#   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 unittest
from functools import partial

import numpy as np

import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.optimizer as optimizer
from paddle.fluid.backward import append_backward
from paddle.fluid.framework import Program, program_guard

paddle.enable_static()


class TestAPICase(unittest.TestCase):
    def test_return_single_var(self):
        def fn_1():
            return paddle.tensor.fill_constant(
                shape=[4, 2], dtype='int32', value=1
            )

        def fn_2():
            return paddle.tensor.fill_constant(
                shape=[4, 2], dtype='int32', value=2
            )

        def fn_3():
            return paddle.tensor.fill_constant(
                shape=[4, 3], dtype='int32', value=3
            )

        main_program = Program()
        startup_program = Program()
        with program_guard(main_program, startup_program):
            x = paddle.tensor.fill_constant(
                shape=[1], dtype='float32', value=0.3
            )
            y = paddle.tensor.fill_constant(
                shape=[1], dtype='float32', value=0.1
            )
            z = paddle.tensor.fill_constant(
                shape=[1], dtype='float32', value=0.2
            )
            pred_2 = paddle.less_than(x, y)  # false: 0.3 < 0.1
            pred_1 = paddle.less_than(z, x)  # true: 0.2 < 0.3

            # call fn_1
            out_0 = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[(pred_1, fn_1), (pred_1, fn_2)], default=fn_3
            )

            # call fn_2
            out_1 = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[(pred_2, fn_1), (pred_1, fn_2)], default=fn_3
            )

            # call default fn_3
            out_2 = paddle.static.nn.control_flow.case(
                pred_fn_pairs=((pred_2, fn_1), (pred_2, fn_2)), default=fn_3
            )

            # no default, call fn_2
            out_3 = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[(pred_1, fn_2)]
            )

            # no default, call fn_2. but pred_2 is false
            out_4 = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[(pred_2, fn_2)]
            )

            place = (
                fluid.CUDAPlace(0)
                if core.is_compiled_with_cuda()
                else fluid.CPUPlace()
            )
            exe = fluid.Executor(place)

            res = exe.run(
                main_program, fetch_list=[out_0, out_1, out_2, out_3, out_4]
            )

            np.testing.assert_allclose(res[0], 1, rtol=1e-05)
            np.testing.assert_allclose(res[1], 2, rtol=1e-05)
            np.testing.assert_allclose(res[2], 3, rtol=1e-05)
            np.testing.assert_allclose(res[3], 2, rtol=1e-05)
            np.testing.assert_allclose(res[4], 2, rtol=1e-05)

    def test_0d_tensor(self):
        def fn_1():
            return paddle.full(shape=[], dtype='int32', fill_value=1)

        def fn_2():
            return paddle.full(shape=[], dtype='int32', fill_value=2)

        def fn_3():
            return paddle.full(shape=[], dtype='int32', fill_value=3)

        main_program = Program()
        startup_program = Program()
        with program_guard(main_program, startup_program):
            x = paddle.full(shape=[], dtype='float32', fill_value=0.3)
            y = paddle.full(shape=[], dtype='float32', fill_value=0.1)
            z = paddle.full(shape=[], dtype='float32', fill_value=0.2)
            pred_2 = paddle.less_than(x, y)  # false: 0.3 < 0.1
            pred_1 = paddle.less_than(z, x)  # true: 0.2 < 0.3

            # call fn_1
            out_0 = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[(pred_1, fn_1), (pred_1, fn_2)], default=fn_3
            )

            # call fn_2
            out_1 = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[(pred_2, fn_1), (pred_1, fn_2)], default=fn_3
            )

            # call default fn_3
            out_2 = paddle.static.nn.control_flow.case(
                pred_fn_pairs=((pred_2, fn_1), (pred_2, fn_2)), default=fn_3
            )

            # no default, call fn_2
            out_3 = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[(pred_1, fn_2)]
            )

