# Copyright (c) 2022 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 typing
import unittest

import numpy as np
import paddle

import autograd
import autograd.numpy as anp
import autograd.scipy as ascipy
import config
import utils


@utils.place(config.DEVICES)
@utils.parameterize(
    (utils.TEST_CASE_NAME, 'fun', 'xs', 'v', 'dtype'),
    (('matmul', paddle.matmul,
      (np.random.rand(2, 3), np.random.rand(3, 2)), None, 'float32'), ))
class TestWithoutProgramGuard(unittest.TestCase):

    @classmethod
    def setUpClass(cls):
        cls.xs = tuple(x.astype(cls.dtype) for x in cls.xs)
        cls._rtol = config.TOLERANCE.get(str(
            cls.dtype)).get("first_order_grad").get("rtol")
        cls._atol = config.TOLERANCE.get(str(
            cls.dtype)).get("first_order_grad").get("atol")

    def setUp(self):
        paddle.enable_static()
        paddle.incubate.autograd.enable_prim()

    def tearDown(self):
        paddle.incubate.autograd.disable_prim()
        paddle.disable_static()

    def test_forward_grad_without_program_guard(self):

        def with_program_guard():
            paddle.incubate.autograd.enable_prim()
            sp = paddle.static.Program()
            mp = paddle.static.Program()
            with paddle.static.program_guard(mp, sp):
                feed, static_xs, static_v = utils.gen_static_data_and_feed(
                    self.xs, self.v, stop_gradient=False)
                ys = self.fun(*static_xs) if isinstance(
                    static_xs, typing.Sequence) else self.fun(static_xs)
                ys_grad = paddle.incubate.autograd.forward_grad(
                    ys, static_xs, static_v)
                paddle.incubate.autograd.prim2orig(mp.block(0))
            exe = paddle.static.Executor()
            exe.run(sp)
            out = exe.run(mp, feed=feed, fetch_list=ys_grad)
            paddle.incubate.autograd.disable_prim()
            return out

        def without_program_guard():
            paddle.incubate.autograd.enable_prim()
            feed, static_xs, static_v = utils.gen_static_data_and_feed(
                self.xs, self.v, stop_gradient=False)
            ys = self.fun(*static_xs) if isinstance(
                static_xs, typing.Sequence) else self.fun(static_xs)
            ys_grad = paddle.incubate.autograd.forward_grad(
                ys, static_xs, static_v)
            sp = paddle.fluid.framework.default_startup_program()
            mp = paddle.fluid.framework.default_main_program()
            exe = paddle.static.Executor()
            exe.run(sp)
            out = exe.run(mp, feed=feed, fetch_list=ys_grad)
            paddle.incubate.autograd.disable_prim()
            return out

        expected = with_program_guard()
        actual = without_program_guard()
        self.assertEqual(type(actual), type(expected))
        np.testing.assert_allclose(np.concatenate(actual),
                                   np.concatenate(expected),
                                   rtol=self._rtol,
                                   atol=self._atol)

    def test_grad_without_program_guard(self):

        def with_program_guard():
            paddle.incubate.autograd.enable_prim()
            sp = paddle.static.Program()
            mp = paddle.static.Program()
            with paddle.static.program_guard(mp, sp):
                feed, static_xs, static_v = utils.gen_static_data_and_feed(
                    self.xs, self.v, stop_gradient=False)
                ys = self.fun(*static_xs) if isinstance(
                    static_xs, typing.Sequence) else self.fun(static_xs)
                xs_grad = paddle.incubate.autograd.grad(ys, static_xs, static_v)
                paddle.incubate.autograd.prim2orig(mp.block(0))
            exe = paddle.static.Executor()
            exe.run(sp)
            out = exe.run(mp, feed=feed, fetch_list=xs_grad)
            paddle.incubate.autograd.disable_prim()
            return out

        def without_program_guard():
            paddle.incubate.autograd.enable_prim()
            feed, static_xs, static_v = utils.gen_static_data_and_feed(
                self.xs, self.v, stop_gradient=False)
            ys = self.fun(*static_xs) if isinstance(
                static_xs, typing.Sequence) else self.fun(static_xs)
            xs_grad = paddle.incubate.autograd.grad(ys, static_xs, static_v)
            sp = paddle.fluid.framework.default_startup_program()
            mp = paddle.fluid.framework.default_main_program()
            exe = paddle.static.Executor()
            exe.run(sp)
            out = exe.run(mp, feed=feed, fetch_list=xs_grad)
            paddle.incubate.autograd.disable_prim()
            return out

        expected = with_program_guard()
        actual = without_program_guard()
        for i, j in zip(actual, expected):
            self.assertEqual(type(i), type(j))
            np.testing.assert_allclose(np.concatenate(i),
                                       np.concatenate(j),
                                       rtol=self._rtol,
                                       atol=self._atol)


@utils.place(config.DEVICES)
@utils.parameterize((utils.TEST_CASE_NAME, 'fun', 'xs', 'v', 'dtype'), (
    ('matmul', paddle.matmul,
     (np.random.rand(2, 3), np.random.rand(3, 2)), None, 'float32'),
    ('multiply', paddle.multiply,
     (np.random.rand(2, 3), np.random.rand(2, 3)), None, 'float64'),
    ('add', paddle.add,
     (np.random.rand(2, 3), np.random.rand(2, 3)), None, 'float32'),
    ('input_not_sequence', paddle.tanh,
     (np.random.rand(5, 5), ), None, 'float64'),
    ('input_gradients_not_none', paddle.matmul,
     (np.random.rand(3, 3), np.random.rand(3, 3)),
     (np.random.rand(3, 3), np.random.rand(3, 3)), 'float64'),
    ('log', paddle.log, (np.random.rand(3, 4), ), None, 'float32'),
    ('abs', paddle.abs, (np.random.uniform(-10, 10,
                                           (10, 10)), ), None, 'float32'),
))
# paddle.where, paddle.pow, paddle.maximum has no double grad definition,
# can not compute forward grad use double trick
class TestForwardGrad(unittest.TestCase):

    @classmethod
    def setUpClass(cls):
        cls.xs = tuple(x.astype(cls.dtype) for x in cls.xs)
        cls._rtol = config.TOLERANCE.get(str(
            cls.dtype)).get("first_order_grad").get("rtol")
        cls._atol = config.TOLERANCE.get(str(
            cls.dtype)).get("first_order_grad").get("atol")

    def setUp(self):
        paddle.enable_static()
        paddle.incubate.autograd.enable_prim()

    def tearDown(self):
        paddle.incubate.autograd.disable_prim()
        paddle.disable_static()

    def test_forward_grad(self):

        def expected():
            paddle.incubate.autograd.disable_prim()
            sp = paddle.static.Program()
            mp = paddle.static.Program()
            with paddle.static.program_guard(mp, sp):
                feed, static_xs, static_v = utils.gen_static_data_and_feed(
                    self.xs, self.v, stop_gradient=False)
                _, ys_grad = paddle.incubate.autograd.jvp(
                    self.fun, static_xs, static_v)
            exe = paddle.static.Executor()
            exe.run(sp)
            out = exe.run(mp, feed=feed, fetch_list=ys_grad)
            paddle.incubate.autograd.enable_prim()
            return out

        def actual():
            paddle.incubate.autograd.enable_prim()
            sp = paddle.static.Program()
            mp = paddle.static.Program()
            with paddle.static.program_guard(mp, sp):
                feed, static_xs, static_v = utils.gen_static_data_and_feed(
                    self.xs, self.v, stop_gradient=False)
                ys = self.fun(*static_xs) if isinstance(
                    static_xs, typing.Sequence) else self.fun(static_xs)
                ys_grad = paddle.incubate.autograd.forward_grad(
                    ys, static_xs, static_v)
                paddle.incubate.autograd.prim2orig(mp.block(0))
            exe = paddle.static.Executor()
            exe.run(sp)
            out = exe.run(mp, feed=feed, fetch_list=ys_grad)
            paddle.incubate.autograd.disable_prim()
            return out

        actual = actual()
        expected = expected()
        self.assertEqual(type(actual), type(expected))
        np.testing.assert_allclose(np.concatenate(actual),
                                   np.concatenate(expected),
                                   rtol=self._rtol,
                                   atol=self._atol)

    def test_prim_disabled(self):
        paddle.incubate.autograd.disable_prim()
        sp = paddle.static.Program()
        mp = paddle.static.Program()
        with self.assertRaises(RuntimeError):
            with paddle.static.program_guard(mp, sp):
                feed, static_xs, static_v = utils.gen_static_data_and_feed(
                    self.xs, self.v, stop_gradient=False)
                ys = self.fun(*static_xs) if isinstance(
                    static_xs, typing.Sequence) else self.fun(static_xs)
                ys_grad = paddle.incubate.autograd.forward_grad(
                    ys, static_xs, static_v)
                paddle.incubate.autograd.prim2orig(mp.block(0))
            exe = paddle.static.Executor()
            exe.run(sp)
            exe.run(mp, feed=feed, fetch_list=ys_grad)
        paddle.incubate.autograd.enable_prim()

