formatting and fix some typing

flair/datasets/sequence_labeling.py

@@ -1,9 +1,9 @@
 import copy
+import gzip
 import json
 import logging
 import os
 import re
-import gzip
 import shutil
 import tarfile
 import tempfile
@@ -5251,12 +5251,13 @@ def __init__(
             in_memory (bool): If True the dataset is kept in memory achieving speedups in training.
             **corpusargs: The arguments propagated to :meth:'flair.datasets.ColumnCorpus.__init__'.
         """
-
         VALUE_NOISE_VALUES = ["clean", "crowd", "crowdbest", "expert", "distant", "weak", "llm"]
-        
+
         if noise not in VALUE_NOISE_VALUES:
-            raise ValueError(f"Unsupported value for noise type argument. Got {noise}, expected one of {VALUE_NOISE_VALUES}!")
-
+            raise ValueError(
+                f"Unsupported value for noise type argument. Got {noise}, expected one of {VALUE_NOISE_VALUES}!"
+            )
+
         self.base_path = flair.cache_root / "datasets" / "noisebench" if not base_path else Path(base_path)
 
         filename = "clean" if noise == "clean" else f"noise_{noise}"
@@ -5270,16 +5271,13 @@ def __init__(
         if not all(files_exist):
             cached_path(f"{self.label_url}/{filename}.traindev", self.base_path / "annotations_only")
             cached_path(f"{self.label_url}/index.txt", self.base_path / "annotations_only")
-            
+
             cleanconll_corpus = CLEANCONLL()
 
             self.cleanconll_base_path = flair.cache_root / "datasets" / cleanconll_corpus.__class__.__name__.lower()
 
             # create dataset files from index and train/test splits
-            self._generate_data_files(
-                filename,
-                cleanconll_corpus.__class__.__name__.lower()
-            )
+            self._generate_data_files(filename, cleanconll_corpus.__class__.__name__.lower())
 
         super().__init__(
             data_folder=self.base_path,
@@ -5294,16 +5292,13 @@ def __init__(
         )
 
     @staticmethod
-    def _read_column_file(filename: Union[str, Path]) -> list[list[str]]:
-        with open(filename, "r", errors="replace", encoding="utf-8") as file:
+    def _read_column_file(filename: Union[str, Path]) -> list[list[list[str]]]:
+        with open(filename, errors="replace", encoding="utf-8") as file:
             lines = file.readlines()
             all_sentences = []
             sentence = []
             for line in lines:
-                if "\t" in line.strip():
-                    stripped_line = line.strip().split("\t") 
-                else:
-                    stripped_line = line.strip().split(" ")
+                stripped_line = line.strip().split("\t") if "\t" in line.strip() else line.strip().split(" ")
 
                 sentence.append(stripped_line)
                 if line.strip() == "":
@@ -5318,15 +5313,17 @@ def _read_column_file(filename: Union[str, Path]) -> list[list[str]]:
         return all_sentences
 
     @staticmethod
-    def _save_to_column_file(filename: Union[str, Path], sentences: list[list[str]]) -> None:
+    def _save_to_column_file(filename: Union[str, Path], sentences: list[list[list[str]]]) -> None:
         with open(filename, "w", encoding="utf-8") as f:
             for sentence in sentences:
                 for token in sentence:
                     f.write("\t".join(token))
                     f.write("\n")
                 f.write("\n")
 
-    def _create_train_dev_splits(self, filename: Path, all_sentences: list = None, datestring: str ="1996-08-24") -> None:
+    def _create_train_dev_splits(
+        self, filename: Path, all_sentences: Optional[list] = None, datestring: str = "1996-08-24"
+    ) -> None:
         if not all_sentences:
             all_sentences = self._read_column_file(filename)
 
@@ -5356,7 +5353,9 @@ def _create_train_dev_splits(self, filename: Path, all_sentences: list = None, d
             train_sentences,
         )
 
-    def _merge_tokens_labels(self, corpus: str, all_clean_sentences: list, token_indices: list) -> list[list[str]]:
+    def _merge_tokens_labels(
+        self, corpus: str, all_clean_sentences: list, token_indices: list
+    ) -> list[list[list[str]]]:
         # generate NoiseBench dataset variants, given CleanCoNLL, noisy label files and index file
 
         noisy_labels = self._read_column_file(self.base_path / "annotations_only" / f"{corpus}.traindev")
@@ -5376,9 +5375,9 @@ def _merge_tokens_labels(self, corpus: str, all_clean_sentences: list, token_ind
             self._save_to_column_file(self.base_path / f"{corpus}.traindev", noisy_labels)
         return noisy_labels
 
-    def _generate_data_files(self, filename: Union[str, Path], origin_dataset_name: str) -> None:
+    def _generate_data_files(self, filename: str, origin_dataset_name: str) -> None:
 
-        with open(self.base_path / "annotations_only" / "index.txt", "r", encoding="utf-8") as index_file:
+        with open(self.base_path / "annotations_only" / "index.txt", encoding="utf-8") as index_file:
             token_indices = index_file.readlines()
             all_clean_sentences = self._read_column_file(self.cleanconll_base_path / f"{origin_dataset_name}.train")