
'''simple docstring'''

import torch

from transformers import AutoModel






class  A  (						torch.nn.Module     ):





	def __init__(       self      :							str   ,				__magic_name__      :							List[Any]="sayef/fsner-bert-base-uncased"				):


				"""simple docstring"""

				super(__magic_name__   ,				self				).__init__()

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]   ,				**__magic_name__      :							Union[str, Any]				):


				"""simple docstring"""

				return self.bert(**__magic_name__				).last_hidden_state





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							Dict				):


				"""simple docstring"""

				return token_embeddings.sum(2   ,				keepdim=__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]   ,				__magic_name__      :							List[str]   ,				__magic_name__      :							List[str]   ,				__magic_name__      :							Optional[int]=1				):


				"""simple docstring"""

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







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]   ,				__magic_name__      :							int   ,				__magic_name__      :							Tuple				):


				"""simple docstring"""

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

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

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

				lowerCAmelCase__										=				None
				lowerCAmelCase__										=				None

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

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

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

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

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

				return p_starts, p_ends









'''simple docstring'''



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int = 10_00				)	->						int:
     __lowerCamelCase  :      Union[str, Any]														=						3
     __lowerCamelCase  :      Dict														=						0
     while a < n:
          if a % 3 == 0 or a % 5 == 0:
               result += a
          elif a % 15 == 0:
               result -= a
          a += 1
     return result


if __name__ == "__main__":
      print(f'''{solution() = }''')






