# coding=utf-8 # Copyright 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 ConvBERT checkpoint.""" import argparse from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert from transformers.utils import logging logging.set_verbosity_info() def convert_orig_tf1_checkpoint_to_pytorch(tf_checkpoint_path, convbert_config_file, pytorch_dump_path): conf = ConvBertConfig.from_json_file(convbert_config_file) model = ConvBertModel(conf) model = load_tf_weights_in_convbert(model, conf, tf_checkpoint_path) model.save_pretrained(pytorch_dump_path) tf_model = TFConvBertModel.from_pretrained(pytorch_dump_path, from_pt=True) tf_model.save_pretrained(pytorch_dump_path) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path." ) parser.add_argument( "--convbert_config_file", default=None, type=str, required=True, help=( "The config json file corresponding to the pre-trained ConvBERT model. \n" "This specifies the model architecture." ), ) parser.add_argument( "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) args = parser.parse_args() convert_orig_tf1_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)