

'''simple docstring'''





from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

UpperCamelCase_							=					TypeVar("""T""")






def 							_lowerCAmelCase (      __magic_name__	:    int	)							->     int:
						return (position - 1) // 2






def 							_lowerCAmelCase (      __magic_name__	:    int	)							->     int:
						return (2 * position) + 1






def 							_lowerCAmelCase (      __magic_name__	:    int	)							->     int:
						return (2 * position) + 2





class 		__SCREAMING_SNAKE_CASE       ( Generic[T]			):







				def __init__(					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       list[tuple[T, int]]								=[]
										lowercase   :       dict[T, int]								={}
										lowercase   :       int								=0







				def __len__(					self    :		List[Any]      ):
										'''simple docstring'''
										return self.elements







				def __repr__(					self    :		List[str]      ):
										'''simple docstring'''
										return str(self.heap      )







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										# Check if the priority queue is empty
										return self.elements == 0







				def    lowerCamelCase_  (					self    :		Dict		,				UpperCAmelCase__    :		T		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										# Add an element with given priority to the queue
										self.heap.append((elem, weight)      )
										lowercase   :       Any								=self.elements
										self.elements += 1
										self._bubble_up(UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										# Remove and return the element with lowest weight (highest priority)
										if self.elements > 1:
																self._swap_nodes(0		,				self.elements - 1      )
										lowercase		,  lowercase   :       List[Any]								=self.heap.pop()
										del self.position_map[elem]
										self.elements -= 1
										if self.elements > 0:
																lowercase		,  lowercase   :       Optional[int]								=self.heap[0]
																self._bubble_down(UpperCAmelCase__      )
										return elem







				def    lowerCamelCase_  (					self    :		List[str]		,				UpperCAmelCase__    :		T		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										# Update the weight of the given key
										lowercase   :       str								=self.position_map[elem]
										lowercase   :       Any								=(elem, weight)
										if position > 0:
																lowercase   :       str								=get_parent_position(UpperCAmelCase__      )
																lowercase		,  lowercase   :       List[Any]								=self.heap[parent_position]
																if parent_weight > weight:
																						self._bubble_up(UpperCAmelCase__      )
																else:
																						self._bubble_down(UpperCAmelCase__      )
										else:
																self._bubble_down(UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Any		,				UpperCAmelCase__    :		T      ):
										'''simple docstring'''
										# Place a node at the proper position (upward movement) [to be used internally
										# only]
										lowercase   :       Optional[int]								=self.position_map[elem]
										if curr_pos == 0:
																return None
										lowercase   :       Optional[int]								=get_parent_position(UpperCAmelCase__      )
										lowercase		,  lowercase   :       str								=self.heap[curr_pos]
										lowercase		,  lowercase   :       List[str]								=self.heap[parent_position]
										if parent_weight > weight:
																self._swap_nodes(UpperCAmelCase__		,				UpperCAmelCase__      )
																return self._bubble_up(UpperCAmelCase__      )
										return None







				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				UpperCAmelCase__    :		T      ):
										'''simple docstring'''
										# Place a node at the proper position (downward movement) [to be used
										# internally only]
										lowercase   :       List[str]								=self.position_map[elem]
										lowercase		,  lowercase   :       Tuple								=self.heap[curr_pos]
										lowercase   :       Union[str, Any]								=get_child_left_position(UpperCAmelCase__      )
										lowercase   :       str								=get_child_right_position(UpperCAmelCase__      )
										if child_left_position < self.elements and child_right_position < self.elements:
																lowercase		,  lowercase   :       int								=self.heap[child_left_position]
																lowercase		,  lowercase   :       int								=self.heap[child_right_position]
																if child_right_weight < child_left_weight and child_right_weight < weight:
																						self._swap_nodes(UpperCAmelCase__		,				UpperCAmelCase__      )
																						return self._bubble_down(UpperCAmelCase__      )
										if child_left_position < self.elements:
																lowercase		,  lowercase   :       int								=self.heap[child_left_position]
																if child_left_weight < weight:
																						self._swap_nodes(UpperCAmelCase__		,				UpperCAmelCase__      )
																						return self._bubble_down(UpperCAmelCase__      )
										else:
																return None
										if child_right_position < self.elements:
																lowercase		,  lowercase   :       Tuple								=self.heap[child_right_position]
																if child_right_weight < weight:
																						self._swap_nodes(UpperCAmelCase__		,				UpperCAmelCase__      )
																						return self._bubble_down(UpperCAmelCase__      )
										return None







