"""Merge the many small Pass A/B1 shard groups of a store into a few large ones.

Pass A flushed a shard group whenever a worker had buffered ~1.2 GB of Fock data, which bounds
parser memory but leaves hundreds of groups per 100k calculations and tens of thousands of
metadata files for a loader to touch. This rewrites a store as, per dataset, the smallest number
of groups whose on-disk size stays under --group-bytes (measured on the p2 side, so p1 and p2 get
the same row layout), and writes consolidated metadata so opening a group is one JSON read.

Row order inside a merged group is the concatenation of the source groups in name order; every
ragged array is concatenated and its offsets rebased; the per-calculation attrs lists are
concatenated. Chunk shapes and codecs come from the source arrays; shard files hold
--chunks-per-shard chunks. Writing is done in whole-shard blocks by a pool of workers (one task
per destination array per block), so memory per worker stays around one shard.

    python merge_store.py --src $PSCRATCH/omol_100k --dst $PSCRATCH/omol_100k_m --group-bytes 32e9
"""
from __future__ import annotations
import argparse, glob, json, os, shutil, sys, time, traceback
import multiprocessing as mp
import numpy as np
import zarr

zarr.config.set({"threading.max_workers": 1, "async.concurrency": 2})
try:
    import numcodecs.blosc
    numcodecs.blosc.set_nthreads(1)
    numcodecs.blosc.use_threads = False
except Exception:
    pass

sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))

DROP_ATTRS = {"repack_verified", "occ_derived_verified", "mo_gbw_root", "n_calc", "calc_id",
              "rel_path", "hftyp"}
LIST_ATTRS = ("calc_id", "rel_path", "hftyp")


def groups_by_dataset(root, side):
    out = {}
    for depth in ("*", "*/*/*"):
        for gp in glob.glob(os.path.join(root, side, depth, "*.zarr")):
            ds = os.path.relpath(os.path.dirname(gp), os.path.join(root, side))
            out.setdefault(ds, []).append(gp)
    return {ds: sorted(v) for ds, v in out.items()}


def dir_bytes(path):
    return sum(os.path.getsize(os.path.join(r, f)) for r, _, fs in os.walk(path) for f in fs)


def plan_groups(src, group_bytes):
    """Split each dataset's shard list (sorted by name) into runs whose p2 bytes stay under budget."""
    p2 = groups_by_dataset(src, "p2")
    plan = []
    for ds, shards in sorted(p2.items()):
        run, run_b = [], 0
        for gp in shards:
            b = dir_bytes(gp)
            if run and run_b + b > group_bytes:
                plan.append((ds, run)); run, run_b = [], 0
            run.append(os.path.basename(gp)); run_b += b
        if run:
            plan.append((ds, run))
    return plan


