"""Generate teacher-distilled Q&A seed data for the 10 Tier-1 Bee domains.

Why this exists
---------------
The Kaggle training cron is producing flat loss (~3.84 across 5 runs) because
the only training data we have is 20 hand-written bootstrap rows about Bee's
identity — all in the "general" domain. With the cron now rotating through
all 10 Tier-1 domains, every domain except "general" will return zero rows
and report `partial`.

Distillation closes the gap: a strong teacher LLM generates realistic
domain-specific Q&A pairs. The trained adapter actually learns domain
patterns, loss drops, benchmark scores improve.

Methodology (auditable, no fake numbers)
----------------------------------------
For each Tier-1 domain in `bee/domains.py:TIER_1_DOMAINS` we ask the teacher
to produce N realistic user-question + high-quality-answer pairs. Each row
records the exact teacher provider + model that produced it (in the `source`
field) so we can reproduce, audit, or revoke specific rows later.

Default teacher: Google Gemini 2.0 Flash ($0.40/M output tokens — cheapest
of the four configured teachers per `bee/teacher_providers.py`). Override
via --provider {anthropic,deepseek,openai,google}.

Cost estimate at default settings:
  10 domains × 200 pairs/domain × ~250 tokens/pair = ~500k output tokens
  Gemini Flash: 500k × $0.40/M = ~$0.20 total
  DeepSeek:     500k × $2.19/M = ~$1.10 total
  Claude:       500k × $15/M   = ~$7.50 total

Output rows
-----------
Same schema as scripts/seed_bee_interactions.py with `kind=distilled` and
`source` pointing at the exact teacher response. Uploaded to
cuilabs/bee-interactions/data/<domain>.jsonl. The Kaggle training kernel's
filter (assistant + not-downvoted + matching domain) picks them up
automatically.

Usage
-----
    HF_TOKEN=hf_xxx \\
    BEE_GOOGLE_API_KEY=AIza... \\
        python scripts/distill_domain_seeds.py [options]

Options
-------
    --domains general,programming,...   Only generate for these (default: all 10 Tier-1)
    --pairs N                           Pairs per domain (default 200)
    --batch N                           Pairs per teacher call (default 25)
    --provider <name>                   Teacher provider (default: deepseek; chain order: deepseek > google > openai > anthropic)
    --dry-run                           Print plan, don't call teachers
    --skip-existing                     Skip domains already in the HF dataset
"""
from __future__ import annotations

import argparse
import datetime
import json
import os
import sys
import tempfile
import threading
import time
import urllib.error
import urllib.request
from concurrent.futures import ThreadPoolExecutor, as_completed
from pathlib import Path
from typing import Iterator

REPO_ROOT = Path(__file__).resolve().parent.parent

# Mirror of bee/domains.py:TIER_1_DOMAINS. We don't import bee.domains here
# because that pulls heavy backend deps; the canonical list lives in
# bee/domains.py and we re-state it here with a comment pointing back.
TIER_1_DOMAINS = [
    "general",
    "programming",
    "ai",
    "cybersecurity",
    "quantum",
    "fintech",
    "blockchain",
    "infrastructure",
    "research",
    "business",
]

# Per-domain teacher tier. "pro" routes to a strong reasoning model
# (DeepSeek V4 Pro, currently 75%-off through May 31, 2026); "flash"
# routes to the cheap workhorse (V4 Flash). Picked by cognitive demand:
# domains that benefit from chain-of-thought reasoning get pro;
# pattern-based / operational domains get flash. Override per-domain
# at runtime by passing --pro-domains or --flash-domains.
DOMAIN_TIER: dict[str, str] = {
    # Strong-reasoning domains — get the pro tier.
    "cybersecurity": "pro",   # threat models, attack chains, deep tradeoffs
    "quantum":       "pro",   # math + algorithm analysis
    "research":      "pro",   # methodology, paper critique, statistical depth
    # Pattern-based / operational — flash is plenty.
    "general":         "flash",
    "programming":     "flash",
    "ai":              "flash",
    "fintech":         "flash",
    "blockchain":      "flash",
    "infrastructure":  "flash",
    "business":        "flash",
}

