



"""simple docstring"""

from __future__ import annotations

from queue import Queue
class 						lowerCAmelCase__	:


	'''simple docstring'''







	def __init__(			self							:					int						)	->  None:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       dict[int, list[int]] 							=    {}





	def  _lowerCAmelCase    (			self							:					Dict						)	->  None:





					"""simple docstring"""


					for i in self.vertices:
									print(_SCREAMING_SNAKE_CASE    ,					' : '    ,					' -> '.join([str(_SCREAMING_SNAKE_CASE						) for j in self.vertices[i]]						)						)





	def  _lowerCAmelCase    (			self							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					int    ,					_SCREAMING_SNAKE_CASE							:					int						)	->  None:





					"""simple docstring"""


					if from_vertex in self.vertices:
									self.vertices[from_vertex].append(_SCREAMING_SNAKE_CASE						)
					else:
									SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    [to_vertex]







	def  _lowerCAmelCase    (			self							:					str    ,					_SCREAMING_SNAKE_CASE							:					int						)	->  set[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[Any] 							=    set()

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

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

					while not queue.empty():
									SCREAMING_SNAKE_CASE	:       Dict 							=    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(_SCREAMING_SNAKE_CASE						)
																	visited.add(_SCREAMING_SNAKE_CASE						)
					return visited


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

							testmod(verbose=True)

							A_	:						Optional[int]				=			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]










"""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





