





'''simple docstring'''
import torch

from transformers import AutoModel

class      snake_case     (    torch.nn.Module       ):




 """simple docstring"""



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

        __UpperCamelCase       =  AutoModel.from_pretrained(__A							,	return_dict=__A	)
        __UpperCamelCase       =  torch.nn.CosineSimilarity(3							,	1e-08	)
        __UpperCamelCase       =  torch.nn.Softmax(dim=1	)






 def        _lowerCamelCase					(       self       :							Tuple							,	**__A       :							Optional[int]	):
        return self.bert(**__A	).last_hidden_state






 def        _lowerCamelCase					(       self       :							Tuple							,	__A       :							Tuple	):
        return token_embeddings.sum(2							,	keepdim=__A	)






 def        _lowerCamelCase					(       self       :							List[Any]							,	__A       :							str							,	__A       :							int							,	__A       :							str=1	):
        return self.softmax(T * self.cos(__A							,	__A	)	)






 def        _lowerCamelCase					(       self       :							Optional[int]							,	__A       :							str							,	__A       :							Any	):
        __UpperCamelCase       =  W_supports['sizes'].tolist()
        __UpperCamelCase       =  W_supports['start_token_id'].item()
        __UpperCamelCase       =  W_supports['end_token_id'].item()

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

        __UpperCamelCase       =  self.BERT(**__A	)
        __UpperCamelCase       =  self.BERT(**__A	)

        __UpperCamelCase       =  None
        __UpperCamelCase       =  None

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

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

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

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

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

        return p_starts, p_ends








"""simple docstring"""




_snake_case          =   '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_snake_case          =   [{'type': 'code', 'content': INSTALL_CONTENT}]
_snake_case          =   {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}

