#!/usr/bin/env python3
"""Generate PosterEval IR from poster PNG/JPEG images.

The public flow intentionally uses two IR prompts:
- content IR for Order, Completeness, and Claim F1.
- figure IR for Local Text-Figure Alignment.
"""

import argparse
import json
import re
from concurrent.futures import ThreadPoolExecutor, as_completed
from pathlib import Path
from typing import Any, Dict, Iterable, List, Optional, Tuple

from openrouter_client import call_openrouter_json


DEFAULT_WORKERS = 8
PROMPT_DIR = Path(__file__).resolve().parent / "prompts"
PARSER_TO_PROMPT = {
    "content": PROMPT_DIR / "content_ir_prompt.md",
    "figure": PROMPT_DIR / "figure_grounding_ir_prompt.md",
}


def normalize_key(text: str) -> str:
    return re.sub(r"[^a-z0-9]+", "", text.lower())


def extract_key(name: str, pattern: Optional[str]) -> str:
    if pattern:
        match = re.search(pattern, name)
        if match:
            return match.groupdict().get("key") or match.group(1)
    if re.fullmatch(r"\d+", name):
        return name
    return normalize_key(name)


def should_ignore(name: str, patterns: Iterable[str]) -> bool:
    return any(re.search(pattern, name) for pattern in patterns)


def choose_image(dir_path: Path, image_filename: Optional[str]) -> Optional[Path]:
    if image_filename:
        candidate = dir_path / image_filename
        if candidate.exists():
            return candidate

    priority = [
        "poster.png",
        "paper.png",
        "poster.jpg",
        "paper.jpg",
        "poster.jpeg",
        "paper.jpeg",
    ]
    for name in priority:
        candidate = dir_path / name
        if candidate.exists():
            return candidate

    images = []
    for pattern in ("*.png", "*.jpg", "*.jpeg"):
        images.extend(dir_path.glob(pattern))
    return sorted(images, key=lambda p: p.name)[0] if images else None


def discover_images(
    input_root: Path,
    layout: str,
    image_filename: Optional[str],
    image_glob: str,
    key_regex: Optional[str],
    ignore_dir_regex: List[str],
) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
    items: List[Dict[str, Any]] = []
    missing: List[Dict[str, Any]] = []

    if layout == "flat":
        for image_path in sorted(input_root.glob(image_glob), key=lambda p: p.name):
            if not image_path.is_file():
                continue
            key = extract_key(image_path.stem, key_regex)
            items.append(
                {
                    "key": key,
                    "source_name": image_path.stem,
                    "image_path": image_path,
                    "input_relpath": image_path.name,
                }
            )
        return items, missing

    for child in sorted(input_root.iterdir(), key=lambda p: p.name):
        if not child.is_dir() or should_ignore(child.name, ignore_dir_regex):
            continue
        key = extract_key(child.name, key_regex)
        image_path = choose_image(child, image_filename)
        if image_path is None:
            missing.append({"key": key, "source_name": child.name})
            continue
        items.append(
            {
                "key": key,
                "source_name": child.name,
                "image_path": image_path,
                "input_relpath": str(image_path.relative_to(input_root)),
            }
        )
    return items, missing


def infer_role(title: str, text_content: str) -> str:
    text = f"{title} {text_content}".lower()
    problem_kw = ["problem", "motivation", "challenge", "background"]
    approach_kw = ["approach", "method", "framework", "model", "pipeline"]
    evidence_kw = ["result", "evaluation", "experiment", "ablation", "analysis"]

    if any(keyword in text for keyword in problem_kw):
        return "Problem"
    if any(keyword in text for keyword in evidence_kw):
        return "Evidence"
    if any(keyword in text for keyword in approach_kw):
        return "Approach"
    return "Approach"


def normalize_figure_ir(data: Dict[str, Any]) -> Dict[str, Any]:
    if not isinstance(data, dict):
        return {"sections": []}

    sections = []
    for section_index, section in enumerate(data.get("sections", []), start=1):
        if not isinstance(section, dict):
            continue

        title = section.get("title", "") or ""
        text_content = section.get("text_content", "") or ""
        out_section = {
            "id": section.get("id") or f"s{section_index}",
            "title": title,
            "bbox": section.get("bbox"),
            "text_content": text_content,
            "meta_role": section.get("meta_role") or infer_role(title, text_content),
            "contains_figures": [],
        }
        figures = section.get("contains_figures") or section.get("figures") or []
        for fig_index, figure in enumerate(figures, start=1):
            if not isinstance(figure, dict):
                continue
            description = figure.get("description", "") or ""
            out_section["contains_figures"].append(
                {
                    "id": figure.get("id") or f"f{section_index}_{fig_index}",
                    "bbox": figure.get("bbox"),
                    "type": figure.get("type", "Chart"),
                    "caption": figure.get("caption") or description,
                    "semantic_summary": figure.get("semantic_summary") or description,
                    "visual_description": figure.get("visual_description") or description,
                }
            )
        sections.append(out_section)

    payload: Dict[str, Any] = {"sections": sections}
    if "meta" in data:
        payload["meta"] = data["meta"]
    return payload


def parser_output_dir(output_root: Path, parser_name: str) -> Path:
    if parser_name == "content":
        return output_root / "content_ir"
    if parser_name == "figure":
        return output_root / "figure_ir"
    raise ValueError(f"Unknown parser: {parser_name}")


