#!/usr/bin/env python3
"""Apply the Phase B adjudication to the val split.

Discipline, because this edits a benchmark AFTER models have been scored on it:
  * Adjudication was BLIND — the judge saw only the raw sentences, never a
    model prediction, never which row was flagged.
  * Only rows where my judgement and the blind judge UNANIMOUSLY agreed on
    DISFLUENCY are relabelled. Disagreements stay CLEAN (precision-first).
  * Spans are generated by the existing compiler from a substring and asserted
    to round-trip, never hand-written as character offsets.
  * Every change is written to an audit file. The original file is preserved.

Usage: adjudicate_val.py [--apply]
"""
import json
import sys

sys.path.insert(0, "/opt/vox/sandbox/scripts")
from compiler import compile_candidate_to_example
from schema import RawCandidate

VAL = "/opt/vox/sandbox/corpus/pilot_v2_val.jsonl"
OUT = "/opt/vox/sandbox/corpus/pilot_v2_val_adjudicated.jsonl"
AUDIT = "/opt/vox/sandbox/benchmarks/phaseB_adjudication_APPLIED.json"

# unanimous DISFLUENCY verdicts only (mine + blind judge agreed)
AGREE = {
    "vxb_v2_00121": ("not something, Curious, ", "self_correction", 0.90),
    "vxb_v2_00150": ("We're just, ", "false_start", 0.75),
    "vxb_v2_00368": ("they're just get, ", "false_start", 0.80),
    "vxb_v2_00308": ("It does, ", "false_start", 0.80),
    "vxb_v2_00228": ("there does seem to be a, right now in verbalizing, ", "false_start", 0.85),
    "vxb_v2_00396": ("yeah yeah yeah ", "repetition", 0.85),
    "vxb_v2_00175": ("I happen to, ", "false_start", 0.80),
    "vxb_v2_00282": (", most awful dull", "repetition", 0.70),
}
# deliberate disagreements -> left CLEAN
KEEP_CLEAN = {
    "vxb_v2_00135": "disagreement: I judged ambiguous/keep-CLEAN, blind judge said DISFLUENCY at "
                   "confidence 0.60 (its own weakest call, and it noted UNCLEAR is defensible). "
                   "Precision-first => stays CLEAN.",
    "vxb_v2_00668": "unanimous CLEAN: sentence-initial 'Well' is a legitimate discourse marker.",
}
# shape defects noted but NOT acted on, so the benchmark is not silently shrunk
SHAPE_DEFECTS = {
    "vxb_v2_00396": "2 sentences + lowercase-initial (invariant 3 shape violation)",
    "vxb_v2_00175": "leading orphaned ')' ')' transcription artifact",
    "vxb_v2_00282": "truncated mid-sentence, ends 'Mrs.'",
}


def main():
    rows = [json.loads(l) for l in open(VAL)]
    by_id = {r["id"]: r for r in rows}
    audit = {"applied": [], "kept_clean": KEEP_CLEAN, "shape_defects_not_acted_on": SHAPE_DEFECTS,
             "policy": "blind adjudication; unanimous DISFLUENCY only; compiler-generated spans",
             "base_val": VAL, "out_val": OUT}

    failed = []
    for rid, (span_text, kind, conf) in AGREE.items():
        r = by_id[rid]
        if span_text not in r["raw_text"]:
            failed.append((rid, "span not a verbatim substring"))
            continue
        # expected clean = deterministic slice cut of that span
        i = r["raw_text"].index(span_text)
        expected = (r["raw_text"][:i] + r["raw_text"][i + len(span_text):])
        cand = RawCandidate(raw_text=r["raw_text"], clean_text=expected,
                            disfluent_spans=[span_text])
        ex, why = compile_candidate_to_example(cand, rid)
        if ex is None:
            failed.append((rid, f"compiler rejected: {why}"))
            continue
        # compiler's own punctuation/whitespace normalisation is authoritative
        gold_clean = ex.clean_text
        ex.id = rid
        ex.category = "DISFLUENCY"
        for s in ex.spans:
            s.origin = f"adjudicated:{kind}"
        by_id[rid] = json.loads(ex.model_dump_json())
        audit["applied"].append({"id": rid, "kind": kind, "blind_confidence": conf,
                                 "span_text": span_text,
                                 "new_clean_text": gold_clean,
                                 "old_category": "CLEAN"})
        print(f"RELABELLED {rid}  {kind:16} span={span_text!r}")

    if failed:
        print("\nFAILED (left as CLEAN):")
        for f in failed:
            print("  ", f)
        audit["failed"] = failed

    out = [by_id[r["id"]] for r in rows]  # preserve original ordering
    with open(OUT, "w") as f:
        for r in out:
            f.write(json.dumps(r) + "\n")
    json.dump(audit, open(AUDIT, "w"), indent=2)

    n_c = sum(1 for r in out if r["category"] == "CLEAN")
    n_d = len(out) - n_c
    print(f"\nwrote {OUT}")
    print(f"  CLEAN {n_c}  DISFLUENCY {n_d}  (was CLEAN 427 / DISFLUENCY 373)")
    print(f"audit: {AUDIT}")


if __name__ == "__main__":
    main()