import cog
import tempfile
from pathlib import Path
import argparse
import shutil
import os
import glob
import torch
from skimage import img_as_ubyte
from PIL import Image
from model.SRMNet import SRMNet
from main_test_SRMNet import save_img, setup
import torchvision.transforms.functional as TF
import torch.nn.functional as F

class Predictor(cog.Predictor):
    def setup(self):
        model_dir = 'experiments/pretrained_models/AWGN_denoising_SRMNet.pth'

        parser = argparse.ArgumentParser(description='Demo Image Denoising')
        parser.add_argument('--input_dir', default='./test/', type=str, help='Input images')
        parser.add_argument('--result_dir', default='./result/', type=str, help='Directory for results')
        parser.add_argument('--weights',
                            default='./checkpoints/SRMNet_real_denoise/models/model_bestPSNR.pth', type=str,
                            help='Path to weights')

        self.args = parser.parse_args()

        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    @cog.input("image", type=Path, help="input image")

    def predict(self, image):

        # set input folder
        input_dir = 'input_cog_temp'
        os.makedirs(input_dir, exist_ok=True)
        input_path = os.path.join(input_dir, os.path.basename(image))
        shutil.copy(str(image), input_path)

        # Load corresponding models architecture and weights
        model = SRMNet()
        model.eval()
        model = model.to(self.device)

        folder, save_dir = setup(self.args)
        os.makedirs(save_dir, exist_ok=True)

        out_path = Path(tempfile.mkdtemp()) / "out.png"
        mul = 16
        for file_ in sorted(glob.glob(os.path.join(folder, '*.PNG'))):
            img = Image.open(file_).convert('RGB')
            input_ = TF.to_tensor(img).unsqueeze(0).cuda()

            # Pad the input if not_multiple_of 8
            h, w = input_.shape[2], input_.shape[3]
            H, W = ((h + mul) // mul) * mul, ((w + mul) // mul) * mul
            padh = H - h if h % mul != 0 else 0
            padw = W - w if w % mul != 0 else 0
            input_ = F.pad(input_, (0, padw, 0, padh), 'reflect')
            with torch.no_grad():
                restored = model(input_)
                
            restored = torch.clamp(restored, 0, 1)
            restored = restored[:, :, :h, :w]
            restored = restored.permute(0, 2, 3, 1).cpu().detach().numpy()
            restored = img_as_ubyte(restored[0])

        save_img(str(out_path), restored)
        clean_folder(input_dir)
        return out_path


def clean_folder(folder):
    for filename in os.listdir(folder):
        file_path = os.path.join(folder, filename)
        try:
            if os.path.isfile(file_path) or os.path.islink(file_path):
                os.unlink(file_path)
            elif os.path.isdir(file_path):
                shutil.rmtree(file_path)
        except Exception as e:
            print('Failed to delete %s. Reason: %s' % (file_path, e))