#   Copyright (c) 2018 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 collections
import functools
import unittest

import paddle
import paddle.fluid as fluid
from paddle.fluid import core

SEED = 1
DTYPE = "float32"
paddle.dataset.mnist.fetch()


# random seed must set before configuring the network.
# fluid.default_startup_program().random_seed = SEED
def cnn_model(data):
    conv_pool_1 = fluid.nets.simple_img_conv_pool(
        input=data,
        filter_size=5,
        num_filters=20,
        pool_size=2,
        pool_stride=2,
        act="relu",
    )
    conv_pool_2 = fluid.nets.simple_img_conv_pool(
        input=conv_pool_1,
        filter_size=5,
        num_filters=50,
        pool_size=2,
        pool_stride=2,
        act="relu",
    )

    # TODO(dzhwinter) : refine the initializer and random seed settting
    SIZE = 10
    input_shape = conv_pool_2.shape
    param_shape = [functools.reduce(lambda a, b: a * b, input_shape[1:], 1)] + [
        SIZE
    ]
    scale = (2.0 / (param_shape[0] ** 2 * SIZE)) ** 0.5

    predict = fluid.layers.fc(
        input=conv_pool_2,
        size=SIZE,
        act="softmax",
        param_attr=fluid.param_attr.ParamAttr(
            initializer=fluid.initializer.NormalInitializer(
                loc=0.0, scale=scale
            )
        ),
    )
    return predict


def get_model(batch_size):
    # Input data
    images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
    label = fluid.layers.data(name='label', shape=[1], dtype='int64')

    # Train program
    predict = cnn_model(images)
    cost = paddle.nn.functional.cross_entropy(
        input=predict, label=label, reduction='none', use_softmax=False
    )
    avg_cost = paddle.mean(x=cost)

    # Evaluator
    batch_size_tensor = paddle.tensor.create_tensor(dtype='int64')
    batch_acc = paddle.static.accuracy(
        input=predict, label=label, total=batch_size_tensor
    )

    inference_program = fluid.default_main_program().clone()
    # Optimization
    opt = fluid.optimizer.AdamOptimizer(
        learning_rate=0.001, beta1=0.9, beta2=0.999
    )

    # Reader
    train_reader = paddle.batch(
        paddle.dataset.mnist.train(), batch_size=batch_size
    )
    test_reader = paddle.batch(
        paddle.dataset.mnist.test(), batch_size=batch_size
    )
    opt.minimize(avg_cost)
    return (
        inference_program,
        avg_cost,
        train_reader,
        test_reader,
        batch_acc,
        predict,
    )


def operator_equal(a, b):
    if a.__str__() != b.__str__():
        raise ValueError("In operator_equal not equal\n")

    for k, v in a.__dict__.items():
        if isinstance(v, fluid.framework.Program) or isinstance(
            v, fluid.framework.Block
        ):
            continue

        elif isinstance(v, core.OpDesc):
            continue

        elif isinstance(v, collections.OrderedDict):
            v0 = sorted(list(v.items()), key=lambda x: x[0])
            v1 = sorted(list(b.__dict__[k].items()), key=lambda x: x[0])

            if v0 != v1:
                raise ValueError("In operator_equal not equal:{0}\n".format(k))

        elif v != b.__dict__[k]:
            raise ValueError("In operator_equal not equal:{0}\n".format(k))

    return True


def block_equal(a, b):
    for k, v in a.__dict__.items():
        if (
            isinstance(v, core.ProgramDesc)
            or isinstance(v, fluid.framework.Program)
            or isinstance(v, core.BlockDesc)
        ):
            continue

        elif k == "ops":
            assert len(a.ops) == len(b.ops)
            for i in range(0, len(a.ops)):
                if not operator_equal(a.ops[i], b.ops[i]):
                    raise ValueError("In block_equal not equal:{0}\n".format(k))

        elif isinstance(v, collections.OrderedDict):
            for key, value in v.items():
                if str(value) != str(b.__dict__[k][key]):
                    raise ValueError("In block_equal not equal:{0}\n".format(k))

        elif v != b.__dict__[k]:
            raise ValueError("In block_equal not equal:{0}\n".format(k))

    return True


def program_equal(a, b):
    for k, v in a.__dict__.items():
        if isinstance(v, core.ProgramDesc):
            continue

