src/transformers/models/xlnet/modeling_tf_xlnet.py/TFXLNetMainLayer/relative_positional_encoding
class TFXLNetMainLayer:    def relative_positional_encoding(self, qlen, klen, bsz=None):
        """create relative positional encoding."""
        freq_seq = tf.range(0, self.d_model, 2.0)
        inv_freq = 1 / (10000 ** (freq_seq / self.d_model))

        if self.attn_type == "bi":
            # beg, end = klen - 1, -qlen
            beg, end = klen, -qlen
        elif self.attn_type == "uni":
            # beg, end = klen - 1, -1
            beg, end = klen, -1
        else:
            raise ValueError(f"Unknown `attn_type` {self.attn_type}.")

        if self.bi_data:
            fwd_pos_seq = tf.range(beg, end, -1.0)
            bwd_pos_seq = tf.range(-beg, -end, 1.0)

            if self.clamp_len > 0:
                fwd_pos_seq = tf.clip_by_value(fwd_pos_seq, -self.clamp_len, self.clamp_len)
                bwd_pos_seq = tf.clip_by_value(bwd_pos_seq, -self.clamp_len, self.clamp_len)

            if bsz is not None:
                assert bsz % 2 == 0, f"With bi_data, the batch size {bsz} should be divisible by 2"
                fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz // 2)
                bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq, bsz // 2)
            else:
                fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq)
                bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq)

            pos_emb = tf.concat([fwd_pos_emb, bwd_pos_emb], axis=1)
        else:
            fwd_pos_seq = tf.range(beg, end, -1.0)
            if self.clamp_len > 0:
                fwd_pos_seq = tf.clip_by_value(fwd_pos_seq, -self.clamp_len, self.clamp_len)
            pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz)

        return pos_emb