            # no default, call fn_2. but pred_2 is false
            out_4 = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[(pred_2, fn_2)]
            )

            place = (
                fluid.CUDAPlace(0)
                if core.is_compiled_with_cuda()
                else fluid.CPUPlace()
            )
            exe = fluid.Executor(place)

            res = exe.run(
                main_program, fetch_list=[out_0, out_1, out_2, out_3, out_4]
            )

            np.testing.assert_allclose(res[0], 1, rtol=1e-05)
            self.assertEqual(res[0].shape, ())
            np.testing.assert_allclose(res[1], 2, rtol=1e-05)
            self.assertEqual(res[1].shape, ())
            np.testing.assert_allclose(res[2], 3, rtol=1e-05)
            self.assertEqual(res[2].shape, ())
            np.testing.assert_allclose(res[3], 2, rtol=1e-05)
            self.assertEqual(res[3].shape, ())
            np.testing.assert_allclose(res[4], 2, rtol=1e-05)
            self.assertEqual(res[4].shape, ())

    def test_0d_tensor_backward(self):
        main_program = Program()
        startup_program = Program()
        with program_guard(main_program, startup_program):
            x = paddle.full(shape=[], dtype='float32', fill_value=-2.0)
            x.stop_gradient = False
            pred = paddle.full(shape=[], dtype='bool', fill_value=0)
            # pred is False, so out = -x
            out = paddle.static.nn.case(
                pred_fn_pairs=[(pred, lambda: x)], default=lambda: -x
            )
            append_backward(out)

        place = (
            fluid.CUDAPlace(0)
            if core.is_compiled_with_cuda()
            else fluid.CPUPlace()
        )
        exe = fluid.Executor(place)

        res = exe.run(main_program, fetch_list=[out.name, x.grad_name])
        np.testing.assert_allclose(
            np.asarray(res[0]), np.array(2.0), rtol=1e-05
        )
        self.assertEqual(res[0].shape, ())
        np.testing.assert_allclose(
            np.asarray(res[1]), np.array(-1.0), rtol=1e-05
        )
        self.assertEqual(res[1].shape, ())

    def test_0d_tensor_dygraph(self):
        paddle.disable_static()

        def fn_1():
            return paddle.full(shape=[], dtype='int32', fill_value=1)

        def fn_2():
            return paddle.full(shape=[], dtype='int32', fill_value=2)

        def fn_3():
            return paddle.full(shape=[], dtype='int32', fill_value=3)

        x = paddle.full(shape=[], dtype='float32', fill_value=0.3)
        y = paddle.full(shape=[], dtype='float32', fill_value=0.1)
        z = paddle.full(shape=[], dtype='float32', fill_value=0.2)
        pred_2 = paddle.less_than(x, y)  # false: 0.3 < 0.1
        pred_1 = paddle.less_than(z, x)  # true: 0.2 < 0.3

        # call fn_1
        out_0 = paddle.static.nn.control_flow.case(
            pred_fn_pairs=[(pred_1, fn_1), (pred_1, fn_2)], default=fn_3
        )

        # call fn_2
        out_1 = paddle.static.nn.control_flow.case(
            pred_fn_pairs=[(pred_2, fn_1), (pred_1, fn_2)], default=fn_3
        )

        # call default fn_3
        out_2 = paddle.static.nn.control_flow.case(
            pred_fn_pairs=((pred_2, fn_1), (pred_2, fn_2)), default=fn_3
        )

        # no default, call fn_2
        out_3 = paddle.static.nn.control_flow.case(
            pred_fn_pairs=[(pred_1, fn_2)]
        )

        # no default, call fn_2. but pred_2 is false
        out_4 = paddle.static.nn.control_flow.case(
            pred_fn_pairs=[(pred_2, fn_2)]
        )

        np.testing.assert_allclose(out_0, 1, rtol=1e-05)
        self.assertEqual(out_0.shape, [])
        np.testing.assert_allclose(out_1, 2, rtol=1e-05)
        self.assertEqual(out_1.shape, [])
        np.testing.assert_allclose(out_2, 3, rtol=1e-05)
        self.assertEqual(out_2.shape, [])
        np.testing.assert_allclose(out_3, 2, rtol=1e-05)
        self.assertEqual(out_3.shape, [])
        np.testing.assert_allclose(out_4, 2, rtol=1e-05)
        self.assertEqual(out_4.shape, [])

