#   Copyright (c) 2022 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.

from __future__ import print_function
import unittest
import numpy as np
import tempfile
import warnings
import json
import paddle
import paddle.nn as nn
from paddle.io import Dataset, DataLoader, BatchSampler, SequenceSampler
import sys
import os


class RandomDataset(Dataset):
    def __init__(self, num_samples):
        self.num_samples = num_samples

    def __getitem__(self, idx):
        image = np.random.random([10]).astype('float32')
        label = np.random.randint(0, 10 - 1, (1, )).astype('int64')
        return image, label

    def __len__(self):
        return self.num_samples


class SimpleNet(nn.Layer):
    def __init__(self):
        super(SimpleNet, self).__init__()
        self.fc = nn.Linear(10, 10)

    def forward(self, image):
        return self.fc(image)


class TestAutoTune(unittest.TestCase):
    def setUp(self):
        self.batch_size = 1
        self.dataset = RandomDataset(10)

    def test_dataloader_use_autotune(self):
        paddle.incubate.autotune.set_config(
            config={"dataloader": {
                "enable": True,
                "tuning_steps": 1,
            }})
        loader = DataLoader(
            self.dataset, batch_size=self.batch_size, num_workers=0)

    def test_dataloader_disable_autotune(self):
        config = {"dataloader": {"enable": False, "tuning_steps": 1}}
        tfile = tempfile.NamedTemporaryFile(mode="w+", delete=False)
        json.dump(config, tfile)
        tfile.close()
        paddle.incubate.autotune.set_config(tfile.name)
        os.remove(tfile.name)
        loader = DataLoader(
            self.dataset, batch_size=self.batch_size, num_workers=2)
        if (sys.platform == 'darwin' or sys.platform == 'win32'):
            self.assertEqual(loader.num_workers, 0)
        else:
            self.assertEqual(loader.num_workers, 2)

    def test_distributer_batch_sampler_autotune(self):
        paddle.incubate.autotune.set_config(
            config={"dataloader": {
                "enable": True,
                "tuning_steps": 1,
            }})
        batch_sampler = paddle.io.DistributedBatchSampler(
            self.dataset, batch_size=self.batch_size)
        loader = DataLoader(
            self.dataset, batch_sampler=batch_sampler, num_workers=2)


class TestAutoTuneAPI(unittest.TestCase):
    def test_set_config_warnings(self):
        with warnings.catch_warnings(record=True) as w:
            config = {"kernel": {"enable": 1, "tuning_range": True}}
            tfile = tempfile.NamedTemporaryFile(mode="w+", delete=False)
            json.dump(config, tfile)
            tfile.close()
            paddle.incubate.autotune.set_config(tfile.name)
            os.remove(tfile.name)
            self.assertTrue(len(w) == 2)


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