#!/usr/bin/env python3
"""
embed_reports.py — embed EQC QA chunks with gemini-embedding-2-preview.

LOCKED: gemini-embedding-2-preview, RETRIEVAL_DOCUMENT, 768-dim, L2-normalized.
IPv4 egress forced (net_ipv4). Key = veretex_api_key in ../.env.

Modes:
  realtime (default) — resumable streaming; on sustained 429 print how to fall
                       back to batch and exit non-zero.
  batch    — submit Gemini Batch API job (schema mirrors marine_rag/embed.py),
             resumable via --mode poll. Sentinel EMBED_DONE written when >=99%.

Usage:
  embed_reports.py                 # realtime
  embed_reports.py --mode batch    # submit batch job
  embed_reports.py --mode poll     # download completed batch job
"""
import argparse
import json
import os
import sys
import time
from pathlib import Path

import numpy as np

# force IPv4 (reuse marine_rag net_ipv4)
sys.path.insert(0, "/Users/dmpantiu/copernicus_mcp/marine_rag")
import net_ipv4  # noqa: F401,E402

ROOT = Path(__file__).resolve().parent
IN = ROOT / "chunks.jsonl"
OUT = ROOT / "chunks_embedded.jsonl"
BATCH_INPUT = ROOT / "batch_embed_input.jsonl"
JOB_FILE = ROOT / "batch_job.txt"
SENTINEL = ROOT / "EMBED_DONE"

MODEL = "gemini-embedding-2-preview"
TASK = "RETRIEVAL_DOCUMENT"
DIM = 768
# Free-tier gemini-embedding-2 quota is tiny/fluctuating and counts per content.
# Keep requests small and well-spaced; the finisher loops passes until complete.
RT_BATCH = 5
RT_SLEEP = 20.0


def log(*a):
    print(*a, file=sys.stderr, flush=True)


def resolve_key() -> str:
    for var in ("GOOGLE_API_KEY", "GEMINI_API_KEY"):
        if os.environ.get(var):
            return os.environ[var]
    for env in (Path("/Users/dmpantiu/copernicus_mcp/.env"),
                Path("/Users/dmpantiu/cmip6/cmip6_gpt/.env")):
        if env.exists():
            for line in env.read_text().splitlines():
                line = line.strip()
                if "api_key" in line.lower() and "=" in line and not line.startswith("#"):
                    return line.split("=", 1)[1].strip().strip('"').strip("'")
    raise SystemExit("No Gemini API key found.")


def get_client():
    from google import genai
    return genai.Client(api_key=resolve_key())


def l2(vec):
    a = np.array(vec, dtype=np.float32)
    n = np.linalg.norm(a)
    return (a / n).tolist() if n > 0 else a.tolist()


def load_chunks():
    return [json.loads(l) for l in open(IN)]


def embedded_keys() -> set:
    keys = set()
    if OUT.exists():
        for line in open(OUT):
            try:
                keys.add(json.loads(line)["chunk_id"])
            except Exception:
                pass
    return keys


def embed_realtime(chunks):
    from google.genai import types
    client = get_client()
    done = embedded_keys()
    if done:
        log(f"resume: {len(done)} already embedded")
    todo = [c for c in chunks if c["chunk_id"] not in done]
    log(f"to embed: {len(todo)} / {len(chunks)}")
    n = 0
    consecutive_429 = 0
    with open(OUT, "a", encoding="utf-8") as fout:
        for b in range(0, len(todo), RT_BATCH):
            batch = todo[b:b + RT_BATCH]
            # genai 2.10: a list[str] is treated as ONE content -> 1 embedding.
            # Wrap each text in a Content object to get one embedding per input.
            contents = [types.Content(parts=[types.Part(text=c["text_with_prefix"])])
                        for c in batch]
            ok = False
            for attempt in range(6):
                try:
                    if attempt > 0:
                        client = get_client()
                    r = client.models.embed_content(
                        model=MODEL, contents=contents,
                        config=types.EmbedContentConfig(
                            task_type=TASK, output_dimensionality=DIM))
                    if len(r.embeddings) != len(batch):
                        raise RuntimeError(
                            f"embedding count mismatch {len(r.embeddings)}!={len(batch)}")
                    for c, e in zip(batch, r.embeddings):
                        c["embedding"] = l2(e.values)
                        fout.write(json.dumps(c, ensure_ascii=False) + "\n")
                        n += 1
                    fout.flush()
                    ok = True
                    consecutive_429 = 0
                    break
                except Exception as e:
                    es = str(e)
                    if "IP address restriction" in es:
                        raise SystemExit(
                            "BLOCKED: Gemini key IP restriction. Whitelist this host's IP.")
                    if any(k in es for k in ("429", "RESOURCE_EXHAUSTED", "Quota exceeded")):
                        wait = 35
                    elif "client has been closed" in es:
                        wait = 2
                    else:
                        wait = min(8 * (2 ** attempt), 60)
                    log(f"  retry {attempt+1}/6 in {wait}s: {repr(e)[:120]}")
                    time.sleep(wait)
            if not ok:
                consecutive_429 += 1
                log(f"  FATAL skip batch of {len(batch)}")
                if consecutive_429 >= 3:
                    log("SUSTAINED 429 — realtime quota exhausted.")
                    log("Fall back to batch: python embed_reports.py --mode batch ; "
                        "then: python embed_reports.py --mode poll")
                    sys.exit(2)
            if n and n % 400 == 0:
                log(f"  [{n}/{len(todo)}]")
            time.sleep(RT_SLEEP)
    finalize(chunks)
    log(f"DONE realtime: {n} newly embedded -> {OUT}")


