fix doc args and clean up parameter space

safe/converter.py

@@ -376,10 +376,9 @@ def decode(
 ):
     """Convert input SAFE representation to smiles
     Args:
-        inp: input SAFE representation to decode as a valid molecule or smiles
+        safe_str: input SAFE representation to decode as a valid molecule or smiles
         as_mol: whether to return a molecule object or a smiles string
         canonical: whether to return a canonical smiles or a randomized smiles
-        standardize: whether to standardize the molecule
         fix: whether to fix the SAFE representation to take into account non-connected attachment points
         remove_added_hs: whether to remove the hydrogen atoms that have been added to fix the string.
         remove_dummies: whether to remove dummy atoms from the SAFE representation

safe/tokenizer.py

@@ -3,6 +3,7 @@
 from typing import Any
 from typing import Iterator
 from typing import Union
+from typing import Dict
 
 import re
 import os
@@ -230,8 +231,7 @@ def train_from_iterator(self, data: Iterator, **kwargs: Any):
 
     def __len__(self):
         r"""
-        Returns: Gets the count of tokens in vocab along with special tokens.
-
+        Gets the count of tokens in vocab along with special tokens.
         """
         return len(self.tokenizer.get_vocab().keys())
 
@@ -511,6 +511,7 @@ def from_pretrained(
         local_files_only: bool = False,
         token: Optional[Union[str, bool]] = None,
         return_fast_tokenizer: Optional[bool] = False,
+        proxies: Optional[Dict[str, str]] = None,
         **kwargs,
     ):
         r"""
@@ -533,7 +534,6 @@ def from_pretrained(
             cache_dir: 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: Whether or not to force the (re-)download the vocabulary files and override the cached versions if they exist.
-            resume_download: Whether or not to delete incompletely received files. Attempt to resume the download if such a file exists.
             proxies: A dictionary of proxy servers to use by protocol or endpoint, e.g.,
                 `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
             token: The token to use as HTTP bearer authorization for remote files.
@@ -555,7 +555,6 @@ def from_pretrained(
         ```
         """
         resume_download = kwargs.pop("resume_download", False)
-        proxies = kwargs.pop("proxies", None)
         use_auth_token = kwargs.pop("use_auth_token", None)
         subfolder = kwargs.pop("subfolder", None)
         from_pipeline = kwargs.pop("_from_pipeline", None)

safe/trainer/data_utils.py

@@ -1,11 +1,17 @@
 from typing import Optional
 from typing import Callable
+from typing import Any
+from typing import Union
+from typing import Dict
+
 from collections.abc import Mapping
 from tqdm.auto import tqdm
 from functools import partial
+
 import itertools
 import upath
 import datasets
+
 from safe.tokenizer import SAFETokenizer
 
 
@@ -14,10 +20,12 @@ def take(n, iterable):
     return list(itertools.islice(iterable, n))
 
 
-def get_dataset_column_names(dataset):
+def get_dataset_column_names(dataset: Union[datasets.Dataset, datasets.IterableDataset, Mapping]):
     """Get the column names in a dataset
+
     Args:
         dataset: dataset to get the column names from
+
     """
     if isinstance(dataset, (datasets.IterableDatasetDict, Mapping)):
         column_names = {split: dataset[split].column_names for split in dataset}
@@ -29,8 +37,8 @@ def get_dataset_column_names(dataset):
 
 
 def tokenize_fn(
-    row,
-    tokenizer,
+    row: Dict[str, Any],
+    tokenizer: Callable,
     tokenize_column: str = "inputs",
     max_length: Optional[int] = None,
     padding: bool = False,

safe/trainer/model.py

@@ -152,8 +152,6 @@ def forward(
             mc_labels (`torch.LongTensor` of shape `(batch_size, n_tasks)`, *optional*):
                 Labels for computing the supervized loss for regularization.
             inputs: List of inputs, put here because the trainer removes information not in signature
-
-        Returns:
         """
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
         transformer_outputs = self.transformer(

safe/utils.py

@@ -288,8 +288,9 @@ def link_fragments(
 
 
 @contextmanager
-def attr_as(obj, field, value):
+def attr_as(obj: Any, field: str, value: Any):
     """Temporary replace the value of an object
+
     Args:
         obj: object to temporary patch
         field: name of the key to change