# Copyright 2022 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 os

import sys

import torch

import argparse

sys.path.append('./DeBERTa/')

from DeBERTa.apps.models import SequenceClassificationModel



def create_model(args, num_labels, model_class_fn):

    # Prepare model

    init_model = args.init_model

    model_config = args.config

    model = model_class_fn(init_model, model_config, num_labels=num_labels, \

        drop_out=0.1, pre_trained = None)

    return model



def get_labels():

    """See base class."""

    return ["contradiction", "neutral", "entailment"]



def run_pth2onnx(args):

    label_list = get_labels()

    device = torch.device('cpu')

    model = create_model(args,len(label_list), SequenceClassificationModel.load_model)

    model = model.to(device)

    model.eval()



    input_ids = torch.zeros([1, 256], dtype=torch.int32).to(device)

    input_mask = torch.zeros([1, 256], dtype=torch.int32).to(device)



    out = model(input_ids, input_mask)

    dynamic_axes={'input_ids': {0: '-1'}, 'input_mask': {0: '-1'}, 'logits':  {0: '-1'}}



    torch.onnx.export(model, (input_ids, input_mask), args.onnx_path,

                     input_names=["input_ids", "input_mask"],

                     output_names=["logits"],

                     opset_version=12,

                     dynamic_axes = dynamic_axes)

    



if __name__ == '__main__':

    parser = argparse.ArgumentParser()

    parser.add_argument('--init_model', type=str, default='./pytorch.model-018407.bin')

    parser.add_argument('--config', type=str, default='./model_config.json')

    parser.add_argument('--onnx_path', type=str, default='./dynamic.onnx')

    args = parser.parse_args()



    run_pth2onnx(args)