

'''simple docstring'''





import torch

from transformers import AutoModel





class 		__SCREAMING_SNAKE_CASE       ( torch.nn.Module			):







				def __init__(					self    :		int		,				UpperCAmelCase__    :		Tuple="sayef/fsner-bert-base-uncased"      ):
										'''simple docstring'''
										super(UpperCAmelCase__		,				self      ).__init__()

										lowercase   :       str								=AutoModel.from_pretrained(UpperCAmelCase__		,				return_dict=UpperCAmelCase__      )
										lowercase   :       Dict								=torch.nn.CosineSimilarity(3		,				1E-08      )
										lowercase   :       Optional[int]								=torch.nn.Softmax(dim=1      )







				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				**UpperCAmelCase__    :		Dict      ):
										'''simple docstring'''
										return self.bert(**UpperCAmelCase__      ).last_hidden_state







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		List[str]      ):
										'''simple docstring'''
										return token_embeddings.sum(2		,				keepdim=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Optional[Any]		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		Optional[Any]=1      ):
										'''simple docstring'''
										return self.softmax(T * self.cos(UpperCAmelCase__		,				UpperCAmelCase__      )      )







				def    lowerCamelCase_  (					self    :		List[str]		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		Dict      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=W_supports['''sizes'''].tolist()
										lowercase   :       Optional[Any]								=W_supports['''start_token_id'''].item()
										lowercase   :       int								=W_supports['''end_token_id'''].item()

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

										lowercase   :       Tuple								=self.BERT(**UpperCAmelCase__      )
										lowercase   :       Dict								=self.BERT(**UpperCAmelCase__      )

										lowercase   :       Optional[Any]								=None
										lowercase   :       str								=None

										lowercase   :       int								=W_supports['''input_ids'''] == start_token_id
										lowercase   :       int								=W_supports['''input_ids'''] == end_token_id

										for i, size in enumerate(UpperCAmelCase__      ):
																if i == 0:
																						lowercase   :       Tuple								=0
																else:
																						lowercase   :       int								=support_sizes[i - 1]

																lowercase   :       List[Any]								=S[s : s + size][start_token_masks[s : s + size]]
																lowercase   :       Tuple								=S[s : s + size][end_token_masks[s : s + size]]

																lowercase   :       int								=torch.matmul(q[i]		,				s_start.T      ).sum(1      ).softmax(0      )
																lowercase   :       Tuple								=torch.matmul(q[i]		,				s_end.T      ).sum(1      ).softmax(0      )

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

										return p_starts, p_ends








'''simple docstring'''





def 							_lowerCAmelCase (      __magic_name__	:    int      ,   __magic_name__	:    Optional[Any]	)							->     str:
						lowercase   :       Optional[Any]								=[0 for i in range(r + 1	)]
						# nc0 = 1
						lowercase   :       Optional[Any]								=1
						for i in range(1      ,   n + 1	):
												# to compute current row from previous row.
												lowercase   :       str								=min(__magic_name__      ,   __magic_name__	)
												while j > 0:
																		c[j] += c[j - 1]
																		j -= 1
						return c[r]







print(binomial_coefficient(n=10, r=5))





