Internal change

PiperOrigin-RevId: 424187808

Author: tensorflower-gardener

official/nlp/modeling/layers/text_layers.py

@@ -14,7 +14,7 @@
 
 """Keras Layers for BERT-specific preprocessing."""
 # pylint: disable=g-import-not-at-top
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Mapping, Optional, Text, Union
 
 from absl import logging
 import tensorflow as tf
@@ -71,8 +71,9 @@ class BertTokenizer(tf.keras.layers.Layer):
 
   def __init__(self, *,
                vocab_file: str,
-               lower_case: bool,
+               lower_case: Optional[bool] = None,
                tokenize_with_offsets: bool = False,
+               tokenizer_kwargs: Optional[Mapping[Text, Any]] = None,
                **kwargs):
     """Initialize a `BertTokenizer` layer.
 
@@ -81,15 +82,18 @@ def __init__(self, *,
         This is a text file with newline-separated wordpiece tokens.
         This layer initializes a lookup table from it that gets used with
         `text.BertTokenizer`.
-      lower_case: A Python boolean forwarded to `text.BertTokenizer`.
+      lower_case: Optional boolean forwarded to `text.BertTokenizer`.
         If true, input text is converted to lower case (where applicable)
         before tokenization. This must be set to match the way in which
-        the `vocab_file` was created.
+        the `vocab_file` was created. If passed, this overrides whatever value
+        may have been passed in `tokenizer_kwargs`.
       tokenize_with_offsets: A Python boolean. If true, this layer calls
         `text.BertTokenizer.tokenize_with_offsets()` instead of plain
         `text.BertTokenizer.tokenize()` and outputs a triple of
         `(tokens, start_offsets, limit_offsets)`
         insead of just tokens.
+      tokenizer_kwargs: Optional mapping with keyword arguments to forward to
+        `text.BertTokenizer`'s constructor.
       **kwargs: Standard arguments to `Layer()`.
 
     Raises:
@@ -111,8 +115,11 @@ def __init__(self, *,
     self._special_tokens_dict = self._create_special_tokens_dict(
         self._vocab_table, vocab_file)
     super().__init__(**kwargs)
-    self._bert_tokenizer = text.BertTokenizer(
-        self._vocab_table, lower_case=lower_case)
+    tokenizer_kwargs = dict(tokenizer_kwargs or {})
+    if lower_case is not None:
+      tokenizer_kwargs["lower_case"] = lower_case
+    self._bert_tokenizer = text.BertTokenizer(self._vocab_table,
+                                              **tokenizer_kwargs)
 
   @property
   def vocab_size(self):