"""Shared SAST engine — stage 1 (detect) and stage 2 (adversarial refute).

Extracted verbatim from the snippet path so both the single-snippet audit and the
whole-repo audit reuse the *same* prompts, schemas and refutation logic. Pure functions:
they take an Outlines `model` + tokenizer, no GPU decorator (callers wrap as needed).
"""

import json
import re

from schemas import Report, Verdict

DETECT_SYS = (
    "You are a security code reviewer. List candidate SECURITY vulnerabilities with a real "
    "attack surface (command/SQL injection, path traversal, XSS, SSRF, deserialization, auth "
    "bypass...). Be inclusive about security issues — an adversarial pass verifies each — but "
    "ignore pure style, quality or error-handling nits. Use 1-based line numbers."
)
REFUTE_SYS = (
    "You are an exploit analyst verifying ONE claimed vulnerability in the given code. Decide if it "
    "is GENUINELY exploitable. Judge ONLY what the code shows.\n"
    "- EXPLOITABLE (exploitable=true): attacker-controlled input reaches the dangerous sink with NO "
    "adequate sanitization in between; give a concrete proof-of-concept input.\n"
    "- FALSE POSITIVE (exploitable=false): adequate protection on the value before the sink — a "
    "prepared/parameterized query, an allow-list of permitted values, a cast to a safe type, proper "
    "escaping (escapeshellarg, htmlspecialchars), or strict value validation (is_numeric on the whole "
    "value). Also FP if the sink is unreachable/dead code, or the input is not attacker-controlled. "
    "If a proper sanitization like that is visible, it IS a false positive — say so.\n"
    "Two traps, do NOT fall for them:\n"
    "1. An EXISTENCE/presence check is NOT sanitization. If attacker input is used to build a path "
    "passed to include/require/file_get_contents/fopen/readfile — EVEN with a fixed directory prefix, "
    "a forced extension (.php), or a file_exists()/is_file() guard — it IS EXPLOITABLE (path traversal "
    "/ LFI): the attacker still controls which file loads, and such prefix/suffix/existence constraints "
    "are routinely bypassed (../, encoded traversal, existing sensitive files).\n"
    "2. isset/empty/strlen do not stop injection.\n"
    "If exploitable, set exploitable=true and give the PoC input."
)

SEV = {"critical": "🔴", "high": "🟠", "medium": "🟡", "low": "⚪"}
_SEV_SET = {"low", "medium", "high", "critical"}

# Flat JSON schema mirroring schemas.Report (Candidate fields) — used as response_format for the
# HF Inference detector, which does NOT do Outlines-style constrained decoding (unlike ZeroGPU).
REPORT_JSON_SCHEMA = {
    "type": "object",
    "properties": {
        "findings": {
            "type": "array",
            "items": {
                "type": "object",
                "properties": {
                    "vuln_type": {"type": "string"},
                    "line": {"type": "integer"},
                    "severity": {"type": "string", "enum": ["low", "medium", "high", "critical"]},
                    "rationale": {"type": "string"},
                },
                "required": ["vuln_type", "line", "severity", "rationale"],
            },
        }
    },
    "required": ["findings"],
}

# Flat JSON schema mirroring schemas.Verdict — response_format for the HF Inference refuter.
VERDICT_JSON_SCHEMA = {
    "type": "object",
    "properties": {
        "exploitable": {"type": "boolean"},
        "reasoning": {"type": "string"},
        "poc": {"type": "string"},
    },
    "required": ["exploitable", "reasoning"],
}


def number_lines(code):
    """Prefix each line with its 1-based number (`N| `) so the detector cites the right line."""
    return "\n".join(f"{i}| {ln}" for i, ln in enumerate(code.splitlines(), 1))


def _usage_tokens(resp):
    u = getattr(resp, "usage", None)
    return (getattr(u, "total_tokens", 0) or 0) if u else 0


def _parse_json(txt):
    """Robust parse: strip ``` fences, else extract the outermost {...}. Returns dict or None."""
    if not txt:
        return None
    txt = txt.strip()
    if txt.startswith("```"):
        txt = re.sub(r"^```[a-zA-Z]*\n?", "", txt)
        txt = re.sub(r"\n?```$", "", txt).strip()
    try:
        return json.loads(txt)
    except json.JSONDecodeError:
        m = re.search(r"\{.*\}", txt, re.S)
        if not m:
            return None
        try:
            return json.loads(m.group(0))
        except json.JSONDecodeError:
            return None


