# Copyright (c) 2019 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 paddle
import paddle.fluid as fluid
import numpy as np
import time
import six
import unittest

EPOCH_NUM = 60
BATCH_SIZE = 32
CLASS_NUM = 10


def random_reader():
    for i in range(BATCH_SIZE * 40):
        image = np.random.random([784])
        label = np.random.random_integers(low=0, high=CLASS_NUM - 1)
        yield image, label


def simple_fc_net(places, use_legacy_py_reader, use_double_buffer):
    startup_prog = fluid.Program()
    main_prog = fluid.Program()
    startup_prog.random_seed = 1
    main_prog.random_seed = 1

    with fluid.unique_name.guard():
        with fluid.program_guard(main_prog, startup_prog):
            image = fluid.layers.data(
                name='image', shape=[784], dtype='float32')
            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
            py_reader = fluid.io.PyReader(
                feed_list=[image, label],
                capacity=4,
                iterable=not use_legacy_py_reader,
                use_double_buffer=use_double_buffer)
            hidden = image
            for hidden_size in [10, 20, 30]:
                hidden = fluid.layers.fc(
                    hidden,
                    size=hidden_size,
                    act='tanh',
                    bias_attr=fluid.ParamAttr(
                        initializer=fluid.initializer.Constant(value=1.0)))

            predict_label = fluid.layers.fc(hidden,
                                            size=CLASS_NUM,
                                            act='softmax')
            loss = fluid.layers.mean(
                fluid.layers.cross_entropy(
                    input=predict_label, label=label))

            optimizer = fluid.optimizer.Adam()
            optimizer.minimize(loss)
    return startup_prog, main_prog, py_reader, loss


class TestBase(unittest.TestCase):
    def run_main(self, use_legacy_py_reader, with_data_parallel, places,
                 use_double_buffer):
        scope = fluid.Scope()
        with fluid.scope_guard(scope):
            startup_prog, main_prog, py_reader, loss = simple_fc_net(
                places, use_legacy_py_reader, use_double_buffer)

            reader = paddle.batch(random_reader, batch_size=BATCH_SIZE)

            ps = places if use_double_buffer else fluid.cpu_places(len(places))
            py_reader.decorate_paddle_reader(
                reader, places=ps if py_reader.iterable else None)

            exe = fluid.Executor(place=places[0])
            exe.run(startup_prog)

            prog = fluid.CompiledProgram(main_prog)
            if with_data_parallel:
                prog = prog.with_data_parallel(
                    loss_name=loss.name, places=places)

            step = 0
            step_list = []
            start_t = time.time()
            if not py_reader.iterable:
                for _ in six.moves.range(EPOCH_NUM):
                    step = 0
                    py_reader.start()
                    while True:
                        try:
                            L, = exe.run(program=prog,
                                         fetch_list=[loss],
                                         use_program_cache=True)
                            step += 1
                        except fluid.core.EOFException:
                            py_reader.reset()
                            break
                    step_list.append(step)
            else:
                for _ in six.moves.range(EPOCH_NUM):
                    step = 0
                    for d in py_reader():
                        assert len(d) == len(places)
                        for i, item in enumerate(d):
                            image = item['image']
                            label = item['label']
                            assert image.shape() == [BATCH_SIZE, 784]
                            assert label.shape() == [BATCH_SIZE, 1]
                            assert image._place()._equals(ps[i])
                            assert label._place()._equals(ps[i])
                        L, = exe.run(program=prog,
                                     feed=d,
                                     fetch_list=[loss],
                                     use_program_cache=True)
                        step += 1
                    step_list.append(step)
            end_t = time.time()
            ret = {"time": end_t - start_t, "step": step_list}
            return ret

    def prepare_places(self, with_data_parallel, with_cpu=True, with_gpu=True):
        places = []
        if with_cpu:
            places.append([fluid.CPUPlace()])
            if with_data_parallel:
                places.append([fluid.CPUPlace()] * 2)

        if with_gpu and fluid.core.is_compiled_with_cuda():
            tmp = fluid.cuda_places()
            assert len(tmp) > 0, "no gpu detected"
            if with_data_parallel:
                places.append(tmp)
            places.append([tmp[0]])
        return places

    def test_main(self):
        for with_data_parallel in [True, False]:
            for p in self.prepare_places(with_data_parallel):
                for use_double_buffer in [False, True]:
                    for use_legacy_py_reader in [False, True]:
                        ret = self.run_main(
                            use_legacy_py_reader=use_legacy_py_reader,
                            with_data_parallel=with_data_parallel,
                            places=p,
                            use_double_buffer=use_double_buffer)


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