#!/usr/bin/env python3
"""Stage E gates, run on the injection plan before anything is injected.

  lint       visit_exists: every planned atom targets an existing visit and a content bucket; single_layer: one
             synthetic layer per fact, no topic across batches; record_has_no_injection: no injected atom in the
             evidence of a record question, by atom id or by text; atom_has_axis: every atom declares the axis that
             will look for it -> lint_report.json
  persona    an LLM check that drops every synthetic question with an atom impossible for this patient (e.g. a
             pregnancy test for a male patient), with its atoms and claim; the persona combines the sex in the visit
             table with the documented conditions
"""
from __future__ import annotations

import argparse
import glob
import json
import sys
from collections import Counter
from pathlib import Path

from pipeline.common import BASE, FIELDS, SEX_MAP, VISIT_TABLE, Progress, model, patient_id
from pipeline.llm import ChatClient
from pipeline.synthetic import load, norm, save

PATIENT = patient_id()
ENGINE = model("generation")


def visit_exists(reg, plan):
    """Every planned atom targets an existing visit and a content bucket."""
    f = []
    chain = {json.loads(l)["visit_number"] for l in open(BASE / "event_chain.jsonl", encoding="utf-8")}
    for a in plan:
        if a.get("visit_number") not in chain:
            f.append(f"atom {a.get('atom_id')} targets visit {a.get('visit_number')}, "
                     f"which does not exist (injection may not create visits)")
        if a.get("field") not in FIELDS:
            f.append(f"atom {a.get('atom_id')} field={a.get('field')!r} is not a content bucket")
    return f


def single_layer(reg, plan):
    """One synthetic layer per fact: no atom anchors on another batch's injected evidence, and no topic is
    reused across batches."""
    f = []
    applied_atoms = {a["atom_id"]: a for a in reg["atoms"]}
    for a in plan:
        for dep in a.get("anchors_on") or []:
            if dep in applied_atoms:
                f.append(f"atom {a.get('atom_id')} anchors on injected atom {dep} "
                         f"(batch {applied_atoms[dep]['batch_id']}); only one synthetic layer per fact")
    topic_batch = {}
    for a in reg["atoms"] + plan:
        t, b = a.get("topic"), a.get("batch_id")
        if not t:
            continue
        if t in topic_batch and topic_batch[t] != b:
            f.append(f"topic {t!r} used by batch {topic_batch[t]!r} and {b!r}; topics may not cross batches")
        topic_batch[t] = b
    return f


def record_has_no_injection(reg, plan):
    """No claim with grounding=record has an injected atom in its evidence, and no record question's evidence text
    contains the text of a planned atom (case and punctuation ignored)."""
    f = []
    planned_ids = {a.get("atom_id") for a in plan}
    for c in reg["claims"]:
        if str(c.get("grounding")) != "record":
            continue
        dep = set(c.get("depends_on_atoms") or []) & (planned_ids | {a["atom_id"] for a in reg["atoms"]})
        if dep:
            f.append(f"{c['qid']} is grounding=record but its evidence includes injected atoms {sorted(dep)}")
    planned = [(a.get("atom_id"), f" {norm(a.get('text') or '')} ") for a in plan if norm(a.get("text") or "")]
    for bank in sorted(glob.glob(str(BASE / "questions_axis*.jsonl"))):
        for l in open(bank, encoding="utf-8"):
            q = json.loads(l) if l.strip() else {}
            if str(q.get("grounding")) != "record":
                continue
            for e in (q.get("derivation") or {}).get("evidence") or []:
                text = f" {norm(e.get('atom') or '')} " if isinstance(e, dict) else ""
                for aid, t in planned:
                    if text.strip() and t in text:
                        f.append(f"{q.get('qid')} is grounding=record but its evidence at v{e.get('visit')} "
                                 f"contains the text of planned atom {aid}")
    return f


def atom_has_axis(reg, plan):
    """Every planned atom declares the axis whose discovery will look for it."""
    f = []
    for a in plan:
        if not a.get("anchors_axis"):
            f.append(f"atom {a.get('atom_id')} declares no anchors_axis, so no question can be built on it")
    return f


