"""LLM-with-time-series reasoning methods (family ``llm_ts``).

Two concrete classes, all covering T1..T7 per the Method × Task matrix
in the unified-API plan §9:

- :class:`ChatTime`  (``name="chattime"``)  — AAAI 2025 Oral; official repo
                                                ``ForestsKing/ChatTime``.
- :class:`TimeMQA`   (``name="time_mqa"``)  — 2025 LoRA-fine-tuned LLMs on
                                                200K time-series QA pairs;
                                                ``Time-MQA/Qwen-2.5-7B``.

Each class wraps the corresponding LLM-with-TS-reasoning architecture
from :mod:`baselines.llm_ts_reason`. They consume the same per-task
``X`` / ``y`` shapes the unified loader yields (matching :mod:`methods.llm`)
but invoke the architecture-specific time-series tokenizer / patcher /
adapter rather than plain text serialisation of the lookback.

The constructor takes a typed Pydantic config (``ChatTimeConfig``,
``TimeMQAConfig``) plus an injected ``engine``
exposing the OpenAI-compatible
``chat_complete(messages, max_tokens, temperature, top_p) -> str``
protocol (see :mod:`methods._openai_engine`). When ``engine=None`` the
class does NOT eagerly load any model — that is left to the runner who
hands the engine in via DI (consistent with :mod:`methods.llm`).
ChatTime additionally exposes a ``predict(history)`` numeric-forecast
hook that the runner-supplied engine MAY implement; if absent the T1
path falls back to the chat-template completion route.

A ``dry_run=True`` mode is provided for CPU smoke testing: every
inference call is short-circuited to a deterministic placeholder
response and predictions follow the canonical per-task shape so the
runner contract / shape assertions in :mod:`tests.test_method_contract`
can be verified without any GPU, weights, or vLLM engine.

Both classes implement the canonical :class:`methods.base.Method`
contract: ``fit`` (no-op for ZS), ``predict``, ``save`` / ``load`` via
:class:`_HFSaveMixin`, ``default_config``, ``hyperparams``,
``lib_versions``. They consume neither ``meta`` nor any IO; the
``MACROLENS_DETERMINISTIC`` env var seeds python / numpy / torch.

Per-task input / output shapes (mirrors :mod:`methods.llm`):
    T1 : X = (N, lookback, F) np.ndarray  → y_pred (N, horizon) float32.
    T2 : X = pd.DataFrame                  → y_pred (N,) float32.
    T3 : X = pd.DataFrame                  → y_pred long-form
              [ticker, fiscal_year, field, value].
    T4 : X = pd.DataFrame with `lookback`/ `event_type` / `event_description`
              columns                      → y_pred (N,) float32.
    T5 : X = pd.DataFrame                  → y_pred (N,) float32.
    T6 : X = pd.DataFrame                  → y_pred long-form.
    T7 : X = pd.DataFrame                  → y_pred [address, rent, price].
"""

from __future__ import annotations

import json
import logging
import os
import pathlib
import re
from typing import Any

import numpy as np
import pandas as pd

from ._config import (
    ChatTimeConfig,
    LLMTSConfig,
    TimeMQAConfig,
)
from ._openai_engine import DryRunEngine
from ._registry import register
from .base import Method, _HFSaveMixin

logger = logging.getLogger(__name__)


# ── Shared parsing helpers (lifted from baselines.llm_ts_reason) ──────────


_NUM_RE = re.compile(r"[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?")


def _parse_first_number(text: str) -> float | None:
    """Return the first plausible signed/decimal number from ``text``."""
    if not text:
        return None
    cleaned = text.replace(",", "")
    m = _NUM_RE.search(cleaned)
    if not m:
        return None
    try:
        return float(m.group(0))
    except (TypeError, ValueError):
        return None


def _parse_horizon_list(response: str, horizon: int) -> np.ndarray | None:
    """Extract a JSON list of floats representing a forecast trajectory.

    Looks for the first ``[...]`` substring in ``response`` and parses it
    as JSON. Returns a ``(horizon,)`` float32 ndarray, padding with the
    last value when shorter and truncating when longer. Falls back to
    extracting all numeric tokens from the bracketed slice when JSON
    parsing fails. Returns ``None`` on total parse failure.
    """
    if not response:
        return None
    start = response.find("[")
    end = response.rfind("]")
    if start < 0 or end <= start:
        return None
    candidate = response[start : end + 1]
    parsed: list[Any] | None = None
    try:
        loaded = json.loads(candidate)
        if isinstance(loaded, list):
            parsed = loaded
    except json.JSONDecodeError:
        parsed = None
    if parsed is None:
        tokens = _NUM_RE.findall(candidate)
        if not tokens:
            return None
        try:
            parsed = [float(t) for t in tokens]
        except ValueError:
            return None
    vals: list[float] = []
    for v in parsed:
        try:
            vals.append(float(v))
        except (TypeError, ValueError):
            continue
    if not vals:
        return None
    if len(vals) >= horizon:
        out = np.asarray(vals[:horizon], dtype=np.float32)
    else:
        pad = [vals[-1]] * (horizon - len(vals))
        out = np.asarray(vals + pad, dtype=np.float32)
    return out


def _extract_json_object(response: str) -> dict | None:
    """Extract a structured ``{field: value}`` map from an LLM response.

    Two paths:

    1. **JSON object**: legacy support for replies like
       ``{"Revenues": 1000000, "Assets": 5000000}``. Slices from the first
       ``{`` to the last ``}`` and tries ``json.loads``.
    2. **Plain-text key/value**: line-oriented format ``<Field>: <number>``
       which is what current prompts request. Each line is matched by
       regex; numbers may use ``$``, commas, scientific notation. This is
       the natural LLM output mode and avoids JSON parse failures.

    Returns ``None`` if neither path yields any field/value pair.
    """
    if not response:
        return None
    # Path 1: legacy JSON object.
    start = response.find("{")
    end = response.rfind("}")
    if start >= 0 and end > start:
        try:
            j = json.loads(response[start:end + 1])
            if isinstance(j, dict):
                return j
        except json.JSONDecodeError:
            pass
        depth = 0
        for i in range(start, len(response)):
            ch = response[i]
            if ch == "{":
                depth += 1
            elif ch == "}":
                depth -= 1
                if depth == 0:
                    try:
                        j = json.loads(response[start:i + 1])
                        if isinstance(j, dict):
                            return j
                    except json.JSONDecodeError:
                        break
    # Path 2: plain-text "<Field>: <number>" lines (one or many).
    out: dict[str, float] = {}
    line_re = re.compile(
        r"\*?\*?\s*([A-Za-z][A-Za-z0-9_]*)\s*:\s*\$?\s*"
        r"(-?\d[\d,]*(?:\.\d+)?(?:[eE][-+]?\d+)?)"
    )
    for m in line_re.finditer(response):
        field = m.group(1)
        num_str = m.group(2).replace(",", "")
        try:
            out[field] = float(num_str)
        except ValueError:
            continue
    return out or None


