import torch
import numpy as np
from pathlib import Path
import matplotlib as mpl
import matplotlib.pyplot as plt

# All of the following are required to load
# a pickled model.
from celeste_ai.celeste import Celeste
from celeste_ai.network import DQN
from celeste_ai.network import Transition


def best_action(
	model_file: Path,
	out_filename: Path,
	*,
	device = torch.device("cpu")
):
	if not model_file.is_file():
		raise Exception(f"Bad model file {model_file}")
	out_filename.parent.mkdir(exist_ok = True, parents = True)

	# Create and load model
	policy_net = DQN(
		len(Celeste.state_number_map),
		len(Celeste.action_space)
	).to(device)
	checkpoint = torch.load(
		model_file,
		map_location = device
	)
	policy_net.load_state_dict(checkpoint["policy_state_dict"])



	# Compute preditions
	p = np.zeros((128, 128), dtype=np.float32)
	with torch.no_grad():
		for r in range(len(p)):
			for c in range(len(p[r])):
				x = c / 128.0
				y = r / 128.0

				k = np.asarray(policy_net(
					torch.tensor(
						[x, y],
						dtype = torch.float32,
						device = device
					).unsqueeze(0)
				)[0])
				p[r][c] = np.argmax(k)


	cmap = mpl.colors.ListedColormap(
		[
			"forestgreen",
			"firebrick",
			"lightgreen",
			"salmon",
			"darkturquoise",
			"sandybrown",
			"olive",
			"darkorchid",
			"mediumvioletred"
		]
	)

	# Plot predictions
	fig, axs = plt.subplots(1, 1, figsize = (20, 20))
	ax = axs
	ax.set(
		adjustable = "box",
		aspect = "equal",
		title = "Best Action"
	)

	plot = ax.pcolor(
		p,
		cmap = cmap,
		vmin = 0,
		vmax = 8
	)
	ax.invert_yaxis()
	cbar = fig.colorbar(plot, ticks = list(range(0, 9)))
	cbar.ax.set_yticklabels(Celeste.action_space)

	

	fig.savefig(out_filename)
	plt.close()