fix Ruff issues

flair/data.py

@@ -3,10 +3,10 @@
 import re
 import typing
 from abc import ABC, abstractmethod
-from collections import Counter, defaultdict, namedtuple
+from collections import Counter, defaultdict
 from operator import itemgetter
 from pathlib import Path
-from typing import Dict, Iterable, List, Optional, Union, cast
+from typing import Dict, Iterable, List, NamedTuple, Optional, Union, cast
 
 import torch
 from deprecated import deprecated
@@ -39,7 +39,11 @@ def _len_dataset(dataset: Optional[Dataset]) -> int:
     return len(loader)
 
 
-BoundingBox = namedtuple("BoundingBox", ["left", "top", "right", "bottom"])
+class BoundingBox(NamedTuple):
+    left: str
+    top: int
+    right: int
+    bottom: int
 
 
 class Dictionary:
@@ -727,7 +731,7 @@ def __init__(
         if isinstance(use_tokenizer, Tokenizer):
             tokenizer = use_tokenizer
 
-        elif type(use_tokenizer) == bool:
+        elif isinstance(use_tokenizer, bool):
             tokenizer = SegtokTokenizer() if use_tokenizer else SpaceTokenizer()
 
         else:
@@ -809,7 +813,7 @@ def _add_token(self, token: Union[Token, str]):
         if isinstance(token, Token):
             assert token.sentence is None
 
-        if type(token) is str:
+        if isinstance(token, str):
             token = Token(token)
         token = cast(Token, token)
 

flair/datasets/base.py

@@ -229,7 +229,7 @@ def __getitem__(self, index: int = 0) -> Sentence:
 
 
 def find_train_dev_test_files(data_folder, dev_file, test_file, train_file, autofind_splits=True):
-    if type(data_folder) == str:
+    if isinstance(data_folder, str):
         data_folder: Path = Path(data_folder)
 
     if train_file is not None:

flair/datasets/sequence_labeling.py

@@ -2976,7 +2976,7 @@ def __init__(
         base_path = flair.cache_root / "datasets" if not base_path else Path(base_path)
 
         # if only one language is given
-        if type(languages) == str:
+        if isinstance(languages, str):
             languages = [languages]
 
         # column format
@@ -3249,7 +3249,7 @@ def __init__(
         in_memory : bool, optional
             Specify that the dataset should be loaded in memory, which speeds up the training process but takes increases the RAM usage significantly.
         """
-        if type(languages) == str:
+        if isinstance(languages, str):
             languages = [languages]
 
         base_path = flair.cache_root / "datasets" if not base_path else Path(base_path)
@@ -3710,7 +3710,7 @@ def __init__(
             ]
 
         # if only one language is given
-        if type(languages) == str:
+        if isinstance(languages, str):
             languages = [languages]
 
         base_path = flair.cache_root / "datasets" if not base_path else Path(base_path)
@@ -3802,7 +3802,7 @@ def __init__(
         base_path = flair.cache_root / "datasets" if not base_path else Path(base_path)
 
         # if only one language is given
-        if type(languages) == str:
+        if isinstance(languages, str):
             languages = [languages]
 
         # column format
@@ -4748,10 +4748,10 @@ def __init__(
         """
         supported_domains = ["WN", "FIC", "ADG"]
 
-        if type(domains) == str and domains == "all":
+        if isinstance(domains, str) and domains == "all":
             domains = supported_domains
 
-        if type(domains) == str:
+        if isinstance(domains, str):
             domains = [domains]
 
         base_path = flair.cache_root / "datasets" if not base_path else Path(base_path)

flair/models/tars_model.py

@@ -195,7 +195,7 @@ def add_and_switch_to_new_task(
             # make label dictionary if no Dictionary object is passed
             if isinstance(label_dictionary, Dictionary):
                 label_dictionary = label_dictionary.get_items()
-            if type(label_dictionary) == str:
+            if isinstance(label_dictionary, str):
                 label_dictionary = [label_dictionary]
 
             # prepare dictionary of tags (without B- I- prefixes and without UNK)

flair/tokenization.py

@@ -256,9 +256,9 @@ def combined_rule_prefixes() -> List[str]:
                 r"/",  # want to split at every slash
                 r"(?<=[0-9])[+\-\*^](?=[0-9-])",
                 rf"(?<=[{char_classes.ALPHA_LOWER}])\.(?=[{char_classes.ALPHA_UPPER}])",
-                r"(?<=[{a}]),(?=[{a}])".format(a=char_classes.ALPHA),
-                r'(?<=[{a}])[?";:=,.]*(?:{h})(?=[{a}])'.format(a=char_classes.ALPHA, h=char_classes.HYPHENS),
-                r"(?<=[{a}0-9])[:<>=/](?=[{a}])".format(a=char_classes.ALPHA),
+                rf"(?<=[{char_classes.ALPHA}]),(?=[{char_classes.ALPHA}])",
+                rf'(?<=[{char_classes.ALPHA}])[?";:=,.]*(?:{char_classes.HYPHENS})(?=[{char_classes.ALPHA}])',
+                rf"(?<=[{char_classes.ALPHA}0-9])[:<>=/](?=[{char_classes.ALPHA}])",
             ]
         )
 

flair/trainers/language_model_trainer.py

@@ -56,7 +56,7 @@ def __len__(self) -> int:
 
     def __getitem__(self, index=0) -> torch.Tensor:
         """Tokenizes a text file on character basis."""
-        if type(self.files[index]) is str:
+        if isinstance(self.files[index], str):
             self.files[index] = Path(self.files[index])
         assert self.files[index].exists()
 
