Extend typing to path-like objects in PretrainedConfig and PreTrainedModel (#8770)

* update configuration_utils.py typing to allow pathlike objects when sensible

* update modeling_utils.py typing to allow pathlike objects when sensible

* black

* update tokenization_utils_base.py typing to allow pathlike objects when sensible

* update tokenization_utils_fast.py typing to allow pathlike objects when sensible

* update configuration_auto.py typing to allow pathlike objects when sensible

* update configuration_auto.py docstring to allow pathlike objects when sensible

* update tokenization_auto.py docstring to allow pathlike objects when sensible

* black

src/transformers/configuration_utils.py

@@ -19,7 +19,7 @@
 import copy
 import json
 import os
-from typing import Any, Dict, Tuple
+from typing import Any, Dict, Tuple, Union
 
 from .file_utils import CONFIG_NAME, cached_path, hf_bucket_url, is_remote_url
 from .utils import logging
@@ -262,13 +262,13 @@ class PretrainedConfig(object):
         self.id2label = {i: "LABEL_{}".format(i) for i in range(num_labels)}
         self.label2id = dict(zip(self.id2label.values(), self.id2label.keys()))
 
-    def save_pretrained(self, save_directory: str):
+    def save_pretrained(self, save_directory: Union[str, os.PathLike]):
         """
         Save a configuration object to the directory ``save_directory``, so that it can be re-loaded using the
         :func:`~transformers.PretrainedConfig.from_pretrained` class method.
 
         Args:
-            save_directory (:obj:`str`):
+            save_directory (:obj:`str` or :obj:`os.PathLike`):
                 Directory where the configuration JSON file will be saved (will be created if it does not exist).
         """
         if os.path.isfile(save_directory):
@@ -281,13 +281,13 @@ class PretrainedConfig(object):
         logger.info("Configuration saved in {}".format(output_config_file))
 
     @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs) -> "PretrainedConfig":
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
         r"""
         Instantiate a :class:`~transformers.PretrainedConfig` (or a derived class) from a pretrained model
         configuration.
 
         Args:
-            pretrained_model_name_or_path (:obj:`str`):
+            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
                 This can be either:
 
                 - a string, the `model id` of a pretrained model configuration hosted inside a model repo on
@@ -297,7 +297,7 @@ class PretrainedConfig(object):
                   :func:`~transformers.PretrainedConfig.save_pretrained` method, e.g., ``./my_model_directory/``.
                 - a path or url to a saved configuration JSON `file`, e.g.,
                   ``./my_model_directory/configuration.json``.
-            cache_dir (:obj:`str`, `optional`):
+            cache_dir (:obj:`str` or :obj:`os.PathLike`, `optional`):
                 Path to a directory in which a downloaded pretrained model configuration should be cached if the
                 standard cache should not be used.
             force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
@@ -346,13 +346,15 @@ class PretrainedConfig(object):
         return cls.from_dict(config_dict, **kwargs)
 
     @classmethod
-    def get_config_dict(cls, pretrained_model_name_or_path: str, **kwargs) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+    def get_config_dict(
+        cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
+    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
         """
         From a ``pretrained_model_name_or_path``, resolve to a dictionary of parameters, to be used for instantiating a
         :class:`~transformers.PretrainedConfig` using ``from_dict``.
 
         Parameters:
-            pretrained_model_name_or_path (:obj:`str`):
+            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
                 The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
 
         Returns:
@@ -366,6 +368,7 @@ class PretrainedConfig(object):
         local_files_only = kwargs.pop("local_files_only", False)
         revision = kwargs.pop("revision", None)
 
+        pretrained_model_name_or_path = str(pretrained_model_name_or_path)
         if os.path.isdir(pretrained_model_name_or_path):
             config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
         elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
@@ -451,12 +454,12 @@ class PretrainedConfig(object):
             return config
 
     @classmethod
-    def from_json_file(cls, json_file: str) -> "PretrainedConfig":
+    def from_json_file(cls, json_file: Union[str, os.PathLike]) -> "PretrainedConfig":
         """
         Instantiates a :class:`~transformers.PretrainedConfig` from the path to a JSON file of parameters.
 
         Args:
-            json_file (:obj:`str`):
+            json_file (:obj:`str` or :obj:`os.PathLike`):
                 Path to the JSON file containing the parameters.
 