def make_group(args):
    """Create one destination group (both sides): metadata, attrs, offsets. Returns write tasks."""
    src, dst, side, ds, gname, members, cps = args
    try:
        srcs = [zarr.open_group(os.path.join(src, side, ds, m), mode="r") for m in members]
        out_path = os.path.join(dst, side, ds, gname)
        if os.path.isdir(out_path):
            shutil.rmtree(out_path)
        os.makedirs(os.path.dirname(out_path), exist_ok=True)
        gd = zarr.open_group(out_path, mode="w")
        attrs = {k: v for k, v in dict(srcs[0].attrs).items() if k not in DROP_ATTRS}
        for g in srcs[1:]:
            for k, v in attrs.items():
                if g.attrs.get(k) != v:
                    raise RuntimeError(f"attr {k} differs between source groups of {ds}")
        for k in LIST_ATTRS:
            attrs[k] = sum((list(g.attrs[k]) for g in srcs), [])
        attrs["n_calc"] = int(sum(int(g.attrs["n_calc"]) for g in srcs))
        attrs["merged_from"] = members
        gd.attrs.update(attrs)
        names = sorted(srcs[0].array_keys())
        for g in srcs[1:]:
            if sorted(g.array_keys()) != names:
                raise RuntimeError(f"array list differs between source groups of {ds}")
        tasks = []
        for name in names:
            arrs = [g[name] for g in srcs]
            if name.endswith("_offsets"):
                parts, base = [np.asarray(arrs[0][...])], int(arrs[0][-1])
                for a in arrs[1:]:
                    o = np.asarray(a[...])
                    parts.append(o[1:] + base); base += int(o[-1])
                offs = np.concatenate(parts)
                z = gd.create_array(name=name, shape=offs.shape, chunks=offs.shape, shards=offs.shape,
                                    dtype=offs.dtype, compressors=arrs[0].compressors)
                z[...] = offs
                continue
            lens = [int(a.shape[0]) for a in arrs]
            total = sum(lens)
            shape = (total,) + tuple(arrs[0].shape[1:])
            chunk0 = int(arrs[0].chunks[0])
            chunks = (max(1, min(total, chunk0)),) + tuple(arrs[0].shape[1:])
            n_chunks = max(1, -(-total // chunks[0]))
            shard_len = chunks[0] * min(cps, n_chunks)
            shards = (shard_len,) + tuple(arrs[0].shape[1:])
            gd.create_array(name=name, shape=shape, chunks=chunks, shards=shards,
                            dtype=arrs[0].dtype, compressors=arrs[0].compressors)
            if total == 0:
                continue
            starts = np.concatenate([[0], np.cumsum(lens)[:-1]])
            for a0 in range(0, total, shard_len):
                a1 = min(total, a0 + shard_len)
                pieces = []
                for m, s0, n in zip(members, starts, lens):
                    lo, hi = max(a0, s0), min(a1, s0 + n)
                    if hi > lo:
                        pieces.append((m, int(lo - s0), int(hi - s0)))
                tasks.append((src, side, ds, out_path, name, int(a0), int(a1), pieces))
        return out_path, tasks, ""
    except Exception:
        return os.path.join(dst, side, ds, gname), [], traceback.format_exc(limit=4)


def write_block(args):
    src, side, ds, out_path, name, a0, a1, pieces = args
    t0 = time.time()
    try:
        chunks = [np.asarray(zarr.open_group(os.path.join(src, side, ds, m), mode="r")[name][lo:hi])
                  for m, lo, hi in pieces]
        block = np.concatenate(chunks) if len(chunks) > 1 else chunks[0]
        z = zarr.open_array(out_path + "/" + name, mode="r+")
        z[a0:a1] = block
        return name, block.nbytes, time.time() - t0, ""
    except Exception:
        return name, 0, time.time() - t0, traceback.format_exc(limit=4)


def verify_group(args):
    """Every array of the merged group equals the concatenation of its sources (full compare for
    arrays under 4 M elements, 3 random 200k windows otherwise); then consolidate metadata."""
    src, dst, side, ds, gname = args
    try:
        gd = zarr.open_group(os.path.join(dst, side, ds, gname), mode="r+")
        members = gd.attrs["merged_from"]
        srcs = [zarr.open_group(os.path.join(src, side, ds, m), mode="r") for m in members]
        rng = np.random.default_rng(abs(hash(gname)) % (2**32))
        for name in gd.array_keys():
            if name.endswith("_offsets"):
                parts, base = [np.asarray(srcs[0][name][...])], int(srcs[0][name][-1])
                for g in srcs[1:]:
                    o = np.asarray(g[name][...]); parts.append(o[1:] + base); base += int(o[-1])
                if not np.array_equal(np.asarray(gd[name][...]), np.concatenate(parts)):
                    raise RuntimeError(f"{name} offsets differ")
                continue
            lens = [int(g[name].shape[0]) for g in srcs]
            starts = np.concatenate([[0], np.cumsum(lens)[:-1]])
            total = int(gd[name].shape[0])
            if total != sum(lens):
                raise RuntimeError(f"{name} length {total} != {sum(lens)}")
            if total == 0:
                continue
            if gd[name].size <= 4_000_000:
                windows = [(0, total)]
            else:
                windows = [(int(s), int(min(total, s + 200_000))) for s in rng.integers(0, total, 3)]
            for w0, w1 in windows:
                got = np.asarray(gd[name][w0:w1])
                exp = []
                for g, s0, n in zip(srcs, starts, lens):
                    lo, hi = max(w0, s0), min(w1, s0 + n)
                    if hi > lo:
                        exp.append(np.asarray(g[name][lo - s0:hi - s0]))
                exp = np.concatenate(exp)
                ok = np.array_equal(got, exp, equal_nan=True) if got.dtype.kind == "f" else np.array_equal(got, exp)
                if not ok:
                    raise RuntimeError(f"{name} content differs in [{w0},{w1})")
        n_ids = len(gd.attrs["calc_id"])
        if n_ids != sum(len(g.attrs["calc_id"]) for g in srcs) or n_ids != int(gd.attrs["n_calc"]):
            raise RuntimeError("calc_id list length")
        zarr.consolidate_metadata(gd.store)
        return gname, side, ds, "ok", ""
    except Exception:
        return gname, side, ds, "fail", traceback.format_exc(limit=4)


