ruff 3 formatting (Rye 0.28)

scripts/prepare_test_weights.py

@@ -4,6 +4,7 @@ Download and convert weights for testing
 To see what weights will be downloaded and converted, run:
 DRY_RUN=1 python scripts/prepare_test_weights.py
 """
+
 import hashlib
 import os
 import subprocess

src/refiners/fluxion/adapters/lora.py

@@ -129,8 +129,7 @@ class Lora(Generic[T], fl.Chain, ABC):
         return loras
 
     @abstractmethod
-    def is_compatible(self, layer: fl.WeightedModule, /) -> bool:
+    def is_compatible(self, layer: fl.WeightedModule, /) -> bool: ...
-        ...
 
     def auto_attach(
         self,

src/refiners/fluxion/layers/chain.py

@@ -256,16 +256,13 @@ class Chain(ContextModule):
         self._modules = generate_unique_names(tuple(modules))  # type: ignore
 
     @overload
-    def __getitem__(self, key: int) -> Module:
+    def __getitem__(self, key: int) -> Module: ...
-        ...
 
     @overload
-    def __getitem__(self, key: str) -> Module:
+    def __getitem__(self, key: str) -> Module: ...
-        ...
 
     @overload
-    def __getitem__(self, key: slice) -> "Chain":
+    def __getitem__(self, key: slice) -> "Chain": ...
-        ...
 
     def __getitem__(self, key: int | str | slice) -> Module:
         if isinstance(key, slice):
@@ -324,16 +321,14 @@ class Chain(ContextModule):
         self,
         predicate: Callable[[Module, "Chain"], bool] | None = None,
         recurse: bool = False,
-    ) -> Iterator[tuple[Module, "Chain"]]:
+    ) -> Iterator[tuple[Module, "Chain"]]: ...
-        ...
 
     @overload
     def walk(
         self,
         predicate: type[T],
         recurse: bool = False,
-    ) -> Iterator[tuple[T, "Chain"]]:
+    ) -> Iterator[tuple[T, "Chain"]]: ...
-        ...
 
     def walk(
         self,

src/refiners/foundationals/latent_diffusion/image_prompt.py

@@ -440,18 +440,15 @@ class IPAdapter(Generic[T], fl.Chain, Adapter[T]):
         self.set_context("ip_adapter", {"clip_image_embedding": image_embedding})
 
     @overload
-    def compute_clip_image_embedding(self, image_prompt: Tensor, weights: list[float] | None = None) -> Tensor:
+    def compute_clip_image_embedding(self, image_prompt: Tensor, weights: list[float] | None = None) -> Tensor: ...
-        ...
 
     @overload
-    def compute_clip_image_embedding(self, image_prompt: Image.Image) -> Tensor:
+    def compute_clip_image_embedding(self, image_prompt: Image.Image) -> Tensor: ...
-        ...
 
     @overload
     def compute_clip_image_embedding(
         self, image_prompt: list[Image.Image], weights: list[float] | None = None
-    ) -> Tensor:
+    ) -> Tensor: ...
-        ...
 
     def compute_clip_image_embedding(
         self,

src/refiners/foundationals/latent_diffusion/model.py

@@ -66,22 +66,18 @@ class LatentDiffusionModel(fl.Module, ABC):
         return self.solver.inference_steps
 
     @abstractmethod
-    def set_unet_context(self, *, timestep: Tensor, clip_text_embedding: Tensor, **_: Tensor) -> None:
+    def set_unet_context(self, *, timestep: Tensor, clip_text_embedding: Tensor, **_: Tensor) -> None: ...
-        ...
 
     @abstractmethod
-    def set_self_attention_guidance(self, enable: bool, scale: float = 1.0) -> None:
+    def set_self_attention_guidance(self, enable: bool, scale: float = 1.0) -> None: ...
-        ...
 
     @abstractmethod
-    def has_self_attention_guidance(self) -> bool:
+    def has_self_attention_guidance(self) -> bool: ...
-        ...
 
