

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    ):







  """simple docstring"""




  def __init__(     self      :	Dict			,			UpperCAmelCase__      :	AutoencoderKL			,			UpperCAmelCase__      :	CLIPTextModel			,			UpperCAmelCase__      :	CLIPTokenizer			,			UpperCAmelCase__      :	UNetaDConditionModel			,			UpperCAmelCase__      :	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]			,			)   ->List[str]:
        """simple docstring"""







        super().__init__()

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


  def 	_lowercase (     self      :	str			,			UpperCAmelCase__      :	List[str]						)   ->Union[str, Any]:
        """simple docstring"""







        with torch.no_grad():
              SCREAMING_SNAKE_CASE	: int					       =				self.vae.encode(tfms.ToTensor()(UpperCAmelCase__						).unsqueeze(0						).to(self.device						) * 2 - 1						)
              SCREAMING_SNAKE_CASE	: Any					       =				0.1_82_15 * latent.latent_dist.sample()
        return latent


  def 	_lowercase (     self      :	int			,			UpperCAmelCase__      :	int						)   ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Tuple					       =				(1 / 0.1_82_15) * latent
        with torch.no_grad():
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.vae.decode(UpperCAmelCase__						).sample
        SCREAMING_SNAKE_CASE	: List[str]					       =				(img / 2 + 0.5).clamp(0			,			1						)
        SCREAMING_SNAKE_CASE	: Any					       =				img.detach().cpu().permute(0			,			2			,			3			,			1						).numpy()
        SCREAMING_SNAKE_CASE	: List[Any]					       =				(img * 2_5_5).round().astype("""uint8"""						)
        return Image.fromarray(img[0]						)


  def 	_lowercase (     self      :	str			,			UpperCAmelCase__      :	int						)   ->Optional[Any]:
        """simple docstring"""







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

        SCREAMING_SNAKE_CASE	: int					       =				self.text_encoder(text_input.input_ids.to(self.device						)						)[0]

        SCREAMING_SNAKE_CASE	: Dict					       =				self.tokenizer(
            """"""			,			padding="""max_length"""			,			max_length=self.tokenizer.model_max_length			,			truncation=UpperCAmelCase__			,			return_tensors="""pt"""			,			)

        SCREAMING_SNAKE_CASE	: List[str]					       =				self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]

        return torch.cat([uncond_embedding, text_embedding]						)




  def __call__(     self      :	Optional[Any]			,			UpperCAmelCase__      :	Image.Image			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	float = 0.3			,			UpperCAmelCase__      :	float = 0.6			,			UpperCAmelCase__      :	float = 0.5			,			UpperCAmelCase__      :	int = 4_2			,			UpperCAmelCase__      :	int = 5_0			,			UpperCAmelCase__      :	float = 7.5			,			)   ->Image.Image:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: str					       =				steps - int(kmin * steps						)
        SCREAMING_SNAKE_CASE	: Any					       =				steps - int(kmax * steps						)

        SCREAMING_SNAKE_CASE	: Any					       =				self.prep_text(UpperCAmelCase__						)

        self.scheduler.set_timesteps(UpperCAmelCase__						)

        SCREAMING_SNAKE_CASE	: Any					       =				img.size
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.encode(UpperCAmelCase__						)

        torch.manual_seed(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)			,			).to(self.device						)

        SCREAMING_SNAKE_CASE	: Optional[int]					       =				self.scheduler.add_noise(
            UpperCAmelCase__			,			UpperCAmelCase__			,			timesteps=self.scheduler.timesteps[tmax]			,			)

        SCREAMING_SNAKE_CASE	: List[str]					       =				torch.cat([latents] * 2						)

        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.scheduler.scale_model_input(UpperCAmelCase__			,			self.scheduler.timesteps[tmax]						)

        with torch.no_grad():
              SCREAMING_SNAKE_CASE	: List[str]					       =				self.unet(
                  UpperCAmelCase__			,			self.scheduler.timesteps[tmax]			,			encoder_hidden_states=UpperCAmelCase__			,			).sample

        SCREAMING_SNAKE_CASE	: Optional[int]					       =				pred.chunk(2						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				pred_uncond + guidance_scale * (pred_text - pred_uncond)

        SCREAMING_SNAKE_CASE	: Dict					       =				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
                          SCREAMING_SNAKE_CASE	: str					       =				self.scheduler.add_noise(
                              UpperCAmelCase__			,			UpperCAmelCase__			,			timesteps=UpperCAmelCase__			,			)

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

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

                    SCREAMING_SNAKE_CASE	: Tuple					       =				self.scheduler.scale_model_input(UpperCAmelCase__			,			UpperCAmelCase__						)

                    with torch.no_grad():
                          SCREAMING_SNAKE_CASE	: List[Any]					       =				self.unet(
                              UpperCAmelCase__			,			UpperCAmelCase__			,			encoder_hidden_states=UpperCAmelCase__			,			).sample

                    SCREAMING_SNAKE_CASE	: List[Any]					       =				pred.chunk(2						)
                    SCREAMING_SNAKE_CASE	: Any					       =				pred_uncond + guidance_scale * (pred_text - pred_uncond)

                    SCREAMING_SNAKE_CASE	: int					       =				self.scheduler.step(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__						).prev_sample

        return self.decode(UpperCAmelCase__						)







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment







class     a__    (				unittest.TestCase    ):







  """simple docstring"""




  def 	_lowercase (     self      :	Optional[int]						)   ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				inspect.getfile(accelerate.test_utils						)
        SCREAMING_SNAKE_CASE	: int					       =				os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ["""scripts""", """test_sync.py"""]						)


  @require_cpu
  def 	_lowercase (     self      :	Optional[int]						)   ->str:
        """simple docstring"""







        debug_launcher(test_sync.main			,			num_processes=1						)


  @require_cpu
  def 	_lowercase (     self      :	Optional[int]						)   ->int:
        """simple docstring"""







        debug_launcher(test_sync.main						)


  @require_single_gpu
  def 	_lowercase (     self      :	str						)   ->Any:
        """simple docstring"""







        test_sync.main()




  @require_multi_gpu
  def 	_lowercase (     self      :	Union[str, Any]						)   ->List[Any]:
        """simple docstring"""







        print(f"Found {torch.cuda.device_count()} devices."						)
        SCREAMING_SNAKE_CASE	: List[str]					       =				["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
        with patch_environment(omp_num_threads=1						):
              execute_subprocess_async(UpperCAmelCase__			,			env=os.environ.copy()						)




