Format

spacy/language.py

@@ -801,8 +801,11 @@ class Language:
         self._components.insert(pipe_index, (name, pipe_component))
         return pipe_component
 
-    def add_pipe_instance(self, component: PipeCallable,
+    def add_pipe_instance(
-        /, name: Optional[str] = None,
+        self,
+        component: PipeCallable,
+        /,
+        name: Optional[str] = None,
         *,
         before: Optional[Union[str, int]] = None,
         after: Optional[Union[str, int]] = None,
@@ -1743,7 +1746,7 @@ class Language:
         meta: Dict[str, Any] = SimpleFrozenDict(),
         auto_fill: bool = True,
         validate: bool = True,
-        pipe_instances: Dict[str, Any] = SimpleFrozenDict()
+        pipe_instances: Dict[str, Any] = SimpleFrozenDict(),
     ) -> "Language":
         """Create the nlp object from a loaded config. Will set up the tokenizer
         and language data, add pipeline components etc. If no config is provided,
@@ -1844,7 +1847,7 @@ class Language:
         # and aren't built by factory.
         missing_components = _find_missing_components(pipeline, pipe_instances, exclude)
         if missing_components:
-            raise ValueError(Errors.E1052.format(", ",join(missing_components)))
+            raise ValueError(Errors.E1052.format(", ", join(missing_components)))
         # If components are loaded from a source (existing models), we cache
         # them here so they're only loaded once
         source_nlps = {}
@@ -1858,9 +1861,7 @@ class Language:
                 if pipe_name in exclude:
                     continue
                 else:
-                    nlp.add_pipe_instance(
+                    nlp.add_pipe_instance(pipe_instances[pipe_name])
-                        pipe_instances[pipe_name]
-                    )
             # Is it important that we instantiate pipes that
             # aren't excluded? It seems like we would want
             # the exclude check above. I've left it how it
@@ -2384,7 +2385,9 @@ class _Sender:
             self.send()
 
 
-def _get_instantiated_vocab(vocab: Union[bool, Vocab], pipe_instances: Dict[str, Any]) -> Union[bool, Vocab]:
+def _get_instantiated_vocab(
+    vocab: Union[bool, Vocab], pipe_instances: Dict[str, Any]
+) -> Union[bool, Vocab]:
     vocab_instances = {}
     for name, instance in pipe_instances.items():
         if hasattr(instance, "vocab") and isinstance(instance.vocab, Vocab):
@@ -2410,8 +2413,8 @@ def _get_instantiated_vocab(vocab: Union[bool, Vocab], pipe_instances: Dict[str,
 
 
 def _find_missing_components(
-    pipeline: List[str],
+    pipeline: List[str], pipe_instances: Dict[str, Any], exclude: List[str]
-    pipe_instances: Dict[str, Any],
-    exclude: List[str]
 ) -> List[str]:
-    return [name for name in pipeline if name not in pipe_instances and name not in exclude]
+    return [
+        name for name in pipeline if name not in pipe_instances and name not in exclude
+    ]

spacy/tests/test_language.py

@@ -801,15 +801,18 @@ def test_component_return():
         nlp("text")
 
 
-@pytest.mark.parametrize("components,kwargs,position", [
+@pytest.mark.parametrize(
-    (["t1", "t2"], {"before": "t1"}, 0),
+    "components,kwargs,position",
-    (["t1", "t2"], {"after": "t1"}, 1),
+    [
-    (["t1", "t2"], {"after": "t1"}, 1),
+        (["t1", "t2"], {"before": "t1"}, 0),
-    (["t1", "t2"], {"first": True}, 0),
+        (["t1", "t2"], {"after": "t1"}, 1),
-    (["t1", "t2"], {"last": True}, 2),
+        (["t1", "t2"], {"after": "t1"}, 1),
-    (["t1", "t2"], {"last": False}, 2),
+        (["t1", "t2"], {"first": True}, 0),
-    (["t1", "t2"], {"first": False}, ValueError),
+        (["t1", "t2"], {"last": True}, 2),
-])
+        (["t1", "t2"], {"last": False}, 2),
+        (["t1", "t2"], {"first": False}, ValueError),
+    ],
+)
 def test_add_pipe_instance(components, kwargs, position):
     nlp = Language()
     for name in components:
@@ -822,7 +825,9 @@ def test_add_pipe_instance(components, kwargs, position):
         assert nlp.pipe_names == pipe_names
     else:
         with pytest.raises(ValueError):
-            result = nlp.add_pipe_instance(evil_component, name="new_component", **kwargs)
+            result = nlp.add_pipe_instance(
+                evil_component, name="new_component", **kwargs
+            )
 
