hypersteeldb 0.2.3

//! **Step 2: tagger finetuning in Rust** (candle) — the port of the reference `spo_tagger.py::train`.
//!
//! Trains the multi-head tagger whose design is fixed in [`crate::tagger_data`]:
//!   * **Head A** — BIO span typing over the spec's *semantic* facets + structural kinds (dims 1, 3, 4)
//!   * **Head B** — the 4-way epistemic reading (dim 5), which is also the infon polarity `i` the
//!     Dempster-Shafer layer consumes
//!
//! The shared encoder is finetuned, not frozen: the pretrained tensors are named `bert.*`, so building
//! the model under `vb.pp("bert")` lets the checkpoint load straight into the trainable `VarMap`, and the
//! two new heads simply have no counterpart in the file (loaded per-tensor, missing names skipped).
//!
//! Loss is token-level cross-entropy on both heads with an ignore mask: padding and sub-token
//! continuations that carry no label are marked `-100` and dropped by gathering valid positions before
//! the CE, which candle's `cross_entropy` does not do for us.
//!
//! Head C (the biaffine relation scorer) is deliberately *not* here — it needs span pooling over the
//! encoder output, so it lands as a second stage once Head A's spans are reliable.

use crate::tagger_data::{head_a_labels, head_b_labels, to_bio, to_epistemic, TaggerExample};
use crate::vocabulary::VocabularySpace;
use candle_core::{DType, Device, IndexOp, Tensor, D};
use candle_nn::{loss, ops::softmax, AdamW, Linear, Module, Optimizer, ParamsAdamW, VarBuilder, VarMap};
use crate::hrm::{HrmConfig, HrmTagger};
use candle_transformers::models::bert::Config as BertConfig;
use serde::Serialize;
use std::path::{Path, PathBuf};
use tokenizers::Tokenizer;

const IGNORE: i64 = -100;

#[derive(Debug, Clone)]
pub struct TrainConfig {
    /// HF snapshot dir holding `config.json` + `model.safetensors` (e.g. bert-tiny).
    pub base_dir: PathBuf,
    /// a `tokenizer.json` compatible with the base's vocab
    pub tokenizer: PathBuf,
    pub epochs: usize,
    pub lr: f64,
    pub batch: usize,
    pub max_len: usize,
    /// weight on Head B's loss relative to Head A
    pub lambda_b: f64,
    pub seed: u64,
}

impl Default for TrainConfig {
    fn default() -> Self {
        TrainConfig {
            base_dir: PathBuf::new(),
            tokenizer: PathBuf::new(),
            epochs: 8,
            lr: 5e-4,
            batch: 8,
            max_len: 128,
            lambda_b: 0.5,
            seed: 0xC0FFEE,
        }
    }
}

/// The tagger is the reference HRM architecture: BERT **embeddings only** + the two-timescale reasoning
/// core + independent heads ([`crate::hrm`]). Earlier revisions of this file used BERT's full encoder with
/// heads attached, which is a different model — capacity there comes from stacked layers, whereas the
/// reference gets it from recurrent refinement at ~4M parameters.
pub type MultiHeadTagger = HrmTagger;

/// Build the HRM config from a HuggingFace base `config.json` (only the embedding-table shapes are used;
/// the encoder settings are irrelevant because the encoder is discarded).
pub fn hrm_config_from(bert: &BertConfig) -> HrmConfig {
    HrmConfig::bert_tiny(bert.vocab_size, bert.hidden_size, bert.max_position_embeddings, bert.type_vocab_size)
}

/// One encoded example: token ids, attention mask, and per-token targets for both heads.
#[derive(Debug, Clone)]
pub struct Encoded {
    pub ids: Vec<u32>,
    pub attn: Vec<u32>,
    pub labels_a: Vec<i64>,
    pub labels_b: Vec<i64>,
}

/// Tokenize + project char-span labels onto tokens, padding to `max_len`. Reuses the offset projection
/// already proven in [`crate::tagger_data`], so alignment semantics are identical between data
/// generation and training.
pub fn encode(spec: &VocabularySpace, tok: &Tokenizer, ex: &TaggerExample, max_len: usize) -> Result<Encoded, String> {
    let enc = tok.encode(ex.text.as_str(), true).map_err(|e| format!("tokenize: {e}"))?;
    let offsets: Vec<(usize, usize)> = enc.get_offsets().to_vec();
    let mut ids: Vec<u32> = enc.get_ids().to_vec();
    let mut labels_a = to_bio(spec, &ex.spans, &offsets);
    let mut labels_b = to_epistemic(&ex.spans, &offsets);
    // specials report (0,0) offsets → already IGNORE from the projections
    let n = ids.len().min(max_len);
    ids.truncate(n);
    labels_a.truncate(n);
    labels_b.truncate(n);
    let mut attn = vec![1u32; n];
    while ids.len() < max_len {
        ids.push(0);
        attn.push(0);
        labels_a.push(IGNORE);
        labels_b.push(IGNORE);
    }
    Ok(Encoded { ids, attn, labels_a, labels_b })
}

/// Cross-entropy over only the positions whose target isn't `IGNORE`. candle's `cross_entropy` has no
/// ignore-index, so valid positions are gathered first; returns `None` when a batch has no labels.
fn masked_ce(logits: &Tensor, labels: &[i64], device: &Device) -> candle_core::Result<Option<Tensor>> {
    let keep: Vec<u32> = labels.iter().enumerate().filter(|(_, l)| **l != IGNORE).map(|(i, _)| i as u32).collect();
    if keep.is_empty() {
        return Ok(None);
    }
    let (b, t, c) = logits.dims3()?;
    let flat = logits.reshape((b * t, c))?;
    let idx = Tensor::from_vec(keep.clone(), keep.len(), device)?;
    let picked = flat.index_select(&idx, 0)?;
    let tgt: Vec<u32> = keep.iter().map(|&i| labels[i as usize] as u32).collect();
    let tgt = Tensor::from_vec(tgt, keep.len(), device)?;
    Ok(Some(loss::cross_entropy(&picked, &tgt)?))
}

