/*
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* Copyright (C) 2018 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "annotator/annotator.h"
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#include <algorithm>
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#include <cctype>
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#include <cmath>
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#include <iterator>
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#include <numeric>
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#include <unordered_map>
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#include "annotator/collections.h"
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#include "annotator/model_generated.h"
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#include "annotator/types.h"
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#include "utils/base/logging.h"
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#include "utils/checksum.h"
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#include "utils/math/softmax.h"
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#include "utils/regex-match.h"
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#include "utils/utf8/unicodetext.h"
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#include "utils/zlib/zlib_regex.h"
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namespace libtextclassifier3 {
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using SortedIntSet = std::set<int, std::function<bool(int, int)>>;
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const std::string& Annotator::kPhoneCollection =
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*[]() { return new std::string("phone"); }();
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const std::string& Annotator::kAddressCollection =
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*[]() { return new std::string("address"); }();
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const std::string& Annotator::kDateCollection =
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*[]() { return new std::string("date"); }();
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const std::string& Annotator::kUrlCollection =
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*[]() { return new std::string("url"); }();
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const std::string& Annotator::kEmailCollection =
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*[]() { return new std::string("email"); }();
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namespace {
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const Model* LoadAndVerifyModel(const void* addr, int size) {
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flatbuffers::Verifier verifier(reinterpret_cast<const uint8_t*>(addr), size);
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if (VerifyModelBuffer(verifier)) {
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return GetModel(addr);
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} else {
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return nullptr;
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}
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}
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// If lib is not nullptr, just returns lib. Otherwise, if lib is nullptr, will
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// create a new instance, assign ownership to owned_lib, and return it.
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const UniLib* MaybeCreateUnilib(const UniLib* lib,
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std::unique_ptr<UniLib>* owned_lib) {
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if (lib) {
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return lib;
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} else {
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owned_lib->reset(new UniLib);
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return owned_lib->get();
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}
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}
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// As above, but for CalendarLib.
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const CalendarLib* MaybeCreateCalendarlib(
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const CalendarLib* lib, std::unique_ptr<CalendarLib>* owned_lib) {
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if (lib) {
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return lib;
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} else {
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owned_lib->reset(new CalendarLib);
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return owned_lib->get();
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}
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}
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} // namespace
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tflite::Interpreter* InterpreterManager::SelectionInterpreter() {
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if (!selection_interpreter_) {
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TC3_CHECK(selection_executor_);
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selection_interpreter_ = selection_executor_->CreateInterpreter();
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if (!selection_interpreter_) {
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TC3_LOG(ERROR) << "Could not build TFLite interpreter.";
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}
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}
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return selection_interpreter_.get();
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}
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tflite::Interpreter* InterpreterManager::ClassificationInterpreter() {
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if (!classification_interpreter_) {
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TC3_CHECK(classification_executor_);
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classification_interpreter_ = classification_executor_->CreateInterpreter();
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if (!classification_interpreter_) {
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TC3_LOG(ERROR) << "Could not build TFLite interpreter.";
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}
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}
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return classification_interpreter_.get();
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}
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std::unique_ptr<Annotator> Annotator::FromUnownedBuffer(
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const char* buffer, int size, const UniLib* unilib,
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const CalendarLib* calendarlib) {
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const Model* model = LoadAndVerifyModel(buffer, size);
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if (model == nullptr) {
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return nullptr;
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}
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auto classifier =
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std::unique_ptr<Annotator>(new Annotator(model, unilib, calendarlib));
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if (!classifier->IsInitialized()) {
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return nullptr;
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}
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return classifier;
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}
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std::unique_ptr<Annotator> Annotator::FromScopedMmap(
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std::unique_ptr<ScopedMmap>* mmap, const UniLib* unilib,
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const CalendarLib* calendarlib) {
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if (!(*mmap)->handle().ok()) {
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TC3_VLOG(1) << "Mmap failed.";
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return nullptr;
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}
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const Model* model = LoadAndVerifyModel((*mmap)->handle().start(),
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(*mmap)->handle().num_bytes());
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if (!model) {
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TC3_LOG(ERROR) << "Model verification failed.";
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return nullptr;
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}
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auto classifier = std::unique_ptr<Annotator>(
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new Annotator(mmap, model, unilib, calendarlib));
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if (!classifier->IsInitialized()) {
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return nullptr;
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}
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return classifier;
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}
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std::unique_ptr<Annotator> Annotator::FromScopedMmap(
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std::unique_ptr<ScopedMmap>* mmap, std::unique_ptr<UniLib> unilib,
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std::unique_ptr<CalendarLib> calendarlib) {
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if (!(*mmap)->handle().ok()) {
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TC3_VLOG(1) << "Mmap failed.";
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return nullptr;
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}
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const Model* model = LoadAndVerifyModel((*mmap)->handle().start(),
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(*mmap)->handle().num_bytes());
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if (model == nullptr) {
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TC3_LOG(ERROR) << "Model verification failed.";
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return nullptr;
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}
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auto classifier = std::unique_ptr<Annotator>(
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new Annotator(mmap, model, std::move(unilib), std::move(calendarlib)));
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if (!classifier->IsInitialized()) {
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return nullptr;
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}
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return classifier;
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}
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std::unique_ptr<Annotator> Annotator::FromFileDescriptor(
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int fd, int offset, int size, const UniLib* unilib,
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const CalendarLib* calendarlib) {
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std::unique_ptr<ScopedMmap> mmap(new ScopedMmap(fd, offset, size));
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return FromScopedMmap(&mmap, unilib, calendarlib);
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}
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std::unique_ptr<Annotator> Annotator::FromFileDescriptor(
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int fd, int offset, int size, std::unique_ptr<UniLib> unilib,
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std::unique_ptr<CalendarLib> calendarlib) {
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std::unique_ptr<ScopedMmap> mmap(new ScopedMmap(fd, offset, size));
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return FromScopedMmap(&mmap, std::move(unilib), std::move(calendarlib));
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}
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std::unique_ptr<Annotator> Annotator::FromFileDescriptor(
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int fd, const UniLib* unilib, const CalendarLib* calendarlib) {
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std::unique_ptr<ScopedMmap> mmap(new ScopedMmap(fd));
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return FromScopedMmap(&mmap, unilib, calendarlib);
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}
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std::unique_ptr<Annotator> Annotator::FromFileDescriptor(
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int fd, std::unique_ptr<UniLib> unilib,
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std::unique_ptr<CalendarLib> calendarlib) {
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std::unique_ptr<ScopedMmap> mmap(new ScopedMmap(fd));
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return FromScopedMmap(&mmap, std::move(unilib), std::move(calendarlib));
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}
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std::unique_ptr<Annotator> Annotator::FromPath(const std::string& path,
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const UniLib* unilib,
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const CalendarLib* calendarlib) {
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std::unique_ptr<ScopedMmap> mmap(new ScopedMmap(path));
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return FromScopedMmap(&mmap, unilib, calendarlib);
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}
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std::unique_ptr<Annotator> Annotator::FromPath(
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const std::string& path, std::unique_ptr<UniLib> unilib,
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std::unique_ptr<CalendarLib> calendarlib) {
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std::unique_ptr<ScopedMmap> mmap(new ScopedMmap(path));
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return FromScopedMmap(&mmap, std::move(unilib), std::move(calendarlib));
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}
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Annotator::Annotator(std::unique_ptr<ScopedMmap>* mmap, const Model* model,
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const UniLib* unilib, const CalendarLib* calendarlib)
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: model_(model),
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mmap_(std::move(*mmap)),
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owned_unilib_(nullptr),
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unilib_(MaybeCreateUnilib(unilib, &owned_unilib_)),
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owned_calendarlib_(nullptr),
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calendarlib_(MaybeCreateCalendarlib(calendarlib, &owned_calendarlib_)) {
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ValidateAndInitialize();
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}
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Annotator::Annotator(std::unique_ptr<ScopedMmap>* mmap, const Model* model,
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std::unique_ptr<UniLib> unilib,
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std::unique_ptr<CalendarLib> calendarlib)
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: model_(model),
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mmap_(std::move(*mmap)),
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owned_unilib_(std::move(unilib)),
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unilib_(owned_unilib_.get()),
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owned_calendarlib_(std::move(calendarlib)),
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calendarlib_(owned_calendarlib_.get()) {
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ValidateAndInitialize();
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}
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Annotator::Annotator(const Model* model, const UniLib* unilib,
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const CalendarLib* calendarlib)
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: model_(model),
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owned_unilib_(nullptr),
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unilib_(MaybeCreateUnilib(unilib, &owned_unilib_)),
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owned_calendarlib_(nullptr),
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calendarlib_(MaybeCreateCalendarlib(calendarlib, &owned_calendarlib_)) {
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ValidateAndInitialize();
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}
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void Annotator::ValidateAndInitialize() {
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initialized_ = false;
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if (model_ == nullptr) {
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TC3_LOG(ERROR) << "No model specified.";
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return;
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}
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const bool model_enabled_for_annotation =
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(model_->triggering_options() != nullptr &&
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(model_->triggering_options()->enabled_modes() & ModeFlag_ANNOTATION));
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const bool model_enabled_for_classification =
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(model_->triggering_options() != nullptr &&
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(model_->triggering_options()->enabled_modes() &
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ModeFlag_CLASSIFICATION));
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const bool model_enabled_for_selection =
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(model_->triggering_options() != nullptr &&
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(model_->triggering_options()->enabled_modes() & ModeFlag_SELECTION));
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// Annotation requires the selection model.
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if (model_enabled_for_annotation || model_enabled_for_selection) {
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if (!model_->selection_options()) {
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TC3_LOG(ERROR) << "No selection options.";
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return;
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}
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if (!model_->selection_feature_options()) {
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TC3_LOG(ERROR) << "No selection feature options.";
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return;
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}
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if (!model_->selection_feature_options()->bounds_sensitive_features()) {
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TC3_LOG(ERROR) << "No selection bounds sensitive feature options.";
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return;
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}
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if (!model_->selection_model()) {
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TC3_LOG(ERROR) << "No selection model.";
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return;
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}
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selection_executor_ = ModelExecutor::FromBuffer(model_->selection_model());
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if (!selection_executor_) {
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TC3_LOG(ERROR) << "Could not initialize selection executor.";
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return;
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}
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selection_feature_processor_.reset(
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new FeatureProcessor(model_->selection_feature_options(), unilib_));
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}
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// Annotation requires the classification model for conflict resolution and
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// scoring.
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// Selection requires the classification model for conflict resolution.
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if (model_enabled_for_annotation || model_enabled_for_classification ||
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model_enabled_for_selection) {
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if (!model_->classification_options()) {
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TC3_LOG(ERROR) << "No classification options.";
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return;
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}
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if (!model_->classification_feature_options()) {
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TC3_LOG(ERROR) << "No classification feature options.";
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return;
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}
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if (!model_->classification_feature_options()
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->bounds_sensitive_features()) {
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TC3_LOG(ERROR) << "No classification bounds sensitive feature options.";
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return;
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}
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if (!model_->classification_model()) {
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TC3_LOG(ERROR) << "No clf model.";
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return;
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}
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classification_executor_ =
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ModelExecutor::FromBuffer(model_->classification_model());
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if (!classification_executor_) {
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TC3_LOG(ERROR) << "Could not initialize classification executor.";
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return;
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}
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classification_feature_processor_.reset(new FeatureProcessor(
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model_->classification_feature_options(), unilib_));
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}
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// The embeddings need to be specified if the model is to be used for
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// classification or selection.
