


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__	(   _A	):
				def __init__(	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase = 0.93  ,   _UpperCAmelCase = True  ,   ):




										"""simple docstring"""




										__magic_name__					:				Tuple				=   mixing_coeff
										__magic_name__					:				int				=   leapfrog_steps

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

										__magic_name__					:				Optional[int]				=   2 ** (len(self.vae.config.block_out_channels   ) - 1)
										__magic_name__					:				Tuple				=   VaeImageProcessor(vae_scale_factor=self.vae_scale_factor   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase = None  ,   _UpperCAmelCase = False   ):




										"""simple docstring"""




										__magic_name__					:				Optional[Any]				=   self.tokenizer(
										    _UpperCAmelCase  ,   padding='max_length'  ,   max_length=self.tokenizer.model_max_length  ,   truncation=_UpperCAmelCase  ,   return_tensors='pt'  ,   )

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

										__magic_name__					:				Union[str, Any]				=   prompt_embeds.to(dtype=self.text_encoder.dtype  ,   device=self.device   )

										if do_classifier_free_guidance:
																__magic_name__					:				str				=   '' if negative_prompt is None else negative_prompt

																__magic_name__					:				str				=   self.tokenizer(
																    _UpperCAmelCase  ,   padding='max_length'  ,   max_length=self.tokenizer.model_max_length  ,   truncation=_UpperCAmelCase  ,   return_tensors='pt'  ,   )

																__magic_name__					:				List[Any]				=   self.text_encoder(uncond_input.input_ids.to(self.device   )   ).last_hidden_state

																__magic_name__					:				int				=   torch.cat([negative_prompt_embeds, prompt_embeds]   )

										return prompt_embeds
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Dict				=   self.mixing_coeff * x + (1 - self.mixing_coeff) * y
										__magic_name__					:				int				=   self.mixing_coeff * y + (1 - self.mixing_coeff) * x

										return [x, y]
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Optional[int]				=   (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
										__magic_name__					:				str				=   (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

										return [x, y]
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				str				=   int(_UpperCAmelCase   )

										__magic_name__					:				Optional[Any]				=   self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

										return alpha_prod, 1 - alpha_prod
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   ):




										"""simple docstring"""




										__magic_name__					:				Optional[int]				=   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

										__magic_name__		,			__magic_name__					:				List[Any]				=   self._get_alpha_and_beta(_UpperCAmelCase   )
										__magic_name__		,			__magic_name__					:				Optional[Any]				=   self._get_alpha_and_beta(_UpperCAmelCase   )

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

										__magic_name__					:				Union[str, Any]				=   (base - b_t * model_output) / a_t

										return model_input, next_model_input.to(base.dtype   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   ):




										"""simple docstring"""




										__magic_name__					:				Tuple				=   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

										__magic_name__		,			__magic_name__					:				Tuple				=   self._get_alpha_and_beta(_UpperCAmelCase   )
										__magic_name__		,			__magic_name__					:				List[Any]				=   self._get_alpha_and_beta(_UpperCAmelCase   )

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

										return model_input, next_model_input.to(base.dtype   )
				@torch.no_grad()
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				int				=   1 / self.vae.config.scaling_factor * latents
										__magic_name__					:				List[str]				=   self.vae.decode(_UpperCAmelCase   ).sample
										__magic_name__					:				Optional[Any]				=   (image / 2 + 0.5).clamp(0  ,   1   )
										return image
				@torch.no_grad()
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase = None  ,   ):




										"""simple docstring"""




										__magic_name__					:				Union[str, Any]				=   guidance_scale > 1.0

										__magic_name__					:				Tuple				=   image.to(device=self.device  ,   dtype=text_embeds.dtype   )
										__magic_name__					:				Optional[Any]				=   self.vae.encode(_UpperCAmelCase   ).latent_dist.sample(_UpperCAmelCase   )

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

										__magic_name__					:				List[Any]				=   [latent.clone(), latent.clone()]

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

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

																						if self.leapfrog_steps:
																												if i % 2 == 0:
																																		__magic_name__		,			__magic_name__					:				Optional[Any]				=   j, k

																						__magic_name__					:				Tuple				=   coupled_latents[j]
																						__magic_name__					:				int				=   coupled_latents[k]

																						__magic_name__					:				str				=   torch.cat([model_input] * 2   ) if do_classifier_free_guidance else model_input

																						__magic_name__					:				Optional[int]				=   self.unet(_UpperCAmelCase  ,   _UpperCAmelCase  ,   encoder_hidden_states=_UpperCAmelCase   ).sample

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

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

																						__magic_name__					:				Union[str, Any]				=   model_input

										return coupled_latents





				@torch.no_grad()
				def __call__(	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase = 3.0  ,   _UpperCAmelCase = 50  ,   _UpperCAmelCase = 0.8  ,   _UpperCAmelCase = None  ,   _UpperCAmelCase = None  ,   _UpperCAmelCase = "pil"  ,   ):




