input_file = "/content/bothcan.txt" # Replace with actual input file path
model_prefix = "botchan" # Replace with desired model save path
import sentencepiece as spm
spm.SentencePieceTrainer.train(
input=input_file,
model_prefix=model_prefix,
vocab_size=1000, # Adjust as needed, this is just an example value
model_type="unigram", # You can use different models like unigram or bpe
)

from sentencepiece import SentencePieceProcessor

model_path = "botchan.model" # Replace with the actual path
sp_model = SentencePieceProcessor(model_file=model_path)
vocab_size = 4000

import os
from logging import getLogger
from typing import List

from sentencepiece import SentencePieceProcessor


logger = getLogger()


class Tokenizer:
    def __init__(self, model_path: str):
        # reload tokenizer
        assert os.path.isfile(model_path), model_path
        self.sp_model = SentencePieceProcessor(model_file=model_path)
        logger.info(f"Reloaded SentencePiece model from {model_path}")

        # BOS / EOS token IDs
        self.n_words: int = self.sp_model.vocab_size()
        self.bos_id: int = self.sp_model.bos_id()
        self.eos_id: int = self.sp_model.eos_id()
        self.pad_id: int = self.sp_model.pad_id()
        logger.info(
            f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id}"
        )
        assert self.sp_model.vocab_size() == self.sp_model.get_piece_size()

    def encode(self, s: str, bos: bool, eos: bool) -> List[int]:
        assert type(s) is str
        t = self.sp_model.encode(s)
        if bos:
            t = [self.bos_id] + t
        if eos:
            t = t + [self.eos_id]
        return t

    def decode(self, t: List[int]) -> str:
        return self.sp_model.decode(t)

tokenizer = Tokenizer(model_path="botchan.model") # Replace with actual model path