     @abstractmethod
     def compute_self_attention_guidance(
         self, x: Tensor, noise: Tensor, step: int, *, clip_text_embedding: Tensor, **kwargs: Tensor
-    ) -> Tensor:
+    ) -> Tensor: ...
-        ...
 
     def forward(
         self, x: Tensor, step: int, *, clip_text_embedding: Tensor, condition_scale: float = 7.5, **kwargs: Tensor

src/refiners/foundationals/latent_diffusion/multi_diffusion.py

@@ -68,8 +68,7 @@ class MultiDiffusion(Generic[T, D], ABC):
         return torch.where(condition=num_updates > 0, input=cumulative_values / num_updates, other=x)
 
     @abstractmethod
-    def diffuse_target(self, x: Tensor, step: int, target: D) -> Tensor:
+    def diffuse_target(self, x: Tensor, step: int, target: D) -> Tensor: ...
-        ...
 
     @property
     def steps(self) -> list[int]:

src/refiners/training_utils/callback.py

@@ -20,56 +20,38 @@ class CallbackConfig(BaseModel):
 
 
 class Callback(Generic[T]):
-    def on_init_begin(self, trainer: T) -> None:
+    def on_init_begin(self, trainer: T) -> None: ...
-        ...
 
-    def on_init_end(self, trainer: T) -> None:
+    def on_init_end(self, trainer: T) -> None: ...
-        ...
 
-    def on_train_begin(self, trainer: T) -> None:
+    def on_train_begin(self, trainer: T) -> None: ...
-        ...
 
-    def on_train_end(self, trainer: T) -> None:
+    def on_train_end(self, trainer: T) -> None: ...
-        ...
 
-    def on_epoch_begin(self, trainer: T) -> None:
+    def on_epoch_begin(self, trainer: T) -> None: ...
-        ...
 
-    def on_epoch_end(self, trainer: T) -> None:
+    def on_epoch_end(self, trainer: T) -> None: ...
-        ...
 
-    def on_batch_begin(self, trainer: T) -> None:
+    def on_batch_begin(self, trainer: T) -> None: ...
-        ...
 
-    def on_batch_end(self, trainer: T) -> None:
+    def on_batch_end(self, trainer: T) -> None: ...
-        ...
 
-    def on_backward_begin(self, trainer: T) -> None:
+    def on_backward_begin(self, trainer: T) -> None: ...
-        ...
 
-    def on_backward_end(self, trainer: T) -> None:
+    def on_backward_end(self, trainer: T) -> None: ...
-        ...
 
-    def on_optimizer_step_begin(self, trainer: T) -> None:
+    def on_optimizer_step_begin(self, trainer: T) -> None: ...
-        ...
 
-    def on_optimizer_step_end(self, trainer: T) -> None:
+    def on_optimizer_step_end(self, trainer: T) -> None: ...
-        ...
 
-    def on_compute_loss_begin(self, trainer: T) -> None:
+    def on_compute_loss_begin(self, trainer: T) -> None: ...
-        ...
 
-    def on_compute_loss_end(self, trainer: T) -> None:
+    def on_compute_loss_end(self, trainer: T) -> None: ...
-        ...
 
-    def on_evaluate_begin(self, trainer: T) -> None:
+    def on_evaluate_begin(self, trainer: T) -> None: ...
-        ...
 
-    def on_evaluate_end(self, trainer: T) -> None:
+    def on_evaluate_end(self, trainer: T) -> None: ...
-        ...
 
-    def on_lr_scheduler_step_begin(self, trainer: T) -> None:
+    def on_lr_scheduler_step_begin(self, trainer: T) -> None: ...
-        ...
 
-    def on_lr_scheduler_step_end(self, trainer: T) -> None:
+    def on_lr_scheduler_step_end(self, trainer: T) -> None: ...
-        ...

src/refiners/training_utils/huggingface_datasets.py

@@ -10,11 +10,9 @@ T = TypeVar("T", covariant=True)
 
 
 class HuggingfaceDataset(Generic[T], Protocol):
-    def __getitem__(self, index: int) -> T:
+    def __getitem__(self, index: int) -> T: ...
-        ...
 
