





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A     ,					_A     ):
									__A	:						Optional[int]       =				None
									__A	:						List[Any]       =				None
									__A	:						Tuple       =				graph

									self._normalize_graph(_A     ,					_A     )
									__A	:						str       =				len(_A     )
									__A	:						str       =				None



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									if sources is int:
														__A	:						int       =				[sources]
									if sinks is int:
														__A	:						Any       =				[sinks]

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

									__A	:						List[Any]       =				sources[0]
									__A	:						Tuple       =				sinks[0]

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

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

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



				def 					UpperCAmelCase_		(			self     ):
									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 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						Any       =				algorithm(self     )





class 						_A:


				"""simple docstring"""


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



				def 					UpperCAmelCase_		(			self     ):
									if not self.executed:
														self._algorithm()
														__A	:						Any       =				True





				def 					UpperCAmelCase_		(			self     ):
									pass





class 						_A(					snake_case__   ):


				"""simple docstring"""


				def __init__(			self     ,					_A     ):
									super().__init__(_A     )
									# use this to save your result
									__A	:						List[Any]       =				-1





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

									return self.maximum_flow





class 						_A(					snake_case__   ):


				"""simple docstring"""


				def __init__(			self     ,					_A     ):
									super().__init__(_A     )

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

									__A	:						str       =				[0] * self.verticies_count
									__A	:						List[str]       =				[0] * self.verticies_count



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Any       =				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
									__A	:						Optional[Any]       =				[
									    i
									    for i in range(self.verticies_count     )
									    if i != self.source_index and i != self.sink_index
									]

									# move through list
									__A	:						Union[str, Any]       =				0
									while i < len(_A     ):
														__A	:						int       =				vertices_list[i]
														__A	:						Dict       =				self.heights[vertex_index]
														self.process_vertex(_A     )
														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(_A     )     )
																			__A	:						int       =				0
														else:
																			i += 1

									__A	:						Optional[Any]       =				sum(self.preflow[self.source_index]     )



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									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(_A     ,					_A     )

														self.relabel(_A     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									__A	:						Union[str, Any]       =				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 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						List[Any]       =				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):
																			__A	:						Union[str, Any]       =				self.heights[to_index]

									if min_height is not None:
														__A	:						List[str]       =				min_height + 1


if __name__ == "__main__":
				UpperCAmelCase						:   int			  =     [0]
				UpperCAmelCase						:   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],
				# ]
				UpperCAmelCase						:   List[str]			  =     [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

				# prepare our network
				UpperCAmelCase						:   int			  =     FlowNetwork(graph, entrances, exits)
				# set algorithm
				flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
				# and calculate
				UpperCAmelCase						:   Optional[Any]			  =     flow_network.find_maximum_flow()

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











import argparse
import json

from tqdm import tqdm







def  _SCREAMING_SNAKE_CASE				(   )      ->      List[Any]:
					__A	:						Tuple       =				argparse.ArgumentParser()

					# Required parameters
					parser.add_argument(
					    '--src_path'					,						type=a					,						default='biencoder-nq-dev.json'					,						help='Path to raw DPR training data'					,						)
					parser.add_argument(
					    '--evaluation_set'					,						type=a					,						help='where to store parsed evaluation_set file'					,						)
					parser.add_argument(
					    '--gold_data_path'					,						type=a					,						help='where to store parsed gold_data_path file'					,						)
					__A	:						Optional[int]       =				parser.parse_args()

					with open(args.src_path					,						'r'  ) as src_file, open(args.evaluation_set					,						'w'  ) as eval_file, open(
					    args.gold_data_path					,						'w'  ) as gold_file:
										__A	:						List[Any]       =				json.load(a  )
										for dpr_record in tqdm(a  ):
															__A	:						Dict       =				dpr_record['question']
															__A	:						Any       =				[context['title'] for context in dpr_record['positive_ctxs']]
															eval_file.write(question + '\n'  )
															gold_file.write('\t'.join(a  ) + '\n'  )


if __name__ == "__main__":
				main()




