"""Surrogate-1 training data sanitizer — drops rows that would leak internals.

Discovered 2026-04-29: v1 LoRA leaked path "/home/hermes/.surrogate/state/orchestrate/77426592/1-README.md"
and "# generated via cerebras:llama3.1-8b" tag in inference response. Root cause: dataset-mirror
+ dataset-enrich ingested rows where the response was an LLM-generated artifact still tagged
with provider attribution + internal filesystem context.

Apply this filter at row-level BEFORE the row is added to training-pairs.jsonl. Drop entire
row if either prompt or response matches any high-risk pattern.
"""
import re

# Patterns that indicate the row contains Surrogate-1 internal pollution.
# Order: most-specific first so re.search short-circuits on hits.
POLLUTION_PATTERNS = [
    # 1. LLM provider attribution lines — often added by llm-burst-generator outputs
    r"^\s*#\s*generated\s+via\s+(cerebras|groq|openrouter|gemini|chutes|samba|kimi|nvidia|hf-router|hf-llama|hf-qwen|hf-mistral)",
    r"<<\s*generated by\s+(cerebras|groq|openrouter|gemini|chutes|samba|kimi)",
    r"\[(cerebras|groq|openrouter|gemini|chutes|samba|kimi):[a-z0-9.-]+\]",

    # 2. Internal filesystem paths — exposing host structure
    r"/home/hermes/(?:\.surrogate|\.hermes|\.codex|\.gemini|\.kaggle)/",
    r"/data/(?:state|logs|memory|skills|sessions|workspace|projects|ollama|training|reflexion|index|surrogate)/",
    r"~/\.surrogate/(?:state|logs|memory|bin|skills|sessions)/",
    r"/Users/Ashira/(?:\.surrogate|\.hermes|\.kaggle|\.oci|\.note|develope|axentx)/",

    # 3. Internal directory names (state-management dirs)
    r"\bstate/orchestrate/\d+/",
    r"\bagentic-discovery/",
    r"\braw-mirrors/[a-z0-9-]+/",
    r"\benriched/[a-z0-9-]+/",
    r"\bbatches/(?:public-merged|mirror-merged)/\d{4}-\d{2}-\d{2}/",

    # 4. Daemon names + commit messages from our pipeline
    r"\b(?:dataset-mirror|dataset-enrich|llm-burst-generator|bulk-ingest-parallel|"
    r"agentic-crawler|github-agentic-crawler|skill-synthesis-daemon|push-training-to-hf|"
    r"surrogate-orchestrate|self-heal-watchdog|hermes-status-server|hermes-discord-bot)"
    r"(?:\.sh|\.py)?\b",

    # 5. Specific axentx repo identifiers — model shouldn't reproduce these
    r"axentx/surrogate-1-(?:training-pairs|pairs-[A-D]|coder-[a-zA-Z0-9-]+-v[\d.]+)",

    # 6. Token / secret-shaped strings (leaked credentials)
    r"\b(?:hf_[A-Za-z0-9]{30,}|sk-or-v\d-[A-Za-z0-9]{40,}|sk-ant-[A-Za-z0-9-]{30,}|"
    r"KGAT_[A-Za-z0-9]{30,}|csk-[A-Za-z0-9]{40,}|cpk_[A-Za-z0-9.]{40,}|"
    r"gsk_[A-Za-z0-9]{40,}|nvapi-[A-Za-z0-9_-]{40,}|fc-[a-f0-9]{32}|"
    r"ghp_[A-Za-z0-9]{30,}|sbp_[a-f0-9]{40}|cfut_[A-Za-z0-9]{30,}|"
    r"AIzaSy[A-Za-z0-9_-]{30,}|xai-[A-Za-z0-9]{40,}|r8_[A-Za-z0-9]{30,}|rnd_[A-Za-z0-9]{20,}|"
    r"sk-kimi-[A-Za-z0-9]{40,})\b",