# ── batch fallback (mirrors marine_rag/embed.py) ─────────────────────────────
def prepare_batch(chunks):
    done = embedded_keys()
    todo = [c for c in chunks if c["chunk_id"] not in done]
    with open(BATCH_INPUT, "w", encoding="utf-8") as f:
        for c in todo:
            f.write(json.dumps({
                "key": c["chunk_id"],
                "request": {
                    "content": {"parts": [{"text": c["text_with_prefix"]}]},
                    "task_type": TASK,
                    "output_dimensionality": DIM,
                }}, ensure_ascii=False) + "\n")
    log(f"batch input: {BATCH_INPUT} ({len(todo)} reqs)")
    return todo


def submit_batch(chunks):
    prepare_batch(chunks)
    client = get_client()
    up = client.files.upload(file=str(BATCH_INPUT),
                             config={"display_name": "eqc_qa_embed", "mime_type": "jsonl"})
    job = client.batches.create_embeddings(
        model=MODEL, src={"file_name": up.name},
        config={"display_name": "eqc_qa_embeddings"})
    JOB_FILE.write_text(job.name)
    log(f"job: {job.name} state: {job.state} (saved {JOB_FILE})")


def _extract_values(resp: dict):
    for path in (("response", "embeddings"), ("response", "embedding"),
                 ("embeddings",), ("embedding",)):
        node = resp; ok = True
        for k in path:
            if isinstance(node, dict) and k in node:
                node = node[k]
            else:
                ok = False; break
        if not ok:
            continue
        if isinstance(node, list) and node and isinstance(node[0], dict) and "values" in node[0]:
            return node[0]["values"]
        if isinstance(node, dict) and "values" in node:
            return node["values"]
    return None


def poll_batch(chunks, wait=True):
    client = get_client()
    name = JOB_FILE.read_text().strip()
    while True:
        job = client.batches.get(name=name)
        state = str(job.state)
        log(f"  job {name}: {state}")
        if any(s in state for s in ("SUCCEEDED", "FAILED", "CANCELLED", "EXPIRED")):
            break
        if not wait:
            return
        time.sleep(30)
    if "SUCCEEDED" not in state:
        log(f"job not successful: {state}"); return
    by_key = {c["chunk_id"]: c for c in chunks}
    dest = getattr(job, "dest", None)
    fn = getattr(dest, "file_name", None) if dest else None
    lines = []
    if fn:
        lines = client.files.download(file=fn).decode("utf-8").strip().split("\n")
    elif dest and getattr(dest, "inlined_responses", None):
        lines = [json.dumps(r) for r in dest.inlined_responses]
    n = 0
    with open(OUT, "a", encoding="utf-8") as fout:
        for line in lines:
            if not line.strip():
                continue
            r = json.loads(line)
            k = r.get("key") or r.get("custom_id")
            vals = _extract_values(r)
            if k in by_key and vals:
                c = dict(by_key[k]); c["embedding"] = l2(vals)
                fout.write(json.dumps(c, ensure_ascii=False) + "\n"); n += 1
    log(f"downloaded {n} embeddings -> {OUT}")
    finalize(chunks)


def finalize(chunks):
    got = embedded_keys()
    frac = len(got) / max(1, len(chunks))
    log(f"coverage: {len(got)}/{len(chunks)} = {frac:.1%}")
    if frac >= 0.99:
        SENTINEL.write_text(f"{len(got)}/{len(chunks)}\n")
        log(f"SENTINEL {SENTINEL} written")


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--mode", choices=["realtime", "batch", "poll"], default="realtime")
    a = ap.parse_args()
    chunks = load_chunks()
    toks = sum(c["token_count"] for c in chunks)
    log(f"chunks={len(chunks):,} tokens={toks:,} est ${toks/1e6*0.25:.2f}")
    if a.mode == "realtime":
        embed_realtime(chunks)
    elif a.mode == "batch":
        submit_batch(chunks)
    elif a.mode == "poll":
        poll_batch(chunks, wait=True)


if __name__ == "__main__":
    main()