[pre-commit.ci] pre-commit suggestions

.pre-commit-config.yaml

@@ -36,7 +36,7 @@ repos:
       - id: detect-private-key
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.8.0
+    rev: v3.14.0
     hooks:
       - id: pyupgrade
         args: [--py38-plus]
@@ -48,20 +48,20 @@ repos:
       - id: nbstripout
 
   - repo: https://github.com/PyCQA/docformatter
-    rev: v1.7.3
+    rev: v1.7.5
     hooks:
       - id: docformatter
         additional_dependencies: [tomli]
         args: ["--in-place"]
 
   - repo: https://github.com/psf/black
-    rev: 23.3.0
+    rev: 23.9.1
     hooks:
       - id: black
         name: Format code
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.0.276
+    rev: v0.0.292
     hooks:
       - id: ruff
         args: ["--fix"]

src/flash/audio/classification/data.py

@@ -42,8 +42,8 @@
 
 
 class AudioClassificationData(DataModule):
-    """The ``AudioClassificationData`` class is a :class:`~flash.core.data.data_module.DataModule` with a set of
-    class methods for loading data for audio classification."""
+    """The ``AudioClassificationData`` class is a :class:`~flash.core.data.data_module.DataModule` with a set of class
+    methods for loading data for audio classification."""
 
     input_transform_cls = AudioClassificationInputTransform
 
@@ -141,6 +141,7 @@ def from_files(
             >>> import os
             >>> _ = [os.remove(f"spectrogram_{i}.png") for i in range(1, 4)]
             >>> _ = [os.remove(f"predict_spectrogram_{i}.png") for i in range(1, 4)]
+
         """
 
         ds_kw = {
@@ -275,6 +276,7 @@ def from_folders(
             >>> import shutil
             >>> shutil.rmtree("train_folder")
             >>> shutil.rmtree("predict_folder")
+
         """
 
         ds_kw = {
@@ -365,6 +367,7 @@ def from_numpy(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
 
         ds_kw = {
@@ -453,6 +456,7 @@ def from_tensors(
             Training...
             >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             Predicting...
+
         """
 
         ds_kw = {
@@ -607,6 +611,7 @@ def from_data_frame(
             >>> shutil.rmtree("predict_folder")
             >>> del train_data_frame
             >>> del predict_data_frame
+
         """
 
         ds_kw = {
@@ -854,6 +859,7 @@ def from_csv(
             >>> shutil.rmtree("predict_folder")
             >>> os.remove("train_data.tsv")
             >>> os.remove("predict_data.tsv")
+
         """
 
         ds_kw = {

src/flash/audio/speech_recognition/data.py

@@ -117,6 +117,7 @@ def from_files(
             >>> import os
             >>> _ = [os.remove(f"speech_{i}.wav") for i in range(1, 4)]
             >>> _ = [os.remove(f"predict_speech_{i}.wav") for i in range(1, 4)]
+
         """
 
         ds_kw = {"sampling_rate": sampling_rate}
@@ -302,6 +303,7 @@ def from_csv(
             >>> _ = [os.remove(f"predict_speech_{i}.wav") for i in range(1, 4)]
             >>> os.remove("train_data.tsv")
             >>> os.remove("predict_data.tsv")
+
         """
 
         ds_kw = {"input_key": input_field, "sampling_rate": sampling_rate}
@@ -424,6 +426,7 @@ def from_json(
             >>> _ = [os.remove(f"predict_speech_{i}.wav") for i in range(1, 4)]
             >>> os.remove("train_data.json")
             >>> os.remove("predict_data.json")
+
         """
 
         ds_kw = {"input_key": input_field, "sampling_rate": sampling_rate, "field": field}
@@ -570,6 +573,7 @@ def from_datasets(
             >>> import os
             >>> _ = [os.remove(f"speech_{i}.wav") for i in range(1, 4)]
             >>> _ = [os.remove(f"predict_speech_{i}.wav") for i in range(1, 4)]
+
         """
 
         ds_kw = {"sampling_rate": sampling_rate}

src/flash/audio/speech_recognition/model.py

@@ -45,6 +45,7 @@ class SpeechRecognition(Task):
         learning_rate: Learning rate to use for training, defaults to ``1e-5``.
         optimizer: Optimizer to use for training.
         lr_scheduler: The LR scheduler to use during training.
+
     """
 
     backbones: FlashRegistry = SPEECH_RECOGNITION_BACKBONES

src/flash/core/adapter.py

@@ -35,6 +35,7 @@ def from_task(cls, task: "flash.Task", **kwargs) -> "Adapter":
         """Instantiate the adapter from the given :class:`~flash.core.model.Task`.
 