										"""simple docstring"""




										__magic_name__					:				List[Any]				=   guidance_scale > 1.0

										__magic_name__					:				Any				=   self.image_processor.preprocess(_UpperCAmelCase   )

										__magic_name__					:				Any				=   self._encode_prompt(_UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   )
										__magic_name__					:				str				=   self._encode_prompt(_UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   )

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

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

										__magic_name__					:				str				=   self.prepare_latents(_UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   )

										for i, t in tqdm(enumerate(_UpperCAmelCase   )  ,   total=len(_UpperCAmelCase   )   ):
																# j - model_input index, k - base index
																for k in range(2   ):
																						__magic_name__					:				List[Any]				=   k ^ 1

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

																						__magic_name__					:				str				=   coupled_latents[j]
																						__magic_name__					:				Dict				=   coupled_latents[k]

																						__magic_name__					:				Tuple				=   torch.cat([model_input] * 2   ) if do_classifier_free_guidance else model_input

																						__magic_name__					:				Tuple				=   self.unet(_UpperCAmelCase  ,   _UpperCAmelCase  ,   encoder_hidden_states=_UpperCAmelCase   ).sample

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

																						__magic_name__		,			__magic_name__					:				Optional[int]				=   self.denoise_step(
																						    base=_UpperCAmelCase  ,   model_input=_UpperCAmelCase  ,   model_output=_UpperCAmelCase  ,   timestep=_UpperCAmelCase  ,   )

																						__magic_name__					:				Union[str, Any]				=   model_input

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

										# either one is fine
										__magic_name__					:				str				=   coupled_latents[0]

										if output_type not in ["latent", "pt", "np", "pil"]:
																__magic_name__					:				int				=   (
																    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'  ,   _UpperCAmelCase  ,   standard_warn=_UpperCAmelCase   )
																__magic_name__					:				int				=   'np'

										if output_type == "latent":
																__magic_name__					:				Dict				=   final_latent
										else:
																__magic_name__					:				str				=   self.decode_latents(_UpperCAmelCase   )
																__magic_name__					:				int				=   self.image_processor.postprocess(_UpperCAmelCase  ,   output_type=_UpperCAmelCase   )

										return image






from __future__ import annotations

import random
import string



class      snake_case__	:
				def __init__(	self  ,   _UpperCAmelCase = None   ):




										"""simple docstring"""




										__magic_name__					:				Union[str, Any]				=   passcode or self.__passcode_creator()
										__magic_name__					:				Any				=   self.__make_key_list()
										__magic_name__					:				Tuple				=   self.__make_shift_key()
				def __str__(	self   ):




										"""simple docstring"""




										return "".join(self.__passcode   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										for i in range(1  ,   len(_UpperCAmelCase   )  ,   2   ):
																iterlist[i] *= -1
										return iterlist
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   string.ascii_letters + string.digits
										__magic_name__					:				Optional[Any]				=   [random.choice(_UpperCAmelCase   ) for _ in range(random.randint(10  ,   20   )   )]
										return password
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   (
										    string.ascii_letters + string.digits + string.punctuation + ' \t\n'
										)

										__magic_name__					:				List[str]				=   []

										# creates points known as breakpoints to break the key_list_options at those
										# points and pivot each substring
										__magic_name__					:				List[Any]				=   sorted(set(self.__passcode   )   )
										__magic_name__					:				list[str]				=   []

										# algorithm for creating a new shuffled list, keys_l, out of key_list_options
										for i in key_list_options:
																temp_list.extend(_UpperCAmelCase   )

																# checking breakpoints at which to pivot temporary sublist and add it into
																# keys_l
																if i in breakpoints or i == key_list_options[-1]:
																						keys_l.extend(temp_list[::-1]   )
																						temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
										return keys_l
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Dict				=   sum(self.__neg_pos([ord(_UpperCAmelCase   ) for x in self.__passcode]   )   )
										return num if num > 0 else len(self.__passcode   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				List[Any]				=   ''

										# decoding shift like Caesar cipher algorithm implementing negative shift or
										# reverse shift or left shift
										for i in encoded_message:
																__magic_name__					:				int				=   self.__key_list.index(_UpperCAmelCase   )
																decoded_message += self.__key_list[
																    (position - self.__shift_key) % -len(self.__key_list   )
																]

										return decoded_message





				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				List[Any]				=   ''

										# encoding shift like Caesar cipher algorithm implementing positive shift or
										# forward shift or right shift
										for i in plaintext:
																__magic_name__					:				Dict				=   self.__key_list.index(_UpperCAmelCase   )
																encoded_message += self.__key_list[
																    (position + self.__shift_key) % len(self.__key_list   )
																]

										return encoded_message






def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:str = "Hello, this is a modified Caesar cipher"					)							->	str:
						__magic_name__					:				List[str]				=   ShuffledShiftCipher()
						return cipa.decrypt(cipa.encrypt(lowercase__					)					)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


