mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-27 10:26:35 +03:00
56fc3bc0f3
* 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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