#   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

import gradient_checker
import numpy as np
from decorator_helper import prog_scope
from op_test import get_places
from utils import dygraph_guard, static_guard

import paddle
import paddle.nn.functional as F
from paddle.base import core


class TestSigmoidTripleGradCheck(unittest.TestCase):
    @prog_scope()
    def func(self, place):
        shape = [2, 3, 7, 9]
        eps = 0.0005
        dtype = np.float64
        x = paddle.static.data('x', shape, dtype=dtype)
        x.persistable = True
        y = F.sigmoid(x)
        x_arr = np.random.random(shape).astype(dtype)
        x_arr[np.abs(x_arr) < 0.005] = 0.002
        gradient_checker.triple_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestSigmoidDoubleGradCheck(unittest.TestCase):
    def sigmoid_wrapper(self, x):
        return F.sigmoid(x[0])

    @prog_scope()
    def func(self, place):
        shape = [2, 3, 7, 9]
        eps = 0.0005
        dtype = np.float64
        x = paddle.static.data('x', shape, dtype=dtype)
        x.persistable = True
        y = F.sigmoid(x)
        x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
        x_arr[np.abs(x_arr) < 0.005] = 0.002
        gradient_checker.double_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.double_grad_check_for_dygraph(
            self.sigmoid_wrapper, [x], y, x_init=x_arr, place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestTanhTripleGradCheck(unittest.TestCase):
    def tanh_wrapper(self, x):
        return paddle.tanh(x[0])

    @prog_scope()
    def func(self, place):
        shape = [2, 3, 7, 9]
        eps = 0.0005
        dtype = np.float64
        x = paddle.static.data('x', shape, dtype=dtype)
        x.persistable = True
        y = paddle.tanh(x)
        x_arr = np.random.random(shape).astype(dtype)
        x_arr[np.abs(x_arr) < 0.005] = 0.002
        from paddle.base import core

        core._set_prim_backward_enabled(True)
        gradient_checker.triple_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.triple_grad_check_for_dygraph(
            self.tanh_wrapper, [x], y, x_init=x_arr, place=place
        )
        core._set_prim_backward_enabled(False)

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestTanhDoubleGradCheck(unittest.TestCase):
    def tanh_wrapper(self, x):
        return paddle.tanh(x[0])

    @prog_scope()
    def func(self, place):
        shape = [2, 3, 7, 9]
        eps = 0.0005
        dtype = np.float64
        x = paddle.static.data('x', shape, dtype=dtype)
        x.persistable = True
        y = paddle.tanh(x)
        x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
        x_arr[np.abs(x_arr) < 0.005] = 0.002
        from paddle.base import core

        core._set_prim_backward_enabled(True)
        gradient_checker.double_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.double_grad_check_for_dygraph(
            self.tanh_wrapper, [x], y, x_init=x_arr, place=place
        )
        core._set_prim_backward_enabled(False)

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestAbsDoubleGradCheck(unittest.TestCase):
    def abs_wrapper(self, x):
        return paddle.abs(x[0])

    @prog_scope()
    def func(self, place):
        shape = [2, 3, 7, 9]
        eps = 0.0005
        dtype = np.float64
        x = paddle.static.data('x', shape, dtype=dtype)
        x.persistable = True
        y = paddle.abs(x)
        x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
        x_arr[np.abs(x_arr) < 0.005] = 0.002
        gradient_checker.double_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.double_grad_check_for_dygraph(
            self.abs_wrapper, [x], y, x_init=x_arr, place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestReluDoubleGradCheck(unittest.TestCase):
    @prog_scope()
    def func(self, place):
        shape = [2, 3, 7, 9]
        eps = 0.005
        dtype = np.float64

        x = paddle.static.data('x', shape, dtype)
        x.persistable = True
        y = F.relu(x)
        x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
        x_arr[np.abs(x_arr) < 0.005] = 0.02

        gradient_checker.double_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestLeakyReluDoubleGradCheck(unittest.TestCase):
    def leaky_relu_wrapper(self, x):
        return paddle.nn.functional.leaky_relu(x[0], negative_slope=0.2)

