Delete tagging_dataloader_test.py

tagging_dataloader_test.py

@@ -1,82 +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.tagging_data_loader."""
-import os
-
-from absl.testing import parameterized
-import numpy as np
-import tensorflow as tf, tf_keras
-
-from official.nlp.data import tagging_dataloader
-
-
-def _create_fake_dataset(output_path, seq_length, include_sentence_id):
-  """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 i 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['label_ids'] = create_int_feature(
-        np.random.randint(10, size=(seq_length)))
-    if include_sentence_id:
-      features['sentence_id'] = create_int_feature([i])
-      features['sub_sentence_id'] = create_int_feature([0])
-
-    tf_example = tf.train.Example(features=tf.train.Features(feature=features))
-    writer.write(tf_example.SerializeToString())
-  writer.close()
-
-
-class TaggingDataLoaderTest(tf.test.TestCase, parameterized.TestCase):
-
-  @parameterized.parameters(True, False)
-  def test_load_dataset(self, include_sentence_id):
-    seq_length = 16
-    batch_size = 10
-    train_data_path = os.path.join(self.get_temp_dir(), 'train.tf_record')
-    _create_fake_dataset(train_data_path, seq_length, include_sentence_id)
-    data_config = tagging_dataloader.TaggingDataConfig(
-        input_path=train_data_path,
-        seq_length=seq_length,
-        global_batch_size=batch_size,
-        include_sentence_id=include_sentence_id)
-
-    dataset = tagging_dataloader.TaggingDataLoader(data_config).load()
-    features, labels = next(iter(dataset))
-
-    expected_keys = ['input_word_ids', 'input_mask', 'input_type_ids']
-    if include_sentence_id:
-      expected_keys.extend(['sentence_id', 'sub_sentence_id'])
-    self.assertCountEqual(expected_keys, 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.assertEqual(labels.shape, (batch_size, seq_length))
-    if include_sentence_id:
-      self.assertEqual(features['sentence_id'].shape, (batch_size,))
-      self.assertEqual(features['sub_sentence_id'].shape, (batch_size,))
-
-
-if __name__ == '__main__':
-  tf.test.main()