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--src", required=True)
    ap.add_argument("--dst", required=True)
    ap.add_argument("--group-bytes", type=float, default=32e9)
    ap.add_argument("--chunks-per-shard", type=int, default=64)
    ap.add_argument("--workers", type=int, default=96)
    ap.add_argument("--datasets", default="", help="comma list to restrict (smoke tests)")
    args = ap.parse_args()
    src, dst = os.path.abspath(args.src), os.path.abspath(args.dst)
    t0 = time.time()
    plan = plan_groups(src, args.group_bytes)
    if args.datasets:
        keep = set(args.datasets.split(","))
        plan = [p for p in plan if p[0] in keep]
    counter = {}
    named = []
    for ds, members in plan:
        k = counter.get(ds, 0); counter[ds] = k + 1
        named.append((ds, f"group_{k:03d}.zarr", members))
    print(f"plan: {sum(len(m) for _, _, m in named)} source groups -> {len(named)} merged groups "
          f"over {len(counter)} datasets  ({(time.time()-t0)/60:.1f} min)", flush=True)
    for ds, n in sorted(counter.items(), key=lambda kv: -kv[1])[:8]:
        print(f"   {ds}: {n} groups", flush=True)

    # phase 1: create groups and gather write tasks (parallel over groups)
    jobs = [(src, dst, side, ds, g, m, args.chunks_per_shard) for ds, g, m in named for side in ("p1", "p2")]
    tasks = []
    with mp.Pool(min(32, len(jobs))) as pool:
        for out_path, t, err in pool.imap_unordered(make_group, jobs):
            if err:
                print(f"FAIL creating {out_path}\n{err}", flush=True); sys.exit(1)
            tasks.extend(t)
    # biggest blocks first so the tail is short
    tasks.sort(key=lambda t: -(t[6] - t[5]))
    print(f"phase 1 done: {len(jobs)} groups created, {len(tasks)} write blocks  "
          f"({(time.time()-t0)/60:.1f} min)", flush=True)

    # phase 2: write blocks
    n_ok = n_fail = 0; nbytes = 0
    with mp.Pool(min(args.workers, max(1, len(tasks)))) as pool:
        for k, (name, nb, dt, err) in enumerate(pool.imap_unordered(write_block, tasks), 1):
            if err:
                n_fail += 1; print(f"FAIL block {name}\n{err}", flush=True)
            else:
                n_ok += 1; nbytes += nb
            if k % 500 == 0 or k == len(tasks):
                el = time.time() - t0
                print(f"  blocks {k}/{len(tasks)} ok={n_ok} fail={n_fail} {nbytes/1e12:.3f} TB "
                      f"{el/60:.1f} min", flush=True)
    if n_fail:
        print("write failures, stopping before verification"); sys.exit(1)

    # phase 3: verify and consolidate
    vjobs = [(src, dst, side, ds, g) for ds, g, _ in named for side in ("p1", "p2")]
    v_ok = v_fail = 0
    with mp.Pool(min(args.workers, len(vjobs))) as pool:
        for gname, side, ds, status, err in pool.imap_unordered(verify_group, vjobs):
            if status == "ok":
                v_ok += 1
            else:
                v_fail += 1; print(f"VERIFY FAIL {side}/{ds}/{gname}\n{err}", flush=True)
    for f in glob.glob(os.path.join(src, "*")):
        if os.path.isfile(f):
            shutil.copy2(f, dst)
    if os.path.isdir(os.path.join(src, "code")):
        shutil.copytree(os.path.join(src, "code"), os.path.join(dst, "code"), dirs_exist_ok=True)
    print(f"\ndone: {len(named)} merged groups per side, verify ok {v_ok} fail {v_fail}, "
          f"{nbytes/1e12:.3f} TB written, wall {(time.time()-t0)/60:.1f} min")


if __name__ == "__main__":
    mp.set_start_method("fork", force=True)
    main()