    @prog_scope()
    def func(self, place):
        shape = [2, 3, 7, 9]
        eps = 0.005
        alpha = 0.2
        dtype = np.float64

        x = paddle.static.data('x', shape, dtype)
        x.persistable = True

        y = paddle.nn.functional.leaky_relu(x, alpha)
        x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
        x_arr[np.abs(x_arr) < 0.005] = 0.02

        gradient_checker.double_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.double_grad_check_for_dygraph(
            self.leaky_relu_wrapper, [x], y, x_init=x_arr, place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestELUDoubleGradCheck(unittest.TestCase):
    def elu_wrapper(self, x):
        return paddle.nn.functional.elu(x[0], alpha=0.2)

    @prog_scope()
    def func(self, place):
        shape = [2, 4, 4, 4]
        eps = 1e-6
        alpha = 0.2
        dtype = np.float64
        SEED = 0

        x = paddle.static.data('x', shape, dtype)
        x.persistable = True

        y = paddle.nn.functional.elu(x, alpha=alpha)
        np.random.RandomState(SEED)
        x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
        gradient_checker.double_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.double_grad_check_for_dygraph(
            self.elu_wrapper, [x], y, x_init=x_arr, place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestCELUDoubleGradCheck(unittest.TestCase):
    def celu_wrapper(self, x):
        return paddle.nn.functional.celu(x[0], alpha=0.2)

    @prog_scope()
    def func(self, place):
        shape = [2, 4, 4, 4]
        eps = 1e-6
        alpha = 0.2
        dtype = np.float64
        SEED = 0

        x = paddle.static.data('x', shape, dtype)
        x.persistable = True

        y = F.celu(x, alpha=alpha)
        np.random.RandomState(SEED)
        x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
        gradient_checker.double_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.double_grad_check_for_dygraph(
            self.celu_wrapper, [x], y, x_init=x_arr, place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestSoftplusDoubleGradCheck(unittest.TestCase):
    def softplus_wrapper(self, x):
        return F.softplus(x[0], beta=1, threshold=20)

    @prog_scope()
    def func(self, place):
        shape = [2, 4, 4, 4]
        eps = 1e-6
        beta = 1
        threshold = 20
        dtype = np.float64
        SEED = 0

        x = paddle.static.data('x', shape, dtype)
        x.persistable = True

        y = F.softplus(x, beta=beta, threshold=threshold)
        np.random.RandomState(SEED)
        x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
        gradient_checker.double_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.double_grad_check_for_dygraph(
            self.softplus_wrapper, [x], y, x_init=x_arr, place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestSqrtDoubleGradCheck(unittest.TestCase):
    def sqrt_wrapper(self, x):
        return paddle.sqrt(x[0])

    @prog_scope()
    def func(self, place):
        shape = [2, 3, 7, 9]
        eps = 0.0001
        dtype = np.float64

        x = paddle.static.data('x', shape, dtype)
        x.persistable = True

        y = paddle.sqrt(x)
        x_arr = np.random.uniform(0.1, 1, shape).astype(dtype)

        gradient_checker.double_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.double_grad_check_for_dygraph(
            self.sqrt_wrapper, [x], y, x_init=x_arr, place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestRsqrtDoubleGradCheck(unittest.TestCase):
    def rsqrt_wrapper(self, x):
        return paddle.rsqrt(x[0])

    @prog_scope()
    def func(self, place):
        shape = [2, 3, 7, 9]
        eps = 0.0001
        dtype = np.float64

        x = paddle.static.data('x', shape, dtype)
        x.persistable = True

        y = paddle.rsqrt(x)
        x_arr = np.random.uniform(0.1, 1, shape).astype(dtype)

        gradient_checker.double_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.double_grad_check_for_dygraph(
            self.rsqrt_wrapper, [x], y, x_init=x_arr, place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestSquareDoubleGradCheck(unittest.TestCase):
    def square_wrapper(self, x):
        return paddle.square(x[0])