#[derive(Debug, Clone, Serialize)]
pub struct EpochReport {
    pub epoch: usize,
    pub loss_a: f64,
    pub loss_b: f64,
    pub total: f64,
}

#[derive(Debug, Clone, Serialize)]
pub struct TrainReport {
    pub examples: usize,
    pub labels_a: usize,
    pub labels_b: usize,
    pub epochs: Vec<EpochReport>,
    /// token-level accuracy of Head A on the training set after the final epoch
    pub train_acc_a: f64,
    pub train_acc_b: f64,
    /// held-out examples (never trained on)
    pub dev_examples: usize,
    /// the numbers that indicate generalisation rather than memorisation
    pub dev_acc_a: f64,
    pub dev_acc_b: f64,
    /// Head A accuracy over only the tokens that carry an entity label (not `O`) — the metric that
    /// matters, since `O` dominates token-level accuracy and inflates it
    pub dev_acc_a_spans: f64,
}

/// Finetune Head A + Head B. Returns the report; the trained weights are left in `varmap` for saving.
pub fn train(
    spec: &VocabularySpace,
    examples: &[TaggerExample],
    cfg: &TrainConfig,
) -> Result<(VarMap, TrainReport, Vec<String>), String> {
    if examples.is_empty() {
        return Err("no training examples".into());
    }
    let device = Device::Cpu;
    let bert_cfg: BertConfig = serde_json::from_slice(
        &std::fs::read(cfg.base_dir.join("config.json")).map_err(|e| format!("base config.json: {e}"))?,
    )
    .map_err(|e| format!("parse base config: {e}"))?;
    let tok = Tokenizer::from_file(&cfg.tokenizer).map_err(|e| format!("tokenizer: {e}"))?;

    let labels_a = head_a_labels(spec);
    let n_a = labels_a.len();
    let n_b = head_b_labels().len();

    // trainable params live in the VarMap; pretrained `bert.*` tensors are loaded into it per-name so the
    // two fresh heads (absent from the checkpoint) are simply left at their init.
    let varmap = VarMap::new();
    let vb = VarBuilder::from_varmap(&varmap, DType::F32, &device);
    let hrm_cfg = hrm_config_from(&bert_cfg);
    let model = HrmTagger::new(vb, &hrm_cfg, n_a, n_b).map_err(|e| format!("build model: {e}"))?;
    {
        // Only the EMBEDDING table is pretrained; the HRM core and heads train from scratch. The reference
        // takes `BertModel.from_pretrained(base).embeddings` and discards the encoder, so encoder tensors in
        // the checkpoint simply have no counterpart here.
        let weights = cfg.base_dir.join("model.safetensors");
        let pre = candle_core::safetensors::load(&weights, &device).map_err(|e| format!("load {}: {e}", weights.display()))?;
        let mut vm = varmap.clone();
        let mut loaded = 0usize;
        for (name, t) in pre.iter().filter(|(n, _)| n.starts_with("bert.embeddings.")) {
            if vm.set_one(name, t).is_ok() {
                loaded += 1;
            }
        }
        if loaded == 0 {
            return Err("no pretrained embedding tensors matched (expected `bert.embeddings.*`)".into());
        }
        eprintln!("loaded {loaded} pretrained embedding tensors; HRM core + heads train from scratch");
    }

    let all: Vec<Encoded> = examples.iter().filter_map(|e| encode(spec, &tok, e, cfg.max_len).ok()).collect();
    if all.len() < 5 {
        return Err("too few examples to split train/dev".into());
    }
    // deterministic 80/20 holdout by stride, so every generator case lands in both halves
    let mut encoded: Vec<Encoded> = Vec::new();
    let mut dev: Vec<Encoded> = Vec::new();
    for (i, e) in all.into_iter().enumerate() {
        if i % 5 == 4 {
            dev.push(e);
        } else {
            encoded.push(e);
        }
    }

    let mut opt = AdamW::new(varmap.all_vars(), ParamsAdamW { lr: cfg.lr, ..Default::default() })
        .map_err(|e| format!("optimizer: {e}"))?;
    let mut epochs_out = Vec::new();

    // deterministic shuffling without an rng dependency (LCG over indices)
    let mut order: Vec<usize> = (0..encoded.len()).collect();
    let mut seed = cfg.seed;
    let mut next = move || {
        seed = seed.wrapping_mul(6364136223846793005).wrapping_add(1442695040888963407);
        (seed >> 33) as usize
    };

    for epoch in 1..=cfg.epochs {
        for i in (1..order.len()).rev() {
            order.swap(i, next() % (i + 1));
        }
        let (mut sum_a, mut sum_b, mut steps) = (0.0f64, 0.0f64, 0usize);
        for chunk in order.chunks(cfg.batch.max(1)) {
            let bs = chunk.len();
            let ids: Vec<u32> = chunk.iter().flat_map(|&i| encoded[i].ids.clone()).collect();
            let attn: Vec<u32> = chunk.iter().flat_map(|&i| encoded[i].attn.clone()).collect();
            let la: Vec<i64> = chunk.iter().flat_map(|&i| encoded[i].labels_a.clone()).collect();
            let lb: Vec<i64> = chunk.iter().flat_map(|&i| encoded[i].labels_b.clone()).collect();
            let ids_t = Tensor::from_vec(ids, (bs, cfg.max_len), &device).map_err(|e| e.to_string())?;
            let attn_t = Tensor::from_vec(attn, (bs, cfg.max_len), &device).map_err(|e| e.to_string())?;
            let (log_a, log_b) = model.forward(&ids_t, &attn_t, true).map_err(|e| format!("forward: {e}"))?;
            let ce_a = masked_ce(&log_a, &la, &device).map_err(|e| e.to_string())?;
            let ce_b = masked_ce(&log_b, &lb, &device).map_err(|e| e.to_string())?;
            let (Some(ce_a), Some(ce_b)) = (ce_a, ce_b) else { continue };
            let total = (&ce_a + (ce_b.affine(cfg.lambda_b, 0.0).map_err(|e| e.to_string())?)).map_err(|e| e.to_string())?;
            opt.backward_step(&total).map_err(|e| format!("backward: {e}"))?;
            sum_a += ce_a.to_scalar::<f32>().map_err(|e| e.to_string())? as f64;
            sum_b += ce_b.to_scalar::<f32>().map_err(|e| e.to_string())? as f64;
            steps += 1;
        }
        let d = steps.max(1) as f64;
        epochs_out.push(EpochReport {
            epoch,
            loss_a: round4(sum_a / d),
            loss_b: round4(sum_b / d),
            total: round4((sum_a + cfg.lambda_b * sum_b) / d),
        });
    }

