import torch
import torch.nn as nn

from timm.layers import create_act_layer, set_layer_config


class MLP(nn.Module):
    def __init__(self, act_layer="relu", inplace=True):
        super(MLP, self).__init__()
        self.fc1 = nn.Linear(1000, 100)
        self.act = create_act_layer(act_layer, inplace=inplace)
        self.fc2 = nn.Linear(100, 10)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.fc2(x)
        return x


def _run_act_layer_grad(act_type, inplace=True):
    x = torch.rand(10, 1000) * 10
    m = MLP(act_layer=act_type, inplace=inplace)

    def _run(x, act_layer=''):
        if act_layer:
            # replace act layer if set
            m.act = create_act_layer(act_layer, inplace=inplace)
        out = m(x)
        l = (out - 0).pow(2).sum()
        return l

    out_me = _run(x)

    with set_layer_config(scriptable=True):
        out_jit = _run(x, act_type)

    assert torch.isclose(out_jit, out_me)

    with set_layer_config(no_jit=True):
        out_basic = _run(x, act_type)

    assert torch.isclose(out_basic, out_jit)


def test_swish_grad():
    for _ in range(100):
        _run_act_layer_grad('swish')


def test_mish_grad():
    for _ in range(100):
        _run_act_layer_grad('mish')


def test_hard_sigmoid_grad():
    for _ in range(100):
        _run_act_layer_grad('hard_sigmoid', inplace=None)


def test_hard_swish_grad():
    for _ in range(100):
        _run_act_layer_grad('hard_swish')


def test_hard_mish_grad():
    for _ in range(100):
        _run_act_layer_grad('hard_mish')
