



'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


snake_case_					:      List[str]    			=    logging.get_logger(__name__)






class 				lowercase__      (				lowercase  ):
	lowercase__    							=     ["""input_features""", """is_longer"""]





	def __init__(					self   :							int					,lowerCamelCase__   :							Any=64					,lowerCamelCase__   :							List[Any]=48000					,lowerCamelCase__   :							Optional[Any]=480					,lowerCamelCase__   :							str=10					,lowerCamelCase__   :							List[Any]=1024					,lowerCamelCase__   :							List[Any]=0.0					,lowerCamelCase__   :							List[str]=False					,lowerCamelCase__   :							float = 0					,lowerCamelCase__   :							float = 14000					,lowerCamelCase__   :							int = None					,lowerCamelCase__   :							str = "fusion"					,lowerCamelCase__   :							str = "repeatpad"					,**lowerCamelCase__   :							List[Any]					,):







							'''simple docstring'''
							super().__init__(
							    feature_size=lowerCamelCase__					,sampling_rate=lowerCamelCase__					,padding_value=lowerCamelCase__					,return_attention_mask=lowerCamelCase__					,**lowerCamelCase__					,)
							_UpperCamelCase   :						Tuple												=  top_db
							_UpperCamelCase   :						int												=  truncation
							_UpperCamelCase   :						Dict												=  padding
							_UpperCamelCase   :						str												=  fft_window_size
							_UpperCamelCase   :						Tuple												=  (fft_window_size >> 1) + 1
							_UpperCamelCase   :						Dict												=  hop_length
							_UpperCamelCase   :						Optional[Any]												=  max_length_s
							_UpperCamelCase   :						str												=  max_length_s * sampling_rate
							_UpperCamelCase   :						int												=  sampling_rate
							_UpperCamelCase   :						Optional[Any]												=  frequency_min
							_UpperCamelCase   :						Dict												=  frequency_max
							_UpperCamelCase   :						List[Any]												=  mel_filter_bank(
							    num_frequency_bins=self.nb_frequency_bins					,num_mel_filters=lowerCamelCase__					,min_frequency=lowerCamelCase__					,max_frequency=lowerCamelCase__					,sampling_rate=lowerCamelCase__					,norm=lowerCamelCase__					,mel_scale='htk'					,)
							_UpperCamelCase   :						Union[str, Any]												=  mel_filter_bank(
							    num_frequency_bins=self.nb_frequency_bins					,num_mel_filters=lowerCamelCase__					,min_frequency=lowerCamelCase__					,max_frequency=lowerCamelCase__					,sampling_rate=lowerCamelCase__					,norm='slaney'					,mel_scale='slaney'					,)





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  copy.deepcopy(self.__dict__					)
							_UpperCamelCase   :						Union[str, Any]												=  self.__class__.__name__
							if "mel_filters" in output:
													del output["mel_filters"]
							if "mel_filters_slaney" in output:
													del output["mel_filters_slaney"]
							return output





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							np.array					,lowerCamelCase__   :							Optional[np.array] = None					):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  spectrogram(
							    lowerCamelCase__					,window_function(self.fft_window_size					,'hann'					)					,frame_length=self.fft_window_size					,hop_length=self.hop_length					,power=2.0					,mel_filters=lowerCamelCase__					,log_mel='dB'					,)
							return log_mel_spectrogram.T





	def       UpperCamelCase_    (					self   :							Union[str, Any]					,lowerCamelCase__   :							str					,lowerCamelCase__   :							str					,lowerCamelCase__   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  np.array_split(list(range(0					,total_frames - chunk_frames + 1					)					)					,3					)
							if len(ranges[1]					) == 0:
													# if the audio is too short, we just use the first chunk
													_UpperCamelCase   :						List[Any]												=  [0]
							if len(ranges[2]					) == 0:
													# if the audio is too short, we just use the first chunk
													_UpperCamelCase   :						Any												=  [0]
							# randomly choose index for each part
							_UpperCamelCase   :						List[Any]												=  np.random.choice(ranges[0]					)
							_UpperCamelCase   :						Union[str, Any]												=  np.random.choice(ranges[1]					)
							_UpperCamelCase   :						Dict												=  np.random.choice(ranges[2]					)

