Delete dual_encoder_dataloader_test.py

dual_encoder_dataloader_test.py

@@ -1,131 +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.dual_encoder_dataloader."""
-import os
-
-from absl.testing import parameterized
-import tensorflow as tf, tf_keras
-
-from official.nlp.data import dual_encoder_dataloader
-
-
-_LEFT_FEATURE_NAME = 'left_input'
-_RIGHT_FEATURE_NAME = 'right_input'
-
-
-def _create_fake_dataset(output_path):
-  """Creates a fake dataset contains examples for training a dual encoder model.
-
-    The created dataset contains examples with two byteslist features keyed by
-    _LEFT_FEATURE_NAME and _RIGHT_FEATURE_NAME.
-
-  Args:
-    output_path: The output path of the fake dataset.
-  """
-  def create_str_feature(values):
-    return tf.train.Feature(bytes_list=tf.train.BytesList(value=values))
-
-  with tf.io.TFRecordWriter(output_path) as writer:
-    for _ in range(100):
-      features = {}
-      features[_LEFT_FEATURE_NAME] = create_str_feature([b'hello world.'])
-      features[_RIGHT_FEATURE_NAME] = create_str_feature([b'world hello.'])
-
-      tf_example = tf.train.Example(
-          features=tf.train.Features(feature=features))
-      writer.write(tf_example.SerializeToString())
-
-
-def _make_vocab_file(vocab, output_path):
-  with tf.io.gfile.GFile(output_path, 'w') as f:
-    f.write('\n'.join(vocab + ['']))
-
-
-class DualEncoderDataTest(tf.test.TestCase, parameterized.TestCase):
-
-  def test_load_dataset(self):
-    seq_length = 16
-    batch_size = 10
-    train_data_path = os.path.join(self.get_temp_dir(), 'train.tf_record')
-    vocab_path = os.path.join(self.get_temp_dir(), 'vocab.txt')
-
-    _create_fake_dataset(train_data_path)
-    _make_vocab_file(
-        ['[PAD]', '[UNK]', '[CLS]', '[SEP]', 'he', '#llo', 'world'], vocab_path)
-
-    data_config = dual_encoder_dataloader.DualEncoderDataConfig(
-        input_path=train_data_path,
-        seq_length=seq_length,
-        vocab_file=vocab_path,
-        lower_case=True,
-        left_text_fields=(_LEFT_FEATURE_NAME,),
-        right_text_fields=(_RIGHT_FEATURE_NAME,),
-        global_batch_size=batch_size)
-    dataset = dual_encoder_dataloader.DualEncoderDataLoader(
-        data_config).load()
-    features = next(iter(dataset))
-    self.assertCountEqual(
-        ['left_word_ids', 'left_mask', 'left_type_ids', 'right_word_ids',
-         'right_mask', 'right_type_ids'],
-        features.keys())
-    self.assertEqual(features['left_word_ids'].shape, (batch_size, seq_length))
-    self.assertEqual(features['left_mask'].shape, (batch_size, seq_length))
-    self.assertEqual(features['left_type_ids'].shape, (batch_size, seq_length))
-    self.assertEqual(features['right_word_ids'].shape, (batch_size, seq_length))
-    self.assertEqual(features['right_mask'].shape, (batch_size, seq_length))
-    self.assertEqual(features['right_type_ids'].shape, (batch_size, seq_length))
-
-  @parameterized.parameters(False, True)
-  def test_load_tfds(self, use_preprocessing_hub):
-    seq_length = 16
-    batch_size = 10
-    if use_preprocessing_hub:
-      vocab_path = ''
-      preprocessing_hub = (
-          'https://tfhub.dev/tensorflow/bert_multi_cased_preprocess/3')
-    else:
-      vocab_path = os.path.join(self.get_temp_dir(), 'vocab.txt')
-      _make_vocab_file(
-          ['[PAD]', '[UNK]', '[CLS]', '[SEP]', 'he', '#llo', 'world'],
-          vocab_path)
-      preprocessing_hub = ''
-
-    data_config = dual_encoder_dataloader.DualEncoderDataConfig(
-        tfds_name='para_crawl/enmt',
-        tfds_split='train',
-        seq_length=seq_length,
-        vocab_file=vocab_path,
-        lower_case=True,
-        left_text_fields=('en',),
-        right_text_fields=('mt',),
-        preprocessing_hub_module_url=preprocessing_hub,
-        global_batch_size=batch_size)
-    dataset = dual_encoder_dataloader.DualEncoderDataLoader(
-        data_config).load()
-    features = next(iter(dataset))
-    self.assertCountEqual(
-        ['left_word_ids', 'left_mask', 'left_type_ids', 'right_word_ids',
-         'right_mask', 'right_type_ids'],
-        features.keys())
-    self.assertEqual(features['left_word_ids'].shape, (batch_size, seq_length))
-    self.assertEqual(features['left_mask'].shape, (batch_size, seq_length))
-    self.assertEqual(features['left_type_ids'].shape, (batch_size, seq_length))
-    self.assertEqual(features['right_word_ids'].shape, (batch_size, seq_length))
-    self.assertEqual(features['right_mask'].shape, (batch_size, seq_length))
-    self.assertEqual(features['right_type_ids'].shape, (batch_size, seq_length))
-
-
-if __name__ == '__main__':
-  tf.test.main()