import pytest
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as tm
from .zoo import tm_models, tmm_models

try:
    from colossalai._analyzer._subclasses import MetaTensorMode, flop_count
except:
    pass


@pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12')
@pytest.mark.parametrize('m', tm_models + tmm_models)
def test_flop_count_module(m):
    x = torch.rand(2, 3, 224, 224)
    with MetaTensorMode():    # save time for testing
        module = m()
    rs_fwd, rs_bwd = flop_count(module, x, verbose=True)
    assert rs_fwd > 0, f'fwd flop count of {m.__name__} is {rs_fwd}'
    assert rs_bwd > 0, f'bwd flop count of {m.__name__} is {rs_bwd}'


odd_cases = [
    (F.relu, (torch.rand(2, 3, 224, 224, requires_grad=True),), {
        'inplace': True
    }),
    (F.max_pool2d, (torch.rand(2, 3, 224, 224, requires_grad=True),), {
        'kernel_size': 3,
        'stride': 2,
        'padding': 1,
        'dilation': 2
    }),
    (torch.where, (torch.rand(2, 3, 224, 224) > 0.5, torch.rand(2, 3, 224, 224, requires_grad=True),
                   torch.rand(2, 3, 224, 224, requires_grad=True)), {}),
]


@pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12')
@pytest.mark.parametrize('func, args, kwargs', odd_cases)
def test_flop_count_function(func, args, kwargs):
    rs_fwd, rs_bwd = flop_count(func, *args, **kwargs, verbose=True)
    assert rs_fwd > 0, f'fwd flop count of {func.__name__} is {rs_fwd}'
    assert rs_bwd > 0, f'bwd flop count of {func.__name__} is {rs_bwd}'


if __name__ == '__main__':
    test_flop_count_module(tm.resnet18, torch.rand(2, 3, 224, 224))
    test_flop_count_function(F.relu, (torch.rand(2, 3, 224, 224, requires_grad=True),), {'inplace': True})
