ci: auto fixes from pre-commit.ci

For more information, see https://pre-commit.ci

src/_bentoml_impl/loader.py

@@ -179,16 +179,16 @@ def import_service(
     try:
         module_name, _, attrs_str = service_identifier.partition(":")
 
-        assert (
-            module_name and attrs_str
-        ), f'Invalid import target "{service_identifier}", must format as "<module>:<attribute>"'
+        assert module_name and attrs_str, (
+            f'Invalid import target "{service_identifier}", must format as "<module>:<attribute>"'
+        )
         module = importlib.import_module(module_name)
         root_service_name, _, depend_path = attrs_str.partition(".")
         root_service = t.cast("Service[t.Any]", getattr(module, root_service_name))
 
-        assert isinstance(
-            root_service, Service
-        ), f'import target "{module_name}:{attrs_str}" is not a bentoml.Service instance'
+        assert isinstance(root_service, Service), (
+            f'import target "{module_name}:{attrs_str}" is not a bentoml.Service instance'
+        )
 
         if not depend_path:
             svc = root_service

src/_bentoml_impl/server/app.py

@@ -258,9 +258,9 @@ def server_request_hook(span: Span, _scope: dict[str, t.Any]) -> None:
             middlewares.append(Middleware(middleware_cls, **options))
         # CORS middleware
         if self.enable_access_control:
-            assert (
-                self.access_control_options.get("allow_origins") is not None
-            ), "To enable cors, access_control_allow_origin must be set"
+            assert self.access_control_options.get("allow_origins") is not None, (
+                "To enable cors, access_control_allow_origin must be set"
+            )
 
             from starlette.middleware.cors import CORSMiddleware
 

src/_bentoml_impl/server/serving.py

@@ -194,9 +194,9 @@ def serve_http(
     if isinstance(bento_identifier, Service):
         svc = bento_identifier
         bento_identifier = svc.import_string
-        assert (
-            working_dir is None
-        ), "working_dir should not be set when passing a service in process"
+        assert working_dir is None, (
+            "working_dir should not be set when passing a service in process"
+        )
         # use cwd
         bento_path = pathlib.Path(".")
     else:

src/bentoml/_internal/batch/spark.py

@@ -68,9 +68,9 @@ def process(
     ) -> t.Generator[RecordBatch, None, None]:
         svc = _load_bento_spark(bento_tag)
 
-        assert (
-            api_name in svc.apis
-        ), "An error occurred transferring the Bento to the Spark worker."
+        assert api_name in svc.apis, (
+            "An error occurred transferring the Bento to the Spark worker."
+        )
         inference_api = svc.apis[api_name]
         assert inference_api.func is not None, "Inference API function not defined"
 

src/bentoml/_internal/client/grpc.py

@@ -86,9 +86,9 @@ def __init__(
         self._interceptors = interceptors
         self._credentials = None
         if ssl:
-            assert (
-                ssl_client_credentials is not None
-            ), "'ssl=True' requires 'ssl_client_credentials'"
+            assert ssl_client_credentials is not None, (
+                "'ssl=True' requires 'ssl_client_credentials'"
+            )
             self._credentials = grpc.ssl_channel_credentials(
                 **{
                     k: load_from_file(v) if isinstance(v, str) else v
@@ -124,9 +124,9 @@ def _create_channel(
         compression: grpc.Compression | None = None,
     ) -> GrpcAsyncChannel:
         if ssl:
-            assert (
-                ssl_client_credentials is not None
-            ), "'ssl=True' requires 'ssl_client_credentials'"
+            assert ssl_client_credentials is not None, (
+                "'ssl=True' requires 'ssl_client_credentials'"
+            )
             return aio.secure_channel(
                 server_url,
                 credentials=grpc.ssl_channel_credentials(
@@ -447,9 +447,9 @@ def __init__(
         self._interceptors = interceptors
         self._credentials = None
         if ssl:
-            assert (
-                ssl_client_credentials is not None
-            ), "'ssl=True' requires 'ssl_client_credentials'"
+            assert ssl_client_credentials is not None, (
+                "'ssl=True' requires 'ssl_client_credentials'"
+            )
             self._credentials = grpc.ssl_channel_credentials(
                 **{
                     k: load_from_file(v) if isinstance(v, str) else v
@@ -483,9 +483,9 @@ def _create_channel(
         compression: grpc.Compression | None = None,
     ) -> GrpcSyncChannel:
         if ssl:
-            assert (
-                ssl_client_credentials is not None
-            ), "'ssl=True' requires 'ssl_client_credentials'"
+            assert ssl_client_credentials is not None, (
+                "'ssl=True' requires 'ssl_client_credentials'"
+            )
             return grpc.secure_channel(
                 server_url,
                 credentials=grpc.ssl_channel_credentials(

