#!/usr/bin/env python3
"""Run and score OpenRouter models on the FinanceGym test split.

The output is resumable: one JSONL file is maintained per model and existing
task/rollout pairs are skipped. Each API call requests one completion because
OpenRouter providers do not consistently support ``n > 1``.

Example:
    python scripts/run_openrouter_rollouts.py data/test-*.parquet
    python scripts/run_openrouter_rollouts.py data/test-*.parquet --limit 2
"""

from __future__ import annotations

import argparse
import base64
import glob
import json
import os
import random
import re
import sys
import threading
import time
from concurrent.futures import FIRST_COMPLETED, ThreadPoolExecutor, wait
from difflib import SequenceMatcher
from pathlib import Path
from typing import Any

import requests

try:
    import pyarrow.parquet as pq
except ImportError:
    sys.exit("Missing dependency: install with `python -m pip install pyarrow requests`.")


DEFAULT_MODELS = [
    "moonshotai/kimi-k2.6",
    "moonshotai/kimi-k3",
    "qwen/qwen3.8-max",
    "bytedance-seed/seed-2-1-turbo"
    ]
OPENROUTER_URL = "https://openrouter.ai/api/v1/chat/completions"
TRANSIENT_HTTP = {408, 409, 425, 429, 500, 502, 503, 504, 522, 524}
SYSTEM_PROMPT = (
    "You are a GUI agent operating a computer via screenshots. At each step, "
    "decide the next UI action and call the `computer_use` tool with the "
    "appropriate action and arguments. Coordinates are on a 0-999 grid. Call "
    "the tool exactly once per step; do not output the action as plain text."
)
COORD_TOL = 50.0
CLICK_VERBS = {
    "left_click", "right_click", "double_click", "triple_click", "mouse_move",
    "left_mouse_down", "left_mouse_up",
}
WRITE_LOCK = threading.Lock()
PRINT_LOCK = threading.Lock()


def load_key(env_file: Path) -> str:
    for name in ("OPENROUTER_API_KEY", "OPENROUTER_KEY"):
        if os.environ.get(name):
            return os.environ[name].strip().strip("\"'")
    if env_file.exists():
        for raw in env_file.read_text().splitlines():
            line = raw.strip()
            for name in ("OPENROUTER_API_KEY", "OPENROUTER_KEY"):
                if line.startswith(name + "="):
                    return line.split("=", 1)[1].strip().strip("\"'")
    raise SystemExit(f"No OPENROUTER_KEY or OPENROUTER_API_KEY found in env or {env_file}")


def data_url(image: Any) -> str:
    if isinstance(image, dict):
        blob = image.get("bytes")
        if blob is None and image.get("path"):
            blob = Path(image["path"]).read_bytes()
    else:
        blob = image
    if isinstance(blob, memoryview):
        blob = blob.tobytes()
    if not isinstance(blob, bytes):
        raise TypeError(f"Unsupported image value: {type(image)!r}")
    return "data:image/jpeg;base64," + base64.b64encode(blob).decode("ascii")


def openai_messages(row: dict[str, Any]) -> list[dict[str, Any]]:
    images = row["images"]
    messages: list[dict[str, Any]] = []
    for source in row["messages"]:
        parts = []
        for part in source.get("content") or []:
            if part.get("type") == "text":
                parts.append({"type": "text", "text": part.get("text") or ""})
            elif part.get("type") == "image":
                parts.append({
                    "type": "image_url",
                    "image_url": {"url": data_url(images[part["image_index"]])},
                })
        message: dict[str, Any] = {"role": source["role"], "content": parts}
        if source.get("tool_calls"):
            message["tool_calls"] = source["tool_calls"]
        if source.get("tool_call_id") is not None:
            message["tool_call_id"] = source["tool_call_id"]
        messages.append(message)
    if messages and messages[0]["role"] == "system":
        messages[0] = {"role": "system", "content": SYSTEM_PROMPT}
    else:
        messages.insert(0, {"role": "system", "content": SYSTEM_PROMPT})
    return messages


