# import matplotlib.patches as patches
import matplotlib.pyplot as plt
import numpy as np
import torch

from dataloader import extract_colors, preprocess_data
from model import ColorTransformerModel

# import matplotlib.colors as mcolors


def make_image(ckpt: str, fname: str, color=True):
    M = ColorTransformerModel.load_from_checkpoint(ckpt)

    # preds = M(rgb_tensor)
    if not color:
        N = 949
        linear_space = torch.linspace(0, 1, N)
        rgb_tensor = linear_space.unsqueeze(1).repeat(1, 3)
    else:
        rgb_tensor, names = extract_colors()

    rgb_values = rgb_tensor.detach().numpy()
    rgb_tensor = preprocess_data(rgb_tensor)
    preds = M(rgb_tensor)
    sorted_inds = np.argsort(preds.detach().numpy().ravel())

    fig, ax = plt.subplots()
    for i in range(len(sorted_inds)):
        idx = sorted_inds[i]
        color = rgb_values[idx]
        ax.plot([i + 0.5, i + 0.5], [0, 1], lw=1, c=color, antialiased=True, alpha=1)
        # rect = patches.Rectangle((i, 0), 1, 5, linewidth=0.1, edgecolor=None, facecolor=None, alpha=1)
        # ax.add_patch(rect)
        ax.axis("off")
        # ax.axis("square")

    plt.savefig(f"{fname}.png", dpi=300)


def create_circle(ckpt: str, fname: str, dpi: int = 150):
    if isinstance(ckpt, str):
        M = ColorTransformerModel.load_from_checkpoint(ckpt)
    else:
        M = ckpt

    rgb_tensor, _ = extract_colors()
    preds = M(rgb_tensor.to(M.device))
    plot_preds(preds, fname=fname, dpi=dpi)


def plot_preds(preds, fname: str, roll: bool = False, dpi: int = 150):
    rgb_tensor, _ = extract_colors()
    rgb_values = rgb_tensor.detach().numpy()
    rgb_tensor = preprocess_data(rgb_tensor)

    if isinstance(preds, torch.Tensor):
        preds = preds.detach().cpu().numpy()
    sorted_inds = np.argsort(preds.ravel())
    colors = rgb_values[sorted_inds]
    if roll:
        # find white in colors, put it first.
        white = np.array([1, 1, 1])
        white_idx = np.where((colors == white).all(axis=1))[0][0]
        colors = np.roll(colors, -white_idx, axis=0)
        # print(white_idx, colors[:2])

    N = len(colors)
    # Create a plot with these hues in a circle
    fig, ax = plt.subplots(figsize=(3, 3), subplot_kw=dict(polar=True))

    # Each wedge in the circle
    theta = np.linspace(0, 2 * np.pi, N + 1) + np.pi / 2
    width = 2 * np.pi / (N)  # equal size for each wedge

    for i in range(N):
        ax.bar(theta[i], 1, width=width, color=colors[i], bottom=0.0)

    ax.set_xticks([])
    ax.set_yticks([])
    ax.axis("off")
    fig.tight_layout()
    plt.savefig(f"{fname}.png", dpi=dpi)
    plt.close()


if __name__ == "__main__":
    # name = "color_128_0.3_1.00e-06"
    import argparse
    import glob

    parser = argparse.ArgumentParser()
    # make the following accept a list of arguments
    parser.add_argument("-v", "--version", type=int, nargs="+", default=[0, 1])
    parser.add_argument(
        "--dpi", type=int, default=150, help="Resolution for saved image."
    )
    args = parser.parse_args()
    versions = args.version
    for v in versions:
        name = f"out/v{v}"
        # ckpt = f"/teamspace/jobs/{name}/work/colors/lightning_logs/version_2/checkpoints/epoch=999-step=8000.ckpt"
        ckpt_path = f"/teamspace/studios/this_studio/colors/lightning_logs/version_{v}/checkpoints/*.ckpt"
        ckpt = glob.glob(ckpt_path)
        if len(ckpt) > 0:
            ckpt = ckpt[-1]
            print(f"Generating image for checkpoint: {ckpt}")
            create_circle(ckpt, fname=name, dpi=args.dpi)
        else:
            print(f"No checkpoint found for version {v}")
        # make_image(ckpt, fname=name + "b", color=False)