src/bentoml/_internal/frameworks/onnx.py

@@ -262,9 +262,9 @@ def forward(self, x, bias):
             break
         except PackageNotFoundError:
             pass
-    assert (
-        _onnxruntime_pkg is not None and _onnxruntime_version is not None
-    ), "Failed to find onnxruntime package version."
+    assert _onnxruntime_pkg is not None and _onnxruntime_version is not None, (
+        "Failed to find onnxruntime package version."
+    )
 
     assert _onnxruntime_version is not None, "onnxruntime is not installed"
     if not isinstance(model, onnx.ModelProto):
@@ -291,7 +291,7 @@ def forward(self, x, bias):
         provided_methods = list(signatures.keys())
         if provided_methods != ["run"]:
             raise BentoMLException(
-                f"Provided method names {[m for m  in provided_methods if m != 'run']} are invalid. 'bentoml.onnx' will load ONNX model into an 'onnxruntime.InferenceSession' for inference, so the only supported method name is 'run'."
+                f"Provided method names {[m for m in provided_methods if m != 'run']} are invalid. 'bentoml.onnx' will load ONNX model into an 'onnxruntime.InferenceSession' for inference, so the only supported method name is 'run'."
             )
 
     run_input_specs = [MessageToDict(inp) for inp in model.graph.input]

src/bentoml/_internal/frameworks/pytorch_lightning.py

@@ -163,9 +163,9 @@ def configure_optimizers(self):
 
     script_module = model.to_torchscript()
 
-    assert not isinstance(
-        script_module, dict
-    ), "Saving a dict of pytorch_lightning Module into one BentoModel is not supported"
+    assert not isinstance(script_module, dict), (
+        "Saving a dict of pytorch_lightning Module into one BentoModel is not supported"
+    )
 
     return script_save_model(
         name,

src/bentoml/_internal/frameworks/transformers.py

@@ -453,9 +453,9 @@ def load_model(bento_model: str | Tag | Model, *args: t.Any, **kwargs: t.Any) ->
 
         kwargs.setdefault("pipeline_class", pipeline.__class__ if pipeline else None)
 
-        assert (
-            task in get_supported_tasks()
-        ), f"Task '{task}' is not a valid task for pipeline (available: {get_supported_tasks()})."
+        assert task in get_supported_tasks(), (
+            f"Task '{task}' is not a valid task for pipeline (available: {get_supported_tasks()})."
+        )
 
         return (
             transformers.pipeline(task=task, model=bento_model.path, **kwargs)
@@ -474,9 +474,9 @@ def load_model(bento_model: str | Tag | Model, *args: t.Any, **kwargs: t.Any) ->
                 protocol: PreTrainedProtocol = cloudpickle.load(f)
             return protocol.from_pretrained(bento_model.path, *args, **kwargs)
         else:
-            assert (
-                len(args) == 0
-            ), "Positional args are not supported for pipeline. Make sure to only use kwargs instead."
+            assert len(args) == 0, (
+                "Positional args are not supported for pipeline. Make sure to only use kwargs instead."
+            )
             with open(bento_model.path_of(PIPELINE_PICKLE_NAME), "rb") as f:
                 pipeline_class: type[transformers.Pipeline] = cloudpickle.load(f)
 
@@ -504,9 +504,9 @@ def load_model(bento_model: str | Tag | Model, *args: t.Any, **kwargs: t.Any) ->
 
             kwargs.setdefault("pipeline_class", pipeline_class)
 
-            assert (
-                task in get_supported_tasks()
-            ), f"Task '{task}' is not a valid task for pipeline (available: {get_supported_tasks()})."
+            assert task in get_supported_tasks(), (
+                f"Task '{task}' is not a valid task for pipeline (available: {get_supported_tasks()})."
+            )
 
             kwargs.update(options.kwargs)
             if len(kwargs) > 0:
@@ -860,7 +860,9 @@ def import_model(
     pretrained = t.cast("PreTrainedProtocol", model)
     assert all(
         hasattr(pretrained, defn) for defn in ("save_pretrained", "from_pretrained")
-    ), f"'pretrained={pretrained}' is not a valid Transformers object. It must have 'save_pretrained' and 'from_pretrained' methods."
+    ), (
+        f"'pretrained={pretrained}' is not a valid Transformers object. It must have 'save_pretrained' and 'from_pretrained' methods."
+    )
     if metadata is None:
         metadata = {}
 
@@ -1001,9 +1003,9 @@ def save_model(
         )
         pretrained_or_pipeline = pipeline
 