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if (model_enabled_for_annotation || model_enabled_for_classification ||
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model_enabled_for_selection) {
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if (!model_->embedding_model()) {
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TC3_LOG(ERROR) << "No embedding model.";
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return;
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}
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// Check that the embedding size of the selection and classification model
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// matches, as they are using the same embeddings.
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if (model_enabled_for_selection &&
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(model_->selection_feature_options()->embedding_size() !=
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model_->classification_feature_options()->embedding_size() ||
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model_->selection_feature_options()->embedding_quantization_bits() !=
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model_->classification_feature_options()
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->embedding_quantization_bits())) {
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TC3_LOG(ERROR) << "Mismatching embedding size/quantization.";
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return;
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}
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embedding_executor_ = TFLiteEmbeddingExecutor::FromBuffer(
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model_->embedding_model(),
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model_->classification_feature_options()->embedding_size(),
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model_->classification_feature_options()->embedding_quantization_bits(),
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model_->embedding_pruning_mask());
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if (!embedding_executor_) {
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TC3_LOG(ERROR) << "Could not initialize embedding executor.";
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return;
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}
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}
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std::unique_ptr<ZlibDecompressor> decompressor = ZlibDecompressor::Instance();
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if (model_->regex_model()) {
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if (!InitializeRegexModel(decompressor.get())) {
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TC3_LOG(ERROR) << "Could not initialize regex model.";
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return;
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}
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}
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if (model_->datetime_model()) {
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datetime_parser_ = DatetimeParser::Instance(
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model_->datetime_model(), *unilib_, *calendarlib_, decompressor.get());
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if (!datetime_parser_) {
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TC3_LOG(ERROR) << "Could not initialize datetime parser.";
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return;
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}
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}
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if (model_->output_options()) {
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if (model_->output_options()->filtered_collections_annotation()) {
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for (const auto collection :
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*model_->output_options()->filtered_collections_annotation()) {
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filtered_collections_annotation_.insert(collection->str());
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}
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}
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if (model_->output_options()->filtered_collections_classification()) {
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for (const auto collection :
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*model_->output_options()->filtered_collections_classification()) {
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filtered_collections_classification_.insert(collection->str());
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}
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}
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if (model_->output_options()->filtered_collections_selection()) {
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for (const auto collection :
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*model_->output_options()->filtered_collections_selection()) {
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filtered_collections_selection_.insert(collection->str());
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}
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}
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}
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if (model_->number_annotator_options() &&
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model_->number_annotator_options()->enabled()) {
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if (selection_feature_processor_ == nullptr) {
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TC3_LOG(ERROR)
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<< "Could not initialize NumberAnnotator without a feature processor";
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return;
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}
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number_annotator_.reset(
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new NumberAnnotator(model_->number_annotator_options(),
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selection_feature_processor_.get()));
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}
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if (model_->duration_annotator_options() &&
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model_->duration_annotator_options()->enabled()) {
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duration_annotator_.reset(
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new DurationAnnotator(model_->duration_annotator_options(),
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selection_feature_processor_.get()));
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}
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if (model_->entity_data_schema()) {
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entity_data_schema_ = LoadAndVerifyFlatbuffer<reflection::Schema>(
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model_->entity_data_schema()->Data(),
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model_->entity_data_schema()->size());
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if (entity_data_schema_ == nullptr) {
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TC3_LOG(ERROR) << "Could not load entity data schema data.";
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return;
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}
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entity_data_builder_.reset(
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new ReflectiveFlatbufferBuilder(entity_data_schema_));
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} else {
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entity_data_schema_ = nullptr;
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entity_data_builder_ = nullptr;
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}
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if (model_->triggering_locales() &&
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!ParseLocales(model_->triggering_locales()->c_str(),
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&model_triggering_locales_)) {
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TC3_LOG(ERROR) << "Could not parse model supported locales.";
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return;
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}
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if (model_->triggering_options() != nullptr &&
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model_->triggering_options()->locales() != nullptr &&
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!ParseLocales(model_->triggering_options()->locales()->c_str(),
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&ml_model_triggering_locales_)) {
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TC3_LOG(ERROR) << "Could not parse supported ML model locales.";
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return;
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}
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if (model_->triggering_options() != nullptr &&
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model_->triggering_options()->dictionary_locales() != nullptr &&
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!ParseLocales(model_->triggering_options()->dictionary_locales()->c_str(),
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&dictionary_locales_)) {
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TC3_LOG(ERROR) << "Could not parse dictionary supported locales.";
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return;
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}
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initialized_ = true;
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}
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bool Annotator::InitializeRegexModel(ZlibDecompressor* decompressor) {
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if (!model_->regex_model()->patterns()) {
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return true;
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}
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// Initialize pattern recognizers.
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int regex_pattern_id = 0;
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for (const auto& regex_pattern : *model_->regex_model()->patterns()) {
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std::unique_ptr<UniLib::RegexPattern> compiled_pattern =
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UncompressMakeRegexPattern(
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*unilib_, regex_pattern->pattern(),
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regex_pattern->compressed_pattern(),
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model_->regex_model()->lazy_regex_compilation(), decompressor);
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if (!compiled_pattern) {
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TC3_LOG(INFO) << "Failed to load regex pattern";
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return false;
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}
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if (regex_pattern->enabled_modes() & ModeFlag_ANNOTATION) {
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annotation_regex_patterns_.push_back(regex_pattern_id);
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}
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if (regex_pattern->enabled_modes() & ModeFlag_CLASSIFICATION) {
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classification_regex_patterns_.push_back(regex_pattern_id);
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}
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if (regex_pattern->enabled_modes() & ModeFlag_SELECTION) {
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selection_regex_patterns_.push_back(regex_pattern_id);
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}
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regex_patterns_.push_back({
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regex_pattern,
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std::move(compiled_pattern),
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});
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++regex_pattern_id;
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}
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return true;
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}
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bool Annotator::InitializeKnowledgeEngine(
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const std::string& serialized_config) {
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std::unique_ptr<KnowledgeEngine> knowledge_engine(
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new KnowledgeEngine(unilib_));
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if (!knowledge_engine->Initialize(serialized_config)) {
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TC3_LOG(ERROR) << "Failed to initialize the knowledge engine.";
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return false;
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}
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knowledge_engine_ = std::move(knowledge_engine);
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return true;
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}
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bool Annotator::InitializeContactEngine(const std::string& serialized_config) {
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std::unique_ptr<ContactEngine> contact_engine(
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new ContactEngine(selection_feature_processor_.get(), unilib_));
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if (!contact_engine->Initialize(serialized_config)) {
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TC3_LOG(ERROR) << "Failed to initialize the contact engine.";
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return false;
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}
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contact_engine_ = std::move(contact_engine);
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return true;
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}
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bool Annotator::InitializeInstalledAppEngine(
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const std::string& serialized_config) {
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std::unique_ptr<InstalledAppEngine> installed_app_engine(
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new InstalledAppEngine(selection_feature_processor_.get(), unilib_));
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if (!installed_app_engine->Initialize(serialized_config)) {
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TC3_LOG(ERROR) << "Failed to initialize the installed app engine.";
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return false;
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}
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installed_app_engine_ = std::move(installed_app_engine);
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return true;
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}
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namespace {
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int CountDigits(const std::string& str, CodepointSpan selection_indices) {
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int count = 0;
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int i = 0;
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const UnicodeText unicode_str = UTF8ToUnicodeText(str, /*do_copy=*/false);
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for (auto it = unicode_str.begin(); it != unicode_str.end(); ++it, ++i) {
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if (i >= selection_indices.first && i < selection_indices.second &&
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isdigit(*it)) {
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++count;
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}
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}
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return count;
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}
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} // namespace
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namespace internal {
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// Helper function, which if the initial 'span' contains only white-spaces,
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// moves the selection to a single-codepoint selection on a left or right side
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// of this space.
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CodepointSpan SnapLeftIfWhitespaceSelection(CodepointSpan span,
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const UnicodeText& context_unicode,
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const UniLib& unilib) {
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TC3_CHECK(ValidNonEmptySpan(span));
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UnicodeText::const_iterator it;
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// Check that the current selection is all whitespaces.
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it = context_unicode.begin();
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std::advance(it, span.first);
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for (int i = 0; i < (span.second - span.first); ++i, ++it) {
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if (!unilib.IsWhitespace(*it)) {
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return span;
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}
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}
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CodepointSpan result;
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// Try moving left.
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result = span;
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it = context_unicode.begin();
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std::advance(it, span.first);
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while (it != context_unicode.begin() && unilib.IsWhitespace(*it)) {
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--result.first;
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--it;
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}
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result.second = result.first + 1;
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if (!unilib.IsWhitespace(*it)) {
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return result;
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}
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// If moving left didn't find a non-whitespace character, just return the
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// original span.
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return span;
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}
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} // namespace internal
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bool Annotator::FilteredForAnnotation(const AnnotatedSpan& span) const {
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return !span.classification.empty() &&
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filtered_collections_annotation_.find(
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span.classification[0].collection) !=
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filtered_collections_annotation_.end();
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}
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bool Annotator::FilteredForClassification(
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const ClassificationResult& classification) const {
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return filtered_collections_classification_.find(classification.collection) !=
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filtered_collections_classification_.end();
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}
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bool Annotator::FilteredForSelection(const AnnotatedSpan& span) const {
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return !span.classification.empty() &&
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filtered_collections_selection_.find(
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span.classification[0].collection) !=
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filtered_collections_selection_.end();
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}
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namespace {
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inline bool ClassifiedAsOther(
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const std::vector<ClassificationResult>& classification) {
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return !classification.empty() &&
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classification[0].collection == Collections::Other();
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}
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float GetPriorityScore(
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const std::vector<ClassificationResult>& classification) {
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if (!classification.empty() && !ClassifiedAsOther(classification)) {
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return classification[0].priority_score;
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} else {
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return -1.0;
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}
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}
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} // namespace
|
|
bool Annotator::VerifyRegexMatchCandidate(
|
const std::string& context, const VerificationOptions* verification_options,
|
const std::string& match, const UniLib::RegexMatcher* matcher) const {
|
if (verification_options == nullptr) {
|
return true;
|
}
|
if (verification_options->verify_luhn_checksum() &&
|
!VerifyLuhnChecksum(match)) {
|
return false;
|
}
|
const int lua_verifier = verification_options->lua_verifier();
|
if (lua_verifier >= 0) {
|
if (model_->regex_model()->lua_verifier() == nullptr ||
|
lua_verifier >= model_->regex_model()->lua_verifier()->size()) {
|
TC3_LOG(ERROR) << "Invalid lua verifier specified: " << lua_verifier;
|
return false;
|
}
|
return VerifyMatch(
|
context, matcher,
|
model_->regex_model()->lua_verifier()->Get(lua_verifier)->str());
|
}
|
return true;
|
}
|
|
CodepointSpan Annotator::SuggestSelection(
|
const std::string& context, CodepointSpan click_indices,
|
const SelectionOptions& options) const {
|
CodepointSpan original_click_indices = click_indices;
|
if (!initialized_) {
|
TC3_LOG(ERROR) << "Not initialized";
|
return original_click_indices;
|
}
|
if (!(model_->enabled_modes() & ModeFlag_SELECTION)) {
|
return original_click_indices;
|
}
|
|
std::vector<Locale> detected_text_language_tags;
|
if (!ParseLocales(options.detected_text_language_tags,
|
&detected_text_language_tags)) {
|
TC3_LOG(WARNING)
|
<< "Failed to parse the detected_text_language_tags in options: "
|
<< options.detected_text_language_tags;
|
}
|
if (!Locale::IsAnyLocaleSupported(detected_text_language_tags,
|
model_triggering_locales_,
|
/*default_value=*/true)) {
|
return original_click_indices;
|
}
|
|
const UnicodeText context_unicode = UTF8ToUnicodeText(context,
|
/*do_copy=*/false);
|
|
if (!context_unicode.is_valid()) {
|
return original_click_indices;
|
}
|
|
const int context_codepoint_size = context_unicode.size_codepoints();
|
|
if (click_indices.first < 0 || click_indices.second < 0 ||
|
click_indices.first >= context_codepoint_size ||
|
click_indices.second > context_codepoint_size ||
|
click_indices.first >= click_indices.second) {
|
TC3_VLOG(1) << "Trying to run SuggestSelection with invalid indices: "
|
<< click_indices.first << " " << click_indices.second;
|
return original_click_indices;
|
}
|
|
if (model_->snap_whitespace_selections()) {
|
// We want to expand a purely white-space selection to a multi-selection it
|
// would've been part of. But with this feature disabled we would do a no-
|
// op, because no token is found. Therefore, we need to modify the
|
// 'click_indices' a bit to include a part of the token, so that the click-
|
// finding logic finds the clicked token correctly. This modification is
|
// done by the following function. Note, that it's enough to check the left
|
// side of the current selection, because if the white-space is a part of a
|
// multi-selection, necessarily both tokens - on the left and the right
|
// sides need to be selected. Thus snapping only to the left is sufficient
|
// (there's a check at the bottom that makes sure that if we snap to the
|
// left token but the result does not contain the initial white-space,
|
// returns the original indices).
