#!/usr/bin/env python3
"""
Normalize CVE JSON files into chunked text documents for downstream embeddings.
"""

from __future__ import annotations

import argparse
import json
import textwrap
from pathlib import Path
from typing import Dict, Iterable, List, Sequence, Tuple

from .config import load_settings


def parse_args() -> argparse.Namespace:
    settings = load_settings()
    parser = argparse.ArgumentParser(description="Prepare CVE corpus.")
    parser.add_argument(
        "--cve-root",
        default=str(settings.cve_root),
        help="Directory containing extracted CVE JSON files.",
    )
    parser.add_argument(
        "--output",
        default=str(settings.corpus_path),
        help="Path to write the normalized JSONL corpus.",
    )
    parser.add_argument(
        "--chunk-size",
        type=int,
        default=700,
        help="Maximum number of characters per chunk.",
    )
    parser.add_argument(
        "--chunk-overlap",
        type=int,
        default=120,
        help="Character overlap between consecutive chunks.",
    )
    return parser.parse_args()


def chunk_text(text: str, chunk_size: int, overlap: int) -> List[str]:
    if not text:
        return []
    text = " ".join(text.split())  # normalize whitespace
    chunks: List[str] = []
    start = 0
    length = len(text)
    while start < length:
        end = min(length, start + chunk_size)
        chunks.append(text[start:end])
        if end == length:
            break
        start = max(0, end - overlap)
    return chunks


def load_json(path: Path):
    with path.open("r", encoding="utf-8") as fh:
        return json.load(fh)


def iter_raw_entries(path: Path) -> Iterable[Dict]:
    data = load_json(path)
    if isinstance(data, dict):
        if "CVE_Items" in data:
            for item in data["CVE_Items"]:
                yield item
        elif "cve" in data:
            yield data
        else:
            yield data
    elif isinstance(data, list):
        for item in data:
            yield item


def select_lang_values(items: Sequence[Dict], prefer_lang: str = "en") -> List[str]:
    preferred = [
        item.get("value", "").strip()
        for item in items
        if item.get("value") and item.get("lang") == prefer_lang
    ]
    if preferred:
        return preferred
    return [
        item.get("value", "").strip()
        for item in items
        if item.get("value")
    ]


def dedupe(seq: Sequence[str]) -> List[str]:
    seen = set()
    result = []
    for item in seq:
        if not item or item in seen:
            continue
        seen.add(item)
        result.append(item)
    return result


def extract_cvss(metrics: Sequence[Dict]) -> Tuple[float | None, str | None, str | None]:
    best_score = None
    best_vector = None
    best_severity = None
    for entry in metrics:
        for key in ("cvssV3_1", "cvssV3_0", "cvssV2_0"):
            if key in entry:
                payload = entry[key]
                score = payload.get("baseScore")
                if score is None:
                    continue
                if best_score is None or score > best_score:
                    best_score = score
                    best_vector = payload.get("vectorString")
                    best_severity = payload.get("baseSeverity")
    return best_score, best_vector, best_severity


def extract_problem_types(problem_types: Sequence[Dict]) -> List[str]:
    labels: List[str] = []
    for entry in problem_types:
        for desc in entry.get("descriptions", []):
            label = desc.get("cweId") or desc.get("description")
            if label:
                labels.append(label)
    return dedupe(labels)


def summarize_affected(affected: Sequence[Dict]) -> Tuple[List[str], List[str]]:
    vendors: List[str] = []
    products: List[str] = []
    for entry in affected:
        vendor = entry.get("vendor")
        product = entry.get("product")
        if vendor:
            vendors.append(vendor)
        if product:
            products.append(product)
    return dedupe(vendors), dedupe(products)


def extract_v5_fields(raw: Dict) -> Dict:
    meta = raw.get("cveMetadata", {})
    cna = raw.get("containers", {}).get("cna", {})
    descriptions = select_lang_values(cna.get("descriptions", []))
    description = " ".join(descriptions).strip()

    references = [
        ref.get("url")
        for ref in cna.get("references", [])
        if ref.get("url")
    ]

    problem_types = extract_problem_types(cna.get("problemTypes", []))
    cwe = next((label for label in problem_types if label.startswith("CWE-")), None)

    vendors, products = summarize_affected(cna.get("affected", []))
    cvss_score, cvss_vector, severity = extract_cvss(cna.get("metrics", []))

    published = meta.get("datePublished")
    last_modified = meta.get("dateUpdated")

    adp_text = []
    for container in raw.get("containers", {}).get("adp", []):
        title = container.get("title")
        notes = select_lang_values(container.get("descriptions", []))
        if title:
            adp_text.append(f"{title}: {' '.join(notes) if notes else ''}".strip())

    body_parts = [
        f"CVE ID: {meta.get('cveId', 'UNKNOWN')}",
        f"State: {meta.get('state', 'N/A')}",
        f"Published: {published or 'N/A'}",
        f"Last Updated: {last_modified or 'N/A'}",
    ]
    if severity or cvss_score:
        body_parts.append(
            f"Severity: {severity or 'N/A'} (CVSS {cvss_score or 'N/A'} {cvss_vector or ''})".strip()
        )
    if vendors:
        body_parts.append(f"Vendors: {', '.join(vendors)}")
    if products:
        body_parts.append(f"Products: {', '.join(products)}")
    if problem_types:
        body_parts.append(f"Problem Types: {', '.join(problem_types)}")