GATES = [("visit_exists", visit_exists), ("single_layer", single_layer),
         ("record_has_no_injection", record_has_no_injection), ("atom_has_axis", atom_has_axis)]


def lint() -> None:
    """Run the gates over the registry and injection_plan.json; exit 1 on any failure."""
    reg = load()
    pf = BASE / "injection_plan.json"
    plan = json.loads(pf.read_text())["atoms"] if pf.exists() else []
    if not plan:
        print(f"note: no {pf}; linting the registry alone (0 planned atoms)")

    print(f"lint, patient {PATIENT}")
    print(f"  registry: {len(reg['batches'])} batches, {len(reg['atoms'])} atoms, {len(reg['claims'])} claims")
    print(f"  plan:     {len(plan)} atoms\n")

    fails, report = [], {}
    for name, fn in GATES:
        f = fn(reg, plan)
        report[name] = f
        fails += f
        print(f"  {'OK  ' if not f else 'FAIL'}  {name:<22} {len(f) or ''}")
        for x in f[:4]:
            print(f"          {x}")

    (BASE / "lint_report.json").write_text(json.dumps(
        {"patient_id": PATIENT,
         "n_planned": len(plan), "pass": not fails, "findings": report}, ensure_ascii=False, indent=1))
    print(f"\n{'PASS: safe to inject' if not fails else f'{len(fails)} FAILURES: do not inject'}")
    print(f"-> {BASE/'lint_report.json'}")
    sys.exit(1 if fails else 0)


def build_persona(chain):
    """Sex from the visit table, the patient's own documented diagnoses and the most frequent tests and
    medications."""
    sex = None
    try:
        import pandas as pd
        df = pd.read_parquet(VISIT_TABLE)
        sub = df[df["record_id"] == PATIENT]
        for col in df.columns:
            if any(k in col for k in ("sex", "gender")):
                v = str(sub[col].dropna().iloc[0]).strip() if len(sub[col].dropna()) else None
                sex = SEX_MAP.get(v, v)
                break
    except Exception as e:
        raise RuntimeError(f"persona: cannot read the patient's sex from {VISIT_TABLE}: {e}") from e
    if not sex:
        raise RuntimeError(f"persona: no sex recorded for the patient in {VISIT_TABLE}")
    dx = Counter()
    for r in chain:
        for d in r.get("diagnosis") or []:
            dx[str(d)[:50]] += 1
    top_dx = [d for d, _ in dx.most_common(15)]
    tm = Counter(str(a)[:50] for r in chain for a in r.get("tests_and_medication") or [])
    return {"sex": sex, "n_visits": len(chain),
            "date_span": f"{min(r['date'] for r in chain)}..{max(r['date'] for r in chain)}",
            "documented_conditions": top_dx,
            "frequent_tests_and_medications": [t for t, _ in tm.most_common(20)]}


def gate(cli, persona, atoms):
    """Ask the LLM which atoms are inconsistent with the persona; returns (ok_ids, violations). A reply without a
    violations list raises; a violation that is not an object naming a listed atom is skipped and reported."""
    idx = {a["atom_id"]: a for a in atoms}
    listing = "\n".join(f'  {a["atom_id"]}: {str(a.get("text"))[:120]}' for a in atoms)
    persona_json = json.dumps(persona, ensure_ascii=False, indent=1)
    sys_p = (
        "You check synthetic clinical facts for consistency with the patient's persona before they are\n"
        "added to the patient's record. Flag only facts that are impossible or contradictory for this\n"
        "specific person: a finding that a person of this sex cannot have, such as pregnancy, menstruation\n"
        "or a Pap test for a man, or the prostate or PSA for a woman; or a fact that flatly contradicts the\n"
        "documented persona, such as an allergy to a drug the patient takes. Do not flag values that are\n"
        "merely unusual, extreme or out of range. Extreme values are fine, because this is a memory test and\n"
        "charts have outliers. Flag only what this person cannot have. Return only JSON.")
    usr = (
        f"Patient persona:\n{persona_json}\n\n"
        f"Synthetic facts (atom id: text):\n{listing}\n\n"
        "Return JSON in this form:\n"
        '{"violations": [{"atom_id": "<id>", "reason": "<why it is impossible for this person>"}]}\n'
        "Return an empty list if all facts are consistent.")
    reply = cli.complete_json(sys_p, usr)
    v = reply.get("violations")
    if not isinstance(v, list):
        raise ValueError(f"persona gate reply lacks a violations list: {str(reply)[:80]}")
    ok = [x for x in v if isinstance(x, dict) and x.get("atom_id") in idx]
    if len(ok) != len(v):
        print(f"  skipped {len(v) - len(ok)} violation item(s) that do not name a listed atom: "
              f"{[x for x in v if x not in ok][:3]}")
    bad = {x["atom_id"] for x in ok}
    return [a for a in idx if a not in bad], ok