        paddle.enable_static()

    def test_return_var_tuple(self):
        def fn_1():
            return paddle.tensor.fill_constant(
                shape=[1, 2], dtype='int32', value=1
            ), paddle.tensor.fill_constant(
                shape=[2, 3], dtype='float32', value=2
            )

        def fn_2():
            return paddle.tensor.fill_constant(
                shape=[3, 4], dtype='int32', value=3
            ), paddle.tensor.fill_constant(
                shape=[4, 5], dtype='float32', value=4
            )

        def fn_3():
            return paddle.tensor.fill_constant(
                shape=[5], dtype='int32', value=5
            ), paddle.tensor.fill_constant(
                shape=[5, 6], dtype='float32', value=6
            )

        main_program = Program()
        startup_program = Program()
        with program_guard(main_program, startup_program):
            x = paddle.tensor.fill_constant(shape=[1], dtype='float32', value=1)
            y = paddle.tensor.fill_constant(shape=[1], dtype='float32', value=1)
            z = paddle.tensor.fill_constant(shape=[1], dtype='float32', value=3)

            pred_1 = paddle.equal(x, y)  # true
            pred_2 = paddle.equal(x, z)  # false

            out = paddle.static.nn.control_flow.case(
                ((pred_1, fn_1), (pred_2, fn_2)), fn_3
            )

            place = (
                fluid.CUDAPlace(0)
                if core.is_compiled_with_cuda()
                else fluid.CPUPlace()
            )
            exe = fluid.Executor(place)
            ret = exe.run(main_program, fetch_list=out)

            np.testing.assert_allclose(
                np.asarray(ret[0]), np.full((1, 2), 1, np.int32), rtol=1e-05
            )
            np.testing.assert_allclose(
                np.asarray(ret[1]), np.full((2, 3), 2, np.float32), rtol=1e-05
            )


class TestAPICase_Nested(unittest.TestCase):
    def test_nested_case(self):
        def fn_1(x=1):
            var_5 = paddle.tensor.fill_constant(
                shape=[1], dtype='int32', value=5
            )
            var_6 = paddle.tensor.fill_constant(
                shape=[1], dtype='int32', value=6
            )
            out = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[
                    (
                        var_5 < var_6,
                        partial(
                            paddle.tensor.fill_constant,
                            shape=[1],
                            dtype='int32',
                            value=x,
                        ),
                    ),
                    (
                        var_5 == var_6,
                        partial(
                            paddle.tensor.fill_constant,
                            shape=[2],
                            dtype='int32',
                            value=x,
                        ),
                    ),
                ]
            )
            return out

        def fn_2(x=2):
            var_5 = paddle.tensor.fill_constant(
                shape=[1], dtype='int32', value=5
            )
            var_6 = paddle.tensor.fill_constant(
                shape=[1], dtype='int32', value=6
            )
            out = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[
                    (var_5 < var_6, partial(fn_1, x=x)),
                    (
                        var_5 == var_6,
                        partial(
                            paddle.tensor.fill_constant,
                            shape=[2],
                            dtype='int32',
                            value=x,
                        ),
                    ),
                ]
            )
            return out

        def fn_3():
            var_5 = paddle.tensor.fill_constant(
                shape=[1], dtype='int32', value=5
            )
            var_6 = paddle.tensor.fill_constant(
                shape=[1], dtype='int32', value=6
            )
            out = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[
                    (var_5 < var_6, partial(fn_2, x=3)),
                    (
                        var_5 == var_6,
                        partial(
                            paddle.tensor.fill_constant,
                            shape=[2],
                            dtype='int32',
                            value=7,
                        ),
                    ),
                ]
            )
            return out

        main_program = Program()
        startup_program = Program()
        with program_guard(main_program, startup_program):
            x = paddle.tensor.fill_constant(
                shape=[1], dtype='float32', value=0.3
            )
            y = paddle.tensor.fill_constant(
                shape=[1], dtype='float32', value=0.1
            )
            z = paddle.tensor.fill_constant(
                shape=[1], dtype='float32', value=0.2
            )
            pred_2 = paddle.less_than(x, y)  # false: 0.3 < 0.1
            pred_1 = paddle.less_than(z, x)  # true: 0.2 < 0.3