				def    lowerCamelCase_  (					self    :		Dict		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										# Swap the nodes at the given positions
										lowercase   :       Optional[Any]								=self.heap[nodea_pos][0]
										lowercase   :       str								=self.heap[nodea_pos][0]
										lowercase		,  lowercase   :       int								=(
										    self.heap[nodea_pos],
										    self.heap[nodea_pos],
										)
										lowercase   :       Dict								=nodea_pos
										lowercase   :       Any								=nodea_pos





class 		__SCREAMING_SNAKE_CASE       ( Generic[T]			):







				def __init__(					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       dict[T, dict[T, int]]								={}
										lowercase   :       int								=0







				def __repr__(					self    :		Dict      ):
										'''simple docstring'''
										return str(self.connections      )







				def __len__(					self    :		int      ):
										'''simple docstring'''
										return self.nodes







				def    lowerCamelCase_  (					self    :		List[Any]		,				UpperCAmelCase__    :		T      ):
										'''simple docstring'''
										# Add a node in the graph if it is not in the graph
										if node not in self.connections:
																lowercase   :       Any								={}
																self.nodes += 1







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		T		,				UpperCAmelCase__    :		T		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										# Add an edge between 2 nodes in the graph
										self.add_node(UpperCAmelCase__      )
										self.add_node(UpperCAmelCase__      )
										lowercase   :       Optional[Any]								=weight
										lowercase   :       List[Any]								=weight






def 							_lowerCAmelCase (      __magic_name__	:    GraphUndirectedWeighted[T]      ,   )							->     tuple[dict[T, int], dict[T, T | None]]:
						lowercase   :       dict[T, int]								={node: maxsize for node in graph.connections}
						lowercase   :       dict[T, T | None]								={node: None for node in graph.connections}

						lowercase   :       MinPriorityQueue[T]								=MinPriorityQueue()
						for node, weight in dist.items():
												priority_queue.push(__magic_name__      ,   __magic_name__	)

						if priority_queue.is_empty():
												return dist, parent

						# initialization
						lowercase   :       Optional[int]								=priority_queue.extract_min()
						lowercase   :       Optional[Any]								=0
						for neighbour in graph.connections[node]:
												if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
																		lowercase   :       Any								=dist[node] + graph.connections[node][neighbour]
																		priority_queue.update_key(__magic_name__      ,   dist[neighbour]	)
																		lowercase   :       List[str]								=node

    # running prim's algorithm
						while not priority_queue.is_empty():
												lowercase   :       str								=priority_queue.extract_min()
												for neighbour in graph.connections[node]:
																		if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
																								lowercase   :       Any								=dist[node] + graph.connections[node][neighbour]
																								priority_queue.update_key(__magic_name__      ,   dist[neighbour]	)
																								lowercase   :       Tuple								=node
						return dist, parent








'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				def __init__(					self    :		Optional[Any]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										super().__init__()

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

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







				@torch.no_grad()
				def __call__(					self    :		List[Any]		,				UpperCAmelCase__    :		int = 1		,				UpperCAmelCase__    :		Optional[Union[torch.Generator, List[torch.Generator]]] = None		,				UpperCAmelCase__    :		float = 0.0		,				UpperCAmelCase__    :		int = 50		,				UpperCAmelCase__    :		Optional[bool] = None		,				UpperCAmelCase__    :		Optional[str] = "pil"		,				UpperCAmelCase__    :		bool = True		,				):
										'''simple docstring'''

										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size		,				UpperCAmelCase__      ):
																lowercase   :       Optional[int]								=(
																    batch_size,
																    self.unet.config.in_channels,
																    self.unet.config.sample_size,
																    self.unet.config.sample_size,
																)
										else:
																lowercase   :       Optional[int]								=(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

										lowercase   :       str								=randn_tensor(UpperCAmelCase__		,				generator=UpperCAmelCase__		,				device=self.device		,				dtype=self.unet.dtype      )

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

										for t in self.progress_bar(self.scheduler.timesteps      ):
																# 1. predict noise model_output
																lowercase   :       Dict								=self.unet(UpperCAmelCase__		,				UpperCAmelCase__      ).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   :       Dict								=self.scheduler.step(
																    UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				eta=UpperCAmelCase__		,				use_clipped_model_output=UpperCAmelCase__		,				generator=UpperCAmelCase__      ).prev_sample

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

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=UpperCAmelCase__      )





