"""Canonical-indices generator.

Reads the immutable benchmark artifacts and produces deterministic,
stratified train/eval index lists per task. Every family runner reads
from here so cross-method comparison is fair: same N, same instances,
same indices.

The cache lives under `experiments/cache/canonical_indices/<key>/`
where `<key>` encodes (n_eval, n_train, seed, stratifier_version) -- so
changing any sample-budget parameter produces a new cache directory.
The cache is an experiment-side speed optimisation; the canonical
dataset tree under `data_small_caps/` contains only raw and derived
benchmark artifacts (which are immutable).

Per-task stratification:
  T1 (TSF):       sector x market_cap_quartile        -> (ticker, anchor_date)
  T2 (Val-PT):    full 30% ticker holdout              -> (ticker, date)
  T3 (Stmt-Gen):  per-(ticker, fiscal_year) holdout    -> (ticker, fiscal_year)
  T4 (Scen-Ret):  sector x mcap_q x event_type        -> (scenario_id, ticker)
  T5 (Val-Priv):  full 30% ticker holdout              -> (ticker, date)
  T6 (Gen-Eval):  per-(ticker, fiscal_year) holdout    -> (ticker, fiscal_year)
  T7 (RE-Val):    property_type x state                -> address
"""

from __future__ import annotations

import logging
from pathlib import Path
from typing import Literal

import numpy as np
import pandas as pd

from .. import config
from . import budgets
from .budgets import EVAL_N_PER_TASK, TRAIN_N_PER_TASK, SEED, Task

logger = logging.getLogger(__name__)


Split = Literal["train", "eval"]


def _cfg_get_lookback(granularity: str) -> int:
    """Return the canonical (shortest) lookback for ``granularity``."""
    return config.get_lookback_windows(granularity)[0] if hasattr(config, "get_lookback_windows") else 63


def _provenance_suffix(granularity: str) -> str:
    """16-char SHA-256-derived suffix encoding the relevant benchmark
    parquets for ``granularity``. Mutating any of those parquets changes
    the cache key, forcing canonical-indices regeneration.
    """
    from ._provenance import sha256_combined

    bench_dir = config.get_benchmark_dir(granularity)
    candidates = [
        bench_dir / "panel_train.parquet",
        bench_dir / "panel_test.parquet",
        bench_dir / "valuation_inputs.parquet",
        bench_dir / "valuation_ground_truth.parquet",
        bench_dir / "private_valuation_inputs.parquet",
        bench_dir / "private_valuation_ground_truth.parquet",
        bench_dir / "generation_inputs.parquet",
        bench_dir / "generation_ground_truth.parquet",
        bench_dir / "generator_eval_inputs.parquet",
        bench_dir / "generator_eval_ground_truth.parquet",
        bench_dir / "scenario_forecast_ground_truth.parquet",
        bench_dir / "scenarios.parquet",
        bench_dir / "re_train_properties.parquet",
        bench_dir / "re_eval_inputs.parquet",
        bench_dir / "re_eval_ground_truth.parquet",
    ]
    existing = [p for p in candidates if p.exists()]
    if not existing:
        return "noprov"
    return sha256_combined(existing)


def _cache_dir(granularity: str, key: str | None = None) -> Path:
    """Return the cache directory for a given budget key.

    Lives under ``experiments/cache/canonical_indices/`` (experiment-side
    speed optimisation, regenerable on miss). The canonical dataset tree
    under ``data_small_caps/`` contains only raw and derived benchmark
    artifacts; experiment-side caches NEVER live there.

