




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


SCREAMING_SNAKE_CASE:    Dict											=   False



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



						def   __a				( self  :      int     ):

								"""simple docstring"""

								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()



						def   __a				( self  :      Union[str, Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       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__     )

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

								SCREAMING_SNAKE_CASE_							=       torch.manual_seed(0     )
								SCREAMING_SNAKE_CASE_							=       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__     )
										SCREAMING_SNAKE_CASE_							=       VersatileDiffusionDualGuidedPipeline.from_pretrained(snake_case__     )

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

								SCREAMING_SNAKE_CASE_							=       generator.manual_seed(0     )
								SCREAMING_SNAKE_CASE_							=       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   __a				( self  :      int     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'     )
								pipe.remove_unused_weights()
								pipe.to(snake_case__     )
								pipe.set_progress_bar_config(disable=snake_case__     )

								SCREAMING_SNAKE_CASE_							=       'cyberpunk 2077'
								SCREAMING_SNAKE_CASE_							=       load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'     )
								SCREAMING_SNAKE_CASE_							=       torch.manual_seed(0     )
								SCREAMING_SNAKE_CASE_							=       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

								SCREAMING_SNAKE_CASE_							=       image[0, 2_53:2_56, 2_53:2_56, -1]

								assert image.shape == (1, 5_12, 5_12, 3)
								SCREAMING_SNAKE_CASE_							=       np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]     )

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





from __future__ import annotations

import random
import string



class       lowercase_		:



						def __init__( self  :      str				,       snake_case__  :      str | None = None     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       passcode or self.__passcode_creator()
								SCREAMING_SNAKE_CASE_							=       self.__make_key_list()
								SCREAMING_SNAKE_CASE_							=       self.__make_shift_key()



						def __str__( self  :      List[Any]     ):

								"""simple docstring"""

								return "".join(self.__passcode     )



						def   __a				( self  :      Union[str, Any]				,       snake_case__  :      list[int]     ):

								"""simple docstring"""

								for i in range(1				,       len(snake_case__     )				,       2     ):
										iterlist[i] *= -1
								return iterlist



						def   __a				( self  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       string.ascii_letters + string.digits
								SCREAMING_SNAKE_CASE_							=       [random.choice(snake_case__     ) for _ in range(random.randint(10				,       20     )     )]
								return password



						def   __a				( self  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       (
								    string.ascii_letters + string.digits + string.punctuation + ' \t\n'
								)

								SCREAMING_SNAKE_CASE_							=       []

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

								# 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   __a				( self  :      Union[str, Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       sum(self.__neg_pos([ord(snake_case__     ) for x in self.__passcode]     )     )
								return num if num > 0 else len(self.__passcode     )



						def   __a				( self  :      str				,       snake_case__  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       ''

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

								return decoded_message






						def   __a				( self  :      Optional[int]				,       snake_case__  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       ''

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

								return encoded_message


def 				_a       (				lowerCAmelCase = "Hello, this is a modified Caesar cipher"   )->  str:
		SCREAMING_SNAKE_CASE_							=       ShuffledShiftCipher()
		return cipa.decrypt(cipa.encrypt(lowerCAmelCase   )   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()