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


class NetM3(nn.Module):
    def __init__(self):
        super(NetM3, self).__init__()
        self.conv1 = nn.Conv1d(1, 128, 80, 4)
        self.bn1 = nn.BatchNorm1d(128)
        self.pool1 = nn.MaxPool1d(4)
        self.conv2 = nn.Conv1d(128, 128, 3)
        self.bn2 = nn.BatchNorm1d(128)
        self.pool2 = nn.MaxPool1d(4)
        self.conv3 = nn.Conv1d(128, 256, 3)
        self.bn3 = nn.BatchNorm1d(256)
        self.pool3 = nn.MaxPool1d(4)
        self.conv4 = nn.Conv1d(256, 512, 3)
        self.bn4 = nn.BatchNorm1d(512)
        self.pool4 = nn.MaxPool1d(4)
        self.avgPool = nn.AvgPool1d(30)  # input should be 512x30 so this outputs a 512x1
        self.fc1 = nn.Linear(512, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(self.bn1(x))
        x = self.pool1(x)
        x = self.conv2(x)
        x = F.relu(self.bn2(x))
        x = self.pool2(x)
        x = self.conv3(x)
        x = F.relu(self.bn3(x))
        x = self.pool3(x)
        x = self.conv4(x)
        x = F.relu(self.bn4(x))
        x = self.pool4(x)
        x = self.avgPool(x)
        x = x.permute(0, 2, 1)  # change the 512x1 to 1x512
        x = self.fc1(x)
        return F.log_softmax(x, dim=2)  # Output: torch.Size([N, 1, 10])


def model_fn(model_dir):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = NetM3()
    if torch.cuda.device_count() > 1:
        print("Gpu count: {}".format(torch.cuda.device_count()))
        model = nn.DataParallel(model)

    with open(os.path.join(model_dir, "model.pth"), "rb") as f:
        model.load_state_dict(torch.load(f))
    return model.to(device)