

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


_UpperCamelCase 			=		transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def 		_lowercase (     lowercase__						):
      if isinstance(lowercase__   ,	torch.Tensor						):
            return image
      elif isinstance(lowercase__   ,	PIL.Image.Image						):
            __lowerCAmelCase					:						List[str]					=					[image]

      __lowerCAmelCase					:						Union[str, Any]					=					[trans(img.convert('''RGB'''						)						) for img in image]
      __lowerCAmelCase					:						Dict					=					torch.stack(lowercase__						)
      return image







class   __lowercase      (_UpperCAmelCase ):
 def __init__(							self      ,							A_      ,							A_					)					->Optional[int]:



       '''simple docstring'''





       super().__init__()

       # make sure scheduler can always be converted to DDIM
       __lowerCAmelCase					:						int					=					DDIMScheduler.from_config(scheduler.config					)

       self.register_modules(unet=A_      ,							scheduler=A_					)
 def 	UpperCamelCase__  (							self      ,							A_					)					->str:



       '''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 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					min(int(num_inference_steps * strength					)      ,							A_					)

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

       return timesteps, num_inference_steps - t_start
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_=None					)					->Optional[Any]:



       '''simple docstring'''





       if not isinstance(A_      ,							(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(A_					)}"""					)

       __lowerCAmelCase					:						List[str]					=					image.to(device=A_      ,							dtype=A_					)

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

       __lowerCAmelCase					:						Optional[int]					=					init_latents.shape
       __lowerCAmelCase					:						Any					=					randn_tensor(A_      ,							generator=A_      ,							device=A_      ,							dtype=A_					)

       # get latents
       print('''add noise to latents at timestep'''      ,							A_					)
       __lowerCAmelCase					:						Optional[int]					=					self.scheduler.add_noise(A_      ,							A_      ,							A_					)
       __lowerCAmelCase					:						Optional[int]					=					init_latents

       return latents





 @torch.no_grad()
 def __call__(							self      ,							A_ = None      ,							A_ = 0.8      ,							A_ = 1      ,							A_ = None      ,							A_ = 0.0      ,							A_ = 50      ,							A_ = None      ,							A_ = "pil"      ,							A_ = True      ,							)					->Union[ImagePipelineOutput, Tuple]:



       '''simple docstring'''





       self.check_inputs(A_					)

       # 2. Preprocess image
       __lowerCAmelCase					:						Dict					=					preprocess(A_					)

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

       # 4. Prepare latent variables
       __lowerCAmelCase					:						List[Any]					=					self.prepare_latents(A_      ,							A_      ,							A_      ,							self.unet.dtype      ,							self.device      ,							A_					)
       __lowerCAmelCase					:						str					=					latents

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

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

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

       return ImagePipelineOutput(images=A_					)





def 		_lowercase (     lowercase__   ,	lowercase__						):

      if density <= 0:
            raise ValueError('''Impossible fluid density'''						)
      if bulk_modulus <= 0:
            raise ValueError('''Impossible bulk modulus'''						)

      return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
 import doctest

 doctest.testmod()


