



from typing import Literal

UpperCAmelCase							=       """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""

def       A_   (						__a  :					str			,		__a  :					str			,		__a  :					Literal["encrypt", "decrypt"]		):

				"""simple docstring"""
				a__					   =		LETTERS if mode == """decrypt""" else key
				a__					   =		key if mode == """decrypt""" else LETTERS
				a__					   =		""""""
				# loop through each symbol in the message
				for symbol in message:
								if symbol.upper() in chars_a:
												# encrypt/decrypt the symbol
												a__					   =		chars_a.find(symbol.upper()		)
												if symbol.isupper():
																translated += chars_b[sym_index].upper()
												else:
																translated += chars_b[sym_index].lower()
								else:
												# symbol is not in LETTERS, just add it
												translated += symbol
				return translated

def       A_   (						__a  :					str			,		__a  :					str		):

				"""simple docstring"""
				return translate_message(__a			,		__a			,		"""encrypt"""		)

def       A_   (						__a  :					str			,		__a  :					str		):

				"""simple docstring"""
				return translate_message(__a			,		__a			,		"""decrypt"""		)

def       A_   (						):

				"""simple docstring"""
				a__					   =		"""Hello World"""
				a__					   =		"""QWERTYUIOPASDFGHJKLZXCVBNM"""
				a__					   =		"""decrypt"""  # set to 'encrypt' or 'decrypt'

				if mode == "encrypt":
								a__					   =		encrypt_message(__a			,		__a		)
				elif mode == "decrypt":
								a__					   =		decrypt_message(__a			,		__a		)
				print(F'''Using the key {key}, the {mode}ed message is: {translated}'''		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()






'''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      lowerCamelCase__     (     _A				):






	'''simple docstring'''

	def __init__(  self		:  Optional[Any]   ,		__A		:  AutoencoderKL   ,		__A		:  CLIPTextModel   ,		__A		:  CLIPTokenizer   ,		__A		:  UNetaDConditionModel   ,		__A		:  DDIMScheduler   ,		__A		:  float = 0.9_3   ,		__A		:  bool = True   ,		)     ->			int:




								'''simple docstring'''





								lowerCAmelCase__  =   mixing_coeff
								lowerCAmelCase__  =   leapfrog_steps

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

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




	def      lowercase__							(  self		:  Optional[int]   ,		__A		:  str   ,		__A		:  Optional[str] = None   ,		__A		:  bool = False					)     ->			List[str]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.tokenizer(
								    __A   ,		padding="""max_length"""   ,		max_length=self.tokenizer.model_max_length   ,		truncation=__A   ,		return_tensors="""pt"""   ,		)

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

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

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

															lowerCAmelCase__  =   self.tokenizer(
															    __A   ,		padding="""max_length"""   ,		max_length=self.tokenizer.model_max_length   ,		truncation=__A   ,		return_tensors="""pt"""   ,		)

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

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

								return prompt_embeds




	def      lowercase__							(  self		:  Optional[Any]   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   self.mixing_coeff * x + (1 - self.mixing_coeff) * y
								lowerCAmelCase__  =   self.mixing_coeff * y + (1 - self.mixing_coeff) * x

								return [x, y]




	def      lowercase__							(  self		:  Dict   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor					)     ->			Union[str, Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
								lowerCAmelCase__  =   (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

								return [x, y]




	def      lowercase__							(  self		:  List[str]   ,		__A		:  torch.Tensor					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   int(__A					)

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

								return alpha_prod, 1 - alpha_prod




	def      lowercase__							(  self		:  List[Any]   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		)     ->			int:




								'''simple docstring'''





								lowerCAmelCase__  =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								lowerCAmelCase__  ,lowerCAmelCase__  =   self._get_alpha_and_beta(__A					)
								lowerCAmelCase__  ,lowerCAmelCase__  =   self._get_alpha_and_beta(__A					)

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

								lowerCAmelCase__  =   (base - b_t * model_output) / a_t

								return model_input, next_model_input.to(base.dtype					)




	def      lowercase__							(  self		:  Tuple   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		)     ->			List[str]:




								'''simple docstring'''





								lowerCAmelCase__  =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								lowerCAmelCase__  ,lowerCAmelCase__  =   self._get_alpha_and_beta(__A					)
								lowerCAmelCase__  ,lowerCAmelCase__  =   self._get_alpha_and_beta(__A					)

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

								return model_input, next_model_input.to(base.dtype					)




	@torch.no_grad()
	def      lowercase__							(  self		:  int   ,		__A		:  torch.Tensor					)     ->			Union[str, Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   1 / self.vae.config.scaling_factor * latents
								lowerCAmelCase__  =   self.vae.decode(__A					).sample
								lowerCAmelCase__  =   (image / 2 + 0.5).clamp(0   ,		1					)
								return image




	@torch.no_grad()
	def      lowercase__							(  self		:  List[str]   ,		__A		:  Image.Image   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  float   ,		__A		:  Optional[torch.Generator] = None   ,		)     ->			str:




								'''simple docstring'''





								lowerCAmelCase__  =   guidance_scale > 1.0

								lowerCAmelCase__  =   image.to(device=self.device   ,		dtype=text_embeds.dtype					)
								lowerCAmelCase__  =   self.vae.encode(__A					).latent_dist.sample(__A					)

								lowerCAmelCase__  =   self.vae.config.scaling_factor * latent

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

								for i, t in tqdm(enumerate(__A					)   ,		total=len(__A					)					):
															lowerCAmelCase__  =   self.noise_mixing_layer(x=coupled_latents[0]   ,		y=coupled_latents[1]					)

															# j - model_input index, k - base index
															for j in range(2					):
																						lowerCAmelCase__  =   j ^ 1

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

																						lowerCAmelCase__  =   coupled_latents[j]
																						lowerCAmelCase__  =   coupled_latents[k]

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

																						lowerCAmelCase__  =   self.unet(__A   ,		__A   ,		encoder_hidden_states=__A					).sample

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

																						lowerCAmelCase__  ,lowerCAmelCase__  =   self.noise_step(
																						    base=__A   ,		model_input=__A   ,		model_output=__A   ,		timestep=__A   ,		)

																						lowerCAmelCase__  =   model_input

								return coupled_latents




	@torch.no_grad()
	def __call__(  self		:  List[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"   ,		)     ->			Any:




								'''simple docstring'''





								lowerCAmelCase__  =   guidance_scale > 1.0

								lowerCAmelCase__  =   self.image_processor.preprocess(__A					)

								lowerCAmelCase__  =   self._encode_prompt(__A   ,		__A   ,		__A					)
								lowerCAmelCase__  =   self._encode_prompt(__A   ,		__A   ,		__A					)

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

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

								lowerCAmelCase__  =   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					):
																						lowerCAmelCase__  =   k ^ 1

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

																						lowerCAmelCase__  =   coupled_latents[j]
																						lowerCAmelCase__  =   coupled_latents[k]

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

																						lowerCAmelCase__  =   self.unet(__A   ,		__A   ,		encoder_hidden_states=__A					).sample

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

																						lowerCAmelCase__  ,lowerCAmelCase__  =   self.denoise_step(
																						    base=__A   ,		model_input=__A   ,		model_output=__A   ,		timestep=__A   ,		)

																						lowerCAmelCase__  =   model_input

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

								# either one is fine
								lowerCAmelCase__  =   coupled_latents[0]

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

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

								return image