    let (acc_a, acc_b, _) = accuracy(&model, &encoded, cfg, &device).map_err(|e| e.to_string())?;
    let (dev_a, dev_b, dev_span) = accuracy(&model, &dev, cfg, &device).map_err(|e| e.to_string())?;
    let report = TrainReport {
        examples: encoded.len(),
        labels_a: n_a,
        labels_b: n_b,
        epochs: epochs_out,
        train_acc_a: round4(acc_a),
        train_acc_b: round4(acc_b),
        dev_examples: dev.len(),
        dev_acc_a: round4(dev_a),
        dev_acc_b: round4(dev_b),
        dev_acc_a_spans: round4(dev_span),
    };
    Ok((varmap, report, labels_a))
}

/// Token-level accuracy on labelled positions for both heads.
fn accuracy(model: &MultiHeadTagger, encoded: &[Encoded], cfg: &TrainConfig, device: &Device) -> candle_core::Result<(f64, f64, f64)> {
    let (mut ok_a, mut ok_b, mut n_a, mut n_b) = (0usize, 0usize, 0usize, 0usize);
    let (mut ok_span, mut n_span) = (0usize, 0usize);
    for e in encoded {
        let ids = Tensor::from_vec(e.ids.clone(), (1, cfg.max_len), device)?;
        let attn = Tensor::from_vec(e.attn.clone(), (1, cfg.max_len), device)?;
        let (la, lb) = model.forward(&ids, &attn, false)?;
        let pa = softmax(&la.i(0)?, D::Minus1)?.argmax(D::Minus1)?.to_vec1::<u32>()?;
        let pb = softmax(&lb.i(0)?, D::Minus1)?.argmax(D::Minus1)?.to_vec1::<u32>()?;
        for (i, &t) in e.labels_a.iter().enumerate() {
            if t != IGNORE {
                n_a += 1;
                if pa[i] as i64 == t {
                    ok_a += 1;
                }
                if t != 0 {
                    // labelled (non-`O`) tokens only: `O` dominates and inflates plain accuracy
                    n_span += 1;
                    if pa[i] as i64 == t {
                        ok_span += 1;
                    }
                }
            }
        }
        for (i, &t) in e.labels_b.iter().enumerate() {
            if t != IGNORE {
                n_b += 1;
                if pb[i] as i64 == t {
                    ok_b += 1;
                }
            }
        }
    }
    Ok((
        ok_a as f64 / n_a.max(1) as f64,
        ok_b as f64 / n_b.max(1) as f64,
        ok_span as f64 / n_span.max(1) as f64,
    ))
}

/// Persist the finetuned tagger: weights plus the label spaces needed to decode it.
pub fn save(varmap: &VarMap, labels_a: &[String], out_dir: &Path) -> Result<(), String> {
    std::fs::create_dir_all(out_dir).map_err(|e| e.to_string())?;
    varmap.save(out_dir.join("tagger.safetensors")).map_err(|e| format!("save weights: {e}"))?;
    let meta = serde_json::json!({
        "head_a_labels": labels_a,
        "head_b_labels": head_b_labels(),
        "ignore_index": IGNORE,
    });
    std::fs::write(out_dir.join("tagger.json"), serde_json::to_vec_pretty(&meta).map_err(|e| e.to_string())?).map_err(|e| e.to_string())?;
    Ok(())
}

fn round4(v: f64) -> f64 {
    (v * 10000.0).round() / 10000.0
}


// ── inference: load a tuned checkpoint and tag real text ────────────────────────────────────────

/// A span predicted by the tuned tagger: byte offsets, its facet (Head A) and epistemic reading
/// (Head B, majority vote over the span's tokens → the infon polarity `i`).
#[derive(Debug, Clone, Serialize, PartialEq)]
pub struct PredictedSpan {
    pub start: usize,
    pub end: usize,
    pub facet: String,
    pub text: String,
    pub negated: bool,
    pub hedged: bool,
    /// the Dempster-Shafer belief level implied by the epistemic reading (±1 / ±0.5)
    pub belief: f32,
}

/// A tuned tagger ready for inference: weights + the label spaces they were trained with.
pub struct TunedTagger {
    model: MultiHeadTagger,
    tok: Tokenizer,
    labels_a: Vec<String>,
    device: Device,
    max_len: usize,
    /// Head C (step 5) + the spec that supplies its type mask. When present, projection emits bound
    /// relation tokens (`rel/<name>/+` on the actor side, `rel/<name>/-` on the target side).
    relations: Option<(crate::relation_train::BiaffineHead, VocabularySpace)>,
}

impl TunedTagger {
    /// Load from a `save()` directory (`tagger.safetensors` + `tagger.json`) plus the base config and a
    /// tokenizer. The label space comes from the checkpoint, never from a hardcoded list, so a model
    /// trained on a different corpus's spec decodes correctly.
    pub fn load(dir: &Path, base_dir: &Path, tokenizer: &Path, max_len: usize) -> Result<TunedTagger, String> {
        let meta: serde_json::Value =
            serde_json::from_slice(&std::fs::read(dir.join("tagger.json")).map_err(|e| format!("tagger.json: {e}"))?)
                .map_err(|e| format!("parse tagger.json: {e}"))?;
        let labels_a: Vec<String> = meta
            .get("head_a_labels")
            .and_then(|v| v.as_array())
            .map(|a| a.iter().filter_map(|s| s.as_str().map(String::from)).collect())
            .ok_or("tagger.json missing head_a_labels")?;
        let n_b = head_b_labels().len();
        let bert_cfg: BertConfig =
            serde_json::from_slice(&std::fs::read(base_dir.join("config.json")).map_err(|e| format!("base config: {e}"))?)
                .map_err(|e| format!("parse base config: {e}"))?;
        let device = Device::Cpu;
        let weights = dir.join("tagger.safetensors");
        let vb = unsafe {
            VarBuilder::from_mmaped_safetensors(&[weights.clone()], DType::F32, &device)
                .map_err(|e| format!("load {}: {e}", weights.display()))?
        };
        let model = HrmTagger::new(vb, &hrm_config_from(&bert_cfg), labels_a.len(), n_b).map_err(|e| format!("build model: {e}"))?;
        let tok = Tokenizer::from_file(tokenizer).map_err(|e| format!("tokenizer: {e}"))?;
        Ok(TunedTagger { model, tok, labels_a, device, max_len, relations: None })
    }

