'''simple docstring'''

import torch

from transformers import AutoModel


class 				_lowercase	(      torch.nn.Module   ):
      '''simple docstring'''


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



         '''simple docstring'''



         super(lowerCamelCase_					,self							).__init__()

         UpperCAmelCase__							:				Union[str, Any]   				=  AutoModel.from_pretrained(lowerCamelCase_					,return_dict=lowerCamelCase_							)
         UpperCAmelCase__							:				str   				=  torch.nn.CosineSimilarity(3					,1e-08							)
         UpperCAmelCase__							:				int   				=  torch.nn.Softmax(dim=1							)




      def 		lowerCAmelCase__					(				self					,**lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         return self.bert(**lowerCamelCase_							).last_hidden_state




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return token_embeddings.sum(2					,keepdim=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_=1							)			->			int:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  W_supports['''sizes'''].tolist()
         UpperCAmelCase__							:				Union[str, Any]   				=  W_supports['''start_token_id'''].item()
         UpperCAmelCase__							:				int   				=  W_supports['''end_token_id'''].item()

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

         UpperCAmelCase__							:				Any   				=  self.BERT(**lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  self.BERT(**lowerCamelCase_							)

         UpperCAmelCase__							:				Any   				=  None
         UpperCAmelCase__							:				Tuple   				=  None

         UpperCAmelCase__							:				Dict   				=  W_supports['''input_ids'''] == start_token_id
         UpperCAmelCase__							:				Any   				=  W_supports['''input_ids'''] == end_token_id

         for i, size in enumerate(lowerCamelCase_							):
            if i == 0:
               UpperCAmelCase__							:				int   				=  0
            else:
               UpperCAmelCase__							:				str   				=  support_sizes[i - 1]

            UpperCAmelCase__							:				Dict   				=  S[s : s + size][start_token_masks[s : s + size]]
            UpperCAmelCase__							:				Union[str, Any]   				=  S[s : s + size][end_token_masks[s : s + size]]

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

            if p_starts is not None:
               UpperCAmelCase__							:				str   				=  torch.vstack((p_starts, p_start)							)
               UpperCAmelCase__							:				Optional[int]   				=  torch.vstack((p_ends, p_end)							)
            else:
               UpperCAmelCase__							:				Union[str, Any]   				=  p_start
               UpperCAmelCase__							:				int   				=  p_end

         return p_starts, p_ends





'''simple docstring'''

def __UpperCamelCase(   _A					:				str  ,			_A					:				str    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  len(_A    )
   UpperCAmelCase__							:				int   				=  len(_A    )
   UpperCAmelCase__							:				int   				=  (
       first_str_length if first_str_length > second_str_length else second_str_length
   )
   UpperCAmelCase__							:				list   				=  []
   for char_count in range(_A    ):
      if char_count < first_str_length:
         output_list.append(first_str[char_count]    )
      if char_count < second_str_length:
         output_list.append(second_str[char_count]    )
   return "".join(_A    )


if __name__ == "__main__":
  print(alternative_string_arrange('AB', 'XYZ'), end=' ')




