
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


lowerCamelCase_						: Union[str, Any]         =  transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def        A__					(						lowerCamelCase						)  ->	int:
				if isinstance(lowerCamelCase     ,    torch.Tensor						):
								return image
				elif isinstance(lowerCamelCase     ,    PIL.Image.Image						):
								UpperCamelCase_:							Optional[int]	=						[image]

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





class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			def __init__(       self :       Dict						,	snake_case_ :       Dict						,	snake_case_ :       Dict      ):
							super().__init__()

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

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

			def       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       List[str]      ):
							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 :       Optional[Any]						,	snake_case_ :       Optional[Any]						,	snake_case_ :       int						,	snake_case_ :       List[str]      ):
							# get the original timestep using init_timestep
							UpperCamelCase_:							Dict	=						min(int(num_inference_steps * strength      )						,	snake_case_      )

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

							return timesteps, num_inference_steps - t_start

			def       lowerCAmelCase__	(       self :       List[str]						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       int						,	snake_case_ :       Optional[Any]						,	snake_case_ :       Optional[int]						,	snake_case_ :       Optional[int]						,	snake_case_ :       Union[str, Any]=None      ):
							if not isinstance(snake_case_						,	(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(snake_case_      )}'''      )

							UpperCamelCase_:							List[str]	=						image.to(device=snake_case_						,	dtype=snake_case_      )

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

							UpperCamelCase_:							Optional[int]	=						init_latents.shape
							UpperCamelCase_:							Optional[int]	=						randn_tensor(snake_case_						,	generator=snake_case_						,	device=snake_case_						,	dtype=snake_case_      )

							# get latents
							print("""add noise to latents at timestep"""						,	snake_case_      )
							UpperCamelCase_:							Optional[int]	=						self.scheduler.add_noise(snake_case_						,	snake_case_						,	snake_case_      )
							UpperCamelCase_:							Tuple	=						init_latents

							return latents




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

							# 2. Preprocess image
							UpperCamelCase_:							Any	=						preprocess(snake_case_      )

							# 3. set timesteps
							self.scheduler.set_timesteps(snake_case_						,	device=self.device      )
							UpperCamelCase_,   UpperCamelCase_:							List[str]	=						self.get_timesteps(snake_case_						,	snake_case_						,	self.device      )
							UpperCamelCase_:							List[str]	=						timesteps[:1].repeat(snake_case_      )

							# 4. Prepare latent variables
							UpperCamelCase_:							Tuple	=						self.prepare_latents(snake_case_						,	snake_case_						,	snake_case_						,	self.unet.dtype						,	self.device						,	snake_case_      )
							UpperCamelCase_:							List[Any]	=						latents

							# 5. Denoising loop
							for t in self.progress_bar(snake_case_      ):
											# 1. predict noise model_output
											UpperCamelCase_:							List[str]	=						self.unet(snake_case_						,	snake_case_      ).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_:							Dict	=						self.scheduler.step(
											    snake_case_						,	snake_case_						,	snake_case_						,	eta=snake_case_						,	use_clipped_model_output=snake_case_						,	generator=snake_case_						,	).prev_sample

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

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

							return ImagePipelineOutput(images=snake_case_      )








import random







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = False						)  ->	dict:
				UpperCamelCase_:							dict	=						{i: [] for i in range(lowerCamelCase						)}

				# if probability is greater or equal than 1, then generate a complete graph
				if probability >= 1:
								return complete_graph(lowerCamelCase						)
				# if probability is lower or equal than 0, then return a graph without edges
				if probability <= 0:
								return graph

				# for each couple of nodes, add an edge from u to v
				# if the number randomly generated is greater than probability probability
				for i in range(lowerCamelCase						):
								for j in range(i + 1     ,    lowerCamelCase						):
												if random.random() < probability:
																graph[i].append(lowerCamelCase						)
																if not directed:
																				# if the graph is undirected, add an edge in from j to i, either
																				graph[j].append(lowerCamelCase						)
				return graph







def        A__					(						lowerCamelCase						)  ->	dict:
				return {
				    i: [j for j in range(lowerCamelCase						) if i != j] for i in range(lowerCamelCase						)
				}


if __name__ == "__main__":
							import doctest

							doctest.testmod()






