



"""simple docstring"""

A_	:						str				=			float('inf')
class 						lowerCAmelCase__	:


	'''simple docstring'''







	def __init__(			self							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       str 							=    [0] * n
					SCREAMING_SNAKE_CASE	:       str 							=    [0] * n
					SCREAMING_SNAKE_CASE	:       str 							=    [0] * n
					SCREAMING_SNAKE_CASE	:       Any 							=    [[] for _ in range(_SCREAMING_SNAKE_CASE						)]





	def  _lowerCAmelCase    (			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					List[Any]    ,					_SCREAMING_SNAKE_CASE							:					int    ,					_SCREAMING_SNAKE_CASE							:					Dict=0						)	->  int:





					"""simple docstring"""


					self.adj[a].append([b, len(self.adj[b]						), c, 0]						)
					self.adj[b].append([a, len(self.adj[a]						) - 1, rcap, 0]						)





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





					"""simple docstring"""


					if vertex == sink or not flow:
									return flow

					for i in range(self.ptr[vertex]    ,					len(self.adj[vertex]						)						):
									SCREAMING_SNAKE_CASE	:       Tuple 							=    self.adj[vertex][i]
									if self.lvl[e[0]] == self.lvl[vertex] + 1:
													SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    self.depth_first_search(e[0]    ,					_SCREAMING_SNAKE_CASE    ,					min(_SCREAMING_SNAKE_CASE    ,					e[2] - e[3]						)						)
													if p:
																	self.adj[vertex][i][3] += p
																	self.adj[e[0]][e[1]][3] -= p
																	return p
									SCREAMING_SNAKE_CASE	:       List[str] 							=    self.ptr[vertex] + 1
					return 0







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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE	:       List[str] 							=    0, source
					for l in range(31						):  # noqa: E741  l = 30 maybe faster for random data
									while True:
													SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE	:       Optional[int] 							=    [0] * len(self.q						), [0] * len(self.q						)
													SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    0, 1, 1
													while qi < qe and not self.lvl[sink]:
																	SCREAMING_SNAKE_CASE	:       List[str] 							=    self.q[qi]
																	qi += 1
																	for e in self.adj[v]:
																					if not self.lvl[e[0]] and (e[2] - e[3]) >> (30 - l):
																									SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    e[0]
																									qe += 1
																									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.lvl[v] + 1

													SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.depth_first_search(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)
													while p:
																	flow += p
																	SCREAMING_SNAKE_CASE	:       List[str] 							=    self.depth_first_search(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)

													if not self.lvl[sink]:
																	break

					return flow
A_	:						List[Any]				=			Dinic(10)
A_	:						Dict				=			0
A_	:						List[str]				=			9
for vertex in range(1, 5):
							graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
							graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
							graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))










"""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





