"""
run_ingest.py — AtmosIQ Phase-2 orchestrator (checkpointed, never-skip build).

GUARANTEE: no file is EVER skipped. Every source is resumable and asserts
100% file completeness before writing its output. If a download can't be
recovered after retries, the run STOPS (raises) — it never drops data.

Feeding modes:
  --local DIR : process files already on disk (use `hf download` for whole repos)
  (default)   : stream each file from HF to scratch, process, delete (for MODIS)

Env: ATMOS_OUT, ATMOS_GRID, ATMOS_SCRATCH, HF_TOKEN
Commands: <source...> | --local DIR | --merge | --check | --no-merge | --force
"""
from __future__ import annotations
import os, sys, glob, math, pathlib, fnmatch, time
os.environ.setdefault("HF_HUB_DISABLE_XET", "1")   # avoid Xet CAS 401 churn
sys.path.insert(0, str(pathlib.Path(__file__).resolve().parent))
sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[1] / "grid"))

import numpy as np
import pandas as pd
import pyarrow as pa, pyarrow.parquet as pq
from aggregation import SPECS, ingest, _to_date, _clean, _select_nearest_ist
from grid_index import GridIndex

OUT     = pathlib.Path(os.environ.get("ATMOS_OUT", "data/ingest"))
GRID    = pathlib.Path(os.environ.get("ATMOS_GRID", "data/grid/grid_nodes.parquet"))
SCRATCH = pathlib.Path(os.environ.get("ATMOS_SCRATCH", "scratch"))
CHUNK   = 500_000
OUT.mkdir(parents=True, exist_ok=True); SCRATCH.mkdir(parents=True, exist_ok=True)
CKPT = OUT / ".ckpt"; CKPT.mkdir(exist_ok=True)

REG = {
 "s5p_no2":  ("Atmos_Pollutants_Particles","data/NO2/*.csv","s5p_no2","stream"),
 "s5p_so2":  ("Atmos_Pollutants_Particles","data/SO2/*.csv","s5p_so2","stream"),
 "s5p_co":   ("Atmos_Pollutants_Particles","data/CO/*.csv","s5p_co","stream"),
 "s5p_o3":   ("Atmos_Pollutants_Particles","data/O3/*.csv","s5p_o3","stream"),
 "s5p_hcho": ("Atmos_Pollutants_Particles","data/HCHO/*.csv","s5p_hcho","stream"),
 "s5p_aer_ai":("Atmos_Pollutants_Particles","data/AER_AI/*.csv","s5p_aer_ai","stream"),
 "s5p_ch4":  ("Atmos_Pollutants_Particles","data/CH4/*.csv","s5p_ch4","stream"),
 "s5p_cloud":("Atmos_Pollutants_Particles","data/CLOUD/*.csv","s5p_cloud","stream"),
 "modis":    ("Atmos_MODIS_AOD","data/*.csv","modis","stream"),
 "viirs":    ("Atmos_VIIRS_AOD","data/*.csv","viirs","stream"),
 "era5_daily":     ("Atmos_ERA5_Meteorology","data/*.csv","era5_daily","centroid"),
 "era5_wind_1330": ("Atmos_ERA5_Meteorology","data/*.csv","era5_wind_1330","centroid"),
 "firms":    ("Atmos_FIRMS_Fire","data/*.csv","firms","stream"),
 "dem":      ("Atmos_DEM_Copernicus","*.tif",None,"dem"),
}
HF_ORG = "AtmosIQ"

def hf_list(repo, subpath):
    from huggingface_hub import HfApi
    api = HfApi(token=os.environ.get("HF_TOKEN"))
    return [f for f in api.list_repo_files(f"{HF_ORG}/{repo}", repo_type="dataset")
            if fnmatch.fnmatch(f, subpath)]

def local_files(root, repo, subpath):
    return sorted(glob.glob(str(pathlib.Path(root) / repo / subpath)))

def _dl_retry(repo, f, tries=10):
    """Download one file with exp backoff. NEVER skips: raises after `tries`
    so the run STOPS rather than dropping data."""
    from huggingface_hub import hf_hub_download
    for a in range(tries):
        try:
            return hf_hub_download(f"{HF_ORG}/{repo}", filename=f, repo_type="dataset",
                                   local_dir=str(SCRATCH), token=os.environ.get("HF_TOKEN"))
        except Exception as e:
            if a == tries - 1:
                raise RuntimeError(f"STOP: {f} failed after {tries} tries ({type(e).__name__}). "
                                   f"Nothing skipped — fix & re-run to resume.") from e
            w = min(120, 3 * 2 ** a); print(f"    retry {a+1}/{tries} {f} in {w}s"); time.sleep(w)

