Automated rollback of commit 0d2f3be5eb

PiperOrigin-RevId: 296320816
Change-Id: Ib8b5857178fa10513755de65ffcde1adf6dabad3

tensorflow/python/keras/layers/preprocessing/BUILD

@@ -303,10 +303,9 @@ cuda_py_test(
 )
 
 tf_py_test(
-    name = "preprocessing_normalization_test",
+    name = "normalization_test",
     size = "small",
     srcs = ["normalization_test.py"],
-    main = "normalization_test.py",
     python_version = "PY3",
     deps = [
         ":normalization",
@@ -317,10 +316,9 @@ tf_py_test(
 )
 
 tf_py_test(
-    name = "preprocessing_text_vectorization_test",
+    name = "text_vectorization_test",
     size = "medium",
     srcs = ["text_vectorization_test.py"],
-    main = "text_vectorization_test.py",
     python_version = "PY3",
     deps = [
         ":preprocessing_test_utils",

tensorflow/python/keras/layers/preprocessing/index_lookup.py

@@ -32,6 +32,7 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops.ragged import ragged_functional_ops
 from tensorflow.python.ops.ragged import ragged_tensor
+from tensorflow.python.platform import gfile
 from tensorflow.python.util import compat
 
 # The string tokens in the extracted vocabulary
@@ -66,7 +67,13 @@ class IndexLookup(base_preprocessing_layer.CombinerPreprocessingLayer):
       1. If this value is more than 1, OOV inputs are hashed to determine their
       OOV value; if this value is 0, passing an OOV input will result in a
       runtime error.
-    vocabulary: An optional list of vocabulary terms.
+    vocabulary: An optional list of vocabulary terms, or a path to a text file
+      containing a vocabulary to load into this layer. The file should contain
+      one token per line. In either case, the vocabulary must be unique; if
+      the list or file contains the same token multiple times, an error will
+      be thrown. Note that when passing a vocabulary - either as a list or as
+      a file - the vocabulary will not be present in the layer's config dict;
+      it will instead be a part of the layer's weights.
     reserve_zero: Whether to reserve the index 0, which indicates pad values in
       the Keras masking system. If True, the output of this layer will be in the
       range `[1...max_tokens+1)`; if False, the output will be in the range
@@ -164,10 +171,38 @@ class IndexLookup(base_preprocessing_layer.CombinerPreprocessingLayer):
     self._inverse_table = None
 
     if vocabulary is not None:
-      self._export_vocab = True
+      if isinstance(vocabulary, str):
+        vocabulary = self._get_vocabulary_from_file(vocabulary)
+
+      vocabulary_set = set(vocabulary)
+      if len(vocabulary) != len(vocabulary_set):
+        repeated_items = [
+            item for item, count in collections.Counter(vocabulary).items()
+            if count > 1
+        ]
+        raise ValueError("The passed vocabulary has at least one repeated "
+                         "term. Please uniquify your dataset before passing "
+                         "it to IndexLookup(). The repeated terms are %s" %
+                         repeated_items)
       self.set_vocabulary(vocabulary)
-    else:
+
-      self._export_vocab = False
+  def _get_vocabulary_from_file(self, vocabulary_path):
+    vocab = []
+    with gfile.GFile(vocabulary_path, "r") as reader:
+      while True:
+        # Get the next line, and break if it is None.
+        text = reader.readline()
+        if not text:
+          break
+
+        # Convert the raw text into UTF8 and strip whitespace.
+        if isinstance(text, str):
+          token = text
+        elif isinstance(text, bytes):
+          token = text.decode("utf-8", "ignore")
+        token = token.strip()
+        vocab.append(token)
+    return vocab
 
   def _get_table_data(self):
     keys, values = self._table.export()
@@ -256,11 +291,10 @@ class IndexLookup(base_preprocessing_layer.CombinerPreprocessingLayer):
     return [x for _, x in sorted(zip(values, keys))]
 
   def get_config(self):
-    vocabulary = self.get_vocabulary() if self._export_vocab else None
     config = {
         "max_tokens": self.max_tokens,
         "num_oov_tokens": self.num_oov_tokens,
-        "vocabulary": vocabulary,
+        "vocabulary": None,
         "reserve_zero": self.reserve_zero,
         "mask_zero": self.mask_zero,
     }

