# 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 inspect
import unittest

from test_imperative_base import new_program_scope

import paddle
from paddle import fluid
from paddle.fluid import framework


class TestTracerMode(unittest.TestCase):
    def setUp(self):
        self.init_mode = True

    def get_tracer_mode(self):
        assert framework.in_dygraph_mode(), "Dygraph mode must be enabled"

    @fluid.dygraph.no_grad
    def no_grad_func(self, a):
        self.assertEqual(self.tracer._has_grad, False)
        return a

    @framework.dygraph_not_support
    def not_support_func(self):
        return True

    def check_not_support_rlt(self, ans):
        try:
            rlt = self.not_support_func()
        except AssertionError:
            rlt = False
        finally:
            self.assertEqual(rlt, ans)

    def test_main(self):
        with fluid.dygraph.guard():
            self.tracer = framework._dygraph_tracer()
            self.tracer._train_mode = self.init_mode

            self.assertEqual(self.no_grad_func(1), 1)
            self.assertEqual(self.no_grad_func.__name__, "no_grad_func")

            def need_no_grad_func(a, b=1):
                return a + b

            decorated_func = fluid.dygraph.no_grad(need_no_grad_func)
            self.assertTrue(
                str(inspect.getfullargspec(decorated_func))
                == str(inspect.getfullargspec(need_no_grad_func))
            )

            self.assertEqual(self.tracer._train_mode, self.init_mode)

        with fluid.dygraph.guard():
            self.check_not_support_rlt(False)

        paddle.enable_static()
        with new_program_scope():
            self.check_not_support_rlt(True)


class TestTracerMode2(TestTracerMode):
    def setUp(self):
        self.init_mode = False


class TestNoGradClass(unittest.TestCase):
    @paddle.no_grad()
    def no_grad_func(self, a):
        self.assertEqual(self.tracer._train_mode, True)
        self.assertEqual(self.tracer._has_grad, False)
        return a

    def test_main(self):
        paddle.disable_static()

        self.tracer = framework._dygraph_tracer()
        self.tracer._train_mode = True
        self.tracer._has_grad = True

        self.assertEqual(self.no_grad_func(1), 1)
        self.assertEqual(self.no_grad_func.__name__, "no_grad_func")

        def need_no_grad_func(a, b=1):
            return a + b

        decorated_func = paddle.no_grad()(need_no_grad_func)
        self.assertEqual(
            str(inspect.getfullargspec(decorated_func)),
            str(inspect.getfullargspec(need_no_grad_func)),
        )

        def test_gen():
            yield from range(3)

        a = 0
        for i in test_gen():
            a += i

        @paddle.no_grad()
        def test_wrapped_gen():
            yield from range(3)

        b = 0
        for i in test_wrapped_gen():
            b += i

        self.assertEqual(a, b)


class TestEnableGradClass(unittest.TestCase):
    @paddle.enable_grad()
    def enable_grad_func(self, a):
        self.assertEqual(self.tracer._train_mode, True)
        self.assertEqual(self.tracer._has_grad, True)
        return a

    def test_main(self):
        paddle.disable_static()

        self.tracer = framework._dygraph_tracer()
        self.tracer._train_mode = True
        self.tracer._has_grad = False

        self.assertEqual(self.enable_grad_func(1), 1)
        self.assertEqual(self.enable_grad_func.__name__, "enable_grad_func")

        def need_enable_grad_func(a, b=1):
            return a + b

        decorated_func = paddle.enable_grad()(need_enable_grad_func)
        self.assertEqual(
            str(inspect.getfullargspec(decorated_func)),
            str(inspect.getfullargspec(need_enable_grad_func)),
        )

        def test_gen():
            yield from range(3)

        a = 0
        for i in test_gen():
            a += i

        @paddle.enable_grad()
        def test_wrapped_gen():
            yield from range(3)

        b = 0
        for i in test_wrapped_gen():
            b += i

        self.assertEqual(a, b)

    def test_stop_gradient(self):
        x = paddle.to_tensor([1.0], stop_gradient=False)
        with paddle.no_grad():
            with paddle.enable_grad():
                y = x * 2
        self.assertTrue(y.stop_gradient is False)
        y.backward()
        self.assertTrue(x.grad is not None)

        # use as decorator
        @paddle.enable_grad()
        def double(x):
            return x * 2

        with paddle.no_grad():
            z = double(x)

        self.assertTrue(z.stop_gradient is False)


class TestSetGradEnabledClass(unittest.TestCase):
    @paddle.set_grad_enabled(True)
    def enable_grad_func(self, a):
        self.assertEqual(self.tracer._train_mode, True)
        self.assertEqual(self.tracer._has_grad, True)
        return a

    def test_main(self):
        paddle.disable_static()

        self.tracer = framework._dygraph_tracer()
        self.tracer._train_mode = True

        self.assertEqual(self.enable_grad_func(1), 1)
        self.assertEqual(self.enable_grad_func.__name__, "enable_grad_func")

        def need_enable_grad_func(a, b=1):
            return a + b

        decorated_func = paddle.set_grad_enabled(True)(need_enable_grad_func)
        self.assertEqual(
            str(inspect.getfullargspec(decorated_func)),
            str(inspect.getfullargspec(need_enable_grad_func)),
        )

        def test_gen():
            yield from range(3)

        a = 0
        for i in test_gen():
            a += i

        @paddle.set_grad_enabled(True)
        def test_wrapped_gen():
            yield from range(3)

        b = 0
        for i in test_wrapped_gen():
            b += i

        self.assertEqual(a, b)

    def test_stop_gradient(self):
        x = paddle.to_tensor([1.0], stop_gradient=False)
        is_train = False
        with paddle.set_grad_enabled(is_train):
            y = x * 2
        self.assertTrue(y.stop_gradient is True)

        paddle.set_grad_enabled(True)
        y = x * 2
        self.assertTrue(y.stop_gradient is False)

        paddle.set_grad_enabled(False)
        y = x * 2
        self.assertTrue(y.stop_gradient is True)


class TestIsGradEnabledClass(unittest.TestCase):
    def test_main(self):
        paddle.disable_static()

        self.tracer = framework._dygraph_tracer()
        self.tracer._train_mode = True
        self.tracer._has_grad = True

        # Dygraph gradient calculation mode is enabled by default.
        flag = paddle.is_grad_enabled()
        self.assertTrue(flag is True)

        with paddle.set_grad_enabled(False):
            flag = paddle.is_grad_enabled()
            self.assertTrue(flag is False)

        flag = paddle.is_grad_enabled()
        self.assertTrue(flag is True)
        paddle.enable_static()


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