Format files with latest ruff

strickvl · strickvl · commit 58cb8900e10a · 2025-05-17T18:53:47.000+02:00
diff --git a/databricks-production-qa-demo/steps/deployment/deployment_deploy.py b/databricks-production-qa-demo/steps/deployment/deployment_deploy.py
@@ -31,14 +31,10 @@
 
 
 @step(enable_cache=False)
-def deployment_deploy() -> (
-    Annotated[
-        Optional[DatabricksDeploymentService],
-        ArtifactConfig(
-            name="databricks_deployment", is_deployment_artifact=True
-        ),
-    ]
-):
+def deployment_deploy() -> Annotated[
+    Optional[DatabricksDeploymentService],
+    ArtifactConfig(name="databricks_deployment", is_deployment_artifact=True),
+]:
     """Predictions step.
 
     This is an example of a predictions step that takes the data in and returns
diff --git a/huggingface-sagemaker/steps/deploying/huggingface_deployment.py b/huggingface-sagemaker/steps/deploying/huggingface_deployment.py
@@ -47,7 +47,9 @@ def deploy_to_huggingface(
     save_model_to_deploy.entrypoint()
 
     logger.info("Model saved locally. Pushing to HuggingFace...")
-    assert secret, "No secret found with name 'huggingface_creds'. Please create one with your `token`."
+    assert secret, (
+        "No secret found with name 'huggingface_creds'. Please create one with your `token`."
+    )
 
     token = secret.secret_values["token"]
     api = HfApi(token=token)
diff --git a/huggingface-sagemaker/steps/promotion/promote_get_metrics.py b/huggingface-sagemaker/steps/promotion/promote_get_metrics.py
@@ -27,12 +27,10 @@
 
 
 @step
-def promote_get_metrics() -> (
-    Tuple[
-        Annotated[Dict[str, Any], "latest_metrics"],
-        Annotated[Dict[str, Any], "current_metrics"],
-    ]
-):
+def promote_get_metrics() -> Tuple[
+    Annotated[Dict[str, Any], "latest_metrics"],
+    Annotated[Dict[str, Any], "current_metrics"],
+]:
     """Get metrics for comparison for promoting a model.
 
     This is an example of a metric retrieval step. It is used to retrieve
diff --git a/llm-complete-guide/steps/eval_retrieval.py b/llm-complete-guide/steps/eval_retrieval.py
@@ -275,9 +275,9 @@ def perform_small_retrieval_evaluation(use_reranking: bool) -> float:
 
 
 @step
-def retrieval_evaluation_small() -> (
-    Annotated[float, "small_failure_rate_retrieval"]
-):
+def retrieval_evaluation_small() -> Annotated[
+    float, "small_failure_rate_retrieval"
+]:
     """Executes the retrieval evaluation step without reranking.
 
     Returns:
@@ -287,9 +287,9 @@ def retrieval_evaluation_small() -> (
 
 
 @step
-def retrieval_evaluation_small_with_reranking() -> (
-    Annotated[float, "small_failure_rate_retrieval_reranking"]
-):
+def retrieval_evaluation_small_with_reranking() -> Annotated[
+    float, "small_failure_rate_retrieval_reranking"
+]:
     """Executes the retrieval evaluation step with reranking.
 
     Returns:
diff --git a/llm-complete-guide/steps/hf_dataset_loader.py b/llm-complete-guide/steps/hf_dataset_loader.py
@@ -23,9 +23,9 @@
 
 
 @step(output_materializers=HFDatasetMaterializer)
-def load_hf_dataset() -> (
-    Tuple[Annotated[Dataset, "train"], Annotated[Dataset, "test"]]
-):
+def load_hf_dataset() -> Tuple[
+    Annotated[Dataset, "train"], Annotated[Dataset, "test"]
+]:
     train_dataset = load_dataset(DATASET_NAME_DEFAULT, split="train")
     test_dataset = load_dataset(DATASET_NAME_DEFAULT, split="test")
     return train_dataset, test_dataset
diff --git a/magic-photobooth/k8s_run.py b/magic-photobooth/k8s_run.py
@@ -326,13 +326,11 @@ def generate_video_frames(
     settings={"orchestrator.kubernetes": kubernetes_settings},
     enable_cache=False,
 )
-def image_to_video() -> (
-    Tuple[
-        Annotated[PILImage.Image, "generated_image"],
-        Annotated[bytes, "video_data"],
-        Annotated[HTMLString, "video_html"],
-    ]
-):
+def image_to_video() -> Tuple[
+    Annotated[PILImage.Image, "generated_image"],
+    Annotated[bytes, "video_data"],
+    Annotated[HTMLString, "video_html"],
+]:
     model_path = f"{TrainConfig().hf_username}/{TrainConfig().hf_repo_suffix}"
 
     pipe = AutoPipelineForText2Image.from_pretrained(
diff --git a/magic-photobooth/modal_run.py b/magic-photobooth/modal_run.py
@@ -308,13 +308,11 @@ def generate_video_frames(
     settings={"step_operator.modal": modal_settings},
     enable_cache=False,
 )
-def image_to_video() -> (
-    Tuple[
-        Annotated[PILImage.Image, "generated_image"],
-        Annotated[bytes, "video_data"],
-        Annotated[HTMLString, "video_html"],
-    ]
-):
+def image_to_video() -> Tuple[
+    Annotated[PILImage.Image, "generated_image"],
+    Annotated[bytes, "video_data"],
+    Annotated[HTMLString, "video_html"],
+]:
     model_path = f"{TrainConfig().hf_username}/{TrainConfig().hf_repo_suffix}"
 
