# 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.
# ============================================================================
import mindspore.common.dtype as mstype
import mindspore.common._monad as monad
from mindspore.common.tensor import Tensor
from mindspore.ops import Primitive
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.ops import _constants as Constants

AssignSub = P.AssignSub()
Mul = P.Mul()
Sub = P.Sub()
make_tuple = Primitive('MakeTuple')
tuple_getitem = Primitive(Constants.kTupleGetItem)
update_state = Primitive('UpdateState')
U = monad.U
BatchNorm = P.BatchNorm()
Cast = P.Cast()
BNTrainingReduce = Primitive('BNTrainingReduce')
BNTrainingUpdate = Primitive('BNTrainingUpdate')
constant0 = Tensor(0.1, mstype.float32)
constant1 = Tensor(0.1, mstype.float32)


class FnDict:
    def __init__(self):
        self.fnDict = {}

    def __call__(self, fn):
        self.fnDict[fn.__name__] = fn

    def __getitem__(self, name):
        return self.fnDict[name]


def test_fused_batch_norm_fusion(tag):
    fns = FnDict()

    @fns
    def before(input0, input1, input2, input3, input4, var0, var1):
        batch_norm = BatchNorm(input0, input1, input2, input3, input4)
        sub0 = Sub(var0, tuple_getitem(batch_norm, 1))
        sub1 = Sub(var1, tuple_getitem(batch_norm, 2))
        mul0 = Mul(sub0, constant0)
        mul1 = Mul(sub1, constant1)
        assign_sub0 = AssignSub(var0, mul0, U)
        u0 = update_state(U, assign_sub0)
        assign_sub1 = AssignSub(var1, mul1, u0)
        u1 = update_state(u0, assign_sub1)
        depend0 = F.depend(tuple_getitem(batch_norm, 0), assign_sub0)
        depend1 = F.depend(depend0, assign_sub1)
        outputs = make_tuple(depend1, tuple_getitem(batch_norm, 3), tuple_getitem(batch_norm, 4), u1)
        return outputs

    @fns
    def before_mix_precision0(input0, input1, input2, input3, input4, var0, var1):
        batch_norm = BatchNorm(input0, input1, input2, input3, input4)
        sub0 = Sub(Cast(var0, mstype.float32), tuple_getitem(batch_norm, 1))
        sub1 = Sub(Cast(var1, mstype.float32), tuple_getitem(batch_norm, 2))
        mul0 = Mul(sub0, constant0)
        mul1 = Mul(sub1, constant1)
        assign_sub0 = AssignSub(var0, Cast(mul0, mstype.float32), U)
        u0 = update_state(U, assign_sub0)
        assign_sub1 = AssignSub(var1, Cast(mul1, mstype.float32), u0)
        u1 = update_state(u0, assign_sub1)
        depend0 = F.depend(tuple_getitem(batch_norm, 0), assign_sub0)
        depend1 = F.depend(depend0, assign_sub1)
        outputs = make_tuple(depend1, tuple_getitem(batch_norm, 3), tuple_getitem(batch_norm, 4), u1)
        return outputs

    @fns
    def before_mix_precision1(input0, input1, input2, input3, input4, var0, var1):
        batch_norm = BatchNorm(input0, input1, input2, input3, input4)
        sub0 = Sub(Cast(var0, mstype.float32), tuple_getitem(batch_norm, 1))
        sub1 = Sub(Cast(var1, mstype.float32), tuple_getitem(batch_norm, 2))
        mul0 = Mul(Cast(sub0, mstype.float32), constant0)
        mul1 = Mul(Cast(sub1, mstype.float32), constant1)
        assign_sub0 = AssignSub(var0, mul0, U)
        u0 = update_state(U, assign_sub0)
        assign_sub1 = AssignSub(var1, mul1, u0)
        u1 = update_state(u0, assign_sub1)
        depend0 = F.depend(tuple_getitem(batch_norm, 0), assign_sub0)
        depend1 = F.depend(depend0, assign_sub1)
        outputs = make_tuple(depend1, tuple_getitem(batch_norm, 3), tuple_getitem(batch_norm, 4), u1)
        return outputs

    @fns
    def after(input0, input1, input2, input3, input4, var0, var1):
        bn_training_reduce = BNTrainingReduce(input0)
        bn_training_update = BNTrainingUpdate(input0, tuple_getitem(bn_training_reduce, 0),
                                              tuple_getitem(bn_training_reduce, 1), input1, input2, var0, var1)
        outputs = make_tuple(tuple_getitem(bn_training_update, 0), tuple_getitem(bn_training_update, 3),
                             tuple_getitem(bn_training_update, 4), U)
        return make_tuple(outputs)

    return fns[tag]