-    assert (
-        pretrained_or_pipeline is not None
-    ), "Please provide a pipeline or a pretrained object as a second argument."
+    assert pretrained_or_pipeline is not None, (
+        "Please provide a pipeline or a pretrained object as a second argument."
+    )
 
     # The below API are introduced since 4.18
     if pkg_version_info("transformers")[:2] >= (4, 18):
@@ -1087,13 +1089,13 @@ def save_model(
                     task_name,
                 )
 
-            assert (
-                task_name in get_supported_tasks()
-            ), f"Task '{task_name}' failed to register into pipeline registry."
+            assert task_name in get_supported_tasks(), (
+                f"Task '{task_name}' failed to register into pipeline registry."
+            )
         else:
-            assert (
-                task_definition is None
-            ), "'task_definition' must be None if 'task_name' is not provided."
+            assert task_definition is None, (
+                "'task_definition' must be None if 'task_name' is not provided."
+            )
 
             # if task_name is None, then we derive the task from pipeline.task
             options_args = t.cast(
@@ -1124,7 +1126,9 @@ def save_model(
         pretrained = t.cast("PreTrainedProtocol", pretrained_or_pipeline)
         assert all(
             hasattr(pretrained, defn) for defn in ("save_pretrained", "from_pretrained")
-        ), f"'pretrained={pretrained}' is not a valid Transformers object. It must have 'save_pretrained' and 'from_pretrained' methods."
+        ), (
+            f"'pretrained={pretrained}' is not a valid Transformers object. It must have 'save_pretrained' and 'from_pretrained' methods."
+        )
         if metadata is None:
             metadata = {}
 

src/bentoml/_internal/frameworks/utils/tensorflow.py

@@ -333,7 +333,7 @@ def _pretty_format_function_call(base: str, name: str, arg_names: t.Tuple[t.Any]
 
 def _pretty_format_positional(positional: t.Optional["tf_ext.TensorSignature"]) -> str:
     if positional is not None:
-        return f'Positional arguments ({len(positional)} total):\n    {"    * ".join(str(a) for a in positional)}'  # noqa
+        return f"Positional arguments ({len(positional)} total):\n    {'    * '.join(str(a) for a in positional)}"  # noqa
     return "No positional arguments.\n"
 
 

src/bentoml/_internal/io_descriptors/file.py

@@ -332,7 +332,7 @@ async def from_proto(
                         )
                 except KeyError:
                     raise BadInput(
-                        f"{field.kind} is not a valid File kind. Accepted file kind: {[names for names,_ in pb_v1alpha1.File.FileType.items()]}",
+                        f"{field.kind} is not a valid File kind. Accepted file kind: {[names for names, _ in pb_v1alpha1.File.FileType.items()]}",
                     ) from None
             content = field.content
             if not content:

src/bentoml/_internal/io_descriptors/image.py

@@ -428,7 +428,7 @@ async def from_proto(self, field: pb.File | pb_v1alpha1.File | bytes) -> ImageTy
                         )
                 except KeyError:
                     raise BadInput(
-                        f"{field.kind} is not a valid File kind. Accepted file kind: {[names for names,_ in pb_v1alpha1.File.FileType.items()]}",
+                        f"{field.kind} is not a valid File kind. Accepted file kind: {[names for names, _ in pb_v1alpha1.File.FileType.items()]}",
                     ) from None
             if not field.content:
                 raise BadInput("Content is empty!") from None

src/bentoml/_internal/io_descriptors/json.py

@@ -191,9 +191,9 @@ def __init__(
         json_encoder: type[json.JSONEncoder] = DefaultJsonEncoder,
     ):
         if pydantic_model is not None:
-            assert issubclass(
-                pydantic_model, pydantic.BaseModel
-            ), "'pydantic_model' must be a subclass of 'pydantic.BaseModel'."
+            assert issubclass(pydantic_model, pydantic.BaseModel), (
+                "'pydantic_model' must be a subclass of 'pydantic.BaseModel'."
+            )
 
         self._pydantic_model = pydantic_model
         self._json_encoder = json_encoder

src/bentoml/_internal/io_descriptors/multipart.py

@@ -176,7 +176,7 @@ def __init__(self, **inputs: IODescriptor[t.Any]):
         self._inputs = inputs
 
     def __repr__(self) -> str:
-        return f"Multipart({','.join([f'{k}={v}' for k,v in zip(self._inputs, map(repr, self._inputs.values()))])})"
+        return f"Multipart({','.join([f'{k}={v}' for k, v in zip(self._inputs, map(repr, self._inputs.values()))])})"
 
     def _from_sample(cls, sample: dict[str, t.Any]) -> t.Any:
         raise NotImplementedError("'from_sample' is not supported for Multipart.")

