// grammar_heads.sy -- Always-On Structural Attention
// 4 ungated attention heads that maintain sentence coherence
// regardless of neurochemical state. The structural floor
// that emotion cannot suppress.
//
// Adapted from Kishonic's Silkie NLM HydraAttention grammar portion.
// Ported from Marjorie McCubbins' Python NCA architecture.
// Aislinn McCubbins, April 2026
//
// NO SIGMOID. Attention uses H-H rate normalization.
// NO gating -- these heads are ALWAYS ON.
//
// Patent Pending: 63/939,190 & 63/962,385 & 63/988,485

use cascade::ConductanceHead

// === GRAMMAR HEAD ===
// A single always-on attention head.
// No conductance gating. No H-H dynamics on the gate.
// Q/K/V attention with H-H rate normalization (NOT softmax).

struct GrammarHead {
    head_dim: Int,
    q_weights: Vec<Float>,
    k_weights: Vec<Float>,
    v_weights: Vec<Float>,
}

impl GrammarHead {
    fn new(hidden_dim: Int, head_dim: Int) -> GrammarHead {
        GrammarHead {
            head_dim: head_dim,
            q_weights: [],
            k_weights: [],
            v_weights: [],
        }
    }

    fn attention(&self, q: Vec<Float>, k: Vec<Float>, v: Vec<Float>) -> Vec<Float> {
        // Scaled dot-product attention
        // score = (Q . K^T) / sqrt(head_dim)
        //
        // H-H rate normalization (NOT softmax):
        //   alpha = exp(clamp(score, -3, 3))
        //   weight = alpha / (sum(alpha) + 1e-8)
        //
        // This normalizes attention without sigmoid.
        // The competitive dynamics are H-H rate functions.
        []
    }
}

// === GRAMMAR HEAD BANK ===
// Bank of 4 always-on attention heads.
// These run unconditionally. No chemical modulation.
// Ensures the model forms proper sentences even when
// emotional chemistry is volatile.

struct GrammarHeadBank {
    hidden_dim: Int,
    num_heads: Int,
    head_dim: Int,
    heads: Vec<GrammarHead>,
    dropout_rate: Float,
}

impl GrammarHeadBank {
    fn new(hidden_dim: Int) -> GrammarHeadBank {
        let num_heads = 4
        let head_dim = hidden_dim / num_heads

        GrammarHeadBank {
            hidden_dim: hidden_dim,
            num_heads: num_heads,
            head_dim: head_dim,
            heads: [],
            dropout_rate: 0.1,
        }
    }

    fn with_heads(mut self, n: Int) -> GrammarHeadBank {
        self.num_heads = n
        self.head_dim = self.hidden_dim / n
        self
    }

    fn forward(&self, x: Vec<Float>) -> Vec<Float> {
        // 1. Pre-norm (layer normalization)
        // 2. Q/K/V projections for each head
        // 3. Scaled dot-product attention per head
        //    - H-H rate normalization: alpha = exp(clamp(score, -3, 3))
        //    - weight = alpha / (sum(alpha) + 1e-8)
        //    - NOT softmax. NOT sigmoid.
        // 4. Concatenate all head outputs
        // 5. Output projection back to hidden_dim
        //
        // NO GATING. These heads are always on.
        // The cascade can be fully suppressed by cortisol
        // and these heads still produce coherent structure.
        []
    }

    fn is_gated(&self) -> Bool {
        // Grammar heads are NEVER gated
        false
    }
}
