


'''simple docstring'''


from __future__ import annotations

from typing import Generic, TypeVar

UpperCAmelCase_		:   Tuple							   =		TypeVar('T')





class     lowercase__  (						Generic[T]					):





							'''simple docstring'''


							def __init__(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      data
											_SCREAMING_SNAKE_CASE :       int     		=      self
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      0





class     lowercase__  (						Generic[T]					):





							'''simple docstring'''


							def __init__(    self  ):
											# map from node name to the node object
											_SCREAMING_SNAKE_CASE :       dict[T, DisjointSetTreeNode[T]]     		=      {}






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											# create a new set with x as its member
											_SCREAMING_SNAKE_CASE :       List[str]     		=      DisjointSetTreeNode(__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											# find the set x belongs to (with path-compression)
											_SCREAMING_SNAKE_CASE :       Any     		=      self.map[data]
											if elem_ref != elem_ref.parent:
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.find_set(elem_ref.parent.data  )
											return elem_ref.parent






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case  ):
											# helper function for union operation
											if nodea.rank > nodea.rank:
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      nodea
											else:
															_SCREAMING_SNAKE_CASE :       Any     		=      nodea
															if nodea.rank == nodea.rank:
																			nodea.rank += 1






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case  ):
											# merge 2 disjoint sets
											self.link(self.find_set(__snake_case  )    ,					self.find_set(__snake_case  )  )





class     lowercase__  (						Generic[T]					):





							'''simple docstring'''


							def __init__(    self  ):
											# connections: map from the node to the neighbouring nodes (with weights)
											_SCREAMING_SNAKE_CASE :       dict[T, dict[T, int]]     		=      {}






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											# add a node ONLY if its not present in the graph
											if node not in self.connections:
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      {}






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											# add an edge with the given weight
											self.add_node(__snake_case  )
											self.add_node(__snake_case  )
											_SCREAMING_SNAKE_CASE :       int     		=      weight
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      weight






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Dict     		=      []
											_SCREAMING_SNAKE_CASE :       List[str]     		=      set()
											for start in self.connections:
															for end in self.connections[start]:
																			if (start, end) not in seen:
																							seen.add((end, start)  )
																							edges.append((start, end, self.connections[start][end])  )
											edges.sort(key=lambda __snake_case  : x[2]  )

											# creating the disjoint set
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      DisjointSetTree[T]()
											for node in self.connections:
															disjoint_set.make_set(__snake_case  )

											# MST generation
											_SCREAMING_SNAKE_CASE :       Dict     		=      0
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      0
											_SCREAMING_SNAKE_CASE :       str     		=      GraphUndirectedWeighted[T]()
											while num_edges < len(self.connections  ) - 1:
															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[str]     		=      edges[index]
															index += 1
															_SCREAMING_SNAKE_CASE :       Tuple     		=      disjoint_set.find_set(__snake_case  )
															_SCREAMING_SNAKE_CASE :       int     		=      disjoint_set.find_set(__snake_case  )
															if parent_u != parent_v:
																			num_edges += 1
																			graph.add_edge(__snake_case    ,					__snake_case    ,					__snake_case  )
																			disjoint_set.union(__snake_case    ,					__snake_case  )
											return graph








'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      UNetaDModel
							A_							:      ScoreSdeVeScheduler






							def __init__(    self    ,					__snake_case    ,					__snake_case  ):
											super().__init__()
											self.register_modules(unet=__snake_case    ,					scheduler=__snake_case  )






							@torch.no_grad()
							def __call__(    self    ,					__snake_case = 1    ,					__snake_case = 2000    ,					__snake_case = None    ,					__snake_case = "pil"    ,					__snake_case = True    ,					**__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.unet.config.sample_size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      (batch_size, 3, img_size, img_size)

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.unet

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      randn_tensor(__snake_case    ,					generator=__snake_case  ) * self.scheduler.init_noise_sigma
											_SCREAMING_SNAKE_CASE :       Any     		=      sample.to(self.device  )

											self.scheduler.set_timesteps(__snake_case  )
											self.scheduler.set_sigmas(__snake_case  )

											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.scheduler.sigmas[i] * torch.ones(shape[0]    ,					device=self.device  )

															# correction step
															for _ in range(self.scheduler.config.correct_steps  ):
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.unet(__snake_case    ,					__snake_case  ).sample
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.scheduler.step_correct(__snake_case    ,					__snake_case    ,					generator=__snake_case  ).prev_sample

															# prediction step
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      model(__snake_case    ,					__snake_case  ).sample
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.scheduler.step_pred(__snake_case    ,					__snake_case    ,					__snake_case    ,					generator=__snake_case  )

															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       int     		=      output.prev_sample, output.prev_sample_mean

											_SCREAMING_SNAKE_CASE :       Tuple     		=      sample_mean.clamp(0    ,					1  )
											_SCREAMING_SNAKE_CASE :       str     		=      sample.cpu().permute(0    ,					2    ,					3    ,					1  ).numpy()
											if output_type == "pil":
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.numpy_to_pil(__snake_case  )

											if not return_dict:
															return (sample,)

											return ImagePipelineOutput(images=__snake_case  )




