spaCy/spacy/tokens/doc.pyi
Sofie Van Landeghem 0737443096
feat: add example stubs (3) (#12801)
* feat: add example stubs

* fix: add required annotations

* fix: mypy issues

* fix: use Py36-compatible Portocol

* Minor reformatting

* adding further type specifications and removing internal methods

* black formatting

* widen type to iterable

* add private methods that are being used by the built-in convertors

* revert changes to corpus.py

* fixes

* fixes

* fix typing of PlainTextCorpus

---------

Co-authored-by: Basile Dura <basile@bdura.me>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
2023-08-02 08:15:12 +02:00

201 lines
6.1 KiB
Python

from pathlib import Path
from typing import (
Any,
Callable,
Dict,
Iterable,
Iterator,
List,
Optional,
Protocol,
Sequence,
Tuple,
Union,
overload,
)
import numpy as np
from cymem.cymem import Pool
from thinc.types import Floats1d, Floats2d, Ints2d
from ..lexeme import Lexeme
from ..vocab import Vocab
from ._dict_proxies import SpanGroups
from ._retokenize import Retokenizer
from .span import Span
from .token import Token
from .underscore import Underscore
DOCBIN_ALL_ATTRS: Tuple[str, ...]
class DocMethod(Protocol):
def __call__(self: Doc, *args: Any, **kwargs: Any) -> Any: ... # type: ignore[misc]
class Doc:
vocab: Vocab
mem: Pool
spans: SpanGroups
max_length: int
length: int
sentiment: float
cats: Dict[str, float]
user_hooks: Dict[str, Callable[..., Any]]
user_token_hooks: Dict[str, Callable[..., Any]]
user_span_hooks: Dict[str, Callable[..., Any]]
tensor: np.ndarray[Any, np.dtype[np.float_]]
user_data: Dict[str, Any]
has_unknown_spaces: bool
_context: Any
@classmethod
def set_extension(
cls,
name: str,
default: Optional[Any] = ...,
getter: Optional[Callable[[Doc], Any]] = ...,
setter: Optional[Callable[[Doc, Any], None]] = ...,
method: Optional[DocMethod] = ...,
force: bool = ...,
) -> None: ...
@classmethod
def get_extension(
cls, name: str
) -> Tuple[
Optional[Any],
Optional[DocMethod],
Optional[Callable[[Doc], Any]],
Optional[Callable[[Doc, Any], None]],
]: ...
@classmethod
def has_extension(cls, name: str) -> bool: ...
@classmethod
def remove_extension(
cls, name: str
) -> Tuple[
Optional[Any],
Optional[DocMethod],
Optional[Callable[[Doc], Any]],
Optional[Callable[[Doc, Any], None]],
]: ...
def __init__(
self,
vocab: Vocab,
words: Optional[List[str]] = ...,
spaces: Optional[List[bool]] = ...,
user_data: Optional[Dict[Any, Any]] = ...,
tags: Optional[List[str]] = ...,
pos: Optional[List[str]] = ...,
morphs: Optional[List[str]] = ...,
lemmas: Optional[List[str]] = ...,
heads: Optional[List[int]] = ...,
deps: Optional[List[str]] = ...,
sent_starts: Optional[List[Union[bool, int, None]]] = ...,
ents: Optional[List[str]] = ...,
) -> None: ...
@property
def _(self) -> Underscore: ...
@property
def is_tagged(self) -> bool: ...
@property
def is_parsed(self) -> bool: ...
@property
def is_nered(self) -> bool: ...
@property
def is_sentenced(self) -> bool: ...
def has_annotation(
self, attr: Union[int, str], *, require_complete: bool = ...
) -> bool: ...
@overload
def __getitem__(self, i: int) -> Token: ...
@overload
def __getitem__(self, i: slice) -> Span: ...
def __iter__(self) -> Iterator[Token]: ...
def __len__(self) -> int: ...
def __unicode__(self) -> str: ...
def __bytes__(self) -> bytes: ...
def __str__(self) -> str: ...
def __repr__(self) -> str: ...
@property
def doc(self) -> Doc: ...
def char_span(
self,
start_idx: int,
end_idx: int,
label: Union[int, str] = ...,
kb_id: Union[int, str] = ...,
vector: Optional[Floats1d] = ...,
alignment_mode: str = ...,
span_id: Union[int, str] = ...,
) -> Span: ...
def similarity(self, other: Union[Doc, Span, Token, Lexeme]) -> float: ...
@property
def has_vector(self) -> bool: ...
vector: Floats1d
vector_norm: float
@property
def text(self) -> str: ...
@property
def text_with_ws(self) -> str: ...
# Ideally the getter would output Tuple[Span]
# see https://github.com/python/mypy/issues/3004
@property
def ents(self) -> Sequence[Span]: ...
@ents.setter
def ents(self, value: Sequence[Span]) -> None: ...
def set_ents(
self,
entities: List[Span],
*,
blocked: Optional[List[Span]] = ...,
missing: Optional[List[Span]] = ...,
outside: Optional[List[Span]] = ...,
default: str = ...
) -> None: ...
@property
def noun_chunks(self) -> Iterator[Span]: ...
@property
def sents(self) -> Iterator[Span]: ...
@property
def lang(self) -> int: ...
@property
def lang_(self) -> str: ...
def count_by(
self, attr_id: int, exclude: Optional[Any] = ..., counts: Optional[Any] = ...
) -> Dict[Any, int]: ...
def from_array(
self, attrs: Union[int, str, List[Union[int, str]]], array: Ints2d
) -> Doc: ...
def to_array(
self, py_attr_ids: Union[int, str, List[Union[int, str]]]
) -> np.ndarray[Any, np.dtype[np.float_]]: ...
@staticmethod
def from_docs(
docs: List[Doc],
ensure_whitespace: bool = ...,
attrs: Optional[Union[Tuple[Union[str, int]], List[Union[int, str]]]] = ...,
) -> Doc: ...
def get_lca_matrix(self) -> Ints2d: ...
def copy(self) -> Doc: ...
def to_disk(
self, path: Union[str, Path], *, exclude: Iterable[str] = ...
) -> None: ...
def from_disk(
self, path: Union[str, Path], *, exclude: Union[List[str], Tuple[str]] = ...
) -> Doc: ...
def to_bytes(self, *, exclude: Union[List[str], Tuple[str]] = ...) -> bytes: ...
def from_bytes(
self, bytes_data: bytes, *, exclude: Union[List[str], Tuple[str]] = ...
) -> Doc: ...
def to_dict(self, *, exclude: Union[List[str], Tuple[str]] = ...) -> bytes: ...
def from_dict(
self, msg: bytes, *, exclude: Union[List[str], Tuple[str]] = ...
) -> Doc: ...
def extend_tensor(self, tensor: Floats2d) -> None: ...
def retokenize(self) -> Retokenizer: ...
def to_json(self, underscore: Optional[List[str]] = ...) -> Dict[str, Any]: ...
def from_json(
self, doc_json: Dict[str, Any] = ..., validate: bool = False
) -> Doc: ...
def to_utf8_array(self, nr_char: int = ...) -> Ints2d: ...
@staticmethod
def _get_array_attrs() -> Tuple[Any]: ...