





from __future__ import annotations

def   a_   (			lowerCAmelCase_				:      str,     lowerCAmelCase_				:      list[str] | None = None,     lowerCAmelCase_				:      dict[str, float] | None = None,     lowerCAmelCase_				:      bool = False,     ):
   __lowerCAmelCase							     =     cipher_alphabet or [chr(lowerCAmelCase_     ) 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)
      __lowerCAmelCase							     =     {
          '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
      __lowerCAmelCase							     =     frequencies_dict

   if not case_sensitive:
      __lowerCAmelCase							     =     ciphertext.lower()

   # Chi squared statistic values
   __lowerCAmelCase							     =     {}

   # cycle through all of the shifts
   for shift in range(len(lowerCAmelCase_     )     ):
      __lowerCAmelCase							     =     ''

      # decrypt the message with the shift
      for letter in ciphertext:
         try:
            # Try to index the letter in the alphabet
            __lowerCAmelCase							     =     (alphabet_letters.index(letter.lower()     ) - shift) % len(
                lowerCAmelCase_     )
            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

      __lowerCAmelCase							     =     0.0

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

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

               # Complete the chi squared statistic formula
               __lowerCAmelCase							     =     ((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
               __lowerCAmelCase							     =     decrypted_with_shift.count(lowerCAmelCase_     )

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

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

   __lowerCAmelCase							     =     min(
       lowerCAmelCase_,     key=lowerCAmelCase_,     )

   # Get all the data from the most likely cipher (key, decoded message)
   (
       (
       __lowerCAmelCase
   )     ,     (
       __lowerCAmelCase
   )     ,     
   )							     =     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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case  :   Optional[Any]						   =  {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   int						   =  ['FNetTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   Tuple						   =  ['FNetTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   List[str]						   =  [
         'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'FNetForMaskedLM',
         'FNetForMultipleChoice',
         'FNetForNextSentencePrediction',
         'FNetForPreTraining',
         'FNetForQuestionAnswering',
         'FNetForSequenceClassification',
         'FNetForTokenClassification',
         'FNetLayer',
         'FNetModel',
         'FNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

     _snake_case  :   str						   =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