    @prog_scope()
    def func(self, place):
        # the shape of input variable should be clearly specified, not include -1.
        shape = [2, 3, 7, 9]
        eps = 0.005
        dtype = np.float64

        x = paddle.static.data('x', shape, dtype)
        x.persistable = True
        y = paddle.square(x)
        x_arr = np.random.uniform(-1, 1, shape).astype(dtype)

        gradient_checker.double_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.double_grad_check_for_dygraph(
            self.square_wrapper, [x], y, x_init=x_arr, place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestLogDoubleGradCheck(unittest.TestCase):
    def log_wrapper(self, x):
        return paddle.log(x[0])

    @prog_scope()
    def func(self, place):
        shape = [2, 3, 7, 9]
        eps = 1e-6
        dtype = np.float64

        x = paddle.static.data('x', shape, dtype)
        x.persistable = True
        y = paddle.log(x)

        x_arr = np.random.uniform(0.1, 1, shape).astype(dtype)

        core._set_prim_all_enabled(True)
        gradient_checker.double_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.double_grad_check_for_dygraph(
            self.log_wrapper, [x], y, x_init=x_arr, place=place
        )
        core._set_prim_all_enabled(False)

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestSinDoubleGradCheck(unittest.TestCase):
    def sin_wrapper(self, x):
        return paddle.sin(x[0])

    @prog_scope()
    def func(self, place):
        shape = [2, 3, 7, 9]
        eps = 0.0005
        dtype = np.float64
        x = paddle.static.data('x', shape, dtype=dtype)
        x.persistable = True
        y = paddle.sin(x)
        x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
        x_arr[np.abs(x_arr) < 0.005] = 0.002
        gradient_checker.double_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.double_grad_check_for_dygraph(
            self.sin_wrapper, [x], y, x_init=x_arr, place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestCosDoubleGradCheck(unittest.TestCase):
    def cos_wrapper(self, x):
        return paddle.cos(x[0])

    @prog_scope()
    def func(self, place):
        shape = [2, 3, 7, 9]
        eps = 0.0005
        dtype = np.float64
        x = paddle.static.data('x', shape, dtype=dtype)
        x.persistable = True
        y = paddle.cos(x)
        x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
        x_arr[np.abs(x_arr) < 0.005] = 0.002
        gradient_checker.double_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.double_grad_check_for_dygraph(
            self.cos_wrapper, [x], y, x_init=x_arr, place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestCosDoubleGradCheck2(unittest.TestCase):
    def _check_cos_double_dynamic(self, place):
        with dygraph_guard():
            x = paddle.randn([64, 64])
            x = paddle.to_tensor(x, place=place, stop_gradient=False)
            y = paddle.cos(x)
            dx = paddle.grad(y, x, create_graph=True)
            dxx_result = paddle.grad(dx, x)[0]
            dxx_expected = -paddle.cos(x)
            np.testing.assert_allclose(
                dxx_result.numpy(),
                dxx_expected.numpy(),
                1e-6,
                1e-6,
            )

    def _check_cos_double_static(self, place):
        x_data = np.random.randn(64, 64).astype("float32")
        with static_guard():
            main_prog = paddle.static.Program()
            startup_prog = paddle.static.Program()
            with paddle.static.program_guard(main_prog, startup_prog):
                x = paddle.assign(x_data)
                x.stop_gradient = False
                y = paddle.cos(x)
                dx = paddle.static.gradients(y, x)
                dxx = paddle.static.gradients(dx, x)[0]

            exe = paddle.static.Executor(place)
            exe.run(startup_prog)
            (dxx_result,) = exe.run(main_prog, fetch_list=[dxx])
            dxx_expected = -np.cos(x_data)
            np.testing.assert_allclose(dxx_result, dxx_expected, 1e-6, 1e-6)

    def test_cos_double_grad(self):
        for place in get_places():
            self._check_cos_double_dynamic(place)
            self._check_cos_double_static(place)


class TestPowDoubleGradCheck1(unittest.TestCase):
    def pow_wrapper(self, x):
        return paddle.pow(x[0], 2)

