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# ============================================================================
import argparse
import importlib
import os

from PIL import Image
import torch
import numpy as np
import torchvision.transforms as transforms


def get_raw_data(params):
    path = params.lr_image
    lr_image = Image.open(path)
    lr_image = np.ascontiguousarray(lr_image)
    lr_image = transforms.functional.to_tensor(lr_image)

    lr_image = torch.reshape(lr_image, (1, lr_image.shape[0], lr_image.shape[1], lr_image.shape[2]))

    return lr_image


def test(parser):
    params = parser.parse_args()
    loc = 'npu:0'
    loc_cpu = 'cpu'
    # torch.npu.set_device(loc)
    checkpoint = torch.load(params.pre_train_model, loc)
    model_module = importlib.import_module('models.wdsr')
    model_module.update_argparser(parser)
    params = parser.parse_args()
    model, criterion, optimizer, lr_scheduler = model_module.get_model_spec(params)
    model = model.to(loc)
    model.load_state_dict(checkpoint['model_state_dict'])
    model.eval()

    inputs = get_raw_data(params)
    inputs = inputs.to(loc, non_blocking=True)
    output = model(inputs)
    output = output.to(loc_cpu)
    output = output.detach().numpy()
    output = output.reshape(output.shape[1], output.shape[2], output.shape[3])
    output = output.transpose((1, 2, 0))
    outImage = Image.fromarray((output * 255.).astype('uint8')).convert('RGB')
    outName = params.lr_image.split("/")[-1]
    if not os.path.exists(params.save):
        os.makedirs(params.save)
    outImage.save(params.save+outName)



if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--pre_train_model',
        help='File path to load checkpoint.',
        default=None,
        type=str,
    )
    parser.add_argument(
        '--lr_image',
        help='input image path.',
        default=None,
        type=str,
    )
    parser.set_defaults(
        dataset="div2k",
        image_mean=0.5,
        num_channels=3,
        scale=2,
        save="output_sr/"
    )
    test(parser)