    body_parts.extend(
        [
            "",
            "Description:",
            description or "No description available.",
        ]
    )

    if adp_text:
        body_parts.append("")
        body_parts.append("Additional Analyst Notes:")
        for note in adp_text:
            body_parts.append(f"- {note}")

    if references:
        body_parts.append("")
        body_parts.append("References:")
        for ref in references:
            body_parts.append(f"- {ref}")

    flat_text = "\n".join(textwrap.dedent(part).strip() for part in body_parts if part is not None)

    return {
        "cve_id": meta.get("cveId", "UNKNOWN"),
        "cwe": cwe,
        "problem_types": problem_types,
        "published": published,
        "last_modified": last_modified,
        "severity": severity,
        "cvss_score": cvss_score,
        "cvss_vector": cvss_vector,
        "vendors": vendors,
        "products": products,
        "references": references,
        "text": flat_text,
        "source_file": str(raw.get("_source_file", "unknown")),
    }


def extract_legacy_fields(raw: Dict) -> Dict:
    cve_id = (
        raw.get("cve", {})
        .get("CVE_data_meta", {})
        .get("ID")
        or raw.get("cve_id")
        or raw.get("id")
    )
    description = ""
    if "cve" in raw:
        desc_data = raw["cve"].get("description", {}).get("description_data", [])
        description = " ".join(item.get("value", "") for item in desc_data)
    elif "description" in raw:
        if isinstance(raw["description"], dict):
            description = raw["description"].get("description_data", "")
        else:
            description = str(raw["description"])

    references = []
    if "cve" in raw:
        refs = raw["cve"].get("references", {}).get("reference_data", [])
        references = [ref.get("url") for ref in refs if ref.get("url")]
    elif "references" in raw and isinstance(raw["references"], list):
        references = [
            ref if isinstance(ref, str) else ref.get("url")
            for ref in raw["references"]
        ]

    cwe = None
    problemtype = raw.get("cve", {}).get("problemtype", {}).get("problemtype_data", [])
    if problemtype:
        descriptions = problemtype[0].get("description", [])
        if descriptions:
            cwe = descriptions[0].get("value")

    published = raw.get("publishedDate") or raw.get("published")
    last_modified = raw.get("lastModifiedDate") or raw.get("last_modified")

    body_parts = [
        f"CVE ID: {cve_id or 'UNKNOWN'}",
        f"CWE: {cwe or 'N/A'}",
        f"Published: {published or 'N/A'}",
        f"Last Modified: {last_modified or 'N/A'}",
        "",
        "Description:",
        description or "No description available.",
    ]

    if references:
        body_parts.append("")
        body_parts.append("References:")
        for ref in references:
            body_parts.append(f"- {ref}")

    flat_text = "\n".join(textwrap.dedent(part).strip() for part in body_parts)

    return {
        "cve_id": cve_id or "UNKNOWN",
        "cwe": cwe,
        "problem_types": [cwe] if cwe else [],
        "published": published,
        "last_modified": last_modified,
        "severity": None,
        "cvss_score": None,
        "cvss_vector": None,
        "vendors": [],
        "products": [],
        "references": references,
        "text": flat_text,
        "source_file": str(raw.get("_source_file", "unknown")),
    }


def normalize_entry(raw: Dict) -> Dict:
    if "containers" in raw and "cveMetadata" in raw:
        return extract_v5_fields(raw)
    return extract_legacy_fields(raw)


def prepare_entries(cve_root: Path) -> Iterable[Dict]:
    json_files = sorted(cve_root.rglob("*.json"))
    for path in json_files:
        for raw in iter_raw_entries(path):
            raw["_source_file"] = path
            yield normalize_entry(raw)


def write_corpus(
    entries: Iterable[Dict], output_path: Path, chunk_size: int, overlap: int
) -> Tuple[int, int]:
    output_path.parent.mkdir(parents=True, exist_ok=True)
    doc_count = 0
    chunk_count = 0
    with output_path.open("w", encoding="utf-8") as writer:
        for entry in entries:
            doc_count += 1
            chunks = chunk_text(entry["text"], chunk_size, overlap)
            for idx, chunk in enumerate(chunks):
                chunk_count += 1
                record = {
                    "cve_id": entry["cve_id"],
                    "chunk_id": idx,
                    "text": chunk,
                    "metadata": {
                        "cwe": entry["cwe"],
                        "problem_types": entry.get("problem_types"),
                        "published": entry["published"],
                        "last_modified": entry["last_modified"],
                        "severity": entry.get("severity"),
                        "cvss_score": entry.get("cvss_score"),
                        "cvss_vector": entry.get("cvss_vector"),
                        "vendors": entry.get("vendors"),
                        "products": entry.get("products"),
                        "references": entry["references"],
                        "source_file": entry["source_file"],
                    },
                }
                writer.write(json.dumps(record) + "\n")
    return doc_count, chunk_count


def main() -> None:
    args = parse_args()
    cve_root = Path(args.cve_root)
    if not cve_root.exists():
        raise FileNotFoundError(
            f"CVE root {cve_root} not found. Run scripts/unzip_cvelist.py first."
        )
    entries = prepare_entries(cve_root)
    docs, chunks = write_corpus(entries, Path(args.output), args.chunk_size, args.chunk_overlap)
    print(f"Wrote {chunks} chunks from {docs} CVE entries to {args.output}")


if __name__ == "__main__":
    main()