"""Write documents as SHADOW text shards: Gemma tokens (EmbeddingGemma 2 tokenizer), <bos> text <eos>, 3 bytes per token."""
import json, pathlib
import numpy as np
from transformers import AutoTokenizer

BOS, EOS, VOCAB = 2, 1, 262144


class Shards:
    def __init__(self, out, name, shard_tokens=100_000_000):
        self.dir = pathlib.Path(out) / name
        self.dir.mkdir(parents=True, exist_ok=True)
        self.name, self.shard_tokens = name, shard_tokens
        self.tok = AutoTokenizer.from_pretrained("google/embeddinggemma-2")
        self.buf, self.n, self.k, self.pending = [], 0, 0, []

    def add(self, text):
        self.pending.append(text)
        if len(self.pending) >= 512:
            self._tokenize()

    def _tokenize(self):
        for ids in self.tok(self.pending, add_special_tokens=False)["input_ids"]:
            self.buf.append(np.asarray([BOS] + ids + [EOS], np.uint32))
            self.n += len(ids) + 2
        self.pending = []
        if self.n >= self.shard_tokens:
            self._write()

    def _write(self):
        if not self.n:
            return
        a = np.concatenate(self.buf)
        b = np.empty((len(a), 3), np.uint8)
        b[:, 0], b[:, 1], b[:, 2] = a & 255, (a >> 8) & 255, (a >> 16) & 255
        stem = self.dir / f"{self.name}_{self.k:05d}"
        stem.with_suffix(".bin").write_bytes(b.tobytes())
        np.savez_compressed(f"{stem}.counts.npz", counts=np.bincount(a, minlength=VOCAB).astype(np.uint32))
        stem.with_suffix(".json").write_text(json.dumps({"tokens": int(len(a)), "documents": len(self.buf), "bytes_per_token": 3, "bos": BOS, "eos": EOS}))
        print(f"{stem.name}: {len(a):,} tokens", flush=True)
        self.buf, self.n, self.k = [], 0, self.k + 1

    def close(self):
        if self.pending:
            self._tokenize()
        self._write()