# 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.
# ============================================================================
"""export checkpoint file into air models"""
import argparse
import numpy as np

from mindspore import Tensor, context, load_checkpoint, export

from src.gcn import GCN
from src.config import ConfigGCN

parser = argparse.ArgumentParser(description="GCN export")
parser.add_argument("--device_id", type=int, default=0, help="Device id")
parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
parser.add_argument("--dataset", type=str, default="cora", choices=["cora", "citeseer"], help="Dataset.")
parser.add_argument("--file_name", type=str, default="gcn", help="output file name.")
parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
parser.add_argument("--device_target", type=str, default="Ascend",
                    choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)")
args = parser.parse_args()

context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
if args.device_target == "Ascend":
    context.set_context(device_id=args.device_id)

if __name__ == "__main__":
    config = ConfigGCN()

    if args.dataset == "cora":
        input_dim = 1433
        class_num = 7
        adj = Tensor(np.zeros((2708, 2708), np.float64))
        feature = Tensor(np.zeros((2708, 1433), np.float32))
    else:
        input_dim = 3703
        class_num = 6
        adj = Tensor(np.zeros((3312, 3312), np.float64))
        feature = Tensor(np.zeros((3312, 3703), np.float32))

    gcn_net = GCN(config, input_dim, class_num)

    gcn_net.set_train(False)
    load_checkpoint(args.ckpt_file, net=gcn_net)
    gcn_net.add_flags_recursive(fp16=True)

    export(gcn_net, adj, feature, file_name=args.file_name, file_format=args.file_format)