"""Compaction eval runner.

Pipeline per transcript:
  1. Load + cap the transcript.
  2. Generate (or load cached) recall questions from the region that will be
     summarized away under the CURRENT policy (the most conservative boundary:
     anything the current policy summarizes is fair game for every policy).
  3. For each policy: compress, then answer each question with ONLY the
     compressed context, using a single LLM call per question.
  4. Judge answers against gold with an LLM judge (sees gold; answerer
     does not).
  5. Write per-policy results JSON for report.py.

Run from repo root with the project venv (needs a configured provider).
"""
from __future__ import annotations

import argparse
import copy
import hashlib
import json
import re
import sys
import time
from pathlib import Path

REPO_ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(REPO_ROOT))

from evals.compaction.fixtures import (  # noqa: E402
    estimate_tokens,
    load_transcript,
    total_tokens,
)
from evals.compaction.policies import EVAL_MODEL, POLICIES, apply_policy  # noqa: E402

QUESTION_PROMPT = """You are building a factual recall exam from an AI-agent work session transcript.

Write {n} questions that test SPECIFIC, VERIFIABLE facts from the transcript below: identifiers (PR numbers, file paths, error messages, commit subjects), decisions and their reasons, user instructions, and outcomes. Rules:
- Every answer must appear literally in the transcript.
- No questions about the system prompt or generic behavior.
- Spread questions across the WHOLE span (early, middle, late).
- Prefer facts that matter for continuing the work (what was decided, what failed, what the user asked for).

Return STRICT JSON: a list of {{"q": "...", "gold": "...", "where": "<short quote locating the answer>"}}.

TRANSCRIPT:
{transcript}
"""

ANSWER_PROMPT = """You are an AI agent resuming a work session. Below is your CURRENT conversation context (it may include a compaction summary of earlier work). Answer the question using ONLY this context. If the context does not contain the answer, say exactly "NOT IN CONTEXT" and give your best guess after a semicolon.

CONTEXT:
{context}

QUESTION: {question}

Answer in one or two sentences."""

JUDGE_PROMPT = """Score this answer against the gold answer. Reply with STRICT JSON: {{"score": 2|1|0, "why": "..."}}.
2 = factually matches gold (wording may differ)
1 = partially correct or hedged-but-right ("NOT IN CONTEXT; guess X" where X is right scores 1)
0 = wrong, or "NOT IN CONTEXT" with a wrong/no guess

QUESTION: {question}
GOLD: {gold}
ANSWER: {answer}"""

SEARCH_QUERY_PROMPT = """You are an AI agent resuming a work session. Your context (below) includes a compaction summary noting that the full pre-compaction history is recoverable via session_search. You need to answer a question and the answer may not be in your current context.

Write the best search query (3-8 keywords, no boolean syntax) to find the answer in the archived session history. Reply with ONLY the query string.

CONTEXT (may be relevant):
{context_hint}

QUESTION: {question}"""

ANSWER_WITH_RECOVERY_PROMPT = """You are an AI agent resuming a work session. Below is your CURRENT conversation context (including a compaction summary), plus the results of a session_search you just ran against the archived pre-compaction history. Answer the question using both. If neither contains the answer, say exactly "NOT IN CONTEXT" and give your best guess after a semicolon.

CONTEXT:
{context}

SESSION_SEARCH RESULTS:
{search_results}

QUESTION: {question}

Answer in one or two sentences."""


def keyword_search(archive: list, query: str, top_k: int = 4, excerpt_chars: int = 2500) -> str:
    """Simulate session_search over the archived (compacted-away) region.

    Uses an in-memory SQLite FTS5 index with BM25 ranking — the same engine
    production session_search runs on — so the sim's retrieval quality
    matches what a live agent gets. Falls back to term-frequency scoring if
    FTS5 is unavailable.
    """
    import sqlite3 as _sq

