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	feat: add example stubs (#12679)
* feat: add example stubs * fix: add required annotations * fix: mypy issues * fix: use Py36-compatible Portocol * Minor reformatting --------- Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> Co-authored-by: svlandeg <svlandeg@github.com>
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			@ -8,6 +8,7 @@ from typing import (
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    List,
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    Optional,
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    Protocol,
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    Sequence,
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    Tuple,
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    Union,
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    overload,
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			@ -134,7 +135,12 @@ class Doc:
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    def text(self) -> str: ...
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    @property
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    def text_with_ws(self) -> str: ...
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    ents: Tuple[Span]
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    # Ideally the getter would output Tuple[Span]
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    # see https://github.com/python/mypy/issues/3004
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    @property
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    def ents(self) -> Sequence[Span]: ...
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    @ents.setter
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    def ents(self, value: Sequence[Span]) -> None: ...
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    def set_ents(
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        self,
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        entities: List[Span],
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			@ -6,6 +6,7 @@ from typing import TYPE_CHECKING, Callable, Iterable, Iterator, List, Optional,
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import srsly
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from .. import util
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from ..compat import Protocol
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from ..errors import Errors, Warnings
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from ..tokens import Doc, DocBin
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from ..vocab import Vocab
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			@ -19,6 +20,11 @@ if TYPE_CHECKING:
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FILE_TYPE = ".spacy"
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class ReaderProtocol(Protocol):
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    def __call__(self, nlp: "Language") -> Iterable[Example]:
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        pass
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@util.registry.readers("spacy.Corpus.v1")
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def create_docbin_reader(
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    path: Optional[Path],
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			@ -26,7 +32,7 @@ def create_docbin_reader(
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    max_length: int = 0,
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    limit: int = 0,
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    augmenter: Optional[Callable] = None,
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) -> Callable[["Language"], Iterable[Example]]:
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) -> ReaderProtocol:
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    if path is None:
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        raise ValueError(Errors.E913)
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    util.logger.debug("Loading corpus from path: %s", path)
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			@ -45,7 +51,7 @@ def create_jsonl_reader(
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    min_length: int = 0,
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    max_length: int = 0,
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    limit: int = 0,
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) -> Callable[["Language"], Iterable[Example]]:
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) -> ReaderProtocol:
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    return JsonlCorpus(path, min_length=min_length, max_length=max_length, limit=limit)
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			@ -63,7 +69,7 @@ def create_plain_text_reader(
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    path: Optional[Path],
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    min_length: int = 0,
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    max_length: int = 0,
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) -> Callable[["Language"], Iterable[Doc]]:
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) -> ReaderProtocol:
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    """Iterate Example objects from a file or directory of plain text
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    UTF-8 files with one line per doc.
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			@ -144,7 +150,7 @@ class Corpus:
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        self.augmenter = augmenter if augmenter is not None else dont_augment
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        self.shuffle = shuffle
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    def __call__(self, nlp: "Language") -> Iterator[Example]:
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    def __call__(self, nlp: "Language") -> Iterable[Example]:
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        """Yield examples from the data.
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        nlp (Language): The current nlp object.
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			@ -182,7 +188,7 @@ class Corpus:
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    def make_examples(
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        self, nlp: "Language", reference_docs: Iterable[Doc]
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    ) -> Iterator[Example]:
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    ) -> Iterable[Example]:
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        for reference in reference_docs:
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            if len(reference) == 0:
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                continue
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			@ -197,7 +203,7 @@ class Corpus:
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    def make_examples_gold_preproc(
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        self, nlp: "Language", reference_docs: Iterable[Doc]
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    ) -> Iterator[Example]:
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    ) -> Iterable[Example]:
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        for reference in reference_docs:
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            if reference.has_annotation("SENT_START"):
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                ref_sents = [sent.as_doc() for sent in reference.sents]
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			@ -210,7 +216,7 @@ class Corpus:
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    def read_docbin(
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        self, vocab: Vocab, locs: Iterable[Union[str, Path]]
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    ) -> Iterator[Doc]:
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    ) -> Iterable[Doc]:
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        """Yield training examples as example dicts"""
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        i = 0
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        for loc in locs:
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			@ -257,7 +263,7 @@ class JsonlCorpus:
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        self.max_length = max_length
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        self.limit = limit
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    def __call__(self, nlp: "Language") -> Iterator[Example]:
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    def __call__(self, nlp: "Language") -> Iterable[Example]:
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        """Yield examples from the data.
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        nlp (Language): The current nlp object.
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			@ -307,7 +313,7 @@ class PlainTextCorpus:
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        self.min_length = min_length
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        self.max_length = max_length
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    def __call__(self, nlp: "Language") -> Iterator[Example]:
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    def __call__(self, nlp: "Language") -> Iterable[Example]:
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        """Yield examples from the data.
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        nlp (Language): The current nlp object.
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										59
									
								
								spacy/training/example.pyi
									
									
									
									
									
										Normal file
									
								
							
							
						
						
									
										59
									
								
								spacy/training/example.pyi
									
									
									
									
									
										Normal file
									
								
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			@ -0,0 +1,59 @@
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from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple
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from ..tokens import Doc, Span
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from ..vocab import Vocab
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from .alignment import Alignment
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def annotations_to_doc(
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    vocab: Vocab,
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    tok_annot: Dict[str, Any],
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    doc_annot: Dict[str, Any],
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) -> Doc: ...
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def validate_examples(
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    examples: Iterable[Example],
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    method: str,
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) -> None: ...
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def validate_get_examples(
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    get_examples: Callable[[], Iterable[Example]],
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    method: str,
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): ...
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class Example:
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    x: Doc
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    y: Doc
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    def __init__(
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        self,
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        predicted: Doc,
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        reference: Doc,
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        *,
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        alignment: Optional[Alignment] = None,
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    ): ...
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    def __len__(self) -> int: ...
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    @property
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    def predicted(self) -> Doc: ...
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    @predicted.setter
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    def predicted(self, doc: Doc) -> None: ...
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    @property
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    def reference(self) -> Doc: ...
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    @reference.setter
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    def reference(self, doc: Doc) -> None: ...
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    def copy(self) -> Example: ...
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    @classmethod
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    def from_dict(cls, predicted: Doc, example_dict: Dict[str, Any]) -> Example: ...
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    @property
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    def alignment(self) -> Alignment: ...
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    def get_aligned(self, field: str, as_string=False): ...
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    def get_aligned_parse(self, projectivize=True): ...
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    def get_aligned_sent_starts(self): ...
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    def get_aligned_spans_x2y(self, x_spans: Sequence[Span], allow_overlap=False) -> List[Span]: ...
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    def get_aligned_spans_y2x(self, y_spans: Sequence[Span], allow_overlap=False) -> List[Span]: ...
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    def get_aligned_ents_and_ner(self) -> Tuple[List[Span], List[str]]: ...
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    def get_aligned_ner(self) -> List[str]: ...
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    def get_matching_ents(self, check_label: bool = True) -> List[Span]: ...
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    def to_dict(self) -> Dict[str, Any]: ...
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    def split_sents(self) -> List[Example]: ...
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    @property
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    def text(self) -> str: ...
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    def __str__(self) -> str: ...
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    def __repr__(self) -> str: ...
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