[AutoTokenizer] Allow creation of tokenizers by tokenizer type (huggi…

…ngface#13668)

* up

* up

src/transformers/models/auto/tokenization_auto.py

@@ -402,6 +402,8 @@ def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
                 facebook/rag-token-base), specify it here.
             use_fast (:obj:`bool`, `optional`, defaults to :obj:`True`):
                 Whether or not to try to load the fast version of the tokenizer.
+            tokenizer_type (:obj:`str`, `optional`):
+                Tokenizer type to be loaded.
             kwargs (additional keyword arguments, `optional`):
                 Will be passed to the Tokenizer ``__init__()`` method. Can be used to set special tokens like
                 ``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``,
@@ -425,8 +427,33 @@ def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
         kwargs["_from_auto"] = True
 
         use_fast = kwargs.pop("use_fast", True)
+        tokenizer_type = kwargs.pop("tokenizer_type", None)
 
-        # First, let's try to use the tokenizer_config file to get the tokenizer class.
+        # First, let's see whether the tokenizer_type is passed so that we can leverage it
+        if tokenizer_type is not None:
+            tokenizer_class = None
+            tokenizer_class_tuple = TOKENIZER_MAPPING_NAMES.get(tokenizer_type, None)
+
+            if tokenizer_class_tuple is None:
+                raise ValueError(
+                    f"Passed `tokenizer_type` {tokenizer_type} does not exist. `tokenizer_type` should be one of "
+                    f"{', '.join(c for c in TOKENIZER_MAPPING_NAMES.keys())}."
+                )
+
+            tokenizer_class_name, tokenizer_fast_class_name = tokenizer_class_tuple
+
+            if use_fast and tokenizer_fast_class_name is not None:
+                tokenizer_class = tokenizer_class_from_name(tokenizer_fast_class_name)
+
+            if tokenizer_class is None:
+                tokenizer_class = tokenizer_class_from_name(tokenizer_class_name)
+
+            if tokenizer_class is None:
+                raise ValueError(f"Tokenizer class {tokenizer_class_name} is not currently imported.")
+
+            return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
+
+        # Next, let's try to use the tokenizer_config file to get the tokenizer class.
         tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
         config_tokenizer_class = tokenizer_config.get("tokenizer_class")
 

tests/fixtures/merges.txt

@@ -0,0 +1,5 @@
+#version: 0.2
+Ġ l
+Ġl o
+Ġlo w
+e r

tests/fixtures/vocab.json

@@ -0,0 +1 @@
+{"l": 0, "o": 1, "w": 2, "e": 3, "r": 4, "s": 5, "t": 6, "i": 7, "d": 8, "n": 9, "Ġ": 10, "Ġl": 11, "Ġn": 12, "Ġlo": 13, "Ġlow": 14, "er": 15, "Ġlowest": 16, "Ġnewer": 17, "Ġwider": 18, "<unk>": 19, "<|endoftext|>": 20}

tests/fixtures/vocab.txt

@@ -0,0 +1,10 @@
+[PAD]
+[SEP]
+[MASK]
+[CLS]
+[unused3]
+[unused4]
+[unused5]
+[unused6]
+[unused7]
+[unused8]

tests/test_tokenization_auto.py

@@ -13,9 +13,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
+import shutil
 import tempfile
 import unittest
 
+import pytest
+
 from transformers import (
     BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
@@ -78,6 +82,39 @@ def test_tokenizer_from_tokenizer_class(self):
         self.assertIsInstance(tokenizer, (BertTokenizer, BertTokenizerFast))
         self.assertEqual(tokenizer.vocab_size, 12)
 
+    def test_tokenizer_from_type(self):
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            shutil.copy("./tests/fixtures/vocab.txt", os.path.join(tmp_dir, "vocab.txt"))
+
+            tokenizer = AutoTokenizer.from_pretrained(tmp_dir, tokenizer_type="bert", use_fast=False)
+            self.assertIsInstance(tokenizer, BertTokenizer)
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            shutil.copy("./tests/fixtures/vocab.json", os.path.join(tmp_dir, "vocab.json"))
+            shutil.copy("./tests/fixtures/merges.txt", os.path.join(tmp_dir, "merges.txt"))
+
+            tokenizer = AutoTokenizer.from_pretrained(tmp_dir, tokenizer_type="gpt2", use_fast=False)
+            self.assertIsInstance(tokenizer, GPT2Tokenizer)
+
+    @require_tokenizers
+    def test_tokenizer_from_type_fast(self):
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            shutil.copy("./tests/fixtures/vocab.txt", os.path.join(tmp_dir, "vocab.txt"))
+
+            tokenizer = AutoTokenizer.from_pretrained(tmp_dir, tokenizer_type="bert")
+            self.assertIsInstance(tokenizer, BertTokenizerFast)
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            shutil.copy("./tests/fixtures/vocab.json", os.path.join(tmp_dir, "vocab.json"))
+            shutil.copy("./tests/fixtures/merges.txt", os.path.join(tmp_dir, "merges.txt"))
+
+            tokenizer = AutoTokenizer.from_pretrained(tmp_dir, tokenizer_type="gpt2")
+            self.assertIsInstance(tokenizer, GPT2TokenizerFast)
+
+    def test_tokenizer_from_type_incorrect_name(self):
+        with pytest.raises(ValueError):
+            AutoTokenizer.from_pretrained("./", tokenizer_type="xxx")
+
     @require_tokenizers
     def test_tokenizer_identifier_with_correct_config(self):
         for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]: