text: # Copyright 2021 The TensorFlow Authors. All Rights Reserved.
  #
  # 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.
  
  """A converter from a tf1 ALBERT encoder checkpoint to a tf2 encoder checkpoint.
  
  The conversion will yield an object-oriented checkpoint that can be used
  to restore an AlbertEncoder object.
  """
  import os
  
  from absl import app
  from absl import flags
  
  import tensorflow as tf
  from official.legacy.nlp.albert import configs
  from official.modeling import tf_utils
  from official.nlp.bert import tf1_checkpoint_converter_lib
  from official.nlp.modeling import models
  from official.nlp.modeling import networks
  
  FLAGS = flags.FLAGS
  
  flags.DEFINE_string("albert_config_file", None,
                      "Albert configuration file to define core bert layers.")
  flags.DEFINE_string(
      "checkpoint_to_convert", None,
      "Initial checkpoint from a pretrained BERT model core (that is, only the "
      "BertModel, with no task heads.)")
  flags.DEFINE_string("converted_checkpoint_path", None,
                      "Name for the created object-based V2 checkpoint.")
  flags.DEFINE_string("checkpoint_model_name", "encoder",
                      "The name of the model when saving the checkpoint, i.e., "
                      "the checkpoint will be saved using: "
                      "tf.train.Checkpoint(FLAGS.checkpoint_model_name=model).")
  flags.DEFINE_enum(
      "converted_model", "encoder", ["encoder", "pretrainer"],
      "Whether to convert the checkpoint to a `AlbertEncoder` model or a "
      "`BertPretrainerV2` model (with mlm but without classification heads).")
  
  
  ALBERT_NAME_REPLACEMENTS = (
      ("bert/encoder/", ""),
      ("bert/", ""),
      ("embeddings/word_embeddings", "word_embeddings/embeddings"),
      ("embeddings/position_embeddings", "position_embedding/embeddings"),
      ("embeddings/token_type_embeddings", "type_embeddings/embeddings"),
      ("embeddings/LayerNorm", "embeddings/layer_norm"),
      ("embedding_hidden_mapping_in", "embedding_projection"),
      ("group_0/inner_group_0/", ""),
      ("attention_1/self", "self_attention"),
      ("attention_1/output/dense", "self_attention/attention_output"),
      ("transformer/LayerNorm/", "transformer/self_attention_layer_norm/"),
      ("ffn_1/intermediate/dense", "intermediate"),
      ("ffn_1/intermediate/output/dense", "output"),
      ("transformer/LayerNorm_1/", "transformer/output_layer_norm/"),
      ("pooler/dense", "pooler_transform"),
      ("cls/predictions", "bert/cls/predictions"),
      ("cls/predictions/output_bias", "cls/predictions/output_bias/bias"),
      ("cls/seq_relationship/output_bias", "predictions/transform/logits/bias"),
      ("cls/seq_relationship/output_weights",
       "predictions/transform/logits/kernel"),
  )
  
  
  def _create_albert_model(cfg):
    """Creates an ALBERT keras core model from BERT configuration.
  
    Args:
      cfg: A `AlbertConfig` to create the core model.
  
    Returns:
      A keras model.
    """
    albert_encoder = networks.AlbertEncoder(
        vocab_size=cfg.vocab_size,
        hidden_size=cfg.hidden_size,
        embedding_width=cfg.embedding_size,
        num_layers=cfg.num_hidden_layers,
        num_attention_heads=cfg.num_attention_heads,
        intermediate_size=cfg.intermediate_size,
        activation=tf_utils.get_activation(cfg.hidden_act),
        dropout_rate=cfg.hidden_dropout_prob,
        attention_dropout_rate=cfg.attention_probs_dropout_prob,
        max_sequence_length=cfg.max_position_embeddings,
        type_vocab_size=cfg.type_vocab_size,
        initializer=tf.keras.initializers.TruncatedNormal(
            stddev=cfg.initializer_range))
    return albert_encoder
  
  
  def _create_pretrainer_model(cfg):
    """Creates a pretrainer with AlbertEncoder from ALBERT configuration.
  
    Args:
      cfg: A `BertConfig` to create the core model.
  
    Returns:
      A BertPretrainerV2 model.
    """
    albert_encoder = _create_albert_model(cfg)
    pretrainer = models.BertPretrainerV2(
        encoder_network=albert_encoder,
        mlm_activation=tf_utils.get_activation(cfg.hidden_act),
        mlm_initializer=tf.keras.initializers.TruncatedNormal(
            stddev=cfg.initializer_range))
    # Makes sure masked_lm layer's variables in pretrainer are created.
    _ = pretrainer(pretrainer.inputs)
    return pretrainer
  
  
  def convert_checkpoint(bert_config, output_path, v1_checkpoint,
                         checkpoint_model_name,
                         converted_model="encoder"):
    """Converts a V1 checkpoint into an OO V2 checkpoint."""
    output_dir, _ = os.path.split(output_path)
  
    # Create a temporary V1 name-converted checkpoint in the output directory.
    temporary_checkpoint_dir = os.path.join(output_dir, "temp_v1")
    temporary_checkpoint = os.path.join(temporary_checkpoint_dir, "ckpt")
    tf1_checkpoint_converter_lib.convert(
        checkpoint_from_path=v1_checkpoint,
        checkpoint_to_path=temporary_checkpoint,
        num_heads=bert_config.num_attention_heads,
        name_replacements=ALBERT_NAME_REPLACEMENTS,
        permutations=tf1_checkpoint_converter_lib.BERT_V2_PERMUTATIONS,
        exclude_patterns=["adam", "Adam"])
  
    # Create a V2 checkpoint from the temporary checkpoint.
    if converted_model == "encoder":
      model = _create_albert_model(bert_config)
    elif converted_model == "pretrainer":
      model = _create_pretrainer_model(bert_config)
    else:
      raise ValueError("Unsupported converted_model: %s" % converted_model)
  
    tf1_checkpoint_converter_lib.create_v2_checkpoint(model, temporary_checkpoint,
                                                      output_path,
                                                      checkpoint_model_name)
  
    # Clean up the temporary checkpoint, if it exists.
    try:
      tf.io.gfile.rmtree(temporary_checkpoint_dir)
    except tf.errors.OpError:
      # If it doesn't exist, we don't need to clean it up; continue.
      pass
  
  
  def main(_):
    output_path = FLAGS.converted_checkpoint_path
    v1_checkpoint = FLAGS.checkpoint_to_convert
    checkpoint_model_name = FLAGS.checkpoint_model_name
    converted_model = FLAGS.converted_model
    albert_config = configs.AlbertConfig.from_json_file(FLAGS.albert_config_file)
    convert_checkpoint(albert_config, output_path, v1_checkpoint,
                       checkpoint_model_name,
                       converted_model=converted_model)
  
  
  if __name__ == "__main__":
    app.run(main)
  