





'''simple docstring'''




import torch

from transformers import AutoModel







class   A_	(  torch.nn.Module			):




 def __init__(					self       :    Union[str, Any]	,			snake_case_       :    Any="sayef/fsner-bert-base-uncased"    ):
      super(snake_case_	,			self    ).__init__()

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




 def      lowercase	(					self       :    Any	,			**snake_case_       :    Union[str, Any]    ):
      return self.bert(**snake_case_    ).last_hidden_state




 def      lowercase	(					self       :    str	,			snake_case_       :    Optional[int]    ):
      return token_embeddings.sum(2	,			keepdim=snake_case_    )




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    Tuple	,			snake_case_       :    str	,			snake_case_       :    Tuple=1    ):
      return self.softmax(T * self.cos(snake_case_	,			snake_case_    )    )




 def      lowercase	(					self       :    List[Any]	,			snake_case_       :    int	,			snake_case_       :    str    ):
      _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(**snake_case_    )
      _UpperCAmelCase     							=   self.BERT(**snake_case_    )

      _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(snake_case_    ):
           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.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
    "kwargs, expected"			,     [
        ({"num_shards": 0, "max_num_jobs": 1}, []),
        ({"num_shards": 10, "max_num_jobs": 1}, [range(10       )]),
        ({"num_shards": 10, "max_num_jobs": 10}, [range(__lowercase			,     i + 1       ) for i in range(10       )]),
        ({"num_shards": 1, "max_num_jobs": 10}, [range(1       )]),
        ({"num_shards": 10, "max_num_jobs": 3}, [range(0			,     4       ), range(4			,     7       ), range(7			,     10       )]),
        ({"num_shards": 3, "max_num_jobs": 10}, [range(0			,     1       ), range(1			,     2       ), range(2			,     3       )]),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      Dict       )		->       List[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   _distribute_shards(**__lowercase       )
     assert out == expected
@pytest.mark.parametrize(
    "gen_kwargs, max_num_jobs, expected"			,     [
        ({"foo": 0}, 10, [{"foo": 0}]),
        ({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
        ({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
        ({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
        ({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Dict			,     __lowercase							:      Optional[Any]			,     __lowercase							:      int       )		->       str:


     '''simple docstring'''



     _UpperCAmelCase     							=   _split_gen_kwargs(__lowercase			,     __lowercase       )
     assert out == expected






@pytest.mark.parametrize(
    "gen_kwargs, expected"			,     [
        ({"foo": 0}, 1),
        ({"shards": [0]}, 1),
        ({"shards": [0, 1, 2, 3]}, 4),
        ({"shards": [0, 1, 2, 3], "foo": 0}, 4),
        ({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
        ({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Optional[Any]			,     __lowercase							:      List[Any]       )		->       List[Any]:


     '''simple docstring'''



     if expected is RuntimeError:
          with pytest.raises(__lowercase       ):
               _number_of_shards_in_gen_kwargs(__lowercase       )
     else:
          _UpperCAmelCase     							=   _number_of_shards_in_gen_kwargs(__lowercase       )
          assert out == expected




