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


def    a      ( a	)     ->Any:




			'''simple docstring'''


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

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





class     lowerCamelCase      (       __lowerCamelCase					):







	def __init__(     self      :List[str]   ,     lowercase      :List[str]   ,     lowercase      :Optional[int]			)		->      Optional[Any]:







				"""simple docstring"""






				super().__init__()

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

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







	def  snake_case__     (     self      :Dict   ,     lowercase      :Union[str, Any]			)		->      Optional[int]:







				"""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  snake_case__     (     self      :Optional[int]   ,     lowercase      :int   ,     lowercase      :Tuple   ,     lowercase      :Any			)		->      Optional[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= min(int(num_inference_steps * strength			)   ,     lowercase			)

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

				return timesteps, num_inference_steps - t_start







	def  snake_case__     (     self      :int   ,     lowercase      :int   ,     lowercase      :Optional[int]   ,     lowercase      :int   ,     lowercase      :Union[str, Any]   ,     lowercase      :Any   ,     lowercase      :Optional[int]=None			)		->      Dict:







				"""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			)}"""			)

				SCREAMING_SNAKE_CASE     						= 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."""			)

				SCREAMING_SNAKE_CASE     						= init_latents.shape
				SCREAMING_SNAKE_CASE     						= randn_tensor(lowercase   ,     generator=lowercase   ,     device=lowercase   ,     dtype=lowercase			)

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

				return latents







	@torch.no_grad()
	def __call__(     self      :List[str]   ,     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 = 5_0   ,     lowercase      :Optional[bool] = None   ,     lowercase      :Optional[str] = "pil"   ,     lowercase      :bool = True   ,     )		->      Union[ImagePipelineOutput, Tuple]:







				"""simple docstring"""






				self.check_inputs(lowercase			)

				# 2. Preprocess image
				SCREAMING_SNAKE_CASE     						= preprocess(lowercase			)

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

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

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

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

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

				return ImagePipelineOutput(images=lowercase			)

from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class     lowerCamelCase      :
	UpperCamelCase_  :					int
	UpperCamelCase_  :					int





class     lowerCamelCase      :







	def __init__(     self      :Dict   ,     lowercase      :int			)		->      Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [[] for _ in range(lowercase			)]
				SCREAMING_SNAKE_CASE     						= size







	def __getitem__(     self      :Union[str, Any]   ,     lowercase      :int			)		->      Iterator[Edge]:







				"""simple docstring"""






				return iter(self._graph[vertex]			)







	@property
	def  snake_case__     (     self      :List[Any]			)		->      str:







				"""simple docstring"""






				return self._size







	def  snake_case__     (     self      :Optional[Any]   ,     lowercase      :int   ,     lowercase      :int   ,     lowercase      :int			)		->      List[Any]:







				"""simple docstring"""






				if weight not in (0, 1):
							raise ValueError('''Edge weight must be either 0 or 1.'''			)

				if to_vertex < 0 or to_vertex >= self.size:
							raise ValueError('''Vertex indexes must be in [0; size).'''			)

				self._graph[from_vertex].append(Edge(lowercase   ,     lowercase			)			)







	def  snake_case__     (     self      :List[str]   ,     lowercase      :int   ,     lowercase      :int			)		->      int | None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= deque([start_vertex]			)
				SCREAMING_SNAKE_CASE     						= [None] * self.size
				SCREAMING_SNAKE_CASE     						= 0

				while queue:
							SCREAMING_SNAKE_CASE     						= queue.popleft()
							SCREAMING_SNAKE_CASE     						= distances[current_vertex]
							if current_distance is None:
										continue

							for edge in self[current_vertex]:
										SCREAMING_SNAKE_CASE     						= current_distance + edge.weight
										SCREAMING_SNAKE_CASE     						= distances[edge.destination_vertex]
										if (
										    isinstance(lowercase   ,     lowercase			)
										    and new_distance >= dest_vertex_distance
										):
													continue
										SCREAMING_SNAKE_CASE     						= new_distance
										if edge.weight == 0:
													queue.appendleft(edge.destination_vertex			)
										else:
													queue.append(edge.destination_vertex			)

				if distances[finish_vertex] is None:
							raise ValueError('''No path from start_vertex to finish_vertex.'''			)

				return distances[finish_vertex]


if __name__ == "__main__":
							import doctest

							doctest.testmod()