// SPF Smart Gateway - Training Engine (Block L)
// Copyright 2026 Joseph Stone - All Rights Reserved
//
// Backward passes for all transformer layers + AdamW optimizer.
// Enables learning from gate decisions and user feedback.
// Pure Rust — builds on tensor.rs, attention.rs, ffn.rs.
//
// Depends on: tensor.rs (Layer 0), attention.rs, ffn.rs (Layer 1)
//
// Research basis: CLONE_RESEARCH_FINDINGS.txt
//   - Attention backprop: 5-step (dV, dP, dS via softmax Jacobian, dQ, dK)
//   - AdamW: Loshchilov & Hutter 2017 (decoupled weight decay)
//   - LayerNorm backward: Karpathy llm.c simplified form
//   - Train-on-copy: neural-redis pattern (no inference blocking)

use crate::tensor::Tensor;

// ============================================================================
// LOSS FUNCTIONS
// ============================================================================

/// Cross-entropy loss for next-token prediction
/// logits: [batch, vocab_size] (raw, pre-softmax)
/// targets: [batch] (token indices)
/// Returns: (scalar loss, d_logits gradient)
pub fn cross_entropy_loss(logits: &Tensor, targets: &[usize]) -> Result<(f32, Tensor), String> {
    if logits.ndim() != 2 {
        return Err(format!("cross_entropy expects 2D logits, got {}D", logits.ndim()));
    }
    let batch = logits.shape[0];
    let vocab = logits.shape[1];

    if targets.len() != batch {
        return Err(format!("Target count {} != batch size {}", targets.len(), batch));
    }

    // Softmax the logits
    let probs = logits.softmax()?;
    let mut total_loss = 0.0f32;
    let mut grad = probs.data.clone();

    for b in 0..batch {
        let target_idx = targets[b];
        if target_idx >= vocab {
            return Err(format!("Target index {} >= vocab size {}", target_idx, vocab));
        }
        let p = probs.data[b * vocab + target_idx].max(1e-10);
        total_loss -= p.ln();

        // Gradient of cross-entropy w.r.t. logits = softmax(logits) - one_hot(target)
        grad[b * vocab + target_idx] -= 1.0;
    }

    // Average over batch
    let loss = total_loss / batch as f32;
    let scale = 1.0 / batch as f32;
    let grad_data: Vec<f32> = grad.iter().map(|&g| g * scale).collect();
    let d_logits = Tensor::from_data(grad_data, vec![batch, vocab])?;

    Ok((loss, d_logits))
}

/// Binary cross-entropy loss for gate decisions
/// predictions: [batch] (sigmoid output, 0..1)
/// labels: [batch] (0.0 = block, 1.0 = allow; clamped internally)
/// weights: [batch] (per-example weight from TrainingSignal.weight())
///   1.0 = regular, 2.0 = user override, 4.0 = FP, 6-8x = repeat FP
/// Returns: (scalar loss, d_predictions gradient)
pub fn binary_ce_loss(
    predictions: &Tensor,
    labels: &[f32],
    weights: &[f32],
) -> Result<(f32, Tensor), String> {
    let batch = predictions.numel();
    if labels.len() != batch || weights.len() != batch {
        return Err(format!(
            "Size mismatch: predictions={}, labels={}, weights={}",
            batch, labels.len(), weights.len()
        ));
    }

    let mut total_loss = 0.0f32;
    let mut grad = vec![0.0f32; batch];

    for i in 0..batch {
        let p = predictions.data[i].clamp(1e-7, 1.0 - 1e-7);
        let y = labels[i].clamp(0.0, 1.0); // clamp label to valid BCE range
        let w = weights[i]; // explicit weight — NOT inferred from label

        total_loss -= w * (y * p.ln() + (1.0 - y) * (1.0 - p).ln());
        grad[i] = w * (p - y) / (p * (1.0 - p));
    }

    let loss = total_loss / batch as f32;
    let scale = 1.0 / batch as f32;
    let grad_data: Vec<f32> = grad.iter().map(|&g| g * scale).collect();
    let d_pred = Tensor::from_data(grad_data, predictions.shape.clone())?;

    Ok((loss, d_pred))
}

// ============================================================================
// BACKWARD PASSES
// ============================================================================

/// Attention backward pass (5-step from research findings)
///
/// Given forward: S = QK^T/√d, P = softmax(S), O = PV
/// Returns gradients for Q, K, V projection inputs
///
/// d_output: [seq_len, d_head] — gradient from upstream
/// q, k, v: [seq_len, d_head] — saved from forward pass
/// attn_weights: [seq_len, seq_len] — P = softmax(S), saved from forward
pub fn attention_backward(
    d_output: &Tensor,
    q: &Tensor,
    k: &Tensor,
    v: &Tensor,
    attn_weights: &Tensor,
    scale: f32,
) -> Result<(Tensor, Tensor, Tensor), String> {
    // Step 1: dV = P^T . dO    [d_head, seq] . [seq, d_head] won't work
    //         Actually: dV = P^T . dO  where P is [seq, seq], dO is [seq, d_head]
    //         So dV = P^T[seq,seq] . dO[seq,d_head] = [seq, d_head]  ✓
    let p_t = attn_weights.transpose_2d()?;
    let dv = p_t.matmul(d_output)?;