    pub fn labels(&self) -> &[String] {
        &self.labels_a
    }

    /// Attach the trained biaffine relation head (Head C) so projection binds arguments and emits
    /// dimension-2 tokens. `dir` is the checkpoint directory holding `relations.safetensors`.
    pub fn enable_relations(&mut self, dir: &Path, spec: &VocabularySpace) -> Result<(), String> {
        let path = dir.join("relations.safetensors");
        if !path.exists() {
            return Err(format!("{} not found — run step 5 first", path.display()));
        }
        let vb = unsafe {
            VarBuilder::from_mmaped_safetensors(&[path.clone()], DType::F32, &self.device)
                .map_err(|e| format!("load {}: {e}", path.display()))?
        };
        let hidden = self.model.hidden_size();
        let head = crate::relation_train::BiaffineHead::new(vb, 2 * hidden, spec.relation_facets.len() + 1)
            .map_err(|e| format!("build head C: {e}"))?;
        self.relations = Some((head, spec.clone()));
        Ok(())
    }

    pub fn has_relations(&self) -> bool {
        self.relations.is_some()
    }

    /// Project a sentence into a [`Situation`] for the roaring index — the bridge from the tuned tagger to
    /// ingest. Emits the six Vocabulary-Space dimensions this tagger can produce:
    ///   * **dim 1** typed entity tokens (`org/raytheon`) from Head A's facets
    ///   * **dim 3** `geo/…` / `time/…` via the deterministic loci normalisers
    ///   * **dim 4** `qty/…` bucket URIs via the units canonicaliser
    ///   * **dim 5** `state/negated` / `state/hedged` from a STATE cue span
    ///
    /// **Negation scope**: an epistemic cue scopes its clause, so a `state/negated` cue sets negative
    /// infon polarity on the *other* tokens of the sentence, not merely on the cue. That is what makes
    /// `Bel`/`Pl` meaningful — "Raytheon does not manufacture Aegis" must not assert `org/raytheon`
    /// positively about that relation.
    pub fn project(&self, sentence: &str) -> Result<crate::projector::Situation, String> {
        use crate::dimensions;
        let spans = self.tag(sentence)?;
        let mut tokens: Vec<String> = Vec::new();
        let mut numbers: Vec<(String, f64)> = Vec::new();
        // clause-level epistemic state from any STATE cue in the sentence
        let (mut negated, mut hedged) = (false, false);
        for sp in spans.iter().filter(|s| s.facet == "state") {
            negated |= sp.negated;
            hedged |= sp.hedged;
        }
        if negated || hedged {
            tokens.push(dimensions::state_uri(negated, hedged).to_string());
        }
        for sp in &spans {
            match sp.facet.as_str() {
                "state" => {}
                "qty" => {
                    if let Some((field, si)) = crate::units::parse_quantity(&sp.text) {
                        if let Some(u) = dimensions::qty_uri(&field, si) {
                            tokens.push(u);
                        }
                        numbers.push((field, si));
                    }
                }
                "time" => tokens.push(dimensions::time_uri(&sp.text).unwrap_or_else(|| format!("time/{}", crate::projector::slug(&sp.text)))),
                "geo" => tokens.push(dimensions::geo_uri(&sp.text)),
                facet => tokens.push(dimensions::entity_uri(facet, &sp.text)),
            }
        }
        // dim 2: bind arguments with Head C. Polarity marks the argument SIDE — `+` actor, `-` target —
        // and a compound token also records which entity filled each role, mirroring the structured
        // projector's `rel/<name>/+/<facet>/<value>` form so both broad and specific queries work.
        if let Some((head, spec)) = self.relations.as_ref() {
            if let Ok(pairs) = self.bind_relations(sentence, &spans, head, spec) {
                for (name, h, t) in pairs {
                    tokens.push(dimensions::rel_uri(&name, dimensions::Role::Actor));
                    tokens.push(dimensions::rel_uri(&name, dimensions::Role::Target));
                    tokens.push(format!("rel/{}/+/{}", crate::projector::slug(&name), dimensions::entity_uri(&h.facet, &h.text)));
                    tokens.push(format!("rel/{}/-/{}", crate::projector::slug(&name), dimensions::entity_uri(&t.facet, &t.text)));
                }
            }
        }
        tokens.sort();
        tokens.dedup();
        // the clause's polarity applies to every projected token (the cue scopes the assertion)
        let level = dimensions::belief_level(negated, hedged);
        let beliefs = if level == 1.0 { Vec::new() } else { tokens.iter().map(|t| (t.clone(), level)).collect() };
        Ok(crate::projector::Situation { tokens, display: vec![sentence.to_string()], numbers, beliefs })
    }


