"""Bootstrap confidence intervals and paired significance tests.

Reads the per-problem diagnosed traces in results/diagnosis/ and, for each
(model, benchmark, quantization) cell, computes:

    - 95% bootstrap CIs for accuracy, average FFS, and ECR (5000 resamples).
    - A full SSR curve CI band (per-depth bootstrap).
    - For (base, restored) pairs: a paired bootstrap significance test on
      ΔAccuracy (uses the SAME sampled problem ids for base and restored in
      each resample, so the null cancels properly).

Emits one *_ci.json next to each existing *_metrics.json, plus one pair-level
*_sig.json per (model, benchmark, method) restoration comparison.

These are real, defensible uncertainty estimates given a SINGLE inference run:
they capture variance across problems, not variance across seeds. For a Q1
paper you want both — this script delivers the first half for free; the
seed-variance half requires re-running inference with NUM_RUNS>=3.
"""

from __future__ import annotations

import argparse
import glob
import json
import os
import re
from typing import Dict, List, Optional, Tuple

import numpy as np


BENCHMARKS = ("gsm8k", "math500", "gpqa")
QUANT_METHODS = ("awq_w4", "gptq_w4", "bnb_nf4_w4")


# ---------------------------------------------------------------------------
# Per-problem metric extraction
# ---------------------------------------------------------------------------

def first_failure_step(steps: List[dict]) -> Optional[int]:
    for s in steps:
        if s.get("is_correct") is False:
            return s.get("index")
    return None


def cascade_rate(steps: List[dict]) -> Optional[float]:
    """Fraction of steps AFTER the first failure that are also incorrect.
    Returns None for problems with no failure (so the aggregate only averages
    over problems that actually failed — matches aggregate_metrics.ecr)."""
    ffs = first_failure_step(steps)
    if ffs is None:
        return None
    tail = [s for s in steps if s.get("index", 0) > ffs]
    if not tail:
        return None
    return sum(1 for s in tail if s.get("is_correct") is False) / len(tail)


def survival_at_depth(steps: List[dict], depth: int) -> Optional[bool]:
    """True if all of steps[0..depth] are correct, None if the trace is shorter."""
    if len(steps) <= depth:
        return None
    return all(s.get("is_correct", True) for s in steps[: depth + 1])


def load_per_problem(jsonl_path: str) -> List[dict]:
    """Return a list of per-problem records with the fields bootstrapping needs."""
    out = []
    with open(jsonl_path) as f:
        for line in f:
            t = json.loads(line)
            steps = t.get("steps", []) or []
            out.append({
                "problem_id":  t.get("problem_id"),
                "is_correct":  bool(t.get("is_correct_final", False)),
                "ffs":         first_failure_step(steps),
                "ecr":         cascade_rate(steps),
                "step_correct": [s.get("is_correct", True) for s in steps],
            })
    return out


# ---------------------------------------------------------------------------
# Bootstrap utilities
# ---------------------------------------------------------------------------

def _ci(samples: np.ndarray, alpha: float = 0.05) -> Tuple[float, float]:
    lo, hi = np.percentile(samples, [100 * alpha / 2, 100 * (1 - alpha / 2)])
    return float(lo), float(hi)


def bootstrap_single(records: List[dict], n_boot: int = 5000,
                     rng: Optional[np.random.Generator] = None) -> dict:
    """Bootstrap accuracy, avg_ffs, ecr, and the SSR curve for one cell."""
    rng = rng or np.random.default_rng(0)
    n = len(records)
    if n == 0:
        return {}

    acc_vec = np.array([r["is_correct"] for r in records], dtype=float)
    ffs_vec = np.array([r["ffs"] if r["ffs"] is not None else np.nan for r in records], dtype=float)
    ecr_vec = np.array([r["ecr"] if r["ecr"] is not None else np.nan for r in records], dtype=float)

    # SSR per depth: for problems with enough steps, is the trace "alive" at depth d?
    max_depth = 30
    alive = np.full((n, max_depth), np.nan)
    for i, r in enumerate(records):
        steps = r["step_correct"]
        for d in range(max_depth):
            if d < len(steps):
                alive[i, d] = float(all(steps[: d + 1]))

    acc_samples = np.empty(n_boot)
    ffs_samples = np.empty(n_boot)
    ecr_samples = np.empty(n_boot)
    ssr_samples = np.full((n_boot, max_depth), np.nan)

    for b in range(n_boot):
        idx = rng.integers(0, n, size=n)
        acc_samples[b] = acc_vec[idx].mean()
        f = ffs_vec[idx]
        ffs_samples[b] = np.nanmean(f) if np.any(~np.isnan(f)) else np.nan
        e = ecr_vec[idx]
        ecr_samples[b] = np.nanmean(e) if np.any(~np.isnan(e)) else np.nan
        for d in range(max_depth):
            col = alive[idx, d]
            valid = ~np.isnan(col)
            if valid.any():
                ssr_samples[b, d] = col[valid].mean()

    def summarize(x):
        x = x[~np.isnan(x)]
        if x.size == 0:
            return None
        lo, hi = _ci(x)
        return {"mean": float(x.mean()), "ci_lo": lo, "ci_hi": hi}