# ---- checkpoint helpers (resume without loss) ------------------------------
def _ckpt_load(name):
    ck = CKPT / f"{name}.ckpt.parquet"                 # new atomic single-file format
    if ck.exists():
        t = pq.read_table(ck); done = set(t.schema.metadata[b"done"].decode().splitlines())
        return done, t.to_pandas().set_index(["node_id", "date"])
    man = CKPT / f"{name}.manifest.txt"; acc = CKPT / f"{name}.acc.parquet"   # backward-compat resume
    if man.exists() and acc.exists():
        return set(man.read_text().splitlines()), pd.read_parquet(acc).set_index(["node_id", "date"])
    return set(), None

def _ckpt_save(name, done, accdf):
    """ONE atomic parquet: accumulator + done-list in metadata. Crash-safe — the
    file is either fully the old checkpoint or fully the new one, never partial,
    so done and accumulator can never drift (no double-count on resume)."""
    ck = CKPT / f"{name}.ckpt.parquet"
    t = pa.Table.from_pandas(accdf.reset_index(), preserve_index=False)
    md = dict(t.schema.metadata or {}); md[b"done"] = ("\n".join(sorted(done))).encode()
    t = t.replace_schema_metadata(md)
    tmp = ck.with_suffix(".tmp"); pq.write_table(t, tmp); os.replace(tmp, ck)


_WORKER_GI = None
def _process_one(args):
    """Worker: one file -> partial groupby (sum_<col> + n_obs). If `repo` is set,
    the worker DOWNLOADS the file (with retry) to scratch, processes, deletes —
    enabling parallel HF streaming. Each worker lazy-loads its GridIndex once
    (Windows spawn-safe)."""
    path, speckey, grid_path, repo, scratch = args
    import os, time, pandas as pd, pathlib
    from aggregation import SPECS, _to_date, _clean
    from grid_index import GridIndex
    global _WORKER_GI
    if _WORKER_GI is None:
        _WORKER_GI = GridIndex.from_parquet(grid_path)
    spec = SPECS[speckey]; gi = _WORKER_GI
    local = path
    if repo is not None:                      # HF: download this file with retry
        from huggingface_hub import hf_hub_download
        for a in range(10):
            try:
                local = hf_hub_download(f"AtmosIQ/{repo}", filename=path, repo_type="dataset",
                                        local_dir=scratch, token=os.environ.get("HF_TOKEN")); break
            except Exception as e:
                if a == 9: raise RuntimeError(f"STOP: {path} failed after 10 tries ({type(e).__name__})") from e
                time.sleep(min(120, 3 * 2 ** a))
    try:
        reader = [pd.read_parquet(local)] if str(local).endswith(".parquet") else pd.read_csv(local, chunksize=500_000)
        acc = None
        for chunk in reader:
            df = _clean(_to_date(chunk, spec), spec)
            if not len(df): continue
            df["node_id"] = gi.assign_node_id(df[spec.lat_col].values, df[spec.lon_col].values)
            df = df[df["node_id"].notna()]
            if not len(df): continue
            g = df.groupby(["node_id","date"])
            part = g[spec.value_cols].sum().add_prefix("sum_"); part["n_obs"] = g.size()
            acc = part if acc is None else acc.add(part, fill_value=0)
    finally:
        if repo is not None:
            try: os.remove(local)
            except OSError: pass
    return pathlib.Path(path).name, acc

# ---- streaming containment (checkpointed, never-skip) ----------------------
class StreamAgg:
    def __init__(self, cols, acc=None): self.cols=cols; self.acc=acc
    def push(self, part): self.acc = part if self.acc is None else self.acc.add(part, fill_value=0)
    def finalize(self, agg):
        df=self.acc.reset_index()
        if agg=="mean":
            for c in self.cols: df[c]=df["sum_"+c]/df["n_obs"]
            df=df.drop(columns=["sum_"+c for c in self.cols])
        elif agg=="count":
            for c in self.cols: df[c]=df["sum_"+c]/df["n_obs"]; df=df.drop(columns=["sum_"+c])
            df=df.rename(columns={"n_obs":"count"})
        return df