|
click_indices = internal::SnapLeftIfWhitespaceSelection(
|
click_indices, context_unicode, *unilib_);
|
}
|
|
std::vector<AnnotatedSpan> candidates;
|
InterpreterManager interpreter_manager(selection_executor_.get(),
|
classification_executor_.get());
|
std::vector<Token> tokens;
|
if (!ModelSuggestSelection(context_unicode, click_indices,
|
detected_text_language_tags, &interpreter_manager,
|
&tokens, &candidates)) {
|
TC3_LOG(ERROR) << "Model suggest selection failed.";
|
return original_click_indices;
|
}
|
if (!RegexChunk(context_unicode, selection_regex_patterns_, &candidates,
|
/*is_serialized_entity_data_enabled=*/false)) {
|
TC3_LOG(ERROR) << "Regex suggest selection failed.";
|
return original_click_indices;
|
}
|
if (!DatetimeChunk(
|
UTF8ToUnicodeText(context, /*do_copy=*/false),
|
/*reference_time_ms_utc=*/0, /*reference_timezone=*/"",
|
options.locales, ModeFlag_SELECTION, options.annotation_usecase,
|
/*is_serialized_entity_data_enabled=*/false, &candidates)) {
|
TC3_LOG(ERROR) << "Datetime suggest selection failed.";
|
return original_click_indices;
|
}
|
if (knowledge_engine_ != nullptr &&
|
!knowledge_engine_->Chunk(context, &candidates)) {
|
TC3_LOG(ERROR) << "Knowledge suggest selection failed.";
|
return original_click_indices;
|
}
|
if (contact_engine_ != nullptr &&
|
!contact_engine_->Chunk(context_unicode, tokens, &candidates)) {
|
TC3_LOG(ERROR) << "Contact suggest selection failed.";
|
return original_click_indices;
|
}
|
if (installed_app_engine_ != nullptr &&
|
!installed_app_engine_->Chunk(context_unicode, tokens, &candidates)) {
|
TC3_LOG(ERROR) << "Installed app suggest selection failed.";
|
return original_click_indices;
|
}
|
if (number_annotator_ != nullptr &&
|
!number_annotator_->FindAll(context_unicode, options.annotation_usecase,
|
&candidates)) {
|
TC3_LOG(ERROR) << "Number annotator failed in suggest selection.";
|
return original_click_indices;
|
}
|
if (duration_annotator_ != nullptr &&
|
!duration_annotator_->FindAll(context_unicode, tokens,
|
options.annotation_usecase, &candidates)) {
|
TC3_LOG(ERROR) << "Duration annotator failed in suggest selection.";
|
return original_click_indices;
|
}
|
|
// Sort candidates according to their position in the input, so that the next
|
// code can assume that any connected component of overlapping spans forms a
|
// contiguous block.
|
std::sort(candidates.begin(), candidates.end(),
|
[](const AnnotatedSpan& a, const AnnotatedSpan& b) {
|
return a.span.first < b.span.first;
|
});
|
|
std::vector<int> candidate_indices;
|
if (!ResolveConflicts(candidates, context, tokens,
|
detected_text_language_tags, options.annotation_usecase,
|
&interpreter_manager, &candidate_indices)) {
|
TC3_LOG(ERROR) << "Couldn't resolve conflicts.";
|
return original_click_indices;
|
}
|
|
std::sort(candidate_indices.begin(), candidate_indices.end(),
|
[&candidates](int a, int b) {
|
return GetPriorityScore(candidates[a].classification) >
|
GetPriorityScore(candidates[b].classification);
|
});
|
|
for (const int i : candidate_indices) {
|
if (SpansOverlap(candidates[i].span, click_indices) &&
|
SpansOverlap(candidates[i].span, original_click_indices)) {
|
// Run model classification if not present but requested and there's a
|
// classification collection filter specified.
|
if (candidates[i].classification.empty() &&
|
model_->selection_options()->always_classify_suggested_selection() &&
|
!filtered_collections_selection_.empty()) {
|
if (!ModelClassifyText(context, detected_text_language_tags,
|
candidates[i].span, &interpreter_manager,
|
/*embedding_cache=*/nullptr,
|
&candidates[i].classification)) {
|
return original_click_indices;
|
}
|
}
|
|
// Ignore if span classification is filtered.
|
if (FilteredForSelection(candidates[i])) {
|
return original_click_indices;
|
}
|
|
return candidates[i].span;
|
}
|
}
|
|
return original_click_indices;
|
}
|
|
namespace {
|
// Helper function that returns the index of the first candidate that
|
// transitively does not overlap with the candidate on 'start_index'. If the end
|
// of 'candidates' is reached, it returns the index that points right behind the
|
// array.
|
int FirstNonOverlappingSpanIndex(const std::vector<AnnotatedSpan>& candidates,
|
int start_index) {
|
int first_non_overlapping = start_index + 1;
|
CodepointSpan conflicting_span = candidates[start_index].span;
|
while (
|
first_non_overlapping < candidates.size() &&
|
SpansOverlap(conflicting_span, candidates[first_non_overlapping].span)) {
|
// Grow the span to include the current one.
|
conflicting_span.second = std::max(
|
conflicting_span.second, candidates[first_non_overlapping].span.second);
|
|
++first_non_overlapping;
|
}
|
return first_non_overlapping;
|
}
|
} // namespace
|
|
bool Annotator::ResolveConflicts(
|
const std::vector<AnnotatedSpan>& candidates, const std::string& context,
|
const std::vector<Token>& cached_tokens,
|
const std::vector<Locale>& detected_text_language_tags,
|
AnnotationUsecase annotation_usecase,
|
InterpreterManager* interpreter_manager, std::vector<int>* result) const {
|
result->clear();
|
result->reserve(candidates.size());
|
for (int i = 0; i < candidates.size();) {
|
int first_non_overlapping =
|
FirstNonOverlappingSpanIndex(candidates, /*start_index=*/i);
|
|
const bool conflict_found = first_non_overlapping != (i + 1);
|
if (conflict_found) {
|
std::vector<int> candidate_indices;
|
if (!ResolveConflict(context, cached_tokens, candidates,
|
detected_text_language_tags, i,
|
first_non_overlapping, annotation_usecase,
|
interpreter_manager, &candidate_indices)) {
|
return false;
|
}
|
result->insert(result->end(), candidate_indices.begin(),
|
candidate_indices.end());
|
} else {
|
result->push_back(i);
|
}
|
|
// Skip over the whole conflicting group/go to next candidate.
|
i = first_non_overlapping;
|
}
|
return true;
|
}
|
|
namespace {
|
// Returns true, if the given two sources do conflict in given annotation
|
// usecase.
|
// - In SMART usecase, all sources do conflict, because there's only 1 possible
|
// annotation for a given span.
|
// - In RAW usecase, certain annotations are allowed to overlap (e.g. datetime
|
// and duration), while others not (e.g. duration and number).
|
bool DoSourcesConflict(AnnotationUsecase annotation_usecase,
|
const AnnotatedSpan::Source source1,
|
const AnnotatedSpan::Source source2) {
|
uint32 source_mask =
|
(1 << static_cast<int>(source1)) | (1 << static_cast<int>(source2));
|
|
switch (annotation_usecase) {
|
case AnnotationUsecase_ANNOTATION_USECASE_SMART:
|
// In the SMART mode, all annotations conflict.
|
return true;
|
|
case AnnotationUsecase_ANNOTATION_USECASE_RAW:
|
// DURATION and DATETIME do not conflict. E.g. "let's meet in 3 hours",
|
// can have two non-conflicting annotations: "in 3 hours" (datetime), "3
|
// hours" (duration).
|
if ((source_mask &
|
(1 << static_cast<int>(AnnotatedSpan::Source::DURATION))) &&
|
(source_mask &
|
(1 << static_cast<int>(AnnotatedSpan::Source::DATETIME)))) {
|
return false;
|
}
|
|
// A KNOWLEDGE entity does not conflict with anything.
|
if ((source_mask &
|
(1 << static_cast<int>(AnnotatedSpan::Source::KNOWLEDGE)))) {
|
return false;
|
}
|
|
// Entities from other sources can conflict.
|
return true;
|
}
|
}
|
} // namespace
|
|
bool Annotator::ResolveConflict(
|
const std::string& context, const std::vector<Token>& cached_tokens,
|
const std::vector<AnnotatedSpan>& candidates,
|
const std::vector<Locale>& detected_text_language_tags, int start_index,
|
int end_index, AnnotationUsecase annotation_usecase,
|
InterpreterManager* interpreter_manager,
|
std::vector<int>* chosen_indices) const {
|
std::vector<int> conflicting_indices;
|
std::unordered_map<int, float> scores;
|
for (int i = start_index; i < end_index; ++i) {
|
conflicting_indices.push_back(i);
|
if (!candidates[i].classification.empty()) {
|
scores[i] = GetPriorityScore(candidates[i].classification);
|
continue;
|
}
|
|
// OPTIMIZATION: So that we don't have to classify all the ML model
|
// spans apriori, we wait until we get here, when they conflict with
|
// something and we need the actual classification scores. So if the
|
// candidate conflicts and comes from the model, we need to run a
|
// classification to determine its priority:
|
std::vector<ClassificationResult> classification;
|
if (!ModelClassifyText(context, cached_tokens, detected_text_language_tags,
|
candidates[i].span, interpreter_manager,
|
/*embedding_cache=*/nullptr, &classification)) {
|
return false;
|
}
|
|
if (!classification.empty()) {
|
scores[i] = GetPriorityScore(classification);
|
}
|
}
|
|
std::sort(conflicting_indices.begin(), conflicting_indices.end(),
|
[&scores](int i, int j) { return scores[i] > scores[j]; });
|
|
// Here we keep a set of indices that were chosen, per-source, to enable
|
// effective computation.
