#   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 eager_op_test import OpTest
from scipy.special import expit, logit

import paddle
from paddle import fluid
from paddle.fluid import Program, program_guard


def loss_wrapper(
    logit, label, pos_weight=None, normalize=False, ignore_index=-100
):
    out = paddle._C_ops.sigmoid_cross_entropy_with_logits(
        logit, label, pos_weight, normalize, ignore_index
    )
    return out


class TestSigmoidCrossEntropyWithLogitsOp1(OpTest):
    """Test sigmoid_cross_entropy_with_logit_op with binary label"""

    def setUp(self):
        self.op_type = "sigmoid_cross_entropy_with_logits"
        self.python_api = loss_wrapper
        batch_size = 64
        num_classes = 20
        self.inputs = {
            'X': logit(
                np.random.uniform(0, 1, (batch_size, num_classes)).astype(
                    "float64"
                )
            ),
            'Label': np.random.randint(0, 2, (batch_size, num_classes)).astype(
                "float64"
            ),
        }

        # Fw Pass is implemented as elementwise sigmoid followed by
        # elementwise logistic loss
        # Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
        sigmoid_X = expit(self.inputs['X'])
        term1 = self.inputs['Label'] * np.log(sigmoid_X)
        term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
        self.outputs = {'Out': -term1 - term2}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class TestSigmoidCrossEntropyWithLogitsOp2(OpTest):
    """Test sigmoid_cross_entropy_with_logit_op with probabalistic label"""

    def setUp(self):
        self.op_type = "sigmoid_cross_entropy_with_logits"
        self.python_api = loss_wrapper
        batch_size = 64
        num_classes = 20
        ignore_index = -1
        self.inputs = {
            'X': logit(
                np.random.uniform(0, 1, (batch_size, num_classes)).astype(
                    "float64"
                )
            ),
            'Label': np.random.randint(-1, 2, (batch_size, num_classes)).astype(
                "float64"
            ),
        }
        self.attrs = {
            'ignore_index': ignore_index,
        }
        # Fw Pass is implemented as elementwise sigmoid followed by
        # elementwise logistic loss
        # Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
        sigmoid_X = expit(self.inputs['X'])
        term1 = self.inputs['Label'] * np.log(sigmoid_X)
        term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
        out = -term1 - term2
        out[np.where(self.inputs['Label'] == ignore_index)] = 0
        self.outputs = {'Out': out}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class TestSigmoidCrossEntropyWithLogitsOp3(OpTest):
    """Test sigmoid_cross_entropy_with_logit_op with probabalistic label"""

    def setUp(self):
        self.op_type = "sigmoid_cross_entropy_with_logits"
        self.python_api = loss_wrapper
        batch_size = 64
        num_classes = 20
        self.inputs = {
            'X': logit(
                np.random.uniform(0, 1, (batch_size, num_classes)).astype(
                    "float64"
                )
            ),
            'Label': np.random.uniform(0, 1, (batch_size, num_classes)).astype(
                "float64"
            ),
        }

        # Fw Pass is implemented as elementwise sigmoid followed by
        # elementwise logistic loss
        # Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
        sigmoid_X = expit(self.inputs['X'])
        term1 = self.inputs['Label'] * np.log(sigmoid_X)
        term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
        self.outputs = {'Out': -term1 - term2}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class TestSigmoidCrossEntropyWithLogitsOp4(OpTest):
    """Test sigmoid_cross_entropy_with_logit_op with probabalistic label"""

    def setUp(self):
        self.op_type = "sigmoid_cross_entropy_with_logits"
        self.python_api = loss_wrapper
        batch_size = 64
        num_classes = 20

        x = logit(
            np.random.uniform(0, 1, (batch_size, num_classes)).astype("float64")
        )
        label = np.random.uniform(0, 1, (batch_size, num_classes)).astype(
            "float64"
        )
        pos_weight = np.random.uniform(0, 1, (batch_size, num_classes)).astype(
            "float64"
        )
        self.inputs = {
            'X': x,
            'Label': label,
            'pos_weight': pos_weight,
        }

        # Fw Pass is implemented as elementwise sigmoid followed by
        # elementwise logistic loss
        term1 = np.maximum(self.inputs['X'], 0)
        term2 = self.inputs['X'] * self.inputs['Label']
        term3 = np.log(1 + np.exp(-1 * np.abs(self.inputs['X']))) * pos_weight
        self.outputs = {'Out': term1 - term2 + term3}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class TestSigmoidCrossEntropyWithNorm(OpTest):
    def setUp(self):
        self.op_type = "sigmoid_cross_entropy_with_logits"
        self.python_api = loss_wrapper
        batch_size = 64
        num_classes = 20
        ignore_index = -1
        self.inputs = {
            'X': logit(
                np.random.uniform(0, 1, (batch_size, num_classes)).astype(
                    "float64"
                )
            ),
            'Label': np.random.randint(-1, 2, (batch_size, num_classes)).astype(
                "float64"
            ),
        }
        self.attrs = {'ignore_index': ignore_index, 'normalize': True}
        sigmoid_X = expit(self.inputs['X'])
        term1 = self.inputs['Label'] * np.log(sigmoid_X)
        term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
        out = -term1 - term2
        out[np.where(self.inputs['Label'] == ignore_index)] = 0
        if self.attrs['normalize']:
            out = out / float(
                np.where(self.inputs['Label'] != ignore_index)[0].size
            )
        self.outputs = {'Out': out}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class TestSigmoidCrossEntropyWithLogitsOp5(OpTest):
    """Test sigmoid_cross_entropy_with_logit_op with probabalistic label"""