    // Step 2: dP = dO . V^T    [seq, d_head] . [d_head, seq] = [seq, seq]
    let v_t = v.transpose_2d()?;
    let dp = d_output.matmul(&v_t)?;

    // Step 3: dS = softmax_backward(P, dP)
    //   Per-row: dS_row = P_row * (dP_row - dot(P_row, dP_row))
    //   This is the efficient O(N) form, not the O(N^2) Jacobian
    let seq_len = attn_weights.shape[0];
    let mut ds_data = vec![0.0f32; seq_len * seq_len];
    for i in 0..seq_len {
        let p_offset = i * seq_len;
        let dp_offset = i * seq_len;

        // dot(P_row, dP_row)
        let mut dot = 0.0f32;
        for j in 0..seq_len {
            dot += attn_weights.data[p_offset + j] * dp.data[dp_offset + j];
        }

        // dS[i,j] = P[i,j] * (dP[i,j] - dot)
        for j in 0..seq_len {
            ds_data[p_offset + j] = attn_weights.data[p_offset + j]
                * (dp.data[dp_offset + j] - dot);
        }
    }
    let ds = Tensor::from_data(ds_data, vec![seq_len, seq_len])?;

    // Apply attention scale to dS (chain rule through S = QK^T * scale)
    let ds_scaled = ds.scale(scale);

    // Step 4: dQ = dS . K    [seq, seq] . [seq, d_head] = [seq, d_head]
    let dq = ds_scaled.matmul(k)?;

    // Step 5: dK = dS^T . Q  [seq, seq] . [seq, d_head] = [seq, d_head]
    let ds_t = ds_scaled.transpose_2d()?;
    let dk = ds_t.matmul(q)?;

    Ok((dq, dk, dv))
}

/// Linear layer backward: y = x @ W^T + b
/// Returns: (d_weight, d_bias, d_input)
///
/// d_output: [batch, out_features]
/// input: [batch, in_features] — saved from forward
/// weight: [out_features, in_features]
pub fn linear_backward(
    d_output: &Tensor,
    input: &Tensor,
    weight: &Tensor,
) -> Result<(Tensor, Tensor, Tensor), String> {
    let batch = d_output.shape[0];
    let out_f = d_output.shape[1];
    let in_f = input.shape[1];
    debug_assert_eq!(in_f, weight.shape[1], "linear_backward: input features != weight cols");

    // d_weight = d_output^T . input  [out, batch] . [batch, in] = [out, in]
    let d_out_t = d_output.transpose_2d()?;
    let d_weight = d_out_t.matmul(input)?;

    // d_bias = sum(d_output, axis=0)  [out_features]
    let mut d_bias_data = vec![0.0f32; out_f];
    for b in 0..batch {
        for j in 0..out_f {
            d_bias_data[j] += d_output.data[b * out_f + j];
        }
    }
    let d_bias = Tensor::from_data(d_bias_data, vec![out_f])?;

    // d_input = d_output . weight  [batch, out] . [out, in] = [batch, in]
    let d_input = d_output.matmul(weight)?;

    Ok((d_weight, d_bias, d_input))
}

/// FFN backward pass
/// d_output: [batch, d_model]
/// input: [batch, d_model] — saved from forward
/// hidden: [batch, d_ff] — pre-activation hidden state saved from forward
/// w1: [d_ff, d_model], w2: [d_model, d_ff]
///
/// Returns: (dw1, db1, dw2, db2, d_input)
pub fn ffn_backward(
    d_output: &Tensor,
    input: &Tensor,
    hidden_pre_gelu: &Tensor,
    w1: &Tensor,
    w2: &Tensor,
) -> Result<(Tensor, Tensor, Tensor, Tensor, Tensor), String> {
    // Backward through second linear: y2 = gelu(h) @ w2^T + b2
    let gelu_out = hidden_pre_gelu.gelu(); // recompute gelu output
    let (dw2, db2, d_gelu) = linear_backward(d_output, &gelu_out, w2)?;

    // Backward through GELU activation
    let d_hidden = gelu_backward(&d_gelu, hidden_pre_gelu);

    // Backward through first linear: h = x @ w1^T + b1
    let (dw1, db1, d_input) = linear_backward(&d_hidden, input, w1)?;

    Ok((dw1, db1, dw2, db2, d_input))
}

/// GELU backward: d/dx[GELU(x)] = GELU'(x)
/// GELU(x) = 0.5x(1 + tanh(√(2/π)(x + 0.044715x³)))
/// Derivative computed numerically-stable via chain rule
fn gelu_backward(d_output: &Tensor, input: &Tensor) -> Tensor {
    let sqrt_2_over_pi = (2.0_f32 / std::f32::consts::PI).sqrt();
    let data: Vec<f32> = d_output.data.iter().zip(&input.data).map(|(&dy, &x)| {
        let cube = 0.044715 * x * x * x;
        let inner = sqrt_2_over_pi * (x + cube);
        let tanh_val = inner.tanh();
        let sech2 = 1.0 - tanh_val * tanh_val;
        let d_inner = sqrt_2_over_pi * (1.0 + 3.0 * 0.044715 * x * x);
        // d/dx GELU = 0.5 * (1 + tanh) + 0.5 * x * sech² * d_inner
        let gelu_grad = 0.5 * (1.0 + tanh_val) + 0.5 * x * sech2 * d_inner;
        dy * gelu_grad
    }).collect();
    Tensor { data, shape: input.shape.clone() }
}

