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- # coding=utf-8
- # Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
- #
- # 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.
- """Tokenization classes for OpenAI GPT."""
-
- from __future__ import (absolute_import, division, print_function,
- unicode_literals)
-
- import sys
- import json
- import logging
- import os
- import re
- from io import open
- import sentencepiece as spm
- import jieba
-
- try:
- from functools import lru_cache
- except ImportError:
- # Just a dummy decorator to get the checks to run on python2
- # because honestly I don't want to support a byte-level unicode BPE tokenizer on python 2 right now.
- def lru_cache():
- return lambda func: func
-
-
- class JIEBATokenizer(object):
-
- def __init__(self, vocab_file, model_file, max_len=None):
- self.max_len = max_len if max_len is not None else int(1e12)
- # self.encoder = json.load(open(vocab_file))
- f = open(vocab_file,'r')
- lines = f.readlines()
- self.encoder = {}
- for line in enumerate(lines):
- key = line[1].split('\t')[0]
- self.encoder[key] = line[0]
-
- self.decoder = {v:k for k,v in self.encoder.items()}
-
- self.sp = spm.SentencePieceProcessor(model_file=model_file)
- self.translator = str.maketrans(" \n", "\u2582\u2583")
-
- self.eod_id = self.encoder['<eod>']
- self.eot_id = self.encoder['<eot>']
- self.pad_id = self.encoder['<pad>']
-
- @property
- def vocab_size(self):
- return len(self.encoder)
-
- def __len__(self):
- return len(self.encoder) + len(self.special_tokens)
-
- @property
- def eod(self):
- return self.eod_id
-
- def tokenize(self, text):
- """ Tokenize a string. """
- seg_list = [x.translate(self.translator) for x in jieba.cut(text, cut_all=False)]
- new_seg = " ".join(seg_list)
- return self.sp.encode(new_seg)
-
- def convert_tokens_to_ids(self, tokens):
- return tokens
-
- def convert_ids_to_tokens(self, ids):
- return self.decode(ids)
-
-
- def encode(self, text):
- res = self.tokenize(text)
- return res
-
- def decode(self, tokens):
- text = self.sp.decode(tokens)
- text = text.replace(' ', '').replace('\u2582', ' ').replace('\u2583', '\n')
- return text
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