#!/usr/bin/env python3
"""Extract attention features without ever storing the full attention set.

Stages 01 and 02 are split: 01 dumps one (L, H, T, T) tensor per sample to
disk, 02 reads them back. That costs ~50 GB per configuration and ~60 GB for
a Yelp-sized split, and it is what makes substantially longer sequence lengths
impractical.

This script fuses them. Samples are processed in chunks: a chunk's attention
is materialised, every family is computed from it, and the tensors are
discarded before the next chunk. Peak attention on disk is CHUNK samples
rather than the whole split, so the cost is bounded by --chunk and not by
the dataset.

The family extractors are called unmodified, on the same inputs in the same
order, so the output files are identical to the two-stage pipeline. Verify
with --verify-against, which compares against an existing extraction.

    python extraction/scripts/05_extract_streaming.py --config <cfg.yaml>
    python extraction/scripts/05_extract_streaming.py --config <cfg.yaml> \
        --chunk 128 --scratch /dev/shm/attn
"""
from __future__ import annotations
import argparse
import shutil
import sys
import time
from pathlib import Path

import numpy as np
import pandas as pd
import torch

ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
sys.path.insert(0, str(ROOT.parent))

from src.utils import load_config, seed_everything, device_from_cfg, ensure_dir
from src.load_data import load_splits
from src.load_model import load_classification_model, move
from src.extract_attention import _build_dataloader
from src.family_specs import write_family_file

M2 = ("graph", "ripser", "template")
TOKTOPO = ("toktopo_pd", "toktopo_graph")


def _dump_chunk(model, batch_texts, tokenizer, cfg, device, scratch: Path,
                idxs: list[int]) -> None:
    """Run the encoder over one chunk and write its attention to scratch."""
    scratch.mkdir(parents=True, exist_ok=True)
    loader = _build_dataloader(batch_texts, tokenizer, cfg["data"]["text_col"],
                               cfg["data"]["label_col"], cfg["model"]["max_length"],
                               cfg["inference"]["batch_size"])
    pos = 0
    with torch.no_grad():
        for batch in loader:
            out = model(input_ids=batch["input_ids"].to(device),
                        attention_mask=batch["attention_mask"].to(device),
                        output_attentions=True)
            attn = torch.stack(out.attentions, dim=1).cpu().numpy().astype(np.float16)
            for b in range(attn.shape[0]):
                seq = int(batch["attention_mask"][b].sum().item())
                np.savez_compressed(scratch / f"{idxs[pos]:06d}.npz",
                                    attn=attn[b, :, :, :seq, :seq], seq_len=seq)
                pos += 1


def main() -> None:
    ap = argparse.ArgumentParser()
    ap.add_argument("--config", required=True)
    ap.add_argument("--splits", nargs="+", default=["train", "validation", "test"])
    ap.add_argument("--chunk", type=int, default=512,
                    help="samples whose attention is on disk at once")
    ap.add_argument("--workers", type=int, default=8)
    ap.add_argument("--scratch", type=Path, default=None,
                    help="where chunk attention goes (default: <output_dir>/_chunk)")
    ap.add_argument("--verify-against", type=Path, default=None,
                    help="an existing outputs/<cfg> dir; compare features and exit")
    args = ap.parse_args()

    cfg = load_config(args.config)
    seed_everything(cfg["seed"])
    cfg_name = cfg["run_name"]
    out_dir = ensure_dir(cfg["paths"]["output_dir"])
    ensure_dir(out_dir / "features")
    device = device_from_cfg(cfg)
    scratch = args.scratch or (out_dir / "_chunk")

    splits_data = load_splits(cfg)
    model, tokenizer = load_classification_model(
        cfg["model"]["pretrained_path"], num_labels=cfg["model"].get("num_labels"),
        base_tokenizer=cfg["model"].get("base_tokenizer"),
        do_lower_case=cfg["model"].get("do_lower_case"),
        is_peft=cfg["model"].get("is_peft", False),
        base_model=cfg["model"].get("base_model"))
    model = move(model, device).eval()

