





from __future__ import annotations



def A_	(		A__		,      A__ = None		,      A__ = None		,      A__ = False		,      )  ->		tuple[int, float, str]:
       a__						:	List[str] =				cipher_alphabet or [chr(A__				) for i in range(97		,      123				)]

       # 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)
              a__						:	int =				{
                  'a': 0.0_84_97,
                  'b': 0.0_14_92,
                  'c': 0.0_22_02,
                  'd': 0.0_42_53,
                  'e': 0.1_11_62,
                  'f': 0.0_22_28,
                  'g': 0.0_20_15,
                  'h': 0.0_60_94,
                  'i': 0.0_75_46,
                  'j': 0.0_01_53,
                  'k': 0.0_12_92,
                  'l': 0.0_40_25,
                  'm': 0.0_24_06,
                  'n': 0.0_67_49,
                  'o': 0.0_75_07,
                  'p': 0.0_19_29,
                  'q': 0.0_00_95,
                  'r': 0.0_75_87,
                  's': 0.0_63_27,
                  't': 0.0_93_56,
                  'u': 0.0_27_58,
                  'v': 0.0_09_78,
                  'w': 0.0_25_60,
                  'x': 0.0_01_50,
                  'y': 0.0_19_94,
                  'z': 0.0_00_77,
              }
       else:
              # Custom frequencies dictionary
              a__						:	Dict =				frequencies_dict

       if not case_sensitive:
              a__						:	int =				ciphertext.lower()

       # Chi squared statistic values
       a__						:	dict[int, tuple[float, str]] =				{}

       # cycle through all of the shifts
       for shift in range(len(A__				)				):
              a__						:	List[Any] =				''

              # decrypt the message with the shift
              for letter in ciphertext:
                     try:
                            # Try to index the letter in the alphabet
                            a__						:	List[Any] =				(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

              a__						:	Optional[int] =				0.0

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

                                   # Get the excepcted amount of times the letter should appear based
                                   # on letter frequencies
                                   a__						:	Optional[int] =				frequencies[letter] * occurrences

                                   # Complete the chi squared statistic formula
                                   a__						:	int =				((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
                                   a__						:	int =				decrypted_with_shift.count(A__				)

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

                                   # Complete the chi squared statistic formula
                                   a__						:	Optional[int] =				((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
              a__						:	str =				(
                  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(A__				) -> tuple[float, str]:
              return chi_squared_statistic_values[key]

       a__						:	int =				min(
           A__		,      key=A__		,      )

       # Get all the data from the most likely cipher (key, decoded message)
       (
           (
           a__
       ) ,							(
           a__
       ) ,							
       )						:	str =				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 ..utils import is_flax_available, is_torch_available


if is_torch_available():
    from .autoencoder_kl import AutoencoderKL
    from .controlnet import ControlNetModel
    from .dual_transformer_ad import DualTransformeraDModel
    from .modeling_utils import ModelMixin
    from .prior_transformer import PriorTransformer
    from .ta_film_transformer import TaFilmDecoder
    from .transformer_ad import TransformeraDModel
    from .unet_ad import UNetaDModel
    from .unet_ad import UNetaDModel
    from .unet_ad_condition import UNetaDConditionModel
    from .unet_ad_condition import UNetaDConditionModel
    from .vq_model import VQModel

if is_flax_available():
    from .controlnet_flax import FlaxControlNetModel
    from .unet_ad_condition_flax import FlaxUNetaDConditionModel
    from .vae_flax import FlaxAutoencoderKL


