# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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_cell_bprop """
import numpy as np
import mindspore.nn as nn
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from mindspore import Parameter
from mindspore.common.tensor import Tensor
import mindspore.common.dtype as mstype
from mindspore.common.initializer import initializer
from mindspore import context
from ....mindspore_test_framework.utils.bprop_util import bprop

import pytest


def setup_module(module):
    context.set_context(mode=context.PYNATIVE_MODE)


class MulAdd(nn.Cell):
    def __init__(self):
        super(MulAdd, self).__init__()

    def construct(self, x, y):
        return 2 * x + y

    def bprop(self, x, y, out, dout):
        # In this test case, The user defined bprop is wrong defined purposely to distinguish from ad result
        return 2 * dout, 2 * y

def test_grad_mul_add():
    mul_add = MulAdd()
    assert C.grad_all(mul_add)(1, 2) == (2, 4)


class InlineMulADD(nn.Cell):
    def __init__(self):
        super(InlineMulADD, self).__init__()
        self.mul_add = MulAdd()
        self.param = Parameter(2, 'param')

    def construct(self, x, y):
        return self.mul_add(x, y) + x + self.param * y

def test_grad_inline_mul_add():
    inline_mul_add = InlineMulADD()
    assert C.grad_all(inline_mul_add)(1, 2) == (3, 6)


class WithParameter(nn.Cell):
    def __init__(self):
        super(WithParameter, self).__init__()
        self.param = Parameter(2, 'param')

    def construct(self, x, y):
        return self.param * x + y

    def bprop(self, x, y, out, dout):
        # In this test case, The user defined bprop is wrong defined purposely to distinguish from ad result
        return self.param * dout, 2 * y

def test_with_param():
    with_param = WithParameter()
    with pytest.raises(RuntimeError):
        C.grad_all(with_param)(1, 2)

class WithNoBprop(nn.Cell):
    def __init__(self):
        super(WithNoBprop, self).__init__()

    def construct(self, x, y):
        return 2 * x + y

def test_with_no_bprop():
    with_no_bprop = WithNoBprop()
    C.grad_all(with_no_bprop)(1, 2) == (2, 1)

def test_grad_in_bprop_1():
    class GradInBprop_1(nn.Cell):
        def __init__(self):
            super(GradInBprop_1, self).__init__()
            self.relu = P.ReLU()
        def construct(self, x, y):
            return self.relu(x)
    class GradInBprop_2(nn.Cell):
        def __init__(self):
            super(GradInBprop_2, self).__init__()
            self.f = GradInBprop_1()
        def construct(self, x, y):
            return self.f(x, y), C.grad_all(self.f)(x, y)
        def bprop(self, x, y, out, dout):
            grads = C.grad_all(self.f)(x, y)
            return out[1][0], grads[1]
    class GradInBprop_3(nn.Cell):
        def __init__(self):
            super(GradInBprop_3, self).__init__()
            self.f = GradInBprop_2()
        def construct(self, x, y):
            return self.f(x, y)
    grad_in_bprop = GradInBprop_3()
    grads = C.grad_all(grad_in_bprop)(Tensor(np.ones([2, 2]).astype(np.float32)),
                                      Tensor(np.ones([2, 2]).astype(np.float32)))
    assert (grads[0].asnumpy() == np.ones([2, 2]).astype(np.float32)).all()
    assert (grads[1].asnumpy() == np.zeros([2, 2]).astype(np.float32)).all()

def test_grad_in_bprop_2():
    class GradInBprop_1(nn.Cell):
        def __init__(self):
            super(GradInBprop_1, self).__init__()
            self.relu = P.ReLU()
        def construct(self, x, y):
            return self.relu(x)
        def bprop(self, x, y, out, dout):
            return x * y, y + x
    class GradInBprop_2(nn.Cell):
        def __init__(self):
            super(GradInBprop_2, self).__init__()
            self.f = GradInBprop_1()
        def construct(self, x, y):
            return self.f(x, y), C.grad_all(self.f)(x, y)
        def bprop(self, x, y, out, dout):
            grads = C.grad_all(self.f)(x, y)
            return out[1][0], grads[1]
    class GradInBprop_3(nn.Cell):
        def __init__(self):
            super(GradInBprop_3, self).__init__()
            self.f = GradInBprop_2()
        def construct(self, x, y):
            return self.f(x, y)
    grad_in_bprop = GradInBprop_3()
    grads = C.grad_all(grad_in_bprop)(Tensor(np.ones([2, 2]).astype(np.float32)),
                                      Tensor(np.ones([2, 2]).astype(np.float32)))
    assert (grads[0].asnumpy() == np.ones([2, 2]).astype(np.float32)).all()
    assert (grads[1].asnumpy() == np.array([[2, 2], [2, 2]]).astype(np.float32)).all()

def test_grad_in_bprop_3():
    class GradInBprop_1(nn.Cell):
        def __init__(self):
            super(GradInBprop_1, self).__init__()
            self.relu = P.ReLU()
        def construct(self, x, y):
            return self.relu(x)
    class GradInBprop_2(nn.Cell):
        def __init__(self):
            super(GradInBprop_2, self).__init__()
            self.f = GradInBprop_1()
        def construct(self, x, y):
            return self.f(x, y), C.grad_all(self.f)(x, y)
        def bprop(self, x, y, out, dout):
            grads = C.grad_all(self.f)(x, y)
            return out[1][0], grads[1]
    class GradInBprop_3(nn.Cell):
        def __init__(self):
            super(GradInBprop_3, self).__init__()
            self.f = GradInBprop_2()
        def construct(self, x, y):
            return self.f(x, y)
        def bprop(self, x, y, out, dout):
            return x + y + y + out[0], x + x + y + y + dout[0]
    grad_in_bprop = GradInBprop_3()
    grads = C.grad_all(grad_in_bprop)(Tensor(np.ones([2, 2]).astype(np.float32)),
                                      Tensor(np.ones([2, 2]).astype(np.float32)))
    assert (grads[0].asnumpy() == np.array([[4, 4], [4, 4]]).astype(np.float32)).all()
    assert (grads[1].asnumpy() == np.array([[5, 5], [5, 5]]).astype(np.float32)).all()

