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# Licensed under the Apache License, Version 2.0 (the "License");
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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"""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.albert import configs
from official.modeling import tf_utils
from official.nlp.modeling import models
from official.nlp.modeling import networks
from official.nlp.tools import tf1_bert_checkpoint_converter_lib

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_bert_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_bert_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_bert_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)
