#   Copyright (c) 2021 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 abc
import unittest

import numpy as np
from scipy.special import erf

from paddle.fluid import core
from paddle.fluid.tests.unittests.eager_op_test import (
    OpTestTool,
    convert_float_to_uint16,
)
from paddle.fluid.tests.unittests.test_activation_op import TestActivation
from paddle.fluid.tests.unittests.test_gelu_op import gelu


@OpTestTool.skip_if_not_cpu_bf16()
class MKLDNNBF16ActivationOp(metaclass=abc.ABCMeta):
    @abc.abstractmethod
    def config(self):
        pass

    @abc.abstractmethod
    def op_forward(self, x):
        pass

    @abc.abstractmethod
    def op_grad(self, dout, x):
        pass

    def set_attrs(self):
        self.attrs = {"use_mkldnn": True}

    def init_data(self):
        self.x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype(np.float32)

    def setUp(self):
        self.dtype = np.uint16
        self.init_data()
        self.config()
        self.set_attrs()
        self.out = self.op_forward(self.x)

        self.inputs = {'X': convert_float_to_uint16(self.x)}
        self.outputs = {'Out': self.out}

    def calculate_grads(self):
        self.dx = self.op_grad(self.out, self.x)

    def test_check_output(self):
        self.check_output_with_place(core.CPUPlace())

    def test_check_grad(self):
        self.calculate_grads()
        self.check_grad_with_place(
            core.CPUPlace(),
            ["X"],
            "Out",
            user_defined_grads=[self.dx],
            user_defined_grad_outputs=[convert_float_to_uint16(self.out)],
        )


class TestMKLDNNSigmoidBF16Op(MKLDNNBF16ActivationOp, TestActivation):
    def config(self):
        self.op_type = "sigmoid"

    def op_forward(self, x):
        return 1 / (1 + np.exp(-x))

    def op_grad(self, dout, x):
        return dout * self.op_forward(x) * (1 - self.op_forward(x))


class TestMKLDNNSqrtBF16Op(MKLDNNBF16ActivationOp, TestActivation):
    def config(self):
        self.op_type = "sqrt"

    def init_data(self):
        self.x = np.random.uniform(1, 2, [2, 4, 3, 5]).astype(np.float32)

    def op_forward(self, x):
        return np.sqrt(x)

    def op_grad(self, dout, x):
        return dout / (2 * np.sqrt(x))


class TestMKLDNNGeluErfBF16Op(MKLDNNBF16ActivationOp, TestActivation):
    def config(self):
        self.op_type = "gelu"

    def op_forward(self, x):
        return gelu(x, False)

    def op_grad(self, dout, x):
        return dout * (
            0.5
            + 0.5 * erf(x / np.sqrt(2))
            + (x / np.sqrt(2 * np.pi) * np.exp(-0.5 * np.power(x, 2)))
        )


class TestMKLDNNGeluErfDim2BF16Op(TestMKLDNNGeluErfBF16Op):
    def init_data(self):
        self.x = np.random.uniform(-1, 1, [11, 17]).astype(np.float32)


class TestMKLDNNGeluTanhBF16Op(MKLDNNBF16ActivationOp, TestActivation):
    def config(self):
        self.op_type = "gelu"

    def op_forward(self, x):
        return gelu(x, True)

    def op_grad(self, dout, x):
        grad_part = np.tanh(
            np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3))
        )
        return (
            dout
            * 0.5
            * (1 + grad_part)
            * (
                1
                + np.sqrt(2 / np.pi)
                * (x + 0.134145 * np.power(x, 3))
                * (1 - grad_part)
            )
        )

    def set_attrs(self):
        self.attrs = {"use_mkldnn": True, "approximate": True}


class TestMKLDNNGeluTanhDim2BF16Op(TestMKLDNNGeluTanhBF16Op):
    def init_data(self):
        self.x = np.random.uniform(-1, 1, [11, 17]).astype(np.float32)


class TestMKLDNNReluBF16Op(MKLDNNBF16ActivationOp, TestActivation):
    def config(self):
        self.op_type = "relu"

