





'''simple docstring'''







import torch

from transformers import AutoModel





class        _UpperCAmelCase				(		torch.nn.Module  ):
		def __init__(    self,__SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"     ):



					'''simple docstring'''

					super(UpperCAmelCase_,self     ).__init__()

					__lowerCAmelCase   		=							AutoModel.from_pretrained(UpperCAmelCase_,return_dict=UpperCAmelCase_     )
					__lowerCAmelCase   		=							torch.nn.CosineSimilarity(3,1e-08     )
					__lowerCAmelCase   		=							torch.nn.Softmax(dim=1     )
		def     lowerCamelCase__     (    self,**__SCREAMING_SNAKE_CASE     ):



					'''simple docstring'''

					return self.bert(**UpperCAmelCase_     ).last_hidden_state
		def     lowerCamelCase__     (    self,__SCREAMING_SNAKE_CASE     ):



					'''simple docstring'''

					return token_embeddings.sum(2,keepdim=UpperCAmelCase_     )
		def     lowerCamelCase__     (    self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=1     ):



					'''simple docstring'''

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







		def     lowerCamelCase__     (    self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE     ):



					'''simple docstring'''

					__lowerCAmelCase   		=							W_supports["""sizes"""].tolist()
					__lowerCAmelCase   		=							W_supports["""start_token_id"""].item()
					__lowerCAmelCase   		=							W_supports["""end_token_id"""].item()

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

					__lowerCAmelCase   		=							self.BERT(**UpperCAmelCase_     )
					__lowerCAmelCase   		=							self.BERT(**UpperCAmelCase_     )

					__lowerCAmelCase   		=							None
					__lowerCAmelCase   		=							None

					__lowerCAmelCase   		=							W_supports["""input_ids"""] == start_token_id
					__lowerCAmelCase   		=							W_supports["""input_ids"""] == end_token_id

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

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

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

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

					return p_starts, p_ends






'''simple docstring'''





def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    return "\n".join(
        f'''{number} * {i} = {number * i}''' for i in range(1  ,number_of_terms + 1							)							)


if __name__ == "__main__":
 print(multiplication_table(number=5, number_of_terms=10))