    /// Tag a sentence → typed spans with epistemic polarity. BIO decoding merges `B-X` + following
    /// `I-X`; each span's epistemic class is the majority vote of its tokens.
    pub fn tag(&self, text: &str) -> Result<Vec<PredictedSpan>, String> {
        let enc = self.tok.encode(text, true).map_err(|e| format!("tokenize: {e}"))?;
        let n = enc.get_ids().len().min(self.max_len);
        let ids: Vec<u32> = enc.get_ids()[..n].to_vec();
        let offsets: Vec<(usize, usize)> = enc.get_offsets()[..n].to_vec();
        let attn = vec![1u32; n];
        let ids_t = Tensor::from_vec(ids, (1, n), &self.device).map_err(|e| e.to_string())?;
        let attn_t = Tensor::from_vec(attn, (1, n), &self.device).map_err(|e| e.to_string())?;
        let (la, lb) = self.model.forward(&ids_t, &attn_t, false).map_err(|e| format!("forward: {e}"))?;
        let pa = softmax(&la.i(0).map_err(|e| e.to_string())?, D::Minus1)
            .and_then(|t| t.argmax(D::Minus1))
            .and_then(|t| t.to_vec1::<u32>())
            .map_err(|e| e.to_string())?;
        let pb_probs = softmax(&lb.i(0).map_err(|e| e.to_string())?, D::Minus1).map_err(|e| e.to_string())?;
        let pb = pb_probs.argmax(D::Minus1).and_then(|t| t.to_vec1::<u32>()).map_err(|e| e.to_string())?;
        // per-token distribution over the 4 epistemic classes, for the STATE-span constraint below
        let pb_dist: Vec<Vec<f32>> = pb_probs.to_vec2::<f32>().map_err(|e| e.to_string())?;

        let mut out: Vec<PredictedSpan> = Vec::new();
        let mut cur: Option<(String, usize, usize, Vec<u32>, Vec<Vec<f32>>)> = None; // (kind, start, end, votes, dists)
        let flush = |cur: &mut Option<(String, usize, usize, Vec<u32>, Vec<Vec<f32>>)>, out: &mut Vec<PredictedSpan>, text: &str| {
            if let Some((kind, s, e, votes, dists)) = cur.take() {
                // CONSISTENCY CONSTRAINT: a STATE span *is* the epistemic cue, so "asserted" is not a
                // possible reading of it. Restrict the choice to the non-asserted classes, summing the
                // span's token distributions — the same masking idea Head C applies to type-invalid pairs.
                let ep = if kind.eq_ignore_ascii_case("state") && !dists.is_empty() {
                    let mut best = (1usize, f32::NEG_INFINITY);
                    for c in 1..4usize {
                        let score: f32 = dists.iter().map(|d| d.get(c).copied().unwrap_or(0.0)).sum();
                        if score > best.1 {
                            best = (c, score);
                        }
                    }
                    best.0 as u32
                } else {
                    majority(&votes)
                };
                let (negated, hedged) = match ep {
                    0 => (false, false),
                    1 => (false, true),
                    2 => (true, true),
                    _ => (true, false),
                };
                out.push(PredictedSpan {
                    start: s,
                    end: e,
                    facet: kind.to_lowercase(),
                    text: text[s..e].to_string(),
                    negated,
                    hedged,
                    belief: crate::dimensions::belief_level(negated, hedged),
                });
            }
        };
        for i in 0..n {
            let (ts, te) = offsets[i];
            if te <= ts {
                continue; // special token
            }
            let label = self.labels_a.get(pa[i] as usize).map(|s| s.as_str()).unwrap_or("O");
            if let Some(kind) = label.strip_prefix("B-") {
                flush(&mut cur, &mut out, text);
                cur = Some((kind.to_string(), ts, te, vec![pb[i]], vec![pb_dist[i].clone()]));
            } else if let Some(kind) = label.strip_prefix("I-") {
                match cur.as_mut() {
                    Some((k, _, e, votes, dists)) if k == kind => {
                        *e = te;
                        votes.push(pb[i]);
                        dists.push(pb_dist[i].clone());
                    }
                    _ => flush(&mut cur, &mut out, text), // orphan I- → drop
                }
            } else {
                flush(&mut cur, &mut out, text);
            }
        }
        flush(&mut cur, &mut out, text);
        Ok(merge_contiguous(snap_to_words(out, text), text))
    }
}

/// Snap span edges out to enclosing word boundaries. Sub-word tokenisation plus a scarce `I-` class makes
/// a tagger clip mid-word ("Aegis" → "Ae", "Thales" → "Tha"); the reference implementation notes the same
/// failure and snaps spans to word boundaries. Purely a decode-time correction over char offsets — it
/// cannot invent a span, only complete the word one already covers.
fn snap_to_words(spans: Vec<PredictedSpan>, text: &str) -> Vec<PredictedSpan> {
    let b = text.as_bytes();
    let alnum = |i: usize| -> bool { (b[i] as char).is_alphanumeric() };
    spans
        .into_iter()
        .map(|mut sp| {
            // walk start left while the previous byte continues a word
            while sp.start > 0 && text.is_char_boundary(sp.start - 1) && alnum(sp.start - 1) && alnum(sp.start.min(b.len() - 1)) {
                sp.start -= 1;
            }
            // walk end right while the next byte continues a word
            while sp.end < b.len() && text.is_char_boundary(sp.end) && alnum(sp.end) {
                sp.end += 1;
            }
            while sp.end < b.len() && !text.is_char_boundary(sp.end) {
                sp.end += 1;
            }
            sp.text = text[sp.start..sp.end].to_string();
            sp
        })
        .collect()
}

