





import torch

from transformers import AutoModel







class     __snake_case       (							torch.nn.Module			):





       '''simple docstring'''







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

         __snake_case:						Union[str, Any]   =						AutoModel.from_pretrained(A							,				return_dict=A		)
         __snake_case:						Union[str, Any]   =						torch.nn.CosineSimilarity(3							,				1E-08		)
         __snake_case:						Union[str, Any]   =						torch.nn.Softmax(dim=1		)




       def 							UpperCAmelCase__ (  self :    Optional[int]							,				**A :    Any		):
         return self.bert(**A		).last_hidden_state




       def 							UpperCAmelCase__ (  self :    str							,				A :    List[str]		):
         return token_embeddings.sum(2							,				keepdim=A		)




       def 							UpperCAmelCase__ (  self :    Union[str, Any]							,				A :    Any							,				A :    Optional[int]							,				A :    Tuple=1		):
         return self.softmax(T * self.cos(A							,				A		)		)






       def 							UpperCAmelCase__ (  self :    Union[str, Any]							,				A :    Optional[int]							,				A :    Union[str, Any]		):
         __snake_case:						Any   =						W_supports["""sizes"""].tolist()
         __snake_case:						Tuple   =						W_supports["""start_token_id"""].item()
         __snake_case:						str   =						W_supports["""end_token_id"""].item()

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

         __snake_case:						int   =						self.BERT(**A		)
         __snake_case:						Optional[int]   =						self.BERT(**A		)

         __snake_case:						List[str]   =						None
         __snake_case:						Union[str, Any]   =						None

         __snake_case:						Tuple   =						W_supports["""input_ids"""] == start_token_id
         __snake_case:						List[str]   =						W_supports["""input_ids"""] == end_token_id

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

           __snake_case:						Optional[Any]   =						S[s : s + size][start_token_masks[s : s + size]]
           __snake_case:						List[Any]   =						S[s : s + size][end_token_masks[s : s + size]]

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

           if p_starts is not None:
             __snake_case:						Union[str, Any]   =						torch.vstack((p_starts, p_start)		)
             __snake_case:						Dict   =						torch.vstack((p_ends, p_end)		)
           else:
             __snake_case:						List[str]   =						p_start
             __snake_case:						str   =						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')






