# Copyright (c) 2020 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 unittest
import paddle
import paddle.fluid as fluid
import paddle.fluid.incubate.fleet.base.role_maker as role_maker
from paddle.fluid.incubate.fleet.collective import CollectiveOptimizer, fleet
import os
import sys

from paddle.distributed.fleet.utils.fs import LocalFS, HDFSClient
import paddle.fluid.incubate.checkpoint.auto_checkpoint as acp
from paddle.fluid.incubate.checkpoint.checkpoint_saver import PaddleModel
from paddle.fluid.framework import program_guard
from paddle.fluid import unique_name

import numpy as np
from paddle.io import Dataset, BatchSampler, DataLoader

from paddle.fluid.tests.unittests.auto_checkpoint_utils import AutoCheckpointBase, get_logger
from paddle.fluid.tests.unittests.test_auto_checkpoint import AutoCheckPointACLBase

paddle.enable_static()
logger = get_logger()


class AutoCheckpointTestMul(AutoCheckPointACLBase):
    def setUp(self):
        get_logger()
        logger.info("enter tests")

        self._old_environ = dict(os.environ)
        proc_env = {
            "PADDLE_RUNNING_ENV": "PADDLE_EDL_AUTO_CHECKPOINT",
            "PADDLE_TRAINER_ID": "0",
            "PADDLE_RUNNING_PLATFORM": "PADDLE_CLOUD",
            "PADDLE_JOB_ID": "test_job_auto_dist_multiple",
            "PADDLE_EDL_HDFS_HOME": "/usr/local/hadoop-2.7.7",
            "PADDLE_EDL_HDFS_NAME": "",
            "PADDLE_EDL_HDFS_UGI": "",
            "PADDLE_EDL_HDFS_CHECKPOINT_PATH": "auto_checkpoint_dist_multiple",
            "PADDLE_EDL_ONLY_FOR_CE_TEST": "1",
            "PADDLE_EDL_FS_CACHE": ".auto_checkpoint_test_dist_multiple",
            "PADDLE_EDL_SAVE_CHECKPOINT_INTER": "0"
        }
        os.environ.update(proc_env)

    def test_multiple(self):
        checker = acp._get_checker()
        fs = HDFSClient(checker.hdfs_home, None)
        fs.delete(checker.hdfs_checkpoint_path)
        self._reset_generator()

        logger.info("begin test_multiple")
        fs = LocalFS()
        save_dir = "./run_save_0"
        fs.delete(save_dir)

        exe, main_prog1, startup_prog1 = self._generate()
        _, main_prog2, startup_prog2 = self._generate()

        compiled1, data_loader1, optimizer1, loss1, image1, label1 = \
            self._init_env(exe, main_prog1, startup_prog1)

        compiled2, data_loader2, optimizer2, loss2, image2, label2 = \
            self._init_env(exe, main_prog2, startup_prog2)

        o = None
        epochs = []
        for i in acp.train_epoch_range(3, 0):
            for data in data_loader1():
                fetch = exe.run(compiled1, feed=data, fetch_list=[loss1])

            for data in data_loader2():
                fetch = exe.run(compiled2, feed=data, fetch_list=[loss2])

            o = acp._get_train_epoch_range()
            self.assertEqual(len(o._exe_status), 2)
            print(o._exe_status)
            epochs.append(i)

        o = acp._get_train_epoch_range()
        self.assertTrue(o == None, "now train epoch must not exits now")
        self.assertEqual(i, 2)
        self.assertEqual(epochs, [0, 1, 2])

        fs.delete(save_dir)
        logger.info("end test_multiple")


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