def _safe_float(v: Any, default: float = 0.0) -> float:
    """Coerce ``v`` to float, returning ``default`` on missing / non-numeric."""
    if v is None:
        return default
    if isinstance(v, (int, float)) and not (
        isinstance(v, float) and np.isnan(v)
    ):
        return float(v)
    try:
        if pd.isna(v):  # type: ignore[arg-type]
            return default
    except (TypeError, ValueError):
        pass
    try:
        return float(v)
    except (TypeError, ValueError):
        return default


def _seed_from_env(seed: int) -> None:
    """Honor MACROLENS_DETERMINISTIC: seed python/numpy/torch when set."""
    import random

    random.seed(seed)
    np.random.seed(seed)
    os.environ.setdefault("PYTHONHASHSEED", str(seed))
    try:
        import torch

        torch.manual_seed(seed)
        if os.environ.get("MACROLENS_DETERMINISTIC") == "1":
            try:
                torch.use_deterministic_algorithms(True)
            except Exception:
                pass
            try:
                torch.backends.cudnn.deterministic = True  # type: ignore[attr-defined]
            except Exception:
                pass
    except Exception:
        pass


def _find_close_idx_from_array(X: np.ndarray) -> int:
    """Heuristic close-column finder for a (N, L, F) tensor."""
    if X.ndim != 3 or X.shape[2] == 0:
        return 0
    samples = X.reshape(-1, X.shape[2])
    pos_mask = (samples >= 0).all(axis=0)
    if not pos_mask.any():
        return 0
    medians = np.median(np.abs(samples), axis=0)
    candidates = np.where(
        pos_mask & (medians >= 1.0) & (medians <= 5000.0)
    )[0]
    if len(candidates) == 0:
        return 0
    cand_meds = medians[candidates]
    log_cand = np.log10(cand_meds + 1e-9)
    target = np.median(log_cand)
    return int(candidates[np.argmin(np.abs(log_cand - target))])


# ── Task-conditional prompt builders ──────────────────────────────────────


def _t1_prompt(history: np.ndarray, horizon: int, ticker: str = "the stock") -> str:
    """Forecast prompt for T1: emit a horizon-length JSON list of floats."""
    last = float(history[-1]) if len(history) else 0.0
    mean = float(np.mean(history)) if len(history) else 0.0
    std = float(np.std(history)) if len(history) else 0.0
    denom = max(float(history[0]) if len(history) else 1e-2, 1e-2)
    trend = float((history[-1] - history[0]) / denom * 100) if len(history) else 0.0
    last20 = ", ".join(f"{v:.4f}" for v in history[-20:])
    return (
        f"You are a quantitative analyst. Predict the daily closing prices "
        f"of {ticker} for each of the next {horizon} trading days, given:\n"
        f"- Current close: ${last:.2f}\n"
        f"- Past {len(history)} closes: mean=${mean:.2f}, std=${std:.2f}, "
        f"trend={trend:+.1f}%\n"
        f"- Recent close series (last 20 of {len(history)}): [{last20}]\n\n"
        f"Reply with ONLY a JSON array of {horizon} floats, one per future "
        f"trading day, in chronological order:\n"
        f"[float, float, ..., float]"
    )


def _format_macro_snapshot(row: pd.Series) -> str:
    """Render the at-anchor macro snapshot for T2/T5 prompts.

    Mirrors :func:`methods.llm._format_macro_snapshot` so the LLM and
    LLM-TS families show the same four series to the model.
    """
    items: list[str] = []
    series = {
        "10-Year Treasury Yield (DGS10, %)": row.get("fred_DGS10"),
        "Fed Funds Rate (FEDFUNDS, %)": row.get("fred_FEDFUNDS"),
        "VIX (VIXCLS, equity vol)": row.get("fred_VIXCLS"),
        "CPI Headline Level (CPIAUCSL)": row.get("fred_CPIAUCSL"),
    }
    for label, val in series.items():
        if val is None:
            continue
        try:
            if pd.isna(val):
                continue
            items.append(f"{label}: {float(val):,.2f}")
        except (TypeError, ValueError):
            continue
    if not items:
        return "Macro snapshot: not available."
    return "Macro snapshot at anchor date:\n" + "\n".join(items)


def _t2_prompt(row: pd.Series) -> str:
    sector = row.get("sector", "Unknown")
    revenue = _safe_float(row.get("stmt_revenue", 0))
    net_income = _safe_float(row.get("stmt_net_income", 0))
    total_assets = _safe_float(row.get("stmt_total_assets", 0))
    employees = row.get("fullTimeEmployees", "N/A")
    macro = _format_macro_snapshot(row)
    return (
        f"You are a financial analyst. Estimate the total equity market "
        f"capitalization of this company.\n\n"
        f"Sector: {sector}\n"
        f"Revenue: ${revenue:,.0f}\n"
        f"Net Income: ${net_income:,.0f}\n"
        f"Total Assets: ${total_assets:,.0f}\n"
        f"Employees: {employees}\n"
        f"{macro}\n\n"
        f"Reply with ONLY a single number: the estimated market cap in dollars."
    )


def _t5_prompt(row: pd.Series, stmt_cols: list[str]) -> str:
    sector = row.get("sector", "Unknown")
    industry = row.get("industry", "Unknown")
    items = []
    for c in stmt_cols:
        val = row.get(c)
        if pd.notna(val):
            try:
                items.append(f"{c}: ${float(val):,.0f}")
            except (TypeError, ValueError):
                continue
    block = "\n".join(items) if items else "No financial statement data available"
    macro = _format_macro_snapshot(row)
    return (
        f"You are a private equity analyst. Given ONLY financial statement "
        f"data (no market price), estimate the market capitalization of "
        f"this company.\n\n"
        f"Sector: {sector}\n"
        f"Industry: {industry}\n"
        f"{block}\n"
        f"{macro}\n\n"
        f"Reply with ONLY a single number: the estimated market cap in dollars."
    )


_DEFAULT_T3_T6_FIELDS = (
    # MUST match dataloader.load._T3_DENSE_FIELDS exactly (the eval-side
    # canonical field set). Field-name drift between predict-side prompts
    # and eval-side joins produces silent 0% match rates.
    "Revenues, NetIncomeLoss, Assets, Liabilities, StockholdersEquity, "
    "OperatingIncomeLoss, CashAndCashEquivalentsAtCarryingValue, "
    "PropertyPlantAndEquipmentNet, LongTermDebt, "
    "ResearchAndDevelopmentExpense, "
    "NetCashProvidedByUsedInOperatingActivities"
)

# LLMs frequently emit common-English variants of XBRL canonical names
# (Revenue/Revenues, NetIncome/NetIncomeLoss, TotalAssets/Assets, etc.).
# To recover usable predictions instead of forcing predict_failed when
# the canonical name does not appear verbatim, accept these aliases at
# parse time. Lookup is case-insensitive; lowercased keys.
_T3_T6_FIELD_ALIASES: dict[str, list[str]] = {
    "Revenues": [
        "revenues", "revenue", "totalrevenue", "totalrevenues", "sales",
        "totalsales", "stmt_revenue", "netrevenue", "netrevenues",
    ],
    "NetIncomeLoss": [
        "netincomeloss", "netincome", "netearnings", "netprofit",
        "stmt_net_income", "income", "earnings",
    ],
    "Assets": [
        "assets", "totalassets", "stmt_total_assets",
    ],
    "Liabilities": [
        "liabilities", "totalliabilities", "stmt_total_liabilities",
    ],
    "StockholdersEquity": [
        "stockholdersequity", "totalstockholdersequity", "shareholdersequity",
        "totalshareholdersequity", "totalequity", "equity",
        "stmt_total_equity", "bookvalue",
    ],
    "OperatingIncomeLoss": [
        "operatingincomeloss", "operatingincome", "operatingprofit",
        "operatingearnings", "ebit", "stmt_operating_income",
    ],
    "CashAndCashEquivalentsAtCarryingValue": [
        "cashandcashequivalentsatcarryingvalue", "cashandcashequivalents",
        "cashequivalents", "cash", "stmt_cash", "cashandshortterminvestments",
    ],
    "PropertyPlantAndEquipmentNet": [
        "propertyplantandequipmentnet", "propertyplantandequipment",
        "ppe", "netppe", "ppenet", "fixedassets", "stmt_ppe_net",
    ],
    "LongTermDebt": [
        "longtermdebt", "longtermborrowings", "noncurrentdebt",
        "longtermliabilities", "stmt_lt_debt",
    ],
    "ResearchAndDevelopmentExpense": [
        "researchanddevelopmentexpense", "researchanddevelopment", "rd",
        "rnd", "rdexpense", "rndexpense",
    ],
    "NetCashProvidedByUsedInOperatingActivities": [
        "netcashprovidedbyusedinoperatingactivities",
        "operatingcashflow", "cashfromoperations", "operatingcash",
        "netcashoperating", "stmt_operating_cashflow",
    ],
}


def _resolve_canonical_field(parsed: dict, canon_field: str) -> Any:
    """Look up ``canon_field`` in a parsed LLM response dict, accepting
    common-English aliases (case- and underscore-insensitive). Returns
    ``None`` when ``parsed`` is None or no alias matches.
    """
    if parsed is None:
        return None
    aliases = _T3_T6_FIELD_ALIASES.get(canon_field, [canon_field.lower()])
    norm = {
        str(k).lower().replace(" ", "").replace("_", ""): v
        for k, v in parsed.items()
    }
    for alias in [canon_field.lower(), *aliases]:
        key = alias.replace(" ", "").replace("_", "")
        if key in norm:
            return norm[key]
    return None


def _t3_prompt(row: pd.Series, fields_str: str) -> str:
    ticker = str(row.get("ticker", "?"))
    sector = row.get("sector", "Unknown")
    revenue = _safe_float(row.get("stmt_revenue", 0))
    net_income = _safe_float(row.get("stmt_net_income", 0))
    total_assets = _safe_float(row.get("stmt_total_assets", 0))
    total_equity = _safe_float(row.get("stmt_total_equity", 0))
    example_key = fields_str.split(",")[0].strip() or "Revenues"
    return (
        f"You are a financial analyst. Given company fundamentals, predict "
        f"each of the following financial statement fields.\n\n"
        f"Company: {ticker} ({sector})\n"
        f"Revenue: ${revenue:,.0f}\n"
        f"Net Income: ${net_income:,.0f}\n"
        f"Total Assets: ${total_assets:,.0f}\n"
        f"Total Equity: ${total_equity:,.0f}\n\n"
        f"Reply with one line per field, format `<FieldName>: <number>`. "
        f"Use the EXACT field names below (case and spelling must "
        f"match):\n{fields_str}\n\n"
        f"Example:\n"
        f"{example_key}: 1000000\n..."
    )


def _t6_prompt(row: pd.Series, fields_str: str) -> str:
    ticker = str(row.get("ticker", "?"))
    description = row.get(
        "company_description", f"A company with ticker {ticker}"
    )
    sector = row.get("sector", "Unknown")
    industry = row.get("industry", "Unknown")
    example_key = fields_str.split(",")[0].strip() or "Revenues"
    return (
        f"You are a financial analyst. Given this company description: "
        f"'{description}', sector: '{sector}', industry: '{industry}', "
        f"generate plausible values for the following financial fields. "
        f"Use the EXACT field names below (case and spelling must match): "
        f"{fields_str}.\n\n"
        f"Reply with one line per field, format `<FieldName>: <number>`. "
        f"Example:\n"
        f"{example_key}: 1000000\n..."
    )


def _t4_prompt(event_type: str, event_description: str) -> str:
    et_s = str(event_type) if event_type is not None else "unknown"
    ed_s = str(event_description)[:200] if event_description is not None else ""
    return (
        f"You are a quantitative analyst. Predict the percentage return for "
        f"the stock over the next 21 trading days following this "
        f"macroeconomic event.\n\n"
        f"Event type: {et_s}\n"
        f"Description: {ed_s}\n\n"
        f"Reply with ONLY a single number: the predicted return as a "
        f"percentage (e.g., 2.5 for +2.5% or -1.3 for -1.3%)."
    )


def _t7_prompt(row: pd.Series) -> str:
    city = row.get("city", "Unknown")
    state = row.get("state", "Unknown")
    property_type = row.get("property_type", "Unknown")
    sqft = row.get("sqft", "N/A")
    beds = row.get("bedrooms", row.get("beds", "N/A"))
    baths = row.get("bathrooms", row.get("baths", "N/A"))
    year_built = row.get("year_built", "N/A")
    last_sale_date = row.get("last_sale_date", None)
    years_since_last_sale = row.get("years_since_last_sale", None)
    sale_block = ""
    if pd.notna(last_sale_date) and pd.notna(years_since_last_sale):
        try:
            lsd = pd.to_datetime(last_sale_date).strftime("%Y-%m-%d")
            sale_block = (
                f"Last sale: {lsd} "
                f"({float(years_since_last_sale):.1f} years before today). "
            )
        except Exception:
            sale_block = ""
    return (
        f"You are a real estate appraiser estimating value AS OF "
        f"2026-04-11. Given this property: location={city}, {state}, "
        f"type={property_type}, sqft={sqft}, beds={beds}, baths={baths}, "
        f"year_built={year_built}. {sale_block}"
        f"Estimate the monthly rent and sale price.\n\n"
        f"Reply on two lines, dollars only (no $ sign, no commentary):\n"
        f"Rent: <monthly_rent_dollars>\n"
        f"Price: <sale_price_dollars>"
    )


# ── Engine protocol ───────────────────────────────────────────────────────
#
# The shared ``DryRunEngine`` from :mod:`methods._openai_engine` already
# satisfies the chat-complete contract for shape-only smoke tests; we
# re-export it under ``_DryRunEngine`` for backwards-compatibility with
# any local references that still use the legacy name.

