# 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 paddle
import paddle.fluid as fluid
import numpy as np
import unittest
import sys

sys.path.append('..')
from op_test import OpTest
from test_bce_with_logits_loss import call_bce_layer, call_bce_functional, test_dygraph, calc_bce_with_logits_loss


def test_static(place,
                logit_np,
                label_np,
                weight_np=None,
                reduction='mean',
                pos_weight_np=None,
                functional=False):
    paddle.enable_static()
    prog = paddle.static.Program()
    startup_prog = paddle.static.Program()
    with paddle.static.program_guard(prog, startup_prog):
        logit = paddle.fluid.data(name='logit',
                                  shape=logit_np.shape,
                                  dtype='float32')
        label = paddle.fluid.data(name='label',
                                  shape=label_np.shape,
                                  dtype='float32')
        feed_dict = {"logit": logit_np, "label": label_np}

        pos_weight = None
        weight = None
        if pos_weight_np is not None:
            pos_weight = paddle.fluid.data(name='pos_weight',
                                           shape=pos_weight_np.shape,
                                           dtype='float32')
            feed_dict["pos_weight"] = pos_weight_np
        if weight_np is not None:
            weight = paddle.fluid.data(name='weight',
                                       shape=weight_np.shape,
                                       dtype='float32')
            feed_dict["weight"] = weight_np
        if functional:
            res = call_bce_functional(logit, label, weight, reduction,
                                      pos_weight)
        else:
            res = call_bce_layer(logit, label, weight, reduction, pos_weight)
        exe = paddle.static.Executor(place)
        static_result = exe.run(prog, feed=feed_dict, fetch_list=[res])
    return static_result[0]


paddle.enable_static()


class TestBCEWithLogitsLoss(unittest.TestCase):

    def test_BCEWithLogitsLoss(self):
        logit_np = np.random.uniform(0.1, 0.8, size=(20, 30)).astype(np.float32)
        label_np = np.random.randint(0, 2, size=(20, 30)).astype(np.float32)
        places = [fluid.MLUPlace(0)]
        reductions = ['sum', 'mean', 'none']
        for place in places:
            for reduction in reductions:
                static_result = test_static(place,
                                            logit_np,
                                            label_np,
                                            reduction=reduction)
                dy_result = test_dygraph(place,
                                         logit_np,
                                         label_np,
                                         reduction=reduction)
                expected = calc_bce_with_logits_loss(logit_np, label_np,
                                                     reduction)
                np.testing.assert_allclose(static_result, expected, rtol=1e-6)
                np.testing.assert_allclose(static_result, dy_result)
                np.testing.assert_allclose(dy_result, expected, rtol=1e-6)
                static_functional = test_static(place,
                                                logit_np,
                                                label_np,
                                                reduction=reduction,
                                                functional=True)
                dy_functional = test_dygraph(place,
                                             logit_np,
                                             label_np,
                                             reduction=reduction,
                                             functional=True)

                np.testing.assert_allclose(static_functional,
                                           expected,
                                           rtol=1e-6)
                np.testing.assert_allclose(static_functional, dy_functional)
                np.testing.assert_allclose(dy_functional, expected, rtol=1e-6)

    def test_BCEWithLogitsLoss_weight(self):
        logit_np = np.random.uniform(0.1, 0.8,
                                     size=(2, 3, 4, 10)).astype(np.float32)
        label_np = np.random.randint(0, 2,
                                     size=(2, 3, 4, 10)).astype(np.float32)
        weight_np = np.random.random(size=(2, 3, 4, 10)).astype(np.float32)
        place = fluid.MLUPlace(0)
        for reduction in ['sum', 'mean', 'none']:
            static_result = test_static(place,
                                        logit_np,
                                        label_np,
                                        weight_np=weight_np,
                                        reduction=reduction)
            dy_result = test_dygraph(place,
                                     logit_np,
                                     label_np,
                                     weight_np=weight_np,
                                     reduction=reduction)
            expected = calc_bce_with_logits_loss(logit_np,
                                                 label_np,
                                                 reduction,
                                                 weight_np=weight_np)
            np.testing.assert_allclose(static_result, expected, rtol=1e-6)
            np.testing.assert_allclose(static_result, dy_result)
            np.testing.assert_allclose(dy_result, expected, rtol=1e-6)
            static_functional = test_static(place,
                                            logit_np,
                                            label_np,
                                            weight_np=weight_np,
                                            reduction=reduction,
                                            functional=True)
            dy_functional = test_dygraph(place,
                                         logit_np,
                                         label_np,
                                         weight_np=weight_np,
                                         reduction=reduction,
                                         functional=True)
            np.testing.assert_allclose(static_functional, expected, rtol=1e-6)
            np.testing.assert_allclose(static_functional, dy_functional)
            np.testing.assert_allclose(dy_functional, expected, rtol=1e-6)

    def test_BCEWithLogitsLoss_pos_weight(self):
        logit_np = np.random.uniform(0.1, 0.8,
                                     size=(2, 3, 4, 10)).astype(np.float32)
        label_np = np.random.randint(0, 2,
                                     size=(2, 3, 4, 10)).astype(np.float32)
        pos_weight_np = np.random.random(size=(3, 4, 10)).astype(np.float32)
        weight_np = np.random.random(size=(2, 3, 4, 10)).astype(np.float32)
        place = fluid.MLUPlace(0)
        reduction = "mean"
        static_result = test_static(place, logit_np, label_np, weight_np,
                                    reduction, pos_weight_np)
        dy_result = test_dygraph(place, logit_np, label_np, weight_np,
                                 reduction, pos_weight_np)
        expected = calc_bce_with_logits_loss(logit_np, label_np, reduction,
                                             weight_np, pos_weight_np)
        np.testing.assert_allclose(static_result, expected)
        np.testing.assert_allclose(static_result, dy_result)
        np.testing.assert_allclose(dy_result, expected)
        static_functional = test_static(place,
                                        logit_np,
                                        label_np,
                                        weight_np,
                                        reduction,
                                        pos_weight_np,
                                        functional=True)
        dy_functional = test_dygraph(place,
                                     logit_np,
                                     label_np,
                                     weight_np,
                                     reduction,
                                     pos_weight_np,
                                     functional=True)
        np.testing.assert_allclose(static_functional, expected)
        np.testing.assert_allclose(static_functional, dy_functional)
        np.testing.assert_allclose(dy_functional, expected)

    def test_BCEWithLogitsLoss_error(self):
        paddle.disable_static()
        self.assertRaises(ValueError,
                          paddle.nn.BCEWithLogitsLoss,
                          reduction="unsupport reduction")
        logit = paddle.to_tensor([[0.1, 0.3]], dtype='float32')
        label = paddle.to_tensor([[0.0, 1.0]], dtype='float32')
        self.assertRaises(ValueError,
                          paddle.nn.functional.binary_cross_entropy_with_logits,
                          logit=logit,
                          label=label,
                          reduction="unsupport reduction")
        paddle.enable_static()


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