lsvbench/parsing.py

"""Pure parsing helpers for benchmark model outputs."""

from __future__ import annotations

import json
import re
from typing import Any, Iterable, Mapping


FENCED_JSON_RE = re.compile(r"^\s*```(?:json|JSON)?\s*(.*?)\s*```\s*$", re.DOTALL)


def strip_markdown_json_fence(text: str) -> str:
    cleaned = str(text or "").strip()
    match = FENCED_JSON_RE.match(cleaned)
    return match.group(1).strip() if match else cleaned


def extract_json_text(text: str) -> str | None:
    cleaned = strip_markdown_json_fence(text)
    if not cleaned:
        return None
    start = cleaned.find("{")
    if start < 0:
        return None
    depth = 0
    in_string = False
    escape = False
    for idx, char in enumerate(cleaned[start:], start=start):
        if in_string:
            if escape:
                escape = False
            elif char == "\\":
                escape = True
            elif char == '"':
                in_string = False
            continue
        if char == '"':
            in_string = True
        elif char == "{":
            depth += 1
        elif char == "}":
            depth -= 1
            if depth == 0:
                return cleaned[start : idx + 1]
    return None


def extract_json_payload(text: str) -> dict[str, Any] | None:
    payload_text = extract_json_text(text)
    if not payload_text:
        return None
    try:
        payload = json.loads(payload_text)
    except Exception:
        return None
    return payload if isinstance(payload, dict) else None


def normalize_step_id(value: Any) -> str | None:
    if value is None:
        return None
    text = str(value).strip()
    if not text or text.lower() == "null":
        return None
    match = re.fullmatch(r"(?:step|s)[_\-\s]*(\d+)", text, flags=re.IGNORECASE)
    if match:
        return match.group(1)
    digits = re.sub(r"\D+", "", text)
    return digits or None


def parse_bool_field(value: Any) -> bool | None:
    if isinstance(value, bool):
        return value
    if value is None:
        return None
    text = str(value).strip().lower()
    if text in {"true", "yes", "y", "1", "correct"}:
        return True
    if text in {"false", "no", "n", "0", "incorrect"}:
        return False
    return None


def parse_step_field(value: Any) -> str | None:
    return normalize_step_id(value)


def _normalize_option(value: Any) -> str:
    return re.sub(r"[\s\-]+", "_", str(value or "").strip().upper())


def strip_answer_tag(text: str) -> str:
    cleaned = str(text or "").strip()
    match = re.fullmatch(r"<answer>\s*(.*?)\s*</answer>", cleaned, flags=re.IGNORECASE | re.DOTALL)
    return match.group(1).strip() if match else cleaned


def parse_choice_option(
    text: str,
    options: Iterable[str],
    *,
    json_keys: Iterable[str] = ("option", "answer", "prediction", "pred_option", "choice", "label", "mistake_type"),
    number_map: Mapping[str, str] | None = None,
) -> str | None:
    option_list = [_normalize_option(option) for option in options]
    option_set = set(option_list)
    normalized_number_map = {
        str(key): _normalize_option(value)
        for key, value in (number_map or {}).items()
    }

    payload = extract_json_payload(text)
    if payload is not None:
        for key in json_keys:
            if key not in payload:
                continue
            parsed = parse_choice_option(
                str(payload[key]),
                option_list,
                json_keys=(),
                number_map=normalized_number_map,
            )
            if parsed is not None:
                return parsed

    cleaned = strip_answer_tag(strip_markdown_json_fence(text))
    upper = _normalize_option(cleaned)
    if upper in option_set:
        return upper
    if upper in normalized_number_map:
        return normalized_number_map[upper]

    first_line = cleaned.splitlines()[0].strip() if cleaned else ""
    first_token = re.split(r"[\s:.)\-\]]+", first_line, maxsplit=1)[0].strip().upper()
    if first_token in normalized_number_map:
        return normalized_number_map[first_token]
    if first_token in option_set:
        return first_token

    pattern = r"\b(" + "|".join(re.escape(option) for option in sorted(option_set, key=len, reverse=True)) + r")\b"
    matches = re.findall(pattern, _normalize_option(cleaned))
    unique = sorted(set(matches))
    return unique[0] if len(unique) == 1 else None


def parse_monitoring_response(text: str) -> tuple[bool | None, str | None]:
    cleaned = strip_markdown_json_fence(text)
    payload = extract_json_payload(cleaned)
    if payload is not None:
        candidate = parse_bool_field(payload.get("candidate_matches_visible_step"))
        step_id = payload.get("observed_step_id")
        step_value = None if step_id in (None, "", "null") else str(step_id)
        return candidate, step_value

    upper = cleaned.upper()
    if re.search(r"\b(TRUE|YES)\b", upper):
        return True, None
    if re.search(r"\b(FALSE|NO)\b", upper):
        return False, None
    return None, None


def parse_next_step_delta_response(text: str) -> dict[str, Any]:
    payload = extract_json_payload(text)
    if payload is None:
        return {
            "pred_current_step_id": None,
            "pred_has_error": None,
            "pred_has_step_skipped": None,
            "pred_error_type": None,
            "pred_error_step_id": None,
            "pred_parse_ok": False,
        }
    current = payload.get("current_step")
    if current in (None, "", "null"):
        current = None
    errors = payload.get("errors")
    if not isinstance(errors, list):
        errors = []
    parsed_errors = [item for item in errors if isinstance(item, dict)]
    error_types = [
        str(item.get("error_type") or "").strip()
        for item in parsed_errors
        if str(item.get("error_type") or "").strip()
    ]
    non_none_error_types = [kind for kind in error_types if kind.lower() != "none"]
    skipped = any(kind.lower() == "step_skipped" for kind in error_types)
    step_ref = None
    for item in parsed_errors:
        if str(item.get("error_type") or "").strip().lower() == "step_skipped":
            step_ref = item.get("step_ref")
            break
    if step_ref in (None, "", "null"):
        step_ref = None
    return {
        "pred_current_step_id": None if current is None else str(current),
        "pred_has_error": bool(non_none_error_types),
        "pred_has_step_skipped": skipped,
        "pred_error_type": non_none_error_types[0] if non_none_error_types else None,
        "pred_error_step_id": None if step_ref is None else str(step_ref),
        "pred_parse_ok": True,
    }