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

import mindspore.common.dtype as mstype
from mindspore import Tensor, context, load_checkpoint, export

from src.finetune_eval_config import ernie_net_cfg
from src.ernie_for_finetune import ErnieCLS
parser = argparse.ArgumentParser(description="Emotect export")
parser.add_argument("--device_id", type=int, default=0, help="Device id")
parser.add_argument("--batch_size", type=int, default=32, help="batch size")
parser.add_argument("--number_labels", type=int, default=3, help="batch size")
parser.add_argument("--ckpt_file", type=str, required=True, help="Bert ckpt file.")
parser.add_argument("--file_name", type=str, default="emotect", help="bert output mindir name.")
parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"],
                    default='MINDIR', 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__":
    net = ErnieCLS(ernie_net_cfg, False, num_labels=args.number_labels)

    load_checkpoint(args.ckpt_file, net=net)
    net.set_train(False)

    input_ids = Tensor(np.zeros([args.batch_size, ernie_net_cfg.seq_length]), mstype.int32)
    input_mask = Tensor(np.zeros([args.batch_size, ernie_net_cfg.seq_length]), mstype.int32)
    token_type_id = Tensor(np.zeros([args.batch_size, ernie_net_cfg.seq_length]), mstype.int32)
    label_ids = Tensor(np.zeros([args.batch_size, ernie_net_cfg.seq_length]), mstype.int32)

    input_data = [input_ids, input_mask, token_type_id]
    export(net.ernie, *input_data, file_name=args.file_name, file_format=args.file_format)