"""Condition D: Vision + grounded annotation — image AND masked text description.

Task 1 — Viewpoint Identification:
  Given a representation of one patent figure, name its viewpoint type.
  Ground truth: parsed from drawing_description text.

Condition D setup:
  The VLM sees BOTH:
    1. The image of the target figure (visual signal)
    2. The drawing description with the target viewpoint MASKED (FIM text context)
       e.g. "FIG. 1 is perspective; FIG. 3 is rear elevational; FIG. 2 is ___ view"
    3. Numeric features (ink density, edge transitions, aspect ratio)

  This is the CadVLM setup: image + structured text together.
  Hypothesis: Condition D > Condition A (image only) because the text context
  scaffolds convention knowledge onto the visual signal.

Comparison targets:
  Condition A (vision only):            23.3% overall, 5.3% front elev
  Condition C (text FIM, no image):     see condition_c_results.json
  Condition D (vision + text FIM):      this script

Usage:
    python scripts/eval/condition_d_grounded.py \
        --enriched data/enriched/enriched_2022.parquet \
        --images /tmp/patent_sample/2022 \
        --n 18 \
        --out results/condition_d_results.json
"""

import argparse
import json
import re
import time
from collections import defaultdict
from pathlib import Path

import pandas as pd

import sys
sys.path.insert(0, str(Path(__file__).parent))
from provider import chat, encode_image, get_client

# ── shared helpers (same as condition_c and track_a) ─────────────────────────

def parse_viewpoint(desc: str, fig_num: int) -> str:
    pat = re.compile(
        rf"FIG\.\s*{fig_num + 1}\s+is\s+(?:a\s+|an\s+)?(.{{5,80}}?)\s*(?:view|thereof|;|\n|$)",
        re.IGNORECASE,
    )
    m = pat.search(desc or "")
    return m.group(1).strip().lower() if m else ""


def mask_viewpoint(desc: str, fig_num: int) -> tuple[str, str]:
    pat = re.compile(
        rf"(FIG\.\s*{fig_num + 1}\s+is\s+(?:a\s+|an\s+)?)(.{{5,80}}?)(\s*(?:view|thereof|;|\n|$))",
        re.IGNORECASE,
    )
    vp = parse_viewpoint(desc, fig_num)
    masked = pat.sub(r"\1___\3", desc or "", count=1)
    return masked, vp


def viewpoint_match(predicted: str, ground_truth: str) -> bool:
    DIRECTIONS = {"front","rear","back","left","right","top","bottom","side",
                  "perspective","plan","elevation","elevational","isometric",
                  "oblique","reference","detail","enlarged","cross","section"}
    pred_w = set(re.findall(r"\w+", predicted.lower())) & DIRECTIONS
    gt_w   = set(re.findall(r"\w+", ground_truth.lower())) & DIRECTIONS
    if not gt_w: return False
    return len(pred_w & gt_w) / len(gt_w) >= 0.5


VP_BUCKETS = [
    ("perspective",   lambda v: "perspective" in v),
    ("front_elev",    lambda v: "front elev" in v),
    ("rear_elev",     lambda v: "rear elev" in v or "rear elevation" in v),
    ("side_elev",     lambda v: "side elev" in v or "side elevation" in v),
    ("top_plan",      lambda v: "top plan" in v or v == "top view"),
    ("bottom_plan",   lambda v: "bottom plan" in v or "bottom" in v),
    ("other",         lambda v: True),
]

def bucket(vp: str) -> str:
    for name, fn in VP_BUCKETS:
        if fn(vp): return name
    return "other"


def find_image(images_dir: Path, image_filename: str) -> Path | None:
    parts = image_filename.split("-D0")
    if len(parts) < 2: return None
    p = images_dir / parts[0] / image_filename
    return p if p.exists() else None


# ── Condition D prompt: image + masked text + numeric features ────────────────

TASK1_D_PROMPT = """\
You are an engineer reading a US design patent.

Patent title: {title}

The patent has {n_figs} figures. Here is the drawing description with one \
figure's viewpoint type replaced by ___:

{masked_desc}

{numeric_context}

The image above shows FIG. {fig_num} of this patent.

Using both the image and the context of what other figures show, what type \
of view is FIG. {fig_num}?
Give the viewpoint label only (2-5 words).
Examples: "front elevational view", "perspective view", "top plan view"
"""


def run(enriched_path: str, images_dir: str, n: int, out_path: str, seed: int = 42):
    import random
    client = get_client()
    images = Path(images_dir)

    df = pd.read_parquet(enriched_path)
    df["vp"] = df.apply(
        lambda r: parse_viewpoint(r.get("drawing_description", ""), r["figure_number"]),
        axis=1,
    )
    df["img_path"] = df["image_filename"].apply(lambda fn: find_image(images, fn))

