1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78

// Copyright 2018 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team.
// Copyright 2019-2020 Guillaume Becquin
// 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.

use crate::preprocessing::vocab::sentence_piece_vocab::{SentencePieceModel};
use crate::{Vocab, Tokenizer, MultiThreadedTokenizer};
use crate::preprocessing::tokenizer::base_tokenizer::{Token, TokenRef, Mask};
use crate::tokenization_utils::{clean_text, decompose_nfkc, lowercase, is_whitespace, split_on_special_tokens};
use crate::preprocessing::vocab::t5_vocab::T5Vocab;

pub struct T5Tokenizer {
    model: SentencePieceModel,
    vocab: T5Vocab,
    lower_case: bool,
}

impl T5Tokenizer {
    pub fn from_file(path: &str, lower_case: bool) -> T5Tokenizer {
        let model = SentencePieceModel::from_file(path);
        let vocab = T5Vocab::from_file(path);
        T5Tokenizer { model, vocab, lower_case }
    }

    pub fn from_existing_vocab_and_model(vocab: T5Vocab, model: SentencePieceModel, lower_case: bool) -> T5Tokenizer {
        T5Tokenizer { model, vocab, lower_case }
    }

}

impl Tokenizer<T5Vocab> for T5Tokenizer {
    fn vocab(&self) -> &T5Vocab { &self.vocab }

    fn tokenize_to_tokens(&self, text: TokenRef) -> Vec<Token> {
        let mut tokens = split_on_special_tokens(text, &self.vocab)
            .into_iter()
            .map(|token| token.to_owned())
            .collect::<Vec<Token>>();

        let mut sub_tokens: Vec<Token> = Vec::new();
        for token in tokens.iter_mut() {
            if token.mask != Mask::Special && token.mask != Mask::Unknown {
                clean_text(token, true);
                decompose_nfkc(token);
                if self.lower_case {
                    lowercase(token);
                }
                token.text = token.text.replace(|c: char| is_whitespace(&c), "\u{2581}");
                if !token.text.starts_with('\u{2581}') {
                    token.text.insert(0, '\u{2581}');
                    token.reference_offsets.insert(0, 0);
                };
                let output = self.model.decode_forward_token_ref(token.as_ref());
                let decoded = self.model.decode_backward(&output);

                let output: Vec<Token> = self.model.parse_nodes_to_tokens(decoded);
                sub_tokens.extend(output)
            } else {
                sub_tokens.push(token.clone());
            }
        }
        sub_tokens
    }


    fn convert_tokens_to_string(&self, tokens: Vec<String>) -> String {
        tokens.into_iter().map(|v| v.replace('\u{2581}', " ")).collect::<Vec<String>>().join("")
    }
}

impl MultiThreadedTokenizer<T5Vocab> for T5Tokenizer {}