
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


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




class        SCREAMING_SNAKE_CASE__					(     lowerCamelCase_ ):





	'''simple docstring'''



	_UpperCAmelCase	:		List[str]          = ['''audio_values''', '''audio_mask''']



	def __init__(  self     :   Any      ,  lowercase     :   str=2_048      ,  lowercase     :   Dict=1      ,  lowercase     :   Dict=[16, 16]      ,  lowercase     :   Dict=128      ,  lowercase     :   Optional[int]=44_100      ,  lowercase     :   Union[str, Any]=86      ,  lowercase     :   Union[str, Any]=2_048      ,  lowercase     :   Union[str, Any]=0.0      ,  **lowercase     :   Dict      ,  ):


				'''simple docstring'''


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

				_snake_case          =      spectrogram_length
				_snake_case          =      num_channels
				_snake_case          =      patch_size
				_snake_case          =      feature_size // self.patch_size[1]
				_snake_case          =      n_fft
				_snake_case          =      sampling_rate // hop_length_to_sampling_rate
				_snake_case          =      sampling_rate
				_snake_case          =      padding_value
				_snake_case          =      mel_filter_bank(
				    num_frequency_bins=1 + n_fft // 2      ,  num_mel_filters=__snake_case      ,  min_frequency=0.0      ,  max_frequency=22_050.0      ,  sampling_rate=__snake_case      ,  norm='slaney'      ,  mel_scale='slaney'      ,  ).T



	def 			A							(  self     :   Any      ,  lowercase     :   np.array     ):


				'''simple docstring'''


				_snake_case          =      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      ,  )
				_snake_case          =      log_spec[:, :-1]
				_snake_case          =      log_spec - 20.0
				_snake_case          =      np.clip(log_spec / 40.0      ,  -2.0      ,  0.0     ) + 1.0
				return log_spec





	def __call__(  self     :   List[str]      ,  lowercase     :   Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,  lowercase     :   Optional[Union[str, TensorType]] = None      ,  lowercase     :   Optional[bool] = True      ,  lowercase     :   Optional[int] = None      ,  lowercase     :   bool = False      ,  lowercase     :   bool = False      ,  **lowercase     :   Dict      ,  ):


				'''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.'     )

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

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

				# Create audio attention mask
				_snake_case          =      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:
							_snake_case          =      [
							    (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
							]
							_snake_case          =      np.array(__snake_case     ).astype(np.floataa     )

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

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

				_snake_case          =      BatchFeature(data=__snake_case      ,  tensor_type=__snake_case     )
				return encoded_inputs

import random




class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	@staticmethod
	def 			A							(  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      [ord(lowercase     ) for i in text]
				_snake_case          =      []
				_snake_case          =      []
				for i in plain:
							_snake_case          =      random.randint(1      ,  300     )
							_snake_case          =      (i + k) * k
							cipher.append(lowercase     )
							key.append(lowercase     )
				return cipher, key





	@staticmethod
	def 			A							(  lowercase     :   list[int]      ,  lowercase     :   list[int]     ):


				'''simple docstring'''


				_snake_case          =      []
				for i in range(len(lowercase     )     ):
							_snake_case          =      int((cipher[i] - (key[i]) ** 2) / key[i]     )
							plain.append(chr(lowercase     )     )
				return "".join(lowercase     )


if __name__ == "__main__":
			_lowerCamelCase   ,					_lowerCamelCase	:    Optional[int]          =      Onepad().encrypt('''Hello''')
			print(c, k)
			print(Onepad().decrypt(c, k))