import argparse, os, sys, glob
import cv2
import torch
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from imwatermark import WatermarkEncoder
from itertools import islice
from einops import rearrange
from torchvision.utils import make_grid
import time
from pytorch_lightning import seed_everything
from torch import autocast
from contextlib import contextmanager, nullcontext

from ldm.util import instantiate_from_config
from ldm.models.diffusion.plms import PLMSSampler
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.dpm_solver import DPMSolverSampler
from ldm.data.build_dataloader import build_dataloader

# from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from transformers import AutoFeatureExtractor
import logging
from torch.nn.functional import adaptive_avg_pool2d
from scipy import linalg
# from pytorch_fid.src.pytorch_fid.inception import InceptionV3
# from pytorch_fid.src.pytorch_fid.fid_score import compute_statistics_of_path
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker

# load safety model
# safety_model_id = "CompVis/stable-diffusion-safety-checker"
# safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
# safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id) 


def chunk(it, size):
    it = iter(it)
    return iter(lambda: tuple(islice(it, size)), ())


def numpy_to_pil(images):
    """
    Convert a numpy image or a batch of images to a PIL image.
    """
    if images.ndim == 3:
        images = images[None, ...]
    images = (images * 255).round().astype("uint8")
    pil_images = [Image.fromarray(image) for image in images]

    return pil_images


def load_model_from_config(config, ckpt, verbose=False):
    print(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cpu")
    if "global_step" in pl_sd:
        print(f"Global Step: {pl_sd['global_step']}")
    sd = pl_sd["state_dict"]
    model = instantiate_from_config(config.model)
    m, u = model.load_state_dict(sd, strict=False)
    if len(m) > 0 and verbose:
        print("missing keys:")
        print(m)
    if len(u) > 0 and verbose:
        print("unexpected keys:")
        print(u)

    model.cuda()
    model.eval()
    return model


def put_watermark(img, wm_encoder=None):
    if wm_encoder is not None:
        img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
        img = wm_encoder.encode(img, 'dwtDct')
        img = Image.fromarray(img[:, :, ::-1])
    return img


def load_replacement(x):
    try:
        hwc = x.shape
        y = Image.open("assets/rick.jpeg").convert("RGB").resize((hwc[1], hwc[0]))
        y = (np.array(y)/255.0).astype(x.dtype)
        assert y.shape == x.shape
        return y
    except Exception:
        return x

def main():
    parser = argparse.ArgumentParser()

    parser.add_argument(
        "--prompt",
        type=str,
        nargs="?",
        default="a painting of a virus monster playing guitar",
        help="the prompt to render"
    )
    parser.add_argument(
        "--outdir",
        type=str,
        nargs="?",
        help="dir to write results to",
        default="outputs/txt2img-samples"
    )
    parser.add_argument(
        "--skip_grid",
        action='store_true',
        help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
    )
    parser.add_argument(
        "--skip_save",
        type=bool,
        default=False,
        help="do not save individual samples. For speed measurements.",
    )
    parser.add_argument(
        "--ddim_steps",
        type=int,
        default=50,
        help="number of ddim sampling steps",
    )
    parser.add_argument(
        "--plms",
        action='store_true',
        help="use plms sampling",
    )
    parser.add_argument(
        "--dpm_solver",
        action='store_true',
        help="use dpm_solver sampling",
    )
    parser.add_argument(
        "--laion400m",
        action='store_true',
        help="uses the LAION400M model",
    )
    parser.add_argument(
        "--fixed_code",
        action='store_true',
        help="if enabled, uses the same starting code across samples ",
    )
    parser.add_argument(
        "--ddim_eta",
        type=float,
        default=0.0,
        help="ddim eta (eta=0.0 corresponds to deterministic sampling",
    )
    parser.add_argument(
        "--n_iter",
        type=int,
        default=2,
        help="sample this often",
    )
    parser.add_argument(
        "--H",
        type=int,
        default=512,
        help="image height, in pixel space",
    )
    parser.add_argument(
        "--W",
        type=int,
        default=512,
        help="image width, in pixel space",
    )
    parser.add_argument(
        "--C",
        type=int,
        default=4,
        help="latent channels",
    )
    parser.add_argument(
        "--f",
        type=int,
        default=8,
        help="downsampling factor",
    )
    parser.add_argument(
        "--n_samples",
        type=int,
        default=3,
        help="how many samples to produce for each given prompt. A.k.a. batch size",
    )
    parser.add_argument(
        "--n_rows",
        type=int,
        default=0,
        help="rows in the grid (default: n_samples)",
    )
    parser.add_argument(
        "--scale",
        type=float,
        default=7.5,
        help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
    )
    parser.add_argument(
        "--from-file",
        type=str,
        help="if specified, load prompts from this file",
    )
    parser.add_argument(
        "--config",
        type=str,
        default="configs/stable-diffusion/v1-inference.yaml",
        help="path to config which constructs model",
    )
    parser.add_argument(
        "--ckpt",
        type=str,
        default="models/ldm/stable-diffusion-v1/model.ckpt",
        help="path to checkpoint of model",
    )
    parser.add_argument(
        "--seed",
        type=int,
        default=42,
        help="the seed (for reproducible sampling)",
    )
    parser.add_argument(
        "--precision",
        type=str,
        help="evaluate at this precision",
        choices=["full", "autocast"],
        default="autocast"
    )
    parser.add_argument(
        "--data_dir",
        type=str,
        default="",
        help="path to data",
    )
    parser.add_argument(
        "--num_sample",
        type=int,
        default=4,
        help="samples num",
    )
    parser.add_argument(
        "--cal_fid",
        type=bool,
        default=False,
    )
    parser.add_argument(
        "--save_npy",
        type=bool,
        default=False,
    )
    parser.add_argument(
        "--timesteps",
        type=str,
        default='',
    )
    opt = parser.parse_args()