    def setUp(self):
        self.op_type = "sigmoid_cross_entropy_with_logits"
        self.python_api = loss_wrapper
        batch_size = [10, 10]
        num_classes = 20
        self.inputs = {
            'X': logit(
                np.random.uniform(
                    0, 1, tuple(batch_size + [num_classes])
                ).astype("float64")
            ),
            'Label': np.random.uniform(
                0, 1, tuple(batch_size + [num_classes])
            ).astype("float64"),
        }

        # Fw Pass is implemented as elementwise sigmoid followed by
        # elementwise logistic loss
        # Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
        sigmoid_X = expit(self.inputs['X'])
        term1 = self.inputs['Label'] * np.log(sigmoid_X)
        term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
        self.outputs = {'Out': -term1 - term2}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class TestSigmoidCrossEntropyWithNorm2(OpTest):
    def setUp(self):
        self.op_type = "sigmoid_cross_entropy_with_logits"
        self.python_api = loss_wrapper
        batch_size = [10, 10]
        num_classes = 20
        ignore_index = -1
        self.inputs = {
            'X': logit(
                np.random.uniform(
                    0, 1, tuple(batch_size + [num_classes])
                ).astype("float64")
            ),
            'Label': np.random.randint(
                -1, 2, tuple(batch_size + [num_classes])
            ).astype("float64"),
        }
        self.attrs = {'ignore_index': ignore_index, 'normalize': True}
        sigmoid_X = expit(self.inputs['X'])
        term1 = self.inputs['Label'] * np.log(sigmoid_X)
        term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
        out = -term1 - term2
        out[np.where(self.inputs['Label'] == ignore_index)] = 0
        if self.attrs['normalize']:
            out = out / float(
                np.where(self.inputs['Label'] != ignore_index)[0].size
            )
        self.outputs = {'Out': out}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')

    class TestSigmoidCrossEntropyWithLogitsOp6(OpTest):
        """Test sigmoid_cross_entropy_with_logit_op with binary label"""

        def setUp(self):
            self.op_type = "sigmoid_cross_entropy_with_logits"
            self.python_api = loss_wrapper
            batch_size = [10, 10]
            num_classes = 20
            self.inputs = {
                'X': logit(
                    np.random.uniform(
                        0, 1, tuple(batch_size + [num_classes])
                    ).astype("float64")
                ),
                'Label': np.random.randint(
                    0, 2, tuple(batch_size + [num_classes])
                ).astype("float64"),
            }

            # Fw Pass is implemented as elementwise sigmoid followed by
            # elementwise logistic loss
            # Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
            sigmoid_X = expit(self.inputs['X'])
            term1 = self.inputs['Label'] * np.log(sigmoid_X)
            term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
            self.outputs = {'Out': -term1 - term2}

        def test_check_output(self):
            self.check_output()

        def test_check_grad(self):
            self.check_grad(['X'], 'Out')

    class TestSigmoidCrossEntropyWithLogitsOpError(unittest.TestCase):
        def test_errors(self):
            with program_guard(Program(), Program()):

                def test_Variable():
                    # the input of sigmoid_cross_entropy_with_logits must be Variable.
                    x1 = fluid.create_lod_tensor(
                        np.array([-1, 3, 5, 5]),
                        [[1, 1, 1, 1]],
                        fluid.CPUPlace(),
                    )
                    lab1 = fluid.create_lod_tensor(
                        np.array([-1, 3, 5, 5]),
                        [[1, 1, 1, 1]],
                        fluid.CPUPlace(),
                    )
                    paddle.nn.functional.binary_cross_entropy_with_logits(
                        x1, lab1
                    )

                self.assertRaises(TypeError, test_Variable)

                def test_dtype():
                    # the input dtype of sigmoid_cross_entropy_with_logits must be float16 or float32 or float64
                    # float16 only can be set on GPU place
                    x2 = paddle.static.data(
                        name='x2', shape=[-1, 3, 4, 5, 6], dtype="int32"
                    )
                    lab2 = paddle.static.data(
                        name='lab2', shape=[-1, 3, 4, 5, 6], dtype="int32"
                    )
                    paddle.nn.functional.binary_cross_entropy_with_logits(
                        x2, lab2
                    )

                self.assertRaises(TypeError, test_dtype)


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