def run_stream(repo, files, use_hf, root, gi, spec):
    done, acc0 = _ckpt_load(spec.name)
    agg = StreamAgg(spec.value_cols, acc0)
    total = len(files); n = len(done)
    JOBS = int(os.environ.get("ATMOS_JOBS", "1"))
    BATCH = int(os.environ.get("ATMOS_BATCH", str(max(JOBS * 4, 32))))  # files per batch
    if JOBS > 1:                                     # parallel, BATCH-WISE (predictable, clean state each batch)
        from multiprocessing import Pool
        gp = str(GRID.resolve())
        todo = [f for f in files if pathlib.Path(f).name not in done]
        # process in fixed batches: download+process a batch in parallel, then
        # checkpoint ONCE, then next batch. Nothing races beyond one batch.
        for bstart in range(0, len(todo), BATCH):
            batch = todo[bstart:bstart + BATCH]
            if use_hf:
                tasks = [(f, spec.name, gp, repo, str(SCRATCH)) for f in batch]
            else:
                tasks = [(f, spec.name, gp, None, None) for f in batch]
            with Pool(min(JOBS, len(batch))) as pool:   # fresh pool per batch = clean workers
                for name, part in pool.imap_unordered(_process_one, tasks, chunksize=1):
                    if part is not None: agg.push(part)
                    done.add(name); n += 1
                    print(f"    [{spec.name}] {n}/{total}", end="\r")
            _ckpt_save(spec.name, done, agg.acc)        # checkpoint after each COMPLETE batch
        if len(done) != total:
            raise RuntimeError(f"[{spec.name}] INCOMPLETE {len(done)}/{total}; refusing to write.")
        out = agg.finalize(spec.agg).rename(columns=spec.rename)
        return out.merge(gi.centroids(), on="node_id", how="left")
    for f in files:
        name = pathlib.Path(f).name
        if name in done:            # already processed in a prior run — resume, NOT a skip
            continue
        path = _dl_retry(repo, f) if use_hf else f
        try:
            reader = [pd.read_parquet(path)] if str(path).endswith(".parquet") else pd.read_csv(path, chunksize=CHUNK)
            for chunk in reader:
                df = _clean(_to_date(chunk, spec), spec)
                if not len(df): continue
                df["node_id"] = gi.assign_node_id(df[spec.lat_col].values, df[spec.lon_col].values)
                df = df[df["node_id"].notna()]
                if not len(df): continue
                g = df.groupby(["node_id","date"])
                part = g[spec.value_cols].sum().add_prefix("sum_"); part["n_obs"]=g.size()
                agg.push(part)
        finally:
            if use_hf:
                try: os.remove(path)
                except OSError: pass
        done.add(name); n += 1
        if n % 50 == 0: _ckpt_save(spec.name, done, agg.acc)
        print(f"    [{spec.name}] {n}/{total}", end="\r")
    _ckpt_save(spec.name, done, agg.acc)
    if len(done) != total:                       # completeness gate — never finish partial
        raise RuntimeError(f"[{spec.name}] INCOMPLETE {len(done)}/{total}; refusing to write. Re-run to resume.")
    out = agg.finalize(spec.agg).rename(columns=spec.rename)
    return out.merge(gi.centroids(), on="node_id", how="left")

def run_centroid(repo, files, use_hf, root, gi, spec):
    frames=[]; total=len(files)
    for i,f in enumerate(files):
        path=_dl_retry(repo,f) if use_hf else f
        try:
            df=pd.read_parquet(path) if str(path).endswith(".parquet") else pd.read_csv(path)
            frames.append(ingest(df,gi,spec))
        finally:
            if use_hf:
                try: os.remove(path)
                except OSError: pass
        print(f"    [{spec.name}] {i+1}/{total}", end="\r")
    if len(frames)!=total:
        raise RuntimeError(f"[{spec.name}] INCOMPLETE {len(frames)}/{total}; refusing to write.")
    return pd.concat(frames, ignore_index=True)

def run_dem(gi, use_hf, root):
    import rasterio
    nodes=gi.centroids().copy(); nodes["elevation_m"]=np.nan
    nodes["tlat"]=np.floor(nodes.centroid_lat).astype(int); nodes["tlon"]=np.floor(nodes.centroid_lon).astype(int)
    def tile(tlat,tlon):
        ns="N" if tlat>=0 else "S"; ew="E" if tlon>=0 else "W"
        fn=f"Copernicus_DSM_COG_10_{ns}{abs(tlat):02d}_00_{ew}{abs(tlon):03d}_00_DEM.tif"
        if use_hf:
            from huggingface_hub import hf_hub_download
            try: return hf_hub_download(f"{HF_ORG}/Atmos_DEM_Copernicus",filename=fn,repo_type="dataset",
                                        local_dir=str(SCRATCH),token=os.environ.get("HF_TOKEN"))
            except Exception: return None
        p=pathlib.Path(root)/"Atmos_DEM_Copernicus"/fn; return str(p) if p.exists() else None
    for (tlat,tlon),grp in nodes.groupby(["tlat","tlon"]):
        p=tile(tlat,tlon)
        if not p: continue
        with rasterio.open(p) as src:
            vals=[v[0] for v in src.sample(list(zip(grp.centroid_lon,grp.centroid_lat)))]
            nd=src.nodata; vals=[np.nan if (nd is not None and v==nd) else float(v) for v in vals]
            nodes.loc[grp.index,"elevation_m"]=vals
        if use_hf:
            try: os.remove(p)
            except OSError: pass
    return nodes[["node_id","elevation_m"]]

