#!/usr/bin/env python3
"""Extract context and prediction panels from a four-grid rollout mp4.

The eval videos produced by eval.py/eval_hyperbolic.py use this layout:

    top-left:     model-controlled rollout frame
    bottom-left:  dataset/reference real trajectory frame
    top-right:    goal frame
    bottom-right: goal frame

This script crops the left panels and saves them with explicit context vs.
open-loop-prediction names so paper figures do not mix the two groups.
"""

from __future__ import annotations

import argparse
import json
from pathlib import Path


def parse_steps(value: str) -> list[int]:
    steps = []
    for item in str(value).split(","):
        item = item.strip()
        if not item:
            continue
        steps.append(int(item))
    if not steps:
        raise argparse.ArgumentTypeError("Expected at least one comma-separated step.")
    return steps


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--video", required=True, type=Path, help="Path to rollout_*.mp4.")
    parser.add_argument(
        "--output-dir",
        type=Path,
        default=None,
        help="Default: <video_parent>/<video_stem>_figure_frames",
    )
    parser.add_argument("--context-steps", type=parse_steps, default=parse_steps("0,5,10"))
    parser.add_argument(
        "--prediction-steps",
        type=parse_steps,
        default=parse_steps("15,20,25,30,35"),
    )
    parser.add_argument(
        "--imagined-label",
        default="imagined",
        help="Name used for the model rollout row. Use 'rollout' if you want stricter wording.",
    )
    parser.add_argument(
        "--save-composite",
        action=argparse.BooleanOptionalAction,
        default=False,
        help="Also save a paper-style composite grid.",
    )
    parser.add_argument("--composite-name", default="context_vs_prediction_grid.png")
    parser.add_argument("--dpi", type=int, default=220)
    return parser.parse_args()


def read_frame(reader, index: int) -> np.ndarray:
    import numpy as np

    try:
        frame = reader.get_data(index)
    except IndexError as exc:
        raise IndexError(f"Video does not contain frame index {index}.") from exc
    frame = np.asarray(frame)
    if frame.ndim != 3 or frame.shape[-1] < 3:
        raise ValueError(f"Unexpected video frame shape at index {index}: {frame.shape}")
    return frame[..., :3].astype(np.uint8, copy=False)


def crop_left_panels(frame: np.ndarray) -> dict[str, np.ndarray]:
    height, width = frame.shape[:2]
    if height < 2 or width < 2:
        raise ValueError(f"Frame is too small to crop: {frame.shape}")
    mid_h = height // 2
    mid_w = width // 2
    return {
        "rollout": frame[:mid_h, :mid_w],
        "real": frame[mid_h : 2 * mid_h, :mid_w],
    }


def save_png(path: Path, frame: np.ndarray) -> None:
    from PIL import Image

    path.parent.mkdir(parents=True, exist_ok=True)
    Image.fromarray(frame).save(path)


def extract_frames(args: argparse.Namespace) -> tuple[Path, dict]:
    video_path = args.video.expanduser().resolve()
    if not video_path.is_file():
        raise FileNotFoundError(f"Video not found: {video_path}")

    output_dir = (
        args.output_dir.expanduser().resolve()
        if args.output_dir is not None
        else video_path.parent / f"{video_path.stem}_figure_frames"
    )
    context_dir = output_dir / "context_input"
    prediction_dir = output_dir / "open_loop_prediction"

    manifest = {
        "video": str(video_path),
        "layout": {
            "source": "four-grid eval mp4",
            "real_panel": "bottom-left",
            "imagined_panel": "top-left",
        },
        "context_steps": list(args.context_steps),
        "prediction_steps": list(args.prediction_steps),
        "frames": [],
    }

    import imageio

    reader = imageio.get_reader(video_path)
    try:
        groups = [
            ("context_input", context_dir, args.context_steps),
            ("open_loop_prediction", prediction_dir, args.prediction_steps),
        ]
        for group_name, group_dir, steps in groups:
            for local_index, step in enumerate(steps, start=1):
                frame = read_frame(reader, int(step))
                panels = crop_left_panels(frame)
                entries = [
                    ("real", panels["real"]),
                    (str(args.imagined_label), panels["rollout"]),
                ]
                for row_name, panel in entries:
                    filename = f"{group_name}_{local_index:02d}_{row_name}_t{int(step):03d}.png"
                    out_path = group_dir / row_name / filename
                    save_png(out_path, panel)
                    manifest["frames"].append(
                        {
                            "group": group_name,
                            "index": int(local_index),
                            "row": row_name,
                            "step": int(step),
                            "path": str(out_path),
                        }
                    )
    finally:
        reader.close()

