"""Render the `Quantitative Results` LaTeX section.

Reads `data/analysis/per_country_per_column.csv` (produced by
`legex-analysis`), restricts it to the twelve evaluated jurisdictions
and the fourteen evaluated fields, then prints the section preamble plus
the headline table that compares the three systems on Acc / Recall /
Hallucination / F1, both over all fields and over the four-field cost
block.
"""

import argparse
import csv
import logging
import sys
from collections import defaultdict
from pathlib import Path

log = logging.getLogger(__name__)


# Jurisdictions that survive the round-2 PDF audit (TW/BR/HK/IN excluded
# because their source PDFs were incomplete; BE/NP/RS excluded because
# inference was intentionally skipped for them).
EVALUATED_COUNTRIES: tuple[str, ...] = (
    "am", "au", "ch", "de", "es", "fr", "ge",
    "nz", "ph", "sg", "uk", "us",
)
H2H_COUNTRIES = EVALUATED_COUNTRIES  # backwards-compat alias

EVAL_FIELDS: tuple[str, ...] = (
    "legal_subject_judgement",
    "trial_start_date",
    "trial_end_date",
    "dispute_value_nominal",
    "plaintiff_loosing_share",
    "court_cost_awarded_nominal",
    "party_compensation_awarded_nominal",
    "plaintiffs_all_count",
    "defendants_all_count",
    "plaintiff_no1_ISIC1_industry_category",
    "defendant_no1_ISIC1_industry_category",
)

COST_BLOCK: tuple[str, ...] = (
    "dispute_value_nominal",
    "plaintiff_loosing_share",
    "court_cost_awarded_nominal",
    "party_compensation_awarded_nominal",
)

# (system slug as written by legex-analysis, LaTeX label). Order = row order
# in the headline table.
SYSTEMS: tuple[tuple[str, str], ...] = (
    ("gemini", r"\texttt{gemini-3.1-flash-lite}"),
    ("gpt",    r"\texttt{gpt-5.4-mini}         "),
    ("harvey", r"\textsc{Harvey}               "),
)

_BUCKETS = ("tp", "mismatch", "missed", "hallucinated", "tn")


def _empty() -> dict[str, int]:
    return {k: 0 for k in _BUCKETS}


def _metrics(c: dict[str, int]) -> dict[str, float]:
    tp, mism, miss, hallu, tn = c["tp"], c["mismatch"], c["missed"], c["hallucinated"], c["tn"]
    total = tp + mism + miss + hallu + tn
    filled_gold = tp + mism + miss
    empty_gold = hallu + tn
    p_denom = tp + mism + hallu
    p = tp / p_denom if p_denom else 0.0
    r = tp / filled_gold if filled_gold else 0.0
    f1 = 2 * p * r / (p + r) if (p + r) else 0.0
    return {
        "accuracy": (tp + tn) / total if total else 0.0,
        "recall_when_filled": r,
        "hallucination_rate": hallu / empty_gold if empty_gold else 0.0,
        "f1": f1,
    }


def _aggregate(
    csv_path: Path,
) -> dict[str, dict[str, dict[str, int]]]:
    """{ model -> { 'all' | 'cost' -> bucket counter } }."""
    out: dict[str, dict[str, dict[str, int]]] = {
        m: {"all": _empty(), "cost": _empty()} for m, _ in SYSTEMS
    }
    h2h = set(H2H_COUNTRIES)
    eval_fields = set(EVAL_FIELDS)
    cost_fields = set(COST_BLOCK)
    models = {m for m, _ in SYSTEMS}

    with open(csv_path, encoding="utf-8", newline="") as f:
        for row in csv.DictReader(f):
            if row["country"] not in h2h or row["model"] not in models:
                continue
            col = row["column"]
            if col not in eval_fields:
                continue
            counts = {k: int(row[k]) for k in _BUCKETS}
            for k in _BUCKETS:
                out[row["model"]]["all"][k] += counts[k]
            if col in cost_fields:
                for k in _BUCKETS:
                    out[row["model"]]["cost"][k] += counts[k]
    return out


