



import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


_lowercase     							=						logging.get_logger(__name__)
class 				__A					( A_      ):
			UpperCamelCase					:Union[str, Any]     =			['''input_features''']





			def __init__(self							,    __magic_name__=80							,    __magic_name__=16000							,    __magic_name__=160							,    __magic_name__=30							,    __magic_name__=400							,    __magic_name__=0.0							,    __magic_name__=False							,    **__magic_name__							,    ):
						super().__init__(
						    feature_size=__magic_name__							,    sampling_rate=__magic_name__							,    padding_value=__magic_name__							,    return_attention_mask=__magic_name__							,    **__magic_name__							,    )
						lowerCamelCase__ :      Tuple								=		n_fft
						lowerCamelCase__ :      List[Any]								=		hop_length
						lowerCamelCase__ :      List[str]								=		chunk_length
						lowerCamelCase__ :      Tuple								=		chunk_length * sampling_rate
						lowerCamelCase__ :      Any								=		self.n_samples // hop_length
						lowerCamelCase__ :      Dict								=		sampling_rate
						lowerCamelCase__ :      Tuple								=		mel_filter_bank(
						    num_frequency_bins=1 + n_fft // 2							,    num_mel_filters=__magic_name__							,    min_frequency=0.0							,    max_frequency=80_00.0							,    sampling_rate=__magic_name__							,    norm="""slaney"""							,    mel_scale="""slaney"""							,    )





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      Tuple								=		spectrogram(
						    __magic_name__							,    window_function(self.n_fft							,    """hann"""				)							,    frame_length=self.n_fft							,    hop_length=self.hop_length							,    power=2.0							,    mel_filters=self.mel_filters							,    log_mel="""log10"""							,    )
						lowerCamelCase__ :      List[Any]								=		log_spec[:, :-1]
						lowerCamelCase__ :      Tuple								=		np.maximum(__magic_name__							,    log_spec.max() - 8.0				)
						lowerCamelCase__ :      List[Any]								=		(log_spec + 4.0) / 4.0
						return log_spec





			@staticmethod
			# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
			def        _snake_case    (__magic_name__							,    __magic_name__							,    __magic_name__ = 0.0				):
						if attention_mask is not None:
									lowerCamelCase__ :      Optional[Any]								=		np.array(__magic_name__							,    np.intaa				)
									lowerCamelCase__ :      Tuple								=		[]

									for vector, length in zip(__magic_name__							,    attention_mask.sum(-1				)				):
												lowerCamelCase__ :      Optional[Any]								=		(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7				)
												if length < normed_slice.shape[0]:
															lowerCamelCase__ :      Dict								=		padding_value

												normed_input_values.append(__magic_name__				)
						else:
									lowerCamelCase__ :      Dict								=		[(x - x.mean()) / np.sqrt(x.var() + 1E-7				) for x in input_values]

						return normed_input_values





			def __call__(self							,    __magic_name__							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = "max_length"							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    **__magic_name__							,    ):

						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."""				)

						lowerCamelCase__ :      Optional[Any]								=		isinstance(__magic_name__							,    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}"				)
						lowerCamelCase__ :      Union[str, Any]								=		is_batched_numpy or (
						    isinstance(__magic_name__							,    (list, tuple)				) and (isinstance(raw_speech[0]							,    (np.ndarray, tuple, list)				))
						)

						if is_batched:
									lowerCamelCase__ :      Optional[Any]								=		[np.asarray([speech]							,    dtype=np.floataa				).T for speech in raw_speech]
						elif not is_batched and not isinstance(__magic_name__							,    np.ndarray				):
									lowerCamelCase__ :      List[Any]								=		np.asarray(__magic_name__							,    dtype=np.floataa				)
						elif isinstance(__magic_name__							,    np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
									lowerCamelCase__ :      Optional[Any]								=		raw_speech.astype(np.floataa				)

