
_A       =					float("inf")





class      _lowerCAmelCase :




				def __init__( self    ,    _UpperCamelCase					)    ->				Tuple:
							lowerCAmelCase_      =     [0] * n
							lowerCAmelCase_      =     [0] * n
							lowerCAmelCase_      =     [0] * n
							lowerCAmelCase_      =     [[] for _ in range(_UpperCamelCase					)]




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase=0					)    ->				Optional[Any]:
							self.adj[a].append([b, len(self.adj[b]					), c, 0]					)
							self.adj[b].append([a, len(self.adj[a]					) - 1, rcap, 0]					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				Optional[int]:
							if vertex == sink or not flow:
										return flow

							for i in range(self.ptr[vertex]    ,    len(self.adj[vertex]					)					):
										lowerCAmelCase_      =     self.adj[vertex][i]
										if self.lvl[e[0]] == self.lvl[vertex] + 1:
													lowerCAmelCase_      =     self.depth_first_search(e[0]    ,    _UpperCamelCase    ,    min(_UpperCamelCase    ,    e[2] - e[3]					)					)
													if p:
																self.adj[vertex][i][3] += p
																self.adj[e[0]][e[1]][3] -= p
																return p
										lowerCAmelCase_      =     self.ptr[vertex] + 1
							return 0




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

													lowerCAmelCase_      =     self.depth_first_search(_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
													while p:
																flow += p
																lowerCAmelCase_      =     self.depth_first_search(_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)

													if not self.lvl[sink]:
																break

							return flow
_A       =					Dinic(10)
_A       =					0
_A       =					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))





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest





class      _lowerCAmelCase (     __a	):
				_lowercase      		=(VQDiffusionScheduler,)




				def 					__a						( self    ,    **_UpperCamelCase					)    ->				List[str]:
							lowerCAmelCase_      =     {
							    "num_vec_classes": 4_097,
							    "num_train_timesteps": 100,
							}

							config.update(**_UpperCamelCase					)
							return config




				def 					__a						( self    ,    _UpperCamelCase					)    ->				str:
							lowerCAmelCase_      =     4
							lowerCAmelCase_      =     8
							lowerCAmelCase_      =     8

							lowerCAmelCase_      =     torch.randint(0    ,    _UpperCamelCase    ,    (batch_size, height * width)					)

							return sample




				@property
				def 					__a						( self					)    ->				List[str]:
							assert False




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Tuple:
							def model(_UpperCamelCase    ,    _UpperCamelCase    ,    *_UpperCamelCase					):
										lowerCAmelCase_     ,  lowerCAmelCase_      =     sample.shape
										lowerCAmelCase_      =     torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)					)
										lowerCAmelCase_      =     F.log_softmax(logits.double()    ,    dim=1					).float()
										return return_value

							return model




				def 					__a						( self					)    ->				str:
							for timesteps in [2, 5, 100, 1_000]:
										self.check_over_configs(num_train_timesteps=_UpperCamelCase					)




				def 					__a						( self					)    ->				Dict:
							for num_vec_classes in [5, 100, 1_000, 4_000]:
										self.check_over_configs(num_vec_classes=_UpperCamelCase					)




				def 					__a						( self					)    ->				Tuple:
							for t in [0, 50, 99]:
										self.check_over_forward(time_step=_UpperCamelCase					)




				def 					__a						( self					)    ->				List[str]:
							pass