|
std::unordered_map<AnnotatedSpan::Source, SortedIntSet>
|
chosen_indices_for_source_map;
|
|
// Greedily place the candidates if they don't conflict with the already
|
// placed ones.
|
for (int i = 0; i < conflicting_indices.size(); ++i) {
|
const int considered_candidate = conflicting_indices[i];
|
|
// See if there is a conflict between the candidate and all already placed
|
// candidates.
|
bool conflict = false;
|
SortedIntSet* chosen_indices_for_source_ptr = nullptr;
|
for (auto& source_set_pair : chosen_indices_for_source_map) {
|
if (source_set_pair.first == candidates[considered_candidate].source) {
|
chosen_indices_for_source_ptr = &source_set_pair.second;
|
}
|
|
if (DoSourcesConflict(annotation_usecase, source_set_pair.first,
|
candidates[considered_candidate].source) &&
|
DoesCandidateConflict(considered_candidate, candidates,
|
source_set_pair.second)) {
|
conflict = true;
|
break;
|
}
|
}
|
|
// Skip the candidate if a conflict was found.
|
if (conflict) {
|
continue;
|
}
|
|
// If the set of indices for the current source doesn't exist yet,
|
// initialize it.
|
if (chosen_indices_for_source_ptr == nullptr) {
|
SortedIntSet new_set([&candidates](int a, int b) {
|
return candidates[a].span.first < candidates[b].span.first;
|
});
|
chosen_indices_for_source_map[candidates[considered_candidate].source] =
|
std::move(new_set);
|
chosen_indices_for_source_ptr =
|
&chosen_indices_for_source_map[candidates[considered_candidate]
|
.source];
|
}
|
|
// Place the candidate to the output and to the per-source conflict set.
|
chosen_indices->push_back(considered_candidate);
|
chosen_indices_for_source_ptr->insert(considered_candidate);
|
}
|
|
std::sort(chosen_indices->begin(), chosen_indices->end());
|
|
return true;
|
}
|
|
bool Annotator::ModelSuggestSelection(
|
const UnicodeText& context_unicode, CodepointSpan click_indices,
|
const std::vector<Locale>& detected_text_language_tags,
|
InterpreterManager* interpreter_manager, std::vector<Token>* tokens,
|
std::vector<AnnotatedSpan>* result) const {
|
if (model_->triggering_options() == nullptr ||
|
!(model_->triggering_options()->enabled_modes() & ModeFlag_SELECTION)) {
|
return true;
|
}
|
|
if (!Locale::IsAnyLocaleSupported(detected_text_language_tags,
|
ml_model_triggering_locales_,
|
/*default_value=*/true)) {
|
return true;
|
}
|
|
int click_pos;
|
*tokens = selection_feature_processor_->Tokenize(context_unicode);
|
selection_feature_processor_->RetokenizeAndFindClick(
|
context_unicode, click_indices,
|
selection_feature_processor_->GetOptions()->only_use_line_with_click(),
|
tokens, &click_pos);
|
if (click_pos == kInvalidIndex) {
|
TC3_VLOG(1) << "Could not calculate the click position.";
|
return false;
|
}
|
|
const int symmetry_context_size =
|
model_->selection_options()->symmetry_context_size();
|
const FeatureProcessorOptions_::BoundsSensitiveFeatures*
|
bounds_sensitive_features = selection_feature_processor_->GetOptions()
|
->bounds_sensitive_features();
|
|
// The symmetry context span is the clicked token with symmetry_context_size
|
// tokens on either side.
|
const TokenSpan symmetry_context_span = IntersectTokenSpans(
|
ExpandTokenSpan(SingleTokenSpan(click_pos),
|
/*num_tokens_left=*/symmetry_context_size,
|
/*num_tokens_right=*/symmetry_context_size),
|
{0, tokens->size()});
|
|
// Compute the extraction span based on the model type.
|
TokenSpan extraction_span;
|
if (bounds_sensitive_features && bounds_sensitive_features->enabled()) {
|
// The extraction span is the symmetry context span expanded to include
|
// max_selection_span tokens on either side, which is how far a selection
|
// can stretch from the click, plus a relevant number of tokens outside of
|
// the bounds of the selection.
|
const int max_selection_span =
|
selection_feature_processor_->GetOptions()->max_selection_span();
|
extraction_span =
|
ExpandTokenSpan(symmetry_context_span,
|
/*num_tokens_left=*/max_selection_span +
|
bounds_sensitive_features->num_tokens_before(),
|
/*num_tokens_right=*/max_selection_span +
|
bounds_sensitive_features->num_tokens_after());
|
} else {
|
// The extraction span is the symmetry context span expanded to include
|
// context_size tokens on either side.
|
const int context_size =
|
selection_feature_processor_->GetOptions()->context_size();
|
extraction_span = ExpandTokenSpan(symmetry_context_span,
|
/*num_tokens_left=*/context_size,
|
/*num_tokens_right=*/context_size);
|
}
|
extraction_span = IntersectTokenSpans(extraction_span, {0, tokens->size()});
|
|
if (!selection_feature_processor_->HasEnoughSupportedCodepoints(
|
*tokens, extraction_span)) {
|
return true;
|
}
|
|
std::unique_ptr<CachedFeatures> cached_features;
|
if (!selection_feature_processor_->ExtractFeatures(
|
*tokens, extraction_span,
|
/*selection_span_for_feature=*/{kInvalidIndex, kInvalidIndex},
|
embedding_executor_.get(),
|
/*embedding_cache=*/nullptr,
|
selection_feature_processor_->EmbeddingSize() +
|
selection_feature_processor_->DenseFeaturesCount(),
|
&cached_features)) {
|
TC3_LOG(ERROR) << "Could not extract features.";
|
return false;
|
}
|
|
// Produce selection model candidates.
|
std::vector<TokenSpan> chunks;
|
if (!ModelChunk(tokens->size(), /*span_of_interest=*/symmetry_context_span,
|
interpreter_manager->SelectionInterpreter(), *cached_features,
|
&chunks)) {
|
TC3_LOG(ERROR) << "Could not chunk.";
|
return false;
|
}
|
|
for (const TokenSpan& chunk : chunks) {
|
AnnotatedSpan candidate;
|
candidate.span = selection_feature_processor_->StripBoundaryCodepoints(
|
context_unicode, TokenSpanToCodepointSpan(*tokens, chunk));
|
if (model_->selection_options()->strip_unpaired_brackets()) {
|
candidate.span =
|
StripUnpairedBrackets(context_unicode, candidate.span, *unilib_);
|
}
|
|
// Only output non-empty spans.
|
if (candidate.span.first != candidate.span.second) {
|
result->push_back(candidate);
|
}
|
}
|
return true;
|
}
|
|
bool Annotator::ModelClassifyText(
|
const std::string& context,
|
const std::vector<Locale>& detected_text_language_tags,
|
CodepointSpan selection_indices, InterpreterManager* interpreter_manager,
|
FeatureProcessor::EmbeddingCache* embedding_cache,
|
std::vector<ClassificationResult>* classification_results) const {
|
return ModelClassifyText(context, {}, detected_text_language_tags,
|
selection_indices, interpreter_manager,
|
embedding_cache, classification_results);
|
}
|
|
namespace internal {
|
std::vector<Token> CopyCachedTokens(const std::vector<Token>& cached_tokens,
|
CodepointSpan selection_indices,
|
TokenSpan tokens_around_selection_to_copy) {
|
const auto first_selection_token = std::upper_bound(
|
cached_tokens.begin(), cached_tokens.end(), selection_indices.first,
|
[](int selection_start, const Token& token) {
|
return selection_start < token.end;
|
});
|
const auto last_selection_token = std::lower_bound(
|
cached_tokens.begin(), cached_tokens.end(), selection_indices.second,
|
[](const Token& token, int selection_end) {
|
return token.start < selection_end;
|
});
|
|
const int64 first_token = std::max(
|
static_cast<int64>(0),
|
static_cast<int64>((first_selection_token - cached_tokens.begin()) -
|
tokens_around_selection_to_copy.first));
|
const int64 last_token = std::min(
|
static_cast<int64>(cached_tokens.size()),
|
static_cast<int64>((last_selection_token - cached_tokens.begin()) +
|
tokens_around_selection_to_copy.second));
|
|
std::vector<Token> tokens;
|
tokens.reserve(last_token - first_token);
|
for (int i = first_token; i < last_token; ++i) {
|
tokens.push_back(cached_tokens[i]);
|
}
|
return tokens;
|
}
|
} // namespace internal
|
|
TokenSpan Annotator::ClassifyTextUpperBoundNeededTokens() const {
|
const FeatureProcessorOptions_::BoundsSensitiveFeatures*
|
bounds_sensitive_features =
|
classification_feature_processor_->GetOptions()
|
->bounds_sensitive_features();
|
if (bounds_sensitive_features && bounds_sensitive_features->enabled()) {
|
// The extraction span is the selection span expanded to include a relevant
|
// number of tokens outside of the bounds of the selection.
|
return {bounds_sensitive_features->num_tokens_before(),
|
bounds_sensitive_features->num_tokens_after()};
|
} else {
|
// The extraction span is the clicked token with context_size tokens on
|
// either side.
|
const int context_size =
|
selection_feature_processor_->GetOptions()->context_size();
|
return {context_size, context_size};
|
}
|
}
|
|
namespace {
|
// Sorts the classification results from high score to low score.