         Returns:
@@ -467,7 +470,7 @@ class PretrainedConfig(object):
         return cls(**config_dict)
 
     @classmethod
-    def _dict_from_json_file(cls, json_file: str):
+    def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):
         with open(json_file, "r", encoding="utf-8") as reader:
             text = reader.read()
         return json.loads(text)
@@ -537,12 +540,12 @@ class PretrainedConfig(object):
             config_dict = self.to_dict()
         return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
 
-    def to_json_file(self, json_file_path: str, use_diff: bool = True):
+    def to_json_file(self, json_file_path: Union[str, os.PathLike], use_diff: bool = True):
         """
         Save this instance to a JSON file.
 
         Args:
-            json_file_path (:obj:`str`):
+            json_file_path (:obj:`str` or :obj:`os.PathLike`):
                 Path to the JSON file in which this configuration instance's parameters will be saved.
             use_diff (:obj:`bool`, `optional`, defaults to :obj:`True`):
                 If set to ``True``, only the difference between the config instance and the default

src/transformers/modeling_utils.py

@@ -697,13 +697,13 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
 
         self.base_model._prune_heads(heads_to_prune)
 
-    def save_pretrained(self, save_directory):
+    def save_pretrained(self, save_directory: Union[str, os.PathLike]):
         """
         Save a model and its configuration file to a directory, so that it can be re-loaded using the
         `:func:`~transformers.PreTrainedModel.from_pretrained`` class method.
 
         Arguments:
-            save_directory (:obj:`str`):
+            save_directory (:obj:`str` or :obj:`os.PathLike`):
                 Directory to which to save. Will be created if it doesn't exist.
         """
         if os.path.isfile(save_directory):
@@ -741,7 +741,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
         logger.info("Model weights saved in {}".format(output_model_file))
 
     @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+    def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs):
         r"""
         Instantiate a pretrained pytorch model from a pre-trained model configuration.
 
@@ -756,7 +756,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
         weights are discarded.
 
         Parameters:
-            pretrained_model_name_or_path (:obj:`str`, `optional`):
+            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`, `optional`):
                 Can be either:
 
                     - A string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co.
@@ -772,11 +772,11 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
                       arguments ``config`` and ``state_dict``).
             model_args (sequence of positional arguments, `optional`):
                 All remaning positional arguments will be passed to the underlying model's ``__init__`` method.
-            config (:obj:`Union[PretrainedConfig, str]`, `optional`):
+            config (:obj:`Union[PretrainedConfig, str, os.PathLike]`, `optional`):
                 Can be either:
 
                     - an instance of a class derived from :class:`~transformers.PretrainedConfig`,
-                    - a string valid as input to :func:`~transformers.PretrainedConfig.from_pretrained`.
+                    - a string or path valid as input to :func:`~transformers.PretrainedConfig.from_pretrained`.
 
                 Configuration for the model to use instead of an automatically loaded configuation. Configuration can
                 be automatically loaded when:
@@ -794,7 +794,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
                 weights. In this case though, you should check if using
                 :func:`~transformers.PreTrainedModel.save_pretrained` and
                 :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
-            cache_dir (:obj:`str`, `optional`):
+            cache_dir (:obj:`Union[str, os.PathLike]`, `optional`):
                 Path to a directory in which a downloaded pretrained model configuration should be cached if the
                 standard cache should not be used.
             from_tf (:obj:`bool`, `optional`, defaults to :obj:`False`):
@@ -881,6 +881,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
 
         # Load model
         if pretrained_model_name_or_path is not None:
+            pretrained_model_name_or_path = str(pretrained_model_name_or_path)
             if os.path.isdir(pretrained_model_name_or_path):
                 if from_tf and os.path.isfile(os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME + ".index")):
                     # Load from a TF 1.0 checkpoint in priority if from_tf

src/transformers/models/auto/configuration_auto.py

@@ -274,7 +274,7 @@ class AutoConfig:
         List options
 
         Args:
-            pretrained_model_name_or_path (:obj:`str`):
+            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
                 Can be either:
 
                     - A string, the `model id` of a pretrained model configuration hosted inside a model repo on
@@ -285,7 +285,7 @@ class AutoConfig:
                       :meth:`~transformers.PreTrainedModel.save_pretrained` method, e.g., ``./my_model_directory/``.
                     - A path or url to a saved configuration JSON `file`, e.g.,
                       ``./my_model_directory/configuration.json``.
-            cache_dir (:obj:`str`, `optional`):
+            cache_dir (:obj:`str` or :obj:`os.PathLike`, `optional`):
                 Path to a directory in which a downloaded pretrained model configuration should be cached if the
                 standard cache should not be used.
             force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
@@ -346,7 +346,7 @@ class AutoConfig:
         else:
             # Fallback: use pattern matching on the string.
             for pattern, config_class in CONFIG_MAPPING.items():
-                if pattern in pretrained_model_name_or_path:
+                if pattern in str(pretrained_model_name_or_path):
                     return config_class.from_dict(config_dict, **kwargs)
 
         raise ValueError(

src/transformers/models/auto/modeling_auto.py

@@ -502,7 +502,7 @@ AUTO_MODEL_PRETRAINED_DOCSTRING = r"""
         deactivated). To train the model, you should first set it back in training mode with ``model.train()``
 
