
# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 							lowercase   (						UpperCAmelCase__  ):



		def __init__(					self							, A_							, A_			)    ->   Optional[Any]:
				"""simple docstring"""
				super().__init__()
				self.register_modules(unet=A_							, scheduler=A_			)



		@torch.no_grad()
		def __call__(					self							, A_ = 1							, A_ = None							, A_ = 50							, A_ = "pil"							, A_ = True							, **A_							, )    ->   Union[ImagePipelineOutput, Tuple]:
				"""simple docstring"""
				UpperCamelCase            =	torch.randn(
				    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							, generator=A_							, )
				UpperCamelCase            =	image.to(self.device			)

				# set step values
				self.scheduler.set_timesteps(A_			)

				for t in self.progress_bar(self.scheduler.timesteps			):
						# 1. predict noise model_output
						UpperCamelCase            =	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
						UpperCamelCase            =	self.scheduler.step(A_							, A_							, A_			).prev_sample

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

				if not return_dict:
						return (image,), "This is a local test"

				return ImagePipelineOutput(images=A_			), "This is a local test"


from __future__ import annotations

from queue import Queue


class 	UpperCamelCase__       :

						'''simple docstring'''



						def __init__(  self	)  ->None:
									UpperCAmelCase__		:dict[int, list[int]] 		=    {}


						def    A__       (  self	)  ->None:
									for i in self.vertices:
												print(A  , ' : '  , ' -> '.join([str(A	) for j in self.vertices[i]]	)	)


						def    A__       (  self  , A  , A	)  ->None:
									if from_vertex in self.vertices:
												self.vertices[from_vertex].append(A	)
									else:
												UpperCAmelCase__		:Union[str, Any] 		=    [to_vertex]


						def    A__       (  self  , A	)  ->set[int]:
									UpperCAmelCase__		:str 		=    set()

									# create a first in first out queue to store all the vertices for BFS
									UpperCAmelCase__		:Queue 		=    Queue()

									# mark the source node as visited and enqueue it
									visited.add(A	)
									queue.put(A	)

									while not queue.empty():
												UpperCAmelCase__		:List[str] 		=    queue.get()

												# loop through all adjacent vertex and enqueue it if not yet visited
												for adjacent_vertex in self.vertices[vertex]:
															if adjacent_vertex not in visited:
																		queue.put(A	)
																		visited.add(A	)
									return visited


if __name__ == "__main__":
						from doctest import testmod

						testmod(verbose=True)

						__snake_case					:							Any           =							Graph()
						g.add_edge(0, 1)
						g.add_edge(0, 2)
						g.add_edge(1, 2)
						g.add_edge(2, 0)
						g.add_edge(2, 3)
						g.add_edge(3, 3)

						g.print_graph()
						# 0  :  1 -> 2
						# 1  :  2
						# 2  :  0 -> 3
						# 3  :  3

						assert sorted(g.bfs(2)) == [0, 1, 2, 3]





