Delete question_answering_dataloader_test.py

question_answering_dataloader_test.py

@@ -1,74 +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.
-
-"""Tests for official.nlp.data.question_answering_dataloader."""
-import os
-
-import numpy as np
-import tensorflow as tf, tf_keras
-
-from official.nlp.data import question_answering_dataloader
-
-
-def _create_fake_dataset(output_path, seq_length):
-  """Creates a fake dataset."""
-  writer = tf.io.TFRecordWriter(output_path)
-
-  def create_int_feature(values):
-    f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
-    return f
-
-  for _ in range(100):
-    features = {}
-    input_ids = np.random.randint(100, size=(seq_length))
-    features['input_ids'] = create_int_feature(input_ids)
-    features['input_mask'] = create_int_feature(np.ones_like(input_ids))
-    features['segment_ids'] = create_int_feature(np.ones_like(input_ids))
-    features['start_positions'] = create_int_feature(np.array([0]))
-    features['end_positions'] = create_int_feature(np.array([10]))
-
-    tf_example = tf.train.Example(features=tf.train.Features(feature=features))
-    writer.write(tf_example.SerializeToString())
-  writer.close()
-
-
-class QuestionAnsweringDataTest(tf.test.TestCase):
-
-  def test_load_dataset(self):
-    seq_length = 128
-    batch_size = 10
-    input_path = os.path.join(self.get_temp_dir(), 'train.tf_record')
-    _create_fake_dataset(input_path, seq_length)
-    data_config = question_answering_dataloader.QADataConfig(
-        is_training=True,
-        input_path=input_path,
-        seq_length=seq_length,
-        global_batch_size=batch_size)
-    dataset = question_answering_dataloader.QuestionAnsweringDataLoader(
-        data_config).load()
-    features, labels = next(iter(dataset))
-
-    self.assertCountEqual(['input_word_ids', 'input_mask', 'input_type_ids'],
-                          features.keys())
-    self.assertEqual(features['input_word_ids'].shape, (batch_size, seq_length))
-    self.assertEqual(features['input_mask'].shape, (batch_size, seq_length))
-    self.assertEqual(features['input_type_ids'].shape, (batch_size, seq_length))
-
-    self.assertCountEqual(['start_positions', 'end_positions'], labels.keys())
-    self.assertEqual(labels['start_positions'].shape, (batch_size,))
-    self.assertEqual(labels['end_positions'].shape, (batch_size,))
-
-
-if __name__ == '__main__':
-  tf.test.main()