




from __future__ import annotations







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ = None ,				lowercase_ = None ,				lowercase_ = False ,				)   ->       tuple[int, float, str]:



       """simple docstring"""

       A__       =    cipher_alphabet or [chr(lowercase_						) 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__       =    {
                  '''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__       =    frequencies_dict

       if not case_sensitive:
              A__       =    ciphertext.lower()

       # Chi squared statistic values
       A__       =    {}

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

              # decrypt the message with the shift
              for letter in ciphertext:
                     try:
                            # Try to index the letter in the alphabet
                            A__       =    (alphabet_letters.index(letter.lower()						) - shift) % len(
                                lowercase_						)
                            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__       =    0.0

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

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

                                   # Complete the chi squared statistic formula
                                   A__       =    ((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__       =    decrypted_with_shift.count(lowercase_						)

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

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

       A__       =    min(
           lowercase_ ,				key=lowercase_ ,				)

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








import os

import pytest
from attr import dataclass


_UpperCAmelCase		:  List[str]	=   "us-east-1"  # defaults region




@dataclass
class       __lowerCAmelCase				:
       _a           =  42
       _a           =  '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
       _a           =  {
           '''task_name''': '''mnli''',
           '''per_device_train_batch_size''': 16,
           '''per_device_eval_batch_size''': 16,
           '''do_train''': True,
           '''do_eval''': True,
           '''do_predict''': True,
           '''output_dir''': '''/opt/ml/model''',
           '''overwrite_output_dir''': True,
           '''max_steps''': 500,
           '''save_steps''': 5500,
       }
       _a           =  {**hyperparameters, '''max_steps''': 1000}






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         if self.framework == "pytorch":
           return [
               {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
               {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
               {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
           ]
         else:
           return [
               {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
               {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
               {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
           ]






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         return F"{self.framework}-transfromers-test"






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         return F"./tests/sagemaker/scripts/{self.framework}"






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         if self.framework == "pytorch":
           return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
         else:
           return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"







@pytest.fixture(scope="class"      )
def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:Union[str, Any]							=  SageMakerTestEnvironment(framework=request.cls.framework      )



