#!/usr/bin/env python
'''
Copyright 2018 Google LLC

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
'''

import torch
import torch.nn
import torch.nn.functional as f
import torch.utils.data
import torch.utils.data.distributed
from torchvision import transforms


class Net(torch.nn.Module):
  """ Network architecture """

  def __init__(self):
    super(Net, self).__init__()
    self.conv1 = torch.nn.Conv2d(1, 10, kernel_size=5)
    self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5)
    self.conv2_drop = torch.nn.Dropout2d()
    self.fc1 = torch.nn.Linear(320, 50)
    self.fc2 = torch.nn.Linear(50, 10)

  def forward(self, x):  # pylint: disable = arguments-differ
    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)


class mnistddpserving():
  def __init__(self):
    self.class_names = ["class:{}".format(str(i)) for i in range(10)]
    self.model = Net()
    # TODO parametrise path to load model, defaulting to GPU
    self.model.load_state_dict(torch.load("/mnt/kubeflow-gcfs/pytorch/model/model_gpu.dat",
                                          map_location='cpu'))
    # Ensure the model is in eval/inference mode
    self.model.eval()

  def predict(self, x, feature_names):
    feature_names = feature_names
    tensor = torch.from_numpy(x).view(-1, 28, 28)
    t = transforms.Normalize((0.1307,), (0.3081,))
    tensor_norm = t(tensor)
    tensor_norm = tensor_norm.unsqueeze(0)
    out = self.model(tensor_norm.float())
    predictions = torch.nn.functional.softmax(out)
    print(predictions)
    return predictions.detach().numpy()