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# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
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#   http://www.apache.org/licenses/LICENSE-2.0
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# KIND, either express or implied.  See the License for the
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# under th

import os
import numpy as np
from PIL import Image

from singa import device
from singa import tensor
from singa import sonnx
import onnx
from utils import download_model, check_exist_or_download

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


def preprocess(img):
    img = img.resize((256, 256))
    img = img.crop((16, 16, 240, 240))
    img = np.array(img).astype(np.float32) / 255.
    img = np.rollaxis(img, 2, 0)
    for channel, mean, std in zip(range(3), [0.485, 0.456, 0.406],
                                  [0.229, 0.224, 0.225]):
        img[channel, :, :] -= mean
        img[channel, :, :] /= std
    img = np.expand_dims(img, axis=0)
    return img


def get_image_labe():
    # download label
    label_url = 'https://s3.amazonaws.com/onnx-model-zoo/synset.txt'
    with open(check_exist_or_download(label_url), 'r') as f:
        labels = [l.rstrip() for l in f]

    # download image
    image_url = 'https://s3.amazonaws.com/model-server/inputs/kitten.jpg'
    img = Image.open(check_exist_or_download(image_url))
    return img, labels


class MyModel(sonnx.SONNXModel):

    def __init__(self, onnx_model):
        super(MyModel, self).__init__(onnx_model)

    def forward(self, *x):
        y = super(MyModel, self).forward(*x)
        return y[0]

    def train_one_batch(self, x, y):
        pass


if __name__ == "__main__":

    url = 'https://github.com/onnx/models/raw/master/vision/classification/shufflenet/model/shufflenet-v2-10.tar.gz'
    download_dir = '/tmp/'
    model_path = os.path.join(download_dir, 'model', 'test_shufflenetv2',
                              'model.onnx')

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

    # inference
    logging.info("preprocessing...")
    img, labels = get_image_labe()
    img = preprocess(img)
    # sg_ir = sonnx.prepare(onnx_model) # run without graph
    # y = sg_ir.run([img])

    logging.info("model compling...")
    dev = device.create_cuda_gpu()
    x = tensor.PlaceHolder(img.shape, device=dev)
    model = MyModel(onnx_model)
    model.compile([x], is_train=False, use_graph=True, sequential=True)

    # verifty the test
    # from utils import load_dataset
    # inputs, ref_outputs = load_dataset(os.path.join('/tmp', 'model', 'test_shufflenetv2',
    #                           'model.onnx'))
    # x_batch = tensor.Tensor(device=dev, data=inputs[0])
    # outputs = sg_ir.run([x_batch])
    # for ref_o, o in zip(ref_outputs, outputs):
    #     np.testing.assert_almost_equal(ref_o, tensor.to_numpy(o), 4)

    logging.info("model running...")
    x = tensor.Tensor(device=dev, data=img)
    y = model.forward(x)

    logging.info("postprocessing...")
    y = tensor.softmax(y)
    scores = tensor.to_numpy(y)
    scores = np.squeeze(scores)
    a = np.argsort(scores)[::-1]
    for i in a[0:5]:
        logging.info('class=%s ; probability=%f' % (labels[i], scores[i]))