# Per-domain prompt context. Honest, real, drawn from how a working
# professional in each domain would actually talk. No invented stats.
DOMAIN_CONTEXT = {
    "general": (
        "general technical assistance — clear, well-grounded answers across "
        "common professional and personal computing topics"
    ),
    "programming": (
        "software engineering — code review, architecture, debugging, "
        "language-specific patterns (Python, TypeScript, Go, Rust, etc.), "
        "build tooling, testing, and CI/CD"
    ),
    "ai": (
        "AI/ML — model architecture, training, inference, evaluation, RAG, "
        "fine-tuning, prompt engineering, LLM tooling (HuggingFace, PyTorch, "
        "vLLM, transformers), and the practical tradeoffs between approaches"
    ),
    "cybersecurity": (
        "cybersecurity — threat modeling, vulnerability analysis, secure code "
        "review, OWASP, network security, cryptography (including post-quantum), "
        "incident response, and security tooling. Focus on defensive use; "
        "refuse weaponizable specifics"
    ),
    "quantum": (
        "quantum computing — Qiskit, circuit design, quantum algorithms (Shor, "
        "Grover, VQE, QAOA), error correction, hardware (IBM Heron, IonQ, "
        "Quantinuum), post-quantum cryptography (FIPS 203/204/205), and "
        "the realistic limits of NISQ-era devices"
    ),
    "fintech": (
        "financial technology — payments, trading systems, market data, "
        "regulatory compliance (PCI-DSS, KYC/AML), accounting concepts, "
        "DeFi mechanics, risk management. Generic explanations only — "
        "explicitly NOT personalized investment advice"
    ),
    "blockchain": (
        "blockchain and distributed ledgers — Bitcoin/Ethereum mechanics, "
        "smart contract design (Solidity, Anchor), L2 scaling, consensus "
        "(PoS, PoW, BFT), cryptographic primitives, MEV, and honest framing "
        "of tradeoffs vs traditional databases"
    ),
    "infrastructure": (
        "cloud + infrastructure — AWS/GCP/Azure, Kubernetes, Terraform, "
        "observability (Prometheus, OpenTelemetry), service mesh, "
        "reliability engineering, capacity planning, and cost optimization"
    ),
    "research": (
        "research methodology — literature review, experimental design, "
        "statistics, reproducibility, paper structure, peer review, and "
        "specifically how to read and critique ML/CS papers from arXiv"
    ),
    "business": (
        "business operations and strategy for technical founders — pricing, "
        "GTM, hiring, fundraising mechanics, term-sheet basics, "
        "incorporation, and how to evaluate technical tradeoffs against "
        "business constraints"
    ),
}

DATASET_REPO = "cuilabs/bee-interactions"
PROMPT_TEMPLATE = """You are generating training data for Bee, a domain-specialized AI assistant built by CUI Labs.

Domain: {domain_label}
Domain context: {domain_context}

Generate {n} distinct user-question + high-quality-answer pairs that a working professional in this domain might genuinely ask an AI assistant.

Requirements:
- Questions must be REALISTIC and SPECIFIC (no generic "what is X?" puffballs).
- Mix difficulty: ~30% beginner, ~50% intermediate, ~20% expert.
- Answers must be ACCURATE, CONCISE (2-6 paragraphs typical), and admit uncertainty when appropriate.
- Include code, equations, or commands where natural — but only if correct.
- Cover a wide range of subtopics within the domain.
- DO NOT invent statistics, dates, or proprietary product claims you cannot verify.
- DO NOT pretend to have personal experiences. Speak as a knowledgeable assistant.
- DO NOT include disclaimers like "I am an AI" — just answer.

Output STRICT JSON, a single object with this exact shape:

{{
  "pairs": [
    {{"prompt": "...", "content": "..."}},
    {{"prompt": "...", "content": "..."}}
  ]
}}

The `pairs` array must contain exactly {n} elements. No markdown fences, no preamble, no trailing text — just the JSON object.

