
from __future__ import annotations






def   lowerCamelCase__							(					a		,		a = None		,		a = None		,		a = False		,		)      ->   List[str]:
   _A:  str					=       cipher_alphabet or [chr(_SCREAMING_SNAKE_CASE      ) 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)
      _A:  int					=       {
          '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
      _A:  int					=       frequencies_dict

   if not case_sensitive:
      _A:  Dict					=       ciphertext.lower()

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

   # cycle through all of the shifts
   for shift in range(len(_SCREAMING_SNAKE_CASE      )      ):
      _A:  Optional[Any]					=       ''

      # decrypt the message with the shift
      for letter in ciphertext:
         try:
            # Try to index the letter in the alphabet
            _A:  Any					=       (alphabet_letters.index(letter.lower()      ) - shift) % len(
                _SCREAMING_SNAKE_CASE      )
            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:  Union[str, Any]					=       0.0

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

               # Get the excepcted amount of times the letter should appear based
               # on letter frequencies
               _A:  Optional[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
         else:
            if letter.lower() in frequencies:
               # Get the amount of times the letter occurs in the message
               _A:  Optional[Any]					=       decrypted_with_shift.count(_SCREAMING_SNAKE_CASE      )

               # 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:  Tuple					=       ((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:  Dict					=       (
          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(
       _SCREAMING_SNAKE_CASE		,		key=_SCREAMING_SNAKE_CASE		,		)

   # Get all the data from the most likely cipher (key, decoded message)
   (
       _A				
   ):  Optional[int]					=       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'''



import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment





@require_huggingface_suite
class 							__UpperCamelCase				(		unittest.TestCase					):




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[Any]					    =  inspect.getfile(accelerate.test_utils							)
						__a			:       List[str]					    =  os.path.sep.join(
						    mod_file.split(os.path.sep							)[:-1] + ['scripts', 'external_deps', 'test_metrics.py']							)

						from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

						__a			:       Union[str, Any]					    =  test_metrics




	@require_cpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						debug_launcher(self.test_metrics.main						,				num_processes=1							)




	@require_cpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						debug_launcher(self.test_metrics.main							)




	@require_single_gpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						self.test_metrics.main()





	@require_multi_gpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						print(f"""Found {torch.cuda.device_count()} devices."""							)
						__a			:       List[Any]					    =  ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
						with patch_environment(omp_num_threads=1							):
											execute_subprocess_async(__a						,				env=os.environ.copy()							)



