

"""simple docstring"""





from __future__ import annotations




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	list[str] | None = None,      UpperCamelCase_					:	dict[str, float] | None = None,      UpperCamelCase_					:	bool = False,      )     ->						tuple[int, float, str]:
   '''simple docstring'''


   __lowercase								=						cipher_alphabet or [chr(UpperCamelCase_) 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)
      __lowercase								=						{
          "a": 0.08_497,
          "b": 0.01_492,
          "c": 0.02_202,
          "d": 0.04_253,
          "e": 0.11_162,
          "f": 0.02_228,
          "g": 0.02_015,
          "h": 0.06_094,
          "i": 0.07_546,
          "j": 0.00_153,
          "k": 0.01_292,
          "l": 0.04_025,
          "m": 0.02_406,
          "n": 0.06_749,
          "o": 0.07_507,
          "p": 0.01_929,
          "q": 0.00_095,
          "r": 0.07_587,
          "s": 0.06_327,
          "t": 0.09_356,
          "u": 0.02_758,
          "v": 0.00_978,
          "w": 0.02_560,
          "x": 0.00_150,
          "y": 0.01_994,
          "z": 0.00_077,
      }
   else:
      # Custom frequencies dictionary
      __lowercase								=						frequencies_dict

   if not case_sensitive:
      __lowercase								=						ciphertext.lower()

   # Chi squared statistic values
   __lowercase								=						{}

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

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

      __lowercase								=						0.0

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

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

               # Complete the chi squared statistic formula
               __lowercase								=						((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
               __lowercase								=						decrypted_with_shift.count(UpperCamelCase_)

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

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

   __lowercase								=						min(
       UpperCamelCase_,      key=UpperCamelCase_,      )

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







"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel




@require_tf
@require_sentencepiece
@require_tokenizers
class  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







 @slow
 def  _lowercase			(      self							:      Optional[Any]     ):
    __lowercase								=						TFCamembertModel.from_pretrained("jplu/tf-camembert-base"     )

    __lowercase								=						tf.convert_to_tensor(
        [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]], dtype=tf.intaa, )  # J'aime le camembert !"

    __lowercase								=						model(UpperCAmelCase__     )["last_hidden_state"]
    __lowercase								=						tf.TensorShape((1, 1_0, 7_6_8)     )
    self.assertEqual(output.shape, UpperCAmelCase__     )
    # compare the actual values for a slice.
    __lowercase								=						tf.convert_to_tensor(
        [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]], dtype=tf.floataa, )
    # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
    # camembert.eval()
    # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

    self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1E-4     )     )




