"""Dual-mind forensic analysis pipeline.

Analyst pass: follows the analysis SOP with a scratchpad.
Skeptic pass: attacks the analyst's conclusions.
Outputs a JSON report.

Usage:
  .venv/bin/python research/analyst.py --file doc.txt --ckpt ckpt/forensic
  cat doc.txt | .venv/bin/python research/analyst.py --ckpt ckpt/forensic
"""

import argparse
import json
import sys
from pathlib import Path

import torch

from model.config import TinyLiquidConfig, CONFIGS
from model.utils import latest_ckpt
from model.tiny_liquid import TinyLiquid
from data.tokenizer import load_tokenizer

SOP = (
    "Follow the analysis protocol exactly. 1) Extract every checkable claim. "
    "2) Separate evidence from assertion; name what is missing. "
    "3) Compare accounts and flag contradictions, ambiguities, and overclaims. "
    "4) Look for patterns across events: clustering, escalation, common cause. "
    "5) State a verdict and a confidence for every conclusion; prefer "
    "'cannot confirm' over speculation. Use the scratchpad before the final answer."
)


def parse_args():
    ap = argparse.ArgumentParser()
    ap.add_argument("--file", default=None)
    ap.add_argument("--ckpt", default="ckpt/forensic")
    ap.add_argument("--tok", default="data/tokenizer.json")
    ap.add_argument("--max-new", type=int, default=220)
    ap.add_argument("--threads", type=int, default=8)
    return ap.parse_args()


def load_model(args):
    torch.set_num_threads(args.threads)
    tok = load_tokenizer(args.tok)
    ckpt = latest_ckpt(args.ckpt)
    assert ckpt, f"no checkpoints in {args.ckpt}"
    sd = torch.load(ckpt, map_location="cpu")
    cfg_dict = dict(sd.get("config", CONFIGS["tiny10m"]))
    cfg = TinyLiquidConfig(vocab_size=tok.get_vocab_size(), **{k: v for k, v in cfg_dict.items() if k != "vocab_size"})
    model = TinyLiquid(cfg)
    model.load_state_dict(sd["model"])
    model.eval()
    return tok, model


def run(model, tok, persona, persona_id, user_text, max_new):
    p_token = {"analyst": "<|analyst|>", "skeptic": "<|skeptic|>"}[persona]
    prompt = p_token + "<|user|>" + user_text + "<|assistant|>"
    ids = tok.encode(prompt).ids
    out = model.generate(tok, ids, persona_id=persona_id, max_new=max_new,
                         temperature=0.6, top_k=40, repetition_penalty=1.4, no_repeat_ngram_size=4)
    return tok.decode(out[len(ids):]).strip()


def main():
    args = parse_args()
    if args.file:
        text = Path(args.file).read_text(encoding="utf-8", errors="ignore")
    else:
        text = sys.stdin.read()
    text = text.strip()
    assert text, "no input text"

    tok, model = load_model(args)
    doc = text if len(text) <= 1200 else text[:1200] + " [truncated]"

    analyst_user = f"{SOP}\n\nMaterial under analysis:\n{doc}"
    analyst = run(model, tok, "analyst", 1, analyst_user, args.max_new)

    skeptic_user = (
        "Act as the skeptic. Tear down the analysis below: find unsupported "
        "conclusions, overclaims, weak sourcing, and alternative explanations. "
        "Keep only what survives.\n\nAnalysis:\n" + analyst
    )
    skeptic = run(model, tok, "skeptic", 2, skeptic_user, max(args.max_new // 2, 100))

    report = {
        "analyst": analyst,
        "skeptic": skeptic,
        "note": "TinyLiquid output is research support, not a verdict. "
                "Every conclusion needs primary-source verification.",
    }
    print(json.dumps(report, indent=2, ensure_ascii=False))
    out = Path("corpus/reports")
    out.mkdir(parents=True, exist_ok=True)
    (out / "latest_report.json").write_text(json.dumps(report, indent=2, ensure_ascii=False),
                                            encoding="utf-8")


if __name__ == "__main__":
    main()