#!/usr/bin/env python3
import argparse
import os
from pathlib import Path

import numpy as np
import torch


def _to_numpy(value):
    if torch.is_tensor(value):
        return value.detach().cpu().numpy()
    return np.asarray(value)


def _load_matplotlib():
    import matplotlib

    matplotlib.use("Agg")
    import matplotlib.pyplot as plt

    return plt


def save_heatmap(path, data, title, cmap="viridis"):
    plt = _load_matplotlib()
    fig, ax = plt.subplots(figsize=(8, 4.8), dpi=160)
    im = ax.imshow(data, cmap=cmap, origin="upper")
    ax.set_title(title)
    ax.set_xlabel("token x")
    ax.set_ylabel("token y")
    fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
    fig.tight_layout()
    fig.savefig(path)
    plt.close(fig)


def save_flow(path, displacement_yx, margin, stride):
    plt = _load_matplotlib()
    h, w, _ = displacement_yx.shape
    yy, xx = np.mgrid[0:h, 0:w]
    sample = (slice(None, None, stride), slice(None, None, stride))
    dx = displacement_yx[..., 1][sample]
    dy = displacement_yx[..., 0][sample]
    confidence = margin[sample]

    fig, ax = plt.subplots(figsize=(9, 5.4), dpi=160)
    ax.imshow(margin, cmap="Greys", origin="upper", alpha=0.35)
    quiver = ax.quiver(
        xx[sample],
        yy[sample],
        dx,
        dy,
        confidence,
        angles="xy",
        scale_units="xy",
        scale=1,
        cmap="magma",
        width=0.0035,
    )
    ax.set_xlim(-0.5, w - 0.5)
    ax.set_ylim(h - 0.5, -0.5)
    ax.set_title(f"current token -> previous short token, stride={stride}")
    ax.set_xlabel("token x")
    ax.set_ylabel("token y")
    fig.colorbar(quiver, ax=ax, fraction=0.046, pad=0.04, label="top1 - top2 score")
    fig.tight_layout()
    fig.savefig(path)
    plt.close(fig)


def visualize(input_path, output_dir, stride):
    artifact = torch.load(input_path, map_location="cpu")
    output_dir = Path(output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)

    displacement_yx = _to_numpy(artifact["displacement_yx"])
    margin = _to_numpy(artifact["margin"])
    magnitude = np.linalg.norm(displacement_yx.astype(np.float32), axis=-1)
    top1_score = _to_numpy(artifact["top1_score"])

    stem = Path(input_path).stem
    save_flow(output_dir / f"{stem}_flow_quiver.png", displacement_yx, margin, stride)
    save_heatmap(output_dir / f"{stem}_displacement_heatmap.png", magnitude, "token displacement magnitude")
    save_heatmap(output_dir / f"{stem}_confidence_heatmap.png", margin, "confidence: top1 - top2 score")
    save_heatmap(output_dir / f"{stem}_top1_score_heatmap.png", top1_score, "top1 raw score")


def main():
    parser = argparse.ArgumentParser(description="Visualize short-history attention token matches.")
    parser.add_argument("input", help="Path to a short_attn_*.pt artifact.")
    parser.add_argument("--output-dir", default=None, help="Directory for PNG outputs. Defaults next to the artifact.")
    parser.add_argument("--stride", type=int, default=4, help="Token stride for sparse flow arrows.")
    args = parser.parse_args()

    output_dir = args.output_dir or os.path.dirname(os.path.abspath(args.input))
    visualize(args.input, output_dir, args.stride)


if __name__ == "__main__":
    main()