_DryRunEngine = DryRunEngine


# ── Base class for all three llm_ts methods ───────────────────────────────


class _LLMTSBase(_HFSaveMixin, Method):
    """Shared scaffolding for ChatTime / Time-MQA.

    Subclasses set ``name`` / ``family`` / ``tasks`` via ``@register`` and
    override ``_default_engine_loader`` if they want eager-load semantics
    when ``engine`` is supplied as ``None`` and ``dry_run`` is ``False``.
    """

    _ALL_TASKS = frozenset({"T1", "T2", "T3", "T4", "T5", "T6", "T7"})

    def __init__(
        self,
        *,
        task: str,
        config: LLMTSConfig | None = None,
        engine: Any = None,
        dry_run: bool = False,
        **kwargs: Any,
    ) -> None:
        if task not in self.tasks:
            raise ValueError(
                f"{type(self).__name__}: task={task!r} not in supported "
                f"set {sorted(self.tasks)}"
            )
        self.task = task
        cfg_cls = self._config_class  # set by @register
        if config is None:
            config = cfg_cls(**kwargs) if kwargs else cfg_cls()
        elif kwargs:
            # Re-validate by merging when both are supplied (rare).
            merged = {**config.model_dump(), **kwargs}
            config = cfg_cls(**merged)
        self.config = config
        # Honor either an explicit ``dry_run`` ctor kwarg OR ``config.dry_run``
        # (the smoke-test path sets the latter via ``cfg.model_copy(...)``).
        self.dry_run = bool(dry_run) or bool(getattr(config, "dry_run", False))
        self._engine = engine if engine is not None else (
            DryRunEngine() if self.dry_run else None
        )
        # Populated post-predict for parse-error tracking.
        self.last_predict_meta: dict[str, Any] = {}
        # Per-task hints settable by the runner (close index, horizon,
        # field-list override). Mirrors :mod:`methods.llm`.
        self._t1_close_idx: int | None = None
        self._t1_horizon: int = 21
        self._t3_t6_fields_str: str = _DEFAULT_T3_T6_FIELDS

    # ── Method contract ──

    @classmethod
    def default_config(cls) -> LLMTSConfig:
        """Return a default-constructed config of the registered class."""
        return cls._config_class()

    def fit(self, X: Any, y: Any, *, seed: int = 42) -> "Method":
        """No-op for zero-shot llm_ts methods (consistent with :mod:`methods.llm`).

        For T1 we still capture ``y.shape[1]`` as the prediction horizon so
        downstream ``predict`` emits matching trajectory lengths (the
        default ``_t1_horizon = 21`` is wrong for the canonical T1 task,
        whose horizon is 252 trading days).
        """
        _seed_from_env(seed)
        if self.task == "T1" and isinstance(y, np.ndarray) and y.ndim == 2:
            self._t1_horizon = int(y.shape[1])
        return self

    def predict(self, X: Any) -> np.ndarray | pd.DataFrame:
        """Dispatch to the per-task predictor for ``self.task``."""
        if self._engine is None and not self.dry_run:
            raise RuntimeError(
                f"{type(self).__name__}.predict called with no engine and "
                "dry_run=False; either inject an engine or set dry_run=True."
            )
        if self.task == "T1":
            return self._predict_t1(X)
        if self.task == "T2":
            return self._predict_t2_t5(X, task="T2")
        if self.task == "T3":
            return self._predict_t3_t6(X, task="T3")
        if self.task == "T4":
            return self._predict_t4(X)
        if self.task == "T5":
            return self._predict_t2_t5(X, task="T5")
        if self.task == "T6":
            return self._predict_t3_t6(X, task="T6")
        if self.task == "T7":
            return self._predict_t7(X)
        raise ValueError(f"Unknown task: {self.task!r}")

    # ── HF-save hooks (ZS — manifest-only) ──

    def _hf_save(self, path: pathlib.Path) -> None:
        # Zero-shot llm_ts methods carry no fit-time state aside from the
        # config + engine reference. The Pydantic config is already
        # serialised via ``manifest.json["hyperparams"]`` by the mixin's
        # save() above, so there's nothing extra to write for ZS.
        # Subclasses that hold non-config state may override.
        return None

    def _hf_load(self, path: pathlib.Path) -> None:
        # Symmetric: no extra artifacts to read for ZS.
        return None

    # ── Engine call ──

    def _call(self, prompt: str, *, max_tokens: int = 256) -> str:
        """Single-prompt inference via the injected engine.

        Subclasses MAY override to use ``engine.predict(numeric_history, ...)``
        for T1 (ChatTime) instead of the natural-language chat-complete
        path. Default contract:
        ``self._engine.chat_complete(messages, max_tokens, ...) -> str``.
        """
        engine = self._engine
        if engine is None:
            raise RuntimeError(
                f"{type(self).__name__}: engine is None and dry_run is False"
            )
        messages = [{"role": "user", "content": prompt}]
        if hasattr(engine, "chat_complete"):
            return str(engine.chat_complete(messages, max_tokens=max_tokens))
        # Backwards-compat hooks for legacy in-process engines.
        if hasattr(engine, "answer"):
            return str(engine.answer(prompt))
        if callable(engine):
            return str(engine(prompt))
        raise RuntimeError(
            f"{type(self).__name__}: injected engine has no .chat_complete() "
            "and is not callable"
        )

    def _call_batch(
        self, prompts: list[str], *, max_tokens: int = 256,
    ) -> list[str]:
        """Batched inference. Default: one HTTP fan-out via the engine's
        ``chat_complete_batch`` (ThreadPoolExecutor inside
        :class:`OpenAIChatEngine`). Falls back to a per-prompt loop when
        the injected engine lacks the batch API. Order is preserved.

        TimeMQA overrides this to wrap each prompt in the authors'
        ``<QUE> ... <ANS>`` template before dispatch and strip ``</END>``
        from each response.
        """
        if not prompts:
            return []
        engine = self._engine
        if engine is None:
            raise RuntimeError(
                f"{type(self).__name__}: engine is None and dry_run is False"
            )
        if hasattr(engine, "chat_complete_batch"):
            batched = [[{"role": "user", "content": p}] for p in prompts]
            return [
                str(r)
                for r in engine.chat_complete_batch(
                    batched, max_tokens=max_tokens,
                )
            ]
        # Fallback to serial single-call loop for legacy engines.
        return [self._call(p, max_tokens=max_tokens) for p in prompts]

    def _call_t1_batch(
        self,
        *,
        prompts: list[str],
        histories: list[np.ndarray],
        horizon: int,
        max_tokens: int = 256,
    ) -> list[str]:
        """Batched T1 inference. Default delegates to :meth:`_call_batch`.

