from __future__ import annotations



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: Dict = None				,  __UpperCamelCase					: Dict = None				,  __UpperCamelCase					: List[str] = False				,  )							->      tuple[int, float, str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   cipher_alphabet or [chr(a__			) for i in range(97				,  1_23			)]

  # If the argument is None or the user provided an empty dictionary
  if not frequencies_dict:
    # Frequencies of letters in the english language (how much they show up)
    SCREAMING_SNAKE_CASE__    							=   {
        """a""": 0.0_8497,
        """b""": 0.0_1492,
        """c""": 0.0_2202,
        """d""": 0.0_4253,
        """e""": 0.1_1162,
        """f""": 0.0_2228,
        """g""": 0.0_2015,
        """h""": 0.0_6094,
        """i""": 0.0_7546,
        """j""": 0.0_0153,
        """k""": 0.0_1292,
        """l""": 0.0_4025,
        """m""": 0.0_2406,
        """n""": 0.0_6749,
        """o""": 0.0_7507,
        """p""": 0.0_1929,
        """q""": 0.0_0095,
        """r""": 0.0_7587,
        """s""": 0.0_6327,
        """t""": 0.0_9356,
        """u""": 0.0_2758,
        """v""": 0.0_0978,
        """w""": 0.0_2560,
        """x""": 0.0_0150,
        """y""": 0.0_1994,
        """z""": 0.0_0077,
    }
  else:
    # Custom frequencies dictionary
    SCREAMING_SNAKE_CASE__    							=   frequencies_dict

  if not case_sensitive:
    SCREAMING_SNAKE_CASE__    							=   ciphertext.lower()

  # Chi squared statistic values
  SCREAMING_SNAKE_CASE__    							=   {}

  # cycle through all of the shifts
  for shift in range(len(a__			)			):
    SCREAMING_SNAKE_CASE__    							=   """"""

    # decrypt the message with the shift
    for letter in ciphertext:
      try:
        # Try to index the letter in the alphabet
        SCREAMING_SNAKE_CASE__    							=   (alphabet_letters.index(letter.lower()			) - shift) % len(
            a__			)
        decrypted_with_shift += (
            alphabet_letters[new_key].upper()
            if case_sensitive and letter.isupper()
            else alphabet_letters[new_key]
        )
      except ValueError:
        # Append the character if it isn't in the alphabet
        decrypted_with_shift += letter

    SCREAMING_SNAKE_CASE__    							=   0.0

    # Loop through each letter in the decoded message with the shift
    for letter in decrypted_with_shift:
      if case_sensitive:
        SCREAMING_SNAKE_CASE__    							=   letter.lower()
        if letter in frequencies:
          # Get the amount of times the letter occurs in the message
          SCREAMING_SNAKE_CASE__    							=   decrypted_with_shift.lower().count(a__			)

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          SCREAMING_SNAKE_CASE__    							=   frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          SCREAMING_SNAKE_CASE__    							=   ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value
      else:
        if letter.lower() in frequencies:
          # Get the amount of times the letter occurs in the message
          SCREAMING_SNAKE_CASE__    							=   decrypted_with_shift.count(a__			)

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          SCREAMING_SNAKE_CASE__    							=   frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          SCREAMING_SNAKE_CASE__    							=   ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
    SCREAMING_SNAKE_CASE__    							=   (
        chi_squared_statistic,
        decrypted_with_shift,
    )

  # Get the most likely cipher by finding the cipher with the smallest chi squared
  # statistic
  def chi_squared_statistic_values_sorting_key(__UpperCamelCase					: Tuple			) -> tuple[float, str]:
    return chi_squared_statistic_values[key]

  SCREAMING_SNAKE_CASE__    							=   min(
      a__				,  key=a__				,  )

  # Get all the data from the most likely cipher (key, decoded message)
  (
      (
      SCREAMING_SNAKE_CASE__	
  )							,  (
      SCREAMING_SNAKE_CASE__	
  )							,  	
  )    							=   chi_squared_statistic_values[most_likely_cipher]

  # Return the data on the most likely shift
  return (
      most_likely_cipher,
      most_likely_cipher_chi_squared_value,
      decoded_most_likely_cipher,
  )

from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"EncodecFeatureExtractor"
   lowerCAmelCase_														=							("T5Tokenizer", "T5TokenizerFast")




   def __init__(					self    :       List[Any] ,			_lowercase    :       int ,			_lowercase    :       Any   ):



     """simple docstring"""






     super().__init__(_lowercase ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.feature_extractor
     SCREAMING_SNAKE_CASE__    							=   False




   def 			__a		(					self    :       Tuple ,			_lowercase    :       str=None ,			_lowercase    :       int=None ,			_lowercase    :       Any=True   ):



     """simple docstring"""






     return self.tokenizer.get_decoder_prompt_ids(task=_lowercase ,			language=_lowercase ,			no_timestamps=_lowercase   )




   def __call__(					self    :       Tuple ,			*_lowercase    :       Tuple ,			**_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






     if self._in_target_context_manager:
       return self.current_processor(*_lowercase ,			**_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""audio""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""sampling_rate""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""text""" ,			_lowercase   )
     if len(_lowercase   ) > 0:
       SCREAMING_SNAKE_CASE__    							=   args[0]
       SCREAMING_SNAKE_CASE__    							=   args[1:]

     if audio is None and text is None:
       raise ValueError("""You need to specify either an `audio` or `text` input to process."""   )

     if text is not None:
       SCREAMING_SNAKE_CASE__    							=   self.tokenizer(_lowercase ,			**_lowercase   )

     if audio is not None:
       SCREAMING_SNAKE_CASE__    							=   self.feature_extractor(_lowercase ,			*_lowercase ,			sampling_rate=_lowercase ,			**_lowercase   )

     if audio is None:
       return inputs

     elif text is None:
       return audio_inputs

     else:
       SCREAMING_SNAKE_CASE__    							=   audio_inputs["""input_values"""]
       if "padding_mask" in audio_inputs:
         SCREAMING_SNAKE_CASE__    							=   audio_inputs["""padding_mask"""]
       return inputs




   def 			__a		(					self    :       Dict ,			*_lowercase    :       Any ,			**_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""audio""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""padding_mask""" ,			_lowercase   )

     if len(_lowercase   ) > 0:
       SCREAMING_SNAKE_CASE__    							=   args[0]
       SCREAMING_SNAKE_CASE__    							=   args[1:]

     if audio_values is not None:
       return self._decode_audio(_lowercase ,			padding_mask=_lowercase   )
     else:
       return self.tokenizer.batch_decode(*_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       List[str] ,			*_lowercase    :       Tuple ,			**_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     return self.tokenizer.decode(*_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       Tuple ,			_lowercase    :       Optional = None   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   to_numpy(_lowercase   )
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   audio_values.shape

     if padding_mask is None:
       return list(_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   to_numpy(_lowercase   )

     # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
     # token (so that the generated audio values are **not** treated as padded tokens)
     SCREAMING_SNAKE_CASE__    							=   seq_len - padding_mask.shape[-1]
     SCREAMING_SNAKE_CASE__    							=   1 - self.feature_extractor.padding_value
     SCREAMING_SNAKE_CASE__    							=   np.pad(_lowercase ,			((0, 0), (0, difference)) ,			"""constant""" ,			constant_values=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   audio_values.tolist()
     for i in range(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   np.asarray(audio_values[i]   )[
           padding_mask[i][None, :] != self.feature_extractor.padding_value
       ]
       SCREAMING_SNAKE_CASE__    							=   sliced_audio.reshape(_lowercase ,			-1   )

     return audio_values
