#   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.
"""Test cloud role maker."""

from __future__ import print_function
import os
import unittest
import paddle.fluid.generator as generator

import time  # temp for debug
import paddle.fluid as fluid
import numpy as np
import paddle
import paddle.fluid.core as core


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "Only test cuda Random Generator")
class TestGeneratorSeed(unittest.TestCase):
    """
    Test cases for cpu generator seed.
    """

    def test_gen_dropout_dygraph(self):
        gen = paddle.seed(12343)

        fluid.enable_dygraph()

        gen.manual_seed(111111111)
        st = paddle.get_cuda_rng_state()

        x = fluid.layers.uniform_random([2, 10],
                                        dtype="float32",
                                        min=0.0,
                                        max=1.0)
        x_again = fluid.layers.uniform_random([2, 10],
                                              dtype="float32",
                                              min=0.0,
                                              max=1.0)
        x_third = fluid.layers.uniform_random([2, 10],
                                              dtype="float32",
                                              min=0.0,
                                              max=1.0)
        print("x: {}".format(x.numpy()))
        print("x_again: {}".format(x_again.numpy()))
        x = x + x_again + x_third
        y = fluid.layers.dropout(x, 0.5)

        paddle.set_cuda_rng_state(st)

        x1 = fluid.layers.uniform_random([2, 10],
                                         dtype="float32",
                                         min=0.0,
                                         max=1.0)
        x1_again = fluid.layers.uniform_random([2, 10],
                                               dtype="float32",
                                               min=0.0,
                                               max=1.0)
        x1_third = fluid.layers.uniform_random([2, 10],
                                               dtype="float32",
                                               min=0.0,
                                               max=1.0)
        x1 = x1 + x1_again + x1_third
        y1 = fluid.layers.dropout(x1, 0.5)
        y_np = y.numpy()
        y1_np = y1.numpy()

        if core.is_compiled_with_cuda():
            print(">>>>>>> dropout dygraph >>>>>>>")
            np.testing.assert_allclose(y_np, y1_np, rtol=1e-05)

    def test_generator_gaussian_random_dygraph(self):
        """Test Generator seed."""
        fluid.enable_dygraph()

        st = paddle.get_cuda_rng_state()
        x1 = paddle.randn([120], dtype="float32")
        paddle.set_cuda_rng_state(st)
        x2 = paddle.randn([120], dtype="float32")
        paddle.set_cuda_rng_state(st)
        x3 = paddle.randn([120], dtype="float32")

        x1_np = x1.numpy()
        x2_np = x2.numpy()
        x3_np = x3.numpy()

        if core.is_compiled_with_cuda():
            print(">>>>>>> gaussian random dygraph >>>>>>>")
            np.testing.assert_allclose(x1_np, x2_np, rtol=1e-05)
            np.testing.assert_allclose(x2_np, x3_np, rtol=1e-05)

    def test_generator_randint_dygraph(self):
        """Test Generator seed."""

        fluid.enable_dygraph()

        paddle.seed(12312321111)
        x = paddle.randint(low=10, shape=[10], dtype="int32")
        st1 = paddle.get_cuda_rng_state()
        x1 = paddle.randint(low=10, shape=[10], dtype="int32")
        paddle.set_cuda_rng_state(st1)
        x2 = paddle.randint(low=10, shape=[10], dtype="int32")
        paddle.seed(12312321111)
        x3 = paddle.randint(low=10, shape=[10], dtype="int32")
        x_np = x.numpy()
        x1_np = x1.numpy()
        x2_np = x2.numpy()
        x3_np = x3.numpy()

        if core.is_compiled_with_cuda():
            print(">>>>>>> randint dygraph >>>>>>>")
            np.testing.assert_allclose(x_np, x3_np, rtol=1e-05)

    def test_gen_TruncatedNormal_initializer(self):
        fluid.disable_dygraph()

        gen = paddle.seed(123123143)
        cur_state = paddle.get_cuda_rng_state()

        startup_program = fluid.Program()
        train_program = fluid.Program()
        with fluid.program_guard(train_program, startup_program):
            # example 1:
            # attr shape is a list which doesn't contain tensor Variable.
            x = fluid.layers.uniform_random(shape=[2, 10])
            result_1 = fluid.layers.fc(
                input=x,
                size=10,
                param_attr=fluid.initializer.TruncatedNormal(loc=0.0,
                                                             scale=2.0))
            result_2 = fluid.layers.fc(
                input=x,
                size=10,
                param_attr=fluid.initializer.TruncatedNormal(loc=0.0,
                                                             scale=2.0))

            exe = fluid.Executor(fluid.CPUPlace())
            exe.run(startup_program)
            out1 = exe.run(train_program,
                           feed={},
                           fetch_list=[result_1, result_2])

        paddle.seed(123123143)
        with fluid.program_guard(train_program, startup_program):
            exe.run(startup_program)
            out2 = exe.run(train_program,
                           feed={},
                           fetch_list=[result_1, result_2])

        out1_res1 = np.array(out1[0])
        out1_res2 = np.array(out1[1])
        out2_res1 = np.array(out2[0])
        out2_res2 = np.array(out2[1])

        if core.is_compiled_with_cuda():
            print(">>>>>>> truncated normal static >>>>>>>")
            np.testing.assert_allclose(out1_res1, out2_res1, rtol=1e-05)
            np.testing.assert_allclose(out1_res2, out2_res2, rtol=1e-05)
            self.assertTrue(not np.allclose(out1_res2, out1_res1))


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