         This includes resolution / creation of backbones / heads and any other provider specific options.
+
         """
 
     def forward(self, x: Any) -> Any:
@@ -73,6 +74,7 @@ class AdapterTask(Task):
     Args:
         adapter: The :class:`~flash.core.adapter.Adapter` to wrap.
         kwargs: Keyword arguments to be passed to the base :class:`~flash.core.model.Task`.
+
     """
 
     def __init__(self, adapter: Adapter, **kwargs):

src/flash/core/data/base_viz.py

@@ -96,6 +96,7 @@ def show(self, batch: Dict[str, Any], running_stage: RunningStage):
         As the :class:`~flash.core.data.io.input_transform.InputTransform` hooks are injected within
         the threaded workers of the DataLoader,
         the data won't be accessible when using ``num_workers > 0``.
+
     """
 
     def _show(

src/flash/core/data/callback.py

@@ -19,6 +19,7 @@ class FlashCallback(Callback):
     Same as PyTorch Lightning, Callbacks can be provided directly to the Trainer::
 
         trainer = Trainer(callbacks=[MyCustomCallback()])
+
     """
 
     def on_per_sample_transform(self, sample: Tensor, running_stage: RunningStage) -> None:
@@ -146,6 +147,7 @@ def from_inputs(
             'val': {},
             'predict': {}
         }
+
     """
 
     batches: dict

src/flash/core/data/data_module.py

@@ -44,8 +44,7 @@
 
 class DatasetInput(Input):
     """The ``DatasetInput`` implements default behaviours for data sources which expect the input to
-    :meth:`~flash.core.data.io.input.Input.load_data` to be a :class:`torch.utils.data.dataset.Dataset`
-    """
+    :meth:`~flash.core.data.io.input.Input.load_data` to be a :class:`torch.utils.data.dataset.Dataset`"""
 
     def load_sample(self, sample: Any) -> Dict[str, Any]:
         if isinstance(sample, tuple) and len(sample) == 2:
@@ -103,6 +102,7 @@ class DataModule(pl.LightningDataModule):
         >>> datamodule = DataModule(train_input, sampler=WeightedRandomSampler([0.1, 0.5], 2), batch_size=1)
         >>> print(datamodule.train_dataloader().sampler)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
         <torch.utils.data.sampler.WeightedRandomSampler object at ...>
+
     """
 
     input_transform_cls = InputTransform
@@ -399,6 +399,7 @@ def configure_data_fetcher(*args, **kwargs) -> BaseDataFetcher:
         """This function is used to configure a :class:`~flash.core.data.callback.BaseDataFetcher`.
 
         Override with your custom one.
+
         """
         return BaseDataFetcher()
 

src/flash/core/data/io/classification_input.py

@@ -25,6 +25,7 @@ class ClassificationInputMixin(Properties):
       targets and store metadata like ``labels`` and ``num_classes``.
     * In the ``load_sample`` method, use ``format_target`` to convert the target to a standard format for use with our
       tasks.
+
     """
 
     target_formatter: TargetFormatter
@@ -46,6 +47,7 @@ def load_target_metadata(
                 rather than inferring from the targets.
             add_background: If ``True``, a background class will be inserted as class zero if ``labels`` and
                 ``num_classes`` are being inferred.
+
         """
         self.target_formatter = target_formatter
         if target_formatter is None and targets is not None:

src/flash/core/data/io/input.py

@@ -108,6 +108,7 @@ def _validate_input(input: "InputBase") -> None:
     Raises:
         RuntimeError: If the ``input`` is of type ``Input`` and it's ``data`` attribute does not support ``len``.
         RuntimeError: If the ``input`` is of type ``IterableInput`` and it's ``data`` attribute does support ``len``.
+
     """
     if input.data is not None:
         if isinstance(input, Input) and not _has_len(input.data):
@@ -122,6 +123,7 @@ def _wrap_init(class_dict: Dict[str, Any]) -> None:
 
     Args:
         class_dict: The class construction dict, optionally containing an init to wrap.
+
     """
     if "__init__" in class_dict:
         fn = class_dict["__init__"]
@@ -153,14 +155,15 @@ def __new__(mcs, name: str, bases: Tuple, class_dict: Dict[str, Any]) -> "_Itera
 
 class InputBase(Properties, metaclass=_InputMeta):
     """``InputBase`` is the base class for the :class:`~flash.core.data.io.input.Input` and
-    :class:`~flash.core.data.io.input.IterableInput` dataset implementations in Flash. These datasets are
-    constructed via the ``load_data`` and ``load_sample`` hooks, which allow a single dataset object to include custom
-    loading logic according to the running stage (e.g. train, validate, test, predict).
+    :class:`~flash.core.data.io.input.IterableInput` dataset implementations in Flash. These datasets are constructed
+    via the ``load_data`` and ``load_sample`` hooks, which allow a single dataset object to include custom loading logic
+    according to the running stage (e.g. train, validate, test, predict).
 
