


"""simple docstring"""




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 __snake_case		(					_SCREAMING_SNAKE_CASE      ):







   """simple docstring"""




   def __init__(		self							:List[Any]				,  UpperCamelCase__							:AutoencoderKL				,  UpperCamelCase__							:CLIPTextModel				,  UpperCamelCase__							:CLIPTokenizer				,  UpperCamelCase__							:UNetaDConditionModel				,  UpperCamelCase__							:Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]				,  ):
          super().__init__()

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




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:str       ):
          with torch.no_grad():
                 _a          =				self.vae.encode(tfms.ToTensor()(UpperCamelCase__       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
                 _a          =				0.18215 * latent.latent_dist.sample()
          return latent




   def SCREAMING_SNAKE_CASE_      (		self							:List[Any]				,  UpperCamelCase__							:Any       ):
          _a          =				(1 / 0.18215) * latent
          with torch.no_grad():
                 _a          =				self.vae.decode(UpperCamelCase__       ).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 SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:List[Any]       ):
          _a          =				self.tokenizer(
              UpperCamelCase__				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  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=UpperCamelCase__				,  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							:Tuple				,  UpperCamelCase__							:Image.Image				,  UpperCamelCase__							:str				,  UpperCamelCase__							:float = 0.3				,  UpperCamelCase__							:float = 0.6				,  UpperCamelCase__							:float = 0.5				,  UpperCamelCase__							:int = 42				,  UpperCamelCase__							:int = 50				,  UpperCamelCase__							:float = 7.5				,  ):
          _a          =				steps - int(kmin * steps       )
          _a          =				steps - int(kmax * steps       )

          _a          =				self.prep_text(UpperCamelCase__       )

          self.scheduler.set_timesteps(UpperCamelCase__       )

          _a   , _a          =				img.size
          _a          =				self.encode(UpperCamelCase__       )

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

          _a          =				self.scheduler.add_noise(
              UpperCamelCase__				,  UpperCamelCase__				,  timesteps=self.scheduler.timesteps[tmax]				,  )

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

          _a          =				self.scheduler.scale_model_input(UpperCamelCase__				,  self.scheduler.timesteps[tmax]       )

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

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

          _a          =				self.scheduler.step(UpperCamelCase__				,  self.scheduler.timesteps[tmax]				,  UpperCamelCase__       ).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(
                                   UpperCamelCase__				,  UpperCamelCase__				,  timesteps=UpperCamelCase__				,  )

                               _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(UpperCamelCase__				,  UpperCamelCase__       )

                        with torch.no_grad():
                               _a          =				self.unet(
                                   UpperCamelCase__				,  UpperCamelCase__				,  encoder_hidden_states=UpperCamelCase__				,  ).sample

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

                        _a          =				self.scheduler.step(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       ).prev_sample

          return self.decode(UpperCamelCase__       )




"""simple docstring"""




import warnings

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


__SCREAMING_SNAKE_CASE					=						logging.get_logger(__name__)





class __snake_case		(					_SCREAMING_SNAKE_CASE      ):







   """simple docstring"""




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