|
void SortClassificationResults(
|
std::vector<ClassificationResult>* classification_results) {
|
std::sort(classification_results->begin(), classification_results->end(),
|
[](const ClassificationResult& a, const ClassificationResult& b) {
|
return a.score > b.score;
|
});
|
}
|
} // namespace
|
|
bool Annotator::ModelClassifyText(
|
const std::string& context, const std::vector<Token>& cached_tokens,
|
const std::vector<Locale>& detected_text_language_tags,
|
CodepointSpan selection_indices, InterpreterManager* interpreter_manager,
|
FeatureProcessor::EmbeddingCache* embedding_cache,
|
std::vector<ClassificationResult>* classification_results) const {
|
std::vector<Token> tokens;
|
return ModelClassifyText(context, cached_tokens, detected_text_language_tags,
|
selection_indices, interpreter_manager,
|
embedding_cache, classification_results, &tokens);
|
}
|
|
bool Annotator::ModelClassifyText(
|
const std::string& context, const std::vector<Token>& cached_tokens,
|
const std::vector<Locale>& detected_text_language_tags,
|
CodepointSpan selection_indices, InterpreterManager* interpreter_manager,
|
FeatureProcessor::EmbeddingCache* embedding_cache,
|
std::vector<ClassificationResult>* classification_results,
|
std::vector<Token>* tokens) const {
|
if (model_->triggering_options() == nullptr ||
|
!(model_->triggering_options()->enabled_modes() &
|
ModeFlag_CLASSIFICATION)) {
|
return true;
|
}
|
|
if (!Locale::IsAnyLocaleSupported(detected_text_language_tags,
|
ml_model_triggering_locales_,
|
/*default_value=*/true)) {
|
return true;
|
}
|
|
if (cached_tokens.empty()) {
|
*tokens = classification_feature_processor_->Tokenize(context);
|
} else {
|
*tokens = internal::CopyCachedTokens(cached_tokens, selection_indices,
|
ClassifyTextUpperBoundNeededTokens());
|
}
|
|
int click_pos;
|
classification_feature_processor_->RetokenizeAndFindClick(
|
context, selection_indices,
|
classification_feature_processor_->GetOptions()
|
->only_use_line_with_click(),
|
tokens, &click_pos);
|
const TokenSpan selection_token_span =
|
CodepointSpanToTokenSpan(*tokens, selection_indices);
|
const int selection_num_tokens = TokenSpanSize(selection_token_span);
|
if (model_->classification_options()->max_num_tokens() > 0 &&
|
model_->classification_options()->max_num_tokens() <
|
selection_num_tokens) {
|
*classification_results = {{Collections::Other(), 1.0}};
|
return true;
|
}
|
|
const FeatureProcessorOptions_::BoundsSensitiveFeatures*
|
bounds_sensitive_features =
|
classification_feature_processor_->GetOptions()
|
->bounds_sensitive_features();
|
if (selection_token_span.first == kInvalidIndex ||
|
selection_token_span.second == kInvalidIndex) {
|
TC3_LOG(ERROR) << "Could not determine span.";
|
return false;
|
}
|
|
// Compute the extraction span based on the model type.
|
TokenSpan extraction_span;
|
if (bounds_sensitive_features && bounds_sensitive_features->enabled()) {
|
// The extraction span is the selection span expanded to include a relevant
|
// number of tokens outside of the bounds of the selection.
|
extraction_span = ExpandTokenSpan(
|
selection_token_span,
|
/*num_tokens_left=*/bounds_sensitive_features->num_tokens_before(),
|
/*num_tokens_right=*/bounds_sensitive_features->num_tokens_after());
|
} else {
|
if (click_pos == kInvalidIndex) {
|
TC3_LOG(ERROR) << "Couldn't choose a click position.";
|
return false;
|
}
|
// The extraction span is the clicked token with context_size tokens on
|
// either side.
|
const int context_size =
|
classification_feature_processor_->GetOptions()->context_size();
|
extraction_span = ExpandTokenSpan(SingleTokenSpan(click_pos),
|
/*num_tokens_left=*/context_size,
|
/*num_tokens_right=*/context_size);
|
}
|
extraction_span = IntersectTokenSpans(extraction_span, {0, tokens->size()});
|
|
if (!classification_feature_processor_->HasEnoughSupportedCodepoints(
|
*tokens, extraction_span)) {
|
*classification_results = {{Collections::Other(), 1.0}};
|
return true;
|
}
|
|
std::unique_ptr<CachedFeatures> cached_features;
|
if (!classification_feature_processor_->ExtractFeatures(
|
*tokens, extraction_span, selection_indices,
|
embedding_executor_.get(), embedding_cache,
|
classification_feature_processor_->EmbeddingSize() +
|
classification_feature_processor_->DenseFeaturesCount(),
|
&cached_features)) {
|
TC3_LOG(ERROR) << "Could not extract features.";
|
return false;
|
}
|
|
std::vector<float> features;
|
features.reserve(cached_features->OutputFeaturesSize());
|
if (bounds_sensitive_features && bounds_sensitive_features->enabled()) {
|
cached_features->AppendBoundsSensitiveFeaturesForSpan(selection_token_span,
|
&features);
|
} else {
|
cached_features->AppendClickContextFeaturesForClick(click_pos, &features);
|
}
|
|
TensorView<float> logits = classification_executor_->ComputeLogits(
|
TensorView<float>(features.data(),
|
{1, static_cast<int>(features.size())}),
|
interpreter_manager->ClassificationInterpreter());
|
if (!logits.is_valid()) {
|
TC3_LOG(ERROR) << "Couldn't compute logits.";
|
return false;
|
}
|
|
if (logits.dims() != 2 || logits.dim(0) != 1 ||
|
logits.dim(1) != classification_feature_processor_->NumCollections()) {
|
TC3_LOG(ERROR) << "Mismatching output";
|
return false;
|
}
|
|
const std::vector<float> scores =
|
ComputeSoftmax(logits.data(), logits.dim(1));
|
|
if (scores.empty()) {
|
*classification_results = {{Collections::Other(), 1.0}};
|
return true;
|
}
|
|
const int best_score_index =
|
std::max_element(scores.begin(), scores.end()) - scores.begin();
|
const std::string top_collection =
|
classification_feature_processor_->LabelToCollection(best_score_index);
|
|
// Sanity checks.
|
if (top_collection == Collections::Phone()) {
|
const int digit_count = CountDigits(context, selection_indices);
|
if (digit_count <
|
model_->classification_options()->phone_min_num_digits() ||
|
digit_count >
|
model_->classification_options()->phone_max_num_digits()) {
|
*classification_results = {{Collections::Other(), 1.0}};
|
return true;
|
}
|
} else if (top_collection == Collections::Address()) {
|
if (selection_num_tokens <
|
model_->classification_options()->address_min_num_tokens()) {
|
*classification_results = {{Collections::Other(), 1.0}};
|
return true;
|
}
|
} else if (top_collection == Collections::Dictionary()) {
|
if (!Locale::IsAnyLocaleSupported(detected_text_language_tags,
|
dictionary_locales_,
|
/*default_value=*/false)) {
|
*classification_results = {{Collections::Other(), 1.0}};
|
return true;
|
}
|
}
|
|
*classification_results = {{top_collection, 1.0, scores[best_score_index]}};
|
return true;
|
}
|
|
bool Annotator::RegexClassifyText(
|
const std::string& context, CodepointSpan selection_indices,
|
std::vector<ClassificationResult>* classification_result) const {
|
const std::string selection_text =
|
UTF8ToUnicodeText(context, /*do_copy=*/false)
|
.UTF8Substring(selection_indices.first, selection_indices.second);
|
const UnicodeText selection_text_unicode(
|
UTF8ToUnicodeText(selection_text, /*do_copy=*/false));
|
|
// Check whether any of the regular expressions match.
|
for (const int pattern_id : classification_regex_patterns_) {
|
const CompiledRegexPattern& regex_pattern = regex_patterns_[pattern_id];
|
const std::unique_ptr<UniLib::RegexMatcher> matcher =
|
regex_pattern.pattern->Matcher(selection_text_unicode);
|
int status = UniLib::RegexMatcher::kNoError;
|
bool matches;
|
if (regex_pattern.config->use_approximate_matching()) {
|
matches = matcher->ApproximatelyMatches(&status);
|
} else {
|
matches = matcher->Matches(&status);
|
}
|
if (status != UniLib::RegexMatcher::kNoError) {
|
return false;
|
}
|
if (matches && VerifyRegexMatchCandidate(
|
context, regex_pattern.config->verification_options(),
|
selection_text, matcher.get())) {
|
classification_result->push_back(
|
{regex_pattern.config->collection_name()->str(),
|
regex_pattern.config->target_classification_score(),
|
regex_pattern.config->priority_score()});
|
if (!SerializedEntityDataFromRegexMatch(
|
regex_pattern.config, matcher.get(),
|
&classification_result->back().serialized_entity_data)) {
|
TC3_LOG(ERROR) << "Could not get entity data.";
|
return false;
|
}
|
}
|
}
|
|
return true;
|
}
|
|
namespace {
|
std::string PickCollectionForDatetime(
|
const DatetimeParseResult& datetime_parse_result) {
|
switch (datetime_parse_result.granularity) {
|
case GRANULARITY_HOUR:
|
case GRANULARITY_MINUTE:
|
case GRANULARITY_SECOND:
|
return Collections::DateTime();
|
default:
|
return Collections::Date();
|
}
|
}
|
|
std::string CreateDatetimeSerializedEntityData(
|
const DatetimeParseResult& parse_result) {
|
EntityDataT entity_data;
|
entity_data.datetime.reset(new EntityData_::DatetimeT());
|
entity_data.datetime->time_ms_utc = parse_result.time_ms_utc;
|
entity_data.datetime->granularity =
|
static_cast<EntityData_::Datetime_::Granularity>(
|
parse_result.granularity);
|
|
flatbuffers::FlatBufferBuilder builder;
|
FinishEntityDataBuffer(builder, EntityData::Pack(builder, &entity_data));
|
return std::string(reinterpret_cast<const char*>(builder.GetBufferPointer()),
|
builder.GetSize());
|
}
|
} // namespace
|
|
bool Annotator::DatetimeClassifyText(
|
const std::string& context, CodepointSpan selection_indices,
|
const ClassificationOptions& options,
|
std::vector<ClassificationResult>* classification_results) const {
|
if (!datetime_parser_) {
|
return false;
|
}
|
|
const std::string selection_text =
|
UTF8ToUnicodeText(context, /*do_copy=*/false)
|
.UTF8Substring(selection_indices.first, selection_indices.second);
|
|
std::vector<DatetimeParseResultSpan> datetime_spans;
|
if (!datetime_parser_->Parse(selection_text, options.reference_time_ms_utc,
|
options.reference_timezone, options.locales,
|
ModeFlag_CLASSIFICATION,
|
options.annotation_usecase,
|
/*anchor_start_end=*/true, &datetime_spans)) {
|
TC3_LOG(ERROR) << "Error during parsing datetime.";
|
return false;
|
}
|
for (const DatetimeParseResultSpan& datetime_span : datetime_spans) {
|
// Only consider the result valid if the selection and extracted datetime
|
// spans exactly match.
|
if (std::make_pair(datetime_span.span.first + selection_indices.first,
|
datetime_span.span.second + selection_indices.first) ==
|
selection_indices) {
|
for (const DatetimeParseResult& parse_result : datetime_span.data) {
|
classification_results->emplace_back(
|
PickCollectionForDatetime(parse_result),
|
datetime_span.target_classification_score);
|
classification_results->back().datetime_parse_result = parse_result;
|
classification_results->back().serialized_entity_data =
|
CreateDatetimeSerializedEntityData(parse_result);
|
classification_results->back().priority_score =
|
datetime_span.priority_score;
|
}
|
return true;
|
}
|
}
|
return true;
|
}
|
|
std::vector<ClassificationResult> Annotator::ClassifyText(
|
const std::string& context, CodepointSpan selection_indices,
|
const ClassificationOptions& options) const {
|
if (!initialized_) {
|
TC3_LOG(ERROR) << "Not initialized";
|
return {};
|
}
|
|
if (!(model_->enabled_modes() & ModeFlag_CLASSIFICATION)) {
|
return {};
|
}
|
|
std::vector<Locale> detected_text_language_tags;
|
if (!ParseLocales(options.detected_text_language_tags,
|
&detected_text_language_tags)) {
|
TC3_LOG(WARNING)
|
<< "Failed to parse the detected_text_language_tags in options: "
|
<< options.detected_text_language_tags;
|
}
|
if (!Locale::IsAnyLocaleSupported(detected_text_language_tags,
|
model_triggering_locales_,
|
/*default_value=*/true)) {
|
return {};
|
}
|
|
if (!UTF8ToUnicodeText(context, /*do_copy=*/false).is_valid()) {
|
return {};
|
}
|
|
if (std::get<0>(selection_indices) >= std::get<1>(selection_indices)) {
|
TC3_VLOG(1) << "Trying to run ClassifyText with invalid indices: "
|
<< std::get<0>(selection_indices) << " "
|
<< std::get<1>(selection_indices);
|
return {};
|
}
|
|
// We'll accumulate a list of candidates, and pick the best candidate in the
|
// end.
