"""simple docstring"""


import torch

from transformers import AutoModel




class  UpperCamelCase					(				torch.nn.Module   ):







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

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







       def 		a_						( self,							**lowerCAmelCase__)      ->      Union[str, Any]:
            return self.bert(**lowerCAmelCase__).last_hidden_state







       def 		a_						( self,							lowerCAmelCase__)      ->      Union[str, Any]:
            return token_embeddings.sum(2,							keepdim=lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__=1)      ->      List[Any]:
            return self.softmax(T * self.cos(lowerCAmelCase__,							lowerCAmelCase__))







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__)      ->      Dict:
            snake_case_					       =					W_supports['sizes'].tolist()
            snake_case_					       =					W_supports['start_token_id'].item()
            snake_case_					       =					W_supports['end_token_id'].item()

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

            snake_case_					       =					self.BERT(**lowerCAmelCase__)
            snake_case_					       =					self.BERT(**lowerCAmelCase__)

            snake_case_					       =					None
            snake_case_					       =					None

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

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

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

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

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

            return p_starts, p_ends






'''simple docstring'''





from collections.abc import Sequence

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase       )       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  __lowerCamelCase       :       Any           = 0.0
  for coeff in reversed(_lowerCAmelCase       ):
    __lowerCamelCase       :       Tuple           = result * x + coeff
  return result


if __name__ == "__main__":
   _UpperCamelCase											=						(0.0, 0.0, 5.0, 9.3, 7.0)
   _UpperCamelCase											=						1_0.0
   print(evaluate_poly(poly, x))
   print(horner(poly, x))






