import importlib

import numpy as np
import taming
import torch
import yaml
from omegaconf import OmegaConf
from PIL import Image
from taming.models.vqgan import VQModel

from utils import get_device
# import discriminator

def load_config(config_path, display=False):
  config = OmegaConf.load(config_path)
  if display:
    print(yaml.dump(OmegaConf.to_container(config)))
  return config

# def load_disc(device):
#     dconf = load_config("disc_config.yaml")
#     sd = torch.load("disc.pt", map_location=device)
#     # print(sd.keys())
#     model = discriminator.NLayerDiscriminator()
#     model.load_state_dict(sd, strict=True)
#     model.to(device)
#     return model
    # print(dconf.keys())

def load_default(device):
    # device = get_device()
    ckpt_path = "logs/2021-04-23T18-11-19_celebahq_transformer/checkpoints/last.ckpt"
    conf_path = "./unwrapped.yaml"
    config = load_config(conf_path, display=False)
    model = taming.models.vqgan.VQModel(**config.model.params)
    sd = torch.load("./vqgan_only.pt", map_location=device)
    model.load_state_dict(sd, strict=True)
    model.to(device)
    del sd
    return model


def load_vqgan(config, ckpt_path=None, is_gumbel=False):
  if is_gumbel:
    model = GumbelVQ(**config.model.params)
  else:
    model = VQModel(**config.model.params)
  if ckpt_path is not None:
    sd = torch.load(ckpt_path, map_location="cpu")["state_dict"]
    missing, unexpected = model.load_state_dict(sd, strict=False)
  return model.eval()

def load_ffhq():
    conf = "2020-11-09T13-33-36_faceshq_vqgan/configs/2020-11-09T13-33-36-project.yaml"
    ckpt = "2020-11-09T13-33-36_faceshq_vqgan/checkpoints/last.ckpt"
    vqgan = load_model(load_config(conf), ckpt, True, True)[0]

def reconstruct_with_vqgan(x, model):
  # could also use model(x) for reconstruction but use explicit encoding and decoding here
  z, _, [_, _, indices] = model.encode(x)
  print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}")
  xrec = model.decode(z)
  return xrec
def get_obj_from_str(string, reload=False):
    module, cls = string.rsplit(".", 1)
    if reload:
        module_imp = importlib.import_module(module)
        importlib.reload(module_imp)
    return getattr(importlib.import_module(module, package=None), cls)

def instantiate_from_config(config):

    if not "target" in config:
        raise KeyError("Expected key `target` to instantiate.")
    return get_obj_from_str(config["target"])(**config.get("params", dict()))

def load_model_from_config(config, sd, gpu=True, eval_mode=True):
    model = instantiate_from_config(config)
    if sd is not None:
        model.load_state_dict(sd)
    if gpu:
        model.cuda()
    if eval_mode:
        model.eval()
    return {"model": model}


def load_model(config, ckpt, gpu, eval_mode):
    # load the specified checkpoint
    if ckpt:
        pl_sd = torch.load(ckpt, map_location="cpu")
        global_step = pl_sd["global_step"]
        print(f"loaded model from global step {global_step}.")
    else:
        pl_sd = {"state_dict": None}
        global_step = None
    model = load_model_from_config(config.model, pl_sd["state_dict"], gpu=gpu, eval_mode=eval_mode)["model"]
    return model, global_step