





def       A__(		__lowerCAmelCase ):
				_snake_case :			list[list[float]]  						=   []
				for data in source_data:
								for i, el in enumerate(__lowerCAmelCase ):
												if len(__lowerCAmelCase ) < i + 1:
																data_lists.append([] )
												data_lists[i].append(float(__lowerCAmelCase ) )
				return data_lists

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			list[list[float]]  						=   []
				for dlist, weight in zip(__lowerCAmelCase	,			__lowerCAmelCase ):
								_snake_case :			str  						=   min(__lowerCAmelCase )
								_snake_case :			Optional[Any]  						=   max(__lowerCAmelCase )

								_snake_case :			list[float]  						=   []
								# for weight 0 score is 1 - actual score
								if weight == 0:
												for item in dlist:
																try:
																				score.append(1 - ((item - mind) / (maxd - mind)) )
																except ZeroDivisionError:
																				score.append(1 )

								elif weight == 1:
												for item in dlist:
																try:
																				score.append((item - mind) / (maxd - mind) )
																except ZeroDivisionError:
																				score.append(0 )

        # weight not 0 or 1
								else:
												_snake_case :			int  						=   F'''Invalid weight of {weight:f} provided'''
												raise ValueError(__lowerCAmelCase )

								score_lists.append(__lowerCAmelCase )

				return score_lists

def       A__(		__lowerCAmelCase ):
				_snake_case :			list[float]  						=   [0 for i in range(len(score_lists[0] ) )]

				for slist in score_lists:
								for j, ele in enumerate(__lowerCAmelCase ):
												_snake_case :			Union[str, Any]  						=   final_scores[j] + ele

				return final_scores

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Dict  						=   get_data(__lowerCAmelCase )
				_snake_case :			Dict  						=   calculate_each_score(__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case :			Optional[Any]  						=   generate_final_scores(__lowerCAmelCase )

				# append scores to source data
				for i, ele in enumerate(__lowerCAmelCase ):
								source_data[i].append(__lowerCAmelCase )

				return source_data













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






