CSS-LM

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# coding=utf-8
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# Copyright 2018 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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#     http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Convert BERT checkpoint."""
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import argparse
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import logging
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import torch
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from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
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logging.basicConfig(level=logging.INFO)
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def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
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    # Initialise PyTorch model
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    config = BertConfig.from_json_file(bert_config_file)
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    print("Building PyTorch model from configuration: {}".format(str(config)))
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    model = BertForPreTraining(config)
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    # Load weights from tf checkpoint
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    load_tf_weights_in_bert(model, config, tf_checkpoint_path)
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    # Save pytorch-model
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    print("Save PyTorch model to {}".format(pytorch_dump_path))
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    torch.save(model.state_dict(), pytorch_dump_path)
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if __name__ == "__main__":
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    parser = argparse.ArgumentParser()
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    # Required parameters
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    parser.add_argument(
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        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
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    )
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    parser.add_argument(
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        "--bert_config_file",
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        default=None,
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        type=str,
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        required=True,
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        help="The config json file corresponding to the pre-trained BERT model. \n"
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        "This specifies the model architecture.",
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    )
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    parser.add_argument(
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        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
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    )
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    args = parser.parse_args()
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    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
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