codingutf8 2020 the huggingface inc team 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 orglicenseslicense2 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 convert funnel checkpoint import argparse import torch from transformers import funnelbasemodel funnelconfig funnelmodel loadtfweightsinfunnel from transformers utils import logging logging setverbosityinfo def converttfcheckpointtopytorchtfcheckpointpath configfile pytorchdumppath basemodel initialise pytorch model config funnelconfig fromjsonfileconfigfile printfbuilding pytorch model from configuration config model funnelbasemodelconfig if basemodel else funnelmodelconfig load weights from tf checkpoint loadtfweightsinfunnelmodel config tfcheckpointpath save pytorchmodel printfsave pytorch model to pytorchdumppath torch savemodel statedict pytorchdumppath if name main parser argparse argumentparser required parameters parser addargument tfcheckpointpath defaultnone typestr requiredtrue helppath to the tensorflow checkpoint path parser addargument configfile defaultnone typestr requiredtrue helpthe config json file corresponding to the pretrained model nthis specifies the model architecture parser addargument pytorchdumppath defaultnone typestr requiredtrue helppath to the output pytorch model parser addargument basemodel actionstoretrue helpwhether you want just the base model no decoder or not args parser parseargs converttfcheckpointtopytorch args tfcheckpointpath args configfile args pytorchdumppath args basemodel coding utf 8 2020 the huggingface inc team 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 convert funnel checkpoint initialise pytorch model load weights from tf checkpoint save pytorch model required parameters
import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()


def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path, base_model):
    
    config = FunnelConfig.from_json_file(config_file)
    print(f"Building PyTorch model from configuration: {config}")
    model = FunnelBaseModel(config) if base_model else FunnelModel(config)

    
    load_tf_weights_in_funnel(model, config, tf_checkpoint_path)

    
    print(f"Save PyTorch model to {pytorch_dump_path}")
    torch.save(model.state_dict(), pytorch_dump_path)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    
    parser.add_argument(
        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
    )
    parser.add_argument(
        "--config_file",
        default=None,
        type=str,
        required=True,
        help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
    )
    parser.add_argument(
        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    parser.add_argument(
        "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
    )
    args = parser.parse_args()
    convert_tf_checkpoint_to_pytorch(
        args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
    )