# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# 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
#
#     http://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.
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import OrderedDict
import os
from tempfile import TemporaryDirectory
import unittest

import torch
from torch import nn

from maskrcnn_benchmark.utils.model_serialization import load_state_dict
from maskrcnn_benchmark.utils.checkpoint import Checkpointer


class TestCheckpointer(unittest.TestCase):
    def create_model(self):
        return nn.Sequential(nn.Linear(2, 3), nn.Linear(3, 1))

    def create_complex_model(self):
        m = nn.Module()
        m.block1 = nn.Module()
        m.block1.layer1 = nn.Linear(2, 3)
        m.layer2 = nn.Linear(3, 2)
        m.res = nn.Module()
        m.res.layer2 = nn.Linear(3, 2)

        state_dict = OrderedDict()
        state_dict["layer1.weight"] = torch.rand(3, 2)
        state_dict["layer1.bias"] = torch.rand(3)
        state_dict["layer2.weight"] = torch.rand(2, 3)
        state_dict["layer2.bias"] = torch.rand(2)
        state_dict["res.layer2.weight"] = torch.rand(2, 3)
        state_dict["res.layer2.bias"] = torch.rand(2)

        return m, state_dict

    def test_from_last_checkpoint_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:

            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # in the same folder
                fresh_checkpointer = Checkpointer(fresh_model, save_dir=f)
                self.assertTrue(fresh_checkpointer.has_checkpoint())
                self.assertEqual(
                    fresh_checkpointer.get_checkpoint_file(),
                    os.path.join(f, "checkpoint_file.pth"),
                )
                _ = fresh_checkpointer.load()

            for trained_p, loaded_p in zip(
                trained_model.parameters(), fresh_model.parameters()
            ):
                # different tensor references
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))

    def test_from_name_file_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:
            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # on different folders
                with TemporaryDirectory() as g:
                    fresh_checkpointer = Checkpointer(fresh_model, save_dir=g)
                    self.assertFalse(fresh_checkpointer.has_checkpoint())
                    self.assertEqual(fresh_checkpointer.get_checkpoint_file(), "")
                    _ = fresh_checkpointer.load(os.path.join(f, "checkpoint_file.pth"))

            for trained_p, loaded_p in zip(
                trained_model.parameters(), fresh_model.parameters()
            ):
                # different tensor references
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))

    def test_complex_model_loaded(self):
        for add_data_parallel in [False, True]:
            model, state_dict = self.create_complex_model()
            if add_data_parallel:
                model = nn.DataParallel(model)

            load_state_dict(model, state_dict)
            for loaded, stored in zip(model.state_dict().values(), state_dict.values()):
                # different tensor references
                self.assertFalse(id(loaded) == id(stored))
                # same content
                self.assertTrue(loaded.equal(stored))


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