-    def __len__(self) -> int:
+    def __len__(self) -> int: ...
-        ...
 
 
 def load_hf_dataset(

src/refiners/training_utils/trainer.py

@@ -335,8 +335,7 @@ class Trainer(Generic[ConfigType, Batch], ABC):
         )
 
     @abstractmethod
-    def compute_loss(self, batch: Batch) -> Tensor:
+    def compute_loss(self, batch: Batch) -> Tensor: ...
-        ...
 
     def compute_evaluation(self) -> None:
         pass

tests/adapters/test_ip_adapter.py

@@ -10,13 +10,11 @@ from refiners.foundationals.latent_diffusion.image_prompt import ImageCrossAtten
 
 
 @overload
-def new_adapter(target: SD1UNet) -> SD1IPAdapter:
+def new_adapter(target: SD1UNet) -> SD1IPAdapter: ...
-    ...
 
 
 @overload
-def new_adapter(target: SDXLUNet) -> SDXLIPAdapter:
+def new_adapter(target: SDXLUNet) -> SDXLIPAdapter: ...
-    ...
 
 
 def new_adapter(target: SD1UNet | SDXLUNet) -> SD1IPAdapter | SDXLIPAdapter:

tests/adapters/test_t2i_adapter.py

@@ -9,13 +9,11 @@ from refiners.foundationals.latent_diffusion.t2i_adapter import T2IFeatures
 
 
 @overload
-def new_adapter(target: SD1UNet, name: str) -> SD1T2IAdapter:
+def new_adapter(target: SD1UNet, name: str) -> SD1T2IAdapter: ...
-    ...
 
 
 @overload
-def new_adapter(target: SDXLUNet, name: str) -> SDXLT2IAdapter:
+def new_adapter(target: SDXLUNet, name: str) -> SDXLT2IAdapter: ...
-    ...
 
 
 def new_adapter(target: SD1UNet | SDXLUNet, name: str) -> SD1T2IAdapter | SDXLT2IAdapter:

tests/foundationals/latent_diffusion/test_sdxl_double_encoder.py

@@ -19,8 +19,7 @@ class DiffusersSDXL(Protocol):
     tokenizer_2: fl.Module
     vae: fl.Module
 
-    def __call__(self, prompt: str, *args: Any, **kwargs: Any) -> Any:
+    def __call__(self, prompt: str, *args: Any, **kwargs: Any) -> Any: ...
-        ...
 
     def encode_prompt(
         self,
@@ -28,8 +27,7 @@ class DiffusersSDXL(Protocol):
         prompt_2: str | None = None,
         negative_prompt: str | None = None,
         negative_prompt_2: str | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: ...
-        ...
 
 
 @pytest.fixture(scope="module")

tests/foundationals/segment_anything/utils.py

@@ -32,19 +32,16 @@ class FacebookSAM(nn.Module):
     prompt_encoder: nn.Module
     mask_decoder: nn.Module
 
-    def __call__(self, batched_input: list[SAMInput], multimask_output: bool) -> list[SAMOutput]:
+    def __call__(self, batched_input: list[SAMInput], multimask_output: bool) -> list[SAMOutput]: ...
-        ...
 
     @property
-    def device(self) -> Any:
+    def device(self) -> Any: ...
-        ...
 
 
 class FacebookSAMPredictor:
     model: FacebookSAM
 
-    def set_image(self, image: NDArrayUInt8, image_format: str = "RGB") -> None:
+    def set_image(self, image: NDArrayUInt8, image_format: str = "RGB") -> None: ...
-        ...
 
     def predict(
         self,
@@ -54,8 +51,7 @@ class FacebookSAMPredictor:
         mask_input: NDArray | None = None,
         multimask_output: bool = True,
         return_logits: bool = False,
-    ) -> tuple[NDArray, NDArray, NDArray]:
+    ) -> tuple[NDArray, NDArray, NDArray]: ...
-        ...
 
 
 @dataclass