# 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 numpy as np
import pytest

import mindspore as ms
from mindspore import nn, context, Tensor
import mindspore.ops as ops

def set_context():
    context.set_context(mode=context.PYNATIVE_MODE)
    context.reset_auto_parallel_context()
    context.set_auto_parallel_context(device_num=8, parallel_mode="auto_parallel", search_mode="sharding_propagation")

class NetMul(nn.Cell):
    def __init__(self):
        super().__init__()
        self.mul = ops.Mul()

    def construct(self, x, y):
        return self.mul(x, y)


class NetMatMul(nn.Cell):
    def __init__(self):
        super().__init__()
        self.matmul = ops.MatMul()

    def construct(self, x, y):
        return self.matmul(x, y)

class Net(nn.Cell):
    def __init__(self, in_axes, out_axes):
        super().__init__()
        self.mul_net = NetMul()
        self.matmul_net = NetMatMul()
        self.mul_net.shard(in_axes=in_axes, out_axes=out_axes)

    def construct(self, x, y):
        out1 = self.matmul_net(x, y)
        out2 = self.matmul_net(x, y)
        return self.mul_net(out1, out2)

def cell_shard_execution(in_axes, out_axes, error_log):

    net = Net(in_axes, out_axes)

    x = Tensor(np.ones([128, 128]), dtype=ms.float32)
    y = Tensor(np.ones([128, 128]), dtype=ms.float32)

    with pytest.raises(Exception) as err:
        _ = net(x, y)
    assert error_log in str(err.value)


def test_in_axes_numbers_check():
    """
    Feature: shard function for cell
    Description: inconsistent input number and in_axes number
    Expectation: throw an exception indicating inconsistent input number and in_axes number
    """
    set_context()
    in_axes = ((8, 1), None, (1, 8))
    out_axes = (None,)
    error_log = "Input numbers: 2 is not equal to in_axes numbers: 3"
    cell_shard_execution(in_axes, out_axes, error_log)


def test_out_axes_numbers_check():
    """
    Feature: shard function for cell
    Description: inconsistent output number and out_axes number
    Expectation: throw an exception indicating inconsistent output number and out_axes number
    """
    set_context()
    in_axes = ((8, 1), None)
    out_axes = (None, (8, 1))
    error_log = "Output number: 1 is not equal to out_axes number: 2"
    cell_shard_execution(in_axes, out_axes, error_log)

def test_in_axes_dimension_check():
    """
    Feature: shard function for cell
    Description: inconsistent input dimension and in_axes dimension
    Expectation: throw an exception indicating inconsistent input_dimension and in_axes dimension
    """
    set_context()
    in_axes = ((8, 1, 1), None)
    out_axes = (None, (8, 1))
    error_log = "Input dimension: 2 is not equal to in_axes dimension: 3 at index 0"
    cell_shard_execution(in_axes, out_axes, error_log)

def test_out_axes_dimension_check():
    """
    Feature: shard function for cell
    Description: inconsistent output dimension and out_axes dimension
    Expectation: throw an exception indicating inconsistent output_dimension and out_axes dimension
    """
    set_context()
    in_axes = ((8, 1), None)
    out_axes = ((8,),)
    error_log = "Output dimension: 2 is not equal to out_axes dimension: 1 at index 0"
    cell_shard_execution(in_axes, out_axes, error_log)

def test_in_axes_format_check():
    """
    Feature: shard function for cell
    Description: unsupported in_axes format
    Expectation: throw an exception indicating an supported in_axes format
    """
    set_context()
    in_axes = ([8, 1], None)
    out_axes = (None,)
    error_log = "in_axes should be a two-dimension tuple"
    cell_shard_execution(in_axes, out_axes, error_log)