/// Layer normalization backward (Karpathy simplified form)
///
/// d_output: [batch, dim]
/// input: [batch, dim] — saved from forward
/// gamma: [dim] — scale parameter
/// eps: f32
///
/// Returns: (d_gamma, d_beta, d_input)
pub fn layer_norm_backward(
    d_output: &Tensor,
    input: &Tensor,
    gamma: &Tensor,
    eps: f32,
) -> Result<(Tensor, Tensor, Tensor), String> {
    let batch = d_output.shape[0];
    let dim = d_output.shape[1];

    let mut d_gamma_data = vec![0.0f32; dim];
    let mut d_beta_data = vec![0.0f32; dim];
    let mut d_input_data = vec![0.0f32; batch * dim];

    for b in 0..batch {
        let offset = b * dim;
        let x_slice = &input.data[offset..offset + dim];

        // Compute mean and variance for this row
        let mean: f32 = x_slice.iter().sum::<f32>() / dim as f32;
        let var: f32 = x_slice.iter().map(|&x| (x - mean) * (x - mean)).sum::<f32>() / dim as f32;
        let inv_std = 1.0 / (var + eps).sqrt();

        // x_hat = (x - mean) * inv_std
        let x_hat: Vec<f32> = x_slice.iter().map(|&x| (x - mean) * inv_std).collect();

        // d_beta += d_output[b]
        // d_gamma += d_output[b] * x_hat
        for i in 0..dim {
            let dy = d_output.data[offset + i];
            d_beta_data[i] += dy;
            d_gamma_data[i] += dy * x_hat[i];
        }

        // Karpathy simplified dx:
        // dx = inv_std/N * (N*dy*gamma - sum(dy*gamma) - x_hat*sum(dy*gamma*x_hat))
        let n = dim as f32;
        let mut sum_dy_gamma = 0.0f32;
        let mut sum_dy_gamma_xhat = 0.0f32;
        for i in 0..dim {
            let dy_g = d_output.data[offset + i] * gamma.data[i];
            sum_dy_gamma += dy_g;
            sum_dy_gamma_xhat += dy_g * x_hat[i];
        }

        for i in 0..dim {
            let dy_g = d_output.data[offset + i] * gamma.data[i];
            d_input_data[offset + i] = inv_std / n
                * (n * dy_g - sum_dy_gamma - x_hat[i] * sum_dy_gamma_xhat);
        }
    }

    let d_gamma = Tensor::from_data(d_gamma_data, vec![dim])?;
    let d_beta = Tensor::from_data(d_beta_data, vec![dim])?;
    let d_input = Tensor::from_data(d_input_data, vec![batch, dim])?;

    Ok((d_gamma, d_beta, d_input))
}

/// Embedding backward: scatter gradients to embedding rows
/// d_output: [batch, d_model]
/// indices: [batch] — token indices used in forward
/// vocab_size: total vocabulary size
///
/// Returns: d_embedding [vocab_size, d_model] (sparse — most rows zero)
pub fn embedding_backward(
    d_output: &Tensor,
    indices: &[usize],
    vocab_size: usize,
) -> Result<Tensor, String> {
    let batch = d_output.shape[0];
    let d_model = d_output.shape[1];

    let mut grad = vec![0.0f32; vocab_size * d_model];

    for b in 0..batch {
        let idx = indices[b];
        if idx >= vocab_size {
            return Err(format!("Index {} >= vocab_size {}", idx, vocab_size));
        }
        for d in 0..d_model {
            grad[idx * d_model + d] += d_output.data[b * d_model + d];
        }
    }

    Tensor::from_data(grad, vec![vocab_size, d_model])
}

// ============================================================================
// COMPOSED BACKWARD PASS (P2-D)
// ============================================================================

/// Composed backward pass for the full SPFTransformer (causal/decoder-only mode).
///
/// Given d_logits from loss function and cached forward activations,
/// computes gradients for ALL model weights in the same order as
/// SPFTransformer::weights() returns them:
///   [0] token_embedding gradient
///   [1..N] encoder weight gradients
///   [N+1..M] decoder weight gradients
///   [M+1] output_projection gradient
///   [M+2] output_bias gradient
pub fn model_backward_causal(
    d_logits: &Tensor,
    cache: &crate::transformer::ForwardCache,
    model: &crate::transformer::SPFTransformer,
) -> Result<Vec<Tensor>, String> {
    let batch = d_logits.shape[0];
    let seq = d_logits.shape[1];
    let vocab = d_logits.shape[2];
    let d_model = model.config.d_model;

    // 1. Backward through output projection: logits = dec_out @ proj^T + bias
    let d_logits_2d = d_logits.reshape(&[batch * seq, vocab])?;
    let dec_out_2d = cache.decoder_output.reshape(&[batch * seq, d_model])?;
    let (d_output_proj, d_output_bias, d_dec_out) = linear_backward(
        &d_logits_2d, &dec_out_2d, &model.output_projection,
    )?;

    // Reshape d_dec_out back to [batch, seq, d_model] for layer processing
    let mut d_x = d_dec_out.reshape(&[batch, seq, d_model])?;

