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

import pytest
import torch

from mmselfsup.models.algorithms.mae import MAE
from mmselfsup.structures import SelfSupDataSample
from mmselfsup.utils import register_all_modules

register_all_modules()

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.,
)
loss = dict(type='MAEReconstructionLoss')
head = dict(type='MAEPretrainHead', norm_pix=False, patch_size=16, loss=loss)


@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
def test_mae():
    data_preprocessor = {
        'mean': [0.5, 0.5, 0.5],
        'std': [0.5, 0.5, 0.5],
        'bgr_to_rgb': True
    }

    alg = MAE(
        backbone=backbone,
        neck=neck,
        head=head,
        data_preprocessor=copy.deepcopy(data_preprocessor))

    fake_data = {
        'inputs': [torch.randn((2, 3, 224, 224))],
        'data_sample': [SelfSupDataSample() for _ in range(2)]
    }
    fake_batch_inputs, fake_data_samples = alg.data_preprocessor(fake_data)
    fake_outputs = alg(fake_batch_inputs, fake_data_samples, mode='loss')
    assert isinstance(fake_outputs['loss'].item(), float)

    fake_feats = alg(fake_batch_inputs, fake_data_samples, mode='tensor')
    assert list(fake_feats.shape) == [2, 196, 768]

    results = alg(fake_batch_inputs, fake_data_samples, mode='predict')
    assert list(results.mask.value.shape) == [2, 224, 224, 3]
    assert list(results.pred.value.shape) == [2, 224, 224, 3]