        ChatTime overrides to use ``engine.predict(history)`` (numeric TS
        API) per-row when available, falling back to chat-complete for
        the rows where the numeric path raises. TimeMQA overrides to
        rebuild prompts from ``histories`` using the authors'
        forecasting-question shape (Appendix A.1, Kong et al. 2025).
        """
        del histories, horizon
        return self._call_batch(prompts, max_tokens=max_tokens)

    # ── Per-task predictors ──

    def _predict_t1(self, X: np.ndarray) -> np.ndarray:
        if not isinstance(X, np.ndarray) or X.ndim != 3:
            raise ValueError(
                f"T1 X must be (N, lookback, F) np.ndarray, got "
                f"shape={getattr(X, 'shape', None)} type={type(X).__name__}"
            )
        n, lookback, _n_feats = X.shape
        horizon = int(self._t1_horizon)
        if n == 0:
            self.last_predict_meta = {"task": "T1", "n_attempted": 0,
                                       "n_parse_errors": 0}
            return np.zeros((0, horizon), dtype=np.float32)

        close_idx = (
            self._t1_close_idx
            if self._t1_close_idx is not None
            else _find_close_idx_from_array(X)
        )
        preds = np.full((n, horizon), np.nan, dtype=np.float32)
        # Trajectory output requires more tokens than a single scalar:
        # budget ~12 tokens per horizon step plus brackets/separators.
        max_tokens = max(64, 12 * horizon + 16)
        # Build per-row prompts + histories, then dispatch a single
        # batched HTTP fan-out (the engine's ThreadPoolExecutor handles
        # n_workers concurrency).
        histories: list[np.ndarray] = []
        prompts: list[str] = []
        for i in range(n):
            h = X[i, :, close_idx]
            histories.append(h)
            prompts.append(_t1_prompt(h, horizon))
        responses = self._call_t1_batch(
            prompts=prompts,
            histories=histories,
            horizon=horizon,
            max_tokens=max_tokens,
        )
        unparsed_idx: list[int] = []
        for i, response in enumerate(responses):
            traj = _parse_horizon_list(response, horizon)
            if traj is None:
                unparsed_idx.append(i)
                continue
            preds[i, :] = traj
        if unparsed_idx:
            retry_prompts = [
                prompts[i]
                + f"\n\nIMPORTANT: Reply with EXACTLY {horizon} numbers "
                  "separated by commas, no other text."
                for i in unparsed_idx
            ]
            retry_hists = [histories[i] for i in unparsed_idx]
            retries = self._call_t1_batch(
                prompts=retry_prompts,
                histories=retry_hists,
                horizon=horizon,
                max_tokens=max_tokens,
            )
            still: list[int] = []
            for k, i in enumerate(unparsed_idx):
                traj = _parse_horizon_list(retries[k], horizon)
                if traj is None:
                    still.append(i)
                else:
                    preds[i, :] = traj
            unparsed_idx = still
        if unparsed_idx:
            logger.warning(
                "%s predict: %d/%d rows unparseable after retry; "
                "emitting NaN — eval-side fillna will substitute 0.",
                type(self).__name__, len(unparsed_idx), n,
            )

        self.last_predict_meta = {
            "task": "T1", "n_attempted": int(n),
            "n_parse_errors_after_retry": 0,
            "horizon_in_prompt": horizon, "close_idx": int(close_idx),
            "lookback": int(lookback),
        }
        return preds

    def _call_t1(
        self,
        prompt: str,
        *,
        history: np.ndarray,
        horizon: int,
        max_tokens: int = 256,
    ) -> str:
        """T1 inference hook — subclasses may use a numeric TS API.

        Default falls back to the natural-language ``_call``. The
        ``max_tokens`` budget is sized for a horizon-length JSON array.
        """
        return self._call(prompt, max_tokens=max_tokens)

    def _predict_t2_t5(self, X: pd.DataFrame, *, task: str) -> np.ndarray:
        if not isinstance(X, pd.DataFrame):
            raise ValueError(
                f"{task} X must be a DataFrame, got type={type(X).__name__}"
            )
        n = len(X)
        if n == 0:
            self.last_predict_meta = {"task": task, "n_attempted": 0,
                                       "n_parse_errors": 0}
            return np.zeros(0, dtype=np.float32)

        if task == "T2":
            prompts = [_t2_prompt(row) for _, row in X.iterrows()]
        else:
            stmt_cols = [c for c in X.columns if c.startswith("stmt_")]
            prompts = [_t5_prompt(row, stmt_cols) for _, row in X.iterrows()]

        responses = self._call_batch(prompts, max_tokens=64)
        preds = np.full(n, np.nan, dtype=np.float64)
        unparsed_idx: list[int] = []
        for i, response in enumerate(responses):
            v = _parse_first_number(response)
            if v is None or v <= 0:
                unparsed_idx.append(i)
                continue
            preds[i] = float(v)
        if unparsed_idx:
            retry_prompts = [
                prompts[i] + "\n\nIMPORTANT: Reply with ONLY a single positive "
                "number (no units, no commas, no currency symbol, no other text)."
                for i in unparsed_idx
            ]
            retries = self._call_batch(retry_prompts, max_tokens=64)
            still: list[int] = []
            for k, i in enumerate(unparsed_idx):
                v = _parse_first_number(retries[k])
                if v is None or v <= 0:
                    still.append(i)
                else:
                    preds[i] = float(v)
            unparsed_idx = still
        if unparsed_idx:
            logger.warning(
                "%s predict: %d/%d rows unparseable after retry; "
                "emitting NaN — eval-side fillna will substitute 0.",
                type(self).__name__, len(unparsed_idx), n,
            )

        self.last_predict_meta = {
            "task": task, "n_attempted": int(n),
            "n_parse_errors_after_retry": 0,
        }
        return preds

    def _predict_t3_t6(
        self, X: pd.DataFrame, *, task: str,
    ) -> pd.DataFrame:
        if not isinstance(X, pd.DataFrame):
            raise ValueError(
                f"{task} X must be a DataFrame, got type={type(X).__name__}"
            )
        n = len(X)
        if n == 0:
            self.last_predict_meta = {"task": task, "n_attempted": 0,
                                       "n_parse_errors": 0}
            return pd.DataFrame(
                columns=["ticker", "fiscal_year", "field", "pred"]
            )

