import argparse, os, sys, glob, datetime, yaml
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) 

import torch
import time
import numpy as np
from tqdm import trange

from omegaconf import OmegaConf
from PIL import Image

 
from unidemoire.models.diffusion.ddim import DDIMSampler
from unidemoire.util import instantiate_from_config

rescale = lambda x: (x + 1.) / 2.

def custom_to_pil(x):
    x = x.detach().cpu()
    x = torch.clamp(x, -1., 1.)
    x = (x + 1.) / 2.
    x = x.permute(1, 2, 0).numpy()
    x = (255 * x).astype(np.uint8)
    if x.shape[2] == 1:
        x = x.squeeze()
        x = Image.fromarray(x, mode='L')
    else:
        x = Image.fromarray(x)
        x = x.convert("RGB") 
    return x

def custom_to_np(x):
    # saves the batch in adm style as in https://github.com/openai/guided-diffusion/blob/main/scripts/image_sample.py
    sample = x.detach().cpu()
    sample = ((sample + 1) * 127.5).clamp(0, 255).to(torch.uint8)
    sample = sample.permute(0, 2, 3, 1)
    sample = sample.contiguous()
    return sample

def logs2pil(logs, keys=["sample"]):
    imgs = dict()
    for k in logs:
        try:
            if len(logs[k].shape) == 4:
                img = custom_to_pil(logs[k][0, ...])
            elif len(logs[k].shape) == 3:
                img = custom_to_pil(logs[k])
            else:
                print(f"Unknown format for key {k}. ")
                img = None
        except:
            img = None
        imgs[k] = img
    return imgs

@torch.no_grad()
def convsample(model, shape, return_intermediates=True,
               verbose=True,
               make_prog_row=False):
    if not make_prog_row:
        return model.p_sample_loop(None, shape,
                                   return_intermediates=return_intermediates, verbose=verbose)
    else:
        return model.progressive_denoising(
            None, shape, verbose=True
        )

@torch.no_grad()
def convsample_ddim(model, steps, shape, eta=1.0):
    ddim = DDIMSampler(model)
    bs = shape[0]
    shape = shape[1:]
    samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, eta=eta, verbose=False,)
    return samples, intermediates

@torch.no_grad()
def make_convolutional_sample(model, batch_size, vanilla=False, custom_steps=None, eta=1.0,):

    log = dict()
    shape = [batch_size,
             model.model.diffusion_model.in_channels,
             model.model.diffusion_model.image_size,
             model.model.diffusion_model.image_size]
    
    with model.ema_scope("Plotting"):
        t0 = time.time()
        if vanilla:
            sample, progrow = convsample(model, shape,
                                         make_prog_row=True)
        else:
            sample, intermediates = convsample_ddim(model,  steps=custom_steps, shape=shape,
                                                    eta=eta)
        t1 = time.time()

    x_sample = model.decode_first_stage(sample)
    log["sample"] = x_sample
    log["time"] = t1 - t0
    log['throughput'] = sample.shape[0] / (t1 - t0)
    return log

def run(model, logdir, batch_size=50, vanilla=False, custom_steps=None, eta=None, n_samples=50000, nplog=None):
    if vanilla:
        print(f'Using Vanilla DDPM sampling with {model.num_timesteps} sampling steps.')
    else:   # Using DDIM sampling with 200 sampling steps and eta=1.0
        print(f'Using DDIM sampling with {custom_steps} sampling steps and eta={eta}')

    tstart = time.time()
    n_saved = len(glob.glob(os.path.join(logdir,'*.png'))) 

    if model.cond_stage_model is None:
        all_images = []
        print(f"Running unconditional sampling for {n_samples} samples")
        for _ in trange(n_samples // batch_size, desc="Sampling Batches (unconditional)"):
            if n_saved >= n_samples:
                print(f'Finish after generating {n_saved} samples')
                break            
            logs = make_convolutional_sample(model, batch_size=batch_size,
                                             vanilla=vanilla, custom_steps=custom_steps,
                                             eta=eta)
            n_saved = save_logs(logs, logdir, n_saved=n_saved, key="sample")
            all_images.extend([custom_to_np(logs["sample"])])
            
    else:
       raise NotImplementedError('Currently only sampling for unconditional models supported.')

    print(f"sampling of {n_saved} images finished in {(time.time() - tstart) / 60.:.2f} minutes.")