 
 def test_add_pipe_instance_to_bytes():
@@ -831,4 +836,3 @@ def test_add_pipe_instance_to_bytes():
     nlp.add_pipe("textcat", name="t2")
     nlp.add_pipe_instance(evil_component, name="new_component")
     b = nlp.to_bytes()
- 

spacy/util.py

@@ -415,7 +415,7 @@ def load_model(
     enable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
     exclude: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
     config: Union[Dict[str, Any], Config] = SimpleFrozenDict(),
-    pipe_instances: Dict[str, Any] = SimpleFrozenDict()
+    pipe_instances: Dict[str, Any] = SimpleFrozenDict(),
 ) -> "Language":
     """Load a model from a package or data path.
 
@@ -427,7 +427,7 @@ def load_model(
     exclude (Union[str, Iterable[str]]):  Name(s) of pipeline component(s) to exclude.
     config (Dict[str, Any] / Config): Config overrides as nested dict or dict
         keyed by section values in dot notation.
-    pipe_instances (Dict[str, Any]): Dictionary of components 
+    pipe_instances (Dict[str, Any]): Dictionary of components
         to be added to the pipeline directly (not created from
         config)
     RETURNS (Language): The loaded nlp object.
@@ -438,7 +438,7 @@ def load_model(
         "enable": enable,
         "exclude": exclude,
         "config": config,
-        "pipe_instances": pipe_instances
+        "pipe_instances": pipe_instances,
     }
     if isinstance(name, str):  # name or string path
         if name.startswith("blank:"):  # shortcut for blank model
@@ -462,7 +462,7 @@ def load_model_from_package(
     enable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
     exclude: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
     config: Union[Dict[str, Any], Config] = SimpleFrozenDict(),
-    pipe_instances: Dict[str, Any] = SimpleFrozenDict()
+    pipe_instances: Dict[str, Any] = SimpleFrozenDict(),
 ) -> "Language":
     """Load a model from an installed package.
 
@@ -478,7 +478,7 @@ def load_model_from_package(
         components won't be loaded.
     config (Dict[str, Any] / Config): Config overrides as nested dict or dict
         keyed by section values in dot notation.
-    pipe_instances (Dict[str, Any]): Dictionary of components 
+    pipe_instances (Dict[str, Any]): Dictionary of components
         to be added to the pipeline directly (not created from
         config)
     RETURNS (Language): The loaded nlp object.
@@ -496,7 +496,7 @@ def load_model_from_path(
     enable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
     exclude: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
     config: Union[Dict[str, Any], Config] = SimpleFrozenDict(),
-    pipe_instances: Dict[str, Any] = SimpleFrozenDict()
+    pipe_instances: Dict[str, Any] = SimpleFrozenDict(),
 ) -> "Language":
     """Load a model from a data directory path. Creates Language class with
     pipeline from config.cfg and then calls from_disk() with path.
@@ -533,7 +533,7 @@ def load_model_from_path(
         enable=enable,
         exclude=exclude,
         meta=meta,
-        pipe_instances=pipe_instances
+        pipe_instances=pipe_instances,
     )
     return nlp.from_disk(model_path, exclude=exclude, overrides=overrides)
 
@@ -548,7 +548,7 @@ def load_model_from_config(
     exclude: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
     auto_fill: bool = False,
     validate: bool = True,
-    pipe_instances: Dict[str, Any] = SimpleFrozenDict()
+    pipe_instances: Dict[str, Any] = SimpleFrozenDict(),
 ) -> "Language":
     """Create an nlp object from a config. Expects the full config file including
     a section "nlp" containing the settings for the nlp object.
@@ -588,7 +588,7 @@ def load_model_from_config(
         auto_fill=auto_fill,
         validate=validate,
         meta=meta,
-        pipe_instances=pipe_instances
+        pipe_instances=pipe_instances,
     )
     return nlp