, ):
							super().__init__(
							    feature_size=UpperCAmelCase_					, sampling_rate=UpperCAmelCase_					, padding_value=UpperCAmelCase_					, **UpperCAmelCase_					, )

							SCREAMING_SNAKE_CASE       :      Optional[Any]							=    spectrogram_length
							SCREAMING_SNAKE_CASE       :      Dict							=    num_channels
							SCREAMING_SNAKE_CASE       :      Tuple							=    patch_size
							SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    feature_size // self.patch_size[1]
							SCREAMING_SNAKE_CASE       :      List[Any]							=    n_fft
							SCREAMING_SNAKE_CASE       :      str							=    sampling_rate // hop_length_to_sampling_rate
							SCREAMING_SNAKE_CASE       :      str							=    sampling_rate
							SCREAMING_SNAKE_CASE       :      Optional[Any]							=    padding_value
							SCREAMING_SNAKE_CASE       :      Optional[int]							=    mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2					, num_mel_filters=UpperCAmelCase_					, min_frequency=0.0					, max_frequency=2_2050.0					, sampling_rate=UpperCAmelCase_					, norm="slaney"					, mel_scale="slaney"					, ).T


					def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	np.array      ):
							SCREAMING_SNAKE_CASE       :      Dict							=    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					, )
							SCREAMING_SNAKE_CASE       :      str							=    log_spec[:, :-1]
							SCREAMING_SNAKE_CASE       :      str							=    log_spec - 20.0
							SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    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_       :	Optional[int]					, ):

							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."      )

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

							# Convert audio signals to log mel spectrograms, truncate by time axis
							SCREAMING_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]					, UpperCAmelCase_      ):
									SCREAMING_SNAKE_CASE       :      Dict							=    [np.asarray(UpperCAmelCase_					, dtype=np.floataa      ) for feature in audio_features]

							# Create audio attention mask
							SCREAMING_SNAKE_CASE       :      Union[str, 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:
									SCREAMING_SNAKE_CASE       :      Optional[int]							=    [
									    (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
									]
									SCREAMING_SNAKE_CASE       :      Optional[Any]							=    np.array(UpperCAmelCase_      ).astype(np.floataa      )

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

							# return as BatchFeature
							if return_attention_mask:
									SCREAMING_SNAKE_CASE       :      List[Any]							=    {"audio_values": padded_audio_features, "audio_mask": audio_mask}
							else:
									SCREAMING_SNAKE_CASE       :      Tuple							=    {"audio_values": padded_audio_features}

							SCREAMING_SNAKE_CASE       :      Any							=    BatchFeature(data=UpperCAmelCase_					, tensor_type=UpperCAmelCase_      )
							return encoded_inputs














snake_case    				=		"""Tobias Carryer"""

from time import time


class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     def __init__(				self       :	List[Any]					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	int=int(time()      )      ):  # noqa: B008
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    multiplier
       SCREAMING_SNAKE_CASE       :      Tuple							=    increment
       SCREAMING_SNAKE_CASE       :      int							=    modulo
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    seed






     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    (self.multiplier * self.seed + self.increment) % self.modulo
       return self.seed


if __name__ == "__main__":
   # Show the LCG in action.
   snake_case    				=		LinearCongruentialGenerator(1_664_525, 1_013_904_223, 2 << 31)
   while True:
      print(lcg.next_number())