     pipe = AutoPipelineForText2Image.from_pretrained(
diff --git a/magic-photobooth/modal_run_using_azure_data.py b/magic-photobooth/modal_run_using_azure_data.py
@@ -300,13 +300,11 @@ def generate_video_frames(
     settings={"step_operator.modal": modal_settings},
     enable_cache=False,
 )
-def image_to_video() -> (
-    Tuple[
-        Annotated[PILImage.Image, "generated_image"],
-        Annotated[bytes, "video_data"],
-        Annotated[HTMLString, "video_html"],
-    ]
-):
+def image_to_video() -> Tuple[
+    Annotated[PILImage.Image, "generated_image"],
+    Annotated[bytes, "video_data"],
+    Annotated[HTMLString, "video_html"],
+]:
     model_path = f"{TrainConfig().hf_username}/{TrainConfig().hf_repo_suffix}"
 
     pipe = AutoPipelineForText2Image.from_pretrained(
diff --git a/oncoclear/steps/model_promoter.py b/oncoclear/steps/model_promoter.py
@@ -44,7 +44,7 @@ def model_promoter(accuracy: float, stage: str = "production") -> bool:
 
     if accuracy < 0.8:
         logger.info(
-            f"Model accuracy {accuracy*100:.2f}% is below 80% ! Not promoting model."
+            f"Model accuracy {accuracy * 100:.2f}% is below 80% ! Not promoting model."
         )
     else:
         logger.info(f"Model promoted to {stage}!")
diff --git a/retail-forecast/pipelines/training_pipeline.py b/retail-forecast/pipelines/training_pipeline.py
@@ -12,14 +12,12 @@
 
 
 @pipeline(name="retail_forecast_pipeline")
-def training_pipeline() -> (
-    Tuple[
-        Annotated[Dict[str, float], "model_metrics"],
-        Annotated[HTMLString, "evaluation_report"],
-        Annotated[HTMLString, "forecast_dashboard"],
-        Annotated[HTMLString, "sales_visualization"],
-    ]
-):
+def training_pipeline() -> Tuple[
+    Annotated[Dict[str, float], "model_metrics"],
+    Annotated[HTMLString, "evaluation_report"],
+    Annotated[HTMLString, "forecast_dashboard"],
+    Annotated[HTMLString, "sales_visualization"],
+]:
     """Simple retail forecasting pipeline using Prophet.
 
     Steps:
diff --git a/sign-language-detection-yolov5/steps/model_loader.py b/sign-language-detection-yolov5/steps/model_loader.py
@@ -21,9 +21,9 @@
 
 
 @step
-def model_loader() -> (
-    Tuple[Annotated[str, "model_path"], Annotated[torch.nn.Module, "model"]]
-):
+def model_loader() -> Tuple[
+    Annotated[str, "model_path"], Annotated[torch.nn.Module, "model"]
+]:
     """Loads the trained models from previous training pipeline runs."""
     training_pipeline = Client().get_pipeline(
         "sign_language_detection_train_pipeline"
diff --git a/vertex-registry-and-deployer/steps/model_promoter.py b/vertex-registry-and-deployer/steps/model_promoter.py
@@ -44,7 +44,7 @@ def model_promoter(accuracy: float, stage: str = "production") -> bool:
 
     if accuracy < 0.8:
         logger.info(
-            f"Model accuracy {accuracy*100:.2f}% is below 80% ! Not promoting model."
+            f"Model accuracy {accuracy * 100:.2f}% is below 80% ! Not promoting model."
         )
     else:
         logger.info(f"Model promoted to {stage}!")
diff --git a/vertex-registry-and-deployer/steps/model_trainer.py b/vertex-registry-and-deployer/steps/model_trainer.py
@@ -90,17 +90,17 @@ def model_trainer(
     # Evaluate the model
     trn_acc = model.score(X_trn, y_trn)
     tst_acc = model.score(X_tst, y_tst)
-    logger.info(f"Train accuracy={trn_acc*100:.2f}%")
-    logger.info(f"Test accuracy={tst_acc*100:.2f}%")
+    logger.info(f"Train accuracy={trn_acc * 100:.2f}%")
+    logger.info(f"Test accuracy={tst_acc * 100:.2f}%")
 
     messages = []
     if trn_acc < min_train_accuracy:
         messages.append(
-            f"Train accuracy {trn_acc*100:.2f}% is below {min_train_accuracy*100:.2f}%!"
+            f"Train accuracy {trn_acc * 100:.2f}% is below {min_train_accuracy * 100:.2f}%!"
         )
     if tst_acc < min_test_accuracy:
         messages.append(
-            f"Test accuracy {tst_acc*100:.2f}% is below {min_test_accuracy*100:.2f}%!"
+            f"Test accuracy {tst_acc * 100:.2f}% is below {min_test_accuracy * 100:.2f}%!"
         )
     else:
         for message in messages:

Original file line number	Diff line number	Diff line change
`@@ -44,7 +44,7 @@ def model_promoter(accuracy: float, stage: str = "production") -> bool:`
`44`	`44`
`45`	`45`	`if accuracy < 0.8:`
`46`	`46`	`logger.info(`
`47`		`- f"Model accuracy {accuracy*100:.2f}% is below 80% ! Not promoting model."`
	`47`	`+ f"Model accuracy {accuracy * 100:.2f}% is below 80% ! Not promoting model."`
`48`	`48`	`)`
`49`	`49`	`else:`
`50`	`50`	`logger.info(f"Model promoted to {stage}!")`