def merge_master(gi):
    daily=[d for d in sorted(OUT.glob("*_daily.parquet"))]
    if not daily: print("  no *_daily.parquet"); return
    master=None
    for d in daily:
        df=pd.read_parquet(d); vc=[c for c in df.columns if c not in ("node_id","date","centroid_lat","centroid_lon","n_obs")]
        df=df[["node_id","date"]+vc]; master=df if master is None else master.merge(df,on=["node_id","date"],how="outer")
    master=master.merge(gi.centroids(),on="node_id",how="left"); master["_ym"]=master["date"].str.slice(0,7)
    mdir=OUT/"node_observations_daily"; mdir.mkdir(exist_ok=True)
    for ym,part in master.groupby("_ym"): part.drop(columns="_ym").to_parquet(mdir/f"{ym}.parquet",index=False)
    print(f"  master: {len(master):,} node-days, {master.shape[1]-1} cols, {master['_ym'].nunique()} partitions -> {mdir}")
    dem=OUT/"dem_static.parquet"
    if dem.exists():
        gn=pd.read_parquet(GRID).drop(columns=["elevation_m"],errors="ignore").merge(pd.read_parquet(dem),on="node_id",how="left")
        gn.to_parquet(GRID,index=False); print("  grid_nodes.elevation_m filled")

SANE={"s5p_no2_column":(-0.002,0.002),"s5p_so2_column":(-0.02,0.02),"s5p_co_column":(0,0.1),
      "s5p_o3_column":(0,0.25),"s5p_hcho_column":(-0.005,0.02),"s5p_aer_ai":(-30,35),
      "s5p_ch4_column":(1300,2400),"s5p_cloud_fraction":(0,1),"modis_aod":(0,5),"viirs_aod":(0,5),
      "era5_temperature":(230,325),"era5_boundary_layer_height":(0,5000),"u_1330ist":(-30,30),
      "v_1330ist":(-30,30),"fire_count":(0,1e6),"elevation_m":(-50,8850)}
def check():
    print("=== range check ===")
    for d in sorted(OUT.glob("*_daily.parquet")):
        df=pd.read_parquet(d); print(f"\n{d.name}: {len(df):,} rows")
        for c in df.columns:
            if c not in SANE: continue
            s=df[c]; lo,hi=SANE[c]; bad=((s<lo)|(s>hi)).sum()
            print(f"  {c}: min={s.min():.4g} max={s.max():.4g} null%={100*s.isna().mean():.1f}{'  ⚠OUT-OF-RANGE' if bad else ''}")

def main(argv):
    if "--check" in argv: check(); return
    use_hf = "--local" not in argv
    root = argv[argv.index("--local")+1] if "--local" in argv else None
    gi = GridIndex.from_parquet(str(GRID))
    print(f"grid: {len(gi)} cells | mode: {'HF-stream' if use_hf else 'local:'+str(root)}")
    if "--merge" in argv: merge_master(gi); return
    skip={"--local","--merge","--check","--no-merge","--force",root}
    only=[a for a in argv if a not in skip and not a.startswith("--")]
    for key,(repo,subpath,speckey,kind) in REG.items():
        if only and key not in only: continue
        outp=OUT/("dem_static.parquet" if kind=="dem" else f"{key}_daily.parquet")
        if outp.exists() and "--force" not in argv: print(f"  skip {key} (exists)"); continue
        if "--force" in argv:   # forced redo: wipe stale checkpoint so it starts clean
            for suf in (".manifest.txt",".acc.parquet",".ckpt.parquet"):
                fp=CKPT/f"{key}{suf}"
                if fp.exists(): fp.unlink()
        if kind=="dem":
            print(f"  {key} [dem]"); out=run_dem(gi,use_hf,root)
        else:
            files=hf_list(repo,subpath) if use_hf else local_files(root,repo,subpath)
            if not files: print(f"  ! {key}: 0 files ({repo}/{subpath})"); continue
            print(f"  {key}: {len(files)} files [{kind}]")
            out=run_stream(repo,files,use_hf,root,gi,SPECS[speckey]) if kind=="stream" \
                else run_centroid(repo,files,use_hf,root,gi,SPECS[speckey])
        out.to_parquet(outp,index=False); print(f"\n    -> {outp} ({len(out):,} rows)")
        # clear this source's checkpoint once its final output is safely written
        for suf in (".manifest.txt",".acc.parquet",".ckpt.parquet"):
            fp=CKPT/f"{key}{suf}"
            if fp.exists(): fp.unlink()
    if "--no-merge" not in argv: merge_master(gi)
    print("done.")

if __name__=="__main__": main(sys.argv[1:])