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Auto-format code with black (#9474)
* Auto-format code with black * Update spacy/pipeline/pipe.pyi Co-authored-by: explosion-bot <explosion-bot@users.noreply.github.com> Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
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@ -56,7 +56,9 @@ class RussianLemmatizer(Lemmatizer):
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if not len(filtered_analyses):
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if not len(filtered_analyses):
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return [string.lower()]
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return [string.lower()]
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if morphology is None or (len(morphology) == 1 and POS in morphology):
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if morphology is None or (len(morphology) == 1 and POS in morphology):
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return list(dict.fromkeys([analysis.normal_form for analysis in filtered_analyses]))
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return list(
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dict.fromkeys([analysis.normal_form for analysis in filtered_analyses])
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)
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if univ_pos in ("ADJ", "DET", "NOUN", "PROPN"):
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if univ_pos in ("ADJ", "DET", "NOUN", "PROPN"):
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features_to_compare = ["Case", "Number", "Gender"]
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features_to_compare = ["Case", "Number", "Gender"]
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elif univ_pos == "NUM":
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elif univ_pos == "NUM":
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@ -87,7 +89,9 @@ class RussianLemmatizer(Lemmatizer):
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filtered_analyses.append(analysis)
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filtered_analyses.append(analysis)
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if not len(filtered_analyses):
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if not len(filtered_analyses):
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return [string.lower()]
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return [string.lower()]
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return list(dict.fromkeys([analysis.normal_form for analysis in filtered_analyses]))
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return list(
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dict.fromkeys([analysis.normal_form for analysis in filtered_analyses])
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)
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def pymorphy2_lookup_lemmatize(self, token: Token) -> List[str]:
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def pymorphy2_lookup_lemmatize(self, token: Token) -> List[str]:
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string = token.text
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string = token.text
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@ -1,5 +1,6 @@
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from pathlib import Path
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from pathlib import Path
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from typing import Any, Callable, Dict, Iterable, Iterator, List, NoReturn, Optional, Tuple, Union
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from typing import Any, Callable, Dict, Iterable, Iterator, List
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from typing import NoReturn, Optional, Tuple, Union
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from ..tokens.doc import Doc
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from ..tokens.doc import Doc
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@ -8,9 +9,14 @@ from ..language import Language
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class Pipe:
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class Pipe:
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def __call__(self, doc: Doc) -> Doc: ...
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def __call__(self, doc: Doc) -> Doc: ...
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def pipe(self, stream: Iterable[Doc], *, batch_size: int = ...) -> Iterator[Doc]: ...
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def pipe(
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self, stream: Iterable[Doc], *, batch_size: int = ...
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) -> Iterator[Doc]: ...
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def initialize(
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def initialize(
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self, get_examples: Callable[[], Iterable[Example]], *, nlp: Language = ...,
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self,
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get_examples: Callable[[], Iterable[Example]],
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*,
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nlp: Language = ...,
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) -> None: ...
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) -> None: ...
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def score(
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def score(
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self, examples: Iterable[Example], **kwargs: Any
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self, examples: Iterable[Example], **kwargs: Any
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@ -26,7 +32,7 @@ class Pipe:
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self, error_handler: Callable[[str, "Pipe", List[Doc], Exception], NoReturn]
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self, error_handler: Callable[[str, "Pipe", List[Doc], Exception], NoReturn]
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) -> None: ...
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) -> None: ...
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def get_error_handler(
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def get_error_handler(
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self
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self,
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) -> Callable[[str, "Pipe", List[Doc], Exception], NoReturn]: ...
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) -> Callable[[str, "Pipe", List[Doc], Exception], NoReturn]: ...
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def deserialize_config(path: Path) -> Any: ...
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def deserialize_config(path: Path) -> Any: ...
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@ -196,7 +196,11 @@ def test_pipe_class_component_model_custom():
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@Language.factory(name, default_config=default_config)
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@Language.factory(name, default_config=default_config)
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class Component:
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class Component:
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def __init__(
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def __init__(
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self, nlp: Language, model: Model, name: str, value1: StrictInt = StrictInt(10)
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self,
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nlp: Language,
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model: Model,
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name: str,
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value1: StrictInt = StrictInt(10),
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):
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):
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self.nlp = nlp
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self.nlp = nlp
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self.model = model
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self.model = model
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@ -22,7 +22,6 @@ class Vocab:
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strings: StringStore
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strings: StringStore
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vectors: Vectors
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vectors: Vectors
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writing_system: Dict[str, Any]
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writing_system: Dict[str, Any]
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def __init__(
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def __init__(
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self,
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self,
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lex_attr_getters: Optional[Dict[str, Callable[[str], Any]]] = ...,
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lex_attr_getters: Optional[Dict[str, Callable[[str], Any]]] = ...,
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