        elif k == 'blocks':
            for i in range(0, len(a.blocks)):
                if not block_equal(a.blocks[i], b.blocks[i]):
                    raise ValueError(
                        "In operator_equal not equal:{0}\n".format(k)
                    )
                    return False
            assert len(a.blocks) == len(b.blocks)
        elif k == '_auto_checkpoint_name':
            continue
        elif v != b.__dict__[k]:
            raise ValueError("In program_equal not equal:{0}\n".format(k))

    return True


class TestCloneWithStopGradient(unittest.TestCase):
    def test_clone_with_stop_gradient(self):
        train_program = fluid.Program()
        startup_program = fluid.Program()
        with fluid.program_guard(train_program, startup_program):
            img = fluid.layers.data(name='image', shape=[784])
            hidden1 = fluid.layers.fc(input=img, size=200, act='relu')
            hidden1.stop_gradient = True
            hidden2 = fluid.layers.dropout(hidden1, dropout_prob=0.5)
            loss = paddle.nn.functional.cross_entropy(
                input=fluid.layers.fc(hidden2, size=10, act='softmax'),
                label=fluid.layers.data(name='label', shape=[1], dtype='int64'),
                reduction='none',
                use_softmax=False,
            )
            avg_loss = paddle.mean(loss)
            test_program = train_program.clone(for_test=False)

        self.assertEqual(
            test_program.block(0).var(hidden1.name).stop_gradient, True
        )
        self.assertEqual(
            test_program.block(0).var(hidden2.name).stop_gradient, False
        )


class TestCloneWithStopGradientInSubBlock(unittest.TestCase):
    def test_clone_with_stop_gradient(self):
        train_program = fluid.Program()
        startup_program = fluid.Program()
        with fluid.program_guard(train_program, startup_program):
            img = fluid.layers.data(name='image', shape=[784])
            true = paddle.ones(shape=[1], dtype="float32")
            hidden1 = fluid.layers.fc(input=img, size=200, act='relu')
            hidden1.stop_gradient = True

            cond = paddle.equal(true, true)

            def true_fn():
                hidden2 = fluid.layers.dropout(hidden1, dropout_prob=0.5)
                hidden2.stop_gradient = True
                return hidden2

            def false_fn():
                hidden2 = fluid.layers.dropout(hidden1, dropout_prob=0.6)
                return hidden2

            hidden2 = fluid.layers.cond(cond, true_fn, false_fn)

            loss = paddle.nn.functional.cross_entropy(
                input=fluid.layers.fc(hidden2, size=10, act='softmax'),
                label=fluid.layers.data(name='label', shape=[1], dtype='int64'),
                reduction='none',
                use_softmax=False,
            )
            avg_loss = paddle.mean(loss)
            test_program = train_program.clone(for_test=False)

        self.assertEqual(
            test_program.block(0).var(hidden1.name).stop_gradient, True
        )
        for var in test_program.block(1).vars.values():
            var2 = train_program.block(1).var(var.name)
            self.assertEqual(var.stop_gradient, var2.stop_gradient)
        for var in test_program.block(2).vars.values():
            var2 = train_program.block(2).var(var.name)
            self.assertEqual(var.stop_gradient, var2.stop_gradient)


class TestCloneWithRaise(unittest.TestCase):
    def test_clone_with_stop_gradient(self):
        train_program = fluid.Program()
        startup_program = fluid.Program()
        with fluid.program_guard(train_program, startup_program):
            img = fluid.layers.data(name='image', shape=[784])
            true = paddle.ones(shape=[1], dtype="float32")
            hidden1 = fluid.layers.fc(input=img, size=200, act='relu')
            hidden1.stop_gradient = True

            cond = paddle.equal(true, true)

            def true_fn():
                hidden2 = fluid.layers.dropout(hidden1, dropout_prob=0.5)
                hidden2.stop_gradient = True
                return hidden2

            def false_fn():
                hidden2 = fluid.layers.dropout(hidden1, dropout_prob=0.6)
                return hidden2

            hidden2 = fluid.layers.cond(cond, true_fn, false_fn)
            loss = paddle.nn.functional.cross_entropy(
                input=fluid.layers.fc(hidden2, size=10, act='softmax'),
                label=fluid.layers.data(name='label', shape=[1], dtype='int64'),
                reduction='none',
                use_softmax=False,
            )
            avg_loss = paddle.mean(loss)
            test_program = train_program.clone(for_test=False)

        self.assertRaises(
            ValueError, train_program._copy_data_info_from, startup_program
        )
        self.assertRaises(
            TypeError,
            train_program._copy_data_info_from,
            startup_program.block(0),
        )


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