





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:	Any = transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Any			)   ->							int:




		'''simple docstring'''
		if isinstance(_lowercase   ,    torch.Tensor			):
				return image
		elif isinstance(_lowercase   ,    PIL.Image.Image			):
				lowercase__					:      Dict      =		[image]

		lowercase__					:      Dict      =		[trans(img.convert('RGB'			)			) for img in image]
		lowercase__					:      Tuple      =		torch.stack(_lowercase			)
		return image





class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	def __init__(						self:						Optional[Any],					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						Tuple			):
			super().__init__()

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

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

	def    snake_case__(						self:						List[Any],					lowerCamelCase_:						Optional[Any]			):
			if strength < 0 or strength > 1:
					raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""			)

	def    snake_case__(						self:						int,					lowerCamelCase_:						List[Any],					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						Tuple			):
			# get the original timestep using init_timestep
			lowercase__					:      Union[str, Any]      =		min(int(num_inference_steps * strength			),					lowerCamelCase_			)

			lowercase__					:      Dict      =		max(num_inference_steps - init_timestep,					0			)
			lowercase__					:      List[str]      =		self.scheduler.timesteps[t_start:]

			return timesteps, num_inference_steps - t_start

	def    snake_case__(						self:						Union[str, Any],					lowerCamelCase_:						int,					lowerCamelCase_:						Dict,					lowerCamelCase_:						int,					lowerCamelCase_:						Optional[int],					lowerCamelCase_:						Any,					lowerCamelCase_:						Optional[Any]=None			):
			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_			)}"""			)

			lowercase__					:      Optional[Any]      =		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."""			)

			lowercase__					:      str      =		init_latents.shape
			lowercase__					:      Union[str, Any]      =		randn_tensor(lowerCamelCase_,					generator=lowerCamelCase_,					device=lowerCamelCase_,					dtype=lowerCamelCase_			)

			# get latents
			print('add noise to latents at timestep',					lowerCamelCase_			)
			lowercase__					:      Optional[int]      =		self.scheduler.add_noise(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_			)
			lowercase__					:      List[str]      =		init_latents

			return latents





	@torch.no_grad()
	def __call__(						self:						Any,					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,					):
			self.check_inputs(lowerCamelCase_			)

			# 2. Preprocess image
			lowercase__					:      List[str]      =		preprocess(lowerCamelCase_			)

			# 3. set timesteps
			self.scheduler.set_timesteps(lowerCamelCase_,					device=self.device			)
			lowercase__   ,				lowercase__					:      Tuple      =		self.get_timesteps(lowerCamelCase_,					lowerCamelCase_,					self.device			)
			lowercase__					:      List[Any]      =		timesteps[:1].repeat(lowerCamelCase_			)

			# 4. Prepare latent variables
			lowercase__					:      Optional[Any]      =		self.prepare_latents(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					self.unet.dtype,					self.device,					lowerCamelCase_			)
			lowercase__					:      Any      =		latents

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

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

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

			return ImagePipelineOutput(images=lowerCamelCase_			)









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

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


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]   ,    _lowercase     : int   ,    _lowercase     : list[bool]			)   ->							list[int]:




		'''simple docstring'''
		lowercase__					:      str      =		True
		lowercase__					:      int      =		[]

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

		order.append(_lowercase			)

		return order


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]   ,    _lowercase     : int   ,    _lowercase     : list[bool]			)   ->							list[int]:




		'''simple docstring'''
		lowercase__					:      Union[str, Any]      =		True
		lowercase__					:      Optional[Any]      =		[vert]

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

		return component





def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]			)   ->							list[list[int]]:




		'''simple docstring'''
		lowercase__					:      Optional[int]      =		len(_lowercase			) * [False]
		lowercase__					:      dict[int, list[int]]      =		{vert: [] for vert in range(len(_lowercase			)			)}

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

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

		lowercase__					:      Dict      =		[]
		lowercase__					:      Optional[Any]      =		len(_lowercase			) * [False]

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

		return components_list