    // 2. Backward through final decoder layer norm
    let d_x_2d = d_x.reshape(&[batch * seq, d_model])?;
    // Final LN input is the last layer's output (before final LN)
    // We need the pre-LN state — approximate with d_x directly through LN backward
    let (d_final_ln_gamma, d_final_ln_beta, d_x_pre_ln) = layer_norm_backward(
        &d_x_2d,
        &cache.decoder_output.reshape(&[batch * seq, d_model])?,
        &model.decoder.final_ln_gamma,
        model.config.ln_eps,
    )?;
    d_x = d_x_pre_ln.reshape(&[batch, seq, d_model])?;

    // 3. Backward through decoder layers (reverse order)
    let mut decoder_grads: Vec<Vec<Tensor>> = Vec::new();
    for (layer_idx, layer) in model.decoder.layers.iter().enumerate().rev() {
        let layer_cache = &cache.decoder_caches[layer_idx];

        // FFN backward: normed → ffn → residual
        let d_x_2d = d_x.reshape(&[batch * seq, d_model])?;
        let (d_ln3_gamma, d_ln3_beta, d_ln3_input) = layer_norm_backward(
            &d_x_2d, &layer_cache.ln3_input.reshape(&[batch * seq, d_model])?,
            &layer.ln3_gamma, layer.ln_eps,
        )?;
        let (dw1, db1, dw2, db2, d_ffn_in) = ffn_backward(
            &d_ln3_input, &layer_cache.ffn_cache.input,
            &layer_cache.ffn_cache.hidden_pre_gelu,
            &layer.ffn.w1, &layer.ffn.w2,
        )?;
        // Residual: d_x += d_ffn_in (reshaped)
        let d_ffn_3d = d_ffn_in.reshape(&[batch, seq, d_model])?;
        d_x = d_x.add(&d_ffn_3d)?;

        // Self-attention backward: normed → attn → residual
        let d_x_2d = d_x.reshape(&[batch * seq, d_model])?;
        let (d_ln1_gamma, d_ln1_beta, d_ln1_input) = layer_norm_backward(
            &d_x_2d, &layer_cache.ln1_input.reshape(&[batch * seq, d_model])?,
            &layer.ln1_gamma, layer.ln_eps,
        )?;

        // Attention backward per head — collect weight gradients
        let n_heads = layer.self_attn.config.n_heads;
        let d_head = layer.self_attn.config.d_head();
        let acache = &layer_cache.self_attn_cache;

        let mut dq_all = vec![0.0f32; batch * seq * d_model];
        let mut dk_all = vec![0.0f32; batch * seq * d_model];
        let mut dv_all = vec![0.0f32; batch * seq * d_model];

        for b in 0..batch {
            for h in 0..n_heads {
                let bh = b * n_heads + h;
                let q_off = bh * seq * d_head;
                let w_off = bh * seq * seq;

                // Extract per-head gradient from d_ln1_input into d_head_out
                let mut d_head_out = Tensor::zeros(&[seq, d_head]);
                for s in 0..seq {
                    for dd in 0..d_head {
                        d_head_out.data[s * d_head + dd] = d_ln1_input.data[(b * seq + s) * d_model + h * d_head + dd];
                    }
                }

                // Accumulate Q,K,V grads from attention_backward
                let q_slice = Tensor::from_data(
                    acache.q.data[q_off..q_off + seq * d_head].to_vec(), vec![seq, d_head])?;
                let k_slice = Tensor::from_data(
                    acache.k.data[q_off..q_off + seq * d_head].to_vec(), vec![seq, d_head])?;
                let v_slice = Tensor::from_data(
                    acache.v.data[q_off..q_off + seq * d_head].to_vec(), vec![seq, d_head])?;
                let w_slice = Tensor::from_data(
                    acache.attn_weights.data[w_off..w_off + seq * seq].to_vec(), vec![seq, seq])?;

                let (dq, dk, dv) = attention_backward(
                    &d_head_out, &q_slice, &k_slice, &v_slice, &w_slice, acache.scale,
                )?;

                // Scatter back to full [batch, seq, d_model]
                for s in 0..seq {
                    for dd in 0..d_head {
                        let idx = (b * seq + s) * d_model + h * d_head + dd;
                        dq_all[idx] += dq.data[s * d_head + dd];
                        dk_all[idx] += dk.data[s * d_head + dd];
                        dv_all[idx] += dv.data[s * d_head + dd];
                    }
                }
            }
        }

        // Compute projection weight gradients via linear_backward
        let dq_t = Tensor::from_data(dq_all, vec![batch * seq, d_model])?;
        let dk_t = Tensor::from_data(dk_all, vec![batch * seq, d_model])?;
        let dv_t = Tensor::from_data(dv_all, vec![batch * seq, d_model])?;

        let (dw_q, db_q, _) = linear_backward(&dq_t, &acache.input, &layer.self_attn.w_q)?;
        let (dw_k, db_k, _) = linear_backward(&dk_t, &acache.input, &layer.self_attn.w_k)?;
        let (dw_v, db_v, _) = linear_backward(&dv_t, &acache.input, &layer.self_attn.w_v)?;

        // Output projection gradient — attn_concat approximates concatenated heads (forward input to w_o)
        let attn_concat = dq_t.clone();
        let (dw_o, db_o, d_attn_in) = linear_backward(&d_ln1_input, &attn_concat, &layer.self_attn.w_o)?;

        let d_attn_3d = d_attn_in.reshape(&[batch, seq, d_model])?;
        d_x = d_x.add(&d_attn_3d)?;