impl TunedTagger {
    /// Score every type-valid ordered span pair with Head C and return the argmax relations
    /// (`(name, head_span, tail_span)`), skipping `none`.
    fn bind_relations(
        &self,
        sentence: &str,
        spans: &[PredictedSpan],
        head: &crate::relation_train::BiaffineHead,
        spec: &VocabularySpace,
    ) -> Result<Vec<(String, PredictedSpan, PredictedSpan)>, String> {
        use crate::tagger_data::{pair_mask, LabeledSpan};
        // entity spans only: a relation binds participants, not epistemic cues
        let ents: Vec<&PredictedSpan> = spans.iter().filter(|s| s.facet != "state").collect();
        if ents.len() < 2 || spec.relation_facets.is_empty() {
            return Ok(Vec::new());
        }
        let enc = self.tok.encode(sentence, true).map_err(|e| format!("tokenize: {e}"))?;
        let n = enc.get_ids().len().min(self.max_len);
        let ids = Tensor::from_vec(enc.get_ids()[..n].to_vec(), (1, n), &self.device).map_err(|e| e.to_string())?;
        let attn = Tensor::from_vec(vec![1u32; n], (1, n), &self.device).map_err(|e| e.to_string())?;
        let hidden = self.model.hidden(&ids, &attn, false).map_err(|e| format!("encode: {e}"))?;
        let offsets: Vec<(usize, usize)> = enc.get_offsets()[..n].to_vec();
        let reps: Vec<Tensor> = ents
            .iter()
            .map(|sp| {
                let ls = LabeledSpan { start: sp.start, end: sp.end, facet: sp.facet.clone(), surface: sp.text.clone(), negated: sp.negated, hedged: sp.hedged };
                crate::relation_train::pool_span(&hidden, &offsets, &ls)
            })
            .collect::<candle_core::Result<Vec<_>>>()
            .map_err(|e| format!("pool: {e}"))?;
        let stacked = Tensor::stack(&reps, 0).map_err(|e| e.to_string())?;
        let logits = head.forward(&stacked).map_err(|e| format!("head C: {e}"))?;
        let mask = pair_mask(spec);
        let n_rel = spec.relation_facets.len() + 1;
        let mut out = Vec::new();
        for i in 0..ents.len() {
            for j in 0..ents.len() {
                if i == j {
                    continue;
                }
                let row = logits.i((i, j)).map_err(|e| e.to_string())?;
                let allow: Vec<f32> = (0..n_rel)
                    .map(|c| {
                        if crate::relation_train::type_allowed(spec, &mask, &ents[i].facet, &ents[j].facet, c) {
                            0.0
                        } else {
                            f32::NEG_INFINITY
                        }
                    })
                    .collect();
                let allow = Tensor::from_vec(allow, n_rel, &self.device).map_err(|e| e.to_string())?;
                let cls = softmax(&(row + allow).map_err(|e| e.to_string())?, D::Minus1)
                    .and_then(|t| t.argmax(D::Minus1))
                    .and_then(|t| t.to_scalar::<u32>())
                    .map_err(|e| e.to_string())? as usize;
                if cls > 0 {
                    if let Some(r) = spec.relation_facets.get(cls - 1) {
                        out.push((r.name.clone(), ents[i].clone(), ents[j].clone()));
                    }
                }
            }
        }
        Ok(out)
    }
}

/// Merge spans of the same facet that are **directly contiguous** in the text (no gap). Sub-word pieces of
/// one word can each be predicted `B-`, which BIO decoding correctly treats as separate spans — e.g.
/// "Aegis" tokenised as `Ae`+`gis` becomes two spans. Only zero-gap neighbours are merged, so two distinct
/// adjacent entities ("Boeing Airbus") are never fused.
fn merge_contiguous(spans: Vec<PredictedSpan>, text: &str) -> Vec<PredictedSpan> {
    let mut out: Vec<PredictedSpan> = Vec::with_capacity(spans.len());
    for sp in spans {
        match out.last_mut() {
            Some(prev) if prev.facet == sp.facet && prev.end == sp.start => {
                prev.end = sp.end;
                prev.text = text[prev.start..prev.end].to_string();
                // a negated/hedged piece makes the whole merged span so
                prev.negated |= sp.negated;
                prev.hedged |= sp.hedged;
                prev.belief = crate::dimensions::belief_level(prev.negated, prev.hedged);
            }
            _ => out.push(sp),
        }
    }
    out
}

fn majority(v: &[u32]) -> u32 {
    let mut counts = [0usize; 8];
    for &x in v {
        if (x as usize) < counts.len() {
            counts[x as usize] += 1;
        }
    }
    counts.iter().enumerate().max_by_key(|(_, c)| **c).map(|(i, _)| i as u32).unwrap_or(0)
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::tagger_data::{Case, LabeledSpan, RelationLabel};
    use crate::vocabulary::{EntityFacet, RelationFacet};

    fn spec() -> VocabularySpace {
        VocabularySpace {
            version: 1,
            corpus: "t".into(),
            entity_facets: vec![
                EntityFacet { name: "org".into(), parent: None, description: "companies".into(), examples: vec![], structural: false },
                EntityFacet { name: "system".into(), parent: None, description: "platforms".into(), examples: vec![], structural: false },
            ],
            relation_facets: vec![RelationFacet { name: "develops".into(), head: "org".into(), tail: "system".into() }],
            gazetteer: vec![],
            metrics: None,
        }
    }

    fn ex(text: &str, a: (usize, usize), b: (usize, usize), negated: bool) -> TaggerExample {
        TaggerExample {
            text: text.into(),
            spans: vec![
                LabeledSpan { start: a.0, end: a.1, facet: "org".into(), surface: text[a.0..a.1].into(), negated, hedged: false },
                LabeledSpan { start: b.0, end: b.1, facet: "system".into(), surface: text[b.0..b.1].into(), negated, hedged: false },
            ],
            relations: vec![RelationLabel { head: 0, tail: 1, name: "develops".into() }],
            case: if negated { Case::Negated } else { Case::Normal },
        }
    }

    fn base_dirs() -> Option<(PathBuf, PathBuf)> {
        let home = std::env::var("HOME").ok()?;
        let g = |p: &str| glob_first(&format!("{home}/{p}"));
        let base = g(".cache/huggingface/hub/models--google--bert_uncased_L-2_H-128_A-2/snapshots/*")?;
        let tokdir = g(".cache/huggingface/hub/models--bert-base-uncased/snapshots/*")?;
        let tok = tokdir.join("tokenizer.json");
        if base.join("model.safetensors").exists() && tok.exists() {
            Some((base, tok))
        } else {
            None
        }
    }

    fn glob_first(pat: &str) -> Option<PathBuf> {
        let (dir, _) = pat.rsplit_once('/')?;
        std::fs::read_dir(dir).ok()?.filter_map(|e| e.ok()).map(|e| e.path()).find(|p| p.is_dir())
    }