						# always return batch
						if not is_batched:
									lowerCamelCase__ :      str								=		[np.asarray([raw_speech]				).T]

						lowerCamelCase__ :      int								=		BatchFeature({"""input_features""": raw_speech}				)

						# convert into correct format for padding

						lowerCamelCase__ :      Optional[Any]								=		self.pad(
						    __magic_name__							,    padding=__magic_name__							,    max_length=max_length if max_length else self.n_samples							,    truncation=__magic_name__							,    pad_to_multiple_of=__magic_name__							,    return_attention_mask=return_attention_mask or do_normalize							,    )

						# zero-mean and unit-variance normalization
						if do_normalize:
									lowerCamelCase__ :      Optional[Any]								=		self.zero_mean_unit_var_norm(
									    padded_inputs["""input_features"""]							,    attention_mask=padded_inputs["""attention_mask"""]							,    padding_value=self.padding_value							,    )
									lowerCamelCase__ :      int								=		np.stack(padded_inputs["""input_features"""]							,    axis=0				)

						# make sure list is in array format
						lowerCamelCase__ :      Union[str, Any]								=		padded_inputs.get("""input_features"""				).transpose(2							,    0							,    1				)

						lowerCamelCase__ :      Optional[int]								=		[self._np_extract_fbank_features(__magic_name__				) for waveform in input_features[0]]

						if isinstance(input_features[0]							,    __magic_name__				):
									lowerCamelCase__ :      List[str]								=		[np.asarray(__magic_name__							,    dtype=np.floataa				) for feature in input_features]
						else:
									lowerCamelCase__ :      str								=		input_features

						if return_attention_mask:
									# rescale from sample (48000) to feature (3000)
									lowerCamelCase__ :      List[Any]								=		padded_inputs["""attention_mask"""][:, :: self.hop_length]

						if return_tensors is not None:
									lowerCamelCase__ :      int								=		padded_inputs.convert_to_tensors(__magic_name__				)

						return padded_inputs





			def        _snake_case    (self				):
						lowerCamelCase__ :      Union[str, Any]								=		copy.deepcopy(self.__dict__				)
						lowerCamelCase__ :      Dict								=		self.__class__.__name__
						if "mel_filters" in output:
									del output["mel_filters"]
						return output








from string import ascii_uppercase

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






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		len(UpperCamelCase )
			lowerCamelCase__ :      int								=		0
			while True:
						if x == i:
									lowerCamelCase__ :      Union[str, Any]								=		0
						if len(UpperCamelCase ) == len(UpperCamelCase ):
									break
						key += key[i]
						i += 1
			return key






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      int								=		""""""
			lowerCamelCase__ :      str								=		0
			for letter in message:
						if letter == " ":
									cipher_text += " "
						else:
									lowerCamelCase__ :      Optional[int]								=		(dicta[letter] - dicta[key_new[i]]) % 26
									i += 1
									cipher_text += dicta[x]
			return cipher_text






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Dict								=		""""""
			lowerCamelCase__ :      Tuple								=		0
			for letter in cipher_text:
						if letter == " ":
									or_txt += " "
						else:
									lowerCamelCase__ :      List[Any]								=		(dicta[letter] + dicta[key_new[i]] + 26) % 26
									i += 1
									or_txt += dicta[x]
			return or_txt






def      _A				()  ->None:



			'''simple docstring'''







			lowerCamelCase__ :      Union[str, Any]								=		"""THE GERMAN ATTACK"""
			lowerCamelCase__ :      Union[str, Any]								=		"""SECRET"""
			lowerCamelCase__ :      List[str]								=		generate_key(UpperCamelCase      ,			UpperCamelCase )
			lowerCamelCase__ :      List[Any]								=		cipher_text(UpperCamelCase      ,			UpperCamelCase )
			print(f"Encrypted Text = {s}" )
			print(f"Original Text = {original_text(UpperCamelCase      ,			UpperCamelCase )}" )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()



