
"""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


_a														=		logging.get_logger(__name__)






class     _UpperCAmelCase(    a__	):
		lowercase__						=      ['input_features', 'is_longer']



		def __init__(					self				,		__a=64				,		__a=4_80_00				,		__a=4_80				,		__a=10				,		__a=10_24				,		__a=0.0				,		__a=False				,		__a = 0				,		__a = 1_40_00				,		__a = None				,		__a = "fusion"				,		__a = "repeatpad"				,		**__a				,		)       ->       Union[str, Any]:






							'''simple docstring'''
							super().__init__(
							    feature_size=UpperCamelCase_				,		sampling_rate=UpperCamelCase_				,		padding_value=UpperCamelCase_				,		return_attention_mask=UpperCamelCase_				,		**UpperCamelCase_				,		)
							_UpperCamelCase							 =  top_db
							_UpperCamelCase							 =  truncation
							_UpperCamelCase							 =  padding
							_UpperCamelCase							 =  fft_window_size
							_UpperCamelCase							 =  (fft_window_size >> 1) + 1
							_UpperCamelCase							 =  hop_length
							_UpperCamelCase							 =  max_length_s
							_UpperCamelCase							 =  max_length_s * sampling_rate
							_UpperCamelCase							 =  sampling_rate
							_UpperCamelCase							 =  frequency_min
							_UpperCamelCase							 =  frequency_max
							_UpperCamelCase							 =  mel_filter_bank(
							    num_frequency_bins=self.nb_frequency_bins				,		num_mel_filters=UpperCamelCase_				,		min_frequency=UpperCamelCase_				,		max_frequency=UpperCamelCase_				,		sampling_rate=UpperCamelCase_				,		norm=UpperCamelCase_				,		mel_scale='''htk'''				,		)
							_UpperCamelCase							 =  mel_filter_bank(
							    num_frequency_bins=self.nb_frequency_bins				,		num_mel_filters=UpperCamelCase_				,		min_frequency=UpperCamelCase_				,		max_frequency=UpperCamelCase_				,		sampling_rate=UpperCamelCase_				,		norm='''slaney'''				,		mel_scale='''slaney'''				,		)



		def 						UpperCAmelCase							(					self)       ->       int:






							'''simple docstring'''
							_UpperCamelCase							 =  copy.deepcopy(self.__dict__)
							_UpperCamelCase							 =  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				,		__a				,		__a = None)       ->       Any:






							'''simple docstring'''
							_UpperCamelCase							 =  spectrogram(
							    UpperCamelCase_				,		window_function(self.fft_window_size				,		'''hann''')				,		frame_length=self.fft_window_size				,		hop_length=self.hop_length				,		power=2.0				,		mel_filters=UpperCamelCase_				,		log_mel='''dB'''				,		)
							return log_mel_spectrogram.T



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a)       ->       List[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  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							 =  [0]
							if len(ranges[2]) == 0:
												# if the audio is too short, we just use the first chunk
												_UpperCamelCase							 =  [0]
							# randomly choose index for each part
							_UpperCamelCase							 =  np.random.choice(ranges[0])
							_UpperCamelCase							 =  np.random.choice(ranges[1])
							_UpperCamelCase							 =  np.random.choice(ranges[2])

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

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



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a)       ->       Tuple:






							'''simple docstring'''
							if waveform.shape[0] > max_length:
												if truncation == "rand_trunc":
																	_UpperCamelCase							 =  True
																	# random crop to max_length (for compatibility) -> this should be handled by self.pad
																	_UpperCamelCase							 =  len(UpperCamelCase_) - max_length
																	_UpperCamelCase							 =  np.random.randint(0				,		overflow + 1)
																	_UpperCamelCase							 =  waveform[idx : idx + max_length]
																	_UpperCamelCase							 =  self._np_extract_fbank_features(UpperCamelCase_				,		self.mel_filters_slaney)[None, :]
												elif truncation == "fusion":
																	_UpperCamelCase							 =  self._np_extract_fbank_features(UpperCamelCase_				,		self.mel_filters)
																	_UpperCamelCase							 =  max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																	_UpperCamelCase							 =  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							 =  np.stack([mel, mel, mel, mel]				,		axis=0)
																						_UpperCamelCase							 =  False
																	else:
																						_UpperCamelCase							 =  self._random_mel_fusion(UpperCamelCase_				,		UpperCamelCase_				,		UpperCamelCase_)
																						_UpperCamelCase							 =  True
												else:
																	raise NotImplementedError(F'''data_truncating {truncation} not implemented''')