    if opt.laion400m:
        print("Falling back to LAION 400M model...")
        opt.config = "configs/latent-diffusion/txt2img-1p4B-eval.yaml"
        opt.ckpt = "models/ldm/text2img-large/model.ckpt"
        opt.outdir = "outputs/txt2img-samples-laion400m"

    seed_everything(opt.seed)
    config = OmegaConf.load(f"{opt.config}")
    model = load_model_from_config(config, f"{opt.ckpt}")  # 加载模型

    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    model = model.to(device)

    # import pdb
    # pdb.set_trace()
    if opt.dpm_solver:
        sampler = DPMSolverSampler(model)  
    elif opt.plms:
        sampler = PLMSSampler(model)
    else:
        sampler = DDIMSampler(model)

    dataloader_info = build_dataloader(config, opt)

    os.makedirs(opt.outdir, exist_ok=True)
    outpath = opt.outdir

    # log
    os.makedirs(outpath, exist_ok=True)
    log_format = '%(asctime)s %(message)s'
    logging.basicConfig(stream=sys.stdout, level=logging.INFO,
        format=log_format, datefmt='%m/%d %I:%M:%S %p')
    fh = logging.FileHandler(os.path.join(outpath, 'log.txt'))
    fh.setFormatter(logging.Formatter(log_format))
    logging.getLogger().addHandler(fh)

    logging.info(str(opt))

    print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
    wm = "StableDiffusionV1"
    wm_encoder = WatermarkEncoder()
    wm_encoder.set_watermark('bytes', wm.encode('utf-8'))

    batch_size = opt.n_samples
    n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
    if not opt.from_file:
        prompt = opt.prompt
        assert prompt is not None
        data = [batch_size * [prompt]]

    else:
        print(f"reading prompts from {opt.from_file}")
        with open(opt.from_file, "r") as f:
            data = f.read().splitlines()
            data = list(chunk(data, batch_size))

    sample_path = os.path.join(outpath, "samples")
    os.makedirs(sample_path, exist_ok=True)
    base_count = len(os.listdir(sample_path))
    grid_count = len(os.listdir(outpath)) - 1

    start_code = None
    if opt.fixed_code:
        start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)

    if opt.timesteps != '':
        timesteps = eval(opt.timesteps)
        if opt.dpm_solver == False:
            timesteps = np.array(timesteps)
    else:
        timesteps = None

    all_txt = []
    precision_scope = autocast if opt.precision=="autocast" else nullcontext
    with torch.no_grad():
        with precision_scope("cuda"):
            with model.ema_scope():
                tic = time.time()
                all_samples = list()
                for itr, batch in enumerate(dataloader_info['validation_loader']):
                    prompts = batch['text']
                    print(prompts)
                    uc = None
                    if opt.scale != 1.0:
                        uc = model.get_learned_conditioning(batch_size * [""])
                    if isinstance(prompts, tuple):
                        prompts = list(prompts)
                    print("3",prompts)
                    all_txt += prompts
                    c = model.get_learned_conditioning(prompts)
                    shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
                    
                    samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
                                                         conditioning=c,
                                                         batch_size=opt.n_samples,
                                                         shape=shape,
                                                         verbose=False,
                                                         unconditional_guidance_scale=opt.scale,
                                                         unconditional_conditioning=uc,
                                                         eta=opt.ddim_eta,
                                                         x_T=start_code,
                                                         sampled_timestep=timesteps)
                    x_samples_ddim = model.decode_first_stage(samples_ddim)
                    x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
                    x_samples_ddim = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy()

                    # x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim)
                    x_checked_image = x_samples_ddim

                    x_checked_image_torch = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2)

                    if opt.cal_fid:
                        for x_sample in x_checked_image_torch:
                            x_sample = x_sample.cpu().numpy()
                            # img = Image.fromarray(x_sample.astype(np.uint8))
                            all_samples.append(x_sample)
                            base_count += 1
                        logging.info('samples: ' + str(len(all_samples)))
                    
                    if base_count > opt.num_sample:
                        break
                for p in range(len(all_txt)):
                    logging.info(str(p) + ': ' + all_txt[p])
                    
                # all_samples = np.array(all_samples)
                # file_name = str(all_samples.shape[0]) + 'x' + str(all_samples.shape[1]) + 'x' + str(all_samples.shape[2]) + 'x' + str(all_samples.shape[3]) + '_samples.npz'
                # np.savez(os.path.join(sample_path, file_name), all_samples)
                
                all_samples_save = 255. * rearrange(all_samples, 'n c h w -> n h w c')
                file_name = str(all_samples_save.shape[0]) + 'x' + str(all_samples_save.shape[1]) + 'x' + str(all_samples_save.shape[2]) + 'x' + str(all_samples_save.shape[3]) + '_samples.npz'
                np.savez(os.path.join(sample_path, file_name), all_samples_save)

    print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
          f" \nEnjoy.")


if __name__ == "__main__":
    main()