def _fmt_pct(v: float) -> str:
    return f"{v * 100:.1f}\\%"


def _fmt_f1(v: float) -> str:
    return f"{v:.3f}"


def _bold_best(values: list[float], formatter, higher_is_better: bool = True) -> list[str]:
    best = max(values) if higher_is_better else min(values)
    return [
        rf"\textbf{{{formatter(v)}}}" if v == best else formatter(v)
        for v in values
    ]


def render_section(agg: dict[str, dict[str, dict[str, int]]]) -> str:
    lines: list[str] = []
    lines.append(r"% Auto-generated by legex-quant-results — do not edit by hand.")
    lines.append(r"\section{Quantitative Results}")
    lines.append("")
    lines.append(
        r"We score the three systems against the human coded dataset: a commercial"
    )
    lines.append(
        r"review-table product by \textsc{Harvey} and two schema-constrained LLM"
    )
    lines.append(
        r"pipelines (\texttt{gpt-5.4-mini} and \texttt{gemini-3.1-flash-lite})."
    )
    lines.append(
        r"Since the dataset contains missing fields, e.g.\ where a court judgement"
    )
    lines.append(
        r"does not issue costs we evaluate our systems against two metrics:"
    )
    lines.append(r"\begin{itemize}")
    lines.append(
        r"    \item \textbf{Accuracy when filled}: how often the system extracts the"
        r" correct value given that the human expert has classified it."
    )
    lines.append(
        r"    \item \textbf{Hallucination rate}: how often the system extracts a"
        r" value given that the human expert has left the field empty."
    )
    lines.append(r"\end{itemize}")
    lines.append("")

    metrics_all = {m: _metrics(agg[m]["all"]) for m, _ in SYSTEMS}
    metrics_cost = {m: _metrics(agg[m]["cost"]) for m, _ in SYSTEMS}

    def _p(model: str, panel: str, key: str) -> str:
        src = metrics_all if panel == "all" else metrics_cost
        return f"{src[model][key] * 100:.1f}"

    lines.append(
        r"\Cref{tab:overall} shows the same trade-off on both panels: the two"
        r" LLM pipelines are more eager extractors, while \textsc{Harvey} is"
        r" more conservative. Across all " + str(len(EVAL_FIELDS)) + r" evaluated"
        r" fields \texttt{gpt-5.4-mini} and \texttt{gemini-3.1-flash-lite}"
        r" reach accuracy when filled of "
        + _p("gpt", "all", "recall_when_filled") + r"\,\% and "
        + _p("gemini", "all", "recall_when_filled")
        + r"\,\%, alongside \textsc{Harvey} at "
        + _p("harvey", "all", "recall_when_filled") + r"\,\%, but the LLM"
        r" pipelines pay for that recall with hallucination rates of "
        + _p("gpt", "all", "hallucination_rate") + r"\,\% and "
        + _p("gemini", "all", "hallucination_rate") + r"\,\% against \textsc{Harvey}'s "
        + _p("harvey", "all", "hallucination_rate") + r"\,\%."
        r" Narrowing to the four cost-block variables, the three systems"
        r" converge on accuracy ("
        + _p("harvey", "cost", "recall_when_filled") + r"--"
        + _p("gemini", "cost", "recall_when_filled") + r"\,\%), and the"
        r" hallucination rates land at "
        + _p("gemini", "cost", "hallucination_rate") + r"\,\% (Gemini), "
        + _p("harvey", "cost", "hallucination_rate") + r"\,\% (Harvey), and "
        + _p("gpt", "cost", "hallucination_rate") + r"\,\% (GPT)."
        r" No single system dominates: the LLM pipelines are preferable when"
        r" the downstream task tolerates noisy extractions in exchange for"
        r" coverage, whereas \textsc{Harvey} is preferable when emitted values"
        r" must be trusted on the non-cost variables."
    )
    lines.append("")

    acc_all = [metrics_all[m]["recall_when_filled"] for m, _ in SYSTEMS]
    hal_all = [metrics_all[m]["hallucination_rate"] for m, _ in SYSTEMS]
    acc_cost = [metrics_cost[m]["recall_when_filled"] for m, _ in SYSTEMS]
    hal_cost = [metrics_cost[m]["hallucination_rate"] for m, _ in SYSTEMS]