@@ -444,7 +444,7 @@ def load_checkpoint(
         corpus: TextCorpus,
         optimizer: Type[Optimizer] = SGD,
     ):
-        if type(checkpoint_file) is str:
+        if isinstance(checkpoint_file, str):
             checkpoint_file = Path(checkpoint_file)
 
         checkpoint = LanguageModel.load_checkpoint(checkpoint_file)

flair/trainers/trainer.py

@@ -139,7 +139,7 @@ def train(
         # evaluation and monitoring
         main_evaluation_metric: Tuple[str, str] = ("micro avg", "f1-score"),
         monitor_test: bool = False,
-        monitor_train_sample: Union[float, int] = 0.0,
+        monitor_train_sample: float = 0.0,
         use_final_model_for_eval: bool = False,
         gold_label_dictionary_for_eval: Optional[Dictionary] = None,
         exclude_labels: List[str] = [],
@@ -211,7 +211,7 @@ def fine_tune(
         # evaluation and monitoring
         main_evaluation_metric: Tuple[str, str] = ("micro avg", "f1-score"),
         monitor_test: bool = False,
-        monitor_train_sample: Union[float, int] = 0.0,
+        monitor_train_sample: float = 0.0,
         use_final_model_for_eval: bool = True,
         gold_label_dictionary_for_eval: Optional[Dictionary] = None,
         exclude_labels: List[str] = [],
@@ -302,7 +302,7 @@ def train_custom(
         # evaluation and monitoring
         main_evaluation_metric: Tuple[str, str] = ("micro avg", "f1-score"),
         monitor_test: bool = False,
-        monitor_train_sample: Union[float, int] = 0.0,
+        monitor_train_sample: float = 0.0,
         use_final_model_for_eval: bool = False,
         gold_label_dictionary_for_eval: Optional[Dictionary] = None,
         exclude_labels: List[str] = [],

flair/training_utils.py

@@ -71,7 +71,7 @@ def to_tsv(self):
     @staticmethod
     def tsv_header(prefix=None):
         if prefix:
-            return "{0}_MEAN_SQUARED_ERROR\t{0}_MEAN_ABSOLUTE_ERROR\t{0}_PEARSON\t{0}_SPEARMAN".format(prefix)
+            return f"{prefix}_MEAN_SQUARED_ERROR\t{prefix}_MEAN_ABSOLUTE_ERROR\t{prefix}_PEARSON\t{prefix}_SPEARMAN"
 
         return "MEAN_SQUARED_ERROR\tMEAN_ABSOLUTE_ERROR\tPEARSON\tSPEARMAN"
 
@@ -99,7 +99,7 @@ class EvaluationMetric(Enum):
 
 class WeightExtractor:
     def __init__(self, directory: Union[str, Path], number_of_weights: int = 10) -> None:
-        if type(directory) is str:
+        if isinstance(directory, str):
             directory = Path(directory)
         self.weights_file = init_output_file(directory, "weights.txt")
         self.weights_dict: Dict[str, Dict[int, List[float]]] = defaultdict(lambda: defaultdict(list))

flair/visual/training_curves.py

@@ -67,7 +67,7 @@ def _extract_evaluation_data(file_name: Union[str, Path], score: str = "F1") ->
 
     @staticmethod
     def _extract_weight_data(file_name: Union[str, Path]) -> dict:
-        if type(file_name) is str:
+        if isinstance(file_name, str):
             file_name = Path(file_name)
 
         weights: Dict[str, Dict[str, List[float]]] = defaultdict(lambda: defaultdict(list))
@@ -86,7 +86,7 @@ def _extract_weight_data(file_name: Union[str, Path]) -> dict:
 
     @staticmethod
     def _extract_learning_rate(file_name: Union[str, Path]):
-        if type(file_name) is str:
+        if isinstance(file_name, str):
             file_name = Path(file_name)
 
         lrs = []

tests/model_test_utils.py

@@ -204,7 +204,7 @@ def test_train_load_use_model_multi_label(
             print(label)
             assert label.value is not None
             assert 0.0 <= label.score <= 1.0
-            assert type(label.score) is float
+            assert isinstance(label.score, float)
 
         del trainer, model, multi_class_corpus
         loaded_model = self.model_cls.load(results_base_path / "final-model.pt")

tests/test_multitask.py

@@ -63,5 +63,5 @@ def test_train_load_use_classifier(results_base_path, tasks_base_path):
     for label in sentence.labels:
         assert label.value is not None
         assert 0.0 <= label.score <= 1.0
-        assert type(label.score) is float
+        assert isinstance(label.score, float)
     del loaded_model