dependencies maintainance updates

flair/data.py

@@ -361,7 +361,7 @@ def get_labels(self, typename: Optional[str] = None):
         if typename is None:
             return self.labels
 
-        return self.annotation_layers[typename] if typename in self.annotation_layers else []
+        return self.annotation_layers.get(typename, [])
 
     @property
     def labels(self) -> List[Label]:
@@ -987,12 +987,10 @@ def get_span(self, start: int, stop: int):
         return self[span_slice]
 
     @typing.overload
-    def __getitem__(self, idx: int) -> Token:
-        ...
+    def __getitem__(self, idx: int) -> Token: ...
 
     @typing.overload
-    def __getitem__(self, s: slice) -> Span:
-        ...
+    def __getitem__(self, s: slice) -> Span: ...
 
     def __getitem__(self, subscript):
         if isinstance(subscript, slice):

flair/datasets/base.py

@@ -182,7 +182,7 @@ def __init__(
             for document in self.__cursor.find(filter=query, skip=start, limit=0):
                 sentence = self._parse_document_to_sentence(
                     document[self.text],
-                    [document[_] if _ in document else "" for _ in self.categories],
+                    [document.get(c, "") for c in self.categories],
                     tokenizer,
                 )
                 if sentence is not None and len(sentence.tokens) > 0:
@@ -225,7 +225,7 @@ def __getitem__(self, index: int = 0) -> Sentence:
             document = self.__cursor.find_one({"_id": index})
             sentence = self._parse_document_to_sentence(
                 document[self.text],
-                [document[_] if _ in document else "" for _ in self.categories],
+                [document.get(c, "") for c in self.categories],
                 self.tokenizer,
             )
             return sentence

flair/datasets/biomedical.py

@@ -2191,11 +2191,8 @@ def patch_training_file(orig_train_file: Path, patched_file: Path):
             3249: '10923035\t711\t761\tgeneralized epilepsy and febrile seizures " plus "\tSpecificDisease\tD004829+D003294\n'
         }
         with orig_train_file.open(encoding="utf-8") as input, patched_file.open("w", encoding="utf-8") as output:
-            line_no = 1
-
-            for line in input:
-                output.write(patch_lines[line_no] if line_no in patch_lines else line)
-                line_no += 1
+            for line_no, line in enumerate(input, start=1):
+                output.write(patch_lines.get(line_no, line))
 
     @staticmethod
     def parse_input_file(input_file: Path):

flair/datasets/sequence_labeling.py

@@ -2762,14 +2762,12 @@ def _create_datasets(self, data_file: Union[str, Path], data_folder: Path):
             with (data_folder / "train.txt").open("w", encoding="utf-8") as train, (data_folder / "test.txt").open(
                 "w", encoding="utf-8"
             ) as test, (data_folder / "dev.txt").open("w", encoding="utf-8") as dev:
-                k = 0
-                for line in file.readlines():
-                    k += 1
+                for k, line in enumerate(file.readlines(), start=1):
                     if k <= train_len:
                         train.write(line)
-                    elif k > train_len and k <= (train_len + test_len):
+                    elif train_len < k <= (train_len + test_len):
                         test.write(line)
-                    elif k > (train_len + test_len) and k <= num_lines:
+                    elif (train_len + test_len) < k <= num_lines:
                         dev.write(line)
 
 

flair/datasets/text_image.py

@@ -63,7 +63,7 @@ def identity(x):
             return x
 
         preprocessor = identity
-        if "lowercase" in kwargs and kwargs["lowercase"]:
+        if kwargs.get("lowercase"):
             preprocessor = str.lower
 
         for image_info in dataset_info:

flair/file_utils.py

@@ -1,4 +1,5 @@
 """Utilities for working with the local dataset cache. Copied from AllenNLP."""
+
 import base64
 import functools
 import io

flair/models/entity_linker_model.py

@@ -108,9 +108,9 @@ def __init__(
         super().__init__(
             embeddings=embeddings,
             label_dictionary=label_dictionary,
-            final_embedding_size=embeddings.embedding_length * 2
-            if pooling_operation == "first_last"
-            else embeddings.embedding_length,
+            final_embedding_size=(
+                embeddings.embedding_length * 2 if pooling_operation == "first_last" else embeddings.embedding_length
+            ),
             **classifierargs,
         )
 

flair/models/language_model.py

@@ -215,9 +215,9 @@ def load_language_model(cls, model_file: Union[Path, str], has_decoder=True):
     def load_checkpoint(cls, model_file: Union[Path, str]):
         state = torch.load(str(model_file), map_location=flair.device)
 
-        epoch = state["epoch"] if "epoch" in state else None
-        split = state["split"] if "split" in state else None
-        loss = state["loss"] if "loss" in state else None
+        epoch = state.get("epoch")
+        split = state.get("split")
+        loss = state.get("loss")
         document_delimiter = state.get("document_delimiter", "\n")
 
         optimizer_state_dict = state.get("optimizer_state_dict")

flair/nn/distance/euclidean.py

@@ -17,7 +17,6 @@
 Source: https://github.com/asappresearch/dynamic-classification/blob/55beb5a48406c187674bea40487c011e8fa45aab/distance/euclidean.py
 """
 