    # 7. Common debug / introspection leakage (when LLM was asked to echo state)
    r"\b(?:LIGHTNING_USER_ID|LIGHTNING_API_KEY|HF_TOKEN|KAGGLE_API_TOKEN|KAGGLE_KEY|"
    r"OPENROUTER_API_KEY|CEREBRAS_API_KEY|GROQ_API_KEY|ANTHROPIC_API_KEY)\s*[=:]\s*['\"]?[A-Za-z0-9_-]{20,}",

    # 8. Discord webhook URLs
    r"https://discord\.com/api/webhooks/\d+/[A-Za-z0-9_-]+",

    # 9. Internal commit messages (from daemons pushing to HF)
    r"^(?:enriched|mirror|chunk):\s+",
    r"^train-ready pusher:",
    r"^clean mirror(?:\s+final)?:",

    # 10. JWT-shaped strings (NVIDIA Brev tokens, etc.)
    r"\beyJ[A-Za-z0-9_-]{50,}\.eyJ[A-Za-z0-9_-]{100,}\.[A-Za-z0-9_=-]{40,}",
]

POLLUTION_RE = re.compile("|".join(f"(?:{p})" for p in POLLUTION_PATTERNS),
                           re.MULTILINE | re.IGNORECASE)


def is_polluted(text: str) -> tuple[bool, str | None]:
    """Return (polluted?, matching_pattern_id_for_log).

    Use the matched substring (truncated) so you can log which type of
    pollution caused the drop. Useful for tuning patterns later.
    """
    if not text or not isinstance(text, str):
        return False, None
    m = POLLUTION_RE.search(text)
    if m:
        return True, m.group(0)[:120]
    return False, None


def is_polluted_pair(prompt: str, response: str) -> tuple[bool, str | None]:
    """Check both fields. Drop the row if either is polluted."""
    p_bad, p_match = is_polluted(prompt)
    if p_bad:
        return True, f"prompt: {p_match}"
    r_bad, r_match = is_polluted(response)
    if r_bad:
        return True, f"response: {r_match}"
    return False, None


# Optional: PII regex set (apply alongside)
PII_PATTERNS = [
    # Email
    r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",
    # Phone (US/intl basic)
    r"\b\+?\d{1,3}[\s.-]?\(?\d{3}\)?[\s.-]?\d{3}[\s.-]?\d{4}\b",
    # SSN
    r"\b\d{3}-\d{2}-\d{4}\b",
    # AWS keys
    r"\bAKIA[0-9A-Z]{16}\b",
    # Stripe keys
    r"\bsk_(?:test|live)_[A-Za-z0-9]{32,}\b",
]
PII_RE = re.compile("|".join(PII_PATTERNS), re.IGNORECASE)


def has_pii(text: str) -> bool:
    return bool(PII_RE.search(text or ""))


# ── Optional NER + secrets scanners (lazy, fail-soft) ──────────────────
# starpii (BigCode) — neural PII NER; better than regex for free-form text.
# detect-secrets (Yelp) — entropy + plugin-based secret detector.
# Both are optional dependencies; if unavailable we fall back to regex above.
_starpii_pipeline = None
_detect_secrets_collection = None


def _load_starpii():
    """Lazy-load BigCode/starpii pipeline. None on failure."""
    global _starpii_pipeline
    if _starpii_pipeline is not None:
        return _starpii_pipeline if _starpii_pipeline is not False else None
    try:
        from transformers import pipeline  # type: ignore
        _starpii_pipeline = pipeline(
            "token-classification",
            model="bigcode/starpii",
            aggregation_strategy="simple",
        )
        return _starpii_pipeline
    except Exception:
        _starpii_pipeline = False  # sentinel: "tried, don't try again"
        return None


def starpii_pii_hits(text: str, threshold: float = 0.8) -> list[dict]:
    """Return [{type, score, span}] for confidently-detected PII spans.
    Empty list if starpii not installed or no hits.
    """
    pipe = _load_starpii()
    if not pipe or not text:
        return []
    try:
        hits = pipe(text[:4000])  # cap input for speed
    except Exception:
        return []
    return [{"type": h["entity_group"], "score": float(h["score"]),
             "span": text[h["start"]:h["end"]][:120]}
            for h in hits if h.get("score", 0) >= threshold]


