






'''simple docstring'''





_UpperCamelCase		:		Optional[int]								=			[chr(i) for i in range(32, 126)]
_UpperCamelCase		:		Optional[int]								=			list(range(len(alphabets)))
_UpperCamelCase		:		Any								=			list(range(len(alphabets)))
_UpperCamelCase		:		Optional[Any]								=			list(range(len(alphabets)))
_UpperCamelCase		:		Any								=			list(reversed(range(len(alphabets))))
_UpperCamelCase		:		int								=			[]
_UpperCamelCase		:		Optional[int]								=			0
def __UpperCAmelCase (			)  -> Tuple:
			global gear_one_pos
			global gear_two_pos
			global gear_three_pos
			UpperCAmelCase_     :   int       =   gear_one[0]
			gear_one.append(A						)
			del gear_one[0]
			gear_one_pos += 1
			if gear_one_pos % int(len(A						)						) == 0:
						UpperCAmelCase_     :   Optional[int]       =   gear_two[0]
						gear_two.append(A						)
						del gear_two[0]
						gear_two_pos += 1
						if gear_two_pos % int(len(A						)						) == 0:
									UpperCAmelCase_     :   List[str]       =   gear_three[0]
									gear_three.append(A						)
									del gear_three[0]
									gear_three_pos += 1
def __UpperCAmelCase (			A			:	Dict						)  -> List[Any]:
			UpperCAmelCase_     :   Dict       =   alphabets.index(A						)
			UpperCAmelCase_     :   Tuple       =   gear_one[target]
			UpperCAmelCase_     :   Dict       =   gear_two[target]
			UpperCAmelCase_     :   str       =   gear_three[target]
			UpperCAmelCase_     :   Tuple       =   reflector[target]
			UpperCAmelCase_     :   List[Any]       =   gear_three.index(A						)
			UpperCAmelCase_     :   Optional[int]       =   gear_two.index(A						)
			UpperCAmelCase_     :   Tuple       =   gear_one.index(A						)
			code.append(alphabets[target]						)
			rotator()


if __name__ == "__main__":
						_UpperCamelCase		:		List[Any]								=			list(input('Type your message:\n'))
						while True:
												try:
																		_UpperCamelCase		:		Optional[int]								=			int(input('Please set token:(must be only digits)\n'))
																		break
												except Exception as error:
																		print(error)
						for _ in range(token):
												rotator()
						for j in decode:
												engine(j)
						print('\n' + ''.join(code))
						print(
						    f'''\nYour Token is {token} please write it down.\nIf you want to decode '''
						    'this message again you should input same digits as token!'
						)










'''simple docstring'''





from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)


class 				snake_case__     (			UpperCamelCase):




					def __init__(       self       :						Optional[int]							,				_A       :						AutoencoderKL							,				_A       :						CLIPTextModel							,				_A       :						CLIPTokenizer							,				_A       :						UNetaDConditionModel							,				_A       :						DDIMScheduler							,				_A       :						float = 0.93							,				_A       :						bool = True							,				)  ->    Dict:
								UpperCAmelCase_     :   Tuple       =   mixing_coeff
								UpperCAmelCase_     :   Union[str, Any]       =   leapfrog_steps

								super().__init__()
								self.register_modules(
								    vae=_A							,				text_encoder=_A							,				tokenizer=_A							,				unet=_A							,				scheduler=_A							,				)

								UpperCAmelCase_     :   Optional[Any]       =   2 ** (len(self.vae.config.block_out_channels     ) - 1)
								UpperCAmelCase_     :   Optional[Any]       =   VaeImageProcessor(vae_scale_factor=self.vae_scale_factor     )




					def 						A     (       self       :						Optional[int]							,				_A       :						str							,				_A       :						Optional[str] = None							,				_A       :						bool = False     )  ->    Dict:
								UpperCAmelCase_     :   int       =   self.tokenizer(
								    _A							,				padding='''max_length'''							,				max_length=self.tokenizer.model_max_length							,				truncation=_A							,				return_tensors='''pt'''							,				)

								UpperCAmelCase_     :   Tuple       =   self.text_encoder(text_inputs.input_ids.to(self.device     )     ).last_hidden_state

								UpperCAmelCase_     :   Tuple       =   prompt_embeds.to(dtype=self.text_encoder.dtype							,				device=self.device     )

								if do_classifier_free_guidance:
											UpperCAmelCase_     :   Optional[int]       =   '''''' if negative_prompt is None else negative_prompt

