text: # Lint as: python3
  # Copyright 2020 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.
  # ==============================================================================
  """Loads dataset for the sentence prediction (classification) task."""
  from typing import Mapping, Optional
  import dataclasses
  import tensorflow as tf
  
  from official.core import input_reader
  from official.modeling.hyperparams import config_definitions as cfg
  from official.nlp.data import data_loader_factory
  
  
  LABEL_TYPES_MAP = {'int': tf.int64, 'float': tf.float32}
  
  
  @dataclasses.dataclass
  class SentencePredictionDataConfig(cfg.DataConfig):
    """Data config for sentence prediction task (tasks/sentence_prediction)."""
    input_path: str = ''
    global_batch_size: int = 32
    is_training: bool = True
    seq_length: int = 128
    label_type: str = 'int'
  
  
  @data_loader_factory.register_data_loader_cls(SentencePredictionDataConfig)
  class SentencePredictionDataLoader:
    """A class to load dataset for sentence prediction (classification) task."""
  
    def __init__(self, params):
      self._params = params
      self._seq_length = params.seq_length
  
    def _decode(self, record: tf.Tensor):
      """Decodes a serialized tf.Example."""
      label_type = LABEL_TYPES_MAP[self._params.label_type]
      name_to_features = {
          'input_ids': tf.io.FixedLenFeature([self._seq_length], tf.int64),
          'input_mask': tf.io.FixedLenFeature([self._seq_length], tf.int64),
          'segment_ids': tf.io.FixedLenFeature([self._seq_length], tf.int64),
          'label_ids': tf.io.FixedLenFeature([], label_type),
      }
      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 example:
        t = example[name]
        if t.dtype == tf.int64:
          t = tf.cast(t, tf.int32)
        example[name] = t
  
      return example
  
    def _parse(self, record: Mapping[str, tf.Tensor]):
      """Parses raw tensors into a dict of tensors to be consumed by the model."""
      x = {
          'input_word_ids': record['input_ids'],
          'input_mask': record['input_mask'],
          'input_type_ids': record['segment_ids']
      }
      y = record['label_ids']
      return (x, y)
  
    def load(self, input_context: Optional[tf.distribute.InputContext] = None):
      """Returns a tf.dataset.Dataset."""
      reader = input_reader.InputReader(
          params=self._params, decoder_fn=self._decode, parser_fn=self._parse)
      return reader.read(input_context)
  