    output_dir.mkdir(parents=True, exist_ok=True)
    with (output_dir / "manifest.json").open("w") as handle:
        json.dump(manifest, handle, indent=2)

    return output_dir, manifest


def _load_manifest_image(frame_entry: dict) -> np.ndarray:
    import numpy as np
    from PIL import Image

    return np.asarray(Image.open(frame_entry["path"]).convert("RGB"))


def save_composite(output_dir: Path, manifest: dict, composite_name: str, dpi: int) -> Path:
    import matplotlib.pyplot as plt

    context_steps = list(manifest["context_steps"])
    prediction_steps = list(manifest["prediction_steps"])
    all_steps = context_steps + prediction_steps
    n_context = len(context_steps)
    n_prediction = len(prediction_steps)
    n_total = len(all_steps)
    gap_units = 0.45

    by_key = {
        (entry["group"], entry["row"], int(entry["index"])): entry
        for entry in manifest["frames"]
    }
    imagined_rows = sorted(
        {
            entry["row"]
            for entry in manifest["frames"]
            if entry["row"] != "real"
        }
    )
    imagined_row = imagined_rows[0] if imagined_rows else "imagined"

    sample = _load_manifest_image(manifest["frames"][0])
    aspect = sample.shape[1] / sample.shape[0]
    tile_h = 1.0
    tile_w = aspect
    fig_w = max(8.0, n_total * 1.35 * aspect + 2.2)
    fig_h = 3.8

    fig = plt.figure(figsize=(fig_w, fig_h))
    left_margin = 0.10
    right_margin = 0.02
    top_margin = 0.20
    bottom_margin = 0.18
    row_gap = 0.055
    available_w = 1.0 - left_margin - right_margin
    available_h = 1.0 - top_margin - bottom_margin
    unit_w = available_w / (n_total + gap_units)
    axis_w = unit_w
    axis_h = (available_h - row_gap) / 2.0

    def x_for_column(column: int) -> float:
        extra_gap = gap_units if column >= n_context else 0.0
        return left_margin + (column + extra_gap) * unit_w

    row_y = {
        "real": bottom_margin + axis_h + row_gap,
        imagined_row: bottom_margin,
    }

    context_entries = [("context_input", i + 1, step) for i, step in enumerate(context_steps)]
    prediction_entries = [
        ("open_loop_prediction", i + 1, step)
        for i, step in enumerate(prediction_steps)
    ]
    column_entries = context_entries + prediction_entries

    for col, (group_name, local_index, step) in enumerate(column_entries):
        for row_name in ("real", imagined_row):
            entry = by_key[(group_name, row_name, local_index)]
            ax = fig.add_axes([x_for_column(col), row_y[row_name], axis_w, axis_h])
            ax.imshow(_load_manifest_image(entry))
            ax.set_xticks([])
            ax.set_yticks([])
            for spine in ax.spines.values():
                spine.set_linewidth(1.2)
                spine.set_color("black")
            if row_name == imagined_row:
                label = f"T = {step}" if col == 0 else f"{step}"
                ax.set_xlabel(label, fontsize=15, labelpad=6)

    context_center = (
        x_for_column(0) + x_for_column(n_context - 1) + axis_w
    ) / 2.0
    prediction_center = (
        x_for_column(n_context) + x_for_column(n_total - 1) + axis_w
    ) / 2.0
    fig.text(context_center, 0.95, "Context Input", ha="center", va="top", fontsize=19)
    fig.text(
        prediction_center,
        0.95,
        "Open Loop Prediction",
        ha="center",
        va="top",
        fontsize=19,
    )
    fig.text(0.045, row_y["real"] + axis_h / 2, "Real", rotation=90, ha="center", va="center", fontsize=17)
    fig.text(
        0.045,
        row_y[imagined_row] + axis_h / 2,
        imagined_row.capitalize(),
        rotation=90,
        ha="center",
        va="center",
        fontsize=17,
    )

    composite_path = output_dir / composite_name
    fig.savefig(composite_path, dpi=dpi, bbox_inches="tight", pad_inches=0.06)
    plt.close(fig)
    return composite_path


def main() -> None:
    args = parse_args()
    output_dir, manifest = extract_frames(args)
    print(f"[extract] wrote frames to {output_dir}", flush=True)
    if args.save_composite:
        composite_path = save_composite(
            output_dir,
            manifest,
            args.composite_name,
            int(args.dpi),
        )
        print(f"[extract] wrote composite figure to {composite_path}", flush=True)


if __name__ == "__main__":
    main()