    ssr_ci_lo = np.nanpercentile(ssr_samples, 2.5, axis=0)
    ssr_ci_hi = np.nanpercentile(ssr_samples, 97.5, axis=0)
    ssr_mean = np.nanmean(ssr_samples, axis=0)

    return {
        "n": int(n),
        "accuracy": summarize(acc_samples),
        "avg_ffs":  summarize(ffs_samples),
        "ecr":      summarize(ecr_samples),
        "ssr_curve_ci": {
            "mean":  [None if np.isnan(v) else float(v) for v in ssr_mean],
            "ci_lo": [None if np.isnan(v) else float(v) for v in ssr_ci_lo],
            "ci_hi": [None if np.isnan(v) else float(v) for v in ssr_ci_hi],
        },
    }


def paired_bootstrap_sig(base: List[dict], rest: List[dict],
                         n_boot: int = 5000,
                         rng: Optional[np.random.Generator] = None) -> dict:
    """Paired bootstrap on ΔAccuracy between base and restored.

    Problems are paired by problem_id. For each bootstrap resample we draw the
    SAME indices in both conditions, compute the delta, and accumulate. The
    two-sided p-value is 2 * min(P(Δ<=0), P(Δ>=0)).
    """
    rng = rng or np.random.default_rng(0)
    base_by_id = {r["problem_id"]: r for r in base}
    rest_by_id = {r["problem_id"]: r for r in rest}
    shared = sorted(set(base_by_id) & set(rest_by_id))
    if not shared:
        return {"n_pairs": 0}

    b_vec = np.array([base_by_id[pid]["is_correct"] for pid in shared], dtype=float)
    r_vec = np.array([rest_by_id[pid]["is_correct"] for pid in shared], dtype=float)

    n = len(shared)
    deltas = np.empty(n_boot)
    for i in range(n_boot):
        idx = rng.integers(0, n, size=n)
        deltas[i] = r_vec[idx].mean() - b_vec[idx].mean()

    observed = float(r_vec.mean() - b_vec.mean())
    p_two = 2 * min((deltas <= 0).mean(), (deltas >= 0).mean())
    lo, hi = _ci(deltas)
    return {
        "n_pairs": int(n),
        "delta_acc_observed": observed,
        "delta_acc_ci_lo": lo,
        "delta_acc_ci_hi": hi,
        "p_value": float(p_two),
    }


# ---------------------------------------------------------------------------
# CLI glue
# ---------------------------------------------------------------------------

def _discover_cells(diagnosis_dir: str):
    """Yield (model, benchmark, quant, jsonl_path) tuples for everything on disk."""
    for quant_dir in sorted(glob.glob(os.path.join(diagnosis_dir, "*"))):
        if not os.path.isdir(quant_dir):
            continue
        quant = os.path.basename(quant_dir)
        for model_dir in sorted(glob.glob(os.path.join(quant_dir, "*"))):
            if not os.path.isdir(model_dir):
                continue
            model = os.path.basename(model_dir)
            for jsonl in sorted(glob.glob(os.path.join(model_dir, "*_run0.jsonl"))):
                base = os.path.basename(jsonl)
                m = re.match(r"(.+)_run0\.jsonl$", base)
                if not m:
                    continue
                bench = m.group(1)
                if bench not in BENCHMARKS:
                    continue
                yield model, bench, quant, jsonl


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--diagnosis", default="results/diagnosis",
                        help="Root diagnosis dir (quant/model/<bench>_run0.jsonl)")
    parser.add_argument("--output", default="results/metrics",
                        help="Where to drop *_ci.json and *_sig.json")
    parser.add_argument("--n-boot", type=int, default=5000)
    parser.add_argument("--seed", type=int, default=0)
    args = parser.parse_args()

    os.makedirs(args.output, exist_ok=True)
    rng = np.random.default_rng(args.seed)

    # --- Phase A: single-cell CIs ------------------------------------------
    cells = list(_discover_cells(args.diagnosis))
    print(f"Found {len(cells)} diagnosis cells to bootstrap")

    per_problem_cache: Dict[Tuple[str, str, str], List[dict]] = {}
    for model, bench, quant, jsonl in cells:
        records = load_per_problem(jsonl)
        per_problem_cache[(model, bench, quant)] = records
        ci = bootstrap_single(records, n_boot=args.n_boot, rng=rng)
        out_path = os.path.join(args.output, f"{model}_{quant}_{bench}_run0_ci.json")
        with open(out_path, "w") as f:
            json.dump(ci, f, indent=2)
    print(f"  wrote {len(cells)} *_ci.json files")

    # --- Phase B: paired significance (base vs restored) --------------------
    n_sig = 0
    for (model, bench, quant), base_recs in per_problem_cache.items():
        if quant.endswith("_restored"):
            continue
        rest_recs = per_problem_cache.get((model, bench, quant + "_restored"))
        if not rest_recs:
            continue
        sig = paired_bootstrap_sig(base_recs, rest_recs, n_boot=args.n_boot, rng=rng)
        out_path = os.path.join(args.output, f"{model}_{quant}_{bench}_run0_sig.json")
        with open(out_path, "w") as f:
            json.dump(sig, f, indent=2)
        n_sig += 1
    print(f"  wrote {n_sig} *_sig.json files (paired base vs restored)")


if __name__ == "__main__":
    main()