src/bentoml/_internal/io_descriptors/pandas.py

@@ -670,7 +670,7 @@ def process_columns_contents(content: pb.Series) -> dict[str, t.Any]:
                 # large tabular data
                 if len(content.ListFields()) != 1:
                     raise BadInput(
-                        f"Array contents can only be one of given values key. Use one of '{list(map(lambda f: f[0].name,content.ListFields()))}' instead."
+                        f"Array contents can only be one of given values key. Use one of '{list(map(lambda f: f[0].name, content.ListFields()))}' instead."
                     ) from None
                 return {str(i): c for i, c in enumerate(content.ListFields()[0][1])}
 

src/bentoml/_internal/runner/container.py

@@ -332,9 +332,9 @@ def batches_to_batch(
     ) -> tuple[ext.PdDataFrame, list[int]]:
         import pandas as pd
 
-        assert (
-            batch_dim == 0
-        ), "PandasDataFrameContainer does not support batch_dim other than 0"
+        assert batch_dim == 0, (
+            "PandasDataFrameContainer does not support batch_dim other than 0"
+        )
         indices = list(
             itertools.accumulate(subbatch.shape[batch_dim] for subbatch in batches)
         )
@@ -348,9 +348,9 @@ def batch_to_batches(
         indices: t.Sequence[int],
         batch_dim: int = 0,
     ) -> list[ext.PdDataFrame]:
-        assert (
-            batch_dim == 0
-        ), "PandasDataFrameContainer does not support batch_dim other than 0"
+        assert batch_dim == 0, (
+            "PandasDataFrameContainer does not support batch_dim other than 0"
+        )
 
         return [
             batch.iloc[indices[i] : indices[i + 1]].reset_index(drop=True)
@@ -365,9 +365,9 @@ def to_payload(
     ) -> Payload:
         import pandas as pd
 
-        assert (
-            batch_dim == 0
-        ), "PandasDataFrameContainer does not support batch_dim other than 0"
+        assert batch_dim == 0, (
+            "PandasDataFrameContainer does not support batch_dim other than 0"
+        )
 
         if isinstance(batch, pd.Series):
             batch = pd.DataFrame([batch])
@@ -402,9 +402,9 @@ def to_payload(
     def get_batch_size(
         cls, batch: ext.PdDataFrame | ext.PdSeries, batch_dim: int
     ) -> int:
-        assert (
-            batch_dim == 0
-        ), "PandasDataFrameContainer does not support batch_dim other than 0"
+        assert batch_dim == 0, (
+            "PandasDataFrameContainer does not support batch_dim other than 0"
+        )
         return batch.shape
 
     @classmethod
@@ -545,9 +545,9 @@ class DefaultContainer(DataContainer[t.Any, t.List[t.Any]]):
     def batches_to_batch(
         cls, batches: t.Sequence[list[t.Any]], batch_dim: int = 0
     ) -> tuple[list[t.Any], list[int]]:
-        assert (
-            batch_dim == 0
-        ), "Default Runner DataContainer does not support batch_dim other than 0"
+        assert batch_dim == 0, (
+            "Default Runner DataContainer does not support batch_dim other than 0"
+        )
         batch: list[t.Any] = []
         for subbatch in batches:
             batch.extend(subbatch)
@@ -559,9 +559,9 @@ def batches_to_batch(
     def batch_to_batches(
         cls, batch: list[t.Any], indices: t.Sequence[int], batch_dim: int = 0
     ) -> list[list[t.Any]]:
-        assert (
-            batch_dim == 0
-        ), "Default Runner DataContainer does not support batch_dim other than 0"
+        assert batch_dim == 0, (
+            "Default Runner DataContainer does not support batch_dim other than 0"
+        )
         return [batch[indices[i] : indices[i + 1]] for i in range(len(indices) - 1)]
 
     @classmethod

src/bentoml/_internal/runner/runner_handle/remote.py

@@ -502,9 +502,9 @@ async def async_run_method(
     ) -> tritongrpcclient.InferResult | tritonhttpclient.InferResult:
         from ..container import AutoContainer
 
-        assert (
-            (len(args) == 0) ^ (len(kwargs) == 0)
-        ), f"Inputs for model '{__bentoml_method.name}' can be given either as positional (args) or keyword arguments (kwargs), but not both. See https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md#model-configuration"
+        assert (len(args) == 0) ^ (len(kwargs) == 0), (
+            f"Inputs for model '{__bentoml_method.name}' can be given either as positional (args) or keyword arguments (kwargs), but not both. See https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md#model-configuration"
+        )
 
         pass_args = args if len(args) > 0 else kwargs