from __future__ import annotations

from dataclasses import dataclass
from typing import Any, Iterable

import numpy as np
import pandas as pd
from gluonts.dataset.common import ListDataset
from gluonts.dataset.split import split
from gluonts.model import Predictor, evaluate_model
from gluonts.time_feature import get_seasonality

from tsfm_bench.data.interface import TimeSeriesDataSource
from tsfm_bench.data.open_meteo import OpenMeteoDataSource
from tsfm_bench.data.ts_bench import TsBenchDataSource
from tsfm_bench.eval.metrics import RESULT_COLUMNS, get_eval_metrics


@dataclass
class OnlineEvalResult:
    dataset: str
    model: str
    metrics: dict[str, float]
    domain: str
    num_variates: int
    data_fetched_at: str
    context_length: int
    prediction_length: int
    forecast_snapshot: dict | None = None


def _records_to_entries(
    record,
    to_univariate: bool,
) -> list[dict[str, Any]]:
    target = np.asarray(record.target, dtype=np.float32)
    start = pd.Period(record.start, freq=record.freq)
    entries: list[dict[str, Any]] = []

    if target.ndim == 1 or not to_univariate:
        entries.append(
            {
                "item_id": record.item_id,
                "start": start,
                "target": target if target.ndim == 1 else target,
                "freq": record.freq,
            }
        )
        return entries

    for idx in range(target.shape[0]):
        entries.append(
            {
                "item_id": f"{record.item_id}_v{idx}",
                "start": start,
                "target": target[idx],
                "freq": record.freq,
            }
        )
    return entries


def resolve_seasonality(freq: str, series_length: int, prediction_length: int) -> int:
    """Cap seasonality so MASE/MSIS scaling is defined on short live windows."""
    nominal = int(get_seasonality(freq))
    train_length = series_length - prediction_length
    # Need at least two seasonal periods in the training window.
    capped = min(nominal, max(1, train_length // 2))
    return max(1, capped)


def evaluate_online_window(
    predictor: Predictor,
    entries: list[dict[str, Any]],
    prediction_length: int,
    freq: str,
) -> dict[str, float]:
    dataset = ListDataset(entries, freq=freq)
    _, test_template = split(dataset, offset=-prediction_length)
    test_data = test_template.generate_instances(
        prediction_length=prediction_length,
        windows=1,
        distance=prediction_length,
    )
    series_length = int(np.asarray(entries[0]["target"]).shape[-1])
    seasonality = resolve_seasonality(freq, series_length, prediction_length)
    evaluated = evaluate_model(
        predictor,
        test_data=test_data,
        metrics=get_eval_metrics(),
        batch_size=64,
        axis=None,
        mask_invalid_label=True,
        allow_nan_forecast=True,
        seasonality=seasonality,
    )
    result = {key: float(value.iloc[0]) for key, value in evaluated.items()}
    return result


def _capture_forecast_snapshot(
    predictor: Predictor,
    entries: list[dict],
    pred_len: int,
    freq: str,
    evaluated_at: str,
    dataset: str,
    model: str,
) -> dict | None:
    """Call predictor once to capture p10/p50/p90 and actuals for visualization.

    Uses Option A: one extra API call per dataset, minimal code invasiveness.
    Returns None silently if anything fails (snapshot is best-effort).
    """
    try:
        ds = ListDataset(entries, freq=freq)
        _, test_template = split(ds, offset=-pred_len)
        test_data = list(test_template.generate_instances(
            prediction_length=pred_len, windows=1, distance=pred_len,
        ))
        if not test_data:
            return None

        input_entry, label_entry = test_data[0]
        context_vals = np.asarray(input_entry["target"], dtype=np.float64).flatten().tolist()
        actuals = np.asarray(label_entry["target"], dtype=np.float64).flatten().tolist()

        input_ds = ListDataset([input_entry], freq=freq)
        forecasts = list(predictor.predict(input_ds))
        if not forecasts:
            return None