    The cache key suffix encodes (i) a SHA-256 over the benchmark
    parquets and (ii) the current ``max(lookback)`` and ``max(horizon)``
    for ``granularity``. Either upstream-data drift or a horizon/lookback
    config change atomically invalidates the cache.
    """
    k = key or budgets.cache_key()
    suffix = _provenance_suffix(granularity)
    max_lb = max(config.get_lookback_windows(granularity))
    max_h = max(config.get_horizons(granularity))
    # Resolve experiments/ as a sibling of dataloader/ (this file lives
    # at projects/.../whatif_bench/dataloader/canonical_indices.py).
    experiments_dir = Path(__file__).resolve().parents[1] / "experiments"
    return (
        experiments_dir
        / "cache"
        / "canonical_indices"
        / granularity
        / f"{k}_prov={suffix}_lb={max_lb}_h={max_h}"
    )


def _stratified_sample(
    df: pd.DataFrame,
    n: int,
    strata_cols: list[str],
    seed: int,
) -> pd.DataFrame:
    """Stratified subsample of `df` to size `n`, preserving the joint
    distribution of `strata_cols` (Cartesian-product strata, with
    proportional allocation and remainder spread by row order).

    Deterministic at fixed `seed`. If `n >= len(df)`, returns df shuffled.
    """
    if n >= len(df):
        return df.sample(frac=1.0, random_state=seed).reset_index(drop=True)

    # Drop rows with NaN in any stratifier column -- they would form a
    # spurious "missing" stratum.
    valid_mask = df[strata_cols].notna().all(axis=1)
    df_valid = df[valid_mask].copy()
    if df_valid.empty:
        # Fall back to uniform random
        return df.sample(n=n, random_state=seed).reset_index(drop=True)

    df_valid["_stratum"] = df_valid[strata_cols].astype(str).agg("|".join, axis=1)

    rng = np.random.RandomState(seed)
    out_rows: list[pd.DataFrame] = []
    total = len(df_valid)

    for stratum, grp in df_valid.groupby("_stratum"):
        # Proportional allocation; at least 1 if stratum has rows.
        q = max(1, round(len(grp) * n / total))
        q = min(q, len(grp))
        out_rows.append(grp.sample(n=q, random_state=rng.randint(0, 2**31 - 1)))

    out = pd.concat(out_rows, ignore_index=True)
    # Trim or top-up to exactly n
    if len(out) > n:
        out = out.sample(n=n, random_state=seed).reset_index(drop=True)
    elif len(out) < n:
        # Top up with non-selected rows (still stratified by selection above)
        remaining = df_valid.loc[~df_valid.index.isin(out.index)]
        extra = remaining.sample(n=min(n - len(out), len(remaining)), random_state=seed)
        out = pd.concat([out, extra], ignore_index=True)

    return out.drop(columns=["_stratum"]).reset_index(drop=True)


# ── Per-task generators ───────────────────────────────────────────────────


def _gen_t1(
    granularity: str,
    n_eval: int,
    n_train: int,
    seed: int,
) -> dict[Split, pd.DataFrame]:
    """T1 TSF: stratified by sector x market_cap_quartile.

    Each (ticker, anchor_date) pair must admit a complete
    ``(lookback, max_horizon)`` window inside the corresponding split's panel,
    so every horizon evaluated by every T1 runner reuses the same anchor set.
    Concretely, for a ticker with ``T`` panel rows we keep only anchor dates
    at per-ticker positions ``[lookback, T - max_horizon - 1]``.

    Returns DataFrames with columns (ticker, anchor_date, sector, mcap_q).
    """
    from .. import config as _cfg

    # Build the canonical anchor pool against the SHORTEST lookback and
    # the LONGEST horizon. The test panel (post-2024-09-03) is ~378
    # trading days; pairing max_lookback (252) with max_horizon (252)
    # exhausts it. Methods that want a longer lookback can request it
    # at load time (load(..., lookback=252)); anchors with insufficient
    # history will be dropped by _build_t1_x_y and counted in
    # ``meta.attrs["n_canonical_dropped"]``.
    lookback = _cfg.get_lookback_windows(granularity)[0]
    max_horizon = max(_cfg.get_horizons(granularity))

    bench_dir = config.get_benchmark_dir(granularity)
    train = pd.read_parquet(
        bench_dir / "panel_train.parquet",
        columns=["ticker", "date", "sector", "derived_market_cap"],
    )
    test = pd.read_parquet(
        bench_dir / "panel_test.parquet",
        columns=["ticker", "date", "sector", "derived_market_cap"],
    )

    # Latest market_cap per ticker for the quartile assignment (across the
    # full panel, so train and eval split on the same definition).
    latest = (
        pd.concat([train, test], ignore_index=True)
        .sort_values("date")
        .groupby("ticker")
        .tail(1)[["ticker", "derived_market_cap"]]
    )
    latest["mcap_q"] = pd.qcut(
        latest["derived_market_cap"].clip(lower=1),
        4, labels=["Q1", "Q2", "Q3", "Q4"], duplicates="drop",
    )
    mcap_q = dict(zip(latest["ticker"], latest["mcap_q"]))

    def _restrict_to_valid_anchors(df: pd.DataFrame) -> pd.DataFrame:
        """Keep only rows at per-ticker positions [lookback, T-max_horizon-1]
        so a complete (lookback + max_horizon) window fits."""
        df = df.sort_values(["ticker", "date"]).reset_index(drop=True)
        df["_row_in_ticker"] = df.groupby("ticker", sort=False).cumcount()
        df["_ticker_len"] = df.groupby("ticker", sort=False)["date"].transform("size")
        valid = (df["_row_in_ticker"] >= lookback) & (
            df["_row_in_ticker"] < df["_ticker_len"] - max_horizon
        )
        return df.loc[valid].drop(columns=["_row_in_ticker", "_ticker_len"])

    out: dict[Split, pd.DataFrame] = {}
    for split, df in (("train", train), ("eval", test)):
        df = _restrict_to_valid_anchors(df)
        df = df.rename(columns={"date": "anchor_date"}).copy()
        df["mcap_q"] = df["ticker"].map(mcap_q)
        n = n_train if split == "train" else n_eval
        sampled = _stratified_sample(df, n, ["sector", "mcap_q"], seed)
        out[split] = sampled[["ticker", "anchor_date", "sector", "mcap_q"]]
    return out


def _gen_t2_t5(
    granularity: str,
    task: str,
    n_eval: int,
    n_train: int,
    seed: int,
) -> dict[Split, pd.DataFrame]:
    """T2 (Val-PT) and T5 (Val-Priv): full 30% ticker holdout for eval;
    latest snapshot per non-holdout ticker for train.

    Returns DataFrames with columns (ticker, date).
    """
    bench_dir = config.get_benchmark_dir(granularity)
    if task == "T2":
        inputs_path = bench_dir / "valuation_inputs.parquet"
        gt_path = bench_dir / "valuation_ground_truth.parquet"
    else:
        inputs_path = bench_dir / "private_valuation_inputs.parquet"
        gt_path = bench_dir / "private_valuation_ground_truth.parquet"
    inputs = pd.read_parquet(inputs_path, columns=["ticker", "date", "sector"])
    gt = pd.read_parquet(gt_path, columns=["ticker", "date"])

    # Restrict the eval pool to (ticker, date) pairs that are present in
    # BOTH inputs and gt. Without this, ~21 quarter-end snapshots per
    # task have inputs but no gt (close or shares_outstanding missing
    # on that date), and the loader silently dropped them at merge
    # time so canonical-eval N came up short of the budget.
    inputs["date"] = pd.to_datetime(inputs["date"])
    gt["date"] = pd.to_datetime(gt["date"])
    eval_pool = inputs.merge(gt, on=["ticker", "date"], how="inner")

    train_panel = pd.read_parquet(
        bench_dir / "panel_train.parquet",
        columns=["ticker", "date", "sector"],
    )

    holdout_tickers = set(inputs["ticker"].unique())
    non_holdout = train_panel[~train_panel["ticker"].isin(holdout_tickers)]
    # Latest snapshot per non-holdout ticker as the train set.
    train_latest = (
        non_holdout.sort_values("date").groupby("ticker").tail(1)
        .reset_index(drop=True)
    )

    rng = np.random.RandomState(seed)
    train_idx = train_latest.sample(
        n=min(n_train, len(train_latest)), random_state=rng.randint(0, 2**31 - 1),
    ).reset_index(drop=True)

    eval_idx = eval_pool.sample(
        n=min(n_eval, len(eval_pool)), random_state=rng.randint(0, 2**31 - 1),
    ).reset_index(drop=True)

    return {"train": train_idx, "eval": eval_idx}


def _gen_t3_t6(
    granularity: str,
    task: str,
    n_eval: int,
    n_train: int,
    seed: int,
) -> dict[Split, pd.DataFrame]:
    """T3 (Stmt-Gen) and T6 (Gen-Eval): per-(ticker, fiscal_year) split.

