"""simple docstring"""





import torch

from transformers import AutoModel


class      UpperCAmelCase_	(    torch.nn.Module):


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

             lowercase__			:		str       =	AutoModel.from_pretrained(a		,			return_dict=a							)
             lowercase__			:		Any       =	torch.nn.CosineSimilarity(3		,			1e-08							)
             lowercase__			:		Union[str, Any]       =	torch.nn.Softmax(dim=1							)


      def 		_UpperCAmelCase       (   self		,			**a							)       ->					List[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							)       ->					str:
             return self.softmax(T * self.cos(a		,			a							)							)


      def 		_UpperCAmelCase       (   self		,			a		,			a							)       ->					str:
             lowercase__			:		Union[str, Any]       =	W_supports['sizes'].tolist()
             lowercase__			:		str       =	W_supports['start_token_id'].item()
             lowercase__			:		str       =	W_supports['end_token_id'].item()

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

             lowercase__			:		Any       =	self.BERT(**a							)
             lowercase__			:		Any       =	self.BERT(**a							)

             lowercase__			:		Tuple       =	None
             lowercase__			:		Dict       =	None

             lowercase__			:		str       =	W_supports['input_ids'] == start_token_id
             lowercase__			:		List[str]       =	W_supports['input_ids'] == end_token_id

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

                    lowercase__			:		Dict       =	S[s : s + size][start_token_masks[s : s + size]]
                    lowercase__			:		int       =	S[s : s + size][end_token_masks[s : s + size]]

                    lowercase__			:		Any       =	torch.matmul(q[i]		,			s_start.T							).sum(1							).softmax(0							)
                    lowercase__			:		int       =	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__			:		Tuple       =	torch.vstack((p_ends, p_end)							)
                    else:
                           lowercase__			:		Optional[int]       =	p_start
                           lowercase__			:		Optional[int]       =	p_end

             return p_starts, p_ends







"""simple docstring"""






import socket







def      _snake_case			(				):
				_lowerCamelCase     :			List[Any]    =    socket.socket(socket.AF_INET							,    socket.SOCK_STREAM		)
				_lowerCamelCase     :			Union[str, Any]    =    socket.gethostname()
				_lowerCamelCase     :			List[Any]    =    12312

				sock.connect((host, port)		)
				sock.send(B'Hello server!'		)

				with open('Received_file'							,    'wb'		) as out_file:
								print('File opened'		)
								print('Receiving data...'		)
								while True:
												_lowerCamelCase     :			int    =    sock.recv(1024		)
												if not data:
																break
												out_file.write(lowercase__		)

				print('Successfully received the file'		)
				sock.close()
				print('Connection closed'		)


if __name__ == "__main__":
			main()