
class 				UpperCAmelCase						:

					def __init__(      self   :       str			,   lowerCAmelCase   :       Any			,   lowerCAmelCase   :       List[Any]			,   lowerCAmelCase   :       Union[str, Any]       ):
											lowercase							:     Optional[Any]									=	None
											lowercase							:     List[Any]									=	None
											lowercase							:     Optional[Any]									=	graph

											self._normalize_graph(lowerCamelCase_			,   lowerCamelCase_       )
											lowercase							:     Tuple									=	len(lowerCamelCase_       )
											lowercase							:     Optional[int]									=	None

					def 							_lowerCAmelCase						(      self   :       Tuple			,   lowerCAmelCase   :       int			,   lowerCAmelCase   :       List[Any]       ):



											if sources is int:
																	lowercase							:     Dict									=	[sources]
											if sinks is int:
																	lowercase							:     str									=	[sinks]

											if len(lowerCamelCase_       ) == 0 or len(lowerCamelCase_       ) == 0:
																	return

											lowercase							:     Optional[Any]									=	sources[0]
											lowercase							:     List[str]									=	sinks[0]

											# make fake vertex if there are more
											# than one source or sink
											if len(lowerCamelCase_       ) > 1 or len(lowerCamelCase_       ) > 1:
																	lowercase							:     List[str]									=	0
																	for i in sources:
																							max_input_flow += sum(self.graph[i]       )

																	lowercase							:     Dict									=	len(self.graph       ) + 1
																	for room in self.graph:
																							room.insert(0			,   0       )
																	self.graph.insert(0			,   [0] * size       )
																	for i in sources:
																							lowercase							:     str									=	max_input_flow
																	lowercase							:     str									=	0

																	lowercase							:     Optional[int]									=	len(self.graph       ) + 1
																	for room in self.graph:
																							room.append(0       )
																	self.graph.append([0] * size       )
																	for i in sinks:
																							lowercase							:     int									=	max_input_flow
																	lowercase							:     Dict									=	size - 1

					def 							_lowerCAmelCase						(      self   :       Tuple       ):



											if self.maximum_flow_algorithm is None:
																	raise Exception('''You need to set maximum flow algorithm before.'''       )
											if self.source_index is None or self.sink_index is None:
																	return 0

											self.maximum_flow_algorithm.execute()
											return self.maximum_flow_algorithm.getMaximumFlow()




					def 							_lowerCAmelCase						(      self   :       Dict			,   lowerCAmelCase   :       Union[str, Any]       ):
											lowercase							:     Tuple									=	algorithm(self       )







class 				UpperCAmelCase						:

					def __init__(      self   :       List[str]			,   lowerCAmelCase   :       int       ):
											lowercase							:     int									=	flow_network
											lowercase							:     Union[str, Any]									=	flow_network.verticesCount
											lowercase							:     int									=	flow_network.sourceIndex
											lowercase							:     Dict									=	flow_network.sinkIndex
											# it's just a reference, so you shouldn't change
											# it in your algorithms, use deep copy before doing that
											lowercase							:     List[str]									=	flow_network.graph
											lowercase							:     int									=	False

					def 							_lowerCAmelCase						(      self   :       List[str]       ):



											if not self.executed:
																	self._algorithm()
																	lowercase							:     Any									=	True




					def 							_lowerCAmelCase						(      self   :       Tuple       ):
											pass







class 				UpperCAmelCase						( lowercase_       ):

					def __init__(      self   :       List[Any]			,   lowerCAmelCase   :       int       ):
											super().__init__(lowerCamelCase_       )
											# use this to save your result
											lowercase							:     int									=	-1




					def 							_lowerCAmelCase						(      self   :       Any       ):



											if not self.executed:
																	raise Exception('''You should execute algorithm before using its result!'''       )

											return self.maximum_flow







class 				UpperCAmelCase						( lowercase_       ):

					def __init__(      self   :       int			,   lowerCAmelCase   :       Any       ):
											super().__init__(lowerCamelCase_       )

											lowercase							:     Any									=	[[0] * self.verticies_count for i in range(self.verticies_count       )]

											lowercase							:     Optional[int]									=	[0] * self.verticies_count
											lowercase							:     Tuple									=	[0] * self.verticies_count

					def 							_lowerCAmelCase						(      self   :       str       ):
											lowercase							:     str									=	self.verticies_count