    Every ticker in the per-task ground-truth file is also in the
    holdout (`*_inputs.parquet` lists holdout tickers only), so a
    plain ``~ticker.isin(holdout)`` train pool would always be empty.
    Instead: per ticker, the **latest** fiscal year is the eval anchor
    and **earlier** fiscal years are train anchors. Train-eval are
    cleanly separated by fiscal year within ticker; both pools are
    non-empty as long as a ticker has >=2 reported fiscal years.

    Eval = unique (ticker, latest_fiscal_year) pairs across all tickers.
    Train = unique (ticker, prior_fiscal_year) pairs across all tickers.
    """
    bench_dir = config.get_benchmark_dir(granularity)
    if task == "T3":
        gt_path = bench_dir / "generation_ground_truth.parquet"
    else:
        gt_path = bench_dir / "generator_eval_ground_truth.parquet"

    gt = pd.read_parquet(gt_path)
    if "fiscal_year" not in gt.columns:
        if "filing_date" in gt.columns:
            gt["fiscal_year"] = pd.to_datetime(gt["filing_date"]).dt.year
        else:
            gt["fiscal_year"] = 0

    pairs = gt[["ticker", "fiscal_year"]].drop_duplicates().reset_index(drop=True)
    pairs["fiscal_year"] = pd.to_numeric(pairs["fiscal_year"], errors="coerce")
    pairs = pairs.dropna(subset=["fiscal_year"]).copy()
    pairs["fiscal_year"] = pairs["fiscal_year"].astype(int)

    # Per-ticker: latest FY -> eval, earlier FYs -> train
    pairs = pairs.sort_values(["ticker", "fiscal_year"]).reset_index(drop=True)
    pairs["_rank_desc"] = pairs.groupby("ticker")["fiscal_year"].rank(
        method="first", ascending=False,
    )
    eval_pairs = pairs[pairs["_rank_desc"] == 1][["ticker", "fiscal_year"]]
    train_pairs = pairs[pairs["_rank_desc"] > 1][["ticker", "fiscal_year"]]

    rng = np.random.RandomState(seed)
    eval_idx = eval_pairs.sample(
        n=min(n_eval, len(eval_pairs)), random_state=rng.randint(0, 2**31 - 1),
    ).reset_index(drop=True)
    train_idx = train_pairs.sample(
        n=min(n_train, len(train_pairs)), random_state=rng.randint(0, 2**31 - 1),
    ).reset_index(drop=True)

    return {"train": train_idx, "eval": eval_idx}


def _gen_t4(
    granularity: str,
    n_eval: int,
    n_train: int,
    seed: int,
) -> dict[Split, pd.DataFrame]:
    """T4 Scen-Ret: stratified by sector x mcap_q x event_type.

    Returns DataFrames with columns (scenario_id, ticker, event_type).
    """
    bench_dir = config.get_benchmark_dir(granularity)
    gt = pd.read_parquet(
        bench_dir / "scenario_forecast_ground_truth.parquet",
        columns=["scenario_id", "ticker", "event_type", "event_date",
                 "actual_return_pct"],
    )
    gt = gt.dropna(subset=["actual_return_pct"])

    # Use the panel-train cutoff as the train/eval split anchor (matches
    # the canonical T1 split semantics).
    panel_train_df = pd.read_parquet(
        bench_dir / "panel_train.parquet", columns=["ticker", "date"],
    )
    panel_test_df = pd.read_parquet(
        bench_dir / "panel_test.parquet", columns=["ticker", "date"],
    )
    panel_train_df["date"] = pd.to_datetime(panel_train_df["date"])
    panel_test_df["date"] = pd.to_datetime(panel_test_df["date"])
    split_date = panel_train_df["date"].max()
    gt["event_date"] = pd.to_datetime(gt["event_date"])

