AutoAndroidController/py/venv/Lib/site-packages/modelscope/models/nlp/gpt3/tokenizer.py

# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import List

from tokenizers import Tokenizer


class JiebaBPETokenizer:
    """SentencePiece BPE tokenizer with Jieba integration"""

    def __init__(self, tokenizer_json_file):
        self.name = 'Jieba BPE Tokenizer'

        self.tokenizer = Tokenizer.from_file(tokenizer_json_file)
        self.eod_id = self.tokenizer.token_to_id('<|endoftext|>')
        try:
            import jieba
            import logging
            jieba.setLogLevel(logging.INFO)
        except ImportError:
            raise ImportError(
                'You need to install jieba to use JiebaTokenizer. '
                'See https://pypi.org/project/jieba/ for installation.')
        self.jieba = jieba
        self.new_line = self.vocab['\n']
        self.sep_token = self.vocab['<sep>']

    @property
    def vocab_size(self):
        return self.tokenizer.get_vocab_size(with_added_tokens=True)

    @property
    def vocab(self):
        return self.tokenizer.get_vocab(with_added_tokens=True)

    @property
    def inv_vocab(self):
        vocab = self.vocab
        inv_vocab = dict()
        for key, val in vocab.items():
            inv_vocab[val] = key
        return inv_vocab

    def tokenize(self, text: str, is_code: bool = False) -> List[int]:
        """
        """
        if not is_code:
            seg_list = [x for x in self.jieba.cut(text)]
            return self.tokenizer.encode(
                seg_list, is_pretokenized=True, add_special_tokens=True).ids
        else:
            return self.tokenizer.encode(
                text, is_pretokenized=False, add_special_tokens=True).ids

    def detokenize(self, token_ids: List[int], early_stop: bool = True) -> str:
        if early_stop and self.sep_token in token_ids:
            token_ids = token_ids[:token_ids.index(self.sep_token)]
        text = self.tokenizer.decode(token_ids, skip_special_tokens=True)
        return text

    @property
    def eod(self):
        return self.eod_id