



'''simple docstring'''





import torch

from transformers import AutoModel






class __A			(  torch.nn.Module						):



 '''simple docstring'''







 def __init__(self ,   A="sayef/fsner-bert-base-uncased"       )    ->     Dict:


      """simple docstring"""



      super(A ,   self       ).__init__()

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



 def 					a__  (self ,   **A       )    ->     List[Any]:


      """simple docstring"""



      return self.bert(**A       ).last_hidden_state



 def 					a__  (self ,   A       )    ->     Tuple:


      """simple docstring"""



      return token_embeddings.sum(2 ,   keepdim=A       )



 def 					a__  (self ,   A ,   A ,   A=1       )    ->     List[str]:


      """simple docstring"""



      return self.softmax(T * self.cos(A ,   A       )       )



 def 					a__  (self ,   A ,   A       )    ->     List[str]:


      """simple docstring"""



      _a      =  W_supports["""sizes"""].tolist()
      _a      =  W_supports["""start_token_id"""].item()
      _a      =  W_supports["""end_token_id"""].item()

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

      _a      =  self.BERT(**A       )
      _a      =  self.BERT(**A       )

      _a      =  None
      _a      =  None

      _a      =  W_supports["""input_ids"""] == start_token_id
      _a      =  W_supports["""input_ids"""] == end_token_id

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

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

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

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

      return p_starts, p_ends










from __future__ import annotations

import typing
from collections import Counter







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    typing.Counter[int]:
  __snake_case:						typing.Counter[int]   =						Counter()
  for base in range(1					,					max_perimeter + 1):
    for perpendicular in range(SCREAMING_SNAKE_CASE__					,					max_perimeter + 1):
      __snake_case:						Dict   =						(base * base + perpendicular * perpendicular) ** 0.5
      if hypotenuse == int(SCREAMING_SNAKE_CASE__):
        __snake_case:						Any   =						int(base + perpendicular + hypotenuse)
        if perimeter > max_perimeter:
          continue
        triplets[perimeter] += 1
  return triplets







def 			A__							(    SCREAMING_SNAKE_CASE__ = 1000)   ->    int:
  __snake_case:						List[str]   =						pythagorean_triple(SCREAMING_SNAKE_CASE__)
  return triplets.most_common(1)[0][0]


if __name__ == "__main__":
      print(f'Perimeter {solution()} has maximum solutions')






