"""Step D. Score one shard with one or both cross-encoders. Identical on the box and on Colab.

    CUDA_VISIBLE_DEVICES=1 python scripts/05_ce_score.py \
        --shard work/shards/test_shard1of4.parquet \
        --model work/ce_model_A --out work/scored/shard1_bge.parquet

The Colab notebook calls exactly this code path through the berx package, so a bug found on
one lane is fixed for all four.

--batch defaults to 0, meaning "probe it". A shared card has an unpredictable amount of free
memory, and a fixed batch size is either too small (wasting the card) or too large (an OOM an
hour into the run). The probe doubles the batch until it OOMs, then backs off one step.
"""
from __future__ import annotations

import argparse
import os
import sys
import time

import numpy as np
import pandas as pd

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from berx import ce_score as CE  # noqa: E402


def probe_batch(tok, model, texts_a, texts_b, device, max_len, cap=1024):
    """Find a batch size that fits, using the LONGEST pairs so the answer is safe for all."""
    import torch
    lens = np.fromiter((len(a) + len(b) for a, b in zip(texts_a, texts_b)), np.int32,
                       len(texts_a))
    worst = np.argsort(lens)[-cap:]
    b, best = 16, 16
    while b <= cap:
        idx = worst[-b:]
        try:
            with torch.inference_mode():
                enc = tok([texts_a[i] for i in idx], [texts_b[i] for i in idx], padding=True,
                          truncation=True, max_length=max_len, return_tensors="pt").to(device)
                with torch.autocast("cuda", dtype=torch.float16,
                                    enabled=device.startswith("cuda")):
                    model(**enc)
            best = b
            b *= 2
        except RuntimeError as e:
            if "out of memory" not in str(e).lower():
                raise
            torch.cuda.empty_cache()
            break
    torch.cuda.empty_cache()
    return max(16, best // 2)                        # one step back: leave the card headroom


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--shard", required=True)
    ap.add_argument("--model", required=True, help="local dir or HF id")
    ap.add_argument("--out", required=True)
    ap.add_argument("--score-col", default="ce")
    ap.add_argument("--device", default="cuda")
    ap.add_argument("--batch", type=int, default=0, help="0 = probe")
    ap.add_argument("--max-len", type=int, default=160)
    ap.add_argument("--ckpt", default="", help="checkpoint prefix; on Colab put it on Drive")
    ap.add_argument("--ckpt-every", type=int, default=200_000)
    ap.add_argument("--compile", action="store_true")
    ap.add_argument("--output", default="logit", choices=["logit", "prob"],
                    help="MUST be 'logit' to match model-4's stored ce column")
    a = ap.parse_args()

    df = pd.read_parquet(a.shard)
    ta, tb = df["text_a"].tolist(), df["text_b"].tolist()
    print(f"shard {a.shard}: {len(df):,} pairs")

    tok, model = CE.load_model(a.model, a.device, compile_model=a.compile)
    batch = a.batch or probe_batch(tok, model, ta, tb, a.device, a.max_len)
    print(f"batch={batch}  max_len={a.max_len}  model={a.model}")

    t0 = time.time()
    s = CE.score_pairs(ta, tb, tok, model, a.device, batch=batch, max_len=a.max_len,
                       ckpt=a.ckpt or None, ckpt_every=a.ckpt_every,
                       output=a.output)
    el = time.time() - t0

    out = df[["q", "c"]].copy()
    out[a.score_col] = s
    os.makedirs(os.path.dirname(os.path.abspath(a.out)) or ".", exist_ok=True)
    out.to_parquet(a.out, index=False, compression="zstd")
    print(f"wrote {a.out}  {el/60:.1f} min  {len(df)/max(el,1e-9):,.0f} pairs/s")
    print(f"  score mean {s.mean():.4f}  min {s.min():.3f}  max {s.max():.3f}  "
          f">0 {(s > 0).mean():.2%}")
    if a.output == "logit" and s.min() >= 0.0 and s.max() <= 1.0:
        print("  WARNING: every score is in [0,1]. That is a probability, not a logit - "
              "check the model head before merging into model-4's ce column.")


if __name__ == "__main__":
    main()