


"""simple docstring"""






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	List[Any]:







								'''simple docstring'''




								super().__init__()
								self.register_modules(unet=lowerCamelCase__	,					scheduler=lowerCamelCase__)







							@torch.no_grad()
							def __call__(						self	,					lowerCamelCase__ = 1	,					lowerCamelCase__ = None	,					lowerCamelCase__ = 50	,					lowerCamelCase__ = "pil"	,					lowerCamelCase__ = True	,					**lowerCamelCase__	,					)   ->	Union[ImagePipelineOutput, Tuple]:







								'''simple docstring'''




								snake_case__  :						Any		=      torch.randn(
								    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)	,					generator=lowerCamelCase__	,					)
								snake_case__  :						Optional[int]		=      image.to(self.device)

								# set step values
								self.scheduler.set_timesteps(lowerCamelCase__)

								for t in self.progress_bar(self.scheduler.timesteps):
									# 1. predict noise model_output
									snake_case__  :						List[str]		=      self.unet(lowerCamelCase__	,					lowerCamelCase__).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									snake_case__  :						List[Any]		=      self.scheduler.step(lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__).prev_sample

								snake_case__  :						Optional[int]		=      (image / 2 + 0.5).clamp(0	,					1)
								snake_case__  :						List[Any]		=      image.cpu().permute(0	,					2	,					3	,					1).numpy()
								if output_type == "pil":
									snake_case__  :						Any		=      self.numpy_to_pil(lowerCamelCase__)

								if not return_dict:
									return (image,), "This is a local test"

								return ImagePipelineOutput(images=lowerCamelCase__), "This is a local test"






"""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			(	__lowerCAmelCase							):



      def __init__(		self       ,     lowercase       ,     lowercase       ,     lowercase       ,     lowercase       ,     lowercase       ,     )      ->     Tuple:





        '''simple docstring'''





        super().__init__()

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



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     int:





        '''simple docstring'''





        with torch.no_grad():
          a__:	List[str]     				=      self.vae.encode(tfms.ToTensor()(lowercase).unsqueeze(0).to(self.device) * 2 - 1)
          a__:	Optional[Any]     				=      0.18215 * latent.latent_dist.sample()
        return latent



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     List[str]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      (1 / 0.18215) * latent
        with torch.no_grad():
          a__:	Optional[int]     				=      self.vae.decode(lowercase).sample
        a__:	Dict     				=      (img / 2 + 0.5).clamp(0       ,     1)
        a__:	List[Any]     				=      img.detach().cpu().permute(0       ,     2       ,     3       ,     1).numpy()
        a__:	Tuple     				=      (img * 2_55).round().astype('uint8')
        return Image.fromarray(img[0])



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





        a__:	Any     				=      self.tokenizer(
            lowercase       ,     padding='max_length'       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowercase       ,     return_tensors='pt'       ,     )

        a__:	Optional[Any]     				=      self.text_encoder(text_input.input_ids.to(self.device))[0]

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

        a__:	Optional[int]     				=      self.text_encoder(uncond_input.input_ids.to(self.device))[0]

        return torch.cat([uncond_embedding, text_embedding])



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





        '''simple docstring'''





        a__:	Optional[Any]     				=      steps - int(kmin * steps)
        a__:	Optional[int]     				=      steps - int(kmax * steps)

        a__:	Optional[int]     				=      self.prep_text(lowercase)

        self.scheduler.set_timesteps(lowercase)

        a__				,       a__:	Union[str, Any]     				=      img.size
        a__:	str     				=      self.encode(lowercase)

        torch.manual_seed(lowercase)
        a__:	Optional[int]     				=      torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)       ,     ).to(self.device)

        a__:	Tuple     				=      self.scheduler.add_noise(
            lowercase       ,     lowercase       ,     timesteps=self.scheduler.timesteps[tmax]       ,     )

        a__:	List[Any]     				=      torch.cat([latents] * 2)

        a__:	List[str]     				=      self.scheduler.scale_model_input(lowercase       ,     self.scheduler.timesteps[tmax])

        with torch.no_grad():
          a__:	Optional[Any]     				=      self.unet(
              lowercase       ,     self.scheduler.timesteps[tmax]       ,     encoder_hidden_states=lowercase       ,     ).sample

        a__				,       a__:	List[str]     				=      pred.chunk(2)
        a__:	Union[str, Any]     				=      pred_uncond + guidance_scale * (pred_text - pred_uncond)

        a__:	int     				=      self.scheduler.step(lowercase       ,     self.scheduler.timesteps[tmax]       ,     lowercase).prev_sample

        for i, t in enumerate(tqdm(self.scheduler.timesteps)):
          if i > tmax:
            if i < tmin:  # layout generation phase
              a__:	List[Any]     				=      self.scheduler.add_noise(
                  lowercase       ,     lowercase       ,     timesteps=lowercase       ,     )

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

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

            a__:	Union[str, Any]     				=      self.scheduler.scale_model_input(lowercase       ,     lowercase)

            with torch.no_grad():
              a__:	Union[str, Any]     				=      self.unet(
                  lowercase       ,     lowercase       ,     encoder_hidden_states=lowercase       ,     ).sample

            a__				,       a__:	Union[str, Any]     				=      pred.chunk(2)
            a__:	Optional[Any]     				=      pred_uncond + guidance_scale * (pred_text - pred_uncond)

            a__:	List[str]     				=      self.scheduler.step(lowercase       ,     lowercase       ,     lowercase).prev_sample

        return self.decode(lowercase)