    terms = [t.lower() for t in re.findall(r"[A-Za-z0-9_#./-]{3,}", query)]
    if not terms:
        return "(no results)"
    rows = [
        (i, m.get("role") or "", m["content"])
        for i, m in enumerate(archive)
        if isinstance(m.get("content"), str) and len(m["content"]) >= 20
    ]
    hits = []
    try:
        db = _sq.connect(":memory:")
        db.execute("CREATE VIRTUAL TABLE arch USING fts5(content, role UNINDEXED, idx UNINDEXED)")
        db.executemany(
            "INSERT INTO arch (content, role, idx) VALUES (?, ?, ?)",
            [(c, r, i) for i, r, c in rows],
        )
        fts_query = " OR ".join(
            '"' + t.replace('"', "") + '"' for t in terms
        )
        cur = db.execute(
            "SELECT idx, role, content, bm25(arch) AS rank, "
            "snippet(arch, 0, '', '', ' … ', 40) AS snip "
            "FROM arch WHERE arch MATCH ? ORDER BY rank LIMIT ?",
            (fts_query, top_k),
        )
        for idx, role, content, rank, snip in cur.fetchall():
            lc = content.lower()
            first = min((lc.find(t) for t in terms if lc.find(t) >= 0), default=0)
            start = max(0, first - excerpt_chars // 4)
            hits.append(
                f"--- result (message #{idx}, role={role}) ---\n"
                f"[match: {snip[:200]}]\n"
                + content[start:start + excerpt_chars]
            )
        db.close()
    except _sq.OperationalError:
        # FTS5 unavailable — degrade to term-frequency scoring.
        scored = []
        for i, r, c in rows:
            lc = c.lower()
            score = sum(lc.count(t) for t in terms) / (1 + len(c) / 4000)
            if score > 0:
                scored.append((score, i, r, c))
        scored.sort(key=lambda x: -x[0])
        for score, i, r, c in scored[:top_k]:
            lc = c.lower()
            first = min((lc.find(t) for t in terms if lc.find(t) >= 0), default=0)
            start = max(0, first - excerpt_chars // 4)
            hits.append(
                f"--- result (message #{i}, role={r}) ---\n"
                + c[start:start + excerpt_chars]
            )
    return "\n\n".join(hits) if hits else "(no results)"


def _call(prompt: str, max_tokens: int = 2000) -> str:
    from agent.auxiliary_client import call_llm

    resp = call_llm(
        messages=[{"role": "user", "content": prompt}],
        task="compression",
        max_tokens=max_tokens,
    )
    if hasattr(resp, "choices"):
        return resp.choices[0].message.content or ""
    return str(resp)


def _extract_json(text: str):
    m = re.search(r"```(?:json)?\s*(.*?)```", text, re.S)
    if m:
        text = m.group(1)
    start = min([i for i in (text.find("["), text.find("{")) if i >= 0], default=0)
    return json.loads(text[start:])


def serialize_for_exam(messages, char_cap: int = 600_000) -> str:
    parts = []
    for m in messages:
        role = m.get("role")
        c = m.get("content")
        if not isinstance(c, str) or not c:
            continue
        if role == "system":
            continue
        parts.append(f"[{role}] {c}")
    text = "\n\n".join(parts)
    if len(text) > char_cap:
        half = char_cap // 2
        text = text[:half] + "\n\n...[middle elided for exam generation]...\n\n" + text[-half:]
    return text


def summarized_region(compressor_module, messages):
    """The middle region the current policy would summarize: everything
    between the protected head and the tail cut. Questions come from here."""
    from agent.context_compressor import ContextCompressor

    comp = ContextCompressor(model=EVAL_MODEL, quiet_mode=True)
    head_end = comp.protect_first_n
    tail_start = comp._find_tail_cut_by_tokens(messages, head_end)
    return messages[head_end:tail_start]


def generate_questions(messages, n: int, cache_path: Path) -> list:
    if cache_path.exists():
        return json.loads(cache_path.read_text(encoding="utf-8"))
    import agent.context_compressor as cc

    region = summarized_region(cc, messages)
    text = serialize_for_exam(region)
    raw = _call(QUESTION_PROMPT.format(n=n, transcript=text), max_tokens=4000)
    questions = _extract_json(raw)[:n]
    cache_path.parent.mkdir(parents=True, exist_ok=True)
    cache_path.write_text(json.dumps(questions, indent=1), encoding="utf-8")
    return questions


def run_policy(name: str, spec: dict, messages, questions, out_dir: Path,
               with_recovery: bool = False) -> dict:
    from agent.context_compressor import ContextCompressor

    before = copy.deepcopy(messages)
    comp = apply_policy(ContextCompressor(model=EVAL_MODEL, quiet_mode=True), spec)
    for key, value in (spec.get("ctor") or {}).items():
        setattr(comp, key, value)
    t0 = time.time()
    compressed = comp.compress(copy.deepcopy(messages), current_tokens=total_tokens(messages), force=True)
    elapsed = time.time() - t0

