

def 	snake_case_      (__A     :						List[str]     )			->	Tuple:
   __lowerCAmelCase  :			Optional[int]					    =						5_3_8_1
   for x in s:
      __lowerCAmelCase  :			List[str]					    =						((hash_value << 5) + hash_value) + ord(__A     )
   return hash_value & 0XFF_FF_FF_FF










"""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      UpperCamelCase_       (				a_     ):



  def __init__(					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = 0.93       ,						snake_case__ = True       ,						)	->	Optional[Any]:



         """simple docstring"""





         UpperCAmelCase					=	mixing_coeff
         UpperCAmelCase					=	leapfrog_steps

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

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



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = None       ,						snake_case__ = False )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	self.tokenizer(
             snake_case__       ,						padding="""max_length"""       ,						max_length=self.tokenizer.model_max_length       ,						truncation=snake_case__       ,						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(
                    snake_case__       ,						padding="""max_length"""       ,						max_length=self.tokenizer.model_max_length       ,						truncation=snake_case__       ,						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 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ )	->	List[Any]:



         """simple docstring"""





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

         return [x, y]



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ )	->	Dict:



         """simple docstring"""





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

         return [x, y]



  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	int(snake_case__ )

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

         return alpha_prod, 1 - alpha_prod



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						)	->	int:



         """simple docstring"""





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

         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )
         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )

         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 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						)	->	str:



         """simple docstring"""





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

         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )
         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )

         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 						UpperCamelCase_ (					self       ,						snake_case__ )	->	str:



         """simple docstring"""





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



  @torch.no_grad()
  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = None       ,						)	->	List[Any]:



         """simple docstring"""





         UpperCAmelCase					=	guidance_scale > 1.0

         UpperCAmelCase					=	image.to(device=self.device       ,						dtype=text_embeds.dtype )
         UpperCAmelCase					=	self.vae.encode(snake_case__ ).latent_dist.sample(snake_case__ )

         UpperCAmelCase					=	self.vae.config.scaling_factor * latent

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

         for i, t in tqdm(enumerate(snake_case__ )       ,						total=len(snake_case__ ) ):
                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(snake_case__       ,						snake_case__       ,						encoder_hidden_states=snake_case__ ).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=snake_case__       ,						model_input=snake_case__       ,						model_output=snake_case__       ,						timestep=snake_case__       ,						)

                       UpperCAmelCase					=	model_input

         return coupled_latents



  @torch.no_grad()
  def __call__(					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = 3.0       ,						snake_case__ = 50       ,						snake_case__ = 0.8       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = "pil"       ,						)	->	str:



         """simple docstring"""





         UpperCAmelCase					=	guidance_scale > 1.0

         UpperCAmelCase					=	self.image_processor.preprocess(snake_case__ )

         UpperCAmelCase					=	self._encode_prompt(snake_case__       ,						snake_case__       ,						snake_case__ )
         UpperCAmelCase					=	self._encode_prompt(snake_case__       ,						snake_case__       ,						snake_case__ )

         self.scheduler.set_timesteps(snake_case__       ,						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(snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ )

         for i, t in tqdm(enumerate(snake_case__ )       ,						total=len(snake_case__ ) ):
                # 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(snake_case__       ,						snake_case__       ,						encoder_hidden_states=snake_case__ ).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=snake_case__       ,						model_input=snake_case__       ,						model_output=snake_case__       ,						timestep=snake_case__       ,						)

                       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"""       ,						snake_case__       ,						standard_warn=snake_case__ )
                UpperCAmelCase					=	"""np"""

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

         return image