    def test_illegal_param(self):
        paddle.incubate.autograd.enable_prim()
        with self.assertRaises(TypeError):
            paddle.incubate.autograd.forward_grad(
                1, paddle.static.data('inputs', shape=[1]))

        with self.assertRaises(TypeError):
            paddle.incubate.autograd.forward_grad(
                paddle.static.data('targets', shape=[1]), 1)
        paddle.incubate.autograd.disable_prim()


where_wrap = lambda x, y: paddle.where(paddle.eye(3, 4) == 1, x, y)


@utils.place(config.DEVICES)
@utils.parameterize(
    (utils.TEST_CASE_NAME, 'fun', 'xs', 'v', 'dtype'),
    (
        ('matmul', paddle.matmul,
         (np.random.rand(2, 3), np.random.rand(3, 2)), None, 'float32'),
        ('multiply', paddle.multiply,
         (np.random.rand(2, 3), np.random.rand(2, 3)), None, 'float64'),
        ('add', paddle.add,
         (np.random.rand(2, 3), np.random.rand(2, 3)), None, 'float32'),
        ('input_not_sequence', paddle.tanh,
         (np.random.rand(5, 5), ), None, 'float64'),
        ('input_gradients_not_none', paddle.matmul,
         (np.random.rand(3, 3), np.random.rand(3, 3)),
         (np.random.rand(3, 3), ), 'float64'),
        ('sin', paddle.sin, (np.random.rand(100, 200), ), None, 'float32'),
        ('cos', paddle.cos, (np.random.rand(200, 90), ), None, 'float32'),
        ('exp', paddle.exp, (np.random.rand(299, 320), ), None, 'float32'),
        # In where op, grad of condition computed by paddle.static.gradients is None,
        # and paddle.incubate.autograd.grad will replace None with zeros while transpose
        # will just return None because cond_dot is unused, that is a diff.
        ('select', where_wrap,
         (np.random.rand(3, 4), np.random.rand(3, 4)), None, 'float32'),
        # pow_p and pow has diff when compute z_dot of 0^0
        ('pow', paddle.pow,
         (np.array([1, 2, 3]), np.array([0, 2, 7])), None, 'float32'),
        # To make max_p consistent with paddle.maximum, be sure x.grad = 0 and y.grad = 1 when x==y.
        ('max', paddle.maximum, (
            np.array([1, 2, 3]),
            np.array([2, 2, 2]),
        ), None, 'float32'),
        ('erf', paddle.erf, (np.random.rand(300, 288), ), None, 'float32'),
        ('gelu', paddle.nn.functional.gelu,
         (np.random.rand(200, 189), ), None, 'float32'),
        ('gelu_approximate', lambda x: paddle.nn.functional.gelu(x, True),
         (np.random.rand(200, 189), ), None, 'float32'),
        ('sum', paddle.sum, (np.random.rand(200, 345), ), None, 'float32'),
        ('sum_with_axis', lambda x: paddle.sum(x, axis=1),
         (np.random.rand(200, 345), ), None, 'float32'),
        ('sum_with_keepdim', lambda x: paddle.sum(x, keepdim=True),
         (np.random.rand(200, 345), ), None, 'float32'),
        ('mean', paddle.mean, (np.random.rand(200, 345), ), None, 'float32'),
        ('mean_with_axis', lambda x: paddle.mean(x, axis=1),
         (np.random.rand(200, 345), ), None, 'float32'),
        ('mean_with_keepdim', lambda x: paddle.mean(x, keepdim=True),
         (np.random.rand(200, 345), ), None, 'float32'),
        ('mean_with_axis_keepdim',
         lambda x: paddle.mean(x, axis=0, keepdim=True),
         (np.random.rand(200, 345), ), None, 'float32'),
        ('abs', paddle.abs, (np.random.uniform(-10, 10,
                                               (200, 345)), ), None, 'float32'),
    ))
class TestGrad(unittest.TestCase):

    def setUp(self):
        paddle.enable_static()
        paddle.incubate.autograd.enable_prim()

    def tearDown(self):
        paddle.incubate.autograd.disable_prim()
        paddle.disable_static()

