Merge pull request #113 from hzhwcmhf/master

fix compatibility with python 3.5.2

pytorch_pretrained_bert/file_utils.py

@@ -45,13 +45,15 @@ def url_to_filename(url: str, etag: str = None) -> str:
     return filename
 
 
-def filename_to_url(filename: str, cache_dir: str = None) -> Tuple[str, str]:
+def filename_to_url(filename: str, cache_dir: Union[str, Path] = None) -> Tuple[str, str]:
     """
     Return the url and etag (which may be ``None``) stored for `filename`.
     Raise ``FileNotFoundError`` if `filename` or its stored metadata do not exist.
     """
     if cache_dir is None:
         cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
+    if isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
 
     cache_path = os.path.join(cache_dir, filename)
     if not os.path.exists(cache_path):
@@ -69,7 +71,7 @@ def filename_to_url(filename: str, cache_dir: str = None) -> Tuple[str, str]:
     return url, etag
 
 
-def cached_path(url_or_filename: Union[str, Path], cache_dir: str = None) -> str:
+def cached_path(url_or_filename: Union[str, Path], cache_dir: Union[str, Path] = None) -> str:
     """
     Given something that might be a URL (or might be a local path),
     determine which. If it's a URL, download the file and cache it, and
@@ -80,6 +82,8 @@ def cached_path(url_or_filename: Union[str, Path], cache_dir: str = None) -> str
         cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
     if isinstance(url_or_filename, Path):
         url_or_filename = str(url_or_filename)
+    if isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
 
     parsed = urlparse(url_or_filename)
 
@@ -158,13 +162,15 @@ def http_get(url: str, temp_file: IO) -> None:
     progress.close()
 
 
-def get_from_cache(url: str, cache_dir: str = None) -> str:
+def get_from_cache(url: str, cache_dir: Union[str, Path] = None) -> str:
     """
     Given a URL, look for the corresponding dataset in the local cache.
     If it's not there, download it. Then return the path to the cached file.
     """
     if cache_dir is None:
         cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
+    if isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
 
     os.makedirs(cache_dir, exist_ok=True)