    def op_forward(self, x):
        return np.maximum(x, 0)

    def op_grad(self, dout, x):
        return dout


class TestMKLDNNMishBF16Op(MKLDNNBF16ActivationOp, TestActivation):
    def config(self):
        self.op_type = "mish"

    def op_forward(self, x):
        return x * np.tanh(np.log(1 + np.exp(x)))

    def op_grad(self, dout, x):
        omega = (
            np.exp(3 * x)
            + 4 * np.exp(2 * x)
            + np.exp(x) * (4 * x + 6)
            + 4 * (x + 1)
        )
        delta = np.exp(2 * x) + 2 * np.exp(x) + 2
        return dout * ((np.exp(x) * omega) / delta**2)


class TestMKLDNNRelu6BF16Op(MKLDNNBF16ActivationOp, TestActivation):
    def config(self):
        self.op_type = "relu6"

    def op_forward(self, x):
        return np.clip(x, 0, 6)

    def op_grad(self, dout, x):
        return np.where((x > 0) & (x <= 6), dout, 0)


class TestMKLDNNLeakyReluBF16Op(MKLDNNBF16ActivationOp, TestActivation):
    def config(self):
        self.op_type = "leaky_relu"

    def op_forward(self, x):
        return np.where(x > 0, x, self.alpha * x)

    def op_grad(self, dout, x):
        return np.where(x > 0, dout, self.alpha * dout)

    def set_attrs(self):
        self.alpha = 0.2
        self.attrs = {"use_mkldnn": True, "alpha": self.alpha}


class TestMKLDNNSwishBF16Op(MKLDNNBF16ActivationOp, TestActivation):
    def config(self):
        self.op_type = "swish"

    def expit(self, val):
        return 1 / (1 + np.exp(-self.beta * val))

    def op_forward(self, x):
        return x * self.expit(x)

    def op_grad(self, dout, x):
        return dout * self.expit(x) * (1 + self.beta * x * (1 - self.expit(x)))

    def set_attrs(self):
        self.beta = 0.2
        self.attrs = {"use_mkldnn": True, "beta": self.beta}


class TestMKLDNNHardSwishBF16Op(MKLDNNBF16ActivationOp, TestActivation):
    def config(self):
        self.op_type = "hard_swish"

    def op_forward(self, x):
        result = np.where(x < -3, 0, x)
        return np.where(result > 3, result, result * (result + 3) / 6)

    def op_grad(self, dout, x):
        result = np.where(x < -3, 0, x)
        return np.where(result > 3, dout, dout * (2 * x + 3) / 6)


class TestMKLDNNTanhBF16Op(MKLDNNBF16ActivationOp, TestActivation):
    def config(self):
        self.op_type = "tanh"

    def op_forward(self, x):
        return np.tanh(x)

    def op_grad(self, dout, x):
        return dout * (1 - np.tanh(x) ** 2)


class TestMKLDNNAbsBF16Op(MKLDNNBF16ActivationOp, TestActivation):
    def config(self):
        self.op_type = "abs"

    def op_forward(self, x):
        return np.absolute(x)

    def op_grad(self, dout, x):
        return dout * np.sign(x)


class TestMKLDNNEluBF16Op(MKLDNNBF16ActivationOp, TestActivation):
    def config(self):
        self.op_type = "elu"

    def op_forward(self, x):
        return np.where(x > 0, x, self.alpha * (np.exp(x) - 1))

    def op_grad(self, dout, x):
        return np.where(x > 0, dout, dout * self.alpha * np.exp(x))

    def set_attrs(self):
        self.alpha = 0.2
        self.attrs = {"use_mkldnn": True, "alpha": self.alpha}


class TestMKLDNNExpBF16Op(MKLDNNBF16ActivationOp, TestActivation):
    def config(self):
        self.op_type = "exp"

    def op_forward(self, x):
        return np.exp(x)

    def op_grad(self, dout, x):
        return dout * np.exp(x)


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