Remove special treatment for custom vocab files (#10637)

* Remove special path for custom vocab files

* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Expand error message
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

src/transformers/tokenization_utils_base.py

@@ -1601,38 +1601,20 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
             logger.info("Offline mode: forcing local_files_only=True")
             local_files_only = True
 
-        s3_models = list(cls.max_model_input_sizes.keys())
         pretrained_model_name_or_path = str(pretrained_model_name_or_path)
         vocab_files = {}
         init_configuration = {}
-        if pretrained_model_name_or_path in s3_models:
-            # Get the vocabulary from AWS S3 bucket
-            for file_id, map_list in cls.pretrained_vocab_files_map.items():
-                vocab_files[file_id] = map_list[pretrained_model_name_or_path]
-            if (
-                cls.pretrained_init_configuration
-                and pretrained_model_name_or_path in cls.pretrained_init_configuration
-            ):
-                init_configuration = cls.pretrained_init_configuration[pretrained_model_name_or_path].copy()
-        else:
-            # Get the vocabulary from local files
-            logger.info(
-                "Model name '{}' not found in model shortcut name list ({}). "
-                "Assuming '{}' is a path, a model identifier, or url to a directory containing tokenizer files.".format(
-                    pretrained_model_name_or_path, ", ".join(s3_models), pretrained_model_name_or_path
-                )
-            )
 
         if os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
             if len(cls.vocab_files_names) > 1:
                 raise ValueError(
-                        "Calling {}.from_pretrained() with the path to a single file or url is not supported."
-                        "Use a model identifier or the path to a directory instead.".format(cls.__name__)
-                    )
-                logger.warning(
-                    "Calling {}.from_pretrained() with the path to a single file or url is deprecated".format(
-                        cls.__name__
+                    f"Calling {cls.__name__}.from_pretrained() with the path to a single file or url is not "
+                    "supported for this tokenizer. Use a model identifier or the path to a directory instead."
                 )
+            warnings.warn(
+                f"Calling {cls.__name__}.from_pretrained() with the path to a single file or url is deprecated and "
+                "won't be possible anymore in v5. Use a model identifier or the path to a directory instead.",
+                FutureWarning,
             )
             file_id = list(cls.vocab_files_names.keys())[0]
             vocab_files[file_id] = pretrained_model_name_or_path
@@ -1652,7 +1634,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
                     else:
                         full_file_name = os.path.join(pretrained_model_name_or_path, file_name)
                     if not os.path.exists(full_file_name):
-                            logger.info("Didn't find file {}. We won't load it.".format(full_file_name))
+                        logger.info(f"Didn't find file {full_file_name}. We won't load it.")
                         full_file_name = None
                 else:
                     full_file_name = hf_bucket_url(
@@ -1672,7 +1654,6 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
             if file_path is None:
                 resolved_vocab_files[file_id] = None
             else:
-                try:
                 try:
                     resolved_vocab_files[file_id] = cached_path(
                         file_path,
@@ -1683,6 +1664,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
                         local_files_only=local_files_only,
                         use_auth_token=use_auth_token,
                     )
+
                 except FileNotFoundError as error:
                     if local_files_only:
                         unresolved_files.append(file_id)
@@ -1715,9 +1697,9 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
                 continue
 
             if file_path == resolved_vocab_files[file_id]:
-                logger.info("loading file {}".format(file_path))
+                logger.info(f"loading file {file_path}")
             else:
-                logger.info("loading file {} from cache at {}".format(file_path, resolved_vocab_files[file_id]))
+                logger.info(f"loading file {file_path} from cache at {resolved_vocab_files[file_id]}")
 
         return cls._from_pretrained(
             resolved_vocab_files, pretrained_model_name_or_path, init_configuration, *init_inputs, **kwargs