    #[test]
    fn encoding_projects_labels_onto_tokens() {
        let Some((_, tok_path)) = base_dirs() else {
            eprintln!("skip: no cached bert");
            return;
        };
        let tok = Tokenizer::from_file(&tok_path).unwrap();
        let s = spec();
        let e = ex("Boeing develops the MQ-28 aircraft.", (0, 6), (20, 25), false);
        let enc = encode(&s, &tok, &e, 32).unwrap();
        assert_eq!(enc.ids.len(), 32);
        assert_eq!(enc.attn.iter().filter(|&&a| a == 1).count(), tok.encode(e.text.as_str(), true).unwrap().get_ids().len());
        let labels = head_a_labels(&s);
        // at least one B-ORG and one B-SYSTEM landed
        let named: Vec<&str> = enc.labels_a.iter().filter(|&&l| l != IGNORE && l != 0).map(|&l| labels[l as usize].as_str()).collect();
        assert!(named.contains(&"B-ORG"), "got {named:?}");
        assert!(named.contains(&"B-SYSTEM"), "got {named:?}");
        // padding is ignored, not class 0
        assert_eq!(enc.labels_a[31], IGNORE);
    }

    /// The real test of a training loop: it must actually fit. Overfitting a handful of examples is the
    /// minimum bar — if loss doesn't fall and accuracy doesn't climb, gradients aren't flowing.
    #[test]
    fn training_overfits_a_tiny_set() {
        let Some((base, tok)) = base_dirs() else {
            eprintln!("skip: no cached bert-tiny");
            return;
        };
        let s = spec();
        let data = vec![
            ex("Boeing develops the MQ-28 aircraft.", (0, 6), (20, 25), false),
            ex("Airbus develops the A400M transport.", (0, 6), (20, 25), false),
            ex("Saab develops the Gripen fighter jet.", (0, 4), (18, 24), false),
            ex("Thales develops the Sonar array system.", (0, 6), (20, 25), false),
            ex("Embraer develops the KC-390 airlifter.", (0, 7), (21, 27), false),
            ex("Lockheed does not develop the F-35 jet.", (0, 8), (30, 34), true),
        ];
        let cfg = TrainConfig { base_dir: base, tokenizer: tok, epochs: 60, lr: 3e-3, batch: 6, max_len: 32, ..Default::default() };
        let (varmap, rep, labels) = train(&s, &data, &cfg).expect("train");
        eprintln!("{}", serde_json::to_string_pretty(&rep).unwrap());
        // derive, don't hardcode: O + BIO x (semantic facets + structural kinds)
        let expect = 1 + 2 * (s.taggable_facets().len() + crate::tagger_data::STRUCTURAL_KINDS.len());
        assert_eq!(labels.len(), expect);
        let first = rep.epochs.first().unwrap().total;
        let last = rep.epochs.last().unwrap().total;
        assert!(last < first, "loss must decrease: {first} → {last}");
        assert!(rep.train_acc_a > 0.8, "Head A should overfit 3 examples (got {})", rep.train_acc_a);
        assert!(rep.train_acc_b > 0.8, "Head B should overfit 3 examples (got {})", rep.train_acc_b);
        // round-trip the checkpoint
        let dir = std::env::temp_dir().join(format!("steeldb-tagger-{}", std::process::id()));
        save(&varmap, &labels, &dir).unwrap();
        assert!(dir.join("tagger.safetensors").exists() && dir.join("tagger.json").exists());
        let _ = std::fs::remove_dir_all(&dir);
    }

    /// Round-trip: train, save, reload via `TunedTagger`, and tag text. Verifies the checkpoint is usable
    /// and that BIO decoding + epistemic voting produce coherent spans (not just that training ran).
    #[test]
    fn tuned_tagger_round_trips_and_tags() {
        let Some((base, tok)) = base_dirs() else {
            eprintln!("skip: no cached bert-tiny");
            return;
        };
        let s = spec();
        let data = vec![
            ex("Boeing develops the MQ-28 aircraft.", (0, 6), (20, 25), false),
            ex("Airbus develops the A400M transport.", (0, 6), (20, 25), false),
            ex("Saab develops the Gripen fighter jet.", (0, 4), (18, 24), false),
            ex("Thales develops the Sonar array system.", (0, 6), (20, 25), false),
            ex("Embraer develops the KC-390 airlifter.", (0, 7), (21, 27), false),
            ex("Lockheed does not develop the F-35 jet.", (0, 8), (30, 34), true),
        ];
        let cfg = TrainConfig { base_dir: base.clone(), tokenizer: tok.clone(), epochs: 60, lr: 3e-3, batch: 6, max_len: 32, ..Default::default() };
        let (varmap, _rep, labels) = train(&s, &data, &cfg).expect("train");
        let dir = std::env::temp_dir().join(format!("steeldb-tagger-rt-{}", std::process::id()));
        save(&varmap, &labels, &dir).unwrap();

        let tt = TunedTagger::load(&dir, &base, &tok, 32).expect("load tuned");
        assert_eq!(tt.labels().len(), labels.len());
        // a memorised sentence must decode to its spans with the right facets
        let spans = tt.tag("Boeing develops the MQ-28 aircraft.").expect("tag");
        eprintln!("TAGGED: {spans:?}");
        assert!(spans.iter().any(|p| p.facet == "org" && p.text.contains("Boeing")), "got {spans:?}");
        assert!(spans.iter().any(|p| p.facet == "system"), "got {spans:?}");
        // the negated training sentence must carry negative belief on its spans
        let neg = tt.tag("Lockheed does not develop the F-35 jet.").expect("tag");
        assert!(neg.iter().any(|p| p.negated && p.belief < 0.0), "negation must set belief<0: {neg:?}");
        // spans are well-formed: non-empty, ordered, inside the text
        for w in spans.windows(2) {
            assert!(w[0].end <= w[1].start, "spans must not overlap: {spans:?}");
        }
        let _ = std::fs::remove_dir_all(&dir);
    }

    /// End-to-end: tuned tagger → Situation → roaring index → typed-facet wildcard + DS belief.
    #[test]
    fn projection_reaches_the_bitmap_with_polarity() {
        let Some((base, tok)) = base_dirs() else {
            eprintln!("skip: no cached bert-tiny");
            return;
        };
        let s = spec();
        let data = vec![
            ex("Boeing develops the MQ-28 aircraft.", (0, 6), (20, 25), false),
            ex("Airbus develops the A400M transport.", (0, 6), (20, 25), false),
            ex("Saab develops the Gripen fighter jet.", (0, 4), (18, 24), false),
            ex("Thales develops the Sonar array system.", (0, 6), (20, 25), false),
            ex("Embraer develops the KC-390 airlifter.", (0, 7), (21, 27), false),
            ex("Lockheed does not develop the F-35 jet.", (0, 8), (30, 34), true),
        ];
        let cfg = TrainConfig { base_dir: base.clone(), tokenizer: tok.clone(), epochs: 60, lr: 3e-3, batch: 6, max_len: 32, ..Default::default() };
        let (varmap, _r, labels) = train(&s, &data, &cfg).expect("train");
        let dir = std::env::temp_dir().join(format!("steeldb-proj-{}", std::process::id()));
        save(&varmap, &labels, &dir).unwrap();
        let tt = TunedTagger::load(&dir, &base, &tok, 32).expect("load");

