from __future__ import annotations

from typing import Any, List

from langchain_text_splitters.base import TextSplitter


class SpacyTextSplitter(TextSplitter):
    """Splitting text using Spacy package.

    Per default, Spacy's `en_core_web_sm` model is used and
    its default max_length is 1000000 (it is the length of maximum character
    this model takes which can be increased for large files). For a faster, but
    potentially less accurate splitting, you can use `pipeline='sentencizer'`.
    """

    def __init__(
        self,
        separator: str = "\n\n",
        pipeline: str = "en_core_web_sm",
        max_length: int = 1_000_000,
        *,
        strip_whitespace: bool = True,
        **kwargs: Any,
    ) -> None:
        """Initialize the spacy text splitter."""
        super().__init__(**kwargs)
        self._tokenizer = _make_spacy_pipeline_for_splitting(
            pipeline, max_length=max_length
        )
        self._separator = separator
        self._strip_whitespace = strip_whitespace

    def split_text(self, text: str) -> List[str]:
        """Split incoming text and return chunks."""
        splits = (
            s.text if self._strip_whitespace else s.text_with_ws
            for s in self._tokenizer(text).sents
        )
        return self._merge_splits(splits, self._separator)


def _make_spacy_pipeline_for_splitting(
    pipeline: str, *, max_length: int = 1_000_000
) -> Any:  # avoid importing spacy
    try:
        import spacy
    except ImportError:
        raise ImportError(
            "Spacy is not installed, please install it with `pip install spacy`."
        )
    if pipeline == "sentencizer":
        from spacy.lang.en import English

        sentencizer: Any = English()
        sentencizer.add_pipe("sentencizer")
    else:
        sentencizer = spacy.load(pipeline, exclude=["ner", "tagger"])
        sentencizer.max_length = max_length
    return sentencizer