    # Restrict the eval/train pools to events whose ticker has at least
    # ``min_history`` panel days BEFORE the event date in the combined
    # panel. Without this filter, ~6.5% of train events sampled at the
    # canonical step cannot produce a valid 63-day lookback at load
    # time and were silently zero-padded then dropped.
    min_history = max(_cfg_get_lookback(granularity), 63)
    panel_full = pd.concat([panel_train_df, panel_test_df], ignore_index=True)
    panel_full = panel_full.drop_duplicates(subset=["ticker", "date"])
    panel_dates_per_ticker = (
        panel_full.sort_values(["ticker", "date"]).groupby("ticker")["date"]
    )
    first_panel_date = panel_dates_per_ticker.first().to_dict()

    def _has_lookback_history(row) -> bool:
        first = first_panel_date.get(row["ticker"])
        if first is None:
            return False
        # need at least min_history trading-day rows prior (use calendar
        # days as a fast upper bound: 252 trading days ~ 365 calendar days).
        return (row["event_date"] - first).days >= int(min_history * 1.45)

    gt = gt[gt.apply(_has_lookback_history, axis=1)].copy()

    train_pool = gt[gt["event_date"] <= split_date].copy()
    eval_pool = gt[gt["event_date"] > split_date].copy()

    # Sector and mcap_q come from the panel (across full date range).
    full_panel = pd.read_parquet(
        bench_dir / "panel_train.parquet",
        columns=["ticker", "sector", "derived_market_cap"],
    )
    latest = full_panel.groupby("ticker").tail(1)[["ticker", "sector", "derived_market_cap"]]
    latest["mcap_q"] = pd.qcut(
        latest["derived_market_cap"].clip(lower=1),
        4, labels=["Q1", "Q2", "Q3", "Q4"], duplicates="drop",
    )
    sector_map = dict(zip(latest["ticker"], latest["sector"]))
    mcap_map = dict(zip(latest["ticker"], latest["mcap_q"]))

    out: dict[Split, pd.DataFrame] = {}
    for split, pool in (("train", train_pool), ("eval", eval_pool)):
        pool = pool.copy()
        pool["sector"] = pool["ticker"].map(sector_map)
        pool["mcap_q"] = pool["ticker"].map(mcap_map)
        n = n_train if split == "train" else n_eval
        sampled = _stratified_sample(
            pool, n, ["sector", "mcap_q", "event_type"], seed,
        )
        out[split] = sampled[["scenario_id", "ticker", "event_type"]]
    return out


def _gen_t7(
    granularity: str,
    n_eval: int,
    n_train: int,
    seed: int,
) -> dict[Split, pd.DataFrame]:
    """T7 RE-Val: stratified by property_type x state."""
    bench_dir = config.get_benchmark_dir(granularity)
    train = pd.read_parquet(bench_dir / "re_train_properties.parquet")
    eval_in = pd.read_parquet(bench_dir / "re_eval_inputs.parquet")
    # ``re_train_properties`` carries 854 duplicate-address rows from
    # multiple RentCast variants of the same listing. Dedup BEFORE
    # sampling so the canonical eval set has unique addresses (the
    # loader otherwise dedups, leaving the canon n short of budget).
    if "address" in train.columns:
        train = train.drop_duplicates(subset="address", keep="first").reset_index(drop=True)
    if "address" in eval_in.columns:
        eval_in = eval_in.drop_duplicates(subset="address", keep="first").reset_index(drop=True)

    def _sample(df: pd.DataFrame, n: int) -> pd.DataFrame:
        ptype_col = next(
            (c for c in ("property_type", "propertyType", "type") if c in df.columns),
            None,
        )
        state_col = next(
            (c for c in ("state", "State") if c in df.columns), None,
        )
        addr_col = next(
            (c for c in ("address", "addressLine1", "Address") if c in df.columns),
            None,
        )
        strata = [c for c in (ptype_col, state_col) if c is not None]
        if not strata or addr_col is None:
            return df.sample(n=min(n, len(df)), random_state=seed).reset_index(drop=True)
        sampled = _stratified_sample(df, n, strata, seed)
        cols_to_keep = [addr_col] + strata
        return sampled[cols_to_keep].rename(columns={addr_col: "address"})

