





from __future__ import annotations

from typing import Generic, TypeVar

lowercase_		:				List[Any]				=						TypeVar('''T''')


class 			lowercase				(							Generic[T]     ):



						"""simple docstring"""

						def __init__(    self			:		List[Any] ,				lowerCamelCase_			:		T					):





										'''simple docstring'''







										_snake_case :			Tuple  						=   data
										_snake_case :			Tuple  						=   self
										_snake_case :			Union[str, Any]  						=   0



class 			lowercase				(							Generic[T]     ):



						"""simple docstring"""

						def __init__(    self			:		str					):





										'''simple docstring'''







										_snake_case :			Optional[Any]  						=   {}







						def 		__UpperCAmelCase       (    self			:		List[Any] ,				lowerCamelCase_			:		T					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   DisjointSetTreeNode(__lowercase					)







						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		T					):





										'''simple docstring'''







										_snake_case :			str  						=   self.map[data]
										if elem_ref != elem_ref.parent:
														_snake_case :			int  						=   self.find_set(elem_ref.parent.data					)
										return elem_ref.parent







						def 		__UpperCAmelCase       (    self			:		List[str] ,				lowerCamelCase_			:		DisjointSetTreeNode[T] ,				lowerCamelCase_			:		DisjointSetTreeNode[T]					):





										'''simple docstring'''







										if nodea.rank > nodea.rank:
														_snake_case :			Optional[int]  						=   nodea
										else:
														_snake_case :			str  						=   nodea
														if nodea.rank == nodea.rank:
																		nodea.rank += 1







						def 		__UpperCAmelCase       (    self			:		List[str] ,				lowerCamelCase_			:		T ,				lowerCamelCase_			:		T					):





										'''simple docstring'''







										self.link(self.find_set(__lowercase					) ,				self.find_set(__lowercase					)					)



class 			lowercase				(							Generic[T]     ):



						"""simple docstring"""

						def __init__(    self			:		int					):





										'''simple docstring'''







										_snake_case :			Optional[Any]  						=   {}







						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		T					):





										'''simple docstring'''







										if node not in self.connections:
														_snake_case :			Optional[int]  						=   {}







						def 		__UpperCAmelCase       (    self			:		List[str] ,				lowerCamelCase_			:		T ,				lowerCamelCase_			:		T ,				lowerCamelCase_			:		int					):





										'''simple docstring'''







										self.add_node(__lowercase					)
										self.add_node(__lowercase					)
										_snake_case :			str  						=   weight
										_snake_case :			Optional[Any]  						=   weight







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										_snake_case :			Dict  						=   []
										_snake_case :			Optional[Any]  						=   set()
										for start in self.connections:
														for end in self.connections[start]:
																		if (start, end) not in seen:
																						seen.add((end, start)					)
																						edges.append((start, end, self.connections[start][end])					)
										edges.sort(key=lambda lowerCamelCase_					: x[2]					)

										# creating the disjoint set
										_snake_case :			List[Any]  						=   DisjointSetTree[T]()
										for node in self.connections:
														disjoint_set.make_set(__lowercase					)

										# MST generation
										_snake_case :			str  						=   0
										_snake_case :			Optional[Any]  						=   0
										_snake_case :			Union[str, Any]  						=   GraphUndirectedWeighted[T]()
										while num_edges < len(self.connections					) - 1:
														_snake_case						,			_snake_case						,			_snake_case :			Tuple  						=   edges[index]
														index += 1
														_snake_case :			Optional[Any]  						=   disjoint_set.find_set(__lowercase					)
														_snake_case :			Optional[Any]  						=   disjoint_set.find_set(__lowercase					)
														if parent_u != parent_v:
																		num_edges += 1
																		graph.add_edge(__lowercase ,				__lowercase ,				__lowercase					)
																		disjoint_set.union(__lowercase ,				__lowercase					)
										return graph













import torch

from transformers import AutoModel


class 			lowercase				(							torch.nn.Module     ):



						"""simple docstring"""

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





										'''simple docstring'''







										super(lowerCamelCase_ ,				self					).__init__()

										_snake_case :			Optional[Any]  						=   AutoModel.from_pretrained(lowerCamelCase_ ,				return_dict=lowerCamelCase_					)
										_snake_case :			str  						=   torch.nn.CosineSimilarity(3 ,				1e-08					)
										_snake_case :			str  						=   torch.nn.Softmax(dim=1					)







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





										'''simple docstring'''







										return self.bert(**lowerCamelCase_					).last_hidden_state







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





										'''simple docstring'''







										return token_embeddings.sum(2 ,				keepdim=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Dict ,				lowerCamelCase_			:		Dict ,				lowerCamelCase_			:		Tuple ,				lowerCamelCase_			:		Optional[Any]=1					):





										'''simple docstring'''







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







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





										'''simple docstring'''







										_snake_case :			int  						=   W_supports['sizes'].tolist()
										_snake_case :			int  						=   W_supports['start_token_id'].item()
										_snake_case :			List[str]  						=   W_supports['end_token_id'].item()

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

										_snake_case :			Optional[int]  						=   self.BERT(**lowerCamelCase_					)
										_snake_case :			Optional[Any]  						=   self.BERT(**lowerCamelCase_					)

										_snake_case :			Optional[int]  						=   None
										_snake_case :			Optional[int]  						=   None

										_snake_case :			List[str]  						=   W_supports['input_ids'] == start_token_id
										_snake_case :			Union[str, Any]  						=   W_supports['input_ids'] == end_token_id

										for i, size in enumerate(lowerCamelCase_					):
														if i == 0:
																		_snake_case :			str  						=   0
														else:
																		_snake_case :			Union[str, Any]  						=   support_sizes[i - 1]

														_snake_case :			Tuple  						=   S[s : s + size][start_token_masks[s : s + size]]
														_snake_case :			Optional[int]  						=   S[s : s + size][end_token_masks[s : s + size]]

														_snake_case :			Tuple  						=   torch.matmul(q[i] ,				s_start.T					).sum(1					).softmax(0					)
														_snake_case :			Union[str, Any]  						=   torch.matmul(q[i] ,				s_end.T					).sum(1					).softmax(0					)

														if p_starts is not None:
																		_snake_case :			Optional[Any]  						=   torch.vstack((p_starts, p_start)					)
																		_snake_case :			List[str]  						=   torch.vstack((p_ends, p_end)					)
														else:
																		_snake_case :			Union[str, Any]  						=   p_start
																		_snake_case :			Any  						=   p_end

										return p_starts, p_ends