def parse_actions(message: dict[str, Any]) -> list[dict[str, Any]]:
    actions = []
    for call in message.get("tool_calls") or []:
        fn = call.get("function") or {}
        value = fn.get("arguments")
        try:
            value = json.loads(value) if isinstance(value, str) else value
        except json.JSONDecodeError:
            continue
        if isinstance(value, dict):
            actions.append(value)
    if actions:
        return actions
    content = message.get("content") or ""
    if isinstance(content, list):
        content = " ".join(p.get("text", "") for p in content if isinstance(p, dict))
    for match in re.finditer(r"<tool_call>\s*(\{.*?\})\s*</tool_call>", content, re.S):
        try:
            value = json.loads(match.group(1))
            value = value.get("arguments", value)
            value = json.loads(value) if isinstance(value, str) else value
            if isinstance(value, dict):
                actions.append(value)
        except (json.JSONDecodeError, AttributeError):
            pass
    return actions


def coord_score(a: Any, b: Any) -> float:
    try:
        distance = ((float(a[0]) - float(b[0])) ** 2 + (float(a[1]) - float(b[1])) ** 2) ** 0.5
        return max(0.0, 1.0 - distance / COORD_TOL)
    except (TypeError, ValueError, IndexError):
        return 0.0


def pair_score(pred: dict[str, Any], gt: dict[str, Any]) -> float:
    if pred.get("action") != gt.get("action"):
        return 0.0
    verb = gt.get("action")
    values = []
    if verb in CLICK_VERBS or "coordinate" in gt:
        values.append(coord_score(pred.get("coordinate"), gt.get("coordinate")))
    if verb == "type":
        a, b = str(pred.get("text") or "").strip(), str(gt.get("text") or "").strip()
        values.append(1.0 if a == b else 0.9 if a.lower() == b.lower() else SequenceMatcher(None, a, b).ratio())
    if verb == "key":
        a, b = [str(x).lower() for x in pred.get("keys") or []], [str(x).lower() for x in gt.get("keys") or []]
        remaining, common = list(b), 0
        for key in a:
            if key in remaining:
                common += 1
                remaining.remove(key)
        values.append(common / (max(len(a), len(b)) or 1))
    if verb == "scroll" and pred.get("pixels") is not None and gt.get("pixels") is not None:
        p, g = float(pred["pixels"]), float(gt["pixels"])
        same_direction = (p >= 0) == (g >= 0)
        magnitude = 1.0 - min(1.0, abs(p - g) / (abs(g) + 1e-6))
        values.append((0.5 if same_direction else 0.0) + 0.5 * max(0.0, magnitude))
    if verb == "terminate":
        values.append(float(pred.get("status") == gt.get("status")))
    return sum(values) / len(values) if values else 1.0


def normalize_drags(actions: list[dict[str, Any]]) -> list[dict[str, Any]]:
    out, index = [], 0
    while index < len(actions):
        action = actions[index]
        if (index + 2 < len(actions) and action.get("action") == "left_mouse_down"
                and actions[index + 1].get("action") == "mouse_move"
                and actions[index + 2].get("action") == "left_mouse_up"):
            out.append({"action": "drag", "start": action.get("coordinate"),
                        "end": actions[index + 2].get("coordinate") or actions[index + 1].get("coordinate")})
            index += 3
        elif action.get("action") == "left_click_drag":
            out.append({"action": "drag", "start": None, "end": action.get("coordinate")})
            index += 1
        else:
            out.append(action)
            index += 1
    return out


def score(message: dict[str, Any], ground_truth: str) -> tuple[float, list[dict[str, Any]]]:
    pred = normalize_drags(parse_actions(message))
    gt = normalize_drags(json.loads(ground_truth))
    if not gt or not pred:
        return 0.0, pred
    total = 0.0
    for index, expected in enumerate(gt):
        if index >= len(pred):
            continue
        actual = pred[index]
        if expected.get("action") == actual.get("action") == "drag":
            values = [coord_score(actual.get("end"), expected.get("end"))]
            if actual.get("start") is not None and expected.get("start") is not None:
                values.append(coord_score(actual["start"], expected["start"]))
            total += sum(values) / len(values)
        else:
            total += pair_score(actual, expected)
    return total / max(len(gt), len(pred)), pred


def retry_delay(response: requests.Response | None, attempt: int, maximum: float) -> float:
    """Honor Retry-After, otherwise use capped exponential backoff + full jitter."""
    if response is not None:
        value = response.headers.get("Retry-After")
        if value:
            try:
                return min(maximum, max(0.0, float(value)))
            except ValueError:
                pass
        if response.status_code == 429:
            # Provider-wide TPM limits need a materially longer pause than a
            # transport failure; short retries create a thundering herd.
            low = min(maximum, 10.0 * (2.0 ** attempt))
            high = min(maximum, 20.0 * (2.0 ** attempt))
            return random.uniform(low, max(low, high))
    ceiling = min(maximum, 2.0 ** attempt)
    return random.uniform(0.5, max(0.5, ceiling))


def api_call(key: str, payload: dict[str, Any], timeout: int, retries: int,
             max_backoff: float) -> tuple[dict[str, Any] | None, str | None, bool]:
    """Return (response, error, retryable).