    has_persp = df.groupby("patent_id")["vp"].apply(lambda x: x.str.contains("perspective").any())
    has_front = df.groupby("patent_id")["vp"].apply(
        lambda x: (x.str.contains("front elev") | x.isin(["front elevation","front plan"])).any()
    )
    has_img = df.groupby("patent_id")["img_path"].apply(lambda x: x.notna().any())
    eligible = has_persp.index[has_persp & has_front & has_img].tolist()

    rng = random.Random(seed)
    rng.shuffle(eligible)
    eval_pids = eligible[:n]
    print(f"Eligible patents with images: {len(eligible)}, evaluating: {len(eval_pids)}")

    results, by_vp = [], defaultdict(lambda: [0, 0])
    total_correct = total_n = 0

    for pid in eval_pids:
        group = df[df["patent_id"] == pid].sort_values("figure_number")
        patent_results = []

        for _, row in group.iterrows():
            img_path = row.get("img_path")
            if img_path is None:
                continue

            desc = str(row.get("drawing_description") or "")
            fig_num = int(row.get("figure_number", 0))
            masked_desc, ground_truth = mask_viewpoint(desc, fig_num)
            if not ground_truth:
                continue

            # Numeric context
            numeric_parts = []
            for col, label in [("ink_frac","ink density"),("edge_transitions","edges"),
                                ("img_aspect","aspect ratio")]:
                val = row.get(col)
                if val is not None and not pd.isna(val):
                    if col == "ink_frac": numeric_parts.append(f"  {label}: {float(val):.2%}")
                    elif col == "edge_transitions": numeric_parts.append(f"  {label}: {int(val):,}")
                    else: numeric_parts.append(f"  {label}: {float(val):.2f}")
            numeric_context = ("Image statistics:\n" + "\n".join(numeric_parts)
                               if numeric_parts else "")

            text_prompt = TASK1_D_PROMPT.format(
                title=str(row.get("patent_title", "")),
                n_figs=int(row.get("n_figures_in_patent", 0)),
                masked_desc=masked_desc.strip(),
                numeric_context=numeric_context,
                fig_num=fig_num + 1,
            )

            # ── TASK 1 (Condition D): image + grounded text ───────────────────
            # Input:  image of the figure (visual) + masked drawing description
            #         (FIM text context) + numeric features
            # Output: predicted viewpoint type
            # Model uses BOTH the visual appearance AND the set context
            b64, media = encode_image(Path(img_path))
            from provider import multi_image_message
            msgs = multi_image_message(
                images=[(b64, media)],
                text_after=text_prompt,
            )
            predicted = chat(client, msgs, max_tokens=30).lower().strip()

            correct = viewpoint_match(predicted, ground_truth)
            vp_label = bucket(ground_truth)
            by_vp[vp_label][1] += 1
            if correct: by_vp[vp_label][0] += 1
            total_correct += int(correct)
            total_n += 1

            patent_results.append({
                "fig": fig_num, "ground_truth": ground_truth,
                "predicted": predicted, "correct": correct, "vp_bucket": vp_label,
            })
            time.sleep(0.3)

        if patent_results:
            n_c = sum(r["correct"] for r in patent_results)
            results.append({"patent_id": pid,
                             "patent_title": str(group["patent_title"].iloc[0]),
                             "figures": patent_results})
            print(f"[{pid}] {str(group['patent_title'].iloc[0])[:45]} — "
                  f"{n_c}/{len(patent_results)} correct")

    print()
    print("=" * 60)
    print("CONDITION D — VISION + GROUNDED ANNOTATION RESULTS")
    print("Input: image + masked drawing description (FIM) + numeric features")
    print("=" * 60)
    print(f"Overall: {total_correct}/{total_n} = {total_correct/max(total_n,1):.1%}")
    print()
    for label, (c, t) in sorted(by_vp.items(), key=lambda x: -x[1][1]):
        if t == 0: continue
        print(f"  {label:<14} {c:>3}/{t:<3} {c/t:>5.1%}")

    output = {
        "condition": "D_vision_plus_text",
        "task": "Task 1 — Viewpoint Identification",
        "input": "image + drawing_description FIM + numeric features",
        "summary": {
            "overall_acc": total_correct / max(total_n, 1),
            "n": total_n,
            "by_viewpoint": {k: {"correct": v[0], "total": v[1], "acc": v[0]/v[1] if v[1] else 0}
                             for k, v in by_vp.items()},
        },
        "results": results,
    }
    Path(out_path).parent.mkdir(parents=True, exist_ok=True)
    with open(out_path, "w") as f:
        json.dump(output, f, indent=2)
    print(f"\nResults → {out_path}")


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--enriched", default="data/enriched/enriched_2022.parquet")
    parser.add_argument("--images",   default="/tmp/patent_sample/2022")
    parser.add_argument("--n",        type=int, default=18)
    parser.add_argument("--out",      default="results/condition_d_results.json")
    args = parser.parse_args()
    run(args.enriched, args.images, args.n, args.out)


if __name__ == "__main__":
    main()