            out_1 = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[(pred_1, fn_1), (pred_2, fn_2)], default=fn_3
            )

            out_2 = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[(pred_2, fn_1), (pred_1, fn_2)], default=fn_3
            )

            out_3 = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[(x == y, fn_1), (x == z, fn_2)], default=fn_3
            )

            place = (
                fluid.CUDAPlace(0)
                if core.is_compiled_with_cuda()
                else fluid.CPUPlace()
            )
            exe = fluid.Executor(place)

            res = exe.run(main_program, fetch_list=[out_1, out_2, out_3])

            np.testing.assert_allclose(res[0], 1, rtol=1e-05)
            np.testing.assert_allclose(res[1], 2, rtol=1e-05)
            np.testing.assert_allclose(res[2], 3, rtol=1e-05)

    def test_nested_0d_tensor(self):
        def fn_1(x=1):
            var_5 = paddle.full(shape=[], dtype='int32', fill_value=5)
            var_6 = paddle.full(shape=[], dtype='int32', fill_value=6)
            out = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[
                    (
                        var_5 < var_6,
                        partial(
                            paddle.full,
                            shape=[],
                            dtype='int32',
                            fill_value=x,
                        ),
                    ),
                    (
                        var_5 == var_6,
                        partial(
                            paddle.full,
                            shape=[],
                            dtype='int32',
                            fill_value=x,
                        ),
                    ),
                ]
            )
            return out

        def fn_2(x=2):
            var_5 = paddle.full(shape=[], dtype='int32', fill_value=5)
            var_6 = paddle.full(shape=[], dtype='int32', fill_value=6)
            out = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[
                    (var_5 < var_6, partial(fn_1, x=x)),
                    (
                        var_5 == var_6,
                        partial(
                            paddle.full,
                            shape=[],
                            dtype='int32',
                            fill_value=x,
                        ),
                    ),
                ]
            )
            return out

        def fn_3():
            var_5 = paddle.full(shape=[], dtype='int32', fill_value=5)
            var_6 = paddle.full(shape=[], dtype='int32', fill_value=6)
            out = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[
                    (var_5 < var_6, partial(fn_2, x=3)),
                    (
                        var_5 == var_6,
                        partial(
                            paddle.full,
                            shape=[],
                            dtype='int32',
                            fill_value=7,
                        ),
                    ),
                ]
            )
            return out

        main_program = Program()
        startup_program = Program()
        with program_guard(main_program, startup_program):
            x = paddle.full(shape=[], dtype='float32', fill_value=0.3)
            y = paddle.full(shape=[], dtype='float32', fill_value=0.1)
            z = paddle.full(shape=[], dtype='float32', fill_value=0.2)
            pred_2 = paddle.less_than(x, y)  # false: 0.3 < 0.1
            pred_1 = paddle.less_than(z, x)  # true: 0.2 < 0.3

            out_1 = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[(pred_1, fn_1), (pred_2, fn_2)], default=fn_3
            )

            out_2 = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[(pred_2, fn_1), (pred_1, fn_2)], default=fn_3
            )

            out_3 = paddle.static.nn.control_flow.case(
                pred_fn_pairs=[(x == y, fn_1), (x == z, fn_2)], default=fn_3
            )

            place = (
                fluid.CUDAPlace(0)
                if core.is_compiled_with_cuda()
                else fluid.CPUPlace()
            )
            exe = fluid.Executor(place)

            res = exe.run(main_program, fetch_list=[out_1, out_2, out_3])

            np.testing.assert_allclose(res[0], 1, rtol=1e-05)
            self.assertEqual(res[0].shape, ())
            np.testing.assert_allclose(res[1], 2, rtol=1e-05)
            self.assertEqual(res[1].shape, ())
            np.testing.assert_allclose(res[2], 3, rtol=1e-05)
            self.assertEqual(res[2].shape, ())


class TestAPICase_Error(unittest.TestCase):
    def test_error(self):
        def fn_1():
            return paddle.tensor.fill_constant(
                shape=[4, 2], dtype='int32', value=1
            )

        main_program = Program()
        startup_program = Program()
        with program_guard(main_program, startup_program):
            x = paddle.tensor.fill_constant(
                shape=[1], dtype='float32', value=0.23
            )
            z = paddle.tensor.fill_constant(
                shape=[1], dtype='float32', value=0.2
            )
            pred_1 = paddle.less_than(z, x)  # true

