# 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 torch

import torch.onnx

import argparse

import os

import sys

sys.path.append('./')





def parse_args(parser):

    """

    Parse commandline arguments.

    """

    parser.add_argument('-i', '--input', type=str, required=True,

                        help='Full path to the WaveGlow checkpoint file')

    parser.add_argument('-o', '--output', type=str, required=True,

                        help='Directory for the exported WaveGlow ONNX model')

    return parser





def export_onnx(parser, args):

    """CPU"""

    dev = torch.device("cpu")

    model = torch.load(args.input, map_location=torch.device('cpu'))['model']

    model.to(dev)

    model = model.remove_weightnorm(model)

    model.eval()



    onnx_path = os.path.join(args.output, "waveglow.onnx")

    mel = torch.randn(1, 80, 1)



    with torch.no_grad():

        model.infer(mel)

        model.forward = model.infer

        forward_input = mel

        opset_version = 13

        input_names = ['mel']

        dynamic_axes = {'mel': {2:'mel_seq'},

                        'output_audio': {1: 'audio_seq'}}

        torch.onnx.export(model, forward_input, onnx_path, input_names=input_names,

                        export_params=True, verbose=True, opset_version=opset_version, 

                        output_names=['output_audio'], dynamic_axes = dynamic_axes)





def main():

    parser = argparse.ArgumentParser(

        description='PyTorch WaveGlow Inference')

    parser = parse_args(parser)

    args, _ = parser.parse_known_args()



    export_onnx(parser, args)



if __name__ == '__main__':

    main()

    print("Onnx converted successfully!")