






from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

SCREAMING_SNAKE_CASE__   :      Tuple				=		(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		[ord(letter) for letter in string.ascii_lowercase]
SCREAMING_SNAKE_CASE__   :      Tuple				=		{ord(char) for char in VALID_CHARS}

SCREAMING_SNAKE_CASE__   :      List[str]				=		["""the""", """be""", """to""", """of""", """and""", """in""", """that""", """have"""]

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[Any]   =   ''''''
				__magic_name__		:List[Any]   =   4_2
				__magic_name__		:str   =   4_2
				__magic_name__		:List[str]   =   4_2

				for keychar, cipherchar in zip(cycle(a__			),	a__			):
								__magic_name__		:Dict   =   cipherchar ^ keychar
								if decodedchar not in VALID_INTS:
												return None
								decoded += chr(a__			)

				return decoded

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:List[str]   =   []
				for key in product(a__,	repeat=3			):
								__magic_name__		:Optional[int]   =   try_key(a__,	a__			)
								if encoded is not None:
												possibles.append(a__			)
				return possibles

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				return [possible for possible in possibles if common_word in possible.lower()]

def   __lowercase       (			snake_case = "p059_cipher.txt"			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   4_2
				__magic_name__		:Union[str, Any]   =   4_2
				__magic_name__		:str   =   4_2
				__magic_name__		:Union[str, Any]   =   4_2
				__magic_name__		:List[Any]   =   Path(a__			).parent.joinpath(a__			).read_text(encoding='''utf-8'''			)

				__magic_name__		:List[Any]   =   [int(a__			) for number in data.strip().split(''','''			)]

				__magic_name__		:Tuple   =   filter_valid_chars(a__			)
				for common_word in COMMON_WORDS:
								__magic_name__		:Any   =   filter_common_word(a__,	a__			)
								if len(a__			) == 1:
												break

				__magic_name__		:str   =   possibles[0]
				return sum(ord(a__			) for char in decoded_text			)


if __name__ == "__main__":
					print(f"{solution() = }")













import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


UpperCamelCase__           =			False

class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):
							pass






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






							def 					_lowerCamelCase		(    self	):
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion"""	)
														# remove text_unet
														pipe.remove_unused_weights()
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      """A painting of a squirrel eating a burger """
														UpperCamelCase__					  =      torch.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=2	,			output_type="""numpy"""	).images

														with tempfile.TemporaryDirectory() as tmpdirname:
																					pipe.save_pretrained(__lowerCAmelCase	)
																					UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained(__lowerCAmelCase	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      generator.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			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 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained(
														    """shi-labs/versatile-diffusion"""	,			torch_dtype=torch.floataa	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      """A painting of a squirrel eating a burger """
														UpperCamelCase__					  =      torch.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			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([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]	)

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