Generate now."""


def call_teacher(provider: str, prompt: str, model_override: str | None = None) -> tuple[str, dict]:
    """Call the configured teacher provider, return (text, telemetry).

    We hit the OpenAI-compatible /chat/completions endpoint for all providers
    except Anthropic. Anthropic uses /v1/messages with x-api-key. This is the
    same logic baked into bee/teacher_providers.py — kept inline here so the
    script doesn't pull the full backend module tree.
    """
    if provider == "anthropic":
        api_key = os.environ["BEE_TEACHER_API_KEY"]
        url = "https://api.anthropic.com/v1/messages"
        # Haiku 4.5 supports up to 16384 output tokens; Sonnet 4 the same.
        # We override model via BEE_ANTHROPIC_MODEL so the script can pick
        # the cheap one (Haiku 4.5) regardless of what the runtime config uses.
        model = model_override or os.environ.get("BEE_ANTHROPIC_MODEL", "claude-haiku-4-5")
        body = {
            "model": model,
            "max_tokens": 16384,
            "messages": [{"role": "user", "content": prompt}],
        }
        headers = {
            "x-api-key": api_key,
            "anthropic-version": "2023-06-01",
            "Content-Type": "application/json",
        }
    elif provider == "deepseek":
        api_key = os.environ["BEE_DEEPSEEK_API_KEY"]
        url = "https://api.deepseek.com/v1/chat/completions"
        # DeepSeek V4 model names: deepseek-v4-flash | deepseek-v4-pro.
        # Legacy aliases (deepseek-chat, deepseek-reasoner) both route to
        # v4-flash now; use explicit names so distillation provenance is
        # honest. Default to flash for cost; override to pro for hardest
        # domains via BEE_DEEPSEEK_MODEL=deepseek-v4-pro.
        # max_tokens: DeepSeek V4 advertises a 1M-token context window
        # and a 384K-token max-output ceiling per call (verified on
        # api-docs.deepseek.com 2026-04-29). We use 128K — comfortable
        # headroom for batch=200 (~140K out tokens) including V4 Pro's
        # reasoning_tokens overhead, without a single runaway response
        # blowing the day's budget. Bump toward 384K only if you need
        # mega-batches (1000+ pairs) per call.
        # response_format=json_object: forces clean JSON, eliminates the
        # parse-recovery code path for the happy case.
        model = model_override or os.environ.get("BEE_DEEPSEEK_MODEL", "deepseek-v4-flash")
        body = {
            "model": model,
            "messages": [{"role": "user", "content": prompt}],
            "max_tokens": 131072,
            "response_format": {"type": "json_object"},
        }
        headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
    elif provider == "openai":
        api_key = os.environ["BEE_OPENAI_API_KEY"]
        url = "https://api.openai.com/v1/chat/completions"
        # GPT-5 (Aug 2025 GA) supports 128K output tokens. Default here
        # matches teacher_providers.py PROVIDERS["openai"].default_model.
        # Bump to gpt-5.5 via BEE_OPENAI_MODEL if a job needs the (3x more
        # expensive) latest. Override max_tokens at call site if needed.
        model = model_override or os.environ.get("BEE_OPENAI_MODEL", "gpt-5")
        body = {
            "model": model,
            "messages": [{"role": "user", "content": prompt}],
            "max_tokens": 131072,
            "response_format": {"type": "json_object"},
        }
        headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
    elif provider == "google":
        api_key = os.environ["BEE_GOOGLE_API_KEY"]
        url = "https://generativelanguage.googleapis.com/v1beta/openai/chat/completions"
        # Gemini 2.5 Flash supports 64K output tokens. gemini-2.0-flash
        # sunsets 2026-06-01 per ai.google.dev/gemini-api/docs/pricing
        # so default is bumped to 2.5. Override to gemini-2.5-pro via
        # BEE_GOOGLE_MODEL for higher quality.
        model = model_override or os.environ.get("BEE_GOOGLE_MODEL", "gemini-2.5-flash")
        body = {
            "model": model,
            "messages": [{"role": "user", "content": prompt}],
            "max_tokens": 65536,
            "response_format": {"type": "json_object"},
        }
        headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
    else:
        raise ValueError(f"unknown provider: {provider}")

