





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 	_UpperCAmelCase						( _lowercase       ):




					def __init__(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase = 0.9_3     ,   __lowerCAmelCase = True     ,   )    ->	Tuple:
							UpperCamelCase__								=						mixing_coeff
							UpperCamelCase__								=						leapfrog_steps

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

							UpperCamelCase__								=						2 ** (len(self.vae.config.block_out_channels							) - 1)
							UpperCamelCase__								=						VaeImageProcessor(vae_scale_factor=self.vae_scale_factor							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase = None     ,   __lowerCAmelCase = False							)    ->	Optional[Any]:
							UpperCamelCase__								=						self.tokenizer(
							    __lowerCAmelCase     ,   padding="""max_length"""     ,   max_length=self.tokenizer.model_max_length     ,   truncation=__lowerCAmelCase     ,   return_tensors="""pt"""     ,   )

							UpperCamelCase__								=						self.text_encoder(text_inputs.input_ids.to(self.device							)							).last_hidden_state

							UpperCamelCase__								=						prompt_embeds.to(dtype=self.text_encoder.dtype     ,   device=self.device							)

							if do_classifier_free_guidance:
									UpperCamelCase__								=						"""""" if negative_prompt is None else negative_prompt

									UpperCamelCase__								=						self.tokenizer(
									    __lowerCAmelCase     ,   padding="""max_length"""     ,   max_length=self.tokenizer.model_max_length     ,   truncation=__lowerCAmelCase     ,   return_tensors="""pt"""     ,   )

									UpperCamelCase__								=						self.text_encoder(uncond_input.input_ids.to(self.device							)							).last_hidden_state

									UpperCamelCase__								=						torch.cat([negative_prompt_embeds, prompt_embeds]							)

							return prompt_embeds




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	Optional[int]:
							UpperCamelCase__								=						self.mixing_coeff * x + (1 - self.mixing_coeff) * y
							UpperCamelCase__								=						self.mixing_coeff * y + (1 - self.mixing_coeff) * x

							return [x, y]




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	str:
							UpperCamelCase__								=						(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
							UpperCamelCase__								=						(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

							return [x, y]




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	int:
							# as self.alphas_cumprod is always in cpu
							UpperCamelCase__								=						int(__lowerCAmelCase							)

							UpperCamelCase__								=						self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

							return alpha_prod, 1 - alpha_prod




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   )    ->	List[Any]:
							UpperCamelCase__								=						timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

							UpperCamelCase__	,      UpperCamelCase__								=						self._get_alpha_and_beta(__lowerCAmelCase							)
							UpperCamelCase__	,      UpperCamelCase__								=						self._get_alpha_and_beta(__lowerCAmelCase							)

							UpperCamelCase__								=						(alpha_prod_t_prev / alpha_prod_t) ** 0.5
							UpperCamelCase__								=						-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

							UpperCamelCase__								=						(base - b_t * model_output) / a_t

							return model_input, next_model_input.to(base.dtype							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   )    ->	Union[str, Any]:
							UpperCamelCase__								=						timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

							UpperCamelCase__	,      UpperCamelCase__								=						self._get_alpha_and_beta(__lowerCAmelCase							)
							UpperCamelCase__	,      UpperCamelCase__								=						self._get_alpha_and_beta(__lowerCAmelCase							)

							UpperCamelCase__								=						(alpha_prod_t_prev / alpha_prod_t) ** 0.5
							UpperCamelCase__								=						-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
							UpperCamelCase__								=						a_t * base + b_t * model_output

							return model_input, next_model_input.to(base.dtype							)




					@torch.no_grad()
					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	Optional[int]:
							UpperCamelCase__								=						1 / self.vae.config.scaling_factor * latents
							UpperCamelCase__								=						self.vae.decode(__lowerCAmelCase							).sample
							UpperCamelCase__								=						(image / 2 + 0.5).clamp(0     ,   1							)
							return image




					@torch.no_grad()
					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase = None     ,   )    ->	Optional[Any]:
							UpperCamelCase__								=						guidance_scale > 1.0

							UpperCamelCase__								=						image.to(device=self.device     ,   dtype=text_embeds.dtype							)
							UpperCamelCase__								=						self.vae.encode(__lowerCAmelCase							).latent_dist.sample(__lowerCAmelCase							)

							UpperCamelCase__								=						self.vae.config.scaling_factor * latent

							UpperCamelCase__								=						[latent.clone(), latent.clone()]

							for i, t in tqdm(enumerate(__lowerCAmelCase							)     ,   total=len(__lowerCAmelCase							)							):
									UpperCamelCase__								=						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__								=						j ^ 1