def _clean(findings, max_findings=None):
    """Normalize HF-Inference findings to the Candidate shape (vuln_type/line/severity/rationale)."""
    out = []
    for f in (findings or []):
        if not isinstance(f, dict):
            continue
        try:
            vt = str(f["vuln_type"]).strip()
            ln = int(f["line"])
        except (KeyError, TypeError, ValueError):
            continue
        if not vt:
            continue
        sev = str(f.get("severity", "medium")).lower()
        if sev not in _SEV_SET:
            sev = "medium"
        out.append({"vuln_type": vt, "line": ln, "severity": sev,
                    "rationale": str(f.get("rationale", "")).strip()})
    return out[:max_findings] if max_findings else out


def _clean_verdict(data):
    return {"exploitable": bool((data or {}).get("exploitable")),
            "reasoning": str((data or {}).get("reasoning", "")).strip(),
            "poc": str((data or {}).get("poc") or "").strip()}


def chat(tok, system, user):
    return tok.apply_chat_template(
        [{"role": "system", "content": system}, {"role": "user", "content": user}],
        tokenize=False, add_generation_prompt=True)


def detect(model, tok, code, lang, max_findings=5):
    """Stage 1 — list candidate vulnerabilities for one piece of code."""
    prompt = chat(tok, DETECT_SYS, f"Language: {lang}\nCode:\n```\n{code}\n```\nList up to "
                                   f"{max_findings} candidate vulnerabilities.")
    try:
        rep = json.loads(model(prompt, output_type=Report, max_new_tokens=512))
    except (json.JSONDecodeError, ValueError):
        # output truncated mid-JSON (large/complex file) → nothing reliable to report
        return []
    return (rep.get("findings") or [])[:max_findings]


def detect_hf(client, model_id, code, lang, max_findings=None):
    """Stage 1 via HF Inference (a large model, e.g. Qwen3-Coder-480B) — an off-GPU network call.

    Sends line-numbered code and asks for ALL candidates (no cap by default: detector recall is
    what matters — the source→sink distance is what defeats the small model; stage-2 refutation
    kills false positives). Prefers a strict json_schema response_format; if the provider rejects
    it, retries once with a strict-JSON prompt + robust parse. Returns (findings, total_tokens),
    tokens being for cost logging by the caller.
    """
    numbered = number_lines(code)
    user = (f"Language: {lang}\nCode (each line is prefixed with `N| ` where N is its 1-based line "
            f"number):\n```\n{numbered}\n```\nList ALL candidate security vulnerabilities you find. "
            f"Cite the 1-based line number from the prefix. Do not limit the number of findings.")
    msgs = [{"role": "system", "content": DETECT_SYS}, {"role": "user", "content": user}]
    tokens = 0
    # 1) preferred: constrained to the schema (when the provider supports json_schema)
    try:
        r = client.chat.completions.create(
            model=model_id, messages=msgs, temperature=0, max_tokens=2048,
            response_format={"type": "json_schema",
                             "json_schema": {"name": "Report", "schema": REPORT_JSON_SCHEMA, "strict": True}},
        )
        tokens += _usage_tokens(r)
        data = _parse_json(r.choices[0].message.content)
        if data is not None:
            return _clean(data.get("findings"), max_findings), tokens
    except Exception:
        pass  # provider may not support response_format → fall through to the strict-JSON retry
    # 2) fallback: strict-JSON instruction, no response_format, one retry
    msgs2 = msgs + [{"role": "system", "content":
                     'Return ONLY a JSON object {"findings":[{"vuln_type":string,"line":int,'
                     '"severity":"low|medium|high|critical","rationale":string}]}. No prose, no markdown.'}]
    try:
        r = client.chat.completions.create(model=model_id, messages=msgs2, temperature=0, max_tokens=2048)
        tokens += _usage_tokens(r)
        data = _parse_json(r.choices[0].message.content) or {}
        return _clean(data.get("findings"), max_findings), tokens
    except Exception:
        return [], tokens


