def element_attention(self, query, key, padding_mask, causal_mask):
        """
        Apply element-wise attention via relu^2 or laplace. Same as original implementation but with standardized
        causal attention mask. Expects the Hugging Face standard attention mask paradigm: 1 for not masked, and 0 for
        masked.
        """
        seq_len = key.size(2)
        if padding_mask is not None:
            # (batch_size X number of chunks X 1)
            lengths = padding_mask.sum(-1, keepdim=True)
            # (batch_size X number of chunks X 1 X 1)
            lengths = lengths.clamp(min=1.0).unsqueeze(-1)
        else:
            lengths = seq_len

        if causal_mask is not None:
            lengths = causal_mask.sum(dim=-1, keepdim=True)

        # (sequence_length X sequence_length)
        bias = self.rel_pos_bias(seq_len)
        if seq_len != query.size(2):
            if query.size(2) != 1:
                raise ValueError("Size mismatch between Q and K in element attention")
            # (1 X sequence_length)
            bias = bias[-1:]

        # (batch_size X number of chunks X sequence_length X sequence_length)
        qk = torch.matmul(query, key.transpose(2, 3)) / lengths + bias

        attn_weights = ACT2FN[self.config.attention_activation](qk).type_as(qk)

        if padding_mask is not None:
            attn_weights = attn_weights * padding_mask.unsqueeze(2)

        if causal_mask is not None:
            attn_weights = attn_weights * causal_mask

        return attn_weights

        Apply element-wise attention via relu^2 or laplace. Same as original implementation but with standardized
        causal attention mask. Expects the Hugging Face standard attention mask paradigm: 1 for not masked, and 0 for
        masked.
        
dpkd/transformers/src/transformers/models/mega/modeling_mega.py