							else:
												_UpperCamelCase							 =  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							 =  int(max_length / len(UpperCamelCase_))
																						_UpperCamelCase							 =  np.stack(np.tile(UpperCamelCase_				,		n_repeat + 1))[:max_length]
																	if padding == "repeatpad":
																						_UpperCamelCase							 =  int(max_length / len(UpperCamelCase_))
																						_UpperCamelCase							 =  np.stack(np.tile(UpperCamelCase_				,		UpperCamelCase_))
																	_UpperCamelCase							 =  np.pad(UpperCamelCase_				,		(0, max_length - waveform.shape[0])				,		mode='''constant'''				,		constant_values=0)

												if truncation == "fusion":
																	_UpperCamelCase							 =  self._np_extract_fbank_features(UpperCamelCase_				,		self.mel_filters)
																	_UpperCamelCase							 =  np.stack([input_mel, input_mel, input_mel, input_mel]				,		axis=0)
												else:
																	_UpperCamelCase							 =  self._np_extract_fbank_features(UpperCamelCase_				,		self.mel_filters_slaney)[None, :]

							return input_mel, longer




		def __call__(					self				,		__a				,		__a = None				,		__a = None				,		__a = None				,		__a = None				,		__a = None				,		**__a				,		)       ->       List[str]:






							'''simple docstring'''
							_UpperCamelCase							 =  truncation if truncation is not None else self.truncation
							_UpperCamelCase							 =  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							 =  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}''')
							_UpperCamelCase							 =  is_batched_numpy or (
							    isinstance(UpperCamelCase_				,		(list, tuple)) and (isinstance(raw_speech[0]				,		(np.ndarray, tuple, list)))
							)

							if is_batched:
												_UpperCamelCase							 =  [np.asarray(UpperCamelCase_				,		dtype=np.floataa) for speech in raw_speech]
							elif not is_batched and not isinstance(UpperCamelCase_				,		np.ndarray):
												_UpperCamelCase							 =  np.asarray(UpperCamelCase_				,		dtype=np.floataa)
							elif isinstance(UpperCamelCase_				,		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(UpperCamelCase_)]

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

							_UpperCamelCase							 =  []
							_UpperCamelCase							 =  []
							for mel, longer in padded_inputs:
												input_mel.append(UpperCamelCase_)
												is_longer.append(UpperCamelCase_)

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

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

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

							_UpperCamelCase							 =  {'''input_features''': input_mel, '''is_longer''': is_longer}
							_UpperCamelCase							 =  BatchFeature(UpperCamelCase_)

							if return_tensors is not None:
												_UpperCamelCase							 =  input_features.convert_to_tensors(UpperCamelCase_)

							return input_features










"""simple docstring"""





import math


def    _snake_case  (  ):
      lowerCAmelCase    :		Union[str, Any]				=      input('''Enter message: '''	)
      lowerCAmelCase    :		Optional[int]				=      int(input(f'''Enter key [2-{len(_snake_case	) - 1}]: '''	)	)
      lowerCAmelCase    :		str				=      input('''Encryption/Decryption [e/d]: '''	)

      if mode.lower().startswith('''e'''	):
            lowerCAmelCase    :		Any				=      encrypt_message(_snake_case       ,      _snake_case	)
      elif mode.lower().startswith('''d'''	):
            lowerCAmelCase    :		Union[str, Any]				=      decrypt_message(_snake_case       ,      _snake_case	)

      # Append pipe symbol (vertical bar) to identify spaces at the end.
      print(f'''Output:\n{text + "|"}'''	)


def    _snake_case  (  _snake_case :	int       ,      _snake_case :	str	):
      lowerCAmelCase    :		Optional[Any]				=      [''''''] * key
      for col in range(_snake_case	):
            lowerCAmelCase    :		Optional[Any]				=      col
            while pointer < len(_snake_case	):
                  cipher_text[col] += message[pointer]
                  pointer += key
      return "".join(_snake_case	)


def    _snake_case  (  _snake_case :	int       ,      _snake_case :	str	):
      lowerCAmelCase    :		Union[str, Any]				=      math.ceil(len(_snake_case	) / key	)
      lowerCAmelCase    :		str				=      key
      lowerCAmelCase    :		Any				=      (num_cols * num_rows) - len(_snake_case	)
      lowerCAmelCase    :		Dict				=      [''''''] * num_cols
      lowerCAmelCase    :		int				=      0
      lowerCAmelCase    :		int				=      0

      for symbol in message:
            plain_text[col] += symbol
            col += 1

            if (
                (col == num_cols)
                or (col == num_cols - 1)
                and (row >= num_rows - num_shaded_boxes)
            ):
                  lowerCAmelCase    :		int				=      0
                  row += 1

      return "".join(_snake_case	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()


