
from string import ascii_uppercase




def 						A_			(				snake_case				: str			,   snake_case				: str			,   snake_case				: bool = False			,   snake_case				: str = ascii_uppercase			)   ->     str:



						'''simple docstring'''

						__UpperCamelCase  	=      keyword.upper()
						__UpperCamelCase  	=      plaintext.upper()
						__UpperCamelCase  	=      set(snake_case			)

						# create a list of unique characters in the keyword - their order matters
						# it determines how we will map plaintext characters to the ciphertext
						__UpperCamelCase  	=      []
						for char in keyword:
												if char in alphabet_set and char not in unique_chars:
																		unique_chars.append(snake_case			)
    # the number of those unique characters will determine the number of rows
						__UpperCamelCase  	=      len(snake_case			)

						# create a shifted version of the alphabet
						__UpperCamelCase  	=      unique_chars + [
						    char for char in alphabet if char not in unique_chars
						]

						# create a modified alphabet by splitting the shifted alphabet into rows
						__UpperCamelCase  	=      [
						    shifted_alphabet[k : k + num_unique_chars_in_keyword]
						    for k in range(0			,   26			,   snake_case			)
						]

						# map the alphabet characters to the modified alphabet characters
						# going 'vertically' through the modified alphabet - consider columns first
						__UpperCamelCase  	=      {}
						__UpperCamelCase  	=      0
						for column in range(snake_case			):
												for row in modified_alphabet:
																		# if current row (the last one) is too short, break out of loop
																		if len(snake_case			) <= column:
																								break

																		# map current letter to letter in modified alphabet
																		__UpperCamelCase  	=      row[column]
																		letter_index += 1

						if verbose:
												print(snake_case			)
						# create the encrypted text by mapping the plaintext to the modified alphabet
						return "".join(mapping[char] if char in mapping else char for char in plaintext			)


if __name__ == "__main__":
				# example use
				print(mixed_keyword("college", "UNIVERSITY"))






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


lowercase__				:				List[str]						=      False






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

							"""simple docstring"""






							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






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



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''      )
													# remove text_unet
													pipe.remove_unused_weights()
													pipe.to(SCREAMING_SNAKE_CASE_      )
													pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

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

													__UpperCamelCase  	=      torch.manual_seed(0      )
													__UpperCamelCase  	=      pipe(
													    prompt='''first prompt'''  ,						image=SCREAMING_SNAKE_CASE_  ,						text_to_image_strength=0.7_5  ,						generator=SCREAMING_SNAKE_CASE_  ,						guidance_scale=7.5  ,						num_inference_steps=2  ,						output_type='''numpy'''  ,						).images

													with tempfile.TemporaryDirectory() as tmpdirname:
																			pipe.save_pretrained(SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      VersatileDiffusionDualGuidedPipeline.from_pretrained(SCREAMING_SNAKE_CASE_      )

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

													__UpperCamelCase  	=      generator.manual_seed(0      )
													__UpperCamelCase  	=      pipe(
													    prompt='''first prompt'''  ,						image=SCREAMING_SNAKE_CASE_  ,						text_to_image_strength=0.7_5  ,						generator=SCREAMING_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'''






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

													__UpperCamelCase  	=      '''cyberpunk 2077'''
													__UpperCamelCase  	=      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''      )
													__UpperCamelCase  	=      torch.manual_seed(0      )
													__UpperCamelCase  	=      pipe(
													    prompt=SCREAMING_SNAKE_CASE_  ,						image=SCREAMING_SNAKE_CASE_  ,						text_to_image_strength=0.7_5  ,						generator=SCREAMING_SNAKE_CASE_  ,						guidance_scale=7.5  ,						num_inference_steps=50  ,						output_type='''numpy'''  ,						).images

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

													assert image.shape == (1, 512, 512, 3)
													__UpperCamelCase  	=      np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]      )

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




