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((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def       lowerCAmelCase_     (		lowercase:							Dict		)    ->				str:







 '''simple docstring'''
 if isinstance(lowercase  ,					torch.Tensor		):
  return image
 elif isinstance(lowercase  ,					PIL.Image.Image		):
  _UpperCamelCase:     Any							= [image]

 _UpperCamelCase:     Tuple							= [trans(img.convert('''RGB'''		)		) for img in image]
 _UpperCamelCase:     str							= torch.stack(lowercase		)
 return image





class  __magic_name__  (		__a    ):





   """simple docstring"""




   def __init__(	self					:  List[str]		,  _lowercase					:  List[Any]		,  _lowercase					:  str							):



    """simple docstring"""



    super().__init__()

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

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



   def   lowerCAmelCase						(	self					:  Any		,  _lowercase					:  Optional[Any]							):



    """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   lowerCAmelCase						(	self					:  str		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  Dict		,  _lowercase					:  int							):



    """simple docstring"""



    _UpperCamelCase:     int							= min(int(num_inference_steps * strength							)		,  _lowercase							)

    _UpperCamelCase:     Tuple							= max(num_inference_steps - init_timestep		,  0							)
    _UpperCamelCase:     Dict							= self.scheduler.timesteps[t_start:]

    return timesteps, num_inference_steps - t_start



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  Optional[int]		,  _lowercase					:  Optional[Any]		,  _lowercase					:  Dict		,  _lowercase					:  List[str]		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  List[Any]=None							):



    """simple docstring"""



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

    _UpperCamelCase:     Dict							= image.to(device=_lowercase		,  dtype=_lowercase							)

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

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

    # get latents
    print('''add noise to latents at timestep'''		,  _lowercase							)
    _UpperCamelCase:     List[Any]							= self.scheduler.add_noise(_lowercase		,  _lowercase		,  _lowercase							)
    _UpperCamelCase:     int							= init_latents

    return latents



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



    """simple docstring"""



    self.check_inputs(_lowercase							)

    # 2. Preprocess image
    _UpperCamelCase:     List[Any]							= preprocess(_lowercase							)

    # 3. set timesteps
    self.scheduler.set_timesteps(_lowercase		,  device=self.device							)
    _UpperCamelCase	,							_UpperCamelCase:     List[Any]							= self.get_timesteps(_lowercase		,  _lowercase		,  self.device							)
    _UpperCamelCase:     int							= timesteps[:1].repeat(_lowercase							)

    # 4. Prepare latent variables
    _UpperCamelCase:     str							= self.prepare_latents(_lowercase		,  _lowercase		,  _lowercase		,  self.unet.dtype		,  self.device		,  _lowercase							)
    _UpperCamelCase:     Dict							= latents

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

    _UpperCamelCase:     Dict							= (image / 2 + 0.5).clamp(0		,  1							)
    _UpperCamelCase:     str							= image.cpu().permute(0		,  2		,  3		,  1							).numpy()
    if output_type == "pil":
     _UpperCamelCase:     Any							= self.numpy_to_pil(_lowercase							)

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

    return ImagePipelineOutput(images=_lowercase							)
from __future__ import annotations




class  __magic_name__  :





   """simple docstring"""




   def __init__(	self					:  Any		,  _lowercase					:  Dict=None							):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= data
    _UpperCamelCase:     int							= None



   def __repr__(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     int							= []
    _UpperCamelCase:     str							= self
    while temp:
     string_rep.append(f"""{temp.data}"""							)
     _UpperCamelCase:     Optional[Any]							= temp.next
    return "->".join(_lowercase							)




def       lowerCAmelCase_     (		lowercase:							list		)    ->				Optional[int]:







 '''simple docstring'''
 if not elements_list:
  raise Exception('''The Elements List is empty'''		)

 _UpperCamelCase:     Optional[Any]							= Node(elements_list[0]		)
 for i in range(1  ,					len(lowercase		)		):
  _UpperCamelCase:     Union[str, Any]							= Node(elements_list[i]		)
  _UpperCamelCase:     Optional[int]							= current.next
 return head




def       lowerCAmelCase_     (		lowercase:							Node		)    ->				None:







 '''simple docstring'''
 if head_node is not None and isinstance(lowercase  ,					lowercase		):
  print_reverse(head_node.next		)
  print(head_node.data		)




def       lowerCAmelCase_     (		)    ->				Tuple:







 '''simple docstring'''
 from doctest import testmod

 testmod()

 _UpperCamelCase:     Any							= make_linked_list([14, 52, 14, 12, 43]		)
 print('''Linked List:'''		)
 print(lowercase		)
 print('''Elements in Reverse:'''		)
 print_reverse(lowercase		)


if __name__ == "__main__":
  main()