# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.

import unittest

import torch
import torch.nn.functional as F

from nni.compression.pytorch.pruning import L1NormPruner

class TorchModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = torch.nn.Conv2d(1, 5, 5, 1)
        self.bn1 = torch.nn.BatchNorm2d(5)
        self.conv2 = torch.nn.Conv2d(5, 10, 5, 1)
        self.bn2 = torch.nn.BatchNorm2d(10)
        self.fc1 = torch.nn.Linear(4 * 4 * 10, 100)
        self.fc2 = torch.nn.Linear(100, 10)

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(self.bn2(self.conv2(x)))
        x = F.max_pool2d(x, 2, 2)
        x = x.view(-1, 4 * 4 * 10)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)


class PrunerTestCase(unittest.TestCase):
    def test_pruner_module_wrapper(self):
        model = TorchModel()
        conv1_weight = model.conv1.weight.data.clone()
        conv2_weight = model.conv2.weight.data.clone()
        config_list = [{'op_types': ['Conv2d'], 'sparsity': 0.8}]
        pruner = L1NormPruner(model, config_list)
        _, masks = pruner.compress()
        model(torch.rand(10, 1, 28, 28))
        assert torch.equal(model.conv1.weight.data, conv1_weight)
        assert torch.equal(model.conv2.weight.data, conv2_weight)
        assert torch.equal(model.conv1.module.weight.data, conv1_weight * masks['conv1']['weight'])
        assert torch.equal(model.conv2.module.weight.data, conv2_weight * masks['conv2']['weight'])

if __name__ == '__main__':
    unittest.main()
