# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

# pyre-unsafe

import torch.nn as nn
from torch.nn import functional as F


# Basic Net for XOR
class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(2, 10)
        self.linear2 = nn.Linear(10, 2)

    def forward(self, x):
        return self.linear2(F.sigmoid(self.linear(x)))


# On device training requires the loss to be embedded in the model (and be the first output).
# We wrap the original model here and add the loss calculation. This will be the model we export.
class TrainingNet(nn.Module):
    def __init__(self, net):
        super().__init__()
        self.net = net
        self.loss = nn.CrossEntropyLoss()

    def forward(self, input, label):
        pred = self.net(input)
        return self.loss(pred, label), pred.detach().argmax(dim=1)