# Copyright 2022 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 variable"""

import numpy as np
from mindspore.ops.composite import GradOperation
from mindspore.common.variable import Variable
from mindspore.common.api import _CellGraphExecutor
from mindspore.ops import operations as P
import mindspore.nn as nn
import mindspore.common.dtype as mstype
from mindspore import Tensor
from mindspore import Parameter


def test_variable_scalar_mul_grad_first():
    """
    Feature: Set Constants mutable.
    Description: Get gradient with respect to the first scalar input.
    Expectation: Get the correct gradient.
    """

    class Net(nn.Cell):
        def construct(self, x, y):
            return x * y

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.grad_op = GradOperation()

        def construct(self, x, y):
            gradient_function = self.grad_op(self.net)
            return gradient_function(x, y)

    x = Variable(2)
    output = GradNet(Net())(x, 3)
    assert output == 3


def test_variable_scalar_mul_grad_all():
    """
    Feature: Set Constants mutable.
    Description: Get gradient with respect to all scalar inputs.
    Expectation: Get the correct gradients.
    """

    class Net(nn.Cell):
        def construct(self, x, y):
            return x * y

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.grad_op = GradOperation(get_all=True)

        def construct(self, x, y):
            gradient_function = self.grad_op(self.net)
            return gradient_function(x, y)

    x = Variable(2)
    y = Variable(3)
    output = GradNet(Net())(x, y)
    assert output == (3, 2)


def test_variable_tuple_or_list_scalar_mul_grad():
    """
    Feature: Set Constants mutable.
    Description: Get gradient with respect to the tuple or list scalar input.
    Expectation: Get the correct gradients.
    """

    class Net(nn.Cell):
        def construct(self, x):
            return x[0] * x[1]

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.grad_op = GradOperation()

        def construct(self, x):
            gradient_function = self.grad_op(self.net)
            return gradient_function(x)

    x = Variable((2, 3))
    output = GradNet(Net())(x)
    assert output == (3, 2)

    x = Variable([2, 3])
    output = GradNet(Net())(x)
    assert output == (3, 2)


def test_variable_dict_scalar_mul_grad():
    """
    Feature: Set Constants mutable.
    Description: Get gradient with respect to the dict scalar input.
    Expectation: Get the correct gradients.
    """

    class Net(nn.Cell):
        def construct(self, x):
            return x['a'] * x['b']

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.grad_op = GradOperation()

        def construct(self, x):
            gradient_function = self.grad_op(self.net)
            return gradient_function(x)

    x = Variable({'a': 2, 'b': 3})
    output = GradNet(Net())(x)
    assert output == (3, 2)


def test_variable_mix_scalar_mul_grad_all():
    """
    Feature: Set Constants mutable.
    Description: Get gradient with respect to the mix scalar input including dict and tuple.
    Expectation: Get the correct gradients.
    """

    class Net(nn.Cell):
        def construct(self, x, y):
            return x['a'] * x['b'] * y[0]

    class GradNet(nn.Cell):
        def __init__(self, net):
            super(GradNet, self).__init__()
            self.net = net
            self.grad_op = GradOperation(get_all=True)

        def construct(self, x, y):
            gradient_function = self.grad_op(self.net)
            return gradient_function(x, y)

    x = Variable({'a': 2, 'b': 3})
    y = Variable((4, 5))
    output = GradNet(Net())(x, y)
    assert output == ((12, 8), (6, 0))


def test_tuple_inputs_compile_phase():
    """
    Feature: Set Constants mutable.
    Description: Test whether the compilation phase for tuple input twice are the same.
    Expectation: The phases are the same.
    """

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

        def construct(self, tuple_input):
            x = tuple_input[0]
            y = tuple_input[1]
            x = x * self.z
            out = self.matmul(x, y)
            return out

    x = Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)
    y = Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)
    p = Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)
    q = Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)
    net = Net()
    _cell_graph_executor = _CellGraphExecutor()
    phase1, _ = _cell_graph_executor.compile(net, (x, y))
    phase2, _ = _cell_graph_executor.compile(net, (p, q))
    assert phase1 != phase2
    phase1, _ = _cell_graph_executor.compile(net, Variable((x, y)))
    phase2, _ = _cell_graph_executor.compile(net, Variable((p, q)))
    assert phase1 == phase2