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# under th

import os
import numpy as np
from PIL import Image
from resizeimage import resizeimage

from singa import device
from singa import tensor
from singa import autograd
from singa import sonnx
import onnx
from utils import download_model, update_batch_size

import logging
logging.basicConfig(level=logging.INFO, format='%(asctime)-15s %(message)s')


def preprocess(img):
    img = resizeimage.resize_cover(img, [224,224], validate=False)
    img_ycbcr = img.convert('YCbCr')
    img_y_0, img_cb, img_cr = img_ycbcr.split()
    img_ndarray = np.asarray(img_y_0)
    img_4 = np.expand_dims(np.expand_dims(img_ndarray, axis=0), axis=0)
    img_5 = img_4.astype(np.float32) / 255.0
    return img_5, img_cb, img_cr

def get_image():
    # download image
    image_url = './159008.jpg'
    img = Image.open(image_url)
    return img

class Infer:

    def __init__(self, sg_ir):
        self.sg_ir = sg_ir
        for idx, tens in sg_ir.tensor_map.items():
            # allow the tensors to be updated
            tens.requires_grad = True
            tens.stores_grad = True
            sg_ir.tensor_map[idx] = tens

    def forward(self, x):
        return sg_ir.run([x])[0]


if __name__ == "__main__":

    url = 'https://github.com/onnx/models/raw/master/vision/super_resolution/sub_pixel_cnn_2016/model/super-resolution-10.tar.gz'
    download_dir = '/tmp/'
    model_path = os.path.join(download_dir, 'super_resolution', 'super_resolution.onnx')

    logging.info("onnx load model...")
    download_model(url)
    onnx_model = onnx.load(model_path)

    # set batch size
    onnx_model = update_batch_size(onnx_model, 1)

    # prepare the model
    logging.info("prepare model...")
    dev = device.get_default_device()
    sg_ir = sonnx.prepare(onnx_model, device=dev)
    autograd.training = False
    model = Infer(sg_ir)

    # preprocess
    logging.info("preprocessing...")
    img = get_image()
    img_y, img_cb, img_cr = preprocess(img)
    
    # inference
    logging.info("model running...")
    x_batch = tensor.Tensor(device=dev, data=img_y)
    img_y = model.forward(x_batch)
    array_img_y = tensor.to_numpy(img_y)
    img_out_y = Image.fromarray(np.uint8((array_img_y[0] * 255.0).clip(0, 255)[0]), mode='L')
    
    # postprocess
    logging.info("postprocessing...")
    final_img = Image.merge(
        "YCbCr", [
            img_out_y,
            img_cb.resize(img_out_y.size, Image.BICUBIC),
            img_cr.resize(img_out_y.size, Image.BICUBIC),
        ]).convert("RGB")
    final_img.show()