import os
import sys
import unittest

import torch
import torchvision.transforms.functional as F
from PIL import Image
from torchvision import models

try:
    from torchvision import _C_tests
except ImportError:
    _C_tests = None


def process_model(model, tensor, func, name):
    model.eval()
    traced_script_module = torch.jit.trace(model, tensor)
    traced_script_module.save("model.pt")

    py_output = model.forward(tensor)
    cpp_output = func("model.pt", tensor)

    assert torch.allclose(py_output, cpp_output), "Output mismatch of " + name + " models"


def read_image1():
    image_path = os.path.join(
        os.path.dirname(os.path.abspath(__file__)), "assets", "encode_jpeg", "grace_hopper_517x606.jpg"
    )
    image = Image.open(image_path)
    image = image.resize((224, 224))
    x = F.pil_to_tensor(image)
    x = F.convert_image_dtype(x)
    return x.view(1, 3, 224, 224)


def read_image2():
    image_path = os.path.join(
        os.path.dirname(os.path.abspath(__file__)), "assets", "encode_jpeg", "grace_hopper_517x606.jpg"
    )
    image = Image.open(image_path)
    image = image.resize((299, 299))
    x = F.pil_to_tensor(image)
    x = F.convert_image_dtype(x)
    x = x.view(1, 3, 299, 299)
    return torch.cat([x, x], 0)


@unittest.skipIf(
    sys.platform == "darwin" or True,
    "C++ models are broken on OS X at the moment, and there's a BC breakage on main; "
    "see https://github.com/pytorch/vision/issues/1191",
)
class Tester(unittest.TestCase):
    pretrained = False
    image = read_image1()

    def test_alexnet(self):
        process_model(models.alexnet(self.pretrained), self.image, _C_tests.forward_alexnet, "Alexnet")

    def test_vgg11(self):
        process_model(models.vgg11(self.pretrained), self.image, _C_tests.forward_vgg11, "VGG11")

    def test_vgg13(self):
        process_model(models.vgg13(self.pretrained), self.image, _C_tests.forward_vgg13, "VGG13")

    def test_vgg16(self):
        process_model(models.vgg16(self.pretrained), self.image, _C_tests.forward_vgg16, "VGG16")

    def test_vgg19(self):
        process_model(models.vgg19(self.pretrained), self.image, _C_tests.forward_vgg19, "VGG19")

    def test_vgg11_bn(self):
        process_model(models.vgg11_bn(self.pretrained), self.image, _C_tests.forward_vgg11bn, "VGG11BN")

    def test_vgg13_bn(self):
        process_model(models.vgg13_bn(self.pretrained), self.image, _C_tests.forward_vgg13bn, "VGG13BN")

    def test_vgg16_bn(self):
        process_model(models.vgg16_bn(self.pretrained), self.image, _C_tests.forward_vgg16bn, "VGG16BN")

    def test_vgg19_bn(self):
        process_model(models.vgg19_bn(self.pretrained), self.image, _C_tests.forward_vgg19bn, "VGG19BN")

    def test_resnet18(self):
        process_model(models.resnet18(self.pretrained), self.image, _C_tests.forward_resnet18, "Resnet18")

    def test_resnet34(self):
        process_model(models.resnet34(self.pretrained), self.image, _C_tests.forward_resnet34, "Resnet34")

    def test_resnet50(self):
        process_model(models.resnet50(self.pretrained), self.image, _C_tests.forward_resnet50, "Resnet50")

    def test_resnet101(self):
        process_model(models.resnet101(self.pretrained), self.image, _C_tests.forward_resnet101, "Resnet101")

    def test_resnet152(self):
        process_model(models.resnet152(self.pretrained), self.image, _C_tests.forward_resnet152, "Resnet152")

    def test_resnext50_32x4d(self):
        process_model(models.resnext50_32x4d(), self.image, _C_tests.forward_resnext50_32x4d, "ResNext50_32x4d")

    def test_resnext101_32x8d(self):
        process_model(models.resnext101_32x8d(), self.image, _C_tests.forward_resnext101_32x8d, "ResNext101_32x8d")

    def test_wide_resnet50_2(self):
        process_model(models.wide_resnet50_2(), self.image, _C_tests.forward_wide_resnet50_2, "WideResNet50_2")

    def test_wide_resnet101_2(self):
        process_model(models.wide_resnet101_2(), self.image, _C_tests.forward_wide_resnet101_2, "WideResNet101_2")

    def test_squeezenet1_0(self):
        process_model(
            models.squeezenet1_0(self.pretrained), self.image, _C_tests.forward_squeezenet1_0, "Squeezenet1.0"
        )

    def test_squeezenet1_1(self):
        process_model(
            models.squeezenet1_1(self.pretrained), self.image, _C_tests.forward_squeezenet1_1, "Squeezenet1.1"
        )

    def test_densenet121(self):
        process_model(models.densenet121(self.pretrained), self.image, _C_tests.forward_densenet121, "Densenet121")

    def test_densenet169(self):
        process_model(models.densenet169(self.pretrained), self.image, _C_tests.forward_densenet169, "Densenet169")

    def test_densenet201(self):
        process_model(models.densenet201(self.pretrained), self.image, _C_tests.forward_densenet201, "Densenet201")

    def test_densenet161(self):
        process_model(models.densenet161(self.pretrained), self.image, _C_tests.forward_densenet161, "Densenet161")

    def test_mobilenet_v2(self):
        process_model(models.mobilenet_v2(self.pretrained), self.image, _C_tests.forward_mobilenetv2, "MobileNet")

    def test_googlenet(self):
        process_model(models.googlenet(self.pretrained), self.image, _C_tests.forward_googlenet, "GoogLeNet")

    def test_mnasnet0_5(self):
        process_model(models.mnasnet0_5(self.pretrained), self.image, _C_tests.forward_mnasnet0_5, "MNASNet0_5")

    def test_mnasnet0_75(self):
        process_model(models.mnasnet0_75(self.pretrained), self.image, _C_tests.forward_mnasnet0_75, "MNASNet0_75")

    def test_mnasnet1_0(self):
        process_model(models.mnasnet1_0(self.pretrained), self.image, _C_tests.forward_mnasnet1_0, "MNASNet1_0")

    def test_mnasnet1_3(self):
        process_model(models.mnasnet1_3(self.pretrained), self.image, _C_tests.forward_mnasnet1_3, "MNASNet1_3")

    def test_inception_v3(self):
        self.image = read_image2()
        process_model(models.inception_v3(self.pretrained), self.image, _C_tests.forward_inceptionv3, "Inceptionv3")


if __name__ == "__main__":
    unittest.main()