|
std::vector<AnnotatedSpan> candidates;
|
|
// Try the knowledge engine.
|
// TODO(b/126579108): Propagate error status.
|
ClassificationResult knowledge_result;
|
if (knowledge_engine_ && knowledge_engine_->ClassifyText(
|
context, selection_indices, &knowledge_result)) {
|
candidates.push_back({selection_indices, {knowledge_result}});
|
candidates.back().source = AnnotatedSpan::Source::KNOWLEDGE;
|
}
|
|
// Try the contact engine.
|
// TODO(b/126579108): Propagate error status.
|
ClassificationResult contact_result;
|
if (contact_engine_ && contact_engine_->ClassifyText(
|
context, selection_indices, &contact_result)) {
|
candidates.push_back({selection_indices, {contact_result}});
|
}
|
|
// Try the installed app engine.
|
// TODO(b/126579108): Propagate error status.
|
ClassificationResult installed_app_result;
|
if (installed_app_engine_ &&
|
installed_app_engine_->ClassifyText(context, selection_indices,
|
&installed_app_result)) {
|
candidates.push_back({selection_indices, {installed_app_result}});
|
}
|
|
// Try the regular expression models.
|
std::vector<ClassificationResult> regex_results;
|
if (!RegexClassifyText(context, selection_indices, ®ex_results)) {
|
return {};
|
}
|
for (const ClassificationResult& result : regex_results) {
|
candidates.push_back({selection_indices, {result}});
|
}
|
|
// Try the date model.
|
//
|
// DatetimeClassifyText only returns the first result, which can however have
|
// more interpretations. They are inserted in the candidates as a single
|
// AnnotatedSpan, so that they get treated together by the conflict resolution
|
// algorithm.
|
std::vector<ClassificationResult> datetime_results;
|
if (!DatetimeClassifyText(context, selection_indices, options,
|
&datetime_results)) {
|
return {};
|
}
|
if (!datetime_results.empty()) {
|
candidates.push_back({selection_indices, std::move(datetime_results)});
|
candidates.back().source = AnnotatedSpan::Source::DATETIME;
|
}
|
|
// Try the number annotator.
|
// TODO(b/126579108): Propagate error status.
|
ClassificationResult number_annotator_result;
|
if (number_annotator_ &&
|
number_annotator_->ClassifyText(
|
UTF8ToUnicodeText(context, /*do_copy=*/false), selection_indices,
|
options.annotation_usecase, &number_annotator_result)) {
|
candidates.push_back({selection_indices, {number_annotator_result}});
|
}
|
|
// Try the duration annotator.
|
ClassificationResult duration_annotator_result;
|
if (duration_annotator_ &&
|
duration_annotator_->ClassifyText(
|
UTF8ToUnicodeText(context, /*do_copy=*/false), selection_indices,
|
options.annotation_usecase, &duration_annotator_result)) {
|
candidates.push_back({selection_indices, {duration_annotator_result}});
|
candidates.back().source = AnnotatedSpan::Source::DURATION;
|
}
|
|
// Try the ML model.
|
//
|
// The output of the model is considered as an exclusive 1-of-N choice. That's
|
// why it's inserted as only 1 AnnotatedSpan into candidates, as opposed to 1
|
// span for each candidate, like e.g. the regex model.
|
InterpreterManager interpreter_manager(selection_executor_.get(),
|
classification_executor_.get());
|
std::vector<ClassificationResult> model_results;
|
std::vector<Token> tokens;
|
if (!ModelClassifyText(
|
context, /*cached_tokens=*/{}, detected_text_language_tags,
|
selection_indices, &interpreter_manager,
|
/*embedding_cache=*/nullptr, &model_results, &tokens)) {
|
return {};
|
}
|
if (!model_results.empty()) {
|
candidates.push_back({selection_indices, std::move(model_results)});
|
}
|
|
std::vector<int> candidate_indices;
|
if (!ResolveConflicts(candidates, context, tokens,
|
detected_text_language_tags, options.annotation_usecase,
|
&interpreter_manager, &candidate_indices)) {
|
TC3_LOG(ERROR) << "Couldn't resolve conflicts.";
|
return {};
|
}
|
|
std::vector<ClassificationResult> results;
|
for (const int i : candidate_indices) {
|
for (const ClassificationResult& result : candidates[i].classification) {
|
if (!FilteredForClassification(result)) {
|
results.push_back(result);
|
}
|
}
|
}
|
|
// Sort results according to score.
|
std::sort(results.begin(), results.end(),
|
[](const ClassificationResult& a, const ClassificationResult& b) {
|
return a.score > b.score;
|
});
|
|
if (results.empty()) {
|
results = {{Collections::Other(), 1.0}};
|
}
|
return results;
|
}
|
|
bool Annotator::ModelAnnotate(
|
const std::string& context,
|
const std::vector<Locale>& detected_text_language_tags,
|
InterpreterManager* interpreter_manager, std::vector<Token>* tokens,
|
std::vector<AnnotatedSpan>* result) const {
|
if (model_->triggering_options() == nullptr ||
|
!(model_->triggering_options()->enabled_modes() & ModeFlag_ANNOTATION)) {
|
return true;
|
}
|
|
if (!Locale::IsAnyLocaleSupported(detected_text_language_tags,
|
ml_model_triggering_locales_,
|
/*default_value=*/true)) {
|
return true;
|
}
|
|
const UnicodeText context_unicode = UTF8ToUnicodeText(context,
|
/*do_copy=*/false);
|
std::vector<UnicodeTextRange> lines;
|
if (!selection_feature_processor_->GetOptions()->only_use_line_with_click()) {
|
lines.push_back({context_unicode.begin(), context_unicode.end()});
|
} else {
|
lines = selection_feature_processor_->SplitContext(context_unicode);
|
}
|
|
const float min_annotate_confidence =
|
(model_->triggering_options() != nullptr
|
? model_->triggering_options()->min_annotate_confidence()
|
: 0.f);
|
|
for (const UnicodeTextRange& line : lines) {
|
FeatureProcessor::EmbeddingCache embedding_cache;
|
const std::string line_str =
|
UnicodeText::UTF8Substring(line.first, line.second);
|
|
*tokens = selection_feature_processor_->Tokenize(line_str);
|
selection_feature_processor_->RetokenizeAndFindClick(
|
line_str, {0, std::distance(line.first, line.second)},
|
selection_feature_processor_->GetOptions()->only_use_line_with_click(),
|
tokens,
|
/*click_pos=*/nullptr);
|
const TokenSpan full_line_span = {0, tokens->size()};
|
|
// TODO(zilka): Add support for greater granularity of this check.
|
if (!selection_feature_processor_->HasEnoughSupportedCodepoints(
|
*tokens, full_line_span)) {
|
continue;
|
}
|
|
std::unique_ptr<CachedFeatures> cached_features;
|
if (!selection_feature_processor_->ExtractFeatures(
|
*tokens, full_line_span,
|
/*selection_span_for_feature=*/{kInvalidIndex, kInvalidIndex},
|
embedding_executor_.get(),
|
/*embedding_cache=*/nullptr,
|
selection_feature_processor_->EmbeddingSize() +
|
selection_feature_processor_->DenseFeaturesCount(),
|
&cached_features)) {
|
TC3_LOG(ERROR) << "Could not extract features.";
|
return false;
|
}
|
|
std::vector<TokenSpan> local_chunks;
|
if (!ModelChunk(tokens->size(), /*span_of_interest=*/full_line_span,
|
interpreter_manager->SelectionInterpreter(),
|
*cached_features, &local_chunks)) {
|
TC3_LOG(ERROR) << "Could not chunk.";
|
return false;
|
}
|
|
const int offset = std::distance(context_unicode.begin(), line.first);
|
for (const TokenSpan& chunk : local_chunks) {
|
const CodepointSpan codepoint_span =
|
selection_feature_processor_->StripBoundaryCodepoints(
|
line_str, TokenSpanToCodepointSpan(*tokens, chunk));
|
|
// Skip empty spans.
|
if (codepoint_span.first != codepoint_span.second) {
|
std::vector<ClassificationResult> classification;
|
if (!ModelClassifyText(line_str, *tokens, detected_text_language_tags,
|
codepoint_span, interpreter_manager,
|
&embedding_cache, &classification)) {
|
TC3_LOG(ERROR) << "Could not classify text: "
|
<< (codepoint_span.first + offset) << " "
|
<< (codepoint_span.second + offset);
|
return false;
|
}
|
|
// Do not include the span if it's classified as "other".
|
if (!classification.empty() && !ClassifiedAsOther(classification) &&
|
classification[0].score >= min_annotate_confidence) {
|
AnnotatedSpan result_span;
|
result_span.span = {codepoint_span.first + offset,
|
codepoint_span.second + offset};
|
result_span.classification = std::move(classification);
|
result->push_back(std::move(result_span));
|
}
|
}
|
}
|
}
|
return true;
|
}
|
|
const FeatureProcessor* Annotator::SelectionFeatureProcessorForTests() const {
|
return selection_feature_processor_.get();
|
}
|
|
const FeatureProcessor* Annotator::ClassificationFeatureProcessorForTests()
|
const {
|
return classification_feature_processor_.get();
|
}
|
|
const DatetimeParser* Annotator::DatetimeParserForTests() const {
|
return datetime_parser_.get();
|
}
|
|
void Annotator::RemoveNotEnabledEntityTypes(
|
const EnabledEntityTypes& is_entity_type_enabled,
|
std::vector<AnnotatedSpan>* annotated_spans) const {
|
for (AnnotatedSpan& annotated_span : *annotated_spans) {
|
std::vector<ClassificationResult>& classifications =
|
annotated_span.classification;
|
classifications.erase(
|
std::remove_if(classifications.begin(), classifications.end(),
|
[&is_entity_type_enabled](
|
const ClassificationResult& classification_result) {
|
return !is_entity_type_enabled(
|
classification_result.collection);
|
}),
|
classifications.end());
|
}
|
annotated_spans->erase(
|
std::remove_if(annotated_spans->begin(), annotated_spans->end(),
|
[](const AnnotatedSpan& annotated_span) {
|
return annotated_span.classification.empty();
|
}),
|
annotated_spans->end());
|
}
|
|
std::vector<AnnotatedSpan> Annotator::Annotate(
|
const std::string& context, const AnnotationOptions& options) const {
|
std::vector<AnnotatedSpan> candidates;
|
|
if (!(model_->enabled_modes() & ModeFlag_ANNOTATION)) {
|
return {};
|
}
|
|
const UnicodeText context_unicode =
|
UTF8ToUnicodeText(context, /*do_copy=*/false);
|
if (!context_unicode.is_valid()) {
|
return {};
|
}
|
|
std::vector<Locale> detected_text_language_tags;
|
if (!ParseLocales(options.detected_text_language_tags,
|
&detected_text_language_tags)) {
|
TC3_LOG(WARNING)
|
<< "Failed to parse the detected_text_language_tags in options: "
|
<< options.detected_text_language_tags;
|
}
|
if (!Locale::IsAnyLocaleSupported(detected_text_language_tags,
|
model_triggering_locales_,
|
/*default_value=*/true)) {
|
return {};
|
}
|
|
InterpreterManager interpreter_manager(selection_executor_.get(),
|
classification_executor_.get());
|
|
// Annotate with the selection model.