							_UpperCamelCase   :						str												=  mel[idx_front : idx_front + chunk_frames, :]
							_UpperCamelCase   :						int												=  mel[idx_middle : idx_middle + chunk_frames, :]
							_UpperCamelCase   :						str												=  mel[idx_back : idx_back + chunk_frames, :]

							_UpperCamelCase   :						Any												=  torch.tensor(mel[None, None, :]					)
							_UpperCamelCase   :						List[str]												=  torch.nn.functional.interpolate(
							    lowerCamelCase__					,size=[chunk_frames, 64]					,mode='bilinear'					,align_corners=lowerCamelCase__					)
							_UpperCamelCase   :						Tuple												=  mel_shrink[0][0].numpy()
							_UpperCamelCase   :						List[Any]												=  np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]					,axis=0					)
							return mel_fusion





	def       UpperCamelCase_    (					self   :							Optional[Any]					,lowerCamelCase__   :							np.array					,lowerCamelCase__   :							str					,lowerCamelCase__   :							int					,lowerCamelCase__   :							int					):







							'''simple docstring'''
							if waveform.shape[0] > max_length:
													if truncation == "rand_trunc":
																			_UpperCamelCase   :						List[str]												=  True
																			# random crop to max_length (for compatibility) -> this should be handled by self.pad
																			_UpperCamelCase   :						int												=  len(lowerCamelCase__					) - max_length
																			_UpperCamelCase   :						Tuple												=  np.random.randint(0					,overflow + 1					)
																			_UpperCamelCase   :						Tuple												=  waveform[idx : idx + max_length]
																			_UpperCamelCase   :						int												=  self._np_extract_fbank_features(lowerCamelCase__					,self.mel_filters_slaney					)[None, :]
													elif truncation == "fusion":
																			_UpperCamelCase   :						str												=  self._np_extract_fbank_features(lowerCamelCase__					,self.mel_filters					)
																			_UpperCamelCase   :						List[str]												=  max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																			_UpperCamelCase   :						Union[str, Any]												=  mel.shape[0]
																			if chunk_frames == total_frames:
																									# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																									# In this case, we just use the whole audio.
																									_UpperCamelCase   :						List[Any]												=  np.stack([mel, mel, mel, mel]					,axis=0					)
																									_UpperCamelCase   :						Union[str, Any]												=  False
																			else:
																									_UpperCamelCase   :						Tuple												=  self._random_mel_fusion(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					)
																									_UpperCamelCase   :						Optional[int]												=  True
													else:
																			raise NotImplementedError(F'data_truncating {truncation} not implemented'					)

							else:
													_UpperCamelCase   :						Union[str, Any]												=  False
													# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
													if waveform.shape[0] < max_length:
																			if padding == "repeat":
																									_UpperCamelCase   :						List[str]												=  int(max_length / len(lowerCamelCase__					)					)
																									_UpperCamelCase   :						Tuple												=  np.stack(np.tile(lowerCamelCase__					,n_repeat + 1					)					)[:max_length]
																			if padding == "repeatpad":
																									_UpperCamelCase   :						Optional[Any]												=  int(max_length / len(lowerCamelCase__					)					)
																									_UpperCamelCase   :						str												=  np.stack(np.tile(lowerCamelCase__					,lowerCamelCase__					)					)
																			_UpperCamelCase   :						Dict												=  np.pad(lowerCamelCase__					,(0, max_length - waveform.shape[0])					,mode='constant'					,constant_values=0					)

													if truncation == "fusion":
																			_UpperCamelCase   :						List[str]												=  self._np_extract_fbank_features(lowerCamelCase__					,self.mel_filters					)
																			_UpperCamelCase   :						Tuple												=  np.stack([input_mel, input_mel, input_mel, input_mel]					,axis=0					)
													else:
																			_UpperCamelCase   :						Optional[Any]												=  self._np_extract_fbank_features(lowerCamelCase__					,self.mel_filters_slaney					)[None, :]

