




"""simple docstring"""




import torch

from transformers import AutoModel

class   UpperCAmelCase_    (			torch.nn.Module		):
		def __init__(						self				,						UpperCamelCase_="sayef/fsner-bert-base-uncased"       )						->       Optional[Any]:
					super(UpperCamelCase_				,						self       ).__init__()

					__lowercase			:  List[str]								=       AutoModel.from_pretrained(UpperCamelCase_				,						return_dict=UpperCamelCase_       )
					__lowercase			:  Any								=       torch.nn.CosineSimilarity(3				,						1E-08       )
					__lowercase			:  Optional[Any]								=       torch.nn.Softmax(dim=1       )
		def 	_lowerCamelCase							(						self				,						**UpperCamelCase_       )						->       Optional[Any]:
					return self.bert(**UpperCamelCase_       ).last_hidden_state
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_       )						->       Tuple:
					return token_embeddings.sum(2				,						keepdim=UpperCamelCase_       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_=1       )						->       Any:
					return self.softmax(T * self.cos(UpperCamelCase_				,						UpperCamelCase_       )       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_       )						->       List[str]:
					__lowercase			:  Tuple								=       W_supports['''sizes'''].tolist()
					__lowercase			:  Union[str, Any]								=       W_supports['''start_token_id'''].item()
					__lowercase			:  Dict								=       W_supports['''end_token_id'''].item()

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

					__lowercase			:  Optional[int]								=       self.BERT(**UpperCamelCase_       )
					__lowercase			:  Union[str, Any]								=       self.BERT(**UpperCamelCase_       )

					__lowercase			:  List[Any]								=       None
					__lowercase			:  List[Any]								=       None

					__lowercase			:  Tuple								=       W_supports['''input_ids'''] == start_token_id
					__lowercase			:  str								=       W_supports['''input_ids'''] == end_token_id

					for i, size in enumerate(UpperCamelCase_       ):
								if i == 0:
											__lowercase			:  int								=       0
								else:
											__lowercase			:  Optional[Any]								=       support_sizes[i - 1]

								__lowercase			:  int								=       S[s : s + size][start_token_masks[s : s + size]]
								__lowercase			:  List[str]								=       S[s : s + size][end_token_masks[s : s + size]]

								__lowercase			:  Optional[Any]								=       torch.matmul(q[i]				,						s_start.T       ).sum(1       ).softmax(0       )
								__lowercase			:  List[str]								=       torch.matmul(q[i]				,						s_end.T       ).sum(1       ).softmax(0       )

								if p_starts is not None:
											__lowercase			:  Optional[Any]								=       torch.vstack((p_starts, p_start)       )
											__lowercase			:  Dict								=       torch.vstack((p_ends, p_end)       )
								else:
											__lowercase			:  Any								=       p_start
											__lowercase			:  int								=       p_end

					return p_starts, p_ends







'''simple docstring'''





import numpy as np

from transformers import Pipeline






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Any ):






			"""simple docstring"""




			__A=	np.max(_SCREAMING_SNAKE_CASE,axis=-1,keepdims=_SCREAMING_SNAKE_CASE )
			__A=	np.exp(outputs - maxes )
			return shifted_exp / shifted_exp.sum(axis=-1,keepdims=_SCREAMING_SNAKE_CASE )






class 						a__					(					a_   ):





			'''simple docstring'''

			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    **lowerCAmelCase_							:			List[str]	)      ->		List[str]:
						__A=	{}
						if "second_text" in kwargs:
									__A=	kwargs['second_text']
						return preprocess_kwargs, {}, {}
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Optional[int]=None	)      ->		int:
						return self.tokenizer(lowerCAmelCase_			,    text_pair=lowerCAmelCase_			,    return_tensors=self.framework	)
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			List[str]	)      ->		Tuple:
						return self.model(**lowerCAmelCase_	)

			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Dict	)      ->		Optional[int]:
						__A=	model_outputs.logits[0].numpy()
						__A=	softmax(lowerCAmelCase_	)

						__A=	np.argmax(lowerCAmelCase_	)
						__A=	self.model.config.idalabel[best_class]
						__A=	probabilities[best_class].item()
						__A=	logits.tolist()
						return {"label": label, "score": score, "logits": logits}