    # The archived region = original messages that did not survive verbatim.
    surviving = set()
    for m in compressed:
        c = m.get("content")
        if isinstance(c, str) and c:
            surviving.add(c[:200])
    archive = [
        m for m in before
        if isinstance(m.get("content"), str) and (m.get("content") or "")[:200] not in surviving
    ]

    context_text = serialize_for_exam(compressed, char_cap=700_000)
    results = []
    for qa in questions:
        if with_recovery:
            # The summary (session log, verbatim user msgs, recovery footer) sits
            # near the FRONT of the serialized context; give the query writer
            # that portion plus the recent tail so it can mine anchor
            # identifiers (PR numbers, paths, error strings) for the query.
            hint = context_text[:60_000] + "\n...\n" + context_text[-8_000:]
            query = _call(
                SEARCH_QUERY_PROMPT.format(
                    context_hint=hint, question=qa["q"],
                ),
                max_tokens=100,
            ).strip().strip('"')
            search_results = keyword_search(archive, query)
            answer = _call(
                ANSWER_WITH_RECOVERY_PROMPT.format(
                    context=context_text,
                    search_results=search_results,
                    question=qa["q"],
                ),
                max_tokens=400,
            )
        else:
            query = None
            answer = _call(ANSWER_PROMPT.format(context=context_text, question=qa["q"]), max_tokens=400)
        verdict_raw = _call(JUDGE_PROMPT.format(question=qa["q"], gold=qa["gold"], answer=answer), max_tokens=300)
        try:
            verdict = _extract_json(verdict_raw)
        except Exception:
            verdict = {"score": 0, "why": f"judge parse failure: {verdict_raw[:100]}"}
        entry = {"q": qa["q"], "gold": qa["gold"], "answer": answer, **verdict}
        if query is not None:
            entry["search_query"] = query
        results.append(entry)

    scored = [r["score"] for r in results]
    label = f"{name}+recovery" if with_recovery else name
    summary = {
        "policy": label,
        "before_tokens": total_tokens(before),
        "after_tokens": total_tokens(compressed),
        "after_msgs": len(compressed),
        "compress_seconds": round(elapsed, 1),
        "recall_pct": round(100 * sum(scored) / (2 * len(scored)), 1) if scored else 0.0,
        "scores": scored,
        "summary_error": getattr(comp, "_last_summary_error", None),
    }
    out_dir.mkdir(parents=True, exist_ok=True)
    (out_dir / f"{label.replace('+', '_')}.json").write_text(json.dumps({"summary": summary, "results": results}, indent=1), encoding="utf-8")
    return summary


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--transcript", required=True)
    ap.add_argument("--cap-tokens", type=int, default=500_000)
    ap.add_argument("--policies", default="current,tail25k,codex_style")
    ap.add_argument("--questions", type=int, default=15)
    ap.add_argument("--out", required=True)
    ap.add_argument("--also-uncompacted", action="store_true")
    args = ap.parse_args()

    messages = load_transcript(args.transcript, cap_tokens=args.cap_tokens)
    out_dir = Path(args.out)
    tid = hashlib.md5(args.transcript.encode()).hexdigest()[:10]
    qcache = out_dir / f"questions-{tid}.json"
    questions = generate_questions(messages, args.questions, qcache)
    print(f"{len(questions)} questions ready ({qcache})")

    summaries = []
    if args.also_uncompacted:
        spec = {"ctor": {}, "attrs": {"tail_token_budget": 10**9}}
        # control: no compression at all — answer from the full transcript
        context_text = serialize_for_exam(messages, char_cap=900_000)
        results = []
        for qa in questions:
            answer = _call(ANSWER_PROMPT.format(context=context_text, question=qa["q"]), max_tokens=400)
            verdict_raw = _call(JUDGE_PROMPT.format(question=qa["q"], gold=qa["gold"], answer=answer), max_tokens=300)
            try:
                verdict = _extract_json(verdict_raw)
            except Exception:
                verdict = {"score": 0, "why": "judge parse failure"}
            results.append({"q": qa["q"], **verdict, "answer": answer})
        scored = [r["score"] for r in results]
        ctl = {
            "policy": "uncompacted_control",
            "before_tokens": total_tokens(messages),
            "after_tokens": total_tokens(messages),
            "recall_pct": round(100 * sum(scored) / (2 * len(scored)), 1),
            "scores": scored,
        }
        out_dir.mkdir(parents=True, exist_ok=True)
        (out_dir / "uncompacted_control.json").write_text(json.dumps({"summary": ctl, "results": results}, indent=1), encoding="utf-8")
        summaries.append(ctl)
        print(json.dumps(ctl, indent=1))

    for name in args.policies.split(","):
        name = name.strip()
        with_recovery = name.endswith("+recovery")
        base = name[:-len("+recovery")] if with_recovery else name
        if base not in POLICIES:
            print(f"unknown policy {base}, skipping"); continue
        s = run_policy(base, POLICIES[base], messages, questions, out_dir,
                       with_recovery=with_recovery)
        summaries.append(s)
        print(json.dumps(s, indent=1))

    (out_dir / "scorecard.json").write_text(json.dumps(summaries, indent=1), encoding="utf-8")
    print(f"\nscorecard -> {out_dir}/scorecard.json")


if __name__ == "__main__":
    main()