
'''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 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Dict					, lowerCamelCase      :     AutoencoderKL					, lowerCamelCase      :     CLIPTextModel					, lowerCamelCase      :     CLIPTokenizer					, lowerCamelCase      :     UNetaDConditionModel					, lowerCamelCase      :     DDIMScheduler					, lowerCamelCase      :     float = 0.93					, lowerCamelCase      :     bool = True					, ):



   """simple docstring"""





   __A		:  str       				=		mixing_coeff
   __A		:  Tuple       				=		leapfrog_steps

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

   __A		:  Any       				=		2 ** (len(self.vae.config.block_out_channels							) - 1)
   __A		:  Dict       				=		VaeImageProcessor(vae_scale_factor=self.vae_scale_factor							)
 def 		lowercase_( self      :     int					, lowerCamelCase      :     str					, lowerCamelCase      :     Optional[str] = None					, lowerCamelCase      :     bool = False							):



   """simple docstring"""





   __A		:  List[Any]       				=		self.tokenizer(
       lowerCamelCase					, padding="""max_length"""					, max_length=self.tokenizer.model_max_length					, truncation=lowerCamelCase					, return_tensors="""pt"""					, )

   __A		:  Optional[Any]       				=		self.text_encoder(text_inputs.input_ids.to(self.device							)							).last_hidden_state

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

   if do_classifier_free_guidance:
     __A		:  Dict       				=		"""""" if negative_prompt is None else negative_prompt

     __A		:  Union[str, Any]       				=		self.tokenizer(
         lowerCamelCase					, padding="""max_length"""					, max_length=self.tokenizer.model_max_length					, truncation=lowerCamelCase					, return_tensors="""pt"""					, )

     __A		:  Tuple       				=		self.text_encoder(uncond_input.input_ids.to(self.device							)							).last_hidden_state

     __A		:  Dict       				=		torch.cat([negative_prompt_embeds, prompt_embeds]							)

   return prompt_embeds
 def 		lowercase_( self      :     List[Any]					, lowerCamelCase      :     torch.Tensor					, lowerCamelCase      :     torch.Tensor							):



   """simple docstring"""





   __A		:  Optional[Any]       				=		self.mixing_coeff * x + (1 - self.mixing_coeff) * y
   __A		:  List[str]       				=		self.mixing_coeff * y + (1 - self.mixing_coeff) * x

   return [x, y]
 def 		lowercase_( self      :     Dict					, lowerCamelCase      :     torch.Tensor					, lowerCamelCase      :     torch.Tensor							):



   """simple docstring"""





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

   return [x, y]
 def 		lowercase_( self      :     List[str]					, lowerCamelCase      :     torch.Tensor							):



   """simple docstring"""





   __A		:  int       				=		int(lowerCamelCase							)

   __A		:  Optional[int]       				=		self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

   return alpha_prod, 1 - alpha_prod
 def 		lowercase_( self      :     Union[str, Any]					, lowerCamelCase      :     torch.Tensor					, lowerCamelCase      :     torch.Tensor					, lowerCamelCase      :     torch.Tensor					, lowerCamelCase      :     torch.Tensor					, ):



   """simple docstring"""





   __A		:  Union[str, Any]       				=		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

   __A				,       __A		:  str       				=		self._get_alpha_and_beta(lowerCamelCase							)
   __A				,       __A		:  Union[str, Any]       				=		self._get_alpha_and_beta(lowerCamelCase							)

   __A		:  int       				=		(alpha_prod_t_prev / alpha_prod_t) ** 0.5
   __A		:  Tuple       				=		-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

   __A		:  Dict       				=		(base - b_t * model_output) / a_t

   return model_input, next_model_input.to(base.dtype							)
 def 		lowercase_( self      :     str					, lowerCamelCase      :     torch.Tensor					, lowerCamelCase      :     torch.Tensor					, lowerCamelCase      :     torch.Tensor					, lowerCamelCase      :     torch.Tensor					, ):



   """simple docstring"""





   __A		:  str       				=		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

   __A				,       __A		:  Any       				=		self._get_alpha_and_beta(lowerCamelCase							)
   __A				,       __A		:  Union[str, Any]       				=		self._get_alpha_and_beta(lowerCamelCase							)

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

   return model_input, next_model_input.to(base.dtype							)
 @torch.no_grad()
 def 		lowercase_( self      :     Any					, lowerCamelCase      :     torch.Tensor							):



   """simple docstring"""