    @prog_scope()
    def func(self, place):
        shape = [2, 3, 7, 9]
        eps = 1e-6
        dtype = np.float64
        x = paddle.static.data('x', shape, dtype=dtype)
        x.persistable = True
        y = paddle.pow(x, 2)
        x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
        gradient_checker.double_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.double_grad_check_for_dygraph(
            self.pow_wrapper, [x], y, x_init=x_arr, place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestPowDoubleGradCheck2(unittest.TestCase):
    def pow_wrapper(self, x):
        return paddle.pow(x[0], 1)

    @prog_scope()
    def func(self, place):
        shape = [2, 3, 7, 9]
        eps = 1e-6
        dtype = np.float64
        x = paddle.static.data('x', shape, dtype=dtype)
        x.persistable = True
        y = paddle.pow(x, 1)
        x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
        gradient_checker.double_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.double_grad_check_for_dygraph(
            self.pow_wrapper, [x], y, x_init=x_arr, place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestSinTripleGradCheck(unittest.TestCase):
    def sin_wrapper(self, x):
        return paddle.sin(x[0])

    @prog_scope()
    def func(self, place):
        shape = [2, 3, 7, 9]
        eps = 0.0005
        dtype = np.float64
        x = paddle.static.data('x', shape, dtype=dtype)
        x.persistable = True
        y = paddle.sin(x)
        x_arr = np.random.random(shape).astype(dtype)
        x_arr[np.abs(x_arr) < 0.005] = 0.002
        gradient_checker.triple_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.triple_grad_check_for_dygraph(
            self.sin_wrapper, [x], y, x_init=x_arr, place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestPowTripleGradCheck1(unittest.TestCase):
    def pow_wrapper(self, x):
        return paddle.pow(x[0], 1)

    @prog_scope()
    def func(self, place):
        shape = [2, 3, 7, 9]
        eps = 1e-6
        dtype = np.float64
        x = paddle.static.data('x', shape, dtype=dtype)
        x.persistable = True
        y = paddle.pow(x, 1)
        x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
        gradient_checker.triple_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.triple_grad_check_for_dygraph(
            self.pow_wrapper, [x], y, x_init=x_arr, place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestPowTripleGradCheck2(unittest.TestCase):
    def pow_wrapper(self, x):
        return paddle.pow(x[0], 2)

    @prog_scope()
    def func(self, place):
        shape = [2, 3, 7, 9]
        eps = 1e-6
        dtype = np.float64
        x = paddle.static.data('x', shape, dtype=dtype)
        x.persistable = True
        y = paddle.pow(x, 2)
        x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
        gradient_checker.triple_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.triple_grad_check_for_dygraph(
            self.pow_wrapper, [x], y, x_init=x_arr, place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestPowTripleGradCheck3(unittest.TestCase):
    def pow_wrapper(self, x):
        return paddle.pow(x[0], 4)

    @prog_scope()
    def func(self, place):
        shape = [2, 3, 7, 9]
        eps = 1e-6
        dtype = np.float64
        x = paddle.static.data('x', shape, dtype=dtype)
        x.persistable = True
        y = paddle.pow(x, 4)
        x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
        gradient_checker.triple_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.triple_grad_check_for_dygraph(
            self.pow_wrapper, [x], y, x_init=x_arr, place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


class TestCosTripleGradCheck(unittest.TestCase):
    def cos_wrapper(self, x):
        return paddle.cos(x[0])

    @prog_scope()
    def func(self, place):
        shape = [2, 3, 7, 9]
        eps = 0.0005
        dtype = np.float64
        x = paddle.static.data('x', shape, dtype=dtype)
        x.persistable = True
        y = paddle.cos(x)
        x_arr = np.random.random(shape).astype(dtype)
        x_arr[np.abs(x_arr) < 0.005] = 0.002
        gradient_checker.triple_grad_check(
            [x], y, x_init=x_arr, place=place, eps=eps
        )
        gradient_checker.triple_grad_check_for_dygraph(
            self.cos_wrapper, [x], y, x_init=x_arr, place=place
        )

    def test_grad(self):
        paddle.enable_static()
        for p in get_places():
            self.func(p)


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