logging

pytorch_pretrained_bert/tokenization_openai.py

@@ -125,16 +125,19 @@ class OpenAIGPTTokenizer(object):
         merges = [tuple(merge.split()) for merge in merges]
         self.bpe_ranks = dict(zip(merges, range(len(merges))))
         self.cache = {}
-        if not special_tokens:
-            self.special_tokens = {}
-        else:
-            self.special_tokens = dict((tok, len(self.encoder) + i) for i, tok in enumerate(special_tokens))
+        self.set_special_tokens(special_tokens)
 
     def __len__(self):
         return len(self.encoder) + len(self.special_tokens)
 
     def set_special_tokens(self, special_tokens):
+        if not special_tokens:
+            self.special_tokens = {}
+            self.special_tokens_decoder = {}
+            return
         self.special_tokens = dict((tok, len(self.encoder) + i) for i, tok in enumerate(special_tokens))
+        self.special_tokens_decoder = {v:k for k, v in self.special_tokens.items()}
+        logger.info("Special tokens {}".format(self.special_tokens))
 
     def bpe(self, token):
         word = tuple(token[:-1]) + ( token[-1] + '</w>',)
@@ -189,6 +192,11 @@ class OpenAIGPTTokenizer(object):
     def convert_tokens_to_ids(self, tokens):
         """Converts a sequence of tokens into ids using the vocab."""
         ids = []
+        if isinstance(tokens, str):
+            if tokens in self.special_tokens:
+                return self.special_tokens[tokens]
+            else:
+                return self.encoder.get(tokens, 0)
         for token in tokens:
             if token in self.special_tokens:
                 ids.append(self.special_tokens[token])
@@ -206,6 +214,9 @@ class OpenAIGPTTokenizer(object):
         """Converts a sequence of ids in BPE tokens using the vocab."""
         tokens = []
         for i in ids:
+            if i in self.special_tokens_decoder:
+                tokens.append(self.special_tokens_decoder[i])
+            else:
                 tokens.append(self.decoder[i])
         return tokens