




import torch

from transformers import AutoModel

class  _lowerCamelCase						(			torch.nn.Module   ):

      """simple docstring"""

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

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







      def       snake_case	(     self					: List[str] ,       **snake_case					: Dict				):
          return self.bert(**__lowerCAmelCase				).last_hidden_state







      def       snake_case	(     self					: Any ,       snake_case					: Dict				):
          return token_embeddings.sum(2 ,       keepdim=__lowerCAmelCase				)







      def       snake_case	(     self					: Optional[Any] ,       snake_case					: List[Any] ,       snake_case					: Optional[Any] ,       snake_case					: Dict=1				):
          return self.softmax(T * self.cos(__lowerCAmelCase ,       __lowerCAmelCase				)				)







      def       snake_case	(     self					: str ,       snake_case					: str ,       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(**__lowerCAmelCase				)
          __UpperCamelCase  		=						self.BERT(**__lowerCAmelCase				)

          __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(__lowerCAmelCase				):
              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











def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_ = False      )       ->	str:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ):
        __UpperCamelCase  		=						F"Expected string as input, found {type(lowercase_      )}"
        raise ValueError(lowercase_      )
    if not isinstance(lowercase_					,     lowercase_      ):
        __UpperCamelCase  		=						F"Expected boolean as use_pascal parameter, found {type(lowercase_      )}"
        raise ValueError(lowercase_      )

    __UpperCamelCase  		=						input_str.split('''_'''      )

    __UpperCamelCase  		=						0 if use_pascal else 1

    __UpperCamelCase  		=						words[start_index:]

    __UpperCamelCase  		=						[word[0].upper() + word[1:] for word in words_to_capitalize]

    __UpperCamelCase  		=						'''''' if use_pascal else words[0]

    return "".join([initial_word, *capitalized_words]      )


if __name__ == "__main__":
 from doctest import testmod

 testmod()