|
std::vector<Token> tokens;
|
if (!ModelAnnotate(context, detected_text_language_tags, &interpreter_manager,
|
&tokens, &candidates)) {
|
TC3_LOG(ERROR) << "Couldn't run ModelAnnotate.";
|
return {};
|
}
|
|
// Annotate with the regular expression models.
|
if (!RegexChunk(UTF8ToUnicodeText(context, /*do_copy=*/false),
|
annotation_regex_patterns_, &candidates,
|
options.is_serialized_entity_data_enabled)) {
|
TC3_LOG(ERROR) << "Couldn't run RegexChunk.";
|
return {};
|
}
|
|
// Annotate with the datetime model.
|
const EnabledEntityTypes is_entity_type_enabled(options.entity_types);
|
if ((is_entity_type_enabled(Collections::Date()) ||
|
is_entity_type_enabled(Collections::DateTime())) &&
|
!DatetimeChunk(UTF8ToUnicodeText(context, /*do_copy=*/false),
|
options.reference_time_ms_utc, options.reference_timezone,
|
options.locales, ModeFlag_ANNOTATION,
|
options.annotation_usecase,
|
options.is_serialized_entity_data_enabled, &candidates)) {
|
TC3_LOG(ERROR) << "Couldn't run RegexChunk.";
|
return {};
|
}
|
|
// Annotate with the knowledge engine.
|
if (knowledge_engine_ && !knowledge_engine_->Chunk(context, &candidates)) {
|
TC3_LOG(ERROR) << "Couldn't run knowledge engine Chunk.";
|
return {};
|
}
|
|
// Annotate with the contact engine.
|
if (contact_engine_ &&
|
!contact_engine_->Chunk(context_unicode, tokens, &candidates)) {
|
TC3_LOG(ERROR) << "Couldn't run contact engine Chunk.";
|
return {};
|
}
|
|
// Annotate with the installed app engine.
|
if (installed_app_engine_ &&
|
!installed_app_engine_->Chunk(context_unicode, tokens, &candidates)) {
|
TC3_LOG(ERROR) << "Couldn't run installed app engine Chunk.";
|
return {};
|
}
|
|
// Annotate with the number annotator.
|
if (number_annotator_ != nullptr &&
|
!number_annotator_->FindAll(context_unicode, options.annotation_usecase,
|
&candidates)) {
|
TC3_LOG(ERROR) << "Couldn't run number annotator FindAll.";
|
return {};
|
}
|
|
// Annotate with the duration annotator.
|
if (is_entity_type_enabled(Collections::Duration()) &&
|
duration_annotator_ != nullptr &&
|
!duration_annotator_->FindAll(context_unicode, tokens,
|
options.annotation_usecase, &candidates)) {
|
TC3_LOG(ERROR) << "Couldn't run duration annotator FindAll.";
|
return {};
|
}
|
|
// Sort candidates according to their position in the input, so that the next
|
// code can assume that any connected component of overlapping spans forms a
|
// contiguous block.
|
std::sort(candidates.begin(), candidates.end(),
|
[](const AnnotatedSpan& a, const AnnotatedSpan& b) {
|
return a.span.first < b.span.first;
|
});
|
|
std::vector<int> candidate_indices;
|
if (!ResolveConflicts(candidates, context, tokens,
|
detected_text_language_tags, options.annotation_usecase,
|
&interpreter_manager, &candidate_indices)) {
|
TC3_LOG(ERROR) << "Couldn't resolve conflicts.";
|
return {};
|
}
|
|
std::vector<AnnotatedSpan> result;
|
result.reserve(candidate_indices.size());
|
AnnotatedSpan aggregated_span;
|
for (const int i : candidate_indices) {
|
if (candidates[i].span != aggregated_span.span) {
|
if (!aggregated_span.classification.empty()) {
|
result.push_back(std::move(aggregated_span));
|
}
|
aggregated_span =
|
AnnotatedSpan(candidates[i].span, /*arg_classification=*/{});
|
}
|
if (candidates[i].classification.empty() ||
|
ClassifiedAsOther(candidates[i].classification) ||
|
FilteredForAnnotation(candidates[i])) {
|
continue;
|
}
|
for (ClassificationResult& classification : candidates[i].classification) {
|
aggregated_span.classification.push_back(std::move(classification));
|
}
|
}
|
if (!aggregated_span.classification.empty()) {
|
result.push_back(std::move(aggregated_span));
|
}
|
|
// We generate all candidates and remove them later (with the exception of
|
// date/time/duration entities) because there are complex interdependencies
|
// between the entity types. E.g., the TLD of an email can be interpreted as a
|
// URL, but most likely a user of the API does not want such annotations if
|
// "url" is enabled and "email" is not.
|
RemoveNotEnabledEntityTypes(is_entity_type_enabled, &result);
|
|
for (AnnotatedSpan& annotated_span : result) {
|
SortClassificationResults(&annotated_span.classification);
|
}
|
|
return result;
|
}
|
|
CodepointSpan Annotator::ComputeSelectionBoundaries(
|
const UniLib::RegexMatcher* match,
|
const RegexModel_::Pattern* config) const {
|
if (config->capturing_group() == nullptr) {
|
// Use first capturing group to specify the selection.
|
int status = UniLib::RegexMatcher::kNoError;
|
const CodepointSpan result = {match->Start(1, &status),
|
match->End(1, &status)};
|
if (status != UniLib::RegexMatcher::kNoError) {
|
return {kInvalidIndex, kInvalidIndex};
|
}
|
return result;
|
}
|
|
CodepointSpan result = {kInvalidIndex, kInvalidIndex};
|
const int num_groups = config->capturing_group()->size();
|
for (int i = 0; i < num_groups; i++) {
|
if (!config->capturing_group()->Get(i)->extend_selection()) {
|
continue;
|
}
|
|
int status = UniLib::RegexMatcher::kNoError;
|
// Check match and adjust bounds.
|
const int group_start = match->Start(i, &status);
|
const int group_end = match->End(i, &status);
|
if (status != UniLib::RegexMatcher::kNoError) {
|
return {kInvalidIndex, kInvalidIndex};
|
}
|
if (group_start == kInvalidIndex || group_end == kInvalidIndex) {
|
continue;
|
}
|
if (result.first == kInvalidIndex) {
|
result = {group_start, group_end};
|
} else {
|
result.first = std::min(result.first, group_start);
|
result.second = std::max(result.second, group_end);
|
}
|
}
|
return result;
|
}
|
|
bool Annotator::HasEntityData(const RegexModel_::Pattern* pattern) const {
|
if (pattern->serialized_entity_data() != nullptr) {
|
return true;
|
}
|
if (pattern->capturing_group() != nullptr) {
|
for (const RegexModel_::Pattern_::CapturingGroup* group :
|
*pattern->capturing_group()) {
|
if (group->entity_field_path() != nullptr) {
|
return true;
|
}
|
}
|
}
|
return false;
|
}
|
|
bool Annotator::SerializedEntityDataFromRegexMatch(
|
const RegexModel_::Pattern* pattern, UniLib::RegexMatcher* matcher,
|
std::string* serialized_entity_data) const {
|
if (!HasEntityData(pattern)) {
|
serialized_entity_data->clear();
|
return true;
|
}
|
TC3_CHECK(entity_data_builder_ != nullptr);
|
|
std::unique_ptr<ReflectiveFlatbuffer> entity_data =
|
entity_data_builder_->NewRoot();
|
|
TC3_CHECK(entity_data != nullptr);
|
|
// Set static entity data.
|
if (pattern->serialized_entity_data() != nullptr) {
|
TC3_CHECK(entity_data != nullptr);
|
entity_data->MergeFromSerializedFlatbuffer(
|
StringPiece(pattern->serialized_entity_data()->c_str(),
|
pattern->serialized_entity_data()->size()));
|
}
|
|
// Add entity data from rule capturing groups.
|
if (pattern->capturing_group() != nullptr) {
|
const int num_groups = pattern->capturing_group()->size();
|
for (int i = 0; i < num_groups; i++) {
|
const FlatbufferFieldPath* field_path =
|
pattern->capturing_group()->Get(i)->entity_field_path();
|
if (field_path == nullptr) {
|
continue;
|
}
|
TC3_CHECK(entity_data != nullptr);
|
if (!SetFieldFromCapturingGroup(/*group_id=*/i, field_path, matcher,
|
entity_data.get())) {
|
TC3_LOG(ERROR)
|
<< "Could not set entity data from rule capturing group.";
|
return false;
|
}
|
}
|
}
|
|
*serialized_entity_data = entity_data->Serialize();
|
return true;
|
}
|
|
bool Annotator::RegexChunk(const UnicodeText& context_unicode,
|
const std::vector<int>& rules,
|
std::vector<AnnotatedSpan>* result,
|
bool is_serialized_entity_data_enabled) const {
|
for (int pattern_id : rules) {
|
const CompiledRegexPattern& regex_pattern = regex_patterns_[pattern_id];
|
const auto matcher = regex_pattern.pattern->Matcher(context_unicode);
|
if (!matcher) {
|
TC3_LOG(ERROR) << "Could not get regex matcher for pattern: "
|
<< pattern_id;
|
return false;
|
}
|
|
int status = UniLib::RegexMatcher::kNoError;
|
while (matcher->Find(&status) && status == UniLib::RegexMatcher::kNoError) {
|
if (regex_pattern.config->verification_options()) {
|
if (!VerifyRegexMatchCandidate(
|
context_unicode.ToUTF8String(),
|
regex_pattern.config->verification_options(),
|
matcher->Group(1, &status).ToUTF8String(), matcher.get())) {
|
continue;
|
}
|
}
|
|
std::string serialized_entity_data;
|
if (is_serialized_entity_data_enabled) {
|
if (!SerializedEntityDataFromRegexMatch(
|
regex_pattern.config, matcher.get(), &serialized_entity_data)) {
|
TC3_LOG(ERROR) << "Could not get entity data.";
|
return false;
|
}
|
}
|
|
result->emplace_back();
|
|
// Selection/annotation regular expressions need to specify a capturing
|
// group specifying the selection.
|
result->back().span =
|
ComputeSelectionBoundaries(matcher.get(), regex_pattern.config);
|
|
result->back().classification = {
|
{regex_pattern.config->collection_name()->str(),
|
regex_pattern.config->target_classification_score(),
|
regex_pattern.config->priority_score()}};
|
|
result->back().classification[0].serialized_entity_data =
|
serialized_entity_data;
|
}
|
}
|
return true;
|
}
|
|
bool Annotator::ModelChunk(int num_tokens, const TokenSpan& span_of_interest,
|
tflite::Interpreter* selection_interpreter,
|
const CachedFeatures& cached_features,
|
std::vector<TokenSpan>* chunks) const {
|
const int max_selection_span =
|
selection_feature_processor_->GetOptions()->max_selection_span();
|
// The inference span is the span of interest expanded to include
|
// max_selection_span tokens on either side, which is how far a selection can
|
// stretch from the click.