def _atom_qid(aid):
    """Plan atom_id 'synthetic-axis7/S000_1' -> (axis '7', qid '<PID>/axis7/S000')."""
    batch, tail = aid.split("/", 1)
    ax = batch.split("axis")[-1]
    suffix = tail.rsplit("_", 1)[0]
    return ax, f"{PATIENT}/axis{ax}/{suffix}"


def filter_plan_and_banks(bad_atom_ids):
    """Drop every question with a persona-impossible atom (its atoms from injection_plan.json, the question from
    its axis bank, its claim from the registry) and return the dropped qids."""
    dead = {_atom_qid(a)[1] for a in bad_atom_ids}
    pf = Path(f"{BASE}/injection_plan.json")
    plan = json.loads(pf.read_text())
    plan["atoms"] = [a for a in plan["atoms"] if _atom_qid(a["atom_id"])[1] not in dead]
    plan["n"] = len(plan["atoms"])
    pf.write_text(json.dumps(plan, ensure_ascii=False, indent=1))
    for bank in glob.glob(f"{BASE}/questions_axis*.jsonl"):
        rows = [json.loads(l) for l in open(bank)]
        keep = [q for q in rows if q.get("qid") not in dead]
        if len(keep) != len(rows):
            with open(bank, "w", encoding="utf-8") as fh:
                for q in keep:
                    fh.write(json.dumps(q, ensure_ascii=False) + "\n")
    reg = load()
    reg["claims"] = [c for c in reg["claims"] if c["qid"] not in dead]
    save(reg)
    return dead


def persona() -> None:
    """Drop synthetic atoms, and their questions, that are impossible for this patient."""
    cli = ChatClient(model=ENGINE, reasoning_effort="medium", max_completion_tokens=3000)

    chain = [json.loads(l) for l in open(f"{BASE}/event_chain.jsonl") if l.strip()]
    persona = build_persona(chain)

    pf = Path(f"{BASE}/injection_plan.json")
    atoms = json.loads(pf.read_text())["atoms"] if pf.exists() else []

    print(f"persona gate, patient {PATIENT}  (injection_plan: {len(atoms)} atoms)")
    print(f"  persona: sex={persona['sex']}, {persona['n_visits']} visits, "
          f"top dx: {', '.join(persona['documented_conditions'][:5])}")
    if not atoms:
        print("  no atoms to gate")
        return

    with Progress("persona gate (LLM)", total=1, out=f"{BASE}/progress_persona.json") as pg:
        _, violations = gate(cli, persona, atoms)
        pg.tick(ok=True)

    (Path(f"{BASE}/persona_gate_report.json")).write_text(json.dumps(
        {"patient_id": PATIENT, "persona": persona, "n_atoms": len(atoms),
         "n_violations": len(violations), "violations": violations,
         "pass": not violations}, ensure_ascii=False, indent=1))
    if violations:
        print(f"  {len(violations)} persona violation(s):")
        for v in violations:
            print(f"     {v['atom_id']}: {v.get('reason')}")
        dropped = filter_plan_and_banks({v["atom_id"] for v in violations})
        print(f"\n  dropped {len(dropped)} question(s) with a persona-impossible atom: {sorted(dropped)}")
        print("  continuing with the filtered plan")
        print(f"-> {BASE}/persona_gate_report.json")
        return
    print(f"  PASS: all {len(atoms)} atoms are consistent with the persona.")
    print(f"-> {BASE}/persona_gate_report.json")


def main():
    ap = argparse.ArgumentParser(description="Stage E gates on the injection plan.")
    sp = ap.add_subparsers(dest="cmd", required=True)
    sp.add_parser("lint")
    sp.add_parser("persona")
    a = ap.parse_args()
    lint() if a.cmd == "lint" else persona()


if __name__ == "__main__":
    main()