    acc_all_s = _bold_best(acc_all, _fmt_pct, True)
    hal_all_s = _bold_best(hal_all, _fmt_pct, False)
    acc_cost_s = _bold_best(acc_cost, _fmt_pct, True)
    hal_cost_s = _bold_best(hal_cost, _fmt_pct, False)

    lines.append(r"\begin{table}[h]")
    lines.append(
        r"\caption{Overall metrics on the "
        + str(len(EVALUATED_COUNTRIES))
        + r" head-to-head jurisdictions, golden-set rows with no"
        r" expert-filled field excluded."
        r" \emph{Acc.\ when filled} is the share of expert-filled cells the"
        r" system extracts correctly; \emph{Hallu.\ rate} is the share of"
        r" expert-empty cells where the system invented a value."
        r" The right block restricts the same metrics to the four cost-block"
        r" variables. Best per column in \textbf{bold} (lower is better for"
        r" Hallu.\ rate)."
        r"}"
    )
    lines.append(r"\label{tab:overall}")
    lines.append(r"\centering\small")
    lines.append(r"\begin{tabular}{@{}lrr@{\hskip 12pt}rr@{}}")
    lines.append(r"\toprule")
    lines.append(
        r" & \multicolumn{2}{c}{All "
        + str(len(EVAL_FIELDS))
        + r" evaluated fields} & \multicolumn{2}{c}{Cost block ("
        + str(len(COST_BLOCK))
        + r" fields)} \\"
    )
    lines.append(r"\cmidrule(lr){2-3}\cmidrule(l){4-5}")
    lines.append(
        r"System & Acc.\ when filled & Hallu.\ rate"
        r" & Acc.\ when filled & Hallu.\ rate \\"
    )
    lines.append(r"\midrule")
    for i, (_, label) in enumerate(SYSTEMS):
        lines.append(
            f"{label} & {acc_all_s[i]} & {hal_all_s[i]}"
            f" & {acc_cost_s[i]} & {hal_cost_s[i]} \\\\"
        )
    lines.append(r"\bottomrule")
    lines.append(r"\end{tabular}")
    lines.append(r"\end{table}")
    lines.append("")
    return "\n".join(lines)


def _print_console_summary(agg: dict[str, dict[str, dict[str, int]]]) -> None:
    """Quick human-readable echo so the user sees the numbers in the terminal too."""
    print(f"{'system':<28}  {'set':<5}  {'Acc.filled':>11}  {'Hallu':>6}")
    for model, _ in SYSTEMS:
        for tag in ("all", "cost"):
            m = _metrics(agg[model][tag])
            print(
                f"{model:<28}  {tag:<5}  "
                f"{m['recall_when_filled']:>11.1%}  {m['hallucination_rate']:>6.1%}"
            )


def main() -> None:
    logging.basicConfig(
        level=logging.INFO,
        format="%(asctime)s [%(levelname)s] %(message)s",
        handlers=[logging.StreamHandler(sys.stderr)],
    )
    parser = argparse.ArgumentParser(
        prog="legex-quant-results",
        description="Render the Quantitative Results section + headline table.",
    )
    parser.add_argument(
        "--input", type=Path,
        default=Path("data/analysis/per_country_per_column.csv"),
        help="per_country_per_column.csv produced by legex-analysis.",
    )
    parser.add_argument(
        "--out", type=Path,
        default=Path("data/analysis/quant_results.tex"),
        help="Where to write the rendered LaTeX section.",
    )
    args = parser.parse_args()

    if not args.input.exists():
        raise SystemExit(
            f"{args.input} not found — run `legex-analysis` first to generate it."
        )
    agg = _aggregate(args.input)
    tex = render_section(agg)
    args.out.parent.mkdir(parents=True, exist_ok=True)
    args.out.write_text(tex, encoding="utf-8")
    log.info(f"wrote {args.out}")
    _print_console_summary(agg)


if __name__ == "__main__":
    main()