




"""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


A_						=				transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

  if isinstance(snake_case__		,   torch.Tensor    ):
    return image
  elif isinstance(snake_case__		,   PIL.Image.Image    ):
    _snake_case							:		Tuple								=  [image]

  _snake_case							:		str								=  [trans(img.convert("""RGB"""    )    ) for img in image]
  _snake_case							:		Dict								=  torch.stack(snake_case__    )
  return image






class 					lowercase(     __a ):







 '''simple docstring'''





 def __init__(				self:		Tuple,      a_:		Optional[Any],      a_:		int		):




   '''simple docstring'''



   super().__init__()

   # make sure scheduler can always be converted to DDIM
   _snake_case							:		Union[str, Any]								=  DDIMScheduler.from_config(scheduler.config		)

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


 def  UpperCamelCase_   (				self:		Tuple,      a_:		Tuple		):




   '''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:		str,      a_:		Union[str, Any],      a_:		Optional[Any],      a_:		int		):




   '''simple docstring'''



   _snake_case							:		str								=  min(int(num_inference_steps * strength		),      a_		)

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

   return timesteps, num_inference_steps - t_start


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		str,      a_:		List[Any],      a_:		str,      a_:		Optional[int],      a_:		str,      a_:		Optional[Any]=None		):




   '''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_		)}"		)

   _snake_case							:		Any								=  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."		)

   _snake_case							:		Any								=  init_latents.shape
   _snake_case							:		List[str]								=  randn_tensor(a_,      generator=a_,      device=a_,      dtype=a_		)

   # get latents
   print("""add noise to latents at timestep""",      a_		)
   _snake_case							:		Dict								=  self.scheduler.add_noise(a_,      a_,      a_		)
   _snake_case							:		str								=  init_latents

   return latents


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




   '''simple docstring'''



   self.check_inputs(a_		)

   # 2. Preprocess image
   _snake_case							:		Dict								=  preprocess(a_		)

   # 3. set timesteps
   self.scheduler.set_timesteps(a_,      device=self.device		)
   _snake_case		,					_snake_case							:		Optional[Any]								=  self.get_timesteps(a_,      a_,      self.device		)
   _snake_case							:		Optional[int]								=  timesteps[:1].repeat(a_		)

   # 4. Prepare latent variables
   _snake_case							:		Dict								=  self.prepare_latents(a_,      a_,      a_,      self.unet.dtype,      self.device,      a_		)
   _snake_case							:		Optional[int]								=  latents

   # 5. Denoising loop
   for t in self.progress_bar(a_		):
     # 1. predict noise model_output
     _snake_case							:		Dict								=  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
     _snake_case							:		Dict								=  self.scheduler.step(
         a_,      a_,      a_,      eta=a_,      use_clipped_model_output=a_,      generator=a_,      ).prev_sample

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

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

   return ImagePipelineOutput(images=a_		)








"""simple docstring"""


import os
from typing import Dict, List, Tuple, TypeVar, Union


A_						=				TypeVar('''T''')

A_						=				Union[List[T], Tuple[T, ...]]
A_						=				Union[T, List[T], Dict[str, T]]
A_						=				Union[str, bytes, os.PathLike]


