# 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 assign add
"""
import numpy as np
import mindspore.nn as nn
from mindspore.ops import operations as P
from mindspore.common.initializer import initializer
from mindspore import Tensor, Parameter
import mindspore as ms
from ..ut_filter import non_graph_engine
from mindspore.common.api import _executor
import mindspore.context as context
import pytest
context.set_context(mode=context.GRAPH_MODE)

class Net(nn.Cell):
    """Net definition"""
    def __init__(self):
        super(Net, self).__init__()
        self.AssignAdd = P.AssignAdd()
        self.inputdata = Parameter(initializer(1, [1], ms.int64), name="global_step")
        print("inputdata: ", self.inputdata)

    def construct(self, x):
        out = self.AssignAdd(self.inputdata, x)
        return out

@non_graph_engine
def test_AssignAdd_1():
    """test AssignAdd 1"""
    import mindspore.context as context
    context.set_context(mode=context.GRAPH_MODE)
    net = Net()
    x = Tensor(np.ones([1]).astype(np.int64)*100)

    print("MyPrintResult dataX:", x)
    result = net(x)
    print("MyPrintResult data::", result)
    expect = np.ones([1]).astype(np.int64)*101
    diff = result.asnumpy() - expect

    print("MyPrintExpect:", expect)
    print("MyPrintDiff:", diff)
    error = np.ones(shape=[1]) * 1.0e-3
    assert np.all(diff < error)

@non_graph_engine
def test_AssignAdd_2():
    """test AssignAdd 2"""
    import mindspore.context as context
    context.set_context(mode=context.GRAPH_MODE)
    net = Net()
    x = Tensor(np.ones([1]).astype(np.int64)*102)

    print("MyPrintResult dataX:", x)
    result = net(x)
    print("MyPrintResult data::", result.asnumpy())
    expect = np.ones([1]).astype(np.int64)*103
    diff = result.asnumpy() - expect

    print("MyPrintExpect:", expect)
    print("MyPrintDiff:", diff)
    error = np.ones(shape=[1]) * 1.0e-3
    assert np.all(diff < error)

class AssignAddNet(nn.Cell):
    """Net definition"""
    def __init__(self):
        super(AssignAddNet, self).__init__()
        self.AssignAdd = P.AssignAdd()
        self.inputdata = Parameter(initializer(1, [1], ms.float16), name="KIND_AUTOCAST_SCALAR_TO_TENSOR")
        self.one = 1

    def construct(self, ixt):
        z1 = self.AssignAdd(self.inputdata, self.one)
        return z1

@non_graph_engine
def test_assignadd_scalar_cast():
    net = AssignAddNet()
    x = Tensor(np.ones([1]).astype(np.int64)*102)
    #_executor.compile(net, 1)
    result = net(x)