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

											UpperCamelCase__								=						coupled_latents[j]
											UpperCamelCase__								=						coupled_latents[k]

											UpperCamelCase__								=						torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

											UpperCamelCase__								=						self.unet(__lowerCAmelCase     ,   __lowerCAmelCase     ,   encoder_hidden_states=__lowerCAmelCase							).sample

											if do_classifier_free_guidance:
													UpperCamelCase__	,      UpperCamelCase__								=						noise_pred.chunk(2							)
													UpperCamelCase__								=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

											UpperCamelCase__	,      UpperCamelCase__								=						self.noise_step(
											    base=__lowerCAmelCase     ,   model_input=__lowerCAmelCase     ,   model_output=__lowerCAmelCase     ,   timestep=__lowerCAmelCase     ,   )

											UpperCamelCase__								=						model_input

							return coupled_latents




					@torch.no_grad()
					def __call__(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase = 3.0     ,   __lowerCAmelCase = 50     ,   __lowerCAmelCase = 0.8     ,   __lowerCAmelCase = None     ,   __lowerCAmelCase = None     ,   __lowerCAmelCase = "pil"     ,   )    ->	Union[str, Any]:
							UpperCamelCase__								=						guidance_scale > 1.0

							UpperCamelCase__								=						self.image_processor.preprocess(__lowerCAmelCase							)

							UpperCamelCase__								=						self._encode_prompt(__lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)
							UpperCamelCase__								=						self._encode_prompt(__lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)

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

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

							UpperCamelCase__								=						self.prepare_latents(__lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)

							for i, t in tqdm(enumerate(__lowerCAmelCase							)     ,   total=len(__lowerCAmelCase							)							):
									# j - model_input index, k - base index
									for k in range(2							):
											UpperCamelCase__								=						k ^ 1

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

											UpperCamelCase__								=						coupled_latents[j]
											UpperCamelCase__								=						coupled_latents[k]

											UpperCamelCase__								=						torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

											UpperCamelCase__								=						self.unet(__lowerCAmelCase     ,   __lowerCAmelCase     ,   encoder_hidden_states=__lowerCAmelCase							).sample

											if do_classifier_free_guidance:
													UpperCamelCase__	,      UpperCamelCase__								=						noise_pred.chunk(2							)
													UpperCamelCase__								=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

											UpperCamelCase__	,      UpperCamelCase__								=						self.denoise_step(
											    base=__lowerCAmelCase     ,   model_input=__lowerCAmelCase     ,   model_output=__lowerCAmelCase     ,   timestep=__lowerCAmelCase     ,   )

											UpperCamelCase__								=						model_input

									UpperCamelCase__								=						self.denoise_mixing_layer(x=coupled_latents[0]     ,   y=coupled_latents[1]							)

							# either one is fine
							UpperCamelCase__								=						coupled_latents[0]

							if output_type not in ["latent", "pt", "np", "pil"]:
									UpperCamelCase__								=						(
									    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"""     ,   __lowerCAmelCase     ,   standard_warn=__lowerCAmelCase							)
									UpperCamelCase__								=						"""np"""

							if output_type == "latent":
									UpperCamelCase__								=						final_latent
							else:
									UpperCamelCase__								=						self.decode_latents(__lowerCAmelCase							)
									UpperCamelCase__								=						self.image_processor.postprocess(__lowerCAmelCase     ,   output_type=__lowerCAmelCase							)

							return image












import itertools
from pathlib import Path

def      a    (   _lowercase :  list[str]							)  ->int:
		UpperCamelCase__								=						[tuple(_lowercase							) for login in logins]

		UpperCamelCase__								=						{char for login in split_logins for char in login}

		for permutation in itertools.permutations(_lowercase							):
				UpperCamelCase__								=						True
				for login in logins:
						if not (
						    permutation.index(login[0]							)
						    < permutation.index(login[1]							)
						    < permutation.index(login[2]							)
						):
								UpperCamelCase__								=						False
								break

				if satisfied:
						return int("""""".join(_lowercase							)							)

		raise Exception("""Unable to find the secret passcode"""							)

def      a    (   _lowercase :  str = "keylog.txt"							)  ->int:
		UpperCamelCase__								=						Path(_lowercase							).parent.joinpath(_lowercase							).read_text().splitlines()

		return find_secret_passcode(_lowercase							)


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





