



"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

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

class 			__SCREAMING_SNAKE_CASE      ( lowercase_  ):


					'''simple docstring'''





					def __init__(			self				:    Optional[int],    lowerCamelCase				:    List[Any],    lowerCamelCase				:    Dict	)->   Dict:
								super().__init__()
								self.register_modules(unet=__UpperCamelCase,    scheduler=__UpperCamelCase	)




					@torch.no_grad()
					def __call__(			self				:    int,    lowerCamelCase				:    int = 1,    lowerCamelCase				:    Optional[Union[torch.Generator, List[torch.Generator]]] = None,    lowerCamelCase				:    int = 1000,    lowerCamelCase				:    Optional[str] = "pil",    lowerCamelCase				:    bool = True,    )->   Optional[Any]:
								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size,    __UpperCamelCase	):
											lowerCamelCase__						:						str						      =(
											    batch_size,
											    self.unet.config.in_channels,
											    self.unet.config.sample_size,
											    self.unet.config.sample_size,
											)
								else:
											lowerCamelCase__						:						Tuple						      =(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if self.device.type == "mps":
											# randn does not work reproducibly on mps
											lowerCamelCase__						:						Optional[Any]						      =randn_tensor(__UpperCamelCase,    generator=__UpperCamelCase	)
											lowerCamelCase__						:						Optional[Any]						      =image.to(self.device	)
								else:
											lowerCamelCase__						:						Union[str, Any]						      =randn_tensor(__UpperCamelCase,    generator=__UpperCamelCase,    device=self.device	)

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

								for t in self.progress_bar(self.scheduler.timesteps	):
											# 1. predict noise model_output
											lowerCamelCase__						:						Optional[Any]						      =self.unet(__UpperCamelCase,    __UpperCamelCase	).sample

											# 2. compute previous image: x_t -> x_t-1
											lowerCamelCase__						:						int						      =self.scheduler.step(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    generator=__UpperCamelCase	).prev_sample

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

								if not return_dict:
											return (image,)

								return ImagePipelineOutput(images=__UpperCamelCase	)











"""simple docstring"""
from __future__ import annotations

from queue import Queue






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							a ^= b
							b ^= a
							a ^= b
							return a, b






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    1
							while (1 << j) < max_node:
														for i in range(1   ,    max_node + 1			):
																					A						=    parent[j - 1][parent[j - 1][i]]
														j += 1
							return parent






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							# u must be deeper in the tree than v
							if level[u] < level[v]:
														A,      A						=    swap(UpperCamelCase   ,    UpperCamelCase			)
							# making depth of u same as depth of v
							for i in range(18   ,    -1   ,    -1			):
														if level[u] - (1 << i) >= level[v]:
																					A						=    parent[i][u]
    # at the same depth if u==v that mean lca is found
							if u == v:
														return u
							# moving both nodes upwards till lca in found
							for i in range(18   ,    -1   ,    -1			):
														if parent[i][u] not in [0, parent[i][v]]:
																					A,      A						=    parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
							return parent[0][u]






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase = 1   ,    ):
							A						=    0
							A						=    Queue(maxsize=UpperCamelCase			)
							q.put(UpperCamelCase			)
							while q.qsize() != 0:
														A						=    q.get()
														for v in graph[u]:
																					if level[v] == -1:
																												A						=    level[u] + 1
																												q.put(UpperCamelCase			)
																												A						=    u
							return level, parent






def    A__							(     ):
							A						=    13
							# initializing with 0
							A						=    [[0 for _ in range(max_node + 10			)] for _ in range(20			)]
							# initializing with -1 which means every node is unvisited
							A						=    [-1 for _ in range(max_node + 10			)]
							A						=    {
							    1: [2, 3, 4],
							    2: [5],
							    3: [6, 7],
							    4: [8],
							    5: [9, 10],
							    6: [11],
							    7: [],
							    8: [12, 13],
							    9: [],
							    10: [],
							    11: [],
							    12: [],
							    13: [],
							}
							A,      A						=    breadth_first_search(UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    1			)
							A						=    create_sparse(UpperCamelCase   ,    UpperCamelCase			)
							print("LCA of node 1 and 3 is: "   ,    lowest_common_ancestor(1   ,    3   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 5 and 6 is: "   ,    lowest_common_ancestor(5   ,    6   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 7 and 11 is: "   ,    lowest_common_ancestor(7   ,    11   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 6 and 7 is: "   ,    lowest_common_ancestor(6   ,    7   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 4 and 12 is: "   ,    lowest_common_ancestor(4   ,    12   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 8 and 8 is: "   ,    lowest_common_ancestor(8   ,    8   ,    UpperCamelCase   ,    UpperCamelCase			)			)


if __name__ == "__main__":
		main()