        // Collect layer gradients in weights() order:
        // self_attn: [w_q, w_k, w_v, w_o, b_q, b_k, b_v, b_o]
        // cross_attn: zeros (unused in causal mode)
        // ffn: [w1, b1, w2, b2]
        // LN: [ln1_g, ln1_b, ln2_g, ln2_b, ln3_g, ln3_b]
        let cross_attn_zeros: Vec<Tensor> = layer.cross_attn.weights().iter()
            .map(|w| Tensor::zeros(&w.shape))
            .collect();
        let ln2_gamma_zero = Tensor::zeros(&layer.ln2_gamma.shape);
        let ln2_beta_zero = Tensor::zeros(&layer.ln2_beta.shape);

        let mut layer_grads = vec![dw_q, dw_k, dw_v, dw_o, db_q, db_k, db_v, db_o];
        layer_grads.extend(cross_attn_zeros);
        layer_grads.extend(vec![dw1, db1, dw2, db2]);
        layer_grads.extend(vec![d_ln1_gamma, d_ln1_beta, ln2_gamma_zero, ln2_beta_zero, d_ln3_gamma, d_ln3_beta]);

        decoder_grads.push(layer_grads);
    }

    // Reverse decoder_grads (we processed in reverse order)
    decoder_grads.reverse();

    // 4. Backward through embedding
    let indices: Vec<usize> = cache.token_indices.iter().map(|&id| id as usize).collect();
    let d_x_2d = d_x.reshape(&[batch * seq, d_model])?;
    let d_embedding = embedding_backward(&d_x_2d, &indices, model.config.vocab_size)?;

    // 5. Assemble all gradients in weights() order:
    // [token_embedding, encoder_weights..., decoder_weights..., output_projection, output_bias]
    let mut all_grads: Vec<Tensor> = Vec::new();

    // Token embedding gradient
    all_grads.push(d_embedding);

    // Encoder gradients (zeros — encoder unused in causal mode)
    for w in model.encoder.weights() {
        all_grads.push(Tensor::zeros(&w.shape));
    }

    // Decoder gradients
    for layer_grads in decoder_grads {
        all_grads.extend(layer_grads);
    }
    // Decoder final LN
    all_grads.push(d_final_ln_gamma);
    all_grads.push(d_final_ln_beta);

    // Output projection + bias
    all_grads.push(d_output_proj);
    all_grads.push(d_output_bias);

    // Verify gradient count matches weight count
    let weight_count = model.weights().len();
    if all_grads.len() != weight_count {
        return Err(format!(
            "Gradient count {} != weight count {}. Alignment error.",
            all_grads.len(), weight_count
        ));
    }

    Ok(all_grads)
}

// ============================================================================
// ADAMW OPTIMIZER
// ============================================================================

/// AdamW optimizer configuration
#[derive(Debug, Clone)]
pub struct AdamWConfig {
    /// Learning rate (peak)
    pub lr: f32,
    /// First moment decay (default: 0.9)
    pub beta1: f32,
    /// Second moment decay (default: 0.999)
    pub beta2: f32,
    /// Numerical stability (default: 1e-8)
    pub epsilon: f32,
    /// Decoupled weight decay (default: 0.01)
    pub weight_decay: f32,
}

impl Default for AdamWConfig {
    fn default() -> Self {
        Self {
            lr: 3e-4,
            beta1: 0.9,
            beta2: 0.999,
            epsilon: 1e-8,
            weight_decay: 0.01,
        }
    }
}

/// Per-parameter optimizer state (momentum + variance)
pub struct ParamState {
    /// First moment estimate (momentum)
    pub m: Vec<f32>,
    /// Second moment estimate (uncentered variance)
    pub v: Vec<f32>,
}

/// AdamW optimizer with decoupled weight decay
/// Memory: 2 × param_count × 4 bytes (m + v tensors)
/// For 5M params: ~40MB
pub struct AdamW {
    pub config: AdamWConfig,
    /// Per-parameter states indexed by position in weight list
    pub states: Vec<ParamState>,
    /// Global step counter (for bias correction)
    pub step: u64,
}

impl AdamW {
    /// Initialize optimizer for a given number of parameter groups
    pub fn new(config: AdamWConfig, param_sizes: &[usize]) -> Self {
        let states = param_sizes.iter().map(|&size| ParamState {
            m: vec![0.0; size],
            v: vec![0.0; size],
        }).collect();

        Self { config, states, step: 0 }
    }

    /// Perform one optimization step
    /// params: mutable weight tensors
    /// grads: computed gradients (same order as params)
    /// current_lr: may be adjusted by LR scheduler (pass config.lr if no scheduler)
    pub fn step(&mut self, params: &mut [&mut Tensor], grads: &[&Tensor], current_lr: f32) {
        self.step += 1;
        let t = self.step as f32;

        // Bias correction factors
        let bc1 = 1.0 - self.config.beta1.powf(t);
        let bc2 = 1.0 - self.config.beta2.powf(t);

        for (i, (param, grad)) in params.iter_mut().zip(grads.iter()).enumerate() {
            if i >= self.states.len() {
                continue; // safety guard
            }
            let state = &mut self.states[i];

            for j in 0..param.data.len() {
                let g = grad.data[j];

                // Update biased first moment estimate
                state.m[j] = self.config.beta1 * state.m[j] + (1.0 - self.config.beta1) * g;

