



"""simple docstring"""

from __future__ import annotations

from queue import Queue
def    __snake_case       (  __A			:	int       ,		__A			:	int    )    ->					tuple[int, int]:






				'''simple docstring'''
				a ^= b
				b ^= a
				a ^= b
				return a, b
def    __snake_case       (  __A			:	int       ,		__A			:	list[list[int]]    )    ->					list[list[int]]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       Optional[int] 							=    1
				while (1 << j) < max_node:
								for i in range(1       ,		max_node + 1    ):
												SCREAMING_SNAKE_CASE	:       Any 							=    parent[j - 1][parent[j - 1][i]]
								j += 1
				return parent
def    __snake_case       (  __A			:	int       ,		__A			:	int       ,		__A			:	list[int]       ,		__A			:	list[list[int]]    )    ->					int:






				'''simple docstring'''
				# u must be deeper in the tree than v
				if level[u] < level[v]:
								SCREAMING_SNAKE_CASE	:       Dict 							=    swap(snake_case__       ,		snake_case__    )
				# making depth of u same as depth of v
				for i in range(18       ,		-1       ,		-1    ):
								if level[u] - (1 << i) >= level[v]:
												SCREAMING_SNAKE_CASE	:       Dict 							=    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]]:
												SCREAMING_SNAKE_CASE	:       Dict 							=    parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
				return parent[0][u]
def    __snake_case       (  __A			:	list[int]       ,		__A			:	list[list[int]]       ,		__A			:	int       ,		__A			:	dict[int, list[int]]       ,		__A			:	int = 1       ,		)    ->					tuple[list[int], list[list[int]]]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       List[Any] 							=    0
				SCREAMING_SNAKE_CASE	:       Queue[int] 							=    Queue(maxsize=snake_case__    )
				q.put(snake_case__    )
				while q.qsize() != 0:
								SCREAMING_SNAKE_CASE	:       int 							=    q.get()
								for v in graph[u]:
												if level[v] == -1:
																SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    level[u] + 1
																q.put(snake_case__    )
																SCREAMING_SNAKE_CASE	:       Any 							=    u
				return level, parent
def    __snake_case       (  )    ->					None:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       Tuple 							=    13
				# initializing with 0
				SCREAMING_SNAKE_CASE	:       Any 							=    [[0 for _ in range(max_node + 10    )] for _ in range(20    )]
				# initializing with -1 which means every node is unvisited
				SCREAMING_SNAKE_CASE	:       List[str] 							=    [-1 for _ in range(max_node + 10    )]
				SCREAMING_SNAKE_CASE	:       dict[int, list[int]] 							=    {
				    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: [],
				}
				SCREAMING_SNAKE_CASE	:       List[str] 							=    breadth_first_search(snake_case__       ,		snake_case__       ,		snake_case__       ,		snake_case__       ,		1    )
				SCREAMING_SNAKE_CASE	:       Tuple 							=    create_sparse(snake_case__       ,		snake_case__    )
				print('LCA of node 1 and 3 is: '       ,		lowest_common_ancestor(1       ,		3       ,		snake_case__       ,		snake_case__    )    )
				print('LCA of node 5 and 6 is: '       ,		lowest_common_ancestor(5       ,		6       ,		snake_case__       ,		snake_case__    )    )
				print('LCA of node 7 and 11 is: '       ,		lowest_common_ancestor(7       ,		11       ,		snake_case__       ,		snake_case__    )    )
				print('LCA of node 6 and 7 is: '       ,		lowest_common_ancestor(6       ,		7       ,		snake_case__       ,		snake_case__    )    )
				print('LCA of node 4 and 12 is: '       ,		lowest_common_ancestor(4       ,		12       ,		snake_case__       ,		snake_case__    )    )
				print('LCA of node 8 and 8 is: '       ,		lowest_common_ancestor(8       ,		8       ,		snake_case__       ,		snake_case__    )    )


if __name__ == "__main__":
							main()










"""simple docstring"""

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Dict     	=   (VQDiffusionScheduler,)





	def  _lowerCAmelCase    (			self							:					Tuple    ,					**_SCREAMING_SNAKE_CASE							:					Optional[int]						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    {
					    'num_vec_classes': 4_097,
					    'num_train_timesteps': 100,
					}

					config.update(**_SCREAMING_SNAKE_CASE						)
					return config





	def  _lowerCAmelCase    (			self							:					Dict    ,					_SCREAMING_SNAKE_CASE							:					Tuple						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    4
					SCREAMING_SNAKE_CASE	:       List[str] 							=    8
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    8

					SCREAMING_SNAKE_CASE	:       Tuple 							=    torch.randint(0    ,					_SCREAMING_SNAKE_CASE    ,					(batch_size, height * width)						)

					return sample





	@property
	def  _lowerCAmelCase    (			self							:					str						)	->  int:





					"""simple docstring"""


					assert False





	def  _lowerCAmelCase    (			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]						)	->  Optional[int]:





					"""simple docstring"""


					def model(_SCREAMING_SNAKE_CASE							:					Optional[Any]    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]    ,					*_SCREAMING_SNAKE_CASE							:					str						):
									SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE	:       Any 							=    sample.shape
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)						)
									SCREAMING_SNAKE_CASE	:       List[str] 							=    F.log_softmax(logits.double()    ,					dim=1						).float()
									return return_value

					return model





	def  _lowerCAmelCase    (			self							:					List[str]						)	->  Dict:





					"""simple docstring"""


					for timesteps in [2, 5, 100, 1_000]:
									self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Union[str, Any]						)	->  int:





					"""simple docstring"""


					for num_vec_classes in [5, 100, 1_000, 4_000]:
									self.check_over_configs(num_vec_classes=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Dict						)	->  List[Any]:





					"""simple docstring"""


					for t in [0, 50, 99]:
									self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE						)







	def  _lowerCAmelCase    (			self							:					Any						)	->  Dict:





					"""simple docstring"""


					pass





