





'''simple docstring'''





from __future__ import annotations



class 				__snake_case  :


    '''simple docstring'''







    def __init__(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= data
        SCREAMING_SNAKE_CASE__       							= None





class 				__snake_case  :


    '''simple docstring'''







    def __init__(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= None



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= Node(lowerCamelCase__    )
        SCREAMING_SNAKE_CASE__       							= self.head
        SCREAMING_SNAKE_CASE__       							= new_node
        return self.head.data




    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.head
        SCREAMING_SNAKE_CASE__       							= self.head
        if self.head:
            while fast_pointer and fast_pointer.next:
                SCREAMING_SNAKE_CASE__       							= fast_pointer.next.next
                SCREAMING_SNAKE_CASE__       							= slow_pointer.next
            return slow_pointer.data
        else:
            print('''No element found.'''    )
            return None


if __name__ == "__main__":
  _A				:							List[Any]							= LinkedList()
  for _ in range(int(input().strip())):
    _A				:							List[Any]							= int(input().strip())
    link.push(data)
  print(link.middle_element())







import torch

from transformers import AutoTokenizer



class 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    def __init__(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= AutoTokenizer.from_pretrained(A_    )



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        if isinstance(A_      ,							A_    ) and all(isinstance(_x      ,							A_    ) for _x in x    ):
            SCREAMING_SNAKE_CASE__       							= None
            for l in x:
                SCREAMING_SNAKE_CASE__       							= self.tokenizer(
                    A_      ,							padding='''max_length'''      ,							max_length=3_84      ,							truncation=A_      ,							return_tensors='''pt'''      ,							)
                SCREAMING_SNAKE_CASE__       							= torch.tensor([len(A_    )]    )
                if d is not None:
                    for k in d.keys():
                        SCREAMING_SNAKE_CASE__       							= torch.cat((d[k], t[k])      ,							0    )
                else:
                    SCREAMING_SNAKE_CASE__       							= t

            SCREAMING_SNAKE_CASE__       							= torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''    )    )
            SCREAMING_SNAKE_CASE__       							= torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''    )    )

        elif isinstance(A_      ,							A_    ) and all(isinstance(_x      ,							A_    ) for _x in x    ):
            SCREAMING_SNAKE_CASE__       							= self.tokenizer(
                A_      ,							padding='''max_length'''      ,							max_length=3_84      ,							truncation=A_      ,							return_tensors='''pt'''      ,							)

        else:
            raise Exception(
                '''Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'''
                ''' strings` for supports are supported.'''    )

        return d




    def    lowercase_				(					self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_=0.70    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= []
        for idx in range(len(W_query['''input_ids''']    )    ):
            SCREAMING_SNAKE_CASE__       							= SCREAMING_SNAKE_CASE__       							= range(p_start.shape[1]    )

            SCREAMING_SNAKE_CASE__       							= []
            for start_id in start_indexes:
                for end_id in end_indexes:
                    if start_id < end_id:
                        output.append(
                            (
                                start_id,
                                end_id,
                                p_start[idx][start_id].item(),
                                p_end[idx][end_id].item(),
                            )    )

            output.sort(key=lambda A_    : (tup[2] * tup[3])      ,							reverse=A_    )
            SCREAMING_SNAKE_CASE__       							= []
            for k in range(len(A_    )    ):
                if output[k][2] * output[k][3] >= thresh:
                    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= output[k][0], output[k][1]
                    SCREAMING_SNAKE_CASE__       							= self.tokenizer.decode(W_query['''input_ids'''][idx][c_start_pos:c_end_pos]    )
                    temp.append((decoded, output[k][2] * output[k][3])    )

            final_outputs.append(A_    )

        return final_outputs