         Args:
-            pretrained_model_name_or_path:
+            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
                 Can be either:
 
                     - A string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co.
@@ -533,7 +533,7 @@ AUTO_MODEL_PRETRAINED_DOCSTRING = r"""
                 weights. In this case though, you should check if using
                 :func:`~transformers.PreTrainedModel.save_pretrained` and
                 :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
-            cache_dir (:obj:`str`, `optional`):
+            cache_dir (:obj:`str` or :obj:`os.PathLike`, `optional`):
                 Path to a directory in which a downloaded pretrained model configuration should be cached if the
                 standard cache should not be used.
             from_tf (:obj:`bool`, `optional`, defaults to :obj:`False`):

src/transformers/models/auto/tokenization_auto.py

@@ -267,7 +267,7 @@ class AutoTokenizer:
         List options
 
         Params:
-            pretrained_model_name_or_path (:obj:`str`):
+            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
                 Can be either:
 
                     - A string, the `model id` of a predefined tokenizer hosted inside a model repo on huggingface.co.
@@ -283,7 +283,7 @@ class AutoTokenizer:
                 Will be passed along to the Tokenizer ``__init__()`` method.
             config (:class:`~transformers.PreTrainedConfig`, `optional`)
                 The configuration object used to dertermine the tokenizer class to instantiate.
-            cache_dir (:obj:`str`, `optional`):
+            cache_dir (:obj:`str` or :obj:`os.PathLike`, `optional`):
                 Path to a directory in which a downloaded pretrained model configuration should be cached if the
                 standard cache should not be used.
             force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):

src/transformers/tokenization_utils_base.py

@@ -1608,13 +1608,13 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
         raise NotImplementedError()
 
     @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, *init_inputs, **kwargs):
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], *init_inputs, **kwargs):
         r"""
         Instantiate a :class:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase` (or a derived class) from
         a predefined tokenizer.
 
         Args:
-            pretrained_model_name_or_path (:obj:`str`):
+            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
                 Can be either:
 
                 - A string, the `model id` of a predefined tokenizer hosted inside a model repo on huggingface.co.
@@ -1626,7 +1626,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
                 - (**Deprecated**, not applicable to all derived classes) A path or url to a single saved vocabulary
                   file (if and only if the tokenizer only requires a single vocabulary file like Bert or XLNet), e.g.,
                   ``./my_model_directory/vocab.txt``.
-            cache_dir (:obj:`str`, `optional`):
+            cache_dir (:obj:`str` or :obj:`os.PathLike`, `optional`):
                 Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the
                 standard cache should not be used.
             force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
@@ -1683,6 +1683,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
         subfolder = kwargs.pop("subfolder", None)
 
         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:
@@ -1904,7 +1905,10 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
         return tokenizer
 
     def save_pretrained(
-        self, save_directory: str, legacy_format: bool = True, filename_prefix: Optional[str] = None
+        self,
+        save_directory: Union[str, os.PathLike],
+        legacy_format: bool = True,
+        filename_prefix: Optional[str] = None,
     ) -> Tuple[str]:
         """
         Save the full tokenizer state.
@@ -1924,7 +1928,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
            modifying :obj:`tokenizer.do_lower_case` after creation).
 
         Args:
-            save_directory (:obj:`str`): The path to a directory where the tokenizer will be saved.
+            save_directory (:obj:`str` or :obj:`os.PathLike`): The path to a directory where the tokenizer will be saved.
             legacy_format (:obj:`bool`, `optional`, defaults to :obj:`True`):
                 Whether to save the tokenizer in legacy format (default), i.e. with tokenizer specific vocabulary and a
                 separate added_tokens files or in the unified JSON file format for the `tokenizers` library. It's only
@@ -1988,7 +1992,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
 
     def _save_pretrained(
         self,
-        save_directory: str,
+        save_directory: Union[str, os.PathLike],
         file_names: Tuple[str],
         legacy_format: bool = True,
         filename_prefix: Optional[str] = None,

src/transformers/tokenization_utils_fast.py

@@ -498,7 +498,7 @@ class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
 
     def _save_pretrained(
         self,
-        save_directory: str,
+        save_directory: Union[str, os.PathLike],
         file_names: Tuple[str],
         legacy_format: bool = True,
         filename_prefix: Optional[str] = None,