



"""simple docstring"""
from __future__ import annotations



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




  '''simple docstring'''







  __UpperCamelCase  						=							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)
    __UpperCamelCase  						=							{
        "a": 0.0_8_4_9_7,
        "b": 0.0_1_4_9_2,
        "c": 0.0_2_2_0_2,
        "d": 0.0_4_2_5_3,
        "e": 0.1_1_1_6_2,
        "f": 0.0_2_2_2_8,
        "g": 0.0_2_0_1_5,
        "h": 0.0_6_0_9_4,
        "i": 0.0_7_5_4_6,
        "j": 0.0_0_1_5_3,
        "k": 0.0_1_2_9_2,
        "l": 0.0_4_0_2_5,
        "m": 0.0_2_4_0_6,
        "n": 0.0_6_7_4_9,
        "o": 0.0_7_5_0_7,
        "p": 0.0_1_9_2_9,
        "q": 0.0_0_0_9_5,
        "r": 0.0_7_5_8_7,
        "s": 0.0_6_3_2_7,
        "t": 0.0_9_3_5_6,
        "u": 0.0_2_7_5_8,
        "v": 0.0_0_9_7_8,
        "w": 0.0_2_5_6_0,
        "x": 0.0_0_1_5_0,
        "y": 0.0_1_9_9_4,
        "z": 0.0_0_0_7_7,
    }
  else:
    # Custom frequencies dictionary
    __UpperCamelCase  						=							frequencies_dict

  if not case_sensitive:
    __UpperCamelCase  						=							ciphertext.lower()

  # Chi squared statistic values
  __UpperCamelCase  						=							{}

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

    # decrypt the message with the shift
    for letter in ciphertext:
      try:
        # Try to index the letter in the alphabet
        __UpperCamelCase  						=							(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

    __UpperCamelCase  						=							0.0

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

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

          # Complete the chi squared statistic formula
          __UpperCamelCase  						=							((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
          __UpperCamelCase  						=							decrypted_with_shift.count(_lowerCAmelCase			)

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

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

  __UpperCamelCase  						=							min(
      _lowerCAmelCase ,key=_lowerCAmelCase ,)

  # Get all the data from the most likely cipher (key, decoded message)
  (
      __UpperCamelCase    
  )  						=							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 random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class        __magic_name__	(				lowerCamelCase__     ,					lowerCamelCase__     ,					unittest.TestCase				):





     """simple docstring"""



     __UpperCamelCase            =  IFInpaintingPipeline
     __UpperCamelCase            =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
     __UpperCamelCase            =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     __UpperCamelCase            =  PipelineTesterMixin.required_optional_params - {'''latents'''}



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        return self._get_dummy_components()



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]      ,   snake_case							:str      ,   snake_case							:List[Any]=0	):





        '''simple docstring'''




        if str(snake_case	).startswith("mps"	):
           A_       :					Optional[Any]        =      torch.manual_seed(snake_case	)
        else:
           A_       :					Any        =      torch.Generator(device=snake_case	).manual_seed(snake_case	)

        A_       :					Optional[Any]        =      floats_tensor((1, 3, 32, 32)      ,   rng=random.Random(snake_case	)	).to(snake_case	)
        A_       :					Any        =      floats_tensor((1, 3, 32, 32)      ,   rng=random.Random(snake_case	)	).to(snake_case	)

        A_       :					List[Any]        =      {
            "prompt": "A painting of a squirrel eating a burger",
            "image": image,
            "mask_image": mask_image,
            "generator": generator,
            "num_inference_steps": 2,
            "output_type": "numpy",
        }

        return inputs



     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()      ,   reason="XFormers attention is only available with CUDA and `xformers` installed"      ,   )
     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        self._test_save_load_optional_components()



     @unittest.skipIf(torch_device != "cuda"      ,   reason="float16 requires CUDA"	)
     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        super().test_save_load_floataa(expected_max_diff=1e-1	)



     def     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        self._test_save_load_local()





     def     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




        self._test_inference_batch_single_identical(
            expected_max_diff=1e-2      ,   )