     Args:
         running_stage: The running stage for which the input will be used.
         *args: Any arguments that are to be passed to the ``load_data`` hook.
         **kwargs: Any additional keyword arguments to pass to the ``load_data`` hook.
+
     """
 
     def __init__(self, running_stage: RunningStage, *args: Any, **kwargs: Any) -> None:
@@ -194,6 +197,7 @@ def load_data(*args: Any, **kwargs: Any) -> Union[Sequence, Iterable]:
         Args:
             *args: Any arguments that the input requires.
             **kwargs: Any additional keyword arguments that the input requires.
+
         """
         return args[0]
 
@@ -203,6 +207,7 @@ def train_load_data(self, *args: Any, **kwargs: Any) -> Union[Sequence, Iterable
         Args:
             *args: Any arguments that the input requires.
             **kwargs: Any additional keyword arguments that the input requires.
+
         """
         return self.load_data(*args, **kwargs)
 
@@ -212,6 +217,7 @@ def val_load_data(self, *args: Any, **kwargs: Any) -> Union[Sequence, Iterable]:
         Args:
             *args: Any arguments that the input requires.
             **kwargs: Any additional keyword arguments that the input requires.
+
         """
         return self.load_data(*args, **kwargs)
 
@@ -221,6 +227,7 @@ def test_load_data(self, *args: Any, **kwargs: Any) -> Union[Sequence, Iterable]
         Args:
             *args: Any arguments that the input requires.
             **kwargs: Any additional keyword arguments that the input requires.
+
         """
         return self.load_data(*args, **kwargs)
 
@@ -230,6 +237,7 @@ def predict_load_data(self, *args: Any, **kwargs: Any) -> Union[Sequence, Iterab
         Args:
             *args: Any arguments that the input requires.
             **kwargs: Any additional keyword arguments that the input requires.
+
         """
         return self.load_data(*args, **kwargs)
 
@@ -240,6 +248,7 @@ def load_sample(sample: Dict[str, Any]) -> Any:
 
         Args:
             sample: A single sample from the output of the ``load_data`` hook.
+
         """
         return sample
 
@@ -248,6 +257,7 @@ def train_load_sample(self, sample: Dict[str, Any]) -> Any:
 
         Args:
             sample: A single sample from the output of the ``load_data`` hook.
+
         """
         return self.load_sample(sample)
 
@@ -256,6 +266,7 @@ def val_load_sample(self, sample: Dict[str, Any]) -> Any:
 
         Args:
             sample: A single sample from the output of the ``load_data`` hook.
+
         """
         return self.load_sample(sample)
 
@@ -264,6 +275,7 @@ def test_load_sample(self, sample: Dict[str, Any]) -> Any:
 
         Args:
             sample: A single sample from the output of the ``load_data`` hook.
+
         """
         return self.load_sample(sample)
 
@@ -272,13 +284,15 @@ def predict_load_sample(self, sample: Dict[str, Any]) -> Any:
 
         Args:
             sample: A single sample from the output of the ``load_data`` hook.
+
         """
         return self.load_sample(sample)
 
     def __bool__(self):
         """If ``self.data`` is ``None`` then the ``InputBase`` is considered falsey.
 
         This allows for quickly checking whether or not the ``InputBase`` is populated with data.
+
         """
         return self.data is not None
 

src/flash/core/data/io/transform_predictions.py

@@ -28,6 +28,7 @@ class TransformPredictions(Callback):
     Args:
         output_transform: The :class:`~flash.core.data.io.output_transform.OutputTransform` to apply.
         output: The :class:`~flash.core.data.io.output.Output` to apply.
+
     """
 
     def __init__(self, output_transform: OutputTransform, output: Output):

src/flash/core/data/transforms.py

@@ -66,6 +66,7 @@ class ApplyToKeys(nn.Sequential):
     Args:
         keys: The key (``str``) or sequence of keys (``Sequence[str]``) to extract and forward to the transforms.
         args: The transforms, passed to the ``nn.Sequential`` super constructor.
+
     """
 
     def __init__(self, keys: Union[str, Sequence[str]], *args):