    OpenRouter may route successive requests to different providers. Transient
    statuses are retried here; if exhausted, retryable=True ensures a later
    program invocation attempts the rollout again.
    """
    headers = {"Authorization": f"Bearer {key}", "Content-Type": "application/json"}
    last_error = None
    retryable = True
    for attempt in range(retries):
        response = None
        try:
            response = requests.post(OPENROUTER_URL, headers=headers, json=payload, timeout=timeout)
            if response.status_code == 200:
                body = response.json()
                if body.get("choices"):
                    return body, None, False
                last_error = f"empty response: {str(body)[:300]}"
            else:
                last_error = f"HTTP {response.status_code}: {response.text[:500]}"
                retryable = response.status_code in TRANSIENT_HTTP
                if not retryable:
                    break
        except (requests.Timeout, requests.ConnectionError) as exc:
            last_error = f"{type(exc).__name__}: {exc}"
            retryable = True
        except requests.RequestException as exc:
            last_error = f"{type(exc).__name__}: {exc}"
            retryable = False
            break
        if attempt + 1 < retries:
            delay = retry_delay(response, attempt, max_backoff)
            with PRINT_LOCK:
                print(f"  retry {attempt + 1}/{retries - 1} in {delay:.1f}s: {last_error[:180]}", flush=True)
            time.sleep(delay)
    return None, last_error, retryable


def count_rows(paths: list[str], limit: int) -> int:
    total = sum(pq.ParquetFile(path).metadata.num_rows for path in paths)
    return min(total, limit) if limit else total


def iter_rows(paths: list[str], limit: int):
    task_id = 0
    for path in paths:
        parquet = pq.ParquetFile(path)
        for batch in parquet.iter_batches(batch_size=8):
            for row in batch.to_pylist():
                row["task_id"] = task_id
                yield row
                task_id += 1
                if limit and task_id >= limit:
                    return


def safe_name(model: str) -> str:
    return re.sub(r"[^A-Za-z0-9._-]+", "__", model)


def normalized_tools(value: Any) -> list[dict[str, Any]]:
    """Return provider-portable JSON Schema for the dataset's native tools."""
    tools = json.loads(value) if isinstance(value, str) else value
    # Round-trip so the Parquet row is never mutated across model calls.
    tools = json.loads(json.dumps(tools))
    for tool in tools:
        properties = (((tool.get("function") or {}).get("parameters") or {})
                      .get("properties") or {})
        for name, schema in properties.items():
            if schema.get("type") == "array" and "items" not in schema:
                schema["items"] = {"type": "number" if name == "coordinate" else "string"}
    return tools


def completed_keys(path: Path) -> set[tuple[int, int]]:
    done = set()
    if path.exists():
        for line in path.read_text().splitlines():
            try:
                item = json.loads(line)
                # Failed attempts remain eligible on the next invocation.
                if item.get("ok") and item.get("score") is not None:
                    done.add((int(item["task_id"]), int(item["rollout"])))
            except (json.JSONDecodeError, KeyError, ValueError):
                pass
    return done


def run_one(key: str, model: str, row: dict[str, Any], rollout: int, args: argparse.Namespace) -> dict[str, Any]:
    payload = {
        "model": model,
        "messages": openai_messages(row),
        "tools": normalized_tools(row["tools"]),
        "tool_choice": "auto",
        "temperature": args.temperature,
        "max_tokens": args.max_tokens,
    }
    body, error, retryable = api_call(
        key, payload, args.timeout, args.retries, args.max_backoff
    )
    if body:
        message = body["choices"][0].get("message") or {}
        value, actions = score(message, row["ground_truth"])
        return {"task_id": row["task_id"], "rollout": rollout, "score": value,
                "ok": True, "actions": actions, "message": message,
                "usage": body.get("usage")}
    return {"task_id": row["task_id"], "rollout": rollout, "score": None,
            "ok": False, "retryable": retryable, "error": error}


def summarize(path: Path, model: str) -> dict[str, Any]:
    records = [json.loads(line) for line in path.read_text().splitlines() if line.strip()]
    good = [r for r in records if r.get("ok") and r.get("score") is not None]
    task_scores: dict[int, list[float]] = {}
    for item in good:
        task_scores.setdefault(item["task_id"], []).append(float(item["score"]))
    per_task = [sum(values) / len(values) for values in task_scores.values()]
    return {"model": model, "successful_rollouts": len(good), "total_records": len(records),
            "tasks_with_success": len(task_scores),
            "average_score": sum(per_task) / len(per_task) if per_task else None}


