import json
import logging
import sys

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

logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
logger.addHandler(logging.StreamHandler(sys.stdout))

# Based on https://github.com/pytorch/examples/blob/master/mnist/main.py
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


def model_fn(model_dir):
    model = Net().to(device)
    model.eval()
    return model


# data preprocessing
def input_fn(request_body, request_content_type):
    assert request_content_type == "application/json"
    data = json.loads(request_body)["inputs"]
    data = torch.tensor(data, dtype=torch.float32, device=device)
    return data


# inference
def predict_fn(input_object, model):
    with torch.no_grad():
        prediction = model(input_object)
    return prediction


# postprocess
def output_fn(predictions, content_type):
    assert content_type == "application/json"
    res = predictions.cpu().numpy().tolist()
    return json.dumps(res)