# 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.

import numpy as np
from mindspore import context
import mindspore.nn as nn
from mindspore.ops import operations as P
from mindspore import Tensor
from tests.ut.python.ops.test_math_ops import VirtualLoss
import mindspore as ms
from mindspore.common import dtype as mstype
from mindspore.common.api import _executor
from mindspore.ops import composite as C
from mindspore.parallel._utils import _reset_op_id as reset_op_id

class NetWithLoss(nn.Cell):
    def __init__(self, network):
        super(NetWithLoss, self).__init__()
        self.loss = VirtualLoss()
        self.network = network

    def construct(self, x, y, z, w):
        predict = self.network(x, y, z, w)
        return self.loss(predict)

class GradWrap(nn.Cell):
    def __init__(self, network):
        super(GradWrap, self).__init__()
        self.network = network

    def construct(self, x, y, z, w):
        return C.grad_all(self.network)(x, y, z, w)

    # model_parallel test
def test_double_star_graph():
    class Net(nn.Cell):
        def __init__(self):
            super().__init__()
            self.matmul1 = P.MatMul()
            self.matmul2 = P.MatMul()
            self.matmul3 = P.MatMul()
            self.cast1 = P.Cast()
            self.cast2 = P.Cast()


        def construct(self, x, y, z, w):
            m1_result = self.matmul1(x, y)
            m2_result = self.matmul2(z, w)
            m3_result = self.matmul3(self.cast1(m2_result, mstype.float16), self.cast2(m1_result, mstype.float16))

            return m3_result

    size = 8
    context.set_auto_parallel_context(device_num=size, global_rank=0)
    
    x = Tensor(np.ones([32, 8]), dtype=ms.float32)
    y = Tensor(np.ones([8, 16]), dtype=ms.float32)
    z = Tensor(np.ones([8, 16]), dtype=ms.float32)
    w = Tensor(np.ones([16, 32]), dtype=ms.float32)

    net = NetWithLoss(Net())
    context.set_auto_parallel_context(parallel_mode="auto_parallel")
    net.set_auto_parallel()
    reset_op_id()

    _executor.compile(net, x, y, z, w, phase='train')
    strategies = _executor._get_strategy(net)
    expected_strategies = {'Default/network-Net/Cast-op1': [[8, 1]],
                           'Default/network-Net/Cast-op3': [[1, 8]],
                           'Default/network-Net/MatMul-op2': [[8, 1], [1, 1]],
                           'Default/network-Net/MatMul-op4': [[1, 1], [1, 8]],
                           'Default/network-Net/MatMul-op0': [[1, 8], [8, 1]]}
    assert strategies == expected_strategies