code: 
  
  
  
  
  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 							_lowerCAmelCase		(     UpperCamelCase__	):
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		)    ->      Any:
  											super().__init__()
  
  											self.register_modules(vae=snake_case_		,		text_encoder=snake_case_		,		tokenizer=snake_case_		,		unet=snake_case_		,		scheduler=snake_case_							)
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      Union[str, Any]:
  											with torch.no_grad():
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.vae.encode(tfms.ToTensor()(snake_case_							).unsqueeze(0							).to(self.device							) * 2 - 1							)
  																		SCREAMING_SNAKE_CASE			: int	     =0.1_8215 * latent.latent_dist.sample()
  											return latent
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      List[Any]:
  											SCREAMING_SNAKE_CASE			: str	     =(1 / 0.1_8215) * latent
  											with torch.no_grad():
  																		SCREAMING_SNAKE_CASE			: Tuple	     =self.vae.decode(snake_case_							).sample
  											SCREAMING_SNAKE_CASE			: List[Any]	     =(img / 2 + 0.5).clamp(0		,		1							)
  											SCREAMING_SNAKE_CASE			: Tuple	     =img.detach().cpu().permute(0		,		2		,		3		,		1							).numpy()
  											SCREAMING_SNAKE_CASE			: int	     =(img * 255).round().astype('''uint8'''							)
  											return Image.fromarray(img[0]							)
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      Union[str, Any]:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.tokenizer(
  											    snake_case_		,		padding='''max_length'''		,		max_length=self.tokenizer.model_max_length		,		truncation=snake_case_		,		return_tensors='''pt'''		,		)
  
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.text_encoder(text_input.input_ids.to(self.device							)							)[0]
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.tokenizer(
  											    ''''''		,		padding='''max_length'''		,		max_length=self.tokenizer.model_max_length		,		truncation=snake_case_		,		return_tensors='''pt'''		,		)
  
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]
  
  											return torch.cat([uncond_embedding, text_embedding]							)
  
  
  
  
  
  
  
  				def __call__( self		,		snake_case_		,		snake_case_		,		snake_case_ = 0.3		,		snake_case_ = 0.6		,		snake_case_ = 0.5		,		snake_case_ = 42		,		snake_case_ = 50		,		snake_case_ = 7.5		,		)    ->      Image.Image:
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =steps - int(kmin * steps							)
  											SCREAMING_SNAKE_CASE			: Any	     =steps - int(kmax * steps							)
  
  											SCREAMING_SNAKE_CASE			: Dict	     =self.prep_text(snake_case_							)
  
  											self.scheduler.set_timesteps(snake_case_							)
  
  											SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Tuple	     =img.size
  											SCREAMING_SNAKE_CASE			: List[Any]	     =self.encode(snake_case_							)
  
  											torch.manual_seed(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Any	     =torch.randn(
  											    (1, self.unet.config.in_channels, height // 8, width // 8)		,		).to(self.device							)
  
  											SCREAMING_SNAKE_CASE			: str	     =self.scheduler.add_noise(
  											    snake_case_		,		snake_case_		,		timesteps=self.scheduler.timesteps[tmax]		,		)
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =torch.cat([latents] * 2							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.scheduler.scale_model_input(snake_case_		,		self.scheduler.timesteps[tmax]							)
  
  											with torch.no_grad():
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.unet(
  																		    snake_case_		,		self.scheduler.timesteps[tmax]		,		encoder_hidden_states=snake_case_		,		).sample
  
  											SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: List[Any]	     =pred.chunk(2							)
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.scheduler.step(snake_case_		,		self.scheduler.timesteps[tmax]		,		snake_case_							).prev_sample
  
  											for i, t in enumerate(tqdm(self.scheduler.timesteps							)							):
  																		if i > tmax:
  																									if i < tmin:  # layout generation phase
  																																SCREAMING_SNAKE_CASE			: List[Any]	     =self.scheduler.add_noise(
  																																    snake_case_		,		snake_case_		,		timesteps=snake_case_		,		)
  
  																																SCREAMING_SNAKE_CASE			: str	     =(mix_factor * latents) + (
  																																    1 - mix_factor
  																																) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
  																																SCREAMING_SNAKE_CASE			: Any	     =torch.cat([input] * 2							)
  
  																									else:  # content generation phase
  																																SCREAMING_SNAKE_CASE			: Any	     =torch.cat([latents] * 2							)
  
  																									SCREAMING_SNAKE_CASE			: Optional[int]	     =self.scheduler.scale_model_input(snake_case_		,		snake_case_							)
  
  																									with torch.no_grad():
  																																SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.unet(
  																																    snake_case_		,		snake_case_		,		encoder_hidden_states=snake_case_		,		).sample
  
  																									SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: List[Any]	     =pred.chunk(2							)
  																									SCREAMING_SNAKE_CASE			: Optional[Any]	     =pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
  																									SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.scheduler.step(snake_case_		,		snake_case_		,		snake_case_							).prev_sample
  
  											return self.decode(snake_case_							)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  _A							    =	{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}
  
  try:
  							if not is_torch_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """RegNetForImageClassification""",
  							    """RegNetModel""",
  							    """RegNetPreTrainedModel""",
  							]
  
  try:
  							if not is_tf_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """TFRegNetForImageClassification""",
  							    """TFRegNetModel""",
  							    """TFRegNetPreTrainedModel""",
  							]
  
  try:
  							if not is_flax_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """FlaxRegNetForImageClassification""",
  							    """FlaxRegNetModel""",
  							    """FlaxRegNetPreTrainedModel""",
  							]
  
  
  if TYPE_CHECKING:
  							from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig
  
  							try:
  														if not is_torch_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_regnet import (
  														    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    RegNetForImageClassification,
  														    RegNetModel,
  														    RegNetPreTrainedModel,
  														)
  
  							try:
  														if not is_tf_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_tf_regnet import (
  														    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    TFRegNetForImageClassification,
  														    TFRegNetModel,
  														    TFRegNetPreTrainedModel,
  														)
  
  							try:
  														if not is_flax_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_flax_regnet import (
  														    FlaxRegNetForImageClassification,
  														    FlaxRegNetModel,
  														    FlaxRegNetPreTrainedModel,
  														)
  
  
  else:
  							import sys
  
  							_A							    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)
  
  
  
  
  
  
  