from pathlib import Path
from typing import Self

class GLiNER:
    @classmethod
    def from_pretrained(
        cls,
        model_id: str,
        revision: str | None = None,
        cache_dir: str | Path | None = None,
        force_download: bool = False,
        proxies: dict[str, str] | None = None,
        resume_download: bool = False,
        local_files_only: bool = False,
        token: str | bool | None = None,
        map_location: str = "cpu",
        strict: bool = False,
        load_tokenizer: bool | None = None,
        resize_token_embeddings: bool | None = True,
        compile_torch_model: bool | None = False,
        load_onnx_model: bool | None = False,
        onnx_model_file: str | None = "model.onnx",
        max_length: int | None = None,
        max_width: int | None = None,
        post_fusion_schema: str | None = None,
        _attn_implementation: str | None = None,
    ) -> Self: ...
    def predict_entities(
        self,
        text: str,
        labels: list[str],
        flat_ner: bool = True,
        threshold: float = 0.5,
        multi_label: bool = False,
    ) -> list[dict[str, object]]: ...