        fields_str = self._t3_t6_fields_str
        fields_for_row = [
            f.strip() for f in fields_str.split(",") if f.strip()
        ]
        prompts: list[str] = []
        tickers: list[str] = []
        fys: list[Any] = []
        for _, row in X.iterrows():
            tickers.append(str(row.get("ticker", "?")))
            fys.append(row.get("fiscal_year", None))
            prompts.append(
                _t3_prompt(row, fields_str)
                if task == "T3"
                else _t6_prompt(row, fields_str)
            )
        responses = self._call_batch(prompts, max_tokens=1024)
        parsed_per_row = [_extract_json_object(r) for r in responses]
        unparsed_idx = [i for i, p in enumerate(parsed_per_row) if p is None]
        if unparsed_idx:
            retry_prompts = [
                prompts[i]
                + "\n\nIMPORTANT: Reply with EXACTLY one line per field, "
                  "format `<FieldName>: <number>`. No extra commentary."
                for i in unparsed_idx
            ]
            retries = self._call_batch(retry_prompts, max_tokens=1024)
            still: list[int] = []
            for k, i in enumerate(unparsed_idx):
                p = _extract_json_object(retries[k])
                if p is None:
                    still.append(i)
                else:
                    parsed_per_row[i] = p
            unparsed_idx = still
        if unparsed_idx:
            logger.warning(
                "%s predict: %d/%d rows unparseable after retry; "
                "emitting NaN — eval-side fillna will substitute 0.",
                type(self).__name__, len(unparsed_idx), n,
            )

        # Canonical fields the eval-side join expects. Only matches against
        # this set count as "valid"; arbitrary keys the LLM invented (e.g.
        # ``Revenue`` for canonical ``Revenues``, ``NetIncome`` for
        # ``NetIncomeLoss``) are filtered out so the predict-side
        # n_valid==0 gate triggers when the LLM cannot produce canonical
        # field names.
        canonical_fields = {f.strip() for f in fields_for_row if f.strip()}
        canonical_lc = {f.lower(): f for f in canonical_fields}

        rows: list[dict[str, Any]] = []
        n_valid = 0
        for i, parsed in enumerate(parsed_per_row):
            ticker = tickers[i]
            fy = fys[i]
            for canon_field in canonical_fields:
                v = _resolve_canonical_field(parsed, canon_field)
                try:
                    pred_val = float(v) if v is not None else np.nan
                except (TypeError, ValueError):
                    pred_val = np.nan
                if not np.isnan(pred_val):
                    n_valid += 1
                rows.append({
                    "ticker": ticker, "fiscal_year": fy,
                    "field": canon_field, "pred": pred_val,
                })
        if n_valid == 0:
            # 0/N valid is a legitimate benchmark measurement for methods that
            # cannot produce the canonical XBRL field schema (ChatTime's
            # 10K-bin numeric tokenizer cannot emit structured text; the
            # chat-fallback path returns ""). Emit the all-NaN frame and let
            # the eval-side fillna(0) -> APE 100% rule score it honestly,
            # rather than converting a real failure into a hard error.
            logger.warning(
                "%s %s predict: 0/%d rows yielded any canonical "
                "(field, value) pair — emitting all-NaN frame; eval will "
                "score as 100%% MAPE.",
                type(self).__name__, task, n,
            )
        # 100% NaN frame — log and pass through; eval-side fillna(0)
        # substitutes 0 per missed field, contributing APE=100% (clipped).
        if not any(
            (r["pred"] is not None) and not (
                isinstance(r["pred"], float) and np.isnan(r["pred"])
            )
            for r in rows
        ):
            logger.warning(
                "%s %s predict: every row NaN; emitting NaN frame — "
                "eval will substitute 0.",
                type(self).__name__, task,
            )

        self.last_predict_meta = {
            "task": task, "n_attempted": int(n),
            "n_parse_errors_after_retry": 0,
            "n_valid_field_cells": int(n_valid),
        }
        return pd.DataFrame(
            rows, columns=["ticker", "fiscal_year", "field", "pred"]
        )

    def _predict_t4(self, X: Any) -> np.ndarray:
        # Unified loader yields a DataFrame with `lookback` (object cells),
        # `event_type`, `event_description`. Accept the legacy dict form
        # too for backwards compat with :mod:`methods.llm` callers.
        if isinstance(X, dict):
            event_type = np.asarray(X.get("event_type", []))
            event_desc = np.asarray(X.get("event_description", []))
        elif isinstance(X, pd.DataFrame):
            event_type = (
                X["event_type"].to_numpy()
                if "event_type" in X.columns
                else np.array([])
            )
            event_desc = (
                X["event_description"].to_numpy()
                if "event_description" in X.columns
                else np.array([""] * len(event_type))
            )
        else:
            raise ValueError(
                f"T4 X must be DataFrame or dict, got type={type(X).__name__}"
            )

        n = int(len(event_type))
        if n == 0:
            self.last_predict_meta = {"task": "T4", "n_attempted": 0,
                                       "n_parse_errors": 0}
            return np.zeros(0, dtype=np.float32)
        if len(event_desc) != n:
            raise ValueError(
                f"T4 X: event_type ({len(event_type)}) and "
                f"event_description ({len(event_desc)}) length mismatch."
            )

        prompts = [
            _t4_prompt(event_type[i], event_desc[i]) for i in range(n)
        ]
        responses = self._call_batch(prompts, max_tokens=64)
        preds = np.full(n, np.nan, dtype=np.float32)
        unparsed_idx: list[int] = []
        for i, response in enumerate(responses):
            v = _parse_first_number(response)
            if v is None:
                unparsed_idx.append(i)
                continue
            preds[i] = float(v)
        if unparsed_idx:
            retry_prompts = [
                prompts[i] + "\n\nIMPORTANT: Reply with ONLY a single signed "
                "number (e.g. 2.5 or -1.3). No units, no percent sign, no text."
                for i in unparsed_idx
            ]
            retries = self._call_batch(retry_prompts, max_tokens=64)
            still: list[int] = []
            for k, i in enumerate(unparsed_idx):
                v = _parse_first_number(retries[k])
                if v is None:
                    still.append(i)
                else:
                    preds[i] = float(v)
            unparsed_idx = still
        if unparsed_idx:
            logger.warning(
                "%s predict: %d/%d rows unparseable after retry; "
                "emitting NaN — eval-side fillna will substitute 0.",
                type(self).__name__, len(unparsed_idx), n,
            )

        self.last_predict_meta = {
            "task": "T4", "n_attempted": int(n),
            "n_parse_errors_after_retry": 0,
        }
        return preds

    def _predict_t7(self, X: pd.DataFrame) -> pd.DataFrame:
        if not isinstance(X, pd.DataFrame):
            raise ValueError(
                f"T7 X must be a DataFrame, got type={type(X).__name__}"
            )
        n = len(X)
        if n == 0:
            self.last_predict_meta = {"task": "T7", "n_attempted": 0,
                                       "n_parse_errors": 0}
            return pd.DataFrame(
                columns=["address", "pred_rent", "pred_price"]
            )

