






'''simple docstring'''







class 	SCREAMING_SNAKE_CASE					:






			def __init__( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			Tuple					)			->			List[str]:
							a_		:  int											=      None
							a_		:  int											=      None
							a_		:  List[str]											=      graph

							self._normalize_graph(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      len(__SCREAMING_SNAKE_CASE					)
							a_		:  int											=      None






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			Optional[Any]:
							if sources is int:
											a_		:  Optional[int]											=      [sources]
							if sinks is int:
											a_		:  Optional[int]											=      [sinks]

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

							a_		:  List[Any]											=      sources[0]
							a_		:  Dict											=      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:
											a_		:  List[str]											=      0
											for i in sources:
															max_input_flow += sum(self.graph[i]					)

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

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






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			List[Any]:
							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 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			Optional[Any]:
							a_		:  Any											=      algorithm(self					)





class 	SCREAMING_SNAKE_CASE					:






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






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Tuple:
							if not self.executed:
											self._algorithm()
											a_		:  Any											=      True







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





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):






			def __init__( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Dict					)			->			Dict:
							super().__init__(__SCREAMING_SNAKE_CASE					)
							# use this to save your result
							a_		:  Dict											=      -1







			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Tuple:
							if not self.executed:
											raise Exception('''You should execute algorithm before using its result!'''					)

							return self.maximum_flow





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):






			def __init__( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			List[str]					)			->			List[Any]:
							super().__init__(__SCREAMING_SNAKE_CASE					)

							a_		:  List[str]											=      [[0] * self.verticies_count for i in range(self.verticies_count					)]

							a_		:  Dict											=      [0] * self.verticies_count
							a_		:  Any											=      [0] * self.verticies_count






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Union[str, Any]:
							a_		:  Dict											=      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[int]											=      [
							    i
							    for i in range(self.verticies_count					)
							    if i != self.source_index and i != self.sink_index
							]

							# move through list
							a_		:  List[Any]											=      0
							while i < len(__SCREAMING_SNAKE_CASE					):
											a_		:  Optional[int]											=      vertices_list[i]
											a_		:  Optional[Any]											=      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					)					)
															a_		:  Dict											=      0
											else:
															i += 1

							a_		:  Dict											=      sum(self.preflow[self.source_index]					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Union[str, Any]:
							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 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			str					)			->			Tuple:
							a_		:  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 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Dict:
							a_		:  List[str]											=      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_		:  Optional[Any]											=      self.heights[to_index]

							if min_height is not None:
											a_		:  Union[str, Any]											=      min_height + 1


if __name__ == "__main__":
		__lowerCAmelCase       		=      [0]
		__lowerCAmelCase       		=      [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],
		# ]
		__lowerCAmelCase       		=      [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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












'''simple docstring'''







import requests
from bsa import BeautifulSoup





def 		_UpperCAmelCase   (							__A							:   str						,		__A							:   dict      ):
				a_		:  Tuple											=      BeautifulSoup(requests.get(__A						,		params=__A      ).content						,		'''html.parser'''      )
				a_		:  List[str]											=      soup.find('''div'''						,		attrs={'''class''': '''gs_ri'''}      )
				a_		:  List[str]											=      div.find('''div'''						,		attrs={'''class''': '''gs_fl'''}      ).find_all('''a'''      )
				return anchors[2].get_text()


if __name__ == "__main__":
		__lowerCAmelCase       		=      {
		    'title': (
		        'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
		        'capacitance, volumetric capacitance, and energy density'
		    ),
		    'journal': 'Chem. Mater.',
		    'volume': 30,
		    'pages': '3979-3990',
		    'year': 2_018,
		    'hl': 'en',
		}
		print(get_citation('https://scholar.google.com/scholar_lookup', params=params))