def refute(model, tok, code, candidate):
    """Stage 2 — adversarially verify ONE candidate (the calibrated, reused step)."""
    prompt = chat(tok, REFUTE_SYS,
                  f"Code:\n```\n{code}\n```\nClaimed vulnerability: {candidate['vuln_type']} "
                  f"at line {candidate['line']} — {candidate['rationale']}\nIs it really exploitable?")
    try:
        return json.loads(model(prompt, output_type=Verdict, max_new_tokens=300))
    except (json.JSONDecodeError, ValueError):
        # couldn't parse a verdict → conservatively treat as not-confirmed (no false alarm)
        return {"exploitable": False, "reasoning": "verification inconclusive (parse error)", "poc": ""}


def refute_hf(client, model_id, code, candidate):
    """Stage 2 via HF Inference (small model) — an off-GPU network call, so a whole-repo scan needs
    no ZeroGPU reservation (no proxy-token expiry on long scans). Same REFUTE_SYS + targeted context
    as the ZeroGPU path. Returns (verdict, total_tokens). `code` is the caller's tight window."""
    user = (f"Code:\n```\n{code}\n```\nClaimed vulnerability: {candidate['vuln_type']} "
            f"at line {candidate['line']} — {candidate['rationale']}\nIs it really exploitable?")
    msgs = [{"role": "system", "content": REFUTE_SYS}, {"role": "user", "content": user}]
    tokens = 0
    try:
        r = client.chat.completions.create(
            model=model_id, messages=msgs, temperature=0, max_tokens=512,
            response_format={"type": "json_schema",
                             "json_schema": {"name": "Verdict", "schema": VERDICT_JSON_SCHEMA, "strict": True}},
        )
        tokens += _usage_tokens(r)
        data = _parse_json(r.choices[0].message.content)
        if data is not None:
            return _clean_verdict(data), tokens
    except Exception:
        pass
    msgs2 = msgs + [{"role": "system", "content":
                     'Return ONLY {"exploitable":bool,"reasoning":string,"poc":string}. No prose, no markdown.'}]
    try:
        r = client.chat.completions.create(model=model_id, messages=msgs2, temperature=0, max_tokens=512)
        tokens += _usage_tokens(r)
        return _clean_verdict(_parse_json(r.choices[0].message.content)), tokens
    except Exception:
        return {"exploitable": False, "reasoning": "verification inconclusive (inference error)", "poc": ""}, tokens


def render_snippet(out, dt):
    """Markdown report for the single-snippet path (unchanged behaviour)."""
    if not out.get("verified"):
        cs = out.get("candidates", [])
        if not cs:
            return f"No candidate vulnerabilities found · {dt:.1f}s"
        lines = [f"### ⚠️ {len(cs)} candidates — **unverified** (raw detector) · {dt:.1f}s",
                 "_Raw guesses — some are false positives. Flip **Verify ON** to refute them._\n"]
        for c in cs:
            lines.append(f"- **{c['vuln_type']}** · line {c['line']} · "
                         f"{SEV.get(c['severity'], '')} {c['severity']} — {c['rationale']}")
        return "\n".join(lines)

    res = out.get("results", [])
    real = [r for r in res if r["verdict"].get("exploitable")]
    fp = [r for r in res if not r["verdict"].get("exploitable")]
    lines = [f"### ✅ Verified · **{len(real)} confirmed**, {len(fp)} refuted · {dt:.1f}s\n"]
    if real:
        lines.append(f"#### ✗ Confirmed ({len(real)})")
        for r in real:
            c, v = r["candidate"], r["verdict"]
            lines.append(f"- **{c['vuln_type']}** · line {c['line']} · {SEV.get(c['severity'], '')} {c['severity']}")
            if v.get("poc"):
                lines.append(f"  - PoC: `{v['poc']}`")
            lines.append(f"  - {v.get('reasoning', '')}")
    if fp:
        lines.append(f"\n#### ✓ Refuted as false positive ({len(fp)})")
        for r in fp:
            c, v = r["candidate"], r["verdict"]
            lines.append(f"- ~~{c['vuln_type']} · line {c['line']}~~ — {v.get('reasoning', '')}")
    if not res:
        lines.append("_Nothing flagged._")
    return "\n".join(lines)