Delete classifier_data_lib_test.py

classifier_data_lib_test.py

@@ -1,95 +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 third_party.tensorflow_models.official.nlp.data.classifier_data_lib."""
-
-import os
-import tempfile
-
-from absl.testing import parameterized
-import tensorflow as tf, tf_keras
-import tensorflow_datasets as tfds
-
-from official.nlp.data import classifier_data_lib
-from official.nlp.tools import tokenization
-
-
-def decode_record(record, name_to_features):
-  """Decodes a record to a TensorFlow example."""
-  return tf.io.parse_single_example(record, name_to_features)
-
-
-class BertClassifierLibTest(tf.test.TestCase, parameterized.TestCase):
-
-  def setUp(self):
-    super(BertClassifierLibTest, self).setUp()
-    self.model_dir = self.get_temp_dir()
-    self.processors = {
-        "CB": classifier_data_lib.CBProcessor,
-        "SUPERGLUE-RTE": classifier_data_lib.SuperGLUERTEProcessor,
-        "BOOLQ": classifier_data_lib.BoolQProcessor,
-        "WIC": classifier_data_lib.WiCProcessor,
-    }
-
-    vocab_tokens = [
-        "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
-        "##ing", ","
-    ]
-    with tempfile.NamedTemporaryFile(delete=False) as vocab_writer:
-      vocab_writer.write("".join([x + "\n" for x in vocab_tokens
-                                 ]).encode("utf-8"))
-    vocab_file = vocab_writer.name
-    self.tokenizer = tokenization.FullTokenizer(vocab_file)
-
-  @parameterized.parameters(
-      {"task_type": "CB"},
-      {"task_type": "BOOLQ"},
-      {"task_type": "SUPERGLUE-RTE"},
-      {"task_type": "WIC"},
-  )
-  def test_generate_dataset_from_tfds_processor(self, task_type):
-    with tfds.testing.mock_data(num_examples=5):
-      output_path = os.path.join(self.model_dir, task_type)
-
-      processor = self.processors[task_type]()
-
-      classifier_data_lib.generate_tf_record_from_data_file(
-          processor,
-          None,
-          self.tokenizer,
-          train_data_output_path=output_path,
-          eval_data_output_path=output_path,
-          test_data_output_path=output_path)
-      files = tf.io.gfile.glob(output_path)
-      self.assertNotEmpty(files)
-
-      train_dataset = tf.data.TFRecordDataset(output_path)
-      seq_length = 128
-      label_type = tf.int64
-      name_to_features = {
-          "input_ids": tf.io.FixedLenFeature([seq_length], tf.int64),
-          "input_mask": tf.io.FixedLenFeature([seq_length], tf.int64),
-          "segment_ids": tf.io.FixedLenFeature([seq_length], tf.int64),
-          "label_ids": tf.io.FixedLenFeature([], label_type),
-      }
-      train_dataset = train_dataset.map(
-          lambda record: decode_record(record, name_to_features))
-
-      # If data is retrieved without error, then all requirements
-      # including data type/shapes are met.
-      _ = next(iter(train_dataset))
-
-
-if __name__ == "__main__":
-  tf.test.main()