# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm

from mmpose.models.backbones import ViPNAS_MobileNetV3
from mmpose.models.backbones.utils import InvertedResidual


def is_norm(modules):
    """Check if is one of the norms."""
    if isinstance(modules, (GroupNorm, _BatchNorm)):
        return True
    return False


def check_norm_state(modules, train_state):
    """Check if norm layer is in correct train state."""
    for mod in modules:
        if isinstance(mod, _BatchNorm):
            if mod.training != train_state:
                return False
    return True


def test_mobilenetv3_backbone():
    with pytest.raises(TypeError):
        # pretrained must be a string path
        model = ViPNAS_MobileNetV3()
        model.init_weights(pretrained=0)

    with pytest.raises(AttributeError):
        # frozen_stages must no more than 21
        model = ViPNAS_MobileNetV3(frozen_stages=22)
        model.train()

    # Test MobileNetv3
    model = ViPNAS_MobileNetV3()
    model.init_weights()
    model.train()

    # Test MobileNetv3 with first stage frozen
    frozen_stages = 1
    model = ViPNAS_MobileNetV3(frozen_stages=frozen_stages)
    model.init_weights()
    model.train()
    for param in model.conv1.parameters():
        assert param.requires_grad is False
    for i in range(1, frozen_stages + 1):
        layer = getattr(model, f'layer{i}')
        for mod in layer.modules():
            if isinstance(mod, _BatchNorm):
                assert mod.training is False
        for param in layer.parameters():
            assert param.requires_grad is False

    # Test MobileNetv3 with norm eval
    model = ViPNAS_MobileNetV3(norm_eval=True)
    model.init_weights()
    model.train()
    assert check_norm_state(model.modules(), False)

    # Test MobileNetv3 forward
    model = ViPNAS_MobileNetV3()
    model.init_weights()
    model.train()

    imgs = torch.randn(1, 3, 224, 224)
    feat = model(imgs)
    assert feat.shape == torch.Size([1, 160, 7, 7])

    # Test MobileNetv3 forward with GroupNorm
    model = ViPNAS_MobileNetV3(
        norm_cfg=dict(type='GN', num_groups=2, requires_grad=True))
    for m in model.modules():
        if is_norm(m):
            assert isinstance(m, GroupNorm)
    model.init_weights()
    model.train()

    imgs = torch.randn(1, 3, 224, 224)
    feat = model(imgs)
    assert feat.shape == torch.Size([1, 160, 7, 7])

    # Test MobileNetv3 with checkpoint forward
    model = ViPNAS_MobileNetV3(with_cp=True)
    for m in model.modules():
        if isinstance(m, InvertedResidual):
            assert m.with_cp
    model.init_weights()
    model.train()

    imgs = torch.randn(1, 3, 224, 224)
    feat = model(imgs)
    assert feat.shape == torch.Size([1, 160, 7, 7])


test_mobilenetv3_backbone()