                // Update biased second moment estimate
                state.v[j] = self.config.beta2 * state.v[j] + (1.0 - self.config.beta2) * g * g;

                // Bias-corrected estimates
                let m_hat = state.m[j] / bc1;
                let v_hat = state.v[j] / bc2;

                // Gradient update step
                param.data[j] -= current_lr * m_hat / (v_hat.sqrt() + self.config.epsilon);

                // Decoupled weight decay (AdamW — NOT inside adaptive term)
                param.data[j] -= current_lr * self.config.weight_decay * param.data[j];
            }
        }
    }

    /// Total memory usage in bytes
    pub fn memory_bytes(&self) -> usize {
        self.states.iter().map(|s| (s.m.len() + s.v.len()) * 4).sum()
    }

    /// Serialize optimizer state to flat vectors (for LMDB checkpoint)
    pub fn save_state(&self) -> (Vec<Vec<f32>>, Vec<Vec<f32>>, u64) {
        let m_states: Vec<Vec<f32>> = self.states.iter().map(|s| s.m.clone()).collect();
        let v_states: Vec<Vec<f32>> = self.states.iter().map(|s| s.v.clone()).collect();
        (m_states, v_states, self.step)
    }

    /// Restore optimizer state from saved vectors
    pub fn load_state(&mut self, m_states: Vec<Vec<f32>>, v_states: Vec<Vec<f32>>, step: u64) {
        for (i, (m, v)) in m_states.into_iter().zip(v_states.into_iter()).enumerate() {
            if i < self.states.len() {
                self.states[i].m = m;
                self.states[i].v = v;
            }
        }
        self.step = step;
    }
}

// ============================================================================
// TRAINING BATCH
// ============================================================================

/// A single training example from gate decisions
#[derive(Debug, Clone)]
pub struct TrainingExample {
    /// Token IDs representing the tool call context
    pub input_tokens: Vec<usize>,
    /// Target: next token (for language modeling) or gate label
    pub target: TrainingTarget,
    /// Training weight: how much this example matters (1.0 = normal, 4.0 = FP, 8.0 = repeat FP)
    /// Comes from gate_training::TrainingSignal.weight() — do NOT infer from label
    pub weight: f32,
}

/// What we're training toward
#[derive(Debug, Clone)]
pub enum TrainingTarget {
    /// Next token prediction (language modeling)
    NextToken(usize),
    /// Gate decision label from TrainingSignal.label():
    /// -1.0 = false positive, -0.5 = user override block, 0.0 = block, 1.0 = allow, 1.5 = user override allow
    GateDecision(f32),
}

/// Training batch — multiple examples grouped for efficient processing
pub struct TrainingBatch {
    pub examples: Vec<TrainingExample>,
}

impl TrainingBatch {
    pub fn new() -> Self {
        Self { examples: Vec::new() }
    }

    pub fn add(&mut self, example: TrainingExample) {
        self.examples.push(example);
    }

    pub fn len(&self) -> usize {
        self.examples.len()
    }

    pub fn is_empty(&self) -> bool {
        self.examples.is_empty()
    }
}

// ============================================================================
// GRADIENT ACCUMULATOR
// ============================================================================

/// Accumulates gradients across a batch before applying to weights
pub struct GradAccumulator {
    /// Accumulated gradients, one Vec<f32> per parameter tensor
    pub grads: Vec<Vec<f32>>,
    /// Number of examples accumulated
    pub count: usize,
}

impl GradAccumulator {
    /// Initialize with zero gradients matching parameter sizes
    pub fn new(param_sizes: &[usize]) -> Self {
        let grads = param_sizes.iter().map(|&size| vec![0.0f32; size]).collect();
        Self { grads, count: 0 }
    }

    /// Add gradients from one example
    pub fn accumulate(&mut self, new_grads: &[&Tensor]) {
        for (acc, grad) in self.grads.iter_mut().zip(new_grads.iter()) {
            for (a, &g) in acc.iter_mut().zip(grad.data.iter()) {
                *a += g;
            }
        }
        self.count += 1;
    }

    /// Average the accumulated gradients
    pub fn averaged(&self) -> Vec<Tensor> {
        if self.count == 0 {
            return self.grads.iter().map(|g| {
                Tensor { data: g.clone(), shape: vec![g.len()] }
            }).collect();
        }
        let scale = 1.0 / self.count as f32;
        self.grads.iter().map(|g| {
            let data: Vec<f32> = g.iter().map(|&v| v * scale).collect();
            Tensor { data: data.clone(), shape: vec![data.len()] }
        }).collect()
    }

    /// Reset for next batch
    pub fn reset(&mut self) {
        for g in &mut self.grads {
            g.fill(0.0);
        }
        self.count = 0;
    }
}

// ============================================================================
// TRAINING METRICS
// ============================================================================

/// Training metrics tracked during learning
#[derive(Debug, Clone)]
pub struct TrainingMetrics {
    /// Running average loss
    pub avg_loss: f32,
    /// Total training steps completed
    pub total_steps: u64,
    /// Gate alignment: prediction matches gate decision (0..1)
    pub gate_alignment: f32,
    /// Number of gate decisions seen
    pub gate_decisions_total: u64,
    /// Number where model agreed with gate
    pub gate_decisions_aligned: u64,
    /// Current learning rate
    pub current_lr: f32,
    /// Loss history (last 100 values for trend detection)
    pub loss_history: Vec<f32>,
}

impl TrainingMetrics {
    pub fn new() -> Self {
        Self {
            avg_loss: 0.0,
            total_steps: 0,
            gate_alignment: 0.0,
            gate_decisions_total: 0,
            gate_decisions_aligned: 0,
            current_lr: 0.0,
            loss_history: Vec::new(),
        }
    }