class OneInputBprop(nn.Cell):
    def __init__(self):
        super().__init__()
        self.op = P.ReLU()
    def construct(self, x):
        return self.op(x)
    def bprop(self, x, out, dout):
        return 5 * x,

def test_grad_one_input_bprop():
    net = OneInputBprop()
    input = Tensor(np.ones([2, 2]).astype(np.float32))
    grad = C.grad_all(net)(input)
    assert (grad[0].asnumpy() == np.array([5, 5]).astype(np.float32)).all()


class TwoInput(nn.Cell):
    def __init__(self):
        super().__init__()
    def construct(self, x, y):
        return x * y

class InlineBpropTwoInput(nn.Cell):
    def __init__(self):
        super().__init__()
        self.f = TwoInput()
    def construct(self, x, y):
        return self.f(x, y), C.grad_all(self.f)(x, y)
    def bprop(self, x, y, out, dout):
        grads = C.grad_all(self.f)(x, y)
        return grads[0] * 2, grads[1] * 2

def test_grad_inline_bprop_two_input():
    net = InlineBpropTwoInput()
    input1 = Tensor(np.ones([2, 2]).astype(np.float32))
    input2 = Tensor(np.ones([2, 2]).astype(np.float32))
    grads = C.grad_all(net)(input1, input2)
    assert (grads[0].asnumpy() == np.array([2, 2]).astype(np.float32)).all()
    assert (grads[1].asnumpy() == np.array([2, 2]).astype(np.float32)).all()
    assert (len(grads) == 2)


class TwoInputBprop(nn.Cell):
    def __init__(self):
        super().__init__()
        self.op = P.Mul()
    def construct(self, x, y):
        return self.op(x, y)
    def bprop(self, x, y, out, dout):
        return 5 * x, 8 * y

class TwoInput(nn.Cell):
    def __init__(self):
        super().__init__()
        self.op = P.Mul()
    def construct(self, x, y):
        return  self.op(x, y)

class TwoInputWithParameter(nn.Cell):
    def __init__(self):
        super().__init__()
        self.op = P.Mul()
        self.inputdata = Parameter(initializer(1, (2,2), mstype.float32),name="global_step")
    def construct(self, x, y):
        x = self.inputdata + x
        return  self.op(x, y)

class TwoInputWithOnlyInitParameterBprop(nn.Cell):
    def __init__(self):
        super().__init__()
        self.op = P.Mul()
        self.inputdata = Parameter(initializer(1, (2,2), mstype.float32),name="global_step")
    def construct(self, x, y):
        return  self.op(x, y)
    def bprop(self, x, y, out, dout):
        return 5*x, 8*y

class InlineMutilTwoInputParameterCell(nn.Cell):
    def __init__(self):
        super().__init__()
        self.f1 = TwoInputBprop()
        self.f2 = TwoInput()
        self.f3 = TwoInputWithParameter()
        self.f4 = TwoInputWithOnlyInitParameterBprop()
    def construct(self, x, y):
        output = self.f1(x,y)+self.f2(x,y)+self.f3(x,y)+self.f4(x,y)
        return output

def test_grad_inline_bprop_multi_input():
    net = InlineMutilTwoInputParameterCell()
    input1 = Tensor(np.ones([2, 2]).astype(np.float32))
    input2 = Tensor(np.ones([2, 2]).astype(np.float32))
    grads = C.grad_all(net)(input1, input2)
    assert (grads[0].asnumpy() == np.array([[12, 12], [12, 12]]).astype(np.float32)).all()
    assert (grads[1].asnumpy() == np.array([[19, 19], [19, 19]]).astype(np.float32)).all()
    assert (len(grads) == 2)

class MulAddWithParam(nn.Cell):
    def __init__(self):
        super(MulAddWithParam, self).__init__()
        self.mul_add = MulAdd()
        self.param = Parameter(Tensor(np.array([[3, 2]], np.float32)), 'param')
    def construct(self, x):
        return self.mul_add(self.param, x)


def test_refkey_bprop():
    net = MulAddWithParam()
    input_data = Tensor(np.array([2, 2], np.float32))
    grads = bprop(net, input_data,
                  grads_wrt_outputs=(Tensor(np.ones([1, 2]).astype(np.float32))),
                  wrt=['params', 'inputs'],
                  params=net.trainable_params())
    assert (grads[0][0].asnumpy() == np.array([4, 4]).astype(np.float32)).all()
    assert (grads[1][0].asnumpy() == np.array([2, 2]).astype(np.float32)).all()


class MulAddWithWrongOutputNum(nn.Cell):
    def __init__(self):
        super(MulAddWithWrongOutputNum, self).__init__()
    def construct(self, x, y):
        return 2 * x + y
    def bprop(self, x, y, out, dout):
        return 2 * dout, 2 * y, out

def test_grad_mul_add_with_wrong_output_num():
    mul_add = MulAddWithWrongOutputNum()
    C.grad_all(mul_add)(1, 2)