#!/usr/bin/env python3
"""Inference via the Codex CLI for GPT-5.x ChatGPT-auth models.

The Codex CLI is run with an isolated temporary CODEX_HOME and temporary
read-only workdirs so inference calls do not persist sessions into the user's
real ~/.codex or the project directory.
"""

import argparse
import atexit
import json
import os
import shutil
import subprocess
import tempfile
from concurrent.futures import ThreadPoolExecutor, as_completed
from pathlib import Path
from threading import Lock


INFER_TASK1 = """You are an expert AI research scientist and a rigorous peer reviewer. Your task is to identify the key ablation research questions that should be investigated to rigorously validate a paper's central methodological claims.

<Research_Context>
(Paper context with ablation-related content removed)
{CONTENT}
</Research_Context>

**Task Instructions:**
1. Read the paper context carefully and infer the most important research questions that should be addressed by ablation or controlled analysis.
2. Identify which components, mechanisms, or assumptions are most scientifically vulnerable.
3. Consider what causal confounders or alternative explanations a skeptical reviewer would raise.
4. Focus on research questions that are necessary to verify whether the claimed gains truly come from the proposed method.
5. Stay at the level of research questions rather than detailed implementation.

**Important Constraints:**
- Do not propose full experimental plans, datasets, hyperparameters, or exact protocols.
- Prefer mechanistically meaningful and causally informative questions over superficial component toggles.
- Identify the 2-6 most critical ablation targets. Prioritize scientific necessity over completeness.

**Output Format:** Each bullet represents one atomic ablation target. Output the most scientifically necessary targets (typically 2-6).

<Think>
[A brief reasoning process explaining the most important scientific vulnerabilities and causal uncertainties.]
</Think>

<Result>
[A list of target modules and their corresponding high-level research questions.]

- Target Module: [Name of the component or design choice]
    - Research Question: [One precise sentence summarizing the exact hypothesis to test]
</Result>

Output only the <Think> and <Result> blocks. Do not run any commands or tools; just write the research questions."""


INFER_TASK2 = """You are an expert AI research scientist specializing in scientific experimental design. Your task is to construct a rigorous and reproducible ablation plan for a given ablation goal, based on the paper's methodology context.

<Research_Context>
{CONTENT}
</Research_Context>

<Ablation_Goal>
{GOAL}
</Ablation_Goal>

**Task Instructions:**
1. Design a scientifically rigorous experimental plan that directly tests the given ablation goal.
2. Define a fair baseline.
3. Specify the most important ablation or control variants.
4. Isolate the intended causal factor as cleanly as possible.
5. Keep unrelated components fixed unless a change is explicitly required.
6. Include the critical protocols and evaluation metrics needed for a reproducible comparison.

**Important Constraints:**
- Focus on causal validity, fairness of comparison, and confounder isolation.
- Avoid introducing unnecessary complexity or speculative variants that are not grounded in the methodology context.
- If a stronger control is needed to rule out a plausible alternative explanation, include it.
- The plan should be specific enough to be executable, but should not invent unsupported details that are absent from the context.

**Output Format:** The response should prioritize scientific rigor, reproducibility, and fairness of comparison.

<Think>
[A brief reasoning process explaining how the ablation goal maps to the key controls, baselines, and confounders.]
</Think>

<Proposed_Plan>
- Objective: [Brief statement of the design goal]
- Baseline Setup: [Clear definition of the control condition]
- Variants: [The main ablation or control conditions and what each one changes]
- Fixed Protocols & Metrics: [Key training constraints, datasets, evaluation settings, and primary metrics in a single paragraph]
</Proposed_Plan>

Output only the <Think> and <Proposed_Plan> blocks. Do not run any commands or tools; just write the plan."""


_write_lock = Lock()


def _isolated_codex_env() -> dict:
    real_home = os.path.expanduser("~/.codex")
    tmp_home = tempfile.mkdtemp(prefix="abforge_codexhome_")
    for name in ("auth.json", "config.toml"):
        src = os.path.join(real_home, name)
        if os.path.isfile(src):
            shutil.copy2(src, os.path.join(tmp_home, name))
    atexit.register(lambda: shutil.rmtree(tmp_home, ignore_errors=True))
    return {**os.environ, "CODEX_HOME": tmp_home}


