





'''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'''
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    'split_dict'				,  [
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'				,  num_bytes=1337				,  num_examples=42				,  dataset_name='my_dataset'     )}     ),
        SplitDict({'train': SplitInfo(name='train'				,  num_bytes=1337				,  num_examples=42     )}     ),
        SplitDict({'train': SplitInfo()}     ),
    ]				,  )
def 				lowercase__   (   __lowercase						:  SplitDict     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  split_dict._to_yaml_list()
       assert len(__lowercase     ) == len(__lowercase     )
       __UpperCamelCase       =  SplitDict._from_yaml_list(__lowercase     )
       for split_name, split_info in split_dict.items():
              # dataset_name field is deprecated, and is therefore not part of the YAML dump
              __UpperCamelCase       =  None
              # the split name of split_dict takes over the name of the split info object
              __UpperCamelCase       =  split_name
       assert split_dict == reloaded


@pytest.mark.parametrize(
    'split_info'				,  [SplitInfo(), SplitInfo(dataset_name=__lowercase     ), SplitInfo(dataset_name='my_dataset'     )]     )
def 				lowercase__   (   __lowercase						:  Dict     ) ->      Any:



       """simple docstring"""






       __UpperCamelCase       =  asdict(SplitDict({'train': split_info}     )     )
       assert "dataset_name" in split_dict_asdict["train"]
       assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
