"""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


_UpperCamelCase								=		logging.get_logger(__name__)
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				['audio_values', 'audio_mask']





			def __init__(			self   ,      snake_case_=2_048   ,      snake_case_=1   ,      snake_case_=[16, 16]   ,      snake_case_=128   ,      snake_case_=44_100   ,      snake_case_=86   ,      snake_case_=2_048   ,      snake_case_=0.0   ,      **snake_case_   ,      ):







										'''simple docstring'''


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

										A__       :     Optional[int]								=			spectrogram_length
										A__       :     Optional[int]								=			num_channels
										A__       :     Any								=			patch_size
										A__       :     Optional[Any]								=			feature_size // self.patch_size[1]
										A__       :     Dict								=			n_fft
										A__       :     int								=			sampling_rate // hop_length_to_sampling_rate
										A__       :     Tuple								=			sampling_rate
										A__       :     Optional[int]								=			padding_value
										A__       :     Optional[Any]								=			mel_filter_bank(
										    num_frequency_bins=1 + n_fft // 2   ,      num_mel_filters=snake_case_   ,      min_frequency=0.0   ,      max_frequency=2_20_50.0   ,      sampling_rate=snake_case_   ,      norm="""slaney"""   ,      mel_scale="""slaney"""   ,      ).T





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Dict								=			spectrogram(
										    snake_case_   ,      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__       :     int								=			log_spec[:, :-1]
										A__       :     List[str]								=			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   ,      snake_case_   ,      snake_case_ = None   ,      snake_case_ = True   ,      snake_case_ = None   ,      snake_case_ = False   ,      snake_case_ = False   ,      **snake_case_   ,      ):







										'''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."""							)

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

										# Create audio attention mask
										A__       :     int								=			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__       :     List[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__       :     Union[str, Any]								=			np.array(snake_case_							).astype(np.floataa							)

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

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

										A__       :     int								=			BatchFeature(data=snake_case_   ,      tensor_type=snake_case_							)
										return encoded_inputs






"""simple docstring"""



def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     List[Any]								=			[1]
							for i in range(2			,  lowerCAmelCase							):
														factorials.append(factorials[-1] * i							)
							assert 0 <= k < factorials[-1] * n, "k out of bounds"

							A__       :     Union[str, Any]								=			[]
							A__       :     int								=			list(range(lowerCAmelCase							)							)

							# Find permutation
							while factorials:
														A__       :     Optional[int]								=			factorials.pop()
														A__				,							A__       :     Union[str, Any]								=			divmod(lowerCAmelCase			,  lowerCAmelCase							)
														permutation.append(elements[number]							)
														elements.remove(elements[number]							)
							permutation.append(elements[0]							)

							return permutation


if __name__ == "__main__":
				import doctest

				doctest.testmod()





