Reformat to make code clearer in tokenizer call (#11497)

* Reformat to make code clearer

* Reformat to make code clearer

src/transformers/tokenization_utils_base.py

@@ -2236,47 +2236,42 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
                 :obj:`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
         """
         # Input type checking for clearer error
-        assert isinstance(text, str) or (
-            isinstance(text, (list, tuple))
-            and (
-                len(text) == 0
-                or (
-                    isinstance(text[0], str)
-                    or (isinstance(text[0], (list, tuple)) and (len(text[0]) == 0 or isinstance(text[0][0], str)))
-                )
-            )
-        ), (
-            "text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) "
-            "or `List[List[str]]` (batch of pretokenized examples)."
-        )
+        def _is_valid_text_input(t):
+            if isinstance(t, str):
+                # Strings are fine
+                return True
+            elif isinstance(t, (list, tuple)):
+                # List are fine as long as they are...
+                if len(t) == 0:
+                    # ... empty
+                    return True
+                elif isinstance(t[0], str):
+                    # ... list of strings
+                    return True
+                elif isinstance(t[0], (list, tuple)):
+                    # ... list with an empty list or with a list of strings
+                    return len(t[0]) == 0 or isinstance(t[0][0], str)
+                else:
+                    return False
+            else:
+                return False
 
-        assert (
-            text_pair is None
-            or isinstance(text_pair, str)
-            or (
-                isinstance(text_pair, (list, tuple))
-                and (
-                    len(text_pair) == 0
-                    or (
-                        isinstance(text_pair[0], str)
-                        or (
-                            isinstance(text_pair[0], (list, tuple))
-                            and (len(text_pair[0]) == 0 or isinstance(text_pair[0][0], str))
-                        )
-                    )
-                )
+        if not _is_valid_text_input(text):
+            raise ValueError(
+                "text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) "
+                "or `List[List[str]]` (batch of pretokenized examples)."
             )
-        ), (
-            "text_pair input must of type `str` (single example), `List[str]` (batch or single pretokenized example) "
-            "or `List[List[str]]` (batch of pretokenized examples)."
-        )
 
-        is_batched = bool(
-            (not is_split_into_words and isinstance(text, (list, tuple)))
-            or (
-                is_split_into_words and isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
+        if text_pair is not None and not _is_valid_text_input(text_pair):
+            raise ValueError(
+                "text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) "
+                "or `List[List[str]]` (batch of pretokenized examples)."
             )
-        )
+
+        if is_split_into_words:
+            is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
+        else:
+            is_batched = isinstance(text, (list, tuple))
 
         if is_batched:
             if isinstance(text_pair, str):