"""Two cognitive minds, one model: the fusion opinion layer (experimental).

Mind 1 (analyst, persona 1): conservative and focal - what does the record say?
Mind 2 (skeptic, persona 2): adversarial - what is the weakest link, what else
explains the same record?

Each mind runs its OWN scratchpad pass (separate prompt + decoding) and writes to
its OWN memory pool (persona-tagged helix strands). The fusion gate combines them:

  AGREE    -> shared verdict; confidence raised to the higher of the two
  RULE     -> deterministic spine wins when it resolves (cannot hallucinate)
  CONFLICT -> calibrated OPINION, not just abstention: lean toward the side with
              the better value citation, else "conflict/LOW"; ALWAYS state the
              discrepancy and what would settle it.

The output is an OPINION: position + evidence + discrepancy + open questions.
Composition is deterministic (suit logic); the Spock voice is generated by the
model from the opinion as context.

Usage (library): from research.fusion import run_two_pass, fuse, opinion_text
"""
import re
import json
from pathlib import Path
from research.helix import rungs, normalize
from research.decision import load_table, calibrated_prob


def _cited(rep):
    c = rep.get("cited")
    if isinstance(c, list):
        return [str(v) for v in c][:5]
    if c:
        return [str(c)]
    return [v for v in rungs(rep.get("reasoning", ""))][:5]


def _gaps(reasoning):
    out = []
    if not reasoning:
        return out
    for s in re.split(r"(?<=[.!?])\s+", reasoning.replace("\n", " ")):
        low = s.lower()
        if any(k in low for k in ("missing", "what would settle", "what would change",
                                  "what is needed", "not in the record", "no record")):
            out.append(s.strip())
    return out[:4]


def _calibrated_merge(a_conf, s_conf, table_path):
    """Merge two confidence labels using calibrated reliability from the table.
    
    Returns (p_mean, bucket) where p_mean is the mean calibrated probability
    and bucket is the display bucket (HIGH/MEDIUM/LOW/cannot assess).
    """
    table = load_table(table_path)
    p_a = calibrated_prob(a_conf, table, unknown=0.0)
    p_s = calibrated_prob(s_conf, table, unknown=0.0)
    p_mean = (p_a + p_s) / 2.0 if (p_a > 0 or p_s > 0) else 0.0
    
    # Map to display bucket
    if p_mean >= 0.66:
        return p_mean, "HIGH"
    if p_mean >= 0.40:
        return p_mean, "MEDIUM"
    if p_mean > 0.0:
        return p_mean, "LOW"
    return p_mean, "cannot assess"


def fuse(analyst, skeptic, rule=None, sources=(), max_gaps=4,
         calibration_table=None):
    """Fuse two minds (+ optional rule spine) into one calibrated opinion.
    
    Args:
        analyst: analyst report dict with verdict, confidence, reasoning
        skeptic: skeptic report dict with verdict, confidence, reasoning
        rule: optional rule spine result dict
        sources: optional list of sources
        max_gaps: max open questions to include
        calibration_table: path to calibration summary JSON (e.g., logs/calib_summary_dpo3_200.json)
    """
    a_v = normalize(analyst.get("verdict", ""))
    s_v = normalize(skeptic.get("verdict", ""))
    a_conf = (analyst.get("confidence") or "LOW").upper()
    s_conf = (skeptic.get("confidence") or "LOW").upper()
    gaps = (_gaps(analyst.get("reasoning", "")) + _gaps(skeptic.get("reasoning", "")))[:max_gaps]

    if rule and rule.get("verdict") in ("supports", "refutes", "not enough information"):
        verdict, conf, basis = rule["verdict"], rule.get("confidence", "HIGH"), "rule"
        pos = ("the record deterministically " +
               ("supports" if verdict == "supports" else "contradicts" if verdict == "refutes"
                else "does not settle") + " the claim")
    elif a_v and a_v == s_v:
        # Both minds agree - use calibrated merge instead of naive confidence raise
        if calibration_table:
            p_mean, conf = _calibrated_merge(a_conf, s_conf, calibration_table)
        else:
            # Fallback: naive confidence raise (but mark as uncalibrated)
            conf = "HIGH" if "HIGH" in (a_conf, s_conf) else "MEDIUM"
        verdict, basis = a_v, "agreed"
        pos = "both minds reach the same verdict"
    elif a_v and s_v:
        cite_a, cite_s = bool(_cited(analyst)), bool(_cited(skeptic))
        if cite_a != cite_s:
            lean, side = (analyst, "analyst") if cite_a else (skeptic, "skeptic")
            verdict, conf, basis = f"leaning: {lean['verdict']}", "MEDIUM", f"leaning-{side}"
            pos = f"the minds conflict, but the {side} mind cites record values"
        else:
            verdict, conf, basis = "conflict", "LOW", "conflict"
            pos = "the two minds conflict on the same record"
    else:
        verdict, conf, basis = "not enough information", "LOW", "insufficient"
        pos = "neither mind can reach a verdict from the record"

    discrepancy = ""
    if basis in ("conflict", "leaning-analyst", "leaning-skeptic"):
        discrepancy = (skeptic.get("reasoning") or "")[:220]

    return {
        "verdict": verdict,
        "confidence": conf,
        "basis": basis,
        "position": pos,
        "discrepancy": discrepancy,
        "cited": _cited(analyst)[:4] + [v for v in _cited(skeptic) if v not in _cited(analyst)][:2],
        "open_questions": gaps,
        "sources": list(sources)[:6],
        "minds": {"analyst": analyst.get("verdict", ""), "skeptic": skeptic.get("verdict", "")},
    }


def opinion_text(op):
    """Turn a fused opinion into a spoken, calibrating statement (suit-composed)."""
    v = op["verdict"]
    conf = op["confidence"]
    line = f"My assessment: {op['position']}. Confidence: {conf}."
    if op.get("discrepancy"):
        line += f" Discrepancy noted: {op['discrepancy']}"
    if op.get("cited"):
        line += " Cited values: " + ", ".join(str(c) for c in op["cited"][:4]) + "."
    if op.get("open_questions"):
        line += " Open: " + "; ".join(op["open_questions"][:3]) + "."
    if op.get("sources"):
        line += " Sources: " + ", ".join(str(s) for s in op["sources"][:4]) + "."
    return line


def run_two_pass(model, tok, doc, memory=None, persona_ids=(1, 2),
                 max_scratch=90, max_reason=50):
    """Mind 1 (analyst) then Mind 2 (skeptic): separate scratchpads, own memory pool."""
    from research.structured import analyst_report
    a = analyst_report(model, tok, doc, persona_id=persona_ids[0],
                       max_scratch=max_scratch, max_reason=max_reason)
    s = analyst_report(model, tok, doc, persona_id=persona_ids[1],
                       max_scratch=max_scratch, max_reason=max_reason)
    if memory is not None:
        for rep, mind in ((a, "analyst"), (s, "skeptic")):
            memory.write(doc, "", rep.get("verdict", ""), rep.get("confidence", ""),
                         rep.get("reasoning", ""), agreed=True, mind=mind)
    return a, s