    @classmethod
    def setUpClass(cls):
        cls.xs = tuple(x.astype(cls.dtype) for x in cls.xs)
        cls._rtol = config.TOLERANCE.get(str(
            cls.dtype)).get("first_order_grad").get("rtol")
        cls._atol = config.TOLERANCE.get(str(
            cls.dtype)).get("first_order_grad").get("atol")

    def test_grad(self):

        def expected():
            paddle.incubate.autograd.disable_prim()
            sp = paddle.static.Program()
            mp = paddle.static.Program()
            with paddle.static.program_guard(mp, sp):
                feed, static_xs, static_v = utils.gen_static_data_and_feed(
                    self.xs, self.v, stop_gradient=False)
                _, ys_grad = paddle.incubate.autograd.vjp(
                    self.fun, static_xs, static_v)
            exe = paddle.static.Executor()
            exe.run(sp)
            out = exe.run(mp, feed=feed, fetch_list=ys_grad)
            paddle.incubate.autograd.enable_prim()
            return out

        def actual():
            paddle.incubate.autograd.enable_prim()
            sp = paddle.static.Program()
            mp = paddle.static.Program()
            with paddle.static.program_guard(mp, sp):
                feed, static_xs, static_v = utils.gen_static_data_and_feed(
                    self.xs, self.v, stop_gradient=False)
                ys = self.fun(*static_xs) if isinstance(
                    static_xs, typing.Sequence) else self.fun(static_xs)
                ys_grad = paddle.incubate.autograd.grad(ys, static_xs, static_v)
                paddle.incubate.autograd.prim2orig(mp.block(0))
            exe = paddle.static.Executor()
            exe.run(sp)
            out = exe.run(mp, feed=feed, fetch_list=ys_grad)
            paddle.incubate.autograd.disable_prim()
            return out

        actual = actual()
        expected = expected()
        self.assertEqual(type(actual), type(expected))
        for i, j in zip(actual, expected):
            np.testing.assert_allclose(i, j, rtol=self._rtol, atol=self._atol)

    def test_illegal_param(self):
        paddle.incubate.autograd.enable_prim()
        with self.assertRaises(TypeError):
            paddle.incubate.autograd.grad(
                1, paddle.static.data('inputs', shape=[1]))

        with self.assertRaises(TypeError):
            paddle.incubate.autograd.grad(
                paddle.static.data('targets', shape=[1]), 1)
        paddle.incubate.autograd.disable_prim()

    def test_disable_prim(self):

        def expected():
            paddle.incubate.autograd.disable_prim()
            sp = paddle.static.Program()
            mp = paddle.static.Program()
            with paddle.static.program_guard(mp, sp):
                feed, static_xs, static_v = utils.gen_static_data_and_feed(
                    self.xs, self.v, stop_gradient=False)
                ys = self.fun(*static_xs) if isinstance(
                    static_xs, typing.Sequence) else self.fun(static_xs)
                ys_grad = paddle.incubate.autograd.grad(ys, static_xs, static_v)
            exe = paddle.static.Executor()
            exe.run(sp)
            out = exe.run(mp, feed=feed, fetch_list=ys_grad)
            paddle.incubate.autograd.enable_prim()
            return out

        def actual():
            paddle.incubate.autograd.disable_prim()
            sp = paddle.static.Program()
            mp = paddle.static.Program()
            with paddle.static.program_guard(mp, sp):
                feed, static_xs, static_v = utils.gen_static_data_and_feed(
                    self.xs, self.v, stop_gradient=False)
                ys = self.fun(*static_xs) if isinstance(
                    static_xs, typing.Sequence) else self.fun(static_xs)
                ys_grad = paddle.static.gradients(ys, static_xs, static_v)
            exe = paddle.static.Executor()
            exe.run(sp)
            out = exe.run(mp, feed=feed, fetch_list=ys_grad)
            paddle.incubate.autograd.enable_prim()
            return out

        actual = actual()
        expected = expected()
        self.assertEqual(type(actual), type(expected))
        for i, j in zip(actual, expected):
            np.testing.assert_allclose(i, j, rtol=self._rtol, atol=self._atol)


def multiply_pd(x):
    x2 = paddle.multiply(x, x)
    x3 = paddle.multiply(x2, x2)
    x4 = paddle.multiply(x3, x)
    return x4


multiply_ag = lambda xs: xs[0] * xs[0] * xs[0] * xs[0] * xs[0]
sin_ag = lambda xs: anp.sin(xs[0])
cos_ag = lambda xs: anp.cos(xs[0])
exp_ag = lambda xs: anp.exp(xs[0])
pow_ag = lambda xs: xs[0]**xs[1]
log_ag = lambda xs: anp.log(xs[0])
erf_ag = lambda xs: ascipy.special.erf(xs[0])


def gelu_ag(x, approximate=False):
    if approximate:
        sqrt_2_over_pi = np.sqrt(2 / np.pi).astype(x.dtype)
        cdf = 0.5 * (1.0 + anp.tanh(sqrt_2_over_pi * (x + 0.044715 * (x**3))))
        return x * cdf
    else:
        return x * (ascipy.special.erf(x / np.sqrt(2)) + 1) / 2