CODEX_ENV = _isolated_codex_env()


def load_jsonl(path: Path) -> list[dict]:
    rows = []
    with path.open(encoding="utf-8") as f:
        for line in f:
            if line.strip():
                rows.append(json.loads(line))
    return rows


def append_jsonl(path: Path, row: dict) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    with _write_lock:
        with path.open("a", encoding="utf-8") as f:
            f.write(json.dumps(row, ensure_ascii=False) + "\n")
            f.flush()


def get_title(row: dict) -> str:
    return ((row.get("meta") or {}).get("title") or "").strip()


def done_titles(path: Path) -> set[str]:
    if not path.exists():
        return set()
    return {title for row in load_jsonl(path) if (title := get_title(row))}


def build_prompt(item: dict, task: str, max_content_chars: int) -> str:
    content = item.get("Content", "") or ""
    if max_content_chars > 0:
        content = content[:max_content_chars]
    if task == "1":
        return INFER_TASK1.replace("{CONTENT}", content)
    return INFER_TASK2.replace("{CONTENT}", content).replace("{GOAL}", item.get("Goal", "") or "")


def build_output_row(item: dict, task: str, response: str, model: str) -> dict:
    if task == "1":
        return {
            "meta": item.get("meta", {}),
            "gt_Candidates": item.get("Candidates", ""),
            "infer_task1_response": response,
            "codex_model": model,
        }
    return {
        "meta": item.get("meta", {}),
        "Goal": item.get("Goal", ""),
        "gt_refined_plan": item.get("refined_standard_plan", ""),
        "gt_Rubric": item.get("Rubric", ""),
        "infer_task2_response": response,
        "codex_model": model,
    }


def call_codex(prompt: str, model: str, reasoning: str, timeout: int) -> str:
    workdir = tempfile.mkdtemp(prefix="abforge_codex_wd_")
    msg_file = os.path.join(workdir, "_last.txt")
    cmd = [
        "codex",
        "exec",
        "--ephemeral",
        "-m",
        model,
        "-s",
        "read-only",
        "--skip-git-repo-check",
        "--color",
        "never",
        "-C",
        workdir,
        "-o",
        msg_file,
        "-c",
        f'model_reasoning_effort="{reasoning}"',
        "-",
    ]
    try:
        proc = subprocess.run(
            cmd,
            input=prompt,
            capture_output=True,
            text=True,
            timeout=timeout,
            env=CODEX_ENV,
        )
        if os.path.exists(msg_file):
            text = Path(msg_file).read_text(encoding="utf-8").strip()
            if text:
                return text
        err = (proc.stderr or proc.stdout or "").strip().replace("\n", " ")
        raise RuntimeError(f"codex exit {proc.returncode}: {err[:500]}")
    finally:
        shutil.rmtree(workdir, ignore_errors=True)


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--task", required=True, choices=["1", "2"])
    parser.add_argument("--input", required=True)
    parser.add_argument("--output", required=True)
    parser.add_argument("--fail-output", default=None)
    parser.add_argument("--model", default="gpt-5.4")
    parser.add_argument("--reasoning", default="medium")
    parser.add_argument("--max-content-chars", type=int, default=60000)
    parser.add_argument("--workers", type=int, default=2)
    parser.add_argument("--timeout", type=int, default=600)
    args = parser.parse_args()

    input_path = Path(args.input)
    output_path = Path(args.output)
    fail_path = Path(args.fail_output) if args.fail_output else None

    rows = load_jsonl(input_path)
    done = done_titles(output_path)
    todo = [row for row in rows if get_title(row) not in done]
    print(
        f"{len(rows)} items, {len(done)} done, {len(todo)} to run, "
        f"task={args.task}, model={args.model} -> {output_path}",
        flush=True,
    )

    def work(index_row: tuple[int, dict]) -> bool:
        index, row = index_row
        label = get_title(row) or f"item-{index}"
        try:
            response = call_codex(
                build_prompt(row, args.task, args.max_content_chars),
                args.model,
                args.reasoning,
                args.timeout,
            )
            append_jsonl(output_path, build_output_row(row, args.task, response, args.model))
            print(f"[ok] {label[:80]}", flush=True)
            return True
        except Exception as exc:
            print(f"[FAIL] {label[:80]}: {exc}", flush=True)
            if fail_path:
                append_jsonl(
                    fail_path,
                    {"meta": row.get("meta", {}), "task": args.task, "error": str(exc)},
                )
            return False

    ok = 0
    with ThreadPoolExecutor(max_workers=args.workers) as executor:
        futures = [executor.submit(work, item) for item in enumerate(todo)]
        for future in as_completed(futures):
            ok += 1 if future.result() else 0

    out_rows = len(load_jsonl(output_path)) if output_path.exists() else 0
    print(f"done: {ok}/{len(todo)} ok; output rows now {out_rows}", flush=True)


if __name__ == "__main__":
    main()