# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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 argparse
import os

import paddle

from paddlenlp.transformers import AutoModelForSequenceClassification

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--params_path",
        type=str,
        required=True,
        default="./checkpoint/model_900",
        help="The path to model parameters to be loaded.",
    )
    parser.add_argument(
        "--output_path", type=str, default="./output", help="The path of model parameter in static graph to be saved."
    )
    args = parser.parse_args()

    # The number of labels should be in accordance with the training dataset.
    label_map = {0: "negative", 1: "positive"}
    model = AutoModelForSequenceClassification.from_pretrained(args.params_path, num_labels=len(label_map))

    model.eval()

    # Convert to static graph with specific input description
    model = paddle.jit.to_static(
        model,
        input_spec=[
            paddle.static.InputSpec(shape=[None, None], dtype="int64"),  # input_ids
            paddle.static.InputSpec(shape=[None, None], dtype="int64"),  # segment_ids
        ],
    )
    # Save in static graph model.
    save_path = os.path.join(args.output_path, "inference")
    paddle.jit.save(model, save_path)
