def create_mask(self, qlen, mlen):
        """
        Creates causal attention mask. Float mask where 1.0 indicates masked, 0.0 indicates not-masked.

        Args:
            qlen: Sequence length
            mlen: Mask length

        ::

                  same_length=False: same_length=True: <mlen > < qlen > <mlen > < qlen >
               ^ [0 0 0 0 0 1 1 1 1] [0 0 0 0 0 1 1 1 1]
                 [0 0 0 0 0 0 1 1 1] [1 0 0 0 0 0 1 1 1]
            qlen [0 0 0 0 0 0 0 1 1] [1 1 0 0 0 0 0 1 1]
                 [0 0 0 0 0 0 0 0 1] [1 1 1 0 0 0 0 0 1]
               v [0 0 0 0 0 0 0 0 0] [1 1 1 1 0 0 0 0 0]

        """
        mask = torch.ones(qlen, qlen + mlen, self.device)
        if self.same_length:
            mask_lo = mask[:, :qlen].tril(-1)
            mask.triu_(mlen + 1)
            mask[:, :qlen] += mask_lo
        else:
            mask.triu_(mlen + 1)

        return mask

        Creates causal attention mask. Float mask where 1.0 indicates masked, 0.0 indicates not-masked.

        Args:
            qlen: Sequence length
            mlen: Mask length

        ::

                  same_length=False: same_length=True: <mlen > < qlen > <mlen > < qlen >
               ^ [0 0 0 0 0 1 1 1 1] [0 0 0 0 0 1 1 1 1]
                 [0 0 0 0 0 0 1 1 1] [1 0 0 0 0 0 1 1 1]
            qlen [0 0 0 0 0 0 0 1 1] [1 1 0 0 0 0 0 1 1]
                 [0 0 0 0 0 0 0 0 1] [1 1 1 0 0 0 0 0 1]
               v [0 0 0 0 0 0 0 0 0] [1 1 1 1 0 0 0 0 0]

        
dpkd/transformers/src/transformers/models/xlnet/modeling_xlnet.py