


import torch

from transformers import AutoModel


class 		_SCREAMING_SNAKE_CASE     (						torch.nn.Module     ):


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

       _UpperCAmelCase		:							Optional[Any]							=     AutoModel.from_pretrained(A_							,					return_dict=A_    )
       _UpperCAmelCase		:							List[Any]							=     torch.nn.CosineSimilarity(3							,					1e-08    )
       _UpperCAmelCase		:							str							=     torch.nn.Softmax(dim=1    )


     def        __snake_case(  self							,					**A_    ):
       return self.bert(**A_    ).last_hidden_state


     def        __snake_case(  self							,					A_    ):
       return token_embeddings.sum(2							,					keepdim=A_    )


     def        __snake_case(  self							,					A_							,					A_							,					A_=1    ):
       return self.softmax(T * self.cos(A_							,					A_    )    )




     def        __snake_case(  self							,					A_							,					A_    ):
       _UpperCAmelCase		:							Any							=     W_supports["""sizes"""].tolist()
       _UpperCAmelCase		:							str							=     W_supports["""start_token_id"""].item()
       _UpperCAmelCase		:							Dict							=     W_supports["""end_token_id"""].item()

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

       _UpperCAmelCase		:							Optional[Any]							=     self.BERT(**A_    )
       _UpperCAmelCase		:							Optional[Any]							=     self.BERT(**A_    )

       _UpperCAmelCase		:							List[Any]							=     None
       _UpperCAmelCase		:							str							=     None

       _UpperCAmelCase		:							Optional[int]							=     W_supports["""input_ids"""] == start_token_id
       _UpperCAmelCase		:							Union[str, Any]							=     W_supports["""input_ids"""] == end_token_id

       for i, size in enumerate(A_    ):
         if i == 0:
           _UpperCAmelCase		:							Dict							=     0
         else:
           _UpperCAmelCase		:							Tuple							=     support_sizes[i - 1]

         _UpperCAmelCase		:							Union[str, Any]							=     S[s : s + size][start_token_masks[s : s + size]]
         _UpperCAmelCase		:							List[str]							=     S[s : s + size][end_token_masks[s : s + size]]

         _UpperCAmelCase		:							Tuple							=     torch.matmul(q[i]							,					s_start.T    ).sum(1    ).softmax(0    )
         _UpperCAmelCase		:							Any							=     torch.matmul(q[i]							,					s_end.T    ).sum(1    ).softmax(0    )

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

       return p_starts, p_ends









SCREAMING_SNAKE_CASE__					:     dict[str, float]       =     {
    "joule": 1.0,
    "kilojoule": 1_000,
    "megajoule": 1_000_000,
    "gigajoule": 1_000_000_000,
    "wattsecond": 1.0,
    "watthour": 3_600,
    "kilowatthour": 3_600_000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_186.8,
    "kilocalorie_nutr": 4_186_800.00,
    "electronvolt": 1.602176634e-19,
    "britishthermalunit_it": 1_055.05_585,
    "footpound": 1.355_818,
}




def    a__    (		snake_case__					:							str						,   snake_case__					:							str						,   snake_case__					:							float     ):
  if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
    _UpperCAmelCase		:							Optional[Any]							=     (
        f'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
        f'''Valid values are: {", ".join(snake_case__     )}'''
    )
    raise ValueError(snake_case__     )
  return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
 import doctest

 doctest.testmod()





