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# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch

from mmpretrain.models.backbones import VGG
from mmpretrain.models.utils import HybridEmbed, PatchEmbed, PatchMerging


def cal_unfold_dim(dim, kernel_size, stride, padding=0, dilation=1):
    return (dim + 2 * padding - dilation * (kernel_size - 1) - 1) // stride + 1


def test_patch_embed():
    # Test PatchEmbed
    patch_embed = PatchEmbed()
    img = torch.randn(1, 3, 224, 224)
    img = patch_embed(img)
    assert img.shape == torch.Size((1, 196, 768))

    # Test PatchEmbed with stride = 8
    conv_cfg = dict(kernel_size=16, stride=8)
    patch_embed = PatchEmbed(conv_cfg=conv_cfg)
    img = torch.randn(1, 3, 224, 224)
    img = patch_embed(img)
    assert img.shape == torch.Size((1, 729, 768))


def test_hybrid_embed():
    # Test VGG11 HybridEmbed
    backbone = VGG(11, norm_eval=True)
    backbone.init_weights()
    patch_embed = HybridEmbed(backbone)
    img = torch.randn(1, 3, 224, 224)
    img = patch_embed(img)
    assert img.shape == torch.Size((1, 49, 768))


def test_patch_merging():
    settings = dict(in_channels=16, out_channels=32, padding=0)
    downsample = PatchMerging(**settings)

    # test forward with wrong dims
    with pytest.raises(AssertionError):
        inputs = torch.rand((1, 16, 56 * 56))
        downsample(inputs, input_size=(56, 56))

    # test patch merging forward
    inputs = torch.rand((1, 56 * 56, 16))
    out, output_size = downsample(inputs, input_size=(56, 56))
    assert output_size == (28, 28)
    assert out.shape == (1, 28 * 28, 32)

    # test different kernel_size in each direction
    downsample = PatchMerging(kernel_size=(2, 3), **settings)
    out, output_size = downsample(inputs, input_size=(56, 56))
    expected_dim = cal_unfold_dim(56, 2, 2) * cal_unfold_dim(56, 3, 3)
    assert downsample.sampler.kernel_size == (2, 3)
    assert output_size == (cal_unfold_dim(56, 2, 2), cal_unfold_dim(56, 3, 3))
    assert out.shape == (1, expected_dim, 32)

    # test default stride
    downsample = PatchMerging(kernel_size=6, **settings)
    assert downsample.sampler.stride == (6, 6)

    # test stride=3
    downsample = PatchMerging(kernel_size=6, stride=3, **settings)
    out, output_size = downsample(inputs, input_size=(56, 56))
    assert downsample.sampler.stride == (3, 3)
    assert out.shape == (1, cal_unfold_dim(56, 6, stride=3)**2, 32)

    # test padding
    downsample = PatchMerging(
        in_channels=16, out_channels=32, kernel_size=6, padding=2)
    out, output_size = downsample(inputs, input_size=(56, 56))
    assert downsample.sampler.padding == (2, 2)
    assert out.shape == (1, cal_unfold_dim(56, 6, 6, padding=2)**2, 32)

    # test str padding
    downsample = PatchMerging(in_channels=16, out_channels=32, kernel_size=6)
    out, output_size = downsample(inputs, input_size=(56, 56))
    assert downsample.sampler.padding == (0, 0)
    assert out.shape == (1, cal_unfold_dim(56, 6, 6, padding=2)**2, 32)

    # test dilation
    downsample = PatchMerging(kernel_size=6, dilation=2, **settings)
    out, output_size = downsample(inputs, input_size=(56, 56))
    assert downsample.sampler.dilation == (2, 2)
    assert out.shape == (1, cal_unfold_dim(56, 6, 6, dilation=2)**2, 32)