

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   UNetaDModel
					UpperCAmelCase__:   KarrasVeScheduler






					def __init__( self    ,       A__    ,       A__			):
						super().__init__()
						self.register_modules(unet=A__    ,       scheduler=A__			)






					@torch.no_grad()
					def __call__( self    ,       A__ = 1    ,       A__ = 50    ,       A__ = None    ,       A__ = "pil"    ,       A__ = True    ,       **A__    ,       ):
						A__							:    Optional[int]        =		self.unet.config.sample_size
						A__							:    List[str]        =		(batch_size, 3, img_size, img_size)

						A__							:    Optional[int]        =		self.unet

						# sample x_0 ~ N(0, sigma_0^2 * I)
						A__							:    Optional[Any]        =		randn_tensor(A__    ,       generator=A__    ,       device=self.device			) * self.scheduler.init_noise_sigma

						self.scheduler.set_timesteps(A__			)

						for t in self.progress_bar(self.scheduler.timesteps			):
							# here sigma_t == t_i from the paper
							A__							:    List[Any]        =		self.scheduler.schedule[t]
							A__							:    Any        =		self.scheduler.schedule[t - 1] if t > 0 else 0

							# 1. Select temporarily increased noise level sigma_hat
							# 2. Add new noise to move from sample_i to sample_hat
							A__   ,   A__							:    Tuple        =		self.scheduler.add_noise_to_input(A__    ,       A__    ,       generator=A__			)

							# 3. Predict the noise residual given the noise magnitude `sigma_hat`
							# The model inputs and output are adjusted by following eq. (213) in [1].
							A__							:    Dict        =		(sigma_hat / 2) * model((sample_hat + 1) / 2    ,       sigma_hat / 2			).sample

							# 4. Evaluate dx/dt at sigma_hat
							# 5. Take Euler step from sigma to sigma_prev
							A__							:    str        =		self.scheduler.step(A__    ,       A__    ,       A__    ,       A__			)

							if sigma_prev != 0:
								# 6. Apply 2nd order correction
								# The model inputs and output are adjusted by following eq. (213) in [1].
								A__							:    List[str]        =		(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2    ,       sigma_prev / 2			).sample
								A__							:    Any        =		self.scheduler.step_correct(
								    A__    ,       A__    ,       A__    ,       A__    ,       step_output.prev_sample    ,       step_output["""derivative"""]    ,       )
							A__							:    Optional[Any]        =		step_output.prev_sample

						A__							:    Dict        =		(sample / 2 + 0.5).clamp(0    ,       1			)
						A__							:    Any        =		sample.cpu().permute(0    ,       2    ,       3    ,       1			).numpy()
						if output_type == "pil":
							A__							:    Dict        =		self.numpy_to_pil(A__			)

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=A__			)






import string

def 						UpperCamelCase					(lowercase_: str					)     ->      None:
	for key in range(len(string.ascii_uppercase					)					):
		A__							:    Dict        =		""""""
		for symbol in message:
			if symbol in string.ascii_uppercase:
				A__							:    Dict        =		string.ascii_uppercase.find(lowercase_					)
				A__							:    Optional[int]        =		num - key
				if num < 0:
					A__							:    Optional[int]        =		num + len(string.ascii_uppercase					)
				A__							:    Any        =		translated + string.ascii_uppercase[num]
			else:
				A__							:    Optional[Any]        =		translated + symbol
		print(f"""Decryption using Key #{key}: {translated}"""					)

def 						UpperCamelCase					()     ->      None:
	A__							:    Optional[Any]        =		input("""Encrypted message: """					)
	A__							:    Optional[Any]        =		message.upper()
	decrypt(lowercase_					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()



