# 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
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.parameter import Parameter
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
from mindspore.nn.optim.momentum import Momentum
from mindspore.ops import operations as P
from mindspore.parallel._utils import _reset_op_id
from mindspore.train import Model
from mindspore.context import ParallelMode
from tests.dataset_mock import MindData

class Dataset(MindData):
    def __init__(self, predict, label, length=3):
        super(Dataset, self).__init__(size=length)
        self.predict = predict
        self.label = label
        self.index = 0
        self.length = length

    def __iter__(self):
        return self

    def __next__(self):
        if self.index >= self.length:
            raise StopIteration
        self.index += 1
        return self.predict, self.label

    def reset(self):
        self.index = 0


class AllToAllNet(nn.Cell):
    def __init__(self, strategy1):
        super(AllToAllNet, self).__init__()
        self.matmul = P.MatMul().shard(((1, 1), (1, 8)))
        self.matmul_weight = Parameter(Tensor(np.ones([128, 256]), dtype=ms.float32), name="weight")
        self.transpose1 = P.Transpose().shard(strategy1)

    def construct(self, x):
        x = self.matmul(x, self.matmul_weight)
        x = self.transpose1(x, (1, 0))
        return x

def all_to_all_net(strategy1):
    return AllToAllNet(strategy1=strategy1)

def all_to_all_common(strategy1):
    learning_rate = 0.1
    momentum = 0.9
    epoch_size = 2

    context.set_context(mode=context.GRAPH_MODE)
    context.reset_auto_parallel_context()
    context.set_auto_parallel_context(parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, device_num=8,
                                      dataset_strategy="full_batch")
    predict = Tensor(np.ones([256, 128]), dtype=ms.float32)
    label = Tensor(np.ones([256]), dtype=ms.int32)
    dataset = Dataset(predict, label, 2)
    net = all_to_all_net(strategy1)

    loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
    loss.softmax_cross_entropy.shard(((8, 1), (8, 1)))
    loss.one_hot.shard(((8, 1), (), ()))
    opt = Momentum(net.trainable_params(), learning_rate, momentum)
    model = Model(net, loss, opt)

    model.train(epoch_size, dataset, dataset_sink_mode=False)

def test_all_to_all():
    strategy1 = ((8, 1),)
    _reset_op_id()
    all_to_all_common(strategy1)

def test_data_parallel_mode():
    _reset_op_id()
    learning_rate = 0.1
    momentum = 0.9
    epoch_size = 2
    context.set_context(mode=context.GRAPH_MODE)
    context.reset_auto_parallel_context()
    context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, full_batch=True)
    predict = Tensor(np.ones([256, 128]), dtype=ms.float32)
    label = Tensor(np.ones([256]), dtype=ms.int32)
    dataset = Dataset(predict, label, 2)
    net = all_to_all_net(None)
    loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
    opt = Momentum(net.trainable_params(), learning_rate, momentum)
    model = Model(net, loss, opt)

    with pytest.raises(RuntimeError):
        model.train(epoch_size, dataset, dataset_sink_mode=False)