






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((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def      __A					(     _lowercase ):

					'''simple docstring'''







					if isinstance(_lowercase					,			torch.Tensor ):
										return image
					elif isinstance(_lowercase					,			PIL.Image.Image ):
										_A								= [image]

					_A								= [trans(img.convert('''RGB''' ) ) for img in image]
					_A								= torch.stack(_lowercase )
					return image





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							def __init__(  self:   Dict		,     __A:   Optional[int]		,     __A:   int   )    ->				Optional[Any]:
												super().__init__()

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

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

							def       __A (  self:   List[Any]		,     __A:   Dict   )    ->				str:
												if strength < 0 or strength > 1:
																	raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}"""   )

							def       __A (  self:   Tuple		,     __A:   str		,     __A:   Tuple		,     __A:   Tuple   )    ->				str:
												# get the original timestep using init_timestep
												_A								= min(int(num_inference_steps * strength   )		,     __A   )

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

												return timesteps, num_inference_steps - t_start

							def       __A (  self:   Union[str, Any]		,     __A:   Optional[int]		,     __A:   List[str]		,     __A:   List[str]		,     __A:   Dict		,     __A:   str		,     __A:   Tuple=None   )    ->				List[Any]:
												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   )}"""   )

												_A								= 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."""   )

												_A								= init_latents.shape
												_A								= randn_tensor(__A		,     generator=__A		,     device=__A		,     dtype=__A   )

												# get latents
												print('''add noise to latents at timestep'''		,     __A   )
												_A								= self.scheduler.add_noise(__A		,     __A		,     __A   )
												_A								= init_latents

												return latents



							@torch.no_grad()
							def __call__(  self:   Dict		,     __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		,     )    ->				Union[ImagePipelineOutput, Tuple]:
												self.check_inputs(__A   )

												# 2. Preprocess image
												_A								= preprocess(__A   )

												# 3. set timesteps
												self.scheduler.set_timesteps(__A		,     device=self.device   )
												_A							,_A								= self.get_timesteps(__A		,     __A		,     self.device   )
												_A								= timesteps[:1].repeat(__A   )

												# 4. Prepare latent variables
												_A								= self.prepare_latents(__A		,     __A		,     __A		,     self.unet.dtype		,     self.device		,     __A   )
												_A								= latents

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

												_A								= (image / 2 + 0.5).clamp(0		,     1   )
												_A								= image.cpu().permute(0		,     2		,     3		,     1   ).numpy()
												if output_type == "pil":
																	_A								= self.numpy_to_pil(__A   )

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

												return ImagePipelineOutput(images=__A   )











__A      =				{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__A      =				{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= True
					_A								= []

					for neighbour in graph[vert]:
										if not visited[neighbour]:
															order += topology_sort(_lowercase					,			_lowercase					,			_lowercase )

					order.append(_lowercase )

					return order




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= True
					_A								= [vert]

					for neighbour in reversed_graph[vert]:
										if not visited[neighbour]:
															component += find_components(_lowercase					,			_lowercase					,			_lowercase )

					return component




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= len(_lowercase ) * [False]
					_A								= {vert: [] for vert in range(len(_lowercase ) )}

					for vert, neighbours in graph.items():
										for neighbour in neighbours:
															reversed_graph[neighbour].append(_lowercase )

					_A								= []
					for i, was_visited in enumerate(_lowercase ):
										if not was_visited:
															order += topology_sort(_lowercase					,			_lowercase					,			_lowercase )

					_A								= []
					_A								= len(_lowercase ) * [False]

					for i in range(len(_lowercase ) ):
										_A								= order[len(_lowercase ) - i - 1]
										if not visited[vert]:
															_A								= find_components(_lowercase					,			_lowercase					,			_lowercase )
															components_list.append(_lowercase )

					return components_list