   __A		:  str       				=		1 / self.vae.config.scaling_factor * latents
   __A		:  Optional[int]       				=		self.vae.decode(lowerCamelCase							).sample
   __A		:  Optional[int]       				=		(image / 2 + 0.5).clamp(0					, 1							)
   return image
 @torch.no_grad()
 def 		lowercase_( self      :     Optional[int]					, lowerCamelCase      :     Image.Image					, lowerCamelCase      :     torch.Tensor					, lowerCamelCase      :     torch.Tensor					, lowerCamelCase      :     float					, lowerCamelCase      :     Optional[torch.Generator] = None					, ):



   """simple docstring"""





   __A		:  Optional[Any]       				=		guidance_scale > 1.0

   __A		:  Any       				=		image.to(device=self.device					, dtype=text_embeds.dtype							)
   __A		:  Union[str, Any]       				=		self.vae.encode(lowerCamelCase							).latent_dist.sample(lowerCamelCase							)

   __A		:  Dict       				=		self.vae.config.scaling_factor * latent

   __A		:  Optional[Any]       				=		[latent.clone(), latent.clone()]

   for i, t in tqdm(enumerate(lowerCamelCase							)					, total=len(lowerCamelCase							)							):
     __A		:  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							):
       __A		:  Dict       				=		j ^ 1

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

       __A		:  Dict       				=		coupled_latents[j]
       __A		:  int       				=		coupled_latents[k]

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

       __A		:  List[Any]       				=		self.unet(lowerCamelCase					, lowerCamelCase					, encoder_hidden_states=lowerCamelCase							).sample

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

       __A				,       __A		:  List[str]       				=		self.noise_step(
           base=lowerCamelCase					, model_input=lowerCamelCase					, model_output=lowerCamelCase					, timestep=lowerCamelCase					, )

       __A		:  Dict       				=		model_input

   return coupled_latents






 @torch.no_grad()
 def __call__( self      :     Union[str, Any]					, lowerCamelCase      :     str					, lowerCamelCase      :     str					, lowerCamelCase      :     Image.Image					, lowerCamelCase      :     float = 3.0					, lowerCamelCase      :     int = 50					, lowerCamelCase      :     float = 0.8					, lowerCamelCase      :     Optional[str] = None					, lowerCamelCase      :     Optional[torch.Generator] = None					, lowerCamelCase      :     Optional[str] = "pil"					, ):



   """simple docstring"""





   __A		:  List[str]       				=		guidance_scale > 1.0

   __A		:  str       				=		self.image_processor.preprocess(lowerCamelCase							)

   __A		:  Dict       				=		self._encode_prompt(lowerCamelCase					, lowerCamelCase					, lowerCamelCase							)
   __A		:  Tuple       				=		self._encode_prompt(lowerCamelCase					, lowerCamelCase					, lowerCamelCase							)

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

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

   __A		:  Optional[Any]       				=		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							):
       __A		:  int       				=		k ^ 1

       if self.leapfrog_steps:
         if i % 2 == 1:
           __A				,       __A		:  Optional[Any]       				=		j, k

       __A		:  List[Any]       				=		coupled_latents[j]
       __A		:  Any       				=		coupled_latents[k]

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

       __A		:  Union[str, Any]       				=		self.unet(lowerCamelCase					, lowerCamelCase					, encoder_hidden_states=lowerCamelCase							).sample

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

       __A				,       __A		:  List[Any]       				=		self.denoise_step(
           base=lowerCamelCase					, model_input=lowerCamelCase					, model_output=lowerCamelCase					, timestep=lowerCamelCase					, )

       __A		:  Union[str, Any]       				=		model_input

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

   # either one is fine
   __A		:  Tuple       				=		coupled_latents[0]

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

   if output_type == "latent":
     __A		:  int       				=		final_latent
   else:
     __A		:  Union[str, Any]       				=		self.decode_latents(lowerCamelCase							)
     __A		:  Any       				=		self.image_processor.postprocess(lowerCamelCase					, output_type=lowerCamelCase							)

   return image





'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


A__ :		Optional[int]					=logging.get_logger(__name__)







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Union[str, Any]					, *lowerCamelCase      :     Dict					, **lowerCamelCase      :     Optional[Any]							):



   """simple docstring"""





   warnings.warn(
       """The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
       """ use ViTImageProcessor instead."""					, lowerCamelCase					, )
   super().__init__(*lowerCamelCase					, **lowerCamelCase							)