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("input", nargs="+", help="Input Parquet file(s); shell globs are accepted")
    parser.add_argument("--models", nargs="+", default=DEFAULT_MODELS)
    parser.add_argument("--rollouts", type=int, default=1)
    parser.add_argument("--output-dir", type=Path, default=Path("openrouter_rollouts"))
    parser.add_argument("--env-file", type=Path, default=Path.home() / ".env")
    parser.add_argument("--concurrency", type=int, default=8)
    parser.add_argument("--temperature", type=float, default=1.0)
    parser.add_argument("--max-tokens", type=int, default=2048)
    parser.add_argument("--timeout", type=int, default=300)
    parser.add_argument("--retries", type=int, default=5)
    parser.add_argument("--max-backoff", type=float, default=60.0,
                        help="Maximum seconds between API retries")
    parser.add_argument("--limit", type=int, default=0, help="Only run the first N tasks (smoke tests)")
    args = parser.parse_args()
    if args.rollouts < 1 or args.concurrency < 1:
        parser.error("--rollouts and --concurrency must be positive")

    paths = sorted({p for pattern in args.input for p in (glob.glob(pattern) or [pattern])})
    missing = [p for p in paths if not Path(p).is_file()]
    if missing:
        parser.error("Input file(s) not found: " + ", ".join(missing))
    key = load_key(args.env_file)
    row_count = count_rows(paths, args.limit)
    args.output_dir.mkdir(parents=True, exist_ok=True)
    print(f"Found {row_count} test tasks in {len(paths)} file(s)", flush=True)

    summaries = []
    for model in args.models:
        path = args.output_dir / f"{safe_name(model)}.jsonl"
        done = completed_keys(path)
        pending_count = row_count * args.rollouts - len(done)
        print(f"{model}: completed={len(done)} pending={pending_count}", flush=True)
        with path.open("a", encoding="utf-8") as output, ThreadPoolExecutor(max_workers=args.concurrency) as pool:
            futures = {}
            completed = 0

            def collect(future) -> None:
                nonlocal completed
                try:
                    record = future.result()
                except Exception as exc:
                    task_id, rollout = futures[future]
                    record = {"task_id": task_id, "rollout": rollout, "score": None,
                              "ok": False, "retryable": True,
                              "error": f"{type(exc).__name__}: {exc}"}
                output.write(json.dumps(record, ensure_ascii=False) + "\n")
                output.flush()
                completed += 1
                if completed % 25 == 0 or completed == pending_count:
                    print(f"  {model}: {completed}/{pending_count} new rollouts", flush=True)

            for row in iter_rows(paths, args.limit):
                for rollout in range(args.rollouts):
                    if (row["task_id"], rollout) in done:
                        continue
                    while len(futures) >= args.concurrency * 2:
                        finished, _ = wait(futures, return_when=FIRST_COMPLETED)
                        for future in finished:
                            collect(future)
                            del futures[future]
                    future = pool.submit(run_one, key, model, row, rollout, args)
                    futures[future] = (row["task_id"], rollout)
            while futures:
                finished, _ = wait(futures, return_when=FIRST_COMPLETED)
                for future in finished:
                    collect(future)
                    del futures[future]
        result = summarize(path, model)
        summaries.append(result)
        print(json.dumps(result, ensure_ascii=False), flush=True)

    summary_path = args.output_dir / "summary.json"
    combined = {}
    if summary_path.exists():
        try:
            combined = {item["model"]: item for item in json.loads(summary_path.read_text())}
        except (json.JSONDecodeError, KeyError, TypeError):
            combined = {}
    combined.update({item["model"]: item for item in summaries})
    summary_path.write_text(json.dumps(list(combined.values()), indent=2) + "\n")
    print(f"Summary written to {summary_path}")


if __name__ == "__main__":
    main()