#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
XERO GPU TRAIN DAEMON — keep BOTH Tesla T4s processing the live corpus, forever.
Author:  Michael Laurence Curzi
Company: ZEDEC AI / 36N9 Genetics LLC
License: MIT (Attribution Required)

Pairs with the polymath ingest daemon: as new knowledge lands in
data/fresh_corpus.jsonl, this folds fresh batches into a dual-GPU,
data-seeded tensor kernel so the cards never sit idle.

    PYTHONPATH=modules python3 tests/gpu_train_daemon.py [--burst 15] [--fraction 0.65]

Honest scope: the organism's knowledge core is updated by the culturer/polymath
(CPU + network). This daemon does NOT pretend to gradient-train that core; it
runs a genuine, data-seeded tensor workload over the ingested corpus to (a)
exercise both T4s at high utilisation and (b) compute a dense representation of
the live corpus. Progress -> testing_logs/GPU_TRAIN.json (read by the monitor).
"""
from __future__ import annotations

import json
import os
import sys
import time

ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, os.path.join(ROOT, "modules"))
LOG = os.path.join(ROOT, "testing_logs")
os.makedirs(LOG, exist_ok=True)
OUT = os.path.join(LOG, "GPU_TRAIN.json")

from xero_resources import detect, saturate_gpus

DIM = 8192


def _num_arg(flag: str, default: float) -> float:
    if flag in sys.argv:
        try:
            return float(sys.argv[sys.argv.index(flag) + 1])
        except Exception:
            return default
    return default


BURST = _num_arg("--burst", 15.0)
FRACTION = _num_arg("--fraction", 0.7)


def _load_texts(limit: int = 4000) -> list:
    texts = []
    try:
        from xero_knowledge_sources import load_corpus
        for u in load_corpus():
            t = getattr(u, "text", None)
            if t:
                texts.append(t)
            if len(texts) >= limit:
                break
    except Exception:
        pass
    fresh = os.path.join(ROOT, "data", "fresh_corpus.jsonl")
    if os.path.exists(fresh):
        try:
            with open(fresh, encoding="utf-8", errors="ignore") as f:
                for line in f:
                    try:
                        t = json.loads(line).get("text")
                        if t:
                            texts.append(t)
                    except Exception:
                        pass
        except Exception:
            pass
    return texts


def _featurize(texts: list) -> list:
    """Hashed byte-trigram histogram -> DIM float vector (the data fold-in seed)."""
    vec = [0.0] * DIM
    total = 0
    for t in texts:
        b = (t or "")[:2000].encode("utf-8", "ignore")
        for j in range(len(b) - 2):
            vec[((b[j] << 16) | (b[j + 1] << 8) | b[j + 2]) % DIM] += 1.0
            total += 1
    if total:
        m = max(vec) or 1.0
        vec = [v / m for v in vec]
    return vec


def main() -> int:
    info = detect()
    print(f"GPU TRAIN DAEMON · backend={info['backend']} · gpus={info['gpus']} · "
          f"vram={info['vram_total_gb']}GB · burst={BURST}s · frac={FRACTION}")
    if info["backend"] != "cuda":
        json.dump({"available": False, "reason": "no cuda backend (install CUDA torch)",
                   "backend": info["backend"],
                   "ts": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())},
                  open(OUT, "w"), indent=2)
        print("  no CUDA backend visible — install CUDA torch to use the T4s. Exiting.")
        return 1

    t0 = time.time()
    bursts = 0
    offset = 0
    cached: list = []
    while True:
        if bursts % 8 == 0 or not cached:
            cached = _load_texts()
        units = len(cached)
        start = offset % max(1, units)
        batch = cached[start:start + 256] or cached[:256]
        offset += 256
        seed = _featurize(batch) if batch else None
        r = saturate_gpus(seconds=BURST, fraction=FRACTION, seed=seed)
        bursts += 1
        rec = {"ts": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
               "bursts": bursts, "corpus_units": units,
               "elapsed_s": round(time.time() - t0, 1), **r}
        with open(OUT, "w") as f:
            json.dump(rec, f, indent=2)
        if r.get("available"):
            print(f"  burst {bursts} · {r['gpus']}xGPU · matrix {r['matrix']} · "
                  f"{r['steps']} steps · {r.get('gflops_est')} GFLOP · "
                  f"vram {r.get('vram_used_gb')}GB · units {units}")
        else:
            print(f"  burst {bursts} · GPU unavailable: {r.get('reason')}")
            time.sleep(5)


if __name__ == "__main__":
    try:
        sys.exit(main())
    except KeyboardInterrupt:
        print("\nGPU train daemon stopped.")