#!/usr/bin/env python3
import argparse,json,re
from pathlib import Path
ap=argparse.ArgumentParser();ap.add_argument("log");a=ap.parse_args()
t=Path(a.log).read_text(errors="replace") if Path(a.log).is_file() else ""
def has(p): return re.search(p,t,re.I|re.S) is not None
root="UNCLASSIFIED"
if has(r"on startup is less than desired GPU memory utilization"):
    root="STARTUP_UTIL_GATE"
elif has(r"Invalid address format:"):
    root="RAY_ADDRESS_FORMAT"
elif has(r"CUDA out of memory|torch\.OutOfMemoryError"):
    oom=min([m.start() for m in re.finditer(r"CUDA out of memory|torch\.OutOfMemoryError",t,re.I)] or [10**18])
    loaded=max([m.start() for m in re.finditer(r"Loading model weights took|Model loading took",t,re.I)] or [-1])
    root="CUDA_OOM_POSTLOAD" if loaded>=0 and loaded<oom else "CUDA_OOM_DURING_MODEL_LOAD"
elif has(r"Out of memory:\s*Killed process|oom-kill|\bSIGKILL\b|exit code -9"):
    root="HOST_OOM_KILL"
elif has(r"nccl(?:System|Internal|Remote)Error|NCCL.*(?:ERROR|unhandled)"):
    root="NCCL_ERROR"
elif has(r"Gloo.*(?:connectFullMesh|failed|error)"):
    root="GLOO_ERROR"
elif has(r"LoRA.*(?:error|failed|unsupported|incompatible)|(?:error|failed).*LoRA"):
    root="LORA_ERROR"
print(json.dumps({"root_cause":root},indent=2))