from transformers import PreTrainedTokenizer
import json
import os

class CognitivessTokenizer(PreTrainedTokenizer):
    def __init__(self, vocab_file, merges_file, **kwargs):
        super().__init__(**kwargs)
        self.vocab_file = vocab_file
        self.merges_file = merges_file
        self.encoder = self.load_vocab(vocab_file)
        self.decoder = {v: k for k, v in self.encoder.items()}

        with open(merges_file, encoding="utf-8") as merges_handle:
            bpe_data = merges_handle.read()
        bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split("\n")[1:-1]]
        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *init_inputs, **kwargs):
        vocab_file = os.path.join(pretrained_model_name_or_path, "vocab.json")
        merges_file = os.path.join(pretrained_model_name_or_path, "merges.txt")
        return cls(vocab_file, merges_file, **kwargs)

    @property
    def vocab_size(self):
        return len(self.encoder)

    def get_vocab(self):
        return dict(self.encoder)

    def _tokenize(self, text):
        return text.split()

    def _convert_token_to_id(self, token):
        return self.encoder.get(token, self.encoder.get(self.unk_token))

    def _convert_id_to_token(self, index):
        return self.decoder.get(index, self.unk_token)

    def convert_tokens_to_string(self, tokens):
        return " ".join(tokens)

    def save_vocabulary(self, save_directory):
        if not os.path.isdir(save_directory):
            os.makedirs(save_directory)
        vocab_file = os.path.join(save_directory, "vocab.json")
        merges_file = os.path.join(save_directory, "merges.txt")

        with open(vocab_file, "w", encoding="utf-8") as vocab_handle:
            json.dump(self.encoder, vocab_handle, ensure_ascii=False)
        with open(merges_file, "w", encoding="utf-8") as merges_handle:
            merges_handle.write("\n".join(" ".join(pair) for pair in self.bpe_ranks.keys()))

        return (vocab_file, merges_file)

    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        bos_token_id = [self.bos_token_id]
        eos_token_id = [self.eos_token_id]
        return bos_token_id + token_ids_0 + eos_token_id

    def load_vocab(self, vocab_file):
        with open(vocab_file, "r", encoding="utf-8") as f:
            return json.load(f)