def write_ir(
    output_root: Path,
    parser_name: str,
    key: str,
    payload: Dict[str, Any],
) -> Path:
    out_dir = parser_output_dir(output_root, parser_name) / key
    out_dir.mkdir(parents=True, exist_ok=True)
    out_path = out_dir / "poster_ir.json"
    out_path.write_text(
        json.dumps(payload, ensure_ascii=False, indent=2) + "\n",
        encoding="utf-8",
    )
    return out_path


def run_one_parser(
    item: Dict[str, Any],
    output_root: Path,
    parser_name: str,
    model: str,
    temperature: float,
    include_paths: bool,
) -> Dict[str, Any]:
    prompt_path = PARSER_TO_PROMPT[parser_name]
    prompt = prompt_path.read_text(encoding="utf-8")

    record = {
        "key": item["key"],
        "source_name": item["source_name"],
        "input_relpath": item["input_relpath"],
        "parser": parser_name,
        "model": model,
        "prompt_file": str(prompt_path.relative_to(PROMPT_DIR.parent)),
        "error": "",
    }
    if include_paths:
        record["image_path"] = str(item["image_path"])

    try:
        raw = call_openrouter_json(
            prompt=prompt,
            model=model,
            image_path=item["image_path"],
            max_tokens=16384,
            temperature=temperature,
            response_format_json=True,
        )
        if raw.get("parse_error"):
            raise RuntimeError(raw.get("error", "model response JSON parse error"))

        payload = normalize_figure_ir(raw) if parser_name == "figure" else raw
        payload.setdefault("_postereval", {})
        payload["_postereval"].update(
            {
                "source_name": item["source_name"],
                "parser": parser_name,
                "model": model,
                "prompt_file": record["prompt_file"],
            }
        )
        if include_paths:
            payload["_postereval"]["image_path"] = str(item["image_path"])
        else:
            payload["_postereval"]["input_relpath"] = item["input_relpath"]

        out_path = write_ir(output_root, parser_name, item["key"], payload)
        record["output_relpath"] = str(out_path.relative_to(output_root))
    except Exception as exc:
        record["error"] = repr(exc)
    return record


def expand_parsers(value: str) -> List[str]:
    if value == "both":
        return ["content", "figure"]
    parsers = []
    for part in value.split(","):
        part = part.strip()
        if part in {"content", "figure"}:
            parsers.append(part)
        elif part in {"content_ir"}:
            parsers.append("content")
        elif part in {"figure_ir"}:
            parsers.append("figure")
        else:
            raise ValueError(f"Unknown parser: {part}")
    return list(dict.fromkeys(parsers))


def run(args: argparse.Namespace) -> None:
    input_root = Path(args.input_root).expanduser()
    output_root = Path(args.output_root).expanduser()
    output_root.mkdir(parents=True, exist_ok=True)

    items, missing = discover_images(
        input_root=input_root,
        layout=args.layout,
        image_filename=args.image_filename,
        image_glob=args.image_glob,
        key_regex=args.key_regex,
        ignore_dir_regex=args.ignore_dir_regex,
    )
    parsers = expand_parsers(args.parser)

    records = []
    with ThreadPoolExecutor(max_workers=args.workers) as executor:
        futures = []
        for item in items:
            for parser_name in parsers:
                futures.append(
                    executor.submit(
                        run_one_parser,
                        item,
                        output_root,
                        parser_name,
                        args.model,
                        args.temperature,
                        args.include_paths,
                    )
                )
        for future in as_completed(futures):
            records.append(future.result())

    records.sort(key=lambda r: (r["parser"], r["key"], r["source_name"]))
    summary = {
        "input_root_exists": input_root.exists(),
        "layout": args.layout,
        "n_images": len(items),
        "parsers": parsers,
        "model": args.model,
        "temperature": args.temperature,
        "n_records": len(records),
        "n_errors": sum(1 for record in records if record.get("error")),
        "missing_images": missing,
        "records": records,
    }
    if args.include_paths:
        summary["input_root"] = str(input_root)

    (output_root / "summary.json").write_text(
        json.dumps(summary, ensure_ascii=False, indent=2) + "\n",
        encoding="utf-8",
    )


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description="Generate PosterEval IR from poster images.")
    parser.add_argument("--input-root", required=True, help="Poster image root.")
    parser.add_argument("--output-root", required=True, help="IR output root.")
    parser.add_argument(
        "--parser",
        default="both",
        help="content / figure / both, or a comma-separated combination.",
    )
    parser.add_argument(
        "--layout",
        choices=["directories", "flat"],
        default="directories",
        help="Input layout: one image per subdirectory, or flat image files.",
    )
    parser.add_argument("--image-filename", help="Expected image filename inside each directory.")
    parser.add_argument("--image-glob", default="*.png", help="Glob used when --layout flat.")
    parser.add_argument("--key-regex", help="Regex with optional named group 'key'.")
    parser.add_argument(
        "--ignore-dir-regex",
        action="append",
        default=[r"^_", r"^\.", r"^__pycache__$"],
        help="Directory regex to skip. Can be repeated.",
    )
    parser.add_argument("--model", default="qwen3-vl-235b", help="OpenRouter model alias or id.")
    parser.add_argument(
        "--temperature",
        type=float,
        default=0.02,
        help="IR parser sampling temperature. Default matches the paper protocol.",
    )
    parser.add_argument("--workers", type=int, default=DEFAULT_WORKERS)
    parser.add_argument(
        "--include-paths",
        action="store_true",
        help="Include absolute local paths in outputs. Keep off for anonymous artifacts.",
    )
    return parser.parse_args()


def main() -> None:
    run(parse_args())


if __name__ == "__main__":
    main()