reraise EnvironmentError in from_pretrained functions of Model and Tokenizer

pytorch_transformers/modeling_utils.py

@@ -473,7 +473,7 @@ class PreTrainedModel(nn.Module):
         # redirect to the cache, if necessary
         try:
             resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
-        except EnvironmentError:
+        except EnvironmentError as e:
             if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
                 logger.error(
                     "Couldn't reach server at '{}' to download pretrained weights.".format(
@@ -486,7 +486,7 @@ class PreTrainedModel(nn.Module):
                         pretrained_model_name_or_path,
                         ', '.join(cls.pretrained_model_archive_map.keys()),
                         archive_file))
-            return None
+            raise e
         if resolved_archive_file == archive_file:
             logger.info("loading weights file {}".format(archive_file))
         else:

pytorch_transformers/tokenization_utils.py

@@ -293,7 +293,7 @@ class PreTrainedTokenizer(object):
                     resolved_vocab_files[file_id] = None
                 else:
                     resolved_vocab_files[file_id] = cached_path(file_path, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
-        except EnvironmentError:
+        except EnvironmentError as e:
             if pretrained_model_name_or_path in s3_models:
                 logger.error("Couldn't reach server to download vocabulary.")
             else:
@@ -303,7 +303,7 @@ class PreTrainedTokenizer(object):
                     "at this path or url.".format(
                         pretrained_model_name_or_path, ', '.join(s3_models),
                         pretrained_model_name_or_path, str(vocab_files.keys())))
-            return None
+            raise e
 
         for file_id, file_path in vocab_files.items():
             if file_path == resolved_vocab_files[file_id]: