import os
import torch
import torch.nn as nn
import numpy as np
import random
from PIL import Image
from tqdm import tqdm
from safetensors.torch import save_file

from interposer import Interposer
from vae import get_vae

# options
target_dev = "cuda"
target_steps = 500000
save_every_n = 50000
latent_src = "v1"
latent_dst = "xl"

class Latent:
	def __init__(self, md5, lat_src, lat_dst, dev):
		if lat_src == "v1": src = os.path.join("latent_v1", f"{md5}.npy")
		if lat_src == "v2": src = os.path.join("latent_v2", f"{md5}.npy")
		if lat_src == "xl": src = os.path.join("latent_xl", f"{md5}.npy")

		if lat_dst == "v1": dst = os.path.join("latent_v1", f"{md5}.npy")
		if lat_dst == "v2": dst = os.path.join("latent_v2", f"{md5}.npy")
		if lat_dst == "xl": dst = os.path.join("latent_xl", f"{md5}.npy")

		self.src = torch.from_numpy(np.load(src)).to(dev)
		self.dst = torch.from_numpy(np.load(dst)).to(dev)

vae = None
def sample_decode(latent, filename, version):
	global vae
	if not vae:
		vae = get_vae(version, fp16=True)
		vae.to("cuda")

	latent = latent.half().to("cuda")
	out = vae.decode(latent).sample
	out = out.cpu().detach().numpy()
	out = np.squeeze(out, 0)
	out = out.transpose((1, 2, 0))
	out = np.clip(out, -1.0, 1.0)
	out = (out+1)/2 * 255
	out = out.astype(np.uint8)
	out = Image.fromarray(out)
	out.save(filename)

if not os.path.isdir("models"): os.mkdir("models")

if __name__ == "__main__":
	print("Loading latents from disk")
	latents = []
	for i in tqdm(os.listdir("images")):
		md5 = os.path.splitext(i)[0]
		latents.append(Latent(md5, latent_src, latent_dst, target_dev))

	if os.path.isfile(f"test_{latent_src}.npy"):
		sample_latent = torch.from_numpy(np.load(f"test_{latent_src}.npy")).to(target_dev)
	else:
		sample_latent = random.choice(latents).src

	model = Interposer()
	model.to(target_dev)

	criterion = torch.nn.MSELoss(size_average=False)
	optimizer = torch.optim.SGD(model.parameters(), lr=1e-8)

	for t in tqdm(range(target_steps)):
		# io = latents[t%len(latents)]
		io = random.choice(latents)

		y_pred = model(io.src) # forward
		loss = criterion(y_pred, io.dst) # loss

		# backward
		optimizer.zero_grad()
		loss.backward()
		optimizer.step()

		# print loss
		if t%1000 == 0: tqdm.write(f"{t} - {loss.data.item():.2f}")

		# sample/save
		if t%save_every_n == 0:
			out = model(sample_latent)
			output_name = f"./models/{latent_src}-to-{latent_dst}_interposer_e{t/1000}k"
			sample_decode(out, f"{output_name}.png", latent_dst)
			save_file(model.state_dict(), f"{output_name}.safetensors")
	# save final output
	output_name = f"./models/{latent_src}-to-{latent_dst}_interposer_e{target_steps/1000}k"
	sample_decode(out, f"{output_name}.png", "v1")
	save_file(model.state_dict(), f"{output_name}.safetensors")