|
const TokenSpan inference_span = IntersectTokenSpans(
|
ExpandTokenSpan(span_of_interest,
|
/*num_tokens_left=*/max_selection_span,
|
/*num_tokens_right=*/max_selection_span),
|
{0, num_tokens});
|
|
std::vector<ScoredChunk> scored_chunks;
|
if (selection_feature_processor_->GetOptions()->bounds_sensitive_features() &&
|
selection_feature_processor_->GetOptions()
|
->bounds_sensitive_features()
|
->enabled()) {
|
if (!ModelBoundsSensitiveScoreChunks(
|
num_tokens, span_of_interest, inference_span, cached_features,
|
selection_interpreter, &scored_chunks)) {
|
return false;
|
}
|
} else {
|
if (!ModelClickContextScoreChunks(num_tokens, span_of_interest,
|
cached_features, selection_interpreter,
|
&scored_chunks)) {
|
return false;
|
}
|
}
|
std::sort(scored_chunks.rbegin(), scored_chunks.rend(),
|
[](const ScoredChunk& lhs, const ScoredChunk& rhs) {
|
return lhs.score < rhs.score;
|
});
|
|
// Traverse the candidate chunks from highest-scoring to lowest-scoring. Pick
|
// them greedily as long as they do not overlap with any previously picked
|
// chunks.
|
std::vector<bool> token_used(TokenSpanSize(inference_span));
|
chunks->clear();
|
for (const ScoredChunk& scored_chunk : scored_chunks) {
|
bool feasible = true;
|
for (int i = scored_chunk.token_span.first;
|
i < scored_chunk.token_span.second; ++i) {
|
if (token_used[i - inference_span.first]) {
|
feasible = false;
|
break;
|
}
|
}
|
|
if (!feasible) {
|
continue;
|
}
|
|
for (int i = scored_chunk.token_span.first;
|
i < scored_chunk.token_span.second; ++i) {
|
token_used[i - inference_span.first] = true;
|
}
|
|
chunks->push_back(scored_chunk.token_span);
|
}
|
|
std::sort(chunks->begin(), chunks->end());
|
|
return true;
|
}
|
|
namespace {
|
// Updates the value at the given key in the map to maximum of the current value
|
// and the given value, or simply inserts the value if the key is not yet there.
|
template <typename Map>
|
void UpdateMax(Map* map, typename Map::key_type key,
|
typename Map::mapped_type value) {
|
const auto it = map->find(key);
|
if (it != map->end()) {
|
it->second = std::max(it->second, value);
|
} else {
|
(*map)[key] = value;
|
}
|
}
|
} // namespace
|
|
bool Annotator::ModelClickContextScoreChunks(
|
int num_tokens, const TokenSpan& span_of_interest,
|
const CachedFeatures& cached_features,
|
tflite::Interpreter* selection_interpreter,
|
std::vector<ScoredChunk>* scored_chunks) const {
|
const int max_batch_size = model_->selection_options()->batch_size();
|
|
std::vector<float> all_features;
|
std::map<TokenSpan, float> chunk_scores;
|
for (int batch_start = span_of_interest.first;
|
batch_start < span_of_interest.second; batch_start += max_batch_size) {
|
const int batch_end =
|
std::min(batch_start + max_batch_size, span_of_interest.second);
|
|
// Prepare features for the whole batch.
|
all_features.clear();
|
all_features.reserve(max_batch_size * cached_features.OutputFeaturesSize());
|
for (int click_pos = batch_start; click_pos < batch_end; ++click_pos) {
|
cached_features.AppendClickContextFeaturesForClick(click_pos,
|
&all_features);
|
}
|
|
// Run batched inference.
|
const int batch_size = batch_end - batch_start;
|
const int features_size = cached_features.OutputFeaturesSize();
|
TensorView<float> logits = selection_executor_->ComputeLogits(
|
TensorView<float>(all_features.data(), {batch_size, features_size}),
|
selection_interpreter);
|
if (!logits.is_valid()) {
|
TC3_LOG(ERROR) << "Couldn't compute logits.";
|
return false;
|
}
|
if (logits.dims() != 2 || logits.dim(0) != batch_size ||
|
logits.dim(1) !=
|
selection_feature_processor_->GetSelectionLabelCount()) {
|
TC3_LOG(ERROR) << "Mismatching output.";
|
return false;
|
}
|
|
// Save results.
|
for (int click_pos = batch_start; click_pos < batch_end; ++click_pos) {
|
const std::vector<float> scores = ComputeSoftmax(
|
logits.data() + logits.dim(1) * (click_pos - batch_start),
|
logits.dim(1));
|
for (int j = 0;
|
j < selection_feature_processor_->GetSelectionLabelCount(); ++j) {
|
TokenSpan relative_token_span;
|
if (!selection_feature_processor_->LabelToTokenSpan(
|
j, &relative_token_span)) {
|
TC3_LOG(ERROR) << "Couldn't map the label to a token span.";
|
return false;
|
}
|
const TokenSpan candidate_span = ExpandTokenSpan(
|
SingleTokenSpan(click_pos), relative_token_span.first,
|
relative_token_span.second);
|
if (candidate_span.first >= 0 && candidate_span.second <= num_tokens) {
|
UpdateMax(&chunk_scores, candidate_span, scores[j]);
|
}
|
}
|
}
|
}
|
|
scored_chunks->clear();
|
scored_chunks->reserve(chunk_scores.size());
|
for (const auto& entry : chunk_scores) {
|
scored_chunks->push_back(ScoredChunk{entry.first, entry.second});
|
}
|
|
return true;
|
}
|
|
bool Annotator::ModelBoundsSensitiveScoreChunks(
|
int num_tokens, const TokenSpan& span_of_interest,
|
const TokenSpan& inference_span, const CachedFeatures& cached_features,
|
tflite::Interpreter* selection_interpreter,
|
std::vector<ScoredChunk>* scored_chunks) const {
|
const int max_selection_span =
|
selection_feature_processor_->GetOptions()->max_selection_span();
|
const int max_chunk_length = selection_feature_processor_->GetOptions()
|
->selection_reduced_output_space()
|
? max_selection_span + 1
|
: 2 * max_selection_span + 1;
|
const bool score_single_token_spans_as_zero =
|
selection_feature_processor_->GetOptions()
|
->bounds_sensitive_features()
|
->score_single_token_spans_as_zero();
|
|
scored_chunks->clear();
|
if (score_single_token_spans_as_zero) {
|
scored_chunks->reserve(TokenSpanSize(span_of_interest));
|
}
|
|
// Prepare all chunk candidates into one batch:
|
// - Are contained in the inference span
|
// - Have a non-empty intersection with the span of interest
|
// - Are at least one token long
|
// - Are not longer than the maximum chunk length
|
std::vector<TokenSpan> candidate_spans;
|
for (int start = inference_span.first; start < span_of_interest.second;
|
++start) {
|
const int leftmost_end_index = std::max(start, span_of_interest.first) + 1;
|
for (int end = leftmost_end_index;
|
end <= inference_span.second && end - start <= max_chunk_length;
|
++end) {
|
const TokenSpan candidate_span = {start, end};
|
if (score_single_token_spans_as_zero &&
|
TokenSpanSize(candidate_span) == 1) {
|
// Do not include the single token span in the batch, add a zero score
|
// for it directly to the output.
|
scored_chunks->push_back(ScoredChunk{candidate_span, 0.0f});
|
} else {
|
candidate_spans.push_back(candidate_span);
|
}
|
}
|
}
|
|
const int max_batch_size = model_->selection_options()->batch_size();
|
|
std::vector<float> all_features;
|
scored_chunks->reserve(scored_chunks->size() + candidate_spans.size());
|
for (int batch_start = 0; batch_start < candidate_spans.size();
|
batch_start += max_batch_size) {
|
const int batch_end = std::min(batch_start + max_batch_size,
|
static_cast<int>(candidate_spans.size()));
|
|
// Prepare features for the whole batch.
|
all_features.clear();
|
all_features.reserve(max_batch_size * cached_features.OutputFeaturesSize());
|
for (int i = batch_start; i < batch_end; ++i) {
|
cached_features.AppendBoundsSensitiveFeaturesForSpan(candidate_spans[i],
|
&all_features);
|
}
|
|
// Run batched inference.
|
const int batch_size = batch_end - batch_start;
|
const int features_size = cached_features.OutputFeaturesSize();
|
TensorView<float> logits = selection_executor_->ComputeLogits(
|
TensorView<float>(all_features.data(), {batch_size, features_size}),
|
selection_interpreter);
|
if (!logits.is_valid()) {
|
TC3_LOG(ERROR) << "Couldn't compute logits.";
|
return false;
|
}
|
if (logits.dims() != 2 || logits.dim(0) != batch_size ||
|
logits.dim(1) != 1) {
|
TC3_LOG(ERROR) << "Mismatching output.";
|
return false;
|
}
|
|
// Save results.
|
for (int i = batch_start; i < batch_end; ++i) {
|
scored_chunks->push_back(
|
ScoredChunk{candidate_spans[i], logits.data()[i - batch_start]});
|
}
|
}
|
|
return true;
|
}
|
|
bool Annotator::DatetimeChunk(const UnicodeText& context_unicode,
|
int64 reference_time_ms_utc,
|
const std::string& reference_timezone,
|
const std::string& locales, ModeFlag mode,
|
AnnotationUsecase annotation_usecase,
|
bool is_serialized_entity_data_enabled,
|
std::vector<AnnotatedSpan>* result) const {
|
if (!datetime_parser_) {
|
return true;
|
}
|
|
std::vector<DatetimeParseResultSpan> datetime_spans;
|
if (!datetime_parser_->Parse(context_unicode, reference_time_ms_utc,
|
reference_timezone, locales, mode,
|
annotation_usecase,
|
/*anchor_start_end=*/false, &datetime_spans)) {
|
return false;
|
}
|
for (const DatetimeParseResultSpan& datetime_span : datetime_spans) {
|
AnnotatedSpan annotated_span;
|
annotated_span.span = datetime_span.span;
|
for (const DatetimeParseResult& parse_result : datetime_span.data) {
|
annotated_span.classification.emplace_back(
|
PickCollectionForDatetime(parse_result),
|
datetime_span.target_classification_score,
|
datetime_span.priority_score);
|
annotated_span.classification.back().datetime_parse_result = parse_result;
|
if (is_serialized_entity_data_enabled) {
|
annotated_span.classification.back().serialized_entity_data =
|
CreateDatetimeSerializedEntityData(parse_result);
|
}
|
}
|
annotated_span.source = AnnotatedSpan::Source::DATETIME;
|
result->push_back(std::move(annotated_span));
|
}
|
return true;
|
}
|
|
const Model* Annotator::model() const { return model_; }
|
const reflection::Schema* Annotator::entity_data_schema() const {
|
return entity_data_schema_;
|
}
|
|
const Model* ViewModel(const void* buffer, int size) {
|
if (!buffer) {
|
return nullptr;
|
}
|
|
return LoadAndVerifyModel(buffer, size);
|
}
|
|
bool Annotator::LookUpKnowledgeEntity(
|
const std::string& id, std::string* serialized_knowledge_result) const {
|
return knowledge_engine_ &&
|
knowledge_engine_->LookUpEntity(id, serialized_knowledge_result);
|
}
|
|
} // namespace libtextclassifier3
|