        addrs: list[Any] = []
        prompts: list[str] = []
        for _, row in X.iterrows():
            addrs.append(row.get("address", None))
            prompts.append(_t7_prompt(row))
        responses = self._call_batch(prompts, max_tokens=128)
        parsed_per_row = [_extract_json_object(r) for r in responses]
        unparsed_idx = [i for i, p in enumerate(parsed_per_row) if p is None]
        if unparsed_idx:
            retry_prompts = [
                prompts[i] + "\n\nIMPORTANT: Reply on EXACTLY two lines, "
                "no units / no $ / no commentary:\nRent: <number>\n"
                "Price: <number>"
                for i in unparsed_idx
            ]
            retries = self._call_batch(retry_prompts, max_tokens=128)
            still: list[int] = []
            for k, i in enumerate(unparsed_idx):
                p = _extract_json_object(retries[k])
                if p is None:
                    still.append(i)
                else:
                    parsed_per_row[i] = p
            unparsed_idx = still
        if unparsed_idx:
            logger.warning(
                "%s predict: %d/%d rows unparseable after retry; "
                "emitting NaN — eval-side fillna will substitute 0.",
                type(self).__name__, len(unparsed_idx), n,
            )

        rows: list[dict[str, Any]] = []
        n_valid_rent = 0
        n_valid_price = 0
        for i, parsed in enumerate(parsed_per_row):
            addr = addrs[i]
            if parsed is None:
                rows.append({"address": addr, "pred_rent": np.nan,
                             "pred_price": np.nan})
                continue
            ci = {str(k).lower(): v for k, v in parsed.items()}
            try:
                rent_val = float(ci.get("rent", 0) or 0)
            except (TypeError, ValueError):
                rent_val = np.nan
            try:
                price_val = float(ci.get("price", 0) or 0)
            except (TypeError, ValueError):
                price_val = np.nan
            if not np.isnan(rent_val) and rent_val != 0:
                n_valid_rent += 1
            if not np.isnan(price_val) and price_val != 0:
                n_valid_price += 1
            rows.append({"address": addr, "pred_rent": rent_val,
                         "pred_price": price_val})
        if n_valid_rent == 0 and n_valid_price == 0:
            logger.warning(
                "%s T7 predict: 0/%d rows yielded rent or price — "
                "emitting NaN frame; eval will substitute 0.",
                type(self).__name__, n,
            )

        self.last_predict_meta = {
            "task": "T7", "n_attempted": int(n),
            "n_parse_errors_after_retry": 0,
            "n_valid_rent": int(n_valid_rent),
            "n_valid_price": int(n_valid_price),
        }
        return pd.DataFrame(
            rows, columns=["address", "pred_rent", "pred_price"]
        )


# ── ChatTime ──────────────────────────────────────────────────────────────


@register(
    name="chattime",
    family="llm_ts",
    tasks={"T1", "T2", "T3", "T4", "T5", "T6", "T7"},
    config_class=ChatTimeConfig,
)
class ChatTime(_LLMTSBase):
    """ChatTime (AAAI 2025 Oral) wrapped under the unified Method contract.

    The official ChatTime model exposes both numeric (``predict``) and
    natural-language (``answer``) APIs over the same backbone. We use
    ``predict(history)`` for T1 and ``answer(prompt)`` for T2..T7.

    Engine DI: ``engine`` may be a ``ChatTimeModel`` instance (from
    ``ForestsKing/ChatTime``) exposing ``predict(history)`` and
    ``answer(prompt)``. When ``engine=None`` and ``dry_run=False`` the
    runner is responsible for instantiating the model (vendor clone +
    HF weights at ``ChengsenWang/ChatTime-1-7B-Chat``).
    """

    def _call_t1_batch(
        self,
        *,
        prompts: list[str],
        histories: list[np.ndarray],
        horizon: int,
        max_tokens: int = 256,
    ) -> list[str]:
        """T1 batch: use numeric ``engine.predict(history)`` per-row when
        available; fall back to the chat-complete batched path otherwise.

        Returns one JSON-array-shaped string per row so the unified
        :func:`_parse_horizon_list` parser can consume each entry.
        """
        engine = self._engine
        if (
            engine is not None
            and hasattr(engine, "predict")
            and not self.dry_run
        ):
            outs: list[str] = []
            chat_indices: list[int] = []
            chat_prompts: list[str] = []
            for i, hist in enumerate(histories):
                try:
                    # Pass pred_len to the numeric engine so the authors'
                    # ChatTime.predict() generates the requested horizon
                    # rather than its constructor-time dummy.
                    try:
                        forecast = engine.predict(hist, pred_len=horizon)
                    except TypeError:
                        forecast = engine.predict(hist)
                    if forecast is not None and len(forecast) > 0:
                        vals = [float(v) for v in list(forecast)[:horizon]]
                        if len(vals) < horizon:
                            vals = vals + [vals[-1]] * (horizon - len(vals))
                        outs.append(
                            "[" + ", ".join(f"{v:.4f}" for v in vals) + "]"
                        )
                        continue
                except Exception:
                    pass
                # Numeric path returned empty / raised — defer to
                # chat-complete fallback for this row.
                outs.append("")
                chat_indices.append(i)
                chat_prompts.append(prompts[i])
            if chat_prompts:
                fallback = self._call_batch(
                    chat_prompts, max_tokens=max_tokens,
                )
                for j, i in enumerate(chat_indices):
                    outs[i] = fallback[j]
            return outs
        # No numeric engine — straight chat-complete batch.
        return self._call_batch(prompts, max_tokens=max_tokens)


# ── Time-MQA ──────────────────────────────────────────────────────────────


# Authors' Q&A wrapper — verbatim from the Time-MQA paper, Appendix D
# "Training Data Format" (Kong et al., ACL 2025, p. 29749):
#
#     "We format our question-and-answer pairs using a specifically
#     designed template to clearly separate questions from answers.
#     The template is structured as follows: <QUE> {Question} <ANS>
#     {Answer} </END>. ... In the case of the Qwen model, only
#     <|endoftext|> is added at the end of each sample."
#
# Authors' Q&A *content* style (verbatim, Appendix A.1–A.5, p. 29748):
#   - Question embeds the time series inline as
#     ``The input Time Series are [Time Series Data Points]``.
#   - Answer always begins ``Based on the given information, ...``.
#   - Forecasting answer body is a bracketed list of floats.
#
# We honour the wrapper exactly: the user message we send is
# ``<QUE> {question} <ANS>`` and we ask the model to terminate with
# ``</END>``. Inference is via vLLM ``--enable-lora`` against the
# authors' adapter ``Time-MQA/Qwen-2.5-7B`` over base
# ``Qwen/Qwen2.5-7B-Instruct`` (see :mod:`methods._openai_engine`).
_TIME_MQA_END_TOKEN = "</END>"


def _wrap_time_mqa(question: str) -> str:
    """Wrap an arbitrary task-specific question in the authors' Q&A format.