    req = urllib.request.Request(
        url,
        data=json.dumps(body).encode("utf-8"),
        headers=headers,
        method="POST",
    )
    # Retry on:
    #   - transient network errors (ConnectionResetError seen back-to-back
    #     from Anthropic on long runs)
    #   - HTTP 429 (rate-limited; DeepSeek dynamically throttles per
    #     api-docs.deepseek.com/quick_start/rate_limit, no fixed RPM cap)
    #   - HTTP 502 / 503 / 504 (gateway / overload — Anthropic + DeepSeek
    #     both surface these under sustained load)
    # Auth / quota / not-found (401, 403, 404, 400) are fatal — no retry.
    # Honors Retry-After header on 429s when the server provides one.
    TRANSIENT_HTTP = {429, 502, 503, 504}
    last_err: Exception | None = None
    raw = ""
    elapsed = 0.0
    for attempt in range(5):  # 5 attempts: 0, 5, 15, 35, 75s default backoff
        try:
            t0 = time.time()
            with urllib.request.urlopen(req, timeout=300) as resp:
                raw = resp.read().decode("utf-8")
            elapsed = time.time() - t0
            last_err = None
            break
        except urllib.error.HTTPError as e:
            if e.code not in TRANSIENT_HTTP:
                raise  # fatal: auth, quota, schema
            last_err = e
            # Honor Retry-After header if the server provided one (seconds).
            retry_after = e.headers.get("Retry-After") if hasattr(e, "headers") else None
            try:
                ra = int(retry_after) if retry_after else None
            except ValueError:
                ra = None
            backoff = ra if ra and ra > 0 else (5 * (2**attempt) if attempt > 0 else 5)
            print(
                f"  http {e.code} ({e.reason}); retry {attempt + 1}/4 in {backoff}s"
                + (" (Retry-After)" if ra else "")
            )
            time.sleep(backoff)
        except (ConnectionResetError, urllib.error.URLError, TimeoutError, OSError) as e:
            last_err = e
            backoff = 5 * (2**attempt) if attempt > 0 else 5
            print(f"  transient error ({type(e).__name__}: {e}); retry {attempt + 1}/4 in {backoff}s")
            time.sleep(backoff)
    if last_err is not None:
        raise last_err
    parsed = json.loads(raw)

    if provider == "anthropic":
        text = "".join(b.get("text", "") for b in parsed.get("content", []) if b.get("type") == "text")
        usage = parsed.get("usage", {})
        telemetry = {
            "model": model,
            "input_tokens": usage.get("input_tokens", 0),
            "output_tokens": usage.get("output_tokens", 0),
            "elapsed_s": round(elapsed, 2),
        }
    else:
        text = parsed["choices"][0]["message"]["content"]
        usage = parsed.get("usage", {})
        telemetry = {
            "model": model,
            "input_tokens": usage.get("prompt_tokens", 0),
            "output_tokens": usage.get("completion_tokens", 0),
            "elapsed_s": round(elapsed, 2),
        }
    return text, telemetry


def parse_pairs(raw: str) -> list[dict]:
    """Extract Q&A pairs from teacher output.

    Happy path (with response_format=json_object set on the request):
    teacher returns `{"pairs": [{"prompt": ..., "content": ...}, ...]}`
    cleanly. We parse and return.

    Recovery path: tolerates code fences AND truncated output. Scans
    for individual `{"prompt": ..., "content": ...}` sub-objects via
    balanced-brace walk, parses each. Survives when max_tokens is hit
    mid-response or the model stuffs JSON into a markdown fence.
    """
    s = raw.strip()
    if s.startswith("```"):
        s = s.split("\n", 1)[1] if "\n" in s else s
        if s.endswith("```"):
            s = s.rsplit("```", 1)[0]
        s = s.strip()
        if s.startswith("json\n"):
            s = s[5:]

