import os
from functools import cache
from itertools import batched
from typing import Generator, Iterator

import numpy as np
from numpy.typing import NDArray
from sentence_transformers import SentenceTransformer


def split(text: str, max_tokens: int = 512) -> Generator[str, None, None]:
    # Naive approach - use opale internal chunking techniques (special tokens count)
    words = text.split()
    if not words:
        return
    for batch in batched(words, max_tokens // 2):  # Assuming 2 tokens per word
        yield " ".join(batch)


@cache
def get_model():
    return SentenceTransformer(
        os.environ["EMBEDDING_MODEL"], revision=os.environ["EMBEDDING_MODEL_REV"]
    )


def embed(texts: Iterator[str], max_tokens: int = 512) -> NDArray:
    res: list[NDArray] = []
    for text in texts:
        embeddings = get_model().encode(
            list(split(text, max_tokens)), show_progress_bar=False
        )
        res.append(np.mean(embeddings, axis=0))
    return np.array(res)