        fc = forecasts[0]
        fallback = fc.mean.tolist()
        quantile_levels = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
        quantiles = {
            f"{level:g}": (
                fc.quantile(level).tolist() if hasattr(fc, "quantile") else fallback
            )
            for level in quantile_levels
        }
        p10 = quantiles["0.1"]
        p50 = quantiles["0.5"]
        p90 = quantiles["0.9"]

        n_context = len(context_vals)
        n_future = len(actuals)

        timestamps = []
        try:
            import pandas as pd
            start_period = input_entry["start"]
            dr = pd.period_range(start=start_period, periods=n_context + n_future, freq=freq)
            timestamps = [str(p.to_timestamp()) for p in dr]
        except Exception:
            pass

        return {
            "dataset": dataset,
            "model": model,
            "evaluated_at": evaluated_at,
            "freq": freq,
            "context_length": n_context,
            "prediction_length": n_future,
            "context": context_vals,
            "actuals": actuals,
            "p50": p50,
            "p10": p10,
            "p90": p90,
            "mean": fallback,
            "quantiles": quantiles,
            "timestamps": timestamps,
        }
    except Exception:
        return None


def run_online_eval_for_dataset(
    source: TimeSeriesDataSource,
    ds_name: str,
    predictor: Predictor,
    prediction_length: int | None = None,
) -> OnlineEvalResult:
    meta = source.get_metadata(ds_name)
    record = next(iter(source.stream(ds_name)))

    if isinstance(source, OpenMeteoDataSource):
        pred_len = prediction_length or source._settings.prediction_length
        fetched_at = source.fetched_at()
    elif isinstance(source, TsBenchDataSource):
        pred_len = prediction_length or source.get_prediction_length(ds_name)
        fetched_at = source.fetched_at()
    else:
        pred_len = prediction_length or 24
        fetched_at = pd.Timestamp.utcnow().isoformat()

    to_univariate = meta.num_variates > 1
    entries = _records_to_entries(record, to_univariate=to_univariate)
    metrics = evaluate_online_window(predictor, entries, pred_len, meta.frequency)

    ds_key = ds_name.split("/")[0].lower()
    if isinstance(source, TsBenchDataSource):
        config_name = source.leaderboard_dataset_name(ds_name)
    else:
        config_name = f"{ds_key}/{meta.frequency}/short"
    model_name = getattr(predictor, "leaderboard_name", predictor.__class__.__name__)
    context_length = int(np.asarray(entries[0]["target"]).shape[-1]) - pred_len
    evaluated_at = pd.Timestamp.utcnow().isoformat()
    forecast_snapshot = _capture_forecast_snapshot(
        predictor,
        entries,
        pred_len,
        meta.frequency,
        evaluated_at,
        config_name,
        model_name,
    )

    return OnlineEvalResult(
        dataset=config_name,
        model=model_name,
        metrics=metrics,
        domain=meta.domain,
        num_variates=1 if to_univariate else meta.num_variates,
        data_fetched_at=fetched_at,
        context_length=context_length,
        prediction_length=pred_len,
        forecast_snapshot=forecast_snapshot,
    )


def result_to_row(result: OnlineEvalResult) -> list[Any]:
    return [
        result.dataset,
        result.model,
        result.metrics["MSE[mean]"],
        result.metrics["MSE[0.5]"],
        result.metrics["MAE[0.5]"],
        result.metrics["MASE[0.5]"],
        result.metrics["MAPE[0.5]"],
        result.metrics["sMAPE[0.5]"],
        result.metrics["MSIS"],
        result.metrics["RMSE[mean]"],
        result.metrics["NRMSE[mean]"],
        result.metrics["ND[0.5]"],
        result.metrics["mean_weighted_sum_quantile_loss"],
        result.domain,
        result.num_variates,
    ]


def results_to_dataframe(results: Iterable[OnlineEvalResult]) -> pd.DataFrame:
    rows = [result_to_row(result) for result in results]
    return pd.DataFrame(rows, columns=RESULT_COLUMNS)