    # Fast path 1: top-level object with "pairs" key (json_object format).
    try:
        obj = json.loads(s)
        if isinstance(obj, dict) and isinstance(obj.get("pairs"), list):
            pairs = []
            for x in obj["pairs"]:
                if isinstance(x, dict):
                    prompt = (x.get("prompt") or "").strip()
                    content = (x.get("content") or "").strip()
                    if prompt and content:
                        pairs.append({"prompt": prompt, "content": content})
            if pairs:
                return pairs
    except json.JSONDecodeError:
        pass

    # Fast path 2 (legacy): top-level array, no wrapper.
    a = s.find("[")
    b = s.rfind("]")
    if a != -1 and b != -1:
        try:
            arr = json.loads(s[a : b + 1])
            if isinstance(arr, list):
                pairs = []
                for x in arr:
                    if isinstance(x, dict):
                        prompt = (x.get("prompt") or "").strip()
                        content = (x.get("content") or "").strip()
                        if prompt and content:
                            pairs.append({"prompt": prompt, "content": content})
                if pairs:
                    return pairs
        except json.JSONDecodeError:
            pass  # fall through to recovery

    # Recovery: walk character-by-character collecting balanced { ... }
    # sub-objects, parse each. Tolerates truncation at the end.
    pairs: list[dict] = []
    i = 0 if a == -1 else a + 1  # start inside the array if we found one
    n = len(s)
    while i < n:
        if s[i] != "{":
            i += 1
            continue
        depth = 0
        in_string = False
        escape = False
        start = i
        end = -1
        while i < n:
            c = s[i]
            if escape:
                escape = False
            elif c == "\\":
                escape = True
            elif c == '"':
                in_string = not in_string
            elif not in_string:
                if c == "{":
                    depth += 1
                elif c == "}":
                    depth -= 1
                    if depth == 0:
                        end = i + 1
                        break
            i += 1
        if end == -1:
            break  # truncated mid-object
        try:
            obj = json.loads(s[start:end])
            prompt = (obj.get("prompt") or "").strip() if isinstance(obj, dict) else ""
            content = (obj.get("content") or "").strip() if isinstance(obj, dict) else ""
            if prompt and content:
                pairs.append({"prompt": prompt, "content": content})
        except json.JSONDecodeError:
            pass
        i = end

    if not pairs:
        raise ValueError(f"no parsable Q&A objects in teacher output (first 200 chars: {raw[:200]!r})")
    return pairs


_print_lock = threading.Lock()


def _emit(s: str) -> None:
    """Thread-safe print so parallel domain workers don't interleave lines."""
    with _print_lock:
        print(s, flush=True)


def resolve_model(provider: str, tier: str) -> str | None:
    """Pick an explicit model name for (provider, tier). None = use the
    provider's default. Currently only DeepSeek has a tier distinction
    that's automatable; other providers fall through to their defaults."""
    if provider == "deepseek":
        return "deepseek-v4-pro" if tier == "pro" else "deepseek-v4-flash"
    # For openai/google/anthropic, tier mapping is not yet wired —
    # use whatever BEE_<PROVIDER>_MODEL or the script default specifies.
    return None


def distill_domain(
    domain: str,
    total: int,
    batch: int,
    provider: str,
    dry_run: bool,
    tier: str = "flash",
) -> tuple[list[dict], dict]:
    """Generate `total` Q&A pairs for `domain` in batches of `batch`.

    `tier` selects model strength when the provider supports it (currently
    DeepSeek: "pro" | "flash"). Each row's `source` field records the
    actual model that produced it, so per-row provenance survives even
    when different domains use different teachers.
    """
    rows: list[dict] = []
    telemetry: dict = {
        "calls": 0, "input_tokens": 0, "output_tokens": 0, "elapsed_s": 0.0,
        "provider": provider, "tier": tier,
    }
    seen_prompts: set[str] = set()
    model_override = resolve_model(provider, tier)

    while len(rows) < total:
        n = min(batch, total - len(rows))
        prompt = PROMPT_TEMPLATE.format(
            domain_label=domain, domain_context=DOMAIN_CONTEXT[domain], n=n
        )
        if dry_run:
            _emit(f"  [dry-run] would call {provider}/{tier} for {n} pairs ({domain})")
            return [], telemetry

