mirror of
				https://github.com/explosion/spaCy.git
				synced 2025-11-04 09:57:26 +03:00 
			
		
		
		
	* correct char_span output type - can be None * unify type of exclude parameter * black * further fixes to from_dict and to_dict * formatting
		
			
				
	
	
		
			199 lines
		
	
	
		
			6.0 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			199 lines
		
	
	
		
			6.0 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
from pathlib import Path
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from typing import (
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    Any,
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    Callable,
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    Dict,
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    Iterable,
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    Iterator,
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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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)
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import numpy as np
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from cymem.cymem import Pool
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from thinc.types import Floats1d, Floats2d, Ints2d
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from ..lexeme import Lexeme
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from ..vocab import Vocab
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from ._dict_proxies import SpanGroups
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from ._retokenize import Retokenizer
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from .span import Span
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from .token import Token
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from .underscore import Underscore
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DOCBIN_ALL_ATTRS: Tuple[str, ...]
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class DocMethod(Protocol):
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    def __call__(self: Doc, *args: Any, **kwargs: Any) -> Any: ...  # type: ignore[misc]
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class Doc:
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    vocab: Vocab
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    mem: Pool
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    spans: SpanGroups
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    max_length: int
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    length: int
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    sentiment: float
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    cats: Dict[str, float]
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    user_hooks: Dict[str, Callable[..., Any]]
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    user_token_hooks: Dict[str, Callable[..., Any]]
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    user_span_hooks: Dict[str, Callable[..., Any]]
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    tensor: np.ndarray[Any, np.dtype[np.float64]]
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    user_data: Dict[str, Any]
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    has_unknown_spaces: bool
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    _context: Any
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    @classmethod
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    def set_extension(
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        cls,
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        name: str,
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        default: Optional[Any] = ...,
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        getter: Optional[Callable[[Doc], Any]] = ...,
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        setter: Optional[Callable[[Doc, Any], None]] = ...,
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        method: Optional[DocMethod] = ...,
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        force: bool = ...,
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    ) -> None: ...
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    @classmethod
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    def get_extension(
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        cls, name: str
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    ) -> Tuple[
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        Optional[Any],
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        Optional[DocMethod],
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        Optional[Callable[[Doc], Any]],
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        Optional[Callable[[Doc, Any], None]],
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    ]: ...
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    @classmethod
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    def has_extension(cls, name: str) -> bool: ...
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    @classmethod
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    def remove_extension(
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        cls, name: str
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    ) -> Tuple[
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        Optional[Any],
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        Optional[DocMethod],
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        Optional[Callable[[Doc], Any]],
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        Optional[Callable[[Doc, Any], None]],
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    ]: ...
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    def __init__(
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        self,
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        vocab: Vocab,
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        words: Optional[List[str]] = ...,
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        spaces: Optional[List[bool]] = ...,
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        user_data: Optional[Dict[Any, Any]] = ...,
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        tags: Optional[List[str]] = ...,
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        pos: Optional[List[str]] = ...,
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        morphs: Optional[List[str]] = ...,
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        lemmas: Optional[List[str]] = ...,
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        heads: Optional[List[int]] = ...,
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        deps: Optional[List[str]] = ...,
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        sent_starts: Optional[List[Union[bool, int, None]]] = ...,
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        ents: Optional[List[str]] = ...,
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    ) -> None: ...
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    @property
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    def _(self) -> Underscore: ...
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    @property
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    def is_tagged(self) -> bool: ...
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    @property
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    def is_parsed(self) -> bool: ...
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    @property
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    def is_nered(self) -> bool: ...
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    @property
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    def is_sentenced(self) -> bool: ...
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    def has_annotation(
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        self, attr: Union[int, str], *, require_complete: bool = ...
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    ) -> bool: ...
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    @overload
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    def __getitem__(self, i: int) -> Token: ...
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    @overload
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    def __getitem__(self, i: slice) -> Span: ...
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    def __iter__(self) -> Iterator[Token]: ...
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    def __len__(self) -> int: ...
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    def __unicode__(self) -> str: ...
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    def __bytes__(self) -> bytes: ...
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    def __str__(self) -> str: ...
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    def __repr__(self) -> str: ...
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    @property
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    def doc(self) -> Doc: ...
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    def char_span(
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        self,
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        start_idx: int,
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        end_idx: int,
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        label: Union[int, str] = ...,
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        kb_id: Union[int, str] = ...,
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        vector: Optional[Floats1d] = ...,
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        alignment_mode: str = ...,
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        span_id: Union[int, str] = ...,
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    ) -> Optional[Span]: ...
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    def similarity(self, other: Union[Doc, Span, Token, Lexeme]) -> float: ...
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    @property
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    def has_vector(self) -> bool: ...
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    vector: Floats1d
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    vector_norm: float
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    @property
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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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    # 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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        *,
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        blocked: Optional[List[Span]] = ...,
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        missing: Optional[List[Span]] = ...,
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        outside: Optional[List[Span]] = ...,
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        default: str = ...
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    ) -> None: ...
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    @property
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    def noun_chunks(self) -> Iterator[Span]: ...
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    @property
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    def sents(self) -> Iterator[Span]: ...
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    @property
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    def lang(self) -> int: ...
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    @property
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    def lang_(self) -> str: ...
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    def count_by(
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        self, attr_id: int, exclude: Optional[Any] = ..., counts: Optional[Any] = ...
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    ) -> Dict[Any, int]: ...
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    def from_array(
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        self, attrs: Union[int, str, List[Union[int, str]]], array: Ints2d
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    ) -> Doc: ...
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    def to_array(
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        self, py_attr_ids: Union[int, str, List[Union[int, str]]]
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    ) -> np.ndarray[Any, np.dtype[np.float64]]: ...
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    @staticmethod
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    def from_docs(
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        docs: List[Doc],
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        ensure_whitespace: bool = ...,
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        attrs: Optional[Union[Tuple[Union[str, int]], List[Union[int, str]]]] = ...,
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    ) -> Doc: ...
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    def get_lca_matrix(self) -> Ints2d: ...
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    def copy(self) -> Doc: ...
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    def to_disk(
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        self, path: Union[str, Path], *, exclude: Iterable[str] = ...
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    ) -> None: ...
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    def from_disk(
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        self, path: Union[str, Path], *, exclude: Iterable[str] = ...
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    ) -> Doc: ...
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    def to_bytes(self, *, exclude: Iterable[str] = ...) -> bytes: ...
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    def from_bytes(self, bytes_data: bytes, *, exclude: Iterable[str] = ...) -> Doc: ...
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    def to_dict(self, *, exclude: Iterable[str] = ...) -> Dict[str, Any]: ...
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    def from_dict(
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        self, msg: Dict[str, Any], *, exclude: Iterable[str] = ...
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    ) -> Doc: ...
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    def extend_tensor(self, tensor: Floats2d) -> None: ...
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    def retokenize(self) -> Retokenizer: ...
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    def to_json(self, underscore: Optional[List[str]] = ...) -> Dict[str, Any]: ...
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    def from_json(
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        self, doc_json: Dict[str, Any] = ..., validate: bool = False
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    ) -> Doc: ...
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    def to_utf8_array(self, nr_char: int = ...) -> Ints2d: ...
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    @staticmethod
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    def _get_array_attrs() -> Tuple[Any]: ...
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