# Copyright 2025 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.

"""BERT model input pipelines."""

import tensorflow as tf, tf_keras


def decode_record(record, name_to_features):
  """Decodes a record to a TensorFlow example."""
  example = tf.io.parse_single_example(record, name_to_features)

  # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
  # So cast all int64 to int32.
  for name in list(example.keys()):
    t = example[name]
    if t.dtype == tf.int64:
      t = tf.cast(t, tf.int32)
    example[name] = t

  return example


def single_file_dataset(input_file, name_to_features, num_samples=None):
  """Creates a single-file dataset to be passed for BERT custom training."""
  # For training, we want a lot of parallel reading and shuffling.
  # For eval, we want no shuffling and parallel reading doesn't matter.
  d = tf.data.TFRecordDataset(input_file)
  if num_samples:
    d = d.take(num_samples)
  d = d.map(
      lambda record: decode_record(record, name_to_features),
      num_parallel_calls=tf.data.experimental.AUTOTUNE)

  # When `input_file` is a path to a single file or a list
  # containing a single path, disable auto sharding so that
  # same input file is sent to all workers.
  if isinstance(input_file, str) or len(input_file) == 1:
    options = tf.data.Options()
    options.experimental_distribute.auto_shard_policy = (
        tf.data.experimental.AutoShardPolicy.OFF)
    d = d.with_options(options)
  return d


def create_pretrain_dataset(input_patterns,
                            seq_length,
                            max_predictions_per_seq,
                            batch_size,
                            is_training=True,
                            input_pipeline_context=None,
                            use_next_sentence_label=True,
                            use_position_id=False,
                            output_fake_labels=True):
  """Creates input dataset from (tf)records files for pretraining."""
  name_to_features = {
      'input_ids':
          tf.io.FixedLenFeature([seq_length], tf.int64),
      'input_mask':
          tf.io.FixedLenFeature([seq_length], tf.int64),
      'segment_ids':
          tf.io.FixedLenFeature([seq_length], tf.int64),
      'masked_lm_positions':
          tf.io.FixedLenFeature([max_predictions_per_seq], tf.int64),
      'masked_lm_ids':
          tf.io.FixedLenFeature([max_predictions_per_seq], tf.int64),
      'masked_lm_weights':
          tf.io.FixedLenFeature([max_predictions_per_seq], tf.float32),
  }
  if use_next_sentence_label:
    name_to_features['next_sentence_labels'] = tf.io.FixedLenFeature([1],
                                                                     tf.int64)
  if use_position_id:
    name_to_features['position_ids'] = tf.io.FixedLenFeature([seq_length],
                                                             tf.int64)
  for input_pattern in input_patterns:
    if not tf.io.gfile.glob(input_pattern):
      raise ValueError('%s does not match any files.' % input_pattern)

  dataset = tf.data.Dataset.list_files(input_patterns, shuffle=is_training)

  if input_pipeline_context and input_pipeline_context.num_input_pipelines > 1:
    dataset = dataset.shard(input_pipeline_context.num_input_pipelines,
                            input_pipeline_context.input_pipeline_id)
  if is_training:
    dataset = dataset.repeat()

    # We set shuffle buffer to exactly match total number of
    # training files to ensure that training data is well shuffled.
    input_files = []
    for input_pattern in input_patterns:
      input_files.extend(tf.io.gfile.glob(input_pattern))
    dataset = dataset.shuffle(len(input_files))

  # In parallel, create tf record dataset for each train files.
  # cycle_length = 8 means that up to 8 files will be read and deserialized in
  # parallel. You may want to increase this number if you have a large number of
  # CPU cores.
  dataset = dataset.interleave(
      tf.data.TFRecordDataset,
      cycle_length=8,
      num_parallel_calls=tf.data.experimental.AUTOTUNE)

  if is_training:
    dataset = dataset.shuffle(100)

  decode_fn = lambda record: decode_record(record, name_to_features)
  dataset = dataset.map(
      decode_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)

  def _select_data_from_record(record):
    """Filter out features to use for pretraining."""
    x = {
        'input_word_ids': record['input_ids'],
        'input_mask': record['input_mask'],
        'input_type_ids': record['segment_ids'],
        'masked_lm_positions': record['masked_lm_positions'],
        'masked_lm_ids': record['masked_lm_ids'],
        'masked_lm_weights': record['masked_lm_weights'],
    }
    if use_next_sentence_label:
      x['next_sentence_labels'] = record['next_sentence_labels']
    if use_position_id:
      x['position_ids'] = record['position_ids']

    # TODO(hongkuny): Remove the fake labels after migrating bert pretraining.
    if output_fake_labels:
      return (x, record['masked_lm_weights'])
    else:
      return x

  dataset = dataset.map(
      _select_data_from_record,
      num_parallel_calls=tf.data.experimental.AUTOTUNE)
  dataset = dataset.batch(batch_size, drop_remainder=is_training)
  dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
  return dataset


def create_classifier_dataset(file_path,
                              seq_length,
                              batch_size,
                              is_training=True,
                              input_pipeline_context=None,
                              label_type=tf.int64,
                              include_sample_weights=False,
                              num_samples=None):
  """Creates input dataset from (tf)records files for train/eval."""
  name_to_features = {
      'input_ids': tf.io.FixedLenFeature([seq_length], tf.int64),
      'input_mask': tf.io.FixedLenFeature([seq_length], tf.int64),
      'segment_ids': tf.io.FixedLenFeature([seq_length], tf.int64),
      'label_ids': tf.io.FixedLenFeature([], label_type),
  }
  if include_sample_weights:
    name_to_features['weight'] = tf.io.FixedLenFeature([], tf.float32)
  dataset = single_file_dataset(file_path, name_to_features,
                                num_samples=num_samples)