tensorflow/python/keras/layers/preprocessing/index_lookup_test.py

@@ -38,6 +38,7 @@ from tensorflow.python.keras.layers.preprocessing import preprocessing_test_util
 from tensorflow.python.keras.saving import save
 from tensorflow.python.keras.utils.generic_utils import CustomObjectScope
 from tensorflow.python.ops.ragged import ragged_factory_ops
+from tensorflow.python.platform import gfile
 from tensorflow.python.platform import test
 
 
@@ -356,7 +357,22 @@ class IndexLookupOutputTest(keras_parameterized.TestCase,
     output_dataset = model.predict(input_array)
     self.assertAllEqual(expected_output, output_dataset)
 
-  def test_int_output_explicit_vocab_from_config(self):
+
+@keras_parameterized.run_all_keras_modes
+class IndexLookupVocabularyTest(keras_parameterized.TestCase,
+                                preprocessing_test_utils.PreprocessingLayerTest
+                               ):
+
+  def _write_to_temp_file(self, file_name, vocab_list):
+    vocab_path = os.path.join(self.get_temp_dir(), file_name + ".txt")
+    with gfile.GFile(vocab_path, "w") as writer:
+      for vocab in vocab_list:
+        writer.write(vocab + "\n")
+      writer.flush()
+      writer.close()
+    return vocab_path
+
+  def test_int_output_explicit_vocab(self):
     vocab_data = ["earth", "wind", "and", "fire"]
     input_array = np.array([["earth", "wind", "and", "fire"],
                             ["fire", "and", "earth", "michigan"]])
@@ -366,10 +382,22 @@ class IndexLookupOutputTest(keras_parameterized.TestCase,
     layer = get_layer_class()(vocabulary=vocab_data)
     int_data = layer(input_data)
     model = keras.Model(inputs=input_data, outputs=int_data)
+    output_dataset = model.predict(input_array)
+    self.assertAllEqual(expected_output, output_dataset)
 
-    with CustomObjectScope({"IndexLookup": get_layer_class()}):
+  def test_int_output_explicit_vocab_from_file(self):
-      new_model = keras.Model.from_config(model.get_config())
+    vocab_list = ["earth", "wind", "and", "fire"]
-    output_dataset = new_model.predict(input_array)
+    vocab_path = self._write_to_temp_file("vocab_file", vocab_list)
+
+    input_array = np.array([["earth", "wind", "and", "fire"],
+                            ["fire", "and", "earth", "michigan"]])
+    expected_output = [[2, 3, 4, 5], [5, 4, 2, 1]]
+
+    input_data = keras.Input(shape=(None,), dtype=dtypes.string)
+    layer = get_layer_class()(vocabulary=vocab_path)
+    int_data = layer(input_data)
+    model = keras.Model(inputs=input_data, outputs=int_data)
+    output_dataset = model.predict(input_array)
     self.assertAllEqual(expected_output, output_dataset)
 
   def test_vocab_appending(self):
@@ -387,6 +415,17 @@ class IndexLookupOutputTest(keras_parameterized.TestCase,
     output_dataset = model.predict(input_array)
     self.assertAllClose(expected_output, output_dataset)
 
+  def test_non_unique_vocab_fails(self):
+    vocab_data = ["earth", "wind", "and", "fire", "fire"]
+    with self.assertRaisesRegex(ValueError, ".*repeated term.*fire.*"):
+      _ = get_layer_class()(vocabulary=vocab_data)
+
+  def test_non_unique_vocab_from_file_fails(self):
+    vocab_list = ["earth", "wind", "and", "fire", "earth"]
+    vocab_path = self._write_to_temp_file("repeat_vocab_file", vocab_list)
+    with self.assertRaisesRegex(ValueError, ".*repeated term.*earth.*"):
+      _ = get_layer_class()(vocabulary=vocab_path)
+
 
 @keras_parameterized.run_all_keras_modes
 class InverseLookupOutputTest(keras_parameterized.TestCase,