#!/usr/bin/env python3
"""Aggregate results/*.jsonl into a comparison table."""

from __future__ import annotations

import json
import sys
from pathlib import Path

import pandas as pd

sys.path.insert(0, str(Path(__file__).resolve().parent / "bench"))
import oolong  # noqa: E402

RESULTS = Path(__file__).resolve().parent / "results"

# Published list prices, USD per million tokens (input, output). Cache reads bill
# at 0.1x input and cache writes at 1.25x input.
PRICES = {
    "claude-haiku-4-5-20251001": (1.0, 5.0),
    "claude-sonnet-4-6": (3.0, 15.0),
    "claude-opus-4-8": (15.0, 75.0),
}


def api_cost(row, model: str) -> float:
    """Cost from raw token counts at list prices.

    The CLI's own total_cost_usd also bills a small hidden Haiku call it makes per
    invocation, so this is the apples-to-apples number for model comparisons.
    """
    pin, pout = PRICES[model.replace("[1m]", "")]
    return (
        row.get("usage_input_tokens", 0) * pin
        + row.get("usage_cache_creation_tokens", 0) * pin * 1.25
        + row.get("usage_cache_read_tokens", 0) * pin * 0.1
        + row.get("usage_output_tokens", 0) * pout
    ) / 1e6


def load(tag: str) -> pd.DataFrame:
    rows = [json.loads(l) for l in (RESULTS / f"{tag}.jsonl").read_text().splitlines() if l.strip()]
    df = pd.json_normalize(rows, sep="_")
    meta = json.loads((RESULTS / f"{tag}.meta.json").read_text())
    df = df.assign(tag=tag, **meta)
    df["api_cost"] = df.apply(lambda r: api_cost(r, meta["model"]), axis=1)

    # Guard the headline number against the answer extractor. `score` uses our
    # clean_final() pre-step; `score_raw` is the official parser on the untouched
    # response. If the two disagree by much, the gap is a parsing artifact rather
    # than a capability difference.
    df["score_raw"] = [
        oolong.score_response({"answer": repr([g]), "answer_type": t}, a)["score"] if a else 0.0
        for g, t, a in zip(df.gold, df.answer_type, df.full_answer)
    ]
    return df


def main(tags: list[str]) -> None:
    tags = tags or sorted(p.stem for p in RESULTS.glob("*.jsonl"))
    df = pd.concat([load(t) for t in tags], ignore_index=True)

    agg = df.groupby(["tag", "mode", "model", "context_len"], as_index=False).agg(
        n=("score", "size"),
        score=("score", "mean"),
        score_raw=("score_raw", "mean"),
        exact=("score", lambda s: (s == 1.0).mean()),
        errors=("error", lambda e: e.notna().sum()),
        cost_per_q=("api_cost", "mean"),
        cli_cost_per_q=("usage_cost_usd", "mean"),
        calls_per_q=("usage_calls", "mean"),
        sec_per_q=("seconds", "mean"),
    )
    for c in ("score", "score_raw", "exact"):
        agg[c] = agg[c].round(3)
    for c in ("cost_per_q", "cli_cost_per_q"):
        agg[c] = agg[c].round(4)
    agg[["calls_per_q", "sec_per_q"]] = agg[["calls_per_q", "sec_per_q"]].round(1)
    print(agg.to_string(index=False))

    print("\nper source dataset (mean score):")
    print(df.pivot_table(index="dataset", columns="tag", values="score", aggfunc="mean").round(2).to_string())
    print("\nper task (mean score):")
    print(df.pivot_table(index="task", columns="tag", values="score", aggfunc="mean").round(2).to_string())


if __name__ == "__main__":
    main(sys.argv[1:])