'''simple docstring'''

from typing import List, Optional, Union

import numpy as np

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


__snake_case       :   Tuple  		=		logging.get_logger(__name__)
class 							lowercase_    (     A__						):
    a_             =   ['''input_values''', '''attention_mask''']







    def __init__(	self       ,     UpperCamelCase__=1       ,     UpperCamelCase__=1_6_0_0_0       ,     UpperCamelCase__=0.0       ,     UpperCamelCase__=False       ,     UpperCamelCase__=True       ,     **UpperCamelCase__       ,     )				->       List[str]:





     """simple docstring"""

     super().__init__(feature_size=UpperCamelCase__       ,     sampling_rate=UpperCamelCase__       ,     padding_value=UpperCamelCase__       ,     **UpperCamelCase__				)
     UpperCAmelCase_				       =					return_attention_mask
     UpperCAmelCase_				       =					do_normalize







    @staticmethod
    def   lowerCamelCase_    (	UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__ = 0.0				)				->       List[str]:





     """simple docstring"""

     if attention_mask is not None:
      UpperCAmelCase_				       =					np.array(UpperCamelCase__       ,     np.intaa				)
      UpperCAmelCase_				       =					[]

      for vector, length in zip(UpperCamelCase__       ,     attention_mask.sum(-1				)				):
       UpperCAmelCase_				       =					(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7				)
       if length < normed_slice.shape[0]:
        UpperCAmelCase_				       =					padding_value

       normed_input_values.append(UpperCamelCase__				)
     else:
      UpperCAmelCase_				       =					[(x - x.mean()) / np.sqrt(x.var() + 1e-7				) for x in input_values]

     return normed_input_values







    def __call__(	self       ,     UpperCamelCase__       ,     UpperCamelCase__ = False       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = False       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     **UpperCamelCase__       ,     )				->       Optional[Any]:





     """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."				)

     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)				))
     )

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

     # convert into correct format for padding
     UpperCAmelCase_				       =					BatchFeature({"input_values": raw_speech}				)

     UpperCAmelCase_				       =					self.pad(
         UpperCamelCase__       ,     padding=UpperCamelCase__       ,     max_length=UpperCamelCase__       ,     truncation=UpperCamelCase__       ,     pad_to_multiple_of=UpperCamelCase__       ,     return_attention_mask=UpperCamelCase__       ,     )

     # convert input values to correct format
     UpperCAmelCase_				       =					padded_inputs["input_values"]
     if not isinstance(input_values[0]       ,     np.ndarray				):
      UpperCAmelCase_				       =					[np.asarray(UpperCamelCase__       ,     dtype=np.floataa				) for array in input_values]
     elif (
         not isinstance(UpperCamelCase__       ,     np.ndarray				)
         and isinstance(input_values[0]       ,     np.ndarray				)
         and input_values[0].dtype is np.dtype(np.floataa				)
     ):
      UpperCAmelCase_				       =					[array.astype(np.floataa				) for array in input_values]
     elif isinstance(UpperCamelCase__       ,     np.ndarray				) and input_values.dtype is np.dtype(np.floataa				):
      UpperCAmelCase_				       =					input_values.astype(np.floataa				)

     # convert attention_mask to correct format
     UpperCAmelCase_				       =					padded_inputs.get("attention_mask"				)
     if attention_mask is not None:
      UpperCAmelCase_				       =					[np.asarray(UpperCamelCase__       ,     dtype=np.intaa				) for array in attention_mask]

     # zero-mean and unit-variance normalization
     if self.do_normalize:
      UpperCAmelCase_				       =					(
          attention_mask
          if self._get_padding_strategies(UpperCamelCase__       ,     max_length=UpperCamelCase__				) is not PaddingStrategy.DO_NOT_PAD
          else None
      )
      UpperCAmelCase_				       =					self.zero_mean_unit_var_norm(
          padded_inputs["input_values"]       ,     attention_mask=UpperCamelCase__       ,     padding_value=self.padding_value				)

     if return_tensors is not None:
      UpperCAmelCase_				       =					padded_inputs.convert_to_tensors(UpperCamelCase__				)

     return padded_inputs

'''simple docstring'''

import random
import sys

__snake_case       :   str  		=    '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''



def lowerCamelCase__						(       ):
 UpperCAmelCase_				       =					input("Enter message: "       )
 UpperCAmelCase_				       =					"LFWOAYUISVKMNXPBDCRJTQEGHZ"
 UpperCAmelCase_				       =					input("Encrypt/Decrypt [e/d]: "       )

 check_valid_key(A_       )

 if resp.lower().startswith("e"       ):
  UpperCAmelCase_				       =					"encrypt"
  UpperCAmelCase_				       =					encrypt_message(A_       ,			A_       )
 elif resp.lower().startswith("d"       ):
  UpperCAmelCase_				       =					"decrypt"
  UpperCAmelCase_				       =					decrypt_message(A_       ,			A_       )

 print(F"""\n{mode.title()}ion: \n{translated}"""       )



def lowerCamelCase__						(       A_       ):
 UpperCAmelCase_				       =					list(A_       )
 UpperCAmelCase_				       =					list(A_       )
 key_list.sort()
 letters_list.sort()

 if key_list != letters_list:
  sys.exit("Error in the key or symbol set."       )



def lowerCamelCase__						(       A_       ,			A_       ):
 return translate_message(A_       ,			A_       ,			"encrypt"       )



def lowerCamelCase__						(       A_       ,			A_       ):
 return translate_message(A_       ,			A_       ,			"decrypt"       )



def lowerCamelCase__						(       A_       ,			A_       ,			A_       ):
 UpperCAmelCase_				       =					""
 UpperCAmelCase_				       =					LETTERS
 UpperCAmelCase_				       =					key

 if mode == "decrypt":
  UpperCAmelCase_       ,       UpperCAmelCase_				       =					chars_b, chars_a

 for symbol in message:
  if symbol.upper() in chars_a:
   UpperCAmelCase_				       =					chars_a.find(symbol.upper()       )
   if symbol.isupper():
    translated += chars_b[sym_index].upper()
   else:
    translated += chars_b[sym_index].lower()
  else:
   translated += symbol

 return translated



def lowerCamelCase__						(       ):
 UpperCAmelCase_				       =					list(A_       )
 random.shuffle(A_       )
 return "".join(A_       )


if __name__ == "__main__":
      main()
