



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 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Optional[Any] )     ->   Union[str, Any]:



 if isinstance(SCREAMING_SNAKE_CASE__ ,				torch.Tensor ):
  return image
 elif isinstance(SCREAMING_SNAKE_CASE__ ,				PIL.Image.Image ):
  _snake_case			:     Union[str, Any] 		=    [image]

 _snake_case			:     Tuple 		=    [trans(img.convert("""RGB""" ) ) for img in image]
 _snake_case			:     int 		=    torch.stack(SCREAMING_SNAKE_CASE__ )
 return image







class 						snake_case  (		_a     ):



   '''simple docstring'''






   def __init__(    self							:	Any ,						lowerCAmelCase							:	List[Any] ,						lowerCAmelCase							:	Any) -> List[str]:

    """simple docstring"""







    super().__init__()

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

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

   def    UpperCamelCase_     (    self							:	Dict ,						lowerCAmelCase							:	List[str]) -> 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    UpperCamelCase_     (    self							:	Optional[Any] ,						lowerCAmelCase							:	Any ,						lowerCAmelCase							:	Optional[Any] ,						lowerCAmelCase							:	List[Any]) -> Optional[Any]:

    """simple docstring"""







    _snake_case			:     Optional[Any] 		=    min(int(num_inference_steps * strength) ,						__lowerCamelCase)

    _snake_case			:     Dict 		=    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							:	List[str] ,						lowerCAmelCase							:	Union[str, Any] ,						lowerCAmelCase							:	str ,						lowerCAmelCase							:	Optional[int] ,						lowerCAmelCase							:	str ,						lowerCAmelCase							:	Dict ,						lowerCAmelCase							:	Optional[int]=None) -> Optional[int]:

    """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)}''')

    _snake_case			:     Tuple 		=    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.''')

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

    # get latents
    print("""add noise to latents at timestep""" ,						__lowerCamelCase)
    _snake_case			:     List[Any] 		=    self.scheduler.add_noise(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)
    _snake_case			:     Tuple 		=    init_latents

    return latents


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

    """simple docstring"""







    self.check_inputs(__lowerCamelCase)

    # 2. Preprocess image
    _snake_case			:     Any 		=    preprocess(__lowerCamelCase)

    # 3. set timesteps
    self.scheduler.set_timesteps(__lowerCamelCase ,						device=self.device)
    _snake_case			:     Dict 		=    self.get_timesteps(__lowerCamelCase ,						__lowerCamelCase ,						self.device)
    _snake_case			:     List[Any] 		=    timesteps[:1].repeat(__lowerCamelCase)

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

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

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

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

    return ImagePipelineOutput(images=__lowerCamelCase)








def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  list[list[int]]	,			__magic_name__					:  int	,			__magic_name__					:  int	,			__magic_name__					:  list[int]					)		->			bool:






	"""simple docstring"""



	if graph[path[curr_ind - 1]][next_ver] == 0:
		return False

	# 2. Validate that next vertex is not already in path
	return not any(vertex == next_ver for vertex in path					)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  list[list[int]]	,			__magic_name__					:  list[int]	,			__magic_name__					:  int					)		->			bool:






	"""simple docstring"""



	if curr_ind == len(__magic_name__					):
		# return whether path exists between current and starting vertices
		return graph[path[curr_ind - 1]][path[0]] == 1

	# Recursive Step
	for next_ver in range(0	,			len(__magic_name__					)					):
		if valid_connection(__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__					):
			# Insert current vertex  into path as next transition
			UpperCamelCase     :str          =	next_ver
			# Validate created path
			if util_hamilton_cycle(__magic_name__	,			__magic_name__	,			curr_ind + 1					):
				return True
			# Backtrack
			UpperCamelCase     :Union[str, Any]          =	-1
	return False

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  list[list[int]]	,			__magic_name__					:  int = 0					)		->			list[int]:






	"""simple docstring"""



	UpperCamelCase     :Union[str, Any]          =	[-1] * (len(__magic_name__					) + 1)
	# initialize start and end of path with starting index
	UpperCamelCase     :Any          =	start_index
	# evaluate and if we find answer return path either return empty array
	return path if util_hamilton_cycle(__magic_name__	,			__magic_name__	,			1					) else []
