chore: fix code formatting and update linter config

- Fix duplicate 'ignore' key in pyproject.toml
- Add extend-exclude for VideoAlign and HPSv3 submodules
- Remove deprecated UP038 rule from ruff config
- Apply ruff formatting to all project files

Author: Harahan

.pre-commit-config.yaml

@@ -4,7 +4,7 @@ repos:
     rev: v0.11.0
     hooks:
       - id: ruff
-        args: [--fix, --respect-gitignore, --config=pyproject.toml, --ignore=UP038]
+        args: [--fix, --respect-gitignore, --config=pyproject.toml]
       - id: ruff-format
         args: [--config=pyproject.toml]
 

genrl/diffusers_patch/wan_pipeline_with_logprob.py

@@ -191,7 +191,7 @@ def wan_pipeline_with_logprob(
 ):
     if callback_on_step_end_tensor_inputs is None:
         callback_on_step_end_tensor_inputs = ["latents"]
-    if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):  # noqa: UP038
+    if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
         callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
 
     self.check_inputs(

genrl/reward/hpsv3.py

@@ -145,9 +145,7 @@ def _fn(
                 # Repeat the general prompt for all frames
                 frame_prompts = [general_prompt] * len(frame_paths)
                 with torch.no_grad(), torch.amp.autocast(device_type=device_type):
-                    frame_rewards_raw = inferencer.reward(
-                        frame_prompts, image_paths=frame_paths
-                    )
+                    frame_rewards_raw = inferencer.reward(frame_prompts, image_paths=frame_paths)
 
                 # Extract mu values (mean scores)
                 # HPSv3 returns a list where each element is [mu, sigma] or a tensor
@@ -226,9 +224,7 @@ def _fn(
                 # Use the same prompt for all frames in the video
                 frame_prompts = [prompt] * len(frame_paths)
                 with torch.no_grad(), torch.amp.autocast(device_type=device_type):
-                    frame_rewards_raw = inferencer.reward(
-                        frame_prompts, image_paths=frame_paths
-                    )
+                    frame_rewards_raw = inferencer.reward(frame_prompts, image_paths=frame_paths)
 
                 # Extract mu values (mean scores)
                 # HPSv3 returns a list where each element is [mu, sigma] or a tensor

genrl/trainer/base_trainer.py

@@ -260,7 +260,7 @@ def log_metrics(
                 # Handle Python native types (int, float)
                 # Note: numpy.float64 is a subclass of float, but has dtype attribute
                 # numpy.int64 is NOT a subclass of int, and has dtype attribute
-                elif isinstance(value, (int, float)) and not hasattr(value, "dtype"):  # noqa: UP038
+                elif isinstance(value, (int, float)) and not hasattr(value, "dtype"):
                     scalar_items[key] = float(value)
                 # Handle numpy scalars (numpy.int64, numpy.float64, etc.)
                 # numpy scalars have .item() method and .dtype attribute

pyproject.toml

@@ -1,12 +1,10 @@
 [tool.ruff]
 line-length = 120
 target-version = "py310"
+extend-exclude = ["genrl/reward/VideoAlign", "genrl/reward/HPSv3"]
 
 [tool.ruff.lint]
 select = ["E", "F", "W", "I", "N", "UP", "B", "C4", "DTZ", "T10", "EM", "ISC", "ICN", "PIE", "T20", "PYI", "PT", "Q", "RSE", "RET", "SIM", "ARG", "PTH", "ERA", "PD", "PGH", "PL", "TRY", "NPY", "RUF"]
-ignore = [
-    "UP038",  # isinstance doesn't support X | Y syntax, must use tuple
-]
 ignore = [
     "PTH",  # Prefer pathlib over os.path (too many changes needed)
     "PLR0913",  # Too many arguments (acceptable for complex functions)