											UpperCAmelCase_     :   Union[str, Any]       =   self.tokenizer(
											    _A							,				padding='''max_length'''							,				max_length=self.tokenizer.model_max_length							,				truncation=_A							,				return_tensors='''pt'''							,				)

											UpperCAmelCase_     :   List[str]       =   self.text_encoder(uncond_input.input_ids.to(self.device     )     ).last_hidden_state

											UpperCAmelCase_     :   Tuple       =   torch.cat([negative_prompt_embeds, prompt_embeds]     )

								return prompt_embeds




					def 						A     (       self       :						Optional[int]							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor     )  ->    Tuple:
								UpperCAmelCase_     :   Union[str, Any]       =   self.mixing_coeff * x + (1 - self.mixing_coeff) * y
								UpperCAmelCase_     :   Any       =   self.mixing_coeff * y + (1 - self.mixing_coeff) * x

								return [x, y]




					def 						A     (       self       :						Union[str, Any]							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor     )  ->    Any:
								UpperCAmelCase_     :   Optional[Any]       =   (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
								UpperCAmelCase_     :   int       =   (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

								return [x, y]




					def 						A     (       self       :						List[str]							,				_A       :						torch.Tensor     )  ->    List[Any]:
								# as self.alphas_cumprod is always in cpu
								UpperCAmelCase_     :   Tuple       =   int(_A     )

								UpperCAmelCase_     :   List[str]       =   self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

								return alpha_prod, 1 - alpha_prod




					def 						A     (       self       :						Dict							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				)  ->    Tuple:
								UpperCAmelCase_     :   str       =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								UpperCAmelCase_  ,     UpperCAmelCase_     :   int       =   self._get_alpha_and_beta(_A     )
								UpperCAmelCase_  ,     UpperCAmelCase_     :   Tuple       =   self._get_alpha_and_beta(_A     )

								UpperCAmelCase_     :   Optional[Any]       =   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
								UpperCAmelCase_     :   Tuple       =   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

								UpperCAmelCase_     :   List[str]       =   (base - b_t * model_output) / a_t

								return model_input, next_model_input.to(base.dtype     )




					def 						A     (       self       :						Any							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				)  ->    Union[str, Any]:
								UpperCAmelCase_     :   Tuple       =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								UpperCAmelCase_  ,     UpperCAmelCase_     :   List[Any]       =   self._get_alpha_and_beta(_A     )
								UpperCAmelCase_  ,     UpperCAmelCase_     :   int       =   self._get_alpha_and_beta(_A     )

								UpperCAmelCase_     :   Any       =   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
								UpperCAmelCase_     :   Optional[int]       =   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
								UpperCAmelCase_     :   List[Any]       =   a_t * base + b_t * model_output

								return model_input, next_model_input.to(base.dtype     )




					@torch.no_grad()
					def 						A     (       self       :						int							,				_A       :						torch.Tensor     )  ->    List[Any]:
								UpperCAmelCase_     :   Union[str, Any]       =   1 / self.vae.config.scaling_factor * latents
								UpperCAmelCase_     :   str       =   self.vae.decode(_A     ).sample
								UpperCAmelCase_     :   Any       =   (image / 2 + 0.5).clamp(0							,				1     )
								return image




					@torch.no_grad()
					def 						A     (       self       :						int							,				_A       :						Image.Image							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						float							,				_A       :						Optional[torch.Generator] = None							,				)  ->    Any:
								UpperCAmelCase_     :   Optional[Any]       =   guidance_scale > 1.0

								UpperCAmelCase_     :   str       =   image.to(device=self.device							,				dtype=text_embeds.dtype     )
								UpperCAmelCase_     :   int       =   self.vae.encode(_A     ).latent_dist.sample(_A     )

								UpperCAmelCase_     :   List[str]       =   self.vae.config.scaling_factor * latent

								UpperCAmelCase_     :   Optional[int]       =   [latent.clone(), latent.clone()]

								for i, t in tqdm(enumerate(_A     )							,				total=len(_A     )     ):
											UpperCAmelCase_     :   List[str]       =   self.noise_mixing_layer(x=coupled_latents[0]							,				y=coupled_latents[1]     )

											# j - model_input index, k - base index
											for j in range(2     ):
														UpperCAmelCase_     :   List[Any]       =   j ^ 1

														if self.leapfrog_steps:
																	if i % 2 == 0:
																				UpperCAmelCase_  ,     UpperCAmelCase_     :   Tuple       =   j, k

