#   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.
import unittest

import numpy as np
from op_test import get_device_place, is_custom_device

import paddle
from paddle import base


class API_Test_Nansum(unittest.TestCase):
    def test_static_graph(self):
        paddle.enable_static()
        startup_program = paddle.static.Program()
        train_program = paddle.static.Program()
        with paddle.static.program_guard(train_program, startup_program):
            input = paddle.static.data(
                name='input', dtype='float32', shape=[2, 4]
            )
            out1 = paddle.nansum(input)
            out2 = paddle.nansum(input, axis=0)
            out3 = paddle.nansum(input, axis=-1)
            out4 = paddle.nansum(input, axis=1, keepdim=True)
            place = base.CPUPlace()
            if base.core.is_compiled_with_cuda() or is_custom_device():
                place = get_device_place()
            exe = base.Executor(place)
            exe.run(startup_program)

            x = np.array(
                [[float('nan'), 3, 5, 9], [1, 2, float('-nan'), 7]]
            ).astype(np.float32)
            res = exe.run(
                train_program,
                feed={'input': x},
                fetch_list=[out1, out2, out3, out4],
            )

            out1_np = np.array(res[0])
            out2_np = np.array(res[1])
            out3_np = np.array(res[2])
            out4_np = np.array(res[3])
            out1_ref = np.array([27]).astype(np.float32)
            out2_ref = np.array([1, 5, 5, 16]).astype(np.float32)
            out3_ref = np.array([17, 10]).astype(np.float32)
            out4_ref = np.array([[17], [10]]).astype(np.float32)

            self.assertTrue(
                (out1_np == out1_ref).all(),
                msg='nansum output is wrong, out =' + str(out1_np),
            )
            self.assertTrue(
                (out2_np == out2_ref).all(),
                msg='nansum output is wrong, out =' + str(out2_np),
            )
            self.assertTrue(
                (out3_np == out3_ref).all(),
                msg='nansum output is wrong, out =' + str(out3_np),
            )
            self.assertTrue(
                (out4_np == out4_ref).all(),
                msg='nansum output is wrong, out =' + str(out4_np),
            )

    # test nansum api with float16

    def test_static_graph_fp16(self):
        if not (base.core.is_compiled_with_cuda() or is_custom_device()):
            return
        paddle.enable_static()
        startup_program = paddle.static.Program()
        train_program = paddle.static.Program()
        with paddle.static.program_guard(train_program, startup_program):
            input = paddle.static.data(
                name='input', dtype='float16', shape=[2, 4]
            )
            out1 = paddle.nansum(input)
            out2 = paddle.nansum(input, axis=0)
            out3 = paddle.nansum(input, axis=-1)
            out4 = paddle.nansum(input, axis=1, keepdim=True)
            place = get_device_place()
            exe = paddle.static.Executor(place)
            exe.run(startup_program)

            x = np.array(
                [[float('nan'), 3, 5, 9], [1, 2, float('-nan'), 7]]
            ).astype(np.float16)
            res = exe.run(
                train_program,
                feed={'input': x},
                fetch_list=[out1, out2, out3, out4],
            )

            out1_np = np.array(res[0])
            out2_np = np.array(res[1])
            out3_np = np.array(res[2])
            out4_np = np.array(res[3])
            out1_ref = np.array([27]).astype(np.float16)
            out2_ref = np.array([1, 5, 5, 16]).astype(np.float16)
            out3_ref = np.array([17, 10]).astype(np.float16)
            out4_ref = np.array([[17], [10]]).astype(np.float16)

            self.assertTrue(
                (out1_np == out1_ref).all(),
                msg='nansum output is wrong, out =' + str(out1_np),
            )
            self.assertTrue(
                (out2_np == out2_ref).all(),
                msg='nansum output is wrong, out =' + str(out2_np),
            )
            self.assertTrue(
                (out3_np == out3_ref).all(),
                msg='nansum output is wrong, out =' + str(out3_np),
            )
            self.assertTrue(
                (out4_np == out4_ref).all(),
                msg='nansum output is wrong, out =' + str(out4_np),
            )

    def test_dygraph(self):
        x = np.array(
            [[float('nan'), 3, 5, 9], [1, 2, float('-nan'), 7]]
        ).astype(np.float32)
        with base.dygraph.guard():
            inputs = paddle.to_tensor(x)
            out = paddle.nansum(inputs)
            out_ref = np.array([27]).astype(np.float32)

            self.assertTrue(
                (out.numpy() == out_ref).all(),
                msg='nansum output is wrong, out =' + str(out.numpy()),
            )


class API_Test_Nansum_ZeroSize(unittest.TestCase):
    def test_dygraph(self):
        x = np.random.random([2, 0, 3]).astype(np.float32)
        with base.dygraph.guard():
            inputs = paddle.to_tensor(x)
            inputs.stop_gradient = False
            out = paddle.nansum(inputs)
            out_ref = np.nansum(x).astype(np.float32)

            self.assertTrue(
                (out.numpy() == out_ref).all(),
                msg='nansum output is wrong, out =' + str(out.numpy()),
            )

            # check grad shape
            loss = paddle.sum(out)
            loss.backward()
            np.testing.assert_allclose(inputs.grad.shape, inputs.shape)


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