added cached tokenizer

src/transformers/tokenization_utils.py

@@ -457,10 +457,27 @@ class PreTrainedTokenizer(PreTrainedTokenizerBase):
     @property
     def added_tokens_encoder(self) -> Dict[str, int]:
         """
-        Returns the sorted mapping from string to index. The added tokens encoder is cached for performance
-        optimisation in `self._added_tokens_encoder` for the slow tokenizers.
+        Returns the sorted mapping from string to index. The cache is dynamically invalidated if `_added_tokens_decoder`
+        has changed since the last computation.
         """
-        return {k.content: v for v, k in sorted(self._added_tokens_decoder.items(), key=lambda item: item[0])}
+        # Check if cache exists and is valid
+        if not hasattr(self, "_cached_added_tokens_encoder") or self._is_decoder_modified():
+            # Recompute and cache the added tokens encoder
+            self._cached_added_tokens_encoder = {
+                k.content: v for v, k in sorted(self._added_tokens_decoder.items(), key=lambda item: item[0])
+            }
+            # Store the current state of `_added_tokens_decoder` for future validity checks
+            self._cached_decoder_state = self._added_tokens_decoder.copy()
+        return self._cached_added_tokens_encoder
+
+    def _is_decoder_modified(self) -> bool:
+        """
+        Check if `_added_tokens_decoder` has been modified since the last computation.
+        Returns:
+            bool: True if modified, False otherwise.
+        """
+        # Compare the current state with the cached state
+        return not hasattr(self, "_cached_decoder_state") or self._cached_decoder_state != self._added_tokens_decoder
 
     @property
     def added_tokens_decoder(self) -> Dict[int, AddedToken]: