



from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowercase		:				Tuple      		=logging.get_logger(__name__)

class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Optional[Any]      =					["input_features", "attention_mask"]

						def __init__(       self ,       __lowercase=8_0 ,       __lowercase=1_6_0_0_0 ,       __lowercase=8_0 ,       __lowercase=0.0 ,       __lowercase=True ,       __lowercase=True ,       __lowercase=True ,       **__lowercase ,       ) ->				Tuple:
									"""simple docstring"""




									super().__init__(feature_size=__lowercase ,       sampling_rate=__lowercase ,       padding_value=__lowercase ,       **__lowercase  )
									a__       :						Tuple							=  num_mel_bins
									a__       :						int							=  do_ceptral_normalize
									a__       :						List[str]							=  normalize_means
									a__       :						Union[str, Any]							=  normalize_vars
									a__       :						Tuple							=  True

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       ) ->				np.ndarray:
									"""simple docstring"""




									a__       :						Optional[Any]							=  waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
									a__       :						List[str]							=  torch.from_numpy(__lowercase  ).unsqueeze(0  )
									a__       :						int							=  ta_kaldi.fbank(__lowercase ,       num_mel_bins=self.num_mel_bins ,       sample_frequency=self.sampling_rate  )
									return features.numpy()

						@staticmethod
						def 							SCREAMING_SNAKE_CASE__(       __lowercase ,       __lowercase ,       __lowercase = True ,       __lowercase = True ,       __lowercase = 0.0 ,       ) ->				np.ndarray:
									"""simple docstring"""




									if normalize_means:
												a__       :						Dict							=  x[:input_length].mean(axis=0  )
												a__       :						int							=  np.subtract(__lowercase ,       __lowercase  )
									if normalize_vars:
												a__       :						str							=  x[:input_length].std(axis=0  )
												a__       :						List[str]							=  np.divide(__lowercase ,       __lowercase  )

									if input_length < x.shape[0]:
												a__       :						List[Any]							=  padding_value

									# make sure array is in float32
									a__       :						Dict							=  x.astype(np.floataa  )

									return x

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None  ) ->				List[np.ndarray]:
									"""simple docstring"""




									a__       :						List[Any]							=  attention_mask.sum(-1  ) if attention_mask is not None else [x.shape[0] for x in input_features]
									return [
									    self.utterance_cmvn(__lowercase ,       __lowercase ,       self.normalize_means ,       self.normalize_vars ,       self.padding_value  )
									    for x, n in zip(__lowercase ,       __lowercase  )
									]

						def __call__(       self ,       __lowercase ,       __lowercase = False ,       __lowercase = None ,       __lowercase = False ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       **__lowercase ,       ) ->				BatchFeature:
									"""simple docstring"""




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

									if is_batched:
												a__       :						Union[str, Any]							=  [np.asarray(__lowercase ,       dtype=np.floataa  ) for speech in raw_speech]
									elif not is_batched and not isinstance(__lowercase ,       np.ndarray  ):
												a__       :						Tuple							=  np.asarray(__lowercase ,       dtype=np.floataa  )
									elif isinstance(__lowercase ,       np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
												a__       :						str							=  raw_speech.astype(np.floataa  )

									# always return batch
									if not is_batched:
												a__       :						Tuple							=  [raw_speech]

									# extract fbank features
									a__       :						List[Any]							=  [self._extract_fbank_features(__lowercase  ) for waveform in raw_speech]

									# convert into correct format for padding
									a__       :						List[str]							=  BatchFeature({"""input_features""": features}  )

									a__       :						List[Any]							=  self.pad(
									    __lowercase ,       padding=__lowercase ,       max_length=__lowercase ,       truncation=__lowercase ,       pad_to_multiple_of=__lowercase ,       return_attention_mask=__lowercase ,       **__lowercase ,       )

									# make sure list is in array format
									a__       :						int							=  padded_inputs.get("""input_features"""  )
									if isinstance(input_features[0] ,       __lowercase  ):
												a__       :						str							=  [np.asarray(__lowercase ,       dtype=np.floataa  ) for feature in input_features]

									a__       :						Any							=  padded_inputs.get("""attention_mask"""  )
									if attention_mask is not None:
												a__       :						Optional[int]							=  [np.asarray(__lowercase ,       dtype=np.intaa  ) for array in attention_mask]

									# Utterance-level cepstral mean and variance normalization
									if self.do_ceptral_normalize:
												a__       :						int							=  (
												    np.array(__lowercase ,       dtype=np.intaa  )
												    if self._get_padding_strategies(__lowercase ,       max_length=__lowercase  ) is not PaddingStrategy.DO_NOT_PAD
												    else None
												)
												a__       :						Optional[int]							=  self.normalize(
												    padded_inputs["""input_features"""] ,       attention_mask=__lowercase  )

									if return_tensors is not None:
												a__       :						Optional[Any]							=  padded_inputs.convert_to_tensors(__lowercase  )

									return padded_inputs









from string import ascii_uppercase

_lowercase		:				str      		={char: i for i, char in enumerate(ascii_uppercase)}
_lowercase		:				Dict      		=dict(enumerate(ascii_uppercase))

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       str)							->	str:







			"""simple docstring"""
			a__       :						Any							=  len(_lowercase)
			a__       :						Optional[int]							=  0
			while True:
						if x == i:
									a__       :						Optional[Any]							=  0
						if len(_lowercase) == len(_lowercase):
									break
						key += key[i]
						i += 1
			return key

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       str)							->	str:







			"""simple docstring"""
			a__       :						Tuple							=  """"""
			a__       :						str							=  0
			for letter in message:
						if letter == " ":
									cipher_text += " "
						else:
									a__       :						List[str]							=  (dicta[letter] - dicta[key_new[i]]) % 26
									i += 1
									cipher_text += dicta[x]
			return cipher_text

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       str)							->	str:







			"""simple docstring"""
			a__       :						int							=  """"""
			a__       :						int							=  0
			for letter in cipher_text:
						if letter == " ":
									or_txt += " "
						else:
									a__       :						Dict							=  (dicta[letter] + dicta[key_new[i]] + 26) % 26
									i += 1
									or_txt += dicta[x]
			return or_txt

def      lowerCAmelCase_					(  )							->	None:







			"""simple docstring"""
			a__       :						List[Any]							=  """THE GERMAN ATTACK"""
			a__       :						List[Any]							=  """SECRET"""
			a__       :						Tuple							=  generate_key(_lowercase						,       _lowercase)
			a__       :						str							=  cipher_text(_lowercase						,       _lowercase)
			print(F'''Encrypted Text = {s}''')
			print(F'''Original Text = {original_text(_lowercase						,       _lowercase)}''')


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()




