[fsmt tokenizer] support lowercase tokenizer (#8389)

* support lowercase tokenizer

* fix arg pos

src/transformers/convert_fsmt_original_pytorch_checkpoint_to_pytorch.py

@@ -133,6 +133,14 @@ def convert_fsmt_checkpoint_to_pytorch(fsmt_checkpoint_path, pytorch_dump_folder
     with open(src_vocab_file, "w", encoding="utf-8") as f:
         f.write(json.dumps(src_vocab, ensure_ascii=False, indent=json_indent))
 
+    # detect whether this is a do_lower_case situation, which can be derived by checking whether we
+    # have at least one upcase letter in the source vocab
+    do_lower_case = True
+    for k in src_vocab.keys():
+        if not k.islower():
+            do_lower_case = False
+            break
+
     tgt_dict = Dictionary.load(tgt_dict_file)
     tgt_vocab = rewrite_dict_keys(tgt_dict.indices)
     tgt_vocab_size = len(tgt_vocab)
@@ -207,6 +215,7 @@ def convert_fsmt_checkpoint_to_pytorch(fsmt_checkpoint_path, pytorch_dump_folder
     tokenizer_conf = {
         "langs": [src_lang, tgt_lang],
         "model_max_length": 1024,
+        "do_lower_case": do_lower_case,
     }
 
     print(f"Generating {fsmt_tokenizer_config_file}")

src/transformers/tokenization_fsmt.py

@@ -154,7 +154,7 @@ class FSMTTokenizer(PreTrainedTokenizer):
             File containing the vocabulary for the target language.
         merges_file (:obj:`str`):
             File containing the merges.
-        do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`):
+        do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`False`):
             Whether or not to lowercase the input when tokenizing.
         unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`):
             The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
@@ -186,6 +186,7 @@ class FSMTTokenizer(PreTrainedTokenizer):
         src_vocab_file=None,
         tgt_vocab_file=None,
         merges_file=None,
+        do_lower_case=False,
         unk_token="<unk>",
         bos_token="<s>",
         sep_token="</s>",
@@ -197,6 +198,7 @@ class FSMTTokenizer(PreTrainedTokenizer):
             src_vocab_file=src_vocab_file,
             tgt_vocab_file=tgt_vocab_file,
             merges_file=merges_file,
+            do_lower_case=do_lower_case,
             unk_token=unk_token,
             bos_token=bos_token,
             sep_token=sep_token,
@@ -207,6 +209,7 @@ class FSMTTokenizer(PreTrainedTokenizer):
         self.src_vocab_file = src_vocab_file
         self.tgt_vocab_file = tgt_vocab_file
         self.merges_file = merges_file
+        self.do_lower_case = do_lower_case
 
         # cache of sm.MosesPunctNormalizer instance
         self.cache_moses_punct_normalizer = dict()
@@ -351,6 +354,9 @@ class FSMTTokenizer(PreTrainedTokenizer):
         #     raise ValueError(f"Expected lang={self.src_lang}, but got {lang}")
         lang = self.src_lang
 
+        if self.do_lower_case:
+            text = text.lower()
+
         if bypass_tokenizer:
             text = text.split()
         else:

tests/test_tokenization_fsmt.py

@@ -151,6 +151,13 @@ class FSMTTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
             decoded_text = tokenizer_dec.decode(encoded_ids, skip_special_tokens=True)
             self.assertEqual(decoded_text, src_text)
 
+    @slow
+    def test_tokenizer_lower(self):
+        tokenizer = FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en", do_lower_case=True)
+        tokens = tokenizer.tokenize("USA is United States of America")
+        expected = ["us", "a</w>", "is</w>", "un", "i", "ted</w>", "st", "ates</w>", "of</w>", "am", "er", "ica</w>"]
+        self.assertListEqual(tokens, expected)
+
     @unittest.skip("FSMTConfig.__init__  requires non-optional args")
     def test_torch_encode_plus_sent_to_model(self):
         pass