    from src.extract_topological_features import recompute_from_attention
    # reuse stage 02's array reshaping so both paths flatten identically
    from importlib.util import spec_from_file_location, module_from_spec
    _sp = spec_from_file_location("_s02", Path(__file__).parent / "02_extract_features.py")
    _s02 = module_from_spec(_sp); _sp.loader.exec_module(_s02)
    _split_topology_arrays = _s02._split_topology_arrays
    from src.extract_toktopo import compute_toktopo
    from src.extract_intra_attn_features import compute_intra_attn_features
    from src.extract_punct_dist import compute_punct_dist

    for split in args.splits:
        preds = pd.read_parquet(out_dir / f"predictions_{split}.parquet")
        idxs = preds["idx"].tolist()
        raw = splits_data[split].reset_index(drop=True)
        raw_m = raw.loc[raw["idx"].isin(idxs)].set_index("idx").loc[idxs].reset_index()
        N = len(idxs)
        acc: dict[str, list] = {}
        cols: dict[str, list] = {}
        t0 = time.time()

        for s in range(0, N, args.chunk):
            e = min(s + args.chunk, N)
            sub_idx = idxs[s:e]
            shutil.rmtree(scratch, ignore_errors=True)
            _dump_chunk(model, raw_m.iloc[s:e], tokenizer, cfg, device, scratch, sub_idx)

            g, r, t = recompute_from_attention(scratch, sub_idx, n_workers=args.workers)
            (rip, rc), (tem, tc), (grp, gc) = _split_topology_arrays(g, r, t, len(sub_idx))
            for fam, arr, c in (("ripser", rip, rc), ("template", tem, tc), ("graph", grp, gc)):
                acc.setdefault(fam, []).append(arr); cols[fam] = c

            tt, tcols = compute_toktopo(scratch, sub_idx, n_workers=args.workers, max_dim=1)
            acc.setdefault("_toktopo", []).append(tt); cols["_toktopo"] = tcols

            ia, iac = compute_intra_attn_features(scratch, sub_idx, device=device)
            acc.setdefault("intra_attn", []).append(ia); cols["intra_attn"] = iac

            pu, puc = compute_punct_dist(scratch, tokenizer, cfg["model"]["max_length"],
                                         raw_m.iloc[s:e].reset_index(drop=True),
                                         cfg["data"]["text_col"], n_workers=args.workers)
            acc.setdefault("punct", []).append(pu); cols["punct"] = puc

            shutil.rmtree(scratch, ignore_errors=True)
            print(f"  [{split}] {e}/{N}  ({time.time()-t0:.0f}s)", flush=True)

        # split the pooled toktopo block into its two families, as stage 02 does
        tt = np.concatenate(acc.pop("_toktopo"), axis=0); tcols = cols.pop("_toktopo")
        pd_cols = [i for i, c in enumerate(tcols) if c.startswith("toktopo_h")]
        gr_cols = [i for i, c in enumerate(tcols)
                   if not c.startswith("toktopo_h") and "_xbc_" not in c]
        write_family_file(out_dir, cfg_name, "toktopo_pd", split,
                          tt[:, pd_cols], [tcols[i] for i in pd_cols], idxs)
        write_family_file(out_dir, cfg_name, "toktopo_graph", split,
                          tt[:, gr_cols], [tcols[i] for i in gr_cols], idxs)
        for fam, parts in acc.items():
            write_family_file(out_dir, cfg_name, fam, split,
                              np.concatenate(parts, axis=0), cols[fam], idxs)
        print(f"[{split}] {N} samples, peak attention on disk = {args.chunk} "
              f"({time.time()-t0:.0f}s)", flush=True)

    if args.verify_against:
        ok = True
        for split in args.splits:
            for fam in list(M2) + list(TOKTOPO) + ["intra_attn", "punct"]:
                a = out_dir / "features" / f"{cfg_name}_{fam}_{split}.npz"
                b = args.verify_against / "features" / f"{cfg_name}_{fam}_{split}.npz"
                if not b.exists():
                    continue
                x = np.load(a, allow_pickle=True)["features"]
                y = np.load(b, allow_pickle=True)["features"]
                same = x.shape == y.shape and np.allclose(x, y, rtol=1e-5, atol=1e-6,
                                                          equal_nan=True)
                ok &= same
                print(f"  {'OK ' if same else 'DIFF'} {fam}_{split}  {x.shape}")
        print("VERIFY", "PASS" if ok else "FAIL")


if __name__ == "__main__":
    main()