        // project a memorised sentence: typed tokens must be hierarchical facet URIs, not flat `ent/`
        let sit = tt.project("Boeing develops the MQ-28 aircraft.").expect("project");
        eprintln!("PROJECTED: {:?}", sit.tokens);
        assert!(sit.tokens.iter().any(|t| t.starts_with("org/")), "typed entity token expected: {:?}", sit.tokens);
        assert!(sit.beliefs.is_empty(), "asserted sentence carries default +1 polarity");

        // ingest into a corpus and query by FACET WILDCARD — only possible because URIs are typed
        let mut corpus = crate::db::Corpus::new_incremental("t", vec!["text".into()], crate::projector::CorpusKind::Csv);
        corpus.add_situation_polar(sit.tokens.clone(), sit.display.clone(), sit.numbers.clone(), sit.beliefs.clone());
        let hits = corpus.query("org/*", 10);
        assert_eq!(hits.count, 1, "facet wildcard must match the typed token");

        let _ = std::fs::remove_dir_all(&dir);
    }

    /// A STATE span is the epistemic cue itself, so "asserted" must be unrepresentable for it — Head A and
    /// Head B cannot disagree about whether a cue is a cue.
    #[test]
    fn state_spans_never_decode_as_asserted() {
        let Some((base, tok)) = base_dirs() else {
            eprintln!("skip: no cached bert-tiny");
            return;
        };
        let s = spec();
        // one negated + one hedged example so both non-asserted classes are learnable
        let mut neg = ex("Lockheed does not develop the F-35 jet.", (0, 8), (30, 34), true);
        neg.spans.push(LabeledSpan { start: 9, end: 17, facet: "state".into(), surface: "does not".into(), negated: true, hedged: false });
        neg.spans.sort_by_key(|x| x.start);
        let mut hedge = ex("Boeing may develop the MQ-28 aircraft.", (0, 6), (23, 28), false);
        hedge.spans.push(LabeledSpan { start: 7, end: 10, facet: "state".into(), surface: "may".into(), negated: false, hedged: true });
        hedge.spans.iter_mut().for_each(|x| { if x.facet != "state" { x.hedged = true; } });
        hedge.spans.sort_by_key(|x| x.start);
        // the dev split needs at least five examples; duplicate the two cases so both classes are learnable
        let data = vec![neg.clone(), hedge.clone(), neg.clone(), hedge.clone(), neg.clone(), hedge];
        let cfg = TrainConfig { base_dir: base.clone(), tokenizer: tok.clone(), epochs: 60, lr: 3e-3, batch: 6, max_len: 32, ..Default::default() };
        let (varmap, _r, labels) = train(&s, &data, &cfg).expect("train");
        let dir = std::env::temp_dir().join(format!("steeldb-state-{}", std::process::id()));
        save(&varmap, &labels, &dir).unwrap();
        let tt = TunedTagger::load(&dir, &base, &tok, 32).expect("load");
        for text in ["Lockheed does not develop the F-35 jet.", "Boeing may develop the MQ-28 aircraft."] {
            for sp in tt.tag(text).expect("tag").iter().filter(|s| s.facet == "state") {
                assert!(sp.negated || sp.hedged, "a state cue decoded as asserted: {sp:?}");
                assert!(sp.belief < 1.0, "asserted belief on a cue span: {sp:?}");
            }
        }
        let _ = std::fs::remove_dir_all(&dir);
    }

    #[test]
    fn snapping_completes_clipped_words() {
        let text = "Thales supplies the Aegis system.";
        let p = |s: usize, e: usize, f: &str| PredictedSpan {
            start: s, end: e, facet: f.into(), text: text[s..e].into(), negated: false, hedged: false, belief: 1.0,
        };
        // clipped first wordpiece → snapped to the whole word
        let out = super::snap_to_words(vec![p(0, 3, "org")], text);
        assert_eq!(out[0].text, "Thales");
        let out2 = super::snap_to_words(vec![p(20, 22, "system")], text);
        assert_eq!(out2[0].text, "Aegis");
        // an already-complete span is unchanged, and snapping never crosses a non-word char
        let out3 = super::snap_to_words(vec![p(0, 6, "org")], text);
        assert_eq!(out3[0].text, "Thales");
        assert_eq!(out3[0].end, 6);
    }

    #[test]
    fn contiguous_subword_spans_merge_but_distinct_entities_do_not() {
        let text = "Aegis and Boeing Airbus";
        let p = |s: usize, e: usize, f: &str| PredictedSpan {
            start: s, end: e, facet: f.into(), text: text[s..e].into(), negated: false, hedged: false, belief: 1.0,
        };
        // "Ae" + "gis" are contiguous sub-words → one span
        let merged = super::merge_contiguous(vec![p(0, 2, "system"), p(2, 5, "system")], text);
        assert_eq!(merged.len(), 1);
        assert_eq!(merged[0].text, "Aegis");
        // "Boeing" + "Airbus" are separated by a space → stay distinct
        let kept = super::merge_contiguous(vec![p(10, 16, "org"), p(17, 23, "org")], text);
        assert_eq!(kept.len(), 2);
        // negation on any piece propagates to the merged span
        let mut a = p(0, 2, "system");
        let mut b = p(2, 5, "system");
        b.negated = true;
        a.belief = 1.0;
        let m2 = super::merge_contiguous(vec![a, b], text);
        assert_eq!(m2.len(), 1);
        assert!(m2[0].negated && m2[0].belief < 0.0);
    }
}