											# push some substance to graph
											for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]       ):
																	self.preflow[self.source_index][nextvertex_index] += bandwidth
																	self.preflow[nextvertex_index][self.source_index] -= bandwidth
																	self.excesses[nextvertex_index] += bandwidth

											# Relabel-to-front selection rule
											lowercase							:     Any									=	[
											    i
											    for i in range(self.verticies_count       )
											    if i != self.source_index and i != self.sink_index
											]

											# move through list
											lowercase							:     Any									=	0
											while i < len(lowerCamelCase_       ):
																	lowercase							:     Union[str, Any]									=	vertices_list[i]
																	lowercase							:     Tuple									=	self.heights[vertex_index]
																	self.process_vertex(lowerCamelCase_       )
																	if self.heights[vertex_index] > previous_height:
																							# if it was relabeled, swap elements
																							# and start from 0 index
																							vertices_list.insert(0			,   vertices_list.pop(lowerCamelCase_       )       )
																							lowercase							:     List[str]									=	0
																	else:
																							i += 1

											lowercase							:     List[str]									=	sum(self.preflow[self.source_index]       )

					def 							_lowerCAmelCase						(      self   :       str			,   lowerCAmelCase   :       str       ):



											while self.excesses[vertex_index] > 0:
																	for neighbour_index in range(self.verticies_count       ):
																							# if it's neighbour and current vertex is higher
																							if (
																							    self.graph[vertex_index][neighbour_index]
																							    - self.preflow[vertex_index][neighbour_index]
																							    > 0
																							    and self.heights[vertex_index] > self.heights[neighbour_index]
																							):
																													self.push(lowerCamelCase_			,   lowerCamelCase_       )

																	self.relabel(lowerCamelCase_       )

					def 							_lowerCAmelCase						(      self   :       int			,   lowerCAmelCase   :       str			,   lowerCAmelCase   :       List[str]       ):
											lowercase							:     str									=	min(
											    self.excesses[from_index]			,   self.graph[from_index][to_index] - self.preflow[from_index][to_index]			,   )
											self.preflow[from_index][to_index] += preflow_delta
											self.preflow[to_index][from_index] -= preflow_delta
											self.excesses[from_index] -= preflow_delta
											self.excesses[to_index] += preflow_delta




					def 							_lowerCAmelCase						(      self   :       Optional[Any]			,   lowerCAmelCase   :       Optional[Any]       ):
											lowercase							:     Dict									=	None
											for to_index in range(self.verticies_count       ):
																	if (
																	    self.graph[vertex_index][to_index]
																	    - self.preflow[vertex_index][to_index]
																	    > 0
																	) and (min_height is None or self.heights[to_index] < min_height):
																							lowercase							:     Optional[int]									=	self.heights[to_index]

											if min_height is not None:
																	lowercase							:     Dict									=	min_height + 1


if __name__ == "__main__":
				snake_case__					=				[0]
				snake_case__					=				[3]
				# graph = [
				#     [0, 0, 4, 6, 0, 0],
				#     [0, 0, 5, 2, 0, 0],
				#     [0, 0, 0, 0, 4, 4],
				#     [0, 0, 0, 0, 6, 6],
				#     [0, 0, 0, 0, 0, 0],
				#     [0, 0, 0, 0, 0, 0],
				# ]
				snake_case__					=				[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

				# prepare our network
				snake_case__					=				FlowNetwork(graph, entrances, exits)
				# set algorithm
				flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
				# and calculate
				snake_case__					=				flow_network.find_maximum_flow()

				print(F'maximum flow is {maximum_flow}')