        try:
            text, tele = call_teacher(provider, prompt, model_override=model_override)
        except (urllib.error.URLError, urllib.error.HTTPError) as e:
            _emit(f"  [{domain}] teacher call failed: {e}; aborting domain")
            break

        try:
            pairs = parse_pairs(text)
        except (ValueError, json.JSONDecodeError) as e:
            _emit(f"  [{domain}] parse failed: {e}; aborting domain")
            break

        for p in pairs:
            if p["prompt"] in seen_prompts:
                continue
            seen_prompts.add(p["prompt"])
            rows.append({
                "messages": [
                    {"role": "user", "content": p["prompt"]},
                    {"role": "assistant", "content": p["content"]},
                ],
                "role": "assistant",
                "prompt": p["prompt"],
                "content": p["content"],
                "feedback": None,
                "source": f"teacher_distillation:{provider}:{tele['model']}",
                "domain": domain,
                "kind": "distilled",
            })
            if len(rows) >= total:
                break

        telemetry["calls"] += 1
        telemetry["input_tokens"] += tele["input_tokens"]
        telemetry["output_tokens"] += tele["output_tokens"]
        telemetry["elapsed_s"] += tele["elapsed_s"]
        _emit(
            f"  [{domain}] +{len(pairs):3d} pairs "
            f"({len(rows):3d}/{total}, +{tele['output_tokens']} out tok, "
            f"{tele['elapsed_s']:.1f}s, model {tele['model']})"
        )

    return rows, telemetry


def write_jsonl(rows: list[dict]) -> str:
    return "\n".join(json.dumps(r, ensure_ascii=False) for r in rows) + "\n"


def upload_domain_jsonl(domain: str, jsonl: str, hf_token: str) -> str:
    from huggingface_hub import HfApi  # type: ignore[import-not-found]

    api = HfApi(token=hf_token)
    with tempfile.TemporaryDirectory() as tmp:
        out = Path(tmp) / f"{domain}.jsonl"
        out.write_text(jsonl, encoding="utf-8")
        api.upload_file(
            path_or_fileobj=str(out),
            path_in_repo=f"data/{domain}.jsonl",
            repo_id=DATASET_REPO,
            repo_type="dataset",
            commit_message=f"distill: teacher-generated {domain} seeds",
        )
    return f"https://huggingface.co/datasets/{DATASET_REPO}/blob/main/data/{domain}.jsonl"


def existing_data_files(hf_token: str) -> set[str]:
    from huggingface_hub import HfApi  # type: ignore[import-not-found]

    api = HfApi(token=hf_token)
    files = api.list_repo_files(repo_id=DATASET_REPO, repo_type="dataset")
    return {Path(f).stem for f in files if f.startswith("data/") and f.endswith(".jsonl")}


def _process_one_domain(
    domain: str, args: argparse.Namespace, hf_token: str | None,
    pro_set: set[str], flash_set: set[str],
) -> tuple[str, list[dict], dict]:
    """Worker: distill one domain end-to-end (generate → upload). Designed
    to be called from ThreadPoolExecutor — only depends on its arguments
    and (thread-safe) module-level state."""
    # Tier resolution: explicit CLI flags > DOMAIN_TIER default > "flash".
    if domain in pro_set:
        tier = "pro"
    elif domain in flash_set:
        tier = "flash"
    else:
        tier = DOMAIN_TIER.get(domain, "flash")