def save_logs(logs, path, n_saved=0, key="sample", np_path=None):
    for k in logs:
        if k == key:
            batch = logs[key]
            if np_path is None:
                for x in batch:
                    img = custom_to_pil(x)
                    imgpath = os.path.join(path, f"{n_saved:07}.png")
                    img.save(imgpath)
                    n_saved += 1
            else:
                npbatch = custom_to_np(batch)
                shape_str = "x".join([str(x) for x in npbatch.shape])
                nppath = os.path.join(np_path, f"{n_saved}-{shape_str}-samples.npz")
                np.savez(nppath, npbatch)
                n_saved += npbatch.shape[0]
    return n_saved

def get_parser():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-r",
        "--resume",
        type=str,
        nargs="?",
        help="load from logdir or checkpoint in logdir",
        default="./data/generated"
    )
    parser.add_argument(
        "-n",
        "--n_samples",
        type=int,
        nargs="?",
        help="number of samples to draw",
        default=20
    )
    parser.add_argument(
        "-e",
        "--eta",
        type=float,
        nargs="?",
        help="eta for ddim sampling (0.0 yields deterministic sampling)",
        default=1.0
    )
    parser.add_argument(
        "-v",
        "--vanilla_sample", 
        default=False,
        action='store_true',
        help="vanilla sampling (default option is DDIM sampling)?",
    )
    parser.add_argument(
        "-l",
        "--logdir",
        type=str,
        nargs="?",
        help="extra logdir",
        default="./data/generated"
    )
    parser.add_argument(
        "-c",
        "--custom_steps",
        type=int,
        nargs="?",
        help="number of steps for ddim and fastdpm sampling",
        default=200      
    )
    parser.add_argument(
        "--batch_size",
        type=int,
        nargs="?",
        help="the bs",
        default=1
    )
    return parser

def load_model_from_config(config, sd):
    model = instantiate_from_config(config)
    model.load_state_dict(sd,strict=False)
    model.cuda()
    model.eval()
    return model

def load_model(config, ckpt, gpu, eval_mode):
    if ckpt:
        print(f"Loading model from {ckpt}")
        pl_sd = torch.load(ckpt, map_location="cpu")
        global_step = pl_sd["global_step"]
    else:
        pl_sd = {"state_dict": None}
        global_step = None
    model = load_model_from_config(config.model,
                                   pl_sd["state_dict"])

    return model, global_step


if __name__ == "__main__":
    now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
    sys.path.append(os.getcwd())    
    command = " ".join(sys.argv)    
    print(75 * "=")
    parser = get_parser()
    opt, unknown = parser.parse_known_args()
    ckpt = None

    if not os.path.exists(opt.resume):
        raise ValueError("Cannot find {}".format(opt.resume))
    
    if os.path.isfile(opt.resume):               
        try:
            logdir = '/'.join(opt.resume.split('/')[:-1])
            print(f'Logdir is {logdir}')
        except ValueError:
            paths = opt.resume.split("/")
            idx = -2  # take a guess: path/to/logdir/checkpoints/model.ckpt
            logdir = "/".join(paths[:idx])
        ckpt = opt.resume
    else:
        assert os.path.isdir(opt.resume), f"{opt.resume} is not a directory"
        logdir = opt.resume.rstrip("/")
        ckpt = os.path.join(logdir, "model.ckpt")

    base_configs = sorted(glob.glob(os.path.join(logdir, "config.yaml")))
    opt.base = base_configs

    configs = [OmegaConf.load(cfg) for cfg in opt.base]
    cli = OmegaConf.from_dotlist(unknown)
    config = OmegaConf.merge(*configs, cli)

    gpu = True
    eval_mode = True

    if opt.logdir != "none":
        locallog = logdir.split(os.sep)[-1]
        if locallog == "": locallog = logdir.split(os.sep)[-2]
        print(f"Switching logdir from '{logdir}' to '{os.path.join(opt.logdir, locallog)}'")
        logdir = os.path.join(opt.logdir, locallog)

    model, global_step = load_model(config, ckpt, gpu, eval_mode)
    print(f"global step: {global_step}")
    print(75 * "=")
    print("logging to:")
    logdir = os.path.join(logdir, now)
    imglogdir = os.path.join(logdir, "moire_patterns")
    os.makedirs(imglogdir)
    print(logdir)
    print(75 * "=")

    # write config out
    sampling_file = os.path.join(logdir, "sampling_config.yaml")
    sampling_conf = vars(opt)

    with open(sampling_file, 'w') as f:
        yaml.dump(sampling_conf, f, default_flow_style=False)

    run(model, imglogdir, eta=opt.eta,
        vanilla=opt.vanilla_sample,  n_samples=opt.n_samples, custom_steps=opt.custom_steps,
        batch_size=opt.batch_size)

    print("done.")