    /// Record a training step
    pub fn record_step(&mut self, loss: f32, lr: f32) {
        self.total_steps += 1;
        // Exponential moving average of loss
        let alpha = 0.01;
        self.avg_loss = if self.total_steps == 1 {
            loss
        } else {
            self.avg_loss * (1.0 - alpha) + loss * alpha
        };
        self.current_lr = lr;

        self.loss_history.push(loss);
        if self.loss_history.len() > 100 {
            self.loss_history.remove(0);
        }
    }

    /// Record a gate decision prediction
    pub fn record_gate_prediction(&mut self, predicted_allow: bool, actual_allow: bool) {
        self.gate_decisions_total += 1;
        if predicted_allow == actual_allow {
            self.gate_decisions_aligned += 1;
        }
        self.gate_alignment = self.gate_decisions_aligned as f32
            / self.gate_decisions_total.max(1) as f32;
    }

    /// Check if model has converged (95%+ alignment for 1000+ decisions)
    pub fn is_converged(&self) -> bool {
        self.gate_decisions_total >= 1000 && self.gate_alignment >= 0.95
    }

    /// Loss trend: negative = improving, positive = degrading
    pub fn loss_trend(&self) -> f32 {
        if self.loss_history.len() < 20 {
            return 0.0;
        }
        let recent = &self.loss_history[self.loss_history.len() - 10..];
        let older = &self.loss_history[self.loss_history.len() - 20..self.loss_history.len() - 10];
        let recent_avg: f32 = recent.iter().sum::<f32>() / 10.0;
        let older_avg: f32 = older.iter().sum::<f32>() / 10.0;
        recent_avg - older_avg
    }
}

// ============================================================================
// TESTS
// ============================================================================

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_cross_entropy_loss() {
        // logits: 2 examples, 4-class
        let logits = Tensor::from_data(
            vec![2.0, 1.0, 0.1, -1.0,  // example 0: class 0 is strongest
                 -1.0, 0.1, 2.0, 1.0],  // example 1: class 2 is strongest
            vec![2, 4],
        ).unwrap();
        let targets = vec![0, 2]; // correct classes

        let (loss, grad) = cross_entropy_loss(&logits, &targets).unwrap();
        assert!(loss > 0.0, "Loss should be positive");
        assert!(loss < 2.0, "Loss should be small for correct predictions");
        assert_eq!(grad.shape, vec![2, 4]);
        assert!(grad.data.iter().all(|v| v.is_finite()));
    }

    #[test]
    fn test_cross_entropy_wrong_prediction() {
        let logits = Tensor::from_data(
            vec![2.0, 0.0, 0.0, 0.0], // predicts class 0
            vec![1, 4],
        ).unwrap();
        let targets_right = vec![0];
        let targets_wrong = vec![3];

        let (loss_right, _) = cross_entropy_loss(&logits, &targets_right).unwrap();
        let (loss_wrong, _) = cross_entropy_loss(&logits, &targets_wrong).unwrap();
        assert!(loss_wrong > loss_right, "Wrong prediction should have higher loss");
    }

    #[test]
    fn test_binary_ce_loss() {
        let predictions = Tensor::from_data(vec![0.9, 0.1], vec![2]).unwrap();
        let labels = vec![1.0, 0.0]; // correct predictions
        let weights = vec![1.0, 1.0]; // uniform weight

        let (loss, grad) = binary_ce_loss(&predictions, &labels, &weights).unwrap();
        assert!(loss > 0.0);
        assert!(loss < 1.0, "Loss should be small for correct predictions");
        assert_eq!(grad.shape, vec![2]);
    }

    #[test]
    fn test_binary_ce_loss_fp_weight() {
        let predictions = Tensor::from_data(vec![0.9, 0.9], vec![2]).unwrap();
        let labels = vec![1.0, 1.0]; // both "allow"
        let weights_normal = vec![1.0, 1.0];
        let weights_fp = vec![4.0, 4.0]; // false positive 4x weight

        let (loss_normal, _) = binary_ce_loss(&predictions, &labels, &weights_normal).unwrap();
        let (loss_fp, _) = binary_ce_loss(&predictions, &labels, &weights_fp).unwrap();
        // FP-weighted loss should be ~4x the normal loss
        assert!((loss_fp / loss_normal - 4.0).abs() < 0.01);
    }

    #[test]
    fn test_attention_backward_shapes() {
        let seq = 4;
        let d_head = 8;
        let d_output = Tensor::randn(&[seq, d_head], 1);
        let q = Tensor::randn(&[seq, d_head], 2);
        let k = Tensor::randn(&[seq, d_head], 3);
        let v = Tensor::randn(&[seq, d_head], 4);
        let attn_w = Tensor::randn(&[seq, seq], 5).softmax().unwrap();
        let scale = 1.0 / (d_head as f32).sqrt();

        let (dq, dk, dv) = attention_backward(&d_output, &q, &k, &v, &attn_w, scale).unwrap();
        assert_eq!(dq.shape, vec![seq, d_head]);
        assert_eq!(dk.shape, vec![seq, d_head]);
        assert_eq!(dv.shape, vec![seq, d_head]);
        assert!(dq.data.iter().all(|v| v.is_finite()));
    }

