

from __future__ import annotations

import random
import string






class 			__magic_name__					:





			def __init__(	self      ,						__snake_case = None      )     ->				None:




						'''simple docstring'''
						__a						   =passcode or self.__passcode_creator()
						__a						   =self.__make_key_list()
						__a						   =self.__make_shift_key()





			def __str__(	self      )     ->				str:




						'''simple docstring'''
						return "".join(self.__passcode      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				list[int]:




						'''simple docstring'''
						for i in range(1      ,						len(__snake_case      )      ,						2      ):
									iterlist[i] *= -1
						return iterlist





			def 	__magic_name__					(	self      )     ->				list[str]:




						'''simple docstring'''
						__a						   =string.ascii_letters + string.digits
						__a						   =[random.choice(__snake_case      ) for _ in range(random.randint(10      ,						20      )      )]
						return password





			def 	__magic_name__					(	self      )     ->				list[str]:




						'''simple docstring'''
						__a						   =(
						    string.ascii_letters + string.digits + string.punctuation + ' \t\n'
						)

						__a						   =[]

						# creates points known as breakpoints to break the key_list_options at those
						# points and pivot each substring
						__a						   =sorted(set(self.__passcode      )      )
						__a						   =[]

						# algorithm for creating a new shuffled list, keys_l, out of key_list_options
						for i in key_list_options:
									temp_list.extend(__snake_case      )

									# checking breakpoints at which to pivot temporary sublist and add it into
									# keys_l
									if i in breakpoints or i == key_list_options[-1]:
												keys_l.extend(temp_list[::-1]      )
												temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
						return keys_l





			def 	__magic_name__					(	self      )     ->				int:




						'''simple docstring'''
						__a						   =sum(self.__neg_pos([ord(__snake_case      ) for x in self.__passcode]      )      )
						return num if num > 0 else len(self.__passcode      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				str:




						'''simple docstring'''
						__a						   =''

						# decoding shift like Caesar cipher algorithm implementing negative shift or
						# reverse shift or left shift
						for i in encoded_message:
									__a						   =self.__key_list.index(__snake_case      )
									decoded_message += self.__key_list[
									    (position - self.__shift_key) % -len(self.__key_list      )
									]

						return decoded_message





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				str:




						'''simple docstring'''
						__a						   =''

						# encoding shift like Caesar cipher algorithm implementing positive shift or
						# forward shift or right shift
						for i in plaintext:
									__a						   =self.__key_list.index(__snake_case      )
									encoded_message += self.__key_list[
									    (position + self.__shift_key) % len(self.__key_list      )
									]

						return encoded_message






def  UpperCamelCase_(							_snake_case     :						str = "Hello, this is a modified Caesar cipher"			):




			"""simple docstring"""






			__a						   =ShuffledShiftCipher()
			return cipa.decrypt(cipa.encrypt(_snake_case			)			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()









import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCAmelCase      :    List[str]						       =    False






@nightly
@require_torch_gpu
class 			__magic_name__					(    unittest.TestCase   ):





			def 	__magic_name__					(	self      )     ->				Optional[Any]:




						'''simple docstring'''
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





			def 	__magic_name__					(	self      )     ->				Tuple:




						'''simple docstring'''
						__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
						# remove text_unet
						pipe.remove_unused_weights()
						pipe.to(__snake_case      )
						pipe.set_progress_bar_config(disable=__snake_case      )

						__a						   =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'      )

						__a						   =torch.manual_seed(0      )
						__a						   =pipe(
						    prompt='first prompt'      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						generator=__snake_case      ,						guidance_scale=7.5      ,						num_inference_steps=2      ,						output_type='numpy'      ,						).images

						with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(__snake_case      )
									__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained(__snake_case      )

						pipe.to(__snake_case      )
						pipe.set_progress_bar_config(disable=__snake_case      )

						__a						   =generator.manual_seed(0      )
						__a						   =pipe(
						    prompt='first prompt'      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						generator=__snake_case      ,						guidance_scale=7.5      ,						num_inference_steps=2      ,						output_type='numpy'      ,						).images

						assert np.abs(image - new_image      ).sum() < 1e-5, "Models don't have the same forward pass"





			def 	__magic_name__					(	self      )     ->				Optional[Any]:




						'''simple docstring'''
						__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
						pipe.remove_unused_weights()
						pipe.to(__snake_case      )
						pipe.set_progress_bar_config(disable=__snake_case      )

						__a						   ='cyberpunk 2077'
						__a						   =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'      )
						__a						   =torch.manual_seed(0      )
						__a						   =pipe(
						    prompt=__snake_case      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						generator=__snake_case      ,						guidance_scale=7.5      ,						num_inference_steps=50      ,						output_type='numpy'      ,						).images

						__a						   =image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						__a						   =np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]      )

						assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2