  # The dataset is always sharded by number of hosts.
  # num_input_pipelines is the number of hosts rather than number of cores.
  if input_pipeline_context and input_pipeline_context.num_input_pipelines > 1:
    dataset = dataset.shard(input_pipeline_context.num_input_pipelines,
                            input_pipeline_context.input_pipeline_id)

  def _select_data_from_record(record):
    x = {
        'input_word_ids': record['input_ids'],
        'input_mask': record['input_mask'],
        'input_type_ids': record['segment_ids']
    }
    y = record['label_ids']
    if include_sample_weights:
      w = record['weight']
      return (x, y, w)
    return (x, y)

  if is_training:
    dataset = dataset.shuffle(100)
    dataset = dataset.repeat()

  dataset = dataset.map(
      _select_data_from_record,
      num_parallel_calls=tf.data.experimental.AUTOTUNE)
  dataset = dataset.batch(batch_size, drop_remainder=is_training)
  dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
  return dataset


def create_squad_dataset(file_path,
                         seq_length,
                         batch_size,
                         is_training=True,
                         input_pipeline_context=None):
  """Creates input dataset from (tf)records files for train/eval."""
  name_to_features = {
      'input_ids': tf.io.FixedLenFeature([seq_length], tf.int64),
      'input_mask': tf.io.FixedLenFeature([seq_length], tf.int64),
      'segment_ids': tf.io.FixedLenFeature([seq_length], tf.int64),
  }
  if is_training:
    name_to_features['start_positions'] = tf.io.FixedLenFeature([], tf.int64)
    name_to_features['end_positions'] = tf.io.FixedLenFeature([], tf.int64)
  else:
    name_to_features['unique_ids'] = tf.io.FixedLenFeature([], tf.int64)

  dataset = single_file_dataset(file_path, name_to_features)

  # The dataset is always sharded by number of hosts.
  # num_input_pipelines is the number of hosts rather than number of cores.
  if input_pipeline_context and input_pipeline_context.num_input_pipelines > 1:
    dataset = dataset.shard(input_pipeline_context.num_input_pipelines,
                            input_pipeline_context.input_pipeline_id)

  def _select_data_from_record(record):
    """Dispatches record to features and labels."""
    x, y = {}, {}
    for name, tensor in record.items():
      if name in ('start_positions', 'end_positions'):
        y[name] = tensor
      elif name == 'input_ids':
        x['input_word_ids'] = tensor
      elif name == 'segment_ids':
        x['input_type_ids'] = tensor
      else:
        x[name] = tensor
    return (x, y)

  if is_training:
    dataset = dataset.shuffle(100)
    dataset = dataset.repeat()

  dataset = dataset.map(
      _select_data_from_record,
      num_parallel_calls=tf.data.experimental.AUTOTUNE)
  dataset = dataset.batch(batch_size, drop_remainder=True)
  dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
  return dataset


def create_retrieval_dataset(file_path,
                             seq_length,
                             batch_size,
                             input_pipeline_context=None):
  """Creates input dataset from (tf)records files for scoring."""
  name_to_features = {
      'input_ids': tf.io.FixedLenFeature([seq_length], tf.int64),
      'input_mask': tf.io.FixedLenFeature([seq_length], tf.int64),
      'segment_ids': tf.io.FixedLenFeature([seq_length], tf.int64),
      'example_id': tf.io.FixedLenFeature([1], tf.int64),
  }
  dataset = single_file_dataset(file_path, name_to_features)

  # The dataset is always sharded by number of hosts.
  # num_input_pipelines is the number of hosts rather than number of cores.
  if input_pipeline_context and input_pipeline_context.num_input_pipelines > 1:
    dataset = dataset.shard(input_pipeline_context.num_input_pipelines,
                            input_pipeline_context.input_pipeline_id)

  def _select_data_from_record(record):
    x = {
        'input_word_ids': record['input_ids'],
        'input_mask': record['input_mask'],
        'input_type_ids': record['segment_ids']
    }
    y = record['example_id']
    return (x, y)

  dataset = dataset.map(
      _select_data_from_record,
      num_parallel_calls=tf.data.experimental.AUTOTUNE)
  dataset = dataset.batch(batch_size, drop_remainder=False)

  def _pad_to_batch(x, y):
    cur_size = tf.shape(y)[0]
    pad_size = batch_size - cur_size

    pad_ids = tf.zeros(shape=[pad_size, seq_length], dtype=tf.int32)
    for key in ('input_word_ids', 'input_mask', 'input_type_ids'):
      x[key] = tf.concat([x[key], pad_ids], axis=0)

    pad_labels = -tf.ones(shape=[pad_size, 1], dtype=tf.int32)
    y = tf.concat([y, pad_labels], axis=0)
    return x, y

  dataset = dataset.map(
      _pad_to_batch,
      num_parallel_calls=tf.data.experimental.AUTOTUNE)

  dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
  return dataset
