# Copyright 2021 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
from mindspore import nn, context
from mindspore import ops as P
from mindspore.train import DatasetHelper, connect_network_with_dataset
import mindspore.dataset as ds
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")

def _exec_preprocess(network, is_train, dataset, dataset_sink_mode, sink_size=-1, epoch_num=1, dataset_helper=None):
    if dataset_sink_mode and not is_train:
        dataset.__loop_size__ = 1

    if dataset_helper is None:
        dataset_helper = DatasetHelper(dataset, dataset_sink_mode, sink_size, epoch_num)

    if dataset_sink_mode:
        network = connect_network_with_dataset(network, dataset_helper)

    network.set_train(is_train)

    return dataset_helper, network


def _eval_dataset_sink_process(network, valid_dataset):
    dataset_helper, eval_network = _exec_preprocess(network, is_train=False, dataset=valid_dataset,
                                                    dataset_sink_mode=True)
    for inputs1, inputs2 in zip(dataset_helper, valid_dataset.create_dict_iterator()):
        outputs = eval_network(*inputs1)
        for elem1, (_, elem2) in zip(outputs, inputs2.items()):
            assert elem1.shape == elem2.shape

def dataset_generator():
    for i in range(1, 10):
        yield (
            np.ones((32, i), dtype=np.float32), np.zeros((32, i, i, 3), dtype=np.int32),
            np.ones((32,), dtype=np.float32),
            np.ones((32, i, 8), dtype=np.float32), np.ones((32, 8, 8), dtype=np.float32))

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.relu = P.ReLU()

    def construct(self, x1, x2, x3, x4, x5):
        x1 = self.relu(x1)
        x1 = self.relu(x1)

        x2 = self.relu(x2)

        x3 = self.relu(x3)
        x3 = self.relu(x3)

        x4 = self.relu(x4)

        x5 = self.relu(x5)
        return x1, x2, x3, x4, x5

@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_getnext_dynamic_pipeline():
    network = Net()
    dataset = ds.GeneratorDataset(dataset_generator, ["data1", "data2", "data3", "data4", "data5"])
    dataset.set_dynamic_columns(columns={"data1": [32, None], "data2": [32, None, None, 3],
                                         "data3": [32], "data4": [32, None, 8], "data5": [32, 8, 8]})
    _eval_dataset_sink_process(network, dataset)