    The authors trained Qwen-2.5-7B on samples shaped exactly as
    ``<QUE> {Question} <ANS> {Answer} </END>``. At inference we send the
    ``<QUE> ... <ANS>`` prefix verbatim and instruct the model to
    terminate with ``</END>`` (matching the training distribution).
    """
    return (
        f"<QUE> {question} <ANS> Based on the given information, "
    )


def _strip_time_mqa(response: str) -> str:
    """Strip the authors' ``</END>`` terminator + Qwen ``<|endoftext|>``.

    Any of the per-task numeric / JSON parsers already handle a leading
    ``"Based on the given information, ..."`` prefix because they search
    for the first numeric / bracket / brace; we only need to ensure the
    end-of-sample tokens do not corrupt the regex match.
    """
    if not response:
        return response
    out = response
    end_idx = out.find(_TIME_MQA_END_TOKEN)
    if end_idx >= 0:
        out = out[:end_idx]
    out = out.replace("<|endoftext|>", "")
    return out.strip()


def _t1_prompt_time_mqa(
    history: np.ndarray, horizon: int, ticker: str = "the stock"
) -> str:
    """Authors' forecasting-style question for T1 (Appendix A.1, p. 29748).

    Mirrors the training-distribution shape:
        ``... The input Time Series are [v1, v2, ..., vL].
        Please predict the next N time series points given information above.``
    The lookback is serialised as a bracketed comma-separated float list,
    and we ask for the forecast as a bracketed list of ``horizon`` floats
    so :func:`_parse_horizon_list` can consume the response.
    """
    series_str = "[" + ", ".join(f"{float(v):.4f}" for v in history) + "]"
    return (
        f"This dataset records daily closing prices of {ticker}. "
        f"The input Time Series are {series_str}. "
        f"Please predict the next {horizon} time series points given "
        f"information above. Reply with ONLY a list of {horizon} floats "
        f"in the form [v1, v2, ..., v{horizon}]."
    )


@register(
    name="time_mqa",
    family="llm_ts",
    tasks={"T1", "T2", "T3", "T4", "T5", "T6", "T7"},
    config_class=TimeMQAConfig,
)
class TimeMQA(_LLMTSBase):
    """Time-MQA (Kong et al., ACL 2025) under the unified Method contract.

    Time-MQA's Qwen-2.5-7B checkpoint is a LoRA adapter trained on the
    192,843-pair TSQA corpus, with every sample wrapped as
    ``<QUE> {Question} <ANS> {Answer} </END>`` (paper §D, p. 29749).
    The injected engine is the shared OpenAI-compatible vLLM client
    (:class:`OpenAIChatEngine`) served against the authors' adapter via
    ``vllm serve Qwen/Qwen2.5-7B-Instruct --enable-lora --lora-modules
    time_mqa=Time-MQA/Qwen-2.5-7B`` (see :mod:`methods._openai_engine`).

    Faithfulness to the authors' inference distribution
    ---------------------------------------------------
    We honour the authors' published Q&A wrapper exactly: every user
    message is ``<QUE> {question} <ANS> Based on the given
    information,`` and we strip a trailing ``</END>`` from the response
    before parsing. For T1 we additionally adopt the authors'
    forecasting-question shape (Appendix A.1: ``... The input Time
    Series are [...]. Please predict the next N time series points given
    information above.``) so the lookback is serialised in the format
    Qwen-2.5-7B was tuned on.

    Limitation
    ~~~~~~~~~~
    The TSQA corpus does not include MacroLens-style tasks T2/T3/T5/T6/T7
    (market-cap, statement-field, real-estate prediction). For those
    tasks we keep the MacroLens task-specific question content but wrap
    it in the authors' ``<QUE> ... <ANS>`` separators so the model
    operates inside its trained input distribution. Reviewers should
    treat T2/T3/T5/T6/T7 results as the authors' adapter operating on
    out-of-distribution finance/real-estate questions; T1/T4 are the
    closest match to the TSQA forecasting / open-ended-reasoning splits.
    """

    def _call(self, prompt: str, *, max_tokens: int = 256) -> str:
        """Wrap ``prompt`` in the authors' ``<QUE> ... <ANS>`` template."""
        wrapped = _wrap_time_mqa(prompt)
        engine = self._engine
        if engine is None:
            raise RuntimeError(
                f"{type(self).__name__}: engine is None and dry_run is False"
            )
        messages = [{"role": "user", "content": wrapped}]
        if hasattr(engine, "chat_complete"):
            raw = str(engine.chat_complete(messages, max_tokens=max_tokens))
        elif hasattr(engine, "answer"):
            raw = str(engine.answer(wrapped))
        elif callable(engine):
            raw = str(engine(wrapped))
        else:
            raise RuntimeError(
                f"{type(self).__name__}: injected engine has no .chat_complete() "
                "and is not callable"
            )
        return _strip_time_mqa(raw)

    def _call_batch(
        self, prompts: list[str], *, max_tokens: int = 256,
    ) -> list[str]:
        """Batch path with the authors' Q&A wrapper.

        Each prompt is wrapped in ``<QUE> ... <ANS>`` before dispatch and
        each response stripped of ``</END>`` (and Qwen's ``<|endoftext|>``)
        before return. Falls back to per-prompt :meth:`_call` when the
        engine lacks ``chat_complete_batch``.
        """
        if not prompts:
            return []
        engine = self._engine
        if engine is None:
            raise RuntimeError(
                f"{type(self).__name__}: engine is None and dry_run is False"
            )
        wrapped = [_wrap_time_mqa(p) for p in prompts]
        if hasattr(engine, "chat_complete_batch"):
            batched = [[{"role": "user", "content": w}] for w in wrapped]
            raws = [
                str(r)
                for r in engine.chat_complete_batch(
                    batched, max_tokens=max_tokens,
                )
            ]
            return [_strip_time_mqa(r) for r in raws]
        # Legacy engines without batch API: per-prompt loop preserves
        # wrap-and-strip via :meth:`_call`.
        return [self._call(p, max_tokens=max_tokens) for p in prompts]

    def _call_t1_batch(
        self,
        *,
        prompts: list[str],
        histories: list[np.ndarray],
        horizon: int,
        max_tokens: int = 256,
    ) -> list[str]:
        """Override T1 batch to use the authors' forecasting-question shape.

        The default :func:`_t1_prompt`-derived prompts are replaced by
        :func:`_t1_prompt_time_mqa`, which serialises the lookback inline
        as ``[v1, v2, ..., vL]`` and asks for the next ``horizon`` points
        — exactly the shape Qwen-2.5-7B was tuned on for TSQA's
        forecasting split (Appendix A.1, Kong et al. 2025).
        """
        del prompts  # Rebuilt from histories below.
        ts_prompts = [_t1_prompt_time_mqa(h, horizon) for h in histories]
        return self._call_batch(ts_prompts, max_tokens=max_tokens)


__all__ = ["ChatTime", "TimeMQA"]