'''simple docstring'''




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	(UnCLIPScheduler,)


					def lowerCamelCase_			(						self				:			List[str]      ,						**lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			{
							    """num_train_timesteps""": 10_00,
							    """variance_type""": """fixed_small_log""",
							    """clip_sample""": True,
							    """clip_sample_range""": 1.0,
							    """prediction_type""": """epsilon""",
							}

							config.update(**lowerCamelCase_						)
							return config


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for timesteps in [1, 5, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							for variance in ["fixed_small_log", "learned_range"]:
									self.check_over_configs(variance_type=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							for clip_sample_range in [1, 5, 10, 20]:
									self.check_over_configs(clip_sample_range=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for prediction_type in ["epsilon", "sample"]:
									self.check_over_configs(prediction_type=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for time_step in [0, 5_00, 9_99]:
									for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
											if prev_timestep is not None and prev_timestep >= time_step:
													continue

											self.check_over_forward(time_step=lowerCamelCase_      ,						prev_timestep=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Dict		=			self.get_scheduler_config(variance_type="""fixed_small_log"""						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler_class(**lowerCamelCase_						)

							assert torch.sum(torch.abs(scheduler._get_variance(0						) - 1.0000e-10						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87						) - 0.0_549_625						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99						) - 0.9_994_987						)						) < 1e-5


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.get_scheduler_config(variance_type="""learned_range"""						)
							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				List[str]		=			0.5

							assert scheduler._get_variance(1      ,						predicted_variance=lowerCamelCase_						) - -10.1_712_790 < 1e-5
							assert scheduler._get_variance(4_87      ,						predicted_variance=lowerCamelCase_						) - -5.7_998_052 < 1e-5
							assert scheduler._get_variance(9_99      ,						predicted_variance=lowerCamelCase_						) - -0.0_010_011 < 1e-5


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				int		=			self.get_scheduler_config()
							SCREAMING_SNAKE_CASE :				List[Any]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler.timesteps

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dummy_sample_deter
							SCREAMING_SNAKE_CASE :				str		=			torch.manual_seed(0						)

							for i, t in enumerate(lowerCamelCase_						):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE :				List[str]		=			model(lowerCamelCase_      ,						lowerCamelCase_						)

									# 2. predict previous mean of sample x_t-1
									SCREAMING_SNAKE_CASE :				Optional[int]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						generator=lowerCamelCase_						).prev_sample

									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			pred_prev_sample

							SCREAMING_SNAKE_CASE :				Optional[int]		=			torch.sum(torch.abs(lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE :				int		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_sum.item() - 252.2_682_495						) < 1e-2
							assert abs(result_mean.item() - 0.3_284_743						) < 1e-3


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Dict		=			self.get_scheduler_config()
							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class(**lowerCamelCase_						)

							scheduler.set_timesteps(25						)

							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler.timesteps

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Tuple		=			self.dummy_sample_deter
							SCREAMING_SNAKE_CASE :				Any		=			torch.manual_seed(0						)

							for i, t in enumerate(lowerCamelCase_						):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE :				int		=			model(lowerCamelCase_      ,						lowerCamelCase_						)

									if i + 1 == timesteps.shape[0]:
											SCREAMING_SNAKE_CASE :				Union[str, Any]		=			None
									else:
											SCREAMING_SNAKE_CASE :				int		=			timesteps[i + 1]

									# 2. predict previous mean of sample x_t-1
									SCREAMING_SNAKE_CASE :				int		=			scheduler.step(
									    lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						prev_timestep=lowerCamelCase_      ,						generator=lowerCamelCase_						).prev_sample

									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			pred_prev_sample

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.sum(torch.abs(lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_sum.item() - 258.2_044_983						) < 1e-2
							assert abs(result_mean.item() - 0.3_362_038						) < 1e-3


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							pass



					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							pass