-
 import torch
 from torch import Tensor, nn
 

flair/nn/model.py

@@ -339,7 +339,7 @@ def evaluate(
             true_values_span_aligned = []
             predicted_values_span_aligned = []
             for span in all_spans:
-                list_of_gold_values_for_span = all_true_values[span] if span in all_true_values else ["O"]
+                list_of_gold_values_for_span = all_true_values.get(span, ["O"])
                 # delete exluded labels if exclude_labels is given
                 for excluded_label in exclude_labels:
                     if excluded_label in list_of_gold_values_for_span:
@@ -348,9 +348,7 @@ def evaluate(
                 if not list_of_gold_values_for_span:
                     continue
                 true_values_span_aligned.append(list_of_gold_values_for_span)
-                predicted_values_span_aligned.append(
-                    all_predicted_values[span] if span in all_predicted_values else ["O"]
-                )
+                predicted_values_span_aligned.append(all_predicted_values.get(span, ["O"]))
 
             # write all_predicted_values to out_file if set
             if out_path:
@@ -700,9 +698,11 @@ def _prepare_label_tensor(self, prediction_data_points: List[DT2]) -> torch.Tens
         else:
             return torch.tensor(
                 [
-                    self.label_dictionary.get_idx_for_item(label[0])
-                    if len(label) > 0
-                    else self.label_dictionary.get_idx_for_item("O")
+                    (
+                        self.label_dictionary.get_idx_for_item(label[0])
+                        if len(label) > 0
+                        else self.label_dictionary.get_idx_for_item("O")
+                    )
                     for label in labels
                 ],
                 dtype=torch.long,

flair/optim.py

@@ -217,8 +217,6 @@ class ReduceLRWDOnPlateau(ReduceLROnPlateau):
             with no improvement, and will only decrease the LR after the
             3rd epoch if the loss still hasn't improved then.
             Default: 10.
-        verbose (bool): If ``True``, prints a message to stdout for
-            each update. Default: ``False``.
         threshold (float): Threshold for measuring the new optimum,
             to only focus on significant changes. Default: 1e-4.
         threshold_mode (str): One of `rel`, `abs`. In `rel` mode,

flair/trainers/language_model_trainer.py

@@ -227,11 +227,9 @@ def train(
                 optimizer.load_state_dict(self.optimizer_state)
 
             if isinstance(optimizer, (AdamW, SGDW)):
-                scheduler: ReduceLROnPlateau = ReduceLRWDOnPlateau(
-                    optimizer, verbose=True, factor=anneal_factor, patience=patience
-                )
+                scheduler: ReduceLROnPlateau = ReduceLRWDOnPlateau(optimizer, factor=anneal_factor, patience=patience)
             else:
-                scheduler = ReduceLROnPlateau(optimizer, verbose=True, factor=anneal_factor, patience=patience)
+                scheduler = ReduceLROnPlateau(optimizer, factor=anneal_factor, patience=patience)
 
             training_generator = DataLoader(self.corpus.train, shuffle=False, num_workers=num_workers)
 

flair/trainers/plugins/functional/anneal_on_plateau.py

@@ -63,7 +63,6 @@ def after_setup(
             patience=self.patience,
             initial_extra_patience=self.initial_extra_patience,
             mode=anneal_mode,
-            verbose=False,
             optimizer=self.trainer.optimizer,
         )
 

flair/trainers/trainer.py

@@ -816,7 +816,7 @@ def train_custom(
 
     def _get_current_lr_and_momentum(self, batch_count):
         current_learning_rate = [group["lr"] for group in self.optimizer.param_groups]
-        momentum = [group["momentum"] if "momentum" in group else 0 for group in self.optimizer.param_groups]
+        momentum = [group.get("momentum", 0) for group in self.optimizer.param_groups]
         lr_info = " - lr: " + ",".join([f"{m:.6f}" for m in current_learning_rate])
         momentum_info = " - momentum: " + ",".join([f"{m:.6f}" for m in momentum])
         self._record(MetricRecord.scalar_list("learning_rate", current_learning_rate, batch_count))

pyproject.toml

@@ -47,6 +47,7 @@ ignore_errors = true
 line-length = 120
 target-version = "py37"
 
+[tool.ruff.lint]
 #select = ["ALL"]    # Uncommit to autofix all the things
 select = [
   "C4",
@@ -95,12 +96,12 @@ unfixable = [
   "F841",   # Do not remove unused variables automatically
 ]
 
-[tool.ruff.per-file-ignores]
+[tool.ruff.lint.per-file-ignores]
 "flair/embeddings/legacy.py" = ["D205"]
 "scripts/*" = ["INP001"]  #  no need for __ini__ for scripts
 "flair/datasets/*" = ["D417"]  # need to fix datasets in a unified way later.
 
-[tool.ruff.pydocstyle]
+[tool.ruff.lint.pydocstyle]
 convention = "google"
 
 [tool.pydocstyle]