Delete question_answering_dataloader.py

question_answering_dataloader.py

@@ -1,115 +0,0 @@
-# Copyright 2024 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 question answering (e.g, SQuAD) task."""
-import dataclasses
-from typing import Mapping, Optional
-
-import tensorflow as tf, tf_keras
-from official.common import dataset_fn
-from official.core import config_definitions as cfg
-from official.core import input_reader
-from official.nlp.data import data_loader
-from official.nlp.data import data_loader_factory
-
-
-@dataclasses.dataclass
-class QADataConfig(cfg.DataConfig):
-  """Data config for question answering task (tasks/question_answering)."""
-  # For training, `input_path` is expected to be a pre-processed TFRecord file,
-  # while for evaluation, it is expected to be a raw JSON file (b/173814590).
-  input_path: str = ''
-  global_batch_size: int = 48
-  is_training: bool = True
-  seq_length: int = 384
-  # Settings below are question answering specific.
-  version_2_with_negative: bool = False
-  # Settings below are only used for eval mode.
-  input_preprocessed_data_path: str = ''
-  doc_stride: int = 128
-  query_length: int = 64
-  # The path to the vocab file of word piece tokenizer or the
-  # model of the sentence piece tokenizer.
-  vocab_file: str = ''
-  tokenization: str = 'WordPiece'  # WordPiece or SentencePiece
-  do_lower_case: bool = True
-  xlnet_format: bool = False
-  file_type: str = 'tfrecord'
-
-
-@data_loader_factory.register_data_loader_cls(QADataConfig)
-class QuestionAnsweringDataLoader(data_loader.DataLoader):
-  """A class to load dataset for sentence prediction (classification) task."""
-
-  def __init__(self, params):
-    self._params = params
-    self._seq_length = params.seq_length
-    self._is_training = params.is_training
-    self._xlnet_format = params.xlnet_format
-
-  def _decode(self, record: tf.Tensor):
-    """Decodes a serialized tf.Example."""
-    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),
-    }
-    if self._xlnet_format:
-      name_to_features['class_index'] = tf.io.FixedLenFeature([], tf.int64)
-      name_to_features['paragraph_mask'] = tf.io.FixedLenFeature(
-          [self._seq_length], tf.int64)
-      if self._is_training:
-        name_to_features['is_impossible'] = tf.io.FixedLenFeature([], tf.int64)
-
-    if self._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)
-    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, y = {}, {}
-    for name, tensor in record.items():
-      if name in ('start_positions', 'end_positions', 'is_impossible'):
-        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
-      if name == 'start_positions' and self._xlnet_format:
-        x[name] = tensor
-    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,
-        dataset_fn=dataset_fn.pick_dataset_fn(self._params.file_type),
-        decoder_fn=self._decode,
-        parser_fn=self._parse)
-    return reader.read(input_context)