# Copyright (c) 2021 PaddlePaddle Authors. 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.
import shutil
from itertools import count

from paddle import nn
from paddle.optimizer import Adam

from paddlespeech.t2s.training.extensions.snapshot import Snapshot
from paddlespeech.t2s.training.trainer import Trainer

# from paddlespeech.t2s.training.updater import StandardUpdater


def _test_snapshot():
    model = nn.Linear(3, 4)
    optimizer = Adam(parameters=model.parameters())

    # use a simplest iterable object as dataloader
    dataloader = count()

    # hack the training proecss: training does nothing except increse iteration
    updater = StandardUpdater(model, optimizer, dataloader=dataloader)
    updater.update_core = lambda x: None

    trainer = Trainer(
        updater, stop_trigger=(1000, 'iteration'), out='temp_test_snapshot')
    shutil.rmtree(trainer.out, ignore_errors=True)

    snap = Snapshot(max_size=5)
    trigger = (10, 'iteration')
    trainer.extend(snap, name='snapshot', trigger=trigger, priority=0)

    trainer.run()

    checkpoint_dir = trainer.out / "checkpoints"
    snapshots = sorted(list(checkpoint_dir.glob("snapshot_iter_*.pdz")))
    for snap in snapshots:
        print(snap)
    assert len(snapshots) == 5
    shutil.rmtree(trainer.out)