    _emit(f"=== {domain} ({args.provider}/{tier}) ===")
    rows, tele = distill_domain(
        domain, args.pairs, args.batch, args.provider, args.dry_run, tier=tier
    )
    if not args.dry_run and rows and hf_token:
        jsonl = write_jsonl(rows)
        url = upload_domain_jsonl(domain, jsonl, hf_token)
        _emit(f"  [{domain}] uploaded {len(rows)} rows → {url}")
    return domain, rows, tele


def main() -> None:
    p = argparse.ArgumentParser()
    p.add_argument("--domains", default="", help="comma-separated subset (default: all 10)")
    p.add_argument("--pairs", type=int, default=200, help="pairs per domain (default 200)")
    p.add_argument("--batch", type=int, default=50, help="pairs per teacher call (default 50 — fits comfortably in 32K max_tokens budget for V4 Pro/Flash)")
    p.add_argument("--provider", default="deepseek", choices=["deepseek", "google", "openai", "anthropic"])
    p.add_argument(
        "--workers", type=int, default=2,
        help="parallel domain workers (default 2). Each worker handles one "
             "domain end-to-end. Increase cautiously to avoid teacher RPM caps.",
    )
    p.add_argument(
        "--pro-domains", default="",
        help="comma-separated list of domains to FORCE onto the pro tier. "
             "Otherwise the per-domain default in DOMAIN_TIER applies.",
    )
    p.add_argument(
        "--flash-domains", default="",
        help="comma-separated list of domains to FORCE onto the flash tier.",
    )
    p.add_argument("--dry-run", action="store_true")
    p.add_argument("--skip-existing", action="store_true",
                   help="skip domains already in cuilabs/bee-interactions/data/")
    args = p.parse_args()

    if args.domains:
        domains = [d.strip() for d in args.domains.split(",") if d.strip()]
        bad = [d for d in domains if d not in TIER_1_DOMAINS]
        if bad:
            sys.exit(f"unknown domains: {bad}. Valid: {TIER_1_DOMAINS}")
    else:
        domains = list(TIER_1_DOMAINS)

    pro_set = {d.strip() for d in args.pro_domains.split(",") if d.strip()}
    flash_set = {d.strip() for d in args.flash_domains.split(",") if d.strip()}

    hf_token = os.environ.get("HF_TOKEN")
    if not hf_token and not args.dry_run:
        sys.exit("HF_TOKEN required (set in env or .env)")

    skip = set()
    if args.skip_existing and not args.dry_run:
        try:
            skip = existing_data_files(hf_token)  # type: ignore[arg-type]
            print(f"skip-existing: dataset already has {sorted(skip)}")
        except Exception as e:
            print(f"could not list existing files: {e}; not skipping any")

    todo = [d for d in domains if d not in skip]
    print(
        f"\nplan: provider={args.provider}, pairs/domain={args.pairs}, "
        f"batch={args.batch}, workers={args.workers}\n"
        f"      todo: {todo}\n"
        f"      tier per domain:"
    )
    for d in todo:
        if d in pro_set:
            tier = "pro (forced)"
        elif d in flash_set:
            tier = "flash (forced)"
        else:
            tier = DOMAIN_TIER.get(d, "flash")
        print(f"        {d:<18} → {tier}")
    print()

    started = datetime.datetime.now(datetime.timezone.utc).isoformat()
    overall = {"calls": 0, "input_tokens": 0, "output_tokens": 0, "elapsed_s": 0.0, "rows": 0}

    # Parallel worker pool. ThreadPoolExecutor is correct here — these
    # workers are I/O-bound (HTTP roundtrips to teacher APIs); the GIL
    # is released during socket reads so we get real concurrency.
    with ThreadPoolExecutor(max_workers=max(1, args.workers)) as ex:
        futures = {ex.submit(_process_one_domain, d, args, hf_token, pro_set, flash_set): d for d in todo}
        for fut in as_completed(futures):
            domain = futures[fut]
            try:
                _, rows, tele = fut.result()
                for k in ("calls", "input_tokens", "output_tokens", "elapsed_s"):
                    overall[k] += tele[k]
                overall["rows"] += len(rows)
            except Exception as e:
                _emit(f"  [{domain}] worker failed: {type(e).__name__}: {e}")

    print(
        f"\nDONE. started={started}\n"
        f"  total rows: {overall['rows']}\n"
        f"  teacher calls: {overall['calls']}\n"
        f"  input tokens: {overall['input_tokens']}, output tokens: {overall['output_tokens']}\n"
        f"  elapsed: {overall['elapsed_s']:.1f}s"
    )


if __name__ == "__main__":
    main()