# -*- Python -*-
# This file is licensed under a pytorch-style license
# See frontends/pytorch/LICENSE for license information.

from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR

import npcomp.frontends.pytorch as torch_mlir
import npcomp.frontends.pytorch.test as test

# RUN: python %s | FileCheck %s

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.maxpool2d = nn.MaxPool2d(2,2)
        #self.dropout1 = nn.Dropout2d(0.25)
        #self.dropout2 = nn.Dropout2d(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = self.maxpool2d(x)
        #x = self.dropout1(x)
        x = x.view((4,9216))
        x = self.fc1(x)
        x = F.relu(x)
        #x = self.dropout2(x)
        x = self.fc2(x)
        output = F.log_softmax(x, dim=1)
        return output


def main():
    model = Net()
    tensor = torch.randn((4, 1, 28, 28))

    # CHECK: PASS! fwd check
    fwd_path = test.check_fwd(model, tensor)

if __name__ == '__main__':
    main()