PathLike (#4479)

Co-authored-by: Evan Pete Walsh <epwalsh10@gmail.com>

allennlp/commands/train.py

@@ -7,7 +7,8 @@
 import argparse
 import logging
 import os
-from typing import Any, Dict, List, Optional
+from os import PathLike
+from typing import Any, Dict, List, Optional, Union
 
 import torch
 import torch.distributed as dist
@@ -106,8 +107,8 @@ def train_model_from_args(args: argparse.Namespace):
 
 
 def train_model_from_file(
-    parameter_filename: str,
-    serialization_dir: str,
+    parameter_filename: Union[str, PathLike],
+    serialization_dir: Union[str, PathLike],
     overrides: str = "",
     recover: bool = False,
     force: bool = False,
@@ -161,7 +162,7 @@ def train_model_from_file(
 
 def train_model(
     params: Params,
-    serialization_dir: str,
+    serialization_dir: Union[str, PathLike],
     recover: bool = False,
     force: bool = False,
     node_rank: int = 0,
@@ -287,7 +288,7 @@ def train_model(
 def _train_worker(
     process_rank: int,
     params: Params,
-    serialization_dir: str,
+    serialization_dir: Union[str, PathLike],
     include_package: List[str] = None,
     dry_run: bool = False,
     node_rank: int = 0,

allennlp/common/file_utils.py

@@ -7,6 +7,7 @@
 import logging
 import tempfile
 import json
+from os import PathLike
 from urllib.parse import urlparse
 from pathlib import Path
 from typing import Optional, Tuple, Union, IO, Callable, Set, List, Iterator, Iterable
@@ -89,7 +90,7 @@ def filename_to_url(filename: str, cache_dir: Union[str, Path] = None) -> Tuple[
 
 
 def cached_path(
-    url_or_filename: Union[str, Path],
+    url_or_filename: Union[str, PathLike],
     cache_dir: Union[str, Path] = None,
     extract_archive: bool = False,
     force_extract: bool = False,
@@ -119,7 +120,7 @@ def cached_path(
     if cache_dir is None:
         cache_dir = CACHE_DIRECTORY
 
-    if isinstance(url_or_filename, Path):
+    if isinstance(url_or_filename, PathLike):
         url_or_filename = str(url_or_filename)
 
     # If we're using the /a/b/foo.zip!c/d/file.txt syntax, handle it here.

allennlp/common/logging.py

@@ -1,6 +1,9 @@
 import logging
 from logging import Filter
 import os
+from os import PathLike
+from typing import Union
+
 import sys
 
 
@@ -54,7 +57,9 @@ def filter(self, record):
         return record.levelno < logging.ERROR
 
 
-def prepare_global_logging(serialization_dir: str, rank: int = 0, world_size: int = 1,) -> None:
+def prepare_global_logging(
+    serialization_dir: Union[str, PathLike], rank: int = 0, world_size: int = 1,
+) -> None:
     root_logger = logging.getLogger()
 
     # create handlers

allennlp/common/params.py

@@ -1,4 +1,5 @@
-from typing import Any, Dict, List
+from os import PathLike
+from typing import Any, Dict, List, Union
 from collections.abc import MutableMapping
 from collections import OrderedDict
 import copy
@@ -456,7 +457,7 @@ def _check_is_dict(self, new_history, value):
 
     @classmethod
     def from_file(
-        cls, params_file: str, params_overrides: str = "", ext_vars: dict = None
+        cls, params_file: Union[str, PathLike], params_overrides: str = "", ext_vars: dict = None
     ) -> "Params":
         """
         Load a `Params` object from a configuration file.

allennlp/common/testing/model_test_case.py

@@ -1,5 +1,6 @@
 import copy
 import json
+from os import PathLike
 from typing import Any, Dict, Iterable, Set, Union
 
 import torch
@@ -48,7 +49,7 @@ def set_up_model(self, param_file, dataset_file):
 
     def ensure_model_can_train_save_and_load(
         self,
-        param_file: str,
+        param_file: Union[PathLike, str],
         tolerance: float = 1e-4,
         cuda_device: int = -1,
         gradients_to_ignore: Set[str] = None,

allennlp/models/archival.py

@@ -1,8 +1,8 @@
 """
 Helper functions for archiving models and restoring archived models.
 """
-
-from typing import NamedTuple
+from os import PathLike
+from typing import NamedTuple, Union
 import atexit
 import logging
 import os
@@ -89,7 +89,9 @@ def extract_module(self, path: str, freeze: bool = True) -> Module:
 
 
 def archive_model(
-    serialization_dir: str, weights: str = _DEFAULT_WEIGHTS, archive_path: str = None
+    serialization_dir: Union[str, PathLike],
+    weights: str = _DEFAULT_WEIGHTS,
+    archive_path: Union[str, PathLike] = None,
 ) -> None:
     """
     Archive the model weights, its training configuration, and its vocabulary to `model.tar.gz`.

allennlp/models/model.py

@@ -5,7 +5,8 @@
 
 import logging
 import os
-from typing import Dict, List, Set, Type, Optional
+from os import PathLike
+from typing import Dict, List, Set, Type, Optional, Union
 
 try:
     from apex import amp
@@ -268,8 +269,8 @@ def _maybe_warn_for_unseparable_batches(self, output_key: str):
     def _load(
         cls,
         config: Params,
-        serialization_dir: str,
-        weights_file: Optional[str] = None,
+        serialization_dir: Union[str, PathLike],
+        weights_file: Optional[Union[str, PathLike]] = None,
         cuda_device: int = -1,
         opt_level: Optional[str] = None,
     ) -> "Model":
@@ -349,8 +350,8 @@ def _load(
     def load(
         cls,
         config: Params,
-        serialization_dir: str,
-        weights_file: Optional[str] = None,
+        serialization_dir: Union[str, PathLike],
+        weights_file: Optional[Union[str, PathLike]] = None,
         cuda_device: int = -1,
         opt_level: Optional[str] = None,
     ) -> "Model":

allennlp/training/util.py

@@ -5,6 +5,7 @@
 import logging
 import os
 import shutil
+from os import PathLike
 from typing import Any, Dict, Iterable, Optional, Union, Tuple, Set, List
 from collections import Counter
 
@@ -168,7 +169,7 @@ def datasets_from_params(
 
 
 def create_serialization_dir(
-    params: Params, serialization_dir: str, recover: bool, force: bool
+    params: Params, serialization_dir: Union[str, PathLike], recover: bool, force: bool
 ) -> None:
     """
     This function creates the serialization directory if it doesn't exist.  If it already exists
@@ -413,7 +414,7 @@ def description_from_metrics(metrics: Dict[str, float]) -> str:
 
 
 def make_vocab_from_params(
-    params: Params, serialization_dir: str, print_statistics: bool = False
+    params: Params, serialization_dir: Union[str, PathLike], print_statistics: bool = False
 ) -> Vocabulary:
     vocab_params = params.pop("vocabulary", {})
     os.makedirs(serialization_dir, exist_ok=True)