def _load_detect_secrets():
    """Lazy-load detect-secrets SecretsCollection. None on failure."""
    global _detect_secrets_collection
    if _detect_secrets_collection is not None:
        return _detect_secrets_collection if _detect_secrets_collection is not False else None
    try:
        from detect_secrets import SecretsCollection  # type: ignore
        from detect_secrets.settings import default_settings  # type: ignore
        _detect_secrets_collection = (SecretsCollection, default_settings)
        return _detect_secrets_collection
    except Exception:
        _detect_secrets_collection = False
        return None


def detect_secrets_hits(text: str) -> list[dict]:
    """Return [{type, line}] for any secret detect-secrets finds.
    Empty list if not installed or none detected.
    """
    loaded = _load_detect_secrets()
    if not loaded or not text:
        return []
    SecretsCollection, default_settings = loaded
    import tempfile, os
    fd, path = tempfile.mkstemp(suffix=".txt")
    try:
        os.write(fd, text.encode("utf-8", "ignore")[:200_000])
        os.close(fd)
        with default_settings():
            sc = SecretsCollection()
            sc.scan_file(path)
        out = []
        for _, secrets in sc.data.items():
            for s in secrets:
                out.append({"type": s.type, "line": s.line_number,
                            "secret_hash": s.secret_hash[:16]})
        return out
    except Exception:
        return []
    finally:
        try: os.unlink(path)
        except OSError: pass


# Quality heuristics — drop if response is too short, identical to prompt, etc.
def is_low_quality(prompt: str, response: str) -> tuple[bool, str | None]:
    if not prompt or not response:
        return True, "empty"
    if len(prompt) < 20:
        return True, "prompt_too_short"
    if len(response) < 30:
        return True, "response_too_short"
    if response.strip().lower() == prompt.strip().lower():
        return True, "response_equals_prompt"
    # Detect when response is just an apology / refusal
    if re.match(r"^\s*i('?m| am)?\s+(sorry|afraid|unable|cannot|cant|can't)\b",
                response.strip(), re.IGNORECASE):
        return True, "refusal"
    # Repeated character spam
    if re.search(r"(.)\1{50,}", response):
        return True, "char_spam"
    return False, None


def filter_pair(prompt: str, response: str,
                deep_scan: bool = False) -> dict:
    """Return verdict: {'keep': bool, 'reason': str|None, 'matched': str|None}.

    deep_scan=True: also runs starpii NER + detect-secrets if installed.
    Slow (model load + per-row scan) — use for the final pre-train pass,
    not for every dedup row. Heuristic (regex) checks always run.
    """
    polluted, p_match = is_polluted_pair(prompt, response)
    if polluted:
        return {"keep": False, "reason": "polluted", "matched": p_match}
    if has_pii(prompt) or has_pii(response):
        return {"keep": False, "reason": "pii_regex", "matched": None}
    low_q, lq_reason = is_low_quality(prompt, response)
    if low_q:
        return {"keep": False, "reason": f"low_quality:{lq_reason}", "matched": None}

    if deep_scan:
        # NER PII
        for field, txt in (("prompt", prompt), ("response", response)):
            hits = starpii_pii_hits(txt)
            if hits:
                return {"keep": False, "reason": f"pii_ner:{field}",
                        "matched": str(hits[:3])[:300]}
        # detect-secrets entropy/plugins
        for field, txt in (("prompt", prompt), ("response", response)):
            hits = detect_secrets_hits(txt)
            if hits:
                return {"keep": False, "reason": f"secrets:{field}",
                        "matched": str(hits[:3])[:300]}

    return {"keep": True, "reason": None, "matched": None}


# CLI helper for testing
if __name__ == "__main__":
    import sys, json
    sample = sys.stdin.read() if not sys.stdin.isatty() else """{"prompt": "fix bug", "response": "# generated via cerebras:llama3.1-8b\\nReadFile path /home/hermes/.surrogate/state/orchestrate/77426592/1-README.md"}"""
    obj = json.loads(sample) if sample.strip().startswith("{") else {"prompt": "test", "response": sample}
    v = filter_pair(obj.get("prompt", ""), obj.get("response", ""))
    print(json.dumps(v, indent=2))