# 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_bprop """
import numpy as np
import mindspore.nn as nn
from mindspore import context
from mindspore.ops import operations as P
from mindspore.common.parameter import Parameter
from mindspore.common import Tensor
from ....mindspore_test_framework.utils.bprop_util import bprop
from mindspore.common.api import ms_function

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

class Net(nn.Cell):
    """ Net definition """
    def __init__(self):
        super(Net, self).__init__()
        self.matmul = P.MatMul()
        self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')

    @ms_function
    def construct(self, x, y):
        x = x * self.z
        out = self.matmul(x, y)
        return x, out

def test_bprop_no_sens():
    grads = bprop(Net(), Tensor(np.ones([2, 3]).astype(np.float32)),
                  Tensor(np.ones([3, 2]).astype(np.float32)), wrt=['inputs'])
    print(grads)

def test_bprop_sens():
    grads = bprop(Net(), Tensor(np.ones([2, 3]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32)),
                  grads_wrt_outputs=(Tensor(np.ones([2, 3]).astype(np.float32)),
                                     Tensor(np.ones([2, 2]).astype(np.float32))), wrt=['inputs'])
    print(grads)

def test_bprop_first_only():
    grads = bprop(Net(), Tensor(np.ones([2, 3]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32)),
                  grads_wrt_outputs=(Tensor(np.ones([2, 3]).astype(np.float32)),
                                     Tensor(np.ones([2, 2]).astype(np.float32))))
    print(grads)

def test_bprop_wrt_params():
    net = Net()
    grads = bprop(net, Tensor(np.ones([2, 3]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32)),
                  grads_wrt_outputs=(Tensor(np.ones([2, 3]).astype(np.float32)),
                                     Tensor(np.ones([2, 2]).astype(np.float32))),
                  wrt=['params'],
                  params=net.trainable_params())
    print(grads)

def test_bprop_wrt_params_no_sens():
    net = Net()
    grads = bprop(net, Tensor(np.ones([2, 3]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32)),
                  wrt=['params'],
                  params=net.trainable_params())
    print(grads)

def test_bprop_wrt_inputs_and_params():
    net = Net()
    grads = bprop(net, Tensor(np.ones([2, 3]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32)),
                  grads_wrt_outputs=(Tensor(np.ones([2, 3]).astype(np.float32)),
                                     Tensor(np.ones([2, 2]).astype(np.float32))),
                  wrt=['inputs', 'params'],
                  params=net.trainable_params())
    print(grads)
