




from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)




class     a__ (      UpperCamelCase__   ):







     def __init__(			self						,	A						,	A						,	A						,	A						,	A						,	)						->					str:



          '''simple docstring'''



          super().__init__()

          self.register_modules(vae=A						,	text_encoder=A						,	tokenizer=A						,	unet=A						,	scheduler=A				)







     def     lowerCAmelCase_       (			self						,	A				)						->					Any:



          '''simple docstring'''



          with torch.no_grad():
               a											=   self.vae.encode(tfms.ToTensor()(A				).unsqueeze(0				).to(self.device				) * 2 - 1				)
               a											=   0.1_8_2_1_5 * latent.latent_dist.sample()
          return latent







     def     lowerCAmelCase_       (			self						,	A				)						->					str:



          '''simple docstring'''



          a											=   (1 / 0.1_8_2_1_5) * latent
          with torch.no_grad():
               a											=   self.vae.decode(A				).sample
          a											=   (img / 2 + 0.5).clamp(0						,	1				)
          a											=   img.detach().cpu().permute(0						,	2						,	3						,	1				).numpy()
          a											=   (img * 255).round().astype("uint8"				)
          return Image.fromarray(img[0]				)







     def     lowerCAmelCase_       (			self						,	A				)						->					Union[str, Any]:



          '''simple docstring'''



          a											=   self.tokenizer(
              A						,	padding="max_length"						,	max_length=self.tokenizer.model_max_length						,	truncation=A						,	return_tensors="pt"						,	)

          a											=   self.text_encoder(text_input.input_ids.to(self.device				)				)[0]

          a											=   self.tokenizer(
              ""						,	padding="max_length"						,	max_length=self.tokenizer.model_max_length						,	truncation=A						,	return_tensors="pt"						,	)

          a											=   self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]

          return torch.cat([uncond_embedding, text_embedding]				)







     def __call__(			self						,	A						,	A						,	A = 0.3						,	A = 0.6						,	A = 0.5						,	A = 42						,	A = 50						,	A = 7.5						,	)						->					Image.Image:



          '''simple docstring'''



          a											=   steps - int(kmin * steps				)
          a											=   steps - int(kmax * steps				)

          a											=   self.prep_text(A				)

          self.scheduler.set_timesteps(A				)

          a						,   a											=   img.size
          a											=   self.encode(A				)

          torch.manual_seed(A				)
          a											=   torch.randn(
              (1, self.unet.config.in_channels, height // 8, width // 8)						,	).to(self.device				)

          a											=   self.scheduler.add_noise(
              A						,	A						,	timesteps=self.scheduler.timesteps[tmax]						,	)

          a											=   torch.cat([latents] * 2				)

          a											=   self.scheduler.scale_model_input(A						,	self.scheduler.timesteps[tmax]				)

          with torch.no_grad():
               a											=   self.unet(
                   A						,	self.scheduler.timesteps[tmax]						,	encoder_hidden_states=A						,	).sample

          a						,   a											=   pred.chunk(2				)
          a											=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

          a											=   self.scheduler.step(A						,	self.scheduler.timesteps[tmax]						,	A				).prev_sample

          for i, t in enumerate(tqdm(self.scheduler.timesteps				)				):
               if i > tmax:
                    if i < tmin:  # layout generation phase
                         a											=   self.scheduler.add_noise(
                             A						,	A						,	timesteps=A						,	)

                         a											=   (mix_factor * latents) + (
                             1 - mix_factor
                         ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                         a											=   torch.cat([input] * 2				)

                    else:  # content generation phase
                         a											=   torch.cat([latents] * 2				)

                    a											=   self.scheduler.scale_model_input(A						,	A				)

                    with torch.no_grad():
                         a											=   self.unet(
                             A						,	A						,	encoder_hidden_states=A						,	).sample

                    a						,   a											=   pred.chunk(2				)
                    a											=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

                    a											=   self.scheduler.step(A						,	A						,	A				).prev_sample

          return self.decode(A				)







from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available




@dataclass
class     a__ (      UpperCamelCase__   ):
     a	:    Union[List[PIL.Image.Image], np.ndarray]


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
   from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
   from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline

