






import torch

from transformers import AutoModel




class 					UpperCamelCase_ (     torch.nn.Module							):



	'''simple docstring'''




	def __init__(			self			,     a="sayef/fsner-bert-base-uncased"   )			->       Optional[int]:
								super(a			,     self   ).__init__()

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




	def 				_UpperCamelCase     (			self			,     **a   )			->       str:
								return self.bert(**a   ).last_hidden_state




	def 				_UpperCamelCase     (			self			,     a   )			->       Optional[Any]:
								return token_embeddings.sum(2			,     keepdim=a   )




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a=1   )			->       Optional[int]:
								return self.softmax(T * self.cos(a			,     a   )   )






	def 				_UpperCamelCase     (			self			,     a			,     a   )			->       Optional[int]:
								snake_case_           =				W_supports['sizes'].tolist()
								snake_case_           =				W_supports['start_token_id'].item()
								snake_case_           =				W_supports['end_token_id'].item()

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

								snake_case_           =				self.BERT(**a   )
								snake_case_           =				self.BERT(**a   )

								snake_case_           =				None
								snake_case_           =				None

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

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

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

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

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

								return p_starts, p_ends













class 					UpperCamelCase_ :



	'''simple docstring'''




	def __init__(			self			,     a   )			->       Tuple:
								snake_case_           =				n
								snake_case_           =				[None] * self.n
								snake_case_           =				0  # index of the first element
								snake_case_           =				0
								snake_case_           =				0




	def __len__(			self   )			->       int:
								return self.size




	def 				_UpperCamelCase     (			self   )			->       bool:
								return self.size == 0




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								return False if self.is_empty() else self.array[self.front]




	def 				_UpperCamelCase     (			self			,     a   )			->       Dict:
								if self.size >= self.n:
															raise Exception('QUEUE IS FULL'   )

								snake_case_           =				data
								snake_case_           =				(self.rear + 1) % self.n
								self.size += 1
								return self






	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								if self.size == 0:
															raise Exception('UNDERFLOW'   )

								snake_case_           =				self.array[self.front]
								snake_case_           =				None
								snake_case_           =				(self.front + 1) % self.n
								self.size -= 1
								return temp





