



"""simple docstring"""






from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


__snake_case							:     Tuple   =					transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def     _lowercase       (						__snake_case			)		->     Union[str, Any]:
   if isinstance(__A      ,torch.Tensor			):
      return image
   elif isinstance(__A      ,PIL.Image.Image			):
      __lowerCAmelCase   :						List[str]				= [image]

   __lowerCAmelCase   :						int				= [trans(img.convert("RGB"			)			) for img in image]
   __lowerCAmelCase   :						Dict				= torch.stack(__A			)
   return image





class     A__     (						_snake_case							):




 '''simple docstring'''





 def __init__(  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Any)       -> Optional[Any]:

    """simple docstring"""

    super().__init__()

    # make sure scheduler can always be converted to DDIM
    __lowerCAmelCase   :						List[str]				= DDIMScheduler.from_config(scheduler.config)

    self.register_modules(unet=lowerCAmelCase__ ,    scheduler=lowerCAmelCase__)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	str)       -> Dict:

    """simple docstring"""

    if strength < 0 or strength > 1:
       raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}""")

 def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	Optional[Any])       -> Union[str, Any]:

    """simple docstring"""

    __lowerCAmelCase   :						int				= min(int(num_inference_steps * strength) ,    lowerCAmelCase__)

    __lowerCAmelCase   :						List[str]				= max(num_inference_steps - init_timestep ,    0)
    __lowerCAmelCase   :						Optional[Any]				= self.scheduler.timesteps[t_start:]

    return timesteps, num_inference_steps - t_start

 def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any] ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	Any=None)       -> Union[str, Any]:

    """simple docstring"""

    if not isinstance(lowerCAmelCase__ ,    (torch.Tensor, PIL.Image.Image, list)):
       raise ValueError(
           F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCAmelCase__)}""")

    __lowerCAmelCase   :						Optional[int]				= image.to(device=lowerCAmelCase__ ,    dtype=lowerCAmelCase__)

    if isinstance(lowerCAmelCase__ ,    lowerCAmelCase__) and len(lowerCAmelCase__) != batch_size:
       raise ValueError(
           F"""You have passed a list of generators of length {len(lowerCAmelCase__)}, but requested an effective batch"""
           F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""")

    __lowerCAmelCase   :						Union[str, Any]				= init_latents.shape
    __lowerCAmelCase   :						str				= randn_tensor(lowerCAmelCase__ ,    generator=lowerCAmelCase__ ,    device=lowerCAmelCase__ ,    dtype=lowerCAmelCase__)

    # get latents
    print("add noise to latents at timestep" ,    lowerCAmelCase__)
    __lowerCAmelCase   :						Optional[Any]				= self.scheduler.add_noise(lowerCAmelCase__ ,    lowerCAmelCase__ ,    lowerCAmelCase__)
    __lowerCAmelCase   :						Any				= init_latents

    return latents







 @torch.no_grad()
 def __call__(  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	Union[torch.FloatTensor, PIL.Image.Image] = None ,    _SCREAMING_SNAKE_CASE:	float = 0.8 ,    _SCREAMING_SNAKE_CASE:	int = 1 ,    _SCREAMING_SNAKE_CASE:	Optional[Union[torch.Generator, List[torch.Generator]]] = None ,    _SCREAMING_SNAKE_CASE:	float = 0.0 ,    _SCREAMING_SNAKE_CASE:	int = 50 ,    _SCREAMING_SNAKE_CASE:	Optional[bool] = None ,    _SCREAMING_SNAKE_CASE:	Optional[str] = "pil" ,    _SCREAMING_SNAKE_CASE:	bool = True ,    )       -> Union[ImagePipelineOutput, Tuple]:

    """simple docstring"""

    self.check_inputs(lowerCAmelCase__)

    # 2. Preprocess image
    __lowerCAmelCase   :						Optional[Any]				= preprocess(lowerCAmelCase__)

    # 3. set timesteps
    self.scheduler.set_timesteps(lowerCAmelCase__ ,    device=self.device)
    __lowerCAmelCase		,			__lowerCAmelCase   :						Tuple				= self.get_timesteps(lowerCAmelCase__ ,    lowerCAmelCase__ ,    self.device)
    __lowerCAmelCase   :						str				= timesteps[:1].repeat(lowerCAmelCase__)

    # 4. Prepare latent variables
    __lowerCAmelCase   :						int				= self.prepare_latents(lowerCAmelCase__ ,    lowerCAmelCase__ ,    lowerCAmelCase__ ,    self.unet.dtype ,    self.device ,    lowerCAmelCase__)
    __lowerCAmelCase   :						Tuple				= latents

    # 5. Denoising loop
    for t in self.progress_bar(lowerCAmelCase__):
       # 1. predict noise model_output
       __lowerCAmelCase   :						Optional[Any]				= 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
       __lowerCAmelCase   :						int				= self.scheduler.step(
           lowerCAmelCase__ ,    lowerCAmelCase__ ,    lowerCAmelCase__ ,    eta=lowerCAmelCase__ ,    use_clipped_model_output=lowerCAmelCase__ ,    generator=lowerCAmelCase__ ,    ).prev_sample

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

    if not return_dict:
       return (image, latent_timestep.item())

    return ImagePipelineOutput(images=lowerCAmelCase__)




"""simple docstring"""






from ..utils import DummyObject, requires_backends



class     A__     (						metaclass=__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			['torch', 'scipy']

	def __init__(  self:	Tuple ,    *_SCREAMING_SNAKE_CASE:	Dict ,    **_SCREAMING_SNAKE_CASE:	str)       -> Union[str, Any]:

				"""simple docstring"""

				requires_backends(self ,    ["torch", "scipy"])

	@classmethod
	def 							_SCREAMING_SNAKE_CASE     (  cls:	Optional[Any] ,    *_SCREAMING_SNAKE_CASE:	Tuple ,    **_SCREAMING_SNAKE_CASE:	Optional[Any])       -> Optional[Any]:

				"""simple docstring"""

				requires_backends(cls ,    ["torch", "scipy"])







	@classmethod
	def 							_SCREAMING_SNAKE_CASE     (  cls:	int ,    *_SCREAMING_SNAKE_CASE:	List[Any] ,    **_SCREAMING_SNAKE_CASE:	Dict)       -> Union[str, Any]:

				"""simple docstring"""

				requires_backends(cls ,    ["torch", "scipy"])