# Copyright 2019 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 disorder """
import functools
import numpy as np
import mindspore.nn as nn
from mindspore import Tensor, Parameter
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from mindspore.common.parameter import ParameterTuple
from ..ut_filter import non_graph_engine
from ....mindspore_test_framework.mindspore_test import mindspore_test
from ....mindspore_test_framework.pipeline.forward.compile_forward \
    import pipeline_for_compile_forward_ge_graph_for_case_by_case_config


class DisOrderTest1(nn.Cell):
    """ DisOrderTest1 definition """

    def __init__(self):
        super(DisOrderTest1, self).__init__()
        weight = Tensor(np.ones([1], np.float32))
        self.s1 = Parameter(weight, name="s1")
        self.s2 = Parameter(weight, name="s2")
        self.s3 = Parameter(weight, name="s3")
        self.s4 = Parameter(weight, name="s4")
        self.mul = P.Mul()
        self.add = P.TensorAdd()

    def construct(self, x):
        return x * (self.s1 * self.s2 + self.s2 * self.s3 + self.s3 * self.s4 + self.s4 * self.s1)


class DisOrderTest2(nn.Cell):
    """ DisOrderTest2 definition """

    def __init__(self):
        super(DisOrderTest2, self).__init__()
        weight = Tensor(np.ones([1], np.float32))
        self.s1 = Parameter(weight, name="s1")
        self.s2 = Parameter(weight, name="s2")
        self.s3 = Parameter(weight, name="s3")
        self.s4 = Parameter(weight, name="s4")
        self.mul = P.Mul()
        self.add = P.TensorAdd()

    def construct(self, x):
        return self.mul(x, (self.add(self.add(self.add(self.mul(self.s1, self.s2), self.mul(self.s2, self.s3)),
                                              self.mul(self.s3, self.s4)), self.mul(self.s4, self.s1))))


class GradNetWrap(nn.Cell):
    """ GradNetWrap definition """

    def __init__(self, net):
        super(GradNetWrap, self).__init__()
        self.net = net
        self.weights = ParameterTuple(net.get_parameters())

    def construct(self, x, sens):
        return C.grad_by_list_with_sens(self.net, self.weights)(x, sens)


test_case_ops = [
    ('DisOrderTest1', {
        'block': GradNetWrap(DisOrderTest1()),
        'desc_inputs': [Tensor(np.ones([1], np.float32)), Tensor(np.ones([1], np.float32))]}),
    ('DisOrderTest2', {
        'block': GradNetWrap(DisOrderTest2()),
        'desc_inputs': [Tensor(np.ones([1], np.float32)), Tensor(np.ones([1], np.float32))]}),
]

test_case_lists = [test_case_ops]
test_exec_case = functools.reduce(lambda x, y: x + y, test_case_lists)
# use -k to select certain testcast
# pytest tests/python/ops/test_ops.py::test_backward -k LayerNorm


import mindspore.context as context


@non_graph_engine
@mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config)
def test_exec():
    context.set_context(mode=context.GRAPH_MODE)
    return test_exec_case