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/*
* Copyright (C) 2018 The Android Open Source Project
*
* 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.
*/
#include "utils/bert_tokenizer.h"
#include <string>
#include "annotator/types.h"
#include "utils/tokenizer-utils.h"
#include "utils/utf8/unicodetext.h"
#include "utils/utf8/unilib.h"
#include "absl/strings/string_view.h"
namespace libtextclassifier3 {
FlatHashMapBackedWordpiece::FlatHashMapBackedWordpiece(
const std::vector<std::string>& vocab)
: vocab_{vocab} {
for (int i = 0; i < vocab_.size(); ++i) {
index_map_[vocab_[i]] = i;
}
}
LookupStatus FlatHashMapBackedWordpiece::Contains(absl::string_view key,
bool* value) const {
*value = index_map_.contains(key);
return LookupStatus();
}
bool FlatHashMapBackedWordpiece::LookupId(const absl::string_view key,
int* result) const {
auto it = index_map_.find(key);
if (it == index_map_.end()) {
return false;
}
*result = it->second;
return true;
}
bool FlatHashMapBackedWordpiece::LookupWord(int vocab_id,
absl::string_view* result) const {
if (vocab_id >= vocab_.size() || vocab_id < 0) {
return false;
}
*result = vocab_[vocab_id];
return true;
}
TokenizerResult BertTokenizer::TokenizeSingleToken(const std::string& token) {
std::vector<std::string> tokens = {token};
return BertTokenizer::Tokenize(tokens);
}
TokenizerResult BertTokenizer::Tokenize(const std::string& input) {
std::vector<std::string> tokens = PreTokenize(input);
return BertTokenizer::Tokenize(tokens);
}
TokenizerResult BertTokenizer::Tokenize(
const std::vector<std::string>& tokens) {
WordpieceTokenizerResult result;
std::vector<std::string>& subwords = result.subwords;
std::vector<int>& wp_absolute_begin_offset = result.wp_begin_offset;
std::vector<int>& wp_absolute_end_offset = result.wp_end_offset;
for (int token_index = 0; token_index < tokens.size(); token_index++) {
auto& token = tokens[token_index];
int num_word_pieces = 0;
LookupStatus status = WordpieceTokenize(
token, options_.max_bytes_per_token, options_.max_chars_per_subtoken,
options_.suffix_indicator, options_.use_unknown_token,
options_.unknown_token, options_.split_unknown_chars, &vocab_,
&subwords, &wp_absolute_begin_offset, &wp_absolute_end_offset,
&num_word_pieces);
if (!status.success) {
return std::move(result);
}
}
return std::move(result);
}
// This replicates how the original bert_tokenizer from the tflite-support
// library pretokenize text by using regex_split with these default regexes.
// It splits the text on spaces, punctuations and chinese characters and
// output all the tokens except spaces.
// So far, the only difference between this and the original implementation
// we are aware of is that the original regexes has 8 ranges of chinese
// unicodes. We have all these 8 ranges plus two extra ranges.
std::vector<std::string> BertTokenizer::PreTokenize(
const absl::string_view input) {
const std::vector<Token> tokens =
TokenizeOnWhiteSpacePunctuationAndChineseLetter(input);
std::vector<std::string> token_texts;
std::transform(tokens.begin(), tokens.end(), std::back_inserter(token_texts),
[](Token const& token) { return std::move(token.value); });
return token_texts;
}
} // namespace libtextclassifier3