    #[test]
    fn test_linear_backward_shapes() {
        let batch = 4;
        let in_f = 8;
        let out_f = 16;
        let d_output = Tensor::randn(&[batch, out_f], 1);
        let input = Tensor::randn(&[batch, in_f], 2);
        let weight = Tensor::randn(&[out_f, in_f], 3);

        let (dw, db, dx) = linear_backward(&d_output, &input, &weight).unwrap();
        assert_eq!(dw.shape, vec![out_f, in_f]);
        assert_eq!(db.shape, vec![out_f]);
        assert_eq!(dx.shape, vec![batch, in_f]);
    }

    #[test]
    fn test_layer_norm_backward_shapes() {
        let batch = 4;
        let dim = 16;
        let d_output = Tensor::randn(&[batch, dim], 1);
        let input = Tensor::randn(&[batch, dim], 2);
        let gamma = Tensor::ones(&[dim]);

        let (dg, db, dx) = layer_norm_backward(&d_output, &input, &gamma, 1e-5).unwrap();
        assert_eq!(dg.shape, vec![dim]);
        assert_eq!(db.shape, vec![dim]);
        assert_eq!(dx.shape, vec![batch, dim]);
    }

    #[test]
    fn test_embedding_backward() {
        let d_output = Tensor::from_data(
            vec![1.0, 2.0, 3.0,  // token 5
                 4.0, 5.0, 6.0], // token 2
            vec![2, 3],
        ).unwrap();
        let indices = vec![5, 2];
        let vocab_size = 10;

        let grad = embedding_backward(&d_output, &indices, vocab_size).unwrap();
        assert_eq!(grad.shape, vec![10, 3]);
        // Row 5 should have [1,2,3], row 2 should have [4,5,6], rest zero
        assert_eq!(grad.data[5 * 3], 1.0);
        assert_eq!(grad.data[2 * 3], 4.0);
        assert_eq!(grad.data[0], 0.0); // row 0 untouched
    }

    #[test]
    fn test_adamw_step() {
        let config = AdamWConfig::default();
        let mut optimizer = AdamW::new(config, &[4]);

        let mut param = Tensor::from_data(vec![1.0, 2.0, 3.0, 4.0], vec![4]).unwrap();
        let grad = Tensor::from_data(vec![0.1, 0.2, 0.3, 0.4], vec![4]).unwrap();

        let original = param.data.clone();
        optimizer.step(&mut [&mut param], &[&grad], 3e-4);

        // Params should have changed
        assert!(param.data != original, "Params should change after optimizer step");
        assert_eq!(optimizer.step, 1);
    }

    #[test]
    fn test_adamw_memory() {
        let config = AdamWConfig::default();
        let optimizer = AdamW::new(config, &[1000, 2000, 500]);
        // 3500 params × 2 states × 4 bytes = 28000
        assert_eq!(optimizer.memory_bytes(), 28000);
    }

    #[test]
    fn test_grad_accumulator() {
        let mut acc = GradAccumulator::new(&[4, 2]);

        let g1 = Tensor::from_data(vec![1.0, 2.0, 3.0, 4.0], vec![4]).unwrap();
        let g2 = Tensor::from_data(vec![10.0, 20.0], vec![2]).unwrap();
        acc.accumulate(&[&g1, &g2]);

        let g3 = Tensor::from_data(vec![3.0, 4.0, 5.0, 6.0], vec![4]).unwrap();
        let g4 = Tensor::from_data(vec![30.0, 40.0], vec![2]).unwrap();
        acc.accumulate(&[&g3, &g4]);

        let avg = acc.averaged();
        assert_eq!(avg[0].data, vec![2.0, 3.0, 4.0, 5.0]);
        assert_eq!(avg[1].data, vec![20.0, 30.0]);
    }

    #[test]
    fn test_training_example_weight() {
        let normal = TrainingExample {
            input_tokens: vec![1, 2, 3],
            target: TrainingTarget::GateDecision(1.0),
            weight: 1.0,
        };
        assert_eq!(normal.weight, 1.0);

        let fp = TrainingExample {
            input_tokens: vec![1, 2, 3],
            target: TrainingTarget::GateDecision(-1.0),
            weight: 4.0,
        };
        assert_eq!(fp.weight, 4.0);
    }

    #[test]
    fn test_training_metrics_convergence() {
        let mut metrics = TrainingMetrics::new();
        // Simulate 1000 aligned decisions
        for _ in 0..1000 {
            metrics.record_gate_prediction(true, true);
        }
        assert!(metrics.is_converged());

        // Not converged with low alignment
        let mut m2 = TrainingMetrics::new();
        for i in 0..1000 {
            m2.record_gate_prediction(i % 2 == 0, true);
        }
        assert!(!m2.is_converged());
    }

    #[test]
    fn test_gelu_backward_finite() {
        let dy = Tensor::randn(&[4, 8], 1);
        let x = Tensor::randn(&[4, 8], 2);
        let dx = gelu_backward(&dy, &x);
        assert!(dx.data.iter().all(|v| v.is_finite()));
        assert_eq!(dx.shape, x.shape);
    }
}