# Copyright (c) OpenMMLab. All rights reserved.
import platform

import pytest
import torch

from mmselfsup.models.algorithms import MAE

backbone = dict(type='MAEViT', arch='b', patch_size=16, mask_ratio=0.75)
neck = dict(
    type='MAEPretrainDecoder',
    patch_size=16,
    in_chans=3,
    embed_dim=768,
    decoder_embed_dim=512,
    decoder_depth=8,
    decoder_num_heads=16,
    mlp_ratio=4.,
)
head = dict(type='MAEPretrainHead', norm_pix=False, patch_size=16)


@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
def test_mae():
    with pytest.raises(AssertionError):
        alg = MAE(backbone=backbone, neck=None, head=head)
    with pytest.raises(AssertionError):
        alg = MAE(backbone=backbone, neck=neck, head=None)
    with pytest.raises(AssertionError):
        alg = MAE(backbone=None, neck=neck, head=head)
    alg = MAE(backbone=backbone, neck=neck, head=head)

    fake_input = torch.randn((2, 3, 224, 224))
    fake_loss = alg.forward_train(fake_input)
    fake_feature = alg.extract_feat(fake_input)
    mask, pred = alg.forward_test(fake_input)
    assert isinstance(fake_loss['loss'].item(), float)
    assert list(fake_feature[0].shape) == [2, 50, 768]
    assert list(mask.shape) == [2, 224, 224, 3]
    assert list(pred.shape) == [2, 224, 224, 3]