							return input_mel, longer





	def __call__(					self   :							Any					,lowerCamelCase__   :							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,lowerCamelCase__   :							str = None					,lowerCamelCase__   :							Optional[str] = None					,lowerCamelCase__   :							Optional[int] = None					,lowerCamelCase__   :							Optional[int] = None					,lowerCamelCase__   :							Optional[Union[str, TensorType]] = None					,**lowerCamelCase__   :							int					,):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  truncation if truncation is not None else self.truncation
							_UpperCamelCase   :						str												=  padding if padding else self.padding

							if sampling_rate is not None:
													if sampling_rate != self.sampling_rate:
																			raise ValueError(
																			    F'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
																			    F' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
																			    F' was sampled 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   :						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}'					)
							_UpperCamelCase   :						Optional[int]												=  is_batched_numpy or (
							    isinstance(lowerCamelCase__					,(list, tuple)					) and (isinstance(raw_speech[0]					,(np.ndarray, tuple, list)					))
							)

							if is_batched:
													_UpperCamelCase   :						int												=  [np.asarray(lowerCamelCase__					,dtype=np.floataa					) for speech in raw_speech]
							elif not is_batched and not isinstance(lowerCamelCase__					,np.ndarray					):
													_UpperCamelCase   :						Tuple												=  np.asarray(lowerCamelCase__					,dtype=np.floataa					)
							elif isinstance(lowerCamelCase__					,np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
													_UpperCamelCase   :						List[str]												=  raw_speech.astype(np.floataa					)

							# always return batch
							if not is_batched:
													_UpperCamelCase   :						Dict												=  [np.asarray(lowerCamelCase__					)]

							# convert to mel spectrogram, truncate and pad if needed.
							_UpperCamelCase   :						int												=  [
							    self._get_input_mel(lowerCamelCase__					,max_length if max_length else self.nb_max_samples					,lowerCamelCase__					,lowerCamelCase__					)
							    for waveform in raw_speech
							]

							_UpperCamelCase   :						Optional[Any]												=  []
							_UpperCamelCase   :						Tuple												=  []
							for mel, longer in padded_inputs:
													input_mel.append(lowerCamelCase__					)
													is_longer.append(lowerCamelCase__					)

							if truncation == "fusion" and sum(lowerCamelCase__					) == 0:
													# if no audio is longer than 10s, then randomly select one audio to be longer
													_UpperCamelCase   :						Any												=  np.random.randint(0					,len(lowerCamelCase__					)					)
													_UpperCamelCase   :						Optional[int]												=  True

							if isinstance(input_mel[0]					,lowerCamelCase__					):
													_UpperCamelCase   :						Tuple												=  [np.asarray(lowerCamelCase__					,dtype=np.floataa					) for feature in input_mel]

							# is_longer is a list of bool
							_UpperCamelCase   :						str												=  [[longer] for longer in is_longer]

							_UpperCamelCase   :						Any												=  {'input_features': input_mel, 'is_longer': is_longer}
							_UpperCamelCase   :						Optional[int]												=  BatchFeature(lowerCamelCase__					)

							if return_tensors is not None:
													_UpperCamelCase   :						Optional[int]												=  input_features.convert_to_tensors(lowerCamelCase__					)

							return input_features







'''simple docstring'''
def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						return base * power(UpperCAmelCase_    ,    (exponent - 1)				) if exponent else 1


if __name__ == "__main__":
					print('Raise base to the power of exponent using recursion...')
					snake_case_					:      int    			=    int(input('Enter the base: ').strip())
					snake_case_					:      Optional[int]    			=    int(input('Enter the exponent: ').strip())
					snake_case_					:      Optional[int]    			=    power(base, abs(exponent))
					if exponent < 0:  # power() does not properly deal w/ negative exponents
										snake_case_					:      List[Any]    			=    1 / result
					print(F"""{base} to the power of {exponent} is {result}""")