														UpperCAmelCase_     :   str       =   coupled_latents[j]
														UpperCAmelCase_     :   List[Any]       =   coupled_latents[k]

														UpperCAmelCase_     :   Optional[int]       =   torch.cat([model_input] * 2     ) if do_classifier_free_guidance else model_input

														UpperCAmelCase_     :   Dict       =   self.unet(_A							,				_A							,				encoder_hidden_states=_A     ).sample

														if do_classifier_free_guidance:
																	UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[int]       =   noise_pred.chunk(2     )
																	UpperCAmelCase_     :   List[str]       =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

														UpperCAmelCase_  ,     UpperCAmelCase_     :   Union[str, Any]       =   self.noise_step(
														    base=_A							,				model_input=_A							,				model_output=_A							,				timestep=_A							,				)

														UpperCAmelCase_     :   str       =   model_input

								return coupled_latents





					@torch.no_grad()
					def __call__(       self       :						Any							,				_A       :						str							,				_A       :						str							,				_A       :						Image.Image							,				_A       :						float = 3.0							,				_A       :						int = 50							,				_A       :						float = 0.8							,				_A       :						Optional[str] = None							,				_A       :						Optional[torch.Generator] = None							,				_A       :						Optional[str] = "pil"							,				)  ->    Union[str, Any]:
								UpperCAmelCase_     :   List[Any]       =   guidance_scale > 1.0

								UpperCAmelCase_     :   Optional[Any]       =   self.image_processor.preprocess(_A     )

								UpperCAmelCase_     :   str       =   self._encode_prompt(_A							,				_A							,				_A     )
								UpperCAmelCase_     :   int       =   self._encode_prompt(_A							,				_A							,				_A     )

								self.scheduler.set_timesteps(_A							,				self.device     )

								UpperCAmelCase_     :   Any       =   num_inference_steps - int(num_inference_steps * strength     )
								UpperCAmelCase_     :   Tuple       =   self.scheduler.timesteps[t_limit:]
								UpperCAmelCase_     :   Any       =   fwd_timesteps.flip(0     )

								UpperCAmelCase_     :   Optional[int]       =   self.prepare_latents(_A							,				_A							,				_A							,				_A							,				_A     )

								for i, t in tqdm(enumerate(_A     )							,				total=len(_A     )     ):
											# j - model_input index, k - base index
											for k in range(2     ):
														UpperCAmelCase_     :   Union[str, Any]       =   k ^ 1

														if self.leapfrog_steps:
																	if i % 2 == 1:
																				UpperCAmelCase_  ,     UpperCAmelCase_     :   Tuple       =   j, k

														UpperCAmelCase_     :   Tuple       =   coupled_latents[j]
														UpperCAmelCase_     :   Optional[int]       =   coupled_latents[k]

														UpperCAmelCase_     :   Union[str, Any]       =   torch.cat([model_input] * 2     ) if do_classifier_free_guidance else model_input

														UpperCAmelCase_     :   int       =   self.unet(_A							,				_A							,				encoder_hidden_states=_A     ).sample

														if do_classifier_free_guidance:
																	UpperCAmelCase_  ,     UpperCAmelCase_     :   List[Any]       =   noise_pred.chunk(2     )
																	UpperCAmelCase_     :   Any       =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

														UpperCAmelCase_  ,     UpperCAmelCase_     :   Any       =   self.denoise_step(
														    base=_A							,				model_input=_A							,				model_output=_A							,				timestep=_A							,				)

														UpperCAmelCase_     :   str       =   model_input

											UpperCAmelCase_     :   str       =   self.denoise_mixing_layer(x=coupled_latents[0]							,				y=coupled_latents[1]     )

								# either one is fine
								UpperCAmelCase_     :   Union[str, Any]       =   coupled_latents[0]

								if output_type not in ["latent", "pt", "np", "pil"]:
											UpperCAmelCase_     :   str       =   (
											    F"the output_type {output_type} is outdated. Please make sure to set it to one of these instead: "
											    '''`pil`, `np`, `pt`, `latent`'''
											)
											deprecate('''Unsupported output_type'''							,				'''1.0.0'''							,				_A							,				standard_warn=_A     )
											UpperCAmelCase_     :   int       =   '''np'''

								if output_type == "latent":
											UpperCAmelCase_     :   Tuple       =   final_latent
								else:
											UpperCAmelCase_     :   Optional[int]       =   self.decode_latents(_A     )
											UpperCAmelCase_     :   str       =   self.image_processor.postprocess(_A							,				output_type=_A     )

								return image


