




class       a_       :





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						None
				SCREAMING_SNAKE_CASE_								=						None
				SCREAMING_SNAKE_CASE_								=						graph

				self._normalize_graph(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						len(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						None





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					Dict:




				"""simple docstring"""



				if sources is int:
							SCREAMING_SNAKE_CASE_								=						[sources]
				if sinks is int:
							SCREAMING_SNAKE_CASE_								=						[sinks]

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

				SCREAMING_SNAKE_CASE_								=						sources[0]
				SCREAMING_SNAKE_CASE_								=						sinks[0]

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

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

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





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				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 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						algorithm(self      )




class       a_       :





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						flow_network
				SCREAMING_SNAKE_CASE_								=						flow_network.verticesCount
				SCREAMING_SNAKE_CASE_								=						flow_network.sourceIndex
				SCREAMING_SNAKE_CASE_								=						flow_network.sinkIndex
				# it's just a reference, so you shouldn't change
				# it in your algorithms, use deep copy before doing that
				SCREAMING_SNAKE_CASE_								=						flow_network.graph
				SCREAMING_SNAKE_CASE_								=						False





	def 						A_(							self      )  ->					List[Any]:




				"""simple docstring"""



				if not self.executed:
							self._algorithm()
							SCREAMING_SNAKE_CASE_								=						True





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				pass




class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Union[str, Any]:




				"""simple docstring"""



				super().__init__(SCREAMING_SNAKE_CASE      )
				# use this to save your result
				SCREAMING_SNAKE_CASE_								=						-1





	def 						A_(							self      )  ->					str:




				"""simple docstring"""



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

				return self.maximum_flow




class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Optional[Any]:




				"""simple docstring"""



				super().__init__(SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						[[0] * self.verticies_count for i in range(self.verticies_count      )]

				SCREAMING_SNAKE_CASE_								=						[0] * self.verticies_count
				SCREAMING_SNAKE_CASE_								=						[0] * self.verticies_count





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						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
				SCREAMING_SNAKE_CASE_								=						[
				    i
				    for i in range(self.verticies_count      )
				    if i != self.source_index and i != self.sink_index
				]

				# move through list
				SCREAMING_SNAKE_CASE_								=						0
				while i < len(SCREAMING_SNAKE_CASE      ):
							SCREAMING_SNAKE_CASE_								=						vertices_list[i]
							SCREAMING_SNAKE_CASE_								=						self.heights[vertex_index]
							self.process_vertex(SCREAMING_SNAKE_CASE      )
							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(SCREAMING_SNAKE_CASE      )      )
										SCREAMING_SNAKE_CASE_								=						0
							else:
										i += 1

				SCREAMING_SNAKE_CASE_								=						sum(self.preflow[self.source_index]      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Optional[Any]:




				"""simple docstring"""



				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(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )

							self.relabel(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						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 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Union[str, Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						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):
										SCREAMING_SNAKE_CASE_								=						self.heights[to_index]

				if min_height is not None:
							SCREAMING_SNAKE_CASE_								=						min_height + 1


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     int		    =       [0]
				SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       [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],
				# ]
				SCREAMING_SNAKE_CASE__     :     int		    =       [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

				print(f"""maximum flow is {maximum_flow}""")






import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
				import jax



@require_flax
class       a_       (     SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= FlaxAutoencoderKL





	@property
	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						4
				SCREAMING_SNAKE_CASE_								=						3
				SCREAMING_SNAKE_CASE_								=						(32, 32)

				SCREAMING_SNAKE_CASE_								=						jax.random.PRNGKey(0      )
				SCREAMING_SNAKE_CASE_								=						jax.random.uniform(SCREAMING_SNAKE_CASE      ,     ((batch_size, num_channels) + sizes)      )

				return {"sample": image, "prng_key": prng_key}





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						{
				    'block_out_channels': [32, 64],
				    'in_channels': 3,
				    'out_channels': 3,
				    'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
				    'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
				    'latent_channels': 4,
				}
				SCREAMING_SNAKE_CASE_								=						self.dummy_input
				return init_dict, inputs_dict