            # The type of 'pred_fn_pairs' in case must be list or tuple
            def type_error_pred_fn_pairs():
                paddle.static.nn.control_flow.case(
                    pred_fn_pairs=1, default=fn_1
                )

            self.assertRaises(TypeError, type_error_pred_fn_pairs)

            # The elements' type of 'pred_fn_pairs' in Op(case) must be tuple
            def type_error_pred_fn_1():
                paddle.static.nn.control_flow.case(
                    pred_fn_pairs=[1], default=fn_1
                )

            self.assertRaises(TypeError, type_error_pred_fn_1)

            # The tuple's size of 'pred_fn_pairs' in Op(case) must be 2
            def type_error_pred_fn_2():
                paddle.static.nn.control_flow.case(
                    pred_fn_pairs=[(1, 2, 3)], default=fn_1
                )

            self.assertRaises(TypeError, type_error_pred_fn_2)

            # The pred's type of 'pred_fn_pairs' in Op(case) must be bool Variable
            def type_error_pred():
                paddle.static.nn.control_flow.case(
                    pred_fn_pairs=[(1, fn_1)], default=fn_1
                )

            self.assertRaises(TypeError, type_error_pred)

            # The function of pred_fn_pairs in case must be callable
            def type_error_fn():
                paddle.static.nn.control_flow.case(
                    pred_fn_pairs=[(pred_1, 2)], default=fn_1
                )

            self.assertRaises(TypeError, type_error_fn)

            # The default in Op(case) must be callable
            def type_error_default():
                paddle.static.nn.control_flow.case(
                    pred_fn_pairs=[(pred_1, fn_1)], default=fn_1()
                )

            self.assertRaises(TypeError, type_error_default)


# when optimizer in case
class TestMutiTask(unittest.TestCase):
    def test_optimizer_in_case(self):
        BATCH_SIZE = 1
        INPUT_SIZE = 784
        EPOCH_NUM = 2

        x = paddle.static.data(
            name='x', shape=[BATCH_SIZE, INPUT_SIZE], dtype='float32'
        )
        y = paddle.static.data(
            name='y', shape=[BATCH_SIZE, INPUT_SIZE], dtype='float32'
        )

        switch_id = paddle.static.data(
            name='switch_id', shape=[1], dtype='int32'
        )

        one = paddle.tensor.fill_constant(shape=[1], dtype='int32', value=1)
        adam = optimizer.Adam(learning_rate=0.001)
        adagrad = optimizer.Adagrad(learning_rate=0.001)

        def fn_1():
            sum = paddle.multiply(x, y)
            loss = paddle.mean(sum, name="f_1_loss")
            adam.minimize(loss)

        def fn_2():
            sum = paddle.multiply(x, y)
            loss = paddle.mean(sum, name="f_2_loss")
            adagrad.minimize(loss)

        paddle.static.nn.control_flow.case(
            pred_fn_pairs=[(switch_id == one, fn_1)], default=fn_2
        )

        exe = fluid.Executor(fluid.CPUPlace())
        exe.run(fluid.default_startup_program())

        for epoch in range(EPOCH_NUM):
            np.random.seed(epoch)
            feed_image = np.random.random(size=[BATCH_SIZE, INPUT_SIZE]).astype(
                'float32'
            )
            main_program = fluid.default_main_program()
            out = exe.run(
                main_program,
                feed={
                    'x': feed_image,
                    'y': feed_image,
                    'switch_id': np.array([epoch]).astype('int32'),
                },
                fetch_list=[],
            )


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