






"""simple docstring"""






class       snake_case_       :



	"""simple docstring"""

	def __init__(      self  ,					__a  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       None
					A__							 =       None
					A__							 =       graph

					self._normalize_graph(__a  ,					__a						)
					A__							 =       len(__a						)
					A__							 =       None


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

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

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

					A__							 =       sources[0]
					A__							 =       sinks[0]

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

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

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


	def       _UpperCAmelCase						(      self						):


					"""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       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       algorithm(self						)





class       snake_case_       :



	"""simple docstring"""

	def __init__(      self  ,					__a						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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






	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					pass





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	def __init__(      self  ,					__a						):


					"""simple docstring"""

					super().__init__(__a						)
					# use this to save your result
					A__							 =       -1






	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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

					return self.maximum_flow





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	def __init__(      self  ,					__a						):


					"""simple docstring"""

					super().__init__(__a						)

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

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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

					# move through list
					A__							 =       0
					while i < len(__a						):
									A__							 =       vertices_list[i]
									A__							 =       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__							 =       0
									else:
													i += 1

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


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""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(__a  ,					__a						)

									self.relabel(__a						)


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       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						):


					"""simple docstring"""

					A__							 =       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__							 =       self.heights[to_index]

					if min_height is not None:
									A__							 =       min_height + 1


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE						:						Optional[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						:						Optional[Any]					   =       [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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










"""simple docstring"""






import torch

from transformers import AutoModel





class       snake_case_       (    torch.nn.Module       ):



	"""simple docstring"""

	def __init__(      self  ,					__a="sayef/fsner-bert-base-uncased"						):


					"""simple docstring"""

					super(__a  ,					self						).__init__()

					A__							 =       AutoModel.from_pretrained(__a  ,					return_dict=__a						)
					A__							 =       torch.nn.CosineSimilarity(3  ,					1E-08						)
					A__							 =       torch.nn.Softmax(dim=1						)


	def       _UpperCAmelCase						(      self  ,					**__a						):


					"""simple docstring"""

					return self.bert(**__a						).last_hidden_state


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return token_embeddings.sum(2  ,					keepdim=__a						)


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a=1						):


					"""simple docstring"""

					return self.softmax(T * self.cos(__a  ,					__a						)						)






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       W_supports['sizes'].tolist()
					A__							 =       W_supports['start_token_id'].item()
					A__							 =       W_supports['end_token_id'].item()

					del W_supports["sizes"]
					del W_supports["start_token_id"]
					del W_supports["end_token_id"]

					A__							 =       self.BERT(**__a						)
					A__							 =       self.BERT(**__a						)

					A__							 =       None
					A__							 =       None

					A__							 =       W_supports['input_ids'] == start_token_id
					A__							 =       W_supports['input_ids'] == end_token_id

					for i, size in enumerate(__a						):
									if i == 0:
													A__							 =       0
									else:
													A__							 =       support_sizes[i - 1]

									A__							 =       S[s : s + size][start_token_masks[s : s + size]]
									A__							 =       S[s : s + size][end_token_masks[s : s + size]]

									A__							 =       torch.matmul(q[i]  ,					s_start.T						).sum(1						).softmax(0						)
									A__							 =       torch.matmul(q[i]  ,					s_end.T						).sum(1						).softmax(0						)

									if p_starts is not None:
													A__							 =       torch.vstack((p_starts, p_start)						)
													A__							 =       torch.vstack((p_ends, p_end)						)
									else:
													A__							 =       p_start
													A__							 =       p_end

					return p_starts, p_ends