@utils.place(config.DEVICES)
@utils.parameterize(
    (utils.TEST_CASE_NAME, 'fun_pd', 'fun_ag', 'xs', 'v', 'dtype'),
    (('multiply', multiply_pd, multiply_ag,
      (np.random.rand(3, 5), ), None, 'float32'),
     ('sin', paddle.sin, sin_ag, (np.random.rand(2, 3), ), None, 'float32'),
     ('cos', paddle.cos, cos_ag, (np.random.rand(3, 4), ), None, 'float32'),
     ('exp', paddle.exp, exp_ag, (np.random.rand(2, 3), ), None, 'float32'),
     ('pow', paddle.pow, pow_ag,
      (np.random.rand(2, 3), np.random.rand(2, 3)), None, 'float32'),
     ('log', paddle.log, log_ag, (np.random.rand(3, 8), ), None, 'float32'),
     ('erf', paddle.erf, erf_ag, (np.random.rand(100, 200), ), None, 'float32'),
     ('gelu', paddle.nn.functional.gelu, lambda xs: gelu_ag(xs[0]),
      (np.random.rand(10, 20, 30), ), None, 'float32'),
     ('gelu_approximate',
      lambda x: paddle.nn.functional.gelu(x, approximate=True),
      lambda xs: gelu_ag(xs[0], approximate=True),
      (np.random.rand(10, 20, 30), ), None, 'float32')))
class TestGradWithHigherOrder(unittest.TestCase):

    def setUp(self):
        paddle.enable_static()
        paddle.incubate.autograd.enable_prim()

    def tearDown(self):
        paddle.incubate.autograd.disable_prim()
        paddle.disable_static()

    @classmethod
    def setUpClass(cls):
        cls.xs = tuple(x.astype(cls.dtype) for x in cls.xs)
        cls._rtol = config.TOLERANCE.get(str(
            cls.dtype)).get("first_order_grad").get("rtol")
        cls._atol = config.TOLERANCE.get(str(
            cls.dtype)).get("first_order_grad").get("atol")

    def test_grad(self):

        def expected():
            egrad = autograd.elementwise_grad
            grad_3 = egrad(egrad(egrad(self.fun_ag)))(self.xs)
            grad_4 = egrad(egrad(egrad(egrad(self.fun_ag))))(self.xs)
            grad_5 = egrad(egrad(egrad(egrad(egrad(self.fun_ag)))))(self.xs)
            # the output of egrad is tuple
            return list(grad_3 + grad_4 + grad_5)

        def actual():
            paddle_grad = paddle.incubate.autograd.grad
            paddle.incubate.autograd.enable_prim()
            main = paddle.static.Program()
            startup = paddle.static.Program()
            with paddle.static.program_guard(main, startup):
                feed, static_xs, static_v = utils.gen_static_data_and_feed(
                    self.xs, self.v, stop_gradient=False)
                ys = self.fun_pd(*static_xs) if isinstance(
                    static_xs, typing.Sequence) else self.fun_pd(static_xs)

                grad1 = paddle_grad(ys, static_xs, static_v)
                grad2 = paddle_grad(grad1, static_xs, static_v)
                grad3 = paddle_grad(grad2, static_xs, static_v)
                grad4 = paddle_grad(grad3, static_xs, static_v)
                grad5 = paddle_grad(grad4, static_xs, static_v)
                paddle.incubate.autograd.prim2orig()

            fetch_list = [grad3, grad4, grad5]

            place = paddle.CPUPlace()
            if paddle.device.is_compiled_with_cuda():
                place = paddle.CUDAPlace(0)
            exe = paddle.static.Executor(place)
            exe.run(startup)
            outs = exe.run(main, feed=feed, fetch_list=fetch_list)
            paddle.incubate.autograd.disable_prim()
            return outs

        actual = actual()
        expected = expected()
        self.assertEqual(type(actual), type(expected))
        for i, j in zip(actual, expected):
            np.testing.assert_allclose(i, j, rtol=self._rtol, atol=self._atol)


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