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

batch_size = 16


class LocalModel(nn.Module):
    def __init__(self, base, predictor):
        super(LocalModel, self).__init__()

        self.base = base
        self.predictor = predictor
        
    def forward(self, x):
        out = self.base(x)
        out = self.predictor(out)

        return out
        

# https://github.com/katsura-jp/fedavg.pytorch/blob/master/src/models/cnn.py
class FedAvgCNN(nn.Module):
    def __init__(self, in_features=1, num_classes=10, dim=1024):
        super().__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_features,
                        32,
                        kernel_size=5,
                        padding=0,
                        stride=1,
                        bias=True),
            nn.ReLU(inplace=True), 
            nn.MaxPool2d(kernel_size=(2, 2))
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(32,
                        64,
                        kernel_size=5,
                        padding=0,
                        stride=1,
                        bias=True),
            nn.ReLU(inplace=True), 
            nn.MaxPool2d(kernel_size=(2, 2))
        )
        self.fc1 = nn.Sequential(
            nn.Linear(dim, 512), 
            nn.ReLU(inplace=True)
        )
        self.fc = nn.Linear(512, num_classes)

    def forward(self, x):
        out = self.conv1(x)
        out = self.conv2(out)
        out = torch.flatten(out, 1)
        out = self.fc1(out)
        out = self.fc(out)
        return out