    return {"train": _sample(train, n_train), "eval": _sample(eval_in, n_eval)}


_GENERATORS = {
    "T1": _gen_t1,
    "T2": lambda g, ne, nt, s: _gen_t2_t5(g, "T2", ne, nt, s),
    "T3": lambda g, ne, nt, s: _gen_t3_t6(g, "T3", ne, nt, s),
    "T4": _gen_t4,
    "T5": lambda g, ne, nt, s: _gen_t2_t5(g, "T5", ne, nt, s),
    "T6": lambda g, ne, nt, s: _gen_t3_t6(g, "T6", ne, nt, s),
    "T7": _gen_t7,
}


# ── Public API ────────────────────────────────────────────────────────────


def get_canonical_indices(
    task: Task,
    split: Split = "eval",
    *,
    granularity: str = "daily",
    n_eval: dict[Task, int] | None = None,
    n_train: dict[Task, int] | None = None,
    seed: int | None = None,
) -> pd.DataFrame:
    """Return the canonical index list for `(task, split)`.

    Reads from cache if available; otherwise generates, persists to cache,
    and returns. The cache key encodes (n_eval, n_train, seed,
    stratifier_version) so non-canonical re-tunes get their own cache dir.

    Smoke-mode override: when ``MACROLENS_N_TRAIN`` and / or
    ``MACROLENS_N_EVAL`` env vars are set (positive int), they replace the
    default budget for every task in this call. This lets the runner do an
    end-to-end smoke (e.g. n_train=2, n_eval=1 across all 22 methods × 7
    tasks) without touching the canonical cache or the CLI signature.
    Explicit ``n_eval`` / ``n_train`` kwargs still take precedence.
    """
    import os as _os
    env_n_eval = _os.environ.get("MACROLENS_N_EVAL")
    env_n_train = _os.environ.get("MACROLENS_N_TRAIN")
    if n_eval is None and env_n_eval is not None:
        try:
            v = int(env_n_eval)
            if v > 0:
                n_eval = {t: v for t in EVAL_N_PER_TASK}
        except ValueError:
            pass
    if n_train is None and env_n_train is not None:
        try:
            v = int(env_n_train)
            if v > 0:
                n_train = {t: v for t in TRAIN_N_PER_TASK}
        except ValueError:
            pass
    n_eval_map = n_eval or EVAL_N_PER_TASK
    n_train_map = n_train or TRAIN_N_PER_TASK
    s = SEED if seed is None else seed

    key = budgets.cache_key(n_eval=n_eval_map, n_train=n_train_map, seed=s)
    cache_dir = _cache_dir(granularity, key)
    cache_path = cache_dir / f"{split}_{task}.parquet"

    if cache_path.exists():
        return pd.read_parquet(cache_path)

    # Cache miss: generate both splits for this task and persist.
    gen = _GENERATORS[task]
    pair = gen(granularity, n_eval_map[task], n_train_map[task], s)
    cache_dir.mkdir(parents=True, exist_ok=True)
    for sp, df in pair.items():
        df.to_parquet(cache_dir / f"{sp}_{task}.parquet", index=False)
        logger.info(
            "Canonical-indices cache write: %s (%d rows)",
            cache_dir / f"{sp}_{task}.parquet", len(df),
        )

    return pair[split]


def build_all(
    granularity: str = "daily",
    *,
    n_eval: dict[Task, int] | None = None,
    n_train: dict[Task, int] | None = None,
    seed: int | None = None,
) -> dict[str, int]:
    """Build canonical indices for every (task, split) pair.

    Returns a summary dict mapping `<task>_<split>` -> n_rows. Idempotent:
    re-running with the same budgets is a no-op (cache hits).
    """
    summary: dict[str, int] = {}
    for task in ("T1", "T2", "T3", "T4", "T5", "T6", "T7"):
        for split in ("train", "eval"):
            df = get_canonical_indices(
                task, split, granularity=granularity,
                n_eval=n